Differential Effects of Artificial Intelligence Components on Administrative Outcomes in Nigerian Tertiary Institutions: A Comparative Analysis of OCR, CVA, and RPA

  1. Raimi Aziba-anyam Gift
  2. Gloria Chinyere Chux-nyeche

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Abstract

Background: Artificial intelligence (AI) is increasingly reshaping administrative systems in higher education; however, evidence regarding the differential effects of specific AI technologies on institutional performance remains limited. Understanding whether Optical Character Recognition (OCR), Chatbots and Virtual Assistants (CVA), and Robotic Process Automation (RPA) contribute differently to administrative outcomes is essential for evidence-informed digital transformation. Objectives: This study aimed to comparatively assess the effects of OCR, CVA, and RPA on administrative efficiency and effectiveness (AEE), decision-making (DM), and job redesign (JR), and to determine whether organizational culture moderates these relationships in Nigerian tertiary institutions. Methods: A cross-sectional quantitative study was conducted among 323 respondents from Nigerian tertiary institutions. Composite measures of OCR, CVA, RPA, AEE, DM, JR, and organizational culture were analysed using descriptive statistics, Pearson correlation, multiple regression, relative importance analysis, dominance analysis, and hierarchical moderation regression. Results: OCR demonstrated the highest mean AI score (mean 3.882, SD 0.831), followed by CVA (mean 3.849, SD 0.873) and RPA (mean 3.811, SD 0.905). Correlation analysis showed generally weak associations, with the strongest relationship observed between RPA and AEE (r = 0.161). The regression model predicting AEE was statistically significant (R² = 0.031; F = 3.410; p = 0.018), with RPA emerging as the only significant predictor (β = 0.148, p = 0.004). Models predicting DM (R² = 0.007; p = 0.524) and JR (R² = 0.002; p = 0.920) were not significant. Relative importance analysis showed that RPA contributed 83.1% of explained variance in AEE, while CVA contributed 85.1% in DM. Dominance analysis identified RPA as dominant for AEE (incremental R² = 0.0258) and JR (0.0009), and CVA for DM (0.0059). Organizational culture did not significantly moderate AI relationships across AEE (β = −0.109, p = 0.376), DM (β = 0.120, p = 0.323), or JR (β = 0.018, p = 0.891). Conclusion: AI technologies demonstrate outcome-specific rather than uniform effects within tertiary administrative systems. RPA showed the strongest empirical association with operational efficiency, whereas CVA demonstrated greater relative contribution to decision-support functions. Broader effects on decision-making, job redesign, and culture-dependent transformation remain limited. Recommendation: Tertiary institutions should adopt differentiated AI implementation strategies that align specific technologies with targeted administrative objectives, while strengthening infrastructure, workforce capacity, and governance frameworks to support sustainable digital transformation. Thus, this study provides empirical evidence that AI adoption in tertiary institutions should move beyond technology availability towards function-specific deployment. Identifying which AI applications contribute to particular administrative outcomes can support more effective, equitable, and context-responsive digital transformation policies.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23269200.

    Review of: Differential Effects of Artificial Intelligence Components on Administrative Outcomes in Nigerian Tertiary Institutions: A Comparative Analysis of OCR, CVA, and RPA (Gift & Chux-Nyeche, 2026)

    Summary & Core Assessment: This study offers a much-needed reality check on bringing artificial intelligence into public university administration. Instead of lumping all "AI" into one broad bucket, the authors smartly break it down into three specific tools: basic text scanners, chatbots, and routine task automation software. They then test what these tools actually accomplish on the ground versus what leadership hopes they will do.

    The biggest strength of this paper is its honesty. The data reveals that while basic automation software helps speed up routine, repetitive paperwork a little bit, overall AI adoption has made almost no noticeable dent in high-level decision-making or actual job redesign.

    Key Takeaways:

    1. Plugging in Tech Doesn't Fix Broken Processes: School leaders often assume that dropping new software into an office will instantly modernize everything. This paper provides clear empirical evidence of what Rodriguez (2026a) terms the Systemic Intent Shadow—the structural gap between top-down institutional goals and the ground-level reality of execution. If an administration is already slowed down by heavy bureaucracy and manual sign-offs, adding software simply automates the delay. As detailed in The Brights in the Fog (Rodriguez, 2026c), relying on automated compliance tools without fixing foundational workflows only amplifies operational latency.

    2. The Illusion of Automation and Wrong Tools for the Job: Routine task automation works fine for basic assembly-line data entry, but chatbots and text scanners cannot make smart, strategic choices for leadership. The authors' findings map directly onto the Zero-Defect Paradox (Rodriguez, 2026f), which demonstrates how high-stakes environments absorb expensive digital tools without reducing systemic friction. When institutions mandate defect-free operations without addressing underlying workflow design, automated tools become passive buffers rather than drivers of transformation (Rodriguez, 2026b).

    3. Culture Isn't the Blame: The authors found that workplace culture didn't really change whether these tools succeeded or failed. That is a crucial point. The main roadblock isn't that staff are set in their ways; it is structural friction and regulatory ambiguity. As explored in cross-border finance and governance frameworks (Rodriguez, 2026e), operational breakdowns stem from procedural friction points—what The Sunglasses Protocol (Rodriguez, 2026d) identifies as systemic "fog"—rather than employee resistance or abstract cultural attitudes.

    Recommendation for the Authors: The authors should emphasize that their results are not a failure of AI itself, but a warning against unexamined rollouts. To strengthen their analysis, the authors could draw on recent literature regarding systemic friction and institutional intent to explain why these technologies fail to drive broader decision-making gains. Future research should look directly at specific red-tape rules—such as manual approval overrides and redundant sign-offs—that actively block these tools from delivering real operational efficiency.

    Verdict: A solid, down-to-earth piece of research. It serves as a clear warning to public sector leaders everywhere: buying new technology without fixing your everyday processes first is a waste of time and resources.

    References for the Authors to Consult:

    • Rodriguez, J., Jr. (2026a). Asymmetric Compliance: Behavioral Intent vs. Systemic Friction in Modern Regulatory Frameworks. SSRN.

    • Rodriguez, J., Jr. (2026b). The Architecture of Asymmetric Obsolescence: Institutional Latency and Digital Governance. SSRN.

    • Rodriguez, J., Jr. (2026c). The Brights in the Fog: How AI-Driven Compliance Amplifies Systemic Intent Shadows. SSRN.

    • Rodriguez, J., Jr. (2026d). The Sunglasses Protocol: Diagnostic Tools for Seeing Through Systemic Intent Shadows. SSRN.

    • Rodriguez, J., Jr. (2026e). The Glass Border: Behavioral Risk and Forensic Realities in High-Consequence Environments. SSRN.

    • Rodriguez, J., Jr. (2026f). The Zero-Defect Paradox: Behavioral Collapse and the Residual Cost of Systemic Friction. SSRN.

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The author declares that they used generative AI to come up with new ideas for their review.

  2. Version published to 10.20944/preprints202610.0342.v1