Purpose Mobile banking (Mbanking) is one of the most widely used mobile technology applications in recent times. This research aims to develop and test a research model by integrating social influence, trust and compatibility along with demographic variables into the original technology acceptance model (TAM) for Mbanking adoption which can be useful for understanding individual behaviours from an international business perspective. Design/methodology/approach Data were collected through a structured survey from 208 Omani Mbanking users and analysed using a two-staged regression and neural network (NN) model. Findings The results showed that perceived ease of use and demographic variables were not statistically significant in the multiple linear regression model, whereas the importance of the aforementioned variables was relatively high in the results obtained from the NN model. Furthermore, other predictors, namely, trust, perceived usefulness, compatibility and social influence included in the proposed research model that were established as significant by the regression model were assigned high relative importance by the NN model as well. Practical implications The study reflects the customer’s opinion from a developing country perspective. In addition, the research makes a significant theoretical contribution by using predictive modelling instead of causal or explanatory modelling for the development of a new and extended TAM model. The findings can be gainfully used by international business to understand Omani customer- and design-appropriate strategies for market penetration. Originality/value This study offers deeper understanding about Mbanking adoption from a developing country perspective and identifies and integrates important variables that influence the adoption in the aforementioned context.

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Purpose – The purpose of this paper is to investigate the quality determinants influencing the adoption of e-government services in Oman and compare the performance of multiple regression and neural network models in identifying the significant factors influencing adoption in Oman. Design/methodology/approach – Primary data concerning service quality determinants and demographic variables were collected using a structured questionnaire survey. The variables selected in the design of the questionnaire were based on an extensive literature review. Factor analysis, multiple linear regression and neural network models were employed to analyze data. Findings – The study found that quality determinants: responsiveness, security, efficiency and reliability are statistically significant predictors of adoption. The neural network model performed better than the regression model in the prediction of e-government services’ adoption and was able to characterize the non-linear relationship of the aforementioned predictors with the adoption of e-government services. Further, the neural network model was able to identify demographic variables as significant predictors. Practical implications – This study highlights the importance of service quality in the adoption of e-government services and suggests that an enhanced focus and investment on improving quality of the design and delivery of e-government services can have a positive impact on the usage of the services, thereby enabling the Oman Government in achieving the governance objectives for which these technologies were employed. Originality/value – Studies in the area of e-government typically focus either on technology adoption problems or service quality problems. The role of service quality in adoption is rarely addressed. The research presented in this paper is of great value to the institutions that are involved in the development of technology-based e-government services in Oman.