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Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities

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  • Patrick Mikalef
  • John Krogstie

Abstract

The potential of big data analytics in enabling improvements in business processes has urged researchers and practitioners to understand if, and under what combination of conditions, such novel technologies can support the enactment and management of business processes. While there is much discussion around how big data analytics can impact a firm’s incremental and radical process innovation capabilities, we still know very little about what big data analytics resources firms must invest in to drive such outcomes. To explore this topic, we ground this study on a theory-driven conceptualisation of big data analytics based on the resource-based view (RBV) of the firm. Based on this conceptualisation, we examine the fit between the big data analytics resources that underpin the notion, and their interplay with organisational contextual factors in driving a firm’s incremental and radical process innovation capabilities. Survey data from 202 chief information officers and IT managers working in Norwegian firms are analysed by means of fuzzy set qualitative comparative analysis (fsQCA). Results show that under different combinations of contextual factors the significance of big data analytics resources varies, with specific configurations leading to high levels of incremental and radical process innovation capabilities.

Suggested Citation

  • Patrick Mikalef & John Krogstie, 2020. "Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities," European Journal of Information Systems, Taylor & Francis Journals, vol. 29(3), pages 260-287, May.
    • Handle: RePEc:taf:tjisxx:v:29:y:2020:i:3:p:260-287
    DOI: 10.1080/0960085X.2020.1740618
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    Cited by:

    1. Showimy Aldossari & Umi Asma’ Mokhtar & Ahmad Tarmizi Abdul Ghani, 2023. "Factor Influencing the Adoption of Big Data Analytics: A Systematic Literature and Experts Review," SAGE Open, , vol. 13(4), pages 21582440231, December.
    2. Ludivine Ravat & Aurélie Hemonnet-Goujot & Sandrine Hollet-Haudebert, 2023. "Data-driven innovation capability of marketing: an exploratory study of its components and underlying processes," Post-Print hal-04151199, HAL.
    3. Ikenna Franklin EZE & Thobekani LOSE, 2023. "Consequences Of Failure And Challenges Of Small Business In South Africa: A Theoretical Review," Business Excellence and Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 13(3), pages 18-32, September.
    4. Lin, Shunzhi & Lin, Jiabao, 2023. "How organizations leverage digital technology to develop customization and enhance customer relationship performance: An empirical investigation," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    5. Plantec, Quentin & Deval, Marie-Alix & Hooge, Sophie & Weil, Benoit, 2023. "Big data as an exploration trigger or problem-solving patch: Design and integration of AI-embedded systems in the automotive industry," Technovation, Elsevier, vol. 124(C).
    6. Quick, Reiner & Münch, M. & Mayer, J. H., 2023. "Lessons Learned from a Case Study: a Diamond Model for Implementing and Scaling Process Mining," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 142479, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Anwar, Muhammad Azfar & Zong, Zupan & Mendiratta, Aparna & Yaqub, Muhammad Zafar, 2024. "Antecedents of big data analytics adoption and its impact on decision quality and environmental performance of SMEs in recycling sector," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    8. Joklan Imelda Camelia Goni & Amy Looy, 2024. "Developing a framework for innovating less-structured business processes: a Delphi study," Information Systems and e-Business Management, Springer, vol. 22(2), pages 385-413, June.
    9. Ludivine Ravat & Aurélie Hemonnet-Goujot & Sandrine Hollet-Haudebert, 2023. "Data-driven innovation capability of marketing for B2B firms: definition and construction process," Post-Print hal-04151228, HAL.
    10. 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.
    11. Mariani, Marcello M. & Machado, Isa & Nambisan, Satish, 2023. "Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda," Journal of Business Research, Elsevier, vol. 155(PB).
    12. Korayim, Diana & Chotia, Varun & Jain, Girish & Hassan, Sharfa & Paolone, Francesco, 2024. "How big data analytics can create competitive advantage in high-stake decision forecasting? The mediating role of organizational innovation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).

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