Volume 45 Number 177,
April-June 2014
The Convergence of Biotechnology Paradigms
and the Strategies of Leading World Groups
Pablo Lavarello*
Date received: July 24, 2013. Date accepted: August 21, 2013

This article aims to analyze how major companies have diversified their technology in response to the emergence of new technology paradigms. Starting with a classification of a patent database, this work questions, first, if these processes give rise to convergence between the knowledge bases and the emergence of a new industrial biotechnology paradigm. Second, this work discusses data from major groups on the international scale to determine if this diversification has produced a coherent knowledge base or if it is simply conglomerate growth, and whether there are still entry opportunities for companies in developing nations.

Keywords: Biotechnology, technology paradigms, technological innovation, technological diversification, technology coherence.

A wide range of literature has analyzed the conditions in which certain developing countries have managed to reduce technology gaps as new technologies emerge (Perez and Soete, 1988: 458; Malerba and Nelson, 2011: 1645). These works have concluded that there are certain transition periods between technology revolutions that provide opportunities (which are both temporary and exceptional) for companies in developing countries to enter new sectors, as long as they have appropriate support from science and technology institutions. Depending on the complementarity between the new technologies and those that already exist, in these time periods, the minimum requirements for investment in productive capacity and experience (both productive and regulatory) may become of secondary importance vis á vis requirements in scientific and technological knowledge (Perez and Soete, 1988). Of course, analyzing possibilities for development requires keeping in mind the specific nature of the knowledge base of each technology revolution in central countries and the response of dominant groups.

Various works have analyzed the technological and regulatory entry requirements specific to certain areas of biotechnology in developing countries (Katz and Bercovich, 1990; Possas, et al., 1994; Guzman et al., 2004). From a different perspective, this article seeks to analyze how knowledge bases are reworked in light of new biotechnologies in a set of sectors in which the principal chemical groups of central countries have managed to maintain their leadership since the end of the nineteenth century (Chandler, Jr., 2005). The end of the twentieth century brought successive waves of revolution in molecular biology (recombinant proteins, monoclonal antibodies, genomics, proteomics, etc.), which have forced the principal pharmaceutical, chemical and food and agriculture conglomerates to diversify their knowledge bases beyond their central capabilities.

These technology revolution dynamics have caused tension at the heart of neo-Schumpeterian approaches between the idea of highly technological, industry-specific activities and the concept of technology diversification. These approaches allow us to appreciate that these different knowledge bases may have areas in common, in line with the emergence of a technology paradigm that transcends specific industries. Finally, there is a third set of works that highlight coherence-related problems within companies as a result of diversification towards rather disperse activities and technologies (Teece et al., 1992: 2; Saviotti, 2002).

Moving on from this discussion, this article sets forth the following question: As a result of this tension between technological convergence and divergence, are we facing a biotechnology paradigm common to various industries? Or, on the contrary, do diverse sector-based paradigms coexist, highly specific (and complementary) to the pre-existing paths in each industry? Given the growing complexity associated with the coexistence of different technologies, a second question would be: Have the leading firms in the biotechnology sector managed to consolidate a coherent knowledge base that will allow them to transform technology opportunities into new products or processes? Or, have they limited themselves to growth in which different technologies are assimilated as assets in a financial portfolio?

To develop answers to these questions, this work uses a methodological approach with patent data from a sample of leading biotechnology companies to measure technology diversification. Section 1 provides a conceptual debate on how the tension between technology specialization and diversification within companies is expressed at the aggregate level in the emergence of new technology paradigms. After the empirical framework has been introduced, section 13 investigates to what extent there is a trend towards convergence around a knowledge base common to the different industries. Section 14 analyzes if this process is manifest in strategies to coherently diversify the knowledge base, or if strategies to explore technology with low linkages between the diverse and varied fields of knowledge predominate, analyzing the effect of these strategies on the rate of biotechnology innovation from companies. Finally, this work presents some conclusions and further research questions.


This article begins with a vision of technological innovation in which innovation involves solving technical-economic problems that must fulfill certain cost requirements to be successful in the marketplace. This brings us to the notion of technology paradigm developed by Dosi (1998: 221), which includes, on the one hand, a resulting knowledge base of various scientific opportunities for future innovations, and, on the other, a limited set of heuristics or search procedures on how to explore these opportunities and ensure their appropriation.

The Theory of Organizational Competencies and Tensions Within Companies

In a context of high marketplace uncertainty, and also uncertainty with respect to the evolution of technology, companies limit their search for solutions to certain scientific principles and technologies. This set of heuristics on “how to do things” and “how to improve them” is part of the organizational routines of companies. In other words, these heuristics exist as certain repetitive problem-solving patterns (Nelson and Winter, 1982: 97). A company can thus be understood as a collection of routines that define its dynamic capacities and competitiveness. This allows us to define the limits of a company beyond transaction costs, internalizing the activities that contain its "central capabilities," that is, activities related to innovation, production and commercialization of a limited set of products that the company "knows how to do well."

However, as Teece ascertains (2008: 509), although this vision fills a vacuum in neoclassical theory by explaining how companies innovate in a context of uncertainty, in certain circumstances with a change of technology paradigm, firms must explore outside of their previous knowledge bases with greater intensity, in order to find new opportunities, orchestrate complementarities and create “new combinations.” As Dosi writes (1988: 1133), in these circumstances, tension arises in company strategy "between efforts to improve the capabilities of doing existing things, monitor existing contracts, allocate given resources, on the one hand, and, on the other, the development of capabilities for doing new things or old things in new ways.”

This tension appears in literature on technological innovation. On the one hand, empirical works inspired by neo-Schumpeterian ideas highlight: i) the cumulative and industry-specific nature of innovation, which can be explained by the technical-practical knowledge that results from the combination of experimentation, experience and interactions within firms or between suppliers and users of new products (Patel and Pavitt, 1997: 141). From this perspective, path-dependent processes would predominate, resulting in the persistence of divergent technological trajectories. On the other hand, there is literature that acknowledges the importance of processes involving ii) diversification of the knowledge base beyond industry-specific knowledge in light of unexploited scientific and technological opportunities and/or problems that remain unresolved with existing technology (Patel, 1999: 8; Von Tunzelmann, 2006: 6). This process of technology diversification from companies in different industries may explain the possibility of a trend towards the convergence of a shared knowledge base and the emergence of a technology paradigm shared by various industries.

Technology Diversification and the Emergence of Technology Paradigms

Technology paradigms encompass various phases, beginning with their emergence, in which, initially, different opportunities and search procedures coexist. At a certain time in their development, a selection takes place for a limited set of problem-solving patterns based on a certain knowledge base.

Diverse works on the evolution of innovative processes over time in manufacturing industries have shown different phases in the spread of technology throughout an industry (Abernathy and Utterback, 1978: 40; Afuah and Utterback, 1997: 183). In the initial phase of a new technology, competition between companies is focused on product features. The search for technical-economic solutions is oriented towards innovating in products based on existing technology processes. In the processing industries, product innovations also require radical processing innovations at the same time; in this case, firms must diversify their knowledge bases to find solutions to problems such as scaling products beyond the laboratory. As the cycle to develop the paradigm advances, the technology becomes more stable and innovation becomes incremental, starting with learning on top of the knowledge base. Practical knowledge in production and regulatory aspects supports central knowledge vis à vis formal knowledge in r&d. Cost advantages help companies gain dominance over the competition. In these stages, innovation becomes strongly dependent on the previous path and entry barriers are raised.

Here it is possible to appreciate how the tension between path dependence, based on previous technology, and company efforts towards technology diversification reappears in the framework of the cycle of technological diffusion. In the phase when a new technology paradigm is emerging, until it is finally in place, companies go from a situation in which they are highly path-dependent in terms of the knowledge base to a situation in which they have more highly diversified technology. This technology diversification process, within a sector, may or may not be accompanied by the convergence of the different sector knowledge bases around a set of heuristics common to all industries. Once the technology paradigm is in place and advancing towards consolidation, this diversification begins to drop off and innovation increases once again in resolving process bottlenecks, reinforcing path dependence.

As a consequence of technology diversification, the technology paradigm is not necessarily restricted to a single sector. Rather, depending on the stage of development, there are various opportunities for it to spread to other sectors, especially with the rise of new industries or the adoption of the paradigm by various preexisting industries, generating new technology systems and/or rejuvenating previously existing systems (Freeman and Perez, 1988: 38). Later, for a set of sector-based technology paradigms to converge into a technology system, the combination of different technologies must allow for the emergence of a common knowledge base and set of R&D heuristics shared among various industries, making it possible for new productive processes and key inputs to emerge that significantly reduce costs.

Coherent Diversification vs. Conglomerate Diversification: The Technology Strategy of Groups Facing New Technology Paradigms

Until now, we have emphasized how companies diversify technology, leaving the complementarities between different technologies internalized by firms more abstract. Teece et al. (1992: 25) argued that in order to prevent displacement by the competition, firms require a certain level of coherence in their knowledge bases.

The concept of coherence can be explained by the localized nature of learning, which is a process involving trials, feedback and evaluation in problem-solving. It is difficult to simultaneously alter multiple parameters of products and productive processes without lowering the rhythm of learning. From this perspective, companies focus on certain technology know-how that defines their central technology capabilities. However, they also integrate a set of secondary technology capabilities complementary to central capabilities. In certain circumstances with significant competitive pressure, secondary capabilities may become principal, serving to put pressure on the company changing its portfolio.

From that perspective, firms diversify their technology capabilities and change their knowledge bases as a function of the complementarities between central and secondary technologies (Coriat and Weinstein, 1995: 126). In this way, large companies change their knowledge bases while maintaining a certain level of coherence beyond a random portfolio of technologies.

The search for technology coherence is not common to all large companies and will depend on the competitive context in which they operate. In recent years, we have witnessed significant mergers and acquisitions of biotechnology companies by leading chemical/pharmaceutical or grain trading companies. These processes lead to conglomerate diversification strategies involving holding companies. From the perspective of coherence theory, these groups can only survive the competition in the context of a low degree of selectivity associated with the presence of industrial and regulatory entry barriers (Dosi et al., 1992: 27).

It is useful to wonder about the role of technology competition in this environment of capital concentration and centralization, in which various industries are dominated by groups with conglomerate strategies. Only in competitive contexts in which groups manage to maintain high regulatory entry barriers or control complementary assets, as is the case with certain pharmaceutical groups, can a conglomerate diversification strategy be viable. In a situation with low entry barriers, the strength of the competition will obligate larger groups to adjust their technology portfolios or lose market share.

As a result of these general considerations, it is useful to ask: what form of technology diversification do biotechnology groups and companies assume? Does coherent diversification or a conglomerate strategy predominate? As may be expected, the more that large companies coherently diversify on the technological and productive levels, the greater their capacity to take advantage of economies of scale and scope, raising dynamic entry barriers to the industry.

Here the question arises as to whether, as a result of the tension between convergence and divergence in the knowledge base, we are facing a biotechnology paradigm common to various industries, or whether, on the contrary, various diverse sectoral paradigms highly specific (and complementary) to the pre-existing paths in each industry coexist. In light of the fierce uncertainty associated with the coexistence of different knowledge bases, a second question to ask would be whether leading companies in the sectors of biotechnology diffusion have been able to consolidate a coherent knowledge base that would allow them to transform technology opportunities into new products and processes, or whether they have been limited to conglomerate expansion in which different technologies are assimilated as mere assets of a financial portfolio.


In order to analyze the degree of convergence among the biotechnology knowledge bases of different industries and the microeconomic responses of companies in terms of technology diversification, this text uses a methodological approach that will employ patent data as an indicator of the composition and evolution of company knowledge bases (Graaf, 2002: Saviotti, 2002). Although the objective of this work is not to provide an exhaustive review of the advantages and disadvantages of patents as an indicator of the knowledge base, a few of the main weak points of this type of indicator should be highlighted (Pavitt, 1988: 123). Among the most common criticism, it is important to mention that the propensity to innovate is not equal among sectors or companies, generating a bias in its estimation. The chemical-pharmaceutical industry does not have the same propensity to register patents as the metal-mechanical industry. This propensity will also vary widely between a large company in a developed nation and an independent enterprise in a peripheral country.

As Griliches (1990) ascertains, despite the limitations of using patents as an indicator, they are a unique source of information, given their broad coverage and the relative homogeneity of the criteria in building information. Patents reflect technology potential and in no way can substitute for information regarding new technologies validated by the market. In this way, a broad range of information regarding the scientific fields involved is of great use in analyzing the knowledge base.

It is also important to emphasize that patents are an indicator of invention, and do not necessarily translate into new products and processes. The knowledge base required for an innovation also requires tacit knowledge resulting from learning in the practice of designing, manufacturing and developing products, as well as learning in crucial areas such as regulatory and commercial factors that firms must take into account when bringing products to market (Brusoni et al., 2000). Although these elements are important, in the specific case of the pharmaceutical and chemical industries, patents are a good approximation of the knowledge base (Saviotti, 2002). In the period of study, this feature is even more significant, as the biotechnology-based industries were focused on absorbing scientific and technological knowledge.

In order to define the biotechnology knowledge base, this work applies the definition of biotechnology provided by the oecd, based on the International Patent Classification (ipc). ipc codes are assigned to patents by examiners. Although perspectives may vary among examiners as to the meanings of the categories, there is an agreement in place with respect to the criteria used for classification. This allowed for the assigned ipc code as a unit of analysis, grouped in different biotechnology areas according to the classification indicated in the appendix (see page 31).

The sample encompasses a selection of 43 companies from different industrial biotechnology applications: human health, food, enzymes and biomass-based biopolymers and other substitutes for chemical-based inputs. 1 Although the selection is focused on diversified large companies, some enterprises specialized in biotechnology that were originally start-ups and are currently fully integrated companies were included. The source of information used was the patents granted by the United States patent offices, organized by the Delphion database.2 The choice of the United States patent office is justified because this economy is the breeding ground for any company with the capabilities and desire to compete on the global scale. Based on this information, this work identified patents granted between 1980 and June 2009 for the pre-selected companies that meet the definition of biotechnology provided by the oecd (see appendix).


The way in which technology is developed, as analyzed in the previous section, would suggest that technology paradigms are characterized by a period of rapid growth in technology opportunities in an industry, followed by a stage of more moderate growth, forming a sort of “S” curve of technology opportunities, followed by a decline (Andersen, 2000: 30). The patents stock is therefore an approximate indicator of the opportunities made possible by the technology, and in this sense, it is different from the curve describing the diffusion of commercialized products.3 It is not possible to establish an average time period during which patents guarantee an advantage for companies as a mechanism to appropriate the outcome of innovation in order to use patents as an approximate indicator of the scientific and technological knowledge of companies, as different strategies and regulations may extend their useful life or increase how they can be used (Barton et al., 2002). In keeping with work by Graaf (2002: 10), this study adopted the ad hoc criteria that 13 years after being granted, patents no longer reflect a technology opportunity for companies.

Figure 1. Industrial Biotechnology: The Accumulated Stock of Patents Granted by upsto by Industry of Application

Note: Calculated patents stock, assuming that patents are no longer part of the stock after 13 years.
Source: Prepared by the author using data on patents granted by the United States Patent and Trademark Office (upsto).

As can be seen in Figure 1, the magnitude and rhythm at which biotechnology opportunities appear is different depending on the various areas of application. The magnitude of opportunities is significantly greater in pharmaceutical biotechnology than in other applications. Biotechnology opportunities also display different evolution over time in the other three sectors. The pattern of technology opportunities for food ingredients and enzymes reveals an initial growth phase until the end of the 1990s, followed by moderate growth and stagnation in growth rates in 2008. Biopolymers showed more or less steady growth in the patents stock (less cyclical) than the rest. The pharmaceutical industry demonstrated significantly different evolution, with successive waves of opportunity that never reach a stage of maturity.

As may be expected, these evolutions in the magnitude of biotechnology opportunities are accompanied by changes in the structure of the knowledge base and field, as some acquire a greater weight than others.

Accumulation and Convergence Among Different Industrial Biotechnology Applications

This section will further develop the question proposed in section 1: to what extent are the knowledge bases subject to cumulative processes with “path-dependency” throughout the entire period of study, or do they rather reveal a pattern of convergence around a knowledge base that would give rise to a common biotechnology paradigm?

The Evolution of the Structure of the Knowledge Base

Before analyzing the processes specific to each industry, it is of interest to show how the composition of the knowledge base taken as a whole has changed in each of the three decades of technology diffusion (see Table 1). This table reveals that the knowledge base has become slightly more diversified, as indicated in the drop in the Hirschman-Herfindalh index. The changes in the structure and hierarchy of different technologies were more notable between the 1980s and 1990s, than between the 1990s and the first decade of the new millennium. This would indicate that following an initial period in which firms incorporate new knowledge, starting in the 1990s, biotechnologies have stabilized into a more or less orderly pattern of heuristics upon which innovations are built.

By analyzing the composition of the knowledge base in detail, it becomes clear that there is a set of biotechnologies that were still insignificant in the 1980s and whose importance later increased significantly until they were among the technologies that draw the most industry interest, as they do today. This group includes recombinant DNA techniques, such as genetic engineering, which went from seventh place in importance to third. To a lesser extent, the importance of peptide developments increased (largely explained by monoclonal and polyclonal antibody developments). Finally, other biotechnologies that were important at the beginning later lost their significance, such as biological measuring or testing devices, which went from being the most important technology in the 1980s to sixth place in the 2000s.

It is important to note that there is some continuity in the knowledge base: certain traditional biotechnologies have maintained a relevant position in the structure of the knowledge base throughout the entire period of study. The identification and use of different microorganisms continues to be central, even after the spread of genetic engineering.4 Enzyme technologies with diverse applications for industrial use and bioprocessing technologies have also maintained an important weight in the knowledge base structure. This shows that regardless of the emergence of genetic engineering, and later genomics, there is a set of complementary technologies where companies count on accumulated capabilities. It is precisely this capacity to take advantage of these complementarities that allows these groups to gradually diversify their knowledge bases.

Accumulation and Path Dependency in Biotechnology Applications

The above analysis allows us to infer that the tension between the path-dependent nature of certain technologies belonging to the previous technology paradigm and the appearance of new technologies, such as recombinant dna, was present to differing degrees throughout the entire period analyzed. In order to systematically analyze this tension, we estimate the statistical correlation ρ between the composition of the industry knowledge base in the 1990s and the 1980s and between the 2000s and the 1990s. If the correlation is close to one and statistically significant, there is a high degree of path dependency in the industry knowledge base. In other words, new developments and/or technologies are highly dependent on the previous knowledge path. This means that, on the one hand, each industry explores technology opportunities in an environment of prior learning, and, on the other, innovative activities yield growing dynamics. When the accumulated knowledge in a certain combination of disciplines is greater, there is a higher probability of obtaining innovation.

We can note that the composition of the knowledge base in human health in the 1990s does not appear very correlated with that of the 1980s. This indicates a restructuring of the knowledge base in this time period and weak path dependency with respect to previous heuristics. Path dependency is relatively greater in the enzymes and food ingredients industries in which the cumulative and localized nature of solution search processes would seem to prevail. By contrast, between the 1990s and the first decade of the 2000s, there is a significant increase in path dependency, reflected for all of the industries with self-correlation indices close to one and significant at 1 percent. This would show that although firms continue to explore new biotechnology fields, the effects of accumulation have an impact in all sectors, with growing yield potential for the applications analyzed. This potential will only be effective, however, as a function of the degree of coherence of the knowledge bases of groups participating in each industry.

The Convergence of the Knowledge Base of Industrial Biotechnology Applications

It could be expected that in the time periods in which one of the industries showed high path dependency, the convergence of different knowledge bases would be limited. By contrast, if diversification leads to the modification of the knowledge base by adopting new technologies, there is a chance for convergence and the emergence of a new technology paradigm. One way to measure the degree of convergence between the knowledge bases of different applications is to estimate the statistical correlation between the knowledge base structures of different industrial applications in each period.

This estimate was made for the three decades, and two rather different situations emerged. On the one hand, industrial biotechnology applications in enzyme production, biopolymers and food showed growing convergence between the 1980s and 1990s, which has been consolidated since the 1990s and into the 2000s. On the other, the biopharmaceutical industry has shown temporary convergence: starting with a knowledge base that is different from the majority of other industries in the 1980s, it advances towards convergence in the 1990s and then diverges again in the first decade of the millennium.

Technology convergence in the 1990s is related to lower accumulation in r&d in all of the industries in this time period and the decade prior. For example, in the 1980s, industrial biotechnology companies manufactured biopolymers and biocatalysts (enzymes) by identifying existing microorganisms using extractive methods. The outbreak of molecular biology and modern genetic engineering techniques at the end of the 1970s in the area of human health led companies in these industries to diversify their science and technology knowledge bases towards these new technologies, previously unknown to them. This made possible, for example, in the 1990s, the production of enzymes from genetically modified microorganisms with levels of productivity significantly higher than those of the extractive methods, taking advantage (and strengthening) their prior knowledge with respect to the behavior of microorganisms acting as systems of expression.5 As a corollary, the diversification of the knowledge base of biopolymer and enzyme applications led to convergence in technology competition with the health industry, while also maintaining complementarities with the previous knowledge base.

In the 2000s, while industrial biotechnology applications converged into a common technology paradigm, this convergence was reversed for human health applications. The pharmaceutical industry increased r&d in biotechnology areas that are secondary in other industries, such as the development of monoclonal antibodies and their medicinal applications. Later, health industries diverged from the knowledge base of other applications, limiting the emergence of a common biotechnology paradigm that could give rise to a set of shared search heuristics. This allows us to ascertain that an "industrial biotechnology paradigm" has emerged, limited to applications in biopolymers, enzymes and food ingredients.


Facing this situation of partial convergence between different biotechnologies at the industry level, companies respond heterogeneously, often as a result of their previous microeconomic trajectories. In a context in which the fields of knowledge needed to develop new products are multiplying, companies may respond in varied ways to the challenges of growing technological complexity. As discussed in section 1, companies push forward strategies to diversify technology, which may be coherent or conglomerate, depending on the capacity to take advantage of complementarities among different technologies. Box 1 defines the indicators of diversification and technology coherence.

Although the productive strategies of different industries involving biotechnology applications are highly heterogeneous, the leading companies in each industry are prone to developing a diversified and coherent knowledge base. Only in the case of large diversified groups in the pharmaceutical industry6 and the food industry and/or grain traders diversified to biopolymers do we see dynamics more associated with expansion by merger-acquisition of companies, where technology diversification does not appear to be accompanied by the development of complementarities among different biotechnologies, resulting in low coherence. To summarize, Figure 2 shows the technology strategies of companies by their level of diversification and coherence.

Figure 2. Industrial Biotechnology: Company Strategies by Diversification and Coherence

Source: Prepared by the author using data on patents granted by the United States Patent and Trademark Office (upsto).

In order to evaluate to what extent innovation was determined by technology diversification or the coherence of the knowledge base, we performed a cross-section estimate of the determinants of biotechnology innovation for the 43 companies from the different industries included in the sample. The model combines information on the innovative performance of these firms, the structure of their knowledge bases and the strategies whose effects on innovation this work seeks to disentangle, always staying within the variables of the companies' own knowledge bases.

Box 1 presents the variables to be used. The dependent variable is the logarithm of patents between 2000 and 2009, considered to be a proxy variable for the competitive performance of the firms.

The diversification of the knowledge base (diversif) and coherence (coherenc) were included as strategic variables. In accordance with the discussion in section 1, it is expected that technology coherence will have a positive effect on the propensity to innovate. The effect of technology diversification on the rhythm of innovation is ambiguous because, on the one hand, when the knowledge base is broadened to new areas, the probability of innovating based on the incorporation of new technology tools increases, but, on the other, this greater diversification has a negative effect on innovative performance as the coherence of the knowledge base decreases. This is due to the difficulty that arises in taking advantage of complementarities between the different biotechnology areas, generating lower efficiency for the innovative process. Taking into account this possibility, a multiplicative variable between coherence and diversification is introduced as the independent variable in an attempt to capture the marginal effect of diversification on the effect of coherence.

Table 4 shows the results of the ordinary least squares estimate. As expected, there is a positive effect of the size of the knowledge base on innovation in the four regressions. Path dependency did not show a positive and significant effect. Also, the previous path does not seem to have an effect on the probability of innovation, which may be industry-specific rather than company-specific. Nor were alliances with companies of equal weight a determining factor for the propensity to innovate, which means that entering into asymmetrical cooperation agreements with laboratories and SMEs does not generate a positive effect.

As expected, technology coherence produces a positive and significant effect on innovation, while the effect of technology diversification strategies reveals that it has a negative and significant effect on the propensity to innovate. In addition, in the second specification, the multiplicative variable between coherence and diversification was significant and negative, which could be interpreted as an indirect negative effect as the effect of the coherence of the knowledge base is reduced. This result confirms the hypotheses deduced from the literature review in section 1, that conglomerate technology strategies or strategies focused on portfolios of non-complementary projects are not sustainable in the framework of relatively demanding selection.

Finally, the sectoral control variables verify that while industrial biotechnology applications produce a positive and significant effect on innovation vis à vis food applications, pharmaceuticals show a negative and non-significant effect. This result is consistent with the conclusions of section 2 in terms of the consolidation of the biotechnology paradigm in industrial applications, which is evident in the greater propensity to innovate seen in these industries.


This work has shed light on a set of results that are relevant when evaluating the possibilities for the expansion of biotechnology in developing countries. Thirty years after the emergence and inter-sectoral diffusion of these technologies, there is no unique technology paradigm, but rather multiple sectoral paths to innovation. This work has shown that the spread of biotechnologies has not been homogenous in terms of time periods or the sectors in which these technologies are applied, which may contribute to determining technology policy in developing countries.

From a sectoral perspective, there is a tendency towards convergence among the technology paradigms of the enzymes, biopolymers and food industries, which reveals the consolidation of strategies in these industries to exploit economies of scope based on technical problem-solving heuristics with common biotechnological areas. Biotechnology applications in the health sector, however, reflect rather different dynamics, as its knowledge base converged with the rest of the industries between the 1980s and 1990s, but starting with the new millennium, a differentiation process began. Biotechnology opportunities in the health sector have shown the most growth. Despite having high path dependency starting in the 2000s with respect to the knowledge base of the previous decade, this sector has undergone important changes in the composition of the knowledge base towards areas that are not of interest (for now) for industrial biotechnology activities.

The evolutionist literature theory that maintains that there is no one-to-one relationship between productive diversification and technology diversification is confirmed here. In this sense, diversified companies with conglomerate strategies and others with coherent diversification coexist. When the effect of the type of strategy on the rhythm of innovation is estimated, it becomes clear that coherence is what most explains the propensity of leading biotechnology companies to innovate. This shows that low usage of complementarities among different technologies within large groups that have conglomerate strategies can limit the creation rate of new technology knowledge.

It would be useful to undertake further research in the future to determine to what extent the greater propensity to innovate among coherent groups translates into significant increases in productivity and cost reductions, creating effective conditions to replace the technical-economic paradigm based on cheap oil and chemical synthesis.

This set of conclusions allows us to infer that given the presence of a consolidated biotechnology paradigm for industrial applications, entry opportunities in these sectors are limited. The dominant groups already have a set of routines, procedures and heuristics that translate into a greater propensity to innovate. However, in the pharmaceutical industry, because a knowledge base in common with the rest of the industries has yet to be consolidated and there are large groups with conglomerate strategies that survive in the framework of regulatory barriers, there are still temporary degrees of freedom for companies to enter in national markets with flexible regulations using strategies that manage to combine a coherent knowledge base with learning in production and the regulatory framework. To further develop these questions, the general analysis given in this article will need to be accompanied by in-depth case studies of medium-size enterprises or peripheral nations to identify the potential and limitations of this type of strategy, taking into account regulatory barriers and opportunities for insertion as suppliers in the value chain.


The oecd definition includes a broad variety of ipc biotechnologies, encompassing everything from recombinant dna techniques to traditional bioremediation techniques. We group the classifications by biotechnology areas based on the classification done by Graaf (2002) and consultation with biological science researchers. Fourteen fields or areas of biotechnology knowledge were determined.


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1 Various companies of chemical origin with diversification more towards agro-biotechnology were excluded from this analysis. Although they are important in analyzing the biotechnology knowledge base, they have specific dynamics that set them apart from industrial activities in terms of productive processes and plant breeder rights that exceed patents as an indicator of their knowledge base. For a more illustrative analysis of these dynamics, see Bisang et al. (2006).

2 The choice of patents granted and not requested is justified in the fact that while the requesting company selects the technology field to which they belong according to more or less subjective (or intentional) criteria, granted patents involve patent office examiners.

3 We should not confuse this curve with the diffusion curve. The latter shows how new products perform in the market, while the opportunity curve merely reflects potential developments indicated by the patent stock.

4 Microorganisms serve as systems of expression for genetic engineering insofar as the multiplication of new molecules is taking place, as is the case for yeasts or bacteria.

5 For an analysis of these aspects, see Gutman et al. (2006).

6 This is the case for Roche, a diversified group with a propensity for conglomerate growth. With low path dependency, associated to a strong change in the composition of their knowledge base between the 1980s and 2000, the company entered into modern biotechnologies with a strategy to acquire specialized biotechnology companies with whom they had prior alliances. However, this modality of growth, fundamentally based on centralization processes, has translated into a biotechnology knowledge base with low coherence.

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