# Investigating Social Contextual Factors
## 2 Materials and Methods
### Predictive Monitoring
As with the methodology used in [14], the training and test set were not temporally disjoint. We chose to utilise the mean absolute error (MAE) to evaluate the accuracy as other measures such as the root mean square error (RSME) are susceptible to outliers and mean percentage error (MAPE) would be skewed towards the end of a case where remaining time tends towards zero [9]. We filter the test set to use only non-censored traces to evaluate the MAE, as these are completed traces, whereas censored traces were abandoned or in-flight as at the time of log extraction. ## 3 Results

### Experimental Results

In the first set of experiments, to explore which of the group centrality factors have the most impact on the cycle, we created a network that visually illustrates the effect high and low values of the grouped centrality measures on case cycle times (see Figure 3a-d below). Each node represents a case, with cases which share common performers connected and the edges weighted by the number of shared performers. The size of the nodes is proportional to the relevant grouped centrality measure. The network appears to show an inverse relationship between cycle time and group degree and eigenvector centrality while the opposite is the case with the group closeness and betweenness centrality measures. We explore these apparent relationships between the group centrality measures and trace cycle times further. Table 2 shows the Spearman rank correlation between each group centrality measure and the trace cycle time (with all the values statistically significant at the 95% confidence level in bold font). This test was selected to determine the strength and direction of the monotonic relationship between these measures. The group closeness centrality is the most strongly correlated measure to the trace cycle time, followed by the group eigenvector centrality. The group betweenness and closeness centrality were generally positively correlated while the group eigenvector centrality was generally negatively correlated. We delve into the team effectiveness literature to sheds some light on these results. As [21] posits, \"maintaining strong ties with people outside the team is an important determinant of team success\". We argue that these results may have implications for team setup as they shed light on the nature of these \"ties\". For example, many organisations create specialised cells or SWAT teams to handle certain types of cases, e.g., complex cases. As a result, these teams could become isolated from other process performers which increases their probability of \"failing\" [32]. The results would seem to imply that connecting the teams to other influential performers in the organisation (high eigenvector centrality) will result in shorter cycle times, perhaps as a result of the ability of these performers to resolve issues relatively quickly. This would suggest that where such cells exist, it would be desirable to work cases \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GB** & **GC** & **GE** & **GD** \\ \hline BPIC 18 & \(\mathbf{-0.058}\) & \(\mathbf{-0.014}\) & \(\mathbf{0.248}\) & \(\mathbf{0.107}\) \\ BPIC 17 & \(\mathbf{0.063}\) & \(\mathbf{0.176}\) & \(\mathbf{0.063}\) & \(\mathbf{0.093}\) \\ BPIC 15(3) & \(\mathbf{0.289}\) & \(\mathbf{0.414}\) & \(\mathbf{-0.208}\) & \(\mathbf{-0.045}\) \\ BPIC 14 & \(\mathbf{0.078}\) & \(\mathbf{0.119}\) & \(\mathbf{-0.183}\) & \(\mathbf{-0.171}\) \\ BPIC 12 & \(\mathbf{0.877}\) & \(\mathbf{0.848}\) & \(\mathbf{-0.475}\) & \(\mathbf{-0.003}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spearman Rank Correlation between group centrality measure and trace cycle time. with performers outside their cell periodically. Intuitively, this will increase the sharing of knowledge and experience across the organisation. On the other hand, lower group betweenness across groups appears linked to lower cycle times.