Artificial Intelligence and Sport Business
Integrating robust datasets, learning algorithms, software programs, and hardware infrastructure, artificial intelligence (AI) has been exponentially elevating human society’s problem-solving capacity and accordingly reshaping the landscape of sport industry. Noted in Russell and Norvig’s seminal Artificial Intelligence: A Modern Approach (2009), AI is the system that acts and thinks as rationally as humans, which has been witnessed in daily sport business operations (PwC, 2019). The sport industry now is embracing AI at an unprecedented rate and this could only be accelerated in the coming years. Salient impacts of AI on sport industry include:
- Large capacity for processing sport data. Sport business produce multitudes of data featured with high volume, high velocity (frequently updated), and high variety (structured-unstructured), but AI enables us to capture and analyze this big data in sport. Noteworthy, AI provides solutions for handling texts and images via groundbreaking techniques such as natural language processing and spectral and spatial transformations. Rich data, combined with appropriate learning algorithms and necessary infrastructure, lay a solid foundation for sound decision-making in sport business operations.
- Robust learning algorithms in mining sport data and modeling sport phenomena. AI techniques generally have loose assumptions when compared with classical statistical models and can perform better analyses when handling big data, especially when the main analytical purpose is to maximize the prediction. A wide spectrum of supervised and unsupervised learning algorithms, such as random forests, gradient boosting, reinforcement learning, recurrent neural networks, and multilayer perceptron, offer cutting-edging analytical solutions to model sport business phenomena.
- Exceptional human-computer interaction. Largely built upon relationship management, sport service quality highlights the interactivity between provision and consumption parties. The latest generative AI systems employing natural language processing and reinforcement learning from human feedback (RLHF) such as ChatGPT by OpenAI have tremendous potential to elevate human-computer interactions in sport consumptions. High quality human-computer interaction is also exemplified with personalized marketing communications in such settings as game attendance, product recommendation, event promotion, social media content provision, and cross-cultural brand management.
- Rich product provision and high-caliber product design. With hardware and software advancement, sophisticated sport data, services, and goods enrich the product provision for both fans and organizations, such as movement tracking systems, AI-enabled referee systems, virtual reality training systems (e.g., STRIVR), and wearable technology. Impacts of AI are even more significant on tech-heavy esports, especially first-person shooter games, real-time strategy games, and sport video games, in which AI systems directly determine the quality of product design and user experience.
In addition to the identified positivities, critical issues and challenges have also been raised in the process of integrating AI into sport industry, including but not limited to data privacy, technology diffusion, consumer autonomy induced by algorithm bias, talent training, legal regulations, and broad social impacts. Though pioneer research efforts have been made to address certain aspects of the aforementioned agenda (e.g., Kunz & Santomier, 2019; Mao, 2021; Naraine & Wanless, 2020; Watanabe, Shapiro, & Drayer, 2021), more systematic and thorough research inquiries are warranted to sustain the prosperity of integrating AI into the sport industry. A special issue with a focus on AI in sport business echoes the IJSMS’s position as the prominent nexus of academia and sport industry. Consistent with the aims and objectives of the IJSMS, this special issue seeks research contributions that could advance the existing knowledge base of leveraging AI for a robust sport industry. A wide spectrum of qualitative, quantitative, or mixed-method research efforts that meet the following general criteria are welcome:
- Being theoretically and/or practically relevant to AI in sport business, preferably with empirical evidence.
- Being innovative in terms of new theoretical perspectives, concepts, technologies, procedures, and/or research approaches. Innovations in other aspects are also welcome.
- Focusing on sport entities that include but not limited to consumers, event organizers, athletes, teams, governing bodies, agency companies, and other sport goods/services providers.
List of topic areas
Topics of interest include, but are not limited to, the following:
- AI, service quality, and service innovation
- Fan behavior under AI settings
- AI and sponsorship activation
- Sportscape and AI
- Data management in sport business
- Data privacy and security in sport business
- Analytics and decision making
- AI for optimizing sports marketing strategies
- Sustainability and AI
- Human-computer interaction in sport
- Gamification and esports design
- Personalized marketing communications in sport business
- AI diffusion in sport business
- Legal and regulatory issues
- AI talent training for sport business
- Social impacts of integrating AI into sport business
- Industry insights
Dr. Jerred Junqi Wang,
Miami University (OH),
Dr. Luke L. Mao,
University of New Mexico,
Dr. Brandon Mastromartino,
San Diego State University,
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Closing date for manuscripts submission: 30 June 2024
Barlow, A., & Sriskandarajah, S. (2019). Artificial intelligence: Application to the sports industry. PricewaterhouseCoopers. https://www.pwc.com.au/industry/sports/artificial-intelligence-application-to-the-sports-industry.pdf
Kunz, R. E., & Santomier, J. P. (2019). Sport content and virtual reality technology acceptance. Sport, Business and Management: An International Journal, 10(1), 83-103.
Mao, L. L. (2021). Understanding retail quality of sporting goods stores: a text mining approach. International Journal of Sports Marketing and Sponsorship, 22(2), 330-352.
Naraine, M. L., & Wanless, L. (2020). Going all in on AI: Examining the value proposition of and integration challenges with one branch of artificial intelligence in sport management. Sports Innovation Journal, 1, 49-61.
Russell Stuart, J., & Norvig, P. (2009). Artificial intelligence: A modern approach. Hoboken, NJ: Prentice Hall.
Watanabe, N. M., Shapiro, S., & Drayer, J. (2021). Big data and analytics in sport management. Journal of Sport Management, 35(3), 197-202.