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INTRODUCTION
Research background
1.1.1 Overview about live stream e-commerce industry
Live streaming is defined as the process of broadcasting real time or live video footage to an audience over the Internet (Cambridge Dictionary 2018) Livestreaming stands out from other forms of video viewing on the Internet because it involves real- time transmission of video content Unlike prerecorded videos, which are stored before being shared, livestreams are simultaneously recorded and transmitted to audiences Thus, real-time interaction between audience members and streamers is enhanced, which often occurs through supporting chat widgets and other chat software made available during the livestream
On the other side, Yang & Lee (2018) described live streaming e-commerce as the combination of live streaming video experience with online shopping experience to supply consumers with commodities-related video content to affect their purchase decisions and promote the conclusion of transactions between buyers and sellers With the development of e-commerce and the widespread popularization and application of the Internet, live streaming plays a key part in retail e-commerce model
The era of live ecommerce traces back when Tabao built a link between an online livestream broadcast and an e-commerce store to allow viewers to watch and shop at the same time (Aurora, 2021) By providing live streaming content, streamers push sales for merchants and help them to increase their brands reputation In the context of live-streaming, streamers themselves could also perform as merchants Consumers are the receivers of information By watching live-streaming video, consumers could not only meet their purchasing needs but also entertainment needs (Yang & Lee,
1.1.2 Current live stream market in Vietnam and buying decision
By 2020, E-commerce live streaming has witnessed a dramatic rise in the live streaming industry under the spread of the coronavirus epidemic, and live streaming used its existing base to penetrate the e-commerce industry (Luyao, 2022) Live streaming has revolutionized the online shopping experience in Vietnam, altering how consumers explore, engage, and buy products Services such as TikTok Shop, Shopee, Lazada, Tiki, and Sendo have emerged as digital marketplaces, offering live showcases of products, enabling real-time inquiries, and facilitating instant purchases According to an article by Asiacircles, Vietnam’s live-streaming e- commerce market is expected to reach $11 billion by 2026, growing at a staggering 28% annually, while over 70% of Vietnamese internet users have watched a live stream, with 50% purchasing during or after the stream Shopee is said to be currently a leading unit in the field of e-commerce in Vietnam by a report from Metric Specifically, in the third quarter of 2023, nearly 70% of the retail sales market share belongs to this platform compared to other competitors in the same industry such as Lazada, Tiki, Sendo, and TikTok
Particularly, in 2022, more than 37 million hours is the amount of time Vietnamese users spent on Shopee Live to interact with their favorite sellers to learn about the products they were interested in before placing orders More value and a more complete online experience from Shopee Live are requested from customers An increasing number of users shared their experiences and comments about purchased products, helping other buyers make better purchasing decisions.
Research motivation
In the ever-evolving landscape of e-commerce, Live-Stream Commerce has emerged as a dynamic and interactive channel that bridges the gap between online shopping and traditional experiences Live streaming has been widely discussed as a new media form that triggers people’s continuous watching behavior (Kaytoue et al., 2012; Sjửblom & Hamari, 2017) Unlike other live-streaming platforms, the live-streaming shopping platform is based on the background of e-commerce and has a strong results-oriented purpose, that is, live viewers are supposed to generate more purchases In 2022, Mai, Q.T.T and Nguyen, T.K.T conducted a survey with 280 Vietnamese users who shop on platforms like Shopee and Lazada and then, they indicated that impulse purchases are common in livestreaming environment
One reason for this phenomenon is that in live streaming e-commerce, streamers (sellers or their employees) provide consumers with product descriptions and information through interpersonal communications and product trials, thereby promoting consumers’ impulse purchases (Hu et al., 2017) Besides, the feeling of being part of a live event, where other viewers are also participating, enhances social presence and can lead to increased impulse purchases The social interaction and community feeling make the experience more immersive and enjoyable (LI, X., Huang, D., Dong, G et al, 2024)
According to Cai et al (2018), impulse buying in livestream is pushed by many factors such as price promotion, streaming media credibility, platform design and interactivity
In my research, I focus on study how external stimuli affect impulse buying decision in livestream So I choose streamer's credibility, interpersonal factors, visual appeal, promotion time limit, and price promotion as independent factors affecting impulse buying in livestream shopping because each of these elements plays a significant role in influencing consumer behavior (Huang, Y & Suo, L., 2021) Streamer's credibility builds trust and persuades viewers through authentic recommendations and demonstrations, which are critical in the real-time, interactive nature of livestreams (Wang et al., 2020; Yang and Lee, 2023) Interpersonal factors, such as the interaction between the streamer and viewers, create a sense of community and social proof, enhancing the likelihood of impulse purchases (Li, M.; Wang, Q.; Cao, 2022) Visual appeal captures attention and engages viewers, making products more attractive and desirable (Huang, Y & Suo, L., 2021) Promotion time limits induce urgency and scarcity, compelling viewers to make quick purchasing decisions to avoid missing out (Xie, 2015) Lastly, price promotions provide immediate financial incentives, making viewers more inclined to take advantage of perceived deals, thus driving impulsive buying behavior (Chen et al., 2021; Li et al., 2021) Together, these factors create a dynamic and persuasive shopping environment that effectively triggers impulse buying
In Vietnam, Shopee is one of the leading e-commerce platforms in Southeast Asia The E-commerce in Southeast Asia 2023 report by Singapore-based market researcher Momentum Works demonstrated that in Vietnam, Shopee had the largest share of 63%, followed by Lazada Meanwhile, Reputa, a Vietnamese social media tracker, pointed out that Shopee and Lazada were the two most popular platforms in the country Analyzing 402,309 entries on social media from January to May, Shopee recorded a total score of 91.03 based on netizens’ sentiments, interactions, discussions, and reach Additionally, Shopee has robust livestreaming capabilities integrated into its platform These features include real-time interactions, product showcases, and instant purchasing options, which are ideal for studying impulse buying triggers With all its advanced live-streaming features, and an extensive user base, Shopee makes it as an ideal platform for studying impulse buying behaviors during livestreams
However, there is a difference in understanding the specific factors that influence impulse buying in the context of Shopee’s livestream platform with other platforms
As a result, the purpose of this research is to fill that gap by identifying and analyzing the key drivers of impulsive buying behavior during Shopee’s live-streaming sessions The findings of this study will not only contribute to the theoretical understanding of impulse buying in livestreams but also provide practical insights for e-commerce platforms and marketers looking to optimize their live streaming stratgies.
Research objectives
This research aims to understand the key drivers behind impulse buying behavior within Shopee's livestream shopping platform It will focus on identifying and analyzing the factors that influence viewers to make unplanned purchases while watching Shopee live streams
By investigating several factors, the research seeks to:
• Identify the most significant influences on impulse buying within Shopee Live
• Understand how these factors interact and contribute to the decision-making process
• Provide valuable insights for Shopee and sellers to optimize their live streams and potentially increase impulse purchases.
Research structure
The research project is divided into five chapters, each playing a crucial role in collecting and analyzing different aspects of the study Chapter 1 serves as the introduction, not only outlining the objectives but also providing an overview of the research structure Next, Chapter 2 will provide overview of independent variables, dependent variables Then, Chapter 3 delves into exploring the existing literature on factors influencing impulse buying on Shopee’s livestreaming, focusing on developing hypotheses and theoretical frameworks for the study Chapter 4 is a detailed description of the research methods chosen, including data collection and sampling Chapter 5 presents the methods and results of data analysis, which is an important step in gaining a deeper understanding of the collected information Finally, Chapter 6 synthesizes the research findings, presents key conclusions, reflects on the limitations of the study, and provides suggestions for future research directions
LITERATURE REVIEW
Theoretical foundation: S – R theory
S-R theory is a theory that explains how customer buying behavior can be influenced by environmental stimuli that trigger their responses (Wei, Wu; Abdullah, Abdul Rashid; Ting, Fu, 2023) S-R theory is based on the idea of conditioning, which is a process whereby a response becomes more frequent or more predictable in a given environment as a result of reinforcement S-R theory was developed by early behaviorists, such as Edward Thorndike, Ivan Pavlov, and John Watson, who focused on observable and measurable behaviors rather than mental processes S-R theory has been applied to various domains of customer buying behavior research, such as learning, motivation, attitude, and habit formation
In customer behaviour, this model posits that marketing and environmental stimuli (such as product, price, place, and promotion) trigger psychological processes within consumers, including perception, motivation, and learning, which ultimately influence their buying behavior (Kotler & Keller, 2016) For instance, a study by Mehrabian and Russell (1974) demonstrated that environmental stimuli in retail settings, such as store layout and music, significantly affect emotional responses and, consequently, shopping behavior Roy and Datta (2022) also applied the S-R theory to examine how consumers decide what to buy and how they make those decisions They explored the basic research on consumer buying behavior, models of consumer buying behavior, factors affecting buying behavior, categories of consumer buying behavior, and consumer decision-making processes They also summarized the research on consumer behavior for simpler understanding and helped in creating the research topic Similarly, Cummins et al (2014) used the S-R theory to explore consumer behavior in the online context They found that online consumers are influenced by various stimuli, such as website design, product information, social media, and reviews, that affect their responses, such as purchase intention, satisfaction, loyalty, and word-of-mouth.
Overview about variables
Impulse buying behavior is considered as an unplanned decision to buy a specific product or a service (Cangelosi & Dill, 1964; Liu & Li, 2008) Impulse buying is characterized by spontaneous, unplanned purchases driven by an immediate desire to fulfill hedonistic needs (Beatty and Elizabeth Ferrell, 1998) With the rise of e- commerce, online impulse buying is demonstrated as the lack of self-control leading to an abrupt online transaction, a concept further supported by Chan and others in
2017, who noted the influence of the online environment Floh and Madlberger in
2013 highlighted the role of online atmospheric cues in this process The rapid expansion of e-commerce has sparked extensive research into impulse buying behaviors, (H Gao et al., 2022)
In the beginning phase of this phenomenon, researchers have recognized that impulse buying can be resulted from various factors such as the economy, location, and timing, which can intertwine under different shopping scenarios Consequently, they have categorized impulse buying into distinct types:
● Pure Impulse Buying: This represents the classic impulse purchase, where a consumer makes an unexpected or novelty buy, deviating from their usual pattern
● Reminder Impulse Buying: This occurs when a shopper’s recollection of past products triggers an impulsive purchase
● Suggestion Impulse Buying: This type arises when a consumer encounters a product for the first time and visualizes a need for it
● Planned Impulse Buying: This happens when a consumer plans to buy specific items but is open to additional purchases based on in-store promotions or discounts
In my research, I incorporated multiple types of impulse buying into the study of factors affecting impulse buying decisions in Shopee’s livestreams to provide a richer, more nuanced understanding of consumer behavior In fact, consumers exhibit diverse behaviors and motivations Some consumers may be more susceptible to impulse buying due to internal factors such as fear of missing out caused by promotion time limit, while others may be influenced by external factors like discounts or peer influence during livestreams (Verhagen & van Dolen, 2011) Besides that, Shopee’s livestream environment has unique features such as real-time interaction, influencer endorsements, and dynamic presentations These factors can differentially impact various types of impulse buying By examining multiple types, researchers can better understand how these contextual elements play a role in influencing consumer behavior during Shopee’s livestreams (Chen & Lin, 2018)
In the offline buying model, impulse purchase decision is usually promoted not only by internal factors, including personality, culture, shopping enjoyment tendency, cognition, etc (A AJB, B AV, 2014), but also by external factors, such as shopping environment, shopping partner, etc
However, with the rapid growth of e-commerce, consumers' purchase behavior gradually breaks the limitations of time and space, reduces the thinking time of consumers' purchase decisions, and is more likely to cause impulse purchases behavior (Liu P, He J, Li A, 2019) In the case of e-commerce impulse buying, customers are also affected by external and internal factors Regarding external dimensions, Louis et al (2016) showed that web page design and promotional activities positively influence consumers' impulse buying behavior The quality and quantity of online comments also have a positive impact on consumers' impulse buying behavior (Li et al, 2018) In addition, internal factors such as emotion, lack of control, and impulsivity are closely related to consumers' impulsive buying behavior (Huang, Y & Suo, L., 2021) As a new retail online method, there is a lack of studies on consumers’ impulse buying on live streaming (Li, X., Huang, D., Dong, G & Wang, B., 2024) Impulse purchasing behavior is common in live streaming, but the cognition of this phenomenon is very scarce
In the context of buying livestreams, a streamer is typically defined as a host, often an influencer or celebrity, who promotes products through a live video broadcast This marketing strategy, known as live shopping, combines the immediacy of live video with the ability to interact with viewers in real-time Streamers showcase products, demonstrate their use, answer questions, and encourage viewers to make purchases during the broadcast (Lu, B., Li, G & Ge, J E, 2023) Streamer’s credibility demonstrates the level of trust customers can count on streamers It can be measured by streamer’s trustworthiness (L Liu, 2022)
Promotional pricing is a prevalent strategy in the marketplace, essentially divided into two main categories: direct and indirect price promotions Direct price promotions are straightforward reductions in the product’s list price, providing immediate monetary benefits to the consumer This category includes various forms of price cuts such as discounts, coupons, and progressive discounts based on the amount spent Indirect price promotions, on the other hand, offer consumers extra value through means other than a direct price cut This could include free additional products, opportunities to win in a draw, or enhanced product guarantees
There are eight promotional methods frequently applied on e-commerce sites, including price reductions, promotional pricing, prize draws, vouchers, volume discounts, shipping concessions, time-sensitive deals, and exclusive promotions (Zhao and Luo, 2018)
In the context of live streaming e-commerce, direct price promotions often contains price cuts, vouchers, scaled discounts, rebates, shopping credits, and flash deals with limited time and limited quantity Meanwhile, indirect price promotions typically involve complimentary items or prize draws as part of the incentive package for viewers to make a purchase
Visual appeal in livestreaming is defined by the aesthetic quality and sensory impact of the visual elements presented during a live broadcast (Rudi, V, 2024) It’s crucial for capturing and retaining the audience’s attention, as well as for enhancing their engagement and emotional connection with the content
Visual appeal in livestreaming is achieved through a combination of factors such as high-quality video, effective lighting, engaging graphics, and professional
The process in which two or more people exchange information, feelings, and meanings using verbal and nonverbal cues is called interpersonal interaction (Zhao et al., 2015) In the context of live streaming e-commerce, interpersonal interaction encompasses two primary dimensions: the real-time conversation between viewers and streamers, and the one among viewers themselves Thus, interpersonal interaction signifies the process of exchanging information over the internet, where consumers engage with streamers as well as with other consumers This exchange is pivotal for building relationships, fostering community, decreasing transactional distance, helping to deep understand each other and enhancing the overall live-streaming experience
According to Cialdini (2009), a promotion has quantitative and time constraints Quantitative limits are aimed at rare products, increasing competitive consumer purchases (Aggarwal et al., 2011) On the other hand, the time limit is primarily intended to increase consumer desire to purchase products (Swain et al., 2006) There will be no time pressure if the time limit does not make customers feel rushed or anxious (Svenson & Maule, 1993) According to Ariely and Zakay (2001), time constraints can cause pressure, affecting a person's psychological and emotional changes With the promotion time limit in live- streaming selling, consumers have limited time to make a purchase decision, as the promotion will end when the live- streaming is over
Hypothesis Development
2.3.1.1 Streamer’s credibility and impulse buying decision in Shopee’s livestream
Within the realm of live e-commerce, the live streamers is considered as social media influencers Numerous subsequent studies have examined the viability of social media influencers (SMIs) as an effective marketing strategy for enhancing consumer purchasing decisions and brand recognition through their influential presence ( Ki, C.-W.C.; Kim, Y.-K,2017) In the digital era, companies implement social media influencer marketing as a highly efficient marketing tool(Stubb, C.; Nystrửm, A.-G.; Colliander, J.,2019) particularly within the context of live e-commerce In the case of livestream e-commerce, the e-commerce streamer is a salesperson and an opinion leader who links up the product and the consumer When shopping, customers focus either on the brand, reputation and quality of the product or on trust in the product and trust in the streamer’s recommendation
Due to the uncertainty of the online environment, the trust in online purchases will be affected by more factors than one purchase Therefore, how to improve consumers' trust in the live shopping scene has become a problem that must be considered According to LI, X., Huang, D., Dong, G et al (2024), The personal charm and professionalism of the streamer will improve the trust in the information source and make consumers feel at ease, thus affecting consumers’ impulse purchase intention
In addition, this trust can affect consumers' decision-making from other aspects, such as reducing their perception of loss and subjective speculation about the results Moreover, in the live streaming situation, consumer trust will play an important role presentation The goal is to create a visually stimulating experience that resonates with viewers and encourages them to stay tuned in and interact with the stream2
Moreover, visual appeal in livestreams is not just about the attractiveness of the visuals; it’s also about the usability and accessibility of the content This means ensuring that the stream is easy to follow and interact with, which can significantly enhance the viewer’s experience and satisfaction in promoting the rapid conclusion of the transaction, which is more conducive to the generation of an impulsive purchase Therefore, the more consumers trust the streamer, the more impulsive purchase behavior will be aroused in the process of watching the live streaming
Particularly, in livestream, consumers are afforded a pre-experience of products through influencers (live streamers) prior to making a purchase When consumers’ actual product experience aligns with the pre-experience conveyed by the live streamers, feelings of trust and identification with the influencer are established Consequently, this leads to positive consumer intentions and consumption behaviors
H1 Streamer’s credibility has a positive impact on impulse buying decision in Shopee’s livestreaming
2.3.1.2 Price promotion and impulse buying decision in Shopee’s livestream
There are two types of price promotion: 1) providing direct discount prices, such as discounts, coupons, and others, and 2) providing indirect price preferences, such as additional prizes, sweepstakes, product quality assurance, and others (Hardesty & Bearden, 2003) Direct discount prices in live-streaming selling typically include discounts, coupons, cashback, free shipping, limited-time discounts, and other perks (Liu et al.,2022)
Based on the result of the studies on impulse purchases, there is a conclusion that price promotions is one of the factors leading consumer towards a impulse purchase (Leblanc-Maridor, 1989) In other words, because promotional incentives could induce consumers’ perceived value, making customers believe that they bought this item at a bargain price (L.Liu, 2022), and then, making them eager to make a good deal Such feelings can lead to impulse buying because they enable the consumer to rationally satisfy his desire and minimise his concerns for the financial risks associated to the purchase and negative feelings that cause the consumer to resist such desires (Campbell and Diamond, 1990) Therefore, price promotion is a key factor that affects the impulse purchase behavior of consumers Li et al (2018) also stated that price promotion has directly or indirectly positive impact on consumers' impulsive buying behavior Dawson and Kim (2010) noted that price discounts and extra giveaways can boost consumers' inclination towards impulse purchases Similarly, Yin (2013) discovered that price discounts positively influence consumers' buying behavior, with varying promotional techniques affecting impulse buying differently
H2: Price promotion has a positive effect in impulse buying decision on Shopee’s livestream
2.3.1.3 Interpersonal Interaction and impulse buying decision in Shopee’s livestream
In traditional online shopping, consumers get detailed information about products via browsing texts and images posted on e-commerce or social media platforms If they want to know more about one items, they will communicate with the sellers by leaving text messages, so it is impossible for consumers to receive a reply immediately In contrast, in live streaming e-commerce, consumers can have real- time interaction with the streamers and other viewers, which makes the shopping process more vivid
Interactions in live streaming selling can take the form of streamer-consumer and consume-consumer interactions Streamers can increase consumer interest in products by interacting with them, increasing their desire to buy (Liaw et al., 2007) Interaction among consumers can also improve the process of exchanging product information, leading to decisions in online transactions
In broadcasting e-commerce, consumers could interact in the real-time with streamers at any time based on their own needs This allows them to gain a thorough understanding of the quality and functionality of products, which helps them make impulse purchase decisions For example, streamers taste the food products, and try on new clothes and cosmetics, allowing consumers to intuitively assess these items through the streamer's verbal descriptions, facial expressions, and body language
Viewers can send screen bullet comments to ask questions about the products, which the streamer then answers These dynamic online interactions between consumers and streamers during live streaming create a sense of immersion and engagement This interactive feature enhances the appeal of live shopping and stimulates consumers' purchasing intentions and behavior in live e-commerce
Besides, a study found that consumers’ direct interactions with streamers boosted their cognitive assimilation, emotional energy, and arousal in live-streaming shopping The change in consumers’ attitude and feeling from this interaction triggers different purchasing behaviours in the live streaming e-commerce (Xu, X., Wu, J H.,
Streamers also organized entertaining activities to maintain ongoing interaction with consumers, encouraging them to stay in live-streaming chat rooms and make purchases (Sun, Y., 2020) Huang and Suo (2021) highlighted that interaction between consumers and streamers significantly reduced consumers' perceived risk regarding the products and stimulated impulsive buying behavior
H3a: Customer – Streamer Interaction has a positive effect in impulse buying in Shopee’s live-streaming
In live streaming shopping, viewers can connect with each other by chatting, sharing their real-time feelings and any positive or negative comments on the products by bullet screen Other viewers can see these instant feedbacks shown on the screen, so they can rely on them to help consumers make purchase decisions This creates interactions between consumers
A research conducted by Larose (2001) proved that The study showed that the feedback of buyers on goods in livestreaming will prompt consumers to make an immediate purchase Herb behaviour, which means that individuals in a group follow others’ behaviours (Yin, S, 2020), is used to explain for this action In live-streaming shopping, consumers often mitigate uncertainty by emulating the purchasing behaviors of others They place trust in product comments shared by other users, making them more likely to make impulse buying decision in live streaming shopping (Guo, L., Hu, X., Lu, J., & Ma, L, 2021)
METHODOLOGY
Research philosophy
In my study, I chose positivism as my research philosophy because positivism emphasizes the use of quantitative data and statistical analysis My research involves collecting numerical data, testing hypotheses, and looking for patterns and correlations, so a positivist approach support these methods effectively Positivism is a philosophical paradigm that is centered on the ontological belief that reality is measurable and encompasses only what one can directly observe (Lincoln and Guba, 1985; Tashakkori et al., 2021) Axiologically, positivists tend to believe that research can and should be without value judgments, thereby emphasizing researcher objectivity (Tashakkori et al., 2021) The role of research in this paradigm is to observe and measure patterns in reality to test hypotheses or make predictions about reality using deductive reasoning and quantitative methods (Hesse-Biber, 2017; Tashakkori et al., 2021) They also use other methods that emphasize researcher objectivity through standardization of research practices, data collection, and analysis (Hesse-Biber, 2017).
Research Methodology
The research will use a quantitative research approach to gather and analyze data An online survey will be conducted, featuring structured and comprehensive questionnaires designed to evaluate impulse buying decisions on Shopee’s livestream and the possible influencing factors Quantitative methods are advantageous for collecting extensive data from a representative sample, ensuring accuracy and reliability of the findings This approach allows for large-scale comparisons of impulse shopping decisions and influencing factors across different demographic groups, such as age, gender, or educational level The use of structured questions in the survey simplifies the analysis and interpretation of results, while also facilitating standardization and statistical testing.
Data Gathering procedure
+ Step 1: The questionnaire was constructed based on previous studies so that validity of the measures was maximized and the results of the research can be compared with previous studies (Martin et al., 2020, Dixson, 2015, Pham et al., 2019, Pham et al., 2018) This questionnaire contains two main sections: background of respondents, research questions related to 7 variables To ensure the right sample, I designed a filter question which is “Have you ever bought a product without initial intention to buy it in Shopee Live?”
+ Step 2: Conducting pretesting We will conduct pre-testing by asking 8 potential people from different academic backgrounds: 2 students, 2 officers and 2 freelancers,
2 others The purpose of pretesting is to check time to complete the survey and collect the comments on the wording and content understanding of the items After that, we will test the data by using SPSS 26 to assess the questionnaire items Based on feedback from pretesting, we will make adjustments to the items pool
+ Step 3: gathered responses by sending the survey links to active Shopee livestream viewers who purchased impulsively in Shopee Live across Vietnam to ensure a diverse demographic representation.
Data Collection
During the data collection process, the researcher employed an online survey method through Google Forms, a convenient and flexible tool for gathering data from a large number of consumers I sent the survey to groups where there are many Shopee users like Nghien Shopee with 1M members, Review Shopee with 195K members I collected the data in 1 month to ensure flexibility and effectiveness in gathering information from consumers This period provided the researcher with sufficient time to amass a significant volume of data Moreover, Google Forms streamlined data management and analysis, ensuring that the research results were both accurate and reliable.
Sample size
In this research, the primary data contains 300 Shopee users used to have impulse buying behaviour in the past The determination of the sample size was based on the formula n = 5m , where n is the population size and m is the number of questions in the questionnaire, as suggested by Comrey (1973) and Bove (2006) My research survey has 31 questions, so the sample size was calculated as follows: n = 5*31 155 Consequently, the study aimed to collect data from a minimum of 155 participants It is noteworthy that the researcher exceeded this minimum requirement by collecting data from 300 consumers The researcher asserted that this sample size adequately represented the Shopee user community in the context of Vietnam, thereby enhancing the robustness and generalizability of the research findings.
Measurement Items
For the purpose of this study, data collection was carried out through the distribution of a two-part questionnaire The initial section comprised multiple-choice questions designed to elicit fundamental personal details, including Gender, Age, and Occupation, Weekly Watching Shopee’s Livestreaming hours, Monthly spending on Shopee’s livestreaming, Purpose of watching Shopee’s livestreaming Subsequently, the second segment of the questionnaire was dedicated to inquiries about the research variables, as outlined in Table 1 In measuring the constructs, I use five-point Likert scale anchored by: 1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree,
4 = agree and 5 = strongly agree due to its perceived ease of respondent engagement, supported by previous research by Babakus and Mangold (1992) and Sachdev and Verma (2004)
I believe in the information that the live streamers provide through live streaming
I can trust live streamers that use live streaming
I believe that live streamers are trustworthy
I don't think that live streamers would take advantage of me
PP1 I am easily attracted by price promotions
When it comes to price promotions, I cannot help buying
The price promotion gave me a strong impulse to buy
I feel that streamers usually give a short promotional period
I feel that the time to decide whether to buy the sale product or not is very short
I do not want to miss the opportunity to buy the sale product
I feel that if the product is not purchased immediately, I may not be able to purchase a product with the same promotion in the future
Streamers respond actively to viewers’ questions
Ma et al (2022) Zhao et al
Streamer gives detailed information and explanation about the products
Streamer helped me to visualize products as in the real world
Live streaming reduced the distance between me and the streamers
While watching live streaming, I feel as if I am interacting with the streamer in- person
Streamer is very happy to communicate with viewers
I can communicate with other consumers on livestreaming smoothly
What other consumers said about the product helped me make my purchase decision
Liu et al (2020); Faradiba (2021), Zhao et al (2015)
Other consumers’ reviews and choices on livestreaning are helpful for me
Other consumers’ comments increase my intention to purchase products
Streamers make a clear presentation of the products for sale
The way the streamers present the products is very attractive
I like the overall layout of the live streaming room
The overall visual effect of the live streaming room is very good
I did not plan it at all until I went into the live streaming room and decided to buy it
I bought the product without thinking it through at all
I was completely influenced by the mood of the moment when I made the purchase
In the process of shopping, I have a strong desire to buy some goods that I would not have intended to buy.
Data Analysis Technique
During the analytical phase, two prominent software tools were utilized The Statistical Package for Social Science (SPSS) version 22 was employed for robust statistical analysis, allowing for a thorough examination of the dataset (Abu-Bader,
2021) Additionally, Smart PLS version 4.0 was used for Partial Least Squares (PLS) analysis, enabling structural equation modeling (SEM) and path analysis to delve deeper into relationships within the data (Wuisan et al., 2023) This combination of software packages enabled researchers to uncover insights and patterns inherent in the dataset, thereby contributing to the study's objectives and enhancing the validity of the findings.
DATA ANALYSIS
Descriptive Static Analysis
Based on the results obtained, there are interesting differences in responses between males and females within the scope of this study Notably, females showed a strong response rate of up to 74.3%, significantly exceeding the observed response rate of 23.3% in males, with the 'Other' category representing only 2.3% of responses This underscores the importance of studying the influence of gender motivation on the study's outcomes
One of the most suitable explanations for this phenomenon is that livestreams can create strong emotional connections through storytelling and live demonstrations Women may be more influenced by these emotional appeals, leading to spontaneous buying decisions
These response rate differences suggest that gender may play a significant role in shaping opinions, attitudes, and behaviors related to the studied topic Understanding these specific gender differences can provide valuable insights about impulse buying decision on livestream, especially in Shopee
The age group representing 90.3% is the age range from 19 to 30 This age group constitutes the majority of the study, indicating a significant interest in impluse buying in Shopee’s livestream This group belongs to Gen Z and Millenials, they are highly active on social media and digital platforms, including livestreams Moreover, this age group also encounter FOMO, so seeing others purchase and the urgency created during livestreams can trigger impulse buys Another reason is many in this age group are in the early stages of their careers, with fewer financial obligations compared to older age group
On the other hand, the age group from 31-40 accounts for 6.3% which indicates that with age, consumer behavior tends to shift towards more planned and considered purchases Individuals in their 30s and 40s often prioritize value, quality, and necessity over spontaneous buying Besides, people in this age range are frequently busy with their careers and family commitments They may have less time to engage in livestream shopping, which reduces opportunities for impulse purchases
2.7% and 0.7% are the percentage of the age group from 41-50 and under 18, respectively 2 groups only makes up for a small amount in total because the former may not be as engaged with livestream shopping compared to younger age groups, leading to fewer opportunities for impulse buys and the latter own limited finance
The highest proportion of participants in the sample, 82.3% is students This group is more likely to be influenced by trends, peer pressure, and social proof, leading to impulsive purchasing behaviors Besides, livestreams often feature time-limited offers and discounts, which can be particularly appealing to students looking for deals
The next group is officer with 13.7% Officers experience less impulse buying compared to students because while students are highly influenced by peer pressure and social trends, officers may be more influenced by practical needs and long-term value, which dampen impulsive buying tendencies
The remaimg belongs to freelancer with 3.7% and others with 0.3%
4.1.4 Weekly watching Shopee’s livestream hours
Figure 4.1.4 Weekly watching Shopee Live hours
The vast majority of viewers fall into this category, watching Shopee's livestream for less than 5 hours per week with 81%, while a smaller yet significant portion of viewers spend between 6 to 10 hours per week watching Shopee's livestream with 14.3%
Only 1.3.% viewers watch Shopee's livestream for 16 to 20 hours weekly and a small but notable group spends more than 20 hours per week watching Shopee's livestream, accounting for 3.3.%
4.1.5 Monthly Spending on Shopee’s livestreaming
Figure 4.1.5: Monthly Spending on Shopee Live
The chart reveals varying spending behaviors among Shopee's livestream viewers, with the largest segment (52.0%) spending 200,000VND – 500,000 VND monthly, reflecting high engagement and a balanced approach to shopping The 33.7% who spend less than 200,000 VND epresent casual or budget-conscious shoppers The 13.7% spending 500,000 VND – 1 million VND indicate loyal and possibly impulsive buyers, while the 0.7% more than 1 million VND who spend the most are likely high-spenders with significant disposable income Understanding these spending patterns can help Shopee tailor its marketing strategies and product offerings to better cater to each group's preferences and behaviors.
Measurement model analysis
From the table 4.2.1, we can see that the mean values for all indicators are around 4, indicating generally positive responses and all standard deviations are below 1.5, showing low variability in the responses Particularly, related to mean values, it ranges from 3.57(IBD4) to 4.22 (SC4), with most means around the 4.00 mark This suggests that participants generally have a favorable view towards the impulse buying on Shopee’s livestreaming The mean values close to 4 indicate that participants perceive the factors influencing impulse purchase decision positively, although not extremely high
About the standard deviations, they range from 0.858 (PTL1) to 1.249 (IBD2) A standard deviation below 1.4776 indicates that the responses are relatively consistent and close to the mean This low variability suggests that there is a general agreement among participants factors influencing impulse buying decision on Shopee’s livestreaming
• High Consistency Indicators: o SC4 (Mean = 4.22, Std Dev = 0.888) shows high consistency in positive responses o PTL1 (Mean = 4.15, Std Dev = 0.858) also indicates high agreement among participants
• Indicators with Slightly Higher Variability: o IBD1 (Mean = 3.83, Std Dev = 1.232) and IBD3 (Mean = 3.48, Std Dev = 1.249) also exhibit greater spread in responses ®Implications:
• The relatively high mean values (around 4) imply that participants generally perceive factors affecting impulse buying on Shopee's livestream positively
However, the fact that these values are not extremely high (i.e., closer to 5) suggests that while the perception is positive, it is not overwhelmingly so
• The low standard deviations (below 1.4776) indicate that there is minimal variability in participants' responses, which suggests a consensus among participants about the factors influencing impulse buying on Shopee's livestream.
Reliability was assessed using composite reliability (CR) and Cronbach's Alpha, both of which evaluate the stability and equivalence of the construct (Hair et al., 2009) Chou (2016) notes that Cronbach's alpha values between 0.7 and 0.9 are deemed acceptable, while values above 0.9 indicate excellent internal consistency As demonstrated in Table 4.2.2, all constructs have composite reliabilities and Cronbach's Alpha values exceeding 0.7, indicating acceptable reliability levels This confirms the internal consistency of the factors CCI, CSI, IBD, PLT, PP, SC, and VA, as supported by various studies cited
Construct validity was determined by the convergent and discriminant validities and measured using the average variance exacted (AVE) (Hair et al., 2009) An AVE value is suggested to be above 0.5 is considered acceptable, indicating that a majority of the variance in the construct is explained by its indicators (Sholihin, 2013) In the current research, the AVE values varied from 0.553 to 0.749 across the constructs These values is larger than the recommended threshold of 0.5, as suggested by Sholihin (2013) Such results provide strong evidence for the convergent validity of the constructs used in this study These values also show the strong positive relationships between the variables, affirming the validity of the measurement model used in this research (Anderson & Gerbing (1988); Hair et al (2010)
CCI CSI IBD PP PTL SC VA
The Heterotrait-Monotrait Ratio of Correlations (HTMT) is a vital indicator for assessing discriminant validity in research This metric estimates the true distinction between two concepts, assuming perfect measurement According to Hair et al
(2017), an ideal HTMT ratio should be less than 1.00 to demonstrate discriminant validity Meeting this threshold ensures that the concepts being studied are sufficiently distinct, indicating that the variables measure different underlying constructs rather than just variations of the same concept
In the context of this study, as illustrated in Table 4.2.3, the HTMT ratios were below the recommended threshold of 1.00 This implies that the concepts under investigation have strong discriminant characteristics, confirming that the variables measure distinct underlying constructs without overlap or distortion Consequently, the results validate the discriminant validity of the constructs used in the research, reinforcing the integrity and distinctiveness of each variable within the conceptual framework This outcome enhances the depth and reliability of the study’s findings, significantly contributing to the existing body of knowledge in the field
4.2.3.2 Discriminant Validity Fornell-Larcker Table
Table 4.2.3.2: Discriminant Validity Fornell-Larcker Table
CCI CSI IBI PP PTL SC VA
The diagonal elements (bold and highlighted) represent the square root of the Average Variance Extracted (AVE) for each construct These values are used to assess discriminant validity by comparing them to the correlations between constructs For all constructs, the square root of AVE (diagonal elements) is greater than the inter- construct correlations (off-diagonal elements), indicating good discriminant validity according to the Fornell-Larcker criterion This means that each construct shares more variance with its own indicators than with other constructs in the model, supporting the distinctiveness of the constructs measured.
Exploratory Factor Analysis – EFA
In this research, both independent and dependent Exploratory Factor Analyses (EFAs) were performed following Hair et al (2010) The Kaiser-Meyer-Olkin (KMO) measure was used to assess sampling adequacy for factor analysis, with a KMO value of 0.5 or higher being considered sufficient A KMO value below 0.5 suggests the data may not be suitable for factor analysis
The Bartlett’s test of sphericity was employed to determine if the observed variables in the factor model are correlated, which is a prerequisite for conducting factor analysis A statistically significant Bartlett’s Test (p < 0.05) indicates that the observed variables are sufficiently correlated for factor analysis
Additionally, a Total Variance Explained of 50% or more suggests that the EFA model is appropriate This measure indicates the percentage of variance accounted for by the extracted factors, reflecting the model’s adequacy
Table 4.3.1: KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .826
The results of the Exploratory Factor Analysis (EFA) show a Kaiser-Meyer-Olkin (KMO) measure of 0.826, confirming the adequacy for conducting factor analysis This indicates that the EFA results are usable and statistically significant, with a significance coefficient (Sig.) 1 and a cumulative variance of 62.378% > 50%
Extraction Method: Principal Component Analysis
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 5 iterations
The factor loading matrix analysis reveals that all factor loadings exceed the essential benchmark of 0.5, validating the strength of the analytical approach and adherence to recognized standards Additionally, the matrix categorizes variables into 6 coherent factor groups, each marked by a unified cluster of contributing variables:
• Factor Group 1 includes variables CSI1 to CSI6, reflecting customer-streamer interaction
• Factor Group 2 contains variables CCI1 to CCI4, reflecting customer- customer interaction
• Factor Group 3 consists of variables VA1 to VA4, demonstrating visual appeal
• Factor Group 4 comprises variables PTL1 to PTL4, emphasizing promotion time limit
• Factor Group 5 involves variables SC1 to SC4, reflecting streamer’s credibility
• Factor Group 6 includes variables PP1 to PP3, reflecting price promotion
This careful arrangement of variables into cohesive groups not only clarifies and aids in interpreting the results but also assists in a thorough grasp of the foundational constructs and their interrelations.
Analyze structural models
Table 4.4.1: Multi collinearity test -VI 1
In the provided paraphrase, it is stated that the multi-collinearity test results, displayed in Table 4.4.1, indicate no significant correlation between independent and dependent variables in the proposed model All Variance Inflation Factor (VIF) values are below
10, adhering to the recommendations of Brace, Kemp, and Snelgar (2003), as well as Diamantopoulos and Siguaw (2000) However, for enhanced reliability, it is suggested that VIF should not surpass 5 (Hait & et al., 2016) Table 4.4.1 reveals that the VIF values for all variables in the model are below this threshold Hence, multi- collinearity does not seem to pose a significant concern in this study
R-square, also known as the coefficient of determination, is a statistic in linear regression that represents the proportion of the variance for the dependent variable that's explained by the independent variables (Lewis-Beck & Skalaban,1990) R- square values range from 0 to 1, with values closer to 1 indicating that the regression model fits the data better (Akossou & Palm, 2013) In this research, the R-square for IBD is 0.566, meaning that the regression model explains approximately 56.6% of the variance in the dependent variable based on IBD
The F-square value serves as a crucial metric for evaluating the impact of exogenous variables when they are excluded from the model As outlined by Cohen (1988), F- square values are categorized as small, medium, and large, corresponding to 0.02, 0.15, and 0.35, respectively If an exogenous variable's effect is below 0.02, it is typically deemed not significant
In this model, it was found that there were two links with the highest level of impact These are the CSI to IBD link with an F- square value of 0.250 and the VA to IBD with an F2 value of 0.227 This suggests that the influence of the factor Customer – Streamer Interaction (CSI) on the Impulse Buying Decision (IBD) and the influence of Visual Appeal (VA) on Impulse Buying Decision (IBD) are both very strong and important in the model It is worth noting This means that all other links have an F- square value greater than 0.02, which indicates that none of the links had a low impact on Impulse Buying Decision This can suggest that in this model, different factors all play an important role and cannot be easily eliminated without affecting the final result
Table 4.4.4 : Structural model path analysis
Table 4.4.4 showed the standardized path coefficient and path significance for each hypothesis With regard to H1, it can be observed that a significant and negative correlation between streamer’s credibility (β= -0.108, p=0.005) and customers impulse buying decision on Shopee’s live streaming, which iss opposite with H1 Concerning H2, price promotion (β= 0.110, p>0.05) has no impact on customers impulse buying decision, Thus, H2 was rejected Similarly, interpersonal interaction including consumer-streamer interaction (β=0.357, p