- محمودی، سید محمد. جعفری، محمد و پیشدار، مهسا. (1403). کاربردها و الزامات بهکارگیری هوش مصنوعی در محصولات نوین خودرویی. مطالعات مدیریت کسبوکار هوشمند. 12(47)، 109-79. doi: 10.22054/ims.2023.72410.2292
- Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in human behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548
- Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205-219. https://doi.org/10.1016/j.indmarman.2021.07.013
- Chung, H. F., Kingshott, R. P., MacDonald, R. V., & Putranta, M. P. (2021). Dynamism and B2B firm performance: The dark and bright contingent role of B2B relationships. Journal of Business Research, 129, 250-259. https://doi.org/10.1016/j.jbusres.2021.02.047
- Davis, F. D. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 984. https://doi.org/10.1287/mnsc.35.8.982
- 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. https://doi.org/10.1016/j.ijpe.2019.107599
- Fornell, C. and D. Larcker, (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 8(1), 39-50. https://doi.org/10.2307/3151312
- García de Blanes Sebastián, M., Sarmiento Guede, J. R., & Antonovica, A. (2022). Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants. Frontiers in Psychology, 13, 993935. https://doi.org/3389/fpsyg.2022.993935
- Gupta, S., Abbas, A. F., & Srivastava, R. (2022). Technology Acceptance Model (TAM): A bibliometric analysis from inception. Journal of Telecommunications and the Digital Economy, 10(3), 77-106.
- https://doi.org/10.18080/jtde.v10n3.598
- Herman, L. E., Sulhaini, S., & Farida, N. (2021). Electronic customer relationship management and company performance: Exploring the product innovativeness development. Journal of Relationship Marketing, 20(1), 1-19. https://doi.org/10.1080/15332667.2019.1688600
- Hair, J., Joseph F, et al., (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) 2. 384.
- Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212. https://doi.org/10.1016/j.techsoc.2019.101212
- Kruger, S., & Steyn, A. A. (2024). Navigating the fourth industrial revolution: a systematic review of technology adoption model trends. Journal of Science and Technology Policy Management, 16(10), 24-56. https://doi.org/10.1108/JSTPM-11-2022-0188
- Kelly, A. E., & Palaniappan, S. (2023). Using a technology acceptance model to determine factors influencing continued usage of mobile money service transactions in Ghana. Journal of Innovation and Entrepreneurship, 12(1), 34. https://doi.org/1186/s13731-023-00301-3
- Lopes, J. M., Silva, L. F., & Massano-Cardoso, I. (2024). AI Meets the Shopper: Psychosocial Factors in Ease of Use and Their Effect on E-Commerce Purchase Intention. Behavioral Sciences, 14(7), 616. https://doi.org/10.3390/bs14070616
- Lutfi, A., Alrawad, M., Alsyouf, A., Almaiah, M. A., Al-Khasawneh, A., Al-Khasawneh, A. L., ... & Ibrahim, N. (2023). Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling. Journal of Retailing and Consumer Services, 70, 103129. https://doi.org/1016/j.jretconser.2022.103129
- Ma, J., Wang, P., Li, B., Wang, T., Pang, X. S., & Wang, D. (2025). Exploring user adoption of ChatGPT: A technology acceptance model perspective. International Journal of Human–Computer Interaction, 41(2), 19. 1431-1445. https://doi.org/1080/10447318.2024.2314358
- Martín-García, A. V., Redolat, R., & Pinazo-Hernandis, S. (2022). Factors influencing intention to technological use in older adults. The TAM model aplication. Research on aging, 44(7-8), 573-588. https://doi.org/10.1177/01640275211063797
- Nawaz, N., Arunachalam, H., Pathi, B. K., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4(1), 100208. https://doi.org/10.1016/j.jjimei.2023.100208
- Rahman, M. S., Bag, S., Gupta, S., & Sivarajah, U. (2023). Technology readiness of B2B firms and AI-based customer relationship management capability for enhancing social sustainability performance. Journal of Business Research, 156, 113525. https://doi.org/10.1016/j.jbusres.2022.113525
- Rejali, S., Aghabayk, K., Esmaeli, S., & Shiwakoti, N. (2023). Comparison of technology acceptance model, theory of planned behavior, and unified theory of acceptance and use of technology to assess a priori acceptance of fully automated vehicles. Transportation research part A: policy and practice, 168, 103565. https://doi.org/1016/j.tra.2022.103565
- Reiman, A., Kaivo-oja, J., Parviainen, E., Takala, E. P., & Lauraeus, T. (2024). Human work in the shift to Industry 4.0: a road map to the management of technological changes in manufacturing. International Journal of Production Research, 62(16), 5613-5630. https://doi.org/10.1080/00207543.2023.2291814
- Rezaei, R., Safa, L., & Ganjkhanloo, M. M. (2020). Understanding farmers’ ecological conservation behavior regarding the use of integrated pest management-an application of the technology acceptance model. Global Ecology and Conservation, 22, e00941. https://doi.org/10.1016/j.gecco.2020.e00941
- Shamout, M., Ben-Abdallah, R., Alshurideh, M., Alzoubi, H., Al 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. https://doi.org/10.5267/j.uscm.2021.11.006
- Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, 101324. https://doi.org/10.1016/j.tele.2019.101324
- Verma, S., Bhattacharyya, S. S., & Kumar, S. (2018). An extension of the technology acceptance model in the big data analytics system implementation environment. Information Processing & Management, 54(5), 791-806. https://doi.org/10.1016/j.ipm.2018.01.004
- Wetzels, M. Odekerken-Schroder, G. Van Oppen, C. (2009). Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration. MIS Quarterly. 33(1), 177-195. https://doi.org/2307/20650284
- Zhang, B. S., Ali, K., & Kanesan, T. (2022). A model of extended technology acceptance for behavioral intention toward EVs with gender as a moderator. Frontiers in Psychology, 13, 1080414. https://doi.org/3389/fpsyg.2022.1080414
|