Quantitative Method for Data-Collecting Survey

THE INFLUENCE OF YOUTUBERS ON CONSUMERS’ PURCHASE INTENTION
A report submitted to the business department in partial fulfilment for final assessment for the
module Business Research Methods
Confidentiality Statement
This document contains confidential information that must not be disclosed to anyone other than
the instructor and the student unless authorised to do so.
DECEMBER 2020
1
LE PHAM NGOC MAI
[e-mail]
VNUK Institute for Research and Executive Education
The University of Danang

Table of Contents
1. SYNOPSIS.....................................................................................................................................2
2. ABSTRACT....................................................................................................................................3
3. INTRODUCTION............................................................................................................................3
3.1. Research Background...........................................................................................................3
3.2. Purpose of the research........................................................................................................4
3.3. Research questions and hypotheses.....................................................................................4
a. Research question...............................................................................................................4
b. Conceptual Framework.......................................................................................................5
c. Hypothesis Development....................................................................................................6
4. LITERATURE REVIEW....................................................................................................................6
2.1 YouTube................................................................................................................................6
2.2. Factors..................................................................................................................................7
2.2.1 Perceived usefulness of information in the videos (PU).................................................7
2.2.2 Perceived attractiveness (PVC).......................................................................................7
2.2.3 Number of views, likes, comments and replies (NVLCR)................................................8
2.2.4 Trustworthiness.............................................................................................................8
2.3 Purchase Intention.................................................................................................................9
5. METHODOLOGY.........................................................................................................................10
5.1. Data Collection and samples...............................................................................................10
5.2 Measures.............................................................................................................................11
6. DATA ANALYSIS..........................................................................................................................15
1. Descriptive statistics..............................................................................................................15
2. Inferential statistics...............................................................................................................20
3. Regression model..................................................................................................................21
7. DISCUSSION & CONCLUSION......................................................................................................23
1. Research results:....................................................................................................................23
2. Research limitations:.............................................................................................................23
3. Development direction:.........................................................................................................24
4. Conclusion.............................................................................................................................24
8. REFERENCES...............................................................................................................................25
9. APPENDIX...................................................................................................................................28
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1. SYNOPSIS
Study Title The influence of Youtube influencers on consumers’ purchase
intention in Hoa Cuong Nam ward in Da Nang city.
Internal ref. no. / short
title
Influence of Youtube influencers on consumers’ purchase intention.
Study Design The method of the research used the quantitative method for data-
collecting survey.
Study Participants The survey’s respondents are people living in Hoa Cuong Nam ward
(especially on Truong Chi Cuong street) in Da Nang city.
Planned Sample Size The sample size was expected to be 200 respondents. However, there
are more than 200 people (in fact, 208 respondents) joining this
survey.
Planned Study Period Autumn 2020-2021
2. ABSTRACT
This research examines the impact of Youtubers on users’ buying intention in the particular
area range, to be more specific, this research aimed at residents living in Hoa Cuong Nam
ward, Da Nang city. It collected data of 208 observations in total by an online questionnaire.
Descriptive statistics, inferential statistics, reliability analysis, factor analysis and regression
model were used to analysed the data. The limitation of the study is source of data so the
model proposed cannot be applied for a large population.
3. INTRODUCTION
3.1. Research Background
The human history has witnessed the fastest – growing development of the Internet in this
new technological age. There is no doubt that the Internet and social media take an
important role in customers’ purchasing intention and their decision-making result. More
and more businesses are using different types of social media sites for the good sake of
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building their brand name’s position since that is where the consumers are able to express
their thoughts, exchange experiences and knowledge as well as approach to their interests
just by a click on their fingertips.
Youtube was not that popular among the Vietnamese until the past few years. It is recorded
that Vietnam is gradually moving towards online, with the Internet penetration of nearly
49,063,762 people, accounting for 52% of the Vietnamese population (Internet Live Stats
2016). According to a research on Vietnamese Consumers’ Online Behaviors in 2014 carried
out by TNS and published by Google, in Vietnam, 93% of people use Internet for information
navigation, amongst which products searching accounts for 69%. On the other hand,
Vietnam is also a potential market with the rising level of purchasing power of consumers.
3.2. Purpose of the research
The thesis’s main purpose is to aim at the relationship between youtubers and how they
affect normal youtube users’ purchase intention. Besides viewers’ benefits, a wide range of
companies could also take advantage of this type of social media sites thanks to the
collaborations of well-known youtubers and the companies. In other words, companies
cooperate with famous youtubers, formally known as Social Media influencers, to extend
their brand name and replace traditional marketing means. Dredge (2016) stated that the
audience feels more genuine connection with Youtubers through engagement, similar
humor and the absence of filters, which partly explains their growing popularity compared
to traditional celebrities.
3.3. Research questions and hypotheses
a. Research question
The core research question of this study is: How do Youtubers influence normal viewers’
purchase intention? In order to answer the core question, there will be several sub-
questions as following:
1. Who are youtube influencers?
2. What is the buyer purchase intention?
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3. How can youtubers affect the purchase intention?
b. Conceptual Framework
In this section, we present the hypotheses and conceptual model based on the previous
discussion on literature review regarding consumers’ buying intention affected by
YouTubers/ Youtube Influencers and additional relevant issues related to variables. There
are many ways to measure the effect on purchase intention, such as through the number of
views, likes, comments and replies (Lee, 2009), perceived usefulness of information in the
videos (Pavlou and Fygenson, 2006), perceived video characteristics (Satgunam et al., 2010)
and the attitude toward purchase (Bouhlel et al.,2010).
YouTube users who seek user-generated content also take into consideration the comments
and the number of comments increase the credibility and usefulness of the videos (Mir and
Rehman, 2013). Ratings are also important for evaluating the credibility of online contents
(Flanagin et al., 2011). While, number of likes affects the credibility of contents in forums
(O'Reilly and Marx, 2011); this effect also applies to YouTube videos and the number of likes
increases the popularity of videos leading to increased credibility and usefulness (Mir and
Rehman, 2013). As proposed by Mir and Rehman (2013) the number of users who view the
content on YouTube is important in the perception of credibility and usefulness. The quality
of the video is also a factor that influences purchase decisions (Satgunam et al., 2010). There
are several studies supporting the relationship between attitude and intention. Mir and
Rehman (2013) proved a positive link between consumers’ attitude toward UGC on YouTube
and intention to use these UGC for purchase decisions.
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Usefulness
(H1)
Attractiveness
(H2)
Trustworthiness
(H3)
Purchase
Intention
Interaction
(H4)

c. Hypothesis Development
There are several hypotheses considered for the research:
H1: Usefulness of Youtube videos affects intention to purchase.
H2: Attractiveness of videos affects intention to purchase.
H3: Trustworthiness of viewers in Youtubers affects intention to purchase.
H4: The interaction between Youtubers and users affects intention to purchase.
4. LITERATURE REVIEW
2.1 YouTube
Video sharing websites provide their users low cost broadcasting tools which are easy to use
and which allow them to share their content on personal profiles and interact with other
users (Rigby, 2008). One of the most popular video sharing sites is YouTube, which was
founded in 2005. YouTube has reached incredible growth in the number of users and videos,
and Time magazine declared it as the invention of 2006 (Jarrett, 2008).
According to a survey in 2009 by Pew Research Center, 69% of American internet users have
watched or downloaded online videos. A report from Cisco reveals that one third of the 50
most visited websites are video sharing websites with YouTube being the most highly visited
one (Snelson, 2011).
The nature of YouTube is clearly defined by its slogan “Broadcast Yourself” which focuses on
users with a “do it yourself” approach and allows them to create and broadcast UGC
(Jarrett, 2008). Personal profiles of YouTube users are called “channels” (Miller, 2011) and
users can choose to share UGC publicly or only with their friend circle (Lange, 2007).
Becoming a YouTube user and creating a channel provides features such as commenting to
videos, subscribing and following other channels, customizing the experience by creating
playlists, etc. (Sahlin and Botello, 2007).
YouTube allows consumers to define their relationships with products or brands freely and
in a creative way (Pace, 2008); and millions of internet users have become self-broadcasting
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consumers (Harris, 2012). Therefore, this study aimed to investigate how broadcasted UGC
on YouTube affects purchase intention of consumers.
2.2. Factors
2.2.1 Perceived usefulness of information in the videos (PU)
According to Davis (1989, p.320) perceived usefulness is defined as “the degree to which a
person believes that using a particular system would enhance his or her job performance”,
and it is related to the expectations that a person hopes to get in the end (McKnight and
Kacmar, 2007). Pavlou and Fygenson (2006) defined perceived usefulness of the information
from websites as the belief that information will enhance efficiency in obtaining product
information. A study related to blogs (Bouhlel et al., 2010) reveals that perceived usefulness
is related to the advantages of using blogs and these advantages can be preventing waste of
time and accessing extra information and different perspectives about products.
According to Technology Acceptance Model (TAM), perceived usefulness affects attitudes
(Mir and Rehman, 2013). Bouhlel et al. (2010) supports this hypothesis for blogs by showing
that perceived usefulness has an effect on attitude toward the blog. Hsu et al. (2013) also
found that the usefulness of blog recommendations has a direct effect on attitudes and an
indirect effect on purchase intention.
2.2.2 Perceived attractiveness (PVC)
Specific features of YouTube videos may have an effect on purchase intentions. First of all,
the quality of the video is a factor that influences purchase decisions (Satgunam et al., 2010)
and high-quality videos increase user engagement. Secondly, the perception about the
length of the video may also be a factor. The results of an analysis on mostly shared Top 50
YouTube videos reveal that the average length for marketing videos is 3-3.5 minutes; but
the wish to share depends more on the strength of emotions that the video elicits from the
viewer. Therefore, not the exact length of the video but the perception of it may be a factor
that affects attitudes and purchase intentions. Finally, the preparation and presentation of
the content is also considered to be influential for consumer purchase intentions. Because,
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these can affect the level of information the consumers obtain and lead to attitude
formation.
2.2.3 Number of views, likes, comments and replies (NVLCR)
Consumers’ perceptions about a content can be influenced by interactions of other users.
Comments on an online content about a product may be perceived as a sign of product
popularity and can affect purchase intentions (Lee, 2009). For consumers who find online
information credible, number of comments increases the credibility of those information
(O'Reilly and Marx, 2011). YouTube users who seek user-generated content also consider
the comments and the number of comments increase the credibility and usefulness of the
videos (Mir and Rehman, 2013).
Ratings are also important for evaluating the credibility of online contents (Flanagin et al.,
2011). While, number of likes affects the credibility of contents in forums (O'Reilly and
Marx, 2011); this effect also applies to YouTube videos and the number of likes increases
the popularity of videos leading to increased credibility and usefulness (Mir and Rehman,
2013).
Lastly, as proposed by Mir and Rehman (2013) the number of users who view the content
on YouTube is important in the perception of credibility and usefulness. Moreover, it is
proposed that the number of replies to the comments by the video owner may also affect
the perception of credibility and usefulness of the information given in the video.
2.2.4 Trustworthiness
Expertise and trustworthiness are cores of credibility (Perloff, 2013). Hung et al. (2011)
pointed out that salient viewers’ communication and interaction with other online users will
lead to high interpersonal trust. Once a vlogger has a high perceived credibility, he or she
will gradually become popular on YouTube. This popularity will attract more viewers to
watch, leave comments, rate and like the vlog, and subscribe to the vloggers’ personal
channel (Mir & Rehman, 2013). Prior study also posits that in a trusting Internet
environment, users are willing to seek more information and feel safe and relaxed
responding to the information to which they are exposed (Hung et al., 2011). Therefore, the
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present research postulated that perceived credibility is a factor that will increase viewers’
time spent watching UGC and the frequency of online interactions with the vloggers.
Furthermore, previous research posited that interpersonal trust and source credibility
enhance users’ engagement in online activities and intentions to act on other consumers’
recommendations (Hung et al., 2011). Ultimately, this study suggested that there was a
positive correlation between UGC perceived credibility and user activities. Furthermore,
more YouTube user activity would lead to stronger effects on users’ future purchasing
intention.
2.3 Purchase Intention
Intentions can be defined as “the person’s motivation in the sense of his or her conscious
plan to exert effort to carry out a behavior” (Eagly & Chaiken, 1993, p.168). Purchase
intention represents “an individual’s conscious plan to make an effort to purchase a brand”
(Spears & Singh, 2004, p. 56).
Facing a wide range of product types, varied brand names and confusing market messages,
it is not an easy task for the consumer to make a purchase decision. Previous studies found
that there are many factors, such as product characteristics, consumers’ individual
characteristics, and environmental characteristics that may influence peoples’ consumption
behaviors (Kwan, 2006). Individual characteristics and environmental characteristics are
regarded as two important factors influencing consumers’ clothing purchase decisions
(Kwan, 2006). According to Jalalkamali and Nikbin (2010), in a complex business
environment, consumers usually make a purchase decision based on prices, quality, brands
of products, advertisements, friends’ and families’ recommendations, and consumers’
previous purchase experiences.
Specifically, interpersonal influence plays a significant role in consumers’ cosmetic
purchasing, because most cosmetics advertisements fail to carry credible information due to
overestimated using effects (Hung et al., 2011). Therefore, consumers are more influenced
by their friends’ and families’ opinions than by advertisements on cosmetics purchasing. As
the Internet has become increasingly accessible, interpersonal influence on purchase
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decisions from online communication has rapidly grown. The Internet provides a wide
choice of approaches for consumers to exchange opinions. These interpersonal interactions
could be conducted via e-mail, homepages, blogs, forums, online communities, chat rooms,
review sites, and social networking sites (Goldsmith & Horowitz, 2006). Consumers use the
new media to give and seek product information and share user experiences. Online
consumer reviews, especially the professional reviews, can significantly influence people’s
purchase intentions (Zhu & Zhang, 2010).
Goldsmith and Horowitz (2006) also identified eight factors for online information seeking
before purchase: (a) perceived risk, (b) influence of others, (c) price consciousness, (d) ease
of use, (e) accidentally (unplanned), (f) it is cool, (g) getting pre-purchase information, and
(h) saw it on TV.
In addition, Geissler and Edison (2005) introduced the notion of “market mavens”, which
refers to consumers who believe they are expert in shopping with knowledge and influence
across a brand range of product categories. They are seen not only as opinion leaders, but
as technologically savvy (Cheong & Morrison, 2008). Geissler and Edison (2005) posited that
market mavens have an affinity for Internet-related communication. In that case, vloggers
who post a product review video and help other consumers to make a purchase decision on
YouTube could be seen as market mavens. Earlier studies indicated that consumers are
influenced by an online review generated by other users, and they think their opinions are
perceived to be the most credible for consumers to seek information about a product (Bae
& Lee, 2011). Therefore, it was safe to postulate that vloggers, as market mavens, could
influence the viewers’ future purchase intentions.
5. METHODOLOGY
5.1. Data Collection and samples
The sample of this study consists of people living in Hoa Cuong Nam ward in Da Nang city.
There were 208 respondents for the online questionnaire. A structured questionnaire was
designed in order to achieve the objective of the study. Descriptive statistics, reliability
analysis, factor analysis (EFA) and regression model have been applied to analyze the data.
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In order to measure the performance of all factors effecting the purchase intention, the
study uses the 6 points Likert Scale and Multiple choices. The collected data was analyzed by
using SPSS software version 20.0.
5.2 Measures
Data Analysis methods
In the study, quantitative methods are used to analyze the data, including descriptive
statistics, reliability analysis, as well as factor analysis (EFA) and regression model. First,
Cronbach’s Alpha is used to analyze the reliability of internal consistency of scales and items
in our questionnaire. In terms of determining if hypothesized factor structure fits observed
data, the study used Exploratory Factor Analysis (EFA) to group variables having similar kind
of characteristics. It identifies underlying factors which are measured by a (much larger)
number of observed variables and determines latent variables that are found in groups of
manifest variables provided information about the numbers of factors required to represent
the data. Then descriptive statistics figured out the distribution of variables in order to show
current tendencies and create first predictions. Results of this test were data of Linear
regression. Regression model determined the strength of predictors to identify the strength
of the effect that the independent variables have on a dependent variable. The regression
analysis can be used to get point estimates.
Reliability analysis (Cronbach’s Alpha)
 Usefulness
A reliability analysis of
Usefulness comprised 3
items. Cronbach’s alpha
showed the questionnaire to reach acceptable reliability with α = 0.785. All items appeared
to be worthy of retention, resulting in a decrease in the alpha if deleted.
11
Item-Total Statistics
Scale Mean if Item
Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha if
Item Deleted
Use Youtube to search for
new products/ services 7,56 6,598 ,716 ,619
Use Youtube to search for
things that are aware of 7,28 7,243 ,607 ,731
Use Youtube to find
discounts/ promotions/
giveaways offered by
Youtubers.
8,46 5,786 ,581 ,783
Reliability Statistics
Cronbach's Alpha N of Items
,785 3

 Attractiveness
In terms of Attractiveness, the reliability analysis consisted of 5 items. The questionnaire
had acceptable reliability because Cronbach’s alpha was 0.815 which is a relatively high
value. If any item was delected, it led to the decrease of alpha, so all items appeared to be
worthy of retention.
 Trustworthiness
In terms of Trustworthiness, the reliability analysis consisted of 2 items. The questionnaire
had acceptable reliability because Cronbach’s alpha was 0.851 which is a relatively high
value. If any item was delected, it led to the decrease of alpha, so all items appeared to be
worthy of retention.
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Item-Total Statistics
Scale Mean if Item
Deleted
Scale Variance
if Item
Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
Pay attention to how Youtubers
get dressed 18,51 14,674 ,571 ,791
Easily attracted by interesting
and unique content 17,86 15,409 ,704 ,756
Look at the thumbnail and the
title 18,19 14,776 ,636 ,769
Prefer a Youtuber with great
sense of humor 18,00 15,433 ,570 ,789
An effective platform 18,57 14,659 ,572 ,790
Reliability Statistics
Cronbach's Alpha N of Items
,815 5
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance if
Item Deleted
Corrected Item-
Total Correlation
Cronbach's Alpha
if Item Deleted
Aware of a product that is
highly-recommended by well-
known Youtubers
3,88 1,944 ,746 .
Believe in a Youtuber because
he/ she always recommends
high-quality products/
services.
3,89 1,540 ,746 .
Reliability Statistics
Cronbach's Alpha N of Items
,851 2

 Interaction
In terms of Interaction, the
reliability analysis consisted of
4 items. The questionnaire had
acceptable reliability because
Cronbach’s alpha was 0.841
which is a relatively high value.
However, if we delete the item ITR1 (Look at the number of likes and comments), the
Cronbach’s alpha will be 0.843 and higher than the recent Cronbach’s Alpha. However, the
corrected Item-Total Correlation of this item is 0.574 ( 0.3) and the Cronbach’s Alpha is
0.841 which is still high so we do not need to eliminate this item. So all items appeared to be
worthy of retention.
Factor analysis (EFA)
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. ,888
Bartlett's Test of Sphericity
Approx. Chi-Square 1528,422
df 91
Sig. ,000
KMO value was 0.888 which is higher than 0.5 and closed to 1 so the results of factor
analysis may be useful with our data. In terms of Barlett's test of sphericity, p-value was 0
which is smaller than values of the significance level (less than 0.05). It also indicated that
the factor analysis may be useful with the data.
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Item-Total Statistics
Scale Mean
if Item
Deleted
Scale Variance
if Item Deleted
Corrected
Item-Total
Correlation
Cronbach's Alpha
if Item Deleted
Look at the number of likes and
comments
13,05 11,162 ,574 ,843
The freedom to discuss or comment on
any video posted
13,46 9,775 ,712 ,783
Like how Youtubers make videos related
to users’ comments/ recommendations
13,07 9,939 ,800 ,743
Pay attention to how Youtubers reply/
react to users’ comments
12,93 11,630 ,634 ,817
Reliability Statistics
Cronbach's Alpha N of Items
,841 4

In the Total Variance Explained table, the first three components extracted Initial
Eigenvalues greater than 1 so these 3 factors were retained. Rotation Sums of Squared
Loadings represented the distribution of the variance after the varimax rotation. It
reallocated the total amount of variance to these 3 factors by maximizing the variance of
each factors. These 3 factors explained 66.564% of the variability, which reachs an
acceptable percent.
There are total of 14 items that affect purchase intention. Each of them has been
rearranged based on the rating given by the respondents and are catagorised into 4
underlying factors which are Usefulness, Attractiveness, Trustworthiness and Interaction.
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In sum up, Factor Analysis devided 14 variables into 4 factors. The first factor is Usefulness
which includes 3 items. Secondly, Attractiveness with 5 items is the second components.
Then, Trustworthiness factor includes 2 variables. Finally, Interaction consists of 4 items.
6. DATA ANALYSIS
1. Descriptive statistics
Half of the participants in this survey are from 18-21 years old with accurately 50% of 104
respondents. 63 people are at the age of 22 to 30 accounting for 30,3%. Others at the age of
31 to 40 and over 40 comprise respectively 8,7% (18 respondents) and 7,2% (15
respondents). And the smallest number of respondents comes from the generation under
18 years old (8 respondents that make up 3,8%).
Age
N Valid 208
Missing 0
Mean 2,65
Std. Error of Mean ,066
Median 2,00
Mode 2
Std. Deviation ,956
Skewness ,978
Std. Error of Skewness ,169
Range 4
Minimum 1
Maximum 5
15
Frequency Percent
Valid
Under 18 8 3,8
18 - 21 104 50,0
22 - 30 63 30,3
31 - 40 18 8,7
Over 40 15 7,2
Total 208 100,0

 The mean is 2,65 so the average age of this sample ranges from 18 to 30 years old.
Meanwhile, Std Error equal to 0.066 is the standard error when using sample mean
to estimate the average value of the population.
 Mode equals to 2 so most of participants are from the age of 18 to 22.
 The minimum age of students surveyed using this sample is under 18 years old, and
the highest is over 40 years old.
 Std. Deviation equals to 0.956 so the variance is 0.956^2
 Skewness is 0.978, greater than 0 but less than 1, so the distribution shape is right-
deviated.
 We can conclude that the generation that participates the most in this survey is
probably young adults.
The number of female participating is 62,5% and the other percentages are male with
37,5%. The gender difference of respondents is significant.
 We can easily see that more women
took part in this survey than men (130
female respondents vs 78 male
respondents)
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Frequency Percent
Valid
Male 78 37,5
Female 130 62,5
Total 208 100,0
Frequency Percent
Valid
Less than 5 million 118 56,7
5 - Under 15 million 60 28,8
15 - under 25 million 16 7,7
Over 25 million 14 6,7
Total 208 100,0

The first largest percentage belongs to that of respondents earning less than 5 million VND
for a month which accounts for 56,7% with 118 responses, followed by the percentage of
participants making from 5 to under 15 million VND which makes up 28,8% with 60
respondents. The third and fourth largest percentage come from people making 15 to under
25 million VND and over 25 million VND and are respectively 7,7% and 6,7%.
Monthly income
N Valid 208
Missing 0
Mean 1,64
Std. Error of Mean ,062
Median 1,00
Mode 1
Std. Deviation ,889
Skewness 1,346
Std. Error of Skewness ,169
Range 3
Minimum 1
Maximum 4
 The mean is 1,64 so the average level of monthly income of this sample ranges from
under 5 million to 15 million VND. Meanwhile, Std Error equal to 0.062 is the
standard error when using sample mean to estimate the average value of the
population.
 Mode equals to 1 so most of participants are earning under 5 million VND per
month.
 The minimum level of monthly income of students surveyed using this sample is
under 5 million VND, and the highest is over 25 million VND.
 Std. Deviation equals to 0.889 so the variance will be 0.889^2.
 Skewness is 1,346, greater than 1, so the distribution shape is significantly right-
deviated.
 Since most of the participants are from the age of 18 to 30 so with this age level, they
are mainly students or at their very earlier phase of building or starting their own career
path. Therefore, it is no doubt that the level of monthly income is not very high and falls
steadily when the choice for monthly income gets higher.
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N Valid 208
Missing 0
Mean 1,62
Std. Error of Mean ,065
Median 1,00
Mode 1
Std. Deviation ,936
Skewness 1,911
Std. Error of Skewness ,169
Range 5
Minimum 1
Maximum 6
More than a half of respondents are student with 58,7% of 122 respondents. 63 people are
employed or having a job and it accounts for 30,3%. The percentage of people who are
unemployed comprises only 3,8% (with 8 respondents). However, there are 12 respondents
who claimed to be self-employed or run their own business (accounts for 5,8%). Finally, the
18
Frequency Percent
Valid
Student 122 58,7
Employed 63 30,3
Unemployed 8 3,8
Self-employed 12 5,8
Retired 2 1,0
Others 1 ,5
Total 208 100,0

rest of them is retired or has another state of education or work. (1% for retired and 0,5%
for others).
 The mean is 1,62 so the average level education or work of this sample is mainly
students and employees. Meanwhile, Std Error equal to 0.065 is the standard error
when using sample mean to estimate the average value of the population.
 Mode equals to 1 so most of participants are students.
 Std. Deviation equals to 0.936 so the variance will be 0.936^2
 Skewness is 1,911, greater than 1, so the distribution shape is significantly right-
deviated.
 This information clarifies the point why most of them choose their average income to
be under 5 million and from 5 to under 15 million VND. Because they are mainly
undergraduates and employees so maybe they do not have chance or opportunity to find
a job for making more stable income.
Frequency Percent
Valid
I have never watched Youtubers’ videos before
buying new products and services 13 6,3
I am watching Youtubers’ videos before buying
new products and services 120 57,7
I used to watch Youtube before buying new
products and services (Not anymore) 75 36,1
Total 208 100,0
With the question “Have you ever watched Youtube before buying new products and
services?”, the survey finds that 120 respondents are watching Youtubers’ videos before
buying new products and services (57,7%) and 75 respondents used to watch Youtube
before buying but now they do not. On the other hand, there are 13 respondents saying
that they have never watched Youtubers’ videos before buying and that accounts for the
rest 6,3%.
 More and more people are getting used to with watching Youtube and taking
advantage of it as a platform for searching information. However, there are people who
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do not watch Youtube to get to know more about a product or a service as they find it
time-consuming and unreliable.
2. Inferential statistics
*With p-value is 0.00 < 0.01 (significance level) so there was sufficient evidence to conclude
that there was a linear relationship between the usefulness of Youtube videos and
purchase intention. Besides, Pearson correlation of usefulness of information and purchase
intention was r = 0,612 which is higher than 0 so the direction of the relationship is positive,
meaning that these variables tend to increase together.
Correlations
Purchase
Intention
Attractiveness
Purchase Intention
Pearson Correlation 1 ,673**
Sig. (2-tailed) ,000
N 195 195
Attractiveness
Pearson Correlation ,673** 1
Sig. (2-tailed) ,000
N 195 195
**. Correlation is significant at the 0.01 level (2-tailed).
*With p-value is 0.00 < 0.01 (significance level), there was sufficient evidence to conclude
that there was a linear relationship between attractiveness of videos and intention to
purchase. Besides, Pearson correlation of attractiveness and purchase intention was r =
20
Correlations
Purchase
Intention
Usefulness
Purchase Intention
Pearson Correlation 1 ,612**
Sig. (2-tailed) ,000
N 195 195
Usefulness
Pearson Correlation ,612** 1
Sig. (2-tailed) ,000
N 195 195

0,673 which is higher than 0 so the direction of the relationship is positive, meaning that
these variables tend to increase together.
Correlations
Purchase
Intention
Trustwort
hiness
Purchase Intention
Pearson Correlation 1 ,798**
Sig. (2-tailed) ,000
N 195 195
Trustworthiness
Pearson Correlation ,798** 1
Sig. (2-tailed) ,000
N 195 195
**. Correlation is significant at the 0.01 level (2-tailed).
*With p-value is 0.00 < 0.01 (significance level), there was sufficient evidence to conclude
that there was a linear relationship between trustworthiness of viewers in Youtubers and
intention to purchase. Besides, Pearson correlation of attractiveness and purchase intention
was r = 0,798 which is higher than 0 so the direction of the relationship is positive, meaning
that these variables tend to increase together.
Correlations
Purchase Intention Interaction
Purchase
Intention
Pearson Correlation 1 ,862**
Sig. (2-tailed) ,000
N 195 195
Interaction
Pearson Correlation ,862** 1
Sig. (2-tailed) ,000
N 195 195
**. Correlation is significant at the 0.01 level (2-tailed).
*With p-value is 0.00 < 0.01 (significance level), there was sufficient evidence to conclude
that there was a linear relationship between the interaction between Youtubers and
users’ intention to purchase. Besides, Pearson correlation of attractiveness and purchase
intention was r = 0,862 which is higher than 0 so the direction of the relationship is positive,
meaning that these variables tend to increase together.
21

3. Regression model
- We have R square = 0.852, which means that the independent variables put into regression
had 85.2% effect on the purchase intention (dependent variable) the remaining 14.8% was
due to other factors and random errors.
- In the Coefficients table, Sig. t test in regression coefficients for the independent variables:
Attractiveness, Trustworthiness and Interaction were less than 0.05 with 0.026 and 0.00
respectively while the sig. of Usefulness was higher than 0.05 (0.358). The regression
coefficients of Attractiveness, Trustworthiness and Interaction were greater than 0 while
Usefulness’s one was less than 0 with -0.037. In addition, VIF coefficents of all the variables
were less than 2.
- With 4 hypotheses laid out initially, 3 hypotheses were accepted: H2, H3, H4
corresponding to the variables: Attractiveness, Trustworthiness and Interaction on Purchase
Intention. Hypotheses H1 was rejected, so the Usefulness on Purchase Intention has no
meaning in the regression model.
- Standardized Coefficient Equation:
Purchase Intention = 0.562 * Interaction + 0.416* Trustworthiness + 0.087 * Attractiveness
 Explaination: A standardized beta coefficient compares the strength of the effect of
each individual independent variable to the dependent variable. The higher the
22

absolute value of the beta coefficient, the stronger the effect is. By this, we can
conclude that the Interaction has the strongest impact on the Purchase Intention
(0.562). After that, the Trustworthiness has the second strongest impact on the
Purchase Intention with the beta coefficient of 0.416. Finally, the Attractiveness has
the weakest impact on the dependent variable Purchase Intention (0.087).
 The results showed that all 3 factors, which are Attractiveness, Trustworthiness and
Interaction, have effects on the purchase intention of Youtube users, except the factor
Usefulness.
 P-value was 0 which less than 0.05 (the significance level), so the independent
variables reliably predict the dependent variable.
7. DISCUSSION & CONCLUSION
1. Research results:
Considering all the results, it is seen that product related user generated content on
YouTube affects purchase intention of consumers significantly and the degree of influence
changes due to some factors. In conclusion, the study finds out that Usefulness has no
impact on the Purchase Intention when it comes to watching Youtube which contradicts to
the results proposed by Mir and Rehman (2013). The Interaction has the strongest impact
on the Purchase Intention. Previous research has shown that the number of views, likes and
comments has a positive effect on perceived credibility which is also confirmed in the
current study., then the Trustworthiness has the second strongest impact on the Purchase
Intention. As mentioned before, credible sources help developing positive attitudes and
trust has a positive effect on attitude and purchase intention. Finally, the Attractiveness has
the weakest impact on the dependent variable Purchase Intention (for this research only).
23

Besides, the study could also check the reliability of all variables and successfully they were
all analyesed and accepted by Cronbach’s Alpha. In other words, all variableds tested are
correlated and appropriate to the study. Besides, factor analysis were useful with data
collected. It rearranged 14 variables that affect purchase intention and catagorised into 4
underlying factors which are proposed.
2. Research limitations:
This research emphasized the impact of the elements related to Youtube/Yotubers on the
consumers’ purchase intention. However, there were still some limitations that restrict the
research. Firrstly, the sample size was quite small with only 208 participants so the research
result might be inaccurate and subjective. Secondly, in terms of research content: just stop
surveying some of the most basic issues about Youtube references of residents in Hoa
Cuong Nam ward. Finally, due to the limited knowledge and skills of researchers has lead to
some difficulties in connecting between theory, practice and research content in the topic.
There may be other factors that are really necessary to learn but have not been mentioned
in the topic.
3. Development direction & recommendations
Although there are many limitations, but it also has some basis suggestions for the next
research and development directions of the topic, if conditions allow in terms of time and
budget, the topic will expand the number of suitable surveyors, ask more reasonable
questions so that the answers can clearly state thoughts and expectations of the
participants to answer and try to add different data collecting methods.
Competition among companies are heating up day by day and marketers are looking for
alternative ways to attract consumers. YouTube has become one of the new social media
platforms that brands and companies use to promote their products. Therefore, it will
be useful for them to identify the factors that affect purchase intention of consumers who
watch product related YouTube videos; and this thought created the starting point of this
study. The overall results of this study indicate that consumers are influenced by the
product related information in YouTube videos, especially when this information is
generated by users rather than companies. For this reason, it is important for brands and
24

companies to deliver their marketing messages through ordinary users. Strategic
alliances between companies and opinion leaders in the YouTube world can be formed to
carry out marketing efforts. Product replacements in the videos, sending the channel owner
some of the products free for their review or even paying them to promote their product
can provide alternative marketing ways for companies. Of course, if the owner of the video
is paid by the company, then this should be stated in the video for the sake of ethics. The
issue of ethics will be an important subject in the near future because of the increasing use
of YouTube videos by companies for marketing purposes.
4. Conclusion
YouTubers are becoming a rapidly increasing and developing phenomenon in this new age.
The study finds out that the key to YouTubers’ influence on their viewers is interaction, trust
and attraction. Through interaction, the invisible gap between influencers and normal users
can possibly be eliminated. By that, viewers can have a positive way of looking at Youtubers’
sharing and tend to believe in them more. That is also related to the trust. If a viewer
trusted a YouTuber, it contributed positively to all aspects of influence on their buying
intention. Lastly, the attractiveness in their contents is also a factor for viewers to determine
whether they should follow the recommendations or not. Usefulness is definitely an
important factor when it comes to Youtube, however, acccording to the survey result,
usefulness is not a thing for people to think about.
25

8. REFERENCES
Internet Live Stats. 2016. Viet Nam Internet Users. Accessed on 12 October 2020. Retrieved
from http://www.internetlivestats.com/internet-users/viet-nam/.
Dredge, S. 2016. Why are Youtube stars so popular? The Guardian. Accessed on 12 October
2020. Retrieved from https://www.theguardian.com/technology/2016/feb/03/why-
youtube-stars-popular-zoella
Rigby, B. (2008). Mobilizing Generation 2.0: A practical guide to using Web 2.0: technologies
to recruit, organize and engage youth. John Wiley & Sons.
Jarrett, K. (2008). Beyond broadcast yourself™: The future of YouTube. Media International
Australia, 126(1), 132-144.
Snelson, C. (2011). YouTube across the disciplines: A review of the literature. MERLOT
Journal of Online Learning and Teaching.
Miller, M. (2011). YouTube for business: Online video marketing for any business. Pearson
Education.
Lange, P. G. (2007). Publicly private and privately public: Social networking on
YouTube. Journal of computer-mediated communication, 13(1), 361-380.
Pace, S. (2008). YouTube: an opportunity for consumer narrative analysis?. Qualitative
Market Research: An International Journal.
Sahlin, D., & Botello, C. (2011). Youtube for dummies. John Wiley & Sons.
Harris, C. G. (2012, April). An Evaluation of Search Strategies for User-Generated Video
Content. In CrowdSearch (pp. 48-53).
Bouhlel, O., Mzoughi, N., Ghachem, M. S., & Negra, A. (2010). Online purchase intention:
Understanding the blogosphere effect. International journal of e-business
management, 4(2), 37.
Mir, I., & Zaheer, A. (1970). Verification of social impact theory claims in social media
context. The Journal of Internet Banking and Commerce, 17(1), 1-15.
Hsu, H. Y., & Tsou, H. T. (2011). Understanding customer experiences in online blog
environments. International Journal of Information Management, 31(6), 510-523.
McKnight, D. H., & Kacmar, C. J. (2007, August). Factors and effects of information
credibility. In Proceedings of the ninth international conference on Electronic
commerce (pp. 423-432).
26

Mir, I. A., & Ur REHMAN, K. (2013). Factors affecting consumer attitudes and intentions
toward user-generated product content on YouTube. Management & Marketing, 8(4).
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS quarterly, 319-340.
Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce
adoption: An extension of the theory of planned behavior. MIS quarterly, 115-143.
Hsu, C. L., Lin, J. C. C., & Chiang, H. S. (2013). The effects of blogger recommendations on
customers’ online shopping intentions. Internet Research.
Satgunam, P., Woods, R. L., Bronstad, P. M., & Peli, E. (2010, May). 8.2: Factors Affecting Image
Quality Preferences. In SID Symposium Digest of Technical Papers (Vol. 41, No. 1, pp. 94-97).
Oxford, UK: Blackwell Publishing Ltd.
Lee, S. H. (2009). How do online reviews affect purchasing intention?. African Journal of
Business Management, 3(10), 576-581.
O'Reilly, K., & Marx, S. (2011). How young, technical consumers assess online WOM
credibility. Qualitative Market Research: An International Journal.
Flanagin, A. J., Metzger, M. J., Pure, R., & Markov, A. (2011, January). User-generated ratings
and the evaluation of credibility and product quality in ecommerce transactions. In 2011
44th Hawaii International Conference on System Sciences (pp. 1-10). IEEE.
Cheong, H. J., & Morrison, M. A. (2008). Consumers’ reliance on product information and
recommendations found in UGC. Journal of Interactive Advertising, 8(2), 38-49.
Bae, S., & Lee, T. (2011). Product type and consumers' perception of online consumer
reviews. Electronic Markets, 21(4), 255-266.
Cheong, H.J., & Morrison, M.A. (2008). Consumers' reliance on product information and
recommendations found in UGC. Journal of Interactive Advertising, 8(2), 38-49.
Eagly, A. H., & Chaiken, S. (1993), The Psychology of Attitudes, New York: Harcourt Brace
College Publishers.
Geissler, G. L., & Edison, S.W. (2005), Market mavens' attitudes towards general Technology:
Implications for marketing communications. Journal of Marketing Communications, 11(2),
73-94.
Goldsmith, R., & Horowitz, D. (2006). Measuring motivations for online opinion seeking.
Journal of Interactive Advertising, 6(2), 1-16.
27

Hung, K., Li, S. Y., & Tse, D. K. (2011). Interpersonal trust and platform credibility in a
Chinese multibrand online community: Effects on brand variety seeking and time spent.
Journal of Advertising, 40(3), 99-112.
Jalalkamali, M., & Nikbin, D. (2010). The effects of motivation on purchase
decision. Interdisciplinary Journal of Contemporary Research in Business, 2(8), 234-245.
Kwan, C. Y. (2006). An investigation on the factors affecting young Chinese consumers'
decision-making behavior towards casual wear purchase. (Doctoral dissertation). Available
from ProQuest Dissertations and Theses database. (Hong Kong Polytechnic University No.
3241063)
Perloff, R. (2013). The Dynamics of Persuasion (5th Ed). Hillsdale, NJ: Erlbaum.
Spears, N., & Singh, S. N. (2004). Measuring attitude toward the brand and purchase
intentions. Journal of Current Issues & Research in Advertising, 26(2), 53-66.
Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating
role of product and consumer characteristics. Journal of Marketing 74(2), 133-148.
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9. APPENDIX
Questionnaire about the influence of
Youtubers
PERSONAL INFORMATION
1. Please state your age (in years): O 40  31 to 40 Under 18  18 to 21  22 to 30
2. Please state your gender: Male  Female  Others. Please specify (if possible):
3. Please state your educational or work status: Student  Employed  Unemployed  Self-employed  Retired Others. Please specify:
4. Please state your monthly income (in million VND): Less than 5  5 - under 15  15 - under 25  Above 25
5. How often do you tend to collect the information that will be useful for
your buying decision? Always  Often  Sometimes  Seldom  Never
EVALUATION OF INFORMATION
1 = Strongly Disagree, 2 = Disagree, 3 = Slightly Disagree, 4 = Slightly Agree, 5 = Agree, 6 =
Strongly Agree
1 2 3 4 5 6
Perceived Usefulness
PU1 I use Youtube to find out about new
products and services
PU2 I use Youtube to seek for reviews of
anything that I am aware of
PU3
I use Youtube to find discounts/
promotions/ giveaways offered by
Youtubers.
29

Attractiveness
ATR1 I pay attention to how Youtubers get
dressed on their video.
ATR2 I am easily attracted by interesting
and unique content.
ATR3
I look at the thumbnail and the title
of video to decide whether I should
watch or not.
ATR4 I prefer a Youtuber with great sense
of humor.
ATR5 Youtube is an effective platform for
products/ services to draw attention
Trustworthiness
TW1
I am aware of a product that is
highly-recommended by well-known
Youtubers.
TW2
I believe in a Youtuber because he/
she always recommends high-quality
products/ services.
Interaction with users
ITR1
I look at the number of likes and
comments of the video to estimate
the popularity of a Youtuber.
ITR2
I use Youtube because of the
freedom to discuss or comment on
any video posted.
ITR3
I like how Youtubers make videos
related to users’
comments/recommendations.
ITR4
I pay attention to how Youtubers
reply/ react to users’ comments
(both positive and negative
comments)
Purchase Intention
PI1
I buy a product because I receive
useful information provided by
Youtubers
PI2 I buy a product because the video
concept is very appealing
PI3 I buy a product because I believe in
Youtubers and their expertise
PI4
I tend to buy a product because
Youtubers are so friendly and willing
to answer all questions that I ask
30