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Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance

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  • Awan, Usama
  • Shamim, Saqib
  • Khan, Zaheer
  • Zia, Najam Ul
  • Shariq, Syed Muhammad
  • Khan, Muhammad Naveed

Abstract

Big data analytics (BDA) is a revolutionary approach for sound decision-making in organizations that can lead to remarkable changes in transforming and supporting the circular economy (CE). However, extant literature on BDA capability has paid limited attention to understanding the enabling role of data-driven insights for supporting decision-making and, consequently, enhancing CE performance. We argue that firms drive decision-making quality through data-driven insights, business intelligence and analytics (BI&A), and BDA capability. In this study, we empirically investigated the association of BDA capability with CE performance and examined the mediating role of data-driven insights in the relationship between BDA capability and decision-making. Data were collected from 109 Czech manufacturing firms, and partial least squares structural equation modeling was applied to analyze the data. The results reveal that BDA capability and BI&A are positively associated with decision-making quality. This effect is stronger when the manufacturer utilizes data-driven insights. The results demonstrate that BDA capability drives decision-making quality in organizations, and data-driven insights do not mediate this relationship. BI&A is associated with decision-making quality through data-driven insights. These findings offer important insights to managers, as they can act as a reference point for developing data-driven insights with the CE paradigm in organizations.

Suggested Citation

  • Awan, Usama & Shamim, Saqib & Khan, Zaheer & Zia, Najam Ul & Shariq, Syed Muhammad & Khan, Muhammad Naveed, 2021. "Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:tefoso:v:168:y:2021:i:c:s0040162521001980
    DOI: 10.1016/j.techfore.2021.120766
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    1. Walter R. Stahel, 2016. "The circular economy," Nature, Nature, vol. 531(7595), pages 435-438, March.
    2. Kristoffersen, Eivind & Blomsma, Fenna & Mikalef, Patrick & Li, Jingyue, 2020. "The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies," Journal of Business Research, Elsevier, vol. 120(C), pages 241-261.
    3. 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.
    4. Wamba, Samuel Fosso & Dubey, Rameshwar & Gunasekaran, Angappa & Akter, Shahriar, 2020. "The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism," International Journal of Production Economics, Elsevier, vol. 222(C).
    5. 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).
    6. 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.
    7. Ghasemaghaei, Maryam & Calic, Goran, 2019. "Does big data enhance firm innovation competency? The mediating role of data-driven insights," Journal of Business Research, Elsevier, vol. 104(C), pages 69-84.
    8. Akhtar, Pervaiz & Khan, Zaheer & Tarba, Shlomo & Jayawickrama, Uchitha, 2018. "The Internet of Things, dynamic data and information processing capabilities, and operational agility," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 307-316.
    9. Akter, Shahriar & Gunasekaran, Angappa & Wamba, Samuel Fosso & Babu, Mujahid Mohiuddin & Hani, Umme, 2020. "Reshaping competitive advantages with analytics capabilities in service systems," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    10. Yang, Yumei & Secchi, Davide & Homberg, Fabian, 2018. "Are organisational defensive routines harmful to the relationship between personality and organisational learning?," Journal of Business Research, Elsevier, vol. 85(C), pages 155-164.
    11. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    12. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    13. 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.
    14. Phanish Puranam & Murali Swamy, 2016. "How Initial Representations Shape Coupled Learning Processes," Organization Science, INFORMS, vol. 27(2), pages 323-335, April.
    15. Shamim, Saqib & Zeng, Jing & Khan, Zaheer & Zia, Najam Ul, 2020. "Big data analytics capability and decision making performance in emerging market firms: The role of contractual and relational governance mechanisms," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    16. Okechukwu Okorie & Konstantinos Salonitis & Fiona Charnley & Mariale Moreno & Christopher Turner & Ashutosh Tiwari, 2018. "Digitisation and the Circular Economy: A Review of Current Research and Future Trends," Energies, MDPI, vol. 11(11), pages 1-31, November.
    17. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    18. Omar Alhawari & Usama Awan & M. Khurrum S. Bhutta & M. Ali Ülkü, 2021. "Insights from Circular Economy Literature: A Review of Extant Definitions and Unravelling Paths to Future Research," Sustainability, MDPI, vol. 13(2), pages 1-22, January.
    19. Usama Awan & Robert Sroufe & Muhammad Shahbaz, 2021. "Industry 4.0 and the circular economy: A literature review and recommendations for future research," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 2038-2060, May.
    20. Linda Argote & Manpreet Hora, 2017. "Organizational Learning and Management of Technology," Production and Operations Management, Production and Operations Management Society, vol. 26(4), pages 579-590, April.
    21. Martin Kowalczyk & Peter Buxmann, 2014. "Big Data and Information Processing in Organizational Decision Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 267-278, October.
    22. Chiappetta Jabbour, Charbel Jose & De Camargo Fiorini, Paula & Wong, Christina W.Y. & Jugend, Daniel & Lopes De Sousa Jabbour, Ana Beatriz & Roman Pais Seles, Bruno Michel & Paula Pinheiro, Marco Anto, 2020. "First-mover firms in the transition towards the sharing economy in metallic natural resource-intensive industries: Implications for the circular economy and emerging industry 4.0 technologies," Resources Policy, Elsevier, vol. 66(C).
    23. Alan Murray & Keith Skene & Kathryn Haynes, 2017. "The Circular Economy: An Interdisciplinary Exploration of the Concept and Application in a Global Context," Journal of Business Ethics, Springer, vol. 140(3), pages 369-380, February.
    24. Wynne W. Chin & Barbara L. Marcolin & Peter R. Newsted, 2003. "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study," Information Systems Research, INFORMS, vol. 14(2), pages 189-217, June.
    25. 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.
    26. Kowalczyk, Martin & Buxmann, Peter, 2014. "Big Data and Information Processing in Organizational Decision Processes: A Multiple Case Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65730, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    27. Gupta, Shivam & Chen, Haozhe & Hazen, Benjamin T. & Kaur, Sarabjot & Santibañez Gonzalez, Ernesto D.R., 2019. "Circular economy and big data analytics: A stakeholder perspective," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 466-474.
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