Yi Huang
Siti Hajar Mohamad
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- 1Graduate School of Management, Management and Science University, Shah Alam, Malaysia
- 2Faculty of Business Management and Professional Studies, Management and Science University, Shah Alam, Malaysia
In recent years, along with the rapid development of Internet technology and drastic changes in consumption patterns, live streaming commerce has gradually become an emerging business model that attracts the participation of consumers. Based on parasocial interaction and social presence created by live streaming commerce, this study uses SmartPLS software to exam the impact of parasocial interaction and social presence on impulsive purchase in live streaming commerce context. According to an empirical analysis of 407 valid questionnaire data from China through an online survey, consumers’ social anxiety and fear of missing out have a significant positive effect on parasocial interaction, while their narrative involvement has a non-significant effect on parasocial interaction. Social presence has a significantly positive effect on impulsive purchase. Consumers’ narrative involvement and smartphone addiction had a significant positive effect on their social presence. This study extends existing research findings regarding consumer impulsive purchase on live streaming commerce. In addition, this study provides evidence that consumers’ parasocial interaction and social presence mediate the relationship between consumer characteristics and impulsive purchase.
1 Introduction
The advent of live streaming technology has resulted in a significant shift in the e-commerce landscape (Hua et al., 2023). Live streaming commerce (LSC) platforms have emerged as dynamic spaces in which influencers engage with audiences in real time, showcasing products and services interactively and engagingly. This digital evolution has given rise to a unique consumer phenomenon, marked by impulsive purchase, creating a rich area for scholarly inquiry. The worldwide LSC market is expected to reach $129.6 billion by 2024, primarily driven by Asia. In China, these events attract millions of viewers and generate substantial revenue (Ghostretail, Ontario, Canada, 2024). McKinsey’s report indicated that Chinese consumers are more likely to engage in impulsive purchase (Zhou et al., n.d.). Many scholars have examined the factors influencing consumers’ impulsive purchase in LSC. Past research has endeavored to clarify the formation of impulsive purchase in LSC, focusing on summarizing factors from diverse perspectives, such as platforms (Zhu et al., 2023), live streamers (Zhou and Huang, 2023; Chen et al., 2023), consumers (Luo et al., 2024), and sellers (Shang et al., 2023; Lu and Duan, 2024).
Academic research continues to focus on thoroughly investigating how consumer characteristics influence the purchasing process in LSC. Recent studies have emphasized social barriers, prompting greater reliance on smartphones for social interaction and consumer engagement during pandemic (Kindred and Bates, 2023). Previous research has recognized social media as an important tool for problem-solving in digital context (Koehler and Vilarinho-Pereira, 2023; Wang et al., 2023). Nevertheless, a thorough understanding of the nuanced effects associated with consumers’ social anxiety and fear of missing out (FOMO) in LSC domain remains to be fully clarified. While the stimulus-organization-response (S-O-R) framework has demonstrated that narrative involvement is a key antecedent of impulsive purchase behavior in digital environment (Vazquez et al., 2020), its behavioral pathways in LSC ecosystems remain theoretically under-explored, which is a key research gap to be addressed in this study. Positioned as a critical influencing factor in contemporary digital ecosystems, smartphone addiction has been extensively studied for its substantial impact on shaping social media usage behaviors, particularly consumption patterns, user engagement dynamics among digital consumers (Chopdar et al., 2022).
The primary advantage of live streaming lies in its capacity to facilitate real-time, bidirectional interactivity between live streamers and viewers, fostering immediate engagement and dynamic communication (Zhang et al., 2022). Live streaming platforms enable real-time, dynamic interactions between live streamers and their audiences, facilitating immediate question-response exchanges and allowing for personalized content delivery, including tailored recommendations and customized solutions based on individual viewer needs (Yang et al., 2022). Parasocial interaction (PI) and social presence have become core elements that affect user participation, emotional connections, and immersive experience (Liao et al., 2023). PI connects consumers and live streamers to form relationships, going beyond traditional one-sided communication to give audiences a sense of connection, intimacy, and companionship (Zhong et al., 2021). Social presence defines the extent to which users perceive the presence of others in a shared virtual space, thus fostering a sense of co-presence and shared interaction (Woo et al., 2024). PI and social presence play key roles in influencing consumer behavior (Kim, 2022). However, most previous studies in the field of LSC have explored parasocial relationships from the perspective of live streamers, failing to discuss PI and social presence from the consumer perspective (Shraf et al., 2023). To address these gaps, this study addresses the following questions:
(1) Is there a significant positive relationship between social anxiety, FOMO, narrative involvement, smartphone addiction, PI, and social presence?
(2) Is there a significant positive relationship between PI, social presence, and impulsive purchase?
(3) Do PI and social presence mediate the relationship between social anxiety, FOMO, narrative involvement, smartphone addiction, and impulsive purchase?
Therefore, this study exams how consumers’ characteristics relate to PI, based on parasocial interaction theory (Horton and Richard Wohl, 1956). Additionally, flow theory was employed to clarify the relationship between narrative involvement and smartphone addiction in consumer behavior (Nakamura and Csikszentmihalyi, 2002). This study contributes significantly to LSC in several ways. First, it enriches the LSC marketing theory by examining consumer characteristics, particularly focusing on innovative variables such as social anxiety and smartphone addiction. Second, it enhances the parasocial interaction theory, social presence, and flow theory. Finally, it empirically identifies the new antecedents of PI and social presence in LSC.
2 Literature review
In the era of digital economy, consumers’ shopping patterns have changed significantly. In particular, the rise of LSC has dramatically changed the traditional shopping scenarios. This study focuses on consumers’ impulsive purchase in LSC context, aiming to deeply examine the relevant influencing factors and the theoretical mechanisms. A systematic review of existing literature not only lays the foundation for understanding this complex consumer phenomenon but also clarifies the research gaps and provides directions for subsequent studies.
2.1 Impulsive purchase
Impulsive purchase refers to the purchasing behavior in which consumers suddenly develop a strong desire to buy and quickly put it into action without prior planning (Luo et al., 2024). Compared with planned purchasing behavior, impulsive purchase has significant characteristics of immediacy, unplanned, emotionally driven (Luo et al., 2024). In previous research, impulsive purchase predominantly takes place within physical retail settings, such as supermarkets and shopping malls. Here, consumers are spurred to make impulsive purchase by factors like on-site product displays and promotional campaigns (Kacen et al., 2012). Planned purchasing behavior is usually based on consumers’ rational needs and decision-making process, and consumers will collect, compare, and evaluate sufficient information before purchasing (Kacen et al., 2012). However, impulsive purchase is more substantially influenced by factors like emotions and situational environments. Its decision-making process is comparatively rapid and lacks premeditation. In LSC context, consumers are bombarded with an overwhelming volume of information and a wide array of stimuli (Kim et al., 2008). This abundance renders it more likely for them to exhibit impulsive purchase. This behavioral paradigm exerts a significant influence not only on consumers’ individual consumption decisions and financial circumstances but also on the formulation of marketing strategies by e-commerce platforms and sellers. Concentrating on impulsive purchase enables a more profound comprehension of consumers’ irrational behavioral models within emerging shopping scenarios. Moreover, it furnishes more targeted recommendations and strategies for the advancement of the e-commerce industry.
2.2 Factors influencing impulsive purchase in LSC
Live streaming commerce, an emerging commerce paradigm, is characterized by its distinct interactivity and real-time nature (Luo et al., 2024). In LSC context, the factors related to live streamers exert a substantial influence on impulsive purchase. A live streamer’s professional ability, combined with proficient sales techniques, significantly impacts consumers’ trust and purchase intentions (Huang et al., 2024). For instance, a live streamer equipped with comprehensive product knowledge and strong communication capabilities is better positioned to present products to consumers, thereby enhancing consumers’ confidence in the products and potentially triggering impulsive purchase (Wang et al., 2022). Interactivity during live streaming, such as lucky draws and question—and—answer sessions, can augment consumer engagement and immersion. This, in turn, encourages consumers to make impulsive purchase decisions (Zhang et al., 2022). The inherent characteristics of products, such as their uniqueness and scarcity, are further accentuated in LSC context, thereby stimulating consumers’ impulsive purchase desires (Miranda et al., 2024). Additionally, the atmosphere created in live stream, such as limited-time and a sense of urgency in the purchasing process, also motivates consumers to make quick purchasing decision (Qu et al., 2023).
2.3 Parasocial interaction theory
Parasocial interaction theory conceptualizes the one-sided psychological connections and perceived relationships that viewers develop with media personalities through mediated communication channels, characterized by asymmetrical intimacy and audience-driven engagement (Horton and Richard Wohl, 1956). Audiences begin to see media influencers as friends even though they have no or limited interaction with them (Giles, 2002). PI involves viewers interacting with people such as talk show hosts, celebrities, and influencers, focusing on how the audience engages with these characters (Makmor et al., 2024).
PI research on new media has not fully kept pace with technological advancements, but there is a growing focus in this area (Jarzyna, 2021). For example, Rubin et al. (1985) proved that loneliness is associated with less interpersonal communication, and loneliness and PI are associated with greater television dependence. Rubin and Step (2000) assessed the effects of motivation, interpersonal attraction, and PI on listening to the public affairs talk radio. Kassing and Sanderson (2009) conducted a thematic analysis of fan posts on the cyclist Floyd Landis website, using a constant comparison method. The results show that Internet communication technology has transformed the nature of PI from one-sided and passive to approximate actual social interactions (Makmor et al., 2024). They also confirmed that viewers could easily engage in PI with influencers. Social media influencers, such as YouTubers and Instagram, play a key role in marketing by introducing products to their audiences. Content created by online influencers in PI and their perceived trustworthiness are associated with purchase intentions (Sokolova and Kefi, 2020). Regardless of the level of product involvement in the social media context, PI has a significant positive impact on followers’ attitudes and behavior (Gong, 2021). Some studies indicate that the interactive orientation of live streamers positively influences audience immersion and PI, thereby enhancing audience purchase intention (Liao et al., 2023; Fu and Hsu, 2023). Considering the dramatic increase in social media usage, this trend is expected to continue. Despite the well-documented empirical evidence and theoretical propositions put forth by researchers in the field, PI remains a crucial social need for social media (Deng et al., 2023). Current research suggests that the strength of parasocial interaction between live streamers and consumers affects impulsive purchase (Li et al., 2023). For example, live streamers establish an emotional connection with consumers by sharing their personal experiences of using the product, which strengthens parasocial interaction and then triggers impulsive purchase (Luo et al., 2025). The previous studies reveal that consumers’ parasocial interaction with celebrities on social media affect their attitudes toward products and purchase decisions, which also applies to LSC (Safrianto et al., 2024). Studies on PI in LSC and its influencing factors remain limited. Given these gaps, this study focuses on consumers’ social needs in LSC. Individuals with social anxiety may seek PI in a live stream to fulfill their social needs (Apaolaza et al., 2019).
2.4 Social presence
Social presence refers to the perceived reality of an individual and its connection with others during media communication (Castellanos-Reyes et al., 2024). Presence refers to the psychological perception of others when people interact in a technology-supported environment (Rodríguez-Ardura and Meseguer-Artola, 2016). In the context of smartphone addiction and digital media use, social presence measures one’s perception of the presence of others and the degree of intimacy or immediacy that simulates social experiences during online interactions (Kreijns et al., 2022).
Current studies indicate that social presence has been used in online shopping sites (Weisberg et al., 2011), live tourism (Zhang et al., 2024), meta-universe tourism (Ghali et al., 2024), online education (Kim et al., 2011; Sobaih et al., 2020) and social media (Lum and Chang, 2023; Yang et al., 2024). Researchers have begun incorporating social presence into LSC research. For instance, Gao et al. (2023) tested that virtual live streamers enhance social presence and telepresence, consequently facilitating purchase intention. When consumers feel a strong sense of social presence in social e-commerce, they feel closer to live streamers and other consumers, and are more likely to be influenced by live streamers’ recommendations and other consumers’ purchasing behaviors and then make impulsive purchase (Ju and Ahn, 2016).
2.5 Flow theory
Flow theory describes an invigorated state of mind in which individuals are fully immersed and completely engaged in the activity itself, losing self-consciousness (Wu and Liang, 2011). The central feature of the flow theory is immersion (Liao et al., 2023). Flow significantly affects continuous viewing and purchase intention (Zheng et al., 2023). Flow theory has become particularly important in the fields of internet consumption and narrative involvement. Narrative involvement primarily originates from consumers’ self-expression and socialization needs, while parasocial interaction stems from consumers’ emotional attachment and identification with media influences (Ahmed et al., 2024; Farivar et al., 2022). Narrative involvement is a two-sided interaction between dual consumers and others (Kang et al., 2020), whereas parasocial interaction is a unilateral emotional investment of consumers in media influencers, and it is often considered as a one-sided interaction (Lou, 2022). This suggests that when individuals are immersed in engaging in digital content, such as narrative involvement or interactions on social media (Chang, 2013), the experience may induce a state of flow (Pelet et al., 2017).
Advancements in communication and internet technology offer new perspectives on e-commerce research. Interactivity enhances consumers’ flow experiences during social searches on Instagram (Cuevas et al., 2021). Furthermore, research has begun to explore the role of flow experience in consumer characteristics and purchasing behavior. Researchers have found that flow experience positively affects purchase intention, satisfaction, and impulsive purchase (Hyun et al., 2022; Guan et al., 2022; Bao and Yang, 2022). Zheng (2023) drew on flow theory and conducted a questionnaire survey on Douyin (similar to Tiktok) users, confirming that flow experience positively moderates the relationship between hedonic value and emotional pleasure as well as between emotional pleasure and purchase intention. It has been found that flow significantly influences consumers’ persistent viewing intention and purchase intention (Liu et al., 2022). Peer opinions on social networking sites have a considerable impact on consumers’ impulsive purchase desires (Huang, 2016). Consumers’ perception of time changes in the state of flow and they are more likely to make purchase decisions driven by impulsive emotions (Sun et al., 2023).
3 Conceptual framework and hypotheses
3.1 Effect of social anxiety
Social anxiety is a common psychological condition marked by heightened fear of being evaluated or judged by others in social situations (Morrison and Heimberg, 2013). Previous research demonstrated a positive relationship between social anxiety and web usage and online shopping behaviors (Pierce, 2009; López-Bonilla et al., 2021). Furthermore, research on PI suggests that individuals with higher social anxiety may be more inclined to develop parasocial relationships with media characters (Keefer et al., 2024; de Bérail et al., 2019), which may lead to higher sensitivity to impulsive purchase in LSC platforms. Based on these findings, we established H1 the follows:
H1. Consumers’ social anxiety positively affects PI in LSC.
3.2 Effect of FOMO
FOMO was first introduced into the media in the early 2010s (Cheng et al., 2023). It consists of both the worry that others are enjoying and the persistent desire to stay connected with others on social networks (Elhai et al., 2021). With the increasing popularity of smartphones, informational and normative social influence affect FOMO, which in turn affects compulsive buying (Mason et al., 2022). FOMO drives individuals to seek continued contact and participation in live streaming context, promotes the establishment of parasocial relationships, and enhances their social presence (Moore and Craciun, 2021). Thus, we propose:
H2. Consumers’ FOMO positively affects PI in LSC.
3.3 Effect of narrative involvement
Narrative involvement refers to the audience’s engagement with the storyline (Hu et al., 2024). In this study, narrative involvement denotes the way LSC viewers interact with live streamers. Social media’s interactive capabilities allow audiences and influencers to communicate directly with their followers (Arora et al., 2019). Hence, we propose:
H3. Consumers’ narrative involvement positively affects PI in LSC.
Interactivity directly affects the construction of social presence, which strongly influences narrative involvement (Fortin and Dholakia, 2005). Considering that narrative involvement enhances one’s emotional and cognitive involvement with a story or content (Green, 2004), we hypothesize that consumers who are highly engaged in the narrative aspects of LSC interactions will also experience a greater sense of social presence. Thus, we propose:
H4. Consumers’ narrative involvement positively affects social presence in LSC.
3.4 Effect of smartphone addiction
The rich variety of functions and applications found on smartphones has given rise to a subset of individuals showing signs of addiction to their mobile devices (Jia et al., 2023). Past research has identified a relationship between smartphone addiction and increased online social interactions (Ihm, 2018). These findings suggest that people addicted to their smartphones may seek out and value social interactions facilitated by their devices. Being addicted to mobile phones leads to a higher frequency of using web pages or software (Barnes et al., 2019). Thus, we propose:
H5. Consumers’ smartphone addiction positively affects social presence in LSC.
3.5 Effect of parasocial interaction
PI is one-sided and individuals experience a sense of connection, familiarity, and immersion (Lee, 2013). In e-commerce, consumers engage in PI with influencers, brands, or other users on social commerce platforms (Jin and Ryu, 2020). These interactions create feelings of trust (Chen et al., 2019), which may influence impulsive purchase. Previous research shows that PI has a positive impact on impulsive purchase (Jarzyna, 2021). E-commerce platforms bridge the gap between users, encouraging them to exchange information like real-life friends, as well as providing them with the opportunity to interact with celebrities, thereby establishing PI (Xiang et al., 2016). Thus, we propose the following hypothesis:
H6. Consumers’ PI positively affects impulsive purchase in LSC.
3.6 Effect of social presence
Social presence refers to the perceived degree of feeling with others (Biocca et al., 2003). LSC enhances social presence through real-time interactions and product demonstrations by live streamers, strengthening the psychological connection with consumers (Li et al., 2022). Prior research demonstrated that social presence promotes impulsive purchase. Ju and Ahn (2016) indicated that social presence in social commerce mimics the retail environment and fosters a sense of shopping, increasing the probability of impulsive purchase. Hence, we propose the following hypothesis:
H7. Consumers’ social presence positively affects impulsive purchase in LSC.
3.7 Mediation effects
Consumers become emotionally attached to and identify with media influencers in their interactions with them, making them more likely to accept the products or services they recommend. This emotional drive makes consumers make purchase decisions without rational thinking. When consumers watch the content of media influencers, they will unconsciously compare their lifestyles with their own, thus generating imitation behavior (Alnoor et al., 2024). In addition, media influencers’ recommendations are often accompanied by positive feedback from other consumers, which further strengthens impulsive purchase (Lajnef, 2023). Combining the above hypotheses, we conclude that there may be a mediating relationship between consumer characteristics and impulse purchase. This study examines whether PI may be a partial mediator because consumer characteristics may bypass PI to directly stimulate consumer impulsive purchase. Thus, we propose the following hypotheses:
H8a. Consumers’ PI mediates social anxiety and impulsive purchase in LSC.
H8b. Consumers’ PI mediates FOMO and impulsive purchase in LSC.
H8c. Consumers’ PI mediates narrative involvement and impulsive purchase in LSC.
Consumers are more likely to be emotionally empathetic and immersed in the shopping experience in environments with high social presence (Saad and Choura, 2024). This immersion weakens consumers’ rational judgment and makes them more susceptible to immediate stimuli. Social presence media, such as live streaming and video content, effectively convey non-verbal cues like facial expressions and gestures. This enables consumers to experience a more authentic and immersive interaction, thereby reducing the rational barriers to purchase decisions (Li et al., 2023). Similarly, social presence may mediate the relationship between consumer characteristics (i.e., social anxiety, FOMO, narrative involvement, and smartphone addiction) and impulsive purchase. In this study, we examined whether social presence may also be a partial mediator, as consumer characteristics may directly influence consumer impulsive purchase. Additionally, consumers with social anxiety, FOMO, narrative involvement, and smartphone addiction are more likely to impulsive purchase.
H9a. Consumers’ social presence mediates narrative involvement and impulsive purchase in LSC.
H9b. Consumers’ social presence mediates smartphone addiction and impulsive purchase in LSC.
The research model is presented in Figure 1.
Figure 1
Figure 1. Research model.
Theoretically, parasocial interaction can enhance consumers’ sense of social presence in mediated environments by fostering stronger emotional connections with media influencers. For instance, when consumers watch live streams, they often perceive a heightened social presence by viewing influencers as real-life social entities during their interactions. Despite the potential influence of parasocial interaction on social presence, the two constructs are typically treated as independent variables in existing models (Tsai et al., 2021). This separation arises for two main reasons: first, the measurement methods for parasocial interaction and social presence differ significantly, making it challenging to accurately capture their interplay within the same model. Second, current theoretical frameworks have not yet fully explored or integrated the relationship between parasocial interaction and social presence, leading to their interconnectedness being overlooked in many models (Kim and Song, 2016).
4 Method
4.1 Measurements
The questionnaire included seven variables: social anxiety (SA), fear of missing out (FOMO), narrative involvement (NI), smartphone addiction (SMA), parasocial interaction (PI), social presence (SP), and impulsive purchase (IP). The items were adapted from prior research (see Appendix). Responses were collected using a 5-point Likert scale, ranging from “1 = completely disagree” to “5 = completely agree.” As the survey was conducted in China, all original items were translated from English to Chinese, and two Chinese researchers reviewed the translations to enhance the reliability and validity of the questionnaire. A pilot was conducted with 50 users and the final questionnaire was obtained.
4.2 Data collecting and descriptive statistics
The recruitment process started on the 15 April 2024, until 15 May 2024. The study population consisted of individuals with experience using LSC platforms, and potential participants were aged 18 years or older. A total of 425 questionnaires were sent to respondents, of which 407 valid questionnaires were analyzed using SmartPLS version 4.1. The characteristics of the sample are presented in Table 1.
Table 1
Table 1. Sample characteristics (N = 407).
Impulsive purchase: R2 = 0.094 (adjusted r2 = 0.089). Parasocial interaction: R2 = 0.11 (adjusted R2 = 0.104). Social presence: R2 = 0.087 (adjusted R2 = 0.082). These results indicate that the model has some explanatory power for the endogenous variables, especially parasocial interaction (R2 = 0.11). FOMO → parasocial interaction: f2 = 0.02 (small effect), narrative involvement → parasocial interaction: f2 = 0.008 (small effect), narrative involvement → social presence: f2 = 0.013 (small effect), parasocial interaction → impulsive purchase: f2 = 0.03 (small effect), smartphone addiction → social presence: f2 = 0.069 (medium effect), social anxiety → parasocial interaction: f2 = 0.046 (small effect), social presence → impulsive purchase: f2 = 0.036 (small effect). These results suggest that smartphone addiction has a strong effect on social presence (f2 = 0.069), while the other paths have smaller effect sizes. Impulsive purchase: q2 = 0.048 (RMSE = 0.982, MAE = 0.79). Parasocial interaction: Q2 = 0.089 (RMSE = 0.959, MAE = 0.809). Social Presence: Q2 = 0.074 (RMSE = 0.968, MAE = 0.755). All Q2 values were greater than 0, indicating that the model has good predictive power.
5 Data analysis and results
5.1 Measurement model
Calculation of the loadings of the measurement model indicates that the loadings of “social anxiety” range from 0.790 to 0.820, “fear of missing out” range from 0.765 to 0.799, “narrative involvement” range from 0.804 to 0.866, and “smartphone addiction” ranged from “0.795 to 0.849,” and the loadings for the latent variables of social interaction, social presence and impulse buying were all greater than 0.6, indicating a high level of internal consistency of the measurement items. The findings are presented in Table 2.
Table 2
Table 2. Results of cross-loadings.
Using the SmartPLS 4.1 software environment, a latent variable model was constructed to assess convergent validity. This validity was evaluated using two key metrics: Average Variance Extracted (AVE) and Cronbach’s alpha. Convergent validity was considered satisfactory if the AVE value for each factor exceeded 0.5 and the Composite Reliability (CR) value surpassed 0.7, indicating strong internal consistency and reliability. The results in Table 3 show that the AVE values for the seven constructs of impulsive purchase, narrative involvement, fear of missing out, smartphone addiction, social presence, social anxiety, and parasocial interaction are 0.708, 0.701, 0.616, 0.685, 0.683, 0.65, and 0.664, with variance explained levels of 70.8, 70.1, 61.6, 68.5, 68.3, 65, and 66.4%, respectively. It is generally accepted that Cronbach’s alpha coefficients are above 0.7 (Beaudart et al., 2017), and the reliability levels of the above variables are greater than 0.7, indicating a high level of internal consistency in the measurement construct.
Table 3
Table 3. Results of convergent validity.
The dimensions are significantly correlated with each other, the absolute value of the correlation coefficient is less than 0.50 and the two-by-two correlation coefficients of impulsive purchase, narrative involvement, fear of missing out, smartphone addiction, social presence, social anxiety, and parasocial interaction constructs are less than the square root of the AVE of the corresponding variables. The square root of the AVE for impulsive purchase is 0.842, and the correlation coefficients between impulsive purchase and the other variables range from 0.166 to 0.258, which is less than the threshold required level of 0.842, indicating ideal discriminant validity. Discriminant validity results are presented in Table 4.
Table 4
Table 4. Results of discriminant validity.
5.2 Structure model
Social anxiety had a positive and significant effect on parasocial interaction (β = 0.221, p < 0.001), fear of missing out had a positive and significant effect on parasocial interaction (β = 0.140, p = 0.002 < 0.01), and there was no positive and significant effect of narrative involvement on parasocial interaction (β = 0.088, p = 0.066 > 0.05). Narrative involvement had a positive and significant effect on social presence (β = 0.111, p = 0.012 < 0.05) and smartphone addiction had a positive and significant effect on social presence (β = 0.254, p < 0.001). There was no positive or significant effect of social presence on impulsive purchase (β = 0.194, p < 0.001), and there was a positive and significant effect of parasocial interaction on impulsive purchase (β = 0.177, p = 0.001 < 0.01) (see Table 5).
Table 5
Table 5. Direct effects.
We further tested the indirect effects to explore how SA, FOMO, NI, and SMA may influence impulsive purchase through significant mechanisms. The path from SA to IP via PI was significant (H8a). The study found no support for the mediating effect of parasocial interaction on the relationship between fear of missing out, narrative involvement, and impulsive purchase (H8b and H8c). The mediating effect of social presence on the relationship between narrative involvement is not supported (H9a). Social presence mediates the effects of smartphone addiction and impulsive purchase (H9b). The results of the research model are illustrated in Figure 2.
Figure 2
Figure 2. Results of the research model.
6 Discussion
Based on parasocial interaction theory and flow theory, this study reveals the dual-path mechanism by which consumer characteristics influence impulsive purchase through media perceptions. The research model clarifies the influence of consumer characteristics on their parasocial interaction and social presence, as well as the mediating role of parasocial interaction and social presence between consumer characteristics and impulsive purchase. The results of the study not only validate some of the presuppositions of the established theory but also reveal several noteworthy findings.
Consistent with expectations, consumers’ social anxiety and fear of missing out had a significant positive effect on parasocial interaction (H1, H2 accepted). This finding aligns with previous research (Deng et al., 2023), suggesting that individuals with high social anxiety prefer to engage in virtual parasocial interaction via live streaming commerce to compensate for anxiety in real social situations. Notably, this study further revealed the unique role of the real-time feedback properties of live streaming in creating virtual intimate compensation (Wu et al., 2023). Social anxious individuals receive instant social rewards through screen interactions (Li et al., 2024), and this operant conditioning mechanism drives impulsive purchase (Djafarova and Rushworth, 2017), which provides a new theoretical perspective for understanding consumption behavior in live streaming commerce context.
However, contrary to the hypothesis, the effect of narrative involvement on parasocial interaction was not significant (H3 reject), despite adequate measurement reliability. This null effect potentially related to our operationalization through the scale may have inadequately captured the dimensions of engagement for specific groups. The observed discrepancies may stem from cross-cultural variance in narrative interpretation frameworks and individual phenomenological differences. In addition, consumers’ exposure to narratives may be influenced by other factors including viewing environment, resulting in insufficient emotional involvement, which in turn weakens the formation of parasocial interaction (Giles, 2002). Future research could further explore measures of narrative involvement and consider other potential factors.
The findings support both H4 and H5, suggesting that consumers’ narrative involvement and smartphone addiction significantly affected their social presence. This suggests that deeper narrative involvement enhances consumers’ perceptions of community atmosphere, while smartphone addiction may exacerbate consumers’ reliance on virtual socialization (Vazquez et al., 2020). In addition, H6 and H7 were also validated that parasocial interaction and social presence had a significant positive effect on consumers’ impulsive purchase, which is in line with the findings of a past study (Fu and Hsu, 2023).
The results of the mediation effects test validated H8a and H9b that parasocial interaction mediates between social anxiety and impulsive purchase, and that social presence mediates between smartphone addiction and impulsive purchase. However, the study unexpectedly found that H8b, H8c, and H9a were not supported. This result may be due to insufficient sample size or the presence of moderating variables. For example, individual differences (e.g., self-control, consumerism, etc.) may have moderated the effects of parasocial interaction and social presence on impulsive purchase (Nawaz et al., 2021). Thus, future research could expand the sample size and incorporate potential moderate variables to further validate the robustness of the research model.
7 Conclusion
7.1 Theoretical and practical implications
Theoretically, this study encompasses the following aspects. Firstly, previous research on consumer impulsive purchase influences has focused on influencers and live streaming technology, with little consideration of individual consumer characteristics. This study confirms that consumer characteristics, such as social anxiety, fear of missing out, and smartphone addiction, have an impact on their impulsive purchase, thus contributing to the improvement of live streaming marketing theory. Secondly, this study identifies the impact of parasocial interaction, social presence provided by new media on consumers’ impulsive purchase in the context of the growing popularity of the digital economy and considering the dual influence of traditional and new media. Finally, the findings of this study do not confirm the positive impact of narrative involvement on consumer impulsive purchase, which differs from previous research and opens possibilities for new research directions (Vazquez et al., 2020). Overall, this study enriches the theoretical framework of live streaming marketing from the perspectives of communication and psychology.
Practically, this study holds significant implications for live streaming commerce platforms and sellers. First, platforms should pay attention to high social anxiety user groups and enhance users’ virtual social experience by optimizing functions such as screen interaction and virtual gifts, thus promoting conversion rate. Second, sellers should focus on the narrative and interactivity of the live streaming content to enhance consumers’ sense of participation and social presence and then stimulate their desire to purchase. Finally, platforms and sellers should guide users to consume rationally and avoid over-reliance on virtual socialization and impulsive purchase.
7.2 Limitations and future research
Despite the great strengths of this study there are still several limitations. First, the study sample is mainly from China, which may limit the applicability of the findings to other countries (Djafarova and Rushworth, 2017). Future studies could expand the sample to test the generalizability of the findings. Second, this study did not consider other potential influencing factors, such as product type and live streamer characteristics. Future research should incorporate additional studies to ascertain whether other potential factors influence consumers’ impulsive purchase, thereby constructing a more comprehensive research model. Additionally, respondents’ answers were based on a recent live streaming experience. There is a certain time lag between the respondents’ answers and the viewing experience. Lastly, respondents held different views on live streaming due to the different live streaming platforms. As a result, respondents may have biased their responses toward emotions. In the future, we plan to address these limitations by inviting respondents to view LSC live, and fill out the questionnaire.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.
Author contributions
YH: Formal analysis, Methodology, Software, Writing – original draft. SM: Project administration, Supervision, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. We would like to thank Management and Science University (MSU) for their support of our study. This research received its funding from the MSU Publication Grant (MPG-013-042024).
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
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References
Ahmed, S., Sharif, T., Ting, D. H., and Sharif, S. J. (2024). Crafting emotional engagement and immersive experiences: comprehensive scale development for and validation of hospitality marketing storytelling involvement. Psychol. Market. 41, 1514–1529. doi: 10.1002/mar.21994
Alnoor, A., Abbas, S., Khaw, K. W., Muhsen, Y. R., and Chew, X. (2024). Unveiling the optimal configuration of impulsive buying behavior using fuzzy set qualitative comparative analysis and multi-criteria decision approach. J. Retail. Consum. Serv. 81:104057. doi: 10.1016/j.jretconser.2024.104057
Apaolaza, V., Hartmann, P., D'Souza, C., and Gilsanz, A. (2019). Mindfulness, compulsive mobile social media use, and derived stress: the mediating roles of self-esteem and social anxiety. Cyberpsychol. Behav. Soc. Network. 22, 388–396. doi: 10.1089/cyber.2018.0681
Arora, A., Bansal, S., Kandpal, C., Aswani, R., and Dwivedi, Y. (2019). Measuring social media influencer index-insights from Facebook, twitter and Instagram. J. Retail. Consum. Serv. 49, 86–101. doi: 10.1016/j.jretconser.2019.03.012
Bao, Z., and Yang, J. (2022). Why online consumers have the urge to buy impulsively: roles of serendipity, trust and flow experience. Manage. Decis. 60, 3350–3365. doi: 10.1108/MD-07-2021-0900
Barnes, S. J., Pressey, A. D., and Scornavacca, E. (2019). Mobile ubiquity: understanding the relationship between cognitive absorption, smartphone addiction and social network services. Comput. Hum. Behav. 90, 246–258. doi: 10.1016/j.chb.2018.09.013
Beaudart, C., Biver, E., Reginster, J. Y., Rizzoli, R., Rolland, Y., Bautmans, I., et al. (2017). Validation of the SarQoL®, a specific health-related quality of life questionnaire for sarcopenia. J. Cachexia Sarcopeni. 8, 238–244. doi: 10.1002/jcsm.12149
Biocca, F., Harms, C., and Burgoon, J. K. (2003). Toward a more robust theory and measure of social presence: review and suggested criteria. Presence Teleoper. Virt. Environ. 12, 456–480. doi: 10.1162/105474603322761270
Castellanos-Reyes, D., Richardson, J. C., and Maeda, Y. (2024). The evolution of social presence: a longitudinal exploration of the effect of online students' peer-interactions using social network analysis. Internet High. Educ. 61:100939. doi: 10.1016/j.iheduc.2024.100939
Chang, C. C. (2013). Examining users′ intention to continue using social network games: a flow experience perspective. Telemat. Inform. 30, 311–321. doi: 10.1016/j.tele.2012.10.006
Chen, H., Dou, Y., and Xiao, Y. (2023). Understanding the role of live streamers in live-streaming e-commerce. Electron. Commer. R. A. 59:101266. doi: 10.1016/j.elerap.2023.101266
Chen, Y., Lu, Y., Wang, B., and Pan, Z. (2019). How do product recommendations affect impulse buying? An empirical study on WeChat social commerce. Inform. Manage Amster. 56, 236–248. doi: 10.1016/j.im.2018.09.002
Cheng, X., Liu, J., Li, J., and Hu, Z. (2023). COVID-19 lockdown stress and problematic social networking sites use among quarantined college students in China: a chain mediation model based on the stressor–strain–outcome framework. Addict. Behav. 146:107785. doi: 10.1016/j.addbeh.2023.107785
Chopdar, P. K., Paul, J., and Prodanova, J. (2022). Mobile shoppers’ response to Covid-19 phobia, pessimism and smartphone addiction: does social influence matter? Technol. Forecast. Soc. 174:121249. doi: 10.1016/j.techfore.2021.121249
Cuevas, L., Lyu, J., and Lim, H. (2021). Flow matters: antecedents and outcomes of flow experience in social search on Instagram. J. Res. Interact. Mark. 15, 49–67. doi: 10.1108/JRIM-03-2019-0041
de Bérail, P., Guillon, M., and Bungener, C. (2019). The relations between YouTube addiction, social anxiety and parasocial relationships with YouTubers: a moderated-mediation model based on a cognitive-behavioral framework. Comput. Hum. Behav. 99, 190–204. doi: 10.1016/j.chb.2019.05.007
Deng, F., Lin, Y., and Jiang, X. (2023). Influence mechanism of consumers’ characteristics on impulsive purchase in E-commerce livestream marketing. Comput. Hum. Behav. 148:107894. doi: 10.1016/j.chb.2023.107894
Dinh, T. C. T., Wang, M., and Lee, Y. (2023). How does the fear of missing out moderate the effect of social media influencers on their followers’ purchase intention? SAGE Open 13:21582440231197259. doi: 10.1177/21582440231197259
Djafarova, E., and Rushworth, C. (2017). Exploring the credibility of online celebrities' Instagram profiles in influencing the purchase decisions of young female users. Comput. Hum. Behav. 68, 1–7. doi: 10.1016/j.chb.2016.11.009
Elhai, J. D., Yang, H., and Montag, C. (2021). Fear of missing out (FOMO): overview, theoretical underpinnings, and literature review on relations with severity of negative affectivity and problematic technology use. Brazil. J. Psychiatr. 43, 203–209. doi: 10.1590/1516-4446-2020-0870
Farivar, S., Wang, F., and Turel, O. (2022). Followers' problematic engagement with influencers on social media: an attachment theory perspective. Comput. Hum. Behav. 133:107288. doi: 10.1016/j.chb.2022.107288
Fortin, D. R., and Dholakia, R. R. (2005). Interactivity and vividness effects on social presence and involvement with a web-based advertisement. J. Bus. Res. 58, 387–396. doi: 10.1016/S0148-2963(03)00106-1
Fu, J. R., and Hsu, C. W. (2023). Live-streaming shopping: the impacts of Para-social interaction and local presence on impulse buying through shopping value. Ind. Manage. Data Syst. 123, 1861–1886. doi: 10.1108/IMDS-03-2022-0171
Gao, W., Jiang, N., and Guo, Q. (2023). How do virtual streamers affect purchase intention in the live streaming context? A presence perspective. J. Retail. Consum. Serv. 73:103356. doi: 10.1016/j.jretconser.2023.103356
Ghali, Z., Rather, R. A., and Khan, I. (2024). Investigating metaverse marketing-enabled consumers’ social presence, attachment, engagement and (re) visit intentions. J. Retail. Consum. Serv. 77:103671. doi: 10.1016/j.jretconser.2023.103671
Ghostretail, Ontario, Canada. (2024). Top live shopping statistics of 2024 for business. Available online at: https://www.ghostretail.com/post/top-live-shopping-statistics (Accessed February 3, 2024).
Giles, D. C. (2002). Parasocial interaction: a review of the literature and a model for future research. Media Psychol. 4, 279–305. doi: 10.1207/S1532785XMEP0403_04
Gong, W. (2021). Effects of parasocial interaction, brand credibility and product involvement on celebrity endorsement on microblog. Asia Pac. J. Market. Logistic. 33, 1437–1454. doi: 10.1108/APJML-12-2019-0747
Green, M. C. (2004). Transportation into narrative worlds: the role of prior knowledge and perceived realism. Discourse Process. 38, 247–266. doi: 10.1207/s15326950dp3802_5
Guan, Z., Hou, F., Li, B., Phang, C. W., and Chong, A. Y. L. (2022). What influences the purchase of virtual gifts in live streaming in China? A cultural context-sensitive model. Inform. Syst. J. 32, 653–689. doi: 10.1111/isj.12367
Horton, D., and Richard Wohl, R. (1956). Mass communication and Para-social interaction: observations on intimacy at a distance. Psychiatry 19, 215–229. doi: 10.1080/00332747.1956.11023049
Hu, J., Wang, H., Li, L., and Guo, L. (2024). How travel vlog audience members become tourists: exploring audience involvement and travel intention. Comput. Hum. Behav. 152:108045. doi: 10.1016/j.chb.2023.108045
Hua, Y., Wang, D., Luo, X. R., Chang, F. K., and Xie, Y. (2023). Discovering the juxtaposed affordances in digitally transformed live streaming e-commerce: a mixed-methods study from a vicarious learning perspective. Eur. J. Inform. Syst. 33, 469–500. doi: 10.1080/0960085X.2023.2178978
Huang, L. T. (2016). Flow and social capital theory in online impulse buying. J. Bus. Res. 69, 2277–2283. doi: 10.1016/j.jbusres.2015.12.042
Huang, Y., Makmor, N., and Mohamad, S. H. (2024). Research progress analysis of live streaming commerce based on cite space. Heliyon 10:e36029. doi: 10.1016/j.heliyon.2024.e36029
Hyun, H., Thavisay, T., and Lee, S. H. (2022). Enhancing the role of flow experience in social media usage and its impact on shopping. J. Retail. Consum. Serv. 65:102492. doi: 10.1016/j.jretconser.2021.102492
Ihm, J. (2018). Social implications of children’s smartphone addiction: the role of support networks and social engagement. J. Behav. Addict. 7, 473–481. doi: 10.1556/2006.7.2018.48
Jarzyna, C. L. (2021). Parasocial interaction, the COVID-19 quarantine, and digital age media. Hum. Arenas 4, 413–429. doi: 10.1007/s42087-020-00156-0
Jia, R., Yang, Q., Liu, B., Song, H., and Wang, Z. (2023). Social anxiety and celebrity worship: the mediating effects of mobile phone dependence and moderating effects of family socioeconomic status. BMC Psychol. 11:364. doi: 10.1186/s40359-023-01405-x
Jin, S. V., and Ryu, E. (2020). I'll buy what she's# wearing: the roles of envy toward and parasocial interaction with influencers in Instagram celebrity-based brand endorsement and social commerce. J. Retail. Consum. Serv. 55:102121. doi: 10.1016/j.jretconser.2020.102121
Ju, J., and Ahn, J. H. (2016). The effect of social and ambient factors on impulse purchasing behavior in social commerce. J. Org. Comput. Electronic Commer. 26, 285–306. doi: 10.1080/10919392.2016.1228353
Kacen, J. J., Hess, J. D., and Walker, D. (2012). Spontaneous selection: the influence of product and retailing factors on consumer impulse purchases. J. Retail. Consum. Serv. 19, 578–588. doi: 10.1016/j.jretconser.2012.07.003
Kang, J. A., Hong, S., and Hubbard, G. T. (2020). The role of storytelling in advertising: consumer emotion, narrative engagement level, and word-of-mouth intention. J. Consum. Behav. 19, 47–56. doi: 10.1002/cb.1793
Kassing, J. W., and Sanderson, J. (2009). You're the kind of guy that we all want for a drinking buddy: expressions of parasocial inter-action on Floydlandis com. Western J. Comm. 73, 182–203. doi: 10.1080/10570310902856063
Keefer, L. A., Brown, F. L., Rothschild, Z. K., and Allen, K. (2024). A distant ally?: mortality salience and parasocial attachment. OMEGA J. death. dying 89, 967–985. doi: 10.1177/00302228221085173
Kim, H. (2022). Keeping up with influencers: exploring the impact of social presence and parasocial interactions on Instagram. Int. J. Advert. 41, 414–434. doi: 10.1080/02650487.2021.1886477
Kim, D. J., Ferrin, D. L., and Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decis. Support. Syst. 44, 544–564. doi: 10.1016/j.dss.2007.07.001
Kim, J., Kwon, Y., and Cho, D. (2011). Investigating factors that influence social presence and learning outcomes in distance higher education. Comput. Educ. 57, 1512–1520. doi: 10.1016/j.compedu.2011.02.005
Kim, J., and Song, H. (2016). Celebrity's self-disclosure on twitter and parasocial relationships: a mediating role of social presence. Comput. Hum. Behav. 62, 570–577. doi: 10.1016/j.chb.2016.03.083
Kindred, R., and Bates, G. W. (2023). The influence of the COVID-19 pandemic on social anxiety: a systematic review. Int. J. Environ. Res. Public Health 20:2362. doi: 10.3390/ijerph20032362
Koehler, A. A., and Vilarinho-Pereira, D. R. (2023). Using social media affordances to support ill-structured problem-solving skills: considering possibilities and challenges. Educ. Tech. Res. 71, 199–235. doi: 10.1007/s11423-021-10060-1
Kreijns, K., Xu, K., and Weidlich, J. (2022). Social presence: conceptualization and measurement. Educ. Psychol. Rev. 34, 139–170. doi: 10.1007/s10648-021-09623-8
Lajnef, K. (2023). The effect of social media influencers' on teenagers behavior: an empirical study using cognitive map technique. Curr. Psychol. 42, 19364–19377. doi: 10.1007/s12144-023-04273-1
Lee, E. J. (2013). Effectiveness of politicians' soft campaign on twitter versus TV: cognitive and experiential routes. J. Commun. 63, 953–974. doi: 10.1111/jcom.12049
Li, M., Cheng, M., Quintal, V., and Cheah, I. (2023). From live streamer to viewer: exploring travel live streamer persuasive linguistic styles and their impacts on travel intentions. J. Travel Tour. Mark. 40, 764–777. doi: 10.1080/10548408.2023.2294071
Li, M., Deng, R., and Gong, B. (2024). Research on the impact of live marketing on consumers’ irrational consumption behavior under the background of the new economic era. J. Knowl. Econ., 1–50. doi: 10.1007/s13132-024-02146-x
Li, X., Guo, M., and Huang, D. (2023). The role of scarcity promotion and cause-related events in impulse purchase in the agricultural product live stream. Sci. Rep. UK 13:3800. doi: 10.1038/s41598-023-30696-8
Li, L., Kang, K., Zhao, A., and Feng, Y. (2022). The impact of social presence and facilitation factors on online consumers' impulse buying in live shopping–celebrity endorsement as a moderating factor. Inform. Technol. People 36, 2611–2631. doi: 10.1108/ITP-03-2021-0203
Liao, J., Chen, K., Qi, J., Li, J., and Yu, I. Y. (2023). Creating immersive and parasocial live shopping experience for viewers: the role of streamers' interactional communication style. J. Res. Interact. Mark. 17, 140–155. doi: 10.1108/JRIM-04-2021-0114
Liu, X., Zhang, L., and Chen, Q. (2022). The effects of tourism e-commerce live streaming features on consumer purchase intention: the mediating roles of flow experience and trust. Front. Psychol. 13:995129. doi: 10.3389/fpsyg.2022.995129
López-Bonilla, L. M., Sanz-Altamira, B., and López-Bonilla, J. M. (2021). Self-consciousness in online shopping behavior. Mathematics 9:729. doi: 10.3390/math9070729
Lou, C. (2022). Social media influencers and followers: theorization of a trans-parasocial relation and explication of its implications for influencer advertising. J. Advert. 51, 4–21. doi: 10.1080/00913367.2021.1880345
Lu, Y., and Duan, Y. (2024). Strategic live streaming choices for vertically differentiated products. J. Retail. Consum. Serv. 76:103582:103582. doi: 10.1016/j.jretconser.2023.103582
Lum, Y., and Chang, C. W. (2023). Modeling user participation in Facebook live by applying the mediating role of social presence. Information 15:23. doi: 10.3390/info15010023
Luo, X., Cheah, J. H., Lim, X. J., Ramayah, T., and Dwivedi, Y. K. (2025). Inducing shoppers’ impulsive buying tendency in live-streaming: integrating signaling theory with social exchange theory. Internet Res. 35, 318–348. doi: 10.1108/INTR-04-2023-0260
Luo, L., Xu, M., and Zheng, Y. (2024). Informative or affective? Exploring the effects of streamers’ topic types on user engagement in live streaming commerce. J. Retail. Consum. Serv. 79:103799. doi: 10.1016/j.jretconser.2024.103799
Luo, X., Cheah, J-H., Hollebeek, L. D., and Lim, X-J. (2024). Boosting customers’ impulsive buying tendency in live-streaming commerce: the role of customer engagement and deal proneness. J. Retail. Consum. Serv. 77:103644. doi: 10.1016/j.jretconser.2023.103644
Lyngdoh, T., El-Manstrly, D., and Jeesha, K. (2023). Social isolation and social anxiety as drivers of generation Z's willingness to share personal information on social media. Psychol. Mark. 40, 5–26. doi: 10.1002/mar.21744
Makmor, N., Hafiz, K. A., Anuar, A., and Sofian, F. (2024). Impact of para-social interaction on impulsive buying through live-streaming shopping website. Environ. Soc. Psychol. 9, 1–15. doi: 10.54517/esp.v9i5.2089
Mason, M. C., Zamparo, G., Marini, A., and Ameen, N. (2022). Glued to your phone? Generation Z's smartphone addiction and online compulsive buying. Comput. Hum. Behav. 136:107404. doi: 10.1016/j.chb.2022.107404
Miranda, S., Borges-Tiago, M. T., Tiago, F., and Tu, X. (2024). To buy or not to buy? The impulse buying dilemma in livestream shopping. Psychol. Market. 41, 989–1005. doi: 10.1002/mar.21967
Moore, K., and Craciun, G. (2021). Fear of missing out and personality as predictors of social networking sites usage: the Instagram case. Psychol. Rep. 124, 1761–1787. doi: 10.1177/0033294120936184
Morrison, A. S., and Heimberg, R. G. (2013). Social anxiety and social anxiety disorder. Annu. Rev. Clin. Psychol. 9, 249–274. doi: 10.1146/annurev-clinpsy-050212-185631
Nakamura, J., and Csikszentmihalyi, M. (2002). “The concept of flow” in Handbook of positive psychology (New York, USA: Oxford University Press), 239–263.
Nawaz, S., Jiang, Y., Nawaz, M. Z., Manzoor, S. F., and Zhang, R. (2021). Mindful consumption, ego-involvement, and social norms impact on buying SHC: role of platform trust and impulsive buying tendency. SAGE Open 11:21582440211056621. doi: 10.1177/21582440211056621
Pelet, J. É., Ettis, S., and Cowart, K. (2017). Optimal experience of flow enhanced by telepresence: evidence from social media use. Inform. Manage Amster. 54, 115–128. doi: 10.1016/j.im.2016.05.001
Pierce, T. (2009). Social anxiety and technology: face-to-face communication versus technological communication among teens. Comput. Hum. Behav. 25, 1367–1372. doi: 10.1016/j.chb.2009.06.003
Qu, Y., Khan, J., Su, Y., Tong, J., and Zhao, S. (2023). Impulse buying tendency in live-stream commerce: the role of viewing frequency and anticipated emotions influencing scarcity-induced purchase decision. J. Retail. Consum. Serv. 75:103534:103534. doi: 10.1016/j.jretconser.2023.103534
Rodríguez-Ardura, I., and Meseguer-Artola, A. (2016). E-learning continuance: the impact of interactivity and the mediating role of imagery, presence and flow. Inform. Manage Amster. 53, 504–516. doi: 10.1016/j.im.2015.11.005
Rubin, A. M., Perse, E. M., and Powell, R. A. (1985). Loneliness, parasocial interaction, and local television news viewing. Hum. Commun. Res. 12, 155–180. doi: 10.1111/j.1468-2958.1985.tb00071.x
Rubin, A. M., and Step, M. M. (2000). Impact of motivation, attraction, and parasocial interaction on talk radio listening. J. Broadcast. Electron. 44, 635–654. doi: 10.1207/s15506878jobem4404_7
Saad, S. b., and Choura, F. (2024). Avatars’ impacts in retail: a study within regulatory engagement theory. Int. J. Retail. Distrib. 52, 689–705. doi: 10.1108/IJRDM-10-2022-0413
Safrianto, A. S., Herawati, H., and Fauzan, R. (2024). Study of consumer behavior on TikTok: motivations, preferences, and their impact on marketing trends. J. Manage. 3, 296–309.
Shang, Q., Ma, H., Wang, C., and Gao, L. (2023). Effects of background fitting of e-commerce live streaming on consumers’ purchase intentions: a cognitive affective perspective. Psychol. Res. Behav. Manage. 16, 149–168. doi: 10.2147/PRBM.S393492
Shraf, A., Hameed, I., and Saeed, S. A. (2023). How do social media influencers inspire consumers' purchase decisions? The mediating role of parasocial relationships. Int. J. Consum. Stud. 47, 1416–1433. doi: 10.1111/ijcs.12917
Sobaih, A. E. E., Hasanein, A. M., and Abu Elnasr, A. E. (2020). Responses to COVID-19 in higher education: social media usage for sustaining formal academic communication in developing countries. Sustainability 12:6520. doi: 10.3390/su12166520
Sokolova, K., and Kefi, H. (2020). Instagram and you tube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. J. Retail. Consum. Serv. 53:101742. doi: 10.1016/j.jretconser.2019.01.011
Sun, B., Zhang, Y., and Zheng, L. (2023). Relationship between time pressure and consumers' impulsive buying—role of perceived value and emotions. Heliyon 9:e23185. doi: 10.1016/j.heliyon.2023.e23185
Tsai, W. H. S., Liu, Y., and Chuan, C. H. (2021). How chatbots' social presence communication enhances consumer engagement: the mediating role of parasocial interaction and dialogue. J. Res. Interact. Mark. 15, 460–482. doi: 10.1108/JRIM-12-2019-0200
Vazquez, D., Wu, X., Niguyen, B., Kent, A., Gutierrez, A., and Chen, T. (2020). Investigating narrative involvement, parasocial interactions, and impulse buying behaviours within a second screen social commerce context. Int. J. Inform. Manage. 53:102135. doi: 10.1016/j.ijinfomgt.2020.102135
Wang, M., Yu, R., and Hu, J. (2023). The relationship between social media-related factors and student collaborative problem-solving achievement: an HLM analysis of 37 countries. Edu. Inf. Technol. 28, 103582, 103582–114089. doi: 10.1007/s10639-023-11763-z
Wang, D., Luo, X., Hua, Y., and Benitez, J. (2022). Big arena, small potatoes: a mixed-methods investigation of atmospheric cues in live-streaming e-commerce. Decis. Support. Syst. 158:113801:113801. doi: 10.1016/j.dss.2022.113801
Weisberg, J., Te'eni, D., and Arman, L. (2011). Past purchase and intention to purchase in e-commerce: the mediation of social presence and trust. Internet Res. 21, 82–96. doi: 10.1108/10662241111104893
Woo, H., Shin, D. C., Kim, N. L., Tong, Z., and Kwon, S. (2024). Can sharing with others whom consumers Can't see increase their sense of community? An examination of social presence on sharing platforms. J. Retail. Consum. Serv. 76:103614. doi: 10.1016/j.jretconser.2023.103614
Wu, C. H. J., and Liang, R. D. (2011). The relationship between white-water rafting experience formation and customer reaction: a flow theory perspective. Tour. Manage. 32, 317–325. doi: 10.1016/j.tourman.2010.03.001
Wu, R., Liu, J., Chen, S., and Tong, X. (2023). The effect of E-commerce virtual live streamer socialness on consumers' experiential value: an empirical study based on Chinese E-commerce live streaming studios. J. Res. Interact. Mark. 17, 714–733. doi: 10.1108/JRIM-09-2022-0265
Xiang, L., Zheng, X., Lee, M. K. O., and Zhao, D. (2016). Exploring consumers’ impulse buying behavior on social commerce platform: the role of parasocial interaction. Int. J. Inform. Manage. 36, 333–347. doi: 10.1016/j.ijinfomgt.2015.11.002
Yang, P., Sheng, H., Yang, C., and Feng, Y. (2024). How social media promotes impulsive buying: examining the role of customer inspiration. Ind. Manage. Data Syst. 124, 698–723. doi: 10.1108/IMDS-05-2023-0343
Yang, J., Zeng, Y., Liu, X., and Li, Z. (2022). Nudging interactive cocreation behaviors in live-streaming travel commerce: the visualization of real-time danmaku. J. Hosp. Tour. Manag. 52, 184–197. doi: 10.1016/j.jhtm.2022.06.015
Zhang, T., Li, B., and Hua, N. (2024). Live-streaming tourism: model development and validations. J. Travel Res. 64, 559–575. doi: 10.1177/00472875231223133
Zhang, M., Liu, Y., Wang, Y., and Zhao, L. (2022). How to retain customers: understanding the role of trust in live streaming commerce with a socio-technical perspective. Comput. Hum. Behav. 127:107052. doi: 10.1016/j.chb.2021.107052
Zheng, C. (2023). Research on the flow experience and social influences of users of short online videos. A case study of DouYin. Sci. Rep. UK 13:3312. doi: 10.1038/s41598-023-30525-y
Zheng, S., Chen, J., Liao, J., and Hu, H. L. (2023). What motivates users' viewing and purchasing behavior motivations in live streaming: a stream-streamer-viewer perspective. J. Retail. Consum. Serv. 72:103240. doi: 10.1016/j.jretconser.2022.103240
Zhong, Y., Shapoval, V., and Busser, J. (2021). The role of parasocial relationship in social media marketing: testing a model among baby boomers. Int. J. Contemp. Hosp. Manage. 33, 1870–1891. doi: 10.1108/IJCHM-08-2020-0873
Zhou, Y., and Huang, W. (2023). The influence of network anchor traits on shopping intentions in a live streaming marketing context: the mediating role of value perception and the moderating role of consumer involvement. Econ. Anal. Policy 78, 332–342. doi: 10.1016/j.eap.2023.02.005
Zhou, J., Poh, F., Zhang, C., and Zipser, D. Mckinsey greater China, Beijing, China. China’s gen Z are coming of age: Here’s what marketers need to know | McKinsey. Mckinsey. Available online at: https://www.mckinsey.com/cn/our-insights/our-insights/chinas-gen-z-are-coming-of-age-heres-what-marketers-need-to-know (Accessed February 3, 2024).
Zhu, P., Liu, Z., Li, X., Jiang, X., and Zhu, M. X. (2023). The influences of livestreaming on online purchase intention: examining platform characteristics and consumer psychology. Ind. Manage. Data Syst. 123, 862–885. doi: 10.1108/IMDS-07-2022-0430
Appendix
Measures of study constructs items.
Keywords: impulsive purchase, live streaming commerce, parasocial interaction, social presence, SmartPLS
Citation: Huang Y and Mohamad SH (2025) Examining the impact of parasocial interaction and social presence on impulsive purchase in live streaming commerce context. Front. Commun. 10:1554681. doi: 10.3389/fcomm.2025.1554681
Edited by:
Tereza Semerádová, Technical University of Liberec, CzechiaReviewed by:
Iuliana Raluca Gheorghe, Carol Davila University of Medicine and Pharmacy, RomaniaHaeyoung Jeong, North Carolina Central University, United States
Copyright © 2025 Huang and Mohamad. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Siti Hajar Mohamad, c2l0aWhhamFyX21vaGFtYWRAbXN1LmVkdS5teQ==; Yi Huang, MDEyMDIxMDkwOTQyQGdzbS5tc3UuZWR1Lm15
†Present address: Yi Huang, School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, China