Examining the Role of Corporate Social Responsibility in Resident Attitude Formation: A Missing Link?

Abstract

Resident attitude toward the development of individual commercial tourism projects (e.g., theme parks and resorts) has not been well analyzed in tourism literature. In this study, the authors explored the role of corporate social responsibility (CSR) in understanding residents’ attitudes toward a high-profile tourism project in its initial building stage. Four models were proposed to evaluate the role of CSR in resident attitude formation. Model comparison results suggest that the moderation model (i.e., CSR moderates the effect of perceived impacts on resident attitude) appears to be statistically and conceptually sound. The results reveal that CSR positively affects resident attitude and enhances the effect of environmental tourism impacts on resident attitude at the city level. Theoretical and empirical implications are discussed.

Keywords

resident attitude corporate social responsibility model comparison tourism impact

Introduction

Nearly all business initiatives require some form of approval from the local government and institutions (Assaf, Josiassen, and Agbola 2015). However, the development of large tourism projects, such as a tourist attraction or hotel, often involves more complex connections with the local community and requires additional grassroots support. As with any business development, major tourism projects, particularly those involving foreign brands, are not always welcomed or immediately appreciated by local communities (Fong, Lee, and Du 2014). Despite the importance of local support to the long-term success of a tourism project, existing studies on resident attitude have focused more on general tourism development; research targeting “large scale resort areas in both the industrialized and developing world” is notably scarce (Sharpley 2014, 46). The new Shanghai Disney Resort (SHDR), a $5.5-billion project, provided researchers a rare opportunity to examine residents’ attitudes toward the construction of a highly visible tourism project from its inception, hence the inspiration for this study.

A key distinction between general tourism impact studies and those focusing explicitly on the impacts of individual tourism projects is that the latter must tie closely to corporate strategies and take corporate- and brand-related factors into consideration. A major construct of interest in this study is residents’ perceptions of corporate social responsibility (CSR), which has not been adequately examined in the tourism and hospitality fields (Farmaki and Farmakis 2018; Serra-Cantallops et al. 2018). CSR refers to “expectations the society has of organizations at a given point in time” (Carroll 1979, p. 500). As stakeholders in tourism industry, residents are likely to expect tourism companies to contribute positively to the well-being of the local community, society, and environment through CSR-related activities (Dahlsrud 2008; Lee, Kim, and Kim 2018). Scholars have argued that CSR is a fundamental component (Lewis 2003) of customers’ appraisal of companies and their products (Marin, Ruiz, and Rubio 2009) as well as employees’ organizational commitment (Lee et al. 2013; Kim 2017). However, the role of CSR in resident attitude formation remains underexplored.

The general consensus of resident attitude studies is that locals’ perceptions of tourism impacts greatly inform their attitudes toward tourism development (Sharpley 2014). To identify the role of CSR in resident attitude formation, the challenge appears to be positioning CSR in the perceived tourism impacts→resident attitude framework. As the link between CSR and tourism impacts is not well established in the existing literature, at least four distinct assumptions of such link can be proposed based on relevant theoretical tenets. This study thus constructed four competing models, including the independent model, moderation model, mediation model, and interaction model (see next section for details). By comparing these models and identifying the best one—a theory-building approach long advocated by researchers of various fields (Bagozzi and Yi 1988; Li and Petrick 2010; MacCallum and Austin 2000; Nunkoo, Ramkissoon, and Gursoy 2013), the authors attempted to uncover the associations among CSR, perceived tourism impacts, and resident attitude. Thus, a key theoretical contribution of this study comes with bridging the CSR literature and resident attitude studies in tourism, which brings a new perspective to resident attitude research.

In light of recent theoretical developments (Li, Hsu, and Lawton 2015), the authors took a dual-theory approach when exploring residents’ attitudes regarding perceived economic, sociocultural, and environmental impacts of a large-scale tourism project. Specifically, the authors applied social exchange theory to understand residents’ perceptions of tourism impacts on their own lives and social representation theory to understand tourism impacts on the local community as a whole (Li, Hsu, and Lawton 2015). Through the dual-theory approach, this study also attempted to explore the differences underlying individual- and community-level resident attitudes. Further, by focusing on residents’ attitudes toward the development of a high-profile tourism project, results of this study also shed light on the planning and management of other large-scale initiatives.

Literature Review

Resident Attitude

Attitude is “a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor” (Eagly and Chaiken 2007, 582). The evaluation process encompasses an assessment of beliefs and thoughts, feelings and emotions, and intentions and overt behavior (Eagly and Chaiken 2007). In this sense, one’s overall attitude toward an object can be regarded as the aggregate of beliefs about the object and one’s corresponding evaluations of those beliefs (Williams and Lawson 2001). Tourism development brings economic, sociocultural, and environmental impacts to the local community (Ap 1990) and to individual residents (Perdue, Long, and Allen 1990; McGehee and Andereck 2004). As resident attitude has been recognized as essential for tourism planning (Harrill 2004) and the sustainability of tourism development (Miller 2001), resident attitude toward tourism development is considered a critical area of tourism research (Xiao and Smith 2006; Sharpley 2014).

Social exchange theory (SET) has served for decades as the chief theoretical foundation of resident attitude research (Nunkoo, Smith, and Ramkissoon 2013). SET recognizes residents as individuals who exchange material, social, or psychological resources for benefits derived from tourism development (Harrill 2004). The theory postulates that individuals evaluate tourism development based on their perceptions of costs and benefits (Andereck et al. 2005). If residents believe they can benefit from an exchange, they will evaluate the exchange more positively and be more willing to support tourism development; otherwise, they will oppose it (Ap 1992; McGehee and Andereck 2004). Although the effects of tourism impacts may vary across cases, empirical evidence has generally supported the relationship between positive tourism impacts and residents’ support of tourism development (i.e., the perceived impacts→resident attitude link) in diverse regions (Harrill 2004).

The popularity of SET has been accompanied by criticism regarding an oversimplification of resident attitude formation (Li, Hsu, and Lawton 2015; Sharpley 2014). Some scholars contend that SET is more applicable to explaining individual-level resident support for tourism development, namely, the relationship between tourism impacts on individual residents and their respective attitudes (Boley et al. 2014; Perdue, Long, and Allen 1990). One shortcoming of SET is its supposition that personal gain is the top priority in an exchange, which is not always the case (McGehee and Andereck 2004). For example, under some circumstances (e.g., a collectivistic culture), residents may support tourism development for benefits to the community as a whole without anticipating significant personal gains (Li, Hsu, and Lawton 2015; Zhou and Ap 2009). Consequently, the inadequacy of SET has compelled researchers to seek new theoretical lenses through which to understand resident attitude, and use other theories in conjunction with SET to explain resident attitude formation (Boley et al. 2014).

Social representation theory (SRT) is proposed as an alternative perspective. Social representations are systems of shared values, ideas, and practices through which a community makes sense of the world and interacts with other community members (Voelklein and Howarth 2005). Developed by Moscovici in 1961, SRT is normally used to explain social psychological phenomena, such as group feelings and behaviors. Social representations influence human perception and behavior; they become part of collective consciousness and are taken for granted once they are woven into tradition (Moscovici 1984). Yet because SRT’s definition is vague and the concept is difficult to quantify, the theory is more controversial and less empirically validated than SET (Li, Hsu, and Lawton 2015).

Resident attitude research often uses SRT as a theoretical framework when examining perceptions and attitudes shared by groups of residents within a community (Andriotis and Vaughan 2003; Fredline and Faulkner 2000; Zhou and Ap 2009). The main objective of these studies is to identify community subgroups that share the same attitudes and to provide policy implications tailored to each cluster of residents (Andriotis and Vaughan 2003). The underlying assumption is that representations shared by residents are not identical, and it is important to recognize heterogeneity within the community. What seems to be missing from the current SRT-based resident attitude research is a recognition of the overarching cultural and social effects underlying resident attitude formation toward tourism projects. For instance, collectivist cultures tend to focus on social cohesion and emphasize benefits to the community, city, and nation. Also, residents of collectivist cultures often prioritize group goals and pay more attention to external processes, rather than internal connotations, in their social behavior (Triandis 2001). Thus, for residents in a collectivist society, community-level tourism impacts will presumably weigh heavily on their attitudes toward tourism development.

The foregoing review suggests SET could effectively explain residents’ perceptions in a personal context, whereas SRT is better suited to making sense of collective resident perceptions. Thus, SET and SRT may play complementary roles when examining residents’ perceptions and attitudes, particularly in cultures that emphasize group values (Li, Hsu, and Lawton 2015). Yutyunyong (2010) employed SET and SRT to investigate the individual and societal facets of Thailand residents’ perceptions of tourism development in Bangkok. Focusing on the individual and collective perspectives on tourism impacts, Li, Hsu, and Lawton (2015) integrated SET and SRT to assess resident attitude formation toward a mega-event, the 2010 Shanghai Expo. In all, the dual-theory approach seems to be effective at capturing a more holistic picture of the resident attitude formation process.

CSR

Since the 1950s, scholars have understood that businesses are not only responsible for earning profits; they are also accountable for the economic, social, and environmental impacts they produce (Davis 1973; Carroll 1999; Dahlsrud 2008; Manente, Minghetti, and Mingotto 2014). A company’s economic, social, and environmental responsibilities compose the “triple bottom line” of running a business (Elkington 2004).

Stakeholders help to shape companies’ CSR initiatives (Caruana and Chatzidakis 2014). Companies engaging in CSR activities make efforts to optimize the impacts of their operations on the local community, society, and environment (Dahlsrud 2008). Multilevel, socially responsible stakeholders (e.g., individuals, families, citizens, governments, and nongovernmental organizations) use CSR performance as one criterion for evaluating a firm (Caruana and Chatzidakis 2014), revealing that CSR is a fundamental addition to appraisal of companies’ brands and reputations (Lewis 2003). Substantial evidence suggests that awareness of a company’s social responsibility initiatives can help people develop positive affective and cognitive responses to the company and its product (Brown and Dacin 1997; Kim 2017). In particular, CSR activities may make a company’s identity more appealing (Marin and Ruiz 2007), build trust and commitment (Lacey and Kennett-Hensel 2010), and generate positive opinions about the company among stakeholders (Marin, Ruiz, and Rubio 2009). Presumably, findings from commercial settings can be equally transferrable to the tourism field. Local residents, as a group of tourism business stakeholders, will likely hold more favorable attitudes toward a firm when they have a more positive perception and evaluation of the firm’s CSR performance (Lee, Kim, and Kim 2018).

Linking CSR with Resident Attitude

Most previous resident attitude studies focused on the impacts of tourism development in general. The impacts of individual tourism projects, such as a major hotel or large-scale resort, have rarely been reported (Sharpley 2014). A company’s projects and CSR initiatives are corporate associations, which could contribute to the company’s overall evaluation (Berens and Van Riel 2004). Residents’ attitudes toward tourism projects may also relate to corporate associations, such as expectations about the company’s CSR performance, due to the context-driven characteristics of resident attitude (Fredline and Faulkner 2000).

Relationships between resident attitude and tourism impacts are well established in academia; however, the role of CSR in the tourism impacts→resident attitude link remains largely unknown as there seems to be a lack of empirical evidence on the relationships between CSR and perceived impacts. Most recently, Lee, Kim, and Kim (2018) proposed that CSR affected resident support toward a gaming company’s casino development via the mediation of perceived benefits from the project. That is, CSR was considered an antecedent of perceived impacts. However, at least three theoretically plausible alternatives exist for the relationship between CSR and perceived tourism impacts, which highlights the importance of establishing competing models to clarify the role of CSR in a traditional resident attitude framework.

Four competing models that make distinct assumptions about the potential relationships between CSR and perceived tourism impacts can be hypothesized based on relevant literature: (1) CSR and perceived impacts jointly but independently affect resident attitude (i.e., independent model); (2) CSR moderates the perceived impacts→resident attitude link (i.e., moderation model); (3) CSR partially mediates the effect of perceived impacts on resident attitude (i.e., mediation model); and (4) CSR affects the perception of tourism impacts and, thus, besides a direct effect, CSR has an indirect effect (through perceived impacts) on resident attitude (i.e., antecedent model) (Figure 1).

Figure 1.

Competing Models.

Independent Model

The independent model is configured such that CSR and perceived tourism impacts separately affect resident support. First, extant CSR and resident attitude literature emphasizes distinct theoretical reasoning approaches. CSR research suggests that CSR is associated with stakeholder attitude toward a company by influencing the company’s reputation and stakeholder–company relationships (Carroll and Shabana 2010; Du, Bhattacharya, and Sen 2010). However, resident attitude literature suggests that perceived tourism impacts affect resident attitude toward tourism development via an assessment of the positive and negative impacts that tourism development generates on an individual or community (Ap 1992; Li, Hsu, and Lawton 2015). In addition, a company’s CSR initiatives may not necessarily influence its stakeholders, whereas tourism development always directly or indirectly impacts local community. For example, travelers flying with American Airlines may not benefit from the company’s participation in the “Change for Good” donation program, which aims to improve and save the lives of children worldwide (UNICEF USA, n.d.). Given the divergent foci of the CSR and resident attitude literature, the independent model assumes CSR and perceived tourism impacts affect resident attitude toward the development of tourism projects independently.

Moderation Model

CSR and perceived impacts are based on residents’ cognitive perceptions (Sharpley 2014; Oppewal, Alexander, and Sullivan 2006). The cognitive system does not work discretely and sequentially but is instead “a structure of mutually and simultaneously influencing change” (Van Gelder and Port 1995, p. 3). Residents may not be able to form their attitudes separately based on the evaluation of tourism companies’ CSR and the impacts of tourism projects; instead, they may hold an overall attitude without contemplating its source. That is, residents’ perceptions of CSR will likely interact with the perception processes of tourism impacts, thereby strengthening or weakening the association between tourism impacts and resident attitude. The moderating role of CSR reputation in the relationship between CSR initiatives and brand evaluation has been supported in several empirical studies (Lii and Lee 2012; Ye, Cronin, and Peloza 2015). Therefore, it is reasonable to assume that when residents have different expectations of a company’s future CSR, their reactions to the company’s tourism projects will vary substantially. The same tourism impacts may exert different levels of effects on residents’ overall attitudes toward tourism projects. Specifically, high expectations regarding a company’s CSR practice can make the audience (i.e., residents) react more favorably toward the same positive impact and less adversely toward the same negative impact (Brown and Dacin 1997; Tuškej, Golob, and Podnar 2013; Vanhamme and Grobben 2009). In this sense, CSR may play a moderating role in the resident attitude framework.

Mediation Model

CSR is broadly defined as residents’ societal expectations that tourism businesses will minimize negative impacts on their local community and society at large. By recognizing the possible impacts of tourism on their community or themselves, residents may expect tourism businesses (i.e., the origin of these impacts) to engage in CSR activities aimed at community development and improvement (Bertels and Peloza 2008). If residents anticipate more negative than positive impacts of tourism development, they will probably expect the company to enhance its CSR efforts to reduce negative impacts and/or generate positive impacts (Basu and Palazzo 2008). On the other hand, if residents perceive more positive than negative impacts, they may come to expect better CSR to guarantee these benefits, which also puts pressure on companies to conform to societal expectations (Bertels and Peloza 2008). In this case, tourism impacts trigger residents’ expectations of CSR activities, which in turn determine resident attitude. Given that the perceived impacts→resident attitude link is well established, CSR is assumed to be a partial mediator.

Antecedent Model

An alternative view suggests that residents’ support for tourism, and their perceptions of tourism impacts, depends on what they value (Jurowski, Uysal, and Williams 1997). For instance, residents who see tourism as a primary means of local economic growth or with a dominant role in the economy are more sensitive to the positive impacts of tourism compared to residents who hold a less favorable opinion of tourism overall (Andereck et al. 2005). In societies where CSR is highly valued, such as in China (Ramasamy and Yeung 2009), CSR may evolve as the underlying value that influences Chinese citizens’ perceptions of tourism impacts, thereby affecting their attitudes toward tourism projects. Indeed, CSR is considered a form of self-regulation for private businesses, which can shape companies’ efforts to generate positive public goods and ameliorate production risks (Sheehy 2015). If residents believe a company has a high degree of self-regulation, they will expect that company to benefit the public rather than harm it. Therefore, the authors of the current study propose that CSR expectations will influence residents’ perceptions of tourism impacts and, by extension, their attitudes toward tourism projects. That is, if residents are optimistic about a company’s CSR, they are more likely to think the company’s CSR activities will generate positive impacts and offset negative impacts (if any). As a result, residents will hold more positive attitudes toward the tourism project and company in general. For instance, Lee, Kim, and Kim (2018) identified a positive association between CSR perceptions and perceived benefits of a gaming company’s casino development.

What’s more, residents’ perceived tourism impacts are influenced by the exchange in which they believe they are involved (Jurowski, Uysal, and Williams 1997). Residents’ decision-making process is “a net through which an array of cues passes” (Bettman 1970, p. 370), including attributes of the object or situation under judgment. When residents evaluate the positive and negative impacts of tourism projects, they need to consider the nature, scope, and characteristics of the company’s business and products. The company’s potential efforts in managing impacts on the local community may be considered as well. Therefore, residents’ beliefs about a tourism company’s CSR practices may influence their perceived tourism impacts. If residents have reason to believe a tourism company will be proactive in CSR, they are likely to expect more positive impacts and fewer negative impacts to be associated with the company.

The preceding discussion justifies four conceptually plausible models. The purpose of the present paper is to test, compare, and identify the best of these models based on empirical findings. SET and SRT are used to clarify relationships between tourism impacts and resident attitude. In each of the four models, tourism impacts consist of three dimensions (economic, sociocultural, and environmental) and two levels (personal and city). Relationships between resident attitude and personal-level tourism impacts follow the premise of SET, whereas relationships between city-level impacts and resident attitude are based on SRT (Li, Hsu, and Lawton 2015). Using the model comparison approach, this article aims to bridge the gap between CSR and resident attitude in the context of a highly visible tourism project.

Methodology

The Study Site

The present study represents the initial phase of a larger, longitudinal research program tracking the tourism impacts of SHDR. The resort spans 963 acres (1.505 square miles; 3.897 km²), approximately three times the size of Hong Kong’s Disneyland resort, and is expected to host three theme parks eventually (Q. Zhang 2016). The SHDR project attracted a total investment of 34 billion RMB (approximately $5.5 billion) (Frater 2016). The Disney brand, size of the project, and host city (Shanghai is one of the fastest-growing and most populous cities in the world) have aroused significant interest in SHDR from the Chinese media and the public since its introduction. After more than five years’ construction, SHDR opened on June 16, 2016. The resort includes Shanghai Disneyland, two theme hotels, a Disney Town, and a Wishing Star Park (The Walt Disney Company 2016). With average daily attendance reaching 27,000 in the first month, the park was considered an instant success (Wang 2016).

Disney, a leader in CSR (Reputation Institute 2017), has announced that its CSR mission is “to promote the happiness and well-being of kids and families and inspire them to join [Disney] in making lasting positive change in their community” (Walt Disney Parks and Resorts 2014, 4). Even during the initial construction stage, SHDR has engaged in socially responsible activities, such as the Disney VoluntEARing event in December 2012 to help students beautify their schools with Disney images (Shanghai Disney Resort 2012), and the celebration of Chinese New Year in February 2013 with Mickey Mouse and Chinese migrant workers at the SHDR project site (Shanghai Disney Resort 2013).

Data Collection

The target population for this study was urban adult Shanghai residents who had lived in Shanghai for at least two years. In fall 2012, eight focus groups were conducted to understand how Shanghai residents viewed SHDR’s construction along with their expectations about SHDR regarding its CSR practices and tourism impacts. Each focus group was professionally moderated, videotaped, and transcribed, and the results informed the subsequent survey design.

A local market research company was hired to conduct phone surveys with Shanghai residents in spring 2013. In total, 1,000 valid respondents were recruited via random-digit dialing to household phone numbers. To ensure the representativeness of the final sample, a quota sampling strategy was used. Quotas were set for respondents’ age, gender, residential district, and household income. The survey field work lasted about 3 weeks; each phone interview took approximately 25–30 minutes. The effective survey response rate among eligible individuals was 31.0%.

The phone survey questionnaire was developed based on focus group interview results and previous literature regarding tourism impacts (Frauman and Banks 2011; Gursoy and Rutherford 2004; Jurowski and Gursoy 2004; Williams and Lawson 2001), resident attitudes (Li, Hsu, and Lawton 2015), and CSR (Tsai, Tsang, and Cheng 2012; Turker 2009). Respondents were first asked about their level of agreement with general statements concerning the economic, sociocultural, and environmental impacts of SHDR on Shanghai (i.e., city-level tourism impacts). Subsequently, they were asked to state how the economic, sociocultural, and environmental consequences affected their own lives either positively or negatively (i.e., personal-level tourism impacts). Then, respondents were asked about their general attitudes toward SHDR’s construction with the statement “Overall, I support the construction of Shanghai Disneyland.” Finally, respondents were asked about their expectations regarding SHDR’s CSR efforts. All scale items were assessed on a 5-point Likert-type scale.

Data Analysis

Four theoretically plausible models, including CSR as an independent variable, a moderator, a mediator, and an antecedent, respectively, were assessed. Formative measurement models were specified for the latent constructs in this study, namely economic, sociocultural, and environmental impacts and CSR (Handajani et al. 2014; Hinz 2009). Formative measurement models were used for two reasons. First, indicators of each latent construct in the present study were considered to be causes of the constructs, consistent with the conceptualization of formative measurement (Jarvis, MacKenzie, and Podsakoff 2003). For example, indicators of city-level economic impacts included perceptions of local employment opportunities, tourism revenue, and investment opportunities, each of which “caused” perceptions of city-level economic impacts rather than the reverse. Second, indicators of each latent construct in the present study were not expected to share common themes or have high covariances (Jarvis, MacKenzie, and Podsakoff 2003). For instance, residents may believe a tourism project can provide job opportunities to the local community but simultaneously not expect the project to generate investment opportunities. As such, formative measurement models were considered more suitable for this study than reflective measurement models.

To assess the developed models, the authors used SmartPLS 3.2.1 software (SmartPLS GmbH, Boenningstedt; Ringle Wende and Becker 2015) to run partial least squares structural equation modeling (PLS-SEM). Compared with covariance-based structural equation modeling (CB-SEM), PLS-SEM has several advantages, including (1) predicting and explaining target constructs with less-developed theories; (2) handling formative measurement models; (3) dealing with single-item constructs without identification problems; (4) testing interaction/moderation effects; and (5) analyzing data that are not normally distributed (Hair et al. 2014). Considering the purpose of this study (i.e., model comparisons for theoretical development) and the characteristics of the data (i.e., formative measurement, non-normal data, and a single-item construct), PLS-SEM was deemed more appropriate than CB-SEM. However, unlike CB-SEM, PLS-SEM does not aim to assess model fit based on discrepancies between observed and estimated variance–covariance matrices. Traditional goodness-of-fit measures used in CB-SEM cannot be calculated and used to assess the goodness-of-fit of PLS-SEM models directly, and there is no well-established goodness-of-fit measure to assess PLS-SEM models (Hair et al. 2014). Given this study’s focus on the role of CSR in resident attitude formation toward tourism projects, the best model was selected based on the extent to which a tested model explained the variance in resident attitude. A five-step procedure was implemented to identify the optimal model among four options. Theoretical and practical implications will be discussed based on estimates of the final model.

Results

Respondent Profiles

Table 1 shows respondents’ demographic characteristics. Slightly more respondents were women (50.2%) than men (49.8%). Most were married (78.4%) with children (78.5%). As for age, 41.1% of respondents were between 35 and 49 years old. Nearly 40% were college graduates (39.4%), and most (60.4%) were employed either full- or part-time. More than half of respondents (59.7%) earned a moderate household income (4,000–9,999 RMB; roughly $600–$1,500). On average, respondents had lived in Shanghai for 26.9 years at the time of the survey.

Table 1.

Demographic Characteristics.

Demographics	Percentage (%)
Gender
Male	49.8
Female	50.2
Marital status
Single/never married	16.7
Married	78.4
Separated/divorced/widowed	4.0
Prefer not to say	0.9
Age, years
18–24	9.5
25–34	18.0
35–49	41.1
50–64	17.3
65 or above	14.1
Education
High school or less	21.5
Technical/vocational high school	6.5
Associate degree or some college	21.6
College graduate	39.4
Graduate work/master’s/doctoral degree	10.6
Something else	0.3
Prefer not to say	0.1
Number of children
0	21.5
1	60.4
2	10.8
3 or more	7.3
Monthly household income (RMB)
Below 3,999	9.5
4,000 to 9,999	59.7
10,000 to 19,999	21.7
20,000 or above	6.0
Prefer not to say	3.1
Employment status
Employed full-time/part-time	60.6
Housewife/homemaker	12.2
Retired	23.1
Student	0.9
Temporarily unemployed	3.0
Other	0.1
Prefer not to say	0.1

Measurement Model Selection: Reflective or Formative?

To empirically determine the measurement model types, confirmatory tetrad analysis in PLS-SEM (CTA-PLS) was run with 5,000 bootstrap subsamples at a given α = 0.05 level (two-tailed). The CTA-PLS procedure requires that each tested construct have at least 4 but no more than 25 manifest variables (Ringle, Wende, and Becker 2015). Thus, all proposed constructs except resident attitude were included in the tests. Results showed that for each formatively measured construct, the Bonferroni-adjusted confidence interval of at least one indicator did not include the parameter value of the null hypothesis (H0: τ = 0), rejecting the appropriateness of using reflective measurement (Gudergan et al. 2008). Thus, following the recommendation by Gudergan et al. (2008), formative measurement of exogenous constructs (i.e., CSR and economic, sociocultural, and environmental impacts at the city and personal levels) was used for further analysis.

Measurement Model Assessment

The tolerance value and variance inflation factor (VIF) were used to assess collinearity issues in the measurement models. The tolerance value of all indicators was greater than 0.20, and the VIF of all indicators was below 5.0, indicating an acceptable level of collinearity (Hair et al. 2014). To ensure convergent validity and indicator validity for each formatively measured construct, criteria were established to identify items to be retained in the measurement model (Hair et al. 2014). Every construct was used to predict a theoretically related construct (in this case, resident attitude). Indicators satisfying one of the following conditions were retained: (1) the indicator had significant outer weights (t value > 1.96); (2) the outer loading of the indicator was above 0.50 if its outer weight was not significant; or (3) its outer loading was significant, and the indicator was theoretically important. If none of these criteria was met, the indicator was deleted. All retained measurement items for the constructs are listed in Appendix A.

To assess discriminant validity, correlations between all latent variables (using retained items) were calculated. The highest correlation was 0.539, between sociocultural impacts at the city level and the individual level. Fornell and Larcker (1981) suggested that the discriminant validity of two constructs is demonstrated when their correlations are lower than the square root of both constructs’ average variance extracted (AVE; AVE scores are normally greater than 0.5). This requirement was satisfied for each pair of constructs in this study; all correlations in the study were lower than 0.71, which did not exceed the square root of constructs’ AVE scores. Hence, discriminant validity was established.

Common method bias was assessed using Harman’s single-factor test (Podsakoff et al. 2003) and the method in Lindell and Whitney (2001). First, Harman’s single-factor test was conducted in SPSS. The single factor was found to explain only a small amount of the variance (17.19%), indicating no systematic bias caused by the data collection method. Next, the Lindell and Whitney (2001) method was applied in SmartPLS. A theoretically unrelated construct, the perceived difference between Chinese and American cultures, was used as the marker variable. Low correlations (<0.21) were found between perceived cultural differences and all constructs used in the formative measurement model. Constructs’ variances explained by the marker variable were less than 0.05, implying that variances caused by the common method were below 5% (i.e., low). Thus, the measurement of all constructs in the tested model was not affected by common method bias.

Figure 2 displays the mean scores of each item measuring tourism impacts at the city level and personal level before any item was dropped. The comparison shows that (1) a majority of the expected tourism impacts were positive; and (2) expected tourism impacts at the city level were more polarized, whereas those at the personal level tended to be more centralized (i.e., closer to the average mean scores of all items).

Figure 2.

Mean scores of tourism impacts at city and personal levels.

Structural Model Assessment

Each of the structural models in PLS-SEM was assessed using the following five steps, as recommended by Hair et al. (2014):

Step 1: SEM collinearity was assessed. The VIF of all independent variables in each model was higher than 0.20 and lower than 5 (see Tables 2 –4), indicating no collinearity problems (Hair et al. 2014).

Step 2: The effects of independent variables on resident attitude were assessed. Significant paths between independent variables and resident attitude were identified as shown in Tables 2 –4. Paths with nonsignificant effects were recommended to be deleted (Hair et al. 2014).

Step 3: The value of R² was assessed for each model. It has been suggested that “in general, R² values of 0.75, 0.50, or 0.25 for the endogenous constructs can be described as respectively substantial, moderate and weak” (Hair et al. 2014, 186). The moderation model has the highest determination coefficient R² (0.246) and adjusted R² (0.236), followed by the independent model (0.229 and 0.224), mediation model (0.187 and 0.182), and antecedent model (0.182 and 0.176).

Step 4: The effect size (f²) of each independent construct in a model was assessed. Effect size (f²) represents the change in R² value when an independent construct is omitted from a model (Hair et al. 2014). The effect size measures to what extent an independent construct exerts a substantial influence on resident attitude (Hair et al. 2014). Values of 0.02, 0.15, and 0.35 indicate that an exogenous construct has a small, medium, or large effect, respectively, on an endogenous construct (Cohen 1988; Hair et al. 2014). The f² results (see Tables 2 –4) indicate that independent constructs significantly influencing resident attitude (bolded in Tables 2 –4) have much larger effect sizes than other independent constructs. Most notably, economic impacts at the city level, sociocultural impacts at the city level, and CSR had the largest effect sizes among all independent constructs in every tested model.

Step 5: The predictive relevance (Q²) of a model and the effect size (q²) of each construct in the model were assessed. The blindfolding procedure in SmartPLS was used to generate Q² values. Q² is a measure of how well the structural model can predict the originally observed values of dependent constructs (i.e., resident attitude, tourism impacts in the antecedent model, or CSR in the mediation model). Results showed that the Q² value of each structural model was greater than 0 (see Tables 2 –4), suggesting that all models had predictive relevance for resident attitude (Hair et al. 2014). The moderation model had the highest Q² value (0.199) followed by the independent model (0.194), mediation model (0.167), and antecedent model (0.163). Similar to the relationship between f² and R², q² reflects the change in Q² value when a specified independent construct is omitted from a model (Hair et al. 2014). According to Hair et al. (2014), q ² values of 0.02, 0.15, and 0.35 respectively indicate small, medium, or large predictive relevance. A q² lower than 0.02 indicates negligible predictive relevance for an independent construct, providing additional support for deleting nonsignificant paths from structural models. Tables 2 –4 and Figure 3 show the PLS-SEM analysis results for the independent, moderation, mediation, and antecedent models (see Appendix B for the direct and indirect effects in the mediation and antecedent models).

Table 2.

Results of Independent and Moderation Models.

	Independent Model						Moderation Model
	β	t	Sig.	f ²	q ²	VIF	β	t	Sig.	f ²	q ²	VIF
EconCity	0.078	2.382	0.017	0.005	0.002	1.472	0.081	2.479	0.013	0.006	0.000	1.556
EnviCity	−0.002	0.070	0.944	0.000	−0.001	1.303	−0.014	0.434	0.665	0.000	0.005	1.348
SociCity	0.295	7.703	0.000	0.062	0.061	1.818	0.315	8.237	0.000	0.066	0.054	2.003
EconPers	0.055	1.344	0.179	0.003	−0.002	1.360	0.065	1.402	0.162	0.004	−0.004	1.425
EnviPers	0.007	0.188	0.851	0.000	−0.002	1.236	0.015	0.401	0.688	0.000	−0.002	1.291
SociPers	0.007	0.183	0.855	0.000	−0.005	1.607	0.018	0.481	0.630	0.000	0.000	1.628
CSR	0.170	4.999	0.000	0.026	0.020	1.447	0.181	5.183	0.000	0.029	0.026	1.507
CSR*EconCity	–	–	–	–	–	–	0.023	0.611	0.541	0.001	−0.002	2.603
CSR*EnviCity	–	–	–	–	–	–	0.097	2.851	0.005	0.010	0.006	1.650
CSR*SociCity	–	–	–	–	–	–	0.036	0.867	0.387	0.001	−0.006	3.118
CSR*EconPers	–	–	–	–	–	–	−0.024	0.558	0.577	0.001	−0.001	2.075
CSR*EnviPers	–	–	–	–	–	–	0.017	0.436	0.663	0.000	−0.001	1.817
CSR*SociPer	–	–	–	–	–	–	−0.071	1.906	0.057	0.005	0.004	2.084
R ²	0.229						0.246
R _adj ²	0.224						0.236
Q ²	0.194						0.199

Note: CSR = corporate social responsibility; Sig. = significance; VIF = variance inflation factor.

Table 3.

Results of Mediation Model.

	CSR						Resident Attitude
	β	t	Sig.	f ²	q ²	VIF	β	t	Sig.	f ²	q ²	VIF
EconCity	0.118	3.378	0.001	0.016	0.001	1.537	0.133	3.425	0.001	0.014	0.013	1.562
EnviCity	0.066	1.992	0.046	0.005	0.003	1.424	0.004	0.098	0.922	0.000	−0.002	1.431
SociCity	0.387	9.604	0.000	0.127	0.009	2.093	0.273	5.320	0.000	0.039	0.040	2.358
EconPers	0.166	4.583	0.000	0.029	0.008	1.665	0.098	2.049	0.041	0.007	0.004	1.714
EnviPers	0.112	3.090	0.002	0.016	0.002	1.354	−0.016	0.398	0.690	0.000	−0.001	1.376
SociPers	−0.006	0.166	0.868	0.000	0.000	1.779	0.017	0.407	0.684	0.000	−0.001	1.779
CSR	–	–	–	–	–	–	1.768	3.856	0.000	0.018	−0.013	1.768
R ²	0.434						0.187
R _adj ²	0.431						0.182
Q ²	0.118						0.167

Note: CSR = corporate social responsibility; Sig. = significance; VIF = variance inflation factor.

Table 4.

Results of Antecedent Model.

	EconCity	EnviCity	SociCity	EconPers	EnviPers	SociPers	Resident Attitude
	β	β	β	β	β	β	β	t	Sig.	f ²	q ²	VIF
EconCity	–	–	–	–	–	–	0.139	3.637	0.000	0.015	0.014	1.560
EnviCity	–	–	–	–	–	–	0.003	0.081	0.935	0.000	−0.004	1.445
SociCity	–	–	–	–	–	–	0.280	5.463	0.000	0.041	0.038	2.340
EconPers	–	–	–	–	–	–	−0.102	2.140	0.032	0.007	0.005	1.723
EnviPers	–	–	–	–	–	–	−0.012	0.297	0.766	0.000	−0.002	1.383
SociPers	–	–	–	–	–	–	0.016	0.362	0.718	0.000	0.000	1.776
CSR	0.454	0.415	0.598	0.493	0.404	0.429	0.142	3.504	0.000	0.014	−0.018	1.784
t	14.224	13.446	22.424	16.465	12.771	13.575
Sig.	0.000	0.000	0.000	0.000	0.000	0.000
f ²	0.259	0.209	0.557	0.322	0.195	0.226
R ²	0.206	0.173	0.358	0.243	0.163	0.184	0.182
R _adj ²	0.205	0.172	0.357	0.243	0.163	0.184	0.176
Q ²	0.056	0.093	0.099	0.092	0.057	0.041	0.163

Note: CSR = corporate social responsibility; Sig. = significance; VIF = variance inflation factor.

Figure 3.

Four competing models.

Model Comparison

The adjusted R square (R_adj²) and Q² for predicting resident attitude were used to compare the four PLS-SEM models. R_adj² is more accurate than the PLS goodness-of-fit index in comparing PLS-SEM models with different exogenous constructs (Hair et al. 2014; Henseler, Ringle, and Sarstedt 2012). PLS-SEM aims to maximize the R² values of the endogenous latent variable(s) in the path model, and R_adj² modifies R² according to the number of exogenous constructs relative to sample size, which minimizes bias toward complex models. In addition, the Q² for resident attitude is helpful when comparing models. The R_adj² and Q² values were each highest for the moderation model (R_adj² = 0.236, Q² = 0.199; see Tables 2 –4). In other words, the moderation model had the best performance among all tested models in explaining the variance and predicting the observed value of resident attitude.

Final Model

The moderation model was re-run after deleting insignificant relationships, and then a final model was obtained (see Table 5 and Figure 4). The final moderation model achieved an R² of 0.236 and adjusted R² of 0.232. A Q² value of 0.210 indicated that exogenous constructs in the final model collectively showed predictive relevance for resident attitude. Overall, resident attitude was positively affected by city-level sociocultural impacts (β = 0.328, t = 9.027), city-level economic impacts (β = 0.094, t = 2.870), and CSR (β = 0.191, t = 6.041); the more favorable beliefs residents held about SHDR’s sociocultural impacts, economic impacts, and CSR performance, the more likely they were to report supportive attitudes toward the project (see Table 5 and Figure 4). The q² values indicated that city-level social impacts and CSR also showed small-to-mediate predictive relevance for resident attitude. According to the cut-off values for f² suggested by Cohen (1988) and Hair et al. (2014), city-level sociocultural impacts (f² = 0.091), and CSR (f² = 0.034) exerted small effects on resident attitude; the effect of city-level economic impacts (f² = 0.008) on resident attitude was trivial. City-level environmental impacts did not significantly affect resident attitude (β = 0.004, t = 0.130); however, the interaction between city-level environmental impacts and CSR significantly influenced resident attitude (β = 0.092, t = 3.028). The effect size for the interaction is 0.013, which is rather small (Cohen 1988; Hair et al. 2014). However, this does not necessarily imply that the interaction effect is unimportant (Chin, Marcolin, and Newsted 2003). Specifically, good CSR enhanced the effect of city-level environmental impacts on resident attitude; one standard deviation increase in CSR not only impacted resident attitude by 0.191 but also increased the effect of city-level environmental impacts on resident attitude by 0.092.

Table 5.

PLS-SEM Results of Final Model.

	Moderation Model
	β	t	Sig.	f ²	q ²	VIF
EconCity	0.094	2.870	0.004	0.008	0.005	1.415
EnviCity	0.004	0.130	0.896	0.000	0.011	1.255
SociCity	0.328	9.027	0.000	0.091	0.082	1.557
CSR	0.191	6.041	0.000	0.034	0.034	1.423
CSR*EnviCity	0.092	3.028	0.002	0.013	0.011	1.102
R ²	0.236
R _adj ²	0.232
Q ²	0.210

Figure 4.

Final model.

Discussion and Implications

This paper explores the role of CSR in understanding residents’ attitudes toward a high-profile tourism project in its initial building stage. Four theoretically plausible models (i.e., the independent model, moderation model, antecedent model, and mediation model) were proposed and compared to identify the best option. Model comparisons suggest that the moderation model, in which CSR moderates the effect of perceived impacts on resident attitude, is statistically and conceptually sound. That is, tourism companies’ CSR has a positive effect on resident attitude toward tourism projects and enhances the effect of city-level tourism on resident attitude.

Our findings highlight the potential of connecting two important areas of study: CSR and resident attitude. Although local residents’ attitudes toward tourism development constitute one of the most heavily studied areas in tourism (Harrill 2004; Nunkoo Smith and Ramkissoon 2013), this line of research is still criticized for being atheoretical (Nunkoo Smith and Ramkissoon 2013) and somewhat irrelevant to mainstream tourism practice (Sharpley 2014). Sharpley (2014) posits that such critiques may stem from the fact that existing studies have often focused on tourism development in general and neglected the impacts of individual tourism projects such as a major resort or theme park. In examining resident attitude toward tourism projects, variables associated with company-level characteristics and corresponding company evaluations should be integrated into a traditional resident attitude framework. The present study contributes to the literature by proposing a link between residents’ perceptions of a tourism developer’s CSR and locals’ attitudes toward the project. However, when addressing the role of other corporate-level constructs in resident attitude formation, the mechanisms required to connect these two lines of research should be carefully examined on a case-by-case basis.

This study reveals that the effects of CSR and tourism impacts on resident attitude are intertwined: CSR interacts with perceived tourism impacts in influencing resident attitude toward the development of large-scale tourism projects. Residents tend to form attitudes toward tourism projects based on holistic evaluations about the effects of CSR and tourism impacts, rather than a simple aggregation of two separate effects or a sequential assessment of the two effects. This finding supports the notion that attitude formation and information processing are not discrete and sequential; multiple mental processes can indeed operate simultaneously and interact (Van Overwalle and Siebler 2005; Thomas and McClelland 2008). The moderation effect of CSR on the relationship between perceived tourism impacts and resident attitude is also consistent with previous studies regarding the role of CSR reputation in consumer attitudinal evaluations (Lii and Lee 2012; Ye, Cronin, and Peloza 2015). Notably, in this study, CSR was measured by expectations that residents had on SHDR’s CSR practice, as data were collected in an early stage of the project. The CSR reputation of the Disney brand, SHDR’s own CSR initiatives, and Shanghai residents’ impressions of foreign companies may have influenced CSR expectations held by residents. Despite the concern that CSR activities may ratchet up stakeholder expectations over time (Bertels and Peloza 2008), this study shows that CSR expectations of residents do benefit tourism companies that generate positive tourism impacts for the local community.

Results of this study indicate that CSR interacts with perceived impacts to influence resident attitude; however, the interaction effect was found to exist only between CSR and city-level environmental impacts. This finding is interesting, particularly when considering the main effect of city-level environmental impacts. City-level environmental impacts of the tourism project did not appear to influence resident attitude significantly, but they did significantly affect resident attitude through an interaction with CSR. When a local community believes the company running a project will engage in good CSR activities, such beliefs may create a halo effect leading to more favorable resident attitude and enhancing the influence of city-level environmental impacts on resident attitude formation. Previous studies have suggested that the halo effect could make consumers more likely to attribute crises to external factors and hence develop more favorable brand evaluations (Klein and Dawar 2004). In the context of this study, respondents might be more likely to attribute unfavorable city-level environmental impacts to external reasons and favorable environmental impacts to internal reasons when holding high (vs. low) expectations about SHDR’s CSR performance. As a result, when CSR expectations were high, perceptions of city-level environmental impacts generated more supportive attitudes among local residents than when CSR expectations were low. Future studies are warranted to better understand the interaction between CSR and perceived tourism impacts.

This study tested economic, sociocultural, and environmental impacts at the city and personal levels, yet analyses indicated that only city-level impacts were significant. Overall, city-level impacts influenced resident attitude more strongly than individual-level impacts, implying that residents’ attitudes were tied more closely to SHDR’s city-level impacts than to individual-level impacts. On one hand, residents may not have anticipated many direct or immediate personal gains from the development of a large-scale tourism project, especially when the survey was conducted three years before the park opened. For instance, in the early stage of the project, locals could gauge the economic impacts of SHDR on the city (e.g., generating new jobs and increasing tourism revenue), but they may not have been able to associate the park with any personal financial gains. On the other hand, the collectivist culture in China emphasizes community pride and group benefits over individual advantages (Li, Hsu, and Lawton 2015; Zhou and Ap 2009); thus, residents may have considered support for a tourism project to be more relevant to the community than to themselves. This finding is consistent with SRT assumptions, suggesting the importance of the collective formation of resident attitude (Fredline and Faulkner 2000), particularly in the project-building stage.

In addition, this study found that among three types of tourism impacts (economic, sociocultural, and environmental), sociocultural impacts are most influential on resident attitude. The insignificant association between environmental impacts on resident attitude has been identified in other studies (Prayag et al. 2013). This result makes sense given the timing of the present study (i.e., roughly 3 years before the park opened), at which point the environmental impacts of SHDR were not readily apparent. Somewhat surprisingly, economic impacts were less influential than sociocultural impacts, presumably because generating economic benefits in the community may be taken for granted in tourism project development and thus not appreciated as much as sociocultural impacts by residents. Finally, the different effects of tourism impacts on resident attitude underscore the need to disaggregate the economic, sociocultural, and environmental components of perceived impacts (Prayag et al. 2013).

Compared with economic and environmental impacts, the sociocultural impacts of Disney, an iconic American cultural brand, have been more widely discussed and debated in the local community since SHDR’s inception. Findings of this study suggest that in the early stages of development for a major tourism project, particularly those carrying foreign investments like SHDR, explaining the project’s positive sociocultural significance would be an effective way of building rapport with the local community. For theme parks such as SHDR, whose major features include world-famous cartoon characters originating from Western culture, preserving these traditions can help the company maintain its identity and popularity. At the same time, tourism developers should consider adapting some features to Chinese culture, as most SHDR visitors are Chinese. For example, characters like Monkey King, Mulan, and Kung Fu Panda are culturally relevant; the key is to determine the extent to which to integrate Western and Eastern cultures to address the sociocultural impact on local residents.

The current study also sheds light on the connection between residents’ perceptions of CSR and resident attitude from a managerial perspective. Findings suggest that tourism developments in China during an initial construction stage can use CSR initiatives to attract grassroots support, particularly when assessing the environmental impacts of tourism projects. CSR has emerged as a key factor behind the success of service enterprises in China (Zhang et al. 2016). Consistent with previous studies, this study suggests that a company’s good CSR record can be transformed into positive corporate evaluations in China, particularly when the company sells experiential products (Tian, Wang, and Yang 2011). Public relations efforts in collectivist cultures like China should emphasize the positive aspects of tourism projects generated for the local community, which can then contribute to the formation of favorable resident attitudes.

Limitations and Future Research

Readers are reminded to interpret this study’s findings with some limitations in mind. First, compared with CB-SEM, PLS-SEM does not have firmly established measures for global model fit tests (Hair et al. 2014), which is a drawback of this approach. When exploring less-developed theories or comparing models with different exogenous constructs, as this study did, PLS-SEM scholars recommend using the adjusted coefficient of determination (adjusted R²) (Hair et al. 2014). In the future, when more scholarly efforts have sought to examine the association between CSR and the link of perceived tourism impact→resident attitude and additional theories and construct relationships have been developed, CB-SEM may be a better methodological choice.

Second, the measurement of economic tourism impacts did not encompass the price of real estate properties, as the survey did not target residents living in the areas near SHDR; respondents were limited to urban Shanghai residents in broad geographic locations. The evaluation of tourism impacts by residents directly involved in the construction of tourism projects may generate different insights. For instance, residents who are economically dependent on the tourism industry or who have relocated because of tourism-induced displacement and resettlement (Xue, Kerstetter, and Buzinde 2015) may hold different opinions of tourism impacts compared to other residents. Subsequent studies may wish to examine the performance of the moderation model among residents who have been influenced substantially by tourism projects. Third, in this study, the tourism impacts and CSR of SHDR were measured based on residents’ expectations before the park opened. As the frequency and quality of individual-level contact with tourists can shape resident attitude (Ward and Berno 2011), it would be interesting to monitor local residents’ attitudes toward SHDR across time, particularly after its official opening. This line of research would require a longitudinal approach, which the research team has been conducting. Additional follow-up research will be reported as it becomes available.

Last but not least, it should be noted that the present study reports findings regarding the role of CSR in the context of Chinese residents’ attitudes toward the development of large-scale tourism projects in their local community. The relationships identified in the study are presumably delimited to the collectivist culture context, and the generalizability of the findings must be tested in other contexts.

Conclusion

CSR has been overlooked in the resident attitude literature, particularly in understanding residents’ attitudes toward a high-profile tourism project. In this study, four competing models were proposed based on different assumptions around the role of CSR in resident attitude formation. Model comparisons indicate that CSR interacts with tourism impacts in shaping resident attitude. Specifically, CSR is an important driving force behind local residents’ attitudes toward a tourism project and can also increase the effects of city-level environmental impacts on resident attitude. These results suggest that in order to cultivate grassroots support from residents, tourism companies should make a point to demonstrate social responsibility to the public, particularly when the public has insufficient information to gauge the potential impacts of tourism projects. Discrepancies in the effects of community-level and personal-level tourism impacts on resident attitude also substantiate the application of a dual-theory approach to clarify resident attitude formation.

For investors launching new businesses in a foreign country, building rapport with the local community is crucial to their sustainable success. This is particularly true of tourism and hospitality businesses, whose operations require constant, open interactions with the local community. In this sense, for multinational corporations, gaining local support is first and foremost a public relations issue with clear strategic purposes. This hence makes a natural connection between understanding local resident attitudes and a company’s CSR strategy and goal, which also distinguishes the current study from most other resident attitude research that focuses on general tourism development and governmental policymaking. This perspective, which has been largely overlooked in the extant literature, carries important implications for tourism and hospitality developers along with the potential to establish a new research direction.

Footnotes

Appendix

Appendix B.

Indirect and Total Effects.

	Mediation Model: Indirect and Total Effects						Antecedent Model: Indirect and Total Effects
	Resident Attitude						Resident Attitude
	Indirect Effect			Total Effect			Indirect Effect			Total Effect
	β	t	Sig.	β	t	Sig.	β	t	Sig.	β	t	Sig.
EconCity	0.019	2.278	0.023	0.152	3.892	0.000	–	–	–	0.139	3.637	0.000
EnviCity	0.011	1.672	0.095	0.014	0.387	0.698	–	–	–	0.003	0.081	0.935
SociCity	0.062	3.613	0.000	0.335	6.947	0.000	–	–	–	0.280	5.463	0.000
EconPers	0.027	3.028	0.002	−0.072	1.521	0.128	–	–	–	−0.102	2.140	0.032
EnviPers	0.018	2.231	0.026	0.002	0.063	0.950	–	–	–	−0.012	0.297	0.766
SociPers	−0.001	0.157	0.875	0.016	0.386	0.699	–	–	–	0.016	0.362	0.718
CSR	–	–	–	0.161	3.856	0.000	0.183	5.563	0.000	0.325	9.727	0.000

Authors’ Note

An earlier draft of this paper was presented at the 2015 Travel and Tourism Research Association Annual Conference, Portland, OR.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Shanghai International Theme Park Company Limited.

Author Biographies

Xiang (Robert) Li, PhD, is a professor at the Department of Tourism and Hospitality Management, Temple University. His research focuses on destination marketing and tourist behavior.

Yuan Wang is a PhD candidate at the School of Sport, Tourism and Hospitality Management and the Fox School of Business, Temple University. Yuan’s research focuses on destination marketing an peer-to-peer accommodations.

Yingsha Zhang, PhD, is a senior lecturer at the Waikato Management School, The University of Waikato. Yingsha’s research mainly focuses on destination marketing and management, consumer behavior, and tourism geography, with special emphasis on theme parks and big data analysis.

Yang Cao, PhD, is a financial advisor at Merrill Lynch Wealth Management, Bank of America Corporation. She received a PhD degree in hospitality management from University of South Carolina.

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