*5.3. Parameter Sensitivity Analysis*

When calculating the comprehensive weight, there is a preference adjustment parameter τ. Therefore, in the process of ranking the candidate construction program managers, the comprehensive perceived utility value p∗ <sup>i</sup> of the construction program managers will be different. This will ultimately affect the ranking of the candidates. The following change parameter τ values τ = 0, τ = 0.2, τ = 0.4, τ = 0.6, τ = 0.8, τ = 1, resulted in different alternative rankings as shown in Figure 3.

**Figure 3.** Ranking of alternative construction program managers.

It can be seen from the sensitivity analysis results that large preference adjustment parameter gives larger weight wj(t) based on Fuzzy-DEMATEL.

When τ = 1, only attribute weights are considered, the first and second rankings in the ranking result will be switched. When τ < 1, it can be found from the figure that as τ decreases to zero, the ranking results of the candidate construction program managers are

consistent and do not change. Based on the above results, on the one hand, when τ = 1, due to ignoring the weight of decision makers, the ranking results change greatly, this indicates that the model may have some instability when only attribute weights are considered, suggesting that decision maker weights should be considered in a comprehensive manner in practical applications. For example, in a construction program, an optimal manager candidate needs to be selected from several alternatives. However, different decision makers may have different views on the importance of the characteristics, so the decision maker weights need to be considered together when ranking. If we only consider the attribute weights and ignore the decision maker weights, it may lead to instability in the ranking results and thus bring negative impact to the selection process. Therefore, in practical application, we need to consider both attribute weights and decision maker weights according to the context in order to better stabilize the ranking results and provide a more credible basis for the selection of construction program managers.

On the other hand, the ranking results did not change when τ < 1. Although, theoretically the different values of the parameter may have an impact on the ranking of the alternative construction program managers. However, if sensitivity analysis of the parameters reveals that the ranking results of the alternatives did not change significantly, this indicates that the model is stable when the parameter changes. This stability may enable decision makers to use the model with more confidence. It is important to note that although the model performs well in such cases, in practical applications, decision makers still need to choose the parameters carefully to ensure reliable results.
