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How Can AI ‐Driven Deep Learning Models Forecast the Sustainability of Esports and Its Transformative Impact on Tourism and Hospitality?

Siyu Zhang et al.

International Journal of Tourism Research2026https://doi.org/10.1002/jtr.70271article
AJG 2ABDC A
Weight
0.50

Abstract

This study develops an AI‐driven forecasting model that leverages deep learning and advanced data analytics to assess the sustainability of the esports ecosystem and its impact on tourism and hospitality. Addressing a critical research gap at the intersection of esports and hospitality management, the model offers actionable insights for industry stakeholders while advancing academic understanding. Using a multidisciplinary approach, it systematically examines economic value creation in esports and its role in enhancing sustainable hospitality practices, including boosted tourism, improved customer engagement, and new revenue streams from events and collaborations. Findings indicate that AI and deep learning significantly improve forecasting accuracy, positioning esports as a catalyst for innovation in tourism and hospitality. This research provides a structured framework for future studies and practical guidance for integrating esports into sustainable tourism and hospitality strategies.

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https://doi.org/https://doi.org/10.1002/jtr.70271

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@article{siyu2026,
  title        = {{How Can AI ‐Driven Deep Learning Models Forecast the Sustainability of Esports and Its Transformative Impact on Tourism and Hospitality?}},
  author       = {Siyu Zhang et al.},
  journal      = {International Journal of Tourism Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/jtr.70271},
}

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How Can AI ‐Driven Deep Learning Models Forecast the Sustainability of Esports and Its Transformative Impact on Tourism and Hospitality?

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.