A perspective on using partial least squares structural equation modelling in data articles

Ringle, C. M., Sarstedt, M., Sinkovics, N. and Sinkovics, R. R. (2023) A perspective on using partial least squares structural equation modelling in data articles. Data In Brief, 48, 109074. (doi: 10.1016/j.dib.2023.109074)

Abstract

This perspective article on using partial least squares structural equation modelling (PLS-SEM) is intended as a guide for authors who wish to publish datasets that can be analysed with this method as stand-alone data articles. Stand-alone data articles are different from supporting data articles in that they are not linked to a full research article published in another journal. Nevertheless, authors of stand-alone data articles will be required to clearly demonstrate and justify the usefulness of their dataset. This perspective article offers actionable recommendations regarding the conceptualisation phase, the types of data suitable for PLS-SEM and quality criteria to report, which are generally applicable to studies using PLS-SEM. We also present adjusted versions of the HTMT metric for discriminant validity testing that broaden its applicability. Further, we highlight the benefit of linking data articles to already published research papers that employ the PLS-SEM method.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sinkovics, Professor Rudolf and Sinkovics, Dr Noemi
• Authors: Ringle, C. M., Sarstedt, M., Sinkovics, N., and Sinkovics, R. R.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Data In Brief
• Publisher: Elsevier
• ISSN: 2352-3409
• ISSN (Online): 2352-3409
• Published Online: 21 March 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Data in Brief 48: 109074
• Publisher Policy: Reproduced under a Creative Commons License