Emotional outcomes of e-learning adoption during compulsory online education

Educ Inf Technol (Dordr). 2022;27(6):7827-7849. doi: 10.1007/s10639-022-10930-y. Epub 2022 Feb 24.

Abstract

Information on the emotional outcomes of e-learning system use and emotional aspects of user experience in higher education is quite limited. Accordingly, the aim of the study is to identify the factors that influence university students' intention to continue using e-learning systems and to examine the emotional outcomes of the continuance intention. The core constructs of the Technology Acceptance Model formed the basis of the proposed model, and the model was extended with a framework of emotions (challenge, achievement, deterrence, loss) and external variables. Data were collected online from 19,530 university students of a state university. For the analysis, Partial Least Squares-Structural Equation Modeling was employed. The proposed model explained 73.5% of continuance intention, 50.3% of achievement, and 52.2% of challenge emotions. In addition, 23 of the 25 tested hypotheses were supported. The findings indicate that perceived usefulness is a decisive factor in creating user experiences that generate emotions such as enjoyment, playfulness and satisfaction. In addition, the results showed that personal innovativeness strongly influenced the core constructs of technology acceptance model and the positive aspects of emotions (achievement and challenge). Accordingly, it can be stated that these findings lead us to the fact that students' value perceptions regarding e-learning systems have a critical role in terms of emotional outcomes. In addition, the findings suggest that both intrinsic-extrinsic motivators, innovativeness characteristics and emotional outcomes should be taken into account in design and development process in order to improve the quality of the user experience. In this direction, implications for research and practice are discussed.

Keywords: Continuance intention; E-learning; Emotions; Higher education; PLS path modeling.