IDEAS home Printed from https://ideas.repec.org/a/bcp/journl/v9y2025issue-9p2435-2452.html

Integrating the MVAICTM Model and Fraud Diamond Theory to Examine Financial Statement Fraud

Author

Listed:
  • Hazlina Hassan

    (National Audit Department, Malaysia)

  • Norlydawaty Hashim

    (Faculty of Accountancy, Universiti Teknologi MARA Cawangan Selangor, Puncak Alam Malaysia)

  • Y Nurli Abu Bakar

    (Faculty of Accountancy, Universiti Teknologi MARA Cawangan Selangor, Puncak Alam Malaysia)

  • Aida Hazlin Ismail

    (Faculty of Accountancy, Universiti Teknologi MARA Cawangan Selangor, Puncak Alam Malaysia)

Abstract

This study investigates the influence of intellectual capital (IC) on the occurrence of financial statement fraud (FSF) among Malaysian public-listed companies, with a specific focus on firms operating within knowledge-based sectors. Drawing on the Fraud Diamond Theory, the research examines how the components of IC: Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), Relational Capital Efficiency (RCE), and Capital Employed Efficiency (CEE) which impact the likelihood of fraudulent financial reporting. The Modified Value-Added Intellectual Coefficient (MVAIC™) model was employed to measure IC, while the Beneish M-score was used to detect potential FSF. A quantitative research design was adopted, using secondary data collected from 61 firms listed on Bursa Malaysia’s Main Market between 2020 and 2022. Multiple linear regression and binary logistic regression analyses were conducted to test the hypothesised relationships. The findings indicate a significant negative relationship between IC and FSF, suggesting that higher IC efficiency contributes to reduced fraudulent financial reporting. Each component of IC also exhibited varying levels of influence, with HCE and RCE showing particularly strong effects. This study contributes to the limited body of empirical research linking IC and FSF, especially in the Malaysian context. It also extends the application of the Fraud Diamond Theory by incorporating IC as a moderating mechanism in mitigating fraud. The findings offer practical insights for policymakers, regulators, and corporate governance advocates by highlighting the importance of IC investment in fraud prevention strategies and organizational integrity.

Suggested Citation

  • Hazlina Hassan & Norlydawaty Hashim & Y Nurli Abu Bakar & Aida Hazlin Ismail, 2025. "Integrating the MVAICTM Model and Fraud Diamond Theory to Examine Financial Statement Fraud," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(9), pages 2435-2452, September.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-9:p:2435-2452
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijriss/Digital-Library/volume-9-issue-9/2435-2452.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijriss/articles/integrating-the-mvaictm-model-and-fraud-diamond-theory-to-examine-financial-statement-fraud/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Messod D. Beneish, 1999. "The Detection of Earnings Manipulation," Financial Analysts Journal, Taylor & Francis Journals, vol. 55(5), pages 24-36, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexander Dyck & Adair Morse & Luigi Zingales, 2024. "How pervasive is corporate fraud?," Review of Accounting Studies, Springer, vol. 29(1), pages 736-769, March.
    2. Sonika Gupta & Sushil Kumar Mehta, 2024. "Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies," Global Business Review, International Management Institute, vol. 25(5), pages 1290-1313, October.
    3. Anas Alqudah & Safa Jaradat & Lara Al-Haddad, 2025. "Financial Consequences of Fraud in Amman Stock Exchange Firms," Advances in Decision Sciences, Asia University, Taiwan, vol. 29(1), pages 83-111, March.
    4. Ángel Beade & Manuel Rodríguez & José Santos, 2024. "Multiperiod Bankruptcy Prediction Models with Interpretable Single Models," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1357-1390, September.
    5. Christi R. Wann & John Trussel & Lisa A. Burke-Smalley, 2025. "Hardship Withdrawals Among Households with Disabilities," Journal of Family and Economic Issues, Springer, vol. 46(3), pages 919-934, September.
    6. Konstantīns Beņkovskis & Ludmila Fadejeva, 2025. "Chasing the shadow: unreported wage payments and income inequality," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 23(4), pages 1089-1120, December.
    7. Almina Doko & Rezarta Shkurti, 2025. "Detecting financial statement manipulation in SMEs: evidence from Albania," Digital Finance, Springer, vol. 7(4), pages 787-813, December.
    8. Cumming, Douglas J. & Javakhadze, David & Rajkovic, Tijana, 2024. "Unlocking Dividends: The impact of managerial social capital on international corporate payouts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 95(C).
    9. Wang, Wenjiao & Sun, Ziyuan & Wang, Lan, 2025. "Does ESG rating divergence exacerbate management tone manipulation? − Empirical evidence based on MD&A text," Journal of Business Research, Elsevier, vol. 197(C).
    10. Cebi, Selcuk & Karakurt, Necip Fazıl & Kurtulus, Erkan & Tokgoz, Bunyamin, 2024. "Development of a decision support system for client acceptance in independent audit process," International Journal of Accounting Information Systems, Elsevier, vol. 53(C).
    11. Flint, Michele S., 2025. "Expansion of analytical methods in auditing education," Journal of Accounting Education, Elsevier, vol. 70(C).
    12. Santoni, Alessandro & Allali, Lamia & Dierick, Nicolas, 2026. "Earnings manipulation and probability of default: insights from AnaCredit and supervisory," Occasional Paper Series 385, European Central Bank.
    13. Ángel Beade & Manuel Rodríguez & José Santos, 2024. "Business failure prediction models with high and stable predictive power over time using genetic programming," Operational Research, Springer, vol. 24(3), pages 1-41, September.
    14. repec:osf:socarx:48g5c_v1 is not listed on IDEAS
    15. Marius-Sorin CIUBOTARIU & Cristina-Raluca CERNOVSCHI, 2024. "Diagnosing The Risk Of Manipulation In Financial Accounting Information. An M-Score Based Approach," European Journal of Accounting, Finance & Business, "Stefan cel Mare" University of Suceava, Romania - Faculty of Economics and Public Administration, West University of Timisoara, Romania - Faculty of Economics and Business Administration, vol. 12(3), pages 134-141, October.
    16. Dan Amiram & Zahn Bozanic & Ethan Rouen, 2015. "Financial statement errors: evidence from the distributional properties of financial statement numbers," Review of Accounting Studies, Springer, vol. 20(4), pages 1540-1593, December.
    17. Zhang, Yi & Liu, Tianxiang & Li, Weiping, 2024. "Corporate fraud detection based on linguistic readability vector: Application to financial companies in China," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    18. Bidisha Chakrabarty & Pamela C. Moulton & Leonid Pugachev & Xu (Frank) Wang, 2025. "Catch me if you can: In search of accuracy, scope, and ease of fraud prediction," Review of Accounting Studies, Springer, vol. 30(2), pages 1268-1308, June.
    19. Dobija, Dorota & Hryckiewicz, Aneta & Zaman, Mahbub & Puławska, Karolina, 2022. "Critical mass and voice: Board gender diversity and financial reporting quality," European Management Journal, Elsevier, vol. 40(1), pages 29-44.
    20. Zhang, Zejun & Wang, Zhao & Cai, Lixin, 2025. "Predicting financial fraud in Chinese listed companies: An enterprise portrait and machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    21. Su, Huishui & Jiang, I-Ming & Liu, Duan, 2025. "Detecting financial fraud risk using machine learning: Evidence based on different categories and matching samples," Finance Research Letters, Elsevier, vol. 85(PA).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bcp:journl:v:9:y:2025:issue-9:p:2435-2452. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Pawan Verma (email available below). General contact details of provider: https://rsisinternational.org/journals/ijriss/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.