Business and Social Sciences

Business and social sciences | Online ISSN 3067-8919
0
Citations
54k
Views
42
Articles
RESEARCH ARTICLE   (Open Access)

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

+ Author Affiliations

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


View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
60
View
0
Share