## 4 Results and Discussion
### Discussion on the Statistical Tests of H3 and H4  
In the following, statistical results concerning our research hypotheses on categories of main type of bike are discussed. In particular, such hypotheses--which are negatives of the null ones reported in Table 8b--state that:  
**Hypothesis 3 (H3). **_The profit distribution varies across categories of bike model._  
**Hypothesis 4 (H4). **_The profitability distribution varies across categories of bike model._  
As a first remark, both profit and profitability distributions vary across categories of main type of bike (Table 8b). In this case, multiple comparisons were performed because the overall test does show significant differences across such categories. In detail, the statistical analysis proves how profit and profitability distributions change according to the main type of bike utilized in cycle logistics projects and, thus, recognizes that some bike models have a relevantly higher impact than others on the likelihood of success of bike delivery businesses. Multiple comparisons concerning profit and profitability distributions across categories of the project-specific variable have been performed in order to understand which types of bike have the most significant impact on profit and profitability. The multiple comparisons have been analyzed by means of adjustment using the Bonferroni correction for multiple tests and they are reported in Figure 2 (concerning profit) and Figure 3 (concerning profitability). In Figure 2, the pairwise comparison for profit shows the highest value for the node representing projects using contemporarily cargo bikes and tricycles. Note that this result is coherent with the data reported in Table 5, where the mean values associated with the two bike models used together are the highest ones for profit and also for profitability. Hence, that appears to be the best modal configuration. Furthermore, tricycles and cargo bikes taken separately prove to have a relatively significant impact on the profit variable and to generate relatively high values for both profit and profitability. By contrast, traditional or trailer bikes seem to not to provide such a relevant impact on profit, even though traditional bikes have a profitability mean value higher than the overall average in Table 5. On the contrary, if we analyze the paired use of different modal configurations, trailer bikes combined with big-sized bike models--i.e., either cargo bikes or tricycles or both of them--appear to be the best matched choice. In particular, trailer bikes combined with cargo bikes and/or tricycles seem to broadly cover the whole demand span. In fact, such configurations of paired bike models can satisfy altogether the delivery needs of different goods-- i.e., small- to big-sized--and cope flexibly with different topological features and network shapes at the same time. As further proof, the trailer-tricycle-cargo bike configuration including all those types of bikes is the one having the highest impact on profit distribution among the three significant configurations in Figure 2. In fact, it covers a more extended demand span than either the trailer-tricycle or trailer-cargo bike configurations. In Figure 3, the pairwise comparison for profitability shows the two highest values for those nodes representing projects exploiting cargo bikes and tricycles together as well as those adopting traditional bikes. Again, note that this result is coherent with the data reported in Table 5, where the mean values associated with the two bike models used together are the highest ones for profitability and also for profit. Analogously, traditional bikes have one of the highest values in terms of average profitability in Table 5. Hence, those appear to be the best modal configurations. The node representing tricycles, taken separately, has a relatively high value as well, but the possible matches with other modal solutions do not show statistical significance.