The effect of perceived benefits and perceived risks on intention to shop apparel online by white - collar women in Viet Nam

 Journal of Science Ho Chi Minh City Open University – VOL. 19 (3) 2016 – October /2016 11 
THE EFFECT OF PERCEIVED BENEFITS AND PERCEIVED RISKS 
ON INTENTION TO SHOP APPAREL ONLINE BY WHITE - 
COLLAR WOMEN IN VIETNAM 
HOANG THI PHUONG THAO 
Ho Chi Minh City Open University, Vietnam - Email: thao.htp@ou.edu.vn 
NGUYEN NGOC THANH HAI 
Ho Chi Minh City Open University, Vietnam - Email: nguyenngoc.thanhhai@gmail.com 
(Received: June 14, 2016; Revised: July 4, 2016; Accepted: October 10, 2016) 
ABSTRACT 
Online apparel shopping has become more and more popular and has attracted many consumers. This research 
aims to study how these perceived benefits and perceived risks influenced Internet users on buying apparel online. A 
survey of 298 white-collar women was carried out to identify the benefits and risks when buying apparel online. 
Five dimensions of perceived benefits (i.e. convenience shopping, abundance and liking product, competitive price, 
enjoyment, and comfortable shopping) and three dimensions of perceived risks (i.e. financial risk, product risk, and 
time risk) were ascertained by exploratory factor analysis. The correlation between these benefits and risks with 
online purchasing intention was explored by multiple regression tests. The result demonstrates that consumers 
perceive benefits more than risks in online apparel shopping. While ‘comfortable shopping’ has the strongest effect 
on respondents’ intention, ‘competitive price’ has the lowest effect. Among the risks, product risk is of highest 
concern, followed by financial risk and time risk. The result also shows that middle-age white-collar women of 31 to 
40 years old have the intention to shop apparel online higher than other groups. 
Keywords: Perceived benefits; perceived risks; online apparel shopping. 
1. Introduction 
Internet has become a platform in 
developing applications and has changed not 
only business methods but also people’s 
communication manners. It offers consumers 
a wide range of products and services. They 
can buy or sell anything, at any time, and 
from anywhere through e-commerce system 
(King et al., 2008). Specially, online apparel 
with abundant types, unique and rare designs, 
has provided customers with a lot of 
information about products and competitive 
prices (Javadi et al, 2012). By that way, 
online apparel websites prove to be more 
advantageous than traditional stores. 
However, it is perceived that risks and 
mistrust in virtual stores are higher than in 
traditional ones, especially security and privacy 
risks (Martin and Camarero, 2009). Consumers 
often worry that what they receive will not be 
as good as what described in the web. Despite 
those risks, the number of online shoppers has 
been increasing. The number of Vietnamese 
online-shoppers increased from 68.4% to 
80.2% in 2015, reaching the second growth 
rate in the Asia Pacific (The Saigon Times, 
2015). This proves that consumers perceive 
significant benefits of this shopping type; for 
example, they can shop from home and at any 
time. According to a report by Vecita (2014), 
58% of Internet users have purchased items 
online, of which the most popular products 
belong to apparel and accessories (taking up 
60% of online shoppers). 
Aiming to provide apparel website owners 
with more information to improve their 
business, this study conducts an analysis of 
dimensions of consumers’ perceived benefits 
12 The effect of perceived benefits and perceived risks on intention to shop apparel... 
and risks and their effects on apparel shopping 
intention of Vietnamese white-collar women. 
2. Literature review 
Shopping intention 
Shopping Intention is an important figure 
for monitoring and mearsuring in 
advertisement and marketing because brands 
want to spend money attracting market 
audience to use their product or service 
(Crespo, 2009). It is an anxious expression for 
buying a product or service in the future. 
Shopping intention is determined as a plan 
purchase products or services in the future 
from consumers who have not purchased that 
type of products or services yet (Martin and 
Camarero, 2009). 
Frosythe’s research (2006) proved that 
consumers who purchased online frequently 
and spent a lot of money for this channel 
perceived more benefits than risks. While 
Wani and Malik (2013) noted that consumers 
perceived risks higher than perceived benefits, 
especially in India. Therefore, it is interesting 
to find out what kinds of benefits and risks of 
online shopping the Vietnamese consumers 
concern most. 
Perceived benefits 
Benefit is a convenience or profit 
achieved from anything. Perceived benefit is a 
trust about positive result in reality or when 
under a threat (Oxford, 1089). Lim’s and 
Ting’s research (2012) showed that 
perceptions have affected significantly to 
attitude and online shopping intention of 
individuals.  ... 0.691 
FINR_22 Information of individual account 
may be disclosed. 
3.61 0.664 0.760 
FINR_23 Information of payment card of 
buyer may be disclosed. 
3.28 0.538 0.745 
FINR_24 Payment may not be returned if 
the product is not similar to what 
described. 
3.85 0.985 0.725 
Cronbach’s Alpha: 0.771 Overall means: 3.434 
P
ro
d
u
ct
 R
is
k
PROR_25 The size of apparel may not tally 
with international standards. 
3.25 0.700 0.674 
PROR_26 I cannot try on the apparel 
online. 
3.42 0.754 0.665 
PROR_27 I cannot touch and feel the 
apparel. 
3.73 0.637 0.685 
PROR_28 I cannot receive the apparel 
online in a few minutes. 
3.49 0.735 0.688 
PROR_29 When receiving apparel, product 
will not be similar to the apparel 
image I saw. 
3.31 0.701 0.648 
Cronbach’s Alpha: 0.720 Overall mean: 3.442 
T
im
e 
R
is
k
TIMR_30 Spending a lot of time learning 
how to order apparel online. 
3.08 0.796 0.742 
TIMR_31 Spending a lot of time searching 
the right products. 
3.18 0.728 0.753 
 Journal of Science Ho Chi Minh City Open University – VOL. 19 (3) 2016 – October/2016 17 
Dimension Code Item Item Statement Mean 
Std. 
Deviation 
Cronbach’s 
Alpha if 
deleted item 
TIMR_32 It takes too long for the product 
to be delivered. 
3.64 0.810 0.755 
TIMR_33 It takes too long to cancel an 
order. 
3.15 0.785 0.771 
TIMR_34 It takes too long to complete the 
procedures for returning a 
product. 
3.44 0.747 0.755 
TIMR_35 It takes too long to compare prices 
when shopping apparel online. 
3.07 0.816 0.759 
Cronbach’s Alpha: 0.788 Overall mean: 3.261 
In
te
n
ti
o
n
 t
o
 s
h
o
p
 a
p
p
ar
el
o
n
li
n
e 
INTD_36 I like to shop apparel online. 2.95 0.439 0.694 
INTD _37 I will purchase apparel in online 
stores more often than in 
traditional stores. 
3.33 0.476 0.580 
INTD _38 I will purchase apparel via the 
Internet in the near future. 
3.15 0.427 0.603 
INTD _39 I will learn how to purchase 
apparel online in the near future. 
3.19 0.495 0.578 
Cronbach’s Alpha: 0.683 Overall mean: 3.156 
Exploratory factor analysis 
An exploratory factor analysis (EFA) is 
performed to evaluate the validity of the 
measurement scales of all variables included 
in the proposed model. Total 36 items are 
entered to analyze exploratory factor and 
include two parts. Part 1 consists of 32 items 
to present perceived benefits and risks in 
online shopping, and part 2 with 4 items 
describes white collar women’s intention to 
shop apparel online. The KMO of dimensions 
of perceived risks in part 1 is 0.808, and its 
Barlett’s test p-value is 0.000. The KMO of 
intention to shop apparel online in part 2 is 
0.700, and its Barlett’s test p-value is 0.000. 
The test values indicate that the data are 
accepted to perform further factor analysis. 
Then in part 1, we use principal 
component analysis method and variable 
maximization rotation to maintain 32 items. 
Their factor loadings are shown in Table 4. 
The table shows eight common factors 
extracted from the remaining 32 items. 
Cumulative extraction sum of squared loading 
is 57.111%. All factor loadings are above 0.5 
and no-cross construct loadings are above 0.3, 
indicating good validity of discrimination. 
These eight variables can be used for multiple 
regression tests. In part 2, we also get one 
common factor and the cumulative extraction 
sums of squared loading are 51.571% as 
shown in Table 4. 
18 The effect of perceived benefits and perceived risks on intention to shop apparel... 
Table 4 
 Factor analysis 
Variable Item Code Factor loading 
Cumulative 
extraction 
sums of 
squared 
loading 
Time risk 
TIMR _31 0.727 17.253% 
TIMR _32 0.590 
TIMR _33 0.667 
TIMR _34 0.581 
TIMR _35 0.657 
TIMR _36 0.609 
Financial 
Risk 
FINR _21 0.675 13.123% 
FINR _22 0.763 
FINR _23 0.534 
FINR _24 0.703 
FINR _25 0.727 
Product Risk 
PROR _26 0.561 5.779% 
PROR _27 0.611 
PROR _28 0.556 
PROR _29 0.697 
PROR _30 0.698 
Abundance 
and liking 
Product 
PROD _06 0.703 4.857% 
PROD _07 0.791 
PROD _08 0.756 
Enjoyment 
ENJY _18 0.725 4.451% 
ENJY _19 0.679 
ENJY _20 0.620 
Competitive 
Price 
PRIC _10 0.785 4.112% 
PRIC _11 0.705 
PRIC _12 0.750 
Convenience 
Shopping 
CONV _01 0.639 3.956% 
CONV _02 0.641 
CONV _03 0.670 
CONV _04 0.698 
 Journal of Science Ho Chi Minh City Open University – VOL. 19 (3) 2016 – October/2016 19 
Variable Item Code Factor loading 
Cumulative 
extraction 
sums of 
squared 
loading 
Comfortable 
Shopping 
COMF _14 0.537 3.579% 
COMF _15 0.730 
COMF _16 0.788 
Intending to 
shop apparel 
online 
INT _37 0.565 
51.571% 
INT _38 0.766 
INT _39 0.749 
INT _40 0.772 
Linear regression analysis 
In Table 5, five factors belonging to 
perceived benefits have standardized beta 
coefficients greater than zero with their 
significance less than 0.01. This means these 
factors have positively affected on intention to 
shop apparel online so an increase of these 
perceived benefits will help enhance intention 
to shop apparel online. 
On the contrary, three standardized 
beta coefficients of perceived risks are less 
than zero with their significance less than 
0.01. These factors have affected the 
dependent variable negatively. This means 
the more these three perceived risks 
increase, the less intention to shop apparel 
online decreases. 
The VIF of each independent variable in 
the regression model is lower than 2, which 
meets the criteria of multicollinearity testing 
(Hair et al., 2010). In other words, there is no 
multicollinearity in the regression model. 
Table 5 
Linear regression 
Independent Variable 
Standardized Beta 
Coefficients 
Sig. VIF 
Convenience shopping 0.234 0.000 1.309 
Abundance and diversify of product 0.225 0.000 1.244 
Competitive price 0.151 0.000 1.275 
Comfortable shopping 0.270 0.000 1.205 
Enjoyment 0.157 0.000 1.310 
Financial risk -0.208 0.000 1.483 
Product risk -0.263 0.000 1.572 
Time risk -0.182 0.000 1.727 
Adjusted R Square = 0.770 
Sig. F = 0.000 
Durbin Watson = 2.114 
20 The effect of perceived benefits and perceived risks on intention to shop apparel... 
The adjusted R square in the regression 
model is 0.770, which means that 77% 
variation of white collar women’s intention to 
shop apparel online is explained by variation 
of 8 factors: convenience shopping, 
abundance and diversify of products, 
competitive price, enjoyment, comfortable 
shopping, financial risk, product risk and time 
risk. 
For perceived benefits, comfortable 
shopping has the strongest effect on online 
shopping intention (β = 0.270) followed by 
convenience shopping (β =0.234), abundance 
and liking of products (β = 0.225), and 
enjoyment (β = 0.157) and competitive price (β 
= 0.151). For perceived risks, product risk has 
the highest effect (β = -0.263), followed by 
financial risk (β =-0.208), and time risk (β = -
0.182). Thus, eight hypotheses are accepted. 
ANOVA test of different age groups 
This study continues exploring difference 
among age groups of white-collar female and 
their intention to shop apparel online. White-
collar female’s ages are divided into three 
groups: 22-30, 31-40, 41-50. The result of 
ANOVA test is showed in Table 6. 
Table 6 
Result of one-way ANOVA analysis 
 (I) (J) N Means 
Mean Difference 
(I – J) 
Sig. 
22 – 30 
31 – 40 
41 – 50 
98 3.1327 -0,2008* 
0,1053 
0,035 
0,126 
31 – 40 
22 – 30 
41 – 50 
136 3.3335 0,2008* 
 0,3062* 
0,035 
0,000 
41 – 50 22 – 30 
31 – 40 
64 3.0273 -0,1053 
 -0,3062* 
0,126 
0,000 
Ages of respondents really affect their 
intentions. The intention of women in 31- 40 
age group is significantly different from that 
of women in 41- 50 age group and women in 
22- 30 age group. The greatest intention 
belongs to women of 31-40 age group (mean 
=3.33), followed by 22-30 age group (mean 
=3.13), and 41-50 age group (mean =3.03). 
 In brief, all eight hypotheses are 
supported, of which 5 hypotheses shows the 
positive correlation between perceived benefits 
and shopping intention and 3 hypotheses show 
the negative correlation between perceived risks 
and shopping intention. The degree effect of 
each benefit and risk on white collar women’s 
intention to shop apparel online is different. 
The age group of 31-40 has strongest 
intention to buy apparel online. 
6. Conclusion and future directions 
Conclusion 
This study determines that there are five 
dimensions of white-collar women’s 
perceived benefits and three dimensions of 
their perceived risks when they consider 
shopping apparel online. All eight dimensions 
give good explanations about the benefits and 
risks when shopping online. Perceived 
benefits have positive influence, on the 
contrary, perceived risks have negative 
influence on white collar women’s intention 
to shop apparel online. Managers should 
concentrate on enhancing the benefits and 
reducing the risks to redouble competitive 
capacity of their websites and attract more 
customers, especially the main market 
segment of white-collar women from 31 to 40 
 Journal of Science Ho Chi Minh City Open University – VOL. 19 (3) 2016 – October/2016 21 
years of age. 
Managers should improve selling process 
to make it easier and more convenient for 
customers. Furthermore, they need to advertise 
their websites widely to help customers 
recognize the convenience of shopping online. 
 To enhance white-collar women to 
shop apparel online, managers should pay 
attention to product’s factors by diversifying 
product styles. More focus should be put on 
fashionable tendency by designing attractive 
interface with understandable navigation to 
make customers comfortable and save time in 
their search for favorite items. Website design 
should be friendly to help consumers stay 
longer in the site and get more shopping 
experience. Information about product 
characteristics and related services should be 
continuously updated and truthful to build 
customer’s trust of e-retailer. 
Online price of apparel should be lower 
than that of physical stores to attract more 
women to purchase apparel online. Women 
often consider product’s price before 
purchasing. They also tend to compare prices 
among known brands so competitive price is a 
key to attract white-collar women to do more 
shopping. Return policy, i.e. allowing 
customers return apparel in suitable time, and 
guiding them to physical place to try out and 
select the most suitable one, will contribute to 
establish website’s reliability. Addition to 
applying modern payment methods like credit 
cards, debit cards, and e-wallet, managers 
should maintain COD delivery system 
because the white-collar women want to touch 
apparel before they pay. Hotline and chat 
room of customer services should be operated 
effectively to relieve consumers’ perceived 
risks. 
Future direction 
Our study provides a new measurement 
of perceived benefits and risks dimensions 
which influence on the intent to shopping 
apparel online of Vietnamese white-collar 
women. This study sets some foundation for 
further research on online shopping intention 
with other investigated objects and other 
online products because specific products and 
objects will have different perceived benefits 
and risks. 
At the same time, a future additional 
proposition of our study lies at considering a 
correlation between consumers’ purchasable 
intention and real purchasing behavior. 
Moreover, future studies can perform larger 
sample size and choose appropriate sampling 
to gain higher generalizability 
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