1. Introduction
Landscape, represented by irreplaceable raw materials on land, is a key resource element as well as primary driving force in tourism [1]. Rural landscape, with its idyllic environment, authentic husbandry, distinctive customs, and pastoral lifestyle, is a magnet for developing rural tourism [2]. Especially during the aftermath of COVID-19, when people were paying special attention to low density, less pollution, open fields, smaller scale, healing environments, etc., rural landscapes became a good option and helped fuel a boom in rural tourism [3].
The coupling of rural landscapes and tourism is attracting growing attention. On the one hand, landscapes are the foundation for tourism development in rural areas, as rural landscapes involve diverse ecosystems (e.g., forest, meadows and wetlands) which are the major aspects of destination attractiveness [4]. On the other hand, rural tourism makes significant contributions to reviving local economies, causing landscape transformation [5]. Thanks to rural tourism, local family-based small firms expand [6], farmers’ livelihoods are diversified, and reliance on land is reduced [7]. The agricultural function of rural landscape is weakened and land plays a more prominent leisure and recreational role [8]. In this sense, rural tourism is a highly effective strategy to further the protection and upgrading of rural landscapes and enhances the eco-sustainability of rural areas [5,8].
Tourists are critical stakeholders of destinations, for they constitute the main tourism activity agent and major service target [9]. Tourist behavior is closely associate with rural landscapes, as landscapes and particularly natural landscape are the essence of rural tourism development [10]. Tourist activities such as orchard picking [11] and hunting [12] conducted in rural areas help promote natural resource conservation and facilitate ecological restoration of rural landscapes [13]. However, increasing tourist involvement has also created many negative effects on rural landscapes, including environmental destruction, resource damage, improper uses and overuse, waste accumulation, harmful emission release, and overcrowding [14,15], all of which slow down the sustainable evolution of rural landscapes and hamper rural tourism development.
Given that rural landscapes are the core asset in rural tourism, and their transformation is a constant and inevitable process, cultivating pro-environmental behavior among tourists appears to be a good solution. Pro-environmental behavior plays an essential role in balancing ecological environment preservation and providing good travel experiences for tourists to access landscape at tourism destinations [9]. Therefore, it has gained attention from scholars and becomes a leading research field [16]. The research literature has covered national scenic areas [17], hotels [18], coasts [19], national parks [20], museums [21], and tour companies [22]. However, the rural context has been insufficiently analyzed.
The research explores pro-environmental behavioral intentions considering behavioral intentions acting as a proxy for actual behavior [23]. To better understand pro-environmental behavioral intentions, attention should be given to identifying its antecedents [24]. Loureiro, Guerreiro, and Han (2021) completed a systematic literature review based on Scopus and Web of Science on pro-environmental behavior in tourism and hospitality and extracted 210 qualified papers between 2002 and 2020, among which the theory of planned behavior (TPB) was the most utilized construct [25]. Consistent with these findings, the current research introduced the three TPB constructs, namely the attitudes toward the behavior, subjective norms and perceived behavioral control to predict pro-environmental behavioral intentions. Additionally, the need for exploring the relationship of destination-specific factors and pro-environmental behavioral intentions has also been noted, including destination eco-friendly reputation [26], social responsibility [27], reputation [28], and pro-environmental destination image [29]. Unfortunately, the effect of destination unique fascination on pro-environmental behavioral intentions has been unexplored.
Destination unique fascination highlights the perceived uniqueness of a destination to fascinate and satisfy tourists. Destination unique fascination occurs when something distinctive and enchanting attracts people’s involuntary attention, and encourages their engagement in exploring the uniqueness and charm of a destination via a fascinating experience process [30]. Therefore, it is deemed as a crucial perception of tourists and key concept in destination branding. Tourism destinations worldwide are promoted or voted on as fascinating places in tourism from time to time [31]. The previous literature has underpinned the impact of fascination on people’s decision-making processes and relevant behaviors including destination loyalty [32] and revisit intention [31]. However, to the best of the authors’ knowledge, the influence of destination unique fascination on pro-environmental outcomes remain unresearched. Nowadays, challenged by increasing homogenization at destinations, tourists favor those who offer highly memorable and impressive experiences in providing distinctive tourism resources and services. Once they are enchanted with the destination, they are expected to be more positively influenced, and more willing to engage in environmentally sustainable development [32].
Regarding positive emotions generated by unique fascination, delight is a more of an affect-based construct. Delight is often associated with emotions containing surprise elements such as joy and pleasure, and leads to both customer and employee’s behavioral outcomes such as customer loyalty, positive mouth-of-word, repurchase intention, and employee engagement [33]. Despite its recognized significance in influencing personal behaviors, studies examining delight in the tourism literature remains scarce to date. Beside the notable benefits when formulating marketing and managerial strategies for destinations, identifying the relationship between destination unique fascination and tourist delight as well as their impacts on pro-environmental behavioral intentions could be valuable in academic exploration, especially in theory construction, which thus provides an opportunity for this research.
To address the theoretical gap, the cognition-affect-behavior (CAB) model, underscoring cognitive and affective impacts and their influence in shaping behavior [34], was employed beside the theory of behavior (TPB), hence forming an integrated framework. The reasons for the extension of TPB were twofold. First, since TPB is a representative of rational theories, it is not perfect in all situations and needs extension [35], especially including emotional determinants [36] which show considerable power to influence and shape behavior formation [37]. Second, viewing destinations as complex, destination-tourist bundling would be treated better from an integrated perspective [38] by stressing destination factors and examining the interaction between cognitive and affective factors and behavioral intentions [39].
The specific research objectives were to: (1) apply the theory of planned behavior and cognition-affect-behavior model to analyze tourist pro-environmental behavioral intentions in rural context; (2) test the mediating role of delight; (3) examine whether the integrated model had a better explanatory power than either the TPB or CAB models; and (4) introduce a cross-validation approach to reexamine the conceptual model in a wetland context.
2. Literature Review and Hypotheses Development
2.1. Theory of Planned Behavior (TPB) and Its Extension
As one of the most favored social-psychology theoretical frameworks to predict and explain behaviors [40], the theory of planned behavior has been extensively employed to examine environmental behaviors, and has become the most widely used model in the pro-environmental behavior domain [41,42]. The contexts covered include scenic areas [9], national parks [20], islands [43], mid-route water source areas [40], green lodging [44], edible insect restaurants [45], households [46], workplaces [47], cities [48], universities [49], and student dormitories [50], while relevant research on rural land remains scant.
The theory of planned behavior consists of three core constructs, namely, attitudes toward the behavior, subjective norms, and perceived behavioral control, all of which influence individual’s behavioral intentions [51].
Attitudes toward the behavior are defined as an individual’s overall evaluation of engaging in a certain behavior [51]. Attitudes reflect an individual’s psychologically comprehensive assessment of whether it is valuable, wise, necessary, beneficial, or not to perform an action. Consistent with the expectation–disconfirmation paradigm, a positive attitude deriving from favorable expectation generates a positive motivational force to drive behavioral intentions, and vice versa [52], i.e., attitudes demonstrate causal links with behavior. For instance, environmental attitudes have been shown to exert a positive impact on pro-environmental intentions [53]. Hence, the first hypothesis of this research was:
Attitudes toward the behavior have a positive and direct influence on tourist pro-environmental behavioral intentions.
Subjective norms are perceived social pressure when performing a behavior [51]. Subjective norms imply that people value and act according to those who they consider to be important [54]. Subjective norms, acting as a source of social pressure, encourage people to adjust behaviors when referring to environmental and social responsibility [55], e.g., subjective norms were found to significantly affect tourists’ responsible environmental behavioral intentions [56]. Accordingly, this research put forward the second hypothesis as:
Subjective norms have a positive and direct influence on tourist pro-environmental behavioral intentions.
Perceived behavioral control is conceptualized as a person’s perception of the ease or difficulty in conducting a behavior [57]. Perceived behavioral control reflects a person’s evaluation of the resources or opportunities as well as difficulties to engage in a specific behavior [51]. Studies suggest that perceived behavioral control is effective in predicting behavior. For instance, a positive connection was observed between traveler’s perceived behavioral control and their pro-environmental behavioral intentions at destinations [58]. Based on the above evidence, the third hypothesis was:
Perceived behavioral control has a positive and direct influence on tourist pro-environmental behavioral intentions.
Although it has been acknowledged that the theory of planned behavior (TPB) has a significant explanatory power for the variance in behavioral intentions [59], it has deficiencies in fully explaining all cases and needs extension [35].
One major criticism centers on its theoretical nature as it has been noted that behavioral intentions are generated through a process combining cognitive (rational) and affective (emotional) systems [60]. The theory of planned behavior, however, is a representative of rational theories [61], neglecting emotional determinants of behavior [36]. As mental states arising from cognitive appraisals of events or thoughts, emotions demonstrate a powerful ability to influence and shape an individual’s decisions [37]. Affective components such as emotions are critical in marketing and consumer decision making by impacting information processing, reacting to appeals, and driving behaviors; failing to explore the role of emotions hinders the understanding of consumer behavior [62]. Therefore, it is suggested that emotions especially positive emotions should be included in pro-environmental behavior research [37]. Empirical studies, albeit far from sufficient, have underscored the importance of emotions in predicting intentions with the extended framework of TPB. For example, emotions added strongly to the prediction of intentions to reduce food waste [63]. Based on the above argument, the researchers decided to integrate positive emotional variables as antecedents for examining pro-environmental behavioral intentions. It is suggested that positive emotions enhance pro-environmental behavioral intentions at tourism destinations (i.e., tourists who have positive emotions are more likely to perform pro-environmental behaviors).
Another concern on the extension of TPB is that destination is large and complex, but a majority of studies using the theory of planned behavior focus on tourist traits or social factors as antecedents of pro-environment behavior, with destination factors being largely ignored [26]. There is a need for more research to reveal the nature of cognitive and affective constructs and their interaction in influencing attitudes and behavioral intentions [39]. Specifically, destination-tourist bundling as a representative of human–environment relationships should be treated from an integrated perspective [38] by taking destination factors into consideration. In fact, destination factors serve important roles in affecting tourist behavioral intentions [64], such as destination fascination which positively impacted tourist intention to revisit Cayman islands [31]. Hence, this research postulates that destination unique fascination reveals tourists’ cognitive side and triggers affective responses that lead to action, hence echoing the influence of the cognition-affect-behavior model.
In summary, on the basis of the discussion above, both conceptual and empirical grounds provide support for the integration of cognition-affect-behavior model and the theory of planned behavior.
2.2. Cognition-Affect-Behavior (CAB) Model and Relationships among Its Components
2.2.1. Cognition-Affect-Behavior Model
The cognition-affect-behavior model is a paradigm that delineates the behavior formation process based on the interaction between an individual and the environment [34]. The model holds that cognition triggers affective responses as either favorable or unfavorable, which in turn shape eventual behavior [65].
The cognition-affect-behavior model has been applied in various contexts, including e-commerce [66], fast-food services [67], e-banking services [68], mobile shopping cart abandonment [65], food industry greenwashing practice [69], e-commerce recommendation systems [70], and tourism destinations [71].
In this research, destination unique fascination is regarded as a cognitive component; tourist delight, as an affective variable and tourist pro-environmental behavioral intentions as a behavioral outcome. It is posited that destination unique fascination works as a cognition component eliciting tourist delight (affect) which further promotes tourist environmentally responsible behavior (behavior).
2.2.2. Cognition: Destination Unique Fascination
As a significant concept in attention restoration theory, fascination plays the core role in a restorative experience [30]. Its key component, unique fascination, showcases the environment-human intervening relationship especially in a destination setting where the destination presents unique attributes as perceived by tourists and adds to their special experiences that cannot be sampled in daily life. Destination unique fascination is measured subjectively by tourists according to their perception of how special the destination is and how difficult to replace it by counterparts. Its importance has been acknowledged and highlighted as the major purpose of destination branding in many studies in which scholars encouraged tourism destinations to enhance tourist positive word-of-mouth and revisit intentions by improving unique fascination [72].
Although destination unique fascination provides a notable insight into evaluating behavioral outcomes, little is known about its effect on tourist behavior. The prior literature has revealed the impact of destination fascination on destination loyalty [32] and revisit intentions [31]. Additionally, it has hinted that fascination with an environment could promote a person’s willingness toward environmentally sustainable development [32]. Based on the above consideration, this research expects that there is a causal link between unique fascination and pro-environmental behavioral intentions with the relevant hypothesis below:
Destination unique fascination has a positive and direct influence on tourist pro-environmental behavioral intentions.
2.2.3. Affect: Tourist Delight
Affect and emotions are interchangeably used in the extant literature [73]. Delight as a positive emotion refers to individuals receiving positive surprises beyond their expectations [74]. Stemming from the convergence of two primary emotions, namely surprise and joy [75], delight occurs when a person’s expectations are surpassed to an unanticipated and surprising degree [76]. The significance of delight has inspired research in the context of hotels [77], restaurants [78], airlines [79], and retailing [80]. In the marketing literature, delight is of great value in driving customer repurchases and positive word-of-mouth [81].
Given that positive emotions are likely to induce pro-environmental behavior, now there is an opportunity to introduce delight as a positive emotion into pro-environmental behavior research. Emotions especially delight can be triggered by many factors including novelty. Unique fascination as a representative of novelty impacts evaluations of destinations, readily awaking positive affection, which furthers the arousal of the more specific emotion of delight [82]. A similar finding was uncovered that tourism resource uniqueness exerted a positive impact on positive emotions [83]. Based on the argument above, the fifth hypothesis was as follows:
Destination unique fascination has a positive and direct influence on tourist delight.
2.2.4. Behavior: Tourist Pro-Environmental Behavioral Intentions
Although the key role of delight has been emphasized in the marketing and hospitality contexts [81], the tourism literature has rarely associated delight with behavior, and specifically pro-environmental behavior.
Pro-environmental behavior is the behavior taken by a person or group to eliminate or minimize negative environmental impacts [84]. In tourism and hospitality, pro-environmental behavior is particularly favored by scholars and tourism operators [25]. The term “pro-environmental behavior” is interchangeably used with equivalent expressions including environmentally responsible behavior, environmentally sustainable behavior, green consumption, green behavior, environment-protective/preserving behavior, environmentally friendly behavior, environmentally concerned/significant behavior, eco-friendly behavior, and ecological/environmental/sustainable behavior [24,85,86]. As a proxy for actual behavior [23], behavioral intentions offer a prediction of future behavior and represent one of the most accurate antecedents in forecasting [87]. Taken together, the current research adopted tourist pro-environmental behavioral intentions (TPEBI) which means a willingness to minimize environmental impacts on destinations.
It has been noted that what and how people do is determined by their emotions, that is to say, emotions are powerful in shaping various behaviors [37]. In tourism, emotions help predict environmental behaviors [88]. In particular, empirical analysis confirms that positive emotions positively impact environmentally responsible behavior [26]. For example, positive emotions of satisfaction and trust were found to positively influence energy saving behavior [71]. According to the broaden-and-build theory [89], positive emotions help broaden thought-action repertoires which nurture an open mindset towards others and the world, and naturally raise awareness of human-environment communication [88]. Thus, this research infers that once tourists feel delighted, they are less self-centered and more concerned about the environment, willing to engage in pro-environmental practices at destinations. Based on these arguments, the sixth hypothesis was:
Tourist delight has a positive and direct influence on tourist pro-environmental behavioral intentions.
2.3. Conceptual Model
Based on the above discussion, this research developed the integrated conceptual model below (Figure 1) to predict tourist pro-environmental behavioral intentions.
3. Method
3.1. Measurement
Table 1 lists the detailed measurements of each construct in the conceptual model with items that have been well identified and validated with modification. To measure attitudes toward the behavior, four items (e.g., ‘I think conserving this destination’s environment is a wise behavior’) were adapted from Liu et al. (2020) [90]. To measure subjective norms, four items (e.g., ‘Most people who are important to me think I should conserve this destination’s environment’) were adapted from Liu, An, & Jang (2020) and Song, You, Reisinger, Lee, and Lee (2014) [90,91]. To measure perceived behavioral control, four items (e.g., ‘I am capable of conserving this destination’s environment’) were adapted from Meng and Choi (2016) [54]. To measure destination unique fascination, five items (e.g., I feel this destination is different from others) were adapted from Liu et al. (2017) [72]. To measure tourist delight, five items (e.g., Overall, I am delighted by the visit.) were adapted from Ahrholdt, Gudergan, and Ringle (2017) [92]. To measure tourist pro-environmental behavioral intentions, four items (e.g., I intend to conserve this destination’s environment) were adapted from Meng & Choi (2016) [54]. The scale of each item ranged from “strongly disagree” to “strongly agree” based on five-point Likert scales.
After the survey was bilingually translated and back translated, a pretest was made with a sample of 45 candidate tourists to ensure the reliability and validity of the scale.
3.2. Data Collection
The data collection was conducted at Yucun Village of Anji County, Zhejiang province, China. Yucun Village is the cradle of the “two mountains” concept (Full version is “lucid waters and lush mountains are invaluable assets”) proposed by Chinese president Xi Jinping [93]. As the demonstration case for national rural revitalization and green development, Yucun Village recently was among 2021 UNWTO Best Tourism Villages [94] and a provincial international cultural exchange base [95]. Since 2015, the total tourist number visiting Yucun Village has mounted to 5,000,000 [95], with around 900,000 annually from 2019 to 2021 [96,97,98]. Based on these recognitions, Yucun Village is qualified for the survey as the rural tourism destination.
Domestic tourists were the predominant respondents in the survey while international tourists were not included, since the number of international tourists was greatly limited due to the perceived health risks, travel restrictions, and quarantine regulations caused by COVID-19. The survey was conducted in the early October, 2021. Three college students were trained as assistants. Together with three researchers, three equal-sized teams were formed. The convenience sampling method was then used in the survey. A brief introduction and detailed instructions were provided to courteously invite potential respondents to participate in the survey. Survey gifts for free included masks, hand sanitizer, and antiseptic wet wipes. If visitors were not willing to participate in the survey or not qualified as domestic tourists, the research teams would look for next available respondent. Following the above procedure, some 440 questionnaires were collected with 403 usable responses, a 91.6% valid response rate. The respondents showed a relatively balanced ratio of males (48.6%) and females (51.4%). Among them, 10.9% were aged below 20; 24.8%, between 20–29 years old; 31.8%, 30–39 years old; 18.6%, 40–49 years old; and 13.9%, 50 years old and above. Some 7.4% only received middle school education or less; 10.7%, high school or vocational/technical secondary school education; 24.6%, vocational college education; 44.4% had Bachelor degrees; and 12.9% were postgraduates and above. The values of both univariate skewness statistics (−1.411 to −0.086) and kurtosis statistics (−0.791 to 0.947) met the requirements [99].
4. Results
4.1. Common Method Variance Analysis
Since cross-sectional data were applied [100], several approaches were adopted to assess the potential problem of common method variance (CMV). The results of exploratory factor analysis generated a multi-factor pattern occupying 76.65% of the total variance. The first factor accounted for 31.93% of the variance, below the 50% threshold. In addition, the results indicated that the proposed measurement model was better than the common factor one (Δχ2 = 4518.275, Δdf = 15, p < 0.001). Thus a CMV problem was not detected [101].
4.2. Measurement Model Analysis
The confirmatory factor analysis (CFA) estimating the measurement reliability and validity was adopted to verify the measurement model [102] with the aid of AMOS. The results presented a good fit (χ2/df = 1.950, RMR = 0.017, RMSEA = 0.049, GFI = 0.906, NFI = 0.929, IFI = 0.964, TLI = 0.959, CFI = 0.964, SRMR = 0.0380).
Table 2 shows the composite reliability (CR) of each construct was above 0.883, higher than the 0.70 threshold [103,104]. Factor loadings of all items were significant (p < 0.001), ranging from 0.688 to 0.929. The average variance extracted (AVE) values ranged from 0.602 to 0.778, surpassing the minimum criterion of 0.50. Thus, convergent validity was satisfied [105]. By comparing the square root of each construct’s AVEs with the correlations between variable pairs [106], discriminant validity was tested and its results (Table 3) demonstrated that the measurement model met the validity requirement.
4.3. Structural Model Analysis
The direct hypotheses were examined through structural equation modeling (SEM). The fit indices suggested a good fit for the model (χ2/df = 2.040, RMR = 0.031, RMSEA = 0.051, GFI = 0.9, NFI = 0.925, IFI = 0.96, TLI = 0.955, CFI = 0.96, SRMR = 0.0636), and all six hypotheses were supported (Table 4). The TPB constructs (i.e., ATT, SN, PBC) had a significant and direct impact on TPEBI, respectively (βATT = 0.229, p < 0.001; βSN = 0.248, p < 0.001; βPBC = 0.231, p < 0.001). Hence, H1, H2 and H3 were supported. DUF exerted a significant and direct influence on TPEBI (β = 0.222, p < 0.001) and TD (β = 0.429, p < 0.001). H4 and H5 were thus confirmed. Additionally, the direct impact of TD on TPEBI (β = 0.141, p < 0.01) verified H6.
4.4. Mediating Effect Analysis
The mediating effect was assessed by AMOS’s bootstrapping method. The number of bootstrap samples was set to 5000 with bias-corrected confidence intervals at 95%. Table 5 shows a significant mediating effect of DUF on TPEBI through TD (β = 0.060; CI = (0.017, 0.116); p < 0.01).
4.5. Explanatory Power of the Conceptual Model
The explanatory power of the conceptual model was analyzed by estimating its endogenic constructs’ R2 values. The large, medium, and small effect of model thresholds for the R2 values were 0.25, 0.09, and 0.01 respectively [107]. Table 6 shows the findings from the squared multiple correlations (SMC = R2) and indicates that the theory of planned behavior (i.e., M0) explained 37.7% of the variance for TPEBI; the CAB model (i.e., M1), 26.6%; the integrated model (i.e., M2), a higher 43.9%. The results implied that compared with the single model, the integrated model was superior in explanatory power.
4.6. Cross-Validation Analysis
Cross-validation aims at furthering the applicability and generalizability of a study by testing it in different contexts [108]. As such, cross-validation was conducted in both rural and wetland tourism destinations for the conceptual model.
The target wetland destination was Xixi National Wetland Park. Located in Hangzhou, capital city of Zhejiang Province (Figure 2). Xixi National Wetland Park has earned multiple recognitions including the first national urban wetland, national top-tier 5A tourist attraction, and one of the top 10 fascinating wetlands in China [109,110]. It has also been officially included in the list of international key wetlands [111]. Considering the above recognitions, Xixi National Wetland Park is suitable for the survey as the wetland tourism destination.
The wetland tourism destination survey also concentrated on domestic tourists. Totally 440 questionnaires were collected with 406 usable responses, indicating a 92.3% usable response rate. The respondents showed a relatively balanced ratio of males (49.5%) and females (50.5%). Among them, 7.6% were aged below 20; 24.4%, between 20–29 years old; 33.7%, 30–39 years old; 20.2%, 40–49 years old; and 14.0%, 50 years old and above. Some 8.1% had middle school education or less; 11.8%, high school or vocational/technical secondary school education; 22.4%, vocational college education; 43.1% had a bachelor’s degree; and 14.5% were postgraduates and above.
All of the normality, validity and reliability tests were made and validated the conceptual model (Table 2 and Table 3). The six hypotheses developed from the conceptual model (Figure 1) were all supported in the wetland destination context (Table 4) and the mediation effect was also found (Table 5). Figure 3 presents the AMOS output results for the rural and wetland contexts.
Respondents were distributed to form a rural destination (n = 403) group and wetland destination (n = 406) group. In line with Su and Swanson (2017) [28], AMOS’ multi-group comparative analysis was adopted by testing both groups’ data in different conditions with good fit (Table 7). No significant statistical differences were noted (p > 0.05) and this supported cross-validation when examining differences between the constrained and the unconstrained models (Table 8).
5. Conclusions, Theoretical Contributions and Managerial Implications
5.1. Conclusions
The research combined the theory of the planned behavior (TPB) and cognition-affect-behavior (CAB) models to predict tourist pro-environmental behavioral intentions (TPEBI) in rural tourism destination. Later, the cross-validation test which took place at a wetland destination affirmed the integrated theoretical framework. The specific findings were as follows.
First, SEM and cross-validation results indicated that aside from the TPB constructs (i.e., attitudes toward the behavior, subjective norms and perceived behavioral control), destination unique fascination as an antecedent positively and significantly impacted tourist pro-environmental behavioral intentions. This finding was consistent with the assumption that destination fascination raises and enhances willingness to engage in environmental protection [32], and extended the literature by revealing the key role that destination unique fascination plays in predicting tourist pro-environmental behavioral intentions.
Second, the empirical analysis showed that tourist delight exerted mediating effects on the relationship between destination unique fascination and tourist pro-environmental behavioral intentions. Following the CAB model, this finding confirmed prior statements that positive emotions mediate the connections between destination factors and pro-environmental behavior [26]. Moreover, this broadened the delight literature into a tourism context by driving pro-environmental behavior [32,81].
Third, whether the SEM or cross-validation results, the finding was the same: the integrated theoretical framework had greater explanatory power in predicting tourist pro-environmental behavioral intentions. This is another empirical confirmation of the necessity for extending the TPB [35], and viewing destination-tourist relationship from an integrated perspective [38], for instance, the interaction of cognitive and affective constructs [39].
Fourth, the cross-validation method proved its suitability for the research by indicating the robustness of the conceptual model. Both the initial survey at a rural land area and the following one with a wetland setting displayed the value of the conceptual model, the latter of which, in the form of cross-validation, improved the generalizability and capacity of this research.
5.2. Theoretical Implications
First, the research applies the theory of planned behavior (TPB) to examine the formation of tourist pro-environmental behavioral intentions in a rural land context. Though TPB has demonstrated its power in various domains [55], this research initiates its application in the destination unique fascination literature and adds to the currently insufficient level of research in rural land settings. By corroborating the causality of endogenous constructs (i.e., attitudes toward the behavior, subjective norms, and perceived behavioral control) and pro-environmental behavioral intentions, TPB retained its robustness through SEM and cross-validation analyses. Thus, the research enlarges TPB’s applicability to analyze rural tourist pro-environmental behavioral intentions in the field of destination unique fascination.
Second, the research extends the application of the cognition-affect-behavior (CAB) model to explore the influence of destination unique fascination in a rural land setting. This model has manifested its power in many settings. However, rural land research has been far from sufficient. Taking destination unique fascination as a cognitive antecedent, and tourist delight as an affective variable, the CAB model demonstrated its potential in successfully predicting pro-environmental behavioral intentions, which was also acknowledged by the cross-validation analysis. Consistent with prior studies [71], the research affirms the value of applying the CAB model in a rural land context. Also, it empirically supports the suggestion of introducing cognitive and affective constructs to predict behavioral intentions [39]. Moreover, it reveals destination unique fascination’s connection with delight as a specific positive emotion, and enriches behavioral outcomes from loyalty-related to pro-environmental behavioral intentions.
Third, the research integrates the TPB and CAB models to examine tourist pro-environmental behavioral intentions. The validity of TPB extension has been frequently confirmed [112], but to the best of the authors’ knowledge, this is the first time that TPB has been combined with the CAB model. The empirical results showed that compared with each single theory, the integrated TPB plus CAB theoretical framework proposed had better predictive power in exploring pro-environmental behavioral intentions, which supports the preceding claims [38,39] and is a worthy perspective for destination-tourist relationship research.
Fourth, the study employed a cross-validation approach and adds to the present literature on destination unique fascination. The cross-validation approach contributes to testing a conceptual model’s stability by analyzing it in different situations [108]. This enhances the generalizability of the integrated model through different samples and contexts. Analytical results supported the invariant model, indicating that both TPB constructs (i.e., attitudes toward the behavior, subjective norms, and perceived behavioral control) and CAB variables (i.e., destination unique fascination and tourist delight) are stable predictors in driving tourist pro-environmental behavioral intentions, making a methodological contribution to the destination unique fascination literature.
5.3. Managerial Implications
First, this empirical analysis suggests that attitudes are influential in predicting pro-environmental behavioral intentions. Therefore, creating positive attitudes should be given a top priority. Once aware of the environment, people will be on the lookout for responsible tourism [113]. Thus, people should be encouraged to self-educate to improve their environmental awareness and establish positive attitudes towards ecological sustainability especially landscape protection at destinations. Government publicity and education should be consistently arranged as well.
Second, different parties in society should make an effort to encourage tourist engagement in pro-environmental behavior. The government should take the lead to organize various communication campaigns, such as launching non-commercials on public transportation systems. Also, popular social media platforms such as TikTok, Red Book (Xiaohongshu), WeChat, and Weibo should follow up to create topics. Schools, communities, institutions, companies and commercial entities such as hotels and restaurants should cooperate in these educational programs. Specially to rural areas, lots of small and medium-sized tourism enterprises, rooting in the local environment, can help create the atmosphere, for they are regarded as the leading force in promoting local sustainabililty [114,115]. Destination working staff as well as tour guides should also advocate the significance of environmental conservation, for perception of a certain group will trigger individual behavioral tendencies [116]. In addition, considering peer’s online reviews is becoming more critical and positive comments are conducive in influencing tourist behaviors [117]. So, destination managers should encourage and reward positive online reviews.
Third, destination management organizations (DMOs) should consider how to reduce the difficulties and costs of people engaging in pro-environmental behaviors. They should assist tourists by means of incentives and infrastructure improvements. For instance, public signs should be increased and positioned at the right places. Garbage bins should be well designed and strategically located, making it inconvenient for people to litter. Incentives such as cultural souvenirs, local delicacies, mascot toys, performance tickets and mobile data, will be welcomed as recognitions of pro-environmental practices.
Fourth, since the SEM and cross-validation results demonstrated the significance of destination unique fascination in driving tourist pro-environmental behavioral intentions, DMOs should devote themselves to enhancing unique fascination by strategically highlighting uniqueness to achieve differentiation in marketing. They should also strive to design fascinating travel experiences, trying to design unique and memorable experiences that can be found only at destinations. One thing they should always bear in mind is that landscape is among the primary values of a destination; incorporating landscape at the core of the tourism development is fundamental, and this can be achieved by coordinating with local actors with the guidelines of sustainable development [1]. For example, rural land destinations should collaborate with experts and residents to improve fascination by renewing village halls into experience centers, launching events combined with natural and cultural resources, cultivating new economies based on local industry, and designing special activities special for all types of visitors. Another example is establishing attractive public transportation in rural areas according to different terrains, such as “soft mobility” and car-free alternatives which are unique selling points (USPs) that help shape unique fascination [118]. These methods not only help strengthen unique fascination, foster tourist affection towards destinations, lead to their strong willingness to protect the environment, but also create a sustainable pattern of landscape transformation, which encourages sustainable tourism development.
Finally, regarding the employee-customer interaction as a major catalyst for delight [119], the focus should be enhancing destination-tourist interactions to trigger greater delight. Destinations should design service environments and atmosphere to optimize the occurrence of delight. For instance, travel experiences will be more attractive by introducing avant-garde high-quality venue technologies (e.g., interactive and immersive experience backed by extended reality), incorporating highly unexpected service features, for example, high quality but unrelated supporting services (e.g., nail beauty services when queuing at scenic areas) can assist in creating tourist delight.
6. Limitations and Directions for Future Research
First, self-reported measurements were adopted in the research, which may have had social desirability effects. Observations or interviews should be considered for future research. Second, the data collected were limited to domestic tourists, not representing the entire tourist population. Future research should pay attention to diverse nationalities as well as more destination types. Third, the theoretical framework could be further enriched based on literatures, for instance, by simultaneously examing negative emotions [26,120] and introducing more interesting variables such as cultural values [121], perceived green image [122], and perceived policy effectiveness [123] to test the robustness of the integrated model. Fourth, more concrete emotions (e.g., gratitude, guilt) and segment pro-environmental behaviors (e.g., resource saving, waste sorting) can be examined to further this research.
Conceptualization, H.Q. and X.Z.; methodology, H.Q. and W.W.; software, H.Q. and W.W.; validation, H.Q. and A.M.M.; formal analysis, H.Q. and X.Z.; investigation, W.Z.; data curation, H.Q. and W.Z.; writing—original draft preparation, W.Z. and H.Q.; writing—review and editing, A.M.M. All authors have read and agreed to the published version of the manuscript.
This research was funded by the Youth Foundation of Humanities and Social Science Project of the Ministry of Education of China (grant number: 19YJC630131); the Youth Key Project of Premier Humanities and Social Science Program for Higher Educational Institutes of Zhejiang Province, China (grant number: 2018QN015); the General Research Project of the Department of Education of Zhejiang Province (grant number: Y202147320); the Research Project of Zhejiang Federation of Humanities and Social Sciences (grant number: 2018B18); and the Cultivation Project of Tourism Top-notch Talent “Zhejiang Young Tourism Experts” (grant number: 2019LYBJ0103).
Not applicable.
Informed consent was obtained from all subjects involved in the study.
Not applicable.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 2. Geographical locations of Yucun Village and Xixi National Wetland Park. (a) Map of the People’s Republic of China; (b) Geographical locations of Anji and Hangzhou in Zhejiang province; (c) Geographical locations of Yucun Village in Anji and Xixi National Wetland Park in Hangzhou.
The measurement items.
| Construct | Item | Source |
|---|---|---|
| Attitudes toward the behavior |
ATT1 I think conserving this destination’s environment is a wise behavior. | Liu et al. (2020) [ |
| ATT2 I think conserving this destination’s environment is a valuable behavior. | ||
| ATT3 I think conserving this destination’s environment is a necessary behavior. | ||
| ATT4 I think conserving this destination’s environment is a beneficial behavior. | ||
| Subjective norms |
SN1 Most people who are important to me think I should conserve this destination’s environment. | Liu et al. (2020); |
| SN2 Most people who are important to me support me to conserve this destination’s environment. | ||
| SN3 Most people who are important to me recommend me to conserve this destination’s environment. | ||
| SN4 Most people who are important to me agree me to conserve this destination’s environment. | ||
| Perceived behavioral control |
PBC1 I am capable of conserving this destination’s environment. | Meng & Choi (2016) [ |
| PBC2 Whether or not I conserve this destination’s environment is up to me. | ||
| PBC3 I have enough resource, time and opportunities to conserve this destination’s environment. | ||
| PBC4 I am confidence that if I want, I can conserve this destination’s environment. | ||
| Destination unique fascination |
DUF1 I feel this destination is different from others. | Liu et al. (2017) [ |
| DUF2 This destination has local features. | ||
| DUF3 This destination has special themed areas. | ||
| DUF4 This destination looks visually different from others. | ||
| DUF5 This destination performs unique style. | ||
| Tourist delight |
TD1 Overall, I am delighted by the visit. | Ahrholdt et al. (2017) [ |
| TD2 I gleefully talk about the visit. | ||
| TD3 I am elated about the visit. | ||
| TD4 I am proud by the visit. | ||
| TD5 The visit has been an unforgettable experience. | ||
| Tourist pro-environmental behavioral intentions |
TPEBI1 I intend to conserve this destination’s environment. | Meng & Choi (2016) [ |
| TPEBI2 I will make an effort to conserve this destination’s environment. | ||
| TPEBI3 I am willing to conserve this destination’s environment. | ||
| TPEBI4 I am planning to conserve this destination’s environment. |
Note: X = Yucun Village as rural tourism destination; Y = Xixi National Wetland Park as wetland tourism destination.
Results of the measurement model.
| Construct | Case1 (Rural Tourism Destination) | Case2 (Wetland Tourism Destination) | ||||||
|---|---|---|---|---|---|---|---|---|
| Loading | t-Values | CR | AVE | Loading | t-Values | CR | AVE | |
| ATT | 0.923 | 0.749 | 0.921 | 0.744 | ||||
| ATT1 | 0.811 | 20.395 | 0.834 | 19.513 | ||||
| ATT2 | 0.891 | 23.998 | 0.908 | 21.983 | ||||
| ATT3 | 0.893 | 24.093 | 0.894 | 21.536 | ||||
| ATT4 | 0.864 | 0.811 | ||||||
| SN | 0.933 | 0.778 | 0.935 | 0.783 | ||||
| SN1 | 0.82 | 21.475 | 0.833 | 22.289 | ||||
| SN2 | 0.929 | 27.295 | 0.937 | 28.121 | ||||
| SN3 | 0.904 | 25.898 | 0.893 | 25.558 | ||||
| SN4 | 0.872 | 0.874 | ||||||
| PBC | 0.906 | 0.706 | 0.907 | 0.709 | ||||
| PBC1 | 0.865 | 19.17 | 0.807 | 18.642 | ||||
| PBC2 | 0.884 | 19.659 | 0.858 | 20.292 | ||||
| PBC3 | 0.819 | 17.904 | 0.877 | 20.885 | ||||
| PBC4 | 0.79 | 0.824 | ||||||
| DUF | 0.909 | 0.667 | 0.9 | 0.643 | ||||
| DUF1 | 0.767 | 16.447 | 0.713 | 15.266 | ||||
| DUF2 | 0.825 | 18.019 | 0.784 | 17.201 | ||||
| DUF3 | 0.85 | 18.685 | 0.838 | 18.741 | ||||
| DUF4 | 0.85 | 18.709 | 0.866 | 19.515 | ||||
| DUF5 | 0.787 | 0.8 | ||||||
| TD | 0.883 | 0.602 | 0.88 | 0.595 | ||||
| TD1 | 0.688 | 14.051 | 0.745 | 16.197 | ||||
| TD2 | 0.775 | 16.149 | 0.735 | 15.927 | ||||
| TD3 | 0.797 | 16.686 | 0.728 | 15.739 | ||||
| TD4 | 0.827 | 17.396 | 0.821 | 18.317 | ||||
| TD5 | 0.784 | 0.822 | ||||||
| TPEBI | 0.896 | 0.684 | 0.906 | 0.709 | ||||
| TPEBI1 | 0.86 | 17.101 | 0.817 | 16.972 | ||||
| TPEBI2 | 0.906 | 17.921 | 0.919 | 19.249 | ||||
| TPEBI3 | 0.8 | 15.876 | 0.872 | 18.252 | ||||
| TPEBI4 | 0.733 | 0.75 | ||||||
Note: ATT, attitudes toward the behavior; SN, subjective norms; PBC, perceived behavioral control; DUF, destination unique fascination; TD, tourist delight; TPEBI, tourist pro-environmental behavioral intentions; CR, composite reliability; AVE, average variance extracted.
Discriminant validity results.
| Construct | Case1 (Rural Tourism Destination) | Case2 (Wetland Tourism Destination) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ATT | SN | PBC | DUF | TD | TPEBI | ATT | SN | PBC | DUF | TD | TPEBI | |
| ATT | [0.865] | [0.863] | ||||||||||
| SN | 0.268 | [0.882] | 0.281 | [0.885] | ||||||||
| PBC | 0.111 | 0.267 | [0.840] | 0.175 | 0.337 | [0.842] | ||||||
| DUF | 0.209 | 0.283 | 0.257 | [0.817] | 0.243 | 0.263 | 0.182 | [0.802] | ||||
| TD | 0.239 | 0.254 | 0.336 | 0.420 | [0.776] | 0.303 | 0.330 | 0.265 | 0.366 | [0.771] | ||
| TPEBI | 0.395 | 0.462 | 0.419 | 0.453 | 0.418 | [0.827] | 0.441 | 0.527 | 0.440 | 0.451 | 0.486 | [0.842] |
Note: ATT, attitudes toward the behavior; SN, subjective norms; PBC, perceived behavioral control; DUF, destination unique fascination; TD, tourist delight; TPEBI, tourist pro-environmental behavioral intentions.
Structural model analysis and hypothesis test result.
| Hypotheses | Path | Case1 (Rural Tourism Destination) | Case2 (Wetland Tourism Destination) | ||||
|---|---|---|---|---|---|---|---|
| Standardized Coefficient | t-Value | Results | Standardized Coefficient | t-Value | Results | ||
| H1 | ATT→TPEBI | 0.229 | 4.883 *** | Supported | 0.222 | 4.936 *** | Supported |
| H2 | SN→TPEBI | 0.248 | 5.091 *** | Supported | 0.28 | 5.922 *** | Supported |
| H3 | PBC→TPEBI | 0.231 | 4.787 *** | Supported | 0.227 | 4.967 *** | Supported |
| H4 | DUF→TPEBI | 0.222 | 4.11 *** | Supported | 0.218 | 4.438 *** | Supported |
| H5 | DUF→TD | 0.429 | 7.628 *** | Supported | 0.377 | 6.804 *** | Supported |
| H6 | TD→TPEBI | 0.141 | 2.808 ** | Supported | 0.204 | 4.366 *** | Supported |
Note: ** p < 0.01, *** p < 0.001. ATT, attitudes toward the behavior; SN, subjective norms; PBC, perceived behavioral control; DUF, destination unique fascination; TD, tourist delight; TPEBI, tourist pro-environmental behavioral intentions.
Mediation test results.
| Mediating Hypothesized Path | Case1 (Rural Tourism Destination) | Case2 (Wetland Tourism Destination) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Indirect Effects | Lower | Upper | p-Value | Results | Indirect Effects | Lower | Upper | p-Value | Results | |
| H7: DUF→TD→TPEBI | 0.060 | 0.017 | 0.116 | 0.005 | Supported | 0.077 | 0.041 | 0.122 | 0.000 | Supported |
Note: DUF, destination unique fascination; TD, tourist delight; TPEBI, tourist pro-environmental behavioral intentions.
Model comparison test results.
| Model Category | Case1 (Rural Destination) | Case2 (Wetland Destination) |
|---|---|---|
| R2: TPEBI | R2: TPEBI | |
| M0: TPB | 0.377 | 0.434 |
| M1: CAB | 0.266 | 0.321 |
| M2: M0 + M1 | 0.439 | 0.502 |
Note: TPB, theory of planned behavior; CAB, cognition-affect-behavior model; TPEBI, tourist pro-environmental behavioral intentions.
Goodness results of fit indices.
| Model | χ2/df | RMR | RMSEA | GFI | NFI | IFI | TLI | CFI |
|---|---|---|---|---|---|---|---|---|
| Unconstrained | 2.057 | 0.032 | 0.036 | 0.900 | 0.925 | 0.960 | 0.954 | 0.960 |
| Measurement weights | 2.026 | 0.032 | 0.036 | 0.898 | 0.923 | 0.960 | 0.956 | 0.960 |
| Structural weights | 2.011 | 0.033 | 0.035 | 0.898 | 0.923 | 0.960 | 0.956 | 0.960 |
| Structural covariances | 1.988 | 0.034 | 0.035 | 0.897 | 0.923 | 0.960 | 0.957 | 0.960 |
| Structural residuals | 1.982 | 0.034 | 0.035 | 0.897 | 0.923 | 0.960 | 0.958 | 0.960 |
| Measurement residuals | 1.970 | 0.034 | 0.035 | 0.895 | 0.920 | 0.959 | 0.958 | 0.959 |
Significance results of tested model compared with unconstrained model.
| Model | DF | χ2 | p | NFI |
IFI |
RFI |
TLI |
|---|---|---|---|---|---|---|---|
| Measurement weights | 20 | 22.604 | 0.309 | 0.001 | 0.001 | −0.001 | −0.001 |
| Structural weights | 26 | 26.167 | 0.454 | 0.002 | 0.002 | −0.002 | −0.002 |
| Structural covariances | 36 | 32.295 | 0.646 | 0.002 | 0.002 | −0.003 | −0.003 |
| Structural residuals | 38 | 32.367 | 0.727 | 0.002 | 0.002 | −0.003 | −0.003 |
| Measurement residuals | 64 | 76.154 | 0.142 | 0.005 | 0.005 | −0.004 | −0.004 |
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; Morrison, Alastair M 3 ; Wei, Wei 4 ; Zhang, Xihua 5 1 School of Tourism and Foreign Languages, Tourism College of Zhejiang, Hangzhou 311231, China;
2 School of Business Administration, Tourism College of Zhejiang, Hangzhou 311231, China; Zhejiang Academy of Culture & Tourism Development, Hangzhou 311231, China
3 Department of Marketing, Events and Tourism, Greenwich Business School, Old Royal Naval College, Park Row, Greenwich, London SE10 9LS, UK;
4 Department of Hospitality Services, Rosen College of Hospitality Management, University of Central Florida, Orlando, FL 32819, USA;
5 School of Cooperative Economics, Zhejiang Institute of Economics and Trade, Hangzhou 310018, China;