When less is more: the impact of macro and micro social media influencers’ disclosure

Abstract

There are growing discussions of social media influencers and their effectiveness in endorsing products. Further, recent policy regulations are requiring social media influencers to disclose sponsored content when using a form of native advertising. This research examined the effect of macro-influencers (high likes) and micro-influencers (low likes) and their disclosure of native advertising sponsorship on consumer evaluations of products. Results from a 2 × 2 experiment first show that consumers exposed to the micro-influencer condition report higher levels of product knowledge, and consumers exposed to the disclosure condition reported the products endorsed by social media influencers to be more attractive. The results also show that when exposed to micro-influencers who disclose, consumers have higher levels of purchase intentions than when exposed to macro-influencers who do not disclose, as well as higher purchase intentions than for posts where sponsorship is not disclosed by influencers. The important findings of this research for theory, practice and policy are discussed.

Full text

When less is more: The impact of macro and micro social media
influencers’ disclosure
Samantha Kaya, Rory Mulcahya*, Joy Parkinsonb
aSchool of Business, University of the Sunshine Coast, Sippy Downs, Australia; b Social
Marketing @ Griffith, Griffith Business School, Griffith University, Brisbane, Australia
*Corresponding author: rmulcahy@usc.edu.au
When less is more: the impact of macro and micro social media influencers’
disclosure
There are growing discussions of social media influencers and their effectiveness in
endorsing products. Further, recent policy regulations are requiring social media
influencers to disclose sponsored content when using a form of native advertising. This
research examined the effect of macro-influencers (high likes) and micro-influencers
(low likes) and their disclosure of native advertising sponsorship on consumer
evaluations of products. Results from a 2 x 2 experiment first show that consumers
exposed to the micro-influencer condition report higher levels of product knowledge,
and consumers exposed to the disclosure condition reported the products endorsed by
social media influencers to be more attractive. The results also show that when exposed
to micro-influencers who disclose, consumers have higher levels of purchase intentions
than when exposed to macro-influencers who do not disclose, as well as higher
purchase intentions than for posts where sponsorship is not disclosed by influencers.
The important findings of this research for theory, practice and policy are discussed.
Keywords: social media influencers; native advertising; disclosure
Summary statement of contribution to knowledge
This research has theoretical, practical and policy implications. In terms of theoretical
contributions, this research is the first to conceptualise and investigate different levels
of social media influencer (macro and micro). It also bridges this classification of social
media influencer with the disclosure of native advertising sponsorship literature to
provide new insight into the impact influencer-endorsement has upon consumer
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outcomes. Mediation analysis shows that influencer type, disclosure and the influencer
type–disclosure interaction indirectly influence purchase intentions via product
knowledge, not product attractiveness. From a practical perspective, this research
provides insights as to which level of social media influencer provides superior
outcomes for product knowledge, product attractiveness and purchase intentions.
Finally, from a policy viewpoint, this study contributes empirical support to the
effectiveness of mandatory disclosure of native advertising regulations and legislation
by demonstrating how this impacts the persuasive effects of social media influencers.
Introduction
Brands have increasingly been using micro-celebrities or so-called ‘social media influencers’
– that is, fitness gurus, food bloggers, beauty bloggers, fashionistas and others – as the face of
their advertisements (Khamis, Ang, & Welling, 2017; Pedroni, 2016). Influencers share
endorsed opinions about products on social media platforms, such as Instagram, which assist
in spreading viral conversations about brands online (De Veirman, Cauberghe, & Hudders,
2017). As a result, they engage in native advertising or sponsored posts (Campbell & Grimm,
2019), allowing payment for what they share on social media platforms. Practitioners are
increasingly recognising the importance of understanding social media influencers and their
impact on marketing activities, as evidenced by numerous online articles (see e.g. Hill, 2019;
Petrofes, 2018), and a Google search using the phrase ‘social media influencers’ returning
139,000,000 results. However, there is little scholarly research discussing the implications of
using social media influencers for marketing purposes.
In contrast, celebrity endorsement (Aleti, Pallant, Tuan, & van Laer, 2019; Choi &
Rifon, 2012; Spry, Pappu, & Cornwell, 2011) and disclosure of sponsorship (Boerman, Van
Reijmersdal, & Neijens, 2014; Campbell, Mohr, & Verlegh, 2013) are well researched
domains; however, there is little work combining these streams, examining the rise of the new
phenomena of ‘social media influencers’ and ‘influencer marketing’. This represents a
considerable gap given social media influencers are being consistently leveraged and paid
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considerable amounts of money from brands and organisations to endorse their products
through native advertising. For example, reports suggest that travel influencers can be paid up
to A$31,000 per post (Wallace, 2018). Thus, there have been calls for greater theoretical and
practical insights into what types of social media influencers should be used and the
transparency of their relationships with firms to understand if being paid or compensated
changes perceptions of transparency and credibility (Diggins, 2019). This research aims to
contribute actionable insights in this area by investigating the differing levels of social media
influencer and the disclosure of native advertising sponsorship.
Despite interchangeable labels used for influencers, there is little insight as to whether
an influencer with a certain level of following outperforms that of another influencer. In
practice, social media influencers are beginning to be segmented based upon their following,
with some researchers suggesting they can be classified as micro-influencers (small
following) and macro-influencers (large following) (Hatton, 2018; Porteous, 2018). Thus,
given the gap in understanding of micro- and macro-influencers, examining if macro-
influencers exert greater persuasion on followers than micro-influencers is important (Casaló,
Flavián, & Ibáñez-Sánchez, 2018).
Another key issue of influencer marketing is the legal requirement to disclose paid
content shared on a social media influencer’s pages and posts (De Jans, Cauberghe, &
Hudders, 2019; Evans, Phua, Lim, & Jun, 2017). For example, the Australian Association of
National Advertisers issued new guidelines for influencer marketing around the disclosure of
commercial sponsored posts in Australia. The US Federal Trade Commission took similar
steps in setting guidelines for disclosure. This practice of mixing sponsored content with non-
sponsored content by social media influencers is known as native advertising, an emerging
area of regulatory and scholarly interest (Campbell & Grimm, 2019). Despite recommended
guidelines and legislation for social media influencers, little is understood as to the effect
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disclosure has upon consumer outcomes, highlighting an important gap in both scholarly and
practical understanding (Audrezet, De Kerviler, & Moulard, 2018). Therefore, this study aims
to investigate whether the disclosure of sponsorship on a macro- and micro-influencer’s
native advertising post influences consumer outcomes.
Social media influencers’ posts have two purposes: to increase their fans’ purchase
intention, and enhance product knowledge or product attractiveness. For example, social
media influencers design posts with testimonials or facts about product features, thus
attempting to enhance information value and product knowledge (Luo & Yan, 2019). They
also attempt to transfer their personal attractiveness to that of the product they are endorsing
through the use of sex appeal and posing (Schouten, Janssen & Verspaget, 2019). However,
how these consumer product perceptions facilitate any potential efforts by social media
influencers to increase purchase intentions is relatively unknown, highlighting an important
gap in the academic literature. Research has frequently focused on the traits of the influencer
as facilitating factors (e.g. trustworthiness and expertise) rather than consumer product
perceptions (Hughes, Swaminathan, & Brooks, 2019; Lou & Yuan, 2019; Schouten, Janssen,
& Verspaget, 2019). Therefore, there is a need to investigate how perceptions of product
knowledge and product attractiveness influence consumer outcomes in social media
influencer marketing.
This research sits at the nexus of the phenomena of macro- and micro-influencers and
native advertising sponsorship disclosure to identify what impact they have upon consumer
product perceptions (product knowledge and product attractiveness) and consumer outcomes
(purchase intentions). This study aims to address three research questions:
RQ1: What is the impact of social media influencer type (macro- vs. micro-
influencer) on consumer product perceptions and consumer outcomes?
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RQ2: What is the influence of sponsorship disclosure on consumer product
perceptions and consumer outcomes?
RQ3: Do consumer product perceptions (product attractiveness and product
knowledge) mediate the interrelationships between social media influencer
type, disclosure and purchase intentions?
In addressing these three research questions, this study contributes to the academic
literature in three ways. First, this study demonstrates ‘less is more’; that is, micro-influencers
can be more effective than their macro-influencer counterparts in influencing consumer
intentions and behaviour. Second, this study demonstrates that disclosure of sponsorship is a
practice that should be embraced by social media influencers. Third, it shows that the
influential roles of product attractiveness and product knowledge on consumers’ purchase
intentions are increased by social media influencer efforts. The remainder of this paper is
structured as follows. First, the paper reviews the social media influencer and disclosure of
native advertising sponsorship literature. Next, the hypotheses are presented, followed by the
study methodology and results. Finally, implications for theory, practice and policy are
discussed as well as directions for future research.
Theoretical background
Macro and micro social media influencers
Social media influencers, like traditional celebrities, have developed a personal brand, also
called a 'human brand’. For the purposes of this paper, we draw from the academic and
practitioner literature to define social media influencers as individuals with big followings
online which attract a large amount of engagement (e.g. likes), and are able to use this
popularity for marketing efforts in a specific industry (see Table 1). An influencer has the
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tools and perceived authenticity to consistently attract many viewers and can motivate others
to expand their social reach. Thus, social media influencers’ audiences are not limited to their
actual followers; they can connect with the followers of their followers who share their
content, extending their potential reach exponentially. Understanding what constitutes an
individual being classified as an ‘influencer’ (Evans et al., 2017) – often referred to using
other terms, such as ‘insta-famous’ (Marwick, 2015), ‘micro-celebrity’ (Khamis et al., 2017),
‘market maven’ and ‘opinion leader’ (Lin, Bruning, & Swarna, 2018) to name a few – is
important from both a theoretical and practitioner perspective. For the remainder of this paper,
we refer to these individuals as ‘social media influencers’ or ‘influencers’ as this is the most
predominant term in both theory and practice, as shown in Table 1.
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Table 1. Overview of terms and definitions used for social media influencers.
Author(s) year Term/label Academia/practice Definition
Senft (2008) Micro-celebrity Academia ‘…involves people “amping up” their
popularity over the Web using techniques
like video, blogs, and social networking
sites’ (p. 25)
Marwick & Boyd (2011) Micro-celebrity Academia ‘…using social media to develop and
maintain an audience’ (p. 140)
Wong (2014) Social Media
Influencer
Practice ‘a form of marketing that identifies and
targets individuals who have influence over
potential buyers’
Evans et al. (2017) Social Media
Influencer
Academia ‘…often amass large followings through
posting aspirational photos using hashtags
and engaging with followers on the site…’
(p. 139)
Khamis et al. (2017) Social Media
Influencer/Micro
-celebrity
Academia ‘any well-known persona who is the subject
of marketing communications efforts’ (p.
193)
Ge & Gretzel (2018) Social Media
Influencer
Academia ‘individuals who are in a consumer’s social
graph and have a direct impact on the
behaviour of that consumer’ (p. 1273)
Kirwan (2018) Social Media
Influencer
Practice ‘Social media influencers are people who
have large audiences of followers on their
social media accounts, and they leverage
this to influence or persuade this following
to buy certain products or services’
InfluencerMarketingHu
b (2019)
Influencer Practice ‘An influencer is an individual who has the
power to affect purchase decisions of others
because of his/her authority, knowledge,
position or relationship with his/her
audience’
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Influencers differ from traditional celebrities through the manner in which they gain
fame in order to be considered a personal brand or celebrity. Traditional celebrities find fame
through pursuits including acting, music, sports and politics, and gain a following through
their work, interviews and media relations (McCracken, 1989). Meanwhile, influencers
develop a personal brand through their posts on social media (De Veirman et al., 2017).
Platforms such as Instagram encourage consumers to develop a self-brand and extend their
potential for fame and, because of this, consumers have become the new brand endorsers over
the past decade due to perceptions of credibility, authenticity and relatability (Booth & Matic,
2011; Evans et al., 2017). This is resultant from the close relationships fans feel they share
with influencers due to the influencer sharing parts of their lives online (Djafarova &
Rushworth, 2017).
There is growing discussion of what constitutes a large number of followers/likes or
acceptable levels of followers/likes to be classified as a social media influencer. For example,
De Vierman et al. (2017) classified a low level of following as 2100 followers and 21,000 as
high followers, whereas a study by Kusumasondjaja and Tjipotono (2019) distinguished
between celebrities and experts for food Instagram posts based upon the average number of
likes their posts attracted. Conversely, other studies refer more broadly to perceived opinion
leadership (Casaló et al., 2018; Lin et al., 2018; Xiong, Cheng, Liang, & Wu, 2018). Xiong et
al. (2018) argue there is no consistent measurement regarding perceived opinion leader status.
This leaves little consensus as to what level of followers or likes segments social media
influencers into distinct groups. Furthermore, Lin et al. (2018) posit there are specific online
opinion leadership roles for different influencers, and that these roles and their influence differ
according to influencers’ social reach (i.e. the number of followers and likes they attract).
Thus, an important gap in the literature is the impact different influencers have on consumer
outcomes.
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While academic literature lacks consistent and specific definitions of what constitutes
a large number of followers, industry sources offer some clarity. In practice, there are varying
classifications of social media influencers. For example, Porteous (2018) suggests three levels
– micro, macro and celebrity – whereas others suggest two levels – micro and macro (Dhanik,
2016; Hatton, 2018). The current study employs a two-level classification for several reasons.
First, previous scholarly investigations appear to align to a two-level classification without
identifying their investigation or using micro- and macro-influencer labels (De Vierman et al.,
2017; Kusumasondjaja & Tjiptono, 2019). Secondly, in practice, the third level of the three-
tier classification appears to refer to traditional mainstream celebrities, which are not the
focus of this study. Finally, the two-level classification appears to be the most widely used and
discussed in both academia and practice, as shown in Table 2.
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Table 2. Overview of two-level and three-level classifications.
Author(s) Two-level classification
(macro and micro)
Three-level classification
(mega, macro and micro)
Dhanik (2016) Y
De Vierman et al. (2017) Y
Hatton (2018) Y
Porteous (2018) Y
Neil (2018) Y
Jin, Muqaddam, & Ryu (2019) Y
Kusumasondjaja & Tjiptono (2019) Y
Schouten et al. (2019) Y
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Using Kusumasondjaja and Tjiptono’s (2019) likes attracted by a post, this study
distinguishes between macro- and micro-influencers. Further, Thomas (2017) argues that
determining social media influencers by their number of followers is easily adapted to focus
on the number of likes influencers receive as well as through the ‘percentage of post[s]’ that
they attract (the percentage of followers that like posts). In fact, this engagement rate may be
a superior indicator of an influencer’s potential worth to a brand if engagement translates to
purchase behaviour. Using likes as an indication of influencer status based upon the number
of likes is also supported by the literature (Hong & Cameron, 2018; Kim & Xu, 2019 ; Reich,
Subrahmanyam, & Espinoza 2012). Kim and Xu (2019) argue that consumers have positive
evaluations of social media content based upon the ‘number of likes’. In addition, both
scholars and practitioners point out that the number of ‘likes’ is often indicative of the size of
an influencer’s followership and labelled as the ‘Like Follower Ratio’ (Kim & Xu, 2019).
Studies also show consumers perceive more credibility in messages based upon the number of
likes (Hong & Cameron, 2018). In further support, other studies suggest that ‘likes’ related to
a certain message are more influential as they indicate the peer groups’ endorsement of that
message (Reich et al., 2012); thus, suggesting likes are a key influential factor for influencer
marketing. As such, leveraging from the aforementioned points, that likes are indicative of the
followership of social media influencers and that ‘likes’ are highly influential (persuasive) on
consumer responses, we use ‘likes’ as our basis to distinguish between different social media
influencer types. For the purposes of this paper, we define micro-influencers as individuals
who attract 1,000 to 100,000 likes, and macro-influencers as those who attract 100,000 to
1,000,000 likes/followers (De Vierman et al., 2017), which we justify shortly in the method
section.
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Disclosure of native advertising endorsement/sponsorship
Native advertising, also referred to as sponsored content, describes any paid advertising that
takes the specific form and appearance of editorial content from the publisher itself
(Wojdynski & Evans, 2016). Another key characteristic of native advertising is its
presentation, which appears alongside, and often intermingled with, non-sponsored content on
the same digital platform, such as social media posts, blogs, reviews, videos and written
articles (Harms, Bijmolt, & Hoekstra, 2017; Wojdynski, Evans, & Hoy, 2018). This practice
leads to native advertising blending in with its surrounding context, making it difficult for
consumers to determine the difference between advertising and non-advertising content
(Campbell & Grimm, 2019). For example, social media influencers intermingle personal posts
and sponsored posts on their profiles (Evans et al., 2017). Campbell and Grimm (2019) posit
the potential for this intermingling between sponsored and non-sponsored content is most
evident in social media influencers. The potential risk of consumer harm in this setting is
much greater as it is difficult for consumers to decipher, as advertisements by social media
influencers can be confused with word-of-mouth, entertainment or editorial programming
content.
Native advertising is attracting prominence in social media influencer marketing and,
thus, is beginning to draw attention from regulators and policy-makers due to its deceptive
nature (Campbell & Grimm, 2019). As a result, regulatory and policy changes are requiring
social media influencers to disclose their endorsements to their followers. Despite these
changes, questions remain about how native advertising by social media influencers is
impacted when they are required to disclose sponsored posts (Wojdynski & Evans, 2016). As
such, this study turns to the disclosure of sponsorship literature to gain insight as to how this
may impact native advertising by social media influencers.
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Disclosure of sponsorship is the communication of sponsored content to consumers
(Boerman et al., 2014). As shown in Table 3, there are two streams of research regarding
sponsorship disclosure. First, studies have focused on disclosure in traditional advertising
settings using television-based advertisements (e.g. Boerman, Van Reijmersdal, & Neijens,
2012, 2014, 2015; Campbell et al., 2013). The second stream focuses on disclosure in native
advertising settings, in particular focusing on its practice by social media influencers
(Boerman, Willensen, & Van Der Aa, 2017; Evans et al., 2017; Hwang & Jeong, 2016). As
influencer marketing is a recent phenomenon, research investigations into sponsorship
disclosure in this domain are few and more are needed. However, the resounding sentiment of
the literature is that sponsorship disclosure results in negative brand attitudes (Boerman et al.,
2012, 2014, 2015; Wojdynski & Evans, 2016) and a reduction in intention to engage in
electronic word-of-mouth (eWOM; Boerman et al., 2017). For example, Boerman et al.’s
(2012) study showed that disclosure results in less favourable brand attitudes, while
Wojdynski and Evans (2016) found that disclosure can negatively impact both attitudes and
behavioural intentions; yet the impact this has for influencers, and specifically different levels
of influencers (macro and micro), is relatively unknown. Furthermore, as shown in Table 3,
little research has combined disclosure and social media influencer levels and investigated
their impact on product knowledge, product attractiveness and purchase intentions.
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Table 3. Chronological overview of related disclosure studies.
Study Traditional/native advertising Disclosure Macro- vs micro-
influencer
Product knowledge/attractiveness Behavioural
intentions
Campbell et al. (2013) Traditional Y
Boerman et al. (2014) Traditional Y
Boerman et al. (2015) Traditional Y
Hwang & Jeong (2016) Native Y
Matthes & Naderer
(2016)
Traditional Y
Wojdynski & Evans
(2016)
Traditional Y Y
Boerman et al. (2017) Native Y Y
Evans et al. (2017) Native Y Y
Ikonen, Luoma-aho, &
Bowen (2017)
Native Y
Campbell & Evans
(2018)
Traditional Y Y
Wojdynski et al. (2018) Traditional Y
De Jans et al. (2019) Native Y Y
This Study Native Y Y Y Y
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Given the lack of studies on social media influencers in this area, it is vital to know
how disclosure influences consumer outcomes to confirm whether the findings are in line
with prior research or nuances exist. For example, Audrezet et al. (2018) point out that tighter
regulation on disclosure stimulates the need for greater research in this area. Of the small
body of studies in this area, there is a suggestion that disclosure is also important for social
media influencers. Thus, while there is tentative evidence to suggest sponsorship disclosure is
important, more evidence is needed to provide greater insight as to whether recent policy
developments and guidelines which enforce and encourage social media influencers to
disclose sponsored content impact consumers’ evaluations. Next, the consumer outcomes
examined in this study are presented and justified.
Consumer product perceptions and outcomes
As shown in Table 3, studies have extensively examined the impact of disclosure on brand
recall and brand attitude. However, influencers can have a more targeted purpose than
building brand attitude and recall, as established in prior studies. The current research sets out
to investigate three additional consumer outcomes to understand if social media influencers
can make products more attractive, enhance consumers’ knowledge of products and how they
can be used, as well as increase purchase intentions.
A review of the literature demonstrates that there are two perspectives to investigating
consumers’ perceptions of influencer marketing. The first focuses on the perception of the
influencer. For example, Hughes et al.’s (2019) review suggests that consumers’ perceptions
of the traits of influencers (or bloggers), such as their source credibility, is one of the most
commonly investigated factors. Other such studies include Schouten et al. (2019) and Luo and
Yuan (2019), which both investigate the source credibility of influencers via trustworthiness,
expertise and attractiveness, finding trustworthiness to be the most significantly important
attribute of influencers. The second focus of consumer perception of influencer marketing
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relates to consumers’ evaluations of the products and brands incorporated within an
influencer’s post.
In this study, we align with the latter and focus on consumers’ product perceptions. This
is supported by Childers, Lemon and Hoy (2018), whose study of marketing agencies using
influencers remains largely uncharted territory, and strategic evaluations and decision-making
relating to their impacts on products and brands require investigation. Furthermore,
investigating consumer product perceptions of products rather than the influencer themselves
is important as it can provide important evidence of the impact of native advertising practices
as called for and needed by policy-makers in this largely uncharted and regulated area of
marketing (Campbell & Grimm, 2019). Next, we define and review the consumer product
perceptions of product knowledge, product attractiveness and purchase intentions.
The first consumer product perception is product knowledge, which refers to
consumers’ perceived level of familiarity and expertise with a product (Biswas, Biswas, &
Das, 2006). Previous literature has suggested celebrity endorsement has a positive influence
on product knowledge (Biswas et al., 2006; Silvera & Austad, 2004; Spry et al., 2011), yet
this is not confirmed for macro- and micro-influencers. Product knowledge is an important
outcome, as research demonstrates that it influences the way in which consumers will recall
and evaluate a product (Hong & Sternthal, 2010; Lee & Lee, 2011). We therefore seek to
determine whether the effect of social media influencers’ endorsement enhances consumers’
product knowledge and whether this differs for macro- and micro-influencers.
Product attractiveness – referring to the visual elements of the product, such as shape
and colour, which make it aesthetically appealing to the consumer (Mathwick, Malhotra, &
Rigdon, 2001) – is the second addition. Understanding the potential of social media
influencers to enhance product attractiveness is important, as attractiveness can be a deciding
factor for consumers regarding whether to terminate or continue searching for a product and
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for the end decision (Dellaert & Häubl, 2012). Most advocates of social media influencers
believe their endorsement should lead to products being perceived as more attractive (Kapitan
& Silvera, 2016; Lou & Yuan, 2019; Martensen, Brockenhuus-Schack, & Zahid, 2018), yet
there is a lack of empirical evidence to support this proposition, particularly regarding their
visual appeal.
Purchase intentions is the third addition and refers to consumers’ willingness to buy a
product. Purchase intentions is an important outcome to explore to determine return on
investment for social media endorsement, as well as the strategic selection of social media
influencer levels and disclosure of sponsorship content. Marketers are naturally interested in
strategies and approaches that enhance consumers’ purchase intentions. This is also the case
for social media influencers’ product endorsement (Evans et al., 2017). While social media
influencers have been used to endorse products, the ability of different levels of influencers as
well as the combination of disclosure practices to influence behavioural outcomes, such as
purchase intentions, is only just emerging (De Jans et al., 2019; Hwang and Jeong, 2016;
Evans et al., 2017). This study therefore investigates three outcomes – product knowledge,
product attractiveness and purchase intentions – to contribute new insight into the impact
disclosure has upon consumer outcomes.
Conceptual model and hypotheses development
The Persuasion Knowledge Model (Friestad & Wright, 1994) underpins the conceptual
model’s network of relationships, as presented in Figure 1, suggesting that once consumers
realise that a particular message has persuasive content and intent (e.g. trying to convince
individuals to buy a product endorsed by a social media influencer) people will resist it
(Friestad & Wright, 1994). The following sections outline the hypotheses tested in the current
study supported by previous literature and the Persuasion Knowledge Model.
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18
Macro- vs
Micro-
Inuencer
Disclosure vs
Non-
disclosure
Social media
inuencer x
Disclosure
Interaction
Product
Knowledge
Product
Attractivenes
s
Purchase
Intentions
=Experimental manipulation
variable
=Self-report measured
variable
Figure 1. Conceptual model.

Practitioners suggest that micro-influencers who engage more with their followers can
be more effective with greater levels of persuasion. For example, Dhanik (2016) argues that
micro-influencers can be more effective as they have a greater personal connection with their
followers and higher engagement rates. Conversely, other practitioner-based research suggests
that as the number of followers increases, the engagement for influencers drops, with
suggestions that micro-influencers – with engagements in the 10,000 to 100,000 – are in the
‘sweet spot’ (Chen, 2017). The practitioner literature contradicts the scholarly literature. For
example, the findings of DeVeirman et al. (2017) suggest increasing numbers of followers for
Instagram influencers leads to higher perceptions of ascribed opinion leadership. Further,
research by Kusumasondjaja and Tjiptono (2019) shows celebrities rather than experts
(distinguished by using the average number of likes per post) create greater levels of pleasure
and arousal in Instagram posts of food. For the purpose of this research, it is suggested that
product knowledge will be higher for micro-influencers than for macro-influencers for several
reasons. First, in line with the practitioner literature, it is proposed that micro-influencers will
have perceptions of authenticity and connection with their audience that will lead to their
communication in their posts being more persuasive than their macro-influencer counterparts.
Secondly, Ilicic and Webster (2016) suggest celebrities who are perceived to be more
authentic have a greater influence on consumer outcomes. Thus, the type of influencer will
significantly influence consumers’ product knowledge.
The Persuasion Knowledge Model supports the premise that micro-influencers are
more effective in increasing product knowledge than macro-influencers. The model describes
that people’s knowledge and responses to persuasive marketing content differ when
consumers realise a marketer’s intent (de Pelsmacker & Neijens, 2012; Stubba and
Collianderb, 2019). In the case of micro- and macro-influencers, a consumer exposed to the
macro-influencer is likely to interpret that the macro-influencer is attempting to be more
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persuasive through their popularity (i.e. number of likes) than the micro-influencer. In
accordance with the Persuasion Knowledge Model, consumers, when exposed to macro-
influencers, will recognize these influencers are attempting to be more persuasive and will
either ignore or resist the attempt, resulting in consumers reporting less product knowledge in
comparison to consumers exposed to micro-influencers. Based upon the previous discussion,
the following hypothesis is proposed:
H1. Product knowledge will be significantly higher for consumers exposed to the
micro-influencer condition than macro-influencer condition.
Disclosure of sponsorship research demonstrates that disclosure affects consumer
outcomes (Boerman et al., 2017; De Jans et al., 2019; Evans et al., 2017). For instance,
Hwang and Jeong (2016) found that ‘simple’ sponsorship disclosure (i.e., stating, ‘this is a
sponsored post’) had negative effects on persuasion. In contrast, De Jans et al. (2019) found
that disclosure in vlogs (video blog content) increases recognition of advertising. Similarly,
Evans et al. (2017) found that the language used by social media influencers for disclosure,
featuring wording such as ‘Paid Ad’ increases ad recognition. Therefore, as the disclosure of
an influencer’s sponsorship will lead to the post being recognised as a form of advertisement,
it is likely that consumers will take greater notice of the message and report higher product
knowledge than in the non-disclosure condition. Thus, a social media influencer disclosing
the sponsored product within their post potentially creates advertising recognition, which
increases consumers’ understanding of and receptiveness to what is being communicated,
which subsequently enhances their knowledge of the product being promoted.
Drawing from the Persuasion Knowledge Model, which proposes responses to
persuasive marketing content differ when consumers realise a marketer’s intent (de
Pelsmacker & Neijens, 2012), the disclosure condition will lead to significantly higher
20

product attractiveness than the non-disclosure condition. When an influencer discloses
sponsorship, consumers will likely perceive this as an attempt to make the post honest and
less persuasive. Whereas, when a social media influencer does not disclose sponsorship and
engages in native advertising, consumers will interpret this as a covert attempt to appear
authentic and be more persuasive through hiding evidence of marketing efforts. Thus, the
following hypothesis is proposed:
H2. Product attractiveness will be significantly higher for consumers exposed to the
social media influencer native advertising sponsorship disclosure condition than the
non-disclosure condition.
Eastman, Iyer, Liao-Troth, Williams and Griffin (2014) found opinion leadership
(similar to influencers) interacts with involvement to influence purchase behaviour.
Additionally, Evans et al. (2017) established the ability of sponsorship disclosure to interact
with advertising recognition, with their findings showing that disclosure memory interacts
with advertising recognition to influence attitude towards the brand and purchase intentions.
Furthermore, we posit when micro-influencers disclose their sponsorship, consumers are
likely to see these posts as genuine and as an attempt to be honest and open with their close
and small(er) number of followers (fans), thus suggesting influencer type and disclosure will
interact. Taken together, micro-influencers will likely exert greater persuasion due to
consumers’ perceptions of them being more genuine in combination with positive perceptions
of disclosing sponsorship. This in turn should lead to heightened levels of purchase intentions.
Conversely, drawing on the Persuasion Knowledge Model, consumers are more likely to try to
resist or ignore posts by macro-influencers which do not disclose sponsorship as they may be
perceived as efforts to be more persuasive. Thus, the third hypothesis builds upon H1 and H2
21

to propose that consumers exposed to micro-influencers who disclose native advertising
sponsorship will have significantly higher purchase intentions:
H3. Purchase intentions will be significantly higher for consumers exposed to the
Micro-Influencer x Disclosure condition.
Product knowledge and product attractiveness have been found to influence the
relationships between influencer type, disclosure and purchase intentions. Social media
influencers often aim to increase consumers’ product knowledge and confidence in using
products by posting testimonials describing product features or ‘how to videos’ in an attempt
to increase purchase intentions. Previous studies show knowledge plays a mediating role
between product knowledge and purchase intention (Claycomb, Cauberghe, & Hudders, 2005;
Kang, Manthiou, Sumarjan, & Tang, 2017; Van Ngyunen, Lu, Hill, & Conduit, 2019),
suggesting that product knowledge is necessary prior to purchase intention. In particular,
brand knowledge has been shown to mediate the relationship between brand leadership and
brand citizenship behaviour (Van Ngyunen et al., 2019). Therefore, to extend previous
findings, the following mediation hypotheses are proposed:
H4. (a) The influencer type–purchase intentions relationship will be mediated by
product knowledge.
(b) The disclosure vs non-disclosure–purchase intentions relationship will be mediated
by product knowledge.
(c) The interaction of influencer type and disclosure–purchase intentions relationship
will be mediated by product knowledge.
The attractiveness of a brand or product design is shown to be an important
consideration for consumers (Elbedweihy, Jayawardhena, Elsharnouby, & Elsharnouby, 2016;
Orth, Campana, & Malkewitz, 2010; Scarpi, Pizzi, & Raggiotto, 2019). Similar to product
22

knowledge, social media influencers can also focus their posts on increasing the attractiveness
of a product. This can be achieved through social media influencers using their attractiveness
or by posing with the product in a particular way to highlight its features. Subsequently, by
attempting to increase the attractiveness of a product, both the social media influencer and the
brand hope that this results in an increased likelihood of purchase. Similar relationships have
been proposed in previous studies, whereby product attractiveness has an influence on
consumer outcomes such as purchase intentions (Scarpi et al., 2019), brand loyalty
(Elbedweihy et al., 2016) and price expectation (Orth et al., 2010). Therefore, the following
mediation hypotheses are proposed:
H5. (a) The influencer type–purchase intentions relationship will be mediated by
product attractiveness.
(b) The disclosure vs non-disclosure–purchase intentions relationship will be mediated
by product attractiveness.
(c) The interaction of influencer type and disclosure–purchase intentions relationship
will be mediated by product attractiveness.
Method
To test the hypotheses, an experiment was undertaken using a 2 x 2 factorial design, where the
two independent variables – sponsorship disclosure (disclosure vs non-disclosure) and
influencer type (macro vs micro) – were manipulated, resulting in four experimental
conditions (1= Disclosure x Macro-Influencer, 2 = Non-Disclosure x Macro-Influencer, 3 =
Disclosure x Micro-Influencer, and 4 = Non-Disclosure x Micro-Influencer).
Materials and procedures
The materials for the experiment were four fictitious female influencer (Instagram handle
@BethanyBeauty) posts containing a real-world beauty product XO Beauty. The four stimuli
23

were identical except for the presence of disclosure and the number of likes to depict the
different influencer type (see Appendix 1). To depict disclosure, the hashtag ‘#sponsored’ was
included, which has been employed successfully in other studies (e.g. Evans et al., 2017). For
influencer type, 11,883 likes for the post were used for the micro-influencer condition and
118,863 were used for the macro-influencer condition. Participants were assigned randomly to
one of the four conditions using a randomised link creator that was embedded in the survey
link. This gave participants an equal probability of being allocated to one of the four
conditions. Measures for product knowledge, product attractiveness and purchase intentions
were collected after participants were exposed to the stimuli. From the random allocation,
n=83 were assigned to the Macro-influencer x Disclosure condition, n=86 were allocated to
the Macro-influencer x No Disclosure condition, n=82 were allocated to the Micro-influencer
x Disclosure condition, and n=83 were allocated to the Micro-influencer x No Disclosure
condition.
The development of the ‘number of likes’ variable began by searching for sources that
have conducted research where the number of likes on a social media post had been
manipulated. However, very little research exists which provides direction as to the
appropriate number of likes. One study by De Veirman et al. (2017) used numbers of varying
values beginning with '21' (210 likes, 2,100 likes, 2,100 followers, and 21,000 followers).
These numbers are comparatively low in at least the beauty influencer segment, with some
influencers having over 10 million followers and over 500,000 likes.
De Veirman et al. (2017) also used a 10% followers to likes ratio (2,100 followers has
210 likes; 21,000 followers has 2,100 likes). After perusing a number of beauty influencer
profiles on Instagram, it was clear that 10% is not an accurate representation of the follower to
likes ratio seen on Instagram. At this point, 20 influencers were selected, their number of
followers recorded, the previous 10 non-sponsored photo posts were looked at, and the
24

number of likes recorded. These numbers were averaged per influencer and then as a whole to
get an average percentage of likes to followers. The percentage found was 5.63%. Appendix 2
shows the recorded number of likes and followers.
Participants
The participants were 334 female consumers aged 18–35 years who indicated they were
interested in beauty and makeup, used Instagram and had not previously purchased or seen
XO Beauty products. Participants were recruited through an online survey panel provider.
This target market was deemed appropriate to operationalise the study for several reasons.
First, this study looks at the beauty industry – an industry with a strong social media presence
for both brands and influencers, as well as having a history of economic stability and
continuous growth (Lopaciuk & Loboda, 2013). In 2017, the cosmetics segment of the beauty
industry generated US$773.8m in Australia (Statista, 2018), and experienced 5% growth
(Statista, 2017). Second, using women for this study is appropriate as the beauty industry has
been predominately targeted towards this market segment. While there has been an increase in
men watching beauty vlogs, they only account for 11% of the viewers (Jankowski, 2018).
Third, this sample is consistent with previous influencer studies with predominant female
samples (e.g. 82% in Evans et al., 2017) or those that allocated participants to match the
influencer’s gender (e.g. De Veirman et al., 2017). Sample characteristics are shown in Table
4.
25

Table 4. Demographics.
Characteristic %
Employment
Full-time
Part-time
Unemployed
Student
Volunteer work
Other
36.8
24.3
19.4
13.5
.3
5.7
Income
Less than $10,000
$10,000–$19,999
$20,000–$29,999
$30,000–$39,999
$40,000–$49,999
$50,000–$59,999
$60,000–$69,999
$70,000–$79,999
$80,000–$89,999
$90,000–$99,999
$100,000–$149,999
More than $150,000
23.1
11.7
8.4
6.9
9.0
9.9
7.2
8.4
2.7
3.6
8.1
1.2
Instagram use per week
Mean 9 hours (SD=XX)
26

Measures
All items used in the study were adapted from previously validated scales. For the
manipulation check of disclosure, two items were adapted from Evans et al.’s (2017) study: ‘I
believe this was a paid advertisement’ and ‘I believe BethanyBeauty has disclosed that this
was a paid advertisement’ (1 = strongly disagree, 5 = strongly agree). The macro- vs micro-
influencer manipulation was measured using one item, ‘How many likes do you believe this
post has?’ (1 = very small, 7 = very large), adapted from De Veirman et al. (2017). Product
knowledge was measured on three items adapted from Spry et al. (2011). Product
attractiveness was measured via three items adapted from Mathwick et al.’s (2001) aesthetic
appeal scale. Purchase intention was measured using four items adapted from Hausman and
Siekpe’s (2009) scale. Product knowledge, product attractiveness and purchase intentions
were each measured on a 7-point scale (1 = strongly disagree, 7 = strongly agree).
Common method bias
To assess the potential for common method bias to influence the results, a Harman’s single-
factor test was undertaken. The test revealed that less than the majority (47.76%) was
explained by one factor, confirming that common method bias had a minimal impact on the
results of the study. Further, as suggested by Podsakoff, MacKenzie, Lee and Podsakoff
(2003), we tried to minimise potential common method bias when designing the study, for
instance, by varying response formats and reassuring respondents of their anonymity.
Covariate variables
Covariates are used as controls in experimental research and are used to ensure the robustness
of the main or interaction effects being tested for. Three covariates were used in this research,
specifically for testing H4–H5, one of which was a demographic measure: income bracket.
27

The remaining two covariates, trustworthiness and authenticity, are justified below.
First, trustworthiness – how believable and honest a consumer perceives an endorser
or source to be – is suggested to be a positive indicator of an effective message from a source
(Amos, Holmes, & Strutton, 2008; Chao, Wührer, & Werani, 2005). Further, trustworthiness
is suggested to be influential in determining a source's credibility and has been extensively
studied (for a review see Moraes, Gountas, Gountas, & Sharma, 2019). Seven items were
adapted from Ohanian’s (1990) scale to measure trustworthiness. Second, consumers have an
increasing desire for authenticity from products and brands, and the use of authenticity in
marketing has been shown to improve message receptivity (Labrecque, Markosm, & Milne,
2011), increase perceived quality (Moulard, Raggio, & Folse, 2016) and increase purchase
intention (Napoli, Dickinson, Beverland, & Farrelly, 2014). Authenticity was measured via
three items adapted from Ilicic and Webster (2016). Given the strong evidence of the
influence of authenticity based upon prior research, it was deemed appropriate to include it as
a covariate in the current study to ensure its presence did not confound the relationships being
examined.
Results
Manipulation checks and instrument validation
First, prior to testing the hypotheses, we undertook manipulation checks to ensure that
disclosure vs non-disclosure and macro- vs micro-influencer conditions were satisfactory for
testing. Participants considered the disclosed condition to be sponsored (M=4.32, SD=2.20)
and the non-disclosed condition to not be sponsored (M=2.55, SD=1.30, t=8.96, p<.000).
Participants also significantly agreed that BethanyBeauty had disclosed in the disclosure
condition (M=3.98, SD=1.98) in comparison to the non-disclosure condition (M=2.95,
SD=1.34, t=5.56, p<.000). It was therefore concluded that the disclosure and non-disclosure
28

manipulations were satisfactory, as confirmed by participant responses. Participants reported a
significantly different amount of likes between the macro- (M=4.92, SD= 1.68) and micro-
influencer (M=1.98, SD= 1.25, t= 6.42, p<.05) conditions, as intended in the stimuli design. It
can therefore be concluded that the manipulation check results provide empirical support for
the stimuli conditions for hypotheses testing.
Prior to testing the hypotheses, reliability and validity tests were undertaken for both
the covariates (trustworthiness and authenticity) and dependent variables (product knowledge,
product attractiveness and purchase intentions). As shown in Table 5, all scales for the
covariates and dependent variables were shown to have high levels of reliability and validity.
Once the manipulation checks and reliability and validity for the measures had been
confirmed, hypotheses testing was undertaken, which is reported next.
29

Table 5. Reliability and validity of measures.
Construct Factor
Loadings
Composite
Reliability
Cronbach’s
Alpha
AVE
Product Knowledge .911 .854 .775
Do you have an interest in beauty and
makeup?
0.746
I am interested in beauty and makeup
products
0.950
Compared to other people, I know more
about makeup and beauty
0.932
My friends consider me an expert on
makeup and beauty
Product Attractiveness .918 .865 .789
The way XO Beauty's products are
displayed is in an attractive way
0.826
XO Beauty's packaging is aesthetically
pleasing
0.907
I like the way XO Beauty's packaging looks 0.928
Purchase Intentions .976 .968 .911
I will definitely buy the product from this
post in the near future
0.945
I intend to purchase through this post in the
near future
It is likely that I will purchase through this
post in the near future
0.949
0.966
I expect to purchase through this post in the
near future
0.958
Note: AVE = average variance explained
30

Hypotheses testing
To test the hypotheses, a series of analysis of variances (ANOVAs) and a PLS-SEM model
were examined. The same independent variables (presence of disclosure and influencer type)
were used when testing all hypotheses.
ANOVA testing (H1–H3)
Hypothesis 1 – product knowledge. We undertook a one-way ANOVA (macro- vs micro-
influencer) to examine the effect of influencer type on product knowledge, which was
computed via an average of the three items measuring the construct. The result of the one-way
ANOVA (Table 6 and Figure 2) shows that there was a significant main effect of influencer
type on product knowledge. The results revealed the micro-influencer condition had a
significantly higher mean (M = 4.42, SD =.12) than the macro-influencer condition (M =
4.02, SD=.12).
To demonstrate rigour, we also investigated the effect of disclosure vs non-disclosure
as well as the interaction effect (Macro- vs Micro-Influencer x Disclosure vs Non-Disclosure).
The results revealed that there were no significant differences between the disclosure and non-
disclosure (p=.889) conditions or a significant interaction effect between influencer type and
disclosure on product knowledge (p=.074).
31

Table 6. Product knowledge test results.
Relationship D
f
f p value Partial Eta
Squared
Observed
Power
H1. Macro- vs micro-
influencer
1 5.00 .026 .015 .607
Disclosure vs no-disclosure 1 .020 .889 .000 .052
Disclosure x Influencer Type 1 3.21 .074 .010 .432
Macro Influencer Micro Influencer
3.8
3.9
4
4.1
4.2
4.3
4.4
4.5
4.02
4.41
Product Knowledge
Product Knowledge
*Note: Measured on 7-point scale
Figure 2. H1 – Product knowledge results.
32

Hypothesis 2 – Packaging attractiveness. To test H2 we undertook a one-way ANOVA
(disclosure vs non-disclosure) to examine the effect of disclosure on packaging attractiveness,
which was computed by averaging the three items used to measure the construct. A significant
main effect was found for disclosure (F=6.05; p = .014; Table 7 and Figure 3). The results
revealed that consumers exposed to the disclosure condition reported significantly higher
product attractiveness (M = 4.96, SD = .086) in comparison to the non-disclosure condition
(M=4.66, SD=0.85), supporting H2.
As per H1, we conducted additional testing for thoroughness in H2. Additional testing
found there was no significant main effect for influencer type (p=.740) or an interaction effect
between influencer type and disclosure (p=.192).
33

Table 7. Packaging attractiveness test results.
Relationship Df f p value Partial eta
squared
Observed
power
H2. Disclosure vs no-
disclosure
1 6.05 .014 .018 .689
Macro- vs micro-
influencer
1 .14 .740 .000 .067
Disclosure x Influencer
Type
1 1.70 .192 .005 .256
Non-disclosure Disclosure
4.3
4.4
4.5
4.6
4.7
4.8
4.9
5
4.57
4.96
Product Attractiveness
Product Attractiveness
*Note: Measured on 7-point scale
Figure 3. H2 testing results.
34

Hypothesis 3 – Purchase intentions. To test H3 we undertook a two-way ANOVA (Disclosure
vs Non-Disclosure x Macro- vs Micro-Influencer). As per product knowledge and product
attractiveness, an average computed score was calculated based upon the items measuring
purchase intentions. As seen in Table 8 and Figure 4, the results reveal a significant
interaction effect with the Disclosure x Micro-Influencer condition having a higher mean (M
= 3.66, SD=1.42) compared to the Non-Disclosure x Macro-Influencer condition (M = 3.27,
SD=1.69), followed by the Non-Disclosure x Macro-Influencer condition (M = 3.18, SD =
1.63) and the Disclosure x Micro-Influencer condition (M= 3.04, SD=1.68).
A follow-up simple effects analysis identified that when social media influencers
disclose native advertising sponsorship, micro-influencers (M=3.66) produce significantly
higher purchase intentions than micro-influencers who do not disclose (M=3.07, p=.014).
Further testing also found that there were no significant main effects for disclosure (p=.129)
or influencer type (p=.482).
35

Table 8. Purchase intentions test results.
Relationship Df f p value Partial eta
squared
Observed
Power
H3 Disclosure x Influencer Type 1 4.03 .045 .012 .517
Macro- vs micro-influencer 1 .495 .482 .002 .108
Disclosure vs no-disclosure 1 2.31 .129 .007 .329
Disclosure x Macro Influencer
Disclosure x Micro Influencer
Non-Disclosure x Macro Influencer
Non-disclosure x Micro-influencer
2.6
2.8
3
3.2
3.4
3.6
3.8
3.18
3.66
3.27
3.07
Purchase Intentions
Purchase Intentions
*Note: Measured on 7-point scale
Figure 4. H3 – Hypothesis testing results
36

Mediation Analysis (H4–H5)
Partial least squares equation modelling (PLS) was employed to test our factorial design
experiment following the procedures of Struekens, Wetzels, Daryanto and De Ruyter (2010).
They point out that ‘a PLS-based approach to experimental designs offers a strong
methodological tool that can be applied in many circumstances’ (p. 568). Furthermore, this
analytical approach has been replicated and shown to be effective in other factorial design
studies (e.g. Baker et al., 2019; Leroi-Werelds, Streukens, Van Vaerenbergh, & Grönroos,
2017; Singh et al. 2016). We ran the PLS-SEM model with 1,000 bootstraps and the results
are summarised in Table 9 and Figure 5.
The results show the macro- vs micro-influencer manipulation had a significant direct
effect on product knowledge (β=.41, p<.01) and purchase intentions (β=.26, p<.01) but not
product attractiveness (β=.00, ns). The results also revealed that the macro- vs micro-
influencer manipulation had a significant indirect effect on purchase intentions (β=.12, p<.01)
when mediated by product knowledge but not product attractiveness (β=.00, ns).
The disclosure versus non-disclosure manipulation had a significant direct effect on
product knowledge (β=.34, p<.05) and purchase intentions (β=.26, p<.01) but not product
attractiveness (β=.16, ns). The results show the disclosure vs non-disclosure manipulation had
a significant indirect effect on purchase intentions when mediated by product knowledge
(β=.10, p<.01) but not product attractiveness (β=.00, ns). The influencer type and disclosure
interaction was shown to have a significant direct effect on purchase intentions (β=-.38,
p<.01) but not product knowledge (β=.42, ns) or product attractiveness (β=.36, ns).
Product knowledge was found to have a positive direct effect on purchase intentions
(β=.29, p<.000) but not product attractiveness (β=.00, ns). Finally, the model provided
moderate levels of explanation of variance for purchase intentions (R2=.53).
37

Table 9. PLS-SEM results
Relationship(s) β T-statistic P
Macro- vs micro-influencerProduct attractiveness .22 1.70 0.08
Macro- vs micro-influencerProduct knowledge .41 2.58 0.01
Macro- vs micro-influencerPurchase Intentions .26 2.31 0.01
Macro- vs micro-influencerProduct attractivenessPurchase intentions .00 0.01 0.99
Macro- vs micro-influencerProduct knowledgePurchase intentions .12 2.47 0.01
Disclosure vs non-disclosureProduct attractiveness .19 1.39 0.16
Disclosure vs non-disclosureProduct knowledge .34 2.12 0.02
Disclosure vs non-disclosurePurchase intentions .26 2.38 0.01
Disclosure vs non-disclosureProduct attractivenessPurchase intentions .00 0.02 0.98
Disclosure vs non-disclosureProduct knowledgePurchase intentions .10 2.07 0.01
InteractionProduct attractiveness -.36 1.98 0.06
Interaction Product knowledge -.42 2.00 0.06
Interaction Purchase intentions -.38 2.49 0.01
Interaction Product attractivenessPurchase intentions .00 0.01 0.99
Interaction Product knowledgePurchase intentions -.12 1.89 0.06
Product attractivenessPurchase intentions .00 0.01 0.98
Product knowledgePurchase intentions .29 0.04 0.00
R2
Packaging attractiveness .35
Product knowledge .13
Purchase intentions .53
38

Figure 5. Overview of PLS-SEM results
39
Macro- vs
Micro-
Inuencer
Disclosure vs
Non-
disclosure
Inuencer
type and
disclosure
interaction
Product
Knowledge
Product
Attractivenes
s
Purchase
Intentions
=Experiment manipulated
variable
=Self-report measured
variable
β= -.38*
Note: Direct paths only shown, refer to
Table 9 for indirect effects
***p<.0001, **p<.01, *p<.05
β= .29***
β= .26**
β= .41**
β= .34*
β= .26**

Discussion
As industry continues to invest in influencer marketing, theorising and examining the
marketer and influencer relationship is important (Diggins, 2019). Therefore, this research
aimed to investigate the influence of social media influencer type and disclosure of
sponsorship in native advertising on consumer outcomes. The results showed that consumers
report significantly higher product knowledge when exposed to micro-influencers, which
challenges previous theoretical assumptions; however, this finding supports practitioners’
suggestions that ‘less is more’, as shown in Table 10. The results also demonstrate disclosure
of sponsorship leads to significantly higher levels of purchase intentions, which contrasts with
prior suggestions from both scholars and practitioners that disclosure of sponsorship leads to
lower levels of purchase intentions (refer to Table 10). Building on the first two findings,
disclosure by micro-influencers was found to lead to higher purchase intentions and this was
significantly higher than for their macro-influencer counterparts, which contrasts with what
would be suggested based upon prior evidence in practice and in the literature. A discussion
and explanation of these findings and their theoretical, policy and practical implications now
follow.
40

Table 10. Comparison of past assumptions with findings of current study.
Assumption Practical Assumption Theoretical Assumption Findings of Current Study
Likes ‘Less is more’ – micro-
influencers can be more
effective (Dhanik, 2016;
Chen, 2017)
‘More is better’ – macro-
influencers can be more
effective (e.g. DeVeirman et
al., 2017; Kusumasondjaja &
Tjiptono, 2019)
‘Less is more’ – micro-
influencers can be more
effective
Disclosure Disclosure leads to negative
outcomes/less persuasion
(Australian Association of
National Advertisers, US
Federal Trade Commission)
Disclosure leads to negative
outcomes/less persuasion (e.g.
Hwang & Jeong, 2016)
Disclosure can lead to positive
outcomes/greater persuasion
Purchase*
Intentions
To achieve purchase
intentions micro-influencers
should not disclose
To achieve higher purchase
intentions macro-influencers
should not disclose
To achieve higher purchase
intentions micro-influencers
should be used and should
disclose sponsorship
*Assumptions based upon combination of literature from likes and disclosure
41

Theoretical implications
This research provides three theoretical contributions to advance theory of social media
influencers and disclosure of sponsorship in native advertising. First, this study begins to shift
practical discussions of segmenting social media influencers into the academic literature by
empirically examining macro- and micro-influencers. An important contribution of this study
is that micro-influencers, rather than macro-influencers, appear to be more effective in
enhancing consumer outcomes. These results challenge prior theorisations and empirical
findings in opinion leadership and celebrity endorsement, which suggest that greater levels of
fandom would significantly enhance the influence of social media influencers (Casaló et al.,
2018; Eastman et al., 2014). In contrast to previous studies on social media influencers (De
Veirman et al., 2017; Kusumasondjaja & Tjiptono, 2019), the results here suggest the opposite
– that ‘biggest is (potentially) not best’ regarding the use of social media influencers. Rather,
our research lends empirical support for arguments put forward by practitioners that micro-
influencers can often offer greater benefits than their macro-influencer counterparts (Chen,
2017; Dhanik, 2016).
One plausible theoretical explanation for micro-influencers having greater influence
than their macro-influencer counterparts is through the Persuasion Knowledge Model, which
suggests consumers attempt to resist or ignore marketing and advertising content which
attempts to be more persuasive (Friestad & Wright, 1994). Consumers thus perceive macro-
influencers attempting to use their greater levels of popularity in comparison to micro-
influencers as attempts to be more persuasive, and subsequently they resist these efforts. The
results examining the differences between micro- and macro-influencers also have important
theoretical implications for the future study of social media influencers as they advocate that
previous theories or findings in the related areas of opinion leadership and celebrity
42

endorsement are potentially not generalisable, demonstrating a need to test theories and
frameworks in this new setting.
A second contribution of this study is the new insights into disclosure of sponsorship
in native advertising by social media influencers. Answering previous calls (Audrezet et al.,
2018; Campbell & Grimm, 2019), this paper examined the impact of sponsorship disclosure
on native advertising. Consistent with prior evidence found in the disclosure of sponsorship in
traditional settings (e.g. television; Boerman et al., 2014, 2015; Campbell et al., 2013;
Matthes & Naderer, 2016), disclosure of sponsorship in native advertising by social media
influencers significantly impacts consumer outcomes. The comparison of stimuli, which
mimicked a normal post (non-disclosure) and a sponsored post (disclosure), allows the
findings from this study to contribute to literature on native advertising by showing how these
practices of social media influencers and legislative changes impact on native advertising
practices. In particular, the results of this study suggest that disclosure can enhance, rather
than detract, from consumer outcomes. The Persuasion Knowledge Model explains this by
suggesting consumers perceive social media influencers as more honest and candid when they
clearly communicate product sponsorship in their posts. This results in positive reactions to
this practice as these social media influencers are actively attempting to reduce their
persuasive power.
The third contribution of this study is the bridging of two streams of literature: social
media influencer level (macro versus micro) and native advertising sponsorship disclosure
(disclosure versus non-disclosure). Combining these two literature streams provides a
theoretical explanation of how these growing phenomena and practices affect consumers’
purchase intentions. To date, research has examined influencer type (e.g. Schouten et al.,
2019) and sponsorship disclosure (e.g. Boerman et al., 2017) in isolation. This study therefore
takes an important step forward to connect these bodies of literature to unpack how these
43

areas impact consumers, social media influencers, and endorsed brands and products.
Leveraging the results of H1 and H2, micro-influencers who do disclose are likely to have a
significantly more positive impact on purchase intentions. This suggests that consumers have
a greater preference for social media influencers who are more relatable (lower levels of
followings and likes) and honest (disclose when posts are sponsored), and are potentially
sceptical as social media influencers’ fandom increases.
A fourth contribution of this study is taking a novel theoretical perspective by
including mediators beyond the traditional social media influencer or celebrity traits, such as
source credibility (trustworthiness, expertise and attractiveness). Instead, this research
investigates mediators which focus on consumers’ perceptions of product traits. This
broadening of models to include mediators from a product, rather than the social media
influencer trait perspective, is important given current discussions and requests for greater
strategic marketing and policy-making insights into this area (Kees & Andrews, 2019). This
expanded approach has generated new insight into the pathways through which social media
influencers do (and do not) increase consumers’ purchase intentions. Specifically, purchase
intentions are shown to indirectly increase via product knowledge rather than product
attractiveness.
Practical and policy implications
There are several practical and policy implications derived from the findings of this study.
First, the results suggest practitioners should carefully consider the type of social media
influencer they are using to endorse their products. The results of this study show micro-
influencers can have a greater influence on consumers and, thus, should be considered by
practitioners when selecting endorsers of their products and brand. This will benefit
organisations and brands that engage with and use social media influencers, as micro-
influencers are more cost-effective, accessible and flexible than their counterparts.
44

Second, the results suggest policy-makers should be concerned by the practices of not
only macro-influencers but also micro-influencers given their ability to influence consumers.
Consistent with Campbell and Grimm’s (2019) argument, this raises important considerations
for policy-makers, as micro-influencers can be more difficult to identify and target. Therefore,
policy-makers and law enforcement should also consider how they will ensure micro-
influencers are complying correctly with legislative frameworks for native advertising
practice.
The results provide important empirical support for research policy changes and
recommendations made by bodies such as the Australian Association of National Advertisers
and the US Federal Trade Commission regarding native advertising and social media
influencers. From the findings of this study, policy-makers should have confidence that the
changes in legislation they are implementing are indeed leading to desired results. Policy-
makers should, however, be cautious of unintended consequences of such legislative changes
requiring social media influencers to disclose sponsorship and that they do not encourage
attempts to find ‘loop holes’. This could be done by using comparative advertising to social
media influencers whereby the results of empirical research such as the current study are
shown to social media influencers. Such comparative advertising efforts should clearly
communicate how non-disclosure of sponsorship leads to counter-intuitive outcomes (i.e. less
effective) and whereby disclosing sponsorship can lead to dual benefits whereby they
conform to advertising regulations whilst also improving their marketing performance
through their social media posts.
Social media influencers can also draw implications from these findings. The results
suggest that social media influencers should view disclosing sponsorship as a potential
opportunity to draw greater support and positive outcomes for the products and brands they
endorse. Therefore, social media influencers should change their perceptions of disclosing
45

sponsorship and actively comply and enact sponsorship practices based upon recommended
guidelines and legislation. This could be achieved through social media influencers hashtags
as shown in this study and communicating clearly that the post is “#sponsored” or
“#advertisement”. There are also current practices whereby social media influencers can buy
followers from “follower farms”, whereby bots are used to artificially increase their
popularity and likes on posts (Iurillo, 2019). Our results stress caution to social media
influencers considering to employ this practice as whilst it is unethical, our results also reveal
such efforts could also be counter intuitive as it appears “less is more” when it comes to likes.
Our results also reveal that influencers and organisations should focus on product knowledge
rather than product attractiveness in their social media efforts. This could be achieved by
social media influencers ensuring they post testimonials or ‘how to’ videos demonstrating the
utility and functionality of the product rather than its aesthetic features. Based upon the results
of this current study, this will be a more useful strategy to employ when designing social
media posts to maximise opportunities to increase consumers’ purchase intentions.
Limitations and future directions for research
As with all research, limitations exist that provide opportunities for future research. First,
while this study draws strength from its experimental design, the cross-sectional nature of the
data limits the ability to examine how disclosure and influencer type impact consumers over
time, providing opportunities for future research. A key strength of the study is that it is one of
the first to empirically examine disclosure and influencer type in a prominent social media
influencer setting of female beauty products in consumer- rather than student-based samples,
as per prior social media influencer studies (e.g. Evans et al., 2017; Kusumasondjaja &
Tjiptono, 2019). However, caution should be taken from generalising the findings, and future
research should seek to examine whether the findings of this study extend into other product
categories and market segments. For example, other product categories could be explored
46

such as fitness and travel as they are often noted for being popular categories for social media
influencers and their followers. Finally, while the use of lifelike social media stimuli for the
study enhances the external validity of the findings, opportunities exist for future research.
This study leverages only one type of post by a social media influencer – a selfie-style
image – and while this is a popular style of post on Instagram, it is not the only option
available to brands and influencers. Instagram also allows for multiple photo posts in a
carousel, video posts, story posts which can be photos, videos of 15 seconds, and longer video
posts via the Instagram TV feature. Understanding the different effects of these types of posts
would be beneficial to understand if the type of post results in more nuanced effects of
disclosure and influencer type.
This study draws from data collected from an experiment where consumers were
exposed to a fictitious influencer. While careful attention was undertaken to ensure as much
realism as possible, future studies could attempt to integrate more real objective data by using
real-world influencers and their posts in a field experiment setting. The integration or
exploration of other concepts and variables could provide useful additions to augment the
results of the current study, such as source credibility, product attachment and brand attitude,
as these factors could be relevant and potentially important. Another interesting avenue
which is yet to be considered in the literature is the type of influence exerted by social media
influencers. What is currently not clear is whether social media influencers leverage based
upon informational or normative influence or a combination of both. Future studies could
seek to explore the predominant type of influence used by social media influencers as well as
how they can design their posts in such a way to leverage a particular type of influence.
The stimuli used in the study were as close to real life as possible, however
respondents may find it difficult to infer trustworthiness and authenticity from a fictitious
individual. Personality and reputation of a social media influencer may play a role in
47

participants’ evaluations and future field experiments and confirmatory qualitative studies
may provide further insights which extend the findings of the current study or identify notable
nuances.
Another interesting avenue for future research exists for disclosure. In the
marketplace, consumers often only become aware of deceit overtime, usually after reports of
dishonest practice by a social media influencer. This research only examined a disclosure
versus non-disclosure scenario, future research should investigate whether the timing of
disclosure has an impact on the relationships observed. For instance, future studies could
investigate whether social media influencers disclosing a past behaviour has an impact on
trustworthiness and authenticity perceptions of the influencer and provide insights into the
antecedents of this impact.
Conclusion
Overall, this study contributes new insight into the growing area of social media influencers,
providing an important foundation for practice and policy-makers as well as for future
research into this fruitful area. We have demonstrated that ‘less is more’, meaning that having
less followers can be more beneficial for brands, challenging the assumption that greater
popularity on social media can lead to greater marketing outcomes, and that disclosure of
sponsorship can also lead to improved outcomes.
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Appendix 1 Experimental stimuli
Condition 1: High Likes & Disclosure Condition 2: High Likes & No Disclosure
Condition 3: Low Likes & Disclosure Condition 4: Low Likes & No Disclosure
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Appendix 2
Table A2.1. Followers/likes conversion used to develop stimuli.
Inuencer Instagram
handle (@name)
Number of
followers
Number of likes Percentage of
followers to
likes
shanigrimmond 1.4 Million 102,047 7.29%
isabella_:ori 627 Thousand 75,086 11.98%
michellecrossan_ 60 Thousand 753 1.26%
kathleenlights 2.1 Million 116,867 5.57%
jackieaina 1 Million 111,039 11.1%
shaaanxo 1.5 Million 29,236 1.95%
nicolconcilio 1.3 Million 34,251 2.63%
alissa.ashley 892 Thousand 87,929 9.86%
desiperkins 3.7 Million 128,337 3.5%
lozcurtis 1.4 Million 42,638 3.05%
jazzi:lipek 66 Thousand 9,003 12.64%
RawBeautyKristi 293 Thousand 21,051 7.18%
chloemorello 1.1 Million 39,722 3.61%
jaclynhill 5.6 Million 348,795 6.23%
katy 2 Million 75,378 3.77%
carlibel 4.9 Million 198,685 4.05%
_sallyjo_ 96 Thousand 2,612 2.72%
jamiegenevieve 1.1 Million 59,452 5.4%
ThatGirlShaexo 79 Thousand 2,738 3.47%
nikkietutorials 10.9 Million 584,824 5.37%
Average
Interaction %
5.63%
57