MAKE A SUBMISSION
The rapid development of Structural Equation Modeling (SEM) techniques has significantly impacted social science research, offering innovative ways to examine complex relationships between variables (Manosuthi et al., 2021). As a widely used statistical analysis technique, SEM encompasses two main approaches: factor-based and composite-based. Factor-based SEM uses common factors to represent underlying conceptual variables, while composite or component-based SEM employs composites to represent conceptual variables (Manosuthi et al., 2021).
Common factors can serve as proxies of conceptual variables when researchers believe that all indicators share a common pattern that the factor can explain. In contrast, composites can be used as stand-ins for conceptual variables when researchers condense all indicators into a single, unidimensional entity. While both methods are crucial to understanding the intricacies of research models, their appropriate application is essential to avoid biased results and ensure the validity of findings. Specifically, if possible, all conceptual variables should be classified as either factors or composites for good practice. For example, in a study by Fakfare et al. (2023), artifacts such as application design (AD) and security (SC) were treated as composites, while patronage intention (PI), which is a consequence of AD and SC, was represented as a factor. Therefore, researchers are encouraged to identify the type of measurement model (factor or composite) and to apply an appropriate statistical analysis (such as IGSCA, PLSc, GSCA, PLS, or CSA).
In recent years, studies have proposed guidelines for the application of SEM such as IGSCA (e.g., Cho et al., 2022; Hwang et al., 2021; and Hwang et al., 2023) and PLSc (e.g., Benitez et al., 2020; and Dijkstra & Henseler, 2015) as unbiased estimators for mixed constructs in research models. However, empirical research addressing these issues remains scarce (Hwang et al., 2023).
To address this gap and promote the standardization of SEM applications in various domains, we invite researchers and scholars to contribute to a special issue focused on the advancements and applications of SEM in general business, education, tourism, sport, recreation, and language studies.
Topics of interest include, but are not limited to:
- A comprehensive understanding of factor-based and composite-based SEM
- The importance of appropriately applying SEM techniques
- The application of IGSCA and PLSc in research models with mixed constructs
- Methodological advancements in SEM
- The role of SEM in business and management research
- The application of SEM in educational research and assessment
- The use of SEM in language studies, including language acquisition and teaching
Additionally, researchers in the field of education are invited to submit papers that explore the integration of technology in education and the application of Item Response Theory (IRT) for assessment and evaluation purposes. We look forward to receiving your contributions to the ABAC Journal.
Paper Submission Deadline – August 31, 2023
1st Screening Notice – September 15, 2023
Paper Acceptance Notice – October 15, 2023
Name your file that starts with SP (that stands for Special Issue)
Example, SP-Recreation GenZ.docx
Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168. https://doi.org/https://doi.org/10.1016/j.im.2019.05.003
Cho, G., Schlaegel, C., Hwang, H., Choi, Y., Sarstedt, M., & Ringle, C. M. (2022). Integrated generalized structured component analysis: On the use of model fit criteria in international management research. Management International Review, 62(4), 569-609. https://doi.org/https://doi.org/10.1007/s11575-022-00479-w
Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS quarterly. MIS quarterly, 39(2), 297-316. https://www.jstor.org/stable/26628355
Fakfare, P., Promsivapallop, P., & Manosuthi, N. (2023). Applying integrated generalized structured component analysis to explore tourists' benefit consideration and choice confidence toward travel appscape. Technological Forecasting and Social Change, 188, 122321. https://doi.org/https://doi.org/10.1016/j.techfore.2023.122321
Hwang, H., Cho, G., Jung, K., Falk, C. F., Flake, J. K., Jin, M. J., & Lee, S. H. (2021). An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis Psychological Methods, 26(3), 273-294. https://doi.org/https://doi.org/10.1037/met0000336
Hwang, H., Sarstedt, M., Cho, G., Choo, H., & Ringle, C. M. (2023). A primer on integrated generalized structured component analysis. European Business Review, ahead-of-print (ahead-of-print). https://doi.org/https://doi.org/10.1108/EBR-11-2022-0224
Manosuthi, N., Lee, J. S., & Han, H. (2021). An innovative application of composite-based structural equation modeling in hospitality research with empirical example. Cornell Hospitality Quarterly, 62(1), 139-156. https://doi.org/https://doi.org/10.1177/1938965520951751
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