“This book provides all the essentials in comprehending, assimilating, applying and explicitly presenting sophisticated structured models in the most simplistic manner for a plethora of Business and Non-Business disciplines.” - Professor Siva Muthaly, Dean of Faculty of Business and Management at APU. Filled with useful illustrations to facilitate understanding, you’ll find this guide a go-to tool when conducting marketing research. Coupled with business examples and downloadable datasets for practice, the guide includes step-by-step guidelines for advanced PLS-SEM procedures in SmartPLS, including: CTA-PLS, FIMIX-PLS, GoF (SRMR, dULS, and dG), HCM, HTMT, IPMA, MICOM, PLS-MGA, PLS-POS, PLSc, and QEM. Ken Kwong-Kay Wong wrote this reference guide with graduate students and marketing practitioners in mind. , In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the. Adjusted R-squared and predicted R-squared use different approaches to help you fight that impulse to add too many. The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields. When applied correctly, PLS can be a great alternative to existing covariance-based SEM approaches. R-squared tends to reward you for including too many independent variables in a regression model, and it doesn’t provide any incentive to stop adding more. Marketers can use PLS to build models that measure latent variables such as socioeconomic status, perceived quality, satisfaction, brand attitude, buying intention, and customer loyalty. Partial least squares is a new approach in structural equation modeling that can pay dividends when theory is scarce, correct model specifications are uncertain, and predictive accuracy is paramount.
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