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Article

Examining the Formation of Resident Support for Tourism: An Integration of Social Exchange Theory and Tolerance Zone Theory

by
Xue Qin
1,
Shun Ye
2,*,
Fuhua Xiang
2 and
Chunyan Wang
3
1
School of Management, Guizhou University, Guiyang 550025, China
2
School of Management, Zhejiang University, Hangzhou 310058, China
3
College of Economic and Management, Tarim University, Alar 843300, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(11), 4921; https://doi.org/10.3390/su17114921
Submission received: 8 April 2025 / Revised: 17 May 2025 / Accepted: 20 May 2025 / Published: 27 May 2025
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Abstract

:
Resident support toward tourism is often analyzed through the lens of the “benefits vs. costs” paradigm within social exchange theory. However, empirical observations have shown instances where residents remain supportive despite costs outweighing benefits, challenging the conventional social exchange logic. To address this paradox, this study introduces the Tolerance Zone Theory. A conceptual framework has been constructed wherein the influence of negative tourism factors is contingent upon residents’ level of tolerance. This tolerance is, in turn, molded by the presence of positive outcomes derived from tourism. This framework was tested using survey data gathered from 514 residents in ethnic villages in Guizhou Province, China. The results validate the moderating effect of tolerance, demonstrating that high tolerance can mitigate the impact of negative tourism outcomes on resident support. Theoretical and practical implications are discussed.

1. Introduction

Residents are key stakeholders in tourism destinations, playing a vital role in their evolution [1]. The residents’ support for tourism initiatives is paramount for ensuring the sustained prosperity and enduring vitality of such locales [2,3,4]. This significance is particularly pronounced in ethnic tourism villages, where residents often embody a “dual identity” (serving simultaneously as both community members maintaining their traditional way of life and tourism service providers participating in the commercial sector). These residents possess firsthand knowledge of the tourism development process, experiencing both its advantages and challenges. In many ethnic tourism settings, local residents, acting as the “living carriers” (individuals who actively preserve and transmit intangible cultural heritage through their daily practices and performances) of traditional cultural elements, actively or passively engage in the evolution of tourism. Their traditions, cultural practices, warm hospitality, and daily interactions serve as major draws for tourists. Consequently, in comparison to other tourism destinations, the level of residents’ backing significantly impacts the prosperity and advancement of the local tourism industry [5,6].
Academics have conducted pervasive research on residents’ support for tourism, frequently utilizing the social exchange theory (SET) as the foundational framework to elucidate the genesis of such support [7]. Within this framework, residents in tourism destinations are typically viewed as “rational actors” whose endorsement of tourism development is based on a careful evaluation of the “benefit–cost” ratio (the perceived balance between economic/social gains and personal/community sacrifices resulting from the development of tourism) [4]. However, empirical evidence has shown instances where residents exhibit support even when there are perceived costs [8,9]. This discrepancy has led researchers to question the sufficiency of the SET framework and propose that factors beyond rational calculation may influence residents’ support for tourism [4,10]. Recent explorations infer that there might be vital determinants or mediatory mechanisms hitherto inadequately examined in comprehending the association between residents’ perceptions of the impacts of tourism and their ensuing support for tourism [11]. These outcomes underscore the intricacy of residents’ support for tourism, urging the necessity to contemplate a more comprehensive selection of influences, extending beyond the confines of traditional theoretical frameworks.
This study endeavors to develop a novel conceptual framework integrating social exchange theory and Tolerance Zone Theory to examine the critical role of tourism tolerance in shaping residents’ support mechanisms. Through this endeavor, the study showcases its innovation in two primary dimensions. Firstly, it delves into the intricate process of residents’ support formation in tourist destinations, thereby broadening and enriching the conventional research paradigm of “residents’ perceived tourism impact—tourism support.” Secondly, it skillfully integrates social exchange theory with Tolerance Zone Theory, thereby providing a comprehensive and insightful analysis of residents’ support for tourism, aligning with prior research appeals for theoretical integration to facilitate a thorough analysis of residents’ backing for tourism [5,12,13].

2. Literature Review

2.1. Ethnic Tourism

Smith defines ethnic tourism as tourism activities involving “exotic peoples”, encompassing visits to native homes, observing traditional dances and ceremonies, and purchasing authentic handicrafts [14]. Rooted in culture, it primarily manifests in ethnic villages or indigenous communities [15], necessitating active resident participation.
The residents play a pivotal role in propelling ethnic tourism forward [16]. The rich tapestry of traditions and lifestyles exhibited by indigenous peoples acts as a major draw for tourists [17]. While studies explore tourism’s impacts on locals, gaps remain in understanding resident attitudes and behaviors from their own perspectives [18], particularly in minority/indigenous contexts [10].

2.2. Resident Support for Tourism

Research on resident support for tourism has traditionally linked it to attitudinal factors, often treating attitudes and support as interchangeable concepts [19,20]. However, scholars have progressively differentiated resident tourism support from attitudes, recognizing it as a distinct construct representing a willingness to engage or pro-tourism behavior [21,22]. As one of tourism research’s most examined topics [7], its theoretical foundations have evolved significantly. While Perdue et al.’s early model lacked strong theoretical grounding [23], Ap’s introduction of social exchange theory marked a turning point by explaining how perceived impacts shape resident support [24].
Social exchange theory has become fundamental in tourism support research. Building on Perdue’s model and Ap’s framework, scholars have developed various analytical models. For instance, Jurowski et al. devised a path model to investigate residents’ support within the social exchange theory context (Jurowski, et al. [25]). Similarly, Ng and Feng [26] and Gursoy et al. [27] formulated structural models based on this rationale, culminating in a substantial body of literature on residents’ support for tourism grounded in social exchange theory. In ethnic tourism contexts, Yang and Wall examined minorities’ perceptions across cultural, social, and economic dimensions [18], while Wang et al. found that residents’ economic and socio-cultural perceptions exert more pronounced effects on their support for tourism, particularly in ethnic minority village settings [10].
Previous studies have generally found a consensus in research on supportive behavior when destination residents perceive benefits (positive impact), with residents’ perceived benefits positively correlating with their support for tourism [7,28]. Nonetheless, inconsistent findings have been observed in research exploring the nexus between perceived costs (or negative impacts) and support for tourism. Some studies denote a negative relationship between residents’ apprehension of potential drawbacks and their inclination to endorse tourism [13,29]. Conversely, other research suggests that no substantial correlation exists between residents’ perception of such costs (negative impacts) and their endorsement of tourism [5,11].
The contentious findings have spurred scholars to question the comprehensive explanatory power of social exchange theory (SET). This skepticism has prompted calls for a deeper understanding through theoretical integration. Notably, Nunkoo and Gursoy merged Identity Theory with SET [30], while the emotional solidarity theory has been integrated by Erul et al. [31], Hasani et al. [32], and Woosnam and Aleshinloye [33]. Li and Wan [34] also incorporated the Theory of Place Attachment into this integrative approach. Gautam combined social exchange theory, emotional solidarity theory, and bottom-up spillover theory to explore the precursors of support for tourism [12]. Hateftabar and Rasoolimanesh blended SET with Weber’s Theory [35]. This growing trend toward theoretical integration reflects a paradigm shift in tourism support research, enabling a more nuanced understanding of its underlying mechanisms.

2.3. Tolerance

Muldoon et al. regard tolerance as the act of tolerating or putting up with something you do not like [36]. McLain posits that the concept of tolerance and intolerance characterizes a spectrum of reactions, which extend along a continuum that stretches from outright rejection to full acceptance [37,38]. Parasuraman et al. have applied this concept to the study of service management and put forward the concept of the zone of tolerance (ZOT) [39]. The zone of tolerance refers to a psychological acceptance span of customers, within which customers find the service they receive acceptable. Customers within the tolerance zone are less sensitive to changes in service quality compared to those outside the zone [40].
In tourism literature, the zone of tolerance has been widely applied to explore service quality in hotels and travel agencies. For instance, Nadiri and Hussain proposed that the zone of tolerance provides a range within which customers are willing to accept variations in service delivery and examined its relationship with customer satisfaction levels in Northern Cyprus hotels [41]. Tsaur, Huang, and Luoh adopted perceived risk and zone of tolerance theory to investigate how travel product types (tour-based vs. ticket-based) and online review directions influence review persuasiveness [42]. Lv, Liu, and Luo et al. demonstrated the positive effect of cuteness design in AI assistants on customers’ tolerance of service failures [43]. Lobo et al., in the context of Singaporean travel retail agencies, explored how the zone of tolerance mediates the relationship between service quality and favorable behavioral intentions [44]. Gilbert and Gao examined the connections between customer tolerance, customer experience, brand trust, and emotions in travel agency businesses [45]. Emmanuel evaluated young customers’ perceptions of service quality in North Cyprus travel agencies using the zone of tolerance theory, noting that while customers benefit when adequate service meets their minimum expectations, they often do not receive superior service matching their desired expectations [46]. In addition, Lee and An (2021) analyzed disabled tourists’ responses to service failures, arguing that when a service failure exceeds customers’ zones of tolerance, it directly triggers complaints [47]. It can be seen that the application of the zone of tolerance in tourism research has primarily focused on tourists as the subject of study, exploring their tolerance levels toward tourism service quality. Few studies have examined this concept from the perspective of destination residents to investigate their tolerance levels [48].

3. Hypothesized Model

3.1. Tourism Impacts and Resident Support: Social Exchange Theory

Social exchange theory, as formulated by Homans [49], posits that humans are rational beings engaging in social interactions characterized by exchanges of tangible and intangible benefits and costs. The fundamental premise of this theory is that human interactions, or exchange behaviors, are rational endeavors aimed at maximizing benefits and minimizing costs, with all human actions being motivated by the pursuit of self-interest [50].
Within this theoretical structure, it is postulated that residents’ backing for tourism materializes from their interpretation of its effects, which tentatively span across various dimensions—namely, economic, social, cultural, and environmental aspects [51,52]. These impacts are commonly divided into positive and negative aspects [13,53]. From a cost–benefit perspective, several researchers categorize the positive impacts of tourism as benefits, while viewing the negative impacts as costs [11,54].
In accordance with the tenets of social exchange theory, scholars propose that the liaisons between local inhabitants and tourism can be conceptualized as a mutual exchange of resources. When residents witness tangible benefits from tourism, their inclination toward promoting its continuous growth intensifies. On the other hand, perceiving the costs or adverse ramifications of the development of tourism could possibly incite them to oppose its further expansion [55]. The study conducted by Munanura and Kline suggests that residents’ perceptions of the impacts of tourism are strong determinants of their support for tourism. The results indicate that residents’ positive perceptions of tourism are positively correlated with the support for tourism, while negative perceptions show a negative correlation with the support for tourism [29]. Viana-Lora’s research also confirms that perceived benefits and costs are antecedent variables of residents’ support for the sustainable development of tourism. Specifically, perceived benefits positively influence the support for tourism, while perceived costs exert a negative impact on such support [56].
Building on the theoretical framework of social exchange theory (SET), this study examines residents’ perception of the impacts of tourism, where support for the development of tourism is contingent upon their assessment of the perceived benefits versus the costs. The following hypotheses are proposed.
H1a. 
There is a positive correlation between residents’ perceptions of the positive impact of tourism and support for tourism.
H1b. 
There is a negative correlation between residents’ perceptions of the negative impact of tourism and support for tourism.

3.2. The Role of Tolerance: Zone of Tolerance Theory

The zone of tolerance theory is widely applied in the field of service management research. It was proposed by Zeithaml, Berry, and Parasuraman; they defined the zone of tolerance as “the extent to which customers recognize and are willing to accept heterogeneity (in service delivery)”. This theory suggests that the consumer’s zone of tolerance is influenced by their expectations of the level of service: the higher the expected service level, the narrower the zone of tolerance [57].
In the field of tourism, Ap and Crompton described tolerance as a slight degree of acceptance, meaning that residents accept the inconvenience or cost associated with the development of tourism [58]. According to the study of Mansfeld and Ginosar, the researchers ascertained that residents of tourist destinations possess an inherent social carrying capacity threshold for the development of tourism [59]. Upon exceeding this threshold, residents begin to manifest their frustration and discontent with the existing state of the development of tourism. This limit is referred to as “the level of tolerance”. Stewart et al. propose that tolerance does not merely encompass the binary question of residents’ acceptance or rejection of the development of tourism [60]. It primarily involves residents’ responses to adverse factors. They prompt a deeper exploration: to what degree and in what manner are residents prepared to tolerate the effects of tourism? Drawing on previous researchers’ definitions of tolerance, as well as definitions from the fields of psychology and service management, and considering the context of ethnic villages, this study defines tolerance for tourism as the reaction of destination residents to the negative effects (or adverse effects) caused by the development of tourism, which spans a continuous range from rejection to acceptance.
Existing studies have explored the influence of residents’ tolerance on their support for tourism, emphasizing its significance in three key aspects.
There is a relationship between the perceived positive impact of tourism and tolerance. Tolerance for tourism is closely linked to residents’ perceptions of its benefits. The argument is that perceived benefits precede and enhance the tolerance of tourism. Qi et al. identified that a positive correlation exists between residents’ tolerance and their perception of the positive impact of tourism [61]. Concurrently, Qi et al. reinforced this notion, uncovering a substantive positive association between perceived benefits and tolerance [11]. This result aligns with the findings of Zajac et al., who corroborated that perceived advantages can efficaciously augment individual acceptance [62]. Lischka et al. also recognized the perception of impacts as a critical factor in acceptance decisions [63].
Tolerance is a key factor in the support for tourism. Some researchers have identified residents’ tolerance as a pivotal factor in fostering support for tourism. For instance, Qiu Zhang et al. suggested that social acceptance reflects local residents’ acceptance and appreciation of tourism and its development [64]. Qi et al. posited that tolerance could indicate the dynamic change in residents’ support for tourism [11]. Qi et al. highlighted that residents’ tolerance has the potential to bolster support for tourism, with higher tolerance levels leading to increased overall support [61].
Tolerance plays a mediating role. Recognizing the aforementioned aspects, some studies have examined the mediating role of tolerance [11,61]. These studies have discovered that tolerance plays the role of a mediator between residents’ perceptions of the impact of tourism and their support for tourism. Empirical outcomes suggest that tolerance partially mediates the relationship between residents’ perceptions of the positive impact of tourism and their endorsement of tourism.
In view of the above analysis, this study reconceptualizes the zone of tolerance (ZOT) theory to explain how residents’ perceptions of tourism’s impacts influence their support for tourism’s development and reveal the underlying psychological mechanisms governing this tolerance. Thus, the following hypotheses are proposed:
H2a. 
Residents’ perception of the positive impact of tourism is positively correlated with residents’ tolerance for tourism;
H2b. 
Residents’ tolerance for tourism is positively correlated with their support for tourism;
H2c. 
Residents’ tolerance for tourism plays a mediating role between residents’ perceptions of the positive impact of tourism and their support for tourism.
In addition, certain studies have underscored the pivotal role that tolerance plays in the relationship between residents’ perceptions of the negative impact of tourism and their endorsement of tourism [4]. Nevertheless, the role of tolerance presents itself as slightly contentious. Some studies postulate that residents’ tolerance performs a mediating role between the perceived negative impact of tourism and support for tourism. Essentially, residents’ perception of the negative impact of tourism influences their support for tourism via the degree of their tolerance [11].
Contradicting the viewpoint that residents’ tolerance is molded by their recognition of tourism’s benefits, several studies propose that a perception of the positive impact of tourism does not directly influence tolerance. These investigations present the argument that tolerance serves as a moderating entity in the relationship between the perceived negative impact of tourism and support for tourism [4]. This perspective aids in demystifying the inconsistent relationship previously observed in studies between the perceived negative impact of tourism and support for tourism. The hypothesis advanced is that the intensity of the negative relationship between the perceived negative impact of tourism and support for tourism hinges on the degree of residents’ tolerance. With a substantial level of tolerance among residents, the negative correlation between their perception of the negative impact of tourism and their support for tourism diminishes in significance. Conversely, when tolerance is below a certain threshold, this negative relationship is more pronounced. Qin et al. demonstrated that tolerance tempers the negative relationship between the perception of the negative impact of tourism and support for tourism [4]. Similarly, Haukeland et al. noted that varying degrees of tolerance can sway the relationship between negative impacts and acceptance [65]. In a parallel vein, Riorini and Barusman probed how tolerance moderates the relationship between satisfaction, trust, inertia, and customer loyalty [66]. Gorla scrutinized the relationship between perceived service quality and user satisfaction, revealing that this association is influenced by tolerance levels [67]. Collectively, these studies underscore the intricate interplay between residents’ perceptions, their tolerance, and their advocacy for tourism.
Following the steps of the above researchers, this study proposes the following hypotheses:
H3. 
The negative relationship between perceived negative impact and support is weaker when residents’ tolerance is high.
The above hypotheses are summarized in Figure 1.

4. Methodology

4.1. Research Sites: Xijiang Miao Village and Zhaoxing Dong Village

To secure the representativeness of the research samples, this study strategically selects two emblematic ethnic minority villages in Guizhou Province as sample sites: Xijiang Miao Village and Zhaoxing Dong Village. Xijiang Miao Village, situated in Leishan County, Guizhou Province, China, stands as the largest Miao village globally, comprising over 1400 households and 6000 residents, with 99.5% belonging to the Miao ethnicity (source: https://www.xjqhmz.com, accessed on 15 January 2025). Referred to as the “Thousand-household Miao Village in Xijiang”, it holds profound cultural significance. On the other hand, Zhaoxing Dong Village, located in Liping County, also within Guizhou, is recognized as the oldest and largest Dong village in China, known as the “First Village in Dong Villages”. This village is home to more than 1200 households, with over 5000 residents, 99.5% of whom are Dong people, predominantly sharing the surname Lu (source: https://www.lp.gov.cn/newsite/ztzl/rdzt/zxjq/202403/t20240308_83902438.html, accessed on 15 January 2025).
The selection of these two villages is underpinned by three primary rationales. Firstly, both Xijiang Miao Village and Zhaoxing Dong Village were officially designated in 2014 by China’s National Ethnic Affairs Commission as among the first batch of “Chinese Minority Characteristic Villages”, a national recognition that certifies their status as prototypical examples of well-preserved ethnic communities with authentic cultural characteristics (source: https://www.neac.gov.cn/seac/xxgk/201410/1079695.shtml, accessed on 15 January 2025). Secondly, tourism plays a pivotal role as the primary economic driver in both locales, significantly shaping the livelihoods and economic landscape of the residents. Lastly, aligning with Butler’s tourism area lifecycle, it is posited that destinations progress through five evolutionary stages: exploration, involvement, development, consolidation/stagnation, and decline/rejuvenation [68]. These two ethnic villages represent distinct stages of development, offering a diverse and comprehensive spectrum of tourist destinations for study and analysis.

4.2. Questionnaire and Measurement

The questionnaire consists of four parts, including basic information about the residents, residents’ perceptions of the impact of tourism, residents’ support for tourism, and their tolerance for tourism. The basic information about residents mainly includes gender, age, income, education level, whether they are local residents, whether they are engaged in tourism-related work, etc. To ensure the reliability and validity of the scale as much as possible, the measurement of residents’ perceptions of the impact of tourism, residents’ support for tourism, and their tolerance for tourism was conducted using mature scales from the existing literature. Additionally, residents’ tolerance for tourism was adjusted based on existing relevant scales and combined with the research context of ethnic villages.
Residents’ perceptions of the impact of tourism were assessed. Drawing upon the extant body of research, this work concurs with the discoveries made by Ko and Stewart [69], Perdue et al. [23], Vargas-Sánchez et al. [70], Nunkoo and Gursoy [30], and Qin et al. [4]. These studies posit that residents’ perceptions of the impacts of tourism can be bifurcated along positive and negative dimensions. As such, our assessment of residents’ perceptions of the impact of tourism encompasses both these dimensions. The perceived positive impacts of tourism are represented by four items, while the negative aspects are captured by three items. Both categories are grounded in and emerged from the existing scholarly literature.
Residents’ support for tourism was assessed. In this research, residents’ support for tourism is interpreted as a form of endorsing behavior, signaling the degree to which residents advocate for the development of tourism within their own communities. The scale employed to gauge residents’ support for tourism development comprises five distinct items, drawn from the research of McGehee and Andereck [71], Látková and Vogt [72], and Qin et al. [4].
Residents’ tolerance for tourism was measured. As per Qin et al., residents’ tolerance for tourism is conceived as the spectrum of responses from inhabitants of tourism destinations to the negative (or adverse) impact elicited by the local development of tourism [4]. This spectrum ranges from outright rejection to open acceptance. Given the paucity of research on the variable of tolerance within the domain of tourism, the measurement of residents’ tolerance for tourism in this investigation primarily borrows from the work of McLain [37,38] in the field of psychology and the illustrious research conducted by Qin et al. in the tourism area [4]. In integrating the above-mentioned sources with the present research context, an initial list of five items was garnered. After soliciting the expertise of scholars in tourism research, one item was eliminated, resulting in a four-item measure to assess residents’ tolerance for tourism.

4.3. Sample and Data Collection

This study mainly focuses on residents living in Xijiang Miao Village and Zhaoxing Dong Village as the sample population. Given that the research was conducted in ethnic minority villages, the local residents primarily communicate in Miao and Dong languages in their daily lives. To facilitate communication with the local residents and maximize their cooperation, the researchers specifically selected four local college students proficient in both Chinese and the local ethnic languages to assist in distributing the survey questionnaires. These students were compensated for their assistance.
To ensure effective survey implementation, we rigorously trained four college student researchers. The training covered (1) the questionnaire content and study objectives, (2) proper administration techniques, and (3) the implementation of two localization strategies for ethnic village contexts. This ensured quality data collection while maintaining cultural appropriateness. We employed dual localization approaches: pilot interviews with 15 residents to verify local relevance, and contextualized explanations during administration (e.g., linking “increased employment opportunities” to Xijiang/Zhaoxing’s tourism jobs).
The survey spanned from 26 January to 6 March 2021. During this period, a total of 550 questionnaires were disseminated, with 300 distributed in Xijiang Miao Village and 250 in Zhaoxing Dong Village. Of these, 550 questionnaires were collected. After excluding those with missing key information and invalid questionnaires filled out randomly, 275 valid questionnaires were obtained from Xijiang Miao Village and 239 from Zhaoxing Dong Village. Thus, a total of 514 valid questionnaires, with an effective rate of 93.45%, were used for analysis.

5. Results

5.1. Descriptive Data Analysis

Table 1 presents the demographic profiles of residents in Xijiang Miao and Zhaoxing Dong villages based on a survey of 514 respondents (275 from Xijiang Miao village and 239 from Zhaoxing Dong village). The majority are male (56.2%), with females comprising 41.1%. Most respondents are aged 18 to 45 (69.6%). Educational levels are generally low, with 40.9% having junior high school education or below, 18.5% holding a bachelor’s degree (95 residents), and 1.2% (6 residents) with a master’s degree.
Income levels have shown improvement with tourism development, which is particularly noticeable in Xijiang Miao Village. The majority of residents now earn over 24,000 RMB annually, with 27.4% earning between 24,001 and 48,000 RMB and 20.4% between 48,001 and 72,000 RMB. This marks a significant increase from an average income of 1700 RMB in 2007, before the development of tourism commenced in 2008 [73]. Approximately 90.5% of respondents were local residents, with over 80% having resided in the area for more than 10 years. However, only 32.5% were engaged in tourism-related occupations.
In accordance with the recommendations made by Hair et al. [74], skewness and kurtosis were assessed to ascertain data normality. The absolute values of skewness for all the measurement items in this study oscillated between 0.35 and 1.19, while those of kurtosis varied from 0.12 to 1.53. These values fall within the recommended critical value range proposed by Kline [75]. Hence, it is considered that the data adhere to the normality assumption.

5.2. Common Method Bias

To preclude the possibility of common method bias, the Harman single-factor test was utilized. All survey items were subjected to exploratory factor analysis without rotation, leading to the emergence of four factors that collectively explained 70.925% of the total variance. The principal factor elucidated 39.413% of the total variance, which lies below the critical threshold of 50%. This indicates that the dataset is not significantly marred by common method bias [76]. By employing the Harman single-factor test and examining the variance explained by each factor, scholars can discern potential instances of common method bias and take appropriate mitigation steps. Consequently, this bolsters the validity and reliability of the study’s findings.

5.3. Reliability and Validity

The assessment of reliability hinges on the calculation of Cronbach’s α coefficient and the Composite Reliability (CR) estimate for each variable. Both Cronbach’s α and CR values ought to reach or exceed the 0.70 threshold [77]. The item “NEG3” from the perceived negative impact of tourism had a Corrected Item–Total Correlation (CITC) value less than the recommended cutoff value of 0.5 proposed by Churchill [78], and, therefore, it was discarded. As shown in Table 2, all the Cronbach’s α coefficients and CR scores exceeded the suggested baseline of 0.70. As for validity, it pertains to the structural configuration of variables and measurement items, encapsulating both convergent and discriminant validity. These forms of validity were probed using confirmatory factor analysis (CFA) [79].
The results of confirmatory factor analysis showed that χ2/df = 2.576, which falls between 1 and 3. RMSEA = 0.055, less than the suggested cutoff value of 0.08. Additionally, GFI = 0.953, NFI = 0.952, RFI = 0.936, TLI = 0.960, and CFI = 0.970, all of which are greater than the critical value of 0.9. PGFI = 0.627, PNFI = 0.716, and PCFI = 0.730; all the values exceed the critical threshold of 0.5, suggesting that the model possesses a commendable degree of fit.
Table 2 corroborates that the factor load for all items surpasses the 0.5 benchmark, and the T-value is in excess of 1.96. The average variance extracted (AVE) value for each construct exceeds 0.5, albeit with the exception of the construct of tolerance for tourism, which is slightly under 0.5. According to Chin’s recommendation, standardized factor loadings for measured items should ideally exceed 0.7, though values above 0.6 are considered acceptable. Consequently, average variance extracted (AVE) values greater than 0.36 are deemed acceptable [80]. Therefore, the measurement model in this study demonstrates adequate convergent validity overall. Furthermore, Table 3 indicates that the square root of the AVE for each construct outperforms its correlation coefficient with other constructs. This certifies commendable discriminant validity for all constructs, aligning with the standards set by Fornell and Larcker [81].

5.4. Hypothesis Test

5.4.1. Main Effect

The primary effects between the perceived impact of tourism and residents’ support for tourism were examined via hierarchical regression analysis using SPSS 23.0. An initial evaluation was undertaken to discern the influence of control variables such as gender, age, education, and annual income. Subsequently, these control variables, together with the independent variables (perceived positive impact of tourism and perceived negative impact of tourism), were incorporated into the regression equation. The results are delineated in Table 4.
Model 1 investigates the influence of control variables, encompassing gender, age, education, and income. Cumulatively, these four variables accounted for 9% of the variance in support for tourism (R2 = 0.09, F = 1.068, p > 0.05), thereby suggesting that demographic factors did not significantly sway support for tourism. In Model 2, the twin independent variables of the perceived positive impact of tourism and the perceived negative impact of tourism were introduced. The model’s ability to explain the variance improved to 19% (R2 = 0.190, F = 17.454, p < 0.05). The rise in explained variance from Model 1 to Model 2 was considerably noticeable (ΔR2 = 0.181, p < 0.05). Essentially, upon controlling for demographic variables, the independent variables of the perceived positive impact of tourism and the perceived negative impact of tourism notably augmented the explained variance of support for tourism by 18.1%. The outcome demonstrated that both the perceived positive impact of tourism (β = 0.336, p < 0.05) and the perceived negative impact of tourism (β = −0.129, p < 0.05) could efficaciously predict support for tourism. Hence, both hypotheses, H1a and H1b, are substantiated.

5.4.2. Mediating and Moderating Effects

The mediating and moderating effects of tolerance for tourism were further examined using AMOS 23 statistical analysis software. To test the hypotheses, the Latent Moderated Structural Equations (LMS) method, utilizing a maximum likelihood estimation approach, was implemented. The resulting outcomes are depicted in Figure 2. Indicative of solid model fitness, the primary fit indices of the model were found to be satisfactory (χ2/df = 1.830, GFI = 0.966, NFI = 0.964, IFI = 9841, RFI = 0.951, TLI = 0.977, CFI = 0.983, PGFI = 0.620, PNFI = 0.707, PCFI = 0.721, and RMSEA = 0.040).
Tolerance for tourism has a mediating effect. The details of the LMS test are illustrated in Figure 2. The variable of the positive perception of the impact of tourism significantly and positively influences tolerance for tourism (β = 0.294, t = 4.998, p < 0.05), supporting H2a. Additionally, tolerance for tourism significantly and positively affects support for tourism (β = 0.414, t = 7.680, p < 0.05), thus supporting H2b. The present study employed the bootstrap resampling method with 5000 iterations to scrutinize the mediating role of residents’ tolerance for tourism in the relationship between their perception of the positive impact of tourism and their support for tourism. More precisely, it was utilized to estimate both the mediation effect value of tolerance for tourism as well as its confidence intervals in the AMOS 23 statistical software application. The findings, as outlined in Table 5, are in line with the standards established by Baron and Kenny [83]. The results substantiate that residents’ tolerance for tourism operates as a significant mediator in the relationship between their perception of the positive impact of tourism and their support for tourism (β = 0.122, SE = 0.032, p < 0.05), thereby confirming hypothesis H2c. Furthermore, the findings suggest a significant direct impact of residents’ perception of the positive impact of tourism on support for tourism (β = 0.336, SE = 0.107, p < 0.05). This implies that tolerance for tourism serves as a partial mediator in this relationship.
Tolerance for tourism has a mediating effect. According to the results of the LMS test, a significant positive effect is demonstrated by the interaction variable of NEG*TOL on support for tourism (β = 0.239, t = 2.549, p < 0.05), thereby supporting H3. This result indicates that a significant moderation occurs in the relationship between the perceived negative impact of tourism (NEG) and support for tourism (SUP) by the tolerance for tourism (TOL). To probe further into the moderating effect of tolerance for tourism, the researchers employed the SPSS PROCESS macro model 1 [84] to test for the role of tolerance for tourism as a moderating variable. Following Dawson‘s recommendation, a simple slope analysis was conducted by dividing the moderating variable into low and high groups using the mean ± 1 standard deviation (SD) [85]. The specifics of this simple slope test are laid out in Table 6. The findings suggest that at low levels of tolerance, a negative perception of tourism has a significant negative impact on support for tourism (β = −0.340, t = −6.794, p < 0.05, CI= [−0.439, −0.242]). Conversely, at the high tolerance level, a negative perception of tourism also has a significant negative impact on support for tourism (β = −0.141, t = −2.728, p < 0.05, CI= [−0.242, −0.039]).
The moderation effect is also depicted in Figure 3. Notably, when tolerance for tourism runs high, it significantly softens the negative interplay between the perceived negative impact of tourism and residents’ support for tourism. Conversely, when tolerance for tourism is low, a considerable negative correlation manifests between the perceived negative impact of tourism and residents’ support for tourism. This suggests that the adverse influence of the perceived impact of tourism on support for tourism progressively lessens as the tolerance for tourism escalates.

6. Discussion

The study sampled residents residing in two representative ethnic minority villages at various stages of tourism lifecycle development, validating the research model and hypotheses. Subsequently, three significant research findings emerge from this study that merit in-depth discussion.
Firstly, grounded in social exchange theory, the equilibrium between benefits and costs directly influences an individual’s engagement in subsequent exchange behavior. Consistent with prior research [5,11,13], this study reaffirms the positive relationship between residents’ perceptions of the positive impacts of tourism and their support for tourism. To translate these findings into practice, we recommend implementing (1) community-based tourism cooperatives that enable direct revenue sharing from tourism activities; and (2) cultural heritage certification programs that provide financial incentives for preserving and showcasing traditional practices while generating tourism income. These findings reinforce the notion that residents’ perceptions of the positive impact of tourism serve as a fundamental driver of the endorsement of tourism.
Secondly, the study reaffirms the complex relationship between residents’ perceptions of the negative impact of tourism and their support for tourism. While our results demonstrate a significant negative correlation—aligning with much of the existing literature [13,21]—they also contrast with recent studies reporting non-significant or context-dependent effects [11,56]. These discrepancies may stem from contextual moderators (e.g., economic dependence on tourism) or measurement differences (e.g., conflating short-term disruptions with chronic costs). To mitigate negative impacts, our research suggests (1) establishing tourism impact mitigation funds, where a percentage of tourism revenues is allocated to address specific community concerns like infrastructure strain or cultural commodification; and (2) developing resident-controlled tourism zoning systems that limit visitor access to culturally sensitive areas while maintaining economic benefits. This study further elucidates the intricate relationship between perceptions of the negative impact of tourism and residents’ support for tourism, within the purview of social exchange theory. It paves the way for a more in-depth exploration of the intermediary mechanisms that govern this relationship.
Thirdly, in response to the contradicting findings concerning the relationship between the perceived negative impact of tourism and support for tourism in existing research, and in concurrence with the sentiments expressed by various researchers [4,31], this study amalgamates the social exchange theory with the zone of tolerance theory. The aim is to dissect the process that forms resident support for tourism. It incorporates residents’ tolerance for tourism into the classic model of “perceived tourism impact—support for tourism” to provide deeper insights into the mechanisms behind supporting tourism. The results suggest that residents’ tolerance for tourism plays a crucial role in shaping their support for tourism. On one hand, tolerance for tourism reveals a partial mediating effect in the relationship between residents’ perceptions of the positive impact of tourism and their support for tourism. The findings suggest that as residents reap benefits from tourism development, their tolerance for tourism escalates, subsequently reinforcing their support for additional tourism initiatives. This result is in line with the research conducted by Qi et al. [61] and Qi et al. [11]. However, according to Zeithaml et al., there might be other factors that determine the width of the zone of tolerance—such as situational factors, expectation, and so on [57]. Lv also argues that an individual’s tolerance level is shaped by three categories of factors: individual characteristics, environmental factors, and interaction factors [43]. However, whether these factors similarly influence residents’ tolerance for tourism in ethnic villages remains to be empirically tested and validated. On the other hand, tolerance for tourism is found to exercise a significant moderating effect on the link between residents’ perceptions of the negative impact of tourism and their support for tourism. The data allow us to infer that tolerance for tourism softens the damaging impact of residents’ perceptions of the negative repercussions of tourism, thereby fortifying their support for tourism. This inference resonates with the conclusions arrived at by Qin et al. [4]. Meanwhile, it provides a possible idea to help us explain why some of the existing studies on residents’ support for tourism have concluded that a negative perception is significantly correlated with support for tourism [13,26], while others have not [5,11].
These findings highlight the limitations of the social exchange theory, a widely employed theoretical framework in prior models investigating the relationship between residents’ perceptions of the impacts of tourism and their ensuing support for tourism. While the positive correlation between a favorable perception of the impacts of tourism and support for tourism is largely agreed upon, the controversy surrounding the linkage between a perceived negative impact of tourism and support for tourism remains unaddressed [77]. Consequently, this study introduces the concept of tolerance for tourism into the research model, drawing from the zone of tolerance theory, to elucidate the process of shaping resident support for tourism. By enhancing the research paradigm from residents’ “perceived tourism impact—support for tourism”, commonly employed in previous studies, this study demonstrates the significant influence of residents’ tolerance for tourism on the relationship between the two constructs.

7. Conclusions

7.1. Key Findings

Drawing upon the social exchange theory and the zone of tolerance theory from service marketing, this study enriches the classical paradigm of “residents’ perceived tourism impact—support for tourism” by incorporating the concept of tolerance for tourism from a process-oriented viewpoint. The findings of this study demonstrate the following:
(1)
Social exchange theory exhibits strong explanatory power in interpreting the relationship between residents’ perceptions of tourism and their support for the development of tourism. Specifically, residents’ positive perceptions of tourism show a significant positive correlation with support for tourism, while negative perceptions demonstrate a significant negative correlation. Our findings affirm the explanatory power of social exchange theory in the context of ethnic tourism, aligning with the conclusions of Nugroho and Numata [13], as well as Munanura and Kline [29]. This supports the widely held view in existing research that social exchange theory is one of the most important and prevalent frameworks for studying residents’ attitudes and behaviors [86,87].
(2)
The study confirms the complex dual role of tolerance—acting as a mediator between positive perceptions and support for tourism, while simultaneously serving as a moderator in the relationship between negative perceptions and support. This finding responds to the research suggestions of Qin et al. and Qi et al. [4,11,61], revealing the intrinsic mechanism of the classic “tourism perception-support” paradigm in existing studies through the introduction of the tolerance construct. Moreover, it verifies that tolerance indeed exerts a significant influence on the relationship between residents’ perceptions of tourism and their support for tourism.
(3)
The integration of social exchange theory and zone of tolerance theory provides an effective framework for analyzing the underlying mechanisms linking residents’ perceptions of tourism and their support behavior. This theoretical synthesis enhances our understanding of the factors influencing residents’ support for the development of tourism. This study aligns with the theoretical integration trend advocated by scholars such as Gursoy et al. [28], Gautam [12], and Hateftabar and Rasoolimanesh [35], demonstrating that combining social exchange theory with the zone of tolerance theory indeed enhances the explanatory power regarding residents’ support for tourism.

7.2. Theoretical Implications

This study makes significant contributions to the understanding of residents’ support for tourism in several key aspects.
It enhances the classic research paradigm. By introducing a conceptual model that incorporates tolerance for tourism, this study advances the classic paradigm of “perceived tourism impact—support for tourism.” This model, grounded in both social exchange theory and the zone of tolerance theory, addresses the limitations of existing research paradigms that rely solely on residents’ perceptions of the impact of tourism. By integrating tolerance for tourism, it adds a new dimension, offering a more comprehensive framework to understand the formation of resident support for tourism.
It provides empirical evidence about the role of tolerance. This study reaffirms, in line with the social exchange theory, the positive relationship between residents’ perceptions of the positive impact of tourism and their resultant support for tourism, as well as the negative relationship between their perceptions of the impact of tourism and their support for tourism. Concurrently, though, it tackles the controversial and occasionally inconsistent negative correlation observed in previous studies between the residents’ perceptions of the negative consequences of tourism and their support for tourism [4,13]. By introducing tolerance, from service marketing, as a moderating factor, the study demonstrates that residents’ tolerance significantly influences this relationship. The empirical evidence underscores that tolerance for tourism not only tempers the influence of a perceived negative impact, but also partially intermediates the relationship between a perceived positive impact and support for tourism. While previous studies have examined the impact of tolerance on support for tourism, they have failed to reveal its complex mechanisms at the micro-level. This study demonstrates that tolerance actually plays a dual role between residents’ tourism perceptions and support: it mediates the relationship between positive perceptions and support for tourism, while simultaneously moderating the linkage between negative perceptions and support for tourism. We are able to infer that residents’ tolerance for tourism could provide insight into why some destinations maintain ongoing support in spite of the residents’ explicit cognizance of the adverse impacts of tourism.
Theories are integrated for a holistic understanding. The study goes beyond the traditional application of social exchange theory in explaining residents’ support for tourism. Recognizing the limitations and critiques of solely relying on social exchange theory, particularly in light of controversial empirical findings, the study integrates it with the zone of tolerance theory. This integration provides a more nuanced understanding of how residents’ support for tourism is shaped. It posits that residents’ support is influenced not only by their perception of the impacts of tourism but also by their tolerance for tourism. By incorporating the zone of tolerance theory from service marketing, the study explores the mediating and moderating effects of residents’ tolerance, offering an effective approach to comprehending the complex process of shaping resident support for tourism in ethnic villages.

7.3. Management Implications

This study provides critical insights with dual implications for both global sustainability objectives and local policymaking in tourism development, with particular relevance for ethnic communities. The findings make substantive contributions to the United Nations Sustainable Development Goals (SDGs), most notably (1) SDG 11 (Sustainable Cities and Communities) through its examination of community-inclusive planning processes, and (2) SDG 8 (Decent Work and Economic Growth) by demonstrating how sustainable tourism generates employment opportunities while maintaining cultural integrity.
Economic participation and benefit-sharing are important. The study demonstrates how residents’ perceptions of tourism’s costs and benefits fundamentally influence their support. For policymakers, we recommend implementing structured benefit-sharing mechanisms, such as allocating a percentage of tourism revenues to ethnic cultural preservation funds—an approach proven successful in Yunnan’s Dai villages and Guizhou’s Miao cooperatives. Additionally, creating targeted employment programs that leverage traditional skills through handicraft cooperatives or cultural performance troupes can significantly enhance income stability. For developers, adopting community-inclusive planning models that engage local elders and ethnic councils in decision-making processes is essential, as is developing authentic tourism products like homestay certification programs and artisan marketplaces that directly benefit residents.
Tolerance management strategies are important. Our findings emphasize the importance of tolerance management strategies, revealing tolerance’s critical role in buffering the negative impacts of tourism. Destination managers should establish early-warning systems through regular community surveys to monitor tolerance levels, while implementing comprehensive “tourism literacy” programs that enhance the understanding of long-term benefits and develop coping strategies for tourism-related disruptions. Policymakers can contribute by developing tiered response protocols for communities showing declining tolerance thresholds and creating cultural exchange platforms that enable residents to voice concerns directly to stakeholders. These evidence-based recommendations provide a practical framework for fostering the sustainable development of tourism while preserving ethnic communities’ cultural integrity and securing resident support.

7.4. Limitations and Future Research

By integrating social exchange theory and the zone of tolerance theory, this study delved into the influence mechanism of support for tourism among minority village residents. While offering theoretical and practical contributions, several limitations warrant acknowledgment to guide future research.
Firstly, this study only considers residents’ perceptions of the impact of tourism in terms of two dimensions: positive and negative. Existing research suggests that these dimensions can be further subdivided based on specific aspects, allowing for a more comprehensive understanding of residents’ perceptions of the impacts of tourism. Hence, future studies should adopt a more exhaustive classification approach.
Secondly, this study focused on tolerance’s role as an intermediary between resident perceptions and support for tourism; thus, we did not examine its mechanisms of formation. However, prior research confirms that tolerance is influenced by individual traits, environmental factors, and interactions [43]. Future work should explore these formation mechanisms to better understand residents’ support for tourism.
Thirdly, while focusing on two southwest Chinese villages allowed depth, it limited generalizability. Future research should (1) expand within China to include diverse ethnic villages, urban destinations, and mass tourism settings; (2) examine indigenous communities internationally (e.g., Australia, U.S.) to compare cultural and governance impacts; and (3) systematically compare ethnic versus non-ethnic tourism contexts. Importantly, future studies should incorporate cultural capital, community decision-making, and ethnic identity alongside tolerance. Methodologically, longitudinal designs and refined cultural measures (beyond demographics) are needed to better understand tolerance thresholds.

Author Contributions

Conceptualization, S.Y.; Methodology, C.W.; Investigation, F.X.; Writing—original draft, X.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Zhejang Universiy-The Hong Kong Poytechnic Unverstly Joint Center (grant number ZUPUC--2023-04HT), Humanities and Social Sciences Research Project of Higher Education Institutions, sponsored by the Department of Education of Guizhou Province (grant number 2024RW187), Humanities and Social Sciences Research Project at Guizhou University (grant number GDYB2024004), and The APC was funded by Research project for introduced talents at Guizhou University (grant number 2024008).

Institutional Review Board Statement

This study is waived for ethical review as, after review by the School of Management at Guizhou University, the questionnaire survey protocol for this study was deemed scientifically sound, fair, and free from harm or risks to participants. Participant recruitment adhered to principles of voluntariness and informed consent, safeguarding participants’ rights and privacy. The research content involves no conflicts of interest or violations of ethical, moral, or legal prohibitions.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ganji, S.F.G.; Johnson, L.W.; Sadeghian, S. The effect of place image and place attachment on residents perceived value and support for tourism development. Curr. Issues Tour. 2021, 24, 1304–1318. [Google Scholar] [CrossRef]
  2. Liang, Z.; Luo, H.; Bao, J. A longitudinal study of residents’ attitudes toward tourism development. Curr. Issues Tour. 2021, 24, 3309–3323. [Google Scholar] [CrossRef]
  3. Munanura, I.E.; Needham, M.D.; Lindberg, K.; Kooistra, C.; Ghahramani, L. Support for tourism: The roles of attitudes, subjective wellbeing, and emotional solidarity. J. Sustain. Tour. 2023, 31, 581–596. [Google Scholar] [CrossRef]
  4. Qin, X.; Shen, H.; Ye, S.; Zhou, L. Revisiting residents’ support for tourism development: The role of tolerance. J. Hosp. Tour. Manag. 2021, 47, 114–123. [Google Scholar] [CrossRef]
  5. Çelik, S.; Rasoolimanesh, S.M. Residents’ attitudes towards tourism, cost–benefit attitudes, and support for tourism: A pre-development perspective. Tour. Plan. Dev. 2023, 20, 522–540. [Google Scholar] [CrossRef]
  6. Li, X.; Boley, B.B.; Yang, F.X. Resident empowerment and support for gaming tourism: Comparisons of resident attitudes pre-and amid-Covid-19 pandemic. J. Hosp. Tour. Res. 2023, 47, 1503–1529. [Google Scholar] [CrossRef]
  7. Santos, E.R.M.d.; Pereira, L.N.; Pinto, P.; Boley, B.B. Imperialism, empowerment, and support for sustainable tourism Can residents become empowered through an imperialistic tourism development model. Tour. Manag. Perspect. 2024, 53, 101270. [Google Scholar] [CrossRef]
  8. Nunkoo, R.; Gursoy, D. Political trust and residents’ support for alternative and mass tourism: An improved structural model. Tour. Geogr. 2016, 19, 318–339. [Google Scholar] [CrossRef]
  9. Vargas-Sanchez, A.; Valle, P.O.d.; Mendes, J.d.C.; Silva, J.A. Residents’ attitude and level of destination development: An international comparison. Tour. Manag. 2015, 48, 199–210. [Google Scholar] [CrossRef]
  10. Wang, Y.; Shen, H.; Ye, S.; Zhou, L. Being rational and emotional: An integrated model of residents’ support of ethnic tourism development. J. Hosp. Tour. Manag. 2020, 44, 112–121. [Google Scholar] [CrossRef]
  11. Qi, R.; So, K.K.F.; Cárdenas, D.A.; Hudson, S. The missing link in resident support for tourism events: The role of tolerance. J. Hosp. Tour. Res. 2023, 47, 422–452. [Google Scholar] [CrossRef]
  12. Gautam, V. Why local residents support sustainable tourism development? J. Sustain. Tour. 2023, 31, 877–893. [Google Scholar] [CrossRef]
  13. Nugroho, P.; Numata, S. Resident support of community-based tourism development: Evidence from Gunung Ciremai National Park, Indonesia. J. Sustain. Tour. 2022, 30, 2510–2525. [Google Scholar] [CrossRef]
  14. Smith, V. Hosts and Guests: The Anthropology of Tourism; University of Pennsylvania Press: Philadelphia, PA, USA, 1977. [Google Scholar]
  15. Zhang, J.; Xu, H.-G.; Xing, W. The host–guest interactions in ethnic tourism, Lijiang, China. Curr. Issues Tour. 2017, 20, 724–739. [Google Scholar] [CrossRef]
  16. Yang, Y.; Wang, S.; Cai, Y.; Zhou, X. How and why does place identity affect residents’ spontaneous culture conservation in ethnic tourism community? A value co-creation perspective. J. Sustain. Tour. 2022, 30, 1344–1363. [Google Scholar] [CrossRef]
  17. Nielsen, N.; Wilson, E. From Invisible to Indigenous-Driven A Critical Typology of Research in Indigenous Tourism. J. Hosp. Tour. Manag. 2012, 19, 67–75. [Google Scholar] [CrossRef]
  18. Yang, L.; Wall, G. Ethnic tourism: A framework and an application. Tour. Manag. 2009, 30, 559–570. [Google Scholar] [CrossRef]
  19. Prayag, G.; Hosany, S.; Nunkoo, R.; Alders, T. London residents’ support for the 2012 Olympic Games: The mediating effect of overall attitude. Tour. Manag. 2013, 36, 629–640. [Google Scholar] [CrossRef]
  20. Dyer, P.; Gursoy, D.; Sharma, B.; Carter, J. Structural modeling of resident perceptions of tourism and associated development on the Sunshine Coast, Australia. Tour. Manag. 2007, 28, 409–422. [Google Scholar] [CrossRef]
  21. Liu, J.; An, K.; Jang, S.C.S. Behaviours not set in stone do residents still adopt pro-tourism behaviours when tourists behave in uncivilized ways. Curr. Issues Tour. 2023, 27, 4503–4521. [Google Scholar] [CrossRef]
  22. Wang, Y.; Zhao, X.; Fan, L.; Zhou, L.; Ye, S. Revisiting residents’ support through collective rationality. J. Hosp. Tour. Manag. 2024, 58, 298–308. [Google Scholar] [CrossRef]
  23. Perdue, R.R.; Long, P.T.; Allen, L. Resident support for tourism development. Ann. Tour. Res. 1990, 17, 586–599. [Google Scholar] [CrossRef]
  24. Ap, J. Residents’ Perceptions of Tourism Impacts. Ann. Tour. Res. 1992, 19, 665–690. [Google Scholar] [CrossRef]
  25. Jurowski, C.; Uysal, M.; Williams, D.R. A Theoretical Analysis of Host Community Resident Reactions to Tourism. J. Travel Res. 1997, 36, 3–11. [Google Scholar] [CrossRef]
  26. Ng, S.L.; Feng, X. Residents’ sense of place, involvement, attitude, and support for tourism: A case study of Daming Palace, a Cultural World Heritage Site. Asian Geogr. 2020, 37, 189–207. [Google Scholar] [CrossRef]
  27. Gursoy, D.; Yolal, M.; Ribeiro, M.A.; Panosso Netto, A. Impact of Trust on Local Residents’ Mega-Event Perceptions and Their Support. J. Travel Res. 2017, 56, 393–406. [Google Scholar] [CrossRef]
  28. Gursoy, D.; Ouyang, Z.; Nunkoo, R.; Wei, W. Residents’ impact perceptions of and attitudes towards tourism development: A meta-analysis. J. Hosp. Mark. Manag. 2019, 28, 306–333. [Google Scholar] [CrossRef]
  29. Munanura, I.E.; Kline, J.D. Residents’ Support for Tourism: The Role of Tourism Impact Attitudes, Forest Value Orientations, and Quality of Life in Oregon, United States. Tour. Plan. Dev. 2023, 20, 566–582. [Google Scholar] [CrossRef]
  30. Nunkoo, R.; Gursoy, D. Residents’ support for tourism: An Identity Perspective. Ann. Tour. Res. 2012, 39, 243–268. [Google Scholar] [CrossRef]
  31. Erul, E.; Woosnam, K.M.; McIntosh, W.A. Considering emotional solidarity and the theory of planned behavior in explaining behavioral intentions to support tourism development. J. Sustain. Tour. 2020, 28, 1158–1173. [Google Scholar] [CrossRef]
  32. Hasani, A.; Moghavvemi, S.; Hamzah, A. The Impact of Emotional Solidarity on Residents’ Attitude and Tourism Development. PLoS ONE 2016, 11, 0157624. [Google Scholar] [CrossRef] [PubMed]
  33. Woosnam, K.M.; Aleshinloye, K.D. Can Tourists Experience Emotional Solidarity with Residents? Testing Durkheim’s Model from a New Perspective. J. Travel Res. 2012, 52, 494–505. [Google Scholar] [CrossRef]
  34. Li, X.; Wan, Y.K.P. Residents’ support for festivals: Integration of emotional solidarity. J. Sustain. Tour. 2016, 25, 517–535. [Google Scholar] [CrossRef]
  35. Hateftabar, F.; Rasoolimanesh, S.M. Crisis-driven shifts in resident pro-tourism behaviour. Curr. Issues Tour. 2023, 27, 4479–4502. [Google Scholar] [CrossRef]
  36. Muldoon, R.; Borgida, M.; Cuffaro, M. The conditions of tolerance. Politics Philos. Econ. 2011, 11, 322–344. [Google Scholar] [CrossRef]
  37. McLain, D.L. The Mstat-I: A New Measure of an Individual’S Tolerance for Ambiguity. Educ. Psychol. Meas. 1993, 53, 183–189. [Google Scholar] [CrossRef]
  38. McLain, D.L. Evidence of the properties of an ambiguity tolerance measure: The Multiple Stimulus Types Ambiguity Tolerance Scale-II (MSTAT-II). Psychol. Rep. 2009, 105, 975–988. [Google Scholar] [CrossRef]
  39. Parasuraman, A.; Berry, L.L.; Zeithaml, V.A. Understanding Customer Expectations of Service. Sloan Manag. Rev. 1991, 32, 39–48. [Google Scholar]
  40. Guo, G.; Meng, J.; Kou, X.-X. A Comprehensive Review and Extension of ZOT Theory. Manag. Rev. 2004, 16, 32–42. [Google Scholar] [CrossRef]
  41. Nadiri, H.; Hussain, K. Diagnosing the zone of tolerance for hotel services. Manag. Serv. Qual. Int. J. 2005, 15, 259–277. [Google Scholar] [CrossRef]
  42. Tsaur, S.-H.; Huang, C.-C.; Luoh, H.-F. Do travel product types matter Online review direction and persuasiveness. J. Travel Tour. Mark. 2014, 31, 884–898. [Google Scholar] [CrossRef]
  43. Lv, X.; Liu, Y.; Luo, J.; Liu, Y.; Li, C. Does a cute artificial intelligence assistant soften the blow The impact of cuteness on customer tolerance of assistant service failure. Ann. Tour. Res. 2021, 87, 103114. [Google Scholar] [CrossRef]
  44. Lobo, A.; Maritz, A.; Mehta, S. Enhancing Singapore travel agencies’ customer loyalty an empirical investigation of customers’ behavioural intentions and zones of tolerance. Interam. J. Tour. Res. 2007, 9, 485–495. [Google Scholar] [CrossRef]
  45. Gilbert, D.; Gao, Y. A failure of UK travel agencies to strengthen zones of tolerance. Tour. Hosp. Res. 2005, 5, 306–321. [Google Scholar] [CrossRef]
  46. Emmanuel, S.O. Evaluating Young Customers’ Perception of Service Quality Offered by Travel Agencies in North Cyprus Using Their Zone of Tolerance; Eastern Mediterranean University: Famagusta, Cyprus, 2009. [Google Scholar]
  47. Lee, B.; An, S.; Suh, J. How do tourists with disabilities respond to service failure an application of affective events theory. Tour. Manag. Perspect. 2021, 38, 100806. [Google Scholar] [CrossRef]
  48. Baksi, A.K. Mapping Zone of Tolerance from Destination Atmospherics. Asia-Pac. J. Innov. Hosp. Tour. 2017, 6, 1–23. [Google Scholar]
  49. Homans, G.C. Social Behavior: Its Elementary Forms; Harcourt, Brace & World: New York, NY, USA, 1961. [Google Scholar]
  50. Gao, L.-K. On the Theory of Coleman’s Social Capital. J. Beihua Univ. (Soc. Sci.) 2005, 6, 13–18. [Google Scholar]
  51. Teng, H.-Y.; Chang, S.-T. Resident perceptions and support before and after the 2018 Taichung international Flora exposition. Curr. Issues Tour. 2021, 24, 2110–2129. [Google Scholar] [CrossRef]
  52. Gong, J.; Shapovalova, A.; Lan, W.; Knight, D.W. Resident support in China’s new national parks: An extension of the Prism of Sustainability. Curr. Issues Tour. 2023, 26, 1731–1747. [Google Scholar] [CrossRef]
  53. Godovykh, M.; Hacikara, A.; Baker, C.; Fyall, A.; Pizam, A. Measuring the perceived impacts of tourism a scale development study. Curr. Issues Tour. 2024, 27, 2516–2532. [Google Scholar] [CrossRef]
  54. Tam, P.S.; Lei, C.K.; Zhai, T. Investigating the bidirectionality of the relationship between residents’ perceptions of tourism impacts and subjective wellbeing on support for tourism development. J. Sustain. Tour. 2023, 31, 852–1868. [Google Scholar] [CrossRef]
  55. Gjerald, O. Sociocultural Impacts of Tourism: A Case Study from Norway. J. Tour. Cult. Change 2005, 3, 36–58. [Google Scholar] [CrossRef]
  56. Viana-Lora, A.; Orgaz-Agüera, F.; Aguilar-Rivero, M.; Moral-Cuadra, S. Does the education level of residents influence the support for sustainable tourism? Curr. Issues Tour. 2024, 27, 3165–3183. [Google Scholar] [CrossRef]
  57. Zeithaml, V.A.; Berry, L.L.; Parasuraman, A. The nature and determinants of customer expectations of service. J. Acad. Mark. Sci. 1993, 21, 1–13. [Google Scholar] [CrossRef]
  58. Ap, J.; Crompton, J.L. Residents’ Strategies for Responding to Tourism Impacts. J. Travel Res. 1993, 32, 47–50. [Google Scholar] [CrossRef]
  59. Mansfeld, Y.; Ginosar, O. Determinants of Locals’ Perceptions and Attitudes Towards Tourism Development in their Locality. Geoforum 1994, 25, 227–248. [Google Scholar] [CrossRef]
  60. Stewart, E.J.; Kirby, V.G.; Steel, G.D. Perceptions of Antarctic tourism: A question of tolerance. Landsc. Res. 2006, 31, 193–214. [Google Scholar] [CrossRef]
  61. Qi, R.; So, K.K.F.; Cardenas, D.; Hudsonn, S.; Meng, F. The Mediating Effects of Tolerance on Residents’ Support Toward Tourism Events. In Proceedings of the Travel and Tourism Research Association: Advancing Tourism Research Globally, Dubai, Saudi Arabia, 3–4 December 2016. [Google Scholar]
  62. Zajac, R.M.; Bruskotter, J.T.; Wilson, R.S.; Prange, S. Learning to Live With Black Bears: A Psychological Model of Acceptance. J. Wildl. Manag. 2012, 76, 1331–1340. [Google Scholar] [CrossRef]
  63. Lischka, S.A.; Riley, S.J.; Rudolph, B.A. Effects of impact perception on acceptance capacity for white-tailed deer. J. Wildl. Manag. 2008, 72, 502–509. [Google Scholar] [CrossRef]
  64. Qiu Zhang, H.; Fan, D.X.F.; Tse, T.S.M.; King, B. Creating a scale for assessing socially sustainable tourism. J. Sustain. Tour. 2017, 25, 61–78. [Google Scholar] [CrossRef]
  65. Haukeland, J.V.; Veisten, K.; Grue, B.; Vistad, O.I. Visitors’ acceptance of negative ecological impacts in national parks comparing the explanatory power of psychographic scales in a Norwegian mountain setting. J. Sustain. Tour. 2013, 21, 291–313. [Google Scholar] [CrossRef]
  66. Riorini, S.V.; Barusman, A.R.P. Zone-of-Tolerance Moderates Satisfaction, Customer Trust and Inertia—Customer Loyalty. 2016. Available online: https://www.researchgate.net/publication/309060830_Zone-of-tolerance_moderates_satisfaction_customer_trust_and_inertia_-_Customer_loyalty (accessed on 15 March 2025).
  67. Gorla, N. Information Systems Service Quality, Zone of Tolerance, and User Satisfaction. J. Organ. End User Comput. 2012, 24, 50–73. [Google Scholar] [CrossRef]
  68. Butler, R.W. The Concept of a Tourist Area Cycle of Evolution: Implications for Management of Resources. Can. Geogr. Géographe Can. 1980, 24, 5–12. [Google Scholar] [CrossRef]
  69. Ko, D.-W.; Stewart, W.P. A structural equation model of residents’ attitudes for tourism development. Tour. Manag. 2002, 23, 521–530. [Google Scholar] [CrossRef]
  70. Vargas-Sánchez, A.; Plaza-Mejía, M.d.l.Á.; Porras-Bueno, N. Understanding Residents’ Attitudes toward the Development of Industrial Tourism in a Former Mining Community. J. Travel Res. 2009, 47, 373–387. [Google Scholar] [CrossRef]
  71. McGehee, N.G.; Andereck, K.L. Factors Predicting Rural Residents’ Support of Tourism. J. Travel Res. 2004, 43, 131–140. [Google Scholar] [CrossRef]
  72. Látková, P.; Vogt, C.A. Residents’ Attitudes toward Existing and Future Tourism Development in Rural Communities. J. Travel Res. 2012, 51, 50–67. [Google Scholar] [CrossRef]
  73. Li, T.; Ma, Y.; Cang, M. Report on Tourism Development of China’s Xijiao Miao Village; Sicial Sciences Academic Press: Beijing, China, 2018. [Google Scholar]
  74. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis, 8th ed.; Cengage Learning, EMEA: Andover, UK, 2019. [Google Scholar]
  75. Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford: New York, NY, USA, 2015. [Google Scholar]
  76. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
  77. Ribeiro, M.A.; Pinto, P.; Silva, J.A.; Woosnam, K.M. Residents’ attitudes and the adoption of pro-tourism behaviours: The case of developing island countries. Tour. Manag. 2017, 61, 523–537. [Google Scholar] [CrossRef]
  78. Churchill, G.A. A Paradigm for Developing Better Measures of Marketing Constructs. J. Mark. Res. 1979, 16, 64–73. [Google Scholar] [CrossRef]
  79. Asmelash, A.G.; Kumar, S. Assessing progress of tourism sustainability: Developing and validating sustainability indicators. Tour. Manag. 2019, 71, 67–83. [Google Scholar] [CrossRef]
  80. Chin, W.W. Commentary Issues and Opinion on Structural Equation Modeling. Manag. Inf. Syst. Q. 1998, 22, 7–16. [Google Scholar]
  81. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  82. Cohen, J.; Cohen, P.; West, S.G.; Aiken, L.S. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd ed.; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 2003. [Google Scholar]
  83. Baron, R.M.; Kenny, D.A. The moderator-mediator variable distinction in social psychological research conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. 1986, 51, 1173–1182. [Google Scholar] [CrossRef]
  84. Hayes, A.F.; Rockwood, N.J. Regression-based statistical mediation and moderation analysis in clinical research Observations, recommendations, and implementation. Behav. Res. Ther. 2017, 98, 37–57. [Google Scholar] [CrossRef]
  85. Dawson, J.F. Moderation in management research: What, Why, When, and How. J. Bus. Psychol. 2014, 29, 1–19. [Google Scholar] [CrossRef]
  86. Chen, N.; Li, F.S.; Ma, J. Dual identity and ambivalent sentiment of border residents Predicting border community support for tourism development. Tour. Manag. 2025, 106, 105000. [Google Scholar] [CrossRef]
  87. Erul, E.; Uslu, A.; Cinar, K.; Woosnam, K.M. Using a value-attitude-behaviour model to test residents pro-tourism behaviour and involvement in tourism amidst the COVID-19 pandemic. Curr. Issues Tour. 2023, 26, 3111–3124. [Google Scholar] [CrossRef]
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Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. The results of the LMS test. Note: since the LMS method only provides unstandardized estimates, all path coefficients in this figure are unstandardized. “→” has no meaning in the LMS model [82]. NEG = positive perception of impacts of tourism; TOL = tolerance for tourism; NEG*TOL represents an interaction variable. *** p < 0.01. ** p < 0.05.
Figure 2. The results of the LMS test. Note: since the LMS method only provides unstandardized estimates, all path coefficients in this figure are unstandardized. “→” has no meaning in the LMS model [82]. NEG = positive perception of impacts of tourism; TOL = tolerance for tourism; NEG*TOL represents an interaction variable. *** p < 0.01. ** p < 0.05.
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Figure 3. The moderating effect of tolerance for tourism. Note: as shown in the figure, high tolerance weakens the negative relationship; NEG = perceived negative impact of tourism; TOL = tolerance for tourism.
Figure 3. The moderating effect of tolerance for tourism. Note: as shown in the figure, high tolerance weakens the negative relationship; NEG = perceived negative impact of tourism; TOL = tolerance for tourism.
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Table 1. Sample profiles (N = 514).
Table 1. Sample profiles (N = 514).
N% N%
Gender Village
Male28956.2Xijiang27553.5
Female21141.1Zhaoxing23946.5
Missing142.7Annual income (RMB)
Age ≤24,00019838.5
<18152.924,001–48,00014127.4
18–3021341.448,001–72,00010520.4
31–4514528.272,001–96,000305.8
46–609718.9>96,000152.9
>60377.2Missing254.9
Missing71.4Duration of residence
Education <1 year142.7
Secondary school21040.91–5 years387.4
High school 103206–10 years336.4
Junior college8316.111–20 years10520.4
College9518.5>20 years31761.7
Post-graduate61.2Missing71.4
Missing173.3Engagement in tourism
Local residents Yes16732.5
Yes46590.5No33264.6
No438.4Missing152.9
Table 2. The results of confirmatory factor analysis (N = 514).
Table 2. The results of confirmatory factor analysis (N = 514).
ItemLoadingVariableCronbach’s αCRAVE
POS10.764 ***Perceived positive impact of tourism0.8130.820.536
POS20.846 ***
POS30.719 ***
POS40.571 ***
NEG10.705 ***Perceived negative impact of tourism0.7110.7000.535
NEG20.757 ***
SUP10.802 ***Support for tourism0.9270.9250.711
SUP20.899 ***
SUP30.919 ***
SUP40.806 ***
SUP50.781 ***
TOL10.723 ***Tolerance for tourism0.7910.7960.495
TOL20.628 ***
TOL30.667 ***
TOL40.787 ***
Notes: *** p < 0.01; POS = perceived positive impact of tourism; NEG = perceived negative impact of tourism; TOL = tolerance for tourism; SUP = support for tourism.
Table 3. Discriminant validity of variables.
Table 3. Discriminant validity of variables.
POSNEGSUPTOL
POS0.732
NEG−0.3430.731
SUP0.242−0.2120.843
TOL0.176−0.1840.1760.704
Note: N = 514; the main diagonal, emphasized in bold, represents the square root of the average variance extracted (AVE).
Table 4. The results of regression analysis.
Table 4. The results of regression analysis.
VariablesSupport for Tourism
Model 1Model 2Collinearity Statistics
βTSigβTSigToleranceVIF
(Constant) 19.5860.000 8.1020.000
Gender−0.029−0.6140.540−0.040−0.9160.3600.9681.033
Age0.0480.8690.385−0.007−0.1380.8900.7241.381
Education0.0040.0810.9360.0010.0290.9770.7141.400
Annual income−0.083−1.7140.087−0.086−1.9600.0510.9511.052
POS 0.336 ***6.1850.0000.6141.630
NEG −0.129 **−2.3680.0180.6101.641
R2 0.009 0.190
△R2 0.009 0.181
F 1.068 17.454 ***
Note: N = 514; *** p < 0.01; ** p < 0.05; β represents standardized coefficients; POS = perceived positive impact of tourism; NEG = perceived negative impact of tourism; Variance Inflation Factor (VIF) serves as an indicator of multicollinearity within the model.
Table 5. Results of mediation test of tolerance for tourism.
Table 5. Results of mediation test of tolerance for tourism.
PathEffectBootBias-Corrected 95% CIPercentile 95% CI
S.ELowerUpperPLowerUpperP
POS → TOL → SUP
Indirect effect0.1220.0320.0670.1890.0000.0670.1900.000
Direct effect0.3360.1070.1340.5520.0030.1370.5560.003
Total effect0.4580.1100.2510.6840.0020.2600.6920.001
Notes: POS = perceived positive impacts of tourism; TOL = tolerance for tourism; SUP = support for tourism; bootstrap = 5000.
Table 6. Simple slope test.
Table 6. Simple slope test.
TOLEffectSET-Valuep-ValueLLCIULCI
Low TOL (M − 1SD)−0.3400.050−6.7940.000−0.439−0.242
TOL(M)−0.2410.037−6.4500.000−0.314−0.167
High TOL (M + 1SD)−0.1410.052−2.7280.007−0.242−0.039
Note: SE denotes Standard Error, while LLCI and ULCI represent the Lower Limit Confidence Interval and Upper Limit Confidence Interval, respectively. TOL = tolerance for tourism; M = mean; SD = standard deviation.
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Qin, X.; Ye, S.; Xiang, F.; Wang, C. Examining the Formation of Resident Support for Tourism: An Integration of Social Exchange Theory and Tolerance Zone Theory. Sustainability 2025, 17, 4921. https://doi.org/10.3390/su17114921

AMA Style

Qin X, Ye S, Xiang F, Wang C. Examining the Formation of Resident Support for Tourism: An Integration of Social Exchange Theory and Tolerance Zone Theory. Sustainability. 2025; 17(11):4921. https://doi.org/10.3390/su17114921

Chicago/Turabian Style

Qin, Xue, Shun Ye, Fuhua Xiang, and Chunyan Wang. 2025. "Examining the Formation of Resident Support for Tourism: An Integration of Social Exchange Theory and Tolerance Zone Theory" Sustainability 17, no. 11: 4921. https://doi.org/10.3390/su17114921

APA Style

Qin, X., Ye, S., Xiang, F., & Wang, C. (2025). Examining the Formation of Resident Support for Tourism: An Integration of Social Exchange Theory and Tolerance Zone Theory. Sustainability, 17(11), 4921. https://doi.org/10.3390/su17114921

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