DIGITAL TRANSFORMATION OF PERFORMING ARTS MANAGEMENT: STRATEGIES, CHALLENGES, AND FUTURE DIRECTIONS

Authors

  • Vinitha M Department of Computer Science, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, India
  • Pushpa Nagini Sripada Professor, Department of English, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, India
  • Saraswati B Department of Computer Science, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, India
  • Baskaran Kuppusamy Scientist, Central Research Laboratory, Meenakshi Medical College Hospital and Research Institute, Meenakshi Academy of Higher Education and Research, India
  • Veda Vijaya T Professor, Department of Pharmacology, Meenakshi Ammal Dental College and Hospital, Meenakshi Academy of Higher Education and Research, India
  • Ankur Singh Bist Graphic Era Hill University, Bhimtal Campus, Centre for Promotion of Research Graphic Era (Deemed to be) University, Dehradun, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i3s.2026.7316

Keywords:

Data-Driven Management, Performing Arts Management, Digital Transformation, Data Analytics, Audience Engagement, Cultural Technology, Smart Arts Management

Abstract [English]

Data-driven digital platform and management practices have caused a massive transformation to the performing art industry. The traditional systems of the performing arts management are typically manual coordinating based, minimal audience analytics and conventional means of promotion that may hinder the growth and involvement of the audience in the organization. New opportunities of efficiency and sustainability of work of performance art organizations emerged due to development of data analytics, cloud computing, artificial intelligence and digital platforms in the last several years. As a recommendation of the current paper, a Data-Driven Performing Arts Management model could be proposed that could also leverage digital transformation to enhance the decision-making of operations, audience involvement, and performance management. Some of the elements that are incorporated in the proposed system architecture include data collection, data storage, data processing, analytic engines, and visualization dash boards. The data on various sources, such as but not limited to ticketing information, social media, online streaming services companies, and the feedback system of the audience, is gathered and processed using advanced analytical software. The analytics layer uses machine learning and predictive modeling to identify the behavioral pattern of the audiences, project attendance and strategize actions of the performances and events. The visualization level further enables the managers and the stakeholders to analyze the analytics of the analytics in an interactive dashboard and reports. In addition, the theoretical framework is dedicated to the role of cloud-based infrastructures and online platforms in development of effective communication between artists, administrators and audiences. This is because digital transformation helps the performing arts institutions to extend further into the physical space to the online performances, virtual engagement, and participation of the global audience. Such technological combination can not only optimise the efficiency of the work, but also to make cultural conservation and the increase of the access to the performing art possible. The findings suggest that a data approach might bring a significant contribution to the decision-making, marketing and resource management in the performing arts organizations.

References

Alharoon, D., and Gillan, D. J. (2020). The Relation of the Perceptions of Aesthetics and Usability. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 64(1), 1876–1880. https://doi.org/10.1177/1071181320641452 DOI: https://doi.org/10.1177/1071181320641452

Askitas, N. (2025). Notes on a World with Generative AI. Bonn: IZA–Institute of Labor Economics. https://doi.org/10.2139/ssrn.5466786 DOI: https://doi.org/10.2139/ssrn.5466786

Brockinton, A., Hirst, S., Wang, R., McAlaney, J., and Thompson, S. (2022). Utilising Online Eye-Tracking to Discern the Impacts of Cultural Backgrounds on Fake and Real News Decision-Making. Frontiers in Psychology, 13, 999780. https://doi.org/10.3389/fpsyg.2022.999780 DOI: https://doi.org/10.3389/fpsyg.2022.999780

Brookes, I., Donald, E., Holmes, A., and O’Neill, M. (Eds.). (2023). Collins English Dictionary (4th ed.). HarperCollins.

Chesher, C., and Albarrán-Torres, C. (2023). The Emergence of Autolography: The “Magical” Invocation of Images from Text Through AI. Media International Australia, 189(1), 57–73. https://doi.org/10.1177/1329878X231193252 DOI: https://doi.org/10.1177/1329878X231193252

Desai, S. C., and Reimers, S. (2019). Comparing the Use of Open and Closed Questions for Web-Based Measures of The Continued-Influence Effect. Behavior Research Methods, 51(3), 1426–1440. https://doi.org/10.3758/s13428-018-1066-z DOI: https://doi.org/10.3758/s13428-018-1066-z

Doi, T., and Murata, A. (2020). Comparative Analysis of Website Usability between United States and Japan. In Advances in Physical, Social and Occupational Ergonomics. Springer. https://doi.org/10.1007/978-3-030-51549-2_66 DOI: https://doi.org/10.1007/978-3-030-51549-2_66

Ebert, N., Scheppler, B., Ackermann, K., and Geppert, T. (2023, April 23–28). QButterfly: Lightweight Survey Extension for Online User Interaction Studies for Non-Tech-Savvy Researchers. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. Hamburg, Germany. https://doi.org/10.1145/3544548.3580780 DOI: https://doi.org/10.1145/3544548.3580780

Gadre, R., Lonkar, H., Gahule, T., Aashtankar, H., and Patle, M. (2025). Design and Fabrication of Burning Block Making Machine from Agriculture Waste. International Journal of Theoretical and Applied Research in Mechanical Engineering, 14(1), 12–16. https://doi.org/10.65521/ijtarme.v14i1.514 DOI: https://doi.org/10.65521/ijtarme.v14i1.514

Hansen, K., and Świderska, A. (2024). Integrating Open- and Closed-Ended Questions on Attitudes Towards Outgroups with Different Methods of Text Analysis. Behavior Research Methods, 56(9), 4802–4822. https://doi.org/10.3758/s13428-023-02218-x DOI: https://doi.org/10.3758/s13428-023-02218-x

Hazarika, C. A. I., Khalfan, J., Ahmed, M., Yousif, A., and Hussain, J. (2024). Role of Fintech as an Enabler to Fulfill HR Requirements and Attain Sustainability. In A. Hamdan and A. Harraf (Eds.), Business development Via AI and Digitalization (Studies in Systems, Decision and Control, Vol. 537). Springer. https://doi.org/10.1007/978-3-031-62106-2_5 DOI: https://doi.org/10.1007/978-3-031-62106-2_5

Jadhav, K. D. (2027). Integrating IoT, Sensors, and Machine Learning for Enhancing Crop Yield and Irrigation Efficiency Systems. In R. R. Janghel, R. Doriya, J. Lachure, and Y. K. Rathore (Eds.), Sustainable Agriculture Production Using Blockchain Technology (1–20). Wiley. https://doi.org/10.1002/9781394248711.ch11 DOI: https://doi.org/10.1002/9781394248711.ch11

Katsaounidou, A., Xylogiannis, P., Baltzi, T., Saridou, T., Papadopoulos, S., and Dimoulas, C. (2025). An AI-Driven News Impact Monitoring Framework Through Attention Tracking. Societies, 15(8), 233. https://doi.org/10.3390/soc15080233 DOI: https://doi.org/10.3390/soc15080233

Li, Y., Karreman, J., and De Jong, M. (2022, July 17–20). Cultural Differences in Web Design on Chinese and Western Websites: A Literature Review. In IEEE International Professional Communication Conference (ProComm). Limerick, Ireland. https://doi.org/10.1109/ProComm53155.2022.00023 DOI: https://doi.org/10.1109/ProComm53155.2022.00023

Liu, P., and Lan, L. (2021). Museum as Multisensorial Site: Story Co-Making and the Affective Interrelationship Between Museum Visitors, Heritage Space, and Digital Storytelling. Museum Management and Curatorship, 36(4), 403–426. https://doi.org/10.1080/09647775.2021.1948905 DOI: https://doi.org/10.1080/09647775.2021.1948905

Melissourgos, G., and Katsanos, C. (2025, September 24–26). UEQ-GR and UEQ-S-GR: Towards a Greek Adaptation of the User Experience Questionnaire and its Short Version. In Proceedings of the 3rd International Conference of the ACM Greek SIGCHI Chapter. Syros, Greece. https://doi.org/10.1145/3749012.3749059 DOI: https://doi.org/10.1145/3749012.3749059

Navaneethakannan, D. V. (2025). A Study on the Impact of Service Quality Dimensions on Consumer Satisfaction Among Electric Two-Wheeler Users in Chennai. International Journal of Research in Digital Marketing and Retailing, 14(2), 79–84. https://doi.org/10.65521/ijrdmr.v14i2.1282 DOI: https://doi.org/10.65521/ijrdmr.v14i2.1282

Novák, J. Š., Masner, J., Benda, P., Šimek, P., and Merunka, V. (2024). Eye Tracking, Usability, and User Experience: A Systematic Review. International Journal of Human-Computer Interaction, 40(8), 4484–4500. https://doi.org/10.1080/10447318.2023.2221600 DOI: https://doi.org/10.1080/10447318.2023.2221600

Pergantis, M., Limniati, L., Lamprogeorgos, A., and Giannakoulopoulos, A. (2025). AI-Powered User Experience Personalization in Academic Digital Art Repositories. In ICERI2025 Proceedings (6781–6789). Valencia: IATED. https://doi.org/10.21125/iceri.2025.1857 DOI: https://doi.org/10.21125/iceri.2025.1857

Rawandale, U. S., and Kolte, M. T. (2021). Design, Development and Analysis of Variable Bandwidth Filter Bank for Enhancing the Performance of Hearing Aid System. International Journal of Intelligent Engineering Systems, 14(6), 391–401. https://doi.org/10.22266/ijies2021.1231.35 DOI: https://doi.org/10.22266/ijies2021.1231.35

Tsita, C., Satratzemi, M., Pedefoudas, A., Georgiadis, C., Zampeti, M., Papavergou, E., Tsiara, S., Sismanidou, E., Kyriakidis, P., Kehagias, D., et al. (2023). A Virtual Reality Museum to Reinforce the Interpretation of Contemporary Art and Increase the Educational Value of User Experience. Heritage, 6(5), 4134–4172. https://doi.org/10.3390/heritage6050218 DOI: https://doi.org/10.3390/heritage6050218

Vasanthan, R., and Nandhini, R. (2019). Kinaesthetic Learning Styles and Activity-Based Content for Tailoring Language Teaching to Diverse Learning Preferences. Man in India, 99(4), 987–995.

Venkata, S. B., Ashirova, A., Karwande, V. S., Aruna, T., Kholiyarov, E., and Singh, S. (2025). Towards Accurate Maritime Surveillance: A Hybrid CNN-Transformer Architecture for Ship Detection in SAR Imagery. In International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3 2025). IEEE. https://doi.org/10.1109/ISAC364032.2025.11156808 DOI: https://doi.org/10.1109/ISAC364032.2025.11156808

Vijayakumar, M., Muniyandy, E., Mirajkar, G., et al. (2026). Intrusion Detection and Localization Using Deep Learning Approaches in VANET Environments. SN Computer Science, 7, 234. https://doi.org/10.1007/s42979-026-04769-0 DOI: https://doi.org/10.1007/s42979-026-04769-0

Țichindelean, M., Țichindelean, M. T., Cetină, I., and Orzan, G. (2021). A Comparative Eye Tracking Study of Usability—Towards Sustainable Web Design. Sustainability, 13(18), 10415. https://doi.org/10.3390/su131810415 DOI: https://doi.org/10.3390/su131810415

Downloads

Published

2026-04-03

How to Cite

M, V., Sripada, P. N., B, S., Kuppusamy, B., T, V. V., & Bist, A. S. (2026). DIGITAL TRANSFORMATION OF PERFORMING ARTS MANAGEMENT: STRATEGIES, CHALLENGES, AND FUTURE DIRECTIONS. ShodhKosh: Journal of Visual and Performing Arts, 7(3s), 14–28. https://doi.org/10.29121/shodhkosh.v7.i3s.2026.7316