Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9, 671-681 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i9.9948 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 10 June 2025; Revised: 1 August 2025; Accepted: 4 August 2025; Published: 12 September 2025 * Correspondence: 22010281@siswa.unimas.my How does E-commerce live streaming affect Chinese consumers' purchase intention Mengjia Qi1*, Janifer Anak Lunyai2 1,2Faculty of Economics and Business, Universiti Malaysia Sarawak (UNIMAS), 943000 Kota Samarahan, Malaysia; 22010281@siswa.unimas.my, 472981841@qq.com (M.Q.) ljanifer@unimas.my (J.A.L.). Abstract: Among many marketing models, e-commerce live streaming has rapidly become a mainstream commercialized marketing approach by integrating consumer attributes of e-commerce with the rapid user acquisition capabilities of live streaming. Although e-commerce live streaming is continually promoted in practice, its effect on online consumption intentions still lacks sufficient theoretical and empirical support. Understanding how e-commerce live streaming influences online purchasing intentions can provide valuable references and suggestions for e-commerce websites and sellers to maximize the marketing potential of live streaming, thereby enhancing market competitiveness. Additionally, this understanding can expand research on the commercialized marketing mode of live streaming, offering a solid theoretical foundation. This study focuses on e- commerce live streaming as the research subject, exploring its impact on consumers' online purchase intentions. It considers the tripartite interaction system formed by consumers, sellers, and live streaming platforms. Based on socio-technical theories, the study analyzes in depth how the technical features and service quality of e-commerce live streaming influence purchase intentions. Data for this research was obtained through a questionnaire survey. Keywords: E-commerce live streaming, Purchase intention, Service quality, Socio-technical theory, Technical feature. 1. Introduction With the rising competition in online retail and growing consumer demands for a better online shopping experience, current marketing models struggle to sustain and attract consumers. Consequently, an increasing number of e-commerce platforms and sellers are introducing new marketing strategies to adapt to the evolving business landscape [1]. E-commerce live streaming has emerged as a popular marketing strategy, blending the interactive nature of live streaming with the consumer engagement of e-commerce. Live streaming has emerged as a significant global social and economic trend in recent years, providing users with essential information and interactive experiences. Major social networking platforms like Facebook, YouTube, and Twitter have incorporated live streaming features since 2017. The Interactive Advertising Bureau's 2024 report indicates that over 71.2% of Internet users have engaged with live streaming, with 47% continuing to watch real-time content [2]. For e-commerce sellers, it is both a development opportunity and a challenge. Understanding consumers' online purchasing intentions in the context of e-commerce live streaming is crucial. This study focuses on e-commerce websites and examines which elements of live streaming sellers can use to attract consumers and drive purchasing [3]. Based on the above background, this study investigates the impact of e-commerce live streaming on consumer purchase intention. Utilising socio-technical theory, it examines technical and social factors to reveal how live streaming technical feature and service quality affect consumer intention. 672 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 671-681, 2025 DOI: 10.55214/2576-8484.v9i9.9948 © 2025 by the authors; licensee Learning Gate This study expands the research scope of evaluating the commercial marketing model of live streaming. Based on the theoretical framework and practical research of existing digital marketing models, this study analyses the commercial marketing model and the impact of live streaming in the current online retail market. The theoretical hypothesis framework is verified by combining practical data. Based on the analysis of the core interactive system of e-commerce live streaming, this study identifies key components that may enhance business performance and conducts empirical research. The findings affirm the critical marketing role played by live streaming in enhancing sales on e-commerce platforms [4]. The findings supplement the theoretical basis of the success of e-commerce live streaming marketing practice and qualitative research, enriching the research implications of digital marketing theory and live streaming business models. The findings can help e-commerce platform sellers establish live streaming marketing and improve market competitiveness through live streaming model innovation. By analysing the development of e- commerce live streaming and the rise in product sales following its introduction on China’s largest e- commerce platform, this study helps online sellers understand mainstream marketing models in today’s online retail environment and supports e-commerce growth amid intense market competition [5]. Sellers should improve their concept and awareness of live streaming marketing and meet their own urgent needs to improve online marketing effects by customising live streaming modes. Distinguishing product types can accurately position the focus of live streaming content and maximise marketing efficiency through live streaming model innovation. 2. Literature Review 2.1. E-commerce Live Streaming Technical Feature In the context of online retail, technical feature of e-commerce refers to how retailers design different characteristics through website content and structure, thereby increasing online consumers' positive emotions and intentional responses. This not only reflects consumers' perception of the website's objective attributes, but also their perception of its subjective attributes [6]. Extensive literature has demonstrated that the technical feature of websites significantly affects consumers' online intentions, such as their intentions for continuous use, purchase intentions, and participation intentions. Since 1996, there has been a growing reliance on e-commerce websites for online transactions, with much research focusing on technical feature [7]. Their study highlighted two crucial aspects of the technical environment: information-task matching and visual appeal. These aspects positively affect consumers' co-creation experiences, including learning value, social integration value, and hedonic value, influencing future participation intentions [8]. 2.2. E-commerce Live Streaming Service Quality E-commerce service quality refers to a specific type of service quality in the context of e-commerce. E-commerce service quality relates to consumers' overall judgment and evaluation of service delivery in virtual markets [9]. E-commerce service quality refers to whether various business activities carried out through the network platform can be efficient and convenient, along with the degree of consumer trust and satisfaction [10]. E-commerce service quality is the result of consumers evaluating the quality of the interaction process and the quality of the outcome. Among them, the interactive interface used by consumers who are not limited to computer networks, and the service content required is not limited to online shopping [11]. 2.3. Purchase Intention Purchase intention as the subjective probability that consumers will buy a particular product or service, shaped by their attitudes, evaluations, and other factors. They perceive purchase intention as a key predictor of consumer intention [12]. Purchase intention forms the foundation of consumer purchase intention [13]. Purchase intention develops when consumers collect information based on 673 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 671-681, 2025 DOI: 10.55214/2576-8484.v9i9.9948 © 2025 by the authors; licensee Learning Gate their experiences, preferences, and external factors, which informs their decision-making process regarding a purchase [14]. 2.4. Socio-Technical Theory Socio-technical theory offers a framework for understanding how new technologies can support and enhance social interactions while improving coordination in both work and personal life [15]. Introduced by Cherns [16] this theory emphasises the integration of technological and social factors. Firstly, socio-technical theory explains user information sharing and donation intention in social media. The technical feature of individual users’ intention to use Weibo and the impact of network factors on users’ psychology and intention through a socio-technical system framework [17]. Considered social commerce to be a socio-technical system [18]. Finally, socio-technical theory explains the intention of users of other new technologies and organisational forms. employees’ organisational citizenship intention can be regarded as social factors, and service innovation and improvement as technical factors, which not only have a direct impact on the value perception of services, but also ultimately affect satisfaction, sustainability and Intention to use [19]. 3. Research Hypotheses 3.1. E-commerce Live Streaming Technical Feature and Purchase Intention 3.1.1. Real-time interactivity and Purchase Intention A significant body of research confirms that the technical feature of websites impact consumers' online intentions, such as willingness to continue using, willingness to purchase, and willingness to participate. interactive real-time features of a website can create positive emotions for consumers and therefore increase their time spent browsing the website and willingness to purchase online [20]. Three technical features of a website—readability, real-time capabilities, and information richness—impact users' willingness to keep using the site. Based on the above, this study proposes the following hypothesis. H1: Real-time interactivity positively affects purchase intention. 3.1.2. Perceived Proximity and Purchase Intention Research on the perceived proximity of technical feature of social business websites is also relatively mature. The impact of interactive and social features of social media on users' online experience and willingness to acquire virtual goods from the perspective of consumers. The study found that the three features—active control, two-way communication, and perceived proximity—have differing degrees of positive effects on cognitive involvement, clear sense involvement, and mind-flow experience [21]. These features increase users' purchasing intention and mind-flow experience to varying degrees, which further improves users' purchasing intention. How technical feature surrounding social commerce influence consumers' virtual experiences, focusing on social support. Their findings revealed that perceived proximity, perceived personalisation, and perceived sociability affect the virtual experience of social support and social presence. Based on the above, this study proposes the following hypothesis. H2: Perceived proximity positively affects purchase intention. 3.1.3. Perceived Authenticity and Purchase Intention During a live stream, consumers can post comments at any time, which are immediately visible to both the seller and other viewers, fostering synchronised communication and enhancing perceived authenticity [22]. Additionally, live streaming offers genuine product information and behind-the- scenes sales scenarios, with its spontaneous and unedited nature. This contributes to higher perceived authenticity compared to pre-recorded videos or static website images [23]. The integration of real- time comments within the live video interface helps create a sense of spatial perceived authenticity by allowing consumers to perceive the presence of others. H3: Perceived authenticity positively affects purchase intention. 674 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 671-681, 2025 DOI: 10.55214/2576-8484.v9i9.9948 © 2025 by the authors; licensee Learning Gate 3.2. E-commerce Live Streaming Service Quality and Purchase Intention 3.2.1. Information Quality and Purchase Intention Consumers with stronger brand connections exhibit a greater impulse purchase intention when they receive negative information about a brand; those with a weaker brand connection showed inhibition in their impulse purchase intention when they received negative information about a brand. Additionally, several scholars have noted that the quality of information provided by anchors is a crucial factor influencing consumer intention when examining anchor traits [24]. The characteristics of anchors as a source of information can impact consumers' trust, intention, and other aspects. Based on the above, this study proposes the following hypothesis. H4: Information quality positively affects purchase intention. 3.2.2. Interaction Quality and Purchase Intention Interaction is among the most common phenomena in interpersonal communication. Interaction quality is playing an increasingly important role in marketing, public service, and other fields, where the role of interactions is receiving more scholarly attention. Interaction quality between employees and customers is an important form of service. It is also the basis of customers' perceived service value, service quality and satisfaction. The interaction between employees and customers has a positive impact on employee work efficiency and customer satisfaction, among other aspects [19]. Interaction quality within communities into informational, interpersonal, and human-computer interactions, indicating that these three types of interactions play a significant role in fostering consumer loyalty. Research on interaction has amply demonstrated that interaction quality affects user satisfaction, loyalty, and other aspects, which, in turn, impact consumers' purchase intention. Based on the above, this study proposes the following hypothesis. H5: Interaction quality positively affects purchase intention. Figure 1 shows that the framework comprises two main structures, namely, the independent and dependent. The independent variable consists of two constructs: e-commerce live streaming technical feature and service quality. The dependent variable is purchase intention. Figure 1. Conceptual framework. 675 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 671-681, 2025 DOI: 10.55214/2576-8484.v9i9.9948 © 2025 by the authors; licensee Learning Gate 4. Methodology 4.1. Survey Instrument This study empirically tested the research model through a survey questionnaire. Based on a review and synthesis of existing literature, a theoretical model was developed to examine how e-commerce live streaming technical feature and service quality influence consumers' purchase intention. To assess the impact of e-commerce live streaming technical feature and service quality on consumers' purchase intention, the questionnaire measures several dimensions. Technical features include real-time interactivity, perceived proximity, and perceived authenticity. For service quality, the questionnaire evaluates dimensions such as credibility, usefulness, and vividness of information quality; responsiveness, real-time, and empathy of interaction quality. 4.2. Measures The questionnaire is divided into three sections as follows. Introduction and Screening: This section provides an overview of the questionnaire and includes screening questions to ensure respondents have relevant experience. A specific question, such as “Do you have experience purchasing goods through live streaming e-commerce platforms?” is used to filter out those without such experience. Demographic Information: This section collects basic respondent information, including gender, age, education level, monthly income, occupation, and frequency of engaging with e-commerce live streaming. Variable Measurement: This section comprises detailed questions that measure the variables of interest. Respondents rate their agreement with each statement on a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). 5. Results and Discussion 5.1. Sample Characteristics Table 1 shows the basic characteristics of the respondents counted.Gender of the survey sample. Among survey respondents, 45.0% were male and 55.0% were female who had watched or purchased on e-commerce live streaming platforms, representing more female than male respondents. Nearly 80% of the respondents in the study sample were under the age of 35, with approximately 50% falling between the ages of 19 and 30. This indicates that the respondents comprised a large portion of younger age groups. Based on the China Internet Network Information Center (CNNIC) [25] audience with the highest participation rate for live shopping is younger generations under the age of 30. Specialist and undergraduate qualifications accounted for more than 70% of the total number of respondents, with only a few having high school or lower qualifications. This finding suggests that e-commerce live shopping as a mode of consumption can be quickly accepted by the highly educated group. In this survey, the proportion of students was the largest, followed by company employees and freelance professionals. Due to the lower income levels of students, most respondents reported a monthly income of less than 3,000 RMB. The distribution of occupation and income aligns with the overall age demographics, ensuring a reasonable and representative sample structure. Most respondents engage with live streaming at least once a week or several times a week. Given its entertainment value, some individuals watch at a higher frequency, reaching once a day or even multiple times a day. The statistical distribution indicates a balanced proportion of both frequent and infrequent viewers, ensuring the survey results are reasonable and representative. 676 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 671-681, 2025 DOI: 10.55214/2576-8484.v9i9.9948 © 2025 by the authors; licensee Learning Gate Table 1. Demographic Variables for Samples. S/N Demographic Variables Frequency Valid % (N = 493) I Gender Male 222 45.0 Female 271 55.0 2 Age under 18 years old 14 2.8 19 to 25 years old 198 40.2 26 to 30 years old 43 8.7 31 to 35 years old 128 26.0 36 years old and above 110 22.3 3 Education Level High school and below 45 9.1 Specialist 156 31.6 Undergraduate 230 46.7 Graduate and above 62 12.6 4 Monthly Income below 3000 RMB 150 30.4 3000 to 4999 RMB 132 26.8 5000 to 7999 RMB 113 22.9 more than 8000 RMB 98 19.9 5 Occupation Student 237 48 Company employee 133 27 Institution worker 37 7.5 Freelance worker 54 11.0 Other 32 6.5 6 Viewing Frequency Several times a day 47 9.5 Once a day 55 11.2 Several times a week 181 36.7 Once a week 98 19.9 Several times a month 67 13.6 Once a month 45 9.1 5.2. Measurement Model Table 2 shows that all the items for constructs have individual factor loadings that are higher than the recommended level of 0.7. Furthermore, the overall constructs have AVE values greater than 0.5 and high composite reliability, with values exceeding 0.7. Therefore, the results show evidence of convergent validity (CV). 677 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 671-681, 2025 DOI: 10.55214/2576-8484.v9i9.9948 © 2025 by the authors; licensee Learning Gate Table 2. Convergent Validity. Variables Items Outer loadings AVE Composite reliability Real-time interactivity (RI) RI1 0.871 0.752 0.938 RI2 0.84 RI3 0.877 RI4 0.847 RI5 0.863 Perceived Proximity (PP) PP1 0.854 0.755 0.925 PP2 0.874 PP3 0.866 PP4 0.849 Perceived Authenticity (PA) PA1 0.884 0.774 0.911 PA2 0.887 PA3 0.878 Credibility (CRE) CRE1 0.865 0.729 0.915 CRE2 0.867 CRE3 0.847 CRE4 0.838 Usefulness (USE) USE1 0.873 0.776 0.933 USE2 0.883 USE3 0.873 USE4 0.885 Vividness (VIV) VIV1 0.866 0.756 0.939 VIV2 0.861 VIV3 0.862 VIV4 0.887 VIV5 0.861 Responsiveness (RES) RES1 0.85 0.731 0.916 RES2 0.846 RES3 0.867 RES4 0.865 Real-time (RTI) RTI1 0.835 0.726 0.93 RTI2 0.862 RTI3 0.841 RTI4 0.843 RTI5 0.855 Empathy (EMP) EMP1 0.903 0.77 0.931 EMP2 0.857 EMP3 0.891 EMP4 0.865 Purchase Intention (PI) PI1 0.89 0.783 0.916 PI2 0.878 PI3 0.887 Moreover, discriminant validity is assessed using the AVE, which should exceed the squared correlations between constructs. Table 3 shows that the AVE for each construct surpasses its corresponding squared inter-scale correlation, confirming discriminant validity. 678 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 671-681, 2025 DOI: 10.55214/2576-8484.v9i9.9948 © 2025 by the authors; licensee Learning Gate Table 3. Discriminant Validity. CRE EMP PA PI PP RES RI RTI USE VIV CRE 0.854 EMP 0.452 0.879 PA 0.446 0.479 0.883 PI 0.478 0.498 0.442 0.885 PP 0.471 0.447 0.421 0.44 0.861 RES 0.517 0.434 0.453 0.483 0.4 0.857 RI 0.462 0.524 0.48 0.472 0.423 0.411 0.86 RTI 0.418 0.452 0.438 0.421 0.441 0.386 0.491 0.847 USE 0.419 0.413 0.482 0.473 0.415 0.408 0.382 0.431 0.878 VIV 0.515 0.398 0.419 0.469 0.538 0.462 0.471 0.446 0.43 0.868 The variance inflation factor (VIF) is used to assess the extent of multicollinearity in a regression analysis. If the VIFs of the inner model, obtained through a full collinearity test, are 3.3 or lower, the model can be regarded as free from common method bias (CMB). Since information quality, and interaction quality are formative indicators, it is necessary to test their dimensions for multicollinearity. Table 4 presents the variance inflation factor (VIF) results for assessing the second-order formative latent variables in this study. The findings indicate that all VIF values range from 1.259 to 1.645, confirming that common method bias (CMB) is not a concern. Therefore, this study validates that information quality, interaction quality, social support, and swift relationship can be conceptualized as formative indicators. Table 4. Formative Variance Inflation Factors Results. Formative Construct Items Outer Weight t-value VIF Information Quality CRE 0.412 6.281 1.464 USE 0.431 5.98 1.346 VIV 0.401 5.97 1.476 Interaction Quality RES 0.417 6.461 1.338 RTI 0.364 5.857 1.32 EMP 0.482 7.632 1.394 5.3. Structural Model We tested the fit of the proposed model using SEM. The results showed a good model fit. Table 5 shows that the SRMR for this study was 0.037, well below the 0.08 threshold, confirming a good model fit. Furthermore, the normative fit index (NFI) was 0.885, where values closer to 1 indicate a better fit. The Q² values PI (0.511), all of which exceed 0, thereby confirming the predictive relevance of the inner model. These findings validate the model’s predictive power and overall acceptability. The R² for PI was 0.568, indicating that the model explains all variance in the construct. This confirms that the models in this study are valid and accepted. Table 5. GoF (SRMR-NFI). Model fit Saturated Model Estimated Model SRMR 0.037 0.054 NFI 0.885 0.875 5.4. Effects Testing Table 6 presents the path coefficients and hypothesis testing results for the direct relationships in H1–H5. Path coefficients range from -1 to +1, with values closer to +1 indicating strong positive associations. While high coefficients generally suggest statistical significance, this study further verifies significance through bootstrapping. A path coefficient is considered significant if it meets the 0.05 679 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 671-681, 2025 DOI: 10.55214/2576-8484.v9i9.9948 © 2025 by the authors; licensee Learning Gate threshold. This requires a t-value above 1.96 and a p-value below 0.05 in a two-tailed test. Therefore, hypothesis testing supports the model for H1 to H4, but it does not support H5. Real-time interactivity positively affects purchase intention. Perceived proximity positively affects purchase intention. Perceived authenticity positively affects purchase intention. Information quality positively affects purchase intention. Interaction quality positively affects purchase intention. Table 6. Testing of Direct Effect Hypotheses. Hypotheses Paths Path coefficient Mean Standard Error T-statistics P-values H1 RI→PI 0.132 0.130 0.063 2.079 0.038* H2 PP→PI 0.120 0.119 0.054 2.211 0.027* H3 PA→PI 0.133 0.132 0.062 2.147 0.032* H4 IQ1→PI 0.136 0.138 0.068 2.006 0.045* H5 IQ2→PI 0.047 0.052 0.072 0.659 0.510 Note: Significant at 0.01** Significant at 0.05*. 6. Conclusion E-commerce live streaming technical feature (real-time interactivity, perceived proximity, and perceived authenticity) positively affects purchase intention. According to socio-technical theory, the impact of new technologies should be evaluated from both technical and social perspectives. As a growing social media format, live streaming has a significant effect on consumer experiences in e-commerce, where both dimensions influence engagement and purchasing decisions. From a technical standpoint, e-commerce live streaming is characterised by real-time interactivity, perceived proximity, and perceived authenticity. Real-time interactivity enables instant communication between hosts and viewers, allowing for immediate responses and engagement. Perceived proximity cultivates a sense of closeness, enhancing trust and connection. Perceived authenticity reinforces credibility by making content appear genuine and unscripted. E-commerce live streaming service quality (information quality and interaction quality), information quality positively affects purchase intention, and interaction quality negatively affects purchase intention. In socio-technical theory, social factors primarily emphasise human behaviour. A review of previous studies on live streaming highlights that consumers are primarily driven by information search and social interaction. Additionally, information quality and interaction quality are widely acknowledged as critical dimensions of service quality. Based on these insights, this study incorporates these two factors to evaluate the quality of e-commerce live streaming services. As a new form of social media and online marketing, e-commerce live streaming differs significantly from traditional e-commerce in terms of information delivery and interaction formats. Existing measurement scales for information and interaction quality fail to capture their unique characteristics. To address this gap, this study redefines and validates the variables and question items for these dimensions by integrating established scales with the specific feature of live commerce. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). https://creativecommons.org/licenses/by/4.0/ 680 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 671-681, 2025 DOI: 10.55214/2576-8484.v9i9.9948 © 2025 by the authors; licensee Learning Gate References [1] C. Qing and S. Jin, "What drives consumer purchasing intention in live streaming e-commerce?," Frontiers in Psychology, vol. 13, 2022. https://doi.org/10.3389/fpsyg.2022.938726 [2] J. H. Yu and Y. 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