Digital
Single
Market
ICT for work:
Digital skills in the
workplace
FINAL REPORT
A study prepared for the European Commission
DG Communications Networks, Content & Technology
by:
This study was carried out for the European Commission by
Authors: Maurizio Curtarelli and Valentina Gualtieri with Maryam Shater Jannati and Vicki
Donlevy
Internal identification
Contract number: 30-CE-0676076/00-14
SMART number: 2014/0048
DISCLAIMER
By the European Commission, Directorate-General of Communications Networks,
Content & Technology.
The information and views set out in this publication are those of the author(s)
and do not necessarily reflect the official opinion of the Commission. The
Commission does not guarantee the accuracy of the data included in this study.
Neither the Commission nor any person acting on the Commission’s behalf may
be held responsible for the use which may be made of the information contained
therein.
ISBN 978-92-79-67761-8
doi:10.2759/498467
© European Union, 2016. All rights reserved. Certain parts are licensed under
conditions to the EU.
Reproduction is authorised provided the source is acknowledged.
1
Content
ABSTRACT .............................................................................................................................. 3
EXECUTIVE SUMMARY ...................................................................................................... 4
CHAPTER 1. SETTING THE SCENE: THE CONTEXT OF THIS STUDY .............. 12
1.1 Definition of the digital economy .............................................................................. 12
1.2 Impacts and implications of the digital economy ............................................... 13
1.3 Digital skills and digital literacy ................................................................................ 16
1.4 Digital skills challenges ................................................................................................. 21
1.5 The digital skills challenge: policies and solutions............................................. 24
1.6 Purpose and scope of this study ............................................................................... 25
1.7 Overview of methodological approach ................................................................... 27
1.8 Structure of the report ................................................................................................. 29
CHAPTER 2. WORKPLACES’ FEATURES IN THE EUROPEAN UNION:
DESCRIBING THE SURVEY POPULATION ................................................................ 31
2.1 Profile of workplaces ...................................................................................................... 31
2.2 Profile of workforce ........................................................................................................ 37
2.3 Summary ........................................................................................................................... 41
CHAPTER 3. ICT AND DIGITAL TECHNOLOGIES FOR WORK .......................... 43
3.1 Digital technologies in European workplaces ........................................................... 43
3.2 Recent trends in the use of digital technologies ..................................................... 45
3.3 Investment strategies in ICT .......................................................................................... 49
3.4 Summary ............................................................................................................................... 59
CHAPTER 4. DIGITAL SKILLS IN EUROPEAN WORKPLACES ........................... 61
4.1 Defining digital skills in the context of this survey ................................................ 61
4.2 Digital skills for work: available evidence from other sources .......................... 62
4.3 What types of digital skills are required in European workplaces? ................. 64
4.4 Digital skills of employees in different jobs .............................................................. 69
4.5 Analysis of specific jobs .................................................................................................... 73
4.6 Summary ............................................................................................................................... 79
CHAPTER 5. THE DIGITAL SKILLS CHALLENGE IN EUROPEAN
WORKPLACES ..................................................................................................................... 81
5.1 Defining digital skills gaps ............................................................................................... 81
5.2 Digital skill gaps in European workplaces ................................................................. 82
5.3 Digital skills gaps density ................................................................................................ 83
5.4 Impacts of digital skills gaps .......................................................................................... 85
5.5 How do workplaces deal with digital skill gaps? ..................................................... 89
5.6 Barriers to initiatives tackling digital skills gaps ..................................................... 93
5.7 Summary ............................................................................................................................... 94
CHAPTER 6. CONCLUSIONS AND RECOMMENDATIONS .................................... 97
6.1 Main results and related learning points ................................................................... 97
2
6.2 Learning points from the validation workshop ...................................................... 101
6.3 Recommendations ............................................................................................................ 102
ANNEX 1. SURVEY METHODOLOGY .......................................................................... 106
A1.1 The target population ................................................................................................... 106
A1.2 Sampling strategy .......................................................................................................... 108
A1.3 The sampling frames .................................................................................................... 115
A1.4 Data collection ................................................................................................................. 115
A1.5 Identification of parameters of interest and estimation phase .................... 116
A1.6 Sample profile ................................................................................................................. 119
A1.7 Data preparation and analysis .................................................................................. 122
A1.8 The survey questionnaire............................................................................................ 123
ANNEX 2. COMPLEMENTARY STATISTICAL EVIDENCE .................................... 138
ANNEX 3. BIBLIOGRAPHIC REFERENCES .............................................................. 188
3
ABSTRACT
The digitisation of the economy is one of the most important drivers behind the
profound transformation of the labour market and the way people work, which is
thought likely to become even more significant in the years to come. This new paradigm
represents a major challenge for employers, workers and public authorities, and the
challenges needs to be fully understood in order to identify the most appropriate policy
options to transform them into opportunities for all. This study "ICT for Work: Digital
skills in the workplace", launched by The European Commission, DG CONNECT, and
carried out by Ecorys and Danish Technological Institute in 2015-2016 has been
conceptualised and implemented in order to examine the transformation of jobs in the
digital economy in the European Union, investigating the penetration of digital
technologies into workplaces, the digital skills required by employers and the digital
skills currently available in workplaces. In this light, an employers’ survey and
qualitative interviews on the impact of ICT on job quality have been carried out, .
Rooted in an extensive review of academic and scientific literature, existing surveys and
data from other statistical sources, this study aims to fill existing research gaps. This
report presents findings from the study.
4
EXECUTIVE SUMMARY
The digitisation of the economy is one of the most important drivers behind the
profound transformation of the labour market and the way people work, with this
digitisation thought likely to become even more significant in the years to come. This
new paradigm represents a major challenge for employers, workers and public
authorities, and the challenges needs to be fully understood in order to identify the
most appropriate policy options to transform them into opportunities for all. This study
"ICT for Work: Digital skills in the workplace", launched by The European Commission,
DG CONNECT, and carried out by Ecorys and Danish Technological Institute has been
conceptualised and implemented in order to examine the transformation of jobs in the
digital economy in the European Union, investigating the penetration of digital
technologies into workplaces, the digital skills required by employers and the digital
skills currently available in workplaces. In this light, a number of research tasks aimed
at gathering primary data on digital skills and digital skill gaps have been carried out,
primarily through an employers’ survey, and through qualitative interviews on the
impact of ICT on job quality. This report focuses primarily on findings from the
employers’ survey (the ‘European Digital Skills Survey’), with these being integrated
where relevant with findings from the qualitative analysis of the impact of digital
technologies on job quality and working conditions (these are presented in a separate
report “The impact of ICT on job quality: evidence from 12 job profiles”1). Rooted in an
extensive review of academic and scientific literature, existing surveys and data from
other statistical sources, this study aims to fill existing research gaps.
The conceptual framework. While the ‘digital economy’ can be broadly defined as the
economy that is extensively based on digital computing technologies, most of the
literature on the impact of digital technologies on the world of work converges in
pointing out that the phenomenon of digitisation is actually of great complexity, and is
changing rapidly to also embrace new technological developments. The concept of
‘digitisation’ covers a wide range of different digital technologies (e.g. computers,
mobile devices, internet and the ‘Internet of Things’, robotics and automation), which
have different implications in terms of their impacts on production and work.
The impacts of digitisation on the labour market are multiple, and have a high degree of
interconnection. The digitisation of the economy is seen in most of the literature as
contributing to the polarisation of the labour market in many countries. On one
hand, digitisation is thought to have led to a significant increase over time in the
demand for high-skilled individuals, equipped with cognitive skills and technical
knowledge to deal with tasks and procedures required by the new technologies. On the
other hand, it has led to a sharp decrease in the demand for the medium-skilled and
(although to a lesser extent) low-skilled or non-skilled workers.
Some other authors see digitisation as resulting in job losses. This is due to
automation, based on the development of a combination of increasingly smart
technologies - such as robotics, numerically-controlled machines,
computerised inventory management software, speech recognition, pattern recognition,
automated language translation, self-driving vehicles, e-commerce - which are
replacing humans in doing their jobs and therefore are contributing to job destruction. 1 Ecorys and Danish Technological Institute (2016) available at https://ec.europa.eu/digital-single-market/en/news/report-shows-digital-skills-are-required-across-all-types-work-also-jobs-outside-office
5
Conversely, according to other scholars, digitisation is favouring the emergence of
new occupations and creating new jobs. Digitisation is leading to a higher demand
of human workers because humans have a comparative advantage over computers
when it comes to ‘cognitive tasks’ which require thinking, improvising creative solutions
and solving unexpected problems, and to also be better than machines with tasks
requiring flexibility to adapt and interpersonal interactions. These human cognitive and
interactive abilities are complementary to the work of computers. In addition,
digitisation is generating the emergence of completely new occupations and jobs.
Digitisation is also seen to bring transformations in existing jobs, by changing (to
different degrees) work practices, the way tasks are carried out, job contents and job
requirements, and, as a consequence, the skills needed to perform the job. Digitisation
has also been shown to impact on working conditions: digital technologies are
contributing significantly to changes in the way we work, with an expected negative
impact on the work-life balance and health and safety of workers (e.g. increased stress,
decreased protection, higher risks related to working remotely), as well as their
remuneration.
While the net impact of digitisation on employment is debatable, there are national-
level studies that indicate that digital technologies may contribute to productivity
increases and economic transformation and with multiplicative effects on non-ICT
sectors, if accompanied by work process reorganisation and skill upgrading. The growth
of digitisation has therefore resulted in an increased demand of digital skills in
recent years (as shown by several studies), which is expected to continue growing due
to the increasing number of jobs requiring employees to use ICT and possess digital
skills. Digital skills are required in many jobs and have become transversal skills.
The nature of demand for digital skills is, however, influenced by the strategies firms
pursue and it is highly dependent on the sector and the market position of the firm.
Given the recognised importance of digital skills for an effective implementation of ICT
in the workplace, the main challenge for employers is to make sure that the supply of
digital skills within the workforce matches its demand at the labour market and
enterprise level, in terms of both required type and level of skills. In other terms, the
digital skill challenge for businesses is twofold. It can refer either to the lack of workers
with the needed skills in the labour market (skills shortages), and/or to the presence of
overskilled or underskilled workers at company level (skills mismatches and skills gaps).
Challenges can therefore be external to the firm (as in the case of shortages) or internal
(as in the case of mismatches or gaps). In terms of impact, the adverse effects of
digital skills gaps (or digital underskilling of employees) are evidenced across
aggregated, company and individual levels. Of particular relevance is the company
level, where skills gaps cause a loss in terms of productivity, increasing labour costs,
slowing growth and adaptation of new technologies down.
Purpose and scope of this study. In this context, the study on ‘ICT for work: Digital
skills in the workplace’ has been conceptualised and implemented in order to examine
the transformation of jobs in the digital economy in the European Union, investigating
the penetration of digital technologies into workplaces, the digital skills required by
employers and the digital skills currently available in workplaces. Overall, the evidence
gathered corroborates existing research demonstrating that digital technologies are
becoming increasingly widespread across a wide range of workplaces, also in economic
sectors not traditionally related to digitisation. The study also confirms (as observed in
6
existing research) that digitisation is resulting in an increasing demand for digital skills
across different types of occupations and jobs in a range of industries, and that
employers are encountered with a shortfall in the availability of appropriate digital
skills.
European workplaces and survey population. The European Digital Skills Survey
was carried out among a representative sample of 7,800 workplaces in six EU member
states (Germany, Finland, United Kingdom, Portugal, Sweden and Slovakia), which are
statistically representative of 4,295,345 workplaces in the six countries as a whole, and
of 13,803,113 workplaces in the whole Europe Union (EU28). Such workplaces operate
in 12 economic sectors with different levels of digital intensity: agriculture;
manufacturing; electricity and gas supply; construction; wholesale and retail trade,
repair of motor vehicles and motorcycles; transportation and storage; accommodation
and food service activities; information and communication; professional, scientific and
technical activities; administrative and support service activities; education; human
health and social work activities. The vast majority of the workplaces represented
employ between two to nine employees and therefore fall in the category of micro-sized
workplaces (more than 80%), with the remaining being mainly small enterprises
(16%); only 0.5% are large enterprises. Workplaces belong mainly to the private sector
(90%) and are not part of a group of companies (85%). The main reference market for
these workplaces is the local or regional market (65%), while a more limited proportion
of workplaces operate or trade at national level (about 21%) and only 13% at
international level.
The 13,803,113 European workplaces (EU28) employ a total of 150,563,540
employees. Overall around 39% of employees are women, 20% are older than 50
years of age and 19% are younger than 30 years of age. Over a quarter of employees
(27%) hold a university degree. Employees are classified in nine occupational
categories identified by the ISCO 1-digit code: managers; professionals; technicians
and associate professionals; clerical support workers; sales, customer or personal
service workers; skilled agricultural, forestry and fishery workers; building, craft and
related trade workers; plant and machine operators and assemblers; and elementary
occupations. Of the 150,563,540 total employees in EU28 workplaces, 7.5 million (or
5%) work as managers. Workers employed as professionals in the workplaces covered
by this survey number 28.5 million (or 19%). Technicians number 25.5 million (17% of
total employees), sales workers number 26 million (17%), clerical workers account for
17 million (11%), and skilled agricultural workers total 1.5 million (1% of employees).
Finally, the survey identified about 17 million building workers (11%), 12 million plant
machine operators (8%) and 15.5 million workers employed in elementary occupations
(10%).
ICT and digital technologies for work. In this context, the survey results show that
digital technologies are widely used by workplaces in the European Union. The vast
majority of European workplaces use desktop computers (93%), broadband technology
to access the internet (94%), portable computers (75%) and other portable devices
(63%). Much smaller proportions of workplaces use an intranet platform (22%), CNC
machine or tools (8%) or programmable robots (5%). Specific sector-based trends,
with the use of certain technologies concentrated in specific sectors can be observed, as
for example the use of CNC machine and robots in the agriculture and manufacturing
sectors and the use of laptops in the information and communication or education and
human health sectors. Large-sized workplaces report the highest use of all the digital
7
technologies listed. The use of ICT in workplaces has increased significantly in the last
five years at EU level with less than 10% of workplaces reporting that there has been
no increase. In this period, micro-sized workplaces were more likely to report no
increase or a limited increase in the use of ICT compared to workplaces of a larger size.
Generally, about 69% of workplaces anticipate that the use of digital technology will
increase in the next five year, while 25% expect no increase and around 7% have no
specific expectation. The current level of use of digital devices seems to have been
supported by specific investment strategies over the last five years, mainly those aimed
at introducing ICTs to improve either the overall efficiency or the business volume of
workplaces. However, investments in ICTs appear to be less common among micro-
sized workplaces, which report more frequently than other workplaces a total lack of
investment in ICT in the recent past. At sectoral level, recent investments in ICTs seem
to be more frequent among workplaces in sectors with traditionally low levels of digital
intensity (e.g. agriculture, manufacturing or construction), most probably in light of
recent changes in the production strategy pursuing higher efficiency, but also in sectors
with higher levels of digital intensity (e.g. information and communication sector).
Digital skills in European workplaces. In the European Union the proportion of
workplaces requiring their employees to possess digital skills varies greatly according to
the type of job and the type of digital skills. The demand for digital skills is clearly
related to the job role of the worker, and the evidence gathered through the European
Digital Skills Survey indicates that in some job categories more than 90% of jobs
require specific types of digital skills. Basic digital skills are the most commonly required
in all the occupations. However, the evidence indicates that this requirement is
particularly the case for high and medium-skilled jobs. Almost all workplaces require
their managers to possess basic digital skills and around 90% of employers state that
professionals, technicians, clerical workers or skilled agricultural workers are required to
possess at least basic digital skills. Eight out of ten workplaces require basic digital
skills for sales workers. Although in much smaller proportions, workplaces also often
require basic digital skills for building workers (almost half of workplaces), plant
machine operators (34% of workplaces) and even employees in elementary occupations
(27% of workplaces). Advanced digital skills are much less required by employers. It is
mostly professionals (54% of workplaces), technicians (52%) and to a lesser extent
clerical workers (45%), managers and building workers (31% of workplaces in both
cases) who are required to have this type of digital skills, while they are considered
much less important for all other occupations. Specialist digital skills are required
mostly for workers employed as professionals and technicians (43% and 44%
respectively), and to a lesser extent as managers (33% of workplaces). Advanced and
specialist digital skills are very much related to specific sectors (in particular
manufacturing and information and communication) and are more likely to be required
in larger workplaces. There are a very high proportion of workplaces which do not
consider digital skills to be important at all for several medium, low or non-skilled
occupations. The proportion of employees equipped with the required digital skills
broadly reflects the level of importance attached by the employers to the specific types
of digital skills in the different job categories.
The digital skills challenge in European workplaces. Nevertheless, 15% of
workplaces report the existence of digital skill gaps in their workforce, indicating that a
proportion of their employees are not fully proficient in carrying out tasks involving the
use of digital technologies. Large workplaces, and workplaces in the manufacturing or
8
construction sectors, are more likely to report digital skill gaps. Overall, the density of
the digital skills gap varies greatly according to the type of digital skills in relation to the
different occupations. Larger digital skills gaps are more likely to be found in the high-
skilled (managers, technicians) and in medium-skilled (clerical workers, sales workers)
occupations, and to a lesser extent in the low-skilled occupations, with the exception of
workers in elementary occupations. Skills gaps related to basic digital skills are more
concentrated among technicians (22%), elementary occupations (21%), sales workers
(20%) and clerical workers (17%). Skill gaps related to advanced digital skills are more
concentrated among sales workers (18%), technicians (17%), plant machine operators
(17%), clerical workers (16%) and elementary occupations (15%). Skills gaps related
to specialist digital skills are more concentrated among sales workers (23%), followed
by elementary occupations (18%) and technicians (16%).
Most workplaces (62%) that report an issue of digital skills gaps do not consider that
existing digital skills gaps have an impact on workplace performance, while more than
one third of workplaces with digital skills gaps express concern about the impact that
gaps could have on the workplace performance (36%). About half of those expressing
concern about the impact on workplace performance expect a major impact (18%) and
the other half a minor impact (19%). Micro-sized and, to a lesser extent, large
workplaces, and workplaces in the manufacturing and construction sectors are among
those more likely to consider that digital skills gaps are not impacting on performance.
The type of impact most frequently reported is a loss of productivity (46%) followed by
an expected decrease in the number of customers (43%), another (unspecified)
negative impact (41%) and a decrease in the number of contracts (32%). Awareness of
the existence of digital skills gaps is frequently not accompanied by initiatives
undertaken to address the issue: 77% of workplaces reporting digital skills gaps have
not undertaken any actions, while only 12% have done so, and 11% plan to. Micro-
sized workplaces have been least active in this respect, with only 9% having taken
action to tackle digital skill gaps and 81% having not undertaken any actions at all.
Overall, training (both in the form of on-the-job training and development programmes
and external training) appears to be the most common action undertaken to tackle the
digital skills gaps, while changes to work organisation and the hiring of new staff appear
to be much less common.
Excessive cost seems to be the main barrier encountered when undertaking actions to
deal with digital skills gaps. Micro-sized workplaces are the most likely to report the
excessive cost of most of the available options. Only limited proportions of large
workplaces encounter difficulties when taking action to tackle the digital skill gaps, with
the exception of digital skill shortages in the overall labour market, which is reported by
37% of large-sized workplaces.
Conclusions and recommendations. The evidence shows that digital technologies
are increasingly and extensively used across the economy. However, digital skills
appear to be currently required mostly for the high-skilled and, to a lesser extent,
medium-skilled employees to perform their job tasks, and are less likely to be required
for the low-skilled or the unskilled (or frequently not required at all, even at basic
level). These polarising trends, confirmed also by other available evidence, draws
attention to the fact that a high share of workers in low-skilled occupations whichdo not
require (or require to a very limited extent) digital skills. This dichotomy risks widening
the digital divide, leaving a proportion of workers lagging behind and at risk of digital
9
exclusion, who would hence benefit from specific attention. Another finding regards the
availability of digital skills, which is not always sufficient to meet employers’ needs, as
demonstrated by the reported existence of digital skills gaps in the workforce, even as
regards basic digital skills. Different factors contribute to this situation. The speed at
which workers are being provided with the right digital skills in the right locations is
frequently slower than the speed at which digital technologies are evolving. As a result,
digital skills are often also more subject to obsolescence. An age-related issue can also
be identified, as older workers are less likely to be equipped with digital skills than
younger workers. Results show as well that even if workplaces report that a proportion
of their workforce is not fully proficient in carrying out tasks involving the use of digital
technologies, they often do not recognise that existing in-house skills gaps impact on
workplace performance and hence often do not take action to deal with the issue.
Another important result regards the relationship between workplace size and access to
digital technologies. For micro and small-sized workplaces, it may not be viable to
invest in order to increase ICT use. Also, for those micro and small-sized employers
who have a high demand for digital skills, simply allocating staff time to acquire them is
both difficult (loss of productive time), and expensive (training and development
programmes need to be brought in). This is less an issue for bigger employers with
more available resources who can manage capacity, develop training programmes or
buy them in. But it is also important to remember that some micro or small-sized
companies consider that they do not need ICT at all, and therefore do not demand
digital skills.
Finally, the skills challenges appear highly dispersed, as different sectors have different
demands, and the balance of supply and demand is different across Member States.
The sectoral analysis indicates that the use of digital technologies is uneven across
economic sectors, particularly concerning the types of digital technologies, their speed
of penetration and also the related demand for digital skills, with some sectors clearly
leading the ‘digital revolution’ and some others following at a slower pace.
The recommendation formulated at the end of the study can be summarised as follows.
1. Raise awareness on digital technologies and the need for digital skills
Awareness-raising campaigns, should be implemented to raise awareness both of the
importance of the use of digital technologies to support and improve business
performance, productivity and internal organisation, and of the need for digital skills in
relation to new digital technologies.
2. Promote access to digital technologies
Many companies, particularly micro and small-sized ones, are not fully aware of the
importance of investing in digital technologies, and often do not have the financial
capacity to do so. Mechanisms (loans, grants etc.) should be used to enhance and
support access to digital technologies, particularly for micro and small-sized companies.
3. Expand the availability of digital skills through the education and training
system
The education and training sector should be supported to develop and adapt its offer to
meet the changing needs of the digital economy. Programmes at all levels and sectors
10
of education should be updated and digital skills should be part of the core competences
required at every level.
4. Promote access to training
Access to training to address digital skills gaps in the existing workforce should be
supported through a variety of means. Information about existing training initiatives
and procedures to access them should be made available to employers through their
professional or sectoral organisations and associations, or through governmental
channels.
5. Build multi-stakeholder partnerships based also on effective social dialogue
to increase the availability of digital skills
Policymakers should support digital skills development within multi-stakeholder
partnerships. Partnerships are proven to generate a more inclusive and targeted
approach to skills development and training provision that is more responsive to labour
market needs, in line with vocational programmes and qualifications. A digital skills
strategy should therefore be the result of a discussion based on effective social
dialogue.
6. Provide access to funding for digital technologies and digital skills
development
Funding is critical to enhance the availability of digital skills in the current workforce.
Employers could benefit from access to funds (including EU funds) to support more
investment in digital technologies and the development of digital skills, especially for
initiatives that are cross-border and which share experiences in the generation and use
of digital skills. Better access to funding should also be provided to social partners.
7. Include digital skills in a wider skills strategy
Although it remains crucial to develop a range of specific digital skills which respond to
the needs of the digital economy, wider digital skills for the whole population should be
embedded in a broader and comprehensive skills strategy in which other transversal
skills relevant to employers such as soft skills and communication skills are also
included. Evidence shows that the most effective means of improving employability and
closing skills gaps are more generic measures aimed at improving the capacity of
workers to acquire new skills and learn in an evolving economy.
8. Consider diversity and avoid the ‘one-size fits all’ approach
Employers require different types and levels of digital skills according to the sector in
which they operate, their size, their market, and the country in which they are based.
In designing a digital skills strategy or any other type of initiative to help employers to
access the required digital skills, diversity needs to be clearly addressed through a
tailored approach.
9. Reduce the digital divide
Policymakers should take action to reduce the existing digital divide, focusing in
particular on the categories of individuals who do not possess digital skills and are
11
consequently at risk of marginalisation not only in the labour market, but also in day-
to-day life, which can contribute to social and economic exclusion.
12
CHAPTER 1. SETTING THE SCENE: THE CONTEXT OF THIS STUDY
The digitisation of the economy, along with market globalisation and demographic
change, is reported in the research literature to be one of the most important drivers
behind the profound transformation of the labour market and the way people work with
this digitisation thought likely to become even more significant in the years to come.
This new paradigm represents a major challenge for employers, workers and public
authorities, and the challenges needs to be fully understood in order to identify the
most appropriate policy options to transform them into opportunities for all.
This introductory chapter summarises the debate, as it is communicated in research
literature and policy documents, around the implications of digitisation for the economy,
and in particular for the world of work. As such, it sets out the general context in which
this study on ‘ICT for work: Digital skills in the workplace’ has been conceptualised and
conducted.
The chapter focuses on the definition of the digital economy, the impacts of digitisation
on the labour market and its implications for employers and policymakers. In particular,
it focuses upon the expected impacts of digitisation in terms of job destruction, job
creation and job transformation, as well as setting out the implications of the latest
developments in needs for digital skills.
In addition, the chapter presents the aims and specific objectives of the employers’
survey and of the wider study. It also presents an overview of the research questions
and the methodology used to respond to the research questions.
1.1 Definition of the digital economy
While the ‘digital economy’ can be broadly defined as the economy that is extensively
based on digital computing technologies, most of the literature on the impact of digital
technologies on the world of work converges in pointing out that the phenomenon of
digitisation is actually of great complexity, and is also changing rapidly to embrace new
technological developments.
The concept of ‘digitisation’ covers a wide range of different digital technologies (e.g.
computers, mobile devices, internet and the ‘Internet of Things’, robotics and
automation), which have different implications in terms of their impacts on production
and work.
The OECD, for example, acknowledges this complexity, stating in recent research that
the digital economy is growing quickly and is now permeating “the world economy from
retail (e-commerce) to transportation (automated vehicles), education (Massive Open
Online Courses), health (electronic records and personalised medicine), social
interactions and personal relationships (social networks). […] ICTs are integral to
professional and personal life; individuals, businesses and governments are increasingly
inter-connected via a host of devices at home and at work, in public spaces and on the
move. These exchanges are routed through millions of individual networks ranging from
residential consumer networks to networks that span the globe. The convergence of
fixed, mobile and broadcast networks, along with the combined use of machine-to-
machine (M2M) communication, the cloud, data analytics, sensors, actuators and
13
people, is paving the way for machine learning, remote control, and autonomous
machines and systems. Devices and objects are becoming increasingly connected to the
Internet of Things, leading to convergence between ICTs and the economy on a grand
scale” (OECD 2015). In line with this description, the ‘digital economy’ has been defined
through its “four specific features: the irrelevance of geographical location, the key role
played by platforms, the importance of network effects and the use of big data. These
features distinguish it from the traditional economy, particularly as a result of the
associated value chain transformations” (Charrié and Janin 2015).
The World Economic Forum’s analysis of the ‘Future of jobs’ defines the digitisation of
the economy, underlining that the “development in genetics, artificial intelligence,
robotics, nanotechnology, 3D printing and biotechnology, to name just a few, [which]
are all building on and amplifying one another, will lay the foundation for extensive
transformations in the way we live and work”. In this context, “smart systems – homes,
factories, farms, grids or cities – will help tackle problems ranging from supply chain
management to climate change. The rise of the sharing economy will allow people to
monetize everything from their empty house to their car” (World Economic Forum
2016). The digitisation of the economy, involving the extensive changes driven and
supported by digital technologies, is also referred to as the ‘Fourth Industrial
Revolution’, characterised by a blend of technologies that is progressively blurring the
boundaries between the physical, digital and biological spheres. It follows the Third
Industrial Revolution, which started in the second half of 20th Century and was defined
by the use of electronics and information technology to automate production (Schwab
2016)2.
1.2 Impacts and implications of the digital economy
The impacts of digitisation on the labour market are multiple, and have a high degree of
interconnection.
First, the digitisation of the economy is seen in most of the literature3 as contributing
to the polarisation of the labour market in many countries. On one hand,
digitisation is thought to have led to a significant increase over time in the demand for
high-skilled individuals, equipped with cognitive skills and technical knowledge to deal
with tasks and procedures required by the new technologies. On the other hand, it has
led to a sharp decrease in the demand for the medium-skilled and (although to a lesser
extent) low-skilled or non-skilled workers (Berger and Frey 2016).
Second, some authors see digitisation as resulting in job losses. This is due to
automation, based on the development of a combination of increasingly smart
technologies - such as robotics, numerically-controlled machines,
computerised inventory management software, speech recognition, pattern recognition,
automated language translation, self-driving vehicles, e-commerce - which are
replacing humans in doing their jobs and therefore are contributing to job destruction,
as extensively discussed by Brynjolfsson and McAfee (2011). Frey and Osborne (2013)
2 According to the author, ‘the First Industrial Revolution used water and steam power to mechanize production’, while the ‘Second used electric power to create mass production’ (Schwab, 2016) 3 For example: Dolphin T (Ed.) (2015); empirical evidence for the US: Autor et al. (2006, 2008) ; Autor and Dorn (2013); for the UK: Goos and Manning (2007); for Germany: Spitz-Oener (2006); Dustmann et al. (2009); for Western Europe: Goos et al. (2009); Michaels et al. (2014); for Japan: Ikenaga (2009); Ikenaga and Kambayashi (2016).
14
predicted that 47% of jobs in the US were at high risk of being automated over the
following decade or two, and could therefore be lost. In particular, workers in the
transportation and logistics sectors, but also those involved in production, office and
administrative support, were considered at risk of being replaced by ICT or computer-
controlled devices (Frey and Osborne 2013). Valsamis et al. (2015) reported Bruegel
think-tank estimations that 40% to 60% of the jobs in the European Union were at risk
due to digitisation-induced automation. They expressed strong doubts that a fully
digitalised economy will produce sufficient demand for labour to compensate expected
job losses (Valsamis et al. 2015). More generally, the existing empirical evidence
indicates that jobs entailing a high degree of routine-based tasks4 are those most at
risk of automation. These tasks are mostly performed as part of their core job tasks by
middle-skilled workers, as pointed out by Autor, Levy and Murnane (2003), and could
also be behind the polarisation of the labour market mentioned previously. Jobs
requiring intellectual, cognitive or creative skills, and jobs requiring human interaction,
would be less at risk. Nevertheless, some authors express concern also for this type of
job, as the most recent technological developments in the area of artificial intelligence
or the development of highly sophisticated software are considered to put other types
of job at risk,– for example, translators, medical doctors, journalists (Staglianò 2016)
and teachers, the latter in connection with the growing importance of MOOCs (Massive
Open Online Courses) (Staglianò 2016, Bainbridge 2015). Job losses can also occur
because digital technologies can support global outsourcing of tasks to another location
or country with different cost and productivity conditions, which results in direct job
losses in one country but job gains in another (OECD 2014).
Third, a number of other authors maintain that digitisation is favouring the emergence
of new occupations and creating new jobs. Bainbridge (2015) for example
underlines that digitisation is leading to a higher demand of human workers because
digital technology “enables enterprises to make existing products better and more
efficiently, and to make new things. […] Small firms and individuals are already
publishing and distributing books and music via the Internet and creating apps for
smartphones, computers and enabled TVs. Three-dimensional printing enables bespoke
manufacturing by small firms. Aside from opening up opportunities, lowering barriers to
entry also reduces risk and so may increase the number of budding entrepreneurs
starting businesses, perhaps encouraged by online ‘crowdsourcing’ sources”
(Bainsbridge 2015). De La Rica (2016) considers humans to have a comparative
advantage over computers when it comes to ‘cognitive tasks’ which require thinking,
improvising creative solutions and solving unexpected problems, and to also be better
than machines with tasks requiring flexibility to adapt and interpersonal interactions.
These human cognitive and interactive abilities can be “complementary to the work of
computers, rather than necessarily substituting for it, and hence computerisation is
likely to increase the demand for people with this skills” (De La Rica 2016). Digitisation
is generating the emergence of completely new occupations and jobs, as pointed out for
example by the OECD (2014). Examples of brand new occupations which emerged
recently in connection to digital technologies include big data architects, internet
engineers, networking specialists, hardware engineers, mobile app developers, data
4 According to Autor and Handel (2013) routine tasks can be both cognitive and manual. On the one hand the routine cognitive tasks involve the importance of repeating the same tasks, the importance of being exact or accurate, and more in general the structured versus unstructured work. On the other hand, the routine manual tasks involve controlling machines and processes, keeping a pace set by machinery or equipment, and spend time making repetitive motions.
15
scientists, and digital marketing specialists. Digitisation can also lead “to job growth in
traditional occupations by supporting the creation of new businesses (e.g.
entrepreneurship) or the expansion of existing firms (e.g. growth from tapping into
foreign/new markets or more effective marketing)” (OECD 2014). Estimates from the
World Economic Forum (2013) indicate that digitisation is responsible for the net
creation of 213,578 jobs in Western Europe. A study found that one in ten companies in
Germany has employees dealing specifically with activities in Web 2.0. The study
estimates that digitisation created about 1.4 million new workplaces in Germany in
2012. Most of the new workplaces (976,000) were created in the service sectors, while
300,000 were created in manufacturing (Bitkom 2014). Nevertheless, it is worth
mentioning that there is no definitive evidence regarding the net impact of digital
technologies on jobs.
Fourth, digitisation brings transformations in existing jobs, by changing (to
different degrees) work practices, the way tasks are carried out, job contents and job
requirements, and, as a consequence, the skills needed to perform the job (OECD 2014,
Valsamis et al. 2015). As pointed out by Berger and Frey (2016) among several other
authors, “as digital technology becomes more heavily integrated into the daily
operations of firms across a wide range of industries, digital literacy will become
critically important for the vast majority of workers” (Berger and Frey 2016). The World
Economic Forum (2016) shares this perspective, stating that “as entire industries
adjust, most occupations are undergoing a fundamental transformation. While some
jobs are threatened by redundancy and others grow rapidly, existing jobs are also going
through a change in the skill sets required to them” (World Economic Forum 2016). This
would be “highly specific to the industry, region and occupation in question as well as
the ability of various stakeholders to manage change” (World Economic Forum 2016).
The growth of digitisation has therefore resulted in an increased demand of digital
skills in recent years, which is expected to continue, as shown by several studies (e.g.
Empirica 2015, World Economic Forum 2016). Overall, the literature shows that an
increasing number of jobs require employees to use ICT and possess digital skills.
Estimates suggest that 90% of jobs need at least basic computer skills (European
Commission 2014). A different estimate based on the OECD definition of “intensive ICT-
using occupations” (OECD 2016) shows these accounted for 22% of the whole economy
in EU15 in 2010 (ibid)5. More recent OECD statistics on employment of “ICT specialists”
displays a growing trend in most OECD countries. In particular, in EU226 the share of
“ICT specialists” employment was on average 3.6% of the total employment in 2014
(and 3.3% in 2011), ranging from as much as 6% in Finland or 5.3% in Sweden to 2%
in Latvia or 1.7% in Greece (OECD 2015). A recent US study for example shows that, in
the middle-skill job market, the world is increasingly divided between jobs that demand
digital skills and those that do not (Burning Glass Technologies 2015). Being able to use
spreadsheets, word processing programs, digitalised systems such as accounting
systems, is required in many middle-skilled jobs, as well as more occupationally specific
digital skills (ibid.). These figures indicate that digital skills are required in many jobs
and have become transversal skills. The nature of demand for digital skills is, however,
influenced by the strategies firms pursue (Danish Technological Institute 2014).
Moreover, it is highly dependent on the sector and the market position of the firm. A
5 This data is based on the broad definition based on the methodology described in OECD Information Technology Outlook 2004 6 Finland, Sweden, Luxembourg, UK, Ireland, Netherlands, Denmark, Estonia, Belgium, Germany, Czech
Republic, Slovenia, Austria, Hungary, Portugal, Spain, France, Italy, Poland, Slovakia, Latvia and Greece.
16
shortage in the supply of digital skills, or the lack of an appropriate level of digital skills
among existing employees, poses serious challenges to employers in terms of
competitive position.
Fifth, digitisation has been shown to impact on working conditions. More specifically,
a recent study by Eurofound (2014) has demonstrated how digital technologies are
contributing significantly to changes in the way we work, with an expected negative
impact on the work-life balance and health and safety of workers (e.g. increased stress,
decreased protection, higher risks related to working remotely), as well as their
remuneration. This is due to working from non-conventional workplaces (as in the case
of mobile work), on poorly paid micro-tasks (as in crowd employment), and long, or so-
called ‘anti-social’, hours to meet clients’ or employers’ demands (Eurofound 2014). The
study by Störmer et al. (2014) also highlights that work is increasingly more networked
and less rigidly focused on a specific workplace or around fixed working hours. Another
impact on working conditions is related to the progressive increase in self-employment,
as digital technologies make outsourcing of specific tasks much easier than in the past.
Self-employment is notoriously a form of employment that has a much lower level of
protection in several areas of working conditions (Eurofound 2010 and 2014).
In addition, while the net impact of digitisation on overall employment levels is
debatable, there are national-level studies that indicate that digital technologies may
contribute to productivity increases and economic transformation and with
multiplicative effects on non-ICT sectors, if accompanied by work process
reorganisation and skill upgrading (OECD 2004). For example, an Italian study shows
that the spread of new information and communication technologies in enterprises has
led to a profound transformation of the Italian production system and has impacted
efficiency and innovative capacity (Di Carlo, et al. 2010). A Danish study found that, if
ICT investments are not accompanied by skill upgrading, ICT investments may even
lead to a reduction in productivity (Danish Technological Institute 2013).
On a final note, as pointed out by Valsamis et al. (2015), entire industries are being
transformed by digital technologies (e.g. the financial sector and the manufacturing
industry). Digitisation can sometimes bring down leading players in the industry, as
happened to Kodak which, at its peak in 1988, had 145,300 employees, and in 2012
this was down to 13,100 (Valsamis et al. 2015). The digitisation of the economy is not
only transforming entire economic sectors, but also the traditional split between
‘industry’ and ‘services’, which has become less relevant than it used to be. As pointed
out by Christophe Degryse (2016), “the ‘Fourth Industrial Revolution’ seems to be
making the frontier between the two sectors much more porous: an emblematic case of
this characteristic merging of industry and services is the so-called intelligent car,
potentially a ‘computer on wheels’ incorporating all the services that mobile applications
are increasingly able to offer the user” (Degryse 2016).
1.3 Digital skills and digital literacy
As seen above, the digitisation of economy has a number of complex effects on the
labour market, work organisation, workers’ skillsets and working conditions. In
particular, it entails a shift in the type of skills required by workers in relation to ICT
utilisation, exploitation and advancement in the workplace. It thus becomes
fundamental that the effective introduction of ICT in the workplace is accompanied by
17
an appropriate upskilling of workers. In this context, it is essential to understand what
digital skills are and how they have been conceptualised to date.
Scholars, governments and international organisations have generally and progressively
recognised the importance of digital skills within knowledge economies, both in the
workplace and more generally. The literature on the conceptualisation and importance
of digital skills is thus broad.
Digital skills and related concepts, such as digital competence, have become key terms
in the discussion on the kind of skills needed by citizens – in Europe and beyond – to
participate and thrive in our society (European Commission 2010; Ferrari 2012;
Gallardo-Echenique et al. 2015), not only in terms of citizens’ social and digital
inclusion, but also in terms of employability and economic growth (European
Commission 2016; Ferrari 2013; Kolding, Robinson, & Ahorlu 2009; Lavin & Kralik
2009; Lanvin & Bassman 2008; Vuorikari et al. 2016).
Despite the overall agreement on the importance of digital skills, no common definition
has been agreed upon, with different terms and interpretations of the content of digital
competence and of the skills, knowledge and abilities it implies. Different terms have
been used over time with a more or less broad scope to identify skills linked to the use
and understanding of ICT (Gallardo-Echenique et al. 201; Ilomaki, Kanotsalo, & Lakkala
2011)7. These concepts are used in policy documents, academic literature and learning
practices to refer to more or less sophisticated skills and to different groups of users,
ranging from the general public to ICT professionals.
The evolution and diversity of definitions used is linked to several factors. The difficulty
in reaching one common definition stems first of all from the fluidity of the concept,
which is continuously expanding and changing as a consequence of the rapid evolution
of information and digital technologies and their use over time (Mutka 2011). The broad
range of terms and definitions used is not however only linked to societal and
technological change, but also reflects different research interests and aims across the
literature Ilomaki, Kanotsalo & Lakkala 2011).
The first definitions used in relation to this type of skills referred to ‘computer or ICT
literacy’ as declarative and procedural knowledge about computer use (Fraillon, Schulz
& Ainley 2013). With time, as technologies have embedded more complex
functionalities (so that they can be used for more advanced purposes) and the use of
ICT has evolved, not least through the internet, broader definitions have emerged, to
reflect the more pervasive and encompassing role that these concepts currently have
(Lavin & Kralik 2009; Mutka 2011). Even though agreement on a definition still does
not exist, a common trend is identifiable in the literature of the past decade, which is
the expansion of the scope of what is meant by digital skills (Lavin & Kralik 2009).
In 2006, European institutions defined digital competence as "the confident and critical
use of ICT for work, leisure, learning and communication", recognising it as one of eight
key competences for lifelong learning (European Parliament and Council of the EU
2006). This definition implies that digital competence does not only encompass the
operational usage of digital devices, but also cognitive skills and attitudes (Martin 2006;
Mutka 2011). It furthers implies that digital competence is a transversal competence
7 For a more detailed overview of how different concepts have been used, see Ilomaki, Kanotsalo, & Lakkala (2011); Gallardo-Echenique et al. (2015); Ecorys (2016).
18
which enables individuals to acquire other competences (Ferrari 2012).8 While this term
has been broadly used in policy documents and academic literature, both in a
descriptive and normative manner, it is still not considered a particularly stable notion
(Ferrari 2012; Ferrari, Punie, & Redecker 2012; Ilomäki, Kantosalo & Lakkala 2011).
Several studies have attempted to operationalise the concept of digital skills, by
identifying the components and elements it consists of and describing in practice the
skills it includes (Eshet-Alkalai 2004; Eshet-Alkalai & Chajut 2010; Ferrari 2012; Martin
2006; OECD 2013). The objective, in many instances, was to allow for an assessment
or a measurement of this competence and to enhance the understanding and the
development of digital competence among citizens, students or other segments of the
population (Ferrari 2012; Ferrari, Punie, & Redecker 2012). The conceptual and
practical frameworks developed have been used for both policy-making purposes and
for the development and assessment of certification and learning practices (see Ferrari,
2012 for an overview). The Digital Agenda for Europe for example, which envisaged the
development of "EU-wide indicators of digital competence and media literacy", led to
the development of the DigComp framework on digital competence (European
Commission 2010a) which was designed to help policymakers formulate appropriate
education and lifelong learning policies.
Overall, digital competence and skills include a range of inter-related concepts (Ecorys
2016). Examining the literature, it is possible to identify three main categories of digital
skills, which are replicated in various ways in different frameworks for the measurement
or development of digital competence. These three categories apply to different types of
abilities, linked to the capacity to carry out tasks of increasing complexity or specialised
nature, thus also applying to different type of users:
a) Basic digital literacy skills empower individuals to become digitally literate; these
skills can be applied both to the workforce and generally to individuals in knowledge
society;
8 Similar definitions are provided by scholars, using terms such as information literacy or digital literacy (Martin 2006)
Digital Competence Framework for Citizens (DigComp)
The Digital Competence Framework for Citizens (DigComp) was published in
2013 by the European Commission. It is a tool to “improve citizens’ digital
competence, help policy-makers formulate policies that support digital competence
building, and plan education and training initiatives to improve the digital competence
of specific target groups” (Vuorikari et al. 2016).
DigComp is based on four dimensions. Dimension 1 and 2 represent a conceptual
reference model identifying the areas to be part of digital competence and the
competence descriptors that belong to each area. Dimensions 3 and 4 relate to the
levels of proficiency for each competence and to examples of knowledge and skills
applicable to the competences (Ferrari 2013). The framework builds on a task based
approach. It can be used to measure digital competence across Europe (European
Commission 2014).
19
b) Digital skills which relate to employment, encompassing basic skills plus skills
which are needed in a workplace and generally are linked to the use of ICT
applications developed by professionals of information technology;
c) Digital skills for ICT professions, which include both categories above and the
skills needed in the ICT sector as well as having an innovative and creating
component, as linked to the ability to develop new digital solutions, products or
services.
Among the frameworks which have operationalised the definition of digital skills, a
number have focused specifically on the skills needed in the workplace, focusing on the
digital skills needed by the workforce and employed as drivers of employability, growth
and competiveness.
Some of these frameworks have only looked at the skills of ICT workforce. The
definition set out by the European e-Skills Forum for example aims to classify the e-
skills of the workforce, with a specific focus on ICT practitioners, to better define the e-
skills gap and policy initiatives to address it across Europe. Building on this framework,
several other works have described the development of e-skills demand and supply with
regard to ICT workforce (Empirica 2009; Empirica 2015). The conceptual framework
distinguishes between ICT user skills, ICT practitioner skills and e-business skills (see
below).
Two other approaches have been developed with the aim of investigating and assessing
the digital skill level of the overall workforce. Cedefop (2015) distinguishes between
basic, moderate and advanced ICT skills, which relate to different levels of competence
and type of tasks carried out by the worker.
European e-Skills Forum – 2004
In 2004, the European e-Skills Forum adopted a definition of the term ‘e-skills’. The
term was then used by the European Commission to respond to the growing demand
for highly-skilled ICT practitioners and users (European Commission 2007; Gallardo-
Echenique et al 2015). The classification put forward distinguished between:
ICT user skills, required for effective use of ICT systems and devices; “ICT users
apply systems as tools in support of their own work, which is, in most cases, not
ICT”;
ICT practitioner skills, required for researching, managing, developing and
designing, consulting, marketing and selling, integrating, installing and
administering, maintaining, servicing ICT systems;
E-business skills (or e-leadership skills), needed to exploit opportunities
provided by ICT, producing more efficient and effective performance of different
types of organisations, exploring possibilities for new ways of conducting business
and organisational processes, establishing new enterprises.
20
Similarly, the OECD (2004) identifies basic users, advanced users and ICT specialists,
following a task-based approach with regard to the employed workforce. More recently,
the organisation has adopted the concept of generic, specialist and complementary
skills to identify the three main lines along which new digital skills are required (OECD
2016).
The OECD Programme for the International Assessment of Adult Competencies (PIAAC)
survey builds on this distinction and offers a direct measurement of adult digital skills.
It has been used to measure the demand for ICT skills at work (OECD 2015), and to
measure and assess level of skills and mismatches in the workplace (Pellizzari, Biagi &
Brecko 2015)9.
9 “The survey provides a rich source of data on adults’ proficiency in literacy, numeracy and problem solving in technology-rich environments – the key information-processing skills that are invaluable in 21st-century economies – and in various “generic” skills, such as co-operation, communication, and organising one’s time.” (OECD, 2013)
The CEDEFOP ESJ Survey
The Cedefop European Skills and Jobs (ESJ) survey (2015) analyses digital skills by
identifying three bundles of ICT skills:
Basic ICT skills: using a PC, tablet or mobile device for email, internet browsing;
Moderate ICT skills: Word-processing, using or creating documents and/or
spreadsheets;
Advanced ICT skills: Developing software, applications or programming; use
computer syntax or statistical analysis packages.
OECD’s Skills for the Digital Economy
The OECD (2004) suggests a distinction between:
ICT specialists: user whose competences cover the “ability to develop, operate
and maintain ICT systems”. ICTs make up for the main part of their job;
Advanced users: this group of users are “competent users of advanced, and
often sector-specific, software tools”. ICT is a tool in a workplace context;
Basic users: basic users are “competent users of generic tools (e.g. office
software, e-mailing and other internet-related tools) needed for the information
society, e-government and working life”.
The OECD (2016) introduced another classification, referring to ICT generic,
complementary and specialist skills. More specifically, “ICT specialist skills [are those
necessary] to programme, develop applications and manage networks; ICT generic
skills to use such technologies for professional purposes; ICT complementary skills to
perform new tasks associated to the use of ICTs at work, e.g. communicate on social
networks, brand products on e-commerce platforms or analyse big data” (OECD
2016, p.5). Similar to e-business skills, complementary skills are thus “skills that are
not related to the capability to use the technology effectively but to carry out the
work within the new environment shaped by ICTs, i.e.: a “technology-rich
environment”” (ibid, p.7).
21
At the national level, the Canadian study “Defining Essential Digital Skills in the
Canadian Workplace” (WDM Consultants 2011) is an example of a sector-wide digital
skill framework which focuses on digital skills in the workplace.
In terms of methods, Van Deursen, Helsper, & Eynon (2014) identify three main basic
methodologies employed to investigate levels of digital skills, describing the main
pitfalls and benefits of each method:
Surveys with questions that are assumed to deliver indirect evidence for
the command of skills: this method is very common with large benchmark
surveys, with the one downfall of not clearly identifying the relation between the use
of an application and the skill;
Surveys with questions that request self-assessments of skills: this is the
most used method, which however can originate bias in terms of overrating and
underrating skills;
Performance tests: these are considered the most reliable methods in terms of
internal validity, but have the main problem of being very costly, time consuming
and difficult to implement on large scale.
1.4 Digital skills challenges
Given the recognised importance of digital skills for an effective implementation of ICT
in the workplace, the main challenge for employers is to make sure that the supply of
digital skills within the workforce matches its demand at the labour market and
enterprise level, in terms of both required type and level of skills. In other words, the
digital skill challenge for businesses is twofold. It can relate to the lack of workers with
the needed skills in the labour market (skills shortages), or to the presence of
overskilled or underskilled workers at company level (skills mismatches and skills gaps).
Challenges can therefore be external to the firm (as in the case of shortages) or internal
(as in the case of mismatches or gaps).
Cedefop (2015a) has provided relevant definitions that are useful to better understand
the digital skills challenges that employers can face, although they do not refer directly
to digital skills but more generally to skills and thus need to be adapted to the specific
reality of digital skills.
Defining Essential Digital Skills in the Canadian Workplace
The framework is based on a survey among a sample of SMEs drawn from various
industry sectors across the country and defines digital skills as a multifaceted
concept which encapsulates four skill clusters: (1) Digital Technical Skills; (2)
Digital Information Processing Skills; (3) Foundational Skills; and (4) Transversal
Skills.
22
A number of academic studies and policy-oriented studies have focused on the supply
and demand of digital skills in the economy, relating to the workforce as a whole, or
more specifically among ICT professionals.
Most studies focus on the shortage of a skilled workforce, pointing at a mismatch
between demand and supply of ICT professionals, both in terms of shortages and skill
mismatches (CEPIS 2014; Empirica 2014, 2015; Hüsing, Werner & Dashja 2015; UK
Digital Skills Taskforce 2014) and an overall shortage of digital skills in the labour
market (Burning Glass Technologies 2015, European Commission 2016). These studies
normally relate to digital skills at the labour market level, focusing on their impact in
terms of overall economic trends, mainly identifying a loss of potential economic growth
as a consequence of the insufficient supply of workers with the right skillset across the
European Union (CEPIS 2014). A smaller number of studies focus on the impact of
shortages at the company level, as a barrier to digital transformation (Capgemini
Consulting 2013) and in terms of mitigating strategies (Ramboll 2014). At individual
level, studies mainly report the importance of this type of skills in terms of
employability as “survival” (Eshet-Alkalai 2004) or “gateway skills” (Van Deursen A.
2010).
Similarly, both the academic literature and policy-oriented research on skill matching
have been largely focused on the problem of overskilling (Cedefop 2015b; Livanos &
Nunez 2016; McGuinness & Ortiz 2016), well-documenting the negative effects of the
phenomenon for both firms and workers (Green & Zhu 2010).
Skills shortages, mismatches and gaps
Skill shortages are usually defined as “instances when the demand for a particular
skill exceeds the supply of available people with that skill at market-clearing wage
rates” (Cedefop 2015a, p.24). This means that a shortage exists when “there are not
enough individuals with the required skills within the economy to fill existing
vacancies at market-clearing wages” (ibid., p. 26).
Skill mismatches on the other hand refer to those situations in which there is a
“(qualitative) discrepancy between the qualifications and skills that individuals
possess and those that are needed by the labour market” (ibid, p. 27). This means
that skills mismatches relate to situations in which the workforce is overskilled or
underskilled when compared to the demand of skills in the labour market and in the
enterprise. As such, the concept is broader than that of skills gap or shortages.
Skills gaps finally are to be interpreted as situations where “the level of skills of the
existing workforce in a firm is less than required to perform a job adequately or to
match the requirements of a job” (ibid, p.27). This definition is important as it allows
us to understand what the focus of the study is. We are looking at a situation in
which the workforce is underskilled at the level of the enterprise. This concept
thus concerns the employed (differently from skills shortages which normally relate
to employers’ difficulty to hire someone who has the skills required for the vacant
job).
23
Fewer studies, on the other hand, deal with digital kills gaps specifically, thus referring
to the workforce employed at the company level and the impact which digital
underskilling can have on the performance of the organisation, leaving a research gap
which this study aims to fill (Pellizzari, Biagi & Brecko 2015).
Overall these studies point out that, as well as the growing demand for this type of
skills, a large part of the workforce still has insufficient digital skills. According to the
European Commission's Digital Scoreboard, for example, in 2016 37 % of the EU labour
force and around 45 % of the EU population had insufficient digital skills (low or no
digital skills at all), , with significant differences across Europe.10 In the UK, the House
of Commons (2016) reported that digital skills shortages persist at all stages in the
education and training pipeline, from schools to the workplace. Different factors can
account for this shortage. These include structural changes, such as the adoption of
new technology, since the educational and lifelong learning systems need time to
produce skills which can keep up with technological change (Quintini 2011). Similarly, a
lack of investment in upskilling employees, out-of-date training systems,
inability/difficulty in attracting workers with adequate skills (also financial), and
information asymmetries can also be influencing factors (Livianos & Nunez 2016).
In terms of impact, the adverse effects of digital skills gaps (underskilling) are
evidenced at both aggregated, company and individual level (OECD 2015). The
problems arising from skills gaps are very similar to those arising from overskilling, i.e.
overall lower productivity, lower salaries or higher risk of being fired for the employee
(Livianos & Nunez 2016). At the aggregated level, the literature shows how the overall
economic output is linked to how well workers can carry out their tasks (OECD 2015).
Economic growth can be negatively affected because of skill shortages at the labour
market level and because of skills gaps within companies. The cost of skills mismatches
has become a particular concern for policymakers nationally and internationally (OECD
2015; ILO 2008; European Commission 2016).
At the company level, skills gaps cause a loss in terms of productivity, increasing labour
costs, slowing growth and adaptation of new technologies down (OECD 2012; OECD
2015; Idea Consult, AIAS/UvA, Ecorys, Wifo, 2015). Organizations are beginning to
recognise the scope of their problem. In Comptia’s IT Skills Gap Report for 2014, IT
skills gaps remain one of the main challenges, with 58% of companies reporting
concerns about the quality and quantity of IT talents available to them (Comptia 2014).
A study carried out by Capgemini (2013) in collaboration with the MIT Center for Digital
Business showed that 77% of companies perceived missing digital skills within their
organisation as the key obstacle to their digital transformation. Over 90% of companies
surveyed on this occasion stated that they did not have the necessary “skills in the
areas of social media, mobile, internal social networks, process automation and
performance monitoring and analysis” (ibid). However, only 46% were investing in the
development of digital skills (ibid). Similarly, in the UK, the House of Commons (2016)
reported that organisations were “not maximising the potential of new digital
technologies or utilising the skills and talents of their employees in the most productive
way” (House of Commons 2016), and that almost 50% of employers had a digital skills
gap (ibid.). The study stated that the economic impact of these gaps was clear, with
10 See https://ec.europa.eu/digital-single-market/en/news/new-comprehensive-digital-skills-indicator (26/01/2017). The Digital Agenda Scoreboard applies a newly constructed Digital Skills Indicator, which is based on the Digital Competence Framework developed by DG EAC and IPTS.
24
SMEs primarily losing out, and thus making it “imperative for businesses to develop the
digital skills of their employees’ being this ‘now a matter of survival” (House of
Commons 2016).
The literature clearly points out that firms may find it more problematic to mitigate
underskilling, compared to overskilling, as this normally requires investing in training
(Cedefop 2016; Livanos & Nunez 2016). Optimal training decisions however require
employers to have accurate information about their workers’ needs (McGuinness & Ortiz
2016). It is therefore becoming evident why developing methods to test and measure
these skills are important.
1.5 The digital skills challenge: policies and solutions
Two approaches can be identified in the literature on how to respond to the digital skills
challenge as described in the previous section: firstly, the need to intervene on the
educational system and, secondly, the need to introduce training for the current
workforce.
Regarding the first point, some authors point to the need to intervene on the
educational system in order to make sure that the required digital skills are available on
the labour market and among the general population. As indicated by Berger and Frey
(2016), “the educational system should be aligned to provide students with basic digital
and ICT skills. Such initiatives are currently being undertaken in several European
countries. […] Integrating digital skills in the curriculum early on will be crucial for
maintaining a competitive labour market in the future” (Berger and Frey 2016).
However, as recognised by the same authors, a narrow focus on digital skills will not be
sufficient, as “the digital skills of today are likely to be obsolete sooner than we may
think” (Berger and Frey 2016) and therefore it is advisable to provide (future) workers
with integrated skillsets of technical, creative and social skills, as “analytical and
creative capabilities will be the core ingredients of successful careers in the future”
(Meyer 2016), with this being more likely to meet employers’ needs .Educational
systems have a strategic part to play to tackle the digital skills challenge, as do
governments and policymakers, as indicated for example by the launch of the European
Commission’s ‘Grand Coalition for Digital Jobs 2013-2016’ “which emphasises the need
for embedding basic ICT training throughout the European educational system” (Berger
and Frey 2016)11.
With reference to the second approach mentioned above, Schwab (2016) suggests
focusing also on today’s workforce, as “while much has been said about the need for
reform in basic education, it is simply not possible to weather the current technological
revolution by waiting for the next generation’s workforce to become better prepared.
Instead it is critical that businesses take an active role in supporting their current
workforces through re-training, that individuals take a proactive approach to their own
lifelong learning and that governments create the enabling environment, rapidly and
creatively, to assist these efforts” (Schwab 2016).
Training aimed at fostering digital competence and digital skills is commonly believed to
be the most appropriate tool to tackle the digital challenge. As pointed out by the
11 https://ec.europa.eu/digital-single-market/en//digital-skills-jobs-coalition
25
OECD, several longitudinal studies have demonstrated that training (in particular on-
the-job training) is “an appropriate policy response to cope with the rapid pace of
technological change that characterises the digital economy” (OECD 2015). In addition,
while on-the-job training is a key policy to maximise skills utilisation and contrast skills
obsolescence, “existing practices at firm level show that firms themselves have great
incentives to provide employees with continuous professional training if they want to
innovate and remain competitive in the market” (OECD 2015).
Challenges are reported to remain regarding the training participation gap that exists in
most countries between “the less-qualified and the more-qualified, prime age and older
workers, those in large and small and medium enterprises” (OECD 2015).
Technological change, combined with the long period out of formal education, requires
older workers in particular to update skills. As displayed by empirical evidence, on-the-
job training increases the proportion of older workers in employment and reduces their
turnover, although these effects do not compensate for the negative age bias
associated with digital technologies (Behangel et al. 2014). Older workers in low-skilled
occupations are reported to have even less opportunities to access training on digital
technologies than older workers with higher skills (Behangel and Greenan 2012).
Workers employed in small and medium enterprises are also considered less likely to
access training to develop digital skills. As pointed out by the OECD (2015) this is due
on the one hand to the fact that small and medium enterprises “lack sufficient resources
to develop training programmes; on the other, training providers have not sufficiently
developed training content specific to the needs of SMEs” (OECD 2015).
In order to make training more widely available, two main types of initiatives are
envisaged, , fully state-funded initiatives to provide training and skills development
programmes based on the example of Korea or Ireland (OECD 2015) and/or the
creation of multi-stakeholder consortia at the sector, local or industry level to share
training costs for the workforce, as been successfully implemented in some countries
(OECD 2015). Multi-stakeholder partnerships (e.g. governments, training providers,
employers and social partners) are considered as contributing to the building of
capacity, and in order to promote a more inclusive and targeted approach to skills
development, as they “encourage training provision that is more responsive to labour
market needs, familiarise with vocational programmes and qualifications and help
vocational trainers to keep up-to-date” (OECD 2015). Also, collaboration between
businesses is seen as a possible way to enhance the provision of training. As pointed
out by Schwab (2016), “business collaboration within industries to create larger pools of
skilled talent will become indispensable, as will multi-sector skilling partnerships that
leverage the very same collaborative models that underpin many of the technology-
driven business changes underway today” (Schwab 2016).
1.6 Purpose and scope of this study
In the context described in the previous sections, this study on ‘ICT for work: Digital
skills in the workplace’ has been conceptualised and implemented in order to examine
the transformation of jobs in the digital economy in the European Union, investigating
the penetration of digital technologies into workplaces, the digital skills required by
employers and the digital skills currently available in workplaces.
26
In this light, a number of research tasks aimed at gathering primary data on digital
skills and digital skill gaps have been carried out, primarily through an employers’
survey, and through qualitative interviews on the impact of ICT on job quality. Rooted
in an extensive review of academic and scientific literature, existing surveys and data
from other statistical sources, the study aims to fill existing research gaps.
This report mostly presents findings from the employers’ survey (the ‘European
European Digital Skills Survey’), integrated where relevant with findings from the
qualitative analysis of the impact of digital technologies on job quality and working
conditions, presented in a separate report “The impact of ICT on job quality: evidence
from 12 job profiles”12.
In order to fill the research gaps identified, the survey was designed to answer the
following research questions:
How many jobs in the EU require digital skills?
What types of digital skills are the most required by employers?
How can the jobs requiring digital skills be classified according to the level of digital
skills required?
What are the differences across EU Member States, economic sectors and
occupations in terms of digital skills required by employers?
What are the most common digital skill gaps in workforce according to employers?
What are the differences across economic sectors and occupations in terms of digital
skill gaps?
What are the actions undertaken by employers to address existing digital skill gaps?
What are the differences across economic sectors and occupations in terms of
actions undertaken by the employers to address the lack of digital skills?
The specific objectives of the survey were to:
Quantify the jobs, in the EU that require digital skills by economic sector;
Provide evidence on the level/type of digital skills required by different jobs and in
different sectors;
Identify the main digital skills gaps in different occupational categories and
economic sectors;
Investigate how employers deal with ICT/digital skills gaps (e.g. providing training,
out-sourcing);
Examine the main bottlenecks/barriers to improved availability of digital skills.
The related variables of interest to investigate were:
Types and level of digital skills existing by occupational category;
Types and level of digital skills gaps by occupational category;
Actions taken by employers to deal with digital skills gaps;
Types of barriers to improved availability of digital skills by occupational categories.
12 Ecorys and Danish Technological Institute (2016) available at https://ec.europa.eu/digital-single-market/en/news/report-shows-digital-skills-are-required-across-all-types-work-also-jobs-outside-office
27
1.7 Overview of methodological approach
The survey was carried out on a sample of 7,800 workplaces, representative of
13,803,113 workplaces across the European Union in the following 12 economic sectors
(in bold):
A - Agriculture, forestry and fishing
B - Mining and quarrying
C - Manufacturing
D - Electricity, gas, steam and air conditioning supply
E - Water supply; sewerage; waste management and remediation activities
F - Construction
G - Wholesale and retail trade; repair of motor vehicles and motorcycles
H - Transporting and storage
I - Accommodation and food service activities
J - Information and communication
K - Financial and insurance activities
M - Professional, scientific and technical activities
N - Administrative and support service activities
O - Public administration and defence; compulsory social security
P - Education
Q - Human health and social work activities
R - Arts, entertainment and recreation
S - Other services activities
T - Activities of households as employers
U - Activities of extraterritorial organisations and bodies
The survey was carried out on six countries, selected according to their level of
digitisation to represent the European Union as a whole. The countries covered are
Finland, Germany, Portugal, Slovakia, Sweden and the United Kingdom.
A two-stage, stratified sampling design was used to select and include workplaces in the
sample. In particular, the primary sampling units were the countries, the secondary
sampling units were the workplaces and the stratification variables were the economic
sector and the workplaces size. To make sure that results could be correctly inferred to
the general target population, calibration estimators were employed, and the
parameters of interest were analysed both at sampled countries level and at EU28 level.
In particular, with the aim of producing estimations both at EU and sampled countries
level, two different calibration weights were calculated. The first allowed the estimation
28
of the amount and the characteristics of workplaces falling in the selected economic
sectors at EU level. The second allowed the estimation of the workplaces in each
country included in the sample.
Using the survey data, it was possible to produce statistics, both at EU and sampled
country level, on the structure and characteristics of workplaces, characteristics of
establishments’ workforce, types of occupations, type and level of digital skills, type and
level of digital skill gaps, and actions and related barriers to reduce digital skill gaps.
Survey data also allowed for the analysis, at EU level, of some specific characteristics of
certain occupations selected by the respondents as being amongst the most relevant for
the day-to-day activities of their business.
All the analyses were carried out at EU level and, wherever possible, estimations were
carried out for individual Member States. Descriptive and multivariate analyses were
carried out. Composite indicators of digital skills, which summarise different pieces of
elementary information describing digital skills, were calculated using appropriate
statistical procedures.
Data were collected using a mixed web and phone approach to boost the response rate
and increase participation, based on a questionnaire designed to meet project
requirements. For each workplace, the respondent was selected among the members of
staff with the best overview of working tasks, normally the human resources manager
or the managing director.
This circumstance is however one of the main limitations of this survey in terms of
accuracy of the information collected, common to all the employers’ surveys which
investigate employees’ skills, attitudes or behaviours. Indeed in employers’ surveys the
selected respondents do not report individual/personal information regarding
themselves, but report information about characteristics of their employees or sub-
groups of them (e.g. the level of skills possessed). Therefore, the reported information
reflects the respondents’ assessment of a perceived characteristic possessed on average
by employees (or sub-groups of employees). This is an issue particularly in large-sized
workplaces, where the human resources manager is requested to respond with
reference to a significant number of employees and can result in a response bias (e.g.
inaccurate answers) or in a high number of missing responses (e.g. “don’t know”). An
additional limitation of this specific survey – despite the rigorous sampling strategy -
comes from the limited sample size, which ensures a high level of representativeness
for the planned domains of study, but does not allow for more granular analyses of
results.
Conversely, the main methodological strength of this survey is the robust sampling
design and estimation phase (calibration weights) adopted, which allows highly reliable
and EU28-level representative results in spite of the limited funds available to carry it
out. In particular, thanks to the calibration procedure, data collected from workplaces
(and their employees) in six Member States can be inferred to workplaces in the whole
EU28: the structure of workplaces and the distribution of employees, in terms of
economic sector and the workplaces size, are constrained to known values proceeding
from official statistics. In other words, results from 7,800 interviews can be inferred to
29
13,803,113 workplaces and 150,563,540 employees in 12 selected economic sectors in
the whole European Union (EU28).
The full survey methodology, including the characteristics of the sample and target
population and the survey questionnaire, are set out in detail in Annex 1.
The findings from both the survey and the qualitative analysis of specific job profiles
was presented and validated by experts and stakeholders in a one-day workshop
(Section 6.2), which allowed identification of the main areas of concern and the policy
recommendations reported in Section 6.3.
The study was carried out by a research team led by Maurizio Curtarelli, which included
researchers from Ecorys (Vicki Donlevy, Maryam Shater Jannati, Mike Blakemore,
Elizabeth Kwaw), the Danish Technological Institute (Martin Eggert Hansen, Hanna
Shapiro, Gwendolyn Carpenter), and a Statistical Expert (Valentina Gualtieri, Head of
Statistical Service, National Institute for the Evaluation of Public Policies). The team was
supported by a Scientific Committee (Irene Mandl, Head of Unit - Employment,
Eurofound; Ferràn Mañé, Associate Professor, University Rovira i Virgili of Tarragona;
Graham Vickery, former Head of OECD Information Economy Group), and the study
benefitted as well from inputs from experts at the validation stage (Barbara
Gestenberger, Head of Unit – Working Life, Eurofound; Konstantinos Pouliakas,
Department for Skills and Labour Market, Cedefop; Katerina Ananiadou, Division for
Policies and Lifelong Learning Systems, UNESCO).
1.8 Structure of the report
In order to reach the aims illustrated above, the report is structured as follows:
This first chapter has presented the available evidence and the discussion around
the digitisation of the economy and its impacts on the labour market in terms of job
destruction, job creation and job transformation. Furthermore it has considered the
implications and challenges for employers in terms of digital skills needs. In this
context, the chapter sets out the main aims of the study and of the employers’
survey, the research questions, and a brief overview of the survey methodology.
Chapter 2 presents and analyses the characteristics of the workplaces in the
economic sectors covered by the survey and their workforce, in order to set the
context for later responses to the research questions.
Chapter 3 discusses the results of the survey in relation to the use of digital
technologies in European workplaces, and presents the investment strategies in ICT
of workplaces across the European Union.
Chapter 4 sets out the results from the survey in relation to the type and level of
digital skills required in specific occupations in workplaces across the European
Union, including a focus on specific jobs requiring digital skills.
Chapter 5 analyses one of the core aspects of this survey. Digital skill gaps (defined
as the proportion of workers not fully proficient to carry out tasks involving the use
of digital technologies) are calculated and presented across different types of digital
skills and different occupations in European Union workplaces. The chapter also
presents the perceived impacts of digital skills gaps on business performance, the
30
actions undertaken to tackle digital skill gaps in the existing workforce and the main
difficulties encountered when taking action to address gaps.
Chapter 6 presents conclusions and recommendations, which have been enriched
with findings from consultation of relevant stakeholders.
In addition:
Annex 1 presents the detailed survey methodology.
Annex 2 contains a number of statistical tables and charts which complement the
data presented in specific sections of this report.
Annex 3 details the bibliographic references for this study.
31
CHAPTER 2. WORKPLACES’ FEATURES IN THE EUROPEAN UNION:
DESCRIBING THE SURVEY POPULATION
This chapter presents key information about the target population of the European
Digital Skills Survey. . In particular, the chapter describes the main characteristics of
the workplaces in terms of sector and size, organisational structure, type of ownership
and main market of reference. The characteristics of the workforce currently employed
in such workplaces are also analysed (looking at gender, age, level of education), with a
specific focus on the existing occupations in workplaces.
This information is then used in the remainder of the report, in combination with all the
information gathered, to present results on the type and level of required digital skills,
on existing digital skill gaps and on the action undertaken to tackle gaps in European
workplaces.
2.1 Profile of workplaces
The European Digital Skills Survey was carried out among a representative sample of
7,800 workplaces in six EU member states (Germany, Finland, United Kingdom,
Portugal, Sweden and Slovakia), which are statistically representative of 4,295,345
workplaces in the six countries as a whole, and of 13,803,113 workplaces in the whole
Europe Union (EU28). Such workplaces operate in 12 economic sectors with different
levels of digital intensity identified by the respective NACE 1-digit code:
A. Agriculture, forestry and fishing;
C. Manufacturing;
D. Electricity, gas, steam and air conditioning supply;
F. Construction;
G. Wholesale and retail trade; repair of motor vehicles and motorcycles;
H. Transportation and storage;
I. Accommodation and food service activities;
J. Information and communication;
M. Professional, scientific and technical activities;
N. Administrative and support service activities;
P. Education;
Q. Human health and social work activities.
Grouping such sectors for issues of representativeness in six macro-sectors, the figure
below (Figure 2.1) displays the distribution of workplaces by macro-sector in the EU28
as a whole.
32
The vast majority of the workplaces surveyed operate in the services; about six
workplaces out of ten belong to the macro-sectors ‘Commerce, transport and
accommodation’, ‘Information and communication’; ‘Professional, scientific and
technical activities’; ‘Administrative and support services’ and, ‘Education and human
health’. Around two workplaces out of ten are active in Agriculture, and roughly one
workplace out of ten in the Manufacturing sector and also in the Construction sector
(Figure 2.1).
Figure 2.1 – Workplaces by economic sector groupings, EU28 (%)
Q10. What is the main area of activity of your workplace? Note: totals from the European Digital Skills Survey correspond mathematically to those from Eurostat Structural Business Statistics, Business Demography Statistics and Farm Structure Statistics (2013) regarding the distribution of workplaces. As a result of the calculation of sample weights according to the calibration procedure as described in Annex One. Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values), Eurostat (2013)
22.2
9.2
10.4
33.6
17.0
7.6 A. Agriculture
CD. Manufacturing and utilities
F. Construction
GHI. Commerce, transport, accommodation andfood service
JMN. Information and communication;professional, scientific and technical activities;Administrative services
PQ. Education and human health
33
About eight out of ten workplaces in the EU28 employ between two to nine employees
and therefore fall in the category of micro-sized workplaces. A much smaller proportion
of workplaces (15%) in the selected sectors have 10 to 49 employees (categorized as
small-sized workplaces). Less than 4% of workplaces fall in either the medium or large
categories: less than 3% of workplaces are in the EU28 medium-sized (50 to 49
employees) and only 0.5% are large-sized (250 employees and more) (Figure 2.2).
Figure 2.2 – Workplaces by size (expressed as number of employees), EU28 (%)
Q6: In total and including yourself, approximately how many employees work in THIS workplace? Q7: Could you please give your best estimate using the following categories? (2-9, 10-49, 50-249, 250+) Note: totals from the European Digital Skills Survey correspond mathematically to those from Eurostat Structural Business Statistics, Business Demography Statistics and Farm Structure Statistics (2013) with regards to the distribution of workplaces, as a result of the calculation of sample weights according to the calibration procedure as described in Annex One. Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values), Eurostat (2013)
The six countries surveyed display proportions at country level which are roughly in line
with the EU average, with the remarkable exception of Germany, where only 62% of
establishments fall in the group 2-9 employees, 29% are classified as small and around
8% of workplaces (more than double than the EU28 average) have 50-249 employees.
Portugal displays a higher than average incidence of micro establishments (89%) and a
relatively smaller proportion of establishments falling in the 10-49 and 50-249
employee categories (9% and 2% respectively).
Detailed tables by sector and size, at country level and for the EU28 as a whole,
reporting both figures and percentages are displayed in Annex 2 (Tables A2.1 and
A2.2).
81.6
15.1
2.9 0.5
2-9
10-49
50 - 249
250 +
34
In terms of organisational structure, the vast majority (86%) of EU workplaces do not
belong to a wider group of workplaces, but are a single office/plant (Figure 2.3). This
breakdown is relatively consistent across the six sampled countries, although small
differences can be observed. These range from Portugal where 94% of the workplaces
are a single-office workplace, to Slovakia where the equivalent figure is 82%.
Figure 2.3 – Workplaces by type of organizational structure in sampled countries and EU28 (%)
Q2: Is your workplace one of many different workplaces belonging to the same organisation, or is it the only workplace your organisation has? Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)
14.2
85.6
0.2
Workplaces belonging to a group Single office Don't know
35
Of the 14% of workplaces belonging to a wider group, almost 70% are the
headquarters of the group, while the rest are a subsidiary site (Figure 2.4). A marked
variability, mostly related to the average size of workplaces in selected sectors, is
displayed at country level (Figure A2.2 in Annex 2).
Figure 2.4 – Workplaces belonging to a group by role within the group in sampled countries and EU28 (%)
Q3: Is your workplace the headquarters of your organisation, or is it a subsidiary site? Number of valid responses: 1,893 N=1,979,446 Source: European Digital Skills Survey (weighted values)
69.5
28.9
1.6
Headquarters Subsidiary site Don't know
36
In terms of ownership (public or private-owned workplaces), at EU level the vast
majority of workplaces (almost 95%) are private sector (Figure 2.5). At country level
this breakdown does not vary significantly from the EU average. It is important to point
out that these figures reflect the exclusion from the survey’s population, as mentioned
in section 1.7 and 2.1 and extensively in the methodology, of workplaces belonging to
NACE sector O - Public administration and defence; compulsory social security, which
has a significant proportion of workplaces belonging to the public sector.
Figure 2.5 - Workplaces by sector (public/private) in sampled countries and EU28 (%)
Q9. A public sector organisation is either wholly owned by the public authorities or they own more than 50%. Is your workplace part of…(the private sector/the public sector/don’t know) Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)
4.5
94.7
0.8
Public sector Private sector Don't know
37
The survey identifies the main market of reference for EU28 workplaces. The majority
operate or trade in local and regional markets (65%) and to a lesser extent in national
and international markets (21% and 13% respectively) (Figure 2.6).
Figure 2.6 - Workplaces by main market of reference, in sampled countries and EU28 (%)
Q11. Which of the following markets is most important for the main activity of your workplace? Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)
The data shows a correlation between market of reference and size and sector of
operation. In particular, micro-sized workplaces are more frequently operating in local
and regional markets (40% and 31%) while small and medium-sized workplaces are
more frequently active in national (28% each) and international markets (27% and
26% respectively). Large-sized workplaces are most likely to operate at international
level (40%) (Table A2.3 in Annex 2).
Results at country level are broadly in line with the EU figure, with the exception of
Sweden, with two thirds of workplaces operating in the local market (66%) and
relatively smaller proportions of workplaces trading in national and international
markets. Workplaces in Slovakia, Germany and Portugal trade more frequently in the
national market, while in Germany and Portugal much higher proportions of workplaces
trade internationally.
2.2 Profile of workforce
The 13,803,113 European workplaces (EU28) described above employ a total of
150,563,540 employees. Gender, age, level of education and occupational composition
of the workforce is explored below.
36.6
29.0
21.3
13.0
0.2
Local Regional National International Don't Know
38
Overall around 39% of employees are women, 20% are older than 50 years of age and
19% are younger than 30 years of age. Over a quarter of employees (27%) hold a
university degree (Figure 2.7).
Figure 2.7 – Employees by specific characteristics in workplaces in the EU by sector and
size (% of total employees)
Q8: Could you please indicate, for this workplace, the number or percentage of employees who…(are female/have a university degree, are younger than 30 years of age/are older than 50 years of age) Number of valid responses: 6,917 (female rate); 6,334 (university degree rate); 6,114 (younger than 30 rate); 6,245 (older than 30 rate) N=12,269,195 (female rate); N=11,596,077 (university degree rate); N=11,365,645 (younger than 30 rate); N=11,829,617 (older than 30 rate) Source: European Digital Skills Survey (weighted values)
Workplaces in the ‘Education and human health’ macro-sector (56%) and in large-sized
workplaces (44%) have the highest proportion of female employees while the lowest
proportions of female employees are reported in the agricultural sector (4%) and in
micro-sized establishments (33%).
The highest proportion of younger employees is reported in the macro-sector
‘Commerce, transportation, accommodation and food service activities’ (22%) and in
small and medium-sized workplaces (21%). Agriculture has the lowest proportions of
younger employees (1%) and in both micro and large-sized establishments (17% in
both cases). Employees older than 50 are more strongly represented in the construction
sector (25%) and in small-sized workplaces (25%), and less well represented in
agriculture (11%) and in medium-sized establishments.
The proportion of employees with a university degree is highest in the ‘Information and
communication’ sector; ‘Professional, scientific and technical activities’; ‘Administrative
and support service activities’ (39%) and ‘Education and human health’ (39%).
Employees with a university degree are also better represented in medium-sized
establishments (32%), and lower in the agricultural sector (9%) and in medium-sized
establishments (32%) (see Figure A2.5, Annex 2).
38.9
26.8
18.9 20.5
Female employees Employees with auniversity degree
Employees younger than30 years of age
Employees older than 50years of age
39
The composition of the workforce in workplaces varies significantly at the country level.
Germany has the highest diversity among employees working in the sectors covered by
the survey, as shown by the high proportion of female employees (46%), of older and
younger employees (34% and 35% respectively) and the highest proportion of
employees with a university degree (44%). Slovakia has the highest overall incidence of
female employees (47%).
Portugal displays a comparatively smaller proportion of female workers (29%), older
and younger workers (11% and 12% respectively). Slovakia, Finland and Portugal have
the lowest proportion of employees with a university degree (22%, 18% and 20%
respectively) (Figure A2.6 and Table A2.4, Annex 2).
Focusing on the occupational structure of the workforce, at the EU level, 41% of
workplaces employ workers in the ‘managers’ category, 30% employ ‘clerical support
workers’, 27% employ ‘sales, customer or personal service workers’, and 23%
‘professionals’. Smaller proportions of workplaces employ workers in the categories of
‘building, craft and related trade workers’ (18%), ‘technicians’ (14%), and ‘skilled
agricultural, forestry and fishery workers’ (12%). Workers in the ‘plant machine
operators and assemblers’ and ‘elementary occupations’ occupational categories are
employed by 7% and 8% respectively of workplaces in the sectors selected for this
study at EU level (Figure 2.8).
Figure 2.8 – Workplaces employing workers in specific occupations in the European Union (%)
Q12: Does your workplace have any employees in any of the following job categories? Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)
From a sectoral viewpoint, managers are relatively more common in the ‘commerce,
transport, accommodation and food service activities’ sectors. Professionals are more
frequently employed by workplaces active in the ‘education and human health’ macro-
40.8
23.0
14.0
29.9
27.2
12.3
17.7
7.4
8.2
Managers
Professionals
Technicians and associate professionals
Clerical support workers
Sales, customer or personal service workers
Skilled agricultural, forestry and fishery workers
Building, craft and related trade workers
Plant and machine operators and assemblers
Elementary occupations
40
sector. Technicians are more often employed by workplaces in the ‘information and
communication, professional, scientific and technical activities, administrative and
support service activities’ macro-sectors. ‘Clerical workers’ are more common in the
‘manufacturing and utilities’ macro-sector. Furthermore, workplaces employing workers
as ‘sales workers’ are more common in the ‘commerce, transportation, accommodation
and food service activities’ sectors, and those employing ‘skilled agricultural workers’
are active mostly in agriculture. Workplaces with ‘building workers’ are concentrated in
the construction sector and those employing ‘plant machine operators’ fall in the
‘manufacturing and utilities’ macro-sector. Finally, workplaces employing workers in the
‘elementary occupations’ are concentrated in the ‘education and human health’ macro-
sector (Table A2.5, Annex 2).
Of the 150,563,540 total employees in EU28 workplaces, 7.5 million (or 5%) work as
managers. Workers employed as ‘professionals’ in the workplaces covered by this
survey number 28.5 million (or 19%). Technicians number 25.5 million (17% of total
employees), sales workers number 26 million (17%), clerical workers account for 17
million (11%), and skilled agricultural workers total 1.5 million (1% of employees).
Finally, the survey identified about 17 million building workers (11%), 12 million plant
machine operators (8%) and workers employed in elementary occupations account for
15.5 million workers (10%). Skilled agricultural workers represent the smallest category
of workers (Table 2.1).
Table 2.1 - Employees in specific occupations, EU28 (total number and % of employees, and average number of workers in specific occupation per workplace)
Q13. Could you please indicate approximately how many employees your workplace has in these job categories? Q14. Could you please provide your best estimate of the approximate percentage of employees in your workplace in these job categories? Note: totals from the European Digital Skills Survey correspond mathematically to those from Eurostat Labour Force Survey (2015) with regards to the distribution of employees, as a result of the calculation of sample weights according to the calibration procedure as described in Annex 1. Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values), Eurostat (2015)
Occupations N %
Managers 7,564,363 5.0
Professionals 28,452,941 18.9
Technicians 25,519,514 16.9
Clerical workers 16,921,402 11.2
Sales workers 25,923,570 17.2
Skilled agricultural workers 1,526,330 1.0
Building workers 16,887,614 11.2
Plant machine operators 12,206,673 8.1
Elementary occupations 15,561,133 10.3
Total number of employees 150,563,540 100.0
Total number of workplaces 13,803,113
41
Looking at job categories by sector, a higher proportion of workers are employed as
skilled agricultural workers (46%) in the agricultural sector, similarly there are higher
proportions of technicians in the manufacturing sector (37%), building workers in the
construction sector (48%), sales workers in macro-sector ‘commerce, transportation,
accommodation and food services activities’ (31%) and of professionals in macro-
sectors ‘information and communication, professional, scientific and technical activities,
administrative and support service activities (28%) and ‘education and human health’
(42%) (Table A2.8, Annex 2).
At a country level, employees working as managers represent comparatively the largest
group in the UK (10%). Sweden has the highest proportion of professionals (26%), and
technicians and clerical workers are more commonly employed in Germany (23% and
14% respectively). Sweden also employs the highest proportion of sales workers
(21%). Skilled agricultural workers are more commonly employed in Portugal (3%),
while building workers and plant machine operators are prevalent in Slovakia (14% and
17%). Overall, elementary occupations have the highest incidence in Portugal (12%)
compared to the other five countries (Table A2.9, Annex 2).
2.3 Summary
This section has described the contextual data from the survey. This analysis will be
used as a basis upon which to address the research questions in the following chapters.
The main points presented in this section are:
The survey was conducted on a sample of 7,800 workplaces in six EU member
states (Germany, Finland, United Kingdom, Portugal, Sweden and Slovakia);
The survey and its findings are representative of 13,803,113 workplaces in 12
economic sectors across the EU;
The sectors of reference are agriculture; manufacturing; electricity and gas supply;
construction; wholesale and retail trade, repair of motor vehicles and motorcycles;
transportation and storage; accommodation and food service activities; information
and communication; professional, scientific and technical activities; administrative
and support service activities; education; human health and social work activities;
The vast majority of the workplaces represented are micro-sized enterprises (more
than 80%), with those remaining being mainly small enterprises (16%); only 0.5%
are large enterprises;
Workplaces belong mainly to the private sector (90%) and are not part of a group
of companies (85%);
The main reference market for these workplaces is the local or regional market
(65%), while a more limited proportion of workplaces operate or trade at national
level (about 21%) and only 13% at international level;
The sample of workplaces employ 150,563,540 employees; less than 40% of them
are women, 27% hold a university degree, about 19% are younger than 30 years of
age and 20% are older than 50 years of age;
Employees are classified in 9 occupational categories identified by the ISCO 1-digit
code: ‘managers’, ‘professionals’, ‘technicians and associate professionals’, ‘clerical
support workers’, ‘sales, customer or personal service workers’, ‘skilled agricultural,
forestry and fishery workers’, ‘building, craft and related trade workers’, ‘plant and
machine operators and assemblers’, ‘elementary occupations’.
42
Of the total number of employees, only 5% are employed as managers, while the
majority of employees work as professionals (19%), technicians (17%) and sales
workers (17%). Smaller proportions of employees work as clerical workers or
building workers (11% in both cases), or in the category of elementary occupations
(10%). Workers employed as plant machine operators represent 8% of total
employees and skilled agricultural workers are 1% of the total;
The breakdown by sector displays a number of sector-specific occupations;
similarly, variations exists across countries;
The analyses have been presented in their breakdown by country wherever possible
and related tables or charts are reported in Annex 2.
43
CHAPTER 3. ICT AND DIGITAL TECHNOLOGIES FOR WORK
As seen in the introductory Chapter 1, the research literature identifies digital
technologies as one of the main drivers of the profound transformation that the labour
market is undergoing in the vast majority of the economies in both the European Union
and in the other most advanced economies. According to several authors, different
types of digital technologies have become increasingly common across all economic
sectors and in most types of organisations.
In order to have a more complete picture of the degree of penetration of digital
technologies in the economy and in the workplace, the European Digital Skills Survey
investigated the use of information and communication technologies (ICT) and specific
digital devices in European workplaces across the 12 economic sectors of interest. The
survey also aimed to investigate the investment strategy in digital technology that
workplaces have undertaken recently, or are planning for the near future. With
reference to the use of ICT, the aim of the survey was to quantify how many
workplaces in the European Union currently use personal computers connected (or not)
to the internet or to an intranet, nettops, portable computers (e.g. laptops, notebooks,
netbooks, tablets) and/or other portable devices (e.g. smartphones, Personal Digital
Assistant (PDA), GPS navigator) and also CNC (Computer Numerical Control) machines
or tools, which are operated by precisely programmed commands encoded on a storage
medium, and programmable robots.
Overall, findings display a very high penetration of digital technologies in workplaces,
regardless of the sector or the size, which is in line with the literature. More than nine
out of ten workplaces in the European Union are currently using desktop computers or
broadband to access the internet, three-quarters are using portable computers, and six
out of ten use other digital portable devices.
3.1 Digital technologies in European workplaces
The vast majority of European workplaces use desktop computers (93%), broadband
technology to access the internet (94%)13, portable computers (75%) and other
portable devices (as defined above) (63%). Much smaller proportions of workplaces use
an intranet platform (22%), CNC machine or tools (8%) or programmable robots (5%)
(Figure 3.1).
13 This finding is in line with the most recent figure released by Eurostat for 2015 on the access to broadband to access the internet (95%) within the Eurostat ‘ICT usage in enterprises’ survey http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_ci_it_en2&lang=en
44
Figure 3.1 – Workplaces by use of computers and other digital devices by type of
device, EU28 (%)
Q15. Please indicate if your workplace currently uses computers, CNC machines or tools, and other digital devices to carry out its main business activity. By digital device an electronic device which uses discrete, numerable data and processes for all its operations should be meant. Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)
A breakdown of the use of types of digital technologies in different economic sectors
can be found in Table A3.1 in Annex 2. Desktop computers are extensively used by
most workplaces regardless of the sector. Nevertheless, workplaces operating in the
‘Education and human health and social work activities’ macro-sector have the highest
incidence in the use of computers (97.9%). This figure should however be treated with
caution, as most of the workplaces that responded to the survey in this macro-sector
are privately-owned (82.5% of the total), while in most countries workplaces in this
sector are mainly publicly-owned. This is important as the use of computers tends to be
less frequent among workplaces in the public sector, with only 1.9% of privately-owned
workplaces in the ‘Education and human health sector’ not using computers, versus
6.7% of those which are publicly-owned.
The use of the broadband to access the internet is reported by almost the totality of
workplaces in the Agriculture sector14, while the use of portable computers, portable
devices and intranet platforms is most common among workplaces in the macro-sector
‘Information and communication, Professional, scientific and technical activities and
Administrative and support service activities’ (85.2%, 79.9% and 37.4% respectively).
The use of CNC machinery and tools, on the other hand, seems to be concentrated
mostly in the ‘Manufacturing and utilities’ macro-sector (21.4%) and to a lesser extent
14 This figure could actually be biased due to the expected prevalence of respondents with a valid email address.
92.7
75.3
63.3
93.6
22.5
7.8
5.2
Desktop computers
Portable computers
Other portable devices
Broadband technology to access the Internet
Intranet platform
CNC machines or tools
Programmable robots
45
in Agriculture (12.4%). The use of programmable robots is concentrated in the
Agriculture (12.3%) and ‘Manufacturing and utilities’ (8.4%) sectors.
While the figures related to the Manufacturing sector are backed by a considerable
corpus of evidence and literature, the findings about the Agricultural sector (Table A3.1,
Annex 2), which could appear as counterintuitive, are actually in line with most recent
evidence15, as also evidenced in the wider study of which the European Digital Skills
Survey is a part16. Relevant stakeholders confirm that the sector is increasingly turning
to digital technologies for agricultural purposes, with changes in the production
systems, productivity and outlook of traditional farming. Digital technologies are used
as well to respond to other management requirements, for example to access the social
security systems for employees, as in many countries these systems have been
digitalised and farmers need ICT skills to employ workers (e.g. seasonal workers)17.
The analysis by size shows a clear correlation between workplace size and use of
desktop computers, portable computers and other portable devices: the use of digital
technologies increases according to the size of the workplaces, with large-sized
workplaces reporting the highest use of all the digital technologies listed (Table A3.1,
Annex 2). The analysis by country shows that Germany leads in the use of desktop
computers (although all other countries with the exception of Sweden have similar
levels), of intranet platforms and CNC machine or tools. Finland leads in the use of
portable computers and other portable devices, in the use of broadband to access the
internet and in the use of programmable robots. Portugal is the country with the lowest
use of portable computers, other portable devices, and intranet platforms. Sweden
displays the lowest incidence of use of computers, but the second highest incidence in
the use of portable computers and of other portable devices (Figure A3.1, Annex 2).
3.2 Recent trends in the use of digital technologies
The use of digital technologies in the last five years has increased in almost nine
workplaces out of ten in the EU28, with less than 10% of workplaces reporting that
there has not been any increase (Figure 3.2). In addition, seven out of ten workplaces
anticipate that the use of digital technology will increase in the next five years; one
15 For example: European Commission, ERANET, 2010. ' Coordination of European Research within ICT and Robotics in Agriculture and related Environmental Issues'. http://cordis.europa.eu/fp7/coordination/docs/ictagri_en.pdf; European Commission and rural development, 2011. 'Structural development in EU agriculture- Brief N° 3 – September 2011'. http://ec.europa.eu/agriculture/rural-areaeconomics/briefs/pdf/03_en.pdf; Ict in Agriculture. 2012. ' Overview of ICT in Agriculture: Opportunities, Access, and Cross-Cutting Themes'. http://www.ictinagriculture.org/sites/ictinagriculture.org/files/final_Module1.pdf; ICT AGRI, 2015. 'ICT-AGRI 2015 Action Plan for implementation of the Strategic Research Agenda with focus on Precision Agriculture. http://ict-agri.eu/sites/ictagri.eu/files/ICT-AGRI_2015_Action_Plan_0.pdf; ICT AGRI, 2014. 'Precision Agriculture: An opportunity for EU Farmers'. http://ictagri.eu/node/14013 JP, 2015: (translated) 'Farmers tired of milking robot go back to milk with their hands'. Jyllands-Posten 11 September 2015. http://jyllandsposten.dk/ECE8012845/Tr%C3%A6t+af+robotter%3A+Flere+landm%C3%A6nd+malker+med+h%C3%A6nderne/ Stienen, Jac; Bruinsma, Wietse & Neuman, Frans, International Institute for Communication and Development (IICD) 2007. 'How ICT can make a difference in agricultural livelihoods'. 16 https://ec.europa.eu/digital-single-market/en/news/report-shows-digital-skills-are-required-across-all-types-work-also-jobs-outside-office 17 Information reported by COPA-COGECA, the European association of farmers and agri-cooperatives represented by Mrs Federica Zolla, at the experts’ workshop which took place on the 7th of October 2016 in Brussels.
46
quarter however think that there will not be any increase in the use of ICT in the near
future, and 6.5% do not know.
Figure 3.2 - Workplaces by trends and importance in the use of ICT in the last and in
next five years, EU28 (%)
Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), that the use of ICT in your workplace…? Number of valid responses: 7,773
N=13,763,547 Source: European Digital Skills Survey (weighted values)
Workplaces in ‘Education and human health’ and ‘Information and communication,
Professional, scientific and technical activities, Administration support activities’ sectors
report more frequently an increase in the use of digital technologies (96% and 97%
respectively). Micro-sized workplaces (10%) are more likely to report no increase at all
in the use of ICT in the last five years compared to workplaces of a larger size. 94% of
small and medium-sized workplaces, and 95% of large workplaces, report a significant
increase in the use of ICT in the recent past (Table A3.2, Annex 2).
The logistic regression model18 below (Figure 3.3) allows the identification of additional
characteristics of the workplace that correlate to the probability that the use of ICT in
the workplace has increased in the last five years (the full model is reported in Table
A3.3 in Annex 2).
18 Logistic regression is used to investigate the effect of two or more independent (or predictor) variables on a two-category (binary) outcome variable. The independent variable can be continuous or categorical (grouped) variable. The parameter estimates from a logistic regression model for each independent variable give an estimate of the effect of that variable on the outcome variable, adjusted for all other independent variables in the model. Logistic regression models the log ‘odds’ of a binary outcome variable. The ‘odds’ of an outcome is the ratio of the probability of it occurring to the probability of it not occurring. The parameter estimates obtained from a logistic regression model have been presented as ‘odds ratios’ for ease of interpretation. Reference categories (reported as ‘omitted’ categories in the subsequent models) are usually chosen on the basis of being the most numerous or suitable category to compare everything against.
9.8
25.0
87.1
68.5
3.1
6.5
The use ofICT has
increased inthe last 5
years
The use ofICT will
increase inthe next 5
years
Not at all Yes Don't know
47
Workplaces in which the use of ICT has increased in the last five years are more likely
to be part of an economic group (in particular being the headquarters) rather than
being a single workplace. In particular, analysing the odds ratios, it can be pointed out
the odds of the headquarters of a group and a subsidiary site reporting an increased
use of ICT in the last five years are 4.8 and 4.0 times more likely than those for a single
workplace (not belonging to a group).
Also, in line with the descriptive analysis above, the likelihood of reporting an increase
in the use of ICT is higher for the macro-sectors ‘Education and human health’ (3.5
times more likely than the agricultural sector) and ‘Information and
communication/Professional, scientific and technical activities/Administrative and
support service activities’ (2.9 times more likely than the agricultural sector).
Workplaces with a higher incidence of female employees are more likely to report an
increase in the use of ICT: compared to workplaces with less than 26% female staff, the
odds of having introduced digital technologies in the last five years are 1.3 times higher
for workplaces where women represent between 26% and 50% of the workforce and
1.4 times higher for 1.4 times for those with a share of women between 51% to 75% of
the total workforce, and 1.3 times more likely for those employing more than 75%
female staff. . While these odds ratios suggest the existence of a positive correlation
between the share of female staff and the use of digital technologies, further research is
needed to identify characteristics of such correlation.
Workplaces employing highly educated staff are more likely to have introduced digital
technologies: workplaces with 75% or more employees holding a university degree are
1.3 times more likely than those with less than one quarter of highly educated
employees to report an increase in the use of ICT in the last five years.
Also workplaces active on the national market are more likely to report an increase in
the use of digital technology compared to workplaces operating mostly at local level (2
times more likely).
On the other hand, lower odds compared to the reference group were seen for
workplaces with over 26% older employees (compared to those with a smaller
proportion), or over 26% younger employees (compared to those with a smaller
proportion), , workplaces in the public sector (compared to those in the private sector)
and those active on the regional market compared to those in the local market.
48
Figure 3.3 – Logistic regression: probability that the use of ICT in the workplace has
increased in the last five years (Odds ratio).
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N:
Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), that the use of ICT in your workplace…? Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Source: elaboration on European Digital Skills Survey
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Only workplace (omitted)
Headquarters
Subsidiary_site
size < 10 (omitted)
size10_49
size50_249
size250
sectorA (omitted)
sectorCD
sectorF
sectorGHI
sectorJMN
sectorPQ
fem_rate<26 (omitted)
fem_rate_26_50
fem_rate_51_75
fem_rate_75
univ_rate<26 (omitted)
univ_rate_26_50
univ_rate_51_75
univ_rate_75
young_rate<26 (omitted)
young_rate_26_50
young_rate_51_75
young_rate_75
old_rate<26 (omitted)
old_rate_26_50
old_rate_51_75
old_rate_75
private (omitted)
public
Local_market (omitted)
Regional_market
National_market
International_market
Wo
rkp
lace
typ
eW
ork
pla
ce s
ize
Sect
or
Fem
ale
rate
Un
iver
sity
rat
eYo
un
g ra
teO
ld r
ate
Ow
ner
sh
ip (
q9
)M
arke
ts (
q11
)
49
In terms of future trends, an increase in the use of ICT is foreseen by 75% of EU28
workplaces, with a higher incidence in the ‘Education and human health’ (85%),
‘Manufacturing and utilities’ (83%), ‘Information and communication/Professional,
scientific and technical activities/Administrative and support service activities’ (80%),
and ‘Construction’ (80%) macro-sectors. While only a limited proportion of workplaces
at EU level reported no increase in the use of ICT in the last five years (around 10%),
workplaces appear more cautious when reporting the expected increase over the next
five years; a quarter of workplaces expect an increase in the use of ICT in the next five
years, with an important degree of variability among sectors. Medium and large-sized
workplaces are more positive regarding their likely increase in the use of ICT compared
to micro-sized workplaces (Table A3.2, Annex 2).
3.3 Investment strategies in ICT
The survey also investigated the investment strategy of European workplaces in digital
technologies in 11 areas related to the improvement of management and business
volume. These areas are listed in Table 3.3 below (right column). For the purposes of
the analysis, they have however been clustered into four meaningful synthetic
indicators related to the main areas of investment identified as ‘improving overall
efficiency’, ‘marketing and sales’, ‘internal organisation’, and ‘delocalisation strategy’
(reported in the left column).
Table 3.3 – Areas of investment in ICT
Area of investment in ICT Specific topic
Improving overall efficiency Improving overall efficiency
Marketing and sales Improving quality of existing products and services
Launching new marketing methods Engaging customers, users, suppliers or other companies
to improve or create products or services Tracking and analysing data from business processes, customers, and transactions to improve or create products or services.
Internal organisation Making the production process leaner Improving work organization or working procedures
Making work easier and less stressful for employees
Delocalisation strategy Delocalising the production of goods or services within the country Delocalising the production of goods or services abroad
Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not
at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree
has your workplace invested in ICT for…? (Please select all that apply)
Source: Elaboration on European Digital Skills Survey
As displayed in Figure 3.4 below, workplaces in the EU28 have mostly invested in ICT in
the past five years with the aim of improving overall efficiency. Almost seven
workplaces out of ten report this as the main important area of investment in digital
technology. Furthermore, a majority of workplaces (56%) report investments in digital
technology in the area of marketing and sales, while less than half of workplaces
invested in ICT to improve the internal organisation. Only 31% of European workplaces
invested in digital technology in line with a delocalisation strategy. This figure is in line
with the data presented in Figure 2.6 in Chapter 2, according to which the vast majority
50
of workplaces operate or trade in local or regional markets, while only a much smaller
proportion have national and international markets as the main markets of reference.
Figure 3.4 - Workplaces by area and importance of investment in ICT in the past five
years, UE28 (%)
Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: the levels of importance of investment have been recoded in a dichotomic variable (No/Yes) Number of valid responses: 7,773 N=13,763,547 Source: European Digital Skills Survey (weighted data)
Workplaces in the macro-sectors ‘Information and communication/Professional,
scientific and technical activities/Administrative and support service activities’,
‘Education and human health’ and ‘Commerce, transport, accommodation and food
service’ are those which invested most in ICT to improve the overall efficiency in the
past five years (80%, 75% and 70% respectively). Investments in digital technologies
in the area of marketing and sales and internal organisation are reported comparatively
more by workplaces in the macro-sectors ‘Information and communication/Professional,
scientific and technical activities/Administrative and support service activities’ and
‘Manufacturing and utilities’ (70% and 62% for marketing and sales and 60% and 54%
for internal organisation, respectively). Finally, Agriculture and ‘Information and
communication/Professional, scientific and technical activities/Administrative and
support service activities’ are the macro-sectors with the highest relative share of
workplaces which invested in ICT in line with a delocalisation strategy (42% and 38%
respectively) (Figure A3.4, Annex 2). Medium-sized and large workplaces invested
comparatively more in digital technologies in all the areas. Micro-sized workplaces (2-9
employees) refer more frequently a lack of investment in ICT in all the areas, compared
to larger-sized workplaces. In particular, 24% of micro-sized workplaces have not
invested at all in the last five years in ICT with the aim of improving the overall
efficiency, 35% have not invested at all in ICT in the area of marketing and sales, 40%
21.7
32.2
37.0
56.4
68.1
55.9
49.5
31.4
10.2
11.9
13.5
12.2
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
No Yes Don't know
51
report a lack of investment in ICT related to internal organisation.Finally, 59% have not
invested at all in ICT in relation to a delocalisation strategy (Table A3.5, Annex 2).
Four logistic regression models have been carried out to identify the characteristics of
workplaces that are more likely to invest in digital technologies in each of the areas.
The first model displayed in Figure 3.5 (the full model is reported in Table A3.6 in
Annex 2) has been calculated with reference to the likelihood of investing in ICT to
improve overall efficiency. In this case, if the workplace belongs to a group, the
likelihood of having invested in ICT to improve overall efficiency is higher than for single
workplaces. More in detail, the odds that headquarters and subsidiary sites have
invested in digital technologies to improve overall efficiency are respectively 7.8 and 5.3
times higher than those for single workplaces. In terms of sector, workplaces operating
in the macro-sectors ‘Education and human health’ or ‘Information and
communication/Professional, scientific and technical activities/Administrative and
support service activities’, have odds of investing in ICT to prove efficiency 5 and 5.1
times higher than those in ‘agriculture’. This is also the case – although to a lesser
extent – of workplaces in the macro-sectors ‘Manufacturing and utilities’, ‘Construction’
or ‘Wholesale and retail trade, repair of motor vehicles and motorcycles/Transportation
and storage/Accommodation and food service activities’ (odds which are respectively
2.3, 2.5 and 3.4 times higher than those in the agricultural sector). Similarly,
workplaces employing more than one quarter of employees holding a university degree
are more likely to have invested in ICT to improve efficiency (odds between 2.1 and 2.3
higher than those with a quarter or less).
The model also shows that workplaces active in the regional, national and especially
international markets are more likely to have invested in digital technologies to improve
the overall efficiency. In particular, workplaces operating in international markets and
those operating mostly at national level are respectively 4.2 and 3 times more likely
than workplaces active locally to have invested in ICT to improve the overall efficiency.
Finally, the regression analysis shows that the demographic characteristics of the
employees can have a weak negative correlation with investment in ICT to improve the
overall efficiency. Workplaces with more than 26% of female staff or staff over 50, and
those with more than 50% of employees aged under 30, are slightly less likely to have
invested in digital technologies to improve overall efficiency.
52
Figure 3.5 – Logistic regression: probability that workplace has invested in ICT to
improve the overall efficiency in the last five years (Odds ratios).
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities;
J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Source: Elaboration on European Digital Skills Survey (weighted values)
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8
Only workplace (omitted)
Headquarters
Subsidiary_site
size < 10 (omitted)
size10_49
size50_249
size250
sectorA (omitted)
sectorCD
sectorF
sectorGHI
sectorJMN
sectorPQ
fem_rate<26 (omitted)
fem_rate_26_50
fem_rate_51_75
fem_rate_75
univ_rate<26 (omitted)
univ_rate_26_50
univ_rate_51_75
univ_rate_75
young_rate<26 (omitted)
young_rate_26_50
young_rate_51_75
young_rate_75
old_rate<26 (omitted)
old_rate_26_50
old_rate_51_75
old_rate_75
private (omitted)
public
Local_market (omitted)
Regional_market
National_market
International_market
Wo
rkp
lace
typ
eW
ork
pla
cesi
zeSe
cto
rFe
mal
e ra
teU
niv
ersi
tyra
teYo
un
g ra
teO
ld r
ate
Ow
ner
ship
(q9
)M
arke
ts (
q1
1)
53
The second logistic model is displayed in Figure 3.6 (the full model is reported in Table
A3.8, Annex 2) and has been calculated with reference to the likelihood of investing in
digital technologies in the area of marketing and sales. This model shows that belonging
to a group as headquarters increases significantly (5.9 times) the odds of having
invested in digital technologies for marketing and sales compared to single workplaces.
Small and medium-sized workplaces are respectively 1.5 and 1.7 times more likely than
micro-enterprises to have invested in ICT in the area of marketing and sales. Also,
workplaces operating in particular in the macro-sectors ‘Information and
communication/Professional, scientific and technical activities/Administrative and
support service activities’ and ‘Manufacturing and utilities’ display higher odds (3 and
2.1 times respectively) of having invested in ICT in the area of marketing and sales
compared to workplaces in the agricultural sector. This is also the case – although to a
lesser extent – of workplace in ‘Education and human health’, ‘Construction’ and
‘Wholesale and retail trade/Repair of motor vehicles and motorcycles/Transportation
and storage, Accommodation and food service activities’ sectors, display respectively
1.6, 2 and 1.5 times higher odds than the agricultural sector to have invested in ICT in
the area of marketing and sales.
Workplaces employing high proportions of employees holding a university degree, are
more likely to have invested in digital technologies for marketing and sales: in
particular, for workplaces with more than three quarters of staff holding a university
degree the likelihood of investing in ICT for marketing and sales is 2.6 times higher
compared to workplaces with less than 26% of highly-educated staff. It is also worth
pointing out that the regression model displays a strong probabilistic correlation
between the market of reference and the investment in ICT for marketing and sales.
Workplaces active in the regional, and in particular in the national and international
markets, have odds that are higher (2, 4.6 and 5.1 times respectively) than those for
workplaces operating mostly locally in terms of having invested in ICT for marketing
and sales. Conversely, workplaces with a proportion of more than 75% of female
employees, or those with 26-75% of older employees, are less likely to have invested in
ICT in the area of marketing and sales.
54
Figure 3.6 – Logistic regression: probability that workplace has invested in ICT in the
area of marketing and sales in the last five years (Odds ratios).
* A: Agriculture; CD: Manufacturing and utilities; F: Construction; GHI: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; JMN: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; PQ: Education and human health and social work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Source: Elaboration on European Digital Skills Survey (weighted values)
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Only workplace (omitted)
Headquarters
Subsidiary_site
size < 10 (omitted)
size10_49
size50_249
size250
sectorA (omitted)
sectorCD
sectorF
sectorGHI
sectorJMN
sectorPQ
fem_rate<26 (omitted)
fem_rate_26_50
fem_rate_51_75
fem_rate_75
univ_rate<26 (omitted)
univ_rate_26_50
univ_rate_51_75
univ_rate_75
young_rate<26 (omitted)
young_rate_26_50
young_rate_51_75
young_rate_75
old_rate<26 (omitted)
old_rate_26_50
old_rate_51_75
old_rate_75
private (omitted)
public
Local_market (omitted)
Regional_market
National_market
International_market
Wo
rkp
lace
typ
eW
ork
pla
ce s
ize
Sect
or
Fem
ale
rate
Un
iver
sity
rat
eYo
un
g ra
teO
ld r
ate
Ow
ner
sh
ip (
q9
)M
arke
ts (
q11
)
55
The third logistic model in Figure 3.7 (full model in Table A3.9 in Annex 2) displays
calculations with reference to the likelihood of investing in ICT to improve internal
organisation. Also in this case workplaces that are the headquarters of a group, or are
small and medium-sized workplaces, have higher odds of having invested in ICT to
improve internal organisation than their reference group. Headquarters have odds 6
times higher than single workplaces in terms of having invested in ICT to improve the
internal organisation. More modest effects are displayed in relation to the size: small
and medium-sized workplaces have odds that are 1.2 and 1.9 times higher than micro-
sized workplaces for the same issue. Furthermore, workplaces in the macro-sectors
‘Information and communication/Professional, scientific and technical
activities/Administrative and support service activities’, ‘Education and human health’
and ‘Wholesale and retail trade/Repair of motor vehicles and
motorcycles/Transportation and storage/Accommodation and food service activities’
manifest higher odds (respectively 2.4, 2.1 and 2 times) of having invested in digital
technology to improve internal organisation compared to workplaces in the agricultural
sector. This is the case also of workplaces operating in the ‘Manufacturing and utilities’
and ‘Construction’, which are respectively 1.7 and 1.4 times more likely than
workplaces in the agricultural sector to have invested in ICT for the internal
organisation. Also workplaces active in the regional, national and international markets
are 2 , 3.5 and 3.3 times (respectively) more likely – compared to those operating
mostly at local level - to have invested in ICT for internal organisation. Also, employing
high proportions of employees holding a university degree has a positive correlation
with the investment in ICT to improve internal organisation in the last five years as
indicated by the odds ratios calculated for workplaces employing between 26% and
50% of highly-educated staff (1.7 times more likely), for workplaces employing 51% to
75% of staff holding a university degree (1.3 times more likely) and for workplaces
employing more than 75% of employees with a university degree (1.4 times more
likely).
On the contrary, odds to have invested in ICT to improve internal organisation
were smaller for workplaces with a proportion of more than 75% of female (0.8
times in comparison to those with 25% or less), younger employees (0.8 times
for those with 75% or more in comparison to those with 25% or less), those
with 25-75% of older employees (0.7 times compared to those with less than
26% older employees), or those belonging to the public sector (0.7 times in
comparison to those that are not),.
56
Figure 3.7 – Logistic regression: probability that workplace has invested in ICT to
improve internal organisation in the last five years (Odds ratios).
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion
of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are
as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%.
Source: Elaboration on European Digital Skills Survey (weighted values)
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Only workplace (omitted)
Headquarters
Subsidiary_site
size < 10 (omitted)
size10_49
size50_249
size250
sectorA (omitted)
sectorCD
sectorF
sectorGHI
sectorJMN
sectorPQ
fem_rate<26 (omitted)
fem_rate_26_50
fem_rate_51_75
fem_rate_75
univ_rate<26 (omitted)
univ_rate_26_50
univ_rate_51_75
univ_rate_75
young_rate<26 (omitted)
young_rate_26_50
young_rate_51_75
young_rate_75
old_rate<26 (omitted)
old_rate_26_50
old_rate_51_75
old_rate_75
private (omitted)
public
Local_market (omitted)
Regional_market
National_market
International_market
Wo
rkp
lace
typ
eW
ork
pla
cesi
zeSe
cto
rFe
mal
e ra
teU
niv
ersi
tyra
teYo
un
g ra
teO
ld r
ate
Ow
ner
ship
(q9
)M
arke
ts (
q1
1)
57
The final logistic model displayed in Figure 3.8 (full model in Table A3.10 Annex 2) has
been calculated with reference to the likelihood of investing in ICT to support a
delocalisation strategy. As it can be expected, this model shows a strong correlation
between the market of reference and the investment in ICT to support the
delocalisation strategy in the last five years. Workplaces active in the national and
particularly in international markets are more likely (respectively 4.8 and 6 times) to
have invested in ICT to support delocalisation strategies compared to workplaces
operating locally. Also, workplaces which are the headquarters of a group are more
likely (3.3 times) than single workplaces to have invested in digital technologies to
support delocalisation strategies. In addition, small or medium-sized workplaces have a
higher probability (1.3 and 1.4 times respectively) of having invested in digital
technology to support delocalisation compared to micro-sized workplaces. Furthermore,
employing high proportions of employees holding a university degree has a positive
correlation with the investment in ICT to support delocalisation strategies in the last five
years. Conversely, negative correlations have been calculated for the rest of
characteristics analysed: workplaces in all macro-sectors (compared to Agriculture),
those with a proportion of 26% to 75% and 75% and more of female employees, those
with more than 75% of younger employees or those with 25% to 75% of older
employees are slightly less likely to have invested in ICT to support a delocalisation
strategy.
58
Figure 3.8 – Logistic regression: probability that workplace has invested in ICT to support
delocalization strategy in the last five years (Odds ratios).
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Source: Elaboration on European Digital Skills Survey (weighted values)
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5
Only workplace (omitted)
Headquarters
Subsidiary_site
size < 10 (omitted)
size10_49
size50_249
size250
sectorA (omitted)
sectorCD
sectorF
sectorGHI
sectorJMN
sectorPQ
fem_rate<26 (omitted)
fem_rate_26_50
fem_rate_51_75
fem_rate_75
univ_rate<26 (omitted)
univ_rate_26_50
univ_rate_51_75
univ_rate_75
young_rate<26 (omitted)
young_rate_26_50
young_rate_51_75
young_rate_75
old_rate<26 (omitted)
old_rate_26_50
old_rate_51_75
old_rate_75
private (omitted)
public
Local_market (omitted)
Regional_market
National_market
International_market
Wo
rkp
lace
typ
eW
ork
pla
ce s
ize
Sect
or
Fem
ale
rate
Un
iver
sity
rat
eYo
un
g ra
teO
ld r
ate
Ow
ner
sh
ip (
q9
)M
arke
ts (
q11
)
59
Finally, the descriptive analysis at country level reported in Figure A3.5 and Table A3.6
in Annex 2 shows that workplaces based in Portugal are those that invested relatively
more in ICT in all the areas while, at the other end of the spectrum, workplaces based
in Sweden are those reporting more frequently a lack of investment in ICT in all the
areas in the last five years. Such results are not surprising, considering that Portugal is
one of the European countries with the lowest level of digitisation. These figures
suggest that Portuguese workplaces have been investing in ICT to put their business
and the country in a better position in the digital map of Europe, in line with the
national government priorities and the recent plan for the “Digital employability”19 for
2015-2020. On the other hand, Sweden is among the countries with the highest level of
digitalization in the European Union, and therefore significant investments in ICT have
been already started much earlier, therefore in the last five years the investments in
ICT have been more incremental or not as significant as in an initial stage of
digitisation.
3.4 Summary
This section has provided an overview of the use of ICT and specific digital devices and
of the investment strategy in ICT in European workplaces across the 12 economic
sectors of interest of the European Digital Skills Survey. The main findings presented in
this section include the following:
The vast majority of European workplaces use desktop computers (92.7%),
broadband technology to access the internet (93.6%), portable computers (75.3%)
and other portable devices (63.3%), while a much smaller share makes use of
intranet platforms (22.5%), CNC machines or tools (7.8%) and programmable
robots (5.2%);
Specific sector-based trends, with the use of certain technologies concentrated in
specific sectors can be observed, as for example the use of CNC machine and
robots in the ‘Agriculture’ and ‘Manufacturing’ sectors and the use of laptops in the
‘Information and communication’ or ‘Education and human health’ sectors;
Large-sized workplaces report the highest use of all the digital technologies listed;
The use of ICT in workplaces has increased significantly in the last 5 years at EU
level with less than 10% of workplaces reporting that there has been no increase.
In this period, micro-sized workplaces were more likely to report no increase or a
limited increase in the use of ICT compared to workplaces of a larger size;
Workplaces in which the use of ICT has increased in the last five years are more
likely to be part of a group (in particular being the headquarters), to belong to the
macro-sectors ‘Education and health’ or ‘Information and communication’, to have a
higher incidence of women employees and of employees holding a university
degree, and operate on national markets;
Workplaces are less likely to report expected increases in ICT investment over the
next five years, although medium and large workplaces were more positive than
smaller ones. Generally, 68.5% of workplaces anticipate that the use of digital
technology will increase in the next five year, while 25% expect no increase and
6.5% have no specific expectation;
In terms of investment strategy, workplaces have invested in ICT in the last five
years mainly to improve their overall efficiency (68% of workplaces in the EU28).
19 http://www.fct.pt/dsi/docs/EstrategiaPlanoAcaoEmpregabilidadeDigital_v0.1.pdf
60
Investments in ICT were linked to a lesser extent to marketing and sales (60%),
internal organization (50%) and delocalisation strategy (31%). Nevertheless a
significant proportion of European workplaces did not invest at all in these areas:
21.7% of workplaces did not invest in the area of improving overall efficiency, 31%
did not invest in marketing and sales, 37% did not invest in internal organization
and as much as 56% did not invest in delocalisation strategy);
Medium and large-sized workplaces invest relatively more at a significant level in
practically all the areas, while micro-sized workplaces invest more frequently due to
an overall lack of investment in ICT;
Four logistic regression models have allowed for the identification of the
characteristics of workplaces that are more likely to influence the probability of
investing in digital technologies;
Differences in the use of ICT are registered across countries. Workplaces based in
Portugal are those that invested relatively more in ICT in all the areas at both
moderate and significant level while, at the other end of the spectrum, workplaces
based in Sweden are those reporting more frequently a lack of investment in ICT in
all the areas in the last five years. This is linked to the current levels of digitisation
of workplaces in these countries (lower in Portugal, with a need to bring businesses
closer to the levels in other EU countries, and high in Sweden).
61
CHAPTER 4. DIGITAL SKILLS IN EUROPEAN
WORKPLACES
As noted in Chapter 1, many researchers observe that the digitisation of the economy is
profoundly transforming the labour market. Digital technologies are changing jobs and
work practices; the way job tasks are performed, job content and requirements, and
the skills needed to perform a job. As digital technologies become more and more
embedded into the daily operations of firms across a large number of industries and
economic sectors, digital skills are becoming increasingly important for a large number
of workers.
The aim of this chapter is to respond to the following research questions:
How many jobs in the European Union (EU) require digital skills?
What types of digital skills are most required by employers?
How can the jobs requiring digital skills be classified according to the level of
digital skills required?
What are the differences across economic sectors and occupations in terms of
digital skills required by employers?
This chapter displays the type and level of digital skills required by employers to carry
out jobs in specific occupations, in different economic sectors and in workplaces of
different sizes. The chapter also looks at the proportion of the workforce currently
employed in different types of occupations by type of digital skills, and also by sector
and size. Finally, it provides details on the types of digital skills required in selected
examples of specific jobs based upon qualitative information gathered through the
research.
Overall, the results confirm that the extensive use of digital technologies in workplaces
(displayed in Chapter Three) is transforming jobs in particular in terms of digital skills
required, and in line with what is suggested in the research literature. Research findings
show that digital skills appear to be required - to a varying degree - across a wide
range of jobs and working contexts regardless of the economic sector or the size of the
workplace. Nevertheless, a correlation between type and level of digital skills required
and jobs in specific high- or medium-skilled occupations has been identified, indicating
that employees in low or unskilled jobs are much less likely to be required to possess
digital skills to perform their job.
4.1 Defining digital skills in the context of this survey
As noted in Section 1.3 of Chapter 1, there is no standard or agreed upon definition of
digital or ICT skills, and efforts are ongoing to characterise the various types of digital
or ICT skills. However, as pointed out by existing literature, digital skills can be broadly
understood as the ability to locate, organise, understand, evaluate, create and share
information using digital technology, at different levels of competence. Using the Job
62
Requirements Approach20 employed by the OECD21 and CEDEFOP22 to assess skills,
digital skills can be defined with reference to the tasks carried out and the level of
competence required.
Following this approach and considering the scope and purposes of this survey, a set of
10 digital skills has been considered, and workplaces were surveyed in relation to each
skill by type of occupation and level of proficiency of employees:
1. Using a word processor (e.g. Word);
2. Create a spreadsheet (e.g. Excel);
3. Search for, collect and process information using ICT (e.g. online/Internet);
4. Communicate through ICT using email;
5. Communicate through ICT using social media, Skype/videocalls;
6. Use software for design, calculation or simulation;
7. Undertake programming and software development;
8. Design and maintain ICT architecture for the workplace;
9. Programme and use CNC machines;
10. Programme and use robots.23
Research carried out by OECD and CEDEFOP (explored in Chapter 1), and the
exploratory analysis undertaken for this research shows correlations between specific
groups of indicators. For the purpose of this report these have been clustered into three
main synthetic indicators:
Basic digital skills: includes the skills one to five from the list above;
Advanced digital skills: includes digital skills listed at points 6, 9 and 10 of the list
above;
Specialist digital skills: covers digital skills reported at points 7 and 8 of the list.
The results of the analysis across the three clusters is presented in this chapter and the
one that follows.
4.2 Digital skills for work: available evidence from other sources
The evidence regarding the availability of digital skills among workers is rather limited.
Nevertheless, existing evidence seems to indicate that digital technologies are
increasingly and steadily penetrating workplaces, bringing along a need for related skills
required by a significant proportion of workers, to perform their day-to-day job tasks.
Data from Cedefop’s 2014 ‘European skills and jobs survey’ carried out on adult
employees (aged 24 to 65), indicate that ‘about seven in 10 EU adult workers need to
20 The Job Requirements Approach allows to assess skills based on the tasks that the workers is required to carry out in a specific job. 21 Mañé, F. (2013). Using the job requirements approach and matched employer-employee data to investigate the content of individuals’ human capital. In: Green, F.; Keese, M. (eds). Job tasks, work skills and the labour market. Paris: OECD Publishing. 22 Cedefop (2015). Skills, qualifications and jobs in the EU: the making of a perfect match? Evidence from Cedefop’s European skills and jobs survey. Luxembourg: Publications Office. 23 The number of digital skills (or tasks requiring digital skills) initially included in the survey’s questionnaire was 12, as the programming and use of CNC machines and robots respectively were investigated separately. For the purposes of the analysis, however, they were ex post clustered, and two new variables (‘Programme and use of CNC machines’ and ‘Programme and use of robots’) were accordingly calculated and used in the analysis presented in the chapter and also in Chapter 5.
63
possess at least a moderate level of ICT skills to be able to do their jobs (52%
moderate level and 14% advanced)’ (Cedefop 2015). This appears strictly related to the
job role. More than 20% of those in managerial and professional roles or working as
technicians need advanced ICT skills for their jobs, and about 70% need basic or (more
frequently) moderate digital skills. More than 70% of workers in clerical roles require at
least a moderate level of digital skills to perform their job. Even 42% of those employed
in elementary occupations need some level of digital skills to be able to do their job
(Cedefop 2015).
Data from Eurofound’s ‘Sixth European Working Conditions Survey’, carried out in 2015
on a representative sample of employed individuals in the EU28 Member States, show
that 56% of European workers work with digital technologies (computers, laptops,
smartphones, etc.)24. This data also seems to show a correlation between type of
occupation and use of digital technologies. High-skilled clerical workers are more likely
to use digital technologies, 49% of workers in this occupational group do so all of their
working time and 36% of them do so for at least between a quarter and three quarters
of their working time. Low-skilled clerical workers are less likely to use digital devices,
40% of them do so all of their working time and 26% between one quarter and three
quarters of their working time. Conversely, manual workers are considerably less likely
to use digital devices at work.
Additional evidence from the most recent Eurostat ‘ICT usage in enterprises’ survey
indicates that in EU28 workplaces 54% of employees use computers to perform their
job, and 88% of them use a computer with access to internet25.
These figures remain under the suggested figure that ‘90% of jobs will require digital
skills’ at some point in the future, reported by many official sources. For example, this
figure is often reported in European Commission documents and websites26 and quoted
by several other reports and sources27 with reference to the EU28, and at the national
level this figure is also often reported28. The timespan of when this is expected to
happen varies, as in some cases this is thought to happen in ‘the near future’ and in
other cases is ‘by 2020’. A study carried out in 2009 by IDC claimed that ‘in five years’
time less than 10% of job roles will require no ICT skills at all’ (Kolding et al. 2009).
While the existing evidence displayed in this section suggests that the 90% figure could
be achieved at some time in the near future, the evidence gathered through the Digital
Skills Survey indicates that in some job categories and in relation to specific types of
digital skills already more than 90% of jobs require digital skills. This will be extensively
illustrated in the following sections.
24 http://www.eurofound.europa.eu/surveys/data-visualisation/sixth-european-working-conditions-survey-2015 25 Data available on Eurostat database http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_ci_cm_pn2&lang=en; survey methodology available at http://ec.europa.eu/eurostat/cache/metadata/en/isoc_e_esms.htm 26 http://ec.europa.eu/europe2020/pdf/themes/2016/digital_single_market_skills_jobs_26105.pdf 27http://www.digitaleurope.org/DesktopModules/Bring2mind/DMX/Download.aspx?Command=Core_Download&EntryId=1089&language=en-US&PortalId=0&TabId=353; http://www.unica-network.eu/news/european-commission-launches-opening 28 For example, the UK Skills Funding Agency, a government body, report the figure in a in a recent report https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/499031/Review_of_Publicly_Funded_Digital_Skills_Qualifications_2016_FINAL.pdf; similarly, the Malta Information Technology Agency https://www.meusac.gov.mt/file.aspx?f=3234
64
4.3 What types of digital skills are required in European workplaces?
The Digital Skills Survey investigated the digital skills existing in European workplaces.
Workplaces were surveyed about the level of importance they assign to the digital skills
possessed by their workforce (measured on a scale from 1 to 5, where 1 is ‘not at all
important’ and 5 is ‘essential’), classified in the nine occupational categories identified
by the ISCO 1-digit code29.
Broadly speaking, the evidence collected shows that significant proportions of
workplaces require their workforce to possess at least basic digital skills to perform
their job tasks (Figure 4.1). Almost all the workplaces (98%) suggest that managers
should possess at least basic digital skills. Also, around nine workplaces out of ten state
that employees working as professionals (94%), technicians and associate professionals
(88%), clerical support workers (98%)or skilled agricultural workers (90%) are required
to possess at least basic digital skills. A slightly lower proportion of workplaces (81%)
report that sales, customer or personal service workers need to possess basic digital
skills. Although in much smaller proportions, workplaces require basic digital skills also
to building workers (almost half of workplaces), plant machine operators (34% of
workplaces) and even to employees in elementary occupation (27% of workplaces).
The basic digital skills more frequently required by employers are those related to
searching for, collecting and processing information via the internet and communicating
using email, and this applied across all the job categories (see Tables A4.1 and A4.2 in
Annex 2). These types of digital skills are often seen as being highly important for
managers, professionals, technicians and clerical workers, moderately important for
sales workers and skilled agricultural workers and of lower importance to other groups
of workers (Table A4.3).
Employers mainly require advanced digital skills among the professionals (54% of
workplaces) and technicians (52%) they employ, and to a lesser extent among clerical
workers (45%), managers and building workers (31% of workplaces each). Advanced
digital skills are much less important for all the rest of occupations, but one quarter of
workplaces require advanced digital skills for sales workers, and for skilled agricultural
workers. One fifth of employers report that plant machine operators should possess
advanced digital skills. Advanced digital skills are required to a moderate to low level of
importance for all the jobs (see Table A4.3, Annex 2).
Finally, regarding the third group of digital skills - specialist digital skills – which
includes programming and software development, and the design and maintaining of
ICT architecture for the workplace, a very significant proportion of workplaces require
workers to possess this type of digital skills where they are employed as professionals
and technicians (43% and 44% respectively), and to a lesser extent as managers (33%
of workplaces). Workplaces require specialist digital skills also among skilled agricultural
workers and clerical workers (25% and 22% respectively). Smaller proportions of
workplaces require individuals to possess specialist digital skills where they are
employed as sales workers (16%), building workers (11%), plant machine operators
(10% of workplaces) or in elementary occupations (7%). Specialist digital skills are
29 “Managers”, “Professionals, “Technicians and associate professionals”, ”Clerical support workers”, “Sales, customer or personal service workers”, “Skilled agricultural, forestry and fishery workers”, “Building, craft and related trade workers”, “Plant and machine operators and assemblers”, ”Elementary occupations”.
65
more frequently required in the ‘design and maintaining of ICT for the workplace’ (see
Tables A4.1, Table A4.2 and A4.3 in Annex 2).
Figure 4.1 – Workplaces by level of importance of digital skills (by type) of employees in specific occupations, EU28 (% of total employers)
Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential,) how important it is for employees in these categories to…: Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (job category: Managers); 3,463,858 (job category: Professionals); 2,045,270 (job category: Technicians); 4,172,004 (job category: Clerical workers); 3,815,976 (job category: Sales workers); 1,740,841 (job category: Skilled agricultural workers); 2,475,089 (job category: Building workers); 1,059,179 (job category: Plant machine operators); 1,164,035 (job category: Elementary occupations) Source: European Digital Skills Survey (weighted values)
2.4
66.7
66.6
5.4
45.7
55.9
12.1
46.8
55.0
1.8
54.9
77.8
19.1
74.5
83.6
10.5
74.7
74.7
50.4
66.7
88.5
65.3
77.3
88.3
72.2
89.5
91.6
97.6
33.1
33.0
94.3
53.9
43.2
87.5
52.2
43.7
98.1
44.6
21.6
80.8
25.1
15.7
89.6
25.3
25.2
49.4
33.1
10.9
34.1
22.0
10.7
27.5
9.9
7.0
Basic
Advanced
Specialist
Basic
Advanced
Specialist
Basic
Advanced
Specialist
Basic
Advanced
Specialist
Basic
Advanced
Specialist
Basic
Advanced
Specialist
Basic
Advanced
Specialist
Basic
Advanced
Specialist
Basic
Advanced
Specialist
Man
ager
sP
rofe
ssio
nal
sTe
chn
icia
ns
Cle
rica
l su
pp
ort
wo
rke
rs
Sale
s, c
ust
om
ero
r p
erso
nal
serv
ice
Skill
edag
ricu
ltu
ral
wo
rke
rsB
uild
ing
wo
rke
rsP
lan
t m
ach
ine
op
erat
ors
Ele
me
nta
ryo
ccu
pat
ion
s
Not important at all Somehow important to essential Don't know
66
Given the limitations around the sample size, additional analysis around the specific
characteristics of the workplaces does not allow for more accurate analysis at, for
example, the sector level.
To explore what types of workplaces consider advanced or specialist digital skills
important for their workforce, two linear regression models have therefore been
developed.
The linear regression models (presented in full in Tables A4.5 and A4.6 in Annex 2 and
in a synthesised version in Figures 4.2 and 4.3 below), show the relationship between
each of the workplace’s characteristics and the importance of advanced or specialist
digital skills.
The importance assigned to advanced digital skill increases significantly (all other
characteristics being equal) for large-sized workplaces, and for workplaces operating in
‘information and communication, professional, scientific and technical activities,
administrative and support service activities’ and ‘manufacturing and utilities’ macro-
sectors (Figure 4.2). The importance placed on advanced digital skills increases
significantly (all other characteristics being equal) if the workplace belongs to a group,
in the role of headquarters, rather than being a single office, when the reference market
of the workplace are the national or the international markets, and when the workplace
is in the public sector. Conversely, workplaces for which advanced digital skills are not
as important are micro-sized workplaces, subsidiary site workplaces, and workplaces
that operate in local markets and employ a high proportion of female (51% to 75% of
staff or 75% and more) or older workers (26% to 50% of total staff).
67
Figure 4.2 – Linear regression: importance for workplaces of advanced digital skills
(Estimated parameters).
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential,) how important it is for employees in these categories to…: Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light
grey. Source: European Digital Skills Survey
The linear regression model (Figure 4.3) shows that in line with the patterns observed
above for advanced digital skills, the importance given to specialist digital skill also
increases significantly (all other characteristics being equal) for workplaces belonging to
a group in the role of headquarters, for large-sized workplaces, and in particular for
workplaces operating in the ‘information and communication, professional, scientific and
technical activities, administrative and support service activities’ macro-sector. Also, the
importance given to advanced digital skills increases significantly (all other
characteristics being equal) for workplaces that are active on international markets,
-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
Only workplace (omitted)
Headquarters
Subsidiary_site
size < 10 (omitted)
size10_49
size50_249
size250
sectorA (omitted)
sectorCD
sectorF
sectorGHI
sectorJMN
sectorPQ
fem_rate<26 (omitted)
fem_rate_26_50
fem_rate_51_75
fem_rate_75
univ_rate<26 (omitted)
univ_rate_26_50
univ_rate_51_75
univ_rate_75
young_rate<26 (omitted)
young_rate_26_50
young_rate_51_75
young_rate_75
old_rate<26 (omitted)
old_rate_26_50
old_rate_51_75
old_rate_75
private (omitted)
public
Local_market (omitted)
Regional_market
National_market
International_market
Wo
rkp
lace
typ
eW
ork
pla
cesi
zeSe
cto
rFe
mal
e ra
teU
niv
ersi
tyra
teYo
un
g ra
teO
ld r
ate
Ow
ner
ship
(q9
)M
arke
ts (
q11
)
68
that are in the public sector or that employ a high proportion of employees holding a
university degree. Workplaces where which advanced digital skills are not especially
important include those employing a high proportion of female workforce.
Figure 4.3 – Linear regression: importance for workplaces of specialist digital skills
(Estimated parameters).
Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential,) how important it is for employees in these categories to…: Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Source: elaboration on European Digital Skills Survey
-0.4 -0.2 0 0.2 0.4 0.6 0.8
Only workplace (omitted)
Headquarters
Subsidiary_site
size < 10 (omitted)
size10_49
size50_249
size250
sectorA (omitted)
sectorCD
sectorF
sectorGHI
sectorJMN
sectorPQ
fem_rate<26 (omitted)
fem_rate_26_50
fem_rate_51_75
fem_rate_75
univ_rate<26 (omitted)
univ_rate_26_50
univ_rate_51_75
univ_rate_75
young_rate<26 (omitted)
young_rate_26_50
young_rate_51_75
young_rate_75
old_rate<26 (omitted)
old_rate_26_50
old_rate_51_75
old_rate_75
private (omitted)
public
Local_market (omitted)
Regional_market
National_market
International_market
Wo
rkp
lace
typ
eW
ork
pla
ce s
ize
Sect
or
Fem
ale
rate
Un
iver
sity
rat
eYo
un
g ra
teO
ld r
ate
Ow
ner
ship
(q
9)
Mar
kets
(q
11)
69
4.4 Digital skills of employees in different jobs
The type of digital skills required by employers mirrors the evidence collected on
employees, in each job category, by the type of digital skills they possess.
The majority of employees in each job category possess at least basic digital skills
(Figure 4.4), with the proportion varying greatly according to the occupation. Almost
nine out of ten workers employed as managers (88%), and more than half of clerical
workers (56%) and skilled agricultural workers (59%)30 possess basic digital skills.
Almost four out of ten workers employed as professionals (38%) or technicians (40%)
possess basic digital skills. About three out of ten workers in the rest of the
occupational groups possess basic digital skills. The basic digital skills more frequently
reported are around the use of internet and email, (Table A4.2 in Annex 2). Advanced
digital skills are more likely to be possessed by managers (19%), skilled agricultural
workers (21%) and to a lesser extent clerical workers (13% of total employees in this
category), followed by those employed as technicians (10%), professionals (9%),
building workers (8%), sales workers and elementary occupations (8% each) and finally
as plant machine operators (7%).
Among the advanced digital skills, skills in using software for design, calculation and
simulation is most common across all the occupational categories, while only very
limited proportions of workers in specific job categories (e.g. technicians) possess the
skills needed to use and programme CNC machines or robots (Table A4.2 in Annex 2).
Finally, specialist digital skills are more common among workers employed as skilled
agricultural workers and managers (25% and 23% respectively). Slightly more than
10% of individuals employed as clerical workers, technicians and professionals (14%,
12%, 10% respectively) have specialist digital skills. A small proportion of employees in
the sales workers, building workers, plant machine operators and elementary
occupations job categories possess specialist digital skills.
30 Figures regarding the proportion of skilled agricultural workers should be interpreted cautiously in absence of further research, as they seem to be anomalous in some cases. The existence of a bias of social desirability could be one possible explanation, although the significant changes and the restructuring of the agricultural sector, which has attracted as well younger and skilled employees, could also contribute to explain such results.
70
Figure 4.4 – Employees in specific occupations by type of digital skills (basic, advanced, specialized), EU28 (% of total employees in specific occupational category)
Q17. Please provide your best estimate of the approximate number or share of employees carrying out such tasks (listed in Q16). Number of employees in each job category (weighted values, EU28)= 7,564,363 (job category: Managers); 28,452,941 (job category: Professionals); 25,519,514 (job category: Technicians); 16,921,402 (job category: Clerical workers); 25,519,514 (job category: Sales workers); 1,526,330 (job category: Skilled agricultural workers); 16,887,614 (job category: Building workers); 12,206,673 (job category: Plant machine operators); 15,561,133 (job category: Elementary occupations) Total number of employees (weighted values, EU28)= 150,563,540 Number of valid responses=4,608 (workplaces) N=5,644,799 (workplaces) Source: European Digital Skills Survey (weighted values)
Illustrative examples of the digital skills required by specific jobs that are not office-
based are shown below with reference to different jobs categories. The qualitative
information has been gathered as part of the ‘ICT for work: Digital skills in the
workplace’ study of which the European Digital Skills Survey is part, with the aim of
providing a picture of how digitisation is increasingly penetrating and transforming
existing jobs and their quality31.
31 The full results and the methodology of such research activity, carried out in 2016 by the consortium Ecorys and Danish Technological Institute, are presented in the report The impact of ICT on job quality: evidence from 12 job profiles. An intermediate report from the study "ICT for work: Digital skills in the workplace – SMART 2014/0048" https://ec.europa.eu/digital-single-market/en/news/report-shows-digital-skills-are-required-across-all-types-work-also-jobs-outside-office
88.3
38.4
40.5
56.3
34.3
59.7
30.6
27.5
35.1
18.7
8.9
9.8
13.2
7.7
21.3
8.3
7.1
7.5
23.3
10.3
11.8
14.1
6.7
25.0
7.7
4.7
5.7
Managers
Professionals
Technicians
Clerical workers
Sales workers
Skilled agric workers
Building workers
Plant machine operators
Elementary occupations
Basic digital skills Advanced digital skills Specialist digital skills
71
For example in the ‘agricultural and forestry production managers’ (ISCO 1311) the
profiling of the specific job ‘dairy farmer’ allowed the following digital skills currently
needed by farmers to perform their day-to-day job tasks to be identified.
Box 4.1 – Digital skills required by Dairy farmers
Job: Dairy farmer
Digital skills required Description / Purpose
Basic digital skills The dairy farmer needs a basic level of digital skills to use the software applications and needs to continually update knowledge
Advanced user skills for herd management software and automatic feeding systems
To track and maintain detailed records on livestock and to schedule vaccinations, testing and breeding events. Skills also needed for automatic feeding systems
Advanced user skills to manage automatic milking systems (AMS)
To manage automated milking systems (although farmer may also buy expert advice from ICT
specialist who installs the system)
Advanced user skills for ERP systems Depending on the size of the farm, may need advanced skills to support planning and management of farm's functions
Source: The impact of ICT on job quality: evidence from 12 job profiles
The following example ‘product and garment designers’ (ISCO 2163) in the job category
‘professionals’ looks specifically at the job profile of an ‘industrial designer in
manufacturing company’. The digital skills required to perform the job tasks, which in
the past were carried out entirely manually, are currently the following.
Box 4.2 – Digital skills required by Industrial designers
Job: Industrial designer
Digital skills required Description / Purpose
Basic and advanced digital skills to use multiple ICT tools efficiently
Importance of familiarity with multiple ICT tools
Advanced user skills for computer aided design (CAD) software
To use computer systems to aid in the creation, modification, analysis or optimisation of a design; use CAD software key to increase productivity of designer, improve quality of the design, improve communications through documentation and create a
database for manufacturing
Advanced user skills for 3D printing To quickly create complex, 3D forms
Advanced user skills for analytical software
To do calculations and simulations and model-based design related to graphic programming and the design process; use of this software is important to research production specifications, costs, production materials and manufacturing methods and provide
cost estimates and itemised production requirements
Source: The impact of ICT on job quality: evidence from 12 job profiles
The ‘specialist physician (internal medicine) also in the job category of ‘professionals’
(ISCO 2212) and in particular the job profile of ‘doctor in a hospital (general internist
physician)’, which currently require the following types of digital skills.
72
Box 4.3 – Digital skills required by Doctors in a hospital
Job: Doctor in a hospital (general internist physician)
Digital skills required Description / Purpose
Basic user skills to apply transcription and translation software
To handle voice recognition software to transcribe dictation; computer based skills for computer based
dialogue systems that help doctors to translate and transcribe interviews with foreign language patients
Advanced user skills to apply digital patient administration systems
To operate key tools used for keeping track of the waiting list, appointments, coding of procedures and
diagnoses, and patient billing
Advanced user skills for patient monitoring systems
To monitor and interpret patient data
Source: The impact of ICT on job quality: evidence from 12 job profiles
In the job category ‘clerical workers’, the ‘transport clerks’ (ISCO 4323) and in
particular the job profile of the ‘transport clerk in transport/logistics company’, needs
the following digital skills to perform their job tasks.
Box 4.4 – Digital skills required by Transport clerks (TCs)
Job: Transport clerk
Digital skills required Description / Purpose
Advanced digital skills for analytical software
To find transport solutions that optimise the decided route, the use of energy resources and best possible utilisation of transport vehicles; TC must also be able to present solutions and various options to the client using these skills
Advanced user skills for shipment tracking software (or package logging)
For the process of tracking shipping containers, mail and parcel post at different points of time during the transportation process; to be able to provide a tracking number/ref to the client
Basic user skills in management system organising all the activities of the transport company
Overall management is not the responsibility of the TC, but to work efficiently within the company a basic competence is required. The management system organises inventory, remote warehouses, activity billing, client agreements, orders etc.
Source: The impact of ICT on job quality: evidence from 12 job profiles
A final illustrative example of how digitisation is changing jobs and occupations is the
‘car mechanic’ in the ‘motor vehicle mechanics and repairers’ (ISCO 7231) in the job
category ‘building workers and craft and related trades workers’, who currently needs
the following digital skills to repair cars and vehicles which are incorporating an
increasing amount of digital technology.
73
Box 4.5 – Digital skills required by Car mechanics
Job: Car mechanic
Digital skills required Description
Advanced user skills to apply analytical software to diagnose vehicles
To diagnose the cause of vehicle operating problems by tracing and locating defects and repairing malfunctions
Advanced user skills for online databases for repair of vehicles
To ensure repair is up to manufacture’s standard
Basic user skills for CRM-systems
Automotive Customer Relation Management (CRM)
CRM specifically made for the industry; to organise
and keep track of all activities and services taking place between the customer and repair shop
Digital skills integrated with
professional/technical skills of car mechanics
To use relevant software tools to carry out work;
digital skills alone are of no value and must be combined with mechanical skill
Source: The impact of ICT on job quality: evidence from 12 job profiles
4.5 Analysis of specific jobs
In order to provide additional evidence on the digital skills required by employers to
workers employed in specific jobs, within the European Digital Skills Survey workplaces
were asked to select up to three jobs amongst the most important for day-to-day
operations. They were asked to assess a number of aspects related to them, such as
the type and level of digital skills of employees in these selected jobs, and changes in
job tasks in the last five years or in the next five years related to the use of ICT. This
section presents the findings of the analysis of this data. Data are reported by economic
sector and are unweighted, since this information is meant to be qualitative in nature,
and also considering the limited number of responses collected and the related issues of
representativeness related to the sample size. A total number of 3,138 out of 7,800
workplaces selected only one job to be evaluated, 2,100 workplaces selected two and
1,026 selected three. A total number of 6,264 workplaces (corresponding to 80% of
total workplaces) undertook the assessment of selected jobs.
The job selected at the top of the ranking across all economic sectors is a managerial
job (Table 4.1). Although in most of the sectors the occupations most frequently
reported are very much sector-specific (e.g. in the agricultural sector forestry and
related workers or field crop and vegetable growers, in the construction sector home
builders, roofers, etc.) some occupations, such as for example managing directors and
chief executives, are reported among the first 10 in all the economic sectors. In
‘agriculture’ sector the ‘agricultural and forestry production managers’ were selected as
the most important for day-to-day operations by 147 workplaces; in the ‘manufacturing
and utilities’ macro-sector ‘manufacturing managers’ were chosen by 215 workplaces;
in the ‘construction’ sector the ‘construction managers’ have been selected by 158
respondents; in both ‘commerce, transport, accommodation and food service’ and
‘information and communication, professional and administrative services’ macro-
sectors the ‘managing directors and chief executives’ have been selected by 131 and
130 workplaces respectively; finally in the ‘education and human health’ macro-sector
‘education managers’ were indicated by 82 workplaces.
74
Table 4.1 – Occupations selected as the most important for day-to-day operations by sector and total number of workplaces (ranking based on number of workplaces reporting
occupation, top 10 occupations)
Sector Rank Job title Total
number of workplaces
A. Agriculture
1 Agricultural and forestry production managers 147
2 Forestry and related workers 116
3 Managing directors and chief executives 98
4 Mixed crop growers 61
5 Mixed crop and animal producers 58
6 Gardeners, horticultural and nursery growers 57
7 Field crop and vegetable growers 57
8 Livestock and dairy producers 47
9 General office clerks 45
10 Animal producers not elsewhere classified 34
CD. Manufacturing
and utilities
1 Manufacturing managers 215
2 Managing directors and chief executives 126
3 Clerical support workers not elsewhere classified 73
4 Sales and marketing managers 68
5 General office clerks 59
6 Building frame and related trades workers not elsewhere classified
45
7 Craft and related workers not elsewhere classified 43
8 Industrial and production engineers 43
9 Finance managers 43
10 Manufacturing supervisors 35
F. Construction
1 Construction managers 158
2 House builders 140
3 Managing directors and chief executives 123
4 Building frame and related trades workers not elsewhere classified
103
5 General office clerks 54
6 Civil engineers 48
7 Plumbers and pipe fitters 44
8 Clerical support workers not elsewhere classified 41
9 Craft and related workers not elsewhere classified 37
10 Carpenters and joiners 32
GHI. Commerce, transport,
accommodation and food service
1 Managing directors and chief executives 131
2 Shop sales assistants 123
3 Retail and wholesale trade managers 95
4 Sales and marketing managers 89
5 Waiters 79
6 Cooks 75
7 General office clerks 64
8 Shopkeepers 63
9 Sales workers not elsewhere classified 61
10 Supply, distribution and related managers 59
JMN. Information and communication;
Professional, scientific and
technical activities;
Administrative services
1 Managing directors and chief executives 130
2 Clerical support workers not elsewhere classified 56
3 Sales and marketing managers 52
4 Accountants 49
5 Information and communications technology service managers 45
6 General office clerks 44
7 Finance managers 38
8 Software developers 33
9 Engineering professionals not elsewhere classified 33
10 Business services and administration managers not elsewhere classified
30
PQ. Education and human
health
1 Education managers 82
2 Managing directors and chief executives 72
3 Specialist medical practitioners 59
75
Sector Rank Job title Total
number of workplaces
4 Health services managers 53
5 General office clerks 47
6 Dentists 43
7 Personal care workers in health services not elsewhere classified 39
8 Generalist medical practitioners 36
9 Health professionals not elsewhere classified 32
10 Early childhood educators 31
Q18. Please select up to THREE specific jobs existing in your workplace which are amongst the most important for your day-to-day operations. If possible, please select them from different job categories. N=6,264 Source: European Digital Skills Survey (unweighted values)
For each selected job, workplaces have assessed the importance of digital skills (by
type) required to perform day-to-day activities (Table 4.3). Overall, basic digital skills
are seen as at least somewhat important for almost all the jobs. All the other types of
digital skills do not appear as relevant across sectors and jobs. In several cases, digital
skills are not felt to be important at all. On the other hand, all the most crucial jobs
(those at the top of the ranking) are considered to require all types of digital skills to
some level of importance, although the basic digital skills are required to a moderate to
higher level of importance (Table 4.3, more detailed information is reported in table
A4.8 in Annex 2).
Table 4.3 – Occupations selected as the most important for day-to-day operations by
sector and by type and level of digital skills of employees in selected jobs (level of
importance 1 = not at all important, 3 = moderately important, 5 = essential)
Sector Rank Job title
Basic
dig
ital
skil
ls
Ad
van
ced
dig
ital skil
ls
Sp
ecia
list
dig
ital skil
ls
A. Agriculture
1 Agricultural and forestry production managers 4 2 2
2 Forestry and related workers 2 1 1
3 Managing directors and chief executives 4 2 2
4 Mixed crop growers 2 1 1
5 Mixed crop and animal producers 2 1 1
6 Field crop and vegetable growers 2 1 1
7 Gardeners, horticultural and nursery growers 2 1 1
8 Livestock and dairy producers 2 1 1
9 General office clerks 4 1 2
10 Animal producers not elsewhere classified 1 1 1
CD. Manufacturing
and utilities
1 Manufacturing managers 4 2 2
2 Managing directors and chief executives 4 2 2
3 Clerical support workers not elsewhere classified 5 1 2
4 Sales and marketing managers 4 2 2
5 General office clerks 4 1 2
6 Building frame and related trades workers not elsewhere classified
2 1 1
7 Finance managers 4 2 2
8 Industrial and production engineers 4 2 3
9 Craft and related workers not elsewhere classified 1 1 1
10 Supply, distribution and related managers 4 2 2
F. Construction
1 Construction managers 3 2 2
2 House builders 2 1 1
3 Managing directors and chief executives 4 2 1
76
Sector Rank Job title
Basic
dig
ital
skil
ls
Ad
van
ced
dig
ital skil
ls
Sp
ecia
list
dig
ital skil
ls
4 Building frame and related trades workers not elsewhere classified
2 1 1
5 General office clerks 4 1 2
6 Civil engineers 4 2 2
7 Plumbers and pipe fitters 2 1 1
8 Clerical support workers not elsewhere classified 5 1 2
9 Craft and related workers not elsewhere classified 1 1 1
10 Carpenters and joiners 2 1 1
GHI. Commerce, transport,
accommodation and food service
1 Managing directors and chief executives 4 2 2
2 Shop sales assistants 3 1 1
3 Retail and wholesale trade managers 4 2 2
4 Sales and marketing managers 4 2 2
5 Waiters 1 1 1
6 Cooks 1 1 1
7 General office clerks 4 1 2
8 Shopkeepers 4 1 1
9 Sales workers not elsewhere classified 3 1 1
10 Supply, distribution and related managers 4 1 2
JMN. Information and communication;
Professional, scientific and
technical activities;
Administrative services
1 Managing directors and chief executives 4 2 2
2 Clerical support workers not elsewhere classified 4 1 2
3 Sales and marketing managers 4 2 2
4 Accountants 4 2 2
5 Information and communications technology service managers
4 4 2
6 General office clerks 4 1 2
7 Finance managers 5 2 2
8 Engineering professionals not elsewhere classified 4 3 2
9 Software developers 4 4 2
10 Human resource managers 5 1 1
PQ. Education and human
health
1 Education managers 4 2 2
2 Managing directors and chief executives 4 2 1
3 Specialist medical practitioners 3 1 2
4 Health services managers 4 2 2
5 General office clerks 4 1 2
6 Dentists 3 2 1
7 Personal care workers in health services not elsewhere classified
3 1 1
8 Generalist medical practitioners 3 1 1
9 Health professionals not elsewhere classified 3 1 1
10 Primary school teachers 4 1 2
Q19. Thinking about these jobs in your workplace, please indicate using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential) how important for day-to-day activities it is for employees in these jobs to…: N=6,264 Source: European Digital Skills Survey (unweighted values)
The use of digital technologies has changed the way job tasks are carried out in most of
the selected jobs for the vast majority of workplaces and in most of the sectors (Table
4.4). This is especially true for the top-ranked jobs, which are considered to have been
modified by ICT according to 90% of workplaces in the ‘commerce, transport,
accommodation and food service’, ‘information and communication’, and ‘education and
human health’ macro-sectors, by 80% of workplaces in the ‘agriculture’ and
‘manufacturing and utilities’ macro-sectors, and by 70% of workplaces in the
‘construction’ sector. Only in the ‘agriculture’, ‘manufacturing and utilities’,
‘construction’ and ‘commerce’ macro-sectors do a majority of workplaces (admittedly
77
limited to a number of specific low-skilled jobs) consider that digital technologies have
not changed at all the way tasks are performed over the last five years. This is the case
for ‘field crop and vegetable growers’ and ‘animal producers not elsewhere classified’ in
‘agriculture’, the ‘craft and related workers not elsewhere classified’ in both the
‘manufacturing and utilities’ and the ‘construction’ sectors and finally ‘cooks’ in
‘commerce, transport, accommodation and food service’ sectors. Furthermore, the
changes reported are often at a moderate level (see Table A4.9 in Annex 2).
The majority of workplaces consider that ICT will continue changing the way job tasks
are performed in the next five years for most of the selected jobs and in most of the
sectors, especially with regards to the top-ranked jobs. Nevertheless, reported changes
will be less intense in the near future, and a lack of change is more frequently reported
in relation to a number of jobs especially in specific sectors.
A majority of workplaces consider that there will be no change at all in relation to
‘forestry and related workers’ ‘mixed crop growers’ ‘mixed crop and animal producers’,
‘gardeners, horticultural and nursery growers’, ‘animal producers not elsewhere
classified’ jobs in the ‘agriculture’ sector; to ‘craft and related workers not elsewhere
classified’ jobs in ‘manufacturing and utilities’ macro-sector; to ‘plumbers and pipe
fitters’, ‘craft and related workers not elsewhere classified’ and ‘carpenters and joiners’
in the ‘construction’ sector, and finally to ‘waiters’ and ‘cooks’ in the ‘commerce,
transport, accommodation and food service’ macro-sector.
On the other hand, the ‘information and communication, professional and
administrative services’ and the ‘education and human health’ macro-sectors
workplaces are more likely to be expecting further changes brought by digital
technologies in relation to all the jobs selected. The vast majority of workplaces expect
some degree of change induced by digital technologies in the way job tasks are
performed.
Table 4.4 – Occupations selected as the most important for day-to-day operations by
type of change in the job tasks due to the use of ICT in LAST and NEXT five years, by
sector (%)
Sector Rank Job title
Last 5 years
Next 5 years
No c
hange a
t all
Changes r
eport
ed
No c
hange a
t all
Changes r
eport
ed
A. Agriculture
1 Agricultural and forestry production managers 16.6 83.4 27.7 72.3
2 Forestry and related workers 44.8 55.2 51.8 48.2
3 Managing directors and chief executives 8.2 91.8 17.5 82.5
4 Mixed crop growers 45.0 55.0 66.7 33.3
5 Mixed crop and animal producers 43.1 56.9 53.4 46.6
6 Field crop and vegetable growers 52.7 47.3 60.7 39.3
7 Gardeners, horticultural and nursery growers 33.9 66.1 51.8 48.2
8 Livestock and dairy producers 31.9 68.1 41.3 58.7
9 General office clerks 11.1 88.9 11.1 88.9
10 Animal producers not elsewhere classified 55.9 44.1 54.5 45.5
CD. Manufacturing 1 Manufacturing managers 22.3 77.7 22.0 78.0
78
Sector Rank Job title
Last 5 years
Next 5 years
No c
hange a
t all
Changes r
eport
ed
No c
hange a
t all
Changes r
eport
ed
and utilities 2 Managing directors and chief executives 6.4 93.6 9.9 90.1
3 Clerical support workers not elsewhere classified
4.1 95.9 1.4 98.6
4 Sales and marketing managers 9.4 90.6 22.6 77.4
5 General office clerks 3.6 96.4 1.8 98.2
6 Building frame and related trades workers not elsewhere classified
22.2 77.8 24.4 75.6
7 Finance managers 4.9 95.1 11.9 88.1
8 Industrial and production engineers 16.7 83.3 11.6 88.4
9 Craft and related workers not elsewhere classified
65.1 34.9 69.0 31.0
10 Supply, distribution and related managers 21.2 78.8 16.1 83.9
F. Construction
1 Construction managers 26.6 73.4 29.7 70.3
2 House builders 39.3 60.7 50.0 50.0
3 Managing directors and chief executives 6.6 93.4 10.0 90.0
4 Building frame and related trades workers not elsewhere classified
40.8 59.2 44.1 55.9
5 General office clerks 11.1 88.9 9.6 90.4
6 Civil engineers 6.2 93.8 8.5 91.5
7 Plumbers and pipe fitters 50.0 50.0 53.5 46.5
8 Clerical support workers not elsewhere classified
9.8 90.2 4.9 95.1
9 Craft and related workers not elsewhere classified
83.8 16.2 88.9 11.1
10 Carpenters and joiners 68.7 31.3 59.4 40.6
GHI. Commerce, transport,
accommodation and food service
1 Managing directors and chief executives 4.6 95.4 16.2 83.8
2 Shop sales assistants 20.5 79.5 32.5 67.5
3 Retail and wholesale trade managers 9.8 90.2 13.5 86.5
4 Sales and marketing managers 16.9 83.1 26.4 73.6
5 Waiters 42.3 57.7 54.7 45.3
6 Cooks 66.7 33.3 77.0 23.0
7 General office clerks 9.5 90.5 8.1 91.9
8 Shopkeepers 6.6 93.4 6.6 93.4
9 Sales workers not elsewhere classified 44.3 55.7 46.6 53.4
10 Supply, distribution and related managers 6.8 93.2 15.5 84.5
JMN. Information and
communication; Professional, scientific and
technical activities;
Administrative services
1 Managing directors and chief executives 7.7 92.3 6.2 93.8
2 Clerical support workers not elsewhere classified
7.1 92.9 5.7 94.3
3 Sales and marketing managers 17.6 82.4 17.6 82.4
4 Accountants 2.0 98.0 14.9 85.1
5 Information and communications technology service managers
15.6 84.4 15.6 84.4
6 General office clerks 9.3 90.7 9.3 90.7
7 Finance managers 8.1 91.9 2.9 97.1
8 Engineering professionals not elsewhere classified
15.6 84.4 15.2 84.8
9 Software developers 15.6 84.4 9.7 90.3
10 Human resource managers 16.7 83.3 20.0 80.0
PQ. Education and human health
1 Education managers 6.2 93.8 6.3 93.7
2 Managing directors and chief executives 7.0 93.0 2.9 97.1
3 Specialist medical practitioners 22.0 78.0 29.3 70.7
4 Health services managers 7.5 92.5 15.4 84.6
5 General office clerks 10.9 89.1 13.3 86.7
79
Sector Rank Job title
Last 5 years
Next 5 years
No c
hange a
t all
Changes r
eport
ed
No c
hange a
t all
Changes r
eport
ed
6 Dentists 9.3 90.7 14.0 86.0
7 Personal care workers in health services not elsewhere classified
13.2 86.8 13.5 86.5
8 Generalist medical practitioners 5.6 94.4 2.8 97.2
9 Health professionals not elsewhere classified 10.0 90.0 17.2 82.8
10 Primary school teachers 6.5 93.5 3.2 96.8
Q20. Thinking about these jobs in your workplace, please indicate if and to what extent the use of ICT has changed the way job tasks are carried out. Please refer to the timespan of the last 5 years. Q21a. Thinking about these jobs in your workplace, please indicate if and to what extent you think the use of ICT will change the way job tasks are carried out. Please refer to the timespan of the next 5 years. N=6,264 Source: European Digital Skills Survey (unweighted values)
4.6 Summary
This section has provided an overview of the demand for and the importance
assigned to different types of digital skills by employers, establishing differences
across occupations in terms of skills required. The key findings are:
Digital skills can be broadly understood as the ability to locate, organise,
understand, evaluate, create and share information using digital technology, at
different levels of competence;
A set of 10 digital skills was considered in the survey and were clustered in 3 main
synthetic indicators, based on the level of competences required to the worker:
Basic digital skills, Advanced digital skills, Specialist digital skills;
The demand for digital skills is clearly related to the job role of the worker;
The evidence gathered through the Digital Skills Survey indicates that in some job
categories and in relation to specific types of digital skills already more than 90% of
jobs require digital skills;
Workplaces were surveyed about the level of importance (measured on a scale
from 1 to 5, where 1 is ‘not at all important’ and 5 is ‘essential’) they assign to
digital skills possessed by their workforce;
The demand for basic digital skills is generally higher across occupations, but with
different degrees of importance. Advanced and specialist digital skills are required
to a moderate to low level of importance;
Basic digital skills are required above all for managers, but also for professionals,
technicians and associate professionals, clerical support workers or skilled
agricultural workers (about 90% of workplaces surveyed report the need); basic
digital skills are less required in occupations such as building workers (almost half
of workplaces), plant machine operators (34%) and elementary occupation (27%);
The basic digital skills more frequently required for all the job categories are those
related to the search for, collection and processing of information via the internet
and communicating using email;
80
Advanced digital skills are required mostly by professionals (54% of workplaces)
and technicians (52%) and to a lesser extent by clerical workers (45%), managers
and building workers (31% each). This type of skill is much less important for other
occupations;
Specialist digital skills are required by a significant proportion of workplaces by
professionals and technicians (43% and 44% respectively), and to a lesser extent
to managers (33% of workplaces), skilled agricultural workers and clerical workers
(25% and 22% respectively);
The specialist digital skills most frequently required across all occupations are
around the ‘design and maintaining of ICT for the workplace’;
Different characteristics of workplaces (such as economic sector, size, market of
reference, characteristics of workforce, etc.) are correlated to the different levels of
importance assigned to the type of digital skills.
The section furthermore provided an overview of digital skills held by workers in
different occupations. The key findings are:
The type of digital skills required by employers mirrors the type of digital skills they
possess;
The majority of employees in each job category possess at least basic digital skills,
but the proportion varies greatly according to the occupation, with the highest
share among managers (90%), clerical workers and skilled agricultural workers
(more than half);
Advanced digital skills are possessed more frequently by workers employed as
managers (19%), skilled agricultural workers (21%) and to a lesser extent by
clerical workers (13%), technicians (10%), professionals (9%), building workers
(8%), sales workers (8%), elementary occupations (7%) and finally as plant
machine operators (7%);
Specialist digital skills are more common among workers employed as skilled
agricultural workers and managers (25% and 23% respectively) and to a lesser
extent among clerical workers, technicians and professionals (14%, 12%, 10%
respectively).
Finally, the chapter presented illustrative examples of the digital skills required
by specific jobs that are not office-based, providing a picture of how digitisation is
increasingly penetrating and transforming existing jobs and their quality, using
qualitative information. The key findings are:
Overall, basic digital skills are required for jobs in all the occupational groups; the
other types of digital skills do not appear as relevant across sectors and jobs;
All the most crucial jobs across sectors are considered to require all types of digital
skills to some level of importance, although basic digital skills are required to a
moderate to higher level of importance;
The use of digital technologies has changed the way job tasks are carried out in
most of the selected jobs for the vast majority of workplaces in most of the sectors;
The majority of workplaces consider that ICT will continue changing the way job
tasks are performed in the next five years, especially with regards to the top-
ranked jobs, even though to some extent future changes are expected to be less
intense for certain occupations.
81
CHAPTER 5. THE DIGITAL SKILLS CHALLENGE IN EUROPEAN
WORKPLACES
This chapter explores the issue of digital skills gaps within the workforce in the
European workplaces covered by this survey. Gaps occur when existing employees lack
proficiency in dealing with digital technologies to perform their job tasks. Such digital
skills gaps can inhibit an establishment’s ability to perform efficiently, therefore
impacting negatively on its productivity and profitability, as seen in the studies and
evidence presented in Chapter 1.
The aim of this chapter is thus to respond to the following research questions:
What are the most common digital skills gaps in the workforce according to
employers?
What are the differences across economic sectors and occupations in terms of
digital skill gaps?
What are the actions undertaken by employers to address existing digital skill
gaps?
What are the differences across economic sectors and occupations in terms of
actions undertaken by the employers to address the lack of digital skills?
The chapter presents the incidence of digital skills gaps in European workplaces,
including at sectoral and workplace-size levels, and the digital skills gap density (that is
the proportion of workforce with digital skills gaps) in relation to specific occupations
and types of digital skills. Furthermore, the chapter also presents the impacts of digital
skills gaps on the overall performance of the workplace, and the outcome of such
impacts from an employer’s perspective. The actions undertaken to address the digital
skills gaps are also presented and discussed, alongside the obstacles and bottlenecks to
increase the availability of digital skills in the workplace.
5.1 Defining digital skills gaps
As noted in Chapter 1, there is not a single definition of digital skills gaps, and existing
definitions of skills gaps have been adapted to the context of this study. In particular, a
skills gap can be defined as “a situation where the level of skills of the existing
workforce in a firm is less than required to perform a job adequately or to match the
requirements of a job” (Cedefop, 2015a, p.27). A skills gap corresponds therefore to
the underskilling of workers, that is “a situation in which an individual lacks the skills
and abilities to perform the current job adequately” (Cedefop, 2015, p. 26).
Adapting such definitions to the context of this survey, a digital skills gap has
therefore to be understood as a situation in which the level of digital skills of the
existing workforce in a workplace is less than required to perform a job
adequately or to match the requirements of a job. In other terms, digital skills
gaps exist in workplaces where the workforce lacks the right skills to deal with digital
technologies to perform their current job adequately.
82
From an operational viewpoint, the digital skills gap density (that is the percentage of
underskilled workforce) has been calculated as the number of employees not proficient
in carrying out job tasks involving the use of digital technologies as a proportion of the
total number of employees carrying out the same job tasks involving the use of digital
technologies. This is calculated for each occupation and in relation to each type of
digital skill:
Digital skills gap density = [Number of employees with a non-proficient level of
digital skill n / Number of employees with digital skill n] *100
The digital skills gaps density is expressed in percentage terms and corresponds to the
proportion of underskilled workforce in the area of use of digital technologies. The
higher the value calculated, the larger the digital skills gap.
5.2 Digital skill gaps in European workplaces
The vast majority of European workplaces (85%) report that all of their employees are
fully proficient at performing job tasks involving the use of digital technologies.
Nevertheless, around one in seven workplaces (15%) consider that some of their staff
are not fully proficient when carrying out tasks using digital technologies at work, and
therefore report digital skills gaps in their workforce32.
Larger employers are more likely than smaller employers to report digital skills gaps, in
part because with a larger workforce it is more probable to have underskilled staff, but
most probably because (as seen in Chapter 3) large workplaces are more likely to use
digital technologies. 57% of large workplaces report issues of digital skills gaps,
followed by the small and medium-sized workplaces (24% and 23% respectively), while
only about 12% of micro-sized workplaces (2 to 9 employees) report this issue.
At sectoral level, the macro-sector ‘manufacturing and utilities’ is the most affected by
digital skills gaps (22%), followed by the ‘construction’ sector (19.5% of workplaces
reporting digital skills gaps), the macro-sectors ‘commerce, transport, accommodation
and food service’ (18%) and ‘education and human health’ (17%). The rest of the
sectors display a proportion of workplaces with issues of digital skills gaps lower than
the average, in particular in the agricultural sector where only 0.6% state they have
digital skills gaps (Figure 5.1).
32 The figure regarding the proportion of workplaces with digital skills gaps was calculated comparing variables Q17 and Q23 of the questionnaire and recoding the ‘Don’t knows’ as missing values. In this way, only those who responded to the question were considered to calculate the proportion of workplaces with digital skills gaps.
83
Figure 5.1 – Workplaces reporting digital skill gaps by sector and size, EU28 (% of workplaces)
Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level. Number of valid responses: 4,569 N= 5,634,045 Source: European Digital Skills Survey (weighted values)
The analysis by sampled countries displays that in Portugal and Finland a relatively
lower proportion of employers than in the rest of the countries consider themselves to
have digital skills gaps (3% and 10% respectively). Nevertheless, larger companies in
Portugal seem particularly concerned with digital skills gaps, as 54% of them report this
issue. Digital skills gaps are reported by 22% of workplaces in Germany, 18% in the
UK, 27% in Sweden and 15% in Slovakia (Figures A5.1 and A5.2 in Annex 2).
5.3 Digital skills gaps density
The digital skills gaps density in European workplaces is now presented by occupation
and type of digital skills. Broadly speaking, at an occupational level, digital skills gaps
are more likely to be found in the high-skilled (managers, technicians) and in medium-
skilled (clerical workers, sales workers) occupations, and to a lesser extent in the low-
skilled occupations, with the exception of workers in elementary occupations. This can
be related to the fact that (as noted in Chapter 4) workers in the high-skilled and the
medium-skilled occupations are more likely to use digital technologies to perform their
job than those in the low-skilled and the unskilled occupations, although the proportion
of workers in elementary occupations for which digital skills are required is also
significant.
12.3
23.9 23.3
56.7
0.6
22.4 19.5 18.0
13.7 17.1
14.8
2-9
10
-49
50
-24
9
25
0 o
r m
ore
A. A
gric
ult
ure
CD
. Man
ufa
ctu
rin
g an
d u
tilit
ies
F. C
on
stru
ctio
n
GH
I. C
om
mer
ce, t
ran
spo
rt, a
cco
mm
od
atio
nan
d f
oo
d s
ervi
ce
JMN
. In
form
atio
n a
nd
co
mm
un
icat
ion
;P
rofe
ssio
nal
, sci
enti
fic
and
tec
hn
ical
acti
viti
es; A
dm
inis
trat
ive
serv
ices
PQ
. Ed
uca
tio
n a
nd
hu
man
he
alth
Tota
l
Size Sector EU28
84
Collectively, as set out in Chapter 2, the first five job categories (managers,
professionals, technicians, clerical workers and sales workers) account for almost 70%
of the total workforce employed by the workplaces covered by this survey.
Figure 5.2, shows that the digital skills gaps density is around 13% for managers, and
about 11% with regards to professionals in all the three categories of digital skills
(basic, advanced, specialist). Technicians display a relatively much higher digital skills
gap density in relation to basic digital skills (22%) than in relation to the other two
categories of digital skills: 17% with regards to advanced digital skills and 16% with
regards to specialist digital skills. Clerical workers are slightly more frequently
underskilled with reference to basic digital skills (17%) and less in relation to the other
two groups of digital skills: 16% for advanced digital skills and 15% for specialist digital
skills.
The digital skills gaps density for sales workers are 19% and 18% in relation to basic
and advanced digital skills, but reaches 23% in relation to specialist digital skills. That
result is influenced by the ‘programming and software development’ component that
accounts significantly for this bundle of digital skills (the detailed breakdown by specific
digital skills is reported in Table A5.1 in Annex 2). Both skilled agricultural workers and
building workers display a comparatively lower digital skills gaps density in two out of
the three categories of digital skills (basic and advanced digital skills). Digital skills gaps
density in the category of plant machine operators is about 15% for all the three
categories of digital skills.
Finally, workers in elementary occupations have a high digital skills gaps density in all
the categories of digital skills, in spite of being required to use digital technologies only
to a limited degree, as indicated in Chapter 4. It is likely that the penetration of digital
technologies into tasks normally performed by unskilled workers (as seen in the studies
reported in Chapter 1) has resulted in a growing need for digital skills in a category of
workers that, in the past, would generally not have needed them at all or at a very low
level.
85
Figure 5.2 – Digital skills gaps density by occupation and type of digital skills, EU28 (%)
Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level. Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (job category: Managers); 3,463,858 (job category: Professionals); 2,045,270 (job category: Technicians); 4,172,004 (job category: Clerical workers); 3,815,976 (job category: Sales workers); 1,740,841 (job category: Skilled agricultural workers); 2,475,089 (job category: Building workers); 1,059,179 (job category: Plant machine operators); 1,164,035 (job category: Elementary occupations) Source: European Digital Skills Survey (weighted values)
5.4 Impacts of digital skills gaps
Digital skills gaps at a workplace, determined by the fact that some workers are not
fully proficient in carrying out tasks involving ICT use, may affect the overall workplace
performance as shown in the available evidence presented in Section 1.4 of Chapter 1.
In the majority of workplaces with digital skills gaps, respondents indicated that they
felt that there was no impact of digital skills gaps (62% of total workplaces with digital
skill gaps). However, about 36% of workplaces with digital skill gaps do report a
negative impact on the overall performance. Of those, about the same proportion
(18%) report either a minor impact or a major impact (Figure 5.3).
13.9
11.6
21.8
17.2
19.5
8.4
5.6
15.9
20.6
13.2
10.4
17.4
15.8
17.6
8.0
10.9
16.5
15.3
13.3
12.8
16.4
14.7
22.8
14.0
12.9
15.3
17.6
Managers
Professionals
Technicians
Clerical workers
Sales workers
Skilled agric workers
Building workers
Plant machine operators
Elementary occupations
Basic digital skills Advanced digital skills Specialist digital skills
86
Figure 5.3 - Workplaces by impact of digital skill gaps on overall performance, EU28 (%)
Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance? (Yes, a major impact/Yes, a minor impact/No/Don’t know/Not applicable-100% proficient) Note: workplaces with fully proficient workers (e.g. without digital skills gaps) are excluded from the analysis Number of valid responses: 6,776 N=12,268,096
Source: European Digital Skills Survey (weighted values)
64% of micro-sized workplaces with digital skills gaps report that there is no impact of
digital skill gaps on their overall performance, followed by 61.5% of large workplaces.
On the other hand, small and medium-sized workplaces appear more concerned with
digital skill gaps; around 45% of both categories report an impact on their overall
performance. Small-sized workplaces most frequently report a major impact (24%, with
22% noting a minor impact), while medium-sized workplaces are less likely to report a
major impact (19%, with 26% noting a minor impact) (Table A5.4, Annex 2). The
analysis at sectoral level shows that only about 25-30% of workplaces with digital skills
gaps in the macro-sectors ‘Manufacturing and utilities’ and ‘Construction’ report an
impact on the overall performance of the workplace. In the other economic sectors,
there are greater concerns about the impact of digital skills gaps; almost half of
workplaces expect impacts on the overall workplace performance in the macro-sectors
‘Information and communication/Professional, scientific and technical
activities/Administrative and support service activities’ and ‘Education and human
health’ (Table A5.4, Annex 2).
17.6
18.5
61.7
2.1
Yes, a major impact
Yes, a minor impact
No
Don't know
87
In order to identify additional characteristics of the workplace correlated to the existing
digital skills gaps having a major impact on overall performance, a logistic regression
model was calculated (Figure 5.4, and the full model is reported in Table A5.5 in Annex
2). Workplaces reporting a major impact of digital skill gaps are more likely to be small
or medium-sized, as seen in the descriptive analysis above. Also, the likelihood of
reporting a major impact is highest for the ‘Information and
communication/Professional, scientific and technical activities/Administration support
activities’ macro-sector. Workplaces with a higher incidence of female employees (more
than 75%), of employees holding a university degree (more than 75%) or of younger
employees (between 51% and 75% of employees aged less than 30 years), and
workplaces active on the international market, are more likely to report a major impact
of digital skills gaps on their overall performance. Conversely, workplaces which are
part of a group as a subsidiary site, those belonging to other economic sectors, and
those with a high proportion of older workers are less likely to report a major impact of
digital skills gaps on the overall performance of the workplace.
88
Figure 5.4 – Logistic regression: probability that digital skills gaps have a major impact
on overall performance (Odds ratios).
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance? (Yes, a major impact/Yes, a minor impact/No/Don’t know/Not applicable-100% proficient) Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: workplaces with fully proficient workers (e.g. without digital skills gaps) are excluded from the analysis Source: Elaboration on European Digital Skills Survey (weighted values)
89
The analysis at country level shows that the majority of workplaces with digital skills
gaps consider that digital skill gaps will not have an impact on the overall performance
of the workplace: Germany (86%), Finland (74%), Slovakia (74%), Sweden (63%) and
the UK (59%). In Portugal, on the other hand, the vast majority (83%) of workplaces
express concern about the impact of digital skill gaps, although half expect a minor
impact (Figure A5.3, Annex 2).
As mentioned in the introduction to this chapter, the impact of digital skill gaps on
workplaces’ overall performance can result in different types of negative outcomes as
pointed out in the literature (see Chapter 1, Section 1.4). Of the total number of
workplaces expecting digital skill gaps to impact on the overall performance of
workplace, a general loss of productivity is the expected impact for more than 45% of
them (corresponding to around 15% of total workplaces). Slightly lower proportions of
workplaces consider a decrease in the number of customers or other negative impacts
as the outcome of digital skill gaps in the workforce (42.5% and 41% respectively).
Finally, only 32.4% of workplaces reporting an impact of digital skill gaps on their
overall performance consider digital skills gap to lead to a decrease in the number of
contracts received (Figure 5.5).
Figure 5.5 - Workplaces reporting impacts of digital skill gaps by expected type of impact on overall performance, EU28 (%)
Q26a. What type of impact does this have on your workplace performance? Number of valid responses: 2,591 N=4,427,804 Source: European Digital Skills Survey (weighted values)
5.5 How do workplaces deal with digital skill gaps?
In spite of the potential for digital skills gaps to impact negatively on workplace
performance, the vast majority of workplaces in the European Union have not taken any
45.6 42.5
41.0
32.4
6.8
Loss of productivity Decrease in thenumber ofcustomers
Other negativeimpact
Decrease in thenumber of contracts
Don't know
90
steps to improve the digital proficiency of employees (77%), while 11% reported not
having taken any steps, although they have plans to. Only 12% of total workplaces
have taken action to tackle with digital skill gaps (Figure 5.6).
Figure 5.6 - Workplaces reporting action to tackle digital skill gaps, EU28 (%)
Q27. Has your workplace taken any steps to improve the proficiency of employees to enable them to carry out the tasks involving ICT use? Number of valid responses: 6,776 N=12,268,023 Source: European Digital Skills Survey (weighted values)
Micro-sized workplaces are most likely to say they have not taken any steps to reduce
digital skills gaps (81%), with a further 10% having plans to take steps to tackle digital
skill gaps. On the other hand, medium and large workplaces are those more likely to
have taken action (36% and 29% respectively). At sector level, the sector with the
highest proportion ofworkplaces having taken action is the ‘Education and human
health’ macro-sector (21%), which also has the highest incidence of workplaces
planning to take steps to tackle gaps (15%). ‘Agriculture’ and ‘Construction’ are the
sectors with the highest proportion of workplaces that have not undertaken any
initiative to reduce digital skill gaps (85% and 81% respectively) (Table A5.6, Annex 2).
A logistic regression model on the likelihood that workplaces have undertaken steps to
tackle digital skills gaps as been calculated (Figure 5.7 – full model in Table A5.7, Annex
2). The model confirms a strong positive correlation between the size of the workplace
and the probability of undertaking steps to tackle digital skills gaps. In addition,
workplaces with a high rate of female workers and those active on the international
markets are more likely to undertake actions to deal with digital skills gaps. Conversely,
workplaces with a high incidence of younger workers, those with a proportion of older
workers between 26% and 50%, and those active on national markets are less likely to
undertake any actions to tackle digital skills gaps.
12.1
11.2
76.7
Yes
No, but have plans to
No
91
Figure 5.7 – Logistic regression: probability that workplace has undertaken steps to
tackle digital skills gaps (Odds ratios)
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q27. Has your workplace taken any steps to improve the proficiency of employees to enable them to carry out the tasks involving ICT use? Note: The regressors ‘Female rate’, ‘University rate’, ‘Young rate’. ‘Old rate’ have four categories, reflecting the proportion of female, university degree holders, younger than 30, older than 50 workers of the total workforce. The four categories are as follows: between 0% and 25%, 26% to 50%, 51% to 75%, more than 75%. Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: workplaces with fully proficient workers (e.g. without digital skills gaps) are excluded from the
analysis Source: Elaboration on European Digital Skills Survey (weighted values)
92
The 12% of workplaces which had taken actions to tackle digital skills gaps reported
that training is the most common means selected to deal with digital skill gaps33. In
particular, on-the-job training was used by 84% of workplaces which had undertaken
action to address digital skills gaps. External training and development programmes are
mentioned by 58% of workplaces which had taken action to deal with digital skills gaps.
The other types of actions undertaken are related to changes in work organisation,
including changes in working practices (reported by 49% of workplaces which had taken
action), and reallocating tasks (indicated by 47% of workplaces which had taken
action). Recruiting new staff with the necessary skills, or hiring temporary staff, was an
option taken up by 39% and 25% of workplaces respectively, while outsourcing tasks
involving ICT was used by almost 30% of workplaces which took some action to tackle
digital skill gaps. The last two options - secondment of employees from other
workplaces within the same organisation (for workplaces part of multi-establishment
organisations) and other (non-specified) actions - were the least popular options,
reported by less than 14% and about 11% of workplaces which had taken action to
reduce digital skills gaps (Figure 5.8).
Figure 5.8 - Workplaces reporting having taken action to tackle digital skill gaps by type of action undertaken, EU28 (% of workplaces with digital skill gaps which
undertook actions)
Q28. Which of the following steps is your workplace taking to overcome the fact that some of its employees
are not fully proficient in carrying out tasks involving ICT use? (Please select all that apply) Number of valid responses: 1,486 N=1,476,489 Source: European Digital Skills Survey (weighted values)
33 Eurofound’s Sixth European Working Conditions Survey (2015) carried out on employed individuals indicates that only 37% of workers in the European Union received external training in the last 12 months, even though the majority for less than 5 days, and 33% of employees received on the job training. Differences can be observed among different countries. It is worth to point out that those who are employed in low-skilled occupations, who need the most training, receive actually less; similarly people who have fixed term contracts and people who work part-time receive less training than people in more stable positions: considering that these types of contracts are increasingly more common, this trend might be problematic (Eurofound, Experts’ workshop, Brussels 7th of October 2016).
11.2
13.8
29.3
24.6
39.0
47.2
48.8
58.3
84.4
Other
Secondment of employees from other…
Outsourcing of tasks involving ICT use
Hiring temporary staff with needed skills…
Recruiting new staff with needed skills
Reallocating tasks
Changing working practices (e.g.task sharing)
External training and development programmes
On the job training and development…
93
Larger workplaces are more likely to provide training (both on-the-job and external) to
their employees to reduce digital skills gaps than smaller workplaces. While almost 80%
of micro-sized workplaces chose on-the-job training to reduce digital skill gaps, 90% of
small-sized, 95% of medium-sized and almost the totality of large workplaces chose
this option. Similarly, only around half (52%) of micro-sized workplaces opted for
external training to reduce digital skill gaps of their employees, while the proportion of
the rest of workplaces which chose this type of action is much higher (69% of small-
sized workplaces, 71% of medium-sized workplaces and 64% of large workplaces).
The interventions on work organisation to reduce digital skills gaps in the workforce,
such as changing working practices or reallocating tasks among employees, are more
common among larger workplaces than smaller organisations (about 40% of micro-
sized workplaces versus about 60% of larger-sized workplaces).
Also, actions such as hiring new staff (both on a permanent or temporary basis),
outsourcing tasks or seconding staff, are more frequently undertaken by larger-sized
workplaces than by micro-sized workplaces. For example, 88% of large-sized versus
32% of micro-sized workplaces recruited new staff. 29% of medium-sized versus 22%
of micro-sized workplaces hired temporary staff. 35% of large-sized versus 28% of
micro-sized workplaces outsourced tasks (Table A5.8 in Annex 2). At sectoral level,
workplaces in the ‘Agriculture’, ‘Manufacturing and utilities’, ‘Information and
communication/Professional, scientific and technical activities/ Administrative and
support service activities’ and the ‘Education and human health’ macro-sectors, are
those which have more frequently undertaken actions to tackle digital skills gaps.
5.6 Barriers to initiatives tackling digital skills gaps
Actions to improve the availability of digital skills do not come without difficulties or
costs. As shown in Figure 5.9 below, 31% of workplaces in the EU which reported
having taken action to deal with digital skill gaps found the costs of training and
development programmes to be excessive, while 26% could not modify work
organisation due to the limited number of employees in the workplace (they therefore
did not have sufficient capacity for staff to ‘absorb’ training). Hiring temporary staff with
the required digital skills, and outsourcing tasks involving ICT use, have been found to
be an overly costly option by about 21% and 19% of the workplaces which undertook
actions to reduce skill gaps. Finally, only around 13% of workplaces reported that
vacancies for jobs involving ICT could not be filled either due to a lack of required skills
or that they stayed open for a long time (Figure 5.9).
94
Figure 5.9 - Workplaces reporting difficulties when taking action to tackle digital skill
gaps by type of difficulty encountered, EU28 (%)
Q29. Which – if any – of the following difficulties has your workplace encountered when taking steps to overcome the fact that some employees are not fully proficient in carrying out tasks
involving ICT use? (Please select all that apply)
Number of valid responses: 1,485 N=1,469,631 Source: European Digital Skills Survey (weighted values)
The size of workplaces appear to be very much correlated to a number of these
barriers, such as the excessive cost of training, of hiring temporary staff or of
outsourcing tasks, or the difficulty to find employees with required skills. That is in
addition to the difficulty of modifying work organisation due to the limited size of the
workplace (as displayed in Table A5.9 in Annex 2). Only small proportions of large
workplaces encounter difficulties when taking action to tackle digital skill gaps, with the
exception of digital skill shortages on the labour market, which is reported by 37% of
large-sized workplaces. At sectoral level, the excessive cost (of training, hiring
temporary staff or outsourcing tasks) is an issue reported mostly by workplaces in the
‘Agriculture’ and in the ‘Construction’ sectors, while digital skills shortages are reported
mostly by workplaces in the construction sector.
5.7 Summary
This section has presented the existing digital skill gaps in EU workplaces and has
quantified them (‘digital skills gaps density’) in relation to specific occupations and
types of digital skills gaps. Bearing in mind the definition of the digital skills gap (“a
31.0
26.2
20.7
18.7
13.2
13.1
Excessive cost of training and developmentprogrammes
Modifications to work organisation are not possibledue to limited number of employees in workplace
Excessive cost of hiring temporary staff with therequired skills
Excessive cost of outsourcing of tasks involving ICTuse
Vacancies for jobs involving ICT are not filled due tolack of required skills
Vacancies for jobs involving ICT stay open for a longtime
95
level of digital skills of the existing workforce in a workplace which is less than required
to perform a job adequately”), this section has also explored the perceived impacts of
digital skill gaps on business performance, the actions undertaken to tackle digital skills
gaps in the workforce and the main difficulties encountered when taking action. Overall,
the main findings include the following:
15% of total workplaces in the European Union report the existence of digital skills
gaps in their workforce;
Larger employers are more likely than smaller employers to report digital skills
gaps. 57% of large workplaces report issues of digital skills gaps, followed by small
and medium-sized workplaces (24% and 23% respectively), while micro-sized
workplaces appear to be less likely to have digital skills gaps (12%);
At sectoral level, digital skills gaps are more frequently reported in the
‘Manufacturing’ (22%), ‘Construction’ (19.5%), ‘Commerce, transport,
accommodation and food’ (18%) and ‘Education and health’ (17%) sectors. The
other sectors have a lower than average proportion of workplaces reporting digital
skills gaps (14.8%), in particular in the agricultural sector where only 0.6%
consider that they have digital skills gaps. At the country level, workplaces in
Portugal and Finland report the lowest digital skills gap (3% and 10% respectively).
Sweden and Germany have the highest proportion of workplaces stating that they
experience digital skills gaps issues (27% and 22%); At the occupational level,
digital skills gaps exist across all occupations and types of digital skills, meaning
that a proportion of workforce in all occupations is not fully proficient in carrying
out tasks involving the use of digital technologies;
Digital skills gaps are, however, more likely to be reported to be found in the high-
skilled (managers, technicians) and in medium-skilled (clerical workers, sales
workers) occupations, and to a lesser extent in the low-skilled occupations, with the
exception of workers in elementary occupations;
While, for certain occupations, the level of the digital skills gap across types of skills
is relatively similar, for some occupations the digital skills gaps density is higher for
certain type of skills. For example, technicians more frequently lack basic digital
skills, while agricultural skilled workers more frequently lack specialist digital skills;
Skills gaps related to basic digital skills are more concentrated among technicians
(21.8%), elementary occupations (20.6%), sales workers (19.5%) and clerical
workers (17.2%). Skill gaps related to advanced digital skills are more
concentrated among sales workers (17.6%), technicians (17.4%), plant machine
operators (16.5%), clerical workers (15.8%) and elementary occupations (15.3%).
Skills gaps related to specialist digital skills are more concentrated among sales
workers (22.8%), followed by elementary occupations (17.6%) and technicians
(16.4%).
For the majority of workplaces reporting digital skills gaps within their workforce,
the existence of such gaps is not accompanied by concern regarding their impact on
workplace performance. Almost 62% of workplaces reporting digital skills gaps
think that these skill gaps will not have any impact on overall workplace
performance;
More than one third of workplaces with digital skills gaps express concern about the
impact these could have on the workplace performance (36%); about half of them
expect a major impact (17.6%) and the other half a minor impact (18.5%);
Micro-sized workplaces are less likely to report an expected negative impact (64%
reports no impact), followed by large workplaces (61.5%);
96
More than two thirds of workplaces in the ‘Manufacturing’, ‘Electricity and gas
supply’ and ‘Construction’ sectors do not expect any impact from digital skill gaps,
versus about half of workplaces in the grouping ‘Information and
communication/Professional scientific and technical activities/Administrative
activities’ and the grouping ‘Education and human health and social work activities’;
According to the logistic regression model, workplaces reporting a major impact of
digital skill gaps are more likely to be small or medium sized (compared to the
micro-sized), belong to the ‘Information and communication’ sector (compared to
the Agricultural sector), have a higher incidence of women employees, of
employees holding a university degree or of younger employees, and are active on
the international market;
At the country level, in Portugal the vast majority of workplaces with digital skills
gaps express concern about the possible impacts on workplace performance,
showing a trend opposite to that in other countries, where the majority consider
there will be no impact;
The type of impact most frequently reported is a loss of productivity (45.6%)
followed by an expected decrease in the number of customers (42.5%), another
(unspecified) negative impact (41%) and a decrease in the number of contracts
(32.4%);
Only 12% of workplaces with digital skill gaps have undertaken actions to tackle
the problem, and 11% have plans to do so;
81% of micro-sized workplaces have not taken any steps to deal with digital skill
gaps, versus around half of all the other workplaces;
Only 9% of micro-sized workplaces have undertaken actions to deal with digital skill
gaps, versus almost one third of medium-sized and large workplaces;
Workplaces in the ‘Education and health’ macro-sector are those most frequently
taking action to deal with digital skills gaps or plan to do so;
Training (both in the form of on-the-job training and development programmes and
external training) is the most common action undertaken to tackle digital skill gaps;
The other types of actions undertaken are related to work organisation changes:
changing working practices was reported by 49% of workplaces which took action,
and reallocating tasks, indicated by 47%. Around one third of workplaces hire
permanent or permanent staff or outsource tasks to reduce digital skill gaps in their
workforce;
The most commonly reported barrier when tackling digital skill gaps is the
excessive cost of most of the available options to enhance the skills of workers. At
sectoral level, the excessive cost (of training, hiring temporary staff or outsourcing
tasks) is an issue reported mostly by workplaces in the ‘Agriculture’ and
‘Construction’ sectors. Micro-sized workplaces are most likely to report the
excessive cost of most of the available options.
Only limited proportions of large workplaces encounter difficulties when taking
action to tackle with digital skill gaps, with the exception of digital skill shortages in
the overall labour market, which is reported by 37% of large-sized workplaces.
97
CHAPTER 6. CONCLUSIONS AND RECOMMENDATIONS
The findings of this study ‘ICT for work: Digital skills in the workplace’ appear to be in
line with the research literature relating to the impact of digitisation on the world of
work. At the same time, they provide robust evidence to respond to the research
questions formulated at the conceptualisation stage of the study, and to fill the research
gaps identified. Such findings were presented, discussed and validated in an experts’
workshop (main points of discussion and characteristics of attendees are reported in
Section 6.2), which also allowed identification of priorities and areas of concern, and to
formulate recommendations. These are presented at the end of this Chapter 6.
6.1 Main results and related learning points
The results of the survey are based on a sample of 7,800 workplaces representative of
13,803,113 workplaces in 12 economic sectors across the European Union. They have
been deliberately chosen from sectors not traditionally related to digital technologies:
agriculture, forestry and fishing; manufacturing; electricity, gas, steam and air
conditioning supply; construction; wholesale and retail trade and repair of motor
vehicles and motorcycles; transportation and storage; accommodation and food service
activities; information and communication; professional, scientific and technical
activities; administrative and support service activities; education; human health and
social work activities.
More than 80% of these workplaces (81.6%) can be classified as micro-sized (2 to 9
employees), 15% are small-sized, 3% are medium-sized, and the remaining 0.5% are
large workplaces. The vast majority of such workplaces belong to the private sector
(90%), and are not part of a corporate group of companies (86%). Those belonging to
a group, on the other hand, represent the headquarters in seven out of ten cases. In
addition, the main geographical reference market for most of these workplaces is the
local or regional market (65%), while a limited proportion of workplaces operates or
trades at national level and only 13% at international level.
The workplaces surveyed are representative of workplaces which employ a total of
150,563,540 employees. 39% of employees are female, 20% are older than 50 years
of age, and 19% are younger than 30 years of age. Almost 27% of the employees hold
a university degree. Only 5% are employed as managers, with largest proportions of
employees surveyed working as professionals (19%), technicians (17%) and sales
workers (17%). Smaller proportions of employees work as clerical workers or building
workers (11% in both cases), or in the category of elementary occupations (10%).
Workers employed as plant machine operators represent 8% of total employees and
skilled agricultural workers are 1% of the total. Almost 41% of workplaces employ
managers.
In this context, the survey results show that digital technologies are widely used by
workplaces in the European Union. Personal computers (both desktop and, to a lesser
extent, portable computers), and broadband technology to access the internet, are
used by 90% of workplaces in the EU. Much less common is the use of portable digital
devices and intranet platforms (most probably due to the reduced size of workplaces),
while the use of other types of digital technologies such as CNC machines and tools or
98
robots is very limited, and very much sector specific, being more common in the
agriculture and manufacturing sectors.
The current level of use of digital devices seems to have been supported by specific
investment strategies over the last five years, aimed at introducing ICTs mostly to
improve the overall efficiency or the business volume of workplaces. However,
investments in ICTs appear to be less common among micro-sized workplaces, which
are more likely than other workplaces to report a total lack of investment in ICT in the
recent past. At sectoral level, recent investments in ICTs seem to be more frequent
among workplaces in sectors with traditionally low levels of digital intensity (e.g.
agriculture, manufacturing or construction), most probably in light of recent changes in
the production strategy pursuing higher efficiency, but also in sectors with higher levels
of digital intensity (e.g. information and communication sector).
In the European Union the proportion of workplaces requiring their employees to
possess digital skills varies greatly according to the type of job and the type of digital
skills. Basic digital skills are the most commonly required in all the occupations, and
particularly for high and medium-skilled jobs. Almost all workplaces require their
managers to possess basic digital skills and around 90% of employers state that
professionals, technicians, clerical workers or skilled agricultural workers are required to
possess at least basic digital skills. Eight out of ten workplaces require basic digital
skills for sales workers. Although in much smaller proportions, workplaces also often
require basic digital skills for building workers (almost half of workplaces), plant
machine operators (34% of workplaces) and even employees in elementary occupations
(27% of workplaces).
Advanced digital skills are much less required by employers. It is mostly professionals
(54% of workplaces), technicians (52%) and to a lesser extent clerical workers (45%),
managers and building workers (31% of workplaces in both cases) who are required to
have these type of digital skills, while they are considered much less important for all
other occupations. Specialist digital skills are required mostly for workers employed as
professionals and technicians (43% and 44% respectively), and to a lesser extent for
managers (33% of workplaces). Advanced and specialist digital skills are very much
related to specific sectors (in particular manufacturing and information and
communication) and are more likely to be required in larger workplaces.
There is a high proportion of workplaces which do not consider digital skills to be
important for several low or non-skilled occupations. The proportion of employees
equipped with the required digital skills broadly reflects the level of importance attached
by the employers to the specific types of digital skills in the different job categories.
Nevertheless, 15% of workplaces report the existence of digital skill gaps in their
workforce, indicating that a proportion of their employees are not fully proficient in
carrying out tasks involving the use of digital technologies. Large workplaces, and
workplaces in the manufacturing or construction sectors, are more likely to report
digital skill gaps. Overall, the density of the digital skills gap greatly varies according to
the type of digital skills in relation to the different occupations. Digital skills gaps are
more likely to be found in the high-skilled (managers, technicians) and in medium-
skilled (clerical workers, sales workers) occupations, and to a lesser extent in the low-
skilled occupations, with the exception of workers in elementary occupations.
99
Most workplaces (62%) that report an issue of digitally underskilled workers, do not
however consider that existing digital skills gaps have an impact on workplace
performance, while similar proportions of the remainder state that digital skill gaps
have either a major or a minor impact on it. Micro-sized and, to a lesser extent, large
workplaces, and workplaces in the manufacturing and construction sectors are more
likely to say that digital skills gaps are not impacting on performance. Those who do say
there is an impact are most likely to say this will result in a loss of productivity or a
decrease in the number of customers.
Awareness of the existence of digital skills gaps is frequently not accompanied by
initiatives undertaken to address the issue: 77% of workplaces reporting digital skills
gaps have not undertaken any actions, while only 12% have done so, and 11% plan to.
Micro-sized workplaces have been least active in this respect, with only 9% having
taken action to tackle digital skill gaps and 81% having not undertaken any actions at
all. Overall, training appears to be the most common action undertaken to tackle the
digital skills gaps, while changes to work organisation and the hiring of new staff appear
to be much less common. Excessive cost seems to be the main barrier encountered
when undertaking actions to deal with digital skills gaps.
Overall, the evidence gathered corroborates existing research demonstrating that digital
technologies are becoming increasingly widespread across a wide range of workplaces,
also in economic sectors not traditionally related to digitisation. The study also confirms
(as observed in existing research) that digitisation is resulting in an increasing demand
for digital skills across different types of occupations and jobs in a range of industries,
and that employers have a shortfall in the availability of appropriate digital skills.
However, the evidence gathered also provides answers to existing gaps in the research
on the topic, providing detailed information on the impact of digital technologies on the
existing workforce and the evolving need for digital skills. Firstly, it provides a more
granular picture of the type of digital technologies currently used by firms in the
European Union and their underpinning investment strategies. Secondly, the study
identifies the digital skills that are most frequently required by employers, including the
type and level of importance in relation to all the occupations and specific jobs covered
by the survey. Thirdly, the findings set out the digital skills which currently exist in the
workplace by type and in relation to specific occupations. Fourthly, the evidence
provides a picture of the digital skills challenge for European workplaces, relating to the
lack of employees who are fully proficient in performing tasks requiring digital
technologies. Finally, the study provides empirical evidence on the perceived impact of
the lack of digital skills on business performance, and identifies the actions undertaken
by employers, and the barriers encountered.
Overall, the vast body of evidence collected provides valuable learning in relation to the
main policy areas for action, which forms the basis for the series of recommendations
set out at the end of this chapter. The main learning points can be summarised as
follows:
First, the evidence shows that digital technologies are increasingly and extensively
used across the economy. However, digital skills appear to be currently required
mostly for the high-skilled and, to a lesser extent, for the medium-skilled
employees to perform their job tasks, while are less likely to be required for the
100
low-skilled or the unskilled (or frequently not required at all, even at basic level).
The polarising trends, confirmed by other available evidence34, draws attention to
the fact that a high share of workers are in occupations which do not require (or
require to a very limited extent) digital skills. This dichotomy risks widening the
digital divide, leaving a proportion of workers lagging behind and at risk of digital
exclusion, who would hence benefit from specific attention.
Second, the availability of digital skills is not always sufficient to meet employers’
needs, as demonstrated by the reported existence of digital skills gaps in the
workforce, even as regards basic digital skills. Different factors contribute to this
situation. Digital skills are available to different degrees across Member States, but
the provision often does not meet employer’s needs. The speed at which workers
are being provided with the right digital skills in the right locations is frequently
slower than the speed at which digital technologies are evolving. As a result, digital
skills are often also more subject to obsolescence. An age-related issue can also be
identified, as older workers are less likely to be equipped with digital skills than
younger workers (and hence likely to be less proficient in carrying out tasks
involving digital technologies).
Third, results show that even if workplaces report that a proportion of their
workforce is not fully proficient in carrying out tasks involving the use of digital
technologies, they often do not feel that these skills gaps impact on workplace
performance and hence often do not take action to deal with the issue.
Fourth, size matters. For micro and small-sized workplaces, it may not be viable to
make investment to increase ICT use. Also, for those micro and small-sized
employers who have a high demand for digital skills, simply allocating staff time to
acquire them is both difficult (loss of productive time), and expensive (training and
development programmes need to be brought in). This is less an issue for bigger
employers with more available resources who can manage capacity, develop
training programmes or buy them in. But it is also important to remember that
some micro or small-sized companies do not need ICT at all, and therefore do not
demand digital skills. In this context, it is important to underline that micro-sized
workplaces represent more than 80% of the workplaces in the European Union.
Fifth, the skills challenges appear highly dispersed, as different sectors have
different demands, and the balance of supply and demand is different across
Member States. The sectoral analysis indicates that the use of digital technologies
is uneven across economic sectors, particularly concerning the types of digital
technologies, their speed of penetration and also the related demand for digital
skills, with some sectors clearly leading the ‘digital revolution’ and some others
following at a slower pace.
34 As pointed out by Eurofound expert at the Experts’ Workshop (Brussels 7th of October 2016, see Section 6.2 for details), an increase in the use of ICT in the workplace was registered compared to 2010 (26% to 37 %). However, even though low ICT intensity is dropping, 44% of workers still reported that they hardly use or do not use digital technologies at work. As the highest increases in the use of ICT between 2010 and 2015 are registered among those workers who were already using ICT intensely in 2010, the expert further explained that it seems possible to argue that the digital divide in workplaces is growing. In other words, the future trend seems to go towards a high share of workers who do not use ICT at all and a share of workers who use it very intensely, without these two groups showing any convergence.
101
6.2 Learning points from the validation workshop
As part of the methodological approach of the study, a workshop took place at the
European Commission’s premises on the 7th of October 2016 with the aim of validating
and discussing the findings of the research, involving stakeholders at European and
national level and international experts from Eurofound, UNESCO and Cedefop.
Participants represented a mix of stakeholders, mostly employer and employee
organisations and associations of training providers at European level, in addition to
representatives from the research team (Ecorys and the Danish Technological Institute)
and the European Commission.
The workshop consisted of three main sessions: the presentation and discussion of the
main preliminary findings of the research; experts’ contributions and panel discussion
on the research, and a plenary discussion aimed at addressing some of the questions
underpinning the study. Overall the discussions provided the research team with
meaningful insights on how to better present and interpret the data collected and to
guide the formulation of the conclusions and, most importantly, the recommendations.
With regard to the scope of the study and its methodology, the main points made
during the workshop were the following:
First, the importance of this study in addressing a key research gap.
Second, the importance of a clear conceptual framework was highlighted,
including a clear definition and classification of digital skills and digital skills
gaps, which ensures consistency across the study and clarifies the way gaps are
calculated, in addition to possible bias and limitations of the study. It was
mentioned in particular that data on the density of digital skills gaps is useful to
provide a more precise and nuanced picture of the situation in European
workplaces, as many employees may be under-skilled but only to a limited
extent.
Third, the difficulty in delivering more detailed data on skills and skills gaps
based on survey methods, given cost and methodological constraints. In
addition, the relative lack of utility of delivering a very detailed picture in terms
of policy repercussions taking into consideration the speed with which the
subject of study changes. In this regard, the point was made was that the best
results in terms of improved employability and in closing skills gaps often stem
from more generic measures aimed at improving people’s capacity to acquire
new skills and learn and general skills levels (e.g. basic skills including numerical
skills).
With regards to the results presented by the research team, experts generally noticed a
convergence between the findings stemming from the work of their organisations –
especially Cedefop and Eurofound - and those of the study. This convergence relates
mostly to the following key issues:
First the increasing proliferation of ICT and digitisation in European jobs.
Second the increased segregation and digital divide within the labour force, as
some groups are more reliant on ICT than others, with a high share of workers
in certain occupations who do not need digital skills at all.
Finally, the role of digitisation in further blurring the distinction between different
types of occupations and in driving change within the labour market.
102
In addition to these overall trends, the discussion on findings was fundamental to enrich
the conclusions of the study with stakeholders’ perspectives on the reasons behind the
results observed in particular regarding digitisation at workplace level.
Employers’ attitudes and heterogeneity (which results in different needs) were
particularly discussed. Stakeholders had different views in terms of whether or not
employers tend to upskill their workforce and in terms of viability for the employer to
invest in training. Stakeholders pointed out that companies in certain sectors and/or
countries may be less aware of their employees’ need to upskill their digital skills, as
there is a general lack of focus on the specific issue. Similarly, it was also pointed out
that SMEs might have more difficulties in digitally upskilling their workforce and may
need specific support to do so. Overall, the conclusion was that diversity in company
sizes, economic sectors and countries should be kept in mind when making policy
recommendations.
This discussion led to useful points on possible action to be taken to increase digital
skills in the workforce. With regard to national policies, it was underlined that countries
seem to be quite responsive in developing digital strategies, in trying to address
digitisation within the training systems, update training programmes, and introduce ICT
as part of key competences. On the other hand, areas in which countries have not been
as efficient relate to investment in teachers’ skills development and training with regard
to their digital skills and the use of ICT in teaching, and providing incentives to at-risk
groups by, for example, supporting them in gaining access to ICT or covering the cost
for them of developing these skills. These areas were thus identified as possible areas
of future action.
The role of social partners was also emphasised. In light of the continuous change in
the labour market in relation to required skills, it is important that investment is made
to fund not only training providers and employers, but also to make funds available to
social partners regionally and nationally (both employee and employer organisations);
these actors should have better access to funding and procedures should be simplified.
In addition to better access to funding, stakeholders also felt that policy-makers should
see digital skills as part of a broader and comprehensive skills strategy, resulting from
effective social dialogue with employers and trade unions. This is necessary as, in
several countries, social dialogue in this respect is still lacking, contributing to lack of
alignment of skills strategies with labour market needs.
All the above points, as well as more general outcomes of the workshop, were taken
into account in the analysis and presentation of the research findings – especially in
terms of formulation of the recommendations – in this final report.
6.3 Recommendations
Based on the above learning points, in particular those emerged from the stakeholder
consultation in the validation workshop, the following recommendations can be
formulated:
Recommendation 1: Raise awareness on digital technologies and the need
for digital skills
103
Awareness-raising campaigns, based on up-to-date research evidence, should be
implemented to raise awareness both on the importance of the use of digital
technologies to support and improve business performance, productivity and
internal organisation, and on the need for digital skills in relation to new digital
technologies. Employers should also be made aware of what digital skills gaps are,
how to recognise them, their possible underlying causes, the consequences of not
properly addressing them, and how to tackle them. Among others, it will be
important to raise awareness on the need for the development of improved digital
skills for workers in medium and low-skilled occupations.
Recommendation 2: Promote access to digital technologies
Mechanisms (loans, grants etc.) should be used to enhance and support access to
digital technologies, particularly for micro and small-sized companies, many of
which are not fully aware of the importance of investing in digital technologies, and
often do not have the financial capacity to do so. Access to digital technologies
would allow the proliferation of digitisation across the economy contributing to the
development of the demand of digital skills, also in the medium and low-skilled
occupations.
Recommendation 3: Expand the availability of digital skills through the
education and training system
In order to expand the current and future availability of digital skills in the economy,
actions should be undertaken within educational systems (including vocational
education and training). The education and training sector should be supported to
develop and adapt its offer to meet the changing needs of the digital economy,
programmes in all levels and sectors of education should be updated and digital
skills should be part of the core competences required at every level. There should
be greater investment in teachers’ and trainers’ skills for using ICT in teaching.
Teaching methodologies to use digital technologies should be increased significantly,
which can act as multipliers and provide a significant contribution to increasing the
digital skills availability.
Recommendation 4: Promote access to training
Access to training to address digital skills gaps in the existing workforce should be
supported through a variety of means. Information about existing training initiatives
and procedures to access them should be made available to employers through their
professional or sectoral organisations and associations, or through governmental
channels.
Recommendation 5: Build multi-stakeholder partnerships based also on
effective social dialogue to increase the availability of digital skills
Policymakers should support digital skills development within multi-stakeholder
partnerships (e.g. involving the European Commission, Member State ministries and
agencies, training providers, employers and social partners). Partnerships are
proven to generate a more inclusive and targeted approach to skills development
and training provision that is more responsive to labour market needs, in line with
vocational programmes and qualifications. A digital skills strategy should therefore
104
be the result of a discussion based on effective social dialogue. In that context,
stakeholders including the European Commission need to work together on a holistic
approach to digital skills development. The recent launch of the Digital Skills and
Jobs Coalition35 is an important step in that direction, but special attention should be
paid to promoting the participation of micro and small-sized companies in order to
better understand their needs.
Recommendation 6: Provide access to funding for digital technologies and
digital skills development
Funding is critical to enhance the availability of digital skills in the current workforce.
Employers could benefit from access to funds (including EU funds) to support more
investment in digital technologies and the development of digital skills, especially for
initiatives that are cross-border and which share experiences in the generation and
use of digital skills. Better access to funding should be provided as well to social
partners regionally and nationally (both employee and employer organisations)
considering co-funding mechanisms to increase their role in the provision of training.
Within the context of the Better Regulation initiative, access to funding should be
simplified to support the dynamic development of the digital skills landscape.
Recommendation 7: Include digital skills in a wider skills strategy
Although it remains crucial to develop a range of specific digital skills which respond
to the needs of the digital economy, wider digital skills for the whole population
should be embedded in a broader and comprehensive skills strategy in which other
transversal skills relevant to employers such as soft skills and communication skills
are also included. Evidence shows that the most effective means of improving
employability and closing skills gaps are more generic measures aimed at improving
the capacity of workers to acquire new skills and learn in an evolving economy.
Opportunities for ensuring that digital competence is further embedded in an
integrated approach to skill development could be provided for example via the
European Commission’s proposal for the “Upskilling Pathways” or its current review
of the 2006 recommendation on key competences for lifelong learning
(2006/962/EC), which form part of the New Skills Agenda for Europe36.
Recommendation 8: Consider diversity and avoid the ‘one-size fits all’
approach
Employers require different types and levels of digital skills according to the sector
in which they operate, their size, their market, and the country in which they are
based. In designing a digital skills strategy or any other type of initiative to help
employers to access the required digital skills, diversity needs to be clearly
addressed through a tailored approach, for example through a sectoral/industry
approach, an occupational approach (e.g. focusing on specific categories of jobs) or
a territorial approach.
Recommendation 9: Reduce the digital divide
35 https://ec.europa.eu/digital-single-market/en/digital-skills-jobs-coalition#Article 36 http://ec.europa.eu/social/main.jsp?catId=1223
105
Policymakers should take action to reduce the existing digital divide, focusing in
particular on the categories of individuals (e.g. older people, the lower-educated,
those employed in low-skilled jobs) who do not possess digital skills and are
consequently at risk of marginalisation not only in the labour market, but also in
day-to-day life, which can contribute to social and economic exclusion. The access
to digital literacy should be ensured for everyone, with a special focus on the most
deprived groups.
106
ANNEX 1. SURVEY METHODOLOGY
This annex presents the detailed methodological approach adopted to carry out the
survey which findings are presented in this report.
The Digital Skills Survey has been carried out by Ecorys Survey Division with the
support of GN Research within the study “ICT for Work: Digital Skills in the Workplace”,
conducted by Ecorys UK and Danish Technological Institute on behalf of the European
Commission, DG CONNECT (project SMART 2014/0048).
The survey covered six EU member states and was carried out on a representative
sample of 7,800 workplaces in 12 economic sectors across the selected member states.
The objective of the survey was to collect evidence on:
the number and type of jobs, in the EU and selected member states, that require
digital skills by economic sector;
the level and type of digital skills required by such jobs;
the digital skills gaps related to such jobs and sectors;
the actions undertaken by employers to deal with digital skills gaps (e.g. providing
training, out-sourcing);
the main bottlenecks/barriers to improved availability of digital skills.
As a result, the methodology described in this Annex was designed and implemented to
produce the findings presented in this report. In particular the Annex presents the
methodology employed and activities carried out in order to:
identify the target population;
design the sampling strategy;
withdraw the sample and carry out data collection activities;
design and carry out the estimation procedure to infer sample results to the
population of workplaces in the selected countries and at EU28 level;
design the survey questionnaire.
A1.1 The target population
In the context of this survey the target population - identified as the set of units to be
surveyed - are the employers whose business falls within economic sectors requiring
digital skills, in particular in relation to specific types of occupation.
The economic sectors of interest and relevance for the purposes this study (identified
through the corresponding code within the NACE classification37) was identified using
the information collected by the Eurostat Survey on ‘ICT use in Enterprises’ data38.
Such survey, carried out yearly, covers a wide range of ICT-related topics39, and allows
classifying the economic sectors according to the intensity of computer use.
37 Detailed description and rationale of the NACE classification system, is available on Eurostat website http://ec.europa.eu/eurostat/statistics-explained/index.php/NACE_background 38 http://ec.europa.eu/eurostat/web/information-society/overview 39 The survey covers the following list of topics: ICT systems and their usage in enterprises; use of the Internet and other electronic networks by enterprises; e-commerce; e-business processes and organisational
107
In particular, for the purposes of this survey, the NACE economic sectors were classified
according to three levels of intensity of computer use: low, medium, and high. Such
levels of intensity were calculated considering the percentage of employees using
computers40 within specific sectors, and subsequently classifying the sectors as follows:
Low intensity: sectors employing up to 33% of employees using computers with or
without access to the Internet;
Medium intensity: sectors employing from 34% to 66% of employees using
computers with or without access to the Internet; and
High intensity: sectors employing 67% and more of employees using computers
with or without access to the Internet.
The results are displayed in the following table at EU-28 level and by economic macro-
sectors41.
Table A1.1: Economic sectors covered by the ICT usage in enterprises survey, by share
of workers using computer and related digital intensity, EU-28.
NACE Rev.2 code
Economic sector Share of workers using computer
Digital intensity of sector
C Manufacturing 0.447847 Medium
D, E Electricity, gas, steam, air conditioning and water supply
0.550406 Medium
F Construction 0.373525 Medium
H Transportation and storage 0.492989 Medium
J Information and communication 0.944047 High
L Real estate activities 0.737925 High
M Professional, scientific and technical activities 0.893166 High
N Administrative and support service activities 0.409335 Medium
Source: elaboration Ecorys/DTI on Eurostat data http://ec.europa.eu/eurostat/web/information-
society/data/comprehensive-database
These results were considered as an initial indication, since the Eurostat Survey on ‘ICT
use in Enterprises’ does not cover all the NACE economic sectors42, and the final
selection of sectors to be surveyed benefitted from the indications of the Steering
Committee’s experts.
aspects; use of ICT by enterprises to exchange information and services with governments and public administrations (e-government); ICT competence in the enterprise and the need for ICT skills; barriers to the use of ICT, the Internet and other electronic networks, e-commerce and e-business processes; ICT expenditure and investment; ICT security and trust; use of ICT and its impact on the environment (Green ICT); access to and use of the Internet and other network technologies for connecting objects and devices (Internet of Things); access to and use of technologies providing the ability to connect to the Internet or other networks from anywhere at any time (ubiquitous connectivity). 40 As surveyed by question A2 of the 2014 ‘ICT use in Enterprises survey’ questionnaire available at the link https://circabc.europa.eu/sd/a/cd0f3e86-d720-407e-88c9-b2f10a64410b/Questionnaire%20ENT2014.pdf 41 Data available on Eurostat online database does not allow for a more detailed extraction, as only data at economic macro-sector are made available online. 42 The sectors covered by the Eurostat Survey on ICT use in Enterprises are: (Section C) “Manufacturing”; (Section D,E) “Electricity, gas and steam, water supply, sewerage and waste management”; (Section F) “Construction”; (Section G) “Wholesale and retail trade; repair of motor vehicles and motorcycles”; (Section H) “Transportation and storage”; (Section I) “Accommodation and food service activities”; (Section J) “Information and communication”; (Section L) “Real estate activities”; (Division 69 -74)“Professional, scientific and technical activities”; (Section N)"Administrative and support activities"; (Group 95.1) “Repair of computers”.
108
The final list of sectors selected to be covered by the survey, which includes a total
number of 12 economic sectors with different levels of digital intensity, is reported in
table A1.2 here below.
Table A1.2: Economic sectors selected for the purposes of this study
NACE
Rev.2 code
Economic sector Inclusion?
A Agriculture, forestry and fishing yes
B Mining and quarrying no
C Manufacturing yes
D Electricity, gas, steam and air conditioning supply yes
E Water supply; sewerage, waste management and remediation
activities no
F Construction yes
G Wholesale and retail trade; repair of motor vehicles and motorcycles
yes
H Transportation and storage yes
I Accommodation and food service activities yes
J Information and communication yes
K Financial and insurance activities no
L Real estate activities no
M Professional, scientific and technical activities yes
N Administrative and support service activities yes
O Public administration and defence; compulsory social security no
P Education yes
Q Human health and social work activities yes
R Arts, entertainment and recreation no
S Other service activities no
T Activities of households as employers; undifferentiated goods- and
services-producing activities of households for own use no
U Activities of extraterritorial organisations and bodies no
Source: elaboration Ecorys/DTI on Eurostat data
A1.2 Sampling strategy
The sampling strategy and related quality assurance mechanisms that were employed
for this survey are detailed here below.
The sample size was fixed at 6 countries and 7,800 employers. The sample size was
defined in order to respect budget constraints as far as the minimum size required by
the selection procedure is guaranteed.
With regards to the sampling design, in the context of this survey, a two-stage,
stratified sampling design was adopted, in which countries are the primary sampling
units and workplaces are the secondary sampling units.
109
In a two-stage sampling design, a sample of primary units is selected at the first stage
and a sample of secondary units within each primary unit is subsequently selected at
the second stage.
First-stage sampling
Based on indications of the DG CONNECT Steering Committee, the first-stage sample is
a non-probabilistic sample43. Six countries were selected within the EU-28 Member
States taking into account a number of aspects such as the relative population size of
the country and related enterprises population size, the level of digitisation in the
country, the need of ensuring a satisfactory geographical coverage and the overall and
specific objectives of this study.
In choosing the sample countries, the ‘Digital skills indicator’44 was used to take into
account the level of digitisation of each EU Member State. The indicator is calculated
according to the new methodology used for the measurement of digital skills in the
Digital Agenda Scoreboard 2014 within the pilot work carried out by DG CONNECT F4 in
relation to Action 62 of the Digital Agenda to propose "EU-wide indicators of digital
competence".
The sample of countries selected (table A1.3) included Sweden, Finland and Slovakia
(amongst the countries with a population of less than 10 million inhabitants) and
Germany, Portugal and the United Kingdom (amongst the countries with a
population of more than 10 million).
Table A1.3 - First stage sampling procedure and proposed non-probabilistic sample of countries
Countries Total Population
(2013)
Digital skills indicator
(2012)45 Sample
Austria 8'451'860 63.3
Denmark 5'602'628 78.1
Finland 5'426'674 73.6
Ireland 4'591'087 54.2
Luxembourg 537'039 78.2
Malta 421'364 51.8
Bulgaria 7'284'552 19.4
Croatia 4'262'140 42.5
Cyprus 865'878 37.8
Estonia 1'320'174 58.2
Latvia 2'023'825 51.7
Lithuania 2'971'905 47.7
43 Non-probability sampling represents a group of sampling techniques that help researchers to select units from a population that they are interested in studying. A core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgement of the researcher, rather than random selection (i.e., probabilistic methods), which is the cornerstone of probability sampling techniques. See also: http://www.statcan.gc.ca/edu/power-pouvoir/ch13/nonprob/5214898-eng.htm 44 The ‘Digital skills indicator’ calculated by country is published in the paper “Measuring Digital Skills across the EU: EU wide indicators of Digital Competence” on pag. 14, http://ec.europa.eu/digital-agenda/en/news/measuring-digital-skills-across-eu-eu-wide-indicators-digital-competence. The indicator can be downloaded from the following link: http://digital-agenda-data.eu. 45 Definition: Persons that have been using internet during last 3 months are attributed a score on four digital competence domains: information, communication, content-creation and problem-solving, depending the activities they have been able to do. The scores are basic, above basic and below basic. Individuals not using internet are classified without digital skills. Source dataset: European Commission, Digital Agenda Scoreboard. We have considered the share of people who have basic and above basic digital skills.
110
Countries Total Population
(2013)
Digital skills indicator
(2012)45 Sample
Slovakia 5'410'836 57.3
Slovenia 2'058'821 49.8
Belgium 11'161'642 56.7
France 65'578'819 62.8
Germany 82'020'578 60.5
Italy 59'685'227 39.8
Netherlands 16'779'575 76.7
Spain 46'727'890 54.2
Sweden 9'555'893 74.5
United Kingdom 63'896'071 57.9
Czech Republic 10'516'125 48.5
Greece 11'062'508 35.1
Hungary 9'908'798 52.8
Poland 38'533'299 41.9
Portugal 10'487'289 44.9
Romania 20'020'074 15.3
Source: Elaboration Ecorys/DTI on Eurostat and DG CONNECT data
Second-stage sampling
The second-stage sample was a random stratified sample. In order to draw this
sample, a number of preliminary activities were undertaken.
a) Definition of stratification variables
The strata were identified by the ‘economic macro-sector’ and ‘enterprise size’
variables. In particular the enterprises falling within the selected NACE economic
sectors were grouped in six macro-categories. Such aggregation was carried out
considering the distribution of employers in the sectors at EU level.
Table A1.4 - Grouping of economic sectors for second stage sampling procedure
Nace code
Economic sectors Economic macro-sectors
A Agriculture (farms) A: Agriculture (farms)
C Manufacturing
C,D: Industry D
Electricity, gas, steam and air conditioning supply
F Construction F: Costruction
G Wholesale and retail trade; repair of motor vehicles and motorcycles
G,H,I: Other services H Transportation and storage
I Accommodation and food service activities
J Information and communication
J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities
M Professional, scientific and technical activities
N Administrative and support service activities
P Education P,Q: Education And Human Health And Social Work Activities
Q Human Health And Social Work Activities
Source: Ecorys/DTI
111
Within each of these six macro-categories, the enterprises were clustered according to
their size in four groups, following the definition by Eurostat46:
micro enterprises (less than 10 employees);
small enterprises (10 to 49 employees);
medium-sized enterprises (50 to 249 employees);
large enterprises (250 or more employees).
In summary, in the second-stage sampling 18-strata47 for each country was produced.
b) Definition of the allocation method
Regarding the sample size in each stratum, the sampling design allowed for the sample
allocation on planned domains of estimates. The domains of study are identified by a
partition of the population under investigation. The domains are usually an aggregation
of elementary strata and the planned domains are specific subpopulations with a pre-
defined level of reliability of estimations.
In the second-stage of the sampling a multi-domain allocation was used, with the
following planned domains
Country
Economic macro-sector
Enterprise size
Concatenation of sampled countries and economic macro-sector;
Concatenation of sampled countries and enterprise size.
The sample size in each stratum was allocated so to ensure the desired level of
reliability of estimates in each domain, which were chosen in advance fixing a
coefficient of variation (CV) value48.
When the coefficient of variation (CV) is set to 0.2, the allocation made planning the
domains of analysis allows estimates to be calculated at the national level by sector or
by enterprise size with a relative sampling error up to 0.2. Also, the sample allocation
on planned domains of estimates allows the various segments of the population with a
limited number of units to be represented appropriately. The planned multi-domain
allocation is useful to make the allocation of the units in the strata more effective. In
addition, it is particularly powerful when compared to the proportional allocation, once
considered the design effect (deff.49).
Within the specific context of this survey methodology, we therefore first set the level of
reliability of the estimates for each domain of analysis, and then we allocated the total
46 http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Enterprise_size 47 For the Agriculture sector and for the Education and Human Health sectors the enterprise size variable is not considered. The official data, downloaded by the Eurostat web site, on the structure of enterprises for these sectors does not have the disaggregation by size. 48 The coefficient of variation (CV) is the ratio of the standard deviation to the mean. The higher the coefficient of variation, the greater the level of dispersion around the mean. It is generally expressed as a percentage. Without units, it allows for comparison between distributions of values whose scales of measurement are not comparable. When we are presented with estimated values, the CV relates the standard deviation of the estimate to the value of this estimate. The lower the value of the coefficient of variation, the more precise the estimate. (http://www.insee.fr/en/methodes/default.asp?page=definitions/coefficient-de-variation.htm) 49 The design effect measures the effect of this sampling design compared to the simple random sampling: to this extent the respective variances of estimators will be compared.
112
sample of employers to the strata, so the sample size for each domain allows to respect
the level of reliability set previously. In other terms, the sample allocation was
conducted taking into account simultaneously each country and total EU population of
employers, considering the enterprise size and sector.
In table A1.5 below the multi-domain allocation of the sample of 7,800 units for this
study is displayed. The level of reliability of estimates in each domain is fixed with a
coefficient of variation (CV) not higher than 0.2 for an estimate of a relative frequency
equal to 14%.
The starting point was the quantification of the target population (total number of
employers in the selected sectors in the country). Data to calculate the population were
downloaded from the Eurostat data warehouse using different sources. The main data
source was the Structural Business Statistics50; for P,Q sectors the data source was
the Business demography (Population of active enterprises in t)51; for the A
sector the source was the Farm structure in the Agriculture52. Sample allocation
phase took place at the beginning of 2015; Eurostat data used to allocate the sample
available at the beginning of 2015 referred to 2011.
Table A1.5 - Population size and sample size and the CV by country
Country n. of employers (population) n. of employers (sample) CV (p=0.14)
Germany 2,539,642 1,240 0.09
Portugal 1,259,922 1,333 0.09
Slovakia 446,843 1,325 0.10
Finland 299,880 1,281 0.09
Sweden 707,899 1,316 0.10
United Kingdom 1,926,857 1,305 0.10
Total 7,181,043 7,800 0.05
Source: Ecorys/DTI and Eurostat (2011)
In table A1.6 the detail of the sample allocation in the strata is displayed.
Table A1.6 - Population size and sample size by country, economic macro-sector and
size, year 2011
County Economic
macro-sector*
Size n. of employers
(population) n. of employers
(sample)
Germany
A n.a. 299,130 154
C,D
Less than 10 employees 130,223 82
10-49 employees 62,330 47
50-249 employees 17,561 51
250 or more employees 4,408 66
F
Less than 10 employees 199,840 124
10-49 employees 35,818 27
50-249 employees 3,055 19
250 or more employees 211 13
J,M,N Less than 10 employees 525,410 126
50 http://ec.europa.eu/eurostat/web/structural-business-statistics 51 http://ec.europa.eu/eurostat/statistics-explained/index.php/Business_demography_statistics 52 http://ec.europa.eu/eurostat/web/agriculture/farm-structure
113
County Economic
macro-sector*
Size n. of employers
(population) n. of employers
(sample)
10-49 employees 57,802 27
50-249 employees 12,099 35
250 or more employees 2,851 43
G,H,I
Less than 10 employees 704,039 107
10-49 employees 150,848 65
50-249 employees 20,791 59
250 or more employees 2,727 41
P,Q n.a. 310,499 154
Total 2,539,642 1,240
Portugal
A n.a. 305,270 154
C,D
Less than 10 employees 61,109 112
10-49 employees 10,608 48
50-249 employees 2,237 64
250 or more employees 282 45
F
Less than 10 employees 98,820 135
10-49 employees 7,123 31
50-249 employees 689 43
250 or more employees 78 42
J,M,N
Less than 10 employees 261,138 149
10-49 employees 3,895 16
50-249 employees 726 21
250 or more employees 217 35
G,H,I
Less than 10 employees 348,158 141
10-49 employees 14,602 60
50-249 employees 1,610 46
250 or more employees 236 38
P,Q n.a. 143,124 154
Total 1,259,922 1,333
Slovakia
A n.a. 24,460 153
C,D
Less than 10 employees 66,695 136
10-49 employees 3,340 38
50-249 employees 1,050 68
250 or more employees 286 68
F
Less than 10 employees 89,436 148
10-49 employees 1,737 20
50-249 employees 239 34
250 or more employees 20 16
J,M,N
Less than 10 employees 80,503 147
10-49 employees 1,703 19
50-249 employees 322 21
250 or more employees 76 18
G,H,I
Less than 10 employees 149,460 140
10-49 employees 6,952 77
50-249 employees 629 41
250 or more employees 118 28
P,Q n.a. 19,817 153
Total 446,843 1,325
114
County Economic
macro-sector*
Size n. of employers
(population) n. of employers
(sample)
Finland
A n.a. 63,870 154
C,D
Less than 10 employees 19,993 112
10-49 employees 3,292 37
50-249 employees 900 54
250 or more employees 229 47
F
Less than 10 employees 39,655 137
10-49 employees 2,562 27
50-249 employees 231 30
250 or more employees 37 26
J,M,N
Less than 10 employees 52,980 135
10-49 employees 3,263 33
50-249 employees 635 38
250 or more employees 139 29
G,H,I
Less than 10 employees 75,102 132
10-49 employees 5,688 57
50-249 employees 665 40
250 or more employees 195 40
P,Q n.a. 30,444 153
Total 299,880 1,281
Sweden
A n.a. 71,090 154
C,D
Less than 10 employees 50,932 123
10-49 employees 5,381 32
50-249 employees 1,525 48
250 or more employees 370 51
F
Less than 10 employees 82,185 139
10-49 employees 4,489 25
50-249 employees 405 28
250 or more employees 40 19
J,M,N
Less than 10 employees 244,453 145
10-49 employees 6,438 35
50-249 employees 1,186 37
250 or more employees 250 35
G,H,I
Less than 10 employees 172,649 133
10-49 employees 11,821 63
50-249 employees 1,682 53
250 or more employees 303 42
P,Q n.a. 52,700 154
Total 707,899 1,316
United Kingdom
A n.a. 186,800 154
C,D
Less than 10 employees 99,203 106
10-49 employees 23,215 34
50-249 employees 6,429 40
250 or more employees 1,438 39
F
Less than 10 employees 246,914 138
10-49 employees 16,012 19
50-249 employees 2,088 28
250 or more employees 322 29
115
County Economic
macro-sector*
Size n. of employers
(population) n. of employers
(sample)
J,M,N
Less than 10 employees 610,103 134
10-49 employees 40,093 41
50-249 employees 7,969 49
250 or more employees 1,952 53
G,H,I
Less than 10 employees 481,018 120
10-49 employees 62,693 64
50-249 employees 8,467 52
250 or more employees 1,876 51
P,Q n.a. 130,265 154
Total 1,926,857 1,305
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Other services: Wholesale and retail
trade, repair of motor vehicles and motorcycles; Transportation and storage; Accommodation and food service
activities; J,M,N: Information and communication; Professional, scientific and technical activities; Administrative
and support service activities; P,Q: Education; Human health and Social work activities
Source: Ecorys/DTI and Eurostat (2011)
c) Definition of the inclusion probability and sample withdrawal
In the second-stage sampling a same-inclusion probability of enterprises within the
strata was used. Therefore, the withdrawal of the sample units (enterprises) from the
population was systematic with a predetermined extraction step. Furthermore, the
representativeness of the second-stage sample was guaranteed by the random
selection procedure, which eliminates the introduction of any bias.
A1.3 The sampling frames
A sampling frame can be defined as “the listing of all units in the population from which
a sample is selected” (Bryman, 200853). For this survey, the sampling frames that have
been used to select the sample of enterprises according to the methodology described
so far by country, NACE economic sector and size, have been purchased from Bureau
van Dyke54 and reflect those used by Eurofound in 2013 to carry out the Third European
Company Survey55.
A1.4 Data collection
Data collection has been carried out by Ecorys Survey Division with the support of GN
Research. The data collection strategy chosen for this survey was a mixed CATI
(Computer Assisted Telephone Interviewing) and CAWI (Computer Assisted Web
Interviewing) approach. This strategy was planned with the aim of increasing any low
response rate. The response rate to the CAWI survey was eventually relatively low
53 Bryman, A. (2008) Social Research Methods, Oxford University Press. 54 http://www.bvdinfo.com/en-gb/home 55 More information about the Third European Company Survey is available here http://www.eurofound.europa.eu/surveys/ecs/2013/index.htm, whereas the information regarding the sampling frames existing in the EU28 and EEA Member States is available here http://www.eurofound.europa.eu/surveys/ecs/2013/documents/ecs2013docs/3rdECS%202013Sampling_2.pdf
116
(around 2% on average), in spite of the communication strategy accompanying the
administering of the survey and the reminders sent, and therefore CATI was extensively
employed in order to achieve the targeted number of interviews. This suggests that the
CAWI technique is not recommendable for employers’ surveys, although CAWI
interviews have the great advantage of being significantly less expensive than CATI
interviews. The table below presents – by country - the number of completed interviews
by type (CAWI/CATI), the number of initial contacts made and the resulting response
rates. On average the duration of phone interviews was 20.35 minutes.
A sampling frame of 39,000 contacts was purchased, and contact data was cleansed to
ensure they were complete in terms of name, telephone number and email address for
the person responsible for HR, which was the person who was asked to respond the
survey questionnaire. Where such data was not available, or was not correctly detailed
in the database, telephone calls were made to the organisations to introduce the survey
and collect the missing information.
Table A1.7 – Completed interviews, contacts made and response rates by type of
interview (CAWI/CATI) and by country (N and %)
Country
Total DE FI UK PT SE SK
CAWI interviews 74 128 76 169 81 349 877
CATI interviews 1,095 1,145 1,177 1,189 1,250 1,067 6,923
Total completed interviews 1,169 1,273 1,253 1,358 1,331 1,416 7,800
Total contacts 6,200 6,405 6,525 6,665 6,580 6,625 39,000
Response rates (A/B):
CAWI response rate (%) 1.2 2.0 1.2 2.5 1.2 5.3 2.2
CATI response rate (%) 17.7 17.9 18.0 17.8 19.0 16.1 17.8
Overall response rate (%) 18.9 19.9 19.2 20.4 20.2 21.4 20.0
Source: Ecorys/DTI
A1.5 Identification of parameters of interest and estimation phase
In this section the areas of interest of this study and the related parameters of the
population and subpopulations to be estimated are illustrated, together with the
procedure to identify the estimators. An estimator is a combination of multiplicative
coefficients, each of them applied to each sample unit to calculate how many units of
target population this represent. The calculation of the estimator is crucial in the overall
process of statistical inference and allow comparison of the survey sample to the target
population, thereby allowing bias to be eliminatedand optimisation of the estimation
efficiency.
The areas of interest are to:
Quantify the jobs in the EU and in the sampled Member States that require digital
skills, by economic sector;
117
Provide evidence on the type and level of digital skills required by different jobs and
in different economic sectors;
Identify the main digital skills gaps in different occupational categories and economic
sectors;
Investigate how employers deal with ICT/digital skills gaps (e.g. providing training,
out-sourcing);
Examine the main bottlenecks/barriers to improved availability of digital skills.
The related parameters of interest are therefore:
Types of digital skills available by occupational category;
Types of digital skills needed by occupational category;
Levels of digital skills available by occupational category;
Levels of digital skills needed by occupational category;
Actions undertaken by employers to deal with digital skills gaps;
Types of bottlenecks/barriers to improved availability of digital skills by occupational
categories.
The parameters of interest have been analysed both at the level of the countries
included in the sample and at EU level. This has been possible through the use of
calibration weights as illustrated below.
The estimation phase is of particular relevance in the inferential procedure. At this
stage it is in fact possible to correct the sample selection bias and the total non-
response bias56. The use of a sample rather than a census means that some procedure
is needed in order to estimate the characteristics of the target population from
responding units.
The techniques used for the construction of the survey estimator are based on the
predictive approach to regression estimator, and involve the construction of an
estimator based on the general category of model-based estimators. Model-based
approaches assume that responses are generated according to a statistical model.
These models typically attempt to use important auxiliary variables (information coming
from external sources and correlated with the parameter of interest) to improve fit and
usability. The main concern with model-based inferences is that population estimates
are totally dependent on model assumptions. Once the model is formulated, standard
statistical estimation procedures such as likelihood-based estimation are then used to
make inferences about the parameters being estimated.
Model-assisted means that a superpopulation model (Dorfman et al. 2002)57 is adopted
which allows the development of calibration estimators (Deville and Särndal 1992)58. In
addition to using auxiliary variables, reducing the sample variance, such a class of
estimators has among its properties the fact that the calibration incorporates estimates
of auxiliary variables (used as regressors) which correspond to known population totals
(Deville and Särndal 1992). In this way it is possible to calibrate the estimated
population with known population totals, broken down according to specific
characteristics.
56 http://www.nss.gov.au/nss/home.nsf/NSS/4354A8928428F834CA2571AB002479CE?opendocument 57 Dorfman A.H., Royall R.M., Valliant R., Finite Population Sampling and Inference: a Prediction Approach, New York, John Wiley & Sons, 2002. 58 Deville J. C., Särndal C. E., (1992), Calibration Estimators, “Survey Sampling”, Journal of the American
Statistical Association, vol. 87, pp. 367-382. http://www.stat.unipg.it/~giovanna/didattica/teo/Deville_sarndal.pdf
118
Calibration estimators are a category of adjustments that have been adapted from
probability sampling where the methods have been studied extensively and shown to
reduce both bias and variance in survey estimates (Deville and Särndal 1992). They
may be useful tools for non-probability samples (and in some circumstances the only
tool). However, they rarely are able to compensate fully for biases due to the
composition of the sample (Dever, Rafferty, and Valliant 2008; Tourangeau, Conrad,
and Couper 201359).The calibration estimator, applied as a multiplying coefficient of the
sample units, will produce estimates of the target population.
In particular, the estimate of total of variable Y is given by the following expression:
�̃� =∑ 𝑦𝑖𝑤𝑖
𝑖∈𝑠
where, for sample s, yi indicates the value of variable Y observed in sample unit ith and
wi the sampling weight related to sample unit ith. The procedure to calculate wi
sampling weights according to the calibration technique is articulated as follows: firstly
design weights di - defined as the inverse of the inclusion probability of sample unit ith
di =1/𝜋𝑖 are calculated; subsequently a correction factor i obtained linking direct
estimates to population totals. Sampling weight wi is therefore calculated multiplying
the design weight by the correction factor: wi = di i.
�̃� =∑ 𝑦𝑖𝑤𝑖
𝑖∈𝑠
=∑ 𝑦𝑖𝑑𝑖𝜔𝑖
𝑙∈𝑠
Correction factors i are calculated solving the following minimum optimisation problem,
in which a distance function between design weights di and sampling weights wi is
minimised; constraints are defined by the condition of equality between sample
estimates of auxiliary variables and known population totals:
{
𝑚𝑖𝑛 {∑ 𝑑𝑖𝑠𝑡 (𝑑𝑖, 𝑤𝑖)
𝑖∈𝑠
}
∑ 𝐱𝐢𝑤𝑖 = 𝐭
𝑖∈𝑠
where t denotes the vector of population totals and xi denotes the vector of auxiliary
variables observed in the ith sample unit. The solution is provided by the following
expression, which defines correction factor i for each sample unit (Deville e
Särndal,1992).
𝜔𝑖 = 1 + (𝐭 −∑𝐱𝐢𝑑𝑖𝑖∈𝑠
)
′
(∑𝐱𝐢𝐱𝐢′𝑑𝑖
𝑖∈𝑠
)
−1
𝐱𝐢
A comprehensive description of calibration weighting methods can be found in Särndal
(2007)60.
59 Dever J. A., Rafferty A.,Valliant R. Internet Surveys: Can Statistical Adjustments Eliminate Coverage Bias. Survey Research Methods 2008;2:47-62. Tourangeau R., Conrad F. G., Couper M. P.The Science of Web Surveys. New York: Oxford University Press; 2013 60 Särndal, C.E. (2007). The Calibration Approach in Survey Theory and Practice. Survey Methodology
33(2):99-119.
119
The known population totals that were used in the calibration procedure are, both at EU
level and at sampled country level:
Total number of enterprises (defined according to the selection criteria of the target
population);
Distribution of enterprises by sector;
Distribution of enterprises by size;
Number of workers by different occupational groups;
Number of workers by economic sector.
The input data for the calibration procedure, used as known population totals, came
from the following external sources:
Eurostat Structural Business Statistics;
Eurostat Business Demography Statistics;
Eurostat Farm Structure Statistics;
Eurostat Labour Force Survey.
Data used for the calibration procedure is the most recent Eurostat data available at the
time of the calculation of the weights (June 2016): statistics related to businesses refer
to 2013, while Labour Force Survey data refers to 2015.
With the aim of producing estimates both at EU and sampled country levels, two
different estimators (weights) have been calculated. The first allows the estimation of
the amount and the characteristics of enterprises falling in the selected NACE economic
sectors at EU level. The second allows the estimation of the enterprises in each country
included in the sample. These two estimators are needed as the survey has to reach a
two-fold objective: on the one hand the phenomenon of interest needs to be analysed
for each country included in the sample, and on the other hand a EU-level perspective,
where the countries included in the sample represent all the EU Member States. As a
consequence, in the first case, each country included in the sample will represent itself
and the related estimator will be employed to represent only the population within the
country whereas, while in the other case, each country included in the sample will help
to represent the EU countries as a whole and the related estimator will be used to
represent the overall population of European employers, with no breakdown by country.
A1.6 Sample profile
Table A1.8 below displays the discrepancies between the planned sample allocation as
described in previous section A1.2 and illustrated in Table A1.6 and the actual sample
allocation. Such difference, which happens very frequently when carrying out a survey,
has been taken into account and was corrected during the estimation phase in order to
produce reliable estimates.
120
Table A1.8 – Planned and actual sample allocation by country, economic macro-sector and size (N)
Co
un
try
Macro
secto
r
Planned sample Actual sample Discrepancy (N)
Size Size Size
2 t
o 9
10
to
49
50
- 2
49
25
0 +
To
tal
2 t
o 9
10
to
49
50
- 2
49
25
0 +
To
tal
2 t
o 9
10
to
49
50
- 2
49
25
0 +
To
tal
DE
A -- -- -- -- 154 98 51 46 3 198 -- -- -- -- 44
CD 82 47 51 66 246 77 43 63 79 262 -5 -4 12 13 16
F 124 27 19 13 183 99 28 13 11 151 -25 1 -6 -2 -32
GHI 107 65 59 41 272 74 68 45 26 213 -33 3 -14 -15 -59
JMN 126 27 35 43 231 103 24 35 35 197 -23 -3 0 -8 -34
PQ -- -- -- -- 154 74 31 24 19 148 -- -- -- -- -6
Total -- -- -- -- 1240 525 245 226 173 1169 -- -- -- -- -71
FI
A -- -- -- -- 154 160 36 5 0 201 -- -- -- -- 47
CD 112 37 54 47 250 151 45 44 15 255 39 8 -10 -32 5
F 137 27 30 26 220 196 41 7 3 247 59 14 -23 -23 27
GHI 132 57 40 40 269 148 42 23 6 219 16 -15 -17 -34 -50
JMN 135 33 38 29 235 138 28 30 14 210 3 -5 -8 -15 -25
PQ -- -- -- -- 153 92 18 25 6 141 -- -- -- -- -12
Total -- -- -- -- 1281 885 210 134 44 1273 -- -- -- -- -8
UK
A -- -- -- -- 154 80 16 26 8 130 -- -- -- -- -24
CD 106 34 40 39 219 127 38 42 23 230 21 4 2 -16 11
F 138 19 28 29 214 125 19 26 26 196 -13 0 -2 -3 -18
GHI 120 64 52 51 287 92 60 61 42 255 -28 -4 9 -9 -32
JMN 134 41 49 53 277 124 41 62 35 262 -10 0 13 -18 -15
PQ -- -- -- -- 154 67 33 51 29 180 -- -- -- -- 26
Total -- -- -- -- 1305 615 207 268 163 1253 -- -- -- -- -52
PT
A -- -- -- -- 154 150 54 26 4 234 -- -- -- -- 80
CD 112 48 64 45 269 84 49 70 53 256 -28 1 6 8 -13
F 135 31 43 42 251 93 38 46 7 184 -42 7 3 -35 -67
GHI 141 60 46 38 285 155 50 52 38 295 14 -10 6 0 10
JMN 149 16 21 35 221 158 25 15 30 228 9 9 -6 -5 7
PQ -- -- -- -- 154 95 28 25 13 161 -- -- -- -- 7
Total -- -- -- -- 1334 735 244 234 145 1358 -- -- -- -- 24
SE
A -- -- -- -- 154 107 38 7 1 153 -- -- -- -- -1
CD 123 32 48 51 254 154 20 33 56 263 31 -12 -15 5 9
F 139 25 28 19 211 151 40 24 9 224 12 15 -4 -10 13
GHI 133 63 53 42 291 125 84 52 20 281 -8 21 -1 -22 -10
JMN 145 35 37 35 252 139 39 31 24 233 -6 4 -6 -11 -19
PQ -- -- -- -- 154 77 20 30 50 177 -- -- -- -- 23
Total -- -- -- -- 1316 753 241 177 160 1331 -- -- -- -- 15
SK
A -- -- -- -- 153 82 22 33 0 137 -- -- -- -- -16
CD 136 38 68 68 310 125 51 91 77 344 -11 13 23 9 34
F 148 20 34 16 218 125 24 38 5 192 -23 4 4 -11 -26
GHI 140 77 41 28 286 126 122 53 30 331 -14 45 12 2 45
JMN 147 19 21 18 205 146 23 30 20 219 -1 4 9 2 14
PQ -- -- -- -- 153 73 49 47 24 193 -- -- -- -- 40
Total -- -- -- -- 1325 677 291 292 156 1416 -- -- -- -- 91
Total
A -- -- -- -- 923 677 217 143 16 1053 -- -- -- -- 130
CD 671 236 325 316 1548 718 246 343 303 1610 47 10 18 -13 62
F 821 149 182 145 1297 789 190 154 61 1194 -32 41 -28 -84 -103
121
Co
un
try
Macro
secto
r
Planned sample Actual sample Discrepancy (N)
Size Size Size
2 t
o 9
10
to
49
50
- 2
49
25
0 +
To
tal
2 t
o 9
10
to
49
50
- 2
49
25
0 +
To
tal
2 t
o 9
10
to
49
50
- 2
49
25
0 +
To
tal
GHI 773 386 291 240 1690 720 426 286 162 1594 -53 40 -5 -78 -96
JMN 836 171 201 213 1421 808 180 203 158 1349 -28 9 2 -55 -72
PQ -- -- -- -- 922 478 179 202 141 1000 -- -- -- -- 78
Total -- -- -- -- 7801 4190 1438 1331 841 7800 -- -- -- -- -1
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Other services: Wholesale and retail
trade, repair of motor vehicles and motorcycles; Transportation and storage; Accommodation and food service
activities; J,M,N: Information and communication; Professional, scientific and technical activities;
Administrative and support service activities; P,Q: Education; Human health and Social work activities
Source: Ecorys/DTI
The figures below (Figure A1.1 and Figure A1.2) show the final sample allocation by
country and macro-sector and by country and company size, respectively.
Figure A1.1 – Actual sample allocation by country and economic macro-sector (%)
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Other services: Wholesale and retail trade, repair of motor vehicles and motorcycles; Transportation and storage; Accommodation and food service activities; J,M,N: Information and communication; Professional, scientific and technical activities; Administrative and support service activities; P,Q: Education; Human health and Social work activities Source: European Digital skills survey
16.9
22.4
12.9
18.2
16.9
12.7
17.2
18.9
13.5
21.7
16.8
11.9
9.7
24.3
13.6
23.4
15.5
13.6
15.8
20.0 19.4
17.2 16.5
11.1 11.5
19.8
16.8
21.1
17.5
13.3
10.4
18.4
15.6
20.4 20.9
14.4
A
CD F
GH
I
JMN
PQ A
CD F
GH
I
JMN
PQ A
CD F
GH
I
JMN
PQ A
CD F
GH
I
JMN
PQ A
CD F
GH
I
JMN
PQ A
CD F
GH
I
JMN
PQ
DE PT SK FI SE UK
122
Figure A1.2 – Actual sample allocation by country and size (%)
Source: European Digital skills survey
A1.7 Data preparation and analysis
In order to prepare collected data for the subsequent calibration and analysis steps,
extensive quality and consistency checks using visual and logic checks and running
simple frequencies were carried out to ensure accuracy and completeness of data. Data
was subsequently cleansed, recoded and labelled as appropriate. A non-invasive
approach was chosen to deal with non-responses (people who responded “Don’t know”
to specific questions in the questionnaire). In other terms, it was chosen to not impute
non-responses (e.g. each missing value is replaced with an observed response from a
“similar” unit) to minimise bias, and as a consequence, non-responses were displayed
as blanks in the dataset and they were taken into account to compute denominators
when calculating incidences and proportions in the data analysis phase.
After data was prepared, the calibration procedure as described in section A1.5 was
carried out and two sample weights were calculated (as detailed in the last paragraph
of section A1.5), one to provide estimates at country level and the other one to provide
estimates at EU-28 level. These two weights were applied in the data analysis phase.
During such phase, the usual preliminary statistical analyses – frequencies, analysis of
variability, analysis of concentration, analysis of dispersion – were carried out on
weighted data. Descriptive analyses, including the calculation of ratios and incidences,
were then carried out in order to identify how the variables of interest vary according to
the occupations, economic sectors, enterprise size and country, and results were
presented in tables and graphs. Multivariate analyses, namely linear regression analysis
and logistic regression analysis, have been carried out to analyse the relationship
between specific variables of interest and the characteristics of the workplaces in terms
of sector, ownership, size, markets of reference and also to specific characteristics of
the workforce employed in the workplaces. Multivariate analyses, namely principal
component analysis, have been carried out also as an exploratory tool to assess
correlations between variables and to identify different pieces of elementary
37.0
29.7
20.6
12.7
55.8
25.6
13.7
4.9
47.9
28.7
17.2
6.1
72.3
16.3
9.3
2.0
58.6
21.0
12.8
7.6
46.1
27.1
19.4
7.3
2 to 9 10 to49
50 to249
250and
more
2 to 9 10 to49
50 to249
250and
more
2 to 9 10 to49
50 to249
250and
more
2 to 9 10 to49
50 to249
250and
more
2 to 9 10 to49
50 to249
250and
more
2 to 9 10 to49
50 to249
250and
more
DE PT SK FI SE UK
123
information to compose possible synthetic indicators. Synthetic indicators have then
been calculated employing the appropriate additive procedures defined ad hoc in
relation in particular to the areas in which ICT investments have been done and also in
the area of digital skills, for a better understanding of types and levels of digital skills
required in different jobs.
A1.8 The survey questionnaire
The survey questionnaire was designed operationalising the research questions
underpinning the survey the overall study, and therefore to collect evidence on the level
and type of digital skills that are required in different jobs and sectors as well as how
the need for such skills has changed over the past few years and is expected to change
in the future. Also, the survey questionnaire was designed to investigate the actions
undertaken by the employers to address digital skill gaps (e.g. training). The survey
questionnaire, designed bearing in mind the type of respondent (establishments) and
surveying technique of reported in the following of this section, is structured as follows:
Module 1 is a set of questions which aims at collecting background information on the
respondents while filtering out those not possessing the required characteristics to be
covered by the survey. In addition to the information on type of respondent (single
workplace or organisation with multiples workplaces), economic sector of activity,
number of employees in the workplace and in the organisation, ownership (public or
private) and main market of reference, a number of questions aimed at collecting
information on the employees and their occupational categories (ISCO 1-digit) are
asked.
Module 2 aims to collect information on ICT use and the digital skills available in the
workplace. More in particular a list of types of ICT and digital devices is presented, and
– for each of the occupational categories (ISCO 1-digit) identified in Module 1 – a list of
digital skills (ranging from basic to advanced and including also some specialised ICT
skills such the use and programming of CNC machines or robots) and the related level
of importance is presented, and the number of employees in each occupational category
for each digital skills (the total number and those proficient) is recorded. The digital
skills are investigated using as a proxy a list of tasks, assuming that those carrying out
a specific task possess as well the required skill to do so. The difference between the
number of employees in a specific occupational category with a specific digital skill and
the proportion of them who have a proficient level of that digital skill allows to identify
the digital skill gap.
Module 3 investigates the impact of digital skill gaps on the workplace performance, and
also the actions undertaken to deal with digital skill gaps. The reasons why workplaces
did not undertake any actions to reduce digital skill gaps.
Module 4 aims to collect information on type and level of digital skills and digital skill
gaps of up to three specific occupations amongst the most important for workplace’s
day-to-day operations. In relation to these occupation, a set of questions aim to identify
recent recruitment, expected impact of ICT on the way tasks are carried out and
estimated risk of disappearance due to ICT in relation to the selected occupations.
124
Module 5 concludes the questionnaire and surveys the reasons and the level of
importance of investment in ICT in the recent past and also the trends in the use of ICT
in the last five years.
The questionnaire was developed by core team members (Maurizio Curtarelli, Valentina
Gualtieri, Vicki Donlevy, Mike Blakemore, Martin Eggert Hansen, Hanne Shapiro) with
the support of the Scientific Committee (Ferrán Mañé, Graham Vickery, Irene Mandl),
taking into account the outcome of the literature and survey review carried out
previously and following the indications of the Steering Committee.
125
ICT for work: Digital skills in the workplace
Survey questionnaire
Module 0: Introduction
Ecorys and the Danish Technological Institute are conducting a study on behalf of the European Commission on Information and Communication Technology (ICT) skills needs for enterprises across the European Union. By ICT skills needs, we mean the skills in the area of ICT that your enterprise needs for its day-to-day activities.
The aim of the survey is to collect evidence of the level and types of ICT skills required in different jobs and sectors and what strategies are proposed to meet your skills needs. We would also like to find out how the need for these specific type of skills has changed over the past few years and how enterprises expect it to change in the future.
We guarantee that your responses will be presented completely anonymously and will never be analysed or displayed individually. The survey length is approximately 15-20 minutes. We would very much appreciate your participation.
Module 1: Background questions
[ASK ALL]
We would like to ask you some questions about the place in which you work. By this, we mean the location in
which you work all or most of the time which could be a factory, office, workshop, farm, warehouse, or other
place of work. For the purpose of this survey we will use the word “workplace” to describe this location.
Before we start with the main questionnaire, we would like to make sure that you are the correct
person at your workplace to consult for this survey.
Q1. We would like to survey the person who has the best overview of working tasks of the
employees in your workplace, for example the person responsible for human resources
issues or the managing director. Are you the correct person to survey in this case?
Yes……………………………………………….. 1
GO TO Q2
No………………………………………...……….. _
CONTINUE
Q1a. Can you please provide the name and contact of the person who has the best overview of working
tasks of the employees in your workplace?
Name ________________________________email ______________________________________
[ASK ALL]
Q2. Is your workplace one of many different workplaces belonging to the same organisation,
or is it the only workplace your organisation has?
One of many different workplaces belonging to the same organisation ……………………………………………………. 1 GO TO Q3
126
It is the only workplace your organisation has …………… 2 GO TO Q6
Don’t know…………………………………………………….. 3 GO TO Q6
Q3. Is your workplace the headquarters of your organisation, or is it a subsidiary site?
Headquarters.......................…………………….. 1 CONTINUE
Subsidiary site..................................................... 2 CONTINUE
Don’t know…………………………………….. 3 CONTINUE
Q4. In total and including yourself, approximately how many employees work in your
organisation?
Please include yourself if you are an employee and any other employees on the payroll of the organisation, any
employed proprietors or owners, employed family members and trainees, but do NOT include the self-
employed, freelance and outside contractors or agency staff.
Number of employees: _____________ 1 IF ANSWER IS 0 OR 1
EXIT, OTHERWISE GO
TO Q5
Don’t know……………...………………………….. 3 CONTINUE
Q5. Could you please give your best estimate using the following categories?
1……………………………………………….. 1 EXIT
2-9…………………………………………….. 2 CONTINUE
10-49………………………………………….. 3 CONTINUE
50-249………………………………………… 4 CONTINUE
250 or more………………………………….. 5 CONTINUE
Q6. In total and including yourself, approximately how many employees work in THIS workplace?
Please include yourself if you are an employee and any other employees on your payroll, any employed
proprietors or owners, but do NOT include the self-employed, freelance and outside contractors or agency staff.
Number of employees: _____________ 1 IF ANSWER IS 0 OR 1
EXIT, OTHERWISE GO
TO Q7
Don’t know……………...………………………….. 2 CONTINUE
Q7. Could you please give your best estimate using the following categories?
1……………………………………………….. 1 EXIT
2-9…………………………………………….. 2 CONTINUE
10-49………………………………………….. 3 CONTINUE
50-249………………………………………… 4 CONTINUE
250 or more………………………………….. 5 CONTINUE
Q8. Could you please indicate, for this workplace, the number or percentage of employees who…
127
% Absolute
number DK
…are female? __ __ 99
…have a university degree? __ __ 99
…are younger than 30 years of age? __ __ 99
…are older than 50 years of age? __ __ 99
Q9. A public sector organisation is either wholly owned by the public authorities or they own more than
50%. Is your workplace part of…
The private sector.................…………………….. 1 CONTINUE
The public sector................................................. 2 CONTINUE
Don’t know……………...………………………….. 3 CONTINUE
Q10. What is the main area of activity of your workplace? [ONLY ONE SELECTION IS POSSIBLE]
Agriculture, forestry and fishing………..………… 1 CONTINUE
Mining and Quarrying…………………………….. 2 EXIT
Manufacturing……………………………………… 3 CONTINUE
Electricity, gas, steam, air conditioning supply…. 4 CONTINUE
Water supply, sewerage, waste management and remediation activities…………………………
5 CONTINUE
Construction………………………………………... 6 CONTINUE
Wholesale and retail trade, repair of motor vehicles and motorcycles………………………….
7 CONTINUE
Transportation and Storage……………………… 8 CONTINUE
Accommodation and Food Service Activities…... 9 CONTINUE
Information and Communication…………………. 10 CONTINUE
Financial and Insurance Activities……………….. 11 EXIT
Real Estate Activities……………………………… 12 EXIT
Professional, Scientific and Technical Activities.. 13 CONTINUE
Administrative and Support Service Activities….. 14 CONTINUE
Public Administration and Defence, Compulsory Social Security……………………………………...
15 EXIT
Education…………………………………………… 16 CONTINUE
Human Health and Social Work Activities………. 17 CONTINUE
Arts, Entertainment and Recreation……………... 18 EXIT
Other Service Activities…………………………… 19
Please specify sector:…………………………….. CONTINUE
Activities of households as employers, undifferentiated goods and services, producing activities of households for own use, activities of extraterritorial organisations and bodies………...
20 EXIT
128
Q11. Which of the following markets is most important for the main activity of your
workplace? (Please select all that apply)
Local (i.e. the town or village in which your workplace is located)………………………..
1
CONTINUE
Regional (i.e. your county or region).…… 2 CONTINUE
National (i.e. your country as a whole) ….. 3 CONTINUE
International………………………………… 4 CONTINUE
Don’t know………………………………….. 5 CONTINUE
Q12. Does your workplace have any employees in any of the following job categories?
(Please select all that apply)
Occupation Examples YES NO
Plant and machine
operators and
assemblers
Examples include transport and mobile
machine drivers, plant and machine
operators, routine operatives (sorters,
assemblers), HGV, van, fork lift, train,
bus and taxi drivers.
1 0 CONTINUE
Building, craft and
related trade workers
Examples include electricians, motor
mechanics, machine repairers, metal
workers, blacksmiths, welders, TV
engineers, plumbers, carpenters,
printers, butchers, furniture makers.
1 0 CONTINUE
Skilled agricultural,
forestry and fishery
workers.
Examples include dairy producers,
landscape gardeners and horticultural
workers.
1 0 CONTINUE
Sales, customer or
personal service workers
Examples include sales assistants and retail cashiers, telesales, call centre agents, customer care occupations. Personal care workers such as those providing care to children, elderly and disabled people, ambulance workers. Personal service workers such as hairdressers, cooks, driving instructors, undertakers, housekeepers, waiters/waitresses and bar tenders. Protective service workers such as
security guards, and junior police, fire
and prison officers.
1 0 CONTINUE
Clerical support workers
Examples include secretaries,
receptionists, telephonists, book-
keepers, credit controllers/wage clerks,
assistants/clerks, communication
operators, market research interviewers,
pension and insurance clerks, office
assistants, database assistants.
1 0 CONTINUE
Technicians and
associate professionals
Examples include science, engineering and IT technicians, accounting technicians, manufacturing/ construction supervisors, draughtspersons, insurance underwriters, finance and investment analysts and advisers, buyers, estate
1 0 CONTINUE
129
agents, pilots, graphic designers, fitness instructors, chefs, junior nurses, therapists, community workers, careers advisors, health and safety officers, housing officers, fitness instructors, police inspectors and detectives, photographers, interior designers, sports players.
Professionals
Examples include professional
engineers, software and IT
professionals, accountants, chemists,
scientific researchers, solicitors and
lawyers, economists, architects,
actuaries, doctors, senior nurses,
midwifes, psychologists, teachers, social
workers, librarians, actors, artists,
authors, writers/journalists, musicians.
1 0 CONTINUE
Managers
Examples include chief executives,
senior officials, legislators, managing
directors, senior business managers,
senior production managers, senior
service managers.
1 0 CONTINUE
Elementary occupations
Examples include labourers, packers,
goods handling and storage staff,
cleaners, shelf fillers, kitchen/catering
assistants, postal workers, road
sweepers, traffic wardens.
1 0 CONTINUE
[ONLY OCCUPATIONS SELECTED IN Q12 APPEAR IN SUBSQUENT QUESTION]
Q13. Could you please indicate approximately how many employees your workplace has in
these job categories?
N DK
Plant and machine operators and assemblers __ 99 CONTINUE
Craft and related trade workers __ 99 CONTINUE
Skilled agricultural, forestry and fishery workers __ 99 CONTINUE
Service and sales workers __ 99 CONTINUE
Clerical support workers __ 99 CONTINUE
Technicians and associate professionals __ 99 CONTINUE
Professionals __ 99 CONTINUE
Managers __ 99 CONTINUE
Elementary occupations __ 99 CONTINUE
[IF 0=99 GO TO Q14]
[ONLY OCCUPATIONS SELECTED IN Q12 APPEAR IN SUBSQUENT QUESTION]
130
Q14. Could you please provide your best estimate of the approximate percentage of
employees in your workplace in these job categories? [TOTAL MUST BE 100%]
% DK
Plant and machine operators and assemblers __ 99 CONTINUE
Craft and related trade workers __ 99 CONTINUE
Skilled agricultural, forestry and fishery workers __ 99 CONTINUE
Service and sales workers __ 99 CONTINUE
Clerical support workers __ 99 CONTINUE
Technicians and associate professionals __ 99 CONTINUE
Professionals __ 99 CONTINUE
Managers __ 99 CONTINUE
Elementary occupations __ 99 CONTINUE
Module 2: ICT use and digital skills in the workplace
[ASK ALL]
We would now like to ask you a number of questions on Information Communication Technology (ICT) use, and existing digital skills in your workplace.
Q15. Please indicate if your workplace currently uses computers, CNC machines or tools, and other digital devices to carry out its main business activity.
By digital device an electronic device which uses discrete, numerable data and processes for all its operations should be meant. This includes personal computers connected or not to the Internet or to an intranet, nettops, portable computers (e.g. laptops, notebooks, netbooks, tablets), or other portable devices (e.g. Smartphones, Personal Digital Assistants (PDA), GPS navigator) and CNC (Computer Numerical Control) machines or tools used in manufacturing (machine tools that are operated by precisely programmed commands encoded on a storage medium)..
YES NO DK
a. Desktop computers.…………………… 1 2 99 CONTINUE
b. Portable computers………………… 1 2 99 CONTINUE
c. Other portable devices…………… 1 2 99 CONTINUE
d. Broadband technology to access the Internet…………………………………. 1 2 99 CONTINUE
e. Intranet platform……………………… 1 2 CONTINUE
f. CNC machines or tools……………… 1 2 99 CONTINUE
g. Programmable robots ……………… 1 2 99 CONTINUE
[IF Q15.a AND Q15.b AND Q15.c AND Q15.d AND Q15.e AND Q15.f AND Q15.g=2 EXIT;
PLEASE NOTE THAT IN THIS CASE RESPONDENT HAS TO BE RECONTACTED BY CATI]
Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1
to 5 (where 1 means not at all important, 3 means moderately important and 5 means
essential,) how important it is for employees in these categories to…:
131
[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONAL GROUPS SELECTED IN Q12]
No
t at
all
imp
ort
an
t
Mo
de
rate
ly
imp
ort
an
t
Essen
tial
DK
a. Use a word processor (e.g. Word)…………….. 1 2 3 4 5 99 CONTINUE
b. Create a spreadsheet (e.g. Excel)…………….. 1 2 3 4 5 99 CONTINUE
c. Search for, collect and process information using ICT (e.g. online/Internet)…………………
1 2 3 4 5 99 CONTINUE
d. Communicate through ICT using email .……… 1 2 3 4 5 99 CONTINUE
e. Communicate through ICT using social media, Skype/video calls…………………………………
1 2 3 4 5 99 CONTINUE
f. Use software for design, calculation or simulation……………………………………...……
1 2 3 4 5 99 CONTINUE
g. Undertake programming and software development……………………………………….
1 2 3 4 5 99 CONTINUE
h. Design and maintain ICT architecture for the workplace…………………………………………..
1 2 3 4 5 99 CONTINUE
i. Programme CNC machines ……………………. 1 2 3 4 5 99 CONTINUE
j. Programme robots………………………………..
k. Use CNC machines ……………………………. 1 2 3 4 5 99 CONTINUE
l. Use programmable robots……………………… 1 2 3 4 5 99 CONTINUE
Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks.
Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level.
[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONAL GROUPS SELECTED IN Q12;
ONLY IF Q16=2 TO 5 CORRESPONDING ITEM IS DISPLAYED]
TASKS NUMBER OF
EMPLOYEES
NUMBER OF
PROFICIENT
EMPLOYEES
N % DK N % DK
a. Use a word processor (e.g. Word)………….. __ __ 99 __ __ 99 CONTINUE
b. Create a spread-sheet (e.g. Excel)…………… __ __ 99 __ __ 99 CONTINUE
c. Search for, collect and process information using ICT (e.g. online/Internet)…………………. __ __ 99 __ __ 99 CONTINUE
d. Communicate through ICT using email ………… __ __ 99 __ __ 99 CONTINUE
e. Communicate through ICT using social media, Skype/video calls …………………………………. __ __ 99 __ __ 99 CONTINUE
f. Use software for design, calculation or simulation………………………………………….. __ __ 99 __ __ 99 CONTINUE
g. Undertake programming and software development………………………………………. __ __ 99 __ __ 99 CONTINUE
h. Design and maintain ICT architecture for the workplace………………………………………….. __ __ 99 __ __ 99 CONTINUE
i. Programme CNC machines ……………………. __ __ 99 __ __ 99 CONTINUE
j. Programme robots………………………………. __ __ 99 __ __ 99 CONTINUE
k. Use CNC machines ……………………………. __ __ 99 __ __ 99 CONTINUE
l. Use programmable robots……………………… __ __ 99 __ __ 99 CONTINUE
132
Module 3: ICT and digital skill gaps in the workplace
In the previous section, we have asked you about the use of ICT in your workplace and the level of proficiency of the employees in your workplace in carrying out tasks requiring ICT. We would now like to ask some questions about the steps your workplace has taken to increase the level of proficiency of your employees in the use of ICT.
[ASK IF Q23.a OR Q23.b OR Q23.c OR Q23.d OR Q23.e OR Q23.f OR Q23.g OR Q23.h OR Q23.i OR Q23.j OR Q23.k OR Q23.l ≠ 100% IN ANY OF THE JOB CATEGORIES]
Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance?
Yes, a major impact…………………………….. 1 GO TO Q26a
Yes, a minor impact…………………………… 2 GO TO Q26a
No…………………………………………….. 3 GO TO Q27
Don’t know………………………………………… 4 GO TO Q27
Not applicable (100% proficient)…………………. 5 GO TO MODULE 4
Q26a. What type of impact does this have on your workplace performance?
Decrease in the number of customers……… 1 CONTINUE
Decrease in the number of contracts………… 2 CONTINUE
Loss of productivity…………………………….. 3 CONTINUE
Other negative impact…………………………. 4 CONTINUE
Don’t know………………………………………… 5 CONTINUE
Q27. Has your workplace taken any steps to improve the proficiency of employees to enable them to carry out the tasks involving ICT use?
Yes…………………..…………………………….. 1 CONTINUE
No, but have plans to…………………………… 2 GO TO MODULE 4
No…………………………………………….. 3 GO TO MODULE 4
[ASK IF Q27 = 1]
Q28. Which of the following steps is your workplace taking to overcome the fact that some of its employees are not fully proficient in carrying out tasks involving ICT use? (Please select all that apply)
YES NO
On the job training and development programmes………. 1 2 CONTINUE
External training and development programmes…………. 1 2 CONTINUE
Changing working practices (e.g. task sharing)…………… 1 2 CONTINUE
Reallocating tasks……………………………………………. 1 2 CONTINUE
Recruiting new staff with needed skills............................. 1 2 CONTINUE
Hiring temporary staff with needed skills (e.g. temporary agency workers)………………………………………………. 1 2 CONTINUE
Outsourcing of tasks involving ICT use………………… 1 2 CONTINUE
Secondment of employees from other workplaces within the same organisation [ONLY FOR MULTI- 1 2 CONTINUE
133
ESTABLISHMENT ORGANISATIONS]
Other…………………………………………………………… 1 2 CONTINUE
[ASK IF Q27 = 1]
Q29. Which – if any – of the following difficulties has your workplace encountered when taking steps to
overcome the fact that some employees are not fully proficient in carrying out tasks involving ICT use?
(Please select all that apply)
YES NO
Excessive cost of training and development programmes. 1 2 CONTINUE
Vacancies for jobs involving ICT stay open for a long time due to low number of applicants with the required skills… 1 2 CONTINUE
Vacancies for jobs involving ICT are not filled due to lack of applicants with the required skills………………………. 1 2 CONTINUE
Modifications to work organisation are not possible due to the limited number of employees in this workplace……… 1 2 CONTINUE
Excessive cost of hiring temporary staff with the required skills…………………………………………………………… 1 2 CONTINUE
Excessive cost of outsourcing of tasks involving ICT use…………………………………………………………… 1 2 CONTINUE
Module 4: ICT use, digital skills and digital skill gaps in specific occupations
We would now like to ask you some questions regarding up to three specific jobs of your choice.
Q18. Please select up to THREE specific jobs existing in your workplace which are amongst
the most important for your day-to-day operations. If possible, please select them from
different job categories.
[FOR EACH OCCUPATIONAL GROUP SELECTED IN Q12, ONLY THE CORRESPONDING
DROP-DOWN MENUS WITH THE LIST OF ISCO 4-DIGIT OCCUPATIONS APPEAR FOR
SELECTION]
[IF NO OCCUPATIONS SELECTED GO TO MODULE 5]
[NOTE: Q19 HAS TO BE ASKED FOR EACH OF THE OCCUPATION SELECTED IN Q18,
THEREFORE MAKE SURE THAT IT IS ASKED MAX 3 TIMES]
134
Q19. Thinking about these jobs in your workplace, please indicate using a scale of 1 to 5
(where 1 means not at all important, 3 means moderately important and 5 means essential)
how important for day-to-day activities it is for employees in these jobs to…:
[IF Q19.a AND Q19.b AND Q19.c AND Q19.d AND Q19.e AND Q19.f AND Q19.g AND Q10.h
AND Q19.i AND Q19.j AND Q19.k AND Q19.l = 1 GO TO Q21]
Q24. Thinking about the existing employees in your workplace employed as [TITLES OF
OCCUPATIONS SELECTED IN Q18 APPEAR], could you indicate if you think they are fully
proficient in carrying out the following tasks involving ICT use? Please note that a proficient
employee is someone who is able to do the job to the required level.
[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONS SELECTED IN Q18; ONLY IF
Q19=2 TO 5 CORRESPONDING ITEM IS DISPLAYED]
YES NO DK
a. Use a word processor (e.g. Word)………………………..…….…… 1 2 99 CONTINUE b. Create a spread-sheet (e.g. Excel)………………………………...…. 1 2 99 CONTINUE c. Search for, collect and process information using ICT (e.g.
online/Internet)……………………………………………………….…. 1 2 99 CONTINUE d. Communicate through ICT using email..…………………………..… 1 2 99 CONTINUE e. Communicate through ICT using social media, Skype/video calls.. 1 2 99 CONTINUE f. Use software for design, calculation or simulation………………..… 1 2 99 CONTINUE g. Undertake programming and software development....................... 1 2 99 CONTINUE h. Design and maintain ICT architecture for the workplace…………… 1 2 99 CONTINUE i. Programme CNC machines…………..………………………………. 1 2 99 CONTINUE j. Programme robots…………………………………………….……….. 1 2 99 CONTINUE k. Use CNC machines.......................................................................... 1 2 99 CONTINUE l. Use programmable robots……………………………………………... 1 2 99 CONTINUE
[GRID BELOW APPEARS FOR THE OCCUPATIONS SELECTED IN Q18]
[NOTE: Q20 IS DISPLAYED ONLY ONCE WITH THE TITLES OF THE OCCUPATIONS
SELECTED IN Q18]
No
t at
all
imp
ort
an
t
Mo
de
rate
ly
imp
ort
an
t
Essen
tial
DK
a. Use a word processor (e.g. Word)……… 1 2 3 4 5 99 CONTINUE
b. Create a spread-sheet (e.g. Excel)………… 1 2 3 4 5 99 CONTINUE
c. Search for, collect and process information using ICT (e.g. online/Internet)………………… 1 2 3 4 5 99 CONTINUE
d. Communicate through ICT using email….…. 1 2 3 4 5 99 CONTINUE
e. Communicate through ICT using social media, Skype/video calls……………………………….. 1 2 3 4 5 99 CONTINUE
f. Use software for design, calculation or simulation……………………………………... 1 2 3 4 5 99 CONTINUE
g. Undertake programming and software development…………………………………… 1 2 3 4 5 99 CONTINUE
h. Design and maintain ICT architecture for the workplace………………………………………… 1 2 3 4 5 99 CONTINUE
i. Programme CNC machines ……………. 1 2 3 4 5 99 CONTINUE
j. Programme robots 1 2 3 4 5 99 CONTINUE
k. Use CNC machines…………………………. 1 2 3 4 5 99 CONTINUE
l. Use programmable robots 1 2 3 4 5 99 CONTINUE
135
Q20. Thinking about these jobs in your workplace, please indicate if and to what extent the
use of ICT has changed the way job tasks are carried out. Please refer to the timespan of the
last 5 years.
No
ch
an
ge a
t al
l
Min
or
chan
ge (
e.g
. IC
T is
no
w s
up
po
rtin
g so
me
task
s)
Mo
de
rate
ch
ange
(e
.g.
som
e t
asks
are
no
w
carr
ied
ou
t in
a d
iffe
ren
t
way
as
con
seq
ue
nce
of
ICT
use
)
Maj
or
chan
ge (
e.g
. mo
st
or
all t
he
tas
ks a
re c
arri
ed
ou
t in
a d
iffe
ren
t w
ay a
s
a co
nse
qu
en
ce o
f IC
T u
se)
DK
a. Occupation 1 1 2 3 4 99 CONTINUE
b. Occupation 2 1 2 3 4 99 CONTINUE
c. Occupation 3 1 2 3 4 99 CONTINUE
[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONS IF Q20a _c=4]
Q21. Thinking about these jobs in your workplace, please indicate if you started recruiting
employees for this job(s) in the last 5 years.
YES NO DK
a. Occupation 1 1 2 99 CONTINUE
b. Occupation 2 1 2 99 CONTINUE
c. Occupation 3 1 2 99 CONTINUE
[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONS SELECTED IN Q18]
Q21a. Thinking about these jobs in your workplace, please indicate if and to what extent you
think the use of ICT will change the way job tasks are carried out. Please refer to the timespan
of the next 5 years.
No
ch
an
ge a
t al
l
Min
or
chan
ge (
e.g
. IC
T
will
su
pp
ort
so
me
tas
ks)
Mo
de
rate
ch
ange
(e
.g.
som
e t
asks
will
be
car
rie
d
ou
t in
a d
iffe
ren
t w
ay a
s
con
seq
ue
nce
of
ICT
use
)
Maj
or
chan
ge (
e.g
. mo
st
or
all t
he
tas
ks w
ill b
e
carr
ied
ou
t in
a d
iffe
ren
t
way
as
a co
nse
qu
en
ce o
f
ICT
use
)
DK
d. Occupation 1 1 2 3 4 99 CONTINUE
e. Occupation 2 1 2 3 4 99 CONTINUE
f. Occupation 3 1 2 3 4 99 CONTINUE
[IF Q21.a OR Q21.b OR Q21.c = 1 DO NOT DISPLAY RELATED OCCUPATION IN Q22]
[GRID BELOW APPEARS FOR EACH OF THE OCCUPATIONS SELECTED IN Q18]
136
Q22. Thinking about these jobs in your workplace, please indicate if you think they will
disappear in the next 5 years due to ICT.
YES NO DK
a. Occupation 1 1 2 99 CONTINUE
b. Occupation 2 1 2 99 CONTINUE
c. Occupation 3 1 2 99 CONTINUE
Module 5: Trends in ICT investment and use in the workplace
[ASK ALL]
In this final section of the survey, we would like to find out how investment in ICT and ICT use has changed over the past few years and how you expect it to change in the near future.
Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply)
No
t a
t a
ll
Mo
de
rate
ly
Sig
nif
ican
tly
DK
Improving overall efficiency……………….……. 1 2 3 4 5 99 CONTINUE
Improving quality of existing products and services……. 1 2 3 4 5 99 CONTINUE
Launching new products and services………… 1 2 3 4 5 99 CONTINUE
Implementing new marketing methods………… 1 2 3 4 5 99 CONTINUE
Engaging customers, users, suppliers or other companies to improve or create products or services…………………………………………. 1 2 3 4 5 99 CONTINUE
Tracking and analysing data from business processes, customers, and transactions to improve or create products or services …… 1 2 3 4 5 99 CONTINUE
Making the production process leaner 1 2 3 4 5 99 CONTINUE
Delocalising the production of goods or services within the country…………………………………. 1 2 3 4 5 99 CONTINUE
Delocalising the production of goods or services abroad……………………………………………… 1 2 3 4 5 99 CONTINUE
Improving work organisation or working procedures……………………………………….. 1 2 3 4 5 99 CONTINUE
Making work easier and less stressful for employees 1 2 3 4 5 99 CONTINUE
137
Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), that the use of ICT in your workplace…?
No
t at
all
Mo
dera
tely
Sig
nif
ican
tly
Has increased in the last 5 years……………………… 1 2 3 4 5 99 CONTINUE
Will increase in the next 5 years……………………… 1 2 3 4 5 99 CONTINUE
Q32. Do you think that ICT will cause a new job profile to emerge in your workplace in the
next 5 years?
Yes………………….………………….…………………. CONTINUE
No……………….………………….……………………… EXIT
Don’t know……………….………………….…………… EXIT
Q33. Can you briefly describe this new job profile?
_________________________________________________________________________________
_________________________________________________________
END OF QUESTIONNAIRE
Many thanks for taking part in our survey. For quality control purposes, we would like you to
provide some details regarding yourself:
Position__________________________________
Gender______
Contact details_________________________________________________________
138
ANNEX 2. COMPLEMENTARY STATISTICAL EVIDENCE
Chapter 2. Workplaces’ characteristics: describing the survey population
Table A2.1 – Workplaces by economic sector groupings, in sampled countries and EU28 (N and %)
Co
un
try
Sectors
A.
Ag
ric
ult
ure
CD
. M
an
ufa
ctu
rin
g a
nd
uti
liti
es
F.
Co
nstr
ucti
on
GH
I.
Co
mm
erce,
tra
nsp
ort,
acco
mm
od
ati
on
an
d f
oo
d
servic
e
JM
N.
In
form
ati
on
an
d
co
mm
un
ica
tio
n;
pro
fessio
nal,
scie
nti
fic a
nd
tech
nic
al
acti
vit
ies;
Ad
min
istr
ati
ve
servic
es
PQ
. E
du
cati
on
an
d h
um
an
healt
h
To
tal
N % N % N % N % N % N % N %
DE 208,824 14.4 153,329 10.6 160,233 11.1 491,697 33.9 274,656 19.0 160,068 11.0 1,448,807 100.0
FI 24,045 18.7 10,554 8.2 19,194 14.9 38,701 30.1 22,451 17.5 13,447 10.5 128,392 100.0
UK 166,439 9.4 123,532 7.0 219,316 12.3 483,033 27.2 658,255 37.1 125,523 7.1 1,776,098 100.0
PT 151,910 31.0 39,320 8.0 37,554 7.7 161,707 33.0 50,263 10.3 48,992 10.0 489,746 100.0
SE 28,315 10.0 24,218 8.5 41,404 14.6 88,671 31.3 80,940 28.5 20,059 7.1 283,607 100.0
SK 6,811 4.0 18,675 11.1 16,597 9.8 70,781 42.0 47,990 28.4 7,841 4.6 168,695 100.0
EU28 3,057,598 22.2 1,264,916 9.2 1,438,086 10.4 4,643,863 33.6 2,346,319 17.0 1,052,331 7.6 13,803,113 100.0
Q10. What is the main area of activity of your workplace? Note: totals from the Digital Skills Survey correspond mathematically to those from Eurostat Structural Business Statistics, Business Demography Statistics and Farm Structure Statistics (2013) with regards to the distribution of workplaces, as a result of the calculation of sample weights according to the calibration procedure as described in Annex 1. Number of valid responses: 7,800 N=13,803,113 (EU28) N=4,295,345 (Six countries) Source: Digital Skills Survey (weighted values), Eurostat (2013)
Table A2.2 – Workplaces by size (expressed in number of employees), in sampled countries and
EU28 (N and %)
Size
Country
DE FI UK PT SE SK EU 28
N % N % N % N % N % N % N %
2-9 893,849 61.7 104,419 81.3 1,421,896 80.1 436,327 89.1 234,752 82.8 142,801 84.7 11,256,481 81.6
10-49 420,724 29.0 16,888 13.2 294,050 16.6 45,633 9.3 34,245 12.1 20,522 12.2 2,082,572 15.1
50 - 249 112,997 7.8 6,228 4.9 49,156 2.8 7,306 1.5 13,084 4.6 4,618 2.7 396,316 2.9
250 + 21,238 1.5 858 0.7 10,996 0.6 478 0.1 1,526 0.5 753 0.4 67,744 0.5
Total 1,448,808 100.0 128,393 100.0 1,776,098 100.0 489,744 100.0 283,607 100 168,694 100.0 13,803,113 100.0
Q6: In total and including yourself, approximately how many employees work in THIS workplace? Q7: Could you please give your best estimate using the following categories? (2-9, 10-49, 50-249, 250+)
Note: totals from the Digital Skills Survey correspond mathematically to those from Eurostat Structural Business Statistics, Business Demography Statistics and Farm Structure Statistics (2013) with regards to the distribution of workplaces, as a result of the calculation of sample weights according to the calibration procedure as described in Annex 1. Number of valid responses: 7,800 N=13,803,113 (EU28) N=4,295,345 (Six countries) Source: Digital Skills Survey (weighted values), Eurostat (2013)
139
Figure A2.1 – Workplaces by type of organizational structure in sampled countries and EU28 (N
and %)
Q2: Is your workplace one of many different workplaces belonging to the same organisation, or is it the only
workplace your organisation has?
Number of valid responses: 7,800 N=13,803,113 (EU28)
N=4,295,345 (Six countries)
Source: Digital Skills Survey (weighted values)
Figure A2.2 – Workplaces belonging to a group by role within the group in sampled countries and
EU28 (N and %)
Q3: Is your workplace the headquarters of your organisation, or is it a subsidiary site?
Number of valid responses: 1,893 N=1,979,446 (EU28)
N=492,359 (Six countries)
Source: Digital Skills Survey (weighted values)
12.8
17.4
11.1
5.6
10.4
17.6
14.2
87.2
82.5
88.6
94.1
89.6
81.5
85.6
0.1
0.2
0.4
0.1
0.9
0.2
DE
FI
GB
PT
SE
SK
EU level
Several office branches Single office Don't know
64.2% 53.7% 58.4%
90.7% 74.7%
54.4% 69.5%
35.8% 42.4% 38.6%
5.8% 25.3%
44.8% 28.9%
4.0% 3.0% 3.5% 0.8% 1.6%
DE FI UK PT SE SK
Country EU28
Headquarters Subsidiary site Don't know
140
Figure A2.3 - Workplaces by sector (public/private) in sampled countries and EU28 (%)
Q9. A public sector organisation is either wholly owned by the public authorities or they own more than 50%.
Is your workplace part of…(the private sector/the public sector/don’t know)
Number of valid responses: 7,800 N=13,803,113 (EU28)
N=4,295,345 (Six countries)
Source: Digital Skills Survey (weighted values)
Figure A2.4 - Workplaces by main market of reference, in sampled countries and EU28 (%)
Q11. Which of the following markets is most important for the main activity of your workplace?
Number of valid responses: 7,800 N=13,803,113 (EU28)
N=4,295,345 (Six countries)
Source: Digital Skills Survey (weighted values)
90.7% 98.1% 96.6% 99.0%
79.2% 95.4% 94.7%
9.3% 1.7% 2.7% 1.0%
15.3%
4.4% 4.5% 0.2% 0.8%
5.4% 0.3% 0.8%
DE FI UK PT SE SK
Country EU28
The private sector? The public sector? Don't know
18.9%
36.3%
43.2%
36.6%
65.8%
26.9%
36.6%
36.4%
33.5%
21.3%
19.8%
18.5%
28.5%
29.0%
27.2%
17.3%
22.7%
26.5%
8.7%
30.3%
21.3%
17.5%
12.1%
12.7%
17.1%
7.1%
14.2%
13.0%
0.8%
0.1%
0.2%
DE
FI
UK
PT
SE
SK
Co
un
try
EU2
8
Local Regional National International Don't know
141
Table A2.3 - Workplaces by main market of reference by sector and size, EU28 (%)
Market of reference
Local Regional National International Don't know
Size
2-9 39.8 30.5 19.8 9.7 0.2
10-49 23.4 21.2 28.0 27.3 0.1
50-249 20.6 25.5 27.6 26.2 0.2
250+ 6.8 33.8 19.8 39.6 0.0
Sector
A. Agriculture 29.7 35.0 22.6 12.7 0.0
CD. Manufacturing
and utilities 25.6 24.5 25.2 24.6 0.1
F. Construction 39.7 40.3 15.6 4.2 0.2
GHI. Commerce,
transport,
accommodation and
food service 44.9 23.7 18.6 12.5 0.3
JMN. Information and
communication;
professional,
scientific and
technical activities;
Administrative
services 29.7 24.0 29.3 16.9 0.2
PQ. Education and
human health 44.5 35.9 14.5 5.1 0.0
Total 36.6 29.0 21.3 13.0 0.2
Q11. Which of the following markets is most important for the main activity of your workplace?
Number of valid responses: 7,800 N=13,803,113 (EU28)
N=4,295,345 (Six countries)
Source: European Digital Skills Survey (weighted values)
142
Figure A2.5 – Employees by specific characteristics in workplaces in the EU by sector and size (%
of total employees)
* A: Agriculture; C,D: Manufacturing and utilities; F: Costruction; G,H,I: Other services: Wholesale and retail trade,
repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities;
J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support
service activities ; P,Q: Education; Human health and social work activities
Q8: Could you please indicate, for this workplace, the number or percentage of employees who…(are female/have a
university degree, are younger than 30 years of age/are older than 50 years of age)
Number of valid responses: 6,917 (female rate); 6,334 (university degree rate); 6,114 (younger than 30 rate); 6,245 (older than 30 rate) N=12,269,195 (female rate); N=11,596,077 (university degree rate); N=11,365,645 (younger than 30 rate);
N=11,829,617 (older than 30 rate)
Source: European Digital Skills Survey (weighted values)
38.9
3.5
33.1
21.9
36.7
35.9
56.3
33.4
37.5
40.6
43.5
26.8
9.0
15.4
21.0
21.6
39.2
39.1
25.7
27.5
31.7
23.3
18.9
0.9
13.1
21.4
21.6
20.7
20.9
16.9
21.2
21.2
17.1
20.5
11.1
14.9
25.3
21.7
20.5
23.3
24.6
20.5
21.4
16.1
A
CD
F
GHI
JMN
PQ
2 - 9
10 - 49
50 - 249
250 +
Tota
lSe
cto
rSi
ze
Female employees Employees with a university degree
Employees younger than 30 years of age Employees older than 50 years of age
143
Figure A2.6 – Employees by specific characteristics in sampled countries (% of total employees)
Q8: Could you please indicate, for this workplace, the number or percentage of employees who…(are
female/have a university degree, are younger than 30 years of age/are older than 50 years of age)
Number of valid responses: 6,917 (female rate); 6,334 (university degree rate); 6,114 (younger than 30 rate); 6,245 (older than 30 rate) N=3,944,489 (female rate); N=3,745,575 (university degree rate); N=3,759,190 (younger than 30 rate);
N=3,799,479 (older than 30 rate)
Source: European Digital Skills Survey (weighted values)
45.7
44.0
29.2
38.0
46.6
35.1
44.1
18.2
19.7
38.2
21.7
28.2
34.2
18.0
11.6
13.2
15.5
20.2
34.8
18.9
11.2
14.3
20.1
20.3
DE
FI
PT
SE
SK
UK
Female employees Employees with a university degree
Employees younger than 30 years of age Employees older than 50 years of age
144
Table A2.4 – Employees by specific characteristics in sampled countries by sector and size (% of
total employees)
Sector Size
A.
Agriculture
CD
. M
anufa
ctu
ring a
nd
utilities
F.
Constr
uction
GH
I. C
om
merc
e,
transport
,
accom
modation a
nd food
serv
ice
JMN
. In
form
ation a
nd
com
munic
ation;
pro
fessio
nal, s
cie
ntific a
nd
technic
al activitie
s;
Adm
inis
trative s
erv
ices
PQ
. Education a
nd h
um
an
health
2 -
9
10 -
49
50 -
249
250 +
Country Total Female employees
DE 45.7 54.5 31.1 40.0 41.1 41.4 71.2 56.4 45.4 42.3 44.8
FI 44.0 14.5 36.0 13.1 26.4 56.6 63.0 30.2 40.7 48.5 48.1
PT 29.2 11.4 37.4 19.0 28.3 30.8 28.6 28.4 40.0 12.3 44.6
SE 38.0 13.9 22.8 13.2 34.3 25.1 58.5 27.5 32.0 42.9 41.5
SK 46.6 37.2 36.4 20.5 39.4 44.4 89.5 41.2 30.9 81.9 30.8
UK 35.1 31.6 19.7 36.4 31.4 38.8 43.0 40.2 35.2 31.0 33.5
Employees with a university degree
DE 44.1 58.7 30.6 43.8 37.4 51.5 61.5 54.9 39.9 44.6 42.7
FI 18.2 2.2 12.6 14.4 11.6 34.9 17.6 16.1 15.9 25.0 13.5
PT 19.7 27.8 17.5 15.9 13.4 30.8 17.7 21.1 28.8 9.4 15.6
SE 38.2 2.4 18.4 8.8 13.0 60.0 58.3 18.8 23.9 41.2 58.1
SK 21.7 13.5 12.1 14.3 24.1 50.8 26.0 31.3 16.2 25.1 11.6
UK 28.2 4.0 10.4 35.8 24.8 41.8 31.2 37.1 26.7 28.4 19.9
Employees younger than 30 years of age
DE 34.2 41.3 26.6 41.2 32.2 30.5 44.4 41.3 34.0 33.3 32.1
FI 18.0 2.3 11.0 23.4 25.8 27.2 9.6 13.2 23.2 25.1 11.1
PT 11.6 0.5 21.4 6.3 11.5 13.7 5.5 9.6 14.6 13.5 7.2
SE 13.2 1.4 8.9 11.3 17.4 9.7 14.6 9.4 18.0 16.1 8.0
SK 15.5 15.4 14.7 9.1 23.5 16.6 6.9 11.8 23.5 14.3 14.1
UK 20.2 6.3 14.0 35.4 24.6 19.8 16.6 26.4 21.7 9.2 22.8
Employees older than 50 years of age
DE 34.8 40.7 30.3 35.9 33.1 30.7 43.5 48.5 33.7 35.0 29.6
FI 18.9 29.8 11.7 28.5 21.1 22.6 16.1 29.2 19.7 18.5 13.7
PT 11.2 16.5 10.1 8.0 15.4 5.2 12.3 12.7 12.4 5.0 17.4
SE 14.3 25.1 11.6 22.4 21.2 17.2 7.4 28.4 20.3 11.9 3.4
SK 20.1 32.6 11.9 15.5 18.3 18.4 38.8 17.2 16.9 39.5 6.6
UK 20.3 45.4 12.2 39.2 28.3 22.1 10.5 36.7 20.1 9.7 13.4
Q8: Could you please indicate, for this workplace, the number or percentage of employees who…(are
female/have a university degree, are younger than 30 years of age/are older than 50 years of age)
Number of valid responses: 6,917 (female rate); 6,334 (university degree rate); 6,114 (younger than 30 rate); 6,245 (older than 30 rate) N=3,944,489 (female rate); N=3,745,575 (university degree rate); N=3,759,190 (younger than 30
rate); N=3,799,479 (older than 30 rate)
Source: European Digital Skills Survey (weighted values)
145
Table A2.5 – Workplaces by specific occupations in the European Union by sectors and number of
employees (%)
Workplaces with…
Managers
Pro
fessio
nals
Technic
ians
Cle
rical
Sale
s
Skille
d a
gric w
ork
ers
Buildin
g w
ork
ers
Pla
nt
machin
e
opera
tors
Ele
menta
ry
occupations
Secto
rs
A. Agriculture 32.3 17.7 0.6 5.7 0.4 54.8 9.5 0.1 0.1
CD. Manufacturing and utilities 45.8 17.2 24.7 45.3 24.9 0.3 42.9 17.7 13.8
F. Construction 41.3 12.7 14.9 39.2 8.7 0.1 63.8 6.6 7.0
GHI. Commerce, transport,
accommodation and food service 47.3 11.9 11.3 33.3 55.2 0.4 12.6 13.1 11.9
JMN. Information and communication;
Professional, scientific and technical
activities; Administrative services
36.8 45.9 27.0 34.2 18.6 0.0 4.3 2.9 6.0
PQ. Education and human health 38.5 57.0 22.0 44.4 28.4 0.2 0.8 1.5 15.1
Siz
e
2 - 9 39.0 21.2 11.5 23.5 25.7 13.8 16.7 5.0 5.2
10 - 49 51.2 28.3 22.4 57.1 32.7 6.8 22.2 17.3 20.1
50 - 249 39.8 42.2 38.9 66.5 39.9 0.7 21.9 21.0 26.8
250 + 14.6 46.0 35.5 48.7 21.0 0.0 18.2 17.0 39.6
Total 40.8 23.0 14.0 29.9 27.2 12.3 17.7 7.4 8.2
Q12: Does your workplace have any employees in any of the following job categories?
Number of valid responses: 7,800 N=13,803,113
Source: European Digital Skills Survey (weighted values)
Table A2.6 – Workplaces by specific occupations in sampled countries (%)
Workplaces with…
Managers
Pro
fessio
nals
Technic
ians
Cle
rical
Sale
s
Skille
d a
gric
work
ers
Buildin
g
work
ers
Pla
nt
machin
e
opera
tors
Ele
menta
ry
occupations
Co
un
try
DE 37.1 26.3 16.5 56.7 27.1 10.5 21.9 8.9 7.5
FI 31.6 13.3 24.5 14.7 30.4 16.7 18.3 6.8 5.9
UK 49.7 26.2 17.4 42.0 27.5 5.0 16.7 6.7 8.6
PT 30.0 35.0 6.5 26.0 20.7 7.4 7.6 5.7 8.1
SE 40.1 20.8 15.8 19.5 31.3 8.5 29.9 5.2 9.4
SK 30.4 35.1 16.8 36.6 35.0 2.2 11.0 12.8 13.4
Total 41.3 26.9 15.9 42.6 27.2 7.6 18.1 7.5 8.3
Q12: Does your workplace have any employees in any of the following job categories?
Number of valid responses: 7,800 N=4,295,345
Source: European Digital Skills Survey (weighted values)
146
Table A2.7 - Employees in specific occupations, EU28 (total number and % of employees, and
average number of workers in specific occupation per workplace)
Q13. Could you please indicate approximately how many employees your workplace has in these job
categories?
Q14. Could you please provide your best estimate of the approximate percentage of employees in your
workplace in these job categories?
Note: totals from the European Digital Skills Survey correspond mathematically to those from Eurostat
Labour Force Survey (2015) with regards to the distribution of employees, as a result of the calculation of
sample weights according to the calibration procedure as described in Annex 1.
Number of valid responses: 7,800 N=13,803,113
Source: European Digital Skills Survey (weighted values), Eurostat (2015)
Occupations N %
Average number
on total
workplaces
Average
number on
workplaces
with employees
in occupation
Managers 7,564,363 5.0 0.5 1.3
Professionals 28,452,941 18.9 2.1 9.0
Technicians 25,519,514 16.9 1.8 13.2
Clerical 16,921,402 11.2 1.2 4.1
Sales 25,923,570 17.2 1.9 6.9
Skilled agric workers 1,526,330 1.0 0.1 0.9
Building workers 16,887,614 11.2 1.2 6.9
Plant machine operators 12,206,673 8.1 0.9 12.0
Elementary occupations 15,561,133 10.3 1.1 13.7
Total number of employees 150,563,540 100.0
Total number of workplaces 13,803,113
147
Table A2.8 - Employees in specific occupations in workplaces by sector and size, EU28 (% of total
employees)
Occupations
Managers
Pro
fessio
nals
Technic
ians
Cle
rical
Sale
s
Skille
d a
gric
work
ers
Buildin
g
work
ers
Pla
nt
machin
e
opera
tors
Ele
menta
ry
occupations
Secto
r
A. Agriculture 31.2 10.0 1.0 1.5 0.7 46.2 9.1 0.2 0.2
CD. Manufacturing and utilities 2.8 7.9 37.2 8.3 5.8 0.0 20.0 12.7 6.0
F. Construction 7.4 9.2 8.6 12.8 4.6 0.0 47.8 6.0 4.8
GHI. Commerce, transport, accommodation and food service
6.7 6.9 6.7 12.7 30.9 0.0 9.0 13.0 15.4
JMN. Information and communication; Professional, scientific and technical activities; Administrative services
5.7 27.6 21.4 15.7 14.7 0.0 4.3 7.9 4.6
PQ. Education and human health 1.9 42.1 13.1 10.5 18.7 0.0 0.3 0.2 14.5
Siz
e
2 - 9 13.4 14.4 8.8 12.8 23.8 3.7 14.1 5.0 4.0
10 - 49 5.6 16.4 9.1 15.8 19.3 0.4 14.6 10.6 8.2
50 - 249 1.4 21.8 15.2 14.3 19.4 0.0 9.1 11.3 7.5
250 + 0.2 22.6 32.1 3.5 8.0 0.0 7.5 6.1 20.0
Total 5.0 18.9 16.9 11.2 17.2 1.0 11.2 8.1 10.3
Q13. Could you please indicate approximately how many employees your workplace has in these job categories? Q14. Could you please provide your best estimate of the approximate percentage of employees in your workplace in these job categories? Number of valid responses: 7,800 N=13,803,113 Source: European Digital Skills Survey (weighted values)
Table A2.9 - Employees in specific occupations in workplaces by country (%)
Occupations
Managers
Pro
fessio
nals
Technic
ians
Cle
rical
Sale
s
Skille
d a
gric
work
ers
Buildin
g
work
ers
Pla
nt
machin
e
opera
tors
Ele
menta
ry
occupations
Co
un
try
DE 3.7 15.5 23.0 13.8 14.5 1.1 13.0 6.7 8.8
FI 3.3 24.7 20.9 6.8 19.2 1.2 9.9 7.2 6.8
UK 10.1 24.4 13.1 11.4 19.7 0.8 6.5 4.8 9.2
PT 4.4 21.9 12.5 10.2 15.7 2.7 11.2 9.9 11.6
SE 5.9 26.3 18.1 7.1 20.8 0.9 8.9 7.0 5.0
SK 3.7 12.0 15.6 10.0 17.9 0.6 13.5 16.9 9.7
Total 6.2 20.2 18.8 12.3 17.3 1.1 10.6 6.7 9.0
Q13. Could you please indicate approximately how many employees your workplace has in these
job categories?
Q14. Could you please provide your best estimate of the approximate percentage of employees in
your workplace in these job categories?
Number of valid responses: 7,800 N=4,295,345
Source: European Digital Skills Survey (weighted values)
148
Chapter 3. Digital technologies for work
Table A3.1 – Workplaces by use of computers and other digital devices by sector and size of
workplace, EU28 (%)
Workplace currently uses…
Deskto
p c
om
pute
rs
Port
able
com
pute
rs
Oth
er
port
able
devic
es
Bro
adband t
echnolo
gy t
o
access t
he I
nte
rnet
Intr
anet
pla
tform
CN
C m
achin
es o
r to
ols
Pro
gra
mm
able
robots
Secto
r
A. Agriculture 91.8 84.8 46.4 99.3 9.6 12.4 12.3
CD. Manufacturing and utilities 94.7 71.8 63.8 91.6 26.4 21.4 8.4
F. Construction 87.3 73.7 71.6 91.1 15.1 5.1 1.7
GHI. Commerce, transport, accommodation and
food service 91.4 64.2 60.8 91.0 21.5 4.5 2.6
JMN. Information and communication; Professional,
scientific and technical activities; Administrative
services
96.4 85.2 79.9 93.9 37.4 4.3 2.4
PQ. Education and human health 97.9 81.5 73.9 94.0 35.8 4.2 3.5
Siz
e
2 - 9 91.4 74.6 60.9 94.3 16.9 5.1 3.5
10 - 49 98.4 76.6 71.8 90.4 41.5 18.7 12.2
50 - 249 97.0 86.5 81.4 91.7 70.3 21.6 13.5
250 + 100.
0 95.8 95.9 97.1 80.7 34.2 32.5
Total 92.7 75.3 63.3 93.6 22.5 7.8 5.2
Q15. Please indicate if your workplace currently uses computers, CNC machines or tools, and other digital
devices to carry out its main business activity. By digital device an electronic device which uses discrete,
numerable data and processes for all its operations should be meant.
Number of valid responses: 7,800 N=13,803,113
Source: European Digital Skills Survey (weighted values)
149
Figure A3.1 – Workplaces by use of computers and other digital devices by type of device, sampled
countries (%)
Q15. Please indicate if your workplace currently uses computers, CNC machines or tools, and other digital
devices to carry out its main business activity. By digital device an electronic device which uses discrete,
numerable data and processes for all its operations should be meant.
Number of valid responses: 7,800 N=13,803,113 (EU28)
N=4,295,345 (Six countries)
Source: European Digital Skills Survey (weighted values)
99
.7
62
.2
59
.0
76
.5
41
.3
14
.0
5.6
96
.9
87
.5
86
.8
97
.4
32
.7
10
.2
8.8
94
.6
56
.7
21
.6
93
.1
11
.0
6.2
4.0
77
.5
81
.0
79
.2
96
.4
18
.8
3.2
1.4
94
.8
78
.2
63
.8
86
.4
25
.5
5.8
1.0
95
.6
61
.8 6
7.6
91
.8
22
.4
2.4
1.7
92
.7
75
.3
63
.3
93
.6
22
.5
7.8
5.2
Desktopcomputers
Portablecomputers
Other portabledevices
Broadbandtechnology to
access theInternet
Intranetplatform
CNC machinesor tools
Programmablerobots
DE FI PT SE SK UK EU28
150
Table A3.2 - Workplaces by trends and importance in the use of ICT in the last and in next five
years by sector and size, EU28 (%, importance on a scale from 1 to 5)
Level of importance
1 N
ot
at
all
2
3 M
odera
tely
4
5 S
ignific
antly
DK
The use of ICT…has increased in the last 5 years
Secto
r
A. Agriculture 14.8 13.4 27.7 23.7 15.9 4.6
CD. Manufacturing and utilities 9.8 14.6 43.6 13.8 15.8 2.4
F. Construction 10.5 25.9 38 10.6 12.4 2.6
GHI. Commerce, transport, accommodation and food service
8.9 24.5 40.8 10.3 12.6 2.9
JMN. Information and communication; Professional, scientific and technical activities; Administrative services
6.4 12 42.3 15.6 21.6 2.2
PQ. Education and human health 5.7 16.9 42.9 13.1 17.8 3.5
Siz
e
2 - 9 10.7 19.9 38.5 14.9 14.1 1.9
10 - 49 6.0 13.0 38.1 13.6 19.8 9.5
50 - 249 5.7 9.9 30.1 18.3 33.2 2.8
250 + 5.3 10.4 42.1 8.7 22.6 10.9
Total 9.8 18.6 38.2 14.8 15.5 3.1
The use of ICT…will increase in the next 5 years
Secto
r
A. Agriculture 37.9 5.5 15.3 23.7 5.1 12.5
CD. Manufacturing and utilities 17.0 14.4 41.2 10.4 12.3 4.7
F. Construction 19.6 23.0 35.4 7.6 9.1 5.3
GHI. Commerce, transport, accommodation and food service
25.2 21.2 32.2 9.4 6.8 5.2
JMN. Information and communication; Professional, scientific and technical activities; Administrative services
20.1 11.3 36.4 12.9 15.4 3.9
PQ. Education and human health 14.9 16.1 40.0 11.1 12.9 5.1
Siz
e
2 - 9 27.3 15.8 29.7 13.5 7.9 5.7
10 - 49 16.1 12.8 35.6 11.3 12.5 11.7
50 - 249 10.1 10.7 39.6 14.3 21.2 4.2
250 + 5.5 11.9 35.1 14.1 22.4 10.9
Total 25.0 15.2 30.9 13.2 9.1 6.5
Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not
at all important, 3 means moderately important and 5 means essential), that the use of ICT in your
workplace…?
Number of valid responses: 7,773 N=13,763,547
Source: European Digital Skills Survey (weighted values)
151
Table A3.3 Logistic regression: probability that the use of ICT in the workplace has increased in the last five years (Odds ratio)
N. obs 7800
Normalized weight
used yes
Model estimates
statistics Only intercept
Intercept and
covariates
AIC 4934.491 4596.947
SC 4941.425 4784.167
-2 Log L 4932.491 4542.947
Test Chi-square DF Pr > ChiSQ
Likelihood ratio 389.5439 26 <.0001
Score 356.1349 26 <.0001
Wald 318.6058 26 <.0001
Parameters B SE Chi-square
(Ward) Pr > ChiSQ Odds Ratio
Intercept 1.6574 0.0927 319.5038 <.0001
Workplace type
Only workplace (omitted)
Headquarters 1.5626 0.229 46.5494 <.0001 4.771
Subsidiary_site 1.3801 0.35 15.5462 <.0001 3.975
Workplace size
size < 10 (omitted)
size10_49 0.1515 0.1428 1.1251 0.2888 1.164
size50_249 0.2356 0.3139 0.5635 0.4529 1.266
size250 0.1374 0.7777 0.0312 0.8597 1.147
Sector
sectorA (omitted)
sectorCD 0.6517 0.1606 16.4776 <.0001 1.919
sectorF 0.823 0.1474 31.1709 <.0001 2.277
sectorGHI 0.7461 0.1185 39.6464 <.0001 2.109
sectorJMN 1.068 0.1501 50.6168 <.0001 2.91
sectorPQ 1.244 0.2249 30.6034 <.0001 3.469
Female rate
fem_rate<26 (omitted)
fem_rate_26_50 0.2942 0.118 6.2134 0.0127 1.342
fem_rate_51_75 0.394 0.1985 3.9393 0.0472 1.483
fem_rate_75 0.2938 0.1715 2.9334 0.0868 1.341
University rate
univ_rate<26 (omitted)
univ_rate_26_50 -0.0547 0.145 0.1423 0.706 0.947
univ_rate_51_75 0.0882 0.2431 0.1318 0.7166 1.092
univ_rate_75 0.3068 0.1632 3.5327 0.0602 1.359
Young rate
young_rate<26 (omitted)
young_rate_26_50 -0.2682 0.1317 4.1444 0.0418 0.765
young_rate_51_75 -0.3745 0.2843 1.7352 0.1877 0.688
young_rate_75 -0.6436 0.2446 6.9221 0.0085 0.525
Old rate
old_rate<26 (omitted)
old_rate_26_50 -0.4369 0.1143 14.6032 0.0001 0.646
old_rate_51_75 -0.4778 0.2116 5.0977 0.024 0.62
old_rate_75 -0.3391 0.1579 4.6125 0.0317 0.712
Ownership (q9) private (omitted)
public -0.8689 0.1693 26.3469 <.0001 0.419
Markets (q11)
Local_market (omitted)
Regional_market -0.6507 0.0904 51.8263 <.0001 0.522
National_market 0.7157 0.1478 23.4438 <.0001 2.046
International_market -0.0814 0.1517 0.2883 0.5913 0.922
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), that the use of ICT in your workplace…? Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Source: Own elaboration on European Digital Skills Survey
152
Figure A3.2 - Workplaces by trends and importance in the use of ICT in the last and in next five
years, sampled countries (%, importance on a scale from 1 to 5)
Q31. Thinking about your workplace as a whole, would you say (using a scale of 1 to 5, where 1 means not at
all important, 3 means moderately important and 5 means essential), that the use of ICT in your workplace…?
Number of valid responses: 7,773 N=13,763,547 (EU 28)
N=4,280,432 (six countries)
Source: European Digital Skills Survey (weighted values)
Figure A3.3 - Workplaces by area and level of importance of investment in ICT in the past five
years, UE28 (%, importance on a scale from 1 to 5)
Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not
at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree
has your workplace invested in ICT for…? (Please select all that apply)
Number of valid responses: 7,773 N=13,763,547
Source: European Digital Skills Survey (weighted data)
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
DE
FI
UK
PT
SE
SK
EU28
DE
FI
UK
PT
SE
SK
EU28
Use
of
ICT
in w
ork
pla
ce in
crea
sed
inla
st 5
yea
rsU
se o
f IC
T in
wo
rkp
lace
will
incr
ease
in n
ext
5 y
ears
1 Not at all 2 3 Moderately 4 5 Significantly DK
21.7
32.2
37.0
56.4
10.3
16.3
17.8
10.5
27.4
20.3
20.5
12.5
16.3
13.0
7.5
4.0
14.2
6.3
3.7
4.4
10.2
11.9
13.5
12.2
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
1 Not at all 2 3 Moderately 4 5 Significantly DK
153
Figure A3.4 - Workplaces by area and level of importance of investment in ICT in the past five years, by
sector, UE28 (%, importance on a scale from 1 to 5)
Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all
important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your
workplace invested in ICT for…? (Please select all that apply)
Note: the levels of importance of investment have been recoded in a dichotomic variable (No/Yes)
Number of valid responses: 7,773 N=13,763,547
Source: European Digital Skills Survey (weighted values)
32.3
42.2
42.0
47.7
21.9
26.1
34.5
51.6
25.3
37.8
42.3
65.2
20.9
32.7
37.9
62.6
11.0
18.7
28.0
49.8
13.0
30.8
34.1
63.2
57.7
47.6
40.2
42.3
68.2
62.2
54.3
36.5
61.4
47.1
42.6
19.2
69.4
55.8
50.7
25.6
80.4
69.2
59.9
37.6
75.0
54.9
51.9
22.1
10.0
10.2
17.8
10.1
9.9
11.7
11.1
11.9
13.3
15.1
15.0
15.6
9.7
11.5
11.4
11.8
8.7
12.1
12.1
12.7
11.9
14.2
14.0
14.6
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
A. A
gric
ult
ure
CD
. Man
ufa
ctu
rin
g an
d u
tilit
ies
F. C
on
stru
ctio
n
GH
I. C
om
mer
ce, t
ran
spo
rt,
acco
mm
od
atio
n a
nd
fo
od
serv
ice
JMN
. In
form
atio
n a
nd
com
mu
nic
atio
n;
Pro
fess
ion
al,
scie
nti
fic
and
tec
hn
ical
acti
viti
es;
Ad
min
istr
ativ
ese
rvic
esP
Q. E
du
cati
on
an
d h
um
anh
ealt
h
No Yes Don't know
154
Table A3.4 - Workplaces by area and level of importance of investment in ICT in the past five years,
by sector, UE28 (%, importance on a scale from 1 to 5)
Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at
all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has
your workplace invested in ICT for…? (Please select all that apply)
Number of valid responses: 7,773 N=13,763,547
Source: European Digital Skills Survey (weighted values)
Level of investment
Total
Sector Area
1 N
ot
at
all
2
3 M
odera
tely
4
5 S
ignific
antly
DK
A. Agriculture
Overall efficiency 32.3 0.9 21.9 22.4 12.5 10.0 100.0
Marketing and sales 42.2 8.3 9.7 24.9 4.7 10.2 100.0
Internal organisation 42.0 1.0 17.3 17.4 4.6 17.8 100.0
Delocalisation 47.7 8.0 17.0 9.6 7.7 10.1 100.0
CD. Manufacturing and utilities
Overall efficiency 21.9 12.0 30.1 12.1 14.0 9.9 100.0
Marketing and sales 26.1 18.4 26.7 9.2 7.9 11.7 100.0
Internal organisation 34.5 19.4 24.8 5.9 4.3 11.1 100.0
Delocalisation 51.6 13.7 14.5 3.0 5.2 11.9 100.0
F. Construction
Overall efficiency 25.3 13.3 26.0 12.1 10.0 13.3 100.0
Marketing and sales 37.8 17.0 19.6 5.5 5.0 15.1 100.0
Internal organisation 42.3 20.3 16.8 2.9 2.6 15.0 100.0
Delocalisation 65.2 8.3 7.7 1.4 1.8 15.6 100.0
GHI. Commerce, transport,
accommodation and food
service
Overall efficiency 20.9 15.4 27.3 14.8 11.9 9.7 100.0
Marketing and sales 32.7 19.9 21.2 9.6 5.2 11.5 100.0
Internal organisation 37.9 27.0 18.7 3.3 1.7 11.4 100.0
Delocalisation 62.6 11.0 9.6 1.9 3.1 11.8 100.0
JMN. Information and
communication; Professional,
scientific and technical
activities; Administrative
services
Overall efficiency 11.0 8.7 32.8 17.0 21.9 8.7 100.0
Marketing and sales 18.7 17.0 28.4 12.9 10.8 12.1 100.0
Internal organisation 28.0 17.9 28.1 7.6 6.3 12.1 100.0
Delocalisation 49.8 12.7 15.9 4.1 4.9 12.7 100.0
PQ. Education and human
health
Marketing and sales 30.8 18.9 23.0 7.4 5.7 14.2 100.0
Internal organisation 34.1 20.9 21.4 4.8 4.8 14.0 100.0
Delocalisation 63.2 9.7 8.6 2.0 1.9 14.6 100.0
155
Table A3.5 - Workplaces by area and level of importance of investment in ICT in the past five
years, by size, UE28 (%, importance on a scale from 1 to 5)
Level of investment
Total
Size Area
1 N
ot
at
all
2
3 M
odera
tely
4
5
Sig
nific
antly
DK
2-9
Overall efficiency 23.9 10.4 27.3 16.6 12.3 9.5 100.0
Marketing and sales 35.5 16.3 18.9 13.2 5.1 11.0 100.0
Internal organisation 39.7 17.5 19.0 7.8 3.1 12.9 100.0
Delocalisation 58.7 10.0 12.2 4.0 3.9 11.2 100.0
10-49
Overall efficiency 12.4 9.8 28.5 13.8 20.6 14.9 100.0
Marketing and sales 17.7 16.4 25.7 11.5 11.0 17.7 100.0
Internal organisation 25.8 19.6 26.2 5.5 5.1 17.8 100.0
Delocalisation 46.0 12.4 13.5 3.9 6.0 18.2 100.0
50-249
Overall efficiency 9.6 8.4 24.9 19.7 32.7 4.7 100.0
Marketing and sales 16.3 15.9 30.8 14.9 15.3 6.9 100.0
Internal organisation 20.2 19.0 32.6 9.9 11.3 7.0 100.0
Delocalisation 46.8 15.1 13.8 6.7 8.4 9.2 100.0
250 or more
Overall efficiency 11.4 11.9 21.5 23.2 21.4 10.6 100.0
Marketing and sales 17.2 26.7 26.4 8.7 10.3 10.8 100.0
Internal organisation 29.3 13.1 25.8 7.0 14.3 10.6 100.0
Delocalisation 48.4 12.0 18.8 0.9 9.3 10.6 100.0
Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1
means not at all important, 3 means moderately important and 5 means essential), in the last 5 years
to what degree has your workplace invested in ICT for…? (Please select all that apply)
Number of valid responses: 7,773 N=13,763,547
Source: European Digital Skills Survey (weighted values)
156
Figure A3.5 - Workplaces by area and level of importance of investment in ICT in the past five
years, by size, UE28 (%, importance on a scale from 1 to 5)
Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not
at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree
has your workplace invested in ICT for…? (Please select all that apply)
Number of valid responses: 7,773 N=4,280,432
Source: European Digital Skills Survey (weighted values)
31.7
30.8
49.3
62.9
5.3
29.7
22.4
63.9
9.1
19.6
19.5
42.9
7.6
17.9
26.3
39.7
52.5
60.4
71.5
74.2
17.4
27.4
38.7
68.4
64.7
65.2
46.5
32.8
75.8
48.2
57.4
16.1
71.8
60.1
60.1
36.7
91.3
80.2
59.9
54.4
35.7
26.7
15.1
12.0
71.2
56.8
45.5
17.8
3.7
4.0
4.2
4.3
18.9
22.1
20.2
19.9
19.1
20.3
20.5
20.4
1.1
1.9
13.8
5.9
11.8
12.8
13.5
13.8
11.4
15.9
15.8
13.8
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
Overall efficiency
Marketing and sales
Internal organisation
Delocalisation
DE
FIU
KP
TSE
SK
No Yes Don't know
157
Table A3.6 - Workplaces by area and level of importance of investment in ICT in
the past five years, by size, UE28 (%, importance on a scale from 1 to 5)
Level of importance
Total
Country Area
1 N
ot
at
all
2
3 M
odera
tely
4
5 S
ignific
antly
DK
DE
Overall efficiency 31.7 14.3 22.6 8.4 19.3 3.7 100.0
Marketing and sales 30.8 16.3 25.3 15.6 8.1 4.0 100.0
Internal organisation 49.3 23.5 15.1 4.6 3.3 4.2 100.0
Delocalisation 62.9 18.2 7.6 3.5 3.5 4.3 100.0
FI
Overall efficiency 5.3 14.0 29.1 24.7 8.0 18.9 100.0
Marketing and sales 29.7 22.9 19.0 5.4 1.0 22.1 100.0
Internal organisation 22.4 23.8 24.0 7.8 1.7 20.2 100.0
Delocalisation 63.9 11.3 3.7 0.8 0.3 19.9 100.0
UK
Overall efficiency 9.1 11.1 28.2 13.4 19.0 19.1 100.0
Marketing and sales 19.6 16.3 24.3 8.0 11.5 20.3 100.0
Internal organisation 19.5 21.3 19.8 6.5 12.5 20.5 100.0
Delocalisation 42.9 11.5 14.2 2.3 8.7 20.4 100.0
PT
Overall efficiency 7.6 18.8 38.9 10.0 23.5 1.1 100.0
Marketing and sales 17.9 13.9 25.9 32.0 8.3 1.9 100.0
Internal organisation 26.3 17.2 27.9 13.2 1.6 13.8 100.0
Delocalisation 39.7 9.7 29.8 2.0 12.9 5.9 100.0
SE
Overall efficiency 52.5 10.7 12.1 4.8 8.1 11.8 100.0
Marketing and sales 60.4 11.5 7.5 4.0 3.7 12.8 100.0
Internal organisation 71.5 6.6 6.3 1.5 0.7 13.5 100.0
Delocalisation 74.2 3.9 4.9 1.1 2.1 13.8 100.0
SK
Overall efficiency 17.4 2.8 37.0 18.6 12.7 11.4 100.0
Marketing and sales 27.4 19.1 28.7 5.3 3.7 15.9 100.0
Internal organisation 38.7 22.4 17.4 2.5 3.1 15.8 100.0
Delocalisation 68.4 7.2 6.5 2.2 1.9 13.8 100.0
Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5,
where 1 means not at all important, 3 means moderately important and 5 means essential),
in the last 5 years to what degree has your workplace invested in ICT for…? (Please select
all that apply)
Number of valid responses: 7,773 N=4,280,432
Source: European Digital Skills Survey (weighted values)
158
Table A3.7 – Logistic regression: probability that workplace has invested in ICT to improve
the overall efficiency in the last five years.
N. obs 7800
normalized
weight used yes
Model estimates
statistics Only intercept
Intercept and
covariates
AIC 7723.619 6665.496
SC 7730.476 6850.634
-2 Log L 7721.619 6611.496
Test Chi-square DF Pr > ChiSQ
Likelihood ratio 1110.1227 26 <.0001 Score 942.8074 26 <.0001
Wald 797.3302 26 <.0001
Parameters B SE
Chi-square
(Ward) Pr > ChiSQ
Odds
Ratio
Intercept -0.2999 0.0716 17.5274 <.0001
Workplace type
Only workplace (omitted)
Headquarters 2.0499 0.1828 125.7528 <.0001 7.767
Subsidiary_site 1.669 0.2207 57.1829 <.0001 5.307
Workplace size
size < 10 (omitted)
size10_49 0.1613 0.1073 2.2577 0.133 1.175
size50_249 0.2894 0.2509 1.3301 0.2488 1.336
size250 -0.4368 0.5622 0.6038 0.4371 0.646
Sector
sectorA (omitted)
sectorCD 0.8638 0.1257 47.2021 <.0001 2.372
sectorF 0.9185 0.1129 66.1909 <.0001 2.506
sectorGHI 1.2246 0.0941 169.4025 <.0001 3.403
sectorJMN 1.6327 0.1205 183.5962 <.0001 5.118
sectorPQ 1.6137 0.1664 94.0531 <.0001 5.021
Female rate
fem_rate<26 (omitted)
fem_rate_26_50 -0.2278 0.0879 6.7106 0.0096 0.796
fem_rate_51_75 -0.2907 0.1332 4.7648 0.029 0.748
fem_rate_75 -0.2486 0.1272 3.8196 0.0507 0.78
University rate
univ_rate<26 (omitted)
univ_rate_26_50 0.8394 0.1273 43.4871 <.0001 2.315
univ_rate_51_75 0.7212 0.196 13.5467 0.0002 2.057
univ_rate_75 0.8146 0.1349 36.454 <.0001 2.258
Young rate
young_rate<26 (omitted)
young_rate_26_50 -0.0919 0.1032 0.7926 0.3733 0.912
young_rate_51_75 -0.4327 0.2224 3.7849 0.0517 0.649
young_rate_75 -0.5586 0.1993 7.8573 0.0051 0.572
Old rate
old_rate<26 (omitted)
old_rate_26_50 -0.7091 0.0872 66.0667 <.0001 0.492
old_rate_51_75 -0.9867 0.1565 39.748 <.0001 0.373
old_rate_75 -0.2959 0.1282 5.3322 0.0209 0.744
Ownership (q9) private (omitted)
public 0.0434 0.1774 0.06 0.8065 1.044
Markets (q11)
Local_market (omitted)
Regional_market 0.4069 0.0713 32.5426 <.0001 1.502
National_market 1.115 0.0949 137.955 <.0001 3.05
International_market 1.427 0.14 103.9116 <.0001 4.166
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social
work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Source: Own elaboration on European Digital Skills Survey (weighted values)
159
Table A3.8 – Logistic regression: probability that workplace has invested in ICT in the area of
marketing and sales in the last five years.
N. obs 7800
normalized weight
used yes
Model estimates
statistics Only intercept
Intercept and
covariates
AIC 8999.634 7526.397
SC 9006.464 7710.814
-2 Log L 8997.634 7472.397
Test Chi-square DF Pr > ChiSQ
Likelihood ratio 1525.2364 26 <.0001
Score 1337.5086 26 <.0001
Wald 1105.9404 26 <.0001
Parameters B SE Chi-square
(Ward) Pr > ChiSQ
Odds Ratio
Intercept -0.9872 0.0717 189.7071 <.0001
Workplace type
Only workplace (omitted)
Headquarters 1.7704 0.1433 152.6849 <.0001 5.873
Subsidiary_site 0.0309 0.1453 0.0452 0.8316 1.031
Workplace size
size < 10 (omitted)
size10_49 0.4275 0.0959 19.8865 <.0001 1.533
size50_249 0.512 0.21 5.9418 0.0148 1.669
size250 0.1224 0.4863 0.0633 0.8013 1.13
Sector
sectorA (omitted)
sectorCD 0.7644 0.1209 39.9887 <.0001 2.148
sectorF 0.3964 0.1078 13.5166 0.0002 1.486
sectorGHI 0.7134 0.0875 66.5575 <.0001 2.041
sectorJMN 1.1164 0.1062 110.6006 <.0001 3.054
sectorPQ 0.4725 0.1407 11.2834 0.0008 1.604
Female rate
fem_rate<26 (omitted)
fem_rate_26_50 0.119 0.0805 2.1817 0.1397 1.126
fem_rate_51_75 0.0312 0.1216 0.0659 0.7974 1.032
fem_rate_75 -0.2879 0.1118 6.6347 0.01 0.75
University rate
univ_rate<26 (omitted)
univ_rate_26_50 0.748 0.1073 48.5778 <.0001 2.113
univ_rate_51_75 0.6439 0.1603 16.1349 <.0001 1.904
univ_rate_75 0.9501 0.1177 65.14 <.0001 2.586
Young rate
young_rate<26 (omitted)
young_rate_26_50 0.1767 0.0942 3.5176 0.0607 1.193
young_rate_51_75 0.0912 0.2083 0.1916 0.6616 1.095
young_rate_75 0.2006 0.1895 1.1199 0.2899 1.222
Old rate
old_rate<26 (omitted)
old_rate_26_50 -0.5453 0.0821 44.1723 <.0001 0.58
old_rate_51_75 -0.7016 0.1544 20.6408 <.0001 0.496
old_rate_75 -0.0871 0.116 0.564 0.4527 0.917
Ownership (q9) private (omitted)
public 0.0139 0.1556 0.0079 0.929 1.014
Markets (q11)
Local_market (omitted)
Regional_market 0.7165 0.0664 116.5994 <.0001 2.047
National_market 1.5327 0.0849 325.7634 <.0001 4.631
International_market 1.6344 0.1179 192.1355 <.0001 5.126
* A: Agriculture; CD: Manufacturing and utilities; F: Construction; GHI: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; JMN: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; PQ: Education and human health and social work activities. . Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Source: Own elaboration on European Digital Skills Survey (weighted values)
160
Table A3.9 – Logistic regression: probability that workplace has invested in ICT to improve
internal organisation in the last five years.
N. obs 7800
Normalized weight
used yes
model estimates
statistics Only intercept
Intercept and
covariates AIC 9187.636 8147.086
SC 9194.463 8331.42
-2 Log L 9185.636 8093.086
Test Chi-square DF Pr > ChiSQ
Likelihood ratio 1092.5498 26 <.0001
Score 989.6332 26 <.0001
Wald 846.6196 26 <.0001
Parameters B SE Chi-square
(Ward) Pr > ChiSQ
Odds
Ratio
Intercept -0.9741 0.0718 183.9287 <.0001
Workplace type
Only workplace (omitted)
Headquarters 1.789 0.1286 193.4274 <.0001 5.984
Subsidiary_site -0.0279 0.1352 0.0426 0.8365 0.972
Workplace size
size < 10 (omitted)
size10_49 0.2091 0.0852 6.0226 0.0141 1.233
size50_249 0.6231 0.1906 10.6884 0.0011 1.865
size250 -0.3102 0.4098 0.5731 0.449 0.733
Sector
sectorA (omitted)
sectorCD 0.5571 0.1148 23.544 <.0001 1.746
sectorF 0.3677 0.1057 12.0998 0.0005 1.444
sectorGHI 0.7018 0.0859 66.7678 <.0001 2.017
sectorJMN 0.8664 0.1006 74.241 <.0001 2.378
sectorPQ 0.7203 0.1352 28.3641 <.0001 2.055
Female rate
fem_rate<26 (omitted)
fem_rate_26_50 -0.0642 0.075 0.7329 0.3919 0.938
fem_rate_51_75 -0.1531 0.1119 1.8709 0.1714 0.858
fem_rate_75 -0.2716 0.1044 6.7624 0.0093 0.762
University rate
univ_rate<26 (omitted)
univ_rate_26_50 0.5228 0.0956 29.8869 <.0001 1.687
univ_rate_51_75 0.2464 0.1412 3.0442 0.081 1.279
univ_rate_75 0.3648 0.1101 10.9863 0.0009 1.44
Young rate
young_rate<26 (omitted)
young_rate_26_50 0.084 0.0863 0.9474 0.3304 1.088
young_rate_51_75 0.2512 0.1895 1.7573 0.185 1.286
young_rate_75 -0.2779 0.1671 2.7643 0.0964 0.757
Old rate
old_rate<26 (omitted)
old_rate_26_50 -0.4078 0.0773 27.8561 <.0001 0.665
old_rate_51_75 -0.4205 0.1448 8.4361 0.0037 0.657
old_rate_75 -0.0623 0.1071 0.3385 0.5607 0.94
Ownership (q9) private (omitted)
public -0.3653 0.1405 6.7609 0.0093 0.694
Markets (q11)
Local_market (omitted)
Regional_market 0.725 0.0646 125.7638 <.0001 2.065
National_market 1.2528 0.0767 266.608 <.0001 3.5
International_market 1.1887 0.1041 130.3802 <.0001 3.283
* A: Agriculture; CD: Manufacturing and utilities; F: Construction; GHI: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; JMN: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; PQ: Education and human health and social work activities. Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply) Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Source: Own elaboration on European Digital Skills Survey (weighted values)
161
Table A3.10 – Logistic regression: probability that workplace has invested in ICT to support
delocalization strategy in the last five years.
N. obs 7800
normalized weight
used
yes
Model estimates
statistics Only intercept
Intercept and
covariates
AIC 8907.536 7539.692
SC 8914.359 7723.923
-2 Log L 8905.536 7485.692
Test Chi-square DF Pr > ChiSQ
Likelihood ratio 1419.8438 26 <.0001
Score 1341.3262 26 <.0001
Wald 1112.429 26 <.0001
Parameters B SE Chi-square
(Ward) Pr > ChiSQ
Odds
Ratio
Intercept -1.2689 0.0759 279.2057 <.0001
Workplace type
Only workplace (omitted)
Headquarters 1.1953 0.0963 153.9627 <.0001 3.304
Subsidiary_site -0.1502 0.1417 1.1237 0.2891 0.86
Workplace size
size < 10 (omitted)
size10_49 0.2791 0.085 10.7838 0.001 1.322
size50_249 0.3013 0.17 3.1436 0.0762 1.352
size250 0.0145 0.3952 0.0013 0.9708 1.015
Sector
sectorA (omitted)
sectorCD -0.313 0.1154 7.3535 0.0067 0.731
sectorF -0.8649 0.1178 53.8703 <.0001 0.421
sectorGHI -0.5154 0.0875 34.7109 <.0001 0.597
sectorJMN -0.2647 0.0989 7.1609 0.0075 0.767
sectorPQ -0.7809 0.1467 28.3441 <.0001 0.458
Female rate
fem_rate<26 (omitted)
fem_rate_26_50 -0.2336 0.0804 8.4331 0.0037 0.792
fem_rate_51_75 -0.2151 0.1188 3.2819 0.07 0.806
fem_rate_75 -0.6144 0.1157 28.2188 <.0001 0.541
University rate
univ_rate<26 (omitted)
univ_rate_26_50 0.4964 0.0938 28.0073 <.0001 1.643
univ_rate_51_75 0.3564 0.1405 6.4302 0.0112 1.428
univ_rate_75 0.6856 0.1002 46.8286 <.0001 1.985
Young rate
young_rate<26 (omitted)
young_rate_26_50 0.113 0.0904 1.5619 0.2114 1.12
young_rate_51_75 0.0959 0.1839 0.2723 0.6018 1.101
young_rate_75 -0.3156 0.1799 3.0759 0.0795 0.729
Old rate
old_rate<26 (omitted)
old_rate_26_50 -0.3009 0.0853 12.4284 0.0004 0.74
old_rate_51_75 -0.7266 0.1757 17.1098 <.0001 0.484
old_rate_75 -0.0703 0.1087 0.4181 0.5179 0.932
Ownership (q9) private (omitted)
public -0.0226 0.1502 0.0226 0.8806 0.978
Markets (q11)
Local_market (omitted)
Regional_market 0.9167 0.0755 147.4195 <.0001 2.501
National_market 1.5649 0.0794 388.0824 <.0001 4.782
International_market 1.7954 0.0974 339.5397 <.0001 6.022
* A: Agriculture; CD: Manufacturing and utilities; F: Construction; GHI: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; JMN: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; PQ: Education and human health and social work activities.
Q30. Thinking about your workplace as a whole, please indicate (using a scale of 1 to 5, where 1 means not at all important, 3 means moderately important and 5 means essential), in the last 5 years to what degree has your workplace invested in ICT for…? (Please select all that apply)
Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey.
Source: Own elaboration on European Digital Skills Survey (weighted values)
162
Chapter 4. Digital skills availability in European workplaces
Table A4.1 – Workplaces by type and level of importance of digital skills of employees in specific
occupations, EU28 (% of total employers)
Occupation Digital skills
Level of importance
1 N
ot
at
all im
port
ant
2
3 M
odera
tely
im
port
ant
4
5 E
ssential
DK
Managers
Use a word processor 5.8 4.2 21.1 18.4 50.2 0.2
Create a spreadsheet 3.8 4.0 21.2 17.5 53.3 0.2
Use of Internet 2.3 2.5 10.0 14.8 70.2 0.2
Email 1.8 1.7 4.2 10.0 82.1 0.1
Social media, video calls 41.9 11.2 22.6 6.9 17.3 0.2
Use software for design, calculation and simulation
48.2 10.5 9.3 9.7 21.7 0.5
Programming and software develop.
81.4 4.9 4.5 2.5 5.6 1.2
Design and maintain ICT 67.7 4.1 11.3 6.7 9.8 0.5
Programme and use CNC machines
96.1 0.4 0.7 0.3 1.0 1.5
Programme and use robots 97.4 0.3 0.2 0.2 0.5 1.5
Professionals
Use a word processor 6.9 3.7 17.6 13.2 58.3 0.3
Create a spreadsheet 7.2 3.9 18.9 13.4 56.4 0.2
Use of Internet 4.4 2.1 8.2 11.7 73.3 0.2
Email 4.4 1.7 4.2 10.0 79.5 0.2
Social media, video calls 37.2 17.0 17.4 7.3 20.7 0.3
Use software for design, calculation and simulation
36.6 4.0 7.2 16.5 35.0 0.6
Programming and software develop.
68.1 3.3 5.6 4.8 17.2 1.0
Design and maintain ICT 60.4 3.6 7.0 13.4 14.6 1.0
Programme and use CNC machines
92.7 1.3 2.1 1.0 1.4 1.5
Programme and use robots 93.8 1.1 1.7 0.8 1.1 1.5
Technicians
Use a word processor 13.9 9.1 16.9 15.6 44.0 0.4
Create a spreadsheet 12.4 7.2 18.6 16.7 44.7 0.4
Use of Internet 8.1 6.1 13.0 13.5 58.7 0.6
Email 8.8 3.6 9.7 11.7 65.8 0.4
Social media, video calls 39.4 13.2 16.7 9.0 21.3 0.5
Use software for design, calculation and simulation
31.2 7.6 12.0 13.1 34.8 1.4
Programming and software develop.
64.4 7.8 5.9 5.8 14.4 1.6
Design and maintain ICT 55.3 8.2 12.2 9.4 12.9 2.0
Programme and use CNC machines
88.1 2.2 2.6 0.6 3.7 2.8
Programme and use robots 91.8 1.7 1.3 0.6 2.0 2.7
Clerical support workers
Use a word processor 4.4 4.6 12.8 17.6 60.4 0.2
Create a spreadsheet 3.1 3.6 13.9 17.3 61.8 0.2
Use of Internet 1.5 3.2 13.7 13.2 68.0 0.3
Email 1.5 1.6 8.4 9.0 79.2 0.3
Social media, video calls 45.7 7.7 15.7 7.6 22.8 0.5
Use software for design, calculation and simulation
41.7 5.5 12.8 8.1 31.2 0.7
Programming and software develop.
81.4 4.9 7.4 1.5 3.3 1.4
Design and maintain ICT 79.2 4.0 8.0 4.0 3.9 0.9
Programme and use CNC machines
92.3 1.4 3.8 0.2 0.5 1.7
163
Occupation Digital skills
Level of importance
1 N
ot
at
all im
port
ant
2
3 M
odera
tely
im
port
ant
4
5 E
ssential
DK
Programme and use robots 93.4 1.0 3.6 0.0 0.4 1.6
Sales, customer or
personal service
Use a word processor 23.7 11.4 22.4 19.1 23.4 0.2
Create a spreadsheet 24.1 11.8 20.1 20.0 23.8 0.3
Use of Internet 12.6 9.7 19.6 21.5 36.4 0.2
Email 14.0 6.9 13.4 16.6 48.6 0.5
Social media, video calls 55.5 13.9 12.3 6.6 11.4 0.3
Use software for design, calculation and simulation
58.5 7.6 10.2 7.0 15.9 0.8
Programming and software develop.
89.6 3.2 2.4 1.1 2.2 1.4
Design and maintain ICT 83.3 3.6 5.2 2.8 4.0 1.0
Programme and use CNC machines
95.6 1.4 0.6 0.3 0.6 1.5
Programme and use robots 96.7 1.0 0.4 0.1 0.3 1.5
Skilled agricultural
workers
Use a word processor 15.5 17.9 25.3 16.3 24.9 0.0
Create a spreadsheet 26.7 17.4 9.1 13.9 32.8 0.0
Use of Internet 9.6 9.4 25.8 14.2 41.0 0.0
Email 1.5 0.9 25.9 16.9 54.7 0.0
Social media, video calls 52.3 30.6 8.5 0.3 8.3 0.0
Use software for design, calculation and simulation
52.7 8.9 22.1 16.2 0.2 0.1
Programming and software develop.
82.7 8.5 8.3 0.2 0.2 0.2
Design and maintain ICT 66.7 8.6 24.3 0.3 0.0 0.1
Programme and use CNC machines
82.9 8.4 0.3 8.1 0.1 0.3
Programme and use robots 91.0 8.4 0.3 0.0 0.0 0.3
Building workers
Use a word processor 51.3 12.8 23.8 4.2 7.5 0.4
Create a spreadsheet 50.4 25.1 10.5 6.3 7.3 0.4
Use of Internet 39.3 23.2 14.3 10.1 12.5 0.5
Email 36.8 10.2 24.4 9.1 19.0 0.4
Social media, video calls 84.0 8.5 3.2 1.1 2.7 0.5
Use software for design, calculation and simulation
71.0 6.9 8.8 4.7 7.9 0.6
Programming and software develop.
90.8 2.6 1.8 1.7 1.4 1.7
Design and maintain ICT 88.7 3.7 3.2 1.8 1.6 0.9
Programme and use CNC machines
89.0 1.8 2.0 1.5 3.7 2.0
Programme and use robots 93.7 1.1 0.5 0.8 1.6 2.3
Plant machine
operators
Use a word processor 68.8 9.5 11.3 2.6 7.1 0.7
Create a spreadsheet 68.0 8.4 11.6 4.5 6.8 0.7
Use of Internet 54.7 8.4 14.8 8.0 13.5 0.7
Email 50.6 8.5 15.1 8.4 16.6 0.8
Social media, video calls 79.7 7.3 5.1 2.6 4.4 0.9
Use software for design, calculation and simulation
77.6 5.2 5.6 3.4 6.9 1.2
Programming and software develop.
89.6 2.1 3.8 0.8 2.3 1.3
Design and maintain ICT 88.6 2.5 4.2 2.4 0.9 1.3
Programme and use CNC machines
80.1 1.7 5.6 1.8 8.7 2.1
Programme and use robots
90.4 1.6 2.9 1.4 2.0 1.7
Elementary occupations
Use a word processor 68.7 11.5 8.6 3.3 7.2 0.7
Create a spreadsheet 70.4 10.1 8.5 3.2 7.1 0.7
164
Occupation Digital skills
Level of importance
1 N
ot
at
all im
port
ant
2
3 M
odera
tely
im
port
ant
4
5 E
ssential
DK
Use of Internet 62.9 12.1 9.6 4.2 10.5 0.7
Email 61.1 11.9 9.3 5.0 12.4 0.3
Social media, video calls 80.7 8.0 3.1 2.2 5.0 0.9
Use software for design, calculation and simulation
83.3 6.0 4.6 0.8 3.8 1.5
Programming and software develop.
92.0 2.3 2.7 0.2 1.2 1.6
Design and maintain ICT 91.1 3.5 1.8 0.2 1.5 1.9
Programme and use CNC machines
91.9 3.0 1.2 0.9 1.3 1.6
Programme and use robots 95.6 1.4 0.5 0.5 0.5 1.6
Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential,) how important it is for employees in these categories to…: Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations) Source: European Digital Skills Survey (weighted values)
Table A4.2 – Workplaces by type and level of importance of digital skills of employees in specific
occupations, EU28 (% of total employers)
Occupation Digital skills
Level of importance
Not
at
all im
port
ant
Som
ehow
im
port
ant
to e
ssential
Don’t k
now
Managers
Use a word processor 5.8 93.9 0.2
Create a spreadsheet 3.8 96.0 0.2
Use of Internet 2.3 97.5 0.2
Email 1.8 98.0 0.1
Social media, video calls 41.9 58.0 0.2
Use software for design, calculation and simulation 48.2 51.2 0.5
Programming and software develop. 81.4 17.5 1.2
Design and maintain ICT 67.7 31.9 0.5
Programme and use CNC machines 96.1 2.4 1.5
Programme and use robots 97.4 1.2 1.5
Professionals
Use a word processor 6.9 92.8 0.3
Create a spreadsheet 7.2 92.6 0.2
Use of Internet 4.4 95.3 0.2
Email 4.4 95.4 0.2
165
Occupation Digital skills
Level of importance
Not
at
all im
port
ant
Som
ehow
im
port
ant
to e
ssential
Don’t k
now
Social media, video calls 37.2 62.4 0.3
Use software for design, calculation and simulation 36.6 62.7 0.6
Programming and software develop. 68.1 30.9 1.0
Design and maintain ICT 60.4 38.6 1.0
Programme and use CNC machines 92.7 5.8 1.5
Programme and use robots 93.8 4.7 1.5
Technicians
Use a word processor 13.9 85.6 0.4
Create a spreadsheet 12.4 87.2 0.4
Use of Internet 8.1 91.3 0.6
Email 8.8 90.8 0.4
Social media, video calls 39.4 60.2 0.5
Use software for design, calculation and simulation 31.2 67.5 1.4
Programming and software develop. 64.4 33.9 1.6
Design and maintain ICT 55.3 42.7 2.0
Programme and use CNC machines 88.1 9.1 2.8
Programme and use robots 91.8 5.6 2.7
Clerical support workers
Use a word processor 4.4 95.4 0.2
Create a spreadsheet 3.1 96.6 0.2
Use of Internet 1.5 98.1 0.3
Email 1.5 98.2 0.3
Social media, video calls 45.7 53.8 0.5
Use software for design, calculation and simulation 41.7 57.6 0.7
Programming and software develop. 81.4 17.1 1.4
Design and maintain ICT 79.2 19.9 0.9
Programme and use CNC machines 92.3 5.9 1.7
Programme and use robots 93.4 5.0 1.6
Sales, customer or personal service
Use a word processor 23.7 76.3 0.2
Create a spreadsheet 24.1 75.7 0.3
Use of Internet 12.6 87.2 0.2
Email 14.0 85.5 0.5
Social media, video calls 55.5 44.2 0.3
Use software for design, calculation and simulation 58.5 40.7 0.8
Programming and software develop. 89.6 8.9 1.4
Design and maintain ICT 83.3 15.6 1.0
Programme and use CNC machines 95.6 2.9 1.5
Programme and use robots 96.7 1.8 1.5
Skilled agricultural workers
Use a word processor 15.5 84.4 0.0
Create a spreadsheet 26.7 73.2 0.0
Use of Internet 9.6 90.4 0.0
Email 1.5 98.4 0.0
Social media, video calls 52.3 47.7 0.0
Use software for design, calculation and simulation 52.7 47.4 0.1
Programming and software develop. 82.7 17.2 0.2
Design and maintain ICT 66.7 33.2 0.1
Programme and use CNC machines 82.9 16.9 0.3
Programme and use robots 91.0 8.7 0.3
Building workers
Use a word processor 51.3 48.3 0.4
Create a spreadsheet 50.4 49.2 0.4
Use of Internet 39.3 60.1 0.5
Email 36.8 62.7 0.4
Social media, video calls 84.0 15.5 0.5
Use software for design, calculation and simulation 71.0 28.3 0.6
Programming and software develop. 90.8 7.5 1.7
Design and maintain ICT 88.7 10.3 0.9
166
Occupation Digital skills
Level of importance
Not
at
all im
port
ant
Som
ehow
im
port
ant
to e
ssential
Don’t k
now
Programme and use CNC machines 89.0 9.0 2.0
Programme and use robots 93.7 4.0 2.3
Plant machine operators
Use a word processor 68.8 30.5 0.7
Create a spreadsheet 68.0 31.3 0.7
Use of Internet 54.7 44.7 0.7
Email 50.6 48.6 0.8
Social media, video calls 79.7 19.4 0.9
Use software for design, calculation and simulation 77.6 21.1 1.2
Programming and software develop. 89.6 9.0 1.3
Design and maintain ICT 88.6 10.0 1.3
Programme and use CNC machines 80.1 17.8 2.1
Programme and use robots 90.4 7.9 1.7
Elementary occupations
Use a word processor 68.7 30.6 0.7
Create a spreadsheet 70.4 28.9 0.7
Use of Internet 62.9 36.4 0.7
Email 61.1 38.6 0.3
Social media, video calls 80.7 18.3 0.9
Use software for design, calculation and simulation 83.3 15.2 1.5
Programming and software develop. 92.0 6.4 1.6
Design and maintain ICT 91.1 7.0 1.9
Programme and use CNC machines 91.9 6.4 1.6
Programme and use robots 95.6 2.9 1.6
Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential,) how important it is for employees in these categories to…: Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations) Source: European Digital Skills Survey (weighted values)
167
Table A4.3 – Workplaces by type and level of importance of digital skills of employees in
specific occupations, EU28 (% of total employers)
Occupation Digital skills
Level of importance
1 N
ot
at
all i
mp
ort
an
t
2
3 M
od
erate
ly i
mp
orta
nt
4
5 E
ssen
tial
DK
Managers
Basic 2.4 8.3 30.4 44.6 14.3 0.1
Advanced 66.7 30.2 1.0 0.7 1.2 0.2
Specialist 66.6 16.1 9.7 2.9 4.3 0.4
Professionals
Basic 5.4 6.7 24.8 45.4 17.4 0.2
Advanced 45.7 48.0 3.5 0.9 1.5 0.3
Specialist 55.9 16.2 9.8 5.6 11.6 0.7
Technicians
Basic 12.1 11.3 23.0 37.9 15.3 0.4
Advanced 46.8 43.7 5.3 0.8 2.4 1.0
Specialist 55.0 14.8 13.1 6.8 9.0 1.2
Clerical support workers
Basic 1.8 7.4 25.4 44.9 20.4 0.2
Advanced 54.9 38.9 4.3 0.5 0.9 0.5
Specialist 77.8 8.5 8.9 1.6 2.6 0.7
Sales, customer or personal service
Basic 19.1 21.0 29.9 23.0 6.9 0.1
Advanced 74.5 22.3 1.2 0.7 0.9 0.3
Specialist 83.6 8.1 4.4 1.5 1.7 0.7
Skilled agricultural workers
Basic 10.5 26.2 38.3 16.8 8.3 0.0
Advanced 74.7 16.7 8.3 0.2 0.1 0.1
Specialist 74.7 16.5 8.4 0.3 0.0 0.1
Building workers
Basic 50.4 27.5 14.3 6.1 1.5 0.3
Advanced 66.7 30.2 1.0 0.7 1.2 0.2
Specialist 88.5 5.6 2.9 1.2 1.2 0.7
Plant machine operators
Basic 65.3 15.4 10.1 7.0 1.6 0.7
Advanced 77.3 13.8 4.8 1.9 1.5 0.7
Specialist 88.3 3.8 5.1 0.9 0.9 1.0
Elementary occupations
Basic 72.2 12.7 6.3 3.9 4.6 0.3
Advanced 89.5 7.8 1.0 0.8 0.3 0.7
Specialist 91.6 3.0 2.5 0.3 1.2 1.4
Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1 to 5
(where 1 means not at all important, 3 means moderately important and 5 means essential,) how
important it is for employees in these categories to…:
Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical
workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building
workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations)
Source: European Digital Skills Survey (weighted values)
168
Table A4.4 – Workplaces by type and level of importance of digital skills of employees
in specific occupations, EU28 (% of total employers)
Occupation Digital skills
Level of importance:
No
t at
all
im
po
rtan
t
So
meh
ow
im
po
rta
nt
to
essen
tial
Do
n't
kn
ow
Managers
Basic 2.4 97.6 0.1
Advanced 66.7 33.1 0.2
Specialist 66.6 33.0 0.4
Professionals
Basic 5.4 94.3 0.2
Advanced 45.7 53.9 0.3
Specialist 55.9 43.2 0.7
Technicians
Basic 12.1 87.5 0.4
Advanced 46.8 52.2 1.0
Specialist 55.0 43.7 1.2
Clerical support workers
Basic 1.8 98.1 0.2
Advanced 54.9 44.6 0.5
Specialist 77.8 21.6 0.7
Sales, customer or personal service
Basic 19.1 80.8 0.1
Advanced 74.5 25.1 0.3
Specialist 83.6 15.7 0.7
Skilled agricultural workers
Basic 10.5 89.6 0.0
Advanced 74.7 25.3 0.1
Specialist 74.7 25.2 0.1
Building workers
Basic 50.4 49.4 0.3
Advanced 66.7 33.1 0.2
Specialist 88.5 10.9 0.7
Plant machine operators
Basic 65.3 34.1 0.7
Advanced 77.3 22.0 0.7
Specialist 88.3 10.7 1.0
Elementary occupations
Basic 72.2 27.5 0.3
Advanced 89.5 9.9 0.7
Specialist 91.6 7.0 1.4
Q16. Thinking about the job categories in your workplace, please indicate, using a scale of 1
to 5 (where 1 means not at all important, 3 means moderately important and 5 means
essential,) how important it is for employees in these categories to…:
Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004
(Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers);
2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary
occupations)
Source: European Digital Skills Survey (weighted values)
169
Table A4.5 - Linear regression model on "How important ADVANCED DIGITAL
SKILLS are?"
Parameters B SE t Pr > |t|
Intercept 1.202415024 0.01879163 63.99 <.0001
Workplace type
Only workplace (omitted)
Headquarters 0.157872126 0.02452857 6.44 <.0001
Subsidiary_site -0.127779057 0.03533397 -3.62 0.0003
Workplace size
size < 10 (omitted)
size10_49 0.155830124 0.02095651 7.44 <.0001
size50_249 0.080852057 0.04335877 1.86 0.0623
size250 0.371274103 0.10121827 3.67 0.0002
Sector
sectorA (omitted)
sectorCD 0.268697838 0.03025655 8.88 <.0001
sectorF 0.198298166 0.03015942 6.57 <.0001
sectorGHI 0.120518218 0.02233051 5.4 <.0001
sectorJMN 0.292783933 0.02542001 11.52 <.0001
sectorPQ 0.126536436 0.03456203 3.66 0.0003
Female rate
fem_rate<26 (omitted)
fem_rate_26_50 -0.00906296 0.0194676 -0.47 0.6416
fem_rate_51_75 -0.052110779 0.02895877 -1.8 0.072
fem_rate_75 -0.116427039 0.02680266 -4.34 <.0001
University rate
univ_rate<26 (omitted)
univ_rate_26_50 0.026657418 0.02370968 1.12 0.2609
univ_rate_51_75 -0.01462403 0.03610693 -0.41 0.6855
univ_rate_75 -0.023931382 0.02526974 -0.95 0.3437
Young rate
young_rate<26 (omitted)
young_rate_26_50 -0.069732574 0.02261662 -3.08 0.0021
young_rate_51_75 -0.014938145 0.04756398 -0.31 0.7535
young_rate_75 0.015479685 0.04370476 0.35 0.7232
Old rate
old_rate<26 (omitted)
old_rate_26_50 -0.106391533 0.02040476 -5.21 <.0001
old_rate_51_75 -0.040525862 0.03969517 -1.02 0.3073
old_rate_75 -0.182940722 0.02746769 -6.66 <.0001
Ownership (q9) private (omitted)
public 0.112623557 0.03460364 3.25 0.0011
Markets (q11)
Local_market (omitted)
Regional_market 0.107557618 0.01821747 5.9 <.0001
National_market 0.204863766 0.01997028 10.26 <.0001
International_market 0.353760667 0.02442895 14.48 <.0001
N. obs 7448
normalized weight used yes
F-test Pr > F <.0001
R 0.328866234
Q16. Thinking about the job categories in your workplace, please indicate, using a scale of
1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means
essential,) how important it is for employees in these categories to…:
Source: European Digital Skills Survey (weighted values)
170
Table A4.6 - Linear regression model on "How important SPECIALIST DIGITAL
SKILLS are?"
Parameters B SE t Pr > |t|
Intercept 1.169857735 0.02592531 45.12 <.0001
Workplace type
Only workplace (omitted)
Headquarters 0.294338023 0.03466174 8.49 <.0001
Subsidiary_site -0.083833934 0.0502513 -1.67 0.0953
Workplace size
size < 10 (omitted)
size10_49 0.067821095 0.02946885 2.3 0.0214
size50_249 0.007719273 0.06171388 0.13 0.9005
size250 0.251685083 0.14326898 1.76 0.079
Sector
sectorA (omitted)
sectorCD 0.22986343 0.04119149 5.58 <.0001
sectorF 0.177557568 0.03881864 4.57 <.0001
sectorGHI 0.175573224 0.03077729 5.7 <.0001
sectorJMN 0.699797332 0.03541365 19.76 <.0001
sectorPQ 0.262952294 0.04863621 5.41 <.0001
Female rate
fem_rate<26 (omitted)
fem_rate_26_50 -0.02981906 0.02715781 -1.1 0.2722
fem_rate_51_75 -0.229181797 0.04123441 -5.56 <.0001
fem_rate_75 -0.341556538 0.03776722 -9.04 <.0001
University rate
univ_rate<26 (omitted)
univ_rate_26_50 0.029761021 0.03359705 0.89 0.3757
univ_rate_51_75 0.03843772 0.05139634 0.75 0.4546
univ_rate_75 0.154764065 0.0355762 4.35 <.0001
Young rate
young_rate<26 (omitted)
young_rate_26_50 -0.035622886 0.03147051 -1.13 0.2577
young_rate_51_75 0.0362865 0.06711124 0.54 0.5887
young_rate_75 -0.024757471 0.06083973 -0.41 0.6841
Old rate
old_rate<26 (omitted)
old_rate_26_50 -0.033693283 0.02813237 -1.2 0.2311
old_rate_51_75 0.007131535 0.05450461 0.13 0.8959
old_rate_75 -0.003930795 0.03750943 -0.1 0.9165
Ownership (q9) private (omitted)
public 0.321606153 0.04871132 6.6 <.0001
Markets (q11)
Local_market (omitted)
Regional_market 0.098321768 0.02470761 3.98 <.0001
National_market 0.159948952 0.02804108 5.7 <.0001
International_market 0.274921547 0.03438102 8 <.0001
N. obs 7770
normalized weight used yes
F-test Pr > F <.0001
R 0.332625916
Q16. Thinking about the job categories in your workplace, please indicate, using a scale of
1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means
essential,) how important it is for employees in these categories to…:
Source: European Digital Skills Survey (weighted values)
171
Table A4.7 – Employees in specific occupations by type of digital skills, EU28 (% of total
employees in specific occupational category)
Type of digital skill
Occupation
Man
ag
ers
Pro
fessio
nals
Tech
nic
ian
s
Cle
ric
al w
orkers
Sale
s w
orkers
Skille
d a
gric
wo
rkers
Bu
ild
ing
wo
rkers
Pla
nt
mach
ine
op
era
tors
Ele
men
tary
occu
pati
on
s
Use a word processor 93.2 41.7 42.2 58.7 35.9 63.9 31.2 28.9 37.6
Create a spreadsheet 95.0 41.5 42.4 59.6 36.5 63.8 33.4 30.3 37.6
Use of Internet 96.6 41.1 43.2 62.2 37.7 64.2 34.6 31.1 38.3
Email 97.3 42.0 42.7 61.8 37.8 64.6 34.9 30.2 38.5
Social media, video calls 57.9 25.4 30.8 38.0 23.4 41.8 19.0 16.7 21.5
Use software for design, calculation and simulation
51.3 25.6 25.8 35.8 21.5 45.9 21.5 19.7 20.8
Programming and software develop.
16.6 11.0 10.1 11.4 5.6 4.8 4.3 2.8 4.9
Design and maintain ICT 29.0 9.3 13.5 15.9 7.4 34.7 10.8 6.4 6.1
Programme and use CNC machines
1.9 0.5 1.9 1.6 0.6 1.7 1.7 1.1 0.6
Programme and use robots 0.8 0.3 1.1 0.5 0.2 0.7 0.6 0.3 0.3
Q17. Please provide your best estimate of the approximate number or share of employees carrying out such tasks (listed in Q16). Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations) Source: European Digital Skills Survey (weighted values)
Table A4.8 – Occupations selected as the most important for day-to-day operations by sector and by type
and level of digital skills of employees in the occupation (level of importance 1 = not at all important, 3
= moderately important, 5 = essential)
Secto
r
Ran
k
Occupation (ISCO 4-digit)
Use a
word
pro
cessor
Cre
ate
a s
pre
asheet
Use o
f In
tern
et
Em
ail
Socia
l m
edia
, vid
eo c
alls
Use s
oft
ware
for
desig
n,
calc
ula
tion a
nd
sim
ula
tion
Pro
gra
mm
ing a
nd
soft
ware
dev
Desig
n a
nd m
ain
tain
ICT
Pro
gra
mm
e a
nd u
se
CN
C m
achin
es
Pro
gra
mm
e a
nd u
se
robots
A.
Agriculture
1 Agricultural and forestry production managers 4 4 4 4 2 3 1 2 1 1
2 Forestry and related workers 2 2 2 3 1 1 1 1 1 1
3 Managing directors and chief executives 4 4 4 5 2 3 1 2 1 1
4 Mixed crop growers 2 2 2 2 1 1 1 1 1 1
5 Mixed crop and animal producers 2 2 2 2 1 1 1 1 1 1
6 Field crop and vegetable growers 2 2 2 2 2 2 1 1 1 1
7 Gardeners, horticultural and nursery growers 2 2 2 3 2 2 1 1 1 1
8 Livestock and dairy producers 2 2 2 2 1 2 1 1 1 1
9 General office clerks 5 5 5 5 2 3 1 1 1 1
10 Animal producers not elsewhere classified 1 1 2 2 1 1 1 1 1 1
CD
.
Manuf
actu
ri
ng
and
utilitie
s
1 Manufacturing managers 4 4 4 4 2 3 2 2 2 1
2 Managing directors and chief executives 4 4 4 5 3 3 2 2 1 1
3 Clerical support workers not elsewhere 5 5 5 5 5 5 1 1 1 1
172
Secto
r
Ran
k
Occupation (ISCO 4-digit)
Use a
word
pro
cessor
Cre
ate
a s
pre
asheet
Use o
f In
tern
et
Em
ail
Socia
l m
edia
, vid
eo c
alls
Use s
oft
ware
for
desig
n,
calc
ula
tion a
nd
sim
ula
tion
Pro
gra
mm
ing a
nd
soft
ware
dev
Desig
n a
nd m
ain
tain
ICT
Pro
gra
mm
e a
nd u
se
CN
C m
achin
es
Pro
gra
mm
e a
nd u
se
robots
classified
4 Sales and marketing managers 5 5 5 5 4 4 2 3 1 1
5 General office clerks 5 5 5 5 3 4 1 1 1 1
6 Building frame and related trades workers not elsewhere classified
2 2 3 3 1 2 1 1 1 1
7 Finance managers 5 5 5 5 3 4 2 2 1 1
8 Industrial and production engineers 4 4 5 5 3 4 2 3 2 2
9 Craft and related workers not elsewhere
classified
1 1 2 2 1 1 1 1 1 1
10 Supply, distribution and related managers 4 4 5 5 3 3 1 2 1 1
F.
Constr
uction
1 Construction managers 3 3 4 4 2 3 1 2 1 1
2 House builders 2 2 2 2 1 1 1 1 1 1
3 Managing directors and chief executives 4 4 4 5 2 2 1 2 1 1
4 Building frame and related trades workers not
elsewhere classified
2 2 2 3 1 2 1 1 1 1
5 General office clerks 5 5 5 5 3 3 1 1 1 1
6 Civil engineers 4 4 5 5 2 5 2 3 1 1
7 Plumbers and pipe fitters 2 2 2 2 1 1 1 1 1 1
8 Clerical support workers not elsewhere
classified
5 4 5 5 4 4 1 1 1 1
9 Craft and related workers not elsewhere
classified
1 1 1 1 1 1 1 1 1 1
10 Carpenters and joiners 2 1 2 2 1 1 1 1 1 1
GH
I. C
om
merc
e,
transport
,
accom
modation a
nd food
serv
ice
1 Managing directors and chief executives 4 5 5 5 3 3 2 2 1 1
2 Shop sales assistants 3 3 3 4 2 2 1 1 1 1
3 Retail and wholesale trade managers 4 4 5 5 2 4 2 2 1 1
4 Sales and marketing managers 4 4 4 5 3 3 2 2 1 1
5 Waiters 1 1 2 2 1 1 1 1 1 1
6 Cooks 1 1 1 2 1 1 1 1 1 1
7 General office clerks 5 5 5 5 3 3 1 1 1 1
8 Shopkeepers 4 3 4 4 2 2 1 1 1 1
9 Sales workers not elsewhere classified 3 4 4 4 2 2 1 1 1 1
10 Supply, distribution and related managers 4 4 5 5 3 3 1 1 1 1
JMN
. In
form
ation a
nd
com
munic
ation;
pro
fessio
nal,
scie
ntific a
nd t
echnic
al activitie
s;
Adm
inis
trative s
erv
ices
1 Managing directors and chief executives 5 5 5 5 4 3 2 3 1 1
2 Clerical support workers not elsewhere
classified
4 4 4 5 3 3 1 1 1 1
3 Sales and marketing managers 4 4 4 5 4 3 2 2 1 1
4 Accountants 4 4 4 4 3 4 2 1 1 1
5 Information and communications technology service managers
4 4 5 5 4 4 3 4 1 1
6 General office clerks 5 5 5 5 4 3 1 1 1 1
7 Finance managers 5 5 5 5 4 4 2 2 1 1
8 Engineering professionals not elsewhere
classified
5 4 5 5 3 4 3 3 1 1
9 Software developers 4 4 5 5 4 5 5 4 1 1
10 Human resource managers 5 5 5 5 4 2 2 1 1 1
PQ
. Education a
nd h
um
an
health
1 Education managers 4 4 4 4 3 3 1 2 1 1
2 Managing directors and chief executives 4 4 4 5 3 2 2 2 1 1
3 Specialist medical practitioners 4 3 4 4 2 2 2 1 1 1
4 Health services managers 4 4 4 4 3 3 2 2 1 1
5 General office clerks 4 4 4 4 2 3 1 1 1 1
6 Dentists 3 3 3 3 2 2 2 2 1 1
7 Personal care workers in health services not
elsewhere classified
3 3 3 3 1 1 1 1 1 1
8 Generalist medical practitioners 3 3 3 3 2 2 1 1 1 1
9 Health professionals not elsewhere classified 3 3 3 3 2 2 1 1 1 1
173
Secto
r
Ran
k
Occupation (ISCO 4-digit)
Use a
word
pro
cessor
Cre
ate
a s
pre
asheet
Use o
f In
tern
et
Em
ail
Socia
l m
edia
, vid
eo c
alls
Use s
oft
ware
for
desig
n,
calc
ula
tion a
nd
sim
ula
tion
Pro
gra
mm
ing a
nd
soft
ware
dev
Desig
n a
nd m
ain
tain
ICT
Pro
gra
mm
e a
nd u
se
CN
C m
achin
es
Pro
gra
mm
e a
nd u
se
robots
10 Primary school teachers 4 4 5 4 3 3 1 1 1 1
Q19. Thinking about these jobs in your workplace, please indicate using a scale of 1 to 5 (where 1 means not at all important, 3 means moderately important and 5 means essential) how important for day-to-day activities it is for employees in these jobs to…: Number of valid responses: 6,264 Source: European Digital Skills Survey (unweighted values)
Table A4.9 – Occupations selected as the most important for day-to-day operations by type of change in the job tasks due to the use of ICT in last five years, by sector (%)
Secto
r
Rank Occupation (ISCO 4-digit)
Type of change
No
ch
an
ge a
t all
Min
or c
han
ge
Mo
derate
ch
an
ge
Majo
r c
han
ge
Ch
an
ge (
fro
m m
ino
r t
o m
ajo
r)
A.
Agriculture
1 Agricultural and forestry production
managers 16.6 29.7 39.3 14.5 83.4
2 Forestry and related workers 44.8 27.6 22.4 5.2 55.2
3 Managing directors and chief executives 8.2 32.7 51.0 8.2 91.8
4 Mixed crop growers 45.0 43.3 10.0 1.7 55.0
5 Mixed crop and animal producers 43.1 37.9 8.6 10.3 56.9
6 Field crop and vegetable growers 52.7 18.2 20.0 9.1 47.3
7 Gardeners, horticultural and nursery
growers 33.9 39.3 19.6 7.1 66.1
8 Livestock and dairy producers 31.9 38.3 21.3 8.5 68.1
9 General office clerks 11.1 8.9 64.4 15.6 88.9
10 Animal producers not elsewhere classified 55.9 20.6 14.7 8.8 44.1
CD
. M
anufa
ctu
ring a
nd u
tilities
1 Manufacturing managers 22.3 18.4 42.2 17.0 77.7
2 Managing directors and chief executives 6.4 16.8 59.2 17.6 93.6
3 Clerical support workers not elsewhere classified
4.1 1.4 68.5 26.0 95.9
4 Sales and marketing managers 9.4 29.7 32.8 28.1 90.6
5 General office clerks 3.6 14.3 55.4 26.8 96.4
6 Building frame and related trades workers
not elsewhere classified 22. 42.2 33.3 2.2 77.8
7 Finance managers 4.9 17.1 61.0 17.1 95.1
8 Industrial and production engineers 16.7 11.9 45.2 26.2 83.3
9 Craft and related workers not elsewhere
classified 65.1 14.0 14.0 7.0 34.9
10 Supply, distribution and related managers 21.2 36.4 27.3 15.2 78.8
F.
Constr
uction 1 Construction managers 26.6 29.7 30.4 13.3 73.4
2 House builders 39.3 32.9 25.0 2.9 60.7
3 Managing directors and chief executives 6.6 37.2 45.5 10.7 93.4
4 Building frame and related trades workers
not elsewhere classified 40.8 34.0 20. 4.9 59.2
5 General office clerks 11.1 5.6 44.4 38.9 88.9
174
Secto
r
Rank Occupation (ISCO 4-digit)
Type of change
No
ch
an
ge a
t all
Min
or c
han
ge
Mo
derate
ch
an
ge
Majo
r c
han
ge
Ch
an
ge (
fro
m m
ino
r t
o m
ajo
r)
6 Civil engineers 6.3 20.8 47.9 25.0 93.8
7 Plumbers and pipe fitters 50.0 28.6 11.9 9.5 50.0
8 Clerical support workers not elsewhere
classified 9.8 9.8 58.5 22.0 90.2
9 Craft and related workers not elsewhere
classified 83.8 10.8 5.4 0.0 16.2
10 Carpenters and joiners 68.8 12.5 12.5 6.3 31.3
GH
I. C
om
merc
e,
transport
,
accom
modation a
nd food
serv
ice
1 Managing directors and chief executives 4.6 20.8 61.5 13.1 95.4
2 Shop sales assistants 20.5 35.2 31.1 13.1 79.5
3 Retail and wholesale trade managers 9.8 29.3 39.1 21.7 90.2
4 Sales and marketing managers 16.9 25.8 37.1 20.2 83.1
5 Waiters 42.3 44.9 10.3 2.6 57.7
6 Cooks 66.7 20.0 10.7 2.7 33.3
7 General office clerks 9.5 4.8 61.9 23.8 90.5
8 Shopkeepers 6.6 37.7 39.3 16.4 93.4
9 Sales workers not elsewhere classified 44.3 9.8 34.4 11.5 55.7
10 Supply, distribution and related managers 6.8 25.4 47.5 20.3 93.2
JMN
. In
form
ation a
nd
com
munic
ation;
pro
fessio
nal,
scie
ntific a
nd t
echnic
al activitie
s;
Adm
inis
trative s
erv
ices
1 Managing directors and chief executives 7.7 14.6 46.9 30.8 92.3
2 Clerical support workers not elsewhere
classified 7.1 23.2 60.7 8.9 92.9
3 Sales and marketing managers 17.6 19.6 39.2 23.5 82.4
4 Accountants 2.0 38.8 36.7 22.4 98.0
5 Information and communications technology
service managers 15.6 6.7 35.6 42.2 84.4
6 General office clerks 9.3 2.3 67.4 20.9 90.7
7 Finance managers 8.1 10.8 35.1 45.9 91.9
8 Engineering professionals not elsewhere
classified 15.6 6.3 37.5 40.6 84.4
9 Software developers 15.6 9.4 25.0 50.0 84.4
10 Human resource managers 16.7 16.7 43.3 23.3 83.3
PQ
. Education a
nd h
um
an
health
1 Education managers 6.3 15.0 55.0 23.8 93.8
2 Managing directors and chief executives 7.0 11.3 67.6 14.1 93.0
3 Specialist medical practitioners 22.0 28.8 28.8 20.3 78.0
4 Health services managers 7.5 26.4 45.3 20.8 92.5
5 General office clerks 10.9 15.2 45.7 28.3 89.1
6 Dentists 9.3 41.9 37.2 11.6 90.7
7 Personal care workers in health services not
elsewhere classified 13.2 34.2 44.7 7.9 86.8
8 Generalist medical practitioners 5.6 8.3 52.8 33.3 94.4
9 Health professionals not elsewhere classified 10.0 30.0 50.0 10.0 90.0
10 Primary school teachers 6.5 9.7 41.9 41.9 93.5
Q20. Thinking about these jobs in your workplace, please indicate if and to what extent the use of ICT has changed
the way job tasks are carried out. Please refer to the timespan of the last 5 years.
Number of valid responses: 6,264 Source: European Digital Skills Survey (unweighted values)
175
Table A4.10 – Occupations selected as the most important for day-to-day operations by type of change in
the job tasks due to the use of ICT in next five years, by sector (%)
Secto
r
Rank Occupation (ISCO 4-digit)
Type of change
No
ch
an
ge a
t all
Min
or c
han
ge
Mo
derate
ch
an
ge
Majo
r c
han
ge
Ch
an
ge (
fro
m m
ino
r t
o m
ajo
r)
A.
Ag
ric
ult
ure
1 Agricultural and forestry production
managers 27.7 24.8 34.0 13.5 72.3
2 Forestry and related workers 51.8 19.6 19.6 8.9 48.2
3 Managing directors and chief executives 17.5 41.2 37.1 4.1 82.5
4 Mixed crop growers 66.7 21.7 8.3 3.3 33.3
5 Mixed crop and animal producers 53.4 24.1 12.1 10.3 46.6
6 Field crop and vegetable growers 60.7 14.3 16.1 8.9 39.3
7 Gardeners, horticultural and nursery
growers 51.8 23.2 12.5 12.5 48.2
8 Livestock and dairy producers 41.3 34.8 17.4 6.5 58.7
9 General office clerks 11.1 8.9 53.3 26.7 88.9
10 Animal producers not elsewhere classified 54.5 18.2 15.2 12.1 45.5
CD
. M
an
ufa
ctu
rin
g a
nd
uti
liti
es 1 Manufacturing managers 22.0 20.5 34.0 23.5 78.0
2 Managing directors and chief executives 9.9 24.8 54.5 10.7 90.1
3 Clerical support workers not elsewhere
classified 1.4 4.2 68.1 26.4 98.6
4 Sales and marketing managers 22.6 32.3 32.3 12.9 77.4
5 General office clerks 1.8 10.5 56.1 31.6 98.2
6 Building frame and related trades workers
not elsewhere classified 24.4 53.3 22.2 0.0 75.6
7 Finance managers 11.9 19.0 57.1 11.9 88.1
8 Industrial and production engineers 11.6 20.9 46.5 20.9 88.4
9 Craft and related workers not elsewhere
classified 69.0 4.8 19.0 7.1 31.0
10 Supply, distribution and related managers 16.1 38.7 16.1 29.0 83.9
F.
Co
nstr
ucti
on
1 Construction managers 29.7 23.2 29.0 18.1 70.3
2 House builders 50.0 20.7 26.4 2.9 50.0
3 Managing directors and chief executives 10.0 50.0 35.8 4.2 90.0
4 Building frame and related trades workers
not elsewhere classified 44.1 32.4 14.7 8.8 55.9
5 General office clerks 9.6 9.6 51.9 28.8 90.4
6 Civil engineers 8.5 19.1 51.1 21.3 91.5
7 Plumbers and pipe fitters 53.5 23.3 11.6 11.6 46.5
8 Clerical support workers not elsewhere
classified 4.9 17.1 56.1 22.0 95.1
9 Craft and related workers not elsewhere
classified 88.9 2.8 8.3 0.0 11.1
10 Carpenters and joiners 59.4 18.8 21.9 0.0 40.6
GH
I.
Co
mm
erce,
tran
sp
ort,
acco
mm
od
ati
on
an
d
foo
d s
ervic
e
1 Managing directors and chief executives 16.2 23.1 54.6 6.2 83.8
2 Shop sales assistants 32.5 29.1 24.8 13.7 67.5
3 Retail and wholesale trade managers 13.5 30.3 43.8 12.4 86.5
4 Sales and marketing managers 26.4 28.7 26.4 18.4 73.6
5 Waiters 54.7 26.7 18.7 0.0 45.3
6 Cooks 77.0 10.8 10.8 1.4 23.0
7 General office clerks 8.1 4.8 48.4 38.7 91.9
8 Shopkeepers 6.6 55.7 19.7 18.0 93.4
9 Sales workers not elsewhere classified 46.6 19.0 29.3 5.2 53.4
176
Secto
r
Rank Occupation (ISCO 4-digit)
Type of change
No
ch
an
ge a
t all
Min
or c
han
ge
Mo
derate
ch
an
ge
Majo
r c
han
ge
Ch
an
ge (
fro
m m
ino
r t
o m
ajo
r)
10 Supply, distribution and related managers 15.5 24.1 46.6 13.8 84.5
JM
N.
In
form
ati
on
an
d
co
mm
un
ica
tio
n;
pro
fessio
nal,
scie
nti
fic a
nd
tech
nic
al
acti
vit
ies;
Ad
min
istr
ati
ve
servic
es
1 Managing directors and chief executives 6.2 16.9 53.1 23.8 93.8
2 Clerical support workers not elsewhere classified
5.7 22.6 54.7 17.0 94.3
3 Sales and marketing managers 17.6 19.6 45.1 17.6 82.4
4 Accountants 14.9 31.9 36.2 17.0 85.1
5 Information and communications
technology service managers 15.6 17.8 31.1 35.6 84.4
6 General office clerks 9.3 4.7 60.5 25.6 90.7
7 Finance managers 2.9 17.1 42.9 37.1 97.1
8 Engineering professionals not elsewhere
classified 15.2 6.1 48.5 30.3 84.8
9 Software developers 9.7 9.7 32.3 48.4 90.3
10 Human resource managers 20.0 16.7 46.7 16.7 80.0
PQ
. E
du
cati
on
an
d h
um
an
healt
h
1 Education managers 6.3 21.5 50.6 21.5 93.7
2 Managing directors and chief executives 2.9 23.2 63.8 10.1 97.1
3 Specialist medical practitioners 29.3 31.0 24.1 15.5 70.7
4 Health services managers 15.4 23.1 34.6 26.9 84.6
5 General office clerks 13.3 15.6 51.1 20.0 86.7
6 Dentists 14.0 55.8 18.6 11.6 86.0
7 Personal care workers in health services
not elsewhere classified 13.5 54.1 27.0 5.4 86.5
8 Generalist medical practitioners 2.8 16.7 44.4 36.1 97.2
9 Health professionals not elsewhere
classified 17.2 27.6 48.3 6.9 82.8
10 Primary school teachers 3.2 25.8 54.8 16.1 96.8
Q21a. Thinking about these jobs in your workplace, please indicate if and to what extent you think the use of ICT will
change the way job tasks are carried out. Please refer to the timespan of the next 5 years.
Number of valid responses: 6,264 Source: European Digital Skills Survey (unweighted values)
177
Chapter 5. The digital skills challenge in European workplaces
Figure A5.1 – Workplaces reporting digital skill gaps in the workforce by size of workplace in
sampled countries (% of workplaces)
Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level.Number of valid responses: 4,569 N= 1,772,219 Source: European Digital Skills Survey (weighted values)
17.6
7.7
17.1
1.3
27.0
12.2
27.7
18.1 18.1
24.1
32.0
22.0 21.5 18.7
29.0
42.6
14.1
43.6 41.4
9.9
33.3
54.0
3.4
18.9
22.1
9.9
17.8
2.9
26.7
14.9
DE FI UK PT SE SK
2-9 10-49 50-249 250 or more Total
178
Figure A5.2 – Workplaces reporting digital skill gaps in the workforce by sector in
sampled countries (% of workplaces)
Q17/Q23. Please provide your best estimate of the approximate number or share of employees
carrying out such tasks and indicate how many of them are fully proficient in carrying out the
tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the
task to the required level.
Number of valid responses: 4,569 N= 1,772,219
Source: European Digital Skills Survey (weighted values)
0.2
1.4
0.7
0.0
27.2
19.4
25.8
15.9
9.2
9.7
51.4
23.0
37.3 17.2
9.6
19.2
8.9
39.0
13.7
16.3
16.7
23.9
15.2
7.2
7.8
28.8
13.3
17.5
5.0
13.6
12.4
22.1
9.9
17.8
2.9
26.7
14.9
DE
FI
UK
PT
SE
SK
A. Agriculture
CD. Manufacturing and utilities
F. Construction
GHI. Commerce, transport, accommodation and food service
JMN. Information and communication; Professional, scientific and technical activities; Administrative services
PQ. Education and human health
Total
179
Table A5.1 – Workforce with digital skill gaps by occupation and type of digital skill, EU28 (%)
Type of digital skills
Occupations
Managers
Pro
fessio
nals
Technic
ians
Cle
rical w
ork
ers
Sale
s w
ork
ers
Skille
d a
gri
c w
ork
ers
Buildin
g w
ork
ers
Pla
nt
machin
e o
pera
tors
Ele
menta
ry o
ccupations
Use a word processor 14.1 11.0 21.9 17.4 21.0 8.2 13.4 16.7 20.7
Create a spreadsheet 13.7 11.0 21.4 17.0 19.1 8.1 12.6 15.9 20.2
Use of Internet 13.6 11.0 21.8 17.4 19.6 9.1 12.5 15.7 20.0
Email 13.6 11.1 20.9 16.1 19.7 9.1 12.9 13.4 19.0
Social media, video calls 14.4 15.8 23.2 18.3 17.3 6.7 10.6 18.6 22.5
Use software for design, calculation and
simulation 12.6 9.7 17.6 15.2 17.2 8.4 10.1 16.3 14.0
Programming and software develop 13.7 10.8 16.4 14.7 23.2 7.2 12.1 16.0 12.3
Design and maintain ICT 12.7 15.5 16.4 14.5 22.0 11.3 12.7 15.4 23.0
Programme and use CNC machines 16.2 18.2 12.4 16.0 18.4 37.5 12.4 16.5 3.0
Programme and use robots 18.7 9.4 10.2 15.0 18.8 93.6 8.2 15.8 4.4
Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out
such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a
proficient employee is someone who is able to do the job/carrying out the task to the required level.
N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers);
3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers);
1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations)
Source: European Digital Skills Survey (weighted values)
Table A5.2 – Workforce with digital skill gaps by occupation, type of digital skill (three levels) and
sector, EU28 (%)
Occupation Sector
Digital skills
Basic
dig
ital skills
Advanced d
igital
skills
Specia
list
dig
ital
skills
Managers
A. Agriculture 0.3 2.0 1.0
CD. Manufacturing and utilities 19.9 17.9 19.4
F. Construction 17.5 14.8 17.0
GHI. Commerce, transport, accommodation and food service 15.3 12.9 13.2
JMN. Information and communication; professional,
scientific and technical activities; Administrative services 13.2 12.0 10.6
PQ. Education and human health 19.0 17.3 23.3
Total 13.9 13.2 13.3
Professionals
A. Agriculture 6.8 5.6 40.4
CD. Manufacturing and utilities 18.6 11.1 8.9
F. Construction 8.3 8.5 12.4
GHI. Commerce, transport, accommodation and food service 11.8 12.9 17.4
JMN. Information and communication; professional,
scientific and technical activities; Administrative services 11.6 9.7 9.9
PQ. Education and human health 9.9 8.4 18.7
Total 11.6 10.4 12.8
Technicians A. Agriculture 2.5 1.6 32.7
180
Occupation Sector
Digital skills
Basic
dig
ital skills
Advanced d
igital
skills
Specia
list
dig
ital
skills
CD. Manufacturing and utilities 19.5 13.3 9.8
F. Construction 18.1 18.0 10.2
GHI. Commerce, transport, accommodation and food service 22.8 23.4 25.3
JMN. Information and communication; professional,
scientific and technical activities; Administrative services 18.0 11.6 9.4
PQ. Education and human health 34.7 19.2 49.0
Total 21.8 17.4 16.4
Clerical workers
A. Agriculture 6.3 1.3 0.4
CD. Manufacturing and utilities 17.3 17.0 18.3
F. Construction 19.0 16.0 15.6
GHI. Commerce, transport, accommodation and food service 17.2 15.1 13.7
JMN. Information and communication; professional,
scientific and technical activities; Administrative services 15.2 14.8 8.5
PQ. Education and human health 18.5 19.0 40.8
Total 17.2 15.8 14.7
Sales workers
A. Agriculture 3.2 0.9 14.8
CD. Manufacturing and utilities 17.7 15.6 19.2
F. Construction 17.9 23.2 18.9
GHI. Commerce, transport, accommodation and food service 19.6 17.7 23.3
JMN. Information and communication; professional,
scientific and technical activities; Administrative services 15.1 11.2 9.6
PQ. Education and human health 30.5 30.8 55.0
Total 19.5 17.6 22.8
Skilled agric workers
A. Agriculture 2.0 9.4 1.1
CD. Manufacturing and utilities 5.2 2.6 4.3
F. Construction 73.6 98.1 94.4
GHI. Commerce, transport, accommodation and food service 12.9 0.0 22.6
JMN. Information and communication; professional,
scientific and technical activities; Administrative services 24.9 35.0 56.8
PQ. Education and human health 100.0 100.0 100.0
Total 8.4 8.0 14.0
Building workers
A. Agriculture 0.0 48.4 90.7
CD. Manufacturing and utilities 3.7 10.8 16.3
F. Construction 8.6 12.7 14.0
GHI. Commerce, transport, accommodation and food service 5.7 10.4 9.4
JMN. Information and communication; professional,
scientific and technical activities; Administrative services 3.8 5.8 3.7
PQ. Education and human health 0.0 7.4 70.1
Total 5.6 10.9 12.9
Plant machine operators
A. Agriculture 86.5 94.7 98.8
CD. Manufacturing and utilities 19.7 18.5 14.7
F. Construction 14.4 14.1 17.5
GHI. Commerce, transport, accommodation and food service 13.8 15.2 16.7
JMN. Information and communication; professional,
scientific and technical activities; Administrative services 7.1 0.6 0.4
PQ. Education and human health 16.7 21.0 35.1
Total 15.9 16.5 15.3
Elementary occupations
A. Agriculture 14.4 8.1 14.2
CD. Manufacturing and utilities 22.8 17.9 9.6
F. Construction 8.1 7.1 13.8
GHI. Commerce, transport, accommodation and food service 20.9 14.6 17.2
JMN. Information and communication; professional,
scientific and technical activities; Administrative services 18.4 16.1 9.4
PQ. Education and human health 24.1 16.3 53.4
Total 20.6 15.3 17.6
Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level. N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations) Source: European Digital Skills Survey (weighted values)
181
Table A5.3 – Workforce with digital skill gaps by occupation, type of digital skill (three levels) and size, EU28 (%)
Occupation Size
Basic
dig
ital skills
Advanced d
igital skills
Specia
list
dig
ital skills
Managers
2-9 11.4 10.6 10.6
10-49 18.1 15.9 15.9
50-249 20.6 26.0 31.0
250 or more 40.6 34.8 44.8
Total 13.9 13.2 13.3
Professionals
2-9 10.8 12.0 16.5
10-49 11.5 7.6 7.3
50-249 12.1 13.0 21.4
250 or more 30.1 28.5 32.8
Total 11.6 10.4 12.8
Technicians
2-9 17.8 20.3 18.1
10-49 22.7 18.9 14.7
50-249 24.9 11.3 19.5
250 or more 28.4 10.2 4.0
Total 21.8 17.4 16.4
Clerical workers
2-9 13.6 11.7 8.1
10-49 17.3 15.1 17.1
50-249 22.9 24.6 25.5
250 or more 40.9 31.3 41.8
Total 17.2 15.8 14.7
Sales workers
2-9 16.3 13.6 17.8
10-49 20.2 17.7 27.1
50-249 30.3 31.9 31.7
250 or more 21.9 13.4 16.9
Total 19.5 17.6 22.8
Skilled agric workers
2-9 7.4 7.6 13.3
10-49 37.9 43.0 43.9
50-249 10.5 7.2 6.1
250 or more 4.4 0.7 0.0
Total 8.4 8.0 14.0
Building workers
2-9 7.4 13.3 11.3
10-49 3.9 10.6 16.5
50-249 4.3 5.7 7.2
250 or more 8.5 8.7 10.1
Total 5.6 10.9 12.9
Plant machine operators
2-9 5.6 7.1 13.8
10-49 16.1 15.1 12.8
50-249 16.3 19.4 25.4
250 or more 59.6 40.4 2.5
Total 15.9 16.5 15.3
Elementary occupations
2-9 28.9 21.3 20.3
10-49 14.4 8.1 8.1
50-249 25.2 25.2 35.1
250 or more 25.4 7.5 7.3
Total 20.6 15.3 17.6
Q17/Q23. Please provide your best estimate of the approximate number or share of employees carrying out such tasks and indicate how many of them are fully proficient in carrying out the tasks. Please note that a proficient employee is someone who is able to do the job/carrying out the task to the required level. Number of valid responses=4,608 (job category: Managers); 2,224 (job category: Professionals); 1,886 (job category: Technicians); 3,929 (job category: Clerical workers); 2,298 (job category: Sales workers); 858 (job category: Skilled agricultural workers); 1,824 (job category: Building workers); 1,145 (job category: Plant machine operators); 1,319 (job category: Elementary occupations) N=5,644,799 (Managers); 3,463,858 (Professionals); 2,045,270 (Technicians); 4,172,004 (Clerical workers); 3,815,976 (Sales workers); 1,740,841 (Skilled agricultural workers); 2,475,089 (Building workers); 1,059,179 (Plant machine operators); 1,164,035 (Elementary occupations) Source: European Digital Skills Survey (weighted values)
182
Table A5.4 - Workplaces reporting impact of digital skills gaps on overall performance, by sector and
size, EU28 (%)
Impact of digital skill gaps on overall
performance
Yes,
a m
ajo
r im
pact
Yes,
a m
inor
impact
No
Don't k
now
Siz
e
2 to 9 16.5 17.6 63.8 2.1
10 to 49 24.4 21.8 51.2 2.5
50 to 249 18.6 25.9 53.6 1.8
250 + 10.4 22.9 61.5 5.2
Secto
r
A. Agriculture 25.8 8.8 60.5 4.9
CD. Manufacturing and utilities 12.5 14.8 71.4 1.3
F. Construction 8.0 15.8 75.2 0.9
GHI. Commerce, transport, accommodation and food service 12.9 24.3 61.6 1.3
JMN. Information and communication; Professional, scientific
and technical activities; Administrative services 28.5 19.4 51.0 1.2
PQ. Education and human health 10.3 29.9 57.8 2.0
Total 17.6 18.5 61.7 2.1
Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance? (Yes, a major impact/Yes, a minor impact/No/Don’t know/Not applicable-100% proficient) Number of valid responses: 6,776 N=12,268,096 Source: European Digital Skills Survey (weighted values)
183
Table A5.5 - Logistic regression: probability that digital skills gaps have a major impact on overall
performance (Odds ratios).
N. obs 4466
normalized weight used yes
model estimates statistics Only intercept Intercept and covariates
AIC 4117.243 3833.566
SC 4123.647 4006.481
-2 Log L 4115.243 3779.566
Test Chi-square DF Pr > ChiSQ
Likelihood ratio 335.6769 26 <.0001
Score 374.0324 26 <.0001
Wald 322.1946 26 <.0001
Parameters B SE Chi-square
(Ward) Pr > ChiSQ Odds Ratio
Interc -1.8291 0.1039 310.0493 <.0001
Workplace type
Only workplace (omitted)
Headquarters -0.2045 0.1535 1.7745 0.1828 0.815
Subsidiary_site -1.4336 0.3116 21.1666 <.0001 0.238
Workplace size
size < 10 (omitted)
size10_49 0.875 0.1141 58.8331 <.0001 2.399
size50_249 0.6272 0.2335 7.2146 0.0072 1.872
size250 0.026 0.6038 0.0019 0.9656 1.026
Sector
sectorA (omitted)
sectorCD -0.7889 0.1924 16.8102 <.0001 0.454
sectorF -0.6616 0.1878 12.409 0.0004 0.516
sectorGHI -0.3583 0.1351 7.0355 0.008 0.699
sectorJMN 0.369 0.1404 6.9068 0.0086 1.446
sectorPQ -0.8143 0.2239 13.2265 0.0003 0.443
Female rate
fem_rate<26 (omitted)
fem_rate_26_50 0.2139 0.1162 3.3853 0.0658 1.238
fem_rate_51_75 0.2228 0.1711 1.6958 0.1928 1.25
fem_rate_75 0.187 0.1594 1.3771 0.2406 1.206
University rate
univ_rate<26 (omitted)
univ_rate_26_50 0.00723 0.1409 0.0026 0.9591 1.007
univ_rate_51_75 0.2298 0.2165 1.1258 0.2887 1.258
univ_rate_75 0.3255 0.1512 4.6311 0.0314 1.385
Young rate
young_rate<26 (omitted)
young_rate_26_50 -0.0763 0.1364 0.313 0.5758 0.927
young_rate_51_75 0.544 0.2459 4.894 0.0269 1.723
young_rate_75 -0.2224 0.2865 0.6025 0.4376 0.801
Old rate
old_rate<26 (omitted)
old_rate_26_50 -0.0563 0.1219 0.2129 0.6445 0.945
old_rate_51_75 -0.1388 0.2588 0.2874 0.5919 0.87
old_rate_75 -0.4031 0.1808 4.97 0.0258 0.668
Ownership (q9) private (omitted)
public -0.1131 0.2134 0.2807 0.5962 0.893
Markets (q11)
Local_market (omitted)
Regional_market -0.2998 0.1152 6.7725 0.0093 0.741
National_market -0.2684 0.1265 4.499 0.0339 0.765
International_market 0.7969 0.126 40.0012 <.0001 2.219
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance? (Yes, a major impact/Yes, a minor impact/No/Don’t know/Not applicable-100% proficient)
Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: workplaces with fully proficient workers (e.g. without digital skills gaps) are excluded from the analysis Source: Elaboration on European Digital Skills Survey (weighted values)
184
Figure A5.3 - Workplaces reporting impact of digital skills gaps on overall performance, by sector
and size, in sampled countries (%)
Q26. Thinking about your workplace as a whole, does the fact that some of your employees are not fully proficient in carrying out the indicated tasks involving ICT use have an impact on your workplace performance? (Yes, a major impact/Yes, a minor impact/No/Don’t know/Not applicable-100% proficient) Number of valid responses: 6,776 N=3,750,539 Source: European Digital Skills Survey (weighted values)
Table A5.6 - Workplaces reporting having or not having taken action to tackle digital skill
gaps, EU28 (% of workplaces with digital skill gaps)
Action undertaken?
Yes No, but have plans to No
Siz
e
2 to 9 9.0 10.0 81.0
10 to 49 23.9 18.3 57.8
50 to 249 36.4 8.1 55.6
250 + 29.3 23.9 46.8
Secto
r
A. Agriculture 0.1 14.6 85.3
CD. Manufacturing and utilities 17.7 8.0 74.3
F. Construction 11.5 7.4 81.1
GHI. Commerce, transport, accommodation and food service
14.1 10.3 75.5
JMN. Information and communication; Professional, scientific and technical activities; Administrative services
18.4 10.6 71.0
PQ. Education and human health
21.4 15.5 63.1
Total 12.1 11.2 76.7
Q27. Has your workplace taken any steps to improve the proficiency of employees to enable them to
carry out the tasks involving ICT use?
Number of valid responses: 6,776 N=12,268,023
Source: European Digital Skills Survey (weighted values)
4.2
14.4
20.0
35.2
12.2
6.0
14.9
7.3
11.4
17.6
48.2
23.3
18.8
17.6
85.6
73.8
59.2
15.6
63.2
73.7
65.0
2.8
0.3
3.3
1.0
1.3
1.5
2.6
DE
FI
UK
PT
SE
SK
Total
Yes, a major impact Yes, a minor impact No Don't know
185
Table A5.7 - Logistic regression: probability that workplace has undertaken steps to tackle digital skills
gaps (Odds ratios)
N. obs 4466
normalized weight used yes
model estimates statistics Only intercept Intercept and
covariates
AIC 5097.372 4799.308
SC 5103.631 4968.314
-2 Log L 5095.372 4745.308
Test Chi-square DF Pr > ChiSQ
Likelihood ratio 350.0636 26 <.0001
Score 378.2455 26 <.0001
Wald 333.2119 26 <.0001
Parameters B SE
Chi-square
(Ward) Pr > ChiSQ Odds Ratio
Intercept -1.4703 0.0904 264.801 <.0001
Workplace type
Only workplace (omitted)
Headquarters 0.2063 0.1285 2.5764 0.1085 1.229
Subsidiary_site 0.1602 0.2001 0.6409 0.4234 1.174
Workplace size
size < 10 (omitted)
size10_49 1.0864 0.1009 115.8162 <.0001 2.964
size50_249 1.1978 0.1963 37.2138 <.0001 3.313
size250 1.3134 0.377 12.1359 0.0005 3.719
Sector
sectorA (omitted)
sectorCD -0.2521 0.1584 2.5334 0.1115 0.777
sectorF 0.0505 0.1441 0.123 0.7258 1.052
sectorGHI 0.1057 0.1153 0.8408 0.3592 1.111
sectorJMN 0.1934 0.1317 2.1554 0.1421 1.213
sectorPQ 0.1069 0.1725 0.3837 0.5357 1.113
Female rate
fem_rate<26 (omitted)
fem_rate_26_50 0.1258 0.1 1.5802 0.2087 1.134
fem_rate_51_75 0.2809 0.1506 3.4776 0.0622 1.324
fem_rate_75 0.3793 0.13 8.5131 0.0035 1.461
University rate
univ_rate<26 (omitted)
univ_rate_26_50 0.0976 0.1204 0.6574 0.4175 1.103
univ_rate_51_75 0.2626 0.1881 1.9481 0.1628 1.3
univ_rate_75 -0.0818 0.1374 0.3549 0.5514 0.921
Young rate
young_rate<26
(omitted)
young_rate_26_50 -0.2036 0.1148 3.1415 0.0763 0.816
young_rate_51_75 -0.5244 0.2398 4.7817 0.0288 0.592
young_rate_75 -0.5837 0.2334 6.2533 0.0124 0.558
Old rate
old_rate<26 (omitted)
old_rate_26_50 -0.3545 0.1044 11.5346 0.0007 0.702
old_rate_51_75 -0.1467 0.2079 0.4982 0.4803 0.864
old_rate_75 -0.01 0.1352 0.0055 0.9408 0.99
Ownership (q9) private (omitted)
public 0.0353 0.1651 0.0458 0.8305 1.036
Markets (q11)
Local_market (omitted)
Regional_market 0.0674 0.0913 0.5441 0.4607 1.07
National_market -0.1849 0.1098 2.8381 0.0921 0.831
International_market 0.8358 0.1178 50.3647 <.0001 2.307
* A: Agriculture; C,D: Manufacturing and utilities; F: Construction; G,H,I: Wholesale and retail trade, repair of motor vehicles and motorcycles, Transportation and storage, Accommodation and food service activities; J,M,N: Information and communication, Professional, scientific and technical activities, Administrative and support service activities ; P,Q: Education and human health and social work activities. Q27. Has your workplace taken any steps to improve the proficiency of employees to enable them to carry out the tasks involving ICT use? Note: Parameters statistically not significant are reported in white, those with a statistically significant positive impact in blue, while those with a statistically significant negative impact are reported in light grey. Note: workplaces with fully proficient workers (e.g. without digital skills gaps) are excluded from the analysis Source: Elaboration on European Digital Skills Survey (weighted values)
186
Table A5.8 - Workplaces reporting having taken action to tackle digital skill gaps by type of
action, size and sector, EU28 (% of workplaces with digital skill gaps which undertook actions)
On t
he job t
rain
ing
Exte
rnal tr
ain
ing
Changin
g w
ork
ing p
ractices
Reallocating t
asks
Recru
itin
g n
ew
sta
ff
Hir
ing t
em
pora
ry s
taff
Outs
ourc
ing o
f ta
sks
Secondm
ent
of em
plo
yees
Oth
er
Siz
e
2 to 9 79.7 51.7 45.6 44.4 32.3 21.8 28.5 10.2 11.2
10 to 49 90.2 68.5 53.5 52.0 47.5 29.1 30.3 17.5 11.3
50 to 249 95.8 71.4 55.6 50.0 51.1 30.1 30.8 27.6 11.2
250 + 99.9 63.6 50.6 58.7 88.3 18.2 35.0 11.8 5.6
Secto
r
A. Agriculture 86.9 85.9 85.2 2.3 72.7 83.8 84.0 1.2 1.1
CD.
Manufacturing
and utilities
89.4 63.6 50.8 48.6 50.0 36.1 38.3 16.4 13.4
F. Construction 80.1 56.9 46.7 46.2 40.4 27.5 30.4 19.4 9.8
GHI. Commerce,
transport,
accommodation
and food service
82.6 51.6 44.8 43.1 34.8 19.7 27.3 11.0 11.7
JMN. Information
and
communication;
Professional, scientific and
technical
activities;
Administrative
services
81.4 60.6 52.5 52.2 38.3 28.4 29.3 13.9 12.2
PQ. Education and
human health 93.0 69.9 53.2 50.9 39.9 17.8 24.9 15.5 6.6
Total 84.4 58.3 48.8 47.2 39.0 24.6 29.3 13.8 11.2
Q28. Which of the following steps is your workplace taking to overcome the fact that some of its employees
are not fully proficient in carrying out tasks involving ICT use? (Please select all that apply)
Number of valid responses: 1,486 N=1,476,489
Source: European Digital Skills Survey (weighted values)
187
Table A5.9 - Workplaces reporting difficulties when taking action to tackle digital skill gaps by
type of difficulty encountered, EU28 (%)
Excessiv
e c
ost
of tr
ain
ing
Vacancie
s s
tay o
pen f
or
a long t
ime
Vacancie
s a
re n
ot
filled d
ue t
o lack o
f skills
Modific
ations t
o w
ork
org
anis
ation a
re n
ot
possib
le
Excessiv
e c
ost
of hir
ing t
em
pora
ry s
taff
Excessiv
e c
ost
of
outs
ourc
ing o
f ta
sks
Siz
e
2 to 9 33.0 14.7 13.3 32.7 23.5 20.8
10 to 49 28.3 10.9 12.0 18.0 17.0 16.9
50 to 249 29.1 9.3 13.2 11.2 15.0 11.6
250 + 7.4 5.4 37.1 2.6 2.7 3.1
Secto
r
A. Agriculture 96.6 13.0 12.7 25.3 25.3 25.7
CD. Manufacturing and utilities 28.5 11.4 9.7 22.1 16.4 14.2
F. Construction 47.2 22.4 20.7 26.3 28.7 22.6
GHI. Commerce, transport, accommodation and
food service 25.6 10.9 13.7 30.6 19.7 21.5
JMN. Information and communication;
Professional, scientific and technical activities;
Administrative services
34.0 15.4 13.4 25.6 23.5 16.9
PQ. Education and human health 30.8 9.6 9.3 18.4 16.6 14.7
Total 31.0 13.1 13.2 26.2 20.7 18.7
Q29. Which – if any – of the following difficulties has your workplace encountered when taking steps to
overcome the fact that some employees are not fully proficient in carrying out tasks involving ICT use?
(Please select all that apply)Number of valid responses: 1,486
N=1,469,631
Source: European Digital Skills Survey (weighted values)
188
ANNEX 3. BIBLIOGRAPHIC REFERENCES
Autor D. H. and Handel M. J. (2013). Putting Tasks to the Test: Human Capital, Job
Tasks, and Wages. Journal of Labor Economics, University of Chicago Press, vol.
31(S1), pages S59–S96.
Autor D. H. and Dorn D. (2013). The Growth of Low-Skill Service Jobs and the
Polarization of the US Labor Market. American Economic Review 103(5), pages 1553–
97.
Autor D. H., Katz L. F. and Kearney M. S. (2006). The Polarization of the U.S. Labor
Market. American Economic Review 96(2), pages 189–94.
Autor, D. H., Katz L. F. and Kearney M. S. (2008). Trends in U.S. Wage Inequality:
Revising the Revisionists. Review of Economics and Statistics 90(2), pages 300–23.
Bainbridge S. (2015). In the future, what will people do?, In Dolphin T. (Ed) (2015)
Technology, globalisation and the future of work in Europe. Essays on employment in a
digitised economy. Institute for Public Policy Research.
Behaghel L. et al. (2014). Age-biased Technical and Organisational Change, Training
and Employment Prospects of Older Workers. Economica, 81(322), pages 368-389.
Behangel L. and Greenan N. (2012). Training and Age-biased Technical Change. Annals
of Economics and Statistics/Annales d’Economie et de Statistique (99/100), pages 317-
342.
Berger T. and Frey C. B. (2016). Bridging the skills gap. In Dolphin T. (Ed) (2015)
Technology, globalisation and the future of work in Europe. Essays on employment in a
digitised economy. Institute for Public Policy Research.
BITKOM (2014). Auswirkung der Digitalisierung,
http://www.bitkom.org/files/documents/BITKOM-
Studie_Digitale_Arbeitswelt__Gesamtwirtschaftliche_Effekte.pdf
Brynjolfsson E. and McAfee A. (2011). Race Against the Machine: How the Digital
Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly
Transforming Employment and the Economy. Lexington, Massachusetts: Digital Frontier
Press.
Burning Glass Technologies (2015). Crunched by the Numbers: The Digital Skiills Gap in
the Workforce. http://burning-glass.com/wp-
content/uploads/2015/06/Digital_Skills_Gap.pdf [30 November 2016].
Capgemini Consulting. (2013). The Digital Talent Gap. Developing Skills for Today's
Digital Organizations. https://www.capgemini.com/resource-file-
access/resource/pdf/the_digital_talent_gap27-09_0.pdf [30 November 2016].
Cedefop. (2015). Skills, qualifications and jobs in the EU: the making of a perfect
match? Evidence from Cedefop’s European skills and jobs survey. Luxembourg:
Publication Office of the European Union.
189
Cedefop. (2015a). Skill shortages and gaps in European enterprises. Striking a balance
between vocational education and training and the labour market. Luxembourg:
Publications Office of the European Union.
Cedefop. (2015b). Mitigating over-qualification in the EU. #ESJsurvey INSIGHT No 2.
Thessaloniki.
Cedefop. (2016). EU workforce: overeducated yet underskilled? #ESJsurvey INSIGHT
No 7. Thessaloniki.
CEPIS. (2014). e-Competence in Europe, Analysing Gaps and Mismatches for a stronger
ICT Profession. European Report. http://cepis.org/media/CEB_European_Report1.pdf
[30 November 2016].
Charrié J. and Janin L. (2015). Le numérique: comment réguler une économie sans
frontière? La note d’analyse 35. Paris: France Stratégie.
Comptia (2014). State of the IT Skills Gap 2014.
https://www.comptia.org/resources/state-of-the-it-skills-gap-2014 [30 November
2016].
Danish Technological Institute (2013) Væksttjek. A background analysis for the Danish
Ministry of Business and Growth (internal policy document in preparation of new growth
driver program in support of digital manufacturing in Danish).
Danish Technological Institute. (2014). SMVer i de globale værdikæder - betydningen af
IKT. Internal policy study for the Danish Agency for Commerce and Trade as
contribution to its ICT growth strategy.
Degryse C. (2016). Digitalisation of the economy and its impact on labour markets.
Working paper 2016.02. Brussels: ETUI.
De La Rica S. (2016). Equality of opportunity: Responding to polarisation in Europe’s
labour market. In Dolphin T. (Ed) (2015) Technology, globalisation and the future of
work in Europe. Essays on employment in a digitised economy. Institute for Public
Policy Research.
Di Carlo C., Santarelli E. (2010). Contribution of ICT to economic growth in Italy: Input
Output analysis. Ministry of Economic Development, Department of Communications
http://www.sviluppoeconomico.gov.it/images/stories/pubblicazioni/Contribution_of_ICT
_to_economic_.pdf [30 November 2016].
Digital Europe (2016). Digital Europe and the EC’s Skills Strategy 2016.
http://www.digitaleurope.org/DesktopModules/Bring2mind/DMX/Download.aspx?Comm
and=Core_Download&EntryId=1089&language=en-US&PortalId=0&TabId=353 [30
November 2016].
Dolphin T (ed) (2015) Technology, globalisation and the future of work in Europe:
Essays on employment in a digitised economy. Institute for Public Policy Research.
http://www.ippr.org/publications/technology-globalisation-and-the-future-of-work-in-
europe [30 November 2016].
190
Dustmann C., Ludsteck J., and Schönberg U. (2009). Revisiting the German Wage
Structure. Quarterly Journal of Economics, 124(2), pages 843–81.
Ecorys (2016). Digital Skills for the UK Economy.
https://www.gov.uk/government/publications/digital-skills-for-the-uk-economy [30
November 2016].
Ecorys and Danish Technological Institute (2016) The impact of ICT on job quality:
evidence from 12 job profiles. An intermediate report from the study “ICT for work:
Digital skills in the workplace – SMART 2014 / 0048, Luxembourg, Publications Office of
the European Union
Empirica & IDCGovernment Insights (2009). Monitoring e-Skills demand and supply in
Europe. http://www.pedz.uni-mannheim.de/daten/edz-h/gdb/10/e-
skills_foresight_brochure_en.pdf [30 November 2016].
Empirica. (2014). E-Skills for Jobs in Europe: Measuring Progress and Moving Ahead.
European Commission. http://eskills-monitor2013.eu/results [30 November 2016].
Empirica. (2015). e-Skills in Europe - Trends and Forecasts for the European ICT
Professional and Digital Leadership Labour Markets (2015-2020). empirica Working
Paper. http://eskills-lead.eu/fileadmin/LEAD/Working_Paper_-
_Supply_demand_forecast_2015_a.pdf [30 November 2016].
Eshet-Alkalai, Y. (2004). Digital Literacy. A Conceptual Framework for Survival Skills in
the Digital Era. Journal of Educational Multimedia & Hypermedia, 13(1), pages 93-106.
Eshet-Alkalai, Y., & Chajut, E. (2010). You can teach old dogs new tricks: The factors
that affect changes over time in digital literacy. Journal of Information Technology
Education, 9, pages 173-181.
Eurofound (2010). Self-employed workers: industrial relations and working conditions.
Dublin: Eurofound
Eurofound (2014) New forms of employment. Dublin: Eurofound
Eurofound (2016) Sixth European Working Conditions Survey 2015. Data visualization.
http://www.eurofound.europa.eu/surveys/data-visualisation/sixth-european-working-
conditions-survey-2015 [30 November 2016].
European Commission and Council of the EU (2000). e-Europe 2002 - An Information
Society for All. Action Plan prepared by the Council and the European Commission for
the Feira European Council. Brussels.
European Commission (2003). eLearning: Better eLearning for Europe. Luxembourg:
Office for Official Publications of the European Communities.
European Commission (2007). E-skills for the 21st century: fostering competitiveness,
growth and jobs. COM(2007) 496 final.
European Commission. (2010). Europe's Digital Competitiveness Report. Luxembourg:
Publication Office of the European Union.
European Commission. (2010a). A Digital Agenda for Europe. COM(2010)245 final.
191
European Commission (2014). Measuring Digital Skills across the EU: EU wide
indicators of Digital Competence. https://ec.europa.eu/digital-single-
market/en/news/measuring-digital-skills-across-eu-eu-wide-indicators-digital-
competence [30 November 2016].
European Commission (2016). Europe’s digital progress report 2016.
https://ec.europa.eu/digital-single-market/en/download-scoreboard-reports [30
November 2016].
European Parliament and Council of the EU (2006). Recommendation of the European
Parliament and of the Council of 18 December 2006 on key competences for lifelong
learning. OJ L 394, 30.12.2006
European e-Skills Forum (2004). E-Skills for Europe: Towards 2010 and Beyond.
Synthesis Report.
Ferrari, A. (2012). Digital Competence in Practice: An analysis of Frameworks. JRC
Technical Report, European Commission - Joint Research Centre. Luxembourg:
Publications Office of the European Union
Ferrari, A. (2013). DIGCOMP: A Framework for Developing and Understanding Digital
Competence in Europe. European Commission Joint Research Centre, Institute for
Prospective Technological Studies.
Luxembourg: Publications Office of the European Union
Ferrari, A., Punie, Y., & Redecker, C. (2012). Understanding Digital Competence in the
21st Century: An Analysis of Current Frameworks. In Ravenscroft, Lindstaedt, D. Kloos,
& Hernández-Leo (Ed.), Proceedings 7th European Conference on Technology Enhanced
Learning (pp. 79–92). New York: Springer.
Fraillon, J., Schulz, W., & Ainley, J. (2013). International Computer and Information
Literacy Study- An Assessment Framework. The Australian Council for Educational
Research.
Frey C. B. and Osborne M. (2013). The future of employment: how susceptible are jobs
to computerisation? Oxford Martin School.
http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pd
f [30 November 2016].
Gallardo-Echenique, E., Minelli de Oliveira, J., Marques-Molias, L. and Esteve-Mon, F.
(2015). Digital Competence in the Knowledge Society. MERLOT Journal of Online
Learning and Teaching, 11(1), pages 1-16.
Goos, M., Manning A. and Salomons A. (2009). Job Polarization in Europe. American
Economic Review: Papers & Proceedings 2009, 99(2), pages 58–63.
Goos, M. and Manning A. (2007). Lousy and Lovely Jobs: The Rising Polarization of
Work in Britain. Review of Economics and Statistics 89(1), pages 118–33.
Green, F. and Zhu, Y. (2010). Overqualification, job satisfaction, and increasing
dispersion in the returns to graduate education. Oxford Economic Papers, 62(4), pages
740-763.
192
House of Commons (2016). Digital skills crisis. Second Report of Session 2016-17,
Science and Technology Committee.
Idea Consult; AIAS/UvA; Ecorys; Wifo. (2015). Labour Market Shortags in the European
Union. European Parliament, Policy Department A: Economic and Scientific Policy.
Brussels: European Union.
Ikenaga, T. (2009). Rodo shijo no nikyokuka: IT no donyu to gyomu naiyo no henka ni
tsuite [Polarization of the Japanese Labor Market: The Adoption of IT and Changes in
Task Contents]. The Japanese Journal of Labour Studies 584, pages 73-90.
Ikenaga, T. and Kambayashi R. (2016). Task Polarization in the Japanese Labor Market:
Evidence of a Long-term Trend” Volume 55, pages 267–293.
ILO (2008). Conclusions on Skills for Improved Productivity, Employment Growth and
Development: International Labour Conference. Geneva: International Labour Office.
Ilomaki, L., Kanotsalo, A., & Lakkala, M. (2011). What is digital competence? In Linked
portal. Brussels: European Schoolnet. http://linked.eun.org/web/guest/in-depth3 [30
November 2016].
Kolding, M., Robinson, C., and Ahorlu, M. (2009). Post Crisis: e-Skills are needed to
drive Europe's Innovation Society. White Paper - IDC Opinion, London: IDC EMEA.
Lanvin, B. and Bassman, P. (2008). Building E-skills for the Information Age in Global
Information Technology Report. Geneva: World Economic Forum/INSEAD.
Lavin, B., & Kralik, M. (2009). E-Skills: Who Made That Big Dent in My Flat World?
Information Technologies and International Development, 5(2), pages 81–84.
Livanos, I. and Nunez, I. (2016). Rethinking under-skilling: evidence from the first
Cedefop European Skills and Jobs Survey.
Mañé, F. (2013). Using the job requirements approach and matched employer-
employee data to investigate the content of individuals’ human capital. In: Green, F.;
Keese, M. (eds). Job tasks, work skills and the labour market. Paris: OECD Publishing.
Martin, A. (2006). A European framework for digital literacy. Nordic Journal of Digital
Literacy, 2, pages 151-161.
McGuinness, S., & Ortiz, L. (2016). Skill gaps in the workplace: Measurement,
determinants and impacts. Industrial Relations Journal, 47(3), pages 253-278.
Michaels, G., Natraj A. and Van Reenen J. (2014). Has ICT Polarized Skill Demand?
Evidence from Eleven Countries over Twenty-Five Years. Review of Economics and
Statistics 96(1), pages 60–77.
Mutka, K. A. (2011). Mapping Digital competence- Towards a Conceptual
understanding. IPTS.
OECD (2004). The ICT productivity paradox- evidence from micro data. OECD Economic
Studies No.38. http://www.oecd.org/eco/growth/35028181.pdf [30 November 2016].
OECD (2004b). OECD Information Technology Outlook. Paris: OECD Publishing
193
OECD (2005). The Definition and Selection of Key Competencies. Paris: OECD
Publishing
OECD (2012). Better Skills, Better Jobs, Better Lives: A strategic Approach to skills.
Paris: OECD Publishing.
OECD (2013). OECD Skills Outlook 2013: First Results from the Survey of Adult Skills.
Paris: OECD Publishing.
OECD (2014). Skills and Jobs in the Internet Economy. OECD Digital Economy Papers
No. 242. Paris: OECD Publishing.
OECD (2015) Digital Economy Outlook 2015. Paris: OECD Publishing
OECD (2016). Key ICT Indicators.
http://www.oecd.org/internet/ieconomy/oecdkeyictindicators.htm [30 November 2016].
OECD (2016b). New Skills for the Digital Economy: Measuring the Demand for ICT Skills
at Work. OECD Digital Economy Papers No. 258, Paris: OECD Publishing.
Pellizzari, M., Biagi, F. And Brecko, B. (2015). E-skills Mismatch: Evidence from PIAAC.
Digital Economy Working Paper, Institute for Prospective Technological Studies.
Quintini, G. (2011). Over-qualified or Under-skilled: A Review of Existing Literature.
Paris: OECD Publishing.
Ramboll (2014). Mapping and Analysing Bottleneck Vacancies on the EU Labour
Markets. European Commission.
Spitz-Oener, A. (2006). Technical Change, Job Tasks, and Rising Educational Demands:
Looking Outside the Wage Structure. Journal of Labor Economics 24(2), pages 235–70.
Staglianò R. (2016). Al posto tuo. Così web e robot ci stanno rubando il lavoro [At Your
Place: How Web and Robot are stealing our Jobs]. Einaudi, Torino.
Schwab K. (2016). The Fourth Industrial Revolution: what it means, how to respond.
https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-
means-and-how-to-respond [30 November 2016].
Skills Funding Ageny (2016) Review of publicly funded digital skills qualifications.
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/49903
1/Review_of_Publicly_Funded_Digital_Skills_Qualifications_2016_FINAL.pdf [30
November 2016].
Störmer et al (2014) The Future of Work: Jobs and skills in 2030 . Evidence Report 84.
UK Commission for Employment and Skills.
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/30333
4/er84-the-future-of-work-evidence-report.pdf [30 November 2016].
UK Digital Skills Taskforce. (2014). Digital Skills for tomorrow's world. The independent
report of the UK Digital Skills Taskforce. http://ukforce.org.uk/wp-
content/uploads/2014/07/Digital-Skills-for-Tomorrows-World.pdf [30 November 2016].
194
Valsamis D. et al. (2015) Employments and skills aspects of the Digital Single Market
Strategy. Study for the EMPL Committee, European Parliament. Brussels: European
Union
Van Deursen, A. (2010). Internet Skills. Vital assets in an information society. Thesis.
http://purl.utwente.nl/publications/75133 [30 November 2016].
Van Deursen, A., Helsper, E. and Eynon, R. (2014). Measuring Digital Skills. From
Digital Skills to Tangible Outcomes project report.
www.oii.ox.ac.uk/research/projects/?id=112 [30 November 2016].
Van Deursen, A., Van Dick, J. and Peters, O. (2012). Proposing a Survey Instrument for
Measuring Operational, Formal, Information and Strategic Internet Skills. International
Journal of Human-Computer Interaction, 28(12), pages 827-837.
Vuorikari, R., Punie, Y., Carretero, S. and Van den Brande, L. (2016). DigComp 2.0: The
Digital Competence Framework for Citizens. Update Phase 1: The Conceptual Reference
Model. Joint Research Centre. Luxembourg: Publication Office of the European Union.
WDM Consultants (2011). Defining Essential Digital Skills in the Canadian Workplace:
Final
Report.http://en.copian.ca/library/research/digi_es_can_workplace/digi_es_can_workpl
ace.pdf [30 November 2016].
World Economic Forum (2013) The Global Information Technology Report 2013: Growth
and Jobs in a Hyperconnected World. Geneva: World Economic Forum.
World Economic Forum. (2016). The Future of Jobs. Employment, Skills and Workforce
Strategy for the Fourth Industrial Revolution. Geneva: World Economic Forum.
195
European Commission
ICT for work: Digital skills in the workplace
Luxembourg, Publications Office of the European Union
2017 - 195 pages ISBN 978-92-79-67761-8
doi: 10.2759/498467
196
doi:10.2759/498467 ISBN 978-92-79-67761-8
KK
-04
-17
-35
9-E
N-N