## 6 Limitations and Future Directions  
Despite the promising results, this study has certain limitations. The sample is limited to Romanian team sports, which may restrict the generalizability of the findings to other contexts or individual sports. Moreover, while the study employed validated tools such as structural equation modeling (SEM) and cluster analysis, qualitative insights from in-depth interviews or case studies could provide a richer understanding of blockchain's real-world applications. While HTMT is considered a more robust indicator of discriminant validity, it was not available in the current version of SmartPLS. Therefore, we relied on the Cornell-Larcker criterion, which showed acceptable discriminant validity across constructs. Future work will incorporate HTMT for enhanced rigor.  
While formal pre-validation was not conducted, the questionnaire was based on previously published frameworks and the lead author's coaching experience. While the sample distribution mirrors actual adoption trends in Romanian team sports, the use of convenience sampling introduces limitations in generalizing the results. Specifically, the imbalance between the football and basketball respondents may constrain the statistical power of subgroup comparisons. This limitation is particularly relevant for the interpretation of comparative analyses across sports and professional roles, as the unequal group sizes may affect variance homogeneity and the robustness of inferential statistics.  
Due to the cross-sectional and perceptual nature of our data--based on the informed opinions of individuals with direct experience using the technology--no causal inferences can be made, although their insights provide valuable practical perspectives.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Blockchain Use Case** & **Sports** & **Finance** & **Healthcare** & **Supply Chain** \\ \hline \multirow{3}{*}{Smart Contracts} & Used for player & Used for cross-border & Used for insurance & Used for automating vendor contracts and management & Used for automating vendor contracts and shipments \\  & contracts and sponsorships & & & & \\  & & & & \\  & Protects athlete & & & \\  & performance and & & & \\  & health data & & & \\  & & & & \\  & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} NFTs and Digital \\ Ownership \\ \end{tabular} } & Used for fan & Used for digital art & Potential for patient & Used for tokenized \\  & engagement and & and music royalties & data ownership via & tracking of \\  & sports collectibles & & & NFTs & high-value goods \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Decentralized \\ Governance \\ \end{tabular} } & Used in fan voting & Used in & & & & \\  & decision-making & Decentralized & Not widely adopted & & \\  & (DAOs) & Finance (DeFi) & & yet & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Blockchain benchmarking against other sectors.  
We acknowledge the potential influence of social desirability bias and model-driven response regularity as a limitation. Future qualitative research would help validate the observed clusters. Variables such as funding differences, coaching practices, or organizational culture may mediate or confound the perceived benefits of blockchain.  
Future research should explore the scalability of blockchain applications across diverse sports disciplines and geographic regions. Additionally, longitudinal studies could examine the sustained impact of blockchain adoption on athletic performance over time. Investigating the ethical and regulatory implications of blockchain in sports, particularly concerning data privacy and intellectual property, would also be valuable.  
By addressing these gaps, future studies can contribute to a more comprehensive understanding of how blockchain technology can revolutionize the sports industry.