## 4 Research Findings
### Agility Models in the Healthcare Industry  
The preceding synthesis analysis laid the foundations for the four agility constructs and shed light on potential observable variables that can infer these constructs at different cluster levels. In essence, we have identified which variables can explain agility. However, a crucial aspect remains to be explored--understanding what enables agility and what agility, in turn, enables. This pertains to the empirical agility models in the 21 selected papers.  
Tables 10-13 provide concise summaries of the models featured in these papers to address this question. Latent variables within each model have been meticulously documented, organized by agility level, and categorized as predictors (variables with a positive effect on agility, essentially independent variables of agility), responders (variables positively influenced by agility, effectively serving as dependent variables of agility), mediators (variables that mediate the impact of another variable on agility), and moderators (variables that moderate the relationship).  
Furthermore, the \"variable order\" column specifies the variable level, where an order one variable denotes a reflective construct, while order two signifies a formative construct. The \"path coefficient\" column quantifies the strength of the relationship between the two variables, representing the extent to which the predictor may elucidate the results of the responder. Finally, the \"significance\" column underscores whether the relationship holds statistical significance. For this analysis, a significance level of 0.05 was selected as the baseline threshold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Clustered** & **Author** & **Latent Variable Name** & **Variable Order** & **Path Coefficient** & **Significance** & **Relationship to** **Agility Construct** \\ \hline \multirow{8}{*}{Organization} & \multirow{8}{*}{[PERSON] et al. (2018)} & Development of staff & \multirow{8}{*}{2} & \multirow{8}{*}{0.320} & \multirow{8}{*}{Yes} & \multirow{8}{*}{Predictor} \\  & & support & & & \\ \cline{1-1}  & & Information technology & 2 & 0.560 & Yes & Predictor \\ \cline{1-1}  & & Action of process & 2 & 0.520 & Yes & Predictor \\ \cline{1-1}  & & Sensitivity & 2 & 0.420 & Yes & Predictor \\ \cline{1-1}  & & Appropriate planning & 2 & 0.550 & Yes & Predictor \\ \cline{1-1}  & & Introduction to new & 2 & 0.520 & Yes & Predictor \\ \cline{1-1}  & & product & & & \\ \cline{1-1}  & & Reduction in costs & 2 & 0.530 & Yes & Predictor \\ \cline{1-1}  & & Customer satisfaction & 2 & 0.490 & Yes & Predictor \\ \cline{1-1}  & & Product quality & 2 & 0.510 & Yes & Predictor \\ \hline \multirow{8}{*}{[PERSON] (2018)} & \multirow{8}{*}{Organizational} & \multirow{8}{*}{1} & \multirow{8}{*}{0.901} & \multirow{8}{*}{Yes} & \multirow{8}{*}{Predictor} \\  & & & capabilities & & 0.418 & Yes & Mediator \\ \cline{1-1}  & & Organizational & & & \\ \cline{1-1}  & & performance & & & \\ \hline \multirow{8}{*}{[PERSON] (2018)} & \multirow{8}{*}{Organizational} & \multirow{8}{*}{1} & \multirow{8}{*}{0.571} & \multirow{8}{*}{Yes} & \multirow{8}{*}{Predictor} \\  & & intelligence & & & \\ \cline{1-1}  & & Organizational learning & & & \\ \cline{1-1}  & & & 0.943 & Yes & Mediator \\ \hline \hline \end{tabular}
\end{table}
Table 10: Latent variables in organizational agility models in healthcare.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Clustered** & **Author** & **Latent Variable Name** & **Variable Order** & **Path Coefficient** & **Significance** & **Relationship to** \\
**Construct** & **Author** & **Latent Variable Name** & **Order** & **Path Coefficient** & **at \(p\) (0.05)** & **Agility Construct** \\ \hline \multirow{8}{*}{**Organization