Inter-Organizational Network Studies—A Literature Review

经济地理学 营销 产业组织 业务 经济
作者
Carsten Bergenholtz,Christian Waldstrøm
出处
期刊:Industry and Innovation [Taylor & Francis]
卷期号:18 (6): 539-562 被引量:126
标识
DOI:10.1080/13662716.2011.591966
摘要

Research on inter-organizational networks is generally fragmented which renders some of the studies incompatible and hinders a greater understanding and coherence of the field. The major distinction—which is not clearly stated in most research—is between the metaphorical description of some type of interaction across organizational boundaries, or whether the term refers to specific social structures between organizations. Whereas the metaphorical approach has previously dominated research, there has been a rise in the use of more structured and quantifiable research, most notably in the use of social network analysis. However, this has not been without serious theoretical and methodological issues. Most notably, a number of the concepts, methods and theories used within the field of inter-organizational networks originate from research in interpersonal and intra-organizational networks where some of the methodological issues (e.g. unit of analysis and boundary specification) are more easily addressed. In order to map the different methodological approaches in the field of inter-organizational networks, this paper presents a large-scale systematic literature review of the last 12 years' research on inter-organizational networks, with a focus on the methodological features. Some of the main variables relate to the unit of analysis, whether social network analysis is applied and what concept of a network is involved. The main findings of this paper are that few of the previous studies have used the full methodological (and thus theoretical) scope of the available data, the most cited papers and those appearing in top-ranked journals are more prone to using social network analysis than papers in general and there is a recent tendency among influential papers to go beyond a narrow application of social network analysis, and rely on multiplex relational data and whole networks.
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