User Acceptance and Interaction Design for AR Guides at Industrial Heritage Sites: Integrating Cultural Identity into a TTF–TAM Framework with Eye-Tracking Evidence
Year: 2026
Type: article
Abstract: Smart guide systems for industrial heritage sites must support spatial navigation, information access, and understanding of heritage content. However, translating user acceptance findings into decisions about visual content, information hierarchy, and interface interaction remains challenging. This study incorporates cultural identity, defined as visitors’ identification with industrial symbols, historical narratives, and cultural values, into an integrated task–technology fit (TTF) and technology acceptance model (TAM) framework. Structural equation modeling of 295 valid questionnaire responses examines factors associated with behavioral intention to use smart guide systems. Cultural identity is significantly and positively associated with perceived usefulness and behavioral intention. Task–technology fit, perceived ease of use, and perceived usefulness are also significantly and positively associated with behavioral intention. Complementary eye-tracking experiments and semi-structured interviews in the context of Wuhan Iron and Steel Group’s industrial heritage examine visual attention and participants’ understanding of heritage content. Fixation heatmaps, scanpaths, and area-of-interest metrics characterize attention patterns, while interviews explore how participants interpret industrial history and cultural meanings. Descriptive findings identify distinctive industrial structures and human figures as prominent objects of attention and suggest the value of coherent and person-centered narratives for content organization. These findings inform task-oriented functions, visual content selection, layered information presentation, and interaction flows, resulting in an augmented reality (AR) smart guide interface prototype. The study integrates findings on cultural identity and behavioral intention with visual attention and interview evidence to inform interaction design, providing an empirical basis for industrial heritage AR guides.
Source: Applied Sciences
DOI: 10.3390/app162111051
Authors: Huilan Wei, Jinyi Wu, Rui Zhu
Institution: Hubei University of Technology
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7225643928
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