Adoption of emerging technologies in a regulated environment
A study in audit companies
DOI:
https://doi.org/10.17524/repec.v19.e3687Keywords:
Adoption of emerging technologies. TOE Structure. Audit.Abstract
Objective:
Analyze the moderation of the environmental context in the relationship between technological and organizational contexts and the adoption of emerging technologies by auditing companies in a regulated environment.
Method
A descriptive study was carried out, through survey research, of a quantitative nature, with the population being audit companies registered with the Securities and Exchange Commission, with a sample of 114 valid responses, through Structural Equation Modeling (PLS -SEM) and moderation using the “product indicator” method.
Results
The findings confirm the basic theory of this investigation, the Technology, Organization and Environment (TOE) structure, demonstrating that the technological, organizational and environmental context of a company influence decisions about the adoption of emerging technologies in auditing firms. Regarding the moderation of the environmental context, the results indicate that coercive and mimetic pressures do not moderate and normative pressure positively moderates the relationship between technological and organizational context and the adoption of new technologies.
Contribution
We highlight the empirical validation of the predictive power of the TOE structure in the adoption of emerging technologies in auditing companies and, in the theoretical field, the advancement and deepening of the discussion of the factors that influence the adoption of new technologies by auditing firms in the Brazilian environment.
Translation
This article is a translation in English of the article:
Adoção de tecnologias emergentes em ambiente regulado
References
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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.
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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.
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.
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https://doi.org/10.1080/00207543.2012.693961.
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https://doi.org/10.1093/rfs/hhz007.
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.
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.
Dagiliene, L. & Klovienė, 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.
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.
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.
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.
Flatten, T. C.; Engelen, A.; Zahra, S. A. & Brettel, M. (2011). A measure of absorptive capacity: Scale development and validation. European Management Journal, 29(2), 98-116.
https://doi.org/10.1016/j.emj.2010.11.002.
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
Freitas, C. A. S. & Guimarães, T. A. (2007). Isomorphism, Institutionalization and Legitimacy: Operational Auditing at the Court of Auditors. RAC, 153 - 175.
https://doi.org/10.1590/S1415-65552007000500008.
Grover, V. & Kohli, R. (2012). Cocreating IT value: New capabilities and metrics for multifirm environments. Mis Quarterly, 36(1).
https://doi.org/10.2307/41410415.
Hair, J. F. J.; Ringle, C. M. & Sarstedt, M. (2014). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning: Prentice-Hall.
http://dx.doi.org/10.1016/j.lrp.2013.01.001.
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.
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.
Henry, B. & Hicks, M. (2015). A Survey of Perspectives on the Future of the Accounting Profession. The CPA Journal; 85(8), 6-9.
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.
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.
Inghirami, I. E. (2018). Accounting Information Systems in the Time of Blockchain. Pávia: ITAIS Conference.
Kenny, D. A. & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96 (1), 201–210.
https://doi.org/10.1037/0033-2909.96.1.201.
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.
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.
Liang, H.; Saraf, N.; Hu, Q. & Xue, Y. (2007). Assimilation of Enterprise Systems: The Effect of Institutional Pressures and the Mediating Role of Top Management. Mis Quarterly, 31(1), 59-87.
https://doi.org/10.2307/25148781.
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.
Majchrzak, A.; Markus, M. L. & Wareham, J. (2016). Designing for digital transformation: Lessons for information systems research from the study of ICT and societal challenges. MIS Quarterly, 40(2), 267-277.
Mckinley, W. & Mone, M. (2003). Organization theory. Oxford University Press – USA.
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.
https://doi.org/10.2308/jeta-10589.
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.
Oliveira, R. C. R. (2017). Adoção de tecnologias da informação em micro, pequenas e médias empresas: um estudo a partir da adaptação do modelo technology, organization and environment (TOE) sob influência de fatores institucionais. Pesquisa de Doutorado - Programa de Pós-Graduação em Administração, Universidade Federal da Bahia, Bahia.
Oliveira, T. & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. The Electronic Journal Information Systems Evaluation, 14 (1), 110-121.
Oliveira, T.; Martins, R.; Sarker, S.; Thomas, M. & Popovič, 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.
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/AAAJ-10-2020-4991.
Pivar, J. (2020). Conceptual Model of Big Data Technologies Adoption in Smart Cities of the European Union. IRENET - Society for Advancing Innovation and Research in Economy, Zagreb, 572-585.
Rozario, A. M. & Vasarhelyi, M. A. (2018). Auditing with smart contracts. International Journal of Digital Accounting Research, 18(1), 1–27.
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Published
2025-03-31
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Lima de Araujo, P. G., Costa de Oliveira Araujo, L., & Façanha Camara, S. (2025). Adoption of emerging technologies in a regulated environment: A study in audit companies. Journal of Education and Research in Accounting (REPeC), 19. https://doi.org/10.17524/repec.v19.e3687
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