Reconstruction of Information System Acceptance Model in the Era of Integrated Artificial Intelligence: A Systematic Literature Review

Authors

• Ilham, UIN Sunan Ampel of Surabaya, IndonesiaFollow
• Merlin Apriliyanti, UIN Sunan Ampel of Surabaya, IndonesiaFollow

Date of this Version

Winter 3-27-2026

Document Type

Article

Citation

Ajzen, I., & Fishbein, M. (1975). Belief, attitude, intention and behavior: An introduction to theory and research . Reading, MA: Addison-Wesley. (Classic book, has no DOI)

Arrieta, A.B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., … Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges. Information Fusion, 58 , 82–115. https://doi.org/10.1016/j.inffus.2019.12.012

Baltrušaitis, T., Ahuja, C., & Morency, L.-P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (2), 423–443. https://doi.org/10.1109/TPAMI.2018.2798607 (doi.org in Bing)

Bhattacherjee, A. (2001). Understanding information systems continuity: An expectation-confirmation model. MIS Quarterly, 25 (3), 351–370. https://doi.org/10.2307/3250921

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating? AI generative tools in higher education. Assessment & Evaluation in Higher Education, 48 (1), 1–15. https://doi.org/10.1080/02602938.2022.2122989 (doi.org in Bing)

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319–340. https://doi.org/10.2307/249008

DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19 (4), 9–30. https://doi.org/10.1080/07421222.2003.11045748 (doi.org in Bing)

Dwivedi, Y.K., Hughes, D.L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J.S., … Williams, MD (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57 , 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28 (4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1 (1), 1–28. https://doi.org/10.17705/1jais.00008 (doi.org in Bing)

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19 (2), 213–236. https://doi.org/10.2307/249689

Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 30 (3), 611–642. https://doi.org/10.2307/25148742

Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews? Research Synthesis Methods, 11 (2), 181–217. https://doi.org/10.1002/jrsm.1378

Harzing, A.-W., & Alakangas, S. (2016). Google Scholar, Scopus and the Web of Science: A longitudinal and cross-disciplinary comparison. Scientometrics, 106 (2), 787–804. https://doi.org/10.1007/s11192-015-1798-9

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering . EBSE Technical Report, Keele University. (Technical report, no DOI)

Laudon, KC, & Laudon, JP (2019). Management information systems: Managing the digital firm (16th ed.). Pearson. ISBN: 9781292296562. (Book, no DOI)

Liao, Q. V., Davis, M., Geyer, W., Muller, M., & Millstein, T. (2018). Questioning AI: Informing design practices for explainable AI user experiences. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–14). ACM. https://doi.org/10.1145/3173574.3174183 (doi.org in Bing)

McKnight, D.H., Carter, M., Thatcher, J.B., & Clay, P.F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems, 2 (2), 12. https://doi.org/10.1145/1985347.1985353 (doi.org in Bing)

Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17 (3), 236–263. https://doi.org/10.1057/ejis.2008.15 (doi.org in Bing)

Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., … Duffy, S. (2006). Guidance on the conduct of narrative synthesis in systematic reviews . ESRC Methods Programme. https://doi.org/10.13140/2.1.1018.4643

Rai, A., Constantinides, P., & Sarker, S. (2019). Next-generation digital platforms: Toward human–AI hybrids. MIS Quarterly, 43 (1), iii–ix. https://doi.org/10.25300/MISQ/2019/13747 (doi.org in Bing)

Ribeiro, M.T., Singh, S., & Guestrin, C. (2020). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 1135–1144. https://doi.org/10.1145/2939672.2939778

Rogers, E.M. (2003). Diffusion of innovations (5th ed.). Free Press. ISBN: 9780743222099. (Book, no DOI)

Russell, S., & Norvig, P. (2021). Artificial Intelligence: A modern approach (4th ed.). Pearson. ISBN: 9781292401133. (Book, no DOI)

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104 , 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27 (6), 2067–2077. https://doi.org/10.1016/j.chb.2011.04.005 (doi.org in Bing)

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6 (2), 144–176. https://doi.org/10.1287/isre.6.2.144 (doi.org in Bing)

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14 (3), 207–222. https://doi.org/10.1111/1467-8551.00375 (doi.org in Bing)

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view.

Abstract

This study aims to explain the rapid development of Artificial Intelligence (AI) which has driven significant transformations in the development and use of information systems. However, most classical information system acceptance models, such as the Technology Acceptance Model (TAM) and  (UTAUT), have not been able to fully explain the unique characteristics of AI-based systems that are autonomous, adaptive, and complex. This study aims to reconstruct the information system acceptance model in the era of integrated AI through a Systematic Literature Review (SLR) approach. This study was conducted using the PRISMA protocol on 130 leading scientific articles indexed by Scopus and Google Scholar. Bibliometric and thematic analyses were conducted to identify publication trends, authors, affiliations, countries, scientific fields, document types, funding sources, and key constructs used in AI-based information system acceptance research. The review results show that publications are dominated by journal articles from the fields of Computer Science and Business & Management, with a significant increase in the adoption of non-traditional constructs such as trust in AI, explainability, algorithm transparency, perceived intelligence, and human-AI collaboration. The originality of this research lies in its synthesis of the literature that integrates user behavior perspectives and AI technology characteristics into a more comprehensive and contextual information system acceptance framework. The research findings provide theoretical contributions in the form of a conceptual reconstruction of an AI-based information system acceptance model and practical contributions as a reference for researchers, system developers, and policymakers in designing and implementing AI systems oriented towards user acceptance.