MUHASEBE MESLEK MENSUPLARININ YAPAY ZEKA UYGULAMALARINI KABULLERİNE YÖNELİK ALGILARININ ÖLÇÜLMESİ

Abstract

Bu araştırma muhasebe meslek mensuplarının yapay zekâ uygulamalarına yönelik tutum ve davranışlarını etkileyen faktörlerin Teknoloji Kabul Modeli çerçevesinde belirlenerek yapay zekâ teknolojilerini kabullerine yönelik algılarının ortaya konmasını amaçlamaktadır. 485 muhasebe meslek mensubundan anket yoluyla toplanan verilerin Yapısal Eşitlik Modellemesi analizi sonucunda teknolojik yenilikçiliğin algılanan kullanım kolaylığı üzerinde; algılanan kullanım kolaylığının algılanan kullanışlılık üzerinde istatistiki olarak anlamlı ve pozitif yönde bir etkisinin olduğu tespit edilmiştir. Algılanan kullanım kolaylığının, kullanıma yönelik tutum üzerinde istatistiki olarak anlamlı bir etkisinin olmadığını gösteren bulgular aynı zamanda algılanan kullanım kolaylığı üzerinde algılanan kullanışlılığın ve uygunluğun istatistiki olarak anlamlı ve pozitif yönde etkilerinin olduğunu göstermektedir. Çalışmanın diğer bulguları yapay zekâ kaygısının kullanıma yönelik tutum üzerinde istatistiki olarak anlamlı ve negatif yönde, davranışsal niyet üzerinde ise kullanıma yönelik tutumun istatistiki olarak anlamlı ve pozitif yönde etkisini göstermektedir.

Keywords

• Muhasebe Meslek Mensupları
• Yapay Zeka
• Yapay Zeka Uygulamaları
• Yapısal Eşitlik Modellemesi

ACCEPTANCE OF ARTIFICIAL INTELLIGENCE APPLICATIONS BY ACCOUNTING PROFESSIONALS

Abstract

This research aims to determine the factors affecting the attitudes and behaviors of accounting professionals towards artificial intelligence applications within the framework of the Technology Acceptance Model and to reveal their perceptions regarding their acceptance of artificial intelligence technologies. As a result of the Structural Equation Modeling analysis of the data obtained from 485 accounting professionals, it was determined that technological innovation has a statistically significant and positive effect on perceived ease of use; and perceived ease of use has a statistically significant and positive effect on perceived usefulness. Findings showing that perceived ease of use does not have a statistically significant effect on attitude towards use also show that perceived usefulness and compatibility have statistically significant and positive effects on perceived ease of use. Other findings of the study show that artificial intelligence anxiety has a statistically significant and negative effect on attitude towards use, and attitude towards use has a statistically significant and positive effect on behavioral intention.

Keywords

• Accountants
• Artificial Intelligence
• Artificial Intelligence Applications
• Structural Equation Modeling

References

1. Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16–23.
2. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall.
3. Aldas-Manzaro, J., Ruiz-Mafe, C., & Sanz-Blas, S. (2009). Exploring individual personality factors as drivers of m-shopping acceptance. Industrial Management & Data Systems, 109(6), 739–757.
4. Al-Gahtani, S.S., & King, M. (1999). Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology. Behaviour & Information Technology, 18(4), 277–297.
5. Ali, F., Rasoolimanesh, S.M., Sarstedt, M., Ringle, C.M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1) 514–538.
6. Amoako-Gyampah, K., & Salam, A.F. (2004). An extension of the technology acceptance model in an erp İmplementation environment. Information and Management, 41(6), 731–745.
7. Anderson, J.C., & Gerbing, D.W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411–423.
8. Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C.H. (2020). AI we trust? Perceptions about automated decision‑makingby artificial intelligence. AI & Society, 35, 611–623.

9. Atkinson, J.W. (1957). Motivational determinants of risk taking behavior. Psychology Review, 64, 359–372.
10. Bagozzi, R.P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(2), 74–94.
11. Baldwin, A.A., Brown, C.E., & Trinkle, B.S. (2006). Opportunities for artificial ıntelligence development in the accounting domain: The case for auditing. Intelligent Systems in Accounting, Finance and Management, 14, 77–86.
12. Başol, O. (2018). OECD ülkelerinde yaşam tatmini üzerine bir değerlendirme. “İŞ, GÜÇ” Endüstri İlişkileri ve İnsan Kaynakları Dergisi, 20(3), 67–86.
13. Bizarro, P.A., & Dorian, M. (2017). Artificial intelligence: The future of auditing. Internal Auditing, 5, 21–26.
14. Borders, A., Earleywine, M. & Huey, S. (2004). Predicting problem behaviours with multiple expectancies: Expanding Expectancy Value Theory. Adolescence. 39, 539–551.
15. Bradley, J., (2009). The Technology Acceptance Model and other user acceptance theories. in Handbook of research in contemporary theoretical models in information systems Editor(s): Yogesh K. Dwivedi, Banita Lal, Michael D. Williams, Scott L. Schneberger and Michael Wade. IGI Global
16. Brock, J.K.-U., & von Wangenheim, F. (2019). Demystifying AI: What digital transformation leaders can teach you about realistic artificial ıntelligence. California Management Review, 153650421986522, 1–25.
17. Brown, A.S., Anne, P.M., Mitzi, M-W., & James R.B. (2002). Do I really have to? User acceptance of mandated technology. European Journal of Information Systems, 11(4), 283–295.
18. Canhoto, A.I. & Clear, F. (2020). Artificial intelligence and machine learning as business tools: a framework for diagnosing value destruction potential. Business Horizons, 63(2), 183–193.
19. Chau, P.Y.K., & Hu, P.J.H. (2002). Investigating healthcare professionals’ decision to accept telemedicine technology: an empirical test of competing theories. Information and Management, 39(4), 297–311.
20. Chen, L., & Tan, J., (2004). Technology adaptation in e-commerce: Key determinants of virtual stores acceptance. European Management Journal, 22(1), 74–86.
21. Chen, Y.-Y. (2012). Why do consumers go ınternet shopping again? Understanding the antecedents of repurchase ıntention. Journal of Organizational Computing and Electronic Commerce, 22(1), 38–63.
22. Cheng, J. M.-S., Sheen, G.-J., & Lou, G.-C. (2006). Consumer acceptance of the internet as a channel of distribution in Taiwan-a channel function perspective. Technovation, 26(7), 856–864.
23. Chiu, C.-M., Chang, C.-C., Cheng, H.-L., & Fang, Y.-H. (2009a). Determinants of customer repurchase ıntention in online shopping. Online Information Review, 33(4), 761–784.
24. Chiu, C.-M., Lin, H.-Y., Sun, S.-Y., & Hsu, M.-H. (2009b). Understanding customers' loyalty intentions towards online shopping: An integration of technology acceptance model and fairness theory. Behavior & Information Technology, 28(4), 347–360.
25. Chollet, F. (2021). Deep Learning with Python, 2nd ed., Shelter Island, Manning, NY.
26. Chukwuani, S., & Johnson, M. (2018). The application of AI technology in accounting: Preventing financial fraud, enhancing information quality, and promoting reform in traditional accounting and auditing. Journal of Accounting Technology and Innovation, 10, 145–60.
27. Chukwuani, V.N., & Egiyi, M.A. (2020). Automation of accounting processes: Impact of artificial intelligence. International Journal of Research and Innovation in Social Science (IJRISS), 4, 444–449.
28. Chukwudi, O., Echefu, S., Boniface, U., & Victoria, C. (2018). Effect of artificial intelligence on the performance of accounting operations among accounting firms in South East Nigeria. Asian Journal of Economics, Business and Accounting, 7, 1–11.
29. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.
30. Çark, Ö. (2019). Kurumsal kaynak planlama uygulama ve sonrasında sistem başarısını ve kullanıcı değerini etkileyen faktörler: Borsa İstanbul 100 araştırması. Doktora Tezi, İstanbul Aydın Üniversitesi, İstanbul.
31. Çelik, H. (2011). Influence of social norms, perceived playfulness and online shopping anxiety on customers' adoption of online retail shopping: An empirical study in the turkish context. International Journal of Retail & Distribution Management, 39(6), 390–413.
32. Dasgupta, S., Granger, M., & McGarry, N. (2002). User acceptance of e-collaboration technology: an extension of the technology acceptance model. Group Decision and Negotiation, 11, 87–100.
33. Davis, F.D. (1985). A technology acceptance model for empirically testing new enduser information systems: Theory and results. Doctoral Dissertation, Massachusetts Institute of Technology.
34. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.
35. Davis, F.D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487.
36. Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
37. Dingli, A., Haddod, F. & Klüver, C. (2021). Artificial Intelligence in Industry 4.0. Studies in Computational Intelligence, 928, 1st ed. Springer International Publishing, Cham.
38. Dönmez Turan, A., & Kır, M. (2019). User anxiety as an external variable of technology acceptance model: A meta-analytic study. Procedia Computer Science, 158, 715–724.
39. Dülgeroğlu, İ. (2017). Son tüketicilerin değer bilinci ve fiyat-kalite ilişkilerinin internet üzerinden satın alma davranışı üzerindeki etkisi: Kırklareli ilinde bir araştırma. Kırklareli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 6(1), 53–63.
40. Eccles J.S., Adler, T.F., Futterman, R., Goff, S.B., Kaczala, C.M., Meece, J.L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75–146). San Francisco, CA: W. H. Freeman.
41. Eren, H. (2010). Üniversite öğrencilerinin sosyal yenilikçilik kapasitelerinin teknolojik yenilikçilik eğilimlerine etkisini ölçmeye yönelik bir model önerisi. Doktora Tezi, Kara Harp Okulu Savunma Bilimleri Enstitüsü, Ankara.
42. Fetzer, J.H. (1990). What is artificial intelligence? In J. H. Fetzer (Ed.), Artificial Intelligence: its Scope and Limits (pp. 3–27). Springer.
43. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, ıntention and behavior: An introduction to theory research. Reading. MA: Addison-Wesley.
44. Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388.
45. Gacar, A. (2019). Yapay Zekâ ve Yapay Zekânın Muhasebe Mesleğine Olan Etkileri: Türkiye’ye Yönelik Fırsat ve Tehditler. Balkan Sosyal Bilimler Dergisi, 8(EUREFE’19), 389–394.
46. Garson, G.D. (2016). Partial Least Squares: Regression and Structural Equation Model, Asheboro, Statistical Associates Publishing.
47. Gefen, D., Karahanna, E., & Straub, D.W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.
48. Gomber, P., Kauffman, R.J., Parker, C., & Weber, B.W. (2018). On the Fintech revolution: Interpreting the forces of ınnovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35(1), 220–265.
49. Gong, M., Yan, X., & Yuecheng, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365–374.
50. Greenman, C. (2017). Exploring the impact of artificial intelligence on the accounting profession. Journal of Research in Business, Economics and Management, 8(3), 1451–1454.
51. Groß, M. (2015). Exploring the acceptance of technology for mobile shopping: An empirical ınvestigation among smartphone users. The Traditional Review of Retail, Distribution and Consumer Research, 25(3), 215–235.
52. Gümüşsoy, Ç.A., & Yeterel, A.C. (2016). Fırsat sitelerinden tekrar satın alma kararını etkileyen faktörlerin araştırılması. Bilişim Teknolojileri Dergisi, 9(3), 275–284.
53. Gyampah, K.A., & Salam, A.F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41, 731–745.
54. Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565–571.
55. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., & Tahtam, R.L. (2006). Multivariate Data Analysis, New Jersey, USA, Pearson Education LTD.
56. Hair, J.F., Hult, G.T.M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM), (2nd Edition), Thousand Oaks: Sage Publications.
57. Hair, J.F., Ringle, C.M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–150.
58. Han, H., Shiwakoti, R.K., Jarvis, R., Mordi, C., & Botchie, D. (2023). Accounting and auditing with blockchain technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems, 48, 1–16.
59. Hemin, A.Q. (2017). Will artificial intelligence brighten or threaten the future science, ethics, and society. Journal of Clean Production, 45(4), 1–21.
60. Henseler, J., Ringle, C.M., & Sinkovics, R.R. (2009). The use of partial last squares path modelling in ınternational marketing. Advances in International Marketing, 20, 277–319.
61. Hu, J-H.P., Lin, C., & Chen, H. (2005). User acceptance of intelligence and security informatics technology: A study of COPLINK. Journal of the American Society for Information Science and Technology, 56(3), 235–244.
62. Huang, E. (2005). The acceptance of women-centric websites. The Journal of Computer Information Systems, 45(4), 75–83.
63. Huq, S.M. (2014). The role of artificial ıntelligence in the development of accounting systems: A review. The IUP Journal of Accounting Research and Audit Practices, 13, 7–19.
64. Hurley, R., & Hult, G. (1998). Innovation, market orientation, and organizational learning: An integration and empirical examination. Journal of Marketing, 62, 42–54.
65. Im, J.Y., & Hancer, M. (2014). Shaping travelers' attitude toward travel mobile applications. Journal of Hospitality and Tourism Technology, 5(2), 177–193.
66. Johnson, D.G., & Verdicchio, M. (2017). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267–2270.
67. Kamarulzaman, Y. (2007). Adoption of travel e-shopping in the UK. International Journal of Retail & Distribution Management, 35(9), 703–719.
68. Karahanna, E., Agarwal, R., & Angst, C.M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30(4), 781–804.
69. Karahanna, E., Straub, W.D., & Norman, L.C. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213.
70. Kim, C., Mirusmonov, M., Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322.
71. Kim, D., & Chang, H. (2007). Key functional characteristics in designing and operating health ınformation websites for user satisfaction: An application of the extended technology acceptance model. International Journal of Medical Informatics, 76, 790–800.
72. Kim, J., & Forsythe, S. (2008). Adoption of virtual try-on technology for online apparel shopping. Journal of Interactive Marketing, 22(2), 45–59.
73. King, W.R., & He, J. (2006). A Meta Analysis of the technology acceptance model. Information & Management, 43, 740–755.
74. Kokina, J., & Davenport, T.H. (2017). The emergence of artificial ıntelligence: How automation ıs changing auditing. Journal of Emerging Technologies in Accounting, 14, 115–122.
75. Krejcie, R.V. & Morgan, D.W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.
76. Kumar Doshi, H.A., Balasingam, S., & Arumugam, D. (2020). Artificial intelligence as a paradoxical digital disruptor in the accounting profession: An empirical study amongst accountants. International Journal of Psychosocial Rehabilitation, 24, 873–885.
77. Kureljusic, M. & Karger, E. (2024). Forecasting in financial accounting with artificial intelligence– A systematic literature review and future research agenda. Journal of Applied Accounting Research, 25(1), 81–104.
78. Lam, M. (2004). Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis. Decision Support Systems, 37, 567–581.
79. Lee, C.S., & Tajudeen, F.P. (2020). Usage and ımpact of artificial ıntelligence on accounting: 213 evidence from Malaysian organisations. Asian Journal of Business and Accounting, 13, 213–240.
80. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191–204.
81. Leong, L.-Y., Hew, T.-S., Tan, G. W.-H., & Ooi, K.-B. (2013). Predicting the determinants of the nfc-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604–5620.
82. Li, C., Haohao, S., & Ming, F. (2020). Research on the impact of artificial intelligence technology on accounting. Journal of Physics Conference Series, 1486(3), 1–6.
83. Li, J., & Huang, J.S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63(9), 1–10.
84. Lin, H.-F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433–442.
85. Lin, J.C.C., & Lu, H. (2000). Towards an understanding of the behavioral intention to use a web site. International Journal of Information Management, 20(3), 197–208.
86. Lu, H.-P., & Su, P. Y.-J. (2009). Factors affecting purchase ıntention on mobile shopping web sites. Internet Research, 19(4), 442458.
87. Lu, J., Yu, C.S.; Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless ınternet. Internet Research, 13(3), 206–222.
88. Lumpkin, G.T., & Dess, G.G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. The Academy of Management Review, 21(1), 135–172.
89. Mathieson, K., (1991). Predicting user intentions: Comparing the technology model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.
90. Mazis, M.B., Ahtola, O.T., & Klippel, R.E. (1975). A Comparison of four multi-attribute models in the prediction of consumer attitudes. Journal of Consumer Research, 2, 38–52.
91. Meservy, R.D., Denna, E.L., & Hansen, J.V. (1992). Application of artificial ıntelligence to accounting, tax, and audit services: Research at Brigham Young University. Expert Systems with Applications7, 4, 213–218.
92. Meuter, M.L., Ostrom, A.L., Bitner, M.J. & Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service Technologies. Journal of Business Research, 56, 899–906.
93. Mohammad, S.J., Hamad, A.K., Borgi, H., Thu, P., Sial, M.S., & Alhadidi, A.A. (2020). How artificial intelligence changes the future of accounting industry. International Journal of Economics and Business Administration, 8, 478–488.
94. Moon, J.W., & Kim, Y.G. (2001). Extending the TAM for a world-wide-web context. Information and Management, 38(4), 217–230.
95. Moon, J-W., & Young-Gul, K. (2001). Extending the TAM for a world-wide-web context. Information & Management, 38(4), 217–230.
96. Moore, G.C., & Benbasat, I. (1996). Integrating diffusion of innovations and theory of reasoned action models to predict utilization of information technology by end-users. In: Kautz, K., Pries-Heje, J. (eds) Diffusion and Adoption of Information Technology. IFIP — The International Federation for Information Processing. Springer, Boston, MA. Nascimento, J.C.H.B. & Macedo, M.A.S. (2016). Structural equation models using partial least squares: An example of the application of Smartpls® in accounting Research. REPeC, 10(3), 282–305.
97. Natarajan, T., Balasubramanian, S.A., & Kasilingam, D.L. (2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37, 8–22.
98. Nunnally, J.C. & Bernstein, I.H. (1994). The Assessment of Reliability. Psychometric Theory, 3, 248–292.
99. O’Leary, D.E. (1995). AI in accounting, finance and management. Intelligent Systems in Accounting, Finance and Management, 4, 149–153.
100. O’Leary, D.E., & O’Keefe, R.M. (1997). The impact of artificial intelligence in accounting work: Expert systems use in auditing and tax. AI & Society, 11, 36–47.
101. O’Shaughnessy, M.R., Schiff, D.S., Varshney, L.R., Rozell, C.J., & Davenport, M.A. (2022). What governs attitudes toward? Artificial intelligence adoption and governance? Science & public policy, August, 1–81.
102. Omoteso, K. (2012). The application of artificial ıntelligence in auditing: Looking back to the future. Expert Systems with Applications, 39, 8490–8495.
103. Otar, İ. (2005). Muhasebede “Muzaaf Usul” L. Pacioli’den önce var mıydı?-1. Muhasebe ve Finansman Dergisi, 28, 97–110.
104. Özbek, A. (2024). Muhasebe meslek mensuplarının yapay zekâ kaygılarının gelecekte istihdam edilebilirlik algıları üzerine bir çalışma. Alanya Akademik Bakış Dergisi, 8(1), 254–267.
105. Özcan, M., & Günlük, M. (2021). Muhasebe meslek mensuplarının Türkiye Finansal Raporlama Standartlarını kabullerinin teknoloji kabul modeli çerçevesinde incelenmesi. İşletme Akademisi Dergisi, 2(1), 76–93.
106. Özer, G., Özcan, M., & Aktaş Güzel, S. (2010). Muhasebecilerin Bilgi Teknolojisi Kullanımın Teknoloji Kabul Modeli TKM ile İncelenmesi. Journal of Yasar University, 5(19), 3278–3293.
107. Parasuraman, A. & Colby, C.L. (2015). An Updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59–74.
108. Phonthanukitithaworn, C., Sellitto, C., & Fong, M.W. (2016). An investigation of mobile payment (M-payment) services in Thailand", Asia-Pacific Journal of Business Administration, 8(1), 37–54.
109. Plouffe, C.R., Hulland, J.S., & Vandenbosch, M. (2001). Research Report: Richness versus parsimony in modeling technology adoption decisions—understanding merchant adoption of a smart card-based payment system. Information Systems Research, 12(2), 208–222.
110. Radhakrishnan, J., & Chattopadhyay, M. (2020). Determinants and barriers of artificial intelligence adoption – a literature review. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Rana, N.P. (eds) Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. TDIT 2020. IFIP Advances in Information and Communication Technology, 617, 89–99.
111. Reddy, P.S., Yasaswi, K.R.K., & Kumar, B.K. (2019). Accounting intelligence-The new era in accounting. Journal of Information and Computational Science, 9, 692–697.
112. Rogers, E.M. (1962). Diffusion of Innovations. Glencoe: Free Press.
113. Rogers, E.M. (1995). Diffusion of innovations (4th ed.), New York: The Free Press.
114. Saade, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42, 317–327.
115. Saadé, R.G., & Kira, D. (2009). Computer Anxiety in E-Learning: The Effect of Computer Self-Efficacy. Journal of Information Technology Education, 8, 177–191.
116. Sarstedt, M., Ringle, C.M., & Hair, J.F. (2017). Partial least squares structural equation modeling (Eds. Christian Homburg, Martin Klarmann, Arnd Vomberg), Handbook of Market Research, Springer International Publishing, 1–40.
117. Serçemeli, M. (2018). Muhasebe ve denetim mesleklerinin dijital dönüşümünde yapay zekâ. Journal of Turkish Studies, 13(30), 369–386.
118. Shih, H.P. (2004). An empirical study on predicting user acceptance of e-shopping on the web. Information & Management, 41, 351–368.
119. Srinivasan, R. (2015). Exploring the impact of social norms and online shopping anxiety in the adoption of online apparel shopping by Indian consumers. Journal of Internet Commerce, 14(2), 177–199.
120. Streukens, S., &Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by step guide to get more out of your bootstrap results. European Management Journal, 34(6), 618–632.
121. Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85–92.
122. Taylor, S., & Todd, P. (1995). Understanding Household Garbage Reduction Behavior: A Test of an Integrated Model. Journal of Public Policy & Marketing, 14(2), 192–204.
123. Ucoglu, D. (2020). Current machine learning applications in accounting and auditing. Pressacademia, 12, 1–7.
124. Ulukapı Yılmaz, H., & Yılmaz, A. (2024). Dijital çağın potansiyel çalışanlarının yapay zekâ kaygılarının belirlenmesi. Business and Economics Research Journal, 15(2), 171–188.
125. Ursavaş, Ö., Şahin, S. & Mcılroy, D. (2014). Technology acceptance measure for teachers: T-TAM / Öğretmenler için teknoloji kabul ölçeği: Ö-TKÖ. Eğitimde Kuram ve Uygulama, 10(4), 885–917.
126. Venkatesan, S., & Sumathi, N. (2019). Technology acceptance of artificial intelligence in banking and its impact on banking efficiency. International Journal of Innovative Technology and Exploring Engineering, 8(7C2), 371–374.
127. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
128. Venkatesh, V., & Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies, Management Science, 46(2), 186–204.
129. Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of ınformation technology: toward a unified view. MIS Quarterly, 27(3), 425–478.
130. Wen, C., Prybutok, V.R., Xu, C. (2011). An integrated model for customer online repurchase intention. Journal of Computer Information Systems, 52(1), 14–23.
131. Wigfield, A. (1994). Expectancy-value theory of achievement motivation: A developmental perspective. Educational Psychology Review, 6(1), 49–78.
132. Wigfield, A., & Eccles, J. (1992). The development of achievement task values: A theoretical analysis. Developmental Review, 12, 265–310.
133. Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719–729.
134. Yang, D.C., & Vasarhelyi, M.A. (1995). The application of expert systems in accounting. http://raw.rutgers.edu/MiklosVasarhelyi/Resume%20Articles/CHAPTERS%20IN%20BOOKS/C10.%20expert%20systems%20app%20in%20act.pdf
135. Yücel, G., & Adiloğlu, B. (2019). Dijitalleşme-yapay zekâ ve muhasebe beklentiler. Muhasebe ve Finans Tarihi Araştırmaları Dergisi, 17, 47–60.
136. Zaltman, G., Duncan, R., & Holbek, J. (1973). Innovations and Organizations. London: John Wiley & Sons.
137. Zemánková, A. (2019). Artificial intelligence and blockchain in audit and accounting: Literature review. WSEAS Transactions on Business and Economics, 16, 568–581.
138. Zhang, B., & Dafoe, A. (2019). Artificial Intelligence: American Attitudes and Trends. SSRN Electronic Journal, January, 1–110.
139. Zhang, Y., Xiong, F., Xie, Y., Fan, X., & Gu, H. (2020). The impact of artificial intelligence and blockchain on the accounting profession. IEEE Access, 8, 110461–110477.