A questionnaire was developed for use in
the data collection process with 12 questions
directly related to our research. Measurements
for attitudes, subjective norms and cognitive
behavioral control are adjusted from previous
studies. For each element designed with three
questions, we used the 5-point Likert scale for
the study. In the process of developing a
questionnaire, we refer to studies that apply TPB
in explaining other human behavior.
The SPSS analysis process applied to the
thesis applied a lot of formulas. Among them, the
formula for determining the minimum sample
size for research is reliable. The size of the
sample applied in the study is based on the
requirements of the Exploratory Factor Analysis
(EFA) and the multivariate regression. Based on
research by Hair, Anderson, Tatham and Black
for reference on expected sample size,
accordingly the minimum sample size is 5 times
the total number of observed variables. This is a
suitable sample size for the study using factor
analysis (Comrey, 1973; Roger, 2006). n = 5 *
m, note that m is the number of questions in the
lesson. So the accepted minimum is 60. For
multivariate regression analysis: With
Tabachnick and Fidell fomular, the minimum
sample size to be obtained is calculated by the
formula n = 50 + 8 * m=74. A total of 78
questionnaires were distributed to respondents on
Facebook. The data were then analyzed using
SPSS version 16. Descriptive analysis, reliability
analysis, factor analysis and regression analysis
were then performed on the data.
Demographic
A total of 78 respondents from Facebook
community, majority of the respondents were
female (43 respondents or 55.1%), between the
ages of 18 and 30 years of age ( 38 respondents or
48.7%), most of them are high school students or
not who have just only finished high school or not
graduated from high school yet (40 respondents or
51.3%) . The background of the respondents is
presented in bellow table ( Table 1).
7 trang |
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65
TẠI SAO NGƢỜI DÙNG LẠI SÁNG TẠO NỘI DUNG - ỨNG DỤNG
CỦA THUYẾT HÀNH VI CÓ KẾ HOẠCH
Zhou Xiao Hong
1
, Bùi Thị Thúy2
Tóm tắt
Lý thuyết về hành vi có kế hoạch (TPB) kể từ khi phát triển khoảng 30 năm trước đã được chứng minh
là một cách tiếp cận mạnh mẽ để giải thích hành vi của con người. Nó đã được áp dụng thành công cho
một loạt các hành vi. Theo lý thuyết, hành vi của người tiêu dùng là một chức năng của ý định thực hiện
hành vi được đề cập; hành vi dựa trên thái độ, chuẩn mực chủ quan và kiểm soát hành vi đối với hành
vi; và các yếu tố này được xác định, tương ứng, bởi thái độ đối cá nhân đối với hành vi, chuẩn mực và
kiểm soát. Với sự phát triển của công nghệ, nội dung do người dùng tạo ra (UGC) được coi là một phần
của truyền miệng điện tử, được tạo ra và chia sẻ giữa người tiêu dùng có tầm quan trọng lớn đối với các
nhà tiếp thị. Nghiên cứu này giải thích lý do tại sao người dùng tham gia vào việc tạo nội dung. Lý
thuyết về hành vi có kế hoạch (TPB) đã được sử dụng để giải thích hành vi này. Thông qua một số câu
hỏi khảo sát đã được thực hiện vào tháng 10/2018, sử dụng SPSS 16 với 78 người đã được hỏi về thông
tin liên quan đến nội dung được tạo và chia sẻ trên internet, kết quả kiểm tra cho thấy ý định tạo nội
dung của người dùng được xác định theo thái độ cá nhân, chuẩn mực và kiểm soát.
Từ khóa: Lý thuyết về hành vi có kế hoạch (TPB), nội dung do người dùng tạo ra (UGC), truyền miệng
điện tử (eWOM).
WHY USERS GENERATE CONTENT
AN APPLICATION OF THE THEORY OF PLANNED BEHAVIOR
Abstract
The theory of planned behavior (TPB) since its apprerance about 30 years ago has been proved to be a
powerful approach to explain human behavior. It has been successfully applied to a variety of
behaviors. According to the theory, the consumer’s behavior is a function of intention to perform the
behavior in question; the behavior is based on attitude, subjective norm, and perceived behavioral
control; and these factors are determined, respectively, by behavioral, normative, and control beliefs.
With the development of technology, the user-generated content (UGC) is considered as a part of
electronic word of mouth created and shared between consumers, which has a major importance to
marketers. This study explains why users are involved in creating content. The theory of planned
behavior (TPB) has been used to explain this behavior. Through survey questionnaires in 10/2018, using
SPSS 16 with 78 respondents who were asked about information related to the generated and shared
content on the internet, the results showe that the user's intention to generate content is determined by
personal attitude, Subject norm, Perceived behavioral control.
Keywords: Theory of planned behavior (TPB), User-generated content (UGC), electronic Word of
mouth (eWOM).
1. Introduction
Vietnam is currently ranked 7th in the
number of Facebook users with about 60 million
users. Zalo currently has about 40 million
monthly users. Mocha of Viettel has about 4.5
million users. According to the 2017 survey
results of Pew Research Institute, Vietnamese
people ranked 4th in the world in terms of
reading news online. (Trong Dat, 2018) With the
development of technology, people need to
change the way they communicate. Electronic
Word-of-mouth (WOM) has been recognized as
one of the most influential resources of
information transmission. With the Internet, even
ordinary Web users can conveniently create and
disseminate media content. The notion of User-
Generated Content captures the user-as-producer
feature and refers to content that is not generated
or published by professionals on the Internet,
unlike traditional media. Defined in terms of
situations where consumers suggest products or
services to other consumers on the Internet,
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66
eWOM is closely related to UGC. (Ye Wang,
Shelly Rodgers, 2011) UGC is related to, but not
identical with, electronic word-of-mouth(eWOM),
which is defined as being “any positive or
negativestatement made by potential, actual, or
former customers about a product or company,
which is made available to a multitude of people
and institutions via the Internet”(Hennig-Thurau
et al.2004, p. 39).
The impact of user generated content is
undeniable. Brand engagement also increases
when users share content. According to a
ComScore study, brand engagement increases by
28% when consumers are exposed to a mixture
of branded and user-generated content
(Comscore, 2012). UGC also works wonders no
matter which generation of your target audience.
When Bazaarvoice asked a pool of Millennials
and Baby Boomers how much user-generated
content played into their purchase decisions the
received answers were: 84% of Millennials said
that UGC had at least some influence; 70% of
Baby Boomers said that UGC had at least some
influence; 20%+ of each generation said that
UGC had a lot of influence. (Bazaarvoice,
2012). Understanding the factors that influence
UGC creation is important for modern
marketing. However, researches in this area are
limited. With the application of TPB and
accreditation to find answers to this problem is
the purpose of this study.
2. Background
2.1. Electronic Word-of-mouth
Social media has impacted various facets of
modern life and it has a profound influence on
interpersonal communication. People need
interaction to fulfill their social needs and social
media has become a preferred medium for
communication with the proliferation of digital
and mobile technologies (Kalpathy, 2017).
People have grown up with the Internet as an
important part of their everyday life and
interaction rituals. They suggest that the reason is
coming from the decrease in the amount of time
they spend interacting face-to-face (Brignall and
Van Valey, 2005). The advances in information
technology and the emergence of online social
network sites have changed the way information
is transmitted and have transcended the
traditional limitations of word of mouth
(Mohammad Reza, 2010).
Electronic word-of-mouth (eWOM)
communication refers to any positive or negative
statement made by potential, actual, or former
customers about a product or company, which is
made available to a multitude of people and
institutions via the Internet (T. Hennig-
Thurau,2004). The web has created both
challenges and opportunities for electronic word-
of-mouth (eWOM) communication (R. E.
Goldsmith, 2006). eWOM allows consumers to
not only obtain information related to goods and
services from the few people they know but also
from a vast, geographically dispersed group of
people, who have experience with relevant
products or services.
2.2. User-Generated Content
We mention definitions and outline for our
understanding of UGC, which is often referred to
within the scope of Web 2.0 and social media.
One of the most quoted definitions of UGC is
provided by the Organization for Economic
Cooperation and Development (OECD) (Vickery
and Wunsch-Vincent 2007). OECD uses the term
of user-created content (UCC), which is
considered synonymous with UGC. According to
Vickery and Wunsch-Vincent (2007), UGC has
three central characteristics: (1) publication
requirement, (2) creative effort, and (3) creation
outside of professional routines and practices.
Base on the features, we can see some kinds
of UGC normal in real life: Video on Youtube
(Review, Parody commercials, Introduction
product, Tutorial), Picture and Video on
Instagram, Post on Facebook, Twitters, Rating
and comments on websites (like E-commercial
Shopee, Lazada or main website of products). In
the past, there have been too many successful
marketing campaigns, we can mention that Old
Spice- Video Responses, Coca-Cola: Share a
coke, Starbucks- White Cup Contest, ect by
focusing on UGC. (Delhi school of internet
marketing, 2016). But some of UGC became
disasters such as McDonalds with hashtag #
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67
McDStories; Kia Sorento with creating Kia-
themed memes; Starbucks with
hashtag#SpreadTheCheer; Walgreens with
hashtag#IloveWalgreens (Duel, 2017). They are
good evidence to show up the power of UGC.
2.3. Theory of planned behaviour
The Theory of Planned Behavior (TPB)
developed by Ajzen (1985) is an explanatory
model that has been widely applied in diverse
studies on behavioral intention (Lee, Cerreto &
Lee, 2010 ). TPB stipulates that voluntary human
behavior is preceded by intention to engage in
such behavior (Shirly & Todd, 2001). Then it
postulates that behavioral intention in turn is
determined by three major determinants –
attitude towards behavior (AB), subjective norm
(SN) and perceived behavioral control (PBC).
Fig 1. Model depicting the theory of planned behavior (Ajzen, 1991)
PBC judgments are determined by beliefs
pertaining to the extent to which one has access
to resources or opportunities necessary to carry
out the behavior effectively, subjected to the
perceived power of each factor to enable or
prevent the behavior (Ajzen, 1991).
3. Conceptual framework and hypotheses
In order to develop our research framework,
we begin by examining the relationships between
each element and UGC that appear in the
literature. Based on the TPB, intention signifies
the motivational components of behavior. It
represents the conscious effort that a person is
willing to invest in a behavior. Human action is
guided by three kinds of readily accessible
beliefs: behavioral beliefs are those about the
likely consequences of the behavior, normative
beliefs are those about the normative
expectations and actions of important referents,
and control beliefs are those about the presence
of factors that may facilitate or impede
performance of the behavior (East, 2000). In
their respective aggregates, behavioral beliefs
bring on a favorable or unfavorable attitude
(ATT) toward the behavior; normative beliefs
give rise to subjective norms (SN) or perceived
social pressure (which also contribute to the
forming of attitudes), and control beliefs result in
perceived behavioral control (PBC).
Fig 2. Proposed research model
Attitude
Subject norm
Perceived Behaviour
control
Intention Behaviour
Attitude to UGC
Subject norm
Perceived Behaviour
control
Intention to create
UGC
Create UGC
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So, base on TPB model, we suggest below
hypotheses:
H1: Consumer UGC attitude affects content
creation on the network.
H2: Social influence has effect on the
content creation behavior on the network.
H3: Behavior control affects the content
creation behavior on the network
4. Methodology
There is no official TPB questionnaire,
however, base on the original research (Ajzen,
1991) and instrument of "Constructing a theory
of planned behavior questionnaire" and sample
questionnaire are provided by Ajzen (2013) we
developed the questionnaire for this research.
We focus on a behavior creat video upload
to facebook then interaction between them and
their facebook friend. To measure for
“Behavior”, we create 3 questions about
frequency of the action record, create Video, Edit
Video and Share Video about a product. We use
Likert 5 point scales with 1= rarely and
5=Usually for measurement behavior. The
"Attitude" scale consists of 3 items that reflect
the implementation of actions towards action, the
respondents feel interesting, value, happy when
share information, we use Likert 5 point scales
with this variation. “Subject norm” shows the
reaction toward the action by other people from
the community, they might friends, another
member from society, so we develop 3 questions
your friend always creates and share contents on
the Internet, your friend react positively
whenever you share contents on the Internet,
your friend appreciated your contents, we use
Likert 5 point scales with this variation with 5 is
highest point for positive reaction. “Control”
mention the ability of respondents when we tend
to do the behavior. So in this case, we develop 3
questions to describe the ability of the user, do
they meet any difficulty when they want to do
the action which is showing up through they
photograph skills, supporting equipment, Editing
skills. And we use Likert 5 point scales with this
variation with 5 was for master skills and 1 for
novice skills.
A questionnaire was developed for use in
the data collection process with 12 questions
directly related to our research. Measurements
for attitudes, subjective norms and cognitive
behavioral control are adjusted from previous
studies. For each element designed with three
questions, we used the 5-point Likert scale for
the study. In the process of developing a
questionnaire, we refer to studies that apply TPB
in explaining other human behavior.
The SPSS analysis process applied to the
thesis applied a lot of formulas. Among them, the
formula for determining the minimum sample
size for research is reliable. The size of the
sample applied in the study is based on the
requirements of the Exploratory Factor Analysis
(EFA) and the multivariate regression. Based on
research by Hair, Anderson, Tatham and Black
for reference on expected sample size,
accordingly the minimum sample size is 5 times
the total number of observed variables. This is a
suitable sample size for the study using factor
analysis (Comrey, 1973; Roger, 2006). n = 5 *
m, note that m is the number of questions in the
lesson. So the accepted minimum is 60. For
multivariate regression analysis: With
Tabachnick and Fidell fomular, the minimum
sample size to be obtained is calculated by the
formula n = 50 + 8 * m=74. A total of 78
questionnaires were distributed to respondents on
Facebook. The data were then analyzed using
SPSS version 16. Descriptive analysis, reliability
analysis, factor analysis and regression analysis
were then performed on the data.
Demographic
A total of 78 respondents from Facebook
community, majority of the respondents were
female (43 respondents or 55.1%), between the
ages of 18 and 30 years of age ( 38 respondents or
48.7%), most of them are high school students or
not who have just only finished high school or not
graduated from high school yet (40 respondents or
51.3%) . The background of the respondents is
presented in bellow table ( Table 1).
5. Result
5.1 Reliability statistic Cronbach’s Alpha
Cronbach‟s Alpha of them from 0.6 to 0.9
(Behavior- 0.845; Attitudes - 0.821; Subjective
Norm – 0.836; Control - 0.780) and Corrected
item- Total correlation >0.4 so and Cronbach‟s
Alpha if item Deleted isn‟t bigger than the Total
Cronbach Alpha so don‟t need to delete any
question (Table 1).
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69
Table 1: Reliability statistic Cronbach’s Alpha
Item-Total Statistics
Scale Mean if
Item Deleted
Scale
Variance if
Item Deleted
Corrected Item-
Total
Correlation
Cronbach's
Alpha if Item
Deleted
Cronbach's
Alpha
BH1 6,87 4,11 0,74 0,76
0,845 BH2 6,88 4,26 0,65 0,84
BH3 6,94 3,49 0,75 0,74
ATT1 7,59 3,78 0,68 0,75
0,821 ATT2 7,37 4,37 0,61 0,82
ATT3 7,42 3,78 0,75 0,68
SN1 7,21 3,26 0,8 0,68
0,836 SN2 7,09 3,33 0,64 0,83
SN3 7,09 3,36 0,66 0,81
TBC1 7,53 2,49 0,65 0,66
0,78 TBC2 7,5 2,77 0,59 0,73
TBC3 7,56 2,66 0,61 0,71
5.2 Exploratory factor analysis
0.5 ≤ KMO≤ 1: KMO coefficient (Kaiser-
Meyer-Olkin) is an index used to consider the
appropriateness of factor analysis. In this
research KMO=0.756 values mean that factor
analysis is appropriate.( Table 5.2.1) The Bartlett
test has statistical significance (Sig. <0.05): This
is a statistical quantity used to consider the
hypothesis that variables are not correlated in the
overall. This test is statistically significant (
0.000 <0.05) so the observed variables are
correlated with each other in the overall.( Table
5.2.1) With 9 input variables, PCA initially
extracts 9 factors (or “components”). Each
component has a quality score called an
Eigenvalue. Only components with high
Eigenvalues are likely to represent a real
underlying factor. A common rule of thumb is to
select components whose Eigenvalue is at least
1. So our 9 variables seem to measure 3
underlying factors. (Table 5.2.2) Percentage of
variance 74.088% > 50%: Shows the percentage
variation of observed variables. This means that
when the variable is 100%, the value indicates
74.088% the factor analysis explains. Factor
loading each item > 0.5 is considered to have
practical significance (Table 2)
Table 2: Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
%
Total
% of
Variance
Cumulative
%
Total
% of
Variance
Cumulative
%
1 3,745 41,61 41,61 3,745 41,61 41,61 2,281 25,346 25,346
2 1,599 17,762 59,372 1,599 17,762 59,372 2,229 24,77 50,116
3 1,324 14,717 74,088 1,324 14,717 74,088 2,158 23,972 74,088
4 0,61 6,774 80,862
5 0,513 5,7 86,561
6 0,392 4,359 90,921
7 0,36 4 94,92
8 0,258 2,864 97,784
9 0,199 2,216 100
Extraction Method: Principal Component Analysis
We use Rotated Component Matrix as below, results show ATT, SN, TBC are Convergent Validity
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Table 3: Rotated Component Matrix
Rotated Component Matrix
a
Component
1 2 3
ATT1 ,787
ATT2 ,787
ATT3 ,907
SN1 ,924
SN2 ,844
SN3 ,760
TBC1 ,842
TBC2 ,796
TBC3 ,803
5.3 Testing hypothesises
Adjusted R Square, also known as R square
correction, it reflects the degree of influence of
the independent variables on the dependent
variable. Specifically, in this case, 3 independent
variables affect 59.4% of the variation of the
dependent variable, the remaining 40.6% is due
to out-of-model variables and random errors.
This value is more than 50%, the study can be
used. If d > dU,α, there is no statistical evidence
that the error terms are positively autocorrelated(
Durbin-Watson Significance Tables) To test for
positive autocorrelation at significance α, the test
statistic d is compared to lower and upper critical
values (dL,α and dU,α): 4-dL>d > dU (4-
1.57>2.112>1.72),α, there is no statistical
evidence that the error terms are positively or
negative autocorrelated. (Table 3)
Table 4: Model summarary
Model R R Square Adjusted R Square
Std. Error of
the Estimate
Durbin-Watson
1 ,781
a
,610 ,594 ,580 2,112
a. Predictors: (Constant), TBC, SN, ATT
b. Dependent Variable: BH
For VIF (variance inflation factor) for each
item <2 is not multicollinear and Sig <0.05 so
the hypothesises are supported (Table 5.3.2)
Hypotheses
H1: Consumer UGC attitude affects content
creation on the network. (Supported)
H2: Social influence has effect on the
content creation behavior on the network.
(Supported)
H3: Behavior control affects the content
creation behavior on the network. (Supported)
6. Conclusion and Limitation
Theoretically, this study lends support to the
theory of planned behavior in explaining
intention to generate digital information as UGC.
All the factors; attitude, subjective norms and
perceived behavioral control, all of them were
tested to be positively influenced the intention to
users generate content. This indicates that
attitude, subjective norms and perceived
behavioral control were predictors of intention to
use digital coupon. Overall, these factors
explained about 59.4% of the variance in
intention while the remaining 40.6% may be
explained by other factors that were not captured
in this model. The attitude was found to be the
strongest predictor of intention to use generate
content followed by perceived behavioral control
and subjective norms.
Limitations
This study faced a number of limitations.
Firstly, data for this study were obtained from a
sample including 78 people. If all the large
samples size were examined, the result could
have been generalized. Secondly, the study
focused only on the consumer behavioral
intention, but actual usage was not measured.
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[21]. Ye Wang, Shelly Rodgers. (2011). Chapter 11 Electronic Word of Mouth and Consumer Generated
Content: From Concept to Application. Digital Media and Advertising, 212-231
Thông tin tác giả:
1. Zhou Xiao Hong
- - Đơn vị công tác: Professor in Nanjing University of Science and Technology
2. Bùi Thị Thúy
- - Đơn vị công tác: Student of Nanjing University of Science and Technology
- Địa chỉ email: buithithuy.neu@gmail.com
Ngày nhận bài: 12/10/2018
Ngày nhận bản sửa: 2/11/2018
Ngày duyệt đăng: 28/12/2018
Các file đính kèm theo tài liệu này:
- tai_sao_nguoi_dung_lai_sang_tao_noi_dung_ung_dung_cua_thuyet.pdf