How to cite this paper
Hatamlah, H., Allan, M., Abu-AlSondos, I., Shehadeh, M & Allahham, M. (2023). The role of artificial intelligence in supply chain analytics during the pandemic.Uncertain Supply Chain Management, 11(3), 1175-1186.
Refrences
Albergaria, M., & Jabbour, C. J. C. (2020). The role of big data analytics capabilities (BDAC) in understanding the challenges of service information and operations management in the sharing economy: Evidence of peer effects in libraries. International Journal of Information Management, 51, 102023.
Altay, N., & Pal, R. (2014). Information diffusion among agents: Implications for humanitarian operations. Production and Operations Management, 23(6), 1015-1027.
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396-402.
Asmussen, C. B., & Møller, C. (2020). Enabling supply chain analytics for enterprise information systems: a topic modelling literature review and future research agenda. Enterprise Information Systems, 14(5), 563-610.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
Bayraktar, E., Demirbag, M., Koh, S. L., Tatoglu, E., & Zaim, H. (2009). A causal analysis of the impact of information systems and supply chain management practices on operational performance: evidence from manufacturing SMEs in Turkey. International Journal of Production Economics, 122(1), 133-149.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. Mis Quarterly, 1165-1188.
Colombo, M. G., Piva, E., Quas, A., & Rossi-Lamastra, C. (2021). Dynamic capabilities and high-tech entrepreneurial ventures’ performance in the aftermath of an environmental jolt. Long range planning, 54(3), 102026.
Crick, J. M., & Crick, D. (2020). Coopetition and COVID-19: Collaborative business-to-business marketing strategies in a pandemic crisis. Industrial Marketing Management, 88, 206-213.
Das, T. K., & Teng, B.-S. (2000). A resource-based theory of strategic alliances. Journal of Management, 26(1), 31-61.
Davenport, T. H. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy & Leadership.
De Haas, M., Faber, R., & Hamersma, M. (2020). How COVID-19 and the Dutch ‘intelligent lockdown’change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transportation Research Interdisciplinary Perspectives, 6, 100150.
DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications: Sage publications.
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.
Dussauge, P., Garrette, B., & Mitchell, W. (2004). Asymmetric performance: the market share impact of scale and link alliances in the global auto industry. Strategic Management Journal, 25(7), 701-711.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., . . . Eirug, A. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
Eckstein, D., Goellner, M., Blome, C., & Henke, M. (2015). The performance impact of supply chain agility and supply chain adaptability: the moderating effect of product complexity. International Journal of Production Research, 53(10), 3028-3046.
Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: what are they? Strategic Management Journal, 21(10‐11), 1105-1121.
Fainshmidt, S., Pezeshkan, A., Lance Frazier, M., Nair, A., & Markowski, E. (2016). Dynamic capabilities and organizational performance: a meta‐analytic evaluation and extension. Journal of Management Studies, 53(8), 1348-1380.
Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics: Sage Publications Sage CA: Los Angeles, CA.
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064.
Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of Management Studies, 58(5), 1159-1197.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.
He, W., Zhang, Z. J., & Li, W. (2021). Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic. International Journal of Information Management, 57, 102287.
Helfat, C. E., & Peteraf, M. A. (2003). The dynamic resource‐based view: Capability lifecycles. Strategic Management Journal, 24(10), 997-1010.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
Homburg, C., Klarmann, M., Reimann, M., & Schilke, O. (2012). What drives key informant accuracy? Journal of Marketing Research, 49(4), 594-608.
Hossain, T. M. T., Akter, S., Kattiyapornpong, U., & Dwivedi, Y. (2020). Reconceptualizing integration quality dynamics for omnichannel marketing. Industrial Marketing Management, 87, 225-241.
Hrebiniak, L. G., & Joyce, W. F. (1985). Organizational adaptation: Strategic choice and environmental determinism. Administrative science quarterly, 336-349.
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922.
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.
Kamalaldin, A., Linde, L., Sjödin, D., & Parida, V. (2020). Transforming provider-customer relationships in digital servitization: A relational view on digitalization. Industrial Marketing Management, 89, 306-325.
Kar, A. K., & Dwivedi, Y. K. (2020). Theory building with big data-driven research–Moving away from the “What” towards the “Why”. International Journal of Information Management, 54, 102205.
Ketchen Jr, D. J., & Craighead, C. W. (2020). Research at the intersection of entrepreneurship, supply chain management, and strategic management: Opportunities highlighted by COVID-19. Journal of Management, 46(8), 1330-1341.
Kohtamäki, M., Rabetino, R., & Möller, K. (2018). Alliance capabilities: A systematic review and future research directions. Industrial Marketing Management, 68, 188-201.
Kumar, N., Stern, L. W., & Anderson, J. C. (1993). Conducting interorganizational research using key informants. Academy of management journal, 36(6), 1633-1651.
Lee, H. L. (2004). The triple-A supply chain. Harvard business review, 82(10), 102-113.
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of applied psychology, 86(1), 114.
Mentzer, J. T., Flint, D. J., & Hult, G. T. M. (2001). Logistics service quality as a segment-customized process. Journal of Marketing, 65(4), 82-104.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276.
Oehmen, J., Locatelli, G., Wied, M., & Willumsen, P. (2020). Risk, uncertainty, ignorance and myopia: Their managerial implications for B2B firms. Industrial Marketing Management, 88, 330-338.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
Prasad, S., Zakaria, R., & Altay, N. (2018). Big data in humanitarian supply chain networks: A resource dependence perspective. Annals of operations research, 270(1), 383-413.
Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso Wamba, S. (2020). Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of operations research, 1-38.
Richard, P. J., Devinney, T. M., Yip, G. S., & Johnson, G. (2009). Measuring organizational performance: Towards methodological best practice. Journal of Management, 35(3), 718-804.
Rosenkopf, L., & Schilling, M. A. (2007). Comparing alliance network structure across industries: observations and explanations. Strategic Entrepreneurship Journal, 1(3‐4), 191-209.
Ross, J. W., Beath, C. M., & Quaadgras, A. (2013). You may not need big data after all. Harvard business review, 91(12), 90-+.
Rothaermel, F. T., & Deeds, D. L. (2006). Alliance type, alliance experience and alliance management capability in high-technology ventures. Journal of business venturing, 21(4), 429-460.
Schilke, O. (2014). On the contingent value of dynamic capabilities for competitive advantage: The nonlinear moderating effect of environmental dynamism. Strategic Management Journal, 35(2), 179-203.
Schilke, O., & Cook, K. S. (2015). Sources of alliance partner trustworthiness: Integrating calculative and relational perspectives. Strategic Management Journal, 36(2), 276-297.
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.
Schreiner, M., Kale, P., & Corsten, D. (2009). What really is alliance management capability and how does it impact alliance outcomes and success? Strategic Management Journal, 30(13), 1395-1419.
Sheng, J., Amankwah‐Amoah, J., Khan, Z., & Wang, X. (2021). COVID‐19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, 32(4), 1164-1183.
Sheth, J. (2020). Business of business is more than business: Managing during the Covid crisis. Industrial Marketing Management, 88, 261-264.
Sirmon, D. G., Hitt, M. A., & Ireland, R. D. (2007). Managing firm resources in dynamic environments to create value: Looking inside the black box. Academy of management review, 32(1), 273-292.
Sousa, R., & Voss, C. A. (2008). Contingency research in operations management practices. Journal of Operations Management, 26(6), 697-713.
Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849-1867.
Volberda, H. W., Van Der Weerdt, N., Verwaal, E., Stienstra, M., & Verdu, A. J. (2012). Contingency fit, institutional fit, and firm performance: A metafit approach to organization–environment relationships. Organization Science, 23(4), 1040-1054.
Wamba, S. F., & Akter, S. (2019). Understanding supply chain analytics capabilities and agility for data-rich environments. International Journal of Operations & Production Management, 39(6/7/8), 887-912.
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.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-f., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
Weerawardena, J., Mort, G. S., Liesch, P. W., & Knight, G. (2007). Conceptualizing accelerated internationalization in the born global firm: A dynamic capabilities perspective. Journal of world business, 42(3), 294-306.
Altay, N., & Pal, R. (2014). Information diffusion among agents: Implications for humanitarian operations. Production and Operations Management, 23(6), 1015-1027.
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396-402.
Asmussen, C. B., & Møller, C. (2020). Enabling supply chain analytics for enterprise information systems: a topic modelling literature review and future research agenda. Enterprise Information Systems, 14(5), 563-610.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
Bayraktar, E., Demirbag, M., Koh, S. L., Tatoglu, E., & Zaim, H. (2009). A causal analysis of the impact of information systems and supply chain management practices on operational performance: evidence from manufacturing SMEs in Turkey. International Journal of Production Economics, 122(1), 133-149.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. Mis Quarterly, 1165-1188.
Colombo, M. G., Piva, E., Quas, A., & Rossi-Lamastra, C. (2021). Dynamic capabilities and high-tech entrepreneurial ventures’ performance in the aftermath of an environmental jolt. Long range planning, 54(3), 102026.
Crick, J. M., & Crick, D. (2020). Coopetition and COVID-19: Collaborative business-to-business marketing strategies in a pandemic crisis. Industrial Marketing Management, 88, 206-213.
Das, T. K., & Teng, B.-S. (2000). A resource-based theory of strategic alliances. Journal of Management, 26(1), 31-61.
Davenport, T. H. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy & Leadership.
De Haas, M., Faber, R., & Hamersma, M. (2020). How COVID-19 and the Dutch ‘intelligent lockdown’change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transportation Research Interdisciplinary Perspectives, 6, 100150.
DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications: Sage publications.
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.
Dussauge, P., Garrette, B., & Mitchell, W. (2004). Asymmetric performance: the market share impact of scale and link alliances in the global auto industry. Strategic Management Journal, 25(7), 701-711.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., . . . Eirug, A. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
Eckstein, D., Goellner, M., Blome, C., & Henke, M. (2015). The performance impact of supply chain agility and supply chain adaptability: the moderating effect of product complexity. International Journal of Production Research, 53(10), 3028-3046.
Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: what are they? Strategic Management Journal, 21(10‐11), 1105-1121.
Fainshmidt, S., Pezeshkan, A., Lance Frazier, M., Nair, A., & Markowski, E. (2016). Dynamic capabilities and organizational performance: a meta‐analytic evaluation and extension. Journal of Management Studies, 53(8), 1348-1380.
Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics: Sage Publications Sage CA: Los Angeles, CA.
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064.
Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of Management Studies, 58(5), 1159-1197.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.
He, W., Zhang, Z. J., & Li, W. (2021). Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic. International Journal of Information Management, 57, 102287.
Helfat, C. E., & Peteraf, M. A. (2003). The dynamic resource‐based view: Capability lifecycles. Strategic Management Journal, 24(10), 997-1010.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
Homburg, C., Klarmann, M., Reimann, M., & Schilke, O. (2012). What drives key informant accuracy? Journal of Marketing Research, 49(4), 594-608.
Hossain, T. M. T., Akter, S., Kattiyapornpong, U., & Dwivedi, Y. (2020). Reconceptualizing integration quality dynamics for omnichannel marketing. Industrial Marketing Management, 87, 225-241.
Hrebiniak, L. G., & Joyce, W. F. (1985). Organizational adaptation: Strategic choice and environmental determinism. Administrative science quarterly, 336-349.
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922.
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.
Kamalaldin, A., Linde, L., Sjödin, D., & Parida, V. (2020). Transforming provider-customer relationships in digital servitization: A relational view on digitalization. Industrial Marketing Management, 89, 306-325.
Kar, A. K., & Dwivedi, Y. K. (2020). Theory building with big data-driven research–Moving away from the “What” towards the “Why”. International Journal of Information Management, 54, 102205.
Ketchen Jr, D. J., & Craighead, C. W. (2020). Research at the intersection of entrepreneurship, supply chain management, and strategic management: Opportunities highlighted by COVID-19. Journal of Management, 46(8), 1330-1341.
Kohtamäki, M., Rabetino, R., & Möller, K. (2018). Alliance capabilities: A systematic review and future research directions. Industrial Marketing Management, 68, 188-201.
Kumar, N., Stern, L. W., & Anderson, J. C. (1993). Conducting interorganizational research using key informants. Academy of management journal, 36(6), 1633-1651.
Lee, H. L. (2004). The triple-A supply chain. Harvard business review, 82(10), 102-113.
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of applied psychology, 86(1), 114.
Mentzer, J. T., Flint, D. J., & Hult, G. T. M. (2001). Logistics service quality as a segment-customized process. Journal of Marketing, 65(4), 82-104.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276.
Oehmen, J., Locatelli, G., Wied, M., & Willumsen, P. (2020). Risk, uncertainty, ignorance and myopia: Their managerial implications for B2B firms. Industrial Marketing Management, 88, 330-338.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
Prasad, S., Zakaria, R., & Altay, N. (2018). Big data in humanitarian supply chain networks: A resource dependence perspective. Annals of operations research, 270(1), 383-413.
Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso Wamba, S. (2020). Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of operations research, 1-38.
Richard, P. J., Devinney, T. M., Yip, G. S., & Johnson, G. (2009). Measuring organizational performance: Towards methodological best practice. Journal of Management, 35(3), 718-804.
Rosenkopf, L., & Schilling, M. A. (2007). Comparing alliance network structure across industries: observations and explanations. Strategic Entrepreneurship Journal, 1(3‐4), 191-209.
Ross, J. W., Beath, C. M., & Quaadgras, A. (2013). You may not need big data after all. Harvard business review, 91(12), 90-+.
Rothaermel, F. T., & Deeds, D. L. (2006). Alliance type, alliance experience and alliance management capability in high-technology ventures. Journal of business venturing, 21(4), 429-460.
Schilke, O. (2014). On the contingent value of dynamic capabilities for competitive advantage: The nonlinear moderating effect of environmental dynamism. Strategic Management Journal, 35(2), 179-203.
Schilke, O., & Cook, K. S. (2015). Sources of alliance partner trustworthiness: Integrating calculative and relational perspectives. Strategic Management Journal, 36(2), 276-297.
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.
Schreiner, M., Kale, P., & Corsten, D. (2009). What really is alliance management capability and how does it impact alliance outcomes and success? Strategic Management Journal, 30(13), 1395-1419.
Sheng, J., Amankwah‐Amoah, J., Khan, Z., & Wang, X. (2021). COVID‐19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, 32(4), 1164-1183.
Sheth, J. (2020). Business of business is more than business: Managing during the Covid crisis. Industrial Marketing Management, 88, 261-264.
Sirmon, D. G., Hitt, M. A., & Ireland, R. D. (2007). Managing firm resources in dynamic environments to create value: Looking inside the black box. Academy of management review, 32(1), 273-292.
Sousa, R., & Voss, C. A. (2008). Contingency research in operations management practices. Journal of Operations Management, 26(6), 697-713.
Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849-1867.
Volberda, H. W., Van Der Weerdt, N., Verwaal, E., Stienstra, M., & Verdu, A. J. (2012). Contingency fit, institutional fit, and firm performance: A metafit approach to organization–environment relationships. Organization Science, 23(4), 1040-1054.
Wamba, S. F., & Akter, S. (2019). Understanding supply chain analytics capabilities and agility for data-rich environments. International Journal of Operations & Production Management, 39(6/7/8), 887-912.
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.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-f., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
Weerawardena, J., Mort, G. S., Liesch, P. W., & Knight, G. (2007). Conceptualizing accelerated internationalization in the born global firm: A dynamic capabilities perspective. Journal of world business, 42(3), 294-306.