The Influencers’ Attributes and Customer Purchase Intention: The Mediating Role of Customer Attitude Toward Brand

Abstract

The main purpose of this study is to discuss whether online influencers can improve customer brand attitude and thus increase purchase intention as both of them are important performance indicators in a live show. Drawing on the persuasion theory, the authors aim to investigate the impacts of influencers’ attributes (professionalism, credibility, interactivity and attractiveness) on customers purchase intention from the perspective of attitude toward brand. Data were collected from 233 customers in China where the influencer centered livestreaming e-commerce is enjoying rapid growth. The hypotheses were tested via structural equation modeling. The findings indicate that the influencers’ credibility and attractiveness increase purchase intention directly. Further, customer brand attitude plays a mediating role in the relationship between the influencers’ attributes (attractiveness, credibility, and interactivity) and purchase intention. While, the professionalism improves neither favorable customer brand attitude nor purchase intention. The findings provide theoretical implications for scholars to rethink the role of internet influencers in influencing customer purchase intention.

Plain Language Summary

The influencers’ attributes and customer purchase intention

The authors aim to investigate the impacts of influencers’ attributes (professionalism, credibility, interactivity and attractiveness) on customers purchase intention from the perspective of attitude toward brand. Data were collected from 233 customers in China where the influencer centered livestreaming e-commerce is enjoying rapid growth. The findings indicate that the influencers’ credibility and attractiveness increase purchase intention directly. Further, customer brand attitude plays a mediating role in the relationship between the influencers’ attributes (attractiveness, credibility, and interactivity) and purchase intention. While, the professionalism improves neither favorable customer brand attitude nor purchase intention.

Keywords

internet influencers’ attributes customer attitude toward brand purchase intention live show influencer marketing

Introduction

Influencer marketing on live streaming platform becomes important for business around the world (B. Tian et al., 2023; Y. Wu et al., 2022). For instance, more than 60% retailers in North America use influencers (eMarketer, 2019). It becomes an important retailing form in Chinese market as well (C. Liu et al., 2020) because of the increasing huge number of online live streaming users. According to the 2022 China Live Streaming Industry Development Status and Market Research and Analysis Report by iiMedia Research, as of June 2021, the number of online live streaming users in China has reached to 638 million which account for 63.1% of the total netizens, an increase of 47.2% compared with the same period in 2020. This new selling form via influencers on live streaming platform brings opportunities for managements to cope with the marketing challenges arouse by the uncertainty, such as the influences of the COVID-19, the new technologies, the increasingly fierce competition. It bypasses traditional intermediate channels such as dealers, and breaks through time and space constraints to achieve consumption upgrades.

Practically, there are two different live shows, brand-centered and internet influencer centered. The internet influencer centered live show normally is embedded in a social media platform (i.e., Douyin, Facebook) which is initiated in the internet influencer-centered communities (Fink et al., 2020). The products sold by the internet influencers in the live shows are not fixed on a specific brand product. While, on the contrary, the brand-centered live-streaming normally initiated by the brand party, and therefore, the influencers just sell one specific brand products. Our study focuses on the internet influencer centered live-streaming, as more and more people become internet influencers and do live-stream marketing successfully. These internet influencers are good at using live broadcast technology to display products. By the effective use of the traffic foundation of the social media platforms, the influencers interact with audience in real time and create a community atmosphere for customers. Consequently, the customers’ shopping experience is embedded with entertainment and social attributes, which provide audience with better scenarios (Ma, 2021). While doing live show, the influencers bring more traffic to the brand, making it easier for influencers to share and forward brand information to audience (Lin et al., 2018), which in turn affects audience’s attitudes toward the brands. Therefore, the internet influencers and the live streaming platform provide new channel for management to publicize and express their brands. For this reason, the live shopping which penetrate deeply into our daily life becomes an effective channel for companies to build and promote their brands and stimulate consumption.

Besides influencing the customers attitudes toward brands via the online live streaming, the influencers greatly arouse customers purchasing intention during the live show as well. The sale data in some of live show support this argument. For instance, some live-stream celebrities such as Dong Yuhui who is a successful influencer in China achieve huge sales in one live show, and have exhibited great commercial value (Ma, 2021). On the evening of January 23, 2024, the two-hour live broadcast by Dong Yuhui in his Douyin account “TIME WITH YUHUI” had a total of 8.95 million viewers, with the highest number of online users reaching nearly 700,000. At the same time, the live broadcast room hung out the subscription link of 12 issues of “People’s Literature” in 2024, and 2,000 sets were sold within 15 min of the broadcast. Some research findings suggest that live-stream shopping can increase customer shopping intention (e.g., C. Liu et al., 2020; Taillon, et al., 2020). Facing the emergence and attraction of legendary sales events by the influencers, several serious marketing issues deserve to discover by marketing and brand managers as well as researchers. A key issue concerning the influencer centered live-streaming is whether and how the influencers’ attributes interact with brand attitude and consequently arouse purchase intention. Although this study focuses on the influencer centered livestreaming, brand attitude is still known as an important factor that cannot be ignored in influencing consumer decisions. Though, so far, very few studies have attempted to consider both the combined impacts of influencer and brand attitudes on purchase intention. For instance, Chetioui et al. (2020) investigated the mediating impacts of brand attitude on the relationships between customers attitudes toward influencers and purchase intention in fashion industry. However, there is still a lack of literature tackling the confluences of influences’ attributes and brand attitude on purchase intention. Further, prior research calls for studies focusing on the deeper mechanism of influencers’ attributes on purchase intention in different customer consumers’ cultural and demographic backgrounds (Masuda et al., 2022; S. Tian et al., 2023), as well as studies of confluence of influencers and brand attitude on purchase intention in different industries (Chetioui et al., 2020).

The purpose of this study is to bridge this gap in literature by examining mediating effects of brand attitude on the relationships between influencers’ attributes on purchase intention, as well as the impacts of influencers’ attributes on brand attitude and purchase intention. Rooted in persuasion theory, influencing people arouse purchase intention is a form of persuasion aiming at changing a person’s attitude or behavior (Dotson & Hyatt, 2000). Thus, this study will answer the following research questions based on persuasion theory:

RQ1: To what extent can the internet influencers’ attributes affect customers attitudes toward brands?

RQ2: Do the live-stream influencers can achieve the goals of traditional brand building, and thereby stimulate customers’ desire to buy?

RQ3: Can the personal characteristics of internet influencers directly affect customers’ shopping choices beyond the brand effects? If not, what role does brand attitude play?

Consequently, this study makes theoretical and practical contributions. First of all, it enriches the literature of the influencer centered livestreaming marketing by investigating how and what influencers’ attributes arouse customer purchase intention via customers attitude toward brand in the context of influencer centered livestreaming. Secondly, our study will enrich the literature of celebrity endorsements’ impact on purchase intention and offer further insights regarding the importance of internet influencers’ attributes. Finally, the findings will also provide constructive suggestions for marketers in selecting and training influencers and designing the live show.

The remainder of this paper is organized as follows. Section “Literature Review and Hypotheses Development” provides a literature review and hypotheses development of this study. This is followed by methodology section which introduce research context, data collection process, the Measurements used in this study, as well as the test of data validity and reliability. Section “Results” presents the results of the hypothesis testing. Section “Discussion” provides an in-depth discussion of the theoretical and practical implications, describes the limitations of this study, and suggests future research directions. Finally, Section “Conclusion” concludes the study.

Literature Review and Hypotheses Development

Literature Review

Internet influencer, is an individual who creates content to influence consumers via social media platforms such as Douyin, YouTube, and Twitter (Khamis et al., 2017; Tseng & Wang, 2023). According to persuasion theory, persuasions targets at changing a person’s attitude or behavior (Masuda et al., 2022). Applying persuasion theory to the influencer marketing in social media, the followers’ attitude (e.g., purchase intention) is impacted by the characteristics of influencers. This argument is reflected in the research on the influence of traditional celebrities on consumer attitudes and the research on the influencers’ characteristics of new media platform on consumer attitude. For instance, some prior research suggested that traditional celebrity attributes can help to build the customer attitude toward brands (S. W. Wang et al., 2017; W. Y. Wu et al., 2012). The personal characteristics of internet influencers in social media platforms are different from those of traditional celebrities who are usually popular stars, such as famous singers, bringing strong star halo effect. Internet influencers are mostly “grassroots” in the beginning. However, they disseminate content that is close to public life, such as Dong Yuhui, the internet influencer in China, which make customers have a closer psychological distance to them (Sands et al., 2022). Further, the internet influencers interact with customers through new media channels such as live broadcasts and discussion groups. They have strong real-time dynamic two-way interaction and can arouse customers’ desire to participate. Some research suggested that the internet influencers and social celebrity increase customers’ purchase intention, with no significant difference (Shi et al., 2021). Therefore, practically, more and more brand managers pay attention to livestreaming marketing, and choose internet influencers to deliver brand information in a live show in order to improve consumers’ attitude toward brands and thus increase sales.

Scholars have carried out some studies on the attributes of internet influencers that affect customer attitude and behavior (T. Y. Chen et al., 2021; Ouvrein et al., 2021; Shabahang et al., 2023), and suggested that influencers’ attributes drive their success (L. Wang et al., 2021). We define internet influencers’ attributes as professionalism, credibility, interactivity, and attractiveness in this study, which originate from the study of F. Liu et al. (2020), and have been applied in some studies, such as the study by L. Wang et al. (2021). Adopting this classification is due to the following two reasons. First, the study of F. Liu et al. (2020) refined the characteristics of internet influencers on the basis of analyzing the barrage data of live-stream influencer captured by the big data Python crawler technology. Meanwhile, the purpose of F. Liu et al.’s (2020) study is focusing on the impacts of internet influencers attributes on customers’ purchase intention which share similarities with our study. The other reason is that the growth of internet live streaming industry is not uniform among different countries. The study by F. Liu et al. (2020) is conducted based on the background of online live streaming marketing in China. Therefore, it is more suitable for this study. In addition, this research results have been adopted by 13 research studies in China. From the perspective of the accuracy, applicability, and research purpose, this classification is very suitable for this study.

Attitude toward brand is defined as individual’s overall evaluation of a brand (Mitchell & Olson, 1981). IT is considered as an effective predictor of customer behaviors (Bartsch et al., 2016). The internet influencers promote brand information during a live show. As they have an informational social influence, they act as opinion leaders in their informal groups among the audience (Gnambs & Batinic, 2013; Ouvrein et al., 2021). This kind of influence makes the target audience accept their attitudes (Kelman, 1961). Therefore, customers’ recognition on the brand is increased, and their awareness of brands is improved.

The Relationship Between Influencers’ Attributes and Customer Attitude Toward Brand

The professionalism is defined as the degree to which information source provides correct information (Bristor, 1990). In the live streaming commerce, professionalism reflects both the experience and the level of professional knowledge of the influencer (Racherla & Friske, 2012). It is a major driver for a person’s social influence (Treadway et al., 2011). The customers’ evaluation of influencers’ professionalism is normally reflected by their expertise, including the knowledge of endorsed products, shopping guidance and ability to engage in two-way interaction with audiences (Zhao et al., 2021). The more professional the information providers are, the more consumers will recognize the influencer (Ismagilova et al., 2020; L. Wang et al., 2021). Thus, the influencer’ professionalism improves the persuasiveness of information conveyed by the influencer. In terms of persuasion theory, the goal of persuasion is changing customers’ attitude toward the targeted purpose. Therefore, we propose the following hypothesis:

H1a: The professionalism of live-stream influencers positively influences customer attitude toward the brand.

Credibility refers to the extent to which a source is trusted to be objective about the subject (s)he addresses (Goldsmith et al., 2000). If customers view an influencer as a highly credible person, (s)he is considered to have a higher level of honesty, believability and integrity and the greater knowledge regarding a specific topic (Erdogan, 1999). It reflects the degree to which information source can be trusted (Sparkman & Locander, 1980). When a credible influencer conveys brand information to audience during a live-stream show, the audience would like to accept the information. Prior research has shown that influencers can be more effective at recommending products or brands than traditional advertising formats, due in part to their perceived credibility (Audrezet et al., 2020; Hughes et al., 2019). Therefore, we propose the following hypothesis:

H1b: The credibility of live-stream influencers positively influences customer attitude toward the brand.

The interactivity during a live-stream show refers to the degree of real-time communication between the influencer and audience (L. Wang et al., 2021), either online or offline, which strengthen the audience’s sense of social presence by the two-way communication. In a live show, the influencers not only provide professional information regarding the brand and products, but also communicate emotionally with customers. They frequently interact with audience, which make the audience view them as “friends” with whom they share common interests in their daily interaction (Sokolova & Perez, 2021). The brand information conveyed by them is more easily accepted by customers because of their strong bonds. In addition, the interaction in a live show normally includes providing barrage, lottery, and small gifts to customers via the live broadcasting platform, which enhance customers’ favorability of brands. Therefore, we propose the following hypothesis:

H1c: The interactivity of live-stream influencers positively influences customer attitude toward the brand.

The attractiveness of influencers is the most important attribute for successful endorsement (Carroll, 2008). It refers to the power of the internet influencers to attract customers to watch, pay attention and follow via their appearance, body, personal charm, talents and specialties and so on during the live show (L. Wang et al., 2021). Thereby, the more attractive the internet influencers are, the more customers pay attention to the brands and products information. In addition, in the influencer-centered community, the audience is the fan of the internet influencers. The fan customers are more likely to be influenced by the attractive information and opinions of internet influencers, thus arousing customers brand recognition recommended by influencers (Patel & Basil, 2017). Therefore, the attractiveness of the internet influencers can enhance customers’ positive attitude toward the brand. From this point of view, we proposed the following hypothesis:

H1d: The attractiveness of live-stream influencers positively influences customer attitude toward the brand.

The Relationship Between Internet Celebrity Attributes and Customer Purchase Intention

The study of online customer purchase intention has always been a hot topic in e-commerce marketing (Moslehpour et al., 2021; Zheng, 2021). With the development of the internet celebrity economy, scholars began to pay attention to the research on purchase intention in the process of online live show. The findings suggested that internet influencers are the key factors affecting the decision-making process of online customers (Akdevelioglu & Kara, 2020; Al-Emadi & Yahia, 2020).

First of all, customers tend to accept information that they know. The internet influencer’s professionalism helps audience to improve their understanding and mastery of the recommended product information. Further, customers would like to pay close attention to the professional information disseminators (Krishnamurthy & Kumar, 2018). Thus, the information delivered by the professional internet influencers is easier to be accepted and consequently stimulating purchase intention. Secondly, the interaction between the internet influencer and the audience is a competitive advantage in increasing purchased intention. The online live show not only provides the internet influencer opportunities to communicate with audience, but also provides opportunities to solve the audience’s questions about the product or brand in a timely manner. Meanwhile, the internet influencers usually not only display the useful product information, but also create interesting contents for entertainment, which provide customers with a sense of entertainment and enhanced shopping pleasure (Park & Lin, 2020). The continuous interaction with customers through the pop-up screen during the live show also enhances the customers involvement. Therefore, the frequent interactions with customers solves customers’ questions, meet their entertainment and emotional needs, and thus significantly improve their purchase intentions. Thirdly, the credibility of the internet influencer is a key factor affecting customer perception in the context of online marketing (Keeling et al., 2010; Lăzăroiu et al., 2020). Therefore, when a credible influencer recommends a product, customers are more likely to prefer the product, which in turn customers’ willingness to buy is increased. Finally, the display of internet attractiveness during the live show enhances customers’ shopping pleasure, which can increase customers’ curiosity and interest in the products and brands recommended by the internet influencer. The more attractive internet influencers are to customers, the more likely they are to be loved and sought after by customers, which make it easier to enhance customers’ purchase intention. Therefore, the following hypotheses are proposed:

H2: The internet influencers’ attributes (professionalism, credibility, interactivity, and attractiveness) positively increase customer purchase intention.

Customer Attitude Toward Brand as a Mediator

Customers’ positive attitude toward a brand will increase their involvement in the brand. When the customer’s brand involvement is higher, the customer’s understanding of the brand will also increase. Thus, they tend to use the brand information they have learned. To meet their own shopping needs and shopping goals, their willingness to buy will also be significantly improved. The positive effect of attitude toward brand on customers’ purchase intention has been explored many times in the context of traditional marketing and e-commerce marketing (e.g., Riley et al., 2015). Combined with the above-mentioned hypotheses of the internet influencers’ attributes and attitude toward brand, we propose the following hypothesis:

H3: Customer attitude toward brand mediates the relationships between the internet influencers’ attributes and customers’ purchase intention.

Methodology

Research Design and Data Collection

The questionnaire is distributed through online research platform of questionnaire star, and is pushed in the form of links through online platforms such as WeChat groups, WeChat Moments and QQ groups. In the questionnaire, we selected the target samples by setting “Have you ever shopped in a non-branded streamer’s room?” In total, 233 customers who are targeted sample of this study answer the questionnaire. After deleting incomplete and invalid responses, data from 207 customer responses were obtained. The demographic information is showed in Table 1. Among them, the location of the respondents was identified by using the IP addresses.

Table 1.

The Demographic Information of the Respondents.

		Frequency	Percent (%)
Gender	Male	60	29.0
Gender	Female	147	71.0
Age (years)	Under 20	22	10.6
	21–30	160	77.3
	31–40	13	6.3
	Over 40	12	5.8
Education	High school and less	5	2.4
	Junior college	23	11.1
	University/college	158	76.3
	Master’s degree or above	21	10.1
Online shopping experience (years)	Within one year	9	4.3
	1–3	60	29.0
	4–5	75	36.2
	Over 5	63	30.4
Location	Hebei	85	36.5
	Shandong	28	12.0
	Shanghai	18	7.7
	Jiangshu	8	3.4
	Anhui	7	3.0
	Guangxi	7	3.0
	Henan	7	3.0
	Hebei	7	3.0
	Shanxi	7	3.0
	Others	59	25.3

The items of all variables in the questionnaire were prepared in English and then translated into Chinese. Following Brislin’s (1980) recommendation, we adopted a back-translation technique for the questionnaires. Minor differences were resolved through a group discussion.

Measures

All constructs were adopted from the existing literature. They were measured on a five-point Likert Scale (from 1 = strongly disagree to 5 = strongly agree).

Internet Influencer’s Attributes

We adopted F. Liu et al.’s (2020) scale which originated from Ohanian’s (1991) measure. The 14-item measure has multi-item subscales corresponding to four dimensions: (1) professionalism (4 items), (2) credibility (4 items), (3) interactivity (3 items), and (4) attractiveness (3 items). Example items include: “I think that the internet influencer I watched has professional skills” (professionalism), “I think the content delivering by the internet influencer I watched in the live show is credible” (credibility), “I think the internet influencer I watched had good interaction with me” (interactivity), and “I followed the internet influencer because of his/her charm” (attractiveness). As the internet influencer’s attribute is commonly operationalized as four sub-constructs which has been used in a number of studies (e.g., L. Wang et al., 2021), we treated professionalism, credibility, interactivity and attractiveness separately. The reliability alpha coefficients for these four scales were .905, .904, .879, and .880, respectively, suggesting good consistency of the constructs used in this study.

Customer Attitude Toward Brand

Three items were adapted from were adopted from McCormick (2016) which originated from Osgood et al. (1957) to measure customer attitude toward brand. One example item was “I have a good impression on the brands recommended by the internet celebrity.” The alpha coefficient was .905.

Customer Purchase Intention

Customer purchase intention was measured by three items adopted from Dodds et al. (1991). One example item was “I’d like to buy the product of this brand recommended by the internet influencer.” The alpha coefficient was .917.

Control Variables

The demographics are the key factors influencing purchase intention in the livestreaming, such as gender (Tong et al., 2022), age (Lv et al., 2022), education background (Zhou et al., 2021), and so on. Thus, we controlled for gender, age, education background, years of online shopping experience, and monthly disposable amount. Gender was dummy coded (1 = male, 2 = female). Age was measured as 1 = under 20 years old, 2 = between 21 and 30 years old, 3 = between 31 and 40 years old, and 4 = 40+years old. Education background was measured 1 = high school education and below, 2 = college degree, 3 = bachelor degree, and 4 = master degree and above. Years of online shopping experience was measured 1 = less than 1 years of online shopping experience, 2 = between 1 and 3 years of online shopping experience, 3 = between 4 and 5 years of online shopping experience, and 4 = 5+years of online shopping experience. Monthly disposable amount was measured 1 = 1,000 CNY and below, 2 = between 1,000 and 3,000 CNY, 3 = between 3,000 and 6,000 CNY, and 4 = 6,000 CNY and above.

Common Method Bias

To reduce common method bias (Podsakoff et al., 2003), we took several measures to mitigate common method bias. For example, in the research design stage, we ensured all the respondents about their confidentiality in data and reports. Further, we did pre-test to reduce the confusing items. Finally, we did Harman’s single factor analysis to test the common method bias. The unrotated percentage of variance explained for one single factor is 11.55%. This value of the general factor is smaller than 40% (Harman,1967), indicating that common method bias does not compromise the reliability of the results.

Results

We conducted a confirmatory factor analysis to test the factor structure of the variables. The factor loadings were all above 0.70 and significant at .001 level. Specifically, the factor loadings for professionalism were 0.805, 0.805, 0.871, 0.867, for credibility were 0.875, 0.900, 0.845, for interactivity were 0.868, 0.895, 0.790, for attractiveness were 0.763, 0.876, 0.849, 0.731, for customer attitude toward brand were 0.787, 0.882, 0.859, for purchase intention were 0.913, 0.885, 0.869. In addition, the structural analysis results of AMOS showed that the fit indices for the structural equation modelling are very good with χ²/df = 426.362/195 = 2.19, CFI = 0.94, IFI = 0.94, TLI = 0.93, and RMSEA = 0.076.

In terms of the validity, we calculate the composite reliability (CR) and average variance extraction (AVE) following the recommendation of Fornell and Larcker (1981). The CR values for professionalism, credibility, interactivity, attractiveness, customer attitude toward brand, and purchase intention were 0.905, 0.904, 0.879, 0.880, 0.905, and 0.917, respectively. All the CR values were higher than 0.7, indicating good internal consistency of the scale (Hair et al., 2010). The AVE values for the above scales were 0.777, 0.841, 0.810, 0.729, 0.841, and 0.857, respectively, which were greater than 0.5. In addition, as showed in Table 2, the square root of AVE values (diagonal bold values) of all the constructs were larger than the correlation coefficients with other constructs (off-diagonal value). Thus, all the scales have a reasonable degree of discrimination (Fornell & Larcker, 1981)

Table 2.

Measurements of Variable, Reliability and validity Results.

Constructs	Items	Factor loading	p	Cronbach’s α	CR	AVE
Professionalism	I think that the online influencer I watched has professional skills.	0.805	***	.905	0.905	0.777
	I think that the online influencer I watched has special skills and expertise.	0.805	***
	I think that the online influencer I watched has rich experience in using the recommended products.	0.871	***
	I think that the online influencer I watched has professional knowledge about the recommended products and brand.	0.867	***
Credibility	I think the content delivering by the online influencer I watched in the live show is credible.	0.875	***	.904	0.904	0.841
	I think the products recommended by online influencer are more reliable.	0.900	***
	I trust the online influencer I watched.	0.845	***
Interactivity	I think the online influencer I watched had good interaction with me.	0.868	***	.879	0.879	0.810
	I think that the content I watch can enable me to participate effectively.	0.895	***
	I think that the content I watch can arouse my interest.	0.790	***
Attractiveness	I watch the live show is because of the appearance of online influencer.	0.763	***	.880	0.880	0.729
	I followed the online influencer because of his/her charm.	0.876	***
	I think the influencer I watched is very humorous and interesting	0.849	***
	You agree with the lifestyle of online influencer.	0.731	***
Customer attitude toward brand	I have a good impression on the brands recommended by the internet celebrity.	0.787	***	.905	0.905	0.841
	I think the brand recommended by the online influencer in the live show can meet my needs.	0.882	***
	I like the brand recommended by the online influencer.	0.859	***
Purchase Intention	It is very likely that I will consider buying the products recommended by the online influencer in the live show.	0.913	***	.917.	0.917	0.857
	I’d like to buy the product of this brand recommended by the online influencer.	0.885	***
	I will recommend the products recommended by the influencer to others.	0.869	***

Note.***p<.001.

Table 3 presents the means, standard deviations, inter-correlations, and the square root of AVE values for all study variables.

Table 3.

Descriptive Statistics.

Variables	Mean	SD	1	2	3	4	5	6	7	8	9	10
Internet influencer’s attributes
1. Professionalism	3.20	0.91
2. Credibility	3.13	0.85	.71**
3. Interactivity	3.20	0.87	.69**	.72**
4. Attractiveness	3.10	0.86	.58**	.61**	.72**
5. Customer attitude toward brand	3.16	0.88	.63**	.77**	.74**	.70**
6. Purchase intention	3.14	0.97	.63**	.73**	.70**	.70**	.81**
7. Gender	1.71	0.46	.13	.14*	.05	.10	.01	−.00
8. Age	2.07	0.63	.12	.10	.15*	.01	.12	.16*	−.12
9. Years of online shopping experience	2.93	0.88	.02	−.01	.00	.00	−.02	.06	.05	.33**
10. Education background	3.02	0.61	.17**	.08	.14*	.06	.12	.12	−.12	.23**	.25**
11. Monthly disposable amount	1.94	0.80	.07	.03	.12	.07	.03	.11	−.23**	.46**	.39**	.19**

Note. N = 207 (listwise).

p < .01. *p < .05 (two-tailed tests).

Using AMOS software to run the structural equation model analysis, the basic hypotheses were evaluated. Figure 1 showed the path analysis results. The results showed that Hypothesis 1b, 1c, and1d were fully supported, and Hypothesis 2 was partly supported. Specifically, the internet influencer’s credibility, interactivity, attractiveness significantly affects customer attitude toward the brand (β_credibility = .635, p < .001; β_{interactivity} = .155, p < .05; β_{attractiveness} = .286, p < .01). However, the internet influencer’s professionalism was not supported. As for the Hypothesis 2, the internet influencer’s credibility and attractiveness significantly affect customer purchase intention (β_credibility = .202, p < .01, β_{attractiveness} = .469, p < .001). While, professionalism and interactivity did not significantly affect customer purchase intention.

Figure 1.

Structural model results.

Hypothesis 3 proposed that customer attitude toward brand would mediate the link between internet celebrity attributes (professionalism, credibility, interactivity, attractiveness) and customer purchase intention. Following Hayes (2017), test the mediating effect by using bootstrapped samples (1,000) procedure with PROCESS model 4 and a CI of 95%, supporting the mediation model. The indirect effect of credibility, interactivity, and attractiveness on customer purchase intention via customer attitude toward brand were all significant (for credibility: β = .538, SE = 0.089, CI [0.360, 0.703]; for interactivity: β = .587, SE = 0.076, CI [0.451, 0.754]; for attractiveness: β = .464, SE = 0.063, CI [0.339, 0.584]).

Discussion

The internet influencers as an opinion leader encourage and incentive customer audience to purchase a specific brand product during a live show. In this study, we propose that internet influencers’ attributes (professionalism, credibility, interactivity, and attractiveness) positively increase customer purchase intention both directly and indirectly via customer attitude toward brand. Surprisingly, the findings showed that internet influencer’s attractiveness and credibility directly increase customer purchase intention. However, the professionalism and interactivity don’t directly influence customer purchase intention. Meanwhile, customer attitude toward brand plays mediating effects between some influencers’ attributes (credibility, interactivity and attractiveness) and customers’ purchase intention. The impact of internet influencer’s professionalism on either customer attitude toward brand or customer purchase intention was not supported. Our findings offer some theoretical and managerial implications.

Theoretical Contributions

Firstly, our study enriches the studies of mechanism between internet influencers’ attributes and purchased intention. Many previous studies have explored the impact of internet influencers’ characteristics on purchase intention from the perspective of prosocial relationships (e.g., T. Y. Chen et al., 2021), product-endorser match (e.g., McCormick, 2016), and the like. Limited studies aiming at the mediating effect of brand attitude on the impacts of influencers on purchase intention are mainly focused on the self-brands (e.g., Tseng & Wang, 2023). Our research focuses on the mediating effect of consumers’ attitudes towards brands promoted by influencers (non-self-brand) on the relationship between influencers’ attributes and purchase intention. The research context in this study is different from previous studies. Therefore, our study enriches the literature of the mechanism of influencers’ attributes on purchase intention in the scenario of influencers promoting the brands of the merchants in the livestreaming marketing.

Secondly, the internet influencer’s credibility, interactivity, attractiveness significantly affects customer attitude towards the brand (β_credibility = .635, p < .001; β_{interactivity} = .155, p < .05; β_{attractiveness} = .286, p < .01). These results indicate the strong bonds between influencers’ attributes and brand attitude. Only several studies have paid attention to the relationship between the attractiveness of influencers and brand attitudes. However, our finding is inconsistent with prior results. For instance, Filieri et al. (2022) suggested that influencer’s attractiveness did not affect brand attitude. As the live streaming marketing no longer grows wildly in China, the relevant rules in terms of code of conduct for internet influencers have been promulgated. From Liu Genhong, who is a legendary fitness influencer, to Dong Yuhui who created the legend of “knowledge flow” because of his broader knowledge, especially the knowledge of traditional Chinese culture, it shows that the public has a new understanding of the desirable attributes of influencers, such as attractiveness. Following the renewed recognitions, the live streaming sale data shows that the customers’ attitude toward brands appeared on the highly attractive influencer’s live streaming rooms have been significantly increased.

Thirdly, it is very remarkable that the credibility and attractiveness significantly increase customer purchase intention. The effect size of credibility on purchase intention is consistent with previous research findings (β = .202 in our study vs. β = .225 in the study of Masuda et al. (2022)). More importantly, the effect size between attractiveness and purchase intention is 0.469 which is higher than credibility. The result is different with some of prior studies. For instance, Wong and Wei (2023) found that influencers’ attractiveness has no significant impact on purchase intention in the context of beauty influencers in China. Although both of our studies are based on the sample in China, the findings are different due to different research contexts. However, this finding is not consistent with the study by Park and Lin (2020) which found that source attractiveness failed to influence purchase intention. We carefully compare the differences between the two studies. Although the different research aims between the two studies, both of them have similar research sample, where 233 respondents in our study and 252 respondents in Park and Lin’s (2020) study. Meanwhile, 75% of the sample in Park and Lin’s (2020) study is from 20 to 29 years old, which is also similar with our study. However, in Park and Lin’s (2020) study, participants were asked to recall the recent experience of live streaming shopping based on two live streaming platforms, Taobao and Yizhibo partnered with Sina weibo. Although we did not limit the shopping platforms, almost half of our respondents answer the questionnaires based on their shopping experience in Douyin and more than half of them also have the live streaming shopping experience in Taobao. Both of the two platforms, especially Douyin which become one of the most popular new social media in China, contain many entertainment and social functions. Since the entertainment and social attributes have been recognized as important and necessary traits of live steaming shopping (Ma, 2021), the attractiveness of internet influencers could bring more pleasure for the customers and consequently become an important indicator in boosting sales. The differences between the two findings provide very important hints about customer preference change. Therefore, we conclude that, in China, the attractiveness of the online influencers can get round the influences of customer attitude toward brand, and directly stimulate consumption, especially in the new social media platform.

Further, the brand attitude mediates the links between credibility, interactivity, and attractiveness and customer purchase. Through the live broadcast of the platform, internet influencers and customers have real-time two-way interaction, so as to ensure information sharing between the two sides. The interactive, credible and attractive influencers would be easier to implement the information sharing and make the brand information delivery successful and the brand information more credible. Therefore, by working on the customers’ understanding and belief of the brand, the purchase desire of customers will be consequently improved. In addition, refreshing, improving and maintaining the characteristics of internet influencers has become a necessary link in delivering brand information to customers during this process.

While, none of the impacts of professionalism of internet influencers on customer’s attitude toward brand, purchase intention, or its mediating effects is significant. In figuring out the causes of this finding, two main issues might explain it. With the development and popularization of online influencers live broadcasting, knowledge and professional skills have become the necessary conditions for most internet influencers to enter the e-commerce live broadcasting industry. Customers have gradually increased their experience of e-commerce live broadcast marketing, and become more familiar with the internet influencers live broadcast’s delivery process. In customers’ cognition, the understanding and mastery of product information by internet influencers is the essential condition and should not be the key factor to promote their purchase intention. The other reason might come from the entertainment and social attributes of the live shopping. Customer seeks to get emotional pleasure and social interaction as well as acquiring appropriate products by live shopping (Ma, 2021). These appeals become even stronger when face-to-face social interaction and group life are reduced due to the epidemic. Such kinds of appeal might weaken or eliminate the impacts of professionalism of internet influencers.

Managerial Implications

From the practical perspective, this study revealed the important role of the internet influencers’ attributes on customer purchase intention and examined its underling mechanism from the perspective of customer attitude toward brand. Specifically, our findings indicate the attractiveness is the most valuable characteristics of the influencers in enhancing audience purchase intention and improving customer positive attitude toward brand. This finding could help marketers to make decisions in selecting the influencers. Identifying the most useful attributes of the influencers for the live broadcast is also helpful for marketers to use the circle traffic of internet celebrity to reach customers directly, and transform the traffic into actual purchasing power while stabilizing and increasing the traffic of platform and business fans. Meanwhile, our findings clarify the direction of training for internet influencers and how to effective improve the performance of the internet influencers.

Secondly, the credibility and interactivity are also critical to improve the live broadcast revenue. Therefore, marketers should pay more attention to improving the credibility of the influencers from the perspective of their appearance. It is also helpful in designing the live broadcast room, and encourage and assist the internet influencers to improve the interaction effect in the live show.

Limitations and Further Research

Our study has two main limitations. With the rapid development of the internet celebrity live broadcast industry, a variety of products are displayed in the live broadcast, and the characteristics of products have an important impact for the internet influencers in effectively operating a live show. Our study is generally aimed at all the products. The same research design could aim at a specific product category in the future or consider the products attributes’ impacts on the mechanism.

Secondly, this study only focuses the impact of internet influencers live broadcast on customer purchase intention from the perspective of influencers’ attributes. However, in addition to the internet influencers themselves, other features of the live broadcast room, such as live broadcast scenes and the interactive application of artificial intelligence during live broadcast, will have an impact on customers’ purchase intention. Future research can comprehensively consider the role of other factors.

Finally, the sample size and sample characteristics has its own limitation. More than half of our sample is female. We compare the live audience analysis results from www.chanmama.com. Chanmama is a professional tool designed for short video platforms such as Douyin and Kuaishou, as well as merchants on e-commerce platforms (such as Taobao and JD.com) for data query, operation and advertising effect monitoring in China. Most of the audience in a live show is female. However, the sample size and sample characteristics still has its own limitation as it cannot be generalized to all situations, especially the live show in other countries. Similar studies are to be conducted with a large size sample in other contexts for comparison of the findings.

Conclusion

In conclusion, this study illustrates in detail the impacts of influencers’ professionalism, credibility, attractiveness, and interactivity on customers purchase intention in the live shows, as well as the mediating role of customer attitude toward brands. Our findings can not only enrich the literature of the impact of online influencers on customer attitude, but also better guide practitioners on how to design the marketing practice of live broadcast from perspectives of influencers’ attributes as well as brand attitudes. Future research can integrate more abundant live broadcast scenes into the research on the impact of live broadcast on customer attitude and behavior.

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 work was supported by the Ministry of Education in China (No. 23YJA630144), the National Natural Science Foundation of China (No. 71831005), and the Liaoning Provincial Social Science Planning Fund Project (No. L23BGL026).

Ethical Approval

I certify that this manuscript is original and has not been published. Informed consent was obtained from all individual participants included in the study.

ORCID iDs

Xin Zhao

Zichang Li

Data Availability Statement

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

References

Akdevelioglu

Kara

(2020). An international investigation of opinion leadership and social media. Journal of Research in Interactive Marketing, 14(1), 71–88.

Al-Emadi

F. A.

Yahia

I. B.

(2020). Ordinary celebrities related criteria to harvest fame and influence on social media. Journal of Research in Interactive Marketing, 14(2), 195–213.

Audrezet

De Kerviler

Moulard

J. G.

(2020). Authenticity under threat: When social media influencers need to go beyond self-presentation. Journal of Business Research, 117, 557–569.

Bartsch

Diamantopoulos

Paparoidamis

Chumpitaz

(2016). Global brand ownership: The mediating roles of consumer attitudes and brand identification. Journal of Business Research, 69, 3629–3635.

Brislin

R. W.

(1980). Translation and content analysis of oral and written material. Handbook of Cross-Cultural Psychology, 2(2), 349–444.

Bristor

J. M.

(1990). Enhanced explanations of word of mouth communications the power of relations. Research in Consumer Behavior, 4(1), 51–83.

Carroll

(2008). Brand communications in fashion categories using celebrity endorsement. Journal of Brand Management, 17(2), 146–158.

Chen

T. Y.

Yeh

T. L.

Lee

F. Y.

(2021). The impact of internet celebrity characteristics on followers’ impulse purchase behavior: The mediation of attachment and parasocial interaction. Journal of Research in Interactive Marketing, 15(3), 483–501.

Chetioui

Benlafqih

Lebdaoui

(2020). How fashion influencers contribute to consumers’ purchase intention. Journal of Fashion Marketing and Management, 24(3), 361–380.

10.

Dodds

W. B.

Monroe

K. B.

Grewal

(1991). Effects of Price, Brand, and Store Information on Buyers' Product Evaluations. Journal of Marketing Research, 28(3), 307–319.

11.

Dotson

M. J.

Hyatt

E. M.

(2000). Religious symbols as peripheral cues in advertising: A replication of the elaboration likelihood model. Journal of Business Research, 48(1), 63–68.

12.

eMarketer. (2019). What retailers need to know about influencer marketing. eMarketer. https://www.emarketer.com/content/what-retailers-need-to-know-about-influencer-marketing.

13.

Erdogan

B. Z.

(1999). Celebrity endorsement: A literature review. Journal of Marketing Management, 15(4), 291–314.

14.

Filieri

Acikgoz

C. Y.

Alguezaui

(2022). Influencers’“organic” persuasion through electronic word of mouth: A case of sincerity over brains and beauty. Psychology & Marketing, 40(2), 347–364.

15.

Fink

Koller

Gartner

Floh

Harms

(2020). Effective entrepreneurial marketing on Facebook-A longitudinal study. Journal of Business Research, 113, 149–157.

16.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39–50.

17.

Gnambs

Batinic

(2013). The roots of interpersonal influence: A mediated moderation model for knowledge and traits as predictors of opinion leadership. Applied Psychology, 62(4), 597–618.

18.

Goldsmith

R. E.

Lafferty

B. A.

Newell

S. J.

(2000). The impact of corporate credibility and celebrity credibility on consumer reaction to advertisements and brands. Journal of Advertisement, 29(3), 43–54.

19.

Hair

J.F.

Black

W.C.

Babin

B.J.

Anderson

R.E.

Tatham

R.L.

2010. Multivariate Data Analysis (7th ed.). Prentice Hall, Englewood Cliffs.

20.

Hayes

A. F.

(2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Publications.

21.

Harman

H. H.

(1967). Modern factor analysis (pp. 235–241). University of Chicago Press.

22.

Hughes

Swaminathan

Brooks

(2019). Driving brand engagement through online social influencers: An empirical investigation of sponsored blogging campaigns. Journal of Marketing, 83(5), 78–96.

23.

Ismagilova

Slade

Rana

N. P.

Dwivedi

Y. K.

(2020). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. Journal of Retailing and Consumer Services, 53, 101736.

24.

Keeling

McGoldrick

Beatty

(2010). Avatars as salespeople: Communication style, trust and intentions. Journal of Business Research, 63(8), 793–800.

25.

Kelman

H. C.

(1961). Processes of opinion change. Public Opinion Quarterly, 25(1), 57–78.

26.

Khamis

Ang

Welling

(2017). Self-branding, micro-celebrity’ and the rise of social media influencers. Celebrity Studies, 8(2), 191–208

27.

Krishnamurthy

Kumar

S. R.

(2018). Electronic word-of-mouth and the brand image: Exploring the moderating role of involvement through a consumer expectations lens. Journal of Retailing and Consumer Services, 43, 149–156.

28.

Lăzăroiu

Neguriţă

Grecu

Mitran

P. C.

(2020). Consumers’ decision-making process on social commerce platforms: Online trust, perceived risk, and purchase intentions. Frontiers in Psychology, 11, 1–7.

29.

Lin

Bruning

P. F.

Swarna

(2018). Using online opinion leaders to promote the hedonic and utilitarian value of products and services. Business Horizons, 61(4), 431–442.

30.

Liu

Zhang

(2020). The impact of self-congruity and virtual interactivity on online celebrity brand equity and fans’ purchase intention. Journal of Product & Brand Management, 29(6), 783–801.

31.

Liu

Meng

Chen

Duan

(2020). The impact of network celebrities’ information source characteristics on purchase intention. Chinese Journal of Management, 17(1), 94–104.

32.

Zhang

Yang

(2022). Exploring how live streaming affects immediate buying behavior and continuous watching intention: A multigroup analysis. Journal of Travel & Tourism Marketing, 39, 109–135.

33.

(2021). To shop or not: Understanding Chinese consumers’ live-stream shopping intentions from the perspectives of uses and gratifications, perceived network size, perceptions of digital celebrities, and shopping orientations. Telematics and Informatics, 59, 1–27.

34.

Masuda

Han

S. H.

Lee

(2022). Impacts of influencer attributes on purchase intentions in social media influencer marketing: Mediating roles of characterizations. Technological Forecasting and Social Change, 174, 121246.

35.

McCormick

(2016). Celebrity endorsements: Influence of a product-endorser match on Millennials attitudes and purchase intentions. Journal of Retailing and Consumer Services, 32, 39–45.

36.

Mitchell

A. A.

Olson

J. C.

(1981). Are product attribute beliefs the only mediator of advertising effects on brand attitude?. Journal of Marketing Research, 18(3), 318–332.

37.

Moslehpour

Ismail

Purba

Wong

W. K.

(2021). What makes GO-JEK go in Indonesia? The influences of social media marketing activities on purchase intention. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), 89–103.

38.

Ohanian

(1991). The impact of celebrity spokespersons’ perceived image on consumers’ intention to purchase. Journal of Advertising Research, 31(1), 46–54.

39.

Osgood

C. E.

Suci

G. J.

Tannenbaum

P. H.

(1957). The measurement of meaning. Richard D. Irwin.

40.

Ouvrein

Pabian

Giles

Hudders

Backer

C. D.

(2021). The web of influencers: A marketing-audience classification of (potential) social media influencers. Journal of Marketing Management, 37, 1313–1342.

41.

Park

H. J.

Lin

L. M.

(2020). The effects of match-ups on the consumer attitudes toward internet celebrities and their live streaming contents in the context of product endorsement. Journal of Retailing and Consumer Services, 52, 1–6.

42.

Patel

Basil

(2017). The effects of celebrity attractiveness and identification on advertising interest. In Krey

Rossi

(Eds.), Back to the future: Using marketing basics to provide customer value. AMSAC 2017. Developments in marketing science: Proceedings of the academy of marketing science (pp. 579–589). Springer.

43.

Podsakoff

P. M.

Mackenzie

S. B.

Lee

J. Y.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

44.

Racherla

Friske

(2012). Perceived “usefulness” of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548–559.

45.

Riley

F. D.

Pina

J. M.

Bravo

(2015). The role of perceived value in vertical brand extensions of luxury and premium brands. Journal of Marketing Management, 31(7–8), 881–913.

46.

Sands

Campbell

C. L.

Plangger

Ferraro

(2022). Unreal influence: Leveraging AI in influencer marketing. European Journal of Marketing, 56(6), 1721–1747.

47.

Shabahang

Aruguete

M. S.

Shim

Huynh

H. P.

Chirani

B. M.

(2023). “Celebrities to lean on!” Perceived celebrity support across two cultures—Iran and the United States. Asian Journal of Social Psychology, 26(3), 363–373.

48.

Shi

Zhou

Sun

Geng

(2021). Online star vs. celebrity endorsements: The role of self-concept and advertising appeal in influencing purchase intention. Frontiers in Psychology, 12, 1–15.

49.

Sokolova

Perez

(2021). You follow fitness influencers on YouTube. But do you actually exercise? How parasocial relationships, and watching fitness influencers, relate to intentions to exercise. Journal of Retailing and Consumer Services, 58, 1–11.

50.

Sparkman

J. R. M.

Locander

W. B.

(1980). Attribution theory and advertising effectiveness. Journal of Consumer Research, 7(3), 219–224.

51.

Taillon

B. J.

Mueller

S. M.

Kowalczyk

C. M.

Jones

D. N.

(2020). Understanding the relationships between social media influencers and their followers: The moderating role of closeness. Journal of Product & Brand Management, 29(6), 767–782.

52.

Tian

Chen

Zhang

Wang

Zhang

(2023). Antecedents and consequences of streamer trust in livestreaming commerce. Behavioral Sciences, 13(4):308.

53.

Tian

Cho

S. Y.

Jia

Sun

Tsai

W. S.

(2023). Antecedents and outcomes of Generation Z consumers’ contrastive and assimilative upward comparisons with social media influencers. Journal of Product & Brand Management. Advance online publication.

54.

Tong

X. J

Chen

Y. A

Zhou

S. S

Yang

S. Q.

(2022). How background visual complexity influences purchase intention in live streaming: The mediating role of emotion and the moderating role of gender. Journal of Retailing and Consumer Services, 67, 103031.

55.

Treadway

D. C.

Breland

J. W.

Williams

L. M.

Cho

Yang

Ferris

G. F.

(2011). Social influence and interpersonal power in organizations: Roles of performance and political skill in two studies. Journal of Management, 39(6), 1529–1553.

56.

Tseng

T. H.

Wang

H. Y.

(2023). Consumer attitudes and purchase intentions towards internet celebrity self-brands: An integrated view of congruence, brand authenticity and internet celebrity involvement. Journal of Product & Brand Management, 32(6), 863–877.

57.

Wang

Zhao

(2021). Assessing word-of-mouth reputation of influencers on B2C live streaming platforms: The role of the characteristics of information source. Asia Pacific Journal of Marketing and Logistics, 34(7), 1544–1570.

58.

Wang

S. W.

Kao

H. Y.

Ngamsiriudom

(2017). Consumers’ attitude of endorser credibility, brand and intention with respect to celebrity endorsement of the airline sector. Journal of Air Transport Management, 60, 10–17.

59.

W. Y.

Linn

C. T.

C. S.

Sukoco

B. M.

(2012). The role of endorsers, framing, and rewards on the effectiveness of dietary supplement advertisements. Journal of Health Communication, 17(1), 54–75.

60.

Nambisan

Xiao

Xie

(2022). Consumer resource integration and service innovation in social commerce: The role of social media influencers. Journal of the Academy of Marketing Science, 50, 429–459.

61.

Wong

Wei

(2023). Persuasive cues and reciprocal behaviors in influencer-follower relationships: The mediating role of influencer defense. Journal of Retailing and Consumer Services, 75, 103507.

62.

Zhao

Y.H.

Hong

Y.L.

Westland

(2021). Understanding Characteristics of Popular Streamers on Live Streaming Platforms: Evidence from Twitch.tv. Journal of the Association for Information Systems, 22(4), 1076–1098.

63.

Zheng

(2021). The classification of online consumer reviews: A systematic literature review and integrative framework. Journal of Business Research, 135, 226–251.

64.

Zhou

Huang

Kong

Campy

K. S.

(2021). Characterizing Chinese consumers’ intention to use live e-commerce shopping. Technology in Society, 67, 101767.