How to cite this paper
Shamout, M., Ben-Abdallah, R., Alshurideh, M., Alzoubi, H., Kurdi, B & Hamadneh, S. (2022). A conceptual model for the adoption of autonomous robots in supply chain and logistics industry.Uncertain Supply Chain Management, 10(2), 577-592.
Refrences
Abubakar, A. M., Behravesh, E., Rezapouraghdam, H., & Yildiz, S. B. (2019). Applying artificial intelligence technique to predict knowledge hiding behavior. International Journal of Information Management, 49, 45-57.
Abubakar, A. M. (2019). Using hybrid SEM-artificial intelligence approach to examine the nexus between boreout, generation, career, life and job satisfaction. Personnel Review. 49(1), 67-86.
Aboelmaged, M. G. (2010). Predicting e-procurement adoption in a developing country. Industrial Management & Data Systems, 110(3), 392–414.
Ahmad, S.Z., Abu Bakar, A.R., & Ahmad, N. (2019). Social media adoption and its impact on firm performance: the case of the UAE. International Journal of Entrepreneurial Behavior & Research, 25(1), 84-111.
Alaali, N., Al Marzouqi, A., Albaqaeen, A., Dahabreh,F., et al., (2021) The Impact of Adopting Corporate Governance Strategic Performance in the Tourism Sector: A Case Study in the Kingdom of Bahrain. Journal of Legal, Ethical and Regulatory Issues, 24 (Special Issue 1), 1-18.
Al Dmour, H., Alshurideh, M., & Shishan, F. (2014). The influence of mobile application quality and attributes on the continuance intention of mobile shopping. Life Science Journal, 11(10), 172-181.
Al-Hamad, M., Mbaidin, H., AlHamad, A., Alshurideh, M., Kurdi, B., & Al-Hamad, N. (2021). Investigating students' behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic. International Journal of Data and Network Science, 5(3), 321-330.
Al-Khayyal, A., Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2021). Factors influencing electronic service quality on electronic loyalty in online shopping context: data analysis approach. In Enabling AI Applications in Data Science (pp. 367-378). Springer, Cham.
Al Kurdi, B., Alshurideh, M., & Salloum, S. A. (2020). Investigating a theoretical framework for e-learning technology acceptance. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6484-6496.
AlSharji, A., Ahmad, S.Z., & Abu Bakar, A.R. (2018). Understanding social media adoption in SMEs: Empirical evidence from the United Arab Emirates. Journal of Entrepreneurship in Emerging Economies, 10(2), 302-328.
Alshurideh, M. T., Al Kurdi, B., & Salloum, S. A. (2021). The moderation effect of gender on accepting electronic payment technology: a study on United Arab Emirates consumers. Review of International Business and Strategy. Review of International Business and Strategy, 31(3), 375-396.
Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2019, October). Examining the main mobile learning system drivers’ effects: A mix empirical examination of both the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM). In International Conference on Advanced Intelligent Systems and Informatics (pp. 406-417). Springer, Cham.
Alshurideh, M. T., Kurdi, B. A., AlHamad, A. Q., Salloum, S. A., Alkurdi, S., Dehghan, A., ... & Masa’deh, R. E. (2021, June). Factors affecting the use of smart mobile examination platforms by universities’ postgraduate students during the COVID 19 pandemic: an empirical study. In Informatics (Vol. 8, No. 2, p. 32). Multidisciplinary Digital Publishing Institute.
Alshurideh, M., Alsharari, N. M., & Al Kurdi, B. (2019). Supply chain integration and customer relationship management in the airline logistics. Theoretical Economics Letters, 9(02), 392-414.
Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., & Salloum, S. (2021). Using machine learning algorithms to predict people’s intention to use mobile learning platforms during the COVID-19 pandemic: machine learning approach. JMIR Medical Education, 7(1), e24032.
Al-Qirim, N.A. (2007). E-commerce adoption in small businesses: cases from New Zealand. Journal of Information Technology Case and Application Research, 9(2), 28–57.
Alzoubi, H. M., Alshurideh, M., & Ghazal, T. M. (2021, June). Integrating BLE Beacon Technology with Intelligent Information Systems IIS for Operations’ Performance: A Managerial Perspective. In The International Conference on Artificial Intelligence and Computer Vision (pp. 527-538). Springer, Cham.
Baker, J. (2011). In Y. Dwivedi, M. Wade, & S. Schneberger (Eds.), The technology organization-environment framework. In Information systems theory: explaining and predicting our digital society (pp. 231–246). New York: Springer.
Borgman, H.P., Bahli, B., Heier, H., & Schewski, F. (2014). Cloudrise: exploring cloud computing adoption and governance with the TOE framework. IEEE Computer Society, 12, 4425–4435.
Carlsen, H., Johansson, L., Wikman-Svahn, P., & Dreborg, K.H. (2014). Co-evolutionary scenarios for creative prototyping of future robot systems for civil protection. Technological Forecasting and Social Change, 84, 93-100.
Chuang, T., Nakatani, K., & Zhou, D. (2009). An Exploratory Study of the Extent of Information Technology Adoption in SMEs: An Application of Upper Echelon Theory, 22(1/2), 183-196.
Spokane, School of Business Administration, Gonzaga University, Washington, DC.
Destephe, M., Brandao, M., Kishi, T., Zecca, M., Hashimoto, K., & Takanishi, A. (2015). Walking in the uncanny valley: importance of the attractiveness on the acceptance of a robot as a working partner. Frontiers in psychology, 6, 1-11.
Eze, S.C., Chinedu-Eze, V.C., Bello, A.O., Inegbedion, H., Nwanji, T., & Asamu, F. (2019). Mobile marketing technology adoption in service SMEs: a multi-perspective framework. Journal of Science and Technology Policy Management.
Gangwar, H. (2018). Understanding the Determinants of Big Data Adoption in India: An Analysis of the Manufacturing and Services Sectors. Information Resources Management Journal (IRMJ), 31(4), 1-22.
Ghobakhloo, M., Arias-Aranda, D., & Benitez-Amado, J. (2011). Adoption of e-commerce applications in SMEs. Industrial Management & Data Systems,111(8), 1238–1269.
Gnambs, T., & Appel, M. (2019). Are robots becoming unpopular? Changes in attitudes towards autonomous robotic systems in Europe. Computers in Human Behavior, 93, 53-61.
Gu, V.C., Cao, Q., & Duan, W. (2012). Unified modeling language (UML) IT adoption—a holistic model of organizational capabilities perspective. Decision Support Systems, 54(1), 257–269.
Hamadneh, S., Pederson, O., Alshurideh, M., Al Kurdi, B., & Alzoubi, H. (2021). An Investigation of the Role of Supply Chain Visibility into the Scottish Bood Supply Chain. Journal of Legal, Ethical and Regulatory Issues, 24 (Special Issue 1), 1-12.
Hansen, B.G. (2015). Robotic milking-farmer experiences and adoption rate in Jæren, Norway. Journal of Rural Studies, 41, 109-117.
Hasani, T., Bojei, J., & Dehghantanha, A. (2017). Investigating the antecedents to the adoption of SCRM technologies by start-up companies. Telematics and Informatics, 34(5), 655-675.
Hofmann, E., Sternberg, H., Chen, H., Pflaum, A. and Prockl, G., 2019. Supply chain management and Industry 4.0: conducting research in the digital age. International Journal of Physical Distribution & Logistics Management. 9(10), 945-955.
International Federation of Robotics (2017). World robotics industrial robots 2017. Frankfurt, Germany: IFR.
Jensen, F. V., & Nielsen, T. D. (2007). Bayesian networks and decision graphs. Berlin: Springer.
Joghee, S., Alzoubi, H. M., Alshurideh, M., & Al Kurdi, B. (2021, June). The Role of Business Intelligence Systems on Green Supply Chain Management: Empirical Analysis of FMCG in the UAE. In The International Conference on Artificial Intelligence and Computer Vision (pp. 539-552). Springer, Cham.
Kachouie, R., Sedighadeli, S., Khosla, R., & Chu, M.T. (2014). Socially assistive robots in elderly care: a mixed-method systematic literature review. International Journal of Human-Computer Interaction, 30(5), 369-393.
Kaya, B., Abubakar, A. M., Behravesh, E., Yildiz, H., & Mert, I. S. (2020). Antecedents of innovative performance: Findings from PLS-SEM and fuzzy sets (fsQCA). Journal of Business Research, 114, 278-289.
Kim, S., & Garrison, G. (2010). Understanding users’ behaviors regarding supply chain technology: Determinants impacting the adoption and implementation of RFID technology in South Korea. International Journal of Information Management, 30(5), 388-398.
Kourouthanassis, P. E., Mikalef, P., Pappas, I. O., & Kostagiolas, P. (2017). Explaining traveler’s online information satisfaction: A complexity theory approach on information needs, barriers, sources and personal characteristics. Information & Management, 54(6), 814-824.
Kurdi, B., & Alshurideh, M. (2020). Employee retention and organizational performance: Evidence from banking industry. Management Science Letters, 10(16), 3981-3990.
Kurdi, B. A., Alshurideh, M., Nuseir, M., Aburayya, A., & Salloum, S. A. (2021, March). The effects of subjective norm on the intention to use social media networks: an exploratory study using PLS-SEM and machine learning approach. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 581-592). Springer, Cham.
Lai, H.M., Lin, I.C., & Tseng, L.T. (2014). High-level managers’ considerations for RFID adoption in hospitals: an empirical study in Taiwan. Journal of Medical Systems, 38(2), 1–17.
Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: an empirical investigation. The International Journal of Logistics Management, 29(2), 676-703.
Langseth, H., & Portinale, L. (2007). Bayesian networks in reliability. Reliability Engineering & System Safety,92(1), 92–108.
Li, X., Troutt, M. D., Brandyberry, A., & Wang, T. (2011). Decision Factors for the Adoption and Continued Use of Online Direct Sales Channels among SMEs. Journal of the Association for Information Systems, 12(1), 1–31.
Lin, C. Y., & Ho, Y. H. (2011). Determinants of green practice adoption for logistics companies in China. Journal of Business Ethics, 98(1), 67–83.
Lin, H. H., & Wang, Y. S. (2005, July). Predicting consumer intention to use mobile commerce in Taiwan. In International Conference on Mobile Business (ICMB'05) (pp. 406-412). IEEE.
Maduku, D. K., Mpinganjira, M., & Duh, H. (2016). Understanding mobile marketing adoption intention by South African SMEs: A multi-perspective framework. International Journal of Information Management, 36(5), 711-723.
Moore, G.C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research, 2, 173-191. http://dx.doi.org/10.1287/isre.2.3.192
Naicker, V., & Van Der Merwe, D. B. (2018). Managers’ perception of mobile technology adoption in the Life Insurance industry. Information Technology & People, 31(2), 507-526.
Naqvi, R., Soomro, T. R., Alzoubi, H. M., Ghazal, T. M., & Alshurideh, M. T. (2021, June). The Nexus Between Big Data and Decision-Making: A Study of Big Data Techniques and Technologies. In The International Conference on Artificial Intelligence and Computer Vision (pp. 838-853). Springer, Cham.
Obeidat, U., Obeidat, B., Alrowwad, A., Alshurideh, M., Masadeh, R., & Abuhashesh, M. (2021). The effect of intellectual capital on competitive advantage: the mediating role of innovation. Management Science Letters, 11(4), 1331-1344.
Oliveira, T., Martins, R., Sarker, S., Thomas, M., & Popovič, A. (2019). Understanding SaaS adoption: The moderating impact of the environment context. International Journal of Information Management, 49, 1-12.
Piçarra, N., & Giger, J. C. (2018). Predicting intention to work with social robots at anticipation stage: Assessing the role of behavioral desire and anticipated emotions. Computers in Human Behavior, 86, 129-146.
Ragu-Nathan, B.S., Apigian, C.H., Ragu-Nathan, T.S., & Tu, Q. (2004). A path analytic study of the effect of top management support for information systems performance. Omega, 32(6), 459-471.
Ramayah, T., Ling, N. S., Taghizadeh, S. K., & Rahman, S. A. (2016). Factors influencing SMEs website continuance intention in Malaysia. Telematics and Informatics, 33(1), 150–164.
Reich-Stiebert, N., & Eyssel, F. (2015). Learning with educational companion robots? Toward attitudes on education robots, predictors of attitudes, and application potentials for education robots. International Journal of Social Robotics, 7(5), 875-888.
Rogers, E. M. (2010). Diffusion of innovations (5th ed.). New York: Free Press.
Savela, N., Turja, T., & Oksanen, A. (2018). Social acceptance of robots in different occupational fields: A systematic literature review. International Journal of Social Robotics, 10(4), 493-502.
Šabanović, S. (2014). Inventing Japan’s ‘robotics culture’: The repeated assembly of science, technology, and culture in social robotics. Social Studies of Science, 44(3), 342-367.
Shamout, M. D. (2020a). The nexus between supply chain analytic, innovation and robustness capability. VINE Journal of Information and Knowledge Management Systems. 51(1), 163-176.
Shamout, M. D. (2020b). Supply chain data analytics and supply chain agility: a fuzzy set (fsQCA) approach. International Journal of Organizational Analysis. 28(5), 1055-1067.
Sweiss, N., Obeidat, Z. M., Al-Dweeri, R. M., Mohammad Khalaf Ahmad, A., M. Obeidat, A., & Alshurideh, M. (2021). The moderating role of perceived company effort in mitigating customer misconduct within Online Brand Communities (OBC). Journal of Marketing Communications, 1-24.
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation, Lexington, MA, Lexington Books.
Tsai, M. C., Lee, W., & Wu, H. C. (2010). Determinants of RFID adoption intention: Evidence from Taiwanese retail chains. Information & Management, 47(5), 255–261.
Zu’bi, Z., Al-Lozi, M., Dahiyat, S., Alshurideh, M., & Al Majali, A. (2012). Examining the effects of quality management practices on product variety. European Journal of Economics, Finance and Administrative Sciences, 51(1), 123-139.
Wang, Y. S., Li, H. T., Li, C. R., & Zhang, D. Z. (2016). Factors affecting hotels' adoption of mobile reservation systems: A technology-organization-environment framework. Tourism Management, 53, 163-172.
Wu, F., & Lee, Y. K. (2005). Determinants of e-communication adoption: the internal push versus external pull factors. Marketing Theory, 5(1), 7–31.
Yadav, R., Sharma, S. K., & Tarhini, A. (2016). A multi-analytical approach to understand and predict the mobile commerce adoption. Journal of enterprise information management, 29(2), 222-237.
Yu, Y., & Buahom, K. (2013). Exploring factors influencing consumer adoption on mobile commerce services. The Business Review, Cambridge, 21(1), 258-265.
Abubakar, A. M. (2019). Using hybrid SEM-artificial intelligence approach to examine the nexus between boreout, generation, career, life and job satisfaction. Personnel Review. 49(1), 67-86.
Aboelmaged, M. G. (2010). Predicting e-procurement adoption in a developing country. Industrial Management & Data Systems, 110(3), 392–414.
Ahmad, S.Z., Abu Bakar, A.R., & Ahmad, N. (2019). Social media adoption and its impact on firm performance: the case of the UAE. International Journal of Entrepreneurial Behavior & Research, 25(1), 84-111.
Alaali, N., Al Marzouqi, A., Albaqaeen, A., Dahabreh,F., et al., (2021) The Impact of Adopting Corporate Governance Strategic Performance in the Tourism Sector: A Case Study in the Kingdom of Bahrain. Journal of Legal, Ethical and Regulatory Issues, 24 (Special Issue 1), 1-18.
Al Dmour, H., Alshurideh, M., & Shishan, F. (2014). The influence of mobile application quality and attributes on the continuance intention of mobile shopping. Life Science Journal, 11(10), 172-181.
Al-Hamad, M., Mbaidin, H., AlHamad, A., Alshurideh, M., Kurdi, B., & Al-Hamad, N. (2021). Investigating students' behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic. International Journal of Data and Network Science, 5(3), 321-330.
Al-Khayyal, A., Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2021). Factors influencing electronic service quality on electronic loyalty in online shopping context: data analysis approach. In Enabling AI Applications in Data Science (pp. 367-378). Springer, Cham.
Al Kurdi, B., Alshurideh, M., & Salloum, S. A. (2020). Investigating a theoretical framework for e-learning technology acceptance. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6484-6496.
AlSharji, A., Ahmad, S.Z., & Abu Bakar, A.R. (2018). Understanding social media adoption in SMEs: Empirical evidence from the United Arab Emirates. Journal of Entrepreneurship in Emerging Economies, 10(2), 302-328.
Alshurideh, M. T., Al Kurdi, B., & Salloum, S. A. (2021). The moderation effect of gender on accepting electronic payment technology: a study on United Arab Emirates consumers. Review of International Business and Strategy. Review of International Business and Strategy, 31(3), 375-396.
Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2019, October). Examining the main mobile learning system drivers’ effects: A mix empirical examination of both the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM). In International Conference on Advanced Intelligent Systems and Informatics (pp. 406-417). Springer, Cham.
Alshurideh, M. T., Kurdi, B. A., AlHamad, A. Q., Salloum, S. A., Alkurdi, S., Dehghan, A., ... & Masa’deh, R. E. (2021, June). Factors affecting the use of smart mobile examination platforms by universities’ postgraduate students during the COVID 19 pandemic: an empirical study. In Informatics (Vol. 8, No. 2, p. 32). Multidisciplinary Digital Publishing Institute.
Alshurideh, M., Alsharari, N. M., & Al Kurdi, B. (2019). Supply chain integration and customer relationship management in the airline logistics. Theoretical Economics Letters, 9(02), 392-414.
Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., & Salloum, S. (2021). Using machine learning algorithms to predict people’s intention to use mobile learning platforms during the COVID-19 pandemic: machine learning approach. JMIR Medical Education, 7(1), e24032.
Al-Qirim, N.A. (2007). E-commerce adoption in small businesses: cases from New Zealand. Journal of Information Technology Case and Application Research, 9(2), 28–57.
Alzoubi, H. M., Alshurideh, M., & Ghazal, T. M. (2021, June). Integrating BLE Beacon Technology with Intelligent Information Systems IIS for Operations’ Performance: A Managerial Perspective. In The International Conference on Artificial Intelligence and Computer Vision (pp. 527-538). Springer, Cham.
Baker, J. (2011). In Y. Dwivedi, M. Wade, & S. Schneberger (Eds.), The technology organization-environment framework. In Information systems theory: explaining and predicting our digital society (pp. 231–246). New York: Springer.
Borgman, H.P., Bahli, B., Heier, H., & Schewski, F. (2014). Cloudrise: exploring cloud computing adoption and governance with the TOE framework. IEEE Computer Society, 12, 4425–4435.
Carlsen, H., Johansson, L., Wikman-Svahn, P., & Dreborg, K.H. (2014). Co-evolutionary scenarios for creative prototyping of future robot systems for civil protection. Technological Forecasting and Social Change, 84, 93-100.
Chuang, T., Nakatani, K., & Zhou, D. (2009). An Exploratory Study of the Extent of Information Technology Adoption in SMEs: An Application of Upper Echelon Theory, 22(1/2), 183-196.
Spokane, School of Business Administration, Gonzaga University, Washington, DC.
Destephe, M., Brandao, M., Kishi, T., Zecca, M., Hashimoto, K., & Takanishi, A. (2015). Walking in the uncanny valley: importance of the attractiveness on the acceptance of a robot as a working partner. Frontiers in psychology, 6, 1-11.
Eze, S.C., Chinedu-Eze, V.C., Bello, A.O., Inegbedion, H., Nwanji, T., & Asamu, F. (2019). Mobile marketing technology adoption in service SMEs: a multi-perspective framework. Journal of Science and Technology Policy Management.
Gangwar, H. (2018). Understanding the Determinants of Big Data Adoption in India: An Analysis of the Manufacturing and Services Sectors. Information Resources Management Journal (IRMJ), 31(4), 1-22.
Ghobakhloo, M., Arias-Aranda, D., & Benitez-Amado, J. (2011). Adoption of e-commerce applications in SMEs. Industrial Management & Data Systems,111(8), 1238–1269.
Gnambs, T., & Appel, M. (2019). Are robots becoming unpopular? Changes in attitudes towards autonomous robotic systems in Europe. Computers in Human Behavior, 93, 53-61.
Gu, V.C., Cao, Q., & Duan, W. (2012). Unified modeling language (UML) IT adoption—a holistic model of organizational capabilities perspective. Decision Support Systems, 54(1), 257–269.
Hamadneh, S., Pederson, O., Alshurideh, M., Al Kurdi, B., & Alzoubi, H. (2021). An Investigation of the Role of Supply Chain Visibility into the Scottish Bood Supply Chain. Journal of Legal, Ethical and Regulatory Issues, 24 (Special Issue 1), 1-12.
Hansen, B.G. (2015). Robotic milking-farmer experiences and adoption rate in Jæren, Norway. Journal of Rural Studies, 41, 109-117.
Hasani, T., Bojei, J., & Dehghantanha, A. (2017). Investigating the antecedents to the adoption of SCRM technologies by start-up companies. Telematics and Informatics, 34(5), 655-675.
Hofmann, E., Sternberg, H., Chen, H., Pflaum, A. and Prockl, G., 2019. Supply chain management and Industry 4.0: conducting research in the digital age. International Journal of Physical Distribution & Logistics Management. 9(10), 945-955.
International Federation of Robotics (2017). World robotics industrial robots 2017. Frankfurt, Germany: IFR.
Jensen, F. V., & Nielsen, T. D. (2007). Bayesian networks and decision graphs. Berlin: Springer.
Joghee, S., Alzoubi, H. M., Alshurideh, M., & Al Kurdi, B. (2021, June). The Role of Business Intelligence Systems on Green Supply Chain Management: Empirical Analysis of FMCG in the UAE. In The International Conference on Artificial Intelligence and Computer Vision (pp. 539-552). Springer, Cham.
Kachouie, R., Sedighadeli, S., Khosla, R., & Chu, M.T. (2014). Socially assistive robots in elderly care: a mixed-method systematic literature review. International Journal of Human-Computer Interaction, 30(5), 369-393.
Kaya, B., Abubakar, A. M., Behravesh, E., Yildiz, H., & Mert, I. S. (2020). Antecedents of innovative performance: Findings from PLS-SEM and fuzzy sets (fsQCA). Journal of Business Research, 114, 278-289.
Kim, S., & Garrison, G. (2010). Understanding users’ behaviors regarding supply chain technology: Determinants impacting the adoption and implementation of RFID technology in South Korea. International Journal of Information Management, 30(5), 388-398.
Kourouthanassis, P. E., Mikalef, P., Pappas, I. O., & Kostagiolas, P. (2017). Explaining traveler’s online information satisfaction: A complexity theory approach on information needs, barriers, sources and personal characteristics. Information & Management, 54(6), 814-824.
Kurdi, B., & Alshurideh, M. (2020). Employee retention and organizational performance: Evidence from banking industry. Management Science Letters, 10(16), 3981-3990.
Kurdi, B. A., Alshurideh, M., Nuseir, M., Aburayya, A., & Salloum, S. A. (2021, March). The effects of subjective norm on the intention to use social media networks: an exploratory study using PLS-SEM and machine learning approach. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 581-592). Springer, Cham.
Lai, H.M., Lin, I.C., & Tseng, L.T. (2014). High-level managers’ considerations for RFID adoption in hospitals: an empirical study in Taiwan. Journal of Medical Systems, 38(2), 1–17.
Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: an empirical investigation. The International Journal of Logistics Management, 29(2), 676-703.
Langseth, H., & Portinale, L. (2007). Bayesian networks in reliability. Reliability Engineering & System Safety,92(1), 92–108.
Li, X., Troutt, M. D., Brandyberry, A., & Wang, T. (2011). Decision Factors for the Adoption and Continued Use of Online Direct Sales Channels among SMEs. Journal of the Association for Information Systems, 12(1), 1–31.
Lin, C. Y., & Ho, Y. H. (2011). Determinants of green practice adoption for logistics companies in China. Journal of Business Ethics, 98(1), 67–83.
Lin, H. H., & Wang, Y. S. (2005, July). Predicting consumer intention to use mobile commerce in Taiwan. In International Conference on Mobile Business (ICMB'05) (pp. 406-412). IEEE.
Maduku, D. K., Mpinganjira, M., & Duh, H. (2016). Understanding mobile marketing adoption intention by South African SMEs: A multi-perspective framework. International Journal of Information Management, 36(5), 711-723.
Moore, G.C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research, 2, 173-191. http://dx.doi.org/10.1287/isre.2.3.192
Naicker, V., & Van Der Merwe, D. B. (2018). Managers’ perception of mobile technology adoption in the Life Insurance industry. Information Technology & People, 31(2), 507-526.
Naqvi, R., Soomro, T. R., Alzoubi, H. M., Ghazal, T. M., & Alshurideh, M. T. (2021, June). The Nexus Between Big Data and Decision-Making: A Study of Big Data Techniques and Technologies. In The International Conference on Artificial Intelligence and Computer Vision (pp. 838-853). Springer, Cham.
Obeidat, U., Obeidat, B., Alrowwad, A., Alshurideh, M., Masadeh, R., & Abuhashesh, M. (2021). The effect of intellectual capital on competitive advantage: the mediating role of innovation. Management Science Letters, 11(4), 1331-1344.
Oliveira, T., Martins, R., Sarker, S., Thomas, M., & Popovič, A. (2019). Understanding SaaS adoption: The moderating impact of the environment context. International Journal of Information Management, 49, 1-12.
Piçarra, N., & Giger, J. C. (2018). Predicting intention to work with social robots at anticipation stage: Assessing the role of behavioral desire and anticipated emotions. Computers in Human Behavior, 86, 129-146.
Ragu-Nathan, B.S., Apigian, C.H., Ragu-Nathan, T.S., & Tu, Q. (2004). A path analytic study of the effect of top management support for information systems performance. Omega, 32(6), 459-471.
Ramayah, T., Ling, N. S., Taghizadeh, S. K., & Rahman, S. A. (2016). Factors influencing SMEs website continuance intention in Malaysia. Telematics and Informatics, 33(1), 150–164.
Reich-Stiebert, N., & Eyssel, F. (2015). Learning with educational companion robots? Toward attitudes on education robots, predictors of attitudes, and application potentials for education robots. International Journal of Social Robotics, 7(5), 875-888.
Rogers, E. M. (2010). Diffusion of innovations (5th ed.). New York: Free Press.
Savela, N., Turja, T., & Oksanen, A. (2018). Social acceptance of robots in different occupational fields: A systematic literature review. International Journal of Social Robotics, 10(4), 493-502.
Šabanović, S. (2014). Inventing Japan’s ‘robotics culture’: The repeated assembly of science, technology, and culture in social robotics. Social Studies of Science, 44(3), 342-367.
Shamout, M. D. (2020a). The nexus between supply chain analytic, innovation and robustness capability. VINE Journal of Information and Knowledge Management Systems. 51(1), 163-176.
Shamout, M. D. (2020b). Supply chain data analytics and supply chain agility: a fuzzy set (fsQCA) approach. International Journal of Organizational Analysis. 28(5), 1055-1067.
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