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

  • Authors:

  • Yucong Liu;Younghwa Lee;Andrew N. K. Chen

  • Affiliations:

  • Accounting and Information Systems, School of Business, University of Kansas, Summerfield Hall, 1300 Sunnyside Avenue, Lawrence, KS 66045, United States;Department of Management, College of Business Administration, The University of Northern Iowa, Cedar Falls, IA 50614, United States;Accounting and Information Systems, School of Business, University of Kansas, Lawrence, KS 66045, United States

  • Venue:
  • Decision Support Systems
  • Year:
  • 2011

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Abstract

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 (IT"eF), and task-individual fit (T"aIF) - 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: IT"eF can enhance but T"aIF 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.