[AISWorld] Special issue: Internet Research Using Partial Least Squares Structural Equation Modeling (PLS-SEM)

[Mac Mac]

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

Special issue call for papers from Internet Research (SSCI)
http://www.emeraldgrouppublishing.com/products/journals/call_for_papers.htm?id=7145

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

Submission Deadline: November 1st, 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: November 1st, 2017
First round reviews: January 1st, 2018
Revisions due: February 15th, 2018
Second round decision: April 1st, 2018
Revisions due: May 1st, 2018
Final editorial decision: May 15th, 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 at 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.