Factors Influencing User Engagement and Usage Behavior in Digital Museums: An Extended TAM Approach

This study aims to improve users’ usage intention to engage with digital museums by providing practical insights for practitioners and new theoretical perspectives for academic researchers. Extending the technology acceptance model (TAM), the study incorporates level challenge as a new factor influencing digital museum usage. Data were collected through questionnaires and analyzed using structural equation modeling (SEM). In addition, the study conducted semi-structured interviews to qualitatively triangulate the SEM findings. The results show that perceived usefulness, perceived ease of use, perceived playfulness, and level challenge are significant factors affecting digital museum usage behavior. Perceived usefulness and perceived ease of use strongly impact users’ usage intention, while perceived playfulness and levels and challenges enhance user engagement and participation. Furthermore, usage intention plays a crucial mediating role in actual usage behavior. This study contributes to TAM by integrating user experience factors specific to digital museums, providing a broader understanding of the determinants of digital museum usage. For museum administrators, the findings suggest that enhancing the platform’s perceived usefulness, perceived ease of use, and perceived playfulness while incorporating levels and challenges, can significantly improve user engagement and retention.

Authors

Institutions

Publication Details

Journal
SAGE Open
Published
2025-10-01
DOI
https://doi.org/10.1177/21582440251396378
Primary Topic
Museums and Cultural Heritage
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors Influencing User Engagement and Usage Behavior in Digital Museums: An Extended TAM Approach

Ying Liu, Yongjun He
SAGE Open
Museums and Cultural Heritage
article

Factors Influencing User Engagement and Usage Behavior in Digital Museums: An Extended TAM Approach

Ying Liu, Yongjun He
article en

Abstract

This study aims to improve users’ usage intention to engage with digital museums by providing practical insights for practitioners and new theoretical perspectives for academic researchers. Extending the technology acceptance model (TAM), the study incorporates level challenge as a new factor influencing digital museum usage. Data were collected through questionnaires and analyzed using structural equation modeling (SEM). In addition, the study conducted semi-structured interviews to qualitatively triangulate the SEM findings. The results show that perceived usefulness, perceived ease of use, perceived playfulness, and level challenge are significant factors affecting digital museum usage behavior. Perceived usefulness and perceived ease of use strongly impact users’ usage intention, while perceived playfulness and levels and challenges enhance user engagement and participation. Furthermore, usage intention plays a crucial mediating role in actual usage behavior. This study contributes to TAM by integrating user experience factors specific to digital museums, providing a broader understanding of the determinants of digital museum usage. For museum administrators, the findings suggest that enhancing the platform’s perceived usefulness, perceived ease of use, and perceived playfulness while incorporating levels and challenges, can significantly improve user engagement and retention.

SAGE OpenVol. 15(4)
Suan Sunandha Rajabhat University (TH), Nanjing University of the Arts (CN)
Openalex Percentile: Top 67%
Museums and Cultural Heritage
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.