Applying partial least squares in tourism and hospitality research /

Ten chapters discuss key aspects of advanced PLS analysis and its practical applications, covering new guidelines and improvements in the use of PLS-PM as well as various individual topics.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Ali, Faizan (Editor ), Rasoolimanesh, S. Mostafa (Editor ), Cobanoglu, Cihan (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bingley, UK : Emerald Publishing Limited, [2019]
Edición:First edition.
Temas:
Hospitality industry > Research.
Accueil (Tourisme) > Recherche.
MATHEMATICS > General.
Least squares.
Structural equation modeling.
Tourism > Mathematical models.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover; Title; Copyright; Contents; Preface; References; Foreword; References; About the Authors; Chapter 1 Minimum Sample Size Estimation in PLS-SEM: An Application in Tourism and Hospitality Research; Introduction; Illustrative Study; Power, Effect Size, and Minimum Sample Size; The Monte Carlo Simulation Method; Classic Methods: 10-Times Rule and Minimum R2; The 10-Times Rule Method; The Minimum R2 Method; New Methods: Inverse Square Root and Gamma-Exponential; The Inverse Square Root Method; The Gamma-Exponential Method; The New Methods in WarpPLS
  • Minimum Sample Size Estimation After Data Collection and AnalysisMinimum Sample Size Estimation Before Data Collection and Analysis; Discussion and Conclusion; Acknowledgments; References; Chapter 2 New Guidelines for the Use of PLS Path Modeling in Hospitality, Travel, and Tourism Research; 1. Introduction; 2. Partial Least Squares Path Modeling; 3. The Use of PLS-PM in Different Types of HTT Research; 3.1. Causal Research; 3.2. Predictive Research; 3.3. Descriptive Research; 3.4. Exploratory Research; 4. Discussion and Implications; Notes; References
  • Chapter 3 Predictions from Partial Least Squares Models1. Introduction; 2. How to Predict from PLS Models; 2.1. Generating Predictions from PLS-PM; 2.2. Earliest Versus Direct Antecedents Approaches; 3. Evaluating Predictions from PLS Models; 3.1. In-sample Versus Out-of-sample; 3.2. Cross Validation; 3.3. Predictive Metrics; 3.4. Benchmarks; 4. Evaluating Predictive Performance; 5. Using PLSpredict (An Empirical Example); 5.1. Predictive Value of Model; 5.2. Predictive Value of Proposed Versus Alternative Models; 5.3. Evaluating the Prediction Residuals; 5.4. Predictive Validity
  • 5.5. Discussion of Empirical Example6. Looking Ahead; Acknowledgments; References; Chapter 4 PLS Path Modeling in Hospitality and Tourism Research: The Golden Age and Days of Future Past; 1. Introduction; 2. Recent Advances in PLS-PM; 2.1. Consistent PLS (PLSc); 2.2. Hierarchical Component Analysis; 2.3. Nonlinear Analysis; 2.4. Multigroup Analysis; 2.5. Moderation-Mediation Analysis; 2.6. Unobserved Heterogeneity; 2.7. Confirmatory Tetrad Analysis (CTA-PLS); 2.8. PLS Predict; 2.9. Importance-Performance Map Analysis; 3. Methodology
  • 4. Critical Issues in the Use of PLS-PM in Hospitality and Tourism Research4.1. Reasons for Using PLS-PM; 4.2. Model Descriptive Statistics; 4.3. Sampling Characteristics; 4.4. Technical Reporting; 4.5. Reported Formative Measurement Metrics; 4.6. Reported Reflective Measurement Metrics; 4.7. Reported Structural Model Metrics; 4.8. Additional Considerations and Supplementary Analyses; 4.9. How to Improve the Use of PLS-PM in Future; 5. Conclusions; References; Chapter 5 Hotel Employees' Use of Smartphones and Performance: Reflective-Formative Estimation Approach; Introduction

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