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Relating supply network structure to productive efficiency: A multi-stage empirical investigation

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  • Kao, Ta-Wei (Daniel)
  • Simpson, N.C.
  • Shao, Benjamin B.M.
  • Lin, Winston T.

Abstract

The potential of Social Network Analysis (SNA) to characterize supply network structure is of growing interest in supply chain management, although the related literature provides few empirical investigations. This study identifies those SNA measures most closely associated with supply chain efficiency, using archival inter-firm relationship data collected from U.S. public companies in multiple industries. In a three-stage procedure, a DEA model is applied to measure firm- and chain-level efficiencies, followed by a correlation analysis to group SNA variables into clusters of high correlation. These clusters are used in a step-wise regression algorithm to identify those variables most relevant to productive efficiency while accounting for multicollinearity. The supply network structural characteristics that emerge as significant are consistent with many hypothesized relationships in the literature, although not without exceptions, such as an interesting tradeoff between the benefit of connectedness and a penalty for closeness.

Suggested Citation

  • Kao, Ta-Wei (Daniel) & Simpson, N.C. & Shao, Benjamin B.M. & Lin, Winston T., 2017. "Relating supply network structure to productive efficiency: A multi-stage empirical investigation," European Journal of Operational Research, Elsevier, vol. 259(2), pages 469-485.
  • Handle: RePEc:eee:ejores:v:259:y:2017:i:2:p:469-485
    DOI: 10.1016/j.ejor.2016.11.008
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    7. Su, Hung-Chung & Kao, Ta-Wei (Daniel) & Linderman, Kevin, 2020. "Where in the supply chain network does ISO 9001 improve firm productivity?," European Journal of Operational Research, Elsevier, vol. 283(2), pages 530-540.
    8. Rasool Lavaei Adaryani & Khalil Kalantari & Ali Asadi & Amir Alambeigi & Hesamedin Gholami & Naser Seifollahi, 2023. "Information sharing antecedents in the supply chain: a dynamic network perspective," Operations Management Research, Springer, vol. 16(2), pages 887-903, June.
    9. Yongbo Li & Amir-Reza Abtahi & Mahya Seyedan, 2019. "Supply chain performance evaluation using fuzzy network data envelopment analysis: a case study in automotive industry," Annals of Operations Research, Springer, vol. 275(2), pages 461-484, April.
    10. Denila Jinny Arulraj & Thillai Rajan Annamalai, 2020. "Firms’ Financing Choices and Firm Productivity: Evidence from an Emerging Economy," International Journal of Global Business and Competitiveness, Springer, vol. 15(1), pages 35-48, June.
    11. Iman Kazemian & S. Ali Torabi & Christopher W. Zobel & Yuhong Li & Milad Baghersad, 2022. "A multi-attribute supply chain network resilience assessment framework based on SNA-inspired indicators," Operational Research, Springer, vol. 22(3), pages 1853-1883, July.
    12. Xuan Wei & Wei Chen, 2019. "How Does A Firm’s Previous Social Network Position Affect Innovation? Evidence from Chinese Listed Companies," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
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