Charting New Waters in Influencer Advertising: Summary of Recent Inquiries

We have witnessed exponential growth in the literature on influencer advertising and influencer marketing over the past few years (e.g., Hudders, De Jans, and De Veirman 2021; Ye et al. 2021). Topics on influencer credibility, parasocial relations, and sponsorship disclosure have been abundant, if not saturated (e.g., Evans et al. 2017; Jin, Muqaddam, and Ryu 2019; Lou and Yuan 2019; Lou 2021). This special issue collection aims to encourage and feature research on underexplored themes and domains in influencer advertising. The 15 accepted manuscripts for this special issue indeed fulfill this goal by extending our understanding of less explored topics and stimulating empirical research on influencer advertising using diverse methodologies.

Untapped and Understudied Issues in Influencer Advertising

The articles included in this special issue article collection have approached the theme of influencer advertising through varied methodology, including content analysis, survey, in-depth interview, experiment, systematic review, social network analysis, and data analytics of existing social media data. They also explored some less studied topics in the current literature, such as green advertising promoted by influencers (Pittman and Milfeld 2023), influencers in health communication about COVID-19 (Buvár et al. 2024; Looi, Kemp, and Song 2023), influencers in a corporate social responsibility (CSR) context (Yang et al. 2023), Twitch streamer influencers (Carter and Hoy 2024), and artificial intelligence (AI)-based virtual influencers (Byun and Ahn 2023; Ham et al. 2023; Yang et al. 2023). Moreover, two articles focused on a less studied consumer segment—Chinese social media users—using online survey (Wei et al. 2022) or existing data (Chan, Hung, and Tse 2023), which brought in valuable new perspectives to the existing and dominant Western context.

Three Clusters: Influencer, Message, and Media

We further categorize the factors that explicate the success of influencer advertising into three clusters pertaining to influencer characteristics (i.e., influencer-brand congruence, influencer-follower relation, influencer charisma, follower size of influencer, influencer-follower interaction frequency/level), message features (i.e., storytelling, sponsored content, influencer touting, portrayals of product engagement, message framing of self-construal), and media (i.e., particular social media contexts and platforms).

Influencer Characteristics

First, several studies examined the role of influencer characteristics in the efficacy of influencer advertising. Wei et al. (2022) explicated how influencer-brand congruence and influencer-follower parasocial relation predict consumers’ online brand engagement. In particular, they conducted an online survey among Chinese Sina Weibo (a counterpart of Twitter) users to examine a proposed model explicating consumers’ online brand engagement in terms of consuming, contributing, and creating brand-related content. They showed that influencer-brand congruence positively predicts users’ consumption of brand-related content, and influencer-follower parasocial relation indirectly predicts users’ contribution to brand-related content via the role of social identification with influencer-sponsored brands. Moreover, both parasocial relation and congruence positively predict users’ content creation via the mediating role of personal identification with influencer-sponsored brands and social identification, respectively. Three articles examined how the follower size of influencers plays a role in persuasion effect across different contexts (i.e., Chan, Hung, and Tse 2023; Gong and Holiday 2023; Himelboim and Golan 2023). For instance, Chan, Hung, and Tse (2023) also focused on Chinese social media users and inspected factors that drive audience engagement with influencers and brand sales. They conducted a content analysis of TikTok videos in China and tabulated them with engagement and sales data collated by a third information provider to explore how micro-influencers (30,000 to 100,000 followers) and macro-influencers (5 million followers) perform in brand sales. They found that influencer charisma matters more for macro-influencers in driving audience shares than for micro-influencers, whereas message content significantly drives audience likes for micro-influencers, but not for macro-influencers. Gong and Holiday (2023) tried to explain the impact of social media influencers’ follower count on advertising outcomes. Using a content analysis and two online experiments, they revealed that high (versus low) level of parasocial interaction exhibited by social media influencers has a significant main effect on consumers’ perceived authenticity of social media influencers and purchase intentions, whereas follower count has no impact on those outcomes. Furthermore, sponsorship disclosure placed at the end of a sponsored video (versus at the beginning of the video) led to increased perceived authenticity, and this finding is only significant for nano-influencers, not for micro-influencers. Using a different approach, Himelboim and Golan (2023) adopted a social network analysis to study the role of social media influencers in veganism cause-related communities on Instagram. They found that nano-influencers (with less than 10 influencers) had significantly greater betweenness centrality values than micro-influencers (10,000 to 100,000 followers). They argue that while micro-influencers may reach more followers, nano-influencers can reach more diverse consumers with varied product interests.

Furthermore, two other articles investigated how the level of interactivity exhibited by influencers (Yang et al. 2023) and influencer-follower interaction frequency (Xiao 2023) interact with other factors (e.g., human versus virtual influencers; comment valence) in influencing advertising outcomes. In particular, Yang et al. (2023) conducted an online experiment to compare the impact of human influencers to that of virtual influencers given the varying level of interactivity in a CSR context. They found that human influencers were rated more credible than virtual influencers, and high level of interactivity (versus low level) exhibited by virtual influencers, but not by human influencers, gave rise to greater perceived credibility (i.e., expertise, trustworthiness, and authenticity). Source credibility mediates the interaction effect of the influencer type and interactivity level on CSR engagement and brand reputation. While we know that negative comments can derogate influencers (Hudders, Lou, and de Brabandere 2022), Xiao (2023) conducted experiments to examine the mitigating role of influencer-follower interaction frequency in the impact of negative comments. He found that negative comments significantly lowered influencer trustworthiness and product attitude, but influencers’ frequent replies to negative comments attenuated their negative effect on those outcomes. These findings offered a potential solution to attenuate the detrimental effect of negative comments on influencers.

Message Features

Second, the role of message features in the effectiveness of influencer advertising was examined by many studies (i.e., Buvár et al. 2024; Ham et al. 2023; Gross, Cui, and von Wangenheim 2023; Looi, Kemp, and Song 2023; Pittman and Milfeld 2023). Specifically, Buvár et al. (2024) examined the negative effect of sponsored content on consumers’ behavioral engagement with social media posts containing COVID-19-related messages. This study examined how consumers react when sponsored content was included in a social media post about COVID-19. Results from a between-subject online experiment indicate that inclusion of sponsored content in COVID-19-related social media posts led to lower perceived influencer credibility and negative attitude toward the post, which serially mediated behavioral engagement. Thus, including sponsored content in COVID-19-related posts lowers the social media posts’ dissemination potential, harming both the COVID-19-related messages and the advertised brand. Using existing Instagram data from an influencer advertising agency, Gross, Cui, and von Wangenheim (2023) explicated how emotional storytelling in influencer-sponsored posts affects audience engagement with micro-influencers and macro-influencers differently. They focused on the roles of tone and intensity of emotional storytelling in those ads on Instagram. They reveal that sponsored posts with emotional storytelling (versus without) and with a more pleasant (versus less pleasant) emotional storytelling led to higher engagement. Emotional storytelling by macro-influencer (versus micro-influencer) elicits lower engagement, and a similar finding emerges for pleasant emotional storytelling by macro-influencers (versus micro-influencers). They argue that the audience may find emotional storytelling by macro-influencers less authentic and more likely to be curated compared to that of micro-influencers.

Looi, Kemp, and Song (2023) conducted an online experiment to examine how health message construal (independent self-construal versus interdependent self-construal versus collective construal) and influencer type (mega versus nano) affect persuasion outcomes. They found that participants perceived higher homophily with a nano-influencer (versus a mega-influencer), which in turn, facilitates more favorable attitudes toward health messages and increased intentions regarding COVID-19 prevention. Messages with a collective construal were viewed more favorably than those with an independent self-construal. Another experimental study conducted by Pittman and Milfeld (2023) offered some intriguing findings regarding the merit of influencers in brands’ green advertising campaigns. Conceptualizing a new strategy called “enviro-bragging” and drawing from the signaling theory, they tested how a brand’s market share and influencers’ enviro-bragging (touting brands’ green initiatives) affect consumers’ reactions. Interestingly, they found that dominant brands (high market share) benefit more from engaging influencers bragging about their green initiatives in reaping more favorable brand attitudes when compared to nondominant brands (low market share). Furthermore, Ham et al. (2023) conducted an experiment to investigate how the portrayals of virtual influencers in sponsored posts affect consumer reactions. They found that when a virtual influencer is alone in a sponsored post, an incremental increase in product engagement (e.g., product in the background, holding the product, and consuming the product) led to increased perceived anthropomorphism and authenticity, whereas such an increase in product engagement resulted in decreased perceived anthropomorphism and authenticity when the virtual brand endorser is with a real human. They argue that too much reality or high level of product engagement of the virtual influencers in sponsored campaigns may not be effective in generating positive reactions among consumers.

Media Factors

Third, when it comes to media factors, two articles examined contexts in two particular social media platforms: TikTok by Flecha-Ortiz et al. (2023); Twitch by Carter and Hoy (2024). Flecha-Ortiz et al. (2023) conducted an online survey among Generation Z TikTok users to test a model explicating how gratifications of TikTok use predict purchase intentions toward influencer-promoted products. In particular, they examined how self-expression and social interaction propel purchase intentions via a serial mediators—user-generated content and a parasocial relation with influencers. Carter and Hoy (2024) examined the underexplored terrain of Twitch as a platform for the consumer-influencer relationship. Using a multimethod approach, they conducted in-depth interviews and an online experiment and found that as Twitch viewers perceive a trans-parasocial relationship with Twitch streamers, they develop stronger feelings of parasocial interaction with streamers.

Finally, Byun and Ahn (2023) performed a systematic comparison of virtual influencers and human influencers via reviewing the current literature on virtual influencers. They suggest that many similarities between virtual influencers and human influencers manifest pertaining to source characteristics, content composition (authenticity versus commercialism), and the relation with audience, but significant differences emerge between the two. In particular, they argue that virtual influencers are highly controllable for content creation and activities and are not bounded by physical constraints (fatigue or burnout), but they are also less influential, less trusted, and more psychologically distant compared to human influencers. In addition, Musiyiwa and Jacobson (2024) conducted semistructured interviews with influencer relation professionals in Canada to examine the role of influencer intermediaries in driving upfront and compliant sponsorship disclosure in influencer marketing. This research identified how influencer intermediaries can support sponsorship disclosure in three areas, including legal contracts, formal and informal content vetting processes, and intermediary knowledge transfer. The authors further discussed how influencer intermediaries have access to forms of social, cultural, and technical capital that may influence best disclosure practices.

Conclusion

We believe our special issue collection will provide helpful conceptual foundations and relevant theoretical frameworks for the future research on influencer advertising from multiple angles (i.e., influencer characteristics, message factor, media factor), as well as stimulate more provocative discussions about the future of interactive advertising in the emerging AI-powered digital marketing environments.

Additional information

Funding

Open Access funding provided by the Qatar National Library.

Notes on contributors

Chen Lou

Chen Lou (Ph.D., Michigan State University) is an Associate Professor, Nanyang Technological University.

S. Venus Jin

S. Venus Jin (Ph.D., University of Southern California) is a Professor, Northwestern University in Qatar.