How to cite this paper
Ayed, S & Tit, A. (2024). Measuring gender disparities in the intentions of startups to adopt artificial intelligence technology: A comprehensive multigroup comparative analysis.Uncertain Supply Chain Management, 12(3), 1567-1576.
Refrences
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Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
Al Anezi, F. Y. (2021, June). Saudi Vision 2030: Sustainable Economic Development through IoT. In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT) (pp. 837-841). IEEE.
Alateeg, S., & Alhammadi, A. (2024). The Impact of Organizational Culture on Organizational Innovation with the Mediation Role of Strategic Leadership in Saudi Arabia. Journal of Statistics Applications & Probability, 13(2), 843-858.
Al-Khalidi Al-Maliki, S. Q. (2021). Increasing non-oil revenue potentiality through digital commerce: the case study in KSA. Journal of Money and Business, 1(2), 65-83.
Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Shishakly, R., Lutfi, A., ... & Al-Maroof, R. S. (2022). Measuring institutions’ adoption of artificial intelligence applications in online learning environments: Integrating the innovation diffusion theory with technology adoption rate. Electronics, 11(20), 3291.
Alserr, N., & Salepçioğlu, M. A. (2021, November). Success Factors Affecting the Adoption of Artificial Intelligence and the Impacts of on Organizational Excellence: A Case to be Studied in the MENA Region, and Turkey in Particular. In International Conference on Business and Technology (pp. 3-16). Cham: Springer International Publishing.
Ben Ayed, R., & Hanana, M. (2021). Artificial intelligence to improve the food and agriculture sector. Journal of Food Quality, 2021, 1-7.
Broom, D. R., Lee, K. Y., Lam, M. H. S., & Flint, S. W. (2019). Gotta Catch ‘em All or Not Enough Time: Users Motivations for Playing Pokémon Go—and Non-users’ Reasons for Not Installing. Health Psychological Resources, 7, 1–9.
Busnatu, Ș., Niculescu, A. G., Bolocan, A., Petrescu, G. E., Păduraru, D. N., Năstasă, I., ... & Martins, H. (2022). Clinical applications of artificial intelligence—An updated overview. Journal of clinical medicine, 11(8), 2265.
Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312.
Davis, F. D. (1986). Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results (Doctoral dissertation, The Sloan School of Management, MIT).
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35, 982–1003.
Dutot, V., Bhatiasevi, V., & Bellallahom, N. (2019). Applying the Technology Acceptance Model in a Three-country Study of Smart Watch Adoption. Journal of High Technology Management Research, 30, 1–14.
Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M. (2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211-230.
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734.
Fagan, M., Kilmon, C., & Pandey, V. (2012). Exploring the Adoption of a Virtual Reality Simulation: The Role of Perceived Ease of Use, Perceived Usefulness and Personal Innovativeness. Campus-Wide Information System, 29, 117–127.
Garaca, Z. (2011). Factors Related to the Intended Use of ERP Systems. Management, 16, 23–42.
Giuggioli, G., & Pellegrini, M. M. (2023). Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behavior & Research, 29(4), 816-837.
Gupta, B. B., Gaurav, A., Panigrahi, P. K., & Arya, V. (2023). Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship. Technological Forecasting and Social Change, 186, 122152.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.
Hoyle, R. H. (Ed.). (1999). Statistical strategies for small sample research. sage.
Hsu, C. L., & Lin, J. C. C. (2015). What Drives Purchase Intention for Paid Mobile Apps? An Expectation Confirmation Model with Perceived Value. Electronic Commerce Research and Applications, 14, 46–57.
Ismatullaev, U. V. U., & Kim, S. H. (2024). Review of the factors affecting acceptance of AI-infused systems. Human Factors, 66(1), 126-144.
Karjaluoto, H., & Leppaniemi, M. (2013). Social Identity for Teenagers: Understanding Behavioral Intention to Participate in Virtual World Environment. Journal of Theoretical and Applied Electronic Commerce Research, 8, 1–16.
Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging technology and business model innovation: the case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44.
Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220–221.
Lim, W.M., & Ting, D.H. (2012). E-Shopping: An Analysis of the Technology Acceptance Model. Modern Applied Science, 6, 49–62.
Nasar, A., Kamarudin, S., Rizal, A. M., Ngoc, V. T. B., & Shoaib, S. M. (2019). Short-term and long-term entrepreneurial intention comparison between Pakistan and Vietnam. Sustainability, 11(23), 6529.
Park, Y., & Chen, J. V. (2007). Acceptance and adoption of the innovative use of smartphone. Industrial Management & Data Systems, 107(9), 1349–1365. doi:10.1108/02635570710834009
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Simon & Schuster.
Santos, A. R. (2022). The Importance of Artificial Intelligence in Start-up, Automation, and Scalation of Business for Entrepreneurs. International Journal of Applied Engineering & Technology, 4(3), 1-5.
Saputra, M. C., & Andajani, E. (2024). Analysis of Factors Influencing Intention to Adopt Battery Electric Vehicle in Indonesia. ADI Journal on Recent Innovation, 5(2), 100-109.
Schulte-Althoff, M., Fürstenau, D., & Lee, G. M. (2021). A scaling perspective on AI startups. In 54th Annual Hawaii International Conference on System Sciences, HICSS 2021 (pp. 6515-6524). Hawaii International Conference on System Sciences (HICSS).
Selamat, Z., Jaffar, N., & Ong, B.H. (2009). Technology Acceptance in Malaysian Banking Industry. European Journal of Economics, Finance and Administrative Sciences, 1, 143–155.
Sevim, N., Yuncu, D., & Hall, E.E. (2017). Analysis of the Extended Technology Acceptance Model in Online Travel Products. Journal of International Applied Management, 8, 45–61.
Sumak, B., Heicko, M., Pusnik, M., & Polancic, G. (2011). Factors Affecting Acceptance and Use of Moodle: An Empirical Study Based on TAM. Informatica, 35, 91–100.
Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36, 157–178.
Wu, W. (2011). Developing an Explorative Model for SaaS Adoption. Expert Systems with Applications, 38, 15057–15064.
Xu, S., Kee, K. F., Li, W., Yamamoto, M., & Riggs, R. E. (2023). Examining the Diffusion of Innovations from a Dynamic, Differential-Effects Perspective: A Longitudinal Study on AI Adoption Among Employees. Communication Research, 00936502231191832.
Yadav, R., & Pathak, G.S. (2017). Determinants of Consumers’ Green Purchase Behavior in a Developing Nation: Applying and Extending the Theory of Planned Behavior. Ecological Economics, 134, 114–122.
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
Al Anezi, F. Y. (2021, June). Saudi Vision 2030: Sustainable Economic Development through IoT. In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT) (pp. 837-841). IEEE.
Alateeg, S., & Alhammadi, A. (2024). The Impact of Organizational Culture on Organizational Innovation with the Mediation Role of Strategic Leadership in Saudi Arabia. Journal of Statistics Applications & Probability, 13(2), 843-858.
Al-Khalidi Al-Maliki, S. Q. (2021). Increasing non-oil revenue potentiality through digital commerce: the case study in KSA. Journal of Money and Business, 1(2), 65-83.
Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Shishakly, R., Lutfi, A., ... & Al-Maroof, R. S. (2022). Measuring institutions’ adoption of artificial intelligence applications in online learning environments: Integrating the innovation diffusion theory with technology adoption rate. Electronics, 11(20), 3291.
Alserr, N., & Salepçioğlu, M. A. (2021, November). Success Factors Affecting the Adoption of Artificial Intelligence and the Impacts of on Organizational Excellence: A Case to be Studied in the MENA Region, and Turkey in Particular. In International Conference on Business and Technology (pp. 3-16). Cham: Springer International Publishing.
Ben Ayed, R., & Hanana, M. (2021). Artificial intelligence to improve the food and agriculture sector. Journal of Food Quality, 2021, 1-7.
Broom, D. R., Lee, K. Y., Lam, M. H. S., & Flint, S. W. (2019). Gotta Catch ‘em All or Not Enough Time: Users Motivations for Playing Pokémon Go—and Non-users’ Reasons for Not Installing. Health Psychological Resources, 7, 1–9.
Busnatu, Ș., Niculescu, A. G., Bolocan, A., Petrescu, G. E., Păduraru, D. N., Năstasă, I., ... & Martins, H. (2022). Clinical applications of artificial intelligence—An updated overview. Journal of clinical medicine, 11(8), 2265.
Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312.
Davis, F. D. (1986). Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results (Doctoral dissertation, The Sloan School of Management, MIT).
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35, 982–1003.
Dutot, V., Bhatiasevi, V., & Bellallahom, N. (2019). Applying the Technology Acceptance Model in a Three-country Study of Smart Watch Adoption. Journal of High Technology Management Research, 30, 1–14.
Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M. (2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211-230.
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734.
Fagan, M., Kilmon, C., & Pandey, V. (2012). Exploring the Adoption of a Virtual Reality Simulation: The Role of Perceived Ease of Use, Perceived Usefulness and Personal Innovativeness. Campus-Wide Information System, 29, 117–127.
Garaca, Z. (2011). Factors Related to the Intended Use of ERP Systems. Management, 16, 23–42.
Giuggioli, G., & Pellegrini, M. M. (2023). Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behavior & Research, 29(4), 816-837.
Gupta, B. B., Gaurav, A., Panigrahi, P. K., & Arya, V. (2023). Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship. Technological Forecasting and Social Change, 186, 122152.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.
Hoyle, R. H. (Ed.). (1999). Statistical strategies for small sample research. sage.
Hsu, C. L., & Lin, J. C. C. (2015). What Drives Purchase Intention for Paid Mobile Apps? An Expectation Confirmation Model with Perceived Value. Electronic Commerce Research and Applications, 14, 46–57.
Ismatullaev, U. V. U., & Kim, S. H. (2024). Review of the factors affecting acceptance of AI-infused systems. Human Factors, 66(1), 126-144.
Karjaluoto, H., & Leppaniemi, M. (2013). Social Identity for Teenagers: Understanding Behavioral Intention to Participate in Virtual World Environment. Journal of Theoretical and Applied Electronic Commerce Research, 8, 1–16.
Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging technology and business model innovation: the case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44.
Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220–221.
Lim, W.M., & Ting, D.H. (2012). E-Shopping: An Analysis of the Technology Acceptance Model. Modern Applied Science, 6, 49–62.
Nasar, A., Kamarudin, S., Rizal, A. M., Ngoc, V. T. B., & Shoaib, S. M. (2019). Short-term and long-term entrepreneurial intention comparison between Pakistan and Vietnam. Sustainability, 11(23), 6529.
Park, Y., & Chen, J. V. (2007). Acceptance and adoption of the innovative use of smartphone. Industrial Management & Data Systems, 107(9), 1349–1365. doi:10.1108/02635570710834009
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Simon & Schuster.
Santos, A. R. (2022). The Importance of Artificial Intelligence in Start-up, Automation, and Scalation of Business for Entrepreneurs. International Journal of Applied Engineering & Technology, 4(3), 1-5.
Saputra, M. C., & Andajani, E. (2024). Analysis of Factors Influencing Intention to Adopt Battery Electric Vehicle in Indonesia. ADI Journal on Recent Innovation, 5(2), 100-109.
Schulte-Althoff, M., Fürstenau, D., & Lee, G. M. (2021). A scaling perspective on AI startups. In 54th Annual Hawaii International Conference on System Sciences, HICSS 2021 (pp. 6515-6524). Hawaii International Conference on System Sciences (HICSS).
Selamat, Z., Jaffar, N., & Ong, B.H. (2009). Technology Acceptance in Malaysian Banking Industry. European Journal of Economics, Finance and Administrative Sciences, 1, 143–155.
Sevim, N., Yuncu, D., & Hall, E.E. (2017). Analysis of the Extended Technology Acceptance Model in Online Travel Products. Journal of International Applied Management, 8, 45–61.
Sumak, B., Heicko, M., Pusnik, M., & Polancic, G. (2011). Factors Affecting Acceptance and Use of Moodle: An Empirical Study Based on TAM. Informatica, 35, 91–100.
Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36, 157–178.
Wu, W. (2011). Developing an Explorative Model for SaaS Adoption. Expert Systems with Applications, 38, 15057–15064.
Xu, S., Kee, K. F., Li, W., Yamamoto, M., & Riggs, R. E. (2023). Examining the Diffusion of Innovations from a Dynamic, Differential-Effects Perspective: A Longitudinal Study on AI Adoption Among Employees. Communication Research, 00936502231191832.
Yadav, R., & Pathak, G.S. (2017). Determinants of Consumers’ Green Purchase Behavior in a Developing Nation: Applying and Extending the Theory of Planned Behavior. Ecological Economics, 134, 114–122.