## 4 Results
### Cluster Analysis  
A dendrogram analysis was performed and three clusters were identified. Then a K-Means clustering (SPSS v.25) was used to analyze perceptions of blockchain advantages and applications. Three distinct clusters emerged:  
\begin{table}
\begin{tabular}{l l l l} \hline
**Variable** & **VIF** & **Variable** & **VIF** \\ \hline AppData & 3.06 & DecentralizationTicket & 2.80 \\ AppDescentralization & 3.84 & EuropePerf & 1.15 \\ AppInfo & 3.67 & FinancialSupport & 2.32 \\ AppRevenue & 3.14 & NatPerf & 1.03 \\ AppSponsor & 2.27 & PlayerTokens & 2.65 \\ AppTickets & 3.80 & RewardingFan & 2.69 \\ BestPerform & 1.12 & Smart tickets & 2.85 \\ CryptoSponsorship & 2.14 & WorldSelect & 1.00 \\ \hline \end{tabular}
\end{table}
Table 4: VIF values for each variable.  
Figure 3: Path coefficients. Source: SmartPLS analysis (reprinted from a free version of SmartPLS software, version 3.3.9, created on 30 June 2024) [37].  
Cluster 1 (39.24%): Athletes and trainers lean towards agreement with blockchain benefits (center = 1.17) but neutral regarding blockchain apps (center = 0.44).  
Cluster 2 (28.66%): Respondents were neutral on both blockchain benefits (center = 0.13) and apps (center = 0.19).  
Cluster 3 (32.08%): Strong agreement with blockchain benefits (center = 1.59) and blockchain apps (center = 1.75).  
On a Likert scale (\(-2\): totally disagree, 0: neutral, and 2: totally agree), most participants in Clusters 1 and 3 expressed favorable attitudes toward blockchain benefits. Notably, Cluster 2 reflected neutrality yet continued use of blockchain tools. Statistical significance was established via ANOVA, with high F-values (blockchain benefits: 273.892; blockchain apps: 429.864) and \(p\)-values below 0.05 (Table 5).  
These findings substantiate **H3**, confirming that perceptions of blockchain's advantages and applications vary across professional roles. This supports the broader implications of blockchain adoption in team sports for advancing technology integration and performance optimization.  
The Multivariate Analysis of Variance (MANOVA) based on CryptoSponsorship, PlayerTokens, Smart tickets, DecentralizationTicket, SecureData, Anti-doping, FinancialSupport, RewardingFan, and Subscription results indicate that sport type (football vs. basketball) has a statistically significant effect on blockchain adoption across multiple variables. The key statistical findings are Wilks' Lambda: 0.342, F = 60.61, \(p\) < 0.0001, meaning that sport type significantly influences blockchain adoption. The Pillai's Trace is 0.658, \(p\) < 0.0001, and indicates a large effect size of sport type on blockchain adoption variables. [PERSON]'s Trace and Roy's Largest Root (1.928 and \(p\) < 0.0001) support our hypothesis. The results confirm significant differences in how blockchain is perceived and implemented in football vs. basketball. Based on previous data trends football shows higher adoption in Smart Tickets (UEFA ticketing experiments), fan tokens (Socios.com adoption by major clubs), and decentralized ticketing (FIFA blockchain trials). Basketball leads in financial support (Crypto sponsorships like FTX), NFT collectibles (NBA Top Shot driving blockchain engagement), and athlete performance analytics (NBA integrating blockchain for player tracking). These findings offer empirical support for Hypothesis 4 (H4), suggesting sport-specific differences in blockchain adoption patterns.  
The clustering helped identify patterns in perception, which were then statistically validated through MANOVA.  
While univariate tests may not reveal significance individually, MANOVA considers the collective variation across variables, thereby detecting multivariate effects.