Examining the Nonlinear Effects in Satisfaction-Loyalty-Behavioral Intentions Model

Extant research has widely investigated linear functional forms in satisfaction and loyalty models. Though complex nonlinear nature of satisfaction loyalty link is suggested by several researchers, few attempts have been made to empirically examine nonlinearity. Moreover, researchers have used divergent functional forms to model nonlinearity and their findings are often inconclusive. In this study we use nonlinear form to describe the relationship between satisfaction, attitudinal loyalty, purchase loyalty and customer behavioral intentions such as willingness to pay more and external and internal complaining responses in the context of business-to-consumer ecommerce. We find modest empirical support for nonlinear effects in the relationship. Results support nonlinearity only in the case of attitudinal loyalty to internal complaining response link. Results also present evidence about the mediating role of attitudinal loyalty in the relationship between satisfaction, purchase loyalty, willingness to pay more and internal complaining responses.

CUSTOMER SATISFACTION MEASUREMENT MODELS: GENERALISED MAXIMUM ENTROPY APPROACH

This paper presents the methodology of the Generalised Maximum Entropy (GME) approach for estimating linear models that contain latent variables such as customer satisfaction measurement models. The GME approach is a distribution free method and it provides better alternatives to the conventional method; Namely, Partial Least Squares (PLS), which used in the context of costumer satisfaction measurement. A simplified model that is used for the Swedish customer satis faction index (CSI) have been used to generate simulated data in order to study the performance of the GME and PLS. The results showed that the GME outperforms PLS in terms of mean square errors (MSE). A simulated data also used to compute the CSI using the GME approach.Generalised Maximum Entropy, Partial Least Squares, Costumer Satisfaction Models.

Alternative smoothing strategies in smooth partial least squares path modelling

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMThe assessment of nonlinear relationships in the context of Partial Least Squares Path Modelling (PLS-PM) has received a growing interest in recent years. One important contribution to this subject has been the work of Henseler, Fassot, Dijkstra and Wilson (2012) on the analysis of four different approaches to quadratic effects. The Smooth Partial Least Squares (PLSs) estimation technique studied in this work removes any assumptions on the structure of the nonlinear relationships between latent variables, by applying smoothing spline techniques to the structural model. Performance results of the PLSs show that it is a powerful tool in the context of predictive research, for instance to support the definition of targeted policies. Building from the hybrid approach to the PLS algorithm introduced by Wold (1982), we compare the performance of alternative spline designs, including natural cubic splines, P-Splines and Thin Plate Regression Splines (TPRS). For this purpose, Monte-Carlo simulations are carried with a conceptual model drawn from a comprehensive set of nonlinear relationships, in different sample sizes. All model configurations are compared using Root Mean Squared Error (RMSE) and absolute bias results. The benchmarking exercise shows that, in most contexts, P-Splines perform slightly better than TPRS and natural cubic splines

A Bayesian Network Estimation of the Service-Profit Chain for Transport Service Satisfaction

Bayesian network methodology is used to model key linkages of the service-profit chain within the context of transportation service satisfaction. Bayesian networks offer some advantages for implementing managerially focused models over other statistical techniques designed primarily for evaluating theoretical models. These advantages are (1) providing a causal explanation using observable variables within a single multivariate model, (2) analysis of nonlinear relationships contained in ordinal measurements, (3) accommodation of branching patterns that occur in data collection, and (4) the ability to conduct probabilistic inference for prediction and diagnostics with an output metric that can be understood by managers and academics. Sample data from 1,101 recent transport service customers are utilized to select and validate a Bayesian network and conduct probabilistic inference

Entrepreneurship by circumstances and abilities: the mediating role of job satisfaction and moderating role of self-efficacy

Prior studies have found that job dissatisfaction and self-efficacy are significant factors influencing individuals’ entrepreneurial propensity. Existing literature on entrepreneurship often regards job dissatisfaction as an entrepreneurial push factor and self-efficacy as an entrepreneurial pull factor. The argument is that individuals who are dissatisfied with their jobs are more likely to seek alternative mode of employment such as self-employment. In other words, poor job circumstances may push individuals to leave their paid employment to start their own businesses. On the other hand, personal abilities such as self-efficacy may pull individuals toward starting their own businesses in areas where they are confident and competent in. Despite the importance of job dissatisfaction and self-efficacy for new venture creation, few if any studies have examined the entrepreneurial phenomena from a holistic perspective. Utilizing concepts from the P-E fit and self-efficacy literatures, this paper argues that the path to entrepreneurship is a multi-faceted interactive process between individuals’ personal attributes and their work environment. We specifically examined how IT professional’s personal attributes such as innovation orientation and self-efficacy condition individuals for an entrepreneurial career in unsatisfactory work environments.

An alternating least square approach for the estimation of a SEM based on ordinal variables

The aim of this paper is to propose an approach to quantify the qualitative variables, within Structural Equation Models (SEM), and in particular of PLS-PM. We propose a new algorithm, called Partial Alternating Least Squares Optimal Scaling- Path Modeling (PALSOS-PM), which through an iterative procedure, computes an optimal quantification, for qualitative variables, and structural parameters of the model chosen

The Role of Logistics Service Quality in Creating Customer Loyalty

The purpose of this dissertation is to examine the loyalty phenomenon and to understand the role of logistics service in creating customer loyalty. The main objective is to help companies assess the impact of logistics service in creating loyalty. Logistics service quality is purported to consist of two separate constructs – relational LSQ and operational LSQ. These elements of LSQ drive satisfaction. This research also explores the loyalty phenomenon, which is conceptualized as a causal relationship between affective commitment and purchasing behavior. The strength of this relationship is proposed to be moderated by calculative commitment, which involves the calculation of costs and benefits and the assessment of the investments made in the relationship, along with the availability of alternatives. Further, satisfaction influences the loyalty relationship differently. This research contends that satisfaction has a linear relationship to affective commitment, but its relationship to purchase behavior is nonlinear, being more significant at the extremes. These constructs are defined and operationalized, and by testing its components, along with calculative commitment and satisfaction, different loyalty types should be identified. Understanding that firms have a portfolio of different customer relationships, the research should ascertain what conditions drive various types of customer relationships. This nomological model should also provide managerial insight to the proposition that there are different loyalty types that would have different strategic implications. Top firms recognize the differences in the needs and desires of major customers and design offerings according to those needs (Zhao, Droge and Stank 1996). Because an important goal for firms is to grow a larger share of the profitable revenue available (Bowersox, Closs and Stank 2000), managers must realize that not all customers are the same. This research should help distinguish different customer segments based on their loyalty profiles. If the loyalty relationship can be better understood, then managers will have more clarity about how to determine what level of logistics service (as well as other services) to provide to different customer groups