00
Days
00
Hrs
00
Min
00
Sec
Submit Your Paper

Understanding the Task-Technology Fit (TTF) Model: Genesis and Conceptual Developments

Authors

Imad-dine Bazine

Abdelmalek Essaâdi University, National School of Business and Management, Tangier, Morocco (MA)

My Abdelouhab Salahddine

Abdelmalek Essaâdi University, National School of Business and Management, Tangier, Morocco (MA)

Article Information

DOI: 10.51583/IJLTEMAS.2025.1408000030

Subject Category: Information Systems Management

Volume/Issue: 14/8 | Page No: 241-247

Publication Timeline

Submitted: 2025-08-28

Published: 2025-08-28

Abstract

Abstract: This study traces the evolution of the Task-Technology Fit (TTF) model from its initial formulation by Goodhue and Thompson (1995) to its enrichment through other major theoretical frameworks, including Davis’s (1989) Technology Acceptance Model (TAM), Ajzen’s (1991) Theory of Planned Behaviour (TPB), DeLone and McLean’s (1992) Information Systems Success Model (ISSM), and Venkatesh et al.’s (2003) Unified Theory of Acceptance and Use of Technology (UTAUT). The TTF model highlights the importance of the alignment between tasks and technological functionalities as a key determinant of individual performance and technology adoption. Successive adaptations have incorporated dimensions such as system quality, information quality, user satisfaction, and social norms, thereby enhancing the understanding of the mechanisms underlying effective technology use. Taken together, these combined models offer a more comprehensive approach to evaluating and predicting the adoption, use, and impact of information systems in various organizational contexts. Overall, this body of work underscores that, while task–technology fit is essential, it must be considered within a broader framework that integrates human, social, and organizational factors to ensure the success of information systems.

Keywords

Task-Technology Fit, technology adoption, individual performance

Downloads

References

1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. [Google Scholar] [Crossref]

2. Baroudi, J. J., & Orlikowski, W. J. (1988). A short-form measure of user information satisfaction: A psychometric evaluation and notes on use. Journal of Management Information Systems, 4(4), 44-59. [Google Scholar] [Crossref]

3. Chen, J., Dai, J., Yu, T., & Wang, C. (2025). Factors influencing the adoption of generative AI in supply chain management: An empirical study based on task-technology fit and the theory of planned behaviour. International Journal of Logistics Research and Applications, 1-22. [Google Scholar] [Crossref]

4. D’Ambra, J., & Wilson, C. S. (2004). Explaining perceived performance of the World Wide Web: Uncertainty and the task-technology fit model. Internet Research, 14(4), 294-310. [Google Scholar] [Crossref]

5. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. [Google Scholar] [Crossref]

6. DeLone, W. H., & McLean, E. R. (2002). Information systems success revisited. In Proceedings of the 35th Annual Hawaii International Conference on System Sciences (pp. 2966-2976). [Google Scholar] [Crossref]

7. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. [Google Scholar] [Crossref]

8. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Reading, MA: Addison-Wesley. [Google Scholar] [Crossref]

9. Goodhue, D. L., Littlefield, R., & Straub, D. W. (1997). The measurement of the impacts of the IIC on the end-users: The survey. Journal of the American Society for Information Science, 48(5), 454-465. [Google Scholar] [Crossref]

10. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236. [Google Scholar] [Crossref]

11. Diar, A. L., Sandhyaduhita, P. I., & Budi, N. F. A. (2018). The determinant factors of individual performance from task technology fit and IS success model perspectives: A case of public procurement plan information system (SIRUP). In 2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 69-74). [Google Scholar] [Crossref]

12. Cane, S., & McCarthy, R. (2009). Analyzing the factors that affect information systems use: A task-technology fit meta-analysis. Journal of Computer Information Systems, 50(1), 108-123. [Google Scholar] [Crossref]

13. Venkatraman, N. (1989). The concept of fit in strategy research: Toward verbal and statistical correspondence. Academy of Management Review, 14(3), 423-444. [Google Scholar] [Crossref]

14. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. [Google Scholar] [Crossref]

15. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles

© 2026 IJLTEMAS · RSIS International. All rights reserved. ISSN 2278-2540.