The value co-creation process comprises various actors, including the beneficiary, who decides the value, while other actors contribute to developing and presenting value proposals (Vargo and Lusch, 2004). Ahn et al. (2019) outlined value co-creation behavior as consumers cooperating with other actors to engage in a series of important value-creation activities. In contrast, CCE refers to the mental state resulting from the customers’ participation in the value co-creation activity (Chen, 2018).

Several scholars have examined the concept of co-creation in the tourism (Hurriyati and Sofwan, 2015; Rachão et al., 2019; Mohammadi et al., 2020) and hospitality sectors (Kallmuenzer et al., 2019; Gibbs et al., 2021), as co-creation complements tourism while providing tourists with the opportunity to participate in services. Ramaswamy and Ozcan (2018) posited that co-creation facilitates interactional creation across interactive system environments (afforded by interactive platforms), entailing agency engagements and structuring organizations. According to Jaakkola et al. (2015), CCE is multidimensional as both a phenomenon and a concept. Merz et al. (2018) highlighted its multidimensional nature using two higher-order factors: customer-owned resources and customer motivation. Verleye (2015) highlighted four dimensions of CCE, namely, hedonic experience (HE), cognitive experience (CoE), social experience (SE), and economic experience (EE).

According to Zhang et al. (2020), hedonic reaction is the positive emotional state resulting in customer satisfaction; HE is seen as the pleasurable experience customers expect from the CCE. Customers participating in the value co-creation process seek fun, pleasure, and entertainment regardless of extrinsic rewards (Chen, 2018). CoE is knowledge about service, products, and technologies that customers expect to gain from a co-creation activity (Verleye, 2015). It helps customers to explore news ways to use products and learn from other participants’ co-creation efforts. Füller (2010) stated that customers seeking CoE are intrinsically motived and stimulated by their desire to generate and implement creative ideas for their own sake. At Chinese hotpot restaurants, participation in value co-creation offers customers the opportunity to keep pace with new ideas and hone their skills and ability to perform a specified task. SE is the benefit of connecting with like-minded people during the co-creation activity. Customers expect to gain better status and social esteem from their value co-creation process. According to Füller (2010), this experience builds on the desire of an individual for social identity, recognition, and development of skills that improve communication with the outside world. While some customers participate in the co-creation activity to hone their skills and meet like-minded people, others expect pragmatic and economic benefits. EE refers to the reduction of risks associated with receiving inappropriate products or services and the compensation in line with the effort made (Verleye, 2015). Verleye (2015) found that all four dimensions positively impact the overall co-creation experience (OCCE).

The scale developed by Verleye (2015) presents higher construct validity, reliability, and internal consistency. She stressed that further research should validate the scale in different co-creation situations; therefore, we utilized the exact dimensions to investigate the relationship between CCE, BT, BP, and BE in a restaurant setting. Recent studies have endorsed Verleye’s dimensions (e.g., Hussain et al., 2020).

Organizations face several challenges because of a highly connected marketplace; they are keen to focus on the smaller but most influential customer category known as brand evangelists (Marticotte et al., 2016). The reason for this focus is their potential ability to actively disseminate brand-related experiences among others, embrace a particular brand intensively, persuade others to experience the brand, and dissuade others from buying rival brands (McConnell and Huba, 2003). Many studies argue that positive word-of-mouth or assertive support behavior, such as brand advocacy and brand defense, is derived from the consumer–brand relationship (Hsu, 2018). Mishra et al. (2021) found that customer–brand relational constructs, including brand trust, brand affect, and brand identification, strongly impact BE in terms of purchase intention and brand referral behavior. Harrigan et al. (2020) highlighted that value co-creation leads to brand advocacy and brand defense, which are intrinsically related to BE.

Sharma et al. (2021) outlined that brand community engagement is related to BE, and BE mediates the relationship between brand community engagement, brand defense, and brand resilience. Matzler et al. (2007) argued that a focus on word-of-mouth communication alone does not reveal customers’ behavior and their power to convince others about the brands they like; therefore, BE has emerged as an important concept in the consumer–brand relationship literature. Samson (2006) posited that BE generates a greater and more effective customer response than word-of-mouth. BE has facilitated small brands and benefited large organizations, including Southwest Airlines, IBM, and Build-A-Bear Workshop (McConnell and Huba, 2003).

According to Badrinarayanan and Becerra (2013), BE is customers’ behavioral support for a particular brand, including purchasing a specific brand, convincing others about it, and recommending it to others. Our study adopted the multidimensional approach of BE developed by Badrinarayanan and Becerra (2013). They found that BE is based on three dimensions: brand purchase intention (BPI), positive brand referrals (PBR), and oppositional brand referrals (OBR). Fishbein et al. (1977) defined BPI as a subjective inclination toward a brand or product. Therefore, it is a measure of the strength of consumer intention to decide to purchase a brand in the future (Yeo et al., 2020). Brand evangelists, considered active supporters of a brand, show behavioral support by purchasing products of that brand. PBR can be defined as the brand evangelists’ active behavioral support for a brand, which they show by disseminating favorable opinions, recommending it to others, and attempting to convince others to engage with the same brand. OBR is a negative attitude toward competing brands, resulting from attachment and loyalty to a brand. The strategies employed by brand evangelists go beyond purchasing the brand to include denigrating rival brands that present a threat (Doss, 2014). According to Badrinarayanan and Becerra (2013), research on the antecedents of BE is still limited, so there is a need to broaden the scope for a better understanding of what leads to evangelistic behaviors.

BT plays a vital role in relational exchange (Riorini and Widayat, 2015; Ling et al., 2021). According to Chaudhuri and Holbrook (2001), the foundation of BT relies on the brand’s ability to perform its stated function; therefore, the reliability of a brand is the root of BT (Ballester and Munuera-Alemán, 2005; Villagra et al., 2021). Trust has become a very influential concept in branding (Husain et al., 2022). Morgan and Hunt (1994) stated that trust exists when the concerned parties have confidence in each other’s integrity and reliability. In the tourism and hospitality sector, Kim et al. (2014) suggested that a well-renowned celebrity is seen as reliable and can be influential in attracting customers’ attention and highlighting a positive image for hotels.

BT has strong positive relations with brand love (Albert and Valette-Florence, 2010) and BP (Gilal et al., 2020). Fehr and Anne (1988) stated that trust relates to affection more than passion in terms of interpersonal relations. However, similar to passion, BT encompasses both cognitive and affective dimensions (Ling et al., 2021). Baumann and Le Meunier-FitzHugh (2013) reported that trust among parties emerges from internally guaranteed certainty, which leads to brand value co-creation and ensures the other party will not act opportunistically. Their study further highlighted that consumers’ BT is a core element that makes consumers brand evangelists.

Several scholars have investigated the relationship between customer experience and BT (Ha and Perks, 2005; Walter et al., 2013; Guan et al., 2021); however, they have largely overlooked the relationship between CCE and BT even though both concepts are fundamental in measuring consumer behavior.

Many scholars have investigated the concept of passion in marketing. Bauer et al. (2007) stated that brand uniqueness is one of the principal drivers of consumers’ BP from a theoretical perspective. According to Matzler et al. (2007), individual factors, such as consumer personality, influence BP. Their findings contradicted those of Bauer et al. (2007), who found that the extroversion trait in consumer personality is highly effective for fostering BP.

Gilal et al. (2018) stressed that passion plays a crucial role in shaping positive word-of-mouth communications. Moreover, passion improves consumers’ desire to pay for a premium brand (Swimberghe et al., 2014; Gilal et al., 2018), consumer brand loyalty (Hemsley-Brown et al., 2016), brand advocacy, brand community engagement, social media support, and purchase intention (Pourazad and Pare, 2015; Mukherjee, 2020). Keh et al. (2007) posited that BP represents the zeal and enthusiasm features of the consumer–brand relationship. Matzler et al. (2007) concluded that when consumers are passionate about a brand, they develop a deep emotional relationship with it and may miss it or feel lost if the brand is unavailable. Therefore, BP is intrinsically related to consumer behavior (D'lima, 2018; Gilal et al., 2021a). Thomson et al. (2005) highlighted that BP emerges from an intense and aroused positive feeling toward a brand. Similar to BT, the literature has overlooked the relationship between CCE and BP. Therefore, this study attempts to address this gap in the literature.

The items and instruments used in the questionnaire to measure the constructs were adapted from previously validated studies to maintain reliability and validity. We used twenty-three items to assess CCE: six items for EE, three items for HE, five items for SE, five items for CoE, and four items for OCCE. All items were adapted from Verleye (2015). We measured the BE dimensions with ten items: four items for BPI, three items for PBR, and three items for OBR, adapted from Badrinarayanan and Becerra (2013). We measured BT using five items adapted from Chaudhuri and Holbrook (2001) and Munuera-Alemán et al. (2003), whereas for BP, we used five items adapted from Mostafa and Kasamani (2020) and Thomson et al. (2005). all the items were slightly modified from the existing literature to suit the study context. For example, the original item “I spread positive word of mouth about the brand” was adapted to “I spread positive word of mouth about my favorite hotpot brand.” Two marketing professors reviewed the questionnaire to ensure face and content validity; we distributed the questionnaire after applying their suggestions. Moreover, to measure all the items, we used a 5-point Likert scale, ranging from 1 = strongly disagree to 5 = strongly agree.

In 2017, there were approximately 300,000 hotpot restaurants across China, and in 2020, 13,000 new restaurants were opened to meet increasing demand (Statista, 2020). Chinese hotpot restaurants provide customers with a complete CCE as customers can cook their own food. These restaurants provide customers with raw food and ingredients, which they mix up and cook according to their tastes and preferences in a boiling broth on the dining table. The entire process is similar to the co-creation service experience, wherein companies allow customers to co-create their own experience (Prahalad and Ramaswamy, 2004). It is similar to customized offerings where actors invite other actors to participate in the production of service offerings (Mathis et al., 2016). We collected data from international travelers who dined at hotpot restaurants during their stay in China.

Accordingly, we developed a structured questionnaire divided into three parts. In the first part, we explained the study purpose and asked respondents to recall their recent experiences at a hotpot restaurant and answer questions on a given scale to describe their experience and satisfaction. We assured them of the confidentiality of the information provided. In the second part, we collected the respondents’ demographic data and their country of origin. The third part comprised items that assessed the variables of interest. The current research conducted an online questionnaire, employing a structured and non-probabilistic convenience sample and cross-sectional survey. We conducted our survey questionnaire on Sojump platform. We shared the link among online group chats of foreigners living in China with the help of group chat administrators as it is the fastest way to access a large sample group in a short period of time. We forwarded the questionnaire along with a message stating that only foreigners who have dined at a Chinese hotpot restaurant within the last 3 months are eligible to fill the survey. Consequently, we distributed 600 self-administered questionnaires and received 521 responses within a month. Of these responses, 68 were excluded from the analysis because they contained unengaged responses and missing information. Therefore, the final sample comprised 453 foreign respondents (response rate of 75.5%) from 61 countries across six continents.

Our sample included 291 men (64.2%) and 158 women (34.9%). The majority of respondents (81%) were aged between 26 and 55 years. Additionally, 96.5% had at least a bachelor’s degree, and 66.7% were employed (public and private sector) or self-employed (businesspersons). Table 1 reports the demographic details of the respondents.

The present research used IBM SPSS Statistics version 26.0 to analyze the demographics and the common method bias. We assessed our measurement and structural model with partial least square structural equation modeling (PLS-SEM) on SmartPLS version 3.3.3. In recent years, PLS has become a very popular and powerful alternative to covariance explanation methods, such as AMOS and LISREL (Wasko and Faraj, 2005). PLS is better suited for explaining complex relationships, and it synchronously estimates the measurement and the structural model. The data were analyzed in two stages: In the first stage, we tested data reliability and validity, and in the second stage, we tested the hypotheses.

Common method bias is a crucial issue in behavioral research. We tested it using Harman’s single-factor approach. The variance extracted by a single factor was 25.099%, which is less than 50%, indicating no common method bias in this study.

Ringle et al. (2015) stated that the measurement model must establish reliability and validity. In the reliability analysis, the alpha and composite reliability were higher than 0.7. For convergent validity, the standardized factor loadings were greater than 0.5. Furthermore, the average variance extracted (AVE) values were greater than 0.5 and met the threshold level. Table 2 presents details on composite reliability (CR), alpha, AVE, and outer loading.

For discriminant validity, we applied two methods, namely, the Fornell Larcker criterion and the heterotrait–monotrait (HTMT) ratio of correlations. In the Fornell and Larcker method, the square root of each latent variable’s AVE was greater than the correlation of its coefficient, which indicates discriminant validity in our study (Fornell and Larcker, 1981). Henseler et al. (2015) stated that the HTMT values must be lower than 0.85, which was the case in our study, indicating discriminant validity (see Table 3). We further examined the variance inflation factor (VIF), and the VIF values did not exceed 5; therefore, multicollinearity was acceptable in this study.

To conduct hypothesis testing, the statistical bootstrap technique was applied with the recommended sample size of 5,000 using SmartPLS software version 3.3.3. (Ringle et al., 2015).

H1’s findings revealed that CCE dimensions, namely, HE (β = 0.96, 0.105, and 0.071), CoE (β = 0.075, 0.099, and 0.091), SE (β = 0.11, 0.133, and 0.068), EE (BPI: β = 0.078 and PBR: β = 0.107), and OCCE (β = 0.06, 0.114, and 0.075) positively impact the BPI, PBR, and OBR dimensions of BE. However, the relationship between EE and OBR is insignificant (β = 0.057).

Furthermore, CCE dimensions, including HE (β = 0.119), CoE (β = 0.134), EE (β = 0.165), and OCCE (β = 0.117) directly contribute to enhancing BT, whereas SE (β = 0.048) was found to be non-significant. The analysis of H3 revealed that CCE dimensions, namely, HE (β = 0.153), CoE (β = 0.167), SE (β = 0.204), EE (β = 0.169), and OCCE (β = 0.119) directly increase BP.

Findings for H4 highlighted that BT strongly impacts BE dimensions, namely, BPI (β = 0.298), PBR (β = 0.333), and OBR (β = 0.287). Additionally, results revealed that BP has a positive and significant impact on the BPI (β = 0,382), PBR (β = 0.321), and OBR (β = 0.38) dimensions of BE. Table 4 illustrates the direct effect.

The mediation effect results demonstrated that BT mediates the relationship between the CCE dimensions, including HE (β = 0.035, 0.04, and 0.034), CoE (β = 0.04, 0.034, and 0.038), EE (β = 0.049, 0.055, and 0.047) and OCCE (β = 0.035, 0.039, and 0.034), and BE dimensions of BPI, PBR, and OBR. However, BT does not mediate the relationship between SE and BE dimensions (H5).

Furthermore, we found that BP mediates the relationship between the CCE dimensions, namely, HE (β = 0.059, 0.049, and 0.058), CoE (β = 0.064, 0.054, and 0.064), SE (β = 0.076, 0.066, and 0.078), EE (β = 0.065, 0.054, and 0.064) and OCCE (β = 0.046, 0.039, and 0.046), and BE dimensions, namely, BPI, PBR, and OBR (H7; see Table 5). The structural model is given in Figure 2.

R-squared is the proportion of an endogenous construct’s variance explained by its predictor constructs. According to Hair et al. (2011), 0.25, 0.50, and 0.75 denote small, medium, and large effects. Table 6 represents the adjusted R² values.

The current study contributes to the branding, hospitality, and tourism literature in multiple ways. First, Im and Qu (2017) stressed that most studies on co-creation are scenario-based. This study examined the proposed conceptual model in a realistic co-creation setting (i.e., hotpot restaurants) that explains how international tourists’ CCE can enhance BT, increase BP, and stimulate brand evangelistic behaviors, offering theoretical grounds for future research on CCE and BE.

Second, extant literature suggested that a positive customer experience is necessary to achieve performance and relational benefits (Hussain et al., 2020). However, such experience must be scrutinized at a dimensional level to understand how each dimension affects consumer behavior. To the best of our knowledge, the current study is the first attempt to reveal how the multidimensional concept of CCE affects the multidimensional concept of BE. Our research is also the first to investigate CCE and branding relationships, including BT and BP.

Third, the post-hoc analysis investigating BT and BP’s mediating role between CCE and BE is a valuable contribution. The findings highlighted that BT has a positive mediation effect on CCE dimensions, including HE, CoE, EE, and OCCE, and BE dimensions, namely, BPI, PBR, and OBR, whereas the SE mediation effect is non-significant. Additionally, BP was found to mediate all dimensions of CCE and BE.

Fourth, research on CCE is limited in the hospitality literature with respect to international tourists’ perspectives. Previous research has investigated the CCE of local diners (i.e., Hussain et al., 2020). The current study investigates international tourists’ CCE and its impact on BE.

Fifth, BE has been investigated in banking service (Riorini and Widayat, 2015), the video game industry (Marticotte et al., 2016), and social media use (Harrigan et al., 2020). The current research introduces the concept of BE in the hospitality industry; specifically, it investigates how the dining experience of international tourists instigates brand evangelistic behaviors.

Finally, BE has been a hot topic in marketing and branding literature (Harrigan et al., 2020). The literature has treated BE as a higher-order construct (e.g., Matzler et al., 2007) or a construct with two dimensions (e.g., Harrigan et al., 2020). This study considered BE as a construct with three dimensions: BPI, PBR, and OBR, further clarifying the BE phenomenon.

As we evolve rapidly to consider CCE as the basis of value, the interaction between the company and the customer becomes the locus of value co-creation (Prahalad and Ramaswamy, 2004). The current study provides valuable insight for an in-depth understanding of foreign tourists with respect to restaurants engaged in the profitable Chinese marketplace.

The findings suggest that firms should engage customers in the co-creation of value where the interaction between customer and employee is crucial to increase customers’ BT, BP, and brand evangelistic behaviors. To stimulate optimized customer experience for brand success, restaurant managers can focus on the various aspects of value co-creation (i.e., HE, CoE, SE, EE, and OCCE). For instance, they can allow tourists to co-create a service experience that suits their context, make the brand engaging and meaningful, and create a better customer experience. Managers can also provide an innovative experience environment for new co-creation experiences (Prahalad and Ramaswamy, 2004) and provide customer support to help customers co-create. Optimizing CCE will cause customers to show their passion for the brand and rely on it. Consequently, customers will develop brand evangelistic behaviors, such as PBR, which help attract additional customers who may otherwise not be attracted via conventional marketing channels.

Pansari and Kumar's (2017) theory of engagement states that the consumer–brand relationship is neither static nor instant. Therefore, restaurant brand managers must stimulate a positive customer experience, which will eventually enhance BP and lead to brand evangelistic behaviors. A passionate customer is an asset to a brand. Brand managers can instill BP by implementing and providing platforms and tools for a pleasurable CCE (HE), as customers participating in the value co-creation activity seek fun, pleasure, and entertainment.

Additionally, restaurant brand managers can offer customers the opportunity to learn about service, products, and technologies (CoE) and provide a suitable platform for them to connect with like-minded people (SE). Customers involved in the co-creation process give suggestions and feedback, which reduce risks associated with offering undesirable products or services. Further, managers can provide compensation commensurate with the effort made during the co-creation process (pragmatic and economic benefit). For instance, they could formulate a reward system (company swag) and focus on sophisticated reward programs.

The current study has several limitations. First, we used a structured questionnaire to assess international tourists’ participation. Given that it may limit tourists’ expression, future research may employ a mixed-methods approach. Second, we focused on co-creation in a restaurant setting; future research could specifically focus on co-creation activities in tourism services and online shopping. Third, we did not consider the control variables’ role in this research. Therefore, future research may include control variables, such as tourists’ gender, age, and the frequency of dining at a hotpot restaurant. Fourth, we associated BT and BP to understand the relationship between CCE and BE. Researchers may use other variables, such as customer perceived value, brand love, and affective commitment to better understand the relationship.