1. bookVolume 11 (2021): Issue 2 (December 2021)
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2182-4924
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30 Apr 2016
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Tourism Impact Dimensions, Residents’ Quality of Life and Support for Tourism in Hunza Valley, Pakistan

Published Online: 29 Jun 2022
Volume & Issue: Volume 11 (2021) - Issue 2 (December 2021)
Page range: 195 - 209
Received: 04 Aug 2020
Accepted: 09 Mar 2020
Journal Details
License
Format
Journal
eISSN
2182-4924
First Published
30 Apr 2016
Publication timeframe
3 times per year
Languages
English
Introduction

Tourism is a unique sector which, rather than providing goods and services to a client, transports clients as a product, where the production of goods coincides with their consumption (Wilson & Ypeij, 2012). Globally, tourism is emerging as one of the main sources of revenue for numerous developing and developed countries as it improves the mutual economy of guests and host nations (Arshad et al., 2018). In FY2018, the overall input of travel and tourism to the world's GDP was USD 8,272.3 billion (WTTC, 2018). Worldwide, a 6% increase was observed in international tourist arrivals (overnight guests) worth USD 1.4 billion (UNWTO, 2019). Simultaneously, travel and tourism statistics report Pakistan as a prospective tourism spot, to contribute PKR 2,349 billion (USD 22,286.3 million) to the world's GDP, and its share is expected to grow by 5.8% (WTTC, 2018). Such potential for growth is likely to create intense competition that causes tourism destinations to apply management and marketing strategies that help them survive in the tourism field (Pırnar & Günlü, 2012). In the tourism literature, however, the majority of studies have focused on the impacts of tourism, concerning substantial marketing implications (Woo et al., 2016). A key reason for the growing interest in this type of study is the knowledge and awareness of tourism development and its effects, both positive and negative, at the local level (Ko & Stewart, 2002).

Studies have found that money generated through tourism impacts economically not only the country at large but also individual communities and their residents, through increased employment opportunities, income levels, and advanced public infrastructures (Sinclair & Gursoy, 2016). Opposite to potential monetary growth, however, is the ecological deterioration and negative sociocultural impact of tourism (Choi & Sirakaya, 2005). The literature review suggests that tourism impacts can be studied from various perspectives including environmental, economic, social, and cultural (K. Kim et al., 2013). By relying on tourism impacts, Dogan et al. (2002) stated that support for tourism (SFT) that is important for community growth, effective operation, competitiveness, and sustainability, can be transformed.

Residents’ SFT is defined as ‘a function of what residents understand about tourism development and how it influences them and their community’ (Telfer & Sharpley, 2015). Over the last four decades, research on residents’ SFT has flourished increasingly (Allen et al., 1988; Lin et al., 2019; Nunkoo & Ramkissoon, 2011; Nunkoo & So, 2016; Pizam, 1978). Owing to the evidence that supportable tourism is likely to address the needs of residents in tourism development, residents’ SFT is seen as a necessity for sustainability (Sharpley, 2014). Since tourism depends profoundly upon the willingness of residents, it is very important to comprehend what factors influence residents’ support for tourism (Yoon et al., 2001).

In their research, Jun et al. (2012) investigated the perceived economic gain of residents, the ecocentric attitude of residents, and tourism-related jobs as antecedents of SFT. In another study, Sinclair et al. (2015) used residents’ trust, their identity, residents’ perception of imperialism, and the residents’ perception of tourism growth as antecedents of residents’ SFT growth. Furthermore, Nunkoo and So (2016) included trust in local government, knowledge of tourism, power in tourism, satisfaction with quality of life (QOL), and personal benefits from tourism as factors influencing residents’ SFT. However, investigation of residents’ SFT concerning residents’ quality of life (QOL) is still latent and has gained little academic attention (Nunkoo & So, 2016; Woo et al., 2015).

A plethora of research has examined affiliations between tourism impacts perceived by residents and their SFT (Dong Wan & Stewart, 2002; Dogan et al., 2002; Jurowski & Gursoy, 2004; Nunkoo & Ramkissoon, 2010) through different mediating factors (Lin et al., 2019; Rasoolimanesh et al., 2019). Under Social Exchange Theory (SET), tourism development factors are found to impact residents’ SFT (Woo et al., 2015). However, these contributions are based on a partial set of constructs and are represented by different, yet inconsistent, propositions (Nunkoo & So, 2016). In the Asian context, trivial research has been conducted regarding residents’ SFT (Jaafar et al., 2015; Jaafar et al., 2017; Rasoolimanesh et al., 2015; Rasoolimanesh et al., 2017; Zamani-Farahani et al., 2012). In the Pakistani context, much of the research has focused on tourism-driven economic growth, tourist spatial attitudes, terrorism, tourism, and religious travelers, branding spiritual tourism, ecotourism, tourism impact on deficit in balance of payment, ethnic minorities, and development of tourism (Chowdhury et al., 2017; Haq & Medhekar, 2013; Haq & Wong, 2013; 2019; Raza & Jawaid, 2013; Rovillé, 1988; Ullah et al., 2010). Despite increasing interest in tourism development research, knowledge about how positive attitudes and behaviors towards further SFT can be fostered among residents of Hunza valley (Pakistan) is still scarce. This paper attempts to fill this gap by focusing on tourism impacts’ dimensions and residents’ QOL. By combining SET with bottom-up spillover theory (Rootenberg, 2012), this paper aims to develop and empirically validate a baseline model for residents’ SFT in Pakistan.

This paper contributes to the existing tourism literature in four significant ways. Firstly, it adds to existing knowledge by empirically testing the impact of tourism on residents’ SFT by focusing on residents’ perception regarding tourism impact dimensions and its influence on residents. Secondly, it investigates residents’ QOL from the perspective of satisfaction with various life satisfaction domains (including material, community, emotional, and health & safety wellbeing). Thirdly, residents’ QOL is usually measured as an outcome in the literature (e.g., Kim et al., 2013; Al-Saad 2018); however, this paper posits QOL as an antecedent of residents’ support for tourism development. Fourthly, this paper examines dynamic interactions of tourism impact dimensions with various life satisfaction domains through the construct of QOL which encompasses various life satisfaction domains (including material, community, emotional, and health & safety well-being), because of the spillover effect of a particular well-being domain on other well-being domains (Kim, 2002; Kim et al., 2013).

Literature review

Earlier studies have observed numerous aspects that can shape residents’ attitudes and perceptions towards tourism development. These aspects include socioeconomic factors, tourism dependency, spatial factors, and residents’ perception of tourism (Gursoy et al., 2018). Though prior research was descriptive and atheoretical, this research area has over the years reached the phase of theoretical maturity as well as methodological sophistication (Nunkoo & Smith, 2013). Among various theories, SET has made important theoretic contributions in the tourism literature (Al-Saad et al., 2018; Ap, 1990; Fredline & Faulkner, 2000; D. Gursoy, Chi, & Dyer, 2009; Nkemngu, 2015). SET posits that residents’ attitudes and their SFT growth will be influenced by their assessment of the significances of tourism for their communities as well as for themselves, and for that exchange tourism is necessary to have in the community (Andereck et al., 2005; Özel & Kozak, 2017). Besides this, Andrews & Withey's (1976) bottom-up spillover theory has assisted in conceptualising residents’ quality of life (Diener, 1984; Sirgy, 2002; Sirgy & Lee, 2006). The theory proposes that QOL is influenced by various life satisfaction domains and sub-domains such as domestic, community/societal, leisure, and healthy life (Kruger, 2012; Neal et al., 2007; Sirgy, 2002; Uysal et al., 2012). These two theories have been used in this paper to hypothesise an association between key research constructs.

Tourism Impact Dimensions and Quality of Life

The economic impact can be described as economically based net changes in the region that can be credited to the industry, event, or policy that would otherwise not exist (Watson et al., 2007). On the other hand, standards of quality of life are those dimensions of life by which individuals undergo the stages of self-satisfaction and disappointment (comfort-discomfort and pleasure-sadness) (Terhune, 1973). Several studies have propounded that host residents commonly have an affirmative view of tourism's monetary impacts (Sadler & Archer, 1975; Andereck et al., 2005; Hassani et al., 2016; Al-Saad et al., 2018). On the other hand, studies are claiming that tourism development causes negative monetary effects; for instance, tourism may augment living costs in a community (Dioko & So, 2017; Al-Saad et al., 2018), raise the price of land and housing, and increase the price of various goods and facilities (Suntikul et al., 2016; Al-Saad et al., 2018). Tourism does not only influence attitudes of residents towards tourism but it also affects their QOL (Uysal et al., 2012; Al-Saad et al., 2018; Andereck & Nyaupane, 2011; Kim et al., 2013; Mathew & Sreejesh, 2017; Woo et al., 2015). In a study, Kim (2002) found that tourism impact dimensions influence residents’ wellbeing in various life satisfaction domains, which in turn affect the QOL of these residents. Kim et al. (2013) and Woo et al. (2015) also found the substantial role of tourism impact dimensions in determining residents’ specific life satisfaction domains and QOL. When tourism is properly managed, it may help residents improve their QOL (Andereck et al., 2007) otherwise, it may bring negative effects on the QOL of residents such as greater cost of living, traffic problems, overcrowding and congestion, and an increase in the price of land (Al-Saad et al., 2018). In line with these studies, it is hypothesised that:

H1: Economic impact of tourism has a significant effect on residents’ quality of life.

The social impact of tourism leads to a substantial effect on community well-being (Kim, 2002; Kim et al., 2013), which is associated with economic, social, cultural and political dimensions involved in preserving a community and gratifying the numerous residents’ needs (Kusel, 1991). It can be recognised through the increase in traffic congestion and overcrowding of individuals (Al-Saad et al., 2018), improvement in public service quality (Eshliki & Kaboudi, 2017), social problems of the community such as an increase in crime (Dioko & So, 2017), difficulty upholding communal privacy, which can bring stranger feelings for the residents at the destination (Madawala, 2017). A review of the literature shows that the social impact of tourism plays an important part in residents’ QOL through various life satisfaction domains (Kim, 2002).

H2: Social impact of tourism has a significant effect on residents’ quality of life.

According to Al-Saad et al. (2018), tourism can lead to progressive change in a society's principles, morals, and cultural performance. Residents observe this effect by noticing the visitors; residents might reshape their way of living (such as eating, dressing, leisure activities, amusement, etc.). On the contrary, Brunt & Courtney (1999) state that this impact can be positively interpreted by promoting the living standard of the residents due to tourism, or it may be interpreted negatively as a symptom of acculturation. Tourism can contribute to the realisation of cultural behaviors, cultural identity, and revitalisation of art and craft and local culture. When residents present their own culture to the tourists it justifies the impression of the worth of living within a community. The effects of tourism could be both positive and negative. For example, Poudel (2017) found that the lifestyle, traditional beliefs, and cultural aspects of residents of Sauraha (Nepal) have been changed in the name of modernisation; and that residents are chasing borrowed standards and aspects of culture. On the other hand, tourism has upgraded the native environment as well as local culture protection by enhancing the region's appearance and substructure (Nunkoo & Ramkissoon, 2012). Woo (2013) found a significant positive relationship between the impact of tourism in life domains (material & nonmaterial life domains) and the satisfaction with life domain (material & nonmaterial life domains). Further, Kim et al. (2013) reported a significant positive relationship between the cultural impact of tourism and residents’ satisfaction in the emotional well-being domain. When tourism allows communities to take better care of their cultural legacy, this may encourage residents of the host community to have greater pride in that legacy, which may positively impact residents’ emotional well-being domain. As a result, residents’ satisfaction in the emotional well-being domain leads to better QOL.

H3: Cultural impact of tourism has a significant effect on the residents’ quality of life.

The natural environment is an important source of tourism (Mason, 2008) and disruption in this environment will lead to a decrease in tourism in the host community (Rootenberg, 2012). Environmental impact may include variations in the natural environment, a load of physical infrastructure, overcrowding, loss of flora and fauna, pollution (soil, air, and water), and renovation of historic buildings and infrastructure. Yang et al. (2017) stated that environmental impact can be insignificant to the host community due to associated consequences on the environment and human healthiness and safety. Eshliki & Kaboudi (2017) found that some items have a strong negative impact on the community because of environmental impact such as seawater pollution, loss of agricultural fields, soil erosion, and decrease in plant life. In their research, Azam et al. (2018) reported that there is an inverse relationship between tourism and environmental pollution in developing economies such as Singapore and Thailand. In Malaysia, however, tourism is significantly positively associated with environmental pollution. Al-Saad et al. (2018) found the negative impact of tourism on the QOL of residents of Aqaba city due to litter and noise pollution.

H4: Environmental impact of tourism has a significant effect on the residents’ quality of life.

In tourist destinations, support for tourism is likely to be high when residents are satisfied with their quality of life. Literature suggests that residents’ QOL influences their perception about SFT (Andereck et al., 2007; Ap, 1992; Croes, 2012). According to Woo et al. (2015), QOL is an important predictor of residents’ support for tourism. Findings of the review explicitly state the positive impact of QOL on residents’ perception of tourism support. Thus, it is argued that:

H5: Residents’ quality of life has a significant effect on their support for tourism.

Quality of Life Mediating Tourism Impact Domains: Residents’ Support for Tourism Relationship

Perdue et al. (1990) argued that residents’ SFT is positively associated with positive tourism impacts and vice versa. In 1997, Jurowski et al. found the direct influence of residents’ perceptions of tourism on ‘eco-centric attitude’ and ‘community attachment’ and indirect influence on their level of SFT. In the later years, studies confirmed a direct positive association between perceived benefits from tourism and residents’ SFT (Andereck & Vogt, 2000; Dogan Gursoy et al., 2002). Stylidis et al. (2014) argued that residents’ place image has an emotional impact on their perceptions of tourism impact, which in turn influences their SFT. In the presence of positive tourism impact dimensions, residents’ are likely to express higher support for tourism and tourism development policies (Brida et al., 2014; Stylidis et al., 2014). From the residents’ point of view, SET postulates that people tend to trade their SFT in exchange for advantages received from tourism. In other words, residents’ SFT will depend on the benefits that they will get in return from that tourism. Therefore it is by assessing social, economic, environmental, and cultural concerns of the residents on which they decide whether to support tourism or not (Lee, 2013). It means, if there are some benefits for them and a comparatively higher quality of life, then the residents are more likely to support tourism development (Ap, 1992a). Correspondingly, it is argued that when controlling for quality of life, tourism impact dimensions positively increase residents’ support for tourism.

H6: The residents’ quality of life mediates the relationship between the economic impact of tourism and residents’ support for tourism.

H7: The residents’ quality of life mediates the relationship between the social impact of tourism and residents’ support for tourism.

H8: The residents’ quality of life mediates the relationship between the cultural impact of tourism and residents’ support for tourism.

H9: The residents’ quality of life mediates the relationship between the environmental impact of tourism and residents’ support for tourism.

Methodology

This paper followed a quantitative research design to test the hypothesised relationship between tourism impact dimensions, residents’ quality of life, and support for tourism. Residents of Hunza valley having diverse occupational status served as the target population. However, the research population was unknown due to the unapproachability of the population statistics and information about the residents of the Hunza valley. Where it is impossible to create a sampling frame, it is a prerequisite for a researcher to practice a non-probability sampling technique, which at least is considered a viable substitute for random sampling (Saunders et al., 2009). Using purposive sampling, an adequate sample size of 384 was found for the generalisation of results to an unknown target population with a 95% confidence interval (Sekaran & Bougie, 2016). Before that, a small-scale test was performed for this study to evaluate its design. Such pilot studies are frequently conducted to minimise the risks involved in largescale survey research (Khan & Khan, 2020). In this paper, primary data was collected through scheduled visits to Hunza valley, while the survey was conducted by distributing questionnaires through paper-based (hard copy) and online (Google forms) means. Table 1 summarises the demographic profile of the research participants.

Respondents’ Profile.

Variable Category Frequency Percentage
Gender Male 384 68.4
Female 177 31.6
Total 561 100.0

Age Under 30 25 4.5
31 – 45 187 33.3
46 – 60 177 31.6
61 – 75 97 17.3
above 76 75 13.4
Total 561 100.0

Education Below Matriculation 127 22.6
Matriculation 87 15.5
Intermediate 90 16.0
Graduation 138 24.6
Masters 99 17.6
MS/M.Phil. 3 .5
Doctorate 17 3.0
Total 561 100.0

Occupation Status Retired/agribusiness 59 10.5
Businessman 109 19.4
Govt. job 65 11.6
Pvt. Job 204 36.4
Tourism business 112 20.0
Articrafts 12 2.1
Total 561 100.0

Monthly Income 20000 or less 186 33.2
20001 – 40000 152 27.1
40001 – 60000 84 15.0
60001 – 80000 53 9.4
80001 – 100000 28 5.0
over 100000 58 10.3
Total 561 100.0

Overall 0% 70 12.5
Household 1% – 30% 405 72.2
Income from 31% – 60% 72 12.8
Tourism 61% – 90% 14 2.5
Total 561 100.0

Years of Residency in Hunza Valley 15 years or less 75 13.4
16 – 30 323 57.6
31 – 45 138 24.6
46 – 60 20 3.6
above 61 5 .9
Total 561 100.0

Following Al-Saad et al. (2018), Kim (2002), Kim et al. (2013), and Nunkoo & So (2016), a literature-based questionnaire was adopted (Table 2). The research instrument was comprised of 61 items divided into three main segments. The first two segments were comprised of 54 items measuring economic impact (8 items), social impact (6 items), cultural impact (7 items), environmental impact (5 items), QOL (22 items) including its four well-being domains, and the outcome variable SFT (6 items). The tourism impact dimensions and residents’ SFT were answered through a 5-point Likert scale ranging from (strongly disagree to strongly agree), whereas residents’ QOL was answered through a 5-point Likert scale ranging from (very unsatisfied to very satisfied).

Research Instrument.

Variable Name Instrument Reference No. of items Scale
Economic Impact Al-Saad et al., (2018); Kim, (2002); Kim et al., (2013). 8 1–5
Social Impact Kim, (2002); Kim et al., (2013). 6 1–5
Cultural Impact Kim, (2002); Kim et al., (2013); Mathew & Sreejesh, (2017). 7 1–5
Environmental Impact Kim, (2002); Kim et al., (2013); Mathew & Sreejesh, (2017). 5 1–5
QOL Kim, (2002); Kim et al., (2013); Mathew & Sreejesh, (2017). 22 1–5
Support for Tourism (SFT) Nunkoo & So, (2016) 6 1–5
Demographic variables Kim, (2002); Kim et al., (2013); Mathew & Sreejesh, (2017). 7 -

A total of 650 questionnaires were distributed, out of which 50 questionnaires were removed because 32 of them were returned completely blank while the rest of the 18 were partially filled. After excluding unusable responses, 600 entries were found for survey analysis. This dataset was further scrutinised through data cleaning and outlier identification. As a result, 39 responses were dropped and finally, 561 responses were used for hypotheses testing. SPSS v.23 was used for descriptive statistics, multi-collinearity, reliability, validity, and linear regression analysis. Also, PROCESS Macro 3.3 was used for parallel mediation analysis. Construct reliability was established through Cronbach's alpha, discriminant validity, and convergent validity using average variance extracted (AVE) and Fornell Larcker criterion. The structural model analysis was done through R-square and path coefficients.

Variables and Measurement

Support for Tourism (SFT) It is a function of what residents understand about tourism development and how it influences them and their community’ (Telfer & Sharpley, 2015). It was measured using a 6-item scale where responses were collected on a five-point Likert scale. Cronbach alpha 0.897 reported good reliability of the instrument.

Quality of Life (QOL) It is the sense of well-being of an individual, his disappointment or self-gratification with life, or his sadness or joy. It is operationalised through material, community, emotional, and health & safety well-being (Dalkey & Rourke, 1973). Reliability for the 22-item instrument was established through a Cronbach alpha value of 0.956.

Economic Impact (ECOI) Following Weisbrod & Weisbrod (1997) is defined as the effect of tourism on the level of monetary activity in a given area. It is measured on a five-point Likert scale using an 8-item instrument. Cronbach alpha for this construct was reported to be 0.853.

Social Impact (SCOI) refers to the way a surrounding community is affected by the actions of organisations, businesses, or individuals. A 6-item scale is used to collect participant responses to the social impact construct on a five-point liker scale. The reliability of this scale was established through a Cronbach alpha value of 0.859.

Cultural Impact (CULI) According to Kim (2002), cultural impact is comprised of local culture preservation, its deterioration, and cultural exchange between the residents of the tourist destinations and visitors. It was measured through a 7-item scale with a Cronbach alpha value of 0.885.

Environmental Impact (ENVI) It is conceptualised as the impact that involves variations in the natural environment, a load of physical infrastructure, overcrowding, flora and fauna, pollution (soil, air, and water), and renovation of historic buildings and infrastructure (Miecskowski, 1995). It was measured using a 5-item scale reported on a five-point Likert scale. Cronbach's alpha for this measure was 0.736.

Findings

Descriptive statistics were used to summarise data and to demonstrate an average level of respondent agreeableness on a particular construct. In the preliminary analysis, the multicollinearity test is suggestible for measuring the extent to which two or more predictors are correlated in a multiple regression model (Hair et al., 2010; Raykov & Marcoulides, 2006; Saunders et al., 2009). The simplest way of diagnosing multicollinearity is to test the correlation coefficients between predictors (Hair et al., 2010). Correlation coefficients for all predictors were within the threshold of <0.9 as can be seen in Table 3. Multicollinearity diagnostics were also performed using Landau and Everitt's (2004) value of tolerance, VIF, and condition index suggesting no potential issues of collinearity in the dataset (Table 4).

Pearson Correlations for Multicollinearity.

ECOI_AVG SOCI_AVG CULI_AVG ENVI_AVG
ECOI_AVG 1 .615 .567 .014
SOCI_AVG .615 1 .639 .052
CULI_AVG .567 .639 1 .018
ENVI_AVG .014 .052 .018 1

Note: Indicate collinearity > 0.9 or above

Multicollinearity table for Tolerance, VIF, and Condition index.

Construct Tolerance VIF Condition index
ECOI_AVG .349 2.864 7.111
SOCI_AVG .474 2.109 9.469
CULI_AVG .543 1.842 12.083
ENVI_AVG .988 1.012 16.546

Note: Indicate significance (tolerance>0.1, VIF<10, condition index < 30)

According to Hair et al. (2012), convergent validity indicates that at least 50% variance of indicators can be explained by the latent variable. To achieve convergent validity, AVE must be higher than 0.5. Table 5 shows that the AVE for all variables was above the threshold.

Convergent Validity.

Variables No. of items AVE (%) AVE
ECOI 8 50.369 0.7097
SOCI 6 59.700 0.7726
CULI 7 59.667 0.7724
ENVI 5 50.994 0.7141
QOL 22 53.363 0.7304
SFT 6 65.942 0.8120

For the discriminant validity, the SQRT of each variable's AVE should be greater than the correlation of a particular variable with any of the other variables in the model and should be at least 0.50 (Fornell & Larcker, 1981; Hair et al., 2012). In this paper, discriminant validity for all constructs was established achieving AVE >0.5 (Table 6).

Discriminant Validity.

Variable ECOI SCOI CULI ENVI QOL SFT
ECOI 0.7097
SCOI 0.4038 0.7726
CULI 0.3636 0.4096 0.7724
ENVI 0.0004 0.000576 0.0004 0.7141
QOL 0.7039 0.4019 0.2862 0.0003 0.7304
SFT 0.664 0.2714 0.1892 0.000169 0.64 0.8120

Note: Square root of AVE on the diagonal

Test of Association

Before hypothesis testing, correlation analysis was performed to determine whether the initial direction of the relation between the variables in a model is associated with the theory. Person's correlation coefficient (r) analysis technique allows quantifying the strength of the linear relationship between two numerical variables (Saunders et al., 2009). Except for the environmental impact variable, Pearson correlation showed statistically significant (p<0.01) strong positive relationship between residents’ SFT, QOL, and the tourism impact dimensions (Table 7).

Correlation Analysis.

QOL_AVG SFT_AVG
ECOI_AVG Pearson Correlation .772** .660**
Sig. (2-tailed) .000 .000
N 561 561

SOCI_AVG Pearson Correlation .593** .520**
Sig. (2-tailed) .000 .000
N 561 561

CULI_AVG Pearson Correlation .501** .432**
Sig. (2-tailed) .000 .000
N 561 561

ENVI_AVG Pearson Correlation −.038* .051
Sig. (2-tailed) .365 .714
N 561 561

QOL_AVG Pearson Correlation - .777**
Sig. (2-tailed) - .000
N - 561

Correlation is significant at the 0.05 level (2-tailed).

Correlation is significant at the 0.01 level (2-tailed).

Regression Analysis

Regression analysis was run to examine the causal relationship among the research variables. Linear regression analysis was carried out to test the hypotheses on the direct effect of economic impact, social impact, cultural impact, and environmental impact on residents’ QOL, and residents’ QOL with their SFT (Table 8).

Regression weights for Direct Effect.

Predictors Estimates R2 Adjusted R2 F t-value Sig. Results
QOL ← ECOI .790 .595 .595 822.070 28.672 .000 Supported
QOL ← SCOI .582 .352 .351 303.791 17.430 .000 Supported
QOL ← CULI .482 .251 .250 187.726 13.701 .000 Supported
QOL ← ENVI −0.05 .001 .000 .821 −.906 .365 Rejected
SFT ← QOL .878 .603 849.932 29.154 .000 .603 Supported

ECOI=economic impact, SCOI= social impact, CULI=cultural impact, ENVI=environmental impact, QOL=quality of life.

First hypothesis argued that the economic impact of tourism has a significant effect on residents’ quality of life. Findings showed that there is a significant economic impact of tourism on residents’ QOL with an estimated value (.790) and p-value (0.000). The first hypothesis was accepted as 59.5% of the variance in residents’ QOL can be explained through tourism's economic impact in the model (F=822.070, R2 =.595, p<0.05). In the second hypothesis, it was argued that tourism's social impact has a significant effect on residents’ quality of life. Regression results reported that social impact significantly affects the QOL of Hunza valley residents with an estimated value (.582) and p-value (0.000). Through regression analysis, 35.2% of the variance in residents’ QOL can be explained through the social impact of tourism (F=303.791, R2 =.352, p<0.05). Based on these findings, hypothesis two was accepted.

The third hypothesis posited a significant effect of the cultural dimension of tourism on the quality of life of residents in Hunza valley; estimate value (.482) and p-value (0.000). Linear regression analysis supported this claim, as 25.1% of the variance in residents’ QOL can be explained through the cultural impact of tourism in the model (F=187.726, R2 =.251, p<0.05). The fourth hypothesis was testing the environmental impact of tourism on residents’ quality of life in the Hunza valley. This hypothesis was rejected, with an estimated value (.482) and p-value (0.365). The fifth hypothesis of this paper proposed a significant impact of residents’ quality of life on their support for tourism in Hunza valley. Regression findings indicated that residents’ QOL significantly influences residents’ SFT with an estimated value (.878) and p-value (0.000). This hypothesis was accepted as 60.3% of the variance in residents’ SFT can be explained through their QOL (F=849.932, R2 =.603, p<0.05).

Mediation Analysis

In this paper, a mediation test was defined through parallel mediation analysis with repeated 5000 bootstrapped samples and with a confidence level of 95% (Preacher & Hayes, 2008). Following Meyers et al., (2012), the strength of the mediated effect was also calculated and reported.

The sixth hypothesis posited that residents’ quality of life mediates between economic impact and residents’ support for tourism. Economic impact of tourism was found to be a significant predictor of residents’ QOL (B = .7897, t = 28.6718, p = 0.000), and residents’ QOL was also a significant predictor of residents’ SFT (B = .7462, t = 15.9408, p = 0.000). These results indicated that both path ‘a’ and ‘b’ were significant. When controlling for residents’ QOL, the indirect effect of the economic impact of tourism on residents’ SFT was also found to be significant (Appendix A). Based on direct and indirect effect findings, a partial mediation was reported at 95% confidence interval with B = .5892, SE = .0454. The relative strength of the mediation analysis indicated that approximately 77.16% of the economic impact of tourism on residents’ SFT is mediated through residents’ QOL in Hunza valley.

The seventh hypothesis argued that residents’ quality of life mediates between social impact and residents’ support for tourism. In the regression analysis, social impact of tourism was reported as a significant predictor of residents’ QOL (B = .5821, t = 17.4296, p = 0.000), and residents’ QOL was as a significant predictor of residents’ SFT (B = .8169, t = 21.9704, p = 0.000), the results indicated that path ‘a’ and ‘b’ were significant. Based on direct and indirect effect testing, partial mediation was reported in support of hypothesis seven (Appendix A). The relative strength of the mediation effect showed that approximately 82.56% of the effect of the social impact of tourism on residents’ SFT is mediated through residents’ QOL.

Eight hypotheses tested residents’ quality of life as a mediator between cultural impact and residents’ support for tourism. Regression analysis reported cultural impact of tourism as a significant predictor of residents’ QOL (B = .4821, t = 13.7013, p = 0.000), and the residents’ QOL as a significant predictor of residents’ SFT (B = .0347, t = 24.3503, p = 0.000). These results indicated that both path ‘a’ and ‘b’ were significant. Findings showed that the direct effect of the cultural dimension of tourism on residents’ SFT was insignificant (B = .0618, t = 1.8522, p = 0.064) when controlling for the mediator, i.e. residents’ QOL. Moreover, the results also revealed that the indirect effect of the cultural impact of tourism on residents’ SFT, when controlling for the mediator, i.e., residents’ QOL, was significant. In light of these findings, hypothesis eight was accepted, as approximately 86.83% of the effect of the cultural impact of tourism on residents’ SFT was mediated through residents’ QOL.

The last hypothesis of this paper was testing the environmental impact of tourism on residents’ SFT via residents’ QOL. Environmental impact of tourism was found to be an insignificant predictor of residents’ QOL (B = −.0576, t = −.9059, p = .3654) while the residents’ QOL was a significant predictor of residents’ SFT (B = .8796, t = 29.2468, p = .000). These results indicated that path ‘a’ was insignificant whereas path ‘b’ was significant. Results for direct and indirect effects were insignificant; thus, providing sufficient information to confirm the null hypothesis (Appendix A).

Discussion

In the existing literature, the majority of studies were descriptive (Al-Saad et al., 2018a; Nkemngu, 2015b; Suntikul, Pratt, I Kuan, et al., 2016), only an individual study with regression analysis was conducted till now (Khizindar, 2012). Explicitly, this paper contributes to the literature by providing empirical evidence on the relationship between tourism impact dimensions and residents’ QOL in Hunza valley, Pakistan.

In the Hunza valley, tourism has long been viewed as a tool for monetary development. The findings of this paper reveal that residents perceived that monetary aspects of tourism exert a positive impact on their support for tourism. This positive relationship between the economic dimension and residents’ QOL could be because (i) tourism has developed into a sizable industry in Hunza valley and (ii) residents of this valley perceive tourism as a vital source of income/revenue. In line with prior studies, findings of this paper exhibited tourism as an economically fruitful activity for local businesses, which not only improves residents’ standard of living but also enhances their quality of life (Al-Saad et al., 2018a; Nkemngu, 2015b; Suntikul, Pratt, I Kuan, et al., 2016).

In their studies, Al-Saad et al. (2018b) and Nkemngu (2015b) reported tourism as a means to increased entertainment facilities that encourage positive residents’ perceptions about tourism. Likewise, the empirical analysis showed the positive social impact of tourism on residents’ quality of life in the Hunza valley. Corresponding to Khizindar (2012a), residents have a positive perception of tourism as it creates social benefits and opportunities including local services and better maintenance of roads. On the cultural impact of tourism, findings revealed that tourism yields cultural benefits to the residents of Hunza valley by encouraging activities that increase demand for local artifacts, restore local traditions, and enhance residents’ pride in their cultural identity. These results are consistent with previously reported findings (Al-Saad et al., 2018a; Khizindar, 2012; Nkemngu, 2015b).

Stylidis (2012) argued that residents recognise the more economic, social, and cultural impact of tourism on their QOL, but have little or no difference in their perceptions of the environmental impact on their QOL. In support of Yang et al. (2017a), the environmental impact of tourism on residents’ QOL was found to be insignificant. SET postulates that residents seem to consider their costs and benefits when estimating an interchange (Ap, 1992b). This proposition is validated by the analysis of results indicating that residents’ QOL brings positive attitudinal changes in residents’ SFT. In the Hunza valley, findings of the survey showed that residents support tourism because they perceive that tourism development is significant for their community. Most of the respondents also agreed that the tourism industry is one of the most essential industries for their community. Thus, their community is likely to attract more tourists, as it benefits local people by improving their standard of living, the standard of community services, and community conditions (Nunkoo & Ramkissoon, 2010a, 2010b).

The findings of this paper reported that residents’ QOL mediates the relationship between three dimensions of tourism impact (economic, social, and cultural) and residents’ SFT. Earlier studies emphasised the association between tourism impacts and residents’ QOL (Al-Saad et al., 2018a; Kim et al., 2013b; Yang et al., 2017b), and between residents’ QOL and their SFT (Woo et al., 2015b). In line with SET and bottom-spillover theory, this paper reported the mediating role of residents’ QOL between tourism impact dimensions and residents’ SFT.

Conclusion

This paper assessed support for tourism from the residents’ perspective and their quality of life along with four dimensions of tourism impact. Analysis of primary data reported a significant association between tourism impact dimensions and residents’ QOL, confirming that residents’ perception regarding tourism impact is correlated with higher levels of satisfaction with their QOL (Al-Saad et al., 2018a; Kim et al., 2013b; Suntikul, Pratt, I Kuan, et al., 2016). These findings suggest that tourism administrators and planners should realise residents’ multidimensional perspective to escalate their gratification with QOL through tourism impact dimensions. By increasing residents’ QOL, tourism administrators, developers, and marketers can make a more competitive and supportable tourist destination.

During analysis, it was found that residents of Hunza valley positively discern the three dimensions (social, cultural, and economic) of tourism impacts. These three dimensions were found to be significant predictors of residents’ QOL and their SFT growth. These findings can help local administrators and tourism planners in promoting a positive perception of residents about tourism in the valley. This can be done by arranging internal learning campaigns to escalate residents’ cognizance about social, cultural, and economic advantages of tourism growth; thereby, increasing residents’ SFT. Local authorities can also run awareness campaigns involving residents, particularly in the planning, organising, and decision-making process of tourism development. Such participation will promote favorable resident attitudes towards tourism by increasing their ownership of and involvement in the promotion of tourism activities. This is significantly important for achieving residents’ support for tourism as a key to the long-term success of the tourism industry in the Hunza valley.

Lastly, this study has its limitations that open avenues for further research on tourism and related indicators. This paper used four well-being domains to examine the influence of residents’ perception about tourism impacts on their QOL and their SFT. Future researchers are encouraged to extend the proposed model by examining the role of other well-being domains such as social life, household life, travel life, and work life. The findings of this paper are based on data collected from Hunza valley residents only. Further research on these variables can be conducted by gathering data from diverse destinations that may show a different level of tourism growth. Moreover, the perception of residents about tourism impacts, QOL, and their SFT may vary based on their demographic information, personality, and destination type. Thus further studies are encouraged to consider residents’ characteristics in the model.

Research Instrument.

Variable Name Instrument Reference No. of items Scale
Economic Impact Al-Saad et al., (2018); Kim, (2002); Kim et al., (2013). 8 1–5
Social Impact Kim, (2002); Kim et al., (2013). 6 1–5
Cultural Impact Kim, (2002); Kim et al., (2013); Mathew & Sreejesh, (2017). 7 1–5
Environmental Impact Kim, (2002); Kim et al., (2013); Mathew & Sreejesh, (2017). 5 1–5
QOL Kim, (2002); Kim et al., (2013); Mathew & Sreejesh, (2017). 22 1–5
Support for Tourism (SFT) Nunkoo & So, (2016) 6 1–5
Demographic variables Kim, (2002); Kim et al., (2013); Mathew & Sreejesh, (2017). 7 -

Multicollinearity table for Tolerance, VIF, and Condition index.

Construct Tolerance VIF Condition index
ECOI_AVG .349 2.864 7.111
SOCI_AVG .474 2.109 9.469
CULI_AVG .543 1.842 12.083
ENVI_AVG .988 1.012 16.546

Discriminant Validity.

Variable ECOI SCOI CULI ENVI QOL SFT
ECOI 0.7097
SCOI 0.4038 0.7726
CULI 0.3636 0.4096 0.7724
ENVI 0.0004 0.000576 0.0004 0.7141
QOL 0.7039 0.4019 0.2862 0.0003 0.7304
SFT 0.664 0.2714 0.1892 0.000169 0.64 0.8120

Regression weights for Direct Effect.

Predictors Estimates R2 Adjusted R2 F t-value Sig. Results
QOL ← ECOI .790 .595 .595 822.070 28.672 .000 Supported
QOL ← SCOI .582 .352 .351 303.791 17.430 .000 Supported
QOL ← CULI .482 .251 .250 187.726 13.701 .000 Supported
QOL ← ENVI −0.05 .001 .000 .821 −.906 .365 Rejected
SFT ← QOL .878 .603 849.932 29.154 .000 .603 Supported

Respondents’ Profile.

Variable Category Frequency Percentage
Gender Male 384 68.4
Female 177 31.6
Total 561 100.0

Age Under 30 25 4.5
31 – 45 187 33.3
46 – 60 177 31.6
61 – 75 97 17.3
above 76 75 13.4
Total 561 100.0

Education Below Matriculation 127 22.6
Matriculation 87 15.5
Intermediate 90 16.0
Graduation 138 24.6
Masters 99 17.6
MS/M.Phil. 3 .5
Doctorate 17 3.0
Total 561 100.0

Occupation Status Retired/agribusiness 59 10.5
Businessman 109 19.4
Govt. job 65 11.6
Pvt. Job 204 36.4
Tourism business 112 20.0
Articrafts 12 2.1
Total 561 100.0

Monthly Income 20000 or less 186 33.2
20001 – 40000 152 27.1
40001 – 60000 84 15.0
60001 – 80000 53 9.4
80001 – 100000 28 5.0
over 100000 58 10.3
Total 561 100.0

Overall 0% 70 12.5
Household 1% – 30% 405 72.2
Income from 31% – 60% 72 12.8
Tourism 61% – 90% 14 2.5
Total 561 100.0

Years of Residency in Hunza Valley 15 years or less 75 13.4
16 – 30 323 57.6
31 – 45 138 24.6
46 – 60 20 3.6
above 61 5 .9
Total 561 100.0

Convergent Validity.

Variables No. of items AVE (%) AVE
ECOI 8 50.369 0.7097
SOCI 6 59.700 0.7726
CULI 7 59.667 0.7724
ENVI 5 50.994 0.7141
QOL 22 53.363 0.7304
SFT 6 65.942 0.8120

Correlation Analysis.

QOL_AVG SFT_AVG
ECOI_AVG Pearson Correlation .772** .660**
Sig. (2-tailed) .000 .000
N 561 561

SOCI_AVG Pearson Correlation .593** .520**
Sig. (2-tailed) .000 .000
N 561 561

CULI_AVG Pearson Correlation .501** .432**
Sig. (2-tailed) .000 .000
N 561 561

ENVI_AVG Pearson Correlation −.038* .051
Sig. (2-tailed) .365 .714
N 561 561

QOL_AVG Pearson Correlation - .777**
Sig. (2-tailed) - .000
N - 561

Pearson Correlations for Multicollinearity.

ECOI_AVG SOCI_AVG CULI_AVG ENVI_AVG
ECOI_AVG 1 .615 .567 .014
SOCI_AVG .615 1 .639 .052
CULI_AVG .567 .639 1 .018
ENVI_AVG .014 .052 .018 1

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