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experiences. These two experiences are family According to money-motivation scale (MMS)
resources that they received and stress from family developed by Rose and Orr (2007), there are five
problems at a young age. Apart from China, fundamental dimensions, which are status,
Durvasula and Lynsonski (2010) found the similarity achievement, worry, security and budget in order to
in these relationships in young adult as well; in understand people’s attitudes towards money. They
addition to the relationships, self-control behaviour find the relationship between money and a sign of
is another concern whether it shapes generation Y’s prestige, a sign of one’s accomplishment, and a level
materialism. Good self-control people tend to save of success. Thus, if considering this MMS, it will
money from every paycheck and have better general utilize to measure money attitudes and apply in this
financial behavior. Also, if they are good self- study to find out whether these variables impact
control, they will feel less worried about their money attitudes or not.
economic problems and have more security in their
financial status in the current and in the future. Family Resource and Family Stress
Therefore, This study aims to investigate how the
childhood family resource and the perceived stress According to the research on the stress and human
from family affects their money attitudes, self- capital life-course from Moschis (2007), the money
control towards materialism. attitudes of the Generation Y in the future can not
only be affected by the stress that they perceived
Materialism from family disruption during childhood, but family
resources are another significant factor as well. The
There are diversifications on finding Materialism in reason is that what children experienced when they
various academics. Richins and Dawson (1992) were young can have effects on human capital
identified materialistic value as expressing the growth and consequently shape up attitudes in the
importance of material things and their procession future. In addition, the stress and human capital life-
for a person as well as finding happiness, satisfaction course theory suggested that the family is a source of
and welfare in persons’ life through these material human capital (Frytak, Harley, and Finch, 2003). If
things and persons tendency to judge own and other one of a parent is missing due to divorce or, probably,
people’s success by means of the number of material employed stationed overseas, the children may not
things acquired. In addition, Shrum et al. (2013) get sufficient either financial or emotional
labelled that “the extent to which individual attempt family support or both of them, which lead to the
to engage in the construction and maintenance of the feeling of insecurity and deprivation and
self through the acquisition and use of products, consequently affect the later-life materialistic values
services and experiences”. Furthermore, materialism (Rindfleisch, Burroughs, and Denton, 1997).
can be explained as a sign of success, and so as to get Additionally, Durvasula and Lysonski (2010)
in touch with society, it is significant to acquire mentioned that young Chinese materialism had been
material things. Material things can show their greatly impacted by power- prestige and anxiety.
wealth through satisfaction and well-being. Duh
(2016) found that perceived stress from disruptive There are many types of research illustrating that
family events at young ages and the materialism in family environment during childhood has influences
their later-life have a positive relationship with each on the attitudes and behavior in the later-life course.
other and can refer to monetary attitudes. Some of the events during childhood, such as single
parenthood, parental separation and divorce are
Money Attitudes likely to cause stresses and insecure feeling for
children. These permanent or temporary separations
In young generation perception, money can generate can lead to the decrease in care, attention and
several emotional, attitude and feeling (Duh, 2016). emotional support from their parents, and these
Their attitudes towards money are that they can get experiences motivate young adult to adopt coping
respect and power and that they have high mechanisms (Moschis, 2007; Hill, Yeung, and
achievement, love and security when they have Duncan, 2001). Materialism is one of the coping
money, yet they can have anxiety and feel insecurity strategies (Rindfleisch et al., 1997). In addition, the
when they do not. To support Duh’s idea, research from Duh (2016) found that the relationship
between perceived stress from disruptive family
On a practical level, people find the spending diary events in childhood and later-life materialism of
an excellent tool for getting a handle on managing generation Y in South Africa is positive.
their outgoings. Physically recording the emotions
surrounding spending can have a more profound
impact. (Quaker Social Action, n.d.).
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Self- Control • H6 relationship between Self-Control and
Money Attitudes.
One behavior that controls people to make the
financial decision is Self-Control. (Baumeister, Research Methodology
2002) Self- Control is typically manifested as our
ability to break bad habits, resist temptation and To enhance the instrument of this study project, this
overcome first impulses. This behavior is a challenge research has adjusted the constructs into the model
to be one of the models of the framework because it by using the questionnaires for testing this study.
is a later life habit which is not children experiences According to Duh (2016) and Rindfeisch et al.
as like other independent variables. Hence, this (1997), the standard procedure for survey translation
journal also educates the self- control in later life of from the questionnaires which is originally derived
Thai Gen Y has in terms of spending money on from 2 journals, which 1) Family structure,
unnecessity stuff, luxury goods and materialist. materialism and compulsive consumption, and 2)
Does self-control predict financial behavior and
Framework financial well- being. After releasing the
questionnaires to the targeted of responding for
The concept model developed by (Duh, 2016) will be sometimes, we conducted a pilot test for 98 people
applied in this study for the explanation that and analyzed the data as per Cronbach’s Alpha
Materialism is caused by Money Attitudes, Family methodology for testing this research’s
Resource, Family Stress and Self- Control. The questionnaires are significant.
materialism is a dependent variables, while the
Family Resource, Family Stress, Money Attitudes The questionnaires consist of six sections. The first
and Self- Control are an independent variable. sectioncovers demographic information such as
Hence, the theoretical framework of this journal will Gender, Age (Gen Y, 1980-1997), Education,
be as follow Occupation and Income. Five sections are Family
Resource (before 18 years), Family Stressor (before
Figure 1 the relationships between independent 18 years), Money Attitudes, Material Possession and
variables and dependent variables Self Control. Each section has five-point ranging
scale rating from 5 (strongly agree) to 1 (strongly
• H1 relationship between Family Resource disagree).
and Money Attitudes. This subject of research is young adult, Gen Y (Age
• H2 relationship between Family Stressor 21-39 years) from Bangkok Metropolitan. This
and Money Attitudes. research collected the data from Young Generation
• H3 relationship between Money Attitudes Y in Bangkok from different educational and
and Materialism. financial backgrounds. The method of this study is to
• H4 relationship between Family Resource send the online survey through several online social
and Money Attitudes. network channels such as Facebook, Twitter, Line
• H5 relationship between Family Stress and and etc. The identification of respondents, including
Money Attitudes. colleagues, friends from bachelor and master level
and friends of a friend from generation Y by using
snowball technique sampling.
The data from analysis has shown the relationship
between Materialism with Family Resource, Family
Stress, Money Attitudes and Self-Control.
Regression Analysis is the method to be used in this
research in order to estimate the relationships
between two or more variables. This study uses
Regression Analysis due to uncomplicated
comprehension. Regression analysis helps this study
to understand how the dependent variable
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changes when one of the independent variables Table 1. Descriptive Statistic
varies and allows to determine which of those
variables really has an impact mathematically. From the Cronbach’s alpha results in Table2,
According to the framework, Materialism is a can be identified that the consistency of
dependent variable which is the main factor of this questionnaires whether these are significant
studying. Independent variables are Family enough to find the relationship or influence of
Resource, Family Stress, Money Attitudes and Self- Family Resource, Money Attitudes, Self-
Control. Control towards Materialism of Generation Y in
Bangkok Metropolitan. Running the variables,
Results and Discussion resulting that Family Resource is significant at
0.864 (0.9 > α ≥ Family Resource Family Stress
Descriptive Statistic for all respondents H1Money Attitudes H2 0.8 = Good). Family
In summary of demographic of 412 respondents Stress is significant at 0.783 (0.8 > α ≥ 0.7 =
from Generation Y in Bangkok Metropolis, it is Acceptable). Money Attitudes 0.876 is
found that the majority of the respondents is 261 significant (0.9 > α ≥ 0.8 = Good). Material
females or 63.35%, 130 males or 31.55%, and Possession is significant at 0.789 (0.8 > α ≥ 0.7
5.10% is intersex. All respondents are generation Y, = Acceptable). Self- Control is also significant
289 of them or around 70.15% are 23 – 30 years old 0.804 (0.9 > α ≥0.8 = Good)
and 123 of them or around 29.85% are 31- 40 years
old. The education of respondents are from high Table 2 Cronbach’s Alpha
school level to masters’ s degree above, 289
respondents or around 70.15% are from Bachelor’s
degree, 96 of them or 23.30% are Master’s degree,
13 or 3.16% are from
College, 10 or 2.43% are from high school and the
minority is Master’ s degree above which is 4
respondents or around 0.97%. The income per month
is averagely from lower than 15,000 THB to more
than THB 65,000. Based on the income demographic
of respondents, 108 or around 26.21% earns THB
15,001 - 25,000, 83 of them or 20.15% earn THB
25,001 - 35,000, 69 of them or 16.75% earn more
than THB 65,001, 56 or 13.59% earn THB 35,001 -
45,000, 46 or 11.17% of them earn THB 45,001 -
55,000 and 35 of them or around 8.50% earn the
income lower than THB 15,000 and 15 or around
3.64% earn THB 55,001- 65,000. In terms of the
occupation of Generation Y min Bangkok, the
majority is of cause Office Worker which is 264
respondents of them or around 64.08%. 73 of the or
17.72& are Business Owner such as the online seller.
35 respondents are Officialdom or around 8.50%. 23
or 5.58% are unemployed. 10 of them or 2.43% are
student which is studying master’s degree level and
above. The less of respondents are Freelance which
is 1.70% from the total of all respondents.
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According to Table 3, the linear relationship between showing that both Family Resource (0.000003) and
Family Stressor and Family Resource is the negative Family Stress (0.001227) have a positive relationship
relationship at (0.208). These two variables are not with Money Attitudes. Significant F showing at
magnitude and have no relationship. In addition, 0.0000014 (below 0.05). The multiple R results from
focusing on the Standard Deviation, the table shows Table 4 also showed the relationship strength at
that Money Attitudes is at 9.54 which is the highest 0.2524340 (the value of multiple R is over than 0
number among all variables. It can be inferred that indicates that the relationship strength. (Cheusheva,
the respondents have different attitudes on Money 2019). However, R square showed at 0.0637 which
division due to the variety of their family experiences is very weak (Evan, 1996). Since R square is not that
and backgrounds. With mean average statistics, significant, this could due to there are other variables
Family Resource is the highest number 3.56. that cause Money attitudes.
Showing that resources (Pocket money, Food,
Clothing, Time/attention and etc.) of generation Y in Table 4 Regression Statistic of Model 1
Bangkok had sufficient support from their parent
when they were young. Regression Statistics
Table 3 Standard Deviation, Mean and Correlation Multiple R 0.2524340
R Square 0.0637229
Adjusted R 0.0591446
Square
Standard Error 0.4877923
Significant F 0.0000014
Table 5 Coefficients Statistic of Model 1
Multiple Regression Analysis Family Coefficients Standard t Stat P-value
Resource > 0.156333758 Error 4.7384595 0.000003
Regression performs two separate models in the Money Attitudes 0.03299252
framework. The first model is the finding of the (H1) 0.126594831 3.2552989 0.001227
relationship of Money Attitudes with Family 0.03888885
Resource (family resources received during Family Stress >
childhood) and Family Stress (perceived stress from Money Attitudes
childhood family disruptions). The second model is (H2)
to find the relationship of Money Attitudes, Family
Resource, Family Stress and Self- Control toward to Model 2
Materialism behavior.
Figure 3 the model 2 framework
Figure 2 the model 1 framework The R Square from Table 6 showed that 0.315266
and Multiple R showed 0.561485 indicated that
Referring to the results, Table 4, presented every model 2 is not appropriated and this research reject
variable is significant due to p-value<0.05. It is the null hypothesis. The significant relationship that
p-value less than 0.05 is H5 and H6. There is a non-
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significant and negative relationship which is H4 and Henchoz et al. (2019) revealed that the dimension
H3 due to p-value is over 0.05 in model 2. instrument measuring attitudes towards money;
achieve objectives, status and respect, freedom,
Table 6 Regression Statistic of Model 2 power, social facilitator, materialism, hedonist
behavior, balanced-type behavior and solvency
Regression Statistics 0.561485 behavior. Another interesting finding is that not only
0.315266 do Money Attitudes and Self- Control have most
Multiple R 0.308536 significant impacts on materialism reaction of the
R Square 0.529924 young adult in Bangkok, but Self-Control is
Adjusted R Square intangible behaviour that does have significant
Standard Error 0.000000 impacts on materialism as well. Therefore, it can
Significance F infer that if people control themselves and cautiously
consider before purchasing the things, they will not
Table 7 Coefficients Statistic of Model 2 be considered as materialism. In cases that people do
not control and thoroughly think before they
Conclusion consume, it can be inferred that they are materialism.
According to the researchers from Durvasula and
Lysonski (2010) in China and Duh (2016) in South
Africa, young adults are important catalysts behind
materialistic values. Considering this research and
other journal, these consumptions have driven the
number of young adults to abuse household debt
increased from credit cards, over expending and
unnecessary demand of Generation Y which is
definitely materialism.
This research investigated the impact of two Limitation and Future Study
childhood family experiences – family resources
received during childhood (Family Resource), The research should replicate this study with more
perceived stress from childhood family (Family diverse variable which effects to materialism e.g.
Stress) on later-life of generation Y to money achieve objectives, status and respect, freedom,
attitudes toward materialism and immediately tested power, social facilitator etc. Also, test the
Self-Control behavior of young adult in Bangkok materialism with their respondent’s background e.g.
Thailand impacts to materialism. Basing on education and income which could include one of the
questionnaires’ results from generation Y in factors of materialism.
Bangkok Metropolis, it can conclude that power
Family Resources and power Family Stress from Acknowledgement
childhood experiences has directly impacted on
Money Attitudes around 6%. It means that when the I would like to express my deep and sincere gratitude
children are growing up, if they have well support to Dr. Thananporn Sethjinda, my research
from their parents such as love, financial support, supervisor, for giving me an opportunity to do my
budget, good family members, so they tend to save first individual research and academic writing in my
the money for their future life and cautiously life. I am not sure whether this research meets the
spending their own budget. However, from this expectation of
research, it shows that in order to better describe
Money Attitudes of the young adult by these two advisors, even I do my best and contribute most of
factors, Family Resource and Family Stress, there my leisure time for this project. I also owe a deep of
should be more variables that can affect Money gratitude to Dr. Nopphon Tangjitprom) for providing
Attitudes of people. According to Henchoz, Coste, me invaluable guidance, especially in the part of
and Wernli, (2019), there are many factors that have methodology and support me on running the SSPS
significant influences on Money Attitudes. Plus, program for this research.
I would like to say thank you to all of the Deutsch
Bank crews and friends for helping me spread
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out this several-pages questionnaires. I am Cheusheva, S( .2019, December 4 .)Linear
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The Influence of Investment Knowledge and Risk Perception on Mutual
Fund Choices among Millennials Investors in Bangkok Metropolitan Area
Muktapa Leelapamornkit
Thananporn Sethjinda
Bangkok, Thailand
Assumption University,Thailand
E-mail: [email protected]
Abstract
Mutual funds are one of the simplest investments and are considered a ‘gateway’ investment for new
investors, which should make them ideal for new investors such as younger Millennials. It is more
diversified portfolios in many sectors than direct investment in one sector. This research paper focused
on investment knowledge and risk perception on mutual funds investment among Millennials investor
which is largest population in Thailand. The research done have been collected from 427 Millennials
mutual fund investors in Bangkok. The objective of this study was to investigate the effects of investor
characteristics on investor knowledge, investor anxiety, risk-taking propensity, and risk aversion on the
choice of mutual funds. Descriptive statistics showed that most of investors making transaction via
mobile application and relying on online social media for source of information. Most frequent sources
of information is online media and most popular sites for young and old millennials are difference.
Popular media in young millennials are longtunman, finnomena and Thai mutual fund. While popular
media in old millennials are asset management website, aomMoney and Finnomena. Moreover, they
had moderate investment anxiety and low to moderate risk-taking propensity, along with moderate
investor knowledge and risk aversion. The result of logistic regression was investment knowledge effects
to equity fund and commodities investment. Investment anxiety effects to fixed income investment. In
term of risk-taking effects to money market fund and equity fund, while risk aversion only effects to
equity fund. Finally, none of the factors had an influence on REIT, balanced funds and FIF
Keywords: investment behavior, millennial, mutual funds, risk, investment knowledge
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Introduction knowledge and above average risk appetite (SET &
Research, 2019).
Over the past few years, the growing influence of
millennial has been recognized both commercially Mutual fund industrial plays an important role in
and academically. By definition, millennials refer to financial industries and economy. In recent year net
the group of people who were born between 1981 to asset value rose to 90 billion (6% growth) with 25%
1996 (Michael, 2019). This generation is currently of GDP (AIMC,2019) . However, there are a gap
dominating the global demographic composition, about limited information and conflict that researcher
currently account for 23% of the world’s population found during search for more information about
(worldmeters, 2020). Moreover, about 58% percent Millennials investment in Thailand.
of millennials live in Asia, fueling consumption,
economic and financial activities in the region Recognizing this gap, this research aims to examine
(Peterson, McCaffrey, & Sillman, 2019). As a result, how investor knowledge (Nguyen, 2017), financial
there have been a wealth of study, attempting to anxiety (also called fear of investing) (Baker &
understand their characteristics and behaviors. Ricciardi, 2014); risk-taking propensity (Baker &
Ricciardi, 2014); and risk aversion (Hanna, Waller,
In particular, various researchers observe that & Finke, 2008) influenced choices of mutual fund
millennials possess different characteristics, among millennials in Bangkok Metropolitan areas.
compared with Baby Boomers or Generation X (Ng Establishing better understanding of influencing
& Johnson, 2015). As the millennials grow up with factors towards investment decision of millennials
technological evolution, they are often identified as could provide insights for policy makers and
digitally savvy, confident, self-directed and goal practitioners.
oriented.
Materials and Methods
However,global investor study and developed
market showed that millennials possessed lower Conceptual framework & Hypothesis
level of confidence in making investment
decision.Older generations are more confident and
risk-ready than Millennials (Schroders, 2017).Two-
thirds of Millennials agreed that they had similar
investing approaches as their parents, that their
parents had the right to investing approach, and its
work well that cause lack of comfort and confidence
to decision making in investment by themselves.
(Wilmington, 2019).
In Thailand, Millennials are the largest group of Thai Hypotheses.
population representing over 22% or 14.3Millions Hypothesis 1: Investment knowledge has an effect
people (NSO, 2018). Thailand research reported by on mutual fund investment choice.
BOT shows that although this generation have higher Hypothesis 2: Investment anxiety has an effect on
financial knowledge more than other generations, mutual fund investment choice.
they lack financial management and do not plan for Hypothesis 3: Risk taking has an effect on mutual
long-term financial goals (BOT, Financial Access fund investment choice.
Survey of Thai Households, 2016). On the other Hypothesis 4: Risk aversion has an effect on mutual
hand, based on the survey of Stock Exchange of fund investment choice.
Thailand (SET) and the Securities and Exchange
Commission (SEC), the study showed that Questionnaire and Pre-test
millennials were the largest group of investors in The questionnaires comprised 4 sections, with
mutual funds and stocks during the past four years.
Investing in mutual funds and stocks were usually the first section, is screening questions selected only
associated with investors’ group with investment people who are invested in mutual funds. Second
section, covering the statements of general
investment behavior by check all that applies. In the
third section, questions relating to Attitude towards
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investment and financial risk. For third more female respondents (52.2%) than male
(47.8%)and more young millennials (age 23 to 33)
sections, a five-point Likert-type scale (63.2%) than old millennials (age 34 to 39) (36.8%).
ranging from5(strongly agree) to1 (strongly Figure 1 Demographic
disagree)was used.The last section collected Preferred transaction method and source of mutual
funds information
demographic information such as age, income level, Most participants preferred to manage their mutual
fund investments by mobile apps (46.4%) or bank
genderand education.Thevalidity of the branches (21.8%).
questionnaires test by the pilot test was performed Figure 2 Preferred transaction methods
among 45 respondents prior to the test Figure 6 Attitude toward Risk
period .
Popular source of information for old millennials is
Sample Design and Data Collection Asset Management Company website while,
Longtunman is popular among young millennials.
The survey questionnaire was targeted at residents of
Bangkok Metropolitan. The questionnaire was
distributed online during Nevember 2019 to
February 2020 and using non-probability
convenience sampling method. Target respondents
were Millennial generations. In total, the
questionnaires were done by 465 respondents. After
screening the responses, only 427 responses could be
used.
Data Analysis
Data analysis will be conducted in SPSS, which is a
reliable tool for statistical analysis of quantitative
datasets Firstly, Cronbach’s Alpha was performed
on the pilot questionnaires. Following by descriptive
statistics to understand and categorized respondents.
Main inferential statistic that will be used in the study
is logistics regression (or logit regression). A binary
logistic model has a dependent variable with two
possible values, which is represented by an indicator
variable, where the two values are labeled "0" and
"1" which can apply to this research. It allows the
researcher to make causal inferences about what
factors may influence the choice (or non-choice) of a
specific type of mutual fund investment, which was
the main objective of the research.
Data Analysis
1.Variance inflation factor(VIF)
All VIF of independent variables were less than
5 showing that there are no problem about
multicollinearity.
2.Descriptive Analysis
Demographics
The final sample size was 427 members .There were
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Results and Discussion
Figure 7 Logistic Regression Analysis
Risk and investment knowledge The findings were consistent with what was
The participants were generally risk-averse. Most of expected from the literature, which did predict that
the participants agreed that they wanted little loss of investment knowledge (Choi, Laibson, & Madrian,
principal, even if they did have the opportunity to 2010); (Pellinen, 2011), investment anxiety
receive a higher return (55.5%) and willing to accept (Eisenbach & Schmalz, 2016), risk taking
moderate risk (53.6%) (Eisenbach & Schmalz, 2016) and risk aversion
(Kapteyn & Teppa, 2011) would potentially have an
Figure 4 Consider loss of principal and risk tolerance influence on the selection of different types of mutual
fund investments. These findings showed that each
Mean participant investment knowledge ranged from of these investor characteristics influenced at least
3.21 to 3.63 (out of 5), which indicates neutral to one type of mutual fund selection. Thus, all of the
good investment knowledge. Mean attitude toward hypotheses, which were proposed in line with the
risk, ranging from 2.89 to 3.52, was also neutral to literature could be accepted. Each of these four
good. investor characteristics tended to lead investors
toward one or more types of investment, while it had
Figure 5 Investment Knowledge little effect on the choice of other investments.
Risk-taking propensity had a negative effect on the
choice of money market funds and positively effect
on equity fund which mean that risk taker less likely
to invest in money market funds but, likely to invest
in equity fund due to they will seeking higher risk for
generate higher return. It is consistence with another
research found that investors who have a high
appetite for risk will seeking for higher risk
instruments (Bailey W. K., 2011) Secondly,
investment anxiety negatively influenced the choice
of fixed income funds which mean that those who are
anxious about investment less likely to fixed income
fund. Interestingly, equity fund were influenced by
investment knowledge (positive) and risk-taking
propensity (positive) and risk aversion (negative). In
the other words, investment knowledge and risk
taking characteristic have effect to decision making
in equity fund investment in the same direction
while, risk aversion characteristic is oppose, those
who are risk aversion less likely to invest in equity
fund. Similarly to study result from Bailey, Kumar,
& Ng shows that investors who score high on
behavioral biases as risk taker tend to invest in funds
with higher risk and return (Bailey W. K., 2011).
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There were some unanswered questions resulting old millennials are difference. Popular media in
from this analysis. For example, it is not clear why young millennials are ลงทนุ แมน , finnomena and Thai
the effect of investment knowledge on the selection
of Commodity was negative, rather than positive as mutual fund. While popular media in old millennials
would have been expected given that these are are asset management website, aomMoney and
higher-risk and more advanced mutual fund types Finnomena. This is consistent with their digital
(Ratanabanchuen & Saengchote, 2018) which preference in information search, media viewing
therefore would have been more attractive to more behavior and online banking (Jennifer Brodmann,
knowledgeable investors.One possible reason for this 2018)
difference from what was expected could be that the 2. Logistic Regression Analysis
investors choosing Commodity was attracted by Binomial logistic regression was used to determine
relatively high returns without considering the risk, what effect these characteristics had on the choice of
which might be the case for investors with lower seven different investment types. Risk-taking
knowledge. Finally, none of the factors had an propensity had a negative effect on the choice of
influence on REIT, balanced funds and FIF. There money market funds and positively effect on equity
were some unanswered questions resulting from this fund which mean that risk taker less likely to invest
analysis. For example, why none of the investor in money market funds but, likely to invest in equity
characteristics had a significant effect on decision fund. Secondly, those who are anxious about
making in investment in REIT, Balanced fund and investment less likely to invest in fixed income fund.
FIF which therefore would have been more attractive Almost all of investor characteristics effect to equity
to knowledgeable and risk taking investors (Nguyen, fund. Investors who have investment knowledge and
2017). risk taker likely to invest in these fund. but, risk
aversion people less likely to invest in it. Finally,
Conclusions none of the factors had an influence on REIT,
balanced funds and FIF.
1. Descriptive statistics In conclusion, investor characteristics like investor
The final sample size was 427 members . the most knowledge, investor anxiety, risk-taking propensity,
frequent respondent to this survey was young female and risk aversion do have an effect on the choice of
millennial (age 23 to 33), single and holding a specific types of mutual funds for Millennial
Bachelor degree, working as a corporate employee investors. These characteristics either attract
for a salary of under 50,000 baht/month. The most investors to specific types of funds or act as
frequent choices of investment were stocks. Most aversions, making them less likely to invest in these
important investment objectives included return on funds. In some cases,this results in interesting
investment and tax benefits. They did not invest a lot conflicts.For example, the fact that high-knowledge
of money on average in non-mutual fund investors were less likely to invest in fixed income
investments, more than half invested 30,000 baht or funds and no effect on complex funds as FIF or
under. commodities. Thus, it could not be assumed that
For mutual fund investment average 2 type of funds these factors are the only influences on the choice of
as equity funds and money market fund. While, mutual funds for investment. However, this study can
commodities and foreign investment funds were the be used for future research into investor
least popular mutual fund choices. They usually characteristics and their influence on mutual fund
invest one to two years’ investment period with choice.
investment amount around 30,000 baht or less and
50,000-70,000 baht respectively. The participants Limitation of study
were generally risk-averse. They wanted little loss of
principal, even if they did have the opportunity to 1. The study relied on self-reporting of investor
receive a higher return and moderate risk-taking. characteristics as previous studies have established
Participants investment knowledge and attitude that individual investors may not be fully aware of
toward risk indicates neutral to good. their own risk tolerances, and they may over-report
Analysis of descriptive statistics showed that knowledge.
millennial investors in Bangkok were relying on 2. The study did not address concerns like
online social media for investment related convenience, familiarity or availability, which are
information. Most frequent sources of information is also known to influence the choice of mutual funds.
online media and most popular sites for young and There are plenty of opportunities for additional
research in this area, since this is a topic that has not
been investigated.
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Future study suggestions Jennifer Brodmann, B. R. (2018). Banking
Characteristics of Millennials. Journal of Economic
1. Investigating investor choice for REIT, balanced Cooperation and Development, 39, 4 (2018), 43-74.
funds and FIF since these appear to be different from
other types of funds. Kapteyn, A., & Teppa, F. (2011). Subjective
2. Identifying what other characteristics could measures of risk aversion, fixed costs, and portfolio
influence the choice of specific mutual funds, for choice. Journal of Economic Psychology, 564–580.
example knowledge, availability, or other fund and
investor characteristics. Michael, D. (2019). Defining generations: Where
3. Increase number of sample size in order to get Millennials end and Generation Z begins.
more information to enhance more accuracy of the
study. Ng, E. S., & Johnson, J. M. (2015). Millennials: Who
4. Compared age group between Millennials and Gen are they, how are they different, and why should we
Z due to there are more populations similar to care?, 1–432.
Millennials.
Nguyen, T. A. (2017). The effects of perceived and
Acknowledgments actual financial knowledge on regular personal
savings: Case of Vietnam. Journal of International
I would like to express my special thanks Studies, 278–291.
of gratitude to my supervisors Dr. Punjamaporn
Sethjinda for providing invaluable guidance, NSO. (2018). National Statistical Office of
comments, motivating me to work harder and Thailand.
suggestions throughout the course of the project.
I would specially thank Dr. Nopphon Tangjitprom Pellinen, A. T. (2011). Measuring the financial
for guidance, suggestions research methodology and capability of investors: A case of the customers of
analysis of my individual research. mutual funds in Finland. International Journal of
Bank Marketing, 107–133.
References Peterson, E. R., McCaffrey, C. R., & Sillman, A.
(2019). Where Are the Global Millennials?
Bailey, W. K. (2011). Behavioral biases of mutual
fund investors. . Journal of Financial Economics, Ratanabanchuen, R., & Saengchote, K. (2018).
102(1),1–27. Essays on open-ended on equity mutual funds n
Thailand.
Baker, H. K., & Ricciardi, V. (2014). Investor
behavior: The psychology of financial planning and Schroders. (2017). Global investor study, 10-11.
investing.
SET, & Research, S. (2019). Gen Y stands out in
BOT. (2016). Financial Access Survey of Thai stock, mutual fund investments
Households. 34-35.
Wilmington. (2019). Millennial Investors :
Choi, J. J., Laibson, D., & Madrian, B. C. (2010). Determined to make an impact. 4-6.
Why does the law of one price fail? An experiment
on index mutual funds. Review of Financial Studies. worldmeters. (2020), World Demographics
Eisenbach, T. M., & Schmalz, M. C. (2016). Anxiety
in the face of risk. Journal of Financial Economics,
414–426.
Hanna, S. D., Waller, W., & Finke, M. (2008). The
concept of risk tolerance in personal financial
planning. Journal of Personal Finance, 96–109.
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Factors Affecting Intention to Purchase Decision Energy Drink in Thailand
Ms. Apirati Pichayadecha1 and Dr. Papitchaya Wisankosol2
1Bangkok, Thailand
2Lecturer, Assumption University, Bangkok, Thailand
*Corresponding author. E-mail: [email protected]
Abstract
The energy drink business is fastest-growing product category in the beverage market. Refer to
globalization, energy drink products are market as an alternative to carbonated products, and it is the
opportunity switch from carbonated product to energy drink category in over the last few years. In Thailand
perspective, the energy drinks industry grew at a CAGR of 5.8% between 2018 and 2019, THB 22.1 billion
in terms of sales value (AC Nielsen, 2020), and there are many companies in this market. Therefore, this
market is very competitive and there are various factors affect intention to purchase decision energy drinks
in Thailand. The main purpose of this study was to identify factors affecting intention to purchase decision
energy drinks in Thailand. The questionnaire was distributed to people who are Thai about 400 participants.
Moreover, convenience sampling, which is a non-probability sampling method, is used for the sampling
procedure with analyzing the level of impact toward independent variables (product, price, place,
promotion, and subjective norm) to the dependent variable (purchase decision). The results of this research
indicated that three independent variables, which are product, promotion, and subjective norm, had
significantly affecting intention to purchase decision energy drink in Thailand. Therefore, the energy drink
business should focus more on the product, promotion, and subjective norm, which creates more attracted
to purchase the product.
Keywords: Product, Price, Place, Promotion, and Subjective norm
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Introduction elements to make customers pay for the product and
the company will generate sales (Eccles et al., 2013).
Energy drinks are non-alcohol beverages help to Therefore, the energy drink business has to provide
boost or stimulate physical, mental and regain more benefits and variety of products to serve
energy. The energy drink business is fastest-growing customers need such as new flavor, giving more
product category in the beverage market. Refer to energy and fresh. In additional, product is mentioned
globalization, the majority of energy drink to other attributes for example, quality, packaging,
consumers are millennial population, the ages brand, and assurance (Isaac, 2000).
between of 18-35 years old. However, Europe is an
emerging market for energy drinks, which is poised Price
to grow at a healthy rate, due to the increasing Price refers to the volume of payment or exchange
consumers adoption rate in the region, as a result of value between money and unit of goods or service.
increased marketing efforts by the key players. (Schindler & Robert M., 2012). Moreover, Price is
Children and adolescents are the main target groups the key influencing to customers make a purchase
of energy drinks business. In Thailand perspective, decision and assess the price of product is worth with
energy drink products sale in Thailand are what they perceived from the product in order to
contributed by the blue-collar workers target group. make a purchase decision (Du Plessis & Rousseau,
Meanwhile, the increasing introduction of premium 2007).
energy drinks are accepted and consumed by the
white-collar group, the contribution of premium Place
energy drinks, which are based on the unit price of Place is very important that help customers find and
above THB 13. Therefore, this market is very keep purchasing the products. If the product is
competitive and there are various factors affect arranged to closest customers eye-level and
intention to purchase decision energy drinks in impactful of the shelf space it can drive to purchase
Thailand. the product due to it creates product awareness and it
Research Objective is the best placement for products in stores. Which
include decision and action related to the transfer of
The research objective is to identify factors affecting goods from producers to customers (Matola, 2009).
intention to purchase decision energy drink in
Thailand in order to stimulate purchasing the Promotion
product, increase its profit, consumers and market Promotion campaign is stimulated to decision and
share. action in order to encourage the purchased product
such as special price, free samples, road shows,
Research Question contest, or other special offers. Thus, promotion is a
part of stimulating the customers to make a purchase
What are the factors affecting intention to purchase product (Peter & Olson, 2008).
decision energy drink in Thailand?
Literature Review Subjective norm
Subjective norm is defined specifically, as a
Intention to purchase decision energy drink in personal’s opinion or perception about what
important others believe the individual should do.
Thailand Subjective norm is the social factors use to determine
Person’s attitude and belief toward the behavior behavior that people will perform or not perform the
behavior in a specific situation (Finlay, Trafimow, &
performance which is associated with positive or Moroi, 1999). In terms of young female consumption
negative of value outcomes (Fishbein & Ajzen, of energy drink more concern on subjective norm as
a significantly implying to them (Kassem, Lee,
2010). In additional, beliefs related to the desired Modeste, and Johnston, 2003).
behaviors or the way of conduct that guide an
individual’s actions including purchase (Hansen,
2008). The behavior beliefs derived from the studies
are named in relation to beverage consumption
behavior were, in order of importance good for my
health, goes well with food, the experience of a Purchase decision
variety of taste and flavor (Thompson, 1995; Zanten, The consumers have decided to buy a product after
2005; James et. al., 2011). collecting information from several sources, evaluate
Product it and decided for where and what to purchase.
The product benefits are an important reason why Consumers purchase the brand or product which they
customers buy the product. The product benefit and give the highest rank in the evaluation stage. The
nutrition of beverage are one of the most powerful
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purchase decision also influenced by the surrounding analyzing by using multiple regression analysis to
environment. (Qazzafi, 2019). combine the data.
Research Framework Results and Discussion
The researcher decided to take five independent Descriptive Analysis of Demographic data
variables into consideration, product, price, place, The data was collected shows that the majority of
promotion, and subjective norm. The conceptual respondents who did the survey were female with the
framework connects between those independent percentage of 61% (245 respondents). While the
variables and the dependent variable that is purchase remaining was male with the percentage of 39% (155
decision to define the factors affecting intention to respondents). The marital status was single at 85%
purchase decision energy drinks in Thailand. After (338 respondents) and 16% (62 respondents) was
that, the researcher set hypotheses to see if there married. From the total 400 respondents, the highest
is/are relationship(s) between independent variables percentage of respondents’ age was 82% (328
and the dependent variable.
respondents) who are 22-39 years old. The highest
Research Hypotheses percentage of the respondent’s income was 33% (133
respondents) who have income between 30,001-
H10: Product does not impact on purchase decision 50,000 baht per month. Another demographic factor
to energy drink in Thailand is education, the highest number of education level
H1a: Product impacts on purchase decision to energy dominated by Bachelor's degree which was 69% (277
drink in Thailand respondents). The last demographic factor is
H20: Price does not impact on purchase decision to occupation field, the most of respondents are office
energy drink in Thailand workers which is 76% (304 respondents).
H2a: Price impacts on purchase decision to energy
drink in Thailand Adjusted R Square
H30: Place does not impact on purchase decision to Adjusted R square value is 0.45 which can explain
energy drink in Thailand that 45.5% of the variation in purchase decision to
H3a: Place impacts on purchase decision to energy energy drink in Thailand can be explained by the
drink in Thailand independent variables in this research which are
H40: Promotion does not impact on purchase product, price, place, promotion, and subjective
decision to energy drink in Thailand norm. Therefore, there are another 54.5% of the
H4a: Promotion impacts on purchase decision to variation in purchase decision to energy drink in
energy drink in Thailand Thailand can be explained by other independent
H50: Subjective norm does not impact on purchase variables.
decision to energy drink in Thailand In this study, the Hypothesis was tested by using
H5a: Subjective norm impacts on purchase decision Multiple Linear Regression. The Result of each
to energy drink in Thailand hypothesis is summarized in the table as follows:
Research Methodology
Methods of Research Used
The survey technique method measured by 5 points
Likert Scale because it most widely uses and could
easily adapt to any part of the questionnaire.
Respondents and Sampling Procedures
The target respondents of this study are people who
are Thai and the sample was collected 400 persons
from target population. This questionnaire consists of
3 parts which were screening questions, demographic
questions, and measuring variables.
Statistical Treatment of Data
The researcher used descriptive analysis for
identifying the respondent’s purchase decision by
using demographic data, the statistical package for
the social science programs (SPSS) as a tool for
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Table 1: The summary of Hypothesis tested by value more than 0.05. So, the null hypothesis (H20
using Multiple Linear Regression and H30) were failed to reject. This can be concluded
that product, promotion, and subjective norm are
Hypothesis Signific Standa Result affecting purchase decision to energy drink in
ant rdized Rejected Thailand.
H10: Product Value Coeffici
does not (P- ent Table 2: The Strength of the variables (product,
impact on Value) Beta promotion, and subjective norm) that have a
purchase 0.00 (β) significant impact on purchase decision to energy
decision to drink in Thailand.
energy drink 0.63 0.17
in Thailand
H20: Price 0.58 Rank Independent Beta
does not 1st Variable 0.48
impact on 0.00 Subjective norm
purchase
decision to 0.00 2nd Product 0.17
energy drink
in Thailand 0.02 Failed to 3rd Promotion 0.15
H30: Place reject
does not Dependent Variable: Purchase decision
impact on -0.02 Failed to The table 2, showed that the highest impact to the
purchase reject lower impact parameter (subjective norm, product,
decision to and promotion) toward purchase decision to energy
energy drink 0.15 Rejected drink in Thailand. The average of Beta is calculated
in Thailand to justify the strength of the impact of each variable
H40: on purchase decision to energy drink in Thailand.
Promotion The average Beta value is 0.20 meaning that all the
does not beta value of the variables that is above 0.20 has the
impact on strongest impact. While the factors that has beta
purchase value below 0.20 tell the researcher that are the weak
decision to impact on purchase decision to energy drink in
energy drink Thailand. According to subjective norm variable has
in Thailand the strongest impact on purchase decision to energy
H50: drink in Thailand followed by product variable,
Subjective which has strong impact, and promotion has the
norm does not weaker impact than the 2 variables above on
impact on purchase decision to energy drink in Thailand.
purchase
decision to 0.48 Rejected Recommendation
energy drink
in Thailand The result of this study is advantageous with
beverage business in energy drink category in
*Note: P-Value < 0.05 Thailand to understand all factors to guideline the
Since Significant value less than 0.05 is indicated company in order to stimulate to purchase the
that independent variables have statistical product, increase its profit, consumers and market
significantly impact on dependent variable. share. Referring to the result which found that
According to table 1, Significant value of product, product, promotion, and subjective norm affect to
promotion, and subjective norm are less than 0.05. purchase decision to energy drink in Thailand. The
Therefore, the null hypothesis (H10, H40, and H50) first factor that need to concern is subjective norm
were rejected while price and place get Significant which is the most influence on purchase decision to
energy drink in Thailand. People around customer
has to approve the product that customer purchase
due to customer need self-expectation, love and
belonging from them. Thus, energy drink business
should consider group of people around customers
such as family, friends, and people who decision-
making power accept or appreciate energy drink
product. The second factor that have influence on
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Trump’s twitter effect on Financial Indexes
Wasawat Dumrongvachiraphan
Nopphon Tangjitprom
Bangkok, 11000 and Thailand
Ramkhamhaeng 24 Ramkhamhaeng Rd., Hua Mak, Bangkok, 10240 and Thailand
*Corresponding author. E-mail: [email protected]
Abstract
This study investigates the impact of Trump’s tweets on abnormal returns and trading volumes of the
S&P 500, using VADER to determine the sentiment of the daily tweets to identify relevant events. Based
on the daily tweets from U.S President Donald Trump’s twitter account from 1st January 2018 to 16th
December 2019, about 20 event samples had been identified. Statistical analysis using event study
techniques demonstrated that only negative tweets could lead to statistically significant abnormal return
and trading volumes over 1 or 2 trading days after the tweets. The study did not find any statistically
significant relationship among positive tweets, abnormal returns, and trading volumes. According to the
analysis, the conclusion of these results demonstrates that Trump’s tweet is still another source of
information used to predict the U.S stock market return.
Keywords: Donald Trump’s tweets, S&P500, Sentimental Analysis, Abnormal return, Trading Volume,
VADER (Times New Roman, 11pt)
Introduction In a finance context, textual analysis has
been applied to financial news sentiments (Barber
Over the past decades, textual analysis has & Odean, 2008), microblogging (Sprenger &
become one of the prominent areas of researches, Welpe, 2014) and twitter account of influential
thanks to digital media evolution and continual leaders (Rayarel, 2018) to determine the level of
advancement in natural language processing tools their influences on investors’ trading or
(Edmans, García, & Norli, 2007; Clayton, 2014; subsequent movements in the markets.
Chen, Cho, & Jang, 2015; Azar & Lo, 2016).
Researchers from various disciplines such as In this light, empirical evidence has been
computer science (Sohangir, Petty, & Wang, accumulating in the developed markets on the
2018), marketing (Hennig, Wiertz, & Feldhaus, possible impacts between sentiments from the
2015) and finance (Fang & Peress, 2009), have news (Fendel, Burggraf, & Huynh, 2019), google
been extracting sentiments from the massive text searching (Born, Myers, & Clark, 2017) and
available in the internet and social media sources social media communication on market
to understand users’ opinions, satisfaction or movements (Rao & Srivastava 2012), trading
reaction to such information. activities (Antweiler & Frank, 2004) and policy
communication (Fenn, 2019).
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Among these studies, investment analysts announced earlier. In reaction, the S&P 500
and finance researchers have been paying declined by 0.9% on that day and further dropped
attention to the twitter account of President by almost 3% over the 3 subsequent days (Liu,
Donald Trump, @realDonaldTrump. Since his 2019). These incidents raised questions on the
accession to the U.S. Presidency in 2017, messages effect of Trump’s Tweets on movement in
posted on @realDonaldTrump twitter account financial markets.
seemed to cause movements in stock prices of the
companies mentioned in his tweets, as well as in On the one hand, market reactions to
the broader indices. In 2017, for instance, Trump’s Tweets are often reported by financial
Trump’s tweets about Nordstrom for unfairly media and observed by practitioners. For
dropping his daughter Ivanka’s brands. As a instance, J.P.Morgan and Citibank had introduced
result of his Tweet, share prices of Nordstrom specific indices to quantify Trump’s effect on the
immediately dropped by 1% for a short period volatility of bond yield and foreign exchange
before rebounding to 4% from the Nordstrom markets. More specifically, J.P. Morgan has
announcement (Tu, 2017). A similar observation introduced the Volfefe index to track the effect of
was made on 2nd April 2018. Amazon stock sank Trump’s Tweet on the volatility of the two-year
by 5% after Trump accused Amazon of taking and five-year bond yields (Alloway, 2019).
advantage of the US Postal Service, and he Having said that, there had been a limited number
suggested that Amazon does not pay its fair share of scholarly articles, confirming the impact of
of tax. (Meyersohn, 2018). Trump’s effects on Stock Markets. Relevant
published works examined the relationship
Apart from the preceding incidents on between the google trend searching on “Donald
individual companies, the media had turned Trump” and stock market movements, while the
attention to Trump’s Tweets about Trade Wars. other focused on the impact of Trump’s Tweets
Starting in late 2018, Trump had been making on political news relevant to trade war and its
headlines on Trade War with China, fueling relationship to the return on S&P 500 and VIX
concerns among major financial markets around (Fendel et al, 2019).
the world. Balji & Burgess (2019), for instance,
observed that approximately US$ 1.36 trillion With the impending question on Trump’s
market value of global stocks had been wiped out effect, further studies are required to better
when Trump announced the additional tariff understand whether there exists the Trump’s
US$200 billion on imported Chinese goods on effect on financial markets. To contribute to the
5th May 2019. empirical discussion, the purpose of this study is
to examine the impact of Trump’s Tweets on the
Further, on 2nd August 2019, there was a S&P 500 from 2018 to 2019. In so doing, this
drop in the S&P 500, when Donald Trump posted study proposed to determine the sentimental level
a series of tweets on his plan to impose 10% of Trump’s Tweets through Valiance Awareness
tariffs on $300 billion worth of imports from Dictionary (VADER) and to analyze the
China, on top of the previous $250 billion, statistical relationship with abnormal return and a
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cumulative return of S&P500. The results of the learning and sentimental lexicons. Logistic
study could render support to the existing Regression, Naïve Bayer, Linear SVW,
literature as well as provide rooms for future TextBlob, SentiWordNet, and VADER are used
studies. to perform and compare the result of the
The objectives of the study sentimental analysis. The result demonstrated that
VADER is the most accurate lexicon-based and
With the frameworks of Born et al. fastest method compared to others. VADER
(2017), Rayarel (2018), and Colonescu (2018), this stands for Valence Aware Dictionary Sentiment
research aims to: Reasoning and was created from a generalized,
1.)Study the Trump’s tweet effect on the S&P500 valenced-based, human-curated gold standard
by analyzing the abnormal return and trading sentimental lexicon. VADER also includes the
volume of S&P500 to Trump’s tweet sentiments. impact of grammatical, syntactical rules,
punctuation, capitalization, conjunction, etc.
2.)Examine the impact of Trump’s tweet by Based on the VADER performance, the text data
conducting the sentimental analysis based on will be assigned the scoring base on the word in
VADER and bag-of-word to the series of the the dictionary and the sentiment score is ranked
S&P500 – whether Trump’s tweet with different between 1 and -1 whereby 1 is considered as
sentiment does provide any excessive abnormal being extremely positive, -1 is considered as
return and volume to the S&P500 at the same being extremely negative and 0 being neutral.
specified interval. With such techniques, it becomes the popular
Literature Review lexicon-based technique for researchers in
analyzing the relationship of sentimental text data
Sentimental analysis can be defined as opinion from social media to other numerical data
determination’s process according to the human’s (Chauhan, Bansal, & Goel 2018; Park & Soe,
emotion and feeling (Cakra & Trisedya, 2015). 2018; Abraham, Higdon, Nelson, & Ibarra,
This process is performed by the text’s 2018).
classification represented as positive, negative, In terms of sentimental analysis on financial
and neutral sentiments. The social media markets, Bollen, Mao, & Zeng (2011) in the early
application such as Facebook, Instagram, and author that applied sentimental analysis to their
Twitter, are a popular platform in analyzing the work. With their use of OpinionFinder and
polarity of messages through sentimental analysis Google-Profile of Mood Stage (GPOMS), the
techniques. Typical approaches to sentiment daily twitter feeds will be assigned as the various
analysis include machine learning (Rao & mood stage. Interestingly, these mood time series
Srivastava, 2012) and sentiment analysis, using can be significantly improved the accuracy of
lexicon approaches (Park & Seo, 2018). DJIA prediction. In the same year, Zhang,
In terms of performance, Sohangir et al. (2018) Fuehres, & Gloor (2011) use keywords (#Hope,
compare sentimental analysis approaches of #Happy, #Fear, #Upset, #Nervous, #Positive
social media data by using different machine #Negative) contained in the tweet message to
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track the sentimental polarity to find the United States. As mentioned in the introduction,
correlation with some aggregated market Donald Trump is considered as the one that
variables (DJIA, NASDAQ, S&P500, and VIX). actively uses twitter as social media to share his
The result indicates that the keywords with opinion. Many publicly traded firms used to be
negative emotional words (#Hope and #Fear) are mentioned in his twitter account during his
significantly negative correlated with DJIA, presidential periods such as Boeing, Toyota, and
NASDAQ, and S&P500 while the significantly Lockheed Martin
positive relationship was only found in VIX. (https://twitter.com/realdonaldtrump).
Also, another aspect of sentimental information is Many researchers studied the relationship of
used to find the correlation with the stock market Trump’s tweets to the financial market. Born et
return. For instance, Edmans et al. (2007) al. (2017) use standard event study techniques to
discover a strong negative stock reaction on the find the relationship of a positive and negative
loss of national soccer teams. Matthias (2011) sentiment of Trump’s tweets to opening and the
demonstrates that negative sentiment on Reuter close stock price of 10 publicly traded firms.
news can be used to predict the stock return, in Demonstrating by average abnormal return
comparison to the positive sentiment. Augby, (AAR), cumulative average abnormal return
Muzwi, & Mezher (2018) study different 25 (CAAR), average abnormal trading volume
articles that studied the effect of social media on (AAV), and google searching activities, the result
the stock market prediction. This study as a whole indicates that the price and trading volume,
can be concluded that social media can be used as combined with the Google Search activity of 10
one of the short-term indicative factors to predict publicly traded firms are correlated with
the movement of stock for less than 1 year. They sentimental content of Trump’s tweet messages.
also found that Twitter is considered as the first Similarly, Rayarel (2018) also apply the same
rank of studying social media however, the technique as Born et al. (2017) to find the effect
different sources of social media such as of Donald Trump’s company-specific tweets on
Facebook can provide various impacts on the stock market. The result reveals that Trump’s
different financial markets in each country. tweet leads to a statistically abnormal return on
Twitter is one of the popular social media that the the company stock price. Interestingly, few
researcher used as a proxy to monitor the predict authors study the relationship of Trump’s tweet
the financial market movement. Numerous papers feeds to the stock market indices. Colonescu
discover significant linkage between the financial (2018) looks at the effect of the daily flow of
market and twitter feeds (Azar & Lo, 2016; Zhang Donald Trump’s tweet on the DJIA and some
et al; Bollen et al. 2011). Also, twitter is still the currency exchange rates. By using AFINN
platform that has been considered as a tool of lexicon, the tweets are assigned the score to
politicians to expand their speeches such as quantify the sentimental analysis. Indicated by
Narendra Modi (the President of India), Barack the regression model, there is some short-time
Obama (the ex-U.S. President) and most notably effect of Trump’s announcement on twitter to
Donald John Trump, the 45th president of the
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DJIA and the US-Canadian currency exchange H1: rejects Hothat Trump’s tweet has an impact
rate. on S&P500
In recent years, many research papers have linked Hypotheses 2
the relationship of Trump’s tweet on company- Ho: claim that Trump’s effect exists only 1 day
specific firms but to the best of my knowledge, on the event date
there still have no research on the effect of Donald H1: rejects Ho that Trump’s effect exists more
Trump’s tweets on an aggregated market variable than 1 days of the next trading day
such as S&P500. This paper would apply the Hypotheses 3
concept of Born et al (2017), Rayarel (2018), and Ho: claim that Trump’s tweet has no impact on
Colonscu (2018) to S&P 500 by using VADER to the trading volume of S&P500
conduct the sentimental analysis. The event study
technique (AAR, CAAR, and AAV) would be H1: rejects Hothat Trump’s tweet has an impact
applied to find the relationship of Trump’s tweet on the trading volume of S&P500
to the financial index (S&P 500). However, this Data Collection
paper would use the more recent period from the
year 2018 to 2019 of Trump’s tweet data to test List of Trump’s tweets
analysis. During these periods, there are many Tweets from Donald Trump’s tweets are
world circumstances like the U.S – China Trade
war that ignite me to study more on the impact of collected starting from 1st January 2018 to 16th
it. December 2019 totaling 10,000 messages via
http://www.trumptwitterarchive.com in which
Hypothesis this website directly gathers the information from
@realDonaldTrump, Trump’s twitter account.
In accordance with Born et al. (2017) and Rayarel Subsequently, retweet and other data unrelated to
(2018), this study examines the following tweets written by Donald Trump are eliminated.
hypotheses. Usually, Donald Trump spread out his opinion on
Table1: Hypotheses Formulas twitter via 2 accounts which are @POTUS, his
Hypotheses No. US president account and @realDonaldTrump,
Hypotheses 1 Ho: AAR = 0 his private account. This study applies
Hypotheses 2 H1: ARR ≠ 0 @realDonaldTrump as a sample to test the
Ho: CAAR = 0 hypothesis because he often uses this account to
share his opinion while @POTUS will be used as
Hypotheses 3 H1: CARR ≠ 0 retweeting of his personal account. Also, the
Ho: AAV = 0 number of followers for @realDonaldTrump is
H1: AAV ≠ 0 twice times compared to @POTUS, his US
president account. This is the reason why in US
Explanation president account will not provide any new
Hypotheses 1 information. To be more realistic, this paper
Ho: claim that Trump’s tweet has no impact on assigns the tweet posting after the market close at
S&P500
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4:00 pm to the next trading day since tweet posted Bullishness and Latent Dirichlet Allocation
after market close should be affected on the stock (LDA) model also are simultaneously applied to
market in the next trading day. those sentimental tweets to identify the bag-of-
Stock market data words related to the sentimental analysis resulting
The financial data of S&P500 in both historical from VADER. Base on chosen the event samples,
closing prices and trading volumes are gathered from abnormal return (AR), cumulative abnormal
2 sources, from 1st January 2018 to 16th December return (CAR) and abnormal trading volume
2019, which are (AV) of the S&P500 are calculated to
demonstrate the impact of Trump’s tweet
https://eikon.thomsonreuters.com/index.html and
https://finance.yahoo.com/. The reason for using 2 sentiment. Finally, the T-test (Brown &
sources of information is to cross-check the right Warner,1995) is used to test the significant degree
information and filling some of the missing data of the result for each element.
belonging to some periods. According to Define event and estimation window
Antweiler & Frank (2004), this research would
apply the closing price of the market index to Estimation Window Event Window
perform the logarithmic daily stock return
whereby the return is calculated by the following T0 T1 t=0 T2
formula Event date
, = ( , ) (1) Figure 1 Timeline of an event
( , −1)
This is defined as the key period of an event
Where ( , ) is the daily return of index at day study. On the event study timeline, = 0 is the
. , is the closing price of stock at day and day in which the occurrence of tweet event. The
, − 1 is the previous day’s closing price for event window is ranged between 1 2
stock . where 1 is the first day of the event window. 2
is the last day of the event window. Usually, there
Methodology is no consensus on the length of the event window
Briefly, the first step consists in defining the as there are different window periods used in
event and estimation window of chosen events. different academic papers. In existing paper, the
This step is to identify the time interval over the event window varies from 1 to 20 days. Sprenger
event’s occurrence. Then, Trump’s tweet data et al. (2010) used 20 days event window to test
would be flowed by the process of sentimental the abnormal return of stock and volume while
analysis by using Valence Aware Dictionary and Born et al. (2017) applied 10 days as an event
Sentiment Reasoner (VADER). The purpose of window. According to the paper of Born et al.
this text mining is to assign a sentimental measure (2017) and Rayarel (2018), they stated that
(Positive or Negative) to each tweet and to Trump’s tweet effect would no longer significant
construct a series of sentiment. After assigning after five trading days. Therefore, this research
the sentimental polarity for each tweet message, will apply 10 days as the event window (5 days
chosen events are defined base on the Degree of before and after event dates). The
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next step is to identify the estimation window 1.Degree of Bullishness
where 0 1 is the interval period of the By applying some of Antweiler & Frank (2004),
estimation window. 1 is the first day of Rao & Srivastava (2012), and Sprenger et al.
estimation window and 0is the last day of the (2014) techniques, the degree of bullishness is
estimation window. The estimation window is the defined as:
period before the event window in which it is used ℎ = (11++ ) (2)
to define the scope of expected return. The estimation
window varies from 30 to 250 days. Sprenger et al. Where and are the number
(2014) and Rayarel (2018), for instance, use of positive and negative tweets on day . This
estimation window 120 days and 250 days
respectively while Fenn (2019) uses 100 days. Logarithm of bullishness measures the
However, there is no standard method to define explanation of surplus degree on that specific day.
the estimation window. Therefore, this paper will The higher bullishness implies the larger number
try 50 days as an estimation window starting from of positive messages in a specific sentiment and
-56 day to -6 day. Intentionally, the gap of 5 days vice versa.
is to prevent the overlapping between the event 2.Beg-of-Word method
window and the estimation window.
Sentiment analysis After the 20 event dates are defined, each tweet
will be decomposed by words (the “bag-of-word”
Valence aware dictionary and sentimental method) to identify the word related to groups of
reasoner (VADER), one of the sentimental sentiment in each event date. Latent Dirichlet
lexicon methods, is used to perform the polarity Allocation (LDA) model, a generative probability
of each Trump’s tweet. VADER would match model for collections of discrete data, is used to
Trump’s tweet content with a social media conduct the bag-of-word method (Colonescu,
dictionary and assign the score to each tweet and 2018). LDA would take a corpus of the
categorize the tweets as positive, negative, or unannotated document as input and produces two
neutral. The general purpose of this process is outputs, a set of “Topics” and assignment of the
used to quantify sentiment. VADER assigns a document to the topics where both are represented
sentimental value in the range of -1 and +1 as a probability distribution.
whereby +1 is considered as being extremely
positive, -1 is considered as being extremely Return Calculation
negative and 0 is treated as neutral.
To analyze the impact of Trump’s tweet on
the S&P500, the event study technique is
Sample Selection performed on 20 event dates. The use of abnormal
After the sentimental score is defined, the next return (AR) on each event date is calculated to
step is to select the event samples. The process of find the effect of Trump’s tweet on that day (t=0).
defining event samples for this study is based on Following the calculating of cumulative
2 methods which are the degree of bullishness and abnormal return (CAR), this method is to find
beg-of-word method. how long Trump’s tweet effect does exist after the
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event date. Finally, abnormal trading volume = 1 ∑ =1 , (5)
(AV), the same calculation as the abnormal
return, is computed for testing an attention-based Where N is the number of an event study. , is
the average abnormal return for event at time .
investment.
When the estimation window is defined and
the sample sizes are selected, the expected returns Next, the cumulative average abnormal return
(CAAR) is calculated to find how quickly the
of each event date are required to generate market indexes react to Trump’s tweets. The
CAAR return is expressed as an only single
abnormal returns. The expected return is used as number from different event windows as formula
as below.
the benchmark return in a normal situation that is
not related to the event of interest. This paper
would apply constant mean returns model (CMR)
to calculate expected return since this method ( 1, 2) = ∑ =2 1 (6)
uses the market price itself that already reflects
the market factors to find expected return (Brown Where 1 is the first day in the event window and
2 is the last day of the event window.
& Warner, 1985). The constant mean returns
model (CMR) is defined as: (3) Trading Volume calculation
= ̅ ,
The average abnormal trading volume using the
Where , is the average 50-day estimation same technique as Rayarel (2018) as the formula
period return (estimation window) where this as follows:
calculation will be started 5 days before the
event period. Then, the logarithm daily return on = 1 ∑ =1 , (7)
the event dates is subtracted by the expected
return to get the abnormal return (AR) whereby
the formula defined as: = ( , ̅ − ̅ ) (8)
, = , − ( , ) (4) Where is the change in abnormal trading
volume for event on day , , is the trading
, is the abnormal return and , is the daily abnormal trading volume for event on day
return for indexes for event at day . ( , ) is and ̅ is the average trading volume of event on
the expected return generating from CMR method day . Then, find the average abnormal trading
starting from -56 to -6 days (50 days prior event volume as the same technique as an average
window). abnormal return.
Due to the large event samples, Born et al. (2017) Significance test for AAR, CAAR, & AAV
and Rayarel (2018) suggest that the abnormal (Brown and Warner T-test)
return of each event date can be combined into a
portfolio and uses average abnormal return to Statistical significance of AAR, CAAR,
define the impact. The average abnormal return & AAV is performed using Brown and Warner
(AAR) is calculated as: (1995) T-Tests. According to Brown and Warner
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(1985) theory, the T-test method can be used to According to Table2, 20 events with the high
test the significant relationship between AAR, bullishness score of Trump’s tweets are examined
CAAR, and AAV. This t-statistic is calculated as the impact on S&P500. The result shows that the
below. – indicate one main formula & state all abnormal return on the first trading day of
this will be applied to AAR, CAAR, and AAV. Trump’s tweet (t=0) is negative which moves in
the opposite direction of the positive sentiment of
= (9) Trump’s tweets. Also, the P-value is statistically
insignificantly different from zero. Moreover,
after the event date, the abnormal return for the
positive tweet is still negative and is not
Where apply for 3 results which are , significant. Therefore, this can be implied that
, . is the standard deviation positive Trump’s tweets have no significant
of , , . impact on S&P 500 and consistent with the null
hypothesis that Trump’s tweet has no impact on
Results and discussion S&P500 during this period.
Table2: Abnormal return of positive event dates
N=20 Table3: Abnormal return of negative event dates
S&P5 Peri AA α T- P-
00 od R stat valu
e
After 5 - 0.00 - 0.40 N=19
S&P5 Peri AA α
Event 0.22 26 0.84 84 T- P-
Date % 55 00 od R stat valu
4 0.24 0.00 1.48 0.15 e
% 16 42 41 After 5 - 0.00 - 0.29
3 0.07 0.00 0.41 0.68 Event 0.17 16 1.08 22
% 17 46 31 Date % 52
2 0.23 0.00 0.96 0.34 4 0.05 0.00 0.30 0.76
% 24 58 63 % 15 25 58
1 - 0.00 - 0.61 3 - 0.00 - 0.96
0.11 21 0.51 22 0.01 21 0.04 30
% 54 % 71
0 - 0.00 - 0.76 2 0.16 0.00 0.98 0.33
0.10 34 0.30 02 % 16 65 70
% 96 1 - 0.00 - 0.79
Befor -1 - 0.00 - 0.45 0.04 16 0.26 18
e 0.23 30 0.76 31 % 80
Event % 60 0 - 0.00 - 0.03
date -2 - 0.00 - 0.18 0.45 20 2.25 66
0.28 21 1.36 84 % 82
% 42 Befor -1 0.04 0.00 0.24 0.81
-3 0.19 0.00 1.18 0.25 e % 17 30 08
% 16 37 11 Event -2 0.15 0.00 1.01 0.32
-4 0.01 0.00 0.05 0.95 date % 15 97 14
% 25 73 49 -3 - 0.00 - 0.53
-5 0.11 0.00 0.53 0.60 0.08 13 0.62 96
% 20 12 15 % 53
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-4 0.15 0.00 0.91 0.37 Event -2 - 0.00 - 0.13
% 17 59 18 date 0.62 40 1.55 64
-5 0.02 0.00 0.11 0.90 % 51
% 16 57 91 -3 - 0.00 - 0.36
Based on the Table3 above, it displays the 0.43 45 0.93 04
% 73
abnormal return for a high negative bullishness -4 - 0.00 - 0.37
0.41 45 0.90 45
score with 19 events in samples. It demonstrates % 95
-5 - 0.00 - 0.60
that on the date that when Trump starts tweeting 0.30 57 0.53 08
% 22
some negative messages, abnormal return on
According to the previous AAR implication, the
S&P500 are seemed to be negative of -0.45% and positive Trump’s tweets do not provide any effect
it is statistically significantly different from zero. to return in S&P500. Therefore, CAAR will be
However, in the next trading day, even there is automatically insignificant for this test (Table
small negative abnormal return, the returns are 10).
likely to be insignificant on P-value. This can be Table5: Cumulative Abnormal return of negative
inferred that Trump’s tweets with negative
event dates
sentiment are likely to provide an impact on
S&P500 on the day (t=0). With a 5% level of
significance, the null hypothesis is rejected on the N=19 T- P-
claims that negative Trump’s tweets do not S&P5 Peri AA α
provide any impact on S&P500 return. 00 od R stat valu
Table4: Cumulative Abnormal return of positive e
event dates After 5 - 0.00 - 0.19
N=20 Event 0.47 35 1.35 24
S&P5 Peri AA α
T- P- Date % 42
00 od R stat valu
4 - 0.00 - 0.30
e
0.30 29 1.05 75
After 5 0.11 0.00 0.16 0.87 % 02
Event % 66 49 07 3 - 0.00 - 0.26
Date 4 0.33 0.00 0.52 0.60 0.35 30 1.15 14
% 62 99 23
% 94
3 0.09 0.00 0.16 0.87 2 - 0.00 - 0.27
% 55 13 36
0.34 30 1.12 63
% 28
2 0.02 0.00 0.03 0.97 1 - 0.00 - 0.06
% 52 16 51
0.49 25 1.98 24
1 - 0.00 - 0.62
0.21 43 0.49 57 % 66
% 59
0 - 0.00 - 0.03
0 - 0.00 - 0.76 0.45 20 2.25 66
0.10 34 0.30 02
% 96 % 82
Befor -1 - 0.00 - 0.19
- 0.00 - 0.38 e 0.41 30 1.35 22
Befor -1 Event % 48
e 0.33 37 0.88 60 date -2 - 0.00 - 0.50
% 73 0.26 38 0.68 30
% 34
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-3 - 0.00 - 0.44 -3 1.20 0.03 0.39 0.70
0.34 44 0.77 94 % 08 06 05
% 33
-4 - 0.02 - 0.37
-4 - 0.00 - 0.69 2.03 22 0.91 01
0.19 47 0.40 29 % 80
% 13
-5 1.02 0.02 0.38 0.70
-5 - 0.00 - 0.74 % 65 38 54
0.17 51 0.32 82
% 60
Table5 indicates the cumulative abnormal return Table7: Abnormal trading volume for Negative
for 19 samples of high negative bullishness score event date.
belongs to Trump’s announcement. According to
the result, the effect of Trump’s tweet is likely to N=19
remain for at least 1 day after the event date
indicated by cumulative abnormal return and S&P5 Peri AA α T- P-
statistical significance of P-value with on 10%
level. Therefore, the test is in line with the 00 od R stat valu
alternative hypothesis that Trump’s effect will
exist more than 1 days in the next trading day. e
Table6: Abnormal trading volume for After 5 5.08
Positive event date.
Event % 0.03 1.43 0.16
Date 53 86 74
4- -
0.91 0.04 0.18 0.85
% 87 75 34
3- -
2.10 0.04 0.51 0.61
% 05 78 09
N=20 2 0.68
S&P5 Peri AA α T- P- % 0.02 0.23 0.81
00 od R stat valu 94 27 86
e 1- -
After 5 4.31 0.02 1.61 0.12 3.84 0.03 1.13 0.27
Event % 67 32 32 % 38 59 09
Date 4 - 0.01 - 0.42 0 4.37
1.44 75 0.82 06 % 0.02 1.83 0.08
% 33 37 88 25
3 - 0.01 - 0.83 Befor -1 - -
0.38 83 0.20 80 e 2.75 0.03 0.76 0.45
% 72 Event % 60 42 46
2 1.28 0.04 0.28 0.77 date -2 2.39
% 47 66 75 % 0.03 0.73 0.47
1 - 0.03 - 0.26 27 15 39
4.30 74 1.14 49 -3 0.12
% 88 % 0.02 0.04 0.96
0 3.81 0.04 0.92 0.36 45 79 23
% 11 76 53 -4 2.69
Befor -1 - 0.04 - 0.60 % 0.04 0.62 0.53
e 2.44 61 0.52 27 30 60 92
Event % 93 -5 - -
date -2 3.25 0.04 0.78 0.44 0.29 0.03 0.07 0.94
% 12 87 00 % 77 63 01
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Table6&7 show positive and negative bullishness results could be from the intervals of data, the
score of abnormal trading volume within the method to analyze sentiment, the analysis
event window related to S&P500. It states that technique, and the difference in indices (Augby et
only the negative bullishness score shows slightly al. 2018).
of abnormal trading volume with 4.37% and 10% Finally, the implication of the transitory price
significance of P-value; however, there is no effect including the increase in trading volume
statistically significant on positive event date of related to Trump’s tweet is that it was the
the whole event window. Consequently, Trump’s primarily small retail investor called noise trader
tweet with negative sentiment can lead to who focus and response to Trump tweet as one of
abnormal trading volume while there is no impact the market indicators. Interestingly, such traders
of positive sentiment on the abnormal trading react to the negative tweet rather than the positive
volume of S&P500. one. Taken as a whole, this study can conclude
Conclusion that sentimental analysis could be considered as
an assistance factor to encrypt Trump’s tweet
In this research, the hypothesis test is to identify impact on the financial index like S&P500.
the impact of Trump’s tweets related to
sentimental analysis upon the short-term Acknowledgments
movement of S&P500. The sentimental analysis I would like to express my sincere
of Trump’s tweet is determined by VADER. In gratitude to my supervisor Dr.Nopphon
response to President Donald Trump’s tweet Tangjitprom & Dr.Panjamaporn Sethjinda for
based on sentimental analysis, the result indicates providing me their guidance, comments, and
that the negative Trump’s tweets appear to have suggestions throughout the research. I would
elicited a significant impact on the specially thank Mr. Thatree Homsirikamol for
movement of S&P500. The negative tweets can providing me the advanced method and
generate an abnormal return on S&P500 on the constantly motivating me to work harder. Finally,
same day as tweeting. However, the impact on I would like to thank MSIAM for providing this
Trump’s tweets is perfectly eliminated within 2 to course as well as the informative preparation
3 trading days according to the result of the including sharing a template to conduct this
cumulative abnormal return. Regarding the research.
positive Trump’s tweet, there is no such a References
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The Impact of Transformational Leadership Styles on
EmployeeEngagement in a Sugar Factory
Wichian Srichaipanya1, Piyathida Praditbatuga2 and Santhiti Treetipbut3
Assumption University of Thailand, Bangkok, 10240 Thailand Corresponding Author
Email: [email protected]
Abstract
The objective of this research is to determine the influence of transformational leadership
styles on employee engagement of a chosen sugar factory. Quantitative survey research was
conducted. A total of 230 sets of questionnaires were distributed to manufacturing employees
in the sugar factory and
180 completed questionnaires were used for data analysis. The mean values indicate that
manufacturing employees in the studied sugar factory agreed with transformational
leadership styles (idealized influence with the mean value of 3.57, intellectual stimulation with
the mean value of 3.55, inspirational motivation with the mean value of 3.52, and individual
consideration with the mean value of 3.21). Manufacturing employees in the studied sugar
factory were engaged with their job, with the mean value of 3.90. Results from Multiple
Linear Regression analysis indicate that transformational leadership styles (idealized
influence, inspirational motivation, intellectual stimulation and individual consideration)
significantly influence employee engagement. Individual consideration has the highest
influence on employee engagement (Beta = 0.205), followed by idealize influence (Beta =
0.145), intellectual stimulation (Beta = 0.027) and inspirational motivation (Beta = - 0.096)
respectively. The selected sugar factory should focus more on individual consideration in order
to create more employee engagement. Two factors of transformational leadership styles that
should also be more focused on are idealized influence and intellectual stimulation as they
have positive influence on employee engagement.
Keywords: Transformational Leadership Styles, Idealized Influence, Inspirational
Motivation, Intellectual Stimulation, Individual Consideration and Employee Engagement
Introduction posited that, if managed and utilized properly,
Employee engagement is an affective way for the workforce can become one of the most vital
organizations to gain competitive advantage as assets to a business. Baumruk (2004) reinforces
people, and be association respective this idea and expressed that a company’s vigour
workforces, are one element of business that is is most significantly impacted upon by employee
impossible to duplicate precisely. Anitha (2014) engagement. Transformational leadership is a
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very efficient style to drive organization with Literature Review
overall change (Lussier, 2006). Accompanied
with employee engagement after applying The literature review in this article comprises 1.
transformational leader, it is a helpful instrument Transformational Leadership Styles, 2.
for any organization striving to gain competitive Employee’s Job Satisfaction, 3. The Influence of
advantage over their competitors. People employ Transformational Leadership on Employee
and express themselves physically, cognitively, Engagement.
and emotionally during role performances with
clearity of direction from this style of leader Transformational Leadership Styles
(Ghadi, Fernando and Caputi, 2013).
Leadership is the process of influencing
This paper proposes to investigate the influence employees to work toward the achievement of
between transformational leadership and organizational objectives. Leadership is perhaps
employee engagement. It is suitable for a sugar the most talked about, researched, and written-
factory to transform employees to gain market about management topic (Lussier, 2006). The
competition and better performance. This ongoing pursuit to find a more complete
industry is changing to be an energy business, leadership approach gave rise to transformational
it is especially important to have some and transactional leadership styles (Bass, 1985;
technique to manage team to gain synergy and Burns, 1978 as cited in Popli and Rizvi, 2015).
meet the new challenge of tasks within a short Transactional leaders are the contingent reward
time. Leaders are being driven into unfamiliar leader (contracts exchange of rewards for effort,
territory and how leaders handle that change has promised rewards for good performance,
a massive impact on a success of their business, recognizes accomplishment), the management by
the morale of their workers the social and exception leader (active - vigilant and searches
economic stability of their country (Sarros and for deviations from rules, regulations, and
Santora, 2001). Transformational leadership is standards, or passive –only intervenes when
therefore a suitable style for time to change standards are not met or are unfulfilled) and
situation. This is because of previous findings laissez-faire leader (relinquishes responsibilities
from the management literature that and avoids making decisions). Transformational
transformational leadership can motivate leaders are the charismatic leader, the
employees to work for longer hours and work inspiration leader, the intellectual stimulation
more than is expected of them (Bass, 1985 as leader and the individualized consideration
cited in Jyoti and Dev, 2015). The leader (gives personal attention, treats each
transformational leadership takes control of employee individually, coaches, advises)
situation by conveying a clear vision of group’s (Kreitner, 2007).
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Social scientists have given much attention to problems as opportunities to learn (Jyoti and
the concept of leadership over the past few Dev, 2015). Individual Consideration: The
decades. There has been a notable shift in focus leader gives personal attention to his or her
from traditional/transactional models of followers by treating them “differently but
leadership to theories which put an emphasis on equitably” (Bass and Avolio, 1990). Intellectual
transformational leadership. (Bass, 1985 as cited Stimulation: The leader presents new ideas to
in Ozaralli, 2003). Researchers have carried out followers and challenges them to think
extensive studies on the concept of creatively (Bass and Avolio, 1990).
transformational leadership in recent years and
found this concept is effective both in terms of Employee Engagement
increasing follower’s performance expectation
and transforming their personal values and self- Employee Engagement focuses on how the
concept into a higher level of needs and
aspirations (Cheung and Wong, 2011). Tichy and psychological experiences of work and work
Devanna (1990) posit that transformational
leaders carry out leader-thinking processes: contexts shape the process of people presenting
“recognizing the need for change, creating a new
vision, and institutionalizing the change” (Nusair and absenting themselves during task
et al., 2012). Whilst contemporary
conceptualizations of the transformational performances. Additionally, engagement is a
leadership model have four
multidimensional construct, meaning that
dimensions: idealized influence, inspirational
motivation, individual consideration, and employees can be emotionally, cognitively, or
intellectual stimulation (Smith et al., 2004).
physically engaged. Emotional and cognitive
A brief description of each dimension is
presented. Idealized Influence: The leader engagements are the two most significant
altruistic role models who engender the
respect, admiration, emulation of followers and dimensions in psychological engagement and
something closely related to charisma
(Antonakis et al., 2003). Inspirational organization behaviours. Emotional
Motivation: The leader communicates a clearer
vision of the possible future; align engagement denotes empathy, concern for
organizational goals, personal goals and treat others’ feelings, and forming meaningful
connections to others. In comparison, cognitive
engagement is identified in individuals who are
acutely aware of their purpose and role in an
organization. Employees can be engaged on one
dimension and not the other. However, the
more engaged an employee is on each
dimension, the higher his or her overall
personal engagement (Luthans and Peterson,
2002). Employee engagement has garnered a lot
of interest over the past few years as a term
that is widely used in organizations, business
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circles, and consulting firms, due in part to an idea of the future that is comprised of values,
strongly supporting evidence of an engagement- hopes, and ideals. Inspirational motivational
profit correlation. Employee engagement has managers build upon this idea to “challenge and
been typically characterized as a unique and inspire subordinates to achieve more than they
distinct construct, comprising of cognitive, thought was possible” (Bass and Riggio, 2006).
emotional, and behavioural aspects related with Thereby, inspirational motivational behavior is
individual role performance. Employees who considered to be linked to the attribute of
are engaged often exhibit deep and positive absorption, the third facet of work engagement
emotional connection with their work and are (Ghadi, Fernando and Caputi, 2013).
more likely to show Intellectually stimulating managers produce a
attentiveness and mental absorption (Evelyn and supportive organizational climate which can play
Hazel, 2015). a role in the development of employees’ feelings
of work engagement. Through this behavior,
The Influence of Transformational leaders stimulate their followers’ effort to be
Leadership on Employee Engagement
Behaviours of transformational leadership that more creative in solving problems by questioning
could be linked to employee engagement are old assumptions and solving problems depending
idealized influence, inspiration motivation, on fresh perspectives (Bass and Bass, 2008).
intellectual stimulation, and individualized When employees perform at a high level and their
consideration. A key dimension displayed in the efforts are not recognized, their intrinsic
transformational leadership style involves acting motivation reduces which might in turn
as a role model and supervisor-displaying influence their self- esteem. Taking into account
idealized influence behavior. Typical role model that engaged workers are highly involved and
leaders inspire loyalty and devotion at the dedicated in work, supervisors who display
expense of their own self-interests. Follower’s intellectual stimulation behavior can influence
sense of value and contribution increases when employees’ involvement in work and thus work
leaders set themselves as role models, resulting with high feelings of dedication (Schaufeli et al.,
in followers wholly engaging themselves in their 2002). Individualized consideration behavior
work (Ghadi, Fernando and Caputi, 2013). confers individualized attention towards
followers. Transformational leaders capitalize on
Inspirational motivation occurs when supervisors this by finding out and responding to followers’
cultivate a vision of the future that subordinates demands, giving special consideration to
find appealing and that makes them feel like follower’s desires for achievement and growth,
an important part of the organization (Ghadi, and displaying care and thought to individual
Fernando and Caputi, 2013). A vision embodies differences (Avolio and Bass, 2002). The series
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of interactions that can occur between two Research
parties (e.g. the manager and followers) will Hypothesis
usually create engagements from one party to
another. When leaders demonstrate consideration H o 1 : There is no influence of
and care for each follower, they are more likely transformational leadership styles
to motivate positive leader-follower relationships (idealized influence, inspiration
to improve their sense of belonging to the motivation, intellectual stimulation, and
organization (Ghadi, Fernando and Caputi, 2013). individualized consideration) on employee
engagement.
Conceptual Framework H a 1: There is an influence of
transformational leadership styles
The conceptual framework of this research (idealized influence, inspiration
is developed based on the study of Ghadi, motivation, intellectual stimulation, and
Fernando and Caputi (2013), Evelyn and individualized consideration) on employee
Hazel (2015) and Raja (2012). engagement.
Transformational leadership includes
idealized influence, inspirational Research Methodology
motivation, intellectual stimulation, and
individualized consideration based on A self-administered questionnaire was conducted
Nusair, Ababneh and Bae (2012); Raja to determine whether transformational
(2012); Ghadi, Fernando and Caputi leadership styles (idealized influence, inspiration
(2013); Indrayanto, Burgess, Dayaram motivation, intellectual stimulation, and
and Noermijati (2014); Bacha (2014); individualized consideration) on employee
Menon (2014); Jyoti and Dev (2015); engagement of the factory workers. All twenty
Evelyn and Hazel (2015). questionnaires items of transformational
leadership styles (individual influence,
Figure 1: Conceptual inspirational motivation, intellectual stimulation,
Framework and individual consideration) were developed
based on the study of Jyoti and Dev (2015). The
nine questionnaires items of employee
engagement were adapted from the study of
Hansen (2009). The respondents were asked to
rate their agreement by using a 5-point Likert
scale ranging from “Strongly agree” (5) to
“Strongly disagree” (1). The target population of
this study was the permanent employees of the
studied sugar factory, both males and females.
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The sample size was based on Krejcie and other factors that may influence employee
Morgan’s (1970) work in determining sample engagement such as interpersonal leadership
size from a given population. At the time of the (Hansen, Byrne and Kiersch, 2014), job
study there were 300 permanent employees in the characteristics, perceived organizational,
factory; a minimum required sample size of 169 perceived supervisor support, rewards and
samples was selected for this research. Two recognition, procedural justice and distributive
hundred and thirty sets of questionnaires were justice (Saks, 2006). The results from this study
distributed to the permanent employees of a are consistent with the study of Raja, 2012;
studied sugar factory in Udonthani Province, Evelyn and Hazel, 2015; Jyoti and Dev, 2015.
Thailand. Moreover, individualized consideration (Beta
The human resource manager of the company value = 0.205) had the strongest influence on
rendered assistance in distributing and collecting employee engagement, followed by idealized
questionnaires. A total number of completed, influence (Beta value = 0.145), intellectual
returned questionnaires was 180 sets. Mean stimulation (Beta value = 0.027) and inspiration
evaluation was used to determine the level of motivation (Beta value = -0.096), respectively.
agreement on transformational leadership Individual consideration was rated at the lowest
attributes including individual influence, mean score (mean score = 3.21) among four
inspirational motivation, intellectual stimulation, attributes of transformational leadership style.
individual consideration, and employee This is especially for the item “my team leader
engagement. The hypothesis of this research was treats me as an individual rather than just a
tested using the multiple regression analysis member of the group” which received a mean
(MLR). score value of 2.86. This item demonstrates that
Findings employees in the studied sugar factory were
unsure about their leader treatment towards
According to the results from the MLR, them. Since most of the respondents are just
transformational leadership styles (idealized labour workers, it might be the job itself and
influence, inspiration motivation, intellectual the position level that causes their relationship
stimulation, and individualized consideration) with leader to be limited. The item “my team
significantly influence employee engagement leader gives personal attention to me when I
of manufacturing employees in the studied seem neglected” has the second lowest mean
sugar factory. In addition, only 14% of score of 3.22. This item demonstrates that
employee engagement can be explained by all manufacturing employees seemed to be unsure
dimensions of transformational leadership styles about whether their leader pays enough attention
(r2 = 0.141). This maybe because there are
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to them. To improve transformational speaking to the respondents, the researchers
leadership, the leader of the studied sugar will have an opportunity to ask the respondents
factory should focus on improving individual to clarify more details about Transformational
consideration by giving each team member Leadership Styles (Idealized Influence,
personal attention and motivates him/her to be Inspiration Motivation, Intellectual Stimulation
all that he/she can be. The studied sugar factory and Individualized Consideration), rather than
should also improve all attributes of the limited details from a fixed questionnaires.
transformational leadership styles in order to Based on the research results of this study,
increase the employee engagement. only 14 % of the changes in employee
Discussions and Recommendations engagement caused by the changes in
Transformational Leadership styles so the other
The results of the research study showed 86% of the changes in employee engagement
transformational leadership styles (idealized caused by other factors. Transformational
influence, inspiration motivation, intellectual Leadership Styles (Idealized Influence,
stimulation, and individualized consideration) Inspiration Motivation, Intellectual Stimulation
significantly influence employee engagement and Individualized Consideration) are not the
of manufacturing employees in the studied only factors to create Employee Engagement.
sugar factory. Therefore, the recommendations Other factors that can create Employee
to increase its employee engagement are to Engagement such as interpersonal leadership
improve all attributes of transformational (Hansen, Byrne and Kiersch, 2014), job
leadership by emphasizing on individual characteristics, perceived organizational,
consideration. There may be some possible perceived supervisor support, rewards and
limitations in this study, which future research recognition, procedural justice and distributive
are recommended to address. The data collection justice (Saks, 2006). Future research may study
in this study was limited to only a studied sugar these factors as the antecedent of employee
company in Udonthani Province in Thailand, engagement.
making the results quite limited in scope. Future
research should expand to study in other Reference
provinces or areas in Thailand. Moreover, this
study used the survey questionnaires to collect Anitha, J. (2014). Determinants of employee
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An empirical study of the impact of active leadership on Employee’s Job
Satisfaction in the Subcontractor Company
Jutharat Reungrit1, Piyathida Praditbatuga2 and Santhiti Treetipbut3 123
Assumption University of Thailand, Bangkok, 10240 Thailand
Corresponding Author Email: [email protected]
Abstract
This research study examines the influence of transactional leadership components (contingent reward,
management by exception (active), and management by exception (passive)) on employee’s job
satisfaction among employees at KTK97 Subcontractor Company. Convenience sampling was used.
A sample size of 300 respondents was drawn from employee who worked in KTK97 Subcontractor
Company. Contingent reward was rated at the strongly agreed level, followed by management by
exception (active) at the agreed level, and management by exception (passive) at the disagreed level.
Simple linear regression analysis was used to test research hypotheses. It was found that among the
transaction leadership components, contingent reward had a significant influence on job satisfaction
while management by exception (active) and management by exception (passive) had no significant
influence on job satisfaction.
Keywords: Transactional Leadership, Contingent Reward, Management by Exception (Active),
Management by Exception (Passive), Overall Job Satisfaction
Introduction often cause from the leadership style. Transactional
have been of great interest to many researchers.
Most firms and businesses are composed of Adopting transactional leadership behavior helps in
employers and employees. There must be the achievement of organizational goals (Laohavichien
collaboration among employers and employees in et al., 2009). Transactional leaders are those who
order to achieve the desired objectives (Morris & lead by way of social exchange, emphasizes on
Bloom, 2002). Employee’s job satisfaction is one interactions between leaders and subordinates.
of the most important factors in the success of
organizations (Cook et al., 1989; Bass, 1990, as Transactional leadership is one of the most
cited in Desa, 2010). It is influenced by the internal effective leadership styles and involves giving
organization environment, which includes rewards to employees for good performances and
organizational climate, leadership types, and punishment for bad actions. This style can
personnel relationships between manager and encourage the workers to be better and to be more
employee (Seashore & Taber 1975). aware of their duty (Paracha, et al. 2012).
To stay competitive in the slowdown of global Transactional leadership is typically classified into
economy and survive in the intensity of competition three dimensions: (1) contingent reward, (2)
environment, recruiting productive new employees, management by exception (active), and (3)
retain effective one and good collaboration among management by exception (passive) (Hater & Bass,
employers and employees is key to success for 1988; Yammarino & Bass, 1990).
every business (Morris & Bloom, 2002). While
financial direct cost and indirect cost associate with This study seeks to investigate the influence of
employee turnover is higher than retain the existing transactional leadership components (contingent
one but employee turnover is continue happens and reward, management by exception (active), and
management by exception (passive)) on employee’s
job satisfaction in the manufacturing worker of
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KTK97 Subcontractor Co., Ltd., (KTK97) which an individual and organization, it will be greatly
in turn expect to understand the cause of turnover, beneficial.
therefore lead to decrease in turnover (Ali et al.,
2014). KTK97 is middle person between According to Bass and Avolio (1995) and Zhou
manufacturing workers and manufacturing at (2012), the three dimensions of transactional
Bangpoo Industrial Estate Thailand. KTK97, offers leadership are contingent reward, management by
manufacturing workers recruitment and exception (active), and management by exception
outsourcing services (KTK97 Subcontractor Co., (passive). Contingent reward means leaders who
Ltd., 2016). specify needs clearly, have performance goals and
using motivates to influence employees’
Literature Review performance and behavior (Bass & Avolio, 1995).
Management by exception (active) refers to active
The literature review in this article c o m p r i leaders who monitor employee behavior, anticipate
ses 1 . Transactional Leaders h i p , 2.Employee’s Job problems, and take corrective actions before the
Satisfaction, 3.Leadership and Employees’ Job behavior creates serious difficulties. Management by
Satisfaction. exception (passive) means leaders who tend to act
only after problems have become severe enough to take
Transactional Leadership corrective action, and usually refrain from making any
There are many types of leadership styles, including decisions (Bass, 1995).
transformational leadership, transactional leadership,
participative leadership, autocratic leadership, and Employee’s Job Satisfaction
laissez-faire leadership (Hashim & Yazdanifard Job satisfaction is a very significant viewpoint for
2014). The leadership style that will be focused on in modern organization and much research work has
this study is transactional leadership, in accordance been performed to increase job satisfaction. Job
with the company policy and leadership of KTK97 satisfaction is described as what an individual feel,
Subcontractor. Transactional leadership is one of the emotionally and psychically, through their work
most effective leadership styles as it rewards (Paracha et al., 2012) defined employee job
employees for good performances and punish satisfaction as an attitude that employees have
employees who have bad performance. This can about their jobs and the companies in which they
encourage followers to do better and be more aware perform these jobs. If organizations want to be
of their duty. There are many benefits in having successful in the competitive business world of
transactional leaders in an organization because in today, a strong relationship between the leader and
the competitive business world of today, employees the followers, as well as the follower’s satisfaction
exhibit reward-seeking behavior. Employees could of working, is very importance. With these factors
also improve their skills, increase their knowledge, of total of satisfaction on employee’s job, the
and motivate themselves to perform efficiently. environment in working place, and the quality of
This could increase the productivity, connection between an employee and the leader, the
profitability, and performance of the organization employees are inspired to be more innovative and
and help it to achieve organizational goals more hard-working in helping to improve the business and
quickly (Hashim & Yazdanifard 2014). In addition, productivity of the organization (Zhou, 2012).
transactional leadership defines a style of leadership
in which the leader champions compliancy of the Leadership and Employees’ Job Satisfaction
employees through both reward and disciplines. In the business world, employee’s job satisfaction is
Transactional leaders manage their business by one of the most important factors in the success of
identify employee’s needs and giving rewards to organizations (Cook et al., 1989; Bass, 1990, as cited
satisfy their needs for certain suitable in Desa, 2010). The principles of organizational
accomplishment (Arnold, 1998). Therefore, leadership is the ideation that an individual’s
transactional leadership is the most effective style of performance is most effective when their needs are
leadership. If an organization finds a leader that fulfilled (Bekele & G.M, 2011). When employees are
motivates passion and innovative performance in perceiving as satisfied, they will increase their
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productivity, which will in turn influence the exception (active) on job satisfaction.
company (Stone & Patterson, 2005). Employee’s Ha2: There is an influence of management by
job satisfaction is influenced by the internal exception (active) on job satisfaction.
organization environment, which includes Ho3: There is no influence of management by
organizational climate, leadership types, and exception (passive) on job satisfaction.
personnel relationships between manager and Ha3: There is an influence of management by
employee (Seashore & Taber 1975). Negative exception (passive) on job satisfaction.
leader and employee relationship has various
negative impacts on the employee and it decreases Research Methodology
their productivity and profitability performance,
decreases the turnover rate of employees in the A self-administered questionnaires was conducted
organization, and increases absenteeism (Keashly to determine the influence of Transactional
et al., 2003). In fact, it has been proven that job leadership (contingent reward, management by
satisfaction is higher among employees whose expectation (active), and management by
leaders emphasize concern, consideration, and expectation (passive)) on overall job satisfaction.
support for their followers (Rafferty et al., 1991). Twelve questionnaires items of transactional
leadership components (contingent reward, ma
Conceptual Framework nage me nt b y e xcep tio ( acive), and management
by exception (passive)), were derived from The
The conceptual framework of this study was Multifactor Leadership Questionnaires (MLQ-5X)
adopted from the previous research of Akhigbe et which is the standard instrument for assessing
al., (2014). The link between transactional transactional leadership behavior (Bass & Avolio,
leadership and job satisfaction is based on Javed et 2004). The overall job satisfaction was measured
by a single-item based on Quinn and Shepard, 1974.
al., (2014), Zhou (2012), Belonio (2011), Ali et The respondents were asked to rate their agreement
al.,(2014), Yavirach (2010), Paracha et al., (2012), by using a 5-point Likert scale ranging from
Bateh & Heyliger (2014), Hashim & Yazdanifard “Strongly agree” (5) to “Strongly disagree” (1).
(2014) and Suwannapirom The target population of this study was the 5 5 0
(2005). manufacturing workers in KTK97 Subcontractor Co.,
Ltd (HR Department of KTK97 Subcontractor,
Figure 1: Conceptual 2016), which does not include office workers in
Framework KTK97 Subcontractor Co., Ltd. The sample
consisted of manufacturing workers age 20-40 years
Research old who have worked for KTK97 Subcontractor
Hypothesis Co., Ltd. The minimum and maximum ages of
employee who can apply to work in KTK97
Ho1: There is no influence of contingent reward Subcontractor Co., Ltd are 20 and 40 years old, as
on job satisfaction. per the company policy. The sample size was based
Ha1: There is an influence of contingent reward on Krejcie and Morgan’s (1970) work in
on job satisfaction determining sample size from a given population. At
Ho2: There is no influence of management by the time of the study the company had 550
manufacturing workers; therefore, a minimum
required sample size was 225. Three hundred sets of
questionnaires were distributed to the manufacturing
workers of KTK 97 Subcontractor Co., Ltd. when
they had a weekly meeting with their manager
before registering their arrival at work at 8 am. The
researcher waited until all 300 questionnaires from
the 300 respondents were returned and checked
that all 300 questionnaires were completed and
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valid. Mean evaluation was used to determine the employee in KTK97. For management by exception
level of agreement on transactional leadership (active), the respondents agreed that their leaders
attributes including contingent reward, monitor employee behavior, anticipate problems, and
management by expectation (active), and take corrective actions before the behavior creates
management by expectation (passive), and serious difficulties. The respondents most agreed
overall job satisfaction. The hypothesis of this with “My supervisor concentrates his/her full
research was tested using the single regression attention on dealing with mistakes, complaints, and
analysis (SLR). failures”, with the highest mean value of 4.03. It
means that manufacturing workers feel that they
Findings receive full and proper attention from leaders of
KTK97 and receive suggestions when fail to meet task
According to the analysis of descriptive statistics, standards efficiently. Regarding management by
the result is consistent with the results from exception (passive) the respondents disagreed that
previous studies of Akhigbe, et al., (2014) and Zhou their leaders tend to act only after problems have
(2012) that contingent reward significantly become severe enough to take corrective action, and
enhances employee satisfaction. However, the usually refrain from making any decisions.
result is different from the previous study of Emery According to the results from the SLR, among the
and Barker (2007) that they found the significant transactional leadership components, contingent
influence of management by exception (active) and reward had a significant influence on job satisfaction
management by exception (passive) on job (β = 0.191). However, management by exception
satisfaction. In addition, contingent reward (active) and management by exception “passive” had
contributed to only 3.6% of the changes in overall no significant influence on job satisfaction among
job satisfaction (r2 = 0.036); however, the manufacturing workers of KTK97 (Summary of the
resultfrom the mean evaluation showed that Hypothesis Test).
manufacturing workers were very satisfied with
their job (mean value = 4.33). This maybe the Table 1: Summary of the Hypothesis Test
reason that transactional leadership is not the only
factor that can influence job satisfaction (Zhou, Hypothesis Sig R2 Beta Result
2012).
H01: There is no 0.01 0.036 .191 Rejecte d
Contingent reward was rated at the strongly influence of contingent H01
agreed level with the mean value of 4.51, followed reward on job
by management by exception (active) with a mean satisfaction.
of 3.99 at the agreed level, and management by
exception (passive) with the mean value of 2.06 at H02: There is no Failed to
the disagreed level. Most respondents strongly influence of 0.09 0.006 - .104 reject
agreed that their leaders specify needs clearly, have management by
performance goals and using motivates to influence expectation (active) on H02
their performance and behavior. The statement that job satisfaction
had the highest mean value of 4.61 was “My
supervisor makes clear what one can expect to H03: There is no 0.46 0.002 - .042 Failed to
receive when performance goals are achieved”. The influence of management reject
policy of KTK97, clearly states that leader are to by expectation (passive) H03
give sufficient rewards to manufacturing workers on job satisfaction.
every month according to t h e i r p e r f o r m a n c e a n
d a t t e n d a n c e . T h e manufacturing workers also The null hypothesis 1 (Ho1): “There is no influence
mentioned to the researcher that they were satisfied of contingent reward on job satisfaction”, was
with the reward that they received; therefore, leaders
should keep using this strategy in order to maintain rejected, which means that contingent reward has a
the standard of work and enhance the satisfaction of significant influence on manufacturing worker’s
job satisfaction. The null hypothesis 2 (Ho2): “There
is no influence of management by exception
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(active)”, failed to be rejected, which means style can enhance employee’s job satisfaction.
management by exception (active) has no Managers and supervisors should apply transactional
significant influence on job satisfaction among leadership style, especially contingent reward, to
manufacturing workers in KTK97. The null result in worker’s job satisfaction. They should
hypothesis 3 (Ho3): “There is no influence of specify needs clearly, have performance goals and
management by exception (passive)”, failed to be using motivates to influence their subordinates’
rejected, which means management by exception performance and behavior.
(passive) has no significant influence on job There may be some possible limitations in this study,
satisfaction among manufacturing workers in which future research are recommended to address.
KTK97. The results from this study cannot be extrapolated to
manufacturing workers who work for other
Discussion and Recommendations companies due to difference in organization
al policies, duties, working environments, and other
In this study, the results showed that there is a factors. Further research should be conducted in other
significant influence of contingent reward on job organizations that may give a more holistic picture on
satisfaction in KTK97. However, both the effectives of transactional leadership. Furthermore,
management by exception (active) and (passive) this study focuses on the influence of transactional
showed no significant influence on job satisfaction. leadership components including contingent
This results from simple linear regression showed reward, management by exception (active), an
that contingent reward significantly affected job d management by exception (passive) on employee’s
satisfaction among manufacturing workers in job satisfaction. Future research should include other
KTK97 by 3.6% (Beta score 0.191).Hence, the factors that may influence job satisfaction such as the
independent variable that can predict transactional working conditions, work itself, supervision, policy
leadership component among manufacturing and adminis t r a t i o n , advancement,
worker in this study is contingent reward. compensation, interpersonal relationships,
Compared with the research of Akhigbe, et al., recognition, and empowerment.
(2014), the results showed that contingent
reward significantly enhances employee Reference
satisfaction. It is also partially consistent with the
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contingent reward significantly influences job Leadership Style and Employee Satisfaction
satisfaction.
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An Integrated Model of Factors Affecting Website Adoption,
Perceived Risk and Trust on Online Shopping Intention in China
Zongwen Xia
Sirion Chaipoopirutana
Graduate School of Business, Assumption University, Bangkok, 10240, Thailand
E-mail: [email protected], [email protected]
ABSTRACT
With the growth of e-commerce platforms, more and more customers are changing their
shopping intention from physical stores to online platforms. China has a substantial population
in the world and also has the completed e-commerce platform. Due to Covid-19 Pandemic,
many consumers changed their behavior to be online shopping. E-commerce still has a large
potential market in near future. Therefore, this research aims to test the influence of website
adoption, perceived risk, and trust on online shopping intention. The researchers collected the
data from online shoppers who bought products service from one of the most famous online
shopping websites in China. The sample of this study was collected from 400 respondents
through online. Non-probability sampling methods including purposive and convenience
sampling was applied to collect the data from the sampling units. The five-point Likert scale
was designed for research instruments. Descriptive analysis and inferential analysis were
applied to analyze the data and multiple linear regression analysis was applied to test all
hypotheses. Based on the findings, the researchers found that perceived usefulness, perceived
ease of use, social influence, and facilitating conditions significantly influenced online
shopping intention. Perceived risk and trust also had a significant influence on online shopping
intention.
Keywords: Website adoption, Perceived risk, Trust, Online shopping intention
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