INTRODUCTION
Research motivation
51.78 million Vietnamese Internet users (approximately 52.6 percent of Vietnamese population) purchase consumer goods via the Internet (Simon, 2022) It is even accelerated by Covid-19 as consumers come to realize the convenience of online shopping Total annual spend on online purchases of Vietnamese consumers is projected to grow 4.5 times from USD 12 billion in 2021 up to USD 56 billion in 2026 (Praneeth et al., 2021) With the growth of the Vietnamese e-commerce market, online impulse buying has been reported as a notable accompanying phenomenon on national TV channels and news It is said that the online shopping environment is more conducive to impulse buying behavior than their offline counterpart because it liberates consumers from the limitations that they would feel during offline shopping activities (e.g shop locations, opening hours and product availability) (Chan et al., 2017)
Live Commerce has revolutionized the online shopping landscape by enabling real-time interaction between consumers and sellers through livestreaming, creating a unique and intimate shopping experience A report by Lazada in 2021 highlighted that revenue from LazLive surged more than eightfold in Q3 compared to the previous year, showcasing the significant impact of this trend This innovative approach is believed to enhance impulse buying, as the live format allows customers to perceive various physical, social, and emotional cues, prompting quick and spontaneous purchasing decisions As Live Commerce continues to gain traction, understanding its influence on online impulse buying behavior becomes increasingly important.
Balancing the interests of sellers and buyers in impulse buying is crucial Sellers often promote additional purchases to boost sales and profits, regardless of whether these items are bought impulsively Conversely, consumers may experience both satisfaction and negative consequences from impulse buying.
Impulse buying can be seen positively as a fun and gratifying shopping behavior, but it also has negative implications, such as financial difficulties and feelings of guilt While it may boost revenue for sellers, it can lead to customer complaints and negative word-of-mouth that harm business success Therefore, it is crucial for e-retailers and e-marketers to understand consumers' perceptions of their post-purchase experiences to make informed adjustments to their business strategies.
Over the past fifty years, researchers have explored impulse buying behavior in both offline and online settings However, the relevance of these studies to live streaming, a relatively new online shopping format, remains questionable (Lo et al., 2022) There is a limited number of studies specifically examining impulse buying in Live Commerce, as illustrated in Table 1.1 below.
Table 1.1 Recent studies investigating factors that drive impulse buying behavior in Live Commerce
The study analyzed the influence of product characteristics on consumer’s impulse buying based on product involvement theory
The study investigated the impact of social presence and sales promotion (stimuli) on impulse buying behavior (response), mediated by the flow experience (organism)
The study explored the effect of social presence (stimuli) on impulse buying (response) with pleasure and arousal as mediating factors (organism) h
The study examined the influence of demand, convenience, interactivity, and playfulness on consumers' perceived enjoyment which then drove consumers’ impulsive buying intention
The study demonstrated how parasocial interaction, social contagion, vicarious experience, scarcity persuasion, and price perception (stimuli) resulted in impulsive buying urge (response) via cognitive-affective processing (organism)
The research explored the impact of social presence on live streaming platforms, focusing on the interactions between viewers, streamers, and telepresence stimuli It found that these factors significantly influenced consumer trust and the flow state, ultimately resulting in impulsive buying behavior.
This study significantly enhances the existing literature by examining how parasocial interaction and emotional contagion influence impulse buying behavior in the context of Live Commerce It highlights the critical role of real-time interactions between livestreamers and viewers, a unique aspect of this platform While parasocial interaction has been frequently explored in social media commerce concerning purchase intentions, its application to Live Commerce and impulse buying is limited Similarly, emotional contagion is underrepresented, with its closest reference being social contagion in prior studies This research expands the understanding of emotional contagion's impact on impulse buying and proposes a novel relationship between emotional contagion and parasocial interaction Utilizing the Stimulus-Organism-Response (S-O-R) framework, it elucidates how these factors translate into impulse buying through the mediation of flow state dimensions, specifically concentration and enjoyment.
Recent systematic literature reviews on impulse buying, including studies by Abdelsalam et al (2020), Chan et al (2017), Iyer et al (2020), and Redine et al (2023), indicate that most research in this area has concentrated on the antecedents or trigger phase of the purchasing journey.
Impulse buying outcomes have been less explored, yet they significantly influence consumer relationships with retailers Understanding these consequences is essential for comprehending the full customer journey, encompassing both pre-purchase and post-purchase phases.
Table 1.2 Related literature in the consequences of impulse buying
The study analyzed the influence of process regret and outcome regret on the review creation after impulse buying
The study employed Cognitive Dissonance Theory as a theoretical framework to examine the factors that contribute to online impulse buying, as well as the subsequent experience of post-purchase regret
The study by Lin et al explored the factors influencing impulse buying behavior and examined how this behavior affects post-purchase outcomes, such as cognitive dissonance and the likelihood of returning purchased items.
The study addressed promotion focus and prevention focus as the key antecedents of impulse buying which resulted in cognitive dissonance, satisfaction, and consumer loyalty
The study examined post-purchase behavior after impulse buying such as regret, complaint and return tendency
The study investigated the persuasion of post-purchase arguments on online impulsive buyers’ satisfaction
Previous research has overlooked the simultaneous experience of satisfaction and regret following impulse buying Contrary to popular belief, these emotions can coexist rather than function in opposition Consumers may recognize their purchase as optimal and satisfying, yet still feel regret if they believe alternative choices could have yielded better results This study aims to explore the dual nature of impulse buying outcomes, focusing on both the satisfaction and regret consumers feel towards the product itself, rather than the purchasing process Additionally, it highlights the need to investigate the two dimensions of regret: process regret and outcome regret.
2023) In order to enrich current literature, this study also explores the two core components of regret and their separate relationships with satisfaction
Recent research by Redine et al (2023) highlights that much of the empirical work on impulse buying has predominantly relied on data from just three countries: the United States, China, and Taiwan This raises concerns about the generalizability of the findings In Vietnam, however, several studies on impulse buying have been undertaken, including notable works by Mai et al (2003, 2013) and Nguyen, contributing valuable insights to this field.
Previous studies have primarily focused on factors related to the Transitional Economy, such as Traditional self and Modern self, while only briefly addressing the consequences of impulse buying without differentiating between online and offline settings This highlights the need for a comprehensive examination of online impulse buying specifically within the Vietnamese context, where the existing literature remains insufficient.
Research objectives and research questions
This study focuses on e-retailers, aiming to identify key factors that drive impulse buying in Live Commerce and to understand consumers' perceptions of their post-purchase experiences By strategically stimulating impulse buying, sellers can enhance customer satisfaction and reduce negative outcomes It is essential to encourage impulse purchases in a way that delights customers, fostering long-term relationships with brands and stores rather than merely facilitating one-time transactions.
This study explores the impact of parasocial interaction and emotional contagion on impulse buying, specifically examining how these factors influence the two dimensions of flow state Additionally, it investigates the relationship between emotional contagion and parasocial interaction, as well as the simultaneous effects of impulse buying on both types of regret and overall satisfaction.
Research scope
This research focuses on Vietnamese consumers who have utilized the livestreaming feature of social commerce platforms (S-commerce) According to Huang & Benyoucef (2013), S-commerce platforms can be categorized into two types: (1) E-commerce websites like Shopee, Lazada, Tiki, and Sendo, which integrate social features to enhance user interaction, and (2) Social networking sites such as Facebook, TikTok, Instagram, and Zalo, which incorporate commercial elements to facilitate product or service sales.
This study focuses on impulse buying specifically within the fashion and makeup sectors, acknowledging that consumer behavior varies across product categories Products are typically divided into two classifications: hedonic and utilitarian Utilitarian products fulfill functional needs, while hedonic products cater to emotional desires, as outlined by Holbrook and Hirschman.
Fashion and makeup products enhance overall appearance instantly, eliciting positive emotions such as fun, pleasure, and excitement, which contribute to their hedonic value and can lead to impulsive purchasing decisions Additionally, these items are among the most widely available product categories sold through online platforms and livestreaming channels.
This study examines young and middle-aged individuals under 40 years old, who have grown up fully immersed in digital technology Given that Vietnam connected to the global internet in 1997, this demographic is likely to embrace the emerging trends of online shopping.
Satisfaction and regret can manifest at various stages of the buying process, both prior to and following the purchase decision In the online shopping context, a "time lag" occurs between the purchase and the actual consumption of the product due to delivery delays This study focuses on examining satisfaction and regret specifically after the product has been received and tried on.
Research structure
The thesis is structured into several key chapters: Chapter 2 introduces essential concepts, formulates hypotheses, and outlines the proposed research model Chapter 3 details the methodology utilized in the study Chapter 4 presents the findings and results derived from data analysis Finally, Chapter 5 concludes the study with a discussion on the findings, their implications, limitations, and recommendations for future research.
LITERATURE REVIEW
Review of S-O-R theory
The Stimulus-Organism-Response (S-O-R) theory is a crucial psychological framework that explains how environmental cues (stimuli) influence individuals' internal states (organism), ultimately shaping their behavior (response) Developed by Mehrabian and Russell (1974), this model has been effectively applied to understand impulse buying behaviors in both physical and online retail environments (Abdelsalam et al., 2020; Chan et al., 2017) By utilizing the S-O-R framework, researchers can pinpoint specific stimuli that trigger impulsive purchases, explore the cognitive and emotional processes involved, and uncover factors that mediate the connection between external stimuli and consumer impulse buying.
This study, grounded in the S-O-R theory, presents a conceptual model that incorporates parasocial interaction and emotional contagion as key stimuli While external stimuli are typically emphasized in the S-O-R framework, internal factors also play a significant role Chan et al (2017) expanded the definition of stimuli to include triggers that arouse consumers, categorizing them into external (such as website and marketing stimuli) and internal (consumer characteristics) types This research specifically examines parasocial interaction as a situational stimulus and emotional contagion as a consumer characteristic The flow state is identified as the organism component, as it represents optimal engagement and enjoyment during the shopping experience, influencing buyer responses and impulse buying behavior In the context of livestreaming, where real-time broadcasts enhance viewer engagement, the flow state is crucial for understanding impulse purchasing decisions Impulse buying is analyzed as actual behavior, providing insights into both the antecedents and outcomes of this phenomenon.
Review of related definitions
2.2.1 Parasocial interaction and Emotional contagion as Stimuli
The rise of parasocial interaction (PI) is linked to the introduction of mass media platforms like television and radio, which fostered a one-sided sense of intimacy between audiences and media personalities (Horton & Wohl, 1956; Rubin & McHugh, 1987) This phenomenon illustrates how viewers develop emotional connections while engaging with content, despite the lack of direct interaction.
Television programs create virtual connections between viewers and performers, allowing for repeated encounters that immerse audiences in the narrative Over time, this engagement fosters emotional closeness and intimacy, making viewers feel as though they share a genuine friendship with the performers.
The concept of parasocial interaction has evolved to encompass online environments, shifting the focus from traditional celebrities to social media influencers (Aw & Chuah, 2021; Chung & Cho, 2017; Hwang & Zhang, 2018; Labrecque, 2014; Lee & Watkins, 2016; Sokolova & Kefi, 2020) Influencers can engage with their audience in real-time by sharing their daily experiences on social media, which allows consumers to form strong emotional bonds and feelings of intimacy despite not knowing the influencers personally (Aw & Chuah, 2021) In the realm of livestreaming, parasocial interaction manifests as a one-sided sense of closeness that viewers develop towards livestreamers, enhanced by the immediacy of the livestreaming format that allows consumers to observe product demonstrations, hear personal stories, and receive prompt feedback from the hosts.
Emotional contagion, as defined by Hatfield et al (1994), refers to the subconscious tendency to imitate the emotions and behaviors of others, which can lead to feelings of happiness in the presence of excited individuals or irritation during contentious discussions This phenomenon occurs when a person with higher emotional levels influences another with lower emotional levels, resulting in emotional convergence, known as the “flow effect.” In today's digital age, emotions are transmitted virtually across online platforms, particularly in Live Commerce, where viewers engage with livestreamers and each other through interactive activities like Q&A sessions, likes, and comments, enhancing the emotional experience.
Flow theory, introduced by Csikszentmihalyi (1977), describes a holistic sensation of complete involvement and immersion in activities However, Koufaris (2002) critiques the concept as overly broad and lacking precise definition due to its varied applications in research In impulse buying contexts, flow is often simplified into a single factor, despite its multidimensional nature (Barta et al., 2022; Hsu et al., 2012; Ming et al., 2021; Wu et al., 2016) Koufaris emphasizes the need for caution in applying flow to online consumer behavior, suggesting that a more nuanced approach is necessary This study proposes treating flow dimensions separately, identifying concentration and enjoyment as key factors influencing impulse buying behavior, drawing on Ghani and Deshpande's (1994) framework.
A state of flow occurs when individuals are deeply focused and derive psychological enjoyment from an activity In the context of Live Commerce, this flow state signifies that viewers are so engrossed in the livestream that external distractions fade away, allowing them to experience pleasure and joy while watching.
There is not a single, agreed-upon definition of impulse purchase in the literature
Initially, unplanned buying and impulse buying were considered synonymous; however, as researchers began to explore the behavioral dimensions of impulse buying, they recognized it as more than just an unplanned purchase Numerous studies have utilized Stern's concept of impulse purchases as a foundational reference in this evolving understanding.
Impulse purchasing, as established by various researchers, can be categorized into four types: planned, pure, reminder, and suggestion Rook (1987) describes impulse buying as a sudden, often intense urge to make an immediate purchase, which can lead to emotional conflict and a lack of consideration for consequences Beatty and Ferrell (1998) further define it as a rapid purchase made without prior intent, typically occurring spontaneously after experiencing a buying urge Importantly, this behavior does not include the purchase of reminder items, which are simply those that are out of stock at home.
Impulse buying is defined as an unplanned purchase characterized by quick decision-making driven by emotions rather than rationality This means that consumers do not have a specific product in mind before shopping; even if they enter e-stores with a general intention to buy, any purchase made is still considered unplanned Additionally, if a consumer recalls previously considering a product and decides to buy it on the spot, it remains an unplanned purchase The speed of the decision-making process is a key factor that differentiates impulse buying from other types of unplanned purchases Ultimately, impulse buyers focus on immediate gratification from acquiring a product, often neglecting to consider the potential consequences or engage in thorough product evaluations, unlike more cautious shoppers.
2.2.4 Satisfaction and Regret as Consequences
Satisfaction and regret are complex concepts that can vary in meaning based on context This article utilizes Oliver's (2014) definitions of satisfaction and regret, which are recognized in consumer behavior research Oliver's study clarifies these concepts by exploring the differences between them through various referent points.
Satisfaction, as defined by Oliver (2014), is the consumer's response to fulfillment, particularly in impulse buying, where the focus is on the buying process rather than the product itself This behavior is driven by emotional arousal and the desire for immediate gratification However, as time passes, consumers' tangible needs become more significant, leading them to prioritize product performance At this juncture, satisfaction with products becomes crucial for influencing repurchase intentions and brand loyalty In this study, satisfaction is evaluated based on consumer perceptions of products, using expected performance as a benchmark for comparison Oliver (1980) suggests that satisfaction arises when actual performance surpasses expectations, while dissatisfaction occurs when it falls short.
Regret, much like satisfaction, can be influenced by various comparison referents, particularly inaction and foregone alternatives (Oliver, 2014) This study focuses on foregone alternatives as the primary referent for comparison As a result, regret arises from comparing actual performance with these missed options The more an individual contemplates alternative choices, the higher the chances of experiencing regret.
Decision Justification Theory (Connolly & Zeelenberg, 2002) suggests that individuals can feel regret by evaluating both the decision-making process and the outcome of their choices In impulse buying, process regret occurs when consumers doubt the reasoning behind their purchase, regardless of the outcome This regret manifests in two main ways: individuals may feel guilty for not following their intended decision-making process, creating a gap between their intentions and actions, or they may regret not having sufficient information to make an informed choice (Zeelenberg & Pieters, 2007) Outcome regret, on the other hand, arises when consumers realize they made a poor decision compared to an alternative choice that might have led to a better result, even if the initial decision seemed reasonable at the time This type of regret can occur even when consumers are unaware of the specific opportunities they have missed.
Review of related studies
This study examines the pre-purchase phase of impulse buying behavior through the lens of the S-O-R framework, referencing two pivotal research papers The concept of impulse buying is further supported by earlier findings from Koufaris, highlighting the psychological triggers that influence consumer decisions.
In 2002, Koufaris examined online consumer behavior through the lens of the flow state, focusing on enjoyment, perceived control, and concentration His research highlighted the impact of information system-related constructs—such as web skills and product involvement—on online unplanned buying, revealing positive relationships between these constructs and flow variables like shopping enjoyment and concentration, though no significant links to impulse buying were found A more recent study by Lo et al (2022) explored the dynamics of livestreaming commerce, identifying key elements such as content, parasocial interaction, and social contagion that foster impulsive buying behavior Unlike emotional contagion, which occurs at an individual level, social contagion operates within social networks, influencing multiple individuals simultaneously This study underscored the interconnectedness of these factors, with cognitive and affective reactions serving as intermediaries; it found that parasocial interaction positively influenced affective reactions, while social contagion had a negligible effect Ultimately, affective reactions significantly impacted impulsive buying urges, reinforcing the notion that emotions primarily drive impulsive purchasing behavior.
This study builds on Tsiros et al (2000), which established a model analyzing the relationship between regret and satisfaction, highlighting that these are distinct constructs that can coexist It proposes that both satisfaction and regret can arise from impulse buying While research on the link between impulse buying and product satisfaction is limited, Barta et al (2023) explored the connection between impulse buying and regret within the broader context of online shopping Their findings indicate that impulse buying behavior (IBB) positively influences process regret, which subsequently affects outcome regret, offering valuable insights for understanding consumer behavior in the post-purchase phase.
In summary, these key papers have provided the author with a solid background to formulate hypotheses for the proposed model.
Hypothesis development
2.4.1 Emotional contagion and Flow state
Viewers with high emotional susceptibility are significantly influenced by the emotions of livestreamers and fellow viewers, often unconsciously mirroring their pleasure and focus This phenomenon is intensified by frequent exposure to expressed emotions on digital media platforms, leading to a strong likelihood that these contagious digital emotions can profoundly affect viewers' emotions and behaviors.
H1: Emotional contagion (EC) is positively associated with concentration (CONC) H3: Emotional contagion (EC) is positively associated with enjoyment (ENJ)
2.4.2 Parasocial interaction and Flow state
Parasocial interaction (PI) allows viewers to form intimate relationships with media personalities, fostering a sense of affective trust (Lo et al., 2022) This trust alleviates negative pre-purchase concerns and uncertainty in live streaming commerce, enhancing enjoyment and focus during the experience (Ming et al., 2021) Research by Xiang et al (2016) indicates that such interactions can significantly boost perceived enjoyment in shopping As viewers engage more deeply with media figures for entertainment, their emotional involvement increases, leading to a more pleasant and enjoyable experience.
H2: Parasocial interaction (PI) is positively associated with Concentration (CONC) H4: Parasocial interaction (PI) is positively associated with Enjoyment (ENJ)
2.4.3 Emotional contagion and Parasocial Interaction
Individuals highly susceptible to emotional contagion exhibit distinct traits: they show great attentiveness to others and can interpret emotional expressions, perceive themselves as interconnected rather than independent, tend to mimic facial and vocal cues, and their emotional experiences are greatly influenced by external feedback These characteristics highlight their unfulfilled need for social interaction and stimulate their motivation for parasocial interactions.
H5: Emotional contagion (EC) is positively associated with Parasocial interaction (PI) 2.4.4 Flow state and Impulse buying
Concentration plays a crucial role in achieving a flow state, where individuals become fully immersed in their activities According to Koufaris (2002), consumers who maintain focus on a web store are more likely to notice and respond to marketing promotions, leading to increased impulse buying Enhanced concentration during the purchasing process also aids in understanding product descriptions and reviews, fostering confidence in making quick purchase decisions Since live streaming commerce is a subset of e-commerce, these principles can similarly apply to this context, highlighting the importance of consumer attention in driving sales.
H6: Concentration (CONC) is positively associated with impulsive buying (IB)
The intrinsic enjoyment experienced while watching a livestream can significantly influence consumer behavior According to Rook (1987), the pleasure derived from such experiences acts as a catalyst for impulse purchases, driving a desire for immediate gratification.
Beatty and Ferrell (1998) found that shoppers who feel positive emotions—such as excitement and enthusiasm—during their shopping trips are more likely to make impulse purchases This suggests that consumer feelings significantly influence impulsive buying behavior When consumers experience intrinsic enjoyment, they engage in exploratory behavior (Ghani & Deshpande, 1994), which further enhances their chances of making unplanned purchases In online retail, positive and enjoyable experiences lead consumers to explore the store more, increasing the likelihood of impulse buying (Koufaris, 2002) Similarly, in livestream shopping, enjoyable experiences may encourage consumers to stay longer, watch more product demonstrations, and impulsively spend on newly discovered items.
H7: Enjoyment (ENJ) is positively associated with impulsive buying (IB)
Impulsive buyers often make purchases without careful consideration, leading to potential disappointment with the quality or suitability of the product According to Rook (1987), this lack of evaluation can result in feelings of regret, even if the product performs well, as consumers reflect on the alternatives they did not choose.
H8: Impulsive buying (IB) is positively associated with outcome regret (OUTREG)
Moreover, customers might feel regretful for not investing sufficient time and effort in researching the required information before making a purchase (Barta et al.,
2023) To be more specific, the consumers may feel regret with the purchase process due to under-consideration Hence:
H9: Impulsive buying (IB) is positively associated with process regret (PROREG)
Outcome regret, particularly following a negative result, prompts consumers to critically evaluate their ineffective behaviors This reflection on past decisions can intensify feelings of process regret, highlighting the importance of decision-making in consumer experiences.
H10: Outcome regret (OUTREG) is positively associated with process regret (PROREG)
One significant drawback of online shopping is the inability for consumers to physically touch or experience products prior to purchase, leading to uncertainty about whether their expectations will be met Research indicates that positive emotions often drive impulse buying, which can result in a lack of thorough evaluation regarding the product's quality and suitability Consequently, consumers may develop inflated expectations about their purchases When the actual product fails to meet these expectations, it can lead to negative disconfirmation, ultimately diminishing overall satisfaction.
H11: Impulsive buying behavior (IBB) is negatively associated with satisfaction (SAT) 2.4.7 Regrets and Satisfaction
Research indicates that higher levels of regret are associated with decreased satisfaction, as shown in studies by Inman et al (1997), Taylor (1997), and Tsiro et al (2000) While these studies primarily examine regret as a whole, it can be inferred that both outcome regret and process regret contribute to lower satisfaction levels In other words, a consumer's satisfaction with a chosen outcome may be influenced by comparisons to alternative outcomes and the regret felt regarding the decision-making process that led to the choice.
H12: Outcome regret (OUTREG) is negatively associated with satisfaction (SAT) H13: Process regret (PROREG) is negatively associated with satisfaction (SAT)
Conceptual model
From the above hypotheses, a conceptual model is proposed as in Figure 2.1 bellowed: h
The study puts forward the following hypotheses:
H1: Emotional contagion (EC) is positively associated with concentration (CONC) H2: Parasocial interaction (PI) is positively associated with concentration (CONC) H3: Emotional contagion (EC) is positively associated with enjoyment (ENJ)
H4: Parasocial interaction (PI) is positively associated with enjoyment (ENJ)
H5: Emotional contagion (EC) is positively associated with parasocial interaction (PI) H6: Concentration (CONC) is positively associated with impulsive buying (IBB) H7: Enjoyment (ENJ) is positively associated with impulsive buying (IBB)
H8: Impulsive buying (IBB) is positively associated with outcome regret (OUTREG) H9: Impulsive buying (IBB) is positively associated with process regret (PROREG)
H10: Outcome regret (OUTREG) is positively associated with process regret (PROREG)
H11: Impulsive buying (IBB) is negatively associated with satisfaction (SAT)
H12: Outcome regret (OUTREG) is negatively associated with satisfaction (SAT) H13: Process regret (PROREG) is negatively associated with satisfaction (SAT) h
RESEARCH METHODOLOGY
Sampling and Data collection
This study employs a non-probability sampling strategy, incorporating both convenience and snowball sampling methods To gather convenience samples, the author utilized various techniques, including sharing the survey among friends and acquaintances, posting it in relevant Facebook groups, and conducting offline surveys in public parks during weekends Additionally, snowball sampling was implemented by asking participants from the targeted population to refer other suitable candidates for the survey.
Convenience sampling and snowball sampling are favored for their time-saving and cost-effective benefits, especially in light of individuals' growing concerns about privacy breaches, which often lead to reluctance in sharing personal information By leveraging her own networks and referrals from participants, the author fostered trust, enabling participants to provide more reliable information Acknowledging the inherent biases of these sampling methods, the author implemented strategies to diversify participant demographics by engaging various social groups, including professional associations and online communities, and encouraging participants to refer individuals with diverse backgrounds and experiences related to the research topic.
The questionnaire, originally drafted in English and translated into Vietnamese, comprises 40 questions organized into four sections The first section prompts respondents to recall any unplanned purchases of fashion or makeup products made on a livestream platform within the last four months, ensuring accurate reflection on recent experiences If they confirm such a purchase, they proceed to the second section, which includes five questions about the overall nature of that purchase The third section features 31 randomized questions about their recalled experience, designed to reduce bias and obscure the study's specific objectives Finally, the survey concludes with four demographic questions to outline the respondents' basic profiles.
A total of 250 responses were initially collected, with 140 valid responses remaining after filtering The majority of valid responses (122 out of 140) were obtained through online distribution via Google Forms, which ensures that all required information is provided by respondents To maintain data integrity, trap questions and reverse-coded items were employed to identify and eliminate inconsistent or unfocused responses, resulting in a refined dataset.
Scale measurements
This study utilizes the 8-item Parasocial Interaction scale by Dibble et al (2016), adapted from Rubin et al (1985), to measure viewer engagement with livestreamers, who often lack celebrity status compared to traditional TV personalities To tailor the scale for Live Commerce, three items related to long-term bonding were removed, retaining those that reflect intimacy and friendliness Emotional contagion was assessed using a shortened 3-item scale from Doherty (1997), focusing on susceptibility to happiness, as positive emotions enhance flow state enjoyment and influence impulse buying Flow state dimensions were measured with Koufaris' (2002) scale, while impulse buying was evaluated using a 4-item scale from Barta et al (2023), based on Rook's (1987) research, emphasizing unplanned purchases and spontaneity Satisfaction was measured with Tsiros et al.'s (2000) scale, and outcome and process regret were assessed using Lee and Cotte's (2000) scales, all adapted to fit the livestream commerce context.
A five-point Likert scale was employed to assess all items, with responses ranging from “strongly disagree” (1) to “strongly agree” (5), except for Emotional Contagion, which used “never” (1) to “always” (5) For a complete overview of the measurement scales, please refer to Appendix 2.
Data analysis methodology
The Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, utilizing SmartPLS 4 Professional – Free trial, was employed to generate descriptive statistics and evaluate the proposed conceptual model PLS-SEM is particularly advantageous for its ability to manage complex models with numerous constructs, indicators, and relationships, as well as its flexibility regarding sample size limitations (Hair et al., 2022) The minimum sample size for PLS-SEM should be based on the greater of two criteria: either ten times the largest number of formative indicators for a single construct or ten times the largest number of structural paths directed at a specific latent construct in the model (Hair et al., 2011, p 144) Consequently, the proposed model requires a minimum sample size that adheres to these guidelines.
30 samples are needed A two-step procedure was adopted as bellowed:
Assessment of measurement model evaluates the quality criteria of the constructs: Reliability and Validity
To evaluate the reliability of indicators within a measurement model, it is essential to examine the outer loadings High outer loadings suggest that the indicators share significant commonality captured by the construct Ideally, standardized outer loadings should be 0.708 or higher, while indicators with loadings below 0.4 should be removed For loadings between 0.4 and 0.708, indicators should only be eliminated if their removal enhances internal consistency reliability or convergent validity beyond the recommended thresholds, while also considering the implications for content validity.
Internal consistency reliability (construct level)
Internal consistency reliability measures the correlation between indicators assessing the same construct, with Cronbach Alpha and Composite Reliability (CR) being the most widely used metrics Satisfactory values for these measures typically range from 0.70 to 0.90 (Hair et al., 2022).
Convergent validity refers to the degree to which a measure correlates positively with alternative measures of the same construct, as defined by Hair et al (2022) It is commonly assessed using the Average Variance Extracted (AVE), with a value of 0.5 or higher indicating that the construct accounts for more than half of the variance in its indicators, thereby establishing convergent validity.
Discriminant validity refers to the degree to which a construct is empirically distinct from other constructs (Hair et al., 2022) Traditionally, the Fornell-Larcker criterion has been used to evaluate this validity, requiring that the square root of the average variance extracted (AVE) for each construct exceeds its correlations with other latent constructs Recently, the HTMT (Heterotrait-Monotrait ratio) has gained approval for providing greater specificity and sensitivity in assessing discriminant validity The literature presents varying thresholds for HTMT, with Henseler et al (2015) suggesting three approaches: a threshold of 0.90 for structurally similar constructs, 0.85 for conceptually distinct constructs, and the use of bootstrap confidence intervals to determine if the HTMT criterion significantly differs from 1.0 The third approach is particularly effective at identifying distinct constructs, even when inter-construct correlations are high.
After meeting the measurement model criteria, the evaluation shifts to the structural model's ability to effectively explain and predict target constructs The first step in this process is to check for multicollinearity by analyzing the Variance Inflation Factor (VIF) A VIF value below 5 suggests that there are no significant levels of collinearity, indicating a weak correlation between that predictor and others (Hair et al., 2022).
The second step involves assessing the expected causal relationships among latent constructs in the structural model This phase focuses on evaluating the size and statistical significance of path coefficients, determining the model's explanatory power through the coefficient of determination (R²) for endogenous variables, and measuring the strength of structural model relationships using the f² effect size According to Cohen (1988), f² values of 0.02, 0.15, and 0.35 indicate small, medium, and large effects of predictor constructs on target constructs, respectively.
ANALYSIS AND RESULTS
Descriptive statistics
The profile of 140 valid respondents is described as bellowed:
The study reveals a significant gender disparity among respondents, with females comprising 84% of the total participants This predominance is expected, given that the research centers on fashion and beauty categories, which tend to appeal more to women than men.
Age: Respondents are quite young with the largest percentage of respondents (59%) from 20-29 and the second largest (26%) from 15-19 years old/
Educational level: The largest number of respondents belong to university and college level (a total of 66%), followed by the group of people who have high school level (20%) and graduate level (14%)
In Vietnam, a significant portion of the population reports low monthly incomes, with 54% of respondents earning less than 5 million VND, likely comprising high school and college students reliant on parental support Conversely, 46% of respondents earn more than 5 million VND, contributing to an average monthly income of 5.95 million VND per person in urban areas as recorded in 2022 (General Statistics Office of Vietnam, 2023).
The description of purchases is described as bellowed:
Frequency Percent Type of item
Type of items: The product type is divided into 3 main groups: Clothing, Accessories and Makeup Clothing makes up the most (62%), followed by accessories and makeup
The perception of item value varies significantly among individuals, influenced by personal preferences, financial circumstances, and subjective assessments A luxury item may be deemed expensive by those with limited resources, while others with higher incomes may find it affordable Rather than imposing a rigid definition of what constitutes expensive or cheap, the study empowers consumers to make their own judgments, highlighting the subjectivity of value perception Consequently, the findings reveal that 98% of items are viewed as neither cheap nor expensive, with only a small percentage categorized as expensive, reflecting the generally low income of respondents.
Cross-selling plays a significant role in influencing consumer behavior, as nearly 50% of impulse purchases occur alongside pre-planned items This strategy effectively introduces customers to products they may not have initially considered, enhancing their overall shopping experience.
Livestreaming on social networking sites (SNS) is significantly more popular than on E-commerce websites (EC Web), likely due to the social nature of SNS, which fosters a friendly, intimate, and entertaining shopping experience for consumers.
The average mean of all items which belong to Parasocial interaction (PI), Emotional contagion (EC), Enjoyment (ENJ), Impulse buying behavior (IBB) and h
The satisfaction (SAT) level among respondents is significantly above 3.0 on a 5-point Likert scale, indicating a generally positive agreement Additionally, the standard deviation for each satisfaction item is below 1, reflecting minimal variation in responses In contrast, the average concentration (CONC) score falls below 3.0, with a standard deviation also under 1, suggesting a low-neutral frequency of concentration and slight variability among respondents.
Outcome regret (OUTREG) and process regret (PROREG) reveal mixed responses among participants Regarding OUTREG, there is a lack of consensus on feelings of regret and the inclination to consider alternative products, with statements such as "I should have chosen something else than the one I bought" reflecting this uncertainty.
If I could go back in time, I would make a different purchasing decision, as I regret my initial choice (OUTREG3) Many respondents acknowledge that better alternatives were available (OUTREG4), reflecting a low standard deviation of less than 1, indicating minimal fluctuation in their responses High agreement was noted for PROREG1 (I could have made a better decision with more information) and PROREG3 (I could have made a better decision with more effort), both exceeding an average mean of 3.0 In contrast, PROREG2 (I did not put enough consideration into buying the product) and PROREG4 (I regret not putting enough thought into my decision) received lower average means of less than 3.0 While respondents recognize that the situation could have been improved under different circumstances, they seem to downplay feelings of regret Additionally, PROREG4 has a standard deviation slightly above 1 (1.06), suggesting a wider spread in responses compared to other items.
Items Mean Median Standard deviation
Assessing the measurement model
The measurement model was assessed by calculating outer loadings and evaluating the reliability of constructs through Cronbach's alpha and composite reliability, as detailed in Table 4.2 All items demonstrated outer loadings that meet the recommended threshold, ensuring the model's robustness.
In the context of livestream shopping, my purchasing behavior is encapsulated by the phrase "I see it, I buy it," with a notable score of 0.708 for IBB3 The analysis revealed that both Cronbach’s alpha and composite reliability (rho_a) exceeded 0.7, demonstrating a strong level of internal consistency reliability.
Table 4.4 Indicator and Construct Reliability
The Average Variance Extracted (AVE) values for all constructs exceeded the 0.5 threshold, indicating established convergent validity (refer to Table 4.3) Although the outer loading for IBB3 is 0.650, which falls below the standardized value, it remains within the acceptable range of 4.0 to 0.708 Given that the AVE value for IBB is greater than 0.5 and IBB3 captures a crucial aspect of impulse buying—spontaneity—the item was retained in the model.
To assess discriminant validity, the study employed two criteria The first criterion, illustrated in Table 4.4 (Fornell-Larcker criterion), demonstrated that the square root of each construct's Average Variance Extracted (AVE) exceeded its correlations with other constructs, confirming a satisfactory level of discriminant validity.
Table 4.6 Discriminant validity - Fornell Larcker criterion
CONC EC ENJ IBB OUT
Next, HTMT is taken into consideration Table 4.5 shows HTMT ratio estimates are all below the threshold of 0.85 except the two constructs OUTREG and PROREG
CONC EC ENJ IBB OUT
The correlation value of 0.947 indicates a strong relationship between Outcome regret and Process regret, raising concerns about discriminant validity Nevertheless, given that both constructs are well-established in existing literature, the decision was made to retain them as separate entities to explore new insights Following the methodology proposed by Henseler et al (2015) and employing a one-tailed bootstrapping test at 5% significance with a subsample of 10,000 as suggested by Hair et al (2022), the results are detailed in Table 4.6 The confidence interval boundaries for the HTMT relationship between OUTREG and discriminant validity are also presented.
1 Therefore, s > value any PROREG does not include two constructs are established
Table 4.8 Discriminant validity – HTMT (bootstrap)
The reliability and validity tests conducted on the measurement model yielded satisfactory results, confirming that all items used to measure the constructs in this thesis are valid and appropriate for estimating parameters in the structural model.
Assessing the structural model
All the variance inflation factor (VIF) values were well below the threshold of 5, suggesting the absence of multicollinearity issue (see Table 4.9) h
Table 4.9 Collinearity statistics (VIF) of exogenous variables
CONC EC ENJ IBB OUT
Common method bias can distort the relationship between constructs, and in PLS-SEM, it is assessed using a full collinearity test where all VIF values in the inner model should be below the 3.3 threshold (Kock, 2015) In this study, all VIF values met the threshold, except for the value between outcome regret and satisfaction, which slightly exceeded 3.3, indicating some degree of common method bias This high VIF may stem from the strong correlation between outcome regret and satisfaction, as individuals who experience regret often report dissatisfaction While completely eliminating bias is difficult, the study implemented measures to reduce common method bias by randomizing questionnaire items, ensuring participants were unaware that they were measuring the same construct.
Running bootstrapping for a one-tailed test at 5% and subsample 10,000 as proposed by (Hair et al., 2022), the results came out as shown bellowed: h
The results from Table 4.8 indicated that 4 out 13 hypotheses were statistically insignificant and thus, unsupported Specifically:
- First, regarding the effects of emotional contagion and parasocial interaction on concentration, only parasocial interaction has a positive impact on concentration H1 is not supported (β = 0.062; p = 152) while H2 is supported (β = 0.676; p = 000)
- Second, regarding the effects of emotional contagion and parasocial interaction on enjoyment, only parasocial interaction has a positive impact on enjoyment H3 is unsupported (β = 0.094; p = 104) while H4 is supported (β = 0.606; p = 000) h
- Third, emotional contagion has a positive impact on parasocial interaction: H5 is supported (β = 0.408; p = 000)
- Fourth, regarding the effects of concentration and enjoyment on impulse buying behavior, both H6 (β = 0.171; p = 047) and H7 (β = 0.340; p = 000) are supported
- Fifth, regarding the effects of impulse buying on outcome regret, process regret and satisfaction, impulse buying only has a positive impact on process regret H9 is supported (β = 0.140; p = 002) while H8 (β = -0.143; p = 072) and H11 (β = 0.028; p
- Sixth, regarding the effect of outcome regret on process regret, the effect of outcome regret on satisfaction, the effect of process regret on satisfaction, H10 (β = 0.842; p
- The model also explained 48.8%, 41.2%, 15.6%, 18.9%, 1.3%, 69% and 44.2% of the variance in CONC, ENJ, PI, IBB, OUTREG, PROREG, and SAT respectively
The findings indicate that EC does not influence CONC and ENJ, but it has a medium impact on PI In contrast, PI significantly affects CONC and ENJ Additionally, both ENJ and CONC exert a minor influence on IBB, which in turn has a small effect on PROREG, with no impact on OUTREG and SAT OUTREG shows a strong effect on PROREG and a minor effect on SAT, while PROREG has a small effect on SAT.
CONC EC ENJ IBB OUT
CONCLUSION
General discussion
This research aims to re-evaluate the impact of interaction-related factors in the context of Live Commerce, highlighting the positive influence of parasocial interaction on flow state dimensions Previous studies have indicated a similar relationship between parasocial interaction and enjoyment, emphasizing the intimate connection between livestreamers and viewers fostered by two-way dialogues, which enhances viewer enjoyment However, a gap exists in empirical evidence linking parasocial interaction to concentration Possible explanations include the heightened sense of connection and familiarity that enhances focus, the perception of social presence that fosters engagement, and emotional responses like empathy and excitement that capture attention and deepen immersion in the livestreaming content.
The findings indicate that emotional contagion (EC) does not correlate with dimensions of flow state, such as concentration or enjoyment, aligning with Lo et al (2022) which found no significant link between social contagion and affective reactions This lack of impact may stem from barriers in emotional transmission during livestreaming commerce, where the interaction often favors the livestreamer's perspective While livestreamers utilize verbal and nonverbal cues, viewers are limited to text-based communication, hindering emotional connections Interestingly, despite its lack of direct influence on flow state, emotional contagion significantly contributes to parasocial interaction, fostering a sense of shared experience and connection between viewers and livestreamers, which is fundamental to parasocial relationships.
Research shows that enjoyment and concentration significantly influence consumers' impulse buying behavior While numerous studies highlight the connection between flow state and impulse buying, as well as the role of enjoyment, evidence linking concentration to impulse buying is limited Unlike Ozkara et al (2017), which found no significant link between concentration and online purchase intention, this study reveals a modest relationship between concentration and online impulse purchases This modest impact may be attributed to the lengthy nature of livestreaming sessions, where distractions such as messages and emails can hinder sustained focus As a result, consumers may only achieve concentrated attention during brief moments, complicating the assessment of concentration's overall effect on impulse buying.
This research aims to investigate the connection between impulse buying, regrets, and satisfaction Consistent with Barta et al (2023), findings indicate that impulse buying does not significantly influence outcome regret but does affect process regret It is noteworthy that the lack of impact on outcome regret is unexpected, given that impulsive decisions often lead to poor choices A possible explanation is that many participants purchased inexpensive items, which may encourage consumers to take risks and experiment with new products without significant concern for potential repercussions Once acquired, as long as the product meets basic expectations, consumers may feel that the time and effort spent contemplating alternatives and regretting past decisions are unnecessary.
In the event of an unfavorable outcome, individuals tend to employ justification as a means to alleviate feelings of regret instead Another possible explanation could be that
Impulse purchases are not inherently wrong, as the effective demonstration and display of products during livestreams can equip consumers with the necessary information to make informed choices, thereby minimizing regret over missed alternatives (Barta et al., 2023, p.8) However, consumers often experience process regret after impulse buying, recognizing that their decision-making may have been flawed A report by Q&Me (2022) highlights that Vietnamese consumers tend to be cautious shoppers who engage in a thorough decision-making process, and when they act against their initial intentions, this inconsistency can lead to regret over their perceived inefficient choices.
Impulse buying does not significantly influence consumer satisfaction with products, as consumers often prioritize the consumption experience over the initial purchase enjoyment While the act of impulse buying can bring joy, particularly for low-value items, the significance of the product diminishes due to minimal attachment and lack of expectations Consequently, customer satisfaction is more influenced by the product's actual performance, perceived value, quality, features, and benefits rather than by any heightened expectations stemming from the impulsive purchase.
Theoretical contributions
Impulse buying in the Live Commerce setting remains an underexplored phenomenon, and this study aims to fill that gap by examining interaction-related factors unique to livestream environments Utilizing S-O-R theory, it highlights how emotional contagion and parasocial interaction influence impulse buying through the flow state Although the study found no significant link between emotional contagion and the two dimensions of flow state, it did uncover a new relationship between emotional contagion and parasocial interaction, offering a more comprehensive understanding of their interconnections and their impact on impulse buying Additionally, the research investigates the roles of enjoyment and concentration within the flow state, revealing that while enjoyment's impact on impulse buying is well-documented, concentration has been less studied The findings indicate a small but noteworthy effect of concentration on impulse buying, providing new insights into its role in live commerce.
Previous research on impulse buying has largely overlooked its consequences This study addresses this gap by examining both the positive and negative effects of impulse purchases It enhances the understanding of regret theory by analyzing specific dimensions of regret associated with impulse buying, shedding light on how individuals evaluate their decisions post-purchase Additionally, the research focuses on product consumption satisfaction, exploring the connection between impulse buying and overall consumer satisfaction, a multifaceted concept in existing literature.
Practical implications
The study highlights crucial insights for e-retailers and e-marketers utilizing livestreaming as a sales strategy, emphasizing its potential to foster real-time customer engagement and interactive shopping experiences Modern livestreaming transcends mere discounts and promotions; to boost sales, marketers should combine emotional contagion and parasocial interaction to cultivate a “flow experience” that encourages impulse buying Key strategies include crafting emotionally resonant content through storytelling and relatable experiences, allowing livestreamers—regardless of celebrity status—to forge connections with viewers Additionally, adopting an informal conversational style and actively responding to audience comments enhances viewer engagement Since viewers communicate primarily through text, leveraging online comments and encouraging likes can amplify positive emotions towards products Rewarding viewers who contribute positive feedback with gifts or vouchers can further stimulate interaction and enhance the overall livestream experience.
Incorporating entertainment into livestream sessions has become essential for engaging viewers, especially during lengthy broadcasts To keep audiences interested, livestreamers should regularly introduce innovative ideas, such as hosting giveaways or participating in cosplay These unique and entertaining elements can foster enjoyment and positively influence purchasing decisions It's important to note that maintaining constant attention isn't necessary; instead, creating memorable moments during peak viewer times is key to maximizing engagement.
To minimize process regret, sellers should provide comprehensive information about product features, benefits, and potential drawbacks, enabling consumers to make informed decisions Rather than manipulating purchases, sellers should offer a variety of options and genuine advice tailored to customer needs While satisfaction is not linked to impulse buying, marketers can enhance overall customer satisfaction by implementing strategies such as including a thank-you note or a small gift with purchases, which can evoke positive feelings and reinforce the joy of the purchase.
Limitations and future research directions
This study acknowledges several limitations, notably the sample size of 140, which, while meeting the minimum requirement set by Hair et al (2013), could be improved for enhanced model robustness It is advisable to aim for a minimum of 155 samples, as suggested by the Inverse Square Root Method (Kock and Haydaya, 2018), to achieve a common power level of 80% and a significance level of 5% Additionally, a key drawback of PLS-SEM is the absence of a globally recognized goodness-of-fit measure (Hair et al., 2022) Future research should consider increasing the sample size to at least 200 to facilitate the application of CB-SEM and to validate the model's fitness.
The study's reliance on convenience and snowball sampling limits the representativeness of the data, primarily due to a significant portion of respondents being low-income students Their impulse-buying behaviors may differ markedly from those of middle-aged consumers with higher incomes To improve the generalizability of findings related to online impulse buying, future research should aim for a more diverse sample that includes various demographic factors, such as age, gender, and income Additionally, considering these variables as control factors will enable researchers to better isolate the effects of impulse buying and assess its impact across different demographic groups.
This study examines consumers' impulse buying behavior over the past four months, revealing a temporal ambiguity in their feelings of satisfaction and regret While individuals may initially feel satisfied with an impulse purchase, this satisfaction can diminish over time as they gain more experience with the product Future research should focus on a shorter timeframe, such as one to two weeks post-purchase, to better capture the nuances in participants' responses.
Exploring the distinctions between impulse buying and planned purchasing can provide valuable insights into consumer behavior Additionally, examining the phenomenon of regret stemming from perceived insignificance reveals how consumers often regret acquiring items they don't truly need, leading them to compare their current situation with their past inaction or status quo.
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Zuo, R., & Xiao, J (2021) Exploring consumers’ impulse buying behavior in live streaming shopping In Proceedings of the Fifteenth International Conference on
Management Science and Engineering Management: Volume 1 15 (pp 610-622)
Springer International Publishing https://doi.org/10.1007/978-3-030-79203- 9_47 h
QUESTIONNAIRE
Mình là Văn Thanh Thảo, học viên của chương trình Thạc sĩ Quản trị Kinh doanh, trường Đại học Việt Nhật - Đại học Quốc gia Hà Nội
Hiện tại, tôi đang thực hiện luận văn tốt nghiệp về hành vi mua hàng qua livestream Bảng khảo sát gồm 40 câu hỏi và dự kiến mất khoảng 7-10 phút để hoàn thành Tôi cam kết bảo mật dữ liệu thu thập và chỉ sử dụng cho mục đích nghiên cứu.
Xin chân thành cảm ơn bạn đã dành thời gian quý báu giúp mình hoàn thành bảng khảo sát này
Email: 21117022@st.vju.ac.vn
My name is Van Thanh Thao, an MBA student from Vietnam Japan University - Vietnam National University, Hanoi
I am currently working on my master thesis which focuses on Online Buying in
The Livestreaming E-commerce survey includes 40 questions and takes about 7-10 minutes to complete All data collected will remain confidential and will be used solely for research purposes.
Thank you for taking your time to complete this survey
Email: 21117022@st.vju.ac.vn
Phần 1: Câu hỏi chọn lọc đối tượng
Trong 4 tháng gần đây, hãy nhớ lại một lần mua hàng livestream cụ thể mà bạn đã thực hiện, bắt đầu từ Tết Dương lịch Lần mua này cần phải đáp ứng hai điều kiện quan trọng.
(1) Bạn đã đặt mua qua livestream, nhận hàng và sử dụng ít nhất một sản phẩm thuộc nhóm Hàng thời trang và Mỹ phẩm trang điểm
Bạn có thể mua sắm một cách ngẫu hứng mà không có kế hoạch trước khi xem livestream, có thể do sự hấp dẫn của sản phẩm, mong muốn ủng hộ streamer, hoặc không muốn bỏ lỡ chương trình khuyến mãi Ngoài ra, bạn cũng có thể phát hiện ra những sản phẩm khác không nằm trong dự định ban đầu nhưng lại bổ sung hoàn hảo cho sản phẩm bạn đã dự định mua.
Nếu bạn đã từng trải qua trải nghiệm này và có thể nhớ lại, hãy tiếp tục với phần tiếp theo của khảo sát Nếu không, bảng hỏi sẽ kết thúc tại đây.
You may have many purchases made via livestream but please recall ONE purchase only within the last 4 months (since New Year's holiday) That purchase must satisfy two following conditions:
(1) You made the order via livestream, received and used at least one item that belongs to the Fashion and Makeup categories
Many viewers find themselves purchasing products during livestreams even when they had no initial intention to buy This impulse can stem from a desire to support the streamer, an attraction to the product's appeal, fear of missing out on a limited-time promotion, or the need to complement another item they were already planning to purchase.
* If the respondent has had this experience and can recall it, he/she will continue to the next section Otherwise, the questionnaire ends here
Phần 2 / Section 2: Mô tả chung về lần mua hàng/ Purchase description
1 Sản phẩm được mua ngoài kế hoạch/ý định của bạn là gì? / What is your unplanned item?
Thời trang (Quần áo) / Fashion items (Clothing)
Phụ kiện thời trang (Giày dép, túi xách, mũ nón, đồng hồ, kính mắt, trang sức, etc.) / Accesorries (Shoes, bags, hats, watch, glasses, jewelry, etc.)
Mỹ phẩm trang điểm / Makeup items
2 Bạn cảm thấy thế nào về trị giá của sản phẩm đó? / How do you perceive the price of that item?
Không đắt không rẻ / Neither cheap nor expensive
Did you purchase that item along with other products you had already intended to buy before the livestream?
4 Bạn đã mua sản phẩm đó trên nền tảng livestream nào? / On which livestreaming platforms did you make the purchase?
5* (Trap question) Sau khi nhận hàng và thử (sử dụng), tôi cảm thấy sản phẩm đó: / After receiving the item and (trying) using it, I perceived that item:
Tệ hơn nhiều so với mong đợi / Much worse than expected
Tệ hơn so với mong đợi / Worse than expected
Đúng như mong đợi / Meet my expectation
Tốt hơn so với mong đợi / Better than expected h
Tốt hơn nhiều so với mong đợi / Much better than expected
Phần 3 / Section 3: Câu hỏi chính / Main questions
*Các câu hỏi trong phần này được xáo trộn thứ tự trong bảng hỏi thực tế / Questions in this section were randomized in the actual online/offline form
Please assess your level of agreement with the following statements using a scale from 1 to 5.
1 Strongly disagree / Rất không đồng ý
3 Neither agree nor disagree / Trung lập
6 Người dẫn livestream khiến tôi cảm thấy thoải mái như thể tôi đang ở cùng với một người bạn / The streamer made me feel comfortable, as if I was with a friend
The streamer comes across as genuine and relatable, embodying a natural and down-to-earth persona Their authenticity shines through, as they avoid theatrics and unrealistic statements, making their content feel real and accessible to viewers.
8 Tôi mong chờ được xem người dẫn livestream trong một livestream khác / I look forward to watching the streamer in another livestream session
9 Nếu người dẫn livestream xuất hiện trong một livestream khác, tôi sẽ xem nó / If the streamer would appear in another livestreaming session, I would watch it
10 Tôi thấy người dẫn livestream rất thu hút và có duyên / I found the streamer to be attractive
11 Tôi bị cuốn sâu vào buổi livestream / I was absorbed intensely in the livestream
12 Mọi sự chú ý của tôi đều tập trung vào buổi livestream / My attention was focused on the livestream
13 Tôi tập trung hoàn toàn vào việc xem livestream / I concentrated fully on the livestream
14 Tôi mải mê xem livestream / I was deeply engrossed in the livestream
15 Tôi cảm thấy buổi livestream rất thú vị, hấp dẫn / The livestream was interesting
16 Tôi cảm thấy buổi livestream rất thoải mái dễ chịu / The livestream was enjoyable
17 Tôi cảm thấy buổi livestream rất hào hứng, sôi nổi / The livestream was exciting h
18 Tôi cảm thấy buổi livestream rất vui vẻ / The livestream was fun
19 Khi xem livestream, tôi đã mua sản phẩm mà tôi chưa bao giờ có ý định mua / When I watched the livestream, I bought the product that I had never intended to buy
During a livestream, I made a purchase influenced by my immediate feelings and emotions at that moment.
"I see it, I buy it" perfectly encapsulates my buying behavior during that particular livestream shopping experience, where I instantly made a purchase decision upon spotting the product.
22 Khi xem livestream, tôi đã mua sản phẩm một cách tự phát bởi tôi đột nhiên muốn làm điều đó / When I watched the livestream, I bought the product spontaneously
Since purchasing the product, I have been consistently satisfied with its performance and quality.
Since purchasing the product, I have consistently felt satisfied with its performance and quality.
Since I received and started using the product, I have consistently felt disappointed with its performance.
26 Lẽ ra tôi nên chọn mua sản phẩm khác thay vì cái tôi đã mua
/ I should have chosen something else than the one I bought
27 Nếu có thể quay ngược thời gian, tôi sẽ chọn mua sản phẩm khác / If I were to go back in time, I would choose something different to buy
28 Tôi hối hận vì đã lựa chọn sản phẩm này / I regret the product choice that I made
29 Tôi nhận ra có những lựa chọn sản phẩm khác tốt hơn / I now realize how much better my other choices were
30 Nếu có nhiều thông tin hơn, có lẽ tôi đã có thể đưa ra một quyết định tốt hơn / With more information, I could have made a better decision
31 Tôi cảm thấy tôi đã không cân nhắc kĩ trước khi mua sản phẩm
/ I feel that I did not put enough consideration into buying the product
32 Nếu tôi nỗ lực hơn (Vd: dành nhiều thời gian công sức tìm hiểu hơn), có lẽ tôi đã có thể đưa ra một quyết định tốt hơn /
With more effort, I could have made a better decision h
33 Tôi hối hận vì đã không suy nghĩ kĩ trước khi quyết định mua
/ I regret not putting enough thought into my decision
Please evaluate the frequency of the events described in the following statements using a scale from 1 to 5.
34 Ở cạnh một người vui vẻ hạnh phúc sẽ vực tôi dậy khi tôi cảm thấy buồn / Being with a happy person picks me up when I'm feeling down
35 Khi ai đó nở một cười ấm áp với tôi, tôi mỉm cười lại và cảm thấy ấm áp trong lòng / When someone smiles warmly at me,
1 smile back and feel warm inside
MEASUREMENT SCALE
Construc t Code Measurement items Source
PI1 The streamer made me feel comfortable, as if I was with a friend
(Rubin et al., 1985; Dibble et al., 2016)
2 PI2 I saw the streamer as a natural, down-to- earth person
3 PI3 I look forward to watching the streamer in another livestream session
4 PI4 If the streamer would appear in another livestreaming session, I would watch it
8 PI5 I found the streamer to be attractive
EC1 Being with a happy person picks me up when I'm feeling down
0 EC2 When someone smiles warmly at me, 1 smile back and feel warm inside
1 EC3 Being around happy people fills my mind with happy thoughts
I was absorbed intensely in the livestream
My attention was focused on the livestream
CON C3 I concentrated fully on the livestream
CON C4 I was deeply engrossed in the livestream
ENJ1 The livestream was interesting
7 ENJ2 The livestream was enjoyable
8 ENJ3 The livestream was exciting
9 ENJ4 The livestream was fun
When I watched the livestream, I bought the product that I had never intended to buy