Factors affecting auditors’ decisions to adopt Big Data analytics: a mixed method study

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Published: Apr 8, 2025
Updated: 2025-04-08
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2025-04-08 (3)
DOI: https://doi.org/10.17163/ret.n29.2025.02
Keywords:
audit transformation, behavioral intentions, big data analytics, perceived ease of use, perceived usefulness, technology acceptance model, Palestine, Big Four

Main Article Content

Moath Abdelkarim Abu Al Rob
Universiti Malaysia Terengganu
https://orcid.org/0009-0005-7764-1126
Mohd Nazli Mohd Nor
Universiti Malaysia Terengganu
https://orcid.org/0000-0002-7946-3475
Zalailah Salleh
Universiti Malaysia Terengganu
https://orcid.org/0000-0001-7246-4160
Alia Majed Khalaf
Universiti Malaysia Terengganu
https://orcid.org/0000-0002-5415-0472

Abstract

The purpose of the present study was to address the primary research question: “To what extent do perceived ease of use (PEOU) and perceived usefulness (PU) explain auditors’ behavioral intentions (BI) to adopt big data analytics (BDA) in auditing firms in Palestine?" A mixed-method approach was utilized, integrating quantitative data from a census survey of 94 auditors at the Big Four accounting firms in Palestine, which achieved an 86% response rate, with qualitative data from semi-structured interviews conducted with 9 auditors at the managerial level or higher. This methodological combination strengthens the validity and reliability of the research outcomes. The findings revealed that PU directly influences auditors' intentions to adopt BDA, and PEOU also affects BI to adopt, but not with the same significance. The study’s findings supported the use of the Technology Acceptance Model (TAM), indicating that auditors are more likely to adopt BDA if they perceive it as useful for their audit firms and if it helps achieve the required efficiency.

Article Details

Issue
Vol. 15 No. 29 (2025): (April-September)
Section
Monographic section
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References

Ab Hamid, M. R., Sami, W. y Sidek, M. M. (2017, September). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series, 890(1), 012163). IOP Publishing. https://doi.org/10.1088/1742-6596/890/1/012163

Abu Al Rob, M., Mohd Nor, M. N. y Salleh, Z. (2024). The influence of big data analytics adoption on auditors' professional skepticism in risk assessment: An empirical study using the technology acceptance model. Journal of Logistics, Informatics and Service Science, 11(11), 158-177. https://doi.org/10.33168/JLISS.2024.1110

Abu Al Rob, M., Mohd Nor, M. N. y Salleh, Z. (2024). The role of training in Big Data Analytics adoption: an empirical study of auditors using the technology acceptance model. Electronic Journal of Business Research Methods, 22(2), 30-45. https://doi.org/10.34190/ejbrm.22.2.3752

Adrianto, Z. (2018). Auditing in the era of big data: a literature review. Jurnal Akuntansi dan Keuangan, 17(1), 1-6.‏

Al Amin, M., Nowsin, N., Hossain, I. y Bala, T. (2020). Impact of social media on consumer buying behaviour through online value proposition: A study on e-commerce business in Bangladesh. Academy of Strategic Management Journal, 19(5), 1-18. bit.ly/3EycBHG

Alyoussef, I. Y. y Al-Rahmi, W. M. (2022). Big data analytics adoption via lenses of Technology Acceptance Model: empirical study of higher education. Entrepreneurship and Sustainability Issues, 9(3), 399.‏ ‏ https://doi.org/10.9770/jesi.2022.9.3(24)

Ajibade, P. (2018). Technology acceptance model limitations and criticisms: Exploring the practical applications and use in technology-related studies, mixed-method, and qualitative researches. Library Philosophy and Practice, 9.‏

Braun, V. y Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77- 1001. https://doi.org/10.1191/1478088706qp063oa

Brock, V. y Khan, H. U. (2017). Big data analytics: does organizational factor matters impact technology acceptance? Journal of Big Data, 4(1), 1-28. https://doi.org/10.1186/s40537-017-0081-8

Bumblauskas, D., Nold, H., Bumblauskas, P. e Igou, A. (2017). Big data analytics: transforming data to action. Business Process Management Journal, 23(3), 703-720. https://doi.org/10.1108/BPMJ-03-2016-0056

Cabrera-Sánchez, J. P. y Villarejo-Ramos, A. F. (2020). Factors affecting the adoption of big data analytics in companies. Revista de Administração de Empresas, 59, 415-429.‏ https://doi.org/10.1590/S0034-759020190607.

Chopdar, P. K., Korfiatis, N., Sivakumar, V. J. y Lytras, M. D. (2018). Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the Unified Theory of Acceptance and Use of Technology. Computers in Human Behavior, 86, 109-128. https://doi.org/10.1016/j.chb.2018.04.017.

Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). SAGE Publications.

Dagilienė, L. y Klovienė, L. (2019). Motivation to use big data and big data analytics in external auditing. Managerial Auditing Journal. https://doi.org/10.1108/MAJ-01-2018-1773

Davis, F. D., Bagozzi, R. P. y Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.‏ https://doi.org/10.1287/mnsc.35.8.982

Davis, F. D. y Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International Journal of Human-Computer Studies, 45(1), 19-45. https://doi.org/10.1006/ijhc.1996.0040

Demoulin, N. T. y Coussement, K. (2020). Acceptance of text-mining systems: The signaling role of information quality. Information & Management, 57(1), 103120.‏ https://doi.org/10.1016/j.im.2018.10.006

DiCicco‐Bloom, B. y Crabtree, B. F. (2006). The qualitative research interview. Medical education, 40(4), 314-321. https://doi.org/10.1111/j.1365-2929.2006.02418.x

Diop, E. B., Zhao, S. y Duy, T. V. (2019). An extension of the technology acceptance model for understanding travelers’ adoption of variable message signs. PLoS one, 14(4), e0216007. https://doi.org/10.1371/journal.pone.0216007.

Eilifsen, A., Kinserdal, F., Messier, W. F. y McKee, T. E. (2020). An exploratory study into the use of audit data analytics on audit engagements. Accounting Horizons, 34(4), 75-103. https://doi.org/10.2308/HORIZONS-19-121.‏

Fornell, C. y Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104

Gangwar, H. (2020). Big Data Analytics usage and business performance: integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. Electronic Journal of Information Systems Evaluation, 23(1), 45-64.‏ https://doi.org/10.34190/EJISE.20.23.1.004

Glasofer, A. y Townsend, A. B. (2020). Determining the level of evidence: Nonexperimental research designs. Nursing 2020 Critical Care, 15(1), 24-27.‏ https://doi.org/10.1097/01.NURSE.0000731852.39123.e1

Grimaldo, J. R. y Uy, C. (2020). Factors affecting recruitment officers' intention to use online tools. Review of Integrative Business and Economics Research, 9, 194-208. https://bit.ly/4hUCQql

Hair, J. F., Risher, J. J., Sarstedt, M. y Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203

Henseler, J., Ringle, C. M. y Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. En New challenges to international marketing (Vol. 20, pp. 277-319). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014

Hossain, S. A., Bao, Y., Hasan, N. y Islam, M. F. (2020). Perception and prediction of intention to use online banking systems: An empirical study using extended TAM. International Journal of Research in Business and Social Science (2147-4478), 9(1), 112-126. https://doi.org/10.20525/ijrbs.v9i1.591

İdil, K. A. Y. A. y Akbulut, D. H. (2018). Big data analytics in financial reporting and accounting. Press Academia Procedia, 7(1), 256-259.‏ https://doi.org/10.17261/Pressacademia.2018.892

Khaldi, K. (2017). Quantitative, qualitative or mixed research: Which research paradigm to use? Journal of Educational and Social Research, 7(2), 15-30. https://doi:10.5901/jesr.2017.v7n2p15

Levy, P. S. y Lemeshow, S. (2013). Sampling of populations: Methods and applications (4th ed.). John Wiley & Sons.

Li, H. L. y Lai, M. M. (2011). Demographic differences and internet banking acceptance. MIS REVIEW: An International Journal, 16(2), 55-92.‏ https://doi.org/10.6131/MISR.201103_16(2).0003

Mei, Y. C. y Aun, N. B. (2019). Factors influencing consumers' perceived usefulness of M-Wallet in Klang valley, Malaysia. Review of Integrative Business and Economics Research, 8, 1-23.‏ https://bit.ly/4hLAhY1

Müller, S. D. y Jensen, P. (2017). Big data in the Danish industry: application and value creation. Business Process Management Journal.‏ https://doi.org/10.1108/BPMJ-01-2016-0017

No, W. G., Lee, K., Huang, F. y Li, Q. (2019). Multidimensional audit data selection (MADS): A framework for using data analytics in the audit data selection process. Accounting Horizons, 33(3), 127-140.‏ https://doi.org/10.2308/acch-52453

Ofori, D. y Appiah-Nimo, C. (2019). Determinants of online shopping among tertiary students in Ghana: An extended technology acceptance model. Cogent Business & Management, 6(1), 1644715.‏ https://doi.org/10.1080/23311975.2019.1644715

Olufemi, J. (2018). Considerations for the adoption of Cloud-based Big Data Analytics in SMALL BUSINESS ENTERPRISES. Electronic Journal of Information Systems Evaluation, 21(2), 63-79. https://bit.ly/4hP5NDW

Shahbaz, M., Gao, C., Zhai, L., Shahzad, F. y Hu, Y. (2019). Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. Journal of Big Data, 6(1), 1-20.‏ https://doi.org/10.1186/2047-2501-2-3.

Tarabasz, A. y Poddar, G. (2019). Factors influencing adoption of wearable devices in Dubai. Journal of Economics and Management, 36(2), 123-143.‏ https://bit.ly/4jKMa1w

Verma, S., Bhattacharyya, S. S. y Kumar, S. (2018). An extension of the technology acceptance model in the big data analytics system implementation environment. Information Processing & Management, 54(5), 791-806.‏ https://doi.org/10.1016/j.ipm.2018.01.004