Profiles and Preferences of On-line Millennial Shoppers in Bulgaria

noodleproudSoftware and s/w Development

Oct 29, 2013 (3 years and 9 months ago)

85 views


Page
1


Profiles and Preferences of

On
-
line

Millennial Shoppers in Bulgaria



Patricia R. Loubeau

Profe
ssor, Hagan School of Business,

Iona College,

715 North Avenue, New Rochelle, NY 10801, USA

e
-
mail:
ploubeau@iona.edu


Elitsa Alexander

(corresponding
author)

Postdoctoral researcher
,
mcm


Institute for Media and Communications Management

University of St .Gallen
,
Blumenbergplatz 9, 9000 St. Gallen, Switzerland

e
-
m
ail: elitsa.alexander@unisg.ch


Robert Jantzen

Professor, Department of Economics, Iona
College

715 North Avenue, New Rochelle, NY 10801, USA

e
-
mail: rjantzen@iona.edu




Page
2

Abstract



This research seeks to develop a better understanding of the factors affecting
on
-
line

purchasing behavior among Generation Y consumers in Bulgaria. An empirical study was
conducted based on a written survey of a

sample

consisting of 367 high school and university
students in Bulgaria.

T
he most important reason why Bulgarian young people
shop on
-
line is the
pursuit of unique products not locally available
,
followed by convenience

and better pricing
, and
the
ir

favorite category of internet purchases is
“Apparel and Accessories.”
Bulgarian millennials
are using the internet to
shop for tren
d
y fashion and to obtain
a variety of brands that are
unavailable locally.


Like

other regions of the world,
concern about financial transactions
security
is

a
major
barrier limiting the willing
ness

to shop
on
-
line

in Bulgaria.

Unlike other
markets where on
-
line music
purchases are growing
,
high levels of digital piracy in Bulgaria
strongly discourage
Bulgarian students
from purchasing
music on
-
line
.

Keywords
:
internet

buying;
on
-
line

shopping; Generation Y; consumer behavior; Eastern Europe





Page
3

1.

Introduction


As internet
access

increases
, with
more than 1.9

billion users worldwide
today

[
4
1
]
,
the
number of
on
-
line
purchase
rs

is

expected to increase steadil
y
, including a proportionate increase
in the
number of young adults buying on
-
line
.

Consumers, includ
ing

millenials, are using the
web to obtain information on
p
roducts
and to obtain better pricing

[
67
]
, which is propelling
globalization and international
trade
.


Nielsen
notes that in the past two years the fraction of the
world
’s internet users that shop on
-
line has increased from 40% to over 85%, with half of
today
’s users making re
gular purchases at least once a month

[
68
]
.
Recent research shows that
Western Europe leads the world in retail
-
e
-
commerce [
2
1
]
, with
France, Germany, Italy, the
Netherlands, Spain, Sweden, and the United Kingdom

constituting the largest on
-
line markets

[
3
4
]
. The relative import of e
-
tailing differs significantly within these markets,
however, with
British consumers
the largest
on
-
line

spenders in Eur
ope,
tallying a third of all internet
purchases

[
6
1
]
.

In contrast,
internet

commerce only represents a
small
fraction of total retail volume
in
Italy and Spain
[
10
1
]
.


Even though the growth rate in
internet

usage
and e
-
commerce
worldwide has been
dramatic,
wide variation in
internet
penetration rates among countries [
4
1
,
3
2
]

offer the
opportunity for substantial future growth.
Such growth is expected to
particularly
come from
regions outside of the United States
[
19
].

1.1.

Generation Y



One group of
internet

users that are especially wired and therefore a significant influence
in the
on
-
line
marketplace are
Generation Y consumers, also called millennials.

This
generation
,
born
between the mid
-
1970s and late 1990s,

consists of influential,

powerful, demanding, highly
wired and knowledgeable consumers
. Their

sheer numbers and spending power
are expected to

Page
4

shape

the market
place for decades to come

[
6
2
,
7
2

&

69
]
.

Sarbu
[
8
1
]

has described

them as the
first generation of digital natives
, techno
-
literate and com
put
er able since childhood
, who
depend
on
the internet

for almost all facets of their daily lives.

In short,

m
illennials

are the first
generation
having grown up
in

the virtual world of the
i
nternet

[
10
]
.


According to a Pew
i
nternet

research report, Gen
-
Y consumers represented 35% of the
internet
-
using population in
2010
[
7
4
]

and
Vahlberg
[
9
3
]

has
determined that almost half
(
48%)
of American teenagers buy things
on
-
line
.

Generation Y consumers also have a fair degree of
disposable income available to spend
on
-
line
, earning
in the United States
for example

$200
billion a year in part
-

or full
-
time jobs
(2006)

and purchas
ing

$190 billion worth of
goods [
89
].

They represent ideal customers, with incomes largely disposable and expenditures resilient to
changing
business

conditions

[
8
3
]
.


As

teen
-
specific payment methods

became available
,
the younger millennial cohort aged
13 to 19

entered the world of e
-
commerce
.


Singh

[
8
3
]

identified the growing popularity of
prepaid cards in 2001 as a
turning point for teenagers to enter the world of e
-
commerce
.


Since
then n
ewer
payment methods such as Splash Plastic,

Smart Creds and Dubit, as well digital
wallets, i.e. virtual debit accounts opened by parents from which their children can shop
,

have
proliferated and
enhanced

the ability of teens to
buy
on
-
line

[
49
,
8
3
]
.

A further
payment iteration
can be found in
BillMyParents, which allow
s

teenagers to select products
on
-
line

and
forward

the bills
for parental a
pproval
[
98
]
.

Linking teens to
on
-
line

payment methods has been a potent
combination

fueling teen spending over
the Web.


Understanding the
on
-
line

buying behavior of millennial consumers
allows retailers to
create
initial relationships with them
and to build them into lucrative long
-
lasting brand
attachments.
With these considerations in mind, this study was designed to analyze
the
on
-
line


Page
5

buying behavior
of

Generation Y consumers in Bulgaria.

Unlike many previous studies, the
research will
examine the factors that influence the decision to shop on
-
line as well as the type of
products purchased.
This research extends the work of Brashear et al.
[
19
]
, Ca
hk

and Ersoy
[
2
3
]

and others and addresses the need for a non
US
-
centric view of
internet

usage

by
investigating

an
emergent
market segment in Eastern Europe for which no prior work has been
published
,

namely Bulgaria. The results of this study should be of value to retailers seeking to understand
bu
ying behaviors, educators interested in consumer behavior, and consumer theorists.

2.

Literature review and research question development


Wh
y
do consumers use the
i
nternet

for

shopping
?
Rohm and Swaminathan
[
79
]

have
suggested that on
-
line s
hoppers can be characterized into four motivational types.
Convenience
shoppers

value the ease of the on
-
line transaction
, while
variety seekers

desire greater access to
differing products and retailers.
Store
-
oriented buyers

are more interested in quickly obtaining
products and the social interaction during purchase
, while
balanced buyers

weigh all three
.


Harris Interactive
’s
recent
large scale survey [
4
] of US consumers in the busy 2012 Christmas
season

found that
the most

important reason for shopping on
-
line was to obtain better prices
(
71%
), followed by greater convenience (53%), better ability to stay within budget (32%) and the
desire to avoid crowds (31%).

A similar PriceGrabber survey conducted in 2011
revealed

eve
n
greater proportions (75%+) citing pricing, convenience and avoiding crowds [
5
].

In earlier
studies
of US consumers,
Ahuja et al. [
3
] found
smaller fractions
(< 25%)
citing convenience,
better prices

and
t
hat it saves time
,
while
Brown et al. [
2
0
]
found that consumers also shopped
on
-
line in order to obtain greater selection and to maintain
their privacy
for
products they would
ordinarily be reluctant to buy in
-
store.



Page
6


Demographic
and personal characteristics
are also important factors that influen
ce the
decision to purchase on the web.
In addition to motivational factors,
Donthu and Garcia
[
3
1
]

demonstrated that older consumers and those with higher incomes were more likely to buy on
-
line than their younger and modest income counterparts.
Bellman et al.
[
1
7
]

have shown that
consumers with greater internet expertise, as reflected in the volume of daily emails received and
months of on
-
line experience
, were more likely to make internet purchases.

Similarly,
Swinyard
and Smith
[
88
]

found that

on
-
line

shoppers are younger, wealthier, better educated, and
more
computer litera
te
.


Since Generation Y shoppers are far more active on
-
line than previous
generations
[
4
4
]
, they are likely to be a dominant factor shaping future e
-
commerce trends.


Some researchers
, like
Brown et al. [
2
0
] and Teo [
9
1
]
,

have also identified a gender
differen
ce in on
-
line shopping preferences
, with males more likely to make to make e
-
purchases
.
Others, however,

like
Ulbrich et al.

[
9
2
]
and
Her
nandez, Jimenez, & Martin [
38
] have
f
ailed to
find
that on
-
line purchasing behavior depends on one’s gender.

Others
like
Lynch and Beck [
59
]
and
Dellner [
29
]
have emphasized that
browsing and purchasing patterns will differ between
countries because of differing cultural beliefs, attitudes and perceptions.
This may be
particularly important for Bulgaria which
,

u
nlike w
ell established and developed mass consumer
societies such as the UK and the US,
has
a more recent engagement with global consumerism.
As a post
-
transition Eastern Bloc country, Bulgaria has a more collectivist heritage and more
limited

exposure to
internet

shopping opportunities.


Two sources of anxiety
,
involving
the chances of
receiving a
n unsatisfactory
product
or

incurring
an
unexpected financial
loss
,
have been found to be the major deterrents to ordering
on
-
line.
Kiran, Sharma, and
Mittal [
4
5
]
have identified the

former,
arising from an

inability to
physically examine the product first hand prior to purchase
,
as the most important factor

Page
7

deterring on
-
line purchases.

Others, including
Taylor Nelson and Sofres [
9
]
,
Vellido et al.

[
9
5
]
,
Ackerman et al. [
1
],
Basso et al. [
1
5
], Callahan &
Koenemann [
2
2
] and Spiekermann et al. [
8
5
]
,

have shown that concerns with credit
-
card fraud a
nd privacy are most important.

3.

The Bulgarian
Internet

Environment


Although lagging behind the EU average (61.3%),
nearly ha
lf (48.8%)
of Bulgaria
’s
population has access to the internet, a rate
similar to Greece (46.9%) and
considerably greater
than in
Romania (39.2
%)

[
4
1
].


The
most active users are between 16 and 24 years old according
to the B
ulgarian National Statistical Institute
[
6
5
]
, and their use is increasing rapidly, with
the
percentage on the global network rising from
41.7% in 2004
to
75.1 % in 2009
.

One factor
fueling th
is

rapid growth is the
growing

popularity of internet and computer gaming clubs in
Bulgaria.


A

2010 EU survey
[
5
5
]

has shown

that
Bulgarian teenagers
are among the most frequent
internet users in Europe
. Bulgarian and Swedish teens
tied for first place among 25 European
countries, with 5 of 6 (83%) of their teenagers using the internet almost every day.


A 2006 study
by the Bulgaria
n National Centre for Study of Public Opinion found that the average time of
using the
internet

by Bulgarian teenagers varied from three hours on weekdays, four hours at
weekends, up to five hours on days during school vacations. Of those questioned, 22% o
f the
respondents said they use the
internet

during

any free time
that
they have [
6
6
].


While
internet

shopping
is increasing
,
e
-
commerce

trade is still underdeveloped

in
Bulgaria.
Bulgarians are among the lowest users of on
-
line shopping with estimates ranging
from

3 to 5% of consumers buying products on the web
[
7
,
9
0
].

B
ulgaria’s bank card holders
,
however,

are
much more likely
(79%)
to make purchases on
-
line, and younger buyers between
18 and 24 account for 45% of th
ose making purchases
[
7
].

Internet

commerce, although still

Page
8

primarily done on foreign sites,
is also being stimulat
ed
by the development of local
e
-
tailers and
on
-
line

payment systems. At the same time, Bulgaria, as a developing market economy, does not
have equal financial footing in terms of consumer spending compared to the US and Western
Europe. The purchasing power or GDP per capita was $12,800 in 2010
[
2
5
]
.


As a post
-
t
ransition Eastern Bloc country

now included in the EU,
Bulgaria
’s six million
potential customers
, ris
ing incomes and increased foreign investment

offer
e
normous
on
-
line

buying p
otential

[
2
7
]
. In addition, the adoption of e
-
commerce services will be an important
element of
Bulgaria’s integration into EU community and ensuing economic
development.

4.

Theoretical Foundation and Hypothetical Model


In light of the discussion above, this study
seeks to answer the following research
questions
:


1.
Why do
Bulgarian
Gen Y consumers decide to buy
on
-
line

and what
kinds of goods and
services do they purchase
?

2.
Wh
at factors are the most important
facto
rs

discouraging

Bulgarian
Gen Y consumers
from purchasing on
-
line
?



In

order to answer these research questions, we

made use of Zhou
et al.
’s

On
-
line
Shopping Acceptance Model

(OSAM)
to identify

the
major cat
egories of factors

that
might
influence t
he decision to shop on
-
line

[
99
]
.
While p
rior research
has focused on the willingness
to adopt new technologies, including on
-
line shopping [
e.g
.,

7
3
]
,

the OSAM model ex
tends

the
analysis
by

incorporat
ing

the particular characteristics of the on
-
line transaction

into the decision
to shop on
-
line
.

Our research framework

(Figure 1) embraces the rationale of OSAM and
includes four categories of
determinants
explaining the
acceptance of on
-
line shopping:
consumer demographics
,
computer knowledge
,
perceived outcome

and

the
perceived risks

of

Page
9

internet buying.
Prior research has found significant, but conflicting, results regarding the
influencing of

consumer demographics

on the on
-
line shopping decision.

C
omputer knowledge

reflects both ability and access, which are
expected to

positively

influence the willingness to
conduct on
-
line transactions.


Perceived outcome

includes factors that measure the

expected
benefits of using a particular shopping platform (e.g., an on
-
line system)
,
such as

better pricing,
convenience, variety and
improved
job performance [
2
6
,
7
3
,
5
6

&
28
].

The final category,
namely
pe
rceived risk
,
refers to
consumer
s’

assessment
of incurring unexpected

financial

losses
or being disappointed
in the product after they have purchased it

[
7
3
].


Figure
1.

Research Framework



















In line with our research framework,
and consistent with the finding of prior research [
2
,
1
4
,
18
,
2
0
,
2
4
,
2
6
,
28
,
3
1
,
3
3
,
4
2
,
4
3
,
4
6
,
47
,
5
0
,
5
1
,
5
2
,
5
3
,
5
6
,
7
1
,
7
3
,
78
,
8
0
,
8
4
,
86
,
87
]
,
we propose
to
test
the following

hypotheses
:

H1. The effects of gender on on
-
line shopping behavior are significant.

H2. The effects of age on on
-
line shopping behavior are significant.

H
3
. The effects of
educational
status

on on
-
line shopping behavior are significant.

Perceived

Risk


Perceived

Outcome

H
6

H
7

H1,
H
2, H3, H4


H
5

Consumer

Demographics



C
omputer

Knowledge

On
-
line

Shopping


Page
10

H
4
. The effects of income on on
-
line shopping behavior are significant.

H
5
. Computer knowledge has a significant positive impact on on
-
line shopping behavior.

H
6
. The effects of perceived outcome on
on
-
line shopping behavior are significant.

H
7
. The effects of perceived risk on on
-
line shopping behavior are significant.


5.


Sample and Methodology


5
.1 The on
-
line shopping survey


To empirically
study the
on
-
line

shopping behav
ior of millennials in Bulgaria, a
survey
was designed and
administered
.
The
On
-
Line Shopping Survey (OSS)
, included in the Appendix,

was produced based on
an extensive literature review, an analysis of previous surveys obtained
from researchers, and consultation with market research practi
ti
oners.
The questions in the
OSS

were
also
informed by
focus groups
conducted with students in the targeted age group.

Several
Bulgarian academics, selected on the basis of their fam
iliarity with on
-
line shopping
,

evaluated
the face validity of the instrument. Minor modifications were made following the
ir

reviews.


The

survey included

nine questions pertaining to
internet

use and
on
-
line

purchasing
behavior. The latte
r included questions
regarding whether

the student shops
on
-
line
,
what
products or services are purchased

and

additional

questions relating to
the
level of computer
expertise and motivation for
on
-
line

shopping. The
on
-
line

shopping
categories
include
d

products and services
, an

approach
similar to that of the
on
-
line
-
shopping research companies,
Forrester and comScor, both leaders in measuring the digital world. The approach was also
consistent with that of Ahuja

et al.

[
3
]
,
Ca
hk

and Ersoy [
2
3
] and
Frasier
and Henry [
3
5
]
, who
focused on
the
general
on
-
line

purcha
sing behavior of individual consumers and why they
choose to buy or not buy
on
-
line
.

To distinguish differing computer abilities, students were
asked to self
-
report their level of computer knowledge on a
5
-
point
ordinal

rating
scale ra
nging


Page
11

from no knowledge to expert.

The survey also inc
luded questions regarding age, gender,
educational level

and income status.
To accommodate disparities in income
levels

between
Bulgaria and
the
US/Western Europe,
the
income categories were defined as

much worse off
than colleagues, worse off than colleagues, same as colleagues, better off than colleagues, and
much better off than colleagues.

5
.2
Methodology


In addition to d
escriptive

statistics, bivariate
and multivariate analys
es

w
ere

utilized to
test this study’s research questions

at
the customary
p



0.05
*, p


0.01** and p


0.005***
significance level
s
.

The analysis included chi
-
square tes
ts
between pa
irs of

categorical
variables

as well as regression analysis.

Specifically, chi
-
squared analysis was used to assess whether
the
type of
on
-
line
purchase was related to
each
of the consumer demographic characteristics.
Chi
-
squared analysis was also employe
d to assess whether the
reasons for shopping and not shopping
on
-
line were

significantly related to the demographic traits.
Because
the decision to shop on
-
line
is a binary categorical
(yes/no)
outcome,
we also utilized
the multivariate
logit

regression
method
to
identify

which
underlying factors determined
the
willingness to shop on
-
line
.
In large
samples like ours, the logit method is superior to
ordinary least squares (OLS) regression
because the latter is likely to generate
biased coeffic
ients, heteroskedastic error terms, unreliable
coefficient t values and nonsensical predicted probabilities

[
7
5
]
.
The specific functional form of
the logit regression
model
is as follows:


(






)













i












(

















i

)

where



is the probability of shopping on
-
line

and



is
the
vector of explanatory variables
(including consumer demographics, computer knowledge,
perceived outcome and perceived risk
factors)
.
Because the logit

regression coefficients
(


)
show the marginal effect
s of each

Page
12

explainer
on the log of the odds of shopping on
-
line, their magnitudes are not directly
interpretable.
Statistically s
ignificant logit coefficient
s do, however,

indicate the direction
(
plus

or
minus
)
of influence of each explainer

on the propensity to shop on
-
line.


To estimate the logit regression, the decision to shop on
-
line was coded as a dummy
variable, with
1 for shopping and 0 for non
-
shopping
. A
mong the

consumer demographic
explainers, gender and education status (university vs. high school student) were also coded as
(1, 0) variables.

Because the age

and income demographics, as well as the
computer knowledge
variable
,

were ordered categorical variable
s, their influence was estimated relative to particular
base groups using multiple 1,0 dummy variables. For example, since age was classified as either
<

18, 18
-
22 or >

22, the regression included two 1,0 dummy variables for the 18
-
22 and >

22
categories a
nd excluded a variable for the <

18 group, thereby making it the comparison base.
This regression specification then allows the results to identify whether there were any
differences in the propensity to shop on
-
line between 18
-
22 year olds and <

18 year o
lds or
between >

22 year
olds and those <

18.

Since i
ncome was
similarly
classified into three groups,
namely worse than peers, same as peers or better than peers, the regression estimated the
differences between the latter two groups relative to the wors
e than peers group. Lastly, the
influence of computer knowledge was estimated by contrasting those with average or above
average expertise with those who had less than average self
-
reported ability.

Whether
students

reported that they
expected to receive

better pricing was also included as
a
1,0 dummy variable,
as were two variables measuring whether the
y

felt
either
anxious about financial risk from
shopping on
-
line or b
eing unable to physically examine the product prior to purchase.






Page
13

6.

S
ample
and
Results

6.
1

Survey participants


A total of 388 pen and paper surveys were distributed
in the
late
Spring
/early

Summer

of
2011
to a convenience sample of high school and university students
from

medium sized cities in
Southern Bulgaria.

This

par
ticipant

group

was
chosen

bec
ause p
revious studies
[
including
7
0

and

5
4
]

have
shown

that
students

can
serve as

a representative sample of the e
-
commerce shopper
population
.

Twenty one
surveys
were eliminated due to incomplete data, leaving a total of 367
usable surveys.


Among

these, w
omen (57.8%) and university students (
53.4%) slightly
outnumbered men (42.2%) and high schoolers (46.6%).
Most (80.1%) were between 18
-
22
years old,
with only 15.3% younger and
4.6% older.
Three in four (76.3%) reported they had
incomes similar to their colleagues, while 6.6% reported lower
incomes and 13.1% higher
incomes.

With respect to computer expertise, two in five
(42.8%) felt

they had above average
computer
knowledge
, nearly half (46.3%) average
literacy

and only a small f
raction (3.3%) felt
they had below average computer
skills
.

6.
2
:
Why do
Gen Y
consumers shop
on
-
line
?


Seven out of ten (
70.6%
)

students surveyed reported that they

had
shopped
on
-
line

at
least once
.


The majority (62.5%) of the
se

internet shoppers were
moderately active, having
made one to three purchases in the
previous

three months.

The most
cited

reason
given by the
259
Bul
garian students
for shopping
on
-
line

is the
ability to obtain
unique products not found in
stores (
46.3
%

of the total).


This finding differs from the
study by
Ahuja et al. [
3
]
on US
consumers
and Kwek, Tan, and Lau [
48
]

of Malaysian shoppers
, who found that convenience
was the most common reason given for
on
-
line

shopping. Our study show
s

that
Bulgarian
students are “variety seekers


because of more
limited opportunit
ies

for purchasing
name
brand
s


Page
14

locally
, even more so i
n
the
remote provincia
l regions of Bulgaria
.

Under the prior socialist
regime the variety of branded products was severely constrained
, which has now begun to
change

as
Bulgaria emerges
as

a new market economy
.

At the present time, however, Bulgarian
millennials appear to be us
ing the
internet

to supplement their buying
in order to obtain products
difficult to obtain locally.

Like

American millennials

who

“shop for fashion, they shop for trend,
and they shop a variety of stores and brands…
,”
Bulgarian millennials seem to be con
stantly
looking for new trends

[
69
].


Variety seeking may
also
be connected to
the
ir

desire
to build
“cool” identi
ti
es by being seen using the latest products

[
37
].


The
next most

important reasons why Bulgarian young peopl
e shop on
-
line are
convenience
(
45.9
%) and better pricing (
44.4
%)
, followed by the ability to save time shopping
(
30.9
%) and to shop any time of the day (
26.3
%)
.
Relatively few respondents cited
on
-
line price

comparison

(
13.1
%), fewer hassles & crowds (
10
.8%) and ease of shopping (
8.5
%
) as important
factors.

While
previous research
on UK and US young people have
shown
that
price is often the
biggest draw to
on
-
line shopping [
8
3
]

and
that
younger consumer are more likely to compare
prices than adults
[e.g.,
58
],
Bulgarian
millennials

seem to be
somewhat
less concerned with
price than their counterparts
.

Price may be a less important factor for Bulgarian
youth

because
they have more
limited opportunity for purchasing

products locally, especially some types of
brand name products
.
Anothe
r

possible

explanation may be that Bulgarian
young people

usually
get a lot of financial support from their parents.
There are no “moral limits” to the amount of
money a
GenYer

could obtain from his or her parents, according to Bulgarian culture.
A typical

comment among Bulgarian parents would be “What I did not have as a child [i.e. during
socialism], my children should have now.


Parental support seems to be a noteworthy factor in
the formation of th
e Bulgarian adolescent consumer
.


Page
15


Additional

chi
-
square
d analysis
, not presented here but available from the authors,
also
revealed the absence of any link between the reasons
why students chose to shop on
-
line

and any
of the
ir personal characteristics.

Specifically, ther
e were no significant (p
-
value
≤ .05)
associations between the reasons given and either
a
ge, gender, education, income
o
r computer
knowledge
.


6.
3
: What
do Gen Y consumers purchase on
-
line

in Bulgaria?


For the
259

students who had shopped on
-
line, the most
frequently
reported

favorite
category

(
54
.4%)
was
“Apparel and Accessories
,


followed by
“Books and Magazines
,

“Computers,”

“Air Travel,”
and “Health & Beauty” with
38.6%,
3
5.1
%,
32.4
%

and
30.5
%
shares respectively.


One

in
four

also reported
purchasing
“Event Tickets,”
“Consumer
Electronics,” and “Videos
,


with smaller

fractions report
ing

either “Music” or “Toy” purchases
(8.2% and 2.5%, respectively).
Th
ese

finding
s

about Bulgarian millennials
correspond to the
recent Gallup research finding that
globally

millennials
, more than any other
generational group
,
acutely respond to changes in fashion by shopping
on
-
line
for clothing and accessorie
s [
69
].

Both
Singh
’s study of UK youth

[
8
3
]
and
Lueg
’s study of US teens

[
58
]

have
also previously
concluded that shopping for clothes
is

the most important category of purchases

for young
consumers
.
The
Bulgarian
GenYers
preference for

on
-
line

apparel can also be explained by
existing price differentials.
If we compare Bulgarian prices to Western
-
Eur
opean and US prices
we note an interesting fact. While consumer prices tend to be significantly higher in Western
Europe and
the
US compared to Bulgaria, there is one exception, i.e. prices of brand name
products (e.g.
, the products of

Levis, Nike, Zara, H
&
M). One summer dress in a chain store
(Zara, H&M), for example, would cost approximately 14% less to buy from a store located in
Western Europe (e.g. Germany, Spain, France) than from a store located in Bulgaria [
6
].
The

Page
16

same product would be even less ex
pensive if bought in the US. This may explain why Bulgarian
students are buying apparel and accessories on
-
line when looking for better prices.
Bulgarian
millennials seem to be looking for
the right styles a
t

the right price
.


Of interest to note is th
e lo
w level of on
-
line
music

purchases.

This is consistent with the
fact that most audio is illegally acquired in Bulgaria. Upon the recommendation of the
International Intellectual Property Alliance (IIPA),
Bulgaria was added to the Special 301 Watch
List in
2003 and again in 2005.

The estimated level of
music
piracy in
Bulgaria

was 83% in 2002
[
4
0
, p.

348]
, and
Bulgaria’s
anti
-
piracy efforts have continued
to be ineffective
. In 2012, after
street protests by internet activists
, B
ulgaria
became

the sixth
Euro
pean
country
refusing

to
support the international Anti
-
Counterfeiting Trade Agreement (ACTA)
.

The agreement was
meant to toughen intellectual property rights enforcement and toughen the legislation against on
-
line audio
-
video piracy

and has been signed by

Australia, Canada, Japan, Morocco, New
Zealand, Singapore, South Korea,
Mexico, Mor
occo,
the US and 22 members of the EU
.

Bulgaria joins Poland,
Cyprus, Estonia, Germany, Netherlands

and
Slovakia
in not signing
the
convention
[
77
]
.


On a global s
cale,
Ahuja et al. [
3
] have found that travel and audio
-
video were the most
popular categories

for on
-
line shoppers
.


Legitimate d
igital music
sales have been growing
rapidly and
account
ed

for
an estimated 32
%

of record
ing

company
2012
global
revenues
,
up
from
29
% in
2010.
In s
ome markets

more than half of
company
revenues
arise from
on
-
line
music sales
, including the US (52%), So
uth Korea (53%) and China (71%) [
39
]
.
Despite
advancements that have been made on a global scale
, digital piracy remains a critical ba
rrier to
on
-
line music sales in Bulgaria.

Beken, Janssens and Vandaele have noted that
l
egal action

Page
17

seems to be able to serve as a last
-
resort
solution, while b
uying music
on
-
line
should be
made
easier (and more affordable) than stealing music on
-
line
[
1
6
]
.

6.
4

Are there significant demographic differences in what Gen Y purchases on
-
line?


T
he types of items Bulgarian
Gen Y consumers purchased on
-
line differed significantly
(p
-
value

<

.005) by age, gender, education and income,
with the age and education
differences
largely mirroring one another
(see Table 1).

Older (

18)

university students
were more likely
(15.7%)
to
purchase a
ir travel tickets
on
-
line

than
younger high
students (
3.2
%).
This would be
expected as university students may require air travel to attend school whereas high school
students would not.
Additionally, high school students cannot use credit cards for paying on
-
line,
except when borrowed from an adult, and air tickets

cannot be purchased using the COD
method
. In addition,
older

university students
were twice as likely to buy
event tickets
on
-
line

compared

to
younger high schoolers (10.4% vs. 5.2%).

In contrast to
American high school
students who spend a considerable

part of their money on
entertainment [
58
, p.19], Bulgarian
high school
ers’
infrequent

purchase of event tickets may arise because most events are located in
the capital of Sofia, making them inaccessible
to

students living in provincial regions.

Younger
high schoolers were, however, more likely to purchase Music and Videos on
-
line than their older
counterparts.



Not surprisingly, sizable gender differences existed in purchasing behavior. Women
were more likely to purchase books &

magazines (16.1% vs. 8.7%), health & beauty items
(16.1% vs. 2.6%) and apparel (21.6% vs. 13.7%) on
-
line, while men were more likely to
purchase computing (16.9% vs. 7.6%) and consumer electronics equipment (12.5% vs. 5%). In
addition, rising income ten
ded to be associated with greater purchases of apparel and fewer
purchases of computers, music, videos, and toys. Somewhat unexpectedly, no significant

Page
18

relationship was found between the type of purchases students made and their level of computer
knowledg
e.

Table 1:
On
-
line Purchases by Demographics




6.
5
: Why do Gen Y consumers decide not to buy on
-
line?



On a descriptive level, t
he most important reason Bulgarian millenials cited for not
shopping on
-
line is the inability
to
see, feel

or try on the item (named by 55.6% of the non
-
shoppers), followed by anxiety about the on
-
line transaction (30.6%) and insufficient
information (17.6%).
T
h
is finding stands

in contrast

with the findings of Taylor Nelson and
Sofres [
9
] and of Vellido et a
l.

[
9
5
]

who found that US consumers
list
security

concerns as the
most important reason they do not shop on
-
line.

Th
e finding is also at odd
s

with the research

of
Ackerman et al. [
1
],
Basso et al. [
1
5
], Callahan &
Koenemann [
2
2
] and Spiekermann et al. [
8
5
]
who
have also shown that trust and privacy concerns predominate.


One possible explanation
might be found in the cohort sampled, namely young people. Lessened concern with transaction
security might arise from
a
dolescent optimism
,

feelings of invincibil
ity
and
inexperience [
11
,
6
0
]

which leads adolescents to embrace, rather than avoid, risk.

Another possible explanation
arises
from
the reliance of Bulgarians on the
Cash o
n Delivery (COD) payment method

to

pay for on
-
line purchases.

Most internet orders
are paid COD

with

consumers go
ing

to the nearest post

Page
19

office, receiv
ing

the product and pay
ing

cash.
On
-
line payment methods
where financial fraud
could be a concern
(e.g. credit card payment systems, digital wallet and stored value payment
systems), while

growing, are still not
widely used for internet purchases
in Bulgaria.

Additional
chi
-
squared analysis
, not presented here but available from the authors,
also
indicated that the
reasons
why millenials
avoid shopping

on
-
line
are not significantly

different

between the a
ge,

g
ender, education, income
o
r computer knowledge

categories
.



Table 2 details the differences between
internet

shoppers and non
-
shoppers
in terms of
demographics and computer knowledge.

On a bivariate basis that fails to control for differences

Table 2.
Internet

Shopper versus Non
-
shopper




Demographic:

Internet

Shoppers




Internet

Non
-
shoppers




Total

Chi
-
square test of

association between On
-
line


No.

(%)

No.

(%)

No.

(%)

Shopping & Demographic

(p
-
value)

Age



<

18



18
-
22


>

22



22

82

4


(
39.3)

(
27.9
)

(
23.5
)


34

40

68


(6
0
.7)

(
72.1
)
(
76.5
)


56

1
22


72


(100.0)

(100.0)
(100.0)


.
198

Gender

Male

Female


115

144


(74.2)

(67.9)


40

68


(25.8)
(32.1)


155

212


(100.0)
(100.0)


.193

Educational Status

High School Student

University Student


98

161


(57.3)

(82.1)


73

35


(42.7)

(17.9)


171

196


(100.0)

(100.0)


<.005***

Income

Less

than colleagues

Same as colleagues


More

than colleagues



1
5

193

40



(
62.5
)


(68.9)

(
83.3
)



9

87

8




(
37.5
)


(31.1)

(
16.7
)



24

280

48



(100.0)

(100.0)

(100.0)




.
088

Computer Knowledge

< Average

knowledge

Average

knowledge

>
Average knowledge



5

111

1
43



(41.7)

(65.3)

(77.
3
)



7

59

42



(58.3)

(34.7)

(22.
7
)



12

170

185



(100.0)

(100.0)

(100.0)




.354

Total

259

(70.6)

108

(29.4)

367

(100.0)



in other factors, only the difference between high school and university students was large
enough to be statistically significant at
t
he customary 5% level
.

Specifically, far more (
82.1%
)

university

students shopp
ed

on
-
line
than

high schoolers

(57%)
.

The percentage who
shopped on
-
line did not vary significantly either by age, gender, income or computer knowledge
.

Page
20

Interestingly,
even though Bulgarian teens rely on the COD payment, as opposed to t
een
-
specific
on
-
line

payment methods such as prepaid cards, digital wallets,
and BillMyParents
,
they
were
somewhat
more likely (57%) to shop on
-
line than their American counterparts (48% in 2010)

[
9
3
]
.



The
results of the logit model estimating how
the
decision to shop on
-
line is influenced
by the
demographic factors
,
perceptions
of better pricing and anxiet
ies about financial security
and
lack of seeing
the product
are contained in Table 3 below
, with the causal pathways
summarized in Figure 2
.
Figure
2. Causal Paths between Consumer Factors and On
-
line Shopping
























The
logit regression’s
likelihood ratio test indicates that the overall model is highly significant
(p
-
value
<

.005) and the model
’s explanatory factors correctly predict the actual on
-
line shopping
Better prices



x


Can

t touch product



Transaction a
nxiety



Perceived

Risk
:

Consumer

Demographics
:


Age


Gender


Education


Income





-




-


+

+




x


x

+
positive relationship

-

negative relationship

x
no relationship


On
-
line


Shopping

+

Perceived

Outcome
:

Computer
Knowledge


Page
21

behavior
for
92.9%
(327 of 352)
of the
surveyed students.

T
he decision to shop
on
-
line was
significantly
(p
-
value



.05)
influenced by
educational status,
computer knowledge,
anxieties
about financial and product risks,
and perceptions of better pricing.

Using a .05 significance level, the
logit
regression results yield

the following conclusions:



H1

is

not sustained

as the
re is insufficient evidence (p
-
value = .
792
) that gender influences
the propensity to shop on
-
line
(see Table
3
).
O
ur findings

are consistent with those of
Ulbrich et al.

[
9
2
]
and

Her
nandez, Jimenez, & Martin [
38
]
who have
also f
ailed to find a
significant on
-
line shopping gender differential.


They do, however,
stand in contrast to
those
of Brown
et al. [
2
0
] and Teo [
9
1
]
,

who
have
found
that on
-
line purchasing behavior
depends
on
one’s
gender
, and to the body of research that has demonstrated a gender difference in the
willingness to adopt new information systems, like
e
-
commerce platforms [
3
0
,
6
4
,
57
, 1
3
,
96
,
8
1
].


The absence of a
significant
gender
difference
in our analysis may be due to the unique
subgroup being analyzed (i.e.,
Bulgarian GenYers) or the
level of analysis.

With respect to
the latter, o
ur study, like

Ulbrich et al.

[
9
2
]
,
found

no significant
relationship

between gender
and on
-
line shopping behavior
at the construct level

(
i.e.,
comparing
shoppers vs. non
-
shoppers).

The significant gender difference
s

identified in other studies may
arise from
their
f
ocus on
g
ender
differences
a
t the feature level

(
comparing

individual features of on
-
line
shopping sites)
.



H
2
is

not sustained

as the
re is insufficient evidence (p=values of .281

and .738) that age
influences o
nline shopping behavior
, i.e., there were no differences between the >

22 and

<

18 cohorts or the 18
-
22 and <

18 cohorts.
Additional

analysis, whereby the age and
education variables were
alternately excluded from the regression, revealed that

the
insignificant

age effect was not the result of multicollinearity between t
he two variables.



Page
22

Our
study
’s
finding contrasts with
those of Shim’s
[
8
2
]

and
Moschis and Churchill [
6
3
]

who
have
demonstrated that older adolescents are
more savvy consumers than younger ones in
terms of price sensitivity, consumer affairs knowledge,
managing personal finances,

the
ability to judge advertising claims and
in obtaining information

from varied sources prior to
making a

purchasing decision.


It is likely, however, that our failure to identify an age
difference might be due to
the
fairly i
mprecise age categories used

in our study
, namely < 18,
18
-
22 and >

22

years old.

Table
3

Logit
regression results

Explanatory
f
acto
r

Coefficient

Standard
e
rro
r

t
-
v
alu
e

p
-
v
alue

Constant

-
1.733

4.204

4.006

< 0
.
00
5***

Gender

0.128

0.485

0.264

0
.792

Age: > 22

-
1.107

1.026

-
1.079

0
.281


18
-
22

0
.198

0
.593

0
.335

0.738


< 18

---

---

---

---

Educational status

1.265

0
.544

2
.325

<

0
.
0
5*

Income: > Peers’

1.124

0
.997

1
.127

0
.257


Same as peers’

0
.762

0
.855

0
.891

0
.373


<

Peers’

---

----

----

----

Computer knowledge:






Above average

2.600

0.982

2
.647

<

0
.
01**


Average

1.679

0
.959

1
.751

<

0
.
05*


Below average

---

----

----

----

Better prices

22.789

0
.6344

3
5
.918

< 0.00
5***

Cannot touch product

-
23.924

0
.338

-
70.823

< 0.00
5***

Transaction a
nxiety

-
24.092

0
.335

-
71.854

< 0.00
5***













On
-
line shopping classification table:





Predicted Status

McFadden R
-
squared


0.705


Actual Status

Did not shop

Shopped

Likelihood Ratio chi
-
square test


302.8


Did not shop

81

23

p
-
value of chi
-
square
test

< .005***


Shopped

2

246




H3

is

sustained

as educational status has proven to have a significant impact on online
sho
pping behavior
(p
-
value of
.021
)
.
Not
surprisingly
,

Bulgarian
students
attending


Page
23

university were
significantly
more likely to shop
on
-
line than those
still in high school
,
reflecting their greater levels of discretionary income
, internet usage

and freedom from direct
parental supervision

[
9
2
]
.

Other
researchers have
also noted that
younger

students are
less

likely to shop on
-
line and
to be able to use
credit cards for
payment
, except when borrowed
from an adult
[
9
3
,
76
,
8
,
8
3
,

97
].




H4 is
not sustained

as the
re is no evidence that income differences significantly influence o
n
-
line shopping behavior
. No significant differences were found between lower and median
income millennials

(p
-
value = .
257
) or between lower and higher income millennials (p
-
value = .
353
).

Our findings
contrast those
of Swinyard and Smith [
88
, p.573]
who

found that
internet sho
ppers are wealthier

at customary significance levels
.



H
5

is

sustained

as computer knowledge has proven to have a significant positive impact on
on
-
line shopping behavior
. Compared to those with less than an average knowledge of
computers, young people reporting either an average level of knowledge (p
-
value = .040)
or

better than average computer proficiency (p
-
value = .00
4
)

were significantly more likely to
shop on
-
line
.
Our findings
agree with
the findings of Swinyard and Smith [
88
, p.573]
who

found that
internet shoppers have higher computer literacy
.



H
6

is

sustained

since the effect of perceived outcome
, namely obtaining a better price,

on on
-
line shopping behavior ha
s

proven to be significant
.
Like consumers in the
UK and
US [
3
,

2
0
]
, t
he belief in garnering a better price is a prime motivating factor encouraging
Bulgarian
millenials
to make on
-
line purchases
(p
-
value < .005).



H
7

is

sustained

as th
e effects of perceiv
ed risk on
internet

shopping behavior have proven to
be significant.

Both
risk factors, namely
“Anxiety about on
-
line transaction


and “Cannot
feel, touch, or try a product on
-
line
,

are highly significant
explainers (p
-
value < .005).


Page
24

Concerns regarding
transactions security and receiving an acceptable product when ordering
on
-
line are serious impediments to increasing internet purchasing f
or s
tudents in Bulgaria,
like other consumers around the world [
1
,

9
,
1
5
,

2
2
,
3
6
,

4
5
,

8
5
,

9
4
]
.



7.

Conclusion


One of the crucial challenges created by global e
-
commerce is u
nderstanding the
similarities and differences
in consumer preferences and concerns that exist in
different regions
.
Micro marketing to countries as niche markets is essential because f
ailing to do so can cause
strategies that succeed in some areas to fail in others.


This study was the first large sample study to
examine
the
on
-
line

shopping behavior

of
Bulgarian millennials.

While convenience is reported as the most
important

reason for
using the
internet to make purchases

worldwide
, t
he most important reason why Bulgarian young people
shop on
-
line
is

the
pursu
it of
unique products not
locally available

(46.3%),
followed by
convenience (45.9%) and better pricing (44.4%)
.

Similar to their American counterparts, m
ore
than half (54.4%) reported that their favorite category of on
-
line purchases (54.4%) was
“Apparel and Accessories.”
Bulgarian millennials
are using the internet to
shop for trend
y
fashion and to obtain
a variety of brands that are unavailable locally.

This finding indicates that
there seems to be a promising niche market for selling
brand name products

to Bulgarian youth
on
-
line. This finding

should be of value to retailers

interested in buying behaviors and emerging
e
-
commerce markets.


Also

noteworth
y is the low level
(8.2%) of Bulgarian students who report purchasing
music on
-
line.
Unlike other markets where on
-
line music purchases are growing and
constitute a
majority of
recording company revenues, digital piracy remains a critical barrier to
on
-
li
ne music

sales
.


Page
25


The types of items Bulgarian Gen Y consumers purchased on
-
line differed significantly
by age, gender, education and income
. Older university students were more likely to purchase
air travel and event tickets than younger

high school
students.

Women were more likely to
purchase books & magazines
,
health & beauty items and apparel on
-
line, while men were more
likely to purchase computing and consumer electronics equipment
.

In addition,
students with
greater family incomes tended to ma
ke more apparel purchases and fewer purchases of
computers, music, videos and toys
.


The
logit regression results demonstrated that, holding other things constant, the most
important
factors determining whether a Bulgarian
millennial

would shop on
-
line are

their
perceptions about
obtaining
better pricing, financial transaction security and the inability to
personally examine the product prior to purchase. Given similar perceptions of the former,
university students and those with greater computer knowledge

were more likely to use the
internet for purchases than high schoolers and those with limited computer skills.
The
importance of financial anxiety is
consistent with
the high relevance of “security concerns” as a
major
on
-
line
-
shopping barrier on a globa
l scale.
To ensure further economic development,
public policy efforts should be targeted towards improving the digital payment infrastructure.

It
is widely acknowledged that the
lack of technology infrastructure

and the
lack of government
initiatives
are
major

hurdles that prevent pervasive
e
-
commerce

adoption in developing
countries
.


Further investigations should address the question of the extent to which
students in
Bulgaria are representative of the general population of
internet

users and consumers who make
on
-
line

purchases.
Such
research should also address changes in
on
-
line

shopping
behavior

as
local product availability increases and ref
orms to the payment mechanism take place
over time.


Page
26

8.

Appendix:
T
he On
-
Line Shopping Survey


Dear Participant,

T
he
following short survey is designed to measure computer use in Bulgaria. Your
responses to

this survey
will remain
anonymous

and
it
will take less than 5 minutes to complete. Thank you
for your participation.


1.

Do you use the Internet?

T

Yes

T

No



2.

Do you have internet connections at home?

F

Yes

F

No




If you do not use the internet
at home, why don’t you?

F

Not Interested

F

Do not have access


Too difficult or frustrating


Too expensive


It is a waste of time


Other_________________________________________________________________

3.

How would you rate your computer knowledge?




1





2



3



4




5

No Knowledge


Some Knowledge


Average


Above
-
Average


Expert


4.

Have you ever shopped on
-
line?

X

Yes


No


If yes, please che
c
k the major reason
(s)

why
you shopped on
-
line?

If
no, please check the major reason
(s)

why
you do not shop on
-

line?







Convenience


Too slow







Easier than store shopping


Insufficient information







Better prices


Anxiety about on
-
line transaction







Unique products not in
stores


Cannot see, feel, touch, or try on item






Saves time


No access to a computer






Can shop anytime of the day


Other


Page
27


Like to compare prices on
-
line








Less hassle / No crowds








PLEASE GO TO QUESTION # 5


PLEASE GO TO QUESTION

# 7


5.

How ma
n
y times have you made purchases on the web in the last three months?

X

Never


1
-

3 times


4
-

6 times


7
-

10 times


11
-

20 times


More than 20 times


6.

Please check ALL of your favorite on
-
line categories


Computers

X

CDs


Books and magazines


Event tickets


Consumer electronics


Videos


Air travel


Health and Beauty sites


Apparel and Accessories


Toys


Other


7.

Your age


Under 18


18




u

㈳O







v潵r den摥r


Male

u

cemale




Marital status


pingle

u

Married


㄰1

b摵cati潮al Attai湭ent

u

iess than high sch潯o


eigh sch潯o gra摵ate


p潭e C潬lege


C潬lege H


Page
28

11.

Income

X

Much worse off than colleagues


Worse off than colleagues


Same as colleagues


Better off than colleagues


Much
better off than colleagues


Th
ank you for completing this survey. Your responses will remain confidential.



REFERENCES


1.

Ackerman, M.,
Cranor, L., & Reagle, J. (1999).

Privacy in e
-
commerce: examining user scenarios
and privacy preferences, i
n
Proceedings

of the 1st ACM conference on electronic commerce
,
Denver, Colorado, 1

8.


2.

Alre
ck, P. and Settle, R. B. (2002).
Gender
e
ffects on
i
nterne
t, catalogue and store shopping
,
Journal of Database Marketing
, 9
(
2
),
150
-
162(13).

3.

Ahuja, M., Gupta, B., & Raman, P. (2
003).
An
e
mpirical
i
nvestigation of
o
n
-
line
c
onsumer
p
urchasing
b
ehavior
,
Communications of the ACM
,
46
(
12
)
, 145
-
151.

4.

Anonymous (2012). "Americans:
o
nline
s
tores
m
ore
a
ppealing for
h
oliday
s
hopping due to the
b
ad
e
conomy."
Business Wire,
5 October

2012.

5.

Anonymous (2011).
"PriceGrabber(R)
s
urvey
p
rojects an
i
ncrease in
m
obile and
o
nline
s
hopping
for the 2011
h
oliday
s
eason."
PR Newswire
,
1 November

2011.

6.

Anonymous (2011).
NUMBEO



Internet database about cost of living worldwide,
http://www.numbeo.com. Accessed 15 April 2011.

7.

Anonymous (2010). On
-
line

purchases on the rise in Bulgaria”,
Dnevnik.bg
,
http://sofiaecho.com/2010/03/24/877914_
on
-
line
-
pu
rchases
-
on
-
the
-
rise
-
in
-
bulgaria.
A
ccessed 19

March 20
11.


8.

Anonymous (2002)
. 80% of
t
ee
ns
n
ow
o
n
-
line
,
Credit Union Journal
,
6(
3
)
.

9.

Anonymous (2001).
Government
o
n
-
line
: an international perspe
ctive international perspective
,
Taylor Nelson & Sofres Annual Global Report
, November 2002,
http://www.epracti
ce.eu/files/media/media_872.pdf. A
ccessed 09

April 20
11.

10.

Apostolov
,

G
. (2008).
Bulgarian
t
eenagers’
b
ehavior
o
n
-
line
: Changing
s
ocial

p
atterns and
g
rowing
v
iolence?

in Barbovschi, M. & Diaconescu, M. (eds.)
,
Teenagers Actions and
Interactions Online in Central and Eastern Europe. Potential

and Empowerment, Risks and
Victimization
,
C
luj University Press
, p.151

11.

Arnett, J. (2000).
Emerging adulthood: A theory of development from
late teens through the
twenties
,
American Psychologist
, 55
(
5
)
, 469
-
480
.


12.

Arnett, J. (1992).
Reckless behavior in
adolescen
ce: A developmental perspective
,
Developmental
Review
, 12, 339
-
373
.


13.

Au, N., Ngai, E., & Cheng, T. (2008).
Extending the understanding of end user information
systems satisfaction formation: an equitable needs fulfillment model approach,
MIS Quar
terly,

32(1), 43

66.

14.

Ba
gchi, K. and Mahmood, M. (2004).
A
l
ongitudinal
s
tudy of
b
usiness
m
odel of
o
n
-
l
ine
s
hopping
b
ehavior
u
sing

a
l
atent
g
rowth
c
urve
a
pproach
,
Proceedings of the Tenth Americas Conference on
Information Systems
, NY.

15.

Basso, A., Goldberg,
D., Gre
enspan, S., & Weimer, D. (2001).
First impressions: emotional and
cognitive factors underlying j
udgments of trust in e
-
commerce, i
n
Proceedings of the 3rd ACM

C
onference on
E
lectronic
C
ommerce
, Tampa, Florida,
137

143.


Page
29

16.

Beken, T., Janssens, J., &

Vandaele, S. (2009).
The
m
usic
i
ndustry on (the)
l
ine? Surviving
m
usic
p
iracy in a
d
igital
e
ra.
European Journal of Crime, Criminal Law and Criminal Justice
, 17(2),
77

96.

17.

Bellman, S., Lohse
, G., & Johnson, E.

(1999).
Predi
ctors of on
-
line buying behavior
,
Communications of the ACM
, 42
(
12
)
, 32
-
38.

18.

Bhatnagar, A
., Misra, S. and Rao, H. (2000).
On
r
isk,
c
onvenience,

and
i
nternet
s
hopping
b
ehavior
,
Communications of the ACM
, 43
(
11
)
, 98
-
105.

19.

Brashear, T., Kashyap, V
.
, Musante, M
.
, & Donthu, N
.

(2009).
A
p
rofile of the
i
nternet

s
hopp
er:
Evidence from
s
ix
c
ountries
,
Journal of Marketing Theory and Practice
, 17
(3),

267
-
8.

20.

Brown, M., P
ope, N., and Voges, K. (2003).
Buying or browsing: An
e
xploration of
o
n
-
line

p
urchasing orientations and
o
n
-
line

p
ur
chase inten
tion
,
European Journal of Marketing
,
37
(
11/12
)
, 1666
-
1684.

21.

Business Wire (2010),
Research and
m
arkets: Reta
il
e
-
c
ommerce in Western Europe
,
24 August

2010, available at: http://www.thefreelibrary.com/Research and Markets: Retail E
-
Commerce
in
Western Europ
e.
-
a0235258054. A
ccessed on 18

March 20
11.

22.

Callahan, F.

& Koenemann, J. (2000)
.

A
c
omparative usability evaluation of user interfaces for
on
-
line

product catalog
,

i
n
Proceedings of the 2nd ACM conference on
E
lectronic
C
ommerce
,
Minneapolis,
Minnesota,
197

206.

23.

Ca
h
k
,
N.

& Ersoy,
F. (2008). On
-
line

s
hopping
b
ehavior and
c
haracteristics of consumers in
Eskisehir, Turkey: Who
,
w
hat
,
h
ow
m
uch and
h
ow
o
ften?

The Business Review
, Cambridge,
10
(
2
)
, Summer 2008, 262
-
268.

24.

Castaneda, J., & Montoro
, F. (2007). The effect of
i
nternet general privacy concern on customer
behavior,
Electronic Commerce Research
, 7(2), 117
-
141.

25.

Cen
tral Intelligence Agency (2011).

The World Factbook, Country Comparison: GDP
-

per
capita,
Central Intelligence Agency
, https
://www.cia.gov/library/publications/the
-
world
-
factbook/rankorder/
2004rank.html. A
ccessed on 19

March 20
11.

26.

Chen, L., Gillenson, M. and Sherrell, D. (2002).
Enticing
o
nline
c
onsumers: An
e
xtended
t
e
chnology
a
cceptance
p
erspective
,
Information & Management
,
39
(
8
)
, 705
-
719.

27.

Credit Suisse (2007).
Bulgaria: EU’s
n
ewest
m
ember
o
ffers
a
ttractive
f
uture
i
nvestment
o
pportunities,
Global Equity Research
, 9 February 2007, https://emagazine.credit
-
uisse.com/index.cfm?fuseaction=OpenArticle&aoid=178717&lang=EN&
coid=177883
. A
ccessed
on 19/3/11.


28.

Davis, F. (1989).
Perceived
u
sefulness,
p
erceived
e
ase of
u
se, and
u
ser
a
ccep
tance of
i
nformation
t
echnology
,
MIS Quarterly
, 13
(
3
)
, 319
-
340.

29.

Dellner, T
.

(2007). European
e
-
c
ommerce
,
Electronic Retailer Magazine
,
www.elect
ronicretailermag.com /info/0607_euro.html, accessed on 08/4/11.


30.

Dittmar, H., Long, K., & Meek, R. (2004). Buying on the Internet: gender differences in on
-
line
and conventional buying motivations.
Sex Roles
, 50(5/6), 423

444.

31.

Donthu, N
.
, & Garcia, A
.

(1999). The
i
nternet
s
hopper
,
Journal of Advertising Research
,

52
-
58.

32.

Eurostat (2011)
.

Individuals using the
i
nternet

for ordering goods or services,
http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=

en&pcode=tin00096.

Acce
ssed on 18/6/11.

33.

Feath
erman, M. and Pavlou, P. (2003).
Predicting
e
-
s
ervices
a
doption:
A

p
e
rceived
r
isk
f
acets
p
erspective
,
International Journal of Human
-
Computer Studies
, 59, 451
-
474.

34.

Forrester Research (2010)
.
Double
-
d
igit
g
rowth
f
or
o
n
-
line

r
eta
il
i
n
t
he US
a
nd Western Europe
,
http://www.forrester.com/ER/Press/Release/0,1769,1330,00.html
. A
ccessed on 15

March 20
11.

35.

Frasier,
S.

& Henry,
L. (2007).
An
e
xploratory
s
tudy of
r
esidentia
l
i
nternet
s
hopping in Barbados
,
Journal of Eastern Caribbean Studies
, 32
(
1
)
, 1
-
20.

36.

Ha, S. J., & Stoel, L. (2012). Online apparel retailing: roles of e
-
shopping quality and experiential
e
-
shopping motives,
Journal of Service Management
, 23(2), 197

215.


Page
30

37.

Hartman J., Shim S
., Barber B., O’Brien M. (2006).
Adolescents’
u
tilitarian a
nd
h
edonic
w
eb
-
c
onsumption
b
ehavior: Hierarchical
i
nfluence of
p
er
sonal
values and i
nnovativeness
,
Psychology
& Marketing
, 23
(1
0
)
, 813
-
839.

38.

Hernandez, B., Jimenez, J., & Martin, M. J. (2011).
Age, gender and income: do they really
mode
rate online shopping
behaviour?,

Online Information Review
, 35(1), 113

133.

39.

IFPI



International Federation of the Phonographic Industry

(2012). Digital
m
u
sic
r
eport 2012.
http://www.ifpi.org/content/library/DMR2012.pdf
. Accessed on 30 January 2013
.

40.

IIPA (2003).
International
Intellectual Property Alliance 2003 Special 301 Report BULGARIA,
http://www.iipa.com/r
bc/2003/2003SPEC301BULGARIA.pdf.

Accessed on 09

April 20
11.

41.

Internet World Stats (2011).

http://www.
Internet
worldstats.
com/
. A
ccessed on 15

March 20
11.

42.

Jarvenpaa, S., Tract
insky, N. and Vitale, M. (1999).
Cons
umer
t
rust in an
i
nternet
s
tore
,
Information Technology and Management
, 1
(
12
)
, 45
-
71.

43.

Joines, J., Scherer, C. and Scheufele, D. (2003).
Exploring
m
otivations for
c
onsumer
w
eb
u
se and
t
h
eir
i
mplications for
e
-
c
ommerce
,
Journal of Consumer Marketing
, 20
(
2
),
90
-
109.

44.

Jones, K. (2008). Gen Y
b
usiest
o
n
-
line
s
hoppers
,
Information Week
,
http://www.informationweek.com/news/
internet
/retail/showArticle.jhtml?articleID=2103000
52&
queryText=Gen%20
Y%20Busiest%20On
-
line%20Shoppers.

Accessed on 19

March 20
11.

45.

Kiran, R
.
, Sharma, A
.
, & Mittal, K.
(2008).
Attitudes,
preferences and p
rofile of
o
n
-
line

b
uyers in
India: Changing
t
rends
,
South Asian Journal of Management
, Jul
-
Sep 2008, 15
(
3
)
, p.55.

46.

Kolsaker, A., Lee
-
Kelley, L. and Choy, P. (2004).
The
r
eluctant Hong Kong
c
onsu
mer:
Purchasing
t
ravel
o
nline
,
International Journal of Consumer Studies
,
28
(
3
),
295
-
304.

47.

Korg
aonkar, P. and Wolin, L. (1999).
A
m
ul
tivariate
a
nalysis of
w
eb
u
sage
,
Journal of
Advertising Research
, 39
(
2
)
, 53
-
68.

48.

Kwek, Choon Ling, Tan, hoi
Piew, & Lau, Teck
-
Chai (2010).
Investigating the
s
hopping
orientations on
o
n
-
line

p
urchase
i
ntention in the e
-
c
ommerce

e
nvironment: A Malaysian
s
tudy
,
Journal of
Internet

Banking and Commerce
, 15
(
2
)
, p.15.

49.

Lanphear, Sue (2000),
Digita
l
w
allets
l
et
t
eens
s
hop
o
n
-
line
,
Credit Union Executive Newsletter
,
26
(
16
)
.

50.

Levy, S., (1999).
How t
he
i
nternet
i
s
c
hanging America
, in Levy:
E
-
Life
, 38
-
42.

51.

Li, H.,

Kuo, C. and Russell, M. (1999).
The
i
mpact of
p
erceived
c
hanne
l
u
tilities,
s
hopping
o
rientations, and
d
emographics on the
c
o
nsumer’s
o
nline
b
uying
b
ehavior
,
Journal of Computer
-
Mediated Communication
, 5
(
2
)
.

52.

Liang,

T. and Jin
-
Shiang, H. (1998),
An
e
mpirical
s
tudy on
c
onsumer
a
cceptance of
p
roducts in
e
lectronic
m
a
rkets:
A

t
ransaction
c
ost
m
odel
,
Decision Support Systems
, 24
(
1
)
, 29
-
43.

53.

Liao, Z. and Cheung, M. (2001).
Internet
-
b
ased
e
-
s
hopping and
c
onsum
er
a
ttitudes
: A
n
e
mpirical
s
tudy
,
Information & Management
,
38(
5
),
299
-
306.

54.

Lightner, N., Yenisey, M., Ozo
k
, A. A., & Salvendy, G. (2002).

Shopping behavior and
preferences in e
-
commerce of Turkish and American university students: implicat
ions from cross
-
cultural design,

Behaviour and Information Technology
, 21
(6)
, 373

385.

55.

Livingstone, S., Haddon, L.,
Görzig,

A., Ólafsson, K. (2010).
Risks and safety on the
internet
:

The perspective of European children, Initial findings from the EU Kids
On
-
line

survey of 9
-
16
year olds and their parents

in 25 countries
,
The London School of Economics and Political
Science
, Co
-
funded by the European Union.

56.

Limayem, M., K
halifa, M. and Frini, A. (2000).
What
m
akes
c
onsumers
b
uy
f
rom
i
nternet? A
l
ongit
udinal
s
tudy of
o
nline
s
hopping
,
IEEE Transactions on Systems, Man, and Cybernetics
-
Part A: Systems and Humans
,
30
(
4
),
421
-
432.

57.

López
-
Bonilla, J. M., & López
-
Bonilla, L. M. (2008).
Sensation seeking and e
-
shoppers.
Electronic Commerce Research

Journal
, 8(3), 143

154.

58.

Lueg, Jason (2001).
American
t
eenagers and the
i
nternet
:
A

c
onsideration of
c
onsumer
e
lectronic
c
ommerce from a
c
on
s
umer
s
ocialization
p
erspective
, a dissertation submitted in partial

Page
31

fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of
Management and Marketing,
University of Alabama
, p.19, p.30, p.

83.

59.

Lynch, P. & Beck, J. (2001).

Profiles of
i
nternet buyers in 20
c
ountries: Evidence for region
-
specific strategies,
Journal of International Business Studies
, 32(4), 725
-
748.

60.

Markus, H., & Nurius, P
. (1987).
Possible selves: The interface between
motivation and the self
concept
, In K. Yardley & T. Honess (Eds.),
Psychological
P
erspectives
, New York: John Wiley
& Sons, Inc., 157
-
172.

61.

McAdam, Carrie (2010),
Shoppers in UK ar
e Europe’s top on
-
line spend
ers,

http://www.heraldscotland.com/news/home
-
news/shoppers
-
in
-
uk
-
are
-
europe
-
s
-
top
-
on
-
line
-
spenders
-
1.1002772.

Accessed on 16

March 20
11.

62.

Morton, Linda P. (2002). Targeting
G
eneration Y
.
Public Relations Quarterly
,

47
(
2
)
, 46
-
48.

63.

Moschis,
G
. and Churchill,
Jr., G

(1979). An
a
nalysis of the
a
dolescent
c
onsumer,
Journal of
Marketing
, 43

(3), 40
-
48
.

64.

Moss, G., Gunn, R., & Kubacki, K. (2008). Gender and

web design: T
he implications of the
mirroring principle for the service branding model,
Journal of Marketing Co
mmunications
, 14(1),
37

57.

65.

National Statistical

Institute [of Bulgaria] (2009).

Survey on ICT usage in households and by
indiv
iduals aged between 16 and 74
, 17.12.2009, http://www.nsi.bg/EPDOCS/ICT_

hh2009_en.pdf, accessed on 19

March
2011, p.2.

66.

NCSPO: Na
tional Center for
Study of Public Opinion (2006).
Children an
d
r
isks in
o
n
-
line
c
ommunication
,
Group Discussions and Quantitative Study

in Sofia, Burgas, Varna, Pleven and
Plovdiv, p.13.

67.

Nie, N. & Erbring, L. (2000).
Internet
u
se
,
Stanford Institute for
the Quantitative Study of Society
,
California.

68.

Nielsen (2008).
Trends in
on
-
line

shopping
: a global Nielsen consumer report
, February,
http://th.nielsen.com/site/documents/Global
On
-
line
ShoppingReportFeb08.pdf
. A
ccessed on 15

March 20
11.

69.

Ott, B. (2011).
What does the Millennial generation want? And how can retailers satisfy the ne
eds
of these fickle youngsters?,

http://institution.gallup.com/gmj/146990/Marketing
-
Tweeters
-
Facebook
-
Friends.aspx
. A
ccessed on 11/4/11, p.12.

70.

Ozok, A. &∙Wei, J. (2010). An empir
ical comparison of consumer usability preferences in on
-
line
shopping using stationary and mobile devices: results from a college student population,
Electronic Commerce Research
, 10, 111

137.

71.

Park, J.
, Lee, D. and Ahn, J., (2004).
Risk
-
f
ocused
e
-
c
ommerce
a
dopti
on
m
odel: a
c
ross
-
country
s
tudy
,
Journal of Global Information Management
, 7,6
-
30.

72.

Paul, P. (2001). Getting inside gen Y.

American Demographics, 23
(9), 42
-
49.

73.

Pavlou, P. (2003).
Consumer
a
cceptance of
e
lectronic
c
ommerce: Integrating
t
rust an
d
r
isk with
the
t
echnology
a
cceptance
m
odel
,
International Journal of Electronic Commerce
, 7
(
3
)
, 101
-
134.

74.

Pew
Internet

and

American Life Project (2010).
Generations 2010,
http://pew
internet
.org/~/media//Files/Reports/2010/PIP_Generations_and_Tech10.pdf
. A
ccessed
15 March 20
11, p.4.

75.

Pindyck, R. and Rubinfeld, D.

(1998).

Econometric Models and Economic Forecasts: 4
th

edition

Boston, MA: McGraw
-
Hill, 299
-
302, 307
-
312.


76.

Quick, R. (1999). On
-
line
: New
w
eb
s
ites
l
et
k
ids
s
hop. Like,
w
ithout
c
redit
c
ards
,
The Wal
l
Street Journal
, (June 14), B1.

77.

Reuters. (2012). Bulgaria refuses to ratify ACTA.
http://rt.com/news/acta
-
bulgaria
-
protesters
-
ratification
-
353/ss
. Accessed on
15 February, 2012
.

78.

Ro
dgers, S. and Harris, M. (2003).
Gender and
e
-
c
ommerce: An
e
xploratory
s
tudy
,
Journal of
Advertising Research
,
43
(
3
),
322
-
330.

79.

Rohm,
A.

&

Swaminathan,
V. (2004).
A
t
ypology of
on
-
line

shopper
s based on shopping
motivations
,
Journal of Business Research
, 57
(
7
)
, 748
-
757.


Page
32

80.

Ruyter, K., Wetzels, M. and Kleijnen, M., (2001).
Customer
a
doption of
e
-
service: An
e
xperimental
s
tudy
,
International Jour
nal of Service Industry Management
,
12(
2
),
184
-
208.

81.

Sarbu, M. (2008), Cum arata
g
eneratia Y
,
Business Magazin
,
http://www.businessmagazin.ro/opinii/cum
-
arata
-
generatia
-
y
-
2507667. Accessed on 3 December
2010

82.

Shim, S
. (1996).
Adolescent
c
onsumer
d
ecision
-
mak
ing
s
tyles: The
c
ons
umer
s
ocialization
p
erspective,

Psychology and Marketing
, 13 (September), 547
-
569.

83.

Singh, S. (2002). Paying and playing on the Net.
Marketing Week
,
25
(22), 34.

84.

Slyke, C., Comunale,
C. and Belanger, F. (2002).
Gender
d
ifferences in
p
e
rceptions of
w
eb
-
b
ased
s
hopping
,
Communications of the ACM
,
45(
7
),
82
-
86.

85.

Spiekermann, S., Grossklags, J., & Berendt, B. (2001).
E
-
privacy in 2nd generation e
-
commerce:
privacy pref
erences versus actual behavior, i
n
Proceedings of the 3rd ACM
C
onference on

E
lectronic
C
ommerce
, Tampa, Florida, 38

47.

86.

Stafford, T., Turan
, A. and Raisinghani, M. (2004).
International and
c
ross
-
c
ultural
i
nfluences on
o
nline
s
hopping
b
ehavior
,
Journal of Global Information Management
, 7(
2
),
70
-
87.

87.

Susskind, A. (2004).
Electronic
c
ommerce and
w
orld
w
ide
w
eb
a
pprehensiveness: An
e
xamination of
c
onsumers’
p
e
rceptions of the
w
orld
w
ide
w
eb
,
Journal of Computer
-
Mediated
Communication
, 9
(
3
)
.

88.

Swinyard, Willi
am R. & Smith Scott M. (2003).
Why
p
eople (don’t) shop
on
-
line
: A lif
estyle of
the
i
nternet consumer
,
Psychology and Marketing

Special Issue, 20
(
7
)
, 567
-
597.

89.

T
apscott, Don (2009)
.
Grown
U
p
D
igital;

H
ow the

N
et
G
eneration is
C
hanging your
W
orld
, New
York: McGraw
-
Hill, 2009, p.188.

90.

Temelkova, K. (2008).
Bulgarians
d
on't
s
hop
o
n
-
line
,

Kuneva
m
akes
e
-
commerce
e
asier
,
The
Standart
, http://paper.standartnews.com/en/article
.php?d=2008
-
06
-
23&article=24239. A
ccessed
on 15

June 20
11.

91.

Teo, T. (2001).
Demographic and
m
otivation variables associated

with
i
nternet
u
sage
a
ctivities
,
Intern
et

Research: Electronic Networking Applications and Policy
, 11
(
2
)
,
125
-
137.

92.

Ulbrich, F., Christensen, T., Stankus, L. (2011).
Gender
-
specific on
-
line shopping preferences,
Electronic Commerce Research
, 11, 181
-
199.

93.

Vahlberg, V. (2010). Fitting
i
nto
t
heir
l
ives:
A s
urvey of
t
hree
s
tudies
a
bout
y
outh
m
edia

u
sage,
http://www.mediamanagementcenter.org/research/fitting.pdf. Accessed on 18 June 2011
, p.12.

94.

Vaithianathan, S., (2010). A review of e
-
commerce literature on India and research agenda for the


future,
Electronic Commerce Research
, 10, 83

97.

95.

Vellido, A., Lisboa, P. , & Meehan, K. (2000). Quantitative characterization and prediction of
o
n
-
line
p
urchasing behavior: A
l
atent variable
a
pproach,
International Journal of Electronic
Comm
erce
, 4(4), 83
-
104.

96.

Venkatesh, V., & Morris, M. (2000). Why don’t men ever stop to ask for directions? Gender,
social influence and their role in technology acceptance and usage behavior,
MIS Quarterly
,
24(1), 115
-
139.

97.

Von Abrams, K. (2010). Retail
e
-
Comm
erce in Western Europe,
http://www.emarketer.com/Reports/All/Emarketer_200679.aspx
. Accessed 16 March 2011.

98.

Wolfe, D. (2009), Payments
c
ompanies
s
eek
e
lusive
t
een
p
urchases,
American Banker
, 174
(112).

99.

Zhou, L., Dai, L. & Zhang, D. (2007). Online
s
hopping
a
cceptance
m
odel


a
c
ritical
s
urvey of
c
onsumer
f
actors in
o
nline
s
hopping,
Journal of Electronic Commerce Research
, 8(1), 41
-
62.