Financial inclusion is challenged by numerous factors that include country regulations and policies, poor infrastructure, lack of financial education and literacy, low income levels, political... Show More

Financial inclusion is challenged by numerous factors that include country regulations and policies, poor infrastructure, lack of financial education and literacy, low income levels, political instability, geographical barriers, reluctance by banks to change from traditional banking models of branch networks, and expensive technology. These challenges could be addressed by leveraging technological innovations like mBanking; however, literature indicates that there is limited understanding of integrating mobile technology into banking to enhance financial inclusion. The study collected data from Vhembe District Municipality in South Africa. Results indicated that of the 17 factors tested, 14 were significant for using mBanking to improve financial inclusion. The study recommends that for financial inclusion improvement, developing countries should design realistic policies and strategies that could enable the use of mBanking, especially mobile money and agent banking, that can reach a wider population at low cost.

Although information technology has positively influenced operations in the Ghanaian financial sector, there is still a high operational cost in performing KYC procedures due to duplication of... Show More

Although information technology has positively influenced operations in the Ghanaian financial sector, there is still a high operational cost in performing KYC procedures due to duplication of efforts during clients' onboarding. The decentralized nature of blockchains makes them ideal for addressing these challenges. In this paper, the blockchain maturity model was used to assess the maturity and readiness of Ghanaian banks to adopt blockchain technology for KYC processes. Using primary data obtained via questionnaires and interviews, the individual components of the blockchain maturity model were assessed. The results indicate that the network, hardware, and software components are at repeatable, defined, and managed stages, respectively, while the people component lags in the initial stage due to a lack of adequate staff training. Finally, security and privacy are at the defined stage, whereas policy and regulations are at the initial stage.

This study aimed to identify the factors affecting customers' adoption and use of e-banking services in Algeria. For this purpose, a theoretical model—based on the technology acceptance model (TAM)... Show More

This study aimed to identify the factors affecting customers' adoption and use of e-banking services in Algeria. For this purpose, a theoretical model—based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT)—was developed. To test the hypotheses, a survey was conducted on 326 Algerian bank customers, and the data were analyzed using structural equation modeling. At the end, the results indicate that perceived usefulness and perceived ease of use significantly positively influence attitudes toward e-banking. In turn, attitudes toward e-banking influence behavioral intention and behavioral intention, facilitating conditions each influence significantly and positively actual e-banking use. The results also found that the relationship between perceived usefulness and attitude toward e-banking use was stronger for women, and the relationship between facilitating conditions and actual e-banking use was significantly stronger for older end-users.

This study aims at re-specifying and extending the DeLone and McLean information systems success model and verifying the extended model in the context of mobile banking applications. The model... Show More

This study aims at re-specifying and extending the DeLone and McLean information systems success model and verifying the extended model in the context of mobile banking applications. The model integrates a relationship-driving indicator, namely perceived value and a condition that permanently predicts the use of technology: usefulness. This framework derives its originality from the integration of these variables and verification in timely and innovative service, namely mobile banking applications. The results allowed to accept the hypotheses assuming the impact of the perception of usefulness and value, as well as system and service quality on satisfaction. The latter has a significant influence on continuance intention. However, the impact of information quality was insignificant, and hypotheses assuming the dependence of continuance intention on usefulness and service quality are rejected. These results are discussed, and the conclusion deduces managerial recommendations and directions for future research.

Many management information systems applications can be used to support businesses. One example is platforms that support electronic commerce, so retailers can sell products and services online. The... Show More

Many management information systems applications can be used to support businesses. One example is platforms that support electronic commerce, so retailers can sell products and services online. The use of electronic commerce applications is so common that virtually every smartphone owner has used them. In this research, the author evaluates the usability of mobile applications for electronic commerce in the Kingdom of Saudi Arabia and introduces a measure with which developers and organizations can evaluate the usability of their mobile applications. Applying quantitative research methods, including structural equation modeling, the research shows that three constructs—application design, application utility, and user interface structure—are the strongest predictors of both continued intention to use a mobile application and loyalty to the mobile application.

Customer satisfaction can be measured using facial expression recognition. The current generation of artificial intelligence systems heavily depends on facial features such as eyebrows, eyes, and... Show More

Customer satisfaction can be measured using facial expression recognition. The current generation of artificial intelligence systems heavily depends on facial features such as eyebrows, eyes, and foreheads. This dependence introduces a limitation as people generally prefer to conceal their genuine emotions. As body gestures are difficult to conceal and can convey a more detailed and accurate emotional state, the authors incorporate upper-body gestures as an additional feature that improves the predicted emotion's accuracy. This work uses an ensemble machine-learning model that integrates support vector machines, random forest classifiers, and logistic regression classifiers. The proposed method detects emotions from facial expressions and upper-body movements and is experimentally evaluated and has been found to be effective, with an accuracy rate of 97% on the EMOTIC dataset and 99% accuracy on MELD dataset.