Factor Analysis of Online Clothes Fashion Purchase on Social Media Instagram

In this modern era, internet is inevitably used by the society to access information. Internet service today has been existed in some regions with the big total of users. Based on the official site of Internet World Stats, it is said that from the internet user side, Indonesia is the eighth rank of all countries in 2012 (Internet World Stats). Ministry of Communication and Informatics says that Indonesian internet users in 2012 reach 63 million people and 95 percent of them use the internet to access the social media (TI newspaper, 2013). Social network in Indonesia is also significantly used by the users as a medium to sell their products as well as their product selling and buying activities. John Kerr, Head of Zeno Asia argues that the businessmen nowadays start to look at the social media to sell their products. According to John, there are 27% promotion media that use the social media,  21% use mobile marketing, 15% use television, 10% use advertising in networking, and the rest of it use other media (Andarningtyas, 2013). Shopping and advertising with using internet becomes a trend today. The innovation in softwares enable the advertiser to determine the costumer who will online purchase, to determine how many customers are, and what margins in each selling (Mullins and walker 2013:356).

The phenomenon of online selling and buying occurs in some social media. In this research, the writer chooses Instagram as the research object since Instagram is one of big ten social media in the world in 2012 based on Silverpop survey result (Prihadi, 2012). The official site of SumAll which is an analyis agency in its newest year-end report in 2013 says that Instagram is the effective social media platform in increasing the business (SumAll, 2013). The phenomenon of online selling and buying is also experienced by the students of Economics and Business Faculty (FEB) as well as Communication and Business Faculty (FKB) of Telkom Univeristy. Instagram is accessed by many students as a medium to buy various types of good like clothes, accessories, foods, shoes, and electronically stuffs. The Instagram application provides easiness for the students to do shopping in everywhere and everytime. The products they desire can be searched only by opening the explore feature and inputing the keyword of the products. The information related to the products which will be searched will be displayed in pictures as well as video. FEB and FKB students of Telkom University also use Instagram as a medium to sell. Instagram eases the students who have desire in business without spending money for the place rent. They just open the online shop by creating an account in Instagram and then posting the products they want to sell.

(Laohapensang, 2009) says that with the development of information search engine in internet since last 20 years, it cannot be denied that the online shopping system will be an alternative way in purchasing products. The online shopping system has undergone a development in relation to various things such as the service, efficiency, security, and popularity. However, the marketing of online media needs to be contionusly fixed if we want to meet the change and development accordance with the needs and expectations of the costumers. Hence, the research about factors which influence the students so that they do online clothes fashion shopping on Instagram as one of trending social media needs to be conducted. Based on the background explained before, this research aims to find out what factors influencing the purchase of online clothes fashion among students of Faculty of Economics and Business as well as Faculty of Communication and Business of Telkom University.

Marketing is a social process which involves an activity that is needed to enable an individual and an organization to gain what they need and desire by exchanging with others and developing the relation of ongoing exchange (Mullins and Walker, 2013).

Mixed marketing is a combination of controled marketing variable which uses a manager to conduct a marketing strategy in pursuing the company goal in particular market target (Mullins and Walker, 2013). McCarthy classifies the marketing activities as the means of mixed marketing from four broad types, namley four P in arketing: product, price, place, and promotion.

The American Marketing Association in (Peter and Olso, 2010) say that Costumer Behaviour is a dynamic interaction between affection and cognition, behaviour, as well as enviorenment where people do the aspects exchange in their lives. The online community and social network users are interesting for some people for some reasons. (Hawkins and Mothersbaugh, 2013) say that the reasons of social network usage by some people are:

1. The customers’ usage in accessing social network is high and it increases in every time.

2. The majority of costumer uses the network social sites to share information, including the information about a brand and a product.

3. The potency of customers’ acquisition tends to be high.

4. The costumer who interacts about a brand through social media are more potential to remember the brand, tends to share information related to the brand to others, tends to feel connected with the brand, and then tends to buy that brand.

^{Based on some previous studies, (Park and Kim, 2003), (Broekhuizen, 2006), (Bigne Alcan iz, et al, 2008), (Lin an Sun, 2009), (Lim, 2013), (Thamizhvanan & Xavier, 2013), there are some variables which influence the online buying. Nine of them are Impulse Purchase Orientation, Attitude to Online Shopping, Service Quality, Perceived Risk, Informativeness, Online Trust, Specific Holdup Cost, Perceived Ease of Used, and Purchase Intention.}

In this research, the writer used nine variables which influence online purchasing, namely impulse purchase orientation, attitude to online shopping, service quality, perceived risk, informativeness, online trust, specific holdup cost, perceived ease of used, purchase intention.

This research employed descriptive research using quantitative approach. The data are from the quisionnaires. The respondents of this reseach are Instagram users among the students of Faculty of Economics and Business and Faculty Communication and Business of Telkom University. The data are form 100 respondents. The data are analysed by using factor analysis.

Pre-test of the quisionnaire are conducted. The result is showed in Table 1.1.

Since the result of reliability shows 0,937, so the items on this research are reliable with the value^{> 0.6 (Sugiyonno 2011:184) [15] so that the questionnaires can be used in this research. Table 2 presents the descriptive analysis from the sample of the research}.

The descriptive analysis in this reseach is conducted to find out the percentage of each variable which is the most dominant in accordance with the repsondent based on the answers of the quisionnaire. The grouping category is divided into four which is conducted with using the same range.

The result of factor analysis done on the fifth data processing reaches 0,763 (0,763>0.5) of the Kaiser- Meyer-Olkin Measure of Sampling Adequacy number and 0.000(0.000<0.05) of the significance number so that the existing indicator can be further analsyed. The MSA number is around zero until one. A variable can be predicted and further analysed if it has >0.5 of MSA (Santoso 2010:66) [14]. The result of anti-image correlation on the table of Anti Image Matrices indicates that all analysed indicators have >0.5 MSA value so the further analysis can be conducted.

On the first KMO and Barlett’s Test of Sphericity Test, the analysed indicatot amount is 26. After the fifth test is conducted, it remains 22 indicators since there are four issued indicators on the previous test, they are IPO1, SQ9, SQ7, and IPO2.

The SPP processing result on the table 6 shows that if 22 variants from nine variables are summarised in a single factor so the variants which can be explained by that single factor will be 8.176/22 x 100%= 37.165%. Hence, a single factor can explain 37.165% from the variability of the nine variables. If the nine variables are extracted into five factors, the five total factors can explain the variability of the nine original variables, which are 37,165% + 9,477% + 7,216%+ 6,696% + 5,679%= 66.233% . Eigen values show the relative importance of each factor in counting the anlysed variants (Santoso 2010:83) [14]. The criteria in determining the formed factor is by using the amounts of eigenvalue that are worth more than one. The eige value which is less than one can not be used as the formed factor. In the table 6, it is obtained that the eigenvalues which is still worth more than one exists in the first until fifth factor. It can be concluded that it can form five new factors.

The result from the data processing in the table 7 shows there are 22 analsyed indicators but not all indicators gives the contribution on the five newly formed factors. There are 17 indicators which gives the contribution on the five newly formed factors and five other indicators do not have contribution since they have <0.55 factor loading. The indicators which have <0.55 factor loading are PR10 for 0.492, PR11 for 0.4888, SHC19 for 0.520, SHC20 for 0.534, and PI26 for 0.508.

Based on the analysis factor, there are five newly formed factors within new indicators. The grouping is done in accordance with the factor loading value. Factor loading is the correlation amount between one variable and a newly formed factor (Santoso 2010:85) [14]. These are the newly formed factors:

1. Perceived Ease of Use. The first factor has 8.176 Eigen value and 37.165% contribution in percentage. This factor covers five variables: the customers can visit the online shops easily (0.720), the customers need online shop to buy products they need (0.720), the customers prefer to choose shopping in inline shop (0.709), the customers feel comfortable when they are purchasing in online shops (0.598), and the customers will recommend others to do online shopping (0.563). These factors are called Perceived Ease of Use since the largest factor loading is from the Perceived Ease of Use factor, which is the customers can visit the online shop easily on media social Instagram (0.720).

2. Online Trust. The second factor has 2.085 Eigen value and 9.477% contribution in percentage. This factor covers four variables: the online shops keep personal data safely (0.787), the online shops have a good reputation (0.665), and the online shops consistenly deliver on the promise (0.560). These factors are called Online Trust since the largest factor loading is from the Online Trust variable which is the online shops on social media Instagram are trusted to sell with honest (0.800).

3. Informativeness. The third factor has 1.5888 Eigen value and 7.216% contribution in percentage. This factor covers four variables: the customers can access information they need (0.769), the customers can plan the purchases with the existing information on the online shop (0.792), the customers can easily find the products they desire on the online shops (0.709), and the customers can access the newest information about the available products on the online shops (0.592). These factors are called Informativeness which is the customers can find the information about the clothes they need (0.796).

4. Online Attitude to Online Shopping. The fourth factor has 1.473 Eigen value and 6.696% contribution in percentage. This factor covers three variables: the customers think that online clothes shopping on social media Instagram is very interesting (0.687), the payment is easy on online shops (0.672), and the customers are accostumed to visit online shops (0.553). These factors are called Online Attitude to Online Shopping since the largest factor loading is from Attitude to Online Shopping variable which is the customers think that online clothes shoppings on social media Instagram are very interesting (0.687).Impuls Purchase Orientation. The fifth factor has 1.249 Eigen value and 5.679% contribution in percentage. This factor is called Impuls Purchase Orientation since it only covers one variable so that the largest factor loading is when the customers visit the online shop which sells clothes on social emdia Instagram, there is possibility that they will do the purchasing (0.734).

Result of this research shows that there are five newly formed factors. Those factors have the addition and subtraction of indicators which are from the different variables. The five newly formed factors are:

1. Perceived Ease Of Use,

2. Online Trust,

3. Informativeness,

4. Attitude To Online Shopping,

5. Impuls Purchase Orientation.

The businessmen today are expected to concern those factors so that they can increase the service quality. The further research is expected to use other variables such as enjoyment, perceived usefulness, and innovativeness or to use another analysis technique like Structural Modelling (SEM).