The role of trust in streamers and live streaming commerce in predicting customer loyalty
Main Article Content
Cite this article:
Kim, B.,
Chen, Y., &
Kim, D.
(2025). The role of trust in streamers and live streaming commerce in predicting customer loyalty.
Social Behavior and Personality: An international journal,
53(1),
e13940.
Abstract
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References
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In recent years live streaming commerce (LSC) has become increasingly prevalent, increasing the importance of understanding the key antecedents of customer loyalty in this new marketplace. To examine how trust in streamers and LSC can enhance customer loyalty, we developed a research model focusing on usefulness, entertainment, and ease of use as the main antecedents of trust, and conducted an online survey of 290 customers of Chinese LSC platforms. The results showed that trust in streamers and trust in LSC were significant positive predictors of customer loyalty, and attractiveness, expertise, and interactivity were significant antecedents to building trust in streamers. Furthermore, entertainment and ease of use were significantly related to trust in LSC, whereas usefulness was not. These findings provide new insight into effective strategies for increasing customer loyalty.
Live streaming commerce (LSC) is a new type of e-commerce based on real-time interactions that has solidified its position as a major marketing and distribution channel through rapid growth. The LSC growth trend is most pronounced in China, where the market has grown to a scale of USD 514 billion in 2022 since the Alibaba Group first launched LSC in 2016 (Goldberg, 2023). At present, LSC plays a crucial role in distribution and marketing, with more than 20% of Chinese online shoppers reportedly making purchases through LSC streamers (Goldberg, 2023).
Streamers play a vital role in the LSC environment by broadcasting themselves online through live video feeds, allowing real-time interaction while providing information about the products and chatting to customers (Chen & Liao, 2022; Kim et al., 2023). In traditional e-commerce, interaction between sellers and consumers is difficult, and information is not shared in real time but rather in the form of preproduced content. However, in the case of LSC, customers can communicate with streamers and participate in transactions through online connections, which enhances their online shopping experience (Kwahk & Kim, 2017). Celebrities and influencers, defined as individuals who have the ability to influence potential buyers of a product or service by promoting or recommending items on social media, are often employed as streamers on social media platforms to leverage large followings. For instance, in China, Wang Hong, who has numerous followers on Chinese social media, is also a mainstreamer. In addition, during the 2022 Singles’ Shopping Festival, Taobao streamer, Jiaqi Li, with 50 million followers, sold RMB 21.5 billion worth of merchandise via LSC streamers (Zhang et al., 2022).
The rapid growth of LSC has led to an increasingly competitive LSC environment. Several Chinese LSC operators, such as Taobao, Douyin, and Pinduoduo, engage in intense competition. A critical challenge for these operators is increasing existing customers’ frequency of visits to their platforms by enhancing their shopping experience. One strategy has been the creation of various shopping discount events to stimulate customers’ shopping activities, for instance, shopping festivals, such as the June 18 Shopping Festival, which is the world’s largest discount event, and the November 11 Singles’ Day Shopping Festival, which is akin to Black Friday in the United States.
The intense competition among LSC platforms has contributed to growing interest in understanding how to retain customers and increase sales within this new commerce environment. However, the current literature on LSC is limited. Previous studies mainly focused on the positive effects of LSC characteristics and trust levels on customer satisfaction and loyalty. Kang et al. (2021) noted that the key feature of LSC is the real-time interaction between streamers and customers, indicating the need to differentiate between customer trust in the shopping platform and trust in streamers. Zhang et al. (2022) examined the effect of trust on LSC and found that streamers had a significant effect on customer loyalty. However, although the link between customers’ trust and loyalty is well-established, few studies have examined the key antecedents of trust formation in the LSC context.
The Current Study
This study examined the factors that influence customer loyalty in China’s fiercely competitive LSC environment through exploring the key antecedents that influence the formation of trust in both LSC and streamers. Our research model is shown in Figure 1.
Flow
Flow refers to a state in which individuals are completely absorbed in an activity, task, or work to the extent that they may not even be aware of their own existence (Csikszentmihalyi, 1990). The experience of flow leads to intense involvement, significantly influencing customer loyalty (Liu et al., 2022). In the context of LSC, the flow experience of customers is enhanced through the provision of real-time information, live chats, and events (Kang et al., 2021). Liu et al. (2022) demonstrated that the flow experience significantly predicts customers’ purchase intention in LSC. Thus, we expected that customers experiencing flow would be likely to improve their loyalty toward LSC. Therefore, we proposed the following hypothesis:
Hypothesis 1: Flow will positively predict customer loyalty.
Figure 1. Research Model
Note. LSC = live streaming commerce.
Trust in Streamers
Trust is defined as the voluntary willingness of one party to believe in and act upon the actions of another party (Mayer et al., 1995). It is considered a key factor in customers’ purchase decision making in e-commerce, with researchers primarily focusing on trust in the platform. Recently, Lu and Chen (2021) reported that customers find it challenging to acquire specialized knowledge, and the overwhelming amount of information leads to uncertainty in their decision making. These authors empirically verified that customers tend to prefer information provided by trusted individuals to avoid the risks associated with misinformation and fraud, with trust in streamers reducing risk avoidance and ultimately increasing purchase intention. These findings are similar to those of Zhou (2012), who showed that customers with a high degree of trust feel sure that they will obtain a good experience. On the basis of these findings, it is reasonable to infer that as the level of trust in streamers increases, customers will improve their flow experience and loyalty toward LSC. Therefore, we proposed the following hypotheses:
Hypothesis 2a: Trust in streamers will positively predict flow.
Hypothesis 2b: Trust in streamers will positively predict customer loyalty.
Characteristics of Streamers
Researchers have identified three main characteristics of streamers that engage customers and have the potential to build trust: their personal attractiveness, expertise, and interactivity (Chen & Liao, 2022; Rungruangjit, 2022). Personal attractiveness is defined as streamers’ ability to capture customers’ hearts, and it encompasses their appearance, voice, and sense of humor, which elicit favorable responses from customers (Chen & Liao, 2022). The more customers perceive streamers to be charming, the more they might trust them (Zhao et al., 2015). Expertise refers to the degree to which streamers have specialized knowledge and experience (Rungruangjit, 2022). In LSC, influencers and celebrities often act as streamers for specific products, sharing a large amount of information about the product’s performance, attributes, and effectiveness. Rungruangjit (2022) showed that streamers’ expertise plays a crucial role in purchase decision making. Therefore, it is reasonable to infer that streamers with higher expertise might earn increased customer trust. Interactivity refers to the exchange of opinions that facilitate effective communication. In LSC, streamers can communicate with customers in real time through live chats, and customers can interact with them by sharing their opinions and using the “like” button. Streamers also encourage increased customer participation by providing coupons and red packets, the latter of which are monetary gifts given during holidays or special occasions, often through online platforms in digital form, during live broadcasts. Given this high level of engagement, interactivity likely plays a crucial role in increasing customer trust in streamers. Therefore, we proposed the following hypotheses:
Hypothesis 3a: Personal attractiveness will positively predict trust in streamers.
Hypothesis 3b: Expertise will positively predict trust in streamers.
Hypothesis 3c: Interactivity will positively predict trust in streamers.
Trust in Live Streaming Commerce
Trust in live streaming commerce is defined as the belief that the LSC platform is honest and reliable. According to social exchange theory, trust reduces perceived risks, such as those relating to fraud and personal information abuse (Lu & Chen, 2021). In the context of LSC, trust tends to enhance customers’ expectation of reliable experiences (Kim et al., 2023). For example, Wongkitrungrueng and Assarut (2020) showed that trust in LSC significantly predicted customer shopping commitment. The more customers trust the products or services sold on an LSC platform, the more they will immerse themselves in and use the LSC platform, contributing to increased loyalty. Therefore, we proposed the following hypotheses:
Hypothesis 4a: Trust in live streaming commerce will positively predict flow.
Hypothesis 4b: Trust in live streaming commerce will positively predict customer loyalty.
Characteristics of Live Streaming Commerce
Researchers have identified usefulness, entertainment, and ease of use as key characteristic elements of LSC (Xie et al., 2023). Usefulness refers to the degree to which an LSC platform is perceived as providing benefits for shopping (Xie et al., 2023). According to the technology acceptance model, for users to adopt new technologies specific purposes must be fulfilled efficiently (Davis, 1989). In an LSC environment, customers can obtain real-time information provided by streamers and receive instant feedback on their inquiries. Thus, usefulness is expected to be a major factor increasing trust in these platforms. Entertainment is another significant factor that drives the growth of LSC. Joo and Yang (2023) demonstrated that enjoyable shopping experiences in LSC play a crucial role in the formation of customers’ positive attitude toward them. Entertainment acts as an intrinsic motivator that stimulates certain behaviors and helps customers build trust in shopping platforms (Liu et al., 2022). Ease of use refers to the ease of performing tasks, such as payment, searching, and accessing the desired information (Davis, 1989). The more convenient customers perceive LSC, the higher the probability of them trusting and continuously accessing the platform. Xie et al. (2023) revealed that the ease of use offered by LSC is the main factor in increasing purchase intention in LSC. Therefore, we proposed the following hypotheses:
Hypothesis 5a: Usefulness will positively predict trust in live streaming commerce.
Hypothesis 5b: Entertainment will positively predict trust in live streaming commerce.
Hypothesis 5c: Ease of use will positively predict trust in live streaming commerce.
Method
Participants and Procedure
We utilized an online survey platform and professional survey company in Mainland China to recruit individuals who had shopped on LSC platforms such as Taobao, Douyin, and Pinduoduo in China and administer our survey. Data collection took place from September 7 to 10, 2023, following standard ethical guidelines for research with human subjects. Of 300 responses we collected, 10 were excluded for insincere responses and respondents having had no experience with the three major LSC platforms; thus, we obtained 290 valid responses. Each respondent who submitted a valid form received RMB 50 as compensation for their participation. The respondents’ ages ranged from 18 to 62 years (M = 30.3, SD = 10.4). Table 1 presents further demographic information.
Table 1. Demographic Data for Respondents
Note. Order proportion of LSC refers to the proportion of instances where purchases were made through live commerce out of the total number of shopping instances.
Measures
We derived the items for our questionnaire from previously validated measures in information systems and marketing settings, modified to fit the LSC context. All items were rated on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Two researchers fluent in Chinese and English reviewed and translated the measurement items into Chinese using a standard translation/back-translation procedure.
The survey requested that respondents focus on their general perceptions of streamers overall and experiences with live commerce. To assess personal attractiveness and interactivity we used eight items from the measure of Chen and Liao (2022). A sample item for personal attractiveness is “The streamers in the LSC were very charming.” A sample item for interactivity is “I was able to communicate with the streamer in a timely manner while watching the LSC.” To assess expertise, we adapted four items from Rungruangjit (2022). A sample item is “The streamers in the LSC had expertise in their field.” To assess usefulness and ease of use, we adapted nine items from Xie et al. (2023). A sample item is “The product information provided during the LSC was detailed and clear.” A sample item for ease of use is “I was able to access and use the LSC platform immediately.” To assess entertainment, trust in streamers, and flow, we used 11 items from Liu et al. (2022). A sample item for entertainment is “The LSC was interesting.” A sample item for trust in streamers is “I believed that the streamers in the LSC were trustworthy.” A sample item for flow is “I was highly immersed while watching the LSC.” To measure trust in LSC, we adapted three items from Zhang et al. (2022). A sample item is “The LSC platform is trustworthy.” Last, to assess customer loyalty data we used three items from Spreng et al. (1996). A sample item is “I continue to purchase products from the LSC platform.”
Data Analysis
We analyzed the research model using Amos 22.0.
Results
Measurement Model
This study evaluated the model fit using three indices: root-mean-square error approximation (RMSEA), comparative fit index (CFI), and nonnormed fit index (NNFI). The three indices indicated a good model fit, RMSEA = .015, CFI = .993, NNFI = .992. We conducted a confirmatory factor analysis to confirm the scales’ convergent validity, reliability, and discriminant validity, with the standardized factor loadings, average variance extracted (AVE), and composite reliability (CR) values exceeding their respective thresholds (AVE > .50, CR > .70). Next, we measured the factor loadings of the measurement items. As shown in Table 2, the items demonstrated satisfactory levels of convergent validity, exceeding .70. Last, to check discriminant validity, we compared the AVE values of the individual constructs with the shared variances between the constructs (Fornell & Larcker, 1981). As shown in Table 3, the square root values of AVE were greater than the correlations with the other constructs, showing the variables had acceptable discriminant validity.
Table 2. Construct Reliability Testing
Note. AVE = average variance extracted; CR = composite reliability.
Table 3. Correlation Matrix and Discriminant Validity Assessment
Note. Diagonal elements are the square roots of average variance extracted.
Structural Equation Modeling and Hypothesis Testing
Table 4 presents a summary of the analysis results. The three indices showed a good model fit, RMSEA = .028, CFI = .973, NNFI = .971. Flow significantly predicted customer loyalty, supporting Hypothesis 1. Trust in streamers significantly predicted flow and customer loyalty, supporting Hypotheses 2a and 2b. Personal attractiveness, expertise, and interactivity significantly predicted trust in streamers, supporting Hypotheses 3a, 3b, and 3c. Trust in LSC was significantly associated with flow and customer loyalty, providing empirical support for Hypotheses 4a and 4b. While these results were consistent with our expectations, usefulness was not significantly associated with trust in LSC; thus, Hypothesis 5a was not supported. However, entertainment and ease of use played significant roles in predicting trust in LSC, supporting Hypotheses 5b and 5c. The research model accounted for 46.2% of the variance in customer loyalty, 36.4% of the variance in trust in streamers, and 33.3% of the variance in trust in LSC. The results of this analysis are shown in Figure 2.
Table 4. Summary of the Results
Note. LSC = live streaming commerce.
Figure 2. Structural Equation Modeling Results
Note. LSC = live streaming commerce.
* p < .05. ** p < .01. *** p < .001.
Discussion
Theoretical Contributions
This study examined the key antecedents influencing customer loyalty in LSC, which is a rapidly growing commerce platform. We differentiated trust in streamers from trust in LSC and examined their impact on customer loyalty alongside other characteristic factors of streamers and the LSC platforms. Our proposed research model explained a significant percentage of the variance in customer loyalty in the context of Chinese LSC platforms. In line with previous research findings (Kang et al., 2021), trust in streamers and LSC had a significant effect on customer loyalty, and flow mediated this relationship.
Next, we found that all three of the key characteristics of streamers we investigated (personal attractiveness, expertise, and interactivity) significantly predicted trust in streamers and explained a significant percentage of the variance. This is reasonable, as one of the most distinctive features that differentiates LSC from traditional online shopping is the importance of the interaction between streamers and customers. Customers trust content creators or influencers who have specialized knowledge in the products they are selling. Consistent with the results of Lu and Chen (2021), we found that customers were likely to develop trust toward streamers who were attractive, demonstrated expertise, and engaged in high levels of interaction.
Last, we explored usefulness, entertainment, and ease of use as the key characteristics of an LSC platform. The results showed that both entertainment and ease of use significantly predicted trust in LSC. In line with the results of Liu et al. (2022), providing enjoyable customer experiences can increase trust in and loyalty to a platform. However, some of our results contradict prior findings in the literature. While Xie et al. (2023) showed that usefulness had a significant effect on consumer purchase decision making, it did not have any significant effect on trust in LSC in our research. One reason may be that the product information provided on the platform may not be trusted because it is provided by noncertified streamers. Since streamers often receive commissions from vendors, there is a higher probability of providing favorable information for the sale of products rather than accurate information.
Practical Implications
These findings have several practical implications. First, we recommend that LSC companies secure trust through providing quality evaluations of the products or services sold on their platforms. Moreover, given the increasing importance of streamers in shaping customer loyalty, we recommend that companies develop marketing and operational strategies that attract renowned streamers to platforms. Companies could also utilize metrics to objectively evaluate the personal innovativeness, expertise, and interactivity of streamers as well as recruiting and managing them effectively. Since enjoyable experiences increase customer loyalty to the platform, companies should host live events and provide interactive experiences through real-time chatting with streamers. As customers are increasingly watching LSC on smartphones, it would also be useful to provide convenient menu structures and simple payment systems to further increase trust in LSC.
Limitations and Future Research Directions
This study has limitations. First, we validated our research model using data from China, where LSC platforms are actively utilized. Future research could reexamine the research model through surveying LSC customers in other countries to increase the generalizability of our findings. Second, this study examined the characteristics of streamers and shopping platforms. However, to increase the explanatory power of customer loyalty, the consideration of other factors, such as service and system quality, would provide further insight. Finally, although each LSC platform has unique characteristics, the unique features of these platforms were not considered in this study. Future research could examine the differences regarding the antecedent factors of customer loyalty according to the attributes of the platforms and individual streamers.
References
Chen, J., & Liao, J. (2022). Antecedents of viewers’ live streaming watching: A perspective of social presence theory. Frontiers in Psychology, 13, Article 839629.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Goldberg, J. (2023, February 10). Is live-streaming commerce living up to its hype in the US? Forbes.
Joo, E., & Yang, J. (2023). How perceived interactivity affects consumers’ shopping intentions in live stream commerce: Roles of immersion, user gratification and product involvement. Journal of Research in Interactive Marketing, 17(5), 754–772.
Kang, K., Lu, J., Guo, L., & Li, W. (2021). The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms. International Journal of Information Management, 56, Article 102251.
Kim, B., Chen, Y., & Kim, D. (2023). Key factors influencing customer loyalty in live commerce: The role of perceived value and perceived risk. Social Behavior and Personality: An international journal, 51(9), Article 12656.
Kwahk, K.-Y., & Kim, B. (2017). Effects of social media on consumers’ purchase decisions: Evidence from Taobao. Service Business, 11(4), 803–829.
Liu, X., Zhang, L., & Chen, Q. (2022). The effects of tourism e-commerce live streaming features on consumer purchase intention: The mediating roles of flow experience and trust. Frontiers in Psychology, 13, Article 995129.
Lu, B., & Chen, Z. (2021). Live streaming commerce and consumers’ purchase intention: An uncertainty reduction perspective. Information and Management, 58(7), Article 103509.
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. The Academy of Management Review, 20(3), 709–734.
Rungruangjit, W. (2022). What drives Taobao live streaming commerce? The role of parasocial relationships, congruence and source credibility in Chinese consumers’ purchase intentions. Heliyon, 8(6), Article e09676.
Spreng, R. A., Mackenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the determinants of consumer satisfaction. Journal of Marketing, 60(3), 15–32.
Wongkitrungrueng, A., & Assarut, N. (2020). The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117, 543–556.
Xie, Q., Mahomed, A. S. B., Mohamed, R., & Subramaniam, A. (2023). Investigating the relationship between usefulness and ease of use of living streaming with purchase intentions. Current Psychology, 42(30), 26464–26476.
Zhang, M., Liu, Y., Wang, Y., & Zhao, L. (2022). How to retain customers: Understanding the role of trust in live streaming commerce with a socio-technical perspective. Computers in Human Behavior, 127, Article 107052.
Zhao, N., Zhou, M., Shi, Y., & Zhang, J. (2015). Face attractiveness in building trust: Evidence from measurement of implicit and explicit responses. Social Behavior and Personality: An international journal, 43(5), 855–866.
Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management, 13(1), 27–37.
Chen, J., & Liao, J. (2022). Antecedents of viewers’ live streaming watching: A perspective of social presence theory. Frontiers in Psychology, 13, Article 839629.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Goldberg, J. (2023, February 10). Is live-streaming commerce living up to its hype in the US? Forbes.
Joo, E., & Yang, J. (2023). How perceived interactivity affects consumers’ shopping intentions in live stream commerce: Roles of immersion, user gratification and product involvement. Journal of Research in Interactive Marketing, 17(5), 754–772.
Kang, K., Lu, J., Guo, L., & Li, W. (2021). The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms. International Journal of Information Management, 56, Article 102251.
Kim, B., Chen, Y., & Kim, D. (2023). Key factors influencing customer loyalty in live commerce: The role of perceived value and perceived risk. Social Behavior and Personality: An international journal, 51(9), Article 12656.
Kwahk, K.-Y., & Kim, B. (2017). Effects of social media on consumers’ purchase decisions: Evidence from Taobao. Service Business, 11(4), 803–829.
Liu, X., Zhang, L., & Chen, Q. (2022). The effects of tourism e-commerce live streaming features on consumer purchase intention: The mediating roles of flow experience and trust. Frontiers in Psychology, 13, Article 995129.
Lu, B., & Chen, Z. (2021). Live streaming commerce and consumers’ purchase intention: An uncertainty reduction perspective. Information and Management, 58(7), Article 103509.
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. The Academy of Management Review, 20(3), 709–734.
Rungruangjit, W. (2022). What drives Taobao live streaming commerce? The role of parasocial relationships, congruence and source credibility in Chinese consumers’ purchase intentions. Heliyon, 8(6), Article e09676.
Spreng, R. A., Mackenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the determinants of consumer satisfaction. Journal of Marketing, 60(3), 15–32.
Wongkitrungrueng, A., & Assarut, N. (2020). The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117, 543–556.
Xie, Q., Mahomed, A. S. B., Mohamed, R., & Subramaniam, A. (2023). Investigating the relationship between usefulness and ease of use of living streaming with purchase intentions. Current Psychology, 42(30), 26464–26476.
Zhang, M., Liu, Y., Wang, Y., & Zhao, L. (2022). How to retain customers: Understanding the role of trust in live streaming commerce with a socio-technical perspective. Computers in Human Behavior, 127, Article 107052.
Zhao, N., Zhou, M., Shi, Y., & Zhang, J. (2015). Face attractiveness in building trust: Evidence from measurement of implicit and explicit responses. Social Behavior and Personality: An international journal, 43(5), 855–866.
Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management, 13(1), 27–37.
Figure 1. Research Model
Note. LSC = live streaming commerce.
Table 1. Demographic Data for Respondents
Note. Order proportion of LSC refers to the proportion of instances where purchases were made through live commerce out of the total number of shopping instances.
Table 2. Construct Reliability Testing
Note. AVE = average variance extracted; CR = composite reliability.
Table 3. Correlation Matrix and Discriminant Validity Assessment
Note. Diagonal elements are the square roots of average variance extracted.
Table 4. Summary of the Results
Note. LSC = live streaming commerce.
Figure 2. Structural Equation Modeling Results
Note. LSC = live streaming commerce.
* p < .05. ** p < .01. *** p < .001.
This study was supported by the 2024 Yeungnam University Research Grant.
The data that support the findings of this study are available on request from the corresponding author.
Daekil Kim, School of Communications and Media, Seoul Women’s University, 621 Hwarangro, Nowon-Gu, Seoul 01797, Republic of Korea. Email: [email protected]
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