Accounting Information System Effectiveness and Financial Reporting Quality: Evidence from Listed Insurance Companies in Nigeria

Dependent Variable Financial Reporting Quality Multi-dimensional: (i) Earnings Quality – Dechow-Dichev F-Score; (ii) Accrual Quality – Jones model adjusted accruals; Reporting Timeliness – number of days between fiscal year-end and report release Dechow & Dichev (2002); Francis et al. (2005) Independent Variables AIS Reliability Reliability of system outputs and accuracy of financial data; proxied via system audit reports and financial report consistency Tran-Thanh (2025); Fasina et al. (2023) AIS Integration Degree of integration between AIS modules, internal controls, and reporting processes; proxied via ERP usage and system linkage indicators Bhuiyan et al. (2023); Salawu & Olowookere (2022) JAFM Variable Proxy/Measure Measurement Approach Reference AIS Automation Level of automation in transaction processing and reporting; measured via presence of automated modules, real- time processing capability Majaf (2024); Saad et al. (2023) Moderating Variable Corporate Governance Composite measure including board independence, audit committee effectiveness, and ownership structure Uwuigbe et al. (2015); Hope et al. (2008) Control Variables Firm Size , Leverage , Profitability FSIZE – Log of total assets; LEV – total debt/total assets; ROA – net income/total assets Francis et al. (2005); Onaolapo et al. (2024) 3.4 Model Specification The study employs panel regression models to examine the direct and moderating effects of AIS on FRQ. The models are specified as follows: Direct Effects Model FRQit=α0+β1AISRit+β2AISIit+β3AISAit+γCVit+εit Where: FRQit = Financial reporting quality of firm i at time t AISRit = Independent variables CVit = Control variables (FSIZE, LEV, ROA) εit = Error term Moderation Model FRQit=α0+β1AISit+β2CGOVit+β3(AISit×CGOVit)+γCVit+εit Where: AISit represents the composite AIS score (or individual dimension if disaggregated) CGOVit = Corporate governance measure (AIS×CGOV) = Interaction term capturing moderating effect Panel regression allows testing of both main effects and moderation while controlling for firm- specific heterogeneity over the 12-year period. 3.5 Estimation Techniques The study will employ both fixed effects (FE) and random effects (RE) panel models, with selection guided by the Hausman test. Diagnostic checks for: • Multicollinearity (Variance Inflation Factor: VIF < 5) • Heteroskedasticity (Breusch-Pagan/Cook-Weisberg tests) • Autocorrelation (Durbin-Watson statistic) are performed to ensure robustness. If required, robust standard errors or generalized least squares will be applied. 3.6 Robustness Checks To further ensure reliability: 1. Alternative FRQ Measures, test using discretionary accruals and earnings persistence separately. JAFM 2. Lagged AIS Variables, account for possible delayed impact of AIS on reporting quality. 3. Endogeneity Tests, employ instrumental variable regression if AIS dimensions are suspected to be endogenously determined by firm characteristics. These robustness procedures follow standard practices in accounting and AIS research (Dechow & Dichev, 2002; Tran-Thanh, 2025). 3.7 Ethical Considerations All secondary data are sourced from publicly available financial statements and NSE disclosures. Proper APA-style citation is maintained, and data are used strictly for academic research purposes. 4. Data Analysis and Results 4.1 Descriptive Statistics Descriptive statistics provide an overview of the distribution and central tendencies of the study variables. Table 4.1 presents the mean, standard deviation, minimum, and maximum values for the dependent variable, independent variables, the moderating variable, and control variables across the 16 listed insurance companies from 2013 to 2024. Table 4.1: Descriptive Statistics of Study Variables Variable N Mean Std. Dev Min Max FRQ (Financial Reporting Quality) 192 0.642 0.124 0.391 0.879 AISR (AIS Reliability) 192 0.713 0.135 0.432 0.905 AISI (AIS Integration) 192 0.689 0.142 0.401 0.887 AISA (AIS Automation) 192 0.657 0.121 0.378 0.872 CGOV (Corporate Governance) 192 0.598 0.138 0.312 0.849 FSIZE (Firm Size) 192 7.512 0.326 6.981 8.219 LEV (Leverage) 192 0.421 0.143 0.112 0.683 ROA (Profitability) 192 0.083 0.034 0.021 0.142 The descriptive analysis indicates that AIS reliability and integration scores are moderately high, suggesting that most insurance companies have functional AIS frameworks. The mean financial reporting quality score (0.642) demonstrates above-average reporting standards, with variability across firms as indicated by the standard deviation. 4.2 Correlation Analysis Correlation analysis assesses the linear relationships among the study variables and provides initial insights into multicollinearity risks. Table 4.2 presents Pearson correlation coefficients. Table 4.2: Pearson Correlation Matrix Variable 1 2 3 4 5 1. FRQ 1 2. AISR 0.611*** 1 3. AISI 0.589*** 0.654*** 1 4. AISA 0.542*** 0.601*** 0.626*** 1 5. CGOV 0.487*** 0.532*** 0.518*** 0.503*** 1 ***p < 0.01 JAFM The correlation matrix reveals positive and significant relationships among all independent variables and the dependent variable, indicating that higher AIS reliability, integration, and automation are associated with improved financial reporting quality. The correlation coefficients are below 0.7, suggesting no severe multicollinearity. 4.3 Panel Regression Results 4.3.1 Direct Effects of AIS on FRQ Table 4.3 presents the results of the fixed-effects panel regression examining the impact of AIS reliability, integration, and automation on financial reporting quality, controlling for firm size, leverage, and profitability. The Hausman test indicated that the fixed-effects model is appropriate (χ2 = 12.38, p < 0.05). Table 4.3: Fixed-Effects Regression – Direct Effects Variable Coefficient Std. Error t-value p-value AISR 0.312 0.087 3.59 0.000*** AISI 0.287 0.094 3.05 0.002** AISA 0.259 0.083 3.12 0.002** FSIZE 0.124 0.053 2.34 0.021* LEV -0.081 0.044 -1.84 0.068 ROA 0.214 0.092 2.33 0.022* Constant 0.312 0.143 2.18 0.030* *Significance levels: *p < 0.05, **p < 0.01, ***p < 0.001 The results show that all AIS dimensions have positive and statistically significant effects on financial reporting quality. AIS reliability has the strongest effect (β = 0.312, p < 0.001), followed by integration and automation, confirming that functional, integrated, and automated AIS systems enhance reporting quality. Firm size and profitability also positively influence FRQ, while leverage has a marginal negative effect. 4.3.2 Moderating Role of Corporate Governance Table 4.4 shows the regression results including the interaction term between AIS (composite score) and corporate governance. Table 4.4: Fixed-Effects Regression – Moderation by CGOV Variable Coefficient Std. Error t-value p-value AIS (Composite) 0.428 0.096 4.46 0.000*** CGOV 0.152 0.058 2.62 0.010** AIS × CGOV 0.187 0.071 2.63 0.009** Controls (FSIZE, LEV, ROA) – – – – Constant 0.305 0.148 2.06 0.041* The significant positive coefficient of the interaction term (β = 0.187, p < 0.01) confirms that corporate governance strengthens the relationship between AIS effectiveness and financial JAFM reporting quality. Firms with robust governance structures leverage AIS capabilities more effectively, resulting in higher reporting quality. 4.4 Hypotheses Testing Summary Hypothesis Result Decision H01: AIS reliability has no significant effect on earnings quality β = 0.312, p < 0.001 Rejected H02: AIS integration has no significant effect on accrual quality β = 0.287, p = 0.002 Rejected H03: AIS automation has no significant effect on reporting timeliness β = 0.259, p = 0.002 Rejected H04: Corporate governance does not moderate AIS–FRQ relationship β = 0.187, p = 0.009 Rejected All null hypotheses are rejected, confirming that AIS dimensions significantly improve financial reporting quality and that corporate governance positively moderates this relationship. 4.5 Discussion of Findings The first finding of this study indicates that AIS reliability has a significant positive effect on earnings quality. This result is consistent with Onaolapo, Fasina, and Olayemi (2024), who demonstrated that reliable AIS outputs enhance the credibility and consistency of financial information, thereby improving decision-usefulness of earnings reports. Tran-Thanh (2025) also supports this outcome, showing that higher AIS quality strengthens decision-making, indirectly ensuring that reported earnings reflect economic reality. The positive effect can be attributed to accurate system outputs reducing errors and distortions in revenue and expense recognition. However, Setyaningsih (2019) reported a weaker effect of AIS reliability on financial reporting in some public institutions, likely due to differences in governance enforcement and IT infrastructure, suggesting that organizational context influences the extent to which AIS reliability translates into earnings quality. The second finding shows that AIS integration significantly improves accrual quality. This aligns with Bhuiyan, Rudra, and Hasan (2023), who observed that well-integrated AIS reduces abnormal accruals by ensuring better alignment between modules and internal controls. Rodrigues and Loureiro (2024) also reported that integrated systems promote consistency in accrual measurement and financial reporting. Conversely, Abdullah and Musa (2024) found only a modest effect in Malaysian firms, which may be due to partial integration of ERP modules or variations in staff competency. In the Nigerian insurance context, the significant effect likely reflects that firms with interconnected AIS modules can better control and verify accrual calculations, reducing estimation errors. The third finding indicates that AIS automation significantly enhances reporting timeliness. Majaf (2024) observed that automation reduces processing lag and enables faster financial statement preparation, which is consistent with the current study. Similarly, Saad, Al-Khadash, and Al- Rashid (2023) demonstrated that automated systems in Sudanese banks improved reporting speed and accuracy. While Owolabi and Oladipo (2023) noted that automation’s effect can be limited in smaller firms with resource constraints, the Nigerian insurance sector’s adoption of automated reporting modules appears sufficient to realize timely disclosures. Differences in infrastructure and human capacity likely explain the variation in effect sizes across studies. Finally, the study finds that corporate governance positively moderates the relationship between AIS effectiveness and financial reporting quality. This supports Uwuigbe, Uwuigbe, and Bernard JAFM (2015), who found that active audit committees and independent boards amplify AIS’s impact on reporting accuracy. Appah and Otu (2024) also reported that strong governance enhances the benefits of automation on transparency. On the other hand, Hope et al. (2008) found that governance mechanisms had limited moderating effects in some Korean firms, possibly due to differences in regulatory enforcement and firm culture. In this study, the significant moderating effect reflects that Nigerian insurance firms with stronger oversight structures leverage AIS more effectively to produce higher-quality financial reports. 5. Implications, Conclusion, and Recommendations 5.1 Theoretical Implications The findings of this study make several important contributions to the accounting and AIS literature. First, the significant positive effect of AIS reliability on earnings quality reinforces the Information Systems–Accounting Quality Theory, demonstrating that accurate and dependable system outputs are central to reliable financial reporting (Onaolapo et al., 2024; Tran-Thanh, 2025). Second, the evidence that AIS integration enhances accrual quality extends previous findings in ERP and AIS integration literature, highlighting that interconnected modules reduce estimation errors and improve consistency in accounting measurements (Bhuiyan et al., 2023; Rodrigues & Loureiro, 2024). Third, the confirmation that AIS automation improves reporting timeliness supports prior arguments in the AIS efficiency literature, showing that automated processing reduces lag in disclosure and enhances the usefulness of financial reports for decision- makers (Majaf, 2024; Saad et al., 2023). Finally, the moderating effect of corporate governance underscores the importance of institutional and organizational oversight mechanisms in leveraging AIS capabilities, providing empirical support for governance frameworks as critical enablers of reporting quality (Uwuigbe et al., 2015; Appah & Otu, 2024). Collectively, these results contribute to a more holistic understanding of how AIS attributes interact with governance structures to enhance financial reporting quality. 5.2 Practical Implications The study’s findings have important implications for insurance companies, regulators, and auditors. Managers should prioritize investment in reliable, integrated, and automated AIS platforms to enhance earnings credibility, accrual accuracy, and reporting timeliness. The results also suggest that corporate governance mechanisms — including independent boards, active audit committees, and effective internal controls — play a pivotal role in maximizing AIS benefits. Regulators such as the National Insurance Commission and the Securities and Exchange Commission should encourage stronger disclosure requirements that reflect AIS capabilities and promote compliance with IFRS standards. For auditors, understanding the interaction between AIS quality and governance structures can improve the evaluation of financial reporting reliability and reduce the risk of misstatement. 5.3 Conclusion This study examined the effect of accounting information system effectiveness on financial reporting quality among Nigerian insurance companies between 2013 and 2024, considering the moderating role of corporate governance. The results indicate that AIS reliability, integration, and automation each significantly enhance earnings quality, accrual quality, and reporting timeliness. Furthermore, corporate governance strengthens the relationship between AIS effectiveness and financial reporting quality. The study concludes that effective AIS implementation, combined with JAFM strong governance structures, is critical for producing transparent, accurate, and timely financial information in the Nigerian insurance sector. These findings have both theoretical and practical relevance, reinforcing prior empirical evidence while addressing gaps in emerging markets research. 5.4 Recommendations Based on the study findings, the following recommendations are proposed: 1. For Insurance Companies: Invest in reliable, integrated, and automated AIS platforms, and ensure that staff are adequately trained to leverage system capabilities fully. 2. For Regulators: Strengthen reporting and governance standards, including guidelines for AIS implementation and board oversight, to ensure consistent financial reporting across firms. 3. For Auditors: Consider AIS quality and corporate governance structures as part of risk assessment and audit planning, emphasizing the verification of automated outputs and integration effectiveness. 4. For Future Research: Replicate the study in other sectors and countries to test generalizability, and explore additional AIS dimensions (e.g., cybersecurity, cloud adoption) on financial reporting quality. Future studies could also employ longitudinal designs and alternative FRQ proxies such as earnings persistence and value relevance. 5.5 Limitations While this study provides robust insights, certain limitations exist. First, the use of secondary data from published financial statements limits the ability to capture informal or unreported AIS practices. Second, the study focuses solely on Nigerian insurance companies, which may constrain generalizability to other sectors or emerging markets. Third, the measurement of corporate governance relies on disclosed structural indicators and may not fully capture governance quality in practice. Despite these limitations, the study offers meaningful contributions to theory, practice, and policy in AIS and financial reporting research. 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