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Firm-level capabilities towards big data value creation

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  • Brinch, Morten
  • Gunasekaran, Angappa
  • Fosso Wamba, Samuel

Abstract

Big data has played an increasingly important role in using data to improve business value. In response to several big data challenges, the purpose of this study is to identify firm-level capabilities required to create value from big data. The adjacent theories of business process management and IT business value underpinned the study, together with an in-depth case study that led to the identification of twenty-four types of capabilities related to IT, process, performance, human, strategic, and organizational practices. The findings confirmed the application of practices and capabilities of adjacent theories, as well as certain practices and attributes that were both changed and reinforced at the intersection of big data. As an outstanding additional support to the extant big data studies, this work empirically confirms and portrays hitherto unexplored capabilities of big data and set their roles, thus providing a holistic overview of firm-level capabilities that are required for big data value creation.

Suggested Citation

  • Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    • Handle: RePEc:eee:jbrese:v:131:y:2021:i:c:p:539-548
    DOI: 10.1016/j.jbusres.2020.07.036
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    References listed on IDEAS

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    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. Radhakrishnan, Abirami & Zu, Xingxing & Grover, Varun, 2008. "A process-oriented perspective on differential business value creation by information technology: An empirical investigation," Omega, Elsevier, vol. 36(6), pages 1105-1125, December.
    3. 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.
    4. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    5. 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.
    6. Wang, Yichuan & Hajli, Nick, 2017. "Exploring the path to big data analytics success in healthcare," Journal of Business Research, Elsevier, vol. 70(C), pages 287-299.
    7. 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.
    8. Janssen, Marijn & van der Voort, Haiko & Wahyudi, Agung, 2017. "Factors influencing big data decision-making quality," Journal of Business Research, Elsevier, vol. 70(C), pages 338-345.
    9. 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.
    10. Dutta, Debprotim & Bose, Indranil, 2015. "Managing a Big Data project: The case of Ramco Cements Limited," International Journal of Production Economics, Elsevier, vol. 165(C), pages 293-306.
    11. 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.
    12. Michael Hammer, 2010. "What is Business Process Management?," International Handbooks on Information Systems, in: Jan vom Brocke & Michael Rosemann (ed.), Handbook on Business Process Management 1, pages 3-16, Springer.
    13. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    14. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    15. 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.
    16. 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.
    17. Braganza, Ashley & Brooks, Laurence & Nepelski, Daniel & Ali, Maged & Moro, Russ, 2017. "Resource management in big data initiatives: Processes and dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 328-337.
    18. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    19. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    20. Kim Hua Tan & Guojun Ji & Chee Peng Lim & Ming-Lang Tseng, 2017. "Using big data to make better decisions in the digital economy," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 4998-5000, September.
    21. 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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