Abed, S. S. (2020). Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs. International Journal of Information Management, 53(October 2019), 102118. https://doi.org/10.1016/j.ijinfomgt.2020.102118
Adem, S. Al, Childerhouse, P., Egbelakin, T., Wang, B., Teerlink, M., Tabassum, R., … Verma, S. (2018). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. Industrial Marketing Management, 226(0123456789), 3–5. https://doi.org/10.1016/j.ijpe.2019.107599
Adeodu, A., Maladzhi, R., Kana-Kana Katumba, M. G., & Daniyan, I. (2023). Development of an improvement framework for warehouse processes using lean six sigma (DMAIC) approach. A case of third party logistics (3PL) services. Heliyon, 9(4), e14915. https://doi.org/10.1016/j.heliyon.2023.e14915
Ahmed, W. A. H., & MacCarthy, B. L. (2023). Blockchain-enabled supply chain traceability – How wide? How deep? International Journal of Production Economics, 263(April), 108963. https://doi.org/10.1016/j.ijpe.2023.108963
Al-Banna, A., Rana, Z. A., Yaqot, M., & Menezes, B. (2023). Interconnectedness between Supply Chain Resilience, Industry 4.0, and Investment. Logistics, 7(3). https://doi.org/10.3390/logistics7030050
Alazab, M. (2024). Industry 4 . 0 Innovation : A Systematic Literature Review on the Role of Blockchain Technology in Creating Smart and Sustainable Manufacturing Facilities.
Albrakat, N. S. A., Al-Hawary, S. I. S., & Muflih, S. M. (2023). Green supply chain practices and their effects on operational performance: An experimental study in Jordanian private hospitals. Uncertain Supply Chain Management, 11(2), 523–532. https://doi.org/10.5267/j.uscm.2023.2.012
Ali, A. A. A., Udin, Z. B. M., & Abualrejal, H. M. E. (2023). The Impact of Artificial Intelligence and Supply Chain Resilience on the Companies Supply Chains Performance: The Moderating Role of Supply Chain Dynamism BT - International Conference on Information Systems and Intelligent Applications (M. Al-Emran, M. A. Al-Sharafi, & K. Shaalan, Eds.). Cham: Springer International Publishing.
Alshawabkeh, R. O., Abu Rumman, A. R., & Al-Abbadi, L. H. (2024). The nexus between digital collaboration, analytics capability and supply chain resilience of the food processing industry in Jordan. Cogent Business and Management, 11(1). https://doi.org/10.1080/23311975.2023.2296608
Alshurideh, M. T., Alquqa, E. K., Alzoubi, H. M., Al Kurdi, B., & Hamadneh, S. (2023). The effect of information security on e-supply chain in the UAE logistics and distribution industry. Uncertain Supply Chain Management, 11(1), 145–152. https://doi.org/10.5267/j.uscm.2022.11.001
Amini, M., & Javid, N. J. (2023). A Multi-Perspective Framework Established on Diffusion of Innovation (DOI) Theory and Technology, Organization and Environment (TOE) Framework Toward Supply Chain Management System Based on Cloud Computing Technology for Small and Medium Enterprises. International Journal of Information Technology and Innovation Adoption, 11(8), 1217–1234.
Andiyappillai, N. (2019). Standardization of System Integrated Solutions in Warehouse Management Systems (WMS) Implementations. International Journal of Computer Applications, 178(13), 6–11. https://doi.org/10.5120/ijca2019918891
Baruffaldi, G., Accorsi, R., Manzini, R., & Ferrari, E. (2020). Warehousing process performance improvement: a tailored framework for 3PL. Business Process Management Journal, 26(6), 1619–1641. https://doi.org/10.1108/BPMJ-03-2019-0120
Belhadi, A., Zkik, K., Cherrafi, A., Yusof, S. M., & El fezazi, S. (2019). Understanding Big Data Analytics for Manufacturing Processes: Insights from Literature Review and Multiple Case Studies. Computers and Industrial Engineering, 137(October 2018), 106099. https://doi.org/10.1016/j.cie.2019.106099
Bell, D. R., Gallino, S., & Moreno, A. (2018). Offline showrooms in omnichannel retail: Demand and operational benefits. Management Science, 64(4), 1629–1651. https://doi.org/10.1287/mnsc.2016.2684
Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165. https://doi.org/10.1016/j.techfore.2020.120557
Cai, F., Correll, J. M., Lee, S. H., Lim, Y., Bothra, V., Zhang, Z., … Lu, W. D. (2019). A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations. Nature Electronics, 2(7), 290–299. https://doi.org/10.1038/s41928-019-0270-x
Chittipaka, V., Kumar, S., Sivarajah, U., Bowden, J. L. H., & Baral, M. M. (2023). Blockchain Technology for Supply Chains operating in emerging markets: an empirical examination of technology-organization-environment (TOE) framework. Annals of Operations Research, 327(1), 465–492. https://doi.org/10.1007/s10479-022-04801-5
Deepu, T. S., & Ravi, V. (2023). A review of literature on implementation and operational dimensions of supply chain digitalization: Framework development and future research directions. International Journal of Information Management Data Insights, 3(1), 100156. https://doi.org/10.1016/j.jjimei.2023.100156
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.
Dzwigol, H., Trushkina, N., & Kwilinski, A. (2021). the Organizational and Economic Mechanism of Implementing the Concept of Green Logistics. Virtual Economics, 4(2), 41–75. https://doi.org/10.34021/ve.2021.04.02(3)
Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. N. (2018). An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges. IEEE Internet of Things Journal, 5(5), 3758–3773. https://doi.org/10.1109/JIOT.2018.2844296
Etemadi, N., Borbon-Galvez, Y., Strozzi, F., & Etemadi, T. (2021). Supply chain disruption risk management with blockchain: A dynamic literature review. Information (Switzerland), 12(2), 1–25. https://doi.org/10.3390/info12020070
Fernando, Y., & Ikhsan, R. B. (2023). A Data-Driven Supply Chain: Marketing Data Sharing, Data Security, and Digital Technology Adoption to Predict Firm’s Resilience. Binus Business Review, 14(1), 99–109. https://doi.org/10.21512/bbr.v14i1.9305
Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: a comparison of four procedures. Internet Research.
Fu, S., Liu, J., Tian, J., & Peng, J. (2023). Impact of Digital Economy on Energy Supply Chain Efficiency : 1–21.
Garay-Rondero, C. L., Martinez-Flores, J. L., Smith, N. R., Caballero Morales, S. O., & Aldrette-Malacara, A. (2020). Digital supply chain model in Industry 4.0. Journal of Manufacturing Technology Management, 31(5), 887–933. https://doi.org/10.1108/JMTM-08-2018-0280
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications. European Journal of Tourism Research, 6(2), 211–213.
Hallikas, J., Immonen, M., & Brax, S. (2021). Digitalizing procurement: the impact of data analytics on supply chain performance. Supply Chain Management, 26(5), 629–646. https://doi.org/10.1108/SCM-05-2020-0201
Hammi, B., Zeadally, S., & Nebhen, J. (2023). Security Threats, Countermeasures, and Challenges of Digital Supply Chains. ACM Computing Surveys, 55(14 S). https://doi.org/10.1145/3588999
Hatamlah, H., Allahham, M., Abu-AlSondos, I. A., Al-junaidi, A., Al-Anati, G. M., & Al-Shaikh, and M. (2023). The Role of Business Intelligence adoption as a Mediator of Big Data Analytics in the Management of Outsourced Reverse Supply Chain Operations. Applied Mathematics and Information Sciences, 17(5), 897–903. https://doi.org/10.18576/AMIS/170516
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. https://doi.org/10.5267/j.uscm.2023.4.005
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.
Iris, Ç., & Lam, J. S. L. (2019). A review of energy efficiency in ports: Operational strategies, technologies and energy management systems. Renewable and Sustainable Energy Reviews, 112, 170–182. https://doi.org/10.1016/j.rser.2019.04.069
Jawabreh, O., Baadhem, A. M., Ali, B. J. A., Atta, A. A. B., Ali, A., Al-Hosaini, F. F., & Allahham, M. (2023). The Influence of Supply Chain Management Strategies on Organizational Performance in Hospitality Industry. Applied Mathematics and Information Sciences, 17(5), 851–858. https://doi.org/10.18576/AMIS/170511
Jordan Chamber of Industry. (2022). No Title. Retrieved from
https://www.jci.org.jo/Chamber/Sector/80066/- The engineering, electrical, and information technology industries.
Katsaliaki, K., Galetsi, P., & Kumar, S. (2022). Supply chain disruptions and resilience: a major review and future research agenda. In Annals of Operations Research (Vol. 319). https://doi.org/10.1007/s10479-020-03912-1
Kim, J. S., & Shin, N. (2019). The impact of blockchain technology application on supply chain partnership and performance. Sustainability (Switzerland), 11(21). https://doi.org/10.3390/su11216181
Kovács, G., & Falagara Sigala, I. (2021). Lessons learned from humanitarian logistics to manage supply chain disruptions. Journal of Supply Chain Management, 57(1), 41–49. https://doi.org/10.1111/jscm.12253
Krejcie, R. V, & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.
Lahiri, M. M., Sarkar, S., & Bhargava, M. (2022). The Role of 3rd Party Logistics In Bringing Efficiency & Effectiveness among their Distribution Centers. Academy of Marketing Studies Journal, 26(6), 1–7.
Lee, K. L., Wong, S. Y., Alzoubi, H. M., Al Kurdi, B., Alshurideh, M. T., & El Khatib, M. (2023). Adopting smart supply chain and smart technologies to improve operational performance in manufacturing industry. International Journal of Engineering Business Management, 15, 1–14. https://doi.org/10.1177/18479790231200614
Li, J. C. F. (2020). Roles of individual perception in technology adoption at organization level: Behavioral model versus toe framework. Journal of System and Management Sciences, 10(3), 97–118. https://doi.org/10.33168/JSMS.2020.0308
Malik, S., Chadhar, M., & Chetty, M. (2021). Factors affecting the organizational adoption of blockchain technology: An Australian perspective. Proceedings of the Annual Hawaii International Conference on System Sciences, 2020-Janua, 5597–5606. https://doi.org/10.24251/hicss.2021.680
Medyński, D., Bonarski, P., Motyka, P., Wysoczański, A., Gnitecka, R., Kolbusz, K., … Machado, J. (2023). Digital Standardization of Lean Manufacturing Tools According to Industry 4.0 Concept. Applied Sciences (Switzerland), 13(10). https://doi.org/10.3390/app13106259
Mohsen, B. M. (2023). Developments of Digital Technologies Related to Supply Chain Management. Procedia Computer Science, 220, 788–795. https://doi.org/10.1016/j.procs.2023.03.105
Nandi, M. L., Nandi, S., Moya, H., & Kaynak, H. (2020). Blockchain technology-enabled supply chain systems and supply chain performance: a resource-based view. Supply Chain Management, 25(6), 841–862. https://doi.org/10.1108/SCM-12-2019-0444
Nasir, M. H., Arshad, J., Khan, M. M., Fatima, M., Salah, K., & Jayaraman, R. (2022). Scalable blockchains — A systematic review. Future Generation Computer Systems, 126, 136–162. https://doi.org/10.1016/j.future.2021.07.035
Nurgazina, J., Pakdeetrakulwong, U., Moser, T., & Reiner, G. (2021). Distributed ledger technology applications in food supply chains: A review of challenges and future research directions. Sustainability (Switzerland), 13(8). https://doi.org/10.3390/su13084206
Olan, F., Liu, S., Suklan, J., Jayawickrama, U., & Arakpogun, E. O. (2022). The role of Artificial Intelligence networks in sustainable supply chain finance for food and drink industry. International Journal of Production Research, 60(14), 4418-4433. https://doi.org/10.1080/00207543.2021.1915510
Oubrahim, I., Sefiani, N., & Happonen, A. (2023). The Influence of Digital Transformation and Supply Chain Integration on Overall Sustainable Supply Chain Performance: An Empirical Analysis from Manufacturing Companies in Morocco. Energies, 16(2). https://doi.org/10.3390/en16021004
Perano, M., Cammarano, A., Varriale, V., Del Regno, C., Michelino, F., & Caputo, M. (2023). Embracing supply chain digitalization and unphysicalization to enhance supply chain performance: a conceptual framework. In International Journal of Physical Distribution and Logistics Management (Vol. 53). https://doi.org/10.1108/IJPDLM-06-2022-0201
Reyes, J., Mula, J., & Díaz-Madroñero, M. (2023). Development of a conceptual model for lean supply chain planning in industry 4.0: multidimensional analysis for operations management. Production Planning and Control, 34(12), 1209–1224. https://doi.org/10.1080/09537287.2021.1993373
Salhab, H. A., Allahham, M., Abu-Alsondos, I. A., Frangieh, R. H., Alkhwaldi, A. F., & Ali, B. J. A. (2023). Inventory competition, artificial intelligence, and quality improvement decisions in supply chains with digital marketing. Uncertain Supply Chain Management, 11(4), 1915–1924. https://doi.org/10.5267/j.uscm.2023.8.009
Sarfaraz, A. (2023). Blockchain-Coordinated Frameworks for Scalable and Secure Supply Chain Networks.
Sarfaraz, Aaliya, Chakrabortty, R. K., & Essam, D. L. (2023). Reputation based proof of cooperation: an efficient and scalable consensus algorithm for supply chain applications. Journal of Ambient Intelligence and Humanized Computing, 14(6), 7795–7811. https://doi.org/10.1007/s12652-023-04592-y
Sayginer, C., & Ercan, T. (2020). Understanding Determinants of Cloud Computing Adoption Using an Integrated Diffusion of Innovation (Doi)-Technological, Organizational and Environmental (Toe) Model. Humanities & Social Sciences Reviews, 8(1), 91–102. https://doi.org/10.18510/hssr.2020.8115
Sharabati, A. A., Allahham, M., Yahiya, A., Ahmad, B., & Sabra, S. (2023). Effects of artificial integration and big data analysis on economic viability of solar microgrids : mediating role of cost benefit analysis. Operational Research in Engineering Sciences: Theory and Applications, 6(3), 360–379.
Skafi, M., Yunis, M. M., & Zekri, A. (2020). Factors influencing SMEs’ adoption of cloud computing services in Lebanon: An empirical analysis using TOE and contextual theory. IEEE Access, 8, 79169–79181. https://doi.org/10.1109/ACCESS.2020.2987331
Sobb, T., Turnbull, B., & Moustafa, N. (2020). Supply chain 4.0: A survey of cyber security challenges, solutions and future directions. Electronics (Switzerland), 9(11), 1–31. https://doi.org/10.3390/electronics9111864
Solfa, F. D. G. (2022). Impacts of Cyber Security and Supply Chain Risk on Digital Operations: Evidence from the Pharmaceutical Industry. International Journal of Technology, Innovation and Management (IJTIM), 2(2), 18–32. https://doi.org/10.54489/ijtim.v2i2.98
Thakur, S., & Breslin, J. G. (2020). Scalable and secure product serialization for multi-party perishable good supply chains using blockchain. Internet of Things (Netherlands), 11, 100253. https://doi.org/10.1016/j.iot.2020.100253
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(May 2020), 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Wang, M., Wang, B., & Abareshi, A. (2020). Blockchain technology and its role in enhancing supply chain integration capability and reducing carbon emission: A conceptual framework. Sustainability (Switzerland), 12(24), 1–17. https://doi.org/10.3390/su122410550
Wang, X., Pan, Z., Li, Z., Ji, W., & Yang, F. (2019). Adaptive information sharing approach for crowd networks based on two stage optimization. International Journal of Crowd Science, 3(3), 284–302. https://doi.org/10.1108/IJCS-09-2019-0020
Weerabahu, W. M. S. K., Samaranayake, P., Nakandala, D., & Hurriyet, H. (2023). Digital supply chain research trends: a systematic review and a maturity model for adoption. Benchmarking, 30(9), 3040–3066. https://doi.org/10.1108/BIJ-12-2021-0782
Weisz, E., Herold, D. M., & Kummer, S. (2020). Revisiting the bullwhip effect: how can AI smoothen the bullwhip phenomenon? International Journal of Logistics Management, 34(7), 98–120. https://doi.org/10.1108/IJLM-02-2022-0078