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Profiting from big data analytics: The moderating roles of industry concentration and firm size

Author

Listed:
  • Elisabetta Raguseo

    (Polito - Politecnico di Torino = Polytechnic of Turin)

  • Claudio Vitari

    (AMU - Aix Marseille Université, CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, AMU ECO - Aix-Marseille Université - Faculté d'économie et de gestion - AMU - Aix Marseille Université)

  • Federico Pigni

    (EESC-GEM Grenoble Ecole de Management)

Abstract

Big data has gained momentum as an Information Technology that is capable of supporting organizational efforts to generate new and better business value. We here contribute to the emerging literature on big data analytic (BDA) solutions by investigating the moderating roles of firm size and industry concentration in the relationship between BDA solutions and firm profitability. Using a unique panel data set that covers 13 years, from 2004 to 2016, which contains information about 176 firms, we provide robust econometric empirical evidence of the negative moderating effects of industry concentration and the positive moderating effects of firm size on the relationship between the use of BDA solutions and firm profitability. Our findings provide strong empirical evidence on the business value of BDA as well as the essential role played by contextual conditions that managers should consider.

Suggested Citation

  • Elisabetta Raguseo & Claudio Vitari & Federico Pigni, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," Post-Print hal-03032504, HAL.
    • Handle: RePEc:hal:journl:hal-03032504
    DOI: 10.1016/j.ijpe.2020.107758
    Note: View the original document on HAL open archive server: https://hal.science/hal-03032504
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    as
    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. Joe S. Bain, 1951. "Relation of Profit Rate to Industry Concentration: American Manufacturing, 1936–1940," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 65(3), pages 293-324.
    3. Claudio Vitari & Elisabetta Raguseo, 2016. "Big data value and financial performance: an empirical investigation [Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data]," Post-Print halshs-01923271, HAL.
    4. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    5. Erik Brynjolfsson, 1996. "The Contribution of Information Technology to Consumer Welfare," Information Systems Research, INFORMS, vol. 7(3), pages 281-300, September.
    6. Gongming Qian & Lee Li, 2003. "Profitability of small‐ and medium‐sized enterprises in high‐tech industries: the case of the biotechnology industry," Strategic Management Journal, Wiley Blackwell, vol. 24(9), pages 881-887, September.
    7. Michael E. Porter & Mariko Sakakibara, 2004. "Competition in Japan," Journal of Economic Perspectives, American Economic Association, vol. 18(1), pages 27-50, Winter.
    8. Jay B. Barney, 1996. "The Resource-Based Theory of the Firm," Organization Science, INFORMS, vol. 7(5), pages 469-469, October.
    9. Anandhi S. Bharadwaj & Sundar G. Bharadwaj & Benn R. Konsynski, 1999. "Information Technology Effects on Firm Performance as Measured by Tobin's q," Management Science, INFORMS, vol. 45(7), pages 1008-1024, July.
    10. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    11. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    12. Elisabetta Raguseo & Claudio Vitari, 2018. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," International Journal of Production Research, Taylor & Francis Journals, vol. 56(15), pages 5206-5221, August.
    13. Jeffrey G. Covin & Dennis P. Slevin & Randall L. Schultz, 1994. "Implementing Strategic Missions: Effective Strategic, Structural And Tactical Choices," Journal of Management Studies, Wiley Blackwell, vol. 31(4), pages 481-506, July.
    14. J. Michael Geringer & Stephen Tallman & David M. Olsen, 2000. "Product and international diversification among Japanese multinational firms," Strategic Management Journal, Wiley Blackwell, vol. 21(1), pages 51-80, January.
    15. Kathleen M. Eisenhardt & Jeffrey A. Martin, 2000. "Dynamic capabilities: what are they?," Strategic Management Journal, Wiley Blackwell, vol. 21(10‐11), pages 1105-1121, October.
    16. Wang, Yichuan & Kung, LeeAnn & Byrd, Terry Anthony, 2018. "Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 3-13.
    17. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    18. Mikalef, Patrick & Boura, Maria & Lekakos, George & Krogstie, John, 2019. "Big data analytics and firm performance: Findings from a mixed-method approach," Journal of Business Research, Elsevier, vol. 98(C), pages 261-276.
    19. Margaret A. Peteraf & Jay B. Barney, 2003. "Unraveling the resource-based tangle," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 309-323.
    20. 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.
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    Keywords

    IT business value; big data analytics; firm profitability; econometric analysis; industry concentration; firm size;
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