In recent years, Big Data Analytics has become a very useful tool for Supply Chain Management by effectively supporting managers (especially those of logistics companies) in time-consuming repetitive tasks such as, among others, demand forecasting, inventory management, transportation optimization, supplier performance analysis, risk management, and environmental impact evaluation. However, the application of Big Data Analytics to Supply Chain Management started late compared to other managerial disciplines, and the development of the related research efforts over time was mainly determined by the contingent needs of the industry: first, efficiency; then, sustainability and, in parallel, supply chain resilience; finally, these two latter branches (sustainability and resilience) merged into one, originating the current research focus on “green supply chain management”. The present paper maps this rather rapid and articulated evolution by means of a systematic review approach, i.e., Main Path Analysis techniques, and then interprets it through the lenses of Affordances-Actualization Theory, Contingency Theory of Organizations, Resource-Based Theory of the Firm, Unified Theory of Acceptance and Use of Technology, and Diffusion of Innovation Model, in order to shed some light not only on the past but also on the most promising future directions of research in this field.

Al-Khatib, A. W. (2022). Can big data analytics capabilities promote a competitive advantage? Green radical innovation, green incremental innovation and data-driven culture in a moderated mediation model. Business Process Management Journal, 28(4), 1025-1046.

Al-Khatib, A. W. (2023). Internet of things, big data analytics and operational performance: the mediating effect of supply chain visibility. Journal of Manufacturing Technology Management, 34(1), 1-24.

Al-Khatib, A. W., & Ramayah, T. (2023). Big data analytics capabilities and supply chain performance: testing a moderated mediation model using partial least squares approach. Business Process Management Journal, 29(2), 393-412.

Al-Khatib, A. W., & Shuhaiber, A. (2022). Green intellectual capital and green supply chain performance: does big data analytics capabilities matter?. Sustainability, 14(16), 10054.

Aragón-Correa, J. A., & Sharma, S. (2003). A contingent resource-based view of proactive corporate environmental strategy. Academy of management review, 28(1), 71-88.

Amit, R., & Schoemaker, P. H. (1993). Strategic Assets and Organizational Rent. Strategic Management Journal, 14, 33-46,

Ashrafi, A., Ravasan, A. Z., Trkman, P., & Afshari, S. (2019). The role of business analytics capabilities in bolstering firms’ agility and performance. International Journal of Information Management, 47, 1-15.

Ayed, A. B., Halima, M. B., & Alimi, A. M. (2015, May). Big data analytics for logistics and transportation. In 2015 4th international conference on advanced logistics and transport (ICALT) (pp. 311-316). IEEE.

Bag, S., Gupta, S., & Kumar, S. (2021a). Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development. International Journal of Production Economics, 231, 107844.

Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021b). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420.

Bag, S., & Rahman, M. S. (2023). The role of capabilities in shaping sustainable supply chain flexibility and enhancing circular economy-target performance: an empirical study. Supply Chain Management: An International Journal, 28(1), 162-178.

Bag, S., Wood, L. C., Mangla, S. K., & Luthra, S. (2020). Procurement 4.0 and its implications on business process performance in a circular economy. Resources, Conservation and Recycling, 152, 104502.

Barberá-Tomás, D., Jiménez-Sáez, F., & Castelló-Molina, I. (2011). Mapping the importance of the real world: The validity of connectivity analysis of patent citations networks. Research policy, 40(3), 473-486.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.

Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202.

Basu, P., & Ray, P. (2022). China-plus-one: expanding global value chains. Journal of Business Strategy, 43(6), 350-356.

Batagelj, V. (2003). Efficient algorithms for citation network analysis. Preprint series, University of Ljubljana, Institute of Mathematics, Physics and Mechanics, Department of Theoretical Computer Science.

Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research, 1-26.

Bendoly, E. (2016). Fit, bias, and enacted sensemaking in data visualization: frameworks for continuous development in operations and supply chain management analytics. Journal of Business Logistics, 37(1), 6-17.

Bowersox, D. J., Closs, D. J., & Cooper, M. B. (2016). Supply chain logistics management (4th ed.). McGraw-Hill.

Bruner, M. W., Erickson, K., McFadden, K., & Côté, J. (2009). Tracing the origins of athlete development models in sport: a citation path analysis. International review of sport and exercise psychology, 2(1), 23-37.

Burton-Jones, A., & Volkoff, O. (2017). How can we develop contextualized theories of effective use? A demonstration in the context of community-care electronic health records. Information Systems Research, 28(3), 468-489.

Butt, A. S. (2021). Strategies to mitigate the impact of COVID-19 on supply chain disruptions: a multiple case analysis of buyers and distributors. The International Journal of Logistics Management.

Carvalho, H., Govindan, K., Azevedo, S. G., & Cruz-Machado, V. (2017). Modelling green and lean supply chains: An eco-efficiency perspective. Resources, Conservation and Recycling, 120, 75-87.

Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86-97.

Chae, B. K. (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.

Chae, B., & Olson, D. L. (2013). Business analytics for supply chain: A dynamic-capabilities framework. International Journal of Information Technology & Decision Making, 12(01), 9-26.

Chae, B., Yang, C., Olson, D., & Sheu, C. (2014). The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective. Decision Support Systems, 59, 119-126.

Chan, C. M., Hackney, R., Pan, S. L., & Chou, T. C. (2011). Managing e-Government system implementation: a resource enactment perspective. European Journal of Information Systems, 20(5), 529-541.

Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2018). Operations management for competitive advantage (13th ed.). McGraw-Hill.

Chemero, A. (2018). An Outline of a Theory of Affordances in How Shall Affordances Be Refined? Four Perspectives. Routledge, 181-195.

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.

Chen, H., Chiang, R. H. L., & Storey, V. C. (2018). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 43(3), 1-21.

Chen, H., & Zhang, C. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.

Chen, J., Liu, X., Zhang, L., & Zhang, H. (2020). Real-time big data analytics in supply chain management: A review. IEEE Access, 8, 108999-109013.

Chiang, C. Y., Kocabasoglu‐Hillmer, C., & Suresh, N. (2012). An empirical investigation of the impact of strategic sourcing and flexibility on firm's supply chain agility. International Journal of Operations & Production Management, 32(1-2), 49-78.

Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1883.

Chopra, S., & Meindl, P. (2016). Supply chain management: Strategy, planning, and operation (6th ed.). Pearson Education.

Christopher, M. (2016). Logistics & supply chain management. Pearson UK.

Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1-13.

Christopher, M., & Ryals, L. (1999). Supply chain strategy: its impact on shareholder value. The international journal of logistics management, 10(1), 1-10.

Chu, C. Y., Park, K., & Kremer, G. E. (2020). A global supply chain risk management framework: An application of text-mining to identify region-specific supply chain risks. Advanced Engineering Informatics, 45, 101053.

Coyle, J. J., Bardi, E. J., & Novack, R. A. (2017). Transportation: A supply chain perspective (9th ed.). Cengage Learning.

Cukier K., The Economist, Data, data everywhere: A special report on managing information, 2010, February 25, Retrieved from http://www.economist.com/node/15557443

Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business Press.

DHL, AI-Driven Big Data Analytics: The Next Big Thing in Supply Chain Management, 2023, Januay 13, Retrieved from https://lot.dhl.com/big-data-analytics-supply-chain-management/

DHL Supply Chain, Supply Chain Management Review, Using Data Analytics to Elevate the Success of Supply Chain Sustainability Initiatives, 2021, September 28, Retrieved from https://www.scmr.com/article/using_data_analytics_to_elevate_the_success_of_supply_chain_sustainability

Diebold, F. X., Cheng, X., Diebold, S., Foster, D., Halperin, M., & Lohr, S. (2012). A personal perspective on the origin (s) and development of ‘big data’: The phenomenon, the term, and the discipline. Second Version.

Donaldson, L. (2006). The contingency theory of organizational design: challenges and opportunities. Organization Design: The evolving state-of-the-art, 19-40.

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.

Drucker, P. (1954). The practice of management. New York: Harper & Row.

Drus, Z., & Khalid, H. (2019). Sentiment analysis in social media and its application: Systematic literature review. Procedia Computer Science, 161, 707-714.

Du, W. D., Pan, S. L., Leidner, D. E., & Ying, W. (2019). Affordances, experimentation and actualization of FinTech: A blockchain implementation study. The Journal of Strategic Information Systems, 28(1), 50-65.

Dubey, R., Gunasekaran, A., & Childe, S. J. (2018). Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility. Management Decision 57(8), 2092-2112.

Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Wamba, S. F., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120-136.

Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., ... & Hazen, B. T. (2020). 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, 107599.

Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2016). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology, 84(1), 631-645.

Dutta, D., & Bose, I. (2015). Managing a big data project: the case of Ramco cements limited. International Journal of Production Economics, 165, 293-306.

Eckerson, W. W. (2007). Predictive analytics. Extending the Value of Your Data Warehousing Investment. TDWI Best Practices Report, 1, 1-36.

El-Kassar, A. N., & Singh, S. K. (2019). Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices. Technological Forecasting and Social Change, 144, 483-498.

Elkington, J., & Rowlands, I. H. (1999). Cannibals with forks: The triple bottom line of 21st century business. Alternatives Journal, 25(4), 42.

Ellram, L. M. (1990). The supplier selection decision in strategic partnerships. Journal of Purchasing and Materials Management, 26(4), 8-14.

Esfahbodi, A., Zhang, Y., & Watson, G. (2016). Sustainable supply chain management in emerging economies: Trade-offs between environmental and cost performance. International Journal of Production Economics, 181, 350-366.

Fajgelbaum, P. D., & Khandelwal, A. K. (2022). The economic impacts of the US–China trade war. Annual Review of Economics, 14, 205-228.

Fiedler, F. (1964). A contingency model of leadership effectiveness. Advances in Experimental Social Psychology, 1, pp. 149–190.

Fontana, R., Nuvolari, A., & Verspagen, B. (2009). Mapping technological trajectories as patent citation networks. An application to data communication standards. Economics of Innovation and New Technology, 18(4), 311-336.

Frazelle, E. H. (2002). Supply chain strategy: the logistics of supply chain management. McGraw-Hill.

Fredericks, E. (2005). Infusing flexibility into business-to-business firms: A contingency theory and resource-based view perspective and practical implications. Industrial Marketing Management, 34(6), 555-565.

Frehe, V., Kleinschmidt, T., & Teuteberg, F. (2014). Big Data in Logistics-Identifying Potentials through Literature, Case Study and Expert Interview Analyses. In GI-Jahrestagung (pp. 173-186).

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

Gattorna, J. (2016). Dynamic supply chain alignment: A new business model for peak performance in enterprise supply chains across all geographies. Kogan Page Publishers.

Ghavami, P. (2019). Big data analytics methods. In Big Data Analytics Methods. de Gruyter.

Ghodsypour, S. H., & O'Brien, C. (1998). A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. International Journal of Production Economics, 56, 199-212.

Gibson, J. J. (1977). The theory of affordances. Hilldale, USA, 1(2), 67-82.

Govindan, K., Kaliyan, M., Kannan, D., & Haq, A. N. (2014). Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. International journal of production economics, 147, 555-568.

Grant, R. M. (1991). The Resource-based Theory of Competitive Advantage. California Management Review, 33(3), 114-135,

Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., & Hazen, B. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317.

Gunasekaran, A., Yusuf, Y. Y., Adeleye, E. O., & Papadopoulos, T. (2018). Agile manufacturing practices: the role of big data and business analytics with multiple case studies. International Journal of Production Research, 56(1-2), 385-397.

Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064.

Heizer, J., & Render, B. (2017). Operations management: sustainability and supply chain management (12th ed.). Pearson.

Henschen, D. (2014). Merck Optimizes Manufacturing with Big Data Analytics. Available at http://www.informationweek.com/strategiccio/executiveinsights-and-innovation/merck-optimizes-manufacturing-with-bigdata-analytics/d/d-id/1127901 (accessed 18 April 2023).

Ho, M. H. C., & Liu, J. S. (2021). The swift knowledge development path of COVID-19 research: the first 150 days. Scientometrics, 126, 2391-2399.

Ho, M. H. C., Liu, J. S., & Chang, K. C. T. (2017). To include or not: the role of review papers in citation-based analysis. Scientometrics, 110(1), 65-76.

Hosseini, S., & Ivanov, D. (2020). Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review. Expert Systems With Applications, 161, 113649.

Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39-63.

Hutchby, I. (2001). Technologies, Texts and Affordances. Sociology, 35(2), 441–456.

Ivanov, D., & Dolgui, A. (2018). Challenges and opportunities of Industry 4.0 and big data for logistics and supply chain management. International Journal of Production Research, 56(8), 2918-2944.

Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846.

Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., & Prakash, A. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. The International Journal of Logistics Management, 29(2), 513-538.

Johnson, M. E., & Whang, S. (2002). E‐business and supply chain management: an overview and framework. Production and Operations management, 11(4), 413-423.

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.

Kappelman, A. C., & Sinha, A. K. (2021). Optimal control in dynamic food supply chains using big data. Computers & Operations Research, 126, 105117.

Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., & Mora, H. (2020). A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. Industrial Marketing Management, 90, 523-537.

Kaur Kapoor, K., K. Dwivedi, Y., & D. Williams, M. (2014). Innovation adoption attributes: a review and synthesis of research findings. European Journal of Innovation Management, 17(3), 327-348.

Khan, N., Alsaqer, M., Shah, H., Badsha, G., Abbasi, A. A., & Salehian, S. (2018, March). The 10 Vs, issues and challenges of big data. In Proceedings of the 2018 international conference on big data and education (pp. 52-56).

Kim, K. I., Xu, Y., & Pak, C. J. (2020). Technological development trajectory of chromatography: Main path analysis based on patent citation network. SN Applied Sciences, 2, 1-12.

Kinra, A., Siekmann, F., & Kotzab, H. (2022). Social Media Analytics in Operations and Supply Chain Management: Opportunities, Challenges, and Paradoxes. Diginomics research perspectives: The role of digitalization in business and society, 185-212.

Kiron, D., & Shockley, R. (2011). Creating business value with analytics. MIT Sloan Management Review, 53(1), 57.

Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 2053951714528481.

Krajewski, L. J., Ritzman, L. P., & Malhotra, M. K. (2018). Operations management: processes and supply chains (12th ed.). Pearson.

Kristoffersen, E., Mikalef, P., Blomsma, F., & Li, J. (2021). The effects of business analytics capability on circular economy implementation, resource orchestration capability, and firm performance. International Journal of Production Economics, 239, 108205.

Kumar, V., & Garg, M. L. (2018). Predictive analytics: a review of trends and techniques. International Journal of Computer Applications, 182(1), 31-37.

Kuo, C. Y., Hwang, B. L., & Huang, C. H. (2022). Exploring the Development Trajectory of Big Data Analytics in Supply Chain Management: A Main Path Analysis. IEEE-ICASI

Kusi-Sarpong, S., Gupta, H., & Sarkis, J. (2019). A supply chain sustainability innovation framework and evaluation methodology. International Journal of Production Research, 57(7), 1990-2008.

Lamba, K., & Singh, S. P. (2017). Big data in operations and supply chain management: current trends and future perspectives. Production Planning & Control, 28(11-12), 877-890.

Lamba, K., & Singh, S. P. (2019). Dynamic supplier selection and lot-sizing problem considering carbon emissions in a big data environment. Technological Forecasting and Social Change, 144, 573-584.

Lamba, K., Singh, S. P., & Mishra, N. (2019). Integrated decisions for supplier selection and lot-sizing considering different carbon emission regulations in Big Data environment. Computers & Industrial Engineering, 128, 1052-1062.

Lambert, D. M., Cooper, M. C., & Pagh, J. D. (2016). Supply chain management: Processes, partnerships, performance (4th ed.). Supply Chain Management Institute.

Laudon, K. C., & Laudon, J. P. (2016). Management information systems: Managing the digital firm (14th ed.). Pearson.

Lee, H. L., Padmanabhan, V., & Wang, W. (2018). Real-time demand fulfillment in omnichannel retailing: A case study approach. Production and Operations Management, 27(5), 876-894.

Lee, J. M. Y., & Wong, E. Y. C. (2021). Suez Canal blockage: an analysis of legal impact, risks and liabilities to the global supply chain. In MATEC web of conferences (Vol. 339, p. 01019). EDP Sciences.

Leonardi, P. M. (2013). When does technology use enable network change in organizations?
A comparative study of feature use and shared affordances. MIS Quarterly, 37(3), 749–775.

Leidner, D. E., Gonzalez, E., & Koch, H. (2018). An affordance perspective of enterprise social media and organizational socialization. The Journal of Strategic Information Systems, 27(2), 117-138.

Li, L., Gong, Y., Wang, Z., & Liu, S. (2023). Big data and big disaster: a mechanism of supply chain risk management in global logistics industry. International Journal of Operations & Production Management, 43(2), 274-307.

Liu, J. S., Chen, H. H., Ho, M. H. C., & Li, Y. C. (2014). Citations with different levels of relevancy: Tracing the main paths of legal opinions. Journal of the Association for Information Science and Technology, 65(12), 2479-2488.

Liu, J. S., & Lu, L. Y. (2012). An integrated approach for main path analysis: Development of the Hirsch index as an example. Journal of the American Society for Information Science and Technology, 63(3), 528-542.

Liu, J. S., Lu, L. Y., & Ho, M. H. C. (2019). A few notes on main path analysis. Scientometrics, 119, 379-391.

Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), 1875-1900.

Mangiaracina, R., Marchet, G., Perotti, S., & Tumino, A. (2015). A review of the environmental implications of B2C e-commerce: a logistics perspective. International Journal of Physical Distribution & Logistics Management, 45(6), 565–591.

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.

Mao, H., & Chen, L. (2021). E-Commerce Enterprise Supply Chain Cost Control under the Background of Big Data. Complexity, 2021, 1-11.

Markus, M. L., & Silver, M. S. (2008). A foundation for the study of IT effects: A new look at DeSanctis and Poole's concepts of structural features and spirit. Journal of the Association for Information systems, 9(10), 5.

Marr, B., The Amazing Ways Volvo Uses Big Data, Machine Learning and Predictive Analytics, 2016, December 16, Retrieved from https://www.linkedin.com/pulse/amazing-ways-volvo-uses-big-data-machine-learning-predictive-marr/

McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 61-67.

Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business Logistics, 22(2), 1-25

Michael, M. (2000). These boots are made for walking...: Mundane technology, the body and
human-environment relations. Body & Society, 6(3-4), 107-126
Michaels, C.F. (2003). Affordances: Four Points of Debate. Ecological Psychology, 15(2), 135-148.

Monczka, R. M., Handfield, R. B., Giunipero, L. C., & Patterson, J. L. (2015). Sourcing and supply chain management. Boston, MA: South-Western Cengage Learning.

Moore, G.C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.

Moosavi, J., Fathollahi-Fard, A. M., & Dulebenets, M. A. (2022). Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies. International Journal of Disaster Risk Reduction, 102983.

Moufaddal, M., Benghabrit, A., & Bouhaddou, I. (2018). Big Data Analytics for Supply Chain Management. In Innovations in Smart Cities and Applications: Proceedings of the 2nd Mediterranean Symposium on Smart City Applications 2 (pp. 976-986). Springer International Publishing.

Mukherjee, S., & Mishra, S. K. (2019). Big data analytics for supply chain management: A literature review and research agenda. Decision Science Letters, 8(1), 103-120.

Myamba, B. M., & Nguni, W. S. (2022). Aligning the risk hedging strategy with supplier collaboration and manufacturing competitiveness: a resource-based and contingency approach. International Journal of Productivity and Performance Management, (ahead-of-print).

Naz, F., Kumar, A., Majumdar, A., & Agrawal, R. (2021). Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research. Operations Management Research, 1-21.

Ng, B., Ferrin, B. G., & Pearson, J. N. (1997). The role of purchasing/transportation in cycle time reduction. International Journal of Operations & Production Management, 17(6), 574-591.

Ni, D., Xiao, Z., & Lim, M. K. (2020). A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics, 11, 1463-1482.

Northouse, P. G. (2019). Leadership: Theory and practice (8th ed.). Sage Publications.

Oh, W., & Pinsonneault, A. (2007). On the assessment of the strategic value of information technologies: conceptual and analytical approaches. MIS Quarterly, 31, 239–265.

Olson, D. L. (2020). A review of supply chain data mining publications. Journal of Supply Chain Management Science, 1(1-2), 15-26.

Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso-Wamba, S. (2017). The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, 1108-1118.

Palopak, Y., Huang, S.-J., & Ratnasari, W. (2022). Knowledge Diffusion Trajectories of Agile Software Development Research: A Main Path Analysis. Information and Software Technology, 107131.

Pan, A. C. (1989). Allocation of order quantity among suppliers. Journal of Purchasing and Materials Management, 25(3), 36-39.

Park, H., & Magee, C. L. (2017). Tracing technological development trajectories: A genetic knowledge persistence-based main path approach. PloS one, 12(1), e0170895.

Pertusa‐Ortega, E. M., Molina‐Azorín, J. F., & Claver‐Cortés, E. (2010). Competitive strategy, structure and firm performance: A comparison of the resource‐based view and the contingency approach. Management Decision, 48(8), 1282-1303.

Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250.

Pujawan, I. N., & Bah, A. U. (2021). Supply chains under COVID-19 disruptions: literature review and research agenda. Supply Chain Forum: An International Journal.

Rajesh, R. (2016). Forecasting supply chain resilience performance using grey prediction. Electronic Commerce Research and Applications, 20, 42-58.

Rajesh, R., & Ravi, V. (2015). Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production, 86, 343–359.

Rao, P., Balasubramanian, S., Vihari, N., Jabeen, S., Shukla, V., & Chanchaichujit, J. (2021). The e-commerce supply chain and environmental sustainability: An empirical investigation on the online retail sector. Cogent Business & Management, 8(1), 1938377.

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.

Robey, D., Anderson, C., & Raymond, B. (2013). Information technology, materiality, and organizational change: A professional odyssey. Journal of the Association for Information Systems, 14(7), 1.

Rodríguez-Espíndola, O., Cuevas-Romo, A., Chowdhury, S., Díaz-Acevedo, N., Albores, P., Despoudi, S., ... & Dey, P. (2022). The role of circular economy principles and sustainable-oriented innovation to enhance social, economic and environmental performance: Evidence from Mexican SMEs. International Journal of Production Economics, 248, 108495.

Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). New York: The Free Press.

Roßmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. (2018). The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study. Technological Forecasting and Social Change, 130, 135-149.

Russo, M. V., & Fouts, P. A. (1997). A Resource-based Perspective on Corporate Environmental Performance and Profitability. Academy of Management Journal, 40(3), 534-59.

Safari, A., & Saleh, A. S. (2020). Key determinants of SMEs’ export performance: a resource-based view and contingency theory approach using potential mediators. Journal of Business & Industrial Marketing, 35(4), 635-654.

Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26-48.

Sedera, D., Lokuge, S., Grover, V., Sarker, S., & Sarker, S. (2016). Innovating with enterprise systems and digital platforms: A contingent resource-based theory view. Information & Management, 53(3), 366-379.

Seidel, S., Recker, J., & Vom Brocke, J. (2013). Sensemaking and sustainable practicing: functional affordances of information systems in green transformations. MIS quarterly, 1275-1299.

Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 7(1), 1-22.

Schendel, D. (1994). Introduction to Competitive Organizational Behavior: Toward an Organizationally Based Theory of Competitive Advantage. Strategic Management Journal, 15, 1-4.

Schöpper, H., & Kersten, W. (2021, May). Using natural language processing for supply chain mapping: A systematic review of current approaches. In 5th International Conference on Computational Linguistics and Intelligent Systems (COLINS 2021) (No. 5, pp. 71-86). RWTH Aachen.

Sheffi, Y. (2018). The resilient enterprise: Overcoming vulnerability for competitive advantage. MIT Press.

Sheffi, Y., & Rice Jr, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan management review, 47(1), 41-48.

Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory management and production planning and scheduling (3rd ed.). Wiley.

Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2007). Designing and managing the supply chain: concepts, strategies, and case studies (2nd ed.). McGraw-Hill.

Singh, S. K., & El-Kassar, A. N. (2019). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner Production, 213, 1264-1273.

Snijders, C., Matzat, U., & Reips, U. D. (2012). " Big Data": big gaps of knowledge in the field of internet science. International journal of internet science, 7(1), 1-5.

Srivastava, S. K. (2007). Green supply‐chain management: a state‐of‐the‐art literature review. International Journal of Management Reviews, 9(1), 53-80.

Stoffregen, T. (2003). Affordances as properties of the animal-environment system. Ecological
Psychology, 15(2), 115-134.
Strong, D. M., Volkoff, O., Johnson, S. A., Pelletier, L. R., Tulu, B., Bar-On, I., Trudel, J., & Garber, L. (2014). A theory of organization-EHR affordance actualization. Journal of the Association for Information Systems, 15(2), 2.

Swain, A. K., & Cao, R. Q. (2019). Using sentiment analysis to improve supply chain intelligence. Information Systems Frontiers, 21, 469-484.

Tan, K. H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, 223-233.

Tersine, R. J., & Hummingbird, E. A. (1995). Lead‐time reduction: the search for competitive advantage. International Journal of Operations & Production Management, 15(2), 8-18.

Thompson, J. D. (1967). Organizations in action: Social science bases of administrative theory. McGraw-Hill.

Tim, Y., Hallikainen, P., Pan, S. L., & Tamm, T. (2020). Actualizing business analytics for organizational transformation: A case study of Rovio Entertainment. European Journal of Operational Research, 281(3), 642-655.

Timmerman, E. (1986). An approach to vendor performance evaluation. Journal of Purchasing and Materials Management, 22(4), 2-8.

Ting, S. L., Tse, Y. K., Ho, G. T. S., Chung, S. H., & Pang, G. (2014). Mining logistics data to assure the quality in a sustainable food supply chain: A case in the red wine industry. International Journal of Production Economics, 152, 200-209.

Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517.

Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: a meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28-43.

Tosi, H. and Slocum, J. (1984). Contingency theory: some suggested directions. Journal of Management, 10, pp. 9–26.

Tseng, M., Lim, M., & Wong, W. P. (2015). Sustainable supply chain management: A closed-loop network hierarchical approach. Industrial Management & Data Systems, 115(3), 436-461.

Turvey, M. (1992). Affordances and prospective control: An outline of the ontology. Ecological
Psychology, 4(3), 173-187.

Van de Ven, A. H., Ganco, M., & Hinings, C. R. (2013). Returning to the frontier of contingency theory of organizational and institutional designs. Academy of Management Annals, 7(1), 393-440.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.

Vieira, A. A., Dias, L. M., Santos, M. Y., Pereira, G. A., & Oliveira, J. A. (2020). Supply chain data integration: A literature review. Journal of Industrial Information Integration, 19, 100161.

Xiao, Y., Lu, L. Y., Liu, J. S., & Zhou, Z. (2014). Knowledge diffusion path analysis of data quality literature: A main path analysis. Journal of Informetrics, 8(3), 594-605.

Walmsley, T., & Minor, P. (2020). US trade actions against China: a supply chain perspective. Foreign Trade Review, 55(3), 337-371.

Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2020). The Performance Effects of Big Data Analytics and Supply Chain Ambidexterity: The Moderating Effect of Environmental Dynamism. International Journal of Production Economics, 222, 107498.

Wang, C. N., Yang, C. Y., & Cheng, H. C. (2019). Fuzzy multi-criteria decision-making model for supplier evaluation and selection in a wind power plant project. Mathematics, 7(5), 417.

Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016a). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.

Wang, N., Liang, H., Zhong, W., Xue, Y., & Xiao, J. (2012). Resource structuring or capability building? An empirical study of the business value of information technology. Journal of Management Information Systems, 29(2), 325-367.

Wang, Z., Huo, B., Qi, Y., & Zhao, X. (2016b). A resource-based view on enablers of supplier integration: evidence from China. Industrial Management & Data Systems.

Waqas, M., Honggang, X., Ahmad, N., Khan, S. A. R., & Iqbal, M. (2021). Big data analytics as a roadmap towards green innovation, competitive advantage and environmental performance. Journal of Cleaner Production, 323, 128998.

WCED, S. W. S. (1987). World commission on environment and development. Our common future, 17(1), 1-91.

Weiss, S.M., & Indurkhya, N. (1998). Predictive Data Mining: A Practical Guide. Morgan
Kaufmann Publishers, Inc.

Weng, W. H., & Weng, W. T. (2013). Forecast of Development Trends in Big Data Industry. Proceedings of the Institute of Industrial Engineers Asian Conference, 1487–1494.

Wenzel, H., Smit, D., & Sardesai, S. (2019). A literature review on machine learning in supply chain management. In Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 27 (pp. 413-441). Berlin: epubli GmbH.

Wernerfelt, B. (1984). A resource‐based view of the firm. Strategic Management Journal, 5(2), 171-180.

Wiengarten, F., Humphreys, P., Cao, G., & McHugh, M. (2013). Exploring the important role of organizational factors in IT business value: Taking a contingency perspective on the resource‐based view. International Journal of Management Reviews, 15(1), 30-46.

Woodward, J. (1965). Industrial organization: Theory and practice. Oxford University Press.

Yu, J., Lin, Y., Liu, W., & Huang, H. (2020). Real-time big data analytics in supply chain management: A systematic review. Journal of Manufacturing Systems, 56, 121-133.

Yu, D., & Sheng, L. (2020). Knowledge diffusion paths of blockchain domain: the main path analysis. Scientometrics, 125(1), 471-497.

Yu, Y., Wang, X., Zhong, R. Y., & Huang, G. Q. (2016). E-commerce logistics in supply chain management: Practice perspective. Procedia Cirp, 52, 179-185.

Zeng, D., Tim, Y., Yu, J., & Liu, W. (2020). Actualizing big data analytics for smart cities: A cascading affordance study. International Journal of Information Management, 54, 102156.

Zhang, D., Pan, S. L., Yu, J., & Liu, W. (2022). Orchestrating big data analytics capability for sustainability: A study of air pollution management in China. Information & Management, 59(5), 103231.

Zhao, R., Liu, Y., Zhang, N., & Huang, T. (2017). An optimization model for green supply chain management by using a big data analytic approach. Journal of Cleaner Production, 142, 1085-1097.