PREDICTIVE AUDIENCE ENGAGEMENT FOR PERFORMING ARTS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7091Keywords:
Audience Engagement, Performing Arts, Predictive Analytics, Human-in-the-Loop Systems, Multimodal Data Analysis, Cognitive ModelingAbstract [English]
The primary indicator of performing arts impact and sustainability is the audience engagement, which is traditionally measured retrospectively and in coarse-grain, as surveys, attendance statistics, and critical reviews. The current paper suggests a human-oriented predictive audience engagement framework incorporating cognitive theory, multimodal behavioral and emotive predictors, and human-in-the-loop analytics into a real-time deployable system framework. Engagement is conceptualized as a multidimensional and temporal construct that is influenced by attentional, affective, interpretive and social processes. Temporal predictive models with uncertainty-sensitive inference are applied to multimodal audience data comprising of visual, acoustic, and contextual cues to predict engagement trajectories and estimate them over time. Key points in the process of human expertise are expert annotation, interpretive validation and ethical oversight, being transparent and having context validity. Experimental testing in a variety of performing arts contexts shows that predictive models based on time and multi-modal effects are superior to more basic predictive baselines in predicting engagement dynamics, especially in the context of prominent changes of performance. The expert evaluation of quality further ascertains the semantic congruence of the patterns of engagement predicted and artistic intent. The findings show that predictive analytics based on cognitive foundations and supplemented by human judgment can deliver useful and interpretable information to help in reflective practice, performance analysis, and audience-conscious artistic development.
References
Awasthi, A., (2024). Use OF Indian Traditional Folk Arts IN Interior Designing ShodhShreejan: Journal of Creative Research Insights,1(1), 7-10, https://doi.org/10.29121/shodhshreejan.v1.i1.2024.4 DOI: https://doi.org/10.29121/shodhshreejan.v1.i1.2024.4
Haque, A. N. M. A., and Naebe, M. (2023). Tensile Properties of Natural Fibre-Reinforced FDM Filaments: A Short Review. Sustainability, 15, Article 16580. https://doi.org/10.3390/su152416580 DOI: https://doi.org/10.3390/su152416580
Kennedy, S. M., Wilson, L. A., and Rb, J. R. (2025). Natural Fiber Filaments Transforming the Future of Sustainable 3D Printing. MethodsX, 14, Article 103385. https://doi.org/10.1016/j.mex.2025.103385 DOI: https://doi.org/10.1016/j.mex.2025.103385
Masood, A., and Hashmi, A. (2019). Cognitive Robotic Process Automation. In Cognitive Computing Recipes ( 225–287). Apress. https://doi.org/10.1007/978-1-4842-4106-6_5 DOI: https://doi.org/10.1007/978-1-4842-4106-6_5
Metal AM. (2023). The Convergence of Additive Manufacturing and Artificial Intelligence: Envisioning a Future That is Closer Than You Think. Shrewsbury, Uk: Metal Am.
Opoz, T. T., Burgess, A., Ahuir-Torres, J. I., Kotadia, H. R., and Tammas-Williams, S. (2024). Effect of Surface Finish and Post-Processing on Mechanical Properties of 17-4 PH Stainless Steel Produced by Atomic Diffusion Additive Manufacturing. International Journal of Advanced Manufacturing Technology, 130, 4053–4066. https://doi.org/10.1007/s00170-024-12949-6 DOI: https://doi.org/10.1007/s00170-024-12949-6
Ribeiro, J., Lima, R., Eckhardt, T., and Paiva, S. (2021). Robotic Process Automation and Artificial Intelligence in Industry 4.0: A Literature Review. Procedia Computer Science, 181, 51–58. Https://Doi.org/10.1016/j.procs.2021.01.104 DOI: https://doi.org/10.1016/j.procs.2021.01.104
Richardson, S. (2020). Cognitive Automation: A New Era of Knowledge Work? Business Information Review, 37(4), 182–189. https://doi.org/10.1177/0266382120974601 DOI: https://doi.org/10.1177/0266382120974601
Rizk, Y., Isahagian, V., Boag, S., Khazaeni, Y., Unuvar, M., Muthusamy, V., and Khalaf, R. (2020). A Conversational Digital Assistant for Intelligent Process Automation. Lecture Notes in Business Information Processing, 393, 85–100. https://doi.org/10.1007/978-3-030-58779-6_6 DOI: https://doi.org/10.1007/978-3-030-58779-6_6
Sturm, L. D. (2020). Cyber-Physical Security for Additive Manufacturing Systems. Blacksburg, VA: Virginia Tech.
Subeshan, B., Atayo, A., and Asmatulu, E. (2024). Machine Learning Applications for Electrospun Nanofibers: A Review. Materials Science, 59, 14095–14140. https://doi.org/10.1007/s10853-024-09994-7 DOI: https://doi.org/10.1007/s10853-024-09994-7
Sönmez, Ö. E., and Börekçi, D. Y. (2020). A Conceptual Study on RPAs as of Intelligent Automation. Advances in Intelligent Systems and Computing, 1029, 65–72. https://doi.org/10.1007/978-3-030-23756-1_10 DOI: https://doi.org/10.1007/978-3-030-23756-1_10
Thomas, D. J. (2022). Advanced Active-Gas 3D Printing of 436 Stainless Steel for Future Rocket Engine Structure Manufacture. Journal of Manufacturing Processes, 74, 256–265. https://doi.org/10.1016/j.jmapro.2021.12.037 DOI: https://doi.org/10.1016/j.jmapro.2021.12.037
Wang, W., Wang, P., Zhang, H., Chen, X., Wang, G., Lu, Y., Chen, M., Liu, H., and Li, J. (2023). A Real-Time Defect Detection Strategy for Additive Manufacturing Processes Based on Deep Learning and Machine Vision. Micromachines, 15(1), Article 28. https://doi.org/10.3390/mi15010028 DOI: https://doi.org/10.3390/mi15010028
Yampolskiy, M., Bates, P., Seifi, M., and Shamsaei, N. (2022). State of Security Awareness in the Additive Manufacturing Industry: 2020 Survey. Progress in Additive Manufacturing, 7, 192–212. https://doi.org/10.1520/STP164420210119 DOI: https://doi.org/10.1520/STP164420210119
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Dr. Shivaji Karbhari Dhage, Naveen Jain, Dipti Ganesh Korwar, Deepti Deshmukh, Amalakarthiga G, Pooja Goel
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
