Igniting brand love: Exploring the role of member–member social interactions,co-creation experiences and affordances within online travel communities

Abstract

The rise of social media and online social interaction has significantly contributed to the field of tourism marketing. However, the underlying mechanism of member–member social interactions (M-MSI) in online tourism communities (OTCs) and their emotional benefits remain underexplored. Grounded in social penetration and social exchange theories, this study investigates how the self-driven mechanism of M-MSI, which comprises online social support (OSS), social capital (OSC), and social cohesion (OSN) enhances co-creation experiences (hedonic, social, and cognitive) fosters brand love. An analysis of survey data from 535 Chinese OTCs members using partial least squares structural equation modeling (PLS-SEM) revealed that OSS promoted OSC and OSN, with OSC further facilitating OSN. Moreover, high-quality M-MSI significantly strengthens brand love through co-creation experiences, a relationship positively moderated by affordances. Additionally, a fuzzy set qualitative comparative analysis (fsQCA) was used to identify six configurational paths to brand love, supplementing linear model limitations. These findings expand the understanding of OTCs experience management and underscore the marketing potential of sustained emotional value co-creation through optimized M-MSI.

Keywords

Online travel communities member-to-member social interactions online co-creation experiences brand love affordances fsQCA

Introduction

The emergence of the internet and, more recently, social media has facilitated the sharing of travel experiences in online travel communities (OTCs), which are platforms specifically designed to enable information exchange, knowledge sharing, and social interaction among community members, marking a significant shift in social behavior (Shen et al., 2020; Xin et al., 2023). With the growth of mobile social networking, OTCs members have created a “self-media,” sharing their perspectives via pictures and texts, thereby shaping perceptions of tourist destinations (Jiang et al., 2025). In particular, China's OTCs services are experiencing rapid growth. Statistics show that the number of OTCs users in China has reached 454 million, accounting for 42.1% of all Internet users (China Internet Network Information Center, 2024). Thus, improving community member retention and motivating members to contribute to community promotion to enhance competitive strength have become major challenges to contemporary OTCs (Li and He, 2023).

To enhance the competitiveness of OTCs, prior research has focused on optimizing community technologies, promoting marketing efforts, meeting tourists’ personalized needs, and improving their visitation motivation and satisfaction to boost community competitiveness (Hernández-Méndez et al., 2024; Sancho-Esper et al., 2023; Wong et al., 2023). However, the existing research has largely overlooked the importance of social interactions among community members in enhancing user experience and fostering brand loyalty (Huang et al., 2020). Member-to-member social interaction (M-MSI) refers to how OTCs members build connections and engage in mutual communication (Lin et al., 2019). Numerous studies acknowledge that social interaction behaviors, such as “posting,” “liking,” and “commenting,” allow members to create value through collaborative engagement, thereby enhancing user experience and serving as key indicators of OTCs’ popularity (Shen et al., 2020; Stehr, 2023). Moreover, social interactions can be regarded as strategic assets that significantly influence brand perception, customer acquisition, and consumer satisfaction (Li et al., 2024a).

Although previous research has investigated the effects of social interaction on online communities, it has primarily focused on social networks and e-commerce contexts (Li et al., 2025; Shahzad et al., 2025). M-MSI in OTCs differs substantially from these domains in several key respects. First, M-MSI in OTCs centers on experience sharing and emotional resonance, often stimulating long-term travel decision-making by transferring tacit knowledge, such as personalized travel tips (Shen et al., 2020). Whereas, social interactions in social networks and e-commerce communities typically focus on social entertainment or driving consumer behavior. Second, M-MSI within OTCs often revolves around destination culture and personalized perceptions of tourism products, requiring higher emotional investment and greater information authenticity (Lin et al., 2019). Such information may be less prominent in general social networks or e-commerce communities, where the emphasis is primarily on social gratification or transactional convenience (Li et al., 2024a). Finally, M-MSI in OTCs has a stronger long-term capacity to shape users’ emotional attachment and loyalty towards destinations or brands, whereas interactions in traditional e-commerce communities or general social platforms tend to be short-term and transaction-oriented (Li et al., 2024a). Thus, directly applying other online community findings to OTCs may fail to capture the unique psychological mechanisms of M-MSI, highlighting the need for specific and in-depth research. This study aims to fill this gap by addressing the following research questions:

RQ1. What are the key components of M-MSI in the context of OTCs, and what kind of self-driven mechanisms do these components exhibit?

RQ2. How does M-MSI enhance the competitiveness of OTCs?

To address these questions, this study adopts social penetration theory (SPT) and social exchange theory (SET) as theoretical frameworks. SPT suggests that as self-disclosure increases among community members, their relationships become closer, leading to deeper interactions (Huang et al., 2020). SET posits that individuals tend to exchange resources during interactions, and that these exchanges foster rapport and trust among customers, thereby strengthening their brand love (Li et al., 2024a). This study proposes that M-MSI's positive internal mechanisms can generate strong social interaction effects, encouraging community members to shift from passive recipients of experiences to active co-creators (Deng et al., 2024). Such a high level of engagement and emotional involvement not only amplifies the value generated through co-creation but also fosters enduring emotional attachment and loyalty to the OTCs brand (Junaid et al., 2020), thereby enabling OTCs to gain strategic advantages in an increasingly competitive market.

Xiaohongshu has emerged as a rapidly growing and highly active online travel community (Wu et al., 2024a), with approximately 82% of travelers relying on the platform for travel-related information and decision-making, heavily influenced by peer-generated content and shared experiences (Tao et al., 2024). Given its substantial influence and developmental potential, this study adopts Xiaohongshu as a representative case to generate several important contributions to the existing tourism literature. First, it elucidates, for the first time, the internal organizational structure and operational mechanisms of M-MSIs within the context of OTCs, thereby extending the applicability of SPT. Second, it clarifies the influential relationships between M-MSI, co-creation experiences, and OTCs brand love, thus enriching the explanatory scope of SET. Third, this study innovatively examines the moderating role of affordances in the OTCs context. Fourth, it enhances the methodological rigor by employing a mixed-methods analyses approach—combining structural equation modeling (SEM), necessary condition analysis (NCA), and fuzzy-set qualitative comparative analysis (fsQCA)—providing robust insights. In particular, employing fsQCA grounded in complexity theory to identify the causal configurations underlying brand love helps uncover the asymmetric effects of potential antecedents. This enhances the predictive power of the research framework and offers context-sensitive strategic insights into stimulating member interactions within the OTCs context (Chi et al., 2024a; Manosuthi et al., 2024). Finally, this study offers practical guidance for OTCs managers seeking effective strategies to enhance community competitiveness.

Literature review

Member–member social interactions

Tourism experiences are typically rooted in social contexts, with interactions with others, especially fellow tourists, regarded as a core component (Li et al., 2023). Numerous studies have demonstrated the positive influence of social interaction on tourists’ relational harmony and trust, as well as its vital role in shaping overall tourist experiences (Fatima, 2023; Shen et al., 2020). However, social interaction is a more nuanced and complex concept that involves specific domains and agents and is facilitated by underlying interaction mechanisms. Currently, two primary approaches are used to classify customer social interactions (Li et al., 2024a). The first divides interactions into verbal (e.g., information sharing and self-disclosure) and nonverbal interactions. Nonverbal interactions rely on facial expressions, body posture, and physical touch to convey thoughts and emotions (Lin et al., 2022) that are difficult to capture in virtual environments. The second classification distinguishes between direct interactions (e.g., information exchange or emotional expression) and indirect interactions (e.g., the accumulation of interpersonal resources and perceived similarity), a framework that demonstrates stronger explanatory validity in the context of online communities (Wei et al., 2022).

Drawing on the interaction paradigm proposed by Wei et al. (2022), this study constructed a multidimensional M-MSI framework comprising three components: online social support (OSS), online social capital (OSC), and online social cohesion (OSN). As a form of direct interaction, OSS refers to the assistance provided among members through information exchange or emotional support, which fosters trust and reduces perceived risk (Stehr, 2023). As forms of indirect social interaction, OSC and OSN refer to strong interpersonal relationships among online members and individuals’ subjective evaluations of those relationships, respectively, which facilitate ongoing and vibrant communication within the community and promote the development of member identity (Lin et al., 2019; Zhang et al., 2022). This innovative framework integrates three dimensions, incorporating behavioral support, capital integration, and identity construction (Stehr, 2023; Vongvisitsin et al., 2024; Moon et al., 2023) to systematically deconstruct the synergistic mechanisms between explicit and implicit elements in online community interactions.

Social penetration theory

To explain the internal mechanisms of M-MSI, this study applies SPT. SPT seeks to understand how interpersonal relationships develop through communication and psychological processes and explains how interpersonal communication gradually evolves from superficial, non-intimate levels to deeper, more intimate exchanges (Liu et al., 2023a). According to SPT, interpersonal relationships evolve through the development of “onion-like” layers, whereby online community members gradually reveal more personal information about themselves, deepening their relationships and fostering greater trust (Huang et al., 2020). In this study, OSS facilitates self-disclosure through information exchange and emotional interactions, contributing to the accumulation of OSC (Yang, 2025). Meanwhile, OSS and OSC collectively enhance self-disclosure through resource sharing and interpersonal resource displays, promoting intimacy among members and fostering OSN (Huang et al., 2020; Luo et al., 2024).

Social exchange theory

SET originates from sociology and psychology and examines participants in an exchange (individuals or organizations), the content of the exchanges (material or symbolic resources), and the relationship between participants. Building on SET, individuals establish reciprocal relationships through the exchange of resources during interactions, thereby enabling sustained interactions and long-term relationships (Homans, 1958). These reciprocal resources include intangible emotional exchanges such as social recognition, emotional support, and self-esteem (Xiao et al., 2025). Drawing on the reciprocity principle, Li et al. (2024b) suggest that such interactions enhance rapport experiences among group members and strengthen their emotional connections with brands. Accordingly, this study posits that M-MSI facilitates resource sharing and emotional resonance within co-creation experiences, thereby fostering enduring brand love for OTCs.

Categories of member–member social interactions

Online social support

With the growth of the social Internet and the resulting shift in social interaction patterns, social support has extended beyond offline environments to encompass a wide range of online domains. OSS provides users with advice, emotional comfort, and a sense of well-being, serving as a key driver of engagement in online social interactions (Stehr, 2023; Zhang et al., 2022). Grounded in the social interaction paradigm, OTCs users can function both as recipients of informational and emotional support and providers of these to others (Huang et al., 2020). Moreover, because online interactions are inherently virtual and involve information exchange, research on OSS has primarily emphasized informational and emotional support (Huang et al., 2020; Zhang et al., 2022).

Online social capital

Social capital is regarded as the cornerstone for understanding interpersonal relationships and social dynamics. From a network perspective, OSC represents the “bonding,” “bridging,” and “linking” between actors, which act as essential channels for establishing and maintaining online interpersonal relationships (Vongvisitsin et al., 2024; Yang, 2025). Virtual environments bring together individuals with shared interests, perspectives, and goals, enabling them to exchange ideas, opinions, and comments. These interactions, spanning interpersonal relationships and organizational networks, represent the primary sources of OSC (Li et al., 2024a). Consequently, the development of OSC encourages users to engage more actively in building online communities and fosters stronger social trust among members (Goolaup et al., 2025).

Online social cohesion

Social cohesion is a factor used to assess the level of connectedness and solidarity within a social group (Moon et al., 2023), demonstrating the close relationships and active social interactions among community members (Boytsun et al., 2011). In tourism, social cohesion fosters a sense of group belonging among tourists, strengthens their willingness to establish connections with other members, and increases both engagement and satisfaction with tourism experiences (Lin et al., 2019). However, research on social cohesion in tourism remains in a nascent phase, with most studies focusing on offline contexts. Given the significant advantages of social cohesion in fostering group interactions, building social networks, and forming emotional bonds with strangers (Luo et al., 2024), extending its application to broader online environments is crucial. Based on the work of Dai et al. (2024) and Luo et al. (2024), this study defines OSC as the collective strength of unity exhibited by community members from diverse social backgrounds during their integration into OTCs, reflecting their perceived level of comfortable and satisfying social interactions on the platform.

Building on SPT, OSS which involves the provision of informational and emotional assistance, can be viewed as the starting point for harmonious relationships among customers (Huang et al., 2020). Such support enables members to understand each other better and strengthen their sense of group belonging (Huang et al., 2020; Lin et al., 2019). Huang et al. (2020) observe that when members receive valuable information from others, they tend to perceive those members as both competent and trustworthy. Furthermore, OSC reflects community members’ willingness to align through shared language, a common vision, social trust, and reciprocity, thereby reinforcing emotional bonds within the group (Yang, 2025). This aligns with SPT's proposition that self-disclosure expands social networks and enhances group solidarity (Hu et al., 2025). Accordingly, the following hypotheses are proposed:

H1. Online social support positively affects online social capital.

H2. Online social support positively affects online social cohesion.

H3. Online social capital positively affects online social cohesion.

Online co-creation experiences

Online co-creation experiences refer to the psychological state that arises when individuals share their experiences and memories during the co-creation process in a virtual environment (Deng et al., 2024). These experiences result from individuals engaging in virtual interactions with various stakeholders, locations, and entities at different stages of the co-creation process, thereby enriching their memories and enhancing the authenticity of their experiences (Deng et al., 2024; Lee et al., 2024a). Although the academic literature has shown increasing interest in co-creation experiences, there is still no consensus regarding their dimensional classification; however, cognitive, social, and emotional components have been consistently emphasized (Xie et al., 2021). Therefore, this study adopted the three-dimensional framework proposed by Xie et al. (2021), categorizing co-creation experiences into cognitive, social, and hedonic dimensions, with the hedonic dimension representing the emotional component.

Grounded in the framework of cost-benefit analysis and the principle of reciprocity, SET posits that individuals seek to maximize benefits and maintain equitable exchange relationships during interactions (Homans, 1958). When customers perceive that their interactions result in pleasant and meaningful experiences, they are more likely to proactively support managers in improving their overall experience quality (Deng et al., 2024; Sabiote-Ortiz et al., 2025). Jiao et al. (2024) emphasize that the social support derived from social interaction strengthens members’ emotional connections with the community and enhances their participation quality. Xie et al. (2021) also notes that social capital built through social interactions fosters friendships and trust among members, motivating them to engage voluntarily in community development. Moreover, Lin et al. (2019) argue that the higher the perceived cohesion among members, the greater their level of participation in activities, thereby improving their experience in complex co-creation environments (Luo et al., 2024; Wu and Cheng, 2020). Based on these insights, the following hypotheses are proposed:

H4a-c. Online social support positively affects hedonic experience, social experience and cognitive experience.

H5a-c. Online social capital positively affects hedonic experience, social experience and cognitive experience.

H6a-c. Online social cohesion positively affects hedonic experience, social experience and cognitive experience.

Brand love for OTCs

Brand love denotes consumers’ passionate emotional attachment to brands, characterized by a sense of passionate attraction and deep connection, and often associated with continued usage, loyalty, and intention to recommend (Junaid et al., 2020; Lin and Ryu, 2023). As marketing strategies increasingly shift from emphasizing unique selling propositions to fostering emotional connections with customers online, brand love has gained growing attention within online contexts (He and Timothy, 2024; Ku et al., 2025). Choi et al. (2024) asserted that brand love is cultivated within online settings by providing users with integrated resources that enable shared identity, thereby fostering a sense of belonging. This perceived sense of belonging can evolve into psychological ownership, leading members to develop emotional responsibility toward the community (Zhang and Zhu, 2024). Accordingly, this study defines brand love as the emotional bond that members of OTCs develop toward the community itself (Choi et al., 2024).

As high-value co-creation sectors, the hospitality and tourism industries rely heavily on tourist participation in the co-creation process, which significantly influences their perceptions, emotions, and decisions regarding brands (Junaid et al., 2020; Yang, 2025). Song et al. (2024) found that contextual and behavioral factors in value co-creation amplify the impact of positive online user experiences, fostering stronger brand connections. Furthermore, SET suggests that individuals tend to exchange resources during reciprocal processes such as emotional bonds and social recognition (Homans, 1958). Drawing on SET, Li et al. (2024b) assert that experiences of rapport between customers and employees enhance brand love. Moreover, Junaid et al. (2020), Lin and Ryu (2023), Shin and Back (2020) each demonstrated that cognitive, hedonic, and social experiences positively contribute to the formation of brand love. Hence, the following hypothesis is proposed:

H7a-c. Hedonic experience social experience and cognitive experience positively affects brand love for OTCs.

Affordances of OTCs

Originally coined by Gibson (1979) in ecological psychology, affordance describes the action possibilities that arise from an individual-environment relationship. Subsequent human-computer interaction research has appropriated the term to explain how specific technological features invite or constrain user behaviour, thereby translating design attributes into latent opportunities for action (Liu et al., 2024) (see Appendix B). Building on prior study, we define the affordances of OTCs as the potential for action emerging from the interaction between the technological features of OTCs platforms and their community members(Jo et al., 2022). Such affordances have proven to serve as effective indicators for evaluating the actual use of tourism-oriented technological interventions in online environments and their resulting behavioral outcomes (Yoon and Nam, 2024). Empirical evidence further shows that high-level affordance strengthens users’ engagement, participation in value co-creation, and ultimately their attachment to the community brand (Li et al., 2024a).

With the continuous innovation and widespread application of information technology, the user experience of OTCs has been progressively enhanced. For instance, high-quality interface design provides visual stimulation that can positively trigger user engagement (Rafi et al., 2025), and gamification features enhance enjoyment and immersion, thus maintaining users’ participation (Zhang et al., 2025a). However, emotional responses are more likely to stem from the depth of social interaction rather than the aesthetics of the platform (Zhou et al., 2023). Moreover, the effects of gamification often diminish rapidly over time, potentially undermining users’ long-term emotional attachment to the community (Jo and Shin, 2025). In contrast, affordance is manifested as the action possibilities embedded within a technological environment, which can directly stimulate users’ autonomous behaviors and elicit emotional responses (Lee et al., 2024a). Its function is similar to a non-monetary incentive, enhancing the enjoyment and engagement in the interaction process, thereby deepening users’ emotional involvement (Shi et al., 2025). Similarly, Lee et al. (2024b) confirmed that affordance significantly influences emotion assessment, indicating that tourists tend to develop strong emotional bonds when they perceive the environment as offering ample opportunities for action.

Affordance has been confirmed as a moderating variable contributing to various fields such as social media and team management (Wu et al., 2024b; Yoon et al., 2024). In the context of online communities, online affordances provide users with improved dialogue, access, risk-return, and transparency practice, thereby fostering co-creation interactions between users and brand communities and keeping them informed about brand updates (Liang and Hsu, 2025). Prior research also confirms that affordance significantly magnifies the effect of enjoyment on user engagement and emotional involvement in livestreamed tourism scenarios (Zhang et al., 2025b). In influencer-based marketing contexts, Jo et al. (2022) demonstrated that high affordance intensifies social interactions formed during dialogue and information sharing, which in turn fosters emotional trust and brand attachment. Similarly, Cha et al.(2024) found that users with high affordance perceptions in virtual settings are more likely to translate perceived experiential value into positive emotions, thereby driving continued platform use and emotional affiliation. Accordingly, the following hypotheses are proposed:

H8a-c. Affordances of OTCs positively moderate the relationships from hedonic experience, social experience, and cognitive experience to brand love for OTCs.

Recognizing that incorporating demographic variables into the research model can enhance the accuracy and depth of the study's insights, gender, age, and education level were included as control variables in the proposed conceptual model (Figure 1).

The optimal configuration for brand love

Complexity theory posits that social phenomena in the tourism field do not result from the linear effect of a single factor, but rather emerge from the complex, nonlinear, and interactive influences of multiple factors (Chi et al., 2024b). Anbumathi et al. (2023) demonstrate that brand love formation is influenced by a variety of intricate factors. Therefore, this study aims to clarify the linear mechanisms underlying brand love formation and effectively complement these findings by exploring its nonlinear configurational mechanisms. Previous studies have demonstrated that fsQCA aligns with the core principles explained by complexity theory and is particularly effective in analyzing outcomes driven by the interaction of multiple causes (Chi et al., 2024b; Manosuthi et al., 2024). Hence, using fsQCA was critical to this study. Based on this rationale, we present the following proposition, with the conceptual framework illustrated in Figure 2.

Proposition 1

Online social support, online social capital, online social cohesion, hedonic experience, social experience, cognitive experience, and affordances of OTCs have the optimal combined impact on brand love for OTCs.

Methodology

Measurement and data collection

To measure OSS, OSC, and OSN, this study adopts the scales used by Lee and Joo (2024), Lin et al. (2019), Huang et al. (2020), and Xie et al. (2021). Co-creation experience was measured using scales from Xie et al. (2021) and Deng et al. (2024), whereas brand love and affordances were based on those of Junaid et al. (2020), Liu et al. (2024), and Liang and Hsu (2025). All items were rated on a seven-point Likert scale (1 = strongly disagree to 7 = strongly agree). Given the potential for semantic drift and measurement bias when applying constructs from prior research to respondents in Chinese OTCs, two bilingual PhD students in tourism followed the back-translation procedure to ensure cultural appropriateness. One translated the original items into Chinese, and the other independently back-translated them into English. Under the supervision of a tourism professor, they then compared the original and back-translated versions, evaluating the semantic equivalence, conceptual relevance to the context of Chinese OTCs, and sensitivity to cultural norms and idiomatic expressions. Subsequently, a pre-test with 20 tourism graduate students led to minor adjustments for clarity, such as tense revisions.

This study initially considered four well-known Chinese OTCs as suitable for our research objectives: a user-generated content-driven platform (Xiaohongshu), an integrated booking platform (Ctrip), a vertical niche community (Tuniu), and a local lifestyle community (Meituan). Based on an analysis of the M-MSI characteristics and the overall fit with our research model, Xiaohongshu was identified as the most appropriate platform for this study. This decision was primarily due to its position as one of the leading Chinese OTCs, with over 300 million registered users generating a vast volume of travel-related content, including reviews, experiences, and shares, exerting significant influence on the travel service marketing sector (Xiong et al., 2024). Moreover, Xiaohongshu relies heavily on user-generated content and employs algorithmic systems to provide personalized content recommendations (Wu et al., 2024a), indicating its strong potential and resources for fostering M-MSI and value co-creation.

Over a period of three weeks in January, 2024, data were collected via wjx.cn, a widely used Chinese survey platform (Chi et al., 2024a). All participants were required to answer qualifying questions before completing the survey questionnaire. The survey explained the research purpose and respondents were informed that their anonymity and confidentiality would be maintained. Each participant was required to have experience with the travel section of the Xiaohongshu platform and could submit the questionnaire only once. After excluding 26 invalid responses, 535 valid responses were obtained. The demographic data are shown in Table 1. Harman's single-factor test showed a 48.2% variance, which is below the 50% threshold, indicating no serious common method bias (Harman, 1967).

Table 1.

Demographic characteristics.

Characteristics		N (535)
Gender	Female	316	59.1%
	Male	219	40.9%
Age (years)	Under 18	10	1.9%
	18–30	319	59.6%
	31–40	168	31.3%
	41–50	28	5.2%
	Over 50	10	1.9%
Occupation	Clerical worker	299	55.9%
	Student	153	28.6%
	Professionals	36	6.7%
	Other	47	8.8%
Level of education	High school or below	70	13.1%
	College	179	33.5%
	Bachelor's degree	207	38.7%
	Master's degree or above	79	14.8%

Data analysis

Multiple software programs were used to analyze the collected data. SPSS 26.0 was used to conduct demographic analysis, common method bias evaluation, and moderation effect analysis. To estimate the proposed theoretical model, SmartPLS 4.0 was utilized to conduct partial least squares structural equation modeling (PLS-SEM). Finally, fsQCA 4.1 was applied to identify the configurational conditions leading to brand love for OTCs. By integrating the complementary analytical strengths of SEM and fsQCA, this study enhanced the reliability and validity of its findings. PLS-SEM examines the direct linear relationships among variables, whereas fsQCA explores nonlinear relationships and complex conditional effects on outcomes. This integrated approach aims to triangulate the results, thereby enabling to compare and contrast findings from multiple analytical methods (Chi et al., 2024a).

Results

Measurement model

Table 2 shows the reliability and validity of the study constructs, assessed via factor loadings (λ), composite reliability (CR), average variance extracted (AVE), and correlations among constructs. The Cronbach's alpha values exceeded 0.7, confirming internal consistency. Factor loadings above 0.5 and AVE values over 0.5 confirm convergent validity. The CR values surpassed the 0.7 threshold, ensuring high measurement reliability (Hair et al., 2019). Discriminant validity was also tested using the HTMT criterion, a more precise method than the Fornell-Larcker criterion (Henseler et al., 2015), showing all HTMT ratios below 0.9 (see Table 3), confirming the discriminant validity of the constructs (Hair et al., 2019).

Table 2.

Summary of measurements and factor loadings for indicator reliability.

Constructs and items	λ	M
Online social support (OSS)
OSS1. Certain members provide me with information to help me solve my problem	0.834	5.03
OSS2. Certain members offer me information to assist in resolving my problem	0.849	5.08
OSS3. Certain members help me figure out why the problem was occurring	0.886	5.27
OSS4. Certain members comfort me when I have a problem	0.821	5.28
OSS5. Certain members encourage me when I have a problem	0.814	5.22
OSS6. Certain members worry about me when I have a problem	0.765	5.02
α = 0.909, CR = 0.914, AVE = 0.687
Online social capital (OSC)
OSC1. I have an understanding of the preferences of certain members	0.820	4.90
OSC2. I am familiar with the interests of certain members	0.815	4.95
OSC3. I feel like I have similar goals with certain members	0.796	5.25
OSC4. I sense that I share similar values with certain members	0.839	5.24
OSC5. I have mutual trust with certain members	0.826	5.16
OSC6. I think certain members keep their word	0.806	5.12
α = 0.901, CR = 0.902, AVE = 0.668
Online social cohesion (OSN)
OSN1. I feel a sense of belonging with other members of the community.	0.878	4.64
OSN2. I consider myself an integral part of the community members.	0.867	4.64
OSN3. I feel enthusiastic and positive about other members of the community.	0.882	4.75
OSN4. I am genuinely happy to join the community and be a part of it.	0.808	4.65
α = 0.881, CR = 0.881, AVE = 0.738
Hedonic experience (HE)
HE1. Engaging with certain members, I find it intriguing	0.895	5.25
HE2. Engaging with certain members makes me feel happy	0.886	5.23
HE3. Engaging with certain members fills me with excitement	0.904	5.23
HE4. Engaging with certain members brings me delight	0.878	5.32
HE5. Engaging with specific members helps me feel relaxed	0.874	5.32
HE6. Engaging with certain members brings me enjoyment	0.873	5.26
α = 0.945, CR = 0.948, AVE = 0.783
Social experience (SE)
SE1. Engaging with certain members strengthens my connection to the online travel community	0.875	5.29
SE2. Engaging with certain members strengthens my rapport with the online travel community	0.857	5.28
SE3. Engaging with certain members increases my sense of belonging to the online travel community	0.876	5.18
SE4. Engaging with certain members strengthens my love for the online travel community	0.874	5.31
SE5. Engaging with certain members broadens my social network	0.868	5.33
SE6. Engaging with certain members enhances my social connections	0.880	5.20
α = 0.937, CR = 0.938, AVE = 0.760
Cognitive experience (CE)
CE1. Engaging with certain members can update certain of what I have learned	0.869	5.16
CE2. Engaging with certain members enhances my cognitive abilities	0.848	5.20
CE3. Engaging with certain members broadens my vision	0.862	5.21
CE4. Engaging with certain members expands my aspect of knowledge	0.849	5.18
CE5. Engaging with certain members helps me master certain skills	0.851	5.12
CE6. Engaging with certain members allows me to understand the specific uses of some products	0.878	5.07
α = 0.929, CR = 0.930, AVE = 0.739
Affordances of OTCs (AOTC)
AOTC1. The online travel community allows me to review products and services about travel	0.812	5.14
AOTC2. The online travel community allows me to share my travel experiences	0.806	5.33
AOTC3. The online travel community allows me to find information about destinations that match my interests	0.818	5.29
AOTC4. The online travel community allows me to see information on planning a trip	0.847	5.39
AOTC5. The online travel community allows me to make friends with others I have never met	0.875	5.28
AOTC6. The online travel community allows me to participate in discussions about travel	0.860	5.21
α = 0.919, CR = 0.996, AVE = 0.700
Brand love for OTCs (BL)
BL1. The online travel community is a wonderful brand	0.764	5.37
BL2. The online travel community makes me feel good	0.771	5.40
BL3. The online travel community is totally awesome	0.726	5.39
BL4. I love the online travel community	0.767	5.37
BL5. I am passionate about the online travel community	0.759	5.22
BL6. I am very attached to the online travel communityα = 0.861, CR = 0.863, AVE = 0.590	0.821	5.53

Note1. λ: Factor loading; M: Mean; α: Cronbach alpha; CR: Composite reliability; AVE: Average variance extracted;

Note2. OTCs: online travel communities.

Table 3.

Discriminant validity using the HTMT criterion.

	OSS	OSC	OSN	HE	CE	SE	AOTC
OSS
OSC	0.783
OSN	0.468	0.470
HE	0.557	0.601	0.466
CE	0.613	0.634	0.451	0.760
SE	0.619	0.641	0.453	0.880	0.792
AOTC	0.413	0.467	0.336	0.412	0.440	0.415
BL	0.767	0.781	0.584	0.685	0.697	0.691	0.649

Note. OSS: Online social support; OSC: Online social capital; OSN: Online social cohesion; HE: Hedonic experience; SE: Social experience; CE: Cognitive experience; AOTC: Affordances of OTCs; BL: Brand love for OTCs; OTCs: online travel communities.

Structural model

Henseler et al. (2015) emphasize that the initial focus of model evaluation should be on the overall goodness of fit; disregarding this foundation may lead to meaningless estimates and raise questions on the validity of the model's conclusions. The results show that the coefficient of determination (R²), which ranged from 0.207 to 0.620, significantly exceeded the recommended level of 0.20. The values of Stone-Geisser's Q² were above zero for all endogenous constructs. The standardized root mean square residual (SRMR) value was 0.066, which is below the 0.08 threshold, thereby satisfying the PLS-SEM goodness-of-fit requirements (Hair et al., 2019).

Testing relationships

The PLS-SEM analysis results in Table 4 demonstrate that OSS positively influences OSC (β_OSS→OSC = 0.710, t = 18.388, p < 0.001) and OSN (β_OSS→OSN = 0.247, t = 4.136, p < 0.001), while OSC positively influences OSN (β_OSC→OSN = 0.245, t = 3.984, p < 0.001), confirming H1-3. OSS also exerts a positive influence on hedonic experience (β_OSS→HE = 0.263, t = 3.660, p < 0.001), social experience (β_OSS→SE = 0.281, t = 4.287, p < 0.001), and cognitive experience (β_OSS→CE = 0.278, t = 4.064, p < 0.001), supporting H4a-c. OSC was shown to have a positive effect on hedonic experience (β_OSC→HE = 0.339, t = 4.815, p < 0.001), social experience (β_OSC→SE = 0.340, t = 5.108, p < 0.001), and cognitive experience (β_OSC→CE = 0.343, t = 5.021, p < 0.001), supporting H5a-c. Similarly, OSN positively affects hedonic experience (β_OSN→HE = 0.339, t = 4.815, p < 0.001), social experience (β_OSN→SE = 0.340, t = 5.108, p < 0.001), and cognitive experience (β_OSN→CE = 0.343, t = 5.021, p < 0.001), confirming H6a-c. Moreover, hedonic experience (β_HE→BL = 0.178, t = 2.000, p < 0.05), social experience (β_SE→BL = 0.263, t = 2.452, p < 0.05), and cognitive experience (β_CE→BL = 0.401, t = 5.124, p < 0.001) were shown to positively influence brand love, supporting H7a-c.

Table 4.

Hypotheses testing results.

Hypothesis	Relationships	Path coefficient	t-value	Results
H1	OSS→OSC	0.710***	18.474	Supported
H2	OSS→OSN	0.247***	4.136	Supported
H3	OSC→OSN	0.245***	3.984	Supported
H4a	OSS→HE	0.203**	3.145	Supported
H4b	OSS→SE	0.276***	4.447	Supported
H4c	OSS→CE	0.276***	4.458	Supported
H5a	OSC→HE	0.328***	5.214	Supported
H5b	OSC→SE	0.330***	5.312	Supported
H5c	OSC→CE	0.319***	5.145	Supported
H6a	OSN→HE	0.206***	4.603	Supported
H6b	OSN→SE	0.157***	4.004	Supported
H6c	OSN→CE	0.159***	3.891	Supported
H7a	HE→BL	0.227**	2.596	Supported
H7b	SE→BL	0.198*	2.117	Supported
H7c	CE→BL	0.303***	4.197	Supported
Control variable
Gender→BL	0.159*	2.444	Supported
Age→BL	0.184**	2.668	Supported
Education level→BL	0.033	0.536	Not supported

Note1. OSS: Online social support; OSC: Online social capital; OSN: Online social cohesion; HE: Hedonic experience; SE: Social experience; CE: Cognitive experience; BL: Brand love for OTCs; OTCs: online travel communities.

Note2. *p < 0.05; **p < 0.01; ***p < 0.001.

Among the control variables, gender and age significantly affected brand love, with standardized path coefficients of 0.159 (p < 0.05) and 0.184 (p < 0.01), respectively. The findings reveal that females and younger individuals exhibit greater brand love for OTCs than males and older individuals.

The moderation results in Table 5 show that affordances of OTCs positively moderate the relationships between hedonic experience (HE*AOTC = 0.103; p < 0.001), social experience (SE*AOTC = 0.106; p < 0.001), and brand love, thus supporting H8a and H8b. However, the moderating role of affordances was not significant for the relationship between cognitive experience(CE*AOTC = 0.012; p > 0.05) and brand love, thus H8c was rejected. Appendix B provides an intuitive representation of these interactions.

Table 5.

Moderating effects of OTC's affordances.

Hypothesis	Relationships	Path coefficient	t-value	Results
H8a	HE (HE*AOTC)→BL	0.103***	5.126	Supported
H8b	SE (SE*AOTC)→BL	0.106***	5.348	Supported
H8c	CE (CE*AOTC)→BL	0.012	0.654	Not Supported

Note1. HE: Hedonic experience; SE: Social experience; CE: Cognitive experience; AOTC: Affordances of OTCs; BL: Brand love for OTCs; OTCs: online travel communities.

Note2. *p < 0.05; **p < 0.01; ***p < 0.001.

fsQCA results

We conducted a necessary condition analysis (NCA) to identify the essential conditions for brand love for OTCs. The NCA results (Table 6) revealed that the consistency values for all the proposed variables were below the 0.9 threshold required for a necessary condition (Manosuthi et al., 2024). However, OSS, hedonic experience, social experience, cognitive experience, and affordances of OTCs all exceeded 0.8, suggesting that these conditions play a critical role in the process of fostering brand love for OTCs (Chi et al., 2024b).

Table 6.

Necessary condition analysis of brand love for OTCs

Outcome (BL)
Antecedent conditions	Consistency	Coverage
OSS	0.818	0.847
∼OSS	0.506	0.634
OSC	0.786	0.634
∼OSC	0.526	0.615
OSN	0.746	0.806
∼OSN	0.511	0.608
HE	0.811	0.827
∼HE	0.491	0.626
SE	0.826	0.853
∼SE	0.491	0.616
CE	0.809	0.855
∼CE	0.515	0.630
AOTC	0.828	0.853
∼AOTC	0.489	0.615

Note1. ∼ indicate the negation condition.

Note2. OSS: Online social support; OSC: Online social capital; OSN: Online social cohesion; HE: Hedonic experience; SE: Social experience; CE: Cognitive experience; AOTC: Affordances of OTCs; BL: Brand love for OTCs; OTCs: online travel communities.

Table 7 presents six antecedent configurations leading to brand love for OTCs, with a consistency value of 0.961 and a coverage value of 0.676, both exceeding the recommended thresholds of 0.8 and 0.5, respectively (Ragin, 2009). These six configurations demonstrate the high explanatory power of the solutions obtained in this study. Collectively, these configurations explain 67.6% of the cases, underscoring their significant interpretive capability to understanding the formation of brand love for OTCs.

Table 7.

Configurations of attributes leading to brand love for OTCs.

Configurations
Conditions	1	2	3	4	5	6
OSS	•	•		•		•
OSC	●	●	●			●
OSN			●	●	●	●
HE		•	•		•	◎
SE	●	●	●	●	●	◎
CE	•			•	•	◎
AOTC	●	●	●	●	●	•
Consistency	0.976	0.976	0.981	0.980	0.977	0.988
Raw coverage	0.564	0.566	0.572	0.520	0.535	0.237
Unique coverage	0.008	0.003	0.003	0.005	0.016	0.018
Solution consistency 0.966
Solution coverage 0.676

Note1. ● = causal condition present; ◎ = causal condition absent; blank space = do not care condition; large circle: core condition; small circle: peripheral condition.

Discussion and implications

Conclusion

The PLS-SEM results reveal that OSS facilitates the continuous integration of OSC, which represents interpersonal resources, by providing emotional and informational support, thereby jointly enhancing the formation of OSN that embodies a sense of unity among members. The M-MSI generated by the close connection of these three factors fosters members’ co-creation experiences in the cognitive, hedonic, and social dimensions, enabling active participation in community building with a sense of ownership and strengthening of the emotional bonds with the brand community. Affordances of OTCs serve as accelerators, streamlining the establishment of brand love. Additionally, fsQCA identifies each variable's contribution and configuration pathways to brand love for OTCs. Overall, the diverse methodologies employed in this study enhance the research rigor and enrich the literature on OTCs interactions, emotions, and behaviors (Shen et al., 2020; Vongvisitsin et al., 2024).

Theoretical implications

First, this study is the first of its kind to construct a ternary self-driven M-MSI framework based on SPT, systematically uncovering the internal motivational paths among OSS, OSC, and OSN. It elucidates the reality that OTCs members are not isolated individuals but rather part of a mutually empowering collaborative network. This finding extends the theoretical boundaries of SPT within the OTCs context by highlighting that M-MSI emerges from continuous mutual self-disclosure and understanding among members, and that the resulting social power fosters community vitality and prosperity (Huang et al., 2020; Liu et al., 2023b). Overall, this study bridges the research gap in prior tourism marketing studies, which have predominantly focused on the individual attributes of OTCs members (e.g., tourist mindset, gender, communication style, and experience) (Li and He, 2023; Liu and Hao, 2024; Wong et al., 2023) shifting the research emphasis from “individual characteristics” to “group synergy,” and uncovering the deeper social interaction mechanisms that support long-term vitality in OTCs.

Second, based on SET, this study reveals the internal mechanism through which M-MSI influences community brand love through co-creation experiences. This finding elevates SET from a static framework of “individual resource reciprocity” to a dynamic three-factor integrated model of “group collaboration—value co-creation—brand identification”. Despite recent studies expanding the SET to include group-based emotional attribution (Li et al., 2024a), value co-creation (Shulga et al., 2021), and brand community building (Cho et al., 2023), most of them emphasize the immediate returns of collaboration or the direct influence of co-creation on emotional commitment, while overlooking the emotional-level collaborative mechanisms among members that may exert deeper, long-term impacts on the platform (Huang et al., 2020; Xiao et al., 2025). By constructing a dynamic three-factor integrative model, this study demonstrates how M-MSI drives a fundamental emotional transformation between users and online communities by using co-creation as a catalyst, thereby completing a dual-cycle reciprocal exchange. These insights refine the applicability boundaries of SET within OTCs and provide a robust theoretical foundation for shifting OTCs from traditional functional optimization and traffic competition toward a brand love strategy focused on emotional empowerment.

Third, this study reveals the positive moderating effect of affordances of OTCs on the relationship between the co-creation experience and brand love. Departing from the traditional linear framework of the “technological feature–behavioral outcome,” this finding repositions affordances as active situational catalysts rather than passive technological attributes, thereby empirically validating their moderating effect for the first time. Notably, affordances did not significantly moderate the relationship between cognitive experience and brand love, suggesting that the effectiveness of affordances is subject to boundary conditions. Hedonic and social experiences are primarily driven by enjoyment and interpersonal interactions (Baruah and Chatterjee, 2024; Karagöz et al., 2025; Xie et al., 2021), which are further enhanced by affordance mechanisms (e.g., likes and real-time comments), strengthening emotional bonding. In contrast, cognitive experience relies more heavily on high-quality objective information and the accumulation of individual knowledge, which aligns with the rational characteristics of the systematic information processing pathway (Choi et al., 2021). According to the heuristic-systematic model, when users engage in systematic processing, their sensitivity to external contextual cues decreases (Xie et al., 2023), thereby weakening the marginal emotional effect of affordances.

Moreover, this study proposes a multi-method analytical framework integrating PLS-SEM and fsQCA, thereby offering a more comprehensive and multidimensional theoretical understanding of user community behavior and emotional mechanisms in digital tourism contexts. This also directly responds to the call by Chi et al. (2024b) and Wang and Guo (2025) for integrating both linear and nonlinear methods to more comprehensively capture complex causal relationships. The PLS-SEM results provide a linear analytical foundation for understanding how M-MSI empowers the emotional transformation process of community members. In contrast, the fsQCA results identify complex causal configurations from a nonlinear perspective. Notably, the NCA results reveal that no single condition is necessary, further underscoring the importance of configuration analysis (Chi et al., 2024b). The identified configurations reveal three pathway types to brand love. The first (Configurations 1–3) highlights how structural ties and affordances of OTCs jointly deepen social interaction (Deng et al., 2022; Xie et al., 2021). The second (Configurations 4–5) shows that OSN and social experience, supported by affordances, foster emotional connection (Lin et al., 2019). The third (Configuration 6) indicates that strong OSC and OSN can sustain brand love even without co-creation, supporting relationship-dominant perspectives (Zhang et al., 2022).

Practical implications

These results indicate that effectively leveraging M-MSI is paramount for OTCs managers. Specifically, managers can employ big data analytics on user interaction networks to identify potential Key Opinion Leaders (KOLs). Through the implementation of a “travel recommendation officer” program and an exclusive KOL reward system, these individuals can be converted into social support nodes. Moreover, by harnessing text-mining techniques, OTCs can capture topic hotspots in real time and automatically generate interactive scenarios that align with user preferences. For instance, when an uptick in the “solo travel” topic is detected, the system automatically triggers an AI-driven group travel matching event. Another step is to establish a “digital–physical” resonance mechanism by designing community activities based on mixed-reality technologies. For example, after community members complete a task within OTCs, they can unlock their eligibility to attend an offline badge-exchange gathering while simultaneously accumulating community points through Internet of Things devices (e.g., NFC wristbands) and redeem them for rewards.

Second, OTCs managers should place greater emphasis on affordances of OTCs and co-creation experiences. Specifically, managers can transform user-designed low-carbon itineraries (e.g., routes with over 70% of travel via intercity rail) into quantifiable carbon-credit nonfungible tokens, thereby visualizing environmental contributions and incentivizing ecofriendly behavior. Additionally, by integrating artificial intelligence with geospatial databases, artificial reality-enhanced responsible tourism guides can be generated in real time. For example, once the system recognizes a user-uploaded beach photograph, it automatically overlays awareness-raising messages regarding beach protection. The third initiative involves co-creating metaverse-based destination ecology projects, wherein community members can redeem their points to obtain the right to plant trees in the metaverse destination, and on-the-ground staff can then handle the actual planting to enhance the immersive co-creation experience. Moreover, to develop a brand love strategy for OTCs, managers can establish a “travel memory bank” mechanism that transforms users’ travel trajectories, reviews, and stories into accumulable and inheritable digital cultural assets. By designing a milestone-based “travel career” growth system, the participation process can be elevated into a narrative vehicle for users’ self-actualization, thereby fostering emotional attachment and cultural identification with the platform.

According to the NCA results, online social support, hedonic experience, social experience, cognitive experience, and affordance all play critical roles in the formation of brand love. Accordingly, OTCs’ managers should ensure that these key elements are embedded within the system architecture. For instance, integrating a “Community Help” button within each post could trigger peer support; embedding interactive photo/video sharing templates with music and mood filters may enhance hedonic and cognitive experiences; and enabling voice comment features or real-time topic matching based on geolocation can make affordances of OTCs more intuitive and user-centric.

In addition, OTCs’ managers may leverage embedded behavioral data to identify user types based on the three fsQCA-configured pathways and develop targeted interventions. For relational builders (Configurations 1–3), the platform could award “Trusted Connector” badges to users who frequently engage in mutual evaluation, create customized travel circles for long-term users, and display milestone interaction records (e.g., “You have chatted with this member 10 times!”) to deepen relational ties. For emotional co-creators (Configurations 4–5), the platform should emphasize emotional resonance. Initiatives could include launching a “Travel Confessions” column, allowing users to tag their stories with emotions rather than themes, or organizing weekly story contests in which community members vote based on the emotional impact of the stories. For structural loyalists (Configuration 6), the platform may introduce long-term service badges (e.g., “Five-Year Community Traveler”), establish legacy pages to showcase user contributions, or automatically send monthly appreciation messages to users with consistent interaction patterns.

Limitations and future research

Despite this study's rigorous empirical methodology in drawing its conclusions, certain limitations remain. This study's conclusions are based on cross-sectional data; hence, the effects of the independent and moderating variables are tentative. Future studies should utilize longitudinal surveys to confirm these findings and enhance the reliability of the results. Second, as this study primarily focuses on OTCs, it remains unclear whether the boundary effect of affordance on cognitive experience applies to other types of communities (i.e., short-video communities or online ticketing platforms). Future research is encouraged to validate the moderating effect of the affordance in diverse platform contexts. Third, this study focuses on members’ brand love toward the OTCs, without further exploring the emotional responses elicited by various entities presented on the platform (e.g., destinations, hotels, or services). Considering the multilayered nesting of the various entities within the OTCs, future research could examine the differences in the formation of brand love across different levels. Moreover, the participants were predominantly young. Thus, future studies should include older participants to extend the applicability of these findings. Finally, this study focuses on OTCs in China. Future studies should conduct cross-cultural comparisons or explore OTCs in other regions, such as North America and Europe.

Footnotes

ORCID iDs

Yuhao Lin

Ji-Hwan Yoon

Xiaoting Chi

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

Appendix A. Xiaohongshu's affordances

Appendix B. Moderating role of the affordances of OTCs

References

Anbumathi

Dorai

Palaniappan

(2023) Evaluating the role of technology and non-technology factors influencing brand love in Online Food Delivery services. Journal of Retailing and Consumer Services 71: 103181. https://doi.org/10.1016/j.jretconser.2022.103181

Baruah

Chatterjee

(2024) Cognitive-affective-behavioral themes in post-purchase attitudes toward virtual tourism experiences: a mixed-method approach. International Journal of Tourism Research 26(6): e2811.

Boytsun

Deloof

Matthyssens

(2011) Social norms, social cohesion, and corporate governance. Corporate Governance: An International Review 19(1): 41–60. https://doi.org/10.1111/j.1467-8683.2010.00829.x

Cha

Kim

Tang

(2024) Gamification in the metaverse: affordance, perceived value, flow state, and engagement. International Journal of Tourism Research 26(2): e2635.

Chi

Cheng

Zhou

, et al. (2024a) Investigation on driving mechanism of heritage tourism consumption: a multi-method analytical approach. Journal of Travel & Tourism Marketing 41(8): 1141–1160.

Chi

Kim

Chiriko

, et al. (2024b) Tourists’ ethically responsible participation in animal-based tourism: a configurational impact assessment. Journal of Vacation Marketing 13567667241268650. https://doi.org/10.1177/13567667241268650

China Internet Network Information Center . (2024) The 53rd statistical report on China's internet development. Available at: https://www.cnnic.com.cn/IDR/ReportDownloads/202405/P020240509518443205347.pdf (accessed 20 December 2024).

Cho

Park

Lee

(2023) Do green practices and green image promote consumers’ participation in social network sites (SNS)? Journal of Hospitality Marketing & Management 32(5): 631–648.

Choi

Lee

Choi

, et al. (2024) From click to fork: an empirical analysis of system and food quality influences in online meal kit consumption. International Journal of Hospitality Management 121: 103808.

10.

Choi

Lee

Choe

(2021) Online travel information filtering: Role of commercial cues in trust and distrust mechanisms. Journal of Travel & Tourism Marketing 38(7): 710–724.

11.

Dai

Yeh

Nguyen

, et al. (2024) Unveiling the dynamics of co-created camping experiences: influence of co-production on family cohesion and resilience via place identity. Journal of Outdoor Recreation and Tourism 47: 100773.

12.

Deng

Lee

, et al. (2024) Co-creation of the tourist experience: a systematic assessment scale. Tourism Management Perspectives 51: 101212.

13.

Deng

Benckendorff

Wang

(2022) From interaction to relationship: rethinking parasocial phenomena in travel live streaming. Tourism Management 93: 104583.

14.

Fatima

(2023) Does it matter to have rapport and social interaction on a group tour? Journal of Vacation Marketing 31(3): 549–561.

15.

Gibson

(1979) The Ecological Approach to Visual Perception. Boston: Houghton Mifflin.

16.

Goolaup

Nunkoo

Cho

(2025) Food tourists social interactions: a social capital and social learning perspective. Tourism Management 108: 105128.

17.

Hair

Risher

Sarstedt

, et al. (2019) When to use and how to report the results of PLS-SEM. European Business Review 31(1): 2–24.

18.

Harman

(1967) A single factor test of common method variance. The Journal of Psychology 35: 359–378.

19.

Timothy

(2024) Tourists’ perceptions of buying souvenirs in online museum shops. International Journal of Tourism Research 26(5): e2761.

20.

Henseler

Ringle

Sarstedt

(2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 43: 115–135.

21.

Hernández-Méndez

Baute-Díaz

Gutiérrez-Taño

(2024) The effectiveness of virtual versus human influencer marketing for tourism destinations. Journal of Vacation Marketing: 13567667241276212. https://doi.org/10.1177/1356766724127621

22.

Homans

(1958) Social behavior as exchange. American Journal of Sociology 63(6): 597–606.

23.

Zhang

(2025) How important is charisma? The impacts of charismatic tour-guiding and service disclosure on tourist trust, self-disclosure, interaction quality, and emotional solidarity. Tourism Management Perspectives 58: 101367.

24.

Huang

Chen

Wong

(2020) Hotel guests’ social commerce intention: the role of social support, social capital and social identification. International Journal of Contemporary Hospitality Management 32(2): 706–729.

25.

Jiang

Meng

Liu

(2025) Unlocking nontransactional value co-creation: insights from online travel communities. Journal of Travel Research 64(5): 1206–1225.

26.

Jiao

Wong

Lin

(2024) Understanding customer multi-interactions, trust, social support and voluntary performance in smart restaurants. Journal of Hospitality and Tourism Technology 15(5): 717–736.

27.

Chung

Hlee

, et al. (2022) Perceived affordances and regret in online travel agencies. Journal of Travel Research 61(5): 1024–1042.

28.

Shin

(2025) Can gamification and augmented reality (AR) revitalize declining destinations? Investigating its impact on tourist attitudes and behavior. Journal of Travel Research: 00472875251332961. https://doi.org/10.1177/00472875251332961

29.

Junaid

Hussain

Asghar

, et al. (2020) An investigation of the diners’ brand love in the value co-creation process. Journal of Hospitality and Tourism Management 45: 172–181.

30.

Karagöz

Kama

Uysal

(2025) Examining the relationships between mindfulness, eudaimonic and hedonic experiences, place attachment, loyalty: the moderating role of flow. Journal of Leisure Research 56(3): 404–429.

31.

Lai

(2025) Elegant with leisurely: luxury tourism consumption and brand love. Journal of Hospitality & Tourism Research 49(4): 750–763.

32.

Lee

Jung

Tom Dieck

, et al. (2024a) Affordance, digital media literacy, and emotions in virtual cultural heritage tourism experiences. Journal of Vacation Marketing 31(4): 1056–1073.

33.

Lee

Joo

(2024) Intermediary organizations as supporters of residents’ innovativeness and empowerment in community-based tourism. Journal of Sustainable Tourism 32(8): 1534–1556.

34.

Lee

Joo

Lee

, et al. (2024b) South Korean DMZ tourists’ experience co-creation explained by motivation, interaction, and emotional solidarity. Journal of Destination Marketing & Management 32: 100872.

35.

(2023) The interaction effect of reward type and mindset on online travel community promotion intention: the mediating roles of identification and community commitment. Journal of Travel & Tourism Marketing 40(4): 294–309.

36.

Kunasekaran

Chia

, et al. (2023) A systematic mapping study on tourist-tourist interactions. Journal of Hospitality and Tourism Management 56: 163–185.

37.

Shao

, et al. (2024a) Unveiling the impact of interpersonal interactions on brand love: insights from a dual-path framework. Journal of Travel Research: 00472875241269977. https://doi.org/10.1177/00472875241269977

38.

Leung

Yang

(2025) How tie strength and face consciousness moderate the impact of social media on impulsive travel intentions: a social-technical perspective. Journal of Hospitality and Tourism Technology 16(3): 479–495.

39.

Akram

, et al. (2024b) Understanding how customer social capital accumulation in brand communities: a gamification affordance perspective. Journal of Retailing and Consumer Services 78: 103761.

40.

Liang

Hsu

(2025) Creating ‘meaningful’tourism experiences through digital affordances and gamification: enhancing urban brand identity. Current Issues in Tourism: 1–19. https://doi.org/10.1080/13683500.2025.2482156

41.

Lin

Ryu

(2023) How product design affects repurchase intention, eWOM, and museum visit intention: museum mystery boxes in China. Journal of Travel & Tourism Marketing 40(5): 434–451.

42.

Lin

Zhang

Gursoy

, et al. (2019) Impact of tourist-to-tourist interaction on tourism experience: the mediating role of cohesion and intimacy. Annals of Tourism Research 76: 153–167.

43.

Lin

Zhang

Gursoy

(2022) Effects of tourist-to-tourist interactions on experience cocreation: a self-determination theory perspective. Journal of Travel Research 61(5): 1105–1120.

44.

Liu

Wang

Zhang

(2024) Exploring social media affordances in tourist destination image formation: a study on China’s rural tourism destination. Tourism Management 101: 104843.

45.

Liu

, et al. (2023a) Engaging customers with online restaurant community through mutual disclosure amid the COVID-19 pandemic: the roles of customer trust and swift guanxi. Journal of Hospitality and Tourism Management 56: 124–134.

46.

Liu

Feng

, et al. (2023b) Is tourism live streaming a double-edged sword? The paradoxical impact of online flow experience on travel intentions. Journal of Travel & Tourism Marketing 40(8): 744–763.

47.

Liu

Hao

(2024) Engaging with avatar in virtual regenerative tourism. Journal of Travel & Tourism Marketing 41(6): 864–879.

48.

Luo

Jian

Yung

, et al. (2024) Pursuing social cohesion in cultural tourism destinations: liminality as a mediator. Current Issues in Tourism 27(15): 2499–2515.

49.

Manosuthi

Meeprom

Leruksa

(2024) Exploring multifaceted pathways: understanding behavioral formation in green tourism selection through fsQCA. Journal of Travel & Tourism Marketing 41(4): 640–658.

50.

Moon

Lee

Jeong

(2023) Examining the relationship between individualism and pro-environmental behavior: The moderating role of social cohesion. Behavioral Sciences 13(8): 661.

51.

Rafi

Gani

Hashim

NMHN

, et al. (2025) The influence of 360-degree VR videos on tourism web usage behaviour: the role of web navigability and visual interface design quality. Tourism Review 80(3): 725–741.

52.

Ragin

(2009) Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press.

53.

Sabiote-Ortiz

Coves-Martínez

ÁL

Peco-Torres

, et al. (2025) Can smart technologies enhance co-creation and destination perceived authenticity? Culture's moderating effect. Journal of Vacation Marketing: 13567667241307393. https://doi.org/10.1177/135676672413073

54.

Sancho-Esper

Ostrovskaya

Rodriguez-Sanchez

, et al. (2023) Virtual reality in retirement communities: technology acceptance and tourist destination recommendation. Journal of Vacation Marketing 29(2): 275–290.

55.

Shahzad

Ahmad

, et al. (2025) Impact of metaverse experiences on consumer behavior in metaverse-enabled hospitality retailing: the role of realism, immersion, convenience, and social interaction. Journal of Hospitality Marketing & Management: 1–22. https://doi.org/10.1080/19368623.2025.2496912

56.

Shen

, et al. (2020) The effect of online interaction and trust on consumers’ value co-creation behavior in the online travel community. Journal of Travel and Tourism Marketing 37(4): 418–428.

57.

Shi

(2025) Affordance design for mitigating digital vulnerability in smart tourism. International Journal of Tourism Research 27(3): e70060.

58.

Shin

Back

(2020) Effect of cognitive engagement on the development of brand love in a hotel context. Journal of Hospitality & Tourism Research 44(2): 328–350.

59.

Shulga

Busser

Bai

, et al. (2021) The reciprocal role of trust in customer value co-creation. Journal of Hospitality & Tourism Research 45(4): 672–696.

60.

Song

(2024) It takes a village! Customer value co-creation behavior in restaurant social media-based brand community. Journal of Hospitality & Tourism Research 48(2): 327–352.

61.

Stehr

(2023) The benefits of supporting others online – how online communication shapes the provision of support and its relationship with wellbeing. Computers in Human Behavior 140: 107568.

62.

Tao

Peiying

Huihui

, et al. (2024) How do affective and cognitive involvement affect tourism intentions: a perspective on blogger impact. Current Issues in Tourism: 1–20. https://doi.org/10.1080/13683500.2024.2392763

63.

Vongvisitsin

Huang

King

(2024) Urban community-based tourism development: a networked social capital model. Annals of Tourism Research 106: 103759.

64.

Wang

Guo

(2025) How does the metaverse tourism experience form tourists’ happiness: a mixed-methods study. Journal of Vacation Marketing: 13567667241307958. https://doi.org/10.1177/13567667241307958

65.

Wei

Zheng

Zhang

, et al. (2022) Leveraging customer-to-customer interactions to create immersive and memorable theme park experiences. Journal of Hospitality and Tourism Insights 5(3): 647–662.

66.

Wong

Lin

, et al. (2023) The transformative virtual experience paradigm: the case of Airbnb’s online experience. International Journal of Contemporary Hospitality Management 35(4): 1398–1422.

67.

Zhao

Wang

, et al. (2024a) Digital hoarding in everyday hedonic social media use: the roles of fear of missing out (FoMO) and social media affordances. International Journal of Human–Computer Interaction 40(18): 5399–5414.

68.

Cheng

(2020) Relationships between experiential risk, experiential benefits, experiential evaluation, experiential co-creation, experiential relationship quality, and future experiential intentions to travel with pets. Journal of Vacation Marketing 26(1): 108–129.

69.

Zhang

, et al. (2024b) Understanding dual effects of social network services on digital well-being and sustainability: a case study of Xiaohongshu (RED). Sustainability 16(15): 1–25.

70.

Xiao

Lee

, et al. (2025) Tourism live streamers as brand ambassadors: investigating their impact on destination brand value through social exchange theory. Journal of Hospitality and Tourism Technology 16(1): 174–193.

71.

Xie

Fan

Zhang

, et al. (2023) The effect of risk message framing on tourist safety behavior: a heuristic-systematic model perspective. Tourism Management Perspectives 48: 101173.

72.

Xie

Guan

Liu

, et al. (2021) The antecedents and consequences of the co-creation experience in virtual tourist communities: from the perspective of social capital in virtual space. Journal of Hospitality and Tourism Management 48: 492–499.

73.

Xin

Qiao

Shao

, et al. (2023) Understanding continuous sharing behavior of online travel community users: a case of TripAdvisor. Journal of Tourism and Cultural Change 21(3): 328–343.

74.

Xiong

Wang

Okumus

, et al. (2024) Beyond experience: uncovering the travel experience of cancer patients. Journal of Hospitality & Tourism Research 48(8): 1302–1315.

75.

Yang

(2025) Social capital and consumer co-creation behaviors on tourism social media: the mediating role of psychological ownership. Tourism Review 80(5): 1141–1155.

76.

Yoon

Piercy

Kim

, et al. (2024) Boundary work and transactive memory systems in teams: moderating effects of the visibility affordance. Management Communication Quarterly 38(2): 359–385.

77.

Yoon

Nam

(2024) Metaverse engagement and Korea travel intentions: understanding affordances, presence, and place attachment among Brazilian ZEPETO users. Journal of Destination Marketing & Management 31: 100865.

78.

Zhang

Yang

Bai

(2022) The effects of photo-sharing motivation on tourist well-being: the moderating role of online social support. Journal of Hospitality and Tourism Management 51: 471–480.

79.

Zhang

Zhu

(2024) Investigating the transformative power of brand love in destination crisis recovery. Journal of Travel Research: 00472875241296086. https://doi.org/10.1177/00472875241296086

80.

Zhang

Shi

Liu

, et al. (2025a) From virtual to reality: how online gamification applications promote travel intention. Journal of Travel & Tourism Marketing 42(6): 832–849.

81.

Zhang

Liang

Xue

, et al. (2025b) Beneficial or troublesome? Revealing the double-edged effects of tourism live streaming affordances on viewers’ engagement. Journal of Travel & Tourism Marketing 42(1): 133–159.

82.

Zhou

Liu

, et al. (2023) The effect of tourist-to-tourist interaction on tourists’ behavior: the mediating effects of positive emotions and memorable tourism experiences. Journal of Hospitality and Tourism Management 55: 161–168.