KnE Social Sciences | The Economies of Balkan and Eastern Europe Countries in the Changed World (EBEEC) | pages: 421-435

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1. Introduction

The economic crisis of the past years has affected all aspects of consumption [9,26,12]. Consumers are adjusted to new data, since there is a decrease of wages, a limitation of consumer loans, etc. [21,22,24,17].

E-commerce is becoming an important factor for business sustainability, since Internet has become an integral part of everyday life [23]. Consumers want the best service with the least effort and companies promote their products and services through the cyberspace [2,7].

Several researchers have studied the relation between age, gender, economical status and the consumer behaviour [25,18,23,20]. Economic crisis is the reason that many consumers worldwide re-evaluate their consuming behaviour [9,10,11,8,6,15,19,16]; in Greece as well [14,1], (Matsaganis, 2011).

As a consequence, the use of e-commerce is rapidly increasing. It is remarkable that 65% of internet users in the EU shopped online in 2015 [13] with the highest proportions to be found in Luxembourg, Denmark, the Netherlands and Finland. Greece is below the EU's average at 47%, while, at the same time, 30% of the Greek citizens have never used a computer [13].

More specifically, 2.4 million Greeks (from the total population of 10.9 millions) have consumed and have spent 1624 euro on average in 2013 [10]. There has been a 20.4% e-commerce growth in 2014. Only 2.25% of the country's total turnover are electronic sales [11].

Thus, it is of crucial importance to study the behaviour of consumers in Greece. The goal of this paper is to impress the consuming behaviour of people in the region of Central Macedonia in Greece. For this purpose, an empirical research is conducted and the results of the statistical analysis are given. Finally, conclusions of this study are indicated.

2. Methodology

The study was conducted in Central Greece from November to December 2015. Data were collected using a structured questionnaire, containing 19 closed type questions. The questionnaires were distributed to a random sample of consumers. The 19 questions were grouped into four sections. Section 1 consisted of five questions related to demographic characteristics of the respondents. Section 2 consisted of four questions regarding the use of e-commerce. Section 3 gathered six questions concerning transactions and finally, section 4 contained four questions regarding safety. For the quantitative analysis a number of descriptive statistics tools were applied using the SPSS package (version 19).

3. Empirical Analysis

The questionnaire was filled in by 100 persons, 45 men and 55 women (Figure 1). The majority of the respondents are young people, and more specifically, forty six are less than 26 years old, twenty six are 26-35 years old, fourteen are 36-45 years old, thirteen are 46-55 years old and only one person is older than 55 years old (Figure 2).

Figure 1

Gender of the respondents.

fig-1.jpg
Figure 2

Age of the respondents.

fig-2.jpg

Regarding the level of education of the respondents, half of them (50) have finished secondary education, forty five hold a Bachelor degree and five hold a master or a PhD (Figure 3).

Figure 3

Level of education of the respondents.

fig-3.jpg

The majority of the respondents are freelancers (41), nineteen are public servants, fifteen are public servants, thirteen are students, nine are unemployed, four are keeping their household and one person is retired (Figure 4).

Figure 4

Occupation of the respondents.

fig-4.jpg

Regarding the monthly income of the respondents, the majority –fifty three people earns less than 600 euro per month. Twenty two earn 601-900 euro, eleven earn 901-1200 euro, thirteen earn 1201-1500 euro and only one earn more than 1500 euro per month (Figure 5).

Figure 5

Monthly income of the respondents.

fig-5.jpg

Seventy two of the respondents use e-commerce and twenty eight of the respondents do not use e-commerce. From the seventy two e-commerce users, 74% consume a few times in a year, 18% consume monthly (with the word consume, we mean the consumption derived from e-shopping), 7% weekly and only 1% daily (Figure 6).

Figure 6

Frequency of consuming.

fig-6.jpg

Twenty nine persons (40%) have consumed 2-5 times during the last semester, twenty four (33%) have consumed once and nineteen (27%) consumed more than 5 times. Twenty eight of the respondents do not use e-commerce at all (Figure 7).

Figure 7

Frequency of consuming for the last semester.

fig-7.jpg

The majority of the respondents (45) have consumed cloths, footwear, accessories, thirty four have consumed gadgets, seventeen hotels/vacations or tickets and the rest purchases are shown in Figure 8. We must note that the respondents could chose up to three goods.

Figure 8

What is purchased online.

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Most of the respondents (39%) spend more than 200 euro per year in e-commerce, while the rest are equally distributed as it is shown in Figure 9.

Figure 9

Annual expenditure in e-purchases.

fig-9.jpg

The majority of the respondents prefer to pay cash on delivery, or they use a debit card. As it is shown in Table 1, they do not prefer to use their credit card, PayPal and deposit money in a banking account.

Table 1

You cover the online purchases ...


Never Rarely Usually
Credit card 47 14 11
Debit card 32 18 22
Cash on delivery 13 17 42
PayPal 53 15 4
Via a bank account 42 20 10

The `traditional' ways of searching for products' information are not preferred from our respondents, as they prefer to search in social networks, the companies' websites, review pages and in websites comparing prices (Table 2).

Table 2

You search in ... before consuming.


Never Rarely Usually
Social networks 27 11 34
Blogs 29 27 16
Review pages 20 23 29
Websites of companies 29 11 32
Websites of comparing prices 24 18 30
Magazines 58 9 5
Shops' brochures 39 22 11
Newsletters 50 18 4

The respondents trust an online store and become its customer when a real store exists, when there is a variety of products and services and there is an easy and fast navigation at the website of a store. The rest of the respondents' replies are shown in Figure 10. We must note that they could choose three reasons at the most.

Figure 10

You trust an online store and become its customer when ...

fig-10.jpg

The respondents would “probably” consume from a store with low prices and good offers even though some of the other criteria are not met, but they are not sure about their reply, since the same number of the respondents declare that they wouldn't do so (Figure 11).

Figure 11

Would you consume from a store with low prices and good offers, even though some of the other criteria are not met?

fig-11.jpg

The low prices, the flexibility of time, the easy and fast use of e-commerce and the possibility of buying goods that are not available on domestic markets, seem to be the main advantages of e-stores (Table 3).

Table 3

How important are the following advantages of e-stores?


Not important at all Not important Neither one nor the other Important Very important
Low prices 2 1 4 32 33
Time flexibility 7 3 12 33 17
Easy and fast use 7 3 10 32 20
Possibility of buying goods not available on domestic market 7 1 12 30 22

The majority of the respondents (89%) trust e-stores (Figure 12).

Figure 12

Do you trust e-stores?

fig-12.jpg

However, at the question “Do you consider that transactions with online stores are not always safe”, 38% of the respondents agree with the statement, while one out of two do not agree nor disagree (Figure 13).

Figure 13

Do you consider that transactions with online stores are not always safe?

fig-13.jpg

One out of four (26%) intend to increase using e-commerce, 40% are going to keep on using it as they did in the past, only 3% will decrease using it and the rest (31%) are pensive regarding the use of e-commerce in the future (Figure 14).

Figure 14

Do you intend to increase or decrease the on-line purchases in the future?

fig-14.jpg

Among the twenty eight of the respondents who do not use e-commerce, thirteen do not trust the transactions in websites and seven do not own a credit card. Other reasons are shown in Figure 15. We point out that in this question too, the respondents could choose two of the given reasons.

Figure 15

Why don't you use e-commerce?

fig-15.jpg

In Figure 16 the distribution of people who use e-commerce regarding their gender is shown, with the majority of them to be women.

Figure 16

Gender of e-commerce users.

fig-16.jpg

Furthermore, we investigate if demographic and other economic factors are related with the e-shopping behavior of the respondents. The frequency of e-shopping in relation with the gender is shown in Table 4. The chi-square test of independence (the test of independence hypothesizes that “frequency” and “gender” are unrelated -that is, that the column proportions are the same across columns, and any observed discrepancies are due to chance variation) is used to determine whether there is a relationship between “frequency of e-shopping” and the “gender of the respondents”. The computed chi-square statistic (the chi-square statistic measures the overall discrepancy between the observed cell counts and the counts you would expect if the column proportions were the same across columns) has a value of 0.701. Since the significance value of the statistic is higher than 0.05, the hypothesis of independence can't be rejected at the 0.05 level. Thus, “frequency of e-shopping” and “gender of the respondents” are not related meaning that the frequency of e-shopping does not differ between two genders (Table 5).

Table 4

Frequency of e-shopping in relation with the gender.


Frequency of consuming for the last semester Total
Once 2-5 times >5 times
Gender Male Count 10 12 10 32
% within Gender 31.3% 37.5% 31.3% 100.0%
Female Count 14 17 9 40
% within Gender 35.0% 42.5% 22.5% 100.0%
Total Count 24 29 19 72
% within Gender 33,3% 40.3% 26.4% 100.0%
Table 5

Fequency of e-shopping in relation with the gender - Independence test. (a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 8.44.)


Value df Asymp. Sig. (2-sided)
Pearson Chi-Square .701 a 2 .704
Likelihood Ratio .698 2 .705
Linear-by-Linear Association .462 1 .497
N of Valid Cases 72
Table 6

Expenditure in e-purchases in relation with the gender.


Expenditure in e-purchases Total
<100 100-200 >200
Gender Male Count 9 11 12 32
% within Gender 28.1% 34.4% 37.5% 100.0%
Female Count 19 5 16 40
% within Gender 47.5% 12.5% 40.0% 100.0%
Total Count 28 16 28 72
% within Gender 38.9% 22.2% 38.9% 100.0%
Table 7

Expenditure in e-purchases in relation with the gender- Independence test. a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 7.11.


Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 5.573 a 2 .062
Likelihood Ratio 5.640 2 .060
Linear-by-Linear Association .642 1 .423
N of Valid Cases 72
Table 8

Expenditure in e-purchases in relation with age.


Expenditure in e-purchases Total
<100 100-200 >200
Age <25 Count 19 10 10 39
% within Age 48.7% 25.6% 25.6% 100.0%
25-35 Count 7 4 8 19
% within Age 36.8% 21.1% 42.1% 100.0%
>35 Count 2 2 10 14
% within Age 14.3% 14.3% 71.4% 100.0%
Total Count 28 16 28 72
% of Total 38.9% 22.2% 38.9% 100.0%
Table 9

Expenditure in e-purchases in relation with age - Independence test. a. 2 cells (22.2%) have expected count less than 5. The minimum expected count is 3.11.


Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 9.405 a 4 .052
Likelihood Ratio 9.563 4 .048
Linear-by-Linear Association 8.267 1 .004
N of Valid Cases 72
Table 10

Frequency of consuming in relation with age.


Frequency of consuming for the last semester Total
Once 2-5 times >5 times
Age <25 Count 16 17 6 39
% within Age 41.0% 43.6% 15.4% 100.0%
25-35 Count 6 7 6 19
% within Age 31.6% 36.8% 31.6% 100.0%
>35 Count 2 5 7 14
% within Age 14.3% 35.7% 50.0% 100.0%
Total Count 24 29 19 72
% within Age 33.3% 40.3% 26.4% 100.0%
Table 11

Frequency of consuming in relation with age - Independence test. a. 2 cells (22.2%) have expected count less than 5. The minimum expected count is 3.69.


Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 7.409 a 4 .116
Likelihood Ratio 7.469 4 .113
Linear-by-Linear Association 6.608 1 .010
N of Valid Cases 72
Table 12

Frequency of consuming in relation with monthly income.


Frequency of consuming in the last semester Total
Once 2-5 times >5 times
Income <600 Count 20 15 5 40
% within Income 50.0% 37.5% 12.5% 100.0%
600-900 Count 3 6 7 16
% within Income 18.8% 37.5% 43.8% 100.0%
>900 Count 1 8 7 16
% within Income 6.3% 50.0% 43.8% 100.0%
Total Count 24 29 19 72
% within Income 33.3% 40.3% 26.4% 100.0%
Table 13

Frequency of consuming in relation with monthly income - Independence test. a. 2 cells (22.2%) have expected count less than 5. The minimum expected count is 4.22.


Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 14.937 a 4 .005
Likelihood Ratio 16.560 4 .002
Linear-by-Linear Association 12.962 1 .000
N of Valid Cases 72
Table 14

Expenditure in e-purchases in relation with monthly income.


Spending Total
<100 100-200 >200
Income <600 Count 20 12 8 40
% within Income 50.0% 30.0% 20.0% 100.0%
600-900 Count 4 3 9 16
% within Income 25.0% 18.8% 56.3% 100.0%
>900 Count 4 1 11 16
% within Income 25.0% 6.3% 68.8% 100.0%
Total Count 28 16 28 72
% of Total 38.9% 22.2% 38.9% 100.0%
Table 15

Expenditure in e-purchases in relation with monthly income- Independence test. a. 2 cells (22.2%) have expected count less than 5. The minimum expected count is 3.56.


Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 14.448 a 4 .006
Likelihood Ratio 15.168 4 .004
Linear-by-Linear Association 9.535 1 .002
N of Valid Cases 72

“Expenditure” and “gender” are related, since the hypothesis of independence can't be rejected at the 0.10 level. The two genders spend differently (in this case, a larger chi-square statistic (5.573) indicates a greater discrepancy between the observed and expected cell counts-greater evidence that the column proportions are not equal, that the hypothesis of independence is incorrect, and, therefore, that “Expenditure” and “gender” are related-) their money when they shop online, with male spending more money than female. This is related with the purchases in products or services they prefer to buy (Tables 6 and 7).

Age seems to be a differentiating factor for the expenditure in e-purchases (Table 8), with the older people to spend bigger amounts than the younger ones (when both table variables are quantitative, Chi-square yields the linear-by-linear association test) (Table 9). Specifically, the computed Linear-by-Linear Association statistic has a value of 8.267. Since, the significance value of the statistic is lower than 0.01, the hypothesis of independence is rejected at the 0.01 level.

Similarly, since the significance value of the computed Linear-by-Linear Association is lower than 0.05, age and frequency of consuming for the last semester are related (Tables 10 and 11).

Monthly income of the respondents has been, also, recoded. From the Linear-by-Linear Association test is evident that “monthly income” and “frequency of consuming in the last semester” are not unrelated, with the lowest incomes to consume less (Tables 12 and 13).

Monthly income seems to be, also, a differentiating factor for the expenditures in e-purchases (significance value of the statistic is equal to 0.002). Specifically, as it was expected, people with low income spend small amounts in purchases comparing to those with higher income (Tables 14 and 15).

4. Conclusions - Discussion

According to the present study, people in Central Macedonia prefer to use e-commerce after a search of information in websites and because of low prices and offers in e-shops. The majority prefer to pay with credit or debit cards. Both genders responded that the flexibility of hours in e-shopping is a very important factor for their preference. Almost all of them trust the e-shops, while they are divided in half regarding the trust in transactions. The main reasons for not realizing e-commerce transactions is the lack of a credit card, the lack of trust in transactions and the economic crisis in general. Almost half of the respondents declare that they will keep on e-consuming in the same way they used to do in the past.

The frequency of e-shopping does not differ between two genders. Two genders spend differently their money when they shop online. Male consumers spend more money than female. Older people spend bigger amounts than the younger ones. There is no difference in the frequency of consuming for the last semester among several age groups. As it was expected, the monthly income is a differentiating factor for the frequency of consuming in the last semester, with the lowest incomes to consume less. Furthermore, people with low income spend small amounts in purchases comparing to the people with high income.

In general, e-commerce is growing in Greece. It has altered the way of consuming and further changes and developments are expected. In a future study, our purpose is to broaden the sample of our data both arithmetically and geographically.

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