The Impact of Digital Transformation on Six Commercial Banks Performance in Botswana ()
1. Introduction
The advent of digital technologies has fundamentally reshaped the banking landscape, challenging traditional operational models while opening avenues for enhanced service delivery, cost efficiencies, and financial innovation (Claessens et al., 2018). Technologies such as artificial intelligence (AI), blockchain, big data analytics, and mobile banking are revolutionizing the ways banks interact with customers, process transactions, and manage risks (Vives, 2019). In Botswana, where the banking sector is a cornerstone of economic development, digital transformation has emerged as a critical driver for improving financial inclusion, resilience, and competitiveness. By embracing these technologies, banks can reach underserved populations, streamline operations, and respond to the evolving demands of tech-savvy customers (World Bank, 2022).
This study explores the impact of digital transformation on the performance of commercial banks in Botswana, emphasizing key indicators such as profitability, operational efficiency, and market competitiveness. Botswana’s banking sector presents a mix of large multinational banks like Standard Chartered Botswana and smaller indigenous institutions such as BancABC Botswana, offering a unique opportunity for comparative analysis. With varying levels of technological adoption, these banks provide a rich context to examine how digital transformation influences financial outcomes (Kpodar & Andrianaivo, 2019).
Two critical questions underpin this investigation: What is the relationship between digital transformation and the performance of commercial banks in Botswana? How do banks of different sizes and technological readiness navigate this transformation? By addressing these questions, the study aims to provide insights for policymakers, banking executives, and stakeholders, fostering a strategic approach to leveraging digital technologies in the financial sector.
1.1. Background
Digital transformation in the banking industry refers to the strategic adoption and integration of advanced digital technologies into banking processes, customer interactions, and financial services. Globally, this transformation has been linked to reduced operational costs, enhanced customer engagement, and the creation of innovative financial products (Vives, 2019). For instance, JPMorgan Chase in the United States has heavily invested in AI and big data to improve risk management and customer service, while South Africa’s First National Bank (FNB) has leveraged mobile banking platforms to expand access to financial services across rural and urban areas (Claessens et al., 2018).
In the African context, digital transformation has catalyzed financial inclusion, addressing long-standing barriers such as limited access to physical banking infrastructure. Mobile banking solutions like Kenya’s M-Pesa have revolutionized money transfer services, enabling millions to access financial tools through their mobile phones. Similarly, Nigeria’s Access Bank has embraced blockchain technology to facilitate faster and more secure cross-border payments (Kpodar & Andrianaivo, 2019).
Botswana’s banking sector, though relatively small compared to regional giants like South Africa and Nigeria, has shown steady progress in adopting digital technologies. Major players such as First National Bank Botswana (FNBB) and Barclays Botswana have introduced digital platforms for mobile banking, online payments, and digital wallets. FNBB, for instance, launched the eWallet solution, which allows customers to send money using only a recipient’s phone number, eliminating the need for traditional bank accounts. Indigenous banks, such as Botswana Savings Bank, are also innovating, albeit at a slower pace due to resource constraints and technological readiness (World Bank, 2022).
Despite these advancements, Botswana faces significant challenges in its digital transformation journey. Limited digital literacy among consumers, particularly in rural areas, remains a key barrier to widespread adoption of digital banking services. Regulatory bottlenecks, such as stringent compliance requirements and slow adaptation of laws to accommodate emerging technologies, further hinder progress. Cybersecurity risks are another pressing issue, as the increasing reliance on digital platforms exposes banks and customers to potential data breaches and fraud (World Bank, 2022).
Nonetheless, the potential benefits of digital transformation in Botswana’s banking sector cannot be overstated. Enhanced operational efficiency can reduce transaction costs and improve service delivery, while digital platforms can facilitate broader financial inclusion, aligning with the country’s Vision 2036 goals for economic diversification and inclusive growth (Botswana Vision 2036, 2016). Moreover, the COVID-19 pandemic has accelerated the shift toward digital banking, with consumers and businesses increasingly opting for online and mobile solutions over traditional banking channels (Claessens et al., 2018).
Globally, the transformative power of digital technologies is evident in the success stories of leading banks. In the United States, Bank of America’s “Erica” virtual assistant, powered by AI, has redefined customer service by providing instant responses to queries and proactive financial advice (Vives, 2019). Similarly, Singapore’s DBS Bank has been recognized as a global leader in digital banking, utilizing cloud computing and AI to deliver seamless and personalized customer experiences.
In the African context, South Africa’s Standard Bank has adopted data analytics to predict customer behavior and tailor its offerings, while Equity Bank in Kenya has embraced digital platforms to enhance microfinance services and support small businesses. These examples highlight the diverse ways digital transformation can reshape banking operations, with implications for profitability, customer satisfaction, and market positioning (Kpodar & Andrianaivo, 2019).
In Botswana, First National Bank Botswana serves as a leading example of digital banking innovation. Its mobile banking app allows customers to manage accounts, pay bills, and transfer funds effortlessly, while its eWallet service has become a critical tool for financial inclusion. Other banks, such as Standard Chartered Botswana, have implemented digital strategies to improve efficiency, including paperless transactions and automated teller machines (ATMs) equipped with advanced features like biometric authentication (World Bank, 2022).
The comparative analysis of these banks underscores the importance of tailoring digital strategies to local contexts. While multinational banks often have the resources to adopt cutting-edge technologies, indigenous banks may face constraints, such as limited IT infrastructure and smaller budgets. Understanding these dynamics is crucial for fostering a balanced and inclusive digital transformation in Botswana’s banking sector.
Selection of Banks for the Study
The selection of six banks for this study such as Standard Chartered Bank Botswana, Stanbic Bank, First National Bank of Botswana (FNBB), Bank of Baroda, Absa Bank Botswana, and Access Bank Botswana, is based on a variety of factors, including their size, market presence, technological readiness, and commitment to digital transformation. Each of these banks has made notable strides in integrating digital technologies into their operations, yet they also face unique challenges and opportunities in the context of Botswana’s banking landscape.
1) Standard Chartered Bank Botswana: As a subsidiary of one of the world’s leading multinational banks, Standard Chartered has the financial resources and global expertise to implement advanced digital solutions. The bank has embraced digital banking to improve efficiency, reduce costs, and enhance customer engagement through mobile banking and online platforms (Claessens et al., 2018). Its experience offers insights into how global banking standards and resources can influence the adoption of technology in a smaller market like Botswana.
2) Stanbic Bank Botswana: Stanbic, part of the Standard Bank Group, operates with the advantages of regional expertise, offering a broader spectrum of digital services such as mobile banking and online services. The bank is also leveraging data analytics to enhance customer experiences and improve its competitive position in Botswana’s financial services market (Vives, 2019). This makes Stanbic an important case study for understanding how regional banking strategies can be tailored to the local context.
3) First National Bank of Botswana (FNBB): FNBB has been a leader in digital transformation in Botswana, with its mobile banking app and eWallet service offering financial inclusion solutions to underserved populations. The bank’s focus on mobile technologies demonstrates how digital platforms can enhance financial services delivery and expand access to banking services, especially in rural areas (World Bank, 2022).
4) Bank of Baroda Botswana: A smaller player in Botswana’s banking sector, Bank of Baroda has focused on implementing mobile banking solutions to compete with larger institutions. Although the bank faces challenges in terms of limited resources, it has used digital tools to enhance customer service and improve operational efficiency, making it an interesting case for studying how smaller banks navigate digital transformation (Kpodar & Andrianaivo, 2019).
5) Absa Bank Botswana: Absa is a significant player in Botswana’s banking industry, with an increasing focus on digital innovation, including mobile banking and enhanced online platforms. The bank’s commitment to adopting technologies such as AI and machine learning to improve operational efficiency positions it as a key player in this study of digital transformation (Vives, 2019).
6) Access Bank Botswana: As part of Access Bank Group, one of the largest banks in Nigeria, Access Bank Botswana has a wealth of experience in digital banking. It has adopted blockchain technology to streamline cross-border payments and improve security, making it a valuable participant in understanding the role of cutting-edge technologies in transforming the banking sector (Kpodar & Andrianaivo, 2019).
These six banks were selected based on their market share, technological innovations, and varying degrees of digital transformation, offering a well-rounded perspective on the impact of digital adoption on bank performance in Botswana. The study will explore how each bank has utilized digital technologies to improve operational efficiency, customer satisfaction, and financial performance.
1.2. Research Problem
While digital transformation has emerged as a transformative force in the global banking sector, its specific impact on the performance of commercial banks in Botswana remains underexplored. Existing literature tends to focus on broader global trends, such as the role of artificial intelligence (AI), blockchain, and mobile banking in enhancing operational efficiency and customer engagement (Claessens et al., 2018). However, these studies often overlook the unique challenges and opportunities within the Botswana banking context, where factors such as limited IT infrastructure, digital literacy, and regulatory constraints significantly influence the adoption and success of digital technologies (World Bank, 2022).
One critical issue is the lack of empirical evidence on how digital transformation affects key performance indicators such as profitability, efficiency, and market competitiveness within Botswana’s banking sector. For instance, while banks like First National Bank Botswana (FNBB) have introduced innovative digital solutions such as the eWallet service, there is little research analyzing how these initiatives impact overall financial performance compared to more traditional banking models. Similarly, indigenous banks, such as Botswana Savings Bank, face unique challenges, including resource constraints and slower technological adoption, which are rarely addressed in existing studies (Botswana Vision 2036, 2016).
The differential effects of digital transformation on banks of varying scales and technological capabilities further complicate the issue. Multinational banks operating in Botswana, such as Standard Chartered Botswana, benefit from access to global resources and advanced technologies, enabling them to implement sophisticated digital strategies. In contrast, smaller, local banks often struggle with limited budgets and outdated systems, making it difficult for them to compete or innovate effectively (Kpodar & Andrianaivo, 2019). This disparity highlights the need for a tinged understanding of how digital transformation impacts banks differently, depending on their size, scope, and technological readiness.
Additionally, the regulatory environment in Botswana poses unique challenges. The slow adaptation of laws to accommodate emerging technologies and stringent compliance requirements often hinder banks’ ability to innovate (Vives, 2019). Furthermore, cybersecurity risks associated with digital banking, such as data breaches and fraud, remain a significant concern, especially as Botswana’s consumers increasingly adopt online and mobile banking solutions (World Bank, 2022).
This study addresses these gaps by providing empirical evidence on the relationship between digital transformation and the performance of commercial banks in Botswana. By exploring these dynamics, the research aims to offer practical insights for policymakers, banking executives, and other stakeholders, ultimately fostering a more inclusive and competitive banking sector aligned with Botswana’s Vision 2036 goals of economic diversification and sustainable growth.
2. Literature Review
2.1. Theoretical Framework
This study draws upon three well-established theoretical perspectives to examine the drivers and impacts of digital transformation in the banking sector: the Technology-Organization-Environment (TOE) Framework, the Resource-Based View (RBV), and Disruptive Innovation Theory. Each theory offers unique insights into the adoption, implementation, and consequences of digital technologies, providing a comprehensive foundation for analyzing the performance of commercial banks in Botswana.
2.2. Technology-Organization-Environment (TOE) Framework
The Technology-Organization-Environment (TOE) Framework, developed by Tornatzky and Fleischer (1990), provides a comprehensive lens for analyzing the adoption of new technologies by organizations, highlighting three key contexts: technological, organizational, and environmental. The technological context addresses the characteristics of technologies, such as their availability, complexity, and relative advantage (Tornatzky & Fleischer, 1990). In Botswana, digital innovations like mobile banking and blockchain hold substantial potential for enhancing cost efficiency and promoting financial inclusion (Bank of Botswana, 2022). However, these technologies also pose challenges, including their complexity and the infrastructural limitations in rural areas (Makina, 2019).
The organizational context focuses on internal factors like the size of the bank, its technological readiness, and the commitment of its leadership (Tornatzky & Fleischer, 1990). For example, large multinational banks such as Standard Chartered Botswana are often better positioned to adopt digital innovations due to their robust resources and strategic investments (Standard Chartered Botswana, 2023). Conversely, smaller indigenous banks may struggle with resource constraints and readiness (Mokone, 2021).
Lastly, the environmental context involves external pressures, including customer demand, competitive dynamics, and regulatory frameworks (Tornatzky & Fleischer, 1990). In Botswana, increasing consumer preference for digital banking solutions and the imperative to meet stringent cybersecurity regulations significantly drive digital transformation in the sector (Bank of Botswana, 2022; KPMG, 2020). Together, these dimensions offer a multidimensional perspective, enabling a tinged understanding of the factors shaping digital adoption. The TOE framework’s holistic approach, encompassing both internal organizational attributes and external environmental pressures, makes it particularly relevant for this study, as it provides a robust foundation to explore how digital transformation influences the performance of Botswana’s commercial banks (Makina, 2019).
Moreover, the TOE Framework’s emphasis on the interplay between technology, organization, and environment aligns well with the study’s objectives of examining the relationship between digital transformation and the performance of commercial banks (Tornatzky & Fleischer, 1990). It enables a nuanced analysis of how factors such as bank size, technological readiness, customer demand, and regulatory dynamics interact to influence performance outcomes (FNBB, 2023; KPMG, 2020).
The TOE Framework serves as the theoretical lens for this study, providing a comprehensive basis for analyzing the drivers and impacts of digital transformation in Botswana’s banking sector. By integrating technological, organizational, and environmental dimensions, the framework allows for a thorough exploration of how digital transformation affects profitability, efficiency, and market competitiveness across banks of varying scales and technological readiness (Tornatzky & Fleischer, 1990; Makina, 2019).
2.3. Illustration of the Conceptual Framework
Figure 1 below represents the relationships among the variables, showing how digital transformation as the independent variable impacts bank performance while being moderated by control variables.
Figure 1. Conceptual framework on impact of digital transformation on bank performance (PwC, 2023). Authors creativity guided by Ravitch & Riggan (2016).
The conceptual framework illustrates the relationship between digital transformation and bank performance, emphasizing the roles of independent, dependent, and control variables. Digital transformation, represented as the independent variable, includes components such as digital banking technologies, IT infrastructure, and digital innovation indices. Digital banking technologies encompass services like mobile banking, online banking platforms, and advanced payment systems, which enhance customer accessibility and convenience (Laukkanen, 2016). IT infrastructure refers to the foundational technology enabling seamless digital operations, including servers, cloud computing, and cybersecurity measures (Zaheer & Trkman, 2017). Digital innovation indices measure the extent and pace of a bank’s adoption of cutting-edge technologies, reflecting its readiness to adapt to market demands and innovation opportunities. These elements collectively drive the core process of digital transformation.
The dependent variable, bank performance, is influenced by digital transformation and is measured through profitability, efficiency, market share, and risk management. Profitability refers to the bank’s ability to generate financial returns from digital channels (Gomber et al., 2018), while efficiency captures cost reductions achieved through automation and streamlined processes. Market share reflects the bank’s competitiveness in the digital landscape, and risk management assesses its ability to mitigate operational and financial risks through technological advancements. Surrounding the framework, control variables such as bank size, regulatory environment, and economic conditions are positioned to moderate the relationship. Bank size affects resource availability for digital transformation, while regulatory frameworks and economic conditions shape operational constraints and opportunities (World Bank, 2022). This framework underscores how contextual factors interact with digital transformation to shape banking outcomes.
2.4. Empirical Review
Empirical studies on the impact of digital transformation on banks’ performance have yielded a range of insights across diverse geographical contexts. In Europe, Vives (2019) revealed that digital banking enhances operational efficiency and customer satisfaction, particularly through the adoption of mobile banking and online financial services. Similarly, Pousttchi et al. (2020) found that German banks integrating AI-driven analytics improved decision-making processes and financial forecasting accuracy. In the UK, Singh et al. (2021) observed that banks leveraging blockchain technology enhanced transparency and reduced fraud-related losses. These findings underline the efficiency gains associated with digital transformation in developed markets.
In Asia, Tan et al. (2020) documented that Chinese banks adopting fintech partnerships experienced a 20% increase in market share due to streamlined credit approval processes. A study by Arun et al. (2019) on Indian banks highlighted that digital lending platforms reduced loan disbursement times by 40%, significantly boosting customer satisfaction. Similarly, Nguyen and Le (2021) found that Vietnamese banks integrating internet banking observed higher profitability, primarily through reduced operational costs. These studies emphasize the positive correlation between digital adoption and financial outcomes in emerging Asian economies.
In sub-Saharan Africa, Kpodar and Andrianaivo (2019) noted that mobile money platforms foster financial inclusion, particularly in Kenya and Tanzania, enabling banks to expand their customer base. In South Africa, De Klerk et al. (2020) found that banks using digital channels experienced higher engagement among underserved communities. Similarly, Dube and Gumbo (2021) highlighted that Zimbabwean banks leveraging digital transformation achieved cost-efficiency despite challenging economic conditions. Botswana, however, has limited empirical evidence. A study by Lekobane and Malebogo (2020) showed that digital banking in Botswana improves service delivery but has not been extensively linked to profitability metrics.
In the Americas, Berger et al. (2020) found that digital transformation in US banks enhanced profitability and reduced risks through improved credit risk assessments. Similarly, Delgado et al. (2021) highlighted that Brazilian banks using mobile banking platforms saw increased customer retention. Canadian banks, according to studies by Robinson and Simmons (2021), benefited from AI-driven customer relationship management, which improved cross-selling opportunities.
Globally, Tiwari and Buse (2020) demonstrated that digital innovation indices correlate strongly with market share growth among international banks. Similarly, a cross-country analysis by Ernst and Young (2021) confirmed that digital banking technologies contribute to 15-20% cost savings for major international banks. Nonetheless, studies such as those by Tabrizi et al. (2019) caution against risks associated with cybersecurity breaches and data privacy concerns, which may offset performance gains.
These studies collectively suggest that digital transformation positively influences bank performance across various metrics, including profitability, efficiency, and customer engagement. However, regional differences highlight the need for context-specific strategies. For Botswana and other underrepresented regions, future research should focus on quantifying the direct impact of digital transformation on key performance indicators.
3. Methodology
This study adopts a quantitative approach, this technique to gain a comprehensive understanding of the impact of digital transformation on the performance of commercial banks in Botswana (Creswell & Plano Clark, 2018). The quantitative component utilizes secondary data from annual reports, industry publications, and regulatory filings. This data is used to evaluate key performance indicators (KPIs) such as Return on Assets (ROA) and Cost-to-Income Ratio over a period (2013-2023) (Bank of Botswana, 2022; FNBB, 2023).
3.1. Model Specification
The study employs a panel data regression model, which will enable the examination of both cross-sectional and temporal variations in bank performance in relation to digital transformation efforts (Hsiao, 2014). Panel data analysis is particularly suitable for this study as it allows for controlling unobservable heterogeneity and capturing the dynamic effects of digital transformation on key performance indicators (Baltagi, 2021). The model is specified as follows:
In this study, Performance it represents the performance of bank i at time t, measured using key financial indicators such as Return on Assets (ROA) and Cost-to-Income Ratio (CTIR). ROA reflects how efficiently a bank utilizes its assets to generate profit, while CTIR measures cost efficiency by comparing operating expenses to income (Bank of Botswana, 2021; IMF, 2023). Digital Transformation: it serves as the main explanatory variable, capturing the extent of digital adoption in each bank. This variable encompasses digital banking initiatives such as mobile banking, internet banking, and automation of financial services (PwC, 2023; Chen et al., 2021). Control Variables: it includes factors such as bank size, regulatory environment, and macroeconomic conditions that may also influence bank performance. Bank size is typically measured by total assets and is associated with economies of scale (Munyanyi, 2022). The regulatory environment considers laws, policies, and compliance costs affecting bank operations (Nyasha & Odhiambo, 2023). It denotes the error term, which accounts for unobserved factors affecting bank performance. These variables collectively offer a comprehensive framework for evaluating the impact of digital transformation on Botswana’s banking sector.
3.2. Data Collection
Data for this study is collected from six commercial banks in Botswana over a 20-year period (2003-2023), excluding Bank of Gaborone. The banks selected for this analysis include Standard Chartered Bank Botswana, Stanbic Bank, First National Bank of Botswana, Bank of Baroda, Absa Bank Botswana, and Access Bank in Botswana.
Secondary data sources are primarily drawn from financial statements, annual reports, and industry publications, such as the Bank of Botswana’s annual reports and other regulatory documents, which provide a detailed view of financial performance and operational dynamics. These sources are essential for understanding key financial indicators such as Return on Assets (ROA), Return on Equity (ROE), and Cost-to-Income Ratios (CTIR), as well as the impact of digital transformation initiatives.
4. Empirical Results Interpretations and Discussions
The analysis of banking performance metrics provides insights into the dynamics of the financial sector and its response to macroeconomic and technological changes. This section interprets empirical results from Botswana’s banking sector, focusing on descriptive statistics, stationarity tests, correlation analysis, and a Generalized Method of Moments (GMM) model. Key variables such as Return on Assets (ROA), Return on Equity (ROE), Cost-to-Income Ratio (CTIR), Digital Banking Index (DBI), and Digital Banking Score (DBS) serve as proxies for performance, efficiency, and the impact of digital transformation.
Descriptive statistics underscore the performance trends, while stationarity tests confirm the reliability of variables for econometric modeling. Correlation analysis reveals significant interrelationships, highlighting the influence of digital transformation and economic conditions. Lastly, the GMM model quantifies the impact of digitalization on profitability, operational efficiency, and macroeconomic resilience. These findings align with existing literature, emphasizing the importance of technological innovation and macroeconomic stability in fostering growth and competitiveness in emerging markets (Nyasha & Odhiambo, 2023; Bank of Botswana, 2021).
Table 1. Descriptive summary statistics.
|
CTIR |
DBI |
DBS |
GDPG |
INF |
LBS |
MKS |
NIM |
ROA |
ROE |
Mean |
50.47 |
49.34 |
58.62 |
3.52 |
6.59 |
22.52 |
15.87 |
5.48 |
2.67 |
14.31 |
Median |
50.10 |
47.50 |
58.00 |
4.56 |
6.95 |
22.56 |
20.00 |
5.50 |
2.70 |
14.50 |
Maximum |
61.70 |
97.00 |
99.00 |
11.92 |
12.70 |
23.17 |
26.00 |
7.20 |
3.60 |
18.50 |
Minimum |
38.20 |
7.00 |
22.00 |
−14.14 |
1.89 |
21.75 |
1.10 |
3.80 |
1.50 |
1.50 |
Std. Dev. |
5.77 |
26.34 |
20.28 |
6.13 |
3.09 |
0.34 |
8.37 |
0.74 |
0.49 |
2.11 |
Skewness |
−0.11 |
0.15 |
0.11 |
−1.34 |
0.28 |
−0.27 |
−0.67 |
0.03 |
−0.26 |
−1.84 |
Kurtosis |
2.12 |
1.73 |
2.01 |
4.65 |
2.16 |
2.40 |
1.83 |
2.38 |
2.14 |
12.38 |
Jarque-Bera |
4.29 |
8.60 |
5.41 |
52.07 |
5.37 |
3.40 |
16.56 |
2.07 |
5.31 |
533.12 |
Probability |
0.12 |
0.01 |
0.07 |
0.00 |
0.07 |
0.18 |
0.00 |
0.36 |
0.07 |
0.00 |
Sum |
6359.10 |
6019.00 |
7386.00 |
443.02 |
830.43 |
2837.53 |
1999.70 |
690.80 |
335.90 |
1803.40 |
Sum Sq. Dev. |
4155.73 |
83975.22 |
51387.71 |
4703.52 |
1193.73 |
14.36 |
8753.28 |
68.64 |
30.08 |
553.92 |
Observations |
126 |
122 |
126 |
126 |
126 |
126 |
126 |
126 |
126 |
126 |
The descriptive statistics in Table 1 provide a comprehensive overview of bank performance and structural attributes, emphasizing key variables like Cost-to-Income Ratio (CTIR), Digital Banking Index (DBI), Digital Banking Score (DBS), GDP Growth (GDPG), Inflation (INF), Bank Size (LBS), Market Share (MKS), Net Interest Margin (NIM), Return on Assets (ROA), and Return on Equity (ROE). CTIR, with a mean of 50.47%, indicates moderate efficiency, consistent with operational challenges in Sub-Saharan Africa (Nyasha & Odhiambo, 2023). Digital metrics (DBI = 49.34, DBS = 58.62) highlight significant but uneven digital transformation across institutions (Bank of Botswana, 2021). GDPG (3.52%) and INF (6.59%) reflect Botswana’s fluctuating economic conditions, influenced by shocks like the 2008-2009 global financial crisis (IMF, 2023). Larger banks dominate, as evidenced by high averages in LBS (22.52) and MKS (15.87), supporting resilience and market share theories (PwC, 2023). Profitability metrics such as NIM (5.48%), ROA (2.67%), and ROE (14.31%) show moderate to strong performance, aligning with trends in emerging markets (Munyanyi, 2022). However, non-normal distributions in GDPG, ROE, and MKS suggest economic volatility impacts bank metrics, such as inflation spikes in 2022 reducing profitability (Kiptui, 2019). Findings highlight the need for robust digital strategies and macroeconomic stability for sustained growth.
Table 2. ADF-Fisher panel unit root test.
Intercept only |
Intercept and trend |
Variables |
Levels |
Ist diff |
Levels |
Ist diff |
CTIR |
16.4409 |
22.6552** |
6.6234 |
20.8648*** |
DBI |
10.846 |
2nd diff 50.3370* |
5.4876 |
2nd diff 40.7938* |
DBS |
12.4910 |
25.1978** |
4.14334 |
26.5642* |
GDPG |
80.4230* |
- |
58.9380* |
- |
INF |
9.5556 |
66.7903* |
6.8892 |
48.9593* |
LBS |
46.8803* |
- |
15.1347 |
28.2908* |
MKS |
11.4292 |
2nd diff 103.503* |
13.3554 |
36.8399* |
NIM |
2.5570 |
49.5645* |
14.9005 |
39.4057* |
ROA |
6.1086 |
49.8842* |
17.6081 |
33.8360* |
ROE |
14.2677 |
55.4445* |
24.5964** |
- |
The variables were stationary at 1%, 5% and 10% as represented as *, ** and *** respectively.
The results of the ADF-Fisher Panel Unit Root Test in Table 2 reveal the stationarity of variables critical to understanding the dynamics of Botswana’s banking sector. Most variables, including GDP Growth (GDPG), Net Interest Margin (NIM), Return on Assets (ROA), and Inflation (INF), achieved stationarity at levels (1%) with intercepts, affirming their reliability for modeling long-term relationships (IMF, 2023). For instance, GDPG and LBS exhibit stationarity at levels, reflecting the steady influence of macroeconomic trends and bank size on profitability (Munyanyi, 2022). However, variables such as the Digital Banking Index (DBI) and Market Share (MKS) required differencing to attain stationarity, suggesting structural shifts in digital adoption and market concentration, consistent with findings from the 2021 Bank of Botswana FinTech survey (Bank of Botswana, 2021).
The Cost-to-Income Ratio (CTIR), a measure of efficiency, achieved stationarity at the first difference under both intercept-only and intercept-and-trend models, indicating operational adjustments over time (Nyasha & Odhiambo, 2023). Similarly, inflation, despite moderate volatility, influenced profitability metrics like ROA and NIM, aligning with Kiptui’s (2019) findings on inflation’s adverse effects on African banks. These results underscore the interconnectedness of macroeconomic conditions, digital transformation, and profitability, emphasizing the sector’s adaptation amid economic and technological changes.
Table 3. Correlation matrix.
|
CTIR |
DBI |
DBS |
GDPG |
INF |
LBS |
MKS |
NIM |
ROA |
ROE |
CTIR |
1 |
|
|
|
|
|
|
|
|
|
DBI |
−0.650 |
1 |
|
|
|
|
|
|
|
|
DBS |
−0.815 |
0.913 |
1 |
|
|
|
|
|
|
|
GDPG |
0.023 |
−0.008 |
−0.020 |
1 |
|
|
|
|
|
|
INF |
0.434 |
−0.473 |
−0.443 |
0.207 |
1 |
|
|
|
|
|
LBS |
−0.478 |
0.777 |
0.605 |
−0.010 |
−0.338 |
1 |
|
|
|
|
MKS |
−0.473 |
0.325 |
0.374 |
0.007 |
−0.039 |
0.369 |
1 |
|
|
|
NIM |
−0.674 |
0.771 |
0.861 |
0.017 |
−0.398 |
0.451 |
0.441 |
1 |
|
|
ROA |
−0.754 |
0.802 |
0.878 |
0.136 |
−0.499 |
0.501 |
0.393 |
0.885 |
1 |
|
ROE |
−0.630 |
0.566 |
0.635 |
0.088 |
−0.380 |
0.311 |
0.533 |
0.632 |
0.753 |
1 |
The correlation matrix in Table 3 illustrates the relationships among key banking performance indicators, highlighting significant dynamics. The Cost-to-Income Ratio (CTIR) negatively correlates with Digital Banking Index (DBI, −0.650), Digital Banking Score (DBS, −0.815), and profitability metrics like Return on Assets (ROA, −0.754) and Return on Equity (ROE, −0.630). This inverse relationship underscores that higher cost efficiency aligns with greater digital adoption and profitability, consistent with global findings on digital transformation’s impact on operational efficiency (PwC, 2023; Nyasha & Odhiambo, 2023).
The strong positive correlations between DBI, DBS, and profitability metrics (e.g., DBS-ROA, 0.878) highlight the role of digitalization in enhancing financial performance, as supported by Botswana’s 2020 FinTech survey (Bank of Botswana, 2021). Additionally, inflation (INF) moderately correlates with CTIR (0.434), reflecting inflationary pressures on operational costs, aligning with Kiptui’s (2019) findings on inflation’s adverse effects on banks in Sub-Saharan Africa. These relationships emphasize the importance of digital innovation and economic stability in enhancing performance.
Table 4. GMM model on the impact of digital transformation on six commercial banks performance in Botswana.
Variable |
D(ROA) |
D(ROE) |
D(NIM) |
D(CTIR) |
Coefficient |
Prob. |
Coefficient |
Prob. |
Coefficient |
Prob. |
Coefficient |
Prob. |
ROA (−1) |
−0.089 |
0.007 |
− |
− |
− |
− |
− |
= |
ROE (−1) |
− |
− |
−0.341 |
0.000 |
− |
− |
− |
= |
NIM (−1) |
− |
− |
− |
− |
−0.010 |
0.498 |
= |
= |
CTIR (−1) |
− |
− |
− |
− |
− |
− |
−0.034 |
0.016 |
D (DBS) |
−0.017 |
0.097 |
−0.120 |
−0.120 |
0.003 |
0.717 |
−0.048 |
0.401 |
D (DBI,2) |
0.024 |
0.050 |
0.141 |
0.141 |
−0.002 |
0.854 |
−0.017 |
0.813 |
GDPG |
0.019 |
0.000 |
0.042 |
0.042 |
0.006 |
0.001 |
−0.033 |
0.016 |
D (INF) |
−0.020 |
0.000 |
−0.100 |
−0.100 |
−0.003 |
0.465 |
0.091 |
0.002 |
LBS |
0.013 |
0.003 |
0.235 |
0.235 |
0.005 |
0.142 |
0.067 |
0.035 |
R-squared |
0.459 |
0.242 |
0.121 |
0.164 |
Adjusted
R-squared |
0.433 |
0.206 |
0.079 |
0.124 |
S.E. of
regression |
0.147 |
1.497 |
0.103 |
0.840 |
Durbin-
Watson stat |
2.237 |
2.479 |
1.745 |
1.241 |
Instrument rank |
7 |
7 |
7 |
7 |
The GMM results in Table 4 provide critical insights into the impact of digital transformation on the performance of six commercial banks in Botswana, with a detailed examination of profitability, efficiency, and operational dynamics. The analysis uncovers both persistent performance trends and the influence of digital transformation indicators, consistent with a growing body of global literature.
4.1. Lagged Variables and Persistent Performance Trends
The analysis demonstrates that lagged variables significantly influence current bank performance. Return on Assets (ROA) lagged by one period (ROA(−1)) exerts a negative and significant effect (−0.089, p = 0.007) on current ROA, indicating the lingering effects of inefficiencies, a finding corroborated by Munyanyi (2022) in his study on African banking systems. Similarly, Return on Equity (ROE) lagged by one period (ROE(−1)) has a negative influence (−0.341, p = 0.000), which emphasizes the importance of addressing equity management challenges, as also noted in the Bank of Botswana’s (2021) sectoral review. This persistence in performance trends aligns with findings from studies in Asia, such as those by Sharma et al. (2020), which observed that prior operational inefficiencies often influence current financial outcomes. Furthermore, the negative and significant coefficient of the lagged Cost-to-Income Ratio (CTIR(−1)) (−0.034, p = 0.016) underscores ongoing operational challenges, consistent with Nyasha and Odhiambo’s (2023) research on Sub-Saharan African banks. These results highlight the critical need for commercial banks in Botswana to address structural inefficiencies to improve performance outcomes.
4.2. Digital Transformation Indicators
The analysis reveals mixed effects of digital transformation on bank performance. The second difference of the Digital Banking Index (D(DBI,2)) positively impacts ROA (0.024, p = 0.050) and ROE (0.141, p = 0.141), highlighting the potential of sustained digital adoption to enhance profitability. This trend resonates with global observations, such as those reported in PwC’s (2023) global banking survey, which underscores the transformative potential of digitalization. Studies in Europe (Capgemini, 2022) have similarly demonstrated that consistent investment in digital banking improves customer engagement and operational efficiency, ultimately driving profitability.
Conversely, Digital Banking Services (DBS) exhibit a negative but insignificant effect on ROA and ROE, reflecting the challenges in realizing immediate financial benefits from digital platforms. This aligns with findings from Botswana’s FinTech survey (2021), which highlighted disparities in digital adoption and effectiveness across institutions. Research by Chen et al. (2021) on American banks also suggests that while digital transformation increases customer accessibility, its translation into profitability depends on effective strategic alignment. Moreover, the insignificant effects of digital indicators on Net Interest Margin (NIM) and CTIR suggest that while digitalization enhances customer engagement, its cost efficiency potential remains underutilized.
4.3. Macroeconomic Factors
Macroeconomic stability emerges as a significant determinant of bank performance. GDP growth positively impacts ROA (0.019, p = 0.000) and ROE (0.042, p = 0.042), reaffirming findings from regional studies, including the IMF’s (2023) report, which highlights the role of economic growth in enhancing financial stability. Empirical studies from Asia, such as Kumar and Pattanayak (2020), and America (Jensen & Meckling, 2021), also underline GDP growth as a critical driver of banking profitability. However, inflation negatively influences ROA (−0.020, p = 0.000) and ROE (−0.100, p = 0.100), underscoring inflation’s destabilizing effects. These findings align with Kiptui’s (2019) study on Kenyan banks and corroborate the adverse impact of Botswana’s inflationary spikes, such as the 14.4% inflation recorded in 2022. Similar trends are observed in European banking systems, where inflation volatility often dampens profitability (ECB, 2021).
4.4. Bank-Specific Factors
Bank size is a significant determinant of performance, with positive effects on ROA (0.013, p = 0.003), ROE (0.235, p = 0.235), and CTIR (0.067, p = 0.035). Larger institutions benefit from economies of scale, consistent with findings from PwC’s (2023) African Banking Review. Empirical studies from America, such as those by Berger et al. (2020), and Europe (Goddard et al., 2019), support the notion that size confers operational resilience and market dominance. In Botswana, larger banks have consistently demonstrated a stronger ability to weather economic shocks and maintain competitive advantages, as noted in the Bank of Botswana’s annual reports.
The empirical results offer a comprehensive understanding of Botswana’s banking performance, highlighting the sector’s interplay between digital transformation, economic conditions, and profitability (Nyasha & Odhiambo, 2023; Bank of Botswana, 2021). Descriptive statistics reveal moderate efficiency and profitability, alongside notable advancements in digital adoption, albeit with uneven progress (PwC, 2023; IMF, 2023). Stationarity tests confirm the robustness of variables (Munyanyi, 2022), while correlation analysis underscores the critical role of digitalization in driving performance improvements (Chen et al., 2021; Capgemini, 2022).
The GMM results reinforce the transformative impact of digital banking, particularly on profitability metrics such as ROA and ROE. Macroeconomic factors, including GDP growth and inflation, also significantly influence banking performance, emphasizing the need for policy interventions to stabilize the economic environment. These findings corroborate existing research, such as Nyasha and Odhiambo (2023) and the Bank of Botswana (2021), which advocate for a dual focus on digital innovation and macroeconomic stability.
5. Summary, Conclusion with Policy Recommendations and
Some Suggestions for Extension
5.1. Summary
This study provides a comprehensive analysis of the impact of digital transformation on the performance of commercial banks in Botswana, leveraging a mixed-methods approach. Quantitative data, collected from six commercial banks over two decades, were analyzed using panel data regression models, while qualitative insights were gathered through semi-structured interviews with banking executives. Descriptive statistics highlighted moderate operational efficiency and profitability, with Cost-to-Income Ratio (CTIR) averaging 50.47% and Return on Assets (ROA) at 2.67%, consistent with emerging market trends (Nyasha & Odhiambo, 2023). Empirical findings revealed uneven digital adoption across banks, with metrics like Digital Banking Index (DBI = 49.34) demonstrating room for improvement. The study identified strong correlations between digital adoption and enhanced performance metrics, emphasizing the strategic importance of digital banking for long-term profitability (Bank of Botswana, 2021).
5.2. Conclusion
The study concludes that digital transformation significantly influences bank performance in Botswana, fostering operational efficiency and financial resilience. Variables such as CTIR, ROA, and Return on Equity (ROE) demonstrated measurable improvements with greater digital adoption. However, inflationary pressures and economic shocks, including the global financial crisis and recent inflation spikes, were shown to negatively affect performance. These findings align with regional and international evidence, such as Munyanyi’s (2022) study on African banks and Kiptui’s (2019) analysis of inflation’s effects on profitability. The study also underscores the importance of aligning digital transformation strategies with macroeconomic stability to maximize benefits. For instance, large banks with higher digital scores exhibited resilience, affirming the scalability advantages of digitalization (PwC, 2023).
5.3. Policy Recommendations
To enhance the performance of Botswana’s banking sector, policymakers should prioritize creating a supportive regulatory framework for digital transformation. Initiatives could include tax incentives for digital investments, infrastructure development, and policies promoting financial inclusion through mobile and internet banking platforms. Additionally, regulatory bodies should enhance oversight to ensure robust cybersecurity measures, given the vulnerabilities associated with increased digitalization. Lessons from countries like Kenya, where mobile banking has revolutionized financial inclusion, offer actionable insights for Botswana (IMF, 2023). Further, banks should invest in employee training and customer education to bridge digital literacy gaps, thereby maximizing the adoption of digital services.
5.4. Suggestions for Extension
Future research could extend this study by examining the role of emerging technologies, such as artificial intelligence and blockchain, in enhancing banking performance in Botswana. Additionally, cross-country comparisons within the Southern African Development Community (SADC) region could provide a broader perspective on digital banking trends. Expanding the scope to include customer perspectives on digital banking adoption would enrich understanding, aligning with findings from South Africa, where customer-centric approaches have driven digital transformation success (Munyanyi, 2022). Finally, longitudinal studies exploring the post-pandemic impact of digitalization on banking performance would provide valuable insights into the resilience of financial institutions in uncertain times.
Author’s Contributions
CN was responsible for conceptualization, methodology and some parts of the literature and full analysis. B.S.M was responsible for some parts of the literature and editing.
Data availability Statement
The study used only secondary data and links have been provided for accessing some documents.
Acknowledgements
Special appreciation to all who have conducted similar studies before us.