Examining the Nexus Between Big Data Analytics Capabilities and Organizational Performance: The Mediating Role of Supply Chain Visibility in Manufacturing Organizations

Authors

  • Shahbaz Sultan https://orcid.org/0000-0003-2411-1218
  • Prof. Abu Bakar Abdul Hamid Professor, Putra Business School, Malaysia.
  • Dr. Ida Yasin Associate Professor, Putra Business School, Malaysia.
  • Muhammad Haroon Malik PhD Scholar, Putra Business School, Malaysia.
  • Saqlain Humayun COMSATS University, Pakistan.

DOI:

https://doi.org/10.55737/qjss.971389566

Keywords:

Big Data, Big Data Analytics, Big Data Analytics Capabilities, upply Chain Visibility, Organizational Performance

Abstract

Big data has revolutionized how businesses operate today, offering unprecedented insights. By harnessing vast amounts of data, companies can optimize performance, enhance decision-making, and drive efficiency. This research examined the relationship between big data analytics capabilities and organizational performance in the context of supply chain visibility based on sample data collected from 400 manufacturing firms operating in Pakistan. The data were gathered from experienced supply chain professionals with the help of a structured questionnaire while Smart PLS 4.0 was employed to evaluate the research model. The research findings indicate that big data analytics capabilities positively affect supply chain visibility and result in improved organizational performance. The study supports the research hypothesis that though big data analytics capabilities may not have a direct impact on organizational performance however, they can both indirectly and positively impact organizational performance when mediated by supply chain visibility. The findings recommend that manufacturing sector organisations should focus on improving supply chain visibility to optimise the benefits of big data analytics capabilities. This research extends the existing literature on big data analytics capabilities from a supply chain perspective, showing how they improve the performance of organisations and hence offer valuable insights to researchers and practitioners.

Author Biography

References

Akhtar, P., Frynas, J. G., Mellahi, K., & Ullah, S. (2019). Big Data‐Savvy Teams’ Skills, Big Data‐Driven Actions, and Business Performance. British Journal of Management, 30(2), 252–271. https://doi.org/10.1111/1467-8551.12333

Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to Improve Firm Performance Using Big Data Analytics Capability and Business Strategy Alignment? International Journal of Production Economics, 182(1), 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018

Al-Khatib, A. W. (2022). Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation and technological intensity. Business Process Management Journal, 28(5/6), 1446–1471. https://doi.org/10.1108/bpmj-07-2022-0332

Baah, C., Acquah, I. S. K., & Ofori, D. (2022). Exploring the influence of supply chain collaboration on supply chain visibility, stakeholder trust, environmental and financial performances: a partial least square approach. Benchmarking: An International Journal, ahead-of-print(ahead-of-print). https://doi.org/10.1108/bij-10-2020-0519

Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

Bechtsis, D., Tsolakis, N., Iakovou, E., & Vlachos, D. (2022). Data-driven secure, resilient and sustainable supply chains: gaps, opportunities, and a new generalised data sharing and data monetisation framework. International Journal of Production Research, 60(14), 1–21. https://doi.org/10.1080/00207543.2021.1957506

Chae, B. (Kevin). (2015). Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research. International Journal of Production Economics, 165, 247–259. https://doi.org/10.1016/j.ijpe.2014.12.037

Christopher, M., & Lee, H. (2004). Mitigating Supply Chain Risk through Improved Confidence. International Journal of Physical Distribution & Logistics Management, 34(5), 388–396. https://doi.org/10.1108/09600030410545436

Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Review Press.

Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2019). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226(1), 107599. https://doi.org/10.1016/j.ijpe.2019.107599

Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10-11), 1105-1121.

Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), 1923–1936. https://doi.org/10.1108/md-07-2018-0825

Gawankar, S. A., Kamble, S., & Raut, R. (2017). An investigation of the relationship between supply chain management practices (SCMP) on supply chain performance measurement (SCPM) of Indian retail chain using SEM. Benchmarking: An International Journal, 24(1), 257–295. https://doi.org/10.1108/bij-12-2015-0123

Ivanov, D., & Dolgui, A. (2021). Stress Testing Supply Chains and Creating Viable Ecosystems. Operations Management Research, 15(1-2). https://doi.org/10.1007/s12063-021-00194-z

Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10–36.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.

McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.

Mikalef, P., & Krogstie, J. (2020). Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities. European Journal of Information Systems, 29(3), 260–287. https://doi.org/10.1080/0960085x.2020.1740618

Papadopoulos, T., Gunasekaran, A., Dubey, R., & Fosso Wamba, S. (2017). Big data and analytics in operations and supply chain management: managerial aspects and practical challenges. Production Planning & Control, 28(11-12), 873–876. https://doi.org/10.1080/09537287.2017.1336795

Razaghi, S., & Shokouhyar, S. (2021). Impacts of big data analytics management capabilities and supply chain integration on global sourcing: a survey on firm performance. The Bottom Line, 34(2), 198-223.

Saunders, M. N., Lewis, P., & Thornhill, A. (1997). Research methods for business students. Pitman.

Schoenherr, T., & Speier-Pero, C. (2015). Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential. Journal of Business Logistics, 36(1), 120–132. https://doi.org/10.1111/jbl.12082

Shi, H., Feng, T., & Zhu, Z. (2023). The impact of big data analytics capability on green supply chain integration: an organizational information processing theory perspective. Business Process Management Journal, 29(2), 550–577. https://doi.org/10.1108/bpmj-08-2022-0411

Sodhi, M. S., & Tang, C. S. (2021). Extending AAA Capabilities to Meet PPP Goals in Supply Chains. Production and Operations Management, 30(3), 625–632. https://doi.org/10.1111/poms.13304

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic Capabilities and Strategic Management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%3C509::AID-SMJ882%3E3.0.CO;2-Z

Teece, D., & Pisano, G. (1994). The dynamic capabilities of firms: An introduction. Industrial and Corporate Change, 3(3), 537–556. https://doi.org/10.1093/icc/3.3.537-a

Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2019). How "big data" can make a big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165(165), 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031

Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70(1), 356–365.

Wu, F., Yeniyurt, S., Kim, D., & Cavusgil, S. T. (2006). The impact of information technology on supply chain capabilities and firm performance: A resource-based view. Industrial Marketing Management, 35(4), 493–504. https://doi.org/10.1016/j.indmarman.2005.05.003

Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2018). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114, 1–15. https://doi.org/10.1016/j.jbusres.2020.03.028

Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114, 1–15. https://doi.org/10.1016/j.jbusres.2020.03.028

Downloads

Published

2024-11-22

Issue

Section

Articles

How to Cite

Sultan, S., Hamid, A. B. A., Yasin, I., Malik, M. H., & Humayun, S. (2024). Examining the Nexus Between Big Data Analytics Capabilities and Organizational Performance: The Mediating Role of Supply Chain Visibility in Manufacturing Organizations. Qlantic Journal of Social Sciences , 5(4), 49-63. https://doi.org/10.55737/qjss.971389566