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

Rosemary Dosu, Victor Agbeve, Patrick Botchwey and Jerome Christopher Atisu
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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

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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

Keywords: community banks; credit unions; predictive analytics; internal-control effectiveness; isk-management capability; inancial governance; enterprise resource planning; Artificial intelligence (search for similar items in EconPapers)
Date: 2023-05-25
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Published in European Journal of Theoretical and Applied Sciences, 2023, 1 (3), pp.568-578. ⟨10.59324/ejtas.2023.1(3).56⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05730863

DOI: 10.59324/ejtas.2023.1(3).56

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