Task–Technology Fit as a Foundational Antecedent of Technology Acceptance: Evidence from Compliance-Intensive Financial Management Information Systems
Keywords:
Task–Technology Fit; Technology Acceptance; Compliance-Intensive Systems; Financial Management Information SystemsAbstract
Financial Management Information Systems (FMIS) are increasingly developed in-house by higher education institutions to accommodate institution-specific governance requirements. However, user acceptance of such systems remains insufficiently understood. Integrating the Technology Acceptance Model (TAM) with Task–Technology Fit (TTF) theory, this study examines behavioral intention toward a self-developed FMIS in a Chinese vocational college. Survey data from 181 users were analyzed using PLS-SEM. The results indicate that TTF significantly affects perceived ease of use (β = 0.599) and perceived usefulness (β = 0.377), and directly influences behavioral intention (β = 0.284). The model accounts for a substantial proportion of variance in behavioral intention (R² = 0.574), indicating strong explanatory capability in institutional financial system contexts. Mediation analysis reveals significant parallel and sequential indirect effects, with cognitive pathways accounting for 56% of the total effect. These findings suggest that in compliance-intensive institutional contexts, user acceptance is structurally anchored in task–technology alignment. This study contributes to technology acceptance theory by demonstrating that TTF functions as a foundational antecedent within TAM in task-constrained environments.
References
Davis, f. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Mis quarterly, 13(3), 319–340. Https://doi.org/10.2307/249008
Dishaw, m. T., & strong, d. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & management, 36(1), 9–21. Https://doi.org/10.1016/s0378-7206(98)00101-3
Gangwar, h., date, h., & ramaswamy, r. (2020). Understanding determinants of cloud computing adoption using an integrated tam–ttf model. Journal of enterprise information management, 33(2), 419–451. Https://doi.org/10.1108/jeim-08-2019-0256
Goodhue, d. L., & thompson, r. L. (1995). Task–technology fit and individual performance. Mis quarterly, 19(2), 213–236.
Https://doi.org/10.2307/249689
Hair, j. F., hult, g. T. M., ringle, c. M., & sarstedt, m. (2019). A primer on partial least squares structural equation modeling (pls-sem) (2nd ed.). Sage publications.
Henseler, j., ringle, c. M., & sarstedt, m. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115–135. Https://doi.org/10.1007/s11747-014-0403-8
He, c. (2020). Research on spoc users' behavioral intention: Based on the integrated model of tam and ttf [master's thesis, zhongnan university of economics and law].
Ma, j., & mao, c. (2024). A study on the influencing factors of chatgpt user acceptance and usage intention: An integrated perspective of tam and ttf. Science & technology communication, *362*(17), 118–124. Https://doi.org/10.16607/j.cnki.1674-6708.2024.17.029
Nunnally, j. C., & bernstein, i. H. (1994). Psychometric theory (3rd ed.). Mcgraw-hill.
Sasongko, a. T., ekhsan, m., & fatchan, m. (2025). Dataset on technology acceptance in e-learning: A pls-sem analysis using extended tam among undergraduate students in indonesia. Telematics and informatics reports, 18, 100192. Https://doi.org/10.1016/j.teler.2025.100192
Tian, y. (2024). Predictors of mobile payment use applications: The role of perceived usefulness and perceived ease of use in shaping behavioral intention. Sage open, 14(2), 21582440241292525. Https://doi.org/10.1177/21582440241292525
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. Https://doi.org/10.2307/30036540
Wei, w., zhang, g., gou, y., wen, x., & wu, t. (2022). Study on continuous use intention of road running app based on tam and ttf integration model. Journal of harbin sport university, *41*(9), 825–831.
Yu, f., & wang, r. (2024). A study of factors influencing college students' willingness to use generative ai consistently based on the tam and ttf integration model. Teaching reform, *24*(6), 80–86.
Xu, f., & huang, l. (2018). Willingness to use smart tourist attractions system: An integrated model based on tam and ttf. Tourism tribune, *33*(8), 108–118. Https://doi.org/10.3969/j.issn.1002-5006.2018.08.017
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