Batam State Polytechnic Case Study: Generation Y in Online Fashion
Shopping
Ria Anastasia and Nanik Lestari
Business Management Department, Politeknik Negeri Batam, Jl.Ahmad Yani, Batam, Indonesia
Keywords: Organizational development, Sustainable performing organization, Human Resources, Decision making,
Alternative Solutions. Intention, Trend, Shopping Enjoyment, Economic Orientation, Brand Value,
Generation Y
Abstract: Generation Y are those who were born from 1981 to 2000. In this era, they are starting to forget about shopping
through stores and prefer shopping online. This research aims to identify and test what perceptions support
generation Y in online fashion shopping. The purchase intention is measured by the perceived trend, perceived
shopping enjoyment, perceived economic orientation, and perceived brand value. This study uses non-
probability sampling method with snowball technique. Data processing techniques using SPSS 22 by
performing multiple regression and independent t-test. This study uses a questionnaire distributed via google
form with the number of respondents as many as 369 Batam State Polytechnic Students. The results found,
first perceived trend, perceived shopping enjoyment, perceived economic orientation, and perceived brand
value has a positive effect on online fashion shopping intentions. Second, there is a difference perceived trend,
perceived shopping enjoyment, perceived economic orientation and perceived brand value between women
and men in online fashion shopping.
1 INTRODUCTION
Generation Y or often referred to as the millennial
generation are those born from 1981 to 2000 (Ladhari
et al., 2019). Generation Y are those who are in the
age of 20 to 39 years, meaning they are in the
productive age. Research conducted by (Eastman et
al., 2013) found that the highest consumption level
was Generation Y compared to Generation X and
Baby Boomers.
In this study, the authors classify the population
by generation, namely, generation Y and those who
like online fashion shopping. Clothing is an important
and meaningful object for many consumers. Research
conducted by (Ulaazizah & Februadi, 2020) revealed
that fashion is a person's identity in expressing
themselves and appearing more confident. The
research findings also reveal that fashion can
encourage individuals to shop online.
Research on online shopping has been extensively
researched and found that many studies have been
conducted with the same theme but have various
objectives and results. Research conducted by
(Ladhari et al., 2019) in Canada aims to classify
Generation Y online fashion shopping according to
their lifestyle, identify graphic and behavioural
characteristics, identify the devices they use to do
online fashion shopping. The results of this study
have implications for online and offline fashion
sellers. This allows them to better understand the
lifestyle of Generation Y, so that they can adapt their
marketing strategies to meet the fashion needs of
Generation Y.
This study is a development of the research
conducted by (Ladhari et al., 2019) in Canada which
has been described previously. There are several
differences made in this study. Previous research
focused on generation Y with female gender, while
this study used a sample of generation Y women and
men. The samples of the Y generation of women and
men will be tested differently.
The formulation of the problem in this study is
first, what perceptions support generation Y women
and men in shopping for fashion online. Second, are
there differences in perceptions that support
generation Y women and men in shopping for fashion
online. This study has 2 objectives, namely, first, to
identify what perceptions support generation Y
women and men in shopping for fashion online.
Second, to examine whether there are differences in
418
Anastasia, R. and Lestari, N.
Batam State Polytechnic Case Study: Generation Y in Online Fashion Shopping.
DOI: 10.5220/0010935400003255
In Proceedings of the 3rd International Conference on Applied Economics and Social Science (ICAESS 2021), pages 418-425
ISBN: 978-989-758-605-7
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

perceptions that support generation Y women and
men in shopping for fashion online.
The theoretical benefits of this research are
expected to be used as a development of the existing
theory and can strengthen the theory used. The
practical use of this research is expected to add insight
or knowledge of writers and readers about Generation
Y in doing online fashion shopping and is beneficial
for sellers. The benefits of this research can also be
used as an online seller's policy in targeting buyers
and adjusting their marketing strategies to meet the
needs of generation Y, especially for fashion
products.
The scope of the respondents in this study were Y
generation women and men (20-39 years) who were
at the Batam State Polytechnic who had filled out the
research questionnaire. Respondents who filled out
the questionnaire were confirmed to have shopped for
fashion online in the last 2 months. The questionnaire
that will be distributed contains several questions
related to trends, shopping enjoyment, economic
orientation, brand value, and purchase intention in
online fashion shopping.
2 LITERATURE REVIEW AND
HYPOTHESIS
2.1 Literature Review
The Generational Cohort Theory (GCT) by
(Grosjean, 1980) claims that the population can be
grouped by year of birth. Generation is symbolized as
the identification of people who have the same year
of birth, experienced the same life events, and the
same technological developments. In this study, the
authors classify the population by generation,
namely, generation Y and those who like online
fashion shopping.
Trend is something that makes someone observe
what is currently happening. (Asmita, Dola Erianjoni,
2019) The results of this qualitative study reveal that
the development of today's fashion trends has a very
large impact on the consumptive behaviour of
students.
Shopping pleasure is a hedonic attitude related to
how much pleasure consumers get from a product.
(Sarjono, 2018) the results of the study indicate that
the variables of shopping enjoyment and site design
indicate that these variables have a positive and
significant effect on online purchase intentions.
Economic orientation is the perception of
consumers in shopping according to the value or price
of a product. The results of the study (Ari Bowo,
2018) concluded that the socioeconomic status of
parents and peers had a significant effect on online
shopping consumption behaviour in SMA 8
Semarang students.
Brand Value is a combination of perceptions,
impressions and feelings that consumers have about a
product when the product is compared to other similar
products. (Ivoni et al., 2015) Brand orientation
variable has a positive but not significant effect on
online fashion purchase intentions.
2.2 Hypothesis Development
2.2.1 Perceptions That Support Generation
Y in Shopping for Fashion Online
Asmita, Dola Erianjoni (2019) revealed that the
development of current fashion trends has a positive
effect on student consumptive behaviour (Sarjono,
2018) found that shopping enjoyment has a positive
and significant effect on online purchase intentions.
(Ari Bowo, 2018) found that price had a significant
effect on the online shopping consumption behaviour
of students. (Ivoni et al., 2015) found that the brand
orientation variable had a positive but not significant
effect on online purchase intention. (Ladhari et al.,
2019) found that trends, shopping enjoyment,
economic orientation, and brand value have a
significant effect on online fashion shopping
purchase intentions. (Bilgihan, 2016) found that the
brand is also the main point of loyalty. Research
conducted by (Saputri, 2016) which shows that
consumer behaviour in the current technological age
significantly influences purchasing decisions at
Zalora Indonesia.
The following four hypotheses will be tested
based on this statement:
H1a: Perception of trend influences Generation Y to
shop for fashion online.
H1b: Perception of shopping enjoyment influences
Generation Y to shop for fashion online.
H1c: Perception of economic orientation influences
Generation Y to shop for fashion online.
H1d: Perceived brand value influences generation Y
to shop for fashion online
2.2.2 Differences in Perception between
Women and Men of Generation Y in
Shopping for Fashion Online
Asmita, Dola Erianjoni, 2019) revealed that the
development of current fashion trends has a positive
effect on student consumptive behaviour. (Sarjono,
Batam State Polytechnic Case Study: Generation Y in Online Fashion Shopping
419

2018) found that shopping enjoyment has a positive
and significant effect on online purchase intentions.
(Ari Bowo, 2018) found that price had a significant
effect on the online shopping consumption behaviour
of students. (Ivoni et al., 2015) found that the brand
orientation variable had a positive but not significant
effect on online purchase intention.
The following four hypotheses will be tested
based on this statement:
H2a: There are differences in the perception of trends
between women and men in online fashion shopping.
H2b: There are differences in the perception of
shopping enjoyment between women and men in
online fashion shopping
H2c: There are differences in perceptions of
economic orientation between women and men in
online fashion shopping
H2d: There are differences in the perception of brand
value between women and men in online fashion
shopping
3 RESEARCH METHOD
This study uses a quantitative approach using primary
data. The population in this study were Batam State
Polytechnic students. The population of all Batam
State Polytechnic students is 4,783 people. Through
the calculation of the slovin formula, the number of
samples in this study was 369 students of the Batam
State Polytechnic. Respondents who filled out the
questionnaire were confirmed to have shopped for
fashion online in the last 2 months. This study uses a
survey technique using a questionnaire instrument
distributed via google form. The data processing
technique in this study used SPSS 22 by conducting
influence tests and different tests. The method used in
this study is non-probability sampling with the
snowball technique, which is a technique that is
initially small and then enlarges.
3.1 Data Analysis
Data analysis was done by descriptive analysis,
validity test, reliability test, and classical assumption
test consisting of normality test, heteroscedasticity
test, autocorrelation test, and multicollinearity test.
Hypothesis testing using multiple regression analysis
with the following model:
Int = 1Tr + 2Enjoy + 3Price + 4Brand +
ε
(1)
Information:
a = Constant
b1 = Regression coefficient
In = Intention
Tr = Trend
Enjoy = Shopping Enjoyment
Price = Economic orientation
Brand = Brand
The next hypothesis test is the T-test difference
test with the first condition, the sample used is large
or > 30 respondents. Second, the data used must be
normally distributed. Third, the sample group whose
data is in the form of intervals or ratios.
4 RESULTS AND DISCUSSION
4.1 Respondent Characteristics
The sample in this study is 20-25 years old because
there are no respondents who are over 25 years old
and have done online fashion shopping in the last 2
months. Data collection was carried out for 43 days
from August 22 to October 04, 2020. There were 446
questionnaires distributed and 369 respondents who
met the criteria or according to the number of samples
required. Questionnaires were distributed to 16 study
programs at the Batam State Polytechnic.
Table 1: Data Dissemination.
Description Total
Distributed questionnaire 446
Outlie
r
s:
Not college student
3
Under 20 years old and not doing
online shopping
14
Under 20 years ol
d
8
Not doing online shopping
41
Double questionnaire
11
Number of outliers
(77)
The
q
uestionnaire use
d
369
Source: Primary Data Processed (2021)
Respondents who have filled out the questionnaire are
then identified based on age, gender, and the
marketplace used in online shopping to find out the
general characteristics of the research respondents.
The general characteristics of the respondents are
shown in table 2 below:
ICAESS 2021 - The International Conference on Applied Economics and Social Science
420

Table 2: Respondent Characteristics.
Criteria Frequency Percentage
Gender
Man
Women
184
185
49,86 %
50,14 %
Number of
sam
p
les
369 100%
Age
20-25 years
26-39 years
369
0
100 %
0 %
Number of
sam
p
les
369 100%
Marketplace
Shopee
Zalora
Tokopedia
Lazada
Buka Lapak
FJB Batam
221
11
101
26
9
1
59,89 %
2,98 %
27,37 %
7,05 %
2,44 %
0,27 %
Number of
samples
369 100 %
Source: Primary Data Processed (2021)
4.2 Validity and Reliability Test
Validity test is conducted to measure whether a
questionnaire is valid or not. The test is carried out by
looking for the correlation value between the scores
of each question item and the total score of each
dimension. The test is done by looking for the
correlation value between the scores of each question
item and the total score of each dimension.
Questionnaire questions are declared valid if r count
> r table. The following is a summary of the results of
the validity test.
Table 3: Validity Test.
Variable Item
r
count
r table
Conclusio
n
Item 1 0.955 0.306 Valid
Intention Item 2 0.980 0.306 Valid
Item 3 0.918 0.306 Valid
Item 1 0.740 0.306 Valid
Item 2 0.548 0.306 Valid
Item 3 0.798 0.306 Valid
Trend Item 4 0.875 0.306 Valid
Item 5 0.714 0.306 Valid
Item 6 0.790 0.306 Valid
Item 1 0.651 0.306 Valid
Item 2 0.617 0.306 Valid
Shopping Item 3 0.622 0.306 Valid
Enjoymen
t
Item 4 0.518 0.306 Valid
Item 5 0.753 0.306 Valid
Item 6 0.603 0.306 Valid
Item 1 0.837 0.306 Valid
Economic
Orientatio
n
Item 2 0.959 0.306 Valid
Item 3 0.973 0.306 Valid
Item 4 0.952 0.306 Valid
Item 1 0.928 0.306 Valid
Brand Item 2 0.758 0.306 Valid
Item 3 0.941 0.306 Valid
Source: Primary Data Processed (2021)
Reliability test is used to determine whether the
indicators used in the questionnaire are reliable or
reliable as a variable measuring instrument. The
results of the reliability test are shown in table 4
below:
Table 4: Reliability Test.
Variable
Total
Item
Alpha
Cron
bach
Cut off
AlphaC
ronbac
h
Conclus
ion
Intention 3 0.947 0.60 Reliable
Tren
d
6 0.831 0.60 Reliable
Shopping
En
j
o
y
ment
6 0.676 0.60 Reliable
Economic
Orientation
4 0.949 0.60 Reliable
Bran
d
3 0.853 0.60 Reliable
Source: Primary Data Processed (2021)
4.3 Descriptive Statistics
Descriptive statistical analysis is used to provide an
overview of the data seen based on data from
questionnaire answers from Batam State Polytechnic
students. The scale used is a 5-point Likert scale,
namely: 1) Strongly Disagree; 2) Disagree; 3)
Disagree; 4) Agree; and 5) Strongly Agree. The
results of descriptive statistical analysis can be seen
in table 5 below:
Batam State Polytechnic Case Study: Generation Y in Online Fashion Shopping
421

Table 5: Descriptive Statistics.
Tren
d
STS TS KS S SS Total
Item 1 13 85 54 171 46 369
Item 2 29 52 83 115 90 369
Item 3 5 35 86 188 55 369
Item 4 9 35 90 184 51 369
Item 5 14 56 76 167 56 369
Item 6 6 15 58 218 72 369
Shopping
En
j
o
y
ment
STS TS KS S SS Total
Item 1 3 32 83 165 86 369
Item 2 4 17 58 227 63 369
Item 3 38 69 71 104 87 369
Item 4 44 85 69 87 84 369
Item 5 13 33 104 179 40 369
Item 6 3 7 67 215 77 369
Economic
Orientation
STS TS KS S SS Total
Item 1 4 11 35 186 369 369
Item 2 0 5 15 223 369 369
Item 3 0 7 20 218 369 369
Item 4 0 8 17 239 369 369
Bran
d
STS TS KS S SS Total
Item 1 3 18 49 183 369 369
Item 2 11 79 70 141 369 369
Item 3 5 13 59 206 369 369
Intention STS TS KS S SS Total
Item 1 1 7 29 209 369 369
Item 2 1 9 33 231 369 369
Item 3 2 6 31 186 369 369
Source: Primary Data Processed (2021)
4.4 Classic Assumption Test
The following are the results of the classical
assumption test in table 6:
Table 6: Classic Assumption Test.
Test Sig Description
Normalit
y
Test 0.175 Normall
y
Distribute
d
Heteroscedasticity
Test
>0.05
Not occur
heteroscedasticit
y
Multicollinearity
Test
<0.8
Not occur
multicollinearity
Autocorrelation
Test
1.391
Not occur
Autocorrelation
Source: Primary Data Processed (2021)
Based on table 6, the results of the normality test
with the transformation of the data into the square
root form show the significance value in the
Kolmogorov-Smirnov test is 0.175 (p > 0.05), so it
can be said that the data is normally distributed.
Heteroscedasticity test of each variable > 0.05 then
the data does not experience heteroscedasticity
problems. Multicollinearity test can be seen that the
correlation coefficient between variables has a value
below 0.8. This indicates that the data in this study
does not occur multicollinearity. Autocorrelation test
shows that Durbin Watson is 1.391, so it can be
concluded that there is no autocorrelation in the
regression model used in this study.
4.5 Hypothesis Test Result
4.5.1 Influence Test
The following are the results of multiple linear
regression processed using SPSS 22:
Table 7: Multiple Linear Regression.
Model
Unstandar
dized
Coefficients
Std.
Error
Sig.
B
H1a
(Constant) 5.174 0,000
Trend 0.114 0.024 0,000
H1b
Shopping
Enjoyment
0.065 0.106 0.000
H1c
Economic
Orientation
0.248 0.044 0.000
H1d
Brand 0.108 0.043 0.012
Source: Primary Data Processed (2021)
The first influence test hypothesis (H1a) is the
perception of trends influencing generation Y to shop
for fashion online. H1a is measured from 3 questions
for the shopping intention variable and 6 questions for
the trend perception variable. The equation of the
model explains that if the trend perception variable
(X1) = 0, then the value of the purchase intention
variable is 5.174. The trend perception regression
coefficient is 0.114, which means that if the trend
perception variable increases by 1%, the value of the
purchase intention variable will increase by 0.114.
The trend perception variable has an influence on the
purchase intention variable, this can be seen from the
significance probability for the trend perception of
0.000 which is smaller than 0.05 and does not violate
the classical assumption test. So, it can be concluded
that H1a is supported, which means that there is a
ICAESS 2021 - The International Conference on Applied Economics and Social Science
422

positive influence between perceived trend towards
online fashion shopping intentions. This hypothesis is
also supported by the results of research (Ladhari et
al., 2019) which reveals that trend perceptions have a
significant effect on online fashion purchase
intentions.
The second influence test hypothesis (H1b) is that
the perception of shopping enjoyment affects
generation Y to shop for fashion online. H1b was
measured from 3 questions for the purchase intention
variable and 6 questions for the shopping enjoyment
perception variable. The variable perception of
shopping enjoyment has an influence on intention,
this can be seen from the significance probability for
the perception of shopping enjoyment of 0.000 which
is smaller than 0.05 and does not violate the classical
assumption test. So, it can be concluded that H1b is
supported, which means that there is a positive
influence between perceptions of shopping
enjoyment and online fashion shopping intentions.
This hypothesis is also supported by research results
(Sarjono, 2018) which show that the perception of
shopping enjoyment has a positive and significant
effect on online purchase intentions.
The third influence test hypothesis (H1c) is that
the perception of economic orientation affects
generation Y to shop for fashion online. H1c is
measured from 3 questions for the purchase intention
variable and 4 questions for the economic orientation
perception variable. The economic orientation
perception variable has an influence on purchase
intention, this can be seen from the price significance
probability of 0.000 which is smaller than 0.05 and
does not violate the classical assumption test. So, it
can be concluded that H1c is supported, which means
that there is a positive influence between perceptions
of economic orientation on online fashion shopping
intentions. This hypothesis is in line with research
conducted by (Ladhari et al., 2019) which shows the
perception of economic orientation has a significant
effect on online fashion purchase intentions.
The fourth influence test hypothesis (H1d) is that
the perception of brand value affects generation Y to
shop for fashion online. H1d is measured from 3
questions for the purchase intention variable and 3
questions for the perceived brand value variable. The
perceived brand value variable has an influence on
purchase intention, this can be seen from the brand
significance probability of 0.012 which is smaller
than 0.05 and does not violate the classical
assumption test. It can be concluded that H1d is
supported, which means that there is a positive
influence of perceived brand value on online fashion
shopping intentions. This hypothesis is also
supported by research (Ivoni et al., 2015) which
shows that the brand value variable has a positive
effect on online fashion purchase intentions.
4.5.2 T-Test
This test is used to determine whether each variable
has a significant difference between female and male
respondents. The following are the results of the
independent T-test processed using SPSS 22:
Table 8: T-Test.
Gende
r
Std. Erro
r
Si
g
.
Trend Man 0.328 0.000
Woman 0.330 0.000
Shopping
Enjoyment
Man
0.302 0.000
Woman 0..302 0.000
Economic
Orientation
Man
0.167 0.019
Woman 0.150 0.019
Brand Man 0.278 0.000
Woman 0.153 0.000
Source: Primary Data Processed (2021)
The first different test hypothesis (H2A) is that
there are differences in the perception of trends
between women and men in shopping for fashion
online. The trend perception variable between women
and men has differences, this can be seen from the
trend significance probability of 0.000 which is
smaller than 0.05 and does not violate the classical
assumption test. So, it can be concluded that H2a is
supported, which means that there are differences in
the perception of trends between women and men in
shopping for fashion online.
The second different test hypothesis (H2b) is that
there are differences in perceptions of shopping
enjoyment between women and men in online fashion
shopping. There are differences in the perception of
shopping enjoyment between women and men, this
can be seen from the significance probability of
shopping enjoyment of 0.000 which is smaller than
0.05 and does not violate the classical assumption
test. It can be concluded that H2b is supported, which
means that there are differences in perceptions of
shopping enjoyment between women and men in
online fashion shopping.
The third different test hypothesis (H2c) is that
there are differences in perceptions of economic
Batam State Polytechnic Case Study: Generation Y in Online Fashion Shopping
423

orientation between women and men in online
fashion shopping. The economic orientation
perception variable between women and men does
not have a significant difference, it can be seen from
the economic orientation significance probability of
0.019 which is smaller than 0.05 and does not violate
the classical assumption test. It can be concluded that
H2c is supported, which means that there is a
significant difference for the economic orientation
variable between women and men in online fashion
shopping.
The fourth different test hypothesis (H2d) is that
there are differences in brand value perceptions
between women and men in online fashion shopping.
The brand value perception variable between women
and men has a significant difference, this can be seen
from the probability of a brand value significance of
0.000 which is smaller than 0.05 and does not violate
the classical assumption test. It can be concluded that
H2d is supported, which means that there is a
significant difference for the brand value variable
between women and men in online fashion shopping.
The following is table 9 a summary of the
results of hypothesis testing:
Table 9: Result Hypothesis.
No Hy
p
othesis Sig. Results
H1a
Test the effect of trend
p
erception
0.000 Supported
H1b
Test the effect of
perceived shopping
en
j
o
y
ment
0.012 Supported
H1c
Test the effect of
perceived economic
orientation
0.000 Supported
H1d
Test the Influence of
Brand Value
Perce
p
tion
0.012 Supported
H2a
Test the difference in
trend
p
erce
p
tion.
0.000 Supported
H2b
Difference test of
p
erce
p
tion difference
0.000 Supported
H2c
Different test of
economic orientation
p
erception
0.019 Supported
H2d
Different test of brand
value perception
0.000 Supported
Source: Primary Data Processed (2021)
5 CONCLUSIONS
Through data processing and hypothesis testing in
this study, the conclusions in this study are, trend
perception, shopping enjoyment, economic
orientation, and brand value have a positive influence
on intentions to shop for fashion online. The positive
meaning is that the better the perception of trends,
shopping enjoyment, economic orientation, and one's
brand value, the higher one's purchase intention to
shop for fashion online. Second, there are differences
in trend perception, shopping enjoyment, economic
orientation and brand value between women and men
in online fashion shopping. In this case, women have
higher shopping intentions compared to men.
Some of the limitations in conducting this
research are as follows: (1) This study only used a
sample of Batam State Polytechnic students. (2) This
study only uses generation Y as the sample. (3) This
study only uses 4 independent variables, namely
perceived trend, perceived shopping enjoyment,
perceived economic orientation, and perceived brand
value. (4) The data collection method in this study
only used a 5-point likert scale questionnaire
instrument.
Based on the results of the research and the
conclusions that have been formulated, some
suggestions are given as follows: (1) Further research
is expected to use a wider sample. Respondents in this
study were students aged between 20-25 years, while
the age range of generation Y was 20 -39 years.
Future research is expected to take a larger sample
with an even age range. (2) Further research is
expected to be able to conduct different tests
regarding the buying interest of generation Y and
generation Z online because each generation has
different characters. (3) Further research is expected
to add other supporting variables such as perceptions
of service quality and perceptions of security in
online fashion shopping. (4) Further research can add
data collection methods such as interviews and
observations.
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