Prediction-oriented modeling in business research by means of PLS path modeling: Introduction to a JBR special section
Under the main theme “ prediction-oriented modeling in business research by means of partial least squares path modeling ” (PLS), the special issue presents 17 papers. Most contributions include content from presentations at the 2nd International Symposium on Partial Least Squares Path Modeling: The...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/71116 |
| Acceso en línea: | https://hdl.handle.net/11441/71116 https://doi.org/10.1016/j.jbusres.2016.03.048 |
| Access Level: | acceso abierto |
| Palabra clave: | Partial least squares Prediction-oriented modeling Business research Quantitative methods |
| Sumario: | Under the main theme “ prediction-oriented modeling in business research by means of partial least squares path modeling ” (PLS), the special issue presents 17 papers. Most contributions include content from presentations at the 2nd International Symposium on Partial Least Squares Path Modeling: The Conference for PLS Users, which took place at the Universidad de Sevilla (Spain) from June 16 to 19, 2015. This conference provided PLS users with a platform for the fruitful exchange of ideas on variance-based structural equation modeling. At the same time, the conference addressed the latest methodological advances and their use in research practice. Finally, the conference resumed and enriched the ongoing discussion on the strengths and weaknesses of PLS. Researchers often emphasize that predictive capabilities is a strength of the PLS method. Nevertheless, methodological advances and applications in this direction are rare. The scienti fi c committee therefore selected high-quality papers that mainly advance PLS and prediction. The special issue editors believe that these special issues will become the starting point for a more intensive use of predictive modeling in the social sciences discipline and for additional advances that will exploit PLS' capabilities in this area |
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