**3.3 Ethical considerations**

Respecting the respondent's rights, needs, values and desires is emphasized when collecting research data (Creswell, 2014). Research which includes human input should ensure that they are well informed and consent sought from relevant authorities. Permission was sought from international tourists, service providers and key informants to carry out research. Various organizations which included the ZTA, CAAZ, Zimbabwe Parks and Wildlife Management Authority, the Ministry of Tourism and the Ministry of Tourism and Hospitality Industry issued letters to the researcher granting him permission to conduct the research at Robert Gabriel Mugabe International Airport. Research assistants were trained to ensure that they behaved ethically as they went about administering research instruments.

#### **4. Results and discussion**

#### **4.1 Response rate**

multiple independent variables. Structural Equation Modeling (SEM) was used to analyze the multiple independent variables which included accessibility, amenities ancillary services and prices as well as dependent variables such as affective image and performance. Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and Bartlett's test were used to test data for validity. Cronbach's Alpha was used to test for data reliability. Overall, the Alpha value was 0.7 and above. SEM is one of the most often used statistical techniques used by researchers to test complex models which involve a number of dependent and independent variables [79]. Similar studies have used SEM [35, 46, 53]. Multivariate analysis was used to test hypotheses because it is optimal for analyzing multiple relationships [75]. Factor analysis was applied on the thirty eight destination image attributes which tourists rated on a Likert scale. These destination attributes were classified into constructs which included price, accessibility, amenities, ancillary services, affective image and performance. This was done in order to facilitate data analysis. Quantitative data was

*Research design adopted in the study. Source: Author's compilation (2018).*

Documentary analysis was adopted to establish the trends which were emerging from international aircraft and passenger movements provided by CAAZ from 2016 and 2018 and the international arrivals provided by ZTA during the same period. Data was first captured on a template before it was cleaned (edited). Qualitative data from key informants and service providers was analyzed using NVivo version 12, thematic coding. The goal was to identify, analyze and describe patterns, or themes, across a data set [77]. Word cloud, Word tree, Hierarchical charts, Word

presented using tables and graphs.

**Figure 4.**

*Tourism*

**110**

Response rate refers to the total number of responses divided by the total number in the sample after ineligible respondents have been excluded [78]. A total of 397 respondents comprising 293 international tourists, 90 service providers and 17 key informants was targeted. However, the actual tally was 319 giving a response rate of 80% which was quite commendable [78]. This total of respondents consisted of 240 international tourists, 62 tourism and hospitality service providers and 17 key informants (**Table 1**).

#### **4.2 Demographic characteristics of respondents**

In a study sample of 319, fifty three percent were males while forty-seven were females. The slight dominance of males could be due to the fact that men traveled more for tourism than their female counterparts and feel more motivated to meet their need for sport and adventure experiences than females [80]. The [80] further noted that there were more men than women in the business world and a lot of


**Table 1.** *Response rate.* business travel occurs across the world and as a result, men tended to travel more than females.

**Scale mean if item deleted**

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

Lodging Prices 12.42 6.011 0.582 0.938

Conference Facilities 11.08 7.261 0.726 0.776

Shopping Facilities 10.67 8.766 0.533 0.857

Cleanliness 16.80 6.030 0.569 0.702 Tourist Information 16.97 5.629 0.602 0.688 Quietness 17.15 5.580 0.548 0.709

ICT Readiness 17.14 6.100 0.431 0.753

Service at Immigration 12.20 4.679 0.685 0.714

Price 0.900

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

Affective Image 0.881 Destination's capacity to Relieve Stress

Destination's Capacity to Provide Relaxation

Destination as a Pleasant

Amenities 0.842

Ancillary Services 0.759

Accessibility 0.801

Value 0.854

Destination as an Arousing Place

Destination as a Provider of Excitement

Facilities for Young

Facilities for People living with Disabilities

Friendliness of Local

Infrastructure at the entry point

People

Zimbabwe's Accessibility as a Destination

Accessibility Destinations

**113**

Children

Prices of Restaurant

Prices of Restaurant Beverages

Prices of Goods and

Food

Services

Place

**Scale variance if item deleted**

12.53 4.969 0.892 0.826

12.51 5.004 0.883 0.830

12.41 5.542 0.767 0.874

16.22 10.162 0.729 0.852

16.16 10.223 0.793 0.838

16.04 11.278 0.682 0.865

16.46 9.779 0.696 0.862

16.41 9.985 0.703 0.859

10.88 7.433 0.729 0.775

10.65 7.841 0.724 0.779

16.72 6.631 0.514 0.725

12.17 5.384 0.524 0.793

12.29 4.295 0.736 0.685

12.00 5.926 0.533 0.789

**Corrected itemtotal correlation** **Cronbach's alpha if item deleted**

The variation suggests that gender can influence perceptions of destination's appeal. This is in line with [81] who asserted that females tended to engage in longhaul travel more than their male counterparts. Respondents aged between 25 and 35 years old formed the largest group (25.2%) followed by those aged between 35 and 44 years old (18.1), 17.2% of the respondents were in age group 45–54 years old, 15.1% of the respondents were in age group 55–65 years old, age group 66 or older constituted 13.4% of respondents, and age group 18-24 years old were 10.9%. The results showed that most of the tourists ranged from young to middle aged. The study findings resonate with those by [82]. A Visitor Exit Survey which was conducted at Zimbabwe's ports of entry by [82] revealed that the majority of visitors to Zimbabwe were young (35–39) years (16.4%) and middle-aged (40–49) years (13.9%). A study which was conducted in Egypt by [83] focusing on cultural dimensions, demographics, and information sources as antecedents to cognitive and affective DI found out that tourists in the age ranges 26–35 and 36–50 were more likely to use the Internet, while younger (aged 18–25) and older (51–65) were less likely to use it. This finding in terms of age was similar to that of tourists in that most of the international tourists were fairly young. This may create a scenario whereby the young tourists are served by young service providers. This can help to create telepathy and rapport between the tourist and the server. This may enhance both employee and customer satisfaction leading to improved firm and destination performance. These results show that most of the respondents were well educated indicating that their responses were given from a position of enlightenment and knowledge.

Most of the tourists (37.5%) received an income of US\$50000 and more before tax per annum followed by those who were earning between US\$10001 and US \$20000 (17.9%), and those who earned between US\$20001 and US\$30000 (11.9%), those who earned between US\$30001 and US\$40000 (10%) and those who earned US\$ 40,001 to US\$50000 (10%). There is limited research which has directly examined the relationship between destination attractiveness and income of the tourists. In [84], it is noted that in a study conducted in Taiwan, it was found that income was an influencer of tourist behavior. Tourists with a higher income tended to travel internationally more and were likely to stay in luxury hotels. On the other hand, travelers with less income tended to be associated with domestic trips rather than international vacations. In that regard, income was found to be an important determinant of destination choice [84].

#### **4.3 Reliability analysis**

Reliability analysis is used to determine the extent of internal consistency that is represented by a set of items in a construct [85]. For this study, reliability analysis was used to determine the extent to which the items within each and every construct were consistent. According to [86], the optimal minimum alpha statistic is 0.7. However, other scholars such as [87] argue that even alpha statistics of 0.6 are still reliable. The reliability tests for each and every construct will be presented.

The Cronbach's Alpha statistic was 0.900, and being greater than 0.7, it follows that the construct price was internally consistent and reliable (**Table 2**). Further, assessing the corrected item-total correlation, none of the items had a coefficient less than 0.3 as recommended by [88] and this means that all the items extracted using PCA were reliable. For affective image, the Cronbach's alpha statistic was 0.881. This was greater than the threshold of 0.7, and thus validates that affective


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

business travel occurs across the world and as a result, men tended to travel more

The variation suggests that gender can influence perceptions of destination's appeal. This is in line with [81] who asserted that females tended to engage in longhaul travel more than their male counterparts. Respondents aged between 25 and 35 years old formed the largest group (25.2%) followed by those aged between 35 and 44 years old (18.1), 17.2% of the respondents were in age group 45–54 years old, 15.1% of the respondents were in age group 55–65 years old, age group 66 or older constituted 13.4% of respondents, and age group 18-24 years old were 10.9%. The results showed that most of the tourists ranged from young to middle aged. The study findings resonate with those by [82]. A Visitor Exit Survey which was conducted at Zimbabwe's ports of entry by [82] revealed that the majority of visitors to Zimbabwe were young (35–39) years (16.4%) and middle-aged (40–49) years (13.9%). A study which was conducted in Egypt by [83] focusing on cultural dimensions, demographics, and information sources as antecedents to cognitive and affective DI found out that tourists in the age ranges 26–35 and 36–50 were more likely to use the Internet, while younger (aged 18–25) and older (51–65) were less likely to use it. This finding in terms of age was similar to that of tourists in that most of the international tourists were fairly young. This may create a scenario whereby the young tourists are served by young service providers. This can help to create telepathy and rapport between the tourist and the server. This may enhance both employee and customer satisfaction leading to improved firm and destination performance. These results show that most of the respondents were well educated indicating that their responses were given from a position of enlightenment and

Most of the tourists (37.5%) received an income of US\$50000 and more before tax per annum followed by those who were earning between US\$10001 and US \$20000 (17.9%), and those who earned between US\$20001 and US\$30000 (11.9%), those who earned between US\$30001 and US\$40000 (10%) and those who earned US\$ 40,001 to US\$50000 (10%). There is limited research which has directly examined the relationship between destination attractiveness and income of the tourists. In [84], it is noted that in a study conducted in Taiwan, it was found that income was an influencer of tourist behavior. Tourists with a higher income tended to travel internationally more and were likely to stay in luxury hotels. On the other hand, travelers with less income tended to be associated with domestic trips rather than international vacations. In that regard, income was found to be an important

Reliability analysis is used to determine the extent of internal consistency that is represented by a set of items in a construct [85]. For this study, reliability analysis was used to determine the extent to which the items within each and every construct were consistent. According to [86], the optimal minimum alpha statistic is 0.7. However, other scholars such as [87] argue that even alpha statistics of 0.6 are still reliable. The reliability tests for each and every construct will be presented. The Cronbach's Alpha statistic was 0.900, and being greater than 0.7, it follows that the construct price was internally consistent and reliable (**Table 2**). Further, assessing the corrected item-total correlation, none of the items had a coefficient less than 0.3 as recommended by [88] and this means that all the items extracted using PCA were reliable. For affective image, the Cronbach's alpha statistic was 0.881. This was greater than the threshold of 0.7, and thus validates that affective

than females.

*Tourism*

knowledge.

determinant of destination choice [84].

**4.3 Reliability analysis**

**112**


a measure of multivariate normality, the p-value ought to be significant at p < 0.05 [88]. These tests were computed and the results are summarized in **Table 3**.

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

mality was met.

**4.5 Modeling process**

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

extraction method.

*4.5.1 Factor extraction*

*4.5.2 Communalities matrix*

*Source: Data Survey.*

*KMO and Bartlett's test.*

**Table 3.**

**115**

The results above show that the KMO statistic was 0.843, and being greater than the minimum 0.5, it follows that the sample adequacy condition was satisfied. On the other hand, the Bartlett's test was significant at p < 0.01 and this confirms the assumption of multivariate normality was met. The results above show that the KMO statistic was 0.843, and being greater than the minimum 0.5, it follows that the sample adequacy condition was satisfied. On the other hand, the Bartlett's test was significant at p < 0.01 and this confirms the assumption of multivariate nor-

The modeling process below looked at price, amenities, conducive environment, affective image, accessibility and performance. The research instrument comprised of 38 items that measured the determinants of destination image recovery and performance of the tourism sector in Zimbabwe. With a view to establishing the principal factors behind these determinants of destination image recovery and destination performance, [91] recommend the use of exploratory factor analysis (EFA) dimension reduction techniques. According to [92], these dimension reduction techniques help in the classification of items that share a common underlying structure into a set of similar items collectively known as components [88]. One of the major dimension reduction methods recommended by scholars is factor analysis and this was considered in this study to be the optimal dimensionality reduction technique as prescribed by [93]. To achieve this dimensionality reduction, the principal component analysis (PCA) was used as the factor analysis component

Because the normality assumption was met, the Principal Component Analysis (PCA) was used in this study as the component extraction method, instead of the principal axis factoring, which works best when the normality assumption is not met [92]. With a view to simplifying the factors extracted, rotation was used. The components were assumed to be uncorrelated and o this effect, orthogonal rotation was done instead of oblimin rotation [90]. For the orthogonal rotation, Varimax was selected and this was done with Kaiser Normalization as prescribed by [89].

Having run PCA, the communalities that emerged are presented in **Table 4**. Generally, the communalities inform us on the extent of correlation between one item and the rest of the other items [88]. The higher the common variance, the

Kaiser-Meyer-Olkin measure of sampling adequacy .843 Bartlett's test of sphericity Approx. Chi-Square 4170.258 Df 703 Sig. .000

#### **Table 2.**

*Reliability analysis.*

image was internally consistent. On the other hand, none of the items had a corrected item-total correlation that was less than 0.3. Effectively, this meant that all the items were internally consistent. The Cronbach's alpha for amenities was computed to be 0.842 and this was greater than 0.7. These results validate that the construct amenities were reliable. Regarding the corrected item to total correlation, the minimum observed was 0.533. This again, does fall below the 0.3 threshold set by scholars. In this regard, the researcher confirmed that amenities as a construct was reliable. With respect to ancillary services, the construct was internally consistent since the alpha statistic was 0.759, which is greater than the minimum expected 0.7. With respect to the corrected item-total correlation, the minimum was 0.431 and being greater than 0.3, none of the items were to be dropped.

From the results above, the Cronbach's alpha for accessibility was 0.801 and being greater than 0.7, it follows, therefore, that the construct was internally consistent and reliable. With respect to the corrected item-total correlation, the lowest observed was 0.524 and because this was greater than the minimum 0.3, the researcher confirms that all the items making up the construct.

accessibility were reliable. The next construct that was tested was value/performance. The corresponding Cronbach's alpha for value/performance was 0.754 and being greater than 0.7, we can confirm that the construct was reliable and internally consistent. With respect to the corrected item-total correlation coefficient, the lowest observed was 0.553 and being greater than 0.3, it followed that all the items were very reliable. The construct attractions had a Cronbach alpha statistic of 0.636, this was less than the expected minimum of 0.7 and effectively, this meant that the construct was not so reliable. This is, however, despite that the corrected item-total correlation coefficients were greater than 0.3. Overall, from the reliability analysis, it was confirmed that the reliable constructs were: Price, Affective Image, Amenities, Ancillary Services, Accessibility and Value/Performance.

#### **4.4 KMO and Bartlett's test-destination image recovery and performance**

In order to ensure that the conditions for the use of factor analysis were satisfied, [89], argue that the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and Bartlett's tests ought to be tested. With respect to the KMO test, which measures the adequacy of the sample, the lower expected threshold should be 0.5, with higher values being more desirable [90]. With respect to the Bartlett's test, which is *Development of a Destination Image Recovery Model for Enhancing the Performance… DOI: http://dx.doi.org/10.5772/intechopen.93854*

a measure of multivariate normality, the p-value ought to be significant at p < 0.05 [88]. These tests were computed and the results are summarized in **Table 3**.

The results above show that the KMO statistic was 0.843, and being greater than the minimum 0.5, it follows that the sample adequacy condition was satisfied. On the other hand, the Bartlett's test was significant at p < 0.01 and this confirms the assumption of multivariate normality was met. The results above show that the KMO statistic was 0.843, and being greater than the minimum 0.5, it follows that the sample adequacy condition was satisfied. On the other hand, the Bartlett's test was significant at p < 0.01 and this confirms the assumption of multivariate normality was met.

#### **4.5 Modeling process**

image was internally consistent. On the other hand, none of the items had a corrected item-total correlation that was less than 0.3. Effectively, this meant that all the items were internally consistent. The Cronbach's alpha for amenities was computed to be 0.842 and this was greater than 0.7. These results validate that the construct amenities were reliable. Regarding the corrected item to total correlation, the minimum observed was 0.533. This again, does fall below the 0.3 threshold set by scholars. In this regard, the researcher confirmed that amenities as a construct was reliable. With respect to ancillary services, the construct was internally consistent since the alpha statistic was 0.759, which is greater than the minimum expected 0.7. With respect to the corrected item-total correlation, the minimum was 0.431

Natural Landscape 4.32 0.591 0.472 . Climate 4.48 0.433 0.472 .

**Scale mean if item deleted**

Value as a Vacation Destination

Value as a Business Destination

Overall Quality of the Destination

**Table 2.** *Reliability analysis.*

*Tourism*

**114**

Attractions 0.636

**Scale variance if item deleted**

7.24 2.248 0.633 0.612

7.44 2.329 0.553 0.711

6.97 2.615 0.571 0.688

**Corrected itemtotal correlation** **Cronbach's alpha if item deleted**

and being greater than 0.3, none of the items were to be dropped.

researcher confirms that all the items making up the construct.

ties, Ancillary Services, Accessibility and Value/Performance.

**4.4 KMO and Bartlett's test-destination image recovery and performance**

In order to ensure that the conditions for the use of factor analysis were satisfied, [89], argue that the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and Bartlett's tests ought to be tested. With respect to the KMO test, which measures the adequacy of the sample, the lower expected threshold should be 0.5, with higher values being more desirable [90]. With respect to the Bartlett's test, which is

From the results above, the Cronbach's alpha for accessibility was 0.801 and being greater than 0.7, it follows, therefore, that the construct was internally consistent and reliable. With respect to the corrected item-total correlation, the lowest observed was 0.524 and because this was greater than the minimum 0.3, the

accessibility were reliable. The next construct that was tested was value/performance. The corresponding Cronbach's alpha for value/performance was 0.754 and being greater than 0.7, we can confirm that the construct was reliable and internally consistent. With respect to the corrected item-total correlation coefficient, the lowest observed was 0.553 and being greater than 0.3, it followed that all the items were very reliable. The construct attractions had a Cronbach alpha statistic of 0.636, this was less than the expected minimum of 0.7 and effectively, this meant that the construct was not so reliable. This is, however, despite that the corrected item-total correlation coefficients were greater than 0.3. Overall, from the reliability analysis, it was confirmed that the reliable constructs were: Price, Affective Image, Ameni-

The modeling process below looked at price, amenities, conducive environment, affective image, accessibility and performance. The research instrument comprised of 38 items that measured the determinants of destination image recovery and performance of the tourism sector in Zimbabwe. With a view to establishing the principal factors behind these determinants of destination image recovery and destination performance, [91] recommend the use of exploratory factor analysis (EFA) dimension reduction techniques. According to [92], these dimension reduction techniques help in the classification of items that share a common underlying structure into a set of similar items collectively known as components [88]. One of the major dimension reduction methods recommended by scholars is factor analysis and this was considered in this study to be the optimal dimensionality reduction technique as prescribed by [93]. To achieve this dimensionality reduction, the principal component analysis (PCA) was used as the factor analysis component extraction method.

#### *4.5.1 Factor extraction*

Because the normality assumption was met, the Principal Component Analysis (PCA) was used in this study as the component extraction method, instead of the principal axis factoring, which works best when the normality assumption is not met [92]. With a view to simplifying the factors extracted, rotation was used. The components were assumed to be uncorrelated and o this effect, orthogonal rotation was done instead of oblimin rotation [90]. For the orthogonal rotation, Varimax was selected and this was done with Kaiser Normalization as prescribed by [89].

#### *4.5.2 Communalities matrix*

Having run PCA, the communalities that emerged are presented in **Table 4**. Generally, the communalities inform us on the extent of correlation between one item and the rest of the other items [88]. The higher the common variance, the



higher is the validity of the item, and [85] recommend communalities to be at least

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

From the results, only one item had a communality that was less than 0.5 and this was safety and security and the respective correlation coefficient was 0.476. Effectively, this was discarded off from the results. The rest of the other coefficients were considered to be significant for accurate factor extraction, with the highest communalities being 0.864 and 0.863 for prices of restaurant food and prices of restaurant beverages respectively. The resultant model is presented in **Figure 5**

The corresponding table with the detailed results is presented in **Table 5** below. From the results above, the strongest relationship was found to exist between ancillary and affective image, whose standardized coefficient was 0.345 and this was seconded by price and affective image, with a standardized coefficient of 0.320. The p-value was less than 0.05 for the relationship between Price and Affective Image (p < 0.01), amenities and affective image (p < 0.05), ancillary services and affective image (p < 0.01) as well as ancillary and value. It should be noted that only one of the four hypotheses linking performance was significant. The conclu-

**Estimate S.E. C.R. P Standardized**

AF <– PR .237 .051 4.681 .000 .320 VA <– PR .114 .065 1.750 .080 .128 AF <– AM .089 .044 1.995 .046 .135 VA <– AM .072 .062 1.173 .241 .091 AF <– AC .025 .046 .543 .587 .036 VA <– AC .070 .066 1.071 .284 .083 AF <– AN .586 .146 4.003 .000 .345 VA <– AN .356 .172 2.066 .039 .175

sions to the research hypotheses are indicated below:

*Structural equation model. Source: Data survey (2018).*

0.5 in magnitude.

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

below.

**Figure 5.**

*Source: Data Survey (2018).*

*Structural equation model - regression weights.*

**Table 5.**

**117**

#### **Table 4.**

*Communalities-destination image recovery and performance.*

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

higher is the validity of the item, and [85] recommend communalities to be at least 0.5 in magnitude.

From the results, only one item had a communality that was less than 0.5 and this was safety and security and the respective correlation coefficient was 0.476. Effectively, this was discarded off from the results. The rest of the other coefficients were considered to be significant for accurate factor extraction, with the highest communalities being 0.864 and 0.863 for prices of restaurant food and prices of restaurant beverages respectively. The resultant model is presented in **Figure 5** below.

The corresponding table with the detailed results is presented in **Table 5** below.

From the results above, the strongest relationship was found to exist between ancillary and affective image, whose standardized coefficient was 0.345 and this was seconded by price and affective image, with a standardized coefficient of 0.320. The p-value was less than 0.05 for the relationship between Price and Affective Image (p < 0.01), amenities and affective image (p < 0.05), ancillary services and affective image (p < 0.01) as well as ancillary and value. It should be noted that only one of the four hypotheses linking performance was significant. The conclusions to the research hypotheses are indicated below:

**Figure 5.** *Structural equation model. Source: Data survey (2018).*


#### **Table 5.**

*Structural equation model - regression weights.*

**Initial Extraction**

Zimbabwe's Accessibility as a Destination 1.000 .606 Infrastructure at the Country's Immigration (entry point used) 1.000 .809 Service at Immigration (entry point used) 1.000 .717 Accessibility of Tourist Destinations within Zimbabwe 1.000 .697 Road Condition 1.000 .600 Inland Transportation/Taxi/Bus 1.000 .526 Natural Landscape 1.000 .730 Climate 1.000 .506 Tourist Attractions 1.000 .653 Opportunities for Learning Ethnic Customs 1.000 .751 Local Cuisine 1.000 .600 Outdoor Activities 1.000 .520 Cleanliness 1.000 .639 Tourist Information 1.000 .665 Quietness (Noise Pollution) 1.000 .565 Friendliness of Local People 1.000 .649 Nightlife/Entertainment 1.000 .644 Attitude of Service Personnel 1.000 .685 Safety and Security 1.000 .476 ICT Readiness 1.000 .624 Conference Facilities 1.000 .660 Facilities for Young Children 1.000 .800 Facilities for People living with Disabilities 1.000 .795 Shopping Facilities 1.000 .617 Lodging Facilities 1.000 .726 Restaurants 1.000 .721 Lodging Prices 1.000 .757 Prices of Restaurant Food 1.000 .864 Prices of Restaurant Beverages 1.000 .863 Prices of Goods and Services 1.000 .735 Destination's capacity to Relieve Stress 1.000 .753 Destination's Capacity to Provide Relaxation 1.000 .798 Destination as a Pleasant Place 1.000 .724 Destination as an Arousing Place 1.000 .712 Destination as a Provider of Excitement 1.000 .734 Value as a Vacation Destination 1.000 .751 Value as a Business Destination 1.000 .689 Overall Quality of the Destination 1.000 .727

Extraction Method: Principal Component Analysis.

*Communalities-destination image recovery and performance.*

*Source: Data Survey (2018).*

**Table 4.**

*Tourism*

**116**
