@article{1431142,
      note = {Includes index.},
      author = {Hair, Joseph F., and Hult, G. Tomas M., and Ringle,  Christian M., and Sarstedt, Marko, and Danks, Nicholas P.,  and Ray, Soumya,},
      url = {http://library.usi.edu/record/1431142},
      title = {Partial least squares structural equation modeling  (PLS-SEM) using R : a workbook /},
      abstract = {Partial least squares structural equation modeling  (PLS-SEM) has become a standard approach for analyzing  complex inter-relationships between observed and latent  variables. Researchers appreciate the many advantages of  PLS-SEM such as the possibility to estimate very complex  models and the method's flexibility in terms of data  requirements and measurement specification. This practical  open access guide provides a step-by-step treatment of the  major choices in analyzing PLS path models using R, a free  software environment for statistical computing, which runs  on Windows, macOS, and UNIX computer platforms. Adopting  the R software's SEMinR package, which brings a friendly  syntax to creating and estimating structural equation  models, each chapter offers a concise overview of relevant  topics and metrics, followed by an in-depth description of  a case study. Simple instructions give readers the  "how-tos" of using SEMinR to obtain solutions and document  their results. Rules of thumb in every chapter provide  guidance on best practices in the application and  interpretation of PLS-SEM},
      doi = {https://doi.org/10.1007/978-3-030-80519-7},
      recid = {1431142},
      pages = {1 online resource (xiv, 197 pages) :},
}