Ebook: Customer Experiences affect Customer Loyalty: An Empirical Investigation of the Starbucks Experience using Structural Equation Modeling : An Empirical Investigation of the Starbucks Experience using Structural Equation Modeling

The study at hand investigates customer experiences at the American coffee company Starbucks and develops a new scale to measure customer experience quality on the basis of four dimensions: Service quality, atmosphere quality, flow quality and learning quality. The study reveals that product quality itself is a separate, but related construct to customer experience quality which alone is not sufficient to create customer loyalty. The effect of customer experience quality and product quality on customer loyalty intentions is found to be fully mediated by perceived value. Moreover, perceived wealth of the customer acts as a moderator and increases the positive effect of customer experience quality on perceived value whereas it weakens the effect of product quality on perceived value. Collectively, the results extend and clarify concepts in the evolving, but inconsistent customer experience management literature. The findings enable managers to stage customer experiences more effectively and more efficiently. Auszug aus dem Text Text sample: Kapitel 3, Methodology & Research Design: In order to manage experiences in a way that customer loyalty intentions are maximized, it is crucial to understand which aspects people really remember from the purchase process. Hoch and Deighton (1989) explain that remembered purchase experiences greatly influence future behavior. That means, when deciding to choose a company, individuals first recall their past experiences. This reasoning is in line with Pine and Gilmore (1998) who claim that experiences should be memorable. Therefore, in contrast to existing studies about customer experience quality (see Table 1) in which the measurements were made right after customers/ respondents have left the store, this study will measure customer experience quality with a significant time lag. This approach ensures that only the remembered, memorable experience will be measured and linked to behavioral intentions. The American Coffee Company Star bucks will serve as an exemplary company to which the questionnaire will be adapted in order to transfer the theory into practice and evaluate the proposed hypotheses. Starbucks was chosen because it has a worldwide network of stores with a consistent corporate design and a global strategy focused on staging experiences. Starbucks is known to many people and has already proven to be a good example in previous research on customer experiences (e.g., Chang and Horng, 2010; Hueiju and Wenchang, 2009). As outlined in the introduction, SEM will be applied in this study. The concept of customer experience quality and the other constructs entirely based on perceptions are all latent constructs, which makes this study a perfect case to apply this advanced technique of multivariate dependence analysis. SEM enables the researcher to assess the measurement properties and test the proposed theoretical relationships in a unified and integrated manner. The analysis of a model in SEM hereby consists of two major parts. First, the measurement model in SEM is similar to a factor analysis. On the basis of theory and/or a preliminary exploratory factor analysis which reveals the underlying structure, the researcher specifies in which way observed variables load on latent factors/constructs in the model. Afterwards, in the form of a confirmatory factor analysis, all of the equations are estimated simultaneously and it is tested whether the measurement theory holds true. Second, to test particular research hypotheses a structural model connects the latent constructs via dependence relationships. Again, the entire model is estimated at once. This procedure is similar to conducting factor analysis and a series of multiple regression analyses at once. Endogenuous constructs in the model can be compared with dependent variables in a regression, exogenuous variables are independent variables. However, this analogy has to be treated with caution. Due to the simultaneous estimation of the entire model some of the endogenuous variables might serve as a dependent variable in one relationship/equation, while being an independent variable in another relationship/equation (Malhotra, 2010). A valuable benefit of SEM is that it explicitly takes measurement error into account, i.e. in how far do the observed variables fail to describe the latent constructs of interest. However, in order to apply SEM appropriately, two crucial prerequisites should be fulfilled. First, SEM demands a sufficiently large sample size in order to estimate the model properly. This is the case because the x^2-Test, which is used to decide either to accept or reject the model, significantly is influenced by sample size (n). The test is based on x^2=(n-1)×F, where F is the fit function between the observed sample covariance matrix and the estimated covariance matrix. Therefore, if the sample size is extremely small, the model is always accepted. In return, if n is extremely large the model is always rejected (Blunch, 2008). Recommendations about specific sample sizes fall short because the absolute required sample size depends on several characteristics of the model and cannot be generalized. As a rule of thumb, Malhotra (2010) suggests sample sizes in the range of 200 and 400 if Maximum Likelihood Estimation is used. Second, the collected data should show multivariate normality. However, this is an assumption seldomly true in practice. Therefore, as compensation if the data deviates from the assumption of multivariate normality the sample size in return should be even larger. Regarding the effects of multivariate nonnormality, Lei and Lomax (2005) found that it does not have a significant effect on the parameter estimates but that nonnormality inflates the x^2 statistics. This finding is in line with Henly (1993) who demonstrated that regardless of the sample size, the rejection frequency of a model is substantially higher if it is tested with a sample that contains nonnormal distributions of variables. Taking these facts into account, the sample size for this study was planned to amount to at least 100 respondents for the exploratory factor analyses and the reliability tests in the pre-test and at least 400 respondents for the final data analysis using structural equation modeling. In the following, the different steps of the study will be described. The theory presented in chapter two serves as the conceptual foundation as visualized in Figure 3 and as summarized in the hypotheses. The analysis will follow the classic approach of conducting SEM as suggested by Malhotra (2010). Biographische Informationen Daniel Gurski, born in 1988, graduated at Maastricht University as Master of Science in International Business with a focus on Strategic Marketing in 2013. He is fascinated by Customer-Centric Service Science and always strives to develop new business strategies that make customers more satisfied and companies more profitable. Daniel hereby tries to bridge the gap between strategy and operations by analyzing business processes from a customer perspective. He worked for global organizations in Germany, the Netherlands and Australia.