Keywords

Preference, Online Shopping, Traditional Market, Demographics, Application.

Introduction

Increasingly stable and faster internet presence makes fundamental changes in human behaviour. Hence, the distance becomes increasingly meaningless. Communication and trade can occur in a short time without any significant pause. The flow of information becomes very fast and can even convey information from any part of the world in a short time. Bourlakis et al. (2008) pointed out that from all aspects of change in the internet age, online shopping behaviour change is the most popular and well accepted change by the marketing community today. More specifically, Ali & Sankaran (2011) point out those electronic interactions through the FAQs' program on online sites can address various consumer information needs that facilitate decision making in aspects of shopping.

Shanthi & Kannaiah (2015) found that although many consumers have begun switching to online stores, most of consumers still choose to shop directly to traditional markets that have a clear physical presence. A person's preference for buying a product, both online and traditional, is influenced by the personality factor possessed by that person. Hence, consumer shopping preferences are defined as the tastes of the consumers measured by the perceptions of the usefulness and benefits of the products offered (Guleria et al., 2015). Shopping preferences are related to someone likes or dislikes shopping methods and are not influenced by consumers' purchasing power. This generally affects purchasing decisions. The focus in understanding consumers is the key in keeping consumers (retain) and making it loyal to the seller. Lubis et al. (2017) states that consumers will only become loyal when they are satisfied. Satisfaction is sourced when we focus on providing services to consumers. This study was conducted to evaluate demographic factors that affect a person's preference in buying products online. The study of demographic influences on online shopping decisions has taken place since 1995 (Fram & Grady, 1997; Kunz, 1997; Mehta & Sivadas, 1995; Sultan & Henrichs, 2000; Stafford et al., 2004; Punj, 2011; Richa, 2012; Rahaman, 2014). This consumer demographic study can help stores make decisions based on the characteristics of their customers whether they should run their businesses online or traditional means. For example, Haver (2008) states that in yearung age groups called 'green shopper' or beginner generation is more popular to use shopping online. Yearung people do not want to spend their time going from store to store to make comparisons. They will shop online whenever possible. Richa (2012) in a study indicates that one's shopping preferences are strongly influenced by demographic factors such as age, income, marital status, number of family members and gender. In addition, this research involves the presence of online store applications on smartphones from consumers in influencing their preference for online shopping. The presence of the app can make the experience of using the internet to access online shopping easier. Ease of use is the basis in choosing to shop online (Mauldin & Arunachalam, 2002). However, not all online stores are willing to invest in funds to create an online store application. Moreover, it is theoretically stated that people's shopping preferences have begun to shift to online shopping (Shanthi & Kannaiah, 2015). Nevertheless, some people practically still survive in using traditional markets at certain times or types of shopping. Many traditional stores are turning into online stores by using online shopping apps. Now, smartphone ownership in urban society in Indonesia, including Medan, is very high. The market in Medan itself has also started to tend to lead to online shopping. Various business and shopping activities are already connected to the internet, with some already having an online store application. This study examines whether it should invest funds to create an official online store application (including being part of an official online store) or simply move through third parties of social media like Facebook or Instagram only. To improve the more fundamental understanding, this study also uses demographic factor as a determinant of consumer preference. This is driven by the theoretical basis that consumers form the market itself. Hence, demographic factors which are very fundamental, is regarded able to reflect the state of the market. Many marketers have an interest in understanding population growth in the market, including consumer characteristics such as age, educational level, household, economy and sociocultural issues of society. Thus, by using demographic factors, this study is expected to gain a deeper understanding of the market, consumer behaviour and other considerations that lead to purchasing decisions and to assist marketers in setting up market penetration strategies.

Literature Review

The presence of online stores today provides many changes in the buying behaviour of the community. The online system makes it easy to communicate and approach customers (Katawetawaraks & Wang, 2011). Wang et al. (2005) states that online stores are becoming more fun for consumers as it comes to consumers 24 hours a day, 7 days a week. Online store applications provide more benefits to consumers. Direct apps help consumers find their options more easily. In addition, online applications help address issues such as security and service guarantees to consumers. The interaction between the consumer and the application system helps to cope with direct contact with the salesperson where some people prefer not to interact with the salesperson (Parks, 2008). Online shopping apps can affect consumer shopping preferences to use online shopping. In this drastically altered marketing context, knowledge of demographic factor will lead to understand how the shopping preferences affect market conditions. Marketing experts always pay attention to major factors such as demographic, economic, socio-cultural, technological and environmental factors in an effort to understand market conditions. Haver (2009) has shown that yearunger consumers are more familiar and choose online shopping preferences. Richa (2012) evaluates more demographic aspects and concludes that this preference is influenced by gender, age, income and marital status. Therefore, demographic factors can affect consumers' preferences in shopping.

An adequate internet network is capable of bringing about revolutionary changes in the goods and services market. Traditional perspective states that the market is essentially a meeting place between the seller and the buyer (Wahyuni & Ginting, 2017), but with the rapid development of internet, buying and selling no longer has to be done when the seller meets directly with the buyer (Nugroho et al., 2017; Budiharseno, 2017). Along with the development of technology, the understanding of the market began to shift. In the beginning the market happens when the buyer and seller meet in person. The development of the era of communication makes the market happen without having to meet directly, but through telephone conversation. This activity developed into tele-marketing. Internet presence makes sellers and buyers do not have to meet directly, can even happen without any meaningful interaction from the seller or buyer. The presence of an online store makes the buying and selling process simpler. The seller markets the product information on the website or online store, then the buyer can compare the product according to their needs or wishes and make a purchase either in consultation with the seller or not. In the end, the internet presence in marketing and online shopping has resulted in three beliefs, which are time-saving, cost-effective and 'best match' to the desired product (Punj, 2011).

Attendance of online shops provides various facilities in the aspects of buying and selling. Buyers can easily find information, make comparisons between alternative products and stores in an effort to meet their needs and wants, as well as find the best alternative of all the options available. The buyer does not even have to make purchases outdoors to buy the products he wants from either the local shop or from any hemisphere. Katawetawaraks & Wang (2013) point out that the ease of marketing communication provided by the online market greatly influences consumer decisions in choosing to shop online. The conveniences offered by online stores to consumers make online shopping grows rapidly, reaching even 231% in 2007 (Rose et al., 2011). Unfortunately, the presence of online stores also eliminates those aspects that are quite important in the buying process in general. For example, a prospective buyer can choose the fabric or outfit offered by the online store as well as the availability of supplies for the various sizes available on the choice of clothing. They are even possible to pre-order products that are not available. However, potential buyers lost the chance to try the product before buying, to check whether the size of the clothes are in accordance with his or her desire or not or cannot feel the quality of the fabric used significantly before the transaction occurred. The presence of online stores makes buyers have high expectations for the products they have purchased and can lead to discontent. High expectations alone become a double-edged sword that can be both advantage and weakness. Expectations, however, is closely related to satisfaction and loyalty (Teviana et al., 2017). This can lead to satisfaction or dissatisfaction depending on the level of product evaluation received. The slightest condition that no physical interaction occurs in online transactions also makes it difficult to conduct consumer behaviour studies directly on online shopping activities (Jiang et al., 2008).

A review of the side of online transactions shows that buyers generally have to pay first the products they want to buy before the products are delivered to them. This is in contrast to traditional markets where generally the handover of goods occurs simultaneously with the handover of money to be paid (Wahyuni & Ginting, 2017). This gap makes the act of fraud in online transactions is quite high. In many cases, buyers receive goods that are not in accordance with the goods they buy. Some cases that occur in online shopping community itself occurs when purchasing pots online, goods received by consumers just cover the pan. Good online stores accept complaints and fix existing errors, but not all behave the way they do. Even the seller can simply close the store and open a store with a new name and a new identity.

Online transactions on some online stores ask prospective customers to enter credit card information that is very confidential (Nugroho et al., 2017). A good store will include third party security on its site. However, such information may be misused by others who have particular ability to process data information on the internet or by phishing, creating a similar predatory site whose primary purpose is to gain access to personal information and commit other crimes. In this aspect, trust plays a role in the smoothness of transactions on online stores. Lusiah et al. (2017) argue that beliefs can moderate behaviours that lead to a person's loyalty level. Traditional markets (some call them offline stores today) and online stores each have its own pros and cons, advantages and disadvantages. Until now, traditional markets are still the choice of many people in the transaction behind the rapid growth of online market and buyers who make online transactions. The main advantage of traditional markets is the strong physical interaction that occurs between the seller, the buyer and the products offered. Direct service becomes an important factor in shaping the decision and loyalty of the buyers (Lubis & Lumbanraja, 2016). Today, shops that initially move on traditional markets are beginning to switch and run online stores. This decision needs to be evaluated since not all consumers prefer to conduct transactions online. The study of online shopping preferences by Vijay & Balaji (2009) indicates that behind the ease with which online shopping is offered, people still prefer to shop traditionally. Budiharseno (2017) identifies that although student participation in shopping shows changes to online shopping, there are still many who still do not choose to shop online at their own discretion.

Research Method

Participants

Participants in this study amounted to 200 respondents consisting of 119 men and 81 women who already had their own income and could decide to make a purchase on their own. The study was conducted in Medan, North Sumatra, involving visitors of big cafes in Medan City to narrow the scope of the participant's search in accordance with the needs of this study.

Demographic Classification of Participants

Participants in this study are also grouped according some classifications. First, based on the income level, the participants' income levels are categorized into three main groups:

1. Low Income: Participants who have an income level below the regional minimum wage of Medan city.

2. Medium Income: Participants who have an income level above the minimum wage but cannot be used for savings.

3. High Income: Participants who have a sufficient level of income to meet all the basic needs and can be used to save.

Participants in this study are also grouped according to the highest level of education they get, that are the level of senior high school, diploma level, graduate level and master degree. The education level indicates the maturity of the education pattern is expected to become the reference in a person's decision-making process so this classification can help in evaluating whether the level of education acquired influences the decision-making process. Moreover, participants were grouped within the age range as a reference in looking at the age effect of shopping preferences from participants. Age ranges are grouped into four classes with a five-year age range ranging from 21 years old, i.e., 21-25; 26-30; 31-35; 36-40 years old.

Participants are also grouped according to their daily life in choosing a place to live. Preference as part of decision making can be influenced by the participants' daily lives. This grouping is based on whether they live alone or with their families, namely:

1. Participants Live Alone: Participants who live daily by renting a house, away from family, not married or have been married but choose to divorce.

2. Participants with Family: Participants who live daily with family or have a family.

Participants are grouped by ownership of online store applications in their smartphones. The online store application is a smartphone app that contains various information from products sold by online stores and payment methods in one touch. In other words, an online store application allows transactions to occur without the need to access the browser from participating smartphones.

Research Procedure

Researchers visited popular cafes in Medan City. Researchers invite visitors to participate in short research. The study begins with a brief extension of the advantages and disadvantages of traditional markets and online markets, including the benefits and risks of each of these markets. It is used so that participants have the same understanding of both forms of the market. Furthermore, this research uses questionnaires as a tool to collect research data. Researchers directly distributed questionnaires to prospective participants. Screening question is given to prospective participants whether they have a smartphone that is always connected to the internet network.

The questionnaire given relates to respondent's preference in doing shopping activities, whether online or traditionally. The questionnaire evaluates the characteristics of respondents who will become the basic reference in assessing the influence of characteristics and application on the tendency or preference of expenditure from participants.

Data Processing

Shopping preferences from participants in the conversion in the form of binary data with a classification of 0 to explain traditional shopping preferences and 1 for online. Dummy variables are used to convert nominal and ordinal data of demographic characteristics of participants in this study. Data were evaluated using a logistic regression model that could predict a participant's tendency in choosing shopping methods.

Data Analysis

This study employed logistic regression in predicting shopping preferences from participants of this study. Predictive variables in this study are ownership of applications and demographic factors covering gender, daily, income level, education level and age group of participants. Ownership of online store applications, gender and daily respondents are dichotomous variables. The level of education, age group and income level are categorical variables and codified with dummy variables according to each data set. Logistic regression was used in this study because researchers attempted to see the simultaneous effects of predictor variables in predicting consumer shopping preferences.

The research instrument is validated using face validity method, with 30 respondents involved in the preliminary study. Face validity ensures that the questionnaire is appropriately understood by the respondents. The questionnaire in this study is a single item construct which means it does not require consistency like reliability test. Thus, content validity is not used in this study. Validation is done by directly asking the respondents’ response in an item of question asked. The results show that out of 30 respondents, all have been able to measure the variables used.

Results

Demographic Characteristics of Respondents

The demographic characteristics of respondents in this study were classified by sex, income level, age group and online store application in participating smartphones. This demographic is cross tabulated with participants' preference on shopping activity. The results of this cross-tabulation are summarized in Table 1.

Table 1 Cross-Tab Preferences with Participant Demographics
			Preference
			Traditional Market	Online Market	Total
Online_Shop_App	Do Not Have App	Count	12	3	15
		% within Online_Shop_App	80.0%	20.0%	100.0%
		% within Preferences	15.4%	2.5%	7.5%
	Have App	Count	66	119	185
		%within Online_Shop_App	35.7%	64.3%	100.0%
		% within Preferences	84.6%	97.5%	92.5%
Livelihood	Single	Count	43	61	104
		% within Livelihood	41.3%	58.7%	100.0%
		% within Preferences	55.1%	50.0%	52.0%
	Family	Count	35	61	96
		% within Livelihood	36.5%	63.5%	100.0%
		% within Preferences	44.9%	50.0%	48.0%
Income	High Income	Count	24	39	63
		% within Income	38.1%	61.9%	100.0%
		% within Preferences	30.8%	32.0%	31.5%
	Medium Income	Count	36	72	108
		% within Income	33.3%	66.7%	100.0%
		% within Preferences	46.2%	59.0%	54.0%
	Low Income	Count	18	11	29
		% within Income	62.1%	37.9%	100.0%
		% within Preferences	23.1%	9.0%	14.5%
Age_Group	21-25 year	Count	18	27	45
		% within Age_Group	40.0%	60.0%	100.0%
		% within Preferences	23.1%	22.1%	22.5%
	25-30 year	Count	33	40	73
		% within Age_Group	45.2%	54.8%	100.0%
		% within Preferences	42.3%	32.8%	36.5%
	31-35 year	Count	14	38	52
		% within Age_Group	26.9%	73.1%	100.0%
		% within Preferences	17.9%	31.1%	26.0%
	36-40 year	Count	13	17	30
		% within Age_Group	43.3%	56.7%	100.0%
		% within Preferences	16.7%	13.9%	15.0%
Gender	Male	Count	12	107	119
		% within Gender	10.1%	89.9%	100.0%
		% within Preferences	15.4%	87.7%	59.5%
	Female	Count	66	15	81
		% within Gender	81.5%	18.5%	100.0%
		% within Preferences	84.6%	12.3%	40.5%
Education	High School	Count	33	32	65
		% within Education	50.8%	49.2%	100.0%
		% within Preferences	42.3%	26.2%	32.5%
	Diploma	Count	8	18	26
		% within Education	30.8%	69.2%	100.0%
		% within Preferences	10.3%	14.8%	13.0%
	Graduate	Count	33	61	94
		% within Education	35.1%	64.9%	100.0%
		% within Preferences	42.3%	50.0%	47.0%
	Postgraduate	Count	4	11	15
		% within Education	26.7%	73.3%	100.0%
		% within Preferences	5.1%	9.0%	7.5%

Table 1 provides information that out of the 200 participants in this study, there were 59.5% of participants with male sex and the remaining 40.5% were female. Based on the ownership classification of online store applications, 92.5% of participants have an online store application and understand how to use the application. Thus, there are only 7.5% of respondents who do not have an online store application. Based on the classification of daily life, as many as 52% of participants generally perform various activities alone while the remaining 48% live with spouse or family. Classification by income level, the majority of participants are classified middle-income (54%), followed by high-income (31.5%) and 14.5% low-income earners. Classification by education level indicates that the majority of participants are at the highest education level of bachelor (47%), followed by high school (32.5%), diploma (13%) and master (7.5%).

The tendency of shopping preferences from participants can also be presented based on how much of the research samples tend to be in their shopping activities, both online and traditional. Table 2 summarizes the percentage of respondents in general shopping preferences.

Table 2 Percentage of Shopping Preferences Based on Demographics and Application Ownership
Scenario	Preference		Sig.
	Online Shopping	Traditional Market
Own Online Store Application	61%	39%	0.001
Gender:			0.000
Men	89.9%	10.1%
Women	18.5%	81.5%
Marital Status:			0.479
Single	58.7%	42.3%
With Spouse/Family	63.5%	36.5%
Age Group:			0.201
21-25 year	60%	40%
26-30 year	54.8%	45.2%
31-35 year	73.1%	16.9%
36-40 year	56.7%	43.3%
Income Level:			0.019
Low	37.9%	62.1%
Medium	66.7%	33.3%
High	61.9%	38.1%
Education:			0.108
High school	49.2%	50.8%
Diploma	69.2%	30.8%
Bachelor	64.9%	35.1%
Master	73.3%	26.7%

Table 2 generally indicates that the majority of participants in this study had a tendency to choose to shop online. Moreover, it also indicates that women, on contrary with men in this study prefer to shop traditionally.

Logistic Regression Results

This study employed logistic regression in predicting shopping preferences, by using predictive variables of applications ownership and demographic factors of respondents covering gender, daily, income level, education level and age group of participants. The research model was evaluated based on the full model and the intercept-only model and found that the models were statistically significant, χ^2=24.84, sig. =0.002. This research model can accurately predict and classify 88.5% of participants who have traditional shopping preferences and 86.9% of participants who have online shopping preferences. The false-positive error rate of online shopping preferences is 11.5% and the traditional false-negative shopping preferences are 13.1%. In general, the model success rate in predicting consumer shopping preferences is 87.5%. The result of logistic regression of predictor variable in this study is summarized in Table 3.

Table 3 Logistic Regression Predicting Shopping Preference Based on Demographic and Application Online Store
Predictor	B	SE	Wald	df	Sig.	Odd Ratio
Online Shop Application	3.142	0.931	11394	1	0.001	23.160
Livelihood (0=Single)	1.177	0.679	3.003	1	0.083	3.246
Income (0=Low Income)			8.235	2	0.016
High Income	2.449	0.888	7.615	1	0.006	11.578
Medium Income	2.108	0.784	7.226	1	0.007	8.231
Age_Group (0=21-25 year)			0.116	3	0.990
26-30 year	-0.102	0.765	0.018	1	0.894	0.903
31-35 year	0.120	0.709	0.028	1	0.866	1.127
36-40 year	0.110	0.731	0.022	1	0.881	1.116
Gender (0=Male)	-4.190	0.537	60844	1	0.000	0.015
Educational Attainment (0=High school)			1.556	3	0.670
Diploma	-420	0.826	0.259	1	0.611	0.657
Graduate	0.344	0.528	0.424	1	0.515	1.410
Master	0.778	0.983	0.627	1	0.428	2.177
Constant	-3.078	1.294	5.661	1	0.017	0.046

Traditional or Online Market?

As Table 2 indicates that only by female sex preferring traditional shopping, the majority of respondents prefer to shop online (61% of participants). Apart from the various risks of online shopping that have been put forward by researchers on research activities, the majority of respondents still prefer to do shopping online. Significance level in Table 2 indicates that a distinct preference difference occurs when a person has an online store application, between the sexes of both men and women, as well as at their income level.

Table 3 shows the logistic regression results, Wald's test and the odd-ratio ratio of each predictor variable in predicting shopping preferences. The significance level used in this study is 5%. At this level, ownership of online shop applications, gender and income level of participants have a significant effect in predicting online shopping preferences. While the variables of age, daily and level of education have no significant effect in the proposed model.

The odd-ratio rate of ownership of the online shop application is 23.16 which indicate that under conditions where other variables in this study are the same, customers who have an online shop application 23.16 times more likely than those who are not in choosing to shop online. These results indicate that it is important for online shops to have an online shopping app or join an online store application. Transactions are more common in those listed in the online store application.

In the daily aspect, the odd-ratio level is 3.246 indicating that those who are with their families or with their partners have a greater chance of 3.246 times more than those who are alone. Table 2 indicates that these predictors do not make a significant difference between themselves and their families. Although there is a greater tendency in using online shopping for those who are married, this difference is not always the case at the 5% level of significance. The income predictors in this study are ordinal data divided into three categories converted into two dummy variables. Table 2 indicates a significant difference between the income groups. Table 3 shows that the odds ratio of the intermediate income group is 8.321, which means that those with middle-income tend to be 8.321 times more likely than those in low incomes. The high-income group's odds ratio is 11.578 indicating that there is an 11.578 times greater chance of online shopping transactions on high-income people than those with low incomes. Thus, those with high income are 1.39 times more likely than those with middle-income. The greater the income of consumers, the greater their tendency to shop online. Predictors by age group indicate that no specific age group significantly predicts a consumer's shopping preferences. Each of the age group members of a fishing opportunity is almost as great both for shopping as well as shopping online.

Predictors by sex provide a negative beta value with reference ratings being male. The inverse value of the odd-ratio of sex predictors is 6.67 which indicate that males tend to be 6.67 times larger in choosing online shopping. Predictors based on the last level of education do not give a significant influence between each group of levels of education. Although there is a tendency for higher online shopping opportunities with higher levels of education, this influence is not significant.

Discussion

In general, the study found that 61% of participants preferred to shop online rather than traditionally, even after understanding the risks of online shopping. According to participants, the risks of online shopping can be avoided by being more careful when shopping. However, the fact that the lack of physical interaction that occurs in online shopping makes participants prefers to shop traditionally. The decision to choose the method of shopping from participants based on this research is partially affected by demographic factors and ownership of online shopping applications. Punj (2011) & Richa (2012) suggest that consumer demographic variables affect their shopping preferences. The results partially support the research. Table 3 explains that although online shopping preferences are significantly influenced by the gender and income per month of participants, their decisions are not significantly influenced by recent educational levels, age ranges and daily life of respondents.

By comparing the Wald's test score of each of the predictors in this study, gender plays the greatest role in influencing consumer decisions in online shopping. Table 3 indicates that the chances of men choosing to shop online are 6.67 times greater than women. This result is very stable in the observations made. Previously, Donthu & Garcia (1999) found that there were no significant differences between online shopping actors by sex. The situation is interesting and continues to grow from year to year. In this study, the differences are more pronounced and this is in line with previous research literature indicating that men are more inclined toward online shopping than women (Rodgers & Harris, 2003; Slyke et al., 2002; Stafford et al., 2004). Stafford et al. (2004) found that men do more online shopping transactions and prefer to shop online (Slyke et al., 2002). Shopping online makes physical interactions in the shopping process less and less even zero. According to Rodgers & Harris (2003), women prefer the direct interaction that occurs between the seller, the buyer and the product offered. Women also prefer to directly bid and try the product first before deciding to buy the product. In other words, women are more skeptical of online shopping than men. On the other hand, men pay less attention to details such as size, materials and even the quality of the product purchased during the product according to the minimum purchase criteria a man has. The buying decision process of men is simpler than women. Table 3 indicates that the monthly income level of consumer consumers has a significant effect on online shopping preferences. In addition, the higher the income of consumers, the greater their tendency to shop online. Punj (2011) states that the income level is closely related to the consumer's decision to buy online. In his research Punj conveyed that when income is increasing, their focus in shopping is how shopping activities can become faster and more time saving. The most noticeable advantage of shopping online is the ease of searching for alternative product information and the ease of the transaction. Along with the increase in consumer income, they increasingly pay less attention to the problem of ‘best match’ of needs and products satisfying those needs. Expensive prices are perceived to be cheaper as earnings increase.

Daily life of respondents has a significant effect on the level of 10%. In other words, at the 10% significance level there is a marked difference between those living alone with the family in shopping preferences? Those with families are more likely to choose to shop online. Richa (2012) in his research indicates that marital status has no significant effect on shopping preferences, but the larger the number of families, the more chance they choose to conduct transactions online. When consumers have colleagues to discuss, the accuracy of discussion becomes easier and time-saving if they use online shopping. Conversely, when sharing their partner's opinion does not exist, they tend to prefer to go directly to the market, evaluate the goods directly and then make a purchase.

The age group and level of education in this study did not indicate preference in a particular direction. The literature of previous research indicates that age plays an important role in the online shopping preferences (Punj, 2011). Schiffman & Kanuk (2003) stated that yearung age is more sensitive to innovation so they are more instrumental in using online shopping innovation. Safitri et al. (2017) states that the Y generation or millennial generation is not yet fully aware of the presence of online stores. This indicates that age differences do not guarantee acceptance of good technology. Studies conducted by Richa (2012) in line with the study's findings that age does not play a major role in consumer shopping preferences. Along with the time, consumer adaptation to technology is already very high. Now the majority of consumers have a smartphone connected to the Internet. Trial and error in everyday use as well as learning that occurs every time makes age is no longer a limitation in the use of technology. In addition to demographic factors, the application plays an important role in helping decision-making shop online. Punj (2011) convey an important factor in shopping online is the convenience of saving time costs and find the best product to meet consumer needs. Applications online store is very helping in achieving such easiness. In addition, the official application has features that enhance the online shopping security and minimize the risks that may occur in shopping online. As a result, people will increasingly choose to shop online with the support of the official app of the online shop.

Conclusion

This study results demonstrate that shopping preferences of an individual both online and traditional are influenced by the demographic and proprietary aspects of an online shopping app. The study concluded that male more prefer to shop online than female. Moreover, the study reveals that the higher the income is, the more the chance they will choose to shop online. Those who have an online store application will tend to opt for online shopping. Put simply, the online consumer profile of shopping is a middle-class or high-income male and having an online store application on their smartphone. Furthermore, no significant difference in any age and education level group is revealed. As implications, this study identifies consumers' shopping preferences and their tendency toward online shopping. Thus, sellers can prepare themselves to move in traditional markets as well as expanding into the online market by setting target consumers and market segments accordingly. Product marketing strategies can be better prepared by producers and sellers in accordance with their target market so that the products offered are more targeted and are able to increase product sales.

There are some limitations in this study that can be considered by future research. This study addresses the general shopping preferences for products. The preference based on product categories is not in depth discussed in this study. Further research is needed to identify the specific shopping preferences of products. Moreover, it is probably very interesting to investigate how consumers also allow to opt for online shopping for local products and to examine the degree of online shopping capable of influencing the local product demand.

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