A Task–Technology Fit Perspective on Trust and User Satisfaction in Artificial Intelligence
TL;DR
TTF theory is extended by demonstrating that technology fit serves as a key mechanism linking AI capabilities with user trust and satisfaction, providing a more comprehensive explanation of value creation in AI-supported task environments.
Abstract
The increasing use of AI has transformed how users perform task-related activities, yet limited research explains how AI creates value through alignment between task-requirements and technological capabilities. This study examines the role of TTF in explaining trust and user satisfaction in AI usage by investigating the effects of TC and TEC on TTF, trust, and satisfaction. A quantitative survey was conducted involving 160 users with experience using AI, and data were analyzed using PLS-SEM. The findings reveal that both TC and TEC significantly influence TTF with TEC showing a stronger effect. TTF significantly enhances trust and users' satisfaction, while trust also positively influences satisfaction. However, task characteristics do not significantly affect trust directly. These findings suggest that trust in AI is shaped more by technology capability than by task complexity, highlighting a technology-driven trust formation mechanism in AI usage. This study extends TTF theory by demonstrating that technology fit serves as a key mechanism linking AI capabilities with user trust and satisfaction, providing a more comprehensive explanation of value creation in AI-supported task environments.