Paper Number
ICIS2025-1238
Paper Type
Short
Abstract
This paper extends Task-Technology Fit (TTF) theory by reintroducing feedback as a dynamic construct shaping the temporal evolution of fit, utilization, and performance outcomes. While TTF has provided a valuable lens for understanding the alignment between tasks and technologies, it has typically been treated as static, overlooking the recursive nature of fit over time. Drawing on Control Theory and Expectation Confirmation Theory, we develop a temporal framework that conceptualizes feedback as a mediated process in which individuals adjust their perceptions of fit after evaluating deviations between expected and actual performance outcomes. We argue that feedback not only shapes perceptions of fit but also reconfigures precursors to utilization and subsequent utilization decisions. Our framework specifies quantifiable, time-sensitive relationships suitable for longitudinal analysis and Hierarchical Linear Modeling. This reconceptualization advances TTF by formalizing feedback as a core mechanism, offering deeper understanding of technology use in rapidly evolving digital environments.
Recommended Citation
Rimando, Artemio and Turetken, Ozgur, "Feedback Extensions to Task-Technology Fit" (2025). ICIS 2025 Proceedings. 3.
https://aisel.aisnet.org/icis2025/is_researchmethods/is_researchmethods/3
Feedback Extensions to Task-Technology Fit
This paper extends Task-Technology Fit (TTF) theory by reintroducing feedback as a dynamic construct shaping the temporal evolution of fit, utilization, and performance outcomes. While TTF has provided a valuable lens for understanding the alignment between tasks and technologies, it has typically been treated as static, overlooking the recursive nature of fit over time. Drawing on Control Theory and Expectation Confirmation Theory, we develop a temporal framework that conceptualizes feedback as a mediated process in which individuals adjust their perceptions of fit after evaluating deviations between expected and actual performance outcomes. We argue that feedback not only shapes perceptions of fit but also reconfigures precursors to utilization and subsequent utilization decisions. Our framework specifies quantifiable, time-sensitive relationships suitable for longitudinal analysis and Hierarchical Linear Modeling. This reconceptualization advances TTF by formalizing feedback as a core mechanism, offering deeper understanding of technology use in rapidly evolving digital environments.
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25-Research