Understanding Task-technology Fit Evolvement: A Conceptual Framework

The current study is about task-technology fit evolution and it suggests that feedback inquiry, individuals’ proactive search for evaluative information relating to their strategy, influences the sustained performance of individuals. The study will undertake on both qualitative and quantitative methods to longitudinally examine the linkage between task-technology fit and individual performance. I theorize that computer self-efficacy interacts with technology characteristics to enhance individuals’ chances to choose attractive execution sequences. Execution sequences are defined as different approaches used for addressing an underlying task (Goodhue, 2006). Once a sequence has been applied and performance effects have been experienced, there will be different kinds of feedback opportunities. Individuals that proactively search for feedback are likely to choose more attractive sequences in the future. The feedback inquiry process is iterative as the loop is theoretically indefinite. Finally, I propose that task complexity is expected to interfere with individuals’ choices of execution sequences, hindering performance.

[1]  Dale Goodhue,et al.  Task-Technology Fit and Individual Performance , 1995, MIS Q..

[2]  Peter A. Bamberger,et al.  Can Surgical Teams Ever Learn? The Role of Coordination, Complexity, and Transitivity in Action Team Learning , 2013 .

[3]  Michael W. Kramer,et al.  Feedback Seeking Following Career Transitions , 1999 .

[4]  Barbara D. Klein,et al.  User evaluations of IS as surrogates for objective performance , 2000, Inf. Manag..

[5]  Adam M. Grant,et al.  The dynamics of proactivity at work , 2008 .

[6]  S. Parker,et al.  Taking Stock: Integrating and Differentiating Multiple Proactive Behaviors , 2010 .

[7]  Sandra A. Vannoy,et al.  Intercultural Communication Competence via IP Services Applications: A Modified Task-technology Fit Perspective , 2012 .

[8]  Varun Grover,et al.  Technostress: Technological Antecedents and Implications , 2011, MIS Q..

[9]  Deborah Compeau,et al.  Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..

[10]  Ephraim R. McLean,et al.  Information Systems Success: The Quest for the Independent Variables , 2013, J. Manag. Inf. Syst..

[11]  Arun Rai,et al.  Knowledge Sharing Ambidexterity in Long-Term Interorganizational Relationships , 2008, Manag. Sci..

[12]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[13]  Diane M. Strong,et al.  Extending the technology acceptance model with task-technology fit constructs , 1999, Inf. Manag..

[14]  Alan R. Dennis,et al.  Understanding Fit and Appropriation Effects in Group Support Systems via Meta-Analysis , 2001, MIS Q..

[15]  S. Ashford,et al.  Self-Regulation of Creativity at Work: The Role of Feedback-Seeking Behavior in Creative Performance , 2011 .

[16]  Ilze Zigurs,et al.  A Theory of Task/Technology Fit and Group Support Systems Effectiveness , 1998, MIS Q..

[17]  Alan R. Dennis,et al.  Does Fit Matter? The Impact of Task-Technology Fit and Appropriation on Team Performance in Repeated Tasks , 2009, Inf. Syst. Res..

[18]  Hsi-Chi Hsiao,et al.  Perceived Social Supports, Computer Self-Efficacy, and Computer Use among High School Students , 2012 .

[19]  R. Wood.  Task complexity: Definition of the construct , 1986 .

[20]  Kieran Mathieson,et al.  Beyond the interface: Ease of use and task/technology fit , 1998, Inf. Manag..

[21]  Terence R. Mitchell,et al.  A cost-benefit mechanism for selecting problem-solving strategies: Some extensions and empirical tests , 1982 .

[22]  Michael J. Davern.  When Good Fit is Bad: The Dynamics of Perceived Fit , 1996, ICIS.