Dissertation (TSAI, I-WEN).pdf

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Msc. Marketing
2016/17
Drivers of Consumer's OBC Engagement in
Building Loyalty and Satisfaction to
Leisure & Entertainment Brands
Student name: Tsai, I-Wen
Student ID: 160275768
Supervisor name: Nima Heirati
Date of submission: Sep. 2017

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Copyright and Data Protection Declaration
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Personal email: …i.tsai@hss16.qmul.ac.uk………………………..….

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Abstract
This study aimed at assessing the relationship between different engagement drivers and the
engagement outcomes in the social media embedded brand community context. The study has
a specific focus of entertainment brand engagement. In order to test the relationship, an online
survey of members from 7 different brand communities on Facebook, Twitter, and Sina Weibo
were conducted with 201 valid responses collected from the members. The study was mainly
based upon the consumer engagement framework proposed by Wirtz et al. (2013). The study
findings identified that entertainment driver, brand identification, and functional benefit have
relationship with engagement outcomes. The study also found that membership length does not
have a moderating effect on engagement results.

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Table of Contents
1. Introduction ............................................................................................................6
1.1 Overview of the Research ....................................................................................6
1.2 Objective of the Research ………………………………………………………8
2. Literature Review ..................................................................................................9
2.1 Consumer Engagement: Theoretical Foundations………………………………9
2.2 Consumer Engagement Outcomes: Loyalty & Satisfaction…………………….10
2.3 OBC Consumer Engagement Model…………………………………………....12
2.4 Consumer Engagement Drivers & Membership Length………………………..15
2.4.1 Brand Related Driver: Brand Identification………………………………....15
2.4.2 Social Driver: Social Benefits..……………………………………………...16
2.4.3 Functional Drivers…………………………………………………………...17
2.4.3.1 Functional Benefits……………………………………………………….17
2.4.3.2 Monetary Incentives……………………………………………………...18
2.4.4 Entertainment Driver………………………………………………………...19
2.4.5 Membership Length………………………………………………………….20
3. Methodology………………………………………………………………………21
3.1 Research Method………………………………………………………….…….21
3.2 Measures………………………………………………………………………...22
3.3 Sampling Design & Data Collection……………………………………………24
3.4 Sample Characteristic…………………………………………………………...25
3.5 Data Analysis Method…………………………………………………………..27

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4. Discussion of Results…………………………………………………………….28
4.1 Relationship between Engagement Drivers and Satisfaction Outcome……....28
4.2 Relationship between Engagement Drivers and Loyalty Outcome…………..29
4.3 Moderator – Membership Length…………………………………………….31
5. Conclusion……………………………………………………………………….32
5.1 Study Implications……………………………………………………………..32
5.2 Limitation & Further Research Directions…………………………………….35
6. Appendix…………………………………………………………………………37
6.1 Appendix A- Questionnaire……………………………………………………37
6.2 Appendix B – SPSS Detailed Results………………………………………….39
7. Reference…………………………………………………………………………42

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1. Introduction
1.1 Overview of the Research
Increasing attention has been drawn to the literature of consumer engagement especially
in the online settings as technology has long been embedded in our daily lives of consumption
and different drivers have jointly contributed to the phenomena (Venkatesh et al, 2012).
Customers rely on social media and virtual brand communities to exchange ideas and thoughts
of the brands (Laroche et al., 2013) as such social media platforms along with online
communities are important communication channels for sellers and users to interact with each
other; furthermore, the online setting can facilitate the value co-creation process (Zwass, 2010).
In the online context, the interaction pattern is different and much more complicated from
the offline context, business needs to know how to utilize website atmospheric cues to help
shape customers’ attitude (Mazaheri et al., 2014). Recent studies show that a successful co-
creation can strengthen the relationship between brands and the customers. Beyond creating
value for customers, co-created value helps firms improve the process of identifying customers'
needs and wants (Vargo & Lusch, 2004). Some other scholars also see customer engagement
as a primary driver of sales growth and profit enhancement (Neff & Voyles, 2007). Therefore,
it is important to know that a positive online engagement can increase customers’ purchase
intention toward a brand.
The general understanding of consumer engagement from the past literature is that the
drivers of engagement collectively identified lead to different engagement outcomes.
Specifically, brand related drivers, social drivers, functional drivers, and entertainment needs
are found from the majority of the literature (Wirtz et al., 2013; Dessart et al., 2015) and will
be included in this research model. Multiple outcomes of engagement are also identified from
the previous studies: loyalty, satisfaction, empowerment, connection and emotional bonds, and
trust and commitment (Algesheimer et al., 2005; Ghodeswar, 2008; Woisetschlägeret et al.,

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2008; Gummerus et al., 2012; Brodie et al., 2013; Wirtz et al., 2013; Dessart et al., 2015; Raïes
et al., 2015). In this research, loyalty and satisfaction with the brand is under examination to
be the outcomes of engagement as the previous studies have found that consumer loyalty and
satisfaction emerge in a number of ways and almost all of the consumers demonstrate their
loyalty to the brand or the brand community they belong to through brand recommendations
or expressing their positive experiences with the brand offerings to others (Dessart el al, 2015).
During the engagement process, membership length of the brand community is also included
to see how it moderates with consumer loyalty and satisfaction as one’s membership duration
appears to have an impact on the causal conditions that are sufficient for brand loyal intentions
of behavior (Raïes et al., 2015). In this study, we will thus explore whether a different level of
membership length leads to distinctive engagement performance.
With the abundance of studies discussing issues relating to brand engagement and brand
community from the literature, however, there is still scant research focusing on the driver of
consumer engagement in the online brand community (OBC) to-date (Brodie et al., 2015). As
such, more research needs to be done in this field. Moreover, from the literature so far,
engagement is always directed at a specific object, most often a “brand” at a time, rather than
including other objects of engagement into examination (Sprott et al., 2009; Kumar et al., 2010;
Hollebeek, 2011; Gambetti et al., 2012; So et al., 2012; Hollebeek, 2013; Franzak et al., 2014;
Hollebeek et al., 2014; Hollebeek and Chen, 2014; Sarkar and Sreejesh, 2014; Wallace et al.,
2014). Therefore, previous study suggests that the objects of engagement can be common,
simultaneous and interrelated, mutually enhancing practices (Dessart et al., 2015), such as
branded organisation, firm, product or service, and other multiple entities, which can be brand
community members, organisational offering or activities, organisational object, consumption
activity or event.
Past research also suggests a study focus or comparison of consumer engagement on

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different social media and across brand categories, as most of the studies take a holistic research
approach and do not specialise on any particular brand category or social media engagement
(Dessart et al., 2015). To address the above-mentioned research gap, in this study, an OBC
engagement model is developed based mainly upon the research foundations of Brodie et al.
(2013), Wirtz et al. (2013), and Dessart et al. (2015) to find out the impact of brand community
engagement drivers on the engagement outcome. Specifically, the engagement objects would
be OBC (embedded across different social media, such as Facebook, Sina Weibo, and Twitter).
Moreover, this research aims to examine the triggers behind consumer engagement within
leisure and entertainment brand category, where top digital trends are identified in a way that
they will drive market growth and consumer retention over the future years (Prweb, 2017).
1.2 Objective of the Research
Given the aforementioned rationale, the objective of the research is: within the scope of
leisure and entertainment brands, to what extent does the moderating role of four main
engagement drivers— brand related driver, social driver, functional driver, and entertainment
driver— influence on consumer engagement outcome in terms of consumer loyalty and
satisfaction with the brand.
This study seeks to respond to Dessart et al’s (2015) observation regarding the need for
further empirical research to focus on the study of a particular brand category across different
social media, and also Wirtz et al.’s (2013) implication for future research to critically test the
drivers of OBC engagement to expand the understandings of OBC engagement and their effect
on consumer behaviour. By using the quantitative methodology, this research aims to provide
insight into consumer engagement within OBCs from a social media perspective.

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2. Literature Review
2.1 Consumer Engagement: Theoretical Foundations
A number of studies have discussed the topics around consumer engagement. The
theoretical roots of consumer engagement concept lie in what Vivek et al. (2012) refer to as
the “expanded domain of relationship marketing.” Patterson et al. (2006) define “customer
engagement” as “the level of a customer's physical, cognitive and emotional presence in their
relationship with a service organization.” This is contrary to Vivek et al.’s (2012) definition
which views “consumer engagement” as “the intensity of an individual's participation and
connection with the organization's offerings and activities initiated by either the customer or
the organization”, while Hollebeek (2011, p. 6) views “customer brand engagement” as “the
level of a customer's motivational, brand-related and context-dependent state of mind
characterized by specific levels of cognitive, emotional and behavioral activity in brand
interactions.”
In Brodie et al.’s study (2011), consumer engagement is defined as “a psychological state
that occurs through interactive, co-creative consumer experiences with a focal agent/object”.
On the other hand, from an online perspective, Mollen and Wilson (2010, p. 5) describe
consumer engagement as “the cognitive and affective commitment to an active relationship
with the brand as personified by the website or other computer-mediated entities designed to
communicate brand value.” When conceptualizing online “brand engagement”, Mollen and
Wilson (2010) scrutinize how the concept differs from “involvement.” They suggest that a
consumer's brand engagement extends beyond mere involvement, as it encompasses an
interactive relationship with the engagement object, and requires the emergence of the
individual's perceived experiential value, in addition to the instrumental value obtained from
specific brand interactions. This perspective is consistent with the view of consumer
engagement having theoretical roots within the expanded domain of relationship marketing,

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which emphasize the notions of interactivity and customer experience (Vivek et al., 2012). As
such, the definition of consumer behaviour by Mollen and Wilson (2010) will be adopted as it
most corresponds with this research and also the two-dimensional view offers a foundation for
further research.
Although subject to various interpretations, consumer engagement is often understood as
a motivational construct, with varying intensity. It involves an object (i.e. a brand community)
and a subject (i.e. the consumer), and it has a valence (positive versus negative) (Brodie et al.,
2011; Hollebeek and Chen, 2014). However, the focus or object of engagement has been
predominantly set on brands (of goods or services), organisations or firms, with limited interest
in the online consumer communities (Algesheimer et al., 2005; Wirtz et al., 2013). Moreover,
evidence from the research shows that engagement with both the OBC and the brand is closely
related, and even intertwined, with each one of them sustaining the other, as engagement in the
online settings, especially throng brand community, affects the effectiveness of brand’s
interactions with consumers on three major engagement dimensions: affective, cognitive
(utilitarain) and behavioural (Dessart et al., 2015), all of which are fundamental elements in
the engagement process.
2.2 Consumer Engagement Outcome: Loyalty & Satisfaction
Beyond intra-community engagement, the consequence of sustained relationship with the
brand from consumer engagement needs to be examined. Previous studies indicate that
participation in the brand community leads to a variety of favorable outcomes for the brand,
including stronger loyalty and purchase intentions (Wiertz and de Ruyter, 2007; Algesheimer
et al. 2010; Blazevic et al., 2013) and is effective for retaining both experienced and novice
consumers (Adjei et al., 2010). Generally, a number of researchers have extensively discussed
the consequences of consumer engagement, which may include the concepts of trust (Casalo

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et al., 2007; Hollebeek, 2011), satisfaction (Bowden, 2009a), commitment, emotional
connection/attachment (Chan and Li, 2010), empowerment, consumer value (Gruen et al., 2006;
Schau et al., 2009), and loyalty (Bowden, 2009a, b). Among these, loyalty and satisfaction are
the most prominent in OBC studies. (Andersen, 2005; Casalo et al., 2007; Schouten et al., 2007)
Studies have identified different forms of consumer engagement outcome of loyalty and
satisfaction. Membership continuance, sharing recommendations with other OBC members
(Algesheimer et al., 2005), or acting as a brand defender in the face of negative content (Kumar
et al., 2010) are all examples which help reaffirm consumer’s love and also reflect a sustainable
consumer relationship with the brand. Consumer’s brand loyalty is also activated by a number
of different ways of interactions with the brand and other online community. Moreover,
consumers who join and actively participate in a brand community tend to increase their
willingness to adopt a firm’s new products and are less likely to embrace competing products
(Thompson and Sinha, 2008). Evidence also shows that successfully engaging consumers with
contents on social media platforms can keep unsatisfied customers loyal and preventing them
from defecting the company (Dessart et al., 2015).
In Wirtz et al’s (2013) study interpretation of consumer engagement outcome, the result
of brand community engagement taps into not only OBC but also the brand simultaneously. A
frequent OBC engagement is believed to enhance the overall brand commitment according to
the general belief of the past literature. While engaging in an OBC, consumers strive and aim
to get useful information and increase their social interaction. Therefore, once an OBC meets
or exceeds consumers’ expectation in achieving these goals, they are likely to be satisfied with
the community (Woisetschläger et al., 2008). Other study also proposed that consumers’
knowledge driven interactions with other members lead to a strengthening in-group
consciousness, and this active engagement results in increased satisfaction (Schouten et al.,
2007), which will normally bring an increased brand loyalty.

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Furthermore, previous study affirms that engagement behaviours are derived from the
gain of benefits if a consumer’s engagement with an OBC creates value, and this will increase
his or her brand satisfaction or loyalty because the engagement directly and positively
contributes to various brand relationship results including satisfaction, affective commitment,
and loyalty (Brodie et al., 2013). Study findings are also found to support the view that brand
loyalty is further strengthened by a higher level of engagement with its brand community
(McAlexander et al., 2002). Kim and Jung (2007) believe that community loyalty and word of
mouth are key potential outcomes of community participation. Hollebeek (2011) also states
that strategic initiatives purporting to elevate relevant consumer brand-engagement levels are
expected to generate enhanced consumer loyalty outcomes. Therefore, in this research, brand
satisfaction and loyalty are the final outcomes of OBC engagement under study.
2.3 OBC Consumer Engagement Model
Many consumer engagement frameworks have been found from the previous literature. In
this research, a model will be proposed mainly based on Wirtz et al.’s (2013) framework of
consumer engagement to investigate the relationship between the consumer engagement
drivers and the consumer engagement outcomes of the brand. In Wirtz et al.’s (2013) research,
they provide a synthesis of the extant OBC literature to further construct the conceptual model
which encompasses the drivers of consumer-OBC engagement, the moderators of the
relationship behind the drivers of OBC engagement and actual engagement, and the outcomes
of OBC engagement for the consumer, the brand and the firm (Wirtz et al., 2013).
Other researchers also propose similar frameworks which cover from engagement
antecedents to engagement outcomes in an OBC context. In Brodie et al.’s (2013) framework,
they proposed a dynamic conceptual model derived from the analysis and interpretation of the
blog posts engagement research. Specific triggers of engagement such as a need to reduce

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information search cost and perceived risk are shown in the study and they may lead to
consumer participation in the online community. This observation parallels de Chernatony and
Christodoulides's (2004) analysis pertaining to the nature and functions of brands in an
interactive environment. Brodie et al.’s (2013) framework develops five specific consumer
engagement process dimensions including “learning,” “sharing,” “advocating,” “socializing”
and “co-developing” and suggests that the consumer engagement process generates consumer
loyalty, satisfaction, empowerment, connection, commitment, and trust. Different from Wirtz
et al.’s (2013) or most of other scholars’ engagement literature, which normally presents the
consumer engagement process through a one-way, causal nature concept, the result of analysis
reveals that the consumer engagement process does not follow an orderly, sequential
progression of phases over time (Brodie et al., 2013). This view is also consistent with
Resnick’s (2001) study which believes that consumer engagement is an interplay, or iteration,
of relevant sub-processes.
Van Doorn et al.'s (2010) study also recognizes that the factors influencing consumer
engagement behaviours can interact with each other and help enhance or inhibit the effect of a
particular focal factor on consumer engagement behaviour (CEB). Rather, they believe that a
subset of factors can directly affect CEB as well as moderate the relationship between CEB
and other antecedents. However, in this research we will concentrate on the one-way
progression of the process model as the research aims to concentrate on the investigation into
what drives consumers to actively engage themselves in the OBCs environment and different
factors (drivers) will be tested through a causal relationship. More specifically, which of the
proposed drivers of engagement has the more dominant role in propelling consumers to
participate in the OBCs is the core focus of the research; therefore, the sub-engagement
dimensions will not be considered and discussed in this research. Dessart et al. (2015) also
proposed an overall framework of OBC engagement based on the social media members’

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interviews, their framework involves three dimensions and seven sub-dimensions of OBC
engagement, as well as their antecedents and outcomes. Although their framework does not
represent a causal model and the progression is only indicative of suggested relationships based
on their research, their findings are largely congruent with a conceptual framework proposed
by Wirtz et al. (2013), highlighting the antecedents and outcomes of OBC engagement. Other
studies also put research emphasis on specific antecedents and consequences of the engagement
process. In van Doorn et al.'s (2010) theoretical model of customer engagement behaviour
(CEB), they focus on specific customer, firm and contextual antecedents and consequences;
the model also provides another valuable theoretical foundation for future research in this area.
Following Wirtz et al.’s (2013) perspective, in this research, five principal categories of
OBC engagement drivers are proposed as the factors influencing the engagement outcomes of
loyalty and satisfaction toward the brand. Specifically, they are brand identification, social
benefit, functional benefit, monetary incentives and entertainment drivers (See Figure 1).
Furthermore, Dessart et al.’s study (2015) expands the conceptualisations by Wirtz et al. (2013)
and indicates that OBC engagement is triggered by a number of drivers, which are derived
from brand-related, social, community value, as well as functional aspects of OBC membership.
In this study, drawn from Dessart et al.’s (2015) research, entertainment benefit is included as
one of the drivers because of the nature of entertainment brand, and its intrinsic relevance in
moderating the relationship with engagement outcome compared to economic benefits, which
can be bonuses or lotteries (Gummerus et al., 2012).

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Figure 1: Consumer OBC Engagement Process Model
Membership length will be explored as the moderator in the model because the
relationship between membership length and engagement outcome remains unclear on the topic
and also consumers who have a longstanding duration of membership may not necessarily
develop loyalty to the brand or have active participation in the OBC. Members from different
stages of engagement can have distinctive engagement purposes and attitudes toward the brand
engagement activities, in this way a member’s drivers of OBC engagement is essentially
different from others and can change throughout the time given to various membership length.
Therefore, in this study, the impact of membership length on consumer loyalty and satisfaction
toward the brand will be examined.
2.4 Consumer Engagement Drivers & Membership Length
2.4.1 Brand Related Driver: Brand Identification
In the brand-related driver dimension, brand identification is believed to act as an
antecedent to a consumer’s participation and affiliation with the community (Wirtz, 2013), and

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an existing identification with the brand is believed to facilitate a consumer’s integration and
identification with the brand community (Algesheimer et al., 2005). Hughes and Ahearne (2010)
regard brand identification as a social construct that involves the integration of perceived brand
identity into self-identity. They conceptualize brand identification as the degree to which a
person defines him- or herself by the same attributes that he or she believes defines a brand.
According to Aaker and Joachimsthaler (2000), brand identity refers to the set of brand
associations from which a person derives functional, emotional, and self-expressive benefits.
Furthermore, Donavan, Janda, and Suh (2006) explore the idea of brand identification in the
context of a sports franchise and find that it leads to heightened self-esteem and an increased
propensity to purchase brand-related merchandise. The finding supports the view that
identifying with the brand and the related OBCs has a positive influence on the engagement
result, including satisfaction and loyalty.
Therefore, in this part, the hypotheses can be formed based on the aforementioned
understanding of brand-related drivers:
Within leisure and entertainment brand domain,
H1a: brand identification is positively related to satisfaction with OBC and the brand
H1b: brand identification is positively related to loyalty with OBC and the brand.
2.4.2 Social Driver: Social Benefit
In the past literature, social benefit is seen to be derived from interaction solely between
the company and the consumer and refer to recognition or even friendship (Gwinner et al.,
1998). However, in virtual brand communities where consumers often participate in to seek
assistance and help from other members (Dholakia et al., 2009), the community interaction
facilitated by the OBC provides a wider set of benefits, often affective, to its members (Muniz
and O’Guinn, 2001). Consumers may seek social enhancement, which derives from the motive

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for feeling recognized or needed in the community (Hars and Ou, 2002; Ho and Dempsey,
2010; Nambisan and Baron, 2010). For instance, members/sellers sharing knowledge, advice
or price trends are commonly seen across various online communities or forums. The
interaction and discussion of seeking support strengthen the bonding amongst OBC members.
Such interactions also increase the social benefits members perceive to receive, and in turn
enhance their engagement in the OBC (Wirtz et al., 2013).
Brand community members can also earn respect in the community by assisting other
members with decision-making or giving suggestions on new product development (Dholakia
et al., 2004; Nambisan & Baron, 2009; Sicilia & Palazón, 2008; Yen et al., 2011). Therefore,
the interaction can promote participants’ self-esteem, which is one of the perceived benefits as
participants of the OBCs strive for better individual reputation and status (Kuo and Feng, 2013).
As such, they are tied to identify more intimately with the community, with a higher brand
satisfaction or loyalty. From the past studies and also real-life examples, OBCs like HOG from
Harley-Davidson have built a strong community while highly passionate consumers connect
and engage online. Brands can also build community around a marketing campaign for a cause,
with members joining because of identification with the cause more than with the brand itself
(Wirtz et al., 2013).
Therefore, here we come to the following hypotheses:
H2a: social benefit is positively related to satisfaction with OBC and the brand
H2b: social benefit is positively related to loyalty with OBC and the brand
2.4.3 Functional Drivers
2.4.3.1 Functional Benefits
Functional benefits are normally derived from the direct, information-based support that

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a customer receives to solve the specific service issues from the OBC community (Dholakia et
al., 2009). Studies also assert that greater functional benefits should increase the participant’s
willingness to help others because of norms of reciprocity that accompany intrinsically
motivated behavior (Dholakia et al., 2004). Examples of this category can be OBC members
providing insight into a range of topics such as whether to make a particular purchase, which
products are recommended and why, potential causes of problems that may come up, or viable
solutions (Dholakia et al., 2009). Consumers also look for support from an established OBC,
information quality is thus another important factor that defines the benefits perceived by
community participants (Dholakia et al., 2009). Broad-based and up-to-date information
facilitates members’ learning, and OBC has an unparalleled ability to facilitate interactive
learning and communications (Porter and Donthu, 2008) for its ease of knowledge gathering
and integration (Wiertz and de Ruyter, 2007).
In general, however, functional benefit is rooted in the motivation to solve consumption
related problems (Ghodeswar, 2008), and also, because of the product attributes of
entertainment brands, which provide less functional utility to the consumers, members can even
feel resistant to get involved in the function related contents. As such, in this study, we argue
that functional benefits will have no positive relationship with consumer loyalty or satisfaction
and can bring a decreasing degree of brand satisfaction or loyalty within the given brand
category.
2.4.3.2 Monetary (Economic) Incentives
Firms often turn to monetary incentives such as loyalty points, lucky draws and price
promotions to encourage participation and engagement in their OBCs (Wirtz et al., 2013).
Monetary incentives, such as deals, sweepstakes or coupons (Dholakia et al., 2004; Wiertz and
de Ruyter, 2007) have been shown to increase short-term participation intentions for all types

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of community members, with a stronger effect observed for passive compared to active
members (Garnefeld et al., 2012). Study result from an online gaming Facebook community
research also shows that while many social media communities focus on competitions and
lotteries as the main attraction to the site, monetary (economic) benefits such as bonuses or
lotteries have no influence on either satisfaction or loyalty toward the brand (Gummerus et al.,
2012). Followers of the entertainment brands may want to turn away from the interactions with
these function-centred contents, and monetary incentives can perhaps inhibit the engagement
between the consumer and the brand. Thus, in this study, this type of incentive is believed to
be insignificant and can pose a negative effect on their long-term engagement intentions (Wirtz
et al., 2013).
As such, in this part of functional drivers, we come to the third and the forth hypotheses:
H3a: functional benefit is negatively related to satisfaction with OBC and the brand
H3b: functional benefit is negatively related to loyalty with OBC and the brand
H4a: monetary incentives are negatively related to satisfaction with OBC and the brand
H4b: monetary incentives are negatively related to satisfaction with OBC and the brand
2.4.4 Entertainment Driver
Entertainment benefits are derived from relaxation and fun (Dholakia et al., 2004) and
can motivate community participation. Entertainment is also an experiential value that
customers derive from using online services (Mathwick et al., 2001; Nonnecke et al., 2006;
Nambisan and Baron, 2009). According to Gummerus et al. (2012), entertainment benefits
mediate the influence of both community and transactional behaviors on satisfaction and
loyalty, especially for the nature of certain brands categories such as entertainment and gaming

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products and achieving this goal should increase customer satisfaction and loyalty (Mathwick
et al., 2001).
Therefore, the following hypothesis is proposed:
H5a: entertainment driver is positively related to satisfaction with OBC and the brand
H5b: entertainment driver is positively related to loyalty with OBC and the brand
2.4.5 Membership Length
“Membership length” refers to the length of the relationship that a member has with a
community (Bolton at al., 2004). A previous study by Langerak et al. (2004) found that an
increase in membership length of an online brand community leads to a change in consumers'
interests, the benefits that consumers search for, and their actions concerning other community
participants as well as the brand. In this way, membership length may lead to distinctive
engagement behavior, which results in a variant degree of engagement outcome. From the
previous study, new members are normally motivated by information search and are less
embedded in the community compared to existing members because they are not familiar with
the community and its rules (Walther, 1995; Kozinets 1999; Langerak et al., 2004).While the
interest of longstanding community members seems to change from a dominating personal
interest in gathering useful information (Wasko & Faraj, 2000; Ridings & Gefen, 2004) to a
feeling of obligation toward the community (Mathwick et al., 2008). Therefore, membership
duration can moderate within the engagement process and thus yield distinctive results.
In this part, we hypothesize that membership length has a positive influence on loyalty
and satisfaction as from the previous study, membership duration is considered to have an
increasing returns-to-scale effect on members’ visit frequency (de Valck et al., 2007). Previous
study result shows that members who are more dependent on the social media platforms tended

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to develop a prolonged engagement in the community which often leads to the formation of
personal and intimate relationships with the brand (Tsai and Men, 2013). This can thus
facilitate the formation of consumer engagement outcomes.
Overall, in this last part, the sixth pair of the hypothesis is:
H6a: membership length is positively related to satisfaction with OBC and the brand
H6b: membership length is positively related to loyalty with OBC and the brand
3. Methodology
3.1 Research Method
As differences in OBC engagement motivation may translate to different type, intensity
and forms of consumer engagement outcome and they may also occur for different brand
categories (Dessart et al., 2015), following these five consumer engagement drivers and also
one moderator discussed in the literature, the primary purpose of this study is to examine a set
of pre-specified hypotheses that signify multiple interrelationships among the constructs of
interest depicted in Figure 1. The positivism paradigm is deemed appropriate and adopted for
the study and the causal research approach is used for identifying the relationship.
Some other studies testing the relationship between engagement drivers and consumer’s
satisfaction or loyalty also used quantitative causal research method to examine the relationship
(eg., Algesheimer et al., 2005; Calder et al., 2009; Gummerus et al., 2012; So et al., 2012;
Baldus et al., 2014; Hollebeek et al.2014; Kang et al., 2014; Raïes et al., 2015;). Because of
the nature of the study which needs large sample size, primary data were collected using
questionnaire through a computer-administered online approach. The use of computer-
administered approach is efficient and cost-effective for the study as it is most suitable for
collecting a large amount of data and reducing interviewer bias. Using questionnaire also helps

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in accommodating a large sample size at a relatively low cost and also facilitating the
administration of questions and answers. Therefore, the study adopted the computer-
administered questionnaire approach to measuring the constructs of engagement model from
the OBC participants. A number of studies related to consumer engagement have also been
conducted using questionnaires as the means of data collection (e.g., Gummerus et al., 2012;
So et al., 2012; Baldus et al., 2014; Hollebeek et al.2014; Kang et al., 2014; Raïes et al., 2015)
to further investigate the causal relationship deriving from the consumer engagement model.
As the study focuses on member’s OBC engagement through social media, the survey was
shared and posted across different online brand communities on Facebook, Twitter, and Sina
Weibo to invite members of the community to participate in the survey.
3.2 Measures
Definitions and measuring items for the seven constructs were found and developed based
on an extensive review of existing literature and presented through Table 1. The ordinal scale
was used in the study as it is most suitable for ranking the level or degree of a person’s
involvement and feelings toward a behavior. Therefore, the seven constructs were all measured
on a five- point Likert scale ranging from strongly disagree to strongly agree to provide a
foundation for measurement development process. Specifically, brand identification was
measured using 5 items from Sirgy et al. (1997), social benefit was measured using 7 items
from Gummerus et al. (2012), functional benefit was measured using 4 items from Dholakia et
al. (2009), monetary incentive was measured using 3 items from Kang et al. (2014),
entertainment benefit was measured using 4 items from Baldus et al. (2015), and finally
satisfaction and loyalty were measured using respectively 4 and 3 items from Gummerus et al.
(2012). The data of membership length was collected by asking respondents how many years
have they participated in this OBC and four ranges of options were given (less than 1 year, 1

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to 3 years, 4 to 7 years, and over 7 years).
In-depth interviews were conducted with 5 respondents to test the reliability of the items
and also improve the wordings of the survey before launching mass data collection, and
findings suggested that some items needed better descriptions to fit the research context. After
completing the revision, the survey was understandable and meaningful. Therefore, the
finalized survey was clear of serious flaws and was conducted after considerable deliberation.
The final questionnaire can be seen in the Appendix A.
Construct Definition Measurement List
Loyalty Loyalty is regarded as a fundamental
reason for brand community
participation, i.e. consumers join brand
communities because they like the
brand and feel loyal to it
(McAlexander et al., 2002)
(Gummerus et al, 2012)
1. I consider this brand as my number one choice of
provider
2. I say positive things about this brand to other
people
3. I would recommend this brand to my friends
Satisfaction Customer satisfaction is recognized as
being highly associated with ‘value’
and is based, conceptually, on the
amalgamation of service quality
attributes with such attributes as price
(Athanassopoulos, 2000, p. 192).
1. I am satisfied with my decision to become a
member/fan of the brand’s social media group
2. I think that I did the right thing when I decided to
become the brand’s social media community
member/fan
3. I am satisfied with my decision to become the
brand’s customer
4. I am satisfied with the brand
Brand
Identification
Brand identification is regarded as the
degree to which a person defines him-
or herself by the same attributes that
he or she believes defines a brand
(Hughes & Ahearne, 2010).
1. The community is consistent with how I (would
like to) see myself
2. The brand community reflects who I am
3. The image of the typical member of this brand
community is congruent (consistent/identical) with
how I see myself
4. This brand community is a mirror image of me
5. I am quite similar (I would like to be perceived as
similar) to the typical member of this brand
community
(Sirgy et al., 1997)
Social
Benefit
Social benefits can be developed by
providing more opportunities for
member-to-member interactions and
1. Because I want to get to know other community
members
2. To help other community members
3. To feel needed by this participated or other

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by adding social features that are
valued by the members.
related community members
4. To get help from other community members
5. To provide information to other community
members
6. To share my ideas with other community
members
7. Because I want to stay in touch with other
community members
Functional
Benefit
Functional benefits are normally
derived from the direct, information-
based support that the customer
receives to solve the specific service
issue(s) from the OBC community
(Dholakia et al., 2009).
1. The information provided by the brand
community is valuable.
2. The information provided by the brand
community is useful.
3. The brand community provides information at an
appropriate level of detail.
4. In this online brand community, there are good
features that help me to accomplish my tasks.
(Dholakia et al., 2009)
Monetary
Incentive
Firms often turn to monetary
incentives such as price promotions to
encourage participation and
engagement in their OBCs
(Wirtz et al., 2013).
1. To obtain discounts or special deals that most
consumers do not get
2. To obtain better prices than other consumers
3. To receive free coupons for the entertainment
brands by becoming a member of the community
on the social media page
(Kang et al., 2014)
Entertainment
Benefit
Entertainment benefits are derived
from relaxation and fun (Dholakia et
al., 2004).and can motivate community
participation
(Gummerus et al, 2012).
1. I like participating in this brand community
because it is entertaining
2. Having fun is my main reason for participating in
this brand community
3. I participate in this brand community because I
think it is fun
4. I find participating in this brand community to be
very entertaining
(Baldus et al., 2015)
Membership
Length
Membership length refers to the length
of the relationship that a member has
with a community
(Bolton et al., 2004).
The length of a member’s relationship with the online
brand community.
Table 1: Measures of the Constructs
3.3 Sampling Design & Data Collection
Regarding the selection of empirical setting, online brand communities on social media
were selected as the empirical setting of this study for these community members have

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demonstrated their commitment to the OBCs and the brand and are most suitable for
investigating the relationship between their engagement drivers and their loyalty toward the
brand. As the study concentrates on OBCs within leisure and entertainment brands, several
brand communities are chosen and under study given their successful popularity among the
members and its strong bonding with customers. Chosen OBCs are Disney, Harley Owners
Group (Harley Davidson), PlayStation, NBA, Lego Ideas (Lego), Universal Studios
Entertainment, and XBOX Ambassador (XBOX), and only active members of these OBCs can
participate in the survey.
Given to the past engagement literature which normally covers at least 150 respondents
in the data analysis, a large number of data should be collected so that a meaningful result can
be generated through the quantitative data analysis. A link of the survey was shared and posted
on the aforementioned OBCs and members were invited to take part in the survey to ease the
process of data collection. The sample was collected in Aug. 2017, and after two weeks of data
collection, in total, 211 questionnaires were gathered through the OBCs embedded in
Facebook, Twitter, and Sina Weibo. Highly involved and active members were invited to the
survey and non-active members were excluded because only active members are driven to
engage with the OBCs and are therefore appropriate under study for the relationship between
their OBC engagement triggers and brand loyalty. Thus, in total 10 respondents were deleted
because 3 respondents were not official members of the OBCs and also 7 of them did not
complete the questions on the constructs. Missing values were allowed for the background
characteristics of age and gender. In conclusion, 201 valid data were collected in this study.
3.4 Sample Characteristic
According to the descriptive statistics of the OBC members, the majority of the
respondents are between the age of 25 to 34 years old, around one-third of the total sample

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(33%), following by respondents aged between 18 to 24 years old (29%). For the membership
length, there are 83 of them reporting their relationship with the brand community to be 4 to 7
years (41%), 53 of them to be between 1 to 3 years (26%), 29 of them to be less than 1 years
(14%), and the remaining 36 respondents reported to have committed their relationship with
the brand community over 7 years (18%) (See Table 2).
Age of Member N (Total:201) %
Below 18 7 0.03
18 to 24 years old 58 0.29
25 to 34 years old 66 0.33
35 to 44 years old 45 0.22
Above 45 years old 25 0.12
Membership
Length (Year)
N (Total:201) %
Less than 1 year 29 0.14
1 to 3 years 53 0.26
4 to 7 years 83 0.41
Over 7 years 36 0.18
Table 2: Descriptive Results of Sample (n=201)
All the variables were tested through Pearson Correlations (See Table 3) to ensure there
is no serious multicollinearity among the constructs. The independent variables show at least
above .3 correlations with the dependent variables. Although the construct social benefit shows
a comparatively lower correlation (correlation=0.253) with dependent variable— loyalty, this
can be explained that a long-term commitment to a brand is not greatly impacted by the
relationship or interactions with other community members. Members may be more interested
in a more direct and self-centric interaction with the brand itself, rather than with other
community members regarding the formulation of brand loyalty outcome. The correlations
between each of the independent variables were also below .7 which is within the accepted
correlation value. The collinearity diagnostic result from the multiple regression also shows
the tolerance value of more than .10 and the VIF value of below 10 which are within the

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commonly accepted cut-off points so that the possibility of multicollinearity is excluded.
Table 3: Correlation among Constructs & Descriptive Statistics
*Correlation is significant at the 0.05 level (2-tailed)
The reliability of the constructs was carefully examined by the Cronbach Alpha analysis,
the constructs all have good internal consistency in the current study, with a Cronbach Alpha
coefficient reported over .7 for the constructs. Skewness and kurtosis of the relationship
constructs were analyzed and were within the recommended limits. Data were checked for
outliers and none were detected and there are no major deviations from normality as the data
are presented in a linear line and normally distributed.
3.5 Data Analysis Method
After completion of the mass data collection and pre-test process, the ordinal data were
transformed into numerical data to facilitate the process of score measurement and data
analysis; afterwards, the collected data of the constructs were summarized with a mean score.
The mean data were then analysed by SPSS using Multiple Regression as the analysis is used
to explore the predictive ability of a set of independent variables on a particular outcome and
N=201 A B C D E F G
Construct A-
Brand Identification
1 .449 .585 .487 .488 .587 .497
Construct B-
Social Benefit
.449 1 .471 .247 .205 .342 .253
Construct C-
Functional Benefit
.585 .471 1 .307 .464 .504 .496
Construct D-
Monetary Incentives
.487 .247 .307 1 .488 .365 .405
Construct E-
Entertainment Driver
.488 .205 .464 .488 1 .733 .711
Construct F- Satisfaction .587 .342 .504 .365 .733 1 .771
Construct G-Loyalty .497 .253 .496 .405 .711 .771 1
Mean 3.12 3.11 3.44 2.98 3.56 3.53 3.57
SD .768 .759 .746 1.041 .912 .764 .901
Cronbach Alpha .884 .882 .889 .921 .931 .913 .921

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it is also able to compare the predictive ability of a particular independent variable and find the
best set of variables to predict a dependent variable. Therefore, the multiple regression analysis
is ideal for the investigation of the causal relationship among the variables and also for this
study with complex quantitative research question.
4. Discussion of Results
4.1 Relationship between Engagement Drivers and Satisfaction Outcome
The regression analysis result of the constructs is presented in Table 4. From the analysis,
we can see that within the 95% confidence interval level, the R square value of the overall
model .624 explains 62.4 percent of the variance in dependent variable Satisfaction. If
comparing the contribution of the respective variable in satisfaction outcome, entertainment
driver has the strongest unique contribution to explaining satisfaction (P value.=.000; beta
coefficient=.609); secondly, brand identification also has a positive relationship with
satisfaction with a lower unique contribution to satisfaction (P value=.000; beta
coefficient=.265). Therefore, we know that hypothesis H5a and hypothesis H1a are supported
as both entertainment driver and brand identification have a positive relationship with
satisfaction, and entertainment driver is most significant of all the independent variables in
predicting satisfaction.
While the social benefit is not statistically significant with satisfaction (P value=.056; beta
coefficient=.099) and the sig. value is slightly over .05 as such we can infer that social benefit
has no influence on satisfaction, and this is different from the hypothesis which was presumed
that social benefit is positively related to satisfaction. Therefore, hypothesis H2a is rejected.
Monetary incentive is also not statistically significant to satisfaction as the sig. value is over .05
(P value=.06; beta coefficient= -.101), indicating that monetary incentives have no influence
on satisfaction and the result rejects the hypothesis H4a as no significant negative relationship

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with satisfaction was shown as well. Furthermore, it should be noted that even if they are
significantly correlated, the existence of monetary incentives can predict a diminished effect
on satisfaction as the beta value shows a negative correlation with satisfaction outcome,
signifying that with one unit increase of standard deviation in monetary incentives, the model
will predict a decreasing score of satisfaction, holding all other independent variables constant.
Lastly, the analysis result of functional benefit (P value=.391; beta coefficient=.051)
reveals that it is not statistically correlated with satisfaction and thus has no effect on the
variance of the dependent variable. Hypothesis H3a is thus rejected as no significant negative
relationship between functional benefit and satisfaction was proved. In conclusion, from the
analysis outcome, social benefit, monetary incentives, and functional benefit do not have an
influence on satisfaction while entertainment driver and brand identification both have a
positive influence on satisfaction outcome.
Construct
(N=201)
Brand
Identification
Social
Benefit
Functional
Benefit
Monetary
Incentives
Entertainment
Benefit
Dependent Variable: Satisfaction
Coefficients (Beta) .265 .099 .051 -.101 .609
Sig. .000 .056 .391 .060 .000
Notes: Model summary: R2
=.624 Adjusted R2
= .615 Std. Error of the Estimate=.474 P=.000
Dependent Variable: Loyalty
Coefficients (Beta) .116 .007 .153 .021 .572
Sig. .087 .905 .018 .715 .000
=.550 Adjusted R2
= .538 Std. Error of the Estimate=.612 P=.000
Table 4–Multiple Regression Results with Satisfaction & Loyalty as the Dependent Variable
4.2 Relationship between Engagement Drivers and Loyalty Outcome
For the relationship between independent variables and the loyalty outcome (See Table
4), within the 95% confidence interval level, R square value .550 explains 55 percent of the
variance in dependent variable Loyalty. If comparing the relationship between each variable
and loyalty outcome, entertainment driver is statistically significant with loyalty and still has
the strongest unique contribution to loyalty outcome (P value=.000; beta coefficient =.572)

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Therefore, hypothesis H5b is supported. Different from the settings of satisfaction as the
dependent variable, functional driver is statistically significant with loyalty outcome (P
value=.018; beta coefficient=.153) as the sig. value is less than .05. However, contrary to what
has been stated in the hypothesis H3b that functional benefit is believed to have a negative
relationship with loyalty, the result shows that there exists a positive relationship between
functional benefit and loyalty. We may infer from the result that functional benefits such as
having problems solved, gathering information or seeking help from other members are what
members constantly care about in a long run while joining a community (even if regarding an
entertainment-type of OBC) and are also a pivotal constituent for members to develop a
longstanding relationship with a brand.
Findings from the analysis also indicate that brand identification is not statistically
significant with loyalty outcome (P value=.087; beta coefficient=.116) in comparison to the
scenario of satisfaction as the dependent variable. Therefore, hypothesis H1b is rejected. This
shows that the role of brand identification to loyalty is not as important as the role of a
contributing factor to satisfaction for the members who have already been the loyal consumers
of the brand. Monetary incentives are also not statistically significant with loyalty (P
value=.715; beta coefficient=.021). The result again is inconsistent with the hypothesis which
believes that monetary incentives has a negative effect on loyalty; thus, H4b is rejected. In
summary, inferring from both results of the engagement outcome, we can conclude that
monetary incentives such as discount, coupons and lotteries do not have an impact on either
satisfaction or loyalty.
Finally, social benefit is not statistically significant with loyalty (P value=.905; beta
coefficient=.007). Hypothesis H2b is therefore again rejected. Therefore, we know that social
benefit does not have a relationship with either satisfaction or loyalty as the engagement
outcome in the study. In conclusion, the result indicates that entertainment driver still shows

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the strongest unique contribution to explaining loyalty, and functional benefit also shows a
positive impact on loyalty; while brand identification, monetary incentives, and social benefit
all have p value over .05 and they do not have a relationship with consumer loyalty.
4.3 Moderator – Membership Length
The moderator is tested by multiple regression analysis. In the context of satisfaction as
the model outcome, the result in Table 5 shows that membership length does not have
interacting effect on the relationship between engagement drivers and satisfaction as the p value
is not statistically significant when the moderator was included as an independent variable in
the regression model (P value of moderator=.408>.05). The R2
value of the model and beta
coefficient of each independent variable do not demonstrate significant variance as well with
membership length included as the moderator in the scenario (R2
=.626 compared to R2
=.624
when excluding the moderator). Entertainment benefit and brand identification remain
statistically significant while social benefit, functional benefit and monetary incentives remain
insignificant to the relationship with satisfaction outcome.
Construct
(N=201)
Membership
Length
Brand
Identification
Social
Benefit
Functional
Benefit
Monetary
Incentives
Entertainment
Benefit
Moderator
Dependent Variable: Satisfaction
Coefficients (Beta) -.021 .267 .103 .044 -.108 .613 .037
Sig. .663 .000 .054 .459 .055 .000 .048
=.626 Adjusted R2
= .612 Std. Error of the Estimate=.476 Moderator= IV*membership length
Dependent Variable: Loyalty
Coefficients (Beta) -.077 .126 .023 .134 -.004 .585 .078
Sig. .132 .062 .686 .039 .942 .000 .107
=.562 Adjusted R2
= .546 Std. Error of the Estimate=.607 Moderator= IV*membership length
Table 5: Moderating Effect Analysis through Multiple Regression –
Satisfaction & Loyalty as the Dependent Variable
There is also no moderating effect of membership length on consumer loyalty (See Table
5). The moderator is not making statistically significant contribution to the relationship with
loyalty. (P value of moderator=.107>.05). The R2
value of the model and beta coefficient of

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each independent variable do not have noticeable variance with membership length included
as the moderator (R2
=.562 compared to R2
=.55 when excluding the moderator); this means that
the existence of moderator—membership length does not provide a better prediction between
the independent variable and dependent variable. Entertainment benefit and functional benefit
remain statistically significant, while brand identification, social benefit, and monetary
incentives are still not statistically related to loyalty. In conclusion, we can thus reject the
hypotheses of both H6a and H6b as membership length does not make a significant
contribution to influence both engagement outcomes— satisfaction and loyalty.
5. Conclusion
5.1 Study Implications
The research outcome of each hypothesis is summarized from the result of multiple
regression analysis and presented in Table 6. In summary, regarding the context of satisfaction
as the engagement outcome, entertainment driver and brand identification are proved to hold a
positive impact on the dependent variable while concerning to loyalty as the engagement
outcome, functional and entertainment driver have a significant contribution to the dependent
variable. The findings of the study provide some practical implications for the OBC
engagement research, and helps marketers understand consumer engagement better concerning
entertainment & leisure brands. Firstly, the result shows that entertainment driver is the most
important factor in developing OBC members’ satisfaction and loyalty toward the brand. This
outcome is not that surprising though but further strengthens the role hedonic driver plays in
the brand community engagement. As the nature and main objective of entertainment brands
are to “entertain” and “amuse” consumers, consumers view their consumption experience with
the brand to be a process of appreciation (Mathwick et al., 2001). Achieving the goal of
providing a desirable experience to the consumers through OBC interactions can actually

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enhance their brand satisfaction and loyalty as have proved in this study. Furthermore,
entertainment benefits holding intrinsic values such as playfulness, relaxation, aesthetic appeal
and personalized experience can be the inner factors which members are looking for as
intangible returns from the entertainment brands (Wasko and Faraj, 2000). Business should
therefore employ these entertainment elements to effectively get consumers involved in the
communications with the brand.
Hypotheses Outcomes
H1-
Brand Identification
H1a: brand identification is positively
related to satisfaction with OBC and the
brand
Supported
H1b: brand identification is positively
related to loyalty with OBC and the brand.
Rejected
H2-
Social Benefit
H2a: social benefit is positively related to
satisfaction with OBC and the brand
Rejected
H2b: social benefit is positively related to
loyalty with OBC and the brand
Rejected
H3-
Functional Benefit
H3a: functional benefit is negatively related
to satisfaction with OBC and the brand
Rejected
H3b: functional benefit is negatively related
to loyalty with OBC and the brand
Rejected
(positive relationship)
H4-
Monetary Incentives
H4a: monetary incentives are negatively
brand
Rejected
H4b: monetary incentives are negatively
related to loyalty with OBC and the brand
Rejected
H5-
Entertainment Benefit
H5a: entertainment driver is positively
brand
Supported
H5b: entertainment driver is positively
Supported
H6-
Membership Length
H6a: membership length is positively
brand
Rejected
H6b: membership length is positively
Rejected
Table 6— Hypothesis Outcome

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Secondly, social support is not a prominent driver in encouraging consumer engagement
behaviours in the OBCs of entertainment brands. The argument is against the findings from
Gummerus’s study (2012) which investigated customer engagement in a Facebook brand
community and proposed that brand community providers should focus on offering social
benefits to their visitors as they were found as significant as entertainment benefits in
enhancing satisfaction and loyalty in the social media brand community. Our study, however,
found that social benefit is not that important to members’ satisfaction and loyalty with the
entertainment brand. Reasons can be that they join the community not from the social benefit
perspectives as merits such as knowing other members, sharing ideas or social supports are not
the top priority for members to participate in an entertainment-centred brand community.
Members are less intended to know or socialize with other members while engaging with the
entertainment brands. Another explanation can be that the activity level on the social media
community is perhaps not high enough for those customers who are seeking social recognition
or affiliation (Odekerken-Schro¨der et al., 2003), thereby leading to an insignificant
relationship with satisfaction and loyalty.
The study result also indicates that members value brand identification relating to
satisfaction. A higher level of satisfaction can derive from a higher level of self-congruity being
fulfilled from these self-expressive brands. As for functional benefit and monetary incentives,
by and large, the drivers providing utilitarian functions do not actively promote or diminish the
performance of the engagement outcome. Because members may expect to approach the
information or text with educational purposes from channels like websites or printed books.
(Choi et al., 2008). Only a positive relationship between functional benefit and loyalty was
discovered in this study, while the result is contrary to the hypothesis, it can imply that members
would still incline to engage more with the online communities of entertainment brands which
possess great functional supports and thus a rather significant relationship with consumer

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loyalty was supported. Reviewing the mean score of each driver, we can find that monetary
incentives (mean=2.98) has a relatively low mean score showing that it does not play a
significant role in the online engagement process compared to other drivers while
entertainment driver (mean=3.56) has the highest mean score and interacting effects. We may
conclude from the overall findings that online social media may not be the optimal channel for
brands to communicate messages with utilitarian functions such as promotion or sales offers
with members as they might prefer to receive monetary incentives from other resources through
a non-monetary approach. In such way, members may feel that the brand is indeed concerned
about them and this can make active participants feel valued and important as a member of an
online community (Kang et al., 2014). Moreover, those visitors who are attracted by discount
or sales promotion to the community do not necessarily develop loyalty toward the brand in a
long run, as such monetary driver would not work as an ideal long-term strategy of social media
marketing.
Finally, findings of the study do not show a significant moderating effect of membership
length on either satisfaction or loyalty; this is contrary to the belief from some of the previous
research which contends that a longer membership duration may facilitate the formation of a
higher degree of satisfaction or loyalty. The results show that newcomers of the community
can be highly brand loyal and have a high degree of commitment or passion toward the brand;
meanwhile for longstanding members, they can also demonstrate a relatively lower degree of
satisfaction or loyalty with the brand. As such, no linear relationship was discovered and thus
the possibility of membership length as a moderator in the model is excluded.
5.2 Limitations and Further Research Directions
It should be noted that there are still some limitations in this study concerning its research
design and resource restrictions. First, although the study focuses on a specific brand industry

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in responding to the previous research which recommends a more specific study concentration
on consumer engagement, there exists discrepancies in the nature of these various
entertainment brands included in the study. Due to the resource constraint, the chosen brand
communities under study were not picked evenly enough across different categories within the
entertainment industry and this can lead to a biased conclusion to the general entertainment
brand industry. Therefore, future studies should include a wider range of brands in this topic
and a larger sample size should be collected as well to mitigate the potential inaccuracy of the
research. Secondly, the research model is drawn upon the foundations of the OBC engagement
model which includes multiple types of engagement behaviours and sub-dimensions, but in
this study, we only explore the relationship between drivers and engagement outcome in a one-
way progression, future studies can therefore further investigate how different types of
engagement behaviours such as sharing, learning, co-developing, socializing, and advocating
can generate distinctive paths of engagement outcome, and also how the engagement factors
interplay with one another in moderating the relationship with engagement behaviours on the
social media platforms.
Finally, the study explores consumer engagement embedded in social media platforms
which include Facebook, Twitter, and Sina Weibo; however, different social media have
distinctive attributes which can greatly affect the way how members communicate with others
and the brand. Future studies can therefore look into consumer engagement in a specific social
media for a more precise insight in this research field.

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6. Appendix
6.1 Appendix A- Questionnaire
Online Brand Community Engagement Study
You are invited to participate in a research study regarding consumer’s online brand community
(OBC) engagement in leisure and entertainment brands. The questionnaire focuses on the drivers
behind consumer’s participation of the online brand communities. By completing and returning this
questionnaire, you will be helping us to identify the relationship between the engagement drivers and
their contribution to brand loyalty and satisfaction.
Main Questions
……………………………………………………………………………………………………………
1. How long have you been the member in this online brand community?
a. Less than 1 year
b. 1 to 3 years
c. 4 to 7 years
d. Over 7 years
2. The following statements relate to your brand’s identification and how you see them. Please
circle the number in each statement that best reflects your views.
Strongly disagree Strongly agree
3. The following statements relate to your reasons behind engaging in this online brand
community from the social benefit perspective. Please circle the number in each statement that
best reflects your views.
4. The following statements relate to how you perceive the functional role of this online brand
community in terms of any forms of direct, information-based support. Please circle the number
in each statement that best reflects your views.
a. The image of the brand community is consistent with how I see myself 1 2 3 4 5
b. The traces of my online engagement with the brand community can
reflect part of who I am
1 2 3 4 5
c. The image of the typical member of this brand community is
congruent with how I see myself
1 2 3 4 5
d. This brand community I participated is an extension of myself 1 2 3 4 5
e. I would like to be perceived as similar to the typical member of this
brand community
1 2 3 4 5
a. Because I want to get to know other community members 1 2 3 4 5
b. To help other community members through answering their questions
or information exchange
1 2 3 4 5
c. To feel a sense of belonging through being needed by other community
members
1 2 3 4 5
d. To get help from other community members 1 2 3 4 5
e. To provide information to other community members 1 2 3 4 5
f. To share my ideas with other community members 1 2 3 4 5
g. Because I want to stay in touch with other community members 1 2 3 4 5
a. The information provided by the brand community is valuable 1 2 3 4 5

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5. The following statements relate to the significance of monetary rewards of this online brand
community such as price promotions to encourage your community participation. Please circle
the number in each statement that best reflects your views.
6. The following statements relate to the entertainment benefits of this online brand community
such as relaxation and fun that motivate your community participation. Please circle the
number in each statement that best reflects your views.
7. The following statements relate to how satisfied you are to the brand of the participated
online brand community. Please circle the number in each statement that best reflects your
views.
8. The following statements relate to your degree of loyalty to this brand of the participated
community. Please circle the number in each statement that best reflects your views.
9. What is your age?
a. below 18
b. 18 to 24
c. 25 to 34
d. 35 to 44
e. 45 or above
b. The information provided by the brand community is useful 1 2 3 4 5
c. The brand community provides information at an appropriate level of
detail
1 2 3 4 5
d. In this online brand community, there are good features that help me
to accomplish my tasks
1 2 3 4 5
a. To obtain discounts or special deals that most consumers do not get 1 2 3 4 5
b. To obtain better prices than other consumers 1 2 3 4 5
c. Through any forms of interactions with the brand community to
receive free coupons for the brand
1 2 3 4 5
a. I like participating in this brand community because the experience is
entertaining
1 2 3 4 5
b. Having fun and getting relaxed are my main reasons for participating
in this brand community
1 2 3 4 5
c. I enjoy the moment of participating in this online brand community
because I think it is fun
1 2 3 4 5
d. I find participating in this brand community to be very entertaining 1 2 3 4 5
a. I am satisfied with my decision to become a member/fan of the brand's
social media group
1 2 3 4 5
b. I think that I did the right thing when I decided to become the brand's
social media community member/fan
1 2 3 4 5
c. I am satisfied with my decision to become the brand's customer 1 2 3 4 5
d. I am satisfied with brand 1 2 3 4 5
a. I consider this brand as my number one choice of provider 1 2 3 4 5
b. I say positive things about this brand to other people 1 2 3 4 5
c. I would recommend this brand to my friends 1 2 3 4 5

39 | P a g e
10. What is your age?
a. Male
b. Female
6.2 Appendix B – SPSS Detailed Results
Multiple Regression – Satisfaction
Model Summaryb
Model R R Square
Adjusted
R Square
Std. Error of
the Estimate
1 .790a
.625 .613 .47559
a. Predictors: (Constant), Brand identification, Social benefit, Functional
benefit, Monetary incentives, Entertainment driver, Membership length
b. Dependent Variable: Satisfaction
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 73.017 6 12.170 53.802 .000b
Residual 43.881 194 .226
Total 116.898 200
a. Dependent Variable: Satisfaction
b. Predictors: (Constant), Brand identification, Social benefit, Functional benefit, Monetary incentives, Entertainment
driver, Membership length
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95.0% Confidence
Interval for B Correlations
Collinearity
Statistics
B Std. ErrorBeta
Lower
Bound
Upper
Bound
Zero-
order Partial Part Tolerance VIF
1 (Constant) .660 .205 3.218 .002 .255 1.064
Membership
Length
-.018 .038 -.022 -.460 .646 -.093 .058 .067 -.033 -.020 .878 1.139
Brand
Identification
.266 .062 .268 4.322 .000 .145 .388 .587 .296 .190 .504 1.986
Social Benefit .105 .053 .105 1.971 .050 .000 .211 .342 .140 .087 .687 1.456
Functional
Benefit
.048 .061 .047 .795 .428 -.072 .168 .504 .057 .035 .548 1.825
Monetary
Incentives
-.079 .041 -.108 -1.941 .054 -.160 .001 .365 -.138 -.085 .622 1.609
Entertainment
Driver
.513 .047 .61310.929 .000 .421 .606 .733 .617 .481 .616 1.623
a. Dependent Variable: Satisfaction

40 | P a g e
Multiple Regression Analysis – Loyalty
Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .745a
.556 .542 .61031
a. Predictors: (Constant), Brand identification, Social benefit, Functional
benefit, Monetary incentives, Entertainment driver, Membership length
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 90.323 6 15.054 40.415 .000b
Residual 72.261 194 .372
Total 162.585 200
a. Dependent Variable: Loyalty
b. Predictors: (Constant), Brand identification, Social benefit, Functional benefit, Monetary
incentive s, Entertainment driver, Membership length
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95.0% Confidence
Interval for B Correlations
Collinearity
Statistics
B Std. Error Beta
Lower
Bound
Upper
Bound
Zero-
order Partial Part Tolerance VIF
1 (Constant) .581 .263 2.207 .028 .062 1.100
Membership
Length
-.076 .049 -.079 -1.554 .122 -.173 .020 -.035 -.111 -.074 .878 1.139
Brand
Identification
.149 .079 .127 1.887 .061 -.007 .305 .497 .134 .090 .504 1.986
Social Benefit .032 .069 .027 .461 .645 -.104 .167 .253 .033 .022 .687 1.456
Functional
Benefit
.170 .078 .141 2.175 .031 .016 .324 .496 .154 .104 .548 1.825
Monetary
Incentives
-.004 .053 -.005 -.085 .932 -.108 .099 .405 -.006 -.004 .622 1.609
Entertainment
Driver
.577 .060 .584 9.577 .000 .458 .696 .711 .567 .458 .616 1.623
a. Dependent Variable: Loyalty

41 | P a g e
Correlations
Brand
identification
Social
benefit
Functional
benefit
Monetary
incentives
Entertainm
ent driver
Membership
length Satisfaction Loyalty
Brand identification Pearson
Correlation
1 .449** .585** .487** .488** .082 .587** .497**
Sig. (2-
tailed)
.000 .000 .000 .000 .247 .000 .000
N 201 201 201 201 201 201 201 201
Social benefit Pearson
Correlation
.449** 1 .471** .247** .205** .193** .342** .253**
Sig. (2-
tailed)
.000 .000 .000 .003 .006 .000 .000
N 201 201 201 201 201 201 201 201
Functional benefit Pearson
Correlation
.585** .471** 1 .307** .464** .018 .504** .496**
Sig. (2-
tailed)
.000 .000 .000 .000 .802 .000 .000
N 201 201 201 201 201 201 201 201
Monetary incentives Pearson
Correlation
.487** .247** .307** 1 .488** -.173* .365** .405**
Sig. (2-
tailed)
.000 .000 .000 .000 .014 .000 .000
N 201 201 201 201 201 201 201 201
Entertainment driver Pearson
Correlation
.488** .205** .464** .488** 1 .044 .733** .711**
Sig. (2-
tailed)
.000 .003 .000 .000 .534 .000 .000
N 201 201 201 201 201 201 201 201
Membership length Pearson
Correlation
.082 .193** .018 -.173* .044 1 .067 -.035
Sig. (2-
tailed)
.247 .006 .802 .014 .534 .344 .625
N 201 201 201 201 201 201 201 201
Satisfaction Pearson
Correlation
.587** .342** .504** .365** .733** .067 1 .771**
Sig. (2-
tailed)
.000 .000 .000 .000 .000 .344 .000
N 201 201 201 201 201 201 201 201
Loyalty Pearson
Correlation
.497** .253** .496** .405** .711** -.035 .771** 1
Sig. (2-
tailed)
.000 .000 .000 .000 .000 .625 .000
N 201 201 201 201 201 201 201 201
Statistics
Membership
length
Brand
identification Social benefit
Functional
benefit
Monetary
incentives
Entertainment
driver Satisfaction Loyalty
N Valid 201 201 201 201 201 201 201 201
Missing 0 0 0 0 0 0 0 0
Mean 2.63 3.1154 3.1102 3.4366 2.9801 3.5609 3.5286 3.5705
Std. Deviation .941 .76877 .75993 .74625 1.04171 .91225 .76452 .90162
Skewness -.245 -.278 -.330 -.425 -.088 -.746 -.595 -.683
Std. Error of Skewness .172 .172 .172 .172 .172 .172 .172 .172
Kurtosis -.803 .343 .457 .601 -.466 .812 .603 .423
Std. Error of Kurtosis .341 .341 .341 .341 .341 .341 .341 .341

42 | P a g e
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