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Data-driven supply chain capabilities and performance: A resource-based view

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Listed:
  • Yu, Wantao
  • Chavez, Roberto
  • Jacobs, Mark A.
  • Feng, Mengying

Abstract

Despite the importance and relevance of data-driven supply chains, there has been very limited empirical research that investigates how big data-driven supply chains affect supply chain capabilities. Drawing on the resource-based view, this study explores the effect of data-driven supply chain capabilities on financial performance. The data for this study were gathered from China’s manufacturing industry and analysed using structural equation modelling. The results indicate that a data-driven supply chain has a significant positive effect on the four dimensions of supply chain capabilities. Coordination and supply chain responsiveness are positively and significantly related to financial performance.

Suggested Citation

  • Yu, Wantao & Chavez, Roberto & Jacobs, Mark A. & Feng, Mengying, 2018. "Data-driven supply chain capabilities and performance: A resource-based view," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 371-385.
    • Handle: RePEc:eee:transe:v:114:y:2018:i:c:p:371-385
    DOI: 10.1016/j.tre.2017.04.002
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    1. Sharma, Subhash & Mukherjee, Soumen & Kumar, Ajith & Dillon, William R., 2005. "A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models," Journal of Business Research, Elsevier, vol. 58(7), pages 935-943, July.
    2. Haim Mendelson, 2000. "Organizational Architecture and Success in the Information Technology Industry," Management Science, INFORMS, vol. 46(4), pages 513-529, April.
    3. David J. Collis, 1994. "Research Note: How Valuable are Organizational Capabilities?," Strategic Management Journal, Wiley Blackwell, vol. 15(S1), pages 143-152, December.
    4. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    5. Armstrong, J. Scott & Overton, Terry S., 1977. "Estimating Nonresponse Bias in Mail Surveys," MPRA Paper 81694, University Library of Munich, Germany.
    6. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    7. Samuel Fosso Wamba, 2012. "Achieving supply chain Integration using RFID Technology: the Case of Emerging Intelligent B to B e-commerce Processes in a Living Laboratory," Post-Print hal-00809258, HAL.
    8. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    9. Philip, George & Booth, Marilyn E., 2001. "A new six 'S' framework on the relationship between the role of information systems (IS) and competencies in 'IS' management," Journal of Business Research, Elsevier, vol. 51(3), pages 233-247, March.
    10. Droge, Cornelia & Vickery, Shawnee K. & Jacobs, Mark A., 2012. "Does supply chain integration mediate the relationships between product/process strategy and service performance? An empirical study," International Journal of Production Economics, Elsevier, vol. 137(2), pages 250-262.
    11. Zimmer, Kirstin, 2002. "Supply chain coordination with uncertain just-in-time delivery," International Journal of Production Economics, Elsevier, vol. 77(1), pages 1-15, May.
    12. Yu, Wantao & Jacobs, Mark A. & Salisbury, W. David & Enns, Harvey, 2013. "The effects of supply chain integration on customer satisfaction and financial performance: An organizational learning perspective," International Journal of Production Economics, Elsevier, vol. 146(1), pages 346-358.
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