Predicting the Intention to Use a Web‐Based Learning System: Perceived Content Quality, Anxiety, Perceived System Quality, Image, and the Technology Acceptance Model

Abstract The aim of this study is to determine the factors affecting blue‐collar workers’ intention to use a web‐based learning system in the preimplementation phase in the automotive industry. For that purpose an extended technology acceptance model (TAM) is proposed, which included factors such as image, perceived content quality, and perceived system quality as additions to the basic model. Data collected from 546 blue‐collar workers were used to test the proposed research model by using Linear Structural Relations software LISREL, Version 8.54. The findings of the study indicate that perceived usefulness is the strongest predictor of behavioral intention to use a web‐based learning system. In addition, a high proportion of perceived usefulness is explained by perceived content quality, and perceived ease of use is explained by perceived system quality and anxiety.