Given how important it is to provide superior value to customers and to maintain customer loyalty for a sustainable competitive advantage, the aim of this paper is to examine the relationships among relationship marketing components of – trust, competency, commitment, communication, and conflict handling, – relationship investment, relationship quality, perceived customer value, satisfaction and loyalty in an integrated framework in the Turkish retail banking industry. Unlike previous studies, this research extends the literature by analysing affective as well as cognitive dimensions in the same model with a holistic view by simultaneously examining the direct and indirect effects of the related concepts. The distinctive nature of this study is its evaluation of customer satisfaction and loyalty from the perspective of actual consumers. The research model was tested using data collected from 685 retail banking customers by applying structural equation modelling. The findings show that relationship marketing induces loyalty through relationship quality, customer value, and satisfaction, which are mainly provided by trust, communication, and relationship investment. Furthermore, relationship investment and relationship quality are the most important factors in the development of customer value, satisfaction, and loyalty. The emotional value dimension, which captures the affective aspects of perceived value, has the strongest effect on both satisfaction and loyalty.

In this study, we propose a generalized Marshall-Olkin exponentiated exponential distribution as a submodel of the family of generalized Marshall-Olkin distribution. Some statistical properties of the proposed distribution are examined such as moments, the moment-generating function, incomplete moment, and Lorenz and Bonferroni curves. We give five estimators for the unknown parameters of the proposed distribution based on maximum likelihood, least squares, weighted least squares, and the Anderson-Darling and Cramer-von Mises methods of estimation. To investigate the finite sample properties of the estimators, a comprehensive Monte Carlo simulation study is conducted for the models with three sets of randomly selected parameter values. Finally, four different real data applications are presented to demonstrate the usefulness of the proposed distribution in real life.

Kullanıcıların bilgi ve tecrübelerini paylaştığı sanal bir ortam olarak tanımlanan sosyal medya son zamanlarda teknolojinin yardımıyla yeni bir boyut kazanmıştır. Tüketiciler evde veya işyerinde daha fazla vakit geçirdiğinde sosyal medya kullanımı artmakta ve reklam aracılığıyla ürün ve hizmetler hakkında daha iyi bilgi edinmektedirler. Araştırmanın amacı, sosyal medya reklamlarında tüketici algılarının tutum, davranış ve satın alma niyetine etkisinin incelenmesi ve tüketici satın alma karar süreci açısından değerlendirilmesidir. Araştırma sonucuna göre, bilgi vericilik, eğlence ve ekonomiye yararlılık faktörleri sosyal medya reklamlarına yönelik tutumu, tutum, davranış ve satın alma niyetini ve son olarak davranış, satın alma niyetini pozitif yönde etkilemektedir. Güvenilirlik faktörünün tutum üzerinde bir etkisinin olmamasına karşın değer yozlaşması faktörü tutumu olumsuz yönde etkilemektedir.

This paper presents the results of a survey conducted to measure the service quality of the high-speed railway system (HSRS) that operates between two cities in Turkey. The analysis had its basis in the impact score technique. The survey was prepared to ask users for satisfaction ratings of 61 attributes, which were constructed for the seven dimensions of service quality for public transportation systems: passenger information, fare level and type, accessibility, station environment, vehicle environment, service delivery, and security. Through analysis of the results, the most problematic areas of the service provided by the HSRS were determined. According to the model results, the operator of the system should provide enhancements especially in the seat design of the vehicles and also regarding the time and the frequency of the service provided.

Scalar-on-function logistic regression, where the response is a binary outcome and the predictor consists of random curves, has become a general framework to explore a linear relationship between the binary outcome and functional predictor. Most of the methods used to estimate this model are based on the least-squares type estimators. However, the least-squares estimator is seriously hindered by outliers, leading to biased parameter estimates and an increased probability of misclassification. This paper proposes a robust partial least squares method to estimate the regression coefficient function in the scalar-on-function logistic regression. The regression coefficient function represented by functional partial least squares decomposition is estimated by a weighted likelihood method, which downweighs the effect of outliers in the response and predictor. The estimation and classification performance of the proposed method is evaluated via a series of Monte Carlo experiments and a strawberry puree data set. The results obtained from the proposed method are compared favorably with existing methods.