旅游决策情境下技术感知与主观规范对大学生生成式人工智能采纳行为的影响研究

Research on the Influence of Technology Perception and Subjective Norms on College Students’ Generative Artificial Intelligence Adoption Behavior in Tourism Decision-Making

摘要: 生成式人工智能(GenAI)已深度嵌入大学生的信息获取与旅游决策过程，但其采纳行为背后的心理机制仍缺乏系统检验。本研究整合技术接受模型(TAM)与计划行为理论(TPB)，基于208份有效问卷数据，运用结构方程模型(SEM)检验感知易用性、感知有用性与主观规范对使用意愿及实际使用行为的影响。研究发现：1) 感知易用性显著正向影响感知有用性，感知有用性显著正向影响使用意愿，感知易用性对使用意愿的直接影响不显著，感知有用性在二者之间发挥中介作用(Bootstrap检验95%置信区间不含0)；2) 主观规范对使用意愿的影响不显著(β = 0.408, p = 0.052)，处于显著性边界；3) 使用意愿显著正向影响实际使用行为。研究表明，感知有用性是连接技术感知与使用意愿的核心变量，技术感知路径的独立解释力相对强于主观规范路径。本研究拓展了技术接受模型在旅游决策情境下的适用边界，可为GenAI旅游产品的功能设计提供参考。

Abstract: Generative artificial intelligence (GenAI) has been deeply embedded in college students’ information acquisition and tourism decision-making, yet the psychological mechanisms underlying their adoption behavior remain under-examined. Integrating the Technology Acceptance Model (TAM) with the Theory of Planned Behavior (TPB) and based on 208 valid questionnaires, this study uses structural equation modeling (SEM) to examine the influence of perceived ease of use, perceived usefulness, and subjective norms on usage intention and actual usage behavior. The results show that: 1) perceived ease of use significantly and positively affects perceived usefulness, which in turn significantly enhances usage intention, while the direct effect of perceived ease of use on usage intention is not significant, indicating that perceived usefulness plays a mediating role (bootstrap 95% CI excluding zero); 2) the effect of subjective norms on usage intention is not significant (β = 0.408, p = 0.052), lying on the boundary of statistical significance; 3) usage intention significantly and positively affects actual usage behavior. The findings indicate that perceived usefulness is the core variable linking technology perception to usage intention, and that the technology perception path exhibits relatively stronger independent explanatory power than the subjective norm path. This study extends the applicability of TAM to tourism decision-making contexts and provides implications for the functional design of GenAI-based tourism products.