Title: Construct modelling, statistical analysis and empirical validation using PLS-SEM: a step-by-step guide of the analysis procedure

Authors: Sushant Kr. Vishnoi; Smriti Mathur; Teena Bagga; Abhishek Singhal; Pankaj Rawal; Sanjeev Sharma; Rajesh Yadav

Addresses: Department of Marketing, Institute of Management Studies, Ghaziabad, UP – 201009, India ' Christ University, Delhi NCR – 201003, India ' Faculty of Management, Department of Management Studies, Jamia Millia Islamia, New Delhi – 110025, India ' Institute of Management Studies, Ghaziabad, UP – 201009, India ' Institute of Management Studies, Ghaziabad, UP – 201009, India ' Christ University, Delhi NCR – 201003, India ' Amity School of Business, AUUP – 201313, India

Abstract: Partial least square-structured equation modelling (PLS-SEM) is a widely accepted tool for statistical analysis in social science research. The complex architecture of PLS-SEM sometimes makes it difficult for users to understand the taxonomy, nomenclature, or process of statistical analysis. This research study proposes summarising the procedure adopted in PLS-SEM for data analysis. Measurement evaluation and structural model was the subject of discussion, with a focus on the statistical techniques employed. Furthermore, the threshold values corresponding to statistical tools under measurement and structural model were also provided. The inference of these threshold values were also discussed with an eye on improving researchers' awareness and understanding. The discussion about the methodology adopted in statistical analysis with the help of PLS-SEM is also reported. Finally, the limitations of the research work were presented, and further study directions were streamlined.

Keywords: PLS-SEM; partial least square-structured equation modelling; smart-PLS; structural model; measurement model.

DOI: 10.1504/IJDATS.2024.137877

International Journal of Data Analysis Techniques and Strategies, 2024 Vol.16 No.2, pp.162 - 180

Received: 10 May 2023
Accepted: 16 Jan 2024
Published online: 05 Apr 2024 *

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