## 3 Methodology of the Systemic Design of the Model
### Model Execution  
The model is first executed with the help of the relevant software for the variable values referred to in Table 3 (Example 1), where the values were derived from the mean scores. We collected data from the calculations of time performed over the program and the interviews of the personnel, thus attributing to the factors specific percentages. Figure 6 shows the initial stage of the model execution based on the values of Table 3, while Figure 7 shows the final stage of model execution for a period of 60 months. All figures are the results of running experiments for different values of the variables in the AnyLogic cloud. The control panel interface enables the user of the model to change the values of the parameters that they are interested in and to see the behavior of the model over time before deciding to make changes to any of them. The end user of the model is anyone who wishes to predict the positive or negative results of any change that they wish to make and decide accordingly. The academicians' transition from the TimeOFF_Academician state to the Motivated_Academician and the TimeON_Academician states over time is demonstrated in the graphs of Figure 8.  
Figure 8: The academicians’ transition from the TimeOFF_Academician state to the Motivated_Academician and the TimeON_Academician states over time. Figure 7: The final stage of model execution for a period of 60 months, where all members of the personnel have become TimeON_Academicians. The tasks' progress over time is depicted in Figure 9.  
To demonstrate the importance of the WOM effect in the transition process and the effects on the tasks' progress, we executed the model for the values of the variables shown in Table 3, but we changed the contact rate from 0, as mentioned in Table 3, to 2 (Example 2). WOM is the positive effect of word-of-mouth information dissemination, which is supposed to be a spontaneous process among colleagues. In this sense, we tested the model, enabling different factors to be more evident according to the conditions of the group of dentist academicians in the Department of Dentistry, while the model was executed. The experiment is still ongoing. To test the reliability of the process, we conducted several experiments with different parameter values. The resulting graphs are depicted in Figures 10 and 11 for Example 2. If we run the model for values presented in Table 3 and for its variation in Example 2, we have the graphs presented in Figures 12 and 13 where red and blue colors suggest differences over time in the execution of the model for the two cases. Figure 11: Graphs showing the tasks over time for the variables in Example 2. Figure 12: Graphs for the comparison of the executions of WOM motivation in Example 1 and 2. In Figure 13, a comparison is given for the TimeOFF academicians, the motivated academicians, the TimeON academicians, and the combination of all three categories. According to Example 1, using the values of Table 3, all TimeOFF academicians become TimeON academicians after 51 months, while, according to Example 2 (values A of Example 2) (Table S1), all Time OFF academicians become Time ON academicians after 16 months. We can observe the much faster transition of the academicians from the TimeOFF to the TimeON state, as well as the larger number of motivated academicians resulting from the contacts and the word-of-mouth effect. As a result, a higher peak can be observed in the original tasks as well as in the progress over time, as shown in Figure 13.  
Next, we present some variations of the A values of Example 2, forming Example 3 of TASM's execution (Table 4). According to Table S2 (Supplementary Materials), using the B values of Table 4, all TimeOFF academicians become TimeON academicians after 16 months, while, according to Table S3 (Supplementary Materials), using the C values of Table 4, all TimeOFF academicians become TimeON academicians after 12 months.