Examining the Effect of the Task-Technology Fit of Game Mechanisms on Learning Outcomes in Online Gamification Platforms

Abstract

The designs of gamification platforms are diverse and constantly evolving. Excessive use of various game mechanisms in learning platforms can distract from the learning process. However, the fit of game mechanisms is still uncertain. Thus, this study investigates the effect of achieving fit when implementing game mechanisms on learning outcomes by applying the well-known task-technology fit theory (TTF). TTF is frequently employed to improve fit between tasks to be completed and the technology applied. The findings indicate that achieving gamification fit can reduce the cognitive load of students and result in enhanced learning performance in terms of learning outcomes. Data collected from 266 participants were analyzed using the technique of the partial least squares to validate the developed research model. The findings of this study can aid educators and educational technology designers in identifying the design mechanisms and characteristics that can be used to ensure design fit on gamification platforms.

From theory to practice: An integrated TTF-UTAUT study on electric vehicle adoption behavior

“…Predominantly, TTF was designed with organizational context in mind, based on technology and employee-handled work activities [ 39 ]. However, in the recent years, TTF has expanded beyond the workplace to encompass a wider range of context and technologies like satisfaction and continuance intention of EV drivers (Cruz-Jesus et al, 2023) [ 41 ], online gamification [ 42 ], e-service quality [ 43 ], m-health [ 44 ], and blockchain food delivery [ 45 ].…”