Non-Accounting Drivers of Forensic Accounting Techniques: Insights from PLS-SEM Analysis
Abstract
Forensic accounting techniques are pivotal in combating financial fraud and enhancing corporate governance. According to Forensic Accounting Theory, both accounting and non-accounting factors influence the intention to adopt these techniques. This study explores the impact of key non-accounting factors i.e. Bonus Contract, Anonymity, and Collapse Avoidance on adoption of forensic accounting techniques by the practitioners, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) and SmartPLS software. Data was collected from professionals across diverse industries utilising forensic accounting services. The results reveal that these non-accounting factors exert varying levels of influence on adoption intentions. This research enriches the existing body of knowledge by offering new perspectives on the role of non-accounting drivers in forensic accounting adoption, providing actionable insights for policy-makers, regulators, and corporate leaders.
References
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[9] Azman, N.L.A. (2021). Structural Equation Modelling (SEM) Analysis in Determining the Mediation Effect of Collapse Avoidance Assurance on Threat Appraisal and Intention in Forensic Accountng. Asian Journal of Accounting and Finance, 3 (1): 1–9.
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[27] Hair, J. F., Risher, J. J., Sarstedt, M. and Ringle, C. M. (2019). When to Use and How to Report the Results of PLS-SEM. European Business Review, 31 (1): 2–24. DOI: https://doi.org/10.1108/EBR-11-2018-0203
[28] Hair, J.F., Sarstedt, M., Pieper, T. M. and Ringle, C. M. (2012). The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications. Long Range Planning, Analytical approaches to strategic management: Partial Least Squares modeling in strategy research, 45 (5): 320–40. DOI: https://doi.org/10.1016/j.lrp.2012.09.008
[29] Henseler, J., Ringle, C. M. and 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): 115–35. DOI: https://doi.org/10.1007/s11747-014-0403-8
[30] Henseler, J., Ringle, C. M. and Sinkovics, R. R. (2009). The Use of Partial Least Squares Path Modeling in International Marketing. In New Challenges to International Marketing, 20:277–319. Emerald Group Publishing Limited. DOI: https://doi.org/10.1108/S1474-7979(2009)0000020014
[31] Henseler, J., et al. (2014). “Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013) David J. Ketchen, Joseph F. Hair, G. Tomas M. Hult, Roger J. Calantone.” DOI:https://journals.sagepub.com/doi/10.1177/1094428114526928
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[33] Hult, G. et al. (2018). Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling. Journal of International Marketing, 26 (3): 1–21. DOI:https://doi.org/10.1509/jim.17.0151
[34] Jain, Dr E., and Lamba, J. (2020). FORENSIC ACCOUNTING: A WAY TO FIGHT, DETER AND DETECT FRAUD. IARS International Research Journal, 10 (1). DOI: https://doi.org/10.51611/iars.irj.v10i1.2020.106
[35] Koppl, R., and Sacks, M. 2013. The Criminal Justice System Creates Incentives for False Convictions. Criminal Justice Ethics, 32 (2): 126–62. DOI: https://doi.org/10.1080/0731129X.2013.817070
[36] Mardia, K. V. (1970). Measures of Multivariate Skewness and Kurtosis with Applications. Biometrika 57 (3): 519–30. DOI: https://doi.org/10.1093/biomet/57.3.519
[37] Maulidi, A. (2020). When and Why (Honest) People Commit Fraudulent Behaviours?: Extending the Fraud Triangle as a Predictor of Fraudulent Behaviours. Journal of Financial Crime, 27 (2): 541–59. DOI:https://doi.org/10.1108/JFC-05-2019-0058
[38] Muthuswamy, G. (2011). Behavioral Intention to Use Forensic Accounting Services for the Detection and Prevention of Fraud by Large Malaysian Companies. AUSTRALIA: Curtin University of Technology.
[39] Ng, S.W, et al. (2017). Behavioural Intention to Use Forensic Accounting Services among Small and Medium Enterprises in Malaysia. Eastern European Business and Economics Journal, 3 (4): 306–49.
[40] Nulty, D. D. (2008). The Adequacy of Response Rates to Online and Paper Surveys: What Can Be Done? Assessment & Evaluation in Higher Education, 33 (3): 301–14. DOI:https://doi.org/10.1080/02602930701293231
[41] Oni, S. O., and Godwin E. O. (2023). Forensic Accounting Evidence: Preservation and Admissibility. National Journal of Multidisciplinary Research and Development, 8 (2): 53–58.
[42] Onyango, G. (2024). How Mafia Like Bureaucratic Cartels or Thieves in Suits Run Corruption Inside the Bureaucracy, or How Government Officials Swindle Citizens in Kenya! Deviant Behavior, 45 (5): 689–707. DOI: https://doi.org/10.1080/01639625.2023.2263136
[43] Ozili, P.K. (2020). Forensic Accounting Theory. In Uncertainty and Challenges in Contemporary Economic Behaviour, edited by Ercan Özen and Simon Grima, 49–60. Emerald Publishing Limited. DOI:https://doi.org/10.1108/978-1-80043-095-220201005
[44] Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y. and Podsakoff, N. P. (2003). Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology, 88 (5): 879–903. DOI: https://doi.org/10.1037/0021-9010.88.5.879
[45] Rasheed, F., Said, J. and Khan, N. I. (2023). Evolution of Fraud-Related Theories: A Theoretical Review. Journal of Nusantara Studies (JONUS), 8 (3): 322–50. DOI:https://doi.org/10.24200/jonus.vol8iss3pp322-350
[46] Reinartz, W., Haenlein, M. and Henseler, J. (2009). An Empirical Comparison of the Efficacy of Covariance-Based and Variance-Based SEM. International Journal of Research in Marketing, 26 (4): 332–44. DOI:https://doi.org/10.1016/j.ijresmar.2009.08.001
[47] Reurink, A. (2018). FINANCIAL FRAUD: A LITERATURE REVIEW. Journal of Economic Surveys, 32 (5): 1292–1325. DOI: https://doi.org/10.1111/joes.12294
[48] Rezaee, Z. (2005). Causes, Consequences, and Deterence of Financial Statement Fraud. Critical Perspectives on Accounting, 16 (3): 277–98. DOI: https://doi.org/10.1016/S1045-2354(03)00072-8
[49] Robson, C., and McCartan, K. (2016). Real World Research. Wiley.
[50] Roszkowska, P. (2021). Fintech in Financial Reporting and Audit for Fraud Prevention and Safeguarding Equity Investments. Journal of Accounting & Organizational Change, 17 (2): 164–96. DOI:https://doi.org/10.1108/JAOC-09-2019-0098
[51] Thompson, R., Barclay, D.W. and Higgins, C.A. (1995). The Partial Least Squares (PLS) Approach to Causal Modelling: Personal Computer Adoption and Use as an Illustration. Technologies Studies, 2 (2): 285–309.
[52] Young, S. D. (2020). Financial Statement Fraud: Motivation, Methods, and Detection. In Corporate Fraud Exposed, edited by H. Kent Baker, Lynnette Purda-Heeler, and Samir Saadi, 321–39. Emerald Publishing Limited. DOI: https://doi.org/10.1108/978-1-78973-417-120201021
[2] Aksoy, T., and Saban U. (2021). Relationship Between Fraud Auditing and Forensic Accounting. In Auditing Ecosystem and Strategic Accounting in the Digital Era, edited by Tamer Aksoy and Umit Hacioglu, 127–46. Contributions to Finance and Accounting. Cham: Springer International Publishing. DOI:https://doi.org/10.1007/978-3-030-72628-7_6
[3] Akwa I. et al. (2023). The Role of Forensic Accounting in Bridging the Audit Expectation Gap of Microfinance Banks in Akwa Ibom State. AKSU Journal of Administration and Corporate Governance, 3 (2): 49–62. DOI:https://doi.org/10.61090/aksujacog.2023.005
[4] Alabdullah, T. T.Y., Alfadhl, M.M.A., Yahya, S. and Rabi, A.M.A. (2013). The Role of Forensic Accounting in Reducing Financial Corruption: A Study in Iraq. International Journal of Business and Management, 9 (1). DOI: https://doi.org/10.5539/ijbm.v9n1p26
[5] Al Ghunaimi, H. (2023). How Forensic Accountants Add Value While Investigate Claims & Fraud. SSRN Electronic Journal, August. DOI: https://doi.org/10.2139/ssrn.4870537
[6] Alshurafat, H., Shbail, M.O.A. and Almuiet, M. (2024). Factors Affecting the Intention to Adopt IT Forensic Accounting Tools to Detect Financial Cybercrimes. International Journal of Business Excellence, 33 (2): 169–90. DOI: https://doi.org/10.1504/IJBEX.2024.139917
[7] Armstrong, A., and Francis, R.D. (2015). Protecting the Whistleblower. In Research Handbook on International Financial Crime, edited by Barry Rider. Edward Elgar Publishing. DOI:https://doi.org/10.4337/9781783475797.00063
[8] Azman, N.L.A., and Vaicondam, Y. (2020). Behavioral Intention in Forensic Accounting Services. International Journal of Psychosocial Rehabilitation, 24 (02): 1837–46. DOI:https://doi.org/10.37200/IJPR/V24I2/PR200485
[9] Azman, N.L.A. (2021). Structural Equation Modelling (SEM) Analysis in Determining the Mediation Effect of Collapse Avoidance Assurance on Threat Appraisal and Intention in Forensic Accountng. Asian Journal of Accounting and Finance, 3 (1): 1–9.
[10] Bagozzi, R. P., and Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16 (1): 74–94. DOI: https://doi.org/10.1007/BF02723327
[11] Bhasin, M. (2015). An Emperical Investigation of the Relevant Skills of Forensic Accountants: Experience of a Developing Economy. SSRN Electronic Journal. DOI: https://doi.org/10.2139/ssrn.2676519
[12] Botes, V., and Saadeh, A. (2018). Exploring Evidence to Develop a Nomenclature for Forensic Accounting. Pacific Accounting Review, 30 (2): 135–54. DOI: https://doi.org/10.1108/PAR-12-2016-0117
[13] Cain, M. K., Zhang, Z. and Yuan, K.-H. (2017). Univariate and Multivariate Skewness and Kurtosis for Measuring Nonnormality: Prevalence, Influence and Estimation. Behavior Research Methods, 49 (5): 1716–35. DOI: https://doi.org/10.3758/s13428-016-0814-1
[14] Chin, W.W. (1998). Issues and Opinion on Structural Equation Modelling. MIS Quarterly 22 (1): vii–xvi.
[15] Chukwu, N., et al. (2019). The Impact of Basic Forensic Accounting Skills on Financial Reporting Credibility among Listed Firms in Nigeria. IOP Conference Series: Earth and Environmental Science, 331 (1): 012041. DOI: https://doi.org/10.1088/1755-1315/331/1/012041
[16] Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York: Routledge. DOI:https://doi.org/10.4324/9780203771587
[17] Coltman, T., Devinney, T. M., Midgley, D. F. and Venaik, S. (2008). Formative versus Reflective Measurement Models: Two Applications of Formative Measurement. Journal of Business Research, 61 (12): 1250–62. DOI: https://doi.org/10.1016/j.jbusres.2008.01.013
[18] Ellili, N., et al. (2024). Emerging Trends in Forensic Accounting Research: Bridging Research Gaps and Prioritizing New Frontiers. Journal of Economic Criminology, 100065, 4 (June). DOI:https://doi.org/10.1016/j.jeconc.2024.100065
[19] Emmanuel, G.O, Enyi, P.E and Olajide, S.D. (2018). Forensic Accounting Techniques and Integrity of Financial Statements: An Investigative Approach. Ournal of African Interdisciplinary Studies (JAIS), 2 (3): 1–23.
[20] Faul, F., Erdfelder, E., Buchner, A. and Lang, A.-G. (2009). Statistical Power Analyses Using G*Power 3.1: Tests for Correlation and Regression Analyses. Behavior Research Methods, 41 (4): 1149–60. DOI:https://doi.org/10.3758/BRM.41.4.1149
[21] Felix, U. O. (2022). Evidence Collecting Processes and Fraud Examination: The Role of an Expert Forensic Accountant. Sian Basic and Applied Research Journal, 4 (1): 394–420.
[22] Fornell, C., and Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18 (1): 39–50. DOI:https://doi.org/10.1177/002224378101800104
[23] Gellman, M.D., ed. (2020). Psychometric Theory. In Encyclopedia of Behavioral Medicine, 1771–1771. Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-39903-0_301545
[24] Guellim, N., et al. n.d. Evaluating the Perceived Value of Forensic Accounting: A Systematic Review Method. Discover Sustainability, 5 (1): 49–55. DOI: https://doi.org/10.1007/s43621-024-00431-y
[25] Hair, J. F., Ringle, C. M. and Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19 (2): 139–52. DOI: https://doi.org/10.2753/MTP1069-6679190202
[26] Hair, J.F., Howard, M.C. and Nitzl C. (2020). Assessing Measurement Model Quality in PLS-SEM Using Confirmatory Composite Analysis. Journal of Business Research, 109 (March):101–10. DOI:https://doi.org/10.1016/j.jbusres.2019.11.069
[27] Hair, J. F., Risher, J. J., Sarstedt, M. and Ringle, C. M. (2019). When to Use and How to Report the Results of PLS-SEM. European Business Review, 31 (1): 2–24. DOI: https://doi.org/10.1108/EBR-11-2018-0203
[28] Hair, J.F., Sarstedt, M., Pieper, T. M. and Ringle, C. M. (2012). The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications. Long Range Planning, Analytical approaches to strategic management: Partial Least Squares modeling in strategy research, 45 (5): 320–40. DOI: https://doi.org/10.1016/j.lrp.2012.09.008
[29] Henseler, J., Ringle, C. M. and 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): 115–35. DOI: https://doi.org/10.1007/s11747-014-0403-8
[30] Henseler, J., Ringle, C. M. and Sinkovics, R. R. (2009). The Use of Partial Least Squares Path Modeling in International Marketing. In New Challenges to International Marketing, 20:277–319. Emerald Group Publishing Limited. DOI: https://doi.org/10.1108/S1474-7979(2009)0000020014
[31] Henseler, J., et al. (2014). “Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013) David J. Ketchen, Joseph F. Hair, G. Tomas M. Hult, Roger J. Calantone.” DOI:https://journals.sagepub.com/doi/10.1177/1094428114526928
[32] Henson, R. K. (2001). Understanding Internal Consistency Reliability Estimates: A Conceptual Primer on Coefficient Alpha. Measurement and Evaluation in Counseling and Development, 34 (3): 177–89. DOI:https://doi.org/10.1080/07481756.2002.12069034
[33] Hult, G. et al. (2018). Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling. Journal of International Marketing, 26 (3): 1–21. DOI:https://doi.org/10.1509/jim.17.0151
[34] Jain, Dr E., and Lamba, J. (2020). FORENSIC ACCOUNTING: A WAY TO FIGHT, DETER AND DETECT FRAUD. IARS International Research Journal, 10 (1). DOI: https://doi.org/10.51611/iars.irj.v10i1.2020.106
[35] Koppl, R., and Sacks, M. 2013. The Criminal Justice System Creates Incentives for False Convictions. Criminal Justice Ethics, 32 (2): 126–62. DOI: https://doi.org/10.1080/0731129X.2013.817070
[36] Mardia, K. V. (1970). Measures of Multivariate Skewness and Kurtosis with Applications. Biometrika 57 (3): 519–30. DOI: https://doi.org/10.1093/biomet/57.3.519
[37] Maulidi, A. (2020). When and Why (Honest) People Commit Fraudulent Behaviours?: Extending the Fraud Triangle as a Predictor of Fraudulent Behaviours. Journal of Financial Crime, 27 (2): 541–59. DOI:https://doi.org/10.1108/JFC-05-2019-0058
[38] Muthuswamy, G. (2011). Behavioral Intention to Use Forensic Accounting Services for the Detection and Prevention of Fraud by Large Malaysian Companies. AUSTRALIA: Curtin University of Technology.
[39] Ng, S.W, et al. (2017). Behavioural Intention to Use Forensic Accounting Services among Small and Medium Enterprises in Malaysia. Eastern European Business and Economics Journal, 3 (4): 306–49.
[40] Nulty, D. D. (2008). The Adequacy of Response Rates to Online and Paper Surveys: What Can Be Done? Assessment & Evaluation in Higher Education, 33 (3): 301–14. DOI:https://doi.org/10.1080/02602930701293231
[41] Oni, S. O., and Godwin E. O. (2023). Forensic Accounting Evidence: Preservation and Admissibility. National Journal of Multidisciplinary Research and Development, 8 (2): 53–58.
[42] Onyango, G. (2024). How Mafia Like Bureaucratic Cartels or Thieves in Suits Run Corruption Inside the Bureaucracy, or How Government Officials Swindle Citizens in Kenya! Deviant Behavior, 45 (5): 689–707. DOI: https://doi.org/10.1080/01639625.2023.2263136
[43] Ozili, P.K. (2020). Forensic Accounting Theory. In Uncertainty and Challenges in Contemporary Economic Behaviour, edited by Ercan Özen and Simon Grima, 49–60. Emerald Publishing Limited. DOI:https://doi.org/10.1108/978-1-80043-095-220201005
[44] Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y. and Podsakoff, N. P. (2003). Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology, 88 (5): 879–903. DOI: https://doi.org/10.1037/0021-9010.88.5.879
[45] Rasheed, F., Said, J. and Khan, N. I. (2023). Evolution of Fraud-Related Theories: A Theoretical Review. Journal of Nusantara Studies (JONUS), 8 (3): 322–50. DOI:https://doi.org/10.24200/jonus.vol8iss3pp322-350
[46] Reinartz, W., Haenlein, M. and Henseler, J. (2009). An Empirical Comparison of the Efficacy of Covariance-Based and Variance-Based SEM. International Journal of Research in Marketing, 26 (4): 332–44. DOI:https://doi.org/10.1016/j.ijresmar.2009.08.001
[47] Reurink, A. (2018). FINANCIAL FRAUD: A LITERATURE REVIEW. Journal of Economic Surveys, 32 (5): 1292–1325. DOI: https://doi.org/10.1111/joes.12294
[48] Rezaee, Z. (2005). Causes, Consequences, and Deterence of Financial Statement Fraud. Critical Perspectives on Accounting, 16 (3): 277–98. DOI: https://doi.org/10.1016/S1045-2354(03)00072-8
[49] Robson, C., and McCartan, K. (2016). Real World Research. Wiley.
[50] Roszkowska, P. (2021). Fintech in Financial Reporting and Audit for Fraud Prevention and Safeguarding Equity Investments. Journal of Accounting & Organizational Change, 17 (2): 164–96. DOI:https://doi.org/10.1108/JAOC-09-2019-0098
[51] Thompson, R., Barclay, D.W. and Higgins, C.A. (1995). The Partial Least Squares (PLS) Approach to Causal Modelling: Personal Computer Adoption and Use as an Illustration. Technologies Studies, 2 (2): 285–309.
[52] Young, S. D. (2020). Financial Statement Fraud: Motivation, Methods, and Detection. In Corporate Fraud Exposed, edited by H. Kent Baker, Lynnette Purda-Heeler, and Samir Saadi, 321–39. Emerald Publishing Limited. DOI: https://doi.org/10.1108/978-1-78973-417-120201021
Published
2025-03-31
How to Cite
DIWAKAR, Richa et al.
Non-Accounting Drivers of Forensic Accounting Techniques: Insights from PLS-SEM Analysis.
Theoretical and Practical Research in Economic Fields, [S.l.], v. 16, n. 1, p. 182 - 193, mar. 2025.
ISSN 2068-7710.
Available at: <https://journals.aserspublishing.eu/tpref/article/view/8787>. Date accessed: 11 apr. 2025.
doi: https://doi.org/10.14505/tpref.v16.1(33).15.
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