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Big data analytics business value and firm performance: linking with environmental context

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  • Claudio Vitari
  • Elisabetta Raguseo

Abstract

Previous studies, grounded on the resource based view, have already explored the relationship between the business value that Big Data Analytics (BDA) can bring to firm performance. However, the role played by the environmental characteristics in which companies operate has not been investigated in the literature. We inform the theory, in that direction, via the integration of the contingency theory to the resource based view theory of the firm. This original and integrative model examines the moderating influence of environmental features on the relationship between BDA business value and firm performance. The combination of survey data and secondary financial data on a representative sample of medium and large companies makes possible the statistical validation of our research model. The results offer evidence that BDA business value leads to higher firm performance, namely financial performance, market performance and customer satisfaction. More original is the demonstration that this relationship is stronger in munificent environments, while the dynamism of the environment does not have any moderating effect on the performance of BDA solutions. It means that managers working for firms in markets with a growing demand are in the best position to profit from BDA.

Suggested Citation

  • Claudio Vitari & Elisabetta Raguseo, 2020. "Big data analytics business value and firm performance: linking with environmental context," International Journal of Production Research, Taylor & Francis Journals, vol. 58(18), pages 5456-5476, September.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:18:p:5456-5476
    DOI: 10.1080/00207543.2019.1660822
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    Cited by:

    1. Ladi Daodu & Prof. Dr. Amiya Bhaumik, 2024. "Impacts of Innovation and Business Analytics on the Performance of the Service Sector in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(6), pages 77-91, June.
    2. Dignity Paradza & Olawande Daramola, 2021. "Business Intelligence and Business Value in Organisations: A Systematic Literature Review," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    3. Sivarajah, Uthayasankar & Kumar, Sachin & Kumar, Vinod & Chatterjee, Sheshadri & Li, Jing, 2024. "A study on big data analytics and innovation: From technological and business cycle perspectives," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    4. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    5. Corallo, Angelo & Crespino, Anna Maria & Del Vecchio, Vito & Gervasi, Massimiliano & Lazoi, Mariangela & Marra, Manuela, 2023. "Evaluating maturity level of big data management and analytics in industrial companies," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    6. Oduro, Stephen & De Nisco, Alessandro & Mainolfi, Giada, 2023. "Do digital technologies pay off? A meta-analytic review of the digital technologies/firm performance nexus," Technovation, Elsevier, vol. 128(C).
    7. Olabode, Oluwaseun E. & Boso, Nathaniel & Hultman, Magnus & Leonidou, Constantinos N., 2022. "Big data analytics capability and market performance: The roles of disruptive business models and competitive intensity," Journal of Business Research, Elsevier, vol. 139(C), pages 1218-1230.
    8. Tugba Karaboga & Cemal Zehir & Ekrem Tatoglu & H. Aykut Karaboga & Abderaouf Bouguerra, 2023. "Big data analytics management capability and firm performance: the mediating role of data-driven culture," Review of Managerial Science, Springer, vol. 17(8), pages 2655-2684, November.

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