Niat Menggunakan Big Data Analytics pada Auditor Eksternal di KAP Kota Semarang

Kharisma Syifa Auliya, Atta Putra Harjanto

Abstract


Big Data Analytics (BDA) is a technology with significant potential to improve audit quality; however, its adoption among Audit Firms (AFs) in Indonesia remains relatively low, with only about 34% having implemented it. This study aims to analyze the factors influencing auditors’ intention to use BDA by using Technology Acceptance Model (TAM) and examining the mediating roles of perceived ease of use and perceived usefulness, which have often been overlooked in prior studies. The sample was obtained using a convenience sampling survey of external auditors working at AFs in Semarang, resulting in 124 questionnaires. The results show that training affects both perceived ease of use and perceived usefulness, while technostress only affects perceived ease of use. Furthermore, perceived usefulness significantly influences auditors’ intention to use BDA, whereas perceived ease of use does not have a significant effect. Mediation analysis indicates that perceived usefulness mediates the effect of training on the intention to use BDA. These findings provide practical implications for AFs to increase auditors’ intention to adopt BDA by investing in training and managing technostress to support optimal technology adoption.


Keywords


Big Data Analytics; Intention to Use; Technology Acceptance Model

References


Abdelwahed, A. S., Abu-Musa, A. A., Moubarak, H., & Badawy, H. A. (2024). The Use of Big Data and Analytics in External Auditing: Does Audit Firm Size Matter? Evidence from a Developing Country. South African Journal of Accounting Research, 38(2), 113–145. https://doi.org/10.1080/10291954.2023.2279751

Al Marafi, S. H., Amran, N. A., & Rasit, M. H. H. (2025). Exploring External Auditors’ Intention to Adopt Big Data‎Analytics: The Moderating Role of Perceived Risk. International Journal of Accounting and Economics Studies, 12(4), 64–73. https://doi.org/10.14419/19qcsm78

Al Rob, M. A., Mohd Nor, M. N., Salleh, Z., & Khalaf, A. M. (2025). Factors Affecting Auditors’ Decisions to Adopt Big Data Analytics: A Mixed Method Study. Retos (Ecuador), 15(29), 29–46. https://doi.org/10.17163/ret.n29.2025.02

Al Rob, M. A., Nor, M. N. M., & 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

Al Rob, M. A., Nor, M. N. M., & 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

Al Shurafat, H., Al-Mawali, H., & Al Shbail, M. O. (2023). The Influence of Technostress on the Intention to Use Blockchain Technology: The Perspectives of Jordanian Auditors. Development and Learning in Organizations, 37(3), 24–27. https://doi.org/10.1108/DLO-06-2022-0103

Alenezi, A. R., Karim, A. M. A., & Veloo, A. (2011). Institutional Support and E-Learning Acceptance: An Extension of the Technology Acceptance Model. International Journal of Instructional Technology and Distance Learning. https://scispace.com/pdf/institutional-support-and-e-learning-acceptance-an-extension-1r8cr8pruu.pdf

Aprilia, A., & Rosadi, S. (2025). Pengaruh Teknologi Informasi dan Integritas Profesionalitas Kerja pada Peningkatan Kinerja Seorang Auditor. Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah, 7(4). https://doi.org/10.47467/alkharaj.v7i4.7733

Arildsen, V. (2018). Big Data Analytics Pays Off, New Research Shows. IT University of Copenhagen. https://en.itu.dk/About-ITU/Press/News-from-ITU/2018/Big-Data-Analytics-pays-off-new-research-shows

Brock, V., & Khan, H. U. (2017). Big Data Analytics: Do Organizational Factors Matter in Technology Acceptance? Journal of Big Data, 4(1), 1–28. https://doi.org/10.1186/s40537-017-0081-8

Christian, C., & Prasetyo, W. (2025). Pengaruh Penggunaan Big Data Analytics, Etika, Audit Fee, Dan Reputasi Kap Terhadap Kualitas Audit. INKUBIS: Jurnal Ekonomi Dan Bisnis. https://doi.org/10.59261/inkubis.v7i1.100

Davis, F. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results [Massachusetts Institute of Technology]. https://www.researchgate.net/publication/35465050

Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008

Davis, F., Bagozzi, R. P., & 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., & 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

Farias, J. S., & Resende, M. M. (2020). Impact of Training on the Implementation of a New Electronic System and Acceptance of New Technologies in a Federal Institution of Higher Education. Revista de Administração Da UFSM, 13(4), 773–791. https://doi.org/10.5902/1983465932624

Gabbiadini, A., Paganin, G., & Simbula, S. (2023). Teaching After the Pandemic: The Role of Technostress and Organizational Support on Intentions to Adopt Remote Teaching Technologies. Acta Psychologica, 236. https://doi.org/10.1016/j.actpsy.2023.103936

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7

Kader, M. A. R. A., Aziz, N. N. A., Zaki, S. M., Ishak, M., & Hazudin, S. F. (2022). The Effect of Technostress on Online Learning Behaviour Among Undergraduates. Malaysian Journal of Learning and Instruction, 19(1), 183–211. https://doi.org/10.32890/mjli2022.19.1.7

Karunarathne, E. A. C. P., & Abeyratne, A. S. M. A. R. (2020). Role of Employee Training and Experience on the Adoption of Computerized Maintenance Management System. Strategic Management, 25(4), 3–16. https://doi.org/10.5937/straman2004003k

Khlaif, Z. N., Sanmugam, M., & Ayyoub, A. (2023). Impact of Technostress on Continuance Intentions to Use Mobile Technology. Asia-Pacific Education Researcher, 32(2), 151–162. https://doi.org/10.1007/s40299-021-00638-x

Kukul, V. (2023). Modelling the Spectrum of Technology Integration from Teacher Training to Usage Intention: Findings from a Two-Phase Study. Technology, Knowledge and Learning, 28(4), 1615–1633. https://doi.org/10.1007/s10758-023-09658-6

Lee, Y. K. (2021). Impacts of Digital Technostress and Digital Technology Self-Efficacy on Fintech Usage Intention of Chinese Gen Z Consumers. Sustainability (Switzerland), 13(9). https://doi.org/10.3390/su13095077

Levi, C., & Angelus, M. (2024). Exploring the Factors That Influence Indonesian Auditors’ Intention to Use Big Data Analytics: Application of the UTAUT Model with Perceived Risk and Trust. Journal of Applied Finance and Accounting, 11(2). https://doi.org/10.21512/jafa.v11i2.12558

Marler, J. H., Liang, X., & Dulebohn, J. H. (2006). Training and Effective Employee Information Technology Use. Journal of Management, 32(5), 721–743. https://doi.org/10.1177/0149206306292388

Mohanty, P. K., Sekhar, S. F. C., & Shahaida, P. (2022). Determinants of ERP Adoption, User Satisfaction, and User Engagement. International Journal of Information System Modeling and Design, 13(1). https://doi.org/10.4018/IJISMD.297044

Mushtaque, I., Awais-E-Yazdan, M., & Waqas, H. (2022). Technostress and Medical Students’ Intention to Use Online Learning During the COVID-19 Pandemic in Pakistan: The Moderating Effect of Computer Self-Efficacy. Cogent Education, 9(1). https://doi.org/10.1080/2331186X.2022.2102118

Olabode, O. E., Boso, N., Hultman, M., & Leonidou, C. N. (2022). Big data analytics capability and market performance: The roles of disruptive business models and competitive intensity. Journal of Business Research, 139, 1218. https://doi.org/10.1016/j.jbusres.2021.10.042

Putra, N. S., Ritchi, H., & Alfian, A. (2023). Hubungan Big Data Analytics Terhadap Kualitas Audit: Penerapan pada Instansi Pemerintah. Jurnal Riset Akuntansi Dan Keuangan, 11(1), 57–72. https://doi.org/10.17509/jrak.v11i1.55139

Putri, V. S., & Mahadian, A. B. (2021). Pengaruh Ekspektasi Kinerja, Ekspektasi Usaha, Pengaruh Sosial, dan Kondisi Pendukung terhadap Minat Pemanfaatan Aplikasi E-Clinic di Kota Bandung. Jurnal Ilmu Komunikasi, 1–12. https://openlibrarypublications.telkomuniversity.ac.id/index.php/management/article/view/14998

Rafael, H., Abednego, H., & Deniswara, K. (2024). Factors of Auditor’s Readiness in Using Big Data Analytics Empirical Study in Indonesia. Kurdish Studies, 12, 331–347. https://doi.org/10.53555/ks.v12i4.2961

Rahadi, D. R. (2023). Pengantar Partial Least Squares Structural Equation Model (PLS-SEM) (Wijonarko, Ed.). Lentera Ilmu Madani.

Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research Online, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784

Sellberg, C., & Susi, T. (2014). Technostress in the Office: A Distributed Cognition Perspective on Human-Technology Interaction. Cognition, Technology and Work, 16(2), 187–201. https://doi.org/10.1007/s10111-013-0256-9

Shbail, M. O. Al, Bani-Khalid, T. O., Ananzeh, H., Al-Hazaima, H., & Shbail, A. Al. (2023). Technostress Impact on the Intention to Adopt Blockchain Technology in Auditing Companies. Journal of Governance and Regulation, 12(3 Special Issue), 285–294. https://doi.org/10.22495/jgrv12i3siart10

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly: Management Information Systems, 27(3), 425–478. https://doi.org/10.2307/30036540




DOI: https://doi.org/10.24167/jab.v24i1.14842

Bookmark and Share


Print ISSN : 1412-775X | online ISSN : 2541-5204 JAB Stats