ArticleFrontiers in sports and active living2025
Algorithmic fandom: how generative AI is reshaping sports marketing, fan engagement, and the integrity of sport.
Article in Frontiers in sports and active living, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of different approaches for performance enhancement in elite sport.Frontiers in artificial intelligence · 2026Pooled it
- Value creation in sport business in the age of AI: a multidimensional framework.Frontiers in sports and active living · 2026Review
- Platform power, athlete branding, generative AI, and the future of sport governance-a systematic review.Frontiers in sports and active living · 2025Review
- The costs of participation in and delivery of community sport in Australia-a narrative review.Frontiers in sports and active living · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (AI) is rapidly transforming the landscape of sports marketing by enabling hyper-personalised fan engagement, real-time content delivery, and data-driven commercial strategies. Leveraging technologies such as machine learning, predictive analytics, and automated content generation, sports organisations are increasingly able to tailor fan experiences, anticipate behaviour, and optimise revenue models. While these advancements offer significant opportunities for enhancing interactivity and commercial growth, they also introduce complex ethical, psychological, and regulatory challenges. This paper critically examines the dual nature of generative AI in sports marketing, with a particular focus on consumer autonomy, data monetisation, and the influence of AI-driven personalisation on fan behaviour. Using narrative literature review approach, the paper draws on emerging research, industry cases, and interdisciplinary literature, and explores how algorithmic recommendation systems can manipulate fan decisions, reinforce digital echo chambers, and marginalise underrepresented sports. Special attention is given to the impact of AI on children and adolescents, who are particularly vulnerable to targeted content, gamification, and AI-curated betting environments. The integration of AI in gambling platforms and the commercialisation of fan data raise significant concerns around privacy, consent, and long-term wellbeing. The paper concludes by offering some implementation guidance for sport ecosystem stakeholders. It also outlines a future research agenda that calls for empirical investigations into the long-term effects of AI on fan behaviour, the development of regulatory frameworks to safeguard consumer rights, and interdisciplinary collaboration to design ethical AI systems. By identifying these critical issues, the paper aims to support a more inclusive, transparent, and integrity-focused application of AI in sport.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.