Entrepreneurial Structural Dynamics in Dedicated Biotechnology Alliance and Institutional System Evolution

Transcription

1 1 Entrepreneurial Structural Dynamics in Dedicated Biotechnology Alliance and Institutional System Evolution Tariq Malik Clore Management Centre, Birkbeck, University of London London WC1E 7HX

2 2 The concept of learning activities is widely perceived to be a phenomenon surrounding organizational and management research. The research pertaining to the learning activities is attributed to the formulation, succession and evolution of the technologies (Nelson & Sampat, 2001) and related systems (Brockhoff et al., 1999). These systems may be institutional or structural, and each is simultaneously an antecedent as well as the consequence of the other (Lundvall, 1998). The co-evolution defines the dynamics or the sclerosis of a system (Edquist & McKelvey, 2000). The streams of perspectives, however, focus on one or the other. The proponents of external technological determinisms see knowledge as the given factor or resource. Accordingly, the allocation of the given knowledge resources determines the predefined outcome. Although the priority is given to the knowledge in the centre stage, two elements are overlooked: (a) a narrow definition of knowledge is adopted, and (b) the internal systems are presumed passive. In reality, such is not the case, so the focus moves to the internal perspective. The internal view of knowledge and learning emphasises on the proactive learning. Accordingly, this approach covers broader views on knowledge and proactive learning. However, this approach has been seen based on the access structure (March & Olsen, 1989). The access structure is where the technological tools are in place and rest is assumed to be effectively taking place. In reality, nevertheless, the knowledge creation and learning (taken together) are contingent upon a system, which is turn is based on the institutional and technological structures (Lundvall, 1998). In enabling conditions and absorptive capacity (willingness and ability) are essential for the dynamics of a technological innovation. The willingness suggest there needs to be some kind of enabling conditions that provide conduce environment (institutional system) between the two actors or systems, and there needs to be ability of systems to provide-and-receive knowledge content and context (Johnson et al., 2002). Hence, both the access structure and decision structures are essential on the one hand, and the absorptive capacity to learn on the other (Cohen & Levinthal, 1990). This entails the importance of the third perspective, the interactive learning and evolution (McKelvey, 1997). The evolutionary perspective has recently gained grounds in the mainstream management and organizational theory, practice and empirical studies since its inception (Nelson & Winter, 1982). Despite the system approach is able to explain better than the either external or internal alone, the bulk of the literature in the Anglo-Saxon models of corporate governance continues to maintain hegemonies. This deficiency of not understanding systems of innovation perhaps can be traced from several reasons. One is that the technological activity is a long term process, and therefore, it requires a longitudinal focus. Researchers generally focus static evidences that are easy to pin-point. Second, because the high technology sector such as biotechnology and pharmaceutical constitute a sophisticated process in which uncertainty and costs are high for research and development, the governance structure (decision structure) and coordination structure (integration structure) are tightly-couple where knowledge is of one dimension and loosely-coupled where the knowledge of another dimension with the economic domains. Focussing on the context of this research, which is the dedicated biotechnology alliance (DBA), I propose a conceptual model on DBA and then test it based on a large

3 3 scale dataset drawn from the qualitative announcements (observations). The model, the dataset and the methodology is different in several ways. First, generally, the economic performance is measured in ex ante (R&D investment) and innovation ex post (patents). The patents can be a proxy for applied research, and the performance for wealth creation. For basic research, R&D expenditures, number of scientists and citations are used, which are less likely to be found in all the biotechnology firms. Hence, the reasonably relevant source is the qualitative announcements. I use the announcements as observations. Second, the approach in the contemporary studies is situated on the incumbents perspectives, because it is relatively easy and plausible to trace patents, R&D expenditures and some level of public announcements. The entrepreneurial biotechnological firms, on the other hand promote their successfulness through public announcements and by seeking deals with other similar and complementary resource owners. Given that knowledge is both the explicit and tacit, the tacit element is the most crucial for high technology sector. Therefore, the entrepreneurial research that focuses on tacit dimensions through social interaction merits some attention. In the learning structure that enables DBA entrepreneurial activities for their survival and growth, it may be asserted that the proposed dependent variable is learning and the explanatory variable is defined through the processes (routines) of organizations. The learning is defined as evolutionary (explicit and tacit); it is predicated on governance and coordination structures; it minimizes the pitfalls attributed to rules-based in the one hand and judgement based prediction in the other; and it maps the sense-making process for researchers and an analytical tool for practitioners. Following the paradigm that learning is a dynamic and co-evolutionary process, I draw a conceptual model and eventually test it with the data gathered from more than observations on alliances, more than 2900 firms and spanning over 11 years ( ).

4 4 Methods & Key Propositions The figure depicts the likely hypothesis and eventually propositions that will be drawn and tested. Considering space constraints, the detail elaboration can be shared during the workshop. Learning process Basic Technology Diversity Of Interaction 2 1 Nucleic Acid III 3 4 Interaction Frequency Of Interaction 6 5 Protein II Technological Innovation 7 Cognitive Routines 8 9 Cell/ Tissue I Sub-technologies of Nucleic Acid, Protein and Cell/Tissue are outlined in the method section

5 5 References Brockhoff, K., Chakrabarti, A. K., & Hauschildt, J. (Eds.) The Dynamics of Innovation: Strategic and Managerial Implications. New York: Springer. Cohen, W. M., & Levinthal, D Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35: Edquist, C., & McKelvey, M. (Eds.) Systems of Innovation: Growth, Competition and Employment. Cheltenham: Edward Elgar. Johnson, B., Lorenz, E., & Lundvall, B.-k Why all this fuss about codified and tacit knowledge? Industrial and Corporate Change, 11(2): Lundvall, B.-A Why study National Systems and National Systels of Innovation. Technology Analysis and Strategic Management, 10(4): March, J. G., & Olsen, J. P Rediscovering Institutions. New York: Free Press. McKelvey, M Using Evolutionary Theory to Define Systems of Innovation. In Edquist, C. (Ed.), Systems of Innovation: London: Pinter. Nelson, R., & Sampat, B Making sense of institutions as a factor in economic growth. Journal of Economic Organization and Behaviour. Nelson, R., & Winter, S An Evolutionary Theory of Economic Change. Cambridge, MA: Harvard University Press.