Influence of Individual-technology-task-environment Fit on University Student Online Learning Performance: The Mediating Role of Behavioral, Emotional, and Cognitive Engagement

Kaifeng Liu; Jingtao Yao; Da Tao; Tianrui Yang

doi:10.1007/s10639-023-11833-2

Influence of Individual-technology-task-environment Fit on University Student Online Learning Performance: The Mediating Role of Behavioral, Emotional, and Cognitive Engagement

Educ Inf Technol (Dordr). 2023 May 4:1-20. doi: 10.1007/s10639-023-11833-2. Online ahead of print.

Authors

Kaifeng Liu^{1

2

3}, Jingtao Yao^{2

3}, Da Tao⁴, Tianrui Yang^{2

3}

Affiliations

¹ Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
² Institute of Applied Psychology, Tianjin University, Tianjin, China.
³ School of Education, Tianjin University, Tianjin, China.
⁴ Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China.

Abstract

The present study aimed to examine whether and to what extent university student online learning performance was influenced by individual-technology fit (ITF), task-technology fit (TTF), environment-technology fit (ETF), and whether the influence was mediated by their behavioral, emotional, and cognitive engagement. A theoretical research model was developed by integrating the extended TTF theory and student engagement framework. The validity of the model was assessed using a partial least squares structural equation modeling approach based on data collected from 810 university students. Student learning performance was influenced by TTF (β = 0.25, p < 0.001), behavioral engagement (β = 0.25, p < 0.001), and emotional engagement (β = 0.27, p < 0.001). Behavioral engagement was affected by TTF (β = 0.31, p < 0.001) and ITF (β = 0.41, p < 0.001). TTF, ITF, and ETF were observed as significant antecedents of emotional engagement (β = 0.49, p < 0.001; β = 0.19, p < 0.001; β = 0.12, p = 0.001, respectively) and cognitive engagement (β = 0.28, p < 0.001; β = 0.34, p < 0.001; β = 0.16, p < 0.001, respectively). Behavioral and emotional engagement served as mediators between fit variables and learning performance. We suggest the need for an extension to the TTF theory by introducing ITF and ETF dimensions and demonstrate the important role of these fit variables in facilitating student engagement and learning performance. Online education practitioners should carefully consider the fit between the individual, task, environment, and technology to facilitate student learning outcomes.

Keywords: Environment-technology fit; Individual-technology fit; Learning performance; Online learning; Student engagement; Task-technology fit.

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.