<p>&nbsp; Handbook of Partial Least Squares<br/>Concepts, Methods and Applications in Marketing and Related Fields<br/>Series: Springer Handbooks of Computational Statistics </p><p>Esposito Vinzi, V.; Chin, W.W.; Henseler, J.; Wang, H. (Eds.) </p><p>2008, Approx. 850 p., Hardcover</p><p>ISBN: 978-3-540-32825-4</p><p></p><p>Not yet published. Available: April 3, 2008 <br/>$319.00 <br/>&nbsp;<br/>About this book |Table of contents<br/>Table of contents<br/>I . Methods: </p><p>PLS Path Modeling: Concepts, Model Estimation and Assessment: Latent Variables and Indicators: Herman Wold's Basic Design and Partial Least Squares.- PLS Path Modeling: Recent Developments and Open Issues for Model Assessment and Improvement.- Bootstrap Cross-Validation Indices for PLS Path Model Assessment. </p><p>PLS Path Modeling: Extensions: How to use PLS Path modeling for analyzing multi-block data sets.- How to use ULS-SEM and PLS-SEM to measure interaction 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 behaviour in tourism.- A Permutation Based Procedure for Multi-Group PLS Analysis: Results of Tests of Differences on Simulated Data and a Cross of Information System Services between Germany and the USA.- Looking at the Antecedents of Perceived Switching Costs – A PLS Path Modeling Approach with Categorical Indicators. </p><p>PLS Path Modeling with Classification Issues: The Finite Mixture Partial Least Squares Approach - Methodology and Application.- Prediction-oriented classification in PLS Path Modelling.- Conjoint use of variables clustering and PLS structural equations modelling. </p><p>PLS Path Modeling for Customer Satisfaction Studies: Design of PLS-based Satisfaction Studies.- Applying Bootstrap and Structural Equation Models to a Customer Satisfaction Model on Mobile Telecommunications Sector.- 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.&nbsp; </p><p>PLS Regression: PLS and Data Mining.- Three-block data modeling by endo- and exo-LPLS Regression.-Regression Modeling Analysis on Compositional Data.- A Modification of the PLSR Method. </p><p>II. Applications to Marketing and Related Areas:</p><p>PLS and Success Factor Studies in Marketing.- Applying Maximum Likelihood and PLS on Different Sample Sizes: Studies on Servqual Model and Emloyee Behaviour Model.- A PLS Model to Study Brand Preference: An Application to a Product Class.- An Application of PLS in Multi-Group Analysis: The need for differentiated corporate-level marketing in the mobile communications industry.- Modelling the Impact of Corporate Reputation on Customer Satisfaction and Loyalty Using PLS.- 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.- Content Strategies in 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. </p><p>III. Tutorials with Didactic Approach:</p><p>How to Write Up and Report PLS analyses.- On the Operalization of Constructs in Multiple Groups.- Evaluation of Structural Equation Models using the Partial Least Squares (PLS-) Approach.- PLS and Confirmatory Tetrad Testing for Formative Measurement Scales in Marketing.- 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.- PLS Regression Modeling with Qualitative Variables and Its Application to Beijing Sand Storm Prevention.- Interpretation of the preferences of automotive customers applied to air conditioning supports by combining GPA and PLS regression.<br/></p>