Analysing the impact of digital technology diffusion on the efficiency and convergence process of the commercial banking industry of Pakistan

Banking efficiency determinants

Relationship between banking efficiency and digitalisation

Electronic banking, or e-banking, is the use of the Internet to conduct financial transactions. In today’s world, electronic banking has made it possible for customers to monitor their finances and make investments from a distance quickly and effortlessly. Unlike traditional banking, which requires a customer to approach a branch physically, digital banking may be executed at any time and from any location. Because of the rapid technological advancements in digital technology, online banking has become more prominent in the e-payments industry. The banking industry has been the subject of previous studies (Ekinci, 2021; Li et al., 2021; Wang et al., 2021; Zuo et al., 2021; Le et al., 2022), which have explored the impact of technological advancements on banking industry efficiency. However, none of the above studies have ever explored the effect of digital services such as ATMs, the Internet, and point-of-sale (POS)-based transactions on banking efficiency. The growing Internet, ATM, and POS-based transactions imply that human resources are being substituted more often with modern digital technologies. These three mediums of digital banking have the potential to overcome the inefficiency challenges of traditional banking firms, such as restrictions on working hours, branches, insufficient expertise, and slower processing times. In today’s competitive banking ecosystem, Pakistani banks with larger human resources are under additional pressure to enhance efficiency. In light of this, ATM, internet, and POS transactions are generally regarded as one of the most practical solutions to substitute human labour and improve banking efficiency. According to Abbas et al. (2015), the three most important characteristics of ATM, internet, and POS-based digital transactions technologies are their dependability, speed, and security. Since a country’s economic standing increases with ICT infrastructure, Abbasi and Weigand (2017) assert that digital technologies improve banking efficiency. Therefore, in response to RQ2, we hypothesise:

H1a- Increased utilisation of digitalisation (ATM-based transactions) positively increases Pakistan’s banking industry efficiency.

H1b- Increased utilisation of digitalisation (Internet-based transactions) positively increases Pakistan’s banking industry efficiency.

H1c- Increased utilisation of digitalisation (point-of-sale-based transactions) positively increases Pakistan’s banking industry efficiency.

H1d- Increased overall utilisation of digitalisation (DIG INDEX) positively increases Pakistan’s banking industry efficiency.

The relationship between banking efficiency and profitability

Banking assets are vital to a bank’s revenues and profit margins. The conventional paradigm holds that banks are more efficient when their revenues are high, and vice versa. Bank efficiency may grow as profitability increases. (Das et al., 2005; Siriopoulos & Tziogkidis, 2010; Ekinci, 2021), utilised net interest margin (NIM), return on assets (ROA), and return on equity (ROE) ratios to assess banking profitability. A bank has a strong NIM when its interest income outweighs its interest costs. For the bank, primary lending and investment provide this profit margin. A higher NIM than the industry norm enhances bank efficiency. Bank efficiency improves with higher ROA due to superior cost control, interest, and other revenue gains. A higher ROE showcases a bank’s capital allocation and utilisation performance. Banks with a higher ROE are more efficient because they are in a stronger position to deliver long-term returns to their shareholders. Thus, we hypothesise:

H2a- The higher profitability (NIM) positively increases Pakistan’s banking industry efficiency.

H2b- The higher profitability (ROE) positively increases Pakistan’s banking industry efficiency.

H2c- The higher profitability (ROA) positively increases Pakistan’s banking industry efficiency.

Relationship between banking efficiency and management cost control ability

The cost-to-income ratio (CIR) often serves as an assessment tool for bank managers’ operational cost control. Several studies (Purohit & Mazumdar, 2003; Olson & Zoubi, 2017) have shown that CIR adversely influences banking efficiency. Consequently, we hypothesise;

H3- An effective cost control managerial ability (i.e., lower CIR) results in higher banking efficiency.

Relationship between banking efficiency and capitalization

The agency paradigm implies that highly leveraged banks ought to be efficient to avoid going insolvent by defaulting on interest payments. Several studies (Lee & Huang, 2016; Mansour & El Moussawi, 2020) suggest that bank capitalization elevates efficiency. A lower equity-to-asset ratio indicates more risk-taking and leverage, which could drive up borrowing costs and negatively impact efficiency. Therefore, we hypothesise;

H4- A higher equity-to-asset ratio (CAPT) results in higher banking efficiency.

Relationship between banking efficiency, AGE, SIZE, and other macroeconomic factors

The relationship between bank AGE and efficiency is complex. Mature banks have gained more knowledge and expertise over their long existence. Over time, they might have developed their business practices, risk assessment, and customer service policies (Chiu & Chen, 2009). Mature banks function more efficiently than newer ones. Thus, we hypothesise:

H5- Bank age has a positive link with banking efficiency.

The bank SIZE, measured as the natural logarithm of total assets, influences the ability of banks to achieve economies of scale across different business domains (Sufian, 2009; Phan et al., 2016). For instance, Sufian (2009) identified that larger Chinese banks are more profitable. While some studies reveal a negative relationship between bank SIZE and efficiency, being more efficient is more likely to be the situation for larger banks due to their larger fixed asset bases, human resources, and branch office networks. Thus, we hypothesise:

H6- Larger banks take advantage of economies of scale, which allows them to operate more efficiently.

We have additionally considered macroeconomic factors such as the inflation rate (INFRATE), interest rate (INTRATE), and gross domestic product growth rate (GDPGR) frequently used in prior studies (Avkiran, 1999; Perera et al., 2007; Anbar & Alper, 2011; Řepková, 2015; Carvallo & Kasman, 2017). The relationship between the inflation rate (INFRATE) and banking efficiency is convoluted. High inflation might make resource allocation challenging for banks. Effective resource allocation enables a bank to optimise revenues and remain profitable. However, inflation makes it challenging to assess investment alternatives, potential returns, and the cost of financing. Unforeseen circumstances might impede banks’ decision-making and efficiency. Thus, we hypothesise;

H7- There is an adverse relationship between the inflation rate and banking efficiency.

Moreover, higher interest rates (INTRATE) might increase loan revenues. Banks can increase revenue by charging higher interest rates on loans. With extra revenue, banks might invest in technology, infrastructure, and business operations, improving performance (Perera et al., 2007; Anbar & Alper, 2011). Thus, we hypothesise;

H8- Interest rate has a positive influence on banking efficiency.

The relationship between banking efficiency and the gross domestic product growth rate (GDPGR) may be complex and sensitive to context. Economic activity such as investment, consumption, and business expansion, which stimulates the appetite to open bank accounts, make deposits, and engage in other financial intermediation, contributes to GDPGR. Taking advantage of the growing economy and opportunities for profitable ventures can help banks enhance efficiency (Perera et al., 2007; Tan & Tan, 2016; Carvallo & Kasman, 2017). Therefore, we hypothesise;

H9- There is a positive association between gross domestic product growth rate GDPGR and banking efficiency.

Relationship between banking efficiency and ownership structure

The ownership structure of a bank might influence its business objectives. Because of their business focus, private banks might be more motivated to improve efficiency and revenue. Monetary inclusion or strategic funding may motivate public banks rather than efficiency. For instance, Ayadi (2014) argued that Tunisian private banks are more efficient than government banks. However, Karas et al. (2010) reported that public banks are more efficient than private ones. Therefore, we included ownership structure dummies to isolate Pakistan’s banking industry heterogeneity.

Banking efficiency convergence

Banking efficiency absolute convergence

As a performance benchmark, banking efficiency research sheds light on the best-practice frontier. However, empirical evidence on banking efficiency convergence/catch-up is crucial. Our analysis of banking efficiency convergence dynamics employs the convergence idea from the economic growth literature (Barro & Sala-i-Martin, 1992). Economic convergence takes place as emerging economies grow faster than developed ones, reducing the income gap. It analyses the economy’s performance globally or regionally, taking into account progress in technology, capital accumulation, monetary and fiscal policy, and productivity. Conversely, convergence in banking efficiency neutralises operational performance gaps between banks in a market over time (Casu & Girardone, 2010; Carvallo & Kasman, 2017). Thus, less efficient banks gradually catch up to their more efficient counterparts. It evaluates micro-level operational efficiency. Integration of operational performance measures, which might be influenced by managerial decisions, technology adoption (Casu et al., 2016), and legislative circumstances (Thota & Subrahmanyam, 2020).

Investigating bank efficiency convergence shows how efficient banking enhances the economy. Efficient banking allows banks to effectively allocate resources, lend funds, and manage risk, thereby stimulating investment and growth. To ensure financial stability, banking efficiency convergence ought to be thoroughly studied. Efficient banks reduce the likelihood of financial crises and downturns. Bank efficiency analysis might allow regulators to prevent financial instability and improve economic growth. Regulators and policymakers view convergence in banking efficiency as a key indicator of sector evolution. The convergence concept is relatively new to banking literature (Weill, 2009; Casu & Girardone, 2010; Matthews & Zhang, 2010; Kasman et al., 2013; Carvallo & Kasman, 2017; Olson & Zoubi, 2017; Chen et al., 2020; Mansour & El Moussawi, 2020; Thota & Subrahmanyam, 2020); however, there are several caveats to consider before applying it from macroeconomic growth to banking efficiency. There is a fundamental difference between the two economic disciplines.

First, macroeconomic convergence theorists (Barro et al., 1991; Barro & Sala-i-Martin, 1992; Quah, 1996) focus on regional or national growth indices. Banking performance convergence theorists (Casu & Girardone, 2010; Matthews & Zhang, 2010; Thota & Subrahmanyam, 2020) operate at the micro-level, and banks vary in scope, framework, and business style. Thus, heterogeneity between banks is vital for micro-level convergence. Second, macroeconomic convergence theories examine regional and national incomes, as well as GDP per capita convergence or divergence. This is dependent on government monetary and fiscal policies, as well as individual decisions. As emerging economies catch up, income disparities might narrow. Government spending and taxation, as well as central bank-supervised monetary policy, have an impact on economic growth. Global trade and technology spillover influence economic growth. Human education, labour participation, and migration decisions all contribute to human capital and economic growth. Managerial decisions beyond growth drive the microeconomic concept of banking efficiency convergence, which aims for long-term profitability, effective resource utilisation, higher productivity, and improved risk control. Managers’ micro-decisions have an impact on banking performance. Strategic decisions influence business activities, technology investments, and risk control. Optimising human resources, capital, and technology is vital for achieving financial objectives and providing high-quality services. To compete in evolving markets, banking management examines short-term trends in response to evolving regulations, technology, and circumstances. Banking efficiency convergence demands an in-depth approach, unlike macroeconomic convergence, which takes a broad context. Third, macroeconomic growth convergence is examined over an extended period of time. Banking efficiency convergence has short-term fluctuations in microeconomic settings due to legislation, technology, or rapidly evolving business circumstances.

Though economic growth and banking efficiency are intricate, convergence theory might be used for banking efficiency convergence in a microeconomic setting. This underscores the intricate similarities and underlying characteristics of macroeconomic trends involving economic growth convergence and microeconomic banking efficiency convergence.

Firstly, both economic growth and banking efficiency convergence share catch-up processes. For the former, developing countries aim for economic growth on par with developed countries, while for the latter, less efficient banks seek efficiency on par with more efficient banks (Casu & Girardone, 2010; Matthews & Zhang, 2010). Both convergence approaches involve catching up to narrow the gap towards convergence.

Secondly, structural reforms allow both convergence patterns to occur. To foster economic convergence, legislators undertake systemic reforms that foster organisational productivity, industry competitiveness, and aggregate output. Similar to banks, systemic changes (Matousek et al., 2015; Thota & Subrahmanyam, 2020) such as improved oversight, risk controls (Fiordelisi et al., 2011; Bryce et al., 2015; Sharma et al., 2015), and advanced technologies (Prakash et al., 2021) might increase efficiency.

Thirdly, technological leapfrogging affects economic growth and banking efficiency convergence. Innovative technology accelerates economic growth, allowing developing countries to catch up to advanced economies (Sala-i-Martin & Barro, 1995). In banking efficiency convergence, banks that employ digital innovation (Ekinci, 2021; Li et al., 2021; Wang et al., 2021; Le et al., 2022), automated processes, and data analytics are more likely to increase operational efficiency and catch up to more efficient competitors. Digitalisation leads to efficiency convergence in the banking sector and a paradigm shift in other sectors. Incorporating digital technology and practices that improve efficiency and growth allows less efficient banks to catch up with more efficient rivals. Economic convergence might result from digital transformation if less developed economies adopt digital technologies at the same rate as more developed ones.

Fourthly, market integration drives economic growth convergence and trade barrier reduction (Ben-David & Loewy, 1998). Market integration is essential to banking efficiency convergence. Banking sector integration facilitates competitiveness, knowledge spillover, and optimal process adoption, increasing efficiency convergence. In the banking industry, benchmark efficient banks transfer best organisational practices, risk control strategies, and client service tactics, which allow low-efficient opponents to catch up (Casu et al., 2016).

Finally, investing in training and education to improve economic growth convergence underlines the significance of human capital (Henderson & Russell, 2005), which also elevates banking efficiency convergence. Skilled decision-makers, advanced risk management, and high-quality service increase efficiency (Iliemena et al., 2019; Rahman & Akhter, 2021), enabling less efficient banks to catch up. Thus, human capital fosters both economic growth and banking efficiency convergence. In summary, this conceptual framework offers an in-depth understanding of macroeconomic convergence dynamics and illustrates the application of convergence theory to the convergence of banking efficiency in microeconomics by focusing on similarities and shared influencing factors. Therefore, we examine Pakistan’s banking industry’s efficiency convergence to discover how banks that were less efficient at utilising their input factors of production caught up to their more efficient counterparts over time. There are two approaches, β-convergence and σ-convergence, proposed by (Barro & Sala-i-Martin, 1992; Quah, 1996) and extensively applied in prior studies (Weill, 2009; Casu & Girardone, 2010; Matthews & Zhang, 2010; Kasman et al., 2013; Carvallo & Kasman, 2017; Olson & Zoubi, 2017; Chen et al., 2020; Mansour & El Moussawi, 2020; Thota & Subrahmanyam, 2020) to investigate banking efficiency convergence.

The concept of absolute β-convergence is one of the generalised aspects of the neoclassical theory of economic growth. In the broader context of banking efficiency convergence, absolute β-convergence represents the gradual narrowing of the efficiency gap between less efficient and more efficient banks. Absolute β-convergence asserts that banks with varying initial efficiency levels converge to a common equilibria if their long-run characteristics are homogenous. In a nutshell, absolute β-convergence applies if the rate of efficiency growth is inversely related to its initial level. Therefore, in response to RQ3, we hypothesise:

H5a– In the long run, initially, less efficient banks in Pakistan’s banking industry would eventually converge to more efficient counterparts, achieving the common equilibrium.

Banking efficiency σ-convergence describes the process of a gradual reduction in efficiency variance from the cross-sectional average over time. In the banking industry, any shock might momentarily widen the efficiency dispersion between banks, even when the banks are catching up to equilibrium. Quah (1996) underlines that β-convergence is a necessary but insufficient criterion for σ-convergence. Our research likewise investigates σ-convergence to confirm if the dispersion of banking efficiency levels from the cross-sectional mean diminishes over time. Therefore, in response to RQ3, we hypothesise:

H5b- In the long run, initially, the banks in Pakistan’s banking industry that experienced more dispersed efficiency levels from the cross-sectional mean would eventually diminish this dispersion and converge to the cross-sectional mean, achieving the common equilibrium.

Banking efficiency conditional β-convergence

Banking efficiency conditional convergence emphasises that banks converge to their own efficiency equilibrium, unlike absolute efficiency convergence. Since banks confront idiosyncratic and structural shocks, which influence them in a peculiar way, they may be at varying points towards efficiency equilibrium. The rate of convergence serves as an indicator of competitive advantage; a slower rate of convergence implies the ability to sustain advantages over an extended duration. In an ideal scenario, banks might maximize long-term efficiency. Economic conditions, varying business practices, industry regulations, and digital technology development influence the banking efficiency convergence process in the long run. We argue that banks exhibit varying efficiency levels due to bank-specific and technological factors. Several conditional factors can result in efficiency growth, such as the fact that in domains where resources are underutilised, digital technology tools help improve efficiency by lowering intermediary costs (Ekinci, 2021). Innovative digital banking technology gives banking managers more flexibility in pricing, reliability, and customer service. According to Abbasi and Weigand (2017) digital information and communication technology (ICT) might improve banking operations and mitigate risks. Meanwhile, (Delgado & Nieto, 2004; Vu, 2011; Hilal, 2014; Ekinci, 2021) found that digital ICT improves banking efficiency. Therefore, in response to our RQ4, we hypothesise:

H5c- In the long run, the utilisation of digital payment channels (ATM, internet, and point-of-sale-based transactions) support initially less efficient banks in Pakistan’s banking industry to converge faster to catch up with their more efficient counterparts.

H5d- In the long run, overall utilisation of digitalisation (DIG INDEX) support initially less efficient banks in Pakistan’s banking industry to converge faster to catch up with their more efficient counterparts.

H5e- In the long run, available digital information and communication technologies (ICT) support initially less efficient banks in Pakistan’s banking industry to converge faster to catch up with more efficient counterparts.

H5f- In the long run, utilisation of digital information and communication technologies (ICT) support initially less efficient banks in Pakistan’s banking industry to converge faster to catch up with more efficient counterparts.