Research on user satisfaction with AIGC assisted museum scenario design

Area of Science:

• Digital Transformation
• Human-Computer Interaction
• Museum Studies

Background:

• Museums are undergoing digital transformation, with artificial intelligence generated content (AIGC) offering new possibilities for smart scene design.
• Existing research lacks a systematic, user-centered framework for integrating AIGC in museum environments.

Purpose of the Study:

• To explore the mechanism of AIGC technology adaptability, user demand fit, scene design innovation, technology acceptance, and user satisfaction in museums.
• To construct a user-centered systematic framework for AIGC in museum smart scene design.
• To propose a design strategy for museums to leverage AIGC for improved user experience.

Main Methods:

• Utilized questionnaire surveys to gather user data.
• Employed structural equation modeling (SEM) to analyze the relationships between key variables.
• Developed and tested a closed-loop model of "demand drive-technology adaptation-scene innovation-acceptance conversion".

Main Results:

• User demand fit demonstrated the strongest direct impact on user satisfaction, emphasizing a user-centered approach.
• AIGC technology adaptability positively influenced satisfaction both directly and indirectly, mediated by the technology acceptance model (TAM).
• Scene design innovation impacts user satisfaction by first being transformed through technology acceptance.

Conclusions:

• The study establishes a closed-loop model for AIGC integration in museums, shifting the focus from "object-centered" to "people-centered" design.
• A design strategy based on cognitive load balance is proposed, providing theoretical and practical guidance for museums.
• Findings highlight the critical role of user-centered design and technology acceptance in maximizing the benefits of AIGC for museum visitor experiences.