Managing Contemporary Art Institutions With Predictive Insights
Bibliographic Data
| ID | 22197210 |
|---|---|
| Authors | Khushboo Khushboo (Noida International University), Ramu K Ramu K, Ramu K (Vinayaka Missions University), M S Pavithra M S Pavithra, M S Pavithra (JSS Academy of Higher Education and Research), Sathyabalaji Kannan (International Institute of Information Technology), Akhilesh Kalia (Chitkara University), Shardul Phansalkar (Parul University) |
| Year | 2025 |
| Volume | 6 |
| Issue | 5s |
| Pages | 384-393 |
| Publication date | 2025-12-28 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ShodhKosh: Journal of Visual and Performing Arts (JOURNAL) |
| Journal identifiers | ISSN: 2582-7472 • E-ISSN: 2582-7472 |
| Publisher | Granthaalayah Publications and Printers (PUBLISHER • IN) |
| DOI | 10.29121/shodhkosh.v6.i5s.2025.6922 |
| OpenAlex | W7117821823 |
| Language | EN |
| References cited | 12 |
Modern art organizations are becoming more and more active in unstable cultural, economic, and digital conditions that require evidence-based decision making, not based on intuitions and ex-post reporting. This paper suggests a predictive analytics system to be used in strategic and operational management of museums, galleries, and hybrid cultural institutions. Combining nonhomogeneous data flows visitor attendance and mobility traces, engagement and ticketing logs, exhibition performance indicator, sales and acquisition and digital interaction signals of websites, social media, and virtual exhibitions the framework allows making a forward-looking decision regarding programming, finance, and resource allocation. The data preprocessing and harmonization, feature engineering to capture temporal, behavioral, and contextual dynamics, deployment of machine learning and time-series models to predict visitor demand, predict engagement, and estimate revenue are all part of the methodology. Optimization modules convert predictions into actions to be taken by the staff, exhibition schedule, marketing expenditure and collection plans. A case study shows how it is applied in the context of a modern art organization, including the nature of its datasets, deployment of the model and its connection to managerial processes. Quantitative analysis demonstrates the better accuracy of the forecasts in terms of attendance and revenues, the minimization of operational inefficiency, and a more suitable exhibition planning, whereas qualitative reciprocation demonstrates increased managerial confidence and decision processing transparency. According to the results, predictive insights can help to balance artistic vision and sustainable management by balancing cultural impact and financial viability
Analytics · Exhibition · General partnership · Revenue · Software deployment · Supply chain · Visitor pattern · Art History and Market Analysis · Cultural Industries and Urban Development · Museums and Cultural Heritage
| Highly cited | No |
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