# Metaverse and Fashion: An Analysis of Consumer Online Interest
## 7 Results
### Kruskal-Wallis Test  
In order to determine whether all the analysed platforms behaved in the same way or not when searching for the terms \"metaverse & fashion\", a Kruskal-Wallis test was carried out. This non-parametric test is used to determine whether a set of data comes from the same population. For this reason, and as shown in Table 4, the first step was to assign the average range to each group, defined as:  
Figure 4: Interest in the concepts “metaverse” and “fashion” in Google News in 2022. Source: Google Trends (2023). Figure 5: Interest in the concepts “metaverse” and “fashion” in Google Shopping in 2022. Source: Google Trends (2023). As the main result and following the data in Table 5, as the \(p\)-value (asymptotic significance) is less than 0.05, the null hypothesis that the population medians are equal is rejected. Therefore, we conclude that, with a significance level of 5%, the data differ between the five corporate communication platforms. That is, the population medians are not equal. The analysis carried out is based on a Kruskal-Wallis test. This is a non-parametric statistical test used to determine whether there are significant differences between two or more independent groups on a continuous or ordinal outcome variable [43]. It is often used as an alternative to the one-way analysis of variance (ANOVA) test when the data do not meet the assumptions of normality and equal variances. The Kruskal-Wallis test ranks the data from all groups together, calculates the sum of ranks for each group, and compares these sums to determine whether they differ significantly [44]. The test is based on the null hypothesis that the medians of all groups are equal. To conduct the Kruskal-Wallis test, we carried out the following steps:  
1. Rank the data from all groups together. 2. Calculate the sum of ranks for each group. 3. Calculate the test statistic, H, using the formula:  
\[\text{H}=[(12/(\text{n}(\text{n}+1)))\times\text{sum}(\text{T}\text{j}^{2})]- 3(\text{n}+1)\]  
where Tj is the sum of ranks in group j and n is the total number of observations. 4. Calculate the degrees of freedom (df) using the formula:  
\[\text{df}=\text{k}-1\]  
where k is the number of groups. 5. Determine the \(p\)-value using the appropriate distribution table on the statistical software. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **V1** & **N** & **Mean Range** \\ \hline \multirow{4}{*}{V2} & 1 & 52 & 176.31 \\ \cline{2-4}  & 2 & 52 & 132.49 \\ \cline{2-4}  & 3 & 52 & 147.78 \\ \cline{2-4}  & 4 & 52 & 97.39 \\ \hline \multirow{4}{*}{V2} & 5 & 52 & 98.53 \\ \cline{2-4}  & Total & 260 & \\ \hline \end{tabular} Source: Authors.  
\end{table} 
Table 4: Mean ranges, Kruskal–Wallis test. \begin{table}
\begin{tabular}{l c} \hline \hline  & V2 \\ \hline Kruskal–Wallis H & 48.322 \\ \hline gl & 4 \\ \hline Asymptotic sig. & 0.000 \\ \hline \end{tabular} \({}^{*}\)Grouping variable: V1. Source: Authors.  
\end{table} 
Table 5: Test statistics, Kruskal–Wallis test *. In our case, the \(p\)-value is less than the level of significance (e.g., 0.05), so we can reject the null hypothesis and conclude that there are significant differences between the analysed platforms. As already indicated, and to conclude, the Kruskal-Wallis H test confirms that Google's five communication platforms do not behave in the same way. Google's platforms exhibit different search behaviours due to various factors. First, each platform is designed to meet different search needs. For example, Google Search is designed to provide quick answers to general questions and queries, while YouTube focuses on video results and Google Maps on geographic information [45]. Moreover, the platforms also use different ranking algorithms and metrics to rank and display search results. Each platform has its own set of ranking factors, which can include relevance, authority, freshness, and geographic location [45]. On the other hand, each platform has different usage patterns and audiences.