Abstract
Compared to traditional e-commerce, the live streaming e-commerce model can provide consumers with a more direct and authentic buying experience, encouraging them to make purchases sooner. Recently, live streaming e-commerce has become a new channel for business development. This study looked at five aspects that influence consumers’ behavior under China’s live streaming e-commerce model, including ease of use, marketing factors, personal attitude, community influence, and consumer behavior. The study employed a quantitative method, with two questionnaires to collect data from 130 consumers who had live streaming buying experiences. The data was analyzed using Structural Equation Model. The results confirmed that ease of use, marketing factors, community influence, personal attitude, and consumer behavior strongly influence consumer purchase behavior. To make the analysis brief, this study investigated theories that would explain consumer behavior, such as the Theory of Reasoned Actions (TRA), Theory of Planned Behavior (TPB), Stimulus-Organism-Response Theory (SOR), Technology Acceptance Model (TAM), and involvement theories. In summary, this study investigated the factors that influence the consumers’ behavior under the live streaming e-commerce mode. In summary, this study confirmed the factors that influence the consumers’ behavior under the live streaming e-commerce mode.
Introduction
In the age of social media, a new type of e-commerce has emerged: social commerce. It shares many of the same qualities as social media, allowing customers to quickly access any product information they want and even engage with manufacturers over the internet. User contribution and social interaction are two major aspects of social commerce that are developed from social media elements (Fu, 2021). In this model, sellers and consumers directly interact, such as by inviting celebrities to endorse products, publishing cryptic social dynamics, and live-streaming sales, all of which can invisibly shape the audience’s attitude.
In 2016, e-commerce live streaming services such as Taobao, Mogujie, and Jumei became a new online marketing technique in Chinese e-commerce websites. The success of such a service may be due to its distinct traits, which we will discuss next, which serve to alleviate lifestyle fit uncertainties for online users. The first distinguishing feature is that the service content is IT-mediated and lifestyle-focused. The second feature is mirrored in an ICT-enabled information retrieval system that allows online viewers to search for and obtain information provided by service content. In a live streaming performance, there is frequently a public scrolling text screen (Chen, Cenfetelli, and Benbasat, 2019). An online viewer can type a message in a dialog box provided by the information retrieval system. The message is shown on a public screen, where the internet celebrity and all online viewers may see it. The online celebrity then answers to the show’s question, providing the viewer with the information they want. Meanwhile, other online viewers who are viewing the show get the information and choose what is relevant to them in order to make a purchase choice.
Global retail e-commerce sales were $4.9 trillion in 2021, and e-tail revenues are expected to climb to $5.4 trillion in 2022, indicating the influencer marketing channel’s enormous potential (International Trade Administration, n.d.). Adding a new influencer marketing channel, however, necessitates paying hefty fees to the influencer and may result in demand cannibalism among current channels (Luo et al., 2020). As a result, merchants should carefully weigh the advantages and disadvantages of using an influencer marketing channel before making a choice.
In recent years, China has seen a rise in live streaming e-commerce. It blends live streaming video with online commerce to give customers commodity-related video content to influence their buying decisions and encourage buyers and sellers to complete deals (Yang and Lee, 2018). The term “live streaming commerce” refers to a new kind of e-commerce that incorporates real-time social engagement via live feeds (Cai et al., 2018). Traditional web stores are gradually disappearing, giving way to a more advanced and interactive social arena where people are increasingly ready to learn about items and services from one another rather than from photographs and text (Ho and Rajadurai, 2020). Many companies are seeing the value of social commerce and live streaming to improve intimacy (social presence and engagement), communication, and co-creation. As a result, live streaming and social interaction have given academia, corporations, and consumers new opportunities and difficulties (Felix et al., 2017).
China’s live-streaming commerce industry reached $305 billion in 2021, accounting for more than 15% of e-commerce sales in 2021 and more than 20% by 2022 (Hallanan, 2020). The fact that live streaming commerce combines social commerce and social media features explains its popularity (Cai and Wohn, 2019). As customers participate in real-time dialogues with e-vendors, live streaming improves digital marketing credibility by pushing consumers to pay attention to the focus product. Small merchants might use live broadcasting to build a stronger relationship with their customers. The introduction of e-commerce live streaming—influencer marketing channels and merchant live broadcast channels—has given e-tailers new ways to sell to customers. It is crucial to remember that having a deeper relationship with e-vendors is critical when purchasing as it stands as an important retail strategy.
In China, e-commerce overtook supermarkets and department shops as the leading sales channel. For instance, online retail sales of physical items accounted for 24.5 percent of overall retail sales of consumer goods in 2021 and contributed 23.6 percent of retail sales growth (The State Council of The People’s Republic of China, 2022). In addition, China’s consumption market observed an upgraded trend in 2021, with customers increasingly favoring healthy, green, and high-quality goods. Sales of smart home devices increased by 90.5 percent year over year in 2021, while sales of organic vegetables increased by 127.6 percent.
E-commerce, as a new economic paradigm, has shown great promise and has become a key measure of a country’s economic success and long-term viability. Today, everyone is familiar with live streaming as a new trend. Due to its growing popularity, many forms, and multiple platforms, it has become a hot issue. China is, in terms of data, a country with a truly mobile Internet, and its network architecture is rather extensive. Live streaming offers all the benefits of network resources, such as speed, convenience, and interactivity, in a world where global e-commerce has taken on a colorful pattern. Audiences are engaged, so they develop interests that are satisfied by using live streaming to market or report items. Surprisingly, the burgeoning live-streaming sector, together with numerous Internet-based enterprises, has grown fast across the world, particularly in China.
Research Aim
The purpose of the research is to identify the behavior under the live streaming e-commerce model of Chinese consumers and put forward suggestions for the development of live streaming e-commerce.
Research Objectives
The exhibiting and game industries were the first content categories for the live streaming platform, and there is limited interest in other fields. Users rapidly lose time and space constraints as mobile Internet and intelligent hardware become more common, and they may use their mobile devices to live stream and watch live streaming at any time. According to previous e-commerce live streaming research, China’s e-commerce live streaming transactions reached $480 billion by the end of 2021 (Koetsier, 2022).
The number of people who create live streaming videos has also risen dramatically. Live streaming platforms have been popular among the post-90s and post-95s generations (Zhang, 2018). The internet live streaming platform has revolutionized the way information is distributed and set a new standard for real-time engagement with disseminated data. E-commerce platforms have steadily been interested in this function of online live broadcasting (Huang and Suo, 2021). Live streaming is a tool for e-commerce platforms to carry out branding and infrastructure creation from a strategic standpoint.
Even though e-commerce live streaming has grown swiftly and effectively in China in just a few years, there is now a tendency to slow down, and possible dangers to its further development are progressively disclosed.
According to Polchlopek (2021), live streaming is one of the new paths for merchants to explore new conversions and monetize traffic. Live streaming e-commerce is still inseparable from combining the three elements of “people, goods, and fields”. The difference is that live e-commerce innovates people and fields and integrates participants such as anchors and MCN institutions. The basis of the “product details page” enriches marketing scenarios and promotes the closer integration of the three elements. Compared with traditional e-commerce, live-streaming e-commerce optimizes “people, goods, and markets”, driving the upgrade of the user shopping experience, and has obvious advantages in terms of marketing effects and user conversion.
Therefore, this research paper will:
- Examine the state of development of the ‘live streaming e-commerce’ model and the consumer status quo, as well as the state of development of the three market routes, centered on “people, product, and market”.
- Research consumers’ purchasing behavior and develop a model of influencing factors of consumers’ purchasing.
- Investigate how consumers make purchasing decisions and develop recommendations for the continued growth of the live streaming e-commerce business.
Influencing Factors
- Perceived ease of use
- Marketing factors
- Community influence
- Personal attitude
- Consumer behavior
Research contribution
The model of “live streaming + e-commerce” has seen great development in 2020. It is no longer a supplement to e-commerce or live streaming and has begun to become independent, refined, and professional. There are a few pieces of research that studied this topic. Although there are a lot of studies on e-commerce and live streaming, “live streaming + e-commerce” is different among the two. Due to the unique experience between online shopping and offline shopping in the “live streaming + e-commerce” model, consumer behavior under the “live streaming + e-commerce” model is different. This paper will adapt to this particularity to carry out investigation and research, understand consumer behavior and its influencing factors under the model, and theoretically enrich the framework and content of consumer behavior research.
The “live streaming + e-commerce” model solves the dilemma of traditional e-commerce and traditional live streaming, and enterprises are willing to apply it widely. From a practical point of view, consumers are the main body of purchasing behavior, and the research on the influencing factors is helpful in understanding the process and to understand the key factors that determine their decision.
Research questions
Based on the objectives following research questions were proposed:
- What are the influencing factors of consumer behavior under the live streaming e-commerce model in China?
- What are the advantages of live streaming e-commerce?
- How do influencing factors impact consumers’ behavior under the live streaming e-commerce model?
Literature Review I – Industry/Sector Overview
The nature of live streaming e-commerce
This study combines many research sources to focus on the factors that influence the consumers’ behavior under live streaming e-commerce in China. The growth of live streaming e-commerce is getting more active with information technology development. There are several definitions of the live streaming e-commerce model based on its academic meaning. Many studies have described live streaming e-commerce by examining its history, essential elements, characteristics, and structure. For instance, Hou (2020) defines e-commerce live streaming as a new concept of economics, allowing consumers to determine all the aspects of the products more directly and improve the shopping experience, attract popular traffic, and realize precision marketing. Meanwhile, Lu and Siegfried (2021) define e-commerce live streaming as a type of online streaming through which product sellers propagate their products, brands and events to achieve brand awareness and product sales objectives.
Live streaming e-commerce provides significant pricing and engagement advantages over traditional e-commerce. Consumers may interact directly with brands and factories under the live streaming e-commerce model, removing the cost of intermediary connections. Comparing these two models, e-commerce has the virtual presence brought by live streaming, which is used to depict the subjective experiences of being engaged in a virtual environment in the same way as offline consumption situations are described (Dong, Zhao and Li, 2022). If traditional e-commerce allows for only one-to-one interaction, live streaming e-commerce allows for real-time multidimensional interaction (Ma, Gao and Zhang, 2022). Moreover, visualization and entertainment are greatly improved in the live streaming e-commerce model, letting consumers interact with the streamers directly.
Relationship among live streaming e-commerce attributes
Most of the research is based on the user-perceived usefulness, satisfaction, and other perspectives. Information Resources Management Association (IRMA) (2021) added perceived ease of use, enjoyment, trust, and cost to the traditional e-commerce model to more comprehensive research on the live streaming e-commerce model. The essential variables influencing users’ mobile e-commerce are customer personalized services and user interaction.
Sociality is an essential aspect of consumer buying, and internet shopping cannot directly provide a solid social interaction between product sellers and customers. From the perspective of social presence, e-commerce is a shopping mode that is used to present products to consumers and, at the same time, respond to their inquiries in real-time through social media platforms (Wang et al., 2021). Wang et al. (2021) also state that e-commerce can replicate the traditional model and change the consumers’ behavior through social presence. The mechanism of social presence includes control factors, reality factors, dispersive factors, and sensory factors. Live streaming e-commerce directly depends on the technological affordability of the consumers. If the business and marketing strategy involves a live streaming e-commerce model to increase the purchasing, technological and IT affordability should be considered at first. Sun et al. (2019) discuss the influence of the live streaming e-commerce model of business from the perspective of IT affordance. They state that information technology affordance has a positive influence on consumers’ purchase intentions through live streaming e-commerce, for which visibility, meta voicing, and guidance shopping are considered the most important aspects.
Live streaming facilitates real-time interactions among sellers, users, and peers online by enabling real-time seller and customer interactions on e-commerce platforms. This is likely to alleviate the physical separation between sellers, users, and products in cyberspace (Wang, X., and Wu, D., 2019). Live streaming has been embraced by the e-commerce industry to break through the interaction boundaries between product sellers and customers. Therefore, it is crucial to look at the topic from both e-tailers and consumers’ perspectives. Zheng, Li, and Na (2022) define e-commerce as the fastest-growing new form of shopping mode, and e-tailers must rethink their digital strategies when selling the products online based on the customers’ behavior and engagement. From the consumer’s perspective, Addo and Fang (2021) explain that because customers participate in real-time dialogues with e-vendors, live streaming improves digital marketing credibility by pushing consumers to pay attention to the focus product. Hu and Chaudhry (2020) also report that consumer involvement in the live streaming e-commerce model is vital for operators to create relationships. Social ties, including interpersonal interactions, also positively enhance customer engagement. Barari et al. (2020) also support the idea that consumers have a more significant impact on others’ purchase decisions, and new technologies, for example, social media, mobile applications, and augmented and virtual reality, are making it possible providing a network for consumers interactions.
Nowadays, commercial ads may be found almost wherever. Many people, however, are resistant to receiving a large volume of advertising material, therefore, the desired effect may not be realized. By providing original advertising material in the form of live streaming and connecting authentically with viewers, e-commerce live streaming has revolutionized the shape of commercials (Yang and Siegfried, 2021). Consumers will benefit from the new method of information transmission not just because it is more current but also because it is more easily accepted. As a result, the efficacy of advertising may be more readily achieved through e-commerce.
E-commerce live streaming may also properly identify the target customer group. The viewers that are most interested in the merchandise are the ones who enter the live room first. As a result, e-commerce live broadcasting successfully collects users who are interested in making the same transaction.
Cross-border e-commerce and live streaming e-commerce
What makes China different in the study of live streaming e-commerce is the government’s involvement in both supporting the independent development of industry and regulating and developing standardized guidelines. From the government’s perspective, the government has shaped economic growth and urban development. Si (2021) states that the government’s plan is to use the urban areas and commercial live streams as economy-stimulating tools. The author also reports that the government of China has already made regional policies regarding live streaming e-commerce and strategies for the development of the industry in certain urban areas.
China’s cross-border e-commerce has matured in recent years, with steady increase in industry size, greater integration of supply chain processes, progressive transition to more refined and localized operation, and continued penetration of innovation models such as new retail and live-streaming marketing (Cheng, 2021). The widespread adoption of online consumption patterns has fueled the rapid growth of the worldwide e-commerce retail business, resulting in a steady increase in penetration and sales. However, as a result of COVID-19, more individuals stayed at home than in prior years, resulting in a surge in demand for live streaming shopping (Guo et al., 2021). As a result, cross-border e-commerce emerged to live streaming e-commerce.
Literature Review II – Research status of consumer behavior and its influencing factors
Research content of consumer behavior
Impulsive purchasing is characterized by a strong, abrupt, and persistent desire to acquire anything without hesitation, quickly, and kinetically. Online buying is more likely than conventional shopping to lead to impulsive purchases (Wu et al., 2020). Online transactions lead to many customers overpaying due to the virtual process providing them with the appearance of not spending their own money. Impulsive buying refers to a quick and hedonically complicated purchasing activity in which the urge that leads to the purchase is undertaken without any consideration of alternative or future implications (Gulfraz et al., 2022). As a result, impulsive purchasing might be considered a form of illogical conduct on the part of clients. Notably, a purchase may be classed as impulsive if it satisfies three criteria: first, it is unplanned and spontaneous (Gulfraz et al., 2022). External stimuli such as product-related marketing and suggestions from past purchasers are noted, causing live streaming shoppers to stand out among online shoppers. Live streamers show every aspect of a product, demonstrate how to use it, and communicate with viewers in real-time (Ming et al., 2021). Because both sides of the screen are tugging in the direction of purchase, this type of interaction between live streamers and viewers, as well as thorough product presentations, can easily stimulate impulsive buying behavior (Wongkitrungrueng and Assarut, 2018).
The media plays an essential role in transmitting information and promoting goods. Hsu (2019) explains that according to the media richness theory, the goal of arranging information processing is to eliminate ambiguity and confusion. The Media Richness theory is based on information processing theory and claims that richer media are more successful than less rich media in coping with ambiguous signals (Song and Liu, 2021). Based on the literature, using media richness as a stimulus in investigating risk perceptions in a real retail environment is relevant for this study.
Liu, Li, and Gao(2022) discuss live streaming e-commerce from the perspective of grey system theory, saying that the theory’s correlation and prediction models have been widely employed in scientific investigations across a wide range of sectors including business. It is a useful mathematical tool for analyzing tiny samples of data and addressing issues with little information. Because live streaming e-commerce is a new business with limited yearly data, it is difficult for China to establish scientific rules. As a result, the employment of grey system theory is critical to the research of the future size of live streaming e-commerce. Consumer evaluations were classified using grey relational degrees, with the classifications being realized using grey relational degrees that had the highest relationship with the reference evaluation terms. Some customer views, on the other hand, may contain material that is relevant to more than one class. Because grey relational degrees reveal levels of similarity to the reference, this approach may be used to assess evaluations that are represented by many classes (Fidan, 2020). In this case, the research can be broadened to yield more than one class.
The idea describes users’ hedonic feelings, which is critical for gadget adoption. A novel integrated model based on flow theory is developed, which contains exogenous antecedents that represent the features of streaming devices and services. Yang and Lee (2018) explain the flow as the condition in which individuals are so engrossed in a task that nothing appears to matter. While perceived usefulness is concerned with users’ extrinsic motivation—the desire to participate in activities because they are thought to lead to valued results, flow is concerned with users’ intrinsic motivation—the want to engage in activities for doing them.
Studies have proposed flow experience theories to explicate consumer behaviors in online shopping as a result of extensive studies on consumers’ online consumption experiences. Consumers’ consumption intentions are directly influenced by flow experience, as evidenced by cognition, attitude, intention, and conduct (Wang et al., 2021). Because of the comparatively cheap switching costs, demanding standards, and utilitarian character of online buying, online customers have a greater position in the purchase process than offline shoppers.
Research on influencing factors of consumer behavior
Theory of reasoned action
Customers’ future desire and plan to acquire the items or services they want is referred to as purchase intention. According to the Theory of Reasoned Action (TRA), a person’s behavioral purpose is influenced by their attitude toward the action and subjective judgements about the action’s execution (Guo et al., 2021). Purchase intention refers to a consumer’s choice to buy cross-border items or services from merchants via live streaming in this article (Sun et al., 2019).
In cross-border live streaming e-commerce, the immediate presentation of commercial information such as brands, items, pricing, and promotional strategies saves customers time searching and comparing and improves their decision-making efficiency. On the other hand, streamers rely on their professional talents to promote cross-border items to consumers (Hu and Chaudhry, 2020), leading consumers to assume that information obtained through live streaming is more reliable than information obtained through standard e-commerce web pages. Cross-border live streaming e-distinctive, commerce’s professional, and tailored services enable consumers to fully comprehend cross-border items’ usage and functional details, easing their purchasing selections.
E-commerce has been exploding in popularity. Future commercial transactions are expected to take place over the Internet. The idea of reasoned action is applied to online transactions between e-sellers and customers. It is believed that confidence in using a credit card, subjective norms, and previous behavior are all important factors in determining online buying intent. As a result, the growth of the Internet alters and affects the way business is performed. Business transactions are expected to shift from marketplace to market space in the future. Without the impediments of international borders, business transactions may be undertaken effortlessly through the Internet. Citizens in one country can acquire items from firms in another country with simplicity and comfort.
Theory of Planned Behavior
Attitude, subjective norms, and perceived behavioral control, according to TPB, impact a person’s behavioral intention. The idea of perceived behavioral control was created to address situations where persons lack total volitional control. Perceived behavioral control is a function of control beliefs and perceived facilitation and relates to people’s perceptions of how easy or difficult it is to accomplish the activity of interest. The impression of the availability or lack of resources and opportunity to carry out the conduct is known as control belief. According to Ajzen and Kruglanski (2019), the theory of planned behavior explains aspects such as physical activity, consumer behavior, and technology adoption, privacy protection.
The TPB model provides more detailed information on why people make their decisions. To capture distinctive diversity in intention, TPB uses social effects along with normative variables. TPB has also been shown to be effective in predicting intents and behaviors (Ajzen, 2020). TPB performs better in describing social interaction-related behavior, according to Cheng (2019). TPB is a good fit for live streaming because of its focus on social interaction.
Stimulus-Organism-Response Theory
The Stimulus-Organism-Response (SOR) theory (Figure 1) states that external circumstances would generate a specific cognitive or emotional reaction, which would then lead to changes in consumer behavior. With the rapid growth of live streaming e-commerce in recent years, experts have investigated the influence of live streaming on customers’ purchase intentions and behaviors using the SOR model (Guo et al., 2021). The concept developed a method to explain human behavior by evaluating how environmental stimuli affect people’s cognitive and emotional states (Ming et al., 2021).
The functions and features of live streaming include the ability to achieve real-time interaction through the comprehensive use of text, sound, and image, as well as the ability to deliver true and reliable information to consumers from multiple perspectives, allowing them to clearly evaluate product performance (Zhou et al., 2018). Cross-border live streaming e-commerce combines the benefits of multiple media, allowing sellers to not only transmit detailed and rich product information in real-time but also to communicate with customers about the feel, appearance, or smell of the products. Consumers feel closer to cross-border items in place and time because of the authenticity, visualization, and engaging performance presented through live streaming.
According to the SOR theory, consumers’ cognitive responses will be influenced by live streaming features as an external stimulus. In nature, cognition may be separated into two dimensions: positive cognition and negative cognition, according to previous research (Guo et al., 2020). Guo (2020) also thinks that when customers make purchasing selections, they consider not just the price, but also the advantages and expenses of various items. As a result, this paper will examine the influence of live streaming features on customers’ buy intentions in cross-border livestreaming e-commerce from the two perspectives of perceived value and perceived uncertainty.
Technology Acceptance Model
Individuals’ adoption of technology is impacted by extrinsic reasons, such as perceived utility. However, TAM omitted a concept that assessed people’s intrinsic motives. Many people seek intrinsic gratification when they utilize specific technologies, according to Camilleri and Falzon (2021). Non-utilitarian gratifications, such as enjoyment and amusement, might influence users’ behavioral intentions to keep using technology like mobile phones.
TAM aims to explain why consumers embrace or reject technology as it was proved to be more appropriate to adopt technology for utilitarian rather than hedonic or intrinsic objectives (Camilleri and Falzon, 2020). In essence, TAM proposes that people’s views of the ease of use and utility of specific technologies may predict whether they would use them again in the future. According to the original model, the user’s attitude has a direct impact on the use of a new information system, the goal of which is to assess the user’s acceptance of the system (Nagy, 2018).
The technology adoption model has been broadened to include information quality, system quality, service quality, and contemporary technology, all of which are connected to online marketing. The importance of current technology, social networks, mobile applications, and contextual advertising is the fundamental theoretical contribution of our research study. It has been proven that using current technology to communicate enhances the possibility of purchase, resulting in a bigger number of visitors (Fedorko, Bacik and Gavurova, 2018).
Involvement theories
The live-streaming business has four distinct characteristics compared to social commerce: interaction, visualization, entertainment, and professionalization. A streamer can interact with customers through virtual face-to-face communication based on live streaming, display a product in all directions, including try-on, organize fun activities like prize draws and cash voucher grabbing, and make numerous professional introductions (Ma et al., 2022). As a result, live-streaming commerce helps to lessen online purchasing uncertainty and boost customer purchase intentions.
The degree to which an individual considers something as significant or meaningful to themselves based on their needs, interests, and values is referred to as involvement (Wang et al., 2021). Consumer preferences, attention, and engagement for products, services, or brands are finally expressed as the linkage of this information with intrinsic requirements. The following can be used to explain the amount of interest and involvement (Wang et al., 2021). Consumers with a high degree of participation analyze providers’ competency, reliability, and other attributes carefully and create valid cognition. Consumers are less engaged in live broadcasting, and their drive for exact information processing decreases at low participation levels, resulting in unfavorable and rough processing (Wang et al., 2021). In this study, customer engagement refers to their purpose in watching live e-commerce programming as well as their frequency and sentiments about utilizing live e-commerce. Furthermore, it reflects customers’ interest in live e-commerce streaming.
Based on the above analysis, this paper proposes a research model on the influencing factors of consumer purchase behavior including the characteristics of live streaming, and summarizes the influencing factors into: perceived ease of use, marketing factors, community influence and personal attitudes, among which marketing factors include promotional factors and anchors. factors, etc.
Conceptual Development
Hypothesis Development
Social presence is a significant feature in mediating communication medium, and it is derived from the social presence mechanism. Moreover, customer trust can be built through social presence (Ye et al., 2019). Recently social presence concept has been used in e-commerce. In live streaming, the streamer and viewers are constantly interacting during live streaming, building an online community, perceiving each other’s presence, triggering emotional responses, and forming social bonds. When streamers recognize the existence of customers by acknowledging them while live streaming, social presence increases and has a favorable impact on live streaming (Lim et al., 2020). Based on the previous statement, the following hypothesis was proposed:
H1: There is a positive correlation between consumer personal attitude and consumer behavior.
Live-streaming business, like social commerce, is known for its interaction. The intensity and depth of the interaction that happens in reciprocal communication between two persons are referred to as interactivity (Kang et al., 2021). A customer can interact with a streamer and other consumers in real-time when shopping via live-streaming commerce. In commerce, interactivity affects the consumer’s cognitive and affective state (Xue et al., 2020). As a result, this study proposes the following hypothesis:
H2: There is a positive correlation between perceived ease of use and consumers’ personal attitudes.
It is crucial for marketers to know when to use specific models of marketing, as well as which strategy is more predictive of the future behavior of consumers or how consumers will behave in different scenarios. Traditional marketing tactics such as print commercials, celebrity endorsement, and internet marketing have developed into influencer marketing (Grafström, Jakobsson, and Wiede, 2018). Before creating ads, marketers should analyze the consumer’s motivation to process information, ability to digest information, and the employed media (Duggal, 2019). Therefore, the research proposes the following hypothesis:
H3: Marketing factors have a positive impact on consumers’ personal attitudes.
In contrast to physical presence, community influence in online media emphasizes communication and interaction (Chen and Liao, 2021). The perception of others’ coexistence, on the other hand, is a requirement for the establishment of social presence, which in its turn, may influence consumers’ personal attitudes. A more systematic community development approach is necessary to accomplish effective social mixing, as both personal attitudes and experience are major drivers of individuals’ social mix preferences (Luu et al., 2021). Consumers may create a stronger relationship with e-vendors and better understand the service/product they want by using social presence and telepresence. Meanwhile, being there can lead to impulsive purchasing, which is prevalent in the live streaming business (Ming et al., 2021). Hence, the following hypothesis is proposed:
H4: Community influence has a positive effect on consumers’ personal attitudes.
Research model
It was previously stated that influencing factors are perceived ease of use, marketing factors, community influence, personal attitude, and consumer behavior. Based on the preceding theoretical investigation, this research argues that marketing factors, perceived ease of use, and community influence personal attitude, which in its turn influences consumer behavior. The research model is presented in detail in Figure 1 below.
Research Methodology
Sample and data collection
The paradigms of communication research can be split into two categories: qualitative research and quantitative research (Basias and Pollalis, 2018). Quantitative approaches are closely tied to statistical analysis and are based on digital data. Qualitative methods, in contrast to quantitative methods, focus on meaning and interpretation to investigate the meaning creation of live streaming e-commerce mode and its significance in shaping consumer behavior. As a result, the qualitative technique can bring out the key differences between occurrences (Basias and Pollalis, 2018). However, both qualitative and quantitative methods have certain limitations. For instance, the data retrieved from the quantitative method of research can be not valuable as it depends on how the respondents understood the context of the topic and questions. Moreover, the quantitative method can have limited outcomes. Meanwhile, the results of the qualitative method of research usually cannot be generalized to the study’s targeted audience. Therefore, the paper focuses the investigation on both quantitative and qualitative research methods to discover if there were any distinctions or fresh viewpoints between the pieces of literature regarding the topic.
The goal of this research is to investigate the characteristics of live streaming e-commerce from the standpoint of different theories and perspectives, as well as their impact on live streaming e-commerce consumers’ behavior in China. According to the official statistics of the government of China, the majority of users of e-commerce live streaming are young people (ZhiYan consulting group, 2018). Therefore, this research paper uses students and groups of young people as the main audience for sample analysis. To collect data and test the study methodology, two online questionnaires were implemented. The content and measurement items of the questionnaires were created based on a literature analysis and the study’s objectives.
A total of 130 questionnaires were collected, and all the respondents came from China. The first questionnaire investigated respondents’ basic information, such as age, gender, education, occupation, and monthly income. It also examined whether the respondents had any experience in watching e-commerce live streaming and how much time and money they spend on shopping via e-commerce live streaming. Overall, the questionnaire consisted of 11 closed questions (Table1).
Table 1: Demographics of residents (n=130)
The second questionnaire (Table 2) investigated respondents’ shopping experience via live streaming e-commerce. It was directed to analyze the influencing factors that impact consumers’ behavior that were mentioned previously. These factors include perceived ease of use, marketing factors, community influence, personal attitude, and consumer behavior. To make it easy to analyze collected data and make a graphical presentation of the answers, the questions were to choose based on their feelings and how much they agreed or disagreed with each answer (1 to 5).
Table 2: Measurement Items
Procedure and Measures
Regarding the first questionnaire (Table 1), in the 130 valid survey samples, there are a total of 60 females and 70 males. More than 88% of the samples have a bachelor’s degree or above, and 72% are people aged 18-25. 92% of all respondents are students, and 69% of the samples have an income lower than 3000 yuan. 117 respondents had experience in watching live streaming e-commerce, which is 90% percent. 78 people watch e-commerce for one 1hour or less, and 56 respondents watch live streaming 2-4 times a month. About 34% and 33% of the respondents spend less than 500 yuan, and 500 to 1000 yuan respectively on shopping live streaming in the past three months. Among the most popular platforms for live streaming e-commerce, TikTok is the most used by the respondents. At the same time, the majority of those who do not watch live streaming e-commerce, which is 25 respondents, have replied they have no free time for doing it.
Based on the results of the second questionnaire (Table 2), the average values for Perceived Ease of Use (EU) – 3.22, for Marketing Factors (MF) – 3.4, for Community Influence (CI) – 2.9, for Personal Attitude – 3.4, and for Consumer Behavior – 3.3.
Empirical Findings & Analysis
The first hypothesis (H1) testing was conducted by implementing tables to produce coefficients, the statistical significance of the relationship, and the model fit index for the research model. As shown in Table 1, the hypothesis testing procedure produced endogenous variables’ variances (R), R Square, Adjusted R Square, and Standard Error of the Estimate. It can be observed from Table 3 that the Adjusted R Square is 0.349, which indicates that the independent variables account for 34.9% of the variance in the dependent variable. These values show that all of the variables in the study model can adequately describe the development of the dependent variables.
Table 3: Model summary
The significance and the path coefficients provide additional support for the constructs in the conceptual model’s empirical validity. In addition, as indicated in Table 4, the structural model’s model fit indices were all satisfactory. According to Table 4, it can be observed that the F-ratio is significant (i.e. p<0.01). This indicates that the overall regression is a good fit for the data, and that the independent variables statistically and significantly predict the dependent variable.
Table 4: ANOVA table for the fit of the data
Based on the information in Table 5, it can be seen that the Personal Attitude (β = 0.898, p < 0.01) has a positive and statistically significant impact on the dependent variable. This indicates that these three factors are predictors of Consumer behavior.
Table 5: Regression analysis results
As expected, all the pathways given in the study hypothesis are supported by the hypothesis study model. In other words, the research results support H1 (β = 0.596, p < 0.01).
Live streaming commerce provides customers a channel where they can enjoy social advantages through a much-increased purchase and socializing process. Regarding Personal Attitude, Xu et al. (2020) support the idea that customers may engage in more socially relevant activities, such as discussing shopping experiences and items throughout their social network, as a result of the strong social features contained in streaming commerce. Moreover, customers may hedonically consume to acquire delight and novelty due to the hedonic nature of streaming commerce. Customer’s emotions may be widely stirred and therefore prompted to engage in impulsive consumption as a result of the passionate mood and strong participation in the shopping environment. In addition, Ahmad et al. (2018) state that both internal and external elements play a part in influencing the customer’s attitude. Inner variables are caused by a person’s unique qualities and characteristics, and they have a significant impact on a person’s immediate purchasing attitude. Buying is regarded as a pleasurable action associated with emotions and psychological motivations.
The hypotheses H2, H3, and H4 are tested by implementing tables to produce coefficients and the model fit index for each hypothesis. From Table 6, it can be observed that the Adjusted R Square is 0.473. This indicates that the independent variables account for 47.3% of the variance in the dependent variable.
Table 6: Model Summary
According to Table 7, it can be seen that the F-ratio is significant (i.e. p<0.01). This indicates that the overall regression is a good fit for the data and that the independent variables statistically predict the dependent variable.
Table 7: ANOVA table for the fit of the data
Based on Table 8, it can be observed that perceived ease of use (β = 0.603, p < 0.01), marketing factors (β= 0.382, p < 0.01), community influence (β = 0.247, p < 0.01) all have a positive and statistically significant impact on the dependent variable. This means that these three factors are predictors of Personal Attitude.
Table 8: Regression analysis results
Perceived ease of use represents the user’s perception of how easy it is to learn new technology. Regarding Perceived ease of use, Yin (2020) states that based on the characteristics of China’s e-commerce live streaming, perceived ease of use significantly impacts purchase intention and attitude. Furthermore, Wang, et al (2021) state that when making a purchase the consumer makes decisions regarding what to buy and where. This means that the customers evaluate both products and online stores during the decision-making process, including perceived ease of use of products.
Regarding Marketing Factors, Mappesona et al. (2020) define marketing as a promotion element that companies use to inform the market about new products through advertising, sales promotion, and personal sales. The corporation needs to boost advertising and direct marketing to influence consumers’ purchasing decisions. In other words, the more influential the company’s marketing strategy, the more sales will grow. Victor et al. (2018) also support the idea that firms now may adjust rates more quickly and target adverts for online customers, as well as personalize pricing for individual clients based on their income, previous purchasing experience, online reviews, and activity on social media. Therefore, dynamic pricing has become a normal thing in e-commerce, which means the cost of altering pricing is lower online, and e-tailers can experiment with different marketing strategies and put different prices to increase their profit margin.
Regarding Community Influence, Kumar and Kumar (2020) state the importance of online brand community-based advantages and community connection in predicting brand community engagement levels. Member’s perceived community connection investment and community participation influence their brand community involvement. Govindan and Alotaibi (2021) discuss the term “community influencer” and its impact on consumers’ behavior. They state that community influencers have become a widely used tool in present marketing strategies. Having a large number of followers online, influencers have a high impact on their audiences. Moreover, community influencers are considered the most cost-effective approach to advertising and promoting a certain product, as the consumers more trust them. The fundamental point of involving community influencers in a particular marketing strategy is that they convince their supporters to purchase services and products.
Considering all the literature review and analysis made above, it can be stated that the hypothesis testing model supports all the hypotheses. All the indexes and values demonstrate that all the variables in the study model can describe the development of dependent factors, which are personal attitude and consumer behavior.
Conclusion
Discussion
Given the importance of the issue of sustainability as a result of e-commerce competition, developments in the elements that enable e-commerce acceptability are necessary to understand. This research intended to categorize the process of e-commerce technology adoption in order to identify the variables that will drive e-commerce acceptance in the future. E-commerce may be more sustainable if the correct strategy is developed in accordance with the variables that drive its acceptability.
The expanding usage of the Internet and communication technology has been one of the most significant changes in recent decades. The Internet and related technologies have a significant influence on how businesses operate. Internet technology and e-business give businesses new ways to compete in the global market and contribute significantly to the global economy. Despite the numerous research in the field of e-business, a more complete examination of e-business competitiveness and indicators that allow for the measurement of e-business competitive capacity is required.
The act of selling things online via live video as customers engage with the company in real-time is known as live commerce. In order to make live commerce a success, technologies like a live video shopping tool, live chat, and shopping cart integration are required for the best customer experience. Enterprises all around the world have adopted Internet technology to conduct business and increase performance.
A total of four hypotheses were provided in this study, each of which was labeled as a “positive” impact. After conducting route analysis with the SEM model, it was discovered that all the factors had a positive and statistically significant impact on the dependent values., with the P value less than 0.01. The effects of perceived ease of use, marketing factors, community influence, personal attitude, and consumer behavior on Chinese consumers’ behavior under the live streaming e-commerce model are investigated in this study. Based on the results of 130 respondents who had experience in using and purchasing on live streaming e-commerce platforms, the model provided in this paper confirmed all the hypotheses proposed previously. To begin with, the findings show that all influencing factors have a direct positive impact on consumers’ purchasing behavior and purchasing decisions.
Implementations
The influencing factors of Chinese consumers’ purchase decisions under the live streaming e-commerce model are discussed in this study. The research discovered some insights and limits from prior studies after gathering a large number of literature linked to living streaming e-commerce. The majority of prior studies on live streaming e-commerce have been theoretical or focused on a single aspect, so this study employed two dependent variables separately to explore this topic in brief. Collected data using a quantitative research approach, and the research findings also suggest that this study is significant. The implications and recommendations for further research are presented in this paper.
The live streaming E-commerce business is undergoing significant growth at the moment. A solid Internet environment not only lowers the entry barrier for live streaming E-commerce, allowing anybody to become a live streamer, but it also encourages customers to watch live streaming and purchase items at any time and from any location. However, because of this, it is impossible to ensure the content and quality of live streaming E-commerce, and live streamers’ capacity to live broadcast and sell is inconsistent. Furthermore, consumers are concerned about the quality of items and after-sales support in live streaming E-commerce, which will have a negative influence on the industry’s long-term growth.
The benefit of live streaming e-commerce for e-tailers and E-commerce platforms is that, when compared to traditional e-commerce, it may draw more visitors, resulting in more advantages for merchants and e-commerce platforms. As a result, traffic conversion is a critical aspect for re-tailers. However, with the proliferation of live streaming e-commerce, consumers’ attention can quickly be diverted. E-tailers and e-commerce platforms must examine how to entice customers to stay longer in the live streaming room and how to motivate them to make purchase choices in a timely manner.
The factors studied in this study, such as ease of use, marketing factors, community influence, personal attitude, and consumer behavior, will aid industries, e-tailers, and e-commerce platforms in understanding the consumer buying decision cause and provide some implications for the long-term viability of the live streaming e-commerce industry, and give some recommendations for merchants and e-commerce platforms to adjust their marketing strategies and promote traffic conversion in live streaming.
Limitations and Recommendations
This study analyzes the key influencing elements of live streaming e-commerce consumers’ behavior in terms of theoretical implications. It compares the effect of each influencing factor on consumers’ behavior as well as their influencing process. This study adds to the body of knowledge on consumers’ behavior under the live streaming e-commerce model and provides a theoretical addition to the live streaming e-commerce study. However, the paper has several limitations. First of all, as the field of live streaming e-commerce is vast, this study only examines the customers from China. Researchers can broaden the scope of the survey and increase the sample size in the future. Second, utilizing perceived risk as a mediating variable, this study examines only the influence of ease of use, marketing factors, community influence, personal attitude, and consumer behavior on consumers’ purchase behavior. Researchers might look at additional impacting aspects in the future, such as attitude toward streaming services, personality with streaming services, etc. Third, in addition to the TRA, TPB, SOR, TAM, and involvement theories that were used in this study, additional relevant theories could be used in future research. Future research should look into the factors that contribute to customer dissatisfaction with live streaming in order to understand the reasons for consumer dissatisfaction better and enhance the services of live streaming operators and merchants.
Reference List
Addo, P. C. and Fang, J. (2021) ‘Customer engagement and purchase intention in live-streaming digital platforms’, Service Industries Journal, 41(2), pp. 1-20.
Ahmad, M. B. et al. (2018) ‘Factors affecting impulsive buying behavior with mediating role of positive mood: an empirical study’, European Online Journal of Natural and Social Sciences, 8(1), pp. 17-35. Web.
Ajzen, I. (2020). ‘The theory of planned behavior: Frequently asked questions’, Human Behavior and Emerging Technologies, 2(4), pp. 312-324. Web.
Ajzen, I. and Kruglanski, A. W. (2019) ‘Reasoned action in the service of goal pursuit’, Psychological Review, 126(5), pp. 774-786. Web.
Barari, M. et al. (2020) ‘A meta-analysis of customer engagement behaviour’, International Journal of Consumer Studies, 45(4), pp. 457-477. Web.
Basias, N. and Pollalis, Y. (2018) ‘Quantitative and qualitative research in business & technology: Justifying a suitable research methodology’, Review of Integrative Business and Economics Research, 7(1), pp. 91-105. Web.
Cai, J. and Wohn, D. Y. (2019) ‘Live streaming commerce: uses and gratifications approach to understanding consumers’ motivations’, HI International Conference on System Sciences. USA: Hawaii. Web.
Cai, J. et al. (2018) ‘Utilitarian and Hedonic motivations for live streaming shopping’, Proceedings of Interactive Experiences for TV and Online Video, pp. 81-88. Web.
Camilleri, M. A. and Falzon, L. (2020) ‘Understanding motivations to use online streaming services: integrating the technology acceptance model (TAM) and the uses and gratifications theory (UGT)’, Spanish Journal of Marketing. Web.
Chen, J. and Liao, J. (2022) ‘Antecedents of Viewers’ Live Streaming Watching: A Perspective of Social Presence Theory’, Frontiers in Psychology. Web.
Chen, Z., Cenfetelli, R. and Benbasat, I. (2019) ‘The influence of e-commerce live streaming on lifestyle fit uncertainty and online purchase intention of experience products’, Hawaii International Conference on System Sciences, USA: Hawaii. Web.
Cheng, D. (2021) ‘How should China’s cross-border e-commerce on the racing track of investment hotspots get prepared for the capital market’, EY. Web.
Cheng, E. W. (2019) ‘Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM)’, Educational Technology Research and Development, 67(1), pp. 21-37. Web.
Dong, X., Zhao, H. and Li, T. (2022) ‘The role of live-streaming e-commerce on consumers’ purchasing intention regarding green agricultural products’, Sustainability, 14, 4374. Web.
Duggal, R. (2019) ‘Consumer attitudes: a small factor that makes a big impact’, Forbes. Web.
Fedorko, I., Bacik, R. and Gavurova, B. (2018) ‘Technology acceptance model in e-commerce segment’, Management & Marketing, 13(4), pp.1242-1256.
Felix, R., Rauschnabel, P. A. and Hinsch, C. (2017) ‘Elements of strategic social media marketing: A holistic framework’, Journal of Business Research, 70(1), pp. 118-126. Web.
Fidan, H. (2020) ‘Grey relational classification of consumers’ textual evaluations in e-commerce’, Journal of Theoretical and Applied Electronic Commerce Research, 15(1). Web.
Fu, K. (2021) ‘The effect of social media on customers’ behaviors in e-commerce: live streaming shopping as an example’, Advances in Social Sciences, Education and Humanities Research, 638. Web.
Grafström, J., Jakobsson, L. and Wiede, P. (2018) ‘The impact of influencer marketing on consumers’ attitudes’, Bachelor’s thesis, Jönköping University. Web.
Guo, L. (2020) ‘Cross-border e-commerce platform for commodity automatic pricing model based on deep learning’, Electron Commerce Research, 22, pp. 1-20. Web.
Guo, J. et al. (2021) ‘How Live Streaming Features Impact Consumers’ Purchase Intention in the Context of Cross-Border E-Commerce? A Research Based on SOR Theory’, Frontiers in Psychology,12. Web.
Gulfraz, M. B. et al. (2022) ‘Understanding the impact of online customers’ shopping experience on online impulsive buying: A study on two leading E-commerce platforms’, Journal of Retailing and Consumer Services, 68. Web.
Hallanan, L. (2020) ‘How To Adapt Your China Marketing Efforts During The Coronavirus Crisis’, Forbes. Web.
Ho, R. C. and Rajadurai, K. G. (2020) ‘Live Streaming Meets Online in the Connected World: Interactive Social Video in Online Marketplace’, Strategies and Tools for Managing Connected Consumers, pp. 130-142. Web.
Hou, F. (2020) ‘Analysis on the Development Trend of E-commerce Live Streaming’, Learning & Education, 9(4), pp. 194-195.
Hsu, K. (2019) ‘Discussion on the Live Broadcast of Social Media and E-Commerce’, The 18th International Conference on Electronic Business, Guilin, China. Web.
Hu, M. and Chaudhry, S. S. (2020) ‘Enhancing consumer engagement in e-commerce live streaming via relational bonds’, Internet Research, 30(3), pp. 1019-1041. Web.
Huang, Y. and Suo, L. (2021) ‘Factors Affecting Chinese Consumers’ Impulse Buying Decision of Live Streaming E-Commerce’, Asian Social Science, 17(5), pp. 16-32. Web.
International Trade Administration. (n.d.) ‘eCommerce Size & Sales Forecast’. Web.
Kang, K. et al. (2021) ‘The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms’, International Journal of Information Management, 56. Web.
Koetsier, J. (2022) ‘Livestream Shopping: A $500 Billion Market In 2022 As Amazon, Google, Facebook, TikTok Jump In?’, Forbes. Web.
Kumar, J. and Kumar, V. (2020) ‘Drivers of brand community engagement’, Journal of Retailing and Consumer Services, 54. Web.
Lim, J. S. et al. (2020) ‘The role of wishful identification, emotional engagement, and parasocial relationships in repeated viewing of live-streaming games: A social cognitive theory perspective’, Computers in Human Behavior, 108. Web.
Liu, J., Li, S. and Gao, P. (2022) ‘A Study on Livestreaming E-Commerce Development Scale in China Based on Grey System Theory’, Mathematical Problems in Engineering, 2022. Web.
Lu, Y. and Siegfried, P. (2021) ‘E-Commerce Live Streaming – An Emerging Industry in China and A Potential Future Trend in the World’, ACC Journal, 27(2), pp.73-89. Web.
Luo, X. et al. (2020) ‘Complementary and Cannibalization of Offline-to-OnlineTargeting: A Field Experiment on Omnichannel Commerce’, Mis Quarterly, 44(2), pp. 957-982.
Luu, D. T., Vo, D. C. and Kim, J. (2021) ‘Personal attitude or experience? Which factors influence residents’ acceptance of mixed-income communities?’, PLoS One, 16(4). Web.
Ma, L., Gao, S. and Zhang, X. (2022) ‘How to Use Live Streaming to Improve Consumer Purchase Intentions: Evidence from China’, Sustainability, 14(2), 1045. Web.
Mappesona, H., Ikhsani, K. and Ali, H. (2020) ‘Customer Purchase Decision Model, Supply Chain Management and Customer Satisfaction: Product Quality and Promotion Analysis’, International Journal of Supply Chain Management, 9(1), pp. 592-600. Web.
Ming, J. et al. (2021) ‘How social presence influences impulse buying behavior in live streaming commerce? The role of S-O-R theory’, International Journal of Web Information Systems, 17(4), pp. 300-320. Web.
Nagy, J. T. (2018) ‘Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Model’, International Review of Research in Open and Distributed Learning, 19(1), Web.
Polchlopek, M. P. (2021) ‘Why Live Commerce? – A New Growth Point of Online Shopping’, PolyMath Ventures. Web.
Si, R. (2021) China Livestreaming E-commerce Industry Insights. Springer Nature.
Information Resources Management Association (IRMA). (2021) Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business. IGI Global.
Song, C. and Liu, Y. (2021) ‘The effect of live-streaming shopping on the consumer’s perceived risk and purchase intention in China’, 23rd Biennial Conference of the International Telecommunications Society. Gothenburg, Sweden. Web.
Sun, Y. et al. (2019) ‘How live streaming influences purchase intentions in social commerce: An IT affordance perspective’, Electronic Commerce Research and Applications, 37. Web.
The State Council of The People’s Republic of China. (2022) ‘China’s overall retail sales of physical goods topped 10t yuan in 2021’. Web.
Victor, V. et al. (2018) ‘Factors Influencing Consumer Behavior and Prospective Purchase Decisions in a Dynamic Pricing Environment – An Exploratory Factor Analysis Approach’, Social Sciences, 7(9), 153. Web.
Wang, H. et al. (2021) ‘An Empirical Study on the Impact of E-Commerce Live Features on Consumers’ Purchase Intention: From the Perspective of Flow Experience and Social Presence’, Information, 12, 324. Web.
Wang, X. and Wu, D. (2019) ‘Understanding User Engagement Mechanisms on a Live Streaming Platform’, Information Systems and Analytics, 6th International Conference, Orlando, FL.
Wang, Y. et al. (2021) ‘How Live Streaming Changes Shopping Decisions in E-commerce: A Study of Live Streaming Commerce’, SSRN Electron, pp. 1-25. Web.
Wongkitrungrueng, A. and Assarut, N. (2018) ‘The role of live streaming in building consumer trust and engagement with social commerce sellers’, Journal of Business Research, 117, pp. 543-556. Web.
Wu, L., Chiu, M. and Chen, K. (2020) ‘Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues’, International Journal of Information Management, 52. Web.
Xu, X., Wu, J. and Li, Q. (2020) ‘What drives consumer shopping behavior in live streaming commerce?’, Journal of Electronic Commerce Research, 21(3), pp. 144-167. Web.
Xue, J. et al. (2020) ‘See now, act now: How to interact with customers to enhance social commerce engagement’, Information & Management, 57(6). Web.
Yang, H. and Lee, H. (2018). ‘Exploring user acceptance of streaming media devices: An extended perspective of flow theory’, Information Systems and e-Business Management, 16(1), pp. 1-27. Web.
Ye, S. et al. (2019) ‘Enhancing customer trust in peer-to-peer accommodation: a “soft” strategy via social presence’, International Journal of Hospitality Management, 79, pp. 1-10. Web.
Yin, S. (2020) A Study on the Influence of E-commerce Live Streaming on Consumer’s Purchase Intentions in Mobile Internet, HCII 2020: HCI International 2020 – Late Breaking Papers: Interaction, Knowledge, and Social Media, pp. 720-732. Web.
Zhang, J. (2018) ‘Research on the Current Situation and Development Strategy of E-commerce Live streaming Platform’, Changchun University of Technology. Web.
Zheng, R., Li, Z. and Na, S. (2022) ‘How customer engagement in the live-streaming affects purchase intention and customer acquisition, E-tailer’s perspective’, Journal of Retailing and Consumer Services, 68. Web.
ZhiYan consulting group. (2018) ‘Market situation analysis and investment prospect forecast report of China live broadcasting industry in 2019-2024’.
Zhou, L. et al. (2018) ‘Perceived information transparency in B2C e-commerce: an empirical investigation’, Information & Management, 55(7), pp. 912-927. Web.