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<p>In the context of digital transformation, Generative AI is reshaping cultural heritage dissemination and museum user experiences. This study develops a value-based adoption model to examine how GenAI’s adaptability, perceived benefits, and perceived costs influence users’ perceived value and adoption intention. Using the British Museum’s “The Living Museum” platform, data were collected from 726 Chinese users and analyzed with PLS-SEM. Results show that semantic relevance and contextual adaptability significantly enhance perceived value. Perceived usefulness, enjoyment, novelty, and relative advantage increase perceived value, while complexity and perceived risk reduce it. Service personalization and habit change exerted no significant effects. Perceived value strongly predicts adoption intention, with perceived innovativeness and interactivity moderating this relationship. Multi-group analysis further reveals differences between professional and non-professional users in how novelty and risk affect perceived value. These findings extend value-based adoption theory in digital heritage contexts and provide practical insights for optimizing GenAI-enabled museum services.</p>

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How generative AI shapes user perceived value and adoption intention in digital museum experiences

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月26日

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  • Xiaoyan Hao,
  • Junping Xu,
  • Ying Wang

摘要

In the context of digital transformation, Generative AI is reshaping cultural heritage dissemination and museum user experiences. This study develops a value-based adoption model to examine how GenAI’s adaptability, perceived benefits, and perceived costs influence users’ perceived value and adoption intention. Using the British Museum’s “The Living Museum” platform, data were collected from 726 Chinese users and analyzed with PLS-SEM. Results show that semantic relevance and contextual adaptability significantly enhance perceived value. Perceived usefulness, enjoyment, novelty, and relative advantage increase perceived value, while complexity and perceived risk reduce it. Service personalization and habit change exerted no significant effects. Perceived value strongly predicts adoption intention, with perceived innovativeness and interactivity moderating this relationship. Multi-group analysis further reveals differences between professional and non-professional users in how novelty and risk affect perceived value. These findings extend value-based adoption theory in digital heritage contexts and provide practical insights for optimizing GenAI-enabled museum services.