Integrating the expanded task-technology fit theory and the technology acceptance model: a multi-wave empirical analysis

University of South Alabama; Howard, Matt C.; Hair, Joseph F.; University of South Alabama

doi:10.17705/1thci.00084

Cited by 27Technology Adoption and User BehaviourInformation Systems Theories and ImplementationUsability and User Interface Design

Integrating the expanded task-technology fit theory and the technology acceptance model: a multi-wave empirical analysis

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University of South Alabama, Howard, Matt C., Hair, Joseph F., University of South Alabama·2023·AIS Transactions on Human-Computer Interaction

DOI 10.17705/1thci.00084

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Abstract

Task-technology fit theory proposes that the match between tasks and technologies, known as task-technology fit, has a positive relation with technology use and performance. Researchers have recently extended task-technology fit theory by conceptualizing task-technology misfit, which describes instances in which technology provides too few (too little) or too many (too much) features to perform a task. We link this newly expanded theory, which we label expanded task- technology fit (E-TTF) theory, with the technology acceptance model (TAM). We conducted a study and found that task- technology fit and too little significantly related to the variables in the TAM and that each ultimately had an indirect effect on use. In contrast, too much did not significantly relate to any variable in the TAM. These results support that E-TTF theory explains meaningful variance in the TAM, which suggests that integrating these theories is important for understanding technology use. Likewise, these results emphasize the importance of the multidimensional conceptualization that the E-TTF theory proposes. Too little (too few features) predicted outcomes beyond task- technology fit and meaningfully improved our model’s predictive abilities. In contrast, too much’s (too many features) relationships lacked significance, which emphasizes the need to distinguish types of task-technology misfit. Therefore, our study provides benefits for research on E-TTF theory, the TAM, and their integration.

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Task-technology fit provides a more comprehensive perspective than TAM by considering both user perceptions and the fit between technology and tasks.
“However, this current study is based on the extension of the TAM model, that is, the task-technology fit (Howard and Hair Jr, 2023), as it provides a more comprehensive perspective by not only considering user perceptions and attitudes toward technology but also the fit between the technology and the tasks performed by the user.”
From Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector (2023) · cites this paper as Howard and Hair Jr, 2023

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Authors: University of South Alabama, Howard, M. C., Hair, J. F., & University of South Alabama
Affiliations: University of South Alabama (US)
ORCID: Howard, Matt C.
Hair, Joseph F.
Venue: AIS Transactions on Human-Computer Interaction
Publisher: Association for Information Systems
Published: 2023-03-31
Volume: 15
Issue: 1
Pages: 83-110
ISSN: 1944-3900 (ISSN-L)
DOI: 10.17705/1thci.00084
OpenAlex: W2572346784
Cited by: 27
References: 146
Citations by year: 2026: 2 · 2025: 1 · 2024: 5 · 2023: 4 · 2022: 5 · 2021: 4 · 2020: 2 · 2019: 1 · 2018: 3
FWCI: 2.64
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: AIS Transactions on Human-Computer Interaction (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
Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords: task-technology fit, technology acceptance model, technology use, technology features, theoretical integration, technology performance

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Integrating the expanded task-technology fit theory and the technology acceptance model: a multi-wave empirical analysis

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