2.1 Customer Experience

Customer experience is considered as the subjective and internal reactions that individuals have to a business's offerings, both directly and indirectly (Lee and Park, 2023; Meyer and Schwager, 2007). Since the introduction of experiential marketing by Schmitt (1999), this concept has gained prominence in marketing research, becoming a central element in shaping effective product and service strategies (Brakus et al., 2009). The need of considering both rational and emotional aspects while examining customer experience was underlined by Schmitt (1999). Building on this, contemporary perspectives on customer behavior suggest that customers are increasingly concerned with the quality of the overall experience rather than merely the functional attributes of products or services (Nysveen et al., 2012). Therefore, scholars have identified cognitive and affective responses as essential internal components of customer experience (Rose et al., 2012), as customers aim to fulfill both mental and emotional objectives in their shopping journeys (Jung and Park, 2021; Sombultawee and Tansakul, 2023). This study adopts this dual framework by examining cognitive and affective experiences as core elements of live-stream commerce. A positive experience can foster customer loyalty and retention (Nazir et al., 2023). Specifically, consumers’ inclination to repurchase is specifically influenced by their cognitive and emotional experiences (Rose et al., 2012), a finding supported by later studies across various business settings (Dandis et al., 2023; Ong et al., 2018; Sombultawee and Tansakul, 2023; Tyrväinen et al., 2020). The combined impacts of cognitive and emotive experiences on repurchase intentions in the setting of live-stream commerce have, however, not been extensively studied.

2.2 Relational Bonds

With its focus on long-term consumer involvement and retention rather than short-term transactions, relationship marketing has emerged as a crucial paradigm in current marketing practices (Chiu et al., 2005). At the heart of this approach are relational bonds, which serve as mechanisms through which firms build, maintain, and enhance customer relationships over time (Berry, 1995). Rather than simply attracting new customers, relationship marketing aims to deepen emotional and behavioral commitment, recognizing that loyal customers are more valuable and cost-effective than those who are price-sensitive or brand indifferent (Chen and Chiu, 2009; Gu et al., 2016). Relational bonds have been shown to improve customer trust, satisfaction, commitment, perceived value, and empowerment in previous studies (Chiu et al., 2005; Gu et al., 2016; Hsieh et al., 2005; Hu and Chaudhry, 2020; Lai et al., 2024; Lin et al., 2003). In live-stream commerce, relational bonding has become a core strategy, as sellers interact directly with viewers, provide exclusive offers, and integrate personalized services to increase customer loyalty and engagement (Hu and Chaudhry, 2020; Lai et al., 2024). Despite growing evidence that customer experience directly influences repurchase intentions, examining how relational bonds moderate this relationship offers deeper insight into the mechanisms that strengthen or weaken experiential effects. Given that live-streaming platforms foster ongoing, real-time interactions, relational bonds may intensify the impact of cognitive and affective experiences by creating a deeper sense of connection and trust. In this context, customers who perceive strong relational ties may be more responsive to positive experiences, translating them into stronger repurchase intentions. Integrating relational bonds as moderating variables offers a meaningful extension of relationship marketing theory, especially in the socially dynamic and highly interactive environment of live-stream commerce.

3.1 Data Collection and Sampling

The online questionnaire was created using the Wenjuanxing platform, a widely used Chinese survey tool. The questionnaire consisted of three sections. Section 1 included screening items to ensure that only participants with prior live-stream shopping experience proceeded to the main survey. Participants were asked to indicate whether they had ever made a purchase through a live-stream platform, identify the platform they most frequently used for purchases, and specify the live streamer from whom they had made the most purchases on that platform. Only respondents who had prior live-stream shopping experience were allowed to continue.

Section 2 contained the main questionnaire, where participants responded based on their experience with the selected live-stream platform and streamer. This approach grounded responses in actual purchasing experiences, ensuring consistency and minimizing recall bias. Section 3 collected demographic information. To ensure data integrity, each survey link was restricted to one submission per WeChat account, and only fully completed questionnaires could be submitted.

To reach a broad and diverse sample, the survey link was distributed through WeChat in multiple ways: sent individually through private messages, shared within group chats, and posted in the WeChat Moments feed visible to all contacts. Participants were also encouraged to share the link with others to further expand the respondent pool. This multi-channel approach allowed access to a wide range of users despite WeChat being primarily a private messaging platform. The combination of screening items, one-response-per-account restriction, and multi-channel distribution ensured that the collected data were both reliable and relevant to the study context. Finally, 369 valid replies were received in total. Table 1

3.2 Measures

To ensure content validity and reliability,

all constructs were measured using established scales from prior research, with all items presented in Table 2

4.1 Measurement Model

The validity and reliability of the measurement model were evaluated through the use of confirmatory factor analysis (CFA). Strong correlations between each item and its corresponding construct were shown by standardized factor loadings over 0.7, as seen in Table 2. The model demonstrated excellent fit, with goodness-of-fit indices as follows: CMIN/DF=1.476, CFI=0.985, IFI=0.985, TLI=0.983, RMSEA=0.036, confirming the adequacy of the measurement structure. Composite reliability (CR) values exceeded 0.9 and average variance extracted (AVE) values were above 0.7 for all constructs, demonstrating strong internal consistency and that constructs captured sufficient variance from their indicators. By contrasting the square roots of AVEs (shown in Table 3

4.2 Common-Method Bias Assessment

Harman’s single-factor test and the unmeasured latent method factor (ULMF) approaches were employed to identify common-method bias (CMB) in considering the cross-sectional design (Podsakoff et al., 2003)¹. According to the first test, the first factor only explained 31.801% of the variation, which is less than the 50% criterion and suggests that CMB is restricted. Second, the ULMF approach was applied within CFA, adding a latent factor for method variance with zero correlation to other constructs. Fit indices for this model showed minimal differences compared to the original model ($\Delta$ CMIN/DF=0.427; $\Delta$ IFI=0.014; $\Delta$ TLI=0.015; $\Delta$ CFI=0.014; $\Delta$ RMSEA=0.024), further indicating that CMB did not substantially affect the data.

4.3 Hypotheses Testing

Hierarchical multiple regression analysis was employed to test the proposed hypotheses. As presented in Table 4

H2a and H2b were supported by the finding that financial bonds positively moderated the association between affective experience and repurchase intention ($\beta=0.099, p<0.05$) and cognitive experience and repurchase intention ($\beta=0.136, p<0.01$). This highlights how discounts, coupons, and limited time offers can magnify the influence of satisfying experiences. Social bonds also positively moderated these relationships ($\beta=0.109, p<0.01$ for both), supporting H3a and H3b, reflecting the value of sustained interaction and a community-like atmosphere fostered by popular Chinese streamers. Structural bonds likewise positively moderated both relationships ($\beta=0.111, p<0.01; \beta=0.108, p<0.01$), confirming H4a and H4b, suggesting that features such as sensory product demonstrations, detailed usage instructions, and real-time problem resolution strengthen the link between experience and repurchase intention.

Overall, these results indicate that in China’s live-stream commerce sector, both cognitive and affective experiences matter, and relational bonds significantly amplify their effects, making them key strategic levers for retaining customers.

5.1 Theoretical Contributions

This research contributes a number of theoretical advances to the research on relationship marketing, experiential marketing, and live-stream commerce. First, by empirically validating the positive effects of both cognitive and affective experiences on repurchase intention, this study advances the understanding of experiential marketing outcomes. Prior research has consistently linked customer experience to favorable behavioral intentions across various contexts (Ong et al., 2018; Nysveen et al., 2012; Rose et al., 2012; Sombultawee and Tansakul, 2023), yet studies in live-stream commerce remain limited. The findings confirm that in the interactive and real-time environment of live-stream commerce, both rational evaluations (cognitive experience) and emotional engagement (affective experience) significantly enhance viewers’ intention to repurchase. This extends experiential marketing theory by demonstrating its applicability in digital, highly socialized retail settings, where purchase decisions are influenced not only by product attributes but also by cognitive and affective experiences.

Second, this study advances prior research on the customer experience-behavior link by introducing relational bonds as moderating mechanisms, thereby extending the theoretical integration of experience marketing and relationship marketing. Whereas earlier studies have primarily examined individual differences or contextual factors such as service channel or demographics (Brun et al., 2017; Huang, 2017; Molinillo et al., 2021), the incorporation of financial, social, and structural bonds highlights how relationship marketing mechanisms actively shape the translation of cognitive and affective experiences into repurchase intention. This contribution moves beyond merely applying existing models to live-stream commerce by conceptualizing relational bonds as boundary conditions that amplify experiential effects. Therefore, the study offers a cross-theoretical framework that refines understanding of how experiential and relational strategies interact to influence consumer behavior in digital and highly interactive retail environments.

Third, this research deepens the conceptualization of relational bonds in relationship marketing by examining the moderating effects of each dimension individually. Financial bonds, such as discounts, coupons, special offers, and premiums, were shown to enhance the influence of both cognitive and affective experiences on repurchase intention, aligning with prior findings that economic incentives increase perceived value (Chiu et al., 2005) and loyalty (Gu et al., 2016). Social bonds, characterized by the streamer’s attention, responsiveness, personalized recommendations, and gestures of care, amplified experience effects on customer’s intention to repurchase. Structural bonds, which involve value-adding services and personalized solutions, also strengthened these relationships, reflecting their role in creating switching barriers and long-term engagement (Hu and Chaudhry, 2020). By separating each kind of relational bond and evaluating its moderating function, this study provides a nuanced theoretical contribution, showing how distinct relational strategies differentially reinforce experiential impacts in live-stream commerce.

Collectively, these contributions move beyond confirming the well-established customer experience-loyalty relationship by offering a more integrative framework that bridges experiential and relationship marketing. By situating this framework within the context of live-stream commerce, the study advances theory in two ways: it demonstrates how customer experiences operate under the immediacy and interactivity of live-stream commerce, and it identifies relational bonds as critical boundary conditions that magnify experiential effects. These insights provide a more precise and contextually grounded understanding of how experience marketing and relationship marketing interact to influence consumer behavior in emerging digital retail environments.

5.2 Managerial Implications

First, the finding that both cognitive and affective experiences significantly enhance repurchase intention underscores the need for live-stream sellers and platforms to deliver experiences that appeal to both rational and emotional dimensions of how customers make decisions (Rose et al., 2012). On the cognitive side, streamers should ensure that product information is comprehensive, accurate, and clearly presented, leveraging demonstrations, comparisons, and detailed explanations to reduce customer uncertainty and build trust (Zhang et al., 2023). On the affective side, streamers should engage in storytelling, use an energetic presentation style, and foster a sense of excitement and entertainment to offer memorable experiences during the session (Gu et al., 2023). For Chinese live-stream commerce, blending product expertise with emotional stimulation can create a holistic experience that encourages immediate purchase while nurturing long-term relationships.

Second, the strategic importance of incorporating relationship marketing strategies into live-streaming operations is highlighted by the moderating influence of relational bonds. Financial bonds, such as exclusive discounts, limited-time coupons, or loyalty point systems, can be timed strategically to coincide with peak engagement moments during the live-stream sessions (Hu and Chaudhry, 2020), amplifying the effect of customer experience on purchase decisions. Social bonds should be cultivated through personalized greetings, recognition of returning customers, and consistent streamer-viewer interaction that builds familiarity and rapport (Hu and Chaudhry, 2020). Structural bonds can be reinforced by offering value-added services, such as personalized product recommendations, after-sales support, or loyalty membership programs, that enhance convenience and create switching barriers (Lai et al., 2024). By embedding these bonds into the live-streaming format, sellers can strengthen customer trust, increase retention, and drive higher repeat purchase rates.

Third, the implications of this study extend beyond the Chinese market. While live-stream commerce has developed most rapidly in China, platforms in other Asia-Pacific countries and Western markets are increasingly adopting similar models (Kawaf and Girotto, 2024). The mechanisms identified in this study are not unique to a single market but reflect generalizable principles of consumer decision-making within interactive digital environments. Accordingly, while Chinese sellers may integrate experiential and relationship marketing into highly promotion-driven ecosystems, international practitioners should adapt these strategies to their specific market contexts. For example, European platforms could prioritize structural bonds through after-sales guarantees to alleviate concerns about cross-border logistics, whereas most Asian platforms might emphasize social bonds to capitalize on collectivist cultural orientations. By tailoring relational strategies to local consumer preferences and institutional environments, managers can apply the study’s insights to cultivate long-term loyalty and achieve sustainable competitive advantage across diverse regions.

Finally, our results highlight that managerial focus should not rest solely on driving immediate transactions but on cultivating repeat patronage through carefully balanced experiential and relational strategies. Platforms can play a pivotal role by equipping streamers with analytics to track viewer engagement, segment customers based on experiential responses, and optimize the use of relational bonds. This alignment between experiential value and relational marketing ensures that live-stream commerce moves beyond price wars, establishing enduring loyalty that secures long-term growth.

5.3 Limitations and Future Research

While this study advances understanding of how customer experience influences repurchase intention in live-stream commerce, several limitations provide opportunities for further research. First, the model focused on direct and moderated relationships, without exploring potential mediating mechanisms. Prior studies suggest that variables such as trust, satisfaction, and brand love act as mediators in the experience-behavior link (Huang, 2017; Molinillo et al., 2021; Rose et al., 2012). These dimensions could be combined in future studies to better understand the psychological mechanisms influencing repurchase intentions.

Second, this study did not differentiate between the categories of products that respondents considered when completing the survey. Consumer responses and repurchase intentions may vary depending on the nature of the products, such as functional search goods (e.g., electronics or household appliances), experiential goods (e.g., food, clothing, or cosmetics), or services (e.g., hotel stays, travel packages, or online courses). Future research could explicitly examine how relational bonds and repurchase intentions differ across these product types, providing more nuanced insights into the moderating effects of product characteristics. Including product category as a variable would also enhance the generalizability and practical relevance of the findings across diverse live-commerce contexts.

Finally, this study was conducted exclusively in the Chinese live-stream commerce market, characterized by high customer interactivity and advanced platform features. The reliance on single-country, single-time-point survey data limits causal inference and may reduce the generalizability of the findings to other contexts. Future research could adopt longitudinal designs or cross-cultural comparisons to examine whether relational bond effects persist across diverse technological and cultural settings, thereby enhancing the robustness and theoretical generalization of these results.