Table 4.9: Standardized weights of destination image scale

Observed Variable Factor

Weight | Observed Variable Factor | Weight | |||||

AT4 | <--- | AT | .555 | AMP2 | <--- | AMP | .733 |

AT3 | <--- | AT | .548 | AMP1 | <--- | AMP | .692 |

AT2 | <--- | AT | .604 | AC5 | <--- | AC | .533 |

AT1 | <--- | AT | .562 | AC4 | <--- | AC | .661 |

PV3 | <--- | PV | .800 | AC3 | <--- | AC | .733 |

PV2 | <--- | PV | .863 | AC2 | <--- | AC | .616 |

PV1 | <--- | PV | .677 | INF5 | <--- | INF | .693 |

AT5 | <--- | AT | .683 | INF4 | <--- | INF | .759 |

AT6 | <--- | AT | .706 | INF3 | <--- | INF | .709 |

AMP5 | <--- | AMP | .605 | INF2 | <--- | INF | .786 |

AMP4 | <--- | AMP | .731 | INF1 | <--- | INF | .653 |

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Source: Author's analysis and synthesis from 2014 survey data

Table 4.10: Discriminant validity results between concepts of destination image scales

Relationship

Estimate | SE | CR | p | |||

AT | <--> | AMP | 0.319 | 0.039 | 11,949 | 0.000 |

PV | <--> | AMP | 0.237 | 0.042 | 14,185 | 0.000 |

PV | <--> | AC | 0.319 | 0.040 | 12,754 | 0.000 |

AT | <--> | PV | 0.687 | 0.040 | 12,804 | 0.000 |

AMP | <--> | AC | 0.666 | 0.041 | 13,056 | 0.000 |

AT | <--> | AC | 0.573 | 0.031 | 8,472 | 0.000 |

AT | <--> | INF | 0.486 | 0.034 | 9,660 | 0.000 |

AMP | <--> | INF | 0.446 | 0.040 | 12,939 | 0.000 |

PV | <--> | INF | 0.494 | 0.037 | 10,818 | 0.000 |

AC | <--> | INF | 0.335 | 0.034 | 9,484 | 0.000 |

Source: Author's analysis and synthesis from 2014 survey data.

* Convergent value of the scale: Gerbring and Anderson [40] stated that the scale achieved convergent value when the standardized weights of the scale were greater than 0.5; and had a significant P-value of less than 0.05. According to the CFA coefficients of the component scales of the destination image presented in the table (Table 4.9 **)** , all observed variables had weights > 0.5, so the scale achieved convergent value.

*Regarding the discriminant value between the research concepts (Table 4.10), it shows that the correlation coefficients along with the standard deviation of each pair of concepts are different from 1 (p< 0.05) at the 95% confidence level. Therefore, the research concepts (5 components) have discriminant value.

4.2.3.2 Loyalty scale (loyalty attitudes and behaviors)

The second data set for the loyalty variables was submitted to a CFA, using multivariate (ML) estimation: the two-factor (component) model fit the data quite well (Table 4.11). Figure 4.3 shows that the Chi-square (χ2) = 25.627, which is significant at p < 0.05, indicating that the model fits the data well. The degrees of freedom (χ2/df = 4.271) are lower than recommended (i.e. < 5.0; Bollen, 1989). The RMSEA value indicates that the two-component model had a tentatively acceptable fit (RMSEA = 0.091; Hu and Bentler, 1999). The SRMR (0.091) was ≤ 0.10; (Kline, 2005). The CFI was 0.984, which is considered acceptable (Kline, 2005). The GFI was 0.979 as indicative of an acceptable model, as was the TLI of 0.960, supporting the finding that the model fits the market data well.

Figure 4.3: CFA results of loyalty scale (standardized) Source: Analytical data surveyed by the author in 2014

Table 4.11: Standardized weights of the loyalty scale

Observation variable

Ingredient | Weight | ||

A1 | <--- | ATL | .777 |

A2 | <--- | ATL | .733 |

A3 | <--- | ATL | .705 |

B1 | <--- | BHL | .892 |

B2 | <--- | BHL | .883 |

B3 | <--- | BHL | .777 |

Source: Author's analysis and synthesis from 2014 survey data.

Table 4.12: Discriminant validity results between loyalty concepts

Relationship

Estimate | SE | CR | p | |||

ATL | <--> | BHL | .707 | 0.032 | 9.020 | 0.000 |

Source: Author's analysis and synthesis from 2014 survey data

The correlation coefficients between the concepts with the accompanying standard deviations (Table 4.12 **)** show us that these coefficients are less than 1 (statistically significant), so the concepts: Attitude loyalty (ATL) (including 3 observed variables) and behavioral intention loyalty (BHL) (including 3 observed variables) have discriminant validity.

Table 4.13: Composite Reliability (CR) and Variance Extracted (AVE) Results

Concept

Observation variable | Alpha Reliability | CR | AVE | |

Destination appeal | 6 | 0.843 | 0.782 | 0.476 |

Tourism infrastructure | 5 | 0.924 | 0.867 | 0.523 |

Tourist atmosphere | 4 | 0.822 | 0.785 | 0.479 |

Accessibility | 4 | 0.738 | 0.733 | 0.410 |

Affordable | 3 | 0.738 | 0.825 | 0.614 |

Attitude of loyalty | 3 | 0.790 | 0.813 | 0.591 |

Loyalty behavior | 3 | 0.872 | 0.872 | 0.697 |

Source: Author's analysis and synthesis from 2014 survey data.

Regarding the composite reliability (CR) and average variance extracted (AVE) (Table 4.13), the results show that the research concepts of the scale all have good composite reliability, but the variance extracted is not high, including the components Destination Attractiveness (AT) and Tourism Atmosphere (AMP) with AVE less than 0.5, however, this value can be temporarily accepted as 5, because the value is close to the requirement, and at the same time, it is a topical topic. However, the component "Accessibility" (AC) has a slightly low AVE, and is also not topical, so it can be considered to remove this component from the proposed model.

**4.2.4 Results of SEM theoretical structural model analysis**

Figure 4.4: SEM structural model

Source: Data analyzed by the author in 2014

Table 4.14: Summary of CFA standards

Standard specifications

Result | Conclude | |

Chi-square has P-value >0.5 | Model fit to market data | |

GFI ≥ 0.90 | 0.887 | |

TLI≥0.90 | 0.905 | |

CFI≥0.90 | 0.917 | |

RMSEA ≤ 0.08 | 0.056 | |

Chi-square/df ≤ 5 | 2,235 | |

Standardized factor weight ≥ 0.5 | ≥ 0.55 | Satisfied |

Composite reliability ≥ 0.60 | ≥ 0.7 | Satisfied |

Variance Extracted (AVE) ≥ 0.35 | ≥ 0.45 | Satisfied |

Source: Author's synthesis and analysis from 2014 survey data.

**4.2.5 Testing the research model**

Testing the discriminant validity between the concepts of destination image and destination loyalty.

The SEM results show that the relationships between the research concepts are all different from 1 (p< 0.05) at the 95% confidence level (Table 4.15). Thus, the concepts of destination image and destination loyalty have discriminant validity.

Table 4.15: Discriminant validity results between the concepts of the image and destination loyalty scales

Relationship

Estimate | SE | CR | p | |||

ATL | <--- | INF | 0.499 | 0.040 | 12,587 | 0.000 |

ATL | <--- | AT | 0.072 | 0.046 | 20,257 | 0.000 |

ATL | <--- | AMP | 0.134 | 0.046 | 19,026 | 0.000 |

ATL | <--- | PV | 0.161 | 0.045 | 18,508 | 0.000 |

ATL | <--- | AC | 0.114 | 0.046 | 19,416 | 0.000 |

BHL | <--- | INF | 0.33 | 0.043 | 15,453 | 0.000 |

BHL | <--- | AT | 0.401 | 0.042 | 14,236 | 0.000 |

BHL | <--- | AMP | 0.034 | 0.046 | 21,043 | 0.000 |

BHL | <--- | PV | 0.122 | 0.046 | 19,259 | 0.000 |

BHL | <--- | AC | 0.015 | 0.046 | 21,447 | 0.000 |

Source: Author's analysis and synthesis from 2014 survey data

Testing research hypotheses

In the linear structure analysis (Table 4.16), the component “Accessibility (AC)” was eliminated from the official research model because the relationship between this concept and Attitude Loyalty (ATL) and Behavior Loyalty (BHL) was not statistically significant at the 90% confidence level. This means that the hypothesis **H4A:** “ *Accessibility” has a positive impact on tourists’ attitude loyalty* ); and the hypothesis **H4B:** “ *Accessibility” has a positive impact on tourists’ behavior loyalty* was rejected at the 90% significance level.

Furthermore, the relationship between the component “Destination Attractiveness (AT)” and “Attitudinal Loyalty (ATL)” is not statistically significant at the 90% confidence level. As well as the relationship between the component “Tourism Atmosphere (AMP)” and Tourist Loyalty Behavior is not statistically significant at the 90% confidence level. This means that the hypothesis **H1A (“ ***Destination Attractiveness” has a positive impact on Tourist Attitude Loyalty* ) and hypothesis **H3B** ( *“Tourism Atmosphere” has a positive impact on Tourist Loyalty Behavior* ) are rejected and will not be considered in the next formal model when analyzing the multi-group SEM structural linear model.

Table 4.16: Test of the relationship of the research model (n=396)

Relationship

E(β) | SE | CR (t) | P | |||

ATL | <--- | INF | .501 | .086 | 5.801 | *** |

ATL | <--- | AT | .079 | .097 | .809 | .418 |

ATL | <--- | AMP | .131 | .057 | 2,300 | .021 |

ATL | <--- | PV | .141 | .052 | 2,692 | .007 |

ATL | <--- | AC | .141 | .113 | 1,244 | .213 |

BHL | <--- | INF | .329 | .081 | 4,071 | *** |

BHL | <--- | AT | .437 | .104 | 4,217 | *** |

BHL | <--- | AMP | .033 | .055 | .596 | .551 |

BHL | <--- | PV | .106 | .051 | 2,086 | .037 |

BHL | <--- | AC | .018 | .110 | .167 | .868 |

Source: Author's analysis and synthesis from 2014 survey data

Table 4.17: Results of testing research hypotheses

Contents of the hypotheses

Test results | |

H1A: “Destination attractiveness” has a positive impact to tourist loyalty | Not accepted at 90% significance level |

H2A: “Tourism infrastructure” has a positive impact to tourist loyalty | Accept at level 99% meaning |

H3A: “Tourism atmosphere” has a positive impact to tourist loyalty | Accept at level 95% significance |

H4A: “Accessibility” has a positive impact on tourist loyalty | Not accepted at 90% significance level |

H5A: “Affordability” has a positive impact on tourist loyalty | Accept at level 95% significance |

H1B: “Destination attractiveness” has a positive impact to tourist loyalty behavior | Accept at level 99% meaning |

H2B: “Tourism infrastructure” has a positive impact to tourist loyalty behavior | Accept at 99% significance level |

H3B: “Tourism atmosphere” has a positive impact to tourist loyalty behavior | Not accepted at 90% significance level |

H4B: “Accessibility” has a positive impact to the loyal behavior of tourists | Not accepted at 90% significance level |

H5B: “Affordability” has a positive impact on tourist loyalty behavior | Accept at level 95% significance |

Source: Author's synthesis from analysis of 2014 survey data

However, the analysis of the linear structural model (SEM) with estimation through the ML method, gives the results of the relationships between “Tourism infrastructure (INF)”; “Tourism atmosphere (AMP)” and “Affordability (PV)” respectively have a positive impact on “Tourist loyalty attitude” at a significance level of over 95% with statistical value (t) > 1.96. Therefore, it can be affirmed that hypotheses H1A, H3A and H4A are accepted. Next, the relationship between the components “Tourism infrastructure (INF)”; Destination attractiveness (AT)”; “Affordability (PV)” has a positive impact on “Tourist loyalty behavior” at a significance level of over 96.3% with statistical value

(t) > 1.96. Therefore, hypotheses H1B, H2B and H4B are accepted.

In summary, the results show that the two components “Tourism Infrastructure (INF) and Affordability (PV)” have positive impacts on both tourists’ attitude and loyalty behavior, especially the component “Destination Attractiveness (AT)” has a significant positive impact on loyalty behavior but has no impact on tourists’ attitude loyalty. On the contrary, the component “Tourism Atmosphere (AMP)” has a significant impact on tourists’ attitude loyalty, but has no impact on tourists’ loyalty behavior. The summary of the hypothesis testing results is shown in Table 4.17.

The official SEM structural model after eliminating hypotheses H1A, H4A, H3B, H4B is shown in Figure 4.5.

Table 4.18: Summary of CFA standards

Standard specifications

Result | Conclude | |

Chi-square has P-value >0.5 | Model fit to market data | |

GFI ≥ 0.90 | 0.894 | |

TLI≥0.90 | 908 | |

CFI≥0.90 | 920 | |

RMSEA ≤ 0.08 | 0.60 | |

Chi-square/df ≤ 5 | 2,442 | |

Standardized factor weight ≥ 0.5 | ≥ 0.55 | Satisfied |

Composite reliability ≥ 0.60 | ≥ 0.80 | Satisfied |

Variance Extracted (AVE) ≥ 0.35 | ≥ 0.476 | Satisfied |

Source: Author's synthesis from analysis of 2014 survey data