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CITATION:
De Vries, N. J., & Carlson, J. (2014). Examining the drivers and brand performance
implications of customer engagement with brands in the social media environment.
Journal of Brand Management, 21(6), 495-515.
Natalie Jane De Vries
Newcastle Business School
University of Newcastle
Level 3, University House
Corner King and Auckland Streets
Newcastle 2300
e natalie.devries@newcastle.edu.au
and
Dr. Jamie Carlson*
Newcastle Business School
University of Newcastle
Level 3, University House
Corner King and Auckland Streets
Newcastle 2300
e jamie.carlson@newcastle.edu.au
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Examining the Drivers and Brand Performance Implications of Customer
Engagement with Brands in the Social Media Environment
ABSTRACT
A key issue for marketers resulting from the dramatic rise of social media is how
brand pages can be leveraged to engage customers and enhance relationships with
brands. The paper examines the role of gratifications consumers derive from brand
pages together with customer-brand relationship characteristics influencing customer
engagement with Facebook brand pages. Data was gathered via a survey from 404
consumers of brand pages and analysed using structural equation modelling.
Findings show that co-creation value, social value, usage intensity and brand
strength influence customer engagement with brand pages. Customer engagement
was also found to influence brand performance outcomes of customer engagement
behaviours directed at the brand page and brand loyalty. The findings are of value to
brand managers of social media sites and focus on how managing critical user
gratifications together with customer-brand relationship variables act as mechanisms
for unlocking customer engagement with brand pages. In addition, the study
examines customer engagement effects on both behaviours centric to the brand
page and brand loyalty outcomes in the research framework.
Keywords: Social Media, Customer Engagement, Consumer Behavior, Brands, Motivation,
Brand Relationships
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INTRODUCTION
In recent years, greater focus has been placed upon customer engagement (CE) in
branding and relationship marketing which has been argued to act as a vehicle for
enhancing consumer relationships, profitability and growth (Brodie, Hollebeek, Juric,
and Ilic, 2011; Hollebeek 2011a; Vivek, Beatty, and Morgan, 2012). CE refers to a
psychological state reflecting customers’ interactive, co-creative experiences with a
firm which highlights the active role of the consumer (Brodie et al. 2011; Verleye,
Gemmel and Rangarajan 2014). CE is distinct relative to traditional concepts such as
consumer involvement which reflects a consumer's level of interest in, and personal
relevance of a brand, whereas CE seeks to explain or predict the dynamics
characterizing focal interactive consumer/brand relationships more explicitly such as
in social media (Brodie et al. 2011; Hollebeek, Glynn and Brodie 2014).
Interest in CE has also attracted attention in the social media environment,
and how brands can leverage new media and online communities on platforms such
as Facebook, Twitter, or YouTube to engage and collaborate with customers due to
social media’s multi-dimensional, two-way peer-to-peer communication properties
(Brodie et al, 2013; Jahn and Kunz, 2012; Hutter, Hautz, Dennhardt and Fuller,
2013; Yan, 2012). Specifically, there has been significant growth in the adoption and
use of brand pages found on the Facebook social networking platform by consumers
to communicate with their favourite brands and with one another. In this sense,
consumers are becoming pivotal authors of brand stories through the easy sharing of
brand experiences due to the triad of communication arising from new dynamic
networks among consumers and brands formed through social media (Gensler,
Völckner, Liu-Thompkins, and Wiertz, 2013). These developments are of significant
interest to brand managers, considering the fact that on the Facebook platform more
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than 1.11 billion users worldwide 'like' and 'comment' over 3.2 billion times a day
(Facebook, 2013) and recent forecasts project that interactive marketing expenditure
by 2016, will reach up to $77 billion in the United States alone (Forrester Research,
2011).
Scholars have begun to argue that by leveraging social media platforms,
organizations can create online brand communities. Perhaps the best known
example of this is Brand Facebook Pages (BFP) found on the Facebook platform. In
practice, users follow a BFP by pressing the “like-button” which indicates to their
social network that they like the brand. This then enables new content to be
automatically sent from the brand to be posted to their personal Facebook news
feed, where customers can interact with a brand via anecdotes, photos, videos, or
other brand-related material which users can like, share or comment on with other
followers of the brand or their own friends (De Vries, Gensler and Leeflang, 2012;
Labrecque et al., 2013; Jahn and Kunz, 2012). Interestingly for marketers, social
media technologies enable brands to co-create brand stories with, and among,
active networked consumers through higher levels of customer-brand interactions
(Gensler et al., 2013). Such interactive communications can act as mechanisms for
value co-creation and extraction possibilities such as improved brand meaning
(Hatch and Schultz, 2010; Gensler et al, 2013) and collaborative product innovation
opportunities (Prahalad and Ramaswamy, 2004; Sawhney, Verona and Prandelli,
2005; Kozinets et al, 2010), thereby enhancing consumer perceptions of CE with
BFP leading to favourable consumer behaviours toward the brand.
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PROBLEM STATEMENT AND PURPOSE
Emerging CE research in the social media context of BFP has shown that
consumers who become more psychologically engaged with these BFP tend to be
more committed and loyal to the brand (Jahn and Kunz, 2012), tend to visit the
physical retail store more, generate more positive word-of-mouth (Hutter et al, 2013),
and are more emotionally attached to the brand than non-brand fans (Dholakia and
Durham, 2010). Despite deployment of BFP quickly increasing by many consumer
brands globally to better reach, interact and serve customers, the rate of theorizing
to understand how to better facilitate and optimize CE with BFP and its effects on
brand loyalty outcomes has not kept pace with this growth in social media marketing.
The issue is of critical importance because little is known about how the interactions
that take place in social media environments contribute to effective brand building
efforts. In response to these concerns and gaps in the literature, we aim to offer a
more in-depth understanding of CE in BFP by focusing on three important issues.
First, we develop a theoretical framework that places greater emphasis on the
identification of relevant antecedents to CE with a BFP than previously studied.
Among the first works to enter the literature, Jahn and Kunz (2012), empirically found
that the intensity of prior usage, seeking social interactions and brand interactions
influence CE levels with a BFP on the Facebook platform. However, further
theorizing and investigation is needed on the notion of co-creation activity which has
been argued to take place between the brand’s BFP and the customer in social
media environments as well as examine its contribution toward the formation of CE
with the BFP. Past social media research has suggested the existence of value co-
creation as a form of benefit derived by the consumer as a result from the BFP
consumption experience (Hennig-Thurau et al, 2010; Sashi, 2012), but explicit
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research on this phenomenon has remained underexposed. In order to assess
whether consumers perceive a BFP to deliver co-creation activities which they
consider of value to them, we posit that the concept of co-creation value may serve
as an antecedent driver of CE with BFP in addition to other variables that have
hitherto been empirically considered in the context of BFP. Further to the inclusion of
value co-creation into the research framework, little is known about the impact
consumers’ perceived strength of the customer-brand relationship and its effect on
the development of CE with BFP, in addition to sought after needs and gratifications
as investigated by Jahn and Kunz (2012). The CE and branding literature has
conceptually argued that a consumer’s relationship with a brand impacts the intensity
of CE towards the brand (e.g. Loureiro, Ruediger and Demetris, 2012; Vivek, et al,
2012) including firm-based activities that personify the brand including websites or
other computer-mediated entities (Mollen and Wilson, 2010), and brand performance
in social media (Gensler et al, 2013). As such, incorporating these theoretical
insights we empirically examine the role of consumer’s perceived strength with the
brand to assess its effect on the development of CE with computer-mediated entities
such as the BFP in the social media environment.
Second, we place greater emphasis on the identification of relevant
consequences of CE with a BFP than previously studied. While Jahn and Kunz
(2012) found that the psychological state of CE with the BFP influences brand loyalty
outcomes including brand commitment, positive WoM and future purchase
intentions, we expand upon this view by drawing in work by Van Doorn et al, (2010)
to incorporate relevant CE behaviours (CEB) toward the BFP. According to Van
Doorn et al, (2010), CEB’s are behavioural manifestations of CE toward a firm after
and beyond purchase which can contribute to firm performance in two ways 1)
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CEB’s in interactions with the firm and their employees – such as giving suggestions
for service improvement, and 2) CEB’s in interactions with other customers such as
spreading word of mouth and/or writing reviews which influence other customer’
attitudes and behaviours. In the social media context, the inclusion of CEB’s toward
the BFP (e.g. sharing, liking and posting brand-related content online) has yet to be
investigated within CE frameworks in the social media marketing literature and in
doing so provides a deeper understanding into the process and practices that
encourage successful CEB management practices.
Thirdly, to test the robustness of our model, we apply it to a broad range of
consumer brands in our empirical study. Furthermore, to account for heterogeneity in
the sample, we differentiate between product and service brand BFP to detect if the
research framework differs by product vs service brands. To this end, this paper is
structured in four key sections. The first section presents a brief review of literature
with specific attention given to customer engagement in the context of social media.
The second section introduces the conceptual model and system of relationships.
Third, there is a discussion of the methodology and subsequent analysis of findings.
Finally, limitations and future research directions are presented.
THEORETICAL BACKGROUND
This section presents the proposed model of this study (Figure 1), illustrating the
antecedent factors that influence CE with BFP and its consequences on CEB with
BFP and brand loyalty. Underpinned by Uses and Gratification theory, branding
theory and consumer engagement theory, the antecedent drivers are argued to be
related to a customers’ usage intensity and CE with a BFP. Finally, the constructs of
usage intensity and CE with a BFP are proposed to influence brand relationship
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outcomes including brand loyalty and CEB’s toward the BFP. The framework
explains that, if the BFP satisfies particular needs of a user, operating together with
the influence of favorable perceptions of the brand relationship, this should both lead
to a higher approach to the BFP, which should in turn lead to higher brand
relationship outcomes.
Figure 1: Proposed Conceptual Model
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The Effect of Functional Value
Consumer motivations for adopting and using a wide range of media can be
explained using the theory of Uses and Gratifications (U&G). One of the main
reasons for adopting and motivations of using a new media type has been that of a
functional or ‘information’ gratification (Luo, Chea and Chen, 2011; De Vries et al,
2012). In the context of BFP, a customer satisfying a functional gratification means to
be able to have access to helpful, functional, practical and useful content (Jahn and
Kunz, 2012). For instance, a customer using the BFP of a service brand may want to
know how the service works, what is involved and other practical information.
Consequently, this study proposes that if a customer satisfies these needs for useful
and practical content, and in doing so satisfies their functional gratification, they are
more likely to use that brand page more intensely. Empirical support for this
assertion can be drawn from Jahn and Kunz (2012) who found a significant, positive
influence from functional value to usage intensity of the BFP. Furthermore, Cvijikj
and Michahelles (2013) found that informational content was one of the main drivers
that led active users to participate in BFP behaviours such as liking, commenting and
sharing the brand’s posts as well as duration of BFP interaction. On this basis,
consumers with higher perceptions of functional value are more likely to influence
higher usage intensity with the BFP. Thus:
Hypothesis 1: Functional value of the BFP positively influences usage intensity of
the BFP
The Effect of Hedonic Value
Prior empirical research into CE in the context BFP, has examined seeking hedonic
value, or satisfying hedonic gratifications such as fun and enjoyment. For instance,
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Madupu and Cooley (2010) found hedonic value to be a major driver of online brand
community participation. In the context of BFP’s, a customer satisfying a hedonic
gratification need requires access to fun, entertaining, and exciting content (Jahn
and Kunz, 2012). For instance, a customer using a BFP may do this in their spare
time knowing that this particular brand uploads interesting and entertaining content in
relation to that brand, and/or topics related to what the brand personifies and
symbolises. This high perceived hedonic value of the BFP as derived by the
customer then leads to that customer using the brand page more frequently (Jahn
and Kunz, 2012). In a similar fashion, Cvijikj and Michahelles (2013) also found that
entertaining content was one of the main drivers of BFP participation and usage.
Thus;
Hypothesis 2: Hedonic value of the BFP positively influences usage intensity of the
BFP
The Effect of Social-Interaction Value
Social value has also been argued to drive the adoption and usage levels of new
media (Hennig-Thurau et al, 2010). Social networking sites such as BFP’s provide
greater opportunities for social interactions facilitated via the Facebook platform
where consumers can derive social value from the computer mediated interactions
with one another. Furthermore, it has been established that customers seek ‘linking
value’ based on peer-to-peer bonds (Libai et al, 2010) which acts as a motivation for
customer-to-customer interactions to take place. In the context of new social media,
for a customer to satisfy a social-interaction gratification, the customer needs to be
able to interact and communicate with other customers, perceive other customers to
be similar to themselves and feel as if they have the opportunity to interact, meet and
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communicate with people similar to themselves (De Choudhury et al, 2010; Jahn and
Kunz, 2012). Therefore, a higher perception of social-interaction value of the BFP
derived by the customer may then lead to that customer using that brand page more
frequently and/or becoming more highly engaged with the BFP. Empirical evidence
for this assertion can be drawn from Jahn and Kunz (2012) who found a significant,
positive influence from social-interaction value to BFP on the Facebook platform. As
such, it is argued in this study that consumers with higher perceptions of the social-
interaction value are more likely to influence higher usage intensity and higher levels
of CE on the BFP. Thus;
Hypothesis 3a: Social interaction value of the BFP positively influences usage
intensity of the BFP
Hypothesis 3b: Social interaction value of the BFP positively influences CE with the
BFP
The Effect of Co-Creation Value
The notion of co-creation of value leading to higher levels of customer engagement
in an online context has been introduced by Sawhney et al, (2005) where the
customer is able to interact, communicate, and in certain cases cooperate to achieve
experiences, services and offerings that serve the customer better. From a branding
perspective, such brand-related conversations enable consumers to integrate their
own experiences and thoughts into the brand story which can contribute to building
awareness, comprehension, empathy, recognition, recall, and enhance meaning to
the brand (Hatch and Schultz, 2010; Singh and Sonnenburg, 2012). In the social
media context, consumers can dynamically interact with brands quickly, on a real-
time basis which means that they can co-create value for themselves by
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communicating, providing and receiving feedback, share and interact with the brand
and therefore actually cooperate with the brand through the social networking
platform (Gensler et al, 2013; Sashi, 2012). Consequently, this study advances the
notion that if a customer derives co-creation value from co-creation enabling
interactions with the BFP, that customer is more likely to use that brand page more
intensely and experience higher levels of CE with the BFP. Drawing on the
arguments found in the consumer behaviour literature and co-creation of value
literature, it appears that customers seek co-creation value with brands on BFP’s. As
such, it is argued in this study that consumers with higher perceptions of co-creation
value are more likely to influence higher usage intensity and higher levels of CE with
the BFP. Thus;
Hypothesis 4a: Co-creation of value on the BFP positively influences usage
intensity of the BFP
Hypothesis 4b: Co-creation of value on the BFP positively influences CE with the
BFP
The Effect of Brand Strength on Customer Engagement
Based on the analysis of literature, it can be concluded that variables that reflect the
consumer’s strength of relationship with the brand impact on attachment and
engagement towards the brand (Loureiro et al, 2012; Vivek et al, 2012) including
firm-based activities that personify the brand such as branded websites and social
media sites (Mollen and Wilson, 2010; Gensler et al, 2013). In this sense, we
theorize that when the relationship between the customer and the brand is perceived
to be strong and the brand has high perceived brand strength in the customer’s
mind, the greater the levels of CE with the BFP. Therefore, to explore the role of the
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brand to act as a mechanism to facilitate CE with BFP’s, we focus here on
developing our term of perceived brand strength which may be viewed through the
lens of involvement and self-brand congruency theory, and that these important
brand variables converge into a multi-dimensional construct, namely; brand strength.
Within consumer behavior, involvement is an important concept where it has
been identified as being at the heart of the person-object relationship, the relational
variable predictive of consumers’ behavior (Evrard and Aurier, 1996) and largely
based on the interaction of consumers with objects or stimuli including e-commerce
websites (Carlson and O’Cass, 2012). In the internet environment, it has been found
that involved consumers are more concerned with a website if the information and
other attributes delivered via the website, are related to the object of involvement
(i.e., the brand) are motivated to gain and process more information about the brand
on the website; possess higher exploratory behavior related to the website, exhibit
more extensive information search habits and conduct online purchases (Balabanis
and Reynolds, 2001; Richard and Chandra, 2005). Building on this view, we
anticipate the same effect to be applicable to social media sites such as BFP where
higher brand involvement will influence higher usage intensity and CE levels.
Regarding a consumer’s attitude towards, and mental image of a brand, the
congruence between self-image and brand image has been extensively examined in
the literature (c.f. Dolich, 1969; Sirgy, 1982; Sirgy, Grewal, and Mangleburg, 2000).
Self-brand congruency refers to the match between a consumers’ self-concept and
the ‘personality’ of a brand perceived by the customer and when the match is high,
will lead to increased brand preference and loyalty (Jamal and Goode, 2001;
Kressman et al, 2006) as well as pre-consumption evaluations such as attitude,
preference, and intention (e.g. Aaker, 1999). Moreover, the saliency of self-image
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congruity was demonstrated in the adoption process of information systems such as
mobile services (Kleijnen, de Ruyter, and Andreassen, 2005) and social networking
sites (Kang, Hong and Lee, 2009). Building on this logic that self-image congruence
between an individual and the brand is an important factor in understanding brand
preference; we anticipate the same effect with BFP’s where it acts as a driver which
influences the propensity for usage intensity and CE with the BFP.
Based on the above discussion, it is theorised in this study that these important
brand variables converge into a multi-dimensional construct of brand strength which
has a positive impact on CE. This is because if a customer has a higher level of
perceived brand strength (i.e. strength of relationship with a particular brand), a
customer is then more likely to experience higher levels of CE and become a more
integrated and participating member of the BFP. Therefore, we argue that
involvement and self-brand congruency are two critical components of brand
strength. Importantly, as consumers participate in contemplative assessments, some
consumers will assess a brand as being significant to their life and congruent with
their self-concept. On that basis, brand strength is likely to in part, contribute to the
formation of a favourable assessment of CE in collateral aspects of the brand’s
activity such as the BFP, as well as behavioural activity toward the BFP such as
increased usage levels. As such, it is argued in this study that consumers with higher
perceptions of a brand’s brand strength are more likely to influence higher levels of
usage intensity and higher levels of CE with the BFP Thus;
Hypothesis 5a: Brand strength positively influences usage intensity
Hypothesis 5b: Brand strength positively influences CE on the BFP
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The Effect of Usage Intensity on CE with BFP
As CE has been defined as a psychological state reflecting customers’ interactive,
and integrative participation with a firm (i.e. a behavioural orientation), (Jahn and
Kunz, 2012; Van Doorn et al, 2011), it is expected to find a correlation between
usage intensity and CE. That is, as a logical consequence of a customer using a
BFP more frequently; is heightened engagement levels with that brand. However, as
Jahn and Kunz (2012) state, it is possible for a consumer to be a frequent user of a
particular BFP without becoming an integrated and participating member.
Nevertheless, it is therefore fruitful to investigate this proposed relationship further.
As such, consistent with Jahn and Kunz (2012), we argue that where a positive
relationship exists between a customer’s usage of the BFP and their engagement, it
would be expected to influence CE with a BFP when the customer uses the BFP
more frequently, often and on a regular basis.
Furthermore, it has been empirically found that users who are in regular contact
with the brand and thus have high levels of usage intensity would positively impact
on their brand relationship and therefore their likelihood for word-of-mouth,
repurchase or other loyalty and brand-related behaviours (Jahn and Kunz, 2012). As
Jahn and Kunz (2012) explain, the link between regular contact with the brand, the
brand relationship and increased likelihood for brand loyalty has previously been
made in involvement theory (see for instance; Olsen, 2007). Following the direction
of Jahn and Kunz (2012) on the link between usage intensity and CE and brand
loyalty with the BFP, we extend this link to also incorporate CEB’s directed at the
BFP and theorize this construct as a consequence of usage intensity. On this basis,
we hypothesise the collective effects of usage intensity on CE, CEB’s directed at
BFP’s and brand loyalty. Based on this reasoning, we expect:
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Hypothesis 6: Usage intensity positively influences CE with BFP
Hypothesis 7a: Usage intensity positively influences brand loyalty
Hypothesis 7b: Usage intensity positively influences CEB with the BFP
The Effect of CE with BFP on Brand Performance Outcomes
Favorable performance outcomes towards the brand as a consequence of CE have
been proposed by multiple authors (Brodie et al, 2013; Hollebeek, 2011b; Vivek et al,
2012). This link has since been empirically supported in the social media context
where CE has influenced offline loyalty behaviours towards the brand (including
purchase intentions, positive offline WoM and willingness to pay more) (Hutter et al,
2013; Jahn and Kunz, 2012). However, the link from CE to CEB’s on the BFP has
yet to be investigated. While not investigating CE specifically, Cvijikj and Michahelles
(2013) argue that active users of the BFP are more likely to participate in CEB like
responses on the BFP such as intentions to ‘like’, ‘share’ and post/share comments.
CEB’s directed at the BFP is particularly pertinent in the social media context given
the potential in the magnitude of social contagion effects of customer-to-customer
interactions amongst social networks of users (Hennig-Thurau et al, 2010;
Labrecque et al, 2013; Libai et al, 2010). Based on the above, we argue that as CE
with the BFP increases, loyalty to the brand will also increase. We further argue that
such favourable outcomes to the brand will also include future CEB’s directed at the
BFP such as intentions to ‘like’, ‘share’ and post/share comments on the BFP, and
continued usage of the brand’s BFP. This reasoning leads to the following
hypotheses:
Hypothesis 8a: CE with the BFP positively influences brand loyalty
Hypothesis 8b: CE with the BFP in the offline channel CEB with the BFP
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METHODOLOGY
Study design
Data was collected via a self-administered paper-and-pencil intercept survey with
students at a large public university in Australia. Advantages of conducting an
intercept survey is that the researcher (or interviewer) comes to the respondents,
making it more efficient and less difficult for the respondent to participate and
respondents can be screened by the researcher for eligibility to participate (Malhotra
2010). Such a sample is considered highly suitable because the majority of
Facebook users are young adults between the ages of 18-30 (Chu and Kim, 2011).
This makes it appropriate to draw inferences from a student based sample on the
population of Facebook users, as this type of sample has the appropriate
demographic and technology usage characteristics to yield useful results (Taylor,
Strutton and Thompson, 2012). Thus, a student sample qualifies as a relevant
sample for the theory-building purposes of this study due to the homogenous nature
of students in regards to both their demographic and behavioural characteristics
(Wyllie et al., 2014).
Items to measure each construct were drawn from the literature and are
shown in Table 1. For instance, items to measure the constructs of functional value,
hedonic value, social-interaction value, usage intensity and customer engagement
were drawn from Jahn and Kunz (2012). A six item measure of co-creation value
was adapted from O’Cass and Ngo (2011). A six item measure of brand involvement
was drawn from Carlson and O'Cass (2012). A five item measure of self-brand
congruency was adapted from Hohenstein et al, (2007). Measures used to assess
brand loyalty were adapted from Zeithaml, Berry and Parasurman (1996) and three
items to measure CEB’s directed at the BFP were adapted from Cvijikj and
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Michahelles (2013). Each item was measured on a seven-point Likert scale
anchored with (1) ‘strongly disagree’ to (7) ‘strongly agree’.
Sample Profile
In total, 452 completed surveys were collected with 404 useable surveys remaining
after screening. Of this sample, 42.3% were males and 57.7% females. The age
ranged from 17 to 49 with an average age of 21.44. Due to the nature of the sample
being a student sample the large majority was between the 18-24 age bracket
(90.4%) followed by the 25-30 bracket (6.4%) and the 30 + bracket (2.7%). When
asked to indicate about their usage of their nominated BFP, the majority responded
once a month or more (43.5%) followed by once a week or more (37.5%) and daily
(19%). The majority of respondents also indicated that they had been a user of the
BFP they indicated for six months or more (60.3%), followed by three to five months
(21.9%), one to two months (11.3%) and less than one month (6.3%). Finally, the
respondents were also asked to identify what device they usually used to access the
Facebook brand page with the majority answering mobile smartphone (54.1%). This
is followed by those using a laptop (30.5%), a home desktop or PC (9.4%), a mobile
tablet (4.1%) and finally a work PC (1.95%). On the whole, the sample of this study
comprises experienced users of BFP with most accessing these pages from
smartphone and laptop devices.
EMPIRICAL RESULTS AND FINDINGS
Structural equation modeling (SEM) using Partial Least Squares (PLS) was
employed for analysis of the survey data. PLS is ideal for studies with smaller
sample sizes (for example; less than 500) (Hair, Hult, Ringle and Sarstedt, 2014)
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and is more suitable to the investigation of relationships in a predictive rather than
confirmatory fashion (Fornell and Bookstein, 1982) such as predicting the
relationships between CE, BFP and its consequences. Furthermore, PLS allows for
the simultaneous analysis for reflective and formative constructed models (Hair et
al., 2014). This is particularly the case where a second-order formative latent
construct (in our case perceived brand strength) is formed by several first-order
reflective latent constructs (i.e. brand involvement, self-brand image congruency).
We modeled the second-order construct by using the hierarchical component model,
where the indicators of the first-order reflective constructs are repeated to measure
the second-order formative construct. SEM using PLS method is also consistent with
previous branding and internet-based studies studying consumer behaviour (e.g.
Loureiro et al, 2012; O’Cass and Carlson, 2012).
We conducted our analyses with statistical software SmartPLS v2.0 (Ringle,
Wende, and Will, 2005) using a two-step procedure of first, evaluating the
measurement model, then second estimating the structural model. While the
measurement model results are akin to that of principal component analyses, the
path coefficients calculated in the structural model can be interpreted in a similar
fashion to that of beta (path) coefficients in an ordinary least squares regression
(Vock van Dolen and de Ruyter, 2013).
Measurement Model Evaluation
In order to assess the psychometric properties of the multiple item scales, we
estimated the measurement model by calculating individual indicator reliabilities,
composite reliability (CR), convergent validity, and discriminant validity (e.g. Hair et
al. (2014). To do so, we ran the PLS algorithm (path weighting scheme) and
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bootstrapping procedure (500 samples). To assess the reliability of individual items,
we checked whether the loading of each item on its respective latent construct was
higher than the recommended threshold of .70. As shown in Table 1, all loadings
exceeded this threshold ranging from 0.73 - 0.96 and were significant (t-statistic >
1.96) and hence retained. To assess the internal consistency, we evaluated the CR
scores for each latent construct. They all exceeded the recommended threshold of
0.70, which is considered satisfactory (Table 2). CR scores are interpreted in a
similar way as Cronbach’s alpha and are calculated in PLS. AVE scores (average
variance extracted) indicate the level of convergent validity as they specify the
amount of shared variance between reflective measurement items and their
respective latent construct. All AVE scores in Table 1 are higher than the
recommended benchmark of .50. In addition, it is recommended to asses any
formative measurement constructs on the basis of convergent validity also and
significance and relevance of outer weights (Hair et al, 2014). Thus, the formative
outer model weights for brand strength are included in Table 1, indicating
satisfactory loading and t-value scores also.
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Table 1 PLS Measurement Model Results
Components and Manifest Variables
AVE
CR
Loading
t
-value
Functional Value
0.80
0.94
The content of the Facebook brand page is helpful for me
0.91
64.25
The content of the Facebook brand page is useful for me
0.92
61.50
The content of the Facebook brand page is functional for me
0.90
54.23
The content of the Facebook brand page is practical
0.84
33.06
Hedonic Value
0.77
0.93
The content of the Facebook brand page is fun
0.89
49.43
The content of the Facebook brand page is exciting
0.91
53.35
The content of the Facebook brand page is pleasant
0.87
34.27
The content of the Facebook brand page is entertaining
0.85
28.22
Co-Creation Value
0.72
0.94
The Facebook brand page interacts with me to serve me better
0.81
28.08
The Facebook brand page works together with me to produce
offerings that better suit me
0.88
50.61
The Facebook brand page interacts with me to design offerings that
meet my needs
0.85
41.85
The Facebook brand page provides services in conjunction with me
0.85
47.90
The Facebook brand page allows my involvement in providing
services to me to get the experience that I want
0.85
39.18
The Facebook brand page provides me with services that I help
create
0.79
26.73
Social Value
0.86
0.96
I can meet people like me on this Facebook brand page
0.93
85.58
I can meet new people like me on Facebook brand page
0.94
77.22
I can find out about people like me on this Facebook brand page
0.92
68.82
I can interact with people like me on this Facebook brand page
0.91
49.20
Brand Involvement
0.70
0.93
This brand means a lot to me
0.87
59.13
This brand is significant to me
0.91
81.42
I consider this brand to be a relevant part of my life
0.88
55.32
For me personally, this brand is important
0.91
81.09
I am interested in this brand
0.72
21.18
I am involved in/with this brand
0.70
19.48
Self-Brand Image Congruency
0.77
0.94
This brand is a lot like me
0.86
39.72
This brand reflects what I am
0.91
80.36
This brand is exactly how I see myself
0.91
70.57
This brand image corresponds to my self-image in many respects
0.90
72.85
Through this brand, I can express what I find important in life
0.80
30.80
Usage Intensity
0.87
0.95
I frequently use the Facebook brand page
0.94
16.12
I often use the Facebook brand page
0.93
61.80
I regularly use the Facebook brand page
0.93
68.72
Customer Engagement with the BFP
0.81
0.95
22
I am an integrated member of this Facebook brand page community
0.88
55.80
I am an engaged member of this Facebook brand page community
0.91
57.56
I am an active member of this Facebook brand page community
0.93
77.52
I am a participating member of this Facebook brand page community
0.88
38.55
I am an interacting member of this Facebook brand page community
0.91
61.90
Brand Loyalty
0.68
0.93
I recommend this brand to other people
0.73
18.46
I introduce this brand to other people
0.78
24.30
I say positive things about this brand to other people
0.84
33.58
I intend to remain loyal to this brand in the future
0.87
54.39
I will not stop supporting this brand
0.85
43.44
I think of myself as a loyal customer/supporter of this brand
0.86
37.42
CEB on the BFP
0.71
0.88
I will share this brand’s Facebook page content in the future
0.86
36.96
I intend to ‘like’ this brand’s Facebook page content
0.81
30.68
I will comment on this brand’s Facebook content in the future
0.85
24.62
(Formative Constructs)
Weights
t-value
Brand Strength (Formative type II outer model) weights
Brand Involvement
0.58
39.59
Self-Brand Image Congruency
0.52
35.31
Discriminant validity was assessed next which is described as the extent to which a
construct is truly distinct from other constructs by empirical standards. As Hair et al.
(2014) explain, the Fornell-Larcker criterion (Fornell and Larcker, 1981) is a widely
accepted approach to assessing discriminant validity where each latent construct
should share more variance with its own block of indicators than with any other latent
variables from the model. Therefore, the AVE score of a latent variable should be
higher than the construct’s squared correlation with any other latent variable, which
is confirmed for our model shown in Table 2.
23
Table 2 Latent Variable Correlations
RESULTS
The adequacy of the hypotheses that are represented in the research model in
Figure 1 was examined by running the PLS algorithm as along with the
bootstrapping procedure to obtain path coefficients, t-values and R2 coefficients of
the endogenous constructs. The quality of PLS models are assessed similar to
multiple regression models where they are evaluated based on the direction and
significance of path coefficients, and the magnitude of R2, which indicate the amount
of variance in a construct that is explained by the predictor variables (Gotz, Liehr-
Gobbers, and Krafft, 2010). Some scholars suggest that the recommended R2
benchmark should exceed 0.10 (cf. Falk and Miller 1992), however a value of 0.20 is
considered high in consumer behavior studies (Vock, et al, 2013).
Our results are shown in Table 3. In line with Hypotheses 1 and 2, functional
value and hedonic value positively impact usage intensity. However, the estimation
of our structural model did not confirm Hypotheses 3a and 4a, which suggested that
Construct
Mean
SD
1
2
3
4
5
6
7
8
9
10
1. Co-Creation
Value
4.12
1.31
(0.94)
0.85
2. CE BFP
3.70
1.55
0.49
(0.95)
0.90
3. BFP CEB
4.93
1.37
0.38
0.50
(0.88)
0.84
4. Functional
Value
5.08
1.13
0.34
0.38
0.36
(0.94)
0.89
5. Hedonic
Value
5.33
1.16
0.24
0.33
0.36
0.32
(0.93)
0.88
6. Brand
Involvement
4.74
1.31
0.28
0.42
0.31
0.52
0.32
(0.93)
0.84
7. Brand
Loyalty
5.32
1.91
0.35
0.43
0.69
0.43
0.42
0.55
(0.93)
0.82
8. Self-Brand
Image
Congruency
3.96
1.42
0.32
0.43
0.30
0.46
0.31
0.65
0.45
(0.94)
0.88
9. Social Value
3.86
1.67
0.34
0.43
0.34
0.32
0.26
0.39
0.31
0.44
(0.96)
0.93
10. Usage
Intensity
4.41
1.54
0.24
0.44
0.38
0.37
0.30
0.36
0.32
0.33
0.20
(0.95)
0.93
24
social value and co-creation value do not have a significant, positive impact on
usage intensity. We hence reject Hypothesis 3a and 4a. We also expected a positive
effect of social value (hypothesis 3b), co-creation value (hypothesis 4b), usage
intensity (hypothesis 5) and brand strength (hypothesis 6b) on CE with BFP. Our
results confirmed our hypotheses with the explained variance in CE with BFP
accounted for by social value, co-creation value, usage intensity and brand strength
was R2 = 0.47 which is considerably higher than for usage Intensity. In line with
Hypothesis 7a, and 8a, CE with BFP and usage intensity positively impact brand
loyalty. The explained variance in brand loyalty accounted for by CE with BFP and
usage intensity was R2 = 0.20. Our results also confirmed our hypotheses for 7b and
8b where CE with BFP and usage intensity positively impact BFP CE behaviours
with the explained variance R2 = 0.28.
Table 3 Structural Model Results
Hypotheses
Predictor Variables
Predicted Variables
β
R2
Critical
Ratio
H1
Functional value
0.20
3.65**
H2
Hedonic value
0.18
3.59**
H3a
Social value
0.09
0.63^
H4a
Co-creation value
0.07
1.44^
H5a
Brand strength
Usage intensity
0.20
0.23
3.09**
H3b
Social Value
0.22
3.50**
H4b
Co-creation value
0.33
5.98**
H5b
Brand strength
0.12
3.28**
H6
Usage intensity
CE w/BFP
0.28
0.47
4.83**
H7a
Usage intensity
0.17
2.90**
H8a
CE w/BFP
CEB w/BFP
0.43
0.28
8.38**
H7b
Usage intensity
0.19
3.03**
H8b
CE w/BFP
Brand loyalty
0.32
0.20
6.32**
AVA
0.30
N.B. AVA = Average Variance Accounted for; **Significant 1.96; ^ Not significant <1.65
25
The predictive relevance of the structural model was assessed via the
Average Variance Accounted for (AVA). The AVA is simply the mean of each
dependent construct R2 within the model and it represents the predictive power of
the structural model without regard to the measurement model (Fornell and
Bookstein, 1982). The results revealed the AVA value is of a satisfactory level for the
inner-structural model at 0.30. Given the indices for predictive relevance of the
structural model are higher than the recommended 0.10 benchmark, the theoretical
soundness of the conceptual model is supported.
Figure 2 Final model
New hypotheses, prior research has not considered these relationships.
Replication hypotheses tested in the literature.
NB: All paths exceed t > 1.96, not significant paths not shown
Functional
Value
Hedonic
Value
Social
Value
Co-Creation
Value
Brand Strength
Usage Intensity
CE with BFP
Brand Loyalty
CEB with BFP
Brand Relationship
Characteristics
Gratifications
26
Post-Hoc Analysis
Differences between Product Brands and Service Brands
Following the hypothesis testing, post-hoc analysis was conducted to explore the
influence of brand type on the research model. In this study, the respondents were
asked to identify a BFP they previously ‘liked’ or engaged with in an open question at
the start of the survey. The sample has been manually coded and these brands have
been categorised as either being a ‘Product’ brand or ‘Service’ brand. As previous
empirical studies investigating CE with BFP (e.g. Jahn and Kunz, 2012), did not
account for differences in brand type, we split the sample according to Product-
brands and Service-Brands. After separation of the total data set into two subsets,
the subset including all product brands contained 103 participants and the subset
including all service brands contained 301 participants. Some examples of product
brands that respondents selected include popular and well-known brands such as
Apple, Nike, Red Bull and Toyota. Some examples of the wide variety of service
brands selected by respondents include the online fashion retail aggregator
ASOS.com, McDonalds, Subway, Jetstar Airlines and NAB Bank.
To examine the differences between the two groups, Product Brands and
Service Brands were compared to determine if differences existed in the direction
and strength of the paths. Comparisons were conducted visually by inspecting the
path coefficients (representing H1 to H8b) across the two models. The results of this
examination are displayed in Table 4 where paths relating to H1, H2, H3a, H4b, and
H5b were stronger for product brands than for service brands. Paths relating H3b,
H5a, H6, H7a, H7b, H8a and H8b were stronger for service brands than for product
brands. Furthermore, H4a was not supported for either brand type which is
consistent with the hypothesis finding in the total sample as displayed in Figure 2.
27
Table 4 Comparison between Product Brands and Service Brands
Hypothesis
Products
Sample
Services
Sample
Result
Paths
t-value
Paths
t-value
H1
0.36
7.21**
0.11
1.85*
+ and stronger for products
H2
0.21
5.62**
0.18
3.05**
+ and stronger for products
H3a
-0.09
1.77*
-0.04
0.73^
- and stronger for products
H3b
0.11
2.86**
0.23
4.37**
+ and stronger for services
H4a
0.05
0.84^
0.06
1.24^
Not supported for either
H4b
0.35
8.16**
0.27
6.46**
+ and stronger for products
H5a
-0.09
1.51**
0.32
5.19**
+ and stronger for services
H5b
0.32
8.00**
0.13
2.26**
+ and stronger for products
H6
0.16
3.68**
0.30
6.30**
+ and stronger for services
H7a
0.11
2.14**
0.23
4.23**
+ and stronger for services
H7b
0.09
1.76*
0.22
3.45**
+ and stronger for services
H8a
0.40
9.23**
0.41
8.31**
+ and stronger for services
H8b
0.28
5.82**
0.35
6.78**
+ and stronger for services
NB: **Significant > 1.96; *Significant > 1.65 ^ Not significant <1.65.
The examination of paths within the research framework reveals differences in their
importance in the context of brand type. For instance, both the effects of functional
value and hedonic value of the BFP content to the customer’s usage intensity, brand
strength to CE with BFP, and co-creation value to CE with BFP were stronger for
product brands than for service brands. Conversely, the effects of co-creation value
to usage intensity, brand strength to usage intensity, usage intensity to CE with BFP,
usage intensity to CEB with BFP and Brand loyalty, and CE with BFP with CEB with
BFP were found to be stronger for service brands than for product brands.
28
Discussion
Following the model proposed by Jahn and Kunz (2012), the aim of this study was to
expand our understanding on the drivers of CE in the social media environment, and
investigate how CE translates to improving key brand performance outcomes. We
find that CE is formed by co-creation and social value together with usage intensity
and brand strength. Moreover, our findings show that CE influences CEB directed at
the BFP and brand loyalty. This study contributes to the academic literature in
several ways.
First, it adds insights to the current literature on CE for online social networks
(Dholakia and Durham, 2010; Jahn and Kunz, 2012) by introducing co-creation value
and brand strength as mechanisms to explain CE with the BFP. In terms of co-
creation value, we empirically validate theoretical arguments by Hennig-Thurau et al,
(2010), Sashi (2012) and others that co-creation activity between the brand the
consumer is an important benefit derived by the consumer as a result from the BFP
consumption experience. With regard to brand strength and its effects, we extend
existing research which has been conducted primarily in the website and social
networking context (e.g. Carlson and O’Cass, 2012; Kang et al, 2009), with our study
demonstrating the viability of the construct influencing CE with BFP’s.
Second, this study adds insights to the influence that CE has on two important
brand performance outcomes; CEB’s with the BFP and brand loyalty. While past
researchers have found that CE with a BFP (Jahn and Kunz, 2012), and CE-like
constructs such as brand page commitment (Hutter et al, 2013), influence brand
loyalty, studies have yet to address non-purchase engagement behaviours which are
centric to the BFP which provide benefits to the firm. These benefits are derived in
the form of 1) customer-firm interactions on the BFP to improve the customer
29
relationship such as posting comments and brand-related communication dialogue,
and 2) customer-to-customer interactions which provide positive brand advocacy and
brand exposure (e.g. awareness, observational learning) opportunities to social
networks through the liking and sharing of brand content. We extend present insights
in the branding and marketing literature by examining the impact of CE with the BFP,
along with usage intensity, as direct predictors of brand loyalty and CEB’s with the
BFP in a unified framework.
Finally, our results underscore the importance of considering the type of brand
in the application of the research framework which has yet to be studied in prior
work. For instance, in an examination of the strength of paths between BFP’s of
product and service brands, the results revealed that functional and hedonic value
acted as more important drivers of usage intensity, with co-creation value and brand
strength acted as more important drivers of CE with the brand page than for service
brands. On the other hand, the links between co-creation value and usage intensity,
brand strength and CE with a BFP, and the collective linkages between usage
intensity, CE with a BFP, CEB and brand loyalty were stronger for service brands. As
such, our study thus provides a first step with preliminary evidence that brand type of
the BFP seems to be guided by different relationship principles and hence
consumers seek out different sources of customer value.
From a managerial perspective, the study has several interesting implications.
Primarily, this study provides a greater understanding and further clarification of
mechanisms which influence the formation of CE with a brand as personified through
a BFP. The importance of developing CE with a BFP was further reinforced in this
study given the strong effects found to CEB’s with BFP and brand loyalty. This has
important implications for brand page moderators since the mechanisms in which to
30
faciltate CE are largely under the control of the marketing manager. As the findings
show, customers derive several gratifications from online content, therefore, the
functional and hedonic content provided by a BFP impacts their customers’
participation and usage intensity of the BFP which in turn influence CE with BFP.
Thus, the content a BFP uploads is entirely moderated by the brand which needs to
be practical, helpful and useful which is entertaining, fun and pleasant. These
aspects are critical, given the information and the subsequent experience it enables
on the BFP informs the vision behind the brand and builds differentiations for it. It
was further found that social-interaction value and co-creation value exhibited a
strong postive influence on CE formation indicating that it is important to provide
opportunities and processes for value co-creation and facilitate open, prompt
communication with/among customers and the brand to allow for greater levels of
interaction, collective cooperation and collaboration. This may present challenges for
firms as such processes and subsequent moderation is demanding, as it requires
balancing the stimulation of CE while steering that engagement in a direction that is
consistent with the company's objectives and brand image. This is further intensified
by the constantly changing interfaces and functionalities of large social networking
platforms (e.g. Facebook, Instagram, Pininterest and Google+ etc.) to create and
manage content with community members.
This study also provides managerial guidance by incorporating the notion of
brand strength into the CE framework. For instance, it is important to recognise the
components which form brand strength in this study, namely; brand involvement and
self-brand image congruency. Thus, brand managers should consider these
concepts when developing their social media strategies. Specifically, it has been
found that when a consumer is more highly involved with a brand, they are more
31
likely to process information about the brand on websites and demonstrate
exploratory behaviours (e.g. Carlson and O’Cass, 2012). Thus, increasing
involvement levels with consumers via the BFP and appealing to mid-to-high
involved consumers to adopt the BFP may yield higher levels of participation and CE
with the BFP, greater learning and comprehension of the brand values and
associations to ultimately achieve brand performance outcomes.
In addition to brand involvement, the aspect of self-image brand-image
congruency also needs to be considered by marketing managers. As such, when
designing social media strategies, brand managers could work towards ensuring that
consumers might prefer BFP that have content and images compatible with their
perceptions of self which may reflect the symbolic properties of the brand. Thus,
brand managers can significantly improve the effectiveness of their brand positioning
strategy of the BFP by measuring the image of their brand and self-images of their
target audience. Furthermore, brand managers could also consider segmenting the
audience in terms of self-image brand-image congruency. Analysing the brand’s
customer base may disclose different clusters of images with various brand-related
content (i.e. imagery, brand information, reward campaigns etc.) and interactivity
requirements (e.g. volume of content, intensity of interactivity and participation)
developed to meet the needs of these segments.
As detailed in the research model in Figure 2, the brand loyalty outcomes of
CE with BFP provide brand managers with implications and reasons to be interested
in the concept of CE. The loyalty outcomes include continued support for the brand,
positive WoM, and introducing and recommending the brand to other people.
Additionally, this study provides practitioners with a new finding linking CE to specific
CEB’s with BFP including higher levels of liking, sharing and commenting on content
32
in the future. Such CEB’s are important in the social media environment due to the
potential for social contagion effects as they encourage the users of BFP to engage
in dialogue and promote/evangilize the brand with their social network to those that
the brand has not yet reached. The findings of this study shed more light on this
behavioural aspect of CEB arising from engaged customers with BFP’s and provides
brand managers deeper insights on how to better elicit CE on their BFP and how this
leads to favorable CEB’s.
Limitations and Further Research
It is important that the limitations of the study are detailed to ensure clarity for
readers and future researchers. Firstly, the Facebook platform has a large global
user base and the external validity of the findings to global segments of users should
be considered. Relating to this, a non-probability convenience sample made up of
students was used in this study which may limit the external validity and
generalizability. Therefore, the results should be interpreted with caution. Secondly,
another limitation of this study arises from the surveys being self-administered
questionnaires. This approach may produce data collection errors and non-response
errors tend to be higher than in interviewed-administered format (Wright, Aquilino,
and Supple, 1998). Thirdly, the sample of this study comprised many different
brands which were assessed on the basis of product brands versus service brands.
However, no analysis of more detailed categories has been conducted in this study
in order to provide more insights into the antecedent drivers of online CE typical to a
particular industry or product. Nevertheless, future researchers should consider
comparing multiple brand categories with greater sample sizes for more conclusive
results across industries and product categories.
33
Despite these limitations, a number of opportunities for future research have
emerged from this study. Firstly, no specific product or industry categories were
examined in this study which leaves many opportunities for future research.
Comparing the same model across different industries or product categories of
different natures, for instance, a comparison across different industries such as the
consumer technology goods industry compared to fashion retail or restaurants and
hospitality sector, would provide the literature with a much greater understanding of
the drivers of CE specific to a particular product or industry category. Findings from
such research may also find different drivers or different importance of the drivers of
CE and this is therefore a rich area of research.
Secondly, as it was also stated in the limitations of this study, a non-random
sample of university students was used for this study. Future research should
consider a larger random sample of respondents in order to reflect the wide variety
of BFP users globally. This could include different demographic, individual consumer
characteristics (e.g. opinion leaders, market mavens, personality traits) and cultural
groups to further enrich the findings. In addition to this, cross-cultural studies or
cross-country studies should also be considered by future research as to prove the
universality of the research framework. This is particularly relevant to inform how
managers can ensure consistent brand meaning on one BFP when it is used by
consumers around the world who might have completely different interpretations of
brand meaning.
Third, there are opportunities to enrich this research using a mixed methods
approach. For example, rather than relying solely on self-reporting via surveys,
future studies could employ qualitative content analysis techniques such as
netnography (Brodie et al, 2011) to analyze actual activity by customers online such
34
as ‘likes’, ‘shares’ and comments on the brand page and its posts. Such analysis
was conducted by Cvijikj and Michahelles (2013) and future research could
incorporate such methods into a study examining the behavioural aspects of CE with
BFP’s. Future research addressing this topic could include analysis of positive and
negative comments about the brand to ascertain direction and valence of these
sentiments which may influence future customer behaviour, as well as the impact on
broader customer-to-customer interactions.
Fourth, in order to capture the real-time nature of customers’ online behaviors,
we suggest for future researchers to possibly adopt different measurements of a
customer’s usage intensity. For instance, measurement items measuring the number
of times a day, week or month that customer accessed, used and interacted with the
BFP would provide a more detailed insight into that customer’s actual usage. An
additional avenue for further research could be to examine the moderating effect of
usage intensity on the research model to determine if differences exist in the
research model under these behavioural conditions.
Finally, given the expected number of U.S. social media users accessing
social networking sites via mobile to exceed 200 million by 2018 (Forrester Research
2013), future studies should also explore the role of mobile devices in how
consumers engage with BFP and the implications this has on brand management,
particularly the possibility of real-time brand interactions taking place in the physical
environment (service experiences, in-store product trials in retail stores) influencing
participation and CE with BFP. Understanding of this issue is becoming of strategic
importance pertinent since the share of time spent on mobile social networking
continues to increase.
35
References
Aaker, J. L. (1999) The malleable self: The role of self-expression in persuasion."
Journal of Marketing Research, (36)1: 45-57.
Balabanis, G., & Reynolds, N. (2001) The impact of involvement, brand attitude,
intemet knowledge, and visit duration on consumers' attitudes towards electronic
shopping web sites. The Journal of Business Strategy, 18(2): 1-22.
Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011) Customer Engagement:
Conceptual Domain, Fundamental Propositions, and Implications for Research.
Journal of Service Research, 14(3): 252-271.
Brodie, R.J. Ilic, A., Juric, B. & Hollebeek, L. (2013) Consumer engagement in a
virtual brand community: An exploratory analysis. Journal of Business Research,
66(1): 105-114.
Carlson, J., and O'Cass, A. (2012) Optimizing the Online Channel in Professional
Sport to Create Trusting and Loyal Consumers: The Role of the Professional Sports
Team Brand and Service Quality. Journal of Sport Management, 26(6): 463-478.
Chu, S. C., and Kim, Y. (2011) Determinants of consumer engagement in electronic
word-of-mouth (eWOM) in social networking sites. International Journal of
Advertising, 30(1): 47-75.
Cvijikj, I. P., and Michahelles, F. (2013) Online engagement factors on Facebook
brand pages. Social Network Analysis and Mining, 3(4): 843-861.
De Choudhury, M., Sundaram, H., John, A., Seligmann, D. and Kelliher, A. (2010)
Birds of a Feather: Does user homophily impact information diffusion in social
media?. arXiv preprint arXiv:1006.1702.
De Vries, L., Gensler, S., and Leeflang, P. S. (2012) Popularity of Brand Posts on
Brand Fan Pages: An Investigation of the Effects of Social Media Marketing. Journal
of Interactive Marketing, 26(2): 83-91.
Dholakia, U. M., and Durham, E. (2010) One café chain’s Facebook experiment.
Harvard Business Review, 88(3): 26.
Dolich, I. J. (1969) Congruence Relationships between Self Images and Product
Brands. Journal of Marketing Research, 6(1): 80-84.
Evrard, Y, and Aurier, P. (1996) Identification and validation of the components ofthe
person-object relationship. Journal of Business Research, 37(2): 127-134.
Facebook. (2013) Facebook. Retrieved from https://www.facebook.com/facebook
Falk, R.F. and Miller, N.B. (1992) A Primer for Soft Modelling, University of Akron
Press, Akron, OH.
36
Fornell, C. and Larcker, D. (1981) Evaluating Structural Equation Models with
Unobservable Variables and Measurement Error. Journal of Marketing Research.
18(1): 39-50.
Fornell, C. and Bookstein, F.L. (1982) Two Structural Equation Models: LISREL and
PLS Applied to Consumer Exit-Voice Theory. Journal of Marketing Research. 19(4):
440-452.
Forrester Research (2011) U.S. Interactive Marketing Forecast, 2011 To 2016.
Accessed at:
http://www.forrester.com/US+Interactive+Marketing+Forecast+2011+To+2016/fulltex
t/-/E-RES59379
Gensler, S., Völckner, F., Liu-Thompkins, Y., and Wiertz, C. (2013) Managing
Brands in the Social Media Environment. Journal of Interactive Marketing, 27(4):
242-256.
Götz, O., Liehr-Gobbers, K. and Krafft, M. (2010) Evaluation of Structural Equation
Models Using the Partial Least Squares (PLS) Approach in Esposito Vinzi., V., Chin
W.W., Hensler, J and Wang, H. (2010). Handbook of Partial Least Squares (1st ed.).
Springer-Verlag, Berlin and Heidelberg.
Hair, J. F., Hult, G. T. M., Ringle, C. M. and Sarstedt, M. (2014) A Primer on Partial
Least Squares Structural Equation Modeling (PLS-SEM) Los Angelos: Sage
Publications Inc.
Hatch, M. and Schultz, M. (2010) Toward a theory of brand co-creation with
implications for brand governance. Journal of Brand Management, 17(8): 590-604.
Hennig-Thurau, T., Malthouse, E. C., Friege, C., Gensler, S., Lobschat, L.,
Rangaswamy, A., and Skiera, B. (2010) The Impact of New Media on Customer
Relationships. Journal of Service Research, 13(3): 311-330.
Hohenstein, N., Sirgy, M. J., Herrmann, A. and Heitmann, M. (2007) Self-congruity:
Antecedents and consequences. In Proceedings of the 34th La Londe International
Research Conference in Marketing Communications and Consumer Behaviour (pp.
118-130). France: France University Paul Cezanne Aix en Province.
Hollebeek, L. (2011a) Exploring customer brand engagement: definitions and
themes. Journal of Strategic Marketing, 19(7): 555-573.
Hollebeek, L. (2011b) Demystifying Customer Brand Engagement: Exploring the
Loyalty Nexus. Journal of Marketing Management, 27(7-8): 785-807.
Hollebeek, L., Glynn, M., and Brodie, R. (2014). Consumer Brand Engagement in
Social Media: Conceptualization, Scale Development and Validation. Journal of
Interactive Marketing. Article in press.
37
Hutter, K., Hautz, J., Dennhardt, S., and Fuller. J. (2013) The impact of user
interactions in social media on brand awareness and purchase intention: the case of
MINI on Facebook. Journal of Product & Brand Management, (22)5/6: 342 – 351.
Jahn, B., and Kunz, W. (2012) How to Transform Consumers into Fans of your
Brand. Journal of Service Management, 23(3): 344-361.
Jamal, A. & Goode, M. M. H. (2001) Consumers and Brands: A Study of the Impact
of Self-Image Congruence on Brand Preference and Satisfaction. Marketing
Intelligence & Planning, 19(7): 482-492.
Kang, Y. S., Hong, S., and Lee, H. (2009) Exploring continued online service usage
behavior: The roles of self-image congruity and regret. Computers in Human
Behavior (25)1: 111-122.
Kleijnen, M., De Ruyter, K., & Andreassen, T. W. (2005) Image congruence and the
adoption of service innovations. Journal of Service Research, 7(4): 343-359.
Kozinets, R. V., De Valck, K., Wojnicki, A. C., and Wilner, S. J. (2010) Networked
Narratives: Understanding Word-of-Mouth Marketing in Online Communities. Journal
of Marketing, 74(2): 71-89.
Kressmann, F., Sirgy, M. J., Herrmann, A., Huber, F., Huber, S. and Lee, D. J.
(2006) Direct and Indirect Effects of Self-Image Congruence on Brand Loyalty,
Journal of Business Research, 59(9): 955–964.
Labrecque, I., vor dem Esche, J., Mathwick, C., Novak, T. P. and Hofacker, C. F.
(2013) Consumer Power: Evolution in the Digital Age. Journal of Interactive
Marketing, (27)4: 257-269.
Laihonen, H., and Lonnqvist, A. (2010) Knowledge-based value creation: grasping
the intangibility of service operations in Finland. International Journal of Knowledge-
based development, 1(4): 331-345.
Libai, B., Bolton, R., Bugel, M. S., de Ruyter, K., Gotz, O., Risselada, H., and
Stephen, A. T. (2010) Customer-to-Customer Interactions: Broadening the Scope of
Word of Mouth Research. Journal of Service Research, 13(3): 267-282.
Loureiro, S., Ruediger, K. and Demetris, V. (2012) Brand emotional connection and
loyalty. Journal of Brand Management. 20: 13–27.
Luo, M. M., Chea, S., and Chen, J. S. (2011) Web-based information service
adoption: A comparison of the motivational model and the uses and gratifications
theory. Decision Support Systems, 51(1): 21-30.
Madupu, V., and Cooley, D. O. (2010) Antecedents and Consequences of Online
Brand Community Participation: A Conceptual Framework. Journal of Internet
Commerce, 9(2): 127-147.
38
Malhotra, N. (2010) Marketing Research: An Applied Orientation. 6 ed. Upper Saddle
River: Pearson Education New Jersey
Merz, M. A., He, Y., and Vargo, S. L. (2009) The evolving brand logic: a service-
dominant logic perspective. Journal of the Academy of Marketing Science, 37(3):
328-344.
Mollen, A. and Wilson, H. (2010) Engagement, telepresence and interactivity in
online consumer experience: Reconciling scholastic and managerial perspectives.
Journal of Business Research, 63(9-10): 919–925.
O’Cass, A. and Carlson, J. (2012). An empirical assessment of consumers’
evaluations of web site service quality: Conceptualizing and testing a formative
model. Journal of Services Marketing, 26(6), 419–434.
O'Cass, A., and Ngo, L. (2011) Examining the firm's value creation process: a
managerial perspective of the firm's value offering strategy and performance. British
Journal of Management, 22(4): 646-671.
Olsen, S.O. (2007), Repurchase loyalty: the role of involvement and satisfaction,
Psychology & Marketing, 24(4): 315-41.
Podsakoff, P., MacKenzie, S., Lee. J. and Podsakoff, N. (2003) Common Method
Biases in Behavioural Research: A Critical Review of the Literature and
Recommended Remedies. Journal of Applied Psychology. 88(5): 879-903.
Prahalad, C. K., and Ramaswamy, V. (2004) Co-creation experiences: The next
Practice in Value Creation. Journal of Interactive Marketing, 18(3): 5-14.
Richard, M., & Chandra, R. (2005) A model of consumer web navigational behavior:
Conceptual development and application. Journal of Business Research, 55(8):
1019-1029.
Ringle, C. M., Wende, S. and Will, A. (2005) SmartPLS (Version 2.0 (beta)).
Hamburg, Germany: University of Hamburg.
Sashi, C. M. (2012) Customer Engagement: Buyer Seller Relationships, and Social
Media. Management Decision, 50(2): 253-272.
Sawhney, M., Verona, G., and Prandelli, E. (2005) Collaborating to Create: The
Internet as a Platform for Customer Engagement in Product Innovation. Journal of
Interactive Marketing, 19(4): 4-17.
Singh, S. and Sonnenburg, S. (2012) Brand performances in social media. Journal of
Interactive Marketing. (26)4: 189-197.
Sirgy, M. J, (1982) Self-concept in consumer behavior: a critical review. Journal of
consumer research, 9(3): 287-300.
39
Sirgy, M. J., Grewal, D., and Mangleburg, T. (2000) Retail environment, self-
congruity, and retail patronage: an integrative model and a research agenda. Journal
of Business Research, 49(2): 127-138.
Taylor, D., Strutton, D., and Thompson, K. (2012) Self-enhancement as a Motivation
for sharing Online Advertising. Journal of Interactive Advertising, 12(2): 13-28.
van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P. and Verhoef, P.
C. (2010). Customer Engagement Behavior: Theoretical Foundations and Research
Directions. Journal of Service Research, 13(3): 253-266.
Verleye, K., Gemmel, P., and Rangarajan, D. (2014) Managing engagement
behaviors in a network of customers and stakeholders evidence from the nursing
home sector. Journal of Service Research, 17(1): 68-84.
Vivek, S. D., Beatty, S. E., and Morgan, R. M. (2012) Customer Engagement:
Exploring Customer Relationships Beyond Purchase. Journal of Marketing Theory
and Practice, 20(2): 127-145.
Vock, M., van Dolen, W., and de Ruyter, K. (2013) Understanding Willingness to Pay
for Social Network Sites. Journal of Service Research, 16(3): 311-325.
Wright, D., Aquilino, W. S., and Supple, A. J. (1998) A comparison of computer-
assisted and paper-and-pencil self-administered questionnaires in a survey on
smoking, alcohol, and drug use. Public Opinion Quarterly, 331-353.
Wyllie, J., Carlson, J. and Rosenberger III, P. J. (2014). Examining the influence of
different levels of sexual-stimuli intensity by gender on advertising effectiveness.
Journal of Marketing Management, (ahead-of-print), 1-22.
Yan, J. (2011) Social media in branding: Fulfilling a need. Journal of Brand
Management. 18(9): 688–696.
Zeithaml, V., Berry, L. and Parasuraman, A. (1996) The behavioral consequences of
service quality. Journal of Marketing, 60 (2).
