Antecedents of Impulse Purchase through Perceived Enjoyment Among Chinese Live Streaming Commerce Consumers

Abstract

Despite the growing live streaming commerce (LSC) in China, a comprehensive framework is required to examine the factors influencing consumer impulse purchases. By applying the Social Impact Theory (SIT) and Stimulus-Organism-Response (S-O-R) framework, the mechanism underlying consumers’ urge to buy impulsively (UTBI) was examined through perceived enjoyment as the mediator. Upon analyzing 380 valid responses, the results revealed that parasocial interaction, telepresence, discounted price, serendipity information, and ubiquity positively influenced consumers’ perceived enjoyment. Meanwhile, parasocial interaction, social presence, discounted price, scarcity, ubiquity and perceived enjoyment positively impacted consumers’ UTBI. Perceived enjoyment partially mediated the correlations between parasocial interaction, discounted price, ubiquity and UTBI, whereas perceived enjoyment fully mediated the correlations between telepresence, serendipity information, and UTBI. Moreover, the findings reveal that serendipity information is the most significant factor in perceived enjoyment, whereas ubiquity is the most impactful antecedent of UTBI. This study enriches the literature on impulse purchase and LSC by incorporating the SIT and S-O-R theories, seven multidimensional antecedents and perceived enjoyment as a mediator. Furthermore, the study provides in-depth insights into consumers’ impulse purchases, enabling live streamers, marketers, and retailers to develop effective marketing strategies. Additionally, the designers of live streaming platforms can benefit from this study by designing more interactive and multifunctional platforms to attract more users.

Keywords

LSC parasocial interaction serendipity information ubiquity UTBI perceived enjoyment

Introduction

Rapid technological advancement, social media proliferation, and platform revolution significantly contributed to the explosive growth of interactive marketing (Wang, 2021). Live streaming enables real-time bilateral communication with high interactivity to engage customers, which emerges as an integral interactive marketing tool. Live streaming commerce (LSC), which was developed as a result of the deep association with live streaming and e-commerce, has transformed the engagement approaches of online retailers and marketers with consumers. A total of 777 million LSC users accounts, comprises of 70.6% of Internet users in China (CNNIC, 2024). In 2023, the Chinese LSC market achieved about 5 trillion yuan and is expected to grow to 8.16 trillion yuan by 2026 (Ou, 2024a). China is the global leader in LSC and the LSC model has profoundly transformed China’s online retail industry, ushering in a new era of enhanced consumer engagement and sales performance (Statista, 2023). Thus, it is essential to study the purchase behavior of Chinese LSC consumers. Meanwhile, LSC consumers’ impulsive purchases substantially contribute to the total consumer market. According to iiMedia Research (2020), 49.5% of LSC consumers made impulsive purchases. Despite the high benefits of LSC and the prevalence of impulsive purchases, not all live streamers profit from LSC, and over 1 million live streamers experience issues with low traffic and poor conversion rates (Luo et al., 2024). Therefore, it is imperative for live streamers to understand how LSC affects consumers’ impulsive buying decisions.

Despite the prosperity of LSC, more empirical studies are required to understand what triggers consumers to make impulse purchases. Previous studies on online shopping discovered several determinants, such as promotion tactics (scarcity and discounted price), social factors (social presence, parasocial interaction, and telepresence), serendipity, and ubiquity, positively impacted impulsive purchase behavior (Bao & Yang, 2022; Chen et al., 2022; Khetarpal & Singh, 2023; Ming et al., 2021; Zafar et al., 2020; X. Zheng et al., 2019). For instance, Bao and Yang (2022) investigated the impact of serendipity on impulse buying; however, the respondents were limited to Tmall.com users in China. Consumers across various shopping platforms might share diverse attitudes toward impulsive purchases. Future studies are recommended to examine impulsive purchases among consumers of other shopping platforms. Past researchers solely concentrated on traditional online commerce, including social and mobile commerce. However, these models fail to account for the unique dynamics of LSC, such as real-time interaction, entertainment-driven shopping experiences, and the role of live streamers as influencers which stands as a research gap for the current study. These distinctive features introduce new factors and mechanisms influencing consumer behavior, which remain underexplored in empirical research. Compared to e-commerce, live streaming commerce can offer a digitalized physical purchasing experience and has four unique characteristics: more fantastic entertainment, more robust interactivity, higher visualization, and more professionalization (Ma et al., 2022), which is more conducive to triggering impulsive purchases among consumers. Similarly, Ou (2024b) revealed that discounted prices are the primary motivation for live streaming shopping and other important incentives comprise entertainment, real-time interactivity and high engagement. Variables such as discounted prices, parasocial interaction, social presence and telepresence are particularly relevant to LSC. Meanwhile, studies on Chinese LSC customers assessed separately critical factors influencing impulsive purchases, such as social presence, popularity cues, and trust (Lou et al., 2022; Ming et al., 2021). For example, Lou et al. (2022) examined the cognitive mechanism of popularity cues on LSC consumers’ impulsive purchases, which suggested that future research considers the influence of other factors through consumers’ affective processes on impulsive purchases. Hence, a more holistic and comprehensive perspective of impulsive purchases needs to be investigated especially the antecedents influencing impulsive purchases. Social Impact Theory (SIT) has been applied in several digital social impact contexts, including Facebook commerce, mobile money payment, and online tutoring platforms (Cao et al., 2021; Dzandu et al., 2022; Leong et al., 2018). Currently, there is no research employing SIT to investigate the impulse purchases of LSC consumers. To address the research gap, the present study integrated the SIT theory and S-O-R framework to comprehensively identify antecedents determining Chinese LSC consumers’ impulse purchases.

Investigating the mediating effect of perceived enjoyment (PE) in the relationships between determinants and impulsive purchases is another research gap to uncover in this research. Mediating analysis is widely utilized in social and business research to improve the explanatory power of research models and expand the knowledge base (Ngah et al., 2021). Consumer psychology, which encompasses both affective and cognitive psychology, could influence relevant decision-making processes or behaviors. However, consumers’ impulse behavior is primarily driven by emotion-driven motives, particularly when combined with strong positive emotions (Chen et al., 2022). According to Lin et al. (2022), PE is a crucial internal psychological stimulation influencing consumers’ intention to purchase impulsively in LSC. Nonetheless, the mediating effect of PE was not thoroughly examined in LSC. Theoretically, insufficient findings motivated the current study to incorporate the S-O-R framework and SIT theory to uncover the mediating effect of PE. Summarily, inadequate insights into impulsive purchases in LSC underscore the necessity to conduct this study. The study sought to discover relevant associations and appraise the mediating role of perceived enjoyment in impacting the antecedents of impulsive purchases. Consequently, this study examines the following four research questions (RQs) to address the above gaps.

RQ1: Do parasocial interaction (PSI), social presence (SP), telepresence (TP), discounted price (DCP), scarcity (SCT), serendipity information (SDI) and ubiquity (UBQ) positively influence perceived enjoyment (PE)?

RQ2: Do PSI, SP, TP, DCP, SCT, SDI, UBQ and PE positively influence the urge to buy impulsively (UTBI)?

RQ3: Does PE mediate the relationships between the antecedents and the UTBI?

Theoretical Background

Stimulus-Organism-Response (S-O-R) Model

The S-O-R Model was developed by Mehrabian and Russell (1974) to delineate the arousal of environmental cues (stimuli) on an individual’s internal state (organism) before triggering the individual’s approach-and-avoidance behaviors (response). Contrary to most other consumer purchasing theories, the S-O-R model is a malleable framework that offers various dimensions without necessitating specific constructs to be included in the model. The framework allows researchers to incorporate different dimensions of variables to pertinently explicate the factors influencing consumer behavior. According to J. Yang et al. (2022), the S-O-R model provides a solid theoretical underpinning, which was extensively utilized to illustrate the consumer decision-making process and comprehend the underlying mechanism. However, the tendency of S-O-R to treat the response as a direct and linear response may not adequately account for social interaction. Therefore, to better understand the dynamics of live streaming e-commerce, integrating the Social Impact Theory (SIT) with the S-O-R model provides a more comprehensive framework

Social Impact Theory (SIT)

Latané (1981) established the SIT theory to illustrate the influence of others’ behaviors on an individual. Social impact refers to any influence on a user’s attitudes, perceptions, or behaviors deriving from interactions with others or others’ presence (Ang et al., 2018). Consumers are readily impacted by other individuals, due to LSC’s distinct digital interaction and shared viewership. Although the SIT theory did not receive sufficient attention in LSC research, the theory was widely applied to various social influence disciplines. For example, Simon et al. (2016) investigated the role of fans number in influencing users and corresponding brand engagement on Facebook. Live streaming viewers would positively influence other users’ perception, feelings, and attitudes (Ang et al., 2018). Thus, the SIT theory could assist in exploring the influence of social actors, including live streamers and peer viewers, on viewers’ perceptions and behavioral responses. SIT provides a robust framework for comprehending impulse purchase behavior in LSC as it highlights the mechanisms of strength (influence of the streamer), immediacy (real-time interaction during the live stream) and number of social influences (the collective influence of audience engagement) inherent in the real-time interactive environment of live streams. In contrast to Social Exchange Theory, which focuses on rational decision-making through cost-benefit analysis, SIT addresses the emotional and spontaneous nature of impulse purchasing decisions. Furthermore, the unique dynamics of live streaming, such as parasocial interaction, social presence and telepresence are better captured by SIT, making it the most solid underpinning theory in developing hypotheses for this study.

Literature Review and Hypothesis Development

Urge to Buy Impulsively (UTBI)

Urge to buy impulsively (UTBI) is described as a state of desire experienced when encountering an object in the environment (Beatty & Ferrell, 1998). Parboteeah et al. (2016) elucidated that the response to impulse purchase contains two dimensions, namely the UTBI and the actual impulsive purchase. The actual impulsive purchase behavior is generally perceived as socially undesirable, which might bias respondents’ answers. Therefore, numerous researchers adopted the UTBI to enhance robustness when investigating impulsive purchases in e-commerce (X. Zheng et al., 2019). The UTBI was examined in this study as a precursor of actual impulsive buying. LSC encourages consumer purchases through the transactional pattern of ‘purchasing while watching,’ which would trigger more impulsive purchases via real-time video, screen-scrolling messaging, personalized engagement, and other informational cues (X. Zhu & Vijayan, 2023).

Perceived Enjoyment (PE)

Perceived enjoyment (PE) refers to consumers’ pleasure derived from various sources of LSC, including interactions with other viewers, marketing tactics, playful functions of the platform, diversified commodities, and streamers’ engaging introductions (Lin et al., 2022; Ma, 2021). Consumers usually engage with live streamers by sending bullet screens and likes, sharing the live streaming room links, and offering virtual gifts to gain more enjoyment (S. Zheng et al., 2023). Numerous viewers subscribe to live streaming channels to reduce boredom and satisfy entertainment needs by following multiple live streamers (Yokoi, 2021). Furthermore, consumers who perceive a high level of enjoyment are more satisfied with the content of Vlog and purchase impulsively (Karim et al., 2021). Therefore, when live streaming is highly enjoyable, users are more inclined to make impulsive purchases. A relevant hypothesis was posited:

H1: PE positively influences UTBI.

Parasocial Interaction (PSI)

Parasocial interaction (PSI) was propounded by Horton and Wohl (1956) to depict audiences’ illusion of face-to-face interaction with personae. PSI was extensively studied as a digital interaction across various media and communication contexts, ranging from mass media to social media and live streaming platforms. Although live streaming allows simultaneous bidirectional communication, the information between live streamers and viewers is asymmetrical, as the viewers could obtain vast amounts of information about the live streamers while live streamers acquire no information about the viewers (McLaughlin & Wohn, 2021). Hence, the interaction acts as a key stimulus in LSC as parasocial, in which PSI delineates unidirectional communications created by live streamers and received by viewers. The parasocial interaction, driven by a sense of emotional closeness or connection to the influencer, can result in a stronger purchase intention.

Current literature indicates a favorable correlation between PSI and PE. Xiang et al. (2016) revealed that consumers’ PSI augmented their PE in social commerce. Emerging research has shown that parasocial cues dramatically increased users’ PE (Chinchilla & Kim, 2024; Wulf et al., 2020). Live streaming is a favorable state that signifies concentration and enjoyment (S. Zheng et al., 2023). By interacting with live streamers, users might have a greater sense of intimacy and fulfill their emotional needs, resulting in positive effects such as PE (Y. Y. Lee & Gan, 2020).

On the other hand, existing literature shows a direct positive connection between PSI and impulsive purchases (Kim et al., 2021). According to Zulfa (2020), PSI significantly increased users’ UTBI on YouTube beauty vloggers. Individuals are more responsive to marketing incentives from live streamers when they perceive intimate parasocial cues, leading to a rise in impulsive buying behavior. To validate whether PSI will also impact consumer’s PE and UTBI in LSC, hypotheses H2a and H2b were proposed:

H2a: PSI positively influences PE.

H2b: PSI positively influences UTB.

Social Presence (SP)

Social presence (SP) is regarded as an essential facilitator in e-commerce purchase behavioral studies. Different SP facets were assessed in online shopping literature, such as the SP of platforms, other consumers, merchants, and merchandise. The SP of platforms designates the degree to which consumers perceive a sense of human warmth and personalness from platforms, while other consumers’ SP refers to the level to which consumers perceive the presence of other consumers. Merchants’ SP denotes the extent to which consumers perceive merchants’ sensitivity and personal characteristics (Ye et al., 2020). Similarly, the social presence of merchandise refers to the degree to which consumers perceive the presence of merchandise (Jiang et al., 2019). As live streaming viewers could readily distinguish other viewers’ screen-scrolling messages and dynamic states, this study focused on other viewers’ social presence. The present study defined SP as the degree to which live streaming viewers perceive peer viewers’ presence levels following Lim’s (2015) approach.

Existing literature suggests a positive connection between SP and PE. Ye et al. (2020) uncovered that consumers’ PE was enhanced through SP in community-based online services. The main psychological effects of SP are related to the experience of enjoyment (Shin et al., 2019; Ming et al., 2021). In this respect, increased SP induced by co-viewers may enhance users’ PE within live streaming content.

On the other hand, limited examinations were performed on the direct correlation between SP and impulsive purchases. SP boosts consumers’ perception of source trustworthiness, transparency, and safety by providing immediate alternate product experiences and facilitating information exchange in LSC, thereby alleviating users’ uncertainty, risk, and hesitancy in making purchases, ultimately increasing impulsive buying tendencies (Shi et al., 2023; S. Zheng et al., 2023). To validate whether SP elicited by the co-viewers will also impact consumers’ PE and UTBI in LSC, hypotheses H3a and H3b were postulated:

H3a: SP positively influences PE.

H3b: SP positively influences UTBI.

Telepresence (TP)

Telepresence (TP) portrays customers’ self-perceptions of physical presence in online marketplaces in a vendor’s virtual location (Ou et al., 2014). LSC is more informative, interactive, immersive, and entertaining than conventional e-commerce, which can attract users to remain longer by offering commodities as physical stores, thereby improving consumers’ telepresence perception. Rajasekar and Aithal (2022) confirmed that live streaming shopping could offer experiences comparable to a physical store. In this study, telepresence refers to a live streaming viewer’s immersive experience related to the perception of presence during live streaming.

According to Y. Li and Peng (2021), a higher level of TP can draw more viewers to engage in the interaction by acting as stimuli for viewers to become fully absorbed in LSC, resulting in a psychological state of pleasure. T. Yang et al. (2021) also proved the positive connection between TP and PE. C. Zhu et al. (2023) further indicated that TP created by the TikTok short video platform positively influenced tourist enjoyment.

Meanwhile, TP allows users to visualize products vividly and closely resemble a real-life experience, positively influencing users’ perceptions and purchasing decisions (Chen et al., 2022). Yu et al. (2022) reported that TP significantly contributed to consumers’ impulsive consumption during live streaming gastronomy shopping. The TP cue generated by LSC enhances customers’ virtual shopping experience. Concurrently, the authenticity of live streaming purchases would impact consumers’ impulsive purchase intention (X. Liu et al., 2023). Thus, Hypotheses H4a and H4b were posited as follows:

H4a: TP positively influences PE.

H4b: TP positively influences UTBI.

Discounted Price (DCP)

Price-related factors are decisive stimuli for consumers’ purchasing decisions. An obvious advantage for live streamers with substantial fan bases is that they have more bargaining power and can haggle for lower prices without sacrificing product quality (Zhong et al., 2022). LSC users are price-sensitive due to the ease of switching between different platforms. Y. Liu et al. (2021) revealed that a discounted price would appeal to 50.3% of users to follow live streaming and 37.8% to purchase products in LSC. The discounted price (DCP) is defined as a price benefit provided by live streamers to viewers in the current study.

Consumers perceive greater savings from the product when a larger discount is offered. Consumers who perceive more financial savings are expected to have positive emotions and be motivated to purchase (J. E. Lee & Yu, 2018). Similarly, research by Haws et al. (2017) demonstrated that DCP creates a sense of excitement and reward, which enhances perceived enjoyment. This effect may be amplified in LSC because viewers often perceive DCP as exclusive opportunities, further elevating excitement and enjoyment. On the other hand, existing research revealed a positive correlation between DCP and impulse buying (Arianty et al., 2024; Noor, 2020). In LSC, the DCP often heightens this effect by reducing the deliberation time available for consumers, pushing them toward immediate purchases. Given the S-O-R paradigm and the above arguments, we assume that the DCP would favor PE and UTBI. Correspondingly, hypotheses H5a and H5b were proposed:

H5a: The DCP positively influences PE.

H5b: The DCP positively influences UTBI.

Scarcity (SCT)

Scarcity (SCT) is a promotional approach that strategically limits product availability to create a perception of limited opportunities to purchase. The construct is particularly relevant to the current study on impulse purchases in LSC, as marketers and streamers frequently employ SCT tactics for product promotion.

Previous researchers documented the positive effect of SCT on PE and impulsive purchases. For instance, Song et al. (2015) observed that scarcity messages positively influenced consumers’ PE in social commerce related to restaurant products. Perceived hedonic value (e.g., enjoyment) increases when scarcity is high (L. Zhang & Phang, 2024). SCT operates primarily through a sense of urgency and exclusivity, which triggers emotional value related to loss aversion. This effect may enhance PE by heightening the excitement of securing a rare opportunity.

Furthermore, the perception of good value derived from SCI triggers joy and passion among consumers, resulting in a shift from systematic to heuristic information processing, which increases their UTBI (Melati et al., 2024). Akram et al. (2018) and Naseebullah et al. (2023) identified SCT as a critical factor in impulsive purchases in social commerce. The SCT creates a tension between availability and demand, driving consumers to act quickly and impulsively. Hence, hypotheses H6a and H6b were propounded:

H6a: SCT positively influences PE.

H6b: SCT positively influences UTBI.

Serendipity Information (SDI)

Serendipity information (SDI) denotes relevant information to viewers’ interests accidentally discovered during live streaming. LSC provides users with abundant content to follow, which increases the serendipity likelihood. Bao and Zhu (2023) corroborated that SDI frequently occurs in LSC. Consumers can experience a positive and joyful feeling when they encounter unexpected beneficial information (Cui et al., 2022). Relevant social commerce studies revealed that SDI positively influenced consumers’ PE (Song et al., 2015). Consumers often face an excess of information while shopping online. Nevertheless, when they inadvertently encounter SDI, it may be an exhilarating experience (Naseebullah et al., 2023). The SDI works through the element of surprise and relevance, which fosters unexpected pleasure when viewers accidentally discover interesting or useful content.

Furthermore, consumers are fascinated and surprised when they come across SDI. Such SDI is more likely to stimulate impulse buying (Thuong, 2020). Online shopping studies also emphasized the significance of SDI in increasing consumers’ impulsive purchases (Bao & Yang, 2022; Prawira & Sihombing, 2021). Due to the unexpected characteristics of SDI, consumers tend to make impulse purchases rather than deliberate searches (Naseebullah et al., 2023). The SDI influences UTBI by triggering curiosity and fascination, leading to impulse purchases out of positive surprise. Therefore, the research anticipates that SDI can enhance consumers’ PE and positively impact UTBI in LSC. Thus, hypotheses H7a and H7b were developed:

H7a: SDI positively influences PE.

H7b: SDI positively influences UTB.

Ubiquity (UBQ)

Ubiquity (UBQ) refers to consumers’ accessibility to perform purchases without physical and time constraints. The concept is highly relevant to studies focusing on the usage of mobile devices. Rodríguez-Torrico et al. (2019) discovered that consumers who benefited from UBQ would perform more purchasing behaviors, which suggested that LSC with the UBQ characteristic could significantly influence consumers’ perceptions and purchasing behaviors. Although limited studies were conducted to investigate the UBQ impact, Choi (2016) demonstrated that individuals were more likely to attain higher PE when provided with ubiquitous connectivity services on social networking services, consistent with the findings of Jung et al. (2018) in social media usage.

Furthermore, Chopdar and Balakrishnan (2020) reported that mobile commerce UBQ significantly contributes to consumer impulsiveness. Elgammal et al. (2023) discovered that mobile commerce UBQ positively influences consumer usage behavior through the mediator of trust. Similarly, by eliminating the constraints of geography and time, LSC enables consumers to interact with live streamers at any place and time, extending the duration of their engagement. The heightened engagement between consumers and live streamers may trigger the consumers’ pleasure and impulse purchases. Accordingly, hypotheses H8a and H8b were proposed:

H8a: UBQ positively influences PE.

H8b: UBQ positively influences UTBI.

The Mediating Role of Perceived Enjoyment

Mehrabian and Russell (1974) recommended that the organism could act as a mediator intervening in the association between stimulus and response in the S-O-R model. Theoretically, PE could mediate the correlations between determinants and UTBI in LSC. Empirically, this study discovered consistent relationships between determinants and PE and between consumers’ PE and UTBI. Thus, PE was proposed to mediate the relationships between determinants and UTBI. Hypotheses H9a to H9g and the research framework (Figure 1) were postulated:

H9: PE mediates the relationships between PSI (H9a), SP (H9b), TP (H9c), DCP (H9d), SCT (H9e), SDI (H9f), UBQ (H9g), and UTBI.

Figure 1.

Research framework.

Methodology

Data Collection

This study used purposive sampling to recruit respondents. To ensure that the respondent group is representative, respondents must meet the criteria of being Chinese LSC users aged between 18 and 60 and having a live streaming shopping experience. Data was collected using an online questionnaire created on the Wenjuanxing website. The questionnaire contained 56 questions, including three screening questions and six questions related to the respondents’ demographic profiles. To address the issue of repetitive submissions, a skip logic question was specifically included in the questionnaire, asking if the respondent had ever answered it before. Additionally, two skip logic questions are designed to filter out participants who have not watched the live broadcast or made any transactions in LSC. Given that society typically views impulse buying behavior as undesirable, potentially skewing respondents’ answers, this study utilized the items of urges to buy impulsively as a proxy for impulse buying and informed respondents that there were no right or wrong answers and that all the information was anonymous, encouraging them to respond based on their genuine thoughts. Data was collected between December 2021 and February 2022, with 500 initial responses obtained. In the post-COVID-19 era, the acceleration of digital technology promotes consumers’ indulgence in online shopping, including live-streaming services. LSC has become the mainstream reach for shopping, social and entertainment. Additionally, the uncertainty of the pandemic fuels a “live in the moment” mentality, increasing consumers’ response of impulse purchases to marketing tactics (e.g., scarcity and serendipity) in the LSC. After eliminating questionnaires completed in under 2 min and with the same IP address, 410 questionnaires proceeded with data screening and cleaning. Ultimately, 380 datasets were finalized for subsequent data analysis, significantly exceeding the minimum sample size of 109 respondents developed by the G*POWER approach and 155 respondents as per the inverse square root approach introduced by Kock and Hadaya (2018).

Measurements

The instrument comprises 47 items evaluated on a 7-point Likert scale. Specifically, PSI comprised seven items adapted and modified from J. E. Lee and Watkins (2016) and Rubin et al. (1985). The SP and TP comprised three items and six items respectively, which were adapted and modified from Ming et al. (2021), Baker et al. (2019), Sun et al. (2019), and Ye et al. (2020) respectively. The DCP and SCT comprised seven items and four items respectively, which were adapted and modified from Shim and Altmann (2016) and Song et al. (2015), and Chen and Yao (2018) respectively. The SDI and UBQ comprised five items, which were adapted from Akram et al. (2018) and Yi et al. (2017), Lee (2005), Okazaki and Mendez (2013), and Chopdar and Balakrishnan (2020) respectively. The PE comprised four items adapted from Ma (2021) and Soares and Pinho (2014), whereas UTBI comprised six items adapted and modified from Parboteeah et al. (2016) and Verhagen and Van Dolen (2011). The measurement items are shown in Table 1.

Table 1.

Measurement Items.

Construct	Code	Measurement item	Sources
Parasocial Interaction	PSI1	It helps me make up my mind when live streamers share their opinions about the product.	J. E. Lee and Watkins (2016); Rubin et al. (1985)
	PSI2	I like to compare my ideas with what live streamers say.
	PSI3	The live streamers make me feel comfortable as if I am with friends.
	PSI4	I look forward to watching my favorite live streamer.
	PSI5	I would watch any live streaming presented by my favorite live streamer.
	PSI6	I would like to meet my favorite live streamer in person.
	PSI7	I find my favorite live streamer to be attractive.
Social Presence	SP1	I am aware of other viewers who are also interested in the product.	Ming et al. (2021)
	SP2	I am aware of other viewers who also share the product information.
	SP3	I am aware of other viewers who have purchased the product.
Telepresence	TP1	I feel that my mind is within the world created by live streamers and viewers.	Sun et al. (2019); Baker et al. (2019); Ye et al. (2020)
	TP2	I feel that I am immersed in the world created by live streamers and viewers.
	TP3	I feel as if I am more in the “live streaming world” than the “real world.”
	TP4	Watching live streaming often makes me forget where I am.
	TP5	Live streaming creates a new world for me, and this world suddenly disappears when I stop watching it.
	TP6	When I quit live streaming, I felt like I came back to the “real world” after a journey.
Discounted price	DCP1	I will probably save more money purchasing products during live streaming than other online store.	Song et al. (2015); Shim and Altmann (2016); Chen and Yao (2018)
	DCP2	I may need to pay less money purchasing products during live streaming than other online stores.
	DCP3	I feel that prices of products during live streaming are cheaper than other online stores.
	DCP4	I feel that products during live streaming are very economical.
	DCP5	I feel that products during live streaming give high performance in cost-effectiveness.
	DCP6	I buy more products because of discounts offered during live streaming.
	DCP7	I enjoy looking for discounts during live streaming.
Scarcity	SCT1	I am concerned about the limited time of sales.	Chen and Yao (2018)
	SCT2	I am worried about the limited quantity of sales.
	SCT3	I am concerned about the period of availability of limited-time products.
	SCT4	I am anxious about sold out sign of a limited product.
Serendipity Information	SDI1	I could get unexpected insights during live streaming.	Akram et al. (2018); Yi et al. (2017)
	SDI2	I could find things that surprise me during live streaming.
	SDI3	I could see the ordinary things in novel ways during live streaming.
	SDI4	I unexpectedly discover by chance things that I want to have before.
	SDI5	I could get some unexpected but valuable findings during live streaming.
Ubiquity	UBQ1	I can access live streaming commerce anytime.	T. Lee (2005); Okazaki and Mendez (2013); Chopdar & Balakrishnan (2020)
	UBQ2	I can access live streaming commerce anywhere.
	UBQ3	The live streaming commerce enables me to buy products “anywhere”, “anytime”.
	UBQ4	I can access live streaming commerce without difficulty wherever I am.
	UBQ5	Watching live streaming fits any location, wherever I go.
Perceived Enjoyment	PE1	I find live streaming shopping to be enjoyable.	Ma (2021); Soares and Pinho (2014)
	PE2	I find live streaming shopping to be fun.
	PE3	I find live streaming shopping to be a good way for relieving boredom.
	PE4	Live streaming shopping provides relief from a stressful life.
Urge to Buy Impulsively	UTBI1	I have the urge to buy things other than or in addition to my specific purchasing goal during live streaming.	Parboteeah et al. (2009); Verhagen and Van Dolen (2011)
	UTBI2	I have a desire to buy things that did not appertain to be my specific purchasing goal during live streaming.
	UTBI3	I am inclined to buy things outside my specific purchasing goal during live streaming.
	UTBI4	I experience several sudden urges to buy things during live streaming.
	UTBI5	I experience strong urges to make unplanned purchases during live streaming.
	UTBI6	I feel a sudden urge to buy products during live streaming.

Data Analysis Techniques

According to Hair et al. (2017), PLS-SEM is preferred for theory development and construct prediction. This research aims to develop SIT theory in the LSC context and predict the causal correlation between constructs. Moreover, the proposed model is complex, comprising nine constructs, making PLS-SEM particularly suitable for handling intricate structural models. Additionally, the PLS-SEM technique can be used to analyze non-normal distribution data. Thus, this study utilized the PLS-SEM technique over covariance-based SEM for data analysis.

Data Analysis

Demographic Characteristics

The respondents’ descriptive statistics are presented in Table 2. Among the 380 respondents, 215 were women (56.6%) and 165 were men (43.4%) consistent with the statistics of J. Yang et al. (2022) with more women preferring live streaming shopping. A total of 208 respondents (55%) were single and the remaining 172 respondents (45%) were married. Respondents aged between 18 and 25 years old, 26 to 30, and 31 to 35 accounted for large proportions of the sample, which were 47.4%, 27.6%, and 11.3% respectively. Meanwhile, 243 respondents (64%) possessed a Bachelor’s degree, followed by 86 respondents (22%) with a Master’s degree or higher. Regarding work status, 190 respondents (50%) were working adults while 139 respondents (37%) were students. Regarding the monthly frequency of live streaming shopping, 242 respondents (64%) reported 1 to 4 times, followed by 80 respondents (21%) 5 to 8 times and 58 respondents (15%) above 9 times.

Table 2.

Respondents’ Demographics (N = 380).

Demographics	Classification	Frequency	Percentage
Gender	Male	165	43.4
Gender	Female	215	56.6
Marital status	Single	208	55
Marital status	Married	172	45
Age	18–25	180	47.4
	26–30	105	27.6
	31–35	43	11.3
	36–40	23	6.1
	41–45	17	4.5
	46–50	7	1.8
	51–55	3	0.8
	56–60	2	0.5
Education level	High School	19	5
	Junior College	32	8
	Bachelor	243	64
	Master	73	19
	PhD	13	3
Work status	Student	139	37
	Working	190	50
	Personal Business	22	6
	Retired	1	0
	Unemployed/Others	28	7
The monthly frequency of live streaming purchases	1–4	242	64
	5–8	80	21
	9 or more	58	15

Assumptions of Multivariate Analysis

Before the model assessment, two preliminary tests were performed, namely common method variance and normality. A full collinearity test was conducted to analyze common method variance (Kock, 2015). All Variance Inflation Factor (VIF) values were below 3.3 (ranging from 1.683 to 3.293), demonstrating that common method variance was not an issue in this study. Nonetheless, the data non-normality was indicated by Mardia’s multivariate kurtosis (β = 122.022) and skewness (β = 7.947).

Measurement Model Analysis

Consistency and validity are typically considered while assessing the measurement model. Upon deleting PSI2 (see Table 3) with a factor loading value of 0.545, all Average Variance Extracted (AVE) values were above 0.5 with all items achieving Cronbach’s alpha and Composite Reliability (CR) values above .7. Hence, the study attained adequate consistency and convergent validity. Discriminant validity was also assessed through the heterotrait-monotrait (HTMT) ratio. All HTMT values were below 0.85 (see Table 4), thus demonstrating sufficient discriminant validity for all constructs (Franke & Sarstedt, 2019).

Table 3.

Measurement Model Assessment.

Constructs and items	Factor loading	Cronbach’s alpha	CR	AVE
Parasocial interaction		.831	0.876	0.544
PSI1	0.661
PSI3	0.777
PSI4	0.869
PSI5	0.719
PSI6	0.627
PSI7	0.808
Social presence		.867	0.919	0.790
SP1	0.873
SP2	0.905
SP3	0.888
Telepresence		.911	0.930	0.688
TP1	0.830
TP2	0.883
TP3	0.878
TP4	0.816
TP5	0.781
TP6	0.782
Discounted price		.936	0.949	0.728
DCP1	0.882
DCP2	0.862
DCP3	0.905
DCP4	0.900
DCP5	0.887
DCP6	0.805
DCP7	0.714
Scarcity		.912	0.939	0.793
SCT1	0.909
SCT2	0.930
SCT3	0.907
SCT4	0.811
Serendipity information		.924	0.943	0.769
SDI1	0.824
SDI2	0.903
SDI3	0.894
SDI4	0.886
SDI5	0.874
Ubiquity		.933	0.949	0.787
UBQ1	0.858
UBQ2	0.872
UBQ3	0.904
UBQ4	0.898
UBQ5	0.904
Perceived enjoyment		.924	0.946	0.815
PE1	0.902
PE2	0.925
PE3	0.901
PE4	0.883
Urge to buy impulsively		.932	0.947	0.748
UTBI1	0.893
UTBI2	0.902
UTBI3	0.874
UTBI4	0.827
UTBI5	0.822
UTBI6	0.867

Note. The PSI2 item was deleted due to low loading.

Table 4.

Discriminant Validity—HTMT.

Constructs	DCP	PE	PSI	SCT	SDI	SP	TP	UTBI
DCP
PE	0.701
PSI	0.632	0.732
SCT	0.690	0.705	0.621
SDI	0.723	0.813	0.716	0.764
SP	0.536	0.577	0.608	0.540	0.637
TP	0.457	0.651	0.692	0.527	0.553	0.452
UTBI	0.709	0.838	0.724	0.734	0.760	0.622	0.592
UBQ	0.542	0.697	0.520	0.594	0.667	0.423	0.461	0.701

Structural Model

The SmartPLS version 3.3.9 software was employed to test the hypotheses. The study confirmed that the data did not have multicollinearity issues with the inner VIF values (see Table 5) below 5 (Hair et al., 2017). The bootstrapping with 10000 subsamples and hypotheses testing outcomes are illustrated in Figure 2 and Table 4 respectively. Resultantly, 11 out of 15 direct relationships were significant: PE → UTBI (β = .316, p < .01), PSI → PE (β = .125, p < .01), PSI → UTBI (β = 0.121, p < .01), SP → UTBI (β = .108, p < .01), TP → PE (β = .188, p < .01), DCP → PE (β = .151, p < .01), DCP → UTBI (β = .128, p < .01), SCT → UTBI (β = .147, p < .01), SDI → PE (β = .280, p < .01), UBQ → PE (β = .215, p < .01), and UBQ → UTBI (β = .189, p < .01). Thus, H1, H2a, H2b, H3b, H4a, H5a, H5b, H6b, H7a, H8a and H8b were supported. Nevertheless, the relationships between SP and PE (β = .027, p > .05), between SCT and PE (β = .063, p > .05), between TP and UTBI (β = .031, p > .05), and between SDI and UTBI (β = .009, p > .05) were insignificant. Hence, H3a, H6a, H4b, and H7b were rejected.

Table 5.

Hypothesis Testing Results.

Hypothesis	Path coefficient (β)	Beta	t-Value	p-Value	LL	UP	Decision	VIF
H1	PE→UTBI	.316***	5.329	.000	0.219	0.412	Supported	3.351
H2a	PSI→PE	.125***	2.869	.001	0.052	0.195	Supported	2.216
H2b	PSI→UTBI	.121**	2.745	.003	0.049	0.195	Supported	2.268
H3a	SP→PE	.027ns	0.674	.250	−0.040	0.093	Rejected	1.617
H3b	SP→UTBI	.108**	2.697	.004	0.043	0.173	Supported	1.619
H4a	TP→PE	.188***	4.714	.000	0.123	0.254	Supported	1.776
H4b	TP→UTBI	.031ns	0.791	.215	−0.033	0.095	Rejected	1.894
H5a	DCP→PE	.151***	3.088	.001	0.070	0.232	Supported	2.153
H5b	DCP→UTBI	.128**	2.588	.005	0.047	0.210	Supported	2.229
H6a	SCT→PE	.063ns	1.154	.124	−0.027	0.153	Rejected	2.322
H6b	SCT→UTBI	.147***	3.290	.001	0.076	0.221	Supported	2.335
H7a	SDI→PE	.280***	4.769	.000	0.182	0.377	Supported	3.098
H7b	SDI→UTBI	.009 ns	0.164	.435	−0.081	0.097	Rejected	3.361
H8a	UBQ→PE	.215***	4.534	.000	0.136	0.292	Supported	1.746
H8b	UBQ→UTBI	.189***	4.140	.000	0.114	0.265	Supported	1.900
H9a	PSI→PE→UTBI	.039**	2.395	.008	0.014	0.068	Partial mediation
H9b	SP→PE→UTBI	.009ns	0.652	.257	−0.012	0.031	No mediation
H9c	TP→PE→UTBI	.059***	3.389	.000	0.033	0.090	Full mediation
H9d	DCP→PE→UTBI	.047**	2.564	.005	0.019	0.080	Partial mediation
H9e	SCT→PE→UTBI	.020ns	1.135	.128	−0.009	0.049	No mediation
H9f	SDI→PE→UTBI	.088***	3.878	.000	0.053	0.127	Full mediation
H9g	UBQ→PE→UTBI	.068***	3.147	.001	0.036	0.106	Partial mediation

LL = Lower level, UL = Upper level, ns p > .05; * p ≤ .05; ** p ≤ .01; *** p ≤ .001.

Figure 2.

Path Coefficient Results.

Meanwhile, the study adopts Hair et al.’s (2022) method to determine the mediation effect through bootstrapping the indirect effect. For H9a, the indirect effect of PSI→PE→UTBI is significant (β = .039, LL = 0.014, UP = 0.068, p < .05), and the direct effect of PSI → UTBI is significant, indicating that PE partially mediates the correlation between PSI and UTBI. H9a is supported. For H9b, the indirect effect of SP→PE→UTBI is insignificant (β = .009, LL = −0.012, UP = 0.031, p > .05). However, the direct effect of SP on UTBI is significant, indicating that PE does not mediate the relationship between SP and UTBI. H9b is rejected. For H9c, the indirect effect of TP→PE→UTBI is significant (β = .059, LL = 0.033, UP = 0.090, p < .05); however, the direct effect of TP→UTBI is insignificant, indicating that PE fully mediates the relationship between TP and UTBI. H9c is supported. For H9d, the indirect effect of DCP→PE→UTBI is significant (β = .047, LL = 0.019, UP = 0.080, p < .05), and the direct effect of DCP→ UTBI is significant, indicating that PE partially mediates the relationship between DCP and UTBI. H9d is supported. For H9e, the indirect effect of SCT→PE→UTBI is insignificant (β = .020, LL = −0.009, UP = 0.049, p > .05), but the direct effect of SCT→UTBI is significant, indicating that PE does not mediate the relationship between SCT and UTBI. H9e is rejected. For H9f, the indirect effect of SDI→PE→UTBI is significant (β = .088, LL = 0.053, UP = 0.127, p < .05), but the direct effect of SDI→UTBI is insignificant, indicating that PE fully mediates the relationship between SDI and UTBI. H9f is supported. For H9g, the indirect effect of UBQ→PE→UTBI is significant (β = .316, LL = 0.219, UP = 0.412, p < .05), and the direct effect of UBQ→UTBI is significant, indicating that PE partially mediates the relationship between UBQ and UTBI. H9g is supported.

The explanatory power (R²) of PE was 0.702 and the R² of UTBI was .719, which suggested that 70.2% of the variance in PE was explained by the antecedents and 71.9% of the variance in UTBI was explained by the antecedents with PE. Resultantly, the current research model reflected substantial explanatory power. In addition, the Stone-Geisser Q² was used to evaluate the model’s prediction power. As presented in Table 6, the Q² value for PE is 0.561, and for UTBI, it is 0.530, showing that the model has a large predictive power.

Table 6.

Predictive Relevance (Q²).

Constructs	SSO	SSE	Q² (=1-SSE/SSO)
PE	1520.000	666.849	0.561
UBI	2280.000	1071.479	0.530

Discussion and Conclusion

The findings supported the hypothesis that PE positively influenced UTBI. Customers would be predisposed to make impulsive purchases when experiencing a high enjoyment level, consistent with earlier studies (Karim et al., 2021). In addition, the PSI was revealed to significantly influence both PE and UTBI, which was consistent with prior research (Zulfa, 2020; Xiang et al., 2016). Specifically, a solid PSI established between live streamers and consumers could enhance consumers’ enthusiasm for live streaming shopping. The positive relationship could be a powerful advertising strategy, with consumers more inclined to follow the live streamers’ recommendations and make impulsive purchases.

Simultaneously, SP was confirmed to positively influence UTBI, which was in line with M. Zhang and Shi (2022). While prior studies focused on static e-commerce, this research highlights the unique dynamics of LSC, where real-time interactions, such as comments, reactions, and flash sales, amplify SP. These interactions foster a stronger sense of community, triggering psychological mechanisms like social comparison and herd behavior. Observing others’ purchases heightens the desire to conform, intensifying UTBI in the dynamic LSC environment. Nevertheless, the positive relationship between SP and PE was not corroborated, which contradicted previous findings (Ye et al., 2020; Shin et al., 2019). The divergent results may be attributed to the significant contextual and behavioral differences in LSC. Unlike general e-commerce or entertainment platforms, LSC integrates multiple dimensions of social presence, including live streamers, platforms, and other viewers, creating a complex and multifaceted social environment. The dominant role of live streamers, who actively engage with viewers and influence purchase decisions, may overshadow the impact of other viewers’ presence. Furthermore, consumer behavior in LSC is often goal-driven, with users prioritizing interactions that directly enhance their shopping experience, such as product information and streamer recommendations. This pragmatic focus might reduce the perceived relevance of peer social presence to enjoyment.

The TP positively influenced PE, which corresponded with the findings of T. Yang et al. (2021). In LSC, real-time interactions, such as live demonstrations, immediate streamer responses, and audience participation, create a more immersive experience than static e-commerce. LSC’s participatory nature, including chats and promotional events, amplifies TP’s emotional and behavioral impact, enhancing PE. Nonetheless, the positive association between TP and UTBI was not supported, which contradicted the existing literature (X. Liu et al., 2023; Yu et al., 2022). The discrepancy may stem from heightened risk perceptions, as consumers are wary of counterfeit items, misleading advertising, and poor customer service due to the lower barriers to entry for live stream shops compared to traditional e-commerce. Negative media coverage further amplifies these concerns, prompting a more cautious approach. Additionally, the dynamic nature of live stream shopping, with real-time interactions, promotions, and demonstrations, may increase cognitive load, shifting consumers’ focus toward rational evaluation over impulsive behavior. Furthermore, the predominantly Gen Y and Z sample, being tech-savvy and accustomed to immersive digital experiences, may view TP as a standard feature rather than a novelty, reducing its emotional impact and ability to drive impulsive purchases.

The results indicated an insignificant relationship between SCT and PE, which contrasted with the past findings (Song et al., 2015). The inconsistency may be attributed to the dual affective impact of SCT. While SCT can evoke excitement and urgency by highlighting exclusivity and limited availability, it can also generate pressure and stress, particularly in the fast-paced LSC environment. In LSC, time-sensitive offers and competitive dynamics may overwhelm consumers, shifting their focus from enjoyment to anxiety or decision-making pressure. Moreover, those consumers who are generally more skeptical of marketing tactics may perceive scarcity-based strategies as manipulative, dampening their enjoyment. Additionally, SCT’s impact may vary based on the type of products promoted and their relevance to consumer goals. Products that lack appeal or alignment with consumer preferences may fail to elicit the excitement needed to enhance PE. Specifically, SCT positively impacted consumers’ UTBI following Akram et al. (2018). In traditional e-commerce, SCT is typically conveyed through static cues like countdown timers or low-stock alerts. However, SCT in LSC is reinforced real-time through interactive elements. Streamers dynamically emphasize limited availability or urge immediate action during live sessions, creating a stronger sense of urgency.

Moreover, SDI was uncovered to positively impact PE, which was in line with earlier research (Song et al., 2015). The finding empirically supported that customers perceive live streaming shopping to be more enjoyable with more SDI. Nonetheless, the insignificant correlation between SDI and UTBI contradicts prior studies (Bao & Yang, 2022; Prawira & Sihombing, 2021). Several factors may explain the lack of a significant positive relationship between SDI and UTBI. While a simple regression analysis revealed a positive effect of SDI on UTBI, stronger predictors, such as PSI, SCT, and PE, likely overshadowed SDI’s impact on UTBI in the full model. Furthermore, while SDI typically evokes excitement through unexpected discoveries, tech-savvy consumers are increasingly aware of the algorithm-driven nature of product recommendations in LSC, which may dampen their impulses. Additionally, for consumers who adopt a goal-directed mindset, the impact of SDI on UTBI may be diminished.

Meanwhile, DCP positively impacted both PE and UTBI, which was consistent with prior studies (Noor, 2020). The results showed that customers frequently observe the availability of discounts, as shopping is regarded as a challenge to overcome. Discounted products would result in increased enjoyment, and the degree to which the live streamer provides discounts will directly influence the level of impulsive purchasing by consumers. This study generated insights into the impact of UBQ. The findings demonstrated that UBQ significantly enhanced customers’ PE and UTBI, which corresponded with earlier studies (Choi, 2016; Chopdar & Balakrishnan, 2020). The findings posited that customers enjoy live streaming shopping and tend to purchase impulsively when considering LSC with a higher UBQ degree. Like general e-commerce platforms, LSC platforms enhance UBQ primarily through accessibility and convenience.

Additionally, this study uncovers that PE plays crucial mediating roles in the correlations between antecedents (excluding SP and SCT) and UTBI, which has rarely been examined in past studies. Specifically, we reveal that PE mediates the influence of TP on UTBI and SDI on UTBI. This indicates that although TP and SDI have no direct effect on UTBI, users who experience TP and SDI will purchase impulsively when they perceive high enjoyment in LSC. Interestingly, PE does not mediate the connections between SP and UTBI or SCT and UTBI. The result can be ascribed to the substantial impact of other peer viewers and scarcity tactics, potent enough to trigger the consumer’s UTBI without necessitating PE efforts.

Implication, Limitation, and Future Research

Theoretical Implications

This study has several theoretical contributions. Firstly, this paper validates the applicability of SIT theory in LSC. The study extends SIT by identifying and empirically testing three live-streaming-specific social influence cues: PSI, SP and TP. It demonstrates how these cues operationalize SIT’s principles of immediacy, strength, and the number of influencers in a live-streaming context. The findings contribute to studies on impulsive purchases by identifying live streaming shopping as a social activity where consumers seek emotional experiences and engage in purchasing behavior. Specifically, the direct and indirect effects of social influence cues on PE and UTBI validate and expand Latané’s (1981) propositions in a novel shopping environment.

Secondly, the study extends the S-O-R framework by integrating seven distinct stimuli that are particularly relevant in the LSC context: PSI, SP, TP, DCP, SCT, SDI, and UBQ. These variables represent diverse aspects of LSC, ranging from social influences (e.g., PSI, SP, TP) to economic and environmental factors (e.g., DCP, SCT, SDI, UBQ). This comprehensive set of stimuli underscores the multidimensional nature of LSC shopping experiences.

Thirdly, it enriches the body of knowledge on impulsive purchases in LSC. Although some emerging research investigated factors influencing consumer impulsive purchases in LSC, such as the characteristics of live streamers and live content (e.g., L. Li, Chen, et al., 2024; Shao, 2023), the direct and indirect impacts of antecedents (i.e., PSI, SP, TP, DCP, SCT, SDI and UBQ) on UTBI in LSC remain unclear. This study differs from previous research that only analyzes a single element of factors influencing impulsive buying. Instead, it creates a comprehensive framework to assess the impact of several factors on UTBI, thereby contributing to the current literature on impulse purchases in LSC.

Fourthly, the study expands upon the existing body of knowledge on PE. Prior research has demonstrated that PE significantly influences UTBI, and several studies have explored the mediating role of PE in different contexts. In LSC, however, PE’s mediating role between antecedents (i.e., PSI, SP, TP, DCP, SCT, SDI, and UBQ) and UTBI has received scant attention. Given the high entertainment value of LSC, this study examines the mediating role of PE and reveals numerous routes to boost UTBI. Thus, the study offers a novel comprehension of PE in LSC.

Managerial Implications

The study provided crucial strategic guidelines for marketers, live streamers, live streaming platform designers, and other stakeholders involved in LSC to maximize the potential for improved business performance. The findings uncovered that PSI effectively induced consumers’ PE and UTBI. Therefore, live streamers should concentrate on developing intimate relationships with followers by increasing the intensity of interaction. For instance, live streamers may address viewers by pseudonyms, answer inquiries timely, and share personal stories to foster PSI. Based on the SP finding, live streamers could encourage the audience to engage in the chatbox and share comments or product-related information during live streaming. Besides, live streamers could motivate the audience to participate in thumbs-up and lucky draw activities to enhance their perceived enjoyment. Based on the DCP finding, LSC consumers are susceptible to DCP. To increase the perceived level of discounts, live streamers could reveal the in-store price of the products before disclosing the live streaming price. The action would elevate viewers’ enjoyment and propensity to perform impulsive purchases. Live streamers and marketers could also conduct SCT promotions to motivate viewers to purchase impulsively. By reminding the viewers of the limited-time sales or supplies for certain products during live streaming, live streamers could attract and incentivize viewers to make unplanned purchases.

Stakeholders involved in LSC should ensure that customers obtain ubiquitous purchase opportunities. Live streamers could establish a pervasive atmosphere by lengthening live streaming sessions or shortening the interval between two live streaming episodes. Additionally, with the assistance of artificial intelligence (AI) live streamers, consumers can make purchases without temporal constraints. Platform designers could optimize live streaming accessibility and smoothness to increase consumers’ UBQ perception. The findings discovered that UTBI could be attained through PE. Live streamers should focus on satisfying consumers’ entertainment needs by arranging interactive activities, such as talent showcases and gifting games, which make the experience enjoyable and engaging. For example, a live streamer can randomly take screenshots of the viewers’ scrolling messages, rewarding participants with free products. Marketers can utilize festival themes and partner with live streamers to develop engaging campaigns, such as Spring Festival-themed live streaming events. On the other hand, designers of live streaming platforms can incorporate gamified elements like fan rank displays. While TP and SDI have no substantial direct effect on customers’ UTBI, they can augment consumers’ UTBI via PE. Designers of live streaming platforms should enhance the immersion and novelty features of live streaming and innovate big data advertising push methods to elevate users’ perceptions of TP and SDI.

Limitations and Future Research

Certain limitations existed in the present study to guide future research. First, this paper examines the influence of SP from a unidimensional perspective. F. Li, Ma, et al. (2024) revealed that the different dimensions of SP influence consumers’ arousal and pleasure differently in the live streaming context. Thus, future researchers could examine the SP as a multidimensional construct, such as the SP of live streaming platforms and streamers. Second, similar to Song et al. (2015), the current research did not involve other mediators, such as perceived usefulness and trust in the framework. Previous researchers opined that perceived usefulness and trust are critical variables influencing consumer behavior (Chen et al., 2020; Xiang et al., 2016). Thereby, future studies could include other mediators. Third, this study solely focused on Chinese users of LSC. Future scholars from other countries could conduct related research, as the findings might not apply to other nations. Ma (2021) suggested that cultural differences may influence consumer behavior. It is therefore necessary to investigate this framework in other countries. Finally, while SIT and the S-O-R framework have proven effective in explaining impulse purchases in LSC, their focus on emotional responses may overlook the role of habitual or automatic behaviors. Other theories, such as Habit Theory, may offer a complementary perspective by highlighting how repeated exposure to stimuli can influence automatic responses. For example, the insignificant effects of SP, SCT, TP, and SDI may reflect habitual behaviors rather than conscious appraisals.

Footnotes

Acknowledgements

None.

ORCID iDs

Xiaoli Zhu

Selvi Kausiliha Vijayan

Ethical Considerations

The ethics approval was granted for this study by the Linyi University Research Ethics Committee (LYU20210156).

Consent to Participate

This study informed respondents about information concerning the research background and aims, as well as confidentiality and anonymity. The paper questionnaires did not contain personal identification data.

Funding

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

Declaration of Conflicting Interests

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

Data Availability Statement

The datasets are not accessible to the public owing to privacy concerns, but they can be obtained from the corresponding author upon a reasonable request.

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