STAP International Journal of Accounting and Business Intelligence

ISSN: 3105-3726

The Moderating Role of Institutional Pressures: Adoption of Emerging Technologies in Audit Firms under a Regulated Environment

by 

Mahmoud jaradat

PDF logoPDF

Published: 2025/05/21

Abstract

This study examines the determinants of emerging technology adoption (e.g., AI, blockchain, RPA, data analytics) in audit firms operating within a regulated environment, drawing on the Technology-Organization-Environment (TOE) framework. It analyzes the direct effects of technological competence, organizational absorptive capacity, and institutional pressures, and investigates the moderating role of the environmental context on these relationships. A quantitative survey design was employed, collecting data from 114 audit professionals. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), with measurement models ensuring reliability and validity and a structural model testing the hypotheses. The results validate the TOE framework, showing that organizational context (absorptive capacity) is the strongest predictor of adoption (β = 0.79, p < 0.001) followed by technological context (β = 0.67, p < 0.001) and environmental pressures (β = 0.34, p < 0.05). Crucially, normative pressure was found to be a significant positive moderator, while coercive and mimetic pressures were not. The study confirms that successful adoption in auditing hinges not just on technology but primarily on organizational learning capabilities and is significantly influenced by professional and regulatory norms. It offers practical insights for firms to prioritize capability building and for regulators to shape effective normative guidance, contributing to theory by integrating institutional and absorptive capacity perspectives into the TOE framework.

Keywords

Emerging TechnologiesTechnology AdoptionTOE FrameworkInstitutional TheoryAbsorptive CapacityAuditingPLS-SEM

The Moderating Role of Institutional Pressures: Adoption of Emerging Technologies in Audit Firms under a Regulated Environment is licensed under CC BY 4.0CCBY

References

  1. Al-Htaybat, K., & Von Alberti-Alhtaybat, L. (2017). Big Data and corporate reporting: impacts and paradoxes. Accounting, Auditing & Accountability Journal, 30(4), 850–873. https://doi.org/10.1108/AAAJ-07-2015-2139
  2. Ali, M., & Park, K. (2016). The mediating role of an innovative culture in the relationship between absorptive capacity and technical and non-technical innovation. Journal of Business Research, 69(5), 1669–1675. https://doi.org/10.1016/j.jbusres.2015.10.036
  3. Appelbaum, D., & Nehmer, R. A. (2017). Using drones in internal and external audits: An exploratory framework. Journal of Emerging Technologies in Accounting, 1, 99–113. http://10.2308/jeta-51704
  4. Arpaci, I., Yardimci, Y. C., Ozkan, S., & Turetken, O. (2012). Organizational, adoption of information technologies: a literature review. International Journal of eBusiness and eGovernment Studies, 4(2), 37–50.
  5. Austin, A., Carpenter, T, Christ, M & Nielson, C. (2019). The data analytics transformation: Evidence from auditors, CPOs, and standard-setters. SSRN. http://10.2139/ssrn.3214140
  6. Baron, R. M., & Kenny, D. A. (1986). The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. DOI: 10.1037//0022-3514.51.6.1173
  7. Barr-Pulliam, D., Brown-Liburd, H. L., & Sanderson, K. A. (2021). The effects of the internal control opinion and use of audit data analytics on perceptions of audit quality, assurance, and auditor negligence. Auditing: A Journal of Practice and Theory, 41(1), 25–48. https://doi.org/10.2308/AJPT-19-064
  8. Bradley, S. W., Shepherd, D. A., & Wiklund, J. (2011). The importance of slack for new organizations facing 'tough' environments. Journal of Management Studies, 48(5), 1071–1097. https://doi.org/10.1111/j.1467-6486.2009.00906.x
  9. Brown-Liburd, H., & Vasarhelyi, M. (2015). Big Data and Audit Evidence. Journal of Emerging Technologies in Accounting, 12(1), 1–16. https://10.2308/jeta-10468
  10. Carpenter, V. L., & Feroz, E. H. (2001). Institutional theory and accounting rule choice: An analysis of four US state governments' decisions to adopt generally accepted accounting principles. Acc. Organ. Soc., 26(7), 565–596. https://doi.org/10.1016/S0361-3682(00)00038-6
  11. Cohen, W., & Levinthal, D. (1990). Absorptive capacity, a new perspective on learning and innovation, Admin. Sci. Quart, 35(1), 128–152. https://doi.org/10.2307/2393553
  12. Cooper, L. A., Holderness, D. K., Sorensen, T. L., & Wood, D. A. (2019). Robotic Process Automation in Public Accounting. Accounting Horizons, 33(4), 15–35. http://dx.doi.org/10.2139/ssrn.3193222
  13. Coraiola, D. M, & Machado-da-Silva, C. L. (2008). A influência dos discursos no isomorfismo organizacional: as mudanças gráficas em jornais brasileiros. Revista Eletrônica de Ciência Administrativa, 7(2), 1-13.
  14. Cruz-Jesus, F., Pinheiro, A., & Oliveira, T. (2019) Understanding CRM adoption stages: Empirical analysis building on the TOE framework. Computers in Industry 109: 1–13. https://doi.org/10.1016/j.compind.2019.03.007
  15. Dagliene, L., & Kloviené, L. (2019). Motivation to use big data and big data analytics in external auditing. Managerial Auditing Journal, 34(7), 750–782. https://doi.org/10.1108/MAJ-01-2018-1773
  16. Dedoulis, E. (2016). Institutional formations and the Anglo-Americanization of local auditing practices: The case of Greece. Accounting Forum, 40(1), 29–44. https://doi.org/10.1016/j.accfor.2015.11.003
  17. Deloitte. (2024). How WestRock harnessed GenAI to enhance internal audit. Deloitte WSJ.
  18. Dimaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101
  19. Ding, K., Peng, X., Lev, B., Sun, T., & Vasarhelyi, M. A. (2020). Machine Learning Improves accounting estimates: evidence from insurance payments. Review of Accounting Studies 25, 1098-1123. https://doi.org/10.1007/s11142-020-09546-9
  20. Eilifsen, A., Kinserdal, F, Messier Jr & Mckee, T. E. (2020). An exploratory study into the use of audit data analytics on audit engagements. Accounting Horizons, 34(4). http://10.2308/HORIZONS-19-121
  21. EY. (2024). AI-based fraud detection in auditing. Financial Times.
  22. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing, 18(1), 39-59. https://doi.org/10.2307/3151312
  23. Glover, J. L., Champion, D., Daniels, K. J., & Dainty, A. J. D. (2014). An Institutional Theory perspective on sustainable practices across the dairy supply chain. International Journal of Production Economics, 152, 102-111. https://doi.org/10.1016/j.ijpe.2013.12.027
  24. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Danks, N. (2021). Partial least squares structural equation modeling (PLS-SEM) using R. Springer. https://doi.org/10.1007/978-3-030-80519-7
  25. Handoko, B. L. (2021). How audit firm size moderate effect of TOE context toward auditor adoption of machine learning. Journal of Theoretical and Applied Information Technology, 99(24), 5972-5980.
  26. Handoko, B. L., & Thomas, G. N. (2021). How audit firm size moderate effect of TOE context toward auditor adoption of technology. Turkish Journal of Computer and Mathematics Education, 12(6), 1518-1526.
  27. Henseler, J., & Fassott, G. (2010). Testing Moderating Effects in PLS Path Models: An Illustration of Available Procedures. Handbook of Partial Least Squares, 713-735.
  28. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  29. Hinings, B., Gegenhuber, T., & Greenwood, R. (2018). Digital innovation and transformation: An institutional perspective. Information and Organization, 28(1), 52–61. https://doi.org/10.1016/j.infoandorg.2018.02.004
  30. Huang, F., & Vasarhelyi, M. A. (2019). Applying robotic process automation (HUU) in auditing: A framework. International Journal of Accounting Information Systems, 35, 1-11. https://doi.org/10.1016/j.accinf.2019.100433
  31. Inghirami, I. E. (2018). Accounting Information Systems in the Time of Blockchain. Pávia: ITAIS Conference.
  32. KPMG. (2024). Most Australian companies now using AI for financial processes. The Australian.
  33. Kroon, N., Alves, M. 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), 2021. https://doi.org/10.3390/joitmc7030163
  34. Li, Q., & Vasarhelyi, M. (2018). Developing a Cognitive Assistant for the Audit Plan Brainstorming Session. The International Journal of Digital Accounting Research, 18, 119–140. https://doi.org/10.4192/1577-8517-v18_5
  35. Low, C., Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial management & data systems, 111(7), 1006-1023. https://doi.org/10.1108/02635571111161262
  36. Martins, R., & Oliveira, T. (2023). Digital literacy, absorptive capacity, and technology adoption in organizations. Information Systems Frontiers. https://doi.org/10.1007/s10796-023-10427-9
  37. Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A. (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1–10. DOI: 10.2308/jeta-10589
  38. Montes, G. A., & Goertzel, B. (2019). Distributed, decentralized, and democratized artificial intelligence. Technological Forecasting and Social Change,141, 354-358. https://doi.org/10.1016/j.techfore.2018.11.010
  39. Oliveira, T., Martins, R., Sarker, S., Thomas, M., & Popovic, A. (2019). Understanding SaaS adoption: The moderating impact of the environment context. International Journal of Information Management, 49, 1-12. https://doi.org/10.1016/j.ijinfomgt.2019.02.009
  40. PCAOB & IFAC. (2024). Guidelines on auditing standards and technology adoption.
  41. Pimentel, E., & Boulianne, E. (2020). Blockchain in Accounting Research and Practice: Current Trends and Future. Opportunities. Accounting Perspectives, 19(4), 325-361. https://doi.org/10.1108/AAAI-10-2020-4991
  42. Pivar, J. (2021) Adoption of big data technologies in smart cities of the European Union: Analysis of the importance and performance of technological factors. Croatian Regional Development Journal, 2(1), 11-29. DOI:10.2478/crdj-2021-0005
  43. Rahman, M. M., & Alsmadi, M. K. (2022). Institutional pressures and technology adoption: Evidence from developing countries. Technological Forecasting & Social Change, 182, 121841. https://doi.org/10.1016/j.techfore.2022.121841
  44. Rosli, K., Siew, E.G., & Yeow, P. H. P. (2016). Technological, Organizational and Environmental Aspects of Audit Technology Acceptance. International Journal of Business and Management, 11(5), 140-145. Doi: 10.5539/ijbm.v11n5p140
  45. Rozario, A. M., & Vasarhelyi, M. A. (2019a). Auditing with smart contracts. International Journal of Digital Accounting Research, 18(1), 1-27. https://doi.org/10.4192/1577-8517-v18_1
  46. Rozario, A. M., & Vasarhelyi, M. A. (2019b). How robotic process automation is transforming accounting and auditing. The CPA Journal, 88, 46-49. https://doi.org/10.20869/AUDITF/2020/160/024
  47. Salijeni, G., Samsonova-Taddei, A., & Turley, S. (2019). Big Data and Changes in Audit Technology: Contemplating a Research Agenda. Accounting and Business Research, 49(1), 95-119. https://doi.org/10.1080/00014788.2018.1459458
  48. Sancho-Zamora, R., Hernandez-Perlines, F., Pena-Garcia, I., & Gutierrez-Broncano, S. (2022). The Impact of Absorptive Capacity on Innovation: The Mediating Role of Organizational Learning. International Journal of Environment Research and Public Health, 19(2), 842. https://doi.org/10.3390/ijerph19020842
  49. Santos, F., Pereira, R., & Vasconcelos, J. B. (2020). Toward robotic process automation implementation: an end-to-end perspective. Business Process Management Journal, 26(2), 405-420. https://doi.org/10.1108/BPMJ-12-2018-0380
  50. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial least squares structural equation modeling: A useful tool for family business researchers. Journal of Family Business Strategy, 13(1), 100521. https://doi.org/10.1016/j.jfbs.2021.100521
  51. Scott, W. (2003). Organisation: Rational, Natural, and Open System (5th ed.). Upper Saddle River, NJ: Prentice Hall.
  52. Siew, E.G., Rosli, K., & Yeow, P.H.P. (2019). Organizational and environmental influences in the adoption of computer-assisted audit tools and tec. International Journal of Accounting Information Systems, 36, 100445. https://doi.org/10.1016/j.accinf.2019.100445
  53. Singh, A., Gupta, S., & Sharma, R. (2023). TOE framework and adoption of digital technologies in auditing. Journal of Business Research, 161, 113810. https://doi.org/10.1016/j.jbusres.2023.113810
  54. Sun, T. (2019). Applying Deep Learning to Audit Procedures: An Illustrative Framework. Accounting Horizons, 33(3), 89-109. https://doi.org/10.2308/acch-52455
  55. Tajudeen, F. P., Jaafar, N. I., & Ainin, S. (2017). Understanding the Impact of Social Media Usage among Organizations. Information & Management, 55(2018), 308–321. https://doi.org/10.1016/j.im.2017.08.004
  56. Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington – MA: Lexington Books.
  57. Tsou, H.-T., & Hsu, S. H.-Y. (2015). Performance effects of technology–organization–environment openness, service co-production, and digital-resource readiness: The case of the IT industry. International Journal of Information Management, 35(1), 1-14. https://doi.org/10.1016/j.jiinfomgt.2014.09.001
  58. Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: An overview. Accounting Horizons, 29(2), 381-396. https://doi.org/10.2308/acch-51071
  59. Vitali, M. (2024). Artificial intelligence in auditing: Opportunities and challenges. Journal of Emerging Technologies in Accounting, 21(2), 45–63. Elsevier. https://doi.org/10.1016/j.accinf.2024.100637
  60. Wamba, S. F., Dubey, R., & Gunasekaran, A. (2023). Dynamic capabilities and digital transformation: Implications for auditing. International Journal of Information Management, 70, 102572. https://doi.org/10.1016/j.ijinfomgt.2023.102572
  61. Warren, J. D., Moffitt, K. C., & Byrnes, P. (2015). How big data will change accounting. Accounting Horizons, 29(2), 397–407. https://doi.org/10.2308/acch-51069
  62. White, B. S., King, C. G., & Hollada, J. (2020). Blockchain security risk assessment and the auditor. Journal of Corporate Accounting & Finance, 31(2), 47–53. https://doi.org/10.1002/jcaf.22433
  63. Widuri, R., Oconnell, B., & Yapa, P. W. W. (2019). Adopting generalized audit software: an Indonesian perspective. Managerial Auditing Journal, 31(8/9), 821-847. https://doi.org/10.1108/MAJ-10-2015-1247
  64. Witte, A. L. (2020). Technology Based Audit Tools: Implications for Audit Quality (Tese de doutorado, Setor de Contabilidade, Bentley University). https://scholars.bentley.edu/cgi/viewcontent.cgi?article=1000&context=etd_2020
  65. Zhu, K., & Kraemer, K. L. (2005). Post-adoption variations in usage and value of e-business by organizations: cross-country evidence from the retail industry. Information systems Research, 16(1), 61–84. https://doi.org/10.1287/isre.1050.0045