CHAPTER 5 : DATA ANALYSIS RESULTS
5.4 FACTOR ANALYSIS: PORT INFRASTRUCTURE PRICING PRACTICES
PRACTICES
5.4.1
EXPLORATORY
FACTOR
ANALYSIS:
FACTORS
INFLUENTIAL TO PORT INFRASTRUCTURE TARIFF
SETTING PRACTICES
Similar to the previous section, a EFA is conducted to analyse the factors influencing port infrastructure tariff setting practices based on data collected from the questionnaire sections E1 to E4 (Appendix X). Again, before undertaking the EFA, a test for sampling adequacy and sphericity was conducted. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy for items E1 to E4 was 0.683, which is above the suggested minimum. Similarly Bartlett's Test of Sphericity Chi-Square test statistics for items was 877.45, which is significant at the 1% significant level. Thus the null hypothesis that the correlation matrix is an identity matrix is rejected. Table 5.11 and Table 5.12 show the results of the EFA concerning the factors affecting the port infrastructure tariff setting practices.
Table 5.11: Factors influential to port infrastructure tariff setting practices.
Factor
Initial Eigenvalues
Total % of Variance Cumulative %
1 3.878 27.698 27.698 2 2.384 17.027 44.725 3 1.592 11.373 56.098 4 1.235 8.824 64.922 5 0.921 6.578 71.500 6 0.753 5.377 76.877 7 0.699 4.990 81.867 : : : : 13 0.192 1.372 99.060 14 0.132 0.940 100.000
Factors one to four (Table 5.11), representing port infrastructure tariff policy, tariff regulation, transparency and stakeholder participation in port infrastructure tariffs respectively, have the initial eigenvalues of 3.878, 2.384 1.592 and 1.235, which are larger than 1 (Table 5.11). These factors explain 64.922% of the total variance of the
127 variables. Thus, according to the Kaiser criterion, these factors can be retained for further analysis.
Table 5.12 reports the rotated, rescaled component matrix for these factors. The values of the Cronbach's Alpha coefficient for the variables in each of the four factors identified as influencing port infrastructure tariff setting practices are 0.807, 0.670, 0.678 and 0.873 respectively.
Table 5.12: Rotated, rescaled factor matrix.
Factors T a ri ff p o li c y T a ri ff R e g u la ti o n T ra n sp a re n c y S ta k e h o ld e r p a rt ic ip at io n
E3.5 Revising tariffs as per port’s competitive position 0.772 -0.052 0.213 0.051 E2.1 Having a policy guideline for tariff design and revision 0.723 0.454 0.000 -0.147 E2.2 Having a policy on rebates and discounts 0.707 -0.033 0.219 0.008 E3.4 Revising tariffs as per port’s strategic plan 0.706 0.207 -0.065 0.029 E4.3 Obtaining inputs fro m co mme rcia l and planning dept . 0.684 0.128 0.134 -0.015 E3.2 Adjusting tariffs with inflation and input cost 0.635 -0.222 0.063 0.144 E2.4 Having a tariff regulatory control 0.067 0.806 0.190 0.139 E1.3 Needing government approval for revised tariffs -0.042 0.793 0.113 0.123 E2.8 Adhering to published tariffs 0.242 0.556 -0.267 0.339 E2.5 Offering a co mposite tariff 0.223 -0.015 0.785 -0.093 E2.7 Offering negotiable tariffs 0.277 -0.013 0.719 0.213 E2.9 Offering a lu mp su m port fee -0.039 0.341 0.704 0.184 E1.4 Obtaining feedback fro m port users 0.032 0.159 0.014 0.926
E1.5 Obtaining feedback fro m port operators 0.007 0.199 0.236 0.870
Re liab ility - Cronbach's Alpha 0.807 0.670 0.678 0.873 Extraction Method: Principa l Co mponent Analysis
Rotation Method: Va rima x with Kaiser Norma lisation a. Rotation converged in 5 iterat ions
Kaiser–Meyer–Olkin measure of samp ling adequacy is 0.683
The first factor, ‘port infrastructure tariff policy’, is associated with six variables: tariff revision as per the port’s competitive position (loading value 0.772), availability of a policy guideline for tariff design and revision (0.723), availability of a clear policy on tariff rebates and discounts (0.707), tariff revision as per port’s strategic plan (0.706), obtaining inputs from commercial and planning department participation in port tariff design (0.684) and adjusting tariffs with inflation and input cost (0.635).
The second factor, ‘tariff regulation’, is associated with two variables: having regulatory control of the port tariff (0.806) and the need for government approval for tariff revision (0.793). However the question on tariff transparency (adhering to
128 published tariffs) has the loading of 0.556 and cannot be included in the tariff regulation factor.
The third factor, ‘transparency’ is associated with three variables: tariff bundling (0.785), tariff negotiation (0.719) and the lump sum payment option (0.704). The fourth factor, ‘stakeholder participation’, is associated with two variables concerned with obtaining feedback from port users of revised tariffs (0.926) and feedback from terminal operators of proposed tariff revision (0.870).
The results of the EFA suggest that ports follow specific tariff policies when they revise their tariffs. A number of factors may influence their tariffs and hence the need to revise their tariffs. These include the port’s competitive position, strategic plans, rebate policy, inflation and input costs. The tariff setting and revision process involves the commercial and planning departments of the port as well as stakeholders including port users, competition regulatory authorities and governments.
5.4.2
CONFIRMATORY
FACTOR
ANALYSIS:
FACTORS
INFLUENTIAL TO PORT INFRASTRUCTURE TARIFF
SETTING PRACTICES
A CFA was conducted in order to further evaluate the four underlying factors identified by the EFA and their relationships (Appendix XI). Figure 5.7 (page 129) shows all the possible relationships between the underlying factors of port infrastructure tariff setting practices and their associated variables with the standard estimates of the regression coefficients respectively.
The relationships between the variables as indicated by the covariance estimates indicate a relationship between tariff policy and other two factors including tariff regulation and transparency. Further, tariff regulation is related to transparency and stakeholder participation, and transparency is related to stakeholder participation.
129 Figure 5.7: Path diagram with standardised estimates for the all relationships
(model fit).
The regression results shown in Table 5.13 (p.130) strongly indicate that all relationships are significant at 1%. However, the Chi-square statistic CMIN/DF is 1.696, the root mean square error of approximation (RMSEA) is 0.103 and PCLOSE is 0.011 (Table 5.14, p.129), and a modification of the model is necessary. Variables with low loadings are excluded first and, based on the values of standardised residual covariances; variables with higher residual covariances (above 0.4) are excluded subsequently.
130 Table 5.13: CFA analysis results for the Figure 7 Model.
Variable Factors Estimate S.E. C.R. P
E4.3 Obtaining inputs from commercial and planning dept. Tariff Policy 1
E3.4 Revising tariffs as per port’s strategic plan Tariff Policy 0.920 0.235 3.906 *** E2.2 Having a policy on rebates and discounts Tariff Policy 1.291 0.297 4.346 *** E2.1 Having a policy guideline for tariff design and revision Tariff Policy 1.303 0.294 4.436 *** E3.5 Revising tariffs as per port’s competitive position Tariff Policy 1.194 0.252 4.737 ***
E2.8 Adhering to published tariffs Tariff Regulation 1
E1.3 Needing government approval for revised tariffs Tariff Regulation 1.864 0.587 3.177 *** E2.4 Having a tariff regulatory control Tariff Regulation 2.157 0.676 3.193 ***
E2.9 Offering a lump sum port fee Transparency 1
E2.7 Offering negotiable tariffs Transparency 1.438 0.408 3.529 ***
E2.5 Offering a composite tariff Transparency 1.244 0.365 3.404 ***
E1.5 Obtaining feedback from port operators Stakeholders 1
E1.4 Obtaining feedback from port users Stakeholders 1.344 0.565 3.804 ***
131 Table 5.14: Model Fit Summary for Figure 5.7: CFA on factors influential to port
infrastructure tariff setting practices. Chi-square statistic (CMIN)
Model NPAR CMIN DF P CMIN/DF
Default model 31 101.736 60 0.001 1.696
Saturated model 91 0.000 0
Independence model 13 348.924 78 0.000 4.473
Root Mean Square Erro r of Appro ximation (RM SEA)
Model RMSEA LO 90 HI 90 PCLOSE
Default model 0.103 0.067 0.136 0.011
Independence model 0.229 0.205 0.254 0.000
Figure 5.8: Path Diagram with Standardised Estimates for the Significant Relationships (model fit).
The CFA results (Figure 5.8) indicate significant relationships between transparency and tariff policy, and transparency and stakeholder participation, and some degree of relationship between tariff policy and stakeholder participation. The tariff regulation factor is not significant as all of its variables that were significant under the EFA are excluded by the CFA.
132 The result of the CFA reveals that the underlying factors influential to port infrastructure tariff setting practices are port tariff policy, stakeholder participation and transparency. Table 5.15 shows that all the variables included in this model are significant.
Table 5.15: CFA analysis results for the Figure 5.8 model.
Variables Factors Estimate S.E. C.R. P
E2.2 Having a policy on rebates and
discounts Tariff Policy 1
E3.5 Revising tariffs as per port’s
competitive position Tariff Policy 1.004 0.219 4.576 *** E2.1 Having a policy guideline for
tariff design and revision Tariff Policy 0.783 0.186 4.219 *** E1.5 Obtaining feedback fro m port
operators Stakeholders 1
E1.4 Obtaining feedback fro m port
users Stakeholders 1.244 0.365 3.894 ***
E2.7 Offering negotiable tariffs Transparency 1.675 0.727 2.304 0.02 E2.9 Offering a lu mp su m port fee Transparency 1
*** = significant at 1% significance level
Table 5.16 indicates that the CMIN/DF value has improved significantly to 0.834. The RMSEA value for the 3 factor model is 0.000, with the 90% confidence interval ranging from 0.000 to 0.108 and the p value (PCLOSE) related to RMSEA indicates a closeness of fit equal to 0.740. The 90% confidence interval indicates that the true RMSEA value in the population falls between the lower (0.000) and upper bounds (0.108).
Table 5.16: Model Fit Summary for figure 5.8: CFA on factors influential to port infrastructure tariff setting practices.
Chi-square statistic (CM IN)
Model NPAR CMIN DF P CMIN/DF
Default model 16 10.010 12 0.615 0.834
Saturated model 28 0.000 0
Independence model 7 151.899 21 0.000 7.233
Root Mean Square Erro r of Appro ximation (RM SEA)
Model RMSEA LO 90 HI 90 PCLOSE
Default model 0.000 0.000 0.108 0.740
133 Given a RMSEA point estimate less than 0.05 and the probability associated with the test of close greater than 0.50, and also the Goodness of Fit Index and Adjusted GFI being their values .961 .909 respectively, it can be concluded that the resulting model has a good fit. Therefore, it can be confirmed that the tariff policy factor is related to the port’s guideline for tariff design and revision, the revision of tariffs as per the port’s competitive position, and the presence of a policy on rebates and discounts. The stakeholder participation factor is related to practices such as obtaining feedback from port operators and users, while the transparency factor is related to tariff setting practices such as offering ships negotiable tariffs and lump sum payment options.
This result suggests that, in contrast to the EFA output, the CFA produces a model of three factors that are influential on port infrastructure tariff setting practices: port tariff policy, stakeholder participation and transparency. The tariff regulatory requirements including government approval and adhering to published tariffs do not have much influence on infrastructure tariff setting practices. More importantly although ports have specific departments such as commercial and port pla nning departments that are responsible for tariff setting, obtaining input solely from these departments is not sufficient for tariff revision. In addition, tariff revision as per the port’s strategic plan should not be the only basis for tariff revision.