The Impact of Knowing the Profile of Online Shoppers on Online Shopping:

Evidence from City of Berat, Albania

ANA BUHALJOTI1, MATEUS HABILI2, ARJAN ABAZI1

1Department of Marketing and Tourism, University of Tirana, Faculty of Economy

Elbasani St, Tirana, 1005, ALBANIA

2Department Management and Marketing, Tirana Business University College,

Street of Kavaja Tiranë 1023, ALBANIA

Abstract: - Electronic commerce is a type of business in which all transactions are carried out online and the seller and

buyer do not meet in person, as in traditional commerce. E-commerce has been steadily increasing over the last decade,

and it is now a popular method of shopping in many parts of the globe. As more businesses are switching to online sales,

their sales strategies are adapting to the needs of their online customers, including customers demographic and

psychographic characteristics. Therefore, in an effort to understand and compare the segments of online shoppers, this

research aims on understanding the profile of online shoppers and their implications on online shopping in Albania. The

particular interest of this study is to understand the characteristics and profiles of the online shoppers for marketers in

virtual business in Albania.

The target respondents of the study were internet users located in the city of Berat and a survey questionnaire was used

to collect the data from 389 respondents. The sample selection is probabilistic with 95% reliability level and 5% margin

of error as the total population of the city of Berat is 119,450 inhabitants. The reliability analysis based on Cronbach

Alpha test and Chi square test were used to test the hypotheses and the statistical program Spss 24 for data processing

and analysis. The findings reveal that most of the online shoppers are females, between the age of 20 to 24 years old,

have a university degree, and an average monthly income of up to 61. 000 All and tend to buy products at average

prices. The research findings on online shoppers profile further indicate that gender does not affect the frequency of

online shopping ; trust does not affect the frequency of online shopping; but price affects the frequency of online

purchases. Thus, online businesses should focus on convenience, price, branding to impact online shoppers. The

implications of the findings for practice are further discussed.

Key-Words: - Ecommerce, online shopping, buyer profile, online shoppers profile, consumer behavior.

Received: September 19, 2021. Revised: May 25, 2022. Accepted: June 17, 2022. Published: July 25, 2022.

1 Introduction

A shopper's profile is a detailed description of a specific

customer or group of customers. It usually describes a

company's target or ideal customer and can include

demographic and geographic data, as well as interests

and purchasing patterns. Everyone shops differently, and

as a result, everyone has a different shopper profile.

Maybe customers prefer to browse slowly, or maybe

they stick to a list and come in and out quickly. Different

buying behaviors and what ultimately drives a

customer's decision to buy are identified by shopper

profiles. By understanding the shopper's profile a firm

can tailor its store's browsing and shopping experience to

individual specific needs and demands and increase

sales. Basically, shopping is unique to each individual,

and so the buyer profile as well. Customers may like to

browse slowly or stick to the list and then exit. The

particular interest of this study is whether the

characteristics and profiles of the Internet shoppers will

have profound implications for marketers in the virtual

business world. Therefore, in an effort to understand and

compare the segments of online shoppers, this research

focuses on understanding the profile of online shoppers

and implications on online shopping.

1.1 Profiles of Online Shoppers

The Online shoppers are internet users who make retail

purchase with the use of Internet connection.[1] Various

studies have acknowledged that online shoppers are

behaviorally different in shopping in comparison to non-

internet shoppers and the online shopper profiles differ

across world regions or markets [2] [3] [4] [5].

Globally, Generation X (age 35-49) makes up

approximately 28% of on line buyers at the same time as

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

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Ana Buhaljoti, Mateus Habili, Arjan Abazi

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Baby Boomers (age 50-64) represent round 10%. The

so-called “Silver surfers” (age 65+) account for simplest

2%. Around 65% spend 16+ mins on a company`s`

internet site earlier than you decide on buying a product.

Categories of online shopper profiles are:[6] Bargain

hunter. Most retailers regularly encounter bargain

hunters in their stores. Bargain hunters, also known as

discount shoppers, are usually armed with coupons or

discount codes, know when the best deal is and rarely

buy without the deal at hand. While some shoppers are

looking for something sentimental, bargain hunter

purchases are driven primarily by a sense of price and

money savings.

Browser. Companies that own a physical store,

especially one that gets a lot of foot traffic, are probably

used to "just looking" shoppers. Browsing customers,

also known as traveling customers, are not seeking for a

particular item or desire to shop at a specific place. They

come across the store and are interested in learning more

about what you have to offer.

Showrooming Customers. This type of shoppers are

particularly common among those looking for furniture,

appliances, or other large and expensive products that

must last. Large companies like Ikea have tailored their

entire business model to cater to showrooming

customers, whereas in small enterprises showrooming

can be a problem as shoppers will simply view the

products in order to buy them at a reduced price from

another retailer.

Impulse customer. Impulse shoppers make unplanned

purchases based on products that are currently attractive.

As lined up at a grocery store or convenience store and

probably received a tip at checkout. This is an impulse

purchase. Impulse purchases usually include the

purchase of small items that get people's attention.

However, it can also be used as shopping therapy or

emotional shopping.

Mission-led shoppers. Mission-driven shoppers are

looking for a particular item or buying from a list. They

are often called "wrist buyers". Because they often come

up with a physical ectenia of what they wish to buy.

Others have labeled them "needs-driven" and "passive"

shoppers since they buy because they have to, not

because they like shopping.

Undecided regular customers. Undecided buyers want to

buy, but hesitate to buy because of price, information

overload, or lack of information. They try things, but

struggle to decide and keep coming up with reasons why

they shouldn't buy the piece.

Educated customers. As access to product information

increases, many of today's buyers fall into an educated or

informed buyer profile. Educated shoppers check

product and store inventory online, read customer

reviews, and scan general pricing information before

going to the store.

Faithful customers. Every retailer cherishes a long-term

consumer or patron. A loyal customer is someone who

visits the store frequently and makes regular purchases.

Retailers recognize them by name and may have a

relationship with them. Faithful customers are a

particularly important subset of buyer profiles because of

their potential profitability. In fact, the most valued

customers are those who remain loyal.

According to commonly reported statistics, loyal

customers spend an average of 33% more on a single

visit than new buyers. When shopping online, consumers

usually tend to inadvertently buy immediately [7] Their

intentions may be related to the simplicity and

complicity of the site [8] From this point of view,

Sharma et al. [9] found that online purchases were

caused by consumer emotions, low cognitive control, or

voluntary behavior. They argue that consumer impulse

buying behavior is driven by attractive objects that force

consumers to buy, without considering the economic and

other consequences of online purchases. Some

researchers also claim that online shoppers as an

individual personality are more spontaneous [10].

According to Poddar et al. [11] a commercial website is

like a store claims that it includes all the features of the

store. Therefore, the business personality structure can

be applied to the personality of a website due to the

similarity between web business and offline business.

The website personality in this study is a spiritual

expression of the website shop in a dimension that shows

similarity and reflects the dimension of human

personality. Compared to traditional shoppers, the

impulse buying trend dominates the online purchase of

sensory products and supports the notion that pleasure

buying motivations influence impulse buying [12] [13].

The stimulus of online marketing avoids the risk of

initial search and purchase for online shoppers [8] ,

making it easier to shop impulsively [14].

2 Methodology

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2.1 Research Questions

This paper aims to give answers to the following

research questions:

1.How much have social media advertisements

influenced your decision to shop online?

2. After your online shopping experience, how much

credibility have you gained?

3. How much would you recommend buying products

online to your acquaintances?

4. How often did you buy things online in a month if

you did?

5. Which items are you inclined to buy online?

2.2 Research Method

The method used is the survey and the instrument used is

the questionnaire. The questionnaire consists of 21

questions. The research sampling method is probabilistic

sample. The sample is statistically representative and

consists of 389 individuals. It is statistically calculated

based on the total population of the city of Berat of

119,450 inhabitants [15] with 95% reliability level and

5% margin of error.

2.3 Data Analysis

The research aims to explore the perception and opinion

of the respondents. The primary data collected tend to

evaluate consumers as rational. The survey questionnaire

consisted of questions on a Likert scale of 1 to 5 where

1- represents not at all, 2-little, 3-neutral, 4-many, 5-

extremely much.

The reliability analysis based on Cronbach Alpha test

and Chi square test were used to test the hypotheses and

the statistical program Spss 24 for data processing and

analysis.

2.4 Research Limitations

The main limitation of the study is that the respondents

live in the city of Berat and there is no extension for all

cities in Albania. If the respondents change their place of

residence, their approach might change as well. Their

access to the online purchasing experience can also be

enhanced or limited, which might impact their

impression and opinion in comparison to the study's

findings.

3 Data Analysis and Research Findings

Table 1. Cronbach's Alpha Test

Reliability Statistics

Cronbach's

Alpha

Cronbach's

Alpha Based on

Standardized

Items

N of Items

.736

.734

Based on the table of significance of the model, it results

that the reliability of the model is 73.6%. This indicates

that the questionnaire assesses the purpose of the study

and improves the degree of effectiveness of using the

results in making decisions related to the study findings.

Table 2. Model reliability

Item-Total Statistics

Scale

Mean if

Item

Deleted

Scale

Varianc

e if

Item

Deleted

Correct

Item-

Total

Correla

tion

Squared

Multipl

Correla

tion

Cronba

ch's

Alpha if

Item

Deleted

How much

have social

media

advertisements

influenced

your decision

to shop online

from 1 to 5 ?

7.0720

5.521

.393

.155

.826

After your

online

shopping

experience,

how much

credibility

have you

gained from 1

to 5?

6.4396

4.412

.655

.514

.541

How much

would you

recommend

buying

products

online to your

acquaintances

from 1 to 5?

6.4781

3.714

.662

.524

.518

It turns out that removing the first variable has no effect

on the model's reliability, since it is 82.6 percent. While

removing the other two variables reduces the model's

reliability. This shows that these variables are very

important in the reliability of the model because

removing them reduces the model reliability to 50%.

This shows that in addition to the demographic and

psychographic data of online shoppers, the reliability

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DOI: 10.37394/23207.2022.19.112

Ana Buhaljoti, Mateus Habili, Arjan Abazi

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Volume 19, 2022

built throughout the online shopping experience is an

impact factor that encourages online purchasing, which

influences the growth of online sales.

To address the research problems and to achieve the

main and specific objectives, this study was performed

by testing the following hypotheses:

Table 3. The relationship between the gender variable

and the frequency of online shopping

H1: Gender affects the frequency of online shopping

Before interpreting the results of the cross table, we need

to test if the Chi Square condition is met and for this we

refer to Table 4 and see that the Chi Square test

condition is met, we can proceed with the interpretation.

Based on the results of Table 4 it turns out that the

Pearson Chi Square coefficient is 6.9 and p value 0.223,

this shows that there is no relationship between the

variables and the hypothesis is not accepted as the value

is less than 5%.

Table 4. Chi Square Hypothesis Tests 1

Chi-Square Tests

Value

Asymptotic

Significance

(2-sided)

Pearson Chi-Square

6.969a

.223

Likelihood Ratio

7.012

.220

Linear-by-Linear

Association

1.747

.186

N of Valid Cases

389

a. 0 cells (0.0%) have expected count less than 5. The

minimum expected count is 13.60.

H2: Trust affects the frequency of online purchases

Table 5. The relationship between the trust variable and

the frequency of online purchases

You are: * If you bought products online how

often did you buy within a month?

Crosstabulation

If you bought products online how

often did you buy within a month?

Tota

time

times

5 time-

Over

5 time

are:

Religi

ous

Count

342

Expecte

d Count

64.2

65.9

98.5

62.4

51.0

342.

Not a

religi

ous

Count

Expecte

d Count

8.8

9.1

13.5

8.6

7.0

47.0

Total

Count

112

389

Expecte

d Count

73.0

75.0

112.0

71.0

58.0

389.

We refer to Table 6 and see that the Chi Square test

condition is met, we can proceed with the interpretation.

Based on the results of Table 6 it turns out that the

Pearson Chi Square coefficient is 3.0 and p value 0.54,

this shows that there is no relationship between the

variables and the hypothesis is not accepted as the value

is less than 5%.

Table 6. Chi Square Hypothesis Tests 2

Chi-Square Tests

Value

Asymptotic

Significance

(2-sided)

Pearson Chi-Square

3.098a

.542

Likelihood Ratio

2.998

.558

Linear-by-Linear

Association

.669

.414

N of Valid Cases

389

a. 0 cells (0.0%) have expected count less than 5. The

minimum expected count is 7.01.

H3: The tendency to buy products based on prices

affects the frequency of purchases

Crosstab

If you bought products online how

often did you buy within a month?

Total

time

times

Over 5

times

What

gender do

you belong

to?

Female

Count

200

Expec

ted

Count

37.

38.6

57.6

36.5

14.4

15.4

200.

Male

Count

189

Expec

ted

Count

35.

36.4

54.4

34.5

13.6

14.6

189.

Total

Count

112

389

Expec

ted

Count

73.

75.0

112.

71.0

28.0

30.0

389.

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Table 7. The relationship between the variable the

tendency to buy products based on prices and the

frequency of online purchases

Table 8. Chi Square Hypothesis Tests 3

Chi-Square Tests

Value

Asymptoti

Significanc

e (2-sided)

Pearson Chi-Square

40.952

.000

Likelihood Ratio

39.280

.000

Linear-by-Linear

Association

24.877

.000

N of Valid Cases

385

a. 0 cells (0.0%) have expected count less

than 5. The minimum expected count is 8.29.

We need to see if the Chi Square test condition is met

and for this we refer to Table 8 and see that the Chi

Square test condition is met, we can proceed with the

interpretation. Based on the results of Table 8 it turns

out that the Pearson Chi Square coefficient is 40.952 and

p value 0.00, this shows that there are relationships

between variables and the hypothesis is accepted

because the condition that the value is less than 5% is

met. This shows that based on buying trends , price

affects the frequency of online shopping. This is related

to which product categories are exposed online and

which are inclined to buy online because many products

consumers despite the price require physical

involvement of individuals in shopping, due to the

reliability and characteristics of the products. To

understand on which elements this relationship is based

please refer to table 7. It shows that based on the

tendency to buy products at low prices it turns out that

the highest frequency is 3 times a month or 1 time in 10

days. Those who buy products with low prices and

online do not buy only based on low price, but also have

the tendency to buy products at average prices. It results

that the highest frequency is 3 once a month or once in

10 days, so those who buy products at average prices

and online is not that they often buy only based on the

average price, while in terms of the trend for buying

products at high prices it turns out that the highest

frequency is 5 times a month or 1 time in 6 days, so

those who buy products at high prices buy more often

online. This shows that the price of products sold online

based on online buyers in Albania does not affect online

shopping. This major finding reinforces the fact that if

someone likes to buy online is not affected by the

tendency to buy products based on prices online.

4 Conclusions

The result of the research study on the profile of online

shoppers in Albania, evidence from the city of Berat

show that the online shoppers fall in the demographic

segment of 20-24 years old, 25-29 years old, mostly

female (51.4 percent) with income level range between

31.000-61.000 All, and have a university education

(53.7%). Psychographically, they spend more on food

category (64.7%), tend to buy products at average prices

(64%), prefer more home-cooked food (61.4%), prefer to

live in the city (74%) and use private transport (61.7%),

are believers (87.9%) and do physical activity (67.6%).

The findings from this study provide interesting insights

for the marketing managers who are involved in online

commerce. These data describe the profile of online

shoppers in the city of Berat, based on demographic and

psychographic data of respondents.

The research findings on online shoppers profile further

indicate that gender does not affect the frequency of

online shopping ; trust does not affect the frequency of

online shopping; but price affects the frequency of

You are more likely to buy products: * If you

bought products online how often did you buy

within a month?

Crosstabulation

If you bought products online

how often did you buy within a

month?

tal

time

times

-over

times

You are

inclined to

buy

products:

Low

price

Count

Expec

ted

Count

15.1

15.6

23.1

14.9

12.2

81.

At an

averag

e price

Count

Expec

ted

Count

46.6

47.9

71.1

45.9

37.5

9.0

Highly

priced

Count

Expec

ted

Count

10.3

10.6

15.7

10.1

8.3

55.

Total

Count

110

Expec

ted

Count

72.0

74.0

110.

71.0

58.0

5.0

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Ana Buhaljoti, Mateus Habili, Arjan Abazi

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Volume 19, 2022

online purchases. Thus, online businesses should focus

on convenience, price, branding to impact online

shoppers.

Online businesses need to understand the distinct

characteristics of the Internet shoppers as the Internet

has become a necessity for the younger generations. The

distinctions in the profile of online shoppers are needed

by the marketing managers to tailor marketing strategies

to the different market segments of the shoppers.

Although the present results have provided meaningful

implications, this research study has a few limitations as

the findings may lack of generalizability in relation to

the whole country of Albania and future research shall

link culture with the characteristics of online shopping

behavior in Albania. Future research might also

investigate if online shopping in Albania is more

adopted in rural areas where physical shops are not as

available as in metropolitan areas. Knowledge regarding

it would be of value to e-commerce delivery practices.

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Contribution of Individual Authors to the Creation

of a Scientific Article (Ghostwriting Policy)

 Dr. Ana Buhaljoti conceptualized the article,

interpreted the data and revised them critically for

important scientific content and for main findings

and recommendations.

 Msc. Mateus Habili designed and performed the

statistical analysis, collected the data and contributed

data analysis tools.

 Prof. Dr. Arjan Abazi was accountable for all

aspects of the work in ensuring that questions related

to the accuracy and integrity of any part of the work

are appropriately investigated and resolved.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the Creative

Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en_US

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DOI: 10.37394/23207.2022.19.112

Ana Buhaljoti, Mateus Habili, Arjan Abazi

E-ISSN: 2224-2899

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Volume 19, 2022