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Cited by 136Museums and Cultural HeritageArt, Technology, and CultureLibraries and Information Services

A systematic review of digital transformation technologies in museum exhibition

Open access
Li, Jingjing, Zheng, Xiaoyang, Watanabe, Ikumu, Ochiai, Yoichi·2024·Computers in Human Behavior
DOI 10.1016/j.chb.2024.108407
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Abstract

Museum exhibitions, both temporary and permanent, form an essential link between a society and its cultural, historical, and artistic heritage sites. Curating artifacts and thematic displays in museum exhibitions can promote dialogue, foster cultural appreciation, and contribute to heritage preservation. The traditional way of holding museum exhibitions, heavily reliant on the expertise of designers and curatorial staff, makes them a labor-intensive process, from conceptualization to visitor engagement analysis. This review systematically compiles and examines how the application of digital transformation technologies (DTTs) has revolutionized museum exhibitions and augmented their future potential. DTTs such as artificial intelligence, immersive technologies, additive manufacturing, the Internet of Things, and cloud computing can help create engaging designs, improve accessibility and inclusivity, enhance educational potential, and allow for sophisticated visitor experience data collection and analyses, improving exhibit management. However, despite multiple specialized studies on DTTs and their roles in museum exhibitions, the connections between technology and application scenarios remain underexplored. By addressing this gap, this study is expected to inform and inspire practitioners in the museum and heritage sectors and present new research avenues for scholars.

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  • Some cutting-edge virtual museums are including meta-commentary on their own reconstruction process (e.g., showing what data is missing, or showing alternate reconstructions).

    “Some cutting-edge virtual museums are indeed doing this by including meta-commentary on their own reconstruction process (e.g., showing what data is missing, or showing alternate reconstructions) [10,36,38,49].”

    From Virtual Museums as Meaning-Modeling Systems in Digital Heritage (2025) · cites this paper as [10]

    The review discusses virtual exhibitions and 3D reconstruction (sections 2.2.1, p. 7, and 2.3.1, p. 9), but does not describe virtual museums showing visitors missing data, alternative reconstructions, or how a reconstruction was made. Searches for reconstruction uncertainty and interpretive choices found no passage addressing that practice. (LLM, conf=0.75)

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References 254 · Cited by 136

Research works printed but not linked: 31

  1. Alsuhly, G., & Khattab, A. (2018). An IoT monitoring and control platform for museum content conservation. In 2018 international conference on computer and applications (pp. 196-201). IEEE. http://refhub.elsevier.com/S0747-5632(2400275-9/sb6)
  2. Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., et al. (2023). Palm 2 : technical report , arXiv preprint arXiv:2305.10403. · arXiv
  3. Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit . O'Reilly Media, Inc.. http://refhub.elsevier.com/S0747-5632(2400275-9/sb18)
  4. Booster, M. (2021). Museum innovation barometer 2021. (pp. 11-14). URL https://museumbooster.com/wp-content/uploads/2021/08/Museum-InnovationBarometer-2021.pdf.
  5. Cuomo, S., De Michele, P., Galletti, A., & Piccialli, F. (2015). A cultural heritage case study of visitor experiences shared on a social network. In 2015 10th international conference on p2P, parallel, grid, cloud and internet computing 3PGCIC, (pp. 539-544). IEEE. http://refhub.elsevier.com/S0747-5632(2400275-9/sb42)
  6. French, A., & Villaespesa, E. (2019). AI, visitor experience, and museum operations: a closer look at the possible. In Humanizing the digital: unproceedings from the MCN 2018 conference (pp. 101-113). http://refhub.elsevier.com/S0747-5632(2400275-9/sb57)
  7. Gibson, I., Rosen, D., Stucker, B., Khorasani, M., Rosen, D., Stucker, B., et al. (2021). Additive manufacturing technologies. vol. 17 , Springer. http://refhub.elsevier.com/S0747-5632(2400275-9/sb67)
  8. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. http://refhub.elsevier.com/S0747-5632(2400275-9/sb70)
  9. Gu, D., Shi, X., Poprawe, R., Bourell, D. L., Setchi, R., & Zhu, J. (2021). Materialstructure-performance integrated laser-metal additive manufacturing. Science , 372 (6545), eabg1487. http://refhub.elsevier.com/S0747-5632(2400275-9/sb72)
  10. Hess, M., Petrovic, V., Meyer, D., Rissolo, D., & Kuester, F. (2015). Fusion of multimodal three-dimensional data for comprehensive digital documentation of cultural heritage sites. 2 , In 2015 digital heritage (pp. 595-602). IEEE. http://refhub.elsevier.com/S0747-5632(2400275-9/sb84)
  11. Holler, J., Tsiatsis, V., Mulligan, C., Karnouskos, S., Avesand, S., & Boyle, D. (2014). Internet of things. Academic Press. http://refhub.elsevier.com/S0747-5632(2400275-9/sb86)
  12. International Council of Museums (2020). Museums, museum professionals and COVID19. (pp. 2-3). URL https://icom.museum/wp-content/uploads/2020/05/ReportMuseums-and-COVID-19.pdf.
  13. International Council of Museums (2021). ICOM report: museums, museum professionals and Covid-19: third survey. (pp. 15-16). URL https://icom.museum/wpcontent/uploads/2021/07/Museums-and-Covid-19\_third-ICOM-report.pdf.
  14. Kaplan, A. (2023). Innovation in artificial intelligence: Illustrations in academia, apparel, and the arts. In Oxford research encyclopedia of business and management . http://refhub.elsevier.com/S0747-5632(2400275-9/sb96)
  15. Lecoutre, A., Negrevergne, B., & Yger, F. (2017). Recognizing art style automatically in painting with deep learning. In Asian conference on machine learning (pp. 327-342). PMLR. http://refhub.elsevier.com/S0747-5632(2400275-9/sb114)
  16. Li, J., Zheng, X., Lu, J.-L., Xanat, V. M., & Ochiai, Y. (2022). Transformation of plants into polka dot arts: Kusama yayoi as an inspiration for deep learning. In Universal access in human-computer interaction. novel design approaches and technologies: 16th international conference, UAHCI 2022, held as part of the 24th HCI international conference, HCII 2022, virtual event, June 26-July 1, 2022, proceedings, part i (pp. 270-280). Springer. http://refhub.elsevier.com/S0747-5632(2400275-9/sb124)
  17. Liao, H.-T., Zhao, M., & Sun, S.-P. (2020). A literature review of museum and heritage on digitization, digitalization, and digital transformation. In 6th international conference on humanities and social science research (pp. 473-476). Atlantis Press. http://refhub.elsevier.com/S0747-5632(2400275-9/sb125)
  18. Markopoulos, E., Ye, C., Markopoulos, P., & Luimula, M. (2021). Digital museum transformation strategy against the Covid-19 pandemic crisis. In Advances in creativity, innovation, entrepreneurship and communication of design: proceedings of the AHFE 2021 virtual conferences on creativity, innovation and entrepreneurship, and human factors in communication of design (pp. 225-234). Springer. http://refhub.elsevier.com/S0747-5632(2400275-9/sb131)
  19. Mitchell, T. M., et al. (2007). vol. 1 , Machine learning . McGraw-hill New York. http://refhub.elsevier.com/S0747-5632(2400275-9/sb141)
  20. Mucci, F., Mucci, N., & Diolaiuti, F. (2020). Lockdown and isolation: psychological aspects of COVID-19 pandemic in the general population. Clinical Neuropsychiatry , http://refhub.elsevier.com/S0747-5632(2400275-9/sb146)
  21. Muthanna, A., Ateya, A. A., Amelyanovich, A., Shpakov, M., Darya, P., & Makolkina, M. (2018). AR enabled system for cultural heritage monitoring and preservation. In Internet of things, smart spaces, and next generation networks and systems: 18th international conference, NEW2AN 2018, and 11th conference, ruSMART 2018, st. petersburg, Russia, August 27-29, 2018, proceedings 18 (pp. 560-571). Springer. http://refhub.elsevier.com/S0747-5632(2400275-9/sb148)
  22. OpenAI (2023). GPT-4 : Technical Report , arXiv:2303.08774. · arXiv
  23. Rener, R. (2017). The 3D printing of tactile maps for persons with visual impairment. In Universal access in human-computer interaction. designing novel interactions: 11th international conference, UAHCI 2017, held as part of hCI international 2017, vancouver, BC, Canada, July 9-14, 2017, proceedings, part II 11 (pp. 335-350). Springer. http://refhub.elsevier.com/S0747-5632(2400275-9/sb170)
  24. Shin, J., Cho, J., & Lee, S. (2020). Please touch color: Tactile-color texture design for the visually impaired. In Extended abstracts of the 2020 CHI conference on human factors in computing systems (pp. 1-7). http://refhub.elsevier.com/S0747-5632(2400275-9/sb185)
  25. Standard, A. (2012). ISO/ASTM 52900: 2015 additive manufacturing-general principles-terminology. ASTM F2792-10e1 . http://refhub.elsevier.com/S0747-5632(2400275-9/sb197)
  26. Tache, A. V., Sandu, I. C. A., POPESCU, O.-C., & PETRIŞOR, A.-I. (2018). UAV solutions for the protection and management of cultural heritage. case study: Halmyris archaeological site. International Journal of Conservation Science , 9 (4). http://refhub.elsevier.com/S0747-5632(2400275-9/sb201)
  27. Taigman, Y., Polyak, A., & Wolf, L. (2016). Unsupervised cross-domain image generation. arXiv preprint arXiv:1611.02200. · arXiv
  28. Trinitatova, D., & Tsetserukou, D. (2019). Touchvr: A wearable haptic interface for VR aimed at delivering multi-modal stimuli at the user's palm. In SIGGRAPH Asia 2019 XR (pp. 42-43). http://refhub.elsevier.com/S0747-5632(2400275-9/sb211)
  29. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems , 30 .
  30. Zheng, X., Watanabe, I., Paik, J., Li, J., Guo, X., & Naito, M. (2024). Textto-microstructure generation using generative deep learning. Small , Article 2402685. http://refhub.elsevier.com/S0747-5632(2400275-9/sb247)
  31. Zheng, X., Watanabe, I., Wang, S., Chen, T.-T., & Naito, M. (2024). Minimal-surfacebased multiphase metamaterials with highly variable stiffness. Materials & Design , 237 , Article 112548. http://refhub.elsevier.com/S0747-5632(2400275-9/sb248)

Authors
Li, J., Zheng, X., Watanabe, I., & Ochiai, Y.
Affiliations
University of Tsukuba (JP)
Corresponding author
Ochiai, Yoichi
ORCID
Venue
Computers in Human Behavior
Publisher
Elsevier BV
Published
2024-12
Volume
161
Pages
108407
ISSN
0747-5632 (ISSN-L)
DOI
10.1016/j.chb.2024.108407
OpenAlex
W4401485040
Cited by
136
References
254
Citations by year
2026: 77 · 2025: 54 · 2024: 5
FWCI
50.11
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers in Human Behavior (publishedVersion, cc-by-nc-nd)
  • Institutional Repositories DataBase (IRDB) (publishedVersion)
  • Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by-nc-nd)
  • NIMS Materials Data Repository (submittedVersion, other-oa)
Funders
Japan Science and Technology Agency (JPMJSP2124)
Topics
  • Social Sciences › Arts and Humanities › Museology › Museums and Cultural Heritage
  • Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
  • Social Sciences › Arts and Humanities › Museology › Libraries and Information Services
Keywords
museum exhibitions, augmented reality, immersive technologies, artificial intelligence, additive manufacturing, Internet of Things, cloud computing, visitor experience, accessibility and inclusion, cultural heritage preservation, museum curation

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