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.

In this study, we propose a new descriptive statistic, coefficient of variation function, for functional data analysis and present its utilization. We recommend coefficient of variation function, especially when we want to compare the variation of multiple curve groups and when the mean functions are different for each curve group. Besides, obtaining coefficient of variation functions in terms of cubic B-Splines enables the interpretation of the first and second derivative functions of these functions and provides a stronger inference for the original curves. The utilization and effects of the proposed statistic is reported on a wellknown data set from the literature. The results show that the proposed statistic reflects the variability of the data properly and this reflection gets clearer than that of the standard deviation function especially as mean functions differ.

This study explores the most important socio-economic variables determining the voting decisions of the provinces in Municipality Elections by using classification trees. We collected data on many potential variables that may affect voting decisions in favor  of a political party. Each province’s economic, geographic and demographic data is taken into consideration as independent variables. The dependent variable is the winner party in 2014 Municipality Elections. Data set consists of 81 provinces’ data on 69 variables. The aim of the study is to find which variables affect voting decision the most and try to find a pattern that may lead political campaigns. Amongst many classification algorithms, we used C5.0 algorithm coded in R. It helps us explore the structure of a set of data, while developing easy to visualize decision rules for predicting a categorical (classification tree) or continuous (regression tree) outcome. The C5.0 algorithm determines the separation criterion with the greatest information gain in each decision node and performs optimal separation. Since our data size is small, we used k=1000 trials (estimations) and then summarized them to provide more robust results. By choosing C5.0 algorithm’s sub-trial size as 5, 5000 trees are formed and the mean of all importance scores of all trees formed are calculated and interpreted. The most important independent variables discriminating the voting decision are found to be the result of the previous elections, mean household population, proportion of population between ages 15 and 19, electricity consumption per person, and proportion of population between ages 55 and 64. Keywords: classification trees, voting decision, C5.0 algorithm, decision trees

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