Stakeholders’ Perspectives of Sustainable Tourism Development

Abstract

The purpose of this study is to examine the perspectives of tourism stakeholders regarding sustainable tourism outcomes in protected areas. We compared the responses of residents with residents, and tourists with tourists, in two protected areas of Nepal, namely, the Annapurna Conservation Area and Chitwan National Park. Tourism sustainability was evaluated with six tourism impact subscales measuring negative and positive ecological, economic, and social impacts. Data were collected using the survey method. Respondents included 230 residents and 205 tourists in Annapurna, and 220 residents and 210 tourists in Chitwan. Data analysis involved a series of multigroup confirmatory factor analyses with Annapurna and Chitwan as comparison groups and tourism impact subscales as latent constructs. Results revealed that residents and tourists perceive positive and negative impacts differently across protected areas. This suggests that the form of tourism development affects the sustainability outcomes in protected areas. Theoretical, methodological, and practical implications are discussed.

Keywords

sustainable tourism tourism stakeholders protected areas measurement invariance multigroup confirmatory factor analysis

Introduction

Protected areas are globally recognized as the only means for in situ conservation of biodiversity (Dudley 2008; Walpole, Goodwin, and Ward 2001). It is also argued that ecosystem services, recreation, and poverty alleviation functions of protected areas are equally important (Dlamini and Masuku 2013; Dudley 2008; Nepal 2000; Ruschkowski et al. 2013; Zube and Busch 1990). As a result, protected areas have proliferated globally, with almost 24.24 million hectares of terrestrial and marine area conserved under 157,897 protected areas as of 2011 (IUCN and UNEP-WCMC 2012). The protected area goals, however, are at odds with each other on many counts (Eagles, McCool, and Haynes 2002; Jones 2013; Thur 2010). For example, the establishment of protected areas threatens the livelihoods of surrounding communities, more specifically that of indigenous and tribal communities, by constraining traditional use rights (e.g., collection of forest products) or displacing these communities from their ancestral territory (Agrawal and Redford 2009). Local residents are often blamed for causing deterioration of the vitality of protected areas by reckless harvesting and illicit collection of resources for self-consumption as well as commercial purpose (Jones 2013). Relatedly, tourism is often criticized for destroying the natural and cultural resources on which it is based (Deng et al. 2003; Dlamini and Masuku 2013; Nepal 2000; Ruschkowski et al. 2013; UNEP and UNWTO 2005). This indicates that the protected areas have to serve several ecological, economic, and social functions, while immunizing themselves from various anthropogenic stressors. However, inadequate funding persists as a major challenge to conduct management interventions necessary for achieving the protected area goals (Baral, Stern, and Bhattarai 2008; Dlamini and Masuku 2013; Eagles 2013; Thur 2010; Walpole et al., 2001). As such, there exist many protected areas, often referred to as paper parks, with minimal or no on-the-ground impact (Eagles et al. 2002; Jones 2013; Thur 2010).

Sustainable tourism development is widely promoted as a panacea to the dilemmas of protected areas (Eagles et al. 2002; Hassanali 2013; Puhakka et al. 2009). Potential benefits of sustainable tourism in protected areas include enhancement of economic opportunities, protection of natural and cultural heritage, and improvement of quality of life of the local communities (Eagles 2013; Fennell and Weaver 2005; Puppim de Oliveira 2005; Strickland-Munro, Allison, and Moore 2010; UNEP and UNWTO 2005). In other words, sustainable tourism provides tangible economic benefits to management authority (e.g., offset the cost of protection) and local communities (e.g., improve people’s livelihoods) while conserving the ecological and sociocultural integrity of the entire protected area system (UNEP and UNWTO 2005; Walpole et al. 2001). The interrelationship between sustainable tourism and protected areas is not static though—there is no one-size-fits-all answer for managing tourism in protected areas (Imran, Alam, and Beaumont 2014). The outcomes depend on the nature of tourism development as determined by the biophysical, socioeconomic, and management characteristics of protected areas (Lai and Nepal 2006; Reinius and Fredman 2007; Ruschkowski et al. 2013). In addition, sustainable management of tourism in protected areas requires cooperation and partnership among tourism stakeholders, including the tourism industry, government agencies, residents, nongovernmental organizations, and the tourists (Byrd 2007; Dlamini and Masuku 2013; Hassanali 2013; Weiler, Moore, and Moyle 2013). This is because the stakeholder groups have a direct interest in and are affected by tourism management decisions (Eagles et al. 2002; Waligo, Clarke, and Hawkins 2013).

Past research has established that tourism stakeholder perceptions regarding impacts of tourism in protected areas vary (Puhakka et al. 2009; Ruschkowski et al. 2013; Thapa 2013). These studies have compared the perceptions of different stakeholders within a protected area. However, there is paucity of research that compares the perceptions of similar stakeholder groups between tourism destinations. Therefore, the main objective of this study was to compare protected areas in terms of sustainable tourism outcomes in the views of tourism stakeholders. In particular, we compared the responses of residents with residents, and tourists with tourists, in two protected areas. Empirical data came from surveys conducted in the Annapurna Conservation Area and Chitwan National Park in Nepal. Literature suggests that the main shortcoming of past cross-group comparison studies was that there had been little effort to examine the equivalence of measurement instruments across the groups (Sass 2011; Vandenberg and Lance 2000). Nonequivalence of measurement instruments may produce biased results and threaten the validity of the research (Budruk 2010; Sass 2011). Thus, before comparing stakeholder perspectives of sustainable tourism across the two protected areas, we first established measurement invariance across comparison groups using a multigroup confirmatory analysis (MCFA) approach.

Literature Review

Tourism in Protected Areas

The link between tourism and protected areas can be traced back to the origin of the protected area paradigm with the establishment of Yellowstone National Park (Nash 2014). The creation of the park was justified on the ground of its recreational and conservation values (Eagles et al. 2002; Jamal and Stronza 2009; Reinius and Fredman 2007; Walpole et al. 2001). Since then understanding tourism within protected areas has been a major area of interest for researchers, planners, and managers. As such, the interrelationship between tourism and protected areas has been extensively investigated in the last three decades (Ahebwa and Duim 2013; Eagles 2013; Hassanali 2013; Moore and Weiler 2009; Nepal 2000; Ostrowski 1984; Thapa 2013; Zube and Busch 1990). Discourses are primarily concentrated on tourism and recreational opportunities provided by protected areas (Reinius and Fredman 2007; Ruschkowski et al. 2013; Weiler et al. 2013), tourism as a source of funding for protected areas (Baral et al. 2008; Buckley 2003; Eagles et al. 2013; Thur 2010; Walpole et al. 2001), tourism and local livelihoods (Ahebwa and Duim 2013; Imran et al. 2014; Nyaupane and Poudel 2011; Strickland-Munro and Moore 2013), and impacts of tourism development (Deng et al. 2003; Moyle, Weiler, and Croy 2013; Nepal 2000).

Several scholars have examined the impacts of tourism development in protected areas (Imran et al. 2014; Lai and Nepal 2006; Nepal 2000; Puhakka et al. 2009; Ruschkowski et al. 2013; Thapa 2013; White 1993; Zube and Busch 1990). These studies typically assessed the perspectives (opinions, perceptions, attitude, preferences, or experiences) of tourism stakeholders regarding sociocultural, economic, and ecological impacts. Much of the impact research has concentrated on comparison of stakeholder responses at a single destination. For example, Puhakka and colleagues (2009) examined local stakeholders’ perspectives of sociocultural sustainability of tourism in Oulanka National Park, Finland. Thapa (2013) surveyed visitor attitudes toward sustainable tourism in the protected areas of Zambia. Imran and colleagues (2014) assessed the differences in environmental orientations of stakeholder groups in the Central Karakoram National Park, Pakistan. Their research is undoubtedly invaluable to uncover the preferences and identify the trade-off positions of tourism stakeholders at a particular destination.

A survey of extant literature shows that the significance of research comparing the perspectives (or perceptions) of individual stakeholder groups among multiple destinations has been vehemently overlooked. Very recently, Ruschkowski et al. (2013) examined the differences and similarities in values among parks and protected area managers in Austria, Germany, and the United States. The research revealed that management priorities and practices in parks and protected areas in Austria and Germany are oriented toward conservation. On the contrary, the policies and actions in the United States are focused on social issues such as carrying capacity, visitor satisfaction, and crowding (Ruschkowski et al. 2013). Similarly, Gorner and Cihar (2013) found many differences in attitudes of local residents on conservation- and tourism-related issues in two categories of protected areas, national park and protected landscape, in the Czech Republic. For instance, local people were more supportive of the notion that tourism raises the cost of living of local residents. While these studies have begun to provide an insight into how stakeholder perspectives might vary across protected areas, additional studies are necessary. It is because the knowledge produced by multisite comparative studies is worthwhile to discern how different stakeholder groups evaluate the state of conservation and tourism development. In addition, it is crucial to ensure that all stakeholders are heard because the decisions made by one party such as tourism experts may not reflect the interests and opinions of other stakeholders. The exclusion of stakeholder groups may pose obstacles toward realizing sustainable tourism development goals. Further, protected area managers and other stakeholders can use this type of information to enhance visitor experience and optimize social, economic, and environmental impacts. Management of such impacts is particularly important at this time when tourism is being criticized for killing the goose that lays the golden eggs. Thus, comparing the perspectives of these two major primary stakeholder groups in different protected area–based tourism systems merits academic attention.

Theoretical Background

This research utilizes the protected area management framework and stakeholder theory as conceptual lens to study the sustainable tourism outcomes in protected areas. With the evolution of the protected area movement, the relationship between conservation and tourism has been elusive and mixed, from adversarial to symbiotic (Nyaupane and Poudel 2011). Major factors shaping the relationship are the management objectives set for protected areas and the nature of tourism development (Ruschkowski et al. 2013). The focus of protected area management has gradually expanded from species protection to biodiversity conservation (species, genetic, and ecosystem diversity), ecosystem services, recreation, and community livelihoods (Eagles et al. 2002). According to Dudley (2008, p. 3), “protected areas exist in an astonishing variety—in size, location, management approaches, and objectives.” This indicates that protected areas are not uniform entities and the one-size-fits-all management approach does not work. The International Union for Conservation of Nature (IUCN) developed a global framework categorizing the variety of protected areas into six management categories (Dudley 2008). The framework outlines the major priorities in each category. For example, “biodiversity conservation” is a universal goal (i.e., it is mandatory in all categories of protected areas). On the other hand, “tourism and recreation” is one of the primary objectives in category II “national park” and category V “protected landscape/seascape” (Eagles et al. 2002). It can be surmised that the category assigned to protected areas and the management approach prescribed accordingly shape the form of tourism development.

Over the past two decades, sustainability has become a major concept guiding the process of tourism planning and development (Bramwell and Lane 2012; Butler 1999; Hunter 1997; Ko 2005; Lu and Nepal 2009; Stoddard, Pollard, and Evans 2012). Sustainable tourism is defined as “tourism that takes full account of its current and future economic, social and environmental impacts, addressing the needs of visitors, the industry, the environment and host communities” (UNEP and UNWTO 2005, p. 12). This definition suggests that sustainable tourism development requires an informed participation of all relevant stakeholders (Hawkins and Cunningham 1996).

A stakeholder is defined as “any group or individual who can affect or is affected by the achievement of an organization’s objectives” (Freeman, 1984, p. 46). Since Freeman’s seminal work on the stakeholder theory (Freeman 1984), the theory has been extensively applied in various fields to study the complex relationships among stakeholder groups with different objectives, interests, expectations, rights, and responsibilities. The discussion on the stakeholder theory has primarily centered around two related streams: defining the concept, and classifying stakeholders and understanding their relationships (Rowley 1997). The theory postulates that all voices should be heard while making a decision regardless of the power or interest held by stakeholder groups (Byrd 2007). Clarkson (1995) classified the stakeholder as primary and secondary stakeholders. Primary stakeholders are ones without whose participation the corporation cannot survive, which include investors, employees, customers, and suppliers, whereas secondary stakeholders are those who influence or affect, or are influenced or affected by, the corporation but are not engaged in transactions with the corporation and not essential for its survival (Clarkson 1995).

In the context of tourism, the main tenet of the stakeholder theory is that all parties interested in or affected by tourism development should have an opportunity to influence its management (Sautter and Leisen 1999; UNEP and UNWTO 2005). This means sustainable tourism entails support and involvement of stakeholders in the entire destination planning process (Byrd, Bosley, and Dronberger 2009; Currie, Seaton, and Wesley 2009; Jamal and Stronza 2009; Waligo et al. 2013). The stakeholder theory has been extensively employed to identify the primary stakeholders who are important for a tourism destination and discover their interests. Research shows that tourism systems consist of diverse stakeholders, including residents, entrepreneurs, government officials, and tourists (Byrd 2007; Murphy 1983; Nyaupane and Poudel 2011). The relationship among these stakeholders is complex and dynamic as the roles of stakeholders are site-speciﬁc, varying in type and extent with time, resources, and leadership (Byrd 2007; Sautter and Leisen 1999). There exist trade-offs among the stakeholders regarding the nature of tourism development (Byrd et al. 2009; Hawkins and Cunningham 1996; Murphy 1983; Sautter and Leisen 1999). This indicates that it is imperative to identify the stakeholders and examine their values, perceptions, and interests given that their roles shape the nature of tourism development in a destination (Imran et al. 2014).

Measuring Tourism Sustainability

The triple bottom line, also referred to as TBL, 3P (people, planet, and profit), or 3E (economy, environment, and equity), framework is one of the most widely used approaches to measure sustainable development (Elkington 1997). Sustainability, according to the framework, requires a balanced development of social, economic, and ecological domains (UNEP and UNWTO 2005). Accordingly, the notion of sustainable tourism suggests that there must be a suitable balance between the ecological, sociocultural, and economic dimensions of tourism development (Stoddard et al. 2012). Several scholars have adapted the triple bottom line framework to assess sustainable tourism development (Cottrell et al. 2004; Cottrell et al. 2007; Deng et al. 2003; Ko 2005; Puhakka et al. 2009; Stoddard et al. 2012; Thapa 2013; UNEP and UNWTO 2005; Yu, Chancellor, and Cole 2009). However, there exist many flaws pertaining to quantitative measurement of sustainable tourism. First, there have been several attempts to develop a global measure of sustainable tourism (Choi and Sirakaya 2005; Yu et al. 2009). The development of such a widely applicable measurement instrument is virtually impossible because tourism destinations vary greatly in terms of biophysical attributes, community characteristics, and institutional arrangements to manage tourism. This suggests that the indicators for sustainable tourism should be developed in consultation with destination-level tourism stakeholders so that they are relevant in the local context. Second, the literature seems indifferent concerning the need for distinction between positive and negative impacts of tourism. We argue that it would be an erroneous practice just to record positive (or negative) impacts, measure both positive and negative impacts but reverse code the items related to negative impact during data analysis, or subtract negative scores from positive scores to get net impacts. No form of tourism exclusively produces positive (or negative) impacts. For example, in rural destinations, increased income is frequently accompanied by inflation. Given that positive and negative impacts are like two faces of a coin, it is crucial to measure them separately. The reverse coding of items incorrectly assumes that positive and negative impacts are mutually exclusive. In many circumstances, reverse coding is not possible (e.g., tourism increases prostitution). Moreover, the presence of equal scores for positive and negative impact items does not mean zero impact. Further, the concept of cumulative impact is invalid unless there exists an apposite approach to weight the items.

Third, in comparative studies there is a tendency to examine the difference between group means on an individual item or a set of items forming a scale (the group means are obtained by averaging the item averages). This approach blatantly ignores the likelihood of errors in measurement of variables, and the researchers rarely examine statistical assumptions such as equality of variances across groups. Methodologists maintain that while comparing the responses of diverse populations, it is imperative to ascertain that the comparison groups interpret the individual questions as well as the underlying latent construct similarly (Vandenberg and Lance 2000). This can be done with the test of measurement invariance, which examines “whether an instrument has the same psychometric properties across heterogeneous groups” (Chen 2007, p. 465). A measurement instrument is called invariant “when members of different populations who have the same standing on the construct being measured received the same observed score on the test” (Schmitt and Kuljanin 2008, p. 211). The MCFA approach has been used to test measurement invariance across groups in structural equation modeling literature in last two decades (Vandenberg and Lance 2000; Sass 2011) and more recently in the tourism literature (Chi 2011; Kyle, Graefe, and Manning 2005; Skibins, Powell, and Hallo 2013; Taff et al. 2013). In this approach, the comparison of latent factor means entails that the measurement instrument is invariant across groups (Millsap and Meredith 2007). In particular, the testing of latent mean difference requires strong factorial invariance, that is, equal unstandardized factor loadings and intercepts/thresholds (Sass 2011; Cheung and Rensvold 2002). It is because latent factor mean is jointly created but differently influenced by factor loadings and intercepts (Muthén and Muthén 1998-2012). Measurement invariance in MCFA is established by running a series of increasingly constrained CFA models and testing whether differences between the nested models are statistically significant (Schmitt and Kuljanin 2008).

Study Purpose and Research Questions

The purpose of this study is to compare the perspectives of stakeholder groups regarding sustainable tourism development in protected areas. The research questions specifically examined are as follows:

Are there significant latent factor mean differences in perceptions of sustainable tourism development between residents of Annapurna and Chitwan?

Are there significant latent factor mean differences in perceptions of sustainable tourism development between tourists visiting Annapurna and Chitwan?

Methods

Study Areas

Data for the study come from two protected areas in Nepal: the Annapurna Conservation Area and Chitwan National Park. The rationale to choose these protected areas is their differences in terms of nature of tourism development, which is primarily influenced by biophysical attributes, socioeconomic characteristics, and protected area governance. Further, these are the two most visited protected areas in Nepal and represent two different categories of protected areas. The Annapurna Conservation Area is the largest protected area of Nepal covering 7,629 km². It is an IUCN category VI protected area (protected area with sustainable use of natural resources) managed by the National Trust for Nature Conservation, an autonomous and not-for-profit organization established by a legislative act. The conservation area adopts the Integrated Conservation and Development Program (ICDP) approach. The programs in Annapurna are targeted toward biodiversity conservation, community livelihoods, and integrated tourism management. The conservation area encloses forests, pastures, barren lands, settlements, and agriculture lands. Accordingly, the area is divided into four management zones: wilderness zone, protected forest/seasonal grazing zone, intensive use zone, and special management zone. While the wilderness zone is strictly protected, the protected forest/seasonal grazing zone allows seasonal and limited use of resources. The intensive use zone is inhabited by more than 90,000 people and allows traditional livelihood activities such as farming, animal husbandry, and forest products collection. The special management zones are popular tourism areas where the programs are oriented toward sustainable tourism development. The number of tourists visiting the area is increasing every year; approximately 105,000 tourists visited the area in 2012. Trekking—a multiday hike along foot trails passing through mountains, valleys, and settlements for the purpose of enjoying the Himalayan landscape and the culture of the indigenous people—is the major tourism product (Poudel and Nyaupane 2013). Tourism establishments in Annapurna are small-scale, locally owned, and family managed. Tourism, emigration, foreign employment, and infrastructure development are major agents of change in the area, producing many positive and negative sociocultural, economic, and environmental impacts.

Chitwan National Park, the first protected area in Nepal, consists of the core area (category II “national park”) extending over 932 km² and the buffer zone (category VI “protected area with sustainable use of natural resources”) that covers 750 km². The national park is an example of a nested protected area where the highly protected core area is surrounded by a less strictly protected buffer zone. The core area is strictly secured by the park authorities with the help of the Nepalese Army to safeguard rare and endangered species of flora and fauna including the great one-horned rhinoceros (Rhinoceros unicornis) and the royal Bengal tiger (Panthera tigris). Use of the core area is limited to nature-oriented tourism activities including bird watching, wildlife viewing, elephant safari, canoeing, and jungle walk. Local residents with the help of park management manage the buffer zone. The programs conducted in the buffer zone are oriented toward conservation, sustainable use of natural resources, and community development. The buffer zone area involves forested areas, cultivated lands, and settlements. The forested areas acts as extended habitat for wildlife and supplies forest products for local people. The buffer zone is densely populated, with a population of 260,000 that includes indigenous people (e.g., Tharu, Majhi, and Musahar) and immigrants from northern hilly areas. Tourism is a mainstay of the local economy, and more than 150,000 tourists visited the area in 2012. Tourism in Chitwan could be characterized as nature-based mass tourism, which is predominantly controlled by outside entrepreneurs. However, it has a good multiplier effect as the tourism establishments employ local residents and buy local produce. Tourism, along with immigration, modernization of agriculture, and industrial development, has resulted in significant sociocultural, economic, and environmental changes in the area.

Measurement Instrument

We developed three scales paralleling social, ecological, and economic dimensions of sustainable development. Each scale consists of two subscales for negative and positive impacts. The subscales are latent constructs consisting of multiple (three or more) items related to destination-level tourism impacts. Our scale development process roughly paralleled the process used by Choi and Sirakaya (2005) to develop the scale to measure residents’ attitudes toward sustainable tourism development. At first, we created a pool of items related to each subscale from the review of previously used scales in measuring tourism impacts and sustainable tourism (e.g., Andereck and Nyaupane 2011; Andereck, Knopf, and Vogt 2005; Andereck and Vogt 2000; Byrd et al. 2009; Choi and Sirakaya 2005, 2006; Nyaupane and Thapa 2004; Yu et al. 2009). Second, we revised the scales through a series of discussions with local-level tourism stakeholders, including park staff, tourism entrepreneurs, tourists, and residents in order to ensure that the scale items are locally relevant. This was an iterative process entailing feedback of tourism stakeholders at both sites. Third, a pilot survey of the subscales was done with a sample of 100 participants. The participants were tourists, tourism entrepreneurs, and local residents from Sauraha in Chitwan and Ghandruk in Annapurna. Based on statistical criteria (i.e., exploratory factor analysis, reliability test) and respondents’ feedback, we decided to use a total of 45 items. There were 6 items in negative ecological, 5 items in positive ecological, 5 items in negative economic, 12 items in positive economic, 12 items in negative social, and 5 items in positive social subscales (Table 1). Each item was rated on a 5-point Likert-type scale with response categories ranging from strongly disagree (1) to strongly agree (5). The instrument also included some sociodemographic questions, including gender, age, income, and education.

Table 1.

Tourism Impact Subscales.

Negative ecological impact subscale
ECL1	Tourism destroys the natural environment
ECL2	Tourism increases air, water, and noise pollution
ECL3	Tourism increases environmental problems such as littering and wastewater discharge
ECL4	Tourism produces long-term negative effects on the environment
ECL5	Construction of hotels and other tourist facilities destroy the natural environment
ECL6	Tourism development encourages deforestation
Positive ecological impact subscale
ECL7	Tourism development strengthens local environmental conservation efforts
ECL8	Tourism promotes greater protection of the natural environment
ECL9	Tourism development promotes positive environmental ethics among all parties that have a stake in tourism
ECL10	Tourism development promotes protection of wildlife and their natural habitat
ECL11	Tourism provides incentive for conservation of natural areas
Negative economic impact subscale
ECO1	Tourism development increases property taxes
ECO2	Tourism increases the price of goods and services
ECO3	Tourism development results in increased cost of living
ECO4	Tourism increases the value of land and house
ECO5	Tourism puts more pressure on local services such as police, fire, utilities, and roads
Positive economic impact subscale
ECO6	Tourism contributes to community development fund
ECO7	Tourism increases income and improves living standards of community people
ECO8	Tourism improves the quality of service in shops, restaurants, and hotels in the area
ECO9	Tourism benefits other businesses and industries in local community
ECO10	Tourism brings new income to local community
ECO11	Tourism attracts additional investment for local businesses
ECO12	Tourism creates new market for local products
ECO13	Tourism businesses purchase goods and services from local community
ECO14	Tourism is needed for the development of local economy
ECO15	Roads, bridges, and other public facilities are kept at a high standard because of tourism
ECO16	Tourism provides entrepreneurial (investment) opportunity to the community residents
ECO17	Tourism development makes local businesses, such as retail stores and restaurants, more profitable
Negative social impact subscale
SOC1	Tourism development increases crime rates
SOC2	Tourism development increases traffic problems such as congestion and accidents
SOC3	Tourism development results in crowded public places
SOC4	Tourism development creates conflict (friction) between visitors and residents
SOC5	Tourism development encourages gambling and other illegal games
SOC6	Tourism development leads to decline in traditions of native people
SOC7	Tourism development results in more thefts and vandalism
SOC8	Tourism promotes drug and alcohol abuse
SOC9	Tourism promotes prostitution and sexual openness
SOC10	Tourism results in loss of tranquility (peacefulness) in the community
SOC11	Tourism leads to deterioration of the quality of life of local people
SOC12	Tourism development erodes social cohesion and disrupts traditional family values
Positive social impact subscale
SOC13	Tourism enhances knowledge of other cultures (communities)
SOC14	Tourism development facilitates intercultural communication and understanding
SOC15	Tourism provides incentives for the preservation of local culture
SOC16	Tourism provides opportunities to participate in local cultural activities
SOC17	Tourism supports for the restoration and maintenance of cultural and historic sites

Participants and Data Collection

The participants were primary tourism stakeholders, that is, tourists and residents, in the study areas. The tourist sample consisted of the visitors who had some familiarity with the study areas. It was achieved in Annapurna by selecting the visitors returning from trekking. In Chitwan, we deliberately excluded the visitors spending less than 24 hours. Given that virtually all of the tourists spend at least two days in the study areas, we believe that our sample would still be unbiased. The tourists were approached while they were resting, mostly after lunch and before dinner in Chitwan, and after dinner in Annapurna. It was up to the tourists whether to return the questionnaire directly to the researchers on the same day or drop it off at their hotel front desk next day. Since tourists are mobile, it was not possible to compile a sampling frame. Instead, we compiled a sampling frame of hotels and restaurants and randomly selected the establishments to be sampled in a particular day at specific time slots at both sites.

The resident sample was composed of tourism entrepreneurs, employees of tourism businesses, members of nongovernmental and community-based organizations, and community members. It was realized that several people belong to more than one stakeholder group. For example, person X runs a hotel and he is on board of a nongovernmental organization as well. Thus, we sought help of local-level tourism stakeholders to classify the residents into different subgroups. Adopting the stratified sampling method, we assigned a quota commensurate with the estimated population size for each subgroup. A systematic sampling procedure was applied within each group (stratum), which involved choosing every kth participant after a random start. The k value is determined on the basis of the size of the stratum. The residents were contacted at their place of work or residence depending on their availability.

The survey method was adopted to administer the questionnaires. This method is considered as the most efficient and effective method to solicit the perceptions of large numbers of people in a limited time (Babbie 2013). Additionally, the method is more appropriate than other popular methods such as personal interview and focus group discussion if we need to collect quantitative data from a large sample. The questionnaires were designed in Nepali (for residents) and English (for tourists), and were self-administered by the respondents. A total of 500 questionnaires were distributed at each site and we received back 435 surveys in Annapurna (response rate 87%) and 430 surveys in Chitwan (response rate 86%). The main reason for denying to participate was reported as lack of time.

Data Analysis

The data were analyzed in Mplus 7.1 (Muthén and Muthén 1998-2012) structural equation modeling software using maximum likelihood estimation approach with robust standard errors. The robust standard errors account for nonnormality, if present, in data. As discussed earlier, the valid comparison of latent factor means across groups requires strong factorial invariance. We followed the sequential process suggested for the MCFA to test measurement invariance (Cheung and Rensvold 2002; Muthén and Muthén 1998-2012; Sass 2011; Vandenberg and Lance 2000). The process includes testing of (1) invariance of measures across comparison groups separately, (2) configural invariance, (3) metric invariance, and (4) scalar invariance. At first, we conducted confirmatory factor analysis on all subscales for each group to see whether both groups (i.e., Annapurna and Chitwan) have the same factor structure. Second, we tested for configural invariance, which examined whether the number of factors and the pattern of indicator factor loading were identical across groups. Strictly speaking, it involved estimation of the same model for both groups simultaneously while allowing all model parameters to vary freely across groups. Therefore, the model having configural invariance is considered as the baseline model against which more restrictive invariance models are compared (Vandenberg and Lance 2000). Third, we tested for metric invariance by constraining unstandardized factor loadings for the same item to be equal across groups. Fourth, we tested for scalar invariance by adding equality constraints on item intercepts.

The overall fit of the CFA models was evaluated with the chi-square (χ²) goodness of fit (Brown 2006; Sass 2011). In contrast to traditional significance testing procedure, a nonsignificant χ² value indicates satisfactory model fit (Byrne, Shavelson, and Muthén 1989). Scholars have noted problems related to the use of the χ² statistic as the sole criterion to assess model fit because several factors including sample size, departure from multivariate normality, model complexity, and size of the correlations in the model affect the test result (Byrne et al. 1989; Vandenberg and Lance 2000). We additionally relied on four practical model fit indices: comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) to evaluate the model fit. Researchers suggest that the model with CFI and TLI values greater than .95, RMSEA less than .06, and SRMR less than.08 could be considered as a good fit (Hu and Bentler 1999; Vandenberg and Lance 2000).

In measurement invariance testing procedure, the successive models are nested within the preceding models (Milfont and Fischer 2010). This indicates that the level of measurement equivalency could be evaluated by comparing the fit of more restrictive models to a baseline model. As suggested in the literature, we used three types of incremental goodness-of-fit statistics to assess measurement invariance: (1) likelihood ratio test, commonly referred as chi-square difference (Δχ²) statistic, (2) change in model fit indices (i.e., ΔCFI, ΔTLI, ΔRMSEA, and ΔSRMR), and (3) modification indices (Byrne et al. 1989; Cheung and Rensvold 2002; Sass 2011). A nonsignificant Δχ² value between two nested models for a given degree of freedom signifies that the measures of compared models are invariant. The Δχ² test used in the analysis is rescaled likelihood ratio test as it used scaling factor produced by robust maximum likelihood estimate to adjust for nonnormality in the data. Given that the Δχ² have the same limitations as the χ² test, we additionally considered changes in practical goodness of fix indices (i.e., ΔCFI, ΔTLI, ΔRMSEA, and ΔSRMR) to compare whether the metric and scalar invariance models were better than the configural model. The nested models with ΔCFI < .010, ΔTLI < .020, ΔRMSEA < .015, and ΔSRMR < .030 suggest that all specified equality constraints are tenable and the models could be considered equivalent (Chen 2007; Cheung and Rensvold 2002; Sharma, Durvasula, and Ployhart 2011).

When full measurement invariance was untenable, we proceeded with the evaluation of partial measurement invariance (Byrne et al. 1989; Schmitt and Kuljanin 2008; Vandenberg and Lance 2000). Assessment of partial invariance involves identifying and then freeing of the parameter constraints contributing to model misfit (Byrne et al. 1989). We inspected modification indices to detect the constraints causing the model to fit poorly. In particular, we freed the parameter constraints that significantly reduce the Δχ² value (i.e., parameter constraint producing modification index larger than 3.84) and improve practical model fit indices (i.e., CFI, TLI, RMSEA, and SRMR). The modification indices are one-degree-of-freedom tests, so the constraints are released one at a time starting with the largest χ² (Byrne et al. 1989). Finding noninvariant parameters is an exploratory, iterative, and post hoc practice. Besides producing a nonsignificant Δχ², this procedure informed which factor loadings and intercepts are noninvariant across comparison groups.

Results

Respondent Characteristics

The demographic characteristics of the study participants are given in Table 2. The sample consisted of 450 residents (230 in Annapurna and 220 in Chitwan) and 415 tourists (205 in Annapurna and 210 in Chitwan). In the resident sample, a majority of the respondents were male (64%). The proportion varied between Annapurna and Chitwan, χ²(1, N = 450) = 8.38, p = .004, with Annapurna having a lower proportion of males (58%) than Chitwan (71%). Average age and average income of the residents were 31.44 years and US$2,189.11, respectively. No significant difference was found between the respondents in Annapurna and Chitwan regarding age, t(448) = 1.17, p = .244, and income, t(303) = 1.74, p = .084. Nearly two-thirds (64%) of the residents reported that their education is less than high school. The respondents with education less than high school were lower in Chitwan (59%) compared to Annapurna (70%), but the difference was nonsignificant, χ²(4, N = 450) = 7.37, p = .118.

Table 2.

Respondents’ Characteristics.

	Resident Data				Tourist Data
Characteristics	Combined (n = 450)	Annapurna (n = 230)	Chitwan (n = 220)	t/χ² Value	Combined (n = 415)	Annapurna (n = 205)	Chitwan (n = 210)	t/χ² Value
Gender				8.38**				2.94
Male	0.64 (0.48)	.58 (.50)	0.71 (0.46)		0.50 (0.50)	0.52 (0.50)	0.49 (0.50)
Female	0.36 (0.48)	.42 (.50)	0.29 (0.46)		0.50 (0.50)	0.48 (0.50)	0.51 (0.50)
Age (years)	31.44 (11.47)	30.83 (12.55)	32.08 (10.21)	1.17	36.25 (13.47)	37.93 (13.24)	34.62 (13.53)	2.52*
Income (US$)	2,189.11 (2,556.01)	1,788.45 (1,711.89)	2,349.00 (2,810.98)	1.74	62,644.72 (129,828.76)	91,188.38 (195,510.42)	44,029.30 (47,035.23)	2.25*
Education				7.37				3.23
Less than high school	0.64 (0.48)	0.70 (0.46)	0.59 (0.49)		0.03 (0.17)	0.03 (0.18)	0.02 (0.15)
High school	0.17 (0.38)	0.13 (0.34)	0.22 (0.41)		0.14 (0.35)	0.11 (0.32)	0.17 (0.37)
Associate’s degree	0.06 (0.23)	0.06 (0.23)	0.06 (0.24)		0.11 (0.31)	0.12 (0.32)	0.10 (0.30)
Undergraduate	0.11 (0.31)	0.10 (0.30)	0.12 (0.32)		0.34 (0.48)	0.36 (0.48)	0.33 (0.47)
Master’s degree	0.02 (0.12)	0.02 (0.13)	0.02 (0.12)		0.31 (0.46)	0.30 (0.46)	0.31 (0.46)
PhD	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)		0.07 (0.26)	0.08 (0.27)	0.07 (0.25)

Note: Values are mean (standard deviation) unless otherwise noted.

p < .05, **p < .01.

In tourist sample, the respondents were equally split in terms of gender, χ²(1, N = 415) = 2.94, p = .624. Average age and average household income of the tourists were 36.25 years and US$62,644.72, respectively. There was a significant difference in age, t(413) = 2.52, p = .012, and income, t(226) = 2.25, p = .02, between Annapurna and Chitwan tourists. On average, the tourists visiting Annapurna were older and richer than the tourists visiting Chitwan. Overall, the tourists were well educated, with nearly three-quarters (72 %) having a bachelor’s or higher degree. No significant difference was found between the tourists visiting Annapurna and Chitwan regarding education, χ²(4, N = 415) = 3.22, p = .666.

Testing of Measurement Invariance

The MCFA approach was used to compare the perceptions of tourism stakeholders between Annapurna and Chitwan samples. We created separate data files to record the responses of residents and tourists. In each data set, we conducted six measurement invariance tests since there are six subscales or constructs (i.e., positive and negative ecological, economic, and social impact subscales) to measure tourism sustainability. We went through the steps outlined in the data analysis section to ascertain that the constructs are invariant among groups. The results of confirmatory factor analysis in our sample reproduced the factor structure suggested by the exploratory factory analysis conducted on the data obtained from the pilot survey (see supplementary material provided for this article). This validates that the selected items are appropriate for cross-group comparisons. Measurement invariance (full or partial) was obtained for all subscales (Tables 3 -8), which indicates that both groups perceived the measurement instrument in a similar fashion and that the degree of bias of the scales is equal among groups.

Table 3.

Measurement Invariance across Annapurna and Chitwan on the Negative Ecological Impact Subscale for Residents and Tourists.

Model	χ²(df)	p Value	Δχ²(df)	p Value	CFI	TLI	RMSEA	SRMR
Resident data
Configural invariance	10.70 (8)	.22	−	−	.991	.978	.039	.027
Metric invariance	15.26 (12)	.23	4.58 (4)	.33	.989	.982	.035	.042
Scalar invariance	24.56 (16)	.08	13.96 (8)	.08	.972	.965	.049	.047
Scalar (partial) invariance^a	16.88 (15)	.33	6.11 (7)	.53	.994	.992	.024	.041
Tourist data
Configural invariance	6.34 (8)	.61	−	−	1.00	1.00	.000	.017
Metric invariance	7.94 (12)	.79	1.18 (4)	.88	1.00	1.00	.000	.026
Scalar invariance	18.36 (16)	.30	12.96 (8)	.11	.995	.993	.027	.036
Scalar (partial) invariance^b	11.27 (15)	.73	4.83 (7)	.68	1.00	1.00	.000	.034

Note: CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

Intercept of the item ECL5 freely estimated across groups. Intercepts of the item ECL5 for Annapurna and Chitwan are 3.13 and 2.98, respectively.

Intercept of the item ECL6 freely estimated across groups. Intercepts of the item ECL6 for Annapurna and Chitwan are 2.92 and 3.16, respectively.

Table 4.

Measurement Invariance across Annapurna and Chitwan on the Positive Ecological Impact Subscale for Residents and Tourists.

Model	χ²(df)	p Value	Δχ²(df)	p Value	CFI	TLI	RMSEA	SRMR
Resident data
Configural invariance	14.05 (9)	.12	−	−	.980	.956	.050	.029
Metric invariance	15.50 (13)	.28	2.31 (4)	.68	.990	.985	.029	.049
Scalar invariance	27.34 (17)	.05	13.26 (8)	.10	.959	.952	.052	.061
Scalar (partial) invariance^a	21.47 (16)	.16	7.69 (7)	.36	.978	.973	.039	.049
Tourist data
Configural invariance	4.97 (9)	.84	−	−	1.00	1.00	.000	.017
Metric invariance	7.66 (13)	.87	2.63 (4)	.62	1.00	1.00	0.00	.047
Scalar invariance	21.10 (17)	.22	15.76 (8)	.05	.987	.985	.034	.052
Scalar (partial) invariance^b	13.25 (16)	.65	8.02 (7)	.33	1.00	1.00	.000	.052

Note: CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

Intercept of the item ECL9 freely estimated across groups. Intercepts of the item ECL9 for Annapurna and Chitwan are 4.05 and 3.84, respectively.

Intercept of the item ECL11 freely estimated across groups. Intercepts of the item ECL11 for Annapurna and Chitwan are 3.76 and 3.55, respectively.

Table 5.

Measurement Invariance across Annapurna and Chitwan on the Negative Economic Impact Subscale for Residents and Tourists.

Model	χ²(df)	p Value	Δχ²(df)	p Value	CFI	TLI	RMSEA	SRMR
Resident data
Configural invariance	2.15 (3)	.54	−	−	1.00	1.00	.000	.013
Metric invariance	5.46 (6)	.49	3.44 (3)	.33	1.00	1.00	.000	.040
Scalar invariance	39.12 (9)	<.001	40.59 (6)	<.001	.868	.824	.122	.072
Scalar (partial) invariance^a	11.32 (8)	.18	9.80 (5)	.08	.986	.978	.043	.037
Tourist data
Configural invariance	2.20 (4)	.70	−	−	1.00	1.00	.000	.014
Metric invariance	11.36 (7)	.12	9.59 (3)	.02	.975	.957	.055	.091
Metric (partial) invariance^b	5.06 (6)	.54	2.92 (2)	.23	1.00	1.00	.000	.052
Scalar (partial) invariance^c	7.93 (8)	.44	5.94 (4)	.20	1.00	1.00	.000	.059

Note: CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

Intercept of the item ECO4 freely estimated across groups. Intercepts of the item ECO4 for Annapurna and Chitwan are 3.22 and 3.78, respectively.

Unstandardized factor loading of the item ECO2 freely estimated across groups. Unstandardized factor loadings of the item ECO2 for Annapurna and Chitwan are .57 and .38, respectively.

Unstandardized factor loading and intercept of the item ECO2 freely estimated across groups. Intercepts of the item ECO2 for Annapurna and Chitwan are 4.06 and 3.99, respectively.

Table 6.

Measurement Invariance across Annapurna and Chitwan on the Positive Economic Impact Subscale for Residents and Tourists.

Model	χ²(df)	p Value	Δχ²(df)	p Value	CFI	TLI	RMSEA	SRMR
Resident data
Configural invariance	84.52 (68)	.09	−	−	.970	.961	.033	.045
Metric invariance	90.23 (77)	.14	6.65 (9)	.67	.976	.972	.028	.066
Scalar invariance	109.74 (86)	.04	25.35 (18)	.12	.957	.955	.035	.076
Scalar (partial) invariance^a	98.71 (85)	.15	14.00 (17)	.67	.975	.974	.027	.070
Tourist data
Configural invariance	95.14 (67)	.01	−	−	.963	.950	.045	.047
Metric invariance	102.24 (76)	.02	7.32 (9)	.60	.965	.959	.041	.073
Metric (partial) invariance^b	97.13 (75)	.04	2.30 (8)	.97	.971	.965	.038	.055
Scalar (partial) invariance^c	112.27 (83)	.02	16.92 (16)	.39	.961	.958	.041	.060

Note: CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

Intercept of the item ECO6 freely estimated across groups. Intercepts of the item ECO6 for Annapurna and Chitwan are 4.08 and 3.79, respectively.

Unstandardized factor loading of the item ECO10 freely estimated across groups. Unstandardized factor loadings of the item ECO10 for Annapurna and Chitwan are .70 and .46, respectively.

Unstandardized factor loading and intercept of the item ECO10 freely estimated across groups. Intercepts of the item ECO10 for Annapurna and Chitwan are 4.11 and 4.02, respectively.

Table 7.

Measurement Invariance across Annapurna and Chitwan on the Negative Social Impact Subscale for Residents and Tourists.

Model	χ²(df)	p Value	Δχ²(df)	p Value	CFI	TLI	RMSEA	SRMR
Resident data
Configural invariance	146.42 (102)	.003	−	−	.957	.944	.044	.049
Metric invariance	162.32 (113)	.002	15.92 (11)	.14	.952	.944	.044	.061
Scalar invariance	180.91 (124)	<.001	35.34 (22)	.04	.945	.941	.045	.063
Scalar (partial) invariance^a	172.42 (123)	.002	24.77 (21)	.06	.952	.948	.042	.063
Tourist data
Configural invariance	104.67 (49)	<.001	−	−	.956	.935	.074	.046
Metric invariance	113.40 (57)	<.001	7.91 (8)	.44	.955	.943	.069	.059
Scalar invariance	127.39 (65)	<.001	21.30 (16)	.17	.950	.945	.068	.067

Note: CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

Intercept of the item SOC7 freely estimated across groups. Intercepts of the item SOC7 for Annapurna and Chitwan are 2.72 and 2.42, respectively.

Table 8.

Measurement Invariance across Annapurna and Chitwan on the Positive Social Impact Subscale for Residents and Tourists.

Model	χ²(df)	p Value	Δχ²(df)	p Value	CFI	TLI	RMSEA	SRMR
Resident data
Configural invariance	4.01 (9)	.91	−	−	1.00	1.00	.000	.020
Metric invariance	7.20 (13)	.89	3.16 (4)	.53	1.00	1.00	.000	.054
Scalar invariance	19.93 (17)	.28	17.90 (8)	.02	.982	.978	.028	.063
Scalar (partial) invariance^a	12.08 (16)	.74	8.77 (7)	.27	1.00	1.00	.000	.065
Tourist data
Configural invariance	1.94 (8)	.98	−	−	1.00	1.00	.000	.011
Metric invariance	5.48 (12)	.94	3.50 (4)	.48	1.00	1.00	.000	.043
Scalar invariance	7.21 (14)	.93	5.32 (6)	.50	1.00	1.00	.000	.042

Note: CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

Intercept of the item SOC15 freely estimated across groups. Intercept of the item SOC15 for Annapurna and Chitwan are 3.66 and 3.94, respectively.

Comparison of Latent Means

Confirmation of strong factorial (full or partial) invariance between two sites (Annapurna and Chitwan) with the help of likelihood ratio test (Δχ²) and the change in practical model fit indices (i.e., ΔCFI, ΔTLI, ΔRMSEA, and ΔSRMR) for all six subscales in both resident and tourist data allowed us to make substantive comparisons between latent means. While comparing latent means, Mplus fixes the mean of one group at zero for model identification purpose and other group mean(s) are freely estimated (Muthén and Muthén 1998-2012). As such, the first group becomes the reference group and the mean for the other group(s) are the deviation from the reference group’s mean. In our analysis, the mean for the Annapurna group was fixed to zero, whereas the mean of the Chitwan group was freely estimated as deviation from the Annapurna mean.

Table 9 shows that two out of the six mean pairs examined in resident data were significantly different. On average, residents in Chitwan perceived significantly higher positive ecological impact compared to Annapurna, mean difference = .213, z = 2.78, p = .005. Similarly, Chitwan residents perceived significantly higher positive social impact compared to Annapurna residents, mean difference = .157, z = 2.25, p = .005. In tourist data, all six mean pairs compared were significantly different. When compared to Annapurna tourists, the tourists in Chitwan scored significantly lower in the negative ecological impact subscale, mean difference = –.279, z = −3.28, p = .001, and higher in the positive ecological impact subscale, mean difference = .433, z = 5.55, p < .001. Chitwan tourists scored significantly higher on both the positive economic impact subscale, mean difference = .302, z = 5.42, p < .001, and the negative economic impact subscale, mean difference = .175, z = 3.40, p = .001, than Annapurna tourists. The mean difference between Chitwan and Annapurna was significant for both the negative social impact subscale, mean difference = –.393, z = −4.51, p < .001, and the positive social impact subscale, mean difference = .144, z = 2.02, p < .04. To sum up, Chitwan residents perceived higher positive ecological and positive social impacts than Annapurna residents. Chitwan tourists perceived higher positive ecological and positive social impacts of tourism, and lower negative ecological and negative social impacts compared to Annapurna tourists. The results were contradictory regarding economic impacts—Chitwan tourists perceived significantly higher positive economic impacts and negative economic impacts compared to Annapurna tourists. The responses of the residents concur with the tourists regarding positive ecological and positive social impacts only.

Table 9.

Results of Mean Comparison across Annapurna and Chitwan on Tourism Impact Subscales.

	Resident Data			Tourist Data
Subscale	Mean Difference (SE)	Z Value	p Value	Mean difference (SE)	Z Value	p Value
Negative ecological	0.165 (0.105)	1.57	.12	−0.279 (0.085)	−3.28	.001
Positive ecological	0.213 (0.077)	2.78	.005	0.433 (0.078)	5.55	<.001
Negative economic	−0.158 (0.107)	−1.48	.14	0.302 (0.056)	5.42	<.001
Positive economic	0.064 (0.067)	0.96	.34	0.175 (0.052)	3.40	.001
Negative social	−0.072 (0.075)	−0.96	.34	−0.393 (0.087)	−4.51	<.001
Positive social	0.157 (0.070)	2.25	.005	0.144 (0.071)	2.02	.04

Discussion

This study compared the latent means on tourism impact scales between Annapurna and Chitwan for residents and tourists. The results were mixed, which is consistent with previous findings (e.g., Gorner and Cihar 2013; Ruschkowski et al. 2013). We found that the latent mean of Chitwan residents on the positive ecological subscale is significantly higher than Annapurna residents. Similarly, the latent mean of Chitwan tourists on positive ecological subscale is significantly higher than Annapurna tourists. In addition, the average score of Chitwan tourists is significantly lower on the negative ecological impact subscale compared to Annapurna tourists. These findings mirror the goals and successes of conservation programs in Chitwan National Park. Recently, the park has been successful in achieving the zero poaching target, which means no rhinos were killed for a year. Similarly, it is reported that the population of royal Bengal tigers in Chitwan increased from 60 breeding individuals in 2000 to 125 in 2012.

The results further revealed that both residents and tourists in Chitwan perceived higher positive social impacts, and the tourists in Chitwan perceived lower negative social impacts compared to their Annapurna counterparts. There have been substantial efforts to manage social impacts in both areas including establishment of museums, promotion of local arts and crafts, support for events and festivals, and performance for tourists. We speculate that the differences in perceptions could be linked to (1) community characteristics: the communities in Annapurna are homogenous whereas those in Chitwan are mixed; and (2) juxtaposition of tourism establishments and communities: tourism establishments are concentrated in a single place named Sauraha in Chitwan while they are located within the communities in Annapurna. With regards to economic impacts, the residents in both areas responded similarly. However, the tourists perceived that both positive and negative economic impacts are higher in Chitwan than in Annapurna. The contradictory findings regarding economic impact seem appropriate given that the tourism businesses in Annapurna are small scale, locally owned, and widely spread along the trekking route, whereas in Chitwan, tourism businesses are large scale and concentrated within a limited area. Overall, both residents and tourists are more favorable of ecological and social impacts of tourism development in Chitwan compared to Annapurna. The study has several managerial, theoretical, and methodological implications.

The findings are useful in management of protected area–based tourism locally as well as governance of protected area systems worldwide. The results could not provide a definitive answer to the question “Which is the best management approach to achieve the goals of biodiversity conservation, local livelihoods, and sustainable tourism development?” Yet the study results can help planners evaluate their strategies and priorities, and help managers improve their actions and practices in the respective protected areas. For example, the park authority with the help of the Nepalese Army and local residents in Chitwan has achieved tremendous success in the conservation of rare and endangered species of wildlife and their habitat. The mega and charismatic animals, such as the one-horned rhinoceros and the royal Bengal tiger, and birds are major tourism attractions in Chitwan. The symbiotic relationship between conservation and tourism has produced several biodiversity, recreational, and economic benefits. In Annapurna, despite the fact that the conservation area harbors some rare and endangered wildlife species including the snow leopard, musk deer, and pheasants, marketing of wildlife as tourist attractions is yet to be materialized. In Annapurna, as a category VI protected area, more focus has been placed on sustainable use of natural resources and community development than biodiversity conservation, so there are always trade-offs between conservation and community development goals among various types of protected areas. Hence, it will be a mistake to search for a one-size-fits-all approach for optimal economic, social, and ecological benefits.

The study findings help address the complex challenge of developing sustainable tourism in protected areas. As such, the destination-level comparative studies inform which interventions are required to achieve the conservation, sustainable tourism, and community livelihoods goals at the macro level. Overall, both residents and tourists better perceived the nature-based mass tourism in Chitwan compared to alternative tourism in Annapurna. The results suggest that it would be naïve to conclude that alternative tourism is always a better option. Further, the results confirm the assertions that the outcomes of recreation and tourism partnerships in protected areas depend on the management model (Eagles 2009; Ruschkowski et al. 2013), the benefits derived by local residents are contingent upon the management approach employed in protected areas (Gorner and Cihar 2013), and the protection status (category) of protected areas influence sustainable tourism outcomes (Reinius and Fredman 2007). This indicates that site-specific biophysical, social, and economic situation should be taken into account while deciding management priorities and approaches of protected area–based tourism systems.

Our research provides empirical support to the tenet of stakeholder theory that all stakeholder groups that have a stake or legitimate interest should collectively manage the protected area–based tourism systems. We observed that the perceptions of local people and tourists partially match regarding sustainable tourism development outcomes across Annapurna and Chitwan. The results support that achievement of sustainable tourism outcomes requires active participation of all relevant stakeholders (Byrd 2007). The stakeholder participation is even more crucial when interests of stakeholders are in conflict (Hawkins and Cunningham 1996). Given that stakeholder participation is not a one-shot procedure, various stakeholder groups should be involved throughout the entire planning, management, and decision-making process. Similarly, the comparison of views and opinions of visitors and residents echoes the similarities and differences in their expectations, preferences, and experiences at the destination. Overall, this body of literature is useful in managing conflicts among stakeholders through communication, cooperation, and collaboration for successful and sustainable tourism development (Yu et al. 2009).

This research contributes to the theory and measurement of sustainable tourism and the broader sustainable development literature. This study supports the notion that the triple bottom line is a useful framework to measure sustainability. However, unlike previous research, we postulated that both negative and positive social, economic, and ecological impacts emerge simultaneously and coexist. Therefore, we devised separate scales to measure each of these positive and negative impacts. As expected, the results provided empirical support for our hypothesis. For example, tourists appreciated tourism-induced positive economic impacts, such as employment generation, entrepreneurial opportunities, increased economic activities, and infrastructure development in Chitwan, but at the same time they were concerned about economic externalities, including price hike, increased cost of living, and higher taxes. The findings, therefore, reject the view that positive and negative impacts are mutually exclusive and subtractable with each other. Recognizing the coexistence of negative and positive impacts, the triple bottom line framework should be revised to include six spheres, that is, positive and negative social, economic, and ecological domains. Our study results, thus, challenge the current sustainable tourism paradigm and suggest that we review relevant tourism-related theories and models, such as the tourist area life cycle model (Butler 1980) that assumes that the negative impacts of tourism increase over time.

Methodologically, this study established measurement invariance prior to comparison of latent factor means in the MCFA framework. It is argued that the testing of measurement invariance should precede latent mean comparison as this process allows researchers to identify and retain invariant items in a measurement scale (Budruk 2010; Sass 2011; Vandenberg and Lance 2000). This process is particularly important in tourism research, which frequently involves comparison of groups from diverse backgrounds (Byrd et al. 2009; Murphy 1983). In addition, it is a common practice in tourism research to use the same measurement instrument with different populations (Thapa 2013). Researchers are required to ascertain that the construct has the same meaning (Budruk 2010) and it is measured in the same manner regardless of sample characteristics (Sass 2011 ). The non-invariance of items in our subscales indicates that it would be naïve to compare the latent means or summed score without establishing cross-group equivalence of measurement instruments. We believe that this paper helps improve the traditional research approach that, literally speaking, is analogous to comparing apples and oranges. More specifically, this study helps improve the methodological quality of tourism research by presenting a data analysis procedure that produces less biased results and consequently more reliable conclusions.

We acknowledge some limitations related to study methods. First, we grossly grouped the respondents into residents and tourists because the sample size was insufficient to classify the residents into different groups. Although the tourism literature grossly—without separating negative and positive impacts—indicates that residents directly involved in the tourism business may have more positive attitude toward tourism development than those who are completely devoid of tourism benefits (Andereck and Nyaupane 2011; Imran et al. 2014), further study with a larger sample size would be helpful to analyze the differences between various resident groups. Similarly, the tourists in the study areas come from different countries and cultures, making the group diverse. It is possible that the tourists may have different perceptions of tourism impacts depending on their cultural background. Further, it would be worthwhile to explore and compare the responses of secondary tourism stakeholders, which were not included in this study. Moreover, the surveys we used for data collections were prepared in English. We think the responses might have been affected by tourists’ level of English fluency.

Conclusions

This study confirms that there exists a reciprocal relationship between protected areas and tourism. We observed that the protected area management strategy affects tourism development, and sustainable development of tourism helps achieve biodiversity conservation goals in protected areas. In addition, the symbiotic relationship between tourism and protected area is beneficial to local people living in and around protected areas. Further, the synergetic relationship generates abundant economic benefits at the local, regional, and national levels. The results support that it is imperative to identify the stakeholders and examine their values, perceptions, and interests given that their roles shape the nature of tourism development in protected areas. We observed that there exist trade-offs among the stakeholders regarding the nature of tourism development. The responses of the residents concurred with the tourists regarding positive ecological and positive social impacts. However, the responses of the residents contradict with the tourists regarding negative ecological, negative social, positive economic, and negative economic impacts. The stakeholder theory is useful in examining how various stakeholder groups perceive impacts differently with regards to sustainable tourism. Destination management organizations and marketers tend to focus on satisfying tourists, whereas local residents focus on their livelihood and quality of life improvements, so having both tourists’ and local residents’ perspectives is crucial in tourism planning and management. We conclude that sustainable tourism could be a vehicle to achieve the seemingly contradictory goals of biodiversity conservation and local livelihoods, and in so doing it is imperative to seek meaningful participation of both tourists and local residents while designing and implementing management interventions. Further, the methodological approach used in this study contributes to the measurement of impacts and outcomes of sustainable tourism development.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplementary Material

The supplementary material is available at .

Author Biographies

Surya Poudel is a PhD Candidate in the School of Community Resources and Development at Arizona State University. Mr. Poudel’s research interests include human-environment interactions in tourism systems, common-pool resources, sustainable tourism, environmental education, and governance in protected areas.

Gyan P. Nyaupane is an associate professor and graduate program director in the School of Community Resources & Development at Arizona State University. He is also affiliated with the Julie Ann Wrigley Global Institute of Sustainability as a senior sustainably scientist. He teaches graduate and undergraduate-level courses on sustainable tourism, tourism planning, and community development. His overarching research theme includes the interactions between the environment, culture, and tourism, particularly in the arenas of nature-based/ecotourism/sustainable tourism and heritage tourism.

Megha Budruk is an Associate Professor in the Parks and Recreation Management Program, Arizona State University. Her research focuses on human relationships with natural settings, specifically the special bonds humans form with natural places, crowding and indicator-based social carrying capacity, beneficial outcomes of natural resource recreation, visitor impacts, experience and management at natural and cultural resource settings such as parks, protected areas, and cultural monuments.

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