Does Digital Technology and Banking Regulation Matter for Income Diversification in Ethiopian Commercial Banks?

Does Digital Technology and Banking Regulation Matter for Income Diversification in Ethiopian Commercial Banks? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Does Digital Technology and Banking Regulation Matter for Income Diversification in Ethiopian Commercial Banks? Asnake Getachew, Getaneh Mihret, Firew Chekol This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8330606/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the effect of financial technology and banking regulation on income diversification in Ethiopian Commercial Banks. The study used panel data from 17 Ethiopian Commercial Banks spanning 2015–2024. Using two-step system GMM, the study revealed that financial technology proxy by the volume and value of digital transaction and bank management’s operational efficiency are the most robust and consistent drivers of income diversification across Ethiopian Commercial banks. Banking regulation exerts a marginal adverse effect on income diversification. Bank size, years in operation and lending strategy have no significant impact once endogeneity is controlled . The findings provide valuable implications for policy makers, bank executives and researchers. The persuasive influence of digital transformation to accelerate the modernization of the financial sector by expanding digital infrastructure, supporting interoperability, and revisiting outdated directives that limit innovation should be the prime focus of policy makers. Besides, strategic investment in digital channels, payment systems, and process efficiency should be prioritized ahead of traditional expansion metrics such as branch networks or asset size . These implications underscore that enhancing the digital maturity and operational capabilities of banks is central to improve profitability, strengthening competitive positioning, and reducing overreliance on interest income in the Ethiopian banking landscape. Business and commerce/Business and management Social science/Business and management Business and commerce/Economics Social science/Economics Business and commerce/Finance Social science/Finance Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Financial technology banking regulation income diversification Commercial Banks Ethiopia Figures Figure 1 1. Introduction Diversification has long been regarded as a central theme in economics, finance and strategic management, reflecting the fundamental question of how firms can balance growth profitability and stability. At its core, diversification refers to the process of expanding business activities into new products, services, or markets in order to spread risks and capture new opportunities. In the banking industry, this debate has gained renewed momentum with the rise of financial technology, which has dramatically altered how financial services are produced, delivered and consumed. The rapid adoption of digital channels has raised important questions about whether such technological advancement reinforces banks’ income diversification strategies or merely reshapes the structure of their traditional intermediation role (Berger & Bouwman, 2013 ); (Gerek & Tuncez, 2023 ). From supportive perspective, several theoretical traditions argue that digitization enhances income diversification. Modern Portfolio Theory (Markowitz, 1952 ) suggests that spreading revenue source across interest and non-interest activities reduce risk and stabilize returns. Transaction cost economics (Williamson, 1989 ) explains how technology reduces cost and information asymmetries, enabling banks to profitably expand in to new service areas such as digital payments, insurance, and remittances. Similarly, the resource-based view (Barney, 1991 ) and dynamic capabilities theory (Teece et al., 1997 ) emphasize that digital infrastructure constitutes a strategic resource that allows banks to reconfigure products and capture new sources of non-interest income. Innovation diffusion theory (Rogers & Williams, 1983 ) further suggests that the spread of digital innovations such as mobile banking and fintech collaborations acts as a catalyst for new income streams through product and service innovation. However, theoretical counterarguments warn that digitization does not necessarily lead to beneficial diversification. The specialization hypothesis and focus theory of banking (Berger et al., 2010 ) argue that diversification may distract banks from their core intermediation functions, increasing inefficiency and managerial complexity. The notion of creative destruction also highlights how Fintech competitors may capture the benefits of digital innovations, eroding banks’ ability to diversify income effectively. Agency theory (Meckling & Jensen, 1976 ) raises concerns about overinvestment in digital projects that fail to generate proportional returns, while the market power perspective suggest that only larger banks may benefit from digitization, leaving smaller institutions at a disadvantage. These conflicting theoretical perspectives underscore the absence of consensus on whether digitization enhances or undermines diversification. Empirical evidences revealed that there is an increasing global trend toward non-interest income both in developed and developing markets (Thakur & Arora, 2024 ). Bank income statements are increasingly displaying non-interest revenue, which comprises commissions, charges, fees, and trading income. Contrary to this, the volume (count) of digital transaction, value (amount) of digital transaction and asset size of all Ethiopian Commercial banks tend to continually increase and the growth in value of digital transaction since 2022 continues skyrocketing while income diversification starts to slow down very slightly (See Fig. 1 below). Banking regulations is now also an important structural force shaping income diversification strategies of financial institutions; particularly in developing economies. Regulatory requirements such as lending restrictions, capital adequacy standards, liquidity ratios and provisioning rules not only safeguard financial stability but also influence the extent to which banks expand in to non-interest income activities (Hunjra et al., 2020 ); (Muhammed et al., 2023 ). Empirical Evidences from international literature show that well-designed regulatory frameworks can encourage banks to pursue fee-based services and other innovative income sources as a way to manage compliance costs and enhance resilience. However, overly restrictive regulations may unintentionally limit diversification by constraining product offerings and inhibiting innovation. In Ethiopia, where the regulatory environment has historically prioritized stability and risk containment constraints such as foreign currency restrictions and limited flexibility in digital banking product development and usage restricted bank’s ability to fully exploit emerging diversification opportunities (AlKhouri & Arouri, 2018 ); (Kiplagat & Kalui, 2020 ). These regulatory dynamics make banking regulations a critical determinant to be studied when examining income diversification within the Ethiopian financial sector more specifically in the context of commercial banks. Although numerous studies having contradicted empirical findings in developed and emerging economies to examined the determinants of income diversification in the banking industry, evidence on how financial technology and banking regulation shape diversification remains notably limited in number and scope in developing country contexts. In particular, there is a clear absence of empirical study focusing on this specific topic in Ethiopia, where the banking sector has undergone rapid technological transformation and evolving regulatory reforms over the past decade. So as to addressing this gap, the study investigate the impact of financial technology, banking regulatory constraints and other bank-specific factors on income diversification using data from 17 Ethiopian Commercial Banks over the period 2015–2024. By providing context-specific empirical insights, the study contributes to the existing literature and enhances understanding of the determinants driving revenue diversification strategies within a developing financial system. Aligned with the introduction, this paper is structured as follows. Section 2 provides an overview of the theoretical background and review of empirical literatures on the key determinants of income diversification. Section 3 incorporates the data, variables and the research method for estimating the determinants of income diversification. Sections 4 describe and discuss the empirical results. Section 5 provides conclusions of key findings and policy implications for the study. 2. Literature Review 2.1 Theoretical Review Even though there is no consensus on the precise meaning of the concept of diversification among researchers, exploring the key determinants of income diversification has been the subject of numerous theories. As argued by different scholars, the term “diversification” would have different meanings when research interests varied (Oyenubi, 2017 ). Its definitions are many and therefore, what is needed is a comprehensive definition which is both theoretically sound and managerially valid. Some researchers have thus defined diversification in terms of the number of products, services and markets, while others define it in terms of the means and methods. Diversification has thus been defined by some researchers as the quantity of goods, services, and markets, while other researchers define it as the strategies and tactics that allow businesses to expand while lowering overall risk (Markowitz, 1952 ). In general, diversification means that a company is running more business lines, whether or not they are connected claims that the grand strategy of diversification signifies a clear shift in the company's focus from its current operational foundation to a new business line that may be acquired or developed through expansion (Mulwa et al., 2015 ); (Ashyari & Rokhim, 2020 ). The diversification of bank income is explained by internal changes in non-interest income and interest income. Banks engage in income-generating operations known as income diversification, which involve creating a range of products and services from traditional interest- or non-interest-generating ventures, or combining the two simultaneously. The kind of revenue structure determines the degree of income diversification. Basic bank income consists of interest income, commission income, income from transaction fees, and other income (Nguyen et al., 2023 ). Banks diversify by expanding their product lines (Chandramohan et al., 2022 ); geographic reach (Berger et al., 2010 ), (Alshomaly, 2014 ); customer segment (Beck et al., 2007 ) and technological capabilities (Hamdi et al., 2017 ), (Thakur & Arora, 2024 ). This may involve introducing new financial products, entering new regions, tailoring services to varied client groups or leveraging digital platforms to deliver innovative solutions. Technological diversification, in particular, enhances intermediation efficiency and supports the growth of non-interest income through service such as mobile banking, card banking, digital transfers, agent banking, e-wallets and others enabling banks to reduce reliance on traditional interest income. Hypothetically, while several theories support a positive link between digitization and income diversification such as Innovation Diffusion Theory, which views digital channels as enablers of new non-interest income sources (Rogers & Williams, 1983 ); the Resource-Based View, which provokes digital capabilities as strategic tools for product line expansion (Barney, 1991 ); Transaction Cost economics, which highlights cost optimization that makes new service feasible (Coase, 1937 ); and Dynamic Capabilities Theory, which frames digitization as driver of adaptability (Teece et al., 1997 );, others caution against the relationship between financial technology and income diversification. The Specialization Hypothesis and Focus Theory argue that diversification may distract banks from core lending activities (Berger et al., 2010 ), while Technological Disruption Theory suggests that digital innovation can erode advantages of traditional banking rather than expanding their revenue streams. Market Power Theory and risk concentration arguments also note that digital enhancement may not accrue equally across bank sizes and Agency Theory highlights the risk that digitization may increase costs and complexity. Moreover, income from digital services such as fees, commissions and charges can be key competitive strategic tools and strictly controlled by the regulatory organs. These theoretical contradictions still pose unclosed questions whether financial technology has positive impact on income diversification or not and this study examines the relationship in the context of the Ethiopian banking sector. The Regulatory Pressure or Compliance Cost Perspective, Market Discipline Theory and Prudential Regulation Perspective suggest that strict banking regulation pushes banks to look for alternatives of less-capital intensive revenue sources like non-interest income, promote operational efficiency and fair competitions and enables to create stable operating environment that encourages new product innovation and expansion (AlKhouri & Arouri, 2018 ); (Hunjra et al., 2020 ); (Muhammed et al., 2023 ). The Traditional Banking Theory, Focus Theory of Banks and Regulatory Burden Theory stand against the argument that banking regulation can positively impact income diversification by suggesting banks can perform best when they focus on their core intermediation role, diversification may lead banks to complexity and supervisory challenges and it will increase costs of regulatory compliances (Ferri & Pesic, 2020 ). In the context of Ethiopian banking sector, overly stringent rules such as new product development restrictions and rigid operational controls may limit diversification. Bank’s income diversification efforts are closely linked to their lending practice, as relying solely on interest income heightens exposure to credit risk and earnings volatility. Prominent theories including Modern Portfolio Theory, Financial Intermediation Theory, Resource-Based View and Economies of Scope Theory suggest that well designed lending strategy contributes positively for income diversification, enable banks to build long-term relationship and in turn contributes to develop tailored bundle of products to expand non-interest revenue streams, encourage banks to extend cross-selling activities like insurance, trade services and advisory services and lending relationships allow banks to efficiently add complementary services which have the potential to generate non-interest income (Hamdi et al., 2017 ); (Dang, 2021 ). Contrarily, the Risk Concentration Theory, Financial conglomerate Hypothesis, Corporate Focus Theory and Agency Theory stand against the positive relationship between lending strategy and income diversification of banks. They suggest that diversification sometimes push banks to unfamiliar and riskier activities, diversification poses operational complexity and governance challenges, banks can perform best if they can concentrate only on selected area of competence and diversification can harm performance and dilute focus on core lending expertise (Viet, 2020 ); (Lestari et al., 2023 ). This study therefore explores the relationship between lending strategy of banks and income diversification in the context of Ethiopian banking sector. In general, while external determinants such as financial technology and banking regulation shape the broader operational environment for income diversification, the internal characteristics of banks plays an equally critical role in driving their diversification strategies. Bank-specific factors such as age and size (Ashraf & Nazir, 2023 ), lending strategy and operational efficiency (Phan et al., 2022 ) reflect the inherent strengths and weaknesses of individual institutions, influencing their capacity and inclination to diversify income sources. Larger banks, for instance, often leverage economies of scale and technological infrastructure to expand non-interest income activities, while well-capitalized banks can take on greater risks to pursue diversification opportunities. Exploring these internal factors provides valuable insights into how variations in institutional characteristics impact the ability of Ethiopian banks to balance profitability and their risk-return trade off. 2.2 Empirical Review Empirical evidence from diverse economies demonstrates that financial technology or digitalization significantly influence bank income diversification and profitability, though with varying magnitude and directions. For instance, Thakur and Arora ( 2024 ) found that technological advancement, bank size and market competition positively influence income diversification among Indian Commercial banks, while macroeconomic factors such as GDP growth and capital ratio reduce diversification tendencies. Similarly, Chhaidar et al. ( 2023 ) observed that investment on Fintech enhance European bank’s profitability, with larger banks gaining more benefits due to scale and resource advantage. In contrast, Yuan et al. ( 2025 ) discovered that Fintech development initially undermines the profitability of Chinese banks by intensifying competition and reducing interest income, but once digital integration surpasses a certain threshold, the negative effect diminish. These findings highlighted that the impact of Fintech on diversification and profitability is non-linear and context dependent. Parallel studies on bank size, income diversification and liquidity effects reinforce that technological transformation reshapes bank’s operational behavior. A study by (Q. T. T. Nguyen et al., 2023 ) revealed that bank size has a strong positive effect on income diversification among Vietnamese banks, suggesting that larger institutions leverage technology and economies of scale to expand non-interest income sources. Tang et al. ( 2024 ), confirmed this by showing that Fintech development increases diversification but reduces liquidity creation in Chinese banks, especially during the COVID 19 pandemic. Furthermore, study by Zhu and Guo ( 2024 ), found that digital inclusive finance not only mitigates the negative impact of traditional inclusive lending but also improves banks’ service efficiency and non-interest income sources. Together, these studies affirm that digitalization promotes diversification by enabling broader service delivery and efficiency gains, though it may temporarily suppress liquidity and traditional profitability channels like interest incomes acquired from lending. Evidence on the risk and stability dimension of digital transformation present a more nuanced perspective. Li et al. ( 2022 ), demonstrate that Fintech can effectively reduce banks’ risk-taking by improving operational efficiency, financial innovation, and risk management, particularly in state-owned banks. Likewise, a study by Hoque et al. ( 2024 ), found that digital transformation in Vietnamese banks significantly reduces credit and insolvency risk by lowering information asymmetry, though it has limited influence on liquidity risk. In contrast, Cevik ( 2024 ), warned of potential systematic risks, noting that Fintech expansion in developing countries may undermine financial stability when dominated by digital lending. Study by Lestari et al. ( 2023 ), further revealed that revenue diversification alone does not enhance bank stability in Asean banks, while digital leadership and strategic governance play critical roles. Collectively, these studies suggest that Finetch and digital transformation can enhance resilience if complemented by sound governance and market discipline. Moreover, the empirical literature reveals that financial technology has reshaped the outcomes of income diversification, profitability and risk-taking in banking industry. The consensus among studies by Thakur and Arora ( 2024 ); Chhaidar et al. ( 2023 ), is that technology adoption strengthens diversification and enhance profitability through operational efficiency and innovation, especially for larger banks. However, divergent evidence from Yuan et al. ( 2025 ); Cevik ( 2024 ), highlights that excessive or poorly regulated financial technology growth can erode banks profitability and stability, particularly in emerging markets. Hence, the relationship between financial technology and income diversification is contingent up on regulatory frameworks bank size and digital maturity levels. In conclusion, diversification has become a major theme in global banking research, the existing literatures reveals a clear gap regarding how financial technology and banking regulatory constraints jointly impact income diversification within developing economies, particularly in Ethiopia. Prior empirical studies have produced mixed and often inconsistent findings on whether digitization improves or undermines diversification, and whether regulatory pressure promotes innovation or restricts product expansion. Despite the rapid growth of digital transactions, evolving regulatory reforms, and significant structural shifts in Ethiopian banking sector, no empirical study has specifically examined how these technological and regulatory forces shape the revenue diversification strategies of Ethiopian Commercial banks. This disconnect between global theoretical debates and the absence of country specific evidence create a critical knowledge gap, which the present study aims to fill by investigating the impact of financial technology, banking regulation, and key bank-specific factors on income diversification across 17 Ethiopian Commercial banks over the period 2015–2024. 3. Conceptual Framework, Econometric Model and Data 3.1 Conceptual framework and econometric model The study analyzed quantitative data using regression technique to examine the relationships between variables supported by descriptive statistics to explore general trends within the balanced panel data set through measurements such as means, minimums, maximums and standard deviations. Unlike many previous studies that relied on ordinary Least Square (OLS), this study employed the System Generalized Method of Moments (SGMM), as it is first proposed by Areliano and Bover ( 1995 ) and further developed by Blundell and Bond ( 1997 ), since it effectively addresses dynamic panel bias and endogeneity concerns. Given the two-way causality commonly observed between income diversification and bank-Specific characteristics highlighted in previous studies such as (Sanya & Wolfe, 2011 ) and (GÜRBÜZ et al., 2013), SGMM offers a more robust estimation approach. The study specifies the following econometric model to examine the impact of financial technology, banking regulation and other bank-specific characteristics on income diversification. $$\:{\varvec{D}\varvec{I}\varvec{V}}_{\varvec{i}\varvec{t}\:=\:}\varvec{\alpha\:}+\varvec{\gamma\:}\:{\varvec{D}\varvec{I}\varvec{V}}_{\varvec{i},\varvec{t}-1}+\varvec{\beta\:}\varvec{{\prime\:}}\:{\varvec{x}}_{\varvec{i}\varvec{t}}+\:{\varvec{\mu\:}}_{\varvec{i}}+\:{\varvec{\lambda\:}}_{\varvec{t}}+{\varvec{\epsilon\:}}_{\varvec{i}\varvec{t}}\dots\:\dots\:\dots\:\dots\:\dots\:.\left(1\right)\:$$ Based on the above theoretical or analytical model, the empirical model of the study specified as follows: $$\:{\varvec{D}\varvec{I}\varvec{V}}_{\varvec{i}\varvec{t}\:=\:}\varvec{\alpha\:}+\varvec{\gamma\:}\:{\varvec{D}\varvec{I}\varvec{V}}_{\varvec{i},\varvec{t}-1}+{\varvec{\beta\:}}_{1}{\text{V}\text{o}\text{D}\text{T}}_{\varvec{i}\varvec{t}}+\:{\varvec{\beta\:}}_{2}{\text{V}\text{a}\text{D}\text{T}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{3}{\text{B}\text{R}\text{C}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{4}{\varvec{L}\varvec{D}\varvec{R}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{5}{\text{L}\text{n}\text{T}\text{A}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{6}{\text{A}\text{G}\text{E}}_{\varvec{i}\varvec{t}}+{\varvec{\beta\:}}_{7}{\text{E}\text{F}\text{F}}_{\varvec{i}\varvec{t}}+{\varvec{\mu\:}}_{\varvec{i}}+\:{\varvec{\lambda\:}}_{\varvec{t}}+{\varvec{\epsilon\:}}_{\varvec{i}\varvec{t}}\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:...\left(2\right)$$ Where : \(\:{DIV}_{it\:\:}\) , \(\:{\text{V}\text{o}\text{D}\text{T}}_{it}\) , \(\:{\text{V}\text{a}\text{D}\text{T}}_{it}\) , \(\:{\text{B}\text{R}\text{C}}_{it}\) , \(\:{LDR}_{it}\) , \(\:{\beta\:}_{5}{\text{L}\text{n}\text{T}\text{A}}_{it},\:\:{\beta\:}_{6}{\text{A}\text{G}\text{E}}_{it},\:\:and\:{\beta\:}_{7}{\text{E}\text{F}\text{F}}_{it}\) represents income diversification, volume (count) of digital transaction, value (amount) of digital transaction, banking regulatory constraints, lending Strategy, total asset, age and efficiency of banks i at time t respectively. α represents the intercept, γ represents the coefficient of the dependent variable, β’ represents the coefficient of independent variables, µ represents bank-specific effects and λ represents time specific effects which are not considered in the model and є represent the idiosyncratic errors of bank i at time t. In the dynamic structural model; \(\:{DIV}_{i,t-1}\) represents income diversification of bank i at time t minus one or difference of the observation in the successive period compared with its respective observation in predecessor period. The main rational to use both the volume and value of transaction as proxy for technology is that, while few service commissions and charges depend on the count or frequency of the digital transactions regardless of its amount others merely depend on the value or amount of transaction of which there may provide minimum thresholds to be transacted for free. 3.2 Data and Source The data covers the balanced panel of 17 Ethiopian Commercial banks over the period from 2015–2024. The study includes all Ethiopian Commercial banks operating in the financial system and the availability of data limits the study period. Data on bank specific factors such as bank size and age, efficiency and lending strategies is retrieved from audited financial statements of sampled banks, which is publicly available from the National Bank of Ethiopia (NBE). Data for digital volume and value of transaction used as proxy of financial technology is retrieved from the National Bank of Ethiopia (NBE) directory of payment system via email. Table 1 Variable definition and data source Variables (Representations) Measurement Expected Signs Income Concentration Ratio (Focus) Square of Share of Interest Income to total income plus square of share of Non-Interest Income to total income N/A Income Diversification (HHI-Herfindal Hirschman Index)/(DIV) 1-Income Concentration Ratio N/A Digital Technology (Usage) (VoDT/VaDT)-Explanatory Total Volume(Count) of Digital Transactions (VoDT) Total Value (Amount) of Digital Transactions (VaDT) Positive(+) Banking Regulatory Constraints (BRC)-Dummy One for periods from 2014–2020 otherwise zero Negative(-) Lending Strategy(LDR) Total Loan & advance /Total Deposit Negative(-) Bank Size (LnTA) Natural Log. Of Total Assets Positive(+) Age of Banks Numbers of years in Operation Positive(+) Efficiency (EFF) Total Non-Interest Income/Total Non-Interest Expense Positive(+) Source: Adopted from various sources Income Diversification (DIV) is measured by using the adjusted Herfindahl-Hirschman Index, which is captured the balance between interest and non-interest income and is widely used in banking studies (Chiorazzo et al., 2008 ); (Meslier et al., 2014 ); (Ashraf & Nazir, 2023 ). Financial technology is defined through the total volume and value of digital transactions including transactions made by card banking, mobile and internet banking and agent banking, reflecting the extent of digital adoption within the banks, consistent with measurement used in recent fintech and banking research (Hakimi, 2012 ); (Tang et al., 2024 ). Banking regulation is captured using a dummy variable representing the restrictive policy period from 2014 − 202, during which National Bank of Ethiopia directive limit private-sector lending and influence bank’s income structure (Fekadu, 2018 ). Lending strategy is measured by using the loan-to –deposit ratio, a standard indicator of how aggressively banks extent credit (Hamdi et al., 2017 ); (Kiptum, 2021 ). Bank size is defined as the logarithm of total assets, while bank age is measured in years since establishment to capture institutional maturity (Stiroh, 2004 ); (Nguyen et al., 2013 ); (Lee et al., 2014 ). Efficiency is represented by the ratio of non-interest-income to non-interest expense, reflecting managerial and operational effectiveness (Dietrich & Wanzenried, 2011 ). Together, these variables complement the key technological and regulatory factors by capturing internal characteristics shaping bank’s ability to diversify income streams. 4. Results and discussions 4.1 Descriptive Results Table 2 Descriptive statistics Variable Obs. Mean Std. Dev. Min Max DIV 170 0.43 0.08 0.023 0.50 VoDT 170 21.955 64.66 0.00 573.177 VaDT 170 87.427 271.11 0.00 2,159.36 BRC 170 0.600 0.491 0.00 1.00 LDR 170 0.733 0.138 .355 1.093 LnTA 170 10.27 1.34 7.042 14.178 AGE 170 19.476 16.603 2.00 82.00 EFF 170 69.334 32.342 1.392 159.552 Note : This table reports descriptive statistics for our main variables, including mean, standard deviation, median, maximum and minimum values. The definitions of these variables can be seen in Table 1 . Table 2 presents descriptive statistics of the key variables used in this study. According to the descriptive statistics presented in Table 2 above, diversification has minimum value of 0.023 (banks with highly concentrated source of income) and maximum value of 0.50 (banks having diversified source of income) with average of 0.43. It has also Standard deviation of 0.08 by which each observation value deviates from the mean. In general, the descriptive statistics revealed that there is notable difference in the variability across variables. Income diversification (DIV) is relatively stable among banks. In contrast, indicators of financial technology, volume (VoDT) and value (VaDT) of digital transactions have extremely high variability, with largest standard deviation of (64.66 and 271.11 respectively) relative to their means, reflecting significant differences in usage levels. Bank size (LnTA) and lending strategy (LDR) are moderately dispersed, whereas bank age (AGE) and management efficiency (EFF), show wider spreads, indicating considerable heterogeneity in industry experience and efficiency across selected banks. Table 3 Matrix of Pearson correlation Variables DIV VoDT VaDT BRC LDR LnTA AGE EFF DIV 1.000 VoDT -0.253 1.000 VaDT -0.188 0.829 1.000 BRC 0.335 -0.326 -0.369 1.000 LDR -0.280 -0.170 -0.010 -0.596 1.000 LnTA -0.405 0.597 0.478 -0.464 0.005 1.000 AGE -0.281 0.593 0.388 -0.144 -0.386 0.835 1.000 EFF 0.626 -0.199 -0.213 0.389 -0.329 -0.526 -0.276 1.000 Note : This table reports the Pearson correlation coefficients. The definition of these variables can be seen in Table 1 . The correlation matrix presented in Table 3 revealed that income diversification has positive and strong relationship with efficiency (0.626) and negative and moderate relationship with bank size (-0.405), age (-0.281), lending strategy of banks (0.280) as well as with volume and value of digital transaction (-0.523 and − 0.188) respectively. Banking regulation constraints has positive and slight relationship (0.335) with income diversification. The matrix revealed there is high relationship between size and age of the bank (0.835) as well as between value and volume of digital transactions (0.829), indicating that there is potential multicollinearity but the variables inflation factor (vif = 4.31) result revealed that the problems is not sever. The slight relationship happens between lending strategy and banking regulatory constraints (-0.563) and between efficiency and bank size (-0.526) respectively. 4.2 Pre-estimation Diagnostics Before estimating the dynamic model, pre-estimation diagnostics were conducted. The IPS and CIPS panel unit root tests confirm that the variables become stationery after first differencing, eliminating concerns of non-stationarity and spurious regressions. The Pesaran CD test also indicates the presence of significant cross-section dependence, implying that banks are affected by common shocks and interbank linkages. These diagnostics justify the use of the two-step System GMM estimator, which is suitable for panels characterized by dynamic relationships, endogeneity, and heteroscedasticity and cross-section dependence. Tables 4 and 5 below present results of panel unit root test and cross-section dependence tests respectively. Table 4 Panel Unit Root Test Results (IPS and CIPS Tests) Variable Level IPS Statistics P-Value First Diff. IPS Statistics P-Value Order of Integration DIV -1.215 0.648 -3.487 0.000*** I(1) VoDT -0.982 0.734 -4.102 0.000*** I(1) VaDT -1.341 0.611 -3.956 0.000*** I(1) BRC -0.423 0.889 -2.951 0.002** I(1) LDR -1.027 0.711 -3.772 0.000*** I(1) LnTA -1.859 0.349 -4.558 0.000*** I(1) AGE -0.512 0.901 -2.855 0.004** I(1) EFF -1.376 0.592 -3.642 0.000*** I(1) Note : *, ** and *** denote significance at 10%, 5% and 1% level respectively. Table 5 Cross-Section Dependence (CSD) Tests Results Test Statistics P-Value Conclusion Pesaran CD Test 2.417 0.016** Cross-Section dependence Exists Breusch-Pagan LM Test (Optional) 11.284 0.045** Cross-Section dependence Exists Pesaran Scaled LM Test (Optional) 1.928 0.054* Weak Cross-Section dependence Note : * and ** denote significance at 10% and 5% level respectively. 4.3 Regression Results The system GMM estimation employed 13 instruments, including collapsed GMM-type instruments for lagged dependent variables and standard instruments for the exogenous variables. Diagnostic tests results of Arellano-Bond test for first order autocorrelation AR(1) and second order autocorrelation AR(2) at P-value (0.092) and (0.221) respectively revealed that the model show acceptable evidence of first-order serial correlation and there is no evidence for second-order serial correlation. The result of Hansen test of over identifying restrictions at P-value (0.708) which is between 0.1 and 0.9 reveals that the model uses valid instruments which are not either invalid or overfitting. Over all, the model fits well and provides reliable evidence on the determinants of income diversification in Ethiopian commercial banks. Table 6 Regression result of two-step System GMM Variables Coefficient Std. Error P-Value L1DIV 0.46904 0.157 0.003*** L2DIV -0.06175 0.149 0.678 VoDT 0.00049 0.000 0.000*** VaDT 0.00005 0.000 0.001*** BRC -0.01676 0.009 0.056* LDR -0.05813 0.075 0.440 LnTA 0.00845 0.015 0.562 AGE 0.00006 0.001 0.956 EFF 0.00153 0.001 0.003*** Constant 0.12060 0.125 0.336 Number of Instruments 13 AR(1) P-value 0.092 AR(2) P-value 0.221 Hansen J-test P-value 0.708 Note : This table reports the impact of Fintech and banking regulation on income diversification using two-step system GMM. The variable, Fintech, is measured by both the volume and value of digital transactions. The definitions of these variables can be seen in Table 1 . *, ** and *** denote significance at 10%, 5% and 1% level respectively. Based on the result presented in Table 6 above, the two-step System GMM results revealed that financial technology plays a decisive role in shaping income diversification positively and significantly at the 1% level, and banking regulation displays negative and marginal level of significance in shaping income diversification of Ethiopian commercial banks. The results strongly demonstrate that financial technology is a key driver of income diversification in Ethiopian commercial banks. Both the volume and value of digital transactions exhibit positive and highly significant effects at the 1% level, confirming that digital transformation substantially widens non-interest revenue streams. This finding aligns with Innovation Diffusion Theory, which posits that technology adoption enhances the efficiency and variety of financial services, enabling banks to expand in to fee-based activities. Likewise, the evidence supports the Resource-Based View and Dynamic Capability Theory, which argue that technological assets strengthen a bank’s capabilities to innovate and reconfigure services in response to market shifts. Empirically, the results are consistent with Thakur and Arora ( 2024 ); Tang et al. ( 2024 ); Chhaidar et al. ( 2023 ); A. P. Nguyen et al. ( 2023 ) and Hamdi et al. ( 2017 ), all of whom emphasize that digital channels substantially enhance income diversification through expanded payment services, digital onboarding, and remote financial access. Collectively, the Ethiopian evidence indicates that fintech adoption rather than structural bank characteristics has become the dominant force shaping income diversification in modern banking system. Banking regulation shows a negative and weakly significant impact on income diversification at 10% level of significance, implying that regulatory obligations may constrain bank’s ability to expand in to diversified product lines. This result aligns with the regulatory constraints Hypothesis, which argues that stringent compliance requirements, high reporting burdens, and product restrictions reduce banks’ flexibility to explore new income generating activities. Empirically, the finding is consistent with studies such as Yusuf and Shikur ( 2023 ); and AlKhouri and Arouri ( 2018 ). Which show that in highly regulated environments, especially, in developing economies, banks face limited room for innovation, thereby dampening diversification. The result stands in contrast, however, to frame like Market power Theory, which suggests that regulations can lower excessive competition and thus indirectly promote diversification. In Ethiopia’s context, the negative association indicates that regulatory rigidity and licensing procedures may hinder the rollout of innovative financial services, thereby restricting the breadth of non-interest revenue. Accordingly, the regulatory environment appears to remain an obstacle rather than an enabler of diversification. Coming to other variables, efficiency emerges with positive and significant impact on income diversification, reinforcing the Transaction Cost Economics argument that operational efficiency enables banks to offer additional services at lower marginal cost, thereby expanding non-interest revenue streams. This supports empirical findings from Hoque et al. ( 2024 ); Li et al. ( 2022 ); Phan et al. ( 2022 ); and Dietrich and Wanzenried ( 2011 ) who argue that efficient banks are better positioned to innovative and diversity. Conversely, variables such as bank size, bank age and lending strategy show insignificant effects, implying that traditional structural characteristics do not materially determine diversification outcomes in the context of Ethiopian Commercial Banks. These results contradict classical views derived from the Economies of Scale and Experience Hypothesis, which expect larger and older banks to diversify more due to accumulated expertise and resource advantages. Instead, the Ethiopian context aligns with evidence from emerging economies where structural attributes matter less than technological agility and cost efficient operations. The mixed results suggest that diversification is not inherently tied to size or maturity but is more dependent on strategic capability and focus on digitization. Overall, the findings highlight that technological capability and operational efficiency, rather than traditional bank characteristics, are the primary determinants of income diversification in the context Ethiopian commercial banks. Digital transformation significantly enhances diversification, supporting both modern theoretical frameworks and global empirical evidence. Meanwhile, regulatory constraints appear to hinder diversification, pointing to a need for regulatory reforms that balance prudential oversight with innovation flexibility. The insignificant impacts of bank size age and lending structure reveal that diversification in Ethiopia is driven more by strategic adoption and technological investment than by structural capability. Collectively, the results underscore that bank embracing digital infrastructure and efficiency enabler systems are better positioned to expand non-interest income, improve resilience, and strengthen long-term financial performance while those constrained by regulation or lacking technological agility may remain limited in diversification potential. 5. Conclusions With a particular emphasis to examine the impact of financial technology and banking regulation with other bank-specific characteristics, the study explores key determinants of income diversification among Ethiopian commercial banks. Using a dynamic panel framework through the two-step system GMM estimator, the study uncovered that digital transformation captured by both the volume (count) and value (amount) of digital transactions together with operational efficiency are the strongest and most consistent predictors of income diversifications. The result also revealed significant persistence in diversification behavior, indicating that past diversification decision strongly shape current outcomes. Contrary to longstanding assumptions in banking literature, traditional structural attributes such as bank size, bank age and lending strategy were found to exert only a marginal negative influence. Overall, the findings substantiate modern theories of innovation, resource capability, and transaction cost reduction, while challenging classical views that emphasize size, scale and specialization as the central drivers of diversification. The findings of this study present several policy implications for regulators and bank executives seeking to promote stronger, more resilient banking performance in Ethiopia. First, the persuasive influence of digital transformation signals an urgent need for policymakers to accelerate the modernization of the financial sector by expanding digital infrastructure, supporting interoperability, and revisiting outdated directives that limit innovation. Regulatory reforms that strike a balance between prudential oversight and flexibility would encourage banks to pursue broader non-interest income opportunities without compromising financial stability. For bank managers, the results suggest that strategic investment in digital channels, payment systems, and process efficiency should be prioritized ahead of traditional expansion metrics such as branch networks or asset size. The marginal negative effect of banking regulation also highlights the necessity for regulators to conduct periodic reviews of directives that may unintentionally constrain product innovation, digital adoption, and diversification potential. Collectively, these implications underscore that enhancing the digital maturity and operational capabilities of banks is central to improve profitability, strengthening competitive positioning, and reducing overreliance on interest income in the Ethiopian banking landscape. Despite offering valuable insights, this study is not without limitations. The analysis relied on secondary bank-level panel data covering a limited number of commercial banks and years, which may not fully capture granular diversification behaviors occurring at product, customer, or branch levels. Additionally, the study focused mainly on internal and regulatory determinants, leaving unexplored other potentially relevant factors such as managerial competence, competitive pressures from fintech firms, customer preferences, and the quality of digital infrastructure across banks. Future research may extend this work by incorporating more detailed micro-level data, exploring the non-linear or threshold effects of digital adoption, or comparing public and private banks to capture heterogeneity in strategic responses. Researchers may also examine how income diversification interacts with broader outcomes such as financial inclusion, credit allocation, asset quality, or systematic stability. Such investigations would enrich understanding of how diversification strategies evolve and how digital transformation continues to reshape banking dynamics in emerging economies like Ethiopia. Declarations Authors Contribution AG: Conceptualization, methodology decision, data collection, analysis of data, manuscript writing and editing for final approval GM: Methodology validation, review, editing and approval of final manuscript, supervisor FC: Review and approval of the final manuscript Data Availability Data is available as per the request for technical check. Conflict of interest We declared that there is no conflict of interest regarding the authorship or publication of the manuscript. All financial and non-financial interests relevant to this work have been fully disclosed. Funding The author declared that there is no any source of fund granted for this study. Ethical Approval and Informed Consent This manuscript does not contain any studies with human participants performed by any of the authors. References AlKhouri, R., & Arouri, H. (2018). The effect of diversification on risk and return in banking sector Evidence from the Gulf Cooperation Council countries. International Journal of Managerial Finance , 15 (1), 100-128. https://doi.org/10.1108/IJMF-01-2018-0024 Alshomaly, I. (2014). Bank Diversification and the Systematic Risk of Equity Portfolio European Scientific Journal , 10 (16). Areliano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components model. Journal of econometrics , 68 , 29-51. 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Sanya, S., & Wolfe, S. (2011). Can Banks in Emerging Economies Benefit from Revenue Diversification? J Financ Serv Res , 40 (1-2), 79-101. https://doi.org/10.1007/s10693-010-0098-z Stiroh, K. J. (2004). Diversification Banking-Is Non-interest income the answer. Journal of money, Credit and Banking Tang, M., Hu, Y., Corbet, S., Hou, Y. G., & Oxley, L. (2024). Fintech, bank diversification and liquidity: Evidence from China. Research in International Business and Finance 67 (2024) 102082 , 67 , 102082. https://doi.org/10.1016/j.ribaf.2023.102082 (Elsevier) Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic management journal , 18 (7), 509-533. Thakur, N., & Arora, S. (2024). Determinants of income diversification: empirical evidence from Indian banks. International Journal of Law and Management 66 , 195-215. https://doi.org/10.1108/IJLMA-05-2023-0113 (Emerald) Viet, D. N. T. N. H. D. (2020). Determinants of non-interest Income: The case of Vietnamese Commecial Banks.pdf. Pacific Business Review International . Williamson, O. E. (1989). Transaction cost economics. Handbook of industrial organization , 1 , 135-182. Yuan, X., Puah, C.-H., & Marikan, D. A. b. A. (2025). An Empirical Study on the Impact of Financial Technology on the Profitability of China’s Listed Commercial Banks. Journal of Risk and Financial Management , 18 (8). https://doi.org/10.3390/jrfm18080440 Yusuf, J. M., & Shikur, A. A. (2023). Bank Regulations and Firm Performance: In the Case of Ethiopian Commercial Banks. International Journal of Finance and Banking Research . https://doi.org/10.11648/j.ijfbr.20230904.11 Zhu, K., & Guo, L. (2024). Financial technology, inclusive finance and bank performance. Finance Research Letters , 60 , 104872. Additional Declarations No competing interests reported. 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07:19:20","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144111,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8330606/v1/d0031d3baa8e434104ca4f77.html"},{"id":99854240,"identity":"67b682e8-cc45-4cbd-89e4-396831ab2893","added_by":"auto","created_at":"2026-01-09 05:11:05","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":181045,"visible":true,"origin":"","legend":"\u003cp\u003eTrends of income diversification, volume and value of digital transactions and asset size of 17 Ethiopian Commercial Banks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003eComputed based on NBE data (2015-2024)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8330606/v1/cfadeb540f796fec076e3766.jpeg"},{"id":104721920,"identity":"74f9d04f-0f3b-40df-ad45-865f0495261f","added_by":"auto","created_at":"2026-03-16 12:28:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1102655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8330606/v1/908bc8a6-d569-4010-895e-80dffde51c9b.pdf"},{"id":99854242,"identity":"702235e4-2c1d-472e-bad2-e24e831cf22a","added_by":"auto","created_at":"2026-01-09 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Introduction","content":"\u003cp\u003eDiversification has long been regarded as a central theme in economics, finance and strategic management, reflecting the fundamental question of how firms can balance growth profitability and stability. At its core, diversification refers to the process of expanding business activities into new products, services, or markets in order to spread risks and capture new opportunities. In the banking industry, this debate has gained renewed momentum with the rise of financial technology, which has dramatically altered how financial services are produced, delivered and consumed. The rapid adoption of digital channels has raised important questions about whether such technological advancement reinforces banks\u0026rsquo; income diversification strategies or merely reshapes the structure of their traditional intermediation role (Berger \u0026amp; Bouwman, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e); (Gerek \u0026amp; Tuncez, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFrom supportive perspective, several theoretical traditions argue that digitization enhances income diversification. Modern Portfolio Theory (Markowitz, \u003cspan class=\"CitationRef\"\u003e1952\u003c/span\u003e) suggests that spreading revenue source across interest and non-interest activities reduce risk and stabilize returns. Transaction cost economics (Williamson, \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e) explains how technology reduces cost and information asymmetries, enabling banks to profitably expand in to new service areas such as digital payments, insurance, and remittances. Similarly, the resource-based view (Barney, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e) and dynamic capabilities theory (Teece et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e) emphasize that digital infrastructure constitutes a strategic resource that allows banks to reconfigure products and capture new sources of non-interest income. Innovation diffusion theory (Rogers \u0026amp; Williams, \u003cspan class=\"CitationRef\"\u003e1983\u003c/span\u003e) further suggests that the spread of digital innovations such as mobile banking and fintech collaborations acts as a catalyst for new income streams through product and service innovation.\u003c/p\u003e\n\u003cp\u003eHowever, theoretical counterarguments warn that digitization does not necessarily lead to beneficial diversification. The specialization hypothesis and focus theory of banking (Berger et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) argue that diversification may distract banks from their core intermediation functions, increasing inefficiency and managerial complexity. The notion of creative destruction also highlights how Fintech competitors may capture the benefits of digital innovations, eroding banks\u0026rsquo; ability to diversify income effectively. Agency theory (Meckling \u0026amp; Jensen, \u003cspan class=\"CitationRef\"\u003e1976\u003c/span\u003e) raises concerns about overinvestment in digital projects that fail to generate proportional returns, while the market power perspective suggest that only larger banks may benefit from digitization, leaving smaller institutions at a disadvantage. These conflicting theoretical perspectives underscore the absence of consensus on whether digitization enhances or undermines diversification.\u003c/p\u003e\n\u003cp\u003eEmpirical evidences revealed that there is an increasing global trend toward non-interest income both in developed and developing markets (Thakur \u0026amp; Arora, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Bank income statements are increasingly displaying non-interest revenue, which comprises commissions, charges, fees, and trading income. Contrary to this, the volume (count) of digital transaction, value (amount) of digital transaction and asset size of all Ethiopian Commercial banks tend to continually increase and the growth in value of digital transaction since 2022 continues skyrocketing while income diversification starts to slow down very slightly (See Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e below).\u003c/p\u003e\n\u003cp\u003eBanking regulations is now also an important structural force shaping income diversification strategies of financial institutions; particularly in developing economies. Regulatory requirements such as lending restrictions, capital adequacy standards, liquidity ratios and provisioning rules not only safeguard financial stability but also influence the extent to which banks expand in to non-interest income activities (Hunjra et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); (Muhammed et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Empirical Evidences from international literature show that well-designed regulatory frameworks can encourage banks to pursue fee-based services and other innovative income sources as a way to manage compliance costs and enhance resilience. However, overly restrictive regulations may unintentionally limit diversification by constraining product offerings and inhibiting innovation. In Ethiopia, where the regulatory environment has historically prioritized stability and risk containment constraints such as foreign currency restrictions and limited flexibility in digital banking product development and usage restricted bank\u0026rsquo;s ability to fully exploit emerging diversification opportunities (AlKhouri \u0026amp; Arouri, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e); (Kiplagat \u0026amp; Kalui, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). These regulatory dynamics make banking regulations a critical determinant to be studied when examining income diversification within the Ethiopian financial sector more specifically in the context of commercial banks.\u003c/p\u003e\n\u003cp\u003eAlthough numerous studies having contradicted empirical findings in developed and emerging economies to examined the determinants of income diversification in the banking industry, evidence on how financial technology and banking regulation shape diversification remains notably limited in number and scope in developing country contexts. In particular, there is a clear absence of empirical study focusing on this specific topic in Ethiopia, where the banking sector has undergone rapid technological transformation and evolving regulatory reforms over the past decade. So as to addressing this gap, the study investigate the impact of financial technology, banking regulatory constraints and other bank-specific factors on income diversification using data from 17 Ethiopian Commercial Banks over the period 2015\u0026ndash;2024. By providing context-specific empirical insights, the study contributes to the existing literature and enhances understanding of the determinants driving revenue diversification strategies within a developing financial system.\u003c/p\u003e\n\u003cp\u003eAligned with the introduction, this paper is structured as follows. Section \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides an overview of the theoretical background and review of empirical literatures on the key determinants of income diversification. Section \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e incorporates the data, variables and the research method for estimating the determinants of income diversification. Sections \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e describe and discuss the empirical results. Section \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e provides conclusions of key findings and policy implications for the study.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Review\u003c/h2\u003e \u003cp\u003eEven though there is no consensus on the precise meaning of the concept of diversification among researchers, exploring the key determinants of income diversification has been the subject of numerous theories. As argued by different scholars, the term \u0026ldquo;diversification\u0026rdquo; would have different meanings when research interests varied (Oyenubi, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Its definitions are many and therefore, what is needed is a comprehensive definition which is both theoretically sound and managerially valid. Some researchers have thus defined diversification in terms of the number of products, services and markets, while others define it in terms of the means and methods. Diversification has thus been defined by some researchers as the quantity of goods, services, and markets, while other researchers define it as the strategies and tactics that allow businesses to expand while lowering overall risk (Markowitz, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1952\u003c/span\u003e). In general, diversification means that a company is running more business lines, whether or not they are connected claims that the grand strategy of diversification signifies a clear shift in the company's focus from its current operational foundation to a new business line that may be acquired or developed through expansion (Mulwa et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); (Ashyari \u0026amp; Rokhim, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe diversification of bank income is explained by internal changes in non-interest income and interest income. Banks engage in income-generating operations known as income diversification, which involve creating a range of products and services from traditional interest- or non-interest-generating ventures, or combining the two simultaneously. The kind of revenue structure determines the degree of income diversification. Basic bank income consists of interest income, commission income, income from transaction fees, and other income (Nguyen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBanks diversify by expanding their product lines (Chandramohan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); geographic reach (Berger et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), (Alshomaly, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); customer segment (Beck et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and technological capabilities (Hamdi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), (Thakur \u0026amp; Arora, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This may involve introducing new financial products, entering new regions, tailoring services to varied client groups or leveraging digital platforms to deliver innovative solutions. Technological diversification, in particular, enhances intermediation efficiency and supports the growth of non-interest income through service such as mobile banking, card banking, digital transfers, agent banking, e-wallets and others enabling banks to reduce reliance on traditional interest income.\u003c/p\u003e \u003cp\u003eHypothetically, while several theories support a positive link between digitization and income diversification such as Innovation Diffusion Theory, which views digital channels as enablers of new non-interest income sources (Rogers \u0026amp; Williams, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1983\u003c/span\u003e); the Resource-Based View, which provokes digital capabilities as strategic tools for product line expansion (Barney, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1991\u003c/span\u003e); Transaction Cost economics, which highlights cost optimization that makes new service feasible (Coase, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1937\u003c/span\u003e); and Dynamic Capabilities Theory, which frames digitization as driver of adaptability (Teece et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1997\u003c/span\u003e);, others caution against the relationship between financial technology and income diversification. The Specialization Hypothesis and Focus Theory argue that diversification may distract banks from core lending activities (Berger et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), while Technological Disruption Theory suggests that digital innovation can erode advantages of traditional banking rather than expanding their revenue streams. Market Power Theory and risk concentration arguments also note that digital enhancement may not accrue equally across bank sizes and Agency Theory highlights the risk that digitization may increase costs and complexity. Moreover, income from digital services such as fees, commissions and charges can be key competitive strategic tools and strictly controlled by the regulatory organs. These theoretical contradictions still pose unclosed questions whether financial technology has positive impact on income diversification or not and this study examines the relationship in the context of the Ethiopian banking sector.\u003c/p\u003e \u003cp\u003eThe Regulatory Pressure or Compliance Cost Perspective, Market Discipline Theory and Prudential Regulation Perspective suggest that strict banking regulation pushes banks to look for alternatives of less-capital intensive revenue sources like non-interest income, promote operational efficiency and fair competitions and enables to create stable operating environment that encourages new product innovation and expansion (AlKhouri \u0026amp; Arouri, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); (Hunjra et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); (Muhammed et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Traditional Banking Theory, Focus Theory of Banks and Regulatory Burden Theory stand against the argument that banking regulation can positively impact income diversification by suggesting banks can perform best when they focus on their core intermediation role, diversification may lead banks to complexity and supervisory challenges and it will increase costs of regulatory compliances (Ferri \u0026amp; Pesic, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the context of Ethiopian banking sector, overly stringent rules such as new product development restrictions and rigid operational controls may limit diversification.\u003c/p\u003e \u003cp\u003eBank\u0026rsquo;s income diversification efforts are closely linked to their lending practice, as relying solely on interest income heightens exposure to credit risk and earnings volatility. Prominent theories including Modern Portfolio Theory, Financial Intermediation Theory, Resource-Based View and Economies of Scope Theory suggest that well designed lending strategy contributes positively for income diversification, enable banks to build long-term relationship and in turn contributes to develop tailored bundle of products to expand non-interest revenue streams, encourage banks to extend cross-selling activities like insurance, trade services and advisory services and lending relationships allow banks to efficiently add complementary services which have the potential to generate non-interest income (Hamdi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); (Dang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Contrarily, the Risk Concentration Theory, Financial conglomerate Hypothesis, Corporate Focus Theory and Agency Theory stand against the positive relationship between lending strategy and income diversification of banks. They suggest that diversification sometimes push banks to unfamiliar and riskier activities, diversification poses operational complexity and governance challenges, banks can perform best if they can concentrate only on selected area of competence and diversification can harm performance and dilute focus on core lending expertise (Viet, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); (Lestari et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study therefore explores the relationship between lending strategy of banks and income diversification in the context of Ethiopian banking sector.\u003c/p\u003e \u003cp\u003eIn general, while external determinants such as financial technology and banking regulation shape the broader operational environment for income diversification, the internal characteristics of banks plays an equally critical role in driving their diversification strategies. Bank-specific factors such as age and size (Ashraf \u0026amp; Nazir, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), lending strategy and operational efficiency (Phan et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reflect the inherent strengths and weaknesses of individual institutions, influencing their capacity and inclination to diversify income sources. Larger banks, for instance, often leverage economies of scale and technological infrastructure to expand non-interest income activities, while well-capitalized banks can take on greater risks to pursue diversification opportunities. Exploring these internal factors provides valuable insights into how variations in institutional characteristics impact the ability of Ethiopian banks to balance profitability and their risk-return trade off.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Empirical Review\u003c/h2\u003e \u003cp\u003eEmpirical evidence from diverse economies demonstrates that financial technology or digitalization significantly influence bank income diversification and profitability, though with varying magnitude and directions. For instance, Thakur and Arora (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that technological advancement, bank size and market competition positively influence income diversification among Indian Commercial banks, while macroeconomic factors such as GDP growth and capital ratio reduce diversification tendencies. Similarly, Chhaidar et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed that investment on Fintech enhance European bank\u0026rsquo;s profitability, with larger banks gaining more benefits due to scale and resource advantage. In contrast, Yuan et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) discovered that Fintech development initially undermines the profitability of Chinese banks by intensifying competition and reducing interest income, but once digital integration surpasses a certain threshold, the negative effect diminish. These findings highlighted that the impact of Fintech on diversification and profitability is non-linear and context dependent.\u003c/p\u003e \u003cp\u003eParallel studies on bank size, income diversification and liquidity effects reinforce that technological transformation reshapes bank\u0026rsquo;s operational behavior. A study by (Q. T. T. Nguyen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) revealed that bank size has a strong positive effect on income diversification among Vietnamese banks, suggesting that larger institutions leverage technology and economies of scale to expand non-interest income sources. Tang et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), confirmed this by showing that Fintech development increases diversification but reduces liquidity creation in Chinese banks, especially during the COVID 19 pandemic. Furthermore, study by Zhu and Guo (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), found that digital inclusive finance not only mitigates the negative impact of traditional inclusive lending but also improves banks\u0026rsquo; service efficiency and non-interest income sources. Together, these studies affirm that digitalization promotes diversification by enabling broader service delivery and efficiency gains, though it may temporarily suppress liquidity and traditional profitability channels like interest incomes acquired from lending.\u003c/p\u003e \u003cp\u003eEvidence on the risk and stability dimension of digital transformation present a more nuanced perspective. Li et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), demonstrate that Fintech can effectively reduce banks\u0026rsquo; risk-taking by improving operational efficiency, financial innovation, and risk management, particularly in state-owned banks. Likewise, a study by Hoque et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), found that digital transformation in Vietnamese banks significantly reduces credit and insolvency risk by lowering information asymmetry, though it has limited influence on liquidity risk. In contrast, Cevik (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), warned of potential systematic risks, noting that Fintech expansion in developing countries may undermine financial stability when dominated by digital lending. Study by Lestari et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), further revealed that revenue diversification alone does not enhance bank stability in Asean banks, while digital leadership and strategic governance play critical roles. Collectively, these studies suggest that Finetch and digital transformation can enhance resilience if complemented by sound governance and market discipline.\u003c/p\u003e \u003cp\u003eMoreover, the empirical literature reveals that financial technology has reshaped the outcomes of income diversification, profitability and risk-taking in banking industry. The consensus among studies by Thakur and Arora (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Chhaidar et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), is that technology adoption strengthens diversification and enhance profitability through operational efficiency and innovation, especially for larger banks. However, divergent evidence from Yuan et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Cevik (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), highlights that excessive or poorly regulated financial technology growth can erode banks profitability and stability, particularly in emerging markets. Hence, the relationship between financial technology and income diversification is contingent up on regulatory frameworks bank size and digital maturity levels.\u003c/p\u003e \u003cp\u003eIn conclusion, diversification has become a major theme in global banking research, the existing literatures reveals a clear gap regarding how financial technology and banking regulatory constraints jointly impact income diversification within developing economies, particularly in Ethiopia. Prior empirical studies have produced mixed and often inconsistent findings on whether digitization improves or undermines diversification, and whether regulatory pressure promotes innovation or restricts product expansion. Despite the rapid growth of digital transactions, evolving regulatory reforms, and significant structural shifts in Ethiopian banking sector, no empirical study has specifically examined how these technological and regulatory forces shape the revenue diversification strategies of Ethiopian Commercial banks. This disconnect between global theoretical debates and the absence of country specific evidence create a critical knowledge gap, which the present study aims to fill by investigating the impact of financial technology, banking regulation, and key bank-specific factors on income diversification across 17 Ethiopian Commercial banks over the period 2015\u0026ndash;2024.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Conceptual Framework, Econometric Model and Data","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Conceptual framework and econometric model\u003c/h2\u003e \u003cp\u003eThe study analyzed quantitative data using regression technique to examine the relationships between variables supported by descriptive statistics to explore general trends within the balanced panel data set through measurements such as means, minimums, maximums and standard deviations. Unlike many previous studies that relied on ordinary Least Square (OLS), this study employed the System Generalized Method of Moments (SGMM), as it is first proposed by Areliano and Bover (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and further developed by Blundell and Bond (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), since it effectively addresses dynamic panel bias and endogeneity concerns. Given the two-way causality commonly observed between income diversification and bank-Specific characteristics highlighted in previous studies such as (Sanya \u0026amp; Wolfe, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and (G\u0026Uuml;RB\u0026Uuml;Z et al., 2013), SGMM offers a more robust estimation approach.\u003c/p\u003e \u003cp\u003eThe study specifies the following econometric model to examine the impact of financial technology, banking regulation and other bank-specific characteristics on income diversification.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{D}\\varvec{I}\\varvec{V}}_{\\varvec{i}\\varvec{t}\\:=\\:}\\varvec{\\alpha\\:}+\\varvec{\\gamma\\:}\\:{\\varvec{D}\\varvec{I}\\varvec{V}}_{\\varvec{i},\\varvec{t}-1}+\\varvec{\\beta\\:}\\varvec{{\\prime\\:}}\\:{\\varvec{x}}_{\\varvec{i}\\varvec{t}}+\\:{\\varvec{\\mu\\:}}_{\\varvec{i}}+\\:{\\varvec{\\lambda\\:}}_{\\varvec{t}}+{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{t}}\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:.\\left(1\\right)\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBased on the above theoretical or analytical model, the empirical model of the study specified as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{D}\\varvec{I}\\varvec{V}}_{\\varvec{i}\\varvec{t}\\:=\\:}\\varvec{\\alpha\\:}+\\varvec{\\gamma\\:}\\:{\\varvec{D}\\varvec{I}\\varvec{V}}_{\\varvec{i},\\varvec{t}-1}+{\\varvec{\\beta\\:}}_{1}{\\text{V}\\text{o}\\text{D}\\text{T}}_{\\varvec{i}\\varvec{t}}+\\:{\\varvec{\\beta\\:}}_{2}{\\text{V}\\text{a}\\text{D}\\text{T}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{3}{\\text{B}\\text{R}\\text{C}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{4}{\\varvec{L}\\varvec{D}\\varvec{R}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{5}{\\text{L}\\text{n}\\text{T}\\text{A}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{6}{\\text{A}\\text{G}\\text{E}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta\\:}}_{7}{\\text{E}\\text{F}\\text{F}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\mu\\:}}_{\\varvec{i}}+\\:{\\varvec{\\lambda\\:}}_{\\varvec{t}}+{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{t}}\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:...\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eWhere\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{DIV}_{it\\:\\:}\\)\u003c/span\u003e \u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{V}\\text{o}\\text{D}\\text{T}}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e,\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{V}\\text{a}\\text{D}\\text{T}}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e,\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{B}\\text{R}\\text{C}}_{it}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{LDR}_{it}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{5}{\\text{L}\\text{n}\\text{T}\\text{A}}_{it},\\:\\:{\\beta\\:}_{6}{\\text{A}\\text{G}\\text{E}}_{it},\\:\\:and\\:{\\beta\\:}_{7}{\\text{E}\\text{F}\\text{F}}_{it}\\)\u003c/span\u003e\u003c/span\u003e represents income diversification, volume (count) of digital transaction, value (amount) of digital transaction, banking regulatory constraints, lending Strategy, total asset, age and efficiency of banks i at time t respectively. α represents the intercept, \u003csub\u003eγ\u003c/sub\u003e represents the coefficient of the dependent variable, β\u0026rsquo; represents the coefficient of independent variables, \u0026micro; represents bank-specific effects and λ represents time specific effects which are not considered in the model and є represent the idiosyncratic errors of bank i at time t. In the dynamic structural model; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{DIV}_{i,t-1}\\)\u003c/span\u003e\u003c/span\u003e represents income diversification of bank i at time t minus one or difference of the observation in the successive period compared with its respective observation in predecessor period.\u003c/p\u003e \u003cp\u003eThe main rational to use both the volume and value of transaction as proxy for technology is that, while few service commissions and charges depend on the count or frequency of the digital transactions regardless of its amount others merely depend on the value or amount of transaction of which there may provide minimum thresholds to be transacted for free.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data and Source\u003c/h2\u003e \u003cp\u003eThe data covers the balanced panel of 17 Ethiopian Commercial banks over the period from 2015\u0026ndash;2024. The study includes all Ethiopian Commercial banks operating in the financial system and the availability of data limits the study period. Data on bank specific factors such as bank size and age, efficiency and lending strategies is retrieved from audited financial statements of sampled banks, which is publicly available from the National Bank of Ethiopia (NBE). Data for digital volume and value of transaction used as proxy of financial technology is retrieved from the National Bank of Ethiopia (NBE) directory of payment system via email.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable definition and data source\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables (Representations)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected Signs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Concentration Ratio (Focus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquare of Share of Interest Income to total income plus square of share of Non-Interest Income to total income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Diversification (HHI-Herfindal Hirschman Index)/(DIV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-Income Concentration Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Technology (Usage)\u003c/p\u003e \u003cp\u003e(VoDT/VaDT)-Explanatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Volume(Count) of Digital Transactions (VoDT)\u003c/p\u003e \u003cp\u003eTotal Value (Amount) of Digital Transactions (VaDT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBanking Regulatory Constraints (BRC)-Dummy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne for periods from 2014\u0026ndash;2020 otherwise zero\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLending Strategy(LDR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Loan \u0026amp; advance /Total Deposit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBank Size (LnTA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural Log. Of Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of Banks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumbers of years in Operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficiency (EFF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Non-Interest Income/Total Non-Interest Expense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource: Adopted from various sources\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIncome Diversification (DIV) is measured by using the adjusted Herfindahl-Hirschman Index, which is captured the balance between interest and non-interest income and is widely used in banking studies (Chiorazzo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); (Meslier et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); (Ashraf \u0026amp; Nazir, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Financial technology is defined through the total volume and value of digital transactions including transactions made by card banking, mobile and internet banking and agent banking, reflecting the extent of digital adoption within the banks, consistent with measurement used in recent fintech and banking research (Hakimi, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); (Tang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Banking regulation is captured using a dummy variable representing the restrictive policy period from 2014\u0026thinsp;\u0026minus;\u0026thinsp;202, during which National Bank of Ethiopia directive limit private-sector lending and influence bank\u0026rsquo;s income structure (Fekadu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLending strategy is measured by using the loan-to \u0026ndash;deposit ratio, a standard indicator of how aggressively banks extent credit (Hamdi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); (Kiptum, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Bank size is defined as the logarithm of total assets, while bank age is measured in years since establishment to capture institutional maturity (Stiroh, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2004\u003c/span\u003e); (Nguyen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); (Lee et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Efficiency is represented by the ratio of non-interest-income to non-interest expense, reflecting managerial and operational effectiveness (Dietrich \u0026amp; Wanzenried, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Together, these variables complement the key technological and regulatory factors by capturing internal characteristics shaping bank\u0026rsquo;s ability to diversify income streams.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and discussions","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Results\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e573.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e271.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,159.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e159.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: This table reports descriptive statistics for our main variables, including mean, standard deviation, median, maximum and minimum values. The definitions of these variables can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics of the key variables used in this study. According to the descriptive statistics presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e above, diversification has minimum value of 0.023 (banks with highly concentrated source of income) and maximum value of 0.50 (banks having diversified source of income) with average of 0.43. It has also Standard deviation of 0.08 by which each observation value deviates from the mean. In general, the descriptive statistics revealed that there is notable difference in the variability across variables. Income diversification (DIV) is relatively stable among banks. In contrast, indicators of financial technology, volume (VoDT) and value (VaDT) of digital transactions have extremely high variability, with largest standard deviation of (64.66 and 271.11 respectively) relative to their means, reflecting significant differences in usage levels. Bank size (LnTA) and lending strategy (LDR) are moderately dispersed, whereas bank age (AGE) and management efficiency (EFF), show wider spreads, indicating considerable heterogeneity in industry experience and efficiency across selected banks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMatrix of Pearson correlation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDIV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVoDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBRC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLnTA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAGE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEFF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNote\u003c/b\u003e: This table reports the Pearson correlation coefficients. The definition of these variables can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe correlation matrix presented in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e revealed that income diversification has positive and strong relationship with efficiency (0.626) and negative and moderate relationship with bank size (-0.405), age (-0.281), lending strategy of banks (0.280) as well as with volume and value of digital transaction (-0.523 and \u0026minus;\u0026thinsp;0.188) respectively. Banking regulation constraints has positive and slight relationship (0.335) with income diversification. The matrix revealed there is high relationship between size and age of the bank (0.835) as well as between value and volume of digital transactions (0.829), indicating that there is potential multicollinearity but the variables inflation factor (vif\u0026thinsp;=\u0026thinsp;4.31) result revealed that the problems is not sever. The slight relationship happens between lending strategy and banking regulatory constraints (-0.563) and between efficiency and bank size (-0.526) respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Pre-estimation Diagnostics\u003c/h2\u003e \u003cp\u003eBefore estimating the dynamic model, pre-estimation diagnostics were conducted. The IPS and CIPS panel unit root tests confirm that the variables become stationery after first differencing, eliminating concerns of non-stationarity and spurious regressions. The Pesaran CD test also indicates the presence of significant cross-section dependence, implying that banks are affected by common shocks and interbank linkages. These diagnostics justify the use of the two-step System GMM estimator, which is suitable for panels characterized by dynamic relationships, endogeneity, and heteroscedasticity and cross-section dependence. Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below present results of panel unit root test and cross-section dependence tests respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePanel Unit Root Test Results (IPS and CIPS Tests)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel IPS Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFirst Diff. IPS Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrder of Integration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: *, ** and *** denote significance at 10%, 5% and 1% level respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCross-Section Dependence (CSD) Tests Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesaran CD Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-Section dependence Exists\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreusch-Pagan LM Test (Optional)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-Section dependence Exists\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesaran Scaled LM Test (Optional)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak Cross-Section dependence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: * and ** denote significance at 10% and 5% level respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Regression Results\u003c/h2\u003e \u003cp\u003eThe system GMM estimation employed 13 instruments, including collapsed GMM-type instruments for lagged dependent variables and standard instruments for the exogenous variables. Diagnostic tests results of Arellano-Bond test for first order autocorrelation AR(1) and second order autocorrelation AR(2) at P-value (0.092) and (0.221) respectively revealed that the model show acceptable evidence of first-order serial correlation and there is no evidence for second-order serial correlation. The result of Hansen test of over identifying restrictions at P-value (0.708) which is between 0.1 and 0.9 reveals that the model uses valid instruments which are not either invalid or overfitting. Over all, the model fits well and provides reliable evidence on the determinants of income diversification in Ethiopian commercial banks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression result of two-step System GMM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL1DIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL2DIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.06175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.01676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Instruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(1) P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2) P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen J-test P-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: This table reports the impact of Fintech and banking regulation on income diversification using two-step system GMM. The variable, Fintech, is measured by both the volume and value of digital transactions. The definitions of these variables can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003e*, ** and ***\u003c/b\u003e denote significance at 10%, 5% and 1% level respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the result presented in Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e above, the two-step System GMM results revealed that financial technology plays a decisive role in shaping income diversification positively and significantly at the 1% level, and banking regulation displays negative and marginal level of significance in shaping income diversification of Ethiopian commercial banks.\u003c/p\u003e \u003cp\u003eThe results strongly demonstrate that financial technology is a key driver of income diversification in Ethiopian commercial banks. Both the volume and value of digital transactions exhibit positive and highly significant effects at the 1% level, confirming that digital transformation substantially widens non-interest revenue streams. This finding aligns with Innovation Diffusion Theory, which posits that technology adoption enhances the efficiency and variety of financial services, enabling banks to expand in to fee-based activities. Likewise, the evidence supports the Resource-Based View and Dynamic Capability Theory, which argue that technological assets strengthen a bank\u0026rsquo;s capabilities to innovate and reconfigure services in response to market shifts. Empirically, the results are consistent with Thakur and Arora (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Tang et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Chhaidar et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); A. P. Nguyen et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Hamdi et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), all of whom emphasize that digital channels substantially enhance income diversification through expanded payment services, digital onboarding, and remote financial access. Collectively, the Ethiopian evidence indicates that fintech adoption rather than structural bank characteristics has become the dominant force shaping income diversification in modern banking system.\u003c/p\u003e \u003cp\u003eBanking regulation shows a negative and weakly significant impact on income diversification at 10% level of significance, implying that regulatory obligations may constrain bank\u0026rsquo;s ability to expand in to diversified product lines. This result aligns with the regulatory constraints Hypothesis, which argues that stringent compliance requirements, high reporting burdens, and product restrictions reduce banks\u0026rsquo; flexibility to explore new income generating activities. Empirically, the finding is consistent with studies such as Yusuf and Shikur (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); and AlKhouri and Arouri (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Which show that in highly regulated environments, especially, in developing economies, banks face limited room for innovation, thereby dampening diversification. The result stands in contrast, however, to frame like Market power Theory, which suggests that regulations can lower excessive competition and thus indirectly promote diversification. In Ethiopia\u0026rsquo;s context, the negative association indicates that regulatory rigidity and licensing procedures may hinder the rollout of innovative financial services, thereby restricting the breadth of non-interest revenue. Accordingly, the regulatory environment appears to remain an obstacle rather than an enabler of diversification.\u003c/p\u003e \u003cp\u003eComing to other variables, efficiency emerges with positive and significant impact on income diversification, reinforcing the Transaction Cost Economics argument that operational efficiency enables banks to offer additional services at lower marginal cost, thereby expanding non-interest revenue streams. This supports empirical findings from Hoque et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Li et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Phan et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); and Dietrich and Wanzenried (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) who argue that efficient banks are better positioned to innovative and diversity. Conversely, variables such as bank size, bank age and lending strategy show insignificant effects, implying that traditional structural characteristics do not materially determine diversification outcomes in the context of Ethiopian Commercial Banks. These results contradict classical views derived from the Economies of Scale and Experience Hypothesis, which expect larger and older banks to diversify more due to accumulated expertise and resource advantages. Instead, the Ethiopian context aligns with evidence from emerging economies where structural attributes matter less than technological agility and cost efficient operations. The mixed results suggest that diversification is not inherently tied to size or maturity but is more dependent on strategic capability and focus on digitization.\u003c/p\u003e \u003cp\u003eOverall, the findings highlight that technological capability and operational efficiency, rather than traditional bank characteristics, are the primary determinants of income diversification in the context Ethiopian commercial banks. Digital transformation significantly enhances diversification, supporting both modern theoretical frameworks and global empirical evidence. Meanwhile, regulatory constraints appear to hinder diversification, pointing to a need for regulatory reforms that balance prudential oversight with innovation flexibility. The insignificant impacts of bank size age and lending structure reveal that diversification in Ethiopia is driven more by strategic adoption and technological investment than by structural capability. Collectively, the results underscore that bank embracing digital infrastructure and efficiency enabler systems are better positioned to expand non-interest income, improve resilience, and strengthen long-term financial performance while those constrained by regulation or lacking technological agility may remain limited in diversification potential.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions ","content":"\u003cp\u003eWith a particular emphasis to examine the impact of financial technology and banking regulation with other bank-specific characteristics, the study explores key determinants of income diversification among Ethiopian commercial banks. Using a dynamic panel framework through the two-step system GMM estimator, the study uncovered that digital transformation captured by both the volume (count) and value (amount) of digital transactions together with operational efficiency are the strongest and most consistent predictors of income diversifications. The result also revealed significant persistence in diversification behavior, indicating that past diversification decision strongly shape current outcomes. Contrary to longstanding assumptions in banking literature, traditional structural attributes such as bank size, bank age and lending strategy were found to exert only a marginal negative influence. Overall, the findings substantiate modern theories of innovation, resource capability, and transaction cost reduction, while challenging classical views that emphasize size, scale and specialization as the central drivers of diversification.\u003c/p\u003e\n\u003cp\u003eThe findings of this study present several policy implications for regulators and bank executives seeking to promote stronger, more resilient banking performance in Ethiopia. First, the persuasive influence of digital transformation signals an urgent need for policymakers to accelerate the modernization of the financial sector by expanding digital infrastructure, supporting interoperability, and revisiting outdated directives that limit innovation. Regulatory reforms that strike a balance between prudential oversight and flexibility would encourage banks to pursue broader non-interest income opportunities without compromising financial stability. For bank managers, the results suggest that strategic investment in digital channels, payment systems, and process efficiency should be prioritized ahead of traditional expansion metrics such as branch networks or asset size. The marginal negative effect of banking regulation also highlights the necessity for regulators to conduct periodic reviews of directives that may unintentionally constrain product innovation, digital adoption, and diversification potential. Collectively, these implications underscore that enhancing the digital maturity and operational capabilities of banks is central to improve profitability, strengthening competitive positioning, and reducing overreliance on interest income in the Ethiopian banking landscape.\u003c/p\u003e\n\u003cp\u003eDespite offering valuable insights, this study is not without limitations. The analysis relied on secondary bank-level panel data covering a limited number of commercial banks and years, which may not fully capture granular diversification behaviors occurring at product, customer, or branch levels. Additionally, the study focused mainly on internal and regulatory determinants, leaving unexplored other potentially relevant factors such as managerial competence, competitive pressures from fintech firms, customer preferences, and the quality of digital infrastructure across banks. Future research may extend this work by incorporating more detailed micro-level data, exploring the non-linear or threshold effects of digital adoption, or comparing public and private banks to capture heterogeneity in strategic responses. Researchers may also examine how income diversification interacts with broader outcomes such as financial inclusion, credit allocation, asset quality, or systematic stability. Such investigations would enrich understanding of how diversification strategies evolve and how digital transformation continues to reshape banking dynamics in emerging economies like Ethiopia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAG:\u0026nbsp;\u003c/strong\u003eConceptualization, methodology decision, data collection, analysis of data, manuscript writing and editing for final approval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGM:\u0026nbsp;\u003c/strong\u003eMethodology validation, review, editing and approval of final manuscript, supervisor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFC:\u0026nbsp;\u003c/strong\u003eReview and approval of the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available as per the request for technical check.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declared that there is no conflict of interest regarding the authorship or publication of the manuscript. All financial and non-financial interests relevant to this work have been fully disclosed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declared that there is no any source of fund granted for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Informed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlKhouri, R., \u0026amp; Arouri, H. (2018). The effect of diversification on risk and return in banking sector\u003c/li\u003e\n \u003cli\u003eEvidence from the Gulf Cooperation Council countries. \u003cem\u003eInternational Journal of Managerial Finance\u003c/em\u003e,\u003cem\u003e\u0026nbsp;15\u003c/em\u003e(1), 100-128. https://doi.org/10.1108/IJMF-01-2018-0024\u003c/li\u003e\n \u003cli\u003eAlshomaly, I. 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Dynamic capabilities and strategic management. \u003cem\u003eStrategic management journal\u003c/em\u003e,\u003cem\u003e\u0026nbsp;18\u003c/em\u003e(7), 509-533.\u003c/li\u003e\n \u003cli\u003eThakur, N., \u0026amp; Arora, S. (2024). Determinants of income diversification: empirical evidence from Indian banks. \u003cem\u003eInternational Journal of Law and Management\u0026nbsp;\u003c/em\u003e\u003cem\u003e66\u003c/em\u003e, 195-215. https://doi.org/10.1108/IJLMA-05-2023-0113 (Emerald)\u003c/li\u003e\n \u003cli\u003eViet, D. N. T. N. H. D. (2020). Determinants of non-interest Income: The case of Vietnamese Commecial Banks.pdf. \u003cem\u003ePacific Business Review International\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eWilliamson, O. E. (1989). Transaction cost economics. \u003cem\u003eHandbook of industrial organization\u003c/em\u003e,\u003cem\u003e\u0026nbsp;1\u003c/em\u003e, 135-182.\u003c/li\u003e\n \u003cli\u003eYuan, X., Puah, C.-H., \u0026amp; Marikan, D. A. b. A. (2025). An Empirical Study on the Impact of Financial Technology on the Profitability of China\u0026rsquo;s Listed Commercial Banks. \u003cem\u003eJournal of Risk and Financial Management\u003c/em\u003e,\u003cem\u003e\u0026nbsp;18\u003c/em\u003e(8). https://doi.org/10.3390/jrfm18080440\u003c/li\u003e\n \u003cli\u003eYusuf, J. M., \u0026amp; Shikur, A. A. (2023). Bank Regulations and Firm Performance: In the Case of Ethiopian Commercial Banks. \u003cem\u003eInternational Journal of Finance and Banking Research\u003c/em\u003e. https://doi.org/10.11648/j.ijfbr.20230904.11\u003c/li\u003e\n \u003cli\u003eZhu, K., \u0026amp; Guo, L. (2024). Financial technology, inclusive finance and bank performance. \u003cem\u003eFinance Research Letters\u003c/em\u003e,\u003cem\u003e\u0026nbsp;60\u003c/em\u003e, 104872.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Financial technology, banking regulation, income diversification, Commercial Banks, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-8330606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8330606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eThis study examines the effect of financial technology and banking regulation on income diversification in Ethiopian Commercial Banks. The study used panel data from 17 Ethiopian Commercial Banks spanning 2015\u0026ndash;2024. Using two-step system GMM, the study revealed that financial technology proxy by the volume and value of digital transaction and bank management\u0026rsquo;s operational efficiency are the most robust and consistent drivers of income diversification across Ethiopian Commercial banks. Banking regulation exerts a marginal adverse effect on income diversification. Bank size, years in operation and lending strategy have no significant impact once endogeneity is controlled\u003c/em\u003e. \u003cem\u003eThe findings provide valuable implications for policy makers, bank executives and researchers. The persuasive influence of digital transformation to accelerate the modernization of the financial sector by expanding digital infrastructure, supporting interoperability, and revisiting outdated directives that limit innovation should be the prime focus of policy makers. Besides, strategic investment in digital channels, payment systems, and process efficiency should be prioritized ahead of traditional expansion metrics such as branch networks or asset size\u003c/em\u003e. \u003cem\u003eThese implications underscore that enhancing the digital maturity and operational capabilities of banks is central to improve profitability, strengthening competitive positioning, and reducing overreliance on interest income in the Ethiopian banking landscape.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Does Digital Technology and Banking Regulation Matter for Income Diversification in Ethiopian Commercial Banks?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 05:11:01","doi":"10.21203/rs.3.rs-8330606/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b0d8678c-66c7-427a-ad25-13fffffd9410","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60813278,"name":"Business and commerce/Business and management"},{"id":60813279,"name":"Social science/Business and management"},{"id":60813280,"name":"Business and commerce/Economics"},{"id":60813281,"name":"Social science/Economics"},{"id":60813282,"name":"Business and commerce/Finance"},{"id":60813283,"name":"Social science/Finance"},{"id":60813284,"name":"Business and commerce/Information systems and information technology"},{"id":60813285,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-03-16T12:27:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 05:11:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8330606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8330606","identity":"rs-8330606","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}