Research on the influence of anchor characteristics on consumer purchase intention—a case study of selected anchors on Dong Yuhui’s “With Hui” Live-stream account

Based on previous research and combined with the S-O-R theoretical model, this study examines the characteristics of anchors on Dong Yuhui’s “With Hui” Live-stream account as independent variables. The characteristics, measured by professionalism, high interactivity, fame, and attractiveness, are analyzed to determine their impact on consumer purchase intention. Perceived trust is set as a mediating variable, anticipated regret as a moderating variable, and purchase intention as the dependent variable to construct a model for reference by anchors on Dong Yuhui’s “With Hui” Live-stream account. Results show that anchor characteristics significantly and positively influence purchase intention; professionalism, high interactivity, fame, and attractiveness all have a notable positive impact on purchase intention. And anchor characteristics significantly and positively impact perceived trust, with professionalism, high interactivity, fame, and attractiveness positively influencing perceived trust. Perceived trust positively affects purchase intention and perceived trust partially mediates the relationship between anchor characteristics and purchase intention, with partial mediation observed between professionalism, high interactivity, fame, and attractiveness on purchase intention. Anticipated regret moderates the relationship between perceived trust and purchase intention, with upward anticipated regret showing a negative moderating effect, while downward anticipated regret shows a positive moderating effect on the perceived trust-purchase intention relationship.

1 Introduction

Since its explosive growth and with the user base continuing to grow in recent years, China’s live-streaming e-commerce industry has seen continuous expansion in market size. With the deepening of the digital era, live-streaming e-commerce has become a widely applied marketing model, offering unprecedented interactive experiences for businesses and consumers (Chen and Lin, 2018; Park and Lin, 2020). In live-streaming e-commerce, anchors serve as central figures, attracting consumers and facilitating purchases through real-time interaction and product promotion. Particularly on China’s leading short-video platforms like Dong Yuhui’s “With Hui” live-stream account, live-streaming e-commerce has rapidly risen to prominence due to its immediacy and high interactivity, becoming an integral part of the country’s e-commerce landscape (Li et al., 2024; Ranran and Juan, 2023). As a leading platform, Dong Yuhui anchors not only act as information disseminators but also influence consumer trust and decision-making through their professionalism, interactivity, attractiveness, and popularity (Li et al., 2024; Ranran and Juan, 2023). These characteristics play a crucial role in enhancing consumers’ purchase intentions (Wang et al., 2021).

Despite researches on live-streaming e-commerce domestically and internationally, many studies primarily focus on the impact of individual characteristics on consumer behavior, lacking systematic and comprehensive analysis (Dong et al., 2022; Li et al., 2024). Furthermore, research on how psychological factors like perceived trust and anticipated regret mediate or moderate the relationship between anchor characteristics and purchase intention remains insufficient (Gao et al., 2022; Sun et al., 2021). Therefore, it is essential to explore the influence of anchor characteristics on purchase intention from multiple dimensions based on the S-O-R theoretical model, providing valuable insights for industry practice and theoretical research (Zhang and Liu, 2023; Zhu et al., 2021).

The SOR theory (Stimulus-Organism-Response), as shown in Figure 1, was first proposed by environmental psychologists Mehrabian and Russell in 1974. This theory suggests that when an individual is exposed to an external stimulus (S), it generates a certain emotion (O) internally, which then leads to the individual’s response (R). The theory indicates that a stimulus is an external influence that can affect a person’s psychological state, prompting them to react. After a series of psychological responses, the receiver will exhibit either an internal or external behavioral response to the stimulus. An internal response usually refers to an individual’s attitude, while a behavioral response generally refers to approach or avoidance behavior. This theory has been widely applied in consumer behavior research to explain how consumers respond to external stimuli, which in turn influences their purchasing decisions and behaviors (Sheth et al., 1991; Song et al., 2022).

Figure 1

Figure 1. S-O-R model.

This study makes several key contributions to existing literature by systematically integrating anchors’ professionalism, interactivity, popularity, and attractiveness within the S-O-R theoretical framework, while combining perceived trust and anticipated regret to comprehensively analyze their collective impact on consumers’ purchase intentions (Liu et al., 2022; Wang et al., 2021). It highlights the critical roles of emotions and psychological factors by examining the mediating effect of perceived trust and the moderating role of anticipated regret, enriching the theoretical perspective (Zhang and Liu, 2023; Li et al., 2024). Addressing the gap in interaction studies, this research constructs a model to explore the interplay among multiple variables, thereby deepening relevant insights. By collecting and analyzing empirical data on Dong Yuhui’s “With Hui” live-stream platform anchors, the study validates its theoretical assumptions and provides actionable guidance for optimizing anchor strategies (Fengjun et al., 2020; Yi and Tianqi, 2022). Moreover, it identifies significant differences in how various anchor characteristics influence purchase intentions, offering valuable directions for personalized operational strategies (Zhu et al., 2021).

The primary objectives of this study are to clarify the key role of anchor characteristics in consumer behavior by analyzing their direct and indirect effects on purchase intentions through theoretical modeling and empirical analysis. It aims to elucidate the mediating mechanism of perceived trust between anchor characteristics and purchase intention, contributing to the understanding of consumer trust formation (Liu et al., 2022; Li et al., 2024). Additionally, the study explores how anticipated regret dynamically moderates consumer decision-making, shedding light on the psychological factors influencing live-streaming e-commerce (Wongkitrungrueng et al., 2020). By addressing the practical needs of platforms like Dong Yuhui’s “With Hui,” it provides actionable recommendations for optimizing anchor content operations, enhancing audience engagement, and improving conversion rates (Yu and Zheng, 2022).

Centering on constructing a comprehensive theoretical model based on the S-O-R theory, this study identifies anchors’ professionalism, interactivity, popularity, and attractiveness as external stimulus variables; perceived trust as a mediating variable; anticipated regret as a moderating variable; and purchase intention as the outcome variable (Liu et al., 2022; Chenglin and Shanyue, 2022). By proposing hypotheses derived from literature reviews and the model, it clarifies the relationships between these variables (Li et al., 2024). Employing a questionnaire survey with a Likert scale covering seven key variables, the study collected 314 valid responses through online channels. Data analysis, conducted using SPSS 27.0, involved descriptive statistics, reliability and validity tests, correlation and regression analyses, and mediation and moderation effect tests, ensuring scientific rigor (Wongkitrungrueng et al., 2020; Sun et al., 2021). Empirical results validate the significant positive impact of anchor characteristics on purchase intention, the mediating role of perceived trust, and the moderating effects of upward and downward anticipated regret (Yingying, 2021a,b). Additionally, comparisons among different anchor types reveal performance differences in characteristics and their varying impacts on purchase intention, providing actionable insights for optimizing operational strategies.

Based on the S-O-R model, this study considers professionalism, high interactivity, popularity, and attractiveness as external stimuli brought about by anchor characteristics. Perceived trust and anticipated regret are set as variables, forming the theoretical model for this study, as shown in Figure 2.

Figure 2

Figure 2. Mechanism model of anchor characteristics’ influence on purchase intention.

2 Research hypotheses

2.1 Impact of anchor characteristics on consumers’ purchase intentions

In the context of live-stream e-commerce, anchor characteristics significantly influence consumer purchase intentions. Anchor professionalism refers to the anchor’s expertise, knowledge of the promoted products, and extensive live-streaming experience. Popular anchors often attract more viewers, increasing product exposure and sales. Study of Liu et al. (2022) demonstrates that professional anchors convey product credibility, reducing consumer uncertainty and fostering purchase behavior. The interaction between the anchor and the consumer, where the anchor serves as the initiator and guide, plays a key role in influencing consumer cognition and behavior. High in involves active communication between the anchor and viewers, where the anchor frequently responds to questions, addresses comments, and builds positive rapport with the audience, enhancing their sense of participation and belonging, and increasing purchase intention. Zhenping (2023) emphasize the role of interactivity in creating emotional bonds that strengthen consumer loyalty and drive purchasing behavior. Popular anchors provide social proof, enhancing trust and credibility. According to Sun et al. (2021), popularity amplifies product exposure and influences consumer perceptions, directly impacting purchase behavior. Attractiveness refers to the personal charisma and image of the anchor; a charismatic anchor can draw more viewers, enhance their viewing experience, and boost their willingness to purchase. Mengru (2023) emphasizes that emotional connections driven by anchor charisma play a pivotal role in enhancing consumer behavior. These anchor characteristics stimulate consumers’ emotions, thereby increasing their purchase intentions. Thus, the following hypotheses are proposed:

H1: Anchor characteristics positively influence consumers’ purchase intentions.

H1a: Professionalism significantly positively influences consumers’ purchase intentions.

H1b: High interactivity significantly positively influences consumers’ purchase intentions.

H1c: Popularity significantly positively influences consumers’ purchase intentions.

H1d: Attractiveness significantly positively influences consumers’ purchase intentions.

2.2 Influence of anchor characteristics on perceived trust

Trust is the phenomenon of relying on others, and trust theory highlights interaction as a fundamental condition for exchange. Anchor professionalism, popularity, high interactivity, and attractiveness impact consumers’ perceived trust, which subsequently affects their purchase intentions. An anchor’s professionalism is a key factor influencing consumers’ perceived trust. When consumers believe that the anchor possesses sufficient expertise and skills regarding the product, they are more likely to trust the anchor. Lu et al. (2016) emphasize that anchors with strong professional abilities foster trust by reducing uncertainty and enhancing perceived credibility. Interaction between the anchor and the consumer is also critical in influencing perceived trust; positive interactions and responses enhance consumers’ sense of participation and belonging, thereby increasing their trust in the anchor. Jianfeng et al. (2022) demonstrate that interactive anchors create an approachable and sincere atmosphere, enhancing trust and consumer satisfaction. The popularity of an anchor can increase consumers’ perceived trust, as a well-known anchor attracts more attention and builds consumer trust. Li and Jian (2024) argue that widespread recognition and a strong follower base provide implicit endorsements, making consumers more likely to trust popular anchors. Duanxiang and Xiang (2023) further assert that popularity positively impacts trust by reinforcing the anchor’s authenticity and reliability. An attractive anchor easily captures consumers’ attention and positive feelings, boosting their trust in the anchor. This sense of trust influences consumers’ purchase intentions. Park and Lin (2020) highlight that attractiveness enhances perceived authenticity, fostering emotional connections that build trust. Kexin and Yixin (2023) confirm that anchors with strong personal appeal are more likely to gain consumer trust and influence their purchase decisions effectively. Thus, we propose the following hypotheses:

H2: Anchor characteristics positively influence perceived trust.

H2a: Professionalism significantly positively influences perceived trust.

H2b: High interactivity significantly positively influences perceived trust.

H2c: Popularity significantly positively influences perceived trust.

H2d: Attractiveness significantly positively influences perceived trust.

H3: Perceived trust positively influences purchase intentions.

2.3 Mediating role of perceived trust

Trust is widely recognized as a vital factor influencing consumer behavior in e-commerce, especially within the context of live-streaming. Lu et al. (2016) highlight that professional anchors enhance trust by reducing uncertainties and providing credible information. Interactivity creates a participatory environment which enhances the consumer’s sense of involvement. Sun et al. (2021) argue that interactivity enhances social presence, a key determinant of trust. Jianfeng et al. (2022) confirm that interactive communication between anchors and consumers positively influences trust, as it fosters transparency and sincerity. Attractiveness strengthens emotional connections with viewers. Mengru (2023) and Liu et al. (2022) argue that attractiveness evokes positive emotional responses, creating a perception of authenticity and relatability. Consumers are more likely to trust and act on recommendations from well-known anchors due to their established credibility and perceived influence. Wongkitrungrueng and Assarut (2020) demonstrate that popularity reinforces trust through perceived social validation. Trust reduces perceived risks and enhances consumer confidence in online shopping decisions. Studies by Zhang and Liu (2023) emphasize that trust acts as a bridge between anchor characteristics and purchase intentions, ensuring that consumers feel secure in their decisions. Accordingly, the following hypotheses are proposed:

H4: Perceived trust mediates the relationship between anchor characteristics and purchase intentions.

H4a: Perceived trust mediates the relationship between professionalism and purchase intentions.

H4b: Perceived trust mediates the relationship between high interactivity and purchase intentions.

H4c: Perceived trust mediates the relationship between attractiveness and purchase intentions.

H4d: Perceived trust mediates the relationship between popularity and purchase intentions.

2.4 Moderating role of anticipated regret

Anticipated regret is a critical psychological factor influencing consumer behavior, particularly in live-stream e-commerce. Before making purchase decisions, consumers may anticipate their feelings of regret regarding their shopping choices. This concept divides into upward and downward anticipated regret, which have opposing effects on decision-making. If consumers anticipate upward regret (regret over making a purchase), they may reduce their current purchase intention to minimize future regret. Conversely, if they anticipate downward regret (regret over not making a purchase), they may increase their current purchase intention to avoid future regret. Zhenping (2023) emphasizes that upward anticipated regret can weaken the effect of trust on purchase intentions by fostering a cautious mindset. Duanxiang and Xiang (2023) suggest that downward regret aligns with the fear of losing favorable opportunities, particularly in live-stream contexts where time-sensitive deals dominate. Thus, this study posits the following hypotheses:

H5: Anticipated regret moderates the relationship between perceived trust and purchase intentions, exerting both positive and negative effects depending on its type.

H5a: Upward anticipated regret negatively moderates the relationship between perceived trust and purchase intentions.

H5b: Downward anticipated regret positively moderates the relationship between perceived trust and purchase intentions.

3 Research design

3.1 Questionnaire design

This study collected data using a questionnaire survey method. The research model includes the main variables: professionalism, high interactivity, attractiveness, popularity, perceived trust, anticipated regret, and purchase intention. The initial filter question, “Have you watched live streams on the Dong Yuhui’s “With Hui” Live-stream account?” was used to ensure valid responses, followed by related questions. Demographic variables such as gender, age, and education level were also measured. All items were rated on a 5-point Likert scale, where 1 indicates “strongly disagree” and 5 indicates “strongly agree” (Appendix).

3.2 Measurement of variables

This study analyzed the influence of anchor characteristics, professionalism; high interactivity; popularity and attractiveness, on consumers’ purchase intentions, with three items per variable, as detailed in Table 1.

Table 1

Table 1. Scale for anchor characteristics.

3.3 Questionnaire data collection

The formal survey was primarily conducted online, distributed through social media platforms like WeChat, QQ, Xiaohongshu, and Weibo. A total of 333 questionnaires were collected, with 314 valid responses, resulting in an effective rate of 94%.

4 Results

4.1 Descriptive statistical analysis of the sample

Table 2 summarizes the basic characteristics of the sample. Among the 314 respondents:

• Live Stream Preference: Dong Yuhui’s live streams were the most watched (55.4%), followed by Hansen (17.2%), Panpan (12.7%), and others (14.6%).

• Gender and Age: Female respondents comprised 57.32% of the sample. The largest age group was 18 years and under (39.81%), followed by 19–40 years (31.21%).

• Education Level: Over half of the respondents (50.96%) held a bachelor’s degree, with an additional 18.47% holding a master’s or higher qualification.

• Spending Patterns: Most respondents (36.94%) spent less than 200 RMB monthly on “With Hui” live streams, while 20.70% spent over 1,001 RMB.

• Viewing Time: The majority watched 30 min to 1 h per week (38.5%), followed by under 30 min (30.9%).

Table 2

Table 2. Basic statistical characteristics of the questionnaire sample.

Overall, the sample was predominantly young, female, and well-educated, showing moderate levels of financial and time investment in “With Hui” live streams.

4.2 Reliability and validity analysis

4.2.1 Reliability analysis

The reliability analysis showed that all variables had Cronbach’s Alpha values above 0.8, indicating strong internal consistency. Key results are summarized in Table 3.

Table 3

Table 3. Reliability analysis of each variable.

4.2.2 Validity analysis

The validity analysis revealed a KMO value of 0.883 and a significant Bartlett’s test of sphericity (p < 0.05), confirming the scale’s high validity. Results are summarized in Table 4.

Table 4

Table 4. Suitability test.

4.2.3 Descriptive analysis

Table 5 summarizes the descriptive statistics of the variables. The average values for professionalism, high interactivity, attractiveness, popularity, perceived trust, and purchase intention range from 3.16 to 3.27, indicating moderate to high respondent perceptions. Attractiveness had the highest mean (3.268), suggesting it is a standout characteristic among live-stream hosts, while perceived trust had the lowest mean (3.161), indicating some reservations among respondents. In summary, the analysis confirms strong reliability and validity of the scales used, with respondents demonstrating generally positive perceptions of anchor characteristics and purchase-related factors (Tables 6–8).

Table 5

Table 5. Descriptive statistics analysis.

Table 6

Table 6. Correlation analysis.

Table 7

Table 7. Linear regression analysis results.

Table 8

Table 8. Linear regression analysis results for purchase intention.

4.3 Correlation and regression analysis

4.3.1 Correlation analysis

The correlation analysis revealed significant relationships between the variables, as summarized in Table 6. Key findings include:

• Professionalism (ρ = 0.428, p < 0.01), high interactivity (ρ = 0.401, p < 0.01), attractiveness (ρ = 0.463, p < 0.01), and popularity (ρ = 0.517, p < 0.01) positively correlated with purchase intention.

• Perceived trust (ρ = 0.543, p < 0.01) showed the strongest positive correlation with purchase intention.

• Upward anticipated regret had a negative correlation with purchase intention (ρ = −0.327, p < 0.01), while downward anticipated regret positively correlated (ρ = 0.374, p < 0.01).

These results suggest that anchor characteristics and perceived trust enhance purchase intention, whereas upward regret discourages it.

4.3.2 Regression analysis

• Model Equation: Perceived Trust = 0.237 + 0.088 (Professionalism) + 0.110 (High Interactivity) + 0.125 (Attractiveness) + 0.587 (Popularity).

The regression analysis examined the effects of professionalism, high interactivity, attractiveness, and popularity on perceived trust. The model explained 66.5% of the variance in perceived trust (adjusted R² = 0.665, F = 156.535, p < 0.001). Key findings are summarized below:

• Professionalism (β = 0.088, t = 2.388, p = 0.018): Positive and significant effect.

• High Interactivity (β = 0.110, t = 3.114, p = 0.002): Positive and significant effect.

• Attractiveness (β = 0.125, t = 3.541, p < 0.001): Positive and significant effect.

• Popularity (β = 0.587, t = 15.735, p < 0.001): Strongest positive effect.

These results confirm that all four anchor characteristics significantly and positively influence perceived trust, supporting hypotheses H2, H2a, H2b, H2c, and H2d.

• Model Equation: Purchase Intention = 0.384 + 0.127 (Professionalism) + 0.139 (High Interactivity) + 0.228 (Attractiveness) + 0.173 (Popularity) + 0.227 (Perceived Trust).

The regression model examined the effects of professionalism, high interactivity, attractiveness, popularity, and perceived trust on purchase intention. The model explained 37.7% of the variance in purchase intention (adjusted R² = 0.377, F = 38.925, p < 0.001). Key results are summarized below:

• Professionalism (β = 0.127, t = 2.154, p = 0.032): Positive and significant effect.

• High Interactivity (β = 0.139, t = 2.427, p = 0.016): Positive and significant effect.

• Attractiveness (β = 0.228, t = 3.991, p < 0.001): Strongest positive effect.

• Popularity (β = 0.173, t = 2.175, p = 0.030): Positive and significant effect.

• Perceived Trust (β = 0.227, t = 2.508, p = 0.013): Positive and significant effect.

These results confirm that all five variables significantly enhance purchase intention, supporting hypotheses H1, H1a-H1d, and H3.

4.4 Test of mediating effects of variables

The mediation analysis tested how perceived trust mediates the relationship between anchor characteristics and purchase intention (Table 9). Key findings are:

• Professionalism = > Perceived Trust = > Purchase Intention: the total effect is significant. The effect of professionalism on perceived trust and the effect of perceived trust on purchase intention are both significant. The mediation effect and the direct effect are also significant, indicating partial mediation.

• High Interactivity = > Perceived Trust = > Purchase Intention: the total effect, path a, and path b coefficients are all significant. The mediation effect with a direct effect shows partial mediation.

• Attractiveness = > Perceived Trust = > Purchase Intention: a mediation effect and a direct effect are observed, indicating that attractiveness significantly partially mediates purchase intention through perceived trust.

• Popularity = > Perceived Trust = > Purchase Intention: the total effect, path a, and path b coefficients are significant, with the largest mediation effect and direct effect, show that popularity also partially mediates purchase intention through perceived trust.

Table 9

Table 9. Summary of mediation effect test results.

All models show that perceived trust partially mediates the effect of anchor characteristics on purchase intention, confirming hypotheses H4, H4a, H4b, H4c, and H4d. Popularity has the strongest mediation effect (β = 0.303\beta = 0.303β = 0.303) (Table 10).

Table 10

Table 10. Moderating effect analysis results.

4.5 Test of moderating effects of variables

The moderating effect of anticipated regret (upward and downward) on the relationship between perceived trust and purchase intention was tested using SPSS 27.0. Key findings are summarized below:

• Model 1: Perceived trust significantly impacts purchase intention (β = 0.632, t = 11.409, p < 0.001).

• Model 2: Adding upward anticipated regret as a predictor reduces the effect of perceived trust (β = 0.569, t = 9.653, p < 0.001).

• Model 3: The interaction term (Perceived Trust * Upward Anticipated Regret) is significant (β = −0.107, t = −1.989, p = 0.048), confirming a negative moderating effect.

The adjusted R² increases slightly from 0.292 (Model 1) to 0.315 (Model 3), indicating improved explanatory power. Upward anticipated regret negatively moderates the relationship between perceived trust and purchase intention, reducing the strength of the positive impact of perceived trust as regret levels increase (Table 11).

Table 11

Table 11. Simple slope analysis.

The analysis indicates that as perceived trust increases, purchase intention also increases, regardless of the level of upward anticipated regret. However, the slope is steeper under low upward anticipated regret, demonstrating that perceived trust has a stronger positive impact on purchase intention when regret levels are low. These findings confirm significant negative moderation by upward anticipated regret (Table 12).

Table 12

Table 12. Moderating effect analysis results.

Model 1 confirms a significant effect of perceived trust (β = 0.632, p < 0.001) on purchase intention. Model 2 shows that adding downward anticipated regret increases explanatory power (R² = 0.325, p < 0.001). Model 3 reveals a significant interaction effect (β = 0.117, t = 2.380, p = 0.018), confirming that downward anticipated regret positively moderates the relationship between perceived trust and purchase intention. Downward anticipated regret enhances the impact of perceived trust on purchase intention, indicating stronger effects of trust when regret levels are higher (Table 13).

Table 13

Table 13. Simple slope analysis.

The analysis shows that perceived trust positively impacts purchase intention across all levels of downward anticipated regret. The slope is steeper for high levels of regret, indicating a stronger positive effect of perceived trust on purchase intention when regret is high. These findings confirm significant positive moderation by downward anticipated regret, supporting hypotheses H5, H5a, and H5b (Table 14).

Table 14

Table 14. ANOVA results.

4.6 Differences among different streamers

A one-way ANOVA was conducted to examine differences across eight variables based on the streamers watched on the Douyin platform. Key findings are summarized below:

• No Significant Differences (p > 0.05): Professionalism, Popularity, Perceived Trust, Upward Anticipated Regret, and Downward Anticipated Regret were consistent across streamer groups.

• Significant Differences (p < 0.05):

• High Interaction (F = 11.844, p = 0.000): Hansen, Panpan, and Others scored higher than Dong Yuhui.

• Attractiveness (F = 3.011, p = 0.030): Dong Yuhui and Hansen scored higher than others.

• Purchase Intention (F = 11.184, p = 0.000): Dong Yuhui scored higher than Hansen, Panpan, and others.

While five aspects showed no variability among streamer groups, High Interaction, Attractiveness, and Purchase Intention varied significantly, highlighting differences in audience perception based on the specific streamer.

5 Discussion

5.1 Key findings and practical implications

This research examines the influence of anchor characteristics, namely, professionalism, high interactivity, attractiveness, and popularity, on consumer purchase intentions, mediated by perceived trust and moderated by anticipated regret. The findings are consistent with prior studies and provide valuable insights into the dynamics of live-streaming e-commerce.

The results confirm that all examined anchor characteristics significantly influence purchase intentions, supporting hypotheses H1 and H1a-H1d. Professionalism, as highlighted in Liu et al. (2022), builds trust through product knowledge and expertise, reducing consumer uncertainty. Similarly, high interactivity fosters real-time engagement, enhancing the shopping experience (Sun et al., 2021). Attractiveness and popularity also positively impact consumer decisions by creating emotional connections and leveraging social proof, aligning with findings from Mengru (2023) and Wang (2023).

Perceived trust partially mediates the relationship between anchor characteristics and purchase intentions (H4, H4a–H4d). This underscores the critical role of trust in live-streaming commerce, echoing Hajli et al. (2017) and Lu et al. (2016). Anchors who demonstrate professionalism, interactivity, and charisma are more likely to establish trust, which, in turn, drives purchase decisions.

The moderating role of anticipated regret (H5, H5a, H5b) reveals that upward regret weakens, while downward regret strengthens, the effect of trust on purchase intentions. This finding is consistent with Zeelenberg and Pieters (2007), emphasizing the emotional considerations consumers weigh during purchase decisions.

ANOVA results show significant differences in high interactivity, attractiveness, and purchase intention among different streamers, with Dong Yuhui’s streams having the highest purchase intentions. This suggests that anchor-specific traits influence consumer behavior, supporting the need for personalized strategies.

Anchors should be trained to improve professionalism and interactivity. For instance, providing product expertise and fostering real-time communication can significantly boost consumer trust and engagement. Platforms should leverage the unique strengths of individual anchors. For example, emphasizing attractiveness or popularity can complement strategies aimed at building trust. Marketing campaigns should address anticipated regret by highlighting product scarcity or time-sensitive discounts to nudge consumers toward purchase decisions.

5.2 Limitations and future research directions

The sample predominantly included young, female, and well-educated respondents, potentially limiting the generalizability of the findings to other demographic groups. This study focused exclusively on Douyin’s “With Hui” Live-stream account, which may not reflect the dynamics of other live-streaming platforms. The reliance on cross-sectional data limits the ability to infer causality. Future studies should adopt longitudinal designs for more robust conclusions.

Expanding the study to include diverse age groups, income levels, and geographic regions can enhance generalizability. Investigating the influence of anchor characteristics across different live-streaming platforms could uncover platform-specific consumer behaviors. Tracking consumer behavior over time would provide insights into the long-term effects of trust and anticipated regret on purchase decisions. Exploring the role of cultural differences in shaping the relationships between anchor characteristics, trust, and purchase intentions would enrich the theoretical framework and practical applications.

By addressing these limitations and exploring new avenues, future research can build on this study to deepen the understanding of live-streaming e-commerce and its impact on consumer behavior and further explore consumer behavior patterns across different cultural contexts and platform environments to enhance the generalizability and applicability of the findings.

6 Conclusion

This study, grounded in the S-O-R theory, systematically explored the mechanism through which anchor characteristics influence consumers’ purchase intentions, while also validating the mediating role of perceived trust and the moderating effect of anticipated regret. Based on empirical analysis of Dong Yuhui’s “With Hui” Live-stream platform data, the key conclusions are as follows:

• The Critical Impact of Anchor Characteristics on Purchase Intentions: Professionalism, interactivity, attractiveness, and popularity significantly enhance consumers’ purchase intentions, with professionalism and attractiveness playing particularly important roles in shaping perceived trust.

• The Mediating Role of Perceived Trust: Perceived trust acts as a crucial bridge between anchor characteristics and purchase intentions, strengthening consumers’ acceptance of products recommended by anchors.

• The Moderating Effect of Anticipated Regret: Downward anticipated regret reinforces the positive relationship between perceived trust and purchase intentions, whereas upward anticipated regret weakens this relationship, revealing the complex psychological processes involved in consumer decision-making.

• Differentiated Impacts of Various Anchor Types: Anchors with high interactivity and high attractiveness have the most significant influence on consumers’ purchase intentions, providing data support for platforms and enterprises to optimize anchor operational strategies.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

RL: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. YC: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. XM: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Appendix

Questionnaire on the influence of anchor characteristics on consumer purchase intentions

Hello! I am currently conducting a survey on how anchor characteristics influence consumer purchase intentions. To better understand why consumers choose to purchase products based on specific anchor characteristics, I would like to conduct a survey with consumers. Please answer the following questions based on your personal experience. This survey is anonymous, only takes about 5 minutes to complete, and your participation is greatly appreciated!

1. Have you watched any livestreams by the Dong Yuhui’s "With Hui" Live-stream Account? (If “No” please directly answer “Question 8”.)[Single Choice] *

□ Yes

□ No

2. Which anchor’s livestream have you watched on the Dong Yuhui’s "With Hui" Live-stream Account? [Single Choice] *

□ Dong Yuhui

□ Hansen

□ Panpan

□ Other

3. Your gender: [Single Choice] *

□ Male

□ Female

□ Other

4. Your age: [Single Choice] *

□ 18 years old or younger

□ 19–40 years old

□ 41–60 years old

□ Over 61 years old

5. Educational level: [Single Choice] *

□ High school or below

□ Vocational college

□ Undergraduate

□ Master's degree or above

6. Monthly spending on the Dong Yuhui’s "With Hui" Live-stream Account on Douyin: [Single Choice] *

□ Below 200 yuan

□ 200–500 yuan

□ 501–1000 yuan

□ Above 1000 yuan

7. Time spent watching Dong Yuhui’s "With Hui" Live-stream Account on Douyin per week: [Single Choice] *

□ Less than 30 minutes

□ 30 minutes to 1 hour

□ 1–2 hours

□ More than 2 hours

8. If you haven’t watched, what is the reason? [Open-Ended] *

9. Professionalism [Matrix Scale] *

10. High Interactivity [Matrix Scale] *

11. Attractiveness [Matrix Scale] *

12. Fame / Popularity [Matrix Scale] *

13. Perceived Trust [Matrix Scale] *

14. Purchase Intention [Matrix Scale] *

15. Upward Anticipated Regret [Matrix Scale] *

16. Downward Anticipated Regret [Matrix Scale] *