## 4 Results on Australian HSR Risks
### Survey Results
#### 4.1.2 Analysing Survey Data Using the AHP Method  
The AHP method used in this research begins by assigning weightings to survey respondent attributes. The background questions include years of experience, field of work, and major project sector in which the participant has experience. Trivially, prioritisation is given to input from more experienced participants, particularly those involved in relevant project management of rail projects. However, this is also a notable limitation of the AHP method as those who are not as experienced could also provide valuable input. Nevertheless, the weightings given to responses from individuals with certain industry and work experience characteristics ensure that these subsets of individuals hold greater significance. Additional details on industry sectors and work experience roles are provided in Table 1. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Criteria** & **Options** & **Multiplier** \\ \hline \multirow{4}{*}{Years of Experience (Y)} & 21+ & 0.558 \\ \cline{2-3}  & 11–20 & 0.263 \\ \cline{2-3}  & 6–10 & 0.122 \\ \cline{2-3}  & 0–5 & 0.057 \\ \hline \multirow{4}{*}{Field of Work (F)} & Project Management & 0.633 \\ \cline{2-3}  & Infrastructure Advisory & 0.260 \\ \cline{2-3}  & Other & 0.106 \\ \hline \multirow{4}{*}{Major Projects Sector (P)} & Rail & 0.418 \\ \cline{2-3}  & Water & 0.212 \\ \cline{2-3}  & Energy & 0.110 \\ \cline{2-3}  & Commercial Buildings & 0.067 \\ \cline{2-3}  & Industrial & 0.110 \\ \cline{2-3}  & Other & 0.067 \\ \cline{2-3}  & Empty & 0.018 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Survey respondent characteristic multipliers. Figure 6: Participants’ demographic. These weightings were then used to construct pairwise comparison matrices of respondent attributes, where the relative importance of different experience levels (e.g., 21+ years vs. 11-20 years) was compared. The comparisons were guided by expert judgement and structured using the standard AHP 9-point scale, where higher scores reflect greater relative importance. For example, participants with over 21 years of experience were considered significantly more influential than those with 11-20 years and were assigned a score of 7. Similarly, those with 11-20 years were rated 5 compared to respondents with 6-10 years, and so on. While these judgments were somewhat subjective, they followed a consistent logic based on perceived expertise and relevance. This process was repeated for each attribute: Years of Experience, Field of Work, and Major Projects Sector. The resulting matrices were used to calculate priority vectors (or multipliers) using the eigenvector method. To calculate the final respondent weightings, each pairwise comparison matrix was normalised by dividing each element by the sum of its respective column. The average of each row in the normalised matrix was then computed to produce a priority weight for each respondent category, representing its relative importance. These weights were subsequently used to adjust the influence of each participant's input in the broader analysis. Although the specific values were based on researcher discretion and are not empirically validated, the primary aim was to establish relative importance across groups. Table 1 presents the final respondent multipliers used to weight responses according to participant attributes. The current weighting system introduces some mathematical inconsistencies. For instance, the weighting for participants with 6-10 years of experience is three times higher than those with 0-5 years. Similarly, the weighting for 11-20 years is three times higher than that for 6-10 years. However, the difference between 11 and 20 years and 0-5 years is fivefold, which creates an imbalance in proportional increases. This reflects the limitations of using expert-driven pairwise comparisons, which can be subjective. A more refined and consistent weighting approach is recommended for future research. Survey responses were collected on a 1-5 Likert scale (1 = low sentiment, 5 = high sentiment).