Empirical analysis reveals indicators enhance cultural innovation and sustainable development in museum digital construction.
At present, the digital construction of museums has created a novel cultural ecosystem that integrates digital preservation of cultural heritage, intelligent management, immersive experiences, and cloud-based services. However, insufficient synergistic integration of technological applications constrains the comprehensive release of the digital construction’s efficacy, while the absence of cultural assessment dimensions hinders the effective articulation of mechanisms whereby digital technology empowers cultural innovation. These concerns collectively constitute the primary impediments hindering museums from attaining sustainable development. The effectiveness of museum digital construction is fully clarified by combining grounded theory qualitative research methods with cultural cycle theory in this study. The Analytic Network Process (ANP) is used to manage interdependent relationships between factors, and cloud models are used to clarify indicator ambiguity, which allows for accurate assessment of digital construction results, consequently bolstering the sustainability of museum digitalization initiatives. The developed ‘qualitative–quantitative’ collaborative evaluation methodology for museum digital construction includes three sub-objectives: technology embedding, value co-creation, and institutional adaptation, as well as five primary indicators and ten secondary indicators. An empirical analysis of the ‘Smart Jiangxi Museum’ digital construction initiative at the Jiangxi Provincial Museum in China indicates that the project has achieved an ‘excellent’ standard. The findings of a previous qualitative study are effectively supported by this conclusion. This study presents a systematic approach for museum evaluation and gives decision-making guidance for museums to attain sustainable use of cultural resources, promote social knowledge transmission, and facilitate green, low-carbon transformation of operational models in the digital era.
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Qi et al. (2025) studied this question.
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