Two Essays on Social Media Usage:
The Impact Social Media Mere Exposure on Brand Choice and The Profile of the
Social Media Maven
by
William F. Humphrey, Jr., M.B.A.
A Dissertation
In
Marketing
Submitted to the Graduate Faculty
of Texas Tech University in
Partial Fulfillment of
the Requirements for
the Degree of
Doctor of Philosophy
Debra A. Laverie, Ph.D.
James B. Wilcox, D.B.A.
Roy D. Howell, Ph.D.
Shannon B. Rinaldo, Ph.D.
Mark Sheridan
Dean of the Graduate School
May, 2015
Copyright 2015, Humphrey, Jr., William
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Texas Tech University, William F. Humphrey, Jr., May, 2015
Dedicated to
the five women in my life who have inspired my career and encouraged
my personal development over the years.
I would not be here without your influence and support.
Joyce Humphrey, Mable Shelnutt, Dr. Louise Luchsinger,
Vicki Freed, and Dr. Debbie Laverie
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Texas Tech University, William F. Humphrey, Jr., May, 2015
ACKNOWLEDGMENTS
Much guidance and support was provided throughout the dissertation and
defense from the committee, and I would like to recognize all four members who
graciously served on this committee: Chairperson Debbie Laverie, Jim Wilcox, Roy
Howell, and Shannon Rinaldo. I would first like to thank the Chairperson Debbie
Laverie for her unwavering support and guidance; her willingness to take on a new
research area of social media was critical to my success. In Essay 1, Studies 1 & 4
were conceived in her consumer behavior seminar, and her encouragement was critical
in the completion of the research program. I also wish to thank her for her counsel on
identity theory and role-identities. Her 2002 Journal of Consumer Research article
inspired Essay 2’s investigation of the social media marketing maven from the
viewpoint of social media influencer as a role-identity. Additionally her ongoing
support of my development as a scholar has been unwavering; I seek to be the leader
and scholar she is as I start my career. Dr. Laverie, thank you for opening up a world
of opportunities.
Second, I wish to thank Jim Wilcox for his support and training on
measurement. The scales used in Essay 2 were refined during his seminar on the
topic, and, his guidance on survey design during my time as his research assistant was
incredibly valuable; I designed an instrument that was effective and efficient with the
respondents’ time based on what I learned during his mentorship. His comments in
the final manuscript refinement were of great help as well, and his support of me from
beginning to end of my program have been vital to my growth. Dr. Wilcox, thank you
for taking a chance on a non-traditional Ph.D. student with equally non-traditional
research interests.
Third, I value Roy Howell’s training in theory testing, particularly in the
method of structural equations modeling. His deep knowledge on this strict test of
theory was incredibly helpful as I tested the relationships of the constructs. As some
of these constructs had not been tested using SEM, his incredibly deep knowledge
helped resolve subtle issues that came up during analysis. His ability to take his vast
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Texas Tech University, William F. Humphrey, Jr., May, 2015
knowledge on this method and make it applicable and understandable was a key
contribution to the completion of this research. Dr. Howell, thank you for teaching me
a method that will serve me throughout my career and always letting me interrupt your
Sunday work-time.
Next, I thank Shannon Rinaldo for her support and mentorship during the
design and execution of the experiments in Study 1. Her wealth of knowledge on
experimental design guided my work, and she challenged me to carefully structure the
studies with the greatest contributions in mind. Additionally, Dr. Rinaldo created the
student research panel that introduces students to the research of the faculty through
participation in studies. Without the creation and supportive structure of the panel, the
studies executed here would have not been possible. She has always been very
supportive with her guidance and generous with her time. Dr. Rinaldo always had
time to guide me, even during the busiest time of her academic career. Dr. Rinaldo,
thank you for pushing me to be better and to establish a research stream to support my
faculty career.
As I move to the next stage of my career with my first faculty position, I know
that the guidance I received from my committee has prepared me for a productive
career in research. Each has mentored me in different ways that will serve me well in
my future career. I cannot overstate the generosity and contribution of Drs. Laverie,
Wilcox, Howell, and Rinaldo. Thank you all!
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Texas Tech University, William F. Humphrey, Jr., May, 2015
TABLE OF CONTENTS
ACKNOWLEDGMENTS ................................................................................... iv
ABSTRACT ........................................................................................................ viii
LIST OF TABLES ................................................................................................ x
LIST OF EXHIBITS ............................................................................................ xi
LIST OF FIGURES ............................................................................................ xii
CHAPTER I ........................................................................................................... 1
Introduction ....................................................................................................... 1
Statement of Problem ........................................................................................ 2
Overview of Essay 1 ......................................................................................... 3
Overview of Essay 2 ......................................................................................... 6
CHAPTER 2 .......................................................................................................... 9
ESSAY 1: BRAND CHOICE VIA INCIDENTAL SOCIAL MEDIA
EXPOSURE ........................................................................................................... 9
Involvement..................................................................................................... 13
Word of Mouth................................................................................................ 15
Types Of Social Media Interactions ................................................................ 15
Brand Updates ........................................................................................... 16
Social Media Ads ...................................................................................... 17
Brand Endorsements and Consumer-Generated Content .......................... 18
From Incidental Brand Exposures to Brand Outcomes................................... 18
Method ............................................................................................................ 19
Study 1: Mere Exposure in Social Media ................................................ 26
Study 2: Ad Units and Brand Stories ....................................................... 28
Study 3: Consumer and Brand Generated Content ................................... 31
Study 4: In-Group Versus Out-Group ...................................................... 33
Discussion and Limitations ............................................................................. 37
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CHAPTER 3 ........................................................................................................ 43
ESSAY 2: IN SEARCH OF THE SOCIAL MEDIA MAVEN: THE ROLE OF
COMMITMENT, APPRAISAL AND IDENTITY SALIENCE IN PROPENSITY
TO SHARE ON SOCIAL MEDIA SITES ........................................................ 43
Introduction ..................................................................................................... 43
Conceptual Framework: Propensity to Share in Social Media and Personal Factors
......................................................................................................................... 45
Methodology ................................................................................................... 57
Sample and Procedures ............................................................................. 57
Construct measures ................................................................................... 58
Analysis ........................................................................................................... 61
Findings ..................................................................................................... 66
Discussion and Limitations ....................................................................... 72
CHAPTER 4 ........................................................................................................ 76
Conclusion ...................................................................................................... 76
BIBLIOGRAPHY ............................................................................................... 81
APPENDIX A: ESSAY 1 CONSTRUCT DEFINITIONS ............................. 92
APPENDIX B: ESSAY 1 SUMMARY OF HYPOTHESES .......................... 93
APPENDIX C: ESSAY 1 MEASURES & CONCEPTUALIZATIONS ....... 94
APPENDIX D: ESSAY 2 CONSTRUCT DEFINITIONS ............................. 95
APPENDIX E: ESSAY 2 SUMMARY OF HYPOTHESES .......................... 96
APPENDIX F: ESSAY 2 MEASURES ............................................................ 97
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Texas Tech University, William F. Humphrey, Jr., May, 2015
ABSTRACT
As consumers rapidly adopt social media, marketers recognize social media
comprises an important part of the integrated marketing communications (IMC) mix.
Social media is used to cultivate relationships with consumers; in addition other
possible outcomes include consumer word of mouth, brand-to-consumer problem
resolution, and influence on consumer attitudes toward a brand, such as purchase
intention. In Essay 1, implications related to mere exposure to brands are explored
through an experiment with four studies conducted with rapid presentation of a social
media site. These studies test differences between types of social media exposure and
brand choice in both high-involvement and low-involvement product scenarios.
Further, content originated by brands for both high- and low-involvement products is
tested against content originated by consumers as it relates to brand choice. Finally,
the impact of reference group is tested on consumer and brand generated content about
high and low involvement product brand choice. In Essay 2, the concept of the market
maven in social media is explored through identity theory. Social media breadth
(including network size, media commitment, and frequency of usage) is investigated
in relation to two types of appraisal (self- and reflected) of perceived influence on
social media sites. These appraisals are examined against identity importance; in the
hierarchy of role-identities, participants who identify that social media is identity
salient are posited to have a propensity to share product information in social media
(as measured by the market maven construct). The outcome of this study provides
initial exploration of antecedents of the social media market mavenism and what
appraisals influence propensity to share product information online. Together, these
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two initiatives apply the theoretical lenses of incidental exposure, social identity, and
market maven theory to better understand how consumers behave in regards to brands
given this new context of social media communities.
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LIST OF TABLES
Essay 1
Table 1: Summary of Hypotheses
Table 2: Summary of Measurement Items
Essay 2
Table 3: Summary of Hypotheses
Table 4: Measurement items for Essay 2
Table 5: Estimation of Structural Model: Modification History
Table 6: Parameter Estimates
Table 7: Factor Loadings
Table 8: Model Fit
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Texas Tech University, William F. Humphrey, Jr., May, 2015
LIST OF EXHIBITS
Essay 1
Exhibit A: Construct Definitions
Exhibit B: Summary of Hypotheses
Exhibit C: Measurement Items
Essay 2
Exhibit D: Construct Definitions
Exhibit E: Summary of Hypotheses
Exhibit F: Measurement Items
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LIST OF FIGURES
Figure 1: Sample social media screen with high-involvement ad unit.
Figure 2: Study 1 Comparison of brand choice
Figure 3: Study 2 Ads, Brand Stories, and Involvement
Figure 4: Study 3 investigation of product involvement and consumer or brandgenerated content
Figure 5: Study 4 investigation of product involvement, content type, reference group,
and brand choice
Figure 6: Conceptual model of antecedents of social media mavenism
Figure 7: SEM Results
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CHAPTER I
Introduction
In relationship marketing, firms seek to establish and maintain profitable
relationships with customers indefinitely (Morgan and Hunt 1994). To cultivate and
grow relationships, marketers have many different ways in which to communicate
with consumers and other partners as part of an integrated marketing communications
(IMC) approach (Garretson and Burton 2005; Naik and Raman 2003). This integrated
approach combines a number of diverse and ongoing marketing communications
methods to engage consumers across channels. An emerging way that marketers
engage consumers is via online social media websites, such as Facebook, Twitter, and
Instagram. As of 2011, 83% of brands engaged consumers through some form of
social media (Naylor, Lamberton, and West 2012), and social media marketing has
been synonymized with word of mouth marketing (Kozinets, Valck, Wojnicki, and
Wilner 2010). In this form of marketing communications, the firm seeks to influence
consumer-to-consumer (C-to-C) brand conversations (Kozinets et al. 2010); a variety
of communication types exist, including advertising and brand-originated content.
Consumers have adopted social media in vast numbers; exemplar social network
Facebook claims over 1.2 billion active users monthly, with sixty percent of users
visiting the site at least every other day (Facebook.com 2014a). Additionally, time
spent on social media websites is nearly double the time spent of other types of
websites (Wilcox and Stephen 2013). As a result of this shift in online media
consumption, brand managers recognize that social media must be considered in
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integrated marketing communications efforts; many are allocating firm resources and
successfully cultivating large communities. Exemplar brands that have cultivated
large online followings include: Coca-Cola – 79 million fans (Coca-Cola 2014),
McDonald’s – 29.6 million fans (McDonald’s 2014), Skittles – 25 million fans
(Skittles 2014), Southwest Airlines – 4.1 million (Southwest Airlines 2014), Secret
Antiperspirant – 1.8 million fans (Secret and Proctor & Gamble 2014).
It can be argued that each of these brands invests resources into social media as
a communication channel as part of a comprehensive customer relationship
management approach. Each brand has cultivated large online networks in social
media, and the respective product categories of each brand likely vary in consumer
involvement; for example, consumers may rate airlines as higher involvement than a
candy brand. From a scholarly research standpoint, the academy has recognized the
importance of research into the emerging social media phenomenon (Kotler 2011;
Sheth 2011). Day (2011) argues that consumer-to-consumer conversations in social
media increase the complexity of marketing information that a firm must process to
adapt its strategy. Despite these calls for research, investigations related to the impact
of social media remain nascent in the marketing literature.
Statement of Problem
While researchers and practitioners both have called for research on social
media consumer behavior because of the rapid consumer adoption, questions on the
impact of social media marketing remain. Recent research on 2,500 of the top social
media brands found that across six of the top seven networks only 0.1% of consumers
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who follow a brand actually interact with that brand in social media (Elliott 2014).
This trend implies that while marketers are cultivating large communities, explicit
engagement with brands (such as commenting or endorsing brand posts) remains
extremely low; justifying a significant investment in human capital for these activities
becomes more difficult. Despite this low explicit engagement, do other measures of
success that researchers can corroborate as resulting from brand social media activities
through academic research exist? Is there an impact on brand attitudes and intentions,
such as brand choice, from mere exposure to a brand in a social media context?
Further, what factors influence the sharing of word of mouth in social media channels
by influential consumers? If brand interactions are low, do other factors exist that
further justify brand participation (and consequently, allocation of resources towards
social media marketing and service)? These two essays seek to tie these ideas together
through an investigation of two key points of brand advocacy, product purchase and
sharing online word of mouth on social media sites, to provide insight on these
questions.
Overview of Essay 1
The first essay explores whether consumers who see a brand in a mere
exposure scenario (incidental exposure without time for conscious processing) are
influenced when faced with a brand choice at a later point in time. Much research
exists on how consumers process information in high-involvement and lowinvolvement states; scholars have explored various durations of exposure as well.
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Additionally, investigations into the impact of reference groups, or consumers
perceived to be similar to the observer, on product choice have been cited in the
literature (Ferraro et al. 2009). To date, an integrative exploration on the impact on
brand choice in a social media context when exposure is incidental (operationalized as
mere exposure) has not been completed. Moreover, it can be argued that not all social
media brand interactions are equal. Some social media brand efforts closely resemble
banner advertising, while others appear as content similar to that shared by a
consumer, known as sponsored content. Researchers have established that incidental
exposure to brands has been associated with later brand choice (Ferraro, Bettman, and
Chartrand 2009) when presented in very controlled photographic images. Some
questions remain. Does this relationship change in a more complex social media
environment where advertising, brand content, and consumer posts all co-exist in the
same space? Are there differences in the likelihood of choosing a brand when viewed
as a traditional online advertisement versus when the content is presented as a story on
the news and updates section of a social media site? How does the influence of
reference group (in-group versus out-group) affect brand choice? How do these
relationships change in relation to a high involvement versus a low involvement
product category? Essay 1 seeks to address these questions.
In Ferraro et al. (2009), photographic images of consumers were presented. A
number of images included a water bottle subtly placed with a logo shown. The
scholars tested whether participants viewing a logo in mere exposure settings
influences brand choice, which was corroborated. Additionally, the influence of
reference group was tested, which indicated that whether the consumers represented in
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the picture (either in-group or out-group) influenced brand choice. Building on this
research, the present investigation employs an experiment that rapidly presents images
with brand mentions or images embedded in the context of social media, in this case a
Facebook-inspired fictitious social network. The studies within this research test
whether incidental exposure in social media is related to future brand choice, while
extending the previous work to include content generated by brands versus consumers
and in scenarios with products pretested as high- versus low-involvement by
consumers. The strength of the effect will be tested against the type of presentation,
including display ad unit, consumer-generated posts, and sponsored ad/contextual
presentation. Additionally, the concept of reference group will be tested.
This research is designed to corroborate the work of Ferraro et al. (2009) that
exposure to brands in mere exposure settings influences brand choice and that the
influence of reference group further influences choice. It extends this work by testing
the mere exposure effects in a visually and socially complex setting of social media
websites. Additionally, it further extends the initial work on mere exposure that
despite exposure to many brands (in the form of ads and brand stories) in social media
settings, consumers are still likely demonstrate an influence in brand choice when
mere exposure to the focal brand occurs. This work also examines the influence in
both high and low involvement product categories, and it tests a variety of content
presentation styles, including visual style (advertising units versus brand stories),
source of content (consumer versus brand-generated posts), and reference group in
consumer generated stories (in-group versus out-group).
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Overview of Essay 2
Continuing the investigation of outcomes beyond explicit consumer
engagement with brands on social media sites, Essay 2 shifts from brand choice to an
investigation of predictors of propensity to share brand-related content in social media
environments. While brand choice is critical for firms, stimulating positive word of
mouth from consumers is gaining significant focus (Brown, Broderick, and Lee 2007;
Keller 2007; Kozinets et al. 2010; Lim and Chung 2011; Zhang, Craciun, Shin 2010).
First, commitment has been defined as “an enduring desire to maintain a valued
relationship” (Moorman et al. 1992 p. 316); it can be argued that the relationships
fostered on social media facilitate this commitment. The concept of commitment to
media consumption has been researched by Laverie, Kleine, and Kleine (2002) as an
index that encapsulating the number of media types consumed by an activity
participant. For the present study, social media participation will be investigated,
including network size, frequency of participation, and media commitment as
measured by the number of social networks on which a consumer participates.
Together, these concepts will be referred to collectively as social media breadth,
representing the degree of commitment that a participant has to online social media.
Next, two types of appraisals will be explored; self appraisal provides insight into how
social media participants view themselves; while reflected appraisal provides insight
on how participants perceives others view them (Laverie et al. 2002). The importance
to the participant’s identity of social media community participation is then explored
through the concept of identity salience, which focuses on the importance of a roleidentity on the global self (Callero 1985; Laverie and Arnett 2000; Laverie et al. 2002;
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Reed II 2004). Finally, the concept of propensity to share online with a social network
as a market maven will be explored (Feick and Price 1987).
For researchers, understanding what factors predict sharing online word of
mouth holds value, particularly insights related to heavy users of social media who
have cultivated large networks. Word of mouth has been cited as important when
consumers lack adequate information for a product purchase (Liu 2006), and social
media and online channels provide a “megaphone” effect (McQuarrie, Miller, and
Phillips 2013); consumers now have capabilities to broadcast messages to not only
their network but also to myriad other consumers online. Accordingly, what influence
does this identity salience have on propensity to share product information online? If
sustained social media participation reinforces role-identity of an influential consumer
in online communities, does that influence specific online behaviors (like sharing
product information) (Wilson, Gosling, and Graham 2012; Zhao, Grasmuck, and
Martin 2008)? If participants appraise their own influence online (and posit that
others view their expertise in a particular light), how do these appraisals influence the
propensity to share product information in social media environments? How does
social media usage, including frequency, network size, and length of time participating
in the communities impact a participant’s appraisal? Essay 2 seeks to provide insight
to these questions.
The contribution of this research ties identity theory to social media
participation and the likelihood of sharing brand information with an online
community, which provides a new perspective in research into predictors of word of
mouth. Further, tying back to the overall research aims of this dissertation, insights
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into word of mouth generators provides an advance in knowledge to compensate for
the existing lack of consumer-brand engagement in social media. As Essay 1
establishes the importance of consumer-generated word of mouth on brand choice
(Studies 3 and 4), Essay 2 further explores the profile of influential diffusers of
product information through social media. Together, these essays aim to increase the
academy’s understanding of the phenomenon of social media usage in brand choice
and word of mouth scenarios.
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CHAPTER 2
ESSAY 1: BRAND CHOICE VIA INCIDENTAL SOCIAL MEDIA
EXPOSURE
Consumers continue to adopt social media in staggering numbers; according to
a Pew Internet Research study in 2013, 71% of U.S. adult consumers participate on
social media sites (Duggan and Smith 2013); this number soars to 94% when
examining U.S. teenage consumers (Madden 2013). Time spent on social media sites
across all users is double that of other online activities (Wilcox and Stephen 2013).
Facebook lists its active user base as exceeding 1.23 billion active participants
monthly, while Twitter boasts 241 million monthly active users (Facebook.com
2014a; Twitter.com 2014). Facebook facilitates frequent sharing and consumption of
shared updates via mobile; over 945 million users access Facebook via mobile
(Facebook.com 2014a). Users are adopting social media in significant numbers, and
their time spent on the activity merits investigation. That said, consumer interactions
with brand-generated content prove minimal; research indicates that only 0.1% of
consumers interact with brand content for the top 2,500 brands on social media (Elliott
2014). Based on this statistic, consumers are indicating brand preference through
following brands on social media sites, but explicit follow-up consumer-to-brand
interactions are rare. Given this fact, are there other tangible benefits for the resources
brands allocate to maintaining a social media marketing strategy as part of a larger
integrated marketing communication strategy?
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Every day, active social media participants log in to their account to interact
with their friends, colleagues, and acquaintances. In some cases, they interact with
brands explicitly, such as the act of liking a brand page on Facebook. In this case,
consumers will see a subset of the updates that a brand releases. In other cases, the
interaction with brands may be incidental and not intentional, such as seeing a brand
advertised or mentioned by people on Facebook. Ferraro et al. (2009) call these brief
brand encounters incidental consumer brand exposures (ICBE), while other scholars
reference these brief exposures as mere exposure (Fang, Singh, and Ahluwalia 2007;
Janiszewski 1993). Most social media participants see brands discussed, advertised,
and mentioned through various sources, including the brand itself, strangers, and
friends, each time they visit a social media site.
The present research seeks to investigate whether incidental exposure to a
brand in a social media context results in brand choice in varying contexts, including
visual presentation, source of message, and interpersonal influence of reference group.
Within social media sites, top consumer brands have opportunities to develop a large
base of online brand advocates, providing an incremental consumer touch point within
a brand’s marketing strategy (either via an opt-in relationship with the consumer or
through incidental exposure). These brand followers are described as ‘liking’ a brand
(Naylor et al. 2012). Within the brand community literature, such online brand
forums have been characterized by traits of traditional communities, such as a sense of
belonging, devotion to the brand, and lack of geographical-constraint (Muñiz Jr and
O’Guinn 2001). Brand-generated content and brand participation in the community
differentiate contemporary social media sites from those described in the initial brand
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community research; early researchers defined initial brand communities as being
built and maintained by the brand’s supporters with minimal formal interaction from
the brand (Muñiz Jr and O’Guinn 2001). Initial research indicates that simply
exposing the composition of a brand’s consumer fan base influences consumer brand
choice (Naylor et al. 2012). While the effect of the composition of a brand’s fan base
has been investigated, the influence of different types of brand exposure in social
media on brand choice has not been considered. The purpose of this paper is to further
Ferraro et al.’s (2009) work related to mere exposure and brand choice, extended into
the context of social media, with consideration given to the source of content. In this
prior research, consumers were shown a number of images for short durations; select
images for some participants showed a branded water bottle, while others did not see
the embedded brand. After viewing the sequence of images, study participants were
offered a bottle of water of their choice as they exited the lab. Their brand choice was
recorded and associated with the condition they viewed (brand v. no brand).
Additionally, later studies added the influence of reference group, where in-group and
out-group were signaled by university logos on clothing in the images. The out-group
was portrayed wearing logo wear apparel from a competitor’s university to measure
whether reference group further influenced brand choice after incidental exposure to a
brand in a series of images. While the aforementioned research held empirical support
of the impact of mere exposure on later brand choice, the complexity of the
information processed by participants was low. Extending research by taking into
account added complexity of different levels of product involvement, content
presentation types (including the more complex visual elements found in social media
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sites), and consumer versus brand generated content allows for an investigation of the
impact of source of message (word of mouth versus brand advertising in story format),
while the experimental design controls for potentially confounding influences. The
present work aims to corroborate and extend the aforementioned research with
investigations related to display ads or consumer-to-consumer conversations and brand
choice, while increasing the visual complexity of factors consumers likely encounter
online (including ads, consumer posts, website navigation elements, and other visual
content).
While investigations related to social media interactions may be an emerging
area of the literature, the consequences of banner advertising online have been
explored previously. Online advertising has been described as inducing “online
clicking, which links the consumer to the brand’s target communication (usually the
target company’s website), and ultimately on to a purchase” (Shamdasani, Stanaland,
and Tan 2001 p. 7). Sherman and Deighton (2001) find that context of the site on
which a banner is placed affects click-though (the act of the consumer clicking the
banner ad to visit the site being advertised) with relevant fit between site content and
visitor interests resulting in the greatest consumer click-through. Brand awareness and
ad recall are proposed as appropriate measures of success for banner advertising,
compared to click-through rates (Varadarajan and Yadav 2009). In their review of the
impact of online marketing communications, Shankar and Batra (2009) call for
additional investigation into advertising effectiveness and processing in light of
consumer-to-consumer online communications, such as those found in social media
conversations. Calder, Malthouse, and Schaedel (2009) argue that context affects
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advertising effectiveness; websites that engage visitors to read content are more likely
to have effective advertising efforts. Additionally, repeated exposure to an online
advertisement has been shown to result in greater brand name memory, while varied
ad executions have been associated with intention to click compared with same ad
repetition (Yaveroglu and Donthu 2008).
Levels of exposure to online advertising have been cited as important for brand
outcomes. Varied attention levels (attentive versus inattentive consumers) to web
advertising have resulted in varying levels of favorable brand attitudes (Yoo 2007). A
positive relationship between level of exposure and brand attitude has been found,
including attitude towards the ad, attitude towards the brand, and purchase intention
(Cho, Lee, and Tharp 2001). To some consumers, research indicates that a
“blindness” to certain ad placements may develop over time, including ads on the right
side of a page like used on Facebook when compared with more centrally placed
advertising (Hsieh, Chen, and Ma 2012; Hsieh and Chen 2011; Owens, Chapparo, and
Palmer 2011).
Involvement
Involvement with brands has been posited as a relevant factor in online
advertising; Shamdasani et al. (2001) propose that relevance of a product ad to its
website environment is most important for high-involvement product advertisements
where reputation is most critical when involvement with a product is low.
Zaichkowsky (1995) outlines three types of involvement that consumers experience,
including:
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1. Personal—inherent interests, valued, or needs that motivate one toward the
object
2. Physical—characteristics of the object that cause differentiation and
increase interest
3. Situational—something that temporarily increases relevance or interest
toward the object. (Bloch and Richins 1983; Houston and Rothschild 1978;
Zaichkowsky 1995 p. 342)
Each of the three types of involvement may influence differences in attitudes and
intended behaviors towards a brand. From a digital perspective, online branded
content may elicit higher levels of involvement (situational) in comparison to banner
ads, which may influence consumer brand attitudes (Becker-Olsen 2003). Further,
this involvement has been shown to offset the consumer processing efforts by
influencing brand attitudes (Becker-Olsen 2003). Mollen and Wilson (2010) posit that
involvement (personal) may extend beyond the traditional definition of consumer
relevance and importance in relations to products; instead, involvement may extend to
the context of an online website environment (situational), as evaluated by the
consumer. The typical website consumer experience is described as low-involvement
(situational) towards ads, as consumers allocate attention to their main activities of
information search and entertainment (Cho 2003; Yoo 2009). Familiarity with a brand
has demonstrated higher product involvement levels (personal) in online exposure
than expected in other contact settings (Wansink and Ray 2010).
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Word of Mouth
A desired outcome for brands allocating resources to social media marketing is
consumer electronic word of mouth. Word of mouth has been associated with
increased sales (Berger and Schwartz 2011), being an antecedent and consequence of
retail sales (Duan, Wenjing, and Whiston 2008a), resulting in greater enjoyment of
product consumption experience (He and Bond 2013), and serving as a surrogate to
personal brand or product experience (Jones, Aiken, and Boush 2009). Online
advertising has been associated with stimulating offline brand advocacy (like word of
mouth), online brand searches, and website visits (Graham and Havlena 2007).
The classic profile of a consumer spreading word of mouth has been described
as diffuser of consumer information related to product experiences and consumption,
also known as the market maven (Feick and Price 1987; Goldsmith, Flynn, and
Goldsmith 2003). These consumers pride themselves on early adoption product
experiences and trust earned through opinions shared with their network (Clark and
Goldsmith 2005). For advertising efforts executed online, the market maven’s
propensity to share with their network is of particular interest. If firms invest in social
media advertising, propensity to share product information as an outcome may hold
positive consequences, beyond the standard advertising effective measures of click
through, purchase, or attitude towards the ad.
Types Of Social Media Interactions
Within this research, the context of the brand exposure will be tested to
determine if there exists a contextual influence on brand choice. To structure this
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experiment, a fictitious Facebook-like social network site was presented as the
exemplar for social media interactions because it mimics the leading social network
and provides a variety of brand communication styles. For the purposes of reviewing
the types of exposure consumers encounter, Facebook is discussed in the coming
sections. Branded “Fan Pages” epitomize the core of a brand’s presence on Facebook;
this page type allows the brand to have a community ‘home’ and a forum for
consumer discussions. Consumers typically show support for brands on Facebook by
selecting the “like” button, and using this feature adds the brand to the user’s profile as
a favorite. Also fundamental to the Facebook experience is the News Feed, which
features status updates from a user’s friends and updates from brands consumers
follow, and specific user-generated activity types (such as becoming a fan of a brand’s
page or writing on a brand’s wall) will show up on that user’s wall (a message boardlike feature that displays a social media participant’s activity on the site and public
notes left by friends) (Facebook.com 2014b).
Brand Updates
A promotional update acts as the most obvious form of brand activity within
social media. The site participant’s News Feed broadcasts these updates, typically
shared by brands to provide a tactical or promotional message to the fan base.
Facebook allows brand fans to provide immediate feedback to the brand through
comments or a “like” option. Additionally, a share feature allows participants to
spread a brand’s updates, along with any comments, to their followers via the News
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Feed, assuming that their followers have not filtered out the participant’s updates
(Facebook.com 2014c)
Social Media Ads
The next type of brand/consumer social media exposure represents paid social
media advertising: the banner advertisement. For owners of a branded fan page, a
link for “Promote with an Ad” leads to these display opportunities, paid through CPC
(Cost Per Click) or CPM (Cost by Impressions). Ads are created within the online
advertising module, and rich targeting opportunities are available. Brands may tailor
ads targeted with exacting precision to users based on demographics, psychographics,
or key features of their social network (Facebook.com 2014c).
A second nascent type of advertising is identified as a sponsored story, which
has not had thorough investigation in the literature like standard banner ads. This form
of brand communication is also known as brand stories or brand-generated posts. In
this form of advertising, a brand update is shown in a social media participant’s News
Feed. The participant may not have liked this page, but the brand has paid to show up
in the consumer’s News Feed incidentally. In this case, the brand update shows up in
the main content feed and is not isolated on the right hand side of the user’s page
(Facebook.com 2014d).
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Brand Endorsements and Consumer-Generated Content
Another, more personal, brand exposure occurs when friends like (or become a
fan of) a particular brand. As discussed earlier, liking a page will add the brand into
the “Likes” section of a Facebook user’s profile page, and Facebook broadcasts this
brand endorsement via the News Feed to the user’s friends while simultaneously
appending the user’s “Recent Activity.” As users discuss brands on Facebook, they
can tag a brand (which provides a link to the brand’s Facebook page) in their posts.
These types of consumer discussions are how electronic word of mouth is shared on
Facebook (Facebook.com 2014e). In the present research, brand endorsements are
portrayed as consumer-generated posts mentioning a brand.
From Incidental Brand Exposures to Brand Outcomes
The conceptual descriptions and definitions provided above detail the most
common ways brand and consumers interact in social media and key potential
outcomes of these interactions; these concepts also outline the avenues by which
consumers may be exposed to brand messages without active participation or opt-in.
Much has been written regarding how consumers process information via central or
peripheral routes of persuasion (Petty and Cacioppo 1986). Each of the identified
social media exposures can be classified into either central route or peripheral routes
of persuasion, depending on the context of exposure (quickly browsing versus
deliberately reading a page of friend updates). Within the research proposed here, the
focus on interactions is narrowed to incidental exposure processed peripherally, while
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taking into account levels of involvement and content presentation style of exposure.
To date, most research has focused on the frequency of exposure to online advertising
in a digital environment versus the duration of exposure (Wang, Shih, and Peracchio
2013). An investigation into the influence on mere exposure to brand outcomes in an
online, social media setting has not been examined in the literature, which the present
research addresses.
Based on prior studies related to social media mere exposure, it is posited that
incidental brand exposures through social media are retained by the consumer,
whether the participant remembers seeing the brand name or not; exposure to the
brand leads to increased brand selection when social media participants are offered a
later choice. As a result, four studies are designed to test the influence of mere
exposure to brands in various advertising and word of mouth executions in various
conditions, including ads, social media posts, source of content, and reference group,
and involvement level.
Method
A two-part pre-test was executed to determine which product categories were
considered high and low involvement and to refine the experiment. The pre-test had
three goals. First, it provided insight into involvement levels by product category.
Second, it also provided input on refining the experiment directions that guided
participants. Finally, it provided corroboration that the research goals were obscured
from the participants. The survey portion of the pre-test was administered to 139
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participants at a Southwestern United States university via Qualtrics online survey
software, while the experiment portion of the pre-test was administered to 15
participants using MediaLab & DirectRT software. In part one, the survey focused on
involvement levels with various product categories to determine high and low
involvement products for Studies 1 to 4. Categories included cellphones, pizza,
athletic shoes, streaming movie services, cruise vacations, and beach vacations. Based
on the findings of the survey portion of the pre-test, beach vacations were chosen for
high involvement and pizza was chosen for low involvement as measured by
(Zaichkowsky 2004, 2005) (M=4.081 v. 3.374, df=35, F=3.47, p<0.00001). The
second portion of the pre-test ran a small sample (N=15) of test participants through
the social media screens using the laboratory software. This exercise focused on
clarity of instructions and resolving any confusion the participants may encounter.
Participants were asked what they thought the research was investigating, and none of
the initial participants guessed the research questions. Additionally, instructions were
refined so that required actions (such as mouse clicks or user input) were evident
without additional verbal instructions during the experiment. As heavy consumers of
social media, viewing a number of social media proved to be familiar task to
university students. The results of the laboratory section of the pre-test were excluded
from analysis and were only used to ensure the studies were executed correctly.
The pre-test and the existing literature in mere exposure informed the four
studies executed within this research. Each study was conducted in a laboratory
setting using a pool of student participants who participate for course credit.
Participants who were part of the pre-test or any study within this research were
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excluded from inclusion in later studies. The hypotheses in this exploration were
tested using data gathered using MediaLab and DirectRT version 2014 research
software, and analysis was performed using STATA 13.1. The data-gathering
software enabled the studies to randomly display various social media screens for
investigation at set durations to millisecond precision to simulate mere exposure and
control for order effects. Because this research focuses on mere exposure, the duration
was set and did not vary within the study or between studies. Ferraro et al. (2009)
tested their studies on mere exposure using duration of several seconds (between 1-3
seconds), and the researchers deemed that duration did not have an impact on their
focal construct of brand choice. Additionally, a meta-analysis of a mere exposure
effects found that durations between one to five seconds showed a significant effect in
tests (Bornstein 1989), and five seconds was selected based on the visual complexity
of the stimuli compared to the work of Ferraro et al. (2009).
The research conducted here employed an adapted version of testing incidental
exposure successfully used by Ferraro et al. (2009) in their review of incidental
exposure within the context of photographs. Instead of photographs, these studies
simulated a number of social media screens to create the incidental brand exposures in
the cited research (Ferraro et al. 2009). Only social media exposures that would be
considered incidental were tested; and any opportunity for active processing of
displayed brand exposure was limited based on the short duration of each screen.
Established constructs from marketing and consumer behavior were adapted
for this research. To measure the difference between ad units and posts on the
fictitious social media news feed, the concepts were drawn from de Vries, Gensler,
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and Leelfang (2012); ad units are represented in a right hand presentation with image
and text, while consumer or brand posts appear in the central column on the page. Ingroup and out-group follow this work of Ferraro et al. (2009), with implied physical
consumer differences shown visually. Whereas the reference literature presents
clothing for determination of reference group, the present work focuses on age as a
differentiator. Unaided recall was tested following existing protocol for online
advertising (Danaher and Mullarkey 2003). The measure of brand choice was drawn
from Karremans et al. (2006), which provided a proxy for product selection used by
Ferraro et al. (2009). Table 1 depicts the hypotheses to be tested in this research,
while Table 2 depicts a summary of measures employed in this research.
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Table 1: Summary of Hypotheses Tested in Essay 1 Studies 1-4
H1
Social media users exposed to a brand via mere exposure are more likely to
choose that brand than those not exposed to the brand.
Social media users exposed to a low involvement product’s brand
H2a sponsored stories via mere exposure are more likely to choose that brand
than those exposed to ad units for the brand.
Social media users exposed to a high involvement product’s brand
H2b sponsored stories via mere exposure are more likely to choose that brand
than those exposed to ad units for the brand.
Social media users exposed to consumer posts about a low involvement
H3a product’s brand via mere exposure are more likely to choose that brand
than those exposed to sponsored stories for the brand.
Social media users exposed to consumer posts about a high involvement
H3b product’s brand via mere exposure are more likely to choose that brand
than those exposed to sponsored stories for the brand.
Social media users exposed to in-group consumer posts about a low
H4a involvement product’s brand via mere exposure are more likely to choose
that brand than those exposed to out-group consumer posts for the brand.
Social media users exposed to in-group consumer posts about a high
H4b involvement product’s brand via mere exposure are more likely to choose
that brand than those exposed to out-group consumer posts for the brand.
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Table 2: Measures and Adapted Measures for Mere Exposure and Brand Choice
Experiment
Construct
Involvement
Measures or method tested
To me, X is important
To me, X is interesting
To me, X is relevant
To me, X is exciting
To me, X is meaningful
To me, X is appealing
To me, X is fascinating
To me, X is valuable
To me, X is needed
Drawn/Adapted from
Zaichkowsky (1994)
In-Group/Out-Group
Represented by age (college versus middle
age)
Ferraro et al. (2008)
Ad Unit/Post
Represented as right-hand brand generated
ad unit versus a post in the news feed
(either consumer-generated or brandgenerated)
de Vries et al. (2012)
Unaided Recall
Please list any brands you remember
seeing from the social network presented to
you.
Danaher and Mullarkey
(2003)
Brand Choice
I would like XYZ at this moment
XYZ is something I would purchase if
available
If given the opportunity, I would choose
XYZ
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Karremans et al. (2006)
Texas Tech University, William F. Humphrey, Jr., May, 2015
Once in the laboratory setting, participants each saw directions guiding them
that they would see twenty screens of a fictitious social media site, and they were
instructed that they should focus on the layout to distract from the actual research
purpose. To avoid a pre-existing bias towards an established social media site (such as
Facebook), participants were told the social network they were viewing was fictitious.
Because the layout of Facebook allows both advertising and posts from consumers and
brands, this design arrangement was mimicked. Visual elements used on the page
included a top navigation bar, a central area for consumer and brand-generated stories
or statuses, and a right-hand section for traditional online advertising units. The layout
used in each of the studies is presented in Figure 1.
Figure 1: Layout of Fictitious Social Media Site
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Any identifying information on the site, such as the social media brand logo
and other marks proprietary to Facebook were edited from images. Additionally,
participants were exposed to two ads per screen, similar to Facebook. For Studies 1
and 2, consumer names and images were removed to avoid effect of reference group
(which was subsequently tested in Studies 3 and 4).
Upon completion of the viewing of screens, participants answered the
manipulation check “What do you think this study was investigating?” before
answering questions related to focal constructs. As a distractor task, participants were
asked which brands they recalled seeing (unaided recall). Next, participants were
shown a logo for the brand being tested (either a pizza restaurant brand or beach resort
brand), and the participants answered three Likert-type questions about brand choice
(1= strongly disagree, 5=strongly agree) drawn from Karremans et al. (2006). The
design of the four studies to test the various conditions is discussed below.
Study 1: Mere Exposure in Social Media
In this initial study, the research seeks to establish that mere exposure to a
brand in social media influences brand choice, thereby corroborating the relationship
established by Ferraro et al. (2009) and providing a basis for the additional three
studies. Testing of this hypothesis informs the subsequent studies that testing on
brand choice via mere exposure extends to a visually complex social media context.
Thus the hypothesis tested in Study 1 is:
H1:
Social media users exposed to a brand via mere exposure are more
likely to choose that brand than those not exposed to the brand.
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Method
Fifty-four participants from the research participant pool completed this study,
and 47% of participants were female. Consistent with the research design described
above, Study 1 participants saw twenty screens of the fictitious social network. Half
of the participants saw content related to a pizza restaurant across five screens, while
half of the participants viewed no content related to that pizza restaurant. Twenty-nine
study participants completed the eight-minute experiment for both conditions during
regular business hours. Once seated, the twenty screens were displayed randomly
using the five-second-display duration. Upon completion of the sequence of screens,
the manipulation check was asked, along with unaided brand recall using five blank
spaces for participants to type in any brands they remembered. Then the five choice
Likert-type items related to brand choice drawn from past mere exposure choice
studies were administered, and the reliability of this construct was measured using
Cronbach’s Alpha (α=0.8087). These items measured the influence on brand choice if
consumers saw the brand messages in a mere exposure setting.
Results
The results of Study 1 indicate that participants who encounter mere exposure
to a brand’s advertising in social media are more likely to choose a brand compared to
those that do not see an that brand incidentally. In other words, brand advertising in
social media was effective. Participants who saw an ad reported a greater desire to
choose the brand tested in the brand choice questions (M=3.075, vs. 2.787; df = 15, F
= 5.6, P < 0.00001). This result is consistent with the findings of Ferraro et al. (2009)
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regarding mere exposure to a brand and extends it into the more complex visual
setting of social media websites with multiple items on which the participant can
focus. Additionally, this initial corroboration of the influence of mere exposure on
brand choice provided validation that Studies 2-4 will test differences in brand choice
under varying conditions, including source of message and reference group.
Study 2: Ad Units and Brand Stories
Study 2 builds on the initial study in that it tests brand choice when brands are
encountered in mere exposure settings, but it expands the test to compare the
effectiveness of traditional advertising units versus social media posts originated by
the brand (also known as brand stories) with low and high involvement products. In a
2x2 design, participants saw either the focal brand in ad units or brand stories for
either high or low involvement products using the two product categories chosen in
the pre-test. This design is represented in Figure 3.
High Involvement
Ad Unit
High Involvement
Brand Story
Low Involvement
Ad Unit
Low Involvement
Brand Story
Figure 3: Study 2 Ads, Brand Stories, and Involvement
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It is posited that brand stories will drive greater brand choice in mere exposure
scenarios due to a number of reasons. First, the amount of space the content occupies
in comparison to ad units is much greater, with the limited exposure to likely exert a
greater influence. Second, brand stories resemble posts originated by a consumer, or
these brand-originated stories may be perceived as content the consumer opted to see.
Finally, taking into consideration ad blindness that has been shown in past studies, I
anticipate the smaller ad units displayed on the right-hand side of the screen to result
in lower brand choice post mere exposure. Finally, this study seeks to integrate
Becker-Olsen (2003)’s examination of involvement and online advertising into a
context of social media sites (versus traditional websites). Consequently, in this study,
I test the following hypotheses related to brand choice, product category involvement
levels, and mere exposure:
H2a: Social media users exposed to a low involvement product’s brand
sponsored stories via mere exposure are more likely to choose that
brand than those exposed to ad units for the brand.
H2b: Social media users exposed to a high involvement product’s brand
sponsored stories via mere exposure are more likely to choose that
brand than those exposed to ad units for the brand.
Method
Participants from the research pool completed the second study; 50% of
participants were female. As with Study 1, twenty social media screens were
displayed in random order. For five of the screens, the tested brand was displayed.
For low involvement conditions, a pizza restaurant was shown, while high
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involvement conditions displayed content for a beach resort. Ad units replaced one of
the right hand ads, while brand stories replaced a social media participant post with a
post originated by the tested brand. Manipulation check, unaided recall, and brand
choice were again tested. For low involvement conditions, thirty participants per
condition completed the experiment, while high involvement included twenty-nine
participants per condition.
Results
Participants who saw an ad for a low involvement product showed a lower
desired brand choice than participants who saw a brand story. (M=2.578 v. 2.944;
df=12 F=1.27, P<0.2533), and the brand choice construct showed acceptable
reliability as measured by Cronbach’s Alpha (α =0.8530). While the hypothesis was
supported, this did not reach significance. The results of Study 2 indicate that ads
influence brand choice more than brand stories in the high involvement scenario
(M=3.022, vs. 2.793; df=12 F = 5.21, P < 0.00001), and reliability for brand choice
was (α =0.8085). Surprisingly, the smaller ad unit performed better on influencing
brand choice in mere exposure settings than the larger brand stories when the product
advertised was classified as high involvement. While neither H2a nor H2b were
supported while reaching significance, these findings provide insights into possible
differences between mere exposure, brand choice, and involvement levels. The low
involvement scenario supported the proposed scenarios but the results were not
significant. This mixed result suggests that additional research as to why differences
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exist in low involvement and high involvement scenarios could further uncover the
nuances on brand choice based on mere exposure in complex settings.
Study 3: Consumer and Brand Generated Content
Building on Study 2, the third study continues the exploration of social media
stories originated by brands in high and low involvement product categories.
However, this study compares these conditions with content about a brand generated
by a consumer to see whether consumer or brand generated content in mere exposure
settings influences brand choice more. It tests the same brand-created content such as
the sponsored posts in Study 2, but this exploration manipulates the source of content.
In essence, I investigate the impact of consumer word of mouth versus brand
advertising in a social media setting and its impact on brand choice where the visual
execution is very similar. In this scenario, images of consumers are included on the
posts, along with fictitious names. The conditions to be tested in Study 3 are
represented in Figure 4.
High Involvement
Brand Story
High Involvement
Consumer Story
Low Involvement
Brand Story
Low Involvement
Consumer Story
Figure 4: Study 3 investigation of product involvement and consumer or brandgenerated content
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Accordingly I posit:
H3a: Social media users exposed to consumer posts about a low involvement
product’s brand via mere exposure are more likely to choose that brand
than those exposed to sponsored stories for the brand.
H3b: Social media users exposed to consumer posts about a high
involvement product’s brand via mere exposure are more likely to
choose that brand than those exposed to sponsored stories for the brand.
Method
As with the prior studies, twenty social media screens were displayed in
random order. Fifty-three participants viewed the low involvement scenarios, while
forty-four saw the high involvement conditions. Participants sourced from the
participant pool were 39% female. For five of the screens, the brand being tested was
displayed. On those five screens, participants either saw a post originated by a
consumer about a brand or a post originated by the brand. The latter were the same
images used in Study 2, and a pizza restaurant and beach vacations served as low and
high involvement brands again, respectively. No ad units featuring the brands tested
were displayed. Manipulation check, unaided recall, and brand choice were tested
consistent with the other studies.
Results
When testing the source of a message about a brand, the low involvement
scenario was not supported comparing consumer versus brand-generated content
(2.881 v. 2.874, df=12, F= 1.2, P<0.293) with reliability for the brand choice construct
of (α =0.8430) as measured by Cronbach’s Alpha. Conversely, the high involvement
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scenario did hold to the hypothesized effects, with consumer generated posts
influencing brand choice more than brand-generated posts (M=3.023 v. 2.793; df=19,
F=2.20, P<0.0075) with a reliability for the brand choice construct of (α =0.8714) as
measured by Cronbach’s Alpha. Thus, in line with Study 2, low involvement did not
demonstrate a difference in brand choice, while high involvement scenarios did. This
finding indicates social media content generated by consumers discussing brands have
a greater impact on brand choice than brand-generated content when the product is
considered high-involvement. This outcome raises interesting implications of
consumer-generated content and the impact on the attitudes of those who view this
content. With the ubiquitous channels of consumer generated reviews and product
feedback, consumers may trust other consumers (even strangers, as in the case with
this research) more than they trust brands related to brand content..
Study 4: In-Group Versus Out-Group
From the posited outcomes of Study 3, consumer-generated content will have a
greater impact on brand choice than brand-originated content (supported in high
involvement scenarios). However, not all consumers hold equal influence with social
media participants, thus it is necessary to further consider the impact of reference
group on choice (leveraging the high and low involvement conditions as with Studies
2 and 3). In this scenario, the relevance of the originator of the content is
hypothesized to have an impact on the effects of the incidental exposure on choice
(Zajonc 2001, 1968). Within a social network, contacts may ranges from close friends
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to acquaintances with which little is shared in common. Both qualify as types of
reference groups, which Bearden, Etzel, and Etzel (1982) define as “a person or group
of people that significantly influences an individual's behavior” (Bearden et al. 1982 p.
184). The social media user’s relationship with the reference group, whether strong or
weak, likely exerts a varying moderating influence on the strength of incidental
exposure on future choice decisions (Ferraro et al. 2009). Furthermore, consumers are
possibly driven to make future product and brand choices to conform to the tastes of
others, which may be attributed to these online incidental social endorsements (Lee,
Cotte, and Noseworthy 2010). In this scenario, it is posited that if a social media
interaction is observed from either a friend or member of a strong reference group
(such as employee of the same firm or alumni of the same university), the rate of
brand choice influenced by incidental exposure will be stronger than for brands not
exposed in this context. Ferraro et al. (2009) operationalized reference groups by
varying university attire in a prior examination of incidental exposure and brand
choice within photographs. Due to the challenges of portraying reference groups
using real acquaintances of participants in an experiment, different age groups were
chosen to portray in- and out-groups. The research design for Study 4 is presented in
Figure 5.
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High Involvement
In-Group Consumer Story
High Involvement
Out-Group Consumer Story
Low Involvement
In-Group Consumer Story
Low Involvement
Out-Group Consumer Story
Figure 5: Study 4 investigation of product involvement, content type, reference group,
and brand choice
Based on the above review, I posit the following related to high involvement product
consumer posts by in-group consumers:
H4a: Social media users exposed to in-group consumer posts about a low
involvement product’s brand via mere exposure are more likely to
choose that brand than those exposed to out-group consumer posts for
the brand.
H4b: Social media users exposed to in-group consumer posts about a low
involvement product’s brand via mere exposure are more likely to
choose that brand than those exposed to out-group consumer posts for
the brand.
See Table 1 for a summary of all hypotheses tested in this research.
Method
Participants in the fourth study were 39% female. Consistent with the prior
three studies, twenty social media screens were displayed in random order with five
screens showing the focal brand. On those five screens, participants either saw a post
originated by a consumer of similar age to the participant or a post originated by a
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consumer who appears to be decades older than the participant. No ad units or brandoriginated stories were displayed for the focal brands. The in-group images were the
same images used in Study 3 as consumer-generated content. A pizza restaurant and
beach resort served as low and high involvement brands again, respectively.
Manipulation check, unaided recall, and brand choice were tested consistent with the
other studies. For low involvement conditions, fifty-three participants per condition
completed the experiment, while high involvement included forty-four participants per
condition.
Results
When testing the reference group influence on brand choice, the low
involvement scenario was not supported comparing in-group versus out-group content
(2.992 v. 2.867, df=10, F= 1.2, P<0.3176), with a reliability of (α =0.8616) using
Cronbach’s Alpha. Conversely, the high involvement scenario did hold to the
hypothesized effects, with in-group posts influencing brand choice more than outgroup generated posts (M=3.319 v. 2.957; df=12 F=2.57, P<0.005), with a reliability
of (α =0.8564) using Cronbach’s Alpha for brand choice. The findings from this
study remain consistent with Studies 2 and 3 with low involvement scenarios not
being supported; and in line with Study 3, high involvement scenarios demonstrate
support. This study also corroborates the findings of Ferraro et al. (2009) that
reference group influences brand choice, and it extends the effects of mere exposure
into the more visually complex context of user-generated social media posts about a
brand.
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Discussion and Limitations
This work substantiates that mere exposure exerts an influence on consumer
attitudes, specifically brand choice, when brand content is seen incidentally on social
media sites. This reinforces and extends the work of Ferraro et al. (2009) regarding
the influence of mere exposure on brand choice into a more complex visual brand
setting with many elements competing for the observer’s attention. Additionally, it
builds upon the prior work related reference group to social media posts as portrayed
by age in both consumer-generated posts (Naylor et al. 2012). This exploration adds
complexity from the referenced research from photos with brand placements to
rendered screens of a proposed fictitious social network. It can be argued that the
content on a social network, including advertising, posts, and other common website
elements, creates a more complex environment for testing mere exposure.
Additionally, this line of research extends the impact of brand exposure to include
high and low involvement product categories, along with a comparison of consumer
word of mouth (consumer posts) compared with both stories posted by the brand and
online advertising appearing on social media sites. As (Naylor et al. 2012)
demonstrate, revealing other brand supporters in an online social media setting has an
influence on brand choice intentions, and this work supports this research in a mere
exposure context. Moreover, this research sought to eliminate potential bias that
participants may have towards an existing social network. Facebook, in particular, has
been cited as losing engagement with teens and college-aged students (Madden 2013),
and the scenario of a fictitious social network under investigation seems to have
controlled this issue (at least for high involvement conditions). Also, these
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investigations respond to calls from the marketing scholar community to investigate
the impact of social media on brand marketing and consumer behavior (Day 2011;
Sheth 2011).
While the present research did not corroborate the differences in social media
brand messaging types in low involvement contexts, it provides some interesting
theoretical implications. First, high involvement products may benefit more from
specific executions in a content-heavy social media site like the one portrayed. The
visual executions that performed best were ads (compared to brand stories), consumer
generated posts (versus brand stories), and in-group consumer-generated posts (versus
out-group posts). Low-involvement products such as those tested here, by their
nature, may not trigger the subconscious processes to demonstrate a difference
between these executions. Thus low involvement products may be better suited to an
alternate visual execution not available on the social media layout tested here, such as
the more isolated content layout of Instagram. This visually simple layout aligns more
closely with the single images tested by Ferraro et al. (2009), which were also limited
to a low involvement product category. The findings from Study 2 related to the
performance of ads versus the more visually prominent brand stories are particularly
intriguing. This outcome runs counter to conventional wisdom that the larger brand
stories would perform better due to prominence in the midst of other visual elements.
Additional studies to determine advertising’s performance against user generated
content and variations in the length and format of brand stories when tested against
traditional banner advertising may yield additional insights.
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As with all research, limitations exist. While college age students are avid
users of social media, they are not representative of other age brackets; an opportunity
for future research exists in establishing any effects on the findings of this research on
various age groups. This varying of participant ages also allows for additional
research into reference group effects with varying methods of operationalization. A
sample including older consumers may also show different effects between advertising
and brand stories due to the lower social media usage patterns. Also, as this research
focuses on mere exposure, not various durations of ad exposure, the impact of duration
of observation is not studied. For a more integrative examination, future research may
wish to extend the present studies with varied duration times of brand exposure on
social media. Further, because social media sites vary in context and format, less
structured environments, such as Twitter or visual network SnapChat, may elicit
different effects. Testing these unstructured environments in future research may
provide value to future researchers. Finally, this research was executed using a
standard web layout as viewed on a computer, and the influence of mere exposure in
the more compact mobile device layout was not tested.
This research holds significant potential for practitioners as well. As firms
allocate resources to stimulate social media conversations and manage advertising on
social media sites, managers may question the return on investment of these efforts.
Standard measures of performance in online environments such as click through and
online purchase conversions may well be supplemented with the investigated brand
intention of future purchase. Also, the previously discussed banner blindness issue
noted in the literature may be not be as prevalent in social media, at least in high
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involvement product categories, as shown in Study 2. Additionally, consumer word of
mouth exhibited greater influence on brand choice than brand-originated posts,
reinforcing the value of positive word of mouth for brands participating on social
media. This finding suggests that brands may wish to leverage customer testimonials
sourced through social media as an authentic source of influence on future brand
choice. Studies 2 through 4 also indicate that high involvement products may benefit
more from specific executions, namely advertising over sponsored stories, word of
mouth over brand posts, and in-group over out-group word of mouth posts. It also
reinforces the value of rich segmentation and targeting to reflect the influence of
reference group in social media. By portraying in groups relevant to target audiences
in social media advertising, these ads may better influence later actions. Most
importantly, this research suggests that brands that simply participate and advertise on
social media may exert an influence on later consumer choice, and the duration of
exposure required by the consumer can be quite short. If consumers are likely to
quickly scroll through content on a social media website without purposely viewing
ads or brand content, this research implies that this type of brief, unfocused exposure
impacts attitudes towards consumer choice. Thus brands may benefit from simply
having an active presence on social media. While not as easy to measure as clickthrough or website conversions, knowing that mere exposure can influence a later
brand choice further validates the allocation of marketing human resources towards
social media community management and advertising.
The findings (both the hypotheses corroborated and not corroborated) raise
interesting future directions for research. First, greater research on low involvement
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products in social media in mere exposure settings may hold value, including varying
the amount of content shown. This goal may also be realized by using less visually
complicated social media sites as a template for the research, such as Instagram, which
includes only a single photo per screen. These alternate approaches may later support
the hypotheses not supported in the present research. Additionally, the findings
related to advertising being more influential than brand posts merits additional
investigation to understand in which conditions this holds true. Further research on
reference group with alternate operationalization of in- and out-groups may provide
additional value to both researchers and practitioners, and varying the method of
reference group presentation holds value. For instance, if a brand uses images of a
reference group in social media stories, how does the influence on brand choice differ
from consumer-generated post from the same reference group? Additionally, varying
content presentations extending the present research to represent other social media
types (such as visual networks like Instagram or video-centric sites like Vine and
SnapChat) merits investigation. Finally, comparing the influence of mere exposure to
brand content on desktop versions of social media sites compared to brand content
viewed mobile layouts is relevant as the majority of social media participants access
these sites using a mobile device.
In conclusion, the research conducted here sought to extend the academy’s
understanding of the impact of different brand exposures on product choice in social
media, which is now a prominent consumer activity. While the a priori hypothesized
outcomes related to low involvement products did not hold, it suggested a number of
relevant research avenues. The findings, however, suggest that simply seeing brand
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messages in varying forms affects consumer decision making related positively to
brand choice. This insight helps justify brand participation, including investing
advertising spend in social media channels, when other metrics of return on
investment may remain unclear. The future research directions prescribed here will
further refine the understanding of both the academy and practitioners to the outcomes
of brand social media exposures.
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CHAPTER 3
IN SEARCH OF THE SOCIAL MEDIA MAVEN: THE ROLE OF
COMMITMENT, APPRAISAL AND IDENTITY SALIENCE IN
PROPENSITY TO SHARE ON SOCIAL MEDIA SITES
Introduction
Relationship marketing theory states that successful firms seek positive
relationships with stakeholders indefinitely (Morgan and Hunt 1994); One of these key
relationship types is between marketers and end-consumers (Hunt, Arnett, and
Madhavaram 2006). More and more, marketers seek the aid of consumers in
strengthening relationships through consumer-to-consumer sharing. Driving
electronic word of mouth (eWOM) has long been cited as a goal for marketers, with
the result of positive and significant word of mouth being greater sales (Liu 2006).
Word of mouth also has another significant consequence, trust. A recent Ad Age
study indicated that consumers trust the endorsement of their own networks versus
endorsements of celebrity spokespersons (Daboll 2011), so tangible advantages exist
to encouraging positive consumer conversations. To facilitate these consumer-toconsumer discussions, many marketers have begun to dedicate resources to managing
conversations in social media. These online communities are now used to build
relationships though customer interactions (Szmigin, Canning, and Reppel 2005).
Online community members share product information, knowledge, product
experience, and identity (Wang, Butt, and Wei 2011).
Traditionally, consumers who view themselves as trusted product experts share
product information. Whether these consumers view themselves as product experts or
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a trusted advisor to their networks, word of mouth is spread. Feick and Price (1987)
investigated the concept of the market maven, which they define as consumers who
have market information (such as product information, location information) and
either initiate conversations or respond to requests for feedback on purchases. These
consumers are viewed as having an influence on purchase decisions by the network of
these mavens Feick and Price (1987). Accordingly, these consumers perceive
themselves as sophisticated consumers and pundits who have influence on their social
networks. In the original formulation of this concept, the social network messaging
was executed offline, via face-to-face, phone, or other traditional communication
means. These consumers signal their product usage and consumption by making
recommendations of the products and services they use. Today, social media sites
provide a rich environment for mavens to share their message. (McQuarrie, Miller and
Phillips (2013) describe the online social media sites as a form of amplification, taking
word of mouth from a maven’s own network to a reach of friends and strangers, a
much greater reach than the original conceptualization of the market maven.
In the present research, I seek to update the concept of the market maven as an
influential consumer with a propensity to share in social media, with a much greater
reach than in prior conceptualizations. As social media participants are connected to
others on various social media channels (such as friends on Facebook and followers
and those they follow on Twitter), market mavenism holds value to an investigation
into the likeliness to share product information to a social network. Central to this
investigation establishing the role of identity salience of social media participation and
propensity to share in social media, as defined by market mavenism, in relation to the
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consumer’s commitment to social media and their contribution of appraisals to their
sense of self.
Conceptual Framework: Propensity to Share in Social Media and
Personal Factors
McQuarrie et al. (2013) explore the megaphone effect, or the ability for
ordinary consumers to reach a wide audience through digital channels. Usage of
social media sites for self-presentation and self-expression has been associated with
positive self-esteem in community participants (Wilcox and Stephen 2013). Word of
mouth has traditionally been defined as one consumer influencing another regarding a
product or service they have tried, and this sharing of information from person to
person has been called the most influential sources of information of all time (Duan,
Bin, and Whinston 2008a; b). Word of mouth marketing defines the scenario further
when marketers encourage these peer-to-peer discussions, often via consumer forums
or on social networks such as Facebook or Twitter (Kozinets et al. 2010). These same
needs fulfilled by word of mouth sharing have been associated with the purchase of
luxury goods as a form of network signaling to foster self-esteem (Wilcox and Stephen
2013). Additionally consumers who lack expert knowledge may try to compensate for
this shortcoming through sharing word of mouth with their network, known as
compensatory knowledge signaling (Packard & Wooten 2013). Word of mouth has
been found to impact the message receiver’s attitude towards a product (Martin and
Lueg 2013). Lovett, Peres, and Shachar (2013) found that sharing online word of
mouth recommendations first fulfilled social needs for the sharer, followed by
functional and emotional needs.
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The classic distributor of word of mouth investigated in the marketing
literature is the market maven. Feick and Price (1987) identified consumers likely to
share shopping and marketplace information to other consumers. The scholars suggest
that these consumers are recognized and trusted by other consumers regarding product
recommendations (Feick and Price 1987). These consumers have also been equated
with “generalized opinion leaders” (Steenkamp and Gielens 2003). If market mavens
can be targeted effectively, research has further shown that messages related to
changes in a product’s marketing mix can be a useful strategy for retailers (Abratt,
Nel, and Nezer 1995). Additionally, mavens have been noted as readily using
coupons for personal use and sharing coupons with their network (Price, Feick, and
Guskey-Federouch 1988). While the initial research on mavenism focused on offline
sharing of word of mouth prior to the existence of the internet and online social
networks, recent research indicates that mavenism spans medium type, both online and
offline (Barnes and Pressey 2012).
In this paper, I investigate key antecedents hypothesized to influence the
updated concept of market mavenism in the context of online social networking sites.
First, I discuss the factors representing commitment to social media activities in the
form of breadth, including network size, social media participation frequency, and
media commitment (as represented by the number of social media sites on which a
user participates). Next, this breadth will be explored in relation to the identity theory
concept of appraisals, including what the participants thinks about their performance
on social media (self-appraisal) and what they perceive others say about their usage of
social media (reflected appraisal). Next, these appraisals are tied to the hierarchical
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concept of the varying importance of identities within the global self, and identity
salience as influenced by these two appraisal types are investigated. Finally, the focal
construct of marketing mavenism in the context of online social network sharing is
investigated in context of the two appraisal types and the identity salience of social
media participation. Drawing on the work of Laverie et al. (2002), this study seeks to
extend research on the role of appraisals and identity salience with propensity to share.
It also seeks to establish a new profile of influential social media participants based on
social media usage factors tied with identity theory. If corroborated, this new social
media influencer profile will inform marketing practitioners as they formulate strategy
related to driving positive word of mouth. The model explored in this paper is
presented in Figure 6.
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Figure 6: Conceptual model of antecedents of social media mavenism
While marketers acknowledge that market mavens hold influence on other
consumers and that online social networks serve as a nascent and growing channel of
communication, little research exists in relation to propensity to share via social
media. Value exists in establishing initial corroboration into what personal
characteristics are related to inclination to share on social media sites (in the form of a
social media maven). Social media usage factors, including observable features of
social media participants (such as network size and social media participation
frequency), in addition to media commitment are referred to as consumer commitment
to social media in the present research. Commitment has been viewed as a recognition
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of the value of an ongoing effort to maintain a relationship evermore (Morgan and
Hunt 1994). One way that commitment to social media has been explored previously
is through the frequency of participation, which is explored in the present research
(Chen 2011; Hall and Pennington 2013; Jansen, Zhang, and Sobel 2009; Junco 2012;
Ong, Ang, Ho, Lim, Goh, Lee, and Chua. 2011; Ryan and Xenos 2011).
A further measure of commitment to social media is measured as media
commitment. Laverie et al. (2002) define media commitment as consumption of
media relevant to salient identities. Put in simpler terms, the number of media types
consumed by the participant that reflects back on their identity can be considered his
or her media commitment. For this paper, media commitment relates to the number of
social networks on which a consumer participates. Thus if one consumer participates
in Facebook, Twitter, and Instagram and another participates on Facebook, Instagram,
Vine, Snapchat, and Google+, the latter consumer is considered to have a higher
media commitment.
A second form of breadth of social media participation would be the size of a
consumer’s network across all social media sites. The number of friends in a study
participant’s network has been studied in relation to personality factors (Moore and
McElroy 2012), positive self-appraisal (Gentile, Twenge, Freeman and Campbell
2012), a source of bonding and social capital (Ellison, Steinfield, and Lampe 2007), a
form of social compensation and self-consciousness (Lee, Moore, Park, and Park
2012), fulfillment of social attention and approval (Bergman, Fearrington, Davenport,
and Bergman 2011), and a source of identity construction (Zhao et al. 2008).
Personality has also been linked to the number of friends a participant has cultivated
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on Facebook (Moore and McElroy 2012). In measurement, network size has been
conceptualized as the total number of ‘friends’ a social media participant has
cultivated on a particular social network site (Gentile et al. 2012).
The final form of social media participation breadth to be investigated is the
frequency with which these consumer participation on various networks. Frequency
of participation on social media has been examined in relation to engagement,
particularly with students; Junco (2012) investigated the link between Facebook
frequency of usage with engagement in other student social activities (Junco 2012).
Junco, Heiberger, and Loken (2011) similarly established a positive relationship
between Twitter usage frequency and student engagement. Wilcox and Stephen
(2013) linked frequency of Facebook usage to negative life consequences such as
higher body mass index (BMI) and credit card debt load, establishing a relationship
between frequency of participation and offline repercussions. The frequency of usage
of Twitter has been tied positively to the gratification of the need for connection with
others (Chen 2011); the implication of the aforementioned study is that social media is
less a cacophony of online voices and more of a sharing and communication medium
(Chen 2011). Additionally, frequent social media users were found to be less
agreeable than less frequent users in a study investigating the Five Factor Model of
Personality (Moore and McElroy 2012). Predicting the frequency of usage, a link
between a extroversion and unconscientiousness and the frequency of social network
usage (Wilson et al. 2010). Thus frequency of usage has a theoretical base for
investigation in relation to the constructs of interest in this research.
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Given the stated importance of self-presentation and perceptions of the self by
others as expressed through social media, it is logical to infer that both self-appraisal
(or how a participant views his- or herself) and reflected appraisal (how a participant
speculates others view his- or herself) holds value to the investigation of social media
site usage. Richins (1991) argues that consumers compare themselves to an idealized
self through advertising images; these same social comparisons can be found in social
media participation (Hogan 2010) or creation of an idealized self as constructed
through curation of social media artifacts such as Facebook Timeline milestones (Belk
2013). These views of the self hold clear importance to the investigation of social
media usage and views of role identity importance. Laverie et al. (2002) define
appraisal as a self-evaluation of identity performance. Identity serves as the answer to
“Whom am I?” assessment versus global self-assessment (Laverie et al. 2002).
(Kleine et al. 1993) posit that roles are consensual behaviors of those participants
within a society, which is derived from social identity. It can be argued that within
social media usage, participants take on roles salient to the identities in which they
wish to enact. In scholarly research related to social media usage, Ong et al. (2011)
contend that adolescents use four features of the social network Facebook to manage
the appraisal of others, including profile picture, status updates, number of friends on
the network, and photo count. The scholars argue that Facebook allows participants to
facilitate identity construction and influence peer acceptance (Ong et al. 2011). Selfpresentation through social networking sites has been attributed with the search for
both self-expression and self-actualization by participants (Livingstone 2008).
Agreeableness and neuroticism have been associated with belongingness motivations
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in social media participation (Seidman 2013), while narcissism, or a sense of inflated
self-concept, has been identified as a motivation for participation on social networks
(Bergman et al. 2011; Gentile et al. 2012; Mehdizadeh 2010; Nadkarni and Hofmann
2012; Ong et al. 2011).
In an exploration of identity salience of mundane possessions, Laverie et al.
(2002) investigated the concept of how appraisal contributes to a consumer’s sense of
self. The authors establish a positive link between the two types of appraisal and the
importance to one’s identity (in these studies mediated by ownership of identityrelated possessions) (Laverie et al. 2002). Drawing from this research, I posit the
following relationships related to social media commitment and self-appraisal:
H1a: The network size of a social media participant is positively related to
self-appraisal.
H1b: The frequency of social media participation of a social media
participant is positively related to self-appraisal.
H1c: The media commitment of a social media participant is positively
related to self-appraisal.
In line with symbolic interactionism, individuals view themselves as others
significant to the individual view them (Felson 1985). While reflected appraisal is
closely related in relationship to self-appraisal, the findings of the research upon which
this present investigation is drawn found that reflected appraisal and self-appraisal are
distinct constructs. Consistent with Laverie et al. (2002) because both measures use
adaptations of the same items, these constructs are assumed to covary. Reflected
appraisal has been associated with a greater attribution of response to positive views
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versus perceived negative appraisals (Felson 1980). Reflected appraisal has also been
investigated in association with the influence of media, indirectly affecting self-esteem
(Milkie 1999). Further reflected appraisals have been viewed as a generalized other
versus perception of appraisal of specific individuals (Felson 1989; Milkie 1999).
Reflected appraisal has been associated with both real and imagined feedback, and the
presence of others is not required (Laverie et al. 2002).
In social media participation, the network or reference group of a participant
serve as the referents for this reflected appraisal process, which in turn impacts views
of the global self. Therefore I posit the following relationships between a consumer’s
commitment to social media and reflected appraisal:
H2a: The network size of a social media participant is positively related to
reflected appraisal.
H2b: The frequency of social media participation of a social media
participant is positively related to reflected appraisal.
H2c: The media commitment of a social media participant is positively
related to reflected appraisal.
With the widespread adoption and frequent usage of social media by
consumers, it can be argued that the activities related to sharing and connecting with
others online has gained importance to participant identities. Revisiting the concept of
self-concept, Callero (1985) states that individuals prioritize different role-identities in
hierarchies based on importance to the self. Important or salient identities are seen as
taking prominent positions within the hierarchy, and identities are seen as remaining
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stable over time (Callero 1985). In marketing, identity salience has been investigated
in relation to establishing long-term business relationships by appealing to salient
identities of prospective customers (Arnett, German, and Hunt 2003). Object
relevance to the salient identities has corroborated the hypothesis that salient identities
can be enacted when marketers attempt to persuade (Reed II 2004). Additionally,
identity salience in relation to race and ethnic heritage has been shown to be related to
favorable evaluation or behavioral shifts in the presence of heritage targeted
advertising (Benjamin, Choi, and Strickland 2010; Chattaraman, Rudd, and Lennon
2009). While salient identities are viewed as stable over time, behaviors based on the
salient identities may change contextually; individuals still retain flexibility in reaction
within a group interaction even though their role-identities do not normally shift
(Hogg, Terry, and White 1995). Laverie et al. (2002) investigated identity salience
(termed identity importance) in relation to pride and shame possessions associated
with participation in fitness activities. The scholars state that self-definition (and
higher identity salience) arises from use and contemplation of possessions in
association with identity-related activities (Laverie et al. 2002).
Reed II, Forehand, Putoni, and Warlop (2012) extend the concept of identity
salience to social media usage. The authors argue, “identity-related discourse in these
groups also affords an efficient selection of concepts (objects, people, places, brands,
slang words, etc.) that come to connote the identity and will receive the associated
affective meaning. The continuously reinforced array of meaningful associations will
in turn strengthen the identity itself” (Reed II et al. 2012 p. 315). Brands and pages
liked in social media can be used to influence others’ impressions of the social media
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participant; self-editing through social media sharing has been posited as a way of
communicating role-identities (Hollenbeck and Kaikati 2012). Identifying with (or
avoiding) specific brands via social media has been shown to be a coping mechanism
to resolve conflicts between actual self and idealized self (Hollenbeck and Kaikati
2012). Establishing a link on appraisal and identity salience, I posit
H3a: The reflected appraisal of a social media participant is positively
related to identity salience.
H3b: The self-appraisal of a social media participant is positively related to
identity salience.
As previously discussed, the classic marketing concept of the marketing maven
focuses on consumers who are closely identified with new products and new product
categories and who choose to share related information to their social networks. An
association between identity salience and market mavenism can be established, as
Feick and Price (1987) classify market maven as a role a consumer adopts. As
Laverie et al. (2002) contend, sustained enacting of a role-identity reinforces the
identity salience, and media commitment has been associated with identity salience
(Kleine et al. 1993). Thus I posit the following relationship between identity salience
and market mavenism as a propensity to share product information via social media:
H4:
The identity salience of a social media participant is positively related
to market mavenism.
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A further link to appraisal and market mavenism can be explored; Clark and
Goldsmith (2005) explore the limits of market mavenism as it relates to appraised
norms. The authors contend that despite market mavens being leaders within their
networks, they will constrain themselves based on perceived normative influences
(Clark and Goldsmith 2005). Further, mavens have been posited to seek to maintain
status through their consumption of conspicuous products, indicating a concern for the
appraisal by others (Goldsmith, Clark, and Goldsmith 2006). In this view, consumers
express a desire for unique products to distance themselves or express dissimilarity
from other consumers who are recipients of the maven’s shopping expertise
(Goldsmith et al. 2006). Related to self-appraisal, self-involvement has been cited as
a motivation for sharing word of mouth online (Hennig-Thurau, Gwinner, Walsh, and
Gremler 2004). Further, word of mouth has been cited as a mechanism for selfenhancement (Engel, Blackwell, and Miniard.; Hennig-Thurau et al. 2004), which can
serve as a way for consumers to potentially enhance self- or reflected appraisal.
Perceived similarity and expertise have been identified as key apparent communicator
characteristics for persuasive word of mouth communications, as enacted by mavens
(Wangenheim and Bayón 2004). Thus, an argument can be made for both types of
appraisals (self- and reflected) to influence propensity to share word of mouth in social
networks by market mavens; I posit:
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H5a: The reflected appraisal of a social media participant is positively
related to market mavenism.
H5b: The self-appraisal of a social media participant is positively related to
market mavenism.
Methodology
Sample and Procedures
This study utilizes a survey design administered to students at a southwestern
United States university. University-aged students are a logical sample frame for this
type of research as they are frequent participants in social media, with estimates of
Facebook participation between 85 and 99% of college students, with 97% of those
using Facebook (Junco 2012). Thus, the wide and deep usage of social media sites by
the target respondent helps ensure the necessary number of relevant respondents.
Students participated in completing the survey in exchange for course credit. In total,
the sample included 317 participants took the survey over a two-month period. Of the
317, a total of 311 participants completed the survey and are included in the analysis
(a 98% completion rate). Ages ranged from 18 to 55, with a mean age of 21.6 and
median age of 21. The participant mix was 39.6% male to 60.4% female. Majors
varied, and students could list a double major. Majors identified included 116
marketing students, 80 management, 12 MIS, 100 accounting, 30 finance students, 7
international business, 14 energy commerce, while 19 claimed another major. To
participate in the survey, respondents had to be a member (active or inactive) of at
least one social media site. For the sample observed, the mean number of sites was
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five. The most popular network reported by participants was Facebook (95%),
followed by Snapchat (82%) Twitter (78%), Instagram (74%), LinkedIn (63%), and
Pinterest (51%). Additionally, 87% of respondents who use Facebook visit the site at
least daily; with 87% of these respondents indicated they check social media more
than once a day. Of these latter participants, 61% estimated checking social media
more than 3 times a day.
Construct measures
As this research draws heavily from Laverie et al. (2002), measures for media
commitment, reflected appraisal, self-appraisal, and identity salience were adapted for
the present study. Consistent with the aforementioned research, self-appraisal and
reflected appraisal items mirror one another, with context adapted by construct. For
accessibility, the bipolar scales (notable, not notable) were adapted to a five item, fivepoint Likert-type scale. Media commitment was measured as the number of social
media sites on which a participant was a member, whether or not they remain active
on the site. Additionally, identity salience was measured with a four-item adaptation
of Callero (1985) on a five-point scale, measuring the importance of an activity to a
specific role-identity. Measures related to network size and frequency of participation
were adapted from Junco (2012). Frequency was measured based on the number of
times a user visited a site, including if he or she visited more than once per day.
Network size was initially measured as a sum total of the size of people in the
participant’s network. Certain social media sites include both a follower count (such
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as the people that see the participant’s content) and following count (such as people
whose content the participant follows). For the present research, only the people who
follow the participant are aggregated in network size. Due to the extreme variance in
network size (with a range of 35 to 4,759 people in a participant’s network), this
measure is reported per 100 people in the study participant’s network, which facilitates
model convergence.
The market maven scale is adapted from the five item Feick and Price (1987)
scale with five points. Each construct was tested via pre-test to assess reliability of the
measures and identify any issues with the survey design. Construct definitions are
summarized in Appendix D, and survey measures are listed in Appendix F.
Hypotheses to be tested are included in Table 3. To test these hypotheses, the
measurement items presented in Table 4 were administered by survey to participants.
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Table 3: Essay 2 hypotheses
Hypotheses
H1a:
The network size of a social media participant is positively related to
self-appraisal.
H1b:
The frequency of social media participation of a social media
participant is positively related to self-appraisal.
H1c:
The media commitment of a social media participant is positively
related to self-appraisal.
H2a:
The network size of a social media participant is positively related to
reflected appraisal.
H2b:
The frequency of social media participation of a social media
participant is positively related to reflected appraisal.
H2c:
The media commitment of a social media participant is positively
related to reflected appraisal.
H3a:
The self-appraisal of a social media participant is positively related to
identity salience
H3b
The reflected-appraisal of a social media participant is positively
related to identity salience
H4:
The identity salience of a social media participant is positively related
to market mavenism.
H5a:
The reflected appraisal of a social media participant is positively
related to market mavenism.
H5b:
The self-appraisal of a social media participant is positively related to
market mavenism.
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Table 4: Measurement items for Essay 2
Construct
Market Maven
Media
Commitment
Measures or method tested
I like introducing new brands and products to my
friends.
I like helping people by providing them with
information about many kinds of products.
People ask me for information about products, places
to shop, or sales.
If someone asked where to get the best buy on several
types of products, I could tell him or her where to
shop.
My friends think of me as a good source of
information when it comes to new products or sales.
Which of the following networks do you actively use?
Choices: Facebook, Twitter, Instagram, Vine,
Foursquare, Snapchat, Pinterest, LinkedIn, Google+,
Other
Drawn/Adapted
from
Feick & Price (1987)
Laverie et al. (2002)
Network Size
How many <insert social media site> friends do you
have? (divided by 100).
Junco (2012)
Frequency of Use
How often do you use an online social network such
as Facebook, Twitter, Instagram, Snapchat, or
Google+?
Junco (2012)
Reflected
Appraisal
How would others describe your social media
influence?
Laverie et al. (2002)
Notable, Excellent, Spectacular, Influential, Leader,
Self Appraisal
How would you describe your social media
influence?
Laverie et al. (2002)
Notable, Excellent, Spectacular, Influential, Leader,
Identity Salience
Using social media is an important part of who I am
Using social media is dear to who I am
I would feel at a loss if I couldn't visit social media
sites
For me, social media is more than just visiting a
website.
Analysis
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Callero (1985)
Texas Tech University, William F. Humphrey, Jr., May, 2015
This analysis was conducted consistent with the two-step method proposed by
Anderson and Gerbing (1988); the measures tested underwent a purification process
(maximum likelihood confirmatory factor analysis) followed by an analysis of the
relationships between constructs (structural equations models). Both the confirmatory
factor analysis and structural equations modeling (SEM) analysis were performed
using STATA 13.1. Table 6 includes parameter estimates, while Table 7 includes
factor loadings of individual items retained post measure purification. Following
Badrinarayanan and Laverie (2011), goodness of fit statistics of Root Mean Square
Error of Approximation (RMSEA) and Comparative Fit Index (CFI), along with
Tucker-Lewis Index (TLI) is reported to assess the fit of the proposed model; a
summary of these fit statistics appear in Table 10. SEM was selected for this analysis
because it is considered a strict theory test, testing the strength of each relationship
given all the other constructs in the model along with guidance as to the fit (or lack of
fit) of the model being tested. A path-by-path analysis is presented in Figure 7.
The measurement model featured a X2 (134) =274.200, with RMSEA of 0.58,
CFI of 0.969, and TLI of 0.970. For the initial structural model, the first loading of
the error term is set to 1, and only latent constructs that have a previous theoretical
relationship were allowed to correlate, specifically reflected and self-appraisal. All
construct reliabilities exceeded 0.84 using Cronbach’s Alpha, indicating the items
within each construct performed consistently with one another. Correlations between
residuals were set to zero. Additionally, tests of assumptions associated with
maximum (skewness and kurtosis) likelihood SEM were performed.
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Texas Tech University, William F. Humphrey, Jr., May, 2015
The initial structural model had a X2 (197)=937.814, P<0.00001, RMSEA =0.110,
CFI =0 .839, and TLI = 0.814. Modification indices were run one at a time for each
iteration of the structural model, and items indicating a high index were reviewed for
conceptual similarity or closely similar wording. In the initial review of the
modification indices, a high index of 134.919 indicated a fit improvement if the
second and third reflected appraisal error terms were correlated. In review of the
items, the terms specular and excellent were closely related, and the adjustment was
made (lowering X2 (196) to 808.332). Similarly, the second and third self-appraisal
items had an index of 102.675, and these items are based on the same descriptors
(excellent and spectacular). Accordingly, these error terms were correlated, bringing
X2 (195) to 707.979. Next, the first and second market maven items had an index of
87.852, and both of these items were worded very closely. Correlating these error
terms lowered X2 (194) to 632.323. Next, items 5 in both reflected appraisal featured
an index of 49.033. As these descriptors mirror one another (leader), these error terms
were allowed to correlate, lowering X2 (193) to 544.165. Similarly, items 1 of
reflected appraisal and self-appraisal (both using the descriptor notable), showed an
index of 40.926, and the correlated error terms resulted in a X2 (192) of 493.229.
Next, identity salience items 4 and 5 showed a 32.234 modification index, which was
likely related to closely worded items. The correlated error terms in the revised model
resulting in a X2 (191) = 460.321. Next, market maven items 4 and 5 presented a
modification index of 34.176. As both items refer to consumers asking for product
information and are closely related, the error terms were correlated (resulting in a X2
(190) = 430.562). To remain consistent with the other modifications allowing error
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Texas Tech University, William F. Humphrey, Jr., May, 2015
correlations between identical descriptors in reflected appraisal and self-appraisal, the
error term between both construct’s item 3 were correlated, with a resulting X2 (189)
of 409.964. Similarly, items 2 (spectacular) of both constructs were correlated, with a
resultant X2 (188) of 408.584. The final error terms of items between self and
reflected appraisals were correlated (items 4), with a X2 (187) = 382.572. Finally,
errors of items 3 and 5 were correlated, based on closely worded items related to
information about products or sales, resulting in a final X2 (186)=37.416. See Table 5
for the estimation of the structural model and modification history and resulting fit
statistics. Consistent with SEM, all estimates were performed simultaneously.
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Texas Tech University, William F. Humphrey, Jr., May, 2015
Table 5: Estimation of Structural Model: Modification History
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Texas Tech University, William F. Humphrey, Jr., May, 2015
Following modification of the structural model guided by theory, the model exhibited
adequate fit with Root Mean Square Error of Approximation (RMSEA) at 0.057, with
a 90% confidence interval lower bound of 0.049 and upper bound lf 0.065. The
Comparative Fit Index (CFI) was 0.959 and Tucker-Lewis Index (TLI) was 0.950,
while X2(186) = 374.816 and these measures of fit are summarized in Table 8.
Findings
Self-Appraisal
Network size (β=0.297, z=4.31, p <0.0001) and frequency of social media
participation (β=0.180, z=3.52, p <0.0001) are related to reflected appraisal, while
media commitment does not relate to self-appraisal (β=0.251, z=0.95, p <0.343).
These findings demonstrate support for hypotheses H1a and H1b, while H1c is not
supported.
Reflected Appraisal
Network size (β=0.030, z=4.86, p <0.0001) and frequency of social media
participation (β=0.166, z=3.53, p <0.0001) are related to reflected appraisal, while
media commitment does not relate to self-appraisal (β=0.011 z=0.47, p <0.632).
Consistent with self-appraisal, these findings demonstrate support for hypotheses H2a
and H2b, while H2c is not supported.
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Texas Tech University, William F. Humphrey, Jr., May, 2015
Identity Salience
Self-appraisal (β=0.292, z=1.87 p <0.05) and reflected appraisal (β=0.466
z=2.6, p <0.009) both positive relate to identity salience, demonstrating support for
hypotheses H3a and H3b.
Market Mavenism
Identity salience (β=0.17, z=3.04 p <0.002) positively related to market
mavenism, providing support for hypothesis 4. Self-appraisal (β=0.077, z=0.60,
p <0.55) did not positively relate to market mavenism, while reflected appraisal
(β=0.363, z=2.39 p <0.02) did positively relate to market mavenism. These results do
not support hypothesis 5a and support hypothesis 5b. Table 5 summarizes these
parameter estimates, while Table 6 highlights the factor loadings for individual items
within a latent variable.
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Texas Tech University, William F. Humphrey, Jr., May, 2015
Table 6: Parameter Estimates
Parameter Estimates
Dependent Variables with
Predictors
Reflected Appraisal
Unstandardized
Standardized
Standard Error
Z
p>Z
Network Size
0.03
0.292
0.006
4.86
0.000
Media
Commitment
Frequency
0.011
0.029
0.024
0.48
0.632
0.165
0.216
0.046
3.58
0.000
Network Size
0.297
0.253
0.007
4.31
0.000
Media
Commitment
Frequency
0.251
0.057
0.027
0.95
0.343
0.18
0.21
0.051
3.52
0.000
Reflected
Appraisal
Self Appraisal
0.466
0.317
0.168
2.6
0.009
0.292
0.224
0.146
1.87
0.050
0.37
0.329
0.153
2.42
0.015
0.077
0.077
0.128
0.6
0.549
0.17
0.223
0.056
3.04
0.002
Self Appraisal
Identity
Salience
Market Mavenism
Reflected
Appraisal
Self Appraisal
Identity
Salience
Note: 0.00 indicates p<0. 0001
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Texas Tech University, William F. Humphrey, Jr., May, 2015
Table 7: Factor Loadings
Parameter Estimates
Constructs and Indicators
Standardized
Unstandardized
Standard Error
Reflected Appraisal
α
0.903
Item 1
0.732
1
constrained
Item 2
0.801
1.001
0.071
Item 3
0.769
0.963
0.072
Item 4
0.876
1.256
0.081
Item 5
0.833
1.244
0.085
Self Appraisal
0.923
Item 1
0.796
1
constrained
Item 2
0.789
0.98
0.06
Item 3
0.827
1.007
0.061
Item 4
0.909
1.24
0.066
Item 5
0.844
1.181
0.069
Identity Salience
0.848
Item 1
0.794
1
constrained
Item 2
Item 3
0.741
0.88
0.046
0.715
0.996
0.095
Item 4
0.715
0.909
0.895
Market Mavenism
0.864
Item 1
0.737
1
constrained
Item 2
0.75
1.021
0.065
Item 3
0.755
1.106
0.11
Item 4
0.651
0.938
0.104
Item 5
0.669
0.924
0.101
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Texas Tech University, William F. Humphrey, Jr., May, 2015
Table 8: Fit Statistics
X2 (187) p<0.0001
RMSEA
90% CI, lower bound
upper bound
381.845
0.058
0.050
0.067
Baseline comparison
Comparative Fit Index
Tucker-Lewis Index
0.957
0.947
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Texas Tech University, William F. Humphrey, Jr., May, 2015
Figure 7: SEM Results
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Texas Tech University, William F. Humphrey, Jr., May, 2015
Discussion and Limitations
This work builds on Laverie et al. (2002)’s investigation of identity salience,
appraisal, and media commitment with a new context of social media users and their
propensity to make product recommendations on social media sites on which they
participate. This research also extends the market maven literature from its initial
incarnation of a limited word of mouth spreader in the Feick & Price (1987)
conceptualization to propensity to share in social media to an audience beyond a small
circle of friends to potentially thousands of acquaintances. Social media users in this
study had a range of friends and acquaintances from 35 to over 4,000. Product
information and recommendations shared to online audiences this size eclipses the
potential reach from the initial impact of the initial conceptualization of the market
maven. Further, with nearly 90% of respondents visiting Facebook at least once a day
or more, the opportunities to share product information recommendations and to be
exposed to social media recommendations are myriad. This research ties these
personal social media usage factors using measures adapted from existing measures
tested in the literature with constructs related to identity theory, and ultimately, the
marketing maven; together, these constructs represent a social media participant’s
propensity to share product experience and consumption feedback in an online social
network setting, bringing the established maven concept and updating it for the new
digitally and socially-connected consumer.
While media commitment, as measured as an index of the number of social
media sites on which a participant holds membership did not show significance,
insight into this audience (and their digital footprint) was gained. Participants on
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Texas Tech University, William F. Humphrey, Jr., May, 2015
average were members of five social media communities, with very little variance,
which may differ in future research extended to different age ranges. Additional
research may yield further clues as to the validity in measuring media commitment in
an online setting for identity theory research, and a different range of ages may result
in greater variance in media commitment compared to the heavy social media users
represented by the university student sample leveraged here. Further, while the
hypothesis related to reflected appraisal and propensity to share online was
corroborated and the hypothesis related to self-appraisal and propensity to share did
not show support, these results provide insight as well. I intuit that from an identity
theory perspective, the perception of role-identities as a participant perceives others
hold greatly informs their propensity to share online. Additionally, appealing to a
social media participant’s self-appraisal may have little impact in stimulating sharing
of product information based on the present findings. In a world where average social
media participant visits at least one social media site more than once day, these
connected consumers receive constant feedback and validation. Content posted may
receive validation through the forms of likes, shares, and comments, and the
importance of reflected appraisal makes intuitive sense.
Limitations on the present research include the potential influence of one social
network having differential contribution to appraisal versus all social media usage in
aggregate. Additionally, a sample that has a greater variety of age ranges may allow
for media commitment to have greater variance and corroborate the hypothesized
relationships with self and reflected appraisal. Future replications of this research may
show higher media commitment reports as well, as new online social media websites
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Texas Tech University, William F. Humphrey, Jr., May, 2015
launch regularly and are added to a social media participant’s regularly visited site.
This, in turn, may affect propensity to share, as spreading the attention of a consumer
across more networks may affect overall sharing (or how content is shared). Finally,
this sample focuses on social media users in the United States. Multinational research
may yield different results, as select countries have limited social media sites available
and restricted use of these sites. For instance, a user in China may not have the same
propensity to share based on cultural norms or restrictions enacted by the government,
and the present research does not encompass these advanced scenarios.
Accordingly, the present research suggests a number of additional areas for
future research. First, replication of the current research with different age ranges can
provide insights into generational differences. Much focus has been given to the
millennial consumer, and a comparison with other generations may yield useful
theoretical insights. Additionally, an extension of the current research by type of
social media site frequented by users could provide value. For example, some social
media sites are visual in nature (such as Pinterest and Instagram), where others focus
on a mix of visual content with both text and images (Facebook and Twitter). A study
looking at propensity to share by type of social media content type could yield deeper
insights into identity salience and propensity to share. Finally, a qualitative look at
what social media participants share (types of products, specific brands, high or low
involvement products) and how they share (social media status, location-sharing,
photography of products, sharing social media updates posted by firms, and usergenerated reviews) may provide additional layers to this research.
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Texas Tech University, William F. Humphrey, Jr., May, 2015
Marketing practice will benefit from a new framework for potential to share
online, as executed by the “social media maven.” By understanding the media
consumption factors and how avid social media users view themselves, more effective
social media messaging strategies can be crafted. If reflected appraisal influences
propensity to share more than a participant’s self views, then practitioners may seek to
test messaging and content that reinforces the reflected view of the social media
maven role-identity. Further, as word of mouth and authentic consumer endorsements
continue to hold value for firms in sales and other key metrics, understanding the
psychology of the emergent social media maven holds merit in further investigation.
With this research, I sought to extend the marketing maven concept into the
twenty first century model of the social media maven, who has a large extended
network with whom to share product experiences, recommendations, and images.
Additionally, I sought to apply identity theory’s concept of salient role-identities to the
influential online consumer who participates regularly in social media. By measuring
social media breadth (through frequency, combined network size, and media
commitment), Laverie et al. (2002)’s work was extended from traditional media to
digital media as it relates to self- and reflected appraisals. While media commitment
did not support the a priori hypotheses related to appraisals, this disconfirmation
provided interesting future directions for research. Also, understanding the
antecedents of propensity to share product information on social media sites provides
new insights into the salient identities of social media users.
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CHAPTER 4
Conclusion
Together, the two essays that comprise this research contribute to the overall
understanding of the phenomenon of the social media user in contemporary marketing.
Social media is a complicated, evolving phenomenon that includes both complex
visual stimuli (with text, images and videos) interpersonal influences from friends,
acquaintances, strangers, and brands. Browsing most social media sites adds to the
complexity and sheer number of brand messages viewed by consumers each day.
These take the form of digital advertisements, posts originated by brands, posts
originated by consumers, social check-ins, etc. Each of these distinct elements
competes for participants’ limited attention, and different influences and psychological
processes are at play. Mere exposure influence mimics the casual scrolling of a social
media participant where exposure to a message is very limited in duration with limited
time to process the message; rare is the occasion when consumers will pause and
thoughtfully process a brand message. Mere exposure has been shown to influence
brand choice, which is a key outcome brands desire. Further, consumers trust word of
mouth of friends and strangers, as evidenced by the numerous travel and dining review
sites, along with product ratings and reviews on ecommerce sites. These
recommendations also exist in social media where participants share their product
opinions through sharing content such as photos, text, location, video, and content
sourced on the Internet. Social media participants who view this shared content make
judgments on the worthiness of the content and message through evaluating in-group
and out-group status of the source. In other words, participants judge the likeness of
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Texas Tech University, William F. Humphrey, Jr., May, 2015
the originator of the content. These reference group influences have been shown to
influence product choice in mere exposure settings and purchase intention as measured
from social media sites.
To date, little research has been done on these complex influences on
consumer behavior based on social media participation, and the present research
sought to investigate these influences. This research tests the impact of mere
exposure, reference group, and source of social media content (whether brand or
consumer) as part of the first study. It extends the work set in offline environments of
Ferraro et al. (2009) and corroborates their findings in a more visually complex social
media setting. It also corroborates the findings of Naylor et al. (2012) on the influence
of reference group on social media brand pages. The present research extends the
works cited and adds in the complexity of various brand message formats (ads versus
brand-originated stories), source of message (brand-originated stories versus
consumer-generated stories), and reference group (in group versus out group) in the
visually crowded environment of a social media site. Together, the studies in Essay 1
provide new knowledge on differences in brand choice in mere exposure settings for
high-involvement and low involvement brands. Surprisingly, it also indicates that in
the tested conditions, traditional rectangular ad units perform better in brand choice
than brand-originated stories. It reinforces the value of word of mouth in high
involvement products, as consumer-generated content performed better at influencing
brand choice than brand-generated content. An additional insight gained from the
present research is that low involvement products did not seem to exhibit a difference
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Texas Tech University, William F. Humphrey, Jr., May, 2015
between brand executions tested in this research on brand choice. This stimulates
additional research questions for future studies.
As consumer generated content in the form of word of mouth demonstrated
greater influence on brand outcomes than similar brand messaging, additional research
on online influencers holds value. Essay 2 aimed to further uncover the psychological
processes at play with these diffusers of product information, and the research
examines the concept of the market maven in context of a connected, frequent social
media user and their propensity to share word of mouth. This extends the work of
Studies 3 & 4 from Essay 1 and provides insight as to what influences propensity to
share product information to an extended network on social media sites. In a surveydesigned study, this research examined social media breadth (as measured by the
number of social media sites on which participants had joined, network size across all
of those networks, and frequency of participation on all social media sites). It tied
these usage factors to identity theory and the concept of role-identities, as measured by
reflected appraisal, self-appraisal, and identity salience. In turn, each of these identity
constructs was examined in relation to the propensity to share word of mouth product
information (as measured by the market maven construct items).
This research provides two key advances in theory. First, it is the first
examination to date tying identity theory to social media usage and propensity to share
product information online. It considers Feick & Price (1987)’s view of the market
maven as a role-identity where product information is shared with a network. The
second contribution is that it re-examines the market maven in the context of
contemporary connected consumers. When the initial concept of the market maven
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Texas Tech University, William F. Humphrey, Jr., May, 2015
was explored, word of mouth was limited to traditional means (in-person, telephone,
written) to a limited audience. Today, social media participants cultivate large
networks ranging from close ties to strangers, and each of these audience members is a
potential recipient of word of mouth recommendations. As McQuarrie et al. (2013)
argue, the content of today’s connected consumer can reach thousands across multiple
messaging platforms. The findings of this research indicate that reflected appraisal, or
how participants assume others view them, directly influences propensity to share
word of mouth, while self-appraisal only affected propensity to share as mediated by
identity salience. This corroborates the underlying assumptions of the research
questions that these new social media mavens spread word of mouth, and that complex
psychological influences inform these sharing behaviors.
As knowledge increases related to the psychological factors influencing
consumer behavior in social media settings, brand social media marketing will cease
to be considered new media and become an established strategy in the marketing
practitioner’s IMC toolkit. By establishing the influence on brand choice in high
involvement, mere exposure settings, it is implied that brands may see positive
outcomes by simply participating and advertising in social media. Extending this
study, the impact of consumer word of mouth was shown to perform better than brandoriginated posts in high involvement settings. The psychological antecedents of why
this happens were explored through an identity-theory based examination of social
media participants who embrace the social media maven role-identity. This provides a
number of theoretically rich avenues for future investigation, which will benefit the
academy’s understanding of this phenomenon. This additional research suggested by
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Texas Tech University, William F. Humphrey, Jr., May, 2015
these two research efforts will benefit practitioners as well by providing tangible
insights on why brands may benefit from participating with consumer on social media,
instead of just relying on the adoption statistics to dictate strategy. As brands
understand what psychological processes influence consumers in social media and in
sharing word of mouth, more skillful marketing with new success measures will be a
likely outcome.
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91
Texas Tech University, William F. Humphrey, Jr., May, 2015
APPENDIX A: ESSAY 1 CONSTRUCT DEFINITIONS
CONSTRUCT
Mere Exposure
Brand Choice
Involvement
Ad Unit (adapted
from Banner
Advertising)
Brand & Consumer
Posts
CITATION
DEFINITIONS
Zajonc, R. B. (1968). ATTITUDINAL EFFECTS OF
MERE EXPOSURE. Journal of Personality and
Social Psychology, 9(2), 1–27.
Ferraro, R., Bettman, J., & Chartrand, T. L. (2009). The
Power of Strangers: The Effect of Incidental
Consumer Brand Encounters on Brand Choice.
Journal of Consumer Research, 35(5), 729–741.
Zaichkowsky, J. L. (1995). Measuring the Involvement
Construct. Journal of Consumer Research,
12(December), 341–352.
Bloch, P. H., & Richins, M. L. (1983). A Theoretical
Model for the Study of Product Importance
Perceptions. Journal of Marketing, 47, 69–81.
Houston, M., & Rothschild, M. (1978). Conceptual and
methodological perspectives on involvement. In
Research frontiers in marketing: Dialogues and
directions (pp. 184–187).
Shamdasani, P. N., Stanaland, A. J. S., & Tan, J. (2001).
Location, Location, Location: Insights for
Advertising Placement on the Web. Journal of
Advertising Research, 41, 7–21.
De Vries, L., Gensler, S., & Leeflang, P. S. H. (2012).
Popularity of Brand Posts on Brand Fan Pages: An
Investigation of the Effects of Social Media
Marketing. Journal of Interactive Marketing, 26(2),
83–91.
92
“Mere repeated exposure of the individual to a stimulus is a
sufficient condition for the enhancement of his attitude toward it”
(P. 1)
Selection of brands influenced after incidental brand exposure
(mere exposure) without conscious awareness or intent
1. Personal—inherent interests, valued, or needs that motivate
one toward the object
2. Physical—characteristics of the object that cause
differentiation and increase interest
3. Situational—something that temporarily increases relevance
or interest toward the object. (Zaichkowsky 1995)
“…Ads induce online clicking, which links the consumer to the
brand’s target communication (usually the target company’s
website), and ultimately on to a purchase.” P.7
Brand posts are content shared by a brand in social media, while
consumer posts are generated by participants on a social media
site.
Texas Tech University, William F. Humphrey, Jr., May, 2015
APPENDIX B: ESSAY 1 SUMMARY OF HYPOTHESES
H1
Social media users exposed to a brand via mere exposure are more likely to
choose that brand than those not exposed to the brand.
Social media users exposed to a low involvement product’s brand sponsored
stories via mere exposure are more likely to choose that brand than those
H2a
exposed to ad units for the brand.
Social media users exposed to a high involvement product’s brand sponsored
stories via mere exposure are more likely to choose that brand than those
H2b
exposed to ad units for the brand.
Social media users exposed to consumer posts about a low involvement
H3a product’s brand via mere exposure are more likely to choose that brand than
those exposed to sponsored stories for the brand.
Social media users exposed to consumer posts about a high involvement
H3b product’s brand via mere exposure are more likely to choose that brand than
those exposed to sponsored stories for the brand.
Social media users exposed to in-group consumer posts about a low
involvement product’s brand via mere exposure are more likely to choose that
H4a
brand than those exposed to out-group consumer posts for the brand.
Social media users exposed to in-group consumer posts about a high
involvement product’s brand via mere exposure are more likely to choose that
H4b
brand than those exposed to out-group consumer posts for the brand.
93
Texas Tech University, William F. Humphrey, Jr., May, 2015
APPENDIX C: ESSAY 1 MEASURES &
CONCEPTUALIZATIONS
Construct
Involvement
Measures or method tested
To me, X is important
To me, X is interesting
To me, X is relevant
To me, X is exciting
To me, X is meaningful
To me, X is appealing
To me, X is fascinating
To me, X is valuable
To me, X is needed
Drawn/Adapted from
Zaichkowsky (1994)
In-Group/Out-Group
Represented by age (college versus middle
age)
Ferraro et al (2008)
Ad Unit/Post
Represented as right-hand brand generated
ad unit versus a post in the news feed
(either consumer-generated or brandgenerated)
de Vries et al (2012)
Unaided Recall
Please list any brands you remember
seeing from the social network presented to
you.
Danaher and Mullarkey
(2003)
Brand Choice
I would like XYZ at this moment
XYZ is something I would purchase if
available
If given the opportunity, I would choose
XYZ
94
Karremans et al (2006)
Texas Tech University, William F. Humphrey, Jr., May, 2015
APPENDIX D: ESSAY 2 CONSTRUCT DEFINITIONS
CONSTRUCT
CITATION
DEFINITIONS
Market Maven
Feick, L. F., & Price, L. L. (1987). Maven: Market A
Diffuser of Information Marketplace. Journal of
Marketing, 51(1), 83–97.
“Individuals who have information about many kinds of
products, places to shop, and other facets of the market,
and initiate discussions with and respond to information
requests from other consumers.”
(P. 1)
Identity Salience
Callero, P. L. (1985). Role-Identity Salience. Social, 48(3),
203–215.
Selection of brands influenced after incidental brand
exposure (mere exposure) without conscious awareness
or intent
Self-Appraisal
ReflectedAppraisal
Media
Commitment
Network Size
Frequency of Use
Laverie, D. A., Kleine, R. E., & Kleine, S. S. (2002).
Reexamination and Extension of Kleine, Kleine, and
Kernan’s Social Identity Model of Mundane
Consumption: the Mediating Role of Appraisal
Process. Journal of Consumer Research, 28(March),
659–669.
Felson, R. B. (1985). Reflected Appraisal and the
Development of Self. Social Psychology Quarterly, 48,
71.
Laverie, D. A., Kleine, R. E., & Kleine, S. S. (2002).
Reexamination and Extension of Kleine, Kleine, and
Kernan’s Social Identity Model of Mundane
Consumption: the Mediating Role of Appraisal
Process. Journal of Consumer Research, 28(March),
659–669.
Gentile, B., Twenge, J. M., Freeman, E. C., & Campbell, W.
K. (2012). The effect of social networking websites on
positive self-views: An experimental investigation.
Computers in Human Behavior, 28(5), 1929–1933.
Junco, R. (2012). The relationship between frequency of
Facebook use, participation in Facebook activities, and
student engagement. Computers and Education, 58,
162–171.
95
“Self-appraisal is a person’s independent, personal
evaluation of her identity-relate actions, especially
applicable to freely chosen identities” (p. 662)
“We come to see us as significant others see us.”(p. 71)
Conceptualized as an extensiveness dimension, media
commitment is the sum of media consumed by a study
participant (how many social media networks).
Conceptualized as the number of “friends” on a social
network site
How often various activities are undertaken on social
media sites (in this case Facebook).
Texas Tech University, William F. Humphrey, Jr., May, 2015
APPENDIX E: ESSAY 2 SUMMARY OF HYPOTHESES
H1a:
H1b:
H1c:
H2a:
H2b:
H2c:
H3a:
H3b:
H4:
H5a:
H5b:
The network size of a social media participant is
positively related to self-appraisal
The frequency of social media participation of a social
media participant is positively related to self-appraisal.
The media commitment of a social media participant is
positively related to self-appraisal.
The network size of a social media participant is
positively related to reflected appraisal.
The frequency of social media participation of a social
media participant is positively related to reflected
appraisal.
The media commitment of a social media participant is
positively related to reflected appraisal.
The reflected appraisal of a social media participant is
positively related to identity salience.
The self-appraisal of a social media participant is
positively related to identity salience.
The identity salience of a social media participant is
positively related to market mavenism.
The reflected appraisal of a social media participant is
positively related to market mavenism.
The self-appraisal of a social media participant is
positively related to market mavenism.
96
Supported
Supported
Not Supported
Supported
Supported
Not Supported
Supported
Supported
Supported
Supported
Not Supported
Texas Tech University, William F. Humphrey, Jr., May, 2015
APPENDIX F: ESSAY 2 MEASURES
Construct
Market Maven
Media
Commitment
Measures or method tested
I like introducing new brands and products to my
friends.
I like helping people by providing them with
information about many kinds of products.
People ask me for information about products, places to
shop, or sales.
If someone asked where to get the best buy on several
types of products, I could tell him or her where to shop.
My friends think of me as a good source of information
when it comes to new products or sales.
Which of the following networks do you actively use?
Choices: Facebook, Twitter, Instagram, Vine,
Foursquare, Snapchat, Pinterest, LinkedIn, Google+,
Other
Drawn/Adapted
from
Feick & Price (1987)
Laverie et al. (2002)
Network Size
How many <insert social media site> friends do you
have? (divided by 100).
Junco (2012)
Frequency of Use
How often do you use an online social network such as
Facebook, Twitter, Instagram, Snapchat, or Google+?
Junco (2012)
Reflected
Appraisal
How would others describe your social media
influence?
Laverie et al. (2002)
Notable, Excellent, Spectacular, Influential, Leader,
Self Appraisal
How would you describe your social media influence?
Laverie et al. (2002)
Notable, Excellent, Spectacular, Influential, Leader,
Identity Salience
Using social media is an important part of who I am
Using social media is dear to who I am
I would feel at a loss if I couldn't visit social media sites
For me, social media is more than just visiting a
website.
97
Callero (1985)