The impact of tour quality and tourist satisfaction on tourist loyalty: The case of Chinese tourists in Korea
TL;DR: In this article, the causal relationship between tourist expectations, tourist motivations, tour quality, tourist satisfaction, tourist complaints and tourist loyalty of Chinese tourists in the Republic of Korea using path analysis was examined.
About: This article is published in Tourism Management.The article was published on 2011-10-01. It has received 319 citations till now. The article focuses on the topics: Loyalty.
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TL;DR: This article developed a theoretical model of the effect of memorable tourism experiences (MTEs) on behavioral intentions by examining the structural relationships between destination image (DI) and destination experience (DI).
Abstract: The current study develops a theoretical model of the effect of memorable tourism experiences (MTEs) on behavioral intentions by examining the structural relationships between destination image (DI...
293 citations
Cites background or result from "The impact of tour quality and tour..."
...However, some researchers have observed a low causal effect of customer satisfaction on loyalty behavior (e.g., Hultman et al. 2015; Jones and Sasser 1995; Keiningham and Vavra 2001; S. Lee, Jeon, and Kim 2011; Park and Jang 2014; Reichheld 1993)....
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...…those of previous researchers who, by identifying a low-level or nonsignificant influence of satisfaction on future behavior, suggested that satisfaction alone is an insufficient predictor of future behavior (Assaker and Hallak 2013; Hultman et al. 2015; S. Lee, Jeon, and Kim 2011; Oliver 1997)....
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TL;DR: In this paper, a two-stage analysis of semi-structured interview data from a theoretical sample of 57 individuals yielded 21 aesthetic dimensions that were categorized into nine themes: Scale, Time, Condition, Sound, Balance, Diversity, Novelty, Shape, and Uniqueness.
269 citations
TL;DR: Wang et al. as discussed by the authors developed an integrated model to examine the antecedents to Chinese domestic tourists' destination loyalty and found that destination familiarity, destination image, perceived value, and tourist satisfaction all influenced tourists' destinations loyalty.
256 citations
TL;DR: In this article, the authors identify the most critical antecedents of destination loyalty formation and develop a series of propositions for the relationships among the antecedent of loyalty formation, and their direct and indirect impacts on loyalty formation.
Abstract: Purpose – The purpose of this paper is to identify the most critical antecedents of destination loyalty formation (DLF) and to develop a series of propositions for the relationships among the antecedents of loyalty formation and their direct and indirect impacts on loyalty formation Design/methodology/approach – This conceptual paper provides a comprehensive review of the previous studies that examined destination loyalty and posits a framework of tourist DLF titled Destination Loyalty Formation Findings – In the proposed conceptual model, the sequential relationships among the antecedents of tourist destination loyalty postulate that previous experiences are the most influential driver that could manipulate tourist destination loyalty Place attachment and involvement constitute the second most influential factors of DLF In addition to the above two variables, destination image is proposed to have direct and indirect effects on perception of service quality and satisfaction Meanwhile, service quality
181 citations
TL;DR: In this paper, the authors empirically test a model linking involvement, experience quality, satisfaction, and recommendation intention and find that experience quality and satisfaction mediate the relationship between involvement and intention in the cultural tourism context.
Abstract: This study aims to empirically test a model linking involvement, experience quality, satisfaction, and recommendation intention. The study also analyzes the mediating effect of experience quality and satisfaction on the relationship between involvement and recommendation intention in a cultural tourism destination context. Data were collected from tourists using a survey from a historical area of Istanbul, the Sultanahmet district. The results reveal that experience quality and satisfaction mediate the relationship between involvement and recommendation intention in the cultural tourism context. This study discusses the theoretical and management implications of these findings. The suggested strategies would diversify and boost the Istanbul tourism industry by targeting different tourist groups.
159 citations
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01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each.
Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations.
Table of Contents
1. Introduction.
I. PREPARING FOR A MULTIVARIATE ANALYSIS.
2. Examining Your Data.
3. Factor Analysis.
II. DEPENDENCE TECHNIQUES.
4. Multiple Regression.
5. Multiple Discriminant Analysis and Logistic Regression.
6. Multivariate Analysis of Variance.
7. Conjoint Analysis.
8. Canonical Correlation Analysis.
III. INTERDEPENDENCE TECHNIQUES.
9. Cluster Analysis.
10. Multidimensional Scaling.
IV. ADVANCED AND EMERGING TECHNIQUES.
11. Structural Equation Modeling.
12. Emerging Techniques in Multivariate Analysis.
Appendix A: Applications of Multivariate Data Analysis.
Index.
37,124 citations
TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
Abstract: I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and Correspondence Analysis V Moving Beyond the Basic Techniques 10 Structural Equation Modeling: Overview 10a Appendix -- SEM 11 CFA: Confirmatory Factor Analysis 11a Appendix -- CFA 12 SEM: Testing A Structural Model 12a Appendix -- SEM APPENDIX A Basic Stats
23,353 citations
Journal Article•
TL;DR: In this paper, the authors describe the development of a 22-item instrument (called SERVQUAL) for assessing customer perceptions of service quality in service and retailing organizations, and the procedures used in constructing and refining a multiple-item scale to measure the construct are described.
21,693 citations
TL;DR: In this article, a general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models, and the importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models.
Abstract: Factor analysis, path analysis, structural equation modeling, and related multivariate statistical methods are based on maximum likelihood or generalized least squares estimation developed for covariance structure models. Large-sample theory provides a chi-square goodness-of-fit test for comparing a model against a general alternative model based on correlated variables. This model comparison is insufficient for model evaluation: In large samples virtually any model tends to be rejected as inadequate, and in small samples various competing models, if evaluated, might be equally acceptable. A general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models. Use of the null model in the context of a procedure that sequentially evaluates the statistical necessity of various sets of parameters places statistical methods in covariance structure analysis into a more complete framework. The concepts of ideal models and pseudo chi-square tests are introduced, and their roles in hypothesis testing are developed. The importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models is also emphasized. Normed and nonnormed fit indices are developed and illustrated.
16,420 citations
TL;DR: The attainment of quality in products and services has become a pivotal concern of the 1980s as discussed by the authors, while quality in tangible goods has been described and measured by marketers, quality in services is la...
Abstract: The attainment of quality in products and services has become a pivotal concern of the 1980s. While quality in tangible goods has been described and measured by marketers, quality in services is la...
16,185 citations