Perceived Architectural Fit in Workplace Reliance on Artificial Intelligence: Evidence from a UTAUT-Based Model

Main Article Content

Eric Strandt
Daniel N. Strandt

Abstract

Artificial intelligence (AI) is increasingly integrated into workplace operations through general-purpose systems, including large language models, foundation-model platforms, and AI assistants. The Unified Theory of Acceptance and Use of Technology (UTAUT) explains adoption through performance expectancy, effort expectancy, social influence, and facilitating conditions; however, the four UTAUT-related constructs used here do not directly represent whether an AI system’s technical and governance supports fit the accountability demands of its intended use. This study tests perceived architectural fit, defined as the perceived match between those supports and the accountability demands of the intended use, as an added predictor in a UTAUT-based model. A quantitative vignette-based survey with prebalanced 2 × 2 conditions and post-vignette perception measures collected 350 responses from U.S. workers with workplace AI exposure and retained 282 after screening. The four UTAUT-related predictors jointly explained 59.7% of the variance in AI reliance intention (ARI). Adding perceived architectural fit increased explained variance by 10.0 percentage points, and perceived architectural fit remained significant in the full model. Participants receiving the enhanced architectural-support description reported higher perceived architectural fit than participants receiving the limited-support description. Assigned conditions did not produce statistically significant differences in perceived legitimacy or ARI. Secondary analyses found that perceived architectural fit was associated with perceived legitimacy and that perceived legitimacy was associated with ARI. A bootstrap analysis also identified a positive indirect association through perceived legitimacy after controlling for the UTAUT-related predictors. The findings provide quantitative evidence that perceived architectural fit may contribute explanatory value to UTAUT-based research on workplace ARI.

Article Details

Section

Regular Paper

How to Cite

Perceived Architectural Fit in Workplace Reliance on Artificial Intelligence: Evidence from a UTAUT-Based Model. (2026). International Journal of Management and Data Analytics (IJMADA), 6(1), 336-353. http://ijmada.com/index.php/ijmada/article/view/155

References

Aguinis, H., & Bradley, K. J. (2014). Best practice recommendations for designing and implementing experimental vignette methodology studies. Organizational Research Methods, 17(4), 351–371. https://doi.org/10.1177/1094428114547952

Autio, C., Schwartz, R., Dunietz, J., Jain, S., Stanley, M., Tabassi, E., Hall, P., & Roberts, K. (2024). Artificial intelligence risk management framework: Generative artificial intelligence profile (NIST AI 600-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.600-1

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., ... Liang, P. (2021). On the opportunities and risks of foundation models. arXiv. https://arxiv.org/abs/2108.07258

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587

European Parliament & Council of the European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L 2024/1689. https://eur-lex.europa.eu/eli/reg/2024/1689/oj

Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630, 625–630. https://doi.org/10.1038/s41586-024-07421-0

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

Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8

Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 57(3), 407–434. https://doi.org/10.1177/0018720814547570

Huynh, M.-T., & Aichner, T. (2025). In generative artificial intelligence we trust: Unpacking determinants and outcomes for cognitive trust. AI & Society, 40, 5849–5869. https://doi.org/10.1007/s00146-025-02378-8

JASP Team. (2025). JASP (Version 0.19.3) [Computer software]. https://jasp-stats.org

Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392

Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024). Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics, 12, 157–173. https://doi.org/10.1162/tacl_a_00638

Malhotra, N. (2008). Completion time and response order effects in web surveys. Public Opinion Quarterly, 72(5), 914–934. https://doi.org/10.1093/poq/nfn050

National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). https://doi.org/10.6028/NIST.AI.100-1

Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253. https://doi.org/10.1518/001872097778543886

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 33–44. https://doi.org/10.1145/3351095.3372873

Scott, W. R. (2014). Institutions and organizations: Ideas, interests, and identities (4th ed.). Sage.

Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. Academy of Management Review, 20(3), 571–610. https://doi.org/10.2307/258788

Thomas, K. A., & Clifford, S. (2017). Validity and Mechanical Turk: An assessment of exclusion methods and interactive experiments. Computers in Human Behavior, 77, 184–197. https://doi.org/10.1016/j.chb.2017.08.038

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Wang, X., Zhong, W., Huang, K., & Liang, B. (2026). High interest but low adoption: Navigating organizations' journey towards generative artificial intelligence implementation. International Journal of Information Management, 87, Article 103009. https://doi.org/10.1016/j.ijinfomgt.2025.103009

Xia, Y., & Chen, Y. (2025). Driving factors of generative AI adoption in new product development teams from a UTAUT perspective. International Journal of Human–Computer Interaction, 41(10), 6067–6088. https://doi.org/10.1080/10447318.2024.2375686