Guest Editors
Professor Gabriel Cepeda Carrión, Universidad de Sevilla, Spain (gabi@us.es)
Professor Joseph F. Hair, University of South Alabama, USA (joefhair@gmail.com)
Professor Christian M. Ringle, Hamburg University of Technology, Germany, and University of Waikato, New Zealand (c.ringle@tuhh.de)
Professor Jose L. Roldán, Universidad de Sevilla, Spain (jlroldan@us.es)
Professor Jeronimo García, Universidad de Sevilla, Spain (jeronimo@us.es) (for editorial issues and management)
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 sport management (Cepeda Carrión & Cepeda, 2018; Sarstedt, Ringle, Raithel, & Gudergan, 2014) , 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., Nitzl, Cepeda Carrión & Roldán, 2016; Richter, Cepeda Carrión, Roldán, & Ringle, 2016).
Recent sport management research focuses more and more on fully understanding and explaining the roles of intervening and contingent variables and relationships amongst variables (Koo & Lee, 2018). For example, greater interest has been placed on unraveling the contingencies that are reflected in differences that characterize subgroups of individuals, sport organizations, or fan satisfactions (Engelberg, Zakus, Skinner, & Campbell, 2012). To understand such contingencies requires confidently assessing observed or unobserved heterogeneity to draw conclusions about contingency effects. In a similar manner, a common factor model conceptualization recognizes that effects are not necessarily constant but might diminish or increase. Therefore, 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 proceedings 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 invariance measurement used in the composite model approach (e.g., Cepeda-Carrión, Henseler, Ringle, & Roldán, 2016; Richter et al., 2016).
The aim of this special issue of International Journal of Sports Marketing and Sponsorship is to introduce these advanced methods to a wider audience in an effort to broaden the understanding of sport management applications. This special issue embraces both, the technical side of PLS-SEM and empirical research using this method. The special issue is tied to 2020 International Conference on Partial Least Squares Structural Equation Modeling to be held in March 2020 in Beijing. 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 conference.
Topics of Interest
The guest editors are looking for high-quality papers with an original perspective and advanced thinking in sport management using PLS-SEM. Supplementary to 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:
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- 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 multi-group 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
- Measurement issues, including confirmatory composite analysis (CCA)
- Prediction using PLS-SEM
- Importance-performance map analysis in PLS-SEM
Deadlines
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- Submission due date: From November 1, 2020 to December 21, 2020
- First round reviews: February 28, 2021
- Revisions due date: March 23, 2021
- Second round decision: May 4, 2021
- Revisions due date: June 4, 2021
- Final editorial decision: June 18, 2021
To submit your research, please visit: https://mc.manuscriptcentral.com/ijsms
Please click here to view the author guidelines for the journal.
References
Cepeda Carrión, G., & Cepeda, I. (2018). How public sport centers can improve sport consumer experience. International Journal of Sport Marketing and Sponsorship, 19, 350-367.
Cepeda Carrión, G., Henseler, J., Ringle, C. M., & Roldán, J. L. (2016). Prediction-oriented modeling in business research by means of PLS path modeling: Introduction to a JBR special section. Journal of Business Research, 69, 4545–4551.
Engelberg, T., Zakus, D. H., Skinner, J. L., & Campbell, A. (2012). Defining and measuring dimensionality and targets of the commitment of sport volunteers. Journal of Sport Management, 26, 192-205.
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., 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.
Höck, C., Ringle, C. M., & Sarstedt, M. (2010). Management of multi-purpose stadiums: Importance and performance measurement of service interfaces. International Journal of Services Technology and Management, 14, 188-207.
Koo, J., & Lee, Y. (2018). Sponsor-event congruence effects: The moderating role of sport involvement and mediating role of sponsor attitudes. Sport Management Review, in print.
Nitzl, C., Roldán, J. L., & Cepeda Carrión, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 116, 1849–1864.
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.
