FACTORS AFFECTING ONLINE SHOPPING BEHAVIOR FOR NET-FACTORS AFFECTING ONLINE SHOPPING BEHAVIOR FOR NET-GENERATION IN JABODETABEK GENERATION IN JABODETABEK

ABSTRACT


INTRODUCTION
In January 2019, the population in Indonesia has reached 268.2 million, in which more than half are active Internet users who engage in online shopping transactions. (Digital 2019 Report: Indonesia, 2019) These findings indicate that online shopping consumers in Indonesia have developed sophisticated preferences.
Since 2013, the number of the population who live in urban area of Indonesia has exceeded the number of those who live in rural areas. In 2020, the Central Bureau of Statistics (BPS) predicted that the urban population will reach 56.7% and in 2035 will grow up to 66.6% of the total population (https://www.bps.go.id/ KegiatanLain/view/id/85). Java is the most densely populated island and has the biggest urban population compared to other islands (Ali and Purwandi, 2016).
In 2018, The Ministry of Women's Empowerment and Child Protection published a report called The Millennial Profile (Profil Generasi Milenial). It is said that there are 88 million people or 33.75% of total population were categorized as The Millenials, -people who were born in 1980 -2000. That number ranks the highest compare to the Generation X (25.75%), Baby Boom + veteran (11.27%) and post-millennial (29.23%). Another interesting finding is that 55% of them live in urban area of Indonesia. However, in this research we refer the Millennial as The Net-Generation in order to stay relevant with the main journal we used written by Kim and Ammater, 2017. The Net-Generation are those who were born after 1980 and just like Millennial, they are considered as the digital-native.
Based on this data we find that it is important to limit our research context by focusing on Net-generation in urban populations that is predicted as the dominant buying group for the next few decades. As the internet and digital environment enabled more access for information, Net-generation has much better expertise about the product and service that they desire compared to the previous generation (Tapscott, 2016). Therefore, to study the dynamic or their purchase behaviour in online shopping must be explored continously to get more understanding.
In Indonesia, Jabodetabek has the largest con-centration of urban population in Indonesia, consisting of migrants from rural regions. Jabodetabek is formed by several administrative regions that are part of 3 main provinces, namely DKI Jakarta, Banten (Tangerang Regency, Tangerang Municipality and South Tangerang Municipality) and the West Java provinces (Bogor Regency and municipality, as well as the Depok Municipality) (Rustiadi et al., 2015). By taking into account the distribution factors of the population in Indonesia, we would like to therefore answer our research question: What are the factors that influence the online purchasing intentions of the Net-generation?
Our research model incorporates four phases of the Internet business transaction process as detailed by Selz and Schubert (1997) from what had done by Kim et al., 2012 on their research in America. The Web Assessment Method (WAM) identifies information, agreement, settlement, and after-sales interactions (community) phase as the four components that can impact online purchase decisions. Kim et al, 2012 drawn upon this work as a basis for categorizing the factors that identify as potentially contributing to e-commerce research and extend it by suggesting that sources of e-commerce inertia can similarly arise from the four the WAM components.
In this research, we added on social motive as a variable in the environment phase, adopted from a journal written by Dharmesti et al., 2019. Social motives described by Elik, 2011 andHill et al., 2013 explained to what extent one would shop online in order to be seen by other people that he or she is indeed participating in online shopping (Elik, 2011). Several studies previously done have also found the effects of social motives within shopping behaviors. This striking juxtaposition between eastern and western cultures will be studied further for Jabodetabek population.
This research may have other factors that could affect the intention of online purchasing behavior. As example, Dharmesti et al., 2019 uses attitude, escapism motive, value motive and online shopping familiarity that modeled to purchase intention of online shopping behaviors. Those factors that have not been addressed properly offer opportunities for future studies.
This research defines online shopping sites as a virtual place where the buying and selling transactions have an integrated payment system in it. If we can identify the specific data of the Net-generation and validate empirically the buying behaviour that comes from the Net-generation group in Jabodetabek area, we can contribute to creating a marketing strategy for online shopping to be more competitive.

Cohort Theory
Market, 2014 stated that segmented marketing that is chronologically based on age is often referred as 'generational marketing'. However, in contrast with the generational concept that has a 20-30-year age range (a cycle in which a person grows up and has offspring), the cohort concept is outlined by the similar events that one experiences collectively until they are established economically.

Net-Generation
The concept of the Net-generation was first coined by Don Tapscott who concluded that those who were born in January, 1977 to December, 1997 are the first generation that have grown up and developed with computers, the Internet and other digital media. Tapscott noted that unlike the previous generation, the Net-generation are more curious, independent, rebellious, are easy to adapt, and are globally oriented (Tapscott, 1998). Unlike Tapscott, Oblinger and Oblinger defined that the people called the Net-generation are those who were born between 1982 to 1991. However, they argued that this definition should not be seen solely as an age phenomenon. People who are not born in this time span, yet have similar fluency in using technology related to Information and Communication can also share similar characteristics with the Net-generation (Oblinger & Oblinger, 2005).
We can conclude that the definition of the Net-generation is still very fluid, notably if being juxtaposed with the term Millennial as described by Howe and Strauss, 1991 In general, the similarity between the Net-generation and Millennial lies in the fact that both groups are raised in a digitally rich environment. For this paper, we re-fer to our main journal reference, written by Kim and Ammeter, 2017 where they define the Net-generation as those who are born after 1980 (Howe & Strauss, 2000). Stressing on the importance that The National Science Foundation funded the Internet in the 1980s creating global participation and for the first time, the Internet began to be used popularly throughout the world from the 1990s (Kim & Ammeter, 2017). Jacoby et al., 1986 stated that consumer's knowledge consists of 2 (two) main components called, familiarity and expertise. Familiarity is defined as the accumulation of experience that one relates to a product. While expertise is defined as the ability to use the product successfully (Palfrey & Gasser, 2008). Tapscott, 2009 mentioned that the Net-generation has the tendency to be a "prosumer", -where they prefer to be involved in creating products and services with the producers. Net-generation has a high level of expertise in online shopping because they are aware of what kinds of products they desire.

Theory Development and Research Model
The Research Model was created based mainly on the journal references written by Kim and Ammeter, 2017 as well as combining 4 phases detailed by Selz andSchubert, 1997 (Sheppard &Vilbert, 2016). These 4 phases include information, agreement, settlement and environment phases which influence the purchase intention, as shown in Figure 1. This research is unique, however, as there is an additional independent variable taken from a journal written by Dharmesti et al., 2019 with contextual coverage on the Jabodetabek area.
In the main journal reference, it is mentioned that the information phase consists of information availability and product variety where consumers gather information about the product to eliminate the uncertainties (Kim et al., 2012;Zhang et al., 2011). The Agreement phase shows the interaction between the seller and the buyer, considering the effect of feedback, personalization, and responsiveness to the online purchasing behaviour (Pathak et al., 2010;Srikumar & Bhasker, 1987). Through the settlement phase, ease of return and prod-uct exchange, as well as acceptance of complaints show how orders are being delivered and the interaction between the sellers and the buyers after the transaction is completed (McAlister, 2009;Kim & Benbasat, 2003;Udo & Marquis, 2002). The Environmental phase shows how the whole transaction process could be summed up through enjoyment and purchase involvement (Enrico et al., 2014;Kirk et al., 2015;Miyake & Norman, 1979;Sheppard & Vilbert, 2016;Wang at al., 2014;Wolfinbarger & Gilly, 2003). We added on social motive as variable in the environment phase, adopted from a journal written by Dharmesti et al., 2019. The purchase intention variable, however, will be tested to whether or not it was influenced by the 10 factors being mentioned. Details of the hypotheses that are being built by each variable construct will be explained in next chapter.

Information Phase
Information abundance discusses the extent to which online shopping consumers can get information that usually comes in the form of a product photo. The limitations to this can only support the visual information, while consumers need more detailed information about the product (Sheppard & Vilbert, 2016). The Consumers' desire to participate in online shopping began by finding product evaluation information, using the knowledge, experience and their external information (Bukhari et al., 2013).
The information quality from the online shopping sites create positive effects on repurchases (Ramanathan, 2010).

H1: Information abundance will positively affect the Net-generations' online purchase intention
Product variety that is being mentioned in this paper discusses the heterogeneity of options upon the online shopping sites. The benefits of online shopping for the customers that involved "the availability of product variety" increased by 7 to 10 times more compared to "cheaper price" (Brynjolfsson & Smith, 2003). The Net-generations live in an era where there is plenty of information available through television and the Internet. They also benefit from several product options that boost their confidence while making decisions (Everard & Galletta, 2006;Tapscott, 2008)

Agreement Phase: Interactivity
Internet communication is a form of communication that is timeless and borderless, thus enabling online shopping sites to keep interacting with consumers around the clock. This kind of interactivity makes them want to create the conditions as a consumer, to be able to ask questions and resolve their problems immediately during their visits to online shopping sites (Torkzadeh & Dhillon, 2002).
Feedback is a mechanism that is needed as it offers information from the consumers owns perspective (Giurgiulescu et al., 2015). Feedback in this research discusses to what extend a consumer gives information about their experience during online shopping that can have a positive influence on other prospective buyers (Park & Lee, 2005). The feedback by consumers who have already used the product gives extra product knowledge to the other consumers, easing and creating better buying decisions (Benlian et al., 2012).

H3: Feedback will positively affect the Net-generations' online purchase intention
Personalization gives users control to navigate web sites allowing them to adjust their navigation based upon their needs (Schewe et al., 2013). Web personalization in the previous research consists of 2 main aspects that are (1) the application that is being used and the potential of commercial value obtained (Tapscott, 2008), and (2) focused technology that can trace, analyze and create conclusions about consumer preferences, behaviors, and that offer a series of products that have been tailored based on the consumers' personal needs (Blasco-Arcas et al., 2013;Lancaster & Stillman, 2002;Napoli & Ewing, 2001;Suh & Han, 2003;Bidgoli, 2010).
This research is focusing on the second aspect, in which personalization is defined as to what extend consumers get product and service recommendations that have been tailored to their needs. Giving information that has been personalized allows consumers to easily find whatever they need (Kim & Ammeter, 2017).

H4: Personalization will positively affect the Net-generations' online purchase intention.
Responsiveness is one of the most important aspects of online shopping as it involves two-way communication. The Sellers' ability to respond influences how consumers behave towards online shopping (Sotiriadis & Zyl, 2013). The most important aspect for the consumer is that they will get responses needed to resolve their problems during transactions (Comegys et al., 2006). The Net-generations have a natural tendency to be social, interactive, and immediate (Olbrich & Holsing, 2011;Tapscott, 2008)

Settlement Phase
Post-purchase service is an important factor that determines the online purchase behaviour and influences consumers' desires to do repurchase.
Ease of return and exchange that is user friendly will encourage buyers to purchase whereas complicated policies will discourage potential buyers to continue with their pur-chase. Net-generations believe that it is buyers' right to be served even after they finish the transaction. They will not accept a product that does not meet their expectations and are not reluctant to return a product that has been purchased. (Khalifa & Liu, 2007).
H6: Ease of return and exchange will positively affect Net-generations' online purchase intention Acceptance of complaints from the consumer who have made a purchase affects their loyalty towards online shopping sites. In online media, there are many complaints about purchases that do not meet consumer's expectations. For the Net-generations, it is important for seller to be able to respond to these complaints (Kim & Ammeter, 2017).

Environment Phase
An online shopping environment that is convenient will encourage consumers to do more online shopping.

Enjoyment
Through shopping, not only to purchase the desired product, but also functioning as a means of entertainment. Based on Babin et al., 1994, the shopping motivations of a consumer can be differentiated in two types, those with hedonistic values and with utilitarian values (Babin et al., 1994). The indication of hedonistic value is when shopping is meant for enjoyment, getting experience, expressing freedom, releasing problems, and realizing imagination (Babin et al., 1994;Holbrook & Hirchman, 1982).

H8: Enjoyment will positively affect the Net-generations' online purchase intention
Purchase involvement is defined as the relevancy felt by consumers in the purchases that are being executed. Laurent and Kapferer reflects involvement in purchase as (1) perceived importance, (2) perceived risks associated with the purchase of a product, (3) signs, and (4) hedonistic values (Laurent & Kapfere, 1985). Purchase involvement for the consumers means that they are looking for plenty of information about product and spend a lot of time to choose a product. The Net-generation that are used to finding information will be more eager to do online shopping as searching online is easier and cheaper than finding a product through the offline shopping.
H9: Purchase involvement will positively affect the Net-generations' online purchase intention Social motives described by Elik, 2011 andHill et al., 2013 explained to what extent one would shop online in order to be seen by other people that he or she is indeed participating in online shopping. Limayem et al., 2000 showed the effects of social influence upon the purchase intention of online shopping. They found that the family has an important role in influencing one's online shopping habits. A supportive environment like friends who shop online also encourages online shopping decisions (Limayem et al., 2000).

RESEARCH METHOD
Once we understood, studied and analyzed the literature gathered from journal references and its relationship with each other, a modification of the 10 (ten) independent variables, and 1 (one) dependent variable was created for this research. We adopted 9 (nine) independent variables from the first main journal, and one additional modified independent variable taken from second journal reference.

Respondents
Data was taken through a survey by using the Google Form application that has a list of measurement questions, similar to what was explained in the previous chapter. We distributed the form through email and communication means such as WhatsApp. In the beginning, there were total of 419 respondents. However, each participant must follow these conditions (1) lives in Jakarta, Bogor, Depok, Tangerang, Tangerang Selatan and Bekasi area; (2) born on and after 1980 and (3) they actively engaged in online shopping at least no more than 6 months prior to the survey. As a result, there are 308 valid respondents who had to be screened again through using valid-ity and reliability testing revealing there were 34 invalid tests, thus leaving us with 274 valid respondents to conduct our research. We could learn about the gender of the respondents by analyzing Table 1 above, where 60.2% of the respondents were female, and while 39.8% were male. The parameter for the birth range shows that 25.5% of the respondents were born between 1981-1984, 31% born between 1985 -1989, 32.1% born between 1990 -1994, 10.09% were born between 1995-1999, and the rest of them, consisting of 0.4% of the respondents were born between 2000-2004.
Based on domicile distribution, 51.8% of the respondents live in Jakarta. Whereas 32.2% of the respondents live in the Tangerang, Tangerang Selatan and Tangerang Regency areas. The rest of the respondents were spread out in Depok, Bogor and Bekasi.
From 274 respondents 56.2% of them have average monthly expenses of online shopping of not more than 1 million rupiah. Whereas 40.9% of the respondents spend between 1 million Rupiah to 5 million Rupiah each month. Only 1.5% of the respondents have an average spending of between 5 -10 million Rupiah monthly, while the rest spend more than 10 million Rupiah monthly.

Instrument Validity and Reliability
In this chapter we will present the analysis result of 46 questions determined by each variable to ensure the consistency of each construct. For this purpose, we run our component analysis by using varimax rotation through SPSS.
From the results of the calculation process as shown in Table 2, for the Kaiser-Meyer-Olkin (KMO) Validity test, the Loading Value is above 0.5, whereas the Composite Eigenvalues is greater than 1. This means it is acceptable and suitable to use in the modeling.
By using SPSS, and the reliability analysis from each construct question to determine the value of using scale in each of the questions is indeed reliable or could be counted on, Hair et al., 1998. This scale could only be considered reliable if reoccurred measurements from each variable resulted in a consistent answer, Malhotra, 2004. For reliability analysis, Crobach's Alpha is often being used to determine the extent of which questionnaire items could be treated as a single latent construct. Normally, the value is greater than 0.6 for reliability to be considered adequate for a survey instrument (Hair Et al., 2006;Malhotra, 2007;Padlee et al. 2018).
As a result, the composite data reliability of Cronbach's Alpha per construct is more than 0.7, which means that the items have indicated reliability in measuring the research model. This also means that the questionnaire is suitable to be used as a survey instrument (Hair et al, 2006) as shown in Table 2 above.

Linear Regression Result and Analysis
The results of the linear regression (R2) of the ten independent variables (Information Abundance, Product Variety, Feedback, Personalization, Responsiveness, Ease of Return and Exchange, Acceptance of complaints, Enjoyment, Purchase Involvement and Social Motives) toward one independent variable of Purchase Intention are shown in Table 3 below:  (Markovic and Jankovic, 2013;Padlee et al. 2018). This can be explained by 5 out of 10 independent variables that significantly influence purchase intention.
Social Motives (H10: β =0.281, p < 0.05) is the variable that has the highest influence which can be proved by its statistical number. Therefore, this independent variable has tremendous effect on purchase intention. Based on the result of previous research by Dharmesti et al., 2019, it was revealed that social motive has the biggest influence on Australian purchase intention, but on the contrary, do not affect much purchase intention in America. The next influential variable was enjoyment, (H8: β =0.269, p < 0.05). Previous research done by Kim and Ammeter, 2017, revealed that the enjoyment variable also gives significant impact on the purchase intention. Purchase involvement was third in line (H9: β =0.180, p < 0.05) that gives significant value towards online purchase intention. This result also resembles previous research by Kim and Ammater, 2017. Furthermore, personalization variable (H4: β =0.107, p < 0.05) gives significant impact towards purchase intention, as well as the Feeback variable (H3: β =0.105, p < 0.05).
Both variables influence the purchase intention that is in line with previous research done by Kim and Ammeter, 2017. Unlike the 5 variables mentioned previously, the result of variable responsiveness, product variety, information abundance, and ease of return and exchange, as well as acceptance of complaints has no significant impact on purchase intention. The variable of responsiveness (H5: β =-0.017, p > 0.05) that has no significant result does not represent the result of previous research by Kim and Ammeter, 2017. Whereas the variable of product variety (H2: β = -0.025, p > 0.05) and the variable of acceptance of complaints (H7: β =0.123, p > 0.05) that both have no significant impact on purchase intention are also different from the Kim and Ammeter research, 2017. Interestingly, the variable of information abundance (H1: β = 0.076, p > 0.05) and variable of ease of return and exchange (H6: β =-0.003, p > 0.05) has the same result with Kim's research that has no significant impact on purchase intention.
The results of the linear regression of these variables, whether or not it has significant impact towards purchase intention, is illustrated The top three variables that have statistical value of p<0.01 are social motives, purchasing involvement and enjoyment, and all are part of the environmental phase. We will further discuss the strong influence of the environmental phase on determining the purchase intention of online shopping consumers who live in Jabodetabek in the next section.

Table 4: Hypothesis Correlation Analysis
The analysis and data procession indicate that the most influential factor affecting purchase intention is social motives. The measurement of the social motives results in the highest beta value compared to other factors. These results are very interesting because social motives as the most significant factor influencing the Net-generation purchase intentions in Jabodetabek were only discovered from the results of this study. We suspect this is because the Net-generation in Jabodetabek has already built trust towards online shopping sites from their previous experience that could be influenced by the reference to role models (idols) and close circles, such as friends and family. This means that the owners of online shopping sites or sellers who market their product through online shopping sites can increase their sales if they are able to create a strong impression of social motives to the Net-generation consumers.
Enjoyment has become the second most influential factor that encourages the Net-generation to make purchases on online shopping sites. Our finding is similar to what Kim and Ammeter, 2017 found, which also concludes that for the Net-generation, visiting online shopping sites can also provide pleasure (Park & Lee, 2005). To enhance this enjoyment, online shopping sites need to offer an exciting, fun and interesting format to their visitors. Previous research revealed that the higher the perception of the perceived web quality, the more it will increase the trust and confidence that will benefit the online shopping sites (Al-Debe et al., 2015).
The quality of online shopping sites is reflected through how user friendly the interface is and how swiftly one could navigate, while remaining interactive for its users (Constantinides et al., 2010).
The result of this study also showed that purchase involvement factor significantly influenced the intention to buy on online shopping sites. This finding is in line with the results of Kim and Ammater in 2017. Based on the study by Keeney, 1999 found that the Net-generations are willing to be involved more deeply in making purchases online because they are accustomed with various information and can process the information quickly. The involvement factor from the Net-generation to purchase intention is reflected in their willingness to spend extra time researching products in order to make appropriate choices (Leung, 2004).
It is interesting to find that the environmental phase, consisting of social motives, enjoyment and purchase involvement, are the top three factors which significantly influence the Net-generation purchase intention in Jabodetabek. This phase should be the main concern of online shopping sites when marketing their products to the Net-generation.
Unlike what we first assumed through our hypothesis, the results show that the information abundance factor, as part of information phase, does not have a significant influence upon the purchase intention of the Jabodetabek Net-generation consumers. In the same phase we have product variety, one of the factors consisting of the availability of a product in terms of quantity variation, as well as options available for consumers that does not have much impact on purchase intention. Factors that are represented in the information phase are not important for the Net-generation.
In the agreement phase, although it has no significant impact in influencing the Net-generation purchase intention, the personalization factor, however, is not considered as the dominant factor. Based on the research by De Pechpeyrou, 2009, he found that personalized products or services generate more visits or clicks compared to those that are not. To increase sales, online shopping sites should offer personalization such as giving product recommendations based personal information and historic sales records, as well as accepting orders especially tailored to a single costumer. Meanwhile, the test results also show that the feedback factor also has significant impact on purchase intentions of the Net-generation. Thus, online shopping sites must have a feedback menu, where the consumer is not only allowed to leave their comments, but also to read and evaluate feedback that is being left by other consumers. Responsiveness factors as the last factor in the agreement phase do not give any significant impact on purchase intention, like what we presumed in our initial hypothesis.
There are 2 factors that are part of the settlement phase, the acceptance of complaints and the ease of return & exchange. Interestingly, the Net-generation in Jabodetabek do not bother with the policy that can ease the return & exchange process, nor do they care about the acceptance of complaints. Thus, we conclude that factors in the settlement phase have had little impact in shaping the Net-genera-tions'' purchase intention motives.

CONCLUSION
The purpose of this research is to study the behaviour of the urban Net-generation as consumers in Indonesia through online shopping activity, and notably the factors that influence their purchase intentions. By processing the data collected throughout the Jabodetabek area, the results of this research complement a similar study that was carried out (1) to provide insights on wider consumer groups based on the Net-generation cohort, instead of the Millennial group that is often used in Indonesia (2) to figure out which hypothesis can or cannot be proven in Indonesia, namely the social motive factor which turn out to be the biggest influence on the purchase intention, against the background of Indonesia's cultural aspects as the largest Internet users in Southeast Asia, and (3) to distinguish that both the information and settlement phases do not have any significant influence on purchase intention.
Learning the purchase behaviour of online shopping consumers in Indonesia with uneven demographic distribution comes with its own limitations and challenges. For this reason, we focus on the Net-generation cohort as consumers in Jabodetabek area that has the largest urban population in Indonesia. As stated by Tapscott, 2009, the Net-generation has a unique characteristic as "prosumers", a group of consumers who would like to get involved in the process creation of a product or service with the help of the information given by networks of N-Fluence on the Internet. They have more access to information as consumers, thus the Net Generation has much better product and service expertise than compared to the previous generation.
This research was conducted to study how differently the factor of online purchase intention works in other cultures. The study helps our understanding about the behavior and the important factors in making purchase decision of the Net-generation in Jabodetabek area. Future research could replicate these findings to a wider range of cultures, generations cohorts, or even a specific product category.
The result of this study could be used by online business practitioners to increase the intensity of sales to the Net-generation in Jabodetabek by applying several marketing strategies with considerations: Firstly, social motives have tremendous impact to encourage sales, therefore it is recommended to sell products that are liked and talked about by the community on social media. Secondly, online shopping could also become a means of relaxation that offers entertainment making consumers revisit the sites. Lastly, online shopping malls that have great quality, are easily accessed, and have interactive features are preferred amongst the Net-generation who are looking to buy products or services online.
Nonetheless, this research informs online business practitioners that the Net-generation in Jabodetabek area do not like too much information that could distract them from finding what they want. They are interested to find out about new products or services and want to get the items immediately without much hassle. (Tapscott, 2008) This study has several limitations that can be assessed for further research: First, we did not examine the age factors in more detail. The Net-generation being studied has a wide age gap, a span of 25 years. Some of the Net-generation that partook in our survey already have a fairly well-established level of work, while some have just entered the workforce. The environmental, growth periods, financial and psychological factors which we are not explored could also influence online shopping. Second, our research does not measure the level of technological exposure. The Net-generation is not only classified in terms of age but also in individual exposure to technology, this is difficult to measure due to its multi-characteristics. Third, we also did not examine the difference in spending categories in our research, for example, whether shopping patterns in the fashion category differ from those in the Food and Beverages category. Fourth, our research does not provide further information about geographically differences and its impact on behaviour between Jakarta, Bogor, Depok, Tangerang, South Tangerang, and Bekasi. Instead, we treat Jabodetabek area as a whole. Fifth and lastly, social motives as the most influential factor on purchase intention can be further investigated to find out the best strategies to increase sales. Subsequently, future studies could explore more about these five limitations.