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International Journal of Environmental Research and Public Health
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6 April 2022

Understanding Consumer Online Impulse Buying in Live Streaming E-Commerce: A Stimulus-Organism-Response Framework

,
and
1
Business School, Qingdao University, Qingdao 266061, China
2
Economics School, Qingdao University, Qingdao 266061, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health2022, 19(7), 4378;https://doi.org/10.3390/ijerph19074378
This article belongs to the Special Issue Consumer Wellbeing and Digitalization: Challenges and Opportunities
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Abstract

With the proliferation of live streaming, there is evidence that online impulse buying is becoming an emerging phenomenon. Although many studies have investigated impulse buying in the context of offline shopping and business-to-consumer e-commerce, online impulse buying in live streaming has attracted little attention. In this study, we aim to explore the effect of social presence in live streaming on customer impulse buying based on the stimulus–organism–response framework. The research model presented here identifies pleasure and arousal as the mediation of impulse buying in live streaming. We use the AMOST and IBM SPSS PROCESS software to estimate our model based on data at the minute level from 189 customers, who watched live streaming in the past three months. The results suggest that the social presence of the broadcaster and the social presence of the live streamer positively affect impulse buying directly and indirectly via pleasure and arousal, promoting consumer online impulse buying in live streaming, but the social presence of the viewers has no significant effect on pleasure and arousal. For practice, our results can help policymakers and operators of the live streaming platform alleviate impulse buying in the digital world.

1. Introduction

The prevalence of live streaming has driven a boom in e-commerce activities, namely live streaming e-commerce [,,,]. This new e-commerce mode uses live streaming to engage customers on e-commerce platforms, where broadcasters leverage the new medium as a direct source for online sales []. According to a report by Deloitte [], China owns the world’s largest live streaming market, which reached USD 4.4 billion in 2018. Especially, Alibaba’s Taobao Marketplace, one of the e-commerce giants in China, generated more than USD 15.1 billion in gross merchandise volume through live streaming in 2018. Several factors explain the rapid development of live-streaming e-commerce in the past years. One factor is that the advanced communication infrastructure such as the 5G network facilitates the broadcasting of high-quality live videos. Another factor is that the isolation caused by the COVID-19 coronavirus urgency has further led to the expansion of live-streaming e-commerce because live streaming as a digital tool empowers vendors and customers around the world to be connected [,].
In typical live streaming e-commerce, the broadcaster can create and deliver real-time video to the customers. The broadcaster can engage potential audiences to join the live stream to consume the content. For example, the broadcasters can talk about the last beauty trend, introduce the product, and try on different items for the viewers. During the content consumption process, the viewers can also ask questions about the products, chat with the broadcaster and other viewers via text, and even send a virtual gift to their appreciated broadcasters. Compared with the traditional web-based e-commerce or social e-commerce, live streaming e-commerce can not only allow the customer to become closer to the products but also to hear the broadcaster describe how the product feels and watch the effect of the broadcaster try it on, thus fostering a more authentic and interactive online shopping experience [,]. Many customers could not help buying the product when watching live streaming e-commerce.
Impulse buying refers to an unplanned purchase, the result of exposure to a stimulus, and deciding to purchase on the spot [,]. According to a report from Ishita [], more than 80% of younger customers conduct impulse buying online, especially for the product categories of groceries and household staples. Compared with web-based e-commerce, live streaming can foster more authenticity and interactivity during online shopping, thus promoting more customers to buy impulsively [,]. For example, Jiaqi Li, one of the Chinese live-streaming broadcasters in Taobao Live, has set a record of selling more than 15,000 lipsticks in 5 min during live streaming e-commerce. As one of Li’s fans said in an interview, “His broadcast with full of passion attracts me the most and arouse my enthusiasm to buy products []. ”Another fan said, “… I don’t understand why I wanted to purchase the product within the first 30 s, and I was even worried that it was out of stock [].” This study attempt to understand which factors influence consumer online impulse buying in live streaming is a relatively new viral e-commerce phenomenon.
During the last decade, adequate research attention has been paid to online impulse buying. Current findings in the context of web-based e-commerce and social e-commerce context may not be suitable for impulse buying in live streaming e-commerce [,,]. Compared to these modes, live streaming e-commerce can foster a more authentic and interactive shopping experience and bridge the distance between customers and presented products [,]. The concept of social presence can capture the virtual shopping experience brought by live streaming e-commerce [,,]. However, some studies only investigated trust, flow state, interactive activities, attractiveness and expertise of the live streamer, and the live streaming purchase convenience on impulse buying in live streaming e-commerce. It is still unclear how social presence in live streaming influences impulse buying through an emotional mechanism, considering that the emotional state is the main driver of the impulse behavior.
To address this question, we use the stimulus–organism–response (SOR) framework to conceptualize our theoretical model because the SOR framework provides a suitable lens to understanding the mechanism for impulse buying via social presence in live streaming. The SOR framework of environmental psychology was proposed by Mehrabian and Russell []. The SOR framework suggests that some environmental aspects resulted in certain behavioral outcomes through provoking an individual’s emotional and cognitive conditions. Therefore, it was widely used in the research of consumer behaviors in the marketing and information system disciplines []. The SOR framework consists of three components: stimulus, organism, and response. In our research, the social presence that captures the overall virtual experience within live streaming e-commerce is the stimulus in our model. Some research used a single dimensional to conceptualize social presence. However, Song et al. [] and Lu et al. [] suggest that the unidimensional conceptualization of social presence is oversimplistic in the context of computer-based communication because individuals not only communicate with the medium but also interact with other participants (e.g., the sellers and other customers). Following the paper by Song et al. [], we used three dimensions to conceptualize social presence in live streaming: the social presence of the live streaming, the social presence of the broadcaster, and the social presence of the viewers. The organism is the customers’ affective and cognitive conditions. In our research, impulsive buying behavior is characterized by consumers’ emotional activation rather than cognitive factors []. Therefore, the emotional factors of pleasure and arousal are the organism. Specifically, pleasure refers to the degree to which individuals feel happy or satisfied, and arousal refers to the degree of stimulation caused by an atmosphere []. Third, impulse buying in live streaming is the response. Impulse buying refers to “a sudden and immediate purchase with no pre-shopping intentions either to buy the specific product category or to fulfill a specific buying task. The behavior occurs after experiencing an urge to buy and it tends to be spontaneous and without a lot of reflection” (i.e., it is “impulsive”) [,].
Based on the SOR framework, we construct and empirically test a structural model that examines impulse buying as a behavioral response caused by the social presence in live streaming e-commerce by using sampling data of 189 consumers from Taobao Live in China. Our study not only contributes to the literature on live streaming e-commerce and social presence theory but also provides practical insights for policymakers and live streaming operators to alleviate impulse buying in live streaming e-commerce.
The remainder of the paper is organized as follows. First, we review three streams of related literature. Second, based on current studies, we propose a theoretical model and our hypotheses. Third, we introduce our research context, sampling and data collection, and measure items. Fourth, we empirically test the research model and hypotheses, followed by a presentation of the research results. Finally, this paper concludes with the theoretical and practical implications and limitations that give rise to further research.

3. Research Model and Hypothesis

3.1. Theoretical Model

Our research model is shown in Figure 1. Specifically, social presence is an element of the live streaming e-commerce experience and can thus be considered as the stimuli. Impulse buying in live streaming can be considered as the behavioral response. According to the model, stimuli of live streaming e-commerce context can influence individuals’ cognitive and emotional states, which may in turn trigger customer behavioral response. Some research evidence has confirmed that the emotional state is the main driver for impulsive behavior. Thus, we consider two basic emotional states of pleasure and arousal as part of the organism that mediates the behavioral response to the stimuli in live streaming e-commerce.
Figure 1. Our research model.

3.2. Hypotheses

In live streaming e-commerce, the broadcaster can introduce and try the products as well as provide personalized services for the audiences []. For example, customers can ask for the broadcaster to try on a specific sized T-shirt for her/him, and then she/he can observe the effect of the product. During the consumption process, the customers can also engage with the broadcasters in real time in various ways, such as chatting, sending likes, and tipping []. These interactions can enhance the social presence of the broadcaster as they are communicating face-to-face, which will thus increase the customers’ shopping pleasure in the live streaming. Prior studies also report that the stimuli in physical shopping can influence shoppers’ pleasure and arousal []. For example, Baker et al. [] found that a friendly salesperson has a positive effect on customer pleasure and arousal in offline settings. Therefore, we propose the following hypothesis:
Hypothesis 1a (H1a).
The social presence of the broadcaster has a positive influence on customers’ arousal in live streaming e-commerce.
Hypothesis 1b (H1b).
The social presence of the broadcaster has a positive influence on customers’ pleasure in live streaming e-commerce.
In live streaming e-commerce, the broadcaster presents various content to interact with the existing customers and attract new viewers into the virtual showroom. The audiences in the live streaming can also interact with the other viewers using text in the chat channel []. Communication among viewers makes online shopping more sociable and sensitive. Moreover, viewers can have a better understanding of the products through other viewers’ word of mouth []. This can make customers co-experience shopping in live streaming e-commerce together, thus enhancing customer viewing experience. When customers are immersed in virtual shopping activities, they tend to experience a state of pleasure and arousal. Thus, it is reasonable to expect that customers who are aware of other viewers in live steaming e-commerce are more likely to experience pleasure and arousal.
Hypothesis 2a (H2a).
The social presence of viewers has a positive effect on customer arousal in live streaming e-commerce.
Hypothesis 2b (H2b).
The social presence of viewers has a positive effect on customer pleasure in live streaming e-commerce.
The social presence of live streaming assesses the subjective characteristics of live streaming e-commerce, which reflect the social side of IT-enabled communication. Prior empirical evidence also suggests that the social presence of media communication can enhance the emotions of pleasure and arousal []. Some research suggests a strong correlation of social presence to pleasure and arousal in the circumstance of online e-commerce []. For example, consumers have a stronger sense of social presence when they are interacting with a computer “tutor” on an e-commerce website [,]. The more warm and friendly the customers feel from the computer “tutor”, the more emotionally satisfied they feel.
Compared with online e-commerce websites, live streaming e-commerce has more human touch and sociability with the broadcasters and other viewers [,]. Thus, the circumstance of live streaming e-commerce will make the customers feel more natural and sociable [,]. In live streaming, the broadcaster delivers content or product to the audiences in the online space in real time. Viewers can join the live streaming to consume the content or purchase the products. This interactive experience makes customers feel that they were physically present during the live streaming e-commerce, thus increasing customer emotions of pleasure and arousal. Therefore, we propose the following hypothesis.
Hypothesis 3a (H3a).
The social presence of live streaming has a positive influence on customer arousal in live streaming e-commerce.
Hypothesis 3b (H3b).
The social presence of live streaming has a positive influence on customer pleasure in live streaming e-commerce.
According to Mehrabian and Russell’s [] model, the emotional reactions (i.e., pleasure and arousal) to the environment determine the individual response (i.e., impulse buying in live streaming e-commerce). First, pleasure can serve as the motivation for impulse buying in live streaming e-commerce []. Shen et al. [] indicated that customers who feel pleasure when they are shopping tend to use this affective state to make a judgment. In other words, the customers who are in a pleasant state are more likely to conduct impulse buying in live streaming e-commerce. Second, if customers are in a pleasant context, arousal predicts the approach behaviors []. The aroused customer will allocate more attention to the live streaming and will tend to continue browsing the broadcaster-recommended products and services []. The extended exploring experience may enhance the impulse buying behaviors in live streaming. In short, the effect of pleasure and arousal on impulse buying behaviors has been investigated in both offline shopping contexts and online contexts (e.g., Internet-based e-commerce). Prior empirical evidence shows that pleasure and arousal as customers’ affective reactions can trigger customer impulse buying []. We suggest that the same logic can be extended into the circumstance of live streaming e-commerce.
Hypothesis 4 (H4).
Consumer arousal is positively related to impulsive buying in live streaming e-commerce.
Hypothesis 5 (H5).
Consumer pleasure is positively related to impulsive buying in live streaming e-commerce.

4. Materials and Methods

4.1. Study Context

In this research, we considered Taobao Live, one of the major e-commerce platforms run by Alibaba in China, as our research context. Taobao Live is the live streaming service integrated into Taobao’s e-commerce App. In 2018, Taobao Live provided over 3000 live broadcasts under the guidance of its live streamers []. Taobao Live is different from other Chinese live streaming platforms (e.g., Douyin Live and Kuaishou Live) in that Taobao Live embeds into Alibaba’s e-commerce platform, while Douyin Live and Kuaishou Live embed e-commerce business into the live streaming platform [].
In typical live streaming e-commerce, broadcasters can build the video streams and display and introduce the products (e.g., clothes and cosmetics) for customers. Customers can interact with the broadcasters and other customers using text. Figure 2 provides screenshots of a typical ongoing live streaming e-commerce in Taobao Live from the customers’ view. This enhanced interactive sociability and contact in live streaming e-commerce can easily trigger customers’ impulsive buying behaviors.
Figure 2. Screenshots of live streaming e-commerce on Taobao Live.

4.2. Sampling and Data Collection

An online survey was conducted to collect data to test our hypotheses. Specifically, we used the Wenjuanxing website (www.wjx.cn, accessed on 17 February 2022) to develop the questionnaire and chose a professional data collection service offered by the website to collect our data. Wenjuanxing has access to a sample database of 26 million Chinese respondents [] and can help to select the live streaming e-commerce customers. Since the survey was conducted in China, the original English items were translated into Mandarin Chinese. To ensure consistency between these two versions, a backward translation was conducted []. Our questionnaire included one screen question, four customer demographic information items (i.e., age, gender, income, and watching frequency), and nineteen measurement items for six variables. To ensure the suitability of all the respondents, a screen question regarding if live streaming e-commerce (such as Taobao Live) was watched within the past month was added. Only those respondents who answered “yes” had access to the following questions. Then, the respondents were instructed to answer the following questions by recalling the last live streaming e-commerce experience. In total, 243 responses were collected and 54 of these responses were deleted for missing data. The final data for analysis included 189 respondents, and the response rate was 77.78%.
To assess for non-response bias, we compared the demographic variables of the early (i.e., the first 50 respondents) and late (i.e., the last 50 respondents) waves of the completed surveys. The Mann–Whitney test shows that the two groups do not differ in age (p = 0.81), gender (p = 0.69), or income (p = 0.52). These results suggest that the nonresponse bias is not a concern in this study.4.3. Measure Items
The multiple-item scales used in our study are designed based on the relevant literature, and minor changes were made to fit the context of live streaming e-commerce (see Table 1). Social presence is measured using three dimensions—the social presence of the broadcaster, the social presence of the viewers, and the social presence of live streaming e-commerce. Measurement items for the social presence of the broadcaster are adapted from [,,]. Measurement items for the social presence of the viewer and the social presence of the live streaming are adapted from [,]. Measurement items for pleasure and arousal are adapted from [,]. Measurement items for pleasure and arousal are adapted from [,]. Measurement items for impulse buying in live streaming e-commerce are adapted from [,]. All constructs were measured using multi-item reflective indicators on a 5-point Likert scale ranging from “1 = strongly disagree” to “5 = strongly agree”.
Table 1. Constructs and items.

5. Results

5.1. Sample Characteristics

The demographic information of surveyed respondents is shown in Table 2. Of the respondents, the majority are females (53.44%), in the age bracket of 26 to 35 years old (58.20%), with an income less than 2000 yuan (47.09%), watching live streaming e-commerce three to five times in the past three months (43.92%).
Table 2. Demographic information of respondents.
As our data were collected from a cross-sectional survey, common method bias (CMB) may exist. Following [], we used a post hoc method to analyze CMB. Specifically, we conducted Harman’s one-factor test on the 18 items to assess for CMB. The result shows that the most observed variance explained by one factor is 23.605%, less than 40%. Therefore, the CMB is not an issue in this study.

5.2. Measurement Model

To examine the reliability and validity of measures in our model, we conducted three types of necessary tests: internal consistency, convergent validity, and discriminant validity. First, we used the composite reliabilities [] to assess the internal consistency of the items. As seen in Table 3, the value of all CR is above the minimum level of 0.7. All the item loading was above the reliability threshold level of 0.7. These results show that the data have internal consistency. Second, we used [] criterion of average variance extracted (AVE) to assess the convergent validity of each construct. The results show that the AVE of all constructs ranges from 0.50 to 0.67, thus exceeding the minimum required of 0.5. Third, we used the inter-construct correlations to assess discriminant validity. As shown in Table 4, the square root of the AVE of each construct is higher than the correlation between constructs, and all correlations are less than the square root of AVE, which establishes discriminant validity. The visual inspection of loadings for this study and cross-loadings further confirm that all constructs had good discriminant validity (see Table A1 in Appendix A).
Table 3. The results of confirmatory factor analysis in the survey.
Table 4. Inter-construct correlations.

5.3. Structural Model

We used AMOST to estimate the path coefficient and the significance of the hypothesized relationship. In AMOST, we used the fit criterion of χ²/df, GFI, RMR, CFI, NFI, and NNFI to evaluate the structure model. Most of the criterions exceed the stipulated threshold except for PNFI: χ²/df = 1.197, GFI = 0.924, RMSEA= 0.032, RMR = 0.031, CFI= 0.984, NFI = 0.913, AGFI = 0.895, and NNFI = 0.981. AGFI is below its threshold (0.9), but it is common that not all fit criteria are perfect in the structural model [,]. The structure model results show a good explanatory power for impulse buying in live streaming e-commerce (R2 = 0.703). The presence of the broadcaster, the presence of the viewer, and the presence of the live streaming proved to be effective in creating customers’ pleasure (R2 = 0.518) and arousal (R2 = 0.658). All these results provide strong support for our structure model.
The estimated path coefficients, related hypotheses, and variances explained are shown in Table 5 and Figure 3. The results show that six out of the eight hypotheses (i.e., H1a, H1b, H3a, H3b, H4, and H5) are supported for the p value less than 0.05. Specifically, SPOB has a direct and positive relationship on arousal (β = 0.324, t = 3.652, p < 0.01) and pleasure (β = 0.564, t = 6.297, p < 0.01). These findings support H1a and H1b. SPOV has no significant effect on arousal (β = 0.049, t = 0.526, p = 0.599) and pleasure (β = 0.022, t = 0.242, p = 0.808). As a result, the findings do not support H2a and H2b. SPOLS has a direct and positive relationship on arousal (β = 0.569, t = 4.694, p < 0.01) and pleasure (β = 0.231, t = 2.254, p < 0.01). These findings support H3a and H3b. The coefficients of social presence of live streaming on both arousal and pleasure are stronger than the coefficients of social presence of the broadcaster on arousal and pleasure.
Table 5. Result summary of path analysis.
Figure 3. Results of the structure mode. Note: ** p < 0.01.
Arousal has a direct and positive relationship on IB (β = 0.567, t = 5.949, p < 0.01). Pleasure has a direct and positive relationship on IB (β = 0.383, t = 4.646, p < 0.01). These findings support H4 and H5. These results are consistent with prior studies; pleasure and arousal were the critical determinants of impulse buying [,]. The results mean that the emotional experience of pleasure and arousal in live streaming e-commerce has strong implications for action tendency. The coefficient of pleasure on impulse buying in live streaming is stronger than the coefficients of arousal on impulse buying.

5.4. Additional Analysis

We further examined the role of pleasure and arousal in mediating the effect of social presence on (i.e., the social presence of the broadcaster, the social presence of the viewers, and the social presence of the live streaming) impulse buying in live streaming e-commerce. We used PROCESS in SPSS [,] to conduct the multiple mediation analysis. Table 6 shows the results of the mediating effects. The indirect effect of the social presence of broadcaster and social presence of live streaming on impulse buying in live streaming e-commerce through pleasure and arousal is significant because the intervals of the bootstrap 95% confidence interval (CI) do not include 0. The direct effects of SPOB and SPOLS on impulse buying are significant; thus, pleasure and arousal partially mediated the effects of SPOB and SPOLS on impulse buying. However, the indirect effect of social presence of viewers on impulse buying in live streaming e-commerce through pleasure and arousal is not significant because the intervals of the bootstrap 95% confidence interval (CI) include 0.
Table 6. Indirect effect results.

6. Discussion

The objective of this study was to understand consumer impulse buying in live streaming. Using the SOR framework as our theoretical lens, we investigated the role of social presence (stimulus) in impulse buying (response) in live streaming e-commerce by considering customer pleasure and arousal (organism).
The findings confirm that the social presence of the broadcaster and the social presence of the broadcaster of the live streaming can predict customer arousal and pleasure. However, our findings suggested that there is no direct effect of the social presence of the viewers on impulse buying in live streaming e-commerce. One plausible explanation is that customers may suspect the motives of revealing the social cues or the authenticity of those social cues from other viewers; thus, these social cues from other viewers are not used by the customer to make purchase decisions []. Another reasonable explanation is that most products sold in live streaming e-commerce are experience products (e.g., cosmetics and clothes), and the information provided by other viewers is not credible until they experience the products [].
Furthermore, consistent with the current literature in both relational marketing and information systems [,], impulse buying in live streaming e-commerce can be predicted by customer arousal and pleasure. Through the multiple mediation analysis, we found that the social presence of viewers can only affect impulse buying indirectly via pleasure and arousal. In contrast, the social presence of the broadcaster and the social presence of the live streaming can affect impulse buying both directly and indirectly via pleasure and arousal. These findings have important theoretical and practical implications.

6.1. Theoretical Implications

The study provides several theoretical implications. First, we contribute to the literature on customers’ well-being in the pandemic period by investigating the impulse buying behaviors in the context of live streaming e-commerce. Impulse buying behaviors are commonplace in live streaming e-commerce all over the world. Moser et al. [] indicated that impulse buying behaviors have many negative impacts, such as feelings of guilt and regret and financial strain. However, with live streaming boosting its fusion with market campaigns, few studies have investigated customer impulse buying in live streaming e-commerce [,]. We closed the gap by investigating how three dimensions of social presences affect customer emotional states of pleasure and arousal, thus leading to impulse buying in live streaming e-commerce.
Second, this study contributes to the social presence theory by identifying a three-dimensional social presence in live streaming e-commerce: the social presence of broadcaster, the social presence of viewers, and the social presence of live streaming. Impulse buying in live streaming is typical IT-related behavior, and social presence is one of the critical design principles for customers’ IT-related behavior. The prior studies mainly examined the role of one or two dimensions of social presence (e.g., social presence and telepresence) in IT-related behaviors [,,]. In our study, we conceptualized the social presence in live streaming from three dimensions of broadcaster, viewer, and live streaming platform and investigated how they affect customers’ impulse buying separately. We can examine the influence of the three dimensions of social presence on customer emotions (i.e., pleasure and arousal), thus affecting customer impulse buying in live streaming e-commerce. Conversely, a multi-dimension of social presence in live streaming can help us understand which antecedent factors affect different social presence dimensions.
Third, our study sheds light on the formation mechanism of impulse buying in live streaming e-commerce by investigating the customer’s emotional state as a mediator. Pleasure and arousal (as a set of emotions) as two key mediating variables have been fruitful in explaining experiential shopping behaviors. Prior studies focused on the effect of specific stimuli such as website quality on customer impulse buying [,,]. In our research, we further extended the use of pleasure and arousal as two key mediating variables by integrating the multi-dimensions of social presence in live streaming e-commerce (i.e., the social presence of broadcaster, the social presence of viewers, and the social presence of live streaming). It can allow us to capture the overall virtual shopping experience brought about by live streaming e-commerce, and it also can show the dimension of social presence as having a significant influence on customers’ emotional state.

6.2. Practical Implications

This study also presents important practical implications. First, in the context of COVID-19 emergence, investment in live streaming e-commerce has grown rapidly []. Live streaming empowers the business owner to promote their product virtually face-to-face with an interested customer, thus leading to serious impulse buying in live streaming e-commerce. Our findings suggest that the social presence of the broadcaster and the social presence of the live stream are effective in improving customer pleasure and arousal, thus stimulating online impulse buying in the live stream. Therefore, a practical implication for policymakers and operators of the live streaming platform is to select and design specific features to reduce the social presence of the live stream. Moreover, the policymakers and operators of the live streaming platform should strengthen the supervision of broadcasters and should not allow broadcasters to send persuasive information to deceive or mislead consumers during the live stream.
Second, although the social presence of viewers seems to be a weaker prediction for customer pleasure and arousal, this does not mean that viewer presence does not matter for online impulse buying in live streaming. Policymakers and operators of the live streaming platform can limit the number of viewers in the live stream to reduce the social presence of other viewers. Conversely, policymakers and operators should pay more managerial effort to prevent these capper consumers from spreading false product information during live streaming.
Third, the findings show that the customers’ pleasure and arousal play a mediate role between the three dimensions of social presence and impulse buying in live streaming. Therefore, a practical implication for operators of the live streaming platform is to design live streaming features (e.g., soft music and pictures) to alleviate customers’ excited emotional state when they are shopping online. At the same time, consumers should warn themselves not to engage in live streaming e-commerce when they are in a pleasant or stimulated emotional state.

7. Conclusions

In conclusion, the main contributions of the study are three-fold. First, during COVID-19, the offline commerce operating modes were seriously interrupted by the emergency. Live streaming has empowered vendors to reach a large audience and to promote their products virtually face-to-face with interested buyers, leading to serious online impulse buying behavior. Based on the SOR framework, we attempted to understand customer impulse buying in live streaming, a relatively new e-commerce phenomenon. Second, we proposed a multi-dimensional conceptualization of social presence in live streaming to overcome the limitation of the one-dimensional social presence construct. Third, we highlighted the importance of three social presence factors in live streaming e-commerce that induce impulse buying via customers’ emotional state (i.e., pleasure and arousal). This study has some limitations. First, the research context is Chinese live streaming e-commerce, which may restrict the external validity of our research findings regarding cultural differences. Future studies can further consider a sample of live streaming users in other countries to examine the external validity of our findings. Second, we are not restricted to a specific market (e.g., clothes or cosmetics) in live streaming e-commerce. It is an empirical question to whether our findings are replicable in a different market. Future studies should modify and apply the current research model to a specific product market. Third, in our research, we focused on social presence in live streaming to conceptualize customers’ virtual experience, which is relevant to inducing impulse buying. Future studies can extend our multi-dimensional construct of social presence that could be induced by live streaming to examine how different constructs may influence impulse buying in different ways. Finally, there are different modes of live streaming e-commerce: live streaming embedded in e-commerce (e.g., Taobao) and e-commerce embedded in live streaming (e.g., Facebook live). Future studies can investigate the effect of social presence on impulse buying in the two different modes of live streaming e-commerce.

Author Contributions

Conceptualization, M.L. and Q.W.; methodology, Y.C.; software, M.L.; validation, M.L., Q.W. and Y.C.; formal analysis, Y.C.; investigation, Y.C.; resources, Y.C.; data curation, Y.C.; writing—original draft preparation, M.L.; writing—review and editing, Q.W.; visualization, Q.W.; supervision, Q.W.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of Shandong province, China, grant number ZR2021QG010.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Item loadings and cross-loadings.
Table A1. Item loadings and cross-loadings.
IBSPOBSPOVPleasureSPOLSArousal
SPOB-10.7750.1760.2480.1760.1540.162
SPOB-20.8060.1760.1940.0710.1090.176
SPOB-30.8240.1490.1610.0710.1540.071
IB-10.2330.7280.210.2190.1220.232
IB-20.1270.8240.1870.0830.1910.174
IB-30.2770.6860.2990.1360.2230.189
Pleasure-10.3550.1940.7920.0040.1350.127
Pleasure-20.2430.3360.6890.1240.040.269
Pleasure-30.1340.1620.8250.1520.1420.126
SPOV-10.2820.010.0020.7780.0480.194
SPOV-20.0330.2480.0540.8110.194−0.027
SPOV-30.0010.0840.1870.8020.1640.091
SPOLS-10.1190.2670.070.1820.6960.183
SPOLS-20.1720.1270.1170.2080.7430.099
SPOLS-30.1040.0730.1010.0490.8130.173
Arousal-10.0630.2760.330.1820.2610.607
Arousal-20.110.3720.2220.0270.2460.639
Arousal-30.2610.1150.0920.1190.1510.825

References

  1. Hu, M.; Chaudhry, S.S. Enhancing consumer engagement in e-commerce live streaming via relational bonds. Internet Res. Electron. Netw. Appl. Policy 2020, 30, 1019–1041. [Google Scholar] [CrossRef]
  2. Lin, Y.; Yao, D.; Chen, X. Happiness begets money: Emotion and engagement in live streaming. J. Mark. Res. 2021, 58, 417–438. [Google Scholar] [CrossRef]
  3. Lu, B.; Chen, Z. Live Streaming Commerce and Consumers’ Purchase Intention: An Uncertainty Reduction Perspective. Inf. Manag. 2021, 58, 103509. [Google Scholar] [CrossRef]
  4. Su, Q.; Zhou, F.; Wu, Y.J. Using virtual gifts on live streaming platforms as a sustainable strategy to stimulate consumers’ green purchase intention. Sustainability 2020, 12, 3783. [Google Scholar] [CrossRef]
  5. Zhang, M.; Qin, F.; Wang, G.A.; Luo, C. The impact of live video streaming on online purchase intention. Serv. Ind. J. 2020, 40, 656–681. [Google Scholar] [CrossRef]
  6. Deloitte. Live Thrives in an Online World. 2018. Available online: https://www2.deloitte.com/content/dam/Deloitte/global/Images/infographics/technologymediatelecommunications/gx-deloitte-tmt-2018-online-world-report.pdf (accessed on 17 February 2022).
  7. Jiang, S.; Zhang, H.; Qi, J.; Fang, B.; Xu, T. Perceiving Social-Emotional Volatility and Triggered Causes of COVID-19. Int. J. Environ. Res. Public Health 2021, 18, 4591. [Google Scholar] [CrossRef]
  8. Chua, G.; Yuen, K.F.; Wang, X.; Wong, Y.D. The Determinants of Panic Buying during COVID-19. Int. J. Environ. Res. Public Health 2021, 18, 3247. [Google Scholar] [CrossRef]
  9. Wongkitrungrueng, A.; Dehouche, N.; Assarut, N. Live streaming commerce from the sellers’ perspective: Implications for online relationship marketing. J. Mark. Manag. 2020, 36, 488–518. [Google Scholar] [CrossRef]
  10. Sun, Y.; Shao, X.; Li, X.; Guo, Y.; Nie, K. How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electron. Commer. Res. Appl. 2019, 37, 100886. [Google Scholar] [CrossRef]
  11. Beatty, S.E.; Ferrell, M.E. Impulse buying: Modeling its precursors. J. Retail. 1998, 74, 169–191. [Google Scholar] [CrossRef]
  12. Piron, F. Defining Impulse Purchasing. Adv. Consum. Res. 1991, 18, 509–514. [Google Scholar]
  13. Ishita. Live Shopping-Why It’s Trending in 2022? Available online: https://www.muvi.com/blogs/live-shopping.html (accessed on 17 February 2022).
  14. Ming, J.; Jianqiu, Z.; Bilal, M.; Akram, U.; Fan, M. How social presence influences impulse buying behavior in live streaming commerce? The role of SOR theory. Int. J. Web Inf. Syst. 2021, 17, 300–320. [Google Scholar] [CrossRef]
  15. Yu, S. E-Commer Live Streaming: A New Hit China. Available online: https://en.pingwest.com/a/2291. (accessed on 17 June 2019).
  16. Akram, U.; Hui, P.; Khan, M.K.; Yan, C.; Akram, Z. Factors affecting online impulse buying: Evidence from Chinese social commerce environment. Sustainability 2018, 10, 352. [Google Scholar] [CrossRef] [Green Version]
  17. Akram, U.; Hui, P.; Khan, M.K.; Hashim, M.; Qiu, Y.; Zhang, Y. Online impulse buying on “double eleven” shopping festival: An empirical investigation of utilitarian and hedonic motivations. In Proceedings of the International Conference on Management Science and Engineering Management, Kanazawa, Japan, 28–31 July 2017; pp. 680–692. [Google Scholar]
  18. Akram, U.; Hui, P.; Khan, M.K.; Saduzai, S.K.; Akram, Z.; Bhati, M.H. The plight of humanity: Online impulse shopping in China. Hum. Syst. Manag. 2017, 36, 73–90. [Google Scholar] [CrossRef]
  19. Johnson, E.K.; Hong, S.C. Instagramming Social Presence: A Test of Social Presence Theory and Heuristic Cues on Instagram Sponsored Posts. Int. J. Bus. Commun. 2020, 2329488420944462. [Google Scholar] [CrossRef]
  20. Biocca, F.; Harms, C.; Burgoon, J.K. Toward a more robust theory and measure of social presence: Review and suggested criteria. Presence Teleoperators Virtual Environ. 2003, 12, 456–480. [Google Scholar] [CrossRef]
  21. Ye, S.; Lei, S.I.; Shen, H.; Xiao, H. Social presence, telepresence and customers’ intention to purchase online peer-to-peer accommodation: A mediating model. J. Hosp. Tour. Manag. 2020, 42, 119–129. [Google Scholar] [CrossRef]
  22. Mehrabian, A.; Russell, J.A. An Approach to Environmental Psychology; MIT Press: Cambridge, UK, 1974. [Google Scholar]
  23. Song, J.; Moon, H.; Kim, M. When do customers engage in brand pages? Effects of social presence. Int. J. Contemp. Hosp. Manag. 2019, 31, 3627–3645. [Google Scholar] [CrossRef]
  24. Lu, B.; Fan, W.; Zhou, M. Social presence, trust, and social commerce purchase intention: An empirical research. Comput. Hum. Behav. 2016, 56, 225–237. [Google Scholar] [CrossRef] [Green Version]
  25. Shen, K.N.; Khalifa, M. System design effects on online impulse buying. Internet Res. Electron. Netw. Appl. Policy 2012, 22, 396–425. [Google Scholar] [CrossRef] [Green Version]
  26. Rook, D.W. The buying impulse. J. Consum. Res. 1987, 14, 189–199. [Google Scholar] [CrossRef]
  27. Cai, J.; Wohn, D.Y.; Mittal, A.; Sureshbabu, D. Utilitarian and hedonic motivations for live streaming shopping. In Proceedings of the 2018 ACM International Conference on Interactive Experiences for TV and Online Video, Aveiro, Portugal, 22–24 June 2018; pp. 81–88. [Google Scholar]
  28. Xu, X.; Wu, J.-H.; Li, Q. What drives consumer shopping behavior in live streaming commerce? J. Electron. Commer. Res. 2020, 21, 144–167. [Google Scholar]
  29. Xue, J.; Liang, X.; Xie, T.; Wang, H. See now, act now: How to interact with customers to enhance social commerce engagement? Inf. Manag. 2020, 57, 103324. [Google Scholar] [CrossRef]
  30. Kang, K.; Lu, J.; Guo, L.; Li, W. The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms. Int. J. Inf. Manag. 2021, 56, 102251. [Google Scholar] [CrossRef]
  31. Chan, T.K.; Cheung, C.M.; Lee, Z.W. The state of online impulse-buying research: A literature analysis. Inf. Manag. 2017, 54, 204–217. [Google Scholar] [CrossRef]
  32. Wells, J.D.; Parboteeah, V.; Valacich, J.S. Online impulse buying: Understanding the interplay between consumer impulsiveness and website quality. J. Assoc. Inf. Syst. 2011, 12, 32–56. [Google Scholar] [CrossRef] [Green Version]
  33. Çakanlar, A.; Nguyen, T. The influence of culture on impulse buying. J. Consum. Mark. 2019, 36, 12–23. [Google Scholar] [CrossRef]
  34. Stern, H. The significance of impulse buying today. J. Mark. 1962, 26, 59–62. [Google Scholar] [CrossRef]
  35. Iyer, G.R.; Blut, M.; Xiao, S.H.; Grewal, D. Impulse buying: A meta-analytic review. J. Acad. Mark. Sci. 2020, 48, 384–404. [Google Scholar] [CrossRef] [Green Version]
  36. Xz, A.; Jm, A.; Feng, Y.A.; Xg, B. Understanding impulse buying in mobile commerce: An investigation into hedonic and utilitarian browsing. Int. J. Inf. Manag. 2019, 48, 151–160. [Google Scholar]
  37. Wongkitrungrueng, A.; Assarut, N. The role of live streaming in building consumer trust and engagement with social commerce sellers. J. Bus. Res. 2020, 117, 543–556. [Google Scholar] [CrossRef]
  38. Lee, C.-H.; Chen, C.-W. Impulse buying behaviors in live streaming commerce based on the stimulus-organism-response framework. Information 2021, 12, 241. [Google Scholar] [CrossRef]
  39. Yin, S. A Study on the Influence of E-commerce Live Streaming on Consumer’s Purchase Intentions in Mobile Internet. In Proceedings of the International Conference on Human-Computer Interaction, Copenhagen, Denmark, 19–24 July 2020; pp. 720–732. [Google Scholar]
  40. Parker, E.B.; Short, J.; Williams, E.; Christie, B. The Social Psychology of Telecommunications. Contemp. Sociol. 1976, 7, 32. [Google Scholar] [CrossRef]
  41. Tu, C.-H. On-line learning migration: From social learning theory to social presence theory in a CMC environment. J. Netw. Comput. Appl. 2000, 23, 27–37. [Google Scholar] [CrossRef]
  42. Osei-Frimpong, K.; McLean, G. Examining online social brand engagement: A social presence theory perspective. Technol. Forecast. Soc. Chang. 2018, 128, 10–21. [Google Scholar] [CrossRef] [Green Version]
  43. Guo, Y.; Goh, K.Y.; Ragab Sayed, M. Mobile live streaming: The roles of broadcasters’ screen presence and dynamic emotions in viewership engagement. In Proceedings of the ICIS Proceedings, Munich, Germany, 15–18 December 2019. [Google Scholar]
  44. Jiang, C.; Rashid, R.M.; Wang, J. Investigating the role of social presence dimensions and information support on consumers’ trust and shopping intentions. J. Retail. Consum. Serv. 2019, 51, 263–270. [Google Scholar] [CrossRef]
  45. Hsieh, S.H.; Lee, C.T.; Tseng, T.H. Branded app atmospherics: Examining the effect of pleasure–arousal–dominance in brand relationship building. J. Retail. Consum. Serv. 2021, 60, 102482. [Google Scholar] [CrossRef]
  46. Liao, C.; To, P.-L.; Wong, Y.-C.; Palvia, P.; Kakhki, M.D. The impact of presentation mode and product type on online impulse buying decisions. J. Electron. Commer. Res. 2016, 17, 153–168. [Google Scholar]
  47. Youn, S.; Faber, R.J. Impulse buying: Its relation to personality traits and cues. Adv. Consum. Res. Assoc. Consum. Res. 2000, 27, 179–185. [Google Scholar]
  48. Baker, J.; Levy, M.; Grewal, D. An experimental approach to making retail store environment decisions. J. Retail. 1992, 68, 445–460. [Google Scholar]
  49. Zhang, H.; Lu, Y.; Gupta, S.; Zhao, L. What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Inf. Manag. 2014, 51, 1017–1030. [Google Scholar] [CrossRef]
  50. Nass, C.; Moon, Y. Machines and mindlessness: Social responses to computers. J. Soc. Issues 2000, 56, 81–103. [Google Scholar] [CrossRef]
  51. Hashmi, H.B.A.; Shu, C.; Haider, S.W. Moderating effect of hedonism on store environment-impulse buying nexus. Int. J. Retail Distrib. Manag. 2018, 48, 465–483. [Google Scholar] [CrossRef]
  52. Fan, X.; Jiang, X.; Deng, N.; Dong, X.; Lin, Y. Does role conflict influence discontinuous usage intentions? Privacy concerns, social media fatigue and self-esteem. Inf. Technol. People 2020, 34, 1152–1174. [Google Scholar] [CrossRef]
  53. Brislin, R.W. Back-translation for cross-cultural research. J. Cross-Cult. Psychol. 1970, 1, 185–216. [Google Scholar] [CrossRef]
  54. Ou, C.X.; Pavlou, P.A.; Davison, R.M. Swift Guanxi in online marketplaces: The role of computer-mediated communication technologies. MIS Q. 2014, 38, 209–230. [Google Scholar] [CrossRef]
  55. Parboteeah, D.V.; Valacich, J.S.; Wells, J.D. The influence of website characteristics on a consumer’s urge to buy impulsively. Inf. Syst. Res. 2009, 20, 60–78. [Google Scholar] [CrossRef]
  56. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
  57. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  58. Gefen, D.; Straub, D.; Boudreau, M.-C. Structural equation modeling and regression: Guidelines for research practice. Commun. Assoc. Inf. Syst. 2000, 4, 7. [Google Scholar] [CrossRef] [Green Version]
  59. Hayes, A.F. An index and test of linear moderated mediation. Multivar. Behav. Res. 2015, 50, 1–22. [Google Scholar] [CrossRef] [PubMed]
  60. Hayes, A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-based Approach; Guilford Publications: New York, NY, USA, 2017; Available online: http://dm.darden.virginia.edu/ResearchMethods/Templates.pdf (accessed on 17 February 2022).
  61. Moser, C.; Schoenebeck, S.Y.; Resnick, P. Impulse buying: Design practices and consumer needs. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK, 4–9 May 2019; pp. 1–15. [Google Scholar]
  62. Akram, U.; Fülöp, M.T.; Tiron-Tudor, A.; Topor, D.I.; Căpușneanu, S. Impact of digitalization on customers’ well-being in the pandemic period: Challenges and opportunities for the retail industry. Int. J. Environ. Res. Public Health 2021, 18, 7533. [Google Scholar] [CrossRef] [PubMed]
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