A Framework for Analyzing Absorptive Capacity Using Social Network Analysis (Case Study: Sharif University Innovation Ecosystem)

Document Type : Research Paper

Authors

1 Associate Prof., College of Management, University of Tehran, Tehran, Iran.

2 Associate Professor, Department of Technology and Innovation Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

3 Ph.D. Candidate, Department of Technology Management, College of Management, University of Tehran, Tehran, Iran.

10.22059/imj.2024.357800.1008049

Abstract

Objective
Due to the importance of innovation ecosystems in the global economic circle, the absorptive capacity concept is important in Iran, too. Sharif University of Technology is important in establishing innovation clusters, because of its scientific status, access to knowledge resources, and being close to various industries and factories. It also provides a platform for the flow of knowledge between academic and business fields.
 
Methods
Sharif University of Technology is a pioneer in establishing a cluster platform for start-ups and accelerators. The growth of innovation clusters is a current policy focus in many countries. This article examines and analyzes the absorption capacity of the ecosystem. The primary variable is the measurement of knowledge flows within clusters, while the ability of units to utilize these knowledge flows is another crucial factor. Knowledge flow and the capacity to leverage it are vital inputs for fostering innovative approaches within the cluster. Although the analysis of absorptive capacity has traditionally focused on individual companies without considering the geographical context and external knowledge connections, this approach has often overlooked the external role of companies. Consequently, the effectiveness of knowledge within each organization has been neglected.
 
Results
In this study, In this respect, the role of technology sectors of the Sharif University Innovation Ecosystem regarding knowledge communication and their cognitive position will be explored. The absorption capacity and models of knowledge connection between technology sectors in the innovation cluster are tested by identifying and extracting the role of each sector of the innovation cluster to use the Social Network Analysis (SNA) method. As the absorptive capacity of technological fields is conducive to their innovative performance, detecting and fostering the knowledge flow between such fields ways is essential to develop the innovative platform of innovation clusters, which, in turn, aims at investigating the current situation in terms of the dimensions of absorptive capacity, namely acquisition, adaptation, transfer, and exploitation in the innovation cluster's scope and its external environment. It picks out and exploits the cognitive stance of every field of technology based on the communication of knowledge. Thus, it will be spotting the input to everyone and the output from it, the types of communication, and the types of knowledge interactions with the outside entities. All these interactions result in roles such as technology gatekeepers, stars, and isolates.
 
Conclusion
The absorption capacity model has been identified by extracting the role of each technology field from within and outside the cluster utilizing the social network analysis method by computing the degree of knowledge input, output, and intermediateness. Through expert opinions and a literature review, the improvement solutions of the knowledge network have been identified and are presented as part of a transition mechanism to enhance the condition.

Keywords

Main Subjects


 
 Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration: Agent-Based Models of Competition and Collaboration. Princeton university press.
Capello, R. & Faggian, A. (2005). Collective learning and relational capital in local innovation processes. Regional studies, 39(1), 75-87.
Dantas, E. & Bell, M. (2009). Latecomer firms and the emergence and development of knowledge networks: the case of Petrobras in Brazil. Research Policy, 38(5), 829-844.
Dawid, H., & Fagiolo, G. (2008). Agent-based models for economic policy design: Introduction to the special issue. Journal of Economic Behavior & Organization, 67(2), 351-354.
Gilbert, N., Ahrweiler, P. & Pyka, A. (2007). Learning in innovation networks: Some simulation experiments. Physica A: Statistical Mechanics and its Applications378(1), 100-109.
Gilbert, N., Ahrweiler, P. & Pyka, A. (Eds.). (2014). Simulating knowledge dynamics in innovation networks (pp. 185-198). Berlin: Springer.
Giuliani, E. & Bell, M. (2005). The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster. Research policy, 34(1), 47-68.
Giuliani, E. (2003). Cluster Absorptive Capacity: a comparative study between Chile and Italy. In Paper presentado en la conferencia an honor de Keith Pavitt What do we Know about Innovation.
Giuliani, E. (2003, January). Knowledge in the Air and its Uneven Distribution: A story of a Chilean Wine Cluster. In DRUID Winter Conference, Aalborg (pp. 16-18).
Giuliani, E. (2007). The selective nature of knowledge networks in clusters: evidence from the wine industry. Journal of economic geography, 7(2), 139-168.
Hosseini, S. M. & Aghdasi, M. (2023). Comparison of Formal and Informal Structures in Organizations Using the Social Network Analysis. Industrial Management Journal, 15(3), 365-385. doi: 10.22059/imj.2023.355111.1008026 (in Persian)
Jaffe, A. B. (1989). Real effects of academic research. The American economic review, 957-970.
Jaffe, A. B., Trajtenberg, M. & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. the Quarterly journal of Economics, 108(3), 577-598.
Keeble, D., & Wilkinson, F. (1999). Collective learning and knowledge development in the evolution of regional clusters of high technology SMEs in Europe. Regional studies, 33(4), 295-303.
Korber, M. & Paier, M. (2011). Exploring the effects of public research funding on biotech innovation: an agent-based simulation approach. In 8th International Conference on Complex Systems (pp. 599-613).
Lichtenthaler, U. & Lichtenthaler, E. (2009). A capability-based framework for open innovation: Complementing absorptive capacity. Journal of management studies, 46(8), 1315-1338.
Matofi, A., Tajdini, K., Aghajani, H.A. & Tajdini, K. (2011). The role of learning orientation on innovation and organizational performance. Management perspective, 9(4), 57-71.
(in Persian)
Zahra, S. A. & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of management review, 27(2), 185-203.