Vol11No3Paper3 To cite this article: Muhammad, F. & Hartono,S. (2021) Marketplace analysis of purchase decision factors for Instagram social media users. Journal of Intelligence Studies in Business. 11 (3) 42-56. Issue URL: https://ojs.hh.se/index.php/JISIB/article/view/JISIB Vol 11 Nr 3 2021 This article is Open Access, in compliance with Strategy 2 of the 2002 Budapest Open Access Initiative, which states: Scholars need the means to launch a new generation of journals committed to open access, and to help existing journals that elect to make the transition to open access. Because journal articles should be disseminated as widely as possible, these new journals will no longer invoke copyright to restrict access to and use of the material they publish. Instead they will use copyright and other tools to ensure permanent open access to all the articles they publish. 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Journal of Intelligence Studies in Business Publication details, including instructions for authors and subscription information: https://ojs.hh.se/index.php/JISIB/index Marketplace analysis of purchase decision factors for Instagram social media users Faris Muhammada,* and Sri Hartonoa aMagister Management Program, Faculty of Economic and Business, Mercubuana University, UMB Jakarta, Indonesia; *farisperpus@gmail.com Journal of Intelligence Studies in Business PLEASE SCROLL DOWN FOR ARTICLE Marketplace analysis of purchase decision factors for Instagram social media users Faris Muhammada,* and Sri Hartonoa aMagister Management Program, Faculty of Economic and Business, Mercubuana University, UMB Jakarta, Indonesia *Corresponding author: farisperpus@gmail.com Received 31 August 2021 Accepted 25 December 2021 ABSTRACT Currently, the role of technology, such as the internet, is very important to support human activities. One of the uses of the internet is as a medium to support online shopping. In addition, the existence of Instagram social media also affects consumers’ decisions in online shopping. This study analyzes the purchasing decision factors of Instagram social media users on the marketplace. The variables used in this study are price, promotion, trust, security, Instagram social media users and purchase decision. The research framework was developed using the theory of reasoned action. The sample in this study is consumers who have done online shopping. A total of 200 questionnaires were distributed via a Google form, of which 102 were returned. The data analysis method in this study used Smart PLS 3.0. The results showed that all variables had a positive and significant relationship with online purchasing decisions. This research provides theoretical and practical implications. This study is useful for Instagram social media users to consider the factors that purchasing decisions in online shopping have on the marketplace. KEYWORDS Instagram users, online shopping, price, promotion, purchase decision, security, trust 1. INTRODUCTION From mid-February 2020 until now (2021), Indonesia was impacted by the Covid-19 virus pandemic, and because of this people have choosen to carry out more shopping online. The choice people have made to shop online is due to the activity policies set by the government (Prabawanti., 2020). During this pandemic, online shopping methods through social media have increased. In June 2020, Facebook researched the increasing trend of online shopping methods during the pandemic and found that shopping through social media increased by up to 37% compared to before the pandemic (Facebook, 2020). This indicates that people are more interested in shopping online during the pandemic for reasons of mental health and safety. Based on data from the Ministry of Home Affairs, by early 2020, Indonesia's population was around 268.5 million, with internet users reaching 175.4 million and smartphone users amounting to 338.2 million people (twice that of internet users). In addition to the internet, several types of social media are often used. By January 2020, there were 160 million social media users in Indonesia (Wearesocial, 2020). The most popular social media are Facebook, WhatsApp, YouTube, Twitter and Instagram, and many more. Based on data from Wearesocial in 2020, the most popular social media used is YouTube at 88% of internet users in Indonesia, followed in popularity by WhatsApp, Facebook and Instagram (Wearesocial, 2020). Some of these social media have benefits for their users, such as providing entertaining, providing or sharing Journal of Intelligence Studies in Business Vol. 11, No. 3 (2021) pp. 42-56 Open Access: Freely available at: https://ojs.hh.se/ 43 information, communicating, or use in business matters. One of the most popular social media platforms today is Instagram. The number of Instagram users in Indonesia in January 2020 was 63 million people with 50.8% female and 49.2% male (Wearesocial, 2020). The role of Instagram in the decision-making process in online shopping is as an intermediary or "bridge" before consumers make purchase transactions in the marketplace. The large number of Instagram users provides an opportunity for rapid economic growth in the digital sector. Some of the reasons people, especially women, do online shopping through Instagram, is because they follow the trends displayed in their Instagram feed and stories columns. Consumers are also influenced by prices of products that are offered even though they don't need them (Fauziah, 2018). In addition, several factors determine the goods consumers will buy in online the shopping marketplace. A survey conducted by IDN Times in the 2019 Indonesia Millennial Report found that 60% of consumers chose price as the main factor in considering the products they would buy online, followed by features and promotional programs ranked second and third, respectively (IDN Research Institute.,2019). In 2013, the International Journal of Engineering Research and Development released the results of research on the main factors influencing people who shop online. They found that trust is the strongest factor that influences online shopping decisions. Trust is an important factor that can influence consumers to buy products online (Mohmed, 2013). This research is reinforced by Nawangsari (2018) who finds that trust has a simultaneous effect on purchasing decisions. Regarding the influence of Instagram social media on purchasing decisions, Fredik (2018) found that Instagram has a 33.2% positive influence on the promotion of product purchase decisions. Regarding the factors that influence peoples’ decisions to shop online, the authors also found several results from previous research related to the analysis of peoples’ decisions to shop online. The research proposed by Njoto (2018) found that promotions, namely advertising, sales promotion, and personal selling, have a significant effect on consumer purchasing decisions in the marketplace. Meanwhile, research from Lin Pan (2019) found that safety is the main factor in determining consumer purchasing decisions. From several existing studies, it was found that there were no consistent results from the research, so this study intends to fill in the existing deficiencies. This research consists of several parts, starting from the background of the study, literature review, research methods, discussion of results, and conclusions. 2. LITERATURE REVIEW 2.1 Industrial History In America, online marketplaces became popular in 1995 with the start of eBay and Amazon. In China, the online marketplace started to get crowded after Jack Ma founded Alibaba, which is now a giant marketplace. While in Indonesia, the beginning of online stores began in 1999 with the establishment of the Kaskus buying and selling forum. However, in the early days of online buying and selling forums, most people only used the platform to show their products. Meanwhile, the transaction process was still done offline. A few years later, Tokobagus.com became OLX (Circle, 2020). Currently, there are many marketplaces with the strengths of their respective industries and the choice of payment methods is also increasingly diverse. The transaction process that was previously limited to debit and credit can now be done via a smartphone. Some marketplaces even provide electronic wallets. This makes more and more consumers prefer to shop at online marketplaces because of the convenience they offers. This growth is said to be able to make e-commerce a major driver of the digital economy. It is predicted that the e-commerce market will account for USD 100 billion by 2025 (Tokopedia, 2019). The marketplace is a new business model that is developing along with the rapid development of information technology infrastructure. The marketplace is designed to minimize complex business processes to create efficiency and effectiveness. With a marketplace, everyone can carry out buying and selling activities easily, quickly, and cheaply because there are no limits on space, distance, or time. Conventionally, the market has several roles including facilitating transactions and providing infrastructure. Indicators of marketplace activity are determined by the marketplace's ability to facilitate transactions, bring together sellers and buyers, and provide infrastructure. The efficiency indicator is related to the conciseness of time and costs 44 provided by the marketplace (L. Alrubaiee, 2012). According to Mulyaningsih (2015), There are several differences between a marketplace and e-commerce. In terms of product provision, the marketplace has many vendors/brands, while e-commerce comes from only one brand. Then in terms of the business model, the marketplace can use the B2B (Business to Business and B2C (Business to Customer) business model, while e-commerce only uses the B2C (Business to Customer) business model, registration of premium brands, and advertisements. Therefore, e-commerce income is derived exclusively from buying and selling transactions with customers. In terms of payment, for a marketplace it depends on the brand's policy on the marketplace as a third party, while e- commerce payments are directly from customers. Regarding the process of shipping goods, for marketplaces, they are sent from the vendor/brand of the product provider, while e- commerce is sent from the same place and with the same method. 2.2 Theoretical Foundation The basic framework of thought in this study uses the theory of reasoned action (TRA). This theory was developed in 1967. The theory was then continuously revised and expanded by Icek Ajzen and Martin Fishbein. Starting in 1980 the theory was used to study human behavior (Trafimow, 2009). The TRA was formulated in 1967 in an attempt to provide consistency in the study of the relationship between behavior and attitudes. Reasoned Action Theory was developed to examine the relationship between attitudes and behavior (Trafimow, 2009). This theory explains that a person's behavior is influenced by intentions, while intentions are influenced by subjective attitudes and norms. Attitudes are influenced by beliefs about the results of past actions. Subjective norms are influenced by belief in the opinions of others and the motivation to obey these rules. Simply put, this theory says that a person will do an action if she or he views the action positively and believes that other people want them to do it (Kayati, 2018). Some of the variables used in this study include price (X1) promotion (X2), trust (X3), security (X4), Instagram users (Y), and purchase decision (Z). So for the framework of thought can be arranged as in Figure 1. 2.3 Hypothesis Development Based on several previous studies and referring to research variables, the following hypotheses can be developed: 2.3.1 Influence of price on purchasing decisions of Instagram social media users An important concept for marketers is price. Pricing is a mechanism for obtaining value for the company. For consumers, price is the amount needed to get a product (Gecit, 2017). Price is an important factor in purchasing decisions, especially for frequently purchased products, and therefore influences the choice of Figure 1 Research framework. 45 stores, products and brands to consider (Albari, 2018). Fair pricing refers to price adjustments that offer the right combination of quality and service at a reasonable price (Kotler and Keller, 2016). Many think that selling online is easier and more practical and economical because we don't need a store or a lot of human resources to do a business. It is enough with gadgets, credit for the internet and individual creativity to attract buyers. However, it turns out that running an online business is not as easy as many people imagine or predict. In reality doing business online turns out to have many obstacles related to competitors (because many people also do business online) and precisely because it is online, people find it easier to compare prices with one another (Gain., 2017). Prices are set by the seller under the quality and service provided. Price is also the most visible element of the marketing mix, and pricing policies are often questioned by consumers. If consumers think that prices are unfair, they can leave the company or spread negative information to other consumers. Price has a major influence on purchasing decisions that occur between sellers and buyers. Al- Salamin's research (2016) shows that most respondents consider price as an important factor that influences their purchasing decisions. This research is similar to that conducted by Muliajaya (2019) which shows that there is a partially significant effect of price on the price of purchasing decisions. The same research was conducted by Chadafi (2016) which showed that price had a positive effect on purchasing decisions. Based on the discussion above, our first hypothesis is: H1: Price has a positive effect on purchasing decisions of Instagram social media users 2.3.2 Influence of promotion on purchasing decisions of Instagram social media users The role of promotion for the development of new products in the company is one of the most vital factors for the success of marketing a goods and services product (Brata., 2017). Promotion is part of a marketing strategy, where the promotion has a function to provide information, persuade, and remind consumers both directly and indirectly about a product being sold (Kotler and Keller, 2012). Research conducted by Panjaitan (2019) shows that promotion has a significant effect on consumer purchasing decisions for Bright Gas products. The results of this study are similar to those conducted by Fredik (2018), Njoto (2018), and Lininati (2018), showing that promotion has a positive effect on purchasing decisions. Based on the discussion above, our second hypothesis is: H2: Promotion has a positive effect on purchasing decisions of Instagram users 2.3.3 Influence of security on purchasing decisions of Instagram social media users Security can control and maintain data provided by a consumer (Kim and Park, 2013). Furthermore, security includes an online store's ability to control and maintain security over data transactions (Raman and Viswanatahan, 2011). Based on several studies, Anandita (2015) shows that there is a significant influence of security guarantees on purchasing decisions through social networking sites for students in Surakarta. A similar study by Fadhila (2017) shows that security has a significant positive effect on customer purchasing decisions in Indonesia. Khanna (2019) shows that in general six factors influence online purchasing decisions: convenience, security and privacy, product- related factors, service-related factors, website- related factors, and personal factors. Based on the discussion above, our third hypothesis is: H3: Security has a positive effect on purchasing decisions of Instagram social media users 2.3.4 Influence of Instagram social media users on purchasing decisions Social media is an online media, where users can easily participate, share, and create content including blogs, social networks, wikis, forums and virtual worlds (Kurniawan, 2017). Social media can also be interpreted as a medium on the internet that allows users to represent themselves and interact, collaborate, share, and communicate with other users and form virtual social bonds. The number of Instagram users in Indonesia in January 2020 was 63 million people with 50.8% female Instagram users and 49.2% male Instagram users (Wearesocial, 2020). One of the reasons people, especially women, shop through Instagram, is because they follow trends that are displayed in the Instagram feed and stories column (Fauziah, 2018). Research on the use of Instagram social media has been conducted by 46 Lininati (2018) and shows that there is a positive and significant relationship between Instagram social media users and purchasing decisions at the food court. Furthermore, similar research conducted by Puspitarini (2019) showed that Instagram social media users had a positive effect on purchasing decisions. Based on the discussion above, our fourth hypothesis is: H4: Instagram social media users have a positive effect on purchasing decisions 3. METHOD 3.1 Research Design and Operational Variables The type of research used here is descriptive research. According to Sekaran (2017:111), descriptive studies aim to help researchers to understand the characteristics of groups in certain situations (for example, explanations of certain market segments), think systematically about aspects in certain situations (for example, factors related to purchasing decisions), provide ideas for further investigation or research, and help make informed decisions. In this study the the dependent variable used is the purchase decision, the independent variables are promotion, price, trust and security and the intervening variable is social media Instagram. The operationalization of variables in this study explains how to measure variables so that they can be operated, by explaining the dimensions, indicators, or variable measurement items in a table (Mercubuana 2020:20). 3.2 Data Collection, Sampling, and Analysis Techniques Data was collected through a Google form questionnaire with the conversion of statements into scores based on the Likert Scale as listed in Table 1. Based on the table, the scale used in the questionnaire ,is 1-5 which represents the answers of each respondent. This was tested for its effect on purchase decision. The population is a generalization area consisting of objects/subjects that have certain qualities and characteristics determined by researchers to be studied and then drawn conclusions (Sugiyono 2018:80). The population in this study are all consumers who have shopped online at the marketplace via Instagram at least once. For the sample itself, 200 questionnaires were distributed via a Google form, of which 102 were returned. The sampling method is the non-probability sampling method with purposive sampling technique, namely the technique of determining the sample with certain considerations and criteria (Sugiyono 2018:85). The data analysis uses SmartPLS 3.0 to test the outer model and the inner model, which tests the validity, reliability, r square, q square, GoF, and hypothesis testing. Table 1 Likert scale. Answer Options Score Strongly Disagree 1 Disagree 2 Neutral 3 Agree 4 Strongly Agree 5 3.3 Description of Respondents and Variabel The majority of respondents were women (62 people, 60.8%). This means that consumers who are active users of Instagram social media are dominated by women. The majority of respondents were 30-34 years old (69 people, 67.6%). This means that the marketplace consumers who shop through Instagram are primarily millennials. In terms of education, most respondents had undergraduate (S1) backgrounds (83 people, 81.4%). This is because it is related to the millennial age who already understand the operation of internet technology. Most respondents had an income between 4,000,000 and 4,900,000 Indonesian Rupiah (65 people, 63.7%). The respondents were primarily employed as private employees (84 people, 82.4%). Respondents in this study have a frequency of opening or using Instagram social media every day (98 people, 96.1%). 3.4 Variable Description The research aims to examine the factors that influence the purchasing decisions of Instagram social media users on the marketplace, with Instagram as the mediating/intervening variable. After the distribution of 102 respondents, the following are the results of descriptive statistics from the research variables. 47 3.4.1 Price Variable Distribution Results The results of the distribution price variable show that the statement "Before buying, I compare product prices on the marketplace with product prices on Instagram" has the highest average value ( 4.520), which means that the average consumer considers product price information on Instagram before deciding to buy. 3.4.2 Promotion Variable Distribution Results The results of the distribution promotion variable show that the statement "I know that the marketplace often holds promotions on Instagram" has the highest average value of (4.578). This shows that average consumer knows about the promotion of the marketplace on Instagram. 3.4.3 Trust Variable Distribution Results The results of the distribution of the trust variable show that the statement "I feel that product information from Instagram provides the information needed by its users" has the highest average value of 4.480. This shows that the average consumer believes that Instagram displays marketplace products needed by its users. 3.4.4 Security Variable Distribution Results The results of the distribution of the security variable show that the statement "In my opinion, the product information displayed on Instagram is correct." has the highest average value, 4.520. This shows that the average consumer feels safe with the official marketplace product information displayed on Instagram. This is reinforced by the official link included by Marketplace on Instagram. 3.4.5 Instagram Variable Distribution Results The results of the distribution of the Instagram user variable show that the statement "I am considering buying a product based on comments/reviews from Instagram users." has the highest average value of 4.637. This shows that the average consumer decides to buy products on the marketplace after they see comments/reviews on Instagram. 3.4.6 Distribution Results of Purchase Decision Variables The results of the distribution of the purchasing decision variables show that the statement "I will recommend others to look for product information on Instagram before buying on the marketplace" has the highest average value of 4.520, which shows that the average consumer will recommend others to seek product information on Instagram before deciding to buy products on Marketplace. 4. DATA ANALYSIS SmartPLS 3.0 4.1 Evaluation of the Measurement Model (Outer Model) The evaluation of the outer model is done by testing the validity and reliability of the measurements of the research model design. Figure 2 Analysis of the Outer Model Source: PLS 3.0 processing results. 48 4.1.1 Validity test The validity test on the indicator is a benchmark that describes the relationship between the reflective indicator score and its latent variable. The validity test consists of convergent validity and discriminant validity. Convergent Validity: Convergent validity is the correlation between the indicator score and its construct score and can be declared valid if the outer loading value > 0.7 and the AVE value > 0.5 (Ghozali & Latan, 2015). Figure 2 shows the results of the data processing algorithm with PLS. All indicators have values or correlations between constructs and variables that meet convergent validity because the outer loading value is > 0.70. This means that the results obtained meet the validity criteria. After the outer loading value, we can see the convergent validity test from the AVE value (Ghozali & Latan, 2015), which is > 0.5. From the data all variables have a value > 0.5 so it can be concluded that all indicators are valid and suitable for use in this study. Discriminant Validity: In the discriminant validity test, the values in the Fornell-Laker criterion and cross-loading tables are used. The Fornell-lacker criterion value shows the correlation value between the variables themselves and other variables. The value of cross-loading shows an indicator, which is said to meet discriminant validity if the correlation value between indicators on the variable is greater than that of other variables (Ghozali & Latan, 2015). Fornell-lacker criterion and cross-loading values can be seen in Table 2. According to the data, we can see that all the correlation values of a variable are greater than the correlation values of these variables to other variables so that all variables can be declared valid. Table 2 Fornell-lacker criterion scores. Source: PLS 3.0 processing results. Price Instagram User’s Purchase Decision Promotion Security Trust Price 0.864 Instagram Users 0.676 0.762 Purchase Decision 0.616 0.709 0.854 Promotion 0.581 0.673 0.522 0.806 Security 0.500 0.645 0.606 0.580 0.889 Trust 0.638 0.700 0.672 0.540 0.549 0.831 Table 3 Cross Loading Value. Source: PLS 3. Processing results. Price Instagram User’s Purchase Decision Promotion Security Trust H1 0.881 0.638 0.559 0.538 0.504 0.604 H2 0.891 0.589 0.580 0.510 0.476 0.589 H3 0.817 0.515 0.448 0.452 0.294 0.446 I1 0.428 0.756 0.494 0.527 0.483 0.472 I2 0.626 0.754 0.599 0.536 0.475 0.583 I3 0.510 0.789 0.548 0.443 0.462 0.509 I4 0.478 0.747 0.509 0.541 0.544 0.559 K1 0.495 0.656 0.886 0.494 0.617 0.565 K2 0.554 0.607 0.876 0.440 0.518 0.634 K3 0.538 0.547 0.798 0.400 0.402 0.523 P1 0.462 0.514 0.415 0.773 0.494 0.358 P2 0.422 0.556 0.434 0.804 0.482 0.418 P3 0.521 0.557 0.415 0.840 0.428 0.524 S1 0.447 0.563 0.559 0.423 0.885 0.492 S2 0.442 0.584 0.519 0.605 0.893 0.485 T1 0.488 0.543 0.495 0.455 0.451 0.831 T2 0.568 0.629 0.644 0.442 0.501 0.856 T3 0.529 0.568 0.525 0.450 0.413 0.804 49 Table 4 Value of Composite Reliability and Cronbach's Alpha. Cronbach's Alpha rho_A Composite Reliability Average Variance Extracted (AVE) Price 0.830 0.840 0.898 0.746 Instagram User’s 0.759 0.760 0.847 0.580 Purchase Decision 0.814 0.823 0.890 0.730 Promotion 0.730 0.732 0.848 0.650 Security 0.735 0.736 0.883 0.791 Trust 0.775 0.780 0.870 0.690 According to the data in Table 3, we can find out if all the correlation values between indicators on the variables are higher than other variables. Therefore, it can be said that each variable has good discriminant validity. 4.1.1 Reliability Test A reliability test is a method of testing the reliability value of indicators on a variable seen from two values, namely composite reliability and Cronbach's alpha. A variable is declared reliable if it has a composite reliability value and Cronbach's alpha > 0.7 (Ghozali & Latan, 2015). Table 4 shows the value of composite reliability and Cronbach's alpha for each variable. According to the data in Table 4, we can find out if the composite reliability and Cronbach's alpha values for all variables > 0.7 have met the requirements and it can be said that the measurements in the study are reliable. 4.2 Evaluation of the Structural Model (Inner Model) 4.2.1 R-squared (R2) value The value of R-squared (R2) on the structural model is a measure of how much influence certain independent latent variables have on the dependent latent variable. Based on Table 5, the R-squared value of the Instagram variable is 0.675. It can be concluded that the effect of price, promotion, trust and security variables on Instagram is 67.5%. The R- squared value of the purchase decision variable is 0.502, so it can be concluded that the influence of the Instagram variable on the purchase decision is 50.2%. 4.2.2 Value of Q2 Predictive Relevance In addition to looking at the magnitude of R- square, the evaluation of the PLS model can also be done by looking at Q2 to represent the synthesis of cross-validation and fitting functions with predictions from observed variables and estimates of construct parameters. Q2 measures how well the observed values generated by the model and also the parameter estimates. The value of Q2 > 0 indicates that the model has predictive relevance, while Q2 < 0 indicates that the model lacks predictive relevance (Ghozali and Latan, 2015). Based on Table 6, it can be seen that the Q2 predictive relevance for Instagram's endogenous latent variable is 0.355 and purchase decision is 0.357. The value of Q2 predictive relevance of the endogenous latent variable is > 0, so it can be concluded that the model already has predictive relevance. Table 5 Value of R-squared (R2). Source: the result of processing smart PLS 3.0 R Square R Square Adjusted Instagram Users 0.675 0.662 Purchase Decision 0.502 0.497 4.2.3 Quality Index PLS path modeling can identify global optimization criteria to determine the goodness of fit with the GoF index. The GoF index developed by Tenenhaus et al. (2004) is used to evaluate measurement models and structural 50 models. In addition, the GoF index also provides a simple measurement for the overall prediction of the model. The criteria for GoF values are 0.10 (GoF small), 0.25 (GoF medium), 0.36 (GoF large) (Ghozali and Latan, 2015). 𝐺𝑜𝐹 = %𝐶𝑜𝑚𝑚𝑢𝑛𝑎𝑙𝚤𝑡𝑦/////////////////// × 𝑅!//// = √0.367 × 0.589 𝐺𝑜𝐹 = 0,465 The GoF value is 0.465, which means it can be concluded that the research model is good and also includes a large GoF. Table 6 Value of Q-squared (Q2). Source: Processing Results Smart PLS 3.0. 4.2.4 Hypothesis Test Hypothesis testing in this study aims to determine the significance of the effect of exogenous variables on endogenous variables. The test is carried out using a bootstrapping process on smartPLS 3.0. The basis for decision-making the influence between variables is considered significant at the level of 5% if the statistical t value compared to the t table value is 1.96. The test results with bootstrapping from the PLS analysis are: Test Hypothesis 1 (Influence of price on Instagram Users) Based on the test results in Table 4, we can see that the correlation of the price variable with Instagram has a path coefficient value of 0.231 and a t value of 2.633. This value indicates that the value of the t statistic is greater than t table (> 1.96). This means that the price variable has a significant effect on Instagram with the first hypothesis, namely price has a positive and significant effect on Instagram. Then hypothesis 1 is accepted. Test Hypothesis 2 (Effect of promotion on Instagram Users) Based on the test results in Table 4, we can see that the correlation of the promotion variable with Instagram has a path coefficient value of 0.251 and a t value of 2.580. This value indicates that the value of the t statistic is greater than t table (> 1.96). This means that the promotion variable has a significant effect on Instagram with the third hypothesis that promotion has a positive and significant effect on Instagram. Then hypothesis 2 is accepted. Test Hypothesis 3 (Effect of trust on Instagram Users) Based on the test results in Table 4, we can see that the correlation of the trust variable with Instagram has a path coefficient value of 0.296 and a t value of 3.330. This value indicates that the value of the t statistic is greater than t table (> 1.96). This means that the trust variable has a significant influence on Instagram with the fifth hypothesis, namely trust has a positive and significant effect on Instagram. Then hypothesis 3 is accepted. Test Hypothesis 4 (Effect of security on Instagram Users) Based on the test results in Table 4, we can see that the correlation between the security variable and Instagram has a path coefficient value of 0.222 and a t value of 2.675. This value indicates that the value of the t statistic is greater than t table (> 1.96). This means that the security variable has a significant effect on Instagram with the fourth hypothesis, namely security has a positive and significant effect on Instagram. Then hypothesis 4 is accepted. Test Hypothesis 5 (Influence of Instagram Users on purchase decision) Based on the test results in Table 4, we can see that the correlation of the Instagram variable with purchase decision has a path coefficient value of 0.709 and a t value of 10.321. This value indicates that the value of the t statistic is greater than t table (> 1.96). This means that the Instagram variable has a significant effect on purchase decisions with the second hypothesis, namely, Instagram has a positive and significant effect on purchase decisions. Then hypothesis 5 is accepted. SSO SSE Q² (=1-SSE/SSO) Price 306.000 306.000 Instagram Users 408.000 263.245 0.355 Purchase Decision 306.000 196.744 0.357 Promotion 306.000 306.000 Security 204.000 204.000 Trust 306.000 306.000 51 4.3 Indirect Effect Based on the results of the Bootstrapping calculation in the Specific Indirect Effects Research above, the following can be generated: • Price has a positive and significant effect on purchase decisions through Instagram because the t statistic’s value is 2.750 which is greater than t table = 1.96 and also the p value is 0.006 which is smaller than 0.05. • Promotion has a positive and significant effect on purchase decisions through Instagram because the t statistic’s value is 2.536 which is greater than t table = 1.96 and also the p value is 0.012 which is smaller than 0.05. • Security has a positive and significant effect on purchase decisions through Instagram because the t statistic’s value is 2.486 which is greater than t table = 1.96 and also the p value is 0.013 which is smaller than 0.05. • Trust has a positive and significant effect on purchase decisions through Instagram because the t statistic’s value is 3.027 which is greater than t table = 1.96 and also the p value is 0.003 which is smaller than 0.05. 5. DISCUSSION Based on the results of the above data processing against the proposed hypothesis, it can be seen that all the hypotheses that have been set by the researchers are accepted. The following is an analysis related to the influence between variables according to the proposed hypothesis: 5.1 The influence of price on purchase decisions through Instagram users After testing the hypothesis, it is known that price has a positive and significant effect on purchasing decisions through Instagram because the t statistic’s value is 2.750 which is greater than t table = 1.96 and also the p value is 0.006 which is smaller than 0.05. This is relevant to the results of the IDN Times survey in the 2019 Indonesia Millennial Report which stated that 60% of consumers chose price as the main factor in considering the products they would buy online. 5.2 The influence of promotions on purchase decisions through Instagram users After testing the hypothesis, it is known that promotion has a positive and significant effect on purchase decisions through Instagram because the t statistic value is 2.536 which is greater than t table = 1.96 and also the p-value is 0.012 which is smaller than 0.05. This is relevant to research by Njoto (2018: 612-618), which found that promotions, namely advertising, sales promotion, and personal selling have a significant effect on consumer purchasing decisions. 5.3 The influence of between security on purchase decisions through Instagram users After testing the hypothesis, it is known that security has a positive and significant effect on purchase decisions through Instagram because the t statistic’s value is 2.486 which is greater than t table = 1.96 and also the p value is 0.013 which is smaller than 0.05. This is relevant to research by Anandita (2015, 203-210), Fadhila (2017, 60-64) and Khanna (2019, 1-9) which show that security has a positive effect on purchasing decisions of Instagram social media users. 5.4 The influence of trust on purchase decisions through Instagram users After testing the hypothesis, it is known that trust has a positive and significant effect on purchase decisions through Instagram because the t statistic’s value is 3.027 which is greater than t table = 1.96 and also the p value is 0.003 which is smaller than 0.05. This is relevant to Chadafi (2016, 1-8), Zatalini (2017, 145-146) and Nawangsari (2018, 61-67) showing that trust has a positive effect on purchasing decisions. 5.5 The influence of Instagram social media on purchase decisions users After testing the hypothesis, it is known that the correlation of the Instagram variable with purchase decision has a path coefficient value of 0.709 and a t value of 10.321. This value indicates that the value of t statistic is greater than t table (> 1.96). This means that the Instagram variable has a significant effect on purchase decisions, which means that Instagram has a positive and significant effect 52 on purchase decisions. This is relevant to the research of Lininati (2018, 97-102), Miranda (2017, 1-15) and Puspitarini (2019, 71-80) which show that Instagram social media has a positive effect on purchasing decisions. 6. CONCLUSION AND SUGGESTIONS 6.1 Conclusion The research aims to analyze the purchasing decision factors of Instagram social media users in the marketplace. The analysis test uses Smart PLS 3.0 to analyze the correlation between these variables. By the analysis of the results and discussion in the previous chapter, the following conclusions can be drawn: • Based on the results of the first analysis, price has a positive and significant effect on Instagram. This means that price is the main factor in considering buying products on the marketplace through Instagram. • Based on the results of the second analysis, promotion has a positive and significant effect on Instagram. This means that the promotion of products on the marketplace is consistent and routine on Instagram, making Instagram users consider buying products on the marketplace through Instagram. • Based on the results of the third analysis, it shows that the security variable has a positive and significant effect on Instagram. This means that security by providing valid information is one of the things that consumers consider when buying products on the marketplace through Instagram. • Based on the results of the fourth analysis, it shows that the trust variable has a positive and significant effect on Instagram. This means that the trust / confidence of a consumer in considering buying a product in the marketplace is very high. • Based on the results of the fifth hypothesis test, it shows that the Instagram variable has a positive and significant effect on purchasing decisions. This is supported by the variety of features provided by Instagram to make it easier for consumers to find and compare the products they want to buy. • The results of the tests carried out state that the promotion, price, trust, security and Instagram variables have a positive and significant effect on purchase decisions on the marketplace. 6.2 Suggestions Based on the results of the study and also the conclusions, the following are suggestions that researchers can give to managerial and further researchers. Advice for academics: from the results of the R-square test, it can be seen that the effect of price, promotion, trust and security variables on Instagram is 67.5%. While the influence of the Instagram variable on the purchase decision is 50.2%. It can be expected that future research can expand the model by examining other aspects that also influence purchasing decisions using Instagram on the marketplace. Further research is also expected to re-examine purchasing decisions in the field of Marketplace and social media with a larger number of samples, and with various other social media. Suggestions for Instagram users / consumers: as consideration and input for Instagram social media users in buying products on the marketplace through information from Instagram, this can be used as material to compare products through social media before deciding to buy on the marketplace. 7. REFERENCES Adani, M, R. (2020). Kenali Apa itu Marketplace beserta Jenis dan Contoh Penerapannya. Diunduh dari: https://www.sekawanmedia.co.id/pengertian- marketplace/. Pada 20 Juni 2021. Aji, N, S. & Djawahir, A, H. (2019). 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