Overcoming the Barriers to Machine Learning Adoption in External Auditing: Insights from Auditors' Perspectives
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Authors
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Fazıl Efstath H
a. College of Business, Zayed University, United Arab Emirates
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Musyokha S
b. Faculty of Business, Higher Colleges of Technology, Zayed University, United Arab Emirates
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Silfa Sain Steva
c. Dell Inc., Av. Industrial Belgraf 400, Eldorado do Sul, Rio Grande do Sul, Brazil
DOI:
Keywords:
Machine learning, Audit, Adoption barriers, Technology, External auditorAbstract
Objective: However, all these practices provide very positive results; auditors still have some barriers in use ML in their work paper, so this study aims not only to surround these barriers, but also to know what auditors think about that. Identifying these barriers is thus critical to the advancement of technology and increased auditing efficiency.
Methods: A qualitative research design was used and auditors from various backgrounds were interviewed using semi-structured interview techniques in order to gain insights. A thematic analysis of the data was conducted to identify the key challenges and perceptions related to ML adoption in auditing.
Results: The results show that although auditors appreciate the opportunities that ML provides in terms of improving audit quality and productivity, there are several organizational, technical, professional barriers to widespread adoption. Some of these factors are the complexity of ML tools, lack of training, resistance to change, and regulatory uncertainty. In addition, the research emphasizes the significance of organizational support and the necessity of customized ML solutions to meet the unique demands of auditing activities.
Novelty: This study's novelty lies in addressing the barriers which have been less reviewed in the literature of ML implementation in external auditing. The study provides valuable insights into auditors' perspectives on the adoption of technology, especially in a rapidly changing professional landscape.
Research Implications: This study offers important implications for policymakers, audit firms, and technology developers, outlining recommendations for overcoming the barriers identified. These involve training programs, communication on ML benefits, and regulatory frameworks that foster technology innovation in auditing.
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References
Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2022). Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges. Computer Science Review, 43, 100452. https://doi.org/https://doi.org/10.1016/j.cosrev.2021.100452 DOI: https://doi.org/10.1016/j.cosrev.2021.100452
Asif, M., Searcy, C., & Castka, P. (2022). Exploring the role of industry 4.0 in enhancing supplier audit authenticity, efficacy, and cost effectiveness. Journal of Cleaner Production, 331, 129939. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.129939 DOI: https://doi.org/10.1016/j.jclepro.2021.129939
Asif, M., Searcy, C., & Castka, P. (2023). ESG and Industry 5.0: The role of technologies in enhancing ESG disclosure. Technological Forecasting and Social Change, 195, 122806. https://doi.org/https://doi.org/10.1016/j.techfore.2023.122806 DOI: https://doi.org/10.1016/j.techfore.2023.122806
Bavaresco, R. S., Nesi, L. C., Victória Barbosa, J. L., Antunes, R. S., da Rosa Righi, R., da Costa, C. A., Vanzin, M., Dornelles, D., Junior, S. C., Gatti, C., Ferreira, M., Silva, E., & Moreira, C. (2023). Machine learning-based automation of accounting services: An exploratory case study. International Journal of Accounting Information Systems, 49, 100618. https://doi.org/https://doi.org/10.1016/j.accinf.2023.100618 DOI: https://doi.org/10.1016/j.accinf.2023.100618
Chong, S., Lee, S., Kim, B., & Kim, J. (2020). Applications of machine learning in metal-organic frameworks. Coordination Chemistry Reviews, 423, 213487. https://doi.org/https://doi.org/10.1016/j.ccr.2020.213487 DOI: https://doi.org/10.1016/j.ccr.2020.213487
COMMERFORD, B. P., DENNIS, S. A., JOE, J. R., & ULLA, J. W. (2022). Man Versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence. Journal of Accounting Research, 60(1), 171–201. https://doi.org/https://doi.org/10.1111/1475-679X.12407 DOI: https://doi.org/10.1111/1475-679X.12407
Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938–985. https://doi.org/10.1007/s11142-022-09697-x DOI: https://doi.org/10.1007/s11142-022-09697-x
Feliciano, C., & Quick, R. (2022). Innovative Information Technology in Auditing: Auditors’ Perceptions of Future Importance and Current Auditor Expertise. Accounting in Europe, 19(2), 311–331. https://doi.org/10.1080/17449480.2022.2046283 DOI: https://doi.org/10.1080/17449480.2022.2046283
Georgiou, D., Trikoili, A., & Kester, L. (2023). Rethinking determinants of primary school teachers’ technology acceptance during the COVID-19 pandemic. Computers and Education Open, 4, 100145. https://doi.org/https://doi.org/10.1016/j.caeo.2023.100145 DOI: https://doi.org/10.1016/j.caeo.2023.100145
Haleem, A., Javaid, M., Singh, R. P., Rab, S., & Suman, R. (2021). Hyperautomation for the enhancement of automation in industries. Sensors International, 2, 100124. https://doi.org/https://doi.org/10.1016/j.sintl.2021.100124 DOI: https://doi.org/10.1016/j.sintl.2021.100124
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, 100598. https://doi.org/https://doi.org/10.1016/j.accinf.2022.100598 DOI: https://doi.org/10.1016/j.accinf.2022.100598
Hartmann, B., Reuter, C., & Strauss, E. (2023). Controlling big data? Unfolding the organisational quest for IT-enabled competitive advantage. Scandinavian Journal of Management, 39(3), 101282. https://doi.org/https://doi.org/10.1016/j.scaman.2023.101282 DOI: https://doi.org/10.1016/j.scaman.2023.101282
Henriques, J., Caldeira, F., Cruz, T., & Simões, P. (2023). A forensics and compliance auditing framework for critical infrastructure protection. International Journal of Critical Infrastructure Protection, 42, 100613. https://doi.org/https://doi.org/10.1016/j.ijcip.2023.100613 DOI: https://doi.org/10.1016/j.ijcip.2023.100613
Hunt, J. O. S., Rosser, D. M., & Rowe, S. P. (2021). Using machine learning to predict auditor switches: How the likelihood of switching affects audit quality among non-switching clients. Journal of Accounting and Public Policy, 40(5), 106785. https://doi.org/https://doi.org/10.1016/j.jaccpubpol.2020.106785 DOI: https://doi.org/10.1016/j.jaccpubpol.2020.106785
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212. https://doi.org/https://doi.org/10.1016/j.techsoc.2019.101212 DOI: https://doi.org/10.1016/j.techsoc.2019.101212
Katebi, A., Homami, P., & Najmeddin, M. (2022). Acceptance model of precast concrete components in building construction based on Technology Acceptance Model (TAM) and Technology, Organization, and Environment (TOE) framework. Journal of Building Engineering, 45, 103518. https://doi.org/https://doi.org/10.1016/j.jobe.2021.103518 DOI: https://doi.org/10.1016/j.jobe.2021.103518
Krieger, F., Drews, P., & Velte, P. (2021). Explaining the (non-) adoption of advanced data analytics in auditing: A process theory. International Journal of Accounting Information Systems, 41, 100511. https://doi.org/https://doi.org/10.1016/j.accinf.2021.100511 DOI: https://doi.org/10.1016/j.accinf.2021.100511
Kroon, N., Alves, M. do C., & Martins, I. (2021). The Impacts of Emerging Technologies on Accountants’ Role and Skills: Connecting to Open Innovation—A Systematic Literature Review. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 163. https://doi.org/https://doi.org/10.3390/joitmc7030163 DOI: https://doi.org/10.3390/joitmc7030163
Liu, Y., Esan, O. C., Pan, Z., & An, L. (2021). Machine learning for advanced energy materials. Energy and AI, 3, 100049. https://doi.org/https://doi.org/10.1016/j.egyai.2021.100049 DOI: https://doi.org/10.1016/j.egyai.2021.100049
Maroun, W., & Duboisée de Ricquebourg, A. (2024). How auditors identify and report key audit matters - An organizational routines perspective. The British Accounting Review, 56(2), 101263. https://doi.org/https://doi.org/10.1016/j.bar.2023.101263 DOI: https://doi.org/10.1016/j.bar.2023.101263
Meredith, K., Blake, J., Baxter, P., & Kerr, D. (2020). Drivers of and barriers to decision support technology use by financial report auditors. Decision Support Systems, 139, 113402. https://doi.org/https://doi.org/10.1016/j.dss.2020.113402 DOI: https://doi.org/10.1016/j.dss.2020.113402
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The Ethical Implications of Using Artificial Intelligence in Auditing. Journal of Business Ethics, 167(2), 209–234. https://doi.org/10.1007/s10551-019-04407-1 DOI: https://doi.org/10.1007/s10551-019-04407-1
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/https://doi.org/10.1016/j.sciaf.2024.e02281 DOI: https://doi.org/10.1016/j.sciaf.2024.e02281
Oala, L., Murchison, A. G., Balachandran, P., Choudhary, S., Fehr, J., Leite, A. W., Goldschmidt, P. G., Johner, C., Schörverth, E. D. M., Nakasi, R., Meyer, M., Cabitza, F., Baird, P., Prabhu, C., Weicken, E., Liu, X., Wenzel, M., Vogler, S., Akogo, D., … Wiegand, T. (2021). Machine Learning for Health: Algorithm Auditing & Quality Control. Journal of Medical Systems, 45(12), 105. https://doi.org/10.1007/s10916-021-01783-y DOI: https://doi.org/10.1007/s10916-021-01783-y
Oluleye, B. I., Chan, D. W. M., & Antwi-Afari, P. (2023). Adopting Artificial Intelligence for enhancing the implementation of systemic circularity in the construction industry: A critical review. Sustainable Production and Consumption, 35, 509–524. https://doi.org/https://doi.org/10.1016/j.spc.2022.12.002 DOI: https://doi.org/10.1016/j.spc.2022.12.002
Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., & Pedreschi, D. (2021). FairLens: Auditing black-box clinical decision support systems. Information Processing & Management, 58(5), 102657. https://doi.org/https://doi.org/10.1016/j.ipm.2021.102657 DOI: https://doi.org/10.1016/j.ipm.2021.102657
Plant, K., Barac, K., & Sarens, G. (2019). Preparing work-ready graduates – skills development lessons learnt from internal audit practice. Journal of Accounting Education, 48, 33–47. https://doi.org/https://doi.org/10.1016/j.jaccedu.2019.06.001 DOI: https://doi.org/10.1016/j.jaccedu.2019.06.001
Qian, T. Y., Yu, B., Matz, R., Luo, L., & Xu, C. (2023). Gamification for consumer loyalty: An exploration of unobserved heterogeneity in gamified esports social live streaming. Telematics and Informatics, 85, 102062. https://doi.org/https://doi.org/10.1016/j.tele.2023.102062 DOI: https://doi.org/10.1016/j.tele.2023.102062
Sawrikar, V., & Mote, K. (2022). Technology acceptance and trust: Overlooked considerations in young people’s use of digital mental health interventions. Health Policy and Technology, 11(4), 100686. https://doi.org/https://doi.org/10.1016/j.hlpt.2022.100686 DOI: https://doi.org/10.1016/j.hlpt.2022.100686
Shant Priya, S., Jain, V., Priya, M. S., Dixit, S. K., & Joshi, G. (2023). Modelling the factors in the adoption of artificial intelligence in Indian management institutes. Foresight, 25(1), 20–40. https://doi.org/10.1108/FS-09-2021-0181 DOI: https://doi.org/10.1108/FS-09-2021-0181
Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960–967. https://doi.org/https://doi.org/10.1016/j.promfg.2018.03.137 DOI: https://doi.org/10.1016/j.promfg.2018.03.137
Taherdoost, H. (2019). What Is the Best Response Scale for Survey and Questionnaire Design; Review of Different Lengths of Rating Scale / Attitude Scale / Likert Scale. International Journal of Academic Research in Management (IJARM), 8(1), 2296–1747. https://hal.science/hal-03741841
Thapa, C., & Camtepe, S. (2021a). Precision health data: Requirements, challenges and existing techniques for data security and privacy. Computers in Biology and Medicine, 129. https://doi.org/10.1016/j.compbiomed.2020.104130
Thapa, C., & Camtepe, S. (2021b). Precision health data: Requirements, challenges and existing techniques for data security and privacy. Computers in Biology and Medicine, 129, 104130. https://doi.org/https://doi.org/10.1016/j.compbiomed.2020.104130 DOI: https://doi.org/10.1016/j.compbiomed.2020.104130
Tiberius, V., & Hirth, S. (2019). Impacts of digitization on auditing: A Delphi study for Germany. Journal of International Accounting, Auditing and Taxation, 37, 100288. https://doi.org/https://doi.org/10.1016/j.intaccaudtax.2019.100288 DOI: https://doi.org/10.1016/j.intaccaudtax.2019.100288
Xia, M., Zhang, Y., & Zhang, C. (2018). A TAM-based approach to explore the effect of online experience on destination image: A smartphone user’s perspective. Journal of Destination Marketing & Management, 8, 259–270. https://doi.org/https://doi.org/10.1016/j.jdmm.2017.05.002 DOI: https://doi.org/10.1016/j.jdmm.2017.05.002
Xiao, T., Geng, C., & Yuan, C. (2020). How audit effort affects audit quality: An audit process and audit output perspective. China Journal of Accounting Research, 13(1), 109–127. https://doi.org/https://doi.org/10.1016/j.cjar.2020.02.002 DOI: https://doi.org/10.1016/j.cjar.2020.02.002
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