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Big data analytics capabilities and firm performance: An integrated MCDM approach

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  • Yasmin, Mariam
  • Tatoglu, Ekrem
  • Kilic, Huseyin Selcuk
  • Zaim, Selim
  • Delen, Dursun

Abstract

This study explores the interdependence of big data analytics (BDA) capabilities and the impact of these capabilities on firm performance using an integrated multicriteria decision-making (MCDM) methodology. Drawing on a rich data set obtained from selected case study firms in Pakistan, three MCDM tools, namely, intuitionistic fuzzy decision-making trial and evolution laboratory (IF-DEMATEL), analytic network process (ANP), and simple additive weighting (SAW), are employed to assess the relative importance of BDA capabilities and the relationship of these capabilities with the firm performance. The results show that BDA capabilities are interdependent, and infrastructure capabilities are the highest-ranked among all, followed by management and human resource capabilities, respectively. The SAW results indicate an association between BDA capabilities and firm performance. Moreover, BDA capabilities are more strongly related to operational performance than to market performance.

Suggested Citation

  • Yasmin, Mariam & Tatoglu, Ekrem & Kilic, Huseyin Selcuk & Zaim, Selim & Delen, Dursun, 2020. "Big data analytics capabilities and firm performance: An integrated MCDM approach," Journal of Business Research, Elsevier, vol. 114(C), pages 1-15.
    • Handle: RePEc:eee:jbrese:v:114:y:2020:i:c:p:1-15
    DOI: 10.1016/j.jbusres.2020.03.028
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    1. Chung, Shu-Hsing & Lee, Amy H. I. & Pearn, W. L., 2005. "Analytic network process (ANP) approach for product mix planning in semiconductor fabricator," International Journal of Production Economics, Elsevier, vol. 96(1), pages 15-36, April.
    2. Rialti, Riccardo & Zollo, Lamberto & Ferraris, Alberto & Alon, Ilan, 2019. "Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    3. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    4. Büyüközkan, Gülçin & Güleryüz, Sezin & Karpak, Birsen, 2017. "A new combined IF-DEMATEL and IF-ANP approach for CRM partner evaluation," International Journal of Production Economics, Elsevier, vol. 191(C), pages 194-206.
    5. Thomas L. Saaty & Luis G. Vargas, 2013. "The Analytic Network Process," International Series in Operations Research & Management Science, in: Decision Making with the Analytic Network Process, edition 2, chapter 0, pages 1-40, Springer.
    6. Thomas L. Saaty & Luis G. Vargas, 2013. "Decision Making with the Analytic Network Process," International Series in Operations Research and Management Science, Springer, edition 2, number 978-1-4614-7279-7, September.
    7. Aydiner, Arafat Salih & Tatoglu, Ekrem & Bayraktar, Erkan & Zaim, Selim & Delen, Dursun, 2019. "Business analytics and firm performance: The mediating role of business process performance," Journal of Business Research, Elsevier, vol. 96(C), pages 228-237.
    8. Amankwah-Amoah, Joseph, 2019. "Big data analytics and business failures in data-Rich environments: An organizing framework," MPRA Paper 91264, University Library of Munich, Germany.
    9. David J Teece, 2003. "Essays in Technology Management and Policy:Selected Papers of David J Teece," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 4545.
    10. Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    11. 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.
    12. 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.
    13. Michael J. Tippins & Ravipreet S. Sohi, 2003. "IT competency and firm performance: is organizational learning a missing link?," Strategic Management Journal, Wiley Blackwell, vol. 24(8), pages 745-761, August.
    14. Steven Ji-fan Ren & Samuel Fosso Wamba & Shahriar Akter & Rameshwar Dubey & Stephen J. Childe, 2017. "Modelling quality dynamics, business value and firm performance in a big data analytics environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5011-5026, September.
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