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The purpose of this paper is to propose the application of artificial neural networks (ANN) to predict overall bank customer satisfaction and to prioritize influencing factors on customer satisfaction.

Data are collected from 436 randomly selected customers at ten different branches of an Iranian bank using a questionnaire consisting of 51 questions. An exploratory factor analysis (EFA) is done on the collected data to determine those factors that influence customer satisfaction. A multilayer perceptron ANN model is developed using the factor scores from the EFA. The ANN model is trained and validated to predict overall bank customer satisfaction. In addition, a linear regression model is developed to predict customer satisfaction. Prediction accuracy of the ANN model is compared with that of the linear regression model. The developed ANN is then used to compare sensitivity of customer satisfaction to each influencing factor.

Nine different influencing factors are extracted by EFA. The factors include Fees and Loans, Prompt Service, Appearance, Technological Service, Responsiveness, Reliability and Trustworthiness, Employees’ Attitudes and Behaviors, Accessibility to Bank and Availability of Service, and Interest Rates. Training and validation results show that the ANN model has 73 percent higher accuracy compared to the linear regression model in predicting overall bank customer satisfaction. Factor prioritization results show that Fees and Loans, Appearance, and Prompt Service have the highest impact on customer satisfaction, respectively; interest rate and accessibility to bank and availability of service are the least dominant factors influencing overall bank customer satisfaction.

This study proposes a more reliable and accurate methodology to predict customer satisfaction when compared with regression-based methods. ANN can also be utilized by bank management systems to prioritize different influencing factors that affect the satisfaction level of bank customers.

This paper advances the knowledge on bank customer satisfaction by proposing application of artificial intelligence methods. A case study is discussed and results of the application of an ANN are compared with those of a commonly used statistical regression model.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

An agile supply chain (ASC) includes companies that are operationally linked to each other, such as supply, design, manufacturing and distribution centers that respond and react quickly and effectively to change markets. Information systems and technology have a main role in achieving this objective. Therefore, the purpose of this paper is to examine the relationship between information integration, information infrastructure flexibility and the ASC in the Iranian power plant industry (IPPI).

The quantitative method was employed in this study. Survey questionnaires were sent to 87 managers in the IPPI to examine the relationship between information integration, information infrastructure flexibility, and the ASC.

The final results indicated that information sharing and responsibility were strongly related with the ASC; accessibility and connectivity had important relations with the ASC; while the relationships between compatibility and adaptableness as IT flexibility variables and ASC were positive but not significant.

This study focussed on the impact of IT on the IPPI specifically companies that manufacture boilers, electronic control tools, turbines, turbine blades, generators and other power plant-related components.

A new research model was developed to assess the impact of the interrelationships among IT capabilities and the ASC and results should assist managers as well as academicians.

An investigation was carried out through this study based on the current situation in IPPI to empirically examine and evaluate the effect of IT integration and flexibility on ASC. Besides, a very limited number of studies have been done on the implementation of information technology in the IPPI.