IDEAS home Printed from https://ideas.repec.org/a/bla/acctfi/v65y2025i3p2918-2934.html

How Does Big Data Analytics Impact Accounting Manipulation?

Author

Listed:
  • Van Anh Thi Pham
  • Lan Anh Nguyen
  • Steven Dellaportas
  • Duc Hong Thi Phan
  • Quan Hong Nguyen

Abstract

This study investigates the impact of Big Data Analytics (BDA) on accounting manipulation in an emerging market context. Using semi‐structured interviews with 30 Vietnamese accounting professionals and applying the New Institutional Sociology (NIS) framework, we find that BDA enhances financial transparency and reduces manipulation by increasing system complexity and limiting manual intervention. However, these effects are not permanent. As users gain expertise, they may exploit system weaknesses, raising new ethical and technical concerns. Our findings suggest that effective safeguards, including segregation of duties and ethics training, are essential to maintaining BDA's integrity. While focused on Vietnam, the results offer insights applicable to other economies undergoing digital transformation.

Suggested Citation

  • Van Anh Thi Pham & Lan Anh Nguyen & Steven Dellaportas & Duc Hong Thi Phan & Quan Hong Nguyen, 2025. "How Does Big Data Analytics Impact Accounting Manipulation?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 65(3), pages 2918-2934, September.
    • Handle: RePEc:bla:acctfi:v:65:y:2025:i:3:p:2918-2934
    DOI: 10.1111/acfi.70024
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/acfi.70024
    Download Restriction: no
    File URL: https://libkey.io/10.1111/acfi.70024?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. repec:eme:maj000:maj-01-2016-1299 is not listed on IDEAS
    2. Frankel, Richard & Jennings, Jared & Lee, Joshua, 2016. "Using unstructured and qualitative disclosures to explain accruals," Journal of Accounting and Economics, Elsevier, vol. 62(2), pages 209-227.
    3. Trevor Hopper & Maria Major, 2007. "Extending Institutional Analysis through Theoretical Triangulation: Regulation and Activity-Based Costing in Portuguese Telecommunications," European Accounting Review, Taylor & Francis Journals, vol. 16(1), pages 59-97.
    4. Yanwei Lyu & Yahui Ge & Jinning Zhang, 2023. "The impact of digital economy on capital misallocation: evidence from China," Economic Change and Restructuring, Springer, vol. 56(5), pages 3475-3499, October.
    5. repec:eme:par000:par-03-2020-0026 is not listed on IDEAS
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    7. Roy-Ivar Andreassen, 2020. "Digital technology and changing roles: a management accountant’s dream or nightmare?," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(3), pages 209-238, September.
    8. Maroufkhani, Parisa & Tseng, Ming-Lang & Iranmanesh, Mohammad & Ismail, Wan Khairuzzaman Wan & Khalid, Haliyana, 2020. "Big data analytics adoption: Determinants and performances among small to medium-sized enterprises," International Journal of Information Management, Elsevier, vol. 54(C).
    9. Keith A. Houghton & Michael Kend & Christine Jubb, 2013. "The CLERP 9 Audit Reforms: Benefits and Costs Through the Eyes of Regulators, Standard Setters and Audit Service Suppliers," Abacus, Accounting Foundation, University of Sydney, vol. 49(2), pages 139-160, June.
    10. repec:eme:maj000:maj-01-2018-1773 is not listed on IDEAS
    11. Lounsbury, Michael, 2008. "Institutional rationality and practice variation: New directions in the institutional analysis of practice," Accounting, Organizations and Society, Elsevier, vol. 33(4-5), pages 349-361.
    12. Alles, Michael & Gray, Glen L., 2016. "Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors," International Journal of Accounting Information Systems, Elsevier, vol. 22(C), pages 44-59.
    13. Lan Anh Nguyen & Brendan O'Connell & Michael Kend & Van Anh Thi Pham & Gillian Vesty, 2021. "The likelihood of widespread accounting manipulation within an emerging economy," Journal of Accounting in Emerging Economies, Emerald Group Publishing Limited, vol. 11(2), pages 312-339, February.
    14. Steven Ji-fan Ren & Samuel Fosso Wamba & Shahriar Akter & Rameshwar Dubey & Stephen J. Childe, 2017. "Modelling quality dynamics, business value and firm performance in a big data analytics environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5011-5026, September.
    15. Cristina Thomas Alberti & Jean C. Bedard & Olof Bik & Ann Vanstraelen, 2022. "Audit Firm Culture: Recent Developments and Trends in the Literature," European Accounting Review, Taylor & Francis Journals, vol. 31(1), pages 59-109, January.
    16. Phuong Thi Nguyen & Michael Kend, 2017. "The perceived motivations behind the introduction of the law on external audit in Vietnam," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 32(1), pages 90-108, January.
    17. Phuong Thi Nguyen & Michael Kend, 2019. "An examination of the Vietnamese emerging market economy: understanding how and why auditors have responded to the audit law reforms," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(3), pages 1553-1583, September.
    18. Parker, Lee D., 2012. "Qualitative management accounting research: Assessing deliverables and relevance," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 23(1), pages 54-70.
    19. Lan Anh Nguyen & Gillian Vesty & Michael Kend & Quan Nguyen & Brendan O'Connell, 2020. "Intertwined institutionalization: pressures on Vietnam’s accounting profession during transition to IFRS," Pacific Accounting Review, Emerald Group Publishing Limited, vol. 32(4), pages 475-493, August.
    20. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    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. Ylinen, Mika & Ranta, Mikko, 2025. "Predicting corporate innovation using machine learning and social media data," Technovation, Elsevier, vol. 148(C).
    2. Modell, Sven & Vinnari, Eija & Lukka, Kari, 2017. "On the virtues and vices of combining theories: The case of institutional and actor-network theories in accounting research," Accounting, Organizations and Society, Elsevier, vol. 60(C), pages 62-78.
    3. Xi Chen & Yang Ha (Tony) Cho & Yiwei Dou & Baruch Lev, 2022. "Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 60(2), pages 467-515, May.
    4. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    5. Shuangshuang Fan & Yichao Li & William Mbanyele & Xiufeng Lai, 2025. "Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1231-1264, March.
    6. Abraham Itzhak Weinberg, 2025. "Hybrid Quantum-Classical Ensemble Learning for S\&P 500 Directional Prediction," Papers 2512.15738, arXiv.org.
    7. Fazlija, Bledar & Ibraimi, Meriton & Forouzandeh, Aynaz & Fazlija, Arber, 2025. "Reasoning with financial regulatory texts via Large Language Models," Journal of Behavioral and Experimental Finance, Elsevier, vol. 47(C).
    8. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    9. Chai, Bailin & Jiang, Fuwei & Lin, Yihao & You, Tian, 2025. "Predicting bond risk premiums with machine learning: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 93(C).
    10. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    11. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    12. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
    13. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    14. Gao, Daquan & Li, Songsong & Tian, Zhihong, 2025. "Geopolitical risk, energy market volatility, and corporate energy dependence: The role of green Total factor productivity and decentralized top management team network," Energy Economics, Elsevier, vol. 148(C).
    15. Ahmed Bouteska & Murad Harasheh, 2026. "Decoding the crypto crowd: how social media sentiment predicts Ethereum’s price," Journal of Asset Management, Palgrave Macmillan, vol. 27(1), pages 1-12, March.
    16. Saketh Aleti & Tim Bollerslev & Mathias Siggaard, 2025. "Intraday Market Return Predictability Culled from the Factor Zoo," Management Science, INFORMS, vol. 71(9), pages 7731-7751, September.
    17. Malakhov, Alexey & Riley, Timothy B. & Yan, Qing, 2024. "Do hedge funds bet against beta?," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 1507-1525.
    18. Kun Wang & Bozhou Li & Lu Qiao & Zhiyi Xia, 2026. "Fintech and Dividend Payouts: Evidence From China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 2964-2979, April.
    19. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    20. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Apr 2026.

    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:bla:acctfi:v:65:y:2025:i:3:p:2918-2934. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/aaanzea.html .

    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.