Handbook of Partial Least Squares Concepts, Methods and Applications /

The "Handbook of Partial Least Squares (PLS) and Marketing: Concepts, Methods and Applications" is the second volume in the series of the Handbooks of Computational Statistics. This Handbook represents a comprehensive overview of PLS methods with specific reference to their use in Marketin...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Esposito Vinzi, Vincenzo (Επιμελητής έκδοσης), Chin, Wynne W. (Επιμελητής έκδοσης), Henseler, Jörg (Επιμελητής έκδοσης), Wang, Huiwen (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.
Σειρά:Springer Handbooks of Computational Statistics
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Editorial: Perspectives on Partial Least Squares
  • METHODS
  • Latent Variables and Indices: Herman Wold#x2019;s Basic Design and Partial Least Squares
  • PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement
  • Bootstrap Cross-Validation Indices for PLS Path Model Assessment
  • A Bridge Between PLS Path Modeling and Multi-Block Data Analysis
  • Use of ULS-SEM and PLS-SEM to Measure a Group Effect in a Regression Model Relating Two Blocks of Binary Variables
  • A New Multiblock PLS Based Method to Estimate Causal Models: Application to the Post-Consumption Behavior in Tourism
  • An Introduction to a Permutation Based Procedure for Multi-Group PLS Analysis: Results of Tests of Differences on Simulated Data and a Cross Cultural Analysis of the Sourcing of Information System Services Between Germany and the USA
  • Finite Mixture Partial Least Squares Analysis: Methodology and Numerical Examples
  • Prediction Oriented Classification in PLS Path Modeling
  • Conjoint Use of Variables Clustering and PLS Structural Equations Modeling
  • Design of PLS-Based Satisfaction Studies
  • A Case Study of a Customer Satisfaction Problem: Bootstrap and Imputation Techniques
  • Comparison of Likelihood and PLS Estimators for Structural Equation Modeling: A Simulation with Customer Satisfaction Data
  • Modeling Customer Satisfaction: A Comparative Performance Evaluation of Covariance Structure Analysis Versus Partial Least Squares
  • PLS in Data Mining and Data Integration
  • Three-Block Data Modeling by Endo- and Exo-LPLS Regression
  • Regression Modelling Analysis on Compositional Data
  • APPLICATIONS TO MARKETING AND RELATED AREAS
  • PLS and Success Factor Studies in Marketing
  • Applying Maximum Likelihood and PLS on Different Sample Sizes: Studies on SERVQUAL Model and Employee Behavior Model
  • A PLS Model to Study Brand Preference: An Application to the Mobile Phone Market
  • An Application of PLS in Multi-Group Analysis: The Need for Differentiated Corporate-Level Marketing in the Mobile Communications Industry
  • Modeling the Impact of Corporate Reputation on Customer Satisfaction and Loyalty Using Partial Least Squares
  • Reframing Customer Value in a Service-Based Paradigm: An Evaluation of a Formative Measure in a Multi-industry, Cross-cultural Context
  • Analyzing Factorial Data Using PLS: Application in an Online Complaining Context
  • Application of PLS in Marketing: Content Strategies on the Internet
  • Use of Partial Least Squares (PLS) in TQM Research: TQM Practices and Business Performance in SMEs
  • Using PLS to Investigate Interaction Effects Between Higher Order Branding Constructs
  • TUTORIALS
  • How to Write Up and Report PLS Analyses
  • Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach
  • Testing Moderating Effects in PLS Path Models: An Illustration of Available Procedures
  • A Comparison of Current PLS Path Modeling Software: Features, Ease-of-Use, and Performance
  • to SIMCA-P and Its Application
  • Interpretation of the Preferences of Automotive Customers Applied to Air Conditioning Supports by Combining GPA and PLS Regression.