## 2 Methodology
This method differs from other modeling approaches as it tests the direct and indirect effects on pre-assumed causal relationships. To verify the model fit, a confirmatory factor analysis was carried out. Confirmatory factor analysis is a statistical technique used to verify the factor structure of a set of observed variables. Using this analysis method, the hypothesis can be tested that a relationship between observed variables and their underlying latent constructs exists. The relationship between the variables is identified and then the hypothesis is tested statistically. Structural equation model evaluation is based on the fit indices for the test of a single path coefficient (i.e., \(p\) value and standard error) and the overall model fit. Generally, the more fit indices applied to the model, the more likely that a miss-specified model will be rejected--suggesting an increase in the probability of good models being rejected. It is recommended to use a combination of at least two fit indices. Structural equation is also known as covariance structural modeling. It consists of a confirmatory factor analysis that explores the relationships among factors with measurement variables, as in the following Equation (1) and multiple regression/path analysis [29; 30; 31]. If the initial model does not meet the goodness-of-fit standard, survey questions are deleted to  
Figure 1: The concept model for the perception on the ripple effects of airports on the region. improve the goodness-of-fit of the model in an iterative manner. Example of the structural equation in this paper is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Question** & **Code** \\ \hline \multirow{8}{*}{Regional economic benefit (R)} & It contributes to an increased activity in airport construction/operation-related industries/service industries. & R-1 \\ \cline{2-3}  & It contributes to the revitalization of related industries & \\  & (travel/accommodation business, the tourism industry, etc.). & R-2 \\ \cline{2-3}  & It contributes to creating new jobs. & R-3 \\ \cline{2-3}  & It contributes to increasing the incomes of local residents. & R-4 \\ \cline{2-3}  & It contributes to overall revitalization & R-5 \\ \cline{2-3}  & (such as increases to resident income or increases in tax revenue). & R-6 \\ \cline{2-3}  & It positively affects regional economic development (misc.). & R-6 \\ \cline{2-3}  & More people will travel to other regions (or abroad). & A-1 \\ \cline{2-3}  & It will be more convenient to travel into and out of the area. & A-2 \\ \cline{2-3}  & It will be more convenient for logistics/shipping/labor, etc., to arrive to or depart from the area. & A-3 \\ \cline{2-3}  & Access from outside to inside the airport and from inside the airport to the city center & A-4 \\ \cline{2-3}  & will be easier than before. & \\ \cline{2-3}  & It will have positive effects on the use of air transportation by local residents (others). & A-5 \\ \hline \multirow{8}{*}{Effects related to increased exchange with other regions (M)} & It helps attract foreign tourists. & M-1 \\ \cline{2-3}  & It makes it easier to export or import goods. & M-2 \\ \cline{2-3}  & It seems that there are more exchanges occurring (such as human resources and knowledge exchanges) with foreign countries compared to areas without an airport. & M-3 \\ \cline{2-3}  & It seems that international content (cultural and industrial) are consumed/distributed more around the airport compared to areas without airports. & M-4 \\ \cline{2-3}  & It has a positive effect by promoting more active exchange with foreign countries (misc). & M-5 \\ \hline \multirow{8}{*}{Effects related to regional development (D)} & It helps develop areas around the airport. & D-1 \\ \cline{2-3}  & It helps expand the local transportation network (roads, bridges, KTX/High-speed rail, etc.). & D-2 \\ \cline{2-3}  & It helps increase the number of tourists in the area. & D-3 \\ \cline{2-3}  & It helps industrial development in the area.