Bridging Regional Talent and Niche Events through Artificial Intelligence
Abstract
This study examines how digital dining in Bengaluru has evolved and how these shifts are influencing consumer behaviour and restaurant operations.
The research uses secondary data from industry reports, academic studies, and market analyses on food delivery, digital payments, and restaurant technologies. A thematic review approach was applied to identify major trends shaping the digital dining ecosystem.
Digital dining in Bengaluru has expanded rapidly due to smartphone use, convenience-driven consumers, and strong platform ecosystems. Restaurants increasingly adopt delivery platforms, digital menus, and data-driven tools. Key challenges include high aggregator commissions, operational pressure, and heavy dependency on platforms.
The study offers a focused understanding of how technology is reshaping urban dining markets, using Bengaluru as a leading example of digital transformation in foodservice.
Findings are based solely on secondary data and may not capture deeper behavioural nuances.
Insights can guide restaurants in planning technology adoption and improving customer experience.
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[3] Biega, A. J., Gummadi, K. P., & Weikum, G. (2018). Equity of attention: Amortizing individual fairness in rankings. Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, 405–414. DOI: https://doi.org/10.1145/3209978.3210063
[4] Braun, V., & Clarke, V. (2022). Conceptual and design thinking for thematic analysis. Qualitative Psychology, 9(1), 3–26. DOI: https://doi.org/10.1037/qup0000196
[5] Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
[6] Cui, X. (2025). Urban tourism management based on artificial neural networks analysis and data mining. Scientific Reports, 15(1), 19709. DOI: https://doi.org/10.1038/s41598-025-01237-2
[7] Deldjoo, Y., Jannach, D., & Bellogín, A. (2024). Fairness in recommender systems: Research landscape and future directions. User Modeling and User-Adapted Interaction, 34(3), 571–615. DOI:https://doi.org/10.1007/s11257-023-09364-z
[8] Duxbury, N. (2021). Cultural and creative work in rural and remote areas: An emerging international conversation. International Journal of Cultural Policy, 27(6), 753–767. DOI:https://doi.org/10.1080/10286632.2020.1837788
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[10] Hassouni, A., & Mellor, N. (2025). AI in the United Arab Emirates’ Media Sector: Balancing Efficiency and Cultural Integrity. Journalism and Media, 6(1), 31. DOI: https://doi.org/10.3390/journalmedia6010031
[11] Johnston, M. P. (2017). Secondary data analysis: A method of which the time has come. Qualitative and Quantitative Methods in Libraries, 3(3), 619–626. DOI: https://doi.org/10.1177/2158244014522633
[12] Keiper, M. C., Dredge, D., & Brown, L. (2023). ChatGPT in practice: Increasing event planning efficiency through conversational AI. Journal of Hospitality, Leisure, Sport & Tourism Education, 33, 100456. DOI:https://doi.org/10.1016/j.jhlste.2023.100456
[13] Klimashevskaia, A., Jannach, D., Elahi, M., & Trattner, C. (2024). A survey on popularity bias in recommender systems. User Modeling and User-Adapted Interaction, 34(4), 1257–1299. DOI:https://doi.org/10.1007/s11257-024-09406-0
[14] Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.
[15] Neuhofer, B., Magnus, B., & Celuch, K. (2021). The impact of artificial intelligence on event experiences: Co-creation and co-destruction perspectives. Electronic Markets, 31(3), 689–705. DOI:https://doi.org/10.1007/s12525-020-00433-4
[16] Nieborg, D. B., & Poell, T. (2018). The platformization of cultural production. New Media & Society, 20(11), 4275–4292. DOI: https://doi.org/10.1177/1461444818769694
[17] Ruggiano, N., & Perron, B. E. (2018). The use of secondary data in social research: Opportunities and challenges. Social Work Research, 42(4), 213–222. DOI: https://doi.org/10.1093/swr/svy026
[18] Silva, S. R., Marques, C. S. E., & Galvão, A. R. (2024). Where is the rural creative class? A systematic literature review about creative industries in low-density areas. Journal of the Knowledge Economy, 15, 6026–6056. DOI: https://doi.org/10.1007/s13132-023-01341-6
[19] Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
[20] UNESCO. (2022). Re|Shaping policies for creativity: Addressing culture as a global public good. UNESCO Publishing. DOI: https://doi.org/10.58337/OILN3726
[21] Wang, Y., Wang, F., He, X., Huang, T., & Chua, T.-S. (2022). A survey on the fairness of recommender systems. ACM Computing Surveys, 55(6), 1–37. DOI: https://doi.org/10.1145/3547333
[22] Zhao, X., Guo, X., & Hao, W. (2024). The impact of the digital economy on creative industries development: Evidence from China. PLOS ONE, 19(5), e0299232. DOI: https://doi.org/10.1371/journal.pone.0299232
Published
2026-02-27
How to Cite
TIVARY, Disha; KANNAN, Ashwin.
Bridging Regional Talent and Niche Events through Artificial Intelligence.
Journal of Environmental Management and Tourism, [S.l.], v. 17, n. 1, p. 30 - 41, feb. 2026.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/9340>. Date accessed: 04 mar. 2026.
doi: https://doi.org/10.14505/jemt.v17.1(81).03.
Section
Article
Copyright© 2025 The Author(s). Published by ASERS Publishing 2025. This is an open access article distributed under the terms of CC-BY 4.0 license.