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Does Employee Diversity Lead to Innovation?

Christian R. Østergaard, Kari Kristinsson and Bram Timmermans

DRUID/IKE, Department of Business Studies, Aalborg University

Fibigerstraede 4, 9220 Aalborg Oe, Denmark

First draft

Keywords: Diversity, Innovation, Education, Gender, Cultural background

1 INTRODUCTION

One of the most important challenges and opportunities for firms today is the increasing diversity insociety. The knowledge base becomes more diverse and so does the cultural and ethnic background

among employees. This increasing diversity in the knowledge base increases the need for interaction

and communication within the firms, while increased cultural diversity might lead to conflict. However,

employee diversity might create a broader search space and make the firm more open towards new

ideas and more creative. Ideally, diversity should increase a firm’s knowledge base and increase the

interaction between different types of competences and knowledge. This creates possibilities for new

combinations of knowledge and innovation.

The relation between a diverse composition of the workforce and the performance of firms was

addressed in Penrose’s work from 1959 where she states that:”It is the heterogeneity of the productive

services available or potentially available from its resources that gives each firm its unique character”

An important part of these resources is the firms’ human capital resources (Penrose, 1959; Barney,

1991). These resources have a cognitive dimension, such as vocational training and experience and a

demographic dimension, such as gender, age and cultural background, which affect the application

and combination of existing knowledge and the communication and interaction between different parts

of the firm.

A growing literature is analysing the relation between diversity among top managers and the

performance of firms. The characteristics of the top managers appear to influence growth, productivity

and revenues (Murray, 1989; Wiersema and Bantel, 1992; Pitcher and Smith, 2001). Others have


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studied the effect of diverse top management teams on innovation based on indicators such as age or

tenure, with mixed results (Bantel and Jackson, 1989; O'Reilly and Flatt, 1989; Zajac et al., 1991).

However, innovation is an interactive process that often involves communication and interaction

among employees in a firm and draws on their different qualities from all levels of the organization

(Lundvall, 1985; 1992). In addition the composition of the top management team does not necessarilyreflect the composition of the larger pool of human capital in the company (Laursen et al., 2005).

Therefore it is not sufficient to look at the top management team when analysing the effect of

diversity on innovation. The purpose of this paper is to analyse the effect of employee diversity on the

innovative performance of firms, based on the characteristics of all employees in the firm, using

several indicators such as age, education, nationality and gender.

The empirical analysis will be based on two types of datasets: The first is a questionnaire based

innovation survey (DISKO4) collected in 2006 focusing on organizational and technical change in

more than 1700 Danish firms in the period 2003-2005. This database is merged with register datafrom the second dataset the Integrated Database for Labour Market Research (IDA) that contains

detailed information on all Danish firms and all individuals on the labour market. Therefore it is

possible to link the diverse composition of firms in terms of gender, age, education, and nationality

with the organisational form, strategy and innovative behaviour of these firms. The analysis shows that

employee diversity has an effect on the innovative performance of firms. We find that employee

diversity with respect to gender, education and nationality has a significant positive effect on firms’

likelihood to innovate, while diversity in age has a significant negative effect. We control for factors like

size, industry, competition, organizational change and external cooperation.

The remainder of the paper is structured as follows. Section 2 contains the theoretical discussion on

diversity and innovation. Section 3 describes the questionnaire, data, variables and estimation

technique used in the analysis. Section 4 presents the results of the logistic regressions and Section 5

concludes with a discussion of the results and suggestions for further research.

2 Diversity and innovation

A growing literature is addressing the relation between diversity and performance. The argument is

that the firm’s knowledge, in the form of human capital, is very important in explaining its performanceand that this human capital is affected by diversity in the composition of and interaction between

employees (Laursen et al., 2005). Employee diversity is often measured by individuals’ demographic

attributes that are used as a proxy for different attitudes, knowledge bases and cognitive models. The

individual employees’ knowledge structures are also affected by group membership, social

interactions, and organisation of the firm (Walsh, 1995).

In their review of 40 years of research on demography and diversity in organisations Williams and

O’Reilly (1998) finds that diversity has both direct and indirect effects on processes and performance

of groups, however, some results points towards a positive effect of diversity, while others stress the

negative effect of increased diversity, thus diversity has two potential effects. They argue that the


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difficulties of finding significant positive effects of diversity might stem from differences defining

performance indicators and the lack of separating the creativity (invention) phase from the

implementation (innovation) phase.

Fagerberg (2005)describes the difference between invention and innovation as: “Invention is the

first occurrence of an idea for a new product or process, while innovation is the first attempt to carry it

out into practice" (Fagerberg, 2005, p. 4). Thus innovation is visualised as being a two-stage process

with the idea phase being separate from the implementation phase. This distinction has also been

supported by research within cognitive psychology. West (2002)suggests that the innovation process

should be split up into (1) the creative stage and (2) the innovation implementation stage. Therefore

an innovation often depends on groups of individuals in the organisation. Woodman et al. (1993)

defines organisational innovation as “the creation of a valuable, useful new product, service, idea,

procedure, or process by individuals working together in a complex social system”. It is in the context

of a complex social system in an organisation, where the different types of individual knowledge come

into play to generate new knowledge or ideas. Therefore the composition of individuals within the firm

is an important factor for understanding innovation, since diversity in the composition of a firm’s

employees contributes to diversity in the knowledge base.

Penrose (1959) describes a firm as a collection of productive resources and it are the services

these resources posses that provide the input for the productive processes of a firm. Subsequently,

employee diversity becomes important for the performance of firms, since heterogeneity of these

productive services provide firms with different characteristics (Penrose, 1959). Barney (1991) made a

distinction between three categories of resources a firm would possess and out of which a unique

character could arise. These three categories are: physical capital resources, human capital resources

and organizational capital resources. However, in the knowledge-based economy a firm relies less on

their tangible and more on their intangible resources (Teece et al., 1997).

Whenever services are the input, it is also the combination of services that influence the

performance of a firm. This combination can also occur within human capital resources where the

services (e.g. knowledge, skills and attitude) contribute to a firm’s performance. A combination of

services can only occur when services are different from each other. Diversity among services is thus

required for combination to take place. This has already been recognized by previous research on the

effect of diversity on firm performance.

Previous studies have focused on the effect of the top management on firm performance. The

upper-echelon framework analyses factors that affect the executive leadership’s strategy formation

and subsequently organisational behaviour and performance. Finkelstein and Hambrick (1990) argues

that functional background and demographic characteristics influence the manager’s interpretation of

problems and tenure is related to strategic inertia, however, they find that the characteristics of the full

management team has greater predicting power of firm performance than the top person. Murray

(1989) analysed how heterogeneity in the top management team influence firm performance. As

measures of the heterogeneous composition of top management team he looked at difference in age,tenure, and occupational and educational background based on different educational levels (i.e.


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graduates, undergraduates, doctorates) in several disciplines (e.g. liberal arts, engineering, science,

business, law, etc.). Murray found a positive effect of diversity on firm performance. Kildfuff et al.

(2000)analysed the demographic component of top management team diversity by gender, age and

race. They found that these characteristics gave a more accurate reflection of how much the team

differs in attitude, values and norms. Pitcher and Smith (2001) also analysed the effect of topmanagement team heterogeneity on firm performance. As proxies for heterogeneity they used team

tenure, functional background, industry experience, age and education. Pitcher and Smith found that

heterogeneity was positive for long-term performance, however, it had some limits if the managers

receive orders from the company headquarter.

There have only been a few studies of diversity of top management and innovation performance.

Bantel and Jackson (1989) analysed how the composition of top management teams in the finance

sector affects innovation defined as the number of products, programs and services that firms had

adopted or developed. They analysed diversity by age, tenure, education and functional background

and found a positive relation between educational level, functional background and innovation.

Many studies only look at the diversity in management team. However, innovation is an interactive

process (Lundvall 1985, 1992) and would thus suggest that more employees rather than only the top

management team are involved. Van der Vegt and Janssen (2003)apply a broader perspective and

analyses the effect of task interdependence and heterogeneity in groups on innovative behaviour.

They also argue that innovation is an interactive process, where employees interact in groups and

develop, discuss, modify and realise new ideas, thus diversity in groups should promote innovation

behaviour. However, they find no direct link between group diversity and innovative behaviour when

they controlled for size and organisation of work (flexibility and task non-routineness). Van de Vegt

and Janssen (2003) conclude that diversity in groups is important, but the characteristics of work

organisation are more important. Laursen et al. (2005) takes a broader perspective and analyses the

composition of all the engineers in Danish engineering consulting firms rather than only the top

management team to see how employee diversity affects firm performance. They argue that firm

performance is not only related to levels of human resources, but also to the composition of these

resources. However, too little and too much diversity should have a negative effect, which implies an

inverted curve linear relationship between diversity and performance. They discovered that combining

fundamental different skills leads to a better competitive advantage, besides, they also find somesupport of a curve linear relationship between diversity and performance i.e. a low level of diversity

and a high level of diversity is positive.

In their overview of research on the effect of diversity on performance Williams and O’Reilly (1998)

shows that diversity has an effect on performance, although some have found negative effects and

others a positive effect of diversity. The positive effects relate to openness, creativity, learning,

flexibility, broader search space, better problem solving, increased absorptive capacity1 and new

combinations of knowledge, while the costs of diversity are related to lack of economies of scale in the

1 (Cohen and Levinthal, 1990)


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knowledge production, distrust, conflict, dissatisfaction and increased transaction costs2, since

interaction and communication between two different knowledge bases and groups might be difficult.

Thus, diversity has a positive side and a negative side. However, in their discussion of why they do not

find any negative effects of diversity Bantel and Jackson (1989) argue that: “This may be because the

dysfunctional effects of heterogeneity occur only when extremely high levels of diversity exist, andsuch extreme diversity is less likely among members of top management teams” (Bantel and Jackson,

1989, p 118). Innovation is an interactive process and that diversity among those who interact

promotes the innovation process, since diversity affects the way knowledge is generated and applied

in the innovation process. Thus, employee diversity should generally have a positive effect on

innovation.

Hypothesis 1: Employee diversity has a positive effect on the likelihood that firms innovate

Diversity can be addressed in multiple ways. According to Stirling (2007)diversity consists of

variety, balance and disparity, where variety is the number of groups, balance is the evenness in thedistribution of the groups and disparity is the distance between the groups. Thus, the effect of diversity

depends on the definition of the groups. In addition a company can be diverse in other dimensions

such as diversity in the number and disparity of the services and products the company produces or

diversity in collaboration with external partners and outsourcing of activities.

3 Sample and Data

The quantitative analysis is based on data from the DISKO43 questionnaire survey on

organisations, employees and research and development strategies in Danish firms and the IDA

register database. Innovation is defined as whether the firm has introduced a new product or service

during the period 2003-2005, excluding minor improvements on already existing products and

services. Diversity is analysed at the firm level investigating the composition of the firm’s employees

based on gender, age, nationality and education. Besides these variables there will be included a

number of control variables that might influence the firm’s innovation and performance.

The DISKO (Danish Innovation System: Comparative analyses of challenges, strength and

bottlenecks) questionnaires focus on organisational and technical change in Danish firms and give a

comprehensive picture of factors that are often associated with change and innovation. Over time fourDISKO surveys have been conducted: DISKO1 in 1996, DISKO2 in 2001, DISKO3 in 2004, and

DISKO4 in 20064. The DISKO4 survey data was collected by sending a questionnaire to the

management of a stratified sample of Danish firms with more than 20 employees in 2006, with

emphasis on firms that previously participated in DISKO surveys and large firms with more than 100

fulltime employees. The questionnaire refers to the period 2003-2005 and has been sent to 4136

companies, 1775 answers have been received, which gives a response rate of 42.9 percent.

2

(Williamson, 1987)3 The DISKO4 survey on organisations, employees and research and development strategies in Danish firms was conducted byStatistics Denmark in 2006 on behalf of four research groups (IKE, CARMA, CIP, and CCWS) from Aalborg University.4 For a more thorough explanation of the DISKO research see http://www.business.aau.dk/ike/data.html


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To obtain detailed background information on the firms in the DISKO survey it was merged with

data from the IDA dataset (Integrated Database for Labour Market Research). IDA is maintained by

Statistics Denmark and contains extensive information on the characteristics of individuals, e.g. age,

gender, level of education, work experience and nationality. These are linked with establishments and

employers. The dataset is longitudinal and has been updated annually since 1980. The DISKO4survey referred to 2003-2005, therefore the DISKO4 data was merged with IDA data for November

2002. For weighting our sample we used the total population of firms that were active both in 2002 and

20045. Hotels & Restaurants and Culture & Sports have high values for the weights in the lower size

groups therefore these are omitted in the logistic regression analysis. In addition region dummies are

omitted since the weights for regions where too high. Furthermore, 168 firms with less than 20

employees in 2002 are included in the category 20-29 employees since these firms that have grown in

the period 2002-2006. Table 1 shows the number of firms for each industry and size group in our

sample.

Table 1: The number of firms in the DISKO4 database based on industry and size.

20-49employees

50-99employees

100-249employees

250 or moreemployees

Manufacturing 198 196 137 64

Construction 129 53 24 6

Wholesale & Retail 245 104 40 12

Hotel & Restaurant 14 18 5 1

Transport 64 37 25 9

Finance & Insurance 14 20 19 16

Business Services 127 54 46 23

Culture and Sports 9 11 7 2

Source: Based on data from Statistics Denmark

3.1 Measures

The dependent variable that will be used is whether or not the firm has had one or more

innovations. This variable has been extracted from the DISKO4 survey where the respondent was

asked how many innovation the firm has had in the period 2003-2005 excluding minor improvements

on already existing products and services. The possible choices where zero, one and more than one.

The last two answers have been collapsed into one to make a distinction between innovative and non-

innovative firms.

The independent variables can be categorised in several groups, such as employee diversity

variables, recruitment variables, collaboration, organisation and strategy variables, and dummies for

industry and size. The employee diversity variables age, education and nationality are based on an

entropy index that measures balance and variation.

5 The most recent dataset is from 2004 but the DISKO4 could only be compared with IDA data from 2002.


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∑i si (ln si) = s1 (ln s1) + s2 (ln s2)+… si (ln si)

∑i si = 1

s = share

The age, education and nationality diversity variables have been categorised in respectively four,

eighteen and six groups. The disparity part of diversity is partly included in the definition of the groups

The age groups are: younger than 25, 25- 40, 41– 55, and 56 and older.

Education consists of a wide array of categories. The focus is diversity among employees with a

higher education since earlier DISKO surveys have shown that firms with employees that have a

higher education are more likely to be innovative. The distinction has been made between Bachelor,

Master and Ph.D. degrees in social science, humanistic, food and medicine, engineering, and natural

science. For Bachelor and Masters there is a category added with teachers and army and the last

group is ‘other education’.

In the entropy index for nationality the groups are: Danish, Nordic, EU15 and Swiss, other

Europeans, other western countries, and the rest of the world.

For diversity in the gender composition firms are grouped together by the size of the share of the

highest represented gender in the firm. Basically the entropy values for gender shares are grouped to

make the results easier to interpret. The five groups are:

• Group 1: 90-100 percent of the same gender





The second set of variables are related to the firm’s recruitment strategies and thus indirectly

related to diversity. It is recruitment of new employees that determines how diverse the composition of

a firm is. Diversity Policy is the first variable in this group. This variable indicates whether or not the

firm has an active approach in hiring older and/or foreign employees. Whenever it has this strategy it

will receive the value one, otherwise it will receive the value zero. Having such an approach would be

a proxy of an open mentality towards diversity, hiring foreigners would increase diversity in a firm and

hiring older people as well since often the tendency is to prefer young workers above old. The second

recruitment variable is called Internal. This variable indicates if internal processes are important for

recruitment e.g. hiring a person known by an employee. Having such a strategy would be detrimental

for diversity, since people tend to interact with those that are equal to them. Such a recruitment

strategy would lead to a higher likelihood of hiring individuals similar to those that are already present

in the firm and thus decrease diversity in the firm. This effect is termed homophily in the sociology


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literature (McPherson et al., 2001). Furthermore is this recruitment also a proxy for the openness of

the company.

The third second group of independent variables deals with collaboration, strategy, organisational

change, and competition, which are taken from the DISKO4 survey. Collaboration is divided in five

categories where each group indicates how many types of collaboration partners the firm has had a

high degree of cooperation with in the period 2003-2005. The types of partners asked for are both

national and international customers and suppliers, universities and other institutes of higher

education and consultancy firms. The categories show if the firm had zero, one, two, three, or four or

more collaboration types during that period. This variable measures the external diversity in the

company.

The strategy variable is based on the question if the firm has had a high priority on product, market,

technology, organisational, or business process development. The variable Divstrat1 adds up these

strategies and will have a value varying between zero and five. Whenever there is a high priority inproduct and market development these are collapsed in a market strategy and the other three are

collapsed into a firm/organisation strategy. The Divstrat2 variable is computed by adding these two

together thereby creating a value for Divstrat2 between zero and two.

The independent variable organisational change indicates whether or not the organisation has

implemented a form of organisational change they consider as important. Earlier DISKO studies have

shown that organisational change is important for innovation. The Competition variable is based on in

what degree the firm experienced an increase in competition on the national or the international level.

Possibilities are that: they did not experience an increase, there was a slight increase or there was a

high increase in competition on the international of national level.

The last independent variables are industry and size dummies. The sample has been split up in

eight industries: manufacturing, construction, wholesale and retail, hotel and restaurant, transport,

finance and insurance, business service, and culture and sports. Two industries are not represented in

the final regressions i.e. hotel and restaurants and culture and sports. To correct for the size of the

firm four groups where used: less than 50, between 50 and 100, between 101 and 249, and 250 or

more.

3.2 Descriptive statistics

Table 2 shows distribution of firms that innovated during the period 2003-2005 based on the size of

the firm. The average size of a firm in the sample is 105.26 where the smallest firm has one employee

and the biggest 4041. The table indicate that larger firms have a higher tendency being innovative

compared to smaller firms. The total number of firms that made an innovation in the survey is 910

while 740 that did not innovate.


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Table 2: Did the organisation innovate (by size).

0-49employees

50-99employees

100-249employees

250 or moreemployees

Total

413 197 94 36 74025.03 11.94 5.7 2.18 44.8555.81 26.62 12.7 4.86

no

54.77 41.21 32.75 27.48341 281 193 95 910

20.67 17.03 11.7 5.76 55.1537.47 30.88 21.21 10.44

Yes

45.23 58.79 67.25 72.52754 478 287 131 1650Total

45.7 28.97 17.39 7.94 100.00

Frequency Missing = 79


Table 3 illustrates whether or not the firm has had an innovation by industry. In Manufacturing,

Wholesale & Retail, Finance & Insurance, Business Service, and Culture & Sports there are more

firms that had at least one innovation. The worst performer in this sense is construction where 73.23

percent indicated not to have had an innovation during the period 2003-2005.

Table 3: Did the organisation innovate (by industry).

manufacturing construction wholesale &retail

hotel &restaurant

transport finance &insurance

businessservice

culture &sports

total

220 145 181 21 69 9 84 11 74013.33 8.79 10.97 1.27 4.18 0.55 5.09 0.67 44.8529.73 19.59 24.46 2.84 9.32 1.22 11.35 1.49

no

38.13 73.23 48.14 55.26 55.20 13.04 35.15 39.29

357 53 195 17 56 60 155 17 91021.64 3.21 11.82 1.03 3.39 3.64 9.39 1.03 55.15

39.23 5.82 21.43 1.87 6.15 6.59 17.03 1.87

yes

61.87 26.77 51.86 44.74 44.80 86.96 64.85 60.71577 198 376 38 125 69 239 28 1650Total34.97 12.00 22.79 2.30 7.58 4.18 14.48 1.70 100



Table 4 indicates if the firm has incorporated what they consider important organisational change.

Within the sample there are many firms that conducted organisational change (1101 firms). Table 5

illustrates that most firms in the sample experienced an increase in competition (92.2 percent). The

major part of these firms thought that the increase they experienced was heavy.


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Table 4: Did the firms conduct organisational change.

Frequency Percent Cumulative Frequency Cumulative Percent

no 628 36.32 628 36.32

yes 1101 63.68 1729 100.00


Table 5: Increased competition (national or international).


no increase 133 7.8 133 7.8slightly increase 447 26.22 580 34.02heavy increase 1125 65.98 1705 100.00



For the external diversity based on collaboration the number of types of collaboration partners has

been measured by adding up national suppliers, national customers, international suppliers,

international customers, university and other institutes of higher education, and consultancies. Table 6

shows how many firms have had how many different types of collaboration partners.

Table 6: Number of high degree collaboration partners.


0 403 23.31 403 23.311 301 17.41 704 40.722 531 30.71 1235 71.433 301 17.41 1536 88.844 or more 193 11.16 1729 100.00


The strategies a firm executed are illustrated in Table 7 and Table 8. Table 7 shows the number of

high priority development strategies without indicating which strategies are included. The spread

between the numbers of strategies is fairly equal as the percentages indicate.

Table 7: Number of high priority development st rategies (divstrat1).


0 208 12.03 208 12.031 228 13.19 436 25.22

2 372 21.52 808 46.733 344 19.90 1152 66.634 294 17.00 1446 83.635 283 16.37 1729 100.00


Table 8 shows how many firms have a market and/or organisation strategy. Most of the firms have

at least one of the two.


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Table 8: Does the firm have a market and or firm/organisation strategy (divstrat2).


Table 9 shows descriptive statistics of the diversity measures. The average of gender group is

slightly above 3, that is, the average distribution of gender is in the 70-80 percent group. For the age

entropy value, the average value is high compared to the potential maximum value. This illustrates

that there is a rather equal distribution of age groups within Danish firms. This is not the case for

nationality since there is, obviously, a high representation of one nationality present (the Danes). The

maximum entropy value also indicates that in this sample the maximum, which is an equal share of all

nationality groups, is not reached. Theoretically the maximum value would have been 1.7918.

Education also has a relative low average entropy value and just as in nationality the maximum is not

present in the sample. Theoretical, the maximum value is 2.8904. The skewness in the distribution of

education might be explained by the fact that the categories are only based on higher educated

persons. In addition there are many categories thus having an equal share of all levels in all

disciplines is almost impossible to achieve. Table 10 provides additional information on the distribution

of the gender groups. There is a rather equal distribution of these gender groups in sample of Danish

firms.

Table 9: Descript ive statistics for the diversity measures.

Observations Mean Std Dev Minimum Maximum

Gender groups 1729 3.01215 1.46027 1 5

Age Entropy 1729 1.08942 0.18303 0 1.38629

Nationality Entropy 1729 0.12671 0.16917 0 1.20054Education Entropy 1729 0.45358 0.43243 0 2.16912


Table 10: Distribution of gender groups in Danish firms.

Frequencies Percent Cumulative Frequency Cumulative PercentGender group 1 375 21.69 375 21.69

Gender group 2 334 19.32 709 41.01

Gender group 3 288 16.66 997 57.66Gender group 4 359 20.76 1356 78.43Gender group 5 373 21.57 1729 100.00

Source: Based on data Statistics Denmark

Table 11 shows the number of firms with a diversity policy i.e. whether the firm has an active

approach for hiring older and/or foreigners. Almost one out of five firms have such an approach. Table

12 illustrates the number of firms that make use of internal processes or recruiting new personal 27

percent of the firms indicate that they have such an approach.


No 490 28.34 490 28.34yes one of them 665 38.46 1155 66.80Both 574 33.20 1729 100.00


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Table 11: Number of firms with an active approach on hiring older and/or foreigners.

Frequencies Percent Cumulative Frequency Cumulative Percent

No 1411 81.61 1411 81.61 At least one 318 18.39 1729 100.00


Table 12: Internal processes for recruiting new personnel.

Frequencies Percent Cumulative Frequency Cumulative Percent

no 1258 72.76 1258 72.76yes 471 27.24 1729 100.00


Table 13 presents a correlation matrix of the independent variables.


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Table 13: Correlation matrix of the independent variables.

1 2 3 4 5 6 7 8 9 10

1 Size

2 OrganisationalChange

0.234 ***

3 Competition 0.124 *** 0.115 ***

4 Gender Group 0.199 *** 0.178 *** 0.072 ***

5 Age Entropy 0.045 -0.078 *** -0.017 -0.135 ***

6 Nation Entropy 0.209 *** 0.084 *** 0.014 0.154 *** -0.064 ***

7 EducationEntropy

0.311 *** 0.193 *** 0.067 *** 0.285 *** -0.238 *** 0.118 ***

8 Divstrat 1 0.117 *** 0.138 *** 0.183 *** 0.091 *** 0.011 0.067 *** 0.094 ***

9 Divstrat 2 0.197 *** 0.222 *** 0.112 *** 0.171 *** -0.091 *** 0.071 *** 0.105 *** 0.166 ***

10 Diversity Policy 0.128 *** 0.061 ** 0.007 0.035 -0.026 0.112 *** 0.071 *** 0.063 *** 0.115 ***

11 Internal -0.010 0.049 ** 0.032 -0.031 -0.069 *** 0.026 -0.019 0.081 *** 0.067 *** 0.048 **

*** Significant at the 1% level. ** Significant at the 5% level. *Significant at the 10% level.


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4 Results

The effect of employee diversity on innovation are analysed in four different logistic regression

models. For each model different variables are added to see how they affect the likelihood for having

an innovation in the firm. Model 1 is the most basic model on which the other models will be build on.Each model will be discussed separately including the effects the variables have on innovation. Table

14 shows the results.

Model 1. This model analyses the likelihood that a firm has innovated based on the diversity

measures for education, age, nationality and gender and organisational change and there will be

controlled for the industry and the size of the firm. The likelihood ratio for the model is highly

significant.

The entropy value for education has a significant (P< 0.001) and positive effect (0.5111) on the

firms’ likelihood of having an innovation. This means that a higher degree of diversity in education

leads to a higher likelihood of having an innovation6.

Diversity in Age has a significant (P


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Organisational change has a positive (0.4797) and significant effect on the likelihood of innovation.

The odds ratio measure shows 160.1 percent higher odds of innovating whenever the firm has

implemented organisational change compared to firms that have not implemented organisational

change. The industry effect is in all cases significant. Many of the industries have a negative

coefficient since manufacturing is the benchmark and it is an innovative industry compared to theothers. The only industry that has higher odds of innovating is business services. The benchmark for

the firm size variable is firms with less than 50 employees. The other firms are not significantly

different regarding having an innovation. Firm size 3, 101-250 employees, is significantly positive. The

effect is, however, weak (P


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compared to gender group 1 decreases from 21.7 percent in model 2 to 3.7 percent. For gender group

4 this decrease is from just over 70 percent to 54.4 percent and in the case of gender group 5 the

decrease is from 114.3 percent to 87 percent. The assumption that a more equal distribution of gender

leads to having a higher likelihood to innovate is still valid although the effect is decreasing by adding

the strategy and collaboration variables.

Model 4. In this last model the two recruitment variables (diversity policy and recruitment) are

added. Diversity policy is an indicator for whether or not the firm has an active policy in hiring older

people and/or foreigners. The effect of such a policy on having an innovation is positive (0.26) and

significant. The odds ratio also indicates that a firm that has such a policy has 68.2 percent higher

odds of having an innovation compared to a firm that does not have such a policy. The other

recruitment variable Internal shows that recruiting based on internal recommendation seems to have a

significant negative effect (-0.0778) on having an innovation. Having such an approach to recruitment

compared to not having it will result in 14.4 percent lower odds of having an innovation. Employee

diversity in age still has a significant negative effect on the likelihood of innovation, while diversity in

education, nationality and gender groups 4 and 5 remain significant and positive.


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5 Effects of employee diversity on innovation

The results of the logistic regressions show an effect of employee diversity on the likelihood that

firms innovate. A high entropy value in education has a positive effect on the likelihood of having an

innovation. There are two types of dynamics that influence this likelihood. Different education types, ora more balance in the education types a firm possesses would increase the likelihood of having an

innovation. There is a bias in the education diversity measure, since it measures diversity within the

highly educated group, meaning the employees with a bachelor degree or higher. All employees with a

degree below bachelor are put in a single category. As a result a higher entropy value can be

explained by having a larger share of employees with a higher education and multiple types of higher

educated people. Having a higher educated employee alone would be positive for innovation

performance, having more different types would increase the likelihood.

The diversity in age within a firm has a negative effect on the likelihood of having an innovation.

Meaning that having only one age group would have a higher effect. As in the case of education this

value does not determine if there is a certain age group that is better or worse to have. However, the

distribution of all age groups is rather equally spread in the firms and the average entropy value is

subsequently rather high. Before using this categorisation of age groups we tried some other

groupings of ages and average age, but this did not change the result, which means that the measure

is rather robust.

Nationality, just like education, appears to have a positive effect on the likelihood of having an

innovation. For a firm it therefore seems to be positive to have more nationalities present in order to

increase the likelihood of having an innovation compared to firms that only have Danes working for

them. Obviously, there is a high share of Danes among the employees and it is hard to reach a

balance between nationalities. Thus the entropy value for nationality is on average very low.

Diversity within gender is defined that the more balance there is between men and woman in the

firm the more diversity there is. The odds of having an innovation increases when more balance is

created 7.1 percent in gender group 2 (80-90 percent of one gender) and 90 percent in gender group

5 (50-60 percent of one gender) compared to gender group 1 (90-100 percent of one gender). Thus it

shows that being more diverse in the composition of gender increases the likelihood of having an

innovation compared to firms with a 90-100 percent gender composition. What also needs to be noted

in the case of diversity in gender is that a high shift in the percentage leads to a high increase in the

odds of having an innovation.

The diversity policy variable would indicate that a firm that has an active approach of wanting to

create diversity within the firm. Actively working on hiring foreigners and/ or older people can be used

as a proxy for a firm with an open culture towards diversity. Having such a policy shows a positive

effect on the likelihood of having an innovation, the odds of having an innovation are 68.2 percent

higher compared to not having it. Putting much emphasis on internal processes for recruitment shows

a negative effect on the likelihood of having an innovation. The reason why this is included in thediversity measures is that people interact with people that are a lot like themselves. So, whenever an


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employee recommends a person there is a larger chance that you hire individuals that are similar to

the ones that are already present, thus lowering the diversity in the firm.

6 Conclusion

Previous studies have focused on the effect of diversity of top management teams on firm

performance. However, the diversity in the composition of these teams might be a poor proxy for the

effect of employee diversity on innovation, since the innovation process involves interaction between

several employees at various levels in the firm. Therefore it is necessary to look at the broader

composition of skills and knowledge in the company. The purpose of this paper is to analyse the effect

of employee diversity on the innovative performance of firms.

Employee diversity in terms of gender, age, education and nationality has an effect on the likelihood

that firms innovate with controls for other factors such as size, industry, external cooperation,

organisational change and competition. Firms with more balanced gender composition are more likely

to innovate compared to firms with high concentration in one gender, likewise are firms with diversity

in nationalities more likely to be innovative. Firms with a higher share of employees with a higher

education and diversity in the types of educations have a higher likelihood of innovating. However,

diversity in age composition appears to be negative, although most firms have employees in all age

groups.

Employee diversity has definitely an effect on innovation, and in most cases this effect is also

positive. However, although innovation and diversity is an interesting field one should not forget that

innovation is only consequential. Eventually this innovation should lead to better performance of firms

in for example profit, turnover, growth, survival etc. Therefore it would be interesting to put diversity

along these performance measures. Before making any strong policy recommendations with regards

to diversity and innovation we would like to stress to make a more thorough analysis. The analysis is a

cross section analysis based on all industries in Denmark, but a longitudinal analysis on the industry

level e.g. a distinction between knowledge intensive and less knowledge intensive industries would

provide a stronger analysis, since the eventual reason to look at diversity is to see what the effect of a

diversified knowledge pool is on the performance of a firm.

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