*4.5.3 Dependent variable: Affective image*

With respect to the dependent variable, affective image, the key hypothesis decisions are summarized below:

*4.5.4 Performance*

services as shown below.

H5: Price significantly influences performance.

The hypothesis is therefore not accepted.

*DOI: http://dx.doi.org/10.5772/intechopen.93854*

The hypothesis is therefore not accepted.

The hypothesis is therefore not accepted.

The hypothesis is therefore accepted.

*4.5.5 Squared multiple correlations*

results are presented in **Table 7**.

*Source: Data Survey (2018).*

*Squared multiple correlations.*

**Table 7.**

**119**

*NOT SIGNIFICANT (CR = -1.759; p = 0.080 > 0.05).*

H7: Ancillary services significantly influence performance.

H8: Accessibility significantly influences performance. *NOT SIGNIFICANT (CR = -1.071; p = 0.284 > 0.05).*

H6: Amenities significantly influence performance. *NOT SIGNIFICANT (CR = 1.173; p = 0.241 > 0.05).*

*SIGNIFICANT (CR = 1.066; p = 0.039 < 0.05).*

With regards to the second dependent variable, that is, value/performance, it emerged that there was only one significant determinant and this was ancillary

*Development of a Destination Image Recovery Model for Enhancing the Performance…*

From the outcome above, accessibility, amenities and price were not significant determinants of performance. However, ancillary services were. One of the key aspects in the ancillary services category was the friendliness of local people. In this regard, it follows that the value of tourists was shaped more buy ancillary subfactors such as friendliness of local people, more than traditionally known factors such as accommodation, amenities and price. The lack of significance of tourism resources such as amenities could be an indication of the evolving nature of the type of tourists now visiting Zimbabwe. Generally, the friendliness of local people is a known attribute that is valued by drifters and explorers, or rather allocentric and near allocentric tourists [94]. The lack of significance of amenities could mean that the nature of the tourists visiting Zimbabwe has drifted from being mass tourists, who from the literature, are divorced from the local people, to being drifters and explorers, who tend to interact with the local people, and will try to blend with the host community. This is further validated by the fact that attractions such as the natural landscape and climate had been dropped as not being valid, again, another indication of the evolving interests of tourists, from focusing on the attractions to showing interest in mixing with the host community. This tends to suggest the need to develop community and cultural tourism. Cultural tourism entails interacting with the local people in order to understand their history, present and future [95].

The researcher went on to evaluate the overall squared multiple correlations for the two dependent variables, that is, affective image and value. The corresponding

VA .204 AF .467

**Estimate**

H1: Price is significantly positively related to affective image. *SIGNIFICANT (CR = 4.681; p = 0.000 < 0.05).* The hypothesis is therefore accepted.

H2: There is a significant and positive relationship between amenities and affective image.

*SIGNIFICANT (CR = 1.995; p = 0.046 < 0.05).* The hypothesis is therefore accepted.

H3: Ancillary services have a significant relationship with affective image. *SIGNIFICANT (CR = 4.003; p = 0.000 < 0.05).* The hypothesis is therefore accepted.

H4: Accessibility has a significant positive influence on affective image. *NOT SIGNIFICANT (CR = 0.543; p = 0.578 > 0.05).* The hypothesis is therefore not accepted.

**Table 6** presents hypothesis testing results.

From these findings, it was established that the significant factors affecting the affective image were price, amenities and ancillary services. Further review into the respective magnitudes, using the critical ratios, the findings above do confirm that the most significant of the three is the issue of price. In other words, lodging prices, prices of restaurant food, prices of restaurant beverages and prices of goods and services play the most significant role towards improving the affective image. On the other hand, ancillary services such as cleanliness, tourist information, quietness, friendliness of local people as well as ICT readiness were found to be the second most important factor that has a significant positive influence on affective image. Amenities, while significant, was not so important, comparing with the above two that is price and ancillary services.


**Table 6.** *Hypothesis testing.* *Development of a Destination Image Recovery Model for Enhancing the Performance… DOI: http://dx.doi.org/10.5772/intechopen.93854*

#### *4.5.4 Performance*

*4.5.3 Dependent variable: Affective image*

The hypothesis is therefore accepted.

The hypothesis is therefore accepted.

The hypothesis is therefore accepted.

that is price and ancillary services.

H2 There is a significant and positive relationship between amenities and affective image

H3 Ancillary services have a significant relationship with affective image

H4 Accessibility has a significant positive influence on affective image

*Key: S: Hypothesis Supported. R: Hypothesis Rejected.*

**Table 6.** *Hypothesis testing.*

**118**

The hypothesis is therefore not accepted. **Table 6** presents hypothesis testing results.

decisions are summarized below:

affective image.

*Tourism*

With respect to the dependent variable, affective image, the key hypothesis

H2: There is a significant and positive relationship between amenities and

H3: Ancillary services have a significant relationship with affective image.

H4: Accessibility has a significant positive influence on affective image.

From these findings, it was established that the significant factors affecting the affective image were price, amenities and ancillary services. Further review into the respective magnitudes, using the critical ratios, the findings above do confirm that the most significant of the three is the issue of price. In other words, lodging prices, prices of restaurant food, prices of restaurant beverages and prices of goods and services play the most significant role towards improving the affective image. On the other hand, ancillary services such as cleanliness, tourist information, quietness, friendliness of local people as well as ICT readiness were found to be the second most important factor that has a significant positive influence on affective image. Amenities, while significant, was not so important, comparing with the above two

H1 Price is significantly positively related to affective image 4681 0,000 p <sup>&</sup>lt; 0,05 H1►<sup>S</sup>

H5 Price significantly influences performance 1759 0,080 p > <0,05 R H6 Amenities significantly influence performance 1173 0,241 p > <0,05 R H7 Ancillary services significantly influence performance 1066 0,039 p < 0,05 S H8 Accessibility significantly influences performance 1071 0,284 p > <0,05 R

**CR p Result Decision**

1995 0,046 p < 0,05 S

4003 0,000 p < 0,05 S

0,543 0,578 p > <0,05 H4►<sup>R</sup>

H1: Price is significantly positively related to affective image.

*SIGNIFICANT (CR = 4.681; p = 0.000 < 0.05).*

*SIGNIFICANT (CR = 1.995; p = 0.046 < 0.05).*

*SIGNIFICANT (CR = 4.003; p = 0.000 < 0.05).*

*NOT SIGNIFICANT (CR = 0.543; p = 0.578 > 0.05).*

With regards to the second dependent variable, that is, value/performance, it emerged that there was only one significant determinant and this was ancillary services as shown below.

H5: Price significantly influences performance. *NOT SIGNIFICANT (CR = -1.759; p = 0.080 > 0.05).* The hypothesis is therefore not accepted.

H6: Amenities significantly influence performance. *NOT SIGNIFICANT (CR = 1.173; p = 0.241 > 0.05).* The hypothesis is therefore not accepted.

H7: Ancillary services significantly influence performance. *SIGNIFICANT (CR = 1.066; p = 0.039 < 0.05).* The hypothesis is therefore accepted.

H8: Accessibility significantly influences performance. *NOT SIGNIFICANT (CR = -1.071; p = 0.284 > 0.05).* The hypothesis is therefore not accepted.

From the outcome above, accessibility, amenities and price were not significant determinants of performance. However, ancillary services were. One of the key aspects in the ancillary services category was the friendliness of local people. In this regard, it follows that the value of tourists was shaped more buy ancillary subfactors such as friendliness of local people, more than traditionally known factors such as accommodation, amenities and price. The lack of significance of tourism resources such as amenities could be an indication of the evolving nature of the type of tourists now visiting Zimbabwe. Generally, the friendliness of local people is a known attribute that is valued by drifters and explorers, or rather allocentric and near allocentric tourists [94]. The lack of significance of amenities could mean that the nature of the tourists visiting Zimbabwe has drifted from being mass tourists, who from the literature, are divorced from the local people, to being drifters and explorers, who tend to interact with the local people, and will try to blend with the host community. This is further validated by the fact that attractions such as the natural landscape and climate had been dropped as not being valid, again, another indication of the evolving interests of tourists, from focusing on the attractions to showing interest in mixing with the host community. This tends to suggest the need to develop community and cultural tourism. Cultural tourism entails interacting with the local people in order to understand their history, present and future [95].

## *4.5.5 Squared multiple correlations*

The researcher went on to evaluate the overall squared multiple correlations for the two dependent variables, that is, affective image and value. The corresponding results are presented in **Table 7**.


From the results above, the r-square for value was 0.204 while that for affective image was 0.467. It follows from the above finding that the independent variables price, amenities, ancillary services, accessibility and attractions explained the greatest variance in affective image (46.7%) than in value (20.4%). What this means is that the independent variables determined more of the destination's capacity to relieve stress, the destination's capacity to provide relaxation, the destination as a pleasant place, the destination as an arousing place as well as the destination as a provider of excitement than they defined the value of the destination.

From the above, ε,β, χ, λ, α, φ, ϑ, η were all weights of the exogenous variables that were used to predict the endogenous variables. κ was the intercept and ε was the error term, or residuals. Nevertheless, upon testing the structural equation model, some of the variables were dropped off after their p-values were found to be non-significant (p > 0.05). In this regard, the original equations were subsequently revised. Upon structural equation modeling, for Eq. 1, accessibility was dropped off as it did not have a significant effect on affective image and the subsequent equation comprised one endogenous variable and three exogenous variables as shown below:

*Development of a Destination Image Recovery Model for Enhancing the Performance…*

On the other hand, for Eq. 2, price, amenities and accessibility did not have a significant impact on value (performance), and in this regard, these were dropped off and the subsequent equation comprised one endogenous variable and one exog-

With a view to testing the validity of a structural equation model above, several goodness-of-fit tests are carried out as prescribed by [96]. There are three broad categories of model fitness tests, and these include absolute fit indices, the relative fit indices as well as the parsimonious fit indices [89]. For the absolute fit indices, the CMIN/DF is the most common, and the chi-square test p-value should be greater than 0.05, while the CMIN/DF ought to be less than 3.0. On the other hand, for the relative fit indices, Goodness-of-Fit Index (GFI), Comparative Fit Index (CFI), Incremental Fit Index (IFI) and Normed Fit Index (NFI) are the most common and this ought to be greater than 0.90. With respect to the parsimonious fit indices, the most common include the Parsimony Normed Fit Index (PNFI), Parsimony Comparative Fit Index (PCFI) as well as the Root Mean Square Error of Approximation (RMSEA) according to [97]. Nevertheless, the most common is RMSEA and according to [98], the maximum acceptable is 0.08. Satisfying the goodness-of-fit at these three levels qualifies the structural model being tested to be accurate and valid [89, 97]. The model fit indices from the study are presented from Table 5.24 to Table 5.27. From the results, with respect to the absolute fit indices, CMIN/DF = 1.730 and this was less than the prescribed maximum of 3.0, and this

Further validation was accomplished by the relative fit indices for which IFI and CFI were 0.941 and 0.940 respectively against the expected minimum threshold of

Regarding the model parsimony, PNFI was 0.755 and PCFI was 0.816 > 0.50. Again, both parsimony measures were greater than the expected minimum 0.50 and this confirmed that the model parsimony was not violated. **Table 10 s**hows the

was the first validation of the model. **Table 8** shows absolute fit.

*AF* ¼ *αPR* þ *φAM* þ *ϑAN* þ *κ*<sup>1</sup> þ *ε*<sup>1</sup> … (1)

*VA* ¼ *χAN* þ *k*<sup>2</sup> þ *ε*<sup>2</sup> … (2)

**Revised Eq. 1:**

**Revised Eq. 2:**

Where: κ: Intercept

*4.5.7 Model fit test*

enous variable as shown below:

*DOI: http://dx.doi.org/10.5772/intechopen.93854*

ε,χ: Path coefficients. AN: Ancillary services. VA: Performance.

0.90. **Table 9** shows relative fit.

parsimony measures.

**121**

#### *4.5.6 Research model equation*

The research model originally comprised of two endogenous variables as well as four main exogenous variables and these are presented in the quotations below: **Initial Eq. 1:**

$$AF = PR\_{ai1\dots ai4} + AM\_{bi1\dots bi4} + AC\_{ci1\dots ci4} + AN\_{di1\dots di5}$$

$$+ \varepsilon\_{ai1\dots ai4} + \varepsilon\_{bi1\dots bi4} + \varepsilon\_{ci1\dots ci4} + \varepsilon\_{di1\dots di5}$$

Price, amenities, and accessibility had four items each, and hence *i1-i4*, while ancillary services had five items, and hence *i1-i5.* The equation in simple terms was,

$$AF = aPR + \eta AM + \theta AN + \eta AC + \kappa\_1 + \varepsilon\_1 \dots [1]$$

Where: i: Items. κ: intercept. ε: Error term. α, φ, ϑ, η: Path coefficients. PR: Price. AM: Amenities. AN: Ancillary services. AC: Accessibility. AF: Affective image. **Initial Eq. 2:**

Again, for Eq. 2, price, amenities, and accessibility had four items each, and hence *i1-i4*, while ancillary services had five items, and hence *i1-i5.*

$$VA = PR\_{ai1\dots ai4} + AM\_{bi1\dots bi4} + AC\_{ci1\dots ei4} + AN\_{di1\dots di5}$$

$$+ \varepsilon\_{ai1\dots ai4} + \varepsilon\_{bi1\dots bi4} + \varepsilon\_{ci1\dots ci4} + \varepsilon\_{di1\dots di5}$$

The equation in simple terms was:

$$\text{VA} = \beta \text{PR} + \varepsilon \text{AM} + \chi \text{AN} + \lambda \text{AC} + k\_2 + \varepsilon\_2 \dots [2]$$

Where: i: Items κ: Intercept. ε: Error term. ε, β, χ, λ: Path coefficients. PR: Price. AM: Amenities. AN: Ancillary services. AC: Accessibility. VA: Performance.

*Development of a Destination Image Recovery Model for Enhancing the Performance… DOI: http://dx.doi.org/10.5772/intechopen.93854*

From the above, ε,β, χ, λ, α, φ, ϑ, η were all weights of the exogenous variables that were used to predict the endogenous variables. κ was the intercept and ε was the error term, or residuals. Nevertheless, upon testing the structural equation model, some of the variables were dropped off after their p-values were found to be non-significant (p > 0.05). In this regard, the original equations were subsequently revised. Upon structural equation modeling, for Eq. 1, accessibility was dropped off as it did not have a significant effect on affective image and the subsequent equation comprised one endogenous variable and three exogenous variables as shown below:

**Revised Eq. 1:**

From the results above, the r-square for value was 0.204 while that for affective image was 0.467. It follows from the above finding that the independent variables price, amenities, ancillary services, accessibility and attractions explained the greatest variance in affective image (46.7%) than in value (20.4%). What this means is that the independent variables determined more of the destination's capacity to relieve stress, the destination's capacity to provide relaxation, the destination as a pleasant place, the destination as an arousing place as well as the destination as a provider of

The research model originally comprised of two endogenous variables as well as four main exogenous variables and these are presented in the quotations below:

> *AF* ¼ *PRai*<sup>1</sup> … *ai*<sup>4</sup> þ *AMbi*<sup>1</sup> … *bi*<sup>4</sup> þ *ACci*<sup>1</sup> … *ci*<sup>4</sup> þ *ANdi*<sup>1</sup> … *di*<sup>5</sup> þ*εai*<sup>1</sup> … *ai*<sup>4</sup> þ *εbi*<sup>1</sup> … *bi*<sup>4</sup> þ *εci*<sup>1</sup> … *ci*<sup>4</sup> þ *εdi*<sup>1</sup> … *di*<sup>5</sup>

Price, amenities, and accessibility had four items each, and hence *i1-i4*, while ancillary services had five items, and hence *i1-i5.* The equation in simple terms was,

*AF* ¼ *αPR* þ *φAM* þ *ϑAN* þ *ηAC* þ *κ*<sup>1</sup> þ *ε*<sup>1</sup> … ½ � 1

Again, for Eq. 2, price, amenities, and accessibility had four items each, and

*VA* ¼ *PRai*<sup>1</sup> … *ai*<sup>4</sup> þ *AMbi*<sup>1</sup> … *bi*<sup>4</sup> þ *ACci*<sup>1</sup> … *ci*<sup>4</sup> þ *ANdi*<sup>1</sup> … *di*<sup>5</sup> þ*εai*<sup>1</sup> … *ai*<sup>4</sup> þ *εbi*<sup>1</sup> … *bi*<sup>4</sup> þ *εci*<sup>1</sup> … *ci*<sup>4</sup> þ *εdi*<sup>1</sup> … *di*<sup>5</sup>

*VA* ¼ *βPR* þ *εAM* þ *χAN* þ *λAC* þ *k*<sup>2</sup> þ *ε*<sup>2</sup> … ½ � 2

hence *i1-i4*, while ancillary services had five items, and hence *i1-i5.*

excitement than they defined the value of the destination.

*4.5.6 Research model equation*

**Initial Eq. 1:**

*Tourism*

Where: i: Items. κ: intercept. ε: Error term.

PR: Price. AM: Amenities. AN: Ancillary services. AC: Accessibility. AF: Affective image.

**Initial Eq. 2:**

Where: i: Items κ: Intercept. ε: Error term.

PR: Price. AM: Amenities. AN: Ancillary services. AC: Accessibility. VA: Performance.

**120**

α, φ, ϑ, η: Path coefficients.

The equation in simple terms was:

ε, β, χ, λ: Path coefficients.

$$AF = aPR + \rho AM + \theta AN + \kappa\_1 + \varepsilon\_1 \dots \tag{1}$$

On the other hand, for Eq. 2, price, amenities and accessibility did not have a significant impact on value (performance), and in this regard, these were dropped off and the subsequent equation comprised one endogenous variable and one exogenous variable as shown below:

**Revised Eq. 2:**

$$\text{VA} = \chi \text{AN} + k\_2 + \varepsilon\_2 \dots \tag{2}$$

Where: κ: Intercept ε,χ: Path coefficients. AN: Ancillary services. VA: Performance.

#### *4.5.7 Model fit test*

With a view to testing the validity of a structural equation model above, several goodness-of-fit tests are carried out as prescribed by [96]. There are three broad categories of model fitness tests, and these include absolute fit indices, the relative fit indices as well as the parsimonious fit indices [89]. For the absolute fit indices, the CMIN/DF is the most common, and the chi-square test p-value should be greater than 0.05, while the CMIN/DF ought to be less than 3.0. On the other hand, for the relative fit indices, Goodness-of-Fit Index (GFI), Comparative Fit Index (CFI), Incremental Fit Index (IFI) and Normed Fit Index (NFI) are the most common and this ought to be greater than 0.90. With respect to the parsimonious fit indices, the most common include the Parsimony Normed Fit Index (PNFI), Parsimony Comparative Fit Index (PCFI) as well as the Root Mean Square Error of Approximation (RMSEA) according to [97]. Nevertheless, the most common is RMSEA and according to [98], the maximum acceptable is 0.08. Satisfying the goodness-of-fit at these three levels qualifies the structural model being tested to be accurate and valid [89, 97]. The model fit indices from the study are presented from Table 5.24 to Table 5.27. From the results, with respect to the absolute fit indices, CMIN/DF = 1.730 and this was less than the prescribed maximum of 3.0, and this was the first validation of the model. **Table 8** shows absolute fit.

Further validation was accomplished by the relative fit indices for which IFI and CFI were 0.941 and 0.940 respectively against the expected minimum threshold of 0.90. **Table 9** shows relative fit.

Regarding the model parsimony, PNFI was 0.755 and PCFI was 0.816 > 0.50. Again, both parsimony measures were greater than the expected minimum 0.50 and this confirmed that the model parsimony was not violated. **Table 10 s**hows the parsimony measures.
