Contextual Acceptance of Artificial Intelligence in Commercial Policy-Making: An Integrated Model of Acceptance, Resistance, and Institutional Factors

Hamidreza Goldouz; Ezatollah Abbasian; Nastaran Hajiheydari

Authors

Hamidreza Goldouz Ph.D. Candidate in Business Administration, Kish International Campus, University of Tehran, Kish Island, Iran Author https://orcid.org/0009-0003-2129-399X
Ezatollah Abbasian Department of Financial Engineering, Faculty of Management, University of Tehran, Tehran, Iran Author https://orcid.org/0000-0001-8364-6461
Nastaran Hajiheydari Department of Business Administration, Faculty of Management, University of Tehran, Tehran, Iran. Author https://orcid.org/0000-0003-3663-5254

Keywords:

artificial intelligence; commercial policy-making; contextual acceptance; digital leadership; institutional trust; technological resistance

Abstract

This study examines how senior decision-makers in Iranian commercial policy institutions evaluate and respond to artificial intelligence (AI), with particular attention to the interplay of adoption, resistance, and institutional considerations. It develops the notion of contextual acceptance to describe the importance of whether AI-supported decisions can be justified to relevant stakeholders. An exploratory sequential mixed-methods design was used. The qualitative phase involved 18 semi-structured interviews with experts in commercial policy, information technology, and academia. The authors report thematic saturation after the fourteenth interview and four additional interviews to consolidate the themes. In the quantitative phase, a 27-item five-point Likert questionnaire was completed by 248 senior managers and specialists selected through stratified random sampling. The reported analysis used partial least squares structural equation modeling (PLS-SEM) in SmartPLS 4. The qualitative analysis identified digital leadership, perceived usefulness, institutional trust, algorithmic opacity, organizational resistance, and perceived risk. In the structural model, digital leadership (β = 0.36, t = 6.3), perceived usefulness (β = 0.34, t = 5.9), and institutional trust (β = 0.28, t = 4.7) were positively associated with attitudes toward AI. Algorithmic opacity (β = −0.22, t = 3.8) and organizational resistance (β = −0.19, t = 3.4) were negatively associated with attitudes. The reported R² values were 0.64 for attitude and 0.69 for behavioral intention, with an overall GOF value of 0.61. All nine hypotheses were reported as supported, although the indirect-effect coefficient and test statistic for mediation were not provided. The findings are consistent with a context-sensitive account of AI acceptance in which perceived institutional accountability and the defensibility of algorithm-supported decisions complement assessments of technical usefulness. Potential implications include clearer accountability arrangements, explainable AI systems, and digital leadership development; causal and policy-effectiveness claims require further study.

Downloads

Download data is not yet available.

References

Alon-Barkat, S., & Busuioc, M. (2023). Human–AI interactions in public sector decision making: "Automation bias" and "selective adherence" to algorithmic advice. Journal of Public Administration Research and Theory, 33(1), 153–169.

Rouhani, A., Amin-Kazemi, B., & Arasteh, M. (2024). Developing scenarios for the use of artificial intelligence in human resource recruitment in Iran. Transactions on Data Analysis in Social Science, 6(3), 185–197. https://doi.org/10.47176/TDASS.2024.185

Ataei, E. (2024). Identifying the Factors Affecting Human Resource Productivity. Transactions on Data Analysis in Social Science, 6(2), 127-139. https://doi.org/10.47176/TDASS.2024.127

Bian, X., Wang, B., & Yang, A. (2025). The trust trifecta: How transparency, ethics, and benefits shape public confidence in government AI. Government Information Quarterly, 42(4), 102083.

Burrell, J. (2016). How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12.

Criado, J. I., Sandoval-Almazán, R., & Gil-Garcia, J. R. (2024). Artificial intelligence and public administration: Understanding actors, governance, and policy from micro, meso, and macro perspectives. Public Policy and Administration, 40(2), 173–184.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., & Kar, A. K. (2023). Opinion paper: So what if ChatGPT wrote it? International Journal of Information Management, 71, 102642.

Grimmelikhuijsen, S. (2023). Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making. Public Administration Review, 83(2), 241–262.

Janssen, M., Hartog, M., Matheus, R., Yi Ding, A., & Kuk, G. (2020). Will algorithmic governance cause the downfall of democracy? Government Information Quarterly, 37(3), 101504.

Kim, H. W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567–582.

Lapointe, L., & Rivard, S. (2005). A multilevel model of resistance to information technology implementation. MIS Quarterly, 29(3), 461–491.

Mergel, I., Edelmann, N., & Haug, N. (2019). Defining digital transformation: Results from expert interviews. Government Information Quarterly, 36(4), 101385.

Mikalef, P., Conboy, K., & Krogstie, J. (2021). Artificial intelligence as an enabler of B2B marketing: A dynamic capabilities micro-foundations approach. Industrial Marketing Management, 101, 80–92.

OECD. (2024). OECD AI Policy Observatory: Government AI Readiness Index 2024. OECD Publishing.

Simon, H. A. (1997). Administrative Behavior (4th ed.). Free Press.

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.

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144.

Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The dark side of artificial intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration, 43(9), 818–829.

Young, M., Bullock, J., & Lecy, J. (2019). Artificial discretion as a tool of governance: A framework for understanding the impact of artificial intelligence on public administration. Perspectives on Public Management and Governance, 2(4), 301–313.