The Service–Profit Chain: A Meta-Analytic Test of a Comprehensive Theoretical Framework

2017 ◽  
Vol 81 (3) ◽  
pp. 41-61 ◽  
Author(s):  
Jens Hogreve ◽  
Anja Iseke ◽  
Klaus Derfuss ◽  
Tönnjes Eller
Keyword(s):  
Service Quality ◽  
Employee Satisfaction ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Meta Analysis ◽  
Customer Relationship ◽  
Effect Sizes ◽  
Comprehensive Test ◽  
Service Managers ◽  
Service Profit Chain

The service–profit chain (SPC) has served as a prominent guidepost for service managers and researchers alike. This meta-analysis provides the first comprehensive test of the SPC, showing that all the proposed links are statistically significant and substantial. However, the effect sizes vary considerably, partly according to the type of service provided. Meta-analytic structural equation models show that internal service quality translates into service performance through various mechanisms beyond employee satisfaction, and they highlight the importance of the service encounter and customer relationship characteristics for customer responses. The findings not only indicate the need to integrate complementary paths in the SPC framework but also challenge the implicit SPC rationale that firms should always maximize employee satisfaction and external service quality to optimize firm performance.

The Robustness of the Likelihood Ratio Chi-Square Test for Structural Equation Models: A Meta-Analysis

2001 ◽  
Vol 26 (1) ◽  
pp. 105-132 ◽  
Author(s):  
Douglas A. Powell ◽  
William D. Schafer
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Type I Error ◽  
Meta Analysis ◽  
Generalized Least Squares ◽  
Error Rates ◽  
Type I ◽  
Chi Square ◽  
Distribution Free ◽  
Projection Techniques

The robustness literature for the structural equation model was synthesized following the method of Harwell which employs meta-analysis as developed by Hedges and Vevea. The study focused on the explanation of empirical Type I error rates for six principal classes of estimators: two that assume multivariate normality (maximum likelihood and generalized least squares), elliptical estimators, two distribution-free estimators (asymptotic and others), and latent projection. Generally, the chi-square tests for overall model fit were found to be sensitive to non-normality and the size of the model for all estimators (with the possible exception of the elliptical estimators with respect to model size and the latent projection techniques with respect to non-normality). The asymptotic distribution-free (ADF) and latent projection techniques were also found to be sensitive to sample sizes. Distribution-free methods other than ADF showed, in general, much less sensitivity to all factors considered.

scholarly journals The Antecedent of Customer Satisfaction and Its Impact on Customer Retention in Tourism as Hospitality Industry

2020 ◽  
Author(s):  
Andala Rama Putra Barusman ◽  
Evelin Putri Rulian ◽  
Susanto Susanto
Keyword(s):  
Customer Satisfaction ◽  
Service Quality ◽  
Structural Equation ◽  
Hospitality Industry ◽  
Customer Retention ◽  
Equation Model ◽  
Customer Relationship ◽  
Global Changes ◽  
The Government ◽  
The Impact

Taking a case study of tourism as hospitality industry in Lampung Province in Indonesia, we analyze the antecedent of customer satisfaction and its impact on customer retention. Using Structural Equation Model (SEM), we find that customer relationship management has a significant impact on service quality, customer satisfaction and customer retention. Moreover, the impact of service quality on customer satisfaction and the one of customer satisfaction on customer retention are also significant. Relying on the findings, we recommend some strategies for the government of Lampung Province, e.g. training local people to behave more friendly in welcoming domestic or international tourists, fixing all lodging facilities, creating more souvenirs with Lampung’s ornaments and developing management system adopting global changes in technology, communication and trend.

Introducing Continuous Time Meta-Analysis (CoTiMA)

2019 ◽  
Vol 23 (4) ◽  
pp. 620-650
Author(s):  
Christian Dormann ◽  
Christina Guthier ◽  
Jose M. Cortina
Keyword(s):  
Continuous Time ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Meta Analysis ◽  
Time Lag ◽  
Time Correlation ◽  
Time Lags ◽  
Correlation Matrices ◽  
Open Questions ◽  
Panel Studies

Meta-analysis of panel data is uniquely suited to uncovering phenomena that develop over time, but extant approaches are limited. There is no straightforward means of aggregating findings of primary panel studies that use different time lags and different numbers of waves. We introduce continuous time meta-analysis (CoTiMA) as a parameter-based approach to meta-analysis of cross-lagged panel correlation matrices. CoTiMA enables aggregation of studies using two or more waves even if there are varying time lags within and between studies. CoTiMA thus provides meta-analytic estimates of cross-lagged effects for a given time lag regardless of the frequency with which that time lag is used in primary studies. We describe the continuous time underpinnings of CoTiMA, its advantages over discrete-time, correlation-based meta-analysis of structural equation models (MASEM), and how CoTiMA would be applied to meta-analysis of panel studies. An example is then used to illustrate the approach. We also conducted Monte Carlo simulations demonstrating that bias is larger for time category–based MASEM than for CoTiMA under various conditions. Finally, we discuss data requirements, open questions, and possible future extensions.

Study on Customer Personality Characteristics and Relationship Outcomes Using SEM Analysis

2013 ◽  
Vol 462-463 ◽  
pp. 841-844 ◽  
Author(s):  
Xi Feng
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Positive Impact ◽  
Sem Analysis ◽  
Personality Characteristics ◽  
Customer Relationship ◽  
Social Affiliation ◽  
Service Companies ◽  
Relationship Outcomes ◽  
Customer Trust

The purpose of this article is to empirically examine the influence of customer personality characteristics (need for social affiliation, customer relationship proneness) on relationship outcomes (customer trust, satisfaction, and loyalty). Online responses from service companies were gathered to assess the influence using Structural Equation Models (SEM). Results indicate that need for social affiliation is a strong determinant of customer relationship proneness and trust. Consumer relationship proneness has directly positive impact on trust and satisfaction. The results confirm the trust-satisfaction-loyalty paradigm in service context.

scholarly journals Improvement Strategy of Competitive Advantage and Marketing Performance of Public Market

2018 ◽  
Vol 4 (2) ◽  
pp. 120
Author(s):  
Endah Budiarti
Keyword(s):  
Service Quality ◽  
Competitive Advantage ◽  
Customer Relationship Management ◽  
Structural Equation ◽  
Relationship Management ◽  
Customer Relationship ◽  
Equation Modeling ◽  
The Public ◽  
Marketing Performance ◽  
Public Market

The purpose of this research is to analyze and prove the influence of customer relationship management (CRM), service quality and entrepreneurship orientation to competitive advantage and marketing performance and to analyze and prove the influence of competitive advantage to marketing performance of the public market in East Java Province. The object of this research is the whole market unit of the public in East Java Province spread over 29 regencies and 9 cities in East Java Province amounting to 335 units of the public market. The unit of analysis (analysis unit) in this research is a revitalized public market that is 67 public market in East Java Province. While the observation unit is a trader or tenant of a community market stand in East Java Province. The number of public market traders in the market that has been revitalized today is 25,000. In this study, researchers measured the marketing performance of the Public market in East Java Province based on the perception of the trader or tenant stand, so the measure of marketing performance is perceptual. The sample used is 190 merchants. The number is spread across 67 units of the public market in East Java Province. The model to be used in this research is the model of causality or relationship. To test the proposed hypothesis, the analysis technique uses SEM (Structural Equation Modeling), with AMOS statistic software. The results of hypothesis testing: Customer relationship management, Quality of service and entrepreneurial orientation significantly influence the competitive advantage of the market public in East Java Province. Customer relationship management and service quality significantly influence the marketing performance of the public market in East Java Province, while the orientation of entrepreneurship has no significant effect on marketing performance of the public market in East Java Province. Competitive advantage influences the marketing performance of public market significantly in East Java Province.

scholarly journals Pengaruh Kualitas Pelayanan Dan Customer Relationship Management Terhadap Loyalitas Nasabah Pada Bank BRI

2020 ◽  
Vol 4 (02) ◽  
pp. 26-38
Author(s):  
Erline Erline ◽  
Boby Saputra
Keyword(s):  
Structural Equation Modeling ◽  
Service Quality ◽  
Customer Relationship Management ◽  
Customer Loyalty ◽  
Structural Equation ◽  
Relationship Management ◽  
Positive Influence ◽  
Customer Relationship ◽  
Equation Modeling ◽  
Branch Office

This study aims to determine whether service quality affects customer loyalty at BRI Bank Muara Teweh Branch. Does Customer Relationship Management affect customer loyalty at BRI Bank Muara Teweh Branch Office This research method uses the data analysis technique used, namely the analysis of Structural Equation Modeling from the IBM SPSS Statistics Amos version 20 program AMOS (Analysis of Moment Structures) analysis program Structural Equation Modeling (SEM) a covariance-based The results of this test show significant results with a CR value of 3.848> 1.96 and the p number is 0.000, this number is far below 0.05 so that H0 is rejected and H1 is accepted, which means that there is an influence between service quality on customer loyalty This test shows significant results with a CR value of 2.322> 1.96 and the p number is 0.006, this figure is far below 0.05 so that H0 is rejected and H1 is accepted, which means that there is an influence between customer relationship management on customer loyalty The results of the study concluded that service quality variables have a significant and positive influence on customer loyalty variables customer relationship management have a significant and positive influence on customer loyalty. For further researchers, it is suggested that the Muara Teweh branch of BRI bank improve the quality of their services to increase customer loyalty.  

scholarly journals Structural Equation Modeling (SEM)-Based Meta-Analysis

2021 ◽  
Author(s):  
Mike W.-L. Cheung
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Meta Analysis ◽  
Equation Model ◽  
Equation Modeling ◽  
Future Directions ◽  
Software Packages ◽  
Meta Analyses

Structural equation modeling (SEM) and meta-analysis are two popular techniques in the behavioral, medical, and social sciences. They have their own research communities, terminologies, models, software packages, and even journals. This chapter introduces SEM-based meta-analysis, an approach to conduct meta-analyses using the SEM framework. By conceptualizing studies in a meta-analysis as subjects in a structural equation model, univariate, multivariate, and three-level meta-analyses can be fitted as structural equation models using definition variables. We will review fixed-, random-, and mixed-effects models using the SEM framework. Examples will be used to illustrate the procedures using the metaSEM and OpenMx packages in R. This chapter closes with a discussion of some future directions for research.

scholarly journals Meta-Analytic Structural Equation Modeling

2021 ◽  
Author(s):  
Mike W.-L. Cheung
Keyword(s):  
Structural Equation Modeling ◽  
Correlation Matrix ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Meta Analysis ◽  
Equation Modeling ◽  
Average Correlation ◽  
Correlation Matrices ◽  
Factor Analytic Model ◽  
Special Cases

Meta-analysis and structural equation modeling (SEM) are two popular statistical models in the social, behavioral, and management sciences. Meta-analysis summarizes research findings to provide an estimate of the average effect and its heterogeneity. When there is moderate to high heterogeneity, moderators such as study characteristics may be used to explain the heterogeneity in the data. On the other hand, SEM includes several special cases, including the general linear model, path model, and confirmatory factor analytic model. SEM allows researchers to test hypothetical models with empirical data. Meta-analytic structural equation modeling (MASEM) is a statistical approach combining the advantages of both meta-analysis and SEM for fitting structural equation models on a pool of correlation matrices. There are usually two stages in the analyses. In the first stage of analysis, a pool of correlation matrices is combined to form an average correlation matrix. In the second stage of analysis, proposed structural equation models are tested against the average correlation matrix. MASEM enables researchers to synthesize researching findings using SEM as the research tool in primary studies. There are several popular approaches to conduct MASEM, including the univariate-r, generalized least squares, two-stage SEM (TSSEM), and one-stage MASEM (OSMASEM). MASEM helps to answer the following key research questions: (a) Are the correlation matrices homogeneous? (b) Do the proposed models fit the data? (c) Are there moderators that can be used to explain the heterogeneity of the correlation matrices? The MASEM framework has also been expanded to analyze large datasets or big data with or without the raw data.

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