Purpose: The purpose of this paper is to investigate the impact of customer satisfaction, service quality, the perceived value of services, corporate image and corporate reputation on customer loyalty and their relationship in the Turkish banking industry. Mediation effects of the perceived value and corporate image and reputation are also studied. Understanding the relationships between the determinants of customer loyalty toward the bank helps management to use corporate image and reputation more effectively in its strategy, thus enhancing the institution’s position in the minds of consumers. Design/methodology/approach: A model is proposed to explore the relationships of service quality and customer satisfaction with a perceived value and their effect on transforming the corporate image and corporate reputation into the form of customer loyalty toward the bank. A survey is designed within this framework and SEM analysis is conducted in order to study the nature of relationships between variables of interest hypothesized to affect customer behavior and customer loyalty. Mediation tests for perceived value and corporate image and reputation are also conducted. Findings: The findings of the survey indicate that corporate image and corporate reputation can be used as a common marketing benchmark to measure a bank’s performance. The results demonstrated that customers perceive quality and satisfaction effects loyalty through perceived value, image and reputation. Research limitations/implications: The study was conducted in Izmir, the third biggest city of Turkey. The sample is composed of regular customers, and the sample size is enough for the study but more studies are needed to generalize the results. Practical implications: The results provide information to bank managers to effectively assist them to offer appropriate customer service levels sustaining satisfaction, quality and value to the customers within the transactions. Originality/value: The paper studies the determinants of customer loyalty in the Turkish banking industry and considers the effects of corporate image and corporate reputation as measured by customer satisfaction, service quality and perceived value, on customer loyalty toward banks in Turkey. This model is not studied in bank marketing in Turkey and also in the banking literature.

The main purpose of this study is to review calculation algorithms for some of the most common non-parametric and omnibus tests for normality, and to provide them as a compiled MATLAB function. All tests are coded to provide p-values for those normality tests, and the proposed function gives the results as an output table.

In today’s competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, ARIMA and ARFIMA models, neural networks, and Adaptive Network-based Fuzzy Inference System (ANFIS). Also, the forecasting performances of some widely used combining methods were evaluated by comparison with the weekly sales data for ten products. According to the best of our knowledge, this study is the first time that the recently developed state space models, also called ETS (Error-Trend-Seasonal) models, and the ANFIS model have been tested within combining methods for forecasting retail sales. Analysis of the results of the single models in isolation indicated that none of them outperformed all the others across all the time series investigated. However, the empirical results suggested that most of the combined forecasts examined could achieve statistically significant increases in forecasting accuracy compared with individual models and with the forecasts generated by the company’s current system.

The signal issued by a control chart triggers the process professionals to investigate the special cause. Change point methods simplify the efforts to search for and identify the special cause. In this study, using maximum likelihood estimation, a multivariate joint change point estimation procedure for monitoring both location and dispersion simultaneously is proposed. After a signal is generated by the simultaneously used Hotelling's T ² and/or generalized variance control charts, the procedure starts detecting the time of the change. The performance of the proposed method for several structural changes for the mean vector and covariance matrix is discussed.

In this study, using maximum likelihood estimation, a considerably effective change point model is proposed for the generalized variance control chart in which the required statistics are calculated with its distributional properties. The procedure, when used with generalized variance control charts, would be helpful for practitioners both controlling the multivariate process dispersion and detecting the time of the change in variance-covariance matrix of a process. The procedure starts after the chart issues a signal. Several structural changes for the variance-covariance matrix are considered and the precision and the accuracy of the proposed method is discussed.