Master’s Thesis
Name: Natalie Henshall Student ID: 11580976
Supervisor: Ewa Maslowska Date of completion: 31-01-2019 Word count: 7,772
Graduate School of Communication
Communication Science: Persuasive Communication
Exploring the links between personality traits and motivations
Abstract
The utilisation of social networking sites has continued to grow in popularity in recent
years, with both individuals and brands adopting this new approach to communicating. This
is particularly true of Instagram, one of the fastest growing social networking sites in the
world. However, if brands are to effectively utilise Instagram as a platform for engaging with
consumers, they need to understand what drives engagement activities. This study
investigates the motivations for consumer-brand engagement on Instagram through
application of uses and gratification theory. Furthermore, it looks at the relationship between
motivations, personality traits and consumer-brand engagement. A survey was developed in
which 178 Instagram users reported on their motivations, brand-related activities and
personality measures relating to the Five Factor Model (Big Five) and Hypersensitive
Narcissism Scale. The results revealed that entertainment, information seeking, social
influence and remuneration all motivate individuals to engage with brands on Instagram.
Two personality traits, namely extraversion and conscientiousness were found to interact
with remuneration. All other interactions explored were not significant. The implications of
these findings, along with directions for future research, are discussed.
Introduction
The rise of social networking sites (SNS) has led to a revolution in how we
communicate. Facebook remains the most popular SNS worldwide with 2.23 billion monthly
active users (Statista, 2018a). However, Instagram has demonstrated that ‘a picture is worth a thousand words’ with its popularity exceeding that of Twitter, LinkedIn and Snapchat, and its user base reaching one billion a month worldwide (Statista, 2018b). Brands are also
recognising Instagram’s popularity. At present, there are more than 25 million business profiles and two million active advertisers (Statista, 2018c). Therefore, it comes as no
surprise that Instagram has become an important platform for brand-related communication
(Azar, Cesar Machado, Vacas-de-Carvalho & Mendes, 2016). While many brands have
established a presence on Instagram, it remains unclear what encourages consumers to
interact with brands on SNS (Lee, Lee, Ho Moon & Sung, 2015; Lee, Hosanagar & Nair,
2013) and what factors drive these motivations to interact.
Brands utilise SNS for a range of purposes – from increasing their exposure and driving website traffic, to developing brand loyalty and gaining marketplace intelligence
(Phua, Jin & Kim, 2016). However, there has been a “significant power shift” on SNS from brands to consumers, who are no longer passive recipients of brand communication but
have become active participants and, in some instances, creators of branded content (Azar
et al., 2016, p. 154). This power shift has been attributed to the rise of social media, which
have not only transformed online consumer behaviour, but also how consumers engage with
brands (Muntinga, Moorman & Smit, 2011; Schivinski, Christodoulides, Dabrowski, 2016).
Brand engagement, which is taken to mean the behavioural manifestations resulting from
the relationship between a consumer and a brand (e.g. writing a post on a brand’s fan page), are a likely predictor of consumer behavioural outcomes (van Doorn et al., 2010). Brands
can yield competitive advantages in the marketplace by leveraging consumer-brand
engagement to stimulate purchases, loyalty or consumption (Calder, Malthouse &
consumer-brand engagement are of significant interest to brands that operate online
(Hollebeek, Glynn & Brodie, 2014; Muntinga, 2013).
To date, research has either focused on motivations that drive consumer
engagement on more established SNS such as Facebook (Azar et al., 2016; Smock, Ellison,
Lampe & Wohn, 2011) and Twitter (Sook Kwon, Kim, Sung & Yun Yoo, 2014;Johnson &
Yang, 2009), or has not differentiated between sites and rather investigated social media as
a single entity (Dolan, Conduit, Fahy & Goodman, 2016; Muntinga, Moorman & Smit, 2011).
However, each SNS is different in that it has its own distinct purpose and features, as
evidenced by the fact that the majority of individuals use multiple SNS simultaneously (Phua,
Jin & Kim, 2016). These distinctions have the potential to generate different patterns and
drivers of behaviour dependent on the platform. Alhabash and Ma’s (2017) research into the differences between Facebook, Twitter, Instagram and Snapchat concluded that there were
indeed variances across the four platforms with respect to user motivations. This conclusion
was reiterated by Voorveld, van Noort, Muntinga and Bronner (2018) in their study into
consumer engagement across social media platforms. They stated that “all social media platforms were experienced uniquely, and each had a distinctive profile” (p. 46). We can therefore postulate that differences between platforms mean that previously identified
motivations on one SNS may not directly translate to another.
Consequently, it is beneficial to dedicate research to the distinctness of SNS and to
study a platform that, to date, is underrepresented in research on consumer motivations.
Studies that consider motivations for consumer-brand engagement on Instagram, the
platform that forms the focus of this research, have only just begun to emerge, and yet they
have the ability to provide valuable insight into not only our theoretical understanding of
consumer engagement, but also for brand and marketing managers whose job it is to
incorporate Instagram into their strategy. Instagram is a free photo and video app available
photos and short videos with their followers, as well as interact with others through the use
of view, comment, like and share functions. What distinguishes Instagram from other SNS is
that it is photo-based and can be accessed via a mobile-only app (Lee et al., 2015).
This research aims to understand the motivations that drive consumers to engage
with brands on Instagram. It is a partial replication of the research done by Muntinga,
Moorman and Smit (2011) into consumers’ online brand related activities (COBRAs) and expands upon the user-centric perspective of social media and Katz’s (1959) uses and gratification theory. The following research question is proposed:
RQ1: What are the motivations that drive consumer-brand engagement on Instagram?
Motivations must not be considered as a stand-alone determinant of consumer-brand
engagement. Previous research has shown that individual variables must also be taken into
consideration when studying engagement behaviour (Hollenbaugh & Ferris, 2014), thus the
role of personality is also considered within this research. Kim and Jeong (2015) argue that
beliefs, attitudes and behaviours can differ as a function of an individual’s personality traits. Ajzen (2005) takes this a step further and states that personality traits and the manifesting
behaviour (e.g. consumer-brand engagement) is “subject to various contingencies” (p.42) and may influence an individual in some situations, but not in others. This highlights that
there is an interaction effect between other variables and personality traits. A study by Ross
et al. (2009) into Facebook use has already demonstrated that personality traits correlated
with several motivations. Consequently, this research also seeks to determine whether
personality traits moderate the relationship between motivations and consumer-brand
engagement on Instagram. This leads to the following research question:
RQ2: How do the motivations to engage with brands on Instagram differ depending on personality traits?
Theoretical framework Consumer-brand engagement
The rise of SNS has caused both academics and practitioners to focus on consumer
engagement and the dynamics that pertain to the consumer-brand relationship within these
platforms (Hollebeek, Glynn & Brodie, 2014). Engagement has been conceptualised in a
number of ways, including the notion that consumer engagement comprises cognitive,
emotional and behavioural activity (Hollebeek, Glynn & Brodie, 2014). While it is
acknowledged that cognitive and emotional components are applicable in some situations,
this research intends to focus on the behavioural indicators of consumer engagement. Thus,
in line with van Doorn et al. (2010), consumer engagement is defined as “behaviours [that] go beyond transactions and may be specifically defined as a customer’s behavioural
manifestations that have a brand or firm focus, beyond purchase, resulting from motivational
drivers” (p.254). Notably, this definition aligns itself well with the theoretical foundations of uses and gratification theory (UGT) in that it recognises the influence motivations can have
on engagement behaviour (Dolan et al., 2016).
These behavioural manifestations allow researchers to differentiate between
individuals on the basis of who does and does not engage (Marbach, Lages & Nunan,
2016). In order to understand the nature of these behavioural manifestations it is beneficial
to examine the research put forward by Muntinga, Moorman and Smit (2011). They
established a framework in order to capture “the diversity of consumers’ online brand-related activities” (Muntinga, 2013, p. 10), which they ultimately termed COBRAs. Within the
COBRA framework a further distinction is made in regard to COBRA typologies, which
measures brand activity on a continuum from high to low, specifically consuming,
contributing and creating brand-related content (Muntinga, Moorman and Smit, 2011).
Consuming represents the lowest level of related activity, such as viewing a
brand-related post. Next is contributing, where an individual may contribute to brand-brand-related
where an individual will produce brand-related content, such as images or videos, and
publish them (Muntinga, Moorman & Smit, 2011). The authors note that despite the COBRA
typologies consisting of three factors, an individual can engage in multiple roles at one time,
depending on their motivation (Azar et al., 2016). The COBRA framework is particularly
relevant for its ability to measure consumer brand-related behaviour on social media and as
such, it is utilised within this research.
Uses and gratifications theory
In order to understand the motivations that drive consumers to engage with brands
on Instagram a user-centric functionalistic perspective has been applied, namely uses and
gratifications theory. UGT is an approach that explains how and why individuals actively
seek out different media to fulfil their specific needs and wants (Katz, Blumler & Gurevitch,
1974). It was one of the first approaches to propose that individuals undertake an active role
in their media choice, implying that, through the search for, and identification with,
individuals consume media to fulfil specific gratification needs (Dolan et al., 2016). Although
UGT initially focused on audience gratifications in relation to traditional media – such as radio, television and print – the approach has been expanded to also include new media (McQuail, 2010), thus continuing its relevance in present-day research.
When applying UGT, researchers commonly distinguish between different
motivations to identify why people consume different media and what fulfilment they receive
from said media. However, the approach is not without its critiques, namely the conceptual
ambiguity concerning what constitutes a motivation (Alhabash & Ma, 2017; Muntinga, 2013).
In order to improve upon the framework’s relevance and overcome this critique, UGT research has taken to distinguishing between antecedents and consequences of media
behaviour. Antecedents of behaviour are taken to mean ‘gratifications sought’, while consequences of behaviour are taken to mean ‘gratifications obtained’ (Rubin, 2002). As motivations are acknowledged as an important driving force of behaviour, they can be
understood as gratification sought (Muntinga, 2013). However, our understanding of
gratifications has not remained stable across time, as the emergence of SNS, with their
unique functionalities, have impacted upon them. Specifically, they have changed our
understanding of gratifications sought and obtained as well as rearranged the prominence of
these in comparison to traditional media (Alhabash & Ma, 2017). Hence, UGT needs to be
examined further within the context of SNS.
UGT and SNS
As previously noted, the emergence of new media has caused a shift in our
understanding of motivations. Nonetheless, there still remains a dominant approach to UGT
categorisation. McQuail (2010) differentiated four motivation categories, namely diversion,
personal relationships, personal identity and surveillance. Since the arrival of social media,
researchers have applied UGT in order to understand the motivations for using SNS,
resulting in a plethora of literature. For example, Alhabash and Ma (2017) applied UGT to
examine how Facebook, Twitter, Instagram and Snapchat differed in terms of time spent on
the platform and usage motivations. While platform specific research by Azar et al. (2016)
employed UGT to identify five motivations to explain why consumers interacted with brands
on Facebook. A number of researchers have confirmed that McQuail’s categorisation remains valid when applied to SNS (Alhabash & Ma, 2017; Muntinga, 2013) but their
relevance specifically to Instagram remains undetermined. Therefore, through drawing on
UGT literature (Dolan et al., 2016; McQuail, 2010) and motivations to interact with brands on
SNS (Azar et al., 2016; Muntinga 2013; Muntinga, Moorman & Smit, 2011) four motivations
associated with Instagram have been selected for this study. Entertainment and information
seeking are included as, according to Muntinga (2013), they are “the basic drivers of all brand related social media use” (p.21) and the new platform of Instagram provides the opportunity to test whether these motivations hold true. In contrast, social influence and
remuneration have emerged as new motivations in social networking discourse and thusly
Entertainment
The entertainment motivation covers a range of media gratifications associated with
escaping from routine or problems, emotional release, the desire to relax, to divert or pass
time and for sexual arousal (McQuail, 2010). Thus, entertainment represents the extent to
which branded content on Instagram is fun and entertaining for individuals (Dolan, 2016).
Within UGT research, entertainment is generally understood as an overall motivation and it
is not divided into sub-motivations such as relaxation or escapism (Muntinga, Moorman &
Smit, 2011). Previous research on the relationship between entrainment and SNS has
shown that individuals frequently use SNS as a form of passing time or to relax (Azar et al.,
2016). For example, Muntinga, Moorman and Smit (2011) determined that the entertainment
motivation was present in all three COBRA typologies and thus was a strong predictor of
consumers engaging in brand-related activities on SNS. This was further evident in research
that revealed content containing an entertaining element was a significant factor in
increasing the number of likes, shares and comments on social media (Cvijikj & Michahelles,
2013). Moreover, Dolan et al. (2016) concluded that if social media content is entertaining,
then an individual’s needs are being met they will consequently demonstrate a positive response towards said content.
Information seeking
The information seeking motivation, also referred to within literature as information
searching or surveillance, is the extent to which gratifications are sought via content
providing individuals with resourceful and helpful information (Dolan et al., 2016). Unlike
entertainment, information seeking contains sub-motivations, namely surveillance,
knowledge, pre-purchase information and inspiration (Muntinga, Moorman & Smit, 2011).
When applied to the context of Instagram, this may be understood as an individual looking at
the latest clothing collection from H&M, or the new flavours from Tony Chocolonely, or
watching behind-the-scene access to their football team. Searching for and consuming
motivate individuals to use SNS (Azar, 2016). Sheldon and Bryant (2016), one of the few
researchers to look at motivations in relation to Instagram, concluded that
surveillance/knowledge was the most influential reason for using Instagram, explaining 36%
of the variance.
Social influence
The social influence motivation is associated with media gratifications related to the
approval or disapproval of others (Azar et al., 2016). When applied in the context of brands
and SNS this may influence consumers in the adoption of products and services (Curran &
Lennon, 2011). Previous research has indicated that individuals make a purchase in order to
make a positive impression on others (Azar et al., 2016). The social element of SNS
provides influencers and opinion leaders with a platform in order to share, and promote,
brand-related opinions with a wide social circle, including other consumers (Chu & Kim,
2011). On Instagram this may include imagery containing brand-related content, hashtags or
paid partnerships with brands. As a result, individuals utilise brands as a means of
constructing and maintaining self-identity (Azar et al., 2016) through the process of following,
liking and creating brand-related content on Instagram. Additionally, research has shown
that social influence is an important factor when examining motivations to create
user-generated content. Muntinga, Moorman and Smit (2011) found that this was particularly
evident in both the contributing and the creating COBRA typology.
Remuneration
The remuneration motivation relates to the degree to which people engage in social
media use in order to obtain some form of gain, such as financial incentives, job-related
benefits, time-saving or giveaways (Dolan et al., 2016). Previous research into remuneration
has shown that individuals use social media in order to gain economic benefits through
participating in competitions or by receiving discounts on goods and services (Gummerus,
less motivated by monetary incentives, but rather by the opportunity to obtain exclusive
content, such as a cookbook signed by a celebrity chef; winnable by commenting on the
chef’s Instagram post. Incentives such as this would have a greater impact on consumers’ motivation to engage with brands online. Dolan et al. (2016) argue however, that both
approaches, either when a brand promotes content containing a monetary incentives or
exclusives, are likely to gratify individuals’ needs for remuneration on Instagram.
On the basis of previous literature, which has shown that motivations are a strong
predictor of consumer-brand engagement on SNS, the following hypothesis is derived:
H1: The motivations of information seeking, social influence, entertainment and remuneration are positively related to consumer-brand engagement on Instagram.
Personality traits
Personality traits act as “a direct driver of individuals’ behaviour and determines their pattern of interaction with the environment” (Islam, Rahman & Hollebeek, 2017, p. 512). Personality traits are generally considered to be stable over time and therefore offer a
consistent way of assessing an individual’s true self (Ajzen, 2005), which make them a relevant area of investigation. There are several models to describe personality, however,
research widely holds that personality can be best explained by the Five Factor Model, also
referred to as the ‘Big Five’ (Costa & McCrae, 1992; Ross et al., 2009; Seidman, 2013). According to the Five Factor Model individuals differ in terms of extraversion,
agreeableness, conscientiousness, neuroticism and openness to experience (Azucar,
Marengo & Settanni, 2018).
The Five Factor Model is particularly relevant as its influence on consumer variables,
such as satisfaction and trust, have been successfully investigated in previous research,
(Islam, Rahman & Hollebeek, 2017). Additionally, due to the robust nature of the Five Factor
Model it has been deemed suitable for predicting different behaviours, including
consumer-brand engagement (Marbach, Lages & Nunan, 2016), which makes it appropriate to
implement within this research. However, despite its popularity, debate exists as to whether
the Five Factor Model is comprehensive enough to describe human behaviour by itself
(Marbach, Lages & Nunan, 2016). Therefore, in addition to the ‘Big Five’, narcissism has also been included. This additional trait has been selected to provide a more comprehensive
insight into personality traits and because of the association previously identified in
narcissism predicting online social activity (Buffardi & Campbell, 2008) and the positive
relationship between narcissism and motivations for using Instagram (Sheldon & Bryant,
2016).
Personality traits and media gratification
At its core, UGT is about need fulfilment (Katz, 1959). This is a distinct process
whereby considering the agency of the individual is vital (de Zuñiga, Diel, Huber & Liu,
2017). As such, when studying antecedents of consumer-brand engagement, one has to
take into consideration the interaction between motivations and individual differences.
Personality traits can be said to serve as sub-motivations, underpinning motivations of
consumer engagement by driving the need for self-fulfilment and self-representation, and
reinforcing personal values (Munting, Moorman & Smit, 2011). As a result, personality traits
have become an important area of research in predicting online behaviours and, more
specifically, in identifying which personality traits facilitate consumer-brand engagement
(Islam, Rahman & Hollebeek, 2017).
Extraversion
Individuals who are extroverts typically display a set of characteristics that are
sociable, such as talkativeness, adventurousness and cheerfulness (Hughes et al., 2012).
activities (Marbach, Lages & Nunan, 2016). Generally, extroverts (compared to introverts)
have an increased number of friends and these friendships are of higher quality. This has
resulted in extraversion being one of the most consistent predictors of social media usage
within literature (de Zuñiga et al., 2017). Extroverts’ online behaviour revolves around the social element, which is evident from the immense value they place on interpersonal
relationships and social atmospheres (Islam, Rahman & Hollebeek, 2017). As a result,
extraversion is positively associated with consumer engagement as individuals feel
comfortable sharing information and their experiences with others (Seidman, 2013). This
was further confirmed in Islam, Rahman and Hollebeek’s (2017) research into the Big Five, which found extraversion to be the strongest driver overall for consumer engagement in
online brand communities.
Agreeableness
Individuals who are agreeable tend to be cooperative, helpful and have an
interpersonal orientation which is reflected in their level of friendliness (Marshall,
Lefringhausen & Ferenczi, 2015). Agreeable individuals are inclined to present a more
honest version of themselves since they perceive a high level of control over their virtual
self-presentation (Seidman, 2013). Their caring attitude towards the welfare of others
extends into online platforms, where they appreciate others’ contributions (Matzler, Pichler, Füller & Mooradian, 2011) and, in return, share their experiences and engage at a high level
with online brand communities (Islam, Rahman & Hollebeek, 2017). Therefore, highly
agreeable individuals may use Instagram to participate in activities that add value for other
consumers, such as showcasing or styling a specific product.
Conscientiousness
Conscientious individuals are described as being organised, responsible and
hard-working (Marshall, Lefringhausen & Ferenczi, 2015). Conscientiousness appears to facilitate
self-perceptions (Seidman, 2013) and, as a result, conscientious individuals are less focused on
interpersonal relationships (Tsao, 2013). Previous research has indicated that conscientious
individuals are motivated by the satisfaction they receive from work-related achievements,
such as meeting a deadline (Ross et al., 2009). Therefore, what drives conscientious
individuals may not correlate with the motivations for engaging with brands on SNS. This
notion is in line with other research which has found a negative association between
conscientious individuals and the amount of time spent on SNS, the adoption of social apps
and consumer engagement overall (Islam, Rahman & Hollebeek, 2017; Marbach, Lages &
Nunan, 2016).
Neuroticism
Individuals who are neurotic are characterised as being anxious and sensitive to
threat and rejection. Those who are highly neurotic may use SNS to seek out support and
attention that may be absent from their offline lives (Marshall, Lefringhausen & Ferenczi,
2015). This absence is likely caused by a tendency to feel distressed within the presence of
physical crowds, which leads them to use SNS as not only an escapism from loneliness, but
as a place for self-expression (Islam, Rahman & Hollebeek, 2017). Arguably, individuals
high in neuroticism may engage in brand-related activities find a sense of belonging and
acceptance from others.
Openness to experience
Individuals who are characterised as being open to experience (versus more closed)
tend to be creative, intellectual and curious (Marshall, Lefringhausen & Ferenczi, 2015).
These individuals are inclined to use SNS as a supplement to offline interactions, as a way
of expanding their interests and learning about new experiences (Seidman, 2013). Previous
research has found a positive relationship between openness to experience and using the
internet for entertainment and browsing for information (Kim & Jeong, 2015). This is
detailed information and, as a result, these individuals are more likely to participate in
consumer-brand engagement to pursue their interests (Islam, Rahman & Hollebeek, 2017;
Marbach, Lages & Nunan, 2016).
Narcissism
Narcissistic individuals tend to have “positive and inflated self-views of agentic traits like intelligence, power, and physical attractiveness, as well as pervasive sense of
uniqueness and entitlement” (Buffardi & Campbell, 2008, p. 1304). They are known to seek attention and admiration through exhibiting themselves or boasting about their
accomplishments, and they place a great deal of importance on physical appearance
(Marshall, Lefringhausen & Ferenczi, 2015). Sheldon and Bryant (2015) suggest that the
narcissism trait is particularly relevant to Instagram as narcissists migrate to SNS that
facilitate superficial relationships and highly controlled environments where the individual
retains complete control over self-presentation. Furthermore, the visual functionality of
Instagram, along with additional editing features, encourages self-presentation and is likely
to be directly related to the narcissism trait. However, while the relationship between
narcissism and SNS usage has been investigated, little is known about how narcissism
interacts with consumer-brand engagement.
Despite theoretical insights into the relationship between personality traits and
consumer-brand engagement, the specific role that personality traits play as a moderator of
motivations on consumer-brand engagement is unclear. One may intuitively be able to
speak to certain relationships between specific variables, such that extraversion may appear
to relate more to social influence than other motivations. However, since extant literature is
short on any clear theoretical or empirical direction, the following research questions are
RQ3: Which motivation(s) are the strongest predictors of consumer-brand engagement on Instagram for (a) extraversion, (b) agreeableness, (c) conscientiousness, (d) neuroticism
and (e) openness to experience?
RQ4: Which motivation(s) are the strongest predictors of consumer-brand engagement on Instagram for narcissists?
Figure 1 shows the relationship between the independent variable (motivations) and the
dependent variable (consumer-brand engagement) and the moderating variable (personality
traits).
Methodology Procedure and sample
An online survey was used to collect data over a 10-day period in November and
December of 2018. Conway and Rubin (1991) have stated that survey research is one of the
principal methodological approaches within UGT. Furthermore, previous research has also
found surveys to be beneficial for analysing variables relating to psychological traits and
media motivations (Conway & Rubin, 1991). As such, given the objective of this research,
the utilisation of a survey is the most appropriate approach.
Participant recruitment took place via postings on SNS such as Facebook and
Instagram, resulting in a convenience sample. Respondents were directed to a Qualtrics
survey by following a hyperlink included in the post. Informed written consent was obtained
from participants prior to participation along with a screening question to ascertain if they
met the selection criterion of the study by being an Instagram user. A total of 215
participants started the survey, of which 187 participants completed it, representing an 87%
completion rate. After eliminating missing data and participants who indicated that they did
not use Instagram, a total of 178 participants made up the final sample. Participants’ ages ranged from 17 to 62 (Mage = 28, SDage = 6.92) and the sample consisted mainly of females
(79%). The majority of participants (32%) spent an average of 30 minutes or less on
Instagram each day. Participants’ demographics and characteristics are presented in table 1.
Measures
Pilot test
Before data collection commenced, a pilot-test was undertaken with an active group
of Instagram users (n = 10, 5 female) in order to evaluate the feasibility and clarity of the
survey. Participants of the pilot-test were asked to eliminate any questions which they felt
engagement and motivation sections. Additionally, those who spoke English as a second
language were asked to identify any terminology that was unclear, which was then modified
before the final survey was circulated.
Table 1
Sample demographics and characteristics (N = 178)
Frequency % Gender Male 36 20.2% Female 141 79.2% Other 1 0.6% Origin Africa 1 0.5% Asia 15 8.4% Europe 140 78.6% North America 12 6.7% Australia/Oceania 10 5.6% Education
Less than high school degree 2 1.1%
High school graduate 12 6.7%
Some college, but no degree 15 8.4%
Associate degree in college (2 years) 5 2.8% Bachelor’s degree in college (3-4 years) 98 55.1%
Master’s degree 43 24.3%
Doctoral degree 3 1.7%
Average time spent on Instagram each day
30 minutes, or less 57 32.0% 31 minutes – 1 hour 50 28.1% 1 – 2 hours 44 24.7% 2 – 3 hours 18 10.1% 3 – 4 hours 4 2.2% 5 hours, or more 5 2.8%
Dependent variable: consumer-brand engagement
Consumer-brand engagement behaviours were operationalised through the
adaptation of Schivinski, Christodoulides and Dabrowski’s (2016) Consumer’s Engagement with Brand-Related Social Media Content (CEBSC) scale. This 17-item scale was created
using the theoretical work of COBRAs by Muntinga, Moorman and Smit (2011), which
accounts for different levels of consumer engagement, specifically that of consumption,
contribution and creation (Schivinski, Christodoulides & Dabrowski, 2016). However, the
CEBSC scale was designed to encompass engagement behaviours across all social media
platforms and consequently seven items had to be eliminated as they did not reflect the
functionalities of Instagram. An additional two items were included following feedback from
the pilot-test, namely, “I have used Instagram to take a screenshot of a brand-related post and share it outside of Instagram” and “I have used Instagram to see how other people (including celebrities) interact with a brand”, resulting in a final scale consisting of 12 items. Participants were asked to indicate the frequency with which they undertook the behaviours
on a 7-point Likert scale ranging from (1) ‘never’ to (7) ‘very often’.
The 12 items were subject to principal axis factor analysis using SPSS version 24.
This showed a Kaiser-Meyer-Olkin measure of .90 and was statistically significant (p < .001).
Unexpectedly, following direct Oblimin rotation the presence of only two factors was
revealed with an Eigenvalue exceeding 1. These two factors explained 61.48% of the
variance. As the purpose of this research is not focused on differentiating between
typologies of consumer-brand engagement and given that the 12 items were found to be
reliable (α = .91) a single consumer-brand engagement scale was calculated (M = 2.93, SD = 1.18, range 1-7).
Independent variable: motivations for engaging with brands on SNS
The four constructs that make up motivations to engage with brands on Instagram – entertainment, information seeking, social influence and remuneration – were measured
through a multi-item scale. The items were derived from existing research into motivations
and social media (Azar et al., 2016; McQuail, 2010; Muntinga, Moorman & Smit, 2011)
which generated a total of 18 items. Participants were asked to indicate how much they
agreed or disagreed with statements such as “I engage with brands on Instagram so that I can occupy my spare time” on a 7-point Likert scale ranging from (1) ‘strongly disagree’ to (7) ‘strongly agree’.
A principal axis factor analysis was conducted using direct Oblimin rotation. This
showed a Kaiser-Meyer-Olkin measure of .90 and was statistically significant (p < .001). The
outcome of the analysis supported the existence of the four motivation constructs, with an
Eigenvalue exceeding 1 which together explained 64.35% of the variance. One item, namely
“I engage with brands on Instagram so that I can feel sexually aroused”, failed to load on any factor and was thus excluded from further analysis. Table 2 illustrates the loadings of the
items on each factor. The first factor, information seeking, explained 37.70% of the variance
and the six items formed a reliable scale, as indicated by its Cronbach's alpha score (α = .89). The second factor, social influence, explained 11.54% of the variance and contained
five items (α = .82). The third factor, entertainment, consisted of four items and explained 8.83% of the variance (α = .80). Finally, the fourth factor, remuneration, consisted of two items and explained 6.30% of the variance. While Cronbach's alpha was at the threshold for
reliability (α = .72), it was deemed acceptable for the purpose of analysis. As a result, scales were computed for each factor by averaging the corresponding items. The final scales
consisted of information seeking (M = 4.63, SD = 1.29, range 1-7), social influence (M =
3.12, SD = 1.27, range 1-7), entertainment (M = 4.00, SD = 1.37, range 1-7) and
remuneration (M = 4.13, SD = 1.66, range 1-7), and a higher score on the scale corresponds
Table 2
Component loading table and reliability
Construct Items Factor loading Cronbach’s α
Information seeking .89
Be quickly updated .813
Find useful information .776 Obtain credible information .757 Better understand the brand .714 Find out how others feel .567
See new things .506
Social influence .82
Part of a community .777 Increase social involvement .681 State my preferences to others .623 Share or promote my opinion .615 Align with the brand’s values .417
Entertainment .80
Occupy my spare time -.731
Feel relaxed -.669
Forget everything around me -.620
Find enjoyment -.542
Remuneration .72
Enter contests .710
Access discounts .704
Moderator: personality traits
In order to measure personality traits, participants completed John and Srivastava’s (1999) Big Five Inventory (BFI). The five personality traits measured using the BFI are
extraversion, agreeableness, conscientiousness, neuroticism and openness to experience.
This approach to conceptualising personality traits was developed by the authors to provide
a concise measure of the Five Factor Model of Personality while retaining sufficient reliability
of the scale (John & Srivastava, 1999). During the development of the BFI scale John and
Srivastava (1999) found the reliability of the scale to be good, with scores ranging from .79
de Zuñiga et al. (2017) who upon using the BFI scale, reported reliability scores ranging
from .69 to .81 (M = .74) for each of the five personality traits. The 44-item inventory asked
participants to rate themselves on a range of characteristics using a 7-point Likert scale
ranging from (1) ‘strongly disagree’ to (7) ‘strongly agree’. Each item is comprised of a short statement, such as “I see myself as someone who worries a lot”. All negative items were reverse coded. The attained Cronbach alphas from this research are presented in table 3.
These results suggest the scales have convergent validity.
As narcissism is separate from the Big Five Model, and thus the BFI, the
Hypersensitive Narcissism Scale was used to measure participants’ narcissism score, which has proven to be reliable in previous research (Hendin & Cheek, 1997). This 10-item scale
mirrors the BFI in that it asks participants to rate themselves on a range of characteristics
using a 7-point Likert scale ranging from (1) ‘strongly disagree’ to (7) ‘strongly agree’. Each item is comprised of a short statement, such as “I see myself as someone who dislikes sharing the credit of an achievement with others”.
A principal axis factor analysis was conducted on each of the six personality traits.
Unexpectedly, there were more than one factor for each trait with an Eigenvalue exceeding
1 following direct Oblimin rotation. An inspection of the screeplot exposed a clear break after
the first factor for each of the six personality traits. Thus, using Catell’s (1966) scree test a decision was made to retain only one factor for each personality trait. This decision was
supported by examining the total variance explained, which was relatively low for the
additional factors. Therefore, scales were computed for each personality trait by averaging
the corresponding items, as shown in table 3. The higher a participant scores on the scale,
the more they exhibit that personality trait (e.g. being open to experience versus being more
Table 3
Reliability of BFI and Hypersensitive Narcissism Scale
Personality traits M SD α Extraversion 4.72 1.05 .86 Agreeableness 5.08 0.80 .75 Conscientiousness 5.40 0.74 .78 Neuroticism 3.89 1.10 .86 Openness to experience 5.06 0.88 .81 Narcissism 4.03 0.95 .79 Results
Motivations and consumer-brand engagement
To test the hypothesis that the four identified motivations are positively related to
consumer-brand engagement on Instagram (H1), the relationship between each of the
variables was examined using Pearson’s correlation coefficient in order to determine the direction and size of the relationship. The outcome of the analysis showed that all four
motivations positively correlated with consumer-brand engagement and that these
relationships were significant. The results of the analysis can be found in table 4.
Table 4
Pearson’s correlation between motivations and consumer-brand engagement (CBE)
Variables 1 2 3 4 5 1. CBE - .58** .56** .61** .42** 2. Information seeking - .52** .50** .47** 3. Social influence - .47** .33** 4. Entertainment - .20** 5. Remuneration -
** Correlation is significant at the 0.01 level (2-tailed).
In order to explain the correlations a hierarchal multiple regression was undertaken
to calculate the ability of the four motivations in predicting consumer-brand engagement
a significant influence, but this influence was small, which is why it was controlled for.
Gender was recoded into a dummy variable, where 1 was male and 0 was female.
Preliminary analyses were conducted to ensure the assumptions of normality,
multicollinearity and homoscedasticity were not violated. Model one was significant, F(2,
174) = 7.69, p = .001, but the strength of the prediction was weak with only 8.1% of the
variance explained (R2 = .081). The second model, with the addition of the four motivations,
was also significant, F(4, 170) = 47.27, p <.001 and explained 56.5% of the variance (R2 =
.565). Entertainment, b = 0.30, b* = 0.35, t = 5.70, p < .001, 95% CI [0.20, 0.41] recorded the
highest beta value, followed by information seeking, b = 0.19, b* = 0.21, t = 3.03, p = .003,
95% CI [0.06, 0.31], social influence, b = 0.18, b* = 0.20, t = 3.12, p < .002, 95% CI [0.07,
0.30] and remuneration, b = 0.13, b* = 0.18, t = 3.11, p = .002, 95% CI [0.05, 0.21]. In
addition, age was statistically significant, b = -0.03, b* = -0.16, t = -3.13, p = .002, 95% CI
[-0.05, -0.01] whereas gender became non-significant b = -0.02, b* = -0.006, t = -0.12, p =
.906, 95% CI [-0.28, 0.31].
Big Five personality traits and narcissism
Pearson’s correlation coefficient was used to examine the relationship between each of the motivations and the personality traits in order to establish the direction and size of the
relationship. Results revealed that there were multiple significant relationships, which can be
Table 5
Pearson’s correlation between motivations and personality traits
Variables 1 2 3 4 5 6 7 8 9 10 1. Entertainment - .50** .47** .20** -.06 -.01 -.05 -.03 -.15 -.11 2. Information seeking - .52** .47** -.06 .15* .06 -.17* -.07 -.12 3. Social influence - .33** -.08 .02 .05 .04 .05 .06 4. Remuneration - -.04 .12 .06 -.09 -.21** -.12 5. Extraversion - .12 -.11 -.33** .20** -.28** 6. Agreeableness - -.09 -.26** .12 -.27** 7. Conscientiousness - .10 .02 .03 8. Neuroticism - .12 .62** 9. Openness - .20** 10. Narcissism -
** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
To further explore the moderating effects and answer RQ3 and RQ4, hierarchical
multiple regression analyses were carried out. Preparation for this involved computing
interaction terms between the motivation and each potential moderator. The interaction term
was then entered into block 2 of the hierarchical regression to check for significance and a
change in variance explained. If a significant interaction was found, then the PROCESS
macro for SPSS (Hayes, 2013) was run, applying model 1 across 5,000 bootstrap samples
and using 95% confidence intervals. Mean centering was applied in PROCESS before
creating the interactions as a way of reducing multicollinearity between the variables and the
terms of the interaction (Hayes, 2005).
Research question 3a asked which motivation(s) were the strongest predictors of
consumer-brand engagement for extraversion. Extraversion significantly interacted with
remuneration,b = 0.17, b* = 0.14, t = 2.07, p = .040, 95% CI [0.01, 0.32], but not
entertainment, b = 0.11, b* = 0.10, t = 1.66, p = .100, 95% CI [-0.20, 0.23], information
seeking, b = 0.07, b* = 0.07, t = 1.08, p = .280, 95% CI [-0.06, 0.20], or social influence, b =
in figure 2, shows the positive effect between extraversion and remuneration on increasing
consumer-brand engagement, with high representing +1 SD and low -1 SD. This means that remuneration content causes an increase in consumer-brand engagement for extraverted individuals.
Figure 2. Interaction between remuneration and extraversion on consumer-brand engagement.
Research question 3b asked which motivation(s) were the strongest predictors of
consumer-brand engagement for agreeableness. Agreeableness did not significantly interact
with entertainment, b = -0.03, b* = -0.03, t = -0.44, p = .663, 95% CI [-0.17, 0.11],
information seeking, b = 0.05, b* = 0.04, t = 0.63, p = .531, 95% CI [-0.10, 0.20], social
influence, b = -0.01, b* = -0.01, t = -0.08, p = .936, 95% CI [-0.16, 0.15], or remuneration, b =
0.02, b* = 0.02, t = 0.24, p = .812, 95% CI [-0.13, 0.17]. Based on these results there is no
moderating effect found for agreeableness.
Research question 3c asked which motivation(s) were the strongest predictors of
consumer-brand engagement for conscientiousness. Conscientiousness significantly
0 1 2 3 4
Low Medium High
Con su m er -b ra n d e n gage m en t Remuneration
interacted with remuneration, b = -.16, b* = -0.14, t = -1,99, p = .048, 95% CI [-0.31, -0.002],
but not entertainment, b = -0.03, b* = -0.03, t = -0.46, p = .646, 95% CI [-0.18, 0.11],
information seeking, b = -0.06, b* = -0.05, t = -0.80, p = .429, 95% CI [-0.21, 0.09], or social
influence, b = -0.04, b* = -0.03, t = -0.50, p = .619, 95% CI [-0.19, 0.11]. Simple slope
analysis, as illustrated in figure 3, shows the relationship between remuneration and
consumer-brand engagement becoming less prominent if an individual is more
conscientious, with high representing +1 SD and low -1 SD. This means that remuneration
content causes a decrease in consumer-brand engagement for individuals who are highly conscientious.
Figure 3. Interaction between remuneration and conscientiousness on consumer-brand engagement.
Research question 3d asked which motivation(s) were the strongest predictors of
consumer-brand engagement for neuroticism. Neuroticism did not significantly interact with
entertainment, b = -0.05, b* = -0.04, t = -0.72, p = .475, 95% CI [-0.18, 0.08], information
seeking, b = -0.07, b* = -0.06, t = -1.04, p = .301, 95% CI [-0.21, 0.06], social influence, b =
0.02, b* = 0.02, t = 0.26, p = .795, 95% CI [0.12, 0.15], or remuneration b = 0.10, b* = -0 1 2 3 4
Low Medium High
Con su m er -b ra n d e n gage m en t Remuneration
0.09, t = -1.34, p = .181, 95% CI [-0.24, 0.05]. Based on these results there is no moderating
effect found for neuroticism.
Research question 3e asked which motivation(s) were the strongest predictors of
consumer-brand engagement for openness to experience. Openness to experience did not
significantly interact with entertainment, b = -0.06, b* = -0.05, t = -0.84, p = .402, 95% CI
[-0.19, 0.08], information seeking, b = -0.04, b* = -0.03, t = -0.50, p = .622, 95% CI [-0.18,
0.11], social influence, b = -0.06, b* = -0.05, t = -0.76, p = .448, 95% CI [-0.20, 0.89], or
remuneration, b = -0.07, b* = -0.06, t = -0.92, p = .357, 95% CI [-0.23, 0.08]. Based on these
results there is no moderating effect found for openness to experience.
Research question 4 asked which motivation(s) were the strongest predictors of
consumer-brand engagement for narcissism. Narcissism did not significantly interact with
entertainment, b = -0.14, b* = -0.14, t = -0.23, p = .819, 95% CI [-0.14, 0.11], information
seeking, b = 0.04, b* = 0.04, t = 0.60, p = .563, 95% CI [0.19, 0.10], social influence, b = -0.04, b* = --0.04, t = -0.60, p = .549, 95% CI [-0.18, 0.10], or remuneration, b = 0.02, b* =
-0.02, t = -0.33, p = .741, 95% CI [-0.17, 0.12]. Based on these results there is no moderating
Table 6
Results
H1 The motivations of information seeking, social influence,
entertainment and remuneration are positively related to consumer-brand engagement on Instagram.
Supported
RQ3a Which motivation(s) are the strongest predictors of consumer-brand engagement on Instagram for extraversion?
Effect on remuneration RQ3b Which motivation(s) are the strongest predictors of consumer-brand
engagement on Instagram for agreeableness?
No effect RQ3c Which motivation(s) are the strongest predictors of consumer-brand
engagement on Instagram for conscientiousness?
Effect on remuneration RQ3d Which motivation(s) are the strongest predictors of consumer-brand
engagement on Instagram for neuroticism?
No effect RQ3e Which motivation(s) are the strongest predictors of consumer-brand
engagement on Instagram for openness to experience?
No effect
RQ4 Which motivation(s) are the strongest predictors of consumer-brand
engagement on Instagram for narcissism?
No effect
Discussion
The aim of this research was to investigate which motivations drive consumer-brand
engagement on Instagram from a UGT perspective. In addition, the moderating roles of the
Big Five personality traits and narcissism were explored. Findings from this study revealed
that Instagram users have four motivations for engaging with brands. Entertainment was
found to be the strongest predictor of consumer-brand engagement, accounting for 37.2% of
the variance, followed by information seeking (10.2%), social influence (4.2%) and
remuneration (2.5%). These results suggest that individuals primarily interact with brands on
Instagram as a way to pass time, to relax or for enjoyment purposes.
Furthermore, findings from this study show that there was a small significant
interaction between extraversion and remuneration, with extraversion strengthening the
relationship between remuneration and consumer-brand engagement. This interaction
however, was not present for extraversion in relation to any of the remaining three
motivations. Similarly, there was a small significant interaction between conscientiousness
between remuneration and consumer-brand engagement. As before, this interaction effect
was not present for conscientiousness in relation to any of the remaining three motivations.
In contrast, agreeableness, neuroticism, openness to experience and narcissism were found
not to moderate the relationship between motivations and consumer-brand engagement on
Instagram. Interestingly, there was no main effect found for any of the six personality traits
on consumer-brand engagement either.
An explanation of these interactions comes from extant literature on the Big Five.
Research shows that extraversion is one of the strongest drivers for consumer engagement
online (Islam, Rahman & Hollebeek, 2017), which explains the positive relationship found on
consumer-brand engagement within this study. Likewise, previous research has uncovered
a negative association between conscientiousness and consumer engagement (Marbach,
Lages & Nunan, 2016), which is reflected in the results of this study; high levels of
conscientiousness weakened the relationship with consumer-brand engagement. It is
unclear, however, why these personality traits interacted with remuneration. One could
postulate that the variations found on remuneration may be due to differences in intrinsic
and extrinsic motivations among participants. Future research would therefore benefit from
more in-depth investigation into the drivers of personality traits, as intrinsic and extrinsic
motivations (arguably relating to conscientious and extraverted individuals respectively) may
lead to different behavioural outcomes where incentives and rewards are concerned (for an
example see: Sook Kwon, Kim, Sung, Yun Yoo, 2014).
Theoretical Implications
This study offers three key theoretical contributions. Firstly, it contributes to the
existing UGT literature by not only empirically testing UGT in the context of the popular, but
still relatively new SNS, Instagram, but also by confirming the underlying principles of UGT,
namely that motivations are vital in understanding individuals’ use of media (Katz, 1959; Muntinga, 2013). The regression models predicted over half of the variance in
consumer-brand engagement (54.1%) in spite of critiques on the applicability of UGT in research
pertaining to new media.
Secondly, through partially replicating the work of Muntinga, Moorman and Smit
(2011), this study provides empirical evidence that furthers the theoretical discussion on
COBRAs. The results of this study are consistent with previous research that found
entertainment to be the strongest predictor of consumer-brand engagement (Cvijikj &
Michahelles, 2013; Muntinga, 2013; Phua, Jin & Kim, 2017). Thereby confirming Muntinga’s (2013) observations that COBRAs are “primarily driven by information and entertainment [motivations], and subsequently by other motivations” (p.19).
Finally, the findings from this study provide continuing support that McQuail’s (2010) categorisation of motivations, considered the dominant approach, retain their relevance
when applied to Instagram. Yet, this observation is in conflict with the findings of Voorveld et
al. (2018) who concluded that engagement is “highly dependent on the platform” (p.50) and that different platforms are thus experienced in different ways. As opposed to Voorveld et
al.’s work (2018), this study finds that the motivations to partake in consumer-brand engagement on Instagram are considerably similar to not only the motivations found to
underlie traditional media, but also other SNS.
Practical Implications
This study also provides valuable practical implications for our understanding of what
motivates individuals to participate in consumer-brand engagement, thus providing brand
and marketing managers with strategic insights into how to facilitate engagement with their
brand. Contrary to the findings of Islam, Rahman and Hollebeek (2017), managers should
not segment their audience on the basis of personality traits. Instead, content type appears
to drive engagement. Given the dominance of the entertainment motivation, brand-related
employing other motivations. However, depending on a brand’s objectives, other motivations should not be disregarded in favour of entertainment if they align with specific outcomes.
Limitations and future research
Despite gained insights into the relationship between motivations, personality traits
and consumer-brand engagement, this research is not without its limitations. The first
limitation is that the sample was recruited via a non-random sampling approach and
therefore any inferences made cannot be generalised to the wider population. Moreover,
since participants completed a self-report survey, they may have submitted socially
desirable responses, leading to a bias in the results since they do not accurately reflect
motivations and personality traits. Furthermore, despite the survey method being an
appropriate and validated choice for investigating motivations and personality traits, it cannot
establish causality. Next, the scale used to measure consumer-brand engagement was a
newly constructed scale based specifically on the functionalities offered by Instagram. While
it was partially constructed based on feedback from the pilot-test and deemed to be reliable,
it may not be a comprehensive representation of activities on Instagram relating to
consumer-brand engagement. Going forward, research should continue to test and develop
this scale in order to increase its validity.
Additionally, the motivations confirmed within this research are only relevant for
Instagram’s current functionality. As Instagram continues to evolve and introduce new features, the motivations that drive consumer-brand engagement are likely to evolve and
change in salience. This evolution has already been witnessed on other SNS, such as
Facebook, where early research into the platform identified motivations which are less
applicable in Facebook’s current state (Allhabash & Ma, 2017). It is therefore recommended that future research should reconfirm the significance of the identified motivations following
Finally, future research should look into additional motivations that may explain the
remaining 45.9% of the variance not explained by the entertainment, information seeking,
social influence and remuneration motivations. Other motivations, such as those from newly
emergent literature on Instagram (e.g. documentation, see Sheldon & Bryan, 2016;
archiving, see Lee et al., 2015) could provide additional insights into what drives
consumer-brand engagement. Moreover, the incorporation of a qualitative element, such as through a
mixed-method approach, could provide a deeper understanding into what underlies
motivations to engage with branded content, such as the intrinsic and extrinsic motivation to
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