Effects of short-form video application users’ guanxi on intention to visit rural tourism destinations: The moderating role of tourism fatigue

Abstract

Short-form video applications (SFV apps) provide an ideal channel for promoting rural tourism. Based on the elaboration likelihood model, this study integrated guanxi, telepresence, destination image, and tourism fatigue to investigate the factors affecting user intentions to travel to rural tourism destinations in the context of SFV apps. Through the use of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the theoretical framework was examined using data collected from 445 SFV app users. The SEM findings revealed that guanxi could directly affect argument quality and source credibility, which further strengthened destination image and telepresence; the travel intention could be affected by destination image and telepresence without guanxi. Also, tourism fatigue played three roles in the formation of travel intention. Additionally, the results of fsQCA illustrated three configurations leading to high and low intention to visit rural tourism destinations.

Keywords

Rural tourism travel intention tourism fatigue SFV app

Introduction

The promotion of rural revitalization has provided opportunities for development in rural China. This strategy has considerably improved rural regions and encouraged numerous urban residents to travel to rural China; consequently, a rural tourism industry has gradually emerged (Shen et al., 2019). According to a report, more than 663 million tourists visited rural tourism destinations in China in 2019 (Wang, 2020). However, the entire tourism industry has been facing great challenges because of the COVID-19 pandemic. As a key indicator of rural tourism sustainability, travel intention has been widely discussed in the literature (Li et al., 2019a). In the digital era, tourists tend to make travel decisions based on information from user-generated content on social media (Ukpabi and Karjaluoto, 2017). Short-form video applications (SFV apps) allow users to create SFVs in less than a few minutes in duration for capturing memorable moments and sharing these videos with others (Xie et al., 2019). Studies have highlighted numerous successful cases of the use of SFV apps for tourism destination promotion (Cao et al., 2021). Thus, for both researchers and managers, the influencing mechanism of SFV apps on tourist intention to visit rural tourism must be determined (Cao et al., 2021; Liao et al., 2020).

In China, traditional culture has been demonstrated to have a strong effect on individual behavioral intention (Dai et al., 2021; Zhang et al., 2020). Chinese society is often regarded to be highly collectivist and governed by guanxi, which has Confucian origins (Dunfee and Warren, 2001). It refers to pervasive and close personal relations based on mutual benefit and frequent social interaction (Zhang and Hartley, 2018). Academics have called for increased empirical research on the role of guanxi in individual behavior in China (Ou et al., 2014; Shao and Pan, 2019). However, few scholars have explored the influence of traditional culture on the intention to visit rural tourism destinations in the context of SFV apps. Individual behavioral intention is commonly affected by both central and peripheral routes (Petty and Cacioppo, 1986). Consistent with research on social media that has used the elaboration likelihood model (ELM), this study introduced two variables (i.e. argument quality and source credibility) to investigate the routes from guanxi to travel intention. Thus, the present study integrates guanxi into an ELM to understand the underlying mechanism of intention to visit rural tourism destinations in the context of SFV apps.

Many studies have examined the effects of online platform features on travel intentions. Telepresence, an important feature of online platforms, is essential when users are making decisions to book accommodation in an online peer-to-peer setting (Ye et al., 2020a). SFV app users can provide new information and experiences to others without the limitations of time and space, thus enabling potential tourists to experience remote destinations before their actual trip (Xie et al., 2019). In most cases, tourists are impressed by destination image perceptions rather than reality when they make decisions (Beritelli and Laesser, 2018). Hence, destination image should be considered a key factor linked to the sustainability of rural tourism (Zhou, 2014). The findings from tourism literature indicate that potential factors influencing the intention to visit rural tourism destinations include telepresence and destination image.

The existing tourism literature has primarily focused on the effects of positive states, including happiness, justice, pleasure, and enjoyment, on travel intentions (Filep and Laing, 2019); however, boundary conditions on the mechanism of visit intention formation may exist (Sun et al., 2020). These boundary conditions could be viewed as the elements that limit the propositions emerging from theoretical frameworks by setting the boundaries of generalizability (Whetten, 1989). Tourists may experience tourism fatigue, which could negatively influence individual attitudes and behavioral intentions (Teichert et al., 2021). Thus, this study proposes a conceptual model integrating tourism fatigue as a possible boundary condition that could highlight the underlying mechanism of travel intention in the context of SFV apps.

Because the underlying mechanisms of tourist behavioral intention are dynamic and complex (Olya et al., 2019), a mixed methods approach with structural equation modeling (SEM) and fuzzy-set comparative qualitative analysis (fsQCA) is appropriate for this research. SEM was used to test the net effects of independent concepts on travel intention (Wu et al., 2014), and the complex combinations resulting in the intention to visit rural tourism were evaluated using fsQCA (Woodside, 2014). This research aimed to (1) assess the relationship between SFV users’ guanxi and intention to visit rural tourism destinations; (2) identify the roles of argument quality, source credibility, telepresence, and destination image in the link between SFV users’ guanxi and travel intention; (3) examine the effect of tourism fatigue in the mechanism of SFV users’ intention to visit rural tourism destinations; and (4) identify the combinations of guanxi, argument quality, source credibility, telepresence, destination image, and tourism fatigue that lead to increased travel intention.

Literature review

Elaboration likelihood model

The ELM, which has been widely applied in marketing and social psychology (Li, 2013), was designed by Petty and Cacioppo (1986) to explain the underlying process of attitudinal change toward received messages (Chang et al., 2020; Han et al., 2018). In the ELM, messages are elaborated through two routes of persuasion that affect the messages’ likelihood of being persuasive, namely the central and peripheral routes (Han et al., 2018; Kim et al., 2016). These two routes differ in terms of the depth of information presented to the receiver (Bhattacherjee and Sanford, 2006; Petty and Cacioppo, 1986). In the central route, a critical analysis of the message's veracity is presented, whereas in the peripheral route, only simple cues that require low cognitive effort to process are presented (Bhattacherjee and Sanford, 2006).

The ELM has been extensively used in studies on users’ behavioral intention in social media contexts (Erkan and Evans, 2016; Gao et al., 2021; Hur et al., 2017; Li et al., 2018). Most relevant studies have conceptualized the central and peripheral routes in terms of argument quality and source credibility, which are concepts we employed to investigate the travel intention of the users of SFV apps. As part of the central route, argument quality is defined as a judgment of how truthful a piece of the received information is based solely on its content (Han et al., 2018). This study conceptualized argument quality as the persuasiveness of information from SFV platforms. On the basis of the literature (Hur et al., 2017; Shankar et al., 2020; Wall and Warkentin, 2019), we adopted four basic characteristics to characterize argument quality in the context of SFV platforms, namely accuracy, relevance, sufficiency, and consistency. Source credibility refers to whether an information source is perceived to be trustworthy, competent, and believable by recipients (Chaiken, 1980; Li, 2013). As indicated by Sussman and Siegal (2003), credible message sources typically exert high levels of information persuasiveness. Because SFV platforms provide user-generated content, we conceptualized source credibility as the perception of the provided information as being trustworthy and believable. On the basis of the literature, the variable of source credibility comprises four key elements, namely trustworthiness, knowledgeability, reliability, and expertise (Filieri et al., 2018; Hur et al., 2017; Kang and Namkung, 2019; Visentin et al., 2019; Wang et al., 2019). In line with the prior research on SFV app user behavioral intention (Liao et al., 2020), this study attempted to present a research model on the basis of ELM by integrating both of argument quality and source credibility.

Guanxi

Guanxi is a ubiquitous Chinese phenomenon that has Confucian origins. Because guanxi involves the unique rules of social exchange that govern Chinese philosophy in interpersonal relationships, social interactions in China differ from those in Western countries (Lisha et al., 2017; Qi, 2013). As a complex concept, guanxi encompasses social status, reciprocity, trust, and emotion toward others in interactions. These factors greatly influence Chinese society (Yen et al., 2017). Drawing on the literature (i.e. Lisha et al., 2017; Michailova and Worm, 2003; Wang, 2007), we define guanxi as a type of social relationship constituted by mutual benefit and overlapping interests in which users can satisfy their needs from others in SFV apps.

Various forms of guanxi exist in Chinese society (Liu et al., 2018; Yen et al., 2011). An individual can develop guanxi with friends, schoolmates, family members, government officials, and business partners (Chen and Wu, 2011; Luo, 2000; Zhang and Hartley, 2018). These guanxi-based relationships are categorized into expressive, instrumental, and mixed ties (Lee, 2001; Lisha et al., 2017). Guanxi among Chinese people mostly involves mixed ties (Lee, 2001; Lisha et al., 2017); affection and friendship are maintained by keeping promises, helping others, and giving gifts (Yen et al., 2007). Moreover, according to Confucian thought, interpersonal trust can be established through frequent social interaction (Chen and Chen, 2004; Yen et al., 2011; Zhuang et al., 2010). Recently, guanxi has been discovered to influence user behavior in the context of social media (Lin et al., 2019; Shao and Pan, 2019), however, its effect on SFV users’ behavioral intention is still unverified. On SFV platforms, with the increase in users, a strong level of guanxi has been established through frequent video sharing, chatting, and other social behaviors. In addition, individual behavior is under the influence of traditional Chinese culture, which means that guanxi may exert influence during the decision-making process of SFV users. Consequently, guanxi was integrated as a key factor that directly or indirectly triggers SFV app users’ intention to travel to rural tourism destinations.

The literature on guanxi is large, but consensus on guanxi's subdimensions is lacking (Ma and Turel, 2019; Shao and Pan, 2019). Yen et al. (2011) suggested that the valid and reliable concept of guanxi comprises the subdimensions of ganqing (emotional attachment), renqing (favor), and xinren (trust); however, Lisha et al. (2017) integrated two of these components (ganqing and renqing) and added an additional factor (mianzi, meaning reputation) from Qian (2007) to conceptualize guanxi. According to Confucian thought, reciprocal exchanges of favors, interpersonal trust, emotional ties, and closeness from social interactions are significant factors in measuring the quality of guanxi (Chen and Chen, 2004; Yen et al., 2011; Zhuang et al., 2010). Moreover, mianzi is typically maintained through the social exchange; thus, the quality of guanxi cannot be evaluated through mianzi alone (Yen et al., 2017). Based on the above discussion, ganqing, renqing, and xinren were originally proposed by Yen et al. (2011), which could be regarded as the subdimensions of guanxi of SFV app users in this research.

Telepresence

Telepresence is a technical concept (Ye et al., 2020b) defined as the degree to which users feel immersed in virtual contexts (Li et al., 2002). It is experienced through various forms of media, such as video, audio, and animation, which in turn enhance users’ sense of immersion (Algharabat et al., 2018). That is, high telepresence could lead to the sense of being in a real environment (Kang, 2020). In prior studies, telepresence has been widely identified as an essential determinant of personal behavioral intention in various online contexts, namely online shopping (Han et al., 2020) and online tourism industry (Ye et al., 2020a). In the context of the current study, the SFV app users could deliver new information and experience to others without the limitation of time and space, thus enabling potential tourists to experience remote destinations before their actual trip (Xie et al., 2019). Hence, telepresence can be a significant determinant or intermediate variable that alters or triggers the travel intention of SFV app users.

Destination image

Destination image refers to the individual subjective interpretation of a destination (Agapito et al., 2013). Since the introduction of the concept of destination image by Gunn (1972), it has been one of the most explored topics in the field of tourism research (Arabadzhyan et al., 2021; Tavitiyaman et al., 2021; Vera and Chang, 2022; Wu and Lai, 2021). Compared with words, visual content (photos and videos) is easier to help potential visitors characterize tourism destinations (Xiao et al., 2022). Due to the rapid development of SFV platforms, SFV is a key way to promote attractions, and a main window for potential tourists to know about scenic spots (Liao et al., 2020), in turn, form the perception of the destination image. Many scholars view destination image as a multidimensional notion with a variety of components including conative, affective, and cognitive images (Gartner, 1993); however, recent literature is more inclined towards conceptualizing it as a first-order concept in examining overall destination image (Kuhzady et al., 2020). Following this line, the present study utilizes overall destination image to measure tourists’ perception of rural tourism destinations.

Tourism fatigue

The notion of fatigue emerged from the psychology field in the 1950s and has attracted increasing research attention in various fields, including sports science, transportation, information system, medicine, and behavioristics (Sun et al., 2020; Teichert et al., 2021; Xie and Tsai, 2021). Based on its definition, fatigue may be nonpathological or pathological fatigue (Rajaratnam and Arendt, 2001). Although the negative influence of fatigue on individual decision-making has been widely recognized, few research has explored tourism fatigue measurement and its effects on tourists’ behavioral intentions (Sun et al., 2020).

Tourists typically invest physical and cognitive effort and feel affectionate, emotional, and motivated when engaging in tourism activities (Kock et al., 2018; Mitas and Bastiaansen, 2018). In this regard, tourism fatigue refers to “tourists’ physiological or psychological states of physical decline, weakening motivation, emotional decline and cognition impairment caused by excessive tourism activities” (Sun et al., 2020: 2). Psychological and physiological fatigue are thereby involved in tourism fatigue. Research has demonstrated that tourists experiencing fatigue have lower intentions of re-travel and recommendations (Akbaba, 2015). In rural areas, few industrialized facilities and numerous rural communities exist (Rosalina et al., 2021), but many agricultural travel options are offered on the basis of available resources, including farm rentals, agricultural tours combining agriculture with tourism, nature education, and do-it-yourself creative spaces. These activities may cause physical and psychological fatigue after a long period of traveling. Therefore, we predict that tourism fatigue is a key variable that moderates the correlation between internal status and behavioral intention.

Hypotheses development

Telepresence, destination image, and travel intention

In the online context, consumers fail to make judgments without knowledge of the physical features of target products. However, the strong sense of presence created through online video or photography information could assist users in reducing uncertainty and increasing confidence in decision-making (Sun et al., 2019). Recently, social media and virtual reality have been gradually introduced into the travel industry, thus leading tourism researchers to focus on tourists’ perceived telepresence in such environments (Weathers et al., 2007). For example, Kang (2020) reported that the telepresence provided by virtual reality causes impulsive thoughts in tourists; it has also been reported to be capable of promoting tourist satisfaction, which in turn strengthens their travel intention (An et al., 2021). In this study, the following hypothesis is proposed:

H1. In SFV apps, telepresence positively affects user intention to travel to rural tourism destinations.

Destination image has been widely confirmed to be a key determinant of travelers’ behavioral intentions (Alipour et al., 2020; Kuhzady et al., 2020). In a study on tourists in Macau, destination image was reported to exert a positive effect on behavioral intention (Liu et al., 2015). Similarly, tourists’ overall perception of destination image was a considerable predictor of their intention to travel to Athens (Papadimitriou et al., 2015). The positive relationship between destination image and intention to travel was confirmed in a meta-analysis of destination image (Afshardoost and Eshaghi, 2020). Accordingly, the following hypothesis is proposed:

H2. In SFV apps, destination image positively affects user intention to travel to rural tourism destinations.

Argument quality, source credibility, telepresence, and destination image

Argument quality denotes the accuracy, timeliness, comprehensiveness, and relevance of the content generated by travelers on SFV platforms (Bhattacherjee and Sanford, 2006; Hussain et al., 2017; Lee and Chung, 2009; Sreejesh et al., 2016; Teng et al., 2014; Zhang et al., 2017). In SFV apps, the content on tourism released by tourists is dynamic and timely, and travelers can instantly update their network about their travel-related experiences (Yang et al., 2017). In other words, travelers share their personal experiences through SFVs, which may provide accurate information, impress other tourists (Zhang et al., 2010), and convey positive features of tourist attractions. Similar to other forms of social media, SFV apps enable users to post SFVs on their travel experiences in an individualized manner (Casaló et al., 2015); such content may be highly immersive for other users. Although no research has examined the correlations between argument quality, telepresence, and destination image, the aforementioned discussion suggests that the argument quality of SFVs about rural tourism may have a positive impact on telepresence and destination image. Therefore, the following hypotheses are proposed:

H3. In SFV apps, argument quality positively affects users’ perceived telepresence.

H4. In SFV apps, argument quality positively affects users’ perceived images of rural tourism destinations.

In the ELM, source credibility is presented as a peripheral route that influences the persuasiveness of information by affecting user message processing. For individuals who are less easily persuaded, source credibility is more influential than argument quality in explaining changes in personal attitudes and behaviors (Stephenson et al., 2001). In tourism studies, the relationship between source credibility and the perceived usefulness of content is significant and positive (Ayeh et al., 2013; Kim et al., 2016). As a form of social media, SFV platforms allow users to share their own travel experiences with other users. These videos are a key source of information for consumers making travel plans. Hence, the credibility of SFV sources may improve tourists’ perceived telepresence and destination image. Accordingly, in this study, the following hypotheses are proposed:

H5. In SFV apps, source credibility positively affects users’ perceived telepresence.

H6. In SFV apps, source credibility positively affects users’ perceived image of rural tourism destinations.

Guanxi, argument quality, source credibility, and travel intention

Although some scholars have reported the effects of guanxi on argument quality, source credibility, and travel intention in the tourism industry, the indirect relationship among these variables has rarely been investigated. In China, guanxi and social networks are largely equivalent (Davison et al., 2013; Lee et al., 2014). Therefore, the dimensions of ganqing, renqing, and xinren involved in guanxi are relational capital (Shao and Pan, 2019). The findings of research on the correlation between guanxi and argument quality have been inconsistent. Regarding research on knowledge-sharing communities, some studies have argued that guanxi is the key determinant of argument quality (Chang and Chuang, 2011), whereas others have reported that the relationship between these two variables is not significant (Chiu et al., 2006; Wasko and Faraj, 2005). Accordingly, the stronger the degree of guanxi among SFV app users, the less ambiguous the information received on rural tourism destinations; also, the argument quality is high. Thus, the following hypothesis is proposed:

H7. In SFV apps, guanxi positively affects the argument quality of videos on rural tourism destinations.

Interactivity is a typical feature of social media (Liao and Mak, 2019; Lin et al., 2019), and therefore, SFV app users can establish an extensive network of guanxi through frequent interactions. Frequent interactions could improve information reliability (Chong et al., 2018; Rousseau et al., 1998; Zhao et al., 2012), which is a key characteristic of source credibility. When individuals establish strong guanxi with other users, they are more likely to feel trust (Cheng and Huang, 2013; Lin et al., 2019). Additionally, users of SFV platforms tend to develop guanxi with celebrities or other users with a high level of homophily; when the information originates from such contacts, the level of source credibility is high. Hence, SFV app users may perceive a source as credible when receiving information on rural tourism destinations from contacts with strong guanxi. Thus, the following hypothesis is proposed:

H8. In SFV apps, guanxi positively affects the source credibility of videos on rural tourism destinations.

Generally, the establishment of guanxi has a precondition; that is, the individuals involved in guanxi have certain social resources to meet the needs of others (Hwang, 1987). The SFV app users with a strong social network are likely to make decisions on the basis of the information offered by others in the same network. Furthermore, guanxi has been widely regarded as a key determinant of individual behavioral intention (Tan et al., 2022). For example, guanxi can stimulate online users’ intention to repurchase (Chong et al., 2018) and is a key factor that triggers user participation in WeChat Moments (Shao and Pan, 2019). On the basis of the aforementioned analysis, guanxi among SFV platform users may play a key role in their decision-making on engaging in rural tourism. Hence, the following hypothesis is proposed:

H9. In SFV apps, guanxi positively affects user intention to travel to rural tourism destinations.

Effect of tourism fatigue on travel intention

As a negative state, fatigue has been widely explored in various contexts (Wei et al., 2023). Prior studies presented that tourism fatigue is negatively related to tourist satisfaction (Teichert et al., 2021), but the correlation between tourism fatigue and behavioral intention is unclear. Individuals who travel frequently are likely to have a more sensitive perception of tourism fatigue, and those who have never traveled to rural regions may have certain expectations after evaluations. The perception and anticipation of travel fatigue reduce people's travel motivation and emotion. Consequently, travel motivation is an important variable in the formation of people's travel intentions. In the tourism context, the predicted effects of motivation on behavior have been widely confirmed (Lu et al., 2016). Cognition and emotion are also recognized as key determinants of personal behavior. The following hypothesis is proposed:

H10. In SFV apps, tourism fatigue negatively affects user intention to travel to rural tourism destinations.

The moderation roles of tourism fatigue on travel intention formation

In rural tourism, travelers gradually experience psychological tourism fatigue if they experience many physiological and psychological problems and fail to deal with them. When tourists are slowly affected psychologically, they tend to feel less positive about their travel intention. As a result, the positive impact of destination image, telepresence, and guanxi on travel intention decrease. Therefore, in this study, we assume the following:

H11. In SFV apps, tourism fatigue negatively moderates the correlation between guanxi and user intention to travel to rural tourism destinations.

H12. In SFV apps, tourism fatigue negatively moderates the correlation between telepresence and user intention to travel to rural tourism destinations.

H13. In SFV apps, tourism fatigue negatively moderates the correlation between destination image and user intention to travel to rural tourism destinations.

The proposed conceptual framework comprises several constructs (argument quality and source credibility) involved in the ELM as well as guanxi, telepresence, destination image, and tourism fatigue, which are essential determinants of travel intention. Figure 1 illustrates the conceptual framework.

Figure 1.

Conceptual framework.

Research method

Questionnaire design

In this study, a three-part questionnaire (Appendix 1) was used to collect data. In the first part of the questionnaire, one screening question, “Have you ever used an SFV platform?,” was formulated to exclude nontargeted respondents. If respondents responded “no” to this question, the survey was automatically terminated for them. Also, the questions on rural tourism information obtained and the frequency to visit rural landscapes were asked in this part. The second part contained the items for measuring the variables involved in the research model. In the third part, the sociodemographic information of the respondents, namely, sex, age group, education background, monthly income, online sources of tourism information, and frequency of visits to recommended destinations in SFV apps, was captured.

The items in the survey were adopted from the existing literature to maximize content validity (Mohamed et al., 2020). The notion of guanxi with three subdimensions (ganqing, renqing, and xinren) was evaluated using the scale of Yen et al. (2007). The scale of Bailey and Pearson (1983) was also adapted. The 4-item scale of Wu and Shaffer (1987) was applied to evaluate source credibility. The variable of telepresence was measured using four items from the work of Kim and Biocca (1997). Three items from the study of Kuhzady et al. (2020) were utilized to measure destination image. The 3-item scale suggested by Chaulagain et al. (2019) was used to measure travel intention. In this study, tourism fatigue is regarded as a second-order concept, which includes cognitive, affective, motivational, and physical fatigue. It was measured herein using the 12-item scale by Sun et al. (2020). These items were assessed on a 7-point Likert scale ranging from “completely disagree” (1) to “completely agree” (7).

Our questionnaire was first formulated in English and subsequently translated into Chinese. We used the back-translation approach, which was recommended by Brislin (1986), to ensure clarity, readability, and linguistic equivalence. In order to make the target individuals more clearly understand this research context, they were required to watch an SFV on rural tourism from Douyin before responding to the questions (Figure 2). We conducted a pilot study, where three experts in the field of social media and 20 users of SFV platforms noted no major problems with the wording and clarity of the Chinese version of the questionnaire. Also, among the 20 users, the shortest time to complete the questionnaire was 280 s in a pilot test.

Figure 2.

Screenshot of SFV.

Data collection and demographic information

Considering the characteristics of the research participants, we adopted the snowball sampling strategy in the study. Data were collected through a self-administered online questionnaire, which was distributed by a Chinese online survey company (www.sojump.com). The database of sojum.com indicates that the company's members comprise enterprise staff (39.2%), students (26.3%), administrative staff (10.2%), public servants (4.2%), freelancers (1.8%), professionals (3.1%), scientific researchers (9.7%), and others (5.5%; Zhang et al., 2019). Thus, the sample obtained from the database was diversified (Li et al., 2019b).

The survey was delivered to target respondents on November 14–30, 2020. The collected samples were screened on the basis of two criteria: (1) had the same response for each item; (2) completed the questionnaire in less than 280 s from the pilot test (Liu et al., 2022). Four hundred forty-five valid samples were finally obtained for data analysis. As Table 1 indicates, 201 respondents (45.2%) were male users, and 244 (54.8%) were female users; 82.2% were aged between 21 and 40 years; the major level of monthly income was ¥3000–4999 (44.7%); 66.3% had associate or bachelor's degrees; 32.1% viewed Douyin as a major online source of tourism information; and most respondents had visited the recommended destinations in SFV apps one to three times (48.3%).

Table 1.

Sample characteristics.

Variable	Category	Frequency	Percentage (%)
Online sources of tourism information	Douyin	143	32.1
	Kuaishou	121	27.2
	Miaopai	57	12.8
	Meipai	50	11.2
	Huoshan	31	7.0
	Xigua	32	7.2
	Other platforms	11	2.5
Frequency to visit the recommended destination in SFV apps in recent 1 year	None	207	46.5
	1–3 times	215	48.3
	4–9 times	22	4.9
	≥10 times	1	0.2
Age group	≤20 years old	43	9.7
	21–30 years old	224	50.3
	31–40 years old	142	31.9
	41–50 years old	35	7.9
	≥50 years old	1	0.2
Sex	Male	201	45.2
Sex	Female	244	54.8
Education background	High school or below	74	16.6
	Associate or bachelor degree	295	66.3
	Master's degree or higher	76	17.1
Monthly income	≤¥2999	163	36.6
	¥3000–4999	199	44.7
	¥5000–9999	68	15.3
	≥¥ 10,000	15	3.4

Research results from SEM

The current study employed partial least squares (PLS)-SEM (Smart PLS 3.0) to examine the underlying mechanism of rural landscape travel intention formation in the context of SFV apps. Compared to covariance-based structural equation modeling, PLS-SEM was more adequate due to the purpose of identifying the key determinants of an outcome variable (Hair et al., 2017). Also, the technique of bootstrap embedded in PLS-SEM was more advantageous for the moderation effects test (Nitzl et al., 2016). The first step was to assess the measurement model in terms of convergent validity, reliability, discriminant validity, and common method bias. The net effects of independent concepts on travel intention in the second step.

Measurement model

The measurement model was examined using the PLS algorithm. As the factor loading score was lower than 0.6 (Iacobucci, 2010), GQ4, NR4, XY4, and RZ4 were dropped. As presented in Table 2, the factor loading values for all the items ranged from 0.812 to 0.922 and were thus higher than the suggested value of 0.6 (Iacobucci, 2010). The Cronbach's α score for each variable was between 0.800 and 0.881, thus exceeding the threshold of 0.7 suggested by Hair et al. (2010). The composite reliability (CR) of the concepts varied from 0.882 to 0.921. The obtained values were higher than the requisite minimum of 0.7 recommended by Nunnally and Bernstein (1994). Moreover, the average variance extracted (AVE) of the constructs varied from 0.713 to 0.796. The obtained values were higher than the requisite minimum of 0.5 suggested by Fornell and Larcker (1981). Thus, the indices of the measurement model showed reliability and convergent validity.

Table 2.

Construct reliability and convergent validity.

Concept	Item	Factor loading	Cronbach's α	CR	AVE
Ganqing (Guanxi)	GQ1	0.854	0.810	0.887	0.724
	GQ2	0.865
	GQ3	0.833
Renqing (Guanxi)	RQ1	0.854	0.885	0.921	0.744
	RQ2	0.895
	RQ3	0.849
	RQ4	0.851
Xinren (Guanxi)	XR1	0.877	0.872	0.921	0.796
	XR2	0.877
	XR3	0.922
Destination image	JD1	0.860	0.835	0.901	0.752
	JD2	0.895
	JD3	0.846
Telepresence	LC1	0.872	0.800	0.882	0.714
	LC2	0.825
	LC3	0.838
Travel intention	LY1	0.864	0.856	0.912	0.776
	LY2	0.904
	LY3	0.875
Argument quality	NR1	0.907	0.858	0.914	0.780
	NR2	0.892
	NR3	0.849
Source credibility	XY1	0.842	0.825	0.896	0.741
	XY2	0.880
	XY3	0.861
Affective/emotional fatigue (Tourism fatigue)	QG1	0.812	0.871	0.912	0.722
	QG2	0.869
	QG3	0.835
	QG4	0.880
Cognitive fatigue (Tourism fatigue)	RZ1	0.865	0.832	0.900	0.749
	RZ2	0.889
	RZ3	0.841
Physical fatigue (Tourism fatigue)	ST1	0.843	0.866	0.908	0.713
	ST2	0.824
	ST3	0.870
	ST4	0.840
Motivational/need fatigue (Tourism fatigue)	JL1	0.843	0.881	0.918	0.736
	JL2	0.879
	JL3	0.871
	JL4	0.839

Table 3 presents the matrix of the correlation values for all the variables in this study. The diagonal elements were the square roots of the AVE values of the concepts, which were higher than the correlation values between any two variables. Thus, all the variables had sufficient discriminant validity (Fornell and Larcker, 1981).

Table 3.

Discriminant validity.

	XY	NR	LJ	LY	JD	LC	GX
XY	0 . 861
NR	0.256	0.883
LJ	−0.132	−0.215	0.711
LY	0.270	0.351	−0.254	0.881
JD	0.466	0.227	−0.199	0.417	0.867
LC	0.195	0.408	−0.172	0.472	0.200	0.845
GX	0.460	0.450	−0.188	0.263	0.275	0.274	0.728

Note. XY = Source credibility, NR = Argument quality, LJ = Tourism fatigue, LY = Travel intention, JD = Destination image, LC = Telepresence, GX = Guanxi.

The diagonal elements in boldface are the square root of AVE.

Common-method bias

Since the data were collected from the same source, common method bias might be a problem in this study (Podsakoff et al., 2003). In line with the approach of Liang et al. (2007), the current study examined common method bias by using Smart PLS 3.0. As Table 4 shows, the average variance explained by the principal concepts (R1²) was 0.745, but the average variance explained by the method concept (R2²) was 0.001. The ratio of R1² to R2² was about 745:1. Additionally, most of the method factor loadings were not significant. Thus, common method bias was not a serious concern in this research, as suggested by Williams et al. (2003).

Table 4.

Common method bias analysis results.

Construct	Items	Substantive factor loading	R₁²	Method factor loading	R₂²
Ganqing (Guanxi)	GQ1	0.902***	0.814	−0.079*	0.006
	GQ2	0.821***	0.674	0.067*	0.004
	GQ3	0.831***	0.691	0.011	0.000
Destination image	JD1	0.890***	0.792	−0.037	0.001
	JD2	0.898***	0.806	−0.004	0.000
	JD3	0.813***	0.661	0.043	0.002
Motivational/need fatigue (Tourism fatigue)	JL1	0.853***	0.728	0.014	0.000
	JL2	0.890***	0.792	0.015	0.000
	JL3	0.844***	0.712	−0.038	0.001
	JL4	0.845***	0.714	0.009	0.000
Telepresence	LC1	0.860***	0.740	0.006	0.000
	LC2	0.857***	0.734	−0.046	0.002
	LC3	0.819***	0.671	0.039	0.002
Travel intention	LY1	0.866***	0.750	−0.010	0.000
	LY2	0.898***	0.806	0.010	0.000
	LY3	0.879***	0.773	−0.001	0.000
Argument quality	NR1	0.904***	0.817	−0.004	0.000
	NR2	0.898***	0.806	−0.013	0.000
	NR3	0.846***	0.716	0.017	0.000
Affective/emotional fatigue (Tourism fatigue)	QG1	0.878***	0.771	0.096	0.009
	QG2	0.862***	0.743	−0.012	0.000
	QG3	0.823***	0.677	−0.017	0.000
	QG4	0.839***	0.704	−0.061*	0.004
Renqing (Guanxi)	RQ1	0.876***	0.767	−0.036	0.001
	RQ2	0.889***	0.790	0.010	0.000
	RQ3	0.840***	0.706	0.014	0.000
	RQ4	0.845***	0.714	0.011	0.000
Cognitive fatigue (Tourism fatigue)	RZ1	0.846***	0.716	−0.023	0.001
	RZ2	0.914***	0.835	0.030	0.001
	RZ3	0.835***	0.697	−0.009	0.000
Physical fatigue (Tourism fatigue)	ST1	0.855***	0.731	0.018	0.000
	ST2	0.836***	0.699	0.015	0.000
	ST3	0.876***	0.767	0.009	0.000
	ST4	0.810***	0.656	−0.042	0.002
Xinren (Guanxi)	XR1	0.880***	0.774	−0.008	0.000
	XR2	0.889***	0.790	−0.022	0.000
	XR3	0.908***	0.824	0.029	0.001
Source credibility	XY1	0.833***	0.694	0.016	0.000
	XY2	0.891***	0.794	−0.017	0.000
	XY3	0.857***	0.734	0.001	0.000
Average		0.862	0.745	0.000	0.001

Note. ***p < .001, **p < .01, *p < .05.

Hypotheses test

Prior to the evaluation of the structural model, the model fit should be assessed by using the indicator of standardized root mean square residual (SRMR). As the results indicated, the value of SRMR was 0.077, which was under the criterion of 0.085 (Henseler et al., 2016), indicating that the fitness of the research model was good. The bootstrapping procedure with 5000 samples, along with the path coefficient (β-value) and significance of the paths (p-value), was performed to assess the structural model. When p-value is lower than .05, the relationship between the two variables is significant (Hulland, 1999). The results of the structural model assessment (Figure 3) revealed that guanxi exerted a positive and strong influence on argument quality (β = 0.450, p < .001) and source credibility (β = 0.460, p < .001), supporting H1 and H2; however, there is no significant association between guanxi and travel intention (β = 0.064, p > .05), rejecting H3. Moreover, argument quality (β = 0.383, p < .001) and source credibility (β = 0.097, p < .05) could play a significant role in the influence of guanxi on telepresence, supporting H4 and H6. Meanwhile, the destination image was found to be influenced by argument quality (β = 0.115, p < .05) and source credibility (β = 0.436, p < .001), supporting H5 and H7. Additionally, telepresence (β = 0.362, p < .001) and destination image (β = 0.323, p < .001) exerted significant and positive effects on travel intention, supporting H8 and H9. Moreover, tourism fatigue was observed to play a negative role in influencing travel intention (β = −0.111, p < .01) and negatively moderate the effects of telepresence (β = −0.119, p < .01) and destination image (β = −0.195, p < .001), except guanxi (β = −0.021, p > .05), on travel intention; thus, H10, H12, and H13, without H11, were supported.

Figure 3.

Hypotheses test results.

Mediation effects evaluation

The mediation effects of argument quality, source credibility, telepresence, and destination image on the association between guanxi and travel intention were also examined. On the basis of the recent critiques by Nitzl et al. (2016), the current research followed the bootstrapping procedure developed by Chin (2010) to test the indirect effects. The results (Table 5) revealed that guanxi affected travel intention through the chain mediating factors of argument quality and telepresence (β = 0.062, SE = 0.012, t = 5.009, p < .001, BCCI [0.039, 0.087]), the chain mediating variables of argument quality and destination image (β = 0.017, SE = 0.007, t = 2.359, p < .05, BCCI [0.004, 0.032]), and the chain mediating elements of source credibility and destination image (β = 0.065, SE = 0.011, t = 6.0083, p < .001, BCCI [0.044, 0.085]). However, source credibility and telepresence (β = 0.016, SE = 0.008, t = 1.956, p > 0.05, BCCI [0.001, 0.033]) were not the chain mediators in the relation between guanxi and travel intention.

Table 5.

Mediation effects analysis results.

Path	IE point estimate	Bootstrapping 5000 times			Percentile 95%
Path	IE point estimate	SE	t-value	p-value	low	upper
GX→NR→LC→LY	0.062	0.012	5.009	.000	0.039	0.087
GX→NR→JD→LY	0.017	0.007	2.359	.018	0.004	0.032
GX→XY→LC→LY	0.016	0.008	1.956	.051	0.001	0.033
GX→XY→JD→LY	0.065	0.011	6.083	.000	0.044	0.085

Note. XY = Source credibility, NR = Argument quality, LY = Travel intention, JD = Destination image, LC = Telepresence, GX = Guanxi.

Predictive relevance assessment

The blindfolding with 7 Omission Distance was applied to evaluated predictive relevance. The predictive relevance values (Q²) for argument quality (0.154), source credibility (0.154), telepresence (0.122), destination image (0.168), and travel intention (0.309) were greater than 0, indicating predictive relevance was not absent.

Research results from fsQCA

The general tendencies were illustrated using PLS-SEM while the causal recipes achieving the desired outcomes were performed by utilizing fsQCA (Kaya et al., 2020). PLS-SEM results suggest that the determinants of high desired outcomes are the opposite of the determinants of low desired outcomes. In fact, many scholars have argued that the determinants of high desired outcomes are not simply the mirror opposites of the determinants of low desired outcomes (Olya et al., 2019; Olya and Akhshik, 2019). As such, the asymmetric relations between high/low outcomes and their determinants were examined using fsQCA in this research. To be specific, the configurational model with calibration, fuzzy truth tabulation, predictivity validity, and complexity theory assessment was evaluated using the software of fsQCA 3.0.

Calibration

The seven constructs of guanxi (fsgx), argument quality (fsnr), source credibility (fsxy), destination image (fsjd), telepresence (fslc), tourism fatigue (fslj), and travel intention (fsly) were examined using fsQCA. As the use of set membership is required for fsQCA, the raw data ranging from 1 to 7 should be transformed into fuzzy sets from 0 to 1 (Ragin, 2008). In line with the work of Xie and Tsai (2021), three anchors (full nonmembership, crossover point, and full membership) were calculated using the quartiles method in the calibration process (Table 6).

Table 6.

The anchors for each variable.

	Fsgx	fsnr	fsxy	fsjd	fslc	fslj	fsly
Full nonmembership	3.500	3.000	3.000	3.330	4.000	3.600	3.000
Crossover point	4.650	4.500	4.500	4.665	5.000	4.800	4.500
Full membership	5.800	6.000	6.000	6.000	6.000	6.000	6.000

Fuzzy truth tabulation

The results of fsQCA illustrated that the causal recipes were sufficient to lead to the high and low scores of travel intention on the basis of the calculation of the complex combination of six conditional variables. As shown in Table 7, the coverage and consistency in the asymmetrical model leading to high travel intention were 0.382 and 0.811, respectively; these values exceeded the cutoffs of 0.2 and 0.8, respectively (Ragin, 2008). A total of three configurations could lead to high travel intention. To be specific, high travel intention was obtained when tourists showed a high level of guanxi, perceived telepresence, perceived argument quality, and low tourism fatigue (L1). When tourists with low tourism fatigue perceived high argument quality, source credibility, destination image, and telepresence, they were likely to exhibit high travel intention (L2). The third causal model showed that the combination of high guanxi, argument quality, source credibility, destination image, and telepresence resulted in a high level of intention to travel (L3).

Table 7.

Causal models leading to high/low level of travel intention.

	Raw coverage	Unique coverage	Consistency
Causal models for high travel intention
fsly = f(fsgxfsnrfsxyfsjdfslcfslj*)
L1: fsgxfsnrfslc*∼fslj	0.284	0.096	0.808
L2: fsnrfsxyfsjdfslc∼fslj	0.221	0.033	0.825
L3: fsgxfsnrfsxyfsjdfslc	0.253	0.065	0.875
Solution coverage: 0.382
Solution consistency: 0.811
Causal models for low travel intention
∼fsly = f(fsgxfsnrfsxyfsjdfslcfslj*)
NL1: ∼fsgx∼fsnr∼fsxy∼fsjd∼fslc	0.260	0.055	0.896
NL2: ∼fsgx∼fsnr∼fsjd∼fslcfslj	0.243	0.038	0.928
NL3: ∼fsgx∼fsnr∼fsxy∼fslcfslj	0.245	0.040	0.934
Solution coverage: 0.338
Solution consistency:0.875

Note. L stands for causal models for high travel intention. NL stands for causal models for low travel intention.

Additionally, this research investigated the configurations explaining the low level of intention to travel. According to the fsQCA results, three configurations resulted in low travel intention (coverage: 0.338, consistency: 0.875). NL1 showed that low travel intention resulted from low guanxi, argument quality, source credibility, destination image, and telepresence. Alternatively, low guanxi, argument quality, destination image, and telepresence and high tourism fatigue led to low travel intention (NL2). According to NL3, it was caused by high tourism fatigue and low guanxi, argument quality, source credibility, and telepresence.

Predictivity validity

The predictive validity of the proposed models was tested. In line with Olya et al. (2019), 445 samples were randomly divided into two subgroups. The causal models for the high level of intention to travel calculated from subgroup 1 were tested on the basis of subgroup 2. As the bottom of Table 8 indicates, the coefficients of coverage (SL1: 0.215, SL3: 0.193) and consistency (SL1: 0.870, SL3: 0.914) exceeded the thresholds suggested by Ragin (2008). Accordingly, the predictivity of the configurations was valid across different subgroups (Gigerenzer and Brighton, 2009). Additionally, the XY plots of SL1 and SL3 from subgroup 2 illustrated the asymmetric relation of the causal models with high travel intention.

Table 8.

Predictive validity test results.

	Raw coverage	Unique coverage	Consistency
Causal models from subgroup 1(n = 222)
Subgroup 1: fsly = f(fsgxfsnrfsxyfsjdfslcfslj*)
SL1: fsgxfsnrfslc*∼fslj	0.282	0.064	0.776
SL2: fsgxfsnr∼fsxy∼fsjdfslc	0.136	0.025	0.830
SL3: fsgxfsnrfsxyfsjdfslc	0.244	0.070	0.867
Solution coverage: 0.387
Solution consistency: 0.801
XY plots of SL1 with subgroup 2 (n = 223)		XY plots of SL3 with subgroup 2 (n = 223)

Note. SL stands for causal models for high travel intention.

The XY plots revealed an asymmetric relation between outcomes and causal recipes.

Complexity theory assessment

On the basis of the six tenets of complexity theory by Woodside (2014), the results of the fsQCA were assessed. First, the solo antecedent was not sufficient to cause continuance intention, thus reflecting that Tenet 1 was supported. Second, the fsQCA results showed that at least four variables were involved in one causal model resulting in high or low travel intention. The “recipe principle” was thus supported. Third, the three alternative configurations from fsQCA leading to high travel intention indicated that each causal model was sufficient but not a necessary condition for high travel intention. Similarly, three causal models were calculated to predict low travel intention. Hence, the “equifinality principle” was supported. Fourth, Table 7 shows that three configurations leading to high travel intention were not the mirror opposite of three casual models resulting in low travel intention. Hence, “causal asymmetry” was supported. Fifth, source credibility was a positive (SL2 in Table 8) and negative (SL5 in Table 8) factor in driving high travel intention, thus offering support to Tenet 5. Finally, the coverage of each configuration was under 1. Hence, Tenet 6 was supported. Overall, the six tenets were supported and identified the complex interactions of guanxi, argument quality, source credibility, destination image, telepresence, and tourism fatigue that led to SFV app users’ intention to travel to rural destinations.

Conclusion

On the basis of the ELM, this research proposed an integrated model that incorporates guanxi, argument quality, source credibility, telepresence, destination image, and tourism fatigue to investigate the underlying formation mechanism of SFV app users’ intention to travel to rural tourism destinations. A total of 445 valid samples were analyzed using a combination of PLS-SEM and fsQCA. The SEM results revealed that guanxi could affect source credibility and argument quality, which could lead to enhanced telepresence and destination image and further influence travel intention. However, a direct association between guanxi and travel intention was not verified. In addition, the indirect effect of guanxi on the intention to travel through chain mediators, including source credibility, argument quality, telepresence, and destination image, was confirmed. Tourism fatigue had three distinct roles in the formation of user travel intention in the context of SFV apps. Moreover, through the use of fsQCA, three causal models were identified as the conditions leading to a high level of intention to travel. The findings are valuable for both academic research and practical application.

Theoretical implications

This study has three major theoretical implications. First, this study enhances the understanding of guanxi in the tourism field. As researchers have noted, guanxi, originating in Chinese culture, exerts a substantial influence on personal behavior, including social media continuance intention (Lisha et al., 2017) and housing demolition (Liu et al., 2021). Guanxi was integrated into the current study to clarify the formation mechanism of user intention to travel to rural tourism destinations in the context of SFV apps. The results emphasize the importance of guanxi as a key indirect determinant of travel intention and indicate the applicability of guanxi in the rural tourism context. The results further confirm the effect of traditional culture on individual behavioral intention in China.

Second, this study identified the mediation influence between guanxi and the intention to travel to rural tourism destinations in the context of SFV apps. Although many mediators of the association between guanxi and social media user behavioral intention (e.g. trust in sellers [Chong et al., 2018] and perceived enjoyment [Lisha et al., 2017]) have been investigated in the literature, the impact of guanxi may differ in various research contexts (Ma and Zhang, 2022). Thus, mediators should be explored according to specific environments. In this study, both argument quality and source credibility were introduced from the perspective of tourism information; telepresence and destination image were integrated from the perspective of user internal status. In addition, these variables were confirmed to be chain mediators in the link between guanxi and travel intention. Hence, this research provides a better understanding of the underlying interaction mechanism of guanxi and travel intention.

Third, this study integrated a boundary condition, that is, tourism fatigue, to explore the antecedents of SFV app users’ travel intentions. To our knowledge, this work is the first attempt to illustrate the three roles of tourism fatigue in the formation of individual travel intention in the context of SFV platforms. The literature indicates that tourism fatigue could decrease personal satisfaction toward tourism destinations (Teichert et al., 2021), but few studies have focused on the direct relationship between tourism fatigue and travel intention. In this study, tourism fatigue was confirmed to be a direct negative predictor of intention to visit rural tourism destinations. In addition, the effects of telepresence and destination image on travel intention were negatively moderated by tourism fatigue. Hence, this study offers new insight for travel intention research that examines both drivers and boundary conditions.

Practical implications

While the current research is limited to China, its findings can have implications not only for the developers and operators of rural tourism in China, but also for entrepreneurs from other countries who are interested in investing in Chinese rural tourism.

First, it is found that SFV apps can be effective communication channels for rural tourism destinations, especially with the support of algorithm technology. In other words, SFV apps can be used to accurately deliver SFV content to the target audiences of rural tourism. The research also clarified the indirect effect of guanxi on travel intention. Therefore, rural tourism managers should focus on scenic spot planning and improve service quality to promote the generation and distribution of SFV app user content based on actual travel experiences. It is worth noting that key opinion leaders (KOLs) have a strong level of guanxi in the online context and even become the major information source for other users. As such, rural tourism developers could strengthen the cooperation and maintain a positive guanxi with KOLs. Inviting KOLs to travel to scenic spots can assist in forming positive perceptions of service quality and capturing much more sufficient content to post SFVs, which is an effective way to trigger their fans’ travel intentions.

Second, the present research indicates that travel intention could decline as individuals’ tourism fatigue increases and that tourism fatigue could reduce the influence of telepresence and destination image on travel intention. Reducing psychological and physiological burdens for tourists is a major aim of rural tourism marketers. To achieve this goal, marketers can use the findings of the current study to influence tourist behavior. Specifically, each scenic spot should create a unique position and distinctive landscape, which could provide a different experience for tourists from other destinations. Additionally, managers can provide tourists with convenient transportation and accommodation options, and design experiences related to intimacy, adventure, and relaxation in the context of rural landscapes. Marketers could also consider posting content on typical features of rural destinations in SFV apps to ensure that users have immersive experiences. The SFV content should highlight different experience types instead of just introducing the geographical characteristics of attractions. By adopting these approaches, tourism marketers can reduce target tourists’ tourism fatigue and strengthen the development of a positive destination image in rural tourism.

Third, due to the impact of COVID-19 pandemic, numerous rural tourism destinations are facing substantial operation pressure. Therefore, rural tourism marketers should rationally allocate resources based on their operational strengths and weaknesses to achieve sustainable development. In accordance with the three causal models leading to a high level of travel intention from fsQCA, rural tourism operators can develop suitable marketing strategies to stimulate users’ travel intention in SFV platforms. For example, since guanxi, argument quality, and telepresence served as positive antecedents in configuration (L1: fsgx*fsnr*fslc*∼fslj), rural tourism managers could hold a competition on SFV generation and distribution by offering high-level rewards to attract numerous users. Such approaches can help SFV app users establish strong guanxi with others and improve the argument quality on scenic spots in rural regions, which further triggers users’ intention to travel to rural tourism destinations.

Limitations and future research

This study has several limitations. First, it primarily focused on the antecedents of travel intention for SFV app users by incorporating guanxi, telepresence, destination image, and tourism fatigue into the ELM. Future studies can investigate the influence of other elements on users’ travel intention in the context of SFV apps. Second, our respondents were a diverse group of SFV app users; user behavior may vary according to the level of user engagement. Hence, future studies can analyze the differences between users with and without travel experience with rural tourism destinations recommended by other SFV app users. Third, to collect data, the current study involved the use of a cross-sectional survey that ignored the changes in the tourism fatigue level experienced by individuals (Sun et al., 2020). Future studies can develop a longitudinal study to better capture the trends of tourism fatigue over time.

Footnotes

Acknowledgments

The authors would like to thank the editor and anonymous reviewers for their helpful comments on this article.

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Social Science Foundation in Fuzhou City, China (Grant No. 2020FZC38).

ORCID iD

Xing-Zheng Xie

Appendix

Appendix 1.

Full set of survey questions and response options.

Item	Questions	Options
Part 1
1	Have you ever used a short-form video platform?	(1) Yes, (continue to the Question 2); (2) No, (end of the survey. Thank you for your time indeed.)
2	which app do you use most frequently to obtain rural tourism information?	(1) Douyin; (2) Kuaishou; (3) Miaopai; (4) Meipai; (5) Huoshan; (6) Xigua; (7) Other platforms
3	In recent one year, how often do you visit the recommended rural destination in short-form video apps?	(1) None; (2) 1–3 times; (3) 4–9 times; (4) more than 10 times
Part 2
XR1	Contacts on SFV app are not only concerned about himself/herself.	(1) Completely disagree; (2) Disagree; (3) Slightly disagree; (4) Neutral; (5) Slightly agree; (6) Agree; (7) Completely agree
XR2	Contacts on SFV app behave in a consistent manner.
XR3	Contacts on SFV app will always keep my interests in mind.
RQ1	SFV app maintains the practice of “give and take” among contacts.
RQ2	I am happy to do a favor for contacts on SFV app in order to tighten up relationships with them.
RQ3	SFV app allows me to return favors to my contacts who have helped me.
RQ4	I feel a sense of obligation to my contacts on SFV app for doing him/her a favor.
GQ1	I care about my contacts’ feelings when releasing dynamic information on SFV app.
GQ2	I care about my contacts’ feelings when giving thumbs-ups or comments on SFV app.
GQ3	I would try my best to help out my contacts when they are in need on SFV app.
GQ4	My contacts and I are able to talk openly as “real-world” friends on SFV app.
AQ1	The travel information obtained from my contacts on SFV app is accurate.
AQ2	The travel information obtained from my contacts on SFV app is relevant.
AQ3	The travel information obtained from my contacts on SFV app is complete.
AQ4	The travel information obtained from my contacts on SFV app is consistent.
SC1	The travel information obtained from my contacts on SFV app is trustworthy.
SC2	The travel information provider on SFV app is knowledgeable.
SC3	The travel information obtained from SFV app is reliable.
SC4	The travel information providers on SFV app are experts.
LC1	While I was surfing the SFV app, I felt I was in a world it created.
LC2	When I left the SFV app, I felt like I came back to the “real world” after a journey.
LC3	The SFV app came to me and created a new world for me, and the world suddenly disappeared when I left the platform.
JD1	The contacts on SFV app builds a more preferable image of rural tourism destinations.
JD2	The contacts on SFV app builds a more favorable image of rural tourism destinations.
JD3	The contacts on SFV app builds a more positive image of rural tourism destinations.
IT1	I intend to travel to the destinations suggested by my contacts on SFV app in the future.
IT2	I predict that I should travel to the destinations suggested by my contacts on SFV app in the future.
IT3	I am willing to visit the destinations suggested by my contacts on SFV app in the future.
ST1	My physical strength is declining
ST2	My steps and movements slowed down
ST3	I feel tired
ST4	I want to sit down and have a rest
JL1	My interest in the remaining attractions is declining
JL2	My curiosity about the remaining attractions is declining
JL3	My desire to continue visiting is diminishing
JL4	I am inactive when I go to the rest of the attractions
QG1	The mystery of this destination for me is declining
QG2	The freshness of this destination for me is declining
QG3	My liking for this destination is declining
QG4	My excitement is declining
RZ1	I think slowly now
RZ2	My attention is dropping
RZ3	My reaction to the outside is becoming dull
RZ4	My brain is tired
Part 3
1	What is your age? (Completion)
2	What is your gender?	(1) Male; (2) Female
3	What is your highest educational qualification?	(1) High school or below; (2) Associate or bachelor degree; (3) Master's degree or higher
4	What is your total monthly income (¥) after deductions for income tax, National Insurance, etc.? (Completion)

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