This research study is conducted to determine consumers' reasons for not shopping online, to explicate their attitudes towards online purchasing behaviors, and to examine whether or not their attitudes towards online purchasing behavior differ in accordance with the descriptive characteristics of the consumers. The survey questionnaires, prepared in compliance with the aim, are conducted on 502 Niğde residents chosen by convenience sampling method. The data obtained from the survey questionnaires are analyzed via SPSS 22.0 computer software. According to the results of the study; the reasons that cause online shopping include price reduction, the economics of time and finding the best product, while the reasons of avoiding online shopping include refraining from giving out identity document and credit card information, and demanding physical product trial. Participants are determined to be such individuals within a certain age range (26-35 years) who have more positive attitudes towards online shopping rather than negative attitudes, have higher education and income levels. In the research study, it is also detected that gender, the location at which the Internet is connected, and the duration of time spent on the Internet are independent of attitudes towards online shopping.
Today, technological developments along with continuous improvement and change are under consideration.One of the most important inventions brought about by technological developments, beyond any doubt, is the Internet.The emergence of the Internet and its subsequent prevalence in the social and everyday lives of the individuals reveal the possibility of shopping over the Internet. Shopping over the Internet, especially along with increasing use of smartphones and different applications of shopping sites, has become easier and widespread with every passing day.
In parallel with the development of the Internet, banking transactions have been conducted over the Internet environment, and the means of e-commerce have been completely facilitated. Both individuals and businesses have come to the point at which they can make payments for any product they would purchase without having to go to the bank office. This situation has spurred the purchasing process for both businesses and individuals. On the other hand, the shopping itself has been situated in the context of the internet environment by courtesy of the widespread e-commerce websites while the exchange is only paid via the Internet. Besides e-commerce sites already open especially for books, stationery and technology products, all other necessities from food toclothing have also become available for purchase over the Internet. In this direction, while many businesses began to move their services into the Internet environment, many online stores and shopping websites are established only for online purchases.
The development of e-commerce has also contributed to the emergence and improvement of many marketing areas such as e-marketing, e-advertising, social media marketing, mobile marketing.[
The individuals’ increasing interest in the Internet has enabled and encouraged the firms to convert their traditional retailing activities into online retail sales by marketing their products over the Internet. Thanks to online devices such as computers, tablets, and smartphones; consumers are able to shop from the companies located all over the world offering online shopping features, without even leaving their doorsteps.[
According to data as of 2016, the volume of global e-commerce has increased from 630 billion to 1.6 trillion dollars within the last four years, and its share in total retail has increased from
It is noteworthy that electronic trade from businesses to individuals constitutes a high proportion of the total electronic trade in Turkey. As of 2016, the Turkish e-trade market volume reached 30.8 billion TL with increasing internet penetration and smartphone usage
The ever-growing online shopping concept also affects attitudes and behaviors of the customers. Consumers may prefer traditional shopping by acting skeptical about online shopping, notwithstanding such advantages of online shopping as transportation, convenience, attractive prices, and various options.[
In the light of all this information, the main objectives of this study are to determine reasons why consumers do/do not prefer online shopping, to examine their attitudes towards online purchasing behaviors and to explicate whether or not their attitudes towards online purchasing behaviors differ according to their descriptive characteristics. Therefore, the answers to the following questions are sought in the research:
What are the most important reasons for the participants to go online shopping?
What are the most important reasons for the participants to avoid online shopping?
What is the extent of the participants’ attitudes towards online purchasing behavior?
Do attitudes of the participants towards online shopping differ according to gender?
Do attitudes of the participants towards online shopping differ according to age range?
Do attitudes of the participants towards online shopping differ according to their educational status?
Do attitudes of the participants towards online shopping differ according to their monthly income levels?
Do attitudes of the participants towards online shopping differ according to their marital status?
Do attitudes of the participants towards online shopping differ according to their occupations?
Do attitudes of the participants towards online shopping differ according to the means through which they are connected to the Internet?
Do attitudes of the participants towards online shopping differ according to the duration of time spent online per day?
Do attitudes of the participants towards online shopping differ according to their online shopping status?
In the most general sense, consumer is; “a real person who buys or is capable of buying marketing components for his/her personal or non-personal desires, wants and needs”[
Changing consumption patterns and lifestyles result in the existence of increasingly more consumers in the virtual environment.[
According to the Household Information Technology Usage Survey of Turkey Statistical Institute conducted in 2017; the prevalence of the Internet use among individuals within the age range of 16-7
As can be seen from the obtained data, the Internet creates a new consumer type in terms of rapid development, convenience and operation. This type of consumer has different characteristics than traditional consumer types. Today, consumers with purchasing power and intention to buy who meet their purchasing needs by connecting to online shopping sites over the Internet can be referred to as either electronic consumers or online consumers.[
Online consumers consist of individuals who are more conscious than traditional consumers, have internet skills and experience, can use information technology well, follow technological developments, take risks, find the best product at the best prices and in the shortest time, share their satisfaction or dissatisfaction quickly in social media.[
Upon considering the literature related to online shopping, it is observed that there has been several attempts such as purchasing on the Internet environment, online shopping, shopping over the Internet, etc. to explain the concept. In its simplest definition, “Online shopping means the process of purchasing products or services through the Internet channel”.[
People are in pursuit of new means to meet their needs due to their hectic business environment and lack of enough time. Consumers try to meet their shopping needs on a busy day in different ways within a short period.Today, shopping on the internet offers great advantages to help consumers who have to make purchasing decisions in a short time. This type of shopping, also called online shopping, means purchasing goods or services over the Internet.[
Consumer behavior is “an applied science that investigates the reasons for the consumer’s behavior in the marketplace”.[
Traditionally, consumers visit the stores to examine, touch and then purchase the products they desire.The most important difference between online shopping and traditional shopping is that consumers do not have to go to the stores to purchase products.Online shopping provides many advantages for consumers, such as not waiting in queues, getting rid of crowded stores,
Online shopping grows and develops day by day. Businesses utilize a variety of methods to gain consumers’ trust regarding their websites and online shopping features. In particular, banks eliminate the disadvantages of providing credit card information from consumers via virtual card applications and indicate that their websites are secure and protected by international programs with special signs.[
In the online shopping process, consumers shop through specific stages, as in the traditional shopping process. However, online shopping stages are slightly different from traditional shopping stages. In particular, during the market research phase, consumers do not actively do research as well as in traditional shopping, but they rather try to obtain information through websites created by companies. Firms offer various campaigns, prizes, price reductions, and noticeable advertising campaigns to attract consumers’ attention to their websites.[
Given the social and psychological aspects of online consumer behavior, the internet environment offers consumers an independent domain that is virtually free of physical environments. Consumer behavior norms viewed in physical stores do not apply in this environment.First of all, consumers are not obligated to purchase anything over the Internet.Secondly, shopping over the Internet is as personal as possible.Finally, lack of physical effort for shopping leads to increased online shopping volume (Enginkaya, 2006: 12).
4.1. Research Model
The research study is conducted with a survey model to examine the participants’ attitudes towards their online purchasing behaviors.
4.2. Population and Sample
The population of the research study is comprised of Niğde Province residents at the age of 18 and older. The sample is composed of 502 individuals who voluntarily agreed to participate in the survey determined by the convenience sampling method.
The data are collected via a survey questionnaire method. The questionnaire is prepared by using the study of İşler- Yarangümümioğlu-Gümülü[
The internal consistency coefficient “Cronbach’s Alpha” is calculated to determine the reliability of the 1
Items | Factor Load |
Receiving after-sales support would stimulate my shopping intention | .960 |
Delivery of the product via a reliable shipping company would stimulate my shopping intention | .958 |
Members-only sale campaigns would stimulate my shopping intention | .951 |
References and certificates (SSL) which enhance the reliability of websites would stimulate my shopping intention | .950 |
Definite date of delivery for the purchased products would increase my interest in the product | .943 |
The existence of detailed content about the products would stimulate my shopping intention | .942 |
Comments on the products would stimulate my shopping intention | .940 |
Instant and daily price reductions for the products would stimulate my shopping intention | .936 |
The existence of other payment options besides credit cards would stimulate my shopping intention | .926 |
Receiving special-interest informative e-mails would stimulate my shopping intention | .926 |
The convenience of websites through which I shop online would stimulate my shopping intention | .915 |
Display of low-priced products on the main page of the website would attract my attention even if I do not need the product | .897 |
Credit card installment plans would stimulate my shopping intention | .896 |
Receiving continuous informative e-mail delivery regarding discount or promotional items would stimulate my shopping intention | .896 |
Total Variance = 67.648% | |
Crombach’s Alpha= 0.988 |
Scree plot graph regarding factor structure is shown below.
Upon examining the Scree Plot graph, the emergence of a fraction after the first factor supports the one-factor structure.
4.4. Statistical Data Analysis
The data obtained in the study are analyzed via SPSS (
5. Findings and Comments
The descriptive characteristics of the participants are given in Table 2.
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Gender | Female | 302 | 60.2 |
Male | 200 | 39.8 | |
Age | 18-25 | 354 | 70.5 |
26-35 | 100 | 19.9 | |
36 and above | 48 | 9.6 | |
Education | High School and below | 160 | 31.9 |
Associate Degree | 212 | 42.2 | |
Undergraduate and above | 130 | 25.9 | |
Monthly Income | 0-1000 TL | 268 | 53.4 |
1001-2000 TL | 88 | 17.5 | |
2001-3000 TL | 86 | 17.1 | |
3001 TL and above | 60 | 12.0 | |
Marital Status | Married | 112 | 22.3 |
Single | 390 | 77.7 | |
Occupation | Public Sector | 68 | 13.5 |
Private Sector | 88 | 17.5 | |
Freelance | 28 | 5.6 | |
Student | 238 | 47.4 | |
Retired | 12 | 2.4 | |
Housewife | 28 | 5.6 | |
Unemployed | 40 | 8.0 |
Upon examining Table 2, of totally 502 participants (consisting of 302 (602%) females and 200 (398%) males); 35
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Device(s) Through Which They Mostly Get Connected to the Internet | Computer | 32 | 6.4 |
Smart Phone | 470 | 93.6 | |
Average of Daily Time Spent Online | 2 Hours and below | 206 | 41.0 |
3-4 Hours | 136 | 27.1 | |
5 Hours and above | 160 | 31.9 | |
Aim(s) of the Internet Use* | Research | 154 | 30.7 |
Shopping | 116 | 23.1 | |
Social Media | 324 | 64.5 | |
Other | 100 | 19.9 |
* Multiple Choice Items
According to data in Table 3; 32 (6.
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Yes | 334 | 66.5 |
No | 168 | 33.5 |
According to data in Table 4, 334 (665%) of the participants shop online, while 168 (335%) do not shop online In
Table 5 : The Reasons to Shop and Not to Shop Online
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Price Reduction | 244 | 48.6 | I do not prefer to give out my personal identity document information | 246 | 49.0 |
Saving time | 126 | 25.1 | I prefer a physical trial of the products | 186 | 37.1 |
Living in a small town | 100 | 19.9 | I do not prefer to give out my credit card information | 120 | 23.9 |
Ability to find the best product | 102 | 20.3 | I believe that I would have problems in return of purchased goods | 92 | 18.3 |
Out of curiosity | 38 | 7.6 | I wish to acquire the product instantly | 48 | 9.6 |
Keeping up with the social environment | 20 | 4.0 | I believe that the purchased item would not be delivered at all. | 14 | 2.8 |
Other | 118 | 23.5 | Do not know about online shopping | 14 | 2.8 |
I believed that the purchased items would not be delivered on time. | 38 | 7.6 |
Upon examining the reasons why the participants prefer online shopping; it is seen that 2
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The convenience of websites through which I shop online would stimulate my shopping intention | 49.8 | 6.4 | 43.8 | 2.940 | 0.967 |
The existence of detailed content about the products would stimulate my shopping intention | 47.8 | 5.2 | 47.0 | 2.990 | 0.975 |
Comments on the products would stimulate my shopping intention | 47.4 | 6.0 | 46.6 | 2.990 | 0.971 |
Display of low-priced products on the main page of the website would attract my attention even if I do not need the product | 50.2 | 8.8 | 41.0 | 2.910 | 0.952 |
Definite date of delivery for the purchased products would increase my interest in the product | 47.0 | 6.8 | 46.2 | 2.990 | 0.966 |
Delivery of the product via a reliable shipping company would stimulate my shopping intention | 45.8 | 6.0 | 48.2 | 3.020 | 0.970 |
Receiving after-sales support would stimulate my shopping intention | 47.0 | 5.6 | 47.4 | 3.000 | 0.973 |
References and certificates (SSL) which enhance the reliability of websites would stimulate my shopping intention | 47.0 | 9.2 | 43.8 | 2.970 | 0.953 |
The existence of other payment options besides credit cards would stimulate my shopping intention | 47.0 | 7.6 | 45.4 | 2.980 | 0.962 |
Instant and daily price reductions for the products would stimulate my shopping intention | 47.8 | 10.0 | 42.2 | 2.940 | 0.948 |
Members-only sale campaigns would stimulate my shopping intention | 47.8 | 6.4 | 45.8 | 2.980 | 0.968 |
Credit card installment plans would stimulate my shopping intention | 50.2 | 9.2 | 40.6 | 2.900 | 0.949 |
Receiving special-interest informative e-mails would stimulate my shopping intention | 49.4 | 8.0 | 42.6 | 2.930 | 0.958 |
Receiving continuous informative e-mail delivery regarding discount or promotional items would stimulate my shopping intention | 51.4 | 10.0 | 38.6 | 2.870 | 0.941 |
Upon examining the data in Table 6, the mean values of the participants’ attitudes towards the factors which are considered to affect the participants’ online shopping behaviors vary between 2,870 and 3,020The minimum mean values belong to the responses “Receiving continuous informative e-mail delivery regarding discount, or promotional items would stimulate my shopping intention” (2,870), and average means “Credit card installment plans would stimulate my shopping intention” (2,900) The maximum mean values belong to the responses “Delivery of the product via a reliable shipping company would stimulate my shopping intention” (3,020), and “Receiving after-sales support would stimulate my shopping intention” (3,000)
Table 7 presents the data on participants’ attitudes towards their online purchasing behaviors.
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502 | 2,948 | 1,394 | 1,000 | 5,000 |
For the data in Table 7, it is determined that the levels of attitudes towards online purchasing behaviors of the participants are moderate with an average of 2,9
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Female | 302 | 2,958 | 1,407 | 0.206 | 0.837 |
Male | 200 | 2,932 | 1,379 |
According to the data in Table 8, attitudes of participants towards their online purchasing behaviors does not exhibit a significant difference in terms of their gender (p> 005) Table 9 shows the results of both ANOVA and the Scheffe tests conducted for examining the differentiation of the participants’ attitudes towards their online purchasing behaviors in terms of age
Table 9: Differentiation of Attitude towards Online Purchasing Behaviors According to Age
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1)18-25 | 354 | 2.819 | 1.366 | 6.148 |
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2) 26-35 | 100 | 3.363 | 1.431 | |||
3) 36 and above | 48 | 3.033 | 1.379 |
Attitudes towards online purchasing behaviors differ significantly according to the age of participants (F= 61
Table 10: Differentiation of Attitudes towards Online Purchasing Behaviors in terms of Educational Status
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1) High School and below | 160 | 2.911 | 1.382 | 10.558 |
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2) Associate degree | 212 | 2.700 | 1.397 | |||
3) Undergraduate and above | 130 | 3.398 | 1.305 |
Upon considering the data in Tablo 10, it is determined that the participants’ attitudes towards their online purchasing behaviors differ significantly (p<0001) according to educational status (F = 10,558; p= 0000<005) The difference stems from the fact that attitudes of those with an undergraduate degree or above (x̄=3,398) towards online purchasing behaviors are more positive than of those both with high school diploma and below (x̄=2,911), and with associate degrees (x̄ = 2,700)
Table 11 presents the results of ANOVA and Scheffe tests performed for differentiation of the participants’ attitudes towards online purchasing behaviors according to their monthly income levels.
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1) 0-1000 TL | 268 | 2,691 | 1,338 | 10.377 |
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2) 1001-2000 TL | 88 | 2,873 | 1,373 | |||
3) 2001-3000 TL | 86 | 3,442 | 1,279 | |||
4) 3001 TL and above | 60 | 3,498 | 1,515 |
Upon considering the data in Tablo 11, the participants’ attitudes towards online purchasing behaviors significantly differaccording to their monthly incomes levels (F= 10,377; p =0000 <005) The difference stems from the fact that attitudes of those participants with a monthly income of 2001-3000 TL (x̄=3,
Table 12 presents the results of the t-test performed for examining the differentiation of the participants’ attitudes towards their online purchasing behaviors according to genders of the participants.
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Married | 112 | 3,481 | 1,365 | 4.684 | 0.000 |
Single | 390 | 2,795 | 1,367 |
Participants’ attitudes towards online purchasing behaviors differ significantly according to their marital status (t( 500)= 468
Table 13 presents the results of ANOVA and Scheffe tests performed for examining the differentiation of the participants’ attitudes towards their online purchasing behaviors according to their occupations.
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1)Public Sector | 68 | 3,492 | 1,384 | 3.293 |
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2)Private Sector | 88 | 3,099 | 1,414 | |||
3)Self-employed | 28 | 3,143 | 1,555 | |||
4)Student | 238 | 2,793 | 1,345 | |||
5)Retired | 12 | 2,179 | 0,991 | |||
6)Housewife | 28 | 2,837 | 1,472 | |||
7)Unemployed | 40 | 2,786 | 1,349 |
Upon considering the datain Table 13, the participants’ attitudes towards their online purchasing behaviors significantly differ according to the participants’ occupations (F= 3,293; p= 0003 <005)The difference stems from the fact that attitudes of those participants who work as public employees (x̄=3,
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Computer | 32 | 3,058 | 1,286 | 0.461 | 0.645 |
Smart Phone | 470 | 2,940 | 1,403 |
Table 14 indicates that the participants’ attitudes towards online purchasing behaviors do not differ significantly according to devices through which they mostly get connected to the Internet (p>005) Table 15 presents the results of ANOVA and Scheffe tests performed for examining the differentiation of the participants’ attitudes towards their online purchasing behaviors according to time spent on the Internet
N | Mean | Stand. Dev. | F | P | |
1) 2 Hours and below | 206 | 3,084 | 1,436 | 1.848 | 0.159 |
2)3-4 Hours | 136 | 2,800 | 1,294 | ||
3) 5 Hours and above | 160 | 2,898 | 1,415 |
Table 15 states that that the participants’ attitudes towards online purchasing behaviors do not differ significantly according to the duration of time spent online (p>005) Table 16 presents the results of a t-test performed for examining the differentiation of the participants’ attitudes towards their online purchasing behaviors according to their online shopping status
Table 16: Differentiation of Attitude towards Online Purchasing Behaviors According to Online Shopping Status
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Yes | 334 | 3.190 | 1.409 | 5.659 | 0.000 |
No | 168 | 2.466 | 1.233 |
Table 16 shows that the participants’ attitudes towards online purchasing behaviors significantly differ according to their online shopping status (t( 500)=5659; p=0000<005) Online shoppers’ attitudes towards online purchasing behavior (x̄= 3,190) are found to be more positive than of those who do not shop online (x̄= 2,
The following are the results of the survey conducted for investigating the reason why consumers do/do not prefer online shopping, determining their attitudes towards online purchasing behaviors, and examining whether or not their attitudes towards online purchasing behaviors differ according to consumers’ descriptive characteristics. It is determined that more than half of the survey participants (60.2%) are female, the majority (70.5%) are within the age range of 18-25 years, nearly half of them (
Uponconsidering the Internet use characteristics of theparticipants, it is found that most of them connect to Internet through smartphones (93.6%),
Upon considering the aims of the Internet use, the top three consists of connecting to social media, doing research and shopping, respectively. This result shows the extent to which social media has entered the lives of individuals. According to social media statistics for 2018, 63% of the population are social media users.[
66.5% of the survey participants shop online. This result indicates the wide prevalence of online shopping in society.
The top reasons why the participants prefer online shopping include the price reductions, saving time and finding the best products. In this case, online shopping for high-priced products is still limited, and the possibility of price reductions seems to be the most attractive reason for shopping online.
The reasons for avoiding online shopping include the unwillingness to give out personal identity document/credit card information and desire to physically try and test the products.Thus, it is seen that the most important reason restricting online shopping is trust, and applications have been made available for consumers to allow them to shop online without giving out identity and credit card information in order to overcome this limitation.
Participants are found to have moderate attitudes towards online purchasing behavior and more positive attitudes than negative attitudes.The most negative attitude is involved with the response “Receiving continuous informative e-mail delivery regarding discount or promotional items would stimulate my shopping intention.” This situation suggests that automated advertisement e-mails sent to everyone would lead consumers to exhibit negative attitudes.Instead of automated promotion e-mails, sending more attentive promotion e-mails focused on the characteristics of the target audience and its areas of interest would lead the companies to succeed in sales.
The attitudes towards online purchasing behaviors differ according to the level of income, and those attitudes are found to be more positive as the level of income increases. This result indicates that online shopping is perceived to be more functional by consumers with higher incomes and that it is considered advantageous for this income group in all aspects of online shopping. As average monthly income level decreases, consumers tend to prefer online shopping since they want to take advantage of the price level, while the consumers with higher income prefer online shopping because they have pleasure during online shopping activities.
In the study, it is detected that consumers with higher education levels tend to develop more positive attitudes towards online purchasing behaviors. It is thought that technological and computer skills improve in parallel with the level of educational status, and in this case, it reflects positively on the attitudes towards online shopping.
It is determined that the attitudes towards online purchasing behaviors do not differ according to the amount of time spent on the Internet and the devices through which consumers are mostly connected to the Internet. Along with the developing technology, it is thought that the increase in the possibilities of getting online through smartphones whenever individuals want is the factor in the emergence of this result. This prediction is supported by the fact that 93.6% of the participants are connected to the Internet via smartphones.
It is determined that the attitudes of consumers participating in the research differ according to their profession so that the public employees have the most positive attitudes.This result can be related to the fact that the public employees are mostly educated individuals in terms of quality and the positive attitudes towards online shopping tend to increase parallel to the educational status.
The survey indicated that respondents within the age range of 26-35 years are likely to develop a more positive attitude than younger people. Consumers within this age range are considered to be employed and usually married, and are considered to have limited time for traditional shopping because of their families for which they feel responsible at a certain level of income. Considering that income level is influential in online shopping attitude and that saving time is one of the most important reasons affecting online shopping, the obtained results are thought to be normal. As a matter of fact, it is determined that married people have a more positive attitude than single individuals. In the survey, it is detected that male and female participants exhibit similar attitudes towards online shopping. Along with the developing technology, it is considered that the prevalence rates of Internet use in both genders are at the same levels.
Online shoppers are found to exhibit more positive attitude than traditional shoppers. Consequently, experience happens to be the most important reason. It is determined that those who have experience have more positive attitudes towards online shopping.[
Consequently, consumers are more likely to shop online, and positive attitudes towards online shopping outweigh their negative attitudes. Businesses are suggested to negotiate with a reliable shipping company with remarkable, convenient and different payment options to increase online shopping rates. In addition, the companies are encouraged to enhance options such as cash on delivery, product change or return, etc. to increase the reliability of the businesses since it is the primary reason for consumers’ avoidance of online shopping. Also, considering that the attitudes of users with different demographic characteristics tend to differ, it is thought that shaping their promotions and websites according to the characteristics of target groups would be a crucial factor in preferring online shopping.