A PICTURE IS WORTH A THOUSAND WORDS
A picture is worth a thousand words;
An online experiment regarding the influence that user
generated content versus brand generated content on
Instagram have on perceived brand quality, brand attributes
and associations.
Robert van Leeuwen
University of Amsterdam
Robert van Leeuwen
Student number: 10009019
Bachelor thesis ‘Persuasive Communication’
Communication Science, University of Amsterdam
Teacher: Charlotte Blom
Date: 10-1-2015
Words: 7942
A PICTURE IS WORTH A THOUSAND WORDS
Abstract
The main subject of this article is the difference in content source on Instagram (user
generated content (UGC) versus brand generated content (BGC)) and how it influences
perceived brand quality and brand attributes and associations (BAA). Variables of
importance in this article include peer influence (many likes versus few likes), amount of
user’ activity on Instagram and perceived content credibility (PCC). Results were collected by
a convenience sampled online experiment with a survey (N=202). The results deriving from
this article are applicable for both PR- and marketing managers with interest in Instagram.
With the knowledge gained from this article, marketers have a framework how to address
their target group via Instagram. Firstly, UGC and BGC do not differ in their influence on
perceived brand quality and BAA. Nevertheless it is important to influence the PCC of the
consumers since this is a strong predictor for perceived brand quality. As PCC increases
perceived brand quality increases. Secondly, peer influence has a direct positive influence on
PCC. Thirdly, the more a consumer is active on Instagram, the more credible they perceive
the content to be, regardless the source.
Keywords: source content, Instagram, peer influence, user activity, content credibility,
brand quality, brand attributions and associations.
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Introduction
Instagram was founded on October the sixth 2010. This Internet service was created
for sharing photos. Instagram provides a free application that makes it possible to capture,
edit and share videos and photos with the whole world. Whereas Facebook enables
uploading images, videos and stories via different platforms (e.g. PC, mobile phone, laptop
etc.), Instagram only allows uploads from your mobile device. It seems like this is a limitation
but in October 2014 the 200 Million monthly active users milestone was reached (Instagram,
2014). Facebook foresaw the hype and bought the company in April 2012. ‘The partnership’
as stated on their website, gives users the opportunity to export their pictures uploaded on
Instagram to Twitter, Facebook and even more social media applications. Instagram users
can ‘like’ photos and videos posted by other users. This content is visible when one follows
the relevant user or searches for the relevant ‘#’ added to the photo or video by the user. By
searching with ‘#’, eg. #love, thousands of pictures will show from users with common
interests such as their partners, flowers, heart shaped pictures etc.. Since September 2014,
Instagram is experimenting with sponsored advertisements in the United States. On the
homepage of an user based in the United States not only the content of users they are
following are provided, also advertisements of brands are visible. These advertisements
contain a stamp saying ‘sponsored’ to show the users they are exposed to an advertisement.
Nowadays there are 1,6 Billion “likes” daily, 60 Million uploads daily with a total of 20 Billion
photo’s uploaded and 65% of the users are outside of the United States, it’s going viral all
across the globe (Instagram, September 2014).
Not all of the online content produced by individuals concerns a brand message but a
lot do contain branding. For instance, a user may upload a picture of a cocktail on the beach
with the captions #cocktail and #bacardi. In the example #cocktail refers to non-brand related
content while #bacardi refers to the rum brand Bacardi, known as brand related content. Now
when one searches for the #cocktail or #bacardi, this image among with other images with
the same # will appear. Not only users are able to add a # to their uploaded content, brands
are able to do this as well. So when one searches for the #bacardi, one will find a lot of
content uploaded by the brand Bacardi itself. Nevertheless when one searches for the
#cocktail, it is possible to find content uploaded by Bacardi as well and other liquor related
brands as long as they added #cocktail to the caption. With this, brands are able to spread
information concerning their brand. This is useful for users with interest in for example
cocktails, to stay on the same topic as the previous example. It is easy to find brands and
products conform with their interests. When users find another user or brand in line with their
interest they are able to follow them and share their pictures. There can be stated that with
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this feature Instagram gives companies the opportunity to spread their brand across the
globe. Not only the companies are spreading their brand message, users are spreading the
brand message as well. This leads to two different ‘posts’: brand generated content (BGC)
and user generated content (UGC). Nevertheless, UGC can be partially brand generated as
well. This is referred to as endorsed UGC. There is a trend were brands pay or instruct users
to promote their products. Users may promote brand products as is described with the
aforementioned example. These endorsed UGC’s may consist from content and/or #
regarding the relevant brand. When users of Instagram are exposed to different UGC’s and
BGC’s, concerning a brand, this may influence their perceived brand equity. According to
Kotler and Keller (2012) brand equity can be influenced by different sources of content, so
UGC and BGC may influence brand equity in different ways. Brand equity consists of
different values including perceived brand quality and brand attributes and associations
(BAA). The goal of this article is to give insight in how the difference in source content
influences perceived brand quality and BAA.
There is another difference between Instagram and other social media, Instagram
only enables uploading photos and video’s but no textual posts. Users post photos or video’s
with or without the intention to promote the brand and this can lead to a different evaluation
of the brand equity then BGC will do, due to the credibility of the content (Metzger &
Flanagin, 2008). In this article, the variable perceived content credibility (PCC) describes how
credible users perceive the content to be. The importance of PCC has been demonstrated in
previous research (e.g. Fogg, Soohoo, Danielson, Marable, Stanford & Trauber, 2003;
Metzger, Flanagin & Medders, 2010; Metzger & Flanagin, 2013). PCC is not completely
dependent on the source. Following from different studies (e.g. Sundar, 2008, Deutsch &
Gerard, 1955; Flanagin & Metzer, 2013) it may be stated that the opinion of others on the
post (like the amount of likes) affects the relation between the source of the advertisement
and the PCC. This moderating variable is defined as ‘peer influence’.
Facebook and Instagram are two examples of social media where peer influence is
visible. The two platforms together combine for at least 1.3 Billion monthly active users
(Facebook, 2014) and since the world has 7.2 Billion inhabitants (United States Census
Bureau, 19-7-2014) there’s a possibility that not everyone is familiar with the social network
sites. Even for the people who are familiar with one of the two platforms there is a distinction
of knowledge and usage of it. The amount of user activity one has on the designated
platform can influence PCC (Donath, 2011; Flanagin & Metzer, 2013). This will be defined as
the users activity on Instagram (Insta-activity) in this article.
The aim of this article is to provide a clear insight into how Instagram posts may
influence perceived brand quality and BAA, from here the research question derives:
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‘How do UGC and BGC on Instagram variate in influencing the perceived brand
quality and BAA and what role do PCC, peer influence and Insta-activity play in this
relationship?’
Since the platform is relatively new there is still a lack of scientific research regarding
the relationship between UGC/ BGC and perceived brand quality, attributes and
associations. Due to the exploration of the opportunities of a relatively new medium
concerning brand equity this article provides significant added value to the science. This
article will also have managerial implications conforming brand exposure and the influence
on perceived brand quality and perceived brand associations and attributes. PR- and
marketing managers are provided with suggestions when to use BGC or endorsed UGC and
the possible effects it has on the perceived brand quality and BAA. The conclusion derived
from analyzing data that was collected by conducting an online experiment followed by a
survey.
Theory
BGC versus UGC
The American Brand Association defines a brand as a name, term, signal, symbol,
design or a combination of them intended to identify the goods or services of one seller or
group of sellers and to differentiate them from those of the competitors (Kotler & Keller,
2012). By marketing a brand, customers may gain knowledge of the differentiated attributes
of the brand. To achieve the desired effect (the knowledge of differentiated brand attributes),
marketers need a variety of marketing activities that consistently reinforce the brand promise
(Iacobucci & Calder, 2003). By using social media platforms like Instagram, companies may
target a different audience than those using traditional media. The uses and gratification
theory (de Boer & Brennecke, 2009) explains this. According to the theory people choose to
use a medium because of its gratifications and different people have different gratifications. A
bigger audience will be reached when including both traditional and new media in a
campaign. This is confirmed by Kotler and Keller (2012), who also found that a brand
conducting a mixed marketing strategy will reach a bigger audience. Following from this it
can be stated that it’s necessary for companies to create online content next to traditional
media. Instagram is an example of online content. Some of the biggest companies like Ben &
Jerry’s, Levi’s and Taco Bell (Instagram.com, September 2014) are already using Instagram
to expose their audience to their brand. These companies upload content, photos and
videos, regarding their brand, this is known as brand generated content (BGC). But not only
the companies itself are uploading pictures of their brands, consumers are also uploading
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pictures of brands. Customers uploading content is referred to as user generated content
(UGC).
According to different studies there has been a rise in UGC as customers not only
receive but also produce information (Bruns, 2008; Ochoa & Duvall, 2008). On Instagram
alone there are 20 billion photo’s shared every day (Instagram.com, September 2014).
According to Muntinga, Moorman and Smit (2011) there are different motivations why
consumers produce or create online content. The main motivations to why consumers create
content are establishing personal identity, integration and social interaction, entertainment
and empowerment. The empowerment motive concerns brand ambassadors who want to
persuade others to consume their favorite brands, this is the main reason users generate
content concerning brands. These customers are aware that they have the influence to let
others buy a product or change the course of a company (Berthon, Pitt & Cambell, 2008 ;
Bronner & De Hoog, 2011). Both consumer and brand have their own reasons to create
brand related content and the practice shows that they are eager to produce. The conclusion
is that every individual active on Instagram is being exposed to two different kinds of content:
brand generated and user generated.
Differentiating the brand
Recalling a brand name refers to the dependent variable brand associations and
attributes (BAA). BAA are when one thinks of a brand when trying to recall attributes of a
general product category. For example, when asking for Coca Cola ®, one may be just
looking for the cola soda. BAA are more complicated to create then awareness. It is more
difficult to recall symbols, logo’s, attributes and images then to just remember the name (
Yoo, Donthu & Lee, 2000), they may differ between brands and/or product categories.
Differentiating in BAA may create a marketing advantage. This is because there is a visible
difference with competitors, at least in the eyes of the consumer. Consumers gain knowledge
of a brands attributes and create associations due to content they are exposed to, UGC and
BGC. BAA together with perceived brand quality forms brand equity (Kotler & Keller, 2012).
Previous research show more different dimensions in brand equity e.g. adding brand loyalty
(Yoo, Donthu & Lee, 2000). In this article this variable will not be researched since brand
loyalty develops with time. This is difficult to measure since the amount of time available for
this article was too short to measure loyalty. Perceived brand quality is conceptualized as the
overall excellence of the brand over competitors (Kotler & Keller, 2012).
Motives to use the social media are the process (e.g. relaxation), the content (e.g.
informational), maintaining or creating interpersonal relations (Papacharissi & Rubin, 2000;
De Boer & Brennecke, 2009). UGC and BGC are two different types of posts available on the
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social media platform Instagram, which may lead to different evaluations of BAA and
perceived brand quality. First of all, customers may notice BGC is persuasive because social
media is a high involvement medium, due to its interactivity. People are more aware of the
contents’ persuasive nature compared to low involvement media (e.g. radio and television).
The high and low involvement terms follow from the Elaboration Likelihood Model (ELM)
(Petty, Brinol & Priester, 2009). Two different routes of processing persuasive content are
described in the model: the high involvement (central route) and low involvement (peripheral
route). The central route involves effortful cognitive activity whereby the recipient of the
content actively generates favorable and/or unfavorable thoughts in response to the content.
If one possesses the ability (knowledge, distraction, repetition etc.) and motivation (personal
relevance, need for cognition, etc.) to process the information, one will take the central route
and if not one will take the peripheral route. In contrast to the central route, the peripheral
route can simply be invoked by simple cues but this will not lead to long term effects, unlike
the central route. Following from this there can be stated that users of Instagram are
processing information through the central route. Instagram is highly interactive and it
requires its users to be motivated and able to process the content. If one is not able to use
the platform, no information will be transferred because a user has to find its own content.
Secondly, UGC may lead to positive BAA and perceived brand quality. While the users may
evaluate BGC as persuasive, UGC may be evaluated as honest by the customers. The
content may be regarded to be placed without intent and due to the interactivity of the
Instagram platform, customers may notice this. Nevertheless some have suggested that
nowadays, there isn’t a clear distinction between what is information and what is
advertisement (Alexander & Tate, 1999). Most of the online content has the same
accessibility and format (Burbules, 1998) and this may lead to source confusion due to the
blurred purposes of the content (Eysenbach, 2008; Metzger & Flanagin, 2013). The studies
regarding the blurred content of informational versus advertising focus mainly on textual
content whereas in this article the focus is on the traditional form of advertisement as
pictures are generated by users and by brands on Instagram. It then can be believed that the
lines are more distinctive as people are more known with sponsored ads and brand
generated content due to higher volume of social media usage (Facebook, September 2014;
Instagram, September 2014).
When consumers feel that brands infiltrate their privacy this may lead to negative
evaluation of the content (Stewart & Pavlou, 2009). BGC may create resistance against the
brand due to the invasion of their privacy and this will influence brand evaluation negatively
(Metzger & Flanagin, 2013; Papacharissi & Rubin, 2000). This may happen when one
searches for a # and is exposed to irrelevant BGC, or when one is exposed to the new
sponsored advertisements Instagram is working with. Customers may feel that their privacy
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is invaded by the brand. They can also feel that brands are trying to persuade them to buy
the product. Commercial messages will not gain an individual’s attention if they are
inconsistent with the individuals purpose in using a medium or it may even spoil the mood if
they know the content is created to persuade (Stewart & Pavlou, 2009). In opposite, one is
exposed to UGC when they want to. This can happen when one follows the relevant user or
searches for the relevant #. It may happen a user uses an irrelevant # as well but in overall
UGC is content users want to be exposed to. This will not lead to a negative evaluation of the
content since users do not find their privacy is infiltrated. Nevertheless there are also
negative aspects of UGC and positive aspects of BGC concerning BAA and perceived brand
quality.
UGC is content which can be created by anyone. First of all, this means anyone who
is able to produce posts may create content concerning brands, including children and
people who are biased. Secondly, anonymity is of importance. One can hide behind a fake
profile and claim they are an expert on the topic. Since UGC may be created by anyone and
users know this, UGC will lead to a more negative evaluation of BAA and perceived brand
quality then BGC. BGC is produced by people who know their product, they have a high
expertise on the topic. This is in line with previous research, the amount of expertise a
source is regarded to have leads to a better evaluation of the BAA and perceived brand
quality (Flanagin & Metzger, 2013). Since UGC will be regarded as having less expertise
then BGC, BGC will lead to more positive BAA and perceived brand quality.
To conclude, BAA and perceived brand quality are being influenced by generated
content and may differ depending on the source that creates the content. Previous research
describes resistance against BGC, motives, uses and gratifications , ELM and expertise.
Since Instagram is a relatively new medium no valence can be given to the different effects
of BGC and UGC posted on the platform on BAA and perceived brand quality due to lack of
research. From here the first two research question derives.
RQ1: In what way do UGC and BGC differ in their influence on BAA?
RQ2: In what way do UGC and BGC differ in their influence on perceived brand
quality
For the perceived brand quality there is more to be said. First of all, quality is based
upon the evaluation of the content credibility (Stewart & Pavlou, 2009). The problem of
evaluating credibility has been a problem in every medium, not only social media. But the
very foundation of e-commerce rests on credibility (Keen, 2000). When people know the
source of the broadcasted content is the brand itself (BGC), that is involved in the content,
people will have strong negative credibility cues (Fogg et all., 2003; Metzger et all, 2010;
Metzger & Flanagin, 2013). In delivering a message, word-of-mouth communication (WOM)
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is regarded as the most credible, unbiased effective form of marketing communication
(Cafferky, 1996; Hoyer & Macinnis, 2001; Kiely, 1993; Rosen, 2000). WOM may be seen as
UGC since users of these products are spreading the word. Cues that are harder to
manipulate are likely to be seen as being more credible since they are difficult or costly to
manipulate. For interactive media, the perceived credibility is likely to play an especially
important role in determining its influence on consumers (Stewart & Pavlou, 2009).
Nevertheless the evaluation of source expertise is also of influence on the PCC according to
the same researchers. Due to the evaluation of expertise of the source, UGC may be
regarded as having less expertise then BGC. This causes a different valence of influence as
described earlier, UGC will lead to a lower PCC then BGC. If marketers fail to deliver a
credible message, this can diminish brand loyalty and perceived product quality.(Kotler &
Keller, 2012). To sum it up; How credible consumers regard the source of content to be,
determines their perceived brand quality. The PCC explains the relationship between
UGC/BGC and brand quality. Deriving from this is the first hypotheses:
H1: PCC mediates the relationship between BGC/UGC and perceived brand quality,
perceived brand quality increases when PCC increases.
The influence of peers
Consumers rely on third parties for additional information concerning credibility (Ba &
Pavlou, 2002; Pavlou & Dimoka, 2006). This conforms with the bandwagon theory (Sundar,
2008). The theory states that if peers think something is correct, then others perceive it as
correct and credible. People tend to be influenced by peers in two ways: 1) Normative social
influence; people tend to up-live to the expectations of others by conforming their opinion. 2)
Informational social influence; people tend to be influenced by peers, not because of
normative pressure but because they believe the information is being perceived as useful.
This effect is even stronger when one has no knowledge of the subject ( Deutsch & Gerard,
1955). The influence of peers is demonstrated by Walther, van der Heide, Hamel and
Shulman (2009). They demonstrated that peer influence on an individual FB profile owner
were more influential in assessments of the individuals' physical attractiveness than selfcomments were. Utz (2010) showed that information generated by others had a higher
impact that self-generated info on communal orientation and according to Zhu, Huberman en
Luon (2011) opinions can be swayed by peers. When this is translated into the content of this
article, many peer influence leads to a higher PCC regardless the source. While one may
find UGC or BGC not credible but there is many peer influence, the PCC will be high. Peer
influence is highly explicit on the Instagram platform. People may like or comment on a
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picture and everybody is able to see this, this makes users very vulnerable to peer influence.
To conclude, regardless the source of the content, people will evaluate PCC higher when
peer influence increases. From here the second hypotheses derives.
H2: Many peer influence leads to a high PCC while little peer influence leads to a low
PCC.
Users’ activity on Instagram
Effects of commercial messages differ substantially depending on how a particular
consumer uses a given medium (Stewart & Pavlou, 2009). Due to experience and knowledge
of a medium people will evaluate the content on the relevant medium different. People with
higher online information provision activity will align more with online generated content. The
users trust, use and positively evaluate more of the information provided online (Flanagin &
Metzer, 2013). In the case of this article there can be stated that the amount of the users’
activity on Instagram will have a positive influence on the relation between the source of the
content and PCC. The more active one is on Instagram, the more credible they perceive the
content to be regardless the source. Due to this give a valence can be given to the third
hypotheses:
H3: A high Insta-activity has a more positive influence on the relation between content
and PCC then a low Insta-activity regardless the source.
All the hypotheses are visualized in a conceptual model, visible in figure 1.
Figure 1 Conceptual model
Method
Research method and motivation
An online experiment was performed and followed up by an online survey to collect
the data. This research is part of a bigger research concerning more variables (e.g. purchase
attention and intention to share). This topic was shared with other students doing their
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bachelor thesis, but only the relevant data was used to answer the research question
concerning this research.
The advantages of using an experiment as method, is that it is performed in a trusted
environment and that it is anonymous so there is less resistance against the survey. This will
lead to more reliable answers in contrast to an interview. In an interview one may give social
desirable answers due to the lack of anonymity. In addition, the survey also leads to
quantative analyzable results which an interview may lack. There are also some
disadvantages to this method. The participants may not understand the questions and have
no opportunity to ask questions. Therefor the process of evaluating the information may be
different due to the context the information is provided in. People may be aware that they are
being tested (Boeije, t Hart & Hox, 2009).
A 2x2 between group design has been used. There are two independent variables (
Source Content versus peer influence) with each two levels ( UGC/BGC and little peer
influence/ much peer influence). This has been visualized in table 1.
Sample and procedure
By spreading the online experiment and survey on Facebook along a period of two
weeks, a convenience sample was conducted. A convenience sample is a sample based on
the accessibility and proximity of the respondents. Friends and relatives targeted on
Facebook were encouraged to spread the experiment and survey among their friends and so
on. This method may also be known as the snowball-sample (Boeije, ‘t Hart & Hox, 2009).
Due to money and time limitations it was best to perform a convenience sample
method/snowball-sample. Spreading among friends led to higher response due to social
obligations and to reaching the relevant target group. The target group is the population of 18
year olds and older, they are relevant for todays’ and the future market so the sample led to
results providing the most applications for marketing managers.
The conducted survey began with general questions (gender, age, Insta-activity and
education) before the manipulation and ended with specific questions after the manipulation
(concerning perceived brand quality, perceived content credibility, brand attributes and
associations,). The manipulation check was performed at the end on the basis of two
questions: “try to remember as good as possible; who placed the post on Instagram?” with
the possible answers of ‘mariestellamaris_offical’ and ‘emma_jansen’, and “try to remember
as good as possible; how many likes did the post have on Instagram?” with the possible
answers ‘3’, ‘11’, ‘251’ and ‘1764’. A short introduction and consent form were provided for all
the participants (N=301) before the experiment in purpose of ethical review. After the
experiment no debriefing was performed. The complete survey is visible in the annex. Due to
the convenience sample method it was better not to debrief due to contamination. Friends
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talking to each other concerning the goals of the experiment may lead to biased results. The
experiment started with 301 participants but there was a big drop out, only 202 participants
completed the survey. An amount of 99 men and 125 women participated. Their education
level was overall very high; as highest attended level of education zero had primary school,
seven had VMBO, ten had HAVO, twelve had VWO, fourteen had MBO, 77 had HBO and
103 had WO. The average age was 32.2 with a standard deviation of 11.6.
Table 1 Experimental Design
Manipulation
Many peer influence
Few peer influence
UGC
Group 1 (n=51)
Group 2 (n=48)
BGC
Group 3 (n=52)
Group 4 (n=51)
Manipulation material
Four different posts on Instagram were created to manipulate the content presented
to the participants of the experiment. By visualizing the combination of the independent
variables it is easier to comprehend the manipulation material. For this table 1 was created.
The manipulation material itself is available in the annex.
Peer influence is personified by likes in the manipulation material. The amount of likes
on the post is the translation of approval and promotion of the product by peers. With 1764
likes the peer influence was manipulated to be high and low was manipulated with 11 likes.
These numbers of likes were chosen because they look ‘real’. Other ‘round’ numbers may be
perceived as unlikely or unreal (e.g. 1500, 3000, 100000 likes). These likes were shown
under a Marie Stella Maris advertisement. Marie Stella Maris is a new water brand. A new
water brand was chosen because of the previous experience participants may have had with
the brand. The brand Marie Stella Maris is a brand not commonly known by people, so it
lacks previous experience among the participants of the experiment. Due to bias not only a
new but also a water product was chosen. Water products have a neutral valence to most in
contrast to soda’s or juices. So the right product for this research to use was Marie Stella
Maris. All the manipulations in the experiment contained the same image of a Marie Stella
Maris water bottle as can be seen in the annex.
UGC/BGC is translated into the name of the uploader whereas UGC is translated into
‘emma_jansen’ (a fictional Instagram user) as uploader and BGC is translated into
‘mariestellamaris_offical’. The name Emma Jansen was chosen because both Emma and
Jansen are common Dutch names. So the four manipulations were emma_jansen with 11
likes, emma_jansen with 1764 likes, mariestellamaris_offical with 11 likes and
mariestellamaris_official with 1764 likes.
There was one neutral comment presented (‘these tropical temperatures call for
hydration’) to make the post look real . Due to the natural setting two hashtags were
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presented in the post (#mariestellamaris and #careforwater). These hashtags and comment
were presented in all of the manipulations, as was the time the picture was uploaded (one
hour ago)
Pilot-test
Before the experiment was distributed online, a pilot-test was performed. The
researchers approached 18 persons in their social environment to evaluate the experiment
and survey. Through this, spelling and neatness mistakes were detected and removed.
Operationalization
Brand attributes and associations are defined as the associations consumers have
when thinking about the product. The combinations Ferrari and speed, McDonalds and
unhealthiness, Volvo and safety are common known examples of these associations. Brand
associations are more complicated to create then awareness. It is more difficult to recall
symbols, logo’s, attributes and images in combination with the brand name then to just
remember the name on its own (Yoo, Donthu & Lee, 2000). The brand attributes and
associations Marie Stella Maris thrives to have are creativity, personality, style, caring, live
changing and clean drinking water (http://www.marie-stella-maris.com, September 2014).
Those brand attributes are being measured separately (e.g. I believe the brand Marie Stella
Maris is creative, I believe the brand Marie Stella Maris has style and I believe the brand
Marie Stella Maris has personality). All the 6 items where measured on a 7-point liker scale
ranging from fully disagree to fully agree. First al the 6 items were analyzed separately.
Table 2 BAA Items
Item
M
SD
Creative
3.61
1.69
Style
4.05
1.76
Personality
3.82
1.74
Influences
3.10
1.59
Caring
2.82
1.55
Clean drinking water
3.32
1.63
To test the reliability and internal validity of the manifest variable a (PCA) factor
analyzes with Varimax-rotation and a Cronbach’s alpha were conducted. After analyzing the
items separately the factor analyses (PCA) with Varimax-rotation was conducted and
revealed that one component has an eigenvalue of 4.12 with an explained variance of
68.64% and a high component valence of all the items (>.64). The reliability of this
component shows to be high (α=.908) whereas the measurement of perceived content
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credibility is reliable and valid (M=3.42, SD=1.40). The higher the participants score on the
scale, the more positive the brand attributes and associations they believe to be.
The perceived brand quality measures the overall excellence of the brand over
competitors. The four items measuring the perceived brand quality are collected from Yoo,
Donthu and Lee (2000) and are measured on a 7-point Likert scale ranging from fully
disagree to fully agree (e.g. the brand Marie Stella Maris is of high quality, the brand Marie
Stella Maris is highly functional and the brand Marie Stella Maris is highly reliable). The
higher the participants score on the scale, the higher they perceive the quality of the brand to
be. Since there are less than five items no factor analyses was conducted. A Cronbach’s
alpha was conducted to test the reliability. The scale is reliable (α=.748) and valid (M=4.00,
SD=1.19).
Perceived content credibility is defined as in what degree the consumer evaluates the
content as unbiased, believable and honest. Beltramini’s (1988) 7-point symantic scale was
used to measure the variable (e.g. dishonest/hones, not authentic/authentic and
unlikely/likely). Since the experiment and survey were conducted in Dutch, an item was
removed due to translation problems. Unbelievable/believable and not credible /credible
overlapped in translation (‘ongeloofwaardig’/ ‘geloofwaardig’) so unbelievable/ believable was
removed. To test the reliability and internal validity a (PCA) factor analyzes with Varimaxrotation and a Cronbach’s alpha were conducted. The factor analyzes of the eight items
(PCA) with Varimax-rotation reveals that 1 component has an eigenvalue of 5.97 with an
explained variance of 74.57% and a high component valence of all the items (>.585). The
reliability of this component shows to be high (α=.949) whereas the measurement of
perceived content credibility is reliable and valid (M=4.03, SD=1.22). The higher the
participants score on the scale the more credible they perceive the content to be.
The user activity on Instagram is defined as the average time spent on Instagram per
visit and the frequency of usage per week. Usage is not defined as only uploading but it also
refers to checking the posts of others. This is measured by the question: ‘How often do you
use Instagram?’ The participants were able to answer one of the following options: more than
once a day, once a day, several times a week, once a week or less than once a week. The
results were used to create a dummy variable. Every value equal and above the median
(Mdn=once a week) were translated into the ‘high Insta-activity’ category (n=80) and every
value under the median (less than once a week) into the ‘low Insta-activity’ (n=68) category.
Plan of analyses
After testing the reliability and internal validity with (PCA) factor analyzes with
Varimax-rotation and with Cronbach’s alpha’s the manifest variables are created. The
manipulation check will be done with a Chi-square test. Hereafter a Pearson correlation-
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matrix needs to be conducted with all the variables in the article (including control variables)
to look for cohesion. The cohesion found between control variables and other variables will
then be tested with fitting tests. To test the hypotheses different tests need to be done.
RQ1(‘In what way do UGC and BGC differ in their influence on BAA?’) and RQ2 (‘In what
way do UGC and BGC differ in their influence on perceived brand quality?’)will be tested with
a one-way ANOVA to look for different means between the categorical groups. H1( ‘PCC
mediates the relationship between BGC/UGC and perceived brand quality, perceived brand
quality increases when PCC increases)’), will be tested with Baron and Kenny’s mediation
analyses and Sobels test. Regression analyzes were conducted to look for relations. The
different regression analyzes were indicated with different letters: a, b and c. ‘a’ represents
the correlation between X and M (content and PCC), ‘b’ represents the correlation between
M and Y (PCC and perceived brand quality) and ‘c’ represents the correlation between
content and perceived brand quality. All correlations have to be significant to conduct a
Sobel’s test. H2 (‘Many peer influence leads to a high PCC while little peer influence leads to
a low PCC’) will be tested with a one-way ANOVA to look for differ means between the two
categorical groups. H3 (‘A high Insta-activity has a more positive influence on the relation
between content and PCC then a low Insta-activity regardless the source.’) will be tested
with a two-way ANOVA to look for an interaction between the categorical group means. The
Levene’s test and η2 are only mentioned when the test results are significant.
Results
Manipulation
The content manipulation has succeeded in this research. There is a significant
difference according to a chi square test, Chi-square (1)=6.57, p<.05 between contents.
75.6% of the participants exposed to BGC (N=90) were able to recall this while 96.5% of the
participants exposed to UGC (N=83) were able to recall this. The manipulation check was
done with the question ‘who posted the content?’ with the possible answers of
‘emma_jansen’ (UGC) and ‘mariestellamaris_offical’ (BGC). The peer influence manipulation
has succeeded as well in this research. There was a significance difference in answers to the
control question concerning peer influence, Chi-square(1)=3.83, p=.05. 84.9% of the
participants exposed to a high amount of peer influence (n=93) were able to recall this while
92.9% of the participants exposed to a low amount of peer influence (n=84) were able to
recall this. The distribution of the sample was equal as is visualized in table 3 (added in the
annex). Due to this the results presented in this article are dependent on the manipulation
and not on the conditions itself’.
There was a Pearson correlation matrix produced for all the variables involved in this
article, including the control variables gender, age, education and being a communication
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A PICTURE IS WORTH A THOUSAND WORDS
student. The matrix provided significant results concerning cohesion between perceived
brand quality and age (r=-.268, p<.001), BAA and age (r=-.261, p<.001), and PCC and age
(r=-.230, p=.002). Due to this additional tests were conducted concerning this variable in the
paragraph ‘control variables and additional tests’.
Testing the research questions and hypotheses
First it needed to be established if any of the BAA items were influenced differently by
UGC and BGC, because this was not the case the manifest variable BAA was created. The
loose items, or latent items, of BAA describe the manifest BAA in total. The influence of
UGC/BGC on these separated items were tested by a one-way ANOVA. The results in table
4 show (added in the annex) that there is no significant difference between UGC/BGC and
the score on any of the items. By conduction a one-way ANOVA the effects between UGC or
BGC and the manifest BAA were examined. There was no significant difference in average
between UGC (M=3.35, SD=1.34, 95%CI[3.07, 3.62]) and BGC (M=3.49, SD=1.46,
95%CI[3.20, 3.78]), F(1, 195)=.52, p=.472. The first research question can be answered with
‘they do not’. UGC and BGC do not differ in their influence on BAA.
Another one-way ANOVA was conducted to search for the difference between UGC
or BGC and their influence on perceived brand quality. There was no significant difference in
average between UGC (M=3.90, SD=1.24, 95%CI[3.64, 4.16]) and BGC (M=4.09, SD=1.14,
95%CI[3.86, 4.32]), F(1, 191)=1.23, p=.269. The second research question can be answered
with ‘they do not’ as well. UGC and BGC do not differ in their influence on perceived brand
quality.
The suggested mediation relation where PCC explains the relation between
BGC/UGC and perceived brand quality was analyzed through the Baron and Kenny’s
mediation analyses method. The first regression analyses gave the correlation between
BGC/UGC and PCC, known as ‘a’ (r=.160, R2=0.026). The table of coefficients told that Ba=.390 (SEa=.181) was a significant value (p=.032). Deriving from this it was stated that
BGC/UGC has a significant effect on PCC. The regression analyses’ following revealed the
correlation between BGC/UGC and perceived brand quality, known as ‘c’ (r=.089, R2=.008)
but according to the table of coefficients produced by these analyses, the Bc=-.213
(SEc=.180) was not significant p=.238. The correlation between PCC and perceived brand
quality, known as ‘b’ (r=.550, R2=.303) was significant, p<.001, with Bb=.541 (SEb=.063).
Due to the insignificance of ‘c’ there can be concluded that the first hypotheses must be
rejected partially. PCC does not mediate, nevertheless as PCC increases the perceived
brand quality increases as well, there is a reasonable relation between PCC and perceived
brand quality.
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A one-way ANOVA was conducted to examine the difference in average between few
and many peer influence on the PCC. There was a significant difference in average between
few peer influence (M=3.83, SD=1.26, 95%CI[3.56, 4.10]) and many peer influence (M=4.21,
SD=1.16, 95%CI[3.98, 4.45]) , F(1, 178)= 4.52 , p=.035. According to these results the third
hypotheses was accepted. Peer influence has a direct effect on PCC, the more peer
influence on Instagram the more credible one perceives the content to be.
A two-way ANOVA was conducted to examine the interaction between content and
Insta-activity on PCC. There was no significant interaction effect (F(1,120)=.230, p=.633) but
there was a direct relation found between Insta-activity and PCC. There was a significant
difference in average between low insta-activity (M=3.81, SD=1.15) and high Insta-activity
(M=4.59, SD=1.05) , F(1, 119)= 15.18 , p<.001 and η2=.113. There was a mediocre relation
between Insta-activity and PCC (η2=.113), PCC can be explained for 11.3% by the variable
Insta-activity. According to the results the second hypotheses was accepted. Insta-activity
has a positive influence on PCC regardless the source of the content.
Control variables and additional tests
The control variable age was found to have direct relations with perceived brand
quality, BAA and PCC. A Pearson correlation-matrix suggested these results as described
earlier. Using a two-way ANOVA the interaction effect between content and age on
perceived brand quality was tested. There was no interaction effect found, F(1, 186)=.09,
p=.761, but there was a direct effect found between age and perceived brand quality, F(1,
186)=14.26, p<.001, η2=.072. The results indicate that young people (M=4.31, SD=1.20)
perceived the brand quality to be better than old people (M=3.67, SD=1.11).
The interaction effect between content and age on BAA was tested with a two-way
ANOVA as well. There was no interaction effect found, F(1, 189)=.15, p=.701, but a direct
effect between age and BAA was found, F(1, 189)=13.57, p<.001, η2=.068. The results
indicated that younger people (M=3.77, SD=1.36) found the BAA more positive than older
people (M=3.05, SD=1.34) .
No interaction effect was found between content and age on PCC using a two-way
ANOVA, F(1, 175)=1.15, p=.285 but again a direct effect between age and PCC was found,
F(1,175)=9.66, p=.002, η2=.053. This results indicated that younger people (M=4.31,
SD=1.17) found the PCC higher than older people (M=3.75, SD=1.22).
Conclusion and discussion
The main subject in this article is the difference in content source on Instagram and
how it influences brand quality and BAA, while taking in account of peer influence, Instaactivity and PCC. Results were collected by an online experiment with a survey. Participants
were collected by an online convenience sample spread among Facebook friends (N=202).
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The goal of this article is to give insight of the possibilities Instagram provides for PR- and
marketing managers. Answering the hypotheses and research questions separately will give
the best insight in ways managers can use Instagram. The first two research questions
asked about the difference between UGC and BGC and how they differ in their influence on
BAA and brand quality. According to the results UGC and BGC do not differ in their influence
on BAA and brand quality. This is partially in line with previous research, there were a lot of
contradictions among these researches. While different researchers stated that BGC
influences the brands evaluation negatively due to invasion of a person’s privacy or repulsion
against the persuasive nature of the content. (Metzger & Flanagin, 2013; Papacharissi &
Rubin, 2000; Stewart & Pavlou, 2009) others stated that BGC may influence the brands
evaluation positively due to expertise evaluation (Flanagin & Metzer, 2013). An explanation
for the results found in this article is that the participants didn’t find BGC persuasive or that it
didn’t invade their privacy, so BGC would not give a negative valence to the brand quality
and BAA. Another explanation of this result is based on expertise. Flanagin and Metzger
(2013) stated that the regarded expertise of the source will lead to different outcomes. In the
matter of this article it can be stated that the participants did not see a difference in expertise
of the source. When UGC is evaluated as having the same expertise as BGC, it will lead to
the same evaluation. No negative valence will be given to the brand quality and BAA due to
the absence of a non-expert evaluation of UGC by the participants. So the absence of
difference in expertise evaluation, privacy breach and persuasive nature may have led to the
congruent outcomes of UGC and BGC on brand quality and BAA. H1 suggests PCC
mediates the relation between content and perceived BQ. According to the results PCC does
not mediate, nevertheless as PCC increases the perceived brand quality increases as well,
there is a reasonable relation between PCC and perceived brand quality. The importance of
PCC has been described in previous research. Keen (2000) stated that the very foundation
of e-commerce rests on credibility, while Kotler and Keller (2012) and Steward and Pavlou
(2009) stated that perceived brand quality is based upon content credibility. H2 suggests that
many peer influence leads to a high PCC while low peer influence leads to a low PCC and
H3 suggests that a high Insta-activity has a more positive influence on the relation between
content and PCC then a low Insta-activity regardless the source. Both hypotheses are
accepted. The relation between peer influence and PCC has been described in previous
research. When peer influence increases PCC increases, which is in line with findings from
different articles (Ba & Pavlou, 2002; Pavlou & Dimoka, 2006). Consumers rely on third
parties concerning evaluation of credibility. This is in line with the bandwagon theory
described by Sundar (2008). The theory states that if peers think something is correct, then
others perceive it as correct and credible. PCC increases while Insta-activity increases
regardless of the source of the content, which is confirmed by previous research (Stewart &
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A PICTURE IS WORTH A THOUSAND WORDS
Pavlou, 2009; Flanagin & Metzger, 2013). There was found that the more a user is active on
a certain platform, the higher the PCC of that platform. The research question ‘How do UGC
and BGC on Instagram variate in influencing the perceived brand quality and BAA and what
role do PCC, peer influence and Insta-activity play in this relation ?’ can be answered briefly.
First of all, UGC and BGC do not differ in their influence on perceived brand quality and BAA
but it is important to influence the PCC of the consumers since this is a strong predictor for
perceived brand quality. As PCC increases perceived brand quality increases. Secondly,
peer influence and Insta-activity positively influence the PCC as they increase. In the
additional tests there is found that the older one gets, the less quality they perceive the brand
to have, the more negative they evaluate the BAA and the more the PCC declines. Deriving
from this conclusion is that it creates applicable information for PR- and marketing managers.
When trying to create better perceived brand quality via Instagram it is important to use
credible content while generating many likes and comments for the product regardless the
source of the content. There can be stated that endorsed UGC is a waste of money since it is
very cost full. The effects that UGC are found to have are the same as the effects of BGC.
The age of the target population is of importance as well, as the age increases the power of
Instagram as medium to persuade decreases. Not only is this article relevant for managers, it
provides new scientific insights in the relatively new medium Instagram and it’s applications.
Since Instagram differs from its fellow social network mediums these insights were needed.
The most important limitations of these articles are the dropout rate, manipulation
succession, education bias, setting and limitations to one brand. There was a big drop out,
303 participants started with the experiment and only 202 continued with the experiment after
the introduction. Nevertheless there were enough participants to analyze the results. Another
limitation is the manipulation, for the BGC condition only 75.6% of the manipulation
succeeded. An explanation for this may be involvement or the setting. The participants were
not actually scrolling on Instagram but they were presented with one post. Due to the lack of
a natural setting bias may occur. Another bias is the education level of the participants. The
education level is relatively high, this occurred due to the distribution of the experiment and
survey among high-educated people and their friends. This may have led to biased results.
The statements this article makes cannot be generalized among other product categories
since only one brand is analyzed. The chosen product was as neutral as possible but this
has to be taken into account when making statements about different types of products with
different target audiences. Nevertheless this article did give insights in the relatively new
social media platform Instagram and external factors. It provides a framework for follow-up
studies. Follow-up studies have to be done concerning a different spectrum of brands and
product categories. For these studies it is preferably better to have a wider range of
participant, concerning the level of education. In this article the foundation for research
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concerning BAA and perceived brand quality was build but in extension the variance in BAA
must be researched further. The focusses of this article were more or less external factors
but it lacked internal factors (regarding content). In this article peer influence was translated
by amount of likes but on the medium Instagram there is also a possibility to comment on a
post. It would be interesting for future studies to categorize different posts by their valence
and other attributes to find how they affect perceived brand quality and BAA. Nevertheless
posts on Instagram have been proven to affect perceived brand quality and there are many
more qualities of Instagram to be discovered.
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Annex
24
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Survey
Amsterdam, 30-10-2014
Geachte heer, mevrouw,
U bent uitgenodigd deel te nemen aan een onderzoek dat wordt uitgevoerd onder
verantwoordelijkheid van onderzoeksinstituut ASCoR, onderdeel van de Universiteit van
Amsterdam. ASCoR doet wetenschappelijk onderzoek naar media en communicatie in de
samenleving.
Het onderzoek waarvoor wij uw medewerking hebben gevraagd, is getiteld
‘Instagram en merken’. Tijdens dit onderzoek zullen wij enkele vragen stellen over een merk
op Instagram. Aan dit onderzoek kunnen alleen mensen ouder dan 18 deelnemen. Het
onderzoek duurt ongeveer 10 minuten.
Omdat dit onderzoek wordt uitgevoerd onder de
verantwoordelijkheid van ASCoR, Universiteit van Amsterdam, heeft u de garantie dat:
1.
Uw anonimiteit is gewaarborgd en dat uw antwoorden of gegevens onder geen enkele
voorwaarde aan derden zullen worden verstrekt, tenzij u hiervoor van tevoren uitdrukkelijke
toestemming hebt verleend. 2. U zonder opgaaf van redenen kunt weigeren mee te doen
aan het onderzoek of uw deelname voortijdig kunt afbreken. Ook kunt u achteraf (binnen 24
uur na deelname) uw toestemming intrekken voor het gebruik van uw antwoorden of
gegevens voor het onderzoek. 3. Deelname aan het onderzoek geen noemenswaardige
risico’s of ongemakken voor u met zich meebrengt, geen moedwillige misleiding plaatsvindt,
en u niet met expliciet aanstootgevend materiaal zult worden geconfronteerd. 4. U uiterlijk 5
maanden na afloop van het onderzoek de beschikking over een onderzoeksrapportage kunt
krijgen waarin de algemene resultaten van het onderzoek worden toegelicht.
Voor meer
informatie over dit onderzoek en de uitnodiging tot deelname kunt u te allen tijde contact
opnemen met de projectleider drs. C. Blom ([email protected]).
Mochten er naar aanleiding
van uw deelname aan dit onderzoek bij u toch klachten of opmerkingen zijn over het verloop
van het onderzoek en de daarbij gevolgde procedure, dan kunt u contact opnemen met het
lid van de Commissie Ethiek namens ASCoR, per adres: ASCoR secretariaat, Commissie
Ethiek, Universiteit van Amsterdam, Postbus 15793, 1001 NG Amsterdam; 020-525 3680;
[email protected]. Een vertrouwelijke behandeling van uw klacht of opmerking is
daarbij gewaarborgd. Wij hopen u hiermee voldoende te hebben geïnformeerd en danken u
bij voorbaat hartelijk voor uw deelname aan dit onderzoek dat voor ons van grote waarde is.
Met vriendelijke groet,
Robert van Leeuwen
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A PICTURE IS WORTH A THOUSAND WORDS
Insta-activity
Hoe vaak gebruik je Instagram? Met gebruik wordt niet alleen uploaden bedoeld maar ook
het checken van posts van anderen.
0
Meer dan één keer per dag.
0
Één keer per dag.
0
Meerdere keren per week.
0
Één keer per week.
0
Minder dan één keer per week.
Brand attributes and associations.
Ik vind Marie Stella Maris creatief.
Helemaal mee oneens 0
0
0
0
0
0
0
Helemaal mee eens
0
0
0
0
0
Helemaal mee eens
Ik vind Marie Stella Maris stijlvol.
Helemaal mee oneens 0
0
Ik vind dat Marie Stella Maris persoonlijkheid heeft.
Helemaal mee oneens 0
0
0
0
0
0
0
Helemaal mee eens
0
0
0
0
Helemaal mee eens
0
0
0
0
Helemaal mee eens
Ik vind Marie Stella Maris zorgzaam.
Helemaal mee oneens 0
0
0
Ik vind Marie Stella Maris invloedrijk.
Helemaal mee oneens 0
0
0
Ik vind dat Marie Stella Maris voor schoon drinkwater zorgt.
Helemaal mee oneens 0
0
0
0
0
0
0
Helemaal mee eens
0
0
0
Helemaal mee eens
0
0
0
Helemaal mee eens
0
0
0
Helemaal mee eens
Perceived brand quality
Het merk Marie Stella Maris is van hoge kwaliteit.
Helemaal mee oneens 0
0
0
0
Het merk Marie Stella Maris is erg functioneel.
Helemaal mee oneens 0
0
0
0
Het merk Marie Stella Maris erg betrouwbaar.
Helemaal mee oneens 0
0
0
0
Het lijkt of het merk Marie Stella Maris van erg slechte kwaliteit is. (recode)
Helemaal mee oneens 0
0
0
0
0
0
0
Helemaal mee eens
Het bericht op Instagram is:
Onbetrouwbaar
0
0
0
0
0
0
0
Betrouwbaar
Niet overtuigend
0
0
0
0
0
0
0
Overtuigend
26
A PICTURE IS WORTH A THOUSAND WORDS
Niet geloofwaardig
0
0
0
0
0
0
0
Geloofwaardig
Misleidend
0
0
0
0
0
0
0
Niet misleidend
Oneerlijk
0
0
0
0
0
0
0
Eerlijk
Niet authentiek
0
0
0
0
0
0
0
Authentiek
Onsympathiek
0
0
0
0
0
0
0
Sympathiek
Twijfelachtig
0
0
0
0
0
0
0
Vastberaden
Manipulatiecheck
Probeert je zo goed mogelijk te herinneren: Door wie was het bericht op Instagram
geplaatst?
0
mariestellamaris_offical
0
emma_jansen
Probeer je zo goed mogelijk te herinneren: hoeveel likes had het bericht op Instagram?
0
3
0
11
0
251
0
1764
27
A PICTURE IS WORTH A THOUSAND WORDS
Table 3 Descriptives manipulation conditions
Age
Gender
Gender
(SD)
(M)
(SD)
31.19
10.72
.47
.50
103
UGC
33.08
12.20
.43
.50
99
Much peer influence
31.61
11.14
.44
.50
103
Few peer influence
32.6
11.87
.44
.50
99
Condition
Age (M)
BGC
n
Table 4 Latent items BAA
UGC
UGC
BGC
BGC
Item
(M)
(SD)
(M)
(SD)
Creative
3.62
1.63
3.60
1.74
.003
193
.957
Stylish
3.97
1.65
4.13
1.87
.528
193
.528
Personality
3.67
1.66
3.97
1.81
.226
192
.226
Caring
2.98
1.48
3.21
1.68
.316
192
.316
Influencial
2.81
1.51
2.84
1.60
.875
192
.875
3.25
1.59
3.39
1.68
.554
190
.554
Clean
water
F
28
df within
groups
p