usage’s influence on consumer behaviour: The image machine
Research Report by Maaike
de Koning, 11745274
Lecturer: Natalie Owens
University of Amsterdam
Course: Research Workshop: Survey
Tutorial group: 7
Over the past few years,
the number of social media users has increased tremendously and still this
number is growing at an incredibly fast rate. According to Statista (2017), “In
2019, it is estimated that there will be around 2.77 billion social media users
around the globe, up from 2.46 billion in 2017”. This rise of social media
usage came with a significant change in company’s strategies for communicating
with costumers (Mangold & Faulds, 2009). An important contributor to the
fast grow of several social media platforms, is the ongoing experimentation in
promotions and advertisements of brands on those platforms (Carah & Shaul,
Instagram is one of
those social media platforms that has gained a lot of users over the past
years. Instagram is a social media platform where people can produce, circulate
and attend to images (Carah & Shaul, 2015). People are able to like and
comment on images and videos on Instagram. Wissinger (2007) has described
Instagram as an “image machine”, that according to Carah and Shaul (2015),
“harnesses the continuous and habitual use of mobile devices to scroll, tap,
and glance at a never-ending flow of images”.
There is one feature for users, which is called “home”, where people see
pictures of the accounts that they follow, but which sometimes also show
“recommended images”, including advertisements. There is a second feature which
is called “explore” and this feature shows a feed that is developed by an
algorithm that is responsive to individual users (Carah and Shaul, 2015).
Currently, Instagram has
over 500 million users (Instagram, 2017). Until late 2014, Instagram did not
generate sponsored and targeted advertisements on the platform. Only posts of
individual accounts of brands served as advertisements. However, according to
Carah and Shaul (2015), since 2014, “selected brands have been able to pay for
sponsored posts targeted at specific users”.
popularity increased at an incredibly fast rate in a relatively short amount of
time, little is known about the effects of advertisements and promotions on
Instagram on consumer behaviour. In addition to the traditional advertisements
and promotions on the social media account of brands, a relatively new concept
in promotions is the role of influencers (Statista, 2016) and therefore also
little is known about the effects of this type of promoting on consumer
behaviour. It is important to fill this gap in knowledge on the effects of
social media promotions and advertisements on consumer behaviour, because this
information can be used to produce strategic and effective advertisements on
social media. This may therefore attract more consumers to certain brands and
Based on previous study, consumer
behaviour is described as …. This latent concept is measured by seven different
indicators: personal involvement
with brand, personal involvement with product, appeal to brand, appeal to
product, likeliness to purchase a product, price acceptability and trigger to
buy a lot of products.
Thus, the fast grow of social media has significantly changed the
communication process between corporates and consumers. Since Instagram is one
of the most fast growing social media platforms and Instagram has been using
sponsored and targeted posts only for three years, little is known about how
the use of Instagram influences consumer behaviour. This leads to the following research question: “How does the use of Instagram influence
In order to answer this
research question, the following hypotheses are proposed.
H0: The mean scores of consumer behaviour do not significantly differ
for the three different groups of Instagram users in the population.
H1: At least two mean scores of consumer behaviour significantly differ
for the three different groups of Instagram users in the population.
This study used a cross-sectional survey design. The
cross-sectional survey design allowed the researchers to obtain data about
demographics, but also about opinions and behaviour of consumers towards social
Since there was aimed at an equal distribution of male
and female respondents in the sample, the researchers used quota sampling as
the sampling method, which is a form of convenience sampling. Due to the short
amount of time that was available, the survey was spread through an anonymous
link that was shared on the researchers’ private social media accounts. The
social media platforms that were used for distribution were Facebook,
Instagram, Snapchat and WhatsApp.
The sample in the present study consisted of 230
respondents with different nationalities, from which in total 140 were
excluded. As 73 participants did not finish the survey, the results of only 157
participants were analysed. A test question was added to the survey to improve
the reliability of the sample. The respondents were asked to select a certain
answer, disregarding what their own answer to the posed question would be. This
way, the researchers were enabled to distinguish respondents that were actively
paying attention and respondents that did not pay enough attention to the
survey questions that were posed. Based on this question, 51 respondents were
excluded from the sample. The researchers were aiming to achieve equality of
gender in the sample. As the residual sample of reliable respondents consisted
of 106 participants, from which 56 females and 50 males, 6 female respondents
were excluded randomly to achieve that 50% of the sample is male and 50 % of
the sample is female. Since 10 participants of the equally distributed sample reported
not to use Instagram, the final sample consisted of 90 participants, of which
42 males and 48 females. In addition, another condition was that the
respondents had to be at least 18 years old to participate. The final sample
enabled the researchers to generalize the results about the population that
consists of Instagram users that are at least 18 years old, worldwide.
In the present study, the dependent variable is
consumer behaviour that is predicted by the independent variable, type of
Instagram user. Based on previous study (Intravia,
Wolff, Paez, & Gibbs, 2017), the independent variable, type of
Instagram user, is categorized in three types of users (0-60 minutes per day =
light Instagram user, 61-120 minutes per day = medium heavy Instagram user,
121-180 minutes per day = heavy Instagram user). The alternative hypothesis in
the present study, claims that type of Instagram user influences consumer
behaviour. Consumer behaviour is the independent variable and this concept is
not directly measurable and therefore considered to be a latent construct.
Consumer behaviour is therefore measured through seven different indicators: ‘personal
involvement with the brand’, ‘personal involvement with the product’, ‘appeal
to the brand’, ‘appeal to the product’, ‘willingness to purchase a product’,
‘price acceptability’ and ‘trigger to buy more products’. These seven
indicators were measured through statements that were answered on a 3-point
Likert scale (1 = agree, 2 = nor agree/nor disagree, 3 = disagree). The 3-point Likert scale was chosen
over the usual 5-point Likert scale, because the researchers aimed to examine
whether the mentioned indicators were predictors of consumer behaviour or not.
There was no interest in the extent to which the indicators predict consumer
The group of respondents that was used in this research (N=90) consisted of 42 males and 18
females. 63.3% of the respondents has a Dutch nationality. The next biggest
represented nationality was the German nationality with 8.9%. The residual
27.8% of the sample consists of 14 different nationalities, amongst others
America and Egypt are represented. Over 75% of the respondents is aged between
18 and 22 years old, approximately 15% is aged between 22 and 27 years old and
the residual +/- 10% of the respondents consisted of people older than 28 years
old. 53 out of 90 respondents claimed to be light Instagram users, 28 claimed
to be medium heavy Instagram users and 9 claimed to be heavy Instagram users.
Statistical analyses to test the hypotheses
First, a selection was made to exclude the respondents that do not use
Instagram. Then a principal axis factor analysis with Direct Oblimin rotation
was conducted with 7 items that measure the latent construct ‘consumer
behaviour’. Both the Eigenvalue-criterion, which indicates that a factor’s
Eigenvalue must be bigger than 1 (Eigenvalue Factor 1 is 3.87 and Eigenvalue Factor
2 is 1.01), and the Scree Plot show that there are two factors. Factor 1
consists of 5 items that measure ‘likeliness to buy’. Factor 2 consists of 2
items that measure ‘personal involvement’. Together, the factors explained
69.8% of the variance in the 7 items. Factor 1 explained 55.3% of the variance
and Factor 2 added 14.5% of explained variance. A reliability test showed that
the reliability for both Factor 1 (? =.83), which represents ‘likeliness to buy’, and for
Factor 2 (? =.88),
which represents ‘personal involvement’. For Factor 1, Cronbach’s Alpha could
be improved by deleting one item, ‘more likely to be willing to purchase a
product that usually lies out of price limit’ (? =.85). However, deleting the item does
not make a lot of difference and therefore is not a necessary change to make.
Cronbach’s Alpha could not be improved for Factor 2. A mean scale was created for Factor 1, ‘likeliness to
buy’ and for Factor 2 ‘personal involvement’.
A one-way ANOVA analysis was conducted to examine the effect of
Instagram use on likeliness to buy. The one-way ANOVA analysis was significant.
The analysis of variance showed a weak effect of Instagram use on likeliness to
buy F(2,87) = .85, p =.423, ?2 = .07. A Bonferroni post hoc test revealed
there are no significant differences found between all of the groups (all p > .05). See
Table 1 for means and standard deviations.
Level of likeliness
A second one-way ANOVA analysis was conducted to examine the effect of
Instagram use on personal involvement. The one-way ANOVA analysis was not
= 3.39, p =.038, ?2 =.02. See Table 2 for means and standard
Level of personal
The main reason for conducting this study, was to
examine how Instagram use has an influence on consumer behaviour. The present
study was conducted using a survey measuring participants consumer behaviour
and measuring the number of minutes one spends on Instagram per day. To find an
answer to the research question, ‘how does Instagram use influence consumer
behaviour?’, the independent variable, Instagram use, was measured by dividing
the participants into three subgroups labelled as ‘light users’, ‘medium heavy users’,
and ‘heavy users’ and the dependent variable, consumer behaviour, was measured
through seven different indicators on a 3-point Likert scale.
The results of the present study may contribute to
gaining a deeper insight in communication between consumer and corporations.
This information may provide corporations with information that can contribute
to enhancing strategic
and effective advertisements on social media. This may therefore help corporations
to attract more consumers to certain brands and products.
A factor analysis and a reliability analysis indicated
that there are two factors, ‘likeliness to buy’ and ‘personal involvement’ that
measure consumer behaviour. Those factors were separately used as dependent
variables in One-way ANOVA analyses, using type of Instagram user as
independent variable. For the dependent variable ‘likeliness to buy’, the
omnibus test was significant, however, Bonferroni post hoc test results
revealed that there is are no significant differences between groups, changing
the potentially significant results to non-significant. For the dependent
variable ‘personal involvement’, the omnibus test was not significant.
Therefore, the proposed alternative hypothesis that at least two mean scores of
consumer behaviour significantly differ for the three different groups of
Instagram users in the population is rejected. The proposed null-hypothesis
that stated that the mean scores of consumer behaviour do not differ for the
three different groups of Instagram users in the population is accepted.
Suggestions and explanations for
An explanation for non-significant results in the
post hoc test (with ‘likeliness
to buy’ as the dependent variable and ‘type of Instagram user’ as the
independent variable) that
measured differences between groups, is that due to the group numbers being less than 30, the Bonferroni post
hoc test changed our potential significant results into non-significant
results. An explanation for the
group numbers being less that 30, is that due to the short amount of time, the
sample of which data was collected was too small. Future research should therefore make sure that groups consist of even
groups and of at least 30 participants in order to gain significant results out
of the post hoc test. This can
be achieved by obtaining a larger sample.
The short amount of time and the
small sample size could also be an explanation for the non-significant results
for the omnibus test with ‘personal involvement’ as the dependent variable and ‘type
of Instagram user’ as the independent variable.
Limitations and future research
One limitation is that the sample mainly consisted of
participants with a Dutch nationality (63.3%). An explanation for this occurrence
is that, due to the short amount of time, convenience sampling was used as the sampling
method. The Dutch researchers shared an anonymous link on several social media
platforms and as these researchers mainly have friends with a Dutch
nationality, this may have caused the unequal balance in nationality of
respondents. This sample therefore is not representative for the wider
population. Future research should therefore try to obtain data from a more
equally distributed sample, regarding nationality, in order to improve external
Another limitation in the present study is that the
research design that was used was a survey, which leads to that the researchers
cannot conclude a causal relationship. Future research should use an
experimental design in order to examine the causality of the relationship.
Carah, N., & Shaul, M. (2015).
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Media & Communication,4(1),
Intravia, J., Wolff, K. T., Paez,
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between social media
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Mangold, W. G., & Faulds, D. J.
(2009). Social media: The new hybrid element of the
promotion mix. Business
Horizons,52(4), 357-365. doi:10.1016/j.bushor.2009.03.002
Statista. (2016). Best social media platforms for influence
marketing according to influencers
in the United States as of
July 2016. Retrieved from Statista: https://www.statista.com/
(2017). Number of social media users
worldwide from 2010 to 2021. Retrieved from Statista:https://www.statista.com/statistics/278414/number-of-worldwide-social- network-users/
Wissinger, E. (2007). Always on Display. The
Affective Turn, 231-260.