Riding the Digital Wave: Exploring Athletes' Adoption of Mixed-Reality Sports Platforms

Citation

Botwina G, Westmattelmann D, Hong HJ & Kolyperas D (2024) Riding the Digital Wave: Exploring Athletes' Adoption of Mixed-Reality Sports Platforms. European Association for Sport Management 2024, Paris, France, 30.09.2024.

Abstract
1. Aim and Research Questions The growing importance of digitally mediated sports underscores the need to understand the factors driving athletes to adopt Mixed-Reality (MR) sports platforms. This insight is crucial for developing strategies that enhance athletes' engagement with these innovative technologies (Hoeber et al., 2023). The aim of the present study is to identify contributing factors to the adoption of these platforms, using the example of the indoor cycling platform Zwift. The research question is as follows: What factors influence athletes' intentions to use and their actual usage behavior of MR sports platforms? 2. Literature Review and Theoretical Background The sports industry's digital transformation has significantly changed how sports activities are conducted, experienced, and valued, occurring in two main waves. The first wave, digitalization, has seamlessly integrated digital technologies into various facets of sports, including training, talent recruiting, refereeing, and fan engagement (e.g., Märtins et al., 2023). The second wave, virtualization, gained prominence, especially highlighted during the Covid-19 pandemic (Rojas-Valverde et al., 2022, Westmattelmann et al., 2021b). This era ushered in the adoption of MR technologies to forge interactive and immersive sports experiences (Smith & Skinner, 2022). Prominent first examples, such as the Virtual Olympic Series 2021 and the Virtual Tour de France, demonstrate that traditional sports stakeholders are increasingly engaging in virtual sports (Westmattelmann et al., 2021b). Building on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2; Venkatesh et al., 2012), our theoretical model examines the determinants of athletes' intentions and actual usage of MR sports platforms. Acknowledging the observed gaps between intention and actual behavior in sports (Theodorakis, 1992), we enhance the UTAUT2 framework by incorporating three contextual factors as antecedents of actual use behavior: performance level, urbanization, and workload. 3. Research Design, Methodology and Data Analysis To address the research question, we investigate the adoption of MR sports platforms, using Zwift as a popular example. Zwift allows athletes to measure their performance using specific interface devices (smart trainers) and translate this data into a virtual world where they are represented by avatars. Beyond training, users can also compete in scheduled races against others or customize their virtual bikes. This study adopts a Mixed-Methods approach following the guidelines provided by Venkatesh et al. (2013). This approach integrates both quantitative (Study I) and qualitative (Study II) research methodologies, facilitating a more comprehensive understanding of the research problem by leveraging the strengths of both methods. In Study I, we test our theoretical model on the adoption of MR sports platforms using survey and In-App Data from 515 Zwift users. For analysis, covariance-based structural equation modeling is utilized (Kline, 2023). In Study II, we conduct 20 semi-structured interviews with Zwift users and perform thematic analysis (Braun & Clarke, 2006) to corroborate the quantitative findings and provide deeper insights into user experiences. 4. Results and Discussion The results from Study I reveal that performance expectancy, hedonic motivation, and price value increase users' intentions to continue using Zwift. These findings suggest that the perceived benefits, enjoyment, and cost-effectiveness of Zwift are key drivers of user retention. Conversely, we did not observe significant effects from effort expectancy, facilitating conditions, and social influence on users' continuance intentions. Furthermore, continuance intention demonstrates a medium effect on the actual use of Zwift. This finding underscores the importance of users' intentions in translating into actual engagement with the platform. Among the context factors examined, users' performance level is the only context factor that increases actual use. This suggests that higher-performing users engage more frequently with Zwift. In contrast, the level of urbanization in the users' place of residence and their workload do not significantly affect the actual use of the platform. The qualitative content in Study II corroborates the quantitative results, providing insight into the factors that encourage participation in Zwift. By exploring participants' motivations for starting and continuing to use Zwift, as well as the benefits that sustain their involvement, the results indicate that local weather and infrastructure initially drive users to engage with the platform. Once engaged, the platform's easy access and training and performance features affect decisions to continue subscriptions. Importantly, many participants not only use the platform for fitness but also engage in rigorous training and racing to prepare for outdoor cycling competitions. In this context, Zwift serves as a critical tool, significantly aiding users in achieving their athletic goals. 5. Conclusion Our mixed-methods study found that perceived benefits, enjoyment, and cost-effectiveness drive user retention on MR sports platforms like Zwift, with user intentions closely translating into actual usage. Theoretically, these findings enhance our understanding of the adoption of highly immersive technologies in sports and the reasons for the intention-behavior gap. Practically, sports organizations can leverage these insights to strategically develop virtual sports, focusing on aspects that enhance user engagement and satisfaction, effectively complementing traditional sports.