1

ACCELERATING THE SPREAD OF CAPABILITIES FOR INNOVATION IN COLOMBIAN FIRMS THROUGH THE USE OF

AN OPEN INNOVATION POLICY FRAMEWORK

March 15, 2013 -DRAFT -

Rafael Vesga Professor, Universidad de los Andes

School of Management rvesga@uniandes.edu.co

INTRODUCTION

Latin American countries, and Colombia in particular, are struggling to find

ways to accelerate the application of modern innovation management strategies by

firms in the private sector. Available indicators of innovation activity by firms show

very low levels. For example, in a large-sample innovation survey in Colombia, only

0.6% of manufacturing firms reported that they developed a new product for the

international markets in 2009-2010 (DANE, 2012). The Colombian government has

earmarked substantial resources for the purpose of increasing innovation in business

organizations, to the extent that a Constitutional amendment was passed in order to

ensure that 10% of the royalties that oil companies pay to the Colombian State are

assigned to enhancing innovation. There is vast work to do ahead to ensure that this

money is put to productive use and the goals are attained.

Colombia enjoys a short window of opportunity to achieve these goals. At the

present time, the country has a favorable position in its balance of payments and can

use these substantial resources to invest in transforming its own future. However,

positive circumstances should not last forever. Sooner or later, the low end of the

commodity price cycle will come back and oil incomes will fall. If Colombia does not

take advantage of the present conditions, dire consequences may become apparent

after only a few years. If Colombian policy makers and business leaders do not figure

2

out how to accelerate a structural shift from commodities towards innovative, high

value products and services, history will repeat itself.

However, appropriation of modern innovation capabilities, processes, and

practices by Colombian firms is low. In recent years, following President Juan Manuel

Santos’ call to make innovation one of the major engines of economic growth, interest

in adopting modern innovation techniques has spread among business managers.

Several leading international thinkers in the field of innovation have visited the country

and engaged in conversations with the business community, explaining the basic

principles of that rule the creation of innovation capabilities in a modern economy.

Many firms have expressed their intention to apply such modern techniques in order

to accelerate change within their organizations. The government, in turn, has

strengthened the public agencies charged with providing support for this task.

However, the fact remains that major change is needed to accelerate the evolution of

capabilities for innovation in Colombian firms.

This essay presents the idea that public policy for innovation in Colombia is

restrained by a limited perspective of the problem. The conceptualization of the

innovation process of private firms is limited and highly influenced by the linear

progression view, where research and development precedes the ideation of

technology, which precedes the launch of innovative products in the market. This

problem of a limited conceptualization of innovation, which is not typical only of

Colombia, does not mean that policy makers actually believe that this linear sequence

happens, or should happen, in reality. In fact, it is widely accepted that the linear

paradigm is obsolete. The problem is that there are quite few alternative models

available for policy makers to use when they set out to achieve the objective of

stimulating innovation in private sector companies. Moreover, since the key metrics in

use to frame the problem of innovation in the policy discussion were developed under

the linear paradigm, the discussion itself ends up being defined by the limitations of

the linear model. Even if policy makers in Latin America and Colombia recognize that

innovation is a systemic problem, the metrics, still locked in the linear paradigm, point

to R&D as the essential tenet of innovation. The metrics frame the discussion and

make it very hard to have a dialogue about innovation policies outside of these limits.

3

A brief consideration of the role of metrics illustrates the point. For example, a

recent review of innovation activity in Latin America by the Inter American

Development Bank (Navarro, Zuñiga, 2011) focused on the backwardness of the

innovation performance of Latin American firms in comparison to those of other

regions of the world. The authors explicitly argue that the linear model is not a good

expression of true innovation dynamics. However, the empirical discussion is focused

on differences in R&D, availability of Ph.D. graduates, and other indicators that are

typical of the linear paradigm. Clearly, this happens because there are no other metrics

available for the authors to gauge progress of private firms towards innovation. Thus,

the state of the art is an analysis where R&D is of paramount importance for examining

innovation in a region where, given the key sectors in the economic structure, R&D is

quite limited. The focus on this kind of metrics is also typical of the innovation

indicators used by the OECD. Although this organization has done substantial efforts

recently to introduce new indicators that reflect other views on innovation (OECD,

2012) such indicators are far from mainstream use.

Meanwhile, business leaders who adopt modern innovation management

techniques set their priorities using very different metrics. Managers talk about the

composition of their innovation portfolios, the contribution to the bottom line of

products launched over the previous 18 to 36 months, the number of ideas in the

pipeline, and so on. Very few business leaders in Latin America, in very few sectors,

have high priorities for R&D, although they may have ambitious innovation goals in

terms of new products, services and business models. In a region where services and

natural resources sectors contribute more than 70% of GDP in several countries,

business people find hard to digest the notion that in order to achieve high aspirations

in innovation they need to focus on R&D.

This creates a substantial gap in the dialogue between policy makers and

business leaders. While the policy discussion remains adhered to linear paradigm

metrics, private sector firms have switched their attention to different ways of

understanding the problem of innovation and to a growing variety of metrics. As a

consequence, what is measured in the policy sphere is of low importance to most

business leaders. Also, what is important for business leaders is difficult to measure at

4

a meso and macro levels. Thus, there are substantial transaction costs in the

interaction between firms and policy makers, which reduce the impact of policy

efforts. This affects the quality of the public-private dialogue and weakens the ability

to make effective progress in the matter.

This is not an academic discussion about intellectual paradigms. It is clear that

accelerating the appropriation of knowledge about innovation management practices

and processes is urgent in Latin America and Colombia. The fact that the discussion

about policy instruments remains confined in an R&D paradigm makes it harder to

accelerate the appropriation of innovation management techniques and tools across

firms. This is truly a gap between distant mental models used by different people who

actually seek the same objectives.

If innovation is not synonymous to R&D for most managers, then, how do they

approach the issue? Innovation is not just a process but a capability, a combination of

routines and systems that need to work work harmoniously within the firm and allow

it to achieve competitive advantage in the market through the generation of distinctive

products and services, creating value for customers through novel combinations of

attributes. Innovation is not a linear process and demands several contradictory

abilities from the firm. Deploying a capability for innovation requires that firms engage

in processes geared towards exploring latent customer needs and designing products

that will only be available in the future. At the same time, firms need to keep a focus

on execution processes, oriented at extracting value from the businesses that they

exploit in the present. Scientific and technological innovation is only a part of this

panorama and is more important in some firms than in others, depending on the

sectors in which they compete. Only some firms care about R&D, but all firms should

care about developing capabilities for innovation and overcoming the tensions

between exploration and exploitation (March, 1991).

This essay sets forth the idea that the adoption of an Open Innovation

framework for policy making could provide a substantial contribution to bridging the

gaps that today separate policy makers and business leaders on these issues. Within an

Open Innovation framework it would be possible to develop a common language and

useful metrics for innovation public policy and business firms. Such an environment

5

would be more effective in helping firms overcome the barriers to the absorption of

modern innovation management tools and techniques.

The Open Innovation framework offers several advantages in dealing with the

problems described above. Open Innovation is “the use of purposive inflows and

outflows of knowledge to accelerate internal innovation, and expand the markets for

external use of innovation, respectively” (Chesbrough, 2006). The focus is on the flows

of knowledge to and from the organization. This concept can be applied to innovation

as defined in different ways, so it is relevant for efforts focused on R&D and also for

initiatives that deal with product and service innovation.

The Open Innovation framework also offers a way out of the “black box”

problem, where the inner workings of private firms are unknowable, or beyond the

scope of analysis, for policy makers. Policy makers tend to understand firms as

monolithic entities. However, firms are anything but monolithic. In fact, they are

constellations of resources and individuals that operate at three levels at the same

time: the firm, the group and the individual. Innovation initiatives and strategies within

firms proceed as systemic affairs which will fail if the weakest link in the chain fails. The

weakest link may reside at the firm, group, or individual level. There is no reason why

public policy should not acknowledge this fact and help firms learn from the

experiences of each other at the different levels. It is critical that both policy makers

and firm leaders are both able to understand this and act accordingly, in order to

analyze weak links and find solutions.

For firms, the appropriation of capabilities for innovation is essentially a

problem of organizational learning, that is, it can be understood and solved only

through the appreciation of past experience and the incorporation of new routines,

overcoming inertia and barriers to change. For policy makers, engaging firms in a

routine of sharing information about these processes and applying lessons is quite

difficult, not only because of confidentiality concerns, but because there is quite little

in terms of a shared language that could help firms codify what they are doing. Within

an Open Innovation policy framework, this common language and the associated

metrics could be developed in a concerted effort by private and public actors.

6

This essay is organized as follows. The first section is this introduction. The

second section presents some evidence showing that the level of absorption of

innovation practices and techniques by Colombian firms is low, although they show

high rates of adoption of managerial techniques geared at the replication of processes.

The third section describes the gaps between the mental models, frameworks and

metrics used by policy makers and business people to deal with innovation and shows

how the absorption of innovation management techniques and tools is a problem of

organizational learning for firms. The fourth section presents an argument favoring the

adoption of the Open Innovation model as a general framework for analyzing and

acting on initiatives to accelerate the development of innovation capabilities by firms

in Colombia.

ABSORPTION OF INNOVATION PRACTICES BY COLOMBIAN FIRMS

The development of innovation management and processes in firms depends

crucially on the ability to assimilate and replicate new knowledge gained from external

sources. Any firm that desires to create competitive new products and services in a

systematic, scalable way needs to open up to knowledge and information coming from

the environment and held by customers, competitors, suppliers, partners and the like.

The level and quality of absorptive capacities are defining characteristics of any firm,

since they are developed over time in a path dependent process, where new abilities

depend on the level and quality of previous abilities; thus, absorptive capacities speak

of the history and achievement of a firm. They are crucial in explaining why some

companies are systematically superior to others in understanding customer needs and

creating winning responses in the form of innovative product and services (Cohen &

Levinthal, 1990).

There is limited available evidence on the level and quality of absorptive

capabilities in Colombian firms. Few standard measures aimed at this purpose are

calculated with any regularity or depth.

7

A first step in approaching the problem is to consider the output of absorptive

capabilities, that is, the extent to which Colombian firms are able to develop

innovative products and present them to customers in the market. Information from a

manufacturing survey on innovation and technological development in Colombian

firms for the years 2009-2010 shows that the performance of Colombian firms in this

area is weak (DANE, 2012). According to this survey, only 0.6% of Colombian

manufacturing firms were classified as innovators “in a strict sense”, a term that

describes firms which developed a new product or service for the international market

in the period. According to the same source, 33.8% of Colombian firms can be

classified as innovators “in a wide sense”, which means that they developed at least

one new product for the domestic market during the period. At the same time, a full

60.5% of manufacturing firms were classified as not innovative, meaning that they did

not develop, and were not working to develop, a new product for any market (see

Table 1).

Table 1 Colombia: Distribution of Manufacturing firms according to

Innovation Performance (% of total)

Source: DANE, 2012

The low proportion of firms working to develop products that can be

considered as new in the international market is particularly worrisome. This

percentage fell from a high of 11.8% in 2005-2006, to 4,6% in 2007-2008, to below 1%

in 2009-2010. Several macro factors may explain this falling performance, including the

fact that the Colombian economy has gone through a number of years of continued

appreciation of the domestic currency, which have pushed firms in the manufacturing

sector to a difficult position in terms of their international competitiveness. This may

2009-2010 2007-2008 2005-2006 2003-2004

Innovators in a "Strict Sense" 0.6 4.6 11.8 2.3

Innovatoris, "Widely Understood" 33.8 33.2 21.9 24.5

Potenitial Innovators 5.1 5.3 9.2 21.2

Not Innovators 60.6 56.8 57.1 52.0

100.0 100.0 100.0 100.0

8

help explain why the interest in creating new products for the international markets is

decreasing among Colombian manufacturing firms. At any rate, these results can be

interpreted as evidence of weakening absorptive capabilities in Colombian

manufacturing. The ability to use knowledge for the purpose of developing products

that can compete in demanding international arenas, through innovative value

propositions, is waning not strengthening with time.

Another approach to the issue of absorptive capabilities in manufacturing firms

is provided on Table 2. Firms in the same survey are asked to report if they collaborate

with others in attaining objectives in different areas. This act of collaboration can be

understood as a proxy for a deliberate policy to enhance the flows of knowledge in and

out of the firm. The percentages of firms declaring that they cooperate with others are

quite low. The highest percentages refer to the management of machinery and

equipment, where 17,2% of the surveyed firms have cooperated with suppliers and

11,8% say that they have held relationships with organizations that offer technical

assistance. Beyond this, the indicators of collaboration presented in Table 2 indicate

that business leaders give a low priority to this kind of effort.

Table 2

Colombia: engagement by firms in cooperation with other organization for developing Science, Technology and Innovation projects

(Percentage of firms that report participating in collaborations with other firms)

Source: DANE, 2012

A third perspective on the problem of absorption can be obtained by using

information from the Global Innovation Index for 2012, or GII-2012 (Dutra, 2012). The

index combines information from a wide variety of sources. These variables are used

for identifying statistical factors that are related to innovation performance. The GII-

Other Firms Suppliers Clients Competitors Consultants Universities Technology

Development

Centers

Research

Centers

Technology

Parks

Regional

Productivity

Centers

International

Organizations

Research & Development 5.0 7.3 8.2 1.3 5.4 6.0 1.5 1.2 0.5 0.6 1.0

Machinery & Equipment 3.6 17.2 2.7 1.1 2.5 0.5 0.3 0.1 0.1 0.1 0.4

Information and Telecommunications Technologies2.6 5.8 2.1 0.5 3.2 1.0 0.4 0.2 0.3 0.2 0.2

Marketing of Innovations 2.7 4.5 9.0 1.6 3.0 1.4 0.3 0.1 0.2 0.3 0.4

Technology Transfer 3.0 3.8 1.6 0.9 2.3 1.2 0.5 0.2 0.3 0.2 0.5

Technical Assistance 4.2 11.8 5.1 0.9 9.8 4.1 1.2 0.7 0.3 0.6 1.1

Engineering and Design 2.3 5.0 3.2 0.6 3.0 2.1 0.4 0.2 0.1 0.3 0.3

Training 3.1 6.3 2.9 0.4 5.5 4.0 1.0 0.3 0.3 0.4 0.9

9

2012 uses data generated by authorized statistical sources in each country, and also

information gathered through a survey of business people in the participating

countries.

Table 3 presents some of these measures. While some of the variables express

absorptive capabilities in an obvious way, others need to be understood as proxies for

underlying absorptive capabilities.

By ordering the variables according to the percentile rank in which Colombian

firms are located in the general classification among the 141 countries under

examination, a revealing perspective begins to emerge.

Table 3A

Colombia: Ranking in variables related to knowledge absorption by firms. Variables ranked above the country’s general position

Global Innovation Index 2012

Source: Dutra (2012)

Rank Percentile

Global Innovation Index - Colombia 65

Firms offering formal training 8 0.93

High-tech imports 13 0.90

Creative services exports 20 0.83

ISO 9001 quality certificates 26 0.82

Foreign direct investment net outflows 23 0.81

Country-code top level domains (ccTLDs) 37 0.74

State of cluster development 38 0.72

ICT and business model creation 38 0.72

University/industry research collaboration 40 0.70

Generic top level domains (gTLDs) 44 0.69

ICT and organizational models creation 46 0.66

Computer and communications service imports 64 0.53

Growth rate of GDP per person engaged 55 0.53

Foreign direct investment net inflows 69 0.51

Creative goods exports 66 0.51

10

Table 3B

Colombia: Ranking in variables related to knowledge absorption by firms. Variables ranked below the country’s general position

Global Innovation Index 2012

Source: Dutra (2012)

Colombia was ranked in the 65th position in the general classification according

to the GII-2012. Table 3A and 3B show a number of selected variables used in the

index. Table 3A shows the variables in which Colombia´s ranking is higher than the

position obtained by the country in the general classification. These variables are

interpreted here as making a positive contribution to the country’s overall position.

Table 3B shows the variables in which Colombia obtained a ranking that is lower than

the position achieved by the country in the general classification. These variables are

interpreted to contribute negatively to the overall position. Thus, variables on Table 3A

are pushing Colombia forward in the general effort to attain higher capabilities for

absorbing and applying knowledge, while variables on Table 3B are acting as brakes in

this process.

Rank Percentile

Global Innovation Index - Colombia 65

Wikipedia monthly edits 64 0.50

Video uploads on YouTube 70 0.50

Joint venture / strategic alliance deals 74 0.48

Royalty and license fees payments 65 0.45

Royalty and license fees receipts 59 0.45

Recreation and culture consumption 56 0.44

Patent Cooperation Treaty applications 64 0.42

Employment in knowledge-intensive services 62 0.41

National office trademark registrations 52 0.41

New business density 61 0.40

National office utility model applications 39 0.38

Computer and communications service exports 83 0.38

High-tech exports 77 0.37

Total computer software spending 63 0.35

Daily newspapers circulation 90 0.34

GERD financed by abroad 63 0.32

Scientific and technical journal articles 98 0.30

GERD performed by business enterprise 66 0.26

GERD financed by business enterprise 67 0.26

National feature films produced 79 0.21

National office patent applications 91 0.17

Share of patents with foreign inventor 102 0.00

11

Table 3A show that Colombian firms rank fairly high in some variables that are

clearly related to the ability to seek and absorb information from the environment. For

example, Colombia appears in the 0.93 percentile rank among 141 countries in the

sample regarding the importance that private firms give to providing formal training

for their employees. In other words, only 7% of the countries in the sample showed a

better performance than Colombia in this variable.

Other indicators show a distinctively positive performance of Colombia in

certain topics related to knowledge absorption by firms. For example, Colombia ranks

above 83% of countries in the sample regarding the performance of creative

industries. Quite significantly, Colombian firms rank above 82% of the countries in the

sample in terms of their ability to obtain ISO 9001 quality certificates. According to the

executives surveyed by the creators of the GII-2012, Colombia also ranks high in terms

of the use of information and communication technologies (ITC) in the development of

new business models, in the state of cluster creation (above 72% of the countries in

the sample in both cases), and in university-industry collaboration (above 70% of the

countries in the sample). All these indicators point to the fact that there must be a

significant number of Colombian firms which are routinely engaged in creating and

strengthening knowledge-based relationships with their environment. Taken as a

whole, these indicators show a country that should be performing in the higher ranks

of the distribution in terms of absorption capabilities.

However, these indicators refer to an ability to absorb knowledge, not

necessarily an ability to produce new knowledge and innovate. Other indicators in the

list (Table 3B) show that Colombian firms are not performing quite well according to

this last perspective.

The GII-2012 incorporates several variables that refer to R&D. The performance

of Colombia in these variables is generally poor. For example, when it comes to

domestic patent applications, Colombia is only above 17% of countries that lay at the

tail of the distribution. Regarding R&D financed and performed by private firms,

Colombia is superior only to 26% of countries at the bottom. In high tech exports,

Colombia’s ranking is somewhat higher, standing before 37% of the countries in the

distribution, which is itself interesting, given that the country’s performance is so poor

12

regarding R&D (other capabilities must be in action around these exports, allowing the

country to achieve a ranking that is significantly above that of R&D).

It is true that R&D should not be regarded as the only or ultimate metric for

innovation. However, other variables that indicate high-level innovation capabilities

show similar results. With regards to employment in knowledge-intensive services, the

country is above 41% of the countries in the distribution. In royalty and license fees

receipts, Colombia is above 45% of the countries in the distribution, while in joint

venture and strategic alliance deals it ranks above 48% of the countries in the list.

Each of these indicators is somewhat isolated and partial. They were compiled

by the authors of the GII with the objective of offering a general view of business

sophistication and the ability to assimilate and use knowledge by countries in the

sample. Therefore, these indicators cannot be interpreted as conclusive evidence in

any way, but rather as an exploratory effort to shed light into a very complex matter.

Having said this, it is also true that a picture of Colombia as a country that is

located at the crossroads of contradictory forces begins to emerge. In brief, the

behavior of some of these indicators reveal a productive sector that is interested in

reaching up to international standards in order to be competitive. This is the case of

the extended adoption of ISO 9001 certificates, or the penetration of formal training

for individuals at work. However, these efforts are not enough to counteract the

strength of other indicators in which Colombia is ranked at low levels, such as

employment in knowledge intensive services, income from royalties, new business

density, national office trademark registrations, or computer and communications

services exports.

A synthesis of this situation could be expressed in this way: Colombian firms are

above average in the international scene when it comes to acquiring knowledge for

replication; are somewhat below average in reference to generating knowledge and

innovation that is not related to R&D; and are definitely at the lower echelons in the

distribution regarding the creation of knowledge through R&D.

13

THE MENTAL MODEL GAP

This section examines the distance that separates the perspectives and metrics

used by policy makers and private business managers when addressing innovation

strategies. The goal is to consider the magnitude and consequences of this gap upon

the objectives of accelerating the absorption of knowledge and developing innovation

capabilities by firms.

The policy-making view

In Latin America and Colombia, the action of government is of great importance

in creating an environment that is supportive of the increase of knowledge-absorption

capabilities for innovation by firms. Thus, there should be growing interest from both

policy leaders and private firm managers in gaining deeper understanding of how each

other sees this problem and makes decisions about it. Referring to the general role of

policy and private actors in the process of creating a modern industrial policy, the

economist Dani Rodrik expressed this condition in the following words (Rodrik, 2004):

“…the task…is as much about eliciting information from the private sector on significant externalities and their remedies, as it is about implementing appropriate policies. The right mode…is not that of an autonomous government applying Pigovian taxes or subsidies, but of strategic collaboration between the private sector and the government with the aim of uncovering where the most significant obstacles to restructuring lie and what type of interventions are most likely to remove them. Correspondingly, the analysis of industrial policy needs to focus not on the policy outcomes—which are inherently unknowable ex ante—but on getting the policy process right. We need to worry about how we design a setting in which private and public actors come together to solve problems in the productive sphere, each side learning about the opportunities and constraints faced by the other, and not about whether the right tool for industrial policy is, say, directed credit or R&D subsidies or whether it is the steel industry that ought to be promoted or the software industry”.

This “coming together” with an agenda focused on identifying and removing

“the most significant obstacles” assumes that there are common grounds for dialogue

between public and private actors in the conversation. However, in this case the

14

language and the metrics used to understand the problem probably contribute more

to separate the actors than to unite them.

Policy makers tend to view the problem from the perspective of a national

innovation system, where key actors in society interact with firms in the private sector,

providing innovation inputs to firms. These firms, in turn, produce innovation outputs,

closely related to R&D, to satisfy market demand. A commonly used synthesis of this

process is provided in Figure 1 (Arnold and Kuhlman, 2001). In this view, the key inputs

are human capital and research, which are transformed by private firms into products

that fit the demand by customers. Other supporting roles correspond to financing and

general activities by government.

Figure 1 A National Innovation System

Source: Arnold and Kuhlman (2001).

A synthesized view of key variables that are associated to inputs and outputs in

this view is presented on Table 4. Inputs are essentially human capital and research

resources, while outputs are innovative products as well as research results.

15

Table 4 Variables used in Public Policy for measuring progress

in Science, Technology and Innovation

Source: Hatzichronoglou, 1997

However, Table 4 leaves out many elements that are relevant in any discussion

of innovation. It is recognized today that innovation activities by firms are not

restricted to R&D and that the process of knowledge absorption that leads to

innovation covers a large variety of activities beyond R&D (Oslo Manual, 2005). The

OECD has acknowledged this fact and is working to develop a new set of indicators

reflecting the degree of innovation that is prevalent in different sectors in the

economy (OECD, 2012). The importance of this shift can be appreciated on Tables 5

and 6 below. Table 5 shows the order of manufacturing sectors according to R&D

intensity that was used by the OECD in presenting its innovation reports until a few

years ago (Hatzichronoglou, 1997). This order reflects an estimation of the

contribution of R&D to value added in the different sectors.

Innovation Inputs Innovation Outputs

Expenditures in Scientific and Technological Activities Scientific papers published in Indexed Journals

R&D Expenditures Patents filed

Number of researchers by Sector Patents granted

Number of researchers by Field of Science Patents acquired

Sources of financing for Scientific and Technological Activities Trademarks

Number of individuals with Masters and PhD degrees working in industry New to Market product innovations

Collaboration on innovation New to the world product innovations

Product Innovations (New to the World, New to the Firm)

Exports by Technology Intensity

16

Table 5 High Innovation sectors according to R&D Intensity, OECD 1996

Source: Hatzichronoglou, 1997

When considered from this perspective, the most important sectors from the

stand point of innovation are Aerospace, Computing, and Drugs and Radio-TV-

Communications equipment. Medium-high sectors include Scientific instruments,

Motor Vehicles, Electrical Machinery and Chemicals. Thus, an implicit assumption

behind Figure 1 is that if countries wish to move forward in innovation, they should

develop these sectors, where there is substantial R&D-driven innovation. This

assessment can be expected to find little echo from business managers in a country

like Colombia, where 62% of GDP is provided by services and where the high and

medium-high technology manufacturing sectors presented in Table 4 hold a minimal

share of GDP.

Recently, the OECD has presented a new, and still experimental, classification

of productive sectors according to Innovation Intensity (OECD, 2012). This

classification opens the way for a broader definition of innovation that overcomes

various limitations of previous measurements (Table 6). The new classification

acknowledges that there are many sectors where, even though scientific research is

not an investment priority (since it does not create competitive advantage), there are

Classification ISIC code Sector

High Technology Industries 3845 Aerospace

3825 Office & computing equipment

3522 Drugs & medicines

3832 Radio,TV&communication equipment

Medium-High Technology Industries 385 Scientific instruments,385

3843 Motor vehicles,3843

383 - 3832 Electrical machines excl. commun.,

351+352-3522 Chemicals excl. Drugs

3842+3844+3849 Other transport,

382 - 3825 Non-electrical machinery

Medium-Low Technology Industries 355+356 Rubber & plastic products

3841 Shipbuilding & repairing

39 Other manufacturing

372 Non-ferrous metals

36 Non-metallic mineral products

381 Metal products

353+354 Petroleum refineries & products

371 Ferrous metals

Low-Technology Industries 34 Paper, products & printing

32 Textiles, apparel & leather

31 Food, beverages & tobacco

33 Wood products & furniture

17

significant innovation capabilities being deployed as a key component of business

strategy. In these sectors, innovations are evident in the development of products,

services, organizational systems and marketing solutions. R&D activities are only one

component within a broad framework. This approach is more faithful to the spirit of

the Oslo Manual, which recognizes that innovation can take different material

expressions as product or process innovations, organizational innovations and

marketing innovations (OECD, 2005).

Table 6 Top-10 Innovation sectors according to Innovation Intensity, OECD 2011

Source: OECD, 2012

This new OECD methodology introduces the criterion of Innovation Intensity,

which considers four key variables: a) product and process innovations, b) expenditure

related to innovation, c) organizational and market innovations, and d) intellectual

property rights. The methodology gives a score to each sector in each of these areas,

and establishes a new order among sectors. This order was obtained empirically, based

on information from firms in OECD member countries, using measurements from the

CIS innovation survey (the Eurostat Community Innovation Survey). Table 6 presents

some of the ranked sectors.

This ranking is different from the R&D-based ranking in Table 5. For example,

manufacturing of motor vehicles, trailers and semi-trailers (ISIC 34) is considered as

highly innovative when using a categorization based on R&D, but ranks only as

moderate (ninth in the overall classification) when the categorization is based on

Innovation Intensity. Similarly, the Computer and Related Activities sector (ISIC 72) is

ISIC Code Sector

73 Research and development

24 Chemicals and chemical products

66 Insurance and pension funding, except compulsory social security

23 Coke, refined petroleum products and nuclear fuel

32 Radio, television and communication equipment

65 Financial intermediation except insurance and pension funding

72 Computer and related activities

33 Medical, precision and optical instruments, watches and clocks

34 Motor vehicles, trailers and semi-trailers

64 Post and telecommunications

18

not listed among the 10 most innovative in the classification based on R&D, but it

reaches position 7 in the Innovation Intensity ranking.

These results make it clear that a new interpretation of Figure 1 is needed. Key

human capital is not limited to Ph.D. degrees. Intellectual property is not only patents,

but includes many other forms of appropriation of the benefits of innovation, such as

brand value and trademarks, and does not flow exclusively from research at

specialized institutes or universities. Many more sectors in the economy are involved,

including several key services sectors. This should lead to a major re-interpretation of

the role and characteristics of innovation actors in the economy.

Given this perspective, and having knowledge absorption for innovation in

mind, a view of different modalities of innovation such as the one presented in Table 7

can be quite useful (Pavitt, 1984; Tidd et al, 2001; Arnold et al, 2012). Innovation can

happen not only in manufacturing, but also in services sectors. Innovation can be

present in several degrees, from incremental to radical, can be driven by different

functions within the firm, and can be protected and appropriated through a diverse

range of mechanisms, not only through the use of patents.

19

Table 7 A taxonomy of Sector Innovation

Sources: Pavitt, 1984; Tidd et al, 2001; Arnold et al, 2012.

This view of the problem of innovation is definitively more comprehensive and

inclusive of the different sectors and modes of innovation that exist in private firms.

Therefore, it represents a significant step forward. A framework like this should be

used to consider the definition of the role of government in fostering innovation and in

creating a set of tools and instruments for this purpose.

Category Main sources of technical

change

Focus of innovative activity

Size of innovating firms Means of

appropriation

Supplier- dominated

. Suppliers

. Production learning

Firms are almost entirely dependent on their suppliers of machinery and other production inputs for new technologies

Limited in-house innovation activity is undertaken but some learning from in-house production activities

Main focus is process innovation in pursuit of cost reductions

Innovation strategy is to use to technology from elsewhere to support competitive advantage.

Process innovations are created in supply sectors and embedded within the inputs to production

Firms are typically small and found within traditional manufacturing sectors such as textiles, agriculture and services

Appropriation is rarely based on technological advantage but instead on professional skills:

. Design

. Trademark

. Advertising

. Marketing

Scale- intensive

. Production engineering

. Production learning

. Design office

. May include in-house R&D

. Suppliers

Innovations are largely developed in-house, which may include an internal R&D. Some innovation also sourced from specialised suppliers of equipment and components

Both product and process innovation but a significant focus on production improvements. Innovation strategy is focused on incremental improvements as implementing radical change on complex products and processes is highly risky

Firms are characterised by large-scale mass production where significant economies of scale and division of labour are present.

The products & production systems are complex integrations of technologies. Sectors include: automobiles, extraction & processing of bulk materials & consumer durables

Appropriation by:

. Process secrecy and know-how . Technical leadership . Some patenting

Specialised- suppliers

. Design function

. Operational knowledge

. Input from advanced users

Innovation focused on product performance improvements. These improvements are often developed to meet the high specification requirements of key users. They are later transferred to other users

They are generally small in size, manufacturing high- performance inputs to other complex products and production processes – inputs such as machinery, components, instrumentation and software

Appropriation by

. Design know-how

. Relationships with, and knowledge of users

. Some use of patents

Science-based . In-house R&D

. Basic research from external sources

. Input from advanced users

These firms invest heavily in internal R&D to create innovative new products and have close ties to the research base to access new knowledge, skills and techniques

Focus on product innovation where fundamental discoveries (in basic science) lead to new products and markets and corresponding new production and organisational processes

Innovation strategy requires monitoring and exploiting developments from the research base

Innovation is highly dependent on developments in the relevant science base and new products are diffused widely as consumer goods or inputs to other sectors

Firms are typically large and in sectors such as pharmaceuticals, chemicals, electronics, materials

Appropriation by:

. R&D know-how

. Patents

. Process secrecy and know-how

. Internal dynamic learning

Information- intensive

. In-house systems & software departments

. Suppliers

Innovation comes from internal and external sources, and is based on IT hardware improvements, software developments and systems integration

The focus of innovation is to improve, and even redefine, methods of service delivery and to create entirely new service products

Firms are in service sectors that rely heavily on technology to process large quantities of information for efficient and effective service delivery: sectors such as finance, retail, insurance, travel and publishing, telecoms

Appropriation by:

. Process know-how

. Software IP (copyright)

. System design know-how

20

The private sector view

Private companies use a wide set of metrics to measure progress towards

innovation goals. Although there are many available metrics, only a small number of

these are generally used. Firms also classify metrics into two large groups: input and

output metrics. Some examples are presented in Table 8.

Table 8 Some Innovation Metrics used in by firms in the private sector

Source: Boston Consulting Group, 2009; Morris, 2011

As can be seen in the table, output metrics are focused on financial and brand

impact results. Input measures, on the other hand, focus on the resources that have

been put to use in the innovation effort and also on efficiency, seeking to identify the

21

amount of resources involved and the possible redundancies or over-costs in the use

of these resources. Beyond this, other innovation indicators seek to characterize

employee interactions, and therefore try to capture information about the state of

culture and team work.

A quick comparison of tables 4 and 8 allows to identify key areas where

indicators used by policymakers and private firms share common grounds, as opposed

to other areas where they do differ substantially. Is it clear that an important common

topic is the interest for measuring if innovations developed by the firm are new to the

world and (or) new to the firm. Beyond this, however, the policy and private sector

metrics split towards different directions. The set of indicators used by policymakers is

focused on human capital and technology-related investments. Meanwhile, the

indicators used by private firms only pay attention to R&D if it is an important

component of the firm’s innovation strategy. As mentioned before, there is no reason

to expect that this would be the case beyond a minority of situations.

The question of which are the right metrics for innovation is generating growing

discussions among firm managers. There is a concern among companies about the

value that is being created through their innovation efforts. A survey of firms in four

continents (Boston Consulting Group, 2009) found that only 52% of respondents were

satisfied with the performance of their investments in innovation and only 32% of

respondents were satisfied with their companies’ innovation metrics. While 73%

believed that innovation efforts should be measured as rigorously as processes in the

core business, a full 63% of these were not sure which metrics to use or believed that

this this issue had not been given the priority they felt it deserved. Finally, the survey

identified that the measures most widely used by firms in the sample were related to

profitability (79% of the respondents used metrics related to this topic), customer

satisfaction (75%), incremental revenue from innovations (73%), time to market (59%),

idea generation (55%), and R&D efficiency (49%).

These results show that there are substantial issues to be solved from the

perspective of firms, regarding the measurement of innovation strategies and

processes. This compounds the problem. Not only the metrics used by policy makers

are seen as irrelevant, but the metrics used by the firms themselves are seen as

22

incomplete and unsuitable. The policy-private information gap cannot be solved

simply by adapting private metrics for policy purposes, because firms are not satisfied

with the metrics that they are using. It seems that a new understanding of the problem

must be achieved and new metrics for innovation must be produced.

If private firms are not quite sure about which metrics to use, what can be done

from a policy perspective? In order to close the gap between the ways in which public

and private actors understand the problem of innovation by firms, it will be necessary

for both to work together to create a common view, a common language and common

metrics. Following Rodrik’s call to action, this is clearly an area in which a new setting

needs to be designed where private and public actors can come together to solve

problems, “each side learning about the opportunities and constraints faced by the

other”.

An answer to this invitation could be found by using the instruments provided

by the Open Innovation approach. However, before exploring that avenue in more

depth, it is important to present some considerations regarding the challenges that are

awaiting in the design of innovation metrics.

There are important problems in measuring innovation management, which

cannot be ignored. As Langdon Morris pointed out (Morris, 2011), it is quite possible

that the very efforts that firms make in order to measure innovation may significantly

impede the process itself. Morris offers three reasons for this. First, the pursuit of

innovation necessarily involves a venture into the unknown, and it is quite possible

that trying to define the unknowns too early in the process may make it harder to

recognize good opportunities or solutions. For example, trying to calculate the value of

every idea too early on in the process may end up generating misleading numbers.

Second, misapplied metrics can undermine the spirit of learning and discovery that an

innovation process requires, because they may lead a team to choose a particular

version of a concept too soon. And third, argues Morris, the discussion of metrics can

easily become a form of intimidation used to demean the ambiguity of the innovation

process, particularly when an organization needs to choose between assigning

resources to innovation initiatives or to the well-known core business.

23

At the center of any innovation process lays a process of discovery. Given the

amplitude of the arenas where innovation can be productively used by firms, the

problem can become extremely complex quite easily. Firms need to make this

complexity manageable. At the same time, they should beware of frameworks which

quickly adopt an overly limited perspective. It can happen that, in order to avoid the

ambiguity and doubt that comes with the discovery process, firms end up closing

possibilities and paths for action before the process has even begun.

During the discovery stages, innovation should not be treated as a linear

process that generates a neatly measurable output. An innovation management

system needs to be conceived as as a set of principles, practices and tools which serves

as an enabler of business strategy and needs to be subject to permanent

experimentation. Metrics are necessary, but no static measures can capture the full

scope of the innovation process at the discovery stages. Managers cannot expect that

by focusing on similar metrics for all their projects they will obtain the desired results.

As a firm moves forward, achieving innovation goals and consolidating its learning of

innovation, the metrics should change, based on the firm’s experience.

Figure 2 illustrates this concept. The two essential questions of business

strategy are where to compete (defining segments, geographies, products,

technologies, channels and the like), and how to compete (that is, the specific,

distinctive approach that will be used to create an engaging value proposition and

obtain competitive advantage). Figure 2 presents these questions in the two axes of

the diagram. As the firm moves away from the origin in Figure 2, it abandons existing

ways of doing things and engages in new practices. This happens by executing specific

projects of different magnitude throughout the organization.

24

Figure 2 The essential questions of business strategy

and the role of innovation

An innovative firm is one that continuously experiments, seeking to push the

limits of the existing way of doing things, in a constant drive away from the origin in

Figure 2. The firm may seek to achieve a position like point A, in which it tries new

answers to the where question and seeks to conquer new segments, geographies,

channels, etc., while maintaining a value proposition that is similar to the preceding

one. Or, it may choose a position like B, developing new approaches to the how

question and presenting new value propositions to existing customers. Or, it may try

new approaches on both of the key questions at the same time, as in position C. The

firm may try many other possible combinations in this map.

The actual way in which is this is done consists in developing new projects that

lead to the creation of innovative products, services, processes or business models.

The firm learns lessons from each of these experiences and applies these lessons in

successive projects that gradually expand the frontier of achievement. Along this

search, a capability for innovation emerges. This is similar to the development of a

muscle by an athlete. It demands intense observation of how the process works in the

present, identification of opportunities and barriers, formulation of solutions,

25

application of new methods, verification of what works, and so on. This cycle becomes

a routine for the organization, allowing it to achieve larger leaps in the path away from

the origin in Figure 2.

The process needs to be kept flexible because it is essentially a discovery and

search process. What works in the move from one position to the next in the map will

not necessarily be equally effective as further steps are taken. Metrics are necessary,

but the application of metrics can become counterproductive if they are associated to

an urge to repeat thoughtlessly what worked in the past.

Therefore, organizations need to reflect deeply about what a process of

discovery really is and why it is different from other forms of learning. The framework

in Figure 3 helps to understand this (Yeung et al, 1999).

Figure 3 Learning Styles of Firms

Source: Yeung et al, 1999

There are four distinct learning styles which companies can use to generate the

capabilities needed to develop new products and services. These four learning styles

appear as the result of combining two ways of using experience for learning; and two

26

ways of focusing attention, either on the exploration of new possibilities or the

exploitation of existing markets.

On the horizontal axis in Figure 3, the framework presents an opposition

between learning from the firm’s own experience or from the experience of others. On

the left end of the axis, workers gain knowledge through their own experience,

performing a set of activities and reflecting on cause-effect relationships. On the right

end, the firm learns from the experience of others, obtaining skills through acquiring

other companies, recruiting experienced workers, hiring consultants and educational

institutions, observing, scanning, benchmarking, and imitating successful products and

processes developed by others. Organizations are more likely to learn from direct

experience when their environments change rapidly, competition is based on product

differentiation, and the future basis of competition appears to be uncertain.

On the vertical axis, the framework presents an opposition between

exploration and exploitation, as outlined by James March (1991). According to this

view, organizations may focus their attention either in the exploration of new

opportunities, or in the exploitation of known resources. Exploration involves

experimentation with new competencies, technologies, and paradigms. Exploitation

seeks to consolidate the use of well known resources and involves leveraging existing

products, processes, and practices.

Organizations differ on the degree to which they engage in exploration as

opposed to exploitation. They they tend to lean towards one of these ends, because to

be successful in any of the two modalities demands intense attention and depletion of

resources. Switching between exploration and exploitation is not easy. Organizations

tend to locate at the exploration end when their industry is young, major technological

change occurs, and competitive advantage is derived from technological leadership

rather than cost leadership (Yeung et al, 1999).

Four learning styles emerge from the crossing of the two axes:

Experimentation, Competency Acquisition, Benchmarking and Continuous

Improvement. The research developed by Yeung et al (1999), which engaged 411 firms

in 40 countries, shows that companies tend to rely on one of these styles to develop

the capabilities that give them advantage in their competitive environments. While

27

Competency Acquisition and Continuous Improvement are the most common

dominant learning styles, Yeung et al (1999) obtained evidence showing that

Experimentation is the learning style that is most effective for enhancing business

performance. It is also the one that is most closely related to innovation.

This discussion is quite relevant to the central topic of this essay. The processes

that lead to innovation need to be deeply ingrained in the routines that determine

organizational learning inside organizations. In order to turn innovation into a

powerful enabler of business strategy, allowing organizations to reach new answers to

the where and how to compete questions, firms need to engage progressively in the

Experimentation learning style, where they commit increasing resources to try new

solutions based on their own experiences. The greatest challenge is to do this

continuously, resisting the pressure to abandon experimentation when early solutions

close to the origin have been achieved.

In fact, some Colombian firms find that going through the discovery process

successfully is one of the most difficult components of an innovation strategy. In a

workshop carried out with a group of representatives from major Colombian

businesses, members of the Private Competitiveness Council were asked to identify

their most urgent concerns regarding innovation. These are companies which have

formal innovation systems in place and could be considered as leaders in the country

in terms of building capabilities for innovation (Vesga, 2012). These were the main

concerns that were discussed in this workshop:

Achieving greater productivity in the discovery processes.

Innovation systems within companies include discovery stages where

needs are identified and creative solutions are shaped. Firm

representatives expressed that these processes take too much time and

are too erratic. Participants in the meeting agreed that for them,

increasing the productivity of their processes at this early discovery stage

is a high priority. This involves improving the methodologies used;

opening larger spaces within the organization's routine for working on

this stage of the process; achieving an increase in the number and quality

of the experiments carried out by innovation teams; improving the

28

accuracy and speed in intellectual property searches; and multiplying the

number of innovative business models that are effectively identified each

year. The key to progress in this area does not begin by setting new

metrics, but by accelerating experimentation initiatives. The metrics are

created afterwards.

Ensuring access to talent. At this time, the number of

people who have the knowledge, skills and experience required to

successfully lead innovation processes is very limited in Colombia. The

difficulties in finding and bringing this talent on board are seen as a

significant barrier to expanding the scale and impact of innovation efforts

by these firms. It is necessary to increase rapidly the availability of people

with these skills, to accelerate and scale up progress towards a greater

volume of innovation projects.

Strengthening capabilities for the execution of innovation

projects. For businesses, it is very important to strengthen execution

capabilities to materialize the fruits of discovery processes. Strengthening

the conditions for the maturation of projects and talent; reducing

development time (time to market); accelerating and deepening the

measurement processes that enable the comparison of results; and

having better mechanisms to improve the availability of detailed and

frequent geographic information; are some of the elements that would

contribute to the achievement of fundamental objectives.

Strengthening Open Innovation activities. For the vast

majority of participants, Open Innovation has become an issue of great

importance within strategy. For these firms it is clear that it is quite

important to engage with other actors from the environment, in order to

advance their own innovation strategies. The issues that were mentioned

in this regard include: strengthening and deepening contact with the

other actors in the ecosystem; strengthening synergies with other

businesses for the purposes of creation; increasing the productivity of

creative spaces in solid, long standing relationships with universities; and

29

achieving more ambitious goals in their links with Academia and

Government.

A public policy that seeks to spread absorption of capabilities for innovation

would probably be wise to assume that these concerns will be widely shared by

Colombian firms. As more firms enter discovery processes and seek to leap away from

the origin in Figure 2, it will be evident that a collective learning process needs to take

place, where firms can learn from each other’s experiences and can move forward to

create relevant metrics and apply them when appropriate. It is necessary to develop

instruments that can effectively create spaces for experimentation, where firms can

learn from their own experience in reference to the experiences of others, moving

quickly from observation to design, test and verification of results, sharpening their

learning cycles.

This environment should operate on the basis of a common, simple and

evolving language, where the basic definitions of what innovation is and how it can be

done are easy to understand. Each firm should be able to relate quickly to this

language, and to visualize how these principles can be applied to reach the next step in

their own path away from the origin in Figure 2. It should always be possible for each

firm to visualize the next step as a goal that lies within reach, regardless of the level of

the firm’s capabilities for innovation at the outset.

Bringing such a policy to reality is a demanding endeavor that cannot be solved

by one brilliant government. It requires concerted, hard work by all the stakeholders.

This is where the tools and instruments of the Open Innovation framework can be

useful to achieve the desired goals of innovation policy in a country like Colombia.

30

THE OPEN INNOVATION MODEL AS A SOLUTION

As Henry Chesbrough defined it, Open Innovation is “the use of purposive

inflows and outflows of knowledge to accelerate internal innovation, and expand the

markets for external use of innovation, respectively” (Chesbrough, 2006). The term

became widely used, with the most important interpretation of its possibilities and

influence referring to the idea that businesses can access a much larger number of

ideas and innovative projects if they open the gates and allow externally generated

innovations, in different stages of development, to access their R&D pipelines. Some

companies like Procter & Gamble exemplified this trend (Huston & Sakab, 2006). It was

essentially a way to achieve a larger output of technology-based innovative products,

with a resource endowment that would be quite similar to that the past.

As time passed, the concept of Open Innovation itself expanded its meanings.

Chesbrough’s work moved beyond technology and studied the implications of Open

Innovation on business models and services (Chesbrough, 2006, 2010). This changed

the terrain of the discussion, from a process focused on enlarging the product

development pipeline to a deeper understanding of the connections between firms,

their business models and strategies, and of the essential nature of products as

services-experiences. Today, businesses are “pushing more of their business issues

into the open innovation domain” (Chesbrough and Euchner, 2011).

One of the reasons why the Open Innovation model can be so powerful is that

it accelerates learning and knowledge absorption (Van Haberbeke et al, 2007; Cohen &

Levinthal, 1990). Firms differ in their ability to appreciate and exploit new knowledge

generated elsewhere and, in this sense, they are path dependent (meaning that their

past and their history determine what they will be able to appreciate and exploit in the

present and the future).

The discovery process that allows firms to move away from the origin in Figure

2 requires an increase in absorptive capacity. In this process, firms need to rise their

capacity to know- what (to recognize and value external knowledge); know-how

(assimilate and use valuable external knowledge); and know-why (understand well

31

how using and commercializing external knowledge allows the firm to achieve its own

organizational objectives). Operating within an open innovation model increases firms’

absorptive capacities (Van Haberbeke, 2007), because it forces firms to expand their

span of attention and endows them with access to larger stocks of knowledge, which

helps them to identify more (and more varied) connections between their present

state and possible evolution alternatives. In particular, the tools of open innovation

allow firms to move faster through the discovery process. Options reasoning, for

example, allows firms to consider more alternatives, select among them and place a

variety of bets while reducing the overall risk of their innovation portfolios.

If it is true that firms operating under an Open Innovation framework can

increase their absorptive capacities for discovery, then many practical advantages of

having large numbers of firms doing the same thing should become apparent. The

following are some examples:

1. In Chesbrough’s definition, Open Innovation refers to “the use of

purposive inflows and outflows of knowledge”. The focus is on the flows of

knowledge to and from the organization, not on the definition of what is

innovation. This concept is relevant for efforts focused on R&D and also for

initiatives that deal with service innovation (and other definitions in between).

This, there is no need to stall on the endless question of what is an innovation

or to fall prey to the traps of limited conceptualizations of innovation. The

essence of the definition refers to the flow knowledge and the value that

knowledge can create. Most of the stakeholders of a country or a region’s

innovation policy could easily agree with such an assertion.

2. In a region endowed with an innovation policy environment

focused on accelerating the spread of absorption capabilities, the key objective

would be to enhance the learning and discovery processes of firms. The

instruments and tools of policy would be chosen because their effectiveness at

expanding the inflows and outflows of knowledge between firms, and between

them and the ecosystem. Metrics, of course, are of key importance, but the

most useful metrics should be defined as part of the process, not assumed to

32

be understood at the outset. The most important element is that the region

should be able to foster a productive conversation among firms and policy

makers regarding strategy, the assumptions that should be met in order to turn

strategic goals into reality, and the role of innovation in strategy. The stronger

emphasis should be devoted to clarifying the strategic issues, not to simplistic

perceptions and metrics.

Firms that have successful in developing powerful strategies and

supporting them with strong capabilities for innovation rely in this kind of

dialogue to configure activity systems and to ensure their effectiveness. An

example of how such mechanism works has been described by AG Lafley,

former CEO of Procter & Gamble (Lafley, 2013):

“We actively fostered this approach to communication at P&G, encouraging dialogue in the strategy review sessions, in one-on-one meetings, and all the way to the boardroom. The goal was to create a culture of inquiry that would surface productive tensions to inform smarter choices. The explicit goal was to create strategists at all levels of the organization1. Over the course of a career, P&G leaders gain practice designing strategy for brands and products lines, categories, channels, customer relationships, countries and regions, and functions and technologies. The idea is to build up strategy muscles over time, in different contexts, so that as managers rise in the organization, they are well prepared for the next strategic task. As they succeed, the reward is a bigger, tougher, and more complex strategic challenge. This practice-makes-perfect approach to learning strategy explains why so many P&G alumns go on to become CEOs”.

Creating this kind of environment for discussion is of critical importance

in any explanation of the performance innovative firms. It is necessary to

create a similar environment for discussion among the stakeholders of

innovation policy. Regions and cities would be wise in “taking a page” from

the innovation routines of companies like P&G and follow this approach to

create solid strategy and innovation capabilities among stakeholders. The task

is to induce a process of discussion and learning among actors in the policy

arena that is similar to the one that takes place in successful innovative firms.

*: Not italicized in the original.

33

An Open Innovation framework for policy making, validated by all the

interested parties, would be an appropriate scenario to set objectives, rules of

engagement and knowledge sharing mechanisms, in order to accelerate the

development of innovation capabilities across firms. It may seem like a

dauntingly difficult task but, surely, none of this was easy in the case of a

USD$83 billon dollar sales global firm like Procter & Gamble either.

3. Solving the innovation policy metrics problem will be easier in

regions that work within an Open Innovation framework. Innovation policy is

caught in a dilemma when it comes to measurement: since the optimal

performance of innovation policy is difficult to measure, then the system

resorts to available indicators , such as the R&D metrics discussed above.

These available measures quickly come to define the whole field of activity.

Actions that may contribute to the optimal state, but whose effects cannot be

easily measured through indicators, fall into disuse. In such a situation, a

measurement bias induces suboptimal performance (Marr, 2008).

The available metrics will necessarily determine how results are

evaluated and how decisions will be made in the future. The fact that there

are limited metrics in the present to establish progress towards an apparently

fuzzy objective, such as the spread of the appropriation of innovation

capabilities by firms, should not lead policy makers to forget that this is one

very important objective. All firms involved should be aware of the dangers of

measurement bias. This awareness is more likely to exist if firms are

participating in an Open Innovation initiative than if each is trying to solve the

problem on its own.

4. If metrics for the appropriation of innovation capabilities by firms

are not readily available, then the concerned regions will have to develop such

metrics. In order to do this, both policy makers and private actors need to keep

a focus in the important questions that need to be answered. Again, policy

actors can adopt a tool that was developed in the space of corporate

performance management: Key Performance Questions, or KPQs. This is a tool

that allows firms to focus on the performance factors that really need to be

34

assessed instead of proceeding mindlessly to the use of performance indicators

that may be standard practice, but do not refer to the specific needs of the

situation. By using KPQs, managers keep a focus on what needs to be discussed

when reviewing performance. The process starts with KPQs and only then

follows to developing indicators. Questions should follow a sequence like this:

‘What do we really need to know? What information do we require? What

are therefore the best Performance Indicators we need to collect to help us

answer our key performance questions? (Marr, 2008).

Creating the appropriate environment to ask the right questions is

critical for innovation. This is how the process is described by Eric Schmidt,

former president of Google:

We run the company by questions, not by answers. So in the strategy process we’ve so far formulated 30 questions that we have to answer [ … ] You ask it as a question, rather than a pithy answer, and that stimulates conversation. Out of the conversation comes innovation. Innovation is not something that I just wake up one day and say ‘ I want to innovate. ’ I think you get a better innovative culture if you ask it as a question (Marr, 2008).

In seeking to scale up this approach from a firm to a region in order to

spur innovation, operating inside an Open Innovation policy environment

would facilitate the process. Actors would follow the rule of focusing on how

knowledge flows in and out of organizations. Proceeding from Key Performing

Questions to new Performing Indicators should be a natural process in this

environment.

5. In the prevalent state of the innovation policy discussion in Latin

America and Colombia, most events and phenomena occurring within the firm,

at the group or individual levels, are considered to be exogenous or beyond the

scope of the analysis. Innovation policy assumes that the firm is monolithic

entity that responds unambiguously to incentives from the environment. The

Open Innovation view allows to move past this intellectual construction and

gives policy stakeholders and firm managers the tools to openly discuss barriers

to the innovation process that exist within the firm. Within the Open

Innovation framework it is possible to analyze phenomena at the firm level and

35

also at the team or individual level (Vanhaverbeke, 2006). Issues such as

barriers to change stemming from team inflexibility and inertia can be

examined carefully and dealt with as an integral part of the task. This is an

extraordinary advantage. It is not unusual that worthy policy initiatives fail

because the push towards action is broken when weak links inside

organizations fail, even after managers of firms have agreed to pursue the

chosen path, grinding the full effort to a stop. Within an Open Innovation

framework, these weak links can be explicitly considered. Complementarities

among actors can be summoned to action in order to solve these barriers. All

the actors can have the necessary elements to understand and follow the

advancement of proposed initiatives and not just assume that important

blockages will be overcome in some unspecified way.

6. When firms try to create a capability for discovery, it often

happens that resources for this purpose are in short supply. Firms find it

difficult to assign resources to the discovery process for many reasons.

Discovery is associated to ambiguity, movable goals and shifts in direction that

need to be undertaken before a clear fit between opportunity, project, and

firm capabilities can be finally configured. Thus, it is difficult for any firm to

engage in this process while at the same time excelling at the execution feats

that are needed for succeeding at day-by-day operations. Acquiring capabilities

for discovery can be a frustrating quest at execution-minded firms, since it is

difficult for individuals to simultaneously perform well in discovery-minded

projects and execution-minded projects. It is difficult to assign financial and

technical resources for both kinds of projects at the same time, because they

perform quite differently when compared using standard criteria for decision

making (particularly when only standard financial metrics are used).

However, if a region has many firms operating within an Open

Innovation framework spurred by policy, some critical resources in the

environment would become visible and available and could be utilized with

lower costs. For example, for execution-minded companies it can be quite hard

and costly to find individuals who are properly skilled and could be leaders of

36

discovery processes. This happens because the human resource units at these

firms are used to hiring individuals for execution processes and have little

experience in selecting for discovery (where the set of required skills include

such things as abilities for acute observation, desire to question the status quo

and fluidity at creating new solutions). Their common processes are useless and

they do not know how to validate these new searches. Within an open

innovation setting, however, individuals who have acquired experience

discovery processes would become easily visible. Small “pockets of talent”

could be on call to attend temporary needs of a variety of businesses, reducing

the costs associated to search and validation for each individual firm. The same

can be said for many fixed costs. Infrastructures such as laboratories, for

example, could be shared among many firms whose demand peaks do not

occur exactly at the same time.

7. The Open Innovation model facilitates the dialogue among firms

(as well as among firms and other institutions) which may be located in very

different positions in a continuum of innovation capabilities. Open Innovation

allows for the collaboration of firms with no regard to the frequency of new

product announcements or the degree of R&D intensity that they manage. The

framework facilitates absorption of knowledge by small firms, or by firms who

are only initiating their development as innovators, without imposing great

requirements on the capabilities that they should have at the outset. In order

to engage in an open innovation model, firms need to do some solid thinking

about how far have they advanced in the process described in Figure 2 and

what kinds of goals can they achieve, given the place where they are located in

the spectrum of possibilities. In other words, firms are forced to interiorize the

state of their path dependency; this self-reflection should increase their success

probabilities of in the long term.

8. Research on Open Innovation is generating a growing inventory

of tools and instruments that can be used by firms when they search for,

utilize and share knowledge on all aspects of operations that may be related to

innovation, from user-led design to guidelines for leveraging shared IP

resources. This makes faster organizational learning possible, where newly

37

identified problems can quickly lead to new interpretations and

conceptualizations, with experimentation and testing of novel solutions

following in quick sequence.

9. The analysis of real options has acquired new significance and

usefulness in the context of Open Innovation. Real options analysis allows firms

to stage their financial commitments in new projects, tying disbursements to

the completion of project milestones. This creates the possibility for firms to

invest in a wider variety of projects and only follow-trough with those that

show the highest promise. In an open innovation environment where there are

many firms, this creates new possibilities for any participant to extract value

from projects that do not make the cut along the development pipeline. For

example, if a firm decides not to follow a project beyond the earliest stages of

development, because other projects in its portfolio have shown more promise,

it can cede the rights to continue with the project to another firm in exchange

for a portion of the revenues that the project would attain if successful. This

opens possibilities for new revenue streams that do not exist under a closed

innovation model, reducing the expected losses attached to investments in

innovation.

10. If a region or a cluster embraces an open innovation approach, it

should be expected that issues of governance would become more explicit -

and could be dealt with through discussion and negotiation. Since an open

innovation environment allows to deal directly with issues that affect firms at

many different levels, it also allows to clarify the interests that each firm has

and creates a richer environment, with many alternatives over the table, where

each actor should find it easier to obtain desired objectives through

negotiation. Open innovation and real options analysis allow each participant

to build more varied negotiation packages and increases the probability of

achieving positive results.

38

CONCLUSION

This essay has developed an argument favoring the use of an Open Innovation

framework for public policy initiatives aimed at accelerating the absorption of

knowledge and the development of innovation capabilities by firms. Some evidence

from the case of Colombia has been presented, but the arguments should be relevant

for several countries in Latin America.

The key argument follows from the realization that a limited perspective on the

problem of innovation has lead to the use of a limited set of metrics, making it hard for

many firms to understand their own position regarding innovation capabilities and to

engage in a productive dialogue on the topic with policy makers. The argument states

that this limited-perspective problem can only be solved if firms find ways to

accelerate experimentation and discovery and learn from their own and their mutual

experiences. This learning should happen faster in an Open Innovation environment,

where firms can relate their own experiences to those of others and can resort to

assets and knowledge developed by other firms.

Capabilities for working in an open environment are diverse and hard to acquire.

However, firms would become more adept at these capabilities with frequent use. In

an Open Innovation environment, collective learning should be codified and offered to

new actors. Public Policy should leverage on firms’ efforts to foster the development of

this environment. Once the basic principles are shared and understood, policy efforts

should focus on the spread of the knowledge developed by pioneers and the tools

derived from this knowledge.

Open Innovation principles could serve as a general framework for fostering

innovation policies in Colombia and other countries in Latin America. This paper

outlines the need for such a general framework and presents some basic elements that

could be taken into account. Developing such a framework in detail will involve

substantial contributions from many actors, from the public and private arenas. Such a

focus on collective learning may offer a productive way ahead. It is certainly worth

examining.

39

REFERENCES

Arnold, E. et al (2012) Knowledge Transfer From Public Research Organisations. Science and

Technology Options Assessment, European Parliament, project carried out by Technopolis

Group.

Arnold, E.; Kuhlman, S. (2001) RCN in the Norwegian Research and Innovation System.

Background Report No. 12 in the evaluation of the Research Council of Norway. Technopolis.

Boston Consulting Group (2009) Measuring Innovation 2009. The Need for Action. Boston

Consulting Group, Bsoston, MA.

Chesbrough, H. and J. Euchner (2011). The Evolution of Open Innovation: An Interview With

Henry Chesbrough, Industrial Research Institute, Inc. 54: 13-18.

Chesbrough, H.W. (2010) Open Services Innovation: Rethinking Your Business to Grow and

Compete in a New Era. . Cambridge, MA: Harvard Business School Publishing.

Chesbrough, H.W. (2006) Open Business Models: How to Thrive in the New Innovation

Landscape. Cambridge, MA: Harvard Business School Publishing.

Cohen, W. M., & Levinthal, D. A. 1990. Absorptive capacity: A new perspective on learning and

innovation. Administrative Science Quarterly, 35: 128-153.

Dutra, S. (2012) The Global Innovation Index 2012. INSEAD, WIPO.

DANE. (2012). Encuesta de Desarrollo e Innovación Tecnológica en la Industria Manufacturera

2009 – 2010, EDIT V. Bogotá D.C. Departamento Administrativo Nacional de Estadística.

Hatzichronoglou, T. (1997) Revision of the High-Technology Sector and Product Classification.

OECD Science, Technology and Industry Working Papers, No. 1997/02.

Huston, L. and N. Sakkab (2006). Connect and Develop. Harvard Business Review 84(3): 58-66.

Lafley, A.G; Martin, R. (2013) Playing to Win: How Strategy Really Works. Harvard Business

Review Press.

Marr. B (2008)Managing and Delivering Performance. Elsevier, Oxford, UK.

March, J. G. 1991. Exploration and exploitation in organizational learning. Organization

Science, 2: 71-87.

Morris, L. (2011) The Innovation Master Plan. Innovation Academy.

Navarro, J.C.; and Zuñiga, F. (2011). The Imperative of Innovation. Creating prosperity in Latin

America and the Caribbean. Inter American Development Bank. Washington, D.C. 2nd Edition.

40

OECD. (2012). OECD Science, Technology and Industry Scoreboard 2011. Paris: Organisation for

Economic Co-operation and Development.

OECD (2005). Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data. 3rd

Edition. OECD Publishing, Luxemburg.

Pavitt, K. (1984) Sectoral patterns of technical change: towards a taxonomy and a theory,

Research Policy, Vol. 13, pp.343-373.

Rodrik, D. (2004). Normalizing Industrial Policy. Working paper, Harvard University.

Rodrik, D. (2004). Industrial Policy for the Twenty-First Century. Working paper, Harvard

University.

Tidd, J., Bessant, J. & Pavitt K., (2001) Managing innovation integrating technological, market

and organisational change. 2nd ed. Wiley, Chapter 5

Van Haverbeke et al (2007) Connecting Absorptive Capacity and Open Innovation. Working

paper.

Van Haverbeke, W (2006). The Interorganizational Context of Open Innovation, in Open

Innovation: Researching an New Paradigm. Edited by Hentry Chesbrough, Wim Vanhaberbeke

and Joel West. Oxford University Press, New York.

Vesga, R. (2012) Discusión sobre la Estrategia Nacional de Competitividad. Notas de la reunión

realizada por invitación del Consejo Privado de Competitividad el 22 de junio de 2012. Rafael

Vesga, facilitador

Yeung, A. et al (1999) Organizational Learning Capability. Oxford University Press.