Task-Technology Fit and Individual Performance
Abstract
A key concern in Information Systems (IS) research has been to better understand the linkage between information systems and individual performance. The research reported in this study has two primary objectives: (1) to propose a comprehensive theoretical model that incorporates valuable insights from two complementary streams of research, and (2) to empirically test the core of the model. At the heart of the new model is the assertion that for an information technology to have a positive impact on individual performance, the technology: (1) must be utilized and (2) must be a good fit with the tasks it supports. This new model is moderately supported by an analysis of data from over 600 individuals in two companies. This research highlights the importance of the fit between technologies and users’ tasks in achieving individual performance impacts from information technology. It also suggests that task-technology fit, when decomposed into its more detailed components, could be the basis for a strong diagnostic tool to evaluate whether information systems and services in a given organization are meeting user needs.
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Task-technology fit (TTF) theory emphasizes alignment between technology capabilities and specific user tasks as the primary driver of utilization and performance benefits.
“Task-technology fit (TTF) theory emphasizes alignment between technology capabilities and specific user tasks as the primary driver of utilization and performance benefits [8, 9]”
Task-Technology-Fit (TTF) theorises that a fit among the task, technology, and users positively influences utilisation and performance.
“Task-Technology-Fit (TTF) theorises that a fit among the task, technology, and users positively influences utilisation and performance”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Goodhue & Thompson, 1995
For employees who use e-HRM rarely, repetitive re-discovery of ways to perform simple tasks complicated by e-HRM technology may result.
“This may result in having to re-discover, repetitively, the ways to perform seemingly simple tasks that have been complicated by the e-HRM technology (Goodhue & Thompson, 1995).”
From Conceptualising the future of HRM and technology research (2016) · cites this paper as Goodhue & Thompson, 1995
The task-technology fit theory suggests that the adoption of technology is influenced by individual and organizational factors.
“The task-technology fit theory also suggests that the adoption of technology is influenced by other factors, such as individual and organizational factors (Goodhue and Thompson, 1995; Lin, 2014; Alyoussef, 2023).”
From Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector (2023) · cites this paper as Goodhue and Thompson, 1995
Earlier studies have generally demonstrated the impact of task characteristics and technology characteristics on task-technology fit in a range of scenarios.
“Earlier studies have generally demonstrated the impact of task characteristics and technology characteristics on TTF in a range of scenarios [39,40,88].”
From Acceptance of e-learning in higher education: the role of task-technology fit with the information systems success model (2023) · cites this paper as [39]
The TTF concept contends that performance and acceptance are influenced by how well a task and the technology being used to complete it fit together.
“The task-technology fit (TTF) concept contends that performance and acceptance are influenced by how well a task and the technology being used to complete it “fit” together [39].”
From Acceptance of e-learning in higher education: the role of task-technology fit with the information systems success model (2023) · cites this paper as [39]
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Zhao, Y., & Bação, F. (2020) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2020.102683 · Cited by 514 · see citing works
Baird, A., & Maruping, L. M. (2021) · MIS Quarterly · doi:10.25300/misq/2021/15882 · Cited by 512 · see citing works
The Effects of Virtual Reality on Consumer Learning: An Empirical Investigation1
Suh, K.-S., & Lee, Y. E. (2005) · MIS Quarterly · doi:10.2307/25148705 · Cited by 509 · see citing works
The impact of business analytics on supply chain performance
Trkman, P., McCormack, K. P., Paulo, M., Oliveira, V., & Ladeira, M. B. (2009) · Cited by 508 · see citing works
Extending the Technology Acceptance Model to assess automation
Ghazizadeh, M., Lee, J. D., & Boyle, L. N. (2011) · Cognition Technology & Work · doi:10.1007/s10111-011-0194-3 · Cited by 498 · see citing works
From Use to Effective Use: A Representation Theory Perspective
Burton‐Jones, A., & Grangé, C. (2012) · Information Systems Research · doi:10.1287/isre.1120.0444 · Cited by 498 · see citing works
Understanding the effect of e-learning on individual performance: The role of digital literacy
Mohammadyari, S., & Singh, H. (2015) · Computers & Education · doi:10.1016/j.compedu.2014.10.025 · Cited by 496 · see citing works
Toward a Deeper Understanding of System Usage in Organizations: A Multilevel Perspective1
Burton‐Jones, A., & Gallivan, M. J. (2007) · MIS Quarterly · doi:10.2307/25148815 · Cited by 496 · see citing works
Park, Y., Sawy, O. A. E., & Fiss, P. C. (2017) · Journal of the Association for Information Systems · doi:10.17705/1jais.00001 · Cited by 488 · see citing works
Lin, W.-S., & Wang, C. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.07.008 · Cited by 474 · see citing works
Dimensions of Information Systems Success
Seddon, P. B., Staples, D. S., Patnayakuni, R., & Bowtell, M. (1999) · Communications of the Association for Information Systems · doi:10.17705/1cais.00220 · Cited by 473 · see citing works
Albayati, H., Kim, S. K., & Rho, J. J. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101320 · Cited by 464 · see citing works
Ammenwerth, E., Iller, C., & Mahler, C. (2006) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-6-3 · Cited by 459 · see citing works
Educational Technology Adoption: A systematic review
Granić, A. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10951-7 · Cited by 455 · see citing works
Xi, N., Chen, J., Gama, F., Riar, M., & Hamari, J. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10244-x · Cited by 454 · see citing works
Product fit uncertainty in online markets: nature, effects, and antecedents
Hong, Y. (., & Pavlou, P. A. (2014) · Information Systems Research · doi:10.1287/isre.2014.0520 · Cited by 453 · see citing works
Mobile Application Usability: Conceptualization and Instrument Development1
Hoehle, H., & Venkatesh, V. (2015) · MIS Quarterly · doi:10.25300/misq/2015/39.2.08 · Cited by 445 · see citing works
A Meta-Analysis of Research on Information Technology Implementation in Small Business
Premkumar, G. (2003) · Journal of Organizational Computing and Electronic Commerce · doi:10.1207/s15327744joce1302_2 · Cited by 444 · see citing works
A conceptual model for the process of IT innovation adoption in organizations
Hameed, M. A., Counsell, S., & Swift, S. (2012) · Journal of Engineering and Technology Management · doi:10.1016/j.jengtecman.2012.03.007 · Cited by 442 · see citing works
Conceptualising the future of HRM and technology research
Bondarouk, T., & Brewster, C. (2016) · The International Journal of Human Resource Management · doi:10.1080/09585192.2016.1232296 · Cited by 438 · see citing works
External Knowledge and Information Technology: Implications for Process Innovation Performance1
Trantopoulos, K., von Krogh, G., Wallin, M. W., & Wörter, M. (2017) · MIS Quarterly · doi:10.25300/misq/2017/41.1.15 · Cited by 438 · see citing works
Virtual Work: Bridging Research Clusters
Raghuram, S., Hill, N. S., Gibbs, J. L., & Maruping, L. M. (2019) · Academy of Management Annals · doi:10.5465/annals.2017.0020 · Cited by 426 · see citing works
Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.12.003 · Cited by 425 · see citing works
strong, & Volkoff, O. (2010) · MIS Quarterly · doi:10.2307/25750703 · Cited by 423 · see citing works
Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations
Pillai, R., & Sivathanu, B. (2020) · Benchmarking: An International Journal · doi:10.1108/bij-04-2020-0186 · Cited by 422 · see citing works
Managing team interpersonal processes through technology: A task-technology fit perspective.
Maruping, L. M., & Agarwal, R. (2004) · Journal of Applied Psychology · doi:10.1037/0021-9010.89.6.975 · Cited by 417 · see citing works
AI-Based Digital Assistants: Opportunities, Threats, and Research Perspectives
Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., Hinz, O., Morana, S., & Söllner, M. (2019) · Business & Information Systems Engineering · doi:10.1007/s12599-019-00600-8 · Cited by 432 · see citing works
Defining, Assessing, and Promoting E‐Learning Success: An Information Systems Perspective*
Holsapple, C. W., & Lee, A. (2006) · Decision Sciences Journal of Innovative Education · doi:10.1111/j.1540-4609.2006.00102.x · Cited by 402 · see citing works
Goodman, P. S., & Darr, E. D. (1998) · MIS Quarterly · doi:10.2307/249550 · Cited by 395 · see citing works
An empirical study on trust in mobile banking: A developing country perspective
Malaquias, R. F., & Hwang, Y. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.08.039 · Cited by 394 · see citing works
Brown, B. C., Gude, W. T., Blakeman, T., van der Veer, S. N., Ivers, N. M., Francis, J., Lorencatto, F., Presseau, J., Peek, N. B., & Daker‐White, G. (2019) · Implementation Science · doi:10.1186/s13012-019-0883-5 · Cited by 394 · see citing works
Xue, L., Rashid, A. M., & Ouyang, S. (2024) · SAGE Open · doi:10.1177/21582440241229570 · Cited by 394 · see citing works
Factors Influencing SMEs Adoption of Social Media Marketing
Dahnil, M. I., Marzuki, K. M., Langgat, J. A., & Fabeil, N. F. (2014) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2014.07.025 · Cited by 389 · see citing works
Research in Information Systems: An Empirical Study of Diversity in the Discipline and Its Journals
Vessey, I., Ramesh, V., & Glass, R. L. (2002) · Journal of Management Information Systems · doi:10.1080/07421222.2002.11045721 · Cited by 386 · see citing works
Adoption of digital payment systems in the era of demonetization in India
Sivathanu, B. (2018) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-07-2017-0033 · Cited by 386 · see citing works
- Authors
- Goodhue, D. L., & Thompson, R. L.
- Affiliations
- University of Minnesota (US)
- University of Vermont (US)
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 1995-06-01
- Volume
- 19
- Issue
- 2
- Pages
- 213-236
- ISSN
- 0276-7783 (ISSN-L)
- 2162-9730
- OpenAlex
- W1587451402
- Cited by
- 6,006
- References
- 54
- Citations by year
- 2026: 542 · 2025: 429 · 2024: 339 · 2023: 326 · 2022: 315 · 2021: 330 · 2020: 269 · 2019: 286 · 2018: 244 · 2017: 224 · 2016: 207 · 2015: 285 · 2014: 263 · 2013: 229 · 2012: 248
- FWCI
- 63.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- MIS Quarterly (publishedVersion)
- Journal of the Association for Information Systems (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Keywords
- task-technology fit, individual performance, information technology use, theoretical model, technology adoption
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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