Scholarlink

<p>As tourism undergoes digital and intelligent transformation, generative AI chatbots such as ChatGPT are becoming important tools for tourists’ information acquisition and decision support. Cultural heritage tourism, which relies heavily on cultural interpretation and personalized information, provides a particularly relevant context for such applications. However, tourists’ continuance intention to use AI chatbots in heritage settings remains underexplored. To address this gap, this study develops a Stimulus–Organism–Response (S–O–R) framework to examine the psychological mechanisms underlying tourist–chatbot interactions in cultural heritage tourism. Using a mixed-methods design that integrates SEM, ANN, NCA, and grounded theory, the study finds that trust, satisfaction, and attitude significantly enhance continuance intention. ANN identifies perceived warmth, competence, anthropomorphism, trust, and satisfaction as key predictors, while NCA reveals warmth, trust, and satisfaction as necessary conditions. The study extends S–O–R theory and offers practical implications for intelligent heritage service design and tourism management in practice.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From perception to behavior: ChatGPT's impact on tourists' motivation and continuance intention in heritage tourism

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月23日

查看全文
  • Mingliang Chu,
  • Musen Liu

摘要

As tourism undergoes digital and intelligent transformation, generative AI chatbots such as ChatGPT are becoming important tools for tourists’ information acquisition and decision support. Cultural heritage tourism, which relies heavily on cultural interpretation and personalized information, provides a particularly relevant context for such applications. However, tourists’ continuance intention to use AI chatbots in heritage settings remains underexplored. To address this gap, this study develops a Stimulus–Organism–Response (S–O–R) framework to examine the psychological mechanisms underlying tourist–chatbot interactions in cultural heritage tourism. Using a mixed-methods design that integrates SEM, ANN, NCA, and grounded theory, the study finds that trust, satisfaction, and attitude significantly enhance continuance intention. ANN identifies perceived warmth, competence, anthropomorphism, trust, and satisfaction as key predictors, while NCA reveals warmth, trust, and satisfaction as necessary conditions. The study extends S–O–R theory and offers practical implications for intelligent heritage service design and tourism management in practice.