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A Unified Model of IT Use Choices: Contributions from TAM, TTF, and CSE

A Unified Model of IT Use Choices: Contributions from TAM, TTF, and CSE. Diane M. Strong* Worcester Polytechnic Institute Invited Presentation First Annual Workshop on HCI Research in MIS Barcelona, Spain 2002 * This is joint work with Mark T. Dishaw, University of Wisconsin Oshkosh.

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A Unified Model of IT Use Choices: Contributions from TAM, TTF, and CSE

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  1. A Unified Model of IT Use Choices: Contributions from TAM, TTF, and CSE Diane M. Strong* Worcester Polytechnic Institute Invited Presentation First Annual Workshop on HCI Research in MIS Barcelona, Spain 2002 *This is joint work with Mark T. Dishaw, University of Wisconsin Oshkosh Diane M. Strong, WPI

  2. General Research Objective • Understand the software utilization choices of end users, by using and extending existing models • Task-technology Fit (TTF) models • Technology Acceptance Model (TAM) • Individual Abilities Constructs, e.g., Experience, Computer Self-efficacy • Conduct a series of studies testing the models and combinations of them Diane M. Strong, WPI

  3. Task-Technology Fit Models Diane M. Strong, WPI

  4. 1. TTF Model Study Operationalize the TTF model in the software maintenance context • Task Model - Vessey's debugging model (planning, knowledge building, diagnosis, modification activities) plus coordination • Technology Model - Henderson & Cooprider Functional Case Technology Model (Production and Coordination functionality) Diane M. Strong, WPI

  5. Dimensions of Fit • Fit along two dimensions • Production Fit: how well the tool’s production functions support software maintenance activities • Coordination Fit: how well the tool’s coordination functions support maintenance coordination activities • Compute Fit using an interaction approach (Venkatramen, 1989) (Dishaw & Strong, 1998) Diane M. Strong, WPI

  6. 2. Add Experience to TTF Operationalize Individual Abilities as: • experience with the task • experience with the technology Tool experience and its interaction with tool characteristics is significant Task experience not significant Adjusted R2 of 0.63 (Dishaw & Strong, Forthcoming) Diane M. Strong, WPI

  7. 3. Combined TAM and TTF • TAM: beliefs about the technology, i.e., perceived usefulness and perceived ease of use • TTF: matching of the technology to the needs of the task to deliver benefits • TAM + TTF: addresses both technology beliefs and rationally computed fit to task • Tool experience as an individual ability • Path model, rather than regression • Fit as latent variable, rather than computed as interaction Diane M. Strong, WPI

  8. TTF-TAM Combined Model Diane M. Strong, WPI

  9. Combined TAM / TTF Results Better results than either TAM or TTF alone Utilization variance explained: • 36% with TAM • 41% with TTF • 51% with TAM/TTF (Dishaw and Strong, 1999) Diane M. Strong, WPI

  10. 4. Add Computer Self-efficacy(Work-in-progress) • CSE may be a better predictor of individual ability for new tools than is tool experience • Generalize TTF assessment beyond software maintenance tasks and tools • Develop an instrument for assessing problem-solving tasks, and the support of such tasks with software • Test previous TTF and TAM/TTF models with a new dataset Diane M. Strong, WPI

  11. Computer Self-Efficacy • Derived from the Social Cognition literature, and is based on Bandura’s work on self-efficacy • A specialized definition of Self-efficacy, i.e., a person’s belief in their ability to accomplish a specific task • A judgment of one’s ability to use a computer Diane M. Strong, WPI

  12. Adding CSE to TTF/TAM Diane M. Strong, WPI

  13. Model Operationalization • Software maintenance TTF is generalized by changing the questionnaire items since • Task model is well grounded in the problem solving and cognitive science literature • Technology model is grounded in the literature on information technology support functionality • Add Compeau & Higgins (1995) 10-item, single factor measure of CSE Diane M. Strong, WPI

  14. Item and Scale Testing • Item Testing using a panel of faculty, advanced students, and professionals • Pilot Study using a small number students and professionals in the university Diane M. Strong, WPI

  15. Data Collection • Use revised instrument • Subjects are students in several classes after the completion of an ordinary assignment • Currently, have 136 data points from: • Operations Management simulation class doing modeling • Programming class doing 3 GL program maintenance • Programming class doing OO program maintenance • Business analysis class doing statistical modeling Diane M. Strong, WPI

  16. Data Analysis Using Amos 4.0, test the models • TTF • TTF plus CSE • Combined TAM/TTF • Combined TAM/TTF plus CSE Have results for Models 1 and 2 Diane M. Strong, WPI

  17. General TTF Model • Chi Sq. 26.77, d.f. 17, p=0.061 • AGFI = 0.89, GFI = 0.95 Diane M. Strong, WPI

  18. General TTF Model with CSE • Chi Sq. 27.24, d.f. 22, p=0.202 • AGFI = 0.91, GFI = 0.96 Diane M. Strong, WPI

  19. Lessons for a Unified Model:Importance of Task • Traditional HCI focuses on Usability, with little or no Task emphasis • TAM adds Usefulness, which implicitly includes Task • TTF has explicit Task focus, which adds to the explanatory power Diane M. Strong, WPI

  20. Lessons for a Unified Model:The Fit Construct • Beyond production and coordination Fit to additional dimensions of Fit • Beyond a point estimate of Fit to a process of Fitting over time (as in implementation) • Beyond individual level models (TTF, TAM) to organizational level models, e.g., for Enterprise systems Diane M. Strong, WPI

  21. Lessons for a Unified Model:Experience and CSE • Measure Experience and Self-efficacy for both Task and Technology • Self-efficacy theory: As Experience increases, Experience dominates abilities as measured by Self-efficacy • Need to better understand relationship between Experience and Self-efficacy Diane M. Strong, WPI

  22. References to the Studies Study 1: Dishaw, M. T. and D. M. Strong, "Supporting Software Maintenance with Software Engineering Tools: A Computed Task-Technology Fit Analysis", Journal of Systems and Software, Vol. 44, No. 2, December 1998, pp. 107-120. Study 2: Dishaw, M. T. and D. M. Strong, "The Effect of Task and Tool Experience on Maintenance CASE Tool Usage", Information Resources Management Journal, Forthcoming. Study 3: Dishaw, M. T. and D. M. Strong, "Extending the Technology Acceptance Model with Task-Technology Fit Constructs", Information & Management, Vol. 36, No. 1, July 1999, pp. 9-21. Study 4 (in-progress): Dishaw, M. T., D. M. Strong, and D. B. Bandy, “Extending the Task-Technology Fit Model with Self-Efficacy Constructs”, Proceedings of the Americas Conference on Information Systems, August 9-11, 2002, Dallas, TX, pp. 1021-1027. Diane M. Strong, WPI

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