Keywords

TAM, Perceived Ease of Use, Perceived Usefulness, Attitude, Purchase Decision, Trust, Online Store, SEM.

Introduction

It is inevitable that information technology has changed the business map, one of which has an impact on new marketing models of products and services. Before there was the internet usually to market a product or service or the process of purchasing goods is done by meeting directly between the seller and the buyer traditionally or offline. In contrast to online stores where transactions do not occur because of face-to-face, so good information that is presented attractively about products by companies can be relied upon and useful to better understand the product and further support their purchasing decisions (Lung et al., 2013). Therefore online marketing can be called a new method in modern marketing and is used to market goods, services, information and ideas using the internet as a medium (El-Gohary, 2010). So that currently marketing with the offline method (physical market) faces a new challenge with the internet because of the emergence of new shopping alternatives namely online shopping methods, online shopping is an alternative place for new shopping now aimed at facilitating all activities in human life to be created (Suleman, 2018). Some of the advantages of shopping at online stores, among others, can reduce costs, time and energy in the purchase process because when shopping can all compare between sellers of goods from several sellers (Hong & Cho, 2011). Regarding the move from offline shopping to online is not talking about the advantages of each other, but more than that how consumers want to adapt to use new technologies in their shopping.

Indonesia is a large market for online shopping, especially in Southeast Asia, with a large population and increasing internet users, one of the factors is also in line with the growth of smart phone users and the like that are an important part of Indonesians to be able to transact online (Julianti, 2017 ). Data shows that Indonesia ranks in the top 3 of 35 countries in Asia in terms of the number of internet users. This should be comparable to the potential growth of the online store market (Suleman et al., 2019). In 2018 there were approximately 143.26 million Indonesians using the internet, equivalent to 54.68% of Indonesia's population currently accessing the internet (Suleman, 2018). Considering the growth of the middle class and Millennials and Generation Z people who were born in the digital era this can be a support for the increasing online transactions in Indonesia in the future. The big shift that is taking place in this new era, especially in the changing marketing model in business which so far has only known one path with the traditional distribution (offline) into two channels now with technology has become part of a new form of business model (online). The choice for consumers to be able to get goods and services now can be through online stores (Çelik, 2011). Consumers are faced with various choices of shopping methods, and consumers can compare them from the price and find complete information beforehand about the items to be purchased and then decide to choose where to shop at online or offline stores (Ha & Stoel, 2009). Online stores are becoming a new alternative now because they do offer a variety of advantages and convenience and freedom of time in making transactions so that it might make consumers consider making decisions to shop at this shopping place (online) rather than the old shopping places (offline) (Suleman et al., 2019). In addition, shopping at online stores has its own advantages and benefits (Kim et al., 2009). Some of the advantages of shopping at online stores First, the convenience of consumers can buy products or services at any time without limitation of time and wherever they are can do transactions as long as you have an internet network. Secondly, shopping at online stores can make consumers save money, effort and time when buying products or services. Thirdly shopping at online stores gives consumers the ability to find and gather more information and with a high level of transparency and convenience.

Online marketing is defined as efforts to market products and services and build relationships with customers through internet media (Kotler et al., 2012). Online media is more focused on the use of the internet used by marketers or companies to offer products or services to potential customers (Utami & Firdaus, 2018). So companies must make optimal use of technology, so they can personalize in selected media to market their products online, this is important as a form of change in the marketing of goods or services to adapt in the new marketing era 4.0 (Young & Burgess, 2010). In consumer behavior mentioned is the process by individuals or groups to choose, buy and use goods, services, ideas or experiences to satisfy their needs and desires (Solomon, 2018). The decision to choose where to shop when a consumer wants to decide to do online transactions (Quack et al., 2010). In this new era there are actually 2 decisions that consumers must make, first the decision to choose the product they want to buy. Then the decision process to decide to shop at an offline or online store.

This study aims to understand the factors that consumers consider to shop online. In several previous studies have tried to modify this TAM to test things related to online shopping, the issue of trust and risk is often used in online store research. One thing that was tried in this study was to combine the TAM model with the two issues above so as to create a new model in research about consumers' shopping decisions specifically for fashion products. And alternative models of research results are expected to be developed and become a reference in subsequent studies. One theory about the behavior of the use of information technology systems used to explain individual acceptance of the use of information technology systems is the Technology Acceptance Model (TAM). There are five main constructs that make up TAM, the five constructs are (1) Perceived Usefulness, (2) Perceived Ease of Use, (3) Attitude Toward Using, (4) Behavioral Intention to Use, (5) Actual System Usage (Davis, 1989). Figure 1 is the TAM theory that was first introduced by (Davis, 1989).

In this TAM theory it is explained that behavior is influenced by attitudes and attitudes are influenced by perceptions of usefulness and convenience. A positive attitude of consumers can attract many consumers to decide where to shop in an online store (Indarsin & Ali, 2017). To measure someone's decision on attitude usually uses three independent variables namely perceived usefulness, perceived ease and trust (Yadav et al., 2016). Perception of perceived usefulness and perceived ease of use is a major factor in accepting the attitude of consumers in online stores (Pavlou, 2003). In another study it was mentioned that perceived usefulness has a significant and positive influence on consumers' attitudes toward shopping at online stores (Delafrooz et al., 2011). Consumers who choose to shop through online stores cannot control for certainty their fulfillment of expectations, when consumers buy products or services via the internet they cannot see directly the goods to be bought, or meet directly the seller who offers their products. This shows that the need to build trust between sellers and consumers (Alwafi & Magnadi, 2016). This trust cannot just happen but must be built from scratch and can be proven by the seller by sending goods or services in accordance with the order; this will make buyers have a positive attitude towards online stores. Karen when someone who wants to make a transaction in an online store, then what the consumer thinks is that the money sent is not lost just like that but will get the desired product or service in accordance with what is displayed and explained in the online store (Aribowo & Nugroho, 2013). Risk is also very important determining factors that influence the attitude of shopping consumers online shopping (Shu-Hung & Bayarsaikhan, 2012). Risk is a thing that forms the attitude of consumers to shop online that needs to be considered because the decision of consumers shopping online because there is also an online store fraud and other events that harm consumers online stores (Hsu et al., 2014).

One important thing that influences online purchases by consumers is the trust factor (Pavlou & Fygenson, 2006). Because in online stores, trust becomes an important part of changing the attitude of positive visitors who will become buyers. When doing online shopping, buyers depend on the promises given by the seller. The most important factor why consumers do not choose where to shop online is because of trust in online shopping, fear because of fraud and the payment process (Solomon, 2018). The advantage of online stores can reduce costs, time and effort in purchasing because shopping can also compare between sellers of goods from several sellers, but also many in the marketplace of fraud incidents and other events that harm consumers online (Hong & Cho, 2011). This shows that the need to build trust between producers and consumers who make purchases online (Alwafi & Magnadi, 2016). When someone who wants to make transactions online, then what should be in his mind is that the money sent is not lost just like that but get a reply Product desired in accordance with what is displayed and explained at the destination online store (Aribowo & Nugroho, 2013) . Risk is something that forms the attitude of consumers to shop at online stores that need to be considered by marketers in an effort to improve consumer decisions to shop (Hsu et al., 2014).

This research is one part of a dissertation research that will provide empirical validation emphasizing on the factors that influence consumers' decision to shop online. This research examines how perceptions of convenience, perceived usefulness, trust and risk to attitudes and decisions of consumers, especially buying fashion products in online stores. Furthermore, this research is structured as follows beginning with a background later with an appropriate literature review and the study model developed will be displayed, after that, will explain the research methodology in terms of the study population and sample, measurement items, questionnaire design, construct validity and reliability, and finally structural models and procedures for testing hypotheses. The results of this study were then reported to be followed by discussion and their implications for theory and practice. Finally, conclusions are presented, limitations and limitations in this study to be a guide for further research.

Theoretical Framework And Hypotheses

The main objective of this research is to predict the consumer decision factors in shopping at online stores, especially for fashion products. As has been written the background, said there about the phenomenon of the decision of consumers who choose to shop at online stores. Online stores have several advantages and conveniences because there are some consumers who choose to shop at online stores. But we know that online stores are a combination of shopping with the use of technology so that there are adaptations about new technologies that consumers will use in conducting online transactions.

Therefore in this research, we will use the Technology Acceptance Model (TAM). This TAM uses two variables: first the usability is related to how the new way will improve performance and second, ease of discussing how the new way can be more easily adapted by users (Davis, 1989). And also other variables determining the formation of consumer attitudes, namely trust and risk in shopping at online stores. can be said that an important factor in consumers' decision to choose where to shop is the attitude of consumers themselves to a shopping place, therefore attitude as the main predictor in shopping decisions in online stores, As shown in Figure 2, this study proposes a model to explore the direct relationship between convenience, usability, trust and risk to the attitude of consumers choosing to shop online. Then in this study also explores the direct relationship between attitudes and decisions of consumers shopping in online stores. Figure 2 is a new model that is examined in this study.

Usefulness

In the TAM it is stated that attitude building is wrong by the construct of Perceived usefulness (Davis, 1989). The research model incorporates key concepts from TAM, trust, and relational exchange, and integrates them through TRA. The results showed how the influence of attitude dimensions with the original TAM theory where there are variables of perceived usefulness and perceived usefulness, the results of this study found that there was a very high and positive and significant relationship between perceived usefulness towards attitudes (Indarsin & Ali, 2017; Lung et al., 2013), (Suleman et al., 2019). Therefore researchers first hypothesized that:

H₁: The usefulness of shopping at online stores has a positive effect on consumer attitudes.

Ease of Use

This variable is an inseparable part of TAM to predict consumer attitudes and has been examined beforehand and it is found that proven perceptions have an influence in predicting attitudes (Suleman et al., 2019; Indarsin & Ali, 2017). This perception of convenience has shown that it has more influence on consumers' attitudes in shopping at online stores (Palvia, 2009). So that it can be concluded in shopping at online stores, consumers with more positive perceptions of convenience will be more likely to have the attitude to shop at online stores, therefore researchers hypothesize a second that:

H₂: The ease of use shopping at online stores has a positive effect on consumer attitudes.

Trust

In another variable used in this study will explore consumer confidence in shopping at online stores. In previous studies it was mentioned that trust is the most powerful and significant factor influencing attitude (Suleman et al., 2019). Several empirical studies have also shown a positive relationship between trust and convenience, besides that it is also mentioned that trust in shopping as a consumer to take risks in transactions and influence attitudes (Indarsin & Ali, 2017), in research (Ha & Stoel, 2009 ) attitude towards online is answered by trust. In research (Suleman et al., 2019; Suleman et al., 2019). It was found that besides having a positive effect on significant trust; it was also a determining factor for consumers' decision to shop online. This is a significant positive affect factor. In accordance with the results of the empirical research above that trust influences attitudes and shopping decisions in online stores. Therefore, the third and fourth hypotheses are proposed:

H₃: Trust in shopping at online stores has a positive effect on consumer attitudes.

H₄: Trust in shopping at online stores has a positive effect on buying decisions.

Attitude to Shop Online

Attitude can be interpreted as the level of positive feelings of consumers about participating in shopping and it is believed that this positive attitude opens up more opportunities for consumers to choose where to shop (Suleman et al., 2019; Hsu et al., 2014). Attitude itself is defined as the tendency to learn to behave consistently in a beneficial or unfavorable manner with respect to a given object (Schiffman et al., 2010). And in the previous research it was concluded that attitudes are significantly positively related to decisions (Wu & Chen, 2014; Lee, 2016; Lung et al., 2013). It was concluded that consumer confidence with a more positive attitude would be more likely to shop at online stores. Therefore, hypothesis five was proposed Table 1:

Table 1:Summarized Definition Of Constructs
Construct	Definition	Reference
Usefulnes	Is a level where someone believes that a way of shopping online will improve performance	(Davis, 1989)
Ease of Use	Levels of consumers believe that online shopping can make it easier for them	(Davis, 1989)
Trust	Positive expectations and as consumers' trust to take risks	(Suleman et al., 2019)
Attitude	Depth of positive feelings consumers shop online	(Davis, 1989)
Decision	Depth of positive feelings consumers shop online	(Schiffman et al., 2010)

H₅: The attitude of shopping at online stores has a positive effect on consumer decisions.

Research Methods

Population and Sample

In this study retrieve data from consumers who have already shopped fashion products online. As is known that the total population of consumers of online fashion products in the Jakarta area is a large population and the number is not known with certainty. Sampling in this study was conducted with non-probability sampling with the criteria Respondents are consumers.

Who have shopped fashion products through online, Respondents are with age from 17 years. Respondents are those who live around the City of DKI Jakarta. The technique used in this research is purposive sampling method. By giving questionnaires the number of 30 questions to each respondent.

SEM is very sensitive to the number of samples, so this research sample will require referring to the criteria proposed by (Hair et al., 2010) namely the Maximum Likelihood Estimation (MLE) technique. A good number of samples according to MLE range from 100-200 samples. The number of samples needed is at least 5 times the number of indicator variables (Ferdinand, 2014). So the research requires a minimum of 150 respondents with a calculation of 5 times the indicators used in this study.

Sample Characteristics

With the number n=150 the following respondents obtained data, 36.4 percent were male and 63.6 percent female; 27.7 percent were under 21 years old, 53.8 were 22 years old - 41 years old and 18.5 were over 42 years old. 39.5 respondents graduated from high school / equivalent, 26.7 percent of graduates of diploma / S1 and 33.8 percent of respondents graduated from S2 / S3. 54.9 percent were private employees. 59.5 respondents have income above 4 million rupiah and 40.5 percent of respondents have income below 4 million rupiah. Below is Table 2 summarizing the demographics of respondents:

Table 2: Demographic Profile Responden
Measure	Categories	Sum	Persen (%)
Gender	Male	56	37.3
	Female	94	62.7
Age	Under 21	44	29.3
	22-41	78	52.0
	Over 42	28	18.7
Education Level	High school	61	40.7
	Diploma/Bachelor’s degree	39	26
	Graduate degree	50	33.3
Income	Less Than IDR. 4.000.000	66	44.0
	More Than IDR. 4.000.000	84	56.0

Can be seen in the table above it can be concluded that the most responses in this study are women and the age group that dominates is the age category of millennial generation with the current condition that they have just graduated from high school and are still in the process of studying in college or have a career. With current income in the Jakarta area, most are above four million rupiah per month.

Questions in the questionnaire from using a likert scale and questionnaire format have been improved in the pre-test process to get a good version used in this survey. a sample of 150 respondents was selected from a population of consumers who shop for fashion products online. The trial results show the reliability and validity of the measurements that are acceptable.

Data Analysis and Result Measurement Items

In this study, using four of the five constructs of the Technology Acceptance Model TAM theory. By looking at the results of previous studies, the study decided to use some adaptations of the research construct related to online stores. Perceptions of ease and perceptions of usefulness are measured using a scale of six indicator items derived from (Davis, 1989). Whereas trust is measured using a six item indicator scale derived from (Suleman et al., 2019). While attitudes in this study were measured using a scale of six indicator items derived from Suleman et al. (2019) and buying decisions on a scale of six indicator items came from (Schiffman et al., 2010). All research constructs were measured at five points Likert type scales ranging from 5 "Strongly Agree" to 1 "Strongly Disagree." A small portion was also included in the questionnaire to study the characteristics of the respondents (Kwek et al., 2010).

The analytical tool in this study uses SEM (Structural Equation Modeling) with AMOS analysis tool version 23. According to (Hair et al., 2010) in SEM, evaluation of the level of data compatibility with the model carried out through several stages starting from the suitability of the whole model (overall model fit), the measurement model fit (measurement model fit) and structural fit (structural model fit). SEM in this study is able to test a theory by testing the compatibility of the model with the data and allows a variable to apply dynamically where a variable can act as a dependent variable and also an independent variable in a model. To assess the internal consistency of each construction, composite reliability A measurement model is said to be a good reliability for measuring each latent variable if the value of its construct reliability (CR) ≥ 0.7 or more than its extracted variance (VE) value is greater than 0.05, stating that in exploratory research, reliability between 0.5 - 0.6 is acceptable (Ferdinand, 2014),

In SEM AMOS testing is conducted in one direction with a confidence level of 95%, the other commonly used critical value is a critical value (CR) of> 1.96 which means that the normality assumption is rejected at the significance level (P) <0.05 (5 %). With the requirements that all observed variables have met the validity and reliability tests Ferdinand (2014) in Figure 3.

To measure the size of the loading factor for each latent variable. Loading factors above 0.70 are highly recommended, however loading factors above 0.60 can still be tolerated as long as the model is still under development. The results of the full loading indicator values are presented in Table 3 below.

Table 3: Measurement Scales
Constructs/items
Decision To Shop Online
DTS30	Ease and comfort of the place
DTS29	Fashion products at this shopping place are available in full
DTS28	Buy fashion products here regularly
DTS27	Flexibility of time of purchase according to needs
DTS26	Completeness of payment methods
DTS25	Payment in this place is safe from fraud
Attitude To Shop Online
ATS24	Love this shopping place
ATS23	This place provides many advantages
ATS22	This place helps wiser shopping
ATS21	This place helps wiser shopping
ATS20	Shopping at this place is much more interesting
ATS19	This place presents interesting product information
Perceived Usefulness
PU1	Time to shop at this place is shorter
PU2	Product information is clear to understand
PU3	Shop at this place for complete product specification information
PU4	Shopping for fashion in this place is easier
PU5	Simple shopping process
PU6	It takes a short time to shop online
Perceived Ease Of Use
PE7	The ordering procedure is not complicated
PE8	This place uses common terms
PE9	The order can be monitored in the process
PE10	Order cancellation can be done
PE11	Product information at this shop is clear
PE12	The product instructions at this shop are easy to understand
Trust to shop online
TR13	This shopping place is able to accept product orders
TR14	This shopping place is not involved in fraud cases
TR15	The seller pays attention to customer questions
TR16	Deal with complaints and offer help
TR17	This shopping mall protects customer data information
TR18	Can maintain the confidentiality of transaction payment data

It was concluded that in Figure 4 above, it was proven that all the Outer Test values were entered. This means that the indicator has good reliability and can build the construct (variable) to which it is intended. The number of indicators that can be used on models that are already FIT is 30 indicators.

Reliability of internal consistency is tested using Cronbach's alpha coefficient; alpha coefficient measures the extent to which various indicators for a construct become one. Cronbach's alpha score Table 4 has been calculated and found above the widely recommended level of adequacy of 0.70. It provides support for internal consistency between measurement items and clear evidence of acceptable reliability. From the results of Table 4, it can be seen that there are still values below 0.60, so the indicator must be excluded so that the results are above 0.60 all.

Table 4: Construct Reliability And Variance Extracted
Constructs	Item Code	Factor loadings	(Error)	VE	Composite Reliability
Decision To Shop Online	DTS30	0.80	0.64	0.50	0.86
	DTS29	0.75	0.56
	DTS28	0.82	0.67
	DTS27	0.77	0.59
	DTS26	0.78	0.61
	DTS25	0.81	0.65
Attitude To Shop Online	ATS24	0.74	0.55	0.52	0.86
	ATS23	0.74	0.55
	ATS22	0.78	0.61
	ATS21	0.74	0.55
	ATS20	0.77	0.59
	ATS19	0.88	0.53
Trust	TR13	0.83	0.48	0.52	0.86
	TR14	0.68	0.46
	TR15	0.77	0.60
	TR16	0.74	0.55
	TR17	0.79	0.63
	TR18	0.71	0.50
Easy Of Use	PE7	0.73	0.53	0.50	0.89
	PE8	0.83	0.66
	PE9	0.79	0.62
	PE10	0.75	0.56
	PE11	0.79	0.54
	PE12	0.62	0.62
Usefulness	PU1	0.81	0.66	0.50	0.86
	PU2	0.82	0.67
	PU3	0.69	0.47
	PU4	0.76	0.58
	PU5	0.70	0.48
	PU6	0.70	0.49

Measurement Model and Structural Model Testing

SEM AMOS In conducting the test, Structural Equation Modeling (SEM) was applied to test the validity of the proposed research model and its hypotheses with the help of Amos version 23 software. Model measurement results can be seen in Table 5 and Table 6 Below:

Table 5: Goodness Of Fit (Gof)
Measure GOF	Measurement		Measurement Result
	Good Fit	Marginal Fit
Absolute fit Model
Goodness of fit Index (GFI)	≥ 0.90	0.80 - <0.90	0.80	Marginal Fit
Root Mean Square Error (RMSEA)	< 0.08		0.04	Good Fit
Incremental Fit Model
Comparative Fit Index (CFI)	≥ 0.90	0.80 - <0.90	1.00	Good Fit
Normal Fit Index (NFI)	≥ 0.90	0.80 - <0.90	0.80	Marginal Fit
Increamental Fit Index ( IFI)	≥ 0.90	0.80 - <0.90	1.00	Good Fit
Relative fit Index (RFI)	≥ 0.90	0.80 - <0.90	0.80	Marginal Fit
Parsimonious Fit Model
Parsimonious normed fit index (PNFI)	≥ 0,90	0.80 - <0.90	0.80	Marginal Fit

It can be seen in Table 6 that AMOS 23 output shows that the absolute fit model fit size (absolute fit size) which aims to determine the degree of prediction of the overall model (structural and measurement models) to the suitability of the data consisting of RMSEA 0.044 <0.08 (model fit) and GFI value of 0.80. So overall it can be concluded that the model being tested approaches the absolute fit model test criteria at a sufficiently good level of test criteria. For incremental fit model fit size consisting of several test instruments in their compatibility, namely the value of CFI=1.00; NFI=0.80; IFI=1.00 and RFI=0.80. The output shows that the structural equation model is said to be good because it is at the level of sufficiently good test criteria and meets the incremental fit model requirements. On the fit size of the parsimonious fit model can be seen in the results of PNFI = 0.80; from the output it is concluded that the model is said to be at the level of the test criteria quite well and meets the requirements of the parsimonious fit model.

Table 6: Hypothesis Test Result
No	Hypotheses	SLF1)	t-value	Conclusion
1	PU€ ATS	0.40	4.8	Supported
2	PE€ ATS	0.40	5.6	Supported
3	TR€ ATS	0.50	6.5	Supported
4	TR€ DTS	0.00	0.6	Not Supported
5	ATS€ DTS	1.1	9.4	Supported

It can be seen in Table 6 that the results of the AMOS SEM outcome show that in this study the hypothesis (H1, H2.H3, and H5) was accepted and H4 was rejected.

In Table 6, the AMOS output results show that four hypotheses except one hypothesis H4 show significant results. Decision making is influential if the results of t-value> t table (1.96) and α <0.05. The test results at H1 that Perceived Usefulness affect positively and significantly on Attitude to Shop Online. With the output value t-value 4.8 is greater than table 1.96 and the magnitude of the correlation coefficient between trust with consumer decisions is 0.40 smaller than α <0.05. Next to H2 that Perceived Ease of use affects positively and significantly on Attitude to Shop Online. With the output value t-value 5.6 is greater than table 1.96 and the magnitude of the correlation coefficient between trust with consumer decisions is 0.40 smaller than α <0.05. Whereas for H3 that Perceived Trust influences positively and significantly on Attitude to Shop Online. With an output value of t-value 6.5 is greater than table 1.96 and the magnitude of the correlation coefficient between trust with consumer decisions by 0.50 is equal to α <0.05.

For those who do not influence but not significantly exist in hypothesis H5 that Attitude to Shop Online influences positively and insignificantly to the decision To Shop Online. Greater than α <0.05. For Hypothesis H4 Perceived Trust does not affect positively and insignificantly to the decision to shop online. With the output value t-value 0.6 is smaller than table 1.96 and the magnitude of the correlation coefficient between trust with consumer decisions is 0.00 smaller than α <0.05.

Discussions and Conclusion

From the results of the above research, conclusions can be drawn to improve consumer attitudes towards online shopping. It is important for marketers in online stores to increase their usefulness, ease of use and trust in consumers to improve and automatically consumers' attitudes towards online stores will be positive and with positive attitudes. These consumers will help in their shopping decisions choosing shopping at online stores. Some findings from the results of this study are categorized as innovations where this is because in research using existing methods but using a new model by combining some of the previous research hypotheses. Some of the results of this study are as follows:

First, the attitude is proven empirically influenced by usefulness, ease of use and trust. Based on the results of this study found that in online shopping trust is a variable that influences the attitude more than the other two variables.

Secondly, trust does not affect consumers' decisions in shopping online, it can be interpreted that consumers today are actually more concerned with their shopping decisions than their attitude rather than just trust in a shopping place. In this case the marketer needs to pay attention that his own attitude is one thing that is formed by several factors that are not just from trust alone. This is a finding in this study that provides another view that it is not always that trust influences decisions.

Third, online shopping is more dominated by female consumers with a scope of millennial generation and more with a minimum income level in the city of Jakarta, Indonesia, it can be said that in the online market, especially fashion products. Marketers need to target this segment as a potential group of their current consumers by better meeting the needs of these millennial who are indeed living in the transition from old technology to new technology, so the emergence of online stores is an alternative shopping as long as the online store has usefulness, ease to use and trust then this will change the pattern of consumer spending later from offline to online stores.

In subsequent studies, it is hoped that it can continue with consumers' perceptions of other products in online shopping in Indonesia. And maybe other factors can be added in terms of consumer attitudes in deciding online shopping that will add to and complement the knowledge in Theory of Planned Behavior that makes one wholeness and reference for future research in order to make a complete picture and become a reference in the world of marketing from the internal side of consumers factors that influence them in making decisions.

References

1. Alwafi, F., Magnadi, R.H. (2016). Pengaruh persepsi keamanan, kemudahan, bertransaksi, kepercayaan terhadap dan pengalaman berbelanja terhadap minat beli secara online pada situs jual beli tokopedia.com. Diponegoro Journal of Management, 5(2), 1-15.
2. Aribowo, D., & Nugroho, M. (2013). Pengaruh trust dan perceiced of risk terhdap niat untuk bertransaksi menggunakan e-commerce. Jurnal Nominal, 2(1), 11-35.
3. Çelik, H. (2011). Influence of social norms, perceived playfulness and online shopping anxiety on customers’ adoption of online retail shopping: An empirical study in the Turkish context. International Journal of Retail & Distribution Management, 39(6), 390-413.
4. Lung, H.C., Chuan L.J., & Hsu, C.L. (2013). The effects of blogger recommendations on customers’ online shopping intentions. Emerald Insight, 23(1), 69-88.
5. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-39.
6. Delafrooz, N., Paim, L.H., & Khatibi, A. (2011). Understanding consumer’s internet purchase intention in Malaysia. African Journal of Business Management, 5(3), 2837-2846.
7. El-Gohary, H. (2010). E-Marketing - A literature Review from a Small Businesses perspective. International Journal of Business and Social Science, 1(1), 216.
8. Ferdinand, A. (2014). Metode Penelitian Manajemen. Semarang: Badan Penerbit Universitas Diponegoro.
9. Ha, S., &  Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 565-571.
10. Hair, J.R., Joseph, F.B., William, C., Babin, B.J., Anderson, R.E. (2010). Multivariate Data Analysis, Seventh Edition. New Jersey: Pearson Prentice Hall.
11. Hong, I., & Cho, H. (2011). The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust. International Journal of Information Management, 469-479.
12. Hsu, M.H., Chuang, L.W., & Hsu, C.S. (2014). Understanding online shopping intention: the roles of four types of trust and their antecedents. Internet Research, 24(3), 332-352.
13. Indarsin, T., & Ali, H. (2017). Attitude toward Using m-Commerce: The Analysis of Perceived Usefulness Perceived Ease of Use, and Perceived Trust: Case Study in Ikens Wholesale Trade, Jakarta - Indonesia. Saudi Journal of Business and Management Studies, 2(11), 995-1007. Jakarta: Erlangga.
14. Julianti, S. (2017). Mastering Packaging for E-commerce. Jakarta: Gramedia Pustaka Utama.
15. Kim, D.J., Ferrin, D.L., & Rao, H.R. (2009). Trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564.
16. Kotler, P., Keller, K.L., & Amstrong, G. (2012). Marketing Management Edition 14.
17. Kwek, C., Tan, H., & Lau, T. (2010). Investigating the Shopping Orientations on Online Purchase Intention in the e-Commerce Environment: A Malaysian Study. Journal of Internet Banking and Commerce, 15(2), 1-22.
18. Lee, H. (2016). Examining neighborhood influences on leisure-time walking in older Korean adults using an extended Theory of Planned Behavior. Landscape and Urban Planning, 148, 51-60. New Jersey: Pearson Education Limited.
19. Palvia, P. (2009). The role of trust in e-commerce relational exchange: A unified model. Information & Management, 213-220.
20. Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model. International Journal of Electronic Commerce, 7(3), 101-134.
21. Pavlou, P., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Quarterly, 30(1), 115-143.
22. Schiffman, L., Kanuk, L., Wisenblit, J. (2010). Consumer behavior. 10th edition. New Jersey: Pearson Prentice Hall.
23. Shu-Hung, H.S., & Bayarsaikhan, B.E. (2012). Factors Influencing on Online Shopping Attitude and Intention of Mongolian Consumers. The Journal of International Management Studies, 7(2), 167-176.
24. Solomon, M.R. (2018). Consumer Behavior: Buying, Having, and Being. 12th Edition.
25. Suleman, D. (2018). Faktor penentu keputusan konsumen Indonesia memilih tempat belanja disebuah e-commerce (Theory of Planned Behavior). Jurnal Doktor Manajemen, 1(1), 1-9.
26. Suleman, D., Ali, H., Nusraningrum, D., & Ali, M.M.A. (2019). Perceived Ease of Use, Trust and Risk toward Attitude and Intention in Shopping for Online Fashion Products in Indonesia. Archives of Business Research, 7(4), 240-253. https://doi.org/10.14738/abr.74.6482
27. Suleman, D., Zuniarti, I., & Sabil. (2019). Consumer Decisions toward Fashion Product Shopping in Indonesia: The effects of Attitude, Perception of Ease of Use, Usefulness, and Trust. Management Dynamics in the Knowledge Economy, 7(2), 133-146; https://doi.org/DOI 10.25019/MDKE/7.2.01
28. Utami, H.N., & Firdaus, I.F.A. (2018). Pengaruh Bauran Pemasaran Terhadap Perilaku Online Shopping: Perspektif Pemasaran Agribisnis. Jurnal Ecodemica, 2(1), 136-146.
29. Wu, S., & Chen, J. (2014). Model of Green Consumption Behavior Constructed by the Theory of Planned Behavior. International Journal of Marketing Studies, 6(5).
30. Yadav, R., Sharma, S., & Tarhini, A. (2016). A multi-analytical approach to understand and predict the mobile commerce adoption. Journal of Enterprise Information Management, 29(2).
31. Young, L., & Burgess, B. (2010). Marketing Technology as a Service: Proven Techniques that Create Value. West Sussex: John Wiley & Sons Ltd.