When Trust Sharpens Skepticism: Large Language Models in Indonesian Audit Practice
DOI:
https://doi.org/10.38035/dijefa.v7i3.7181Keywords:
trust, large language model, professional skepticism, transparency, explainability, social influenceAbstract
This study examines the effects of perceived transparency, explainability, and social influence on auditors' trust in Large Language Models, and the effect of that trust on their professional skepticism at Indonesian public accounting firms. The gap between global acceptance and trust levels toward artificial intelligence systems suggests that technology adoption is not always matched by adequate evaluation, a condition relevant to auditors, who must remain critical toward Large Language Models given their tendency to produce inaccurate answers. This study used a quantitative approach with Partial Least Squares Structural Equation Modeling, involving 102 auditors selected through purposive sampling. Results show that all three antecedent variables positively and significantly affect auditors' trust in Large Language Models, with perceived transparency contributing most, while trust in Large Language Models also positively and significantly affects professional skepticism, a direction opposite to the reliance pattern reported in prior audit automation literature. These findings suggest that trust in artificial intelligence based technology can form in a calibrated manner, coexisting with auditors' awareness of system limitations rather than diminishing their professional skepticism.
References
Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
Afsay, A., Tahriri, A., & Rezaee, Z. (2023). A meta-analysis of factors affecting acceptance of information technology in auditing. International Journal of Accounting Information Systems, 49, 100608. https://doi.org/10.1016/j.accinf.2022.100608
Akhunova, S., Tuychiyeva, O., Tukhtasinova, D., & Akhunova, M. (2024). An innovative navigating system design along with AI tech integration for modern system implementation for business. 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 462–467. https://doi.org/10.1109/icacite60783.2024.10616591
Anh, N. T. M., Hoa, L. T. K., Thao, L. P., Nhi, D. A., Long, N. T., Truc, N. T., & Ngoc Xuan, V. (2024). The Effect of Technology Readiness on Adopting Artificial Intelligence in Accounting and Auditing in Vietnam. Journal of Risk and Financial Management, 17(1), 27. https://doi.org/10.3390/jrfm17010027
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2019). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Atf, Z., & Lewis, P. R. (2025). Is Trust Correlated With Explainability in AI? A Meta-Analysis. IEEE Transactions on Technology and Society, 7(1), 70–77. https://doi.org/10.1109/TTS.2025.3558448
Balasubramaniam, N., Kauppinen, M., Rannisto, A., Hiekkanen, K., & Kujala, S. (2023). Transparency and explainability of AI systems: From ethical guidelines to requirements. Information and Software Technology, 159, 107197. https://doi.org/10.1016/j.infsof.2023.107197
Cacioppo, J. T., Petty, R. E., & Geen, T. R. (1989). Attitude Structure and Function: From the Tripartite to the Homeostasis Model of Attitudes. In Attitude Structure and Function. Psychology Press.
Capestro, M., Rizzo, C., Kliestik, T., Peluso, A. M., & Pino, G. (2024). Enabling digital technologies adoption in industrial districts: The key role of trust and knowledge sharing. Technological Forecasting and Social Change, 198, 123003. https://doi.org/10.1016/j.techfore.2023.123003
Cerf, V. G. (2023). Large language models. Communications of the ACM, 66(8), 7–7. https://doi.org/10.1145/3606337
Chen, C.-J., Tsai, P.-H., & Tang, J.-W. (2022). How informational-based readiness and social influence affect usage intentions of self-service stores through different routes: An elaboration likelihood model perspective. Asia Pacific Business Review, 28(3), 380–409. https://doi.org/10.1080/13602381.2021.1872912
Chen, Q., Yin, C., & Gong, Y. (2023). Would an AI chatbot persuade you: An empirical answer from the elaboration likelihood model. Information Technology & People, 38(2), 937–962. https://doi.org/10.1108/ITP-10-2021-0764
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). L. Erlbaum Associates.
Commerford, B., Eilifsen, A., Hatfield, R. C., Holmstrom, K. M., & Kinserdal, F. (2024). Control Issues: How Providing Input Affects Auditors’ Reliance on Artificial Intelligence (SSRN Scholarly Paper No. 4847775). Social Science Research Network. https://doi.org/10.1111/1911-3846.12974
Dam, H. K., Tran, T., & Ghose, A. (2018). Explainable software analytics. Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results, ICSE-NIER ’18, 53–56. https://doi.org/10.1145/3183399.3183424
Deloitte. (2024a). Generative AI in accounting: Opportunities and risks to assess today. Deloitte. https://www.deloitte.com/us/en/services/audit/blogs/accounting-finance/generative-ai-auditing.html
Deloitte. (2024b, November 13). Questions about implementing GenAI? Deloitte provides insights from its own AI journey. https://www2.deloitte.com/us/en/blog/accounting-finance-blog/2024/answering-ai-questions-in-audit-assurance.html
Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective. International Journal of Medical Informatics, 141, 104164. https://doi.org/10.1016/j.ijmedinf.2020.104164
Ding, R., Chen, X., Wei, S., & Wang, J. (2025). What drives trust building in live streaming E-commerce? From an elaboration likelihood model perspective. Industrial Management & Data Systems, 125(3), 969–999. https://doi.org/10.1108/IMDS-03-2024-0273
Ding, W., Abdel-Basset, M., Hawash, H., & Ali, A. M. (2022). Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey. Information Sciences, 615, 238–292. https://doi.org/10.1016/j.ins.2022.10.013
Ding, W., Wu, Y., & Wang, J. (2026). Beyond Risk Reduction: Vigilant Trust in Artificial Intelligence Based on Evidence from China. Behavioral Sciences, 16(1), 95. https://doi.org/10.3390/bs16010095
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes (pp. xxii, 794). Harcourt Brace Jovanovich College Publishers.
Eagly, A. H., & Chaiken, S. (2007). The Advantages of an Inclusive Definition of Attitude. Social Cognition, 25(5), 582–602. https://doi.org/10.1521/soco.2007.25.5.582
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630(8017), 625–630. https://doi.org/10.1038/s41586-024-07421-0
Fazio, R. H. (1989). On the Power and Functionality of Attitudes: The Role of Attitude Accessibility. In Attitude Structure and Function. Psychology Press.
Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Towards Transparency by Design for Artificial Intelligence. Science and Engineering Ethics, 26(6), 3333–3361. https://doi.org/10.1007/s11948-020-00276-4
Ferri, L., Spanò, R., Ginesti, G., & Theodosopoulos, G. (2020). Ascertaining auditors’ intentions to use blockchain technology: Evidence from the Big 4 accountancy firms in Italy. Meditari Accountancy Research, 29(5), 1063–1087. https://doi.org/10.1108/MEDAR-03-2020-0829
Fotoh, E., & Mugwira, T. (2025). Exploring Large Language Models in External Audits: Implications and Ethical Considerations. SSRN. https://doi.org/10.2139/ssrn.4932003
Gefen, Karahanna, & Straub. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, 27(1), 51. https://doi.org/10.2307/30036519
Gilpin, L. H., Testart, C., Fruchter, N., & Adebayo, J. (2019). Explaining Explanations to Society (arXiv:1901.06560). arXiv. https://doi.org/10.48550/arXiv.1901.06560
Greenwald, A. G. (1989). Why Attitudes are Important: Defining Attitude and Attitude Theory 20 Years Later. In Attitude Structure and Function. Psychology Press.
Gunning, D., & Aha, D. W. (2019). DARPA’s Explainable Artificial Intelligence Program. AI Magazine, 40(2), 44–58. https://doi.org/10.1609/aimag.v40i2.2850
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7
Hamm, P., Klesel, M., Coberger, P., & Wittmann, H. F. (2023). Explanation matters: An experimental study on explainable AI. Electronic Markets, 33(1), 17. https://doi.org/10.1007/s12525-023-00640-9
Hardies, K., Janssen, S., Vanstraelen, A., & Zehms, K. M. (2025). Using Field-Based Evidence to Understand the Antecedents to Auditors’ Skeptical Actions. AUDITING: A Journal of Practice & Theory, 44(1), 105–135. https://doi.org/10.2308/AJPT-2021-141
He, G., Kuiper, L., & Gadiraju, U. (2023). Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–18. https://doi.org/10.1145/3544548.3581025
He, W., Sidhu, B., & Taylor, S. (2019). Audit quality and properties of analysts’ information environment. Journal of Business Finance & Accounting, 46(3–4), 400–419. https://doi.org/10.1111/jbfa.12358
Heo, S., & Na, S. (2025). Ready for departure: Factors to adopt large language model (LLM)-based artificial intelligence (AI) technology in the architecture, engineering and construction (AEC) industry. Results in Engineering, 25, 104325. https://doi.org/10.1016/j.rineng.2025.104325
Hooda, A., Gupta, P., Jeyaraj, A., Giannakis, M., & Dwivedi, Y. K. (2022). The effects of trust on behavioral intention and use behavior within e-government contexts. International Journal of Information Management, 67, 102553. https://doi.org/10.1016/j.ijinfomgt.2022.102553
Hurtt, R. K. (2010). Development of a Scale to Measure Professional Skepticism. AUDITING: A Journal of Practice & Theory, 29(1), 149–171. https://doi.org/10.2308/aud.2010.29.1.149
IAASB. (2025). Handbook of International Quality Management, Auditing, Review, Other Assurance, and Related Services Pronouncements. www.iaasb.org
Kahraman, F., Aktas, A., Bayrakceken, S., Çakar, T., Tarcan, H. S., Bayram, B., Durak, B., & Ulman, Y. I. (2024). Physicians’ ethical concerns about artificial intelligence in medicine: A qualitative study: “The final decision should rest with a human.” Frontiers in Public Health, 12. https://doi.org/10.3389/fpubh.2024.1428396
Kalai, A. T., Nachum, O., Vempala, S. S., & Zhang, E. (2025). Why Language Models Hallucinate (arXiv:2509.04664). arXiv. https://doi.org/10.48550/arXiv.2509.04664
Kenesei, Z., Kökény, L., Ásványi, K., & Jászberényi, M. (2025). The central role of trust and perceived risk in the acceptance of autonomous vehicles in an integrated UTAUT model. European Transport Research Review, 17(1), 8. https://doi.org/10.1186/s12544-024-00681-x
Kim, Y., Blazquez, V., & Oh, T. (2024). Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model. Behavioral Sciences, 14(11), 1035. https://doi.org/10.3390/bs14111035
Kim, Y. J., Choi, J. H., & Fotso, G. M. N. (2024). Medical professionals’ adoption of AI-based medical devices: UTAUT model with trust mediation. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100220. https://doi.org/10.1016/j.joitmc.2024.100220
Klingbeil, A., Grützner, C., & Schreck, P. (2024). Trust and reliance on AI — An experimental study on the extent and costs of overreliance on AI. Computers in Human Behavior, 160, 108352. https://doi.org/10.1016/j.chb.2024.108352
Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Challenges and opportunities for artificial intelligence in auditing: Evidence from the field. International Journal of Accounting Information Systems, 56, 100734. https://doi.org/10.1016/j.accinf.2025.100734
Kuen, L., Westmattelmann, D., Bruckes, M., & Schewe, G. (2023). Who earns trust in online environments? A meta-analysis of trust in technology and trust in provider for technology acceptance. Electronic Markets, 33(1), 61. https://doi.org/10.1007/s12525-023-00672-1
Küper, A., & Krämer, N. (2024). Psychological Traits and Appropriate Reliance: Factors Shaping Trust in AI. International Journal of Human–Computer Interaction, 1–17. https://doi.org/10.1080/10447318.2024.2348216
Lee, C., & Cha, K. (2024). Toward the Dynamic Relationship Between AI Transparency and Trust in AI: A Case Study on ChatGPT. International Journal of Human–Computer Interaction, 41(13), 8086–8103. https://doi.org/10.1080/10447318.2024.2405266
Leocádio, D., Malheiro, L., & Reis, J. C. G. dos. (2024). Auditors in the digital age: A systematic literature review. Digital Transformation and Society, 4(1), 5–20. https://doi.org/10.1108/DTS-02-2024-0014
Li, X. (2022). Behavioral challenges to professional skepticism in auditors’ data analytics journey. Maandblad Voor Accountancy En Bedrijfseconomie, 96(1/2), 45–54. https://doi.org/10.5117/mab.96.78525
Lombardi, D. R., Sipior, J. C., & Dannemiller, S. (2023). Auditor Judgment Bias Research: A 50-Year Trend Analysis and Emerging Technology Use. Journal of Information Systems, 37(1), 109–141. https://doi.org/10.2308/ISYS-2020-079
Mansoor, M. (2021). Citizens’ trust in government as a function of good governance and government agency’s provision of quality information on social media during COVID-19. Government Information Quarterly, 38(4), 101597. https://doi.org/10.1016/j.giq.2021.101597
Market.us. (2024). Global AI in Audit Market. https://market.us/report/ai-in-audit-market/
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), 1–25. https://doi.org/10.1145/1985347.1985353
Müller, A. P. R., Tangi, L., & Lerusse, A. (2025). Understanding the Adoption of Artificial Intelligence in Local Government Decision-Making: The Influence of Institutional Pressures and Managerial Perceptions. Public Administration, n/a(n/a). https://doi.org/10.1111/padm.70033
Murikah, W., Nthenge, J. K., & Musyoka, F. M. (2024). Bias and ethics of AI systems applied in auditing—A systematic review. Scientific African, 25, e02281. https://doi.org/10.1016/j.sciaf.2024.e02281
Nguyen, P. T., Kend, M., & Le, D. Q. (2024). Digital transformation in Vietnam: The impacts on external auditors and their practices. Pacific Accounting Review, 36(1), 144–160. https://doi.org/10.1108/par-04-2023-0051
Oldeweme, A., Märtins, J., Westmattelmann, D., & Schewe, G. (2021). The Role of Transparency, Trust, and Social Influence on Uncertainty Reduction in Times of Pandemics: Empirical Study on the Adoption of COVID-19 Tracing Apps. Journal of Medical Internet Research, 23(2), e25893. https://doi.org/10.2196/25893
Pemer, F. (2021). Enacting Professional Service Work in Times of Digitalization and Potential Disruption. Journal of Service Research, 24(2), 249–268. https://doi.org/10.1177/1094670520916801
Peters, C. P. H. (2022). Auditor Automation Usage and Professional Skepticism. Auditor Automation Usage and Professional Skepticism.
Petty, R. E., & Cacioppo, J. T. (1986). THE ELABORATION LIKELIHOOD MODEL OF PERSUASION.
Putri, J. W. (2024). Indonesia and ASEAN chairmanship in 2023: Leading the region in strengthening relations with China. International Journal of Law and Politics Studies, 6(1), 96–106. https://doi.org/10.32996/ijlps.2024.6.1.11
Rajak, M., & Shaw, K. (2021). An extension of technology acceptance model for mHealth user adoption. Technology in Society, 67, 101800. https://doi.org/10.1016/j.techsoc.2021.101800
Rana, N. P., Pillai, R., Sivathanu, B., & Malik, N. (2024). Assessing the nexus of Generative AI adoption, ethical considerations and organizational performance. Technovation, 135, 103064. https://doi.org/10.1016/j.technovation.2024.103064
Rathnayake, A. S., Nguyen, T. D. H. N., & Ahn, Y. (2025). Factors Influencing AI Chatbot Adoption in Government Administration: A Case Study of Sri Lanka’s Digital Government. Administrative Sciences, 15(5), 157. https://doi.org/10.3390/admsci15050157
Robinson, S. N., Curtis, M. B., & Robertson, J. C. (2018). Disentangling the Trait and State Components of Professional Skepticism: Specifying a Process for State Scale Development. Auditing: A Journal of Practice & Theory, 37(1), 215–235. https://doi.org/10.2308/ajpt-51738
Schnackenberg, A. K., & Tomlinson, E. C. (2016). Organizational Transparency: A New Perspective on Managing Trust in Organization-Stakeholder Relationships. Journal of Management, 42(7), 1784–1810. https://doi.org/10.1177/0149206314525202
Seethamraju, R., & Hecimovic, A. (2023). Adoption of artificial intelligence in auditing: An exploratory study. Australian Journal of Management, 48(4), 780–800. https://doi.org/10.1177/03128962221108440
Shin, D. (2020). User perceptions of algorithmic decisions in the personalized AI system:perceptual Evaluation of fairness, accountability, transparency, and explainability. Journal of Broadcasting & Electronic Media, 64(4), 541–565. https://doi.org/10.1080/08838151.2020.1843357
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
Shin, D., & Park, Y. J. (2019). Role of fairness, accountability, and transparency in algorithmic affordance. Computers in Human Behavior, 98, 277–284. https://doi.org/10.1016/j.chb.2019.04.019
Statista. (2025a). Artificial intelligence (AI) adoption, risks, and challenges. https://www.statista.com/study/132103/artificial-intelligence-ai-adoption-risks-and-challenges/
Statista. (2025b). Indonesia: GenAI market size 2030. Statista. https://www-statista-com.unpad.idm.oclc.org/forecasts/1493407/indonesia-genai-market-size
Sullivan, V., & Weger, K. (2025). Transparency and Explainability in AI-Assisted Decision Making: Effects on Trust, Perceived Reliability, Confidence, and Ease of Understanding. 69(1).
Surahman, R. H., & Legowo, N. (2024). Factors that affecting firm performance mediated by digital transformation in Indonesia’s state-owned enterprises plantation and forestry industries. Edelweiss Applied Science and Technology, 8(6). https://doi.org/10.55214/25768484.v8i6.2810
Tian, Y., Zhang, H., Jiang, Y., & Yang, Y. (2021). Understanding trust and perceived risk in sharing accommodation: An extended elaboration likelihood model and moderated by risk attitude. Journal of Hospitality Marketing & Management, 31(3), 348–368. https://doi.org/10.1080/19368623.2022.1986190
Vitali, S., & Giuliani, M. (2024). Emerging digital technologies and auditing firms: Opportunities and challenges. International Journal of Accounting Information Systems, 53, 100676. https://doi.org/10.1016/j.accinf.2024.100676
von Eschenbach, W. J. (2021). Transparency and the Black Box Problem: Why We Do Not Trust AI. Philosophy & Technology, 34(4), 1607–1622. https://doi.org/10.1007/s13347-021-00477-0
Vössing, M., Kühl, N., Lind, M., & Satzger, G. (2022). Designing transparency for effective human-ai collaboration. Information Systems Frontiers, 24(3), 877–895. https://doi.org/10.1007/s10796-022-10284-3
Wang, W., & Benbasat, I. (2016). Empirical Assessment of Alternative Designs for Enhancing Different Types of Trusting Beliefs in Online Recommendation Agents. Journal of Management Information Systems, 33(3), 744–775. https://doi.org/10.1080/07421222.2016.1243949
Yu, L., & Li, Y. (2022). Artificial Intelligence Decision-Making Transparency and Employees’ Trust: The Parallel Multiple Mediating Effect of Effectiveness and Discomfort. Behavioral Sciences, 12(5), 127. https://doi.org/10.3390/bs12050127
Zhang, T., Tao, D., Qu, X., Zhang, X., Zeng, J., Zhu, H., & Zhu, H. (2020). Automated vehicle acceptance in China: Social influence and initial trust are key determinants. Transportation Research Part C: Emerging Technologies, 112, 220–233. https://doi.org/10.1016/j.trc.2020.01.027
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., … Wen, J.-R. (2023). A survey of large language models. https://arxiv.org/abs/2303.18223
Zhou, T. (2012). Understanding users’ initial trust in mobile banking: An elaboration likelihood perspective. Computers in Human Behavior, 28(4), 1518–1525. https://doi.org/10.1016/j.chb.2012.03.021
Zhou, T., & Wu, X. (2025). Examining generative AI user disclosure intention: An ELM perspective. Universal Access in the Information Society, 24(2), 1209–1220. https://doi.org/10.1007/s10209-024-01130-1
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Kamal Amarullah, Hamzah Ritchi, Ahmad Zakie Mubarrok
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish their manuscripts in this journal agree to the following conditions:
- The copyright on each article belongs to the author(s).
- The author acknowledges that the Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) has the right to be the first to publish with a Creative Commons Attribution 4.0 International license (Attribution 4.0 International (CC BY 4.0).
- Authors can submit articles separately, arrange for the non-exclusive distribution of manuscripts that have been published in this journal into other versions (e.g., sent to the author's institutional repository, publication into books, etc.), by acknowledging that the manuscript has been published for the first time in the Dinasti International Journal of Economics, Finance & Accounting (DIJEFA).
