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       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
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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.
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       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
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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
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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.
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       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.
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       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)

                                           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

       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
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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)

                                    Other Firms   Suppliers   Clients Competitors Consultants Universities    Technology    Research   Technology    Regional International
                                                                                                             Development     Centers        Parks Productivity Organizations
                                                                                                                  Centers                             Centers

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 Technologies
                                            2.6         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



            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-
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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

                                                                 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

                      Source: Dutra (2012)
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                                                Table 3B
                Colombia: Ranking in variables related to knowledge absorption by firms.
                         Variables ranked below the country’s general position
                                     Global Innovation Index 2012

                                                                    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

                         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.
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       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
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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.
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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

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


         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.
16


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

                  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


                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
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


                        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


                      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
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

                        Main sources of technical                                                                                        Means of
  Category                       change                   Focus of innovative activity          Size of innovating firms               appropriation
  Supplier-         . Suppliers                          Main focus is process                Firms are typically small and        Appropriation is rarely
  dominated         . Production learning                innovation in pursuit of cost        found within traditional             based on technological
                                                         reductions                           manufacturing sectors such as        advantage but instead on
                   Firms are almost entirely
                                                         Innovation strategy is to use to     textiles, agriculture and            professional skills:
                   dependent on their suppliers of
                                                         technology from elsewhere to         services                            . Design
                   machinery and other production
                   inputs for new technologies           support competitive advantage.                                           . Trademark
                   Limited in-house innovation           Process innovations are created                                          . Advertising
                   activity is undertaken but some       in supply sectors and embedded
                                                                                                                                  . Marketing
                   learning from in-house production     within the inputs to production
                   activities


  Scale-           .   Production engineering            Both product and process             Firms are characterised by          Appropriation by:
  intensive        .   Production learning               innovation but a significant focus   large-scale mass production         . Process secrecy and
                                                         on production improvements.          where significant economies of      know-how
                   .   Design office
                                                         Innovation strategy is focused on    scale and division of labour are    . Technical leadership
                   .   May include in-house R&D          incremental improvements as          present.                            . Some patenting
                   .   Suppliers                         implementing radical change on       The products & production
                   Innovations are largely               complex products and processes       systems are complex
                   developed in-house, which may         is highly risky                      integrations of technologies.
                   include an internal R&D. Some                                              Sectors include: automobiles,
                   innovation also sourced from                                               extraction & processing of bulk
                   specialised suppliers of                                                   materials & consumer durables
                   equipment and components

  Specialised-         . Design function                 Innovation focused on product        They are generally small in size,   Appropriation by
  suppliers            . Operational knowledge           performance improvements.            manufacturing high-                 . Design know-how
                                                         These improvements are often         performance inputs to other
                       . Input from advanced users                                                                                . Relationships with, and
                                                         developed to meet the high           complex products and                   knowledge of users
                                                         specification requirements of        production processes – inputs
                                                         key users. They are later            such as machinery,                  . Some use of patents
                                                         transferred to other users           components, instrumentation
                                                                                              and software


  Science-based    . In-house R&D                        Focus on product innovation          Innovation is highly dependent      Appropriation by:
                   . Basic research from external        where fundamental discoveries        on developments in the              . R&D know-how
                       sources                           (in basic science) lead to new       relevant science base and new       . Patents
                   . Input from advanced users           products and markets and             products are diffused widely as
                                                         corresponding new production         consumer goods or inputs to         . Process secrecy and
                   These firms invest heavily in         and organisational processes         other sectors                           know-how
                   internal R&D to create innovative                                                                              . Internal dynamic
                                                         Innovation strategy requires         Firms are typically large and in
                   new products and have close ties                                                                                   learning
                                                         monitoring and exploiting            sectors such as
                   to the research base to access
                                                         developments from the                pharmaceuticals, chemicals,
                   new knowledge, skills and
                                                         research base                        electronics, materials
                   techniques

  Information-     . In-house systems & software         The focus of innovation is to        Firms are in service sectors that   Appropriation by:
  intensive        departments                           improve, and even redefine,          rely heavily on technology to       . Process know-how
                   . Suppliers                           methods of service delivery          process large quantities of         . Software IP
                   Innovation comes from internal        and to create entirely new           information for efficient and          (copyright)
                   and external sources, and is based    service products                     effective service delivery:
                                                                                              sectors such as finance, retail,    . System design
                   on IT hardware improvements,                                                                                      know-how
                   software developments and                                                  insurance, travel and
                   systems integration                                                        publishing, telecoms




             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.
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




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Arnold, E.; Kuhlman, S. (2001) RCN in the Norwegian Research and Innovation System.
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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
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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.
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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
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Marr. B (2008)Managing and Delivering Performance. Elsevier, Oxford, UK.

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

Navarro, J.C.; and Zuñiga, F. (2011). The Imperative of Innovation. Creating prosperity in Latin
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OECD. (2012). OECD Science, Technology and Industry Scoreboard 2011. Paris: Organisation for
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OECD (2005). Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data. 3rd
Edition. OECD Publishing, Luxemburg.

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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
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Van Haverbeke et al (2007) Connecting Absorptive Capacity and Open Innovation. Working
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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
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ACCELERATING THE SPREAD OF CAPABILITIES FOR INNOVATION IN COLOMBIAN FIRMS THROUGH THE USE OF AN OPEN INNOVATION POLICY FRAMEWORK

  • 1. 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. 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. 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. 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. 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. 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. 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) 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 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
  • 8. 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) Other Firms Suppliers Clients Competitors Consultants Universities Technology Research Technology Regional International Development Centers Parks Productivity Organizations Centers Centers 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 Technologies 2.6 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 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-
  • 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 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 Source: Dutra (2012)
  • 10. 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 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 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.
  • 11. 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. 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. 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. 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. 15 Table 4 Variables used in Public Policy for measuring progress in Science, Technology and Innovation 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 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.
  • 16. 16 Table 5 High Innovation sectors according to R&D Intensity, OECD 1996 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 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
  • 17. 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 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 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
  • 18. 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. 19 Table 7 A taxonomy of Sector Innovation Main sources of technical Means of Category change Focus of innovative activity Size of innovating firms appropriation Supplier- . Suppliers Main focus is process Firms are typically small and Appropriation is rarely dominated . Production learning innovation in pursuit of cost found within traditional based on technological reductions manufacturing sectors such as advantage but instead on Firms are almost entirely Innovation strategy is to use to textiles, agriculture and professional skills: dependent on their suppliers of technology from elsewhere to services . Design machinery and other production inputs for new technologies support competitive advantage. . Trademark Limited in-house innovation Process innovations are created . Advertising activity is undertaken but some in supply sectors and embedded . Marketing learning from in-house production within the inputs to production activities Scale- . Production engineering Both product and process Firms are characterised by Appropriation by: intensive . Production learning innovation but a significant focus large-scale mass production . Process secrecy and on production improvements. where significant economies of know-how . Design office Innovation strategy is focused on scale and division of labour are . Technical leadership . May include in-house R&D incremental improvements as present. . Some patenting . Suppliers implementing radical change on The products & production Innovations are largely complex products and processes systems are complex developed in-house, which may is highly risky integrations of technologies. include an internal R&D. Some Sectors include: automobiles, innovation also sourced from extraction & processing of bulk specialised suppliers of materials & consumer durables equipment and components Specialised- . Design function Innovation focused on product They are generally small in size, Appropriation by suppliers . Operational knowledge performance improvements. manufacturing high- . Design know-how These improvements are often performance inputs to other . Input from advanced users . Relationships with, and developed to meet the high complex products and knowledge of users specification requirements of production processes – inputs key users. They are later such as machinery, . Some use of patents transferred to other users components, instrumentation and software Science-based . In-house R&D Focus on product innovation Innovation is highly dependent Appropriation by: . Basic research from external where fundamental discoveries on developments in the . R&D know-how sources (in basic science) lead to new relevant science base and new . Patents . Input from advanced users products and markets and products are diffused widely as corresponding new production consumer goods or inputs to . Process secrecy and These firms invest heavily in and organisational processes other sectors know-how internal R&D to create innovative . Internal dynamic Innovation strategy requires Firms are typically large and in new products and have close ties learning monitoring and exploiting sectors such as to the research base to access developments from the pharmaceuticals, chemicals, new knowledge, skills and research base electronics, materials techniques Information- . In-house systems & software The focus of innovation is to Firms are in service sectors that Appropriation by: intensive departments improve, and even redefine, rely heavily on technology to . Process know-how . Suppliers methods of service delivery process large quantities of . Software IP Innovation comes from internal and to create entirely new information for efficient and (copyright) and external sources, and is based service products effective service delivery: sectors such as finance, retail, . System design on IT hardware improvements, know-how software developments and insurance, travel and systems integration publishing, telecoms 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.
  • 20. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.
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