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.[ 1 ]
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.[ 2 ] Due to various advantages and prevalence of the Internet usage in Turkey, as in other countries, many small and large-scale companies tried to sell their products and services over the Internet.[ 3 ]
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 4 .2% to 8.5%.
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 retail portion of the market has grown by an annual average of 3 4 % since 2013 by increasing from 7.3 billion to 17. 5 billion TL in 2016 (Kantarcı et al., 2017: 4 4).As can be noticed from the obtained data, presence and importance of the online shopping phenomenon are drastically felt in today’s economic, commercial and social life.
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.[ 6 ] Thus, it is highly crucial for firms to learn the reasons why consumers do/do not prefer online shopping and to improve their services in this manner in order to reach the wider masses. Businesses with online shopping features would find ways to pioneer in competition by maintaining their existence in the market if they can comprehend how online shopping should be in terms of preserving their existing clientele and attracting new ones.
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?
2. Consumer and Online Consumer Concepts
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”[ 7 ] and is a person who pays a monetary price for goods and services bought, benefiting from many products and services without having to pay for them. The formation of a consumer identity does not require a person’s purchase of a product or service at a given price. Being able to utilize services in various fields from art to environment without paying a monetary price is sufficient for the formation of consumer identity.[ 8 ]
Changing consumption patterns and lifestyles result in the existence of increasingly more consumers in the virtual environment.[ 9 ] The new communication devices that emerge due to technological developments, especially computers and the Internet, influence almost all aspects of individuals and the social structure along with the rapid flow of information. All people, institutions, and organizations that have the opportunity to talk to everyone at any location in the world, to learn how to do a job or how to do it differently, to compare alternatives, and of being independent of time and space have become parts of the virtual environment.[ 10 ]
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 11 ] 4 years is found to be 68.8%, meaning that every eight out of ten households have the Internet access. The percentage of online shoppers is 2 4 .9%. 62.3% of online shoppers purchased clothing and sports equipment; 25.3% purchased household goods (furniture, toys, domestic appliances, etc., excluding consumer electronics); 24.1% purchased travel tickets, car rentals; 21.9% purchased food and daily necessities; and 19% purchased electronic devices (mobile phones, cameras, radios, TVs, DVD players, etc.) over the Internet during the twelve months period covering April 2016 and March 2017.[
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.[ 12 ]
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.[ 13 ]
3. Online Shopping and Factors Affecting Consumer Behaviors Through this Process
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”.[ 14 ] Online shopping is defined as a new marketing and sales channel, providing consumers with a different shopping environment in which they can get different people’s opinions about products without having to get tired and get bored.[ 15 ]
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.[ 16 ] “Online shopping has been used by consumers at an increasing rate, adding new dynamics to business and marketing”.[ 17 ]
Consumer behavior is “an applied science that investigates the reasons for the consumer’s behavior in the marketplace”.[ 18 ] Along with the developing online technologies, the Internet environment in which the companies offer online sales can be evaluated as an electronic marketplace. Consumers visit consumer markets to meet their never-ending and unlimited needs. They seek profits while producing goods and services to meet the needs of the consumers. As is well-known, the main objective of businesses is to make profits. Marketing is one of the crucial managerial functions in making profits. In modern marketing concept, marketing functions include marketing research to understand consumers’ wishes and anticipations, producing and selling goods towards the target audience, and activities related to consumer behavior before and after sales.[ 19 ]
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, 24 -hour shopping opportunities, and a wide range of products.[ 16 ]In other words, online shopping has provided the consumers with many conveniences by eliminating temporal and spatial constraints.[ 20 ] Despite all of these advantages of online shopping, some of the customers who prefer traditional shopping are reluctant to purchase online since they think it may be problematic in terms of security issues.[ 21 ] The top reasons why consumers do not prefer online shopping include security concerns about credit card payments;[ 22 ] concerns about personal identity information that would be captured;[ 23 ] the desire to see the true dimensions of the product, to touch it physically, and to test it; and the longer duration of time for the purchased product to arrive.[ 24 ]
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.[ 14 ]
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.[ 25 ]
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. “ The survey model is a research approach that aims to describe a situation either in the past or at the present as it is. An event, an individual or an object, which is the subject of the research, is tried to be defined as it is within its own conditions. Any attempt to change or influence them cannot be made. Something that is wanted to be known exists. To be able to “observe” and identify it appropriately is essential”.[ 26 ]
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[ 13 ] entitled “A Case Study on Factors Affecting Online Consumer Purchasing Behaviors: An Application in Isparta Province.” The questionnaires are collected online in July 2018. In the first part of the questionnaire, questions to determine the descriptive characteristics of the participants are included such as participants’ age, gender, etc. In the second part, questions are asked concerning the devices through which the participants connect to the Internet, the amount of time spent online, and whether or not the participants prefer online shopping. In the third part, questions are asked about the reasons why the participants do/do not prefer online shopping. In the fourth part, there is a five - point Likert - type scale (1: I strongly disagree, ......, 5: I strongly agree) consisting of 1 4 items to measure attitudes towards online purchasing behaviors. The increase/decrease in the average of the scores taken from the scale indicates the positive/negative attitudes of consumers towards online shopping.
The internal consistency coefficient “Cronbach’s Alpha” is calculated to determine the reliability of the 1 4 items on attitude towards online purchasing behavior scale. The overall reliability of the scale is found to be very high (alpha = 0.988). Explanatory factor analysis method is applied to reveal the structural validity of the scale. The results of the Barlett test (p= 0.000 < 0.05) revealed the existence of a correlation between the variables analyzed by factor analysis. As a result of the test (KMO= 0.968 > 0.60), the sample size is found to be sufficient for performing factor analysis. In the factor analysis application, the varimax method is chosen so that the structure of the relationship between the factors remains the same. As a result of the factor analysis, variables are collected under the single factor with a total variance of 86.737%. According to the value of alpha and the variance associated with the reliability, it is understood that the attitude scale towards Online Purchasing Behavior is a valid and reliable instrument. The factor structure of the scale is shown below.
Table 1 : The factor structure of attitude scale towards online purchasing behaviors
|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.
Figure 1: Scree Plot Graph
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 ( Statistical Package for Social Sciences ) for Windows 22.0 software. Number, percentage, mean, standard deviation are used as descriptive statistical methods in the evaluation of the data. t-test is performed for comparison of continuous quantitative data between two independent groups, while the One-way ANOVA test is performed for comparison of continuous quantitative data between more than two independent groups. The Scheffe test is performed as a complementary post-hoc analysis to determine the differences following the ANOVA test.
5. Findings and Comments
The descriptive characteristics of the participants are given in Table 2.
Table 3 : Descriptive Statistics
|Variables||Groups||Frequency (n)||Percentage (%)|
|36 and above||48||9.6|
|Education||High School and below||160||31.9|
|Undergraduate and above||130||25.9|
|Monthly Income||0-1000 TL||268||53.4|
|3001 TL and above||60||12.0|
Upon examining Table 2, of totally 502 participants (consisting of 302 (602%) females and 200 (398%) males); 35 4 (705%) participants are found to be within the age range of 18-25 years, 100 (199%) participants are within the age range of 26-35 years, and the remaining 4 8 (96%) are 36 years of age or older160 of the participants (319%) have high school diplomas, or below, 212 (422%) have associate degrees, and 130 (259%) have undergraduate degrees and above 268 (534%) participants have monthly incomes of 0-1000 TL, 881 (175%) have monthly incomes of 1001-2000 TL, 86% (171%) have monthly incomes of 2001-3000 TL, and 601 (120%) have monthly incomes of 3001 TL and above112 (223%) participants are married, while 390 (777%) of them are single 68 (135%) participants are public employees, 88 (175%) are private sector employees, 28% (56%) are self-employed, 238 (474%) are students, 12 (24%) are retired, 28 (56%) are housewives, and 40 (80%) are unemployedThe participants’ characteristics in terms of the Internet use are given in Table 3
Table 4 : Characteristic in terms of The Internet Use
|Responses||Frequency (n)||Percentage (%)|
|Device(s) Through Which They Mostly Get Connected to the Internet||Computer||32||6.4|
|Average of Daily Time Spent Online||2 Hours and below||206||41.0|
|5 Hours and above||160||31.9|
|Aim(s) of the Internet Use*||Research||154||30.7|
* Multiple Choice Items
According to data in Table 3; 32 (6. 4 %) of the participants get connected to the Internet through their computers and 4 70 (93.6%) get connected through their smartphones. 206 of them spend 2 hours or less time daily on the Internet, 136 (27.1%) participants spend 3-4 hours and 160 (31.9%) spend 5 hours or more online a day.Upon examining the participants’ objectives of using the Internet; it is seen that 154 (30.7%) of the participants went online for conducting research, 116 (23.1%) for shopping, 324 (64.5%) for connecting to social media, and 100 (19.9%) for other reasons. Table 4 presents the data on online shopping status of the participants.
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 for the participants to shop and not to shop online are given
Table 5 : The Reasons to Shop and Not to Shop Online
|Reasons to Shop Online*||N||%||Reasons to Avoid Online Shopping*||N||%|
|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|
* Multiple Choice Items
Upon examining the reasons why the participants prefer online shopping; it is seen that 2 4 4 (20.9%) of the participants shop online because of reduced prices, 126 (25.1%) shop online to save time, 100 (19.9%) shop online since they live in a small town, 102 (20.3%) shop online to find the best product, 38 (7.6%) shop online out of curiosity, 20 (4.0%) shop online to keep up with their social environment, and 118 (23.5%) shop online for other reasons. Upon examining the reasons why participants avoid shopping online; it is found that 146 (% 49.0) of the participant do not want to give out personal identity document information, 186 (% 37.1) prefer physical trial of the products, 120 (23.9%) do not want to give out their credit card information, 92 (18.3%) believe that they would have problems in returning purchased products, 48 (9.6%) want to have products instantly, 14 (2.8%) believe that products would never be delivered at all, 14 (2.8%) do not know how to shop online, and 38 (7.6%) believed that the purchased products would not be delivered on time. Table 6 indicates the distributions of the responses given related to the factors affecting the online purchasing behavior of the survey participants. The total of “I agree” and “I strongly agree” responses given to the reporters are considered as a positive attitude, whereas the total of “I disagree” and “I strongly disagree” responses are considered as a negative attitude.
Table 5 : The Distributions of the Responses Given Related to the Factors Affecting the Online Purchasing Behavior
|Positive attitude (%)||Indecisive (%)||Negative attitude (%)||Mean||Stand. Dev.|
|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.
Table 6 : The Mean Scores of the Factors Affecting the Online Purchasing Behavior
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 4 8 ± 1,39 4 (Min= 1, Max= 5).Table 8 indicates results of t-test conducted to examine the differentiation of attitudes towards online purchasing behaviors according to the gender of the participants.
Table 7 : The Differentiation of Attitudes towards Online Purchasing Behaviors According to Gender
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
|3) 36 and above||48||3.033||1.379|
Attitudes towards online purchasing behaviors differ significantly according to the age of participants (F= 61 4 8; p= 0002 <005), as shown in Table 9 The difference stems from the fact that attitudes of those within the age range of 26-35 years (x̄=3,363) towards online purchasing behaviors are more positive than of those within the age range of 18-25 years (x̄= 2,819) Table 10 presents the results of ANOVA and Scheffe tests performed for the differentiation of the participants’ attitudes towards online purchasing behaviors in terms of their educational status
Table 10: Differentiation of Attitudes towards Online Purchasing Behaviors in terms of Educational Status
|1) High School and below||160||2.911||1.382||10.558||0.000||3>1 3>2|
|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.
Table 8 : Differentiation of Attitudes towards Online Purchasing Behaviors in terms of Monthly Income Levels
|1) 0-1000 TL||268||2,691||1,338||10.377||0.000||3>1 4>1 3>2 4>2|
|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, 4 4 2) and 3001 TL and above (x̄=3,498) towards online purchasing behaviors are more positive than of those with monthly incomes of 0-1000 TL (x̄=2,691) and 1001-2000 TL (x̄=2,873)
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.
Table 9 : Differentiation of Attitude towards Online Purchasing Behaviors According to Marital Status
Participants’ attitudes towards online purchasing behaviors differ significantly according to their marital status (t( 500)= 468 4 ; p= 0000 <005) The attitudes of married individuals towards online purchasing behavior (x̄ = 3, 4 81) are found to be more positive than of the single individuals (x̄ = 2,795)
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.
Table 10 : Differentiation of Attitude towards Online Purchasing Behaviors According to Occupations
|1)Public Sector||68||3,492||1,384||3.293||0.003||1>4 1>5 2>5 3>5 1>6 1>7|
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, 4 92) towards online purchasing behaviors are more positive than of those participants who are students (x̄=2,793), retirees (x̄=2,179), housewives (x̄=2,837), and unemployed (x̄=2,786) In addition, attitudes of the retired participants (x̄=2,179) towards their online purchasing behaviors are found to be more negative than of those who work as private sector employees (x̄= 3,099) and self-employed persons (x̄=3,1 4 3)Table 14 presents the results of t-test pa erformed for examining the differentiation of the participants’ attitudes towards their online purchasing behaviors according to the devices through which they mostly get connected to the Internet
Table 11 : Differentiation of Attitude towards Online Purchasing Behaviors According to Devices through Which the Participants Mostly Get Connected to the Internet
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
Table 12 : Differentiation of Attitude towards Online Purchasing Behaviors According to Duration of Time Spent Online
|1) 2 Hours and below||206||3,084||1,436||1.848||0.159|
|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
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, 4 66)
6. Conclusion and Recommendations
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 ( 4 2.2%) have associate degrees and more than half of them (53. 4 %) have monthly incomes of 0-1000 TL. Upon examining the participants’ occupations, it is found that nearly half of them (47.4%) are students, in parallel with the low-income level, and the majority (77.7%) are single.
Uponconsidering the Internet use characteristics of theparticipants, it is found that most of them connect to Internet through smartphones (93.6%), 4 1.0% of them spend 2 hours a day or less time on the Internet, 27.1% spend 3- 4 hours a day, and 31.9% spend 5 and more hours a day online. This situation indicates that the participants spend considerable amounts of time on the Internet.
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.[ 27 ]
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.[ 9 ; 28 ] Online shopping experience is thought to reduce the consumers’ risk perception towards online shopping and lead to a positive attitude 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.
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