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Streams of digital data and competitive advantage: The mediation effects of process efficiency and product effectiveness

Author

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  • E. Raguseo

    (DIGEP - Department of Management and Production Engineering [Politecnico di Torino] - Polito - Politecnico di Torino = Polytechnic of Turin)

  • Pigni, F.

    (EESC-GEM Grenoble Ecole de Management)

  • Claudio Vitari

    (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)

Abstract

Firms can achieve a competitive advantage by leveraging real-time Digital Data Streams (DDSs). The ability to profit from DDSs is emerging as a critical competency for firms and a novel area for Information Technology (IT) investments. We examine the relationship between DDS readiness and competitive advantage by studying the mediation effect of product effectiveness and process efficiency. The research model is tested with data obtained from 302 companies, and the results confirm the existence of the mediation effects. Interestingly, we confirm that competitive advantage is more significantly impacted by IT investments affecting product effectiveness than those affecting process efficiency

Suggested Citation

  • E. Raguseo & Pigni, F. & Claudio Vitari, 2021. "Streams of digital data and competitive advantage: The mediation effects of process efficiency and product effectiveness," Post-Print hal-03323663, HAL.
    • Handle: RePEc:hal:journl:hal-03323663
    Note: View the original document on HAL open archive server: https://hal.science/hal-03323663
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    References listed on IDEAS

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    1. Tiernan, Chris & Peppard, Joe, 2004. "Information Technology:: Of Value or a Vulture?," European Management Journal, Elsevier, vol. 22(6), pages 609-623, December.
    2. Claudio Vitari, 2009. "It Dynamic Capability Development In The Context Of Data Genesis Capability," Grenoble Ecole de Management (Post-Print) hal-00463282, HAL.
    3. Paul A. Pavlou & Omar A. El Sawy, 2006. "From IT Leveraging Competence to Competitive Advantage in Turbulent Environments: The Case of New Product Development," Information Systems Research, INFORMS, vol. 17(3), pages 198-227, September.
    4. Bram Klievink & Bart-Jan Romijn & Scott Cunningham & Hans Bruijn, 2017. "Big data in the public sector: Uncertainties and readiness," Information Systems Frontiers, Springer, vol. 19(2), pages 267-283, April.
    5. William H. DeLone & Ephraim R. McLean, 1992. "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research, INFORMS, vol. 3(1), pages 60-95, March.
    6. Jörg Henseler & Marko Sarstedt, 2013. "Goodness-of-fit indices for partial least squares path modeling," Computational Statistics, Springer, vol. 28(2), pages 565-580, April.
    7. Claudio Vitari & Elisabetta Raguseo & Federico Pigni, 2020. "Taxonomy for real-time digital data initiatives," Post-Print hal-03026850, HAL.
    8. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    9. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    10. 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.
    11. Claudio Vitari & Elisabetta Raguseo & Federico Pigni, 2020. "Taxonomy for real-time digital data initiatives," Grenoble Ecole de Management (Post-Print) hal-03026850, HAL.
    12. Claudio Vitari, 2009. "It Dynamic Capability Development In The Context Of Data Genesis Capability," Post-Print hal-00463282, HAL.
    13. Bullen, Christine V. & Rockart, John F., 1981. "A primer on critical success factors," Working papers 1220-81. Report (Alfred P, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    14. Bram Klievink & Bart-Jan Romijn & Scott Cunningham & Hans Bruijn, 0. "Big data in the public sector: Uncertainties and readiness," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    15. 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.
    16. Claudio Vitari & Elisabetta Raguseo & Federico Pigni, 2020. "Taxonomy for real-time digital data initiatives," Post-Print hal-03511357, HAL.
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    Keywords

    Streams of big data; process efficiency; product effectiveness; competitive advantage;
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