The influencing factors of vietnamese customers’ engagement and purchase intention in live streaming shopping a study in hanoi

BANKING ACADEMY OF VIETNAM FACULTY OF BUSINESS ADMINISTRATION GRADUATION THESIS Topic: The influencing factors of Vietnamese customers’ engagement and purchase intention in live strea

The urgency of the topic

Vietnam's e-commerce sector has demonstrated remarkable resilience and growth, even amidst global economic fluctuations The COVID-19 pandemic significantly altered consumer shopping habits, leading to an increase in digital engagement and a surge in e-commerce According to the Vietnam eCommerce and Digital Economy Agency, e-commerce growth rates reached 18% and 16% during the pandemic, with market size expanding from 11.8 billion USD in 2020 to 13.7 billion USD in 2021 This upward trajectory continued as the economy improved, with e-commerce revenues climbing to 16.4 billion USD in 2022 and projected to reach 20.5 billion USD in subsequent years.

2023 Based on Metric's comprehensive report on Vietnam's online retail market in

In 2023, product units traded on Vietnam's leading e-commerce platforms, such as Shopee, Lazada, Tiki, Sendo, and Tiktok Shop, surged by 52.3% compared to 2022, marking the highest growth rate in three years This remarkable increase highlights the growing importance of e-commerce, particularly livestreaming e-commerce, in driving sustainable economic growth in Vietnam.

In 2023, Vietnam emerged as one of the top three countries with the highest percentage of social network users engaging in live shopping events, according to Statista A report by Coc Coc, titled "New Trends of Vietnamese Users," published on January 17, 2024, revealed that 77% of survey participants had watched livestreams, with 71% of those viewers having made purchases during these sessions Key factors driving customer purchases during livestreams include the availability of preferred products (47%) and attractive promotional offers (41%).

In the evolving e-commerce landscape, livestreaming has emerged as a powerful communication tool and a unique online shopping experience, allowing customers to actively engage with presenters and express their views This interactive environment significantly influences purchasing decisions, as evidenced by La Roche Posay's remarkable fivefold increase in views and double income boost on Shopee Live within a month Businesses benefit from livestreaming by connecting effortlessly with potential customers, understanding their preferences, and streamlining the buying process The growing popularity of livestream shopping has led numerous brands and individuals to leverage this medium for marketing and revenue generation, particularly during major sales events Prominent brands like Coolmate and Unilever conduct extensive live broadcasts, while collaborations with social media influencers, such as Ha Linh and Pham Thoai, further enhance viewer engagement and attraction.

Livestreaming has become a significant trend in e-commerce, greatly contributing to increased financial gains It is essential for entrepreneurs and professionals in this field to understand the various aspects of livestreaming and its effects on customer engagement and purchase intentions This knowledge is crucial for enhancing competitiveness, attracting more customers, and promoting shopping behavior.

Various research papers have been published worldwide on the topic, such as the livestreaming’s impact on immediate buying behavior and continuous (Lv et al,

Recent studies highlight the significant influence of customer trust on engagement and its role in promoting impulsive purchasing during live streaming (Guo et al., 2021; Luo et al., 2024) In Vietnam, research on this subject is limited, with notable works including Nguyen's (2023) exploration of TikTok livestream sales activities and their impact on purchasing intentions through the S-O-R model, as well as Tran et al.'s (2023) investigation into factors influencing impulsive buying of fashion products via TikTok livestream.

In the evolving landscape of Vietnam, understanding the factors that shape customer behavior, particularly in the realm of live streaming commerce, has become essential This research focuses on the determinants of customer engagement and purchase intention in live streaming shopping, highlighting its significance as a new area of study The author aims to offer practical recommendations for businesses to achieve positive results in this dynamic market Consequently, the research topic is centered on “The Influencing Factors of Vietnamese Customers’ Engagement and Purchase Intention in Live Streaming Shopping: A Study in Hanoi.”

Research objectives

This research aims to explore the complex relationship between various factors affecting customer engagement and purchase intentions in livestream e-commerce Understanding these influences is crucial for businesses looking to effectively harness the potential of this expanding e-commerce channel.

This study examines how perceived product quality, perceived information quality, deal proneness, streamer credibility, and streamer interaction quality affect purchase decisions and customer engagement in livestream shopping By analyzing these factors, the research seeks to provide streamers and organizations with essential insights to improve their livestream e-commerce strategies.

This research aims to explore the role of customer engagement as a mediator between various factors and purchase intent Specifically, it will analyze how perceived product quality, perceived information quality, deal proneness, streamer credibility, and streamer interaction quality influence consumers' intentions to make a purchase.

This study will explore the role of brand equity as a moderating factor in the relationship between customer engagement and purchase intention By analyzing this moderation, we aim to uncover insights into how varying levels of customer engagement influence purchasing decisions in the context of livestream e-commerce.

This research aims to enhance the existing body of knowledge by applying established theories and models to the emerging field of livestream e-commerce It seeks to deepen our understanding of customer behavior and inform the development of effective business strategies in the dynamic digital landscape.

Research subjects and scope

Research subject

The factors influencing customer engagement and purchase intention in livestreaming commerce.

Research scope

Spatial scope: Hanoi citizens engaging in livestream shopping

Research content scope: Focus primarily on examining the factors influencing customer engagement and purchase intention

Time scope: The secondary data gathered by the author comprises domestic and international articles published in periodicals and newspapers, primarily between

2019 and 2023 Concurrently, the author collected primary data between February and April of 2024.

Research methodology

The article predominantly employs two primary methodologies: qualitative and quantitative research approaches

The author conducted qualitative research by gathering secondary sources from newspapers, magazines, and books, focusing on key terms such as livestream e-commerce, product quality, information quality, customer engagement, and purchase intention, to establish a comprehensive theoretical foundation.

The author conducted quantitative research by collecting 350 online survey samples, which were recalibrated and encoded using Excel Descriptive statistics were performed on the sample with SPSS 20 before analyzing the research model Measurement and structural model analyses were executed in Smart-PLS 3, where the measurement model was evaluated for Cronbach's Alpha, composite reliability, outer loadings, AVE, HTMT, and VIF, leading to the exclusion of variables that did not meet the criteria Subsequently, the author tested the relationships between variables in the research model through structural model analysis, utilizing Smart-PLS 3 for both analyses.

The thesis is structured into five main sections, alongside essential components such as an introduction, table of contents, lists of diagrams, tables, figures, acronyms, a conclusion, and references.

Chapter 1: Literature review and theoretical basis

Chapter 2: Research model and hypotheses

LITERATURE REVIEW AND THEORETICAL BASIS

Definitions

Electronic commerce, which gained prominence in the late 1990s alongside the rise of the Internet, has rapidly evolved into a vital aspect of modern business practices Despite its significance, there is no universally accepted definition of e-commerce, as various authors have offered differing interpretations.

As defined by Holsapple and Singh (2000), e-commerce is typically defined as the utilization of technology to facilitate buy-sell transactions

E-commerce, as defined by Turban et al (2010), encompasses electronic transactions that involve the buying, selling, transferring, or exchanging of goods, services, and data via computer networks, primarily utilizing the Internet and intranets.

According to OECD (2011), electronic commerce, or e-commerce, refers to the activities and services involved in buying and selling products or services online

Rahayu and Day (2016) defined e-commerce as the use of networked information and communication technologies (ICT), particularly focusing on Internet technology, to facilitate business operations.

Laudon and Traver (2017) define e-commerce as the execution of business activities via mobile applications and browsers on mobile devices, encompassing transactions conducted over the Internet and the Web This definition highlights the role of digital platforms in facilitating commercial transactions between organizations and individuals.

E-commerce is defined as the electronic transaction of goods and services over the Internet, utilizing information technology to facilitate exchanges between businesses and individuals.

Live-streaming commerce is revolutionizing online retail by merging live video streaming technology with e-commerce platforms, creating an interactive digital marketplace This innovative trend marks a significant shift in how consumers engage with products and make purchases online.

Cai et al (2018) define livestream commerce as an innovative e-commerce model that integrates real-time social interaction via live streaming, creating an engaging platform that enhances and simplifies the shopping experience.

Wang et al (2022) describe live-streaming commerce as an e-commerce service that allows vendors to interact with customers in real-time through live-streaming, while simultaneously enabling customers to place orders within the same platform.

Live-streaming commerce is an innovative online shopping experience that combines live video streaming with e-commerce, allowing streamers to showcase and promote products while engaging with customers in real-time to address their inquiries (Chen et al., 2023).

Luo et al (2023) define live-streaming commerce as a specialized area within e-commerce that merges digital marketing with real-time social interaction, significantly improving customer engagement, promoting products, streamlining transactions, and enriching the online shopping experience.

The thesis will focus on the perspective of Chen et al (2023) regarding live streaming commerce, highlighting its unique characteristics as a standalone form of online buying and selling, distinct from traditional e-commerce Additionally, it underscores the role of live streaming commerce as a real-time communication platform that facilitates direct interaction between live streamers and their customers.

Customer engagement is the level of an individual's involvement and connection with an organization's products or services, driven by either the customer or the organization (Vivek et al., 2012).

Brodie et al (2013) expanded the definition of customer engagement, highlighting its multidimensional nature, which includes cognitive, emotional, and behavioral aspects This refined concept is essential in the relational exchange process, where various relational factors act as both precursors and outcomes of ongoing engagement within the brand community.

In live-streaming commerce, customer engagement is defined as the cognitive, affective, and behavioral involvement of both existing and prospective customers across multiple channels and touchpoints, impacting various industries and sectors (Wongkitrungrueng & Assarut, 2020).

Customer engagement is a multifaceted concept that includes cognitive, emotional, behavioral, and social dimensions, as highlighted by Luo et al (2024) It emphasizes the significance of multilateral relationships between customers and streamers within online communities, underscoring the critical role of social engagement alongside cognitive, emotional, and behavioral aspects.

Overview of previous international research

1.2.1 Overview of research on customer engagement

The emergence of livestream e-commerce marks a transformative shift in online shopping, merging entertainment, real-time interaction, and commerce into a cohesive experience This innovative approach captivates global audiences, fostering higher engagement levels compared to traditional e-commerce platforms As this trend gains traction, researchers from various disciplines are exploring the factors that drive customer engagement in livestream e-commerce and its impact on both businesses and consumers.

Kang et al (2021) investigate the role of tie strength in mediating the effects of interactivity and customer engagement in live-streaming commerce Utilizing text mining and the S-O-R framework, their study analyzes real-time online reviews from a major Chinese platform, revealing an inverse U-shaped relationship between tie strength and interactivity This suggests that while interactivity can enhance community connections, excessive interaction may hinder deeper relationships Additionally, tie strength positively impacts customer engagement behaviors such as hands up, e-WOM sharing, and product referrals, with interactivity's influence on engagement fully mediated by tie strength The research also explores how membership tenure and popularity moderate these dynamics, finding that tenure strengthens the U-shaped effects of interactivity, while popularity weakens them Despite these insights, the study's limitations include its focus on Weibo Live data, which may limit generalizability to other cultural contexts and platforms, and its neglect of emotional content in reviews, as well as the potential impact of spam reviews on the findings.

Figure 1.1 Research model in Kang et al (2021)’s study

Live streaming enhances customer shopping experiences by enabling real-time interactions and fostering trust among broadcasters, community members, and products Research by Guo et al (2021) highlights that trust in broadcasters positively affects trust in both community members and products, with community trust further enhancing product confidence The study reveals that "swift guanxi" significantly influences customer engagement, primarily driven by trust in broadcasters, which fosters strong relationships by addressing customer needs This relationship underscores the importance of quickly establishing trust through live streaming, leading to proactive customer behaviors like purchasing and sharing recommendations However, the study's limitations include its focus on non-branded fashion items, lack of differentiation among various types of live streamers, and its exclusive context within Chinese live streaming, which may not apply to other cultural settings or platforms Additionally, data collection was confined to the Taobao platform, excluding other major e-commerce sites and Western platforms.

Figure 1.2 Research model in Guo et al (2021) ’s study

Qin et al (2022) highlighted the critical role of social support in enhancing customer engagement in live streaming commerce, with emotional support proving more influential than informational support The study underscored the importance of real-time interaction, proximity, and authenticity as key attributes that positively impact both types of support, particularly emphasizing the significance of real-time interaction for informational support These attributes not only foster engagement but also indirectly fulfill customers' informational and emotional needs through social support from sellers, streamers, and the community However, the research identified several limitations, including the lack of consideration for product type's moderating effects on perceived attributes and engagement, and the exclusive focus on social support as a mediating variable Additionally, it did not explore potential antecedents of social factors, such as identification, nor did it account for support from other platforms or consumers The study's scope was limited to live streams featuring sellers and streamers, and it primarily employed quantitative methods, overlooking qualitative insights into consumer behavior Furthermore, the framework failed to incorporate mediators and moderators like gender, generation, and user experience, and did not consider the impact of usage frequency or consumer attributes on engagement levels, ultimately limiting the understanding of viewer requirements and the advancement of live streaming commerce.

Figure 1.3 Research model in Qin et al (2022)’s study

In light of the critical role of customer engagement in the sustainability of livestream purchasing, Ma (2024) established a comprehensive framework to analyze influential factors The study revealed a positive correlation between customer engagement and both informational and emotional support, indicating that assistance from livestreamers enhances non-transactional engagement Interestingly, immediate feedback did not show a positive link with emotional support, likely due to inadequate feedback speed and lack of high-quality conversational interaction The research also confirmed that multiple indicators and personalization improve both types of support, while social identification emerged as a significant factor, highlighting the importance of similarities between streamers and viewers in fostering emotional and informational connections Despite successfully identifying key social and technical elements affecting customer engagement through support mechanisms, the study faced limitations, including a narrow focus on support from livestream hosts, the absence of mediators and moderators like gender and user experience, and neglecting the impact of usage frequency on social support Additionally, it did not consider variations in consumer attributes that could influence engagement levels and relied solely on quantitative methods, overlooking the richer insights qualitative approaches could provide Lastly, the framework's emphasis on social and technical factors failed to address viewer needs, which could enhance understanding of livestream commerce and customer retention.

Figure 1.4 Research model in Ma (2024)’s study

In 2024, Luo et al conducted research that significantly advanced the field of live streaming commerce by utilizing the Elaboration Likelihood Model (ELM) of persuasion Their study examined how live streaming influences customer engagement and impulse buying behavior, highlighting the role of deal proneness as a moderating factor.

Figure 1.5 Research model in Luo et al (2024)’s study

Research highlights three key pathways influencing customer information processing in live-streaming commerce: streamer credibility, product information quality, and streamer interaction quality, with the latter having the most significant impact Increased customer engagement is linked to review consistency, though its peripheral effects are less pronounced The study identifies customer engagement as a strong predictor of impulse buying tendencies, with deal proneness potentially moderating this relationship Limitations include the exclusive data collection in China, raising questions about cultural applicability and generalizability due to purposive sampling The research also explored various ELM components but did not consider additional factors like brand awareness, platform credibility, or streamer attractiveness that might influence consumer behavior Moreover, while it examined central and peripheral route factors, it overlooked other elements, such as product categories and streamer types, that could affect impulse buying tendencies Lastly, the study's focus on streamer credibility did not fully address its role in persuasion through both central and peripheral pathways.

1.2.2 Overview of research on purchase intention

Zhang et al (2020) investigated the interplay between information quality, interaction quality, swift guanxi, and customers' purchase intentions The study defines "swift guanxi" as a rapidly established interpersonal relationship between buyers and sellers, rooted in mutual favors exchanged by both parties.

Figure 1.6 Research model in Zhang et al (2020)’s study

This research enhances our understanding of how various aspects of perceived information quality and interactions impact customers' purchase intentions in live-streaming e-commerce The authors highlight that high-quality information and interactions foster online relationships between retailers and customers, thereby boosting purchase intentions Additionally, the study introduces the concept of "swift guanxi" to the field of online relationship marketing However, it also identifies several limitations, such as the need for broader sample diversity beyond college students to improve the generalizability of the findings The research emphasizes the roles of interaction and information quality as precursors to swift guanxi, suggesting future studies could explore additional factors that strengthen retailer-customer relationships on live streaming platforms Moreover, it notes the absence of potential moderating factors, like product characteristics and individual traits, which could influence the relationship between information quality, interaction quality, and purchase intentions Finally, the study points out that customer interactions significantly affect both information and interaction quality, highlighting the importance of customer-to-customer dynamics in the live streaming context.

Gao et al (2021) explored how viewers process information in live streaming commerce through the Elaboration Likelihood Model (ELM), identifying central factors like information completeness, accuracy, and currency, alongside peripheral factors such as streamer trustworthiness, attractiveness, co-viewer involvement, and bullet-screen consistency Their findings revealed that perceived persuasiveness enhances both purchase and response intentions, with information completeness and currency positively influencing perceived persuasiveness Additionally, co-viewer involvement, streamer attractiveness, and trustworthiness also elevated perceived persuasiveness However, while perceived persuasiveness moderated response intention, it did not affect purchase intention, and neither information accuracy nor bullet-screen consistency significantly impacted perceived persuasiveness The study faced limitations, including generalizability concerns due to its focus on a single location in China and a cross-sectional design that captured self-reported data at one point in time, overlooking behavioral changes over longer viewing periods The research model, while comprehensive, did not consider all potential antecedents, indicating a need for further investigation into the effects of information accuracy and bullet-screen consistency in various live-streaming scenarios.

Figure 1.6 Research model in Gao et al (2021)’s study

A study by Chen et al (2022) revealed that customer loyalty and purchase intent in livestream commerce are significantly influenced by trust, particularly in the product, which enhances willingness to pay more While trust in the streamer does not directly affect willingness to pay, it indirectly boosts purchase intent by increasing confidence in the product, demonstrating a trust transference effect Key factors influencing streamer trust include perceived product expertise, endorsements, and value similarity, while brand awareness does not significantly impact product trust The findings emphasize the importance of establishing streamer credibility to drive sales, although the research faced limitations such as the use of convenience sampling, a narrow focus on Chinese user behaviors, and a lack of consideration for variations among product categories Future research should also differentiate between sellers and streamers in terms of their roles in product promotion.

Figure 1.7 Research model in Chen et al (2022)’s study

Hossain et al (2023) explored the factors influencing customer engagement, swift guanxi, purchase intentions, and actual purchasing behavior in live stream purchasing Their findings revealed that customer response capability, source credibility, and platform interactivity significantly impacted both swift guanxi formation and customer engagement While customer response capability had a strong effect on engagement, swift guanxi did not notably influence it as initially expected The study highlighted that expeditious guanxi and customer engagement were critical in shaping purchase intentions and behaviors Additionally, customer engagement mediated the relationships between source credibility, platform interactivity, and purchase intentions, though this was not the case for response capability The research identified limitations such as potential biases from convenience sampling, the inability to predict changes in consumer reactions due to the cross-sectional data collection, and a sample predominantly composed of young adults aged 20 to 24 The authors emphasized the need for qualitative research to better understand the various factors affecting livestream purchasing behaviors.

Figure 1.8 Research model in Hossain et al (2023)’s study

Liao et al (2023) revealed that streamers' interaction-oriented communication significantly enhances viewer immersion and parasocial interaction, with immersion having the most notable impact on purchase intention While streamer expertise positively influenced immersion, it did not moderate the effects of communication on parasocial interaction Contrary to initial hypotheses, streamer attractiveness did not affect either immersion or parasocial interaction The study emphasizes that the interactive communication style of streamers in live-stream commerce fosters viewer engagement and connections, thereby increasing purchase intentions Furthermore, the findings highlight the importance of streamer attributes, particularly expertise, in enhancing immersive experiences However, the research has limitations, including its focus solely on data from China, which restricts the generalizability of the findings across different contexts, and the need for further exploration of how product types may influence the relationship between communication styles and purchase intention.

Overview of previous domestic research

Tran et al (2018) identified six key factors influencing the perceived value and impulsive buying behavior of young consumers in e-commerce livestreams: scarcity messaging, customer training, entertainment motivation, visual appeal, and social interaction Scarcity messages, such as limited-time discounts, effectively triggered impulsive purchases by enhancing the shopping experience through risk reduction and easy returns The visual presentation of products, especially in fashion, was crucial for attracting buyers Building an interactive community, engaging sellers, and providing prompt responses fostered customer trust, which, while less impactful on impulsive buying, remained essential for transaction security and accurate product descriptions However, the study faced limitations, including convenience sampling and a small sample size, which affected the generalizability of the findings Overall, the perceived value and impulsive purchasing behaviors of young shoppers in e-commerce livestreams were shaped by trust-building strategies, entertainment value, scarcity tactics, and visual attractiveness.

The study's findings indicate a low degree of representativeness, as it focused solely on a limited subset of factors influencing impulsive purchasing Additionally, many other factors impacting impulsive buying behavior are mediated through TikTok livestreams by influencers in Vietnam.

Figure 1.10 Research model in Tran et al (2018)’s study

A study by Nguyen and Tang (2021) highlights that Vietnamese youth rapidly adopted live-streaming fashion purchasing in the digital era The research identified key motivators influencing purchase intentions, including social motivators, practical value, hedonic value, perceived utility, and perceived ease of use Notably, practical value emerged as the most significant factor affecting purchase intentions, demonstrating the importance of functional information provided during live streams Additionally, perceived utility was found to positively influence both utilitarian and hedonic values, suggesting it plays a mediating role in the relationship between purchase intentions and live-stream engagement motives The authors propose actionable strategies to enhance customer acceptance of live-stream clothing purchasing in Vietnam, although the study acknowledges limitations such as the use of convenience sampling and a narrow focus on impulsive purchasing factors.

Figure 1.11 Research model in Nguyen and Tang (2021)’s study

Nguyen (2023) identified several significant findings concerning the variables influencing purchase intent during livestreams on TikTok in Vietnam The research validated that livestreaming has a motivating effect on customers' purchase intention

Engaging with customers during livestreams significantly enhances trust, with seller professionalism playing a crucial role in this relationship This increased trust directly influences purchase intent, aligning with previous research in social commerce Notably, the connection between trust and purchase intention is strengthened by product familiarity, suggesting that sellers can leverage existing trust to boost sales, especially for well-known products Overall, the findings highlight the importance of engagement, professionalism, and product knowledge in driving purchase intentions on social media platforms like TikTok.

Figure 1.12 Research model in Nguyen (2023)’s study

Le et al (2023) made a scholarly contribution by investigating how Generation

Z engages in online purchasing through live stream viewing on social networks In doing so, they validated the practicality of the uses and gratifications theory

The study revealed that perceived usefulness significantly impacts the intention to view live streams, with social interaction, leisure time, entertainment, and ease of use also playing crucial roles Viewers are motivated to watch live streams due to the opportunity to access product information and make informed purchasing decisions The interactive nature of two-way livestreams enhances viewer engagement, while the entertaining aspects and leisure perception positively influence viewing intentions Additionally, the research expands on previous findings by demonstrating how livestream viewing affects online purchasing behavior For sellers, the insights highlight the importance of emphasizing utility, facilitating interaction, providing entertainment, and ensuring accessibility to attract viewers and maximize the benefits of livestreams.

Generation Z exhibits distinct social media usage patterns and preferences that influence their online interactions Despite the findings presented, the article acknowledges existing limitations that need to be addressed Primarily, it concentrates on the behaviors of social media users within this demographic.

Generation Z primarily consists of students, highlighting the need for additional research to expand the sample size and enhance the study's validity and reliability However, since the article focuses solely on TikTok, its findings may not be broadly applicable to customer purchasing behavior across other social networks.

Limitations of previous research

A review of existing research on customer engagement and purchase intention in live-streaming commerce indicates a substantial body of literature globally However, the author notes certain gaps in both domestic and international academic publications.

A significant limitation in the study of customer engagement and purchase intention in live-streaming commerce is the predominance of research conducted by Chinese authors, reflecting the sector's rapid growth in China In contrast, Vietnam's live-streaming commerce is a newer phenomenon, leading to a lack of scholarly articles that explore the factors influencing purchase intention and a notable absence of research on customer engagement in this context.

Research on the determinants of live-streaming commerce in Vietnam is limited, leading to a lack of comprehensive evaluations of factors such as broadcaster characteristics, information quality, and product standards Additionally, while deal proneness could significantly affect customer engagement and purchasing behavior in live streams, there is currently no empirical evidence exploring the connections between deal proneness, customer engagement, and purchase intention.

Previous studies by foreign authors have primarily focused on how factors like trust, swift guanxi, persuasiveness, and customer engagement influence purchase intention through mediators, with limited research on the direct effects of these factors Additionally, domestic research has often overlooked the inclusion of moderating and mediating variables, particularly customer engagement and brand equity, in their models Addressing these gaps could enhance future research and provide a more comprehensive understanding of the dynamics at play.

Current research must incorporate additional variables, including perceived product quality, perceived information quality, and deal proneness, to better understand their impact on customer engagement and purchase intention Additionally, it is crucial to emphasize the importance of brand-related variables, particularly brand equity, in examining the relationship between customer engagement and purchase intention, enabling brands and businesses to gain deeper insights.

SUMMARY OF THE FIRST CHAPTER

In Chapter 1, the author defines key concepts such as e-commerce, live-streaming commerce, customer engagement, and purchase intention Additionally, a thorough review of previous research on the determinants of customer engagement and purchase intention is presented, synthesizing both domestic and international studies The chapter concludes by identifying limitations and opportunities for future research development.

RESEARCH MODEL AND HYPOTHESES

Theoretical basis

2.1.1 The ELM in investigating the impact on purchase intention and willingness to pay (Chen et al., 2022 )

Customer loyalty and purchase intent in livestream commerce are significantly influenced by trust, particularly trust in the product, which enhances willingness to pay more The streamer's positive impact on product trust suggests a trust transference effect, with perceived product quality and streamer trust preceding product trust, where streamer trust plays a more critical role Notably, product trust affects willingness to pay more more than streamer trust does Factors such as perceived streamer product expertise, endorsements, and value similarity contribute to building streamer trust Ultimately, the transfer of trust from the streamer to the product is crucial for boosting purchase intention and willingness to pay, highlighting the importance of establishing the streamer's credibility to drive sales on this interactive platform.

Figure 2.1 Research model of Chen et al (2022)

2.1.2 The ELM of persuasion to explore the impact of live streaming on customers' engagement and impulse buying behavior (Luo et al., 2024)

The study identified three key factors influencing customer information processing in live-streaming commerce: streamer credibility, product information quality, and streamer interaction quality, with the latter having the strongest effect on customer engagement It was found that consistent reviews enhanced customer engagement via the central route, while the peripheral route had no significant impact Additionally, customer engagement was shown to be a strong predictor of impulse buying tendencies, with deal proneness acting as a potential moderating factor in this relationship.

Research model

The authors propose a research model that combines two established theories: the Elaboration Likelihood Model (ELM) to assess its influence on purchase intention and willingness to pay (Chen et al., 2022), and the ELM of persuasion to investigate the effects of live streaming on customer engagement and impulse buying behavior (Luo et al., 2024) Despite the extensive application of these theories by international scholars, domestic researchers have shown limited engagement in conducting thorough studies on customer engagement and purchase intention using both frameworks.

The proposed model integrates perceived product quality and includes key variables such as deal proneness, streamer credibility, product information quality, and streamer interaction quality Incorporating deal proneness as an independent variable is crucial due to its significant influence on the behavior of live broadcast viewers.

An analysis of previous research indicated that specific variables significantly impact customer engagement and purchase intent These variables were subsequently incorporated into the proposed research model Additionally, earlier studies revealed that customer engagement mediates the effect on purchase intention, which was a crucial consideration in developing the research paradigm.

The author recognized that brand equity could play a moderating role in the relationship between customer engagement and purchase intention, leading to its inclusion as a moderating variable in the research model.

The proposed model includes five independent variables: perceived product quality, perceived information quality, deal proneness, streamer credibility, and streamer interaction quality It also features customer engagement as a mediating variable, brand equity as a moderating variable, and purchase intention as the dependent variable A detailed representation of this research model is illustrated in the following figure.

Note: Dashed-line boxes symbolize constructs or dimensions of a lower order: USE

(Usefulness); BEL (Believability); VIV (Vividness); RTI (Real-time Interaction); RES (Responsiveness); EMP (Empathy); TRU (Trustworthiness); EXP (Expertise); AFE (Affective Engagement); BEE (Behavioral engagement); COE (Cognitive engagement); SOE (Social engagement).

Perceived product quality

Perceived product quality refers to the evaluation customers make regarding a product's overall performance based on limited knowledge of its attributes (Chinomona et al., 2013; Yu et al., 2018; Solin & Curry, 2023) Additionally, customers' assessments of a product's functionality or effectiveness depend on their personal preferences and needs (Tsiotsou, 2005).

Customer engagement refers to the various acts that customers undertake while viewing live feeds, including commenting, liking, and sharing (Zheng et al.,

Customer engagement extends beyond social media interaction, as highlighted by research indicating that customers who are informed about product quality through live streams tend to be more satisfied and willing to engage High-quality products often motivate customers to connect with the brand's community, sharing experiences on social media and participating in events or alternative live-streaming shows Perceived quality plays a significant role in influencing customer engagement, leading to the hypothesis proposed by the author.

H1: Perceived product quality positively affects customer engagement

Perceived product quality significantly influences purchase intentions, as highlighted by Tsiotsou (2005) Research by Sun et al (2016) indicates that customers are more inclined to make purchases when they perceive high value and benefits from a product Live streaming enhances customers' understanding by visually presenting key features and demonstrating usage, which helps clarify the product's value and quality (Snoj et al., 2004; Ng et al., 2022) Additionally, customers place greater importance on product quality when making buying decisions, leading to a positive correlation between perceived product quality and purchase intentions in livestream settings (Chen et al., 2022).

H2: Perceived product quality positively affects purchase intention.

Perceived information quality

Perceived information quality is crucial in e-commerce, as it reflects customers’ views on whether the information meets or exceeds their expectations (Mun et al., 2013; Zhu et al., 2020) Unlike traditional stores, online shoppers cannot physically interact with products, making the quality of online product information essential Live streaming enhances this experience by allowing customers to see products clearly, participate in interactive discussions, and obtain immediate answers to their inquiries This direct engagement enables customers to personally experience the product alongside the live streamer Previous studies have identified three key factors for evaluating perceived information quality: utility, vividness, and believability (Zhang et al., 2020; Luo et al., 2024).

Research has demonstrated a strong link between the perceived quality of product information and customer engagement levels When customers receive detailed, accurate, and valuable information about products, their interactions during livestreams significantly improve (Islam and Rahman, 2017; Saima and Khan, 2020) High-quality product information not only enhances customer satisfaction but also boosts engagement (Abror et al., 2020; Mofokeng, 2021) Additionally, the visual richness of livestreaming serves to further stimulate customer involvement (Zhao et al., 2023) A recent study by Luo et al (2024) confirms that product information quality is a crucial factor in fostering client engagement within livestream e-commerce Therefore, the hypothesis is proposed:

H3: Perceived information quality positively affects customer engagement

Previous research indicates that high-quality information positively influences customers' purchase intentions (Jones & Kim, 2010; Bebber et al., 2017) When customers perceive the product information during a live stream as accurate, reliable, and valuable, it effectively persuades them and enhances their likelihood of making a purchase (Gao et al., 2021) Additionally, a clear presentation of product details can engage customers more deeply, significantly affecting their purchasing desires (Sun et al., 2019) Furthermore, the perceived quality of information fosters trust among customers, further motivating their purchasing intentions (Yi et al., 2013; Hong & Cha, 2013) Based on these observations, the author proposes the following hypothesis:

H4: Perceived information quality positively affects purchase intention.

Deal proneness

Deal proneness is the inclination of customers to prioritize the value of transactions, particularly through special offers and promotions (Gázquez-Abad & Sánchez-Pérez; Luo et al., 2024) Customers who are deal-prone are more attracted to benefits that present themselves as deals rather than merely lower prices Consequently, the rising popularity of live streaming is particularly enticing for these deal-oriented consumers, as it offers numerous opportunities to explore a diverse array of promotions and discounts (DelVecchio, 2005).

Livestreams offer targeted pricing promotions that attract deal-seeking customers, leading to increased satisfaction and higher engagement through likes and comments (Hanayasha, 2017) In the context of sales promotions, these streaming activities significantly boost customers' purchase intentions (Kaveh et al., 2021) A 2023 poll by Coc Coc revealed that 41% of Vietnamese respondents were motivated to buy products primarily due to the availability of discount coupons, highlighting the strong tendency among Vietnamese consumers to pursue attractive deals.

H5 Deal proneness positively affects customer engagement

H6: Deal proneness positively affects purchase intention.

Streamer credibility

The source credibility model highlights the importance of perceived reliability in communication, significantly affecting how customers accept messages (Yang et al., 2019) In the context of livestream e-commerce, streamers serve as key information providers, with customers' trust in these individuals directly influencing their product assessments Consequently, the credibility of a streamer plays a crucial role in shaping consumer behavior and ultimately impacts purchasing decisions (Wang et al., 2022).

Li et al., 2024) This thesis aims to investigate the credibility of streamers using Ohanian's (1990) source credibility model as prior studies with three main factors: trustworthiness, expertise, and attractiveness

The credibility of a streamer, encompassing trustworthiness and knowledge, significantly enhances their persuasive abilities and fosters positive customer engagement (Luo et al., 2024) Trustworthy streamers improve para-social interactions and relationships, further boosting customer engagement (Tsai & Men, 2017; Yang et al., 2019; Sheng et al., 2023) Additionally, research by Hossain et al (2023) confirms that a credible source positively affects customer engagement Therefore, the author proposes the following hypothesis.

H7: Streamer credibility positively affects customer engagement

Research by Zhou and Lou (2023) reveals that expertise, trustworthiness, and attractiveness are essential qualities of streamer characters that drive customer purchasing decisions These traits enhance streamer credibility, significantly influencing consumer choices Supporting this, Gao et al (2021) found that a streamer's trustworthiness and attractiveness effectively persuade customers, boosting their purchase intentions Furthermore, Liao et al (2023) highlight that streamers who demonstrate expertise and engage in interactive communication can effectively captivate customers, increasing their likelihood of making a purchase Based on these insights, the author proposes a hypothesis.

H8: Streamer credibility positively affects purchase intention.

Streamer interaction quality

Live streaming has transformed how streamers convey product information and engage with customers, enabling effective communication regardless of distance (Chen et al., 2023) The quality of streamer interaction is defined by three key dimensions: real-time interaction, responsiveness, and empathy (Zhang et al., 2020; Luo et al., 2024) When streamers communicate in a courteous and enthusiastic manner, it encourages audience participation and fosters meaningful interactions Additionally, streamers enhance engagement by responding to questions, discussing products, and conducting live demonstrations, prompting customers to actively join the conversation and connect with the content (Noort et al., 2012).

Consistent and prompt responses from streamers lead to increased customer satisfaction and enthusiasm, resulting in higher engagement in livestream activities and content sharing (Xue et al., 2020) Research highlights that customers perceive high-quality engagement, marked by timely, empathetic, and personalized interactions, as enhancing both informational and emotional value (Xue et al., 2020; Kang et al., 2021) Furthermore, Luo et al (2024) found that the quality of interaction with streamers significantly influences customer engagement.

H9: Streamer interaction quality positively affects customer engagement

Positive interactions between streamers and customers create an engaging livestream atmosphere that encourages active participation and enhances viewers' intentions to continue watching and making purchases Streamers provide valuable information and expertise about products, aiding customers in making informed buying decisions Additionally, effective communication fosters mutual understanding and strong relationships, further increasing the likelihood of purchases Therefore, in analyzing the shopping intentions of Vietnamese customers, the following hypothesis is proposed.

H10: Streamer interaction quality positively affects purchase intention.

The mediating role of customer engagement

Research indicates a strong link between customer engagement and purchase intention Kaveh et al (2020) found that customer engagement directly boosts purchase intention by increasing perceived value and customer satisfaction, especially during live streams with discount offers Furthermore, engagement behaviors like visits, likes, and comments are significant predictors of purchase intention in live streaming contexts (Zheng et al., 2022).

In 2023, research highlighted that customer engagement plays a crucial role in influencing purchase intention By fostering a sense of closeness, brands can enhance viewers' positive emotions, improve their perception of the product, and increase engagement with live streams, ultimately boosting their likelihood of making a purchase (Shen et al., 2022) Additionally, customer engagement not only directly affects purchase intention but also acts as a mediator between various factors and the intention to buy.

H11: Customer engagement positively affects purchase intention

H12: Customer engagement mediates the relationship between perceived product quality and purchase intention

H13: Customer engagement mediates the relationship between perceived information quality and purchase intention

H14: Customer engagement mediates the relationship between deal proneness and purchase intention

H15: Customer engagement mediates the relationship between streamer credibility and purchase intention

H16: Customer engagement mediates the relationship between streamer interaction quality and purchase intention.

The moderating role of brand equity

Brand equity, as defined by Keller (2016), refers to the marketing effects directly linked to a brand, with positive brand equity resulting from successful marketing efforts that lead to distinct outcomes It plays a crucial role in enhancing customer satisfaction and strengthening a company's reputation, which in turn affects customer engagement (Cambra-Fierro et al., 2021) Furthermore, well-known brands with a strong perception of superior quality can significantly drive purchase intentions (Majeed et al., 2021) Additionally, the brand's image and reputation are critical factors influencing customers' purchasing behavior (Agmeka et al.).

2019) Active customer participation and interaction with broadcasters during a live stream can enhance the brand value of a well-established product and encourage purchase intention Thus, the author suggests the hypothesis:

H17: The brand equity moderates the relationship between customer engagement and purchase intention

SUMMARY OF THE SECOND CHAPTER

In Chapter 2, the author presented the research model concerning customer engagement and purchase intention determinants, as Chen et al (2022) and Luo et al

The author created a research model featuring five independent variables—perceived product quality, perceived information quality, deal proneness, streamer credibility, and streamer interaction quality—alongside one intermediate variable (customer engagement), one dependent variable (purchase intention), and one moderating variable (brand equity) The next chapter will outline the research methodologies used to develop and evaluate the scales measuring these concepts.

RESEARCH METHODOLOGY

Research methods

The author opted to employ a mixed-methods approach incorporating qualitative and quantitative research techniques to investigate the determinants of customer engagement and purchase intention within live-streaming commerce

The author compiled secondary data sources through research articles and papers during the research to identify research gaps and incorporate additional characteristics into the research model

This research article utilizes quantitative methods to analyze data collected through an online survey targeting Vietnamese customers in Hanoi who engage in online purchasing via livestream platforms The research questionnaire, designed using Google Forms, aims to assess the current state of livestream shopping in Hanoi and identify factors influencing customer purchase intentions and engagement Employing a five-point Likert scale, the questionnaire items are crafted for clarity, ensuring respondents easily comprehend each question The survey was distributed not only to family and friends but also shared through hyperlinks in social media groups, focusing on quick cumulative and convenient sampling among diverse age groups with high interaction levels.

Item measurement of research variables

To maintain the accuracy of responses in accordance with Vietnam's unique context, the authors effectively reinterpreted the variables They developed a detailed questionnaire encompassing eight key concepts, based on 65 observed variables The influence of additional variables sheds light on important concepts, including perceived information quality, streamer credibility, streamer interaction quality, and customer engagement.

The constructed scales for this study were based on previous research, notably the work of Luo et al (2024), which focused on factors such as perceived product quality, information quality, streamer credibility, streamer interaction quality, and customer engagement Additionally, contributions from Wu and Jang (2013) and Li and Peng (2021) were pivotal in developing scales for measuring perceived product quality and streamer trustworthiness The measures for perceived information quality and streamer interaction quality were sourced from Zhang et al (2020) Further insights were provided by Flacandji and Vlad (2022), as well as Chen et al.

(2022), and Majeed et al (2021) made significant contributions to the development of scales for deal proneness, buy intention, and brand equity a Perceived product quality

PQ1 I think this product on livestream can satisfy my demands

PQ2 I think the quality of this product on livestream appears as advertised

PQ3 I think the total performance of this product on livestream is excellent

Table 3.1 The scale of Perceived product quality b Perceived information quality

According to Luo et al (2024) and Wu and Jang (2020), perceived information quality is explained by three dimensions: usefulness, believability, and vividness

USE1 The product information on livestream is valuable Luo et al

USE2 The product information on the livestream is informative

USE3 The product information on livestream is helpful

USE4 The product information on livestream is useful

Table 3.2 The scale of Usefulness

BEL1 The product information on livestream is reliable Luo et al

BEL2 The product information on livestream is believable

BEL3 The product information on livestream is trustworthy

BEL4 The product information on livestream is sincere

Table 3.3 The scale of Believability

VIV1 The product information on livestreaming has stimulated my senses

VIV2 The product information on livestream is clear

VIV3 The product information on livestream is concrete

VIV4 The product information on livestream is realistic

VIV5 The product information on livestreaming is colorful

Table 3.3 The scale of Vividness c Deal proneness

DP1 Beyond the money I save, buying products on a deal makes me happy

DP2 I feel the product is a good buy when it offers a special promotion

DP3 I feel like a smart shopper when I purchase products that offer special promotions

DP4 I love special promotional offers for products

Table 3.4 The scale of Deal proneness d Streamer Credibility

Luo et al (2024) and Li and Peng (2020) investigated streamer credibility through three main dimensions: trustworthiness, expertise, and attractiveness

TRU1 I feel that the live streamer is dependable Luo et al

TRU2 I feel that the live streamer is honest

TRU3 I feel that the live streamer is sincere

TRU4 I feel that the live streamer is reliable

TRU5 I feel that the live streamer is trustworthy

Table 3.5 The scale of Trustworthiness

EXP1 I feel that the live streamer is an expert Luo et al

EXP2 I feel that the live streamer is experienced

EXP3 I feel that the live streamer is knowledgeable

EXP4 I feel that the live streamer is qualified

EXP5 I feel that the live streamer is skilled

Table 3.6 The scale of Expertise

ATT1 I feel that the live streamer is classy Luo et al

ATT2 I feel that the live streamer is beautiful

ATT3 I feel that the live streamer is elegant

ATT4 I feel that the live streamer is sexy

Table 3.7 The scale of Attractiveness e Streamer interaction quality

Luo et al (2024) and Zhang et al (2020) suggested analyzing streamer interaction quality through three dimensions: real-time interaction, responsiveness, and empathy

RTI1 Livestream allows me to interact with live streamers to receive information

RTI2 Livestream has interactive features to meet my needs

RTI3 Live streamers help me to easily find the desired information

RTI4 The interaction with the live streamer on livestream is efficient

Table 3.8 The scale of Real-time Interaction

RES1 Live streamers are always happy to talk with me Luo et al

RES2 Live streamers always answer my question and requests in time

RES3 Live streamers’ response is closely related to my problem and requests

RES4 Live streamers can provide relevant information for my inquiry in time

Table 3.9 The scale of Responsiveness

EMP1 Live streamers give me individual attention

EMP2 Live streamers understand my specific needs Luo et al

EMP3 Live streamers have my best interests in mind

EMP4 Live streamers offer personalized service to me

Table 3.10 The scale of Empathy f Customer engagement

Based on the research of Luo et al (2024, customer engagement consists of four dimensions: affective engagement, cognitive engagement, behavioral engagement, and social engagement

AFE1 I find live-stream shopping is interesting Luo et al,

AFE2 I am interested in anything about live-streaming shopping

AFE3 When interacting with people during live-stream, I feel happy

Table 3.11 The scale of Affective engagement

COE1 I spend more time on the live-stream shopping Luo et al,

COE2 Time flies when I am interacting with people on the live stream

Table 3.12 The scale of Cognitive engagement

BEE1 I share my ideas with others in the livestream Luo et al,

BEE2 I seek ideas or information from others in the live stream

BEE3 I am likely to recommend live streamers to my friends

BEE4 I am likely to become a fan and a follower of the live streamer

BEE5 I am likely to keep track of the activities of live streamers

Table 3.13 The scale of Behavioral engagement

SOE1 I like sharing my personal shopping experience with other viewers

SOE2 I enjoy live-streaming shopping more when I am with other viewers

SOE3 Live-streaming shopping is more fun when other people around me do it too

Table 3.14 The scale of Social engagement g Purchase intention

PI1 I will consider livestreaming shopping as my first shopping choice

PI2 I intend to purchase products or services through live streams

PI3 I expect that I will purchase products or services through live streams

Table 3.15 The scale of Purchase intention h Brand equity

BQ1 I can easily recognize this brand Majeed et al (2021)

BQ2 I trust the company who makes this brand

BQ3 This brand would be my first choice

BQ4 In comparison to alternative brands, the quality of this brand is high.

Sample selection and data collection

To determine the minimum sample size for multivariate regression analysis, Tabachnick and Fidell (2013) recommend using the formula n = 50 + 8m, where m represents the number of independent variables For instance, with five independent variables, the minimum sample size required is 90 Hair et al (2014) further emphasize that while a minimum of 50 samples is necessary, aiming for at least 100 is preferable Additionally, a ratio of 5:1 or 10:1 for observations to variables is advised Consequently, for a study involving eight constructs with 66 items, the author must achieve a minimum sample size of 330.

The author conducted a survey targeting individuals aged 18 to 36, as this demographic is known for its high engagement in livestream online shopping The selection criteria included factors such as occupation, age, and gender to highlight variations in purchasing behavior Due to time constraints, the survey focused primarily on participants located in Hanoi.

During the two-month investigation period from February to April 2024, the author collected 350 responses Due to time constraints, all respondents were required to complete every question before submitting the questionnaire, ensuring that all responses met the necessary criteria for inclusion in further research.

350 valid responses were obtained for the author’s survey, thus meeting the minimum sample requirements.

Data estimation technique

The data analysis methodology involves an initial assessment of participants' demographic information using SPSS (version 20) To explore the hypothesized relationships, Smart-PLS version 3 is employed for PLS-SEM analysis, chosen for its ability to handle complex, multidimensional relationships inherent in linear structural models The proposed model is a high-order variable model, featuring a second-order factor represented by multiple first-order factors, including perceived information quality, streamer credibility, streamer interaction quality, and customer engagement, as noted in previous research (Rindskopf and Rose, 1988) The analysis using Smart-PLS encompasses both the measurement and structural models For the measurement model, the Algorithm is executed to evaluate scale quality, reliability, convergence, and discrimination In analyzing the structural model, the Bootstrapping technique is utilized to obtain critical results.

These results should include P-values that determine the significance level of those interconnections and coefficients that indicate the degree of correlation between pairings of variables in the model

3.4.1 Measurement model analysis method a Evaluate internal consistency reliability

Internal consistency reliability, as defined by Hair et al (2017), measures how well items evaluating the same construct yield similar scores Establishing the construct validity of latent variables with multiple indicators is essential, and Cronbach's alpha coefficient is the most commonly used measure for this purpose (Cronbach, 1951) This coefficient ranges from 0 to 1, with higher values indicating greater reliability While there is no definitive cutoff, a Cronbach's alpha above 0.7 is generally considered acceptable (Hair et al., 2017) However, caution is advised, as excessively high values (over 0.9) may indicate redundancy among items (Streiner, 2003) For exploratory research or newly created scales, a Cronbach's alpha of 0.6 or higher is regarded as satisfactory (Hair et al., 2017) Additionally, composite reliability is another important measure, typically recommended to be at least 0.7 (Bagozzi & Yi, 1988; Hair et al., 2017).

In PLS-SEM, the reliability of the measurement model is evaluated by examining the outer loadings of indicators on their latent variables, with higher outer loading values indicating a stronger correlation (Hair et al., 2017) Standardized outer loadings should ideally be at least 0.708, while a more lenient threshold of 0.6 is often used in exploratory research (Hulland, 1999; Hair et al., 2017) Indicators with outer loadings below these thresholds may not yield reliable estimates of their associated latent variables and should be considered for removal from the measurement model.

When determining whether to retain or eliminate indicators, it is crucial to recognize that outer loadings should not be the sole factor considered Factors such as content validity and the Average Variance Extracted (AVE) of the latent variable must also be evaluated (Hair et al., 2017) Indicators with lower outer loadings may still be retained if their removal would compromise the construct's content validity or if their inclusion raises the AVE above the recommended threshold of 0.5 (Fornell & Larcker, 1981; Hair et al., 2017) Additionally, it is essential to assess convergent validity.

Convergent validity refers to the degree to which different indicators measure the same underlying construct (Hair et al., 2017) This evaluation is performed using Average Variance Extracted (AVE), which assesses how much variance in a latent variable is due to measurement error (Fornell & Larcker, 1981) For a latent variable to effectively account for at least 50% of the average variance in its indicators, its AVE must be 0.5 or higher (Henseler et al., 2009; Hair et al., 2017).

A high Average Variance Extracted (AVE) value indicates that the measurement model's indicators effectively represent the intended latent variable, enhancing its convergent validity Conversely, an AVE value below the recommended threshold of 0.5 suggests that the latent variable fails to explain most of the variability in its indicators, raising potential validity concerns.

While a high average variance extracted (AVE) is desirable, it should be evaluated in conjunction with other validity and reliability measures such as discriminant and composite reliability In cases where the construct is theoretically robust and other validity indicators are satisfactory, a lower AVE may still be deemed acceptable It is also important to assess discriminant validity to ensure the construct's effectiveness.

Discriminant validity, as defined by Hair et al (2017), refers to the extent to which a specific construct is genuinely distinct from other constructs in a model This aspect of construct validity is crucial for ensuring that latent variables accurately measure unique phenomena, thus avoiding conceptual overlap Common techniques used to assess this validity include analyzing cross-loadings and the Heterotrait-Monotrait ratio (HTMT).

Hair et al (2017) emphasized that in cross-loading evaluations, each indicator should exhibit a stronger loading on its designated latent variable than on any other latent variables.

HTMT values are critical for assessing discriminant validity in research, with Kline (2011) recommending a conservative threshold of 0.85 and Henseler et al (2015) suggesting a more lenient limit of 0.90 When HTMT values surpass these thresholds, it signals potential overlap between constructs, indicating they may not be sufficiently distinct from one another.

The HTMT approach is recognized for its superior resilience compared to traditional methods like the Fornell-Larcker criterion and cross-loadings, as it effectively addresses the issue of inflated correlations between constructs (Henseler et al., 2015) Additionally, it is essential to assess the level of multicollinearity in the analysis.

Multicollinearity refers to the high correlations among predictor variables in a regression model, which can lead to unreliable and unstable estimates of regression coefficients (Hair et al., 2011) In the context of PLS-SEM, multicollinearity is assessed using outer VIF values.

Outer Variance Inflation Factor (VIF) values reflect how much the variability of a formative indicator is explained by other indicators within the same latent variable, as noted by Hair et al (2017) Higher VIF values suggest increased multicollinearity among indicators Typically, it is advised that outer VIF values remain below 5, with values above 5 potentially indicating multicollinearity issues (Hair et al., 2011; Ringle et al., 2015).

Addressing multicollinearity is crucial, as excessive multicollinearity can lead to unreliable estimates of relationships within a structural model, complicating the assessment of predictor variables' importance (Hair et al., 2017) When multicollinearity is detected, corrective measures should be considered, such as removing redundant indicators, combining them into a single indicator, or developing higher-order constructs (Hair et al., 2017; Ringle et al., 2015) Additionally, it is essential to evaluate the statistical significance of the weights involved.

In PLS-SEM, outer weights reflect the contribution of formative indicators to their associated latent variables (Hair et al., 2017) By evaluating the statistical significance of these outer weights, one can ascertain the relative importance of each indicator in defining the construct.

RESEARCH RESULTS

Sample descriptive statistics

The sample's demographic characteristics will be unveiled through descriptive statistical analyses and presented in the Table 4.1

Monthly Income Below 5 million VND 121 34,6%

10 million VND – below 15 million VND

Frequency of online shopping via livestream

At least 5 times per month

Preferred platforms for online shopping via livestream (*)

Table 4.1 Demographic statistic Note: (*) means that respondents were allowed to choose multiple options in the list

In the realm of e-commerce, 57.4% of women participate in online shopping compared to 42.6% of men This disparity can be attributed to women's desire for a wider selection of products, particularly in categories such as apparel, cosmetics, and skincare.

The survey revealed a diverse age distribution among participants, with the majority being younger individuals Specifically, those aged 18-25 represented the largest group, accounting for 63.5% of the responses with 213 votes This was followed by the 26-35 age group, which garnered 32.2% of the votes (112 total), while participants aged 35 and older made up only 7.1% with 25 votes These findings clearly indicate that young people are the most engaged demographic in online livestream shopping.

The survey revealed that 44.9% of respondents, totaling 157 individuals, identified as students, making it the largest occupational group Following this, 29.1% of participants, or 102 individuals, were office workers, while freelancers accounted for 26% of the responses with 91 individuals identifying as such.

The survey revealed that 34.6% of participants, or 121 individuals, earned below 5 million Meanwhile, 37.7% of respondents, totaling 132 individuals, reported incomes ranging from 5 million to just under 10 million Additionally, 15.7% of the sample, equivalent to 55 respondents, had incomes between 10 million and below 15 million Finally, 12% of the participants, or 42 individuals, earned 15 million or more.

A recent survey reveals that a substantial 53.9% of respondents engage in livestream online purchasing two to four times a month, with 162 individuals participating in this frequency Additionally, 28.9% of participants, totaling 101 individuals, make purchases once a month, while 87 respondents, or a smaller segment, report making at least five online purchases each month.

Regarding the preferred livestream commerce platform, Shopee leads with

310 selections, representing 46.5% TikTok is ranked second, having been selected by 217 users, accounting for 32.6% Lazada and Tiki are relatively unpopular platforms, with a respondent base of 11% and 9.9%.

Measurement model analysis

The author employed a disjoint two-stage approach to analyze the research model using Smart-PLS 3, as dictated by the HOC model In the first stage, the focus is on evaluating the measurement model related to LOC variables, while the second stage assesses the measurement models for HOC variables and other latent variables.

4.2.1 The first stage of the disjoint two-stage approach for LOC variables

To assess the dependability, the author applied the PLS Algorithm to obtain Cronbach's Alpha and construct reliability

The author assessed reliability characteristics using two primary indicators: Cronbach's Alpha and Composite Reliability, as detailed in the accompanying table All indices met the established criteria, with Cronbach's Alpha and Composite Reliability both achieving values of 0.7 or higher, in accordance with the standards set by Hair et al (2017).

To ensure the quality of indicators, observed variables must have Outer Loading coefficients of 0.7 or higher (Hair et al., 2017) As indicated in Tables 4.3 and 4.4, all observed variables meet these quality standards, except for BEE4 (0.694) and VIV4 (0.631) However, if the Average Variance Extracted (AVE) is greater than 0.5, variables with outer loadings between 0.4 and 0.7 can still be considered acceptable (Hair et al., 2017).

Table 4.2 presents AVE indicators, which assess convergent validity by measuring how well a latent construct explains the variability in its associated indicators An AVE coefficient of 0.5 or higher indicates that the model meets the convergence criteria, confirming the adequacy of the outcomes listed in the table.

As a result, despite the fact that BEE4 and VIV4 possess outer loadings below 0.7, these variables are retained temporarily to continue the evaluation

AFE ATT BEE BEL COE EMP

Table 4.3 LOC variables’ outer loadings

EXP RES RTI SOE TRU USE VIV

Table 4.4 LOC variables’ outer loadings

The author utilizes the HTMT index and Cross Loading to evaluate discriminant validity In the provided table, the outer-loading coefficients are highlighted in gray, while the uncolored sections represent the cross-loading coefficients Notably, all observed variables exhibit cross-loading coefficients that are lower than their respective outer-loading coefficients.

AFE ATT BEE BEL COE EMP

Table 4.5 LOC variables’ cross-loading

EXP RES RTI SOE TRU USE VIV

EXP1 0.71 0.152 0.214 0.364 0.566 0.466 0.491 EXP2 0.909 0.307 0.339 0.516 0.698 0.597 0.655 EXP3 0.875 0.261 0.305 0.535 0.701 0.527 0.602 EXP4 0.852 0.247 0.226 0.568 0.733 0.444 0.596 EXP5 0.808 0.294 0.398 0.455 0.62 0.631 0.647 RES1 0.263 0.829 0.571 0.232 0.202 0.222 0.253 RES2 0.307 0.849 0.636 0.265 0.286 0.234 0.286 RES3 0.207 0.835 0.524 0.263 0.174 0.157 0.211 RES4 0.235 0.809 0.445 0.217 0.174 0.187 0.194 RT1 0.272 0.544 0.86 0.218 0.249 0.349 0.308 RT2 0.248 0.501 0.835 0.272 0.225 0.264 0.291

RT3 0.293 0.51 0.836 0.254 0.266 0.327 0.3 RT4 0.33 0.581 0.696 0.246 0.304 0.281 0.327 SOE1 0.447 0.178 0.186 0.849 0.466 0.186 0.329 SOE2 0.424 0.265 0.262 0.755 0.444 0.288 0.36 SOE3 0.592 0.289 0.314 0.886 0.515 0.38 0.493 TRU1 0.742 0.232 0.339 0.54 0.906 0.573 0.591 TRU2 0.652 0.198 0.19 0.511 0.851 0.372 0.43 TRU3 0.694 0.241 0.311 0.447 0.829 0.538 0.596 TRU4 0.675 0.22 0.295 0.48 0.879 0.478 0.576 TRU5 0.707 0.221 0.264 0.506 0.87 0.446 0.559 USE1 0.591 0.269 0.37 0.291 0.549 0.858 0.635 USE2 0.425 0.114 0.224 0.201 0.31 0.727 0.484 USE3 0.543 0.173 0.29 0.305 0.422 0.832 0.571 USE4 0.505 0.211 0.327 0.34 0.506 0.862 0.587 VIV1 0.553 0.207 0.302 0.32 0.548 0.586 0.81 VIV2 0.6 0.243 0.282 0.429 0.548 0.518 0.83 VIV3 0.607 0.222 0.317 0.423 0.531 0.637 0.837 VIV4 0.395 0.197 0.264 0.329 0.387 0.36 0.631 VIV5 0.64 0.252 0.323 0.374 0.46 0.619 0.789

Table 4.6 LOC variables’ cross-loading

Henseler et al (2015) state that a factor's discriminant is compromised when the HTMT index between two factors exceeds 0.9 However, as indicated in Tables 4.7 and 4.8, all values remain below this threshold, confirming that discrimination between factors is achievable.

AFE ATT BEE BEL COE EMP

EXP RES RTI SOE TRU USE VIV

The author assessed the HTMT index through Bootstrapping outcomes at a 95% confidence level, analyzing whether the value 1 falls within the 2.5% to 97.5% percentile range The inclusion of 1 in this range would suggest a lack of discrimination among variables However, the results shown in Table [Appendix 1] reveal that no percentile segment contains the value 1, confirming that the variables can be effectively distinguished.

In summary, all LOC variables successfully meet the quantitative model evaluation criteria, ensuring that none are excluded Consequently, during the second stage, the values of each LOC variable will be incorporated into the Latent Variable analysis.

4.2.2 The second stage of the disjoint two-stage approach for other variables a Measurement model analysis for formative model

The HOC variable model is formative, and its evaluation in the measurement model relies on the levels of multicollinearity and the statistical significance of the weights The outer Variance Inflation Factors (VIFs) for all LOC variables of HOC are below five, confirming the absence of multicollinearity among the causally observed variables.

Table 4.9 HOC variables’ outer VIF

The results presented in Table 4.10 reveal that the variables BEE, COE, EMP, and USE do not demonstrate statistical significance, as their outer weights exceed the 0.05 threshold Therefore, it is essential to focus on the outer loading coefficients of these four variables for further analysis.

Table 4.10 HOC variables’ Outer Weight

The retention of the variables BEE, EMP, and USE was justified based on their outer loading values exceeding 0.5 Conversely, COE was omitted due to peripheral loading being less than 0.5

Table 4.11 HOC variables’ outer loading values b Measurement model analysis for reflective model

The author analyzed the reliability attributes using two key metrics: Composite Reliability and Cronbach's Alpha, with the findings detailed in Table 4.12 All indices obtained meet the established standards and requirements for both Cronbach's Alpha and Composite Reliability.

To ensure the quality of indicators, the Outer Loading coefficients for observed variables should be 0.7 or higher (Hair et al., 2017) The observed variables have been developed according to established quality standards, as demonstrated by the data in Table 4.13.

Table 4.13 Other variables’ outer loadings

Moreover, all AVE coefficients are more significant than 0.5, as shown in Table 4.12; this demonstrates that the model meets the convergence criteria

The table illustrates that the uncolored section signifies the cross-loading coefficient, while the gray section denotes the outer-loading coefficient Notably, all observed variables exhibit cross-loading coefficients that are lower than their corresponding outer-loading coefficients.

Table 4.14 Other variables’ cross-loading

Additionally, as shown in the Table 4.15, all HTMT values are below 0.9, ensuring that discrimination is possible

In evaluating the HTMT ratio using Bootstrapping at a 95% confidence level, it is essential to check if the value 1 lies within the 2.5% to 97.5% percentile range A quantile value of 1 indicates that discrimination between variables cannot be assured However, the findings presented in Table 4.16 reveal that none of the percentile segments include the value 1, thereby confirming the ability to distinguish between the variables effectively.

Table 4.16 Other variables’ HTMT in Bootstrapping 4.3 Structural model analysis

An evaluation of multicollinearity among predictor constructs reveals that all inner VIF values are below 5, suggesting that multicollinearity is not a significant concern within the model structure (Hair et al., 2014; Hair et al., 2017).

The author examines the direct impacts on customer engagement and purchase intention, which are dependent variables through Table 4.18

DISCUSSION AND RECOMMENDATIONS

Discussion

This research aimed to explore the factors influencing customer engagement and purchase intentions in livestream e-commerce The findings highlight the significant roles of perceived information quality, perceived product quality, deal proneness, streamer credibility, streamer interaction quality, and brand equity in shaping customer engagement and purchase decisions Additionally, the study reveals that customer engagement mediates the relationship between various antecedents and purchase intention, with brand equity acting as a moderating factor Specifically, perceived product quality positively affects both customer engagement and purchase intention.

Research indicates that customer engagement in livestream e-commerce is positively affected by perceived product quality This finding is consistent with previous studies highlighting the importance of product quality perceptions in fostering customer engagement online (Tzeng et al., 2020; Zheng et al., 2022) When customers view products as high-quality, they are more likely to engage actively during livestream sessions, as shown by their increased inquiries, interest, and sustained attention throughout the broadcast.

Research in customer behavior indicates a strong correlation between purchase intention and perceived product quality When customers view products as high-quality, they are more likely to develop positive purchase intentions, linking quality to greater value, reduced risk, and enhanced brand trust and loyalty This connection is further supported by studies in the field of livestream e-commerce (Sun et al., 2016; Chen et al., 2022).

Businesses must prioritize comprehensive and honest product demonstrations through live streams, ensuring they provide detailed information about materials, features, and functionality Engaging with live viewer questions and showcasing close-up visuals will effectively highlight product quality This approach significantly enhances perceived information quality, leading to increased customer engagement and higher purchase intentions.

The study confirms that perceived information quality significantly enhances customer engagement in livestream e-commerce, supporting previous research on the importance of information quality in digital environments Customers are more actively engaged during livestreams when they find the information to be accurate, comprehensive, relevant, and timely, leading to a higher likelihood of them asking questions, seeking clarifications, and sharing their thoughts.

This study reinforces the positive link between perceived information quality and purchase intention, as highlighted by previous research (Jones & Kim, 2010; Bebber et al., 2017; Sun et al., 2019; Gao et al., 2021) High-quality information helps customers make informed decisions by minimizing ambiguity and perceived risks tied to online shopping Consequently, when customers view information shared during live streams as trustworthy and advantageous, they are more likely to develop favorable purchase intentions.

To enhance credibility during live streams, businesses must deliver clear and accurate product information while citing reputable sources and expert evaluations Engaging knowledgeable hosts or experts for real-time interactions can significantly improve the reliability of the information shared, ultimately influencing viewers' purchase intentions.

Research indicates that deal proneness significantly enhances purchase intention in livestream e-commerce settings This aligns with previous studies showing a strong link between the desire for discounts and increased purchase intent (Kaveh et al., 2021) In the context of livestream e-commerce, where time-sensitive offers and exclusive promotions are common, customers who are more eager to seize deals are more likely to intend to buy Consequently, businesses should utilize live streaming to showcase exclusive, limited-time promotions, creating a sense of urgency that encourages customers to make purchases during the event.

Research confirms that customer engagement in livestream e-commerce is significantly enhanced by the credibility of the streamer This aligns with previous studies highlighting the importance of credible sources in influencing customer attitudes and behaviors Customers are more likely to engage with livestream content when they view streamers as credible, knowledgeable, and trustworthy, considering their information and recommendations as valuable Therefore, businesses should carefully select streamers with established credibility and a strong connection to their audience, while also providing them with thorough training to ensure accurate product knowledge and engaging presentation skills.

In livestream e-commerce, high-quality interaction from streamers significantly enhances customer engagement, aligning with previous research highlighting the importance of social presence and interactivity (Ghahtarani et al., 2020; Xue et al., 2020; Kang et al., 2021; Luo et al., 2024) Customers are more likely to engage actively when streamers address inquiries promptly, personalize responses, and foster a sense of community This active participation is evident through sharing opinions, engaging in discussions, and maintaining attention during the session Consequently, businesses should prioritize fostering two-way communication by encouraging streamers to respond quickly to comments and feedback, creating an immersive and captivating experience for viewers.

Research indicates a positive correlation between customer engagement and purchase intention, supporting previous studies that link engagement to beneficial behaviors such as loyalty and increased purchase intentions (Kaveh et al., 2020; Zheng et al., 2020; Hossain et al., 2023; Shen et al., 2023) Active participation in Livestream sessions fosters positive attitudes and intentions to buy, enhancing the connection between customers, brands, products, and live streamers.

Customer engagement plays a crucial role in mediating the relationship between perceived information quality and purchase intention in livestream e-commerce High-quality information provided during live streams enhances customer engagement, which positively impacts purchase intentions By delivering accurate, relevant, and timely information, live streamers can effectively boost customer engagement, leading to higher purchase intentions among their audience.

This study posits that the relationship between streamer credibility and purchase intention is influenced by customer engagement It reveals that viewers are more likely to engage with livestream content when they find the streamers credible and trustworthy This heightened engagement, in turn, enhances their intention to make purchases Therefore, establishing credibility among livestreamers is essential for fostering customer engagement, which plays a critical role in shaping purchasing decisions.

In livestream e-commerce, customer engagement serves as a crucial mediator between the quality of streamer interactions and purchase intent This finding suggests that live streamers can enhance customer engagement and boost purchase intentions by delivering high-quality interactions, personalized experiences, and fostering responsiveness and community Actively engaged customers are more likely to develop positive attitudes and intentions toward the products or services showcased Additionally, brand equity plays a moderating role in the relationship between customer engagement and purchase intention, further influencing consumer behavior.

The study reveals that in livestream e-commerce, brand equity plays a significant role in shaping the relationship between customer engagement and purchase intention Specifically, strong brand equity—characterized by high brand awareness, positive associations, and perceived quality—can reduce the likelihood of purchase intentions despite increased customer engagement Customers with favorable views of a brand's equity are more inclined to make purchases, regardless of their level of interaction during the livestream Thus, the findings underscore the importance of strong brand equity as a competitive advantage for businesses in the dynamic landscape of livestream e-commerce.

Implications

This study enhances the understanding of customer engagement and purchase intentions in livestream e-commerce, particularly in Vietnam It broadens the application of established theories by investigating the relationship between purchase intention and perceived product quality in this emerging market.

Recent research underscores the significant impact of perceived product quality and information quality on customer engagement and purchase intentions, aligning with earlier studies (Zhang et al., 2020; Chen et al., 2022; Luo et al., 2024) By confirming these relationships in the realm of livestream e-commerce, this study enhances our understanding of the critical role that information quality plays in Vietnam's rapidly evolving digital landscape.

This study enhances the understanding of deal proneness and its impact on customer behavior in Vietnam and globally Consistent with prior research (Kaveh et al., 2021), it reinforces the positive relationship between deal proneness and purchase intention in livestream e-commerce, highlighting the significance of catering to deal-seeking tendencies in this industry.

This research enhances the understanding of source credibility theories in the context of livestream e-commerce in Vietnam, demonstrating that streamer credibility significantly boosts customer engagement This finding highlights the importance of leveraging trustworthy sources in the fast-paced and interactive digital landscape.

This research deepens the understanding of how social presence and interactivity foster customer engagement in Vietnam's virtual environments (Zhang et al., 2020; Chen et al., 2023; Luo et al., 2024) The findings highlight a positive relationship between the quality of streamer interactions and customer engagement, emphasizing the need to nurture community feelings and provide high-quality interactions in livestream e-commerce.

This study explores the mediating role of customer engagement in the relationship between purchase intention and its antecedents, including perceived information quality, streamer credibility, and streamer interaction quality By focusing on the livestream e-commerce sector in Vietnam, the research provides valuable insights into how these factors influence purchase intentions, thereby enriching the existing literature on customer engagement.

This study explores how brand equity moderates the relationship between customer engagement and purchase intention The findings reveal that strong brand equity reduces the impact of customer engagement on purchase intention, enhancing our understanding of how brand equity and customer engagement interact in shaping purchasing decisions.

The research findings highlight the theoretical implications for the emerging field of livestream e-commerce in Vietnam, emphasizing the relevance of established frameworks and theories Key constructs such as interactivity, customer engagement, brand equity, perceived product quality, and perceived information quality are reaffirmed as vital components in navigating this dynamic digital landscape.

The results significantly value organizations seeking to improve customer engagement and stimulate purchase intentions

To enhance customer trust and engagement, businesses must tackle quality concerns by leveraging endorsements from influencers and experts, as well as incorporating user-generated content Furthermore, offering genuine product demonstrations that align with promotional claims is essential for building authentic connections and increasing purchase intent among consumers.

To foster genuine customer engagement and enhance purchase intentions during livestreams, businesses must deliver detailed, accurate, and transparent information This includes offering comprehensive product details, specifications, and practical use cases, supported by credible sources or endorsements from experts.

Businesses can leverage live streams to showcase exclusive, limited-time offers, creating a sense of urgency that drives viewers to make purchases during the event Additionally, partnering with popular streamers or influencers can help tap into their loyal fan bases, utilizing social influence to attract target customers and enhance their purchasing intentions.

To enhance customer engagement and influence purchase intentions through livestream shopping, businesses must prioritize streamer credibility, interaction quality, and seamless integration with broader marketing strategies When partnering with outsourced streamers, it's essential to select reputable individuals with large, engaged audiences, leverage their influence through sponsorships, provide thorough product training, and ensure alignment with overall marketing goals For in-house streamers, companies should recruit and train charismatic brand representatives to build authentic connections with viewers Additionally, conducting performance evaluations and analyzing viewer data is crucial for optimizing strategies and managing live streaming teams effectively.

Businesses with strong brand equity often do not need extensive engagement strategies to drive purchase intentions in livestream commerce, as their favorable reputation already fosters intent among viewers However, livestreams present valuable opportunities to enhance customer experiences through interactive elements, exclusive content, product demonstrations, and influencer collaborations In contrast, brands with lower equity must prioritize customer engagement to boost conversions, necessitating significant investments in interactive livestream experiences that both educate and entertain audiences Key strategies include gamification, influencer endorsements, exclusive offers, interactive polls and chats, and seamless transactions, which are crucial for generating excitement and purchase intent While engagement plays a vital role in building lasting brand equity, it can only temporarily offset a lack of equity.

To boost sales in the emerging e-commerce landscape, businesses should focus on creating engaging livestream experiences that enhance customer interaction and leverage the credibility of streamers Key tactics include ensuring high-quality interactions, providing valuable product information, and reinforcing brand equity when relevant.

The study's results provide streamers engaged in livestream e-commerce with significant insights into improving customer engagement and stimulating purchase intentions throughout their live streams

Streamers should prioritize transparency and authenticity in showcasing product quality by providing in-depth, candid demonstrations that feature close-up visuals and detailed information Incorporating live viewer Q&A sessions and product trials into streams allows audiences to see the product in action, enhancing perceptions of quality and meeting customer expectations This approach not only fosters genuine engagement but also increases the likelihood of purchase intent among viewers.

Limitations

Although the study offers significant insights into customer behaviors in live- streaming commerce, specific limitations suggest potential avenues for future research

The study's limited duration and scope led to a lack of diversity in participant demographics, particularly concerning their place of residence Future research should aim to expand the geographical scope to include a wider array of cities, focusing on customer engagement and purchase intentions This approach would enhance the applicability of the findings and offer valuable insights into potential variations in Vietnamese customer behavior within the realm of livestream e-commerce.

The study lacks a comprehensive analysis of key factors affecting customer engagement and purchase intentions, such as the live streamer's physical appearance, demeanor, and voice characteristics Further research is essential to delve deeper into these elements, as they may significantly impact viewer perceptions, credibility assessments, and overall engagement during live stream sessions A detailed exploration of these variables could provide a more complete understanding of the intricate dynamics that shape customer behavior in this interactive commerce environment.

The study did not consider variations among different product categories, which can significantly influence customer behavior Factors such as perceived product quality, information quality, deal proneness, streamer credibility, and interaction quality may differ based on the product type Therefore, further research into customer behavior specific to each product category could provide deeper insights, enabling the creation of targeted strategies that cater to the unique characteristics and needs of customers within those categories.

The study's limitation lies in its lack of analysis regarding how demographic variables such as age, gender, income, and education affect customer engagement and purchase intentions in live stream commerce Ignoring these factors may hinder the generalizability of the findings, as different demographic segments exhibit varying behaviors and preferences For instance, younger audiences may prefer interactive and engaging livestreams, while older individuals might prioritize informative content and product quality Additionally, those with higher socioeconomic status may show unique engagement patterns and decision-making processes compared to lower-income groups Therefore, further research is essential to understand the influence of demographic factors on customer behavior in specific market segments.

Recognizing and addressing existing limitations, while expanding the scope of investigation, can significantly improve our understanding of the intricate factors influencing customer engagement and purchase intentions in the dynamic landscape of live stream commerce.

The COVID-19 pandemic has accelerated the rise of livestream e-commerce, prompting consumers to explore new ways to connect with brands and shop from home This shift in consumer behavior highlights the necessity for businesses to quickly adapt and leverage live streaming to create engaging and interactive experiences with their target audiences, ensuring they remain competitive and capitalize on this growing trend.

This research enhances the understanding of livestream e-commerce in Vietnam by applying established frameworks and theories It validates hypotheses related to key factors such as customer engagement, purchase intention, perceived product quality, information quality, deal proneness, streamer credibility, and interaction quality.

This study provides essential insights for businesses on the impact of key factors in livestream e-commerce, allowing them to refine their strategies In a competitive landscape, organizations that effectively leverage these insights will gain a significant advantage, leading to increased customer engagement, higher purchase intentions, and an enhanced market presence.

This research deepens the understanding of the intricate factors influencing customer engagement and purchase intentions in the rapidly growing field of livestream e-commerce Despite some limitations, it lays a crucial foundation for further exploration of this emerging and dynamic e-commerce platform, particularly in the urgent context of Vietnam.

1 Abror, A., Patrisia, D., Engriani, Y., Evanita, S., Yasri, Y., & Dastgir, S

(2019) Service quality, religiosity, customer satisfaction, customer engagement and Islamic Bank’s Customer Loyalty Journal of Islamic Marketing, 11(6), 1691–1705 Available at: https://doi.org/10.1108/jima-03-

2 Agmeka, F., Wathoni, R N., & Santoso, A S (2019) The influence of discount framing towards brand reputation and brand image on purchase intention and actual behaviour in e-commerce Procedia Computer Science,

3 Akkaya, M (2021) Understanding the impacts of lifestyle segmentation & perceived value on Brand Purchase Intention: An empirical study in different product categories European Research on Management and Business

Economics, 27(3), 100155 Available at: https://doi.org/10.1016/j.iedeen.2021.100155

4 Alcántara-Pilar, J M., Rodriguez-López, M E., Kalinić, Z., & Liébana- Cabanillas, F (2024) From likes to loyalty: Exploring the impact of influencer credibility on purchase intentions in TikTok Journal of Retailing and

Consumer Services, 78, 103709 Available at: https://doi.org/10.1016/j.jretconser.2024.103709

5 Bagozzi, R P., & Yi, Y (1988) On the evaluation of structural equation models Journal of the academy of marketing science, 16, 74-94 Available at: https://doi.org/10.1007/BF02723327

6 Barari, M., Ross, M., Thaichon, S., & Surachartkumtonkun, J (2020) A meta‐ analysis of customer engagement behaviour International Journal of Consumer Studies, 45(4), 457–477 Available at:10.1111/ijcs.12609

7 Bawack, R E., Wamba, S F., Carillo, K D., & Akter, S (2022) Artificial Intelligence in e-commerce: A bibliometric study and literature review

Electronic Markets, 32(1), 297–338 Available at: https://doi.org/10.1007/s12525-022-00537-z

8 Brodie, R J., Ilic, A., Juric, B., & Hollebeek, L (2013) Consumer engagement in a Virtual Brand Community: An exploratory analysis Journal of Business Research, 66(1), 105–114 Available at: https://doi.org/10.1016/j.jbusres.2011.07.029

9 Cai, J., Wohn, D Y., Mittal, A., & Sureshbabu, D (2018, June) Utilitarian and hedonic motivations for live streaming shopping In Proceedings of the

2018 ACM international conference on interactive experiences for TV and online video (pp 81-88)

10 Cambra-Fierro, J J., Fuentes-Blasco, M., Huerta-Álvarez, R., & Olavarría, A

(2021) Customer-based brand equity and customer engagement in Experiential Services: Insights from an emerging economy Service Business,

11 Chen, C D., Zhao, Q., & Wang, J L (2022) How livestreaming increases product sales: role of trust transfer and elaboration likelihood model Behaviour & Information Technology, 41(3), 558-573 Available at: https://doi.org/10.1080/0144929X.2020.1827457

12 Chen, H., Dou, Y., & Xiao, Y (2023) Understanding the role of live streamers in live-streaming e-commerce Electronic Commerce Research and

Applications, 59, 101266 Available at: https://doi.org/10.1016/j.elerap.2023.101266

13 Chen, J V., Pham, D T., & Tran, S T (2024) Building consumer engagement in live streaming on social media: A comparison of facebook and Instagram live International Journal of Human–Computer Interaction, 1–21 Available at: https://doi.org/10.1080/10447318.2024.2313276

14 Chevalier, S (2024) Social media users watching Livestream Shopping 2023 Retrieved from https://www.statista.com/statistics/1455143/share-social- media-users-watch-livestream-by- country/?fbclid=IwZXh0bgNhZW0CMTAAAR2U90NpnDGLqfjUqj5CTuUS934NubVAvNgUFSKjHJ2AT6ak78IxIHdu6-Y_aem_AeEYbjzF6sS1sYS- hDcux1A4JWAVlIFHLwX3ht3DI2V1Fr8F3pAWdUz6jIRSCcUvOhU8qOe4iXhT0soE22JuR7w6

15 Chinomona, R., Okoumba, L., & Pooe, D (2013) The impact of product quality on perceived value, trust and students’ intention to purchase electronic gadgets Mediterranean Journal of Social Sciences Available at: https://doi.org/10.5901/mjss.2013.v4n14p463

16 Cronbach, L.J (1951) Coefficient alpha and the internal structure of tests Psychometrika 16, 297–334 Available at: https://doi.org/10.1007/BF02310555

17 DelVecchio, D (2005) Deal-prone consumers’ response to promotion: The effects of relative and absolute promotion value Psychology and Marketing,

22(5), 373–391 Available at: https://doi.org/10.1002/mar.20064

18 Dodds, W B., Monroe, K B., & Grewal, D (1991) Effects of Price, Brand, and Store Information on Buyers’ Product Evaluations Journal of Marketing

19 Fandos, C., & Flavián, C (2006) Intrinsic and extrinsic quality attributes, loyalty and buying intention: An analysis for a PDO product British Food

Journal, 108(8), 646–662 Available at: https://doi.org/10.1108/00070700610682337

20 Fornell, C., & Larcker, D F (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error Journal of Marketing

21 Gao, X., Xu, X.-Y., Tayyab, S M., & Li, Q (2021) How the live streaming commerce viewers process the persuasive message: An elm perspective and the moderating effect of mindfulness Electronic Commerce Research and

Applications, 49, 101087 Available at: https://doi.org/10.1016/j.elerap.2021.101087

22 Gázquez-Abad, J C., & Sánchez-Pérez, M (2009) Characterising the deal- proneness of consumers by analysis of price sensitivity and brand loyalty: An analysis in the retail environment The International Review of Retail, Distribution and Consumer Research, 19(1), 1–28 Available at: https://doi.org/10.1080/09593960902780922

23 Ghahtarani, A., Sheikhmohammady, M., & Rostami, M (2020) The impact of social capital and social interaction on customers’ purchase intention, considering knowledge sharing in social commerce context Journal of Innovation & Knowledge, 5(3), 191–199 Available at: https://doi.org/10.1016/j.jik.2019.08.004

24 Ghahtarani, A., Sheikhmohammady, M., & Rostami, M (2020) The impact of social capital and social interaction on customers’ purchase intention, considering knowledge sharing in social commerce context Journal of

Innovation & Knowledge, 5(3), 191-199 Available at: https://doi.org/10.1016/j.jik.2019.08.004

25 Guo, L., Hu, X., Lu, J., & Ma, L (2021) Effects of customer trust on engagement in live streaming commerce: Mediating role of Swift Guanxi

Internet Research, 31(5), 1718–1744 Available at: https://doi.org/10.1108/INTR-02-2020-0078

26 Hair, J F., Gudergan, S P., Ringle, C M., & Sarstedt, M (2018) Advanced issues in partial least squares structural equation modeling Los Angeles:

27 Hair, J F., Jr., Hult, G T M., Ringle, C., & Sarstedt, M (2014) A primer onpartial least squares structural equation modeling (PLS-SEM) Los

28 Hair, J F., Ringle, C M., & Sarstedt, M (2011) PLS-SEM: Indeed a silver bullet Journal of Marketing theory and Practice, 19(2), 139-152 Available at: https://doi.org/10.2753/MTP1069-6679190202

29 Hair, J.F., Black, W.C., Babin, B.J and Anderson, R.E (2010) Multivariate Data Analysis 7th Edition, Pearson, New York

30 Hair, J.F., Hult, G.T.M., Ringle, C.M and Sarstedt, M (2017) A Primer on

Partial Least Squares Structural Equation Modeling (PLS-SEM) 2nd Edition,

Sage Publications Inc: Thousand Oaks

31 Hanaysha, J R (2017) Impact of social media marketing, Price Promotion, and corporate social responsibility on customer satisfaction Jindal Journal of

Business Research, 6(2), 132–145 Available at: https://doi.org/10.1177/2278682117715359

32 Henseler, J., Ringle, C M., & Sarstedt, M (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling Journal of the academy of marketing science, 43, 115-135 Available at: https://doi.org/10.1007/s11747-014-0403-8

33 Holsapple, C W., & Singh, M (2000) Electronic commerce: From a definitional taxonomy toward a knowledge-management view Journal of Organizational Computing and Electronic Commerce, 10(3), 149–170

Available at: https://doi.org/10.1207/s15327744joce1003_01

34 Hong, I B., & Cha, H S (2013) The mediating role of Consumer Trust in an online merchant in predicting purchase intention International Journal of Information Management, 33(6), 927–939 Available at: https://doi.org/10.1016/j.ijinfomgt.2013.08.007

35 Hossain, Md A., Kalam, A., Nuruzzaman, Md., & Kim, M (2023) The power of live-streaming in consumers’ purchasing decision SAGE Open, 13(4)

Available at: https://doi.org/10.1177/21582440231197903

36 Hulland, J (1999) Use of partial least squares (PLS) in Strategic Management Research: A review of four recent studies Strategic Management Journal, 20(2), 195–204 Available at: https://doi.org/10.1002/(SICI)1097- 0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7

37 Islam, J U., & Rahman, Z (2017) The impact of online brand community characteristics on customer engagement: An application of Stimulus- Organism-Response paradigm Telematics and Informatics, 34(4), 96-109 Available at: https://doi.org/10.1016/j.tele.2017.01.004

38 Jones, C., & Kim, S (2010) Influences of retail brand trust, off‐line patronage, clothing involvement and website quality on online apparel shopping intention International Journal of Consumer Studies, 34(6), 627-637 Available at: https://doi.org/10.1111/j.1470-6431.2010.00871.x

39 Kang, J W., & Namkung, Y (2019) The information quality and source credibility matter in customers’ evaluation toward food O2O commerce International Journal of Hospitality Management, 78, 189-198 Available at: https://doi.org/10.1016/j.ijhm.2018.10.011

40 Kang, K., Lu, J., Guo, L., & Li, W (2021) The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms International Journal of Information

Management, 56, 102251 Available at: https://doi.org/10.1016/j.ijinfomgt.2020.102251

41 Kaveh, A., Nazari, M., van der Rest, J.-P., & Mira, S A (2020) Customer engagement in sales promotion Marketing Intelligence & Planning, 39(3),

424–437 Available at: https://doi.org/10.1108/mip-11-2019-0582

42 Keller, K L (2016) Brand equity The Palgrave Encyclopedia of Strategic

Management, 1–5 Available at: https://doi.org/10.1057/978-1-349-94848-

43 Kline, R B (2011) Principles and practice of structural equation modeling (3rd ed.) Guilford Press

44 Laudon, K C., & Traver, C G (2017) E-commerce business technology society (13th ed.) Boston, Massachusetts: Pearson

45 Li, X., Huang, D., Dong, G., & Wang, B (2024) Why consumers have impulsive purchase behavior in live streaming: the role of the streamer BMC psychology, 12(1), 129 Available at: https://doi.org/10.1186/s40359-024-