基于技术接受模型的AIGC满意度影响因素分析

Analysis of Factors Influencing AIGC User Satisfaction Based on the Technology Acceptance Model

摘要: 本研究整合了技术接受模型(TAM)、信息系统成功模型(D&M IS Success Model)和期望确认模型(ECM)，构建了一个旨在解释AIGC (生成式人工智能)工具用户满意度的影响因素模型。研究通过在线问卷调查收集了239份有效样本，并采用结构方程模型(SEM)进行数据分析和假设检验。研究揭示，在AIGC使用情境下，感知易用性处于影响链条的中心位置——用户对工具“是否好用”的判断比“是否有用”的认知更能决定其满意度。该结论为AIGC工具优化设计提供了“优先保障基础性能、降低使用门槛”的明确方向。

Abstract: This study integrates the Technology Acceptance Model (TAM), the DeLone and McLean Information Systems Success Model (D&M IS Success Model), and the Expectation Confirmation Model (ECM) to construct a model for explaining the influencing factors of user satisfaction with AIGC (Artificial Intelligence Generated Content) tools. A total of 239 valid samples were collected via online questionnaire surveys, and structural equation modeling (SEM) was adopted for data analysis and hypothesis testing. The study reveals that perceived ease of use occupies a central position in the influence chain within the AIGC usage context: users’ judgment on whether a tool is “easy to use” exerts a greater effect on their satisfaction than their perception of whether it is “useful”. This finding provides clear guidance for the optimized design of AIGC tools to “prioritize guaranteeing basic performance and lowering barriers to use”.