Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 1635-1649 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: abd.azizunzah@gmail.com The effect of trust and social presence on consumer purchase interest in B2C e-commerce in Indonesia Abd Aziz1*, Moh. Abd Rahman2, Poppy Rachman3, Wahyu Lestari4 1Department of Islamic Education, Faculty of Tarbiyah, Islamic University of Zainul Hasan Genggong, Probolinggo, Indonesia; abd.azizunzah@gmail.com (A.A.). 2Department of Economics, Faculty of Tarbiyah, Islamic University of Zainul Hasan Genggong, Probolinggo, Indonesia. 3Department of Islamic Education Management, Faculty of Tarbiyah, Islamic University of Zainul Hasan Genggong, Probolinggo, Indonesia. 4Department of Mathematics Educations, Faculty of Educational Scientific, Islamic University of Zainul Hasan Genggong, Probolinggo, Indonesia. Abstract: This study aims to examine the impact of social presence and trust dimensions, including integrity, predictability, capability, and benevolence, on consumer purchase intentions in business-to- consumer (B2C) e-commerce, with a focus on Indonesian consumers. The research employs a quantitative methodology, gathering data through validated and reliable questionnaires distributed via Google Forms to individuals who have completed transactions through e-commerce websites or applications. The data is analyzed using Structural Equation Modeling (SEM) with AMOS version 22 software. The findings indicate that social presence has a significant positive effect on trust dimensions, such as integrity, predictability, capability, and benevolence. In addition, consumer purchase intentions are significantly influenced by their trust in the vendor's integrity, predictability, capability, and benevolence. The study suggests that e-commerce vendors can enhance their social presence by incorporating features such as personal chat options with customer service, as well as integrating multimedia elements like images, videos, or emoticons. These efforts can help create a warmer, more engaging online environment, reflecting the vendor's capabilities and boosting consumer trust and purchase intentions. Keywords: Business B2C E-commerce, Consumer trust, Purchase intention, Social presence, Social presence, Purchase intention. 1. Introduction E-commerce applications have become an integral part of daily life in today's digitized world, enabling a range of community activities and revolutionizing consumer purchasing behavior. The rise of e-commerce has significantly boosted economic growth in many countries, especially in the wake of the 2008 financial crisis [1]. The global market for business-to-consumer (B2C) e-commerce has expanded significantly, and many studies have demonstrated the importance of mobile banking and e-commerce applications in driving financial activity in many industries [2]. In addition to changing consumer shopping habits, the explosive growth of e-commerce has created new business opportunities aimed at improving the customer experience and increasing profitability [3]. As online fraud and scams increase, the importance of trust in e-commerce is becoming more evident. Trust is a crucial factor in the success of e-commerce platforms, influencing consumer purchasing intentions [4]. The necessity for trust has increased with the advent of e-commerce platforms, particularly given the surge in online fraud and victimization. The extant literature demonstrates that customer trust is a critical factor influencing the intention to make an online purchase. [5], [6]. The four principal 1636 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate elements of trust in e-commerce are ability, compassion, integrity, and predictability. The concept of "ability" pertains to the conviction that a provider is capable of successfully addressing the needs of the customer, thereby fostering a pattern of repeat business. The term "benevolence" denotes the vendor's altruistic disposition and suggests they will not exploit others. The term "predictability" is used to describe a vendor's consistent behavior over time, which instills confidence in the customer. The concept of "integrity" encompasses the notion of operating with moral rectitude and adherence to established standards [7], [8]. These elements serve as the foundation of a reliable connection between the customer and the online retailer, which in turn affects the customer's likelihood of making a purchase [9]. While trust is undoubtedly a crucial factor in consumer behavior, it is often the case that face-to-face interactions, which can play a pivotal role in fostering trust in traditional retail settings, are lacking on e-commerce platforms. One potential avenue for e-commerce platforms to bridge this gap is through the establishment of a social presence, which can be defined as the extent to which a customer perceives the website as warm and interactive. The incorporation of socially rich written content, heartfelt greetings, and multimedia components such as audio and video can facilitate the communication of social presence, which in turn enhances customer perception of human engagement and mitigates transaction uncertainty in the digital domain. [10]. Recent research indicates that social presence aspects can significantly influence customer trust and purchase intentions in B2C e-commerce environments [11]. Despite extensive research on social presence and trust in international e-commerce contexts, there has been a paucity of studies examining their impact on consumer purchasing intentions in the Indonesian market. Given the rapid growth of e-commerce in Indonesia, particularly on platforms such as Traveloka, this study seeks to address the aforementioned gap by examining how customer purchase intentions in the country's B2C e-commerce industry are shaped by social presence and trust [12], [13]. It is of the utmost importance for e-commerce enterprises operating in Indonesia to gain an understanding of these dynamics, as cultural norms place a high value on interpersonal and group relationships [14], [15]. This underscores the significance of having a robust social media presence on digital platforms [16], [17]. To address this question, the following research issues are considered in the study: (1) What elements influence consumers' intentions to make e-commerce purchases? (2) What impact do social presence and trust have on these intentions? (3) How might the aforementioned hypotheses be tested using the structural equation modeling (SEM) method? (4) What is the impact of social presence and specific trust dimensions on consumers' intentions to purchase on Traveloka and similar platforms? The following is a description of the organization of the paper. A review of pertinent research on social presence and trust in e-commerce is presented in Section 2. The methodology employed, including the procedures for data collection and analysis, is delineated in Section 3. The results of the study are presented in Section 4. Section 5 concludes the investigation and proposes avenues for further research. 2. Literature Review Social presence theory (SPT) examines how well a medium facilitates users' perception of others' psychological presence, which helps to understand the impact of social purchasing features in online markets. The ability of a medium to elicit feelings of warmth, sensitivity, and human interaction is known as social presence (SP) [18]. Further research has developed this idea by indicating that SP is connected to communication media's capacity to convey social cues, which results in emotions of psychological closeness and intimacy. These characteristics of SP are often interpreted as indications of benevolence and affability, which foster a sense of interpersonal connection in the medium [19], [20]. Examples of social presence in the context of e-commerce include personalized welcomes, live chat capabilities, and multimedia material. Such elements have the potential to enhance the online experience for customers and facilitate more engaging interactions [21]. As trust is typically established through positive interactions with others, whether in person or through other forms of communication, the concepts of social presence and trust are closely interrelated. 1637 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate The integration of communication channels that project a robust sense of social presence can facilitate the establishment of trust, even in e-commerce contexts where direct, face-to-face interactions are not feasible. The utilization of these media enables the establishment of a high degree of perceived human touch and social connection, which subsequently fosters trust [22]. For example, an online vendor with a robust social media presence on their website or application is more likely to be perceived as a reliable source by potential customers. Conversely, a paucity of social media activity may engender a lack of confidence in the veracity or quality of the vendor's services [23]. Prior research has demonstrated a positive correlation between social presence and trust. A substantial body of research indicates that a social media presence has a significant impact on consumer trust. This is achieved by reducing the perceived risks associated with online purchases and by enhancing customer confidence in the legitimacy of the vendor [24]. It is not uncommon for customers to place a significant degree of trust in the social cues provided by internet platforms, such as user evaluations and interaction features. These cues serve to build trust and reduce uncertainty for customers, thereby enhancing their overall experience [25]. Moreover, research has demonstrated that social presence has a positive impact on various dimensions of trust, including compassion, predictability, and integrity [26]. An elevated degree of social presence can positively influence customer perceptions of a vendor's honesty and expertise, thereby enhancing the probability of repeat purchases [27]. Moreover, the establishment of a sense of community and emotional connection through social presence serves to strengthen consumer relationships with e-commerce platforms, thereby fostering trust [28]. A substantial body of research indicates that elevated levels of social presence among customers are associated with a greater likelihood of perceiving the seller as benevolent and empathetic. This, in turn, has been shown to reinforce the customer's belief in the vendor's intentions [29]. It is therefore crucial for businesses engaged in B2C e-commerce to enhance their social presence through the incorporation of features such as personalized recommendations, real-time communication, and user- generated content. This is essential for fostering and maintaining confidence among consumers [30]. Table 1. Indicators and sub-indicators of social presence, trust, and purchase intentions. Indicator Sub-indicator Social presence (SP) SP1 Sense of human interaction to communicate SP2 Interested in Traveloka products SP3 Providing information about Traveloka products SP4 Communication via the web and applications feels warm (friendly) Integrity (IN) IN1 Charge Internet shoppers more for Internet shopping IN2 Honest with their customers IN3 Act sincerely in dealing with customers. IN4 Overcharged by Internet merchants during sales transactions Predictability (PR) PR1 Predict the performance of most e-commerce websites from their experience with the website. PR2 Positively related on most trading websites. PR3 Relax when I'm dealing with e-commerce websites that I've previously had good experiences with Ability (AB) AB1 Sufficient skills and resources to do business on the Internet AB2 Sufficient knowledge to manage their business on the Internet AB3 A good reputation AB4 Know how to provide the best service Benevolence (BE) BE1 Help me to make purchasing decisions BE2 Provides products and services based on my needs BE3 Provides products and services based on my requests 1638 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate BE4 Companies consider my rights. Purchase intentions (PI) PI1 Buy products from Traveloka PI2 Consider buying products from Traveloka in the future. PI3 Intend to buy products from Traveloka. PI4 Intend to use the Traveloka website/application. Based on the above literature review above, we propose a research framework, as illustrated in Figure 1. This framework highlights the relationships between the various indicators and sub-indicators outlined in Table 1. Based on this framework, we have formulated the following hypotheses. Figure 1. Research framework. H1 = Consumer trust in the integrity-vendor is positively impacted by social presence on the website. H2 = The inclusion of social media on websites positively impacts consumers' trust in the predictability of online sellers. H3 = Consumer trust in ability vendors is positively impacted by the social presence on the website. H4 = Consumer trust in charitable vendors is positively impacted by social media presence on the website. H5 = The intention of customers to make an online purchase from an e-vendor is positively impacted by their level of trust in the e-vendor's honesty. H6 = The intention of customers to make online purchases from these e-providers is positively impacted by their level of trust in the predictability of these vendors. H7 = Online buyers' interest in products from these e-vendors is positively impacted by consumer trust in their abilities. H8 = The desire to make an online purchase from an e-vendor is positively impacted by consumer trust in charitable e-vendors. 3. Material and Methodology 3.1. Type of Research and Samples This is a quantitative research project that uses ordinal data as its data source. The population used in this study is college students as customers who are purchasing something from the Traveloka e- commerce application. The research sample is 250 respondents taken by random sample techniques. A 1639 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate total of 23 questionnaire samples were used to determine the validity and reliability of the instrument. Furthermore, the questionnaire was distributed to 5 classes, which are Class A with 51 students, class B with 52 students, class C with 50 students, class D with 49 students, and Class E with 48 students with a total of 250 students in semester 3-5 of the 2022/2023 Academic Year Department of Islamic Education, Faculty of Tarbiyah, Islamic University of Zainul Hasan Genggong, Probolinggo, Indonesia. The respondents are students who joined the Advanced Economy course and Advanced Islamic Fiqh course and agreed to participate in this research. The selection of this sample was permitted by the Research Ethics Committee - Faculty of Tarbiyah, Islamic University of Zainul Hasan Genggong, Probolinggo, Indonesia. The sample selection uses proportional random sampling for determining each class concerning the research proposal, which has been reviewed by an independent ethics commission, namely SREC (Social Research Ethics Committee) of the Islamic University of Zainul Hasan Genggong, Probolinggo, Indonesia, number 2568/UNZAH/LL/2023. The research sample is designed to reflect the characteristics of Indonesian e-commerce consumers, particularly within the context of trust and social presence in the Traveloka platform. All the collected data will be analyzed by using structural equation modeling. These respondents are asked to complete a questionnaire via Google Form, which includes several questions about consumer satisfaction and purchasing interest in Traveloka e- commerce. The 28-item questionnaire asks participants about various aspects of their experience with Traveloka e-commerce. These questions cover topics such as user interface ease-of-use, speed of the product search process, clarity of product descriptions, and the variety of products available. Additionally, it probes the transparency of price information, security of payment methods, and comfort with sharing personal information on the platform. Respondents are also asked about the ease of the payment process, the competitiveness of Traveloka's prices, and their trust in the platform's ability to deliver on its promises. The survey further investigates customer support responsiveness, the effectiveness of problem resolution, and the availability of helpful resources like FAQs or guides. Customer satisfaction with the overall quality of services provided by Traveloka, including comparisons to other applications, is also assessed. Questions regarding social presence focus on interactions through features like chat or notifications, and whether social elements such as emoticons or images make the experience more friendly. Finally, the survey explores loyalty and recommendation behaviors, as well as the influence of reviews on social media and the level of control users feel when making purchasing decisions. 3.2. Instruments and Data Collection The data collection process involved the creation of documented, uniform, and structured formal instruments to collect information from respondents. The primary instrument is a questionnaire with the following Likert scale measurements: 5 = Strongly Agree (SA), 4 = Agree (A), 3 = Neutral (N), 2 = Disagree (D), and 1 = Strongly Disagree (SD). The questionnaire should receive validation from the validator and meet the validity, reliability, and normality criteria. The validation process ensures that the instrument captures all relevant aspects of trust and social presence, which are the focus of this research in the context of consumer purchase interest. This stage also includes the pilot testing phase with 23 initial samples to ensure clarity and relevance of the questions, reflecting past studies' suggestions for improving instrument design in consumer behavior research. 3.3. Data Analysis Thus, after obtaining the data from the questionnaire distribution, the data were processed using Structural Equation Modeling (SEM), which is an advanced statistical technique designed to explore complex relationships among variables such as trust, social presence, and consumer purchase interest. The eight assumptions were tested using SEM analysis data obtained from the AMOS version 22 application. AMOS was the SEM software used in this study. The hypothesis is written as a pair of the null hypothesis (H0) and alternative hypothesis (H1). The following conditions must be met to evaluate the acceptance of the hypothesis: if the value of Sig. (2 Tailed) > 0.05, H0 is accepted and H1 is rejected; 1640 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate if the value of Sig. (2 Tailed) < 0.05, H0 is rejected and H1 is accepted. Unlike simpler statistical techniques used in previous studies, SEM enables this research to capture not only direct but also indirect effects between the studied variables. SEM also provides an effective way to model latent constructs such as trust and social presence, which are not directly observable but significantly influence consumer purchase interest in e-commerce settings. By applying SEM, this study can offer a deeper insight into the role of these factors in the specific context of B2C e-commerce in Indonesia, particularly with Traveloka. 4. Results and Discussion This section presents the results of a study on the impact of social presence and trust on consumer interest in B2C e-commerce in Indonesia. The Traveloka application was chosen for this study. The primary data used in this study is the result of online questionnaires via Google form from 250 respondents who have done transactions on the Traveloka application. By using statistical analysis, we have the following results in Figure 3. Meanwhile, the reliability test is considered reliable if the construct reliability value is greater than 0.7. As can be seen in Figure 3, the test results show that the construct reliability value of each variable is greater than 0.7. These results allow us to conclude that the research instrument as a whole is reliable enough to be used in this research. Figure 2. The results of the validity and reliability tests. 1641 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate Figure 3. The results of the validity and reliability test for each factor. Table 2. Results of the validity tests for the goodness of fit index by variable. The goodness of the fit index Cut-off value Social presence Integrity Predictability Ability Benevolence Purchase intention Significant probability ≥ 0.05 0.911 0.555 0.935 0.271 0.060 0.044 RMSEA ≤ 0.08 0.000 0.000 0.000 0.035 0.085 3.134 GFI ≥ 0.90 1.000 0.998 1.000 0.995 0.989 0.930 AGFI ≥ 0.90 0.998 0.988 0.999 0.975 0.945 0.936 CMIN/DF ≤ 2.0 0.093 0.590 0.068 1.307 2.814 3.134 TLI ≥ 0.90 1.004 1.002 1.004 0.999 0.991 0.989 CFI ≥ 0.90 1.000 1.000 1.000 1.000 0.997 0.996 Furthermore, after we have completed the reliability test, we should perform the normality test in the tables below. 1642 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate Table 3. Normality test. Variable Min. Max. Skew C.R. Kurtosis C.R. SP1 1.000 6.000 -0.356 -2.300 -.592 -1.912 SP2 1.000 6.000 -0.393 -2.538 -.493 -1.591 SP3 1.000 6.000 -0.332 -2.142 -.620 -2.001 SP4 1.000 6.000 -0.244 -1.574 -.778 -2.511 IN1 1.000 6.000 -0.247 -1.593 -.785 -2.533 IN2 1.000 6.000 -0.371 -2.398 -.559 -1.806 IN3 1.000 6.000 -0.283 -1.829 -.529 -1.707 IN4 1.000 6.000 -0.343 -2.215 -.548 -1.770 PR1 1.000 6.000 -0.380 -2.454 -.678 -2.189 PR2 1.000 6.000 -0.315 -2.037 -.675 -2.179 PR3 1.000 6.000 -0.373 -2.406 -.632 -2.040 AB1 1.000 6.000 -0.313 -2.023 -.865 -2.790 AB2 1.000 6.000 -0.352 -2.275 -.745 -2.404 AB3 1.000 6.000 -0.304 -1.960 -.810 -2.613 AB4 1.000 6.000 -0.286 -1.843 -.803 -2.592 BE1 1.000 6.000 -0.338 -2.180 -.563 -1.817 BE2 1.000 6.000 -0.322 -2.076 -.589 -1.902 BE3 1.000 6.000 -0.266 -1.715 -.734 -2.370 BE4 1.000 6.000 -264 -1.702 -.722 -2.329 PI1 1.000 6.000 -0.276 -1.782 -.851 -2.745 PI2 1.000 6.000 -0.313 -2.017 -.460 -1.483 PI3 1.000 6.000 -0.215 -1.389 -.813 -2.624 PI4 1.000 6.000 -0.229 -1.481 -.940 -3.034 Multivariate -3.641 -0.849 Table 3 shows that the critical ratio values for skewness and kurtosis are in the range of approximately 2.58, indicating that the univariate normality test shows a normal distribution. In addition, multivariate outliers can be assessed using the AMOS Mahalanobis Distance output. The p 0.001 threshold is where the requirements are applied. Multiplying X2 by the total number of measurable variables in the study gives the distance. In this case, the Insert - Function - CHIINV sub- menu of the Excel application was used, resulting in a variable of 23 and several measured variables of 49,728. Accordingly, any data or cases with a value greater than 49.728 are considered to be multivariate outliers. Table 4. Outlier test. Observation number Mahalanobis d-squared p1 p2 116 46.850 0.002 0.443 235 39.586 0.017 0.928 138 37.298 0.030 0.982 121 36.524 0.036 0.982 120 36.500 0.037 0.953 96 24.331 0.386 0.442 169 24.176 0.394 0.500 144 24.165 0.394 0.456 1643 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate The table shows the results of the outlier test, and the processed data does not contain any discernible values higher than 49.728. Therefore, there are no outliers in the data. The CFA validity test results, as shown in Table 3, indicate that all variable items have factor loading values greater than 0.5 and each variable has construct reliability values greater than 0.7. These results imply that all variables are considered reliable all items are considered valid, and the results of the analysis can be applied to subsequent tests. One way to tell if a problem has been identified is to look at the estimation results. Only if the model identification results indicate that the model is overidentified can SEM analysis be performed. This is done by examining the df value of the model. Table 5. Computation of degrees freedom (Default model). Several unique sample moments: 276 Number of unique parameters to be calculated: 54 Degrees of freedom (276 - 54): 222 The results show that the df value of the model is 222. The model is classified as over-identified as its df value is positive. The data analysis can now proceed to the next stage. Finding out whether the proposed model 'fits' or matches the sample data is the main objective of SEM. The goodness of fit results are shown in the data below. Table 6. Test results for the goodness of fit index. The goodness of the fit index Cut-off value Research model Model Chi-Square Small 389.973 Good fit Significant probability ≥ 0.05 0.000 Marginal fit RMSEA ≤ 0.08 0.027 Good fit GFI ≥ 0.90 0.878 Marginal fit AGFI ≥ 0.90 0.849 Marginal fit CMIN/DF ≤ 2.0 1.757 Good fit TAG ≥ 0.90 0.976 Good fit CFI ≥ 0.90 0.979 Good fit From the data in the table above, it can be seen that the research model is approaching a good model. Since the chi-square in this research model has a value of 389.973, it is considered to be good. Therefore, the Chi-Square value is quite high. A simple measure of fit called CMIN/DF contrasts a model's goodness of fit with the number of predicted coefficients needed to achieve goodness of fit. The CMIN/DF for this study was 1.757, indicating that the research model was a good fit. By comparing the squared residuals of the predicted model with the actual data, the Goodness of Fit Index (GFI) calculates the overall appropriateness of the model. The GFI for this model is 0.878. A marginally fit research model is indicated by a number around the suggested threshold of 0.90. In a large sample, the Chi-square value is explained by the RMSEA index. The RMSEA value of this study is 0.055, but the suggested value is 0.08. This clearly shows the good fit of the research model. The GFI modified by the ratio of the proposed degrees of freedom to the degrees of freedom of the null model is called the AGFI. The AGFI for this model is 0.849. The recommended value, 0.90, is close to this value. This indicates that the study model fits the data only sporadically. The TLI is a unit of measurement of conformity that is not affected by sample size. The TLI for this study was 0.976, while a recommended value of 0.90 was used. This demonstrates the fit of the research model. The CFI index is comparatively unaffected by model complexity and sample size. The research 1644 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate model is moderately fit, as indicated by the CFI value of 0.979 in this study, which is lower than the required value of 0.90. The significant probability index, with a value of < 0.05, is an index indicating that the research model is not good based on the overall goodness of fit measurement mentioned above. However, as the RMSEA, TLI, CFI, and CMIN/DF values meet the goodness of fit requirements, the model proposed in this study is still appropriate. In addition, the figure below shows the graph generated as a result of the data processing of the SEM model. Figure 4. Figure of structural equation model. The purpose of this hypothesis test is to examine the developed structural model. The value of the standardized regression coefficient indicates whether the proposed hypothesis is tested. If the CR value for the p-value is greater than 1.96 and less than 0.05, it can be concluded from the data processing results that there is a positive association between the variables [8]. The table below shows the results of using AMOS version 22 to test the hypothesis: Table 7. Hypothesis test results. Regression weights: (Group number 1 - Default model) Relationship between variables Estimates S.E. C.R. P Information IN ← SP 0.888 0.048 18.477 0.000 Received PR ← SP 0.790 0.055 14.367 0.000 Received AB ← SP 0.988 0.048 20.617 0.000 Received BE ← SP 0.887 0.048 18.466 0.000 Received PI ← IN 0.267 0.068 3.909 0.000 Received PI ← PR 0.151 0.055 2.756 0.006 Received PI ← AB 0.287 0.070 4.087 0.000 Received PI ← BE 0.323 0.064 5.090 0.000 Received 1645 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate The results of the regression weight test describe the coefficient of the effect between the related variables based on the information in the table above. The following conclusions can be drawn from the regression weight analysis: 4.1. Social media presence on a website influences buyers' trust in trustworthy sellers The calculated parameter value of the regression weight coefficient is 0.888. The value of this coefficient suggests that integrity and social presence are positively correlated. This means that the honesty of a supplier increases with its social media presence. 0.000 (p 0.05) is the probability value when examining the association between the two variables. 4.2. Social presence on the website affects consumer confidence in the predictability of e-vendors The estimated value of the regression weight coefficient is 0.790. According to this coefficient, predictability and social presence are positively correlated. This implies that predictability increases with the social media presence of the supplier. If there is a direct correlation between social presence and e-vendor predictability, then the hypothesis that greater social presence on the website affects consumer confidence in e-vendor predictability is supported and can be stated. Testing the relationship between the two variables yields a probability value of 0.000 (p 0.05). 4.3. Social presence on the website affects consumer trust in the ability-vendors The calculated parameter for the regression weight coefficient value is 0.988. The coefficient value suggests that ability and social presence are positively correlated. This implies that a vendor's ability increases with their social media presence. Upon analyzing the association between the two variables, 0.000 (p 0.05) is the probability value. 4.4. Social presence on the website affects consumer confidence in benevolence e-vendor The calculated parameter for the regression weight coefficient value is 0.887. The value of this coefficient suggests that virtue and social presence are positively correlated. This implies that a vendor's goodness increases with their social presence. Upon analyzing the association between the two variables, 0.000 (p 0.05) is the probability value. 4.5. Effect of Consumer Trust in e-vendor Integrity on Online Buying Interest The estimated parameter value of the regression weight coefficient is 0.267, indicating that the relationship between integrity and purchase intention is positive. This means that the greater the integrity of the seller, the greater the interest in buying online. When analyzing the relationship between the two variables, the probability value is 0.000 (p 0.05). 4.6. The effect of consumer trust on the predictability of e-vendors on online buying interest The estimated parameter value of the regression weight coefficient is 0.151, indicating a positive correlation between purchase interest and predictability. This means that the more reliable the supplier is, the more interested customers are in buying online. 0.006 (p 0.05) is the probability value for testing the relationship between the two variables. 4.7. The Effect of consumer trust in Ability vendors on online buying interest The estimated parameter value of the regression weight coefficient is 0.287, indicating a positive correlation between purchase interest and capability. This means that the more capable the e-merchant is, the more interested the customer is in buying online. When analyzing the relationship between the two variables, the probability value is 0.000 (p 0.05). 1646 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate 4.8. The effect of Consumer Trust in Benevolence-Vendors on Online Purchase Intentions Friendliness and purchase intention are positively related, as indicated by the estimated parameter value of the regression weight coefficient of 0.323. This means that the more charitable the supplier, the more interested the customer is in making an online purchase. When the two variables are tested, a probability value of 0.000 (p 0.05) is obtained. The context of the research should be described in the analysis and discussion of the findings by identifying the environment, circumstances, or industry. Show how the research has developed. Subsections are organized to 'respond' to the objectives proposed by the thesis. It can be broken down into subsections to address the objectives proposed by the work. 5. Discussion This study's results confirm the significant influence of trust and social presence on consumer purchase interest in B2C e-commerce, particularly in Indonesia. Social presence enhances consumer perceptions of trust in the online marketplace, reinforcing their purchase intention. These findings align with those of Hassan, Iqbal, and Khanum [16], who demonstrated that social presence significantly influences consumers' purchase intentions through the mediation of trust, especially in social commerce contexts [16]. Similarly, Lu, Zeng, and Fan found that trust in the marketplace, shaped by the perceived competence and benevolence of sellers, plays a critical role in driving purchase behavior in social commerce platforms [17]. Furthermore, our findings suggest that social presence creates a more interactive and engaging environment for consumers, fostering trust and a sense of security in their online transactions. This resonates with Hassan et al.'s [16] assertion that social presence encourages consumers to engage more actively in online shopping environments. Additionally, trust in the marketplace mediates the relationship between social presence and purchase intention, consistent with the findings of Lu et al. [17], who emphasized the role of trust in reducing perceived risks and promoting consumer engagement in social commerce. Contradicting some previous studies, however, our research suggests that trust and social presence may have a more profound impact in emerging markets like Indonesia, where consumers rely more on social cues and interpersonal trust. In contrast, Lu et al. [17] observed that in more developed markets, consumers prioritize system quality and efficiency over social interactions. In conclusion, our study contributes to the growing body of literature on e-commerce by demonstrating the critical role of trust and social presence in shaping consumer behavior in B2C e- commerce platforms in Indonesia. These findings are particularly relevant in the context of emerging markets, where trust in the seller and the platform is essential for consumer engagement. 6. Conclusion This study supports hypotheses H1 to H4 by finding that social presence on a website significantly increases consumer trust in the integrity, reliability, ability, and compassion of the seller. Increased social media presence confirms H5, as it increases perceived vendor integrity, which in turn influences online purchase intention. Confirming H6, trust in a seller's predictability also increases the likelihood of making an online transaction. Similarly, customer confidence in a seller's competence and friendliness increases purchase intention, supporting H7 and H8. In general, social presence is essential for building customer trust and influencing online purchase decisions. 7. Implication The findings of this study have significant implications for e-commerce platforms, particularly those operating in the Indonesian market. Enhancing social presence on platforms like Traveloka is essential to build consumer trust, which directly influences purchase intentions. Practical steps such as integrating live chat support, personalized recommendations, and interactive content like videos or emoticons can make the platform more engaging and reliable in the eyes of consumers. Additionally, 1647 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1635-1649, 2024 DOI: 10.55214/25768484.v8i6.2289 © 2024 by the authors; licensee Learning Gate marketing strategies should emphasize the platform’s reliability and vendor integrity. By highlighting secure payment methods, positive customer testimonials, and a history of successful transactions, platforms can effectively reassure potential buyers and foster a trustworthy environment. 8. Limitations However, this study is not without limitations. The sample, which consists solely of students from the Islamic University of Zainul Hasan Genggong, may not fully represent the broader population of Indonesian e-commerce users, potentially limiting the generalizability of the findings. Additionally, the study's focus on the Indonesian market means that the results may not apply to other regions with different cultural or economic contexts. Methodologically, the reliance on a quantitative approach may overlook deeper insights that qualitative methods, such as interviews or focus groups, could provide. 9. Future Research Suggestions Future research could address these limitations by exploring additional dimensions of trust, such as emotional and transactional trust, to better understand their interaction with social presence in influencing purchase intentions. Longitudinal studies are also recommended to observe how trust and social presence evolve and their impact on consumer loyalty and retention in B2C e-commerce. Furthermore, investigating the role of emerging technologies like artificial intelligence (AI) and augmented reality (AR) in enhancing social presence and trust could provide valuable insights. Finally, cross-platform comparisons could be conducted to identify specific features or practices that are most effective in building trust and encouraging purchases across different e-commerce platforms. Funding: This research is supported by LITAPDIMAS of the year 2022 which has given financial support such that this research can be completed. We also thank all colleagues in our department of the School of Business, Islamic University of Zainul Hasan, Indonesia. Institutional Review Board Statement: The Ethical Committee of the Islamic University of Zainul Hasan, Indonesia, approved this study on May 29, 2024 (2568/UNZAH/LL/2024). Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] Yazid, Z. E., Zainol, Z., & Bakar, J. A. (2023). E-Commerce via mobile banking: Contemporary Shariah issues and ways to address them. International Journal of Professional Business Review, 8(1). https://doi.org/10.26668/businessreview/2023.v8i1.1258 [2] Prasad, K., Tomar, A. S., De, T., & Soni, H. (2022). 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