Interactivity and Professionalism to increase Purchase Intention in LiveStreaming Distribution Channel: The Mediation Effect of Trust in Sellers and Platform

Abstract

Purpose: This study explores the impact of interactivity and professionalism on consumer trust and purchase intentions in live-streaming distribution channels, explicitly focusing on Shopee Live in Indonesia. While prior research has examined trust as a general mediator between live-streaming features and consumer behavior, this study focuses on the distinct effects of trust in sellers versus in platforms. Utilizing the S-O-R framework, this research provides a novel exploration of how these trust dimensions act as an intermediary between live-streaming features and purchase intentions. Research Methods: Data were collected from 373 Shopee Live users who purchased fashion products via online surveys. The research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed model and hypotheses. Results: Findings reveal that interactivity positively affects trust in both sellers and platforms but does not directly influence purchase intentions. Conversely, professionalism directly impacts purchase intentions and enhances trust. Trust in sellers and platforms significantly mediates the relationship between interactivity, professionalism, and purchase intentions. Specifically, trust in sellers substantially affects purchase intentions more than trust in platforms. Conclusion: The study concludes that while interactivity builds essential trust, professionalism directly drives purchase intentions, highlighting the importance of professional conduct in live-streaming distribution channel contexts.

1. Introduction

In the current era of globalization, rapid advancements in information technology are reshaping industries across the globe, impacting both developed and developing nations alike. One significant transformation has been the rise of electronic commerce (e-commerce), which facilitates online business transactions and profoundly influences business practices worldwide. E-commerce has emerged as a pivotal tool, enabling businesses and micro, small, and medium enterprises (MSMEs) in Indonesia to harness digital platforms for promoting and enhancing sales. According to Statista Market Insights, the number of e-commerce usersin Indonesia reached 178.94 million in 2022, marking a 12.79% increase from the previous year. This trend is expected to continue, with projections estimating 196.47 million users by the end of (Mustajab, 2023).

One of the most recent trends in e-commerce is the new form of distribution channel, also known as live-streaming commerce, which began in China in 2016 and gained significant traction worldwide due to the COVID-19 outbreak. (Lee & Chen, 2021). Live-streaming commerce is an e-commerce model that builds real-time communication between sellers and customers on the e-commerce distribution platforms, providing a more personalized and engaging buying experience than other online buying processes. (Lee & Chen, 2021; Wongkitrungrueng & Assarut, 2020; Zhou et al., 2019). The most popular platform for live-streaming shopping in Indonesia is Shopee Live (Suhartadi, 2024). Shopee is a leading Indonesian e-commerce company that sells items from everyday necessities to luxury goods. With ShopeePay, a digital wallet service, Shopee has improved its customer experience. Shopee has also built a nationwide distribution logistical network to deliver goods promptly. Despite fierce competition, Shopee has continuously ranked as Indonesia's most visited platform (Suhartadi, 2024). Therefore, Shopee has become a crucial component of Indonesia's e-commerce ecosystem, offering consumers a total shopping experience that combines price, convenience, and an extensive range of products. Shopee Live allows sellers to make live sales, sell their products, and answer buyers' queries in real-time. This interactive method not only gives the consumers chances to ask questions and perceive the products more clearly but also allows the selling side to enhance the level of consumer trust and purchase intentions (Ngo et al., 2023).

Although live streaming commerce is becoming more and more popular, it has several issues mainly related to consumers’ trust issues (Ashraf et al., 2022). Trust is a critical element in e-commerce sales because consumers cannot directly touch or examine merchandise before buying it (Tuncer, 2021). In prior research, trust has been identified as an intervening variable between live streaming characteristics and consumers’ purchase intentions; particularly, trust is critical in ensuring a pleasant shopping experience (Tuncer, 2021; Zhang et al., 2023). However, more specific consumer trusts, namely trust in the seller and trust in the platform, have not been fully explored, with previous research suggesting further exploration (Luo et al., 2023).

Thus, there is a gap in the literature concerning the live-streaming commerce concept, with very little research on its adoption in the Indonesian market, especially Shopee Live. Although the literature highlighted live-streaming shopping in such markets as China and through such platforms as TikTok (Chen & Liao, 2022; Zhong et al., 2022), the Indonesian context has not been adequately researched. Moreover, within an integrated research model, most prior research has not explored the mediating role of trust in the seller and the platform on purchase intentions.

To fill these gaps, this research employs the S-O-R framework for the context of Shopee Live in Indonesia concerning fashion products. This model assumes that there is an interaction between a person and their environment and that this interaction triggers a set of responses from a person (Mehrabian & Russell, 1974). The S-O-R model is especially applicable to studying how live-streaming facilities of e-commerce platforms can influence consumer trust and purchasing decisions (Hossain, 2018). This study combines variables like interactivity and professionalism features with two types of trust, trust in the seller and trust in the platform, to examine their joint effects on purchase intentions. Therefore, this research aims to identify factors that affect purchase intentions to help e-commerce sellers improve consumers’trust and participation in live-streaming platforms. The study results are expected to enhance the general knowledge of consumers’ behavior in the digital environment and the appropriate application of live streaming as a management tool in the e-commerce domain.

2. Literature Review

2.1. The S-O-R model

The S-O-R model provides a theoretical background on consumers’ behavior in live-streaming commerce (Huang, 2016). The Stimulus-Organism-Response (S-O-R) model, first formulated by Mehrabian and Russell (1974), offers a comprehensive framework for examining consumer behavior by partitioning the process into three main components: stimuli (S), organism (O), and response (R). According to this paradigm, external stimuli impact people's internal states, influencing their behavioral reactions. In live-streaming commerce, various stimuli, including interactivity and professionalism (S), significantly impact consumers' psychological states. The internal states, specifically the level of trust in both the seller and the platform (O), play a vital role in influencing consumers' purchase intentions (R). The dynamic interaction of these elements can be effectively examined through the S-O-R model, asit providesinsight into how internal psychological processes convert stimuli into reactions. Prior studies employing the S-O-R model have also demonstrated the significant influence of trusts as mediating factors in the relationship between live-streaming features and purchase intentions (Meng & Lin, 2023; Tuncer, 2021; Zhang et al., 2023; Zhong et al., 2022).

2.2. Interactivity

In live-streaming commerce, interactivity is the informational and communicative exchange between the streamer and the viewers. This interaction can involve reactions to comments and questions and even show the products in real-time. Thus, the levels of interactivity can be very high, which will positively affect the viewer, making the shopping process more interesting and unique. Meng and Lin (2023) pointed out that interactivity is one of the most effective ways of building trust in live stream. It enables consumers to overcome their uncertainties and better understand the products. This trust that comes from the experience of interactive communication then leads to an increased probability of purchase intention (Zhong et al., 2022). Furthermore, it has been established that interactivity has a positive effect on consumer trust and involvement, which are key determinants of consumers’ purchasing decisions in social commerce contexts (Sun et al., 2019; Tuncer, 2021; Wongkitrungrueng &Assarut, 2020; Zhong et al., 2022).

2.3. Professionalism

Professionalism in live streaming commerce can be described in terms of the streamer’s information, appearances, and stream quality. Professional streamers are considered more believable; thus, they can positively influence consumer trust and purchase intention. According to Sun et al. (2019), professionalism positively impacts trust in live streaming since consumers who are informed and presented with good information from the streamer are likely to gain confidence in the purchase process. In the same vein, Zhang et al. (2023) have established that professional conduct while conducting live streaming sessions enhances the consumer’s trust and makes them repeat customers. Also, Meng and Lin (2023) pointed out that the perceived expertise and professionalism of the streamer are the significant factors that affect consumer perception and trust, which would determine the purchase intention of the consumers (Wongkitrungrueng & Assarut, 2020).

2.4. Trust in Seller and Platform

In the case of live streaming characteristics, trust in the seller and platform is a mediator in the relationship between independent and dependent variables. It is worth defining trust as the consumer’s confidence in the seller and the platform (Chandrruangphen et al., 2022) the latter will protect the consumer’s interests and provide reliable and accurate information on the product, while the former will create proper conditions for the transaction. Wongkitrunggrueng and Assarut (2020) noted that trust in the seller is developed through communication whereby the participants are informed through live streaming sessions. Tuncer (2021) also states that trust in the platform is crucial because it is the foundation for the entire shopping experience’s security and reliability. Meng and Lin (2023) and Zhang et al. (2023)’sresearch also revealsthat trust fully mediates the impact of interactivity, professionalism, and price promotion on the purchase intention, and thus is a critical factor for the live streaming commerce business model to succeed (Sun et al., 2019).

2.5. Hypothesis development

Interactivity plays a major role in influencing purchase intention through enhancing the shopping experience. When the customer can ask the streamer questions or get an answer immediately, it builds trust in the seller. This real-time interaction minimizes uncertainties and increases the perceived credibility of the product offerings. Zhong et al. (2022) found that high interactivity levels influence consumer trust, and trust promotes purchase intention. Also, Wongkitrungrueng and Assarut (2020) reported that the live chat and instant feedback provide a sense of community for the viewers, enhancing their confidence in the seller and the products, and they are likely to buy. Meng and Lin (2023) also agree with this view, showing that incorporating interactive features in live streaming strongly influences consumer engagement and purchase intent. High levels of interaction between the streamer and audience can promote authenticity, transparency, and sense of engagement—all crucial elements of trust in online transaction (Sebastianelli & Tamimi, 2018). For example, real-time questioning, prompt feedback, and product demonstrations lower perceived risks and increase platform trust. Furthermore, the trust developed by this direct contact reduces the uncertainties often connected to online shopping and promotes purchasing decisions (Sung et al., 2023). Based on the previous studies mentioned above, three hypotheses are formulated:

H1a: Interactivity positively influences trust in the seller.

H1b: Interactivity positively influencestrust in the platform.

H1c: Interactivity positively influences purchase intention.

The element of professionalism in live streaming commerce acts to build streamer credibility but also greatly influences the consumer buying decision. Streamers can build trust with their viewers When they are experts in their fields, explain content well, and have high stream quality. Sun et al. (2019) have established that professionalism in live streaming reduces the perceived risk of product quality and its authenticity, thus enhancing trust and purchase intent. Zhang et al. (2023) also stated that viewers are more willing to buy products from streamers who demonstrate professional behavior because it makes consumers trust the streamer and the product's legitimacy. Furthermore, Meng (2023) emphasizes that professionalism is a significant factor that creates a specific image and trust among consumers and thus forces them to make decisions to purchase products (Wongkitrungrueng & Assarut, 2018). Therefore, three hypotheses on professionalism are:

H2a: Professionalism positively influencestrust in the seller.

H2b: Professionalism positively influences trust in the platform.

H2c: Professionalism positively influences purchase intention.

Trust in the seller and the platform mediates the relationship between interactivity and professionalism toward purchase intentions in live-streaming commerce. This trust includes the consumers’ confidence and dependency on the seller, their honesty in the transactions, and the efficiency of the online selling platform (Çelik et al., 2023; Luo et al., 2023). Wongkitrungrueng and Assarut (2020) states that live streaming is another way that sellers can directly interact with consumers and provide answers to their questions, show that products are authentic, and make shopping personal, which, in turn, implies a decrease in perceived risk and an increase in purchase intention. Trust is also determined to be crucial in the platform by Ma et al. (2022) the platform must have security features that guarantee safe user experiences in payment, smooth interfaces, and efficient customerservices. The two forms of trust, in the seller and the platform, act as the link between the interactivity and professionalism stimuli and the consumer’s ultimate decision to purchase. As pointed out by Tuncer (2021), this mediation mechanism helps explain consumer behavior in live-streaming commerce since it reveals how the external stimulus is manifested as internal trust that eventually triggers the purchase intention. Therefore, the hypotheses about these relationships are:

H3a: Trust in the seller positively influences purchase intention.

H3b: Trust in the platform positively influences purchase intention.

H4: Trust in the seller mediates the relationship between interactivity, professionalism, and purchase intention.

H5: Trust in the platform mediates the relationship between interactivity, professionalism, and purchase intention.

As such, these hypotheses will systematically address the relationship between interactivity and professionalism in live-streaming commerce and the impact of trust and purchase intentions, which can benefit theoretical and empirical studies and practical implementations.

3. Research Methods

The variables used in this study have been explored in previous studies; therefore, measuring the constructsin live-streaming commerce aligns with the literature. In this paper, interactivity is indicated by four items, as highlighted by Zhong et al. (2022) and Zhang et al. (2023). Similarly, the professionalism is assessed by four items (Ma et al., 2022; Zhong et al., 2022). Four items capture trust in the seller (Tuncer, 2021; Zhang et al., 2023). Three factors determine trust in the platform (Elsholiha et al., 2023; Tuncer, 2021). While four factors measure purchase intention (Sun et al., 2019; Zhong et al., 2022). The constructs of the instruments are presented in Table 2 below. All the constructs regarding interactivity, professionalism, trust in the seller, trust in the platform, and purchase intention are measured on 5-point Likert scale where 5 = strongly agree and 1 = strongly disagree.

This research adopted purposive sampling as the method of data collection. Purposive sampling, a type of non-probability sampling, was chosen because it allows for sampling participants based on the characteristics and points of view pertinent to live-streaming commerce (Hair et al., 2021). The selection criteria included the following: The target respondents had viewed Shopee Live and purchased a fashion product via Shopee Live within the month before this survey. The data were collected through Google Forms online, and the respondents were reached through social media tools such as Instagram and WhatsApp groups. The data collection took place in April 2024 and lasted for three weeks. From the 401 responsesthat were obtained, 373 were deemed appropriate for analysis.

To validate the proposed model, this study measures the construct validity and reliability through Partial Least Squares (PLS)-based Structural Equation Modeling (SEM). It is also used to confirm the proposed method of verifying the model. The argument for using PLS isthat thistechnique enables a researcher to assess the latent constructs using a small and medium sample and non-normality distributed data (Chin et al., 2008). Moreover, SEM-PLS is a widely recognized method in terms of estimating the coefficient path in structural models (Hair et al., 2021).

4. Results and Discussion

4.1. Respondent demographics

The data was gathered from respondents who completed surveys posted on Google Forms on social media networks like X (Twitter), Instagram, and TikTok. The demographic information that was obtained included gender and age as well as occupation. Moreover, data on how often they buy fashion products from Shopee Live was also gathered.

Table 1: Respondents Demographics

4.2. Measurement Model

This research employs two assessment phases to assess the proposed model. The first stage checks the measurement model by assessing the AVE, outer loading, and CR in ascertaining the discriminant and convergent validity of the measurement model and establishing the construct reliability. At this stage, two indicators, INT4 and PRO1, are removed from the analysis because these two variables have a high level of correlation, which in turn negatively affects the reliability and validity of the model (Hair et al., 2022). The convergent validity test (Table 2) reflects that there is convergent validity as all factors’ AVE is above 0. 5 and the factor loadings are greater than 0. 6 (Hair et al., 2010). Consequently, this outcome fulfils the criterion of convergent validity of the construct variables.

Table 2: Validity and Reliability

The next step is discriminant validity analysis. The discriminant validity is an attempt to prove that they reflect construct has a higher correlation with its indicators than with other constructs in the PLS path model (Hair et al., 2022). The method of ascertaining discriminant validity is by applying the Fornell-Lacker Criterion or the HeteroitraitMonotrait Ratio Test (HTMT). However, (Hair et al., 2022) Hair et al. discuss that the Fornell-Larcker Criterion is relatively non-sensitive to discriminant validity problems, particularly within contexts of a high degree of construct correlation; HTMT is therefore suggested as more appropriate for assessing discriminant validity.

Based on Henseler et al. (2015), to have a valid estimation, the HTMT value should not exceed 0.90 to ensure the discriminant validity of two reflective constructs.

The examination of Table 3 above suggests that all correlation coefficients are below 0.90 and exhibit acceptable discriminant validity values.

Table 3: HTMT Result

4.3. Structural Model

After establishing the measurement model, it is time to calculate the structural (inner) model. Hair et al. (2021) mention that the structural or inner model defines the relationship between the latent variables. It means that to assess whether the proposed model fits the observed data, one must utilize the Goodness-of-fit index, R², and Q² (Chin et al., 2008).

Table 4: Goodness of Fit

The R² values indicate that interactivity and professionalism can explain 36,2% trust in the seller and 24,2% in the platform. However, trust in the seller and platform can explain the purchase intention of up to 48,3%. These R² values indicate that the model has moderate predictive ability for these constructs. Moreover, Q² measures the model's predictive performance with the help of the Stone-Geisser criterion. Table IV indicates that the Q² statistics are positive, proving that the model helps predict the constructs. GoF is a Global fit measure in PLS-SEM that involves the measurement model and the structural model. Given a GoF of 0,602, one can deduce that the model fits well, which means that a considerable proportion of the variance is explained.

4.4. Path Analysis

Path Analysis is used to analyze the hypothesis of the relationship of the variables. In analyzing the data, a significance level of 5% is utilized to decide whether the t-value is more than 1,96 and the p-value is less than 0,05. As stated by Hair et al. (2021), the hypothesis is positive if it has a path coefficient > 0 x < 1.

The findings of the path analysis reveal various notable associations between the studied variables in this research. These results show that interactivity and professionalism positively impact the trust in the seller and the platform (Interactivity: H1a: t = 4.386, p < 0.01; H1b: t = 5.073, p < 0.01, respectively). However, interactivity does not have a statistically significant effect on the purchase intent directly (β = 0.013, p = 0.84). In contrast, professionalism has a statistically significant positive direct effect on purchase intention (β = 0.249, p < 0.01).

In addition, the effect of both trust in the seller and trust in the platform on purchase intention are significant, with β= 0.337 (t = 4.205, p < 0.01) and 0.242 (t = 3. 373, p < 0.01), respectively.

The indirect effects describe the relationship between interactivity, professionalism, and purchase intention through trust in the seller and the use of the online platform. The mediating analysis also shows that the indirect effects from interactivity through trust in the seller to purchase intention are significant (β = 0.077, p < 0.01). Likewise, the path from professionalism to trust in the seller's purchase intention is substantial (β = 0.146, p < 0.01). Also, trust in the platform mediates the effect of interactivity on purchase intention (t = 2.019, p < 0.01, R² = 0.076) as well as professionalism (t = 2.078, p < 0.05, R² = 0.057).

Table 5: Hypothesis Test Results

Note: *) Not accepted

This indicates that trust in the seller is a significant mediating variable that fully mediates the impact of professionalism and interactivity on purchase intention. This suggests that the impact of these variables on purchase intention is entirely accounted for by the seller's trustworthiness, which wholly accounts for the impact of these variables on purchase intention. However, these variables still have a substantial direct effect on purchase intention, indicating that while trust in the platform partially accountsfor the influence, it does not fully explain it. Partial mediation concerns trust in the platform's ability to mediate the relationship between professionalism, interactivity, and purchase intention.

Figure 1: The Structural Model

4.5. Discussion

Therefore, this study's findings support the importance of trust in mediating the relationship between interactivity and professionalism toward purchase intentions in live-streaming commerce. Altogether, we have explained the direct and indirect effects of relationships affecting consumer behavior in thisfast-moving retail context through path analysis by SmartPLS.

Surprisingly, the results reveal that interactivity does not directly affect purchase intention. This finding departs from the existing work that posits a direct relationship between interactive features and consumers’ subsequent behaviors. (Ngo et al., 2023). Nevertheless, this finding aligns with the research conducted by Zhong et al. (2022), which found that interactivity does not directly impact purchase intention. Instead, it exhibits an indirect influence through the formation of trust in the seller and trust in the platform. The mediated model (Interactivity → Trust in Platform → Purchase Intention, and Interactivity → Trust in Sellers → Purchase Intention) provides evidence that the interactivity impacts on purchase intentions are mediated by trust. This has revealed that though interactivity cannot predict purchase, it is crucial in establishing trust that leads to live-streaming decision-making. In line with McLean et al. (2020), elements like live chats, real-time replies and demonstrations, and virtual product tours enhance the customers’ trust in the shopping process, thus indirectly increasing purchase intentions.

The professionalism factor shows that professionalism has a direct positive impact on purchase intentions, asit also suggests in other studies that store appearance and manners in an online retail context are crucial. Also, professionalism has a significant effect on enhancing trust in the platform and sellers. The mediated effects (Professionalism → Trust in Platform → Purchase Intention, and Professionalism → Trust in Sellers → Purchase Intention, also support the role of trust as a mediator, which strengthens the relationship between professional behaviors and purchase intentions. This corresponds with the studies by Chen et al. (2020), who posited that knowledgeable and courteous professional conduct affected consumers’ trust and purchase intent.

Results of the mediation analysis show that trust in the platform and trust in the sellers are two of the most influential factors that correlate with the intentions to purchase. Analysis of the results showed that trust in the platform has a positive influence on purchase intentions as well as trust in sellers. This is in line with Ma et al. (2022), who highlighted that safe and smooth user encounters are achieved through the help of reliable platforms. Additionally, the effect of trust in sellers issomewhat higher than trust in the platform, which implies that personal trust in the seller, who acts as an intermediary and interacts with buyers directly, may play a more significant role in consumers’ decisions. This is supported by the study conducted by Zhao et al. (2021), where they noted that trust in individual sellers plays a significant role in online purchase intentions because it directly impactsthe perceived risks that affect consumers’ confidence.

Theoretical Implications. In line with the above literature review, the findings of this study enrich the knowledge base by applying the trust mediation model to demonstrate how interactivity and professionalism affect purchase intentions through trust in live-streaming commerce. In doing so, the research draws out trust in the platform and trust in the sellers as two distinct mediating factors, which offer a complex picture of consumers’ behavior in online markets. It highlights the indirect influence of interactivity on purchase intentions through the mediating variable of trust, explaining the significance of the interactive features in developing the trust necessary for online purchases, hence expanding the interactivity-trust-purchase intention model (Yang et al., 2023). Moreover, the substantial direct and indirect impact of professionalism underlines the effects of professional conduct in online retailing, thus expanding the understanding of the literature on service quality and consumer trust concerning a traditional and an electronic setting (Chen et al., 2020). The comparison of trust in the platform and trust in sellers provides valuable information about the role of such types of trust in consumer decision-making concerning multi-vendor platforms and marketplaces and contributes to the development of the stream of research associated with the trust dynamics in the online transactions (Ma et al., 2022; Zhao et al., 2021).

Implicationsfor Practice. The findings of this study offer valuable insights for practitioners in live-streaming commerce. Enhancing interactivity: Streamers should actively engage with viewers through live interactions, Q&A sessions, and personalized responses. This dialogue fosters audience engagement, nurtures a sense of community, and builds trust. Maintaining professionalism: Streamers must demonstrate it through structured presentations, extensive product knowledge, and dependable customer service. Streamers can significantly improve their presentation abilities and product expertise through training programs. Building Trust in Platforms: Platforms must prioritize robust security protocols, effective customer support, and a smooth user interface. Allocating resources to these areas might greatly enhance consumer confidence and satisfaction. Establishing a Trustworthy Environment: It is crucial to prioritize creating an atmosphere of confidence in both the seller and the platform. Employing this two-pronged strategy can reduce perceived risks and boost consumer trust, ultimately resulting in increased purchase intentions. Customer apprehension often arises around the possible postponement in product delivery or the probability of obtaining faulty merchandise. An optimal distribution logistics system mitigates these concerns, minimizing purchase barriers and increasing the likelihood of successfully completing a transaction. Crucial to thistrust isthe efficiency and reliability of the distribution network. Consumer confidence in their goods' punctual and impeccable delivery enhances their faith in the vendor and the broader e-commerce distribution system.

Limitations and Future Research. Although this present study offers useful information, it also has limitations. The cross-sectional design limits the possibility of inferring causation from the results obtained. Future research may build upon these ideas in different ways, one of which is to research trust over time using longitudinal research designs. At the same time, increasing the sample size and including other demographic categories may increase the external validity of the research. Further, studies could look at other possible mediator variables like perceived value or enjoyment that could give a richer perspective on consumers’ behavior in live streaming commerce. For instance, research by Wu and Huang (2023) postulatesthat perceived value and enjoyment also significantly influence online purchase intentions and should be further explored.

5. Conclusion

This research focused on understanding how the interactivity and professionalism of live-streaming e-commerce affect consumer trust and purchasing behavior, given the case of Shopee Live in Indonesia. Using the S-O-R model, we examined the mediating roles of trust in sellers and platforms, respectively.​​​​​​​

The research results show that interactivity increases trust in sellers and platforms and does not directly affect the purchase intention. On the other hand, professionalism has a positive relationship with both trust variables and purchase intentions. The findings also revealed that trust in sellers played a more distinctive role than trust in the platforms, which underlined the importance of seller trust for consumers’ purchasing decisions.

The study's findings underscore the need to enhance the interactive and professional features of live-streaming distribution channels to address the problem of consumer trust. Efficient distribution logistics are crucial for strengthening this trust and guaranteeing that customers receive their goods promptly and reliably. These findings emphasize the need for prompt communication, suitable conduct, and strong logistical systems to establish a trustworthy and attractive customer shopping environment. Improving distribution efficiency decreases perceived risks and increases the intention to purchase, thereby supporting live-streaming distribution platforms' long-term viability and expansion.

Appendix