Purpose-This study investigates factors affecting e-banking usage based on electronic service (eservice) quality, attitude and customer satisfaction. Design/methodology/approach-A conceptual model to investigate factors that influence e-banking usage was developed based on review of existing literature. The model employed e-services quality variable, diffusion of innovation construct and self-efficacy to better reflect the users' views of ebanking usage. Data collected from 254 e-banking users was used the test the model. The data were analyzed based on PLS-SEM using SmartPLS 3.0. Findings-Our result reveals that perceived e-service quality has a strong influence on customer satisfaction and use of e-banking, which means that greater quality of e-service has the potential to increase satisfaction and consequently in more use of e-banking. In this research findings, competence of e-service support staff, system availability, service portfolio, responsiveness and reliability, in that order, were found to be most significant in rating e-service quality. Practical implications-This offers financial institutions and professional relevant information ebanking services that will promote greater customer satisfaction and use of e-banking. Originality/value-This paper contribute to knowledge advancement in bank marketing by providing insight into motivational factors of e-banking services quality and personal characteristics.

Mobile cloud computing (MCC) is a relatively new concept that leverages the combination of cloud technology, mobile computing, and wireless networking to enrich the usability experiences of mobile users. Many field of application such as mobile health, mobile learning, mobile commerce and mobile entertainment are now taking advantage of MCC technologies. Since MCC is new, there is need to advance research in MCC in order to deepen practice. Currently, what exist are mostly descriptive literature reviews in the area of MCC. In this paper, a systematic literature review (SLR), which offers a structured, methodical, and rigorous approach to the understanding of the trend of research in MCC, and the least and most researched issue is presented. The objective of the study is to provide a credible intellectual guide for upcoming researchers in MCC to help them identify areas in MCC research where they can make the most impact. The SLR was limited to peer-reviewed conference papers and journal articles published from 2002 to 2014. The study reveals that privacy, security and trust in MCC are the least researched, whereas issues of architecture, context awareness and data management have been averagely researched, while issues on operations, end users, service and applications have received a lot of attention in the literature.

Agriculture is one of the major forces to reckon with in the employment rate and overall economy of any nation. E-agriculture is not yet fully known to all farmers in Nigeria, hence affecting adversely production and the overall business chain. The acceptance and adoption of e-agriculture can make life better and advance the economy faster. This work investigated the acceptance of e-agriculture together with its adoption in Nigeria using questionnaires for data collection. This study seeks to discover to which extent e-agriculture is adopted by diverse categories of people with basic interest on the direct determinants of usage intention and behavior; direct determinant of user behavior, and impact moderators. The Unified Theory of Acceptance and Use of Technology model was adopted and SmartPLS 3.0 was used for the analysis of the collected data. The study establishes that performance expectancy, effort expectancy, social influence and habit were discovered as variables that have significant effect on behavioral intention for the acceptance and adoption of e-agriculture while performance expectancy was discovered to be the most significant factor that influences the usage of e-agriculture in Nigeria. It is recommended that new factors like, quality of service, privacy concerns, and enhanced farmer support can be added as new factors in future works.

The rate at which banks looses funds to loan beneficiaries due to loan default is alarming. This trend has led to the closure of many banks, potential beneficiaries deprived of access to loan; and many workers losing their jobs in the banks and other sectors. This work uses past loan records based on the employment of machine learning to predict fraud in bank loan administration and subsequently avoid loan default that manual scrutiny by a credit officer would not have discovered. However, such hidden patterns are revealed by machine learning. Statistical and conventional approaches in this direction are restricted in their accuracy capabilities. With a large volume and variety of data, credit history judgement by man is inefficient; case-based, analogy-based reasoning and statistical approaches have been employed but the 21st century fraudulent attempts cannot be discovered by these approaches, hence; the machine learning approach using the decision tree method to predict fraud and it delivers an accuracy of 75.9 percent.

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