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Optimization of Accounting information System in Public Sector for Sustainable Risk Management Under Big Data Analytics. Does forensic Accountants’ Skill Generate Differences?

Author

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  • HUY Pham Quang

    (University of Economics Ho Chi Minh City (UEH), School of Accounting, Chi Minh City, VIETNAM)

  • PHUC Vu Kien

    (University of Economics Ho Chi Minh City (UEH), School of Accounting, Vinh Long Campus, Vinh Long, VIETNAM)

Abstract

This article sets its sight to allot an intelligible picture of how to optimize accounting information system (AIS) in public sector organization (PSO) for sustainable risk management (SRM) under the big data analytics (BDA) and offers in-depth understandings concerning to the role of forensic accountants’ skill (FAS) on these aforementioned interconnections. The structural equation modeling (SEM) and multi-group SEM were wielded to testify the hypothesized model rested on cross-sectional data formulated by a closed-ended questionnaire survey distributed to convenience and snowball sample of 683 respondents in PSOs. All the proffered hypotheses in the theoretical model were authenticated by the soundly statistical evidences. The observations of the current study also generated the numerous practical implications for the practitioners in organizational management and policymakers in building up the strategies and promulgating rules in relation to digital initiatives adoption, accounting practices and risk management toward sustainable development within PSOs.

Suggested Citation

  • HUY Pham Quang & PHUC Vu Kien, 2024. "Optimization of Accounting information System in Public Sector for Sustainable Risk Management Under Big Data Analytics. Does forensic Accountants’ Skill Generate Differences?," Foundations of Management, Sciendo, vol. 16(1), pages 67-82.
    • Handle: RePEc:vrs:founma:v:16:y:2024:i:1:p:67-82:n:1005
    DOI: 10.2478/fman-2024-0005
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    References listed on IDEAS

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    1. Qaisar Ali & Hakimah Yaacob & Shazia Parveen & Zaki Zaini, 2021. "Big data and predictive analytics to optimise social and environmental performance of Islamic banks," Environment Systems and Decisions, Springer, vol. 41(4), pages 616-632, December.
    2. James A. DiGabriele, 2009. "Implications of regulatory prescriptions and audit standards on the evolution of forensic accounting in the audit process," Journal of Applied Accounting Research, Emerald Group Publishing Limited, vol. 10(2), pages 109-121, September.
    3. Stephen Oduro & Leul Girma Haylemariam, 2019. "Market orientation, CSR and financial and marketing performance in manufacturing firms in Ghana and Ethiopia," Sustainability Accounting, Management and Policy Journal, Emerald Group Publishing Limited, vol. 10(3), pages 398-426, July.
    4. Adrian Gepp & Martina K. Linnenluecke & Terrence J. O’Neill & Tom Smith, 2018. "Big data techniques in auditing research and practice: Current trends and future opportunities," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 40(1), pages 102-115, February.
    5. Kevin L. Papiorek & Martin R.W. Hiebl, 2023. "Information systems quality in management accounting and management control effectiveness," Journal of Accounting & Organizational Change, Emerald Group Publishing Limited, vol. 20(3), pages 433-458, June.
    6. Baljinder Kaur & Kiran Sood & Simon Grima, 2022. "A systematic review on forensic accounting and its contribution towards fraud detection and prevention," Journal of Financial Regulation and Compliance, Emerald Group Publishing Limited, vol. 31(1), pages 60-95, June.
    7. Côrte-Real, Nadine & Oliveira, Tiago & Ruivo, Pedro, 2017. "Assessing business value of Big Data Analytics in European firms," Journal of Business Research, Elsevier, vol. 70(C), pages 379-390.
    8. Barnes, Stuart J., 2020. "Information management research and practice in the post-COVID-19 world," International Journal of Information Management, Elsevier, vol. 55(C).
    9. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    10. Abubakar, A. Mohammed & Behravesh, Elaheh & Rezapouraghdam, Hamed & Yildiz, Selim Baha, 2019. "Applying artificial intelligence technique to predict knowledge hiding behavior," International Journal of Information Management, Elsevier, vol. 49(C), pages 45-57.
    11. Muhammad Fiaz & Amir Ikram & Asad Ilyas, 2018. "Enterprise Resource Planning Systems: Digitization of Healthcare Service Quality," Administrative Sciences, MDPI, vol. 8(3), pages 1-12, July.
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    More about this item

    Keywords

    accounting information system; big data analytics; forensic accountants; skill; sustainable risk management; public sector;
    All these keywords.

    JEL classification:

    • H83 - Public Economics - - Miscellaneous Issues - - - Public Administration
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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