## 6 Model Building and Analysis
### Analytic Hierarchy Process (AHP)  
The Analytic Hierarchy Process (AHP), developed by [PERSON] in 1977, is a multi-criteria decision-making process of qualitative factors when arranged in a hierarchical process [44]. The process allows for making complex decisions by aiding users in organizing information pertaining to thoughts, knowledge, and judgment into a hierarchical framework and quantifying the effect of the qualitative factors by a sequence of pairwise comparison judgments [44]. In this study, AHP was used to evaluate the significance of qualitative factors on organizational performance. The procedure to carry out AHP, as shown in Figure 6, consists of the following steps:  
1. First, the hierarchy of factors is established, as well as the selection of criteria. The top-level shows the goal of the problem \"Organizational Performance.\" The intermediate levels contain the qualitative or non-financial parameters that are categorized into four main categories (i.e., Administrative and Legal, Technical, Management, and Market and Finance). The next level or sub-level includes 18 sub-factors (i.e., Clear Vision; Mission and Goals; Competitive Strategy; Organizational Structure; Political Conditions; Number of Full-Time Employees; Usage of International Aspects (ISO); Availability of knowledge Usage of IT; Business Experience; Product Maintenance; Employee Culture Environment; Employee Compensation and Motivation; Applying Total Quality Management; Training; Quick Liquid Assets; Feedback Evaluation; Research and Development; Market Conditions/Customer Engagement. The layout of the hierarchy helps decision-makers to assess the relationship between factors, as well as showing if the factors have the same magnitude [45]. 2. The second step involves the priority setting of criteria by pairwise comparison matrices for the main factors. Based on the impact rating of the 18 factors, a matrix is assigned with an overall rating for four main factors--i.e., Administrative and Legal, Technical, Management and Market and Finance, and their subfactors. Figure 7 shows the analysis of pairwise comparison matrices for average values of main factors and their sub-factors. 3. The third step is assigning priorities and establishing a pairwise comparison for sub-factors within each main category. This step involves the average values of the sub-factor within one main factor. The AHP methodology applied to these matrices gives the weight factor of each factor (Wi). Table 1 shows the weights of factors. 4. The fourth step is Consistency Analysis. This step verifies the consistency of the pairwise comparison matrix. Weights can be accepted if the matrix is consistent. Therefore, consistency index (CI) and the consistency ratio (CR) are calculated using Equations (2) and (3) [45]. \[\text{CI}\ =\ \frac{\lambda_{max}\ -\ m}{m-1}\] (2) \[\text{CR}=\frac{\text{CI}}{\text{RI}}\] (3) where CI is the matrix consistency index, \(m\) is matrix size, and \(\lambda_{max}\) is the maximum eigen value. 5. Table 2 shows CI and CR for the main factors. It also shows that CI for main factors is 0.00000149, and CR is 0.00, which is less than 0.10. This means that the primary matrix is consistent, and the weight vectors generated for this matrix are acceptable. 6. The process is repeated for sub-factors. Table 3 shows the weights for the main factors and sub-factors, followed by a calculation of Average Decomposed weight. The decomposed weight is calculated by multiplying the main factor weight by its sub-factor weight. The decomposed weight represents the overall weight of each of the sub-factors [45]. Overall Sub-factor Decomposed Weight is calculate using Equation (4). \[SDWij=W_{i}*\big{(}V_{ij}\big{)}\] (4)where \(W_{i}\) is the weight of factor \(i\) and \(V_{ij}\) is the weight of sub-factor \(j\) within the factor \(i\). 7. The graphical presentation in Figure 8 shows the Average decomposed weights. It can be seen that the weight of factors are ranked very carefully.