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Artificial Intelligence–Enabled Enterprise Resource Planning Systems and Financial Governance in Texas Community Financial Institutions: Examining Risk Management Capability and Internal Control Effectiveness

Author

Listed:
  • Rosemary Dosu

    (Systems Accountant, Finance and Accounts Department, Ghana National Gas Company Limited, Ghana)

  • Victor Agbeve

    (United Bank for Africa, Ghana)

  • Patrick Botchwey

    (Klynveld Peat Marwick Goerdeler (KPMG), Ghana)

  • Jerome Christopher Atisu

    (Kwame Nkrumah University of Science and Technology, School of Business, Ghana)

Abstract

The study was explanatory and predictive, employing a quantitative approach and including 384 respondents from the various strata (accounting, finance, risk management, internal auditing, compliance, information technology, cybersecurity, and executive management). Confirmatory Factor Analysis, Structural Equation Modelling, Mediation Analysis (bootstrapping) and Machine- learning algorithms are employed to analyse the data. Reliability and AVE values of the measurement model range from 0.93 to 0.95 and 0.64 to 0.69, respectively, indicating that the model is highly reliable and valid. The relationships with artificial intelligence–enabled enterprise resource planning capability on financial-governance quality (p

Suggested Citation

  • Rosemary Dosu & Victor Agbeve & Patrick Botchwey & Jerome Christopher Atisu, 2023. "Artificial Intelligence–Enabled Enterprise Resource Planning Systems and Financial Governance in Texas Community Financial Institutions: Examining Risk Management Capability and Internal Control Effectiveness," Post-Print hal-05730863, HAL.
    • Handle: RePEc:hal:journl:hal-05730863
    DOI: 10.59324/ejtas.2023.1(3).56
    as

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