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. 2023 Jun 22;56:124–134. doi: 10.1016/j.jhtm.2023.06.019

Engaging customers with online restaurant community through mutual disclosure amid the COVID-19 pandemic: The roles of customer trust and swift guanxi

Min Liu a, Jie Xu a,∗, Shuhao Li b, Min Wei a
PMCID: PMC10285204

Abstract

Online restaurant communities have emerged as an important channel for restaurants to engage customers and increase sales amid the COVID-19 pandemic in China. According to social penetration theory and social exchange theory, this study investigates how mutual disclosures between servers and customers influence customers' social influence and knowledge-sharing engagement through customer trust and swift guanxi. Using questionnaire data collected from 340 customers, this study finds that server disclosure facilitates customer disclosure, and both server and customer disclosures positively predict customer trust and swift guanxi. Furthermore, customer trust can in turn promote swift guanxi and customers’ social influence engagement, while swift guanxi promotes both kinds of customer engagement. The outstanding theoretical implications of the present research lie in that it depicts the formation mechanism of customer engagement from the new lens of mutual disclosure and guanxi in Chinese culture. Practical implications can help restaurants improve performance.

Keywords: Online restaurant community, Mutual disclosure, Customer engagement, Swift guanxi, Customer trust

1. Introduction

The widespread lockdown and mobility constraints due to the COVID-19 pandemic have drastically reduced foot traffic in the restaurant industry (Jeong et al., 2021). In order to decrease customer acquisition costs, lots of restaurants have built their online communities in China, typically based on instant messaging social platforms, such as WeChat (Ziran Desgin, 2022). Specifically, supported by online restaurant communities built on free social platforms, restaurants can easily reach their customers at a much lower cost, more directly and closely than traditional business-to-consumer e-commerce platforms, such as Meituan, Dianping (Daxueconsulting, 2020). Such online restaurant communities are usually based on customers’ real-world social networks, in which customer power is quite strong (Cao et al., 2021). Typical scenarios of online restaurant communities are shown in Appendix. Online restaurant communities are perceived as important platforms for brand marketing and customer participation. These communities provide restaurants with a range of advantages, including facilitating customer relationship management (Ibrahim, 2023), future purchase frequency (Halloran & Lutz, 2021), customer citizenship behavior and customer engagement behavior (Song et al., 2022). Hence, it is significant for restaurants to set up an engagement-based market through online communities to transform consumers from simple consumers to value co-creators (Itani et al., 2019).

Customer engagement can be described from a behavioral manifestation as customers' non-transactional behaviors, including two well-explored types—customer's social influence engagement and knowledge-sharing engagement (Kumar & Pansari, 2016; Li et al., 2021, 2021, 2021). Existing literature indicates that consumer engagement can enhance customer-perceived value (Touni et al., 2022), foster memorable customer experiences (Tsaur et al., 2023), and strengthen brand loyalty (Li et al., 2020). Consequently, it is frequently adopted as a pivotal metric for assessing the efficacy of brand communication strategies, particularly in social media dissemination (Wang et al., 2023). Restaurants can obtain more value from the consumer by cultivating customer engagement (Shin, 2023). Accordingly, cultivating customer engagement through online communities is important for restaurants (Tsaur et al., 2023).

However, research on online restaurant communities is relatively limited, with even less research focusing on the phenomenon of customer engagement in restaurant communities, except for Gruss et al. (2020) and Kang et al. (2014, 2015) on restaurants' Facebook fan pages (a type of restaurant brand communities). Nevertheless, the above studies focused merely on the unilateral role of restaurants or customers and ignored the two-way interactions between restaurants and customers in the online restaurant community. By contrast, the restaurant communities of interest in this study provide a good platform for timely mutual disclosures between restaurant servers and customers. With the help of online restaurant communities, customers can disclose food preferences, personal information, and timely feedback, whereas servers can disclose food recommendations, provide promotion information, and accurately seize customers’ needs (Cao et al., 2021).

Existing studies on the online restaurant community have analyzed the facilitators of customer engagement in both restaurant and customer dimensions. For example, Sashi et al. (2019) used Twitter data from quick service restaurants and concluded that restaurant effort and customer commitment could foster customer engagement behaviors. Kim et al. (2020) analyzed the impact of technological features of social media and customers' personal characteristics on customer engagement from the perspective of customer flow experience. Habib et al. (2022) found that the interactive features of food delivery platforms and consumers’ self-concept can facilitate online customer engagement. Unfortunately, scarce studies have linked mutual disclosures of servers and customers with customer engagement in the online restaurant community context. To bridge this gap, the present research aims to explore how customer engagement can be promoted in online restaurant communities from the perspective of mutual disclosure between servers and customers.

Customer trust is introduced to unpack the effect mechanism of mutual disclosure on customer engagement. According to social penetration theory, self-disclosure helps to promote trust between two parties (Hwang et al., 2015). During the COVID-19 outbreak, customers' concerns about food safety have increased (Jeong et al., 2021); thus, cultivating customers' trust is crucial for restaurants. As a link between restaurants and customers, online restaurant communities facilitate mutual disclosures between restaurants and customers, which can help promote customer trust. From another aspect, customer trust can be regarded as a valuable intangible resource for interpersonal exchange based on social exchange theory (Cropanzano & Mitchell, 2005). When customers build trust toward restaurant servers, customer engagement is more likely to be triggered as a reward to the restaurant. This study, therefore, aims to test the role of customer trust in linking server (customer) disclosure with customer's social influence and knowledge-sharing engagement.

In addition to customer trust, this study introduces swift guanxi to reveal the mechanism underlying the effect of mutual disclosure on customer engagement. In the Chinese context, guanxi exerts a critical and unique function in relationship marketing associated with corporate performance, as well as customer loyalty (Ou et al., 2014). In contrast to relationship marketing based on formal rules and company interest in the Western, guanxi is often built on personal benefits and social networks in the Chinese context (Wang, 2007). As an extension of Chinese guanxi in the online marketplace, swift guanxi is conceptualized as customers' awareness of a quickly formed relationship with sellers (Ou et al., 2014). Swift guanxi has been well employed to understand the online customer–enterprise relationship in the Chinese context (Shi et al., 2018). However, as far as we know, few studies have paid attention to swift guanxi in the online hospitality context. Based on Lin et al. (2017), the current research proposes that customer trust resulting from mutual disclosure can facilitate the cultivation of customers’ swift guanxi with servers in the online restaurant community. Furthermore, following the reciprocity proposition of social exchange theory (Cropanzano & Mitchell, 2005), customers who establish such swift guanxi with the restaurant are conducive to adopting engagement behaviors as a reward to the restaurant (Guo et al., 2021). Hence, we herein introduce the roles of customer trust and swift guanxi to cover the mechanism of the restaurant–customer disclosures on customer engagement.

To sum up, this study aims to construct a conceptual model grounded on social penetration theory and social exchange theory to reveal how mutual disclosures of servers and customers affect customer's social influence engagement and knowledge-sharing engagement. The concrete aims of the current research are as follows: (a) to explore the impact of server disclosure on customer disclosure; (b) to explore the influence of server and customer disclosures on customer trust; (c) to explore the impact of server and customer disclosures on customers' swift guanxi with servers; (d) to test the relationship between customer trust and swift guanxi; (e) to investigate the influences of server and customer disclosures, customer trust, and swift guanxi on customer's social influence engagement and knowledge-sharing engagement, respectively. This study theoretically enriches the research on online restaurant communities by revealing how restaurants use online communities to promote customer engagement. In addition, the study extends the predictors of swift guanxi from the theoretical lens of social penetration theory and expands the antecedences of customer engagement from the perspective of online guanxi in the Chinese context. In practical terms, the present research provides restaurants with useful suggestions on how to engage customers with online restaurant communities.

2. Literature review and research hypotheses

2.1. Social penetration theory

Social penetration theory is proposed to elucidate the process of close relationship evolution (Altman & Taylor, 1973), which can be mirrored in the degree of self-disclosure (Lei et al., 2023). Self-disclosure is essential to developing relationships between parties (Baack et al., 2000). The key requirement that takes a relationship from superficial to intimate is knowing each other through constantly sharing information concerning themselves (Hwang et al., 2015). More specifically, with individuals gradually revealing themselves to each other, their relationships evolve, often getting deeper and more trustworthy (Baack et al., 2000). Initially, social penetration theory was used to investigate offline interpersonal relationships, such as romantic relationships (Altman & Taylor, 1973). Presently, this theory has been widely applied to online relationships (Lei et al., 2023). The studies of online communities in the tourism and hospitality context have also employed this theory. For instance, Huang et al. (2020) explored hotel customers’ social commerce behaviors in online communities based on social penetration theory. In this study, social penetration theory is employed to unpack whether and how server and customer disclosures affect customer trust and their swift guanxi with service providers in the setting of online restaurant communities.

2.2. Social exchange theory

As a classical theory explaining social interactions between two parties from the perspective of resource exchange, social exchange theory argues that individuals are generally selfish and that their behaviors are determined by calculating benefits and sacrifices in interpersonal exchange activities (Homans, 1958). Namely, human social behaviors are aimed at maximizing self-interest during the social exchange (Li et al., 2021). Economic resources like money and commodities are used for social exchange, as well as socioemotional resources like love and status (Foa & Foa, 1980). Additionally, social exchange theory involves several propositions, of which the reciprocity proposition is the most valued and most applied principle (Cropanzano & Mitchell, 2005). The reciprocity proposition argues that people will reward others if they benefit from them to maintain their reciprocal exchange (Blau, 1964). Social exchange theory has attracted considerable attention in the tourism and hospitality field (Fu et al., 2022). In this study, consumer trust and swift guanxi can be considered valuable resources in interpersonal exchanges. Therefore, the present research applies social exchange theory to interpret how consumer trust and consumers' swift guanxi with service providers affect customer's social influence engagement and knowledge-sharing engagement in online restaurant communities.

2.3. Mutual disclosure

Mutual disclosure refers to mutual communications between two partners (Hwang et al., 2013), such as sharing personal attitudes, thoughts, and plans (Bechtiger et al., 2021). Both sides gradually know and become acquainted with each other through such reciprocal disclosure (Hwang et al., 2015). In the service delivery scenario, mutual disclosure is described as disclosure between the server and the consumer, that is, server disclosure and consumer disclosure (Hwang et al., 2013). Specifically, server disclosure refers to servers disclosing themselves or suggestions to customers (Hwang et al., 2013). In the restaurant industry, for example, servers share food information, provide recommendations on menu choices, or reveal personal backgrounds about themselves. By contrast, customer disclosure describes the behaviors of customers revealing themselves or their needs to servers (Hwang et al., 2013). For example, customers tell servers their personal information, such as food preferences, price budget, and dining evaluation.

Grounded on social penetration theory, server disclosure can be regarded as the beginning of a harmonious server–customer relationship, through which customers get to know the service provider better. As the servers’ self-disclosure to the customer increases, the relationship between them will gradually deepen, which can elicit customers to volunteer to disclose themselves to servers (Hwang et al., 2015). In addition, server disclosure can be recognized as a relationship marketing tactic that creates an atmosphere of openness and sincerity in service (Kim & Jang, 2023). Such an atmosphere can encourage customers to be more open and willing to disclose themselves (Niemi & Pullins, 2021). For instance, customers are more willing to disclose their food preferences to servers when they are told menu choices and given enthusiastic recommendations (Hwang et al., 2013). Furthermore, Hwang et al. (2015) indicated that when servers disclose information to consumers, customers tend to share their needs and feedback with the servers in offline full-service restaurants. Accordingly, we advance the following hypothesis.

H1

Server disclosure poses a positive effect on customer disclosure.

2.4. Customer trust

Customer trust is defined as customers' confident belief in the reliability and honesty of a seller (Morgan & Hunt, 1994). As a fundamental concept of relationship marketing (Fu et al., 2022), customer trust is crucial for companies to foster and keep an enduring connection with customers (Hwang et al., 2015). During the COVID-19 outbreak, consumers' concerns about food safety and hygiene in the dining environment have increased owing to the potential risk of contracting the virus (Jeong et al., 2021). According to Chen (2006), customer trust is a key factor in predicting customers' behaviors under the uncertainty and risk triggered by COVID-19. Therefore, cultivating customers’ trust in restaurants is increasingly important.

In building consumer trust and loyalty, mutual disclosures between servers and consumers play a crucial role and deserve special attention (Hwang et al., 2015). The current research proposes that mutual disclosures between servers and customers can promote customer trust for the following reasoning. In accordance with social penetration theory, self-disclosure contributes to the cultivation of intimate relationships (Altman & Taylor, 1973). In other words, with individuals progressively presenting themselves to each other, their relationship changes, usually getting deeper and more trustworthy (Li et al., 2022). Servers' sincere online disclosure enables them to share their preference and interest with customers, which is conducive to building trust with customers (Hwang et al., 2015). From another aspect, trust is caused by positive customer evaluations and is a key criterion for customers to assess the performance of the service provider (Leung et al., 2023). When servers offer genuine advice in the online restaurant community from customers' standpoint, customers may improve their evaluations of restaurants’ service and further promote their trust. Hence, the following hypothesis is advanced.

H2

Server disclosure is positively associated with customer trust.

The current study considers that customer trust is able to be predicted by customer disclosure as well. Crosby et al. (1990) stated that unilateral and unreciprocated disclosure would result in an unhealthy relationship between two parties. Therefore, customer disclosures are also noteworthy for harmonious relationships and trust building. When customers are reluctant to disclose their personal preferences about food choices to the servers, it may be difficult for them to get their problems resolved promptly, thereby increasing their mistrust (Crosby et al., 1990). By contrast, customers share their food preferences (e.g., spicy or sweet) and personal information (e.g., occupation, hobbies, and hometown) in an online restaurant community, enabling servers to deliver more customized and personal service to customers and resulting in a high degree of customer trust (Huang & Chang, 2008). Besides, previous research validated the driving effect of customer disclosure on customer trust (Chen et al., 2021; Hwang et al., 2015). Hence, the current research posits the hypothesis below.

H3

Customer disclosure has a positive influence on customer trust.

2.5. Swift guanxi

Guanxi, a close interpersonal relationship based on social networks and individual interests (Wang, 2007), is a crucial factor in buyer–seller transactions in China. It also contributes to facilitating business trade by lubricating business relationships via personal social bonds (Lin et al., 2017). Grounded on the notion of guanxi in the Chinese traditional business setting, Ou et al. (2014) introduced swift guanxi to extend traditional guanxi to the online marketplace. Swift guanxi is defined as customers' awareness of a quickly-formed interpersonal relationship with salespersons or enterprises (Ou et al., 2014). Unlike conventional guanxi, swift guanxi in online markets is facilitated by online communication technologies and can be formed quickly without costly investments and face-to-face contact (Guo et al., 2021; Shi et al., 2018). Swift guanxi consists of three components: mutual understanding, reciprocal favors, and harmonious relations. Mutual understanding means that buyers and sellers understand each other's demands. Reciprocal benefits are the positive mutual benefits from buyer–seller contact. Relationship harmony describes bilateral respect and avoiding collision (Ou et al., 2014).

In the online restaurant community, the mutual disclosures of servers and customers help foster customers' swift guanxi with servers because of the following reasoning. In line with social penetration theory, the evolution of close relationships is presented by the level of self-disclosure (Lei et al., 2023). As people gradually make disclosures about themselves to each other, their relationships usually change, often becoming closer (Li et al., 2022). Therefore, we infer that as disclosure between servers and customers increases, their relationship continues to deepen, further driving the formation of guanxi in the online marketplace. In addition, online server disclosure means providing advice to the consumer about the food choice, and online customer disclosure implies that the restaurant can better understand customers' needs (Hwang et al., 2013). Such instant two-way communication can help better capture each party's demands and promote mutually beneficial and harmonious relationships between servers and customers in the online marketplace (Huang & Chang, 2008). Besides, according to Hwang et al. (2013), both server and customer disclosures are predictors of rapport relationships. Hence, we herein propose the following hypotheses.

H4

Server disclosure positively influenced the customer's swift guanxi with the server.

H5

Customer disclosure positively influenced the customer's swift guanxi with the server.

This study argues that customer trust is also an antecedent of customers’ swift guanxi with the server. Previous research identified trust as a key predictor of customer exchange relationships with enterprises (Morgan & Hunt, 1994). Customers generally possess positive evaluations of the company they trust (Chen & Li, 2021), on which basis customers are more willing to build amicable relationships (Ou et al., 2014). Specifically, trust is conducive to building swift guanxi via providing preconditions in which the buyer and seller are able to swiftly establish reciprocal understanding, and realize mutual favors and rapport (Lin et al., 2017). In addition, Ou et al. (2014) argued that trust could be formed in accordance with the features of the trustee before the relationship is developed. In other words, in the online restaurant community, when consumers perceive the servers of the restaurant as trustworthy based on server disclosure, they would like to cultivate swift guanxi with servers. Moreover, previous research indicated that trust antecedes swift guanxi (Guo et al., 2021; Lin et al., 2017; Ou et al., 2014). Thus, we pose the hypothesis below.

H6

Customer trust positively influenced the customer's swift guanxi with the server.

2.6. Customer engagement

During the last decade, customer engagement has been widely explored in the marketing field as a fresh direction in customer management (Touni et al., 2022) and an important predictor of firm performance (Kumar & Pansari, 2016). Nonetheless, no consistent view on the construct of customer engagement exists. Some scholars argued that customer engagement is a psychological process (Paul & Roy, 2023) that leads to repeat purchases and long-term relationships with firms (Li et al., 2020). Other researchers conceptualize customer engagement as a behavioral manifestation of firms beyond trading behaviors (Li et al., 2021). This research describes customer engagement as non-transactional behaviors. Because in an increasingly information-based society, it is easy for customers to interact with enterprises or other customers via online communities (Touni et al., 2022). Therefore, comprehending customer engagement through a behavioral lens is necessary and has been adopted by a large body of literature (Guo et al., 2021; Wang et al., 2023).

Customer engagement is a complex concept requiring multidimensional interpretation (Itani et al., 2019; Kumar & Pansari, 2016), of which customer's social influence engagement and knowledge-sharing engagement are two key types (Itani et al., 2020; Li et al., 2021). Customer's social influence engagement describes that customer impacts others' attitudes and decisions by sharing information about companies or products on social media, such as product information and consuming experience (Kumar & Pansari, 2016). According to Itani et al. (2020), customer's social influence engagement promotes customer-generated content (e.g., online rankings, consumer reviews, and community forums) and contributes to companies' reputation. By contrast, customer's knowledge-sharing engagement refers to customers providing feedback, advice, and complaints to companies (Kumar & Pansari, 2016). For companies, knowledge is a critical asset that can help them to understand customers' needs and preferences, further helping to improve existing offerings and develop new products to better satisfy customers (Itani et al., 2020).

The current research considers that customer's social influence engagement and knowledge-sharing engagement can be positively predicted by customer trust. In line with the social exchange theory, this study regards customer trust as a type of relational resource in interpersonal exchange that helps to increase customers' confidence in long-term mutual benefits (Chen & Li, 2021). Grounded on the reciprocity proposition in social exchange theory, customers would like to reciprocate to their trusted service providers of the restaurant, such as recommending restaurants to friends and relatives and suggesting improvements to restaurants, which can promote restaurants' reputation and performance (Itani et al., 2020; Kumar & Pansari, 2016). Additionally, previous studies verified that customer trust can drive customer engagement. For instance, Yin et al. (2023) argued that customer trust positively predicts their engagement behaviors in AI environments. The above analyses lead to the following hypotheses.

H7

Customer trust positively affects customer's social influence engagement.

H8

Customer trust positively affects customer's knowledge-sharing engagement.

This study argues that swift guanxi can promote customer's social influence engagement and knowledge-sharing engagement for the following reasoning. Swift guanxi can be considered an intangible and valuable resource, indicating a harmonious and reciprocal relationship between customers and servers. In conformity with the reciprocity rule in social exchange theory, persons who benefit from others will tend to do something beneficial to them as a reward (Homans, 1958). Accordingly, when customers build swift guanxi with restaurant servers, they are more likely to do what benefits restaurants, such as positively talking about restaurants and providing suggestions and feedback to help restaurants promote positive word-of-mouth and product quality (Itani et al., 2020). In addition, previous research has verified that a great customer–server relationship promotes customer engagement (Guo et al., 2021; Itani et al., 2020). The arguments hereinabove support the following hypotheses. Research model is shown in Fig. 1 .

H9

Swift guanxi positively affects customer's social influence engagement.

H10

Swift guanxi positively affects customer's knowledge-sharing engagement.

Fig. 1.

Fig. 1

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Research model.

3. Methodology

3.1. Measures

In this study, all the variables in this model were assessed with scales adapted from existing research. Specifically, server and customer disclosures both were measured with four items from Hwang et al. (2015). Four items borrowed from Fu et al. (2022) were employed to assess customer trust. Three items were used to measure swift guanxi, drawing on Lin et al. (2018). Customer's social influence engagement and knowledge-sharing engagement were assessed with four items modified from Li et al. (2021). All items of the six constructs were evaluated by a seven-point Likert scale (1 = extremely disagree; 7 = extremely agree).

Given that the current study was carried out in China, all the subjects of the research were Chinese, and a strict translation and back-translation process was performed (Brislin, 1980). Subsequently, three scholars helped check the questionnaire for translation consistency and intelligibility. Then, we conducted a pilot study in which 74 questionnaires were recovered and 70 were valid. The pretest results showed satisfactory reliability, with Cronbach's alpha varying between 0.670 and 0.924.

3.2. Data collection

This study gathered data from consumers who had joined online restaurant communities to empirically examine the proposed hypotheses. WeChat Group has been widely applied for community marketing in China (Daxueconsulting, 2020). Establishing an online community via WeChat is a trending option for Chinese restaurants, especially in the wake of the COVID-19 outbreak (Guo & Wang, 2020; Tan & Chen, 2021). Therefore, questionnaires were distributed in the restaurants' WeChat Groups for customers following Ballantine and Stephenson's (2011) study.

We conducted the survey in 11 restaurants' WeChat Groups with a convenience sampling method. The restaurant communities in this study were built primarily based on customers' real, private rather than public social networks. Therefore, the researchers selected the target online restaurant communities mainly based on their social networks, borrowing from Cao et al.’s (2021) research on social commerce in the WeChat group. Then, the researchers contacted these restaurant owners to obtain approval for the survey through face-to-face negotiations. Finally, 11 online restaurant communities were identified. Of these, two were chain restaurants, and the others were non-chain. Moreover, the number of members in each restaurant community ranged from 200 to 500. From October 21 and October 31, 2022, the two well-trained researchers led questionnaire collection in these 11 online restaurant communities. The questionnaire distribution is either entrusted to restaurant supervisors or released by researchers under the authorization of restaurant owners. Before filling out the questionnaire, respondents were notified that: (a) the questionnaire was anonymous, (b) it would take them 3–5 min on the questionnaires, and (c) they would receive 3CNY in return if the questionnaires were carefully completed. Finally, 400 questionnaires were collected, and 340 questionnaires were valid after excluding careless respondents or with short response times, with a validity rate of 85%.

4. Results

4.1. Sample profiles

The demographic profiles of the research samples are presented in Table 1 . Females accounted for 57.4%. The majority of the respondents were aged 18–24, and half of them possess a master's degree or above. This sample distribution is acceptable as young people are the main participants in the online community (iResearch, 2017). The majority earned CNY 3000 (USD 426) or less. 28.2% of the respondents had joined the restaurant's online community for one to three months, and 31.8% visited the restaurant three or four times in the past half year.

Table 1.

Demographic profiles (N = 340).

Profiles Category Number Percent (%)
Gender Male 145 42.6
Female 195 57.4
Age 18–24 277 81.5
25–34 55 16.2
35–44 6 1.8
≥45 2 0.6
Level of education Junior high school or below 2 0.6
Senior high/technical secondary school 3 0.9
Junior college 5 1.5
Undergraduate 159 46.8
Master's degree or above 171 50.3
Monthly income CNY 3000 (USD 426) or less 282 82.9
CNY 3001 (USD 427) to CNY 5000 (USD 710) 25 7.4
CNY 5001 (USD711) to CNY 7000 (USD 994) 19 5.6
CNY 7001 (USD995) to CNY 9000 (USD 1279) 4 1.2
CNY 9001 (USD 1280) or more 10 2.9
Visit frequency in the past half year 2 or below 89 26.1
3–4 108 31.8
5–10 72 21.2
11 or above 71 20.9
Duration of membership Within a month 58 17.1
More than one month to three months 96 28.2
More than three months to six months 63 18.5
More than half a year to one year 72 21.2
More than one year 51 15.0
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4.2. Common method variance

All of the survey data were reported by customers themselves, so common method variance may be a serious concern. Procedurally, vague concepts and unfamiliar terms were averted before the questionnaire was distributed. In addition, the questionnaire was anonymized to reduce respondents' evaluation concerns. After data collection, Harman's single-factor method was employed to test common method variance with SPSS 26.0. The results presented six factors with eigenvalues above 1. 40.3% of the total variance could be explained by the first one, suggesting that the common method variance was not serious (Podsakoff et al., 2003).

4.3. Measurement model

SPSS 26.0, AMOS 23.0, and MS Excel were employed to examine the measurement model. The result of Kaiser-Meyer-Olkin measure of sample adequacy was 0.91, implying that it is highly appropriate for factor analysis (Kaiser, 1974). The goodness-of-fit indices (χ2 = 528.682, df = 215, χ2/df = 2.459, GFI = 0.869, NFI = 0.893, IFI = 0.934, TLI = 0.921, CFI = 0.933, RMSEA = 0.066, RMR = 0.114) indicated a good model fit (Hu & Bentler 1999). As shown in Table 2 , the composite reliability and Cronbach's alpha were larger than 0.70, indicating good reliability (Fornell & Larcker, 1981; Nunnally, 1978). Factor loadings of all items were significant at the level of 0.001 and exceeded the threshold of 0.60 (Anderson & Gerbing, 1988). Meanwhile, most values of average variance extracted (AVE) were above 0.5, supporting satisfying convergent validity (Fornell & Larcker, 1981; Lam, 2012).

Table 2.

Measurement model.

Items Mean Standard deviation Factor loading Composite reliability AVE Cronbach's alpha
Server disclosure 0.766 0.462 0.756
When (If) there are mistakes during the services delivery process, the employee of this restaurant tells (would tell) me in the WeChat Group. 4.980 1.472 0.665
The employee of this restaurant tells me about his/her personal opinion (e.g., food taste, food price, portion size) in the WeChat Group. 4.440 1.591 0.814
The employee of this restaurant gives appropriate advice to my menu choice in the WeChat Group. 4.640 1.508 0.745
The employee of this restaurant shares food information with me in the WeChat Group. 5.570 1.266 0.433
Customer disclosure 0.762 0.450 0.754
I express thanks to the employee of this restaurant for his/her services on the WeChat Group. 5.360 1.400 0.494
I tell the employee of this restaurant about my preference (e.g., food taste, food price, portion size) on the WeChat Group. 4.840 1.668 0.725
I tell the employee of this restaurant that I am a regular customer of this restaurant on the WeChat Group. 3.960 1.656 0.710
I share personal information with the employee of this restaurant (e.g., food allergy, vegetarian) in the WeChat Group. 4.330 1.751 0.725
Customer trust 0.938 0.791 0.937
I think the employee of this restaurant is reliable. 5.290 1.111 0.893
I have confidence in the employee of this restaurant. 5.180 1.115 0.916
The employee of this restaurant is trustworthy. 5.220 1.152 0.877
I think the employee of this restaurant has high integrity. 5.350 1.144 0.870
Swift guanxi 0.876 0.702 0.872
The employee of this restaurant in the WeChat Group and I can understand each other. 5.170 1.182 0.873
The employee of this restaurant in the WeChat Group and I treat each other as we treat our friends. 4.530 1.368 0.791
The employee of this restaurant in the WeChat Group and I have harmonious relationships. 5.090 1.198 0.848
Customers' social influence engagement 0.812 0.526 0786
I will talk about my positive experience at this restaurant with others. 5.480 1.153 0.798
I will discuss the benefits that I get from this restaurant with others. 5.390 1.138 0.766
I will actively mention this restaurant in my conversations. 5.070 1.30 0.787
I will actively discuss this restaurant on different media platforms. 4.230 1.574 0.511
Customers' knowledge-sharing engagement 0.900 0.693 0.896
I am willing to provide feedback about my experience with this restaurant. 5.260 1.225 0.756
I am willing to provide suggestions for improving the performance of the restaurant's products/services. 5.190 1.266 0.829
I am willing to provide suggestions/feedback about the new product/services to this restaurant. 5.260 1.241 0.918
I am willing to provide feedback/suggestions for developing new products/services for this restaurant. 5.050 1.392 0.818
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As presented in Table 3 , the correlation coefficients between the constructs were below the squared root of AVEs of the related variables, implying satisfactory discriminant validity (Fornell & Larcker, 1981).

Table 3.

Discriminant validity.

Constructs Server disclosure Customer disclosure Customer trust Swift Guanxi Customers' social influence engagement Customers' knowledge-sharing engagement
Server disclosure 0.680
Customer disclosure 0.520 0.671
Customer trust 0.467 0.512 0.889
Swift guanxi 0.494 0.531 0.722 0.838
Customers' social influence engagement 0.473 0.489 0.669 0.663 0.725
Customers' knowledge-sharing engagement 0.419 0.437 0.508 0.632 0.555 0.832
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Note: The diagonal values are the square roots of AVE.

4.4. Structural model

Amos 23.0 was employed to examine the structural model. The model fit indices (χ2 = 499.210, df = 218, χ2/df = 2.90, GFI = 0.883, NFI = 0.899, IFI = 0.941, TLI = 0.930, CFI = 0.940, RMSEA = 0.062, RMR = 0.132) presented satisfied model fit (Hu & Bentler, 1999). The model explained 27%, 32%, 58%, 55%, and 42% of the variation in customer disclosure, customer trust, swift guanxi, customer's social influence engagement, and customer's knowledge-sharing engagement, respectively (see Fig. 2 ).

Fig. 2.

Fig. 2

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Structural model.

As depicted in Fig. 2, server disclosure positively affected customer disclosure (β = 0.570, p < 0.001). Therefore, H1 was supported. Server disclosure and customer disclosure positively affected customer trust (β = 0.273, p < 0.001; β = 0.372, p < 0.001) and swift guanxi (β = 0.155, p = 0.014; β = 0.179, p = 0.006). Therefore, H2, H3, H4, and H5 were validated according to the empirical results. Furthermore, customer trust positively affected swift guanxi (β = 0.559, p < 0.001). Therefore, H6 was supported. Customer trust significantly contributes to customer's social influence engagement (β = 0.387, p < 0.001) but not to customer's knowledge-sharing engagement (β = 0.092, p = 0.235). H7 was supported whereas H8 was not. These results implied that when consumers trusted the restaurant service provider, they were more likely to highly evaluate this restaurant in their conversations with others. However, they would not necessarily provide feedback and advice for improving the performance of restaurants. In addition, swift guanxi was significantly associated with customer's social influence engagement (β = 0.413, p < 0.001) and customer's knowledge-sharing engagement (β = 0.582, p < 0.001). Hence, H9 and H10 were empirically validated.

Finally, we compared the differences in the effects of server disclosure and customer disclosure, where the standardized total effects of server disclosure on trust, swift guanxi, and both kinds of customer engagement were 0.466, 0.508, 0.338, and 0.390, respectively, while those of customer disclosure were 0.372, 0.387, 0.259, and 0.304, respectively. The above results indicate that server disclosure has stronger contributions to customer trust, swift guanxi, and customer engagement than customer disclosure.

5. Conclusions and discussion

5.1. Conclusions

The current research constructs a conceptual model grounded on social penetration theory and social exchange theory to reveal whether and how mutual disclosures of servers and customers in online restaurant communities affect customer engagement. The empirical findings indicate that server disclosure significantly predicts customer disclosure, and both server and customer disclosures positively affect customer trust and customers' swift guanxi with servers. In addition, customer trust can further boost swift guanxi and customer's social influence engagement, whereas swift guanxi can in turn promote customer's social influence engagement and knowledge-sharing engagement. Besides, compared to customer disclosure, server disclosure has stronger positive effects on trust, swift guanxi, and customer engagement.

5.2. Theoretical implications

The current study theoretically advances the literature as follows. First, it contributes to the research on online restaurant communities by revealing the roles of server and customer disclosures in promoting customer engagement grounded on social penetration theory and social exchange theory. COVID-19 accelerated the digital transformation of the restaurant industry, with the rapid development of restaurant online communities being one of the typical manifestations (Habib et al., 2022). However, existing studies on online restaurant communities are relatively limited. Specifically, Kang et al. (2014, 2015) and Gruss et al. (2020) focused on restaurants’ Facebook fans page, an online restaurant brand community. However, they addressed the unilateral role of the restaurant or the customer, ignoring the two-way interactions between restaurants and customers in the online restaurant community. Timely two-way interactions are the most typical feature of the instant messaging-based online restaurant communities that this study focuses on. To address this gap, the present research not only covered how to promote customer engagement in online restaurant communities from the lens of two-way interactions between servers and customers but also compared the differences in the impacts of server disclosure and customer disclosure, thereby expanding the knowledge of online communities in the hospitality context.

Second, the present study enriches the knowledge of swift guanxi by validating the role of swift guanxi in promoting customer engagement with server–customer mutual disclosure. Different from relationship marketing, which is impersonal and based on firm-level interest in the Western context, guanxi is typically strengthened with individuals' social networks and benefits in the Chinese context (Wang et al., 2007). Swift guanxi has not been thoroughly investigated in the hospitality industry as an extension of the conventional Chinese guanxi in the internet marketplace, although it has received considerable research attention (Lin et al., 2017; Ou et al., 2014). As far as we know, we are the pioneers in discussing swift guanxi in the hospitality context. Meanwhile, this study explores the formation mechanisms of swift guanxi from a new theoretical perspective—social penetration theory, finding that server and customer disclosures contribute to customers' swift guanxi with servers. This result echoes Shi et al.’s (2018) call for more exploration of the causes of swift guanxi in the Internet market. Besides, different from Guo et al. (2021), who considered customer engagement a unidimensional variable and examined its relationship with swift guanxi, we further validated the heterogeneous influences of swift guanxi on the two forms of customer engagement, respectively, deepening the knowledge on the influence of swift guanxi on non-transactional behaviors.

Finally, this study expands the antecedents of customer engagement by constructing a conceptual model grounded on social penetration theory and social exchange theory that links server–customer mutual disclosure, customer trust, and swift guanxi with customer engagement together. Existing research implied that the two-way disclosure between customers and servers contributes to rapport and customer loyalty but failed to further explore their relationship with customer engagement (Hwang et al., 2013, 2015), a typical customer's non-transactional behavior. To address this gap, the present research sheds light on the underlying mechanism of how mutual disclosures of servers and customers affect customer engagement in online restaurant communities. The finding supports the indirect driving effects of server and customer disclosure on customer's social influence engagement and knowledge-sharing engagement, echoing the assertion that engagement can be nourished by interaction (Xie et al., 2022). Furthermore, the present study validated the direct influence of swift guanxi on customer engagement, providing a deep sight into the antecedences of customer engagement from the lens of online guanxi in the Chinese context.

5.3. Practical implications

The study contributes practically to restaurants as follows. First, the results suggest that server disclosure can induce customer disclosure. Both server and customer disclosures can promote customer trust, swift guanxi, and customer engagement, and the impact of server disclosure is even greater. Thus, restaurants are supposed to encourage servers to self-disclose. Specifically, restaurants can conduct service training to encourage server disclosures. Service training can help servers possess rich information and knowledge about restaurants and make them learn how to deliver satisfactory service, such as appropriate meal recommendations. Restaurants are recommended to hire persons with outgoing personalities because they will be inclined to share and disclose themselves actively and induce customer disclosure (Hwang et al., 2013). Furthermore, restaurants are recommended to reward servers who actively participate and interact with customers in online restaurant communities. Besides, customer disclosure is also an important factor in promoting customer trust. Creating a friendly and warm community atmosphere is a good way to promote customer disclosure (Niemi & Pullins, 2021). In such an atmosphere, customers will be more comfortable and casual and thus more active in the interaction and information-sharing in online restaurant communities.

Second, the findings validated the role of customer trust in promoting customers' swift guanxi with servers and customer engagement. Customer trust is regarded as a strong factor in predicting customers' behaviors under uncertainty (Chen, 2006) and is crucial for restaurants. During the COVID-19 pandemic, consumers are paying more attention to food safety and hygiene (Jeong et al., 2021). Therefore, restaurants should strictly manage food safety and hygiene, which is the basis for gaining customers' trust (Jeong et al., 2021). Furthermore, information about the safety and hygiene of food is encouraged to be shared in online restaurant communities to enhance consumers' trust in the restaurants. Moreover, servers should put themselves in their customers’ shoes and be sincere in their interactions with customers, such as recommending appropriate menus to customers instead of excessive and expensive food choices.

Third, the study verifies that customers' swift guanxi with servers can significantly stimulate customer's social influence engagement and knowledge-sharing engagement. Swift guanxi, developed by extending conventional guanxi to the online market (Guo et al., 2021), is characterized by mutual understanding, reciprocal favors, and relationship harmony. Specifically, adequate communication with customers is suggested to ensure that customers' needs and opinions are properly understood to promote mutual understanding and relationship harmony. Then, servers can provide an appropriate and quick response. In addition, restaurants can conduct discount activities in the online communities to warm up the community atmosphere and give preferential benefits to the community members, such as holiday discounts, coupons, and group red envelopes. Such discount activities can help to achieve mutual benefit and harmonious relationships between customers and restaurants, thereby achieving swift guanxi with customers. When consumers establish swift guanxi with restaurant servers in online communities, they are more willing to generate customer engagement, like actively discussing the restaurants' food, sharing food knowledge, and generating favorable word-of-mouth.

5.4. Limitations and recommendations for future directions

The present research acknowledges the following shortcomings. First, this study paid attention to online restaurant communities in the Chinese context, thus the conceptual model was tested with Chinese samples. Given the cultural differences between different countries and regions, the findings may have limited applicability to restaurants in other countries or regions. Second, this research might be affected by sample selection bias. We have tried our best to select diverse communities for the survey. However, considering the wide applicability of the online restaurant communities, we cannot guarantee that our results apply to all restaurants. Third, all data in this study were self-reported and collected in one wave, which may lead to common method variance and have limitations in inferring causal relationships between variables. Although procedures and statistics remedies have ensured that common method variance does not pose a serious threat to research results, multi-resources or longitudinal data can be considered to reduce common method variance and to perform more rigorous causal inference in the future. Fourth, the current research employed a convenience sampling technique to gather data, which could impact the representativeness of the sample. Therefore, more strict sampling techniques, such as random sampling, are encouraged in the future. Finally, boundary conditions, such as restaurant types and customer involvement in the community, may influence the results. Future studies can consider these factors.

Declarations of interest

No potential conflict of interest was reported by the authors.

Funding

This research is supported by The Social Science Foundation of Guizhou Province, China (22GZZB03).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

We would like to thank the reviewers for their beneficial suggestions that helped improve this paper.

Biographies

Min Liu is a PhD candidate at the Department of Tourism and Hospitality Management, School of Management, Xiamen University. Her research interests include destination marketing and tourist behavior.

Jie Xu is a PhD candidate at the Department of Tourism and Hospitality Management, School of Management, Xiamen University. Her research interests include tourism impact and tourist behavior.

Shuhao Li is an associated Professor of School of Tourism and Geography Science, Qingdao University. His research interests include tourism impact and tourist behavior.

Min Wei is a Professor at the Department of Tourism and Hospitality Management, School of Management, Xiamen University. His research interests include tourism impact and tourism economics.

Appendix.

Typical scenarios of online restaurant communities (based on instant messaging social platforms (WeChat)) of a small restaurant and a chain restaurant.

Image 1

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  • a)

    Customer orders food, discloses food preference and shares foot traffic of the restaurant in the online restaurant community of a small restaurant.

  • b)

    Customers ask the restaurant staff when the restaurant is open and express their appreciation of the restaurant's cuisine in the online restaurant community.

  • c)

    Customers and restaurant staff wish each other well on the Lantern Festival in the online restaurant community, and restaurant staff give red packets to customers in return.

  • d)

    Restaurant staff offers exclusive discounts to group members, customers order food and ask staff about the progress of the food preparation in the online restaurant community.

Image 2

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  • e)

    and f) Restaurant staff provides exclusive Mother's Day promotion for the group members in the online restaurant community of a chain restaurant, and customers express their anticipation of the promotion.

References

  1. Altman I., Taylor D.A. Holt, Rinehart & Winston; 1973. Social penetration: The development of interpersonal relationships. [Google Scholar]
  2. Anderson J.C., Gerbing D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin. 1988;103(3):411–423. doi: 10.1037/0033-2909.103.3.411. [DOI] [Google Scholar]
  3. Baack D., Fogliasso C., Harris J. The personal impact of ethical decisions: A social penetration theory. Journal of Business Ethics. 2000;24(1):39–49. doi: 10.1023/A:1006016113319. [DOI] [Google Scholar]
  4. Ballantine P.W., Stephenson R.J. Help me, I'm fat! Social support in online weight loss networks. Journal of Consumer Behaviour. 2011;10(6):332–337. doi: 10.1002/cb.374. [DOI] [Google Scholar]
  5. Bechtiger L., Steinhoff A., Buchmann M., Shanahan L. Developmental associations between sympathy and mutual disclosure in friendships from mid‐adolescence to early adulthood. Journal of Research on Adolescence. 2021;31(2):368–383. doi: 10.1111/jora.12602. [DOI] [PubMed] [Google Scholar]
  6. Blau P.M. Wiley; 1964. Exchange and power in social life. [Google Scholar]
  7. Brislin R.W. Expanding the role of the interpreter to include multiple facets of intercultural communication. International Journal of Intercultural Relations. 1980;4(2):137–148. doi: 10.1016/01471767(80)90025-5. [DOI] [Google Scholar]
  8. Cao H., Chen Z., Cheng M., Zhao S., Wang T., Li Y. You recommend, I buy: How and why people engage in instant messaging based social commerce. Proceedings of the ACM on Human-Computer Interaction. 2021;5(CSCW1):1–25. doi: 10.1145/3449141. [DOI] [Google Scholar]
  9. Chen C. Identifying significant factors influencing consumer trust in an online travel site. Information Technology & Tourism. 2006;8(3):197–214. doi: 10.3727/109830506778690849. [DOI] [Google Scholar]
  10. Chen S., Joseph), Waseem D., Xia Z., Raymond ), Tran K.T., Li Y., Yao J. To disclose or to falsify: The effects of cognitive trust and affective trust on customer cooperation in contact tracing. International Journal of Hospitality Management. 2021;94 doi: 10.1016/j.ijhm.2021.102867. Article 102867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chen G., Li S. Effect of employee-customer interaction quality on customers' prohibitive voice behaviors: Mediating roles of customer trust and identification. Frontiers in Psychology. 2021;12 doi: 10.3389/fpsyg.2021.773354. Article 773354. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cropanzano R., Mitchell M.S. Social exchange theory: An interdisciplinary review. Journal of Management. 2005;31(6):874–900. doi: 10.1177/0149206305279602. [DOI] [Google Scholar]
  13. Crosby L.A., Evans K.R., Cowles D. Relationship quality in services selling: An interpersonal influence perspective. Journal of Marketing. 1990;54(3):68–81. doi: 10.1177/002224299005400306. [DOI] [Google Scholar]
  14. Daxueconsulting . 2020. Private traffic in China: Own your customer traffic.https://daxueconsulting.com/private-traffic-in-china/ Retrieved from. [Google Scholar]
  15. Foa U.G., Foa E.B. In: Social exchange: Advances in theory and research. Gergen K.J., Greenberg M.S., Willis R.H., editors. Plenum Press; 1980. Resource theory: Interpersonal behavior as exchange; pp. 77–94. [Google Scholar]
  16. Fornell C., Larcker D. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research. 1981;18(1):39–50. doi: 10.2307/3151312. [DOI] [Google Scholar]
  17. Fu T., Li S., Xu J., Liu M., Yu K. Exploring antecedents and consequences of tourists' perceived relationship investment from new perspectives in the tour group context. Asia Pacific Journal of Tourism Research. 2022;27(9):925–939. doi: 10.1080/10941665.2022.2142622. [DOI] [Google Scholar]
  18. Gruss R., Kim E., Abrahams A. Engaging restaurant customers on Facebook: The rower of belongingness appeals on social media. Journal of Hospitality & Tourism Research. 2020;44(2):201–228. doi: 10.1177/1096348019892071. [DOI] [Google Scholar]
  19. Guo L., Hu X., Lu J., Ma L. Effects of customer trust on engagement in live streaming commerce: Mediating role of swift guanxi. Internet Research. 2021;31(5):1718–1744. doi: 10.1108/INTR-02-2020-0078. [DOI] [Google Scholar]
  20. Guo J., Wang W. 疫情危机下,数字化转型助力西贝餐饮“自救”[Digital transformation helps Xibei restaurant “save itself” amid the COVID-19 crisis]. WeChat. 2020. https://mp.weixin.qq.com/s/lyfH39NScQ7CKajev5Ia0Q Retrieved from.
  21. Habib A., Irfan M., Shahzad M. Modeling the enablers of online consumer engagement and platform preference in online food delivery platforms during COVID-19. Future Business Journal. 2022;8 doi: 10.1186/s43093-022-00119-7. Article 6. [DOI] [Google Scholar]
  22. Halloran T.J., Lutz R.J. Let's give them something to talk about: Which social media engagements predict purchase frequency? Journal of Interactive Marketing. 2021;56:83–95. doi: 10.1016/j.intmar.2021.05.003. [DOI] [Google Scholar]
  23. Homans G.C. Social behavior as exchange. American Journal of Sociology. 1958;63(6):597–606. doi: 10.1086/222355. [DOI] [Google Scholar]
  24. Huang Y., Chang F. Exploring travel agency relationship quality and relationship outcomes in China. Proceedings of the Annual Meeting of the Association of Collegiate Marketing Educators. 2008:443–455. [Google Scholar]
  25. Huang G.I., Chen Y.V., Wong I.A. Hotel guests' social commerce intention: The role of social support, social capital and social identification. International Journal of Contemporary Hospitality Management. 2020;32(2):706–729. doi: 10.1108/IJCHM-04-2019-0380. [DOI] [Google Scholar]
  26. Hu L.-t., Bentler P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. 1999;6(1):1–55. doi: 10.1080/10705519909540118. [DOI] [Google Scholar]
  27. Hwang J., Han H., Kim S. How can employees engage customers? Application of social penetration theory to the full-service restaurant industry by gender. International Journal of Contemporary Hospitality Management. 2015;27(6):1117–1134. doi: 10.1108/IJCHM-03-2014-0154. [DOI] [Google Scholar]
  28. Hwang J., Kim S.S., Hyun S.S. The role of server–patron mutual disclosure in the formation of rapport with and revisit intentions of patrons at full-service restaurants: The moderating roles of marital status and educational level. International Journal of Hospitality Management. 2013;33:64–75. doi: 10.1016/j.ijhm.2013.01.006. [DOI] [Google Scholar]
  29. Ibrahim B. Social media marketing and restaurant purchase intention: Do online brand community identification and gender matter? International Journal of Internet Marketing and Advertising. 2023;18(2–3):286–309. doi: 10.1504/IJIMA.2023.129668. [DOI] [Google Scholar]
  30. Itani O.S., El Haddad R., Kalra A. Exploring the role of extrovert-introvert customers' personality prototype as a driver of customer engagement: Does relationship duration matter? Journal of Retailing and Consumer Services. 2020;53 doi: 10.1016/j.jretconser.2019.101980. [DOI] [Google Scholar]
  31. Itani O.S., Kassar A.-N., Loureiro S.M.C. Value get, value give: The relationships among perceived value, relationship quality, customer engagement, and value consciousness. International Journal of Hospitality Management. 2019;80:78–90. doi: 10.1016/j.ijhm.2019.01.014. [DOI] [Google Scholar]
  32. Jeong M., Kim K., Ma F., DiPietro R. Key factors driving customers' restaurant dining behavior during the COVID-19 pandemic. International Journal of Contemporary Hospitality Management. 2021;34(2):836–858. doi: 10.1108/IJCHM-07-2021-0831. [DOI] [Google Scholar]
  33. Kaiser H.F. An index of factorial simplicity. Psychometrika. 1974;39(1):31–36. doi: 10.1007/BF02291575. [DOI] [Google Scholar]
  34. Kang J., Tang L., Fiore A.M. Enhancing consumer-brand relationships on restaurant Facebook fan pages: Maximizing consumer benefits and increasing active participation. International Journal of Hospitality Management. 2014;36:145–155. doi: 10.1016/j.ijhm.2013.08.015. [DOI] [Google Scholar]
  35. Kang J., Tang L., Rebecca), Fiore A.M. Restaurant brand pages on Facebook: Do active member participation and monetary sales promotions matter? International Journal of Contemporary Hospitality Management. 2015;27(7):1662–1684. doi: 10.1108/IJCHM-02-2014-0075. [DOI] [Google Scholar]
  36. Kim M., Jang J. I know you, you know me: The effects of customer empathy and employee self-disclosure on customer citizenship behavior. Journal of Service Theory and Practice. 2023;33(1):23–45. doi: 10.1108/JSTP-01-2022-0018. [DOI] [Google Scholar]
  37. Kim B., Yoo M., Yang W. Online engagement among restaurant customers: The importance of enhancing flow for social media users. Journal of Hospitality & Tourism Research. 2020;44(2):252–277. doi: 10.1177/1096348019887202. [DOI] [Google Scholar]
  38. Kumar V., Pansari A. Competitive advantage through engagement. Journal of Marketing Research. 2016;53(4):497–514. doi: 10.1509/jmr.15.0044. [DOI] [Google Scholar]
  39. Lam L.W. Impact of competitiveness on salespeople's commitment and performance. Journal of Business Research. 2012;65(9):1328–1334. doi: 10.1016/j.jbusres.2011.10.026. [DOI] [Google Scholar]
  40. Lei X., Wu H., Deng Z., Ye Q. Self-disclosure, social support and postpartum depressive mood in online social networks: A social penetration theory perspective. Information Technology & People. 2023;36(1):433–453. doi: 10.1108/ITP-12-2020-0825. [DOI] [Google Scholar]
  41. Leung X.Y., Zhang H., Lyu J., Bai B. Why do hotel frontline employees use service robots in the workplace? A technology affordance theory perspective. International Journal of Hospitality Management. 2023;108 doi: 10.1016/j.ijhm.2022.103380. [DOI] [Google Scholar]
  42. Li S., Liu M., Wei M. Host sincerity and tourist environmentally responsible behavior: The mediating role of tourists' emotional solidarity with hosts. Journal of Destination Marketing & Management. 2021;19 doi: 10.1016/j.jdmm.2020.100548. [DOI] [Google Scholar]
  43. Lin J., Li L., Yan Y., Turel O. Understanding Chinese consumer engagement in social commerce: The roles of social support and swift guanxi. Internet Research. 2018;28(1):2–22. doi: 10.1108/IntR-11-2016-0349. [DOI] [Google Scholar]
  44. Lin J., Yan Y., Chen S. Understanding the impact of social commerce website technical features on repurchase intention: A Chinese guanxi perspective. Journal of Electronic Commerce Research. 2017;18(3):225–244. https://www.proquest.com/scholarly-journals/understanding-impact-social-commerce-website/docview/1933856286/se-2 [Google Scholar]
  45. Li S., Qu H., Wei M. Antecedents and consequences of hotel customers' psychological ownership. International Journal of Hospitality Management. 2021;93 doi: 10.1016/j.ijhm.2020.102773. [DOI] [Google Scholar]
  46. Li M.-W., Teng H.-Y., Chen C.-Y. Unlocking the customer engagement-brand loyalty relationship in tourism social media: The roles of brand attachment and customer trust. Journal of Hospitality and Tourism Management. 2020;44:184–192. doi: 10.1016/j.jhtm.2020.06.015. [DOI] [Google Scholar]
  47. Li X., Xie J., Feng Z., Chan H. Exploring residents' helping and tolerant behavior through the lens of cultural intelligence. Journal of Hospitality and Tourism Management. 2022;50:232–244. doi: 10.1016/j.jhtm.2022.02.008. [DOI] [Google Scholar]
  48. Morgan R., Hunt S. The commitment-trust theory of relationship marketing. Journal of Marketing. 1994;58(3):20–38. doi: 10.2307/1252308. [DOI] [Google Scholar]
  49. Niemi J., Pullins E.B. Tell me more: How salespeople encourage customer disclosure. Journal of Business & Industrial Marketing. 2021;36(5):717–728. doi: 10.1108/JBIM-11-2019-0482. [DOI] [Google Scholar]
  50. Nunnally J.C. 2nd ed. McGraw-Hill; 1978. Psychometric theory. [Google Scholar]
  51. Ou C.X., Pavlou P.A., Davison R.M. Swift guanxi in online marketplaces: The role of computer-mediated communication technologies. MIS Quarterly. 2014;38(1):209–230. doi: 10.25300/MISQ/2014/38.1.10. [DOI] [Google Scholar]
  52. Paul I., Roy G. Tourist's engagement in eco-tourism: A review and research agenda. Journal of Hospitality and Tourism Management. 2023;54:316–328. doi: 10.1016/j.jhtm.2023.01.002. [DOI] [Google Scholar]
  53. Podsakoff P.M., MacKenzie S.B., Lee J.Y., Podsakoff N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology. 2003;88(5):879–903. doi: 10.1037/0021-9010.88.5.879. [DOI] [PubMed] [Google Scholar]
  54. Research 新消费趋势下社群发展白皮书[White paper on community development under new consumer trends]. WeChat. 2017. https://mp.weixin.qq.com/s/xgWUZ28UxeQB2CdxnweI1Q Retrieved from.
  55. Sashi C.M., Brynildsen G., Bilgihan A. Social media, customer engagement and advocacy: An empirical investigation using Twitter data for quick service restaurants. International Journal of Contemporary Hospitality Management. 2019;31(3):1247–1272. doi: 10.1108/IJCHM-02-2018-0108. [DOI] [Google Scholar]
  56. Shi S., Mu R., Lin L., Chen Y., Kou G., Chen X.-J. The impact of perceived online service quality on swift guanxi: Implications for customer repurchase intention. Internet Research. 2018;28(2):432–455. doi: 10.1108/IntR-12-2016-0389. [DOI] [Google Scholar]
  57. Shin H. Exploring open innovation engagement process of hotel brand community members: Motivation, empowerment and consequences. International Journal of Contemporary Hospitality Management. 2023 doi: 10.1108/IJCHM-07-2022-0842. [DOI] [Google Scholar]
  58. Song J., Qu H., Robert, Li X. It takes a village! Customer value co-creation behavior in restaurant social media-based brand community. Journal of Hospitality & Tourism Research. 2022 doi: 10.1177/10963480221095721. [DOI] [Google Scholar]
  59. Tan S., Chen W. Building consumer trust in online food marketplaces: The role of WeChat marketing. The International Food and Agribusiness Management Review. 2021;24(5):845–862. doi: 10.22434/IFAMR2020.0159. [DOI] [Google Scholar]
  60. Touni R., Kim W.G., Haldorai K., Rady A. Customer engagement and hotel booking intention: The mediating and moderating roles of customer-perceived value and brand reputation. International Journal of Hospitality Management. 2022;104 doi: 10.1016/j.ijhm.2022.103246. [DOI] [Google Scholar]
  61. Tsaur S.-H., Teng H.-Y., Han T.-C., Tu J.-H. Can perceived coolness enhance memorable customer experience? The role of customer engagement. International Journal of Contemporary Hospitality Management. 2023 doi: 10.1108/IJCHM-08-2022-1031. [DOI] [Google Scholar]
  62. Wang C.L. Guanxi vs. relationship marketing: Exploring underlying differences. Industrial Marketing Management. 2007;36(1):81–86. doi: 10.1016/j.indmarman.2005.08.002. [DOI] [Google Scholar]
  63. Wang X., Cheng M., Li S., Jiang R. The interaction effect of emoji and social media content on consumer engagement: A mixed approach on peer-to-peer accommodation brands. Tourism Management. 2023;96 doi: 10.1016/j.tourman.2022.104696. Article 104696. [DOI] [Google Scholar]
  64. Xie L., Guan X., He Y., Huan T.-C. Wellness tourism: Customer-perceived value on customer engagement. Tourism Review. 2022;77(3):859–876. doi: 10.1108/TR-06-2020-0281. [DOI] [Google Scholar]
  65. Yin D., Li M., Qiu H. Do customers exhibit engagement behaviors in AI environments? The role of psychological benefits and technology readiness. Tourism Management. 2023;97 doi: 10.1016/j.tourman.2023.104745. Article 104745. [DOI] [Google Scholar]
  66. Ziran Design 疫情反复, 餐饮行业怎么搭建自己的私域流量做社群运营？[Repeated epidemic, the catering industry how to build their private domain flow to do community operation?] WeChat. 2022. https://mp.weixin.qq.com/s/IYCw1E_HZQiIWEXiLsd9KA

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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