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The surge in internet usage has generated widespread speculation and optimism regarding its potential impact on the accessibility to financial services. The aim of this study is to investigate the effect of internet penetration on the accessibility of banking services in developed and developing countries.

Panel data regression methods are used to estimate the impact of internet penetration on accessibility to banking services in a sample of 74 countries from Global Findex survey waves of 2011, 2014, 2017 and 2021. To mitigate potential issues related to heteroscedasticity, autocorrelation and cross-sectional dependence, the study has implemented cluster robust standard errors testing. Furthermore, as a sensitivity check, the sample has been segregated into developed and developing country groups.

The study finds a significant positive correlation between internet penetration and banking access in full sample. Subsample analysis reveals that this relationship is statistically significant in developed countries, but not in developing ones, despite being positive. The research discusses the implications of these findings for both country groups.

Research to date has largely investigated the link between information and communication technology (ICT) and financial inclusion, often treating internet penetration as one component of ICT, which obscures its individual influence. This study, however, isolates internet penetration to specifically analyze its distinct effects on banking accessibility across developed and developing countries.

As internet banking service marketing platforms continue to advance, customers exhibit distinct behaviors. Given the extensive array of options and minimal barriers to switching to competitors, the concept of customer churn behavior has emerged as a subject of considerable debate. This study aims to delineate the scope of feature optimization methods for elucidating customer churn behavior within the context of internet banking service marketing. To achieve this goal, the author aims to predict the attrition and migration of customers who use internet banking services using tree-based classifiers.

The author used various feature optimization methods in tree-based classifiers to predict customer churn behavior using transaction data from customers who use internet banking services. First, the authors conducted feature reduction to eliminate ineffective features and project the data set onto a lower-dimensional space. Next, the author used Recursive Feature Elimination with Cross-Validation (RFECV) to extract the most practical features. Then, the author applied feature importance to assign a score to each input feature. Following this, the author selected C5.0 Decision Tree, Random Forest, XGBoost, AdaBoost, CatBoost and LightGBM as the six tree-based classifier structures.

This study acclaimed that transaction data is a reliable resource for elucidating customer churn behavior within the context of internet banking service marketing. Experimental findings highlight the operational benefits and enhanced customer retention afforded by implementing feature optimization and leveraging a variety of tree-based classifiers. The results indicate the significance of feature reduction, feature selection and feature importance as the three feature optimization methods in comprehending customer churn prediction. This study demonstrated that feature optimization can improve this prediction by increasing the accuracy and precision of tree-based classifiers and decreasing their error rates.

This research aims to enhance the understanding of customer behavior on internet banking service platforms by predicting churn intentions. This study demonstrates how feature optimization methods influence customer churn prediction performance. This approach included feature reduction, feature selection and assessing feature importance to optimize transaction data analysis. Additionally, the author performed feature optimization within tree-based classifiers to improve performance. The novelty of this approach lies in combining feature optimization methods with tree-based classifiers to effectively capture and articulate customer churn experience in internet banking service marketing.

This study aims to understand the reasons for individuals switching from traditional banks to Internet-only banks and examine how switching intentions differ between Generation X and Generation Z. Notably, Generation Z, being digital natives, exhibits distinct characteristics compared to Generation X, who often referred to as digital immigrants. Given the technology-driven nature of Internet-only banks, a multi-group analysis between these two generations was conducted.

This study utilizes Bansal’s push–pull–mooring model as a framework to analyze switching intention. The study collected survey data from 383 Korean participants, consisting of 198 participants from Generation Z and 185 participants from Generation X.

The findings indicate that low satisfaction and discomfort are factors that push people to leave traditional banks. Specifically, Generation Z shows a significantly higher inclination to leave traditional banks due to discomfort. On the other hand, relative advantage, compatibility, observability and trialability are factors that pull people to switch to Internet-only banks. Generation X is more likely to consider adopting Internet-only banks when compatibility is high and complexity is low.

This study is the first to explore unique motivators for Generation Z, such as their discomfort with interpersonal interactions in the retail banking sector. These findings challenge earlier research emphasizing human interaction’s importance in technology adoption, offering insights into their future adoption of contactless services.

The purpose of this study is to analyse cyberfraud in the South African banking industry using a multiple regression approach and develop a predictive model for the estimation and prediction of financial losses due to cyberfraud.

To mitigate the occurrence of cyberfraud, this study uses the multiple regression approach to correlate the relationship between financial loss and cyberfraud activities. The cyberfraud activities in South Africa are classified into three, namely, digital banking application, online and mobile banking fraud. Secondary data that captures the rate of cyberfraud occurrences within these three major categories with their resulting financial losses were used for the multiple regression analysis that was carried out in the Statistical Package for Social Science (SPSS, 2022 environment).

The results obtained indicate that the South African financial institutions still incur significant financial losses due to cyberfraud perpetration. The two main independent variables used to estimate the magnitude of financial loss in the South Africa’s banking industry are online (internet) banking fraud (X2) and mobile banking fraud (X3). Furthermore, a multiple regression model equation was developed for the prediction of financial loss as a function of the two independent variables (X2 and X3).

This study adds to the literature on cyberfraud mitigation. The findings may promote the combat against cyberfraud in the South Africa’s financial institutions. It may also assist South Africa’s financial institutions to predict the financial loss that financial institutions can incur over time. It is recommended that South Africa’s financial institutions pay attention to these two key variables and mitigate any associated risks as they are crucial in determining their profitability.

Existing literature indicated significant financial losses to cyberfraud perpetration without establishing any relationship between the magnitude of losses incurred and the prevalent forms of cyberfraud. Thus, the novelty of this study lies in the analysis of cyberfraud in the South African banking industry using a multiple regression approach to link financial losses to the perpetration of the prevalent forms of cyberfraud. It also develops a predictive model for the estimation and projection of financial losses.

This study aims to explore how the shift from traditional to digital banking transforms the nature of trust between banks and their younger clients (aged 18–35) from the perspective of bank employees.

Qualitative semi-structured interviews with representatives of Ukrainian classical banks and neobanks were conducted. The interviews were analysed using the theoretical approach of institution-based and social network-based trust to identify the key distinctions between the nature of trust in traditional and digital banking.

The employees of the banks reported that digitalization processes have helped to mitigate trust issues; as a result, their banks have not experienced any difficulties in this regard among young people. Furthermore, social networks, particularly social approval, were found to be significant factors for establishing trust in digital banking among young people.

The results of this study could assist bank managers in adapting their strategies for cultivating trust among younger clients and aiding international law regulators and government institutions in preventing unintended circumstances in financial services. These contributions were shaped by the study’s limitations, including its focus on only two concepts of trust building: institution-based and social network-based, as well as its specific Ukrainian context.

This study highlights social approval as a valuable constituent of the trust-building process that influences trust in institutions. Furthermore, while gaining social approval – particularly through digital platforms – can promote trust-building among young people, this “easy way” may have negative societal consequences by endorsing unscrupulous institutions.

This paper aims to illuminate a mechanism of positive word of mouth (PWOM) toward mobile banking (m-banking) by extending the fairness theory (FAT) with satisfaction (SAT) and value-in-use (VIU).

A research model is examined by obtaining data from a web-based survey of 398 respondents who have used m-banking in Vietnam. AMOS 21.0 software is applied to analyze the hypothetical model with covariance-based structural equation modeling.

The results reveal that price fairness (PRI) is of primary importance of fairness perception (FAI) in m-banking, followed by informative fairness, procedural fairness, interactional fairness and distributive fairness. Furthermore, SAT and VIU play a vital role in developing PWOM. In addition, FAI exerts a positive influence on SAT and VIU.

This study would help practitioners have the deep insights into affective and behavioral responses among customers and develop effective marketing trajectories to spur SAT, VIU and PWOM toward m-banking. The results hint that marketers may be tempted to simultaneously focus on the important dimensions of FAIs as they are indispensable to derive SAT and VIU from a customer perspective. This research assists governments and banks to adopt training programs and policies that will incite customer behaviors toward m-banking.

The value of the work lies in the combination between FAT with SAT to enlighten FAI and PWOM toward m-banking in a developing country. To the best of the authors’ knowledge, this work is among the first to consider VIU that motivates PWOM. Moreover, this study links PRI to FAI to understand PWOM among Vietnamese customers.

This study aims to develop a customer-centric model based on an online customer experience (OCE) construct relating to e-loyalty, e-trust and e-satisfaction, resulting in improved Net Promoter Score for Indian digital banks.

This study used an online survey method to gather data from a sample of 485 digital banking users, from which usable questionnaires were obtained. The obtained data were subjected to thorough analysis using partial least squares structural equation modelling to further investigate the research hypotheses.

The main factors determining digital banks’ OCE were perceived customer centrality, perceived value and perceived usability. Additionally, relevant constructs were evaluated using importance-performance map analysis.

This study used convenience sampling for the urban population using digital banking services; therefore, the outcome may be generalized to a limited extent. To further strengthen digital banking, it would be valuable to imitate studies in other countries.

There is a lack of research on digital banking and OCE in India; thus, this study will help rectify this issue while providing valuable insights. This study differs from others in that it examines the connections between online customer satisfaction, loyalty, trust and the bottom line of financial institutions using these factors as dependent variables instead of traditional measures.

This study aimed to explore the impact of Artificial Intelligence (AI) characteristics, namely Perceived Animacy (PAN), perceived intelligence (PIN), and perceived anthropomorphism (PAI), on user satisfaction (ESA) and continuous intentions (CIN) by integrating Expectation Confirmation Theory (ECT), with a particular focus on Generation Y and Z.

Using a quantitative method, the study collected 495 data from Gen Y (204) and Z (291) respondents who were users of digital banking apps through structured questionnaires that were analysed using PLS-SEM. The latter helped investigate the driving forces of AI characteristics and user behavioural intentions as well as reveal generation-specific features of digital banking engagement.

The study revealed that PAN and PIN have significant positive effects on the anthropomorphic perceptions of digital banking apps, which in turn increases perceived usefulness, satisfaction, and continuous intentions. In particular, the influence of these AI attributes varies across generations; Gen Y’s loyalty is mostly based on the benefits derived from AI features, whereas Gen Z places a greater value on the anthropomorphic factor of AI. This marked a generational shift in the demand for digital banking services.

The specificity of Indian Gen Y and Z users defines the scope of this study, suggesting that demographic and geographical boundaries can be broadened in future AI-related banking research.

The results have important implications for bank executive officers and policymakers in developing AI-supported digital banking interfaces that appeal to the unique tastes of millennial customers, thus emphasising the importance of personalising AI functionalities to enhance user participation and loyalty.

This study enriches the digital banking literature by combining AI attributes with ECT, offering a granular understanding of AI’s role in modulating young consumers' satisfaction and continuance intentions. It underscores the strategic imperative of AI in cultivating compelling and loyalty-inducing digital banking environments tailored to the evolving expectations of Generations Y and Z.

The purpose of this study is to examine the effect of innovation in e-service delivery on trust and loyalty of Indian customers in the banking sector.

The stated relationships were drawn on the grounded theories by developing a conceptual model. Purposive sampling technique was used to collect the data from 400 bank customers who were availing the e-innovation services.

The findings of the study reveal that e-service innovation has a direct impact on e-service delivery and trust. The existence of a positive relationship between e-service delivery, trust and loyalty in the banking sector of India has also been found.

The findings of the study would help the practitioners and experts in the related fields to understand and adopt the innovative management practices in financial services in developing country like India.

Continuous e-innovation can create a distinct competitive advantage and avert the risk of vanishing from the market. The study contributes in terms of e-service innovation and e-service delivery in the banking sector in India. The impact of e-service innovation on banking outcomes begins with e-service and trust, these factors positively influences e-service innovation. Furthermore, e-service innovation exerts a positive effect on e-service delivery, trust and loyalty, thereby improving organizational value.

Commercial banks are the catalysts for meeting the financing needs of small and medium enterprises (SMEs). However, not all commercial banks are equally attractive to SMEs because of differences in banking services’ key attributes. Moreover, customers’ preferences vary between Islamic and conventional banks. This paper aims to identify factors motivating SMEs to establish business ties with banks in Oman.

The authors collected data from 217 SMEs through a questionnaire survey. The data were analyzed using a t-test and structural equation modeling (SEM). In addition, the research applies the theory of planned behavior as a theoretical framework.

The t-test results show that SMEs place greater emphasis on electronic banking, convenient locations, religious beliefs and favorable terms and conditions. The results from the SEM analysis show that the SMEs in Oman consider attractive packages, including favorable rates, transaction processing time, fees and the availability of technology-enabled services, when choosing a bank. Moreover, customers who are aware of Islamic banking products are optimistic about the future of Shariah-based banking.

As a Muslim-majority country, Oman lags behind its Gulf Cooperative Council peers in terms of the development of the Islamic banking system. For the success of this mode of financing, it is essential to know which factors SMEs prioritize to establish ties with Islamic banks. Hence, the research is expected to provide new information for bank management to devise financial products attractive to investors.