Digital technologies and economic performance of Italian museums

Alessandro Bellocchi et al.

Journal of Cultural Economics2026https://doi.org/10.1007/s10824-026-09578-0article
AJG 2ABDC A
Weight
0.50

Abstract

This paper investigates the relationship between the adoption of digital technologies and the economic performance of Italian museums. We build a novel dataset with microdata for the near-universe of Italian museums in 2018 and 2022, enriched with municipal-level information on economic conditions, tourist flows and transport infrastructure. Our analysis highlights distinct economic roles for different classes of digital technologies. We find that while digital supports , such as interactive displays and mobile applications, are mainly associated with increased museum visits, web services , particularly online ticketing, primarily contribute to higher revenue. In contrast, remote-access tools , such as virtual tours, are generally associated with lower on-site revenue, suggesting a partial substitution away from paid visits. We then show that governance structures and infrastructural accessibility significantly moderate these relationships. Notably, directly managed museums, public institutions and those located in remote areas benefit the most from digital investments. These results emphasize the importance of tailored digital strategies, aligned with institutional characteristics and broader contextual factors, to enhance the economic performance of Italian museums.

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https://doi.org/https://doi.org/10.1007/s10824-026-09578-0

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@article{alessandro2026,
  title        = {{Digital technologies and economic performance of Italian museums}},
  author       = {Alessandro Bellocchi et al.},
  journal      = {Journal of Cultural Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10824-026-09578-0},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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