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Continued engagement intention with
social media influencers: the role
of experience
Ameet Pandit
Marketing and Tourism Group, Newcastle Business School,
College of Human and Social Futures, The University of Newcastle,
Newcastle, Australia
Fraser McLeay
Sheffield University Management School, The University of Sheffield, Sheffield, UK
Moulik M. Zaveri
Department of Marketing, Monash University, Melbourne, Australia
Jabir Al Mursalin
Australian Catholic University, Melbourne, Australia, and
Philip J. Rosenberger III
Marketing and Tourism Group, Newcastle Business School,
College of Human and Social Futures, The University of Newcastle,
Newcastle, Australia and
Marketing and Tourism Group, College of Human and Social Futures,
The University of Newcastle, Newcastle, Australia
Abstract
Purpose –The emergence of social media platforms has revolutionized how brands develop partnerships with
social media influencers (SMIs). However, users are seeking more meaningful engagement with SMIs, and little
is known about how brands can shift their focus from transient engagements to continued engagement that builds
long-term brand–consumer relationships. Extant research has provided inconsistent findings regarding
consumer engagement behavior. To address this knowledge deficit, we contribute to the consumer engagement
literature by developing and testing a conceptual model that explores and explains the relationships between the
factors that influence continued engagement intention (CEI), a form of behavioral intention.
Design/methodology/approach –A literature review was conducted to identify gaps and develop a
theoretically informed conceptual model and hypotheses. Survey data from 604 Instagram SMI followers were
analyzed using partial least squares structural equation modeling using SmartPLS 3.3.3 to assess the structural
model relationships and conduct post hoc analysis.
Findings –The findings suggest that it is important to positively influence consumer responses to elicit CEI.
Furthermore, homophily attitudes toward SMIs moderate the relationship between SMI experience and CEI.
Practical implications –Brands must work with SMIs to create positive SMI experiences and develop
CEI. Furthermore, SMIs should focus on brands that fit their lifestyles to enhance homophily attitudes and forge CEI.
Originality/value –This study contributes to the literature by combining social exchange and flow theories to
develop and test a holistic framework for examining CEIs regarding SMIs and brands. The findings show that
creating positive SMI experiences benefits brands seeking CEI.
Keywords Continued engagement intention, Consumer experience, Homophily attitudes, Luxury fashion,
Social media influencers
Paper type Research paper
Internet Research
1
© Ameet Pandit, Fraser McLeay, Moulik M. Zaveri, Jabir Al Mursalin and Philip J. Rosenberger III.
Published by Emerald Publishing Limited. This article is published under the Creative Commons
Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works
of this article (for both commercial and non-commercial purposes), subject to full attribution to the
original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/
licences/by/4.0/legalcode
This research has been supported by a Faculty Grant from the Newcastle Business School, University
of Newcastle, Australia.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1066-2243.htm
Received 3 December 2023
Revised 29 April 2024
21 July 2024
13 October 2024
Accepted 14 October 2024
Internet Research
Vol.35 No. 7, 2025
pp. 1-29
Emerald Publishing Limited
1066-2243
DOI 10.1108/INTR-12-2023-1105
1. Introduction
Social media influencers (SMIs) are widely recognized as online opinion leaders (Geng et al.,
2024). However, their potential role in enhancing brand performance remains
under-researched (S�
anchez-Fern�
andez and Jim�
enez-Castillo, 2021). The global value of
SMI marketing is estimated at US$1.64 billion and is expected to grow in the coming years
(Statista, 2023). Brands are increasingly turning to SMIs to engage consumers (Aw et al.,
2022) by posting branded content, opinions, recommendations, and experiences to drive post-
engagement through views, likes, comments, shares, visibility, virality, and persuasion (Sheng
et al., 2024). For instance, PewDiePie garnered over 322 million views on YouTube in a single
month (Rohde and Mau, 2021). Social media creates opportunities for interactions among
SMIs, brands, and consumers for entertainment, hedonic experiences, and economic rewards
(Jahn and Kunz, 2012). However, creating and maintaining a long-term engaging experience is
challenging for many brands (Giakoumaki and Krepapa, 2020). Research on technology
acceptance suggests that understanding users’ engagement behavior is more important than
their adoption because it exemplifies long-term use beyond initial adoption (Kim et al., 2013).
Thus, brands need to create continued engagement between users and SMI by enhancing their
experiences to ensure the long-term usage of their products beyond the initial adoption.
Consumer engagement (CE) refers to the interaction between a consumer as an engagement
subject and a focal engagement object (Hollebeek et al., 2014;Hollebeek and Macky, 2019), such
as SMI. The cognitive and emotional absorption experienced by consumers through CE with a
focal object such as SMIs (Brodie et al., 2013) is anticipated to influence outcomes such as
ongoing search behavior and repurchase intentions (Cheung et al., 2021), word-of-mouth (WOM)
(De Oliveira Santini et al., 2020;Wang and Lee, 2020), sharing intention, and recommendations.
Recognizing the interactive nature of CE has become more crucial (Sheng et al., 2024), especially
when users experience fatigue when repeatedly exposed to substantial volumes of information
SMIs and brands (Bright et al., 2015), often opting to disengage when overwhelmed by excessive
content in their social media feeds (Bright and Logan, 2018). Typically, 51% of users scroll past
posts from SMIs in their feeds (Carter, 2023). Instagram engagement rates have decreased from
1.67% in 2020 to 1.18% in 2022 (Amoyo, 2022). Thus, there is a distinct lack of understanding of
what drives consumers’ continued engagement with SMIs (Dolan et al., 2019;Pezzuti et al.,
2021), and few studies have investigated the drivers that affect continued engagement activities
(Vander Shee et al., 2020). Moreover, as firms increasingly leverage influencers for promotional
activities, it is imperative to comprehend the dynamics of CE with SMIs (Pradhan et al., 2023).
However, the results of extant research provide contrasting positive, negative, insignificant, and
significant findings between CE and its outcomes (Beckers et al., 2018), emphasizing a lack of
consistency in the results (De Oliveira Santini et al., 2020). For example, some studies indicate a
strong relationship between CE and WOM (Halaszovich and Nel, 2017), whereas other studies
observe a weak relationship (Badrinarayanan et al., 2015). Research has been inconsistent
regarding the relationship between content-related antecedents (e.g. hedonic and informative
content) and CE behavior (Kefi and Maar, 2020;Kulikovskaja et al., 2023;Hollebeek et al.,
2014). To address this knowledge deficit, we contribute to the CE literature in this study by
developing and testing a conceptual model that explores and explains the relationships between
the factors that influence continued engagement intention (CEI), a form of behavioral intention
(Hepola et al., 2020) in the SMI context. Various theories have examined continued engagement
intentions, such as the theory of planned behavior (Madden et al., 1992) and the unified theory of
acceptance and use of technology (Venkatesh et al., 2003).
In this study, we adopted a theory-integration approach by integrating the flow theory and
social exchange theory to advance the understanding of CEI in the SMI context. Furthermore,
several studies have focused on homophily attitudes in the context of social media. For
example, Ladhari et al. (2020) focused on the three dimensions of homophily (i.e. attitude,
values, and appearance) of SMIs. In the luxury fashion context, Lee and Watkins (2016)
revealed a strong positive effect of parasocial interaction determined by attitude on luxury
brand perceptions and purchase intention.
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Luxury brands have been slower to adopt social media channels (Hughes et al., 2019),
given their desire for exclusivity (Chandon et al., 2016). Despite this initial reluctance, such
brands now regularly use social media (Kim and Ko, 2012), where platforms such as Instagram
offer opportunities for visual extensions of benefits to luxury fashion brands (Filieri et al.,
2023). Luxury fashion brands, such as Louis Vuitton, Burberry, and Calvin Klein, live stream
content from the catwalk via social channels (Plangger et al., 2021), which has allowed these
brands to strengthen consumer–brand engagement (Dhaoui, 2014).
However, there is a limited understanding of luxury brands and the effectiveness of
marketing efforts on social media platforms (Oc et al., 2023), and CEI research in the luxury
domain is also limited (Giakoumaki and Krepapa, 2020). Moreover, there is a commonly held
belief that the luxury sector has not experienced substantial advantages from social media,
given the distinctive traits of luxury brands, such as exclusivity, scarcity, and prestige, which
run counter to social media’s emphasis on widespread accessibility (Zha et al., 2023).
Furthermore, personalized physical interactions and services offered in brick-and-mortar
stores, integral to the luxury shopping experience, are difficult to replicate online (Hoang et al.,
2022). This study examines the factors that drive continued engagement between SMIs and
consumers to address these knowledge gaps and to understand the factors that elicit positive
consumer responses in the context of luxury goods on Instagram.
Many brands prefer Instagram as their marketing communication platform owing to its 1.3
billion monthly active users, with 79% of marketers considering Instagram an important part of
their campaigns (Santora, 2022). Instagram provides the context for this research because it is the
preferred social media platform for luxury fashion brands and consumers (Santora, 2022). In the
luxury space, Chiara Ferragni, an Italian blogger and fashion designer, has 27.7 million Instagram
followers and 755,000 X/Twitter followers (DePino, 2023). However, studies on the use of SMIs
by luxury brands remain scarce despite the number of luxury brands that now use social media
(Athwal et al., 2019). To address these shortcomings, we develop and test a conceptual framework
that examines consumers’ experiences with SMIs and how these experiences influence CEI. We
also examine the influence of homophily attitudes toward SMIs on the relationship between these
experiences and CEI and investigate the impact of CEI on consumer responses.
Accordingly, this study contributes significantly to the literature in several key respects.
First, we focus on the challenges brands face in creating CEI and the drivers of CEI (Yuan and
Lou, 2020). Second, we contribute to the growing but scarce literature on SMI and luxury
branding marketing by responding to calls for further research on how SMIs can foster CEI
(Hughes et al., 2019). Third, we examined the relationship between CEI and consumer
responses, including purchase intention, intention to share, and adoption of the SMI
recommendations. SMI-related consumer behavior and the mechanisms enabling positive
behavioral response formation can be considered complex phenomena that cannot be
explained by a single theory-driven model (Basha et al., 2022). Therefore, we developed and
tested a holistic conceptual framework by drawing on a theory-integration approach (Cheung
et al., 2022) that combines social exchange theory (SET) and flow theory.
2. Conceptual framework
2.1 Continued engagement intention
Scholars have been focusing on the concept and drivers of CE for several years (Pradhan et al.,
2023;Wang and Huang, 2023), which can be considered a form of behavioral intention
(Hepola et al., 2020). Thus, CEI can be conceptualized as a set of measurable consumer
intentions in response to content posted on social media by SMIs (Barger et al., 2016). CEI is
an important factor in enhancing the interactions between consumers and brands in a social
media context. CEI contributes to enhanced consumer outcomes such as self-brand
connection, heightened empowerment, value co-creation (Giakoumaki and Krepapa, 2020),
brand attachment and loyalty (Hollebeek et al., 2014), and consumer–brand relationships
(Pansari and Kumar, 2017). Table 1 summarizes the selected studies that focus on engagement
and behavioral outcomes from a social media perspective.
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However, SMI–brand partnerships also have disadvantages. For instance, SMIs and brands
are subject to “cancel culture” (Jones et al., 2022). Cancel culture refers to the act of
“canceling” someone, which involves rejecting, ignoring, and publicly opposing their views or
actions. It often entails depriving individuals of time and attention and even impeding their
ability to sustain a livelihood (Goldsbrough, 2020). SMIs and their branded content can be
scrutinized in detail. Thus, any missteps in terms of an SMI’s conduct or posts can lead to a
drop in followers and their being “canceled,” which means the SMI would lose lucrative
brand-partnership opportunities (Wei et al., 2022). The lucrative nature of these partnerships
has prompted several SMIs to resort to unethical methods to amass followers and exert their
influence (Tsapatsoulis et al., 2019). This includes tactics such as spoofing or stealing content
from more popular profiles or deceiving followers who believe they are engaging with
legitimate accounts (Marwick and Boyd, 2011). Furthermore, if their followers realize that
Table 1. Selected studies that focus on engagement and behavioral outcomes
Study Main theories Antecedents Moderators Mediators Outcomes
This
manuscript
Flow theory
Social exchange
theory (SET)
Consumer
experience
with Social
Media
Influencer
Attitudes
toward
SMI
Continued
engagement
intention
Recommendation
adoption
Purchase intention
Sharing intention
Cheung
et al.
(2020)
Social media
marketing
Consumer–brand
engagement
Brand knowledge
Social media
marketing
Consumer–brand
engagement
Brand awareness
Brand image
Ib�
a~
nez-
S�
anchez
et al.
(2022)
Associative network
memory model
Collaboration
of the
influencer with
a renowned vs
non-renowned
brand
Service vs
product
Attitude toward
the message
Purchase intention
Intention to search
for information
Credibility of the
influencer
Koay et al.
(2020)
S-O-R model Perceived
social media
activities
Co-
creation
behavior
Brand experience Customer-based
brand equity
Leite and
Baptista
(2022)
Consumer–influencer
relationship
Influencer–brand
meaning transfer
Consumer–brand
relationship
Intimate self-
disclosure
Parasocial
relationship
Source credibility
Brand trust
Purchase intention
S�
anchez-
Fern�
andez
and
Jim�
enez-
Castillo
(2021)
Emotional attachment Emotional
attachment
Perceived
information
value
Perceived
influencer
Positive word-of-
mouth (WOM)
Positive WOM
Intention to
purchase
recommended
brands
Waqaset al.
(2021)
Consumer culture
theory
Openness to
experience
Branded content
experience
Consumer
engagement with
branded content
Yu and
Yuan
(2019)
Brand attributes
Brand experience
Brand attachment
Brand trust
Utilitarian
value
Hedonic value
Brand experience
Brand attachment
Brand trust
Value equity
Brand equity
Relationship
equity
Source(s): Authors’ own work
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commercial motivations drive an SMI’s involvement with a brand, it can have negative effects
on the SMI’s trustworthiness and reputation (Singh et al., 2020). Consequently, any negative
publicity for an SMI can put a brand at risk of being “guilty by association,” leaving it
vulnerable to any criticism that the SMI may face (Pantano, 2021). Brands want to focus on
developing long-term interactions beyond initial ones (Chen et al., 2020). Therefore, we posit
that CEI is a better indicator of engagement than a conventional approach (Chen et al., 2020),
where CEI allows brands to create, maintain, and develop long-term relationships with
consumers and, thus, build long-term competitive advantage (See, 2018) as opposed to
singular engagement platforms (Roy, 2016). There are value-added benefits to brands and
SMIs that affect CEI (Chae et al., 2002;De Moor et al., 2010;Kim and Han, 2011;Pihlstr€
om,
2007). These value-added benefits could be social, utilitarian, or hedonically driven and could
serve user needs (Revels et al., 2010) and encourage CEI (Lee and Kim, 2010). For example,
SMIs, by holding Gucci handbags or wearing Prada on their social media posts, can illustrate a
hedonic-driven lifestyle. Consequently, CEI has emerged as a pivotal metric for marketers,
offering insights into whether consumers will maintain their relationship with a brand (Kwon
et al., 2014).
2.2 Role of social media influencers and consumer experiences
Social media has changed how brands create and distribute content and how they communicate
with and among consumers (Tsai and Men, 2013). Brands are aware of the potential benefits of
cooperating with SMIs (Munnukka et al., 2019), including enhanced brand attitudes and
perceptions and increased purchase intention (Lee and Watkins, 2016) and engagement (Sheng
et al., 2024). This study focuses on consumers who follow SMIs (i.e. SMI followers) to better
understand the relationship between consumers’ SMI experiences and their CEI for a specific
SMI. Following Prentice et al. (2019), we conceptualize the SMI experience as a higher-order
construct consisting of affective, intellectual and sensory experience dimensions evoked in
consumers’ interactions with an SMI. In the context of Instagram, SMI experiences are evoked
by a range of sensory experiences through SMI-created texts, visuals, colors, sounds, and
videos. Sensory brand cues have the capacity to be presented in profoundly emotional terms,
thereby potentially evoking more intense affective responses (Tafesse, 2016), that is, affective
experiences. Intellectual experiences occur when SMIs provoke thoughts, stimulate curiosity,
and influence problem-solving abilities, that is, cognitive processing (Prentice et al., 2019).
Brand posts that leverage media-rich and interactive capabilities to foster a comprehensive
brand experience are more likely to attract heightened CE (Tafesse, 2016). Homophily
attitudes toward SMIs can be formed based on whether consumers perceive that SMIs think,
behave, and are like them in many respects (Ladhari et al., 2020). Thus, SMIs seek to develop
and maintain meaningful consumer interactions by enhancing consumers’ experiences (see
Table 2) and homophily attitudes toward the SMI.
The experiences and homophily attitudes toward an SMI will determine the extent of
engagement consumers are likely to have with an SMI, potentially leading to purchase
intention, sharing, and recommendations. We use SETin parallel with flow theory to provide a
conceptual underpinning for examining the role of SMI experiences and homophily attitudes
toward SMI in cultivating CEI and consumer responses.
2.3 Social exchange theory (SET)
SET represents an interdependent relationship between the SMI and the follower, based on the
principles of reciprocity and rewarding actions initiated by the SMI (Cortez and Johnston,
2020). Users commence and maintain relationships with positive exchange interactions (e.g.
positive experiences and CEI), resulting in expected rewards such as financial benefits,
friendships, and emotional satisfaction interactions (Lambe et al., 2001). For example, 61% of
consumers have engaged with brands on social media in exchange for an incentive (Wiley,
2022). Users engage and interact with SMI, believing they will benefit from sharing and
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Table 2. Synthesis of social media influencer (SMI) literature
Study
Study area (yes 5U)
Experiences Attitudes
Engagement
behavior
Adoption of the SMI
recommendation
Purchase
intention
Sharing
intention Main theory
Country/
Context
This manuscript U U U U U U Flow theory
Social Exchange Theory
(SET)
Australia
Barry and Graça (2018) U U Incongruity theory
Superiority and Relief
theory
US
Barta et al. (2023) U U Stimuli-Organism-
Response (SOR)
Spain
Carlson et al. (2019) U U U Customer perceived
value/benefit framework
China
Chen et al. (2021) U U Parasocial identification China
Dinh and Lee (2022) UMeaning-transfer model US
Florenthal (2019) UUses and gratifications
(U&G) theory
Technology acceptance
model (TAM)
Generation Y
and Z
Giakoumaki and
Krepapa (2020)
UBrand engagement in
self-concept
Luxury
Hudders and De Jans
(2022)
U U Meaning-transfer model Gender
Jim�
enez-Castillo and
S�
anchez-Fern�
andez
(2019)
U U Media dependency
theory
Spain
Kim and Kim (2020) U U SET US
S�
anchez-Fern�
andez and
Jim�
enez-Castillo (2021)
USocial influence theory Spain
Seeler et al. (2019) UExperience New Zealand/
Industry
(continued )
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Table 2. Continued
Study
Study area (yes 5U)
Experiences Attitudes
Engagement
behavior
Adoption of the SMI
recommendation
Purchase
intention
Sharing
intention Main theory
Country/
Context
Shan et al. (2020) U U U Source credibility model
Source attractiveness
model
China
Tafesse and Wood
(2022)
USocial influence theory Indonesia
Vrontis et al. (2021) U U U Antecedents-
Consequences logic
US
Source(s): Authors’ own work
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exchange (Shiau and Luo, 2012). Usually, if the SMI provides a benefit to a user (e.g. a positive
experience), the user will reply in kind by engaging in positive reciprocating actions (Whitener
et al., 1998), such as CEI (Cropanzano et al., 2017), and eventually enhance positive consumer
responses.
2.4 Flow theory
Flow theory can help explain the factors that influence consumer experience (Novak et al.,
2000). Flow is a cognitive state that enhances a consumer’s experience (Csikszentmihalyi,
1990) because of the heightened level of concentration, energy, and focus when engaging in a
very specific activity (Novak et al., 2000), such as interacting with SMIs. This leads to high
enjoyment, involvement, and concentration, making an activity interesting, gratifying, and
compelling (Hyun et al., 2022). For example, a positive experience with SMI allows followers
to experience flow, which leads to CEI and positive consumer responses. The SMI experience
is so enjoyable that followers lose track of time and become cognitively locked into watching
the SMI (Kim et al., 2020), resulting in CEI. Thus, the flow theory is appropriate for examining
the impact of SMI experiences on consumer responses. In line with this discussion, we propose
our conceptual model (Figure 1).
3. Hypothesis development
3.1 Consumers’ experience with social media influencers and their continued engagement
intention
Consumer experiences with SMIs on various platforms constitute intricate interactions
involving SMI-generated texts, visuals, colors, sounds, and videos, collectively shaping
sensory experiences (Rose et al., 2012). These experiences, both tangible (e.g. features of a
luxury handbag) and intangible (e.g. the emotional resonance of owning a luxury brand
handbag; Ong et al., 2018), contribute to overall consumer perception. Consumers actively
engage in the cognitive, affective, and sensory processing of information derived from these
brand-post interactions, forming lasting impressions in their memory (Rose et al., 2012).
Sensory
SMI Experience
Continued
Engagement
Intention (CEI)
Homophily Attitude
toward the SMI
(ATT)
Purchase
Intentions (PI)
Sharing
Intentions
(SI)
Adoption of the SMI
Recommendations
Intellectual
Consumer Responses
Flow Theory SET Theory
Affective
Figure 1. Conceptual framework
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Consequently, SMIs serve as pivotal conduits for brand interactions, potentially influencing
the determination of positive consumer experiences (Buzeta et al., 2020). A flow experience in
social media is characterized by a consumer’s cognitive state featuring a seamless sequence of
responses, intrinsic enjoyment, loss of self-consciousness, and self-reinforcement during
network navigation (Hoffman and Novak, 1996). Thus, a flow experience can explain how
consumers will likely spend more time than they intend on SMI-related content.
A positive sensory flow experience triggered by SMIs will likely evoke favorable
emotions, feelings, and sentiments that constitute affective experiences. Simultaneously,
intellectual flow experiences unfold as SMIs prompt thoughtful consideration, stimulate
curiosity, and influence consumers’ problem-solving abilities through cognitive processing
(Prentice et al., 2019). Thus, a positive flow experience can be important in discerning
continued engagement with SMIs. Based on flow theory, we posit that a positive SMI
experience fosters CEI with SMIs, thereby shaping ongoing interactions. This leads to the
following hypothesis:
H1. SMI experience has a positive effect on consumers’ CEI with SMIs.
Purchase intention is a direct indicator of eventual purchasing actions (Adelaar et al., 2003)
and is an “individual’s conscious plan to make an effort to purchase a brand” (Spears and
Singh, 2004, p. 56). In the context of SET, purchase intention denotes the intention to act
favorably in response to information stimuli related to a brand or product (Kim and Johnson,
2016). However, recent studies have found that potential consumers are inclined to purchase a
product promoted by SMI (Kemp, 2023). Given that flow is a cognitive condition that is
intrinsically enjoyable and creates a very favorable experience (Novak et al., 2000), it leads to
CEI and the intention to purchase (Hossain et al., 2018;Liu and Shiue, 2014). Therefore,
individuals with a high CEI are more inclined to purchase. This suggests that followers who are
deeply connected or exhibit a sense of attachment to an SMI are more likely to engage in
purchasing behavior than those with lower attachment (Sokolova and Kefi, 2020). We posit
that consumers exposed to luxury brands promoted by SMIs are more likely to purchase them
owing to continued engagement with a brand’s SMI. Therefore, we hypothesize as follows:
H2. CEI has a positive effect on consumers’ purchase intention.
Moreover, SMI marketing relies heavily on the spread of branded content being shared with
SMI followers’ network of friends and family on social media platforms. The ability to share
content, gain higher brand exposure, and acquire more brand followers is the central tenet of
SMI marketing. Sharing intention refers to the retransmission of information generated by
others, where content is shared in its original form beyond the reach of the current followers of
the original poster, thereby developing a new level of exposure and new SMI followers (Kwak
et al., 2010). For example, the unique features of Instagram facilitate users’ sharing of
information with others by tapping on the heart symbol to show “love” for the posts made by
other users or “sharing” by commenting on the post or sharing the post with other users
privately by tapping the send icon. Sharing intention manifests when the SMI’s followers
perceive that the posts are worth sharing with friends and family, resulting in their ability to
view the posts (Chen and Lee, 2014). Therefore, we hypothesize that:
H3. Consumers’ CEI has a positive effect on their sharing intention.
The adoption of an SMI recommendations is the process whereby the SMI’s followers
intentionally engage with information use (Cheung et al., 2008) and consider the brand(s)
recommended by the SMI based on the post (Rietveld et al., 2020). The information presented
to SMI followers increases their brand knowledge, making purchasing decisions easier (Filieri
et al., 2015). However, excessive repetition can be detrimental when followers perceive
sameness in SMI content. Notably, 47% of consumers worldwide experience fatigue from
repetitive SMI content, leading them to be less inclined to follow SMI recommendations
(Concannon, 2023). Thus, followers who are deeply connected to the influencer look for
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recommendations compared with those with lower levels of attachment (Sokolova and Kefi,
2020). If an SMI’s followers believe the posts are reliable, they are more likely to adopt the
SMI’s recommendations (Filieri et al., 2015). Therefore, we hypothesize that the more SMI
followers engage with an SMI, the more likely they will be to follow the SMI’s
recommendations.
H4. Consumers’ CEI has a positive effect on their adoption of SMI recommendations.
3.2 The moderating role of homophily attitudes toward the social media influencer
Homophily is “the degree to which people who interact are similar in beliefs, education, social
status, and the like” (Eyal and Rubin, 2003, p. 80). The concept of homophily suggests that
individuals prefer associating with others they perceive as sharing similar values and status
(Lazarsfeld and Merton, 1954). This tendency influences how people seek opinions, respond
to content, and react to content generated by SMIs (Chu and Kim, 2011;Ismagilova et al.,
2020). Furthermore, consumers are more likely to be influenced by recommendations from
SMIs and purchase the products or brands (Ladhari et al., 2020) that share similarities with
them (Ismagilova et al., 2020). Homophily attitude reflects the extent to which a person
perceives that another person shares his or her attitudes (thinks, behaves, is similar, is like)
(Ladhari et al., 2020). Perceiving a more homophily attitude (McGuire, 1985) creates feelings
of connection, affection, and passion toward the SMI. Thus, understanding consumer
homophily attitudes toward SMIs is crucial for comprehending the impact of SMI experiences
on CEIs and the resulting consumer responses. We propose that if consumers have a favorable
homophily attitude toward the SMI, they are more likely to intend to engage with it
continually, which would then lead to positive consumer responses. Therefore, we
hypothesize that:
H5. The influence of the SMI experience on CEI is moderated by consumers’ homophily
attitudes toward the SMI.
3.3 Mediating effect of continued engagement intention on consumer responses
The influence of CEI on consumer responses, such as purchase intention, sharing intention,
and adoption of the SMI recommendations, is based on flow theory and SET, whereby CEI
mediates the relationship between positive experiences and consumer responses. We propose
that if the SMI experience is positive, consumers will likely continue to engage with SMI. This
will lead to positive consumer responses, such as the intention to share, purchase, and adopt the
SMI’s recommendations. This leads to the following hypotheses:
H6a. SMI experience has a positive effect on consumers’ sharing intention through
mediation by CEI.
H6b. SMI experience has a positive effect on consumers’ adoption of the SMI
recommendations through mediation by CEI.
H6c. SMI experience has a positive effect on consumers’ purchase intention through
mediation by CEI.
4. Method
After obtaining institutional ethics approval, we tested the proposed hypotheses using data
from an online survey of 604 Australian respondents. We first discuss the measurement items
and the development of the survey instrument. Next, we discuss the sampling and data analysis
procedures.
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4.1 Measurement items and survey instrument
We adopted multi-item scales from marketing literature as measures for the constructs of the
study (see Table 3). SMI experience is a multidimensional construct that is reflective-
formative in nature (Jarvis et al., 2003). We drew upon items from Prentice et al. (2019) to
measure the three SMI experience dimensions—affective, intellectual, and sensory. These
dimensions explain how consumers are likely to spend more time than they intended to with
the SMI-related content to evoke favorable emotions, feelings, and sentiments, constituting
affective experiences. CEI was measured as the degree of intended future engagement with the
SMI’s posts (H€
ogberg et al., 2019). The items for CEI were adapted from H€
ogberg et al.
(2019). We define purchase intention as an individual’s conscious plan to make an effort to
Table 3. Measurement items and loadings
Measurement items Mean SD Loadings
Affective experience (AffExp)
I am having a strong emotion for this Instagram influencer 4.539 1.447 0.916
This Instagram influencer induces feelings and sentiments 4.758 1.436 0.904
This Instagram influencer is an emotional brand 4.438 1.466 0.824
Intellectual experience (IntExp)
The posts from this Instagram influencer engaged me in a great deal of thinking 4.485 1.513 0.879
The posts from this Instagram influencer stimulate my curiosity and problem-
solving
4.425 1.539 0.905
The posts from this Instagram influencer make me think 4.457 1.522 0.887
Sensory experience (SenExp)
This influencer makes a strong impression on my visual sense or other senses 4.886 1.480 0.880
I find the influencer interesting in a sensory way 4.848 1.446 0.911
This Instagram influencer appeals to my senses 4.689 1.449 0.909
Continued engagement intention (CEI)
I will gladly continue following this Instagram influencer 4.586 1.526 0.912
I will visit this Instagram influencer’s profile more frequently rather than less
frequently
4.373 1.549 0.925
I will actively look for posts from this Instagram influencer on luxury fashion
brands
4.434 1.543 0.910
Purchase intention (PurInt)
My likelihood of purchasing from the brands endorsed by this Instagram
influencer is high
4.452 1.565 0.919
The probability that I will consider buying the brands endorsed by this Instagram
influencer is high
4.483 1.539 0.930
My willingness to buy brands endorsed by this Instagram influencer is high 4.495 1.557 0.913
Adoption of SMI recommendation (RecAdo)
This Instagram influencer’s posts make it easier for me to make a purchase
decision
4.570 1.524 0.891
I will accept the recommendations made by this Instagram influencer while
making purchases
4.566 1.580 0.910
This Instagram influencer’s posts contribute to my knowledge of brands 4.869 1.469 0.865
Sharing intention (ShaInt)
The post of this Instagram influencer is worth sharing with others 4.573 1.541 0.916
I will recommend posts of this Instagram influencer to others 4.455 1.609 0.939
I wish my friends and relatives would watch the posts of this Instagram influencer 4.184 1.560 0.889
Note(s): For SmartPLS, weighting scheme 5factor, iteration 51,000, complete bootstrapping with 5,000
bootstrapping samples, test type 5two-tailed. Indicators are anchored at 1 5strongly disagree and
75strongly agree
Source(s): Authors’ own work
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purchase a brand. The items for purchase intention were adapted from Fritz et al. (2017). The
adoption of recommendation is when the SMI’s followers intentionally engage with
information use (Cheung et al., 2008) and consider the brand(s) recommended by the SMI
based on the post (Rietveld et al., 2020). The measures were adapted from Filieri et al. (2015).
The measures of sharing intention, which is the retransmission of information generated by
others, where the content is shared in its original form beyond the reach of the current
followers, were adopted from Chen and Lee (2014). Homophily attitude has been
characterized as the extent to which a person perceives that another person shares his or her
attitudes toward the SMI (Ladhari et al., 2020). To measure homophily attitude toward the
SMI, we adopted items from Ladhari et al. (2020). All measurement items used a 7-point
Likert scale (1 5strongly disagree, 7 5strongly agree). Several control variables, such as
gender, income, educational qualifications, and frequency of Instagram access, were also
included to account for respondent heterogeneity (Carlson et al., 2021). We also incorporated a
three-item marker variable, socially desirable responses, to test for potential common method
variance (Donavan et al., 2004).
Three screening questions qualified respondents for the survey: those less than 18 years old,
those not having an Instagram account, and those not interested in luxury brands were screened
out. To test respondent attentiveness, we included a check question based on the SMI profile
shown. Those who failed to answer the check question correctly were excluded.
Following an approach similar to that of Delbaere et al. (2020) and Belanche et al. (2021),
we focused on popular fashion influencers on Instagram. Respondents were randomly shown
one of two images of Instagram SMIs (one male and one female with a similar number of
followers). For our SMIs, we selected Instagram accounts in the fashion industry with many
followers who were growing in popularity (Casal�
oet al., 2020). The Instagram SMI profiles
noted the luxury brands they endorsed and the number of followers and featured a styled photo
with several comments posted by their followers (see Table 4).
The survey instrument was pre-tested using 60 respondents to ensure the interpretability of
the questions, survey flow, validity, and integrity of the survey instrument. The full survey was
administered once the survey instrument was deemed sound.
Table 4. Instagram profiles
Ali Gordon (@aligordon) Elle Ferguson (@elle_ferguson)
736K followers
Endorses luxury brands such as Hugo Boss,
Ralph Lauren, and Tod’s.
683K followers
Endorses luxury brands such as Louis Vuitton,
Prada, Dior, and Spell.
Source(s): Authors’ own work
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35,7
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4.2 Data collection and sample profile
We administered an online survey using an online panel from a reputable commercial market
research company. The survey yielded 604 complete Australian responses with the following
characteristics: 66% female (34% male) respondents, with an average annual income of AU
$50,001 to 100,000, and an average age of 27–36 years. In terms of the highest level of
education, 32.1% held a bachelor’s degree, whereas 6.5% held a postgraduate certificate,
11.1% a master’s degree, and 2.5% a PhD/doctorate). In terms of the frequency of Instagram
usage, 77.6% of respondents indicated that they logged in to their respective Instagram
accounts five times or more per week, with 22.4% logging in less than five times a week (see
Table 5).
4.3 Data analysis and results
We followed the data analysis procedure used in previous studies with multidimensional
reflective-formative constructs in the path model (Merz et al., 2018). First, we analyzed the
reliability and validity of the measurements for all constructs in the proposed path model
(MacKenzie et al., 2011). Next, we examined whether evidence of common method bias
affected the responses, statistically tested the significance of the hypothesized structural model
relationships, and examined the mediating effect of continued engagement intention on
consumer responses. Finally, we assessed the moderation effect of homophily attitudes on the
relationship between SMI experience and CEI.
Table 5. Demographic profile of respondents
Percent
Age
18–26 years old 31.3
27–36 years old 34.8
37–46 years old 19.7
47–56 years old 9.6
Above 56 years old 4.6
Total 100
How often do you log into your Instagram account?
≥5 times/week 77.6
<5 times/week 22.4
Total 100.0
Highest level of education completed
Less than year 12 (e.g. school certificate) 6.6
Year 12 (e.g. high school certificate) 16.6
TAFE (e.g. certificate, diploma) 24.7
Bachelor’s degree 32.1
Postgraduate certificate 6.5
Master’s degree 11.1
PhD/Doctorate 2.5
Total 100.0
Income per year
0–AU$50000 26.7
AU$50001–AU$100000 45.4
AU$100001–AU$150000 20.0
Above AU$150000 7.9
Total 100.0
Source(s): Authors’ own work
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We conducted partial least squares-structural equation modeling (PLS-SEM) using
SmartPLS 3.3.3 (Ringle et al., 2015) to evaluate the measurement model and estimate and
assess the structural model relationships, including the moderating and mediating effects.
First, PLS-SEM offers more reliable estimations with greater precision when the path model
includes both reflective and formative constructs with many measurement items (Hair et al.,
2017). Next, an analysis of normality indicated that most variables in this study were not
normally distributed. PLS-SEM does not make any assumptions regarding the normality of the
data and can produce good results when the data distributions are non-normal (Hair et al.,
2017). Finally, PLS-SEM is a causal-predictive approach to SEM that produces useful
estimates when a study aims to predict dependent variables (Carlson et al., 2021). As this study
aimed to predict purchase intention, adoption of the SMI recommendations, and sharing
intention of consumers from their experience with the SMI, we used PLS-SEM because this
method would offer important estimates regarding the predictive quality of the model.
4.4 Reliability and validity of the measures
4.4.1 Assessment of social media influencer experience measurement model. The
measurement model of SMI experience follows the Type II reflective-formative
hierarchical component modeling approach using the repeated-indicator approach (Hair
et al., 2017). Following Hair et al.’s (2017) hierarchical guidelines for assessing the
measurement model of SMI experience, we first assessed the reliability and validity of the
lower-order constructs (i.e. affective, intellectual, and sensory experience) and subsequently
examined the higher-order construct (i.e. SMI experience). The results indicate the reliability
of the lower-order constructs (see Tables 6 and 7): the item loadings for all reflective measures
of the lower-order constructs exceeded 0.708, and the Cronbach’s alpha, rho_A, and
composite reliability scores exceeded 0.70. Moreover, all the average variance extracted
(AVE) scores exceeded 0.50, and all the heterotrait–monotrait (HTMT) ratio values were
below 0.90 (see Table 6), indicating that convergent validity and discriminant validity had
been achieved (Hair et al., 2019).
For the formative higher-order construct, SMI experience, the outer weights of the lower-
order constructs were meaningful in size (0.365, 0.364, and 0.373, for affective, intellectual,
and sensory experience, respectively) and significant (p< 0.001; see Table 6). These results
indicate that all lower-order constructs are significant and contribute almost equally to the
formation of the higher-order construct (Hair et al., 2017). Moreover, the variance inflation
factor (VIF) values for intellectual and sensory experience were less than 3, and for affective
experience, less than 5, indicating that the measurement model did not suffer from
Table 6. Social media influencer experience: reliability, convergent, and discriminant validity statistics
Dimensions
Cronbach’s
alpha rho_A CR AVE VIF
Discriminant validity
(HTMT)
First-order
weights and
significance
(p-value)AffExp IntExp SenExp
Affective
experience
(AffExp)
0.857 0.866 0.913 0.779 3.466 0.365
(0.000)
Intellectual
experience
(IntExp)
0.869 0.871 0.920 0.793 2.358 0.866 0.364
(0.000)
Sensory
experience
(SenExp)
0.883 0.887 0.928 0.810 2.734 0.757 0.755 0.373
(0.000)
Source(s): Authors’ own work
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multicollinearity (Diamantopoulos and Winklhofer, 2001). Hence, this analysis supports the
reliable and valid measurement of SMI experience as a higher-order formative construct.
The reliability and validity of the variables, CEI, purchase intention, adoption of the SMI
recommendations, sharing intention (see Table 7), and homophily attitude toward SMI (see
Table 8), were assessed following the guidelines of Hair et al. (2019). The outer loadings of all
corresponding reflective indicators exceeded the minimum threshold of 0.708; all the
Cronbach’s alpha, rho_A, and composite reliability scores exceeded 0.70, and all the AVE
scores exceeded 0.50, indicating the reliability and convergent validity of the measures. The
reliability and validity analysis results showed that both reliability and convergent validity
were achieved for the moderating variable (Cronbach’s alpha 50.913, rho_A 50.914, and
composite reliability 50.945), which was greater than the minimum threshold of 0.70, while
the AVE 50.852 was greater than 0.50. All the HTMT ratios were less than or equal to 0.90,
indicating that the constructs were empirically distinct from each other (Henseler et al., 2015).
4.4.2 Assessment of common method variance. We employed several techniques to test for
potential common method variance. First, following the partialling out a marker variable
technique, we examined the changes in path coefficients and R
2
values of the endogenous
constructs after including the marker variable in the path model (Podsakoff et al., 2003). The
results (see Table 9) indicate that the path coefficients and R
2
values both changed very slightly
owing to the inclusion of the marker variable, indicating that estimates are not affected by
common method variance (Podsakoff et al., 2003). Second, we estimated the correlations
between the marker variable and all other constructs in the path model (Lindell and Whitney,
Table 8. Measurement model assessment and reliability, convergent validity, and discriminant validity
statisticsfor the moderator attitude homophily
Measurement model assessment for the moderator attitude homophily
Measurement items Mean SD Loadings
I think this Instagram influencer thinks like me 3.67 1.65 0.922
*
I think this Instagram influencer behaves like me 3.64 1.69 0.940
*
I think this Instagram influencer is like me 3.48 1.80 0.907
*
Cronbach’s
alpha rho_A CR AVE
HTMT ratio
INFEXP CEI PurInt RecAdo ShaInt
Attitude
homophily
0.913 0.914 0.945 0.852 0.538 0.552 0.617 0.589 0.628
Source(s): Authors’ own work
Table 7. Endogenous variables: reliability, convergent validity, and discriminant validity statistics
Variable Cronbach’s alpha rho_A CR AVE
Discriminant validity (HTMT)
CEI PurInt RecAdo ShaInt
CEI 0.904 0.904 0.940 0.839
PurInt 0.910 0.910 0.944 0.848 0.852
RecAdo 0.867 0.874 0.919 0.790 0.877 0.892
ShaInt 0.902 0.905 0.939 0.837 0.894 0.838 0.900
Note(s): For SmartPLS, weighting scheme 5factor, iteration 51,000, complete bootstrapping with 5,000
bootstrapping samples. CEI 5Continued Engagement Intention, PurInt 5Purchase Intention,
RecAdo 5adoption of SMI recommendation, ShaInt 5Sharing Intention
Source(s): Authors’ own work
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2001). All correlation coefficients were less than 0.30 (see Table 9), which implies that the
constructs in the path model were not significantly related to the marker variable, and common
method variance was not a concern in this study (Lindell and Whitney, 2001). Finally, we
examined the VIF values of all constructs. The low VIF scores (<5) also support the notion that
the study was not seriously affected by common method variance (Kock, 2015).
4.5 Path analysis and hypothesis testing
4.5.1 Assessment of the structural model. We began the assessment of the structural model
using the SmartPLS bootstrapping procedure with 5,000 bootstrapping samples and the bias-
corrected and accelerated bootstrap (BCa) method (Hair et al., 2017) to examine the
standardized path coefficient (β), t-values, p-values, and f
2
effect size. The results presented in
Table 10 show that SMI experience has a significant and positive influence on CEI, with a large
Table 9. Test for potential common method variance
Partialling out of a marker variable technique
Path
Path coefficient
Change
Construct R
2
original R
2
with marker Change
Original
With
marker CEI 0.589 0.590 �0.001
CEI →PurInt 0.773 0.770 0.003 INFEXP 1.000 1.000 0.000
CEI →RecAdo 0.779 0.779 0.000 PurInt 0.598 0.600 �0.002
CEI →ShaInt 0.808 0.804 0.004 RecAdo 0.607 0.607 0.000
INFEXP →CEI 0.768 0.766 0.002 ShaInt 0.653 0.655 �0.002
Partialling of a marker variable technique
Variable pair Correlation coefficient t-statistic p-values
INFEXP–CMVMarker 0.065 1.371 0.085
CEI–CMVMarker 0.080 1.773 0.038
PurInt–CMVMarker 0.105 2.200 0.014
RecAdo–CMVMarker 0.062 1.322 0.093
ShaInt–CMVMarker 0.113 2.398 0.008
Note(s): For SmartPLS, weighting scheme 5factor, iteration 51,000, complete bootstrapping with 5,000
bootstrapping samples, test type 5two-tailed. INFEXP 5Influencer experience, CEI 5continued engagement
intention, PurInt 5purchase intention, RecAdo 5adoption of SMI recommendation, ShaInt 5sharing
intention, CMVMarker 5the marker variable (socially desirable responding)
Source(s): Authors’ own work
Table 10. Path analysis and hypothesis testing results
Path
Standardized
estimate (β)
Standard
deviation
(SD)
t-values
(β/SD)p-values
f-square
(effect
size) Outcome
INFEXP →CEI 0.768 0.025 31.093 0.000 1.434 H1: Supported
CEI →PurInt 0.773 0.023 33.082 0.000 1.486 H2: Supported
CEI →RecAdo 0.779 0.022 34.840 0.000 1.545 H3: Supported
CEI →ShaInt 0.808 0.018 45.118 0.000 1.880 H4: Supported
Note(s): For SmartPLS, weighting scheme 5path, iteration 51,000, complete bootstrapping with 5,000
bootstrapping samples, test type 5two-tailed. INFEXP 5influencer experience, CEI 5continued engagement
intention, PurInt 5purchase intention, RecAdo 5adoption of SMI recommendation, ShaInt 5sharing
intention. f-square effect size: 0.005 5small, 0.01 5medium, 0.025 5large
Source(s): Authors’ own work
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f
2
effect size (β50.768; t531.093; p50.000; f
2
effect size 51.434). Similarly, the results
also indicate that CEI has a significant and positive influence on purchase intention (β50.773;
t533.082; p50.000; f
2
effect size 51.486), adoption of SMI recommendations (β50.779;
t534.840; p50.000; f
2
effect size 51.545) and sharing intention (β50.808; t545.118;
p50.000; f
2
effect size 51.880). Thus, these results support H1,H2,H3, and H4.
Next, we examined the variance in the endogenous variables explained by the model (R
2
),
which is a key criterion for assessing the quality of the structural model in PLS-SEM (Hair et al.,
2019). Henseler et al. (2015) described an R
2
value of more than 0.67 as substantial, more than
0.33 as moderate, and more than 0.19 as weak. Our results indicate that the model has
moderate explanatory power for all the endogenous variables because it accounts for 59%
(R
2
adjusted 50.588) of the variance of CEI, 60% (R
2
adjusted 50.597) of the variance of
purchase intention, 61% (R
2
adjusted 50.606) of the variance of adoption of SMI
recommendations, and 65% (R
2
adjusted 50.652) of the variance of sharing intention.
Moreover, because all R
2
values were less than 0.90, it can be concluded that the model did not
suffer from overfitting (Hair et al., 2019). Furthermore, we applied the PLS
predict
procedure with
10 folds and 10 repetitions to evaluate the predictive performance of the model (Hair et al.,
2019). The results show that the Q
2predict
values for the indicators of all endogenous variables
are larger than 0 (minimum Q
2predict
50.359; maximum Q
2predict
50.513). Moreover, by
comparing the root mean squared error values produced in the PLS-SEM analysis with those
produced by the naive LM benchmark model, we found that in most cases, PLS-SEM produced
smaller values than the naive LM estimations. Hence, from these results, we conclude that the
model offers excellent predictive performance (Hair et al., 2019).
4.5.2 Moderation analysis. To test the moderation effect (Hair et al., 2019), we followed the
PLS-SEM moderation analysis procedure by examining the significance of the interaction
term (i.e. SMI experience 3homophily attitude toward SMI). As presented in Table 11, the
interaction term had a significant negative effect on CEI (effect 5�0.066; t52.115;
p50.034), meaning that with a more favorable homophily attitude toward SMI, the effect of
SMI experience on CEI decreased. Conversely, with a less favorable homophily attitude
toward SMI, the effect of SMI experience on CEI increased. Overall, these results empirically
support H5 and affirm that homophily attitude toward SMI has a significant negative effect on
the relationship between SMI experience and CEI.
4.5.3 Mediation analysis. As part of the mediation analysis, we evaluated the significance
of both direct and indirect effects using the PLS bootstrapping procedure (Hair et al., 2017).
The results presented in Table 12 show that the direct effect of SMI experience on purchase
intention (0.256; t55.28; p50.000) and the indirect effect of SMI experience on purchase
intention through CEI (0.443; t512.176; p50.000) were both significant. Therefore, this
result empirically supports the notion that CEI partially mediates the relationship between SMI
experience and purchase intention. Moreover, because both effects are positive, we can
conclude that mediation is complementary (Zhao et al., 2010). Similarly, we found that both
Table 11. Moderation analysis
Significance of the interaction term
Interaction term Original effect SD t-value p-value
INFEXP
*
attitude homophily→CEI �0.066 0.032 2.115 0.034
Note(s): For SmartPLS, weighting scheme 5path, iteration 51,000, complete bootstrapping with 5,000
bootstrapping samples, test type 5two-tailed. INFEXP 5influencer experience, CEI 5continued engagement
intention, PurInt 5purchase intention, RecAdo 5adoption of SMI recommendation, ShaInt 5sharing
intention, Attitude homophily 5attitude toward influencer. Modeling of the interaction term 5two-stage
approach.
*
indicates that the loadings are significant at p< 0.000
Source(s): Authors’ own work
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Table 12. Significance analysis of direct and indirect effect
Relationship
Direct
effect
95% CI of the
direct effect t-values p-values
Indirect effect
(moderated by CEI)
95% CI of the
indirect effect t-values p-values
Evidence of mediation
effect?
INFEXP →PurInt 0.256 [0.166,0.354] 5.280 0.000 0.443 [0.369–0.510] 12.176 0.000 Yes, complementary
mediation
INFEXP →RecAdo 0.294 [0.198,0.402] 5.630 0.000 0.425 [0.343–0.501] 10.725 0.000 Yes, complementary
mediation
INFEXP →ShaInt 0.268 [0.179,0.359] 5.887 0.000 0.462 [0.397–0.528] 13.743 0.000 Yes, complementary
mediation
Note(s): For SmartPLS, weighting scheme 5path, iteration 51,000, complete bootstrapping with 5,000 bootstrapping samples, test type 5two-tailed. CI 5confidence interval,
INFEXP 5influencer experience, CEI 5continued engagement intention, PurInt 5purchase intention, RecAdo 5adoption of SMI recommendation, ShaInt 5sharing intention.
Estimates are made using the latent variable of the constructs
Source(s): Authors’ own work
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the direct effect of SMI experience on sharing intention (0.268; t55.887; p50.000) and the
indirect effect of SMI experience on sharing intention through CEI (0.462; t513.743;
p50.000) were positive and significant. Conversely, both the direct effect of SMI experience
on the adoption of the SMI recommendations (0.294; t55.63; p50.000) and the indirect
effect of SMI experience on the adoption of the SMI recommendations through CEI (0.425;
t510.725; p50.000) were also positive and significant. Therefore, the results empirically
support that CEI represents complementary mediation of the relationship from SMI
experience to purchase intention, sharing intention, and the adoption of the SMI
recommendations.
5. Discussion and theoretical implications
Despite the increasing role that SMIs play in working with luxury brands to generate CE, the
factors influencing CEI in the SMI context and related outcomes remain under-researched.
A brand–SMI collaboration may not always be successful owing to many reasons, such as the
prevalence of cancel culture (Jones et al., 2022), SMI misconduct (Wei et al., 2022), unethical
behavior (Tsapatsoulis et al., 2019), negative motivations (Singh et al., 2020), or influencer
fatigue (Bright et al., 2015). However, our study shows CEI can be a key factor in facilitating
long-term relationships between SMIs and consumers, in which engagement is behavior-
based and stretches beyond brand purchase. Furthermore, our results suggest that CEI is
beyond intention—consumers are engaged and will drive future behavior (Kim et al., 2013).
Moving beyond the theory of planned behavior (Madden et al., 1992) and the unified theory
of acceptance and use of technology (Venkatesh et al., 2003), and drawing on SET and flow
theory, we developed and tested a new conceptual framework and examined the relationship
between consumers’ experiences with an SMI and their CEI with a luxury brand. We also
investigated the moderating role of homophily attitudes toward SMIs. Our study shows that
brands should focus on long-term interactions beyond initial interactions (Chen et al., 2020),
which may create value for consumers and subsequently drive involvement and loyalty (See,
2018). We also examined the mediating role of CEI on the relationship between SMI
experience and consumer responses, such as adoption of SMI recommendations, purchase
intention, and sharing intention. Our findings suggest that consumers’ SMI experience has
positive effects on their CEI with a brand. This result suggests that because consumers have a
positive experience with SMI, they forge a stronger relationship with that SMI and are less
likely to defect from that relationship (Kwon et al., 2014).
Moreover, CEI has a positive mediation effect on the relationship between the SMI
experience, their adoption of the SMI recommendations, and their sharing and purchase
intention. CEI plays a critical role in consumer responses, such as the intention to purchase or
share and the adoption of the SMI recommendations. Our data also suggest that the homophily
attitude toward the SMI moderates the relationship between the SMI experience and CEI.
Interestingly, the strength of the influence of SMI experience on CEI decreases when a
consumer possesses a more positive homophily attitude toward an SMI. Therefore, consumers
may demonstrate some level of CEI owing to their more positive homophily attitude toward an
SMI, irrespective of their SMI experience. Collectively, our findings show that creating
positive SMI experiences benefits brands that seek continued engagement with their
consumers. This research addresses calls for research by adding to the scarce literature on SMI
marketing on how SMIs can foster CEI and the resulting outcomes.
6. Managerial implications
SMIs have become integral to brands’ online marketing strategies that attempt to enhance
consumers’ CEI. This study provides two important managerial implications in the context of
luxury brands. First, we present some notable implications for practitioners planning to
implement SMI marketing strategies. Brand managers must focus on developing long-term
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relationships that could be positively affected by consumers’ positive experiences with SMIs.
Marketers can attempt to ensure that all the information and content provided by SMIs are
relevant to consumers. Thus, a key objective for marketing practitioners should be working in
partnership with SMIs to create positive SMI experiences (e.g. intellectual, affective, and
sensory experiences) to foster CEI. Second, SMIs should focus on brands that fit their
lifestyles and values (Kim and Kim, 2020) to increase CEI, enhance consumers’ homophily
attitudes toward them (Belanche et al., 2021) and forge a stronger relationship with their
followers. In the context of luxury brands, SMIs that focus on the newest trends, high-fashion
styles, and desirable lifestyles are likely to exert a positive influence on consumer homophily
attitudes to impress, delight, and attract audiences (Yan et al., 2023), leading to CEI, which, in
turn, can have significant positive effects on consumer responses. For example, Balenciaga
collaborated with Fortnite, allowing players to acquire digital outfits mirroring real-life
Balenciaga fashion through a virtual boutique (Duggal, 2022).
7. Limitations and future research directions
This study is cross-sectional in its design, and it comes with several limitations, which, in turn,
present opportunities for future research. First, this cross-sectional study was limited to the use
of Instagram in Australia. Future studies could replicate this study in other contexts using
different social media platforms, such as Facebook, X (formerly known as Twitter), or TikTok,
in other countries and longitudinally to assess the generalizability of the model. As this study
focuses on Australian Instagram users interested in luxury brands, this can limit the
generalizability of the findings to other cultural contexts or social media users with different
interests. The choice of social media platforms may indicate different experiences with SMIs
and consumer responses. This study analyzed followers’ perceptions of and responses to two
SMIs in the luxury market. Thus, caution should be exercised when generalizing these results.
Future studies could examine SMIs who present different characteristics and audiences with
different profiles to ensure the generalizability of the results. Furthermore, the focus of the
study was limited to luxury brands because they have become a widespread phenomenon in
social media marketing. In addition, the study did not consider respondents’ nationalities and
cultural backgrounds (Nikolinakou and Phua, 2024). Lastly, from a theoretical perspective,
future studies could examine the consequences of CEI on other consumer responses, such as
brand commitment and brand loyalty.
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