Banks in emerging economies are central to driving economic growth, maintaining financial stability, and enhancing institutional efficiency. In Nigeria, tier-One banks such as Zenith Bank, Access Bank, First Bank, United Bank for Africa (UBA), and Guaranty Trust Holding Company (GTCO) serve as the primary intermediaries and credit generators, significantly contributing to market capitalization. Key performance indicators like return on Assets (ROA) and Profit after Tax (PAT) reflect how effectively these banks utilise their assets to generate profits. Recent fluctuations in the ROA of these top banks have raised concerns about their long-term sustainability and competitiveness in a rapidly changing economy influenced by volatile macroeconomic conditions, regulatory changes, and evolving customer expectations.

Amid these changes, digitalisation has played a crucial role in enhancing operational performance, minimising risks, and improving customer experience. Specifically, mobile banking has seen remarkable growth in Nigeria. According to the Nigeria Inter-Bank Settlement System (NIBSS, 2024), mobile banking transactions soared to over ₦19.4 trillion in 2023, marking a 35% increase from the prior year. This shift not only highlights increased consumer adoption but also reflects Tier-One banks' strategic investments in financial technologies aimed at reducing costs and tapping into underserved markets. The incorporation of mobile   platforms into   banking   systems   has     transformed    cost    structures, boosted   transaction    volumes,   and redefined revenue streams presenting both opportunities and competitive challenges.

This bank performance- digitalisation interface merits academic attention. Performance is inherently tied to profitability and efficiency; however, digitalisation introduces a technological aspect that influences a bank' s reasoning. The rise of mobile banking services provides an empirical benchmark for assessing how technology adoption can lead to economic advantages. Consequently, examining how mobile banking transaction volumes impact ROA and PAT establishes a basis for evaluating the effectiveness of digital investments. This analytical relationship is essential, particularly for Tier- One banks, which are leaders in digital transformation, as they navigate the balance between innovation costs and financial returns.

Nonetheless, several challenges exist. Firstly, there is growing concern over the asymmetric benefits of digitalisation amidst increasing transaction volumes. While some banks have reported minimal improvements in ROA and PAT, this suggests that digital growth may not always correlate with enhanced performance. Secondly, infrastructural issues- such as network reliability, cyber threats, and digital illiteracy- tend to hinder the success of mobile banking. Thirdly, a lack of transparency in digital investment reporting obstructs stakeholders' ability to assess cost and value implications. Additionally, the current regulatory and inflationary climate distorts the perceived advantages of digital channels and necessitates a careful examination of the presumed linear relationship between digitalisation and bank profitability.

Previous studies have attempted to explore this interface but have fallen short in scope, structure, or analytical rigour. Only Onaolapo and Odetayo  focused on adoption through ATMs alone, neglecting broader digital channels. Akinola relied on descriptive statistics without regression analysis, limiting causal inference. Nwankwo and Osho used cross- country data that lacked strong local relevance. Ishola and Oladele also failed to consider bank- tier segmentation, drawing inferences across heterogeneous institutions. Ene and Ezeani relied on pre- pandemic data, which may no longer be applicable following the digital surge post- COVID. Oyelami did not distinguish mobile banking from overall e- banking, obscuring certain channel influences. Uche and Madueke used small sample sizes, limiting their findings' external validity. Addressing these gaps calls for more stringent empirical analysis regarding specific mobile banking transaction volumes and their measurable effects on Tier- One banks' ROA in Nigeria, utilising available financial data from the post- pandemic period and applying frontier econometrics.

The link between digitalisation and bank performance has become a pressing topic in both business and academic circles, particularly in developing economies like Nigeria. Faced with challenges around profitability and innovation, banks are increasingly adopting digital technologies- especially mobile banking- as a strategic response to market volatility, rising competition, and evolving consumer preferences. This review focuses on the conceptual, theoretical, and empirical foundations of digitalisation and bank performance, specifically examining how the value of mobile banking transactions affects the performance of Nigerian Tier-One banks, measured by return on assets (ROA) and profit after tax (PAT).

Banking digitalisation involves implementing digital technologies in financial services to boost efficiency, reach, and scalability. This encompasses various innovations such as online banking, automatic teller machines (ATMs), and, more recently, mobile banking platforms. In Nigeria, mobile banking has rapidly transitioned from a supplementary service to a primary transactional method. Customers can now transfer funds, pay bills, purchase airtime, and check balances via their mobile phones, eliminating the need for physical bank visits. This shift has disrupted traditional banking practices and transformed bank-customer interactions, enhancing speed, convenience, and accessibility.

Mobile banking transaction volumes are a key indicator of customer engagement with online banking. In 2023, over 40% of retail electronic transactions occurred through mobile channels, underscoring their strategic importance. Increased reliance on mobile channels could lead to greater customer engagement, elevated account activity, and potential fee-based revenue streams. However, the profitability of mobile banking hinges on its integration into the broader banking ecosystem and the bank’s capacity to manage digital risks. Researchers argue that the true value of mobile banking lies in its potential to promote financial inclusion and deliver services on a large scale at minimal cost, which may subsequently enhance overall financial performance.

Furthermore, the rise of mobile banking through digitalisation can impact operational costs and process efficiency. By automating transactions and minimising paper-based manual tasks, banks can reduce transaction expenses and enhance profit margins. Tier-One banks that have made significant investments in advanced mobile platforms are likely to experience greater benefits from decreased dependence on physical branches and staff for everyday transactions. This simplification of operations allows for reallocating resources toward providing higher value services such as credit risk assessment, investment planning, and strategic development. Mobile banking promotes a streamlined operational framework, which theoretically should improve financial performance. However, assessing the success of these digital investments requires a comprehensive analysis of the financial metrics they are designed to enhance, especially those reflecting a bank's capability to generate returns on its assets.

Key metrics like PAT and ROA illustrate how effectively a bank utilises its assets to produce profits. As mobile banking further establishes itself as a standard service delivery method, we expect that increased transaction volumes will align with improved financial performance, provided that cost management and strategic alignment are effectively maintained. ROA and PAT serve not only as indicators of profitability but also demonstrate the effectiveness of management in leveraging both physical and intangible assets. Therefore, the link between mobile banking and these metrics is essential in determining whether digitalization yields significant financial advantages or merely updates service delivery while failing to improve core performance indicators.

The existing literature reports conflicting evidence on the impact of mobile banking on ROA and PAT. While some studies indicate that mobile transactions enhance ROA and PAT through increased fee-based income and reduced operating costs, other research argues that the negative aspects of digitalization are influenced by factors such as investment expenses, regulatory hurdles, or low platform usage. This discrepancy highlights the need for a deeper investigation into the causal link between mobile banking transaction levels and profitability in Nigeria's banking sector, especially among Tier-One banks that have the most developed digital infrastructure and customer engagement.

Theoretical Review

An explanation of the relationship between digitalization and bank performance must be grounded in a viable theoretical foundation that is capable of describing how technological innovation is converted into quantifiable financial performance. Some of them, such as economics, innovation, and strategic management, are viable models to use in investigating this relationship. Three of them are delineated here because they constitute the conceptual and empirical foundation upon which mobile banking transaction volume's effect on the ROA and PAT of Nigeria's Tier-One banks is investigated.

Technology Acceptance Model (TAM)

According to the Technology Acceptance Model (TAM), articulated by Davis,  perceived usefulness and ease of use are critical factors influencing users' willingness to adopt new technology. Mobile banking exemplifies this, as TAM offers insights into customer behaviour and adoption that directly affect transaction volumes. When customers find mobile banking platforms easy and convenient, they are more likely to use them in their financial transactions, leading to increased volume and frequency of transactions. For banks, this shift signifies enhanced digital operations, less reliance on physical branches, and potential cost efficiencies. Despite its strengths, TAM faces criticism for prioritising user- related factors while overlooking important institutional, infrastructural, and systemic dynamics. While it effectively explains micro- level acceptance in mobile platforms, its applicability for predicting macro- level financial performance metrics, like Return on Assets (ROA) and Profit After Tax (PAT), is limited unless paired with performance- based models.

Beyond its original propositions, TAM has undergone several expansions, notably through the Unified Theory of Acceptance and Use of Technology (UTAUT), which incorporates additional variables such as social influence and facilitating conditions. These advancements represent a growing understanding of the complexities surrounding technology adoption, particularly in contexts like banking. In Nigeria, where there are significant challenges like infrastructural deficiencies, trust issues, and cybersecurity concerns, incorporating these external factors is essential to better explain mobile banking adoption. Consequently, transaction volumes may not solely reflect ease of use or perceived usefulness; they also embody customers' confidence in digital infrastructures and institutional readiness. Therefore, banks must extend their focus beyond just platform design and invest in security measures, customer education, and support systems to encourage broader and deeper technology adoption. This approach is vital for shaping usage patterns that ultimately affect the bank' s performance metrics over time.

Resource-Based View (RBV)

The Resource-Based View (RBV), further developed by Barney [3], argues that a firm’s long-term competitive advantage hinges on owning and effectively utilizing valuable, rare, inimitable, and non-substitutable (VRIN) resources. Mobile banking infrastructure and electronic competencies serve as strategic resources; when optimally leveraged, they enable banks to distinguish themselves in a competitive market. Tier-One Nigerian banks can enhance operational excellence and customer-facing innovations by investing in proprietary digital platforms, cybersecurity systems, and data analytics tools. These competencies can subsequently drive essential performance metrics, including ROA and PAT. However, the RBV emphasizes that merely acquiring digital assets is not sufficient; the strategic integration and utilization of these assets are crucial for driving performance outcomes [18]. Therefore, banks must invest in mobile technologies while also developing complementary organizational competencies to extract financial value from them.

In addition to considering digital competencies as VRIN assets, the RBV highlights the necessity for dynamic capabilities the firm's ability to integrate, build, and adjust internal and external competencies in response to rapidly changing environments. This perspective is particularly relevant for Nigerian Tier-One banks, as digital ecosystems evolve amid increasing regulatory pressures, cyber threats, and competition from fintechs. Relying solely on mobile banking infrastructure is insufficient; banks need to continuously innovate and adapt. This includes updating legacy systems, enhancing employees' digital literacy, responding to customer feedback, and implementing AI-powered service delivery. Thus, the RBV presents not merely a static view of resource ownership but a dynamic framework for capability renewal, essential for translating digital initiatives into sustainable profitability and robust ROA and PAT performance.

Additionally, the theory categorizes adopters into groups innovators, early adopters, early majority, late majority, and laggards based on their responsiveness to new technologies. In the diverse Nigerian banking landscape, these categories reflect various demographic, geographic, and socio-economic divisions. Young urban individuals may fall into the early adopters category, while older customers and rural communities might belong to the late majority or laggards. For banks aiming to expand mobile banking usage, understanding these adoption profiles can aid in segmentation, product customization, and marketing strategies. Furthermore, partnerships with telecommunications companies and government policies significantly influence the broader social system that facilitates innovation diffusion. Although the DOI model offers valuable insights into adoption processes, it has limitations as it tends to operate independently of organizational strategy and financial performance; thus, it requires integration with frameworks like the RBV to illustrate how adoption trends ultimately affect performance outcomes such as ROA and PAT.

Diffusion of Innovation Theory

Rogers' Theory of Diffusion of Innovation (DOI) offers a broad overview of how new technologies spread across a social system over time. When applied to the banking sector, the theory suggests that the rate of adoption of mobile banking by customers and institutions hinges on the characteristics of the innovation, communication channels, timing, and features of the social system. In Nigeria, a high level of mobile penetration and digital literacy has propelled the growth of mobile banking services, particularly among semi- urban and urban populations. The more customers utilise mobile banking, the more transaction volumes increase, likely enhancing bank performance through a better cost- to- income ratio and wider market reach. However, the theory also recognises obstacles such as infrastructure challenges, digital divides, and regulatory uncertainties that can inhibit adoption and jeopardise potential performance gains. DOI emphasises the regional aspects of digitalisation and its varied effects on profitability indicators like ROA and PAT.

Additionally, DOI classifies adopters into categories I innovators, early adopters, early majority, late majority, and laggards based on their openness to new technology [15]. In Nigeria' s diverse banking environment, these categories are evident across different demographic, geographic, and socio- economic segments. Urban youth typically represent early adopters, while rural residents and older customers fall into the late majority or laggards. For banks aiming to boost mobile banking transactions, comprehending these adoption profiles can inform their segmentation, product customisation, and marketing strategies. Furthermore, government policies and telecom collaborations significantly influence the wider social environment through which innovation proliferates. While the DOI model provides valuable insights into adoption behaviours, its shortcoming lies in not fully integrating with organizational strategy and financial performance, highlighting the need to combine it with models such as RBV to clarify how adoption trends affect performance metrics like ROA and PAT.

Theoretical Framework

Among the three theories reviewed, the Resource-Based View (RBV) is most pertinent to this research. In contrast to the Technology Adoption Model (TAM), which emphasises individual user adoption, and the Diffusion of Innovations (DOI) theory, which primarily addresses technology adoption within a social context, RBV operates at the firm level and aligns closely with the research's performance objectives. It positions digitalisation including mobile banking services and digital assets as strategic resources that can enhance competitive advantage and profitability when integrated effectively into core operations [3]. For Tier-One Nigerian banks, leveraging mobile banking platforms as a unique and inimitable asset can result in cost efficiencies, improved customer loyalty, market share growth, and ultimately, increased financial performance as evidenced by Return on Assets (ROA) and Profit After Tax (PAT).

Moreover, RBV fosters a better understanding of how digital transformation initiatives not only automate processes but also drive value creation and profitability. By conceiving digital assets as integral organizational capabilities rather than mere technological tools, the theory elucidates the necessary mechanisms, managerial competencies, and strategic alignment essential for translating higher volumes of mobile banking transactions into increased asset returns and profitability. Consequently, this theoretical framework provides a solid foundation for empirically investigating how different approaches to deploying digital assets among Tier-One banks in Nigeria may explain variations in their financial outcomes. Thus, RBV stands as a significant reference point for this study, offering valuable insights regarding the dynamics of mobile banking innovations and their long-term impact on financial performance.

Empirical Review

Okoro investigated how digitalization affects the performance of Deposit Money Banks (DMBs) in Nigeria. By estimating variables such as Web pay, the Nigeria Inter-Bank Settlement System (NIBSS) Instant Payment (NIP), Point of Sale (PoS), and mobile payments with the Dynamic Ordinary Least Squares (DOLS) method, the researchers concluded that mobile payment transactions had a positive impact on bank profitability. In contrast, PoS and Web pay transactions contributed positively but insignificantly to liquidity ratios. The study highlighted that technological innovation is crucial for enhancing bank profitability and advised DMBs to continue utilizing digital channels to improve performance.

Mboto [1] assessed the effects of electronic banking adoption on Nigeria's deposit money banks' performance. Utilizing a descriptive study design and panel data analysis with variables including ATM, Internet banking, PoS, and mobile banking, the research indicated that all electronic banking channels positively influenced bank performance, as evidenced by data obtained from the CBN Financial Report (2021). The authors claimed that adopting e-banking technology enhances performance and encouraged commercial banks to invest in emerging technologies for efficient service delivery.

Daniyan-Bagudu analyzed how mobile banking affects the performance of Nigerian commercial banks through a survey design. They distributed standardized questionnaires to 22 commercial banks and analyzed the collected data using descriptive statistics and graphs. The findings indicated a positive and significant impact of mobile banking on financial performance. The researchers concluded that mobile banking is essential for maintaining bank competitiveness and recommended increasing the use of mobile services to meet rising customer demand.

Demaki explored the relationship between electronic banking and the performance of deposit money banks in Nigeria through an ex-post facto research design. They employed quarterly time-series data from 2009 to 2019, utilizing co-integration and error correction models, with variables such as mobile banking, ATM, Internet banking, PoS, and ROA. Results showed that mobile banking, ATM, and PoS significantly influenced financial performance, whereas Internet banking had no statistical significance. The study advised banks to promote mobile banking and assess customers' technical backgrounds before implementing advanced technologies.

Origin and Rapuluchukwu studied the impact of electronic banking on Nigerian commercial banks' performance from 2010 to 2019, utilizing the Autoregressive Distributive Lag (ARDL) model. They gathered data on PoS, USSD, Web banking, and ROA from Central Bank of Nigeria and NDIC reports. Their analysis revealed that digital banking positively but minimally affects performance. The authors acknowledged that while electronic banking has great potential, its current contribution to performance is still insufficient, suggesting further investment and outreach strategies may be required.

Madugba eexamined the impact of electronic banking on the financial performance of Nigerian deposit money banks using an ex-post facto research design. They collected data from the Central Bank of Nigeria and the National Bureau of Statistics. The regression analysis found that ATM had a positive and significant effect on EPS and ROA, while PoS and NEFT significantly influenced ROA; however, the effect of Web banking was not significant. The research concluded that electronic banking positively impacts financial performance and recommended enhanced customer education on e-banking products.

Areghan researched the effects of mobile banking adoption on customer satisfaction, loyalty, and retention in Nigeria. Using a survey research design, they distributed 150 questionnaires across five major banks, returning 100 responses. ANOVA regression analysis revealed that mobile banking has strong and positive effects on customer satisfaction and loyalty. The authors recommended improving the mobile banking platform by enhancing internet access and USSD code efficiency to attract and retain customers. 

Raymond examined the impact of electronic banking on the performance of Nigerian banks from 2009 to 2017. Using the Johansen Co-integration and Vector Error Correction Method (VECM), the authors analysed factors including Mobile pay, Web pay, and PoS in relation to Net Interest Margin (NIM). The findings indicated that Mobile pay and Web pay exhibit a negative correlation with NIM, while PoS demonstrates a significantly strong positive relationship.

The research indicated that banks need to increase PoS and internet transaction services in locations for better performance. 

Lohven and Felicia investigated how mobile banking affects customer satisfaction at First Bank Plc in Jos, utilizing a survey design with a sample of 397 customers. Data analysis was conducted using simple regression and E-Views software. The study revealed a significant relationship between customer satisfaction and mobile banking, particularly regarding loyalty. It emphasized that banks must address security concerns, network issues, and transaction errors to enhance customer satisfaction.

Etuk et al. [6] researched the effects of electronic banking on the marketing performance of deposit money banks in Uyo, Akwa Ibom State. They employed a survey research design to collect primary data from senior staff at four banks: First Bank Plc, Zenith Bank Plc, United Bank for Africa Plc, and Guaranty Trust Bank Plc. The focus was on electronic banking proxies such as ATMs, PoS, internet banking, and mobile banking, assessing customer acquisition as the performance measure. Regression analysis indicated that electronic banking positively impacts marketing performance, facilitating customer acquisition. The authors noted that leveraging electronic banking innovations supports this process and recommended the continued use of these services for competitiveness.

Ukwubile and Uche explored the relationship between electronic banking services and customer satisfaction among selected Nigerian deposit money banks. They gathered data through a survey design and questionnaires, analyzing it with ordinary least squares regression. The findings highlighted that while the reliability of electronic banking services did not positively influence customer satisfaction, transaction efficiency and service performance did. The authors recommended that banks improve the reliability, performance, and quality of their electronic banking channels to enhance customer satisfaction and remain competitive.

Oyedeko and Kolawole [15] investigated the impact of digital marketing on the performance of deposit money banks in Nigeria. Utilizing a cross-sectional survey design, they collected data from bank customers and employees across 26 banks, resulting in 384 samples. Multiple and hierarchical regression analyses revealed that email marketing, social media marketing, and mobile marketing positively affect bank performance. The study concluded that digital marketing boosts consumer patronage and advocated for prioritizing digital channels over traditional marketing methods.

Onoh et al. [13] assessed how the digitalization of operations impacts the performance of deposit money banks in Enugu State, employing a descriptive survey design. Data were collected from 232 entrepreneurs via questionnaires and analyzed using Z-tests. The findings showed that mobile apps significantly improve customer retention, while transactional internet banking positively influences the volume of loans attracted. The authors suggested marketing mobile apps to enhance efficiency and customer interaction.

Ezie, Musa, & Joshua [8] analyzed the impact of electronic banking on the performance of Nigerian deposit money banks from 2009Q1 to 2023Q1. Using the Dynamic Ordinary Least Squares (DOLS) methodology, the study examined variables like PoS transactions and cheque usage alongside Return on Assets (ROA). Results indicated that PoS and cheque transactions positively affect banks' long-term performance. The study recommended implementing suitable technology to replicate digital banking segments and encouraging consumers to use electronic banking channels.

Ngbede [10] investigated agency banking and mobile money businesses in Benue State through a survey involving 1,350 registered mobile money agents and management staff. Multiple regression analysis revealed that mobile banking and PoS significantly contribute to bank performance. The study found that agency banking impacts financial inclusion and suggested that banks intensify efforts towards rural penetration.

Review-Gap

Despite a growing body of research exploring the connection between digitalization and bank performance in Nigeria, there remain notable gaps in concepts, scope, and methodology. Conceptually, many studies tend to narrowly define digitalization by focusing on specific e- banking channels such as ATM, PoS, mobile banking, or internet banking (e. g., Okoro et al., 2023; Mboto et al., 2021), rather than adopting a broader framework that encompasses the full integration of digital processes, real- time analytics, artificial intelligence, or blockchain technologies. Furthermore, most of the research priorities aspects like customer loyalty, marketing performance, and satisfaction [6], with significantly less emphasis on key financial performance metrics such as Profit After Tax (PAT) and Return on Assets (ROA) for Tier- One banks. These large- capital institutions form the core of Nigeria' s financial system and have greater opportunities to adopt and improve digital innovations; however, few studies specifically analyse this group in a systematic empirical manner. While some research indicates positive correlations between digital technologies and profitability, the underlying causal processes and mechanisms through which digitalization enhances profitability are often poorly theorized and vaguely characterized.

Methodologically, the existing literature lacks consistency in research designs and statistical techniques, complicating efforts to make comparisons and generalizations. Some studies utilize survey- based designs [10], while others employ time- series [10] or panel data study designs, but often without adequate justification for their design choices in relation to the research aims. Many studies rely on data collected at uneven time intervals, thereby limiting longitudinal analysis of the impact of digitalization on financial performance. Additionally, although a few studies apply advanced econometric methods like ARDL, DOLS, and VECM [10], they seldom compare these results with other estimation methods to demonstrate robustness. Most critically, none of the analysed literature considers the individual estimation of the collective effect of digitalization on PAT and ROA over an extended post- digital policy reform timeframe (2014–2023) within Tier- One banks. This represents a significant methodological gap that the current research aims to address through a rigorous longitudinal econometric approach, thereby contributing to the empirical literature.

The study utilises an ex post facto design, ideal for examining how the independent variable digitalisation affects dependent variables such as ROA and PAT, without manipulating any variables. This approach is particularly relevant in financial research that analyses historical data to uncover cause-effect relationships. By focusing on events from the past during the period of 2014 to 2023, the ex post facto method facilitates a thorough analysis aligned with observable trends and patterns. Additionally, this research design is consistent with previous empirical studies concerning technology adoption and performance in Nigerian banks [10].

The studies focus on Tier-One deposit money banks in Nigeria, including Zenith Bank, Access Bank, First Bank, GTB, and UBA. Due to the limited population, a census sampling method was employed to select these institutions. The selection criteria included asset size, customer base, digital maturity, and their frequent mentions in the CBN's financial reports. Evaluating Tier-One banks aims to identify the most technologically advanced and financially sound institutions, as they are likely to pursue ambitious digital banking initiatives. Additionally, their comprehensive financial disclosures and audited annual reports provide a reliable basis for data collection and verification.

The data utilised in the study is secondary panel data sourced from the audited annual financial reports of the chosen banks, CBN statistical bulletins, and NIBSS reports. The sample spans ten years, from 2014 to 2023, offering ample temporal depth to trace the digitalisation trajectory of the Nigerian banking sector. The independent variable, digitalisation, is estimated using mobile banking transaction volumes, which have been widely recognised in previous studies as a solid indicator of technology adoption in banking [12-14]. The dependent variables are Profit After Tax (PAT) and Return on Assets (ROA), essential financial performance indicators. ROA indicates how effectively a bank utilises its assets to generate returns, while PAT provides insight into the bank's profitability post-tax.

The research employs both descriptive and inferential statistical analysis methods. Descriptive statistics, such as mean, standard deviation, and trend analysis, are utilized to explore the central tendency and variability of the variables over time. Inferential analysis employs multiple regression to assess the impact of digitalisation, specifically mobile transaction volume, on ROA and PAT. The regression equations are derived from research utilising the linear estimation technique to investigate the relationships between financial variables (Gujarati & Porter, 2009). Diagnostic tests, including multicollinearity checks, autocorrelation assessments (using the Durbin-Watson test), and tests for heteroskedasticity (Via the breusch-Pagan test), are conducted to ensure the stability and validity of the regression estimates. The regression equations are expressed as

ROAit = β₀ + β₁(MBTVol)it + β2INTRit + εit

3.1

PATit = β₀ + β₁(MBTVol)it + β2INTRit + εit

3.2

where MBTVol represents mobile banking transaction volume, INTR represents interest rate and ε is the error term. β0 is intercept term, β₁-β2 are coefficients, i is individual banks, t is time-period.

The rationale for using multiple regression stems from its ability to estimate how much and in what direction a predictor variable affects outcome variables, holding time effects constant. Additionally, it is a common method in empirical finance and accounting studies when examining performance measures over a time series. Statistical analysis will be performed using EViews version 12, which enables accurate estimation, visualization, and interpretation of results. Ethical considerations were upheld by strictly using publicly available secondary data, thus avoiding issues related to confidentiality and participant consent. All information was verified against other sources to ensure reliability and validity. This study's findings are expected to provide actionable insights for policymakers, financial institutions, and researchers focusing on the impact of digitalization on bank performance in emerging economies.

The descriptive statistics presented in Table 1 are used to draw insightful inferences regarding how digitalisation affects the performance of banks, here, in PAT and ROA for Tier-One Banks in Nigeria over the period 2014-2023. The mean PAT of ₦163.7 billion, which arises from large standard deviation of ₦149.9 billion, portrays gross heterogeneity in profitability of banks, which could be explained in terms of varying degrees of digital investments, operating efficiency, and customer relationship management practices among the banks. The big value for skewness (2.26) and kurtosis (7.63) illustrates PAT distribution with heavy tails and asymmetry, which suggests the presence of very few banks with highly high profit. This might mean that banks possessing superior digital foundation or prior application of fintech technologies (i.e., mobile banking, data analytics, online channels) perhaps performed superior, thus creating such disparity. Statistically significant Jarque-Bera test (p < 0.01) verifies non-normality in the distribution of PAT, thus justifying the adoption of robust regression methods in following analysis.

For ROA, the average of 2.46% is indicative of moderate effectiveness in generating returns from assets characteristic of emerging economies' levels. Yet, as there is a fairly high standard deviation (1.32%) and moderate skewness (0.88), it indicates significant variation between banks. This variation may be attributed to how effectively the banks implement digital technologies in fundamental activities like credit scoring, onboarding the customer, and cost management. Specifically, NTR variable, where the mean stands at 13.78% and low standard deviation (2.28%), appears very stable across the sample, aligning it for a macroeconomic control. The MBTV, perhaps an indicator of digital investment or uptake, is highly scattered with a gigantic standard deviation, implying differential degrees of digitalisation in banks. 

This aligns with the very research focus: whether such differentials in digital uptake have any material influence on ROA and PAT. Because all variables exhibit a normal distribution, logarithmic transformation was thus conducted (Table 1).

Table 1: Descriptive Results

Source: E-Views 12

The correlation results summarized in Table 2 reveal interesting experiences on how the correlation between digitalisation as MBTV with bank performance indicators (PAT, and ROA) for Tier-One banks in Nigeria operates. The fact that PAT is correlated and strongly (positively) dependent on MBTV (r = 0.461, p = 0.001) implies that high volumes of mobile banking transactions tend to mean high profit declaration by banks. This is in support of the assumption that digitalisation, and indeed via mobile banking channels, propels better financial performance by increasing customers, lowering cost of operations, and improving delivery of services. Digital transformation hence becomes a source of revenue generation, primarily in the Nigeria competitive banking industry where mobile banking is part of the delivery of services.

Table 2: Correlations

However, the connection between PAT and INTR is negative but weak statistically (r = -0.253, p = 0.077). Even if this finding is not statistically significant at conventional levels, it describes a weak negative relationship: as interest rates rise, PAT declines, though in a marginal sense. This might be explained by increasing interest charges for banks or decreasing loan requests from clients that perhaps offsets overall profitability. Yet, the absence of significance implies that other determinants, i.e., efficiency of digital services (as measured by MBTV), would be more important in determining profitability than macroeconomic controls such as interest rates.

Considering ROA, there is a negative and weak relationship with MBTV (r = -0.074, p = 0.607), indicating that there is no significant relationship between return on assets and mobile banking trade volume. This is a sign that even though digitalisation can boost levels of profit in general (as seen in PAT), it may not necessarily enhance the efficiency of banks to leverage assets in order to bring returns. It may also represent costs of short-term digital investment that decrease ROA over the short run. Likewise, ROA and interest rate (r = 0.031, p = 0.832) are unrelated, meaning that monetary policy changes hardly influence asset efficiency in this regard. In general, the results show that digitalisation (through MBTV) is more closely associated with profitability (PAT) than efficiency (ROA), and interest rates play a lesser role, thereby making MBTV a better predictor of financial performance than INTR in this research.

The outcome of the Correlated Random Effects-Hausman Test gives a Chi-Square statistic value of 0.5284 and a corresponding p-value of 0.4673, which means that it is not possible to reject the null hypothesis. This implies that the variance between the fixed effects and random effects estimators is not important, and thus it warrants the application of the random effects model to examine the correlation between digitalisation (e.g., MBTV) and bank performance (ROA and PAT) in Nigeria's Tier-One banks. The implication is that the unobserved heterogeneity between banks is not related to the explanatory variables in the model, and the random effects model is consistent and efficient. This is consistent with a wider interpretation of the effect of interest rates and mobile banking on bank profitability and efficiency throughout the panel data set, which offers more robustness to the study's results on the contribution of digitalisation to financial performance.

Table 3: Correlated Random Effects - Hausman Test

The EGLS regression results with PAT as the dependent variable and MBTV and INTR as explanatory variables provide strong evidence regarding the influence of digitalisation on the performance of Nigerian Tier-One banks. The MBTV coefficient is 0.1045 and is statistically significant (p = 0.0009), suggesting that the growth in mobile banking transactions positively and notably boosts profit after tax. This finding emphasises the crucial role of online banking in improving profitability by enhancing customer access, lowering transaction costs, and generating increased revenue for banks. It supports the hypothesis that digitalisation, represented by MBTV, is a key factor in financial performance within contemporary banking (Table 4).

Table 4: Random Effect Regression

Source: E-Views 12, Dependent Variable: PAT, Method: Panel EGLS (Cross-section random effects), Date: 04/20/25   Time: 14:16, Sample: 2014 2023, Periods included: 10, Cross-sections included: 5, Total panel (balanced) observations: 50, Swamy and Arora estimator of component variances

In contrast, the INTR variable shows a negative coefficient, although it is statistically insignificant (-0.5192, p = 0.3780). This suggests that changes in interest rates do not have a meaningful direct impact on PAT during the sample period. This may indicate that Nigerian Tier-One banks have successfully mitigated potential negative effects of interest rate fluctuations by leveraging diverse income streams and efficient asset-liability management. Consequently, mobile banking digital innovation appears to provide a more reliable prediction of bank profitability than macroeconomic factors like interest rates in this context (Table 4).

Furthermore, the model demonstrates a generally good fit, with an R-squared value of 0.476 and an adjusted R-squared of 0.445, suggesting that nearly 45% of the variation in PAT can be explained by the selected variables. The F-statistic of 8.958 (p = 0.0005) confirms the model's joint statistical significance, reinforcing the hypothesis that digitalisation, specifically mobile banking, significantly contributes to the performance of Nigeria's prominent banks. The Durbin-Watson value of 1.60 indicates no substantial autocorrelation issues within the model, further enhancing the reliability of the findings. In summary, the analysis demonstrates that the use of mobile banking technology is strategically advantageous for boosting profitability in Nigeria's banking sector (Table 4).

Table 5:   Regression results showing the impact of MBTV and INTR on ROA using Panel EGLS (Random Effects) method

Source: E-Views 12, Dependent Variable: ROA, Method: Panel EGLS (Cross-section random effects), Date: 04/20/25   Time: 14:30, Sample: 2014 2023, Periods included: 10, Cross-sections included: 5, Total panel (balanced) observations: 50, Swamy and Arora estimator of component variances

Table 5's panel regression results reveal the effects of digitalisation represented by MBTV and INTR on ROA in Tier-One Nigerian Banks. The MBTV coefficient is statistically insignificant (0.0846) and positive (p = 0.2508), indicating that mobile banking activities may have a slight positive effect on bank efficiency, but not significantly on ROA during the analysed period. Therefore, the increasing digitalisation of Tier-One banks in Nigeria has not yet meaningfully impacted their ability to convert assets into net profits.

Conversely, the INTR shows a negative coefficient (-1.1715) and is also statistically insignificant (p = 0.4183). This implies that changes in interest rates did not substantially influence Tier-One banks' ROA during the specified timeframe. While interest rate fluctuations typically affect banks' profitability through loan pricing and investment returns, these findings suggest that other significant factors also play a role in determining ROA within the Nigerian context.

Moreover, the model does not fit well, as indicated by an R-squared of 0.152 and an adjusted R-squared of 0.111, showing that MBTV and INTR account for only about 11% of the variance in ROA. The F-statistic of 0.7856 and a p-value of 0.462 further demonstrate that the model lacks joint statistical significance. This reinforces the idea that while digitalisation represents a crucial innovation in Nigerian banking, its complete potential for asset-based performance metrics like ROA remains unfulfilled. To unlock greater performance benefits, banks must further leverage their digital strategies to enhance core business and asset management outcomes.

The histogram of standardized residuals, along with the descriptive statistics, suggests that the regression model's residuals are reasonably normally distributed. Both the mean and median are close to zero, skewness is minimal (−0.210811), kurtosis falls within an acceptable range (3.51), and there are no significant outliers affecting normality. Furthermore, the Jarque-Bera test yields a statistic of 0.912383 and a p-value of 0.633899, which significantly exceeds the 0.05 significance threshold, indicating that we cannot reject the null hypothesis of normality. This reinforces the assumption of normality for the residuals, confirming the reliability of the regression estimates used to analyse the impact of digitalisation on bank performance in Nigeria's Tier-One banks (Figure 1).

H01: Mobile banking transaction volumes do not have a significant impact on the Profit After Tax (PAT) of Tier-One banks in Nigeria.

Figure 1: Histogram Normality Test

The regression estimates for PAT displayed in Table 5 reveal that the coefficient for MBTV is 0. 1045, with a p-value of 0. 0009, significantly lower than the 0. 01 level. This strong statistical evidence suggests that MBTV positively and significantly impacts PAT among Tier- One banks in Nigeria. Therefore, we reject the null hypothesis (H 01) in favor of the alternative hypothesis, which posits that mobile banking transaction volumes significantly influence PAT. This indicates that digitalisation efforts, particularly in mobile banking, contribute positively to the overall profitability of major Nigerian banks by boosting transaction volumes, lowering operational costs, and expanding customer outreach through digital channels.

However, while mobile transaction volumes are increasing, this growth has not yet meaningfully increased returns on bank assets. One potential reason is the initial infrastructure and maintenance costs associated with mobile banking platforms, which may hinder early financial returns. Furthermore, the gap between digital adoption and effective performance may explain this statistical insignificance [12]. Scholars like Adebayo and Salisu [1] argue that increased customer reach via digital banking does not necessarily lead to higher returns on assets without policies to enhance service utilization. Moreover, Okoye and Eze [7,11] stress that digitalisation initiatives should be paired with robust risk and credit management systems to positively affect ROA. Onuoha and Ajayi [14] also highlight that operational efficiency, rather than transaction volume, is vital for asset efficiency. Finally, Eze and Nwachukwu [7] assert that ROA serves as a better long- term measure of profitability than a short- term indicator of digital success, recommending time- lagged analysis in future research. Thus, despite the centrality of digitalisation, the observed zero correlation between MBTV and ROA aligns with existing literature, suggesting that digital platforms require time and consistent strategies to impact ROA effectively.

H02: Mobile banking transaction volumes do not have a significant impact on the Return on Asset (ROA) of Tier-One banks in Nigeria.

The regression analysis for ROA presented in Table 5 reveals that MBTV has a coefficient of 0. 0.0846 and a p- value of 0. 0.2508, which exceeds common significance levels (0. 01, 0. 05, or 0. 0.10). This indicates no statistically significant relationship between MBTV and ROA among the studied Nigerian Tier- One banks, leading us to retain the null hypothesis (H 02). In other words, the evidence suggests that mobile banking transaction volumes do not significantly impact Return on Assets. While digitalisation via mobile banking may offer other advantages, it has not yet positively influenced the asset efficiency of these banks.

Conversely, although the PAT regression analysis is not included, it is expected to show a positive significant effect of mobile banking transaction value on profit after tax (PAT) in Tier- One banks. This finding points to a more immediate financial benefit of online banking, where increased mobile transactions not only reduce transaction costs but also enhance efficiency directly affecting profits [7,12]. Online channels allow banks to serve more customers at a lower marginal cost, leading to higher revenues and profits. Additionally, Adebayo and Salisu [1] note that greater use of mobile banking boosts revenue from transactions, improving banks' profitability margins. Etuk, Akpan, and Awah (2023) also highlight that digital channels enhance marketing and cross- selling opportunities, which contribute positively to PAT. This aligns with findings by Oyedeko and Kolawole [15], who confirmed that digital marketing and mobile banking correlate with improved financial performance for Nigerian banks. Ngbede [10] further supports this by stating that agency banking and mobile money reduce overhead costs and increase revenues, leading to higher profitability ratios. Thus, MBTV' s significant influence on PAT underscores that digitalisation serves as a revenue- generating tool, shaping the short- term profitability of mobile banking in Nigeria' s banking sector.

Overall, while mobile banking has not significantly boosted ROA, it has positively impacted PAT, highlighting a distinction between maximising efficiency and focusing on profitability metrics. This indicates that digitalisation currently facilitates gross profitability more effectively than optimally utilising bank assets a gap that could be bridged by deeper integration of digital solutions into banks' core business and asset management practices.

The research analysed how digitalisation, indicated by mobile banking transaction volumes, affects the financial performance of Nigerian Tier-One banks, using ROA and PAT as key metrics. The descriptive, correlation, and regression analyses reveal an increasing reliance on digital channels in Nigeria's banking sector, emphasising mobile banking as a strategic asset in today's competitive financial environment. The results illustrate that while digitalisation is rising as a primary customer service delivery channel and engagement tool, its economic impact varies depending on the performance measure examined. Findings align with the RBV theory, which posits that long-term competitive sustainability derives from valuable, rare, and inimitable internal resources, highlighting digital infrastructure, particularly mobile banking systems, as vital.

Based on the empirical evidence, the conclusion is that mobile banking transaction volumes significantly boost PAT but have no statistically significant effect on ROA. This suggests that while digitalisation enhances banks' overall profitability, it may not yet translate into better asset utilisation. The strong positive correlation between mobile banking and PAT indicates that digital channels contribute to revenue growth and reduce transaction costs, thereby improving profit margins. However, the lack of impact on ROA points to the need for further strategic alignment between digital systems and essential operational processes to enhance asset productivity. Therefore, banks should focus not only on developing digital channels but also on integrating these technologies into their asset management and operational efficiency frameworks to unlock their full potential for performance improvement.

• Based on the findings, the study recommends that:

• Tier-One banks in Nigeria should integrate mobile banking into their core banking and asset management strategies rather than treating it as a separate service. Given mobile banking's impact on Profit After Tax (PAT), these banks can further boost efficiency by linking digital channels to data analytics, credit scoring models, and internal auditing functions. This approach will enhance profitability and improve Return on Assets (ROA) through better asset utilization.

Additionally, to preserve and enhance the profitability achieved through digitalisation, banks need to invest in user-friendly mobile banking technologies and improve financial literacy among their customers. Streamlining mobile banking applications and educating customers about digital financial services can increase transaction volumes, foster customer loyalty, and create more cross-selling opportunities. This dual focus on innovation and empowering customers will allow banks to maximise the value of their digital resources, in line with Resource-Based View (RBV) principles.

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