The technology acceptance scale: Its Bayesian psychometrics assessed in a factor analysis via Markov chain Monte Carlo models

Abstract

The technology acceptance scale (TAS) by van der Laan, Heino, and De Waard (1997) measures the psychological construct of the same term as a sum of attitudes of an operator toward a specific complex sociotechnical system. The TAS has been claimed to comprise two subscales, usefulness and satisfaction. However, recent empirical work has found evidence for only one underlying factor. To provide further insight into the factor structure of the TAS, this study adopts a Bayesian exploratory factor analysis (BEFA) to analyse the data of a flight simulation study regarding single pilot operations. A series of Markov chain Monte Carlo (MCMC) models is used to assess the latent factor structure of the TAS for the two different crewing conditions and their corresponding workstation and cockpit setups of the copilot. A reliable step-by-step data analysis of the MCMC models provides evidence for a one-factor solution of the scale. The divergence to the previous studies which claim two factors can be due to the different applications as well as due to different statistical paradigms and methodological issues in exploratory factor analysis.

A Bayesian Inference Model for Sustainable Crowd Source Logistics for Small and Medium Scale Enterprises (SME) in Africa

“…Based on existing literature (Schmid, 2022;Paucar et al, 2021;Liao et al, 2018) this study adopts the Bayesian inference using Markov Chain Monte Carlo (MCMC) approach to analysis the data. Monte Carlo method provides a numerical approach for solving complicated functions.…”