Impact of Celebrity,Micro-Celebrity,and Virtual Influencers on Chinese Gen Z’s Purchase Intention Through Social Media

Abstract

Social media advertising strategies, including using traditional celebrity endorsers and micro-celebrity influencers, are prevalent marketing tools. However, the trend of using virtual influencers to endorse products is a novel potential way to attract young consumers. This present study aims to analyze the influence of the three types of endorsers (traditional celebrity, micro-celebrity, and virtual influencers) source credibility (i.e., physical attractiveness, expertise, and trustworthiness) on the purchase intention of the Chinese Generation Z, under varying levels of product involvement, through the mediating effect of emotional attachment. The Hayes Process Macro was used as a statistical analysis tool for our research propositions. Overall, our findings highlight the effectiveness of attachment theory in social media endorsement advertisements. Furthermore, these findings can guide marketers, who desire to respond to the purchase trends of Generation Z, to adjust their marketing strategies accordingly.

Keywords

celebrity endorsement micro-celebrity influencer virtual influencer source credibility attachment theory

Introduction

The rapid growth of interactive digital technology has impacted almost every aspect of Generation Z’s (Gen Z) daily lives. Gen Z is considered the first global generation because they were born in the digital era from 1995 to 2010 and are digital natives, highly educated, creative, innovative, and technology savvy (Priporas et al., 2017). Based on the report of Barclays Bank (Patel & Morrison, 2019), Gen Z will play a significant role in future economic development because they represent 40% of consumers in BRIC, Europe, and the United States. It is also estimated by Euromonitor’s (2018) report that Gen Z will be the largest consumer group by 2030. So, understanding how to stimulate Gen Z to purchase is essential to marketers because of the difference in purchasing behavior between Generations X and Y.

Gen Z is active online consumers due to their intensive social media use and positive attitudes toward social media advertising (Croes & Bartels, 2021). Social media advertising has been the most effective way for brands to communicate with these young audiences, who spend most of their time online, using all types of digital or mobile devices (Schouten et al., 2020). As a result, they are highly exposed to digital advertising on social media and care more about engaging online with their favorite brands (Djafarova & Bowes, 2021). In addition, they prefer tracking their social media account daily, browsing, liking, and sharing visual content, such as photos, videos, memes, infographics, illustrations, or video blogging across all forms of new media (Priporas et al., 2017).

In China, 18 to 24-year-olds (Gen Z), constitute about 15% of the country’s population, and are believed to be the next driver of domestic consumption growth (Zhou et al., 2021). They consider social media platforms as essential sources of product information because these channels broadcast the most advertisements. Furthermore, 70% of Gen Z in China are induced to purchase from social media platforms (Sun et al., 2022). Endorsers strongly influence the increasing trend of young Chinese buying branded products through social media (KPMG Huazhen LLP, 2017).

Many Gen Z admire endorsers and idolize their fashion, lifestyles, and talents (Daxue Consulting, 2020). They follow endorsers’ advertisements on social media via liking, posting messages, subscribing to their channel, and sharing the endorser’s post with others. They even want to know more about the brands or products the endorsers are using so they can imitate and follow in their footsteps. A high congruence between endorsers and endorsed products is a sensible and efficient way to reach these young consumers. Thomson’s (2006) study indicated the existence of consumer and human brand attachment bonds, specifically among young consumers. So, we propose the attachment theory of Bowlby (1969), stating that when consumers are familiar with the product endorser, they are likely to establish an emotional attachment between the endorser and the product; this influences their purchasing behavior (Djafarova & Bowes, 2021). It is vital for firms to understand how endorsers can influence emotions, in order to identify the factors influencing young people’s purchase preferences.

Based on prior research, Gen Z tends to be the most involved in the lives of endorsers (e.g., celebrities or influencers). However, few studies have focused on the influence of celebrity (Chen et al., 2013) and micro-celebrity endorsers (Sun et al., 2022) via social media, on the Chinese Gen Z, although many social media advertisements are targeting this generation of consumers. To the best of our knowledge, only two studies (e.g., S. V. Jin et al., 2019; Schouten et al., 2020) directly compared the source credibility of these two types of endorsers. However, celebrity and micro-celebrity influencers may cause risks associated with transgression (e.g., Viya, a top Chinese influencer for tax evasion) and reputation damage (e.g., Kris Wu, a top celebrity for sexual misconduct) (Sands et al., 2022). Recent trends show that many brands have been exploring virtual influencer (VI) endorsement marketing. As young consumers express significant interest in anime, comics, and game content, many brands use virtual influencers to endorse their products and attract fans (Nan, 2021). In order to enrich our academic knowledge about endorsers in social media, we explore the recent trend of virtual influencer endorsements and their effects on Gen Z’s behavioral intentions. Additionally, for brands to avoid the controversy and volatility of human influencers, we compare the characteristics of a virtual influencer with celebrity and micro-celebrity endorsers.

Some academic papers have examined how VI is reshaping marketing practices and influencing consumer behavior. Sands et al. (2022) stated that consumer reactions to VIs have been both positive and negative. So, the purpose of this research goes a step further by comparing the source credibility (i.e., attractiveness, expertise, and trustworthiness) of the three different endorsers (i.e., traditional celebrity endorser (TCE) vs. micro-celebrity influencer (MCI) vs. virtual influencer (VI)), and how it influences Gen Z’s emotional attachment and purchase intention for high- and low-involvement products. Therefore, testing the proposed framework will fill a significant knowledge gap to deepen our understanding of which type of endorsers on social media is appropriate for influencing the young generation. The rest of the study is presented as follows: Section 2 focuses on the relevant literature and research propositions for analysis. Section 3 describes the research method. Section 4 introduces empirical results for three studies. Finally, we present the discussions, implications, and future research.

Literature Review and Hypotheses Development

Endorser Type

The new generation spends more time with their digital devices, whose behavior is constantly affected by many irrational sources of influence, such as endorsers (Croes & Bartels, 2021). Firms need to select the appropriate endorsers to advertise products for Gen Z. Endorsement advertisement is one of the most prevalent marketing strategies, especially with the development of social media. The purpose of endorsers is to make the communication process more efficient through advertisements.

Due to their appearing in advertisements or creating marketing content on social media, endorsers are highly visible to the public. Firms have traditionally used endorsers to associate brands with their attributes and perceived connections with consumers to drive sales. Meaningful “transfer of endorsement” begins when the social and cultural symbols of an endorser’s identity and traits move beyond the person, onto the brand, product, service, or firm and, finally, to the consumer’s life, through advertising and different marketing channels (McCracken, 1989). As a result, endorsers can draw more attention through the clutter of competing advertisements, significantly affecting consumer attitudes, purchase intention, and loyalty (Schimmelpfennig & Hunt, 2020).

Thomson (2006) stated that consumers develop solid emotional bonds, that is, “intimacy at a distance” with “human brands,” as brandable personas, whose image is the effect of marketing effort. Presentation as human brands has happened across various forms of endorsement, such as, through celebrities (Saldanha et al., 2020), social media influencing (Ki et al., 2020), and anthropomorphism (Portal et al., 2018). Compared to all forms of endorsement, TCEs have been widely used by companies for a higher degree of attention, recall, and recognition of brands and are widely studied by scholars (Yu & Hu, 2020).

However, the proliferation of new media technologies has allowed endorsers to connect to a global audience directly, enabling ordinary people to brand themselves commercially as influencers. So, traditional celebrities and influencers entered social media by sharing content curated from their daily lives. Recently, VIs, an artificial intelligence character, have started acting just like real celebrities and influencers by posting photos, creating relatable content, and serving as ambassadors of sponsor brands. VIs can do almost everything like human endorsers, such as promoting brands with creative content and posting their daily activities (e.g., meeting friends, volunteering to community service) through images or videos (Arsenyan & Mirowska, 2021). Drenten and Brooks (2020) stated that brands are drawn to VIs’ infallibility because they don’t exist in reality. Unlike the fallibility of TCEs and MCIs, when an endorser lands a scandal or is in trouble, endorsements suffer and negatively impact companies. At present, there is a gap in the literature explaining VIs’ effectiveness and what source credibility makes them persuasive. As the distinction between human endorsers and virtual ‘influencers, with human-like functionality blurs, we argue that the latter can persuade consumers’ purchase intention like humans do, via source credibility.

With the popularity of social networks in recent years, even ordinary people or artificial intelligence characters can become famous and influential endorsers. Different types of endorsers wield innovative marketing strategies in assisting brands to effectively reach and actively engage target consumers. It is essential to understand the differences because these endorsers are unique in their own right, which best matches the brand’s needs and preferences. Each type of endorser attribute makes independent contributions depending on the targeted consumers, influencing their behavior. As a result, consumers feel emotionally attached to endorsers, which positively impacts their purchase likelihood.

Emotional Attachment

Originating from the attachment theory, emotional attachment is a special emotional connection between an individual and a target or an object (Bowlby, 1982). Bowlby (1969, p. 194) defined attachment as a “lasting psychological connectedness between human beings,” meaning a nature to look for proximity and connection with an individual who is the target of attachment. Emotional attachment, accumulated over time through experiences, encompasses feelings and affections that influence an individual’s judgment, decisions, and purchases (Bagozzi et al., 1999). Numerous scholars concluded that consumers build emotional attachments toward celebrities (Saldanha et al., 2020) and social media influencers (Ladhari et al., 2020; Sánchez-Fernández & Jiménez-Castillo, 2021). Young individuals like to use social media to connect with celebrities (e.g., search for news, follow and interact with the products and services the celebrities are endorsing) (Kowalczyk & Pounders, 2016). The endorsers can generate relationships by expressing themselves more personally and by developing a real emotional connection with consumers through new media (Rios Marques et al., 2020) .

Hence, endorsers are assets for companies; that is why marketers often choose endorsers who are physically attractive, skilled, and perceived as unbiased, which may result in deeper emotional connection which leads to the consumer feeling emotionally attached to a brand. So, retailers consider Gen Z consumers’ feelings that will guide their purchase decisions (Dabija et al., 2019). Apart from directly impacting purchase intention, emotional attachment also mediates the relationship between the credibility of the celebrity endorser and intention to purchase (Burnasheva & Suh, 2022). Thus, we assume that emotional attachment can also mediate the relationship between MCI’s and VI’s credibility and purchase intention.

Source Credibility and Product Involvement

Based on Rossiter-Percy Grid (Rossiter et al., 1991), “product involvement” defines involvement purely in terms of perceived risk, that is, high-involvement products are associated with perceived risks (e.g., financial, functional, operational, psychological, and social) high enough to deserve deep-level information processing, while low-involvement products have low perceived risks so the consumer can simply try the brand and check. It demonstrates consumers’ connection with a product and their perception of it as relevant (Zaichkowsky, 1985).

Consumer involvement refers to consumer fondness, interest, and passion for a particular product (Goldsmith & Emmert, 1991). The pioneering study by Krugman (1965) recognized consumer involvement as one of the significant factors in determining the effects of television advertisements on receivers. Moreover, Greenwald and Leavitt’s (1984) research on involvement stated that persuasive communication depends on audience involvement, and print media can generate audience responses as the readers are highly involved in the advertised products. Roozen and Claeys (2010) supported the significance of classifying the high- and low-involvement products in analyzing endorser effects on print media. Also, in modern days, social media advertising enables consumers to initiate and actively participate in brands’ product-related communication, which is a conducive source of information for highly involved consumerith high product information needs (Gruner et al., 2019).

The Elaboration Likelihood Model (ELM) shows that an individual’s level of involvement during advertisement processing is crucial in determining the type of route to persuasion (Petty et al., 1983). Heuristic Systematic Model states that when consumers’ ability, motivation, and opportunity to process information are higher, persuasion will be via the central route (Chaiken et al., 1989). Jones and Reynolds (2006) point out that consumers highly involved with a given product, are more likely to develop heightened behavioral motivation and stronger emotional attachment to the retailer, leading to purchase intention. High product involvement tends to exert cognitive effort and engender central processing, while consumers use the peripheral route to process persuasive arguments under low-involvement conditions.

ELM provides a significant theoretical foundation in testing the influence of source credibility (SC) on consumers’ attitudes and purchase intentions through the levels of involvement. Firms frequently adopt endorsers to lend their persona to a brand. As mentioned, endorsers are credible sources of information about the brand or product. The source credibility (SC) model assumes that the persuasiveness and efficacy of an advertising message depend on the sender’s credibility (i.e., attractiveness, expertise, and trustworthiness) (Ohanian, 1991).

Even though ELM provides the theoretical foundation for SC effects on consumers’ intention, various scholars have mixed findings on the adopted routes (e.g., peripheral or central). Several studies supported that under low-involvement conditions, SC influences consumers’ attitudes and intentions (Kang & Herr, 2006; Kruglanski & Thompson, 1999; Y. Lee & Koo, 2016; Petty et al., 1983). For example, when the compelling advertising arguments are too complex to understand, peripheral cues like endorsement campaigns can influence consumers’ purchase decisions. Some studies found that SC may influence consumers’ attitudes, using the central route (El Hedhli et al., 2021; J. G. Lee & Thorson, 2008; Roozen & Claeys, 2010; Yilmaz et al., 2011). However, the vast majority of previous studies that tested SC in high product involvement were scrutinized for measuring the three dimensions of SC independently. So, we take a step further to simultaneously test all three dimensions of SC, in both, high- and low-involvement products, to understand its role. So, following the previous findings, this study argues that because of the multidimensional nature of the SC concept, it can play multiple roles in the persuasion process (Kelman, 1961). Therefore, we assumed that with increased product involvement, consumers are likely to develop higher levels of interest in endorsement advertisements on social media and are more likely to concentrate on the credibility of the endorser at a deeper level. Then, consumers connect with the endorser, resulting in a positive attitude through emotional attachment, directly influencing purchase intention.

Endorser’s Attractiveness

Physically attractive endorsers are well-liked and can induce arousal that affects information processing toward advertising and brands. J. G. Lee and Thorson (2008) show that in high-involvement conditions, there is a congruence between TCE attractiveness and the endorsed product. So, we argue that TCEs’ source of attractiveness is positively stronger than that of MCIs and VIs in influencing consumer attitudes: First, TCEs are prevalent in all forms of media, from magazines and television to social networks, so the constant visual presentation of their physical ideology of what is attractive, is projected across the media, for example, the Kardashians reinforce an ideal image to women audience (Brown & Tiggemann, 2016); on the other hand, MCIs and Vis are prevalent only on social media platforms. Lastly, Kamins (1990) stated that advertisers have often chosen TCEs to endorse their products based on their physical attractiveness and appeal, particularly in beauty-related products and fashion items. Hence, we propose the following hypotheses for high-involved consumers:

H1A: The physical attractiveness of all three types of endorsers influences Gen Z’s emotional attachment and purchase intention for both, high- and low-involvement products.

H1B: Among the three types of endorsers, TCE’s physical attractiveness has the strongest impact on Gen Z’s purchase intention for both, high- and low-involvement products.

H1C: Emotional attachment significantly mediates the relationship between the attractiveness of the three types of endorsers and Gen Z’s purchase intention for both, high- and low-involvement products.

Endorser’s Expertise

Consumers are inclined to depend on endorsers’ knowledge, skills, and expertise when the brand information is unfamiliar or complicated and/or the products are expensive (El Hedhli et al., 2021). In this study, we argue that, based on their specialized interests, MCIs possess greater expertise than TCEs in their chosen field: First, MCIs create their foundation by committing themselves to a specific domain of interest, then, become experts in their chosen field (Marwick, 2015), and can communicate with like-minded consumers (Healy, 2021). Second, MCIs are known as content generators or creators who are experts in a specific area (Moustakas et al., 2020). Third, some highly professional influencers or trained specialists, such as doctors, lawyers, or scientists, are perceived as professionally knowledgeable and trusted to provide reliable information about the endorsed brand or product. Lastly, Schouten et al. (2020) stated that endorsers’ expertise influences consumers’ attitudes toward advertisements, products, and purchase intention because they are more knowledgeable about the products than TCEs or VIs. So, we proposed the following hypothesis for high-involved consumers:

H2A: The expertise of the three types of endorsers influences Gen Z’s emotional attachment and purchase intention for both, high- and low-involvement products.

H2B: Compared to the other types of endorsers, MCI’s expertise has the strongest impact on Gen Z’s purchase intention for both, high- and low-involvement products.

H2C: Emotional attachment significantly mediates the relationship between the expertise of the three types of endorsers and Gen Z’s purchase intention for both, high- and low-involvement products.

Endorser’s Trustworthiness

Wang and Scheinbaum’s (2018) study showed that, regardless of the type of media, the source trustworthiness induces a more persuasive effect in influencing consumers’ purchase intention toward endorsed brands. El Hedhli et al. (2021) argue that trustworthiness in a high-involvement situation is a sound argument for consumers because trustworthy endorsers induce greater agreement with the message, which is very significant for high-involved consumers to internalize an endorsement message. From a digital media perspective, trustworthiness was also one of the significant attributes of MCIs’ credibility in systematic and heuristic information processing cues (Xiao et al., 2018). In the present study, we argue that MCIs are more trustworthy in endorsing the product than TCEs and VIs: First, it is widely known that the company pays TCEs for endorsements, which may be perceived as less authentic, and likely to reduce consumers’ trust (S. S. Lee et al., 2021). Second, MCIs are known to share their personal experiences, recommendations, and product reviews; even most of the content is generated by the company that sponsors the endorsements (Schouten et al., 2020). Third, MCIs endorse the brand by demonstrating the brand’s product in real-life settings compared to TCEs and VIs (Ladhari et al., 2020). Therefore, we propose the following hypotheses for high-involved consumers:

H3A: The trustworthiness of the three types of endorsers influences Gen Z’s emotional attachment and purchase intention for both, high- and low-involvement products.

H3B: Among the three types of endorsers, MCI’s trustworthiness has the strongest impact on Gen Z’s purchase intention for both, high- and low-involvement products.

H3C: Emotional attachment significantly mediates the relationships between the trustworthiness of the three types of endorsers and Gen Z’s purchase intention for both, high- and low-involvement products.

Based on previous literature reviews, the conceptual model is proposed in Figure 1.

Figure 1.

Proposed framework.

Research Method

Product and Social Media Platform Selection

We choose the product, based on the Rossiter-Percy Grid (Rossiter et al., 1991) conceptualization because its conceptual rigor shows how purchase decisions and product evaluation vary according to consumers’ levels of involvement. Based on the Rossiter-Percy Grid, we choose daily essentials or the most frequently purchased items for young consumers. For instance, deodorant soap can be considered a low-involvement product. In contrast, beauty-care facial soap is considered a high-involvement product. Hence, soap products were suitably selected for this study. The brand name of the high-involvement soap was created and named “ProBeauty,” while the low-involvement soap was named “SecureClean.” After choosing the type of product, we created a mock product advertisement of the soap on social media to minimize the effects of the brand on consumer preferences, prior knowledge, and familiarity with the brand. In this study, we used “Xiaohongshu,” known as “Little Red Book” (https://www.xiaohongshu.com/), as our social media platform; its function is similar to “Instagram” and “Pinterest” in China. “Xiaohongshu” is a lifestyle social media and e-commerce platform. According to Statista (2021), “Xiaohongshu” has over 300 million registered users and an estimated 100 million active users, of which 46% are in the 18 to 24 years age group while 12% are below 18 years old. Through “Xiaohongshu,” all users can share and post lifestyle stories and product reviews through photos and videos.

Endorser Selection

Due to the tested beauty-related products such as soaps, locating female TCEs, MCIs, and VIs is more appropriate. For TCE, Forbes China (2020) enlists top-ranked female celebrities, that is, (1) Dongyu Zhou, (2) Mini Yang, and (3) Liying Zhao. For MCI, Launchmetrics (2021) reports top-ranked female beauty influencers, (1) Doudou Babe, (2) Ximen, and (3) Late Night Teacher Xu, in China. About the VI selection, a report from Vogue Business (2021) showed that top-ranked human-like female virtual idols, (1) Ling and (2) Ayayi, are the most popular in China. Following the reports above, 50 Gen Z were invited to fill the questionnaire about their familiarity with TCE, MCI, and VI. The results showed that the celebrity, “Dongyu Zhou,” the influencer, “Doudou Babe,” and the virtual idol, “Ling” are the three most familiar endorsers. The means of familiarity for the three types of endorsers are quite similar and have no significant difference (Mean_TCE = 6.02, Mean_MCI = 5.88, and Mean_VI = 5.90, F(2, 147) = 0.210, p = .811). Therefore, this study selected these three famous endorsers, that is, (1) TCE: Dongyu Zhou, (2) MCI: Doudou Babe, and (3) VI: Ling, for the empirical tests,

Sample Selection

The sample generation cohort of this study is Generation Z consumers. Based on the Kantar Millward Brown study (Ray & Hickey, 2017), Gen Z is skeptical about advertising which makes content marketing such as endorsements, more attractive for this generation, as compared to preceding ones. So, for this study, we first measure whether the sample Gen Z respondents are highly involved consumers. This research adopted the Jones and Reynolds (2006) measurement scales for consumers who are highly involved with a particular product. The four items are: The product advertisement of this soap on social media (1) is important to me, (2) does matter much to me, (3) is relevant to me, and (4) I care a lot.

Respondents were invited to check the six soap advertisements respectively (three low- and three high-involvement soap product advertisements) on “Xiaohongshu.” To ensure valid responses, respondents with low-level involvement attitudes were eliminated from the dataset. Using the Likert scale from 1 (strongly disagree) to 7 (strongly agree) point, the results show that the average of highly involved consumers of the three studies was similar, and higher than the overall average of each study.

The demographic summary of the three studies is shown in Table 1. Study 1 focuses on the effects of the attractiveness of different types of endorsers on highly-involved Gen Z consumers’ emotional attachment and purchase intention for high- and low-involvement products. Study 1 was designed to test the hypotheses, H1A, H1B, and H1C. The average for separating high/low levels of involved Gen Z consumers is 21. Out of the total sample size of 435, there are 279 valid samples. Of the 279 valid samples, 48% were male, and 52% were female. The majority of the sample were in the 20 to 21 years age bracket (51%), followed by 22 to 23 years (27%), 24 years (14%), and 18 to 19 years (8%). Most Gen Z respondents were undergraduate students (65%), 34% were graduate students, and 1% were high school students.

Table 1.

Demographic Profile of Respondents for the Three Studies.

Profile	Item	Study 1 (n = 279)		Study 2 (n = 246)		Study 3 (n = 256)
Profile	Item	Freq.	%	Freq.	%	Freq.	%
Gender	Male	133	48	114	46	147	57
Gender	Female	146	52	132	54	109	43
Age (years old)	18–19	22	8	59	24	13	5
	20–21	142	51	79	32	110	43
	22–23	75	27	81	33	97	38
	24	39	14	27	11	36	14
Education	High School	3	1	0	0	3	1
	Undergraduate	181	65	165	67	200	78
	Graduate School	95	34	81	33	54	21

Study 2 focuses on the effects of the expertise of different types of endorsers on high-involved Gen Z consumers’ emotional attachment and purchase intention for high- and low-involvement products. Study 2 replicates the empirical design, procedures, and measures of Study 1 to test hypotheses, H2A, H2B, and H2C about the expertise of the three types of endorsers. The average for separating high/low levels of involved Gen Z consumers is 24. Out of the total sample size of 512, there were 246 valid samples. Of 246 samples, 46% were male, and 54% were female. The age brackets of the respondents were 18 to 19 (24%), 20 to 21 (32%), 22 to 23 (33%), and age 24 (11%). More than half of the respondents were undergraduate students (67%), and the remaining 33% were graduate students. Study 2 focuses on the effects of different types of endorsers’ trustworthiness on high-involved Gen Z consumers’ emotional attachment and purchase intention under high- and low-involvement products.

Study 3 incorporated procedures identical to Studies 1 and 2, to resolve the hypotheses, H3A, H3B, and H3C. The average for separating high/low levels of involved Gen Z consumers is 23. Out of the total sample size of 407, there were 256 valid samples. Of 256 respondents, 57% were male, and 43% were female. The age range of the respondents was 18 to 19 (5%), 20 to 21 (43%), 22 to 23 (38%), and age 24 (14%). An overwhelming majority, 78% of Gen Z, were undergraduate students, 53% were graduate students, and 1% were high school students.

Instrument Development

Based on the relevant previous literature, the measurement scales of the questionnaire were modified to examine each variable’s reliability and validity. Five variables were investigated, namely the three dimensions of SC, emotional attachment, and purchase intention. Each variable is composed of five dimensions, derived from the following studies: (1) Attractiveness includes the source being physically attractive, charming, appealing, good-looking, and beautiful (Ohanian, 1991). (2) Expertise includes expert, experienced, skilled, reliable, and qualified in promoting the brand or product (Folse et al., 2013). (3) Trustworthiness includes the source being trustworthy, ethical, genuine, sincere, and honest (Touré-Tillery & McGill, 2015). (4) Emotional attachment includes having affection, or being attached, bonded, captivated, and connected (Thomson, 2006). (5) Purchase intention includes would try, would seek out, very likely, probable, and would consider (Sierra et al., 2009).

The questionnaire was divided into three sections. The first section focuses on the demographic profile of the respondents, such as gender, age, and education. The second part shows the social media advertisement of the fictitious brands (i.e., “ProBeauty” and “SecureCare” soap advertisement) with three different endorsers. The last part illustrates the theoretical constructs with 25 items from SC, emotional attachment, and purchase intention variables.

The questionnaire in all three studies was first translated into simplified Chinese by two native Chinese scholars and then translated back into English by a native bilingual Chinese marketing professor to ensure the consistency of the meaning of the English and the Chinese version. After the questionnaires were finalized and minor translation differences were corrected, structured online-based surveys were designed to test the research questions. First, a pre-test was conducted by giving the three sets of questionnaires to a marketing agency. Then, the marketing agency distributed the three sets of the survey to three groups with 30 to 40 respondents in each group. The goal was to test the clarity of the questionnaire items and wording, based on the respondents’ feedback. Then, the marketing agency disseminated the three sets of questionnaires online for approximately 45 days, for data collection.

Reliability and Validity Analysis

All items across the three questionnaires were measured using the Likert scale from 1 (strongly disagree) to 7 (strongly agree). Thus, the scores of each variable were derived by summing up the responses per item within each variable. Cronbach’s alpha (α) was measured for each variable to provide the internal consistency for each scale used; all Cronbach’s alpha coefficients were >.7, ranging from .803 to .924. This implies that the measurements in this study are reliable, and the items measure consistent characteristics. We conducted Orthogonal Varimax rotation via exploratory factor and principal component analyses, and retained the items with eigenvalue >1.0. All the Kaiser-Meyer-Olkin Measure (KMO) of the sampling results of the three sets of questionnaires were p > .5, ranging from 0.739 to 0.935. It met the required minimum KMO score, implying that this study’s data set was appropriate for factor analysis. Bartlett’s Test of Sphericity was significant at p < .01. Therefore, all variables were uncorrelated in the population correlation matrix. In addition, all items’ standardized factor loadings (SFL) were >.5, so there was no deleted item (Hair et al., 1998). Convergent validity implies how closely a measure is associated with other measures of the same construct. To measure convergent validity, it is necessary to consider composite reliability (CR) and the average variance extracted (AVE). Here, CR is also a measure of internal consistency for scale items. AVE measures the variance amount obtained from a construct associated with the variance amount because of measurement error. All composite reliability (CR) coefficients of variables fit the minimum acceptable requirement (i.e., >0.7) (Nunnally & Bernstein, 1994), ranging from 0.829 to 0.938. Also, all the AVE values met the requirements of convergent validity >0.5 (Hair et al., 1998). Hence, convergent validity was adequate. Discriminant validity represents a construct that is empirically different from another, by measuring the differences between the overlapping constructs. The square roots of average variance extracted (AVE) per construct were higher than the correlation coefficients of each variable, ranging from .706 to .868 (Farrell, 2010). The results meet the requirement of discriminant validity. In summary, the results support the reliability, convergent validity, and discriminant validity of the construct.

Statistical Analysis

All three studies use the Hayes Process Macro (Preacher & Hayes, 2008) in SPSS to examine the proposed hypotheses about various intervening factors by which causal effects operate. In this study, the Hayes Process Macro is beneficial in analyzing three different effects, estimating the path coefficients using multiple regression. First, the direct effect is the relationship between an independent, and dependent variable, through the presence of a mediating variable. Second, the indirect effect is the relationship of an independent variable with a mediating variable, and then, with a dependent variable. Lastly, the total effect is the combined influence of the direct and indirect effects through the mediator. Process Macro “Model 4” was adopted to evaluate the mediation effect. In running the bootstrap analysis, we followed the 95% confidence intervals and 5,000 bias number corrected bootstrap samples to estimate the path coefficients. Another statistical method to evaluate the significance of mediation is the Sobel test (Sobel, 1982) which examines the product of a and b path coefficients for the path of mediation and estimates the standard error of the product of a (SE_a) and b (SE_b) path. The p-values are drawn from the two-tailed z-test of the normal distribution, so to be statistically significant, the z-value should be > +1.96 or < −1.96.

Results

Study 1 Results—Endorsers’ Attractiveness

Results for High-Involvement Product

Table 2 presents the relationship among all variables. The results show that the TCE’s attractiveness (A_TCE) significantly influences the emotional attachment (EA) (b_High = 0.59, p < .01), and EA significantly affects purchase intention (PI) of Gen Z (b_High = 0.38, p < .01). Thus, the effect of A_TCE on EA and the effect of EA on PI are statistically significant. With regard to the attractiveness of MCI (A_MCI), Gen Z’s respondents indicated that A_MCI significantly influences Gen Z’s EA (b_High = 0.45, p < .01), and EA affects Gen Z’s PI positively and significantly (b_High = 0.42, p < .01). Based on Gen Z’s views toward VI, the attractiveness of VI (A_VI) significantly influences Gen Z’s EA (b_High = 0.46, p < .01), and EA positively and significantly influences Gen Z’s PI (b_High = 0.44, p < .01). Concerning the direct effect of independent variables on the dependent variable, A_TCE (b_High = 0.27, p < .01) has a significantly higher effect on Gen Z’s PI toward high-involvement products than A_MCI (b_High = 0.24, p < .01) and A_VI (b_High = 0.14, p < .01). Moreover, it shows the effect of EA as a mediator. The results show that all total effects are statistically significant along three paths (1) A_TCE→EA→PI_High (b_High = 0.50, p < .01); (2) A_MCI→EA→PI_High (b_High = 0.43, p < .01); and (3) A_VI→EA→PI_High (b_High = 0.34, p < .01). The Sobel test shows that EA plays a significant mediating role between independent variables (i.e., A_TCE, A_MCI, and A_VI) and the dependent variable (i.e., PI).

Table 2.

Study 1 Results—The Attractiveness of Endorsers.

Constructs	Relationship	Coeff.	SE	t	p	LLCI	ULCI	Sobel
High-Involvement Product
Attractiveness-Traditional Celebrity Endorser (A_TCE)
A_TCE→EA→PI	A_TCE→EA	0.5862	0.0630	9.3081	.0000	0.4621	0.7103
	EA→PI	0.3768	0.0510	7.3900	.0000	0.2763	0.4772
	DE	0.2797	0.0578	4.8424	.0000	0.1659	0.3935
	TE	0.5006	0.0547	9.1453	.0000	0.3927	0.6084
	IE	0.2209	0.0427			0.1389	0.3070	5.7860**
Attractiveness-Micro-Celebrity Influencer (A_MCI)
A_MCI→EA→PI	A_MCI→EA	0.4529	0.0683	6.6335	.0000	0.3184	0.5874
	EA→PI	0.4214	0.0479	8.7976	.0000	0.3270	0.5158
	DE	0.2404	0.0548	4.3857	.0000	0.1324	0.3484
	TE	0.4313	0.0579	7.4494	.0000	0.3172	0.5453
	IE	0.1908	0.0410			0.1156	0.2770	5.2953**
Attractiveness-Virtual Influencer (A_VI)
A_VI→EA→PI	A_VI→EA	0.4676	0.0570	8.1999	.0000	0.3553	0.5800
	EA→PI	0.4389	0.0510	8.6006	.0000	0.3383	0.5394
	DE	0.1389	0.0508	2.7371	.0067*	0.0389	0.2389
	TE	0.3441	0.0512	6.7174	.0000	0.2432	0.4451
	IE	0.2052	0.0430			0.1282	0.2974	5.9379**
Low-Involvement Product
Attractiveness-Traditional Celebrity Endorser (A_TCE)
A_TCE→EA→PI	A_TCE→EA	0.5842	0.0538	10.8523	.0000	0.4782	0.6901
	EA→PI	0.3771	0.0483	7.8041	.0000	0.2820	0.4723
	DE	0.3175	0.0517	6.1434	.0000	0.2158	0.4193
	TE	0.5378	0.0477	11.2643	.0000	0.4438	0.6318
	IE	0.2203	0.0397			0.1452	0.3036	6.6339**
Attractiveness-Micro-Celebrity Influencer (A_MCI)
A_MCI→EA→PI	A_MCI→EA	0.4910	0.0598	8.2132	.0000	0.3734	0.6087
	EA→PI	0.4225	0.0454	9.2999	.0000	0.3331	0.5119
	DE	0.2928	0.0504	5.8091	.0000	0.1936	0.3921
	TE	0.5003	0.0517	9.6746	.0000	0.3985	0.6021
	IE	0.2075	0.0372			0.1358	0.2807	6.1539**
Attractiveness-Virtual Influencer (A_VI)
A_VI→EA→PI	A_VI→EA	0.5060	0.0506	10.0038	.0000	0.4064	0.6056
	EA→PI	0.4245	0.0485	8.7464	.0000	0.3290	0.5201
	DE	0.2188	0.0477	4.5897	.0000	0.1250	0.3127
	TE	0.4336	0.0461	9.4073	.0000	0.3429	0.5243
	IE	0.2148	0.0377			0.1461	0.2931	6.5861**

Note. DE = direct effect; TE = total effect; IE = indirect effect.

p < .01. *p < .05

Results for Low-Involvement Product

Similarly, for the low-involvement product, attractiveness is significant for all three types of endorsers. Table 2 shows that A_TCE significantly influences Gen Z’s EA (b_Low = 0.59, p < .01), which, in turn, significantly affects Gen Z’s PI (b_Low = 0.38, p < .01). Thus, the relationship is statistically significant. In relation to A_MCI, the responses indicated that it significantly influences Gen Z’s EA (b_Low = 0.49, p < .01), which significantly affects Gen Z’s PI (b_Low = 0.42, p < .01). With regard to VI, the paths are supported because A_VI influences Gen Z’s EA (b_Low = 0.51, p < .01), which affects Gen Z’s PI (b_Low = 0.42, p < .01). Concerning the direct effects of the attractiveness of the three types of endorsers on Gen Z’s PI for the low-involvement product, A_TCE (b_Low = 0.32, p < .01) is greater than A_MCI (b_Low = 0.29 p < .01) and A_VI (b_Low = 0.22, p < .01). Also, it presents the mediation effect of EA using the Sobel test. The results show that all total effects are significant (1) A_TCE→EA→PI_Low (b_Low = 0.54, p < .01); (2) A_MCI→EA→PI_Low (b_Low = 0.50, p < .01); and (3) A_VI→EA→PI_Low (b_Low = 0.43, p < .01). So, it shows that EA significantly plays a mediating role between the independent and dependent variables.

Study 2 Results—Endorsers’ Expertise

Results for High-Involvement Product

For the high-involvement product, Table 3 shows TCE’s expertise (E_TCE) significantly influences Gen Z’s EA (b_High = 0.37, p < .01), which, in turn, significantly influences Gen Z’s PI (b_High = 0.23, p < .01). Also, the expertise of MCI (E_MCI) significantly affects Gen Z’s EA (b_High = 0.38, p < .01), which affects Gen Z’s PI (b_High = 0.20, p < .01). In addition, the expertise of VI (E_VI) significantly influences Gen Z’s EA (b_High = 0.73, p < .01), which affects Gen Z’s PI (b_High = 0.38, p < .01). With regard to the direct effects of independent variables on PI, E_MCI (b_High = 0.57, p < .01) is greater than E_TCE (b_High = 0.38, p < .01). However, E_VI (b_high = 0.01, p = .9536) has no significant direct effect on Gen Z’s PI toward the high-involvement product. Furthermore, it shows that all total effects are statistically significant (1) E_TCE→EA→PI_High (b_High = 0.46, p < .01); (2) E_MCI→EA→PI_High (b_High = 0.64, p < .01); and (3) E_VI→EA→PI_High (b_High = 0.28, p < .01). The Sobel test shows that it is clear that EA significantly plays a mediating role among the three endorsers’ expertise.

Table 3.

Study 2 Results—Expertise of Endorsers.

Constructs	Relationship	Coeff.	SE	t	p	LLCI	ULCI	Sobel
High-Involvement Product
Expertise-Traditional Celebrity Endorser (E_TCE)
E_TCE→EA→PI	E_TCE→EA	0.3652	0.0556	6.5625	.0000	0.2556	0.4748
	EA→PI	0.2288	0.0604	3.7858	.0000	0.1097	0.3478
	DE	0.3814	0.0570	6.6942	.0000	0.2692	0.4936
	TE	0.4649	0.0539	8.6191	.0000	0.3587	0.5712
	IE	0.0835	0.0307			0.0280	0.1486	3.2815**
Expertise-Micro-Celebrity Influencer (E_MCI)
E_MCI→EA→PI	E_MCI→EA	0.3810	0.0647	5.8841	.0000	0.2535	0.5085
	EA→PI	0.2010	0.0554	3.6272	.0003	0.0919	0.3102
	DE	0.5655	0.0599	9.4412	.0000	0.4475	0.6835
	TE	0.6421	0.0574	11.1800	.0000	0.5289	0.7552
	IE	0.0766	0.0295			0.0263	0.1417	3.0889**
Expertise-Virtual Influencer (E_VI)
E_VI→EA→PI	E_VI→EA	0.7269	0.0501	14.5042	.0000	0.6282	0.8256
	EA→PI	0.3822	0.0827	4.6191	.0000	0.2192	0.5452
	DE	0.0051	0.0884	0.0582	.9536	−0.1690	0.1792
	TE	0.2829	0.0674	4.1971	.0000	0.1502	0.4157
	IE	0.2778	0.0641			0.1497	0.4041	4.4035**
Low-Involvement Product
Expertise-Traditional Celebrity Endorser (E_TCE)
E_TCE→EA→PI	E_TCE→EA	0.5544	0.0468	11.8467	.0000	0.4622	0.6466
	EA→PI	0.5328	0.0608	8.7667	.0000	0.4131	0.6525
	DE	0.2877	0.0551	5.211	.0000	0.1791	0.3962
	TE	0.5830	0.0501	11.6281	.0000	0.4842	0.6818
	IE	0.2954	0.0520			0.1999	0.4019	7.0450**
Expertise-Micro-Celebrity Influencer (E_MCI)
E_MCI→EA→PI	E_MCI→EA	0.6380	0.0552	11.5493	.0000	0.5292	0.7469
	EA→PI	0.4866	0.0580	8.3870	.0000	0.3723	0.6008
	DE	0.4234	0.0615	6.8834	.0000	0.3022	0.5446
	TE	0.7339	0.0559	13.1256	.0000	0.6237	0.8440
	IE	0.3104	0.0577			0.2046	0.4317	6.7895**
Expertise-Virtual Influencer (E_VI)
E_VI→EA→PI	E_VI→EA	0.7105	0.0519	13.6950	.0000	0.6083	0.8127
	EA→PI	0.7436	0.0681	10.9170	.0000	0.6094	0.8778
	DE	−0.0268	0.0726	−0.3689	.7125	−0.1699	0.1163
	TE	0.5015	0.0664	7.5520	.0000	0.3707	0.6323
	IE	0.5283	0.0593			0.4119	0.6438	8.5364**

Note. DE = direct effect; TE = total effect; IE = indirect effect.

p < .01. *p < .05.

Results for Low-Involvement Product

For the low-involvement product, Table 3 demonstrates that E_TCE significantly influences Gen Z’s EA (b_Low = 0.55, p < .01), which significantly impacts Gen Z’s PI (b_Low = 0.53, p < .01). Thus, they are statistically significant. For E_MCI, it shows that Gen Z’s respondents indicated that E_MCI significantly influences Gen Z’s EA (b_Low = 0.64, p < .01), and EA significantly affects Gen Z’s PI (b_Low = 0.49, p < .01). Regarding E_VI, it significantly influences Gen Z’s EA (b_Low = 0.71, p < .01), and EA significantly affects Gen Z’s PI (b_Low = 0.74, p < .01). Hence, they are significant. For the direct effects, the results show that E_MCI (b_Low = 0.42, p < .01) is greater than E_TCE (b_low = 0.29, p < .01). However, E_VI (b_Low = −0.03, p = .71) has an insignificantly direct effect on Gen Z’s PI toward the low-involvement product which is similar to the results of the high-involvement product. It also presents the mediation effect of EA. The results show that all total effects are significant; (1) E_TCE→EA→PI_Low (b_Low = 0.58, p < .01); (2) E_MCI→EA→PI_Low (b_Low = 0.73, p < .01); and (3) E_VI→EA→PI_Low (b_Low = 0.50, p < .01). Based on the Sobel test, significant indirect effects are found between the independent and dependent variables.

Study 3 Results

Results for High-Involvement Product

Table 4 illustrates that TCE’s trustworthiness (T_TCE) significantly influences Gen Z’s EA (b_High = 0.39, p < .01), which significantly affects Gen Z’s PI of the high-involvement product (b_High = 0.15, p < .01). Regarding the trustworthiness of MCI (T_MCI), Gen Z’s respondents indicated that T_MCI significantly influences Gen Z’s EA (b_High = 0.40, p < .01). Also, EA significantly affects Gen Z’s PI (b_High = 0.09, p < .05). Regarding the trustworthiness of VI (T_VI), it significantly influences Gen Z’s EA (b_High = 0.23, p < .01). Furthermore, it shows that EA significantly affects Gen Z’s PI (b_High = 0.18, p < .01). For the direct effects, T_MCI (b_High = 0.15, p < .01) is greater than T_TCE (b_High = 0.11, p < .01). However, the direct effect of T_VI (b_Low = −0.02, p = .59) is insignificant. Furthermore, it shows that the total effects, (1) T_TCE→EA→PI_High (b_High = 0.15, p < .01) and (2) T_MCI→EA→PI_High (b_High = 0.18, p < .01), are statistically significant. However, T_VI→EA→PI_High (b_High = 0.02, p = .59) is statistically insignificant. Based on the Sobel test, it shows that all indirect effects are statistically significant.

Table 4.

Study 3 Results—Trustworthiness of Endorsers.

Constructs	Relationship	Coeff.	SE	t	p	LLCI	ULCI	Sobel
High-Involvement Product
Trustworthiness-Traditional Celebrity Endorser (T_TCE)
T_TCE→EA→PI	T_TCE→EA	0.3912	0.0530	7.3775	.0000	0.2867	0.4956
	EA→PI	0.1452	0.0446	3.2592	.0013	0.0575	0.2330
	DE	0.1147	0.0401	2.8590	.0046	0.0357	0.1938
	TE	0.1479	0.0364	4.0604	.0001	0.0762	0.2197
	IE	0.0332	0.0181			0.0008	0.0725	2.9787**
Trustworthiness-Micro-Celebrity Influencer (T_MCI)
T_MCI→EA→PI	T_MCI→EA	0.3963	0.0490	8.0827	.0000	0.2997	0.4928
	EA→PI	0.0945	0.0467	2.0261	.0439	0.0026	0.1864
	DE	0.1458	0.0400	3.6483	.0003	0.0671	0.2245
	TE	0.1833	0.0357	5.1389	.0000	0.1130	0.2535
	IE	0.0375	0.0186			0.0023	0.0756	1.9630*
Trustworthiness-Virtual Influencer (T_VI)
T_VI→EA→PI	T_VI→EA	0.2301	0.0493	4.6689	.0000	0.1330	0.3272
	EA→PI	0.1815	0.0443	4.0920	.0001	0.0941	0.2688
	DE	−0.0227	0.0354	−0.6421	.5214	−0.0924	0.0470
	TE	0.0191	0.0350	0.5450	.5862	−0.0498	0.0879
	IE	0.0418	0.0136			0.0182	0.0717	3.0791**
Low-Involvement Product
Trustworthiness-Traditional Celebrity Endorser (T_TCE)
T_TCE→EA→PI	T_TCE→EA	0.2597	0.0525	4.9443	.0000	0.1562	0.3632
	EA→PI	0.1364	0.0439	3.1063	.0021	0.0499	0.2229
	DE	0.1434	0.0375	3.8239	.0002	0.0695	0.2173
	TE	0.1788	0.0364	4.9175	.0000	0.1072	0.2505
	IE	0.0354	0.0136			0.0122	0.0653	2.6311**
Trustworthiness-Micro-Celebrity Influencer (T_MCI)
T_TCE→EA→PI	T_TCE→EA	0.3084	0.0511	6.0296	.0000	0.2077	0.4092
	EA→PI	0.1355	0.0453	2.9915	.0031	0.0463	0.2247
	DE	0.1217	0.0385	3.1597	.0018	0.0458	0.1976
	TE	0.1635	0.0365	4.4813	.0000	0.0916	0.2354
	IE	0.0418	0.0154			0.0150	0.0737	2.6801**
Trustworthiness-Virtual Influencer (T_VI)
T_MCI→EA→PI	T_MCI→EA	0.2290	0.0491	4.6603	.0000	0.1322	0.3258
	EA→PI	0.1930	0.0450	4.2917	.0000	0.1044	0.2816
	DE	−0.0154	0.0357	−0.4322	.6660	−0.0859	0.0550
	TE	0.0287	0.0355	0.8108	.4183	−0.0411	0.0986
	IE	0.0442	0.0140			0.0202	0.0738	3.1570**

Note. DE = direct effect; TE = total effect; IE = indirect effect.

p < .01. *p < .05.

Results for Low-Involvement Product

For the analyses of the low-involvement product, Table 4 shows that T_TCE significantly influences Gen Z’s EA (b_Low = 0.26, p < .01), which significantly affects Gen Z’s PI (b_Low = 0.14, p < .01). Thus, this path is statistically significant. T_MCI significantly influences Gen Z’s EA (b_Low = 0.31, p < .01), which significantly affects PI (b_Low = 0.14, p < .01). Regarding T_VI, it significantly influences Gen Z’s EA (b_Low = 0.23, p < .01), which, in turn, has a significant effect on Gen Z’s PI (b_Low = 0.19, p < .01). Regarding the direct effect, both T_TCE (b_Low = 0.14, p < .01) and T_MCI (b_Low = 0.12, p < .01) significantly influence Gen Z’s PI, but T_VI shows an insignificant direct effect on Gen Z’s PI (b_Low = −0.02, p = .67). Moreover, it shows that the total effect for the paths T_TCE→EA→PI_Low (b_Low = 0.18, p < .01) and T_MCI→EA→PI_Low (b_Low = 0.16, p < .01) are statistically significant. However, the path T_VI→EA→PI_Low (b_Low = 0.03, p = .42) is statistically insignificant. Furthermore, the Sobel test shows that all indirect effects are statistically significant, which implies findings similar to high-involvement conditions.

Discussions

Brands are competing to woo young consumers, mainly the Gen Z market, to boost their post-pandemic rebound. Gen Z is familiar with the technology for shopping and are highly involved with brands’ product stories throughout their shopping journey. Gen Z is the most likely to shop via social media and engage in more research for a reliable source of information on the product before making a purchase decision. They are overzealous with endorsements on social media and more invested in following their favorite endorsers. Chinese Gen Z, in particular, is one of the earliest adopters of this trend.

In traditional media marketing, all celebrities were considered influencers. However, with the new media marketing measurement, consumers follow TCE based on consumers’ admiration of their attractiveness or talent or entertainment products, such as movies, music, or sports. Consumers follow MCIs either for their attractiveness, or relevant expertise in relation to the product they specialize in (e.g., cooking, fashion, or fitness). Partnerships between brands, and TCs, and MCIs are not new. However, with the emergence of a new type of computer-generated artificial influencer, VI, it is even more critical for brands to decide how to identify the use of the right TC, MCI, and VI mix. People follow VI based on their attractiveness, and the advantage of less probability of scandals. Brands must understand the differences in order to identify the proper endorser that best matches their product. In addition, brands must tailor their marketing strategies depending on whom they are trying to reach and product category and use different endorsers (e.g., TCE, MCI, or VI) accordingly.

The studies on Gen Z consumers’ involvement with a particular product have been underrated in endorsement literature as many scholars focus on millennials and consider them as a prime construct in their paper. It may be because the Gen Z market is still in the discovery phase for brands and scholars to study. So, the concept of product involvement unexplored in this field would be particularly valuable, since considering the derived risks depends on the importance Gen Z consumers attach to the endorsed product. We proposed that the effects of the endorser’s source credibility and endorser-product match are more pronounced if Gen Z is involved in the advertised product. So, this study bridges the gaps in existing research and attempts to explore the impact of different types of endorsers on highly involved Gen Z by incorporating the product levels of involvement and the effect of emotional attachment as mediators. We may gain a better understanding of the dynamics of product endorsement in social media.

Study 1 Discussions

The results of Study 1 answer hypothesis H1A that physical attractiveness, regardless of the type of endorser, affects both, EA and PI of Gen Z consumers, regardless of whether the products are high- or low-involvement. In addition, the results also supported previous studies (e.g., Kamins, 1990; Ladhari et al., 2020; Petty et al., 1983; Roozen & Claeys, 2010; Till & Busler, 2000) that the beauty-care match-up hypothesis suggests that when advertising attractiveness-related products, endorsers’ physical attractiveness is relevant because it functions as an associative link to product features or arguments.

Regarding comparing TCEs, MCIs, and VIs’ physical attractiveness, the results show that the H1B argument that TCE’s physical attractiveness exerts the most substantial influence in endorsing different beauty-related products’ levels of involvement in which our result significantly supports the study of Praxmarer (2011) that attractive beauty products endorsers are persuasive. Also, celebrities who are known, liked by, and have similarities with consumers are considered attractive and, to an extent, persuasive (Brown & Tiggemann, 2016).

Concerning the nature of the product, the physical attractiveness of the three types of endorsers is more significant in the low-involvement, rather than high-involvement product, which supports previous studies (e.g., Y. Lee & Koo, 2016; Petty et al., 1983). Furthermore, Roozen and Claeys (2010) stated that attractive endorsers would also be more effective for products associated with low financial risk such as soap.

In addition, the results show that Gen Z’s EA has a mediating effect on the relationship between different types of endorsers’ physical attractiveness and PI, which answered the proposition of H1C. The result supports the finding of Bagozzi et al. (1999) on the role of emotions as a mediator for various relationships. Study 1’s findings further strengthen previous literature that the physical attractiveness of endorsers, whether TCE, MCI, or VI, is significantly relevant in product endorsement and is a valuable attribute that marketers need to consider for promoting beauty-related products.

Study 2 Discussions

Concerning endorsers’ expertise as proposed in H2A, Study 2 results show that the expertise of TCEs and MCIs is a significant driver of Gen Z’s EA and willingness to purchase high- and low-involvement products. However, VIs’ expertise does not directly affect Gen Z’s PI, regardless of different products’ levels of involvement. So, the result supports the argument of Healy (2021) that consumers question whether VIs genuinely provide an authentic opinion, testimony, or content of the endorsed products because they don’t actually use the products they endorse, unlike their human counterparts; this posed a significant challenge for VIs’ endorsing beauty-related products.

Regarding research hypothesis H2B, the results show that MCIs’ expertise has the strongest influence on Gen Z’s decision to purchase regardless of different products’ levels of involvement. This contradicts Schouten et al. (2020) findings, stating that there is no significant difference between TCE’s and MCI’s expertise. An explanation of our findings could be that Gen Z respondents see MCIs as experts in a specific domain, such as beauty soap. Therefore, consumers may consider them to be more knowledgeable in providing product reviews, honest opinions, and recommendations (Rios Marques et al., 2020).

Our study also reports that the expertise of MCIs and TCEs has a greater influence on Gen Z’s PI for the high-involvement product than for the low-involvement product for beauty-related products. These results seem to contradict previous findings stating that endorsers’ expertise has little effect on non-technical products (Kamins, 1990; Till & Busler, 2000). One possible explanation may be that beauty-related products like soaps involve psychological risk (Roozen & Claeys, 2010); soap may contain harmful materials that might irritate the skin. So, Gen Z consumers rely more on the expertise of the endorser for reliable information and how to use them effectively. The endorser, in turn, provides content or opinion regularly.

Furthermore, Gen Z’s EA significantly mediates the relationship between the three types of endorsers’ expertise and willingness to purchase high- and low-involvement products. So, again, as for the research hypotheses for Studies 1 and 2, H2C supports previous literature (e.g., Bagozzi et al., 1999) on the role of emotions as a mediator of consumer responses. So, we can summarize that regardless of the levels of product involvement, endorsers’ expertise is critical in persuading Gen Z to buy beauty-related products.

Study 3 Discussions

For hypothesis H3A, Study 3 results show that the trustworthiness of the three types of endorsers is significantly related to Gen Z’s EA and PI high- and low-involvement products which support previous findings (e.g., Park & Lin, 2020; Schouten et al., 2020). It also shows that TCE’s and MCI’s trustworthiness directly affects PI, which supports Qian and Park’s (2021) findings that the trustworthiness of human endorsers is a valuable contributor to perceptions about high-end products. However, VI’s trustworthiness has an insignificant direct effect on PI for varying levels of product involvement. It may be because VI, a computer-generated image, cannot provide genuine reviews or honest recommendations of the merits of a product, because they cannot try out the product, so there are uncertainties about consumers’ trust. Therefore, it can be concluded that the physical embodiment of a VI is not a valuable factor in eliciting favorable consumer trust.

Regarding hypothesis H3B, Study 3 shows that MCIs’ trustworthiness had the strongest effect on Gen Z’s decision to purchase high-end soap. Therefore, the more Gen Z consumers can identify with the MCI, the more their buying intention will be influenced (Munnukka et al., 2016). Also, MCI endorses the products and provides two-sided views, such as positive and negative, creating transparency (Wiedmann & von Mettenheim, 2021). TCEs’ trustworthiness, as compared to that of MCI and VI, greatly influenced Gen Z’s decision to purchase low-end soap. The reason behind this may be that Gen Z are fascinated by famous celebrities, and they may consider them role models. The results of this study contradict the findings of Schouten et al. (2020) that consumers trust MCI more than TCE, which partly explained that the different types of endorsers’ trustworthiness effects might be dependent on the nature of the product. It also contradicts the study of Pornpitakpan (2003) that TCE trustworthiness does not affect consumers’ buying intentions. The discrepancies in the results may be attributed to the difference in the cultural perspectives of the respondents. However, the trustworthiness of VI has no significant direct effect on high- and low-involvement products, which contradicts the findings of S. A. A. Jin and Sung (2010) that spokes-avatars, who are perceived as trustworthy generate higher consumer brand attitudes and satisfaction. Gen Z consumers may not trust VIs, who emerge from a make-believe world, as they cannot try products like soap, and provide an honest opinion. There’s a need to enhance facilitating mood monitoring (e.g., emotional expressions, voice traits, and social interaction) of VIs throughout the consumer’s online shopping journey in order to gain trust from consumers (Kliestik, Kovalova, & Lăzăroiu, 2022)

Lastly, for hypothesis H3C, for high- and low-involvement products, the indirect effect of Gen Z’s EA significantly mediates the relationship between the trustworthiness of two types of endorsers’ (e.g., TCE and MCI) and PI. However, the indirect effect between VI’s trustworthiness and PI is weak. This finding provides new evidence that even though Gen Z is the most targeted audience for VI endorsements, the latter’s impact on consumers’ attitudes and behavior is still doubtful.

Conclusion

Theoretical Contributions

This study has several implications for theory and research. First, it contributes to the literature on endorsement, SC, consumer and product involvement, and attachment theory, by building on previous research and analyzing consumers’ perceptions of social media endorsement advertising. Most of the studies in this field focus on a single endorser. To the best of our knowledge, the present research is the first to directly compare three types of endorsers (TCE vs. MCI vs. VI) in terms of their SC. Although the three SC elements have been predominantly used in previous studies on TCEs and MCIs, two studies (e.g., S. V. Jin et al., 2019; Schouten et al., 2020) found that MCIs are more effective endorsers. However, it has not examined the attributes of VI empirically. Moreover, only a few studies focus on the effectiveness of VI (e.g., Arsenyan & Mirowska, 2021; Kádeková & Holienčinová, 2018; Moustakas et al., 2020). So, previous literature has limited insights regarding the value of VI’s SC to consumers, which has not yet been exploited. This virtual technology still being in its initial stages, and firms are currently not able to analyze the impact of VI on consumer behavior comprehensively. Therefore, there are many research opportunities to be explored. Therefore, this study provides new insights into the difference between various endorsers in social media advertisement or as marketing tools.

Second, most previous studies focus on congruence between endorsers’ SC and product attributes (Kamins, 1990; McCormick, 2016; Till & Busler, 2000). So, we propose the attachment theory of Bowlby (1969), stating that the degree of consumers’ emotional attachment to a human brand, predicts their motivation, judgment, and consumption decision. As a result of our empirical findings, endorsers’ SC can elicit consumers’ emotional states, which culminate in favorable responses. Also, EA is treated as a mediation factor, providing a significant link between the endorser’s SC and buying intention by creating a beneficial emotional experience for the consumers that can trigger the decision to purchase (Nica et al., 2022). The result also reports the direct effect of EA on PI, which supports previous findings (e.g., Burnasheva & Suh, 2022).

Third, we used ELM as our theoretical framework to understand how Gen Z processes various SC elements in social media advertising of beauty-related products. Our study took further steps to demonstrate that both product types (high and low-involvement) affect the nature of Gen Z central processing, which extends the results of the previous scholars (e.g., El Hedhli et al., 2021; M. T. Lee & Theokary, 2021; Yilmaz et al., 2011).

Fourth, our results contradict Zaichkowsky’s (1985) findings showing that with the increasing level of consumer product involvement, the impact of the TCE on purchase decisions lessens, and vice versa. Our results show that TCE and MCI have a direct effect on purchase intention for high- and low levels of product involvement. The endorsement effect plays a vital role in the effectiveness of social media advertisement and all dimensions of endorsers’ SC are valuable factors in persuading Gen Z to purchase.

Fifth, it is well known in advertisements for low-involvement products that the physical attractiveness of endorsers is sufficient for favorable consumer responses (Y. Lee & Koo, 2016; Petty et al., 1983). Our findings show that the physical attractiveness of different endorsers consistently influences Gen Z consumers’ PI regardless of the level of product involvement. So, physical attractiveness should be integrated as a critical requirement for choosing an endorser of a beauty-related product.

Lastly, another novelty of this study is the scope of Gen Z consumers, who are the most targeted market for social media advertisement, even though their purchasing power is low due to their age. Previous studies are mainly limited to examining TCE’s influence on Gen Y’s PI (McCormick, 2016) and MCI’s impact on Gen Z’s buying behavior (Croes & Bartels, 2021). In addition, there is limited study on the impact of different endorsers on Gen Z’s consumption decisions. Knowing the extent to which we could understand which endorsers’ SC has the most vital links to Gen Z’s EA and purchasing behavior would enable us to extend the existing research on endorsement advertising on social media. So, this study introduced research propositions and new arguments from previous studies. For example, different endorsers can impact Gen Z’s EA and intention to purchase endorsed beauty-related products, contradicting earlier studies (e.g., Schouten et al., 2020), that young consumers consist primarily of students, who identify more with MCIs than TCEs. So, the significant similarities and differences of our study with previous research, regarding the perceptions of Gen Z toward social media endorsement advertisement, contribute new knowledge to existing literature.

Practical Implications

The findings of this study provide significant managerial implications for social media marketers and retailers to optimize the use of different endorsers on social media. First, a firm’s use of endorsers is sufficient to drive Gen Z’s purchase intention. Marketers need an in-depth understanding of how Gen Z potentially forms an emotional attachment with endorsers’ SC, which is crucial for an effective social media endorsement advertising strategy. Second, despite the current trends about MCI, TCE is still a reliable marketing strategy for the young generation in China. They are more susceptible to endorsers’ influence, especially in a highly fragmented digital environment; hiring TCE still guarantees media attention and drives traffic. Companies should use localized celebrities from the same ethnic group as Chinese consumers (Yu & Hu, 2020). Third, Table 5 shows that only VI’s physical attractiveness is significant for both product types, while its expertise and trustworthiness are insignificant. We can see that Chinese Gen Z consumers are still more interested in human agents (e.g., TCE and MCI) to endorse beauty-related products, rather than human-like virtual influencers who, they believe, cannot provide an honest opinion because they cannot test the products. Overall, there is still huge uncertainty among marketing practitioners about the use of VI. This study helps marketers overcome the limitations of VI with their storytelling ability. Hence, VI’s physical attractiveness is more persuasive to consumers and may present a unique opportunity for the current generation. Also, VI experiential marketing is still in its infancy, with many technical flaws. So, it is recommended that marketers tailor the services of VI to create an effective emotional shopping experience (Hopkins, 2022) by using articulate quality content production to enhance capturing the young generations’ purchase decisions (Kliestik, Zvarikova, & Lăzăroiu, 2022).

Table 5.

Summary of Findings—Comparison of Source Credibility of Endorsers.

Product Type	Comparison of source credibility of endorsers
High-involvement product	A_TCE > A_MCI > A_VI
	E_MCI > E_TCE
	T_TCE > T_MCI
Low-involvement product	A_TCE > A_MCI > A_VI
	E_MCI > E_TCE
	T_TCE > T_MCI

Note. E_VI and T_VI are insignificant.

Fourth, the high importance of physical attractiveness for all types of endorsers, as shown in Table 5, may be good news for firms selling various beauty-related products because endorsers provide visual evidence of the effectiveness of the product (Wiedmann & von Mettenheim, 2021). Fifth, for MCI, as shown in Table 5, companies need to incorporate credible or expert endorsers, with maximum use and knowledge of the product within the niche market. Gaining Gen Z’s trust in their expertise is extremely important to marketers, especially for high-involvement products (Xiao et al., 2018). Similar to Park and Lin’s (2020) study, consumers tend to rely on endorsers’ professional knowledge.

Sixth, for firms focusing on purchasing intention, the firm should be utilizing human agents with whom Gen Z is emotionally attached. For instance, currently, Chinese Gen Z purchases products endorsed by celebrity idols and famous social media influencers. Seventh, marketers can utilize this information to prioritize specific marketing activities based on the endorsement goal, allowing marketers to obtain more fine-grained perspectives on understanding Chinese Gen Z’s opinions toward purchasing endorsed products. In addition, firms should leverage popular digital platforms in China to establish an end-to-end marketing strategy, which is vital to create an emotional connection with Gen Z.

Lastly, firms can have a complementary strategy of employing TCE, MCI, and VI together, because different types of endorsers may result in distinct interactions with consumers at varying levels of emotional attachment. Also, each endorser has a distinct personality, specialization, and target niche, so they can complement each other in terms of the meaning they transfer to a brand for endorsement content to feel authentic, to reach wider audiences, and to increase brand awareness. Therefore, a brand needs to look for endorsers who could connect with Gen Z closely to affect their purchase behavior.

Limitations and Future Research

Even though this study has been conducted with in-depth rigor, it is subject to some limitations. First, this study was conducted in China; thus, it may pose limitations for the generalizability of the results. Future research can examine consumers’ perceptions from diversified cultural backgrounds. Second, this research does not measure the demographic difference between gender social media patterns and geographic differences among different tier cities in China. Further research is needed to test the validity of these proposed models to understand Gen Z consumers’ differences across diversified demographic, geographical, and psychological perspectives to come up with more precise insights on the Gen Z market. Third, using the Rossiter-Percy Grid, this study only focused on one type of product, soap, which is considered a hedonic product dimension (e.g., beauty-related products) that emphasizes self-expression, pleasure, and experience. So, to make the study more comprehensive, researchers can compare different product types (e.g., hedonic vs. utilitarian or search vs. experiential products). Fourth, future studies may also delve into different types of VIs (e.g., human-like and animation-like) compared to TCE and MCI. Given the virtual and interactive marketing trend, we believe virtual influencers’ research deserves further attention. Lastly, replications of this research using various marketing tools, such as live streaming on social media, should also provide further evidence.

Footnotes

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.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received the research funding from Wenzhou-Kean University: KY2021071500003112.

ORCID iDs

Candy Lim Chiu

Han-Chiang Ho

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