Special Issue on “Partial Least Squares Structural Equation Modeling (PLS-SEM) in Business and Management Research”

Partial least squares structural equation modeling (PLS-SEM), also known as PLS Path Modeling, is a widely used method for multivariate data analysis among business and social science scholars. It is primarily employed to examine models with latent variables, providing a robust means to analyze complex frameworks. Although originally developed by Herman Wold (Wold, 1982) and later extended by Jan-Bernd Lohmöller (Lohmöller, 1989), PLS-SEM has recently been extensively applied across various disciplines, including hospitality and tourism, education, supply chain management, human resource management, information systems, international management, sports management, quality management, industrial management, computer science, engineering, environmental science, medicine, political science, psychology, and sociology. The widespread acceptance of PLS-SEM can be attributed to its user-friendly visual interface, which enables researchers to simultaneously analyze relationships between observed and latent variables in complex models (Ringle et al., 2020; Memon et al., 2021; Cheah et al., 2024). Furthermore, it allows for multiple robustness assessments, such as endogeneity tests, while accounting for measurement error inherent in the evaluation of abstract concepts (Memon et al., 2021; Becker et al., 2023). Consequently, PLS-SEM has become a standard method for assessing multivariate models globally.

Aim and Objectives

This special issue aims to advance the field of Partial Least Squares Structural Equation Modeling (PLS-SEM) by showcasing the latest advancements, methodological innovations, and practical applications in business and management research. Our objectives are:

  • Cutting-Edge Empirical Research: Presenting empirical studies that employ the most recent PLS-SEM techniques to address complex problems in various business and management domains, such as human resources, organizational behavior, strategic management, marketing, and supply chain management.
  • Advancing Methodological Rigor: Encouraging the development and dissemination of new techniques, best practices, and comprehensive guidelines for applying PLS-SEM, ensuring robust and reliable research outcomes.
  • Fostering Interdisciplinary Collaboration: Bridging gaps between different disciplines by demonstrating the versatility and applicability of PLS-SEM across diverse business and management fields, thereby promoting interdisciplinary research and collaboration.

What does this special issue seek to explore?

  • Original Research Articles: Applying PLS-SEM in business and management research (e.g., HR, organizational behavior, strategic management, marketing, supply chain management),
  • Review Papers: Systematic literature reviews on PLS-SEM in business and management research,
  • Methods Papers: Proposing new techniques or enhancing existing techniques for better understanding and use of PLS-SEM in business and management research.

When developing their manuscripts, researchers are encouraged to follow the latest guidelines on the use of PLS-SEM (see Cheah, Amaro & Roldán, 2023; Becker et al., 2023; Richter et al., 2023; Hauff et al., 2024) and ensure methodological rigor (see Memon et al., 2023; Guenther et al., 2023; Sarstedt et al., 2024; Danks et al., 2024; Shela et al., 2024).

Themes and Scope

  1. Applications of PLS-SEM in Business and Management Research: Empirical studies showcasing the use of advanced PLS-SEM techniques in areas such as HR, organizational behavior, strategic management, marketing, supply chain management, and more.
  2. Methodological Articles: Articles providing comprehensive guidelines and best practices for applying PLS-SEM in business and management research.
  3. Systematic Literature Reviews: Systematic literature reviews of existing literature on PLS-SEM and its applications in business and management research.

Key Dates

Submissions Open: September 10, 2024
Submissions Close: December 31, 2024

All submissions will undergo a rigorous peer-review process to ensure the highest standards of scientific quality and relevance. Manuscripts should be prepared according to the journal’s submission guidelines. We look forward to your valuable contributions to this special issue, which will significantly impact the field of PLS-SEM in business and management research. Submission guidelines are available on the journal’s website: https://jasemjournal.com/.

2024 International Conference on Partial Least Squares Structural Equation Modeling (PLS-SEM)

This special issue is specifically proposed for the 2024 International Conference on Partial Least Squares Structural Equation Modeling (PLS-SEM), which will be held from September 3-7, 2024, at Sunway Business School, Malaysia (https://sunwayuniversity.edu.my/PLS2024). During the conference, the editors will be available to discuss potential topics related to this call for papers and to provide guidance on aligning the working papers with the special issue.

Guest Editors

Mumtaz Ali Memon, Sohar University, Sohar, Oman (mumtazutp@gmail.com)
Ramayah Thurasamy, Universiti Sains Malaysia, Penang, Malaysia (ramayah@usm.my)
Hiram Ting, i-CATS University College, Kuching, Malaysia (hiramparousia@gmail.com)

References

Becker, J.-M., Cheah, J.-H., Gholamzade, R., Ringle, C.M., & Sarstedt, M. (2023). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 35(1), 321-346.
Cheah, J. H., Amaro, S., & Roldán, J. L. (2023). Multigroup analysis of more than two groups in PLS-SEM: A review, illustration, and recommendations. Journal of Business Research, 156, 113539.
Cheah, J. H., Magno, F., & Cassia, F. (2024). Reviewing the SmartPLS 4 software: the latest features and enhancements. Journal of Marketing Analytics, 12(1), 97-107.
Danks, N., Ray, S., & Shmueli, G. (2024). The composite overfit analysis framework: Assessing the out-of-sample generalizability of construct-based models using predictive deviance, deviance trees, and unstable paths. Management Science, 70(1), 647–669
Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2023). Improving PLS-SEM use for business marketing research. Industrial Marketing Management, 111, 127-142.
Hauff, S., Richter, N. F., Sarstedt, M., & Ringle, C. M. (2024). Importance and performance in PLS-SEM and NCA: Introducing the combined importance-performance map analysis (cIPMA). Journal of Retailing and Consumer Services, 78, 103723.
Memon, M., Thurasamy, R., Cheah, J., Ting, H., Chuah, F., & Cham, T. (2023). Addressing common method bias, operationalization, sampling, and data collection issues in quantitative research: Review and recommendations. Journal of Applied Structural Equation Modeling, 7(2), 1-14.
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. International Journal of Human Resource Management, 31(12), 1617-1643.
Sarstedt, M., Adler, S. J., Ringle, C. M., Cho, G., Diamantopoulos, A., Hwang, H., & Liengaard, B. D. (2024). Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling. Journal of Product Innovation Management. https://doi.org/10.1111/jpim.12738
Shela, V., Danks, N. P., Ramayah, T., & Ahmad, N. H. (2024). An application of the COA Framework: Building a sound foundation for organizational resilience. Journal of Business Research, 179, 114702.