Journal of marketing, business and management (jmbm)

Methodology. Sampling and data collection. The impact of population perceptions on tourism can be analyzed using hypotheses based on the following factors.

Community consolidation

Public relations affects the perceptions of the population, as evidenced by many studies (for example, [8,19]), including studies in the most visited regions of Uzbekistan, such as Samarkand, Bukhara and Khorezm. confirms the following conclusions.

H1a. There is a positive relationship between Level of attachment and perceived positive tourism impacts.

H1b. There is a positive relationship between Level of attachment and perceived negative tourism impacts.

Distance from tourist area

The role of distance from tourist zones in the perceived tourist effect has been considered in several studies (e.g. [5, 9,19]). More favorable prospects for tourism development have been shown by residents near the tourist center. The following hypotheses were formulated:

H2a. There is a negative relationship between the distance from tourist zones and perceived positive tourism impacts.

H2b. There is a positive relationship between the distance from tourist zones and perceived negative tourism impacts

Gender

In a study conducted in the regions, men are more likely than women to experience more positive effects on tourism development. They also found that women had a more negative view of tourism development than men. Thus, the following hypotheses were identified:

H3a. Women are less optimistic about positive tourism impacts than men.

H3b. Women are more concerned about negative tourism impacts than men.

Age

Young residents are more optimistic about the impact of economic tourism. They see the tourism industry as an opportunity to work, and have found that perceptions of the elderly population are less positive. Thus, the following hypotheses were developed:

H4a. There is a negative relationship between age and perceived positive tourism impacts.

H4b. There is a negative relationship between age and perceived negative tourism impacts.

Tourism-related jobs

Numerous studies have examined the importance of work experience in tourism and how this factor can predict the positive and negative effects of tourism development (e.g., [7,19]). On the contrary, others have stated the opposite. From this, the following assumptions can be made:

H5a. Residents that have a tourism-related job perceive more positive tourism impacts.

H5b.  Residents that have a tourism-related job perceive fewer negative tourism impacts.

Level of education

Studies show that citizens with higher education are more receptive to the impact of tourism. Conversely, it may be due to the fact that people without higher education do not have the opportunity to benefit economically from tourism. Studies have found that the higher the level of knowledge of the population, the more positive socio-cultural and economic influences they perceive. It also showed that the uneducated population was less aware of the development of tourism and its benefits. Accordingly, the following hypotheses were formed:

H6a. There is a positive relationship between level of education and perceived positive tourism impacts.

H6b. There is a positive relationship between level of education and perceived negative tourism impacts.

The data on the attitude of the population to tourism were collected from the population of Samarkand, Bukhara and Khorezm regions, who were at least 18 years old. After reviewing the previously mentioned literature and participating in the Code of Ethics and Practices established by the American Public Opinion Research Association [10], a questionnaire was developed. Data collection was done via telegram messenger, email address and similar social networks.

Exploratory factor analysis (R-type) with varimax rotated principal component analysis was used to generate general groups of residents’ perceptions toward tourism impacts. The assumptions for factor analysis are not violated. The Kaiser–Meyer–Olkin statistic is 0.816, which surpasses the recommended cut-off of 0.06 [11,19], and the correlation between these items is convenient for factor analysis. The data reveal that Bartlett’s test of Sphericity is significant (approximately chi-square = 4649.167, df = 300, p < 0.001). Items with a coefficient value of less than 0.4 were deleted. To improve the results of the analysis, six items were deleted that either had low loadings or were loaded on several factors. These items were excluded from further analysis. The items’ scored factor loadings range from 0.498 to 0.845. The refined (regression) method was used to calculate each factor score. This method maximizes validity and provides true factor scores [12,19]. These scores were used later as dependent variables in the regression analysis to test the effect of several independent variables on residents’ perceptions. Accordingly, five clean domains were produced from the data after several runs. Factors’ eigenvalues are greater than one and explain 56.464% of the total variance in the data.

We created a five-question questionnaire among the population of Samarkand, Bukhara and Khorezm regions, which was used to assess the attitude of the population to tourism.

Domains	Items	Factor Loading		Mean		SD
PSC					2.92
α (0.732) e (6.200) VE (24.800)	Tourism provides entertainment opportunities for the local community	0.757		2.76		1.408
	Tourism helps to creates more local associations	0.584		2.83		1.229
	Tourism helps to improve the government provided facilities (Health centres, better schools, post office, sport centres, etc.)	0.656		2.96		1.351
	Tourism helps to preserve local traditions	0.663		2.74		1.363
PEn					3.02
α (0.730) e (3.220) VE (12.879)	Tourism helps to protect the environment	0.736		3.07		1.243
	Tourism help to creates more natural parks	0.549		2.91		1.290
	Tourism encourages people to protect surrounding environment	0.704		2.99		1.269
	Tourism helps to keep my village\city clean	0.784		3.11		1.278
PE					2.92
α (0.819) e (1.834) VE (7.335)	Tourism increases my family incomes	0.544		2.47		1.249
	Tourism creates better public transportation infrastructure	0.674		2.82		1.308
	Tourism helps to build more roads	0.802		2.93		1.267
	Tourism helps to creates business	0.805		3.15		1.325
	Tourism helps to creates more jobs	0.725		3.22		1.379
NSEn					2.93
α (0.809) e (1.447) VE (5.789)	Tourism increases the uses of alcohol	0.598		3.34		1.413
	Tourism increases the amount of crime	0.756		2.76		1.373
	Tourism reduces my outdoor recreation	0.643		2.61		1.207
	Tourism makes crowding of public spaces and facilities	0.549		2.99		1.216
	Tourism hazards the citizen rights by using the lands and properties to create more hotels and borders from national parks	0.594		3.06		1.283
	Tourism creates more social needs	0.595		2.87		1.175
	Tourism negatively affects the family relationships	0.498		2.78		1.211
	Tourism increases pollution (noise, air, etc.)	0.596		3.12		1.304
	Tourism hazard the natural landscape	0.542		3.21		1.378
NE					3.08
α (0.829) e (1.415) VE (5.661)	Tourism increases the price of properties	0.845		3.40		1.393
	Tourism increases the cost of living	0.804		3.51		1.343
	Tourism generates seasonal unemployment	0.665		3.53		1.390

Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.816; total variance explained data = 56.464%; α: Cronbach’s Alpha; e: Eigenvalues; VE: percentage of variance explained.

Shows the factor loadings and Cronbach’s Alpha for the extracted domains, namely, domain 1: Positive socio cultural (PSC); domain 2: Positive environment (PEn); domain 3: Positive economic (PE); domain 4: Negative socio-environment (NSEn); domain 5: Negative economic (NE). The five domains extracted from the factor analysis were then tested for reliability. Cronbach’s Alpha coefficient values from this study range from 0.730 to 0.829, indicating that the variables present a high correlation with their factor aggregations and that there is an internal consistency of the items.[19] An examination of the correlation matrix indicates that none of the correlations among the constructs are higher than 0.50.

We thought it would be possible to include five models in the list of dependent variables and use the following independent variables (attachment, gender, place of residence, age, employed, education level). The average score was calculated based on the average scores of the public attachment elements to create a variable score.

Results

The GLM model results indicate that all five domains were significant (p < 0.01) explaining the variation of (6.3% of PCS), (10.6% of PEn), (14% of PE), (8.6% of NSEn), and (7.7% of NE). Linear regression was applied to examine whether the perceptions’ domains influence the residents’ satisfaction.[19] The model was significant (F = 39.524; p = 0.000) and explained 30% of the variation. Perceived positive economic impact was the strongest predictor of satisfaction (β = 0.307) followed by positive environment (β = 0.243) and positive socio-culture domain (β = 0.196). Negative perceived impacts were found not to be a significant predictor of the residents’ satisfaction. It should be noted that only significant results are shown further on

DV	IV	B	Hypothesis	Supported?
PSC
^a R^2 0.064 R^2 adjusted 0.0479 F 4.289	Distance	0.240 ***	H2a	yes
	Education	0.120 **	H6a	yes
	Attachment	0.071 *	H1a	yes
	T-employed	–0.500 **	H5a	no
PEn
R^2 0.107 R^2 adjusted 0.094 F 7.856	Distance	0.211 ***	H2a	yes
	Attachment	0.340 ***	H1a	yes
	T-employed	–0.587 **	H5a	no
	Age	0.082 *	H4a	yes
PE
R^2 0.139 R^2 adjusted 0.126 F 10.721	Distance	0.191 ***	H2a	yes
	Age	–0.079 **	H4a	no
	Gender	–0.675 ***	H3a	yes
NSEn
R^2 0.091 R^2 adjusted 0.162 F 6.201	T-employed	0.340 *	H5b	no
	Distance	0.345 ***	H2b	no
	Education	0.152 **	H6b	yes
	Gender	–0.462 **	H3b	no
NE
R^2 0.079 R^2 adjusted 0.065 F 5.493	Education	0.091 *	H6b	yes
	Attachment	0.171 ***	H1b	yes

Hypothesis 1 predicted that when there is a greater level of attachment, the perceived negative and positive impacts increase.[19] This hypothesis was supported in terms of negative economic impacts (β = 0.171, p < 0.01); positive socio-culture (β = 0.071, p < 0.1); and positive environment (β = 0.340, p < 0.01). Hypothesis 2 hypnotized that the further away residents live from the touristic area, the more they are worried about negative impacts and the less they favor positive impacts. Findings support H2a in terms of PSC (β = 0.240, p < 0.01), PEn (β = 0.211, p < 0.01), and PE (β = 0.191, p < 0.01), and H2b was rejected in terms of NSEn (β = 0.345, p < 0.01). Hypothesis 3 predicted that females perceived more negative impacts and less positive impacts than males do. Results of the PE domain support hypothesis H3a (β = –0.675, p < 0.01) Conversely, the results of NSEn (β = –0.462, p = 0.003) reject hypothesis H3b.

Hypothesis 4a was supported by results of PEn domain (β = 0.082, p < 0.1). It was predicted that the greater the age of a participant, the more negatively they perceived positive tourism impacts. Moreover, in terms of the PE domain, it was rejected (β = –0.079, p < 0.05). There were no significant findings in the negative domains. It was hypothesized that having a job related to tourism positively influences perceived tourism impacts. Thus, H5a was rejected in terms of PSC (β = –0.500, p < 0.05) and PEn (β = –0.587, p < 0.05). H5b also was rejected by the results of NSEn (β = 0.340, p < 0.1). According to hypothesis 6, it was hypothesized that the higher education level the residents have, the higher they perceive negative and positive impacts. H6a was supported in term of PSC (β = 0.120, p < 0.05), and H6b was supported by the results of NE (β = 0.091, p < 0.1) and NSEn (β = 0.152, p < 0.05).

As an example of the results, the β value (regression coefficient) of gender in the PE domain tells us that females perceive fewer positive economic impacts than males, with an average score of 0.565. Likewise, the β value of the age in the PEn domain indicates that as the age group increases (e.g., from 25–34 to 35–44), the perceived positive environmental impacts increase by 0.076.In general, the results give an idea of ​​the impact of the population on tourism in Samarkand, Bukhara and Khorezm regions. Regardless of the theoretical support of the hypothesis results, the addition was found to have the highest regression coefficient in the positive environment, the distance in the negative socio-environment, and the highest age in the positive economic field. those who are employed in the tourism industry (those who are employed) have the highest level in a positive environment. Based on the results of the above analysis, we selected the following hypothesis to assess the impact of domestic tourism on women’s employment.

Hypothesis zero: H₀ - Domestic tourism analysis affects women’s employment rate. Alternative Hypothesis: H₁ - Domestic tourism analysis does not affect women’s employment rate. Analysis of domestic tourism - the interdependence of women's employment.

Analysis of women's employment and domestic tourism.



catch

internal tourism consumption, in billion United States dollars;

female employment in services, percent;

error term;

slope coefficient.

Below the results of the linear regression using MS Excel are presented. Statistical parameters¹.