Business and Social Sciences
Strategic Integration of Big Data Analytics to Enhance Risk Management, Sustainable Supply Chains, and Business Competitiveness in the United States
Md. Rezaul Haque1*, Md Nazmuddin Moin Khan2
Business and Social Sciences 1 (1) 1-8 https://doi.org/10.25163/business.1110498
Submitted: 05 April 2023 Revised: 05 June 2023 Accepted: 09 June 2023 Published: 11 June 2023
Abstract
Background: Big Data Analytics (BDA) has emerged as a strategic enabler for improving risk resilience, operational sustainability, and competitive advantage. But American companies show different levels of organizational readiness and integration capabilities.
Methods: A structured survey was conducted among 275 U.S. industry professionals from manufacturing, logistics, retail, and finance sectors. The quantitative analysis used descriptive statistics together with regression analysis and correlation and ANOVA testing to evaluate the relationships between variables. The Cronbach's alpha test results showed that the instrument achieved high reliability with a score above 0.88. Data analysis in SPSS 26 included calculations of average responses and percentage-based statistical explanations together with sectoral distribution patterns.
Results: Findings show that BDA integration positively influences all outcome variables, explaining 65% of total variance in performance. The finance and manufacturing sectors exhibited the highest adoption rates (86.8% and 85.6%), while logistics and retail reported moderate integration levels (79%). The regression analysis shows that BDA Competitiveness stands as the most influential factor through its significant positive coefficient (β = 0.64; p = 0.001). The ANOVA test revealed major variances between different sectors (F = 9.43; p = 0.001) together with post-adoption improvements between 30% and 34% that appeared in all main operational indicators.
Conclusion: The study outcomes show that BDA strategic integration principals to better risk management and supply chain sustainability while improving organizational competitiveness through data-based decision-making systems.
Keywords: Sustainable Supply Chains, Big Data Analytics, Business Competitiveness, Digital Transformation
References
Abane, J. A., Adamtey, R., & Ayim, V. O. (2022). Does organizational culture influence employee productivity at the local level? A test of Denison’s culture model in Ghana’s local government sector. Future Business Journal, 8(1). https://doi.org/10.1186/s43093-022-00145-5
Al-Benna, S., Al-Ajam, Y., Way, B., & Steinstraesser, L. (2009). Descriptive and inferential statistical methods used in burns research. Burns, 36(3), 343–346. https://doi.org/10.1016/j.burns.2009.04.030
Awaisu, A., Bakdach, D., Elajez, R. H., & Zaidan, M. (2014). Hospital pharmacists’ self-evaluation of their competence and confidence in conducting pharmacy practice research. Saudi Pharmaceutical Journal, 23(3), 257–265. https://doi.org/10.1016/j.jsps.2014.10.002
Awan, U., Kanwal, N., Alawi, S., Huiskonen, J., & Dahanayake, A. (2021). Artificial intelligence for supply chain success in the era of data Analytics. In Studies in computational intelligence (pp. 3–21). https://doi.org/10.1007/978-3-030-62796-6_1
Bag, S., Gupta, S., & Wood, L. (2020). Big data analytics in sustainable humanitarian supply chain: barriers and their interactions. Annals of Operations Research, 319(1), 721–760. https://doi.org/10.1007/s10479-020-03790-7
Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2019). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources Conservation and Recycling, 153, 104559. https://doi.org/10.1016/j.resconrec.2019.104559
Barbosa, M. W., De La Calle Vicente, A., Ladeira, M. B., & De Oliveira, M. P. V. (2017). Managing supply chain resources with Big Data Analytics: a systematic review. International Journal of Logistics Research and Applications, 21(3), 177–200. https://doi.org/10.1080/13675567.2017.1369501
Batistic, S., & Van Der Laken, P. (2019). History, Evolution and Future of Big Data and Analytics: A bibliometric analysis of its relationship to performance in organizations. British Journal of Management, 30(2), 229–251. https://doi.org/10.1111/1467-8551.12340
Bi, Z., & Cochran, D. (2014). Big data analytics with applications. Journal of Management Analytics, 1(4), 249–265. https://doi.org/10.1080/23270012.2014.992985
Buchholz, P., Schumacher, A., & Barazi, S. A. (2022). Big data analyses for real-time tracking of risks in the mineral raw material markets: implications for improved supply chain risk management. Mineral Economics, 35(3–4), 701–744. https://doi.org/10.1007/s13563-022-00337-z
Cetindamar, D., Shdifat, B., & Erfani, E. (2021). Understanding big data analytics capability and sustainable supply chains. Information Systems Management, 39(1), 19–33. https://doi.org/10.1080/10580530.2021.1900464
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management. Journal of Management Information Systems, 32(4), 4–39. https://doi.org/10.1080/07421222.2015.1138364
Cheng, T. C. E., Kamble, S. S., Belhadi, A., Ndubisi, N. O., Lai, K., & Kharat, M. G. (2021). Linkages between big data analytics, circular economy, sustainable supply chain flexibility, and sustainable performance in manufacturing firms. International Journal of Production Research, 60(22), 6908–6922. https://doi.org/10.1080/00207543.2021.1906971
Cozzoli, N., Salvatore, F. P., Faccilongo, N., & Milone, M. (2022). How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review. BMC Health Services Research, 22(1). https://doi.org/10.1186/s12913-022-08167-z
Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., Roubaud, D., & Foropon, C. (2019). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110–128. https://doi.org/10.1080/00207543.2019.1582820
Jiwat, R., & Zhang, Z. (2022b). Adopting big data analytics (BDA) in business-to-business (B2B) organizations – Development of a model of needs. Journal of Engineering and Technology Management, 63, 101676. https://doi.org/10.1016/j.jengtecman.2022.101676
Kamble, S. S., & Gunasekaran, A. (2019). Big data-driven supply chain performance measurement system: a review and framework for implementation. International Journal of Production Research, 58(1), 65–86. https://doi.org/10.1080/00207543.2019.1630770
Kassahun, T., Eshetie, T., & Gesesew, H. (2016). Factors associated with glycemic control among adult patients with type 2 diabetes mellitus: a cross-sectional survey in Ethiopia. BMC Research Notes, 9(1). https://doi.org/10.1186/s13104-016-1896-7
Koo, T. K., & Li, M. Y. (2016). A Guideline of selecting and Reporting Intraclass correlation coefficients for Reliability research. Journal of Chiropractic Medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012
Kumar, N., Kumar, G., & Singh, R. K. (2021b). Big data analytics application for sustainable manufacturing operations: analysis of strategic factors. Clean Technologies and Environmental Policy, 23(3), 965–989. https://doi.org/10.1007/s10098-020-02008-5
Maheshwari, S., Gautam, P., & Jaggi, C. K. (2020). Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), 1875–1900. https://doi.org/10.1080/00207543.2020.1793011
Mandal, S. (2018). Exploring the influence of big data analytics management capabilities on sustainable tourism supply chain performance: the moderating role of technology orientation. Journal of Travel & Tourism Marketing, 35(8), 1104–1118. https://doi.org/10.1080/10548408.2018.1476302
Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2017). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547–578. https://doi.org/10.1007/s10257-017-0362-y
Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing & Management, 58(6), 102725. https://doi.org/10.1016/j.ipm.2021.102725
Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of Cleaner Production, 224, 10–24. https://doi.org/10.1016/j.jclepro.2019.03.181
Shan, S., Luo, Y., Zhou, Y., & Wei, Y. (2018). Big data analysis adaptation and enterprises’ competitive advantages: the perspective of dynamic capability and resource-based theories. Technology Analysis and Strategic Management, 31(4), 406–420. https://doi.org/10.1080/09537325.2018.1516866
Talwar, S., Kaur, P., Wamba, S. F., & Dhir, A. (2021). Big Data in operations and supply chain management: a systematic literature review and future research agenda. International Journal of Production Research, 59(11), 3509–3534. https://doi.org/10.1080/00207543.2020.1868599
Vassakis, K., Petrakis, E., & Kopanakis, I. (2017). Big Data Analytics: applications, prospects and challenges. In Lecture notes on data engineering and communications technologies (pp. 3–20). https://doi.org/10.1007/978-3-319-67925-9_1
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2016). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Zheng, T., Ardolino, M., Bacchetti, A., & Perona, M. (2020). The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review. International Journal of Production Research, 59(6), 1922–1954. https://doi.org/10.1080/00207543.2020.1824085