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Author's personal copy Applied Research Quality Life (2016) 11:105–123 DOI 10.1007/s11482-014-9357-8

Residents’ Perceived Quality Of Life in a Cultural-Heritage Tourism Destination Myunghee Mindy Jeon & Myunghwa (Michelle) Kang & Edward Desmarais

Received: 1 August 2014 / Accepted: 20 August 2014 / Published online: 28 August 2014 # Springer Science+Business Media Dordrecht and The International Society for Quality-of-Life Studies (ISQOLS) 2014

Abstract Seasonal phenomena for a tourism destination may be key factors influencing residents’ perceived quality of life, in particular, during peak tourism seasons. Furthermore, these challenges may influence residents’ attitudes toward tourism support in the host community. Numerous studies have discussed the major impacts of tourism, such as economic benefits, social concerns, environmental sustainability, and their associations with residents’ attitudes toward tourism support in the host community. However, few studies have incorporated attributes of seasonal factors into variables regarding residents of the area, including dissatisfaction of living in the community, unsafe feeling due to rise in crime, frustration with traffic, and disruption of quality of life during peak tourism seasons. Therefore, this study attempts to investigate influences of seasonal attributes on residents’ perceptions of tourism impacts and, in turn, residents’ perceived quality life in a cultural-heritage tourism destination. Salem, Massachusetts, was selected for the study site, due to this city’s rich history and cultural heritages that draw tourists from around the world. For example, Salem attracts more than four times its population during the entire month of October, due to the wide range of tourism resources, such as month-long events (Haunted Happenings) in its correlation with the historical event of the witch trials in 1692. Data analysis supported all six hypotheses. Results confirmed seasonal factor attributes adversely affected residents’ perceptions of economic benefits; seasonal attributes positively affected residents’ perceived social costs; seasonal attributes inversely influenced residents’ perceptions of environment sustainability; perceptions of economic benefits positively impacted residents’ perceived quality of life; perceived social costs adversely affected residents’ M. M. Jeon (*) : E. Desmarais Salem State University, Bertolon School of Business, 352 Lafayette St, Salem, MA 01970, USA e-mail: [email protected] M. (M.) Kang University of Nebraska-Lincoln, Lincoln, NE 68583, USA

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perceived quality of life; and perceived environment sustainability positively affected residents’ perceived quality of life. Findings from this study could assist tourism decision-makers and planners when establishing local tourism planning and provide strategies to ensure residents’ quality of life year round. More specified managerial implications are discussed as well as limitations of this study and suggestions for future study. Keywords Seasonal factor . Perceived economic benefits . Environment sustainability . Perceived social costs . Perceived residents’ quality of life

Introduction Public policy-makers and community planners have considered tourism one of many significant contributors to a hosting community by increasing the community’s income, foreign exchanges, new local employment, tax revenue, and multiplier effects to the community’s economy (Ko and Stewart 2002; Yu et al. 2011a). Tourism decisionmakers in the host community attempt to increase residents’ quality of life and satisfaction (Yu et al. 2011a) by promoting tourism businesses and attempting to attract more tourists to the host community. Although tourism provides tourists with memorable vacation experiences (Nawijin 2011; Nawijn et al. 2010) and residents with economic benefits, tourism also brings multiple social concerns to the local community. Increased crowds and crime during the tourist season interrupt the residents’ quality of life. Increased property costs in the community, due to the influx of tourists, may displace residents to other communities. A number of studies indicate tourism has negative social impacts on the local culture and natural resources (Gursoy et al. 2002; McCool, and Martin 1994; Sirakaya et al. 2001). Residents’ concerns of negative social impacts on the local resources increase, particularly during peak tourism seasons. Many cultural and heritage tourism destinations are located in rural or suburban areas, usually quiet and peaceful residential settings during off-peak tourist seasons. During a peak tourist season or a special event in a host community, residents confront the quantity of tourists exceeds the destination’s infrastructure and resource capacities (Gursoy et al. 2002). Although residents appreciate benefits that tourism brings to the community, they confront challenges of living their daily lives, while competing with tourists for seasonally-created scarce resources. Seasonal phenomena in a tourism destination or attraction may be key factors that influence residents in the perception of their quality of life, in particular, during the peak tourist season. Furthermore, these challenges may influence residents’ attitudes toward tourism, which range from a continuum from pro-tourism to passive acceptance to anti-tourism. Therefore, all tourism stakeholders in a host community, whether the public or private sector, should monitor the negative influences on the community’s well-being over time and seek ways to decrease residents’ negative attitudes, while increasing residents’ satisfaction with the community (Sirgy et al. 2010). Numerous studies have discussed the major aspects of tourism, such as economic benefits, social concerns, environmental sustainability, and their impacts on residents’ attitudes or perceptions toward tourism in the host community (Ap 1992; Choi and Sirakaya 2005; Gursoy et al. 2002; Ko and Stewart 2002; Long et al. 1990; Murphy

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1985; Roehl 1999; Sirakaya-Turk et al. 2008a; Yu et al. 2011a; Yu et al. 2011b). In contrast, there is little research on the seasonal effects of residents’ quality of life in a tourism destination (Russo 2002). Hence, this study attempts to investigate how the seasonal factor, that brings tourist influx during the peak season, affects residents’ perceptions on major tourism aspects (economic benefits, social concerns, and environmental sustainability) and how these major tourism impacts influence residents’ perceived quality life in a cultural heritage tourism destination. It is expected findings from this study could provide tourism policy-makers and community planners with good strategies to operate and maintain a healthy and sustainable tourism environment; thus, benefiting all stakeholders in tourism, such as residents, visitors, and tourism enterprises in the community.

Literature Review Sustainable Tourism and the SUSTAS Tourism development should be implemented in line with sustainability of tourism strategies and equally distributed for social, environmental, and economic benefits to all the community’s stakeholders (Li and Wan 2013; Nunkoo and Gursoy 2012). On the other hand, tourism development should be able to take into account residents’ perceived quality of life in the host community (Gursoy et al. 2002; McCool and Martin 1994; Sirgy et al. 2010)). When planning a new tourism development or renovating an existing tourism project, local governments and tourism-related agencies should avoid or mitigate factors that may disrupt residents’ perceived quality of life, especially during peak tourism seasons, when combined resident and tourist demands exceed carrying capacity of the infrastructure and local resources. Ultimately, any approaches to tourism development should be taken with a thoughtful consideration of sustainability in the tourism destination (Sirakaya-Turk et al. 2008b). Sustainability of tourism development has been actively discussed among numerous researchers (Hunter 1997; McIntyre 1993; Sharpley 2000; Sirakaya-Turk et al. 2008a; Russo 2002; Yu et al. 2011b). Sustainable tourism is considered an alternative form of tourism that contributes to improving or maintaining visitors’ quality of experiences, residents’ quality of life, as well as protecting the environment (McIntyre 1993). Therefore, maintaining a healthy, sustainable tourism environment is crucial to ensure visitors’ satisfaction and residents’ quality of life in the host community (Yu et al. 2011b). In an effort to measure residents’ attitudes toward sustainable tourism, Choi and Sirakaya (2005) proposed a sustainable tourism attitude scale (SUSTAS), consisting of 44 items initially. The SUSTAS was tested in cross-cultural settings with a new sample and the scale’s number of items was reduced to 33 items. It was further reduced to 27 resident attitude measures with a study on a rural tourism site in the U.S. that minimally compromises the psychometric properties of the original SUSTAS with results supporting the construct validity and internal consistency of the shortened version of the SUSTAS (Sirakaya-Turk et al. 2008a). Since 2005, numerous studies have applied SUSTAS in different research contexts (i.e., Hung et al. 2011; Kvasovan 2011; Sirakaya-Turk 2007; Sirakaya-Turk and Gursoy 2013; Sirakaya-Turk et al. 2008b).

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Researchers (Yu et al. 2011b) further refined the instrument and dimensions to adapt the individual constructs and showed similar factors are valid for measuring the scales. The SUSTAS comprises of seven factors, including (1) perceived social costs, (2) environmental sustainability, (3) long-term planning, (4) perceived economic benefits, (5) community-centered economy, (6) ensuring visitor satisfaction, and (7) maximizing community participation. In this study, three constructs (perceived economic benefits, perceived social costs, and environmental sustainability) were selected and adopted into the survey instrument to measure residents’ perceptions on tourism impacts of the host community. Attributes of Seasonal Factor Many tourism destinations are facing seasonal fluctuations of tourists (Gursoy et al. 2002). Depending upon the major characteristics of tourism in the host community, particular season (s) may draw more tourism compared to other seasons. Seasonal tourists influx in certain locations, such as a climate zone (i.e., beach resorts, ski resorts), sporting events (i.e., Olympics, sports championship games, NASCAR), festivals (i.e., Mardi Gras, New Year’s Eve), cultural or historically-related events (i.e., Haunted Happenings, Civil War re-enactment), may significantly affect residents’ lives, when living in such tourism destinations. Specifically, an extreme influx of tourists in a tourism site may cause seasonal attributes that influence the satisfaction levels of residents in the host community because seasonal influx leads to increased population, increased crime (Gursoy et al. 2002; McCool and Martin 1994), displacement of residents by new tourism developments, and potential damage of tourism’s cultural and natural resources. Rapid, unplanned tourism development that leads to an influx of a large number of tourists to a community causes adverse impacts on cultural and natural resources (Sirakaya et al. 2001). Therefore, whether it is the public or private sector, tourism planners should monitor community well-being over time to ensure the level of residents’ satisfaction with the community remains high (Sirgy et al. 2010). Tourism that minimizes the negative impacts of tourist’s influx sustains the host community’s environment and positively influences residents’ quality of life. Seasonal influx of tourists during peak seasons can cause a sudden increase in pollution, litter, noise, traffic congestion, and potential crimes, and is harmful to the environment and wildlife. In this study, seasonal attributes have been driven by results of seasonal influx of tourists that directly affect residents’ perceptions of local tourism, including dissatisfaction of living in the community, unsafe feeling due to crime increase, frustration with traffic, and disruption of quality of life during the peak season. Perceived Residents’ Quality of Life Numerous studies have examined impacts of tourism on residents’ quality of life within the relationship of tourism in the host community (Gilbert and Abdullah 2004; Nawijin 2011; Sirgy et al. 2011) because tourism affects residents’ perceptions of well-being as well as quality tourism experiences of tourists within a host community (Andereck and Jurowski 2006). Previously, the quality of life was commonly measured by economic measurements using economic wealth that impacts residents’ quality of life in a

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community (Andereck and Jurowski 2006). However, the value of economic growth is questionable, when it results in a lower quality of life where ensuing costs exceed the benefits. The economic growth from tourism does not necessarily have always a positive impact on residents’ well-being. Costs of tourism’s economic benefits for residents’ quality of life include loss of cultural identity and historical resources, as well as environmental degradation, crowds, noise, litter, traffic and parking problems, water pollution, increased crime, increased cost of living, friction between residents and tourists, and changes in residents’ ways of life (Bastias-Perex and Var 1995; McCool and Martin 1994; Ross 1992). Recently, residents’ quality of life has been increasingly recognized as the perception of happiness of well-being with their life domains, such as economic, social, environmental, consumer, and health domains that are measured by objective and subjective methods (Uysal et al. 2012). Subjective well-being refers to residents’ overall sense of well-being measured by a variety of concepts, including life satisfaction, perceived quality of life, life domain satisfaction (i.e. satisfaction in leisure life, social life, family life, work life, etc.), positive/negative affect, and overall happiness, while objective well-being refers to the actual circumstances related to residents’ economic, social, environmental, and health well-being (Sirgy et al. 2010; Uysal et al. 2012). Therefore, residents’ quality of life should be conceptualized with an aggregation of residents’ perception of economic, social, and environmental conditions as well as comprehensive perception of well-being in the host community, embracing residents’ subjective well-being and objective well-being. In an attempt to measure community residents’ perceived quality of life at large, Sirgy et al. (2010) developed a community well-being composite index, based upon impact of community services and conditions in various life domains. To measure individuals’ responses to life experiences, subject well-being (SWB) measures have been utilized widely (Nawijn et al. 2012). Previous studies (i.e., Gursoy et al. 2002; Roehl 1999; Yu et al. 2011a) examined relationships between tourism’s benefits and residents’ quality of life in a tourism destination, as well as tourism’s costs and residents’ quality of life. Consequently, residents’ positive attitudes toward tourism showed a significant influence on tourism development policy. Since residents’ attitudes toward tourism are critical for leading to successful tourism in a host community (Li and Wan 2013; Nunkoo and Gursoy 2012), it is important to have a tool to measure attitudes toward tourism development (Andereck and Jurowski 2006; Yu et al. 2011a). Some researchers recognized residents are major stakeholders in the tourism development process because residents are usually directly affected by tourism (Ap 1992; Gunn 1994; Murphy 1985). On the other hand, detrimental impacts on residents’ perceived quality of life may lead to a lack of support for tourism. Therefore, the importance of maintaining a high quality of life in the host community is essential to positively increase residents’ attitudes toward tourism, which, in turn, leads to sustaining economic and social contributions to the community. Perceived Economic Benefits Tourism is considered an easy means to make a community a better place to live (Choi and Sirakaya 2005) and a major industry that contributes to creation of local employment, increased tax revenues, among other various economic development

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opportunities (Gursoy et al. 2002; Jurowski et al. 1997; Perdue et al. 1990; Uysal et al. 2012; Yu et al. 2011a). Also, tourism is perceived as one of the primary industries that can assist local communities in developing economic diversity (Aref 2010). Tourism scholars also have recognized public policy and government expenditures have focused on creating a tourism economy with a goal of enhancing the residents’ quality of life in the hosting community (Uysal et al. 2012). Studies in the past found perceived economic benefits, such as job opportunity growth, had a positive correlation with residents’ quality of life (Roehl 1999). Perceived Social Costs Tourism brings the host community both positive and negative impacts (Gilbert and Abdullah 2004; Nawijin 2011; Sirgy et al. 2011). Roehl (1999) suggested not all residents perceive tourism’s impacts in similar ways. Those who directly benefit from tourism through employment are more likely to support it. Tourism creates community issues, such as inconvenience to residents, competition with tourists for limited resources, exceeding the infrastructure’s carrying capacity (i.e., traffic congestion, lack of parking space), and increased crime. The issues of increased crime and traffic congestions are the most frequently examined negative impacts of tourism on the host community. Residents tend to perceive traffic congestion as a major problem created by tourism activities (Jurowski et al. 1997; Long et al. 1990). It has been identified a positive relationship exists between the demographic characteristics of residents and their perceived quality of life (i.e., Perdue et al. 1999; Roehl 1999). Roehl found perceived social costs are negatively correlated with residents’ quality of life. Perceived Environment Sustainability The sustainable tourism perspective has been widely embraced as an alternative to conventional mass tourism that causes various negative impacts in tourism host communities (Sirakaya-Turk et al. 2008a). Previous research focused on two facets of environmental sustainability. (1) An eco-centric perspective focuses on allocating resources to protect and preserve the environment (Jurowski et al. 1997), and (2) a sustainable perspective focuses on minimizing negative impacts on the tourism destinations, while generating benefits for local residents and visitors (Sirakaya-Turk et al. 2008b; Yu et al. 2011a). Tourism contributes to environment sustainability when it brings communities both the improvement of local infrastructure, and the protection of the environment and wildlife (Liu and Var 1986). Conversely, tourism’s inadequate planning and management negatively impact a community (Choi and Sirakaya 2005). Environment sustainability is at risk during peak tourism seasons when tourist influx exceeds the carrying capacity of the host community. Therefore, understanding residents’ attitudes towards the environment is particularly important for the sustainability of protected areas and hosting communities (Nicholas et al. 2009). This study assumes seasonal attributes will negatively affect residents’ perceived economic benefits, as well as their perception of environment sustainability. In contrast, the seasonal factor is assumed to have a positive association with residents’ perceived

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social costs in the host community during the peak season. Figure 1 illustrates the study framework. Hence, it is posited: H1: H2: H3: H4: H5: H6:

Seasonal factors affect residents’ perceptions on economic benefits. Seasonal factors affect residents’ perceptions on social costs. Seasonal factors affect residents’ perceptions on environmental sustainability. Perceived economic benefits positively affect residents’ quality of life. Perceived social costs inversely affect residents’ quality of life. Perceived environmental sustainability positively affects resident quality of life.

Methodology Study Site Salem, Massachusetts is an historic seaside community located approximately 16 miles north of Boston. Its land area is approximately 8 square miles, a population of 40,407, and dominated by the Caucasian race (87 %), according to the U.S. 2000 census in (USDC 2014). Salem is a historic tourism destination, registered as an Essex National Heritage Area (NTHR 2005), where the scenic landscapes begin and continue up the Atlantic coastline to Maine. It presents rich historical resources, including the Salem Maritime National Historic Site and Pioneer village (America's oldest living history museum), Salem Wax Museum, Pirate Museum, Salem Witch Village, and more (Destination Salem 2014; National Parks Service 2014). Salem’s historical legacy began as one of the earliest landing sites for the English colonists and is recognized as the first major port in the United States. A well-known blemish on this legacy involves the Salem Witchcraft Trials of 1692 (Mass.gov 2014; NTHR 2005). Ironically, this infamous legacy has become the major attraction of tourism that brings tax revenues to the city. According to the U.S. Department of Commerce, revenues generated in this tourism-related industry (retail sales, accommodations, and food services) has been nearly a doubled amount ($642.7 millions) of the other industries in Salem, such as manufacturers, shipping, and merchant wholesalers ($324 millions) in 2007 (USDC 2014).

Economic Benefits

Seasonal Factor

Social Costs

Environment Sustainability

Fig. 1 The seasonal attributes (factor), major tourism aspects, and perceived quality of life

Quality of Life

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Salem is also rich in literary and cultural heritage, known as a home of Nathaniel Hawthorne, author of The Scarlet Letter and The House of the Seven Gables. Open to the public, the House of the Seven Gables Settlement site includes the famous mansion and Hawthorne’s birthplace. Peabody Essex Museum houses art collections from around the world. In the mid-nineteenth century, Salem evolved into an important manufacturing and retail center. Irish and French Canadian immigrants poured into Salem to work in its new leather and shoe factories. Then, Italian and Eastern European immigrants began arriving in the early 1900s to take advantage of Salem’s prosperity (Destination Salem 2014). With its historic legacy, modern Salem thrives on tourism and the hospitality industry. Today, the city is considered as the educational, medical, legal, cultural, and banking hub of the North Shore of Boston, MA (MA State Guide 2013). Characterized as a comfortable suburb, unique in its architectural blend of different time periods (MA State Guide 2013), Salem consistently attracts more than 500,000 visitors annually (NBCVB 2014) from around the world. Visitors to Salem range from school educational tour groups, Senior groups in coach tour, family, couples, to ghost tourists. Due to its historical background correlated with the witch trials, the city has been the site of the Haunted Happenings during the entire month of October and Halloween week that draw almost 200,000 visitors during the month of October (NBCVB 2014). Compared to its population of approximately 40,000, tourist influx during the month of October could cause residents a significant lack of resources in their daily lives. As a good example, Salem hotels and inns usually sell out 9 to 12 months in advance (Salem News 2010). Salem provides very limited accommodation capacity for visitors during the month of October with only two local hotels (Hawthorne Hotel has 93 rooms, and the Waterfront Hotel has 86 rooms) and seven Inns/Bed and Breakfasts, including Salem Inn with 42 rooms. Additional accommodations include a camp ground in the Salem Willow Park and hotels/Inns in neighboring towns, such as Peabody, Danvers, and Woburn (MA State Guide 2013; NBCVB 2014) located within a 5–10-mile radius from downtown Salem. The month of October is a very special time for Salem residents. Residents are well aware of the benefits of tourism revenues to the local community because the city heavily depends on the tourism industry. Unlike other tourist destinations, where a special event or a festival hosted for a short time period from a day or two to several days, such as NY Marathon, Boston Marathon, and Mardi Gras, the Salem holds month-long serial events continuously to attract tourists for the entire month until the Halloween Day finale. During these continuous events in October, there are frequent roads blocks to accommodate pedestrians attending the parades. Residents should plan ahead when driving to avoid limited traffic for parades. If they are not vigilant of the road situations on a daily basis during October, drivers will get stuck in traffic for many hours until the parades are over and roads are cleared. These phenomena intrigued the researchers. Various questions came to mind, such as how do residents feel about crowding and traffic challenges; are residents satisfied with living in Salem; do they want to stay away from Salem during October; and, finally, what are the residents’ perceptions on their quality of life during October? Therefore, the seasonal factor, which draws an extreme tourist influx during the month of October to Salem, is the center for this study. This is a unique opportunity to examine relationships with residents’ perceptions on major tourism impacts (benefits,

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costs, environment sustainability) and residents’ perceived quality of life in Salem, MA. Measurement Instrument The instrument consists of two sections. The first section contains questions to measure the seasonal factor, residents’ perceived tourism impacts, and residents’ perceived quality of life. The second section consists of socio-demographic-related questions, including gender, age, ethnic origin, education level, occupation, household income, average length of residency in the community, etc. The first section was constructed using a total of 27 items. Nineteen items under three constructs (seven items of perceived economic benefits, six items of perceived social costs, and six items of environmental sustainability) measured three major constructs of tourism impacts, and four items for the resident quality of life construct. Particularly, four items of attributes of seasonal factor attributes were newly constructed for this study to measure their influence on the seasonal factor on residents’ perceptions of tourism impacts. Four of the 27 items were removed, due to low factor loading (less than .5) or cross loading as a result of CFA. A total of 23 items were retained for further testing. More specifically, four items were retained for attributes of seasonal factors, seven items for perceived economic benefits, four items for perceived environmental sustainability, four items for perceived social costs, and another four items for perceived residents’ quality of life. Especially, two items of quality of life construct were adopted from Wilkin’s (2006) study and two items of quality of life (QOL3 & QOL4) were newly added for this study (see Table 1). A previous study developed a shorten version of the SUS-TAS scale (see Yu et al. 2011a) and used only two questions to measure quality of life adopted from another study (see Wilkin 2006). The factor loading was low, due to the small number of questions. To mitigate this issue, this study added two questions in the questionnaire to secure a stable factor loading to measure overall residents’ quality of life, based upon the suggestion “the separate factor structures of SUS-TAS allow for adopting and adjusting separate parts according to one’s purpose” (Sirakaya-Turk et al. 2008a, p. 420). Findings from the aforementioned study (Yu et al. 2011a) confirmed construct validity of seven dimensions of SUS-TAS, their liability, and their discriminant validity by testing Confirmative Factor Analysis (CFA), Cronbach’s Alpha (α), and the Average Variances Extracted (AVEs). Data Collection A pilot test was conducted using 32 respondents living in the study site city and its neighboring towns considered affected by visitors of the study site. Based on pilot data analysis comments made by pilot respondents, researchers modified and updated the survey. The study’s target population was local residents of the city of Salem and its four adjacent towns—Peabody, Danvers, Beverly, and Marblehead, Massachusetts. From January 11th through 25th, 2013, printed survey questionnaires were hand-delivered to 750 potential respondents using a convenience sampling method. The leading researcher and three graduate/undergraduate students usually met people downtown in cafés,

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Table 1 Description of measurement variables Construct/Item Seasonal attributes (Seasonal factor)

Loading .88 (composite reliability)

S/F 1

During the peak tourist season (e.g. Haunted Happenings / Halloween), I am dissatisfied with living in my community.

0.81

S/F 2

During the peak season (e.g. Haunted Happenings / Halloween), I feel unsafe (e.g. due to crime).

0.65

S/F 3

During the peak season (e.g. Haunted Happenings / Halloween), I am frustrated with traffic.

0.87

S/F 4

During the peak season (e.g. Haunted Happenings / Halloween), tourists disrupt my quality of life.

0.88

Perceived economic benefits

.90

PEB 1 I believe tourism brings important economic benefits to the residents of the community.

0.78

PEB 2 I think tourism creates employment opportunities for residents in the community.

0.83

PEB 3 I like tourism because it brings new income to my community.

0.82

PEB 4 I believe local businesses benefit the most from tourists.

0.56

PEB 5 I think tourism brings more investment to the community’s economy.

0.81

PEB 6 I believe tourism generates tax revenues for local governments.

0.73

PEB 7 I believe tourism helps improve the economic situation for many residents in this community.

0.72

Perceived social costs

.83

0.55 PSC 1 During the peak tourism season, I find it harder to get tickets for the theater, movies, cultural events, or other venues and events. PSC 2 Tourism results in limited parking space for local residents.

0.85

PSC 4 Tourism results in unpleasantly overcrowded restaurants for local residents.

0.88

PSC 5 My community’s recreational resources are overused by tourists.

0.68

Perceived environment sustainability

.83

PES 1

Diversity of cultural heritage is valued and protected in my community.

0.64

PES 2

Tourism development in my community always protects cultural-heritage resources.

0.80

PES 3

My community’s cultural heritage is being protected now and for the future.

0.83

PES 4

Tourism in my community is developed in harmony with the natural environment and cultural-heritage resources.

0.71

Quality of life

.82

QOL 1 Overall, I am satisfied with living in my community.

0.67

QOL 2 Overall, I am satisfied with the cleanliness in my community.

0.82

QOL 3 Overall, I feel safe from crimes in my community.

0.80

QOL 4 Overall, I can deal with the traffic situation in my community.

0.61

retail shops, grocery stores, churches, city council meetings opened to public, and on the streets to explain the purpose of the study and distribute the questionnaire to

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residents of Salem and its four neighboring towns. A screening question was asked to validate the dwelling town of the potential respondents. Some respondents showed a keen interest in this study and offered help to distribute questionnaires to their friends and acquaintances, who lived in the target areas. The survey included self-administering instructions and a self-addressed, stamped envelope for return. A total of 376 respondents returned their completed surveys, which yielded a 50.13 % response rate. Using face-to-face delivery with explanation of the research yielded a considerable response rate. Among 376 replies, 29 incomplete surveys were removed, resulting in a total of 347 useful surveys for the sample population. Responses arriving after the end of January 2013 were not included in the data analysis. Data Analysis Descriptive analysis, reliability, and an exploratory factor analysis (EFA) were conducted using the SPSS version 20. Before the proposed model test, internal consistency and uni-dimensionality were checked by examining loading values, composite reliability, and convergent and discriminant validity. Then, a confirmatory factor analysis (CFA) was conducted to test the goodness-of-fit of the measurement model. Further assessment with the goodness-of-fit indices to evaluate a model fit using χ2, the normed fit index (NFI), the comparative fit index (CFI) (Raykov et al. 1991), and the root mean square error of approximation (RMSEA) were also utilized (Hair et al. 1998). To test the six hypothetical relationships, the structural equation modeling (SEM) method was adopted, using LISREL 8.8 to obtain maximum likelihood estimates of the parameters based upon the covariance matrix.

Results and Discussion Descriptive Statistics Table 2 displays the descriptive statistics of the respondents. Seventy-two percent of the respondents currently reside or previously resided either in or outside downtown Salem, while the remaining 27 % of the respondents resided in the four adjacent towns. There were more Salem-related participants due to the convenience sampling that targeted mainly the residents in Salem. Fifty-eight percent of the respondents were older than 41, while 2 % of the respondents were 18 and 21 years old. Nearly 61 % of respondents have lived in the area longer than 10 years, while 39 % lived shorter than 10 years. Consistent with the demographic profile of the community reported by the USDC (2014) 85 % Caucasian, the largest ethnic group reported in the survey was Caucasian (83.6 %). Around 16 % of the respondents appeared to be involved in the hospitality/ tourism business. Nearly 31.5 % or respondents were involved in tourism/hospitality industry and retail businesses for tourists and residents including souvenir shops and convenient stores (Table 2).

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Table 2 Respondents’ profile Item

Description

Respondents

Percent (%)

Gender (n=340)

Male

137

40.3

Female

203

59.7

Age (n=342)

Up to 20

6

1.8

between 21 – 30

97

28.4

between 31 – 40

38

11.1

between 41 – 50

53

15.5

between 51 – 60

61

17.8

between 61 – 70

41

12.0

Length of residence (n=347)

Ethnicity (n=330)

Highest level of education (n=327)

Current occupation (n=330)

Hospitality related job (n=303)

older than 70

46

13.5

Shorter than 1 year

15

4.3

Longer than 1 year to 5 years

65

18.7

Longer than 5 years – 10 years

53

15.3

Longer than 10 years – 20 years

79

22.8

Longer than 20 years – 30 years

60

17.3

Longer than 30 years – 40 years

18

5.2

Longer than 40 years – 50 years

17

4.9

Longer than 50 years

40

11.5

Caucasian

276

83.6

Asian–American

18

5.5

Hispanic–American

16

4.8

African–American

9

2.7

Others

11

3.3

Up to junior high school graduated

4

1.2

High school diploma

78

23.9

Associate degree

47

14.4

Bachelor degree

139

42.5

Master’s degree

47

14.4

Doctoral degree

12

3.7

Manager/Executive

36

10.9

Clerical

12

3.6

Sales/Marketing

19

5.8

Military

2

.6

professional/Technical

51

15.5

Farming/Fishing

1

.3

Homemaker

8

2.4

Owner/Self-employed

32

9.7

Retired

57

17.3

Student

51

15.5

Teacher/Educator

9

2.7

Other

52

15.8

Yes

49

16.2

No

254

83.8

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Table 2 (continued) Item

Description

Respondents

Percent (%)

Industry involved (n=322)

Tourism/Hospitality

32

14.4

Retail (for tourists/residents)

38

17.1

Other

152

68.5

Measurement Model The measurement model exhibited a good model fit (χ2 (220)=550.36, p=0.000; RMSEA=0.066; NFI=0.94; and CFI=0.97). An adequate internal consistency and uni-dimensionality were indicated by measures such as loading values and composite reliability, convergent, and discriminant validity. The internal consistency of the measurement model was verified with reliabilities ranging from 0.83 to 0.90. As shown in Table 3, the AVE for the five constructs exceeded 0.50. The squared multiple correlation (SMC), loading values (all items were higher than .56), and t-values (all items were higher than 3.27 and 1 item was higher than 1.67) indicated convergent validity of the measurement model. Discriminant validity was also achieved. All AVEs from the five constructs ranged from 0.54 to 0.66 and exceeded the largest squared correlation (0.50) (Fornell and Larcker 1981). With identification of the adequacy of measurement for the five latent constructs in the structural model, this study proceeded with confidence to the structural equation model analysis. Table 3 exhibits the correlation matrix of construct, squared multiple correlations (SMC), and measurement properties. Overall Fit of the Hypothesized Structural Model The results from the estimation of the research model indicated model fit indices of χ2 (224) =578.85 at p=0.000. However, the normalized Chi-squared statistic (χ2/df) was 2.58, which indicated an adequate fit (Gefen and Straub 2000). The other model fit indices indicated RMSEA=0.068, NFI=0.94, and CFI=0.96). The values of NFI and CFI higher than 0.90 are considered as good fit, and RMSEA is close to the cut-off value .06 (Hu and Bentler 1999) and is considered an acceptable model fit or upper limit of 0.07 (Steiger 2007). The squared multiple correlations (SMC) assessed the extent to which the model explains the variance in the data set. The model explained fairly well all variables perceived economic benefit (SMC=0.83), perceived social costs (SMC=0.49), perceived environmental sustainability (SMC=0.90), and perceived resident quality of life (SMC=0.77). Parameter Estimates Analysis The summary of the parameter estimates and significance tests is displayed in Fig. 2. The seasonal factor variable inversely influenced residents’ perceptions of economic benefits (β=−.41, t=− 6.86) and perceived environmental sustainability (β=−.31, t=− 4.93), while showing a positive effect on residents’ perceptions of social costs (β=.71,

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Table 3 Correlations, squared multiple correlations, and measurement properties (n=347) Measurement properties and correlations between latent constructs (SMC) Measures

S/F

S/F

1.00

PEB

−.39** (.15)

PEB

PSC

PES

QOL

AVE 0.66

1.00

0.57

PSC

.71** (.50)

−.34**(.12)

1.00

PES

−.30**(.09)

.42** (.18)

−.20* (.04)

1.00

0.57

QOL

−.38**(.14)

.37** (.14)

−.29**(.08)

.41**(.17)

0.56 1.00

Mean

4.82

4.24

3.06

3.50

3.88

SD

0.80

0.18

0.65

0.14

0.28

Composite reliability

0.88

0.90

0.83

0.83

0.82

0.54

Model fit (χ2 =550.36, d/f=220, p=0.000, RMSEA=0.066, NFI=0.94, CFI=0.97) **t-value: if t>3.291, significant at p1.64, significant at p