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Data capital investment strategy in competing supply chains

Author

Listed:
  • Baogui Xin

    (Shandong University of Science and Technology)

  • Yue Liu

    (Shandong University of Science and Technology)

  • Lei Xie

    (Shandong University)

Abstract

Digitalization strategy can conduce to improving sustainable competitive advantage for supply chains. However, digital transformation requires relentless data capital investment (DCI), often constrained by a dilemma that imposes a stark tradeoff between investment cost and returns. A three-stage non-cooperative game of data capital-embedded competing supply chains solves this dilemma. From the game model, we find neither manufacturer invests in data capital when the DCI is costly. In contrast, both manufacturers invest in data capital when the DCI benefits are relatively significant. And an asymmetric equilibrium with one investing and the other not will be achieved when the DCI advantages and the DCI cost disadvantage match each other. Under some circumstances, a prisoner’s dilemma exists where the two manufacturers implement DCI, even though they will be better off if neither implements. Moreover, DCI in each supply chain has two spillover effects: the positive vertical spillover effect and the negative horizontal spillover effect.

Suggested Citation

  • Baogui Xin & Yue Liu & Lei Xie, 2024. "Data capital investment strategy in competing supply chains," Annals of Operations Research, Springer, vol. 336(3), pages 1707-1740, May.
    • Handle: RePEc:spr:annopr:v:336:y:2024:i:3:d:10.1007_s10479-023-05334-1
    DOI: 10.1007/s10479-023-05334-1
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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Zhao, Xuan & Shi, Chunming, 2011. "Structuring and contracting in competing supply chains," International Journal of Production Economics, Elsevier, vol. 134(2), pages 434-446, December.
    3. Pan Liu & Shu-ping Yi, 2018. "A study on supply chain investment decision-making and coordination in the Big Data environment," Annals of Operations Research, Springer, vol. 270(1), pages 235-253, November.
    4. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
    5. Li, Mingwei & Jia, Suling & Du, Wenyu (Derek), 2019. "Fans as a source of extended innovation capabilities: A case study of Xiaomi Technology," International Journal of Information Management, Elsevier, vol. 44(C), pages 204-208.
    6. Gilbert, Stephen M. & Cvsa, Viswanath, 2003. "Strategic commitment to price to stimulate downstream innovation in a supply chain," European Journal of Operational Research, Elsevier, vol. 150(3), pages 617-639, November.
    7. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    8. Peter Goodridge & Jonathan Haskel & Harald Edquist, 2022. "We See Data Everywhere Except in the Productivity Statistics," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(4), pages 862-894, December.
    9. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    10. Anderson, Edward J. & Bao, Yong, 2010. "Price competition with integrated and decentralized supply chains," European Journal of Operational Research, Elsevier, vol. 200(1), pages 227-234, January.
    11. Jianhua Ma & Xingzheng Ai & Wen Yang & Yanchun Pan, 2019. "Decentralization versus coordination in competing supply chains under retailers’ extended warranties," Annals of Operations Research, Springer, vol. 275(2), pages 485-510, April.
    12. Timothy W. McGuire & Richard Staelin, 1983. "An Industry Equilibrium Analysis of Downstream Vertical Integration," Marketing Science, INFORMS, vol. 2(2), pages 161-191.
    13. Yang, Rui & Tang, Wansheng & Zhang, Jianxiong, 2021. "Technology improvement strategy for green products under competition: The role of government subsidy," European Journal of Operational Research, Elsevier, vol. 289(2), pages 553-568.
    14. Xingzheng Ai & Jing Chen & Jianhua Ma, 2012. "Contracting with demand uncertainty under supply chain competition," Annals of Operations Research, Springer, vol. 201(1), pages 17-38, December.
    15. Gangshu (George) Cai & Yue Dai & Sean X. Zhou, 2012. "Exclusive Channels and Revenue Sharing in a Complementary Goods Market," Marketing Science, INFORMS, vol. 31(1), pages 172-187, January.
    16. Côrte-Real, Nadine & Oliveira, Tiago & Ruivo, Pedro, 2017. "Assessing business value of Big Data Analytics in European firms," Journal of Business Research, Elsevier, vol. 70(C), pages 379-390.
    17. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    18. Li, Jin & Yang, Shilei & Shi, Victor & Zhai, Senjing, 2020. "Partial vertical centralization in competing supply chains," International Journal of Production Economics, Elsevier, vol. 224(C).
    19. Guan, Zili & Zhang, Xumei & Zhou, Maosen & Dan, Yiran, 2020. "Demand information sharing in competing supply chains with manufacturer-provided service," International Journal of Production Economics, Elsevier, vol. 220(C).
    20. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    21. Guowei Liu & Jianxiong Zhang & Wansheng Tang, 2015. "Joint dynamic pricing and investment strategy for perishable foods with price-quality dependent demand," Annals of Operations Research, Springer, vol. 226(1), pages 397-416, March.
    22. Daniel Schallmo & Christopher A. Williams & Luke Boardman, 2017. "Digital Transformation Of Business Models — Best Practice, Enablers, And Roadmap," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(08), pages 1-17, December.
    23. Albert Y. Ha & Shilu Tong, 2008. "Contracting and Information Sharing Under Supply Chain Competition," Management Science, INFORMS, vol. 54(4), pages 701-715, April.
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