Determinant Factors in Influencing Purchase Intention and Word of Mouth in Social Commerce: An Indonesia Case Study

Social media becomes the new reality perceived in the present generation. Further, the role of social network has extended to which the people can do business transactions in its platform. The present research explores the role of purchase intention and word of mouth to understand better how consumers behave. A total of 9 factors are used in this research model. A total of 8 hypotheses are investigated. The present study uses factor analysis with the confirmatory type to see the correlational factors. The Structural Equation Model (SEM) is used to see the path and hypotheses validation. A total of 231 respondents have participated in this research. The result shows that 5 out of 8 hypotheses are accepted. The present model exposes a total of 70 and 51 percent R2 for both PI and WOM, which indicates the overall model can capture the excellent representation of consumer behavior assessment. Practical and implication insights are further discussed in this paper.


INTRODUCTION
The digital business is the inevitable reality that every nation must aware of. Supported by the rapid development of technology, the lifestyle has changed into an internet-based lifestyle. Modern societies surely bring new challenges especially from a business point of view. The societies are provided with enormous choices, which cause the tight competition among the businesses owner. The business owner, sometimes, performs the creative approaches to engage with the consumers, such as through social media.
Shifting from the gathering focus, social media is now being expanded from its function into the semimarketplace. The utilization of business transactions on social media is currently known as social commerce (scommerce). Unlike electronic commerce (e-commerce), where all the transaction infrastructures are prepared well, s-commerce tends to offer a social networking point of view. However, this phenomenon brings another problem that needs to be considered carefully. The classical problem on online transactions is the trust perspective. The consumers are wanted to be sure that online retail, the product, or the service are reliable. Unlike the robust payment system of e-commerce, scommerce rely on the classical transaction such as bank transfer. This transaction relatively has a high risk. Not to mention on how manual data management of online retail makes the large load transactions unreliable.
Considering that trust in purchase transactions becomes a crucial aspect, online retail should fully understand this situation. They should creatively find the innovation on how to please the consumers from their trust. Thus, the present research will start from the trust perspective in revealing the consumers' determinant factors in regards to purchasing intention and word of mouth (WOM). This research uses the model from Kim & Park's research [1] and Ashari [2]. This research brings the beneficial from the s-commerce provider and online retail in the s-commerce platform. This research is also helping the consumers to better understand themselves, which reduces the anxiety of the trust issue. Indonesia is selected as the case study due to the massive population in general and in social media. The Indonesia people also considered a heavy user of social media. Several social media platforms are detected to be used as the media by Indonesia's small-medium enterprise retail. Thus, the case is suitable to be explored as the representation of the s-commerce population.
The rest of this paper is prepared as follows. Chapter 2 explains the necessary information on s-commerce. The development of hypotheses based on the Kim & Park model is also stated. Chapter 3 describes the methodology in conducting this research. The scope and methods are shown in detail. Chapter 4 reveals the data primer analysis.
This chapter also discusses the managerialimplications.
Chapter 5 concludes the overall research by showing the significant findings, state the limitations and potential for future research.

Social Commerce
Social commerce (s-commerce) is a form of business online in which it combines the features of e-commerce and social media in offering the products or services to consumers [1]. S-commerce is a relatively new concept because the social media platform is initially not used as business transactions. Thus the activity brings the new business model to the retails. Considering the use of Omni-channel offering, social media now becomes necessary in the daily consumer-retail business transactions. However, the trends also inspire several social media providers to integrate business transactions in the form of marketplace. Nowadays, most of the transactions are facilitated through retail in the marketplace. Hence, social media and the marketplace become an excellent duocombination.

Conceptual Development
The present research contributes to the case study point of view. The model proposed by Kim & Park [1] is used. In more detail, nine factors are explored. The nine factors consist of reputation (R), information quality (IQ), transaction security (TS), communication (C), the feasibility of economic (EF), WOM reference (WOMR), trust (T), intention to purchase (PI), and WOM intention (WOMI). The model is depicted as eight hypotheses. The entire hypotheses are projected to have positive influences as follow:

METHODOLOGY
The present research uses the primary data to test the hypotheses. The instrument data collection is performed through an online questionnaire. The online questionnaire is conducted with multiple cross-sectional approaches. The questionnaire is containing two-section, which the first section is asking the demographic and profile of the respondents. The second section measures the latent variables. The nine latent variables are projected with 36 observed questions. The questions are instrumented with a 7-type Likert interval scale, ranging from 1 as "I completely disagree" to 7 as "I completely agree." The sample in this research is the people who experience the use of s-commerce. The estimation of 231 respondents is determined. This number is exceeding the minimum number of Slovin's approaches with a 10% margin error, which is 167 sample

Figure 1 Hypotheses Model
The sample was taken from Indonesia people between September 2016 to January 2017. Non-probability with a judgmental approach is used. The first judgmental criterion is the exposure of respondent to s-commerce. The second judgmental criterion is the age of the respondent, which the young generation of Z. Z generation is considered as the tech-savvy generation. Z generation in this research is limited to whom has the legal aspect of doing the online transaction. The third judgmental criterion is gender, in which this research limited the sample into the woman. The woman is considered to be more open and show more passion related to s-commerce. Themodel is analyzed by using the Structural Equation Model.
The instrument of indicators is projected into

RESULT AND DISCUSSION
The respondents are in the age group between 17 to 24 years old. The proportion consists of 1.7 percent in 17 years old; 13.4 percent in 18 years old; 16.9 percent in 19 years old; 13.9 percent in 20 years old; 39.4 percent in 21 years old; 11.3 percent in 22 years old; 1.7 percent each for both 23 and 24 years old. From the reliability and convergent validity, the six exogenous and three endogenous mostly show robust and valid values. However, some of the observed variables show the value below the standard of rule of thumbs. The rule of thumb considers the Factor Loadings (FL), Composite Reliability (CR), Cronbach α, and AVE for 0.7; 0.7; 0.7; and 0.5 respectively [3][4][5][6]. Thus the re-specification by eliminating the improper variables is performed. The final re-specification values can be seen in Table 2. The complete reliability and convergent validity values in the second iteration are above the threshold. The next test is the model fit test. The entire data show promising values, where the Comparative Fit Index (CFI) and Incremental Fit Index (IFI) reveal the score0.9. Both scores are following some rule of thumbs [2][3][4][5][6]. This situation expresses how the data can be considered reliable, fit, and good enough to be used for representing the field condition. The SEM result reveals that 5 out of 8 hypotheses are positive signs, as can be seen in Table 3. Thus three hypotheses are rejected. Based on the SEM analysis, PI and WI have the R2 of 0.70 and 0.51, respectively. These values indicate how the model able to depict over half of the total predictor factors of PI and WI. Several recommendations are proposed from this research. The first-high value is between economic feasibility and trust, in which the price consideration is suggested. The online retailer can give support such as free delivery, loss leader strategy, penetration price, bundling price, and multiple-unit price. The second-high value is between WOM reference to trust. Several practical approaches such as endorsement, gift for followers, brand image, and reseller opportunity are suggested. The third-high beta value between communication to trust suggest the engagement approach from online retail to consumers such as customer service, regular broadcasting, greeting card, and full communication service on failure transaction.

CONCLUSION
The present research reveals several key factors that influence the PI and WOM Intention. Three essential factors in s-commerce, which are economic feasibility, WOM reference, and communication, show the highest influence values. The relationship between these three factors to trust is positive, which means it will drive the trust for every incremental value in these three predictors.
Based on this research, the very first step that online retail must achieve is the consumers' trust. The online retail not only promotes for selling their products, but also considering other aspects such as customer service, economic feasibility, and economic value to gain better trust. This research has several limitations, such as the gender sample. Mixing the gender between female and male will add the accuracy of the overall population representation. Exploring the possibility of integrating with other factors outside this model is highly suggested.

ACKNOWLEDGMENT
The authors would like to thanks all the stakeholders who contribute to this research.