Brain and behavior · 2025
Enhancing Marathon Enthusiast Engagement Through AI: A Quantitative Study on the Role of Social Media in Sports Communication.
СтатьяНазвание и аннотация: Перевод в очереди
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Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Cheng Wei, Tian Yu, Na Meng
Аннотация
PURPOSE: This study explores the impact of AI-driven personalization, interactive features, and real-time feedback on user engagement and experience among marathon enthusiasts.
METHOD: By integrating uses and gratifications theory (UGT), self-determination theory (SDT), and the technology acceptance model (TAM), the research examines how these AI-driven elements influence user behavior on marathon-related social media platforms. A quantitative approach using partial least squares structural equation modeling (PLS-SEM) was applied to data from 400 Chinese marathon enthusiasts.
FINDINGS: The findings reveal that AI-driven personalized content significantly enhances user engagement and experience, with user engagement partially mediating this relationship. Interactive features are crucial for building a sense of community but have a less direct impact on user experience. Real-time feedback significantly improves user engagement, particularly for users with higher technological proficiency.
CONCLUSION: This research contributes to the understanding of user engagement in AI-enhanced environments and provides practical insights for designing more personalized and interactive platforms for marathon enthusiasts. Future studies should explore the long-term effects, cultural factors, and ethical considerations of AI-driven personalization.
METHOD: By integrating uses and gratifications theory (UGT), self-determination theory (SDT), and the technology acceptance model (TAM), the research examines how these AI-driven elements influence user behavior on marathon-related social media platforms. A quantitative approach using partial least squares structural equation modeling (PLS-SEM) was applied to data from 400 Chinese marathon enthusiasts.
FINDINGS: The findings reveal that AI-driven personalized content significantly enhances user engagement and experience, with user engagement partially mediating this relationship. Interactive features are crucial for building a sense of community but have a less direct impact on user experience. Real-time feedback significantly improves user engagement, particularly for users with higher technological proficiency.
CONCLUSION: This research contributes to the understanding of user engagement in AI-enhanced environments and provides practical insights for designing more personalized and interactive platforms for marathon enthusiasts. Future studies should explore the long-term effects, cultural factors, and ethical considerations of AI-driven personalization.