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