Factors Affecting the Adoption of Artificial Intelligence: A Systematic Literature Review and Policy Perspectives in the Digital Age
DOI:
https://doi.org/10.35877/454RI.qems4590Keywords:
AI Adoption, Cognitive Factors, Emotional Factors, Ethical Considerations, Public Policy, Systematic Literature Review, Technology AcceptanceAbstract
The adoption of Artificial Intelligence (AI) has become a global strategic agenda, but its acceptance and implementation levels vary greatly between countries, sectors, and populations. This study presents a systematic literature review (SLR) of 41 empi rical studies to map the factors influencing AI adoption, with a particular focus on cognitive, emotional, socio-ethical, and technological dimensions. Using the PRISMA approach, this study identifies cognitive (AI literacy, perceived usefulness) and emotional (trust, anxiety) factors as key determinants, while socio-ethical considerations (fairness, transparency, accountability) are increasingly crucial in the context of public policy. Recent studies in Indonesia confirm this pattern by demonstrating the strong mediating role of emotions, but also reveal independent cognitive and ethical pathways that operate without emotional mediation—findings that challenge the assumptions of traditional technology acceptance models. From a policy perspective, the results of this SLR indicate the need for a multidimensional approach that focuses not only on technological infrastructure, but also on improving AI literacy, managing public emotional responses, and developing a transparent ethical framework. This article contributes to the literature by providing a comprehensive mapping of state-of-the-art AI adoption research and identifying crucial research gaps, particularly regarding affect-independent pathways mechanisms, cultural context variations, and the longitudinal dynamics of AI adoption.
References
Abid, M., Ben-salha, O., Gasmi, K., Hamed, N., & Alnor, A. (2024). The Impact of Artificial Intelligence on Unemployment among Educated People with Disabilities: An Empirical Analysis. 3 (2022), 1–11. https://doi.org/10.57197/JDR-2024-0008
Al-abdullatif, A. M. (2024). Modeling Teachers’ Acceptance of Generative Artificial Intelligence Use in Higher
Education: The Role of AI Literacy, Intelligent TPACK, and Perceived Trust.
Alzebda, S., & Matar, M. A. I. (2024). Factors affecting citizen intention toward AI acceptance and adoption: the moderating role of government regulations. Competitiveness Review, 35 (2), 434–455. https://doi.org/10.1108/CR-06-2023-0144
Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2020). Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust. January.
Calisto, F. M., Schulz, P. J., Lwin, M. O., & Kee, K. M. (2023). Modeling the influence of attitudes, trust, and beliefs
on endoscopists’ acceptance of artificial intelligence applications in medical practice.
,https://doi.org/10.3389/fpubh.2023.1301563
Chang, P., Zhang, W., Cai, Q., Guo, H., Chang, P., & Zhang, W. (2024). Does AI-Driven Technostress Promote or Hinder Employees’ Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy. 1578 . https://doi.org/10.2147/PRBM.S441444
Choi, Y. (2025). A study of employee acceptance of artificial intelligence technology. 30 (3), 318–330. https://doi.org/10.1108/EJMBE-06-2020-0158
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13 (3), 319–339. https://doi.org/10.2307/249008
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57 . https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Frank, D., Chrysochou, P., Mitkidis, P., Otterbring, T., & Ariely, D. (2024). Technology in Society Navigating uncertainty: Exploring consumer acceptance of artificial intelligence under self-threats and high-stakes decisions. Technology in Society, 79 (August), 102732. https://doi.org/10.1016/j.techsoc.2024.102732
Gerlich, M. (2024). Public Anxieties About AI: Implications for Corporate Strategy and Societal Impact.
Guedes, L. (2024). Artificial intelligence adoption in public organizations: a case study 1. 1–26.
Gursoy, D., Hengxuan, O., Lu, L., & Nunkoo, R. (2019). International Journal of Information Management Consumers acceptance of artificially intelligent ( AI ) device use in service delivery. 49 (February), 157–169.
Hartyándi, M. J. (2025). Distrust and disillusionment toward generative artificial intelligence: Psychodramatic exploration of employee trust in organizational technology acceptance. 47 , 235–255. https://doi.org/10.1556/204.2025.00002
Ho, M.-T., Le, N.-T. B., Mantello, P., Ho, M.-T., & Ghotbi, N. (2023). Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude. Technology in Society, 72 , 102166. https://doi.org/https://doi.org/10.1016/j.techsoc.2022.102166
Horvath, L., James, O., Banducci, S., & Beduschi, A. (2023). Citizens’ acceptance of artificial intelligence in public services: Evidence from a conjoint experiment about processing permit applications. Government Information Quarterly, 40 (4), 101876. https://doi.org/10.1016/j.giq.2023.101876
Jang, Y., Choi, S., & Kim, H. (2022). Development and validation of an instrument to measure undergraduate students’ attitudes toward the ethics of artificial intelligence (AT-EAI) and analysis of its difference by gen... In Education and Information Technologies (Issue May). Springer US. https://doi.org/10.1007/s10639-022- 11086-5
Kim, Y., & Blazquez, V. (2024). Determinants of Generative AI System Adoption and Usage Behavior in Korean
Companies : Applying the UTAUT Model.
Kinney, M., Anastasiadou, M., Naranjo-zolotov, M., & Santos, V. (2024). Heliyon Expectation management in AI : A framework for understanding stakeholder trust and acceptance of artificial intelligence systems. Heliyon, 10 (7), e28562. https://doi.org/10.1016/j.heliyon.2024.e28562
Lazarus, R. S. (1991). Emotion and Adaptation. Oxford University Press. https://books.google.co.id/books?id=- ltMCAAAQBAJ
M´endez-Su´arez, M., Monfort, A., & Hervas-Olive, J.-L. (2023). Are you adopting artificial intelligence products? Social-demographic factors to explain customer acceptance. 29 (September). https://doi.org/10.1016/j.iedeen.2023.100223
Norzelan, N. A., Mohamed, I. S., & Mohamad, M. (2024). Technology acceptance of artificial intelligence (AI) among heads of finance and accounting units in the shared service industry. Technological Forecasting and Social Change, 198 , 123022. https://doi.org/https://doi.org/10.1016/j.techfore.2023.123022
Parthasarathy, P. R., Patil, S. R., Dawasaz, A. A., Abdul, F., Baig, H., & Karobari, M. I. (2024). Unlocking the Potential: Investigating Dental Practitioners’ Willingness to Embrace Artificial Intelligence in Dental Practice. 16 (2). https://doi.org/10.7759/cureus.55107
Rinki, M., & R, D. (2025). “The Evolution and Impact of Human-AI Interaction: A Multi-dimensional Analysis of User Experience in the Digital Age (2020-2025).” IOSR Journal of Business and Management, 14th, 9–1 7. https://doi.org/10.9790/487X-conf0917
Sadiq, S., Kaiwei, J., Aman, I., & Mansab, M. (2025). European Research on Management and Business Economics Examine the factors influencing the behavioral intention to use social commerce adoption and the role of AI
in SC adoption. European Research on Management and Business Economics, 31 (1), 100268. https://doi.org/10.1016/j.iedeen.2024.100268
Shevtsova, D., Ahmed, A., Boot, I. W. A., Sanges, C., Hudecek, M., Jacobs, J. J. L., Hort, S., & Vrijhoef, H. J. M. (2024). Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. 11 . https://doi.org/10.2196/47031
Song, Y., Qiu, X., & Liu, J. (2025). The Impact of Artificial Intelligence Adoption on Organizational Decision- Making: An Empirical Study Based on the Technology Acceptance Model in Business Management. 1–15.
Sousa, S., Lamas, D., Cravino, J., & Martins, P. (2023). Human-Centered Trustworthy Framework: A Human– Computer Interaction Perspective. https://doi.org/10.1109/MC.2023.3287563
Stevens, A. F., & Stetson, P. (2023). Theory of trust and acceptance of artificial intelligence technology ( TrAAIT ): An instrument to assess clinician trust and acceptance of artificial intelligence. Journal of Biomedical Informatics, 148 (August), 104550. https://doi.org/10.1016/j.jbi.2023.104550
Sudirman, I., Setiawan, R., Alfitra, N. F., & Bellani, E. (2025). Multidimensional Exploration in Promoting Artificial Intelligence Adoption in the Digital Society Era.
Sudirman, I., Setiawan, R., Bellani, E., & Page, C. (2023). Multi-Dimensional Exploration in Promoting AI Adoption in the Digital Society.
Topsakal, Y. (2024). How Familiarity, Ease of Use, Usefulness, and Trust Influence the Acceptance of Generative Artificial Intelligence (AI)-Assisted Travel Planning. International Journal of Human–Computer Interaction, 0 (0), 1–14. https://doi.org/10.1080/10447318.2024.2426044
Tsz, D., Ng, K., Wu, W., Ka, J., Leung, L., Kin, T., Chiu, F., Kai, S., & Chu, W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioral, cognitive, and ethical approach. February 2023, 1082– 1104. https://doi.org/10.1111/bjet.13411
Verma, R., & Kapoor, S. (2024). Exploring barriers to acceptance of artificial intelligence in social welfare schemes of governments in India – a systematic literature review. International Journal of System Assurance Engineering and Management, 15 (11), 5139–5156. https://doi.org/10.1007/s13198-024-02498-2
Yin, J., Ngiam, K. Y., & Teo, H. H. (2021). Role of Artificial Intelligence Applications in Real-Life Clinical
Practice : Systematic Review Corresponding. 23 . https://doi.org/10.2196/25759
Yu, T., Tian, Y., Chen, Y., Huang, Y., & Pan, Y. (2025). How Do Ethical Factors Affect User Trust and Adoption Intentions of AI-Generated Content Tools? Evidence from a Risk-Trust Perspective. 1–32.
Yuan, C., Yin, Y., Wang, S., Zhang, Z., & Moon, H. (2025). Exploring technology, organization and environmental factors driving firms' generative AI adoption intention: the moderating role of trans-parasocial relation. Journal of Business & Industrial Marketing. https://doi.org/10.1108/JBIM-04-2024-0257
Al-abdullatif, A. M. (2024). Modeling Teachers’ Acceptance of Generative Artificial Intelligence Use in Higher
Education: The Role of AI Literacy, Intelligent TPACK, and Perceived Trust.
Alzebda, S., & Matar, M. A. I. (2024). Factors affecting citizen intention toward AI acceptance and adoption: the moderating role of government regulations. Competitiveness Review, 35 (2), 434–455. https://doi.org/10.1108/CR-06-2023-0144
Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2020). Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust. January.
Calisto, F. M., Schulz, P. J., Lwin, M. O., & Kee, K. M. (2023). Modeling the influence of attitudes, trust, and beliefs
on endoscopists’ acceptance of artificial intelligence applications in medical practice.
,https://doi.org/10.3389/fpubh.2023.1301563
Chang, P., Zhang, W., Cai, Q., Guo, H., Chang, P., & Zhang, W. (2024). Does AI-Driven Technostress Promote or Hinder Employees’ Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy. 1578 . https://doi.org/10.2147/PRBM.S441444
Choi, Y. (2025). A study of employee acceptance of artificial intelligence technology. 30 (3), 318–330. https://doi.org/10.1108/EJMBE-06-2020-0158
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13 (3), 319–339. https://doi.org/10.2307/249008
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57 . https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Frank, D., Chrysochou, P., Mitkidis, P., Otterbring, T., & Ariely, D. (2024). Technology in Society Navigating uncertainty: Exploring consumer acceptance of artificial intelligence under self-threats and high-stakes decisions. Technology in Society, 79 (August), 102732. https://doi.org/10.1016/j.techsoc.2024.102732
Gerlich, M. (2024). Public Anxieties About AI: Implications for Corporate Strategy and Societal Impact.
Guedes, L. (2024). Artificial intelligence adoption in public organizations: a case study 1. 1–26.
Gursoy, D., Hengxuan, O., Lu, L., & Nunkoo, R. (2019). International Journal of Information Management Consumers acceptance of artificially intelligent ( AI ) device use in service delivery. 49 (February), 157–169.
Hartyándi, M. J. (2025). Distrust and disillusionment toward generative artificial intelligence: Psychodramatic exploration of employee trust in organizational technology acceptance. 47 , 235–255. https://doi.org/10.1556/204.2025.00002
Ho, M.-T., Le, N.-T. B., Mantello, P., Ho, M.-T., & Ghotbi, N. (2023). Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude. Technology in Society, 72 , 102166. https://doi.org/https://doi.org/10.1016/j.techsoc.2022.102166
Horvath, L., James, O., Banducci, S., & Beduschi, A. (2023). Citizens’ acceptance of artificial intelligence in public services: Evidence from a conjoint experiment about processing permit applications. Government Information Quarterly, 40 (4), 101876. https://doi.org/10.1016/j.giq.2023.101876
Jang, Y., Choi, S., & Kim, H. (2022). Development and validation of an instrument to measure undergraduate students’ attitudes toward the ethics of artificial intelligence (AT-EAI) and analysis of its difference by gen... In Education and Information Technologies (Issue May). Springer US. https://doi.org/10.1007/s10639-022- 11086-5
Kim, Y., & Blazquez, V. (2024). Determinants of Generative AI System Adoption and Usage Behavior in Korean
Companies : Applying the UTAUT Model.
Kinney, M., Anastasiadou, M., Naranjo-zolotov, M., & Santos, V. (2024). Heliyon Expectation management in AI : A framework for understanding stakeholder trust and acceptance of artificial intelligence systems. Heliyon, 10 (7), e28562. https://doi.org/10.1016/j.heliyon.2024.e28562
Lazarus, R. S. (1991). Emotion and Adaptation. Oxford University Press. https://books.google.co.id/books?id=- ltMCAAAQBAJ
M´endez-Su´arez, M., Monfort, A., & Hervas-Olive, J.-L. (2023). Are you adopting artificial intelligence products? Social-demographic factors to explain customer acceptance. 29 (September). https://doi.org/10.1016/j.iedeen.2023.100223
Norzelan, N. A., Mohamed, I. S., & Mohamad, M. (2024). Technology acceptance of artificial intelligence (AI) among heads of finance and accounting units in the shared service industry. Technological Forecasting and Social Change, 198 , 123022. https://doi.org/https://doi.org/10.1016/j.techfore.2023.123022
Parthasarathy, P. R., Patil, S. R., Dawasaz, A. A., Abdul, F., Baig, H., & Karobari, M. I. (2024). Unlocking the Potential: Investigating Dental Practitioners’ Willingness to Embrace Artificial Intelligence in Dental Practice. 16 (2). https://doi.org/10.7759/cureus.55107
Rinki, M., & R, D. (2025). “The Evolution and Impact of Human-AI Interaction: A Multi-dimensional Analysis of User Experience in the Digital Age (2020-2025).” IOSR Journal of Business and Management, 14th, 9–1 7. https://doi.org/10.9790/487X-conf0917
Sadiq, S., Kaiwei, J., Aman, I., & Mansab, M. (2025). European Research on Management and Business Economics Examine the factors influencing the behavioral intention to use social commerce adoption and the role of AI
in SC adoption. European Research on Management and Business Economics, 31 (1), 100268. https://doi.org/10.1016/j.iedeen.2024.100268
Shevtsova, D., Ahmed, A., Boot, I. W. A., Sanges, C., Hudecek, M., Jacobs, J. J. L., Hort, S., & Vrijhoef, H. J. M. (2024). Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. 11 . https://doi.org/10.2196/47031
Song, Y., Qiu, X., & Liu, J. (2025). The Impact of Artificial Intelligence Adoption on Organizational Decision- Making: An Empirical Study Based on the Technology Acceptance Model in Business Management. 1–15.
Sousa, S., Lamas, D., Cravino, J., & Martins, P. (2023). Human-Centered Trustworthy Framework: A Human– Computer Interaction Perspective. https://doi.org/10.1109/MC.2023.3287563
Stevens, A. F., & Stetson, P. (2023). Theory of trust and acceptance of artificial intelligence technology ( TrAAIT ): An instrument to assess clinician trust and acceptance of artificial intelligence. Journal of Biomedical Informatics, 148 (August), 104550. https://doi.org/10.1016/j.jbi.2023.104550
Sudirman, I., Setiawan, R., Alfitra, N. F., & Bellani, E. (2025). Multidimensional Exploration in Promoting Artificial Intelligence Adoption in the Digital Society Era.
Sudirman, I., Setiawan, R., Bellani, E., & Page, C. (2023). Multi-Dimensional Exploration in Promoting AI Adoption in the Digital Society.
Topsakal, Y. (2024). How Familiarity, Ease of Use, Usefulness, and Trust Influence the Acceptance of Generative Artificial Intelligence (AI)-Assisted Travel Planning. International Journal of Human–Computer Interaction, 0 (0), 1–14. https://doi.org/10.1080/10447318.2024.2426044
Tsz, D., Ng, K., Wu, W., Ka, J., Leung, L., Kin, T., Chiu, F., Kai, S., & Chu, W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioral, cognitive, and ethical approach. February 2023, 1082– 1104. https://doi.org/10.1111/bjet.13411
Verma, R., & Kapoor, S. (2024). Exploring barriers to acceptance of artificial intelligence in social welfare schemes of governments in India – a systematic literature review. International Journal of System Assurance Engineering and Management, 15 (11), 5139–5156. https://doi.org/10.1007/s13198-024-02498-2
Yin, J., Ngiam, K. Y., & Teo, H. H. (2021). Role of Artificial Intelligence Applications in Real-Life Clinical
Practice : Systematic Review Corresponding. 23 . https://doi.org/10.2196/25759
Yu, T., Tian, Y., Chen, Y., Huang, Y., & Pan, Y. (2025). How Do Ethical Factors Affect User Trust and Adoption Intentions of AI-Generated Content Tools? Evidence from a Risk-Trust Perspective. 1–32.
Yuan, C., Yin, Y., Wang, S., Zhang, Z., & Moon, H. (2025). Exploring technology, organization and environmental factors driving firms' generative AI adoption intention: the moderating role of trans-parasocial relation. Journal of Business & Industrial Marketing. https://doi.org/10.1108/JBIM-04-2024-0257
Downloads
Published
2026-02-28
How to Cite
Sudirman, I., Setiawan, R. ., & Astuti, N. P. . (2026). Factors Affecting the Adoption of Artificial Intelligence: A Systematic Literature Review and Policy Perspectives in the Digital Age. Quantitative Economics and Management Studies, 7(1), 1–14. https://doi.org/10.35877/454RI.qems4590
Issue
Section
Articles
License
Copyright (c) 2026 Indriaty Sudirman, Romi Setiawan, Niken Probondani Astuti
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
