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Abstract

In the digital era, livestreaming e-commerce has emerged as a new frontier in the industry due to its unique advantages. However, human anchors face time, energy, and sustained high-intensity output limitations. The emergence of virtual digital personas offers a new solution for the livestreaming e-commerce industry. Unlike human anchors, virtual anchors possess advantages such as greater controllability, high personalization, and 24/7 uninterrupted livestreaming, making them favored by platforms. While the industry continues to increase the practical application of virtual anchors, academic exploration of their mechanisms in influencing consumer purchasing behavior remains insufficient, necessitating theoretical and empirical responses. Based on the Stimulus-Organism-Response (S-O-R) theoretical framework, this study investigates the impact of virtual anchor characteristics (visibility, metavoicing, guidance shopping, and trading) on consumer purchase intention and its internal mechanisms. Data was collected through questionnaires, yielding 441 valid responses. The Partial Least Squares (PLS) method was used for data analysis. The results show that the visibility, metavoicing, and guidance shopping of virtual anchors significantly positively influence consumer purchase intention through brand authenticity, brand attachment, and sense of community. However, trading only affects purchase intention through brand attachment and sense of community, with the mediating effect of brand authenticity being insignificant. This study extends the application of S-O-R theory in the context of virtual anchors, enriches the theoretical understanding of how virtual service providers influence consumer behavior, and provides practical insights for optimizing virtual anchor images in livestreaming e-commerce.

Plain Language Summary

Virtual Anchors in Livestream Shopping: How They Influence Consumers’ Purchase Decisions

This study explores how virtual anchors (computer-generated hosts) in livestream shopping influence consumers’ intent to purchase products. It examines four key characteristics of virtual anchors: visibility, interactive guidance, shopping assistance, and transaction facilitation. The study finds that visibility, interactive guidance, and shopping assistance positively impact consumers’ perception of brand authenticity, emotional attachment to the brand, and sense of community with other consumers. These factors, in turn, increase purchase intention. However, an overemphasis on transactions by the virtual anchor can negatively affect perceived brand authenticity. The study suggests that to optimize virtual anchors, platforms should focus on creating lifelike, interactive, and intelligent hosts, while brands should design anchors that match their image and foster consumer communities. Virtual anchors offer new opportunities for engaging consumers, but their use should be balanced to avoid damaging trust.

Keywords

virtual anchor livestreaming e-commerce purchase intention stimulus-organism-response (S-O-R) theory affective schema theory

Introduction

In the digital era, livestreaming e-commerce has rapidly transformed the retail landscape, especially in China, where the transaction volume soared to 4.9 trillion yuan in 2023, reflecting a remarkable 35% increase from the previous year (iResearch, 2024). Platforms like Douyin E-commerce reported an annual Gross Merchandise Volume (GMV) approaching 2 trillion yuan, with an impressive growth rate nearing 60% (Global Marketing Service Exchange, 2023). This burgeoning industry has seen a continued rise in penetration, with the transaction scale expected to exceed 5 trillion yuan in 2025 (Qian, 2023). However, alongside this rapid expansion, significant challenges have emerged, including the prevalence of counterfeit goods, misleading advertising, and price fraud, all of which pose risks to consumer rights and trust. A pivotal moment came with the exposure of the Xin Youzhi fake goods scandal during China’s March 15th Consumer Rights Day Gala in 2021, which severely undermined the industry’s credibility and consumer confidence. As the market matures, there has been a striking increase in sign-on fees and commission rates for top anchors, leading to a disparity between the high demand for quality anchors and their limited supply. The COVID-19 pandemic further complicated this landscape by hindering the availability of offline anchors and disrupting livestreaming events and operations.

In light of these challenges, the emergence of virtual anchors presents a compelling solution. Virtual anchors can operate around the clock, significantly enhancing livestream coverage and providing brands with a unique and customizable presence that aligns with their tone and identity (Agatha et al., 2023). Major players such as Alibaba, JD.com, and Kwai, along with virtual anchor companies, are actively exploring this innovative approach, with predictions indicating that the GMV from virtual anchor livestreaming could reach hundreds of billions in the next few years (Zhao et al., 2023). To better understand the development and current state of research in this field, Table 1 provides an overview of the existing research streams in livestreaming e-commerce and virtual anchors literature, revealing several key themes and findings across different research periods.

Table 1.

Summary of Research Streams in Livestreaming E-commerce and Virtual Anchors Literature.

Research stream	Key themes	Representative early studies	Recent studies	Key findings
Development & Characteristics of Livestreaming E-commerce	- Market evolution - Platform features - Industry challenges	Lu et al. (2010) Eroglu et al. (2001)	Shi and Dou, (2023) Wu et al., (2023)	- Real-time interaction enhances trust - Strong social presence - Supply chain integration challenges
Consumer Behavior in Livestreaming	- Purchase motivation - Trust formation - Decision process	Sun et al. (2019)	P. Xu, Cui, et al. (2022) Zhang (2023)	- Utilitarian and hedonic benefits - Anchor credibility impacts trust - Impulse buying behavior
Virtual Anchors and Virtual Idols	- Technology integration - Marketing innovation - Consumer response	Berryman et al. (2021)	Davlembayeva et al. (2024,Kim and Kim (2023) Davlembayeva et al. (2024)	- AI capabilities enhance interaction - Brand personification - Emotional connection building
Brand Relationship Development	- Brand authenticity - Brand attachment - Community building	Park et al. (2010)	Chen and Wang (2022) Kang and Shin (2016)	- Trust building through authenticity - Emotional bonds formation - Value co-creation in communities

While the literature review reveals significant progress in understanding various aspects of livestreaming e-commerce and virtual anchors, several important gaps remain. Despite the growing significance of virtual anchors, the academic literature has yet to catch up. Research has predominantly focused on human anchors, largely overlooking the unique contributions and mechanisms of virtual anchors in influencing consumer behavior (Wongkitrungrueng & Assarut, 2020; P. Xu et al., 2022). Moreover, emotional factors such as brand authenticity, attachment, and a sense of community, which could foster deeper consumer connections with virtual anchors, have not been thoroughly investigated (N. Chen & Yang, 2023). This study addresses these gaps by examining how virtual anchors impact consumer purchase intentions and exploring the underlying mechanisms. To achieve these objectives, this study will address the following research questions (RQs):

RQ1: Which characteristics of virtual anchors influence consumer purchase intention? What are the key factors?

RQ2: What roles do brand authenticity, brand attachment, and sense of community play in the process of virtual anchors influencing purchase intention? Can they become emotional bonds between virtual anchors and consumers?

RQ3: How can platforms and merchants optimize the technology and operation of virtual anchors to enhance their attractiveness and conversion power, thereby achieving sales growth?

Theoretically, this study aims to establish the key mediating roles of brand authenticity, brand attachment, and sense of community in the context of virtual anchor marketing. This will provide new insights into consumer behavior and enrich existing theories. From a practical standpoint, the study will recommend strategies for enhancing the interactivity and intelligence of virtual anchors while urging brands to develop virtual IPs that resonate with target audiences, thereby strengthening emotional connections and increasing purchase intention. By systematically examining the mechanisms through which virtual anchors influence consumer purchases, this research not only addresses pressing questions for practitioners but also contributes to the academic discourse surrounding the integration of virtual entities in commerce, promoting the continued growth and sophistication of livestreaming e-commerce.

Theoretical Foundation

Stimulus-Organism-Response Theory

The Stimulus-Organism-Response (SOR) model, rooted in psychology, posits that external stimuli (S) affect an organism’s internal states (O), which in turn lead to specific responses (R) such as approach or avoidance behaviors (Eroglu et al., 2001). In the e-commerce context, the SOR framework has been widely applied to examine how environmental cues influence consumers’ psychological and behavioral responses (Shiau et al., 2022).

This study adopts the SOR model to investigate the relationship between virtual anchor characteristics and consumer purchase intention in livestream e-commerce. Drawing on Dong and Wang’s (2018) research, we identify virtual anchors’ technology affordances—visibility, Metavoicing, guidance shopping, and trading—as environmental stimuli (S). Integrating affective schema theory, we consider brand authenticity, brand attachment, and sense of community as components of consumers’ positive affective schemas and organism states (O). Purchase intention serves as the response variable (R). Specifically, virtual anchors’ visibility, Metavoicing, guidance shopping, and trading act as stimuli (S) that influence consumers’ perceptions of brand authenticity, brand attachment, and sense of community (O), which subsequently impact their purchase intention (R). We propose that brand authenticity, brand attachment, and sense of community mediate the relationship between virtual anchor characteristics and purchase intention.

Affordance Theory

Affordance refers to “the possibilities for goal-oriented action offered by technology to specific audience groups,” emphasizing the dynamic interaction between technology and users (Proctor, 2020). This concept highlights how user experience and motivation shape product design. In the realm of social e-commerce, the characteristics of social media technologies have been a focal point. Dong and Wang (2018) identified six dimensions of affordances—visibility, Metavoicing, guidance shopping, connectivity, triggered attending, and trading—that facilitate interactions between buyers and sellers. Building on this framework, Sun et al. (2019) specifically examined the affordances relevant to livestreaming e-commerce, particularly highlighting visibility, Metavoicing, and guidance shopping. Zhang (2023) further categorized these affordances into guidance shopping, communality, interactivity, and navigability. Notably, previous research has predominantly addressed “livestreaming platforms” while overlooking the distinct role of “virtual anchors.” Virtual anchor livestreaming rooms embody the key characteristics of social commerce platforms, prompting this study to focus on their specific technology affordances, aligning with Dong & Wang (2018) framework. The relevant affordance dimensions are shown in Table 2.

Table 2.

Affordance Dimensions in Virtual Anchor Livestream E-commerce.

Affordance Dimension	Description	Relevance to Virtual Anchor Livestreaming
Visibility	Access to product and user information	• Reduces perceived risk • Enables real-time interactions between consumers and virtual anchors
Metavoicing	Evaluation and feedback mechanisms	• Promotes public discussions • Facilitates real-time responses between buyers and sellers
Guidance shopping	Informed purchase decision support	• Provides personalized recommendations • Leverages live comment interactions
Trading	Transaction facilitation	• Offers diverse payment options • Provides clear order guidance • Includes features like account binding and password-free payments • Incorporates various discount options

Note. These dimensions from Dong and Wang (2018) original framework were excluded due to their irrelevance to virtual anchor livestreaming environments.

The decision to exclude the dimensions of “connectivity” and “triggered attending” from Dong and Wang’s model stems from their focus on capabilities that are not present in virtual anchor livestreaming environments. “Connectivity” involves establishing two-way relationships via features such as friend additions and homepage views, while “triggered attending” pertains to notifications about product information changes—elements typically absent in these settings. In the SOR model, these affordances act as environmental stimuli (S), influencing consumers’ emotional responses (O), such as brand authenticity, brand attachment, and sense of community, which in turn affect purchase intention (R).

Affective Schema Theory

This study introduces affective schema theory to explore how virtual anchor livestreaming rooms’ technology affordances evoke positive affective schemas from physiological, meaning, and relational aspects. Affective schemas describe emotional responses to situational stimuli, often using a hierarchical model (Abdolmanafi et al., 2019). Building on the work of Song (2021) and Tabesh et al. (2024), this study constructs variables explaining consumers’ emotional states in virtual anchor livestreaming rooms.

Using Tirch et al.’s (2014) affective schema framework, brand authenticity, brand attachment, and sense of community are introduced, corresponding to the physiological, meaning, and relational levels, respectively. Brand authenticity reflects consumers’ perception of honesty in the brand’s information delivered through the virtual anchor (Morhart et al., 2015). Brand attachment refers to the emotional connection consumers develop with the brand, believing it expresses their self and helps achieve personal meaning (Park et al., 2010). Sense of community refers to consumers’ connections with other consumers, developing identification with the brand’s consumer group (Burnasheva et al., 2019). This study examines how the technology affordance of virtual anchors evokes positive affective schemas toward brands, influencing consumers’ purchase intention. By integrating these concepts under the affective schema framework, the study aims to provide a comprehensive understanding of the emotional pathways through which virtual anchors impact consumer behavior.

Research Model and Hypotheses

Research Model

Drawing on the SOR theory, this study develops a research model to investigate the relationship between virtual anchor characteristics and consumer purchase intention. Virtual anchor technology affordances—visibility, Metavoicing, guidance shopping, and trading—are identified as environmental stimulus variables (S). Brand authenticity, brand attachment, and sense of community, as key components of positive affective schema based on affective schema theory, are treated as organism state variables (O). Purchase intention is viewed as the organism response variable (R). Figure 1 illustrates the hypothesized path model.

Figure 1.

The hypothesized path model.

Hypothesis Development

Physiological Level of Positive Affective Schema: Brand Authenticity

Virtual anchor livestreaming represents a novel approach to e-commerce that fundamentally changes how consumers perceive brand authenticity (Zhou & Li, 2023). In this context, brand authenticity refers to consumers’ perception that the brand’s information is honest and supports their true selves (Li & Ng, 2023). Drawing on authenticity theory, this manifests in two key aspects: standardized high-quality product presentations and alignment with consumers’ self-perceptions and expectations (Lou et al., 2023). Unlike human anchors, whose performance fluctuates, virtual anchors enhance brand authenticity through technological consistency and data-driven interactions. Traditional e-commerce lacks lifelike shopping experiences, but virtual anchor livestreaming compensates through three primary mechanisms: visibility enables precise 3D product demonstrations with consistent quality across sessions; Metavoicing provides AI-driven, objective recommendations based on consumer data rather than personal preferences; and guidance shopping integrates real-time market data that human anchors cannot readily access (Kim & Kim, 2023). Leading platforms demonstrate these advantages effectively. Taobao’s virtual anchors deliver standardized product presentations while displaying real-time inventory data and user feedback, creating a transparent shopping environment (Li et al., 2024). This systematic approach drives consumer authenticity perception more effectively than human-led livestreams (Wongkitrungrueng & Assarut, 2020), while consistent interactions reduce product quality uncertainty and increase purchase intention (Zhai & Chen, 2023). The technology affordance of virtual anchors thus creates brand authenticity through technological consistency rather than human personality factors (D. Wang, 2024). Through this technological foundation, virtual anchors effectively alleviate consumer distrust and provide pleasant emotional experiences, establishing brand authenticity as the primary level of consumers’ positive affective response. Based on these theoretical foundations and empirical observations, we propose:

H1a: The visibility of virtual anchors is positively correlated with brand authenticity.

H1b: The metavoicing of virtual anchors is positively correlated with brand authenticity.

H1c: The guidance shopping of virtual anchors is positively correlated with brand authenticity.

H1d: The trading of virtual anchors is positively correlated with brand authenticity.

Meaning Level of Positive Affective Schema: Brand Attachment

Brand attachment in virtual anchor livestreaming represents the emotional connection consumers develop with brands through technological interaction (Park et al., 2010). Drawing on attachment theory (Thomson et al., 2005), this encompasses three dimensions: affection, passion, and connection. Virtual anchor technology creates unique opportunities for emotional bonding through consistent presence and personalized interactions (Lin & Ku, 2023). Virtual anchors overcome human anchors’ limitations in emotional engagement through advanced AI algorithms and personalized interaction design. While human anchors experience fatigue and emotional fluctuations, virtual anchors maintain consistent engagement through technological capabilities (Davlembayeva et al., 2024). The technological foundation enables precise emotional resonance through data analysis, processing consumer preferences, browsing patterns, and interaction history to deliver highly personalized content that strengthens emotional bonds (Ahmadian et al., 2023). This technological advantage manifests through four key affordances that facilitate brand attachment. First, visibility provides consistent brand image presentation. Second, metavoicing enables data-driven emotional interactions. Third, guidance shopping creates value-based connections. Fourth, trading reinforces positive associations through smooth transactions (Ghorbanzadeh & Rahehagh, 2021). A notable example is Xing Tong, whose ability to instantly change outfits to match product themes, like coordinating with Liuliumei’s packaging colors, demonstrates how these affordances create innovative and engaging interactions that resonate with viewers (Gao, 2023). This systematic approach distinguishes virtual anchors’ emotional connection building from human counterparts. As brands gain social recognition through virtual anchors, consumers develop a sense of achievement and personal meaning through these interactions. Thus, brand attachment represents the second level of consumers’ positive affective schema in virtual anchor livestreaming rooms. Based on these theoretical foundations and empirical observations, we propose:

H2a: The visibility of virtual anchors is positively correlated with brand attachment.

H2b: The metavoicing of virtual anchors is positively correlated with brand attachment.

H2c: The guidance shopping of virtual anchors is positively correlated with brand attachment.

H2d: The trading of virtual anchors is positively correlated with brand attachment.

Relational Level of Positive Affective Schema: Sense of Community

In virtual anchor livestreaming rooms, sense of community refers to consumers’ feelings of belonging and connection within the brand community facilitated by virtual anchors (Blanchard & Markus, 2002; McMillan & Chavis, 1986). Following Bergkvist & Bech-Larsen (2010) framework, this encompasses perceived affinity toward brand-associated individuals, particularly fellow consumers. Unlike traditional human-led livestreams where community interactions may be inconsistent, virtual anchors create structured environments through technological integration (G. Chen & Wang, 2022). The technology affordance of virtual anchors enables community building through four key mechanisms. First, visibility enables consumer identification through synchronized viewing experiences. Second, metavoicing facilitates structured community discussions. Third, guidance shopping creates shared experiences. Fourth, trading builds community trust through transparent group-buying mechanisms (Kang & Shin, 2016). These technological capabilities allow virtual anchors to foster community building through innovative approaches that differentiate them from human anchors. Virtual anchors implement systematic community management through targeted content and interaction strategies. Ling, a hyper-realistic virtual anchor on Douyin, exemplifies this approach by integrating local culture and tourism elements. Through her “Streets of Chengdu” short videos and “Thousand Miles of Mountains and Rivers” series, she creates unique community bonds through cultural resonance (Gao, 2023). Similarly, Xing Tong demonstrates effective community engagement by aligning content with audience characteristics, featuring instant outfit changes and incorporating popular internet memes in her livestreaming sessions, which attracted over 400,000 viewers during her Calvin Klein promotion (Gao, 2023). Building on these technological foundations, virtual anchors extend beyond traditional product promotion to create diverse engagement formats. Kwai’s virtual anchor, Guan Xiaofang, illustrates this evolution through brand co-streaming sessions and gaming interactions, fostering deeper community connections (Gao, 2023). These innovative approaches establish meaningful interpersonal relationships, representing the social relationship aspect of positive affective schema. Virtual anchor livestreaming rooms thus serve as platforms for brand-consumer communication, facilitating connections and identification within the community. This establishment of community bonds represents the third level of consumers’ positive affective schema in virtual anchor settings. Based on these theoretical foundations and empirical observations, we propose:

H3a: The visibility of virtual anchors is positively correlated with users’ sense of community.

H3b: The metavoicing of virtual anchors is positively correlated with users’ sense of community.

H3c: The guidance shopping of virtual anchors is positively correlated with users’ sense of community.

H3d: The trading of virtual anchors is positively correlated with users’ sense of community.

Purchase Intention as Organism Response (R)

In the context of virtual anchor livestreaming rooms, purchase intention reflects consumers’ intention to purchase brand products after watching brand livestreams or participating in interactions, falling within the category of approach-oriented behavioral intentions. Therefore, this study views consumer purchase intention in virtual anchor livestreaming rooms as the response (R) in the SOR model.

Brand Authenticity and Purchase Intention

A brand possessing high authenticity implies that the brand is honest to both itself and consumers through the virtual anchor. Authenticity can enhance consumers’ trust in the brand, which may, in turn, help consumers construct positive brand attitudes and purchase intentions (Chaihanchanchai et al., 2024). In virtual anchor livestreaming rooms, consumers form judgments about the authenticity and reliability of products and brands based on the host’s promotion, subsequently making shopping decisions among this brand and other similar brands, thus influencing purchase intention (Tarabieh et al., 2024). Accordingly, we propose:

H4: In the virtual anchor livestreaming scenario, brand authenticity is positively correlated with purchase intention, and brand authenticity mediates the relationship between technology affordance and purchase intention.

Brand Attachment and Purchase Intention

The emotional attachment consumers develop toward a brand can evoke consumer preference and purchasing behavior for that brand, as well as an exclusive tendency to use products of that brand over similar products from other brands. In virtual anchor livestreaming rooms, attached consumers tend to invest more time and energy in following the brand and are willing to make purchases from it, thus demonstrating purchase intention toward the brand (Davlembayeva et al., 2024; Gunawan & Yasser Iqbal Daulay, 2024). Based on this, we propose:

H5: In the virtual anchor livestreaming scenario, brand attachment is positively correlated with purchase intention, and brand attachment mediates the relationship between technology affordance and purchase intention.

Sense of Community and Purchase Intention

E-commerce has enhanced consumer agency, with brand community consciousness driving users and brands to co-create value. As consumers immerse themselves in the process of value co-creation, they feel the necessity to purchase the brand’s products. Thus, consumers’ sense of community indirectly contributes to the increase in consumer purchase intention (Otero-Gómez & Giraldo-Pérez, 2021). In the context of virtual anchor livestreaming rooms, individual consumers confirm the existence of other brand users through interactive comments, while rituals such as red packet passwords and screen-filling bullet comments enhance the sense of community as fans of the same brand (Y. Wang & Hajli, 2014). Consumers construct their purchase intention by experiencing the dual benefits of material discounts and identity recognition that come with being a brand follower. Therefore, we propose:

H6: In the virtual anchor livestreaming scenario, users’ sense of community is positively correlated with purchase intention, and sense of community mediates the relationship between technology affordance and purchase intention.

Methods

Questionnaire Design and Variable Measurement

This study collected data through a questionnaire survey. The first part of the questionnaire measures consumers’ demographic characteristics, including gender, age, education level, and monthly income. The second part consists of scenario-based questions. It begins with a text and image definition of a virtual anchor livestreaming room, followed by questions about whether the respondent has watched virtual anchor livestreams and their experiences with them. Respondents are asked to recall and answer questions about their most recent virtual anchor livestreaming shopping experience (livestream name, time, products of interest, interaction content, whether a purchase was made), as well as their virtual anchor livestream viewing habits (frequency, duration per session). This section screens out respondents with “no virtual anchor livestream viewing experience” to ensure that all survey participants have watched virtual anchor livestreams and that their memories of the viewing context are evoked.

The third part of the questionnaire surveys consumers’ perceptions of the technology affordance characteristics of virtual anchor livestreaming rooms. It adapts the affordance scale developed by Dong and Wang (2018) to measure visibility (3 items), Metavoicing (4 items), guidance shopping (5 items), and trading (3 items). The fourth part of the questionnaire investigates consumers’ emotions and behaviors. It adapts Bruhn et al.’s brand authenticity scale (5 items), Thomson et al.’s brand attachment scale (5 items), Bergkvist and Bech-Larsen (2010)‘s sense of community scale (3 items), and a self-developed purchase intention scale (5 items) based on the research of Dodds et al. (1991), Spears and Singh (2004), and Baker et al. (2016). All measurement items in the questionnaire use a 5-point Likert scale, with 1 to 5 representing “strongly disagree” to “strongly agree” respectively. Through the measurement of these variables, this study can examine how the technology affordance characteristics of virtual anchor livestreaming rooms (visibility, Metavoicing, guidance shopping, and trading) influence consumers’ brand authenticity perception, brand attachment, and sense of community, and subsequently affect their purchase intention.

Research Subjects and Design

This study employed a questionnaire survey method, distributing questionnaires in two rounds to match the current user profile of virtual anchor livestream e-commerce in terms of product categories, age, and gender. The survey was published on the Wenjuanxing platform and distributed through social media. The first round targeted the main consumer product categories in virtual anchor livestream e-commerce, namely skincare, men’s and women’s clothing, food and beverages, and digital products. Brands with large followings and virtual anchor livestreams on the Taobao platform, such as Xiaomi, Li-Ning, Three Squirrels, and Estée Lauder, were selected. The initial questionnaire was distributed in these brands’ Weibo super topics and virtual anchor livestream super topics. Results from the first round revealed underrepresentation of certain age groups and genders, affecting the sample’s fit with the virtual anchor livestream e-commerce user profile. Therefore, a second round of distribution was conducted using the snowball method in WeChat groups and Moments to complete the sample fitting in terms of gender and age.

A total of 441 valid questionnaires were collected, with demographic characteristics shown in Table 3. The sample demonstrates good representativeness of virtual anchor livestream e-commerce users, with balanced gender distribution, concentration in young adult age groups (18–34 years), and product categories aligned with typical platform offerings. The respondents showed high education levels and were primarily employed professionals with middle-income levels. Most participants demonstrated active engagement with virtual anchors through regular viewing and purchasing behaviors.

Table 3.

Sample Characteristics (N = 441).

Category	Option	Frequency	%
Gender	Male	211	47.846
Gender	Female	230	52.154
Age	18–24	163	36.961
	25–34	213	48.299
	35–44	54	12.245
	>45	11	2.494
Education	High school and below	42	9.524
	Associate degree	157	35.601
	Bachelor’s degree	169	38.322
	Master’s degree and above	73	16.553
Occupation	Student	25	5.669
	Corporate/company employee	255	57.823
	Public institution/government employee	122	27.664
	Freelancer	29	6.576
	Other	10	2.268
Monthly Income (RMB/Yuan)	<3,000	5	1.134
	3,000–5,999	220	49.887
	6,000–9,999	161	36.508
	>10,000	55	12.472
Average Daily Internet Usage (Hours)	<1	65	14.739
	1–3	159	36.054
	3–5	160	36.281
	>5	57	12.925
Viewing Frequency	Daily	22	4.989
	2–3 times per week	106	24.036
	Once per week	128	29.025
	1–2 times per month	122	27.664
	Almost never	63	14.286
Product Purchase Frequency	Often (more than once per week)	18	4.082
	Sometimes (1–2 times per month)	121	27.438
	Rarely (less than once per quarter)	200	45.351
	Never purchased	102	23.129
Virtual Anchor Understanding	Very familiar	11	2.494
	Quite familiar	142	32.200
	Moderately familiar	208	47.166
	Not very familiar	61	13.832
	Not familiar at all	19	4.308

Data Analysis

For data analysis, this study employed a two-stage analytical approach using SPSS 28.0 and SmartPLS 4.0. The preliminary analysis through SPSS included descriptive statistics for demographic characteristics, reliability tests using Cronbach’s alpha, and examination of data normality and potential common method bias. Subsequently, Partial Least Squares Structural Equation Modeling (PLS-SEM) was chosen for hypothesis testing through SmartPLS 4.0, as it is particularly suitable for exploratory research and theory development, effectively handles complex models with multiple mediating relationships, does not require strict normal distribution of data, and is appropriate for smaller sample sizes. The PLS-SEM analysis followed a two-step process, first assessing the measurement model through construct reliability, convergent validity, and discriminant validity, then evaluating the structural model through path coefficients, R² values, effect sizes, and predictive relevance. Bootstrapping with 5,000 resamples was performed to test the statistical significance of path coefficients.

Results

Measurement Model

Reliability and Validity Analysis

This study assesses internal consistency reliability using Cronbach’s Alpha (CA) and Composite Reliability (CR). CA measures the questionnaire’s pre-test reliability, with values above .7 indicating reliability and above 0.9 indicating high reliability. CR represents construct indicators’ internal consistency, with 0.7 as an acceptable threshold (Hair, 2009) and values above 0.6, as Fornell and Larcker (1981) suggested. Average Variance Extracted (AVE) is used to evaluate reliability and convergent validity. AVE measures the proportion of variance in the latent variable explained by the observed variables, calculated as the sum of squared factor loadings divided by the sum of squared factor loadings plus error variances. An AVE value ≥0.50 indicates good composite validity (Hair Jr et al., 2016). Table 4 presents the reliability and validity analysis results.

Table 4.

Analysis of Reliability and Validity.

Measurements	Path	Factor loading	T	p	Cronbach’s alpha	CR	AVE
Visibility (VI)	VI1←VI	0.847	59.609	.000	0.841	0.843	0.758
	VI2←VI	0.874	81.529	.000
	VI3←VI	0.891	85.340	.000
Metavoicing (ME)	ME1←ME	0.872	86.473	.000	0.875	0.879	0.726
	ME2←ME	0.845	64.985	.000
	ME3←ME	0.845	57.964	.000
	ME4←ME	0.846	55.941	.000
Guidance shopping (GS)	GS1←GS	0.846	59.031	.000	0.889	0.890	0.692
	GS2←GS	0.846	59.904	.000
	GS3←GS	0.814	49.192	.000
	GS4←GS	0.823	49.625	.000
	GS5←GS	0.830	54.896	.000
Trading (TR)	TR1←TR	0.871	67.401	.000	0.825	0.825	0.740
	TR2←TR	0.862	69.058	.000
	TR3←TR	0.848	54.616	.000
Brand authenticity (BU)	BU1←BU	0.845	63.801	.000	0.899	0.899	0.712
	BU2←BU	0.836	60.955	.000
	BU3←BU	0.826	54.470	.000
	BU4←BU	0.850	69.500	.000
	BU5←BU	0.861	64.578	.000
Brand attachment (BT)	BT1←BT	0.826	53.224	.000	0.880	0.880	0.675
	BT2←BT	0.807	58.639	.000
	BT3←BT	0.813	47.022	.000
	BT4←BT	0.838	58.799	.000
	BT5←BT	0.822	55.410	.000
Sense of community (SC)	SC1←SC	0.869	66.207	.000	0.822	0.826	0.737
	SC2←SC	0.866	82.801	.000
	SC3←SC	0.841	52.878	.000
Purchase intention (PI)	PI1←PI	0.815	52.216	.000	0.878	0.879	0.672
	PI2←PI	0.804	48.914	.000
	PI3←PI	0.808	54.125	.000
	PI4←PI	0.829	66.438	.000
	PI5←PI	0.843	68.327	.000

Discriminant Validity

Discriminant validity analysis ensures constructs are statistically distinct and items are not highly correlated (correlation > 0.85), as high correlations suggest overlapping constructs. This study assesses discriminant validity using the AVE method. Fornell and Larcker (1981)state that for each factor, the square root of AVE should exceed the correlation coefficients between variable pairs. Table 5 confirms discriminant validity, with the diagonal showing each factor’s square root of AVE, which is greater than the off-diagonal standardized correlation coefficients. The lower triangular portion presents the correlation coefficients.

Table 5.

Discriminant Validity.

Category	VI	ME	GS	TR	BU	BT	SC	PI
VI	0.871
ME	0.297	0.852
GS	0.371	0.313	0.832
TR	0.455	0.379	0.417	0.860
BU	0.528	0.507	0.537	0.439	0.844
BT	0.493	0.448	0.541	0.556	0.513	0.822
SC	0.544	0.386	0.520	0.500	0.518	0.496	0.859
PI	0.517	0.428	0.512	0.456	0.634	0.595	0.583	0.820

Structural Equation Model

Path Coefficient Significance

Path coefficient magnitude and significance assess hypothesized relationships. Path coefficients range from −1 to 1, with values closer to 1 (−1) indicating strong positive (negative) correlations. The T-value is the path coefficient divided by the standard deviation. For samples > 30, critical normal distribution quartile values determine significance: 1.96 (5%), 2.57 (1%), and 3.29 (0.1%) (Hair Jr et al., 2016). This study uses bootstrapping with 5,000 samples to calculate path coefficients and T-values. Figure 2 and Table 6 shows the structural model’s path coefficients.

Figure 2.

Path analysis results for the structural model.

Table 6.

Path Coefficients of the PLS Structural Equation Model.

Path	O	M	STDEV	T	p	2.50%	97.50%
VI→BU	0.297	0.297	0.039	7.567	.000	0.221	0.374
VI→BT	0.205	0.206	0.042	4.823	.000	0.122	0.287
VI→SC	0.313	0.312	0.041	7.577	.000	0.229	0.392
ME→BU	0.300	0.300	0.037	8.153	.000	0.227	0.371
ME→BT	0.195	0.196	0.039	4.974	.000	0.119	0.272
ME→SC	0.133	0.133	0.041	3.235	.001	0.052	0.212
GS→BU	0.307	0.307	0.039	7.823	.000	0.227	0.383
GS→BT	0.292	0.292	0.041	7.109	.000	0.211	0.373
GS→SC	0.284	0.285	0.039	7.262	.000	0.203	0.356
TR→BU	0.062	0.063	0.043	1.456	.145	−0.022	0.147
TR→BT	0.267	0.267	0.041	6.502	.000	0.186	0.349
TR→SC	0.189	0.189	0.042	4.511	.000	0.107	0.274
BU→PI	0.353	0.354	0.041	8.615	.000	0.273	0.432
BT→PI	0.286	0.286	0.042	6.793	.000	0.201	0.366
SC→PI	0.258	0.258	0.042	6.107	.000	0.171	0.337

Based on the analysis results, the following conclusions can be drawn:

Visibility (VI), Metavoicing (ME), and guidance shopping (GS) of virtual anchors significantly positively impact brand authenticity (BU), brand attachment (BT), and sense of community (SC), supporting H1a-c, H2a-c, and H3a-c. Trading (TR) of virtual anchors significantly impacts BT and SC but not BU, supporting H2d and H3d but not H1d.

Mediation Effect Analysis

To demonstrate the mediating effects, this study employs the bootstrap mediation effect test with a 95% bias-corrected confidence interval and 5,000 repeated samplings to assess the significance of the mediating effects. The mediation effect test results are presented in Table 7.

Table 7.

Bootstrap Mediation Effect Test.

Path	O	M	STDEV	T	p	2.50%	97.50%
VI→BU→PI	0.105	0.105	0.019	5.422	.000	0.071	0.148
VI→BT→PI	0.059	0.059	0.015	3.797	.000	0.032	0.093
VI→SC→PI	0.081	0.081	0.018	4.536	.000	0.049	0.119
ME→BU→PI	0.106	0.106	0.018	5.945	.000	0.074	0.145
ME→BT→PI	0.056	0.056	0.014	3.934	.000	0.031	0.087
ME→SC→PI	0.034	0.034	0.012	2.903	.004	0.014	0.061
GS→BU→PI	0.109	0.109	0.019	5.845	.000	0.075	0.150
GS→BT→PI	0.084	0.083	0.018	4.749	.000	0.053	0.122
GS→SC→PI	0.073	0.073	0.015	4.764	.000	0.045	0.105
TR→BU→PI	0.022	0.022	0.016	1.410	.158	−0.007	0.055
TR→BT→PI	0.076	0.076	0.015	5.026	.000	0.050	0.109
TR→SC→PI	0.049	0.049	0.014	3.550	.000	0.025	0.080

The Bootstrap mediation effect test results show that: The visibility (VI), Metavoicing (ME), and guidance shopping (GS) of virtual anchors significantly mediate purchase intention (PI) through brand authenticity (BU), brand attachment (BT), and sense of community (SC), with the confidence intervals not including 0. The trading (TR) of virtual anchors has significant mediating effects on PI through BT and SC but not through BU, as the confidence interval for the TR→BU→PI path includes 0.

Thus, the visibility, Metavoicing, and guidance shopping of virtual anchors significantly mediate purchase intention through brand authenticity, brand attachment, and sense of community. Trading only significantly mediates through brand attachment and sense of community. Therefore, hypotheses H4, H5, and H6 are supported by the data.

Discussion and Implications

Discussion of Key Findings

We obtained several essential findings from the current study. Firstly, the visibility, Metavoicing, and guidance shopping of virtual anchors positively influence consumers’ purchase intention through brand authenticity, brand attachment, and sense of community, while trading only affects purchase intention through brand attachment and sense of community, with the mediating effect of brand authenticity being insignificant. This finding aligns with and differs from previous studies on human anchors. The study confirms the basic path that anchors’s characteristics influence purchase intention through consumers’ emotional responses, supporting existing research (J. Xu, Lee, et al., 2022). However, this study finds no significant correlation between virtual anchors’ trading and brand authenticity. This difference can be explained by several empirically-supported reasons:

Firstly, the persuasive and urgent sales behaviors emphasized in trading might make consumers perceive virtual anchors as overly utilitarian, thereby damaging brand authenticity. This is supported by research on consumer psychology and brand authenticity. For instance, Morhart et al. (2015) found that commercialized messages could significantly reduce perceived brand authenticity, as consumers tend to value genuine and sincere brand communications over purely commercial ones. Similarly, (Fritz et al., 2017) demonstrated through structural equation modeling that excessive commercial focus could weaken the relationship between brand communication and perceived authenticity.

Secondly, excessive emphasis on transactions may lead consumers to believe that the brand focuses more on short-term sales rather than long-term brand image, which is unfavorable for establishing brand authenticity (Lodish & Mela, 2007). This assertion is supported by research in livestreaming commerce. For example, (X. Xu et al., 2020) found that when livestreaming platforms overly emphasize transaction-oriented features, it negatively impacts consumers’ trust and authenticity perceptions. Additionally, (Moulard et al., 2016) revealed that when consumers perceive a brand’s behavior as commercially motivated rather than intrinsically motivated, they rate the brand as less authentic.

Thirdly, overtly apparent persuasive intentions may provoke consumer aversion, generating psychological reactance and hindering the establishment of brand authenticity. This phenomenon is well-documented in consumer behavior research. Studies by (Van Reijmersdal et al., 2016) demonstrated that when consumers recognize explicit persuasion attempts in digital environments, their skepticism increases, and brand evaluations become more negative. These findings align with reactance theory and are supported by subsequent research showing that transparent selling tactics can trigger resistance in consumers (Lou & Yuan, 2019).

In contrast, the visibility, Metavoicing, and guidance shopping of virtual anchors are more likely to make consumers perceive the brand’s humanized characteristics, reducing psychological distance and establishing brand authenticity. This positive relationship is supported by recent empirical evidence. Studies in livestreaming commerce have shown that when streamers focus on providing hedonic value and building relationships rather than purely utilitarian benefits, they generate stronger viewer engagement and trust (Cai et al., 2018). This aligns with (Schouten et al., 2020) findings that humanized interactions in social media marketing lead to significantly higher engagement and trust compared to purely transactional approaches.

Second, the mediation effect test results reveal that visibility, Metavoicing, and guidance shopping of virtual anchors have significant mediating effects on purchase intention through brand authenticity, brand attachment, and sense of community, while trading only has significant mediating effects through brand attachment and sense of community. This suggests the impact pathways of the first three characteristics are more comprehensive, while trading’s influence pathway is relatively singular and potentially negative. While existing research has largely focused on the direct impact of anchor characteristics on purchase intention (İNal & BiL, 2023), this study clarifies their internal mechanisms through these mediating variables. Furthermore, this study extends previous findings about positive emotional factors’ effects on purchase intention (Vrtana & Krizanova, 2023) from human anchors to virtual anchors. By confirming these relationships hold true in the virtual anchor context, this research not only enhances the external validity of existing theories but also provides valuable insights for virtual anchor marketing practices.

Third, our research reveals distinct patterns in how virtual anchor characteristics function across different aspects of consumer emotional engagement. Drawing on affective schema theory’s three-level framework (physiological, meaning, and relational), we find that virtual anchors’ technological capabilities create unique advantages in each dimension. At the physiological level (brand authenticity), virtual anchors demonstrate superior consistency in information delivery compared to human anchors (Lou et al., 2023; Wongkitrungrueng & Assarut, 2020). The technological affordances (visibility, Metavoicing, and guidance shopping) enable standardized, high-quality product presentations and objective information sharing. For example, our findings show that virtual anchors’ visibility feature allows for precise 3D product demonstrations with consistent quality across sessions (supported by path coefficients VI→BU = 0.297, p < .001), while Metavoicing provides AI-driven recommendations based on consumer data (ME→BU = 0.300, p < .001). At the meaning level (brand attachment), virtual anchors exhibit unique capabilities in fostering emotional connections through technological personalization (Davlembayeva et al., 2024). The path analysis reveals strong relationships between virtual anchor characteristics and brand attachment (GS→BT = 0.292, p < .001; TR→BT = 0.267, p <.001), suggesting that virtual anchors successfully create emotional bonds through their technological features. Unlike human anchors, who may experience fatigue or emotional fluctuations, virtual anchors maintain consistent emotional engagement through advanced AI algorithms and personalized interaction design (Lin & Ku, 2023; Moulard et al., 2016). At the relational level (sense of community), virtual anchors demonstrate effectiveness in community building through structured technological integration (G. Chen & Wang, 2022). The significant path coefficients (VI→SC=0.313, GS→SC = 0.284, both p < .001) indicate that virtual anchors successfully facilitate community development through synchronized viewing experiences and structured community discussions (Kang & Shin, 2016). This systematic approach to community building differs from the more spontaneous but potentially inconsistent community management style of human anchors (Souki et al., 2022).

These findings highlight the unique advantages of virtual anchors in creating emotional connections through technological means rather than trying to replicate human anchors’ interpersonal approach. The success of virtual anchors lies in their ability to leverage technological capabilities to create new forms of authentic, meaningful, and community-oriented consumer experiences. This understanding is particularly valuable given our sample’s demographic characteristics, which show that the primary users of virtual anchor livestreaming are young, digitally-native consumers (85.26% aged 18–34) who may be more receptive to technologically-mediated emotional connections.

Theoretical Implications

This study makes several significant theoretical contributions to the understanding of virtual anchor marketing and consumer behavior. First and foremost, this study makes a pioneering contribution by extending the Stimulus-Organism-Response (SOR) theory into the emerging domain of virtual anchor marketing. We identify and validate four distinct virtual anchor characteristics (visibility, Metavoicing, guidance shopping, and trading) as environmental stimuli, expanding beyond traditional marketing stimuli. Unlike previous research that treated environmental stimuli uniformly, our findings reveal that different virtual anchor characteristics trigger distinct consumer response patterns. Specifically, visibility, Metavoicing, and guidance shopping demonstrate consistent positive effects across all emotional pathways, while trading shows limited effectiveness, only working through brand attachment and sense of community. This challenges the universal applicability of SOR theory by demonstrating that in virtual environments, the effectiveness of stimuli varies based on their technological nature. Our work extends Seymour et al.‘s (Seymour et al., 2020) research by providing empirical evidence that virtual stimuli operate differently from traditional marketing cues.

Secondly, this study makes a significant theoretical advancement by being the first to integrate affective schema theory into virtual marketing research systematically. We develop a novel three-level framework (physiological, meaning, and relational) for analyzing consumer emotional responses to virtual entities, extending beyond the single-dimensional approaches prevalent in previous studies (Kumar & Aravamudhan, 2023). The research uniquely captures the progressive development of consumer emotional responses, from initial sensory perception through emotional connection to social bonding, advancing existing theoretical models that typically treat emotional responses as static outcomes. By successfully bridging affective schema theory with digital marketing concepts, we create a new theoretical foundation for understanding emotional responses in virtual environments. This integration provides a more nuanced understanding than previous single-theory approaches.

Thirdly, this study significantly extends brand authenticity theory into the digital realm by challenging traditional authenticity theory and demonstrating that technological features can effectively create and convey brand authenticity, expanding beyond Kühle’s (2020) human-centric conceptualization. Our findings reveal that visual and interactive elements are more crucial in establishing brand authenticity than traditional interpersonal factors in virtual contexts, fundamentally challenging existing theoretical assumptions about authenticity formation. Moreover, we identify important boundary conditions for brand authenticity in virtual environments, specifically highlighting how excessive transaction-focused interactions can undermine authenticity perceptions, a phenomenon not previously identified in the literature.

Furthermore, the research significantly advances the understanding of emotional and community marketing in virtual contexts. We extend existing community theory by demonstrating how technology-mediated interactions can create genuine community bonds, advancing beyond traditional human-centric community models (Hur et al., 2011). Our findings reveal unique mechanisms through which virtual entities can foster emotional attachment, challenging Vlachos et al.’s (2010) assumption that genuine emotional bonds require human presence. Importantly, we identify significant generational differences in virtual marketing effectiveness, particularly highlighting stronger effects among younger consumers, adding a crucial demographic dimension to existing theories.

Finally, this study introduces several methodological advances in researching virtual marketing. We develop and validate a comprehensive methodological framework for analyzing multiple concurrent emotional pathways in virtual marketing contexts. Additionally, we introduce new measurement approaches for quantifying virtual anchor characteristics and their effects, providing valuable tools for future research in this domain. These methodological innovations not only enhance our ability to study virtual marketing phenomena but also provide a robust foundation for future research in digital marketing and consumer behavior.

Practical Implications

Drawing from the research findings, this study offers several prioritized practical recommendations for livestreaming e-commerce platforms and brands to optimize virtual anchor strategies. First and foremost, platforms should focus on enhancing the visibility, Metavoicing, and guidance shopping attributes of virtual anchors, rather than overemphasizing transaction conversion. Platforms should prioritize leveraging technologies such as motion capture, facial recognition, semantic analysis, emotional computing, knowledge graphs, and recommendation algorithms to create vivid, interactive, and intelligently recommending virtual anchors while reasonably controlling promotional frequency and intensity to avoid excessively disturbing the user experience. Secondly, when creating virtual anchor IPs, brands should shape distinct personas that align with the brand tone and target audience, designing personality traits, behavioral styles, and even fashion styles that resonate with the target customer group, seamlessly integrating the virtual anchors with the brand image to become relatable brand ambassadors rather than generic copies.

Moreover, brands should harness virtual anchors to manage brand communities and enhance consumer identification with the brand. By cultivating intimate interactions between virtual anchors and fans and fostering UGC (User-Generated Content) activities such as collaborative creations, brands can accelerate community growth and increase consumer sense of belonging; simultaneously, inviting consumers to participate in refining virtual anchor personas can deepen brand impressions and contribute to brand equity accumulation. Furthermore, the design and marketing strategies of virtual anchors should fully consider the characteristics of the target audience, especially the preferences of young consumer groups. The visual image, content planning, and promotional channels of virtual anchors should cater to the aesthetic tastes, topics of interest, and gathering places of young people, such as short video and social platforms; concurrently, based on brand positioning, differentiated virtual anchors should be developed to provide personalized services for niche groups such as anime enthusiasts or lifestyle pursuers.

Finally, brands need to reasonably define the service boundaries of virtual anchors and avoid over-reliance or improper marketing. Brands should rationally plan the service content and frequency of virtual anchors, setting clear service boundaries for them and avoiding over-promising to manage consumer expectations; simultaneously, human-machine collaboration should be strengthened, matching human customer service based on business complexity to ensure service quality. Additionally, brands must enhance control over the content and timing of virtual anchor marketing, avoiding abrupt selling and truly creating value for consumers, organically combining virtual and real elements, and refraining from blindly treating virtual anchors as a panacea.

By prioritizing key anchor attributes, shaping distinct personas that align with the brand tone, empowering brand communities through virtual anchors, customizing marketing strategies for target audiences, and balancing the usage boundaries of virtual anchors, platforms, and brands can fully leverage the unique advantages of virtual anchors in fostering emotional connections and shaping brand assets, ultimately driving consumer purchases. Progressing step by step and persistently advancing actions consistent with strategic priorities is the key to optimizing the effectiveness of virtual anchor marketing.

Limitations and Future Research

This study has several limitations that future research should address. First, although multiple methods were used to ensure reliability and validity, the data primarily relied on subjective consumer evaluations. Future studies should incorporate objective data (e.g., sales records, interaction data) and diverse methods (e.g., content analysis, experiments) for cross-validation. Second, the model did not consider the moderating effects of consumer characteristics such as demographics, psychological traits, and motivations on virtual anchor acceptance and reactions. Incorporating these variables could provide a more nuanced understanding. Third, the cross-sectional design did not capture dynamic changes in perceptions and attitudes over time. Longitudinal approaches like tracking surveys could reveal how responses evolve as interactions with virtual anchors deepen. Moreover, virtual anchors in livestream e-commerce are still in the early stages, with rapidly evolving technologies and business models. Future research should stay attentive to emerging developments, such as the integration of virtual anchors with VR/AR, 5G, and blockchain, which may lead to innovative applications and consumer experiences. Scholars should expand research topics and perspectives to keep pace with these advancements.

Footnotes

ORCID iD

Yiwei Zhao

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

Abdolmanafi

Winter

Ghorban Jahromi

Farahani

Peixoto

M. M.

Nobre

(2019). Psychometric properties of the Persian version of the Questionnaire of Cognitive Schema Activation in Sexual Context (QCSASC) in unsuccessful sexual situations. Sexual and Relationship Therapy, 34(1), 109–120. https://doi.org/10.1080/14681994.2017.1412418

Agatha

Emilio

Rahman

(2023). Pengaruh flow experience dan parasocial interaction pada Dorongan Pembelian Impulsif di livestreaming commerce. Kajian Branding Indonesia, 5(2), 99. https://doi.org/10.21632/kbi.5.2.99-125

Ahmadian

Sahraei

Khosro

S. K.

(2023). Brand attachment, brand experience, brand image, percieved quality, perceived value, and brand loyalty. Journal of Humanities Social Sciences And Business (JHSSB), 3(1), 257–269. https://doi.org/10.55047/jhssb.v3i1.863

Baker

A. M.

Donthu

Kumar

(2016). Investigating how Word-of-Mouth Conversations about Brands Influence Purchase and Retransmission Intentions. Journal of Marketing Research, 53(2), 225–239. https://doi.org/10.1509/jmr.14.0099

Bergkvist

Bech-Larsen

(2010). Two studies of consequences and actionable antecedents of brand love. Journal of Brand Management, 17(7), 504–518. https://doi.org/10.1057/bm.2010.6

Berryman

Abidin

Leaver

(2021). A topography of virtual influencers. AoIR Selected Papers of Internet Research. https://doi.org/2013

Blanchard

A. L.

Markus

M. L.

(2002). Sense of virtual community—Maintaining the experience of belonging [Conference session]. Proceedings of the 35th Annual Hawaii International Conference on System Sciences, pp. 3566–3575. https://doi.org/10.1109/HICSS.2002.994449

Burnasheva

Suh

Y. G.

Villalobos-Moron

(2019). Sense of community and social identity effect on brand love: Based on the online communities of a luxury fashion brands. Journal of Global Fashion Marketing, 10(1), 50–65. https://doi.org/10.1080/20932685.2018.1558087

Cai

Wohn

D. Y.

Mittal

Sureshbabu

(2018). Utilitarian and hedonic motivations for livestreaming shopping. TVX ’18: Proceedings of the 2018 ACM International Conference on Interactive Experiences for TV and Online Video, Association for Computing Machinery, New York, NY, USA.

10.

Chaihanchanchai

Anantachart

Ruangthanakorn

(2024). Unlocking the persuasive power of virtual influencer on brand trust and purchase intention: A parallel mediation of source credibility. Journal of Marketing Communications, Adavance online publication. https://doi.org/10.1080/13527266.2023.2301390

11.

Chen

Wang

(2022). The influence of virtual brand community on consumer loyalty. Advances in Economics and Management Research, 1(2), 34. https://doi.org/10.56028/aemr.1.2.34

12.

Chen

Yang

(2023). The role of influencers in livestreaming E-commerce: Influencer trust, attachment, and consumer purchase intention. Journal of Theoretical and Applied Electronic Commerce Research, 18(3), 1601–1618. https://doi.org/10.3390/jtaer18030081

13.

Curtis

Arnaud

Waguespack

B. P.

(2017). Advertising effect on consumer emotions, judgements, and purchase intent. Asian Journal of Business Research, 7(2), 57–73. https://doi.org/10.14707/ajbr.170037

14.

Davlembayeva

Chari

Papagiannidis

(2024). Virtual influencers in consumer behaviour: A social influence theory perspective. British Journal of Management, 36(1), 202–222 https://doi.org/10.1111/1467-8551.12839

15.

Dodds

W. B.

Monroe

K. B.

Grewal

(1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of Marketing Research, 28(3), 307–319. https://doi.org/10.1177/002224379102800305

16.

Dong

Wang

(2018). Social tie formation in Chinese online social commerce: The role of IT affordances. International Journal of Information Management, 42, 49–64. https://doi.org/10.1016/j.ijinfomgt.2018.06.002

17.

Eroglu

S. A.

Machleit

K. A.

Davis

L. M.

(2001). Atmospheric qualities of online retailing. Journal of Business Research, 54(2), 177–184. https://doi.org/10.1016/S0148-2963(99)00087-9

18.

Fornell

Larcker

D. F.

(1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.1177/002224378101800313

19.

Fritz

Schoenmueller

Bruhn

(2017). Authenticity in branding – exploring antecedents and consequences of brand authenticity. European Journal of Marketing, 51(2), 324–348. https://doi.org/10.1108/EJM-10-2014-0633

20.

Gao

(2023). The rise of virtual livestream salespeople in 2023 left nothing behind. The Paper. https://www.thepaper.cn/newsDetail_forward_25782098

21.

Ghorbanzadeh

Rahehagh

(2021). Emotional brand attachment and brand love: The emotional bridges in the process of transition from satisfaction to loyalty. Rajagiri Management Journal, 15(1), 16–38. https://doi.org/10.1108/RAMJ-05-2020-0024

22.

Global Marketing Service Exchange. (2023). 2023 Annual development of Douyin E-commerce industrial belt. http://m.gmse.com.cn/new/show-615.html

23.

Gunawan

Yasser Iqbal Daulay

(2024). Celebrity Entrepreneurship: Understanding effect of self congruence, credibility, social media interaction and brand attachment in celebrity product purchase intention. Proceedings of International Conference on Finance Economics and Business (ICOFEB), 1, 0010. https://doi.org/10.29103/icofeb.v1i-.438

24.

Hair

J. F.

(2009). Multivariate data analysis. Pearson Prentice Hall. https://doi.org/10.1007/978-3-642-04898-2_395

25.

Hair Jr

J. F.

Hult

G. T. M.

Ringle

Sarstedt

(2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

26.

Hur

Ahn

Kim

(2011). Building brand loyalty through managing brand community commitment. Management Decision, 49(7), 1194–1213. https://doi.org/10.1108/00251741111151217

27.

İNal

BiL

(2023). Mediating role of consumer purchase intention on the relationship between influencer’s perceived characteristics and consumer Purchase behavior. Yönetim Bilimleri Dergisi. https://doi.org/10.35408/comuybd.1359816

28.

iResearch. (2024). 2023 China livestream E-commerce industry research report. https://www.jiemian.com/article/9224815.html

29.

Kang

Shin

D.-H.

(2016). The effect of customers’ perceived benefits on virtual brand community loyalty. Online Information Review, 40(3), 298–315. https://doi.org/10.1108/OIR-09-2015-0300

30.

Kim

D. Y.

Kim

H.-Y.

(2023). Social media influencers as human brands: An interactive marketing perspective. Journal of Research in Interactive Marketing, 17(1), 94–109. https://doi.org/10.1108/JRIM-08-2021-0200

31.

Kühle

(2020). The emotional dimension to sensory perception. In Gatzia

D. E.

Brogaard

(Eds.), The epistemology of non-visual perception (1st ed., pp. 236–255). Oxford University Press. https://doi.org/10.1093/oso/9780190648916.003.0011

32.

Kumar

Aravamudhan

(2023). Investigating the effects of virtual personas on brand image. In Business, Management and Economics (Vol. 0). IntechOpen. https://doi.org/10.5772/intechopen.112704

33.

Zhang

(2024). Research on the application of virtual anchors based on artificial intelligence technology in livestreaming e-commerce. Journal of Electrical Systems, 20(3), 1232–1240. https://doi.org/10.52783/jes.3529

34.

(2023). The impact of E-commerce livestreaming on the purchasing behavior of Chinese Nationals. Advances in Economics, Management and Political Sciences, 24(1), 137–143. https://doi.org/10.54254/2754-1169/24/20230426

35.

Lin

F.-L.

T.-H.

(2023). Effect of digital brand experience on luxury fashion brand authenticity, attachment and loyalty. South African Journal of Business Management, 54(1), 1–8. https://doi.org/10.4102/sajbm.v54i1.3583

36.

Lodish

L. M.

Mela

C. F.

(2007). If brands are built over years, why are they managed over quarters? Harvard Business Review, 85(7/8), 104.

37.

Lou

Kiew

S. T. J.

Chen

Lee

T. Y. M.

Ong

J. E. C.

Phua

(2023). Authentically fake? How consumers respond to the influence of virtual influencers. Journal of Advertising, 52(4), 540–557. https://doi.org/10.1080/00913367.2022.2149641

38.

Lou

Yuan

(2019). Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19(1), 58–73. https://doi.org/10.1080/15252019.2018.1533501

39.

Zhao

Wang

(2010). From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers’ purchase intention. Electronic Commerce Research and Applications, 9(4), 346–360. https://doi.org/10.1016/j.elerap.2009.07.003

40.

McMillan

D. W.

Chavis

D. M.

(1986). Sense of community: A definition and theory. Journal of Community Psychology, 14(1), 6–23. https://doi.org/10.1002/1520-6629(198601)14:1<6::AID-JCOP2290140103>3.0.CO;2-I

41.

Morhart

Malär

Guèvremont

Girardin

Grohmann

(2015). Brand authenticity: An integrative framework and measurement scale. Journal of Consumer Psychology, 25(2), 200–218. https://doi.org/10.1016/j.jcps.2014.11.006

42.

Moulard

J. G.

Raggio

R. D.

Folse

J. A. G.

(2016). Brand authenticity: Testing the antecedents and outcomes of brand management’s passion for its products. Psychology & Marketing, 33(6), 421–436. https://doi.org/10.1002/mar.20888

43.

Otero-Gómez

M. C.

Giraldo-Pérez

(2021). Los consumidores y la co-creación de valor: El caso de una comunidad de marca virtual. Revista CEA, 7(15), e1847. https://doi.org/10.22430/24223182.1847

44.

Park

C. W.

Macinnis

D. J.

Priester

Eisingerich

A. B.

Iacobucci

(2010). Brand attachment and brand attitude strength: Conceptual and empirical differentiation of two critical brand equity drivers. Journal of Marketing, 74(6), 1–17. https://doi.org/10.1509/jmkg.74.6.1

45.

Proctor

(2020). The social production of internet space: affordance, programming, and virtuality. Critical Theory, 22(3), 1–19. https://doi.org/10.1093/ct/qtz036

46.

Qian

(2023). 2023 E-commerce industry analysis report: Current status and future trends of douyin e-commerce. https://www.qianzhan.com/analyst/detail/220/231227-8b34c170.html

47.

Schouten

A. P.

Janssen

Verspaget

(2020). Celebrity vs. Influencer endorsements in advertising: The role of identification, credibility, and Product-Endorser fit. International Journal of Advertising, 39(2), 258–281. https://doi.org/10.1080/02650487.2019.1634898

48.

Seymour

Yuan

Dennis

Riemer

(2020). Facing the Artificial: Understanding Affinity, Trustworthiness, and Preference for More Realistic Digital Humans [Conference session]. Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2020.574

49.

Shi

Dou

(2023). How broadcasters enhance rapport with viewers in livestreaming commerce: A genre-based discourse analysis. Pragmatics. Quarterly Publication of the International Pragmatics Association (IPrA), 33(4), 592–617. https://doi.org/10.1075/prag.22009.shi

50.

Shiau

W.-L.

Zhou

Liu

(2022). Understanding the formation mechanism of consumers’ behavioral intention on Double 11 shopping carnival: Integrating S-O-R and ELM theories. Frontiers in Psychology, 13, 984272. https://doi.org/10.3389/fpsyg.2022.984272

51.

Song

(2021). Facial expression emotion recognition model integrating philosophy and machine learning theory. Frontiers in Psychology, 12, 759485. https://doi.org/10.3389/fpsyg.2021.759485

52.

Souki

G. Q.

Chinelato

F. B.

Gonçalves Filho

(2022). Sharing is entertaining: The impact of consumer values on video sharing and brand equity. Journal of Research in Interactive Marketing, 16(1), 118–136. https://doi.org/10.1108/JRIM-03-2020-0057

53.

Spears

Singh

S. N.

(2004). Measuring Attitude toward the Brand and Purchase Intentions. Journal of Current Issues & Research in Advertising, 26(2), 53–66. https://doi.org/10.1080/10641734.2004.10505164

54.

Sun

Shao

Guo

Nie

(2019). How livestreaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 37, 100886. https://doi.org/10.1016/j.elerap.2019.100886

55.

Tabesh

Zia-Tohidi

Firoozi

Farahani

(2024). Decomposing the variance in early maladaptive schemas: The major role of one general factor, the minor role of domains, and their differential relations to facial emotion recognition. Frontiers in Psychology, 15, 1342480. https://doi.org/10.3389/fpsyg.2024.1342480

56.

Tarabieh

Gil

Galdón Salvador

J. L.

Faisal Ahmad AlFraihat

(2024). The new game of online marketing: How social media influencers drive online repurchase intention through brand trust and customer brand engagement. Intangible Capital, 20(1), 103. https://doi.org/10.3926/ic.2515

57.

Thomson

MacInnis

D. J.

Whan Park

(2005). The Ties That Bind: Measuring the Strength of Consumers’ Emotional Attachments to Brands. Journal of Consumer Psychology, 15(1), 77–91. https://doi.org/10.1207/s15327663jcp1501_10

58.

Tirch

Schoendorff

Silberstein

L. R.

(2014). The ACT practitioner’s guide to the science of compassion: Tools for fostering psychological flexibility. New Harbinger Publications.

59.

Van Reijmersdal

E. A.

Fransen

M. L.

Van Noort

Opree

S. J.

Vandeberg

Reusch

Van Lieshout

Boerman

S. C

. (2016). Effects of disclosing sponsored content in blogs: how the use of resistance strategies mediates effects on persuasion. American Behavioral Scientist, 60(12), 1458–1474. https://doi.org/10.1177/0002764216660141

60.

Vlachos

P. A.

Theotokis

Pramatari

Vrechopoulos

(2010). Consumer-retailer emotional attachment: Some antecedents and the moderating role of attachment anxiety. European Journal of Marketing, 44(9/10), 1478–1499. https://doi.org/10.1108/03090561011062934

61.

Vrtana

Krizanova

(2023). The power of emotional advertising appeals: Examining their influence on consumer purchasing behavior and brand–customer relationship. Sustainability, 15(18), 13337. https://doi.org/10.3390/su151813337

62.

Wang

(2024). E-commerce livestreaming interaction quality, immersion experience and consumer purchase intention. Applied Mathematics and Nonlinear Sciences, 9(1), 20241401. https://doi.org/10.2478/amns-2024-1401

63.

Wang

Hajli

(2014). Co-creation in branding through social commerce: The role of social support, relationship quality and privacy concerns. roceedings of twentieth Americas conference on information systems, Savannah, Georgia.

64.

Wongkitrungrueng

Assarut

(2020). The role of livestreaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117, 543–556. https://doi.org/10.1016/j.jbusres.2018.08.032

65.

Liu

Chen

Tong

(2023). The effect of E-commerce virtual live streamer socialness on consumers’ experiential value: An empirical study based on Chinese E-commerce livestreaming studios. Journal of Research in Interactive Marketing, 17(5), 714–733. https://doi.org/10.1108/JRIM-09-2022-0265

66.

Lee

Wang

Wen

Kim

(2022). A study on the effects of the influencer characteristics on purchase intention. Asia-Pacific Journal of Convergent Research Interchange, 8(3), 27–36. https://doi.org/10.47116/apjcri.2022.03.03

67.

Cui

Lyu

(2022). Influence of streamer’s social capital on purchase intention in livestreaming E-commerce. Frontiers in Psychology, 12, 748172. https://doi.org/10.3389/fpsyg.2021.748172

68.

J.-H.

(2020). What drives consumer shopping behavior in livestreaming commerce? Journal of Electronic Commerce Research, 21(3), 144–167.

69.

Zhai

Chen

(2023). How do relational bonds affect user engagement in e-commerce livestreaming? The mediating role of trust. Journal of Retailing and Consumer Services, 71, 103239. https://doi.org/10.1016/j.jretconser.2022.103239

70.

Zhang

(2023). Product presentation in the live-streaming context: The effect of consumer perceived product value and time pressure on consumer’s purchase intention. Frontiers in Psychology, 14, 1124675. https://doi.org/10.3389/fpsyg.2023.1124675

71.

Zhao

Zhou

Wang

Gao

(2023). Prediction of purchase decision-making on digital livestreaming Interface on MT: A comparative research of multi-modal Human-Machine Interaction (

) [Conference session]. 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). https://doi.org/10.54941/ahfe1003439

72.

Zhou

(2023). Research on the communication of virtual anchors in E-commerce platforms from the perspective of Embodied Theory—Taking the livestreaming sales of the virtual idol Luo Tianyi as an example. New Media and Mass Communication. https://doi.org/10.7176/NMMC/104-05

Digital Emotional Bonds: How Virtual Anchor Characteristics Drive User Purchase Intention in Livestreaming E-commerce

Abstract

Plain Language Summary

Keywords

Introduction

Theoretical Foundation

Stimulus-Organism-Response Theory

Affordance Theory

Affective Schema Theory

Research Model and Hypotheses

Research Model

Hypothesis Development

Physiological Level of Positive Affective Schema: Brand Authenticity

Meaning Level of Positive Affective Schema: Brand Attachment

Relational Level of Positive Affective Schema: Sense of Community

Purchase Intention as Organism Response (R)

Brand Authenticity and Purchase Intention

Brand Attachment and Purchase Intention

Sense of Community and Purchase Intention

Methods

Questionnaire Design and Variable Measurement

Research Subjects and Design

Data Analysis

Results

Measurement Model

Reliability and Validity Analysis

Discriminant Validity

Structural Equation Model

Path Coefficient Significance

Mediation Effect Analysis

Discussion and Implications

Discussion of Key Findings

Theoretical Implications

Practical Implications

Limitations and Future Research

Footnotes

ORCID iD

Funding

Declaration of Conflicting Interests

Data Availability Statement

References