The relationship between the application of Generative Artificial Intelligence and audit quality of companies listed on the Stock Exchange of Thailand
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This study aims to (1) examine the relationships and (2) test the effects of the application of Generative Artificial Intelligence (Generative AI) on audit quality of listed companies on the Stock Exchange of Thailand. This research adopts a quantitative approach. The sample consists of 131 auditors approved by the Securities and Exchange Commission of Thailand, determined using Yamane’s formula and selected through simple random sampling. The research instrument was a questionnaire with acceptable content validity. The item discrimination indices ranged from 0.479–0.803 and 0.556–0.805, and the overall reliability coefficients ranged from 0.762–0.972. Data were analyzed using percentage, mean, standard deviation, multiple correlation analysis, and multiple regression analysis. The findings indicate that the application of Generative AI in deep data analytics, professional judgment support, and audit process efficiency is positively related to and has a statistically significant impact on audit quality at the 0.05 level. In contrast, the application of Generative AI in automated document review, communication and reporting, and auditors’ learning and knowledge development shows no significant relationship or impact on audit quality. These results suggest that Generative AI enhances audit quality only in specific dimensions, particularly in data analytics, decision support, and process efficiency, while no significant effects are observed in other dimensions. Therefore, the implementation of Generative AI in auditing should focus on areas that yield positive outcomes in order to improve audit effectiveness and strengthen stakeholders’ confidence in financial reporting.
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ฐิติพร แสนสุริวงค์, และ สายทิพย์ จะโนภาษ. (2568). ผลกระทบของการประยุกต์ใช้เทคโนโลยีดิจิทัลในการสอบบัญชีที่มีต่อคุณภาพการสอบบัญชีของผู้สอบบัญชีรับอนุญาตในประเทศไทย. วารสารมนุษยศาสตร์และสังคมศาสตร์ มหาวิทยาลัยนครพนม, 15(2), 172–185.
บุญชม ศรีสะอาด. (2554). การวิจัยเบื้องต้น (พิมพ์ครั้งที่ 8). กรุงเทพฯ: บุญศิริ.
สมบัติ ท้ายเรือคำ. (2552). ระเบียบวิธีวิจัยสำหรับมนุษยศาสตร์และสังคมศาสตร์ (พิมพ์ครั้งที่ 8). มหาสารคาม: มหาวิทยาลัยมหาสารคาม.
สุมินทร เบ้าธรรม. (2558). วิจัยทางการบัญชี (พิมพ์ครั้งที่ 1). กรุงเทพมหานคร: ทริปเพิ้ล เอ็ดดูเคชั่น.
สำนักงานคณะกรรมการกำกับหลักทรัพย์และตลาดหลักทรัพย์. (2567). รายงานประจำปี 2566. กรุงเทพฯ: สำนักงานคณะกรรมการกำกับหลักทรัพย์และตลาดหลักทรัพย์.
สำนักงานคณะกรรมการกำกับหลักทรัพย์และตลาดหลักทรัพย์. (2568). ผู้สอบบัญชีที่ได้รับความเห็นชอบโดยสำนักงานคณะกรรมการกำกับหลักทรัพย์และตลาดหลักทรัพย์. สืบค้นเมื่อ 19 กันยายน 2568, จาก https://market.sec.or.th/public/orap/AUDITOR01.aspx?lang=th
Aaker, D. A., Kumar, V., & Day, G. S. (2001). Marketing research (7th ed.). John Wiley & Sons.
Abdullah, A. A. H. (2024). The impact of artificial intelligence and Industry 4.0 on accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity, 10, 1–20.
Anica-Popa, I.-F., Vrîncianu, M., Anica-Popa, L.-E., Cismasu, I.-D., & Tudor, C.-G. (2024). Framework for integrating generative AI in developing competencies for accounting and audit professionals. Electronics, 13(13), Article 2621. https://doi.org/10.3390/electronics13132621
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1–27.
Bierstaker, J. L., Janvrin, D. J., & Lowe, D. J. (2021). What factors influence auditors’ use of information technology? Advances in Accounting, 52, 100516. https://doi.org/10.1016/j.adiac.2021.100516
Black, K. (2006). Business statistics for contemporary decision making (4th ed.). John Wiley & Sons.
Brown-Liburd, H., Issa, H., & Lombardi, D. (2021). Behavioral implications of big data’s impact on audit judgment and decision making and future research directions. Accounting Horizons, 35(1), 85–99. https://doi.org/10.2308/HORIZONS-19-116
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
DeAngelo, L. E. (1981). Auditor independence, “low balling,” and disclosure regulation. Journal of Accounting and Economics, 3(2), 113–127.
Deloitte. (2023). Generative AI in accounting: Opportunities and risks to assess today. https://www.deloitte.com
Deloitte. (2024). Generative AI enables us to enhance our digital audit. https://www.deloitte.com
Ditkaew, K., & Suttipun, M. (2023). The impact of audit data analytics on audit quality and audit review continuity in Thailand. Asian Journal of Accounting Research, 8(3), 269–278.
EY. (2025). How artificial intelligence can transform accounting. https://www.ey.com
Francis, J. (2011). A framework for understanding and researching audit quality. Auditing: A Journal of Practice & Theory, 30(2), 125–152.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2006). Multivariate data analysis (6th ed.). Pearson.
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122.
KPMG. (2023). Generative AI in the modern workplace. https://kpmg.com/xx/en/our-insights/ai-and-technology/generative-ai-in-the-modern-workplace.html
Noordin, N. A., Hussainey, K., & Hayek, A. F. (2022). The use of artificial intelligence and audit quality: An analysis from the perspectives of external auditors in the UAE. Journal of Risk and Financial Management, 15(8), 339. https://doi.org/10.3390/jrfm15080339
PwC. (2024). Generative AI: The next frontier in the transformation of global capability centres for pharma and life sciences. https://www.pwc.in/assets/pdfs/generative-ai-next-frontier-transformation-global-capability-centres-pharma-life-sciences.pdf
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Tojiboyev, N., Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2022). Basics of SQL for audit data retrieval and analysis. Journal of Emerging Technologies in Accounting, 19(1), 237–265.
Yamane, T. (1973). Statistics: An introductory analysis (2nd ed.). Harper & Row.
Zhang, S. (2024). Research on the application of AI technology in auditing. Economic Management & Global Business Studies, 3(1), Article 13.
