Therefore, to promote businesses to use AI influencers, in this research paper, the authors investigated the relationships between AI influencers' anthropomorphism and purchase intention
INTRODUCTION
R esearch background and statement of the problem
In the 4.0 era, the rapid advancement of artificial intelligence (AI) has sparked significant digital transformation, introducing innovative technologies such as avatars and content-generation AI that enhance social connectivity (Ahn et al., 2022) This period, often referred to as "The era of AI," showcases the potential applications of AI across various sectors, including business, healthcare, and transportation, with a particular emphasis on marketing Marketing has evolved beyond traditional advertising to harness the power of influential figures on social media platforms.
Influencer Marketing is a strategic approach where influencers promote a company's products or services, significantly enhancing brand image and customer purchasing behavior This method has been effectively utilized by hotels, restaurants, and tourist attractions, leading to improved financial performance (Zhang and Wei, 2021) Recently, it has gained traction among businesses targeting younger demographics, with a report indicating that 72% of Vietnamese youth trust influencer recommendations The influencer marketing industry reached $21.1 billion in 2023, up 29% from $16.4 billion the previous year, according to Influencer Marketing Hub Furthermore, research by Vrontis revealed that nearly half of internet users followed at least one influencer in 2019, with 40% having made purchases influenced by content on Instagram or YouTube.
In recent years, an increasing number of businesses, including small and medium-sized enterprises (SMEs) and family-owned firms, have turned to influencers for promoting their products and services on social media This trend is not exclusive to multinational corporations, highlighting the widespread adoption of influencer marketing across various sectors (Obermayer et al., 2022).
The influencer marketing industry is experiencing significant growth, yet it faces notable challenges According to the Influencer Marketing Hub Report, approximately 67% of brands express concerns about influencer fraud, while nearly 30% of marketers struggle to measure the ROI of their campaigns Moreover, even well-known celebrities can tarnish a brand's reputation overnight due to public scandals However, the rise of Artificial Intelligence (AI) technologies presents a new opportunity in the marketing landscape, particularly with the emergence of AI influencers (Jang, 2022).
AI Influencers, or computer-generated personas, have surged in popularity on online platforms, attracting a significant following (Miyake, 2023) They offer unique advantages over human influencers, including constant availability, easy management, and reduced scandal risk (Ameen et al., 2023; Drenten & Brooks, 2020; Duffy & Hund, 2019) For brands, AI Influencers provide exceptional adaptability, the ability to foster robust brand communities (Sands, Ferraro, et al., 2022), and endless storytelling opportunities (Moustakas et al., 2020) Notably, they achieve higher engagement rates, averaging 2.84% compared to 1.72% for human influencers (Influencer Marketing Hub, 2023) Consequently, AI Influencers are seen as innovative extensions of influencer marketing (Laszkiewicz et al., 2023) Major brands like Chanel, Calvin Klein, and Prada have collaborated with Lil Miquela, a prominent AI Influencer with around 8 million followers, who has also been recognized as one of Time Magazine’s “Most Influential People on the Internet” (TIME).
In recent years, Vietnamese businesses have increasingly embraced AI Influencers for their marketing strategies Notably, in 2020, Toe Tien A.I Clear Head emerged as Vietnam's first virtual brand ambassador for the Clear brand, uniquely modeled after the real-life singer Toe Tien Additionally, the country's first virtual model, “E.M,” was launched at the end of 2020, followed by Vic, the first virtual finance expert from VIB International Bank, introduced in August 2022 These developments highlight how businesses are adapting to trends and recognizing the significant advantages of incorporating AI Influencers into their marketing campaigns.
The rapid growth of AI Influencers in virtual marketing presents significant challenges for brand marketers in understanding customer responses to these digital entities It is crucial to investigate the factors shaping Vietnamese customers' perceptions of AI Influencers, as existing research is limited and often overlooks the impact of these perceptions on marketing strategies With Vietnam's increasing population of tech-savvy individuals, particularly among the 18-34 age group—which constitutes a substantial portion of social media users—this demographic is vital for brands Gen Y and Gen Z, known for their mobile-oriented habits, primarily engage with social media through smartphones and apps Therefore, the study titled "AI Influencers in Marketing: How AI Influencers’ Anthropomorphism Impacts on Consumer Intention among Young People in Ho Chi Minh City" aims to explore the opportunities and challenges that AI Influencers present in the Vietnamese market.
This study seeks to enhance the limited research on customer perceptions regarding the effectiveness of AI Influencer marketing Specifically, it aims to provide empirical insights into how consumers view the impact of AI-driven influencers in marketing strategies.
1 Identify the relationships between AI Influencers Anthropomorphism and Purchase Intention.
2 Measure the impact of AI Influencers’ Anthropomorphism and other mediators on young customers' Purchase Intention (18-34 years old) in Ho Chi Minh City.
3 Show the potential and provide recommendations for enterprises in Vietnam to apply AI Influencers in marketing strategy in the future.
R esearch objects
Research subjects: How AI Influencers' Anthropomorphism Impacts on Consumer Intention through AI Influencers’ Credibility as a mediator.
Survey subjects: Young people in Ho Chi Minh City already know about AI Influencers. Ỉ.3.2 Scope of study
About space: This survey was performed in Ho Chi Minh City Vietnam.
About lime: Data was collected in 7 days in 2024 ( 31st January & 6th February
2024) In total, the research was performed from the end of January 2024 to the middle of February 2024.
The study employs quantitative research methods, beginning with data collection through a questionnaire created on Khaosat.mc, which was distributed to friends and acquaintances in Ho Chi Minh City via social media platforms such as Facebook, Instagram, and Zalo Once the required sample size was achieved, the data was analyzed using SPSS 25 and AMOS 24 software The authors evaluated the impact of AI Influencers on the purchasing intentions of young consumers in Ho Chi Minh City, ultimately drawing conclusions that offer management implications and actionable solutions for businesses to enhance the effectiveness of AI Influencers in their marketing strategies.
The research includes 5 chapters with specific contents as below:
Chapter 01: Introduction: This chapter presents the reason for forming the research topic, and also provides the goals, questions, subjects, and scope of the research on purchase intention ofproducts endorsed by Al Influencers of young people in Ho Chi Minh City and factors impact on it.
Chapter 02: Literature review and hypothesis development: This section presents the theoretical basis and previous research related to the research topic - AI Influencers, thereby synthesizing and providing an appropriate research model.
Chapter 03: Research methodology: This chapter demonstrates the method used for the current thesis, consisting of the research process, measurement scale, questionnaire design, sample and data collection.
Chapter 04: Data analysis and results: This section delves into the data, examining demographic characteristics of the sample, assessing measurement scales, and finally, analyzing the proposed structural model.
Chapter 05: Discussion and conclusion: This concluding chapter critically examines the research’s central findings, acknowledging its inherent limitations and proposing valuable recommendations for future investigations
LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT 12 2.1 R elevant theories
T he rise of AI I nfluencers
Social media has become an essential part of daily life, leading to the rise of social media influencers as a key advertising strategy The Collins dictionary defines an influencer as "any person or thing that exerts an influence," which includes AI Influencers—computer-generated characters that exist solely online and mimic human behavior These AI Influencers not only have human-like appearances but can also express opinions and engage in activities typical of real people A 2019 survey revealed that 42% of Gen Z and millennials followed influencers without realizing they were AI creations Since AI Influencers are virtual and free from human issues like aging or scandals, they offer brands a reliable marketing alternative Major companies across various industries, including McDonald's, LVMH, Samsung, and Porsche, have successfully integrated AI Influencers into their advertising campaigns, showcasing their unique advantages and establishing them as a viable substitute for human influencers.
D efinitions
According to Dabiran et al (2022), anthropomorphism plays a vital role in social media marketing for AI influencers This concept involves attributing human-like traits to non-human entities, such as animals, AI systems, and robots (Guthrie, 1993; Epley et al., 2007) The integration of human-like features—such as voice, motion, and appearance—enhances the anthropomorphic qualities of technological agents (Kim et al., 2023) Consequently, the level of human likeness serves as a measurable indicator of an entity's attributes and personalities, making it a valuable focus in modern research (Gammoh et al., 2018; Tsai et al., 2021).
Arsenyan and Mirowska (2021) investigated the different levels of anthropomorphism in AI influencers, finding that while some resemble realistic humans, others take on cartoonish features with exaggerated facial traits Their study highlighted that certain virtual influencers go beyond conventional human-like appearances, suggesting that the design diversity among AI influencers significantly influences audience perception, particularly with human-like representations being more susceptible to anthropomorphism.
Social presence refers to an individual's perception of connection with others, influenced by various factors According to Savicki and Kelley (2000), this perception can arise from experiences like hearing a voice on the phone or watching someone on television (Lee, 2004) Key elements that enhance social presence include technology-related aspects such as modality, content, and vividness, as well as user characteristics like personality and social dynamics (Lee).
& Nass, 2005; Lombard & Ditton, 1997) Social presence is also termed “co-presence” by
According to Biocca et al (2003), social presence refers to the experience of being in the same environment as others through computer-mediated communication, even without physical interaction (Grewal et al., 2020) The rise of humanoid robots and digital assistants has made human-robot interaction increasingly prevalent in society, highlighting the importance of social presence in these interactions (Kim & Park, 2024).
Parasocial Interaction (PSI) refers to the one-sided imaginative relationship that audiences form with media characters, a concept introduced by Horton and Wohl in 1956 Although inherently one-sided, PSI allows individuals to experience a sense of connection and kinship with their chosen media figures, creating an illusion of a real-world relationship (Ferchaud et al., 2018; Hartmann et al., 2008; Horton & Wohl).
Over time, viewers develop imaginary interpersonal relationships with media characters, resembling friendships and intimacy This ongoing exposure leads to realistic psychological engagement and mutual awareness, akin to feelings experienced toward friends in real life Social media influencers, like other media figures, effectively cultivate parasocial interactions (PSI) with their followers through direct and consistent communication Audiences can engage instantly by commenting, liking, and sharing, which fosters a sense of connection Notably, even virtual influencers can evoke similar PSI dynamics as their human counterparts, demonstrating the powerful nature of these interactions.
The Source Credibility Theory (SCT) posits that individuals are more likely to be persuaded by sources that exhibit credibility (Hovland et al., 1953) This theory highlights that higher source credibility enhances persuasiveness, making it a crucial factor in consumer acceptance of influencers Source credibility encompasses positive attributes that establish a source as trustworthy for delivering accurate and valuable information (Ohanian, 1990) It comprises three key dimensions: trustworthiness, expertise, and attractiveness, with trustworthiness specifically relating to the honesty, integrity, and reliability of AI influencers (Ohanian, 1990).
1990) Expertise refers to the knowledge, skill, and competence of AI Influencer (Ohanian,
Attractiveness, which encompasses physical appearance, charisma, and likability, plays a significant role in the effectiveness of AI influencers (Ohanian, 1990) Moreover, the credibility of these influencers is essential in cultivating positive consumer attitudes towards their recommendations (Belanche et al., 2021) When consumers trust an AI influencer, they are more inclined to take their product endorsements seriously, leading to increased acceptance of the promoted brands (Hu et al., 2019) Research by Shamim and Islam (2022) further indicates that the credibility of AI influencers significantly enhances purchasing intent within social media commerce.
2.3.5 Mimetic Desire rhe consumer's doppelganger effect (Ruvio et al., 2013) refers to the ability of mimetic desire to influence a customer's purchasing decision According to this effect, individuals who have followed a person and perceive that person as a role model tend to imitate that person's consumption behaviors because they love and want to look, behave like that person (Ruvio et al., 2013) Therefore, when individuals harbor close sentiments towards others, they demonstrate a strong inclination to emulate the consumption behaviors ofthose individuals (Ruvio et al., 2013) And that's how influencer marketing works, social media influencers not only amass a large following but also possess the ability to reinforce the mimetic behaviors oftheir followers (Okazaki et al., 2014) Prominent influencers share their lifestyles with followers and showcase their desirable traits in social media communities As a result, imitation frequently occurs among followers if influencers' posts reflect the desired personal attributes, as the essence of mimetic desire lies not only in the desire to resemble others but also in acquiring the desirable qualities of others (Elliott,1997).
Brand trust is the consumer's confidence in a brand's ability to fulfill its promises (Chaudhuri & Holbrook, 2001) It helps reduce consumer vulnerability by enhancing the perception of brand credibility (Doney & Cannon, 1997) Trust can be built through direct interactions with the brand or via social media platforms (Habibi et al., 2014), leading to a stronger consumer-brand relationship Additionally, brands can benefit from trust transfer, where positive experiences with brand representatives, such as influencers and marketers, contribute to building trust in the brand itself (Liu et al., 2018).
Faced with uncertainty, Luhmann (1989) points out that brand trust acts as a mental shortcut, simplifying customers' choices Therefore, the more credibility the customer feels the more brand trust they perceive.
Intention is often used to understand how attitudes translate into behavior hinges on examining an individual's intention to act (Huang et al., 2004) Similarly, Stevenson el al.,
Customer intentions play a crucial role in influencing their purchasing actions, as they reflect the belief that positive attitudes towards a product or service will result in actual purchases (Bergkvist et al., 2016) This concept also encompasses how perceived reactions to a product can create opportunities for buying (Dodds et al., 1991) Additionally, factors such as a favorable attitude towards a brand (Wang et al., 2019) and the appeal of endorsers significantly impact consumers' purchase intentions (Osei-Frimpong et al., 2019).
This study examines the purchase intention of users in relation to products or services promoted by influencers, focusing specifically on the impact of Al Influencers on the young demographic in Ho Chi Minh City.
PRIOR relevant studies
Table 2 J Summary of prior research
Participants perceived more human-like influencers as conscious and interacted more, leading to better similar branding outcomes as a real human in brand perception and purchase intention.
Virtual influencers' perceived anthropomor phism
Cognitive Response (Perceived Usefulness, Credibility);
Affective response (Perceived Ẹnoymcnt, Flow)
Human-like virtual influence boosts consumer satisfaction with ads, thanks to their positively perceived authenticity and relatability, which make AI influencers become a key marketing tool.
Parasocial interaction, self congruity, perceived authenticity
Source credibility of influencer (Trustworthiness, Expertise),
Destination brand trust, Trust in
The noble point of this study is to examine how parasocial interaction and self- congruity further influence influencer credibility.
Physical attractiveness , attitude homophily, social attractiveness
This study shows how credibility, parasocial interaction, and influencer traits like shared values, appearances, and social appeal influence purchase intention.
Influencer credibility, Brand trust/Audicncc comments
This study reveals how parasocial relationships with an influencer can ultimately translate into brand trust, mitigating purchase uncertainty associated with the endorsed brand
Influencer credibility (expertise, trustworthiness, attractiveness, authenticity) /Interactivity
This study explores how virtual influencers with human-like interactions can improve their credibility and how credibility influences brand reputation which makes CSR messages more effective.
Design of multi-product category c- commerce based on specialist generalist allribulion via self introduction and assignment to dedicated product zones
Perceived social presence of website Perceived message trustworthiness Website trust (Ability,
The study explores how social cues (like self-introduction and dedicated zones) designed for virtual product advisors impact positively user- pcrccivcd expertise and trustworthiness via a multi-category e commerce website.
R esearch framework and hypothesis development
Intention to follow the account;
Intention to follow the advice;
Intention to recommend the influencer.
The present study aims to clarify how Instagram-based influencers might strengthen their relationships with their followers through the followers' reactions toward the influencer.
Product-endorser fit with the brand product
This research aims to gain a more profound comprehension ofhow the appeal of virtual influencers can trigger consumer imitation, foster a robust connection to the brand, and impact buying choices.
2.5.1 The relationship between A! Influencers9 Anthropomorphism and Social Presence
Social presence and anthropomorphism are closely linked, with studies showing a positive correlation between them (Blut et al., 2021) Research by Nowak and Biocca (2003) highlights anthropomorphism as a crucial factor in enhancing social presence, indicating that human-like attributes promote a stronger sense of interaction.
Research indicates that human-like attributes can foster a sense of social presence, primarily through non-verbal cues such as facial expressions, voice, body language, and friendliness (1997) Tsai et al (2021) discovered that chatbots featuring anthropomorphic traits significantly enhance social presence and generate positive consumer responses Similarly, Munnukka et al (2022) found that robot anthropomorphism markedly increases social presence Hyper-realistic AI influencers, such as Miquela, exemplify high levels of anthropomorphism in both appearance and behavior, aligning with users' evolving expectations and further enhancing social presence Based on these findings, the authors propose the following hypotheses.
Hl: AI Influencers' Anthropomorphism positively impacts on Social Presence.
2.5.2 The relationship between AI Influencers’ Anthropomorphism and Parasocial
The Computers are Social Actors (CASA) paradigm, proposed by Nass et al (2000), posits that humans instinctively apply social norms to computers due to their human-like attributes such as language and interactivity This framework allows for the examination of how human-like AI influencers impact Parasocial Interaction (PSI) with their followers through anthropomorphism (Ruijten et al., 2018) PSI refers to the psychological bonds audiences form with media figures, including celebrities and influencers, and is often assessed through inquiries about the intent to engage with these characters This connection reflects the social motivations behind anthropomorphism, as explored in the research by Ma and Li (2024).
In 2024, respondents indicated that they experienced greater anthropomorphism when interacting with more humanlike virtual influencers, which resulted in enhanced parasocial interactions, improved brand attitudes, and increased purchase intentions Based on these findings, the authors propose the following hypothesis.
H2: Al Influencers’ Anthropomorphism positively impacts on Parasocial Interaction.
2.5.3 The Relationship between Social Presence and AI Influencers ’ Credibility
Research by Chung et al (2020) indicates that an agent's communication skills significantly enhance its perceived credibility, with more effective communicators achieving higher credibility ratings Similarly, Kim et al (2022) explored the impact of an instructional agent's voice and identified a correlation between perceived social presence and credibility, where increased social presence leads to greater perceived credibility Their findings revealed that when an AI newscaster is perceived to possess high social presence, it correlates with elevated credibility, a stronger intent to seek information, and enhanced behavioral intentions during weather forecasts Consequently, the authors proposed several hypotheses based on these insights.
H3: Social Presence positively impacts on AI Influencers' Credibility.
2.5.4 The relationship between AI Influencers ’ Anthropomorphism and AI Influencers ’ Credibility
Credibility, distinct from subjective emotional factors in social influence, encompasses expertise, reliability, trustworthiness, attractiveness, and activity (Gass, 2015; Giffin, 1967) AI influencers with high physical attractiveness and trustworthiness are likely to foster positive consumer perceptions Trustworthiness is a critical factor in shaping consumer attitudes towards AI agents, potentially leading to favorable outcomes in human-computer interaction (Song et al., 2022) The credibility of virtual influencers is further demonstrated through their authenticity in endorsing products, which enhances consumer trust (Alboqami, 2023) Additionally, research indicates a positive correlation between the human likeness of computer-generated avatars and their perceived credibility (Nowak & Rauh, 2005) This relationship between anthropomorphic virtual influencers and credibility is supported by the "Computers are Social Actors" theory, establishing a foundation for the current study's hypothesis.
H4: AI Influencers' Anthropomorphism positively impacts on the AI Influencers'Credibility.
2.5.5 The Relationship between Parasocial Interaction and AI Influencers' Credibility
Research indicates that as parasocial interaction (PSI) with influencers increases, so does their perceived credibility, including trustworthiness, attractiveness, and expertise (Reinikainen et al., 2020; Munnukka et al., 2019; Djafarova and Rushworth, 2017) Consumers' positive perceptions of influencers' credibility are significantly influenced by the parasocial relationships they develop (Ding & Qiu, 2017) Additionally, influencers facilitate a heightened level of parasocial interaction, leading to long-term associations with AI influencers, which positions them as credible sources and opinion leaders (Dibble et al., 2016) Based on these findings, the authors propose the following hypothesis.
H5 Parasocial Interaction positively impacts on the AI Influencers’ Credibility.
2.5.6 The Relationship between AI Influencers' Credibility and Mimetic Desire
Social learning theory, introduced by Bandura in 1977, suggests that individuals learn by imitating the attitudes, behaviors, and values of others, influenced by cognitive and environmental factors This imitation extends to how consumers select products, brands, or stores, often viewing credible figures as role models Influencers play a crucial role in shaping their followers' purchasing decisions, as their suggestions can significantly impact consumer behavior Consequently, the credibility of AI influencers is directly linked to the likelihood of inspiring mimetic desire among consumers Based on these insights, the authors propose the following hypothesis.
H6: AI Influencers’ Credibility positively impacts on Mimetic Desire.
2.5.7 The Relationship between AI Influencers' Credibility and Brand Trust
Brand trust refers to the confidence consumers place in a brand's capability to fulfill its promises, a vital component for building robust relationships between brands and their customers.
Research indicates that the credibility of endorsements significantly enhances consumer trust in brands Spry et al (2011) highlighted that credible endorsements can positively influence brand perception, while Chung and Cho (2017) found that a spokesperson's credibility directly affects consumer trust in the endorsed brand Additionally, Leite & Baptista (2022) demonstrated that the credibility of social media influencers positively impacts trust in new body lotion brands in the beauty and fashion sectors Collectively, these studies emphasize the strong link between influencer credibility and brand trust.
2015) The final hypothesis is therefore suggested as follows:
H7 AI Influencers’ Credibility positively impacts on Brand Trust.
2.5.8 The Relationship between Mimetic Desire and Purchase Intention
The doppelganger effect theory posits that individuals consciously emulate the consumption choices of admired figures to fulfill their desire for resemblance (Ruvio et al., 2013) This behavior aligns with social comparison theory, which illustrates how consumers evaluate themselves against others to shape their social identities (Festinger, 1957; Hogg, 2000) These theories highlight the established tendency of individuals to compare themselves with peers, influencing their behaviors and life choices Furthermore, mimetic desire can provide consumers with valuable insights, such as attentiveness and trust, aiding their purchasing decisions (Darani et al., 2023) Based on these findings, the authors propose a hypothesis.
H8: Mimetic Desire positively impacts on Purchase Intention.
2.5.9 The Relationship between AI Influencers’ Credibility and Purchase Intention
In online environments, the freedom to express feelings necessitates the expertise and trustworthiness of contributors, influencing customers' decisions to adopt or reject information (Cheung et al., 2008) Ohanian (1990) highlights that source credibility—comprised of trustworthiness, attractiveness, and perceived expertise—affects audience outcomes, including purchase intention (Gunawan and Huarng, 2015) Additionally, Gong and Li (2017) found that influencer credibility mediates the effectiveness of endorsements, impacting attitudes towards advertising and products, as well as purchase intentions Furthermore, established research indicates a direct positive relationship between source credibility and purchase intention (Wang et al., 2017) Thus, the research hypothesis is established.
H9: AI Influencers' Credibility positively impacts on Purchase Intention.
2.5.10 The Relationship between Brand Trust and Purchase Intention
Research indicates that brand trust significantly enhances the intention to purchase, fostering customer loyalty and driving sales (Aydin et al., 2014; Sanny et al., 2020; Chaudhuri & Holbrook, 2001) Additionally, brand trust serves as a crucial moderating factor in sales promotions, further shaping consumers' purchasing decisions (Soni & Verghese).
Brand trust is crucial in shaping purchase intentions, as it reflects consumers' willingness to buy goods or services from a specific brand (Dodds et al., 1991) It helps reduce uncertainty when consumers face choices among various brands (Chaudhuri & Holbrook, 2001; Lee et al., 2011) In online shopping, the influence of brand trust on buying decisions is well-documented (Soni & Verghese, 2018) Buyers tend to rely on trusted companies during uncertain situations, hoping for favorable outcomes (Lau & Lee, 1999) Additionally, a strong correlation exists between brand trust and purchase intention, indicating that higher brand trust leads to increased purchase intentions (Aydin et al., 2014; Sanny et al., 2020).
H10: Brand Trust positively impacts on Purchase Intention.
Figure 2 J The proposed research model
S ummary
This chapter outlines the research framework based on a comprehensive literature review and nine relevant studies, leading to the formulation of ten hypotheses The initial five hypotheses explore the relationship between AI Influencers' Anthropomorphism and Credibility, with Social Presence and Parasocial Interaction acting as mediators, grounded in the Computer as Social Actors theory The subsequent five hypotheses examine how AI Influencers' Credibility impacts Mimetic Desire and Brand Trust, ultimately enhancing Purchase Intention through the consumer's doppelganger effect The following chapter will detail the research methodology employed in this study.
RESEARCH METHODOLOGY
P rocedure
The research process includes the following steps as illustrated in Figure 3.1.
The authors begin by reviewing existing scientific literature on AI Influencers to establish a foundational model for their research They create an initial English questionnaire based on these studies, which is then translated into Vietnamese To refine the questionnaire, a group discussion is held to ensure clarity and logical coherence for each question The authors further adapt the scales to align with the cultural context of Ho Chi Minh City, focusing on the application of AI Influencers in marketing Following the development of the scale and questionnaire, a pilot test is conducted using SPSS 25 and AMOS 24 software for necessary adjustments The official quantitative research is then carried out using the finalized questionnaire, distributed online via Khaosat.me, to assess the measurement and structural models Collected data undergoes processing to enhance quality, followed by analysis in SPSS 25 for reliability testing (using Cronbach's Alpha) and Exploratory Factor Analysis (EFA), along with Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) in AMOS 24 to validate the proposed model The authors conclude with insights and implications based on their analysis results.
Q uantitative methods
The study will utilize a non-probability sampling method for data collection, which is suitable for exploratory research and hypothesis testing despite not being representative of the entire population This approach is advantageous due to its lower cost, reduced time requirements, and ability to meet research objectives (Tat Khai Minh, 2022) Given the challenges of identifying all AI influencers in Ho Chi Minh City, a precise sample size calculation is impractical Therefore, a convenient non-probability sampling method will be employed Researchers recommend a minimum sample size of 100 to 200 for Maximum Likelihood estimation (Nguyen Đinh Tho and Nguyen Thi Mai Trang, 2007; Hair et al., 1998) With 29 observed variables in the research model, the minimum sample size should be at least 145, based on the guideline of five times the number of observed variables To ensure robust analyses and mitigate risks, a sample size of 300 has been selected for this study.
Following a group discussion, the authors refined the questionnaire for better semantic clarity The research team conducted a survey targeting young individuals living and working in Ho Chi Minh City, reaching out to friends and acquaintances The data collection process was executed in a systematic manner.
Conducting a comprehensive survey questionnaire through the Khaosat.me platform.
Step 2: Determining the sample size
The research model outlined in Chapter 2 includes seven latent variables and 29 observed variables, necessitating a minimum sample size of 145 participants, calculated as 29 multiplied by 5 To achieve the most accurate results in the SEM analysis using AMOS 24, the study aims to recruit approximately 350 participants.
The authors distributed a survey link through various social media platforms, including Facebook, Zalo, and Instagram Out of 350 questionnaires sent, 300 responses were collected, resulting in a response rate of 85.71% Data collection occurred between January 31 and February 6.
This study utilizes Structural Equation Modeling (SEM) with AMOS 24 software to test proposed hypotheses, leveraging SEM's evolution from General Linear Models (GLM) to analyze complex causal relationships effectively SEM's robust framework is increasingly favored across various research fields for estimating both the Measurement Model and Structure Model of multivariate theoretical issues Through Confirmatory Factor Analysis (CFA), SEM enables the identification of the most appropriate model among the alternatives, allowing authors to not only assess the acceptance or rejection of hypothesized relationships but also to evaluate their relative strength and potential weaknesses.
3.2.4 1 Data entry, validation, and descriptive statistics analysis
After synthesizing the responses, the data is refined by eliminating invalid answer sheets and unfilled variables This is followed by coding and entering the data into SPSS 25 and AMOS 24 for further analysis Descriptive statistical analysis is performed using SPSS 25 to outline the characteristics of the research sample.
3.2.4.2 Assessing the reliability of scales with Cronbach’s Alpha
Cronbach's Alpha, developed by Lee Cronbach in 1951, is a statistical measure used to evaluate the internal consistency reliability of observable variables based on their correlations It operates under the assumption that all observed variables possess the same reliability In this study, Cronbach’s Alpha was utilized to assess the reliability of the scales for both independent and dependent variables, aiming to determine if the observed variables effectively measure the same underlying concept Additionally, the Corrected Item-Total Correlation coefficient was employed to illustrate the relationship between each observed variable and the other variables within the same scale, allowing researchers to identify and eliminate inconsistently observed variables from the research model.
Cronbach's Alpha coefficient values range from 0 to 1, with Hair et al (2010) suggesting that a reliable scale should have a Corrected Item-Total Correlation coefficient exceeding 0.3 Additionally, Hoang Trong and Chu Nguyen Mong Ngoc (2008) indicate that Cronbach's Alpha coefficients above 0.6 are considered acceptable This summary highlights the significance of interpreting Cronbach's Alpha values in reliability analysis.
Table 3.1 Summary of Cron ba clĩ ’s Alpha coefficient value ranges
Cronbach’s Alpha value range Interpretation
This study employs Principal Axis Factoring with Promax rotation in its exploratory factor analysis (EFA) to investigate the relationships among factors and uncover latent structures As outlined by Hair et al (2010), this method serves as a standard for conducting EFA analysis.
The KMO coefficient, or Kaiser-Meyer-Olkin index, is a crucial measure for assessing the suitability of factor analysis For factor analysis to be deemed appropriate, the KMO value should fall between 0.5 and 1 A KMO score below 0.5 indicates that factor analysis may not be suitable for the given dataset.
Bartlett's test of sphericity is a statistical measure used to determine the correlation among observed variables in a factor analysis A significant result, indicated by a p-value less than 0.05, confirms that the observed variables are indeed correlated with one another.
• Total Variance Explained > 50% indicates that the EFA model is used appropriately.
In Exploratory Factor Analysis (EFA), the Eigenvalue serves as a key criterion for determining the number of factors to retain Factors with an Eigenvalue greater than one are considered significant, as they are capable of summarizing the most information from the dataset.
Factor Loading indicates the correlation between observed variables and underlying factors, with higher values signifying a stronger relationship According to Hair et al (2009), a quality observed variable should have a loading factor of at least 0.5 in Multivariate Data Analysis, while the minimum acceptable threshold is 0.3.
Following the Exploratory Factor Analysis (EFA), it is essential to conduct Confirmatory Factor Analysis (CFA) to assess the model's fit, reliability, and both convergent and discriminant validity of the measurement scale During this phase, any observed variables that do not meet the required standards are excluded from the model.
According to Hu & Bentler (1999), Indexes to verify Model Fit include:
CMIN/df < 3 is good, CMIN/df < 5 is acceptable.
CFI > 0.9 is good, CFI > 0.95 is excellent.
GFI > 0.9 is good, GFI > 0.95 is excellent, GFI > 0.8 is acceptable.
RMSEA < 0.06 is good, RMSEA < 0.08 is acceptable.
PCLOSE > 0.05 is good, PCLOSE > 0.01 is acceptable.
Reanalysis of the Cronbach Alpha index for the group of factors whose observed variables were excluded al the EFA analysis step, Cronbach Alpha > 0.7.
Composite Reliability (CR) > 0.7 is acceptable.
However, if CR > 0.95, it needs to be reconsidered because of the possibility of variables carrying the same content together.
According to Hair et al (2010), Indexes to verify include
Maximum Shared Variance (MSV) < Average Variance Extracted (AVE).
Square Root of AVE (SQRTAVE) > Inter - Construct Correlations.
Following the CFA analysis and the elimination of any unsatisfactory variables, the remaining variables are incorporated into the SEM analysis using AMOS The model identifies seven factors and ten hypotheses, which will be assessed through SEM analysis This evaluation will be presented in two primary results tables: Regression Weights and Standardized Regression Weights.
• Regression Weights p-valuc (Sig.) < 0.05: The relationship between the variables is statistically significant and accepted. p-value (Sig.) > 0.05: The variables have no impact.
DATA ANALYSIS AND RESULTS
T he features of sample size
A study conducted with 300 respondents familiar with Al Influencers in Ho Chi Minh City reveals key demographic insights, including gender, income, and age distribution Table 4.1 illustrates that all 300 participants provided responses to the survey questions.
• Regarding gender, the authors team saw that 55.7% are male with 167 people, and 44.3% were female with 133 people.
• About income groups, people with under 5 million VND accounted for 60% with
180 individuals, while the 5-11 million VND group had 92 individuals with 30.7% The number of people in 11-16 million was 22 with 7.3% while people in the over
16 million group were 6 individuals with 2%.
The study revealed that individuals aged 18-25 comprised the majority, representing 88.3% with 265 participants, while the 26-30 age group accounted for 11.7%, totaling 35 individuals.
As of recent data, Facebook leads social media usage with 275 users, accounting for 27.9% of the total, followed closely by Instagram at 254 users, or 25.8% TikTok follows with 233 users, representing 23.7%, while X (formerly Twitter) and Zalo have significantly lower engagement, each with 49 users, making up just 5.0%.
Table 4.1 Demographic characteristic of sample
Influencers Frequency Percent % Gender Frequency Percent %
Minh City Frequency Percent % Total 300 100.0
Social Media of use Frequency Percent % From 5-11 million VND
T esting of scales
4.2,1 The results of Assessing the reliability of scales with Cron bach’s Alpha
To analyze how AI Influencer's Anthropomorphism impacts on purchase intention; we conducted a scale reliability analysis - Cronbach’s Alpha for 29 observed variables in 7 constructs.
Table 4.2 Results of Assessing the Reliability of Scales with Cron bach's Alpha
The analysis using Cronbach’s Alpha indicates that all scales with coefficients above 0.7 are considered reliable, with Corrected Item-Total Correlation values exceeding 0.3 Among the seven factors analyzed, the Social Presence factor exhibits the lowest Cronbach's Alpha coefficient at 0.826.
Parasocial Interaction has the largest Cronbach's Alpha coefficient at 0.942 Therefore, all scales of the study arc sufficiently reliable for the following analysis.
4.2.2 The results of Exploratory Factor Analysis (EFA)
Following the preliminary assessment of scale components using Cronbach’s Alpha coefficients, we identified 29 satisfactory observed variables across seven factor groups To refine these variables into more meaningful factors, we performed Exploratory Factor Analysis (EFA) utilizing Principal Axis Factoring and Promax Rotation Additionally, the compatibility of the survey samples was evaluated using Kaiser-Meyer-Olkin and Bartlett’s Test.
Table 4.3 KMO and Bartlett’s test results
Factors to be assessed Result Compare
KMO = 0.921 shows that EFA analysis is appropriate.
Sig (Bartlett's Test) = 0.000 (sig < 0.05) proves that the observed variables are correlated with each other.
Eigenvalue = 1.152 > 1 represents the variation explained by each factor, the factor drawn has the best summary meaning.
The analysis reveals that seven factors extracted from the Exploratory Factor Analysis (EFA) account for 69.697% of the total variation among the 29 observed variables, each exhibiting a Factor Loading coefficient greater than 0.5.
Extraction Method: Principal Axis Factoring.
Rotation Method: Promax with Kaiser Normalization3
CONFIRMATORY F actor A nalysis (CFA)
Following the exploratory factor analysis (EFA), the research team incorporated 29 observed variables into the confirmatory factor analysis (CFA) The findings of the CFA are detailed in Appendix E To assess the suitability of the research model, the authors focused on the Model Fit output table, highlighting key indicators summarized in the accompanying table.
Table 4.5 Summary of key indicators in CFA analysis the second time
Model CM1N/DF GFI CFI RMSEA PCLOSE
The results based on the summary table show:
• Chi-square/DF (CMIN/DF) 1.552 < 3, good result.
• GFI (Goodness-of-Fit Index) 0.887 > 0.8, acceptable result.
• CFI (Comparative Fit Index) 0.968 > 0.9, excellent result.
• RMSEA (Root mean square error ofapproximation) 0.043 < 0.06, good result.
• PCLOSE (Probability Close to Zero) 0.954 > 0.05, good result.
The evaluation indicators indicate that the model is appropriate for the research data, although the GF1 index, which should ideally be greater than 0.9 for good results according to Hu & Bentler (1999), only reached 0.882 in our study This issue has been noted in other research, where GF1 values often exceed 0.8 but fall short of 0.9, largely due to sample size influences Despite efforts to enhance the index by connecting strongly correlated observed variables, it remained below the desired threshold Nonetheless, a minimum GF1 value of 0.8 is deemed acceptable, as supported by studies from Baumgartner and Homburg (1995) and Doll, Xia, and Torkzadeh (1994).
The authors team then measured the Composite Reliability (CR), Convergent, andDiscriminant Validity of the variables The results are shown in the table below.
Table 4.6 The results of the inspection of the reliability and validity
PSI MD AIC PI AIA BT SP
The results indicate that the composite reliability (CR) of all variables exceeds 0.7, confirming the scale's reliability Additionally, the average variance extracted (AVE) for the variables is above 0.5, ensuring the study's convergence Furthermore, all maximum shared variance (MSV) values are lower than the AVE, and the square root of the AVE (SQRAVE) values surpass all inter-construct correlations, thereby demonstrating the discriminant validity of the research.
S tructural E quation M odeling (SEM)
Structural Equation Modeling (SEM) is a valuable tool for authors to assess the statistical significance of hypotheses based on p-values A p-value below 5% indicates statistical significance, allowing for the acceptance of overlapping concepts As shown in Table 4.7, most p-values are represented by ***, signifying they are less than 0.001 and meet the criteria of being under 0.05 However, the effects of AI A on SP and SP on AIC, with values of 0.101 and 0.875 respectively, exceed 0.05, indicating they are not statistically significant Consequently, out of 10 hypotheses tested, only 8 are accepted while hypotheses H1 and H3 are rejected.
In this analysis, we assess the influence of independent variables on the dependent variable using standardized regression coefficients, as detailed in Table 3.7 Among the three variables affecting AIA, the relationship between AIA and SP was not supported However, AIA demonstrated a stronger effect on PSI (0.621) compared to AIC (0.182) When evaluating the impact of these three factors on IC, the influence is ranked in descending order, with PSI showing the highest impact at 0.548.
The analysis reveals that among the three factors influencing the Pl-dependent variable, the degree of impact decreases in the following order: AIC (0.554), MD (0.187), and BT (0.171) Consequently, the most substantial and significant influence is observed in the relationship: AIA > PSI > AIC on Pl.
Thus, after completing CFA and SEM, the authors rejected the two hypotheses because they did not achieve the p-value condition (Sig.) less than 0.05.
Table 4.9 Summarize the results of hypothesis testing
Hl AI Influencers' Anthropomorphism positively impacts on
H2 AI Influencers' Anthropomorphism positively impacts on
H3 Social Presence positively impacts on AI Influencers'
H4 AI Influencers* Anthropomorphism positively impacts on the AI Influencers’ Credibility.
H5 Parasocial Interaction positively impacts on the AI
H6 AI Influencers’ Credibility positively impacts on Mimetic
H7 AI Influencers' Credibility positively impacts on Brand
H8 H8: Mimetic Desire positively impacts on Purchase
H9 AI Influencers’ Credibility positively impacts on Purchase
H10 Brand Trust positively impacts on Purchase Intention Supported
DISCUSSION AND CONCLUSION
DISCUSSION of research
The study titled "AI Influencers in Marketing: How AI Influencers’ Anthropomorphism Impacts on Consumer Intention among Young People in Ho Chi Minh City" was conducted over nearly two months, utilizing a 7-scale questionnaire with 29 observed variables Employing a quantitative research method, the authors designed a survey based on a 5-level Likert scale and conducted a group discussion to refine the questionnaire's wording and logic The research gathered responses from 350 individuals, ultimately resulting in 300 valid participants (an 85.71% response rate), all of whom were aged 18-34, familiar with AI Influencers, and residing in Ho Chi Minh City Data analysis was performed using SPSS 25 and AMOS 24 software.
The study revealed that a significant majority of respondents, 88.3%, were aged 18-25, primarily consisting of pupils and students Most of these individuals reported a monthly income of under 5 million, with 60% earning in this bracket, while 30.7% earned between 5 and 11 million This young demographic is expected to be more receptive to digital trends and innovations.
AI influencers are reshaping consumer behavior by easily adapting to new technologies and social trends, leading many to rely on their recommendations for purchases Shopping has become a popular hobby, indicating that factors beyond income level significantly influence consumer intentions Interestingly, older age groups, particularly those aged 30-34, show less concern for these influences compared to younger consumers.
The reliability of the proposed research scales was confirmed through the evaluation of Cronbach's Alpha coefficient and Exploratory Factor Analysis (EFA), demonstrating high values and reliability Subsequently, the model and hypotheses were tested using Structural Equation Modeling (SEM), yielding significant results.
The results of Structural Equation Modeling (SEM) indicate the correlations of AI Influencers’ Anthropomorphism, Parasocial Interaction, AI Influencers Credibility,
Mimetic Desire and Brand Trust significantly impact Purchase Intention, particularly through the relationship between AI Influencers' Anthropomorphism, Parasocial Interaction, and Credibility Young consumers engage in one-way interactions with AI Influencers through posts, captions, and advertisements, allowing them to connect with shared interests and feel empathy through comments and views, regardless of the influencers' virtual nature This connection enhances the perceived credibility of AI Influencers, ultimately boosting the likelihood of purchasing products they endorse.
Research indicates that AI Influencer Credibility significantly impacts young people's Purchase Intention for products promoted by AI Influencers in Ho Chi Minh City, with an estimated influence score of 0.554 This highlights that consumers prioritize the trustworthiness of both the products and the information shared by virtual influencers Consequently, the ability of these computer-generated influencers to deliver accurate and unbiased information stands out as their primary advantage.
The findings indicate a lack of correlation between AI influencers' anthropomorphism and their social presence, as well as between social presence and the credibility of AI influencers This may be attributed to the varying levels of anthropomorphism among virtual influencers, which can influence consumers' perceptions of their social presence.
AI influencers like Lil Miquela and Shudu exhibit a more human-like appearance, while others such as Zoe and Knox Frost adopt a more cartoonish or stylized look This variation in anthropomorphism can significantly influence how individuals perceive these virtual personalities, as preferences, expectations, and motivations differ among audiences.
The lack of correlation between Social Presence and the credibility of AI influencers may stem from the diverse sources of credibility that virtual influencers possess, which are not necessarily linked to their social presence For instance, influencers like Bermuda and Noonoouri derive their credibility from their expertise in fashion and beauty, while others like Imma and Blawko are recognized for their authenticity and trustworthiness This indicates that the credibility of virtual influencers is shaped more by their communication of values, opinions, and endorsements rather than their social engagement Thus, it is essential to consider the multidimensional nature of credibility and its various determinants when examining this relationship.
P ractical implications
This research provides essential insights for brand marketing managers utilizing virtual influencers in social media advertising The findings offer specific criteria to help select virtual influencers that align effectively with branded products and campaign goals.
Marketing has long relied on celebrities and influencers to promote products and brands Recently, the emergence of AI Influencers presents a new challenge, as users are accustomed to human influencers However, by designing AI Influencers to closely resemble humans, this innovative approach can effectively bridge the gap and ultimately serve as a viable alternative to traditional influencer marketing.
To enhance user engagement, brands must strive to make virtual influencers exhibit human-like traits, such as gentle smiles, eye contact, and positive physical cues like leaning forward and nodding, which foster openness and trust Users often perceive AI influencers as emotionless, leading to reluctance in interaction Therefore, brands should incorporate a range of facial expressions and body gestures that convey emotions such as anger and sadness, creating a more natural and intimate experience However, achieving this level of realism in AI influencers presents challenges that necessitate a skilled design team well-versed in artificial intelligence, including Natural Language Processing and Machine Learning.
The anthropomorphization of AI influencers enhances their human-like perception, sparking curiosity among users about their gestures and actions This curiosity drives increased follower engagement and interactions, ultimately benefiting brands by boosting product recognition and revenue To foster this curiosity, brands should focus on creating authentic and intimate content, moving beyond mere product promotions Sharing glimpses of AI influencers' daily activities, such as eating and socializing, along with personal stories through Instagram stories, can strengthen connections with followers Additionally, hosting interactive livestreams between AI influencers and users can further enhance engagement and empathy.
Brands must ensure that AI Influencers respond to user comments swiftly and positively, using natural language that mimics human interaction This approach helps create a more authentic experience for users, making them feel as though they are engaging with a real person.
Research by Chung and Cho (2017) indicates that the intimacy and understanding fostered through parasocial relationships with AI influencers can enhance the credibility of celebrity information sources Frequent interactions lead users to perceive AI influencers as trustworthy, impacting their brand trust and purchase intentions However, not all interactions will drive users to imitate or purchase the products promoted by these influencers To effectively stimulate mimetic desire, brands should analyze the preferred attributes of AI influencers through user surveys before launching promotional strategies This includes identifying which traits of AI influencers resonate more with users compared to human influencers, and adapting their appearance, lifestyle, and fashion accordingly Additionally, strengthening brand trust through the strategic use of AI influencers is essential for successful marketing.
Brands must choose AI influencers that align with their values and image to enhance credibility While paid partnerships are common, strategic collaborations based on shared values, campaign themes, and content formats can yield benefits that go beyond financial returns and foster brand trust To ensure effective product advertising, brands should develop and continuously refine the communication strategies of virtual influencers, making them feel natural and authentic Just like human influencers, AI influencers must be influential, trusted, and admired to attract customers By building and maintaining a perception of credibility among their followers, AI influencers can positively influence customer attitudes towards both themselves and the brands they represent, ultimately enhancing consumers' purchase intentions.
L imitations and further research
Future research should focus on expanding data sets and utilizing longitudinal studies to evaluate the long-term effectiveness of anthropomorphic AI influencers, moving beyond the limitations of a single self-administered questionnaire used in initial studies Additionally, incorporating data collected directly from actual marketing campaigns would enhance the insights gained.
The research is limited to Vietnam, specifically Ho Chi Minh City, which restricts its generalizability and depth Additionally, the digital personas and characteristics of virtual influencers in social media advertisements may vary by country Analyzing virtual influencer endorsements across different nationalities could uncover significant cultural factors that influence the choice of endorsers and the development of product content.
This study focuses on the limited presence of virtual influencers in Vietnam, indicating that the findings may vary based on the number and quality of AI influencers To enhance the generalizability of the results, future research should consider exploring contexts where Vietnamese audiences are more accustomed to computer-generated influencers.
Further investigation into the relationship between AI influencers, their anthropomorphic traits, and purchase intention may yield different insights than those presented in this study Future research could explore how virtual influencers develop their social presence by expressing political views, emotions, and attitudes, as well as other attributes that enhance their human-like qualities.
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BẢNG CÂU HÔI KHẢO SÁT
Chúng tôi là nhóm sinh viên nghiên cứu từ Đại học Kinh tế TP Hồ Chí Minh (UEH), đang thực hiện nghiên cứu về "Ảnh hưởng của AI Influencers giống người đến hành vi tiêu dùng qua sự hiện diện xã hội và tương tác giả tưởng trên nền tảng truyền thông xã hội: Trường hợp giới trẻ tại TP HCM" Mục tiêu của nghiên cứu là khảo sát độ tin tưởng của người dùng đối với AI Influencer và thương hiệu mà họ quảng cáo tại Việt Nam Chúng tôi rất mong nhận được sự hỗ trợ từ mọi người bằng cách dành ít thời gian để hoàn thành khảo sát dưới đây Những câu trả lời của các bạn sẽ đóng góp quan trọng vào kết quả nghiên cứu Chúng tôi cam kết bảo mật thông tin thu thập từ khảo sát và chỉ sử dụng cho mục đích nghiên cứu Cuối khảo sát, chúng tôi sẽ gửi đến các bạn một số tài liệu học tập hữu ích hy vọng sẽ phục vụ cho nhu cầu học tập của các bạn.
Chân thành cám ơn bạn vỉ đã dành thời gian quý báu đe thực hiộn khảo sát này.
Giói thiêu SO’ lược về AI Influencer
Imma là người mẫu thời trang áo đầu tiên trên thế giới, được phát triển bởi công ty đồ họa máy tính ModelingCafe của Nhật Bản từ năm 2019 Với vẻ ngoài nổi bật và mái tóc hồng, Imma đã đạt được nhiều thành công đáng kể kể từ khi ra mắt và được vinh danh trong ngành công nghiệp thời trang.
“Phụ nữ của năm 2020" do tạp chí Forbes Women cùa Forbes Poland bình chọn Đên năm
2022, Imma trớ thành đại sứ thương hiệu áo đầu tiên tại châu Á, đồng hành trong các chiến dịch quảng bá cho dòng V25 Series của Vivo.
Vivo khẳng định tầm nhìn tiên phong trong công nghệ bằng cách hợp tác với nhân vật ảo Imma để quảng bá dòng sản phẩm V25 series, đồng thời hòa cùng xu hướng trí tuệ nhân tạo AI đang thu hút sự chú ý toàn cầu Imma sẽ ghi lại những khoảnh khắc ấn tượng tại các thành phố ở Châu Á, trong đó có Việt Nam, nơi giới trẻ năng động đang thể hiện cá tính và sự sáng tạo qua ống kính của vivo V25 series.
1.1 Bạn có biêt dên AI Influencer? o Có o Không
1.2 Bạn có sinh sống tại thành phố Hồ Chí Minh không? o Cỏ o Không
Bạn sử dụng mạng xà hội nào dưới đây? (có thổ chọn nhiều câu trá lời)
Trong phần này, chúng ta sẽ khám phá suy nghĩ và độ tin cậy của bạn khi tiếp nhận thông tin từ các bài viết và video của những AI Influencer trên mạng xã hội Xin vui lòng đọc kỹ từng phát biểu dưới đây và đánh giá mức độ đồng ý của bạn bằng cách chọn một ô từ 1 đến 5 trong bảng đánh giá phía dưới.
Hoàn toàn không đồng ý Không đồng ý Trung lập Đồng ý Hoàn toàn đồng ý
1 Tôi tin răng AI Influencer có cám xúc riêng cùa họ 1 2 3 4 5
2 Tôi tin rằng AI Influencer cỏ tinh cách riêng của họ 1 2 3 4 5
3 Tôi tin rằng AI Influencer có vé ngoài như một con người 1 2 3 4 5
4 Tôi tin rằng AI Influencer có tính sáng tạo và có trí tường tượng riêng của họ.
5 Tôi cám thấy như mình đang ớ cùng một không gian với AI
6 Tôi cám thấy AI Influencer có tồn tại và cho tôi cám giác thân thuộc 1 2 3 4 5
7 Tôi cám thây mình như đang trò chuyện với AI Influencer 1 2 3 4 5
8 Tôi muốn được làm bạn với AI Influencer 1 2 3 4 5
9 Tôi câm nhận được mối liên hệ sâu sắc với AI Influencer kể cã khi họ không phái là con người thật.
10 Tôi muốn được gặp AI Influencer 1 2 3 4 5
11 Tôi càm thấy minh là một phần trong thế giới cùa AI Influencer 1 2 3 4 5
12 Tôi muổn có mối quan hệ với AI Influencer 1 2 3 4 5
13 AI Influencer có khả năng truyên đạt lôt nhừng suy nghĩ và cảm xúc của tôi.
14 Tôi có thẻ tin tường AI Influencer 1 2 3 4 5
15 AI Influencer trông có vé trung thực 1 2 3 4 5
16 AI Influencer trông có vé đáng tin cậy 1 2 3 4 5
17 AI Influencer trông có vé chân thành 1 2 3 4 5
18 Tôi khao khát lối sổng cùa AI Influencer 1 2 3 4 5
19 Lấy càm hứng từ AI Influencer, tôi muốn trớ nên phong cách như họ.
20 Lấy cảm hứng lừ AI Influencer, tôi muốn trờ nên họp mốt như họ 1 2 3 4 5
21 Lấy cám hứng từ AI Influencer, lôi muốn có lối sống giống như họ 1 2 3 4 5
22 Tôi tin tường thương hiệu được chửng thực bời AI Influencer 1 2 3 4 5
23 Tôi mong đợi vào thương hiệu được chứng thực bời AI Influencer 1 2 3 4 5
24 Thương hiệu được chứng thực bới AI Influencer thì có thế tin cậy 1 2 3 4 5
25 Thương hiệu được chứng thực bời AI Influencer thi an toàn 1 2 3 4 5
26 Nẻu có thổ tôi se mua những sán phẩm đen từ thương hiộu được quáng cáo bởi AI Influencer.
27 Tôi sè mua nhừng sàn phâm đên từ thương hiệu được quáng cáo bời
AI Influencer trong tương lai.
28 Tôi sẽ mua sân phâm nêu được chứng thực bới AI Influencer 1 2 3 4 5
29 Tôi có ý định mua sàn phẩm được quàng cáo bời AI Influencer 1 2 3 4 5
PHẦN C: THỒNG TIN CÁ NHÂN
Xin bạn vui lòng cho biết thông tin cá nhân cùa minh:
1 Giói tính của bạn là gì? o Nam o Nữ
2 Bạn thuộc nhóm tuổi nào dưới đây? o 18-25 tuổi o 26-30 tuổi o 31-35 tuổi
3 Bạn có sinh sống tại thành phố Hồ Chí Minh không? o Có o Không
4 Tống thu nhập bình quân hàng tháng của bạn là bao nhiêu? o Dưới 5 triệu đông o Từ 5-11 triệu đồng o Từ trên 11-16 triệu đông o Từ trên 16 triệu đồng
XIN CHÂN THÀNH CẢM ƠN sụ GIÚP ĐÕ CỦA BẠN.
In Ho Chi Minh City
APPENDIX C: THE RESULTS OF ASSESSING THE RELIABILITY OF SCALES WITH CRONBACH’S ALPHA
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
APPENDIX D: THE RESULTS OF EXPLORATORY FACTOR ANALYSIS
Table IT KMO and Bartlett's Test
Kaiscr-Mcycr-Olkin Measure of Sampling
Table III Total Variance Explained
Initial Eigenvalues Extraction Sums ofSquared
Extraction Method: Principal Axis Factoring.
Extraction Method: Principal Axis Factoring.
Rotation Method: Promax with Kaiser Normalization3
APPENDIX E: THE RESULTS OF COMFIRMATORY FACTOR ANALYSIS
Figure I The results of Confirmatory Factor Analysis the first time
Figure Ỉ/ The results of Confirmatory Factor Analysis the second times
Table V The results of the inspection of the reliability and validity
CR AVE MSV MaxR(H) PSI MD IC PI IA BT SP
APPENDIX F: THE RESULTS OF STRUCTURAL EQUATION MODELING
I'able VI Regression Weights: (Group number I - Default model)
Table VII Standardized Regression Weights: (Group number Ỉ - Default model)