Applying an extended task-technology fit for establishing determinants of mobile learning: An instant messaging initiative

The high proliferation of mobile instant messaging (MIM) among university students creates opportunities for a new wave of mobile learning. However, correlational methods for assessing factors that influence student performance impacts on MIM platforms for learning are blurry. The task-technology fit theory has been widely used in the past in predicting performance impacts of users after using new technology. Despite the momentum gained by this framework in the information systems community, it lacks focus on user characteristics. The purpose of this study is to develop an extended model for task-technology fit through an integration of individual antecedent characteristics. Data were collected from 223 participants using a survey questionnaire. The analysis was performed using the partial least squares approach to structural equation modelling. The findings of the study confirmed the original task-technology fit hypotheses considered in this study. Study findings associated with individual antecedent characteristics indicate that perceived ease of use and perceived usefulness of the academic use of the MIM positively influence task-technology fit, while social influence was found to have no significant bearing on task-technology fit.