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Internet Research Using Partial Least Squares Structural Equation Modeling (PLS-SEM)


Special issue call for papers from Internet Research

Guest Editors

Prof. Wen-Lung Shiau, Ming Chuan University, Taiwan (mac@mail.mcu.edu.tw)
Prof. Marko Sarstedt, Otto-von-Guericke-University Magdeburg and University of Newcastle, Australia (Marko.Sarstedt@ovgu.de)
Prof. Joseph F. Hair Jr., University of South Alabama, USA (joefhair@gmail.com)

Submission Deadline: December 31st, 2017

Motivation and Aim of the Special Issue

Partial least squares structural equation modeling (PLS-SEM) has recently gained considerable attention in a variety of disciplines including management information systems (Ringle, Sarstedt, & Straub, 2012; Shiau & Chau, 2016; Huma, Hussain, Thurasamy, & Malik, 2017), marketing (Hair, Sarstedt, Ringle, & Mena, 2012), strategic management (Hair, Sarstedt, Pieper, & Ringle, 2012), operations management (Peng & Lai, 2012), and organizational research (Sosik, Kahai, & Piovoso, 2009). PLS is a composite-based approach to SEM, which aims at maximizing the explained variance of dependent constructs in the path model (e.g., Hair, Hult, Ringle, & Sarstedt, 2017). Compared to other SEM techniques, PLS allows researchers to simultaneously estimate complex interrelationships involving a variety of constructs and indicators with their direct, indirect, or moderating relationships that would otherwise not be easy to disentangle and examine (e.g., Hair, Ringle, & Sarstedt, 2011).

Recent Internet and information systems research focuses on more fully understanding and also explaining the roles of intervening and contingent variables and relationships amongst variables. For example, greater interest has been placed on unraveling the contingencies that are reflected in differences that characterize subgroups of individuals, organizations, or environments. To understand such contingencies requires confidently assessing observed or unobserved heterogeneity to draw conclusions about contingency effects. In a similar vein, a common conceptualization recognizes that effects are not necessarily constant but that they might diminish or increase such that researchers need to move beyond linear modeling to nonlinear modeling.

This emergence of more complex modeling requirements goes hand-in-hand with and underlines the critical importance of advanced analytical methods. Notable advances in PLS-SEM include, for example, confirmatory tetrad analysis to empirically assess the mode of measurement, new approaches for testing discriminant validity, prediction-oriented segmentation analysis to identify and treat unobserved heterogeneity, and invariance testing by means of the measurement invariance of composite models approach (e.g., Hair, Sarstedt, Ringle, & Gudergan, 2017).

The aim of this special issue of Internet Research is to introduce these advanced methods to a wider audience in an effort to broaden the understanding of Internet and information systems applications. This special issue embraces both, the technical side of PLS-SEM and empirical research using the technique. The special issue is tied to the 9th International Conference on PLS and Related Methods (PLS’17) to be held 17-19 June 2017 in Macau, China and the 2017 International Symposium on Applied Structural Equation Modeling to be held 10-14 October 2017 in Kuching, Malaysia. Outstanding papers presented at these conferences will be invited for submission. However, the guest editors also welcome submissions that have not been submitted to or presented at the conferences.

Topics of Interest

The guest editors are looking for high-quality papers with an original perspective and advanced thinking in Internet and information systems using PLS-SEM. Supplementing PLS-SEM applications, the special issue seeks for methodological papers that strongly emphasize empirical illustrations and the practical relevance of the proposed methods. Topics of interest of the special issue include, but are not limited to the following:

  • Applications and advancements of the original PLS-SEM algorithm (e.g., extended PLS, consistent PLS),
  • Analysis of complex model relationships involving nonlinear effects, multiple mediation, and/or moderated mediation,
  • Invariance assessment and multigroup analysis,
  • Applications and advancements of latent class procedures (e.g., FIMIX-PLS, PLS-Gas, PLS-POS, PLS-IRRS),
  • Common method bias assessment,
  • Endogeneity assessment and treatment,
  • Longitudinal data analysis,
  • Model comparisons,
  • Use of PLS-SEM in experimental research,
  • Application and development of novel prediction metrics,
  • Application of PLS-SEM with archival (secondary) data, and
  • Measurement issues including confirmatory composite analysis (CCA)

Deadlines

  • Submission due date: December 31st, 2017
  • First round reviews: February 28th, 2018
  • Revisions due: March 23rd, 2018
  • Second round decision: May 4th, 2018
  • Revisions due: June 4th, 2018
  • Final editorial decision: June 18th, 2018

Author Guidelines

Submissions to Internet Research are made using ScholarOne Manuscripts, the online submission and peer review system. Registration and access is available at http://mc.manuscriptcentral.com/intr.

If you are unable to find the information you need in the author guidelines or our author resources (http://emeraldgrouppublishing.com/authors/index.htm) section, please email manuscriptcentral@emeraldinsight.com for assistance.

References

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd Edition. Thousand Oaks: Sage.
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM – Indeed a Silver Bullet. Journal of Marketing Theory & Practice, 19, 139–151.
  • Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications. Long Range Planning, 45, 320–340.
  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling. Thousand Oaks: Sage.
  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An Assessment of the Use of Partial Least Squares Structural Equation Modeling in Marketing Research. Journal of the Academy of Marketing Science, 40, 414–433.
  • Huma, Z., Hussain, S., Thurasamy, R., & Malik, M. I. (2017). Determinants of Cyberloafing: A Comparative Study of a Public and Private Sector Organization. Internet Research, 27, 97–117.
  • Peng, D. X. & Lai, F. (2012). Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research. Journal of Operations Management, 30, 467–480.
  • Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A Critical Look at the Use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36, iii–xiv.
  • Shiau, W. L. & Chau, Y. K. (2016). Understanding Behavioral Intention to Use a Cloud Computing Classroom: A Multiple Model-comparison Approach. Information & Management, 53, 355–365
  • Sosik, J. J., Kahai, S. S., & Piovoso, M. J. (2009). Silver Bullet or Voodoo Statistics? A Primer for Using the Partial Least Squares Data Analytic Technique in Group and Organization Research. Group Organization Management, 34, 5–36.