Customer engagement (CE) is a marketing concept of great importance and the rise of social media has further amplified the importance of this concept. Yet, our understanding of the progress of CE research remains limited due to the absence of a one-stop state-of-the-art overview of the concept that considers its manifestation on social media. To address this gap, we review CE research on social media since the beginning of the present millennium using the PRISMA protocol for systematic reviews. The outcome of our review reveals the antecedents, decisions, and outcomes; the theories, contexts, and methods; and the ways forward for advancing knowledge, improving representation, and enhancing rigor with respect to future research on CE and social media.

1. Journal of Business Research 148 (2022) 325–342
Available online 11 May 2022
0148-2963/© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Customer engagement and social media: Revisiting the past to inform
the future
Weng Marc Lim a,b
, Tareq Rasul c,*
a
Swinburne University of Technology, School of Business, Law and Entrepreneurship, John Street, 3122 Hawthorn, Victoria, Australia
b
Swinburne University of Technology, Faculty of Business, Design and Arts, Jalan Simpang Tiga, 93350 Kuching, Sarawak, Malaysia
c
Australian Institute of Business, Department of Marketing, King William Street, 5000 Adelaide, South Australia, Australia
A R T I C L E I N F O
Keywords:
Brand engagement
Business engagement
Consumer engagement
Customer engagement
Marketing
Relationship marketing
Social media
Systematic literature review
Systematic review
A B S T R A C T
Customer engagement (CE) is a marketing concept of great importance and the rise of social media has further
amplified the importance of this concept. Yet, our understanding of the progress of CE research remains limited
due to the absence of a one-stop state-of-the-art overview of the concept that considers its manifestation on social
media. To address this gap, we review CE research on social media since the beginning of the present millennium
using the PRISMA protocol for systematic reviews. The outcome of our review reveals the antecedents, decisions,
and outcomes; the theories, contexts, and methods; and the ways forward for advancing knowledge, improving
representation, and enhancing rigor with respect to future research on CE and social media.
1. Introduction
Customers today expect brands to connect with them and do more for
them than simply sell them a product—indicating a shift from trans­
actional to relationship marketing, which has occurred in the recent
decades post the new millennium (Coviello et al., 2002; Dall’Olmo-Riley
& De Chernatony, 2000; Islam et al., 2019; Rosenbaum et al., 2017;
Vivek et al., 2012). Of particular interest in relationship marketing is the
concept of customer engagement (CE), which is widely regarded as a
vital agenda that marketers today must actively pursue if they wish to
build long-term customer interactions and relationships and solidify
customer loyalty for their brands (Lim, Kumar, Pandey, Rasul, & Gaur,
2022; Lim, Rasul, Kumar, & Ala, 2022; Kumar, 2020; Rosenbaum et al.,
2017). In line with this, global professional marketing bodies such as the
Marketing Science Institute (2018) have included CE into their priority
list (Tier 1) for 2018 to 2020 to assist marketing professionals identify
the most effective strategies to build sustainable engagement with their
customers. Having considered the importance of CE for brands in
building customer loyalty, the Marketing Science Institute (2020) have
once again included CE into their priority list (Tier 1) for 2020 to 2022.
Brands around the world have also realized the significance of invest­
ment in the digital space, which mostly consists of a variety of social
media platforms (Hride et al., 2022; Husain et al., 2022; Sajid et al.,
2022). Accordingly, their spending globally in the digital space have
increased from $380.75 billion in 2020 to $491.70 billion in 2021 and
this is forecasted to grow to an estimated $785.08 billion in 2025
(Bhattacharjee, 2020; Cramer-Flood, 2021).
In essence, CE is a multidimensional concept that places a great
emphasis on cognitive, affective, and behavioral aspects of customer-
brand relationships (Brodie et al., 2011; Hollebeek, 2011a, 2011b;
Hollebeek et al., 2014; Kumar, 2020; van Doorn et al., 2010). Many
studies have reported that CE often functions as a brand’s desire to
connect and relate with its customers (France et al., 2016), and when
present, customers have been reported to develop favorable attitudes
toward that brand and form longer-term relationships with the brand
(Moliner et al., 2018; Pinto et al., 2019; Prentice et al., 2020).
In research, CE is considered to be an emerging field of study (Lim,
Rasul, Kumar, & Ala, 2022). Yet, in order for our understanding of CE to
progress, it is important to pursue a stock take of existing research in
order for future research to gain an up-to-date understanding of the state
of the literature and to build on past trajectories to continue to enrich
our understanding of the concept (Lim, Kumar, & Ali, 2022). Indeed,
several reviews on CE have recently appeared in the literature—some
reviews were general (e.g., Barari et al., 2021; Islam & Rahman, 2016;
* Corresponding author.
E-mail addresses: lim@wengmarc.com, marclim@swin.edu.au, wlim@swinburne.edu.my (W.M. Lim), tareq.rasul@aib.edu.au, tfrasul@gmail.com (T. Rasul).
Contents lists available at ScienceDirect
Journal of Business Research
journal homepage: www.elsevier.com/locate/jbusres
https://doi.org/10.1016/j.jbusres.2022.04.068
Received 13 May 2021; Received in revised form 22 April 2022; Accepted 30 April 2022

2. Journal of Business Research 148 (2022) 325–342
326
Lim, Rasul, Kumar, & Ala, 2022; Ng et al., 2020; Rosado-Pinto &
Loureiro, 2020), whereas others were reviewed in conjunction with
sectoral domains, such as hospitality and tourism (e.g., Hao, 2020; So,
Li, & Kim, 2020) and services (e.g., Chandni & Rahman, 2020). Some
scholars such as Ajiboye et al. (2019) have attempted to systematically
review CE on social media, but the resulting insights remain limited (e.
g., five uncategorized antecedents only). To this end, no review, to date,
has considered CE on social media in a holistic, rigorous, and systematic
way using a well-structured framework, and we argue that a new review
in this direction is highly warranted in order to thoroughly consolidate
and advance knowledge in the field. This contention is in line with (1)
the authoritative guides for systematic reviews by Lim, Kumar, and Ali
(2022) and Paul et al. (2021), which recommended reviews using
organizing frameworks due to its ability to comprehensively provide a
structured representation of the literature and its utility for curating and
positioning new research in the field, as well as (2) the call by Lim,
Rasul, Kumar, and Ala (2022) in the Journal of Business Research for new
reviews on CE that consider the theories, contexts, constructs (ante­
cedents, decisions, outcomes), and methods underpinning CE research
in emergent domains such as social media. A review of CE on social
media also has practical relevance given that the advent of the Internet
and the proliferation of smart devices have been closely associated with
the evolution of social media, holding great promise and immense po­
tential for curating and scaling CE (Carlson et al., 2018; Riley, 2020;
Santini et al., 2020; Shawky et al., 2020).
In essence, “social media” has been defined as “tools for social
interaction, using highly accessible and scalable communication tech­
niques – such as web-based, mobile technologies – to turn communi­
cation into interactive dialogue” (Coulson, 2013, p. 1). The main
differences between traditional offline and contemporary online (i.e.,
social media-based) CE processes are best appreciated by focusing on the
differences in CE facilitated by analogue and digital communication
(Eigenraam et al., 2018; Zook & Smith, 2016). Specifically, the tradi­
tional offline CE process relies on one-way or linear communication,
which enables brands to engage with customers but limits the ability of
customers to engage with brands (Greve, 2014), whereas the multi-way
or non-linear communicative dynamics entailing social media reflect the
strength of the contemporary online (i.e., social media-based) CE
strategy that allows brands and customers to reciprocally engage beyond
location and time boundaries (Barari et al., 2021). Noteworthily, social
media allows brands to build CE quickly through direct and flexible
communication and provides them with analytical functions that enable
them to assess the impact and efficacy of their communication in
engaging with their target customers (Greve, 2014; Labrecque, 2014;
Zook & Smith, 2016), thereby highlighting its potency for CE.
While many studies demonstrate that traditional offline CE is useful
(Gilpin, 2019; Harmeling et al., 2017; Shawky, Kubacki, Dietrich, &
Weaven, 2020), other studies report that many brands have benefitted
by enhancing their engagement with target customers using digital
technologies, especially through social media, which is instantaneously
available, highly accessible, and widely used by society today (Brodie,
Ilić, Jurić, & Hollebeek, 2013; Calder, Malthouse, & Schaedel, 2009;
Hennig-Thurau et al., 2010; Hollebeek et al., 2014; Lim, Ahmad, Rasul,
& Parvez, 2021). In this regard, we contend that a review of CE on social
media using a well-structured framework should produce new insights,
thereby extending the contributions of past reviews (e.g., Lim, Rasul,
Kumar, & Ala, 2022). That is to say, such a review as we will pursue
herein should help to clarify how CE is manifested on social media,
including its antecedents and consequences. In doing so, we hope to
contribute to a deeper understanding of CE from a social media
perspective, thereby advancing marketing theory and practice in this
area.
To this end, the goal of this article is to review conceptual and
empirical studies on CE on social media published in academic journals
since the beginning of the current (or third) millennium spanning from
2000 to 2020. In line with the recommendations by Lim, Kumar, and Ali
(2022) and Paul et al. (2021) and the work of Lim, Yap, and Makkar
(2021), the present review will take an integrated framework-based
approach using the antecedents, decisions, and outcomes (ADO)
framework (Paul & Benito, 2018) and the theories, contexts, and
methods (TCM) framework (Paul et al., 2017) to answer the following
research questions:
• What are the antecedents, decisions, and outcomes of CE on social
media?
• What are the theories, contexts, and methods used to study CE on
social media?
• What are the pathways for advancing knowledge, improving repre­
sentation, and enhancing rigor with respect to future research on CE
on social media?
The rest of this article is organized as follows. First, we provide a
sharp and succinct overview of the emerging field of CE and social
media, its key works, and leading approaches. Next, we describe the type
and procedure of the review with reference to seminal reviews and
references. This is followed by a presentation of the major findings
covering the antecedents, decisions, and outcomes of CE on social media
and the theories, contexts, and methods employed by past studies to
develop those findings. Finally, we present an agenda for future research
on CE on social media.
2. Theoretical background
2.1. Customer engagement
Serious attention to CE, as well as its systematic conceptualization,
began around 2010. The most widely acknowledged definitions of CE
were developed by scholars such as Brodie et al. (2011,2013), Hollebeek
(2011a, 2011b), van Doorn et al. (2010), and Vivek et al. (2012). In
particular, van Doorn et al. (2010, p. 253) approached CE as a behav­
ioral construct, defining it as “the customers’ behavioral manifestation
toward a brand or firm, beyond purchase, resulting from motivational
drivers.” In contrast, Brodie et al. (2011, p. 260) defined CE as a psy­
chological state that “occurs by virtue of interactive, co-creative
customer experiences with a focal agent/object (e.g., a brand) in focal
service relationships.” Elaborating on the psychological nature of CE,
Hollebeek (2011a, p. 790) explained CE as “the level of an individual
customer’s motivational, brand-related, and context-dependent state of
mind characterized by specific levels of cognitive, emotional, and
behavioral activity in direct brand interactions.” Emphasizing the
participatory and experience-based aspect of CE, Vivek et al. (2012, p.
133) view CE as “the intensity of an individual’s participation in and
connection with an organization’s offerings or organizational activities,
which either the customer or the organization initiates.” France et al.
(2016) suggest that these works not only provide the theoretical foun­
dations for understanding CE but also offer a strong framework for
model development. Indeed, subsequent works in the field have offered
a range of models and scales that further specified, operationalized, and
validated CE dimensions (e.g., Bowden, 2009; Dessart et al., 2016;
Hollebeek & Chen, 2014; Hollebeek et al., 2014; Hollebeek et al., 2019;
Marbach et al., 2016).
In most instances, the dimensions of CE typically included cognitive,
affective, and behavioral aspects of engagement (Dessart et al., 2016;
Islam & Rahman, 2017). However, in some instances, the dimensions of
CE appear to vary quite substantially. For example, the variant of CE by
van Doorn et al. (2010) differs from other works in the field as it pro­
poses five dimensions in the form of valence, modality, scope, nature of
impact, and customer goals, whilst emphasizing the overall behavioral
manifestation of CE. Similarly, So et al. (2014, 2016) proposed five di­
mensions of CE in the form of identification, attention, absorption,
enthusiasm, and interaction. As a result, the recognition of CE as
multidimensional has led to the call by Dwivedi (2015) to consider CE as
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3. Journal of Business Research 148 (2022) 325–342
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a second-order construct. More recently, Lim, Rasul, Kumar, and Ala
(2022, p. 441) have sought to reconcile the varied manifestations of CE,
noting that “CE is a concept that can accommodate and be approached
from diverse perspectives as long as the perspective captures and ex­
plains the “nature of interaction” (e.g., type, characteristic) that cus­
tomers exhibit, which can then be extrapolated for scrutiny against
marketing actions in the pursuit of encouraging desired (e.g., brand
loyalty) or discouraging undesired (e.g., brand switching) customer
behavior,” in which this article subscribes to due to its logic, inclusivity,
recency, and relevance to the present review.
Moving on to the application of CE, most marketing studies have
examined CE interactions with other customer-related concepts. Among
the notable concepts that have attracted significant scientific attention
are brand attachment, commitment, involvement, loyalty, satisfaction,
trust, and value (e.g., Alvarez-Milán et al., 2018; France et al., 2016;
Dwivedi, 2015; Hollebeek, 2011b; Kumar & Nayak, 2019; Leckie et al.,
2016; Nysveen & Pedersen, 2014; Prentice et al., 2020; Solem, 2016;
Thakur, 2016; Vivek et al., 2012). The empirical analysis and discussion
presented in these works make numerous attempts to decide which of
the related marketing concepts should be considered as the antecedents
of CE and which ones should be seen as the consequences of CE. The
outcomes of research endeavors in this area are particularly relevant for
branding and marketing as they allow marketers to plan their activities
strategically based on an informed understanding of the chain of CE and
its implications for brands. To this end, it is clear that there are multiple
ways of approaching CE. Thus, the manifestations of CE will need to be
systematically reported, which we will endeavor to do through our
systematic review.
2.2. Customer engagement and social media
The proliferation of new technologies in general and the ever-rising
popularity of social media in particular have played a significant role in
the emergence and evolution of relationship marketing (Steinhoff et al.,
2019; Thaichon et al., 2020). On the one hand, the adoption of smart
devices and the accessibility to high-speed Internet have enabled cus­
tomers to access brand-related information within their fingertips
(Lamberton & Stephen, 2016; Papakonstantinidis, 2017). On the other
hand, the ease of setting up and using social media have allowed cus­
tomers today to easily and openly express their attitudes and opinions
toward various brands—be it through comments, likes, or shares (Buzeta
et al., 2020; Hennessy, 2018). Such instances indicate the need for
marketers to search for innovative ways to connect with customers, with
CE poised to play a prominent role in the online environment, most
notably via social media. Indeed, existing research has demonstrated
that CE in the online environment has a positive impact on numerous
brand- and customer-related outcomes, such as brand evaluations, loy­
alty, and trust, as well as customer purchase intentions and satisfaction
(Brodie et al., 2013; Harrigan et al., 2017, 2018; So et al., 2014, 2016;
Tsai & Men, 2013).
In practice, social media is a popular and widely-used tool for
building customer-brand relationships. Facebook, Instagram, Pinterest,
Snapchat, TikTok, and Twitter are among the social media that brands
commonly use to engage with their customers (Arora et al., 2019; Phua
et al., 2017). Facebook, in particular, has emerged as the main channel
for many brands for customer-brand interactions (Brodie et al., 2013;
Simon & Tossan, 2018). Nonetheless, social media has made CE more
complex and dynamic as it allows not only direct customer-brand ex­
changes but also exchanges between customers themselves (Carlson
et al., 2018; Cova & Pace, 2006; Prentice et al., 2020; Sawhney et al.,
2005). In this regard, social media has also contributed to the trans­
formation of customers into active participants (Hollebeek et al., 2014)
and co-creators of brand stories (de Vries & Carlson, 2014), all of which
are relevant insights on CE that we will endeavor to explore in greater
detail through our review, which we will describe in the next section.
3. Methods
3.1. Review method
A systematic review is an established method of study that aims to
consolidate existing knowledge to drive future agenda on a subject (i.e.,
domain, theory, or method) in a logical and systematic way (Palmatier
et al., 2018). The authoritative guide by Lim, Kumar, and Ali (2022)
stipulate a typology of review methods that could be undertaken to
achieve that goal, namely bibliometric, framework, thematic, meta-
analytical, and meta-systematic methods. Given that this article is
interested to unpack the questions of “what do we know,” “how do we
know,” and “where we should be heading” with respect to CE on social
media, we have decided to adopt an integrated framework-based review
method in line with Lim, Yap, and Makkar (2021). In particular, we
adopt and integrate two frameworks typically used in systematic re­
views in the form of the ADO framework (Paul & Benito, 2018), which
grounds our review of the antecedents, decisions, and outcomes of (or
what do we know about) CE on social media, and the TCM framework
(Paul et al., 2017), which underpins our review of the theories, contexts,
and methods used to study (or how do we know about) CE on social
media. Insights from the review predicated on these two frameworks
will inform the extant gaps and the future research directions (or where
we should be heading) with respect to CE on social media.
3.2. Review procedure
The review in this article adopts the widely used PRISMA protocol
for reporting items for systematic reviews (Moher et al., 2009). The
PRISMA protocol was originally developed for reviews in healthcare
research, but the protocol has been proven to be equally useful for re­
views in other fields, including marketing studies (Huurne et al., 2017;
Lim, Yap, & Makkar, 2021). In essence, the protocol recommends four
stages for developing a transparent and rigorous scientific review. The
stages are identification, screening, eligibility, and inclusion. The steps
taken in each of these stages (Fig. 1), including their rationale, will be
explained in the next sections.
3.2.1. Identification
In the identification stage, we searched for articles from 2000 to
2020 using Google Scholar as a search engine. The period of review was
limited to two decades—and more specifically, up to the time of search
(i.e., October 20, 2020). In order to account for the most recent de­
velopments of CE on social media, Google Scholar was chosen as the
search engine. It is one of the most comprehensive and sophisticated
search engines that indexes and returns articles in a very timely manner
(Gusenbauer, 2019). It is in line with a similar timeframe criterion
employed in prior studies by Lim (2021) and Paul and Mas (2020).
In order to conduct the search, we chose (1) “customer engagement”
and “social media”, (2) “consumer engagement” and “social media”, (3)
“brand engagement” and “social media”, and (4) “business engagement”
and “social media” as exact keywords for the search because of the
centrality of these concepts to our review (i.e., customer = consumer,
business). We checked the “title of the article” option in “advanced
search” in order to produce finer-grained search results. The keywords
were consistent in both American and British English, and thus, alter­
native spellings were not considered. Moreover, the decision to use only
four combinations of keywords for the search (i.e., one combination for
one search) is in line with the recommendation of a recent systematic
review by Lim, Yap, and Makkar (2021). This echoed the adequacy,
appropriateness, and logic of using few but meaningful combinations of
keywords. In total, 155 articles (i.e., 97 articles from “customer
engagement” and “social media” and “brand engagement” and “social
media”, 58 articles from “consumer engagement” and “social media,”
and zero articles from “business engagement” and “social media”) were
returned from the search. We then progressed to the screening stage.
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4. Journal of Business Research 148 (2022) 325–342
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3.2.2. Screening
In the screening stage, we screened articles in terms of source type,
followed by language and duplication. This was deemed to be most
efficient based on the deliberation of the authors. In particular, we
screened the source of publication and chose to exclude 79 articles (i.e.,
46 articles from “customer engagement” and “social media” and “brand
engagement” and “social media”, and 33 articles from “consumer
engagement” and “social media”) that were not published in journals (e.
g., books, book chapters, conferences, working papers). This is in line
with Paul et al. (2021), who suggested that non-journal articles may be
excluded as they often lack rigor and a thorough peer-review process.
We then screened the remaining 76 articles for language and duplica­
tion. Of these, we removed five non-English articles (i.e., three articles
from “customer engagement” and “social media” and two articles from
“consumer engagement” and “social media”). We further removed four
duplicates (i.e., three duplicates from “customer engagement” and “so­
cial media” and one duplicate from “consumer engagement” and “social
media”) in order to avoid double counting. In total, 67 articles met the
screening criteria, and their full texts were retrieved via databases (e.g.,
EBSCO, ScienceDirect) and publishers (e.g., Emerald, Springer, Taylor &
Francis) before progressing to the next stage of eligibility assessment.
3.2.3. Eligibility
In the eligibility stage, we assessed the full text of 67 articles for
article type and source quality. In particular, we considered conceptual
and empirical articles only as they typically serve as the foundation of
knowledge. Editorials and book reviews were excluded as they might not
engage in knowledge building as extensively and as rigorously as con­
ceptual and empirical articles. We also excluded systematic reviews (e.
g., meta-analysis) to avoid duplication in reporting. In total, two non-
conceptual and non-empirical articles were excluded from our review.
Following that, we assessed the remaining articles for source quality. In
this process, we included only articles ranked in the Australian Business
Deans Council (ABDC) journal ranking list. Unlike Lim, Yap, and Makkar
(2021), we did not filter articles based on journal ranks (e.g., “A*” or
“A”) due to the relatively small number of articles in the area, a practice
deemed acceptable for niche fields by Paul et al. (2021). In total, we
removed 31 articles that did not meet this threshold. This resulted in 34
articles published in ABDC-ranked journals that were considered for
review in the next stage.
3.2.4. Inclusion
In the inclusion stage, using content analysis, we reviewed 34 con­
ceptual and empirical articles that were published in ABDC-ranked
journals. In particular, we extracted key information with respect to
Fig. 1. Review procedure based on the PRISMA protocol. Note: * = up to October 20, 2020.
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5. Journal of Business Research 148 (2022) 325–342
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the antecedents, decisions, and consequences, as well as the theories,
contexts, and methods used to study CE on social media. In this regard,
we followed the integrated ADO (Paul & Benito, 2018) and TCM (Paul
et al., 2017) frameworks for systematic reviews in line with Lim, Yap,
and Makkar (2021). The content analysis and thematic categorization in
the review, which was guided by these frameworks, were conducted by
both authors. We reached an inter-rater agreement score of 92%, which
is well above the acceptable level of 80%, with discrepancies discussed
and resolved (Belur et al., 2021). The summary of the four-pronged re­
view process is presented in Fig. 1. The outcome of the review is pre­
sented in the next sections.
4. What do we know
CE on social media is an area of research that has gained increasing
academic attention in recent years. Though the period of search for ar­
ticles began in 2000, the review indicates that research on CE on social
media has only made its debut in ABDC-ranked journals in 2012 (Fig. 2).
Yet, most articles in the area have only appeared very recently, with
73.53% (n = 25) of the total number of articles reviewed (n = 34)
appearing in 2019 and 2020.
Most articles on CE on social media have appeared in the field of
marketing (n = 24), with few articles appearing in the fields of infor­
mation systems (n = 4), management (n = 3), and tourism (n = 3)
(Table 1). Interestingly, most articles have appeared in journals ranked
“A*” and “A” in the ABDC journal ranking list, which is a positive sign of
high-quality research. Yet, the spread of articles in these journals
appeared to be relatively thin, as the journals ranked first and second on
the list have only three and two articles, respectively, with the rest of the
journals housing only one article each on the topic, regardless of the
field of research (as of October 20, 2020) (Table 2).
The top-cited articles on CE on social media appear to be dominated
by five articles, namely Ashley and Tuten (2015), de Vries and Carlson
(2014), Hollebeek et al. (2014), Lee et al. (2018), and Sashi (2012). In
particular, Hollebeek et al. (2014) remained in top spot for both the total
number of citations received and the average citations received per year,
whereas movements in ranks are observed for the other articles
(Table 3).
The next sections of this article build upon the general bibliometric
insights by shedding light on the antecedents, decisions, and outcomes
of CE on social media. In total, the review of 34 articles reveals eight
categories of antecedents with a total of 41 constructs, one category of
decision with a total of three constructs, and four categories of outcomes
with a total of 18 constructs. This article provides an overview of these
categories and constructs in Fig. 3 and presents the ensuing relation­
ships, which are guided by the ADO framework (Paul & Benito, 2018),
alongside the foundation of marketing knowledge (i.e., first-order
knowledge encapsulating the conceptualization and operationalization
of concepts, second-order knowledge pertaining to the associations be­
tween concepts, and third-order knowledge relating to the causes and
effects between concepts; Rossiter, 2001, 2002) in Table 4. ADO stands
for antecedents, decisions, and outcomes, wherein “antecedents” refer to
the reasons for engaging or non-engaging behavior, followed by “de­
cisions,” which clarify the types of behavioral performance or non-
performance, and finally “outcomes,” as the name implies, demon­
strate the results following decisions (Lim, Yap, & Makkar, 2021; Paul &
Benito, 2018). The nature of relationships (i.e., positive, negative, or
non-significant) and the foundation of marketing knowledge underpin­
ning these relationships (i.e., the number of votes received as per the
number of studies supporting those relationships at the first-, second-,
and third-order knowledge level) will be detailed in the next sections.
4.1. Antecedents
Antecedents are the direct precursors of decisions and indirect pre­
cursors of outcomes—they directly explain why a decision is made or
not made, and indirectly explain why an outcome avails or do not avail
(Lim, Yap, & Makkar, 2021; Paul & Benito, 2018; Paul et al., 2021). In
the present review, eight categories of antecedents were uncovered, and
they were related to (1) brands, (2) customers, (3) industries, (4) mar­
keters, (5) messages, (6) platforms, (7) societies, and (8) values. There
was also a total of 57 positive, six negative, and seven non-significant
relationships that were unpacked between the antecedents and the de­
cision of CE on social media, which will be detailed in the next sections.
4.1.1. Brand-related antecedents
Brand-related antecedents encapsulate the characteristics of brands
0
1
0
2 2 2
1 1
11
14
0
2
4
6
8
10
12
14
16
Article(s)
Year(s)
Fig. 2. Articles included in the review (n = 34).
Table 1
Articles included in the review according to journal rank.
Field n A* A B C
Marketing 24 16.67 58.33 16.67 8.33
Information systems 4 75.0 25.0
Management 3 33.3 33.3 33.3
Tourism 3 33.3 66.7
Note: Figures in percentages for journal ranks.
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6. Journal of Business Research 148 (2022) 325–342
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that could influence CE on social media. In total, four brand-related
antecedents were uncovered based on five votes from second-order
knowledge and one vote from third-order knowledge: brand analytics,
brand expressiveness, brand incentives, and brand organizational
characteristics. In general, brand-related antecedents have a positive
impact on CE on social media with five positive (out of six) votes. Brand
analytics enable brands to gain customer insights that can be used to
curate and manage CE (Garg et al., 2020). Such insights can also inform
how brands could effectively organize, position, and express themselves
to customers on social media (Algharabat et al., 2020; Oliveira & Fer­
nandes, 2020; Rasul & Hoque, 2020; Rasul, Zaman, & Hoque, 2020),
especially with respect to commitment and competence, as suggested by
Guesalaga (2016). Nevertheless, the incentives that brands provide will
need to be carefully curated, as CE, when viewed organically, was
revealed to be at its highest when an external reward was not offered,
implying that CE is likely to be intrinsically motivated (Quach et al.,
2019). Instead, brand incentives were found to be more effective when
call to actions were in place (e.g., discounts, recognition, and rewards to
incentivize sign ups or participations—e.g., contests) (Ashley & Tuten,
2015). More importantly, further research is required, especially with
regards to third-order knowledge, in order to strengthen the body of
marketing knowledge emerging from brand-related antecedents.
4.1.2. Customer-related antecedents
Customer-related antecedents pertain to the characteristics of cus­
tomers that could explain CE on social media. In total, 10 customer-
related antecedents were revealed based on one vote from first-order
knowledge, 13 votes from second-order knowledge, and three votes
from third-order knowledge: avant gardism, advice seeking, customer
(brand) knowledge, customer (brand) involvement, customer partici­
pation, customer personality, customer profile, customer sentiment, self-
concept, and self-image expression. In general, customer-related ante­
cedents have a positive impact on CE on social media with 17 positive
(out of 20) votes. In particular, customers who are knowledgeable about
the brand (Carlson, Gudergan, Gelhard, & Rahman, 2019) and who
perceive their participation and involvement, solicited and unsolicited,
as important to value creation (Algharabat et al., 2020; Hollebeek et al.,
2014; Oliveira & Fernandes, 2020; Quach et al., 2019; Samala et al.,
2019; Solem & Pedersen, 2016) are likely to engage with brands on
social media. In addition, customer profiling (e.g., delighted, enthusi­
astic, loyal, reserved, transactional, and unpassionate customers, fans;
Sashi, 2012; So, Wei, & Martin, 2020) was observed to be potentially
useful, as customers with different avant gardism (Carlson, Gudergan,
Gelhard, & Rahman, 2019), advice seeking needs (Wang & Lee, 2020),
emotional bonds (e.g., high, low; Sashi, 2012), personalities (e.g., ex­
traversion, conscientiousness, openness, and neuroticism; Dodoo &
Padovano, 2020), self-concepts (Giakoumaki & Krepapa, 2020), self-
image expressions (Wang & Lee, 2020), and relational exchanges (e.g.,
high, low; Sashi, 2012) help to explain the magnitude of CE on social
media. Despite providing very rich insights, further research that
empirically validates these associations in a causal way should be pur­
sued to strengthen the foundation of marketing knowledge (i.e., third-
order knowledge) with respect to customer-related antecedents.
4.1.3. Industry-related antecedents
Industry-related antecedents relate to the factors influenced or sha­
ped by the industry that could explain CE on social media. In total, two
industry-related antecedents were uncovered based on one vote each
from first- and second-order knowledge: fashion involvement (i.e.,
shaped by the fashion industry) and technology orientation (i.e., shaped
by the technology industry). The results appear to be mixed, as the
motivation to be involved in fashion, which is an inherent attribute in
the fashion industry, had no significant impact, on its own, on CE on
social media (Wang & Lee, 2020), whereas in the context of technology,
the technologies that are set up to require involvement of customers and
salespeople is poised to set the tone for CE on social media (Agnihotri,
2020). Nevertheless, the knowledge foundation for industry-related
antecedents is relatively weak, and its study across a broader range of
industries remains underexplored, and thus, deserves additional
investigation.
4.1.4. Marketer-related antecedents
Marketer-related antecedents denote the activities and characteris­
tics of marketers that could explain CE on social media. In total, three
marketer-related antecedents were revealed based on two votes each
from first- and second-order knowledge: engagement effort, salesperson
behavior, and salesperson characteristic. In general, marketer-related
antecedents have a positive impact on CE on social media with four
positive (out of four) votes. Indeed, the efforts of marketers to engage
Table 2
List of journals in the review.
Rank Journal title Field Article (n
= 34)
1 Journal of Retailing and Consumer
Services
Marketing 3
= 2 Industrial Marketing Management Marketing 2
= 2 International Journal of Information
Management
Information
Systems
2
= 2 Journal of Business Research Marketing 2
= 2 Journal of Promotion Management Marketing 2
= 2 Psychology & Marketing Marketing 2
= 7 Academy of Marketing Studies Journal Marketing 1
= 7 Journal of Marketing Marketing 1
= 7 Asia Pacific Journal of Marketing and
Logistics
Marketing 1
= 7 European Journal of Marketing Marketing 1
= 7 Information & Management Information
Systems
1
= 7 International Journal of Contemporary
Hospitality Management
Tourism 1
= 7 International Journal of Electronic
Marketing and Retailing
Marketing 1
= 7 International Journal of Internet
Marketing and Advertising
Marketing 1
= 7 Journal of Asia Pacific Business Management 1
= 7 Journal of Brand Management Marketing 1
= 7 Journal of Electronic Commerce
Research
Information
Systems
1
= 7 Journal of Hospitality and Tourism
Management
Tourism 1
= 7 Journal of Interactive Marketing Marketing 1
= 7 Journal of Islamic Marketing Marketing 1
= 7 Journal of Marketing Management Marketing 1
= 7 Journal of Service Theory and Practice Marketing 1
= 7 Journal of Strategic Marketing Marketing 1
= 7 Journal of Travel Research Tourism 1
= 7 Management Decision Management 1
= 7 Management Science Management 1
= 7 Marketing Intelligence & Planning Marketing 1
Table 3
Top-cited articles for customer engagement on social media.
Rank Author(s) Total
citations
(n =
4,351)
Rank Author(s) Citations
per year
(n = 130.4)
1 Hollebeek et al.
(2014)
1,512 1 Hollebeek et al.
(2014)
216
2 Sashi (2012) 1,324 2 Ashley and
Tuten (2015)
161.3
3 Ashley and
Tuten (2015)
968 3 Sashi (2012) 147.1
4 de Vries and
Carlson (2014)
287 4 Lee et al. (2018) 86.7
5 Lee et al. (2018) 260 5 de Vries and
Carlson (2014)
41
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7. Journal of Business Research 148 (2022) 325–342
331
with customers on social media (e.g., number of postings, comments;
Lee et al., 2020) along with their ability to adapt and demonstrate
customer orientation and product-market knowledge in committed,
competent, and responsive ways (Agnihotri, 2020; Guesalaga, 2016)
will contribute to greater CE on social media. Nevertheless, further
research is needed to explore new marketer-related antecedents (i.e.,
first-order knowledge) and to test its potential effectiveness to garner CE
on social media (i.e., second- and third-order knowledge).
4.1.5. Message-related antecedents
Message-related antecedents consist of the attributes of messages
that marketers design and disseminate to customers that could impact
the latter’s engagement on social media. In total, 10 message-related
antecedents were uncovered based on 16 votes from second-order
knowledge and two votes from third-order knowledge: communication
style, content message, content source, customization, electronic word
of mouth, entertainment, information richness, interaction, regulatory
focus, and trendiness. In general, most message-related antecedents
could be manipulated to curate CE on social media, with 15 positive
votes received out of 20 votes. For example, messages with a friendly
communication style were found to increase both the number of com­
ments and their positive tone among customers on social media, whereas
an authoritative communication style had no such effect (Wu et al.,
2019). With such a communication style, messages that are fresh,
frequent, informative (e.g., brand, product, or deal mentions), rich, and
persuasive (e.g., remarkable facts) are likely to be more effective (Ashley
& Tuten, 2015; Bai & Yan, 2020; Lee et al., 2020). Interestingly, the use
of informative content, on its own, may be less effective to solicit CE, but
may become highly effective when paired with emotional or personality
expressive content (e.g., emotion, humor) (Lee et al., 2018), especially
when the intention is to build brand communities rather than to simply
disseminate information (Wu et al., 2019), as witnessed through higher
brand engagement levels among customers who interact more with
content posted by other users as compared to brands (Cheung et al.,
2020; Giakoumaki & Krepapa, 2020; Hollebeek, 2011b), whose brand
customized messages had no significant effects in most instances
(Cheung et al., 2020; Liu et al., 2019). Indeed, messages on social media
need to be interactive and trendy (Cheung et al., 2020; Liu et al., 2019;
Ningthoujam et al., 2020), with prevention-focused messages gaining
more traction than promotion-focused messages, especially among
customers who score high on extraversion and neuroticism (Dodoo &
Padovano, 2020). More importantly, the manipulative nature of
message-related antecedents demands more investigations that could
contribute to third-order knowledge so as to influence the curation of
finer-grained message content and design that would yield the greatest
amount of desired CE on social media.
4.1.6. Platform-related antecedents
Platform-related antecedents encapsulate the characteristics of social
media platforms that could influence CE on social media. In total, five
platform-related antecedents were revealed based on five votes from
second-order knowledge and one vote from third-order knowledge:
effort expectancy, performance expectancy, perceived enjoyment,
perceived risk, and perceived trust. The effect of platform-related an­
tecedents on CE on social media is dependent on the nature of the
antecedent, with three votes each for positive and negative effects. In
particular, social media platforms that require greater effort expectancy
and places greater risks (e.g., privacy concerns) on customers are likely
to deter CE, whereas social media platforms that are able to perform as
expected and that could provide enjoyment in a trustworthy manner will
likely motivate customers to engage on social media (Al Mamun et al.,
2020; Quach et al., 2019). Indeed, technology features of social media
Fig. 3. State-of-the-art overview of the antecedents, decisions, and outcomes of customer engagement on social media.
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8. Journal of Business Research 148 (2022) 325–342
332
platforms appear to be quite pervasive for this group of antecedents,
though albeit limited, and thus, further application of technology
acceptance models (e.g., technology acceptance model, uses and grati­
fications theory) could further enrich our understanding of platform-
related antecedents that goes beyond customer adoption of and into
CE on social media.
4.1.7. Social-related antecedents
Social-related antecedents consist of social influences that impact
upon CE on social media. In total, one social-related antecedent was
revealed based on one vote from second-order knowledge: social influ­
ence. This antecedent that was uncovered was relatively general, as it
simply looks at the influence of family and friends on CE on social media
(Al Mamun et al., 2020). Given the lack of richness of social-related
antecedents, further exploratory research is necessary (i.e., first-order
knowledge), which could then be employed for empirical testing in
correlational (i.e., second-order knowledge) and causal (i.e., third-order
knowledge) ways.
4.1.8. Value-related antecedents
Value-related antecedents relate to the benefits that customers stand
to gain when they engage on social media. In total, six value-related
antecedents were uncovered based on 10 votes from second-order
knowledge and one vote from third-order knowledge: co-creation,
functional, emotional (or hedonic), innovativeness, relationship build­
ing, and social value. In general, value-related antecedents have a pos­
itive impact on CE on social media, with a whopping 11 positive (out of
11) votes. Indeed, customers may engage on social media for a variety of
reasons, and when they are presented with a value that resonates with
them (e.g., co-creation, functional, emotional [or hedonic], innova­
tiveness, relationship building, and social value), they are likely to
engage on social media (Carlson, Rahman, Taylor, & Voola, 2019; de
Vries & Carlson, 2014; Loureiro & Lopes, 2019; Quach et al., 2019).
While the concept of value is typically associated with costs, it is
interesting to see the absence of the cost aspect of value with regards to
CE on social media, which may be due to the fact that engagement on
social media is virtually free, and its effort expectancy and perceived risk
are platform-related antecedents that are typically associated with the
social media platform rather than the engagement itself. Nonetheless,
most findings in this area are correlational (i.e., second-order knowl­
edge), and thus, future research should focus on testing the correlations
in a casual manner, so as to develop finer-grain understanding on when
value-related antecedents could or could not work to encourage CE on
social media (i.e., third-order knowledge).
4.2. Decisions: Customer engagement on social media
Decisions encapsulate the engagement that customers partake or do
not partake on social media, and thus, they serve as a direct response to
antecedents and a precursor of outcomes (Lim, Yap, & Makkar, 2021;
Paul & Benito, 2018; Paul et al., 2021). That is to say, the decision under
study herein this review pertain to CE on social media (i.e., only one
decision).
Upon detailed scrutiny, the review uncovers several manifestations
of CE that could transpire on social media. In general, CE has been
examined as a unidimensional (e.g., Ashley & Tuten, 2015; Guesalaga,
2016; Shah et al., 2019) and a multidimensional (e.g., Carlson, Guder­
gan, Gelhard, & Rahman, 2019; Liu, Shin, & Burns, 2019; Quach, Shao,
Ross, & Thaichon, 2019; So, Wei, & Martin, 2020; Wang & Lee, 2020)
construct (Table 5). As a unidimensional construct, CE is often observed
in a behavioral form (e.g., number of customer comments and posts in a
week; Bai & Yan, 2020). As a multidimensional construct, CE has
manifested as “customer brand engagement,” which consists of cogni­
tive processing, affection, and activation (Hollebeek et al., 2014), or
Table 4
Knowledge map of antecedents, decisions, and consequences of customer engagement on social media.
Note: n.s. = non-significant. green = 10 votes or more. yellow = 5 to 9 votes. red = less than 5 votes. votes = number of studies providing construct-level support.
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9. Journal of Business Research 148 (2022) 325–342
333
cognitive, emotional, and behavioral / physical engagement (Leckie
et al., 2016); “customer engagement,” which consists of identification,
enthusiasm, attention, absorption, and interaction (So et al., 2014), or
cognitive, emotional, and behavioral engagement (e.g., consumption,
contribution, creation—e.g., like, share, comment) (Schivinski et al.,
2016); “customer engagement behavior,” which consists of calculative
and affective commitment observed through behavioral manifestations
(e.g., interaction, recommendation) (van Doorn et al., 2010); “customer
engagement cycle,” which consists of connection, interaction, satisfac­
tion, retention, commitment, and advocacy stages (Sashi, 2012); and
“customer engagement value,” which consists of customer referral,
customer social-influence, and customer knowledge value (Kumar et al.,
2010).
While “customer brand engagement,” “customer engagement,” and
“customer engagement behavior” are relatively easy to understand and
may very well be simplified into three basic dimensions of cognitive (e.
g., interests—e.g., reading, thinking), affective (e.g., feelings—e.g.,
attachment, sense of belonging), and behavioral (e.g., interaction—e.g.,
comment, like, post, share) aspects of engagement, “customer engage­
ment cycle” and “customer engagement value” may be a little bit more
complex. In particular, “customer engagement cycle” posits that CE can
be viewed over a number of stages, where engagement truly happens at
the end when customers become immerse into the activities on social
media (Sashi, 2012), whereas “customer engagement value” is not
“value” per se (and thus not a value-related antecedent) but rather a
proxy to measure the cognitive, affective, and behavioral aspects of
engagement, particularly in research relying on secondary data (e.g.,
customer-generated social media content, online ratings, product and
service feedback, prospect conversation) (Agnihotri, 2020). In that
sense, both “customer engagement cycle” and “customer engagement
value” may still fit into the three basic aspects of engagement, but
nonetheless, require further exploration empirically (i.e., second- and
third-order knowledge) given that these two concepts are underpinned
only by first-order knowledge (Agnihotri, 2020; Sashi, 2012).
4.3. Outcomes
Outcomes refer to the evaluations that emerge as a consequence of
decisions (Lim, Yap, & Makkar, 2021; Paul & Benito, 2018; Paul et al.,
2021), and in this case, as a result of CE or customer non-engagement on
social media. The review reveals four major outcomes, namely business-,
brand-, customer-, and social media-related outcomes.
4.3.1. Business-related outcomes
Business-related outcomes relate to the consequences of business
activities on the performance of the business. In most instances, this
usually transcends brand performance given that a business may acquire
and own numerous brands (e.g., Procter and Gamble, Unilever). The
review herein reveals a single business outcome that receives two pos­
itive (out of two) votes from two votes of second-order knowledge:
business performance. In particular, business performance was treated
as a multidimensional business-related outcome (e.g., financial perfor­
mance, market performance, and net profits/returns) as a result of CE on
social media (Bai & Yan, 2020; Garg et al., 2020). Notwithstanding the
two studies contributing to second-order knowledge, further research is
necessary to delineate the impact of CE on social media on a range of
business-related outcomes beyond financial and market performance (e.
g., market capitalization), wherein the characteristics of businesses are
controlled for (e.g., firm age and size), which in turn, could make sig­
nificant advancement contributing to third-order knowledge.
4.3.2. Brand-related outcomes
Brand-related outcomes pertain to the consequences of brand ac­
tivities experienced by the brand, and in this case, the consequences for
brands as a result of their engagement with customers on social media.
In total, 13 brand-related outcomes were uncovered based on two votes
from first-order knowledge and 15 votes from second-order knowledge:
brand advocacy, brand attachment, brand awareness, brand (self)
connection, brand experience, brand image, brand intimacy, brand
knowledge, brand loyalty, brand responsiveness, brand sales, brand
trust, and brand usage. In general, CE on social media creates positive
brand-related outcomes, as evidenced by 26 positive (out of 31) votes. In
particular, CE on social media enables brands to better connect with
customers (Hollebeek et al., 2014), creates better brand image and
responsiveness in the eyes of customers (Loureiro & Lopes, 2019; Oli­
veira & Fernandes, 2020; Panagiotopoulos et al., 2015), and solicit
brand attachment (Li et al., 2020; Loureiro & Lopes, 2019), intimacy
(Wang & Lee, 2020), trust (Loureiro & Lopes, 2019), usage (Hollebeek
et al., 2014), and loyalty (Algharabat et al., 2020; de Vries & Carlson,
2014; Ningthoujam et al., 2020; Oliveira & Fernandes, 2020; Samala
et al., 2019; So, Wei, & Martin, 2020; Solem & Pedersen, 2016) among
customers. In doing so, brands will be able to acquire and develop a good
customer base who are not only aware (Cheung et al., 2020; Loureiro &
Lopes, 2019) and knowledgeable (Cheung et al., 2020) about their
brands, but would also be good advocates for their brands (Sashi et al.,
2019). Indeed, brand-related outcomes are the most prominent out­
comes as a result of CE on social media. Nevertheless, third-order
knowledge remains weak, and thus, further research that sheds light
on instances of causality would be highly encouraged so that brands
could forecast brand measures and performance more reliably based on
the conditions of CE on social media.
4.3.3. Customer-related outcomes
Customer-related outcomes refer to the consequences of CE on social
media on the customers themselves. In total, two customer-related
outcomes were revealed based on one vote each from second- and
third-order knowledge—i.e., customer lifetime value and customer sat­
isfaction—which were positively related to CE on social media, as seen
through two positive votes (Meire et al., 2019; Shah et al., 2019).
Interestingly, customer-related outcomes were not prominently seen in
the review, and given the importance of customers to businesses and
Table 5
Manifestations of customer engagement on social media.
Construct n Seminal source(s)
Multidimensional (n = 25)
Customer brand engagement
(i.e., cognitive processing,
affection, and activation;
cognitive, emotional, and
behavioral / physical)
6 Hollebeek et al. (2014) and Leckie
et al. (2016)
Customer engagement
(i.e., identification, enthusiasm,
attention, absorption, and
interaction;
consumption, contribution, and
creation; cognitive, emotional, and
behavioral [e.g., like, share,
comment])
13 So et al. (2014) and Schivinski et al.
(2016)
Customer engagement behavior
(i.e., calculative and affective
commitment observed through
behavioral manifestations [e.g.,
interaction, recommendation])
4 van Doorn et al. (2010)
Customer engagement cycle
(i.e., connection, interaction,
satisfaction, retention, commitment,
and advocacy)
1 Sashi (2012)
Customer engagement value
(i.e., customer referral value,
customer social-influence value, and
customer knowledge value)
1 Kumar et al. (2010)
Unidimensional (n = 9)
Customer behavioral form
(e.g., number of customer
comments and posts in a week)
9 Ashley and Tuten (2015), Bai and
Yan (2020), Guesalaga (2016) and
Shah et al. (2019)
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10. Journal of Business Research 148 (2022) 325–342
334
brands, it is important that future research dive deeper on the outcomes
of CE on social media from the customer perspective. Exploration of
such outcomes could contribute to new first-order knowledge, which
could then be tested for correlation and causality, thereby contributing
to second- and third-order knowledge on customer-related outcomes,
respectively.
4.3.4. Social media-related outcomes
Social media-related outcomes encapsulate the consequences of CE
on social media on the social media itself. In total, two social media-
related outcomes were revealed based on two votes from second-order
knowledge: social media perceived quality and social media use in
sales. In general, CE on social media was observed to produce no sig­
nificant effect in (re)shaping customer perceptions of social media
quality in the consumer market (Algharabat et al., 2020). Yet, this
engagement could motivate the use of social media for sales, as seen in
the business market (Guesalaga, 2016). Needless to say, social media-
related outcomes remain underexplored, and thus, further research
that uncovers additional outcomes using correlational and causal ways
is therefore warranted to enrich our understanding in this area.
5. How do we know?
Building on the rich insights that avail from the extant literature, this
section will dive into the theories, contexts, and methods that have been
employed to inform our understanding of CE on social media.
5.1. Theories
Theories are essential to advance fields of knowledge (Lim, Yap, &
Makkar, 2021, Paul et al., 2017,2021). Guided by an integration of
theories in the form of the ADO and TCM frameworks (Lim, Yap, &
Makkar, 2021; Paul & Benito, 2018; Paul et al., 2017,2021), the review
sheds light on three pertinent insights on the theories used to inform past
conceptual and empirical research on CE on social media (Table 6).
First, the review uncovers 23 different theories that have been
employed in past studies on CE on social media (i.e., communication
theory, configuration theory, consumer culture theory, consumption
value theory, customer engagement theory, dynamic capability theory,
economic theory, emotional attachment theory, interactional
psychology theory, personal construct theory, probabilistic theory,
regulatory focus theory, relationship marketing, resource exchange
theory, self-concept theory, self-determination theory, self-schema the­
ory, service dominant logic, social comparison theory, social exchange
theory, social interaction theory, unified theory of acceptance and use of
technology, and uses and gratifications theory), which indicate that
research in this area is not short on theories.
Second, the review reveals that 22 studies have applied theories to
guide their research on CE and social media, with 15 studies employing a
single theory (e.g., Algharabat et al., 2020), five studies employing two
theories (e.g., Li et al., 2020), and two studies employing three theories
(e.g., Wang & Lee, 2020), which suggest that theoretical integration is a
potential means that could be exploited by future research, as seen by
past research in the area.
Third, the review also noted 12 studies that have not relied on any
theories to guide their research irrespective of fields (e.g., marketing
[Ashley & Tuten, 2015], management [Sashi, 2012], information sys­
tems [Oh et al., 2017], tourism [Sashi et al., 2019]) and journal ranks (e.
g., “A*” [Agnihotri, 2020], “A” [Liu et al., 2019], “B” [Loureiro & Lopes,
2019], and “C” [Ningthoujam et al., 2020]). Though this observation
suggests that publishing without relying on theories is possible, it is
arguably better to employ theories to inform research, as its presence
would arguably strengthen the theoretical foundation of the study,
especially in the case of conceptual, quantitative, and experimental
research. Future research could rely on the theories identified herein,
and if necessary, integrate a number of them so as to enrich and solidify
its theoretical foundation.
5.2. Contexts
Contexts refer to the circumstances that characterize a study (Lim,
Yap, & Makkar, 2021; Paul et al., 2017,2021). The review considers
three main contexts where CE on social media transpired in the 34 ar­
ticles under study: country, customer, and social media (Table 7).
In terms of country, the review indicates that past research on CE on
social media (n = 23) have been carried out in nine different countries,
namely China (including Hong Kong and Taiwan), Greece, India,
Ireland, Jordan, Malaysia, Norway, United Kingdom, and United States,
whereas 12 studies have considered their research as an international
phenomenon (e.g., Hollebeek et al., 2014; Oliveira & Fernandes, 2020),
which is no surprise given that brands can be multinational, and thus,
acquiring and serving customers beyond their country of origin and to
Table 6
List of theories for customer engagement on social media research.
Theory n articles
With theory (n = 22; 1 theory = 15, 2 theories = 5, 3 theories = 2)
Communication theory 1
Configuration theory 1
Consumer culture theory 3
Consumption value theory 2
Customer engagement theory 1
Dynamic capability theory 1
Economic theory 1
Emotional attachment theory 1
Interactional psychology theory 1
Personal construct theory 1
Probabilistic theory 1
Regulatory focus theory 1
Relationship marketing 2
Resource exchange theory 1
Self-concept theory 1
Self-determination theory 1
Self-schema theory 1
Service dominant logic 2
Social comparison theory 1
Social exchange theory 2
Social interaction theory 1
Unified theory of acceptance and use of technology 1
Uses and gratifications theory 1
Without theory (n = 12) 12
Table 7
Contextual coverage of customer engagement on social media research.
Context n articles
Country
China (including 1 each from Hong Kong and Taiwan) 6
Greece 1
India 3
Ireland 1
Jordan 1
Malaysia 1
Norway 1
United Kingdom 1
United States 8
International 12
Customer
Business 6
Consumer 28
Social media
Facebook 13
Instagram 3
LinkedIn 1
Sina Weibo 4
Twitter 4
YouTube 1
General 11
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335
customers worldwide. Only one studied had explicitly collected samples
in two countries (i.e., Ireland and United Kingdom) (Panagiotopoulos
et al., 2015), which could inspire other studies to do the same in the
future, thereby allowing for cross-country and cross-culture
comparisons.
In terms of customers, the review shows that past research on CE on
social media have generally considered two types of customers: business
and consumer. In particular, most studies in the area have focused on
customers in consumer markets (n = 28) (e.g., Carlson, Rahman, Taylor,
& Voola, 2019; Quach et al., 2019; So, Wei, & Martin, 2020; Wang &
Lee, 2020), with few studies shedding light on customers in business
markets (n = 6) (e.g., Agnihotri, 2020; Bai & Yan, 2020; Guesalaga,
2016; Panagiotopoulos et al., 2015). This observation, in turn, evidences
that social media is not only relevant for consumers, but also businesses,
not only as sellers, but also as buyers (or customers).
In terms of social media, the review reveals that past research on CE
on social media have been conducted on six different types of social
media platforms (n = 26), namely Facebook, Instagram, LinkedIn, Sina
Weibo, Twitter, and YouTube, whereas 11 studies have considered so­
cial media platforms in general in their research (e.g., Garg et al., 2020;
Loureiro & Lopes, 2019; Shah et al., 2019). Apart Sina Weibo, which is
widely used in China (e.g., Bai & Yan, 2020; Carlson, Gudergan, Gel­
hard, & Rahman, 2019; Wu et al., 2019), the other social media plat­
forms, such as Facebook and Twitter, are very much Western-centric,
but nonetheless, remain widely-used even in non-Western countries,
such as India (e.g., Garg et al., 2020; Ningthoujam et al., 2020; Samala
et al., 2019) and Jordan (e.g., Algharabat et al., 2020).
5.2.1. Business situations and functions
Given the importance and relevance of business situations and
functions to the understanding of context (Anthony, Majid, & Romli,
2017; Lim, Yap, & Makkar, 2021; Paul et al., 2017,2021), this sub-
section will shed light on the business situation and function entailing
each article covered in the present review of CE on social media.
Noteworthily, each article in the review has been classified in terms of
the business situation(s) and function(s) that took center stage in its
study. Specifically, three business situations—i.e., business-to-customer
(B2C), business-to-business (B2B), and both B2C and B2B—and two
business functions—i.e., marketing and sales—are covered in the review
(Table 8).
A total of 28 out of 34 studies concentrated on B2C. The majority of
these studies (n = 25) focused on marketing, whereas two studies
focused on sales, and one study focused on both marketing and sales.
B2C studies with a marketing focus indicated that CE on social media
assisted brands to configure and share marketing messages more effec­
tively with existing and potential customers (Carlson, Gudergan, Gel­
hard, & Rahman, 2019; Carlson, Rahman, Taylor, & Voola, 2019;
Giakoumaki & Krepapa, 2020; Lee et al., 2018; Lee et al., 2020; Pan­
agiotopoulos et al., 2015). Such studies also reported that CE on social
media enriches brand value and brand loyalty (Helme-Guizon & Mag­
noni 2019; Li et al., 2020; Ningthoujam et al., 2020; Samala et al., 2019;
Solem & Pedersen, 2016). In contrast, B2C studies with a sales focus
indicated that CE on social media increases a brand’s economic per­
formance (Al Mamun et al., 2020; Oh et al., 2017), whereas the B2C
study that focused on both marketing and sales highlighted the value of
CE on social media in influencing customers’ advocacy for the brand and
its products (Sashi et al., 2019).
Three studies each were devoted to B2B and both B2C and B2B. All
three studies that concentrated on B2B were also focused on sales. These
studies reported that CE on social media enhances a brand’s perfor­
mance by increasing its sales, and in turn, its profits (Agnihotri, 2020;
Garg et al., 2020; Guesalaga, 2016). The remaining three studies
concentrated on both B2C and B2B, wherein two of these studies focused
on marketing, whereas one study focused on both marketing and sales.
These studies reported the utility of social media to increase a brand’s
engagement with their existing and potential customers, as well as
stakeholders, resulting in improved overall brand performance (Bai &
Yan, 2020; Loureiro & Lopes, 2019).
5.3. Methods
Methods denote the nature of empirical evidence on which studies
are predicated upon (Lim, Yap, & Makkar, 2021; Paul et al., 2017,2021).
The review considers two major attributes that characterize the 34 ar­
ticles under study: research approach (Table 9) and research data
(Table 10).
In terms of research approach, past research on CE on social media
have generally used four approaches: conceptual, qualitative, quanti­
tative, and experimental approach. More than 80% of articles in the
review (n = 29) have taken a quantitative approach to study, with
structural equation modeling appearing to be highly popular (n = 15).
Table 8
Business situations and functions in customer engagement on social media
research.
Business
situation
Business function and related studies
B2C *Al Mamun et al. (2020), **
Algharabat et al. (2020), **
Ashley and
Tuten (2015), **
Carlson, Gudergan, Gelhard, and Rahman (2019),
**
Carlson, Rahman, Taylor, and Voola (2019), **
Cheung et al.
(2020), **
de Vries and Carlson (2014), **
Dodoo and Padovano
(2020), **
Giakoumaki and Krepapa (2020), **
Helme-Guizon and
Magnoni (2019), **
Hollebeek et al. (2014), **
Lee et al. (2018), **
Lee et al. (2020), **
Li et al. (2020), **
Liu et al. (2019), **
Ningthoujam et al. (2020), *Oh et al. (2017), **
Oliveira and
Fernandes (2020), **
Panagiotopoulos et al. (2015), **
Quach et al.
(2019), **
Samala et al. (2019), **
Sashi (2012), *,**
Sashi et al.
(2019), **
Shah et al. (2019), **
So et al. (2020), **
Solem and
Pedersen (2016), **
Wang and Lee (2020), and **
Wu et al. (2019).
B2B *Agnihotri (2020), *Garg et al. (2020), and *Guesalaga (2016).
B2C and B2B *,**
Bai and Yan (2020), **
Loureiro and Lopes (2019), and **
Meire
et al. (2019).
Note: B2C = business-to-consumer. B2B = business-to-business.
*
= business function discussed pertain to sales.
**
= business function discussed pertain to marketing.
Table 9
Research approach in customer engagement on social media research.
Approach n article
(s)
Conceptual (n = 2 studies)
Critical review 2
Empirical (n = 32; single method = 28, multi-method = 4 [2 methods = 3, 3
methods = 1])
Qualitative (n = 6)
Case study analysis 1
Content analysis 2
Fuzzy-set qualitative comparative analysis (fsQCA) 3
Quantitative (n = 29)
Big data analysis 1
Correlation analysis 1
Econometric modeling 1
Factor analysis (exploratory, confirmatory) 1
Fixed and random effects modeling 1
Latent profile analysis 1
Regression analysis (hierarchical = 2, logistic = 1, multiple = 3,
OLS = 1)
7
Structural equation modeling (CB SEM = 7, PLS-SEM = 8) 15
t-test 1
Experiment (n = 3)
Single 2
Multiple 1
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12. Journal of Business Research 148 (2022) 325–342
336
Some qualitative studies avail, with many taking a rigorous qualitative
approach in the form of fuzzy-set qualitative comparative analysis
(fsQCA) (n = 3). Critical reviews were used for two conceptual articles,
whereas single experiments (n = 2) were most common in experimental
studies.
In terms of research data, past research on CE on social media have
generally relied on two sources of data: primary and secondary data.
Most articles in the review have relied on primary data (n = 24), with 22
studies relying on a single data source, and one study each relying on
two and three data sources for primary data, respectively. Interestingly,
most articles that have employed primary data have opted for online
surveys (n = 18), which may be due to the quick turnaround time in data
collection that typifies online survey panels such as MTurk, Norstat,
Qualtrics, SoJump, Survey Cake, and Survey Monkey. Self-collected
surveys appear to be equally distributed regardless of whether it is off­
line (n = 7) or online (n = 6). Only three qualitative studies avail, with
two articles using individual interviews and one article using focus
groups. Whereas, web crawling of social media data (e.g., event pages,
Facebook comments, movie releases, microblogs, tweets) were highly
popular secondary data, with one study opting for a third-party provider
(e.g., Facebook Insights).
6. Where should we be heading?
Charting pathways for future research is an important agenda for
systematic reviews (Lim, Kumar, & Ali, 2022). In this article, we identify
several pertinent pathways that we would highly encourage future
research to consider in order to build upon the findings of past research
on CE and social media that we have reviewed. These pathways are
segmented into three main areas, namely pathways to advance knowl­
edge (i.e., focusing on theory), pathways to improve representation (i.e.,
focusing on context), and pathways to enhance rigor (i.e., focusing on
method).
6.1. Pathways to advance knowledge (theory)
In terms of theory, this article offers three potentially fruitful path­
ways that future researchers could consider to contribute new knowl­
edge on CE on social media.
First, the review accentuated the various manifestations of CE (i.e.,
customer brand engagement, CE, CE behavior, CE cycle, and CE value).
We opine that this diversity in conceptualization, on the one hand, en­
riches the emerging field but, on the other hand, creates the challenge of
confusion. This diversity could be attributed to the different dimensions
suggested by numerous scholars (e.g., Hollebeek et al., 2014; Hollebeek
et al., 2019; Kumar et al., 2010; Leckie et al., 2016; Rasul, Hoque, &
Arefin, 2020; Sashi, 2012; So et al., 2014; Schivinski et al., 2016; van
Doorn et al., 2010) but may nonetheless be consolidated into three
major dimensions, namely, cognitive, affective, and behavioral
engagement, as we have explained in our review.
The key point of the debate that remains unresolved, however, is
how cognitive, affective, and behavioral engagement could manifest
across a lifecycle perspective (e.g., connection, interaction, satisfaction,
retention, commitment, and advocacy), as in the case of the “customer
engagement cycle” (Sashi, 2012). Moreover, in most instances, the
different manifestations of CE are often “behavioral” (e.g., consumption,
contribution, creation; like, share, comment) (Schivinski et al., 2016),
with the “cognitive” and “affective” dimensions of CE remaining
underexplored. This may be due to the lack of clarity on how the latter
two dimensions could be operationalized beyond self-reported
measures.
Though we are confident of the tripartite classification of CE (i.e.,
cognitive, affective, and behavioral), we concede that we were not able
to confidently code and classify the fragmented dimensions that tran­
spired in our review, such as identification and absorption (So, Wei, &
Martin, 2020). These dimensions could span all three dimensions of CE.
That is to say, the fragmented dimensions could be accurately classified
only when they are described more thoroughly (e.g., cognitive [identify
with beliefs, perspectives, and opinions shared about the brand on social
media]; affective [identify with feelings shared about the brand on so­
cial media]).
More importantly, we contend that it may be possible to capture
“cognitive” and “affective” engagement via “behavioral” engagement,
wherein behaviors are analyzed in terms of manifestations of rationality
and emotion, as in the case of social media comments and posts that are
captured as secondary measures. Thus, this article calls for future
research that conducts in-depth investigations that expand on the different
cognitive, affective, and behavioral engagement manifestations. This includes
the possibility of cognitive and affective engagement manifestations tran­
spiring in behavioral engagement on social media. This further explains how
such manifestations might remain constant or evolve as customers gain more
experience in engaging on social media over time. We opine that such a
pathway would further consolidate the disparate fragments of CE and
enrich its operational manifestations substantially.
Second, the review demonstrated a plethora of antecedents and
consequences of CE on social media. Indeed, the breadth of categories
uncovered provides good coverage of the possible scope that could be
studied. Yet, the depth amongst categories is relatively inconsistent,
with some categories researched more richly than others, regardless of
whether they are categories of antecedents or consequences.
For example, customer- and message-related categories were some of
the most researched antecedents in our review, with 10 constructs un­
covered in both cases. However, industry- and social-related anteced­
ents collectively revealed only three constructs in our review. Similarly,
the brand-related category was the most studied consequence in our
review, with 13 constructs revealed. However, business-, customer-, and
social media-related consequences collectively uncovered only five
constructs in our review.
In light of these observations predicated on the ADO framework, this
article calls for greater investigation of the categories of antecedents and
consequences that remain underexplored, namely brand-, industry-,
marketer-, platform-, social-, and value-related antecedents, as well as
business-, customer-, and social media-related consequences. We believe
such a pathway would add “more meat to the bones” in our under­
standing of CE on social media.
Third, the review indicated that many studies have employed only a
single theory to inform their investigation of CE on social media (e.g.,
Table 10
Research data in customer engagement on social media research.
Data n article(s)
Primary (n = 24—single data = 22, multi-data = 2 [2 data sources = 1, 3 data
sources = 1])
Interviews (n = 3)
Focus group 1
Individual 2
Online survey (n = 18)
MTurk 4
Norstat 1
Qualtrics 2
SoJump 2
Survey Cake 1
Survey Monkey 1
Unnamed online panel 1
Self-collected 6
Offline survey (self-collected) (n = 7) 7
Secondary (n = 8)
Third party (Facebook Insights) (n = 1) 1
Web crawling (n = 7)
Event pages 1
Facebook comments 1
Movie releases 1
Microblogs 2
Tweets 2
No data (n = 2) 2
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13. Journal of Business Research 148 (2022) 325–342
337
Algharabat et al., 2020; Bai & Yan, 2020; Lee et al., 2018; Shah et al.,
2019). A few other studies relied on a combination of theories (e.g.,
Hollebeek et al., 2014; Solem & Pedersen, 2016; Wang & Lee, 2020). We
believe that research predicated on theory is arguably better than
research without a guiding theory. This is because a theory typically
provides an underlying foundation for supporting purported proposi­
tions and sensemaking. We also opine that research that is informed by a
collection of theories would arguably produce richer insights than
research relying on only a single theory (Hollebeek et al., 2019). In
particular, theoretical integration—or the combination and application
of multiple theories—provides the opportunity for research to effec­
tively craft a more holistic scope of investigation. This allows multiple
perspectives to be investigated, thereby enabling cross-perspective
comparisons.
For example, a study that integrates the theories of economics, social
exchange, and self-schema could reveal whether customers are more
likely to engage in the presence of economic or social benefits and
whether such motivations would differ among customers with different
personalities and experiences with the brand and social media. Simi­
larly, a study that relies on the theories of social comparison and tech­
nology acceptance could reveal whether the technological aspects of
social media would be more, less, or as important as the social dynamics
that transpire in CE on social media. This, in turn, could dictate where
brands and marketers should invest their effort, time, and resources to
curate and enhance engagement with customers on social media.
Thus, this article calls for future research that pursues theoretical inte­
gration as a means to showcase a “big picture” rather than a “jigsaw piece”
of myriad aspects of CE on social media. We opine such a pathway would
dismantle disciplinary silos and propel multidisciplinary research,
thereby enriching insights (and mitigating piecemeal findings).
6.2. Pathways to improve representation (context)
In terms of context, this article offers three enriching pathways that
future research may consider if they wish to rely on context as a means
to contribute to our understanding of CE on social media. Indeed,
context holds the potential to deliver meaningful contributions when the
unique circumstances that characterize CE on social media are cate­
gorically delineated (e.g., country, customer, social media platform) in
ways that improves representation.
First, the review revealed that most studies on CE on social media
have been conducted in a single country rather than in multiple coun­
tries (e.g., de Vries & Carlson, 2014; Giakoumaki & Krepapa, 2020;
Quach et al., 2019; Wu et al., 2019). In addition, the geographic
coverage has so far been scarce, with only nine countries receiving
serious research attention, thereby limiting the generalizability of
research findings to the larger population.
Nonetheless, around 30% of the studies reviewed have considered
customers as international as opposed to associating them to any one
particular country (e.g., Agnihotri, 2020; Lee et al., 2020; Liu et al.,
2019; Sashi et al., 2019). Though this approach may, to a certain extent,
improve generalizability, it may, however, ignore the reality that cus­
tomers may inherently differ based on country and culture (e.g.,
engagement practices accepted in the Western context may be inter­
preted as culturally insensitive in and thus inappropriate for the Eastern
context), thereby overlooking the need for representative samples in
order to truly establish generalizability more broadly (e.g., over
continents).
In this regard, this article calls for future research that considers cross-
country studies, which in turn, would enable cross-cultural comparison of CE
on social media between customers originating from different countries—a
pathway that we believe should help to improve the generalizability of
findings when consistent effects are observed, as well as the represen­
tation of findings when inconsistent effects are discovered as a result of
cross-cultural differences.
Second, the review indicated that customers may be individuals who
represent an organization or themselves, whereby the former represents
customers from business markets, whereas the latter represents cus­
tomers from consumer markets. Most studies have concentrated on CE
on social media in consumer markets (e.g., Ashley & Tuten, 2015;
Helme-Guizon & Magnoni, 2019; Sashi et al., 2019), with few studies
focusing on such engagement in business markets (e.g., Agnihotri, 2020;
Garg et al., 2020; Panagiotopoulos et al., 2015).
Though we opine that both customers are equally important, as
brands and marketers stand to gain a bigger return on a single trans­
action from business markets and a greater diversity in customer base
from consumer markets (Hollebeek et al., 2019), we would like to issue a
rallying call for future research that considers CE on social media in business
markets alongside consumer markets, which should shed light on whether CE
strategies on social media could be undifferentiated or should be differenti­
ated for brands that serve customers from both business and consumer
markets—a pathway that we believe would elevate business insights on
the topic to the level that we have witnessed with respect to consumer
insights, thereby enriching our understanding on the extent to which
consumer insights may be generalizable to other customer (e.g., busi­
ness) markets.
Third, the review showed that research on CE on social media have
investigated numerous social media platforms, such as Facebook,
Instagram, LinkedIn, Sina Weibo, Twitter, and YouTube. Similar to the
context of country, research in the area has also considered social media
platforms in general (e.g., Al Mamun et al., 2020; Guesalaga, 2016;
Loureiro & Lopes, 2019), which to us, may provide little insights on
whether CE on social media will differ or remain consistent across
different social media platforms.
In addition, most social media platforms that have been investigated
are Western-centric, with the exception of Sino Weibo, though many of
them are also widely used around the world. More importantly, the
variety of social media platforms that CE is investigated will need to be
expanded given the emergence and widespread adoption of new social
media platforms, such as social feed platforms (e.g., TikTok), social
gathering platforms (e.g., Clubhouse), social messaging platforms (e.g.,
Telegram, WhatsApp, and WeChat), social shopping platforms with
community and live streaming functions (e.g., Lazada, Shopee), and
social world platforms (e.g., Meta [metaverse]).
Thus, this article encourages future research that compares and con­
trasts CE across different types of social media platforms—a pathway that
should create better alignment in the nature of CE that brands and
marketers would like to solicit and the types of social media platforms
that could satisfy such engagement goals among different customer
markets.
6.3. Pathways to enhance rigor (method)
In terms of method, this article offers three noteworthy pathways
that should improve the rigor of research focusing on CE on social
media, as observed through the foundation of marketing knowledge (i.
e., first-, second-, and third-order knowledge).
First, the review discovered two studies that have contributed to first-
order knowledge with respect to CE on social media, whereby the crit­
ical review approach was adopted to conceptually develop two mani­
festations of CE, namely CE cycle (Sashi, 2012) and CE value (Agnihotri,
2020).
However, one of the shortcomings of critical reviews is the absence
of a procedure that would systematically organize the underpinnings of
prior literature, which is arguably needed to accentuate research gaps
and to justify proposals for (re)conceptualization and (re)operationali­
zation that typically avail in research contributing to first-order
knowledge.
Thus, to improve the rigor of first-order knowledge, this article calls
for future research seeking to enrich our understanding of concepts relating to
CE on social media to consider conducting systematic reviews using alter­
native review methods such as bibliometric and meta-systematic methods
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(Lim, Kumar, & Ali, 2022) guided by rigorous review protocols (e.g.,
PRISMA; Moher et al., 2009; SPAR-4-SLR; Paul et al., 2021)—a pathway
that should deliver conceptual insights in a more systematic and
rigorous way.
Second, the review indicated that most studies on CE on social media
contributed to second-order knowledge, with most studies adopting a
quantitative (e.g., Cheung et al., 2020; Li et al., 2020) as opposed to a
qualitative research approach (e.g., Panagiotopoulos et al., 2015). In
addition, most studies relied only on a single method (e.g., Liu et al.,
2019; So, Wei, & Martin, 2020). We opine that future research could
benefit by taking a mix-methods approach, which could consist of
rigorously conducted qualitative and quantitative studies (e.g., Ashley &
Tuten, 2015; Helme-Guizon & Magnoni, 2019; Lang et al., 2022).
For example, the fuzzy-set qualitative comparative analysis is a state-
of-the-art qualitative analysis that is increasingly employed by qualita­
tive researchers today (Kumar et al., 2022). Insights from the qualitative
study could then inform the design of the quantitative study, which
could rely on the highly popular structural equation modeling, including
a multi-group analysis that builds upon a latent profile analysis.
Needless to say, a research that conducts and reports on multiple
studies using different research approaches would be substantially more
rigorous than a research that reports on the results of only a single study.
In that sense, this article calls for future research that adopts mix-methods
to develop multiple studies to improve the rigor of conclusions about CE on
social media—a pathway that will enable triangulation and thus
strengthen the rigor of second-order knowledge.
Third, the review revealed that few studies that begun to use an
experimental approach to study CE on social media (e.g., Quach et al.,
2019). This, to us, is promising, and must be highly encouraged given
that experimental research allows causality to be established, which
represents the third-order and strongest form of marketing knowledge.
Yet, the majority of experimental studies in the review had employed
only a single experiment (e.g., Dodoo & Padovano, 2020; Giakoumaki &
Krepapa, 2020), which suggests that the potential for confounding ef­
fects may not have been rigorously tested, as in the case with multiple
experiments, and the difference between chronic disposition and situa­
tional priming cannot be ascertained with confidence (Lim, 2015).
Moreover, the experiments in the review have not kept abreast with
the latest advances in experimental research, such as the use of neuro­
scientific tools (e.g., electroencephalogram, eye tracker, galvanic sen­
sors), which could potentially offer very powerful insights beyond self-
report measures (Lim, 2018a, 2018b). Undeniably, the running of ex­
periments is often resource intensive, but new solutions, such as data
partitioning, avail in order to maximize the ways in which data could be
utilized and to minimize the cost associated with experiments (Holle­
beek et al., 2019; Lim, Ahmed, & Ali, 2019, 2022).
In this regard, this article calls for future research that adopts an
experimental approach to consider designing multiple experiments, measuring
chronic and primed dispositions, and using neuroscientific tools in order to
more accurately identify or rule out potential confounding effects that could
distort our understanding of the effects of manipulated antecedents on CE on
social media and its ensuing outcomes—a pathway that would result in
substantially stronger third-order marketing knowledge in the area.
7. Conclusion
In summary, this article has provided a state-of-the-art overview of
CE on social media using a systematic review that was predicated upon
the integrated ADO-TCM framework (Lim, Yap, & Makkar, 2021; Paul &
Benito, 2018; Paul et al., 2017,2021).
Using the ADO framework, the review herein this article has
discovered eight categories of antecedents consisting of 41 constructs,
one decision in the form of CE on social media, four categories of out­
comes consisting of 18 constructs, and 107 votes of positive, negative,
and non-significant relationships among the 59 different constructs,
which emerged from eight votes from first-order knowledge, 73 votes
from second-order knowledge, and nine votes from third-order
knowledge.
Using the TCM framework, the review herein this article sheds light
on 23 different theories, nine different countries, two types of customers,
six variations of social media platforms, one approach for conceptual
research, 14 approaches for empirical research, and five types of data
sources that past scholars have relied upon to contribute to our under­
standing of CE on social media. Moreover, the TCM framework was
employed as a guiding framework to chart the nine pathways, with three
pathways each for advancing knowledge (theory), improving repre­
sentation (context), and enhancing rigor (method).
Notwithstanding the contributions of our review, we concede that
we had to make several but necessary concessions to keep our review
manageable. First, our review did not consider CE in online commu­
nities, as individuals in such communities may not necessarily be cus­
tomers (e.g., soccer fans who do not purchase club merchandises), the
latter which is the focus of our review. Second, our review did not
consider research where “customer engagement,” “consumer engage­
ment,” “brand engagement,” or “business engagement” and “social
media” did not appear in the title of the article, though this is generally
an acceptable practice in systematic reviews (Paul & Criado, 2020).
Thus, we believe the concessions we made were pragmatic and thus
upholding our representation of the state of the field in a legitimate way.
CRediT authorship contribution statement
Weng Marc Lim: Conceptualization, Data Curation, Formal Anal­
ysis, Investigation, Validation, Visualization, Writing – Original Draft,
Writing – Review & Editing. Tareq Rasul: Conceptualization, Data
Curation, Formal Analysis, Investigation, Validation, Visualization,
Writing – Original Draft, Writing – Review & Editing.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Appendix. List of articles reviewed.
1. Agnihotri, R. (2020). Social media, customer engagement, and
sales organizations: A research agenda. Industrial Marketing
Management, 90, 291–299.
2. Al Mamun, A., Nawi, N. B. C., Nasir, N. A. B. M., & Fazal, S. A.
(2020). Social media and consumer engagement: The case of
Malaysian student entrepreneurs. Journal of Asia-Pacific Business,
21(3), 185–206.
3. Algharabat, R., Rana, N. P., Alalwan, A. A., Baabdullah, A., &
Gupta, A. (2020). Investigating the antecedents of customer
brand engagement and consumer-based brand equity in social
media. Journal of Retailing and Consumer Services, 53, 101767.
4. Ashley, C., & Tuten, T. (2015). Creative strategies in social media
marketing: An exploratory study of branded social content and
consumer engagement. Psychology & Marketing, 32(1), 15–27.
5. Bai, L., & Yan, X. (2020). Impact of firm-generated content on
firm performance and consumer engagement: Evidence from so­
cial media in China. Journal of Electronic Commerce Research, 21
(1), 56–74.
6. Carlson, J., Gudergan, S.P., Gelhard, C., & Rahman, M.M. (2019).
Customer engagement with brands in social media platforms:
Configurations, equifinality and sharing. European Journal of
Marketing, 53(9), 1733–1758.
7. Carlson, J., Rahman, M. M., Taylor, A., & Voola, R. (2019). Feel
the VIBE: Examining value-in-the-brand-page-experience and its
impact on satisfaction and customer engagement behaviours in
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mobile social media. Journal of Retailing and Consumer Services,
46, 149–162.
8. Cheung, M. L., Pires, G., & Rosenberger, P. J. (2020). The influ­
ence of perceived social media marketing elements on consum­
er–brand engagement and brand knowledge. Asia Pacific Journal
of Marketing and Logistics, 32(3), 695–720.
9. Dodoo, N. A., & Padovano, C. M. (2020). Personality-based
engagement: An examination of personality and message fac­
tors on consumer responses to social media advertisements.
Journal of Promotion Management, 26(4), 481–503.
10. de Vries, N., & Carlson, J. (2014). Examining the drivers and
brand performance implications of customer engagement with
brands in the social media environment. Journal of Brand Man­
agement, 21(6), 495–515.
11. Garg, P., Gupta, B., Dzever, S., Sivarajah, U., & Kumar, V. (2020).
Examining the relationship between social media analytics
practices and business performance in the Indian retail and IT
industries: The mediation role of customer engagement. Interna­
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12. Giakoumaki, C., & Krepapa, A. (2020). Brand engagement in self-
concept and consumer engagement in social media: The role of
the source. Psychology & Marketing, 37(3), 457–465.
13. Guesalaga, R. (2016). The use of social media in sales: Individual
and organizational antecedents, and the role of customer
engagement in social media. Industrial Marketing Management, 54,
71–79.
14. Helme-Guizon, A., & Magnoni, F. (2019). Consumer brand
engagement and its social side on brand-hosted social media:
How do they contribute to brand loyalty? Journal of Marketing
Management, 35(7–8), 716–741.
15. Hollebeek, L., Glynn, M., & Brodie, R. (2014). Consumer brand
engagement in social media: Conceptualization, scale develop­
ment and validation. Journal of Interactive Marketing, 28,
149–165.
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