Analyzing the Impact of Firm’s Embeddedness in a Centralized Supply Network Structure on Relational Capital Outcomes

Open access


This research looks at the different effects of firms’ network structural positions in an upstream supply network upon the firms’ level of relational capital outcomes. Previous research largely focused on the context of decentralized network structure. However, the supply network is a centralized network because of the existence of the focal firm. The existence of the focal firm may influence the impact of relational capital outcomes. Hence, the objective of this research is to determine the type of network structural positions required to obtain a reasonable relational capital outcome in upstream supply network. This study found that network structural positions, i.e. degree centrality contributed to firms’ level of relational capital trust. Hence, a firm embedded in upstream supply network benefits differently in terms of relational capital through different degree of embeddedness. The firm resources should be re-aligned to match the benefits of the different network structural positions.

1. Borgatti & Li 2009, ‘On Social Network Analysis in a Supply Chain Context*’, Journal of Supply Chain Management, vol. 45, no. 2, pp. 5-22.

2. Borgatti, S & Molina, J 2003, ‘Ethical and strategic issues in organizational social network analysis’, The Journal of Applied Behavioral Science, vol. 39, no. 3, p. 337.

3. Borgatti, SP, Jones, C & Everett, MG 1998, ‘Network measures of social capital”, Semantic Pajek Networks Software, vol. 21, no. 2, pp. 27-36.

4. Choi & Kim, Y 2008, ‘Structural Embeddedness and Supplier Management: A Network Perspective’, Journal of Supply Chain Management, vol. 44, no. 4, pp.5-13.

5. Choi & Krause 2006, ‘The supply base and its complexity: Implications for transaction costs, risks, responsiveness, and innovation’, Journal of Operations Management, vol. 24, no. 5, pp. 637-52.

6. Coleman, JS 1988, ‘Social Capital in the Creation of Human Capital’, American journal of sociology, vol. 94, no. ArticleType: research-article / Issue Title: Supplement: Organizations and Institutions: Sociological and Economic Approaches to the Analysis of Social Structure / Full publication date: 1988 / Copyright © 1988 The University of Chicago Press, pp. S95-S120.

7. Corteville, L & Sun, M 2009, An interorganizational social network analysis of the Michigan diabetes outreach networks: Measuring relationships in community networks, Lansing, MI: Michigan Department of Community Health.

8. Ford, EW, Wells, R & Bailey, B 2004, ‘Sustainable network advantages: A game theoretic approach to community-based health care coalitions’, Health Care Management Review, vol. 29, no. 2, p. 159.

9. Freeman, LC 1979, ‘Centrality in social networks conceptual clarification’, Social Networks, vol. 1, no. 3, pp. 215-39.

10. Granovetter 1985, ‘Economic action and social structure: the problem of embeddedness’, American journal of sociology, pp. 481-510.

11. Granovetter, M 1985, ‘Economic action and social structure: the problem of embeddedness’.

12. Gulati, R 1995, ‘Does Familiarity Breed Trust? The Implications of Repeated Ties for Contractual Choice in Alliances’, The Academy of Management Journal, vol. 38, no. 1, pp. 85-112.

13. Gulati, R & Gargiulo, M 1999, ‘Where do interorganizational networks come from? 1’, American journal of sociology, vol. 104, no. 5, pp. 1439-93.

14. Gulati, R & Gargiulo, M 1999, ‘Where do interorganizational networks come from? 1’, American journal of sociology, vol. 104, no. 5, pp. 1398-438.

15. Knoke, D & Kuklinski, J 1982, Network analysis, Sage Publications, Inc.

16. Krackhardt, D 1999, ‘The ties that torture: Simmelian tie analysis in organizations’, Research in the Sociology of Organizations, vol. 16, no. 1999, pp. 183-210.

17. Krauss, M, Mueller, N & Luke, D 2004, ‘Interorganizational relationships within state tobacco control networks: a social network analysis, Preventing Chronic Disease, vol. 1, no. 4, p. A08.

18. Lusher, D 2011, ‘Masculinity, educational achievement and social status: a social network analysis, Gender and Education, vol. 23, no. 6, pp. 655-75.

19. Lusher, D & Robins, G 2010, ‘A social network analysis of hegemonic and other masculinities’, The Journal of Men’s Studies, vol. 18, no. 1, pp. 22-44.

20. Lusher, D, Robins, G & Kremer, P 2010, ‘The application of social network analysis to team sports’, Measurement in physical education and exercise science, vol. 14, no. 4, pp. 211-24.

21. McEvily, B, Perrone, V & Zaheer, A 2003, ‘Trust as an organizing principle’, Organization Science, pp. 91-103.

22. McEvily, B & Zaheer, A 1999, ‘Bridging ties: a source of firm heterogeneity in competitive capabilities’, Strategic Management Journal, vol. 20, no. 12, pp.1133-56.

23. Podolny, JM & Page, KL 1998, ‘Network forms of organization’, Annual review of sociology, pp. 57-76.

24. Powell, W 1996, ‘ Neither market nor hierarchy: network forms of organization’, in Te al (ed.), Markets, Hierarchies and Networks Understanding Governance RAW Rhodes,, Buckingham, Open University Press, vol., pp. 265-76. 29 ... 35

25. Reagans, R, Zuckerman, E & McEvily, B 2004, ‘How to make the team: Social networks vs. demography as criteria for designing effective teams’, Administrative Science Quarterly, vol. 49, no. 1, pp. 101-33

26. Robins, G, Pattison, P, Kalish, Y & Lusher, D 2007, ‘An introduction to exponential random graph models for social networks’, Social Networks, vol. 29, no. 2, pp. 173-91.

27. Robins, G, Pattison, P & Wang, P 2009, ‘Closure, connectivity and degree distributions: Exponential random graph (p*) models for directed social networks’, Social Networks, vol. 31, no. 2, pp. 105-17.

28. Scott, J 1988, ‘Social network analysis’, Sociology, vol. 22, no. 1, p. 109.

29. Uzzi, B 1997, ‘Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness’, Administrative science quarterly, vol. 42, no. 1, pp. 35-67.

30. Wang, P, Robins, G & Pattison, P 2006a, ‘Pnet: a program for the simulation and estimation of exponential random graph models’, University of Melbourne.

31. Wang, P, Robins, G & Pattison, P 2006b, ‘PNet: Program for the estimation and simulation of p* exponential random graph models, User Manual’, Department of Psychology, University of Melbourne.

32. Womack, JP 1990, Machine that changed the world, Scribner.

33. Wu, Z, Choi, TY & Rungtusanatham, MJ 2010, ‘Supplier–supplier relationships in buyer–supplier–supplier triads: Implications for supplier performance’, Journal of Operations Management, vol. 28, no. 2, pp. 115-23.

Journal Information


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 168 116 12
PDF Downloads 93 71 4