Tourism Impact and Stakeholders’ Quality of Life

Abstract

This article reports a study testing the hypothesis that, compared with community residents who are not affiliated with the tourism industry, residents affiliated with tourism are likely to perceive tourism impact more positively, and the more positive their perceptions of tourism development, the more likely they feel satisfied with their lives. The study involved a survey of community residents of four tourist destinations in the United States. A total of 407 responses were used for data analysis. The results provided support for the notion that the influence of community residents’ perceptions of tourism impact and their life satisfaction is dependent on whether the residents are affiliated or not affiliated with the tourism sector.

Keywords

quality of life life satisfaction material well-being nonmaterial well-being impact on stakeholders’ quality of life

The tourism industry is one of the world’s largest industries, having long experienced an almost constant and rapid annual increase in revenues and employment (Pırnar & Günlü, 2012). With such growth, competition has become very fierce, and only tourism destinations that apply creative and effective management and marketing strategies prosper. As such, marketers of tourism destination sites have a vested interest in highly effective marketing and management strategies. Much research has focused on the impact of tourism development as an important area of research with significant marketing implications. A focal point in this research stream is the impact of tourism development on the quality of life of community residents (e.g., Ap & Crompton, 1998; Choi & Sirakaya, 2005; Delamere, 2001; Gursoy, Jurowski, & Uysal, 2002; Lankford & Howard, 1994; Madrigal, 1993; Uysal, Woo, & Singal, 2012). One has to recognize that the quality of life of residents in any community that is a tourist destination is significantly influenced by the influx of tourism in that community. Depending on the nature of the impact, residents’ support can change—positive impact induces residents to provide support for politicians and public policy initiatives reinforcing tourism development, whereas negative impact may cause residents to withdraw their support. Residents’ support of tourism in their community is essential for tourism development, competitiveness, and sustainability (Gursoy et al., 2002; Nunkoo & Ramkissoon, 2010; Woosnam, 2012).

Early research on tourism impacts on communities has focused on identifying perceptions of impact of tourism development (e.g., Ap & Crompton, 1998; Choi & Sirakaya, 2005; Delamere, 2001; Draper, Woosnam, & Norman, 2011; Gursoy et al., 2002; Lankford & Howard, 1994; Madrigal, 1993; Nunkoo, Smith, & Ramkissoon, 2013; Sharpley, 2014; Woosnam, 2012). Specifically, community residents’ perceptions of impact involve economic, social, cultural, political, and environmental outcomes of tourism development, which play a significant role in residents’ overall opinion about the living conditions of the community. Residents’ perceptions of tourism impact influence not only residents’ attitude toward tourism but also their overall life satisfaction (e.g., Andereck & Jurowski, 2006; Khizindar, 2012; Lipovčan, Brajša-Žganec, & Poljanec-Borić, 2014; Nawijn & Mitas, 2012; Perdue, Long, & Kang, 1999; Uysal, Woo, & Singal, 2012). In that vein, there seems to be limited research on making the distinction between residents affiliated with the tourism industry and those not affiliated with the industry. A study by King, Pizam, and Milman (1993) found that residents of communities dependent on tourism can clearly differentiate between its economic benefits and the social costs. The study further pointed out that awareness of certain negative consequences may not lead to opposition toward further tourism development. One would assume that perceptions of tourism impact are likely to be more positive for residents affiliated with the tourism industry from those not affiliated with the industry (King, Pizam, & Milman, 1993; Kuvan & Akan, 2012; Sirakaya, Teye, & Sonmez, 2001; Teye, Sonmez, & Sirakaya, 2002). Residents affiliated with the tourism industry may include members of tourism-related associations and councils, convention and visitors bureaus, employees in hotels and restaurants, and business leaders who benefit from tourists as customers (Freeman, 1984; Yoon, 2002). Residents not affiliated with the tourist industry are essentially the remaining residents, namely residents who are not employed in the tourism industry and believe that they do not benefit directly from the tourism trade. Therefore, the goal of this study is to examine differences in perceived impact of tourism development between residents affiliated with the tourism sector and those not affiliated. Our expectation is that residents affiliated with the tourism industry are likely to perceive the impact of tourism development more favorably in terms of quality of life than those who are not affiliated with the industry.

Literature Review

Tourism researchers have examined issues of quality of life in tourism from two different perspectives: (1) quality of life of residents at a tourism destination and (2) quality of life of tourists (Uysal, Perdue, & Sirgy, 2012). This present study is related to quality-of-life issues related to residents at a tourism destination. Residents’ quality of life has been measured using subjective and objective indicators. Most of the past research in tourism have used subjective indicators (e.g., Allen, Hafer, Long, & Perdue, 1993; Allen, Long, Perdue, & Kieselbach, 1988; Andereck & Nyaupane, 2011; Andereck & Vogt, 2000; Bachleitner & Zins, 1999; Carmichael, Peppard, & Boudreau, 1996; Kim, 2002; Lankford, 1994; Nichols, Stitt, & Giacopassi, 2002; Perdue et al., 1999; Roehl, 1999). However, several studies used objectives indicators (e.g., Meng, Li, & Uysal, 2010; Perdue & Gustke, 1991). Examples of objective quality-of-life indicators include income, physical health, standard of living, and crime; whereas a prime example of subjective indicators is life satisfaction. The strength of objective indicators is that they can usually be defined and quantified without relying on individual perceptions. However, the greatest limitation of objective indicators is that they may not accurately reflect people’s experience of well-being (e.g., Andrews & Withey, 1976). Subjective quality-of-life indicators measure satisfaction that individuals experience in their lives (e.g., Andereck & Jurowski, 2006; Diener & Suh, 1997; Phillips, 2006). Given that our study focuses on residents’ perceptions, subjective indicators of quality of life were used.

Allen et al. (1988), for example, explored changes in resident perception of seven dimensions (public services, economics, environment, medical services, citizen involvement, formal education, and recreation services) of community life across 20 communities varying in their degree of tourism development (measured using percentage of retail sales derived from tourism). The results support the tourism development cycle theory (Allen et al., 1988). That is, lower-to-moderate levels of tourism development community residents tend to perceive tourism as beneficial. But as development continues residents’ perceptions of community life become increasingly unfavorable. Another example is the study conducted by Perdue et al. (1999) that explored the impact of gaming tourism on residents’ quality of life across different stages of tourism development. These authors collected data from five different communities: a nongaming community, three early-stage gaming communities, and a late-stage gaming community. The results provided support for social disruption theory in that residents’ quality of life initially declined with greater tourism development but then improved in the later stages when residents became more adapted to the new situation (Perdue et al., 1999). Similarly, Roehl (1999) examined the relationship between resident characteristics, their perception of the impact of gaming and their perceptions of quality of life. The results indicated that perceived social costs of tourism are associated with low quality of life, whereas perceived job growth is associated with high quality of life. That is, quality of life of community residents was high when the residents perceived the economic benefits of casino tourism to the community as high with minimum social costs. Conversely, residents’ quality of life was low when residents perceived the social costs of casino tourism to be high with minimum economic benefits. Other studies still followed. Andereck and Nyaupane (2011) investigated the relationship between residents’ perception of the role of tourism and quality of life. The results showed that the economic benefits of tourism development on community quality of life are mediated by residents’ perceptions of tourism benefits. Kim, Uysal, and Sirgy (2012) also reported a study that established the relationship between community residents’ perception of tourism impact and residents’ perceptions of quality of life. Specifically, the study provided support to the notion that community residents’ perceptions of tourism impact (economic, social, cultural, and environmental) play a significant role in predicting residents’ satisfaction in particular life domains (material well-being, community well-being, emotional well-being, and health and safety) and overall life.

The strength of these relationships with respect to life domains may differ between residents affiliated with the tourism industry and residents who are not affiliated with tourism (Allen & Gibson, 1987; Byrd, Bosley, & Dronberger, 2009; Jurowski & Brown, 2001; Lankford, 1994; Sirakaya et al., 2001; Sirakaya, Ingram, & Harrill, 2008; Teye, Sonmez, & Sirakaya, 2002; Thomason, Crompton, & Kamp, 1979; Weiermair & Peters, 2012). Lankford (1994), for example, examined the impact of tourism development on business owners, paid government officials, elected, appointed officials, and residents of the Columbia River Gorge region of Oregon and Washington. He found that residents were more negative about tourism (or rather more cautious about the benefits of tourism) than were government employees, elected/appointed leaders, or business owners. Business owners, elected/appointed leaders, and government employees seem to be in agreement concerning the support for tourism developed in the Columbia River Gorge. A study with mixed results reported by Teye, Sonnmez, and Sirakaya (2002) compared attitudes of residents in the two communities to identify similarities and differences. The results indicated that residents’ expectations from tourism development were not met and also residents who are working in related businesses were more negative about the tourism industry.

Given the overall pattern we conclude that most of the past studies show that compared with residents who are not affiliated with the tourism industry, residents affiliated with tourism are likely to perceive tourism impact more positively, and the more positive their perceptions of tourism development, the more likely they feel satisfied with their lives (e.g., Allen & Gibson, 1987; Byrd et al., 2009; Jurowski & Brown, 2001; Lankford, 1994; Thomason et al., 1979; Weiermair & Peters, 2012). Although the link between affiliation with tourism and support for tourism development has been well supported, the link with life satisfaction needs further testing and empirical support. This latter hypothesis essentially motivated the study reported in this article.

Conceptual Development

The primary goal of this study was to determine differences between two groups of residents (those affiliated with the tourism industry and those not affiliated) in their perception of tourism impact on their quality of life. Specifically, residents’ satisfaction with life in general derives from their satisfaction with particular life domains such as satisfaction with material life, satisfaction with leisure life, satisfaction with family life, and so on; and their satisfaction with particular life domains, in turn, is also affected by their perception of tourism impact on the community at large. This assertion is supported by much research in quality-of-life studies using bottom-up spillover theory (e.g., Diener, 1984; Diener, Suh, Lucas, & Smith, 1999; Sirgy, 2002; Sirgy & Lee, 2006). The basic premise of bottom-up spillover theory is that overall life satisfaction is affected by satisfaction with important life domains and life events affecting those domains. Life satisfaction is considered to be on top of a satisfaction hierarchy. For instance, overall life satisfaction is influenced by satisfaction with family, social, leisure and recreation, health, work, financial, and travel. Satisfaction with a particular life domain (e.g., leisure life) is influenced by lower levels of life concerns (or life events) affecting that domain (e.g., Kruger, 2012). Satisfaction with a hospital stay, for example, affects satisfaction with health life and community life, which in turn contributes to overall life satisfaction (e.g., Sirgy, Hansen, & Littlefield, 1994). See the conceptual model in Figure 1.

Figure 1

Theoretical Model and Hypotheses

Residents affiliated with the tourism industry may perceive the tourism impacts on community economic and noneconomic well-being differently than residents not affiliated with tourism. In other words, residents affiliated with tourism (e.g., government officials, business leaders, and other community residents employed in the tourism sector) may be more sensitive to the economic impact of tourism on community well-being than other residents not affiliated with tourism. Residents affiliated with tourism are likely to have perceptions of tourism impact on community economic well-being that are strongly associated with their perceptions of community economic well-being, compared with residents not affiliated with tourism. Similarly, perceptions of tourism impact on community noneconomic well-being (i.e., leisure and social well-being of community residents) for residents affiliated with tourism (i.e., residents are employed by hospitality or tourism firms) are likely to be strongly associated with their perceptions of community noneconomic well-being (i.e., leisure and social well-being of community residents). Stakeholder theory and social exchange theory may help us better understand these relationships.

Stakeholder theory suggests that an organization is characterized by its relationships with various groups and individuals, including employees, customers, suppliers, governments, and members of the communities (Freeman, 1984). Common examples of tourism stakeholders may include chambers of commerce, tourism authorities, local agencies, tourism-related educators and professionals, and local residents and tourists (e.g., Byrd et al., 2009; Yoon, 2002). Stakeholder theory posits that the various groups can and should have a direct influence on managerial decision making, and consideration should be given to each stakeholder group, regardless of the relative power or interest held by each (Sautter & Leisen, 1999). In addition, different types of stakeholders might have different opinions and perceptions depending on stakeholders’ attitudes about costs and benefits.

Social exchange theory, on the other hand, can be defined as “a general sociological theory concerned with understanding the exchange of resources between individuals and groups in an interaction situation” (Ap, 1992, p. 668). From a tourism development perspective, social exchange theory assumes that stakeholders’ attitudes toward and support for tourism in their community is influenced by their evaluation of the actual and perceived outcomes that tourism has in their community (Andereck, Valentine, Knopf, & Vogt, 2005). This theory suggests that people evaluate an exchange based on the costs and benefits incurred as a result of that exchange. If the individual perceives benefits from an exchange, he or she is likely to evaluate it positively; however, if he or she perceives costs, he or she is likely to evaluate it negatively. Thus, depending on the costs and benefits of the social exchange related to tourism, community residents’ sense of well-being might be positive or negative. If residents perceive tourism impact positively, then their lives are likely to be also positively affected by tourism; however, if they perceive tourism impact negatively, then their lives might also be negatively affected.

The present study attempts to investigate stakeholders’ perception of tourism impact in life domains (residents employed in the tourism sector vs. those who are not affiliated with tourism), their satisfaction with particular life domains, and their overall life satisfaction. Our expectation is that depending on the type of stakeholder group, community residents’ perception of tourism impact on community quality of life might be different. Specifically, we expect that the relationship between residents’ perception of tourism impact on community economic well-being and residents’ perception of community economic well-being (i.e., satisfaction with material life) to be stronger for residents affiliated with tourism than residents not affiliated. Similarly, we expect that the relationship between residents’ perception of tourism impact on community noneconomic well-being and residents’ perception of community noneconomic well-being (i.e., satisfaction with leisure life, family life, social life, etc.) to be stronger for residents affiliated with the tourism sector than those who are affiliated. The following is a list of hypotheses that will be tested in this study:

Hypothesis 1: Perceptions of tourism impact in material life on satisfaction with material life is more positive for residents affiliated with the tourism industry than those who are not affiliated.

Hypothesis 2: Perceptions of tourism impact in nonmaterial life on satisfaction with nonmaterial life is more positive for residents affiliated with the tourism industry than those who are not affiliated.

Hypothesis 3: Satisfaction with material life is a significant and positive predictor of overall life satisfaction. That is, the greater the satisfaction with material life the greater the overall life satisfaction.

Hypothesis 4: Satisfaction with nonmaterial life is a significant and positive predictor of overall life satisfaction. That is, the greater the satisfaction with nonmaterial life the greater the overall life satisfaction.

Method

In this section, we will describe aspects of the study method. This includes sampling and study sites, measurement of the constructs, and data collection.

Sampling and Study Sites

The target population of this study involved two different stakeholder groups in selected tourism destinations: Group 1—related to the hospitality and tourism industry, whether employed, self-employed, or a business owner; and Group 2—unrelated to the hospitality and tourism industry. Stakeholders who were at least 18 years old and have lived in their community for at least a year were considered as potential participants. The minimum sample size was determined to be at least 200 (or more) to ensure the effective use of the structural equation modeling and to minimize the chance of getting exaggerated goodness-of-fit indices due to small sample size (Anderson & Gerbing, 1988). The total target sample size for this study was 500 (250 for each group: Group 1 and Group 2).

Butler (1980) has long argued that the level of the development of a tourist destination influences residents’ and tourists’ experiences. The level of development is defined in terms of the number of visitors and the level of infrastructure. To control the level of tourism development the current study selected five locations with a similar stage of tourism development (with difference target markets) based on the number of tourists (U.S. Census Bureau): New York City, New York (metro area); Orlando, Florida (theme park); Las Vegas, Nevada (entertainment/gambling); Hawaii (beach/leisure activities); and Virginia (historic/cultural cites). The goal was to select communities and destinations with large numbers of tourists signaling that these communities are in the mature stages of tourism development. For the pretest, data were collected from residents living in New York City; and for the main test, data were collected from remaining four sites.

According to the U.S. Census Bureau, 86 million visitors came to New York in 2010—the greatest number in the United States, followed by Florida (58 million), California (56 million), Nevada (25 million), and Hawaii (21 million). Even within the same state, each city or county has a different level of tourism development and attracts a different number of tourists. For instance, 97% of New York tourists visited New York City, 96% of Nevada visitors came to Las Vegas and 89% of Florida tourists traveled to Orlando, Thus, specific cities (e.g., New York City, Las Vegas, and Orlando) were selected as study sites. However, for Hawaii and Virginia, the entire states were considered as study sites because, compared with other study sites, Hawaii has a small number of residents and all of the Hawaiian islands are considered to be a popular tourism destination (U.S. Census Bureau). Virginia is considered an historical tourism destination, and most tourism attractions are located in different parts of the state (Kim, 2002). Therefore, the entire state of Virginia was considered as a study site.

Measurement of Constructs

The measurement scales and survey questionnaire were developed in several stages following the procedures recommended by Churchill Jr (1979) and DeVellis (1991). See all the constructs and survey items in the appendix. We generated a list of quality-of-life indicators from the literature (e.g., Andrews & Withey, 1976; Cummins, 1996; Kim et al., 2012; Sirgy, 2001). After generating a list of indicators, four professional experts were asked to evaluate and add or delete important indicators in each life domain. After the indicators were developed, they were pretested for content adequacy. Definitions of life domains were listed on the left-hand side of each page and indicators were listed on the right-hand side. For this step, naïve respondents were required (Hinkin, Tracey, & Enz, 1997). Approximately 200 university students were asked to match indicators with corresponding life domains. In total, 124 completed questionnaires were collected. The retained indicators were factor analyzed and then incorporated into a survey instrument.

The survey instrument consisted of four parts:

Part I: Demographic information

Part II: Perceptions of tourism impacts in various economic and noneconomic life domains

Part III: Residents’ life domain satisfaction

Part IV: Resident’s overall life satisfaction

All the survey sections consisted of items involving 5-point rating scales: (a) “not at all affected” to “very affected,” (b) “very unsatisfied” to “very satisfied,” or (c) “strongly disagree” to “strongly agree” (see items and scales in the appendix).

To measure perception of tourism impact on a specific life domain and satisfaction with the same life domain, the same exact list of indicators was used. For instance, to measure “perceptions of tourism impact on material life” and “material life satisfaction” the same seven material life indicators were used. However, each construct was operationalized in a different way. For the perceptions of tourism impact on material life, respondents were asked, “How does the impact of tourism affect each of the material life indicators?” For material life satisfaction, respondents were asked, “How satisfied are you with each of material life indicators?” See all measurement items in the appendix.

The material life domain was measured by specific material life indicators; meanwhile, the nonmaterial life domain involved three subdimensions, and each subdimension was measured by specific indicators. The material life domain was measured by three items for cost of living and four items for income and employment (Kim et al., 2012). Thus, to measure two constructs of “perceptions of tourism impact on material life domain,” and “satisfaction with material life,” seven indicators were used. To measure the nonmaterial life domain, three subdimensions were used: community life, emotional life, and health/safety. Each subdimension was measured by specific indicators. Five items were used based on the large-scale studies of Andrews and Withey (1976), Cummins (1996), and Kim (2002). Emotional life domain was investigated by four leisure life indicators and three spiritual indicators (Andrews & Withey, 1976; Cummins, 1996; Kim, 2002; Sirgy, 2001, 2002). With respect to health and safety, we used five items to capture health well-being and three items to capture safety (Cummins, 1997). Last, overall life satisfaction was measured using six items (Diener, Emmons, Larsen, & Griffin, 1985; Diener, Horwitz, & Emmons, 1985; Sirgy, 2002). See all measurement items in the appendix.

Data Collection

A pretest was conducted to test the validity of measurement items. Based on the scale development procedure previously described, 27 items were developed for material, community, emotional, and health/safety life domains. In addition, six items were developed to measure overall life satisfaction. The pretest was conducted using these 33 items. The survey data collection was conducted by a commercial survey company (www.surveymonkey.com). The company e-mailed the survey invitation letters to the target population. Potential respondents accessed and participated in the survey through the company’s website. Data were collected from residents who live in New York City. A total of 389 visits were logged, 289 were discarded because they were incomplete. The remaining 100 were used for data analysis.

To identify construct dimensionality, an exploratory factor analysis with a principal component method was conducted for each construct. Factor analysis of three constructs (perception of material life, material life satisfaction, and overall life satisfaction and six subdimensions (perceptions of community life, perceptions of emotional life, and perceptions of health/safety, community life satisfaction, emotional life satisfaction, and health/safety satisfaction) were examined. The results of the exploratory factor analysis and reliability coefficients showed that all dimensions were unidimensional producing a satisfactory score of .7 or higher. Therefore, all items used to measure the main constructs were considered to be reliable.

The current study selected four locations based on the number of tourists: Hawaii; Las Vegas, Nevada; Orlando, Florida; and Virginia. Data were collected using the same marketing research company (Surveymonkey.com). An online panel survey was conducted through the company’s website in February 2013. The company e-mailed invitation letters to their consumer panel. The survey invitations were sent to 4,000 respondents. Within 1 week, 1,790 respondents participated in the survey. Among these respondents, some of the respondents were filtered based on a screening question (residency). Only people who live in the selected destinations (Virginia, Las Vegas, Orlando, or Hawaii) could participate in the survey. Furthermore, 814 respondents were filtered out at the beginning of the survey because of their residency and 50 responses were not completed; hence, these were deleted. Next, the unusable responses that tended to answer in a certain direction were deleted.

A total of 407 completed responses were used for data analysis. Of the 407 respondents, 227 (55.8 %) were female and 187 (44.2%) were male. Among the 407 respondents, 92 respondents reported that they reside in Virginia, 79 in Hawaii, 109 in Las Vegas, and 127 in Orlando. Among the 407 respondents, 95 reported working in the hospitality and tourism industry, 161 reported working outside the hospitality and tourism industry, and 151 reported that they are retired, unemployed, or students.

Results

To test the hypotheses of this study we used two different analytic techniques: hierarchical multiple regressions and structural equation modeling (SEM). To test the moderating effects of t\he two stakeholder groups: community residents affiliated versus nonaffiliated with the tourism sector (Hypotheses 1 and 2) hierarchical multiple regression was applied. SEM was used to test Hypotheses 3 and 4.

Confirmatory Factor Analysis

Confirmatory factor analysis (CFA) allows identification and clustering of the observed variables in a prespecified, theory-driven hypothesized model to evaluate to what extent a particular collected data set confirms what is theoretically believed to be its underlying constructs. The CFA approach examines whether or not the collected data are consistent with a highly constructed hypothesized model, or a priori specified model (Hair, Black, Babin, Anderson, & Tatham, 2010). Therefore, CFA was used to test the measurement model specifying the hypothesized relationships of the observed variables to the underlying constructs.

Before conducting an overall CFA, a single CFA was conducted in relation to the five measurement constructs. CFA results for the three constructs (perceptions of tourism impact on nonmaterial life domain, satisfaction with nonmaterial life domain, and overall life satisfaction) were found to be acceptable. However, the results of the CFA for the perceptions of tourism impact on material life domain and satisfaction with material life domain were not acceptable. Therefore, after reviewing the t value, standard error, modification indices, squared multiple correlations, and completely standardized loadings, three indicators (“the real estate taxes,” “the cost of living in general,” and “the cost of basic necessities such as food, housing, and clothing”) were deleted because of their low t value, high standard error, and low explained variance.

The overall measurement model consisted of five major constructs and 20 observed indicators (see Table 1). Specifically, perceptions of tourism impact on material life domain were measured using four indicators, and so was material life satisfaction. Perceptions of tourism impact on nonmaterial life and nonmaterial life satisfaction were also measured by three indicators each. Specifically, perceptions of tourism impact on nonmaterial life were captured through three indicators: perceptions of tourism impact on community life, perceptions of tourism impact on emotional life, and perception of tourism impacts on health/safety. Similarly, nonmaterial life satisfaction was captured through three indicators: satisfaction with community life, satisfaction with emotional life, and satisfaction with health and safety. Overall life satisfaction was captured using six indicators (see Table 1).

Table 1

Confirmatory Factor Analysis Results for the Overall Measurement Model (n = 407)

Constructs and Indicators	Standardized Loading (Li)	Reliability (Li²)	Error/Variance Extracted
Perception of tourism impacts on material life		.93^a	.78^b
PM1	.90	.81	.19
PM2	.91	.82	.18
PM3	.86	.74	.26
PM4	.87	.76	.24
Perception of tourism impacts on nonmaterial life		.87^a	.68^b
PNM1	.87	.76	.24
PNM2	.74	.55	.45
PNM3	.86	.74	.26
Material life satisfaction		.90^a	.69^b
SM1	.89	.78	.22
SM2	.77	.59	.41
SM3	.83	.69	.31
SM4	.84	.71	.29
Nonmaterial life satisfaction		.83^a	.62^b
SNM1	.87	.76	.24
SNM2	.71	.50	.50
SNM3	.77	.60	.40
Overall life satisfaction		.92^a	.66^b
OLS1	.84	.70	.30
QLS2	.90	.81	.19
QLS3	.89	.80	.20
QLS4	.81	.65	.35
QLS5	.70	.49	.51
QLS6	.73	.53	.47

Note: PM1 = Perception of tourism impact on income at your current job; PM2 = Perception of tourism impact of economic security of your job; PM3 = Perception of tourism impact on family income; PM4 = Perception of tourism impact on the pay and fringe benefits you get; PNM1 = Perception of tourism impact on community life; PNM2 = Perception of tourism impact on emotional life; PNM3 = Perception of tourism impact on health/safety life; SM1 = Satisfaction with income at your current job; SM2 = Satisfaction with the economic security of your job; SM3 = Satisfaction with family income; SM4 = Satisfaction with the pay and fringe benefits you get; SNM1 = Community life satisfaction; SNM2 = Emotional life satisfaction; SNM3 = Health/safety life satisfaction; QLS1 = I am satisfied with my life as whole; QLS2 =The conditions of my life are excellent; QLS3 = In most ways my life is close to ideal; QLS4 = So far I have gotten the important things I want in life; QLS5 = If I could live my life over, I would change almost nothing; QLS6 = In general, I am a happy person.

Composite reliability. ^bVariance extracted estimate.

The initial estimation of the overall measurement model resulted in a good model fit (Hair et al., 2010): χ² = 327.04(degrees of freedom [df] = 160, p = .00); goodness-of-fit index (GFI) = .92; root mean square residual (RMSR) = .047; root mean square error of approximation (RMSEA) = .057; comparative fit index (CFI) = .96; normed fit index (NFI) = .94; nonnormed fit index (NNFI) = .96; adjusted goodness-of-fit index (AGFI) = .89; parsimony normed fit index (PNFI) = .79; parsimony goodness-of-fit index (PGFI) = .70.

The next step was to examine the extent to which the measurement model is adequately represented by the observed variables. The squared-multiple correlation (R²) values were used to determine whether the measurement model is adequately represented by the observed variables. The squared-multiple correlation also represents the indicators’ reliability. Examination of the Li² values reported in Table 1 reveals that the measures are strong. After measuring the adequacy of the individual items, the composite reliability score and variance extracted estimate for each latent factor was measured. As shown in Table 1, all the composite reliabilities were greater than .80, ranging between .83 and .93. All the variance extracted estimates were also greater than .50, which indicate satisfactory results of fit indices.

Testing the Hypotheses (Hypotheses 3 and 4)

The relationships between main constructs in the proposed model were then tested using SEM. The review of the theoretical structural model demonstrated that the chi-square value was 553.54 with 166 df (p < .000). The remaining goodness-of-fit statistics indicated that the theoretical model was a well-fitting model to the data: GFI = .90; AGFI = .85; PGFI = .70; NFI = .92; NNFI = .92; PNFI = .80; CFI = .95; IFI = .93; RFI = .89; RMR = .17; RMSEA = .068.

The relationships between the constructs were tested based on t values associated with path coefficients. Hypothesis 3 states that satisfaction with the material life domain influences stakeholders’ overall life satisfaction—the greater the satisfaction with material life the greater the life satisfaction (or overall life satisfaction). The hypothesis was supported by the LISREL analysis (see Table 2). Satisfaction with the material life domain significantly predicted overall life satisfaction (t = 5.13, p < .001). The results indicate that as residents’ satisfaction with material life (in terms of their family income and economic security) increased, their overall life satisfaction increased too. Hypothesis 4 states that nonmaterial life satisfaction is a positive and significant predictor of overall life satisfaction. The structural coefficient and t values associated with this path provide support for this hypothesis (t = 4.59, p < .001). This finding suggests that the more satisfaction with the nonmaterial life domain residents experienced in terms of community life, emotional life, and health/safety, the more satisfied they are with their lives overall (see Table 2).

Table 2

Summary of the Hypothesis Testing

Hypothesis	Hypothesized Path	Standardized Coefficients	t Value	Results
H3	Satisfaction with material life → Overall life satisfaction	.26	5.13**	Supported
H4	Satisfaction with nonmaterial life → Overall life satisfaction	.41	4.59**	Supported

p < .001 (2.58).

Testing of the Moderating Effect of Stakeholder Groups (Hypotheses 1 and 2)

The basic premise is that the extent to which perceptions of tourism impact on material life (and nonmaterial life) influence satisfaction with material life (nonmaterial life) depends on whether the residents are affiliated or not affiliated with the hospitality/tourism industry. Specifically, we expected that the predictive influence of perceptions of tourism impact on material life on satisfaction with material life is positive for residents affiliated with tourism than those who are nonaffiliated (Hypothesis 1). We also expected that the predictive influence of perceptions of tourism impact in nonmaterial life on satisfaction with nonmaterial life is positive for residents affiliated with tourism than those who are not affiliated (Hypothesis 2). We used hierarchical multiple regression to examine these moderating effects (Cohen, Cohen, West, & Aiken, 2002). The regression procedure recommended by Cohen and Cohen (1983) entailed the following steps:

Center the independent variable (perceptions of tourism impact) by creating a new variable in which the mean of this variable is subtracted from each person’s score on the variable.

Multiply the centered independent variable by the dummy variable (types of stakeholders) to create cross-product terms.

Regress the dependent variable (satisfaction with life domains) on the independent variable of interest, using simultaneous regression. Use the centered version of relevant variables, but exclude the interaction terms.

Add, in a sequential fashion, the interaction term.

The moderating effect was tested by observing the statistical significance of Δr². If Δr² is significant, this indicates that the dummy variable affects the dependent variable.

Each dependent variable was regressed on an independent variable and a moderator, with the type of stakeholders (residents affiliated vs. nonaffiliated with the tourism industry) recoded as a dummy variable. Residents who are affiliated with tourism were coded as “0” and community residents not affiliated with tourism were coded as “1.” The independent variable (centered perceptions of tourism impact on material life) and dummy variable (stakeholder type) were added in the first model to examine whether the two variables registered main effects. The results showed that perceptions of tourism impact on material life significantly predicts satisfaction with material life: adjusted R² = .020, F(2, 404) = 5.08, p < .005. Next, the interaction effect (Centered perception of tourism impact on material life domain * Stakeholder type) was added. The ΔR² (.01) was found to be statistically significant (p < .05). This means that the interaction effect increased the predictive power of the regression model: adjusted R² = .028, F(1, 403) = 4.35, p < .005. Specifically, for the residents who are affiliated with tourism, the relationship between their perceptions of tourism impact on material life and their satisfaction with material life was positive (as hypothesized). Conversely, for residents not affiliated with hospitality and tourism, the relationship was negative (see Table 3). These results provide support for Hypothesis 1. Also see interaction plot in Figure 2.

Table 3

Coefficients of Moderating Effects on Satisfaction With Material Life

		Unstandardized Coefficients		Standardized Coefficients
Model	Variables	Β	Standard Error	β	t	Significance
1	(Constant)	2.889	.107		27.007	.000
	Perceptions of tourism impact on material life	−1.33	.043	−.181	−3.104	.002
	Dummy variable	−1.34	.127	−.062	−1.062	.289
2	(Constant)	2.625	.166		15.837	.000
	Perceptions of tourism impact on material life	.083	.112	.113	.741	.459
	Dummy variable	.117	.174	.054	.671	.503
	Perceptions of tourism impact on material life * Dummy variable	−.253	.121	−.271	−2.087	.038

Note: Dummy variable: Residents who are affiliated with tourism were coded as “0” and community residents not affiliated with tourism were coded as “1.”

Figure 2

Scatter Plots for Two Groups’ Perceptions of Material Life Satisfaction

With respect to Hypothesis 2 (stakeholder type moderating the relationship between the perceptions of tourism impact on nonmaterial life and satisfaction with nonmaterial life), the same procedure was applied. The results showed that the perception of tourism impact on nonmaterial life did not predict satisfaction with nonmaterial life: adjusted R² = .008, F(2, 404) =2.549, p > .005. Next, the interaction effect (Centered perception of tourism impacts on nonmaterial life * Stakeholder type) was added in the second step. The addition of the interaction term did partially lead to a statistically significant increase (ΔR² = .007, p = .087). In other words, the pattern of interaction was as expected (although not statistically significant). That is, increased perceptions of tourism impact on nonmaterial life for residents affiliated with the tourism industry were associated with increased satisfaction with nonmaterial life. However, for residents not affiliated with tourism, increased perceptions of tourism impact on nonmaterial life were associated with decreased satisfaction with nonmaterial life (see Table 4 and Figure 3).

Table 4

Coefficients of Moderating Effects on Satisfaction With the Nonmaterial Life

		Unstandardized Coefficients		Standardized Coefficients
Model	Variables	Β	Standard Error	β	t	Significance
1	(Constant)	3.228	.075		43.160	.000
	Perceptions of tourism impact on nonmaterial life	−.031	.038	−.041	−.803	.422
	Dummy variable	.158	.086	.094	1.834	.067
2	(Constant)	3.168	.083		38.367	.000
	Perceptions of tourism impact on nonmaterial life	.104	.087	.139	1.191	.234
	Dummy variable	.214	.092	.127	2.330	.020
	Perceptions of tourism impact on nonmaterial life * Dummy variable	−.166	.097	−.194	−1.714	.087

Note: Dummy variable: Residents who are affiliated with tourism were coded as “0” and community residents not affiliated with tourism were coded as “1.”

Figure 3

Scatter Plots for Two Groups’ Perceptions of Nonmaterial Life Satisfaction

As mentioned in the measurement part, the nonmaterial life domain was measured by three subdimensions: community life, emotional life, and health/safety life; and the indicators of the each subdimension were summed. Therefore, perceptions of tourism impact on nonmaterial life domain and nonmaterial life satisfaction were decomposed into its constituent dimensions to test the interaction effect with each dimension separately. Specifically, to test the relationship between perceptions of tourism impact on community life and satisfaction with community life, the same procedure was applied. The results indicated that perceptions of tourism impact on community life did not predict satisfaction with community life: adjusted R² = .002, F(2, 404) = 1.494, p > .05. However, the addition of the interaction effect did partially lead to a statistically significant increase (ΔR² = .007, p = .086). Specifically, the relationship between the perceptions of tourism impact on community life domain and community life satisfaction was positive for residents who are affiliated with hospitality and tourism industry; whereas, for residents who are not affiliated with hospitality and tourism this relationship was negative (see Figure 4).

Figure 4

Scatter Plots for Two Groups’ Perceptions of Community Life Satisfaction

The relationship between perceptions of tourism impact on emotional life and emotional life satisfaction was examined following the same procedure. The results showed that the perceptions of tourism impact on emotional life did not affect emotional life satisfaction: adjusted R² = .004, F(2, 404) = 1.806, p > .005. Moreover, the interaction effect also did not statistically increase the predictive power of regression model (ΔR² = .002, p = .367). In other words, the moderation effect was not evident here.

Last, the effect of perceptions of tourism impact on health/safety satisfaction was examined. The relationship was significant: adjusted R² = .029, F(2, 404) = 7.049, p < .05. Next, the interaction effect was added in the second step. The addition of the interaction term did lead to a statistically significant increase (ΔR² = .012, p = .028). That is, we found a statistically significant difference between the two groups (see Figure 5). Specifically, for residents who are affiliated with tourism this relationship was positive; however, for residents not affiliated with hospitality and tourism this relationship was negative.

Figure 5

Scatter Plots for Two Groups’ Perceptions of Health/Safety Life Satisfaction

Discussion

This study contributes to the scholarly literature of tourism by shedding light on the relationship between community residents’ perceptions of the impact of tourism on community well-being and residents’ satisfaction with certain life domains and life at large. The overall pattern of results was mostly supportive of our general hypothesis (see Table 5). That is, the influence of community residents’ perceptions of tourism impact and their life satisfaction is dependent on whether the residents are affiliated or not affiliated with the tourism sector. This result is also consistent with the notion that those residents whose livelihood depends on tourism activities show stronger support for tourism development in general (e.g., Ap & Crompton, 1998; Choi & Sirakaya, 2005; Delamere, 2001; Gursoy et al., 2002; Lankford & Howard, 1994; Madrigal, 1993; Uysal, Woo, & Singal, 2012). Those who are affiliated with the tourism sector (e.g., residents working at hotels, tourist attractions, restaurants) are likely to perceive tourism impact on community economic well-being positively, which spills over to their own sense of material well-being. Similarly, perceptions of tourism impact on nonmaterial life spills over unto the sense of well-being in nonmaterial life domains. In turn, community residents’ satisfaction with their material and nonmaterial life plays an important role in influencing their life satisfaction overall.

Table 5

Summary of the Hypothesis Testing

Hypothesis	Results
H1: Perceptions of tourism impact in material life on satisfaction with material life is more positive for residents affiliated with the tourism industry than those who are not affiliated.	Supported
H2: Perceptions of tourism impact in nonmaterial life on satisfaction with nonmaterial life is more positive for residents affiliated with the tourism industry than those who are not affiliated.	Partially supported
H3: Satisfaction with material life is a significant and positive predictor of overall life satisfaction. That is, the greater the satisfaction with material life the greater the overall life satisfaction.	Supported
H4: Satisfaction with nonmaterial life is a significant and positive predictor of overall life satisfaction. That is, the greater the satisfaction with nonmaterial life the greater the overall life satisfaction.	Supported

This study also contributes to theory development in tourism by demonstrating how theories such as bottom-up spillover theory of life satisfaction, social exchange theory, and stakeholder theory can explain complex interactions among constructs such as perceptions of tourism impact, domain satisfaction, and life satisfaction. Social exchange theory and stakeholder theory have been applied in tourism to better understand stakeholders’ perceptions (Byrd et al., 2009; Freeman, 1984; Yoon, 2002); however, bottom-up spillover theory has not.

The model developed and tested in this study can be used to compare communities at different stages of tourism development to determine stakeholders’ quality of life. Moreover, the model can be applied to different types of tourism destinations and cultural environments. In addition, the proposed quality-of-life model may constitute a theoretical foundation for the examination of the effect of perceptions of the impact of tourism on domain satisfaction and overall life satisfaction.

Once a community becomes a tourism destination, the lives of stakeholders in that community are affected in numerous ways. Tourism not only affects their attitude toward tourism development but also their quality of life. If stakeholders benefit from tourism, they are likely to feel a greater sense of satisfaction and better quality of life (compared with those who do not benefit from tourism). As such, community residents affiliated with tourism are more likely to support the development of tourism in their community to enhance the community’s tourism competitiveness. In other words, if political support is needed to bolster tourism efforts in the community, tourism officials should turn to community residents affiliated with the tourism industry. On the other hand, larger benefits of tourism should be better articulated to generate support among residents who are not affiliated with tourism.

The relationship between perceptions of impact of tourism in material life and satisfaction with material life was highly evident for residents affiliated with the tourism industry, but not residents not affiliated with the industry. In other words, residents affiliated with the tourism industry perceive the impact of tourism in a positive way because they benefit directly from it through their employment. Therefore, they are more favorable to the impact of tourism and its effects on their material life. In contrast, residents not affiliated with tourism believe that they do not experience much material and nonmaterial benefits from tourism. As such, when their perceptions of the impact of tourism in material and nonmaterial life increase, their satisfaction with material and nonmaterial life decreases. For example, they might think that tourism development increases their cost of living and this may lead to a decrease in satisfaction with material life.

Residents not affiliated with tourism may not experience direct benefits from tourism, but they could benefit indirectly. Tourism development can provide employment opportunities, generate foreign exchange earnings, and increase income for the destination community in the form of tax revenue (Kim et al., 2012; Uysal, Woo & Singal, 2012). These benefits can improve their community’s quality of life; thus, enhancing their own quality of life. Therefore, tourism officials should help residents better understand how tourism development may improve their quality of life (e.g., by creating access to better amenities, open and green space, better fire protection, and/or greater safety and security).

Even though the study contributes to theory development in the tourism literature and has practical policy implications, we have to acknowledge that there are some study limitations. First, to control for the possible confounding effect of level of tourism development, four destinations (Hawaii, Orlando, Las Vegas, and Virginia) were selected based on the number of tourists—tourist destinations in the mature stages of tourism development. One may argue that the number of tourists may not be a sufficient indicator of tourism development (Uysal, Woo, & Singal, 2012). Other indicators should have been used. Examples may include the level of infrastructure, tourism receipts, capacity levels, visitor days, types of visitors, and types of accommodation (Butler, 1980; Debbage, 1990; Hovinen, 1982; Ioannides, 1992). Future research should replicate this study using a more comprehensive set of indicators of tourism development.

Second, the survey data were collected only from residents who live in Hawaii, Orlando, Las Vegas, and Virginia to control the level of tourism development. Given that we selected four different types/size of destinations we checked whether there is any difference between the destinations before testing the model. No significant differences regarding the hypotheses were detected. However, if this study had collected data from different types of destinations, the strength of the relationship between perceptions of impact of tourism and satisfaction with life domains could have revealed some variation. For instance, if data were collected from the beginning stage of tourism development with different size destinations the results might have been more evident. Therefore, future research should replicate the study results using destinations in other stages of tourism development.

A third study limitation is related to the sampling frame. The U.S. online panel was used in the final survey, which is essentially a convenience sampling technique. Hence, one can argue that the study results are not truly representative of community residents at large. However, given the normality structure of the data generated, one could make the argument that the sample is somewhat representative. However, for the future research, the study should be replicated with a probabilistic sampling frame.

Fourth, this study investigated the effect of the perception of tourism impact on satisfaction with life domains and overall life satisfaction. Two major life domains (material and nonmaterial life) were selected and tested in this study. However, there might be other domains such as family life, social life, travel, and work. Future research could replicate the study results with a more comprehensive list of life domains.

Furthermore, this study is essentially correlational in design. That is, we cannot infer causality based on the findings of this study. Future research should use longitudinal study designs in an attempt to establish causality. Ideally, one can track residents’ perceptions of tourism impact and their own quality of life through a long time span that may reflect the tourism development cycle—the growth stages versus the mature stages.

Footnotes

Appendix

Constructs and Measures

Material life domain

1. The real estate taxes

2. The cost of living in your community

3. The cost of basic necessities such as food, housing, and clothing

4. Income at your current job

5. The economic security of your job

6. Family income

7. The pay and fringe benefits you get

Community life domain

1. The conditions of your community environment (air, water, land)

2. The people who live in your community

3. The service and facilities you get in your community

4. Community life

5. Public transportation

Emotional life domain

1. Spare time

2. Leisure activity in your community

3. Leisure life

4. Religious services in your community

5. The way culture is preserved in your community

6. The leisure life in the community

7. The spiritual life in the community

Health/safety life domain

1. Health facilities in your area

2. Health service quality in your area

3. Water quality in your area

4. Air quality in your area

5. Environmental quality in your area

6. Environmental cleanness in your community

7. Safety and security in your community

8. Accident rate or crime rate in your community

Overall life satisfaction

1. I am satisfied with my life as a whole

2. The conditions of my life are excellent

3. In most ways my life is close to ideal

4. So far I have gotten the important things I want in life

5. If I could live my life over, I would change almost nothing

6. In general, I am a happy person

Note: All the survey sections consisted of items involving 5-point rating scales: (a) Perception of tourism impacts in life domain was measured “how tourism impacts affect life indicators” [“not at all affected” (1) to “very affected” (5)]. (b) Domain satisfaction was measured “how satisfied you are with each of these life indicators” [“very unsatisfied” (1) to “very satisfied” (5)]. (c) Overall life satisfaction was measured “how much you agree or disagree with each statement [“strongly disagree” (1) to “strongly agree” (5)].

Acknowledgements

This study was supported by 2015 Research Grant from Kangwon National University.

Eunju Woo, PhD (e-mail: ejwoo@kangwon.ac.kr), is an assistant professor in the Department of Tourism Administration, College of Business Administration, at the Kangwon National University, Chuncheon, Republic of Korea. Muzaffer Uysal, PhD (e-mail: muysal@isenberg.umass.edu), is department chair at the Department of Hospitality & Tourism Management in the Isenberg School of Management at University of Massachusetts, Amherst, MA. M. Joseph Sirgy, PhD (e-mail: sirgy@vt.edu), is a professor in the Department of Marketing, Pamplin College of Business, at Virginia Tech, Blacksburg, Virginia.

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