Evaluating the effects of task–individual–technology fit in multi-DSS models context: A two-phase view

We investigate the effects of individual difference with the framework of task–individual–technology fit under a multi-DSS models context using a two-phase view. Our research question is: in addition to task–technology fit, does individual–technology fit or individual–task fit matter in users' attitude and performance in the multi-tasks and multi-DSS models context? We first divide the concept of task–individual–technology fit into three dimensions – task–technology fit (TTF), individual–technology fit (ITeF), and task–individual fit (TaIF) – so that we can explore mechanisms and effects of interaction among these factors (i.e., task, individual difference, and technology). We then propose a two-phase view of task–individual–technology fit (i.e., pre-paradigm phase and paradigm phase) based on Kuhn's concept of revolutionary science. We conducted a controlled laboratory experiment with multiple DSS models and decision-making tasks to test our hypotheses. Results confirmed our arguments that in the paradigm phase, the effects of individual differences on user attitudes toward DSS models can be ignored and that in the pre-paradigm phases individual differences play an important role. In addition, we found that effects of individual difference can be a two-blade sword: ITeF can enhance but TaIF can diminish users' attitude to DSS model (i.e., technology). Our results also suggested that different dimensions of fit may affect performance directly or indirectly.

Research Highlights
► We conducted an experiment to examine effects of task-individual-technology fit.
► Increasing task-technology fit and individual-technology fit will improve attitude.
► Increasing task-individual fit will decrease attitude.
► Increasing task-technology fit will improve decision agreement and decision time.
► Increasing individual-technology fit will reduce decision time.