Dataset: UTAUT, Combining PLS-SEM and selected machine learning algorithms

This dataset accompanies the manuscript by Richter/Tudoran on "Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms" (at the time of upload, in-press at: Journal of Business Research). In this manuscript, to illustrate the combined use of PLS-SEM and selected ML algorithms, we used PLS-SEM on a Unified Theory of Acceptance and Use of Technology (UTAUT) model to create latent variable scores. The analysis made use of raw indicator data provided by Al-Gahtani, S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information & Management, 44, 681-691. The dataset uploaded contains the latent variable scores for two endogenous constructs, the behavioral intention (BI) and the use behavior (USE), as well as four exogenous constructs, performance expectancy (PE), effort expectancy (EE), social influence (SN), and facilitating conditions (FC).