The Consequences of Servicescape of Live-streaming on Luxury Goods Purchase Intention

The Consequences of Servicescape of Live-streaming on Luxury Goods Purchase Intention | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Consequences of Servicescape of Live-streaming on Luxury Goods Purchase Intention Junying Yu, Jing Gao, Jiarui Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4483569/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the influence of the servicescape in live-streaming on consumer buying behavior regarding luxury goods. We examine the mediating role of perceived value and consumer trust, considering the prior luxury purchasing experience of consumers. Additionally, we explore the interplay between the live-streaming servicescape and conspicuous consumption in shaping the perceived luxury value. Our findings reveal intriguing results: Perceived value mediates the relationship between servicescape and purchase intention for consumers without prior luxury purchase experience, while consumer trust does not exhibit a mediating effect. Conversely, for individuals with prior luxury purchase experience, the mediating effect is reversed. Furthermore, we identify the interplay between the live-streaming servicescape and conspicuous consumption as a moderator of perceived value. These insights indicate that luxury brands can strategically enhance their live-streaming servicescape to cater to different consumer segments and strengthen their marketing endeavors. Overall, this study contributes valuable insights into the servicescape theory in the context of live-streaming. Live-streaming Servicescape Luxury brands Conspicuous consumption Consumer trust Perceived value of luxury Figures Figure 1 Introduction Live-streaming can provide consumers with richer information through real-time videos 1 and has experienced a remarkable surge in recent years, revolutionizing the way products are sold and promoted 2 . Live-streaming has emerged as a new digital marketing mode 3 that seamlessly integrates live-streaming platforms with e-commerce, offering customers an instant and interactive servicescape 4 . Live-streaming e-commerce combines real-time interaction, authenticity, and social engagement 5 , resulting in increased consumer trust, brand exposure, and sales conversion 6 . As technology continues to advance, and consumer preferences evolve, live commerce is expected to further flourish, offering both brands and consumers new opportunities and a more immersive shopping experience 7 . Live-streaming e-commerce has emerged as a transformative force in the retail industry. The market for live-streaming e-commerce has been particularly gratifying for luxury brands seeking to transition to online retailing in China, especially during the economic winter brought on by the COVID-19 pandemic. In an attempt to harness data flows and realize profit transformation via live-streaming technology, luxury brands such as Louis Vuitton, Gucci, Chanel, and Hermès have made significant strides in online marketing and digitalization 8 . The adoption of live streaming by affordable luxury brands like Michael Kors, Coach, and Kate Spade has also attracted significant industry attention 9 . However, it is still a challenge for luxury brands to unlock the power of live e-commerce, luxury brand marketers remain cautious in testing waters in this previously unexplored market, due to lacking in-depth understanding of the impact mechanism of the live-streaming servicescape 10 , 11 . Luxury brands are known for their creativity, uniqueness, artistry, accuracy, modernity, high quality, and premium 12 – 15 . These characteristics provide consumers with a sense of satisfaction for possessing exclusive goods and provide psychological satisfaction in the form of high status, high prestige, and social concern 16 , 17 . In a highly dynamic technological change background, new demands are placed on Luxury brands’ marketing and communication leveraging digital and social media technologies and AI 18 . However, although live-streaming e-commerce is becoming increasingly popular among luxury brands 9 , the results of live-streaming by luxury brands remain mixed due to various uncertain factors concerning the adopted mode of live-streaming e-commerce. The perceived luxury value is usually inseparable from the servicescape of physical stores 19 . The value perception and trust building of luxury goods are often easier to achieve with a physical store experience 20 . For instance, the excellent materials and exquisite craftsmanship of luxury goods are easier to examine in physical stores 21 , where consumers can trust the brand. Physical luxury store service also plays an important role as the gatekeeper and instructor of the luxury class, which helps in building customer trust and loyalty. Online shopping for luxury goods poses higher risks and worries due to safety and trust issues, and the lack of product inspection 22 , especially in live-streaming. Therefore, obtaining a thorough understanding of consumers' reactions to the live-streaming of luxury goods, as well as a grasp of the mediating effect mechanism of the perception and the trust of luxury goods value, is one of the most important challenges faced by luxury marketers. To delve into how the live-streaming of luxury goods influences consumer purchase intention, we utilize servicescape theory research. Servicescape refers to the environment in which customer and employee interactions occur, encompassing all tangible elements that facilitate service delivery 23 . With the advent of internet technology, servicescape theory has been extended to online channels, giving rise to e-servicescape theory 24 . Researchers have studied the characteristics of live streamers, information sources 25 , interactions, social presence 26 , and media richness of live streaming. However, given the complexity and evolving experience of the servicescape of live-streaming, and the different backgrounds of customers, it is important to determine how these factors influence purchase intention and the role of perceived value and consumer trust. Our study also draws on experiential learning theory (ELT) 27 , which suggests that customers without prior luxury purchase experience value the item's worth, while customers with such experience prioritize trust in live-streaming. This study adds to the servicescape theory and expands live-streaming research to the luxury goods category. It provides guidance and recommendations for designing the live-streaming servicescape for luxury brands. Furthermore, it presents a new framework that effectively utilizes digital technology to achieve targeted and distinctive marketing outcomes for various consumer segments. The findings of this research contribute to the understanding of the mechanisms that drive consumers' purchase intention of luxury goods in the live-streaming context. It provides valuable insights for luxury brands and marketers on how to optimize the servicescape, build consumer trust, and enhance perceived value to effectively engage consumers and drive their intention to purchase luxury goods in a live-streaming setting. Based on SOR theory, a comprehensive theoretical model is constructed (see Fig. 1 ). This framework represents three dimensions of the servicescape of live-streaming as the stimulus, consumer trust, and perceived value as organism evaluation, to pursue intention as a reaction. We formulated three hypotheses. The effects of the live-streaming servicescape on purchase intention are significant, and the purchase intention of consumers with prior luxury purchasing experience is more likely to be influenced by social factors(a), technical factors(b), and physical factors than that of consumers without such experience (H1). Consumer trust plays a more significant intermediary role between the live-streaming servicescape and purchase intention among consumers with prior luxury purchasing experience than those without it (H2). The impact of the live-streaming servicescape on purchase intention is mediated by the perceived luxury value among consumers without prior luxury purchase experience but not among consumers with such experience (H3). Conspicuous consumption serially moderates the effects of live-streaming servicescape on the perceived luxury value and purchase intention (H4). Materials and Methods Scenario and Administration Procedure To assess consumers' evaluation of perceived value and environmental trust in a live-streaming servicescape for luxury goods, along with their purchase intention, we utilized three scenario stimuli: situational recall, scenario display, and simulation methods. The questionnaire commenced with queries about a situational recall for live-streaming and an identification question to ascertain respondents' exposure to live-streaming sales. This screening process ensured that the samples possessed familiarity with the marketing approach, enabling us to obtain qualified participants with live-streaming experience. Subsequently, respondents were prompted to evaluate the social aspects of existing live-streaming tools based on their encounters. To precisely target the intended population and ensure respondents had genuine live-streaming experience, we included the question " Have you ever watched any live-streaming e-commerce?" to exclude individuals lacking exposure to live-streaming viewing. For luxury live-streaming, we presented a 20-second video extracted from a live stream on Taobao, featuring Helena, a French cosmetics brand. By utilizing a specific brand in the scenario, we aimed to ensure the video's authenticity and brand presence. Following the video, respondents were instructed to evaluate the physical factors by observing another live stream featuring a luxury product. We chose cosmetics brands as our focus since we believed they possess a higher market penetration rate and are subject to less gender influence compared to clothing brands. Before measuring consumer purchase intention, we provided a comprehensive introduction to the featured cosmetics brands and products, including their respective prices. To gauge perceptions and purchasing behaviors related to luxury goods, we included the question "Have you ever purchased luxury goods, such as shoes, bags, or accessories costing more than 1,000 yuan?" in the questionnaire. This enabled us to differentiate between consumers with and without prior luxury purchase experience. Both groups were then asked to list the luxury brands they were familiar with or had purchased. The sample size of respondents with luxury purchase experience was approximately 6:4, facilitating an effective comparative study. Additionally, the questionnaire collected demographic information, encompassing gender, income level, age, geographic location, and educational background. Measurement Items This research utilized questionnaires as a data collection method. To ensure the credibility and accuracy of the questionnaire data, various factors were incorporated from established scales, including physical factors 28 , social factors 28 , 29 , perceived luxury value 30 , consumer trust 31 , conspicuous consumption 32 , and purchase intention 31 . These factors were then tailored to fit the context of live-streaming e-commerce and combined with the item contents. All items were rated on a 7-level Likert scale (1 represents strongly disagree, while 7 represents strongly agree). Following the initial draft of the questionnaire, pretesting was conducted, and 90 groups of data were gathered, with 65 samples being valid. After analyzing reliability and validity, items with low reliability scores were adjusted. Data collection and sample We obtained samples from China to investigate the differences in attitudes towards luxury goods between consumers with and without prior luxury purchase experience during live-streaming. The study utilized a snowball sampling method to collect and compare the samples, each containing 300 data points. During the screening period, we sent well-informed invitation letters describing the objective and protocols of the study, and respondents who were willing to participate will fill in the next step questionnaire. After screening, we obtained 380 valid data points, with 232 samples having luxury purchase experience and 148 samples without. The questionnaire collection process was successful. Table 1 . presents the sample characteristics. The majority of respondents were between 18–30 years old (67.9%), with men accounting for 30.5% (116 people) and women for 69.5% (264 people) of the total, aligning with the user characteristics of China's live-streaming e-commerce. Table 1 Demographic characteristics of the sample (N = 380) characteristics Items Sample N % Gender male 116 30.5% female 264 69.5% Age 40 16 4.2% City first-tier 153 40.3% provincial capital 141 37.1% else 86 22.6% Education junior college 83 21.8% undergraduate 157 41.3% Postgraduate and above 88 23.2% else 52 13.7% Monthly income no income 28 7.4% 100001 6 1.6% Reliability, Validity test, and Common Method Variance Test The results of the reliability and validity tests show that the Cronbach's α coefficients of all variables are greater than 0.7, indicating that the overall reliability of the questionnaire is very good. Moreover, the KMO values of all variables are greater than 0.6, indicating that the scale is suitable for factor analysis. After revising and checking the scales, the minimum factor load was calculated as 0.498, and the minimum cumulative variance explained was 63.60%, both of which were acceptable, as shown in Table 2 . Table 2 Factor analysis (N = 380) Variables Code Cronbach's α KMO Minimum factor load Cumulative variance explained Social factors SF 0.928 0.925 0.572 63.60% Technique factors TF 0.898 0.871 0.739 77.9% Physical factors PF 0.893 0.871 0.643 70.14% Perceived luxury value PV 0.720 0.624 0.592 64.57% Consumer trust CT 0.876 0.825 0.588 73.00% Conspicuous consumption CC 0.937 0.890 0.683 80.07% Purchase intention PI 0.913 0.836 0.737 79.34% To address potential common method bias, Harman's single-factor test was conducted to assess the presence of a single dominant factor that could account for the majority of the variance in the data. The test revealed that the first factor explained 36.02% of the total variance. However, this percentage did not exceed the commonly accepted threshold of 50%, indicating that common method bias was unlikely to be a significant concern in our study. Ethics approval and consent to participate The study was reviewed and approved by the Science and Technology Ethics Committee of Donghua University (No. DHUEC-GL-2024-07). All methods were performed under the relevant guidelines and regulations and complied with the 1975 Helsinki Declaration on ethics in medical research. The study Informed consent was obtained from all subjects and/or their legal guardian(s). Before accessing the questionnaire, participants were informed about the purpose of this study, and that their participation was voluntary and confidential, with guaranteed anonymity and the option to withdraw at any time. Results As expected, our analysis of the overall sample, including both the basic model and the model with control variables (See, Table 3.), revealed significant and positive effects of the servicescape on purchase intentions. This implies that the live-streaming servicescape positively influences purchase intentions, regardless of whether consumers have prior experience with luxury purchases (R2=0.589, F=179.75, p<0.001). Thus, our findings generally support hypothesis H1. DV= PI Total Sample （ N=380 ） Model 1 Model 2 with Controls B (SE) T B (SE) T Constant -.151(.250) -0.604 .152(.362) 0.42 SF .277(.277) 4.190 *** .271(.067) 4.074 *** TF .431(.431) 6.489 *** .429(.067) 6.394 *** PF .301(.301) 5.393 *** .284(.057) 4.965 *** Age .003(.041) 0.068 Earning .050(.047) 1.049 Education -.055(.039) -1.403 Sex .031(.080) 0.387 City -.081(.049) -1.665 R 2 0.586 0.587 F 179.75 *** 68.28 *** Table 3. Three dimensions effect of servicescape on Purchase Intention To better comprehend the distinction between the two samples, we conducted an independent sample t-test of variables. As demonstrated in Table 4 . We have observed noteworthy differences in the effect of luxury goods purchase experience on the focal variables. These results are indicative of the fact that the impact of luxury goods purchasing experience is a crucial factor that needs to be taken into account when analyzing the influence of variables Variables Sample with prior luxury purchasing experience （ N=232 ） Sample without prior luxury purchase experience （ N=148 ） t servicescape 5.906±0.75 5.356±0.782 6.853 *** Physical factors 5.876±0.853 5.318±0.932 6.001 *** Social factors 5.905±0.835 5.346±0.886 6.209 *** Conspicuous consumption 5.606±1.215 4.655±1.356 6.936 *** Consumer trust 5.74±1.06 5.104±1.109 5.604 *** Perceived luxury value 5.801±0.84 5.164±0.795 7.364 *** Purchase intention 5.864±0.889 5.108±1.108 6.990 *** *p<0.05， **p<0.01， ***p<0.001 Table 4 . Comparison table of direct effect test Additionally, according to Table 5., we found that within the sample of consumers with prior luxury purchasing experience, the physical, social, and technical factors of the live-streaming servicescape had significant positive effects on purchase intention (R2=0.705, F=184.67, p<0.001). In the sample without experience in luxury goods purchasing, the technical factors and physical also had significant positive effects on purchase intention (t=4.311, p<0.001; and t=2.194, p0.05). These results confirm hypotheses H1a and H1c but reject H1b. Furthermore, when considering the regression analysis with control variables, the inclusion of these variables did not affect the significant effects of the three factors (physical, social, and technical) on purchase intention. Thus, the presence of control variables did not alter the impact of these factors on purchase intentions. In summary, our analysis of the overall sample, as well as the subgroups based on prior luxury purchasing experience, consistently demonstrates the significant influence of the physical, social, and technical factors within the live-streaming servicescape on purchase intentions. However, the social factor effect appears to be not significant for consumers without prior experience in luxury goods purchasing. These findings hold even when controlling for other variables, indicating the robustness of the observed effects. DV= PI with prior luxury purchasing experience（N=232） without prior luxury purchase experience（N=148） Base Model with Controls Base Model with Controls B (SE) T B (SE) T Constant -.025（.388） -0.074 1.123(.883) 1.272 CF .385（.07） 5.497 *** .120(.121) 0.985 TF .323（.067） 4.820 *** .502(.127) 3.953 *** PF .296（.061） 4.816 *** .247(.103) 2.396 * Age .060（.035） 1.706 -.103(.103) -0.997 Earning .018（.048） 0.378 .003(.107) 0.029 Education -.023（.038） -0.605 -.122(.093) -1.312 Sex -.085（.071） -1.191 .183(.184) 0.992 City -.001（.046） -0.022 -.128(.104) -1.232 R 2 0.706 0.371 F 70.28 *** 11.83 *** Table 5. Three dimensions effect of servicescape on Purchase Intention of two samples We utilized PROCESS Model 4, with 10,000 bootstrapped samples, to test for dual mediation. In the sample of individuals with luxury goods purchasing experience, the mediating effect of the perceived luxury value (B=0.07, SE=0.08, 95% CI=[-0.0953, 0.2261]) was found to be non-significant, while the mediating effect of consumer trust (B=0.28, SE=0.12, 95% CI= [0.0644, 0.5201]) was found to be significant. In the sample of individuals without prior luxury purchase experience, the mediating effect of the perceived luxury value (B=0.4025, SE=0.40, 95% CI=[0.1244, 0.6865]) was found to be significant, while the mediating effect of consumer trust (B=0.24, SE=0.24, 95% CI= [-0.0321, 0.5024]) was found to be non-significant. These results lend support to H2 and H3. According to the PROCESS Model 8 conducted by Hayes in 2018, which involved 10,000 bootstrap samples, with the three factors (physical, social, and technical) as independent variables, perceived luxury value as the mediator, and purchase intention as the dependent variable, the interaction between the three factors and conspicuous consumption did not have a significant effect on the mediator variable (perceived luxury value) and purchase intention. Specifically, only in the sample of participants with prior luxury purchasing experience, the interaction between social factors and conspicuous consumption significantly influenced purchase intention(β=-.11, SE=.03, 95% [CI]=[-.1688, -.0589]). However, this interaction had a negative moderating effect. To better understand this interaction, we conducted a floodlight analysis to observe the range of conspicuous consumption for which the effect of anxiety on purchase intention was significant. There are no statistical significance transition points within the observed range of the moderator found using the Johnson-Neyman method. As shown in Table 6., we found that as conspicuous consumption becomes stronger, the effect value gradually decreases. This suggests that in the presence of both social factors and conspicuous consumption, purchase intention may decrease. This could be because consumers in this sample place more importance on the social symbolic meaning of luxury goods rather than conspicuous consumption itself. It is important to note that although the interaction between the three factors and conspicuous consumption did not have a significant effect on the mediator variable (perceived luxury value), it does not imply that these factors do not influence purchase intention. They may still have direct or indirect effects on purchase intention through other pathways, separate from the mediator of perceived value. In conclusion, the interaction between social factors and conspicuous consumption has a significant effect on purchase intention, particularly in the sample of participants with prior luxury purchasing experience. However, this interaction has a negative moderating effect, indicating that purchase intention may decrease when both social factors and conspicuous consumption are present. CC Effect se t p LLCI ULCI 1.0000 1.1735 .1316 8.9172 .0000 .9142 1.4328 1.2857 1.1410 .1242 9.1896 .0000 .8963 1.3856 1.5714 1.1084 .1168 9.4910 .0000 .8783 1.3386 1.8571 1.0759 .1095 9.8256 .0000 .8601 1.2917 2.1429 1.0434 .1023 10.1977 .0000 .8418 1.2450 2.4286 1.0108 .0953 10.6121 .0000 .8231 1.1985 2.7143 .9783 .0883 11.0734 .0000 .8042 1.1524 3.0000 .9458 .0816 11.5855 .0000 .7849 1.1066 3.2857 .9132 .0752 12.1494 .0000 .7651 1.0614 3.5714 .8807 .0690 12.7610 .0000 .7447 1.0167 3.8571 .8482 .0633 13.4056 .0000 .7235 .9729 4.1429 .8156 .0581 14.0500 .0000 .7013 .9300 4.4286 .7831 .0535 14.6327 .0000 .6777 .8886 4.7143 .7506 .0499 15.0562 .0000 .6524 .8488 5.0000 .7181 .0473 15.1944 .0000 .6249 .8112 5.2857 .6855 .0459 14.9297 .0000 .5950 .7760 5.5714 .6530 .0459 14.2142 .0000 .5625 .7435 5.8571 .6205 .0473 13.1111 .0000 .5272 .7137 6.1429 .5879 .0500 11.7689 .0000 .4895 .6864 6.4286 .5554 .0537 10.3515 .0000 .4497 .6611 6.7143 .5229 .0582 8.9818 .0000 .4081 .6376 7.0000 .4903 .0634 7.7277 .0000 .3653 .6153 Notes: Figures in boldface highlight Johnson–Neyman points, identifying the range of hope for which the effect of anxiety on adoption intentions was significant. Table 6. Conditional effect of focal predictor at values of the moderator: Discussion First, the live-streaming servicescape has a significant positive impact on Chinese consumers' purchase intentions for luxury goods. Firstly, the physical factors such as the ambiance and aesthetics of the servicescape can play a significant role in luxury goods purchases. Luxurious and visually appealing environments can create a sense of exclusivity and enhance the perceived value of the products. For example, a high-end boutique with tasteful interior design, elegant lighting, and premium displays can create a luxurious atmosphere that aligns with the brand image, attracting customers and influencing their purchasing decisions. However, social factors such as the level of personalized customer service provided by the anchor within the servicescape can greatly impact luxury goods purchases only on the consumers with prior luxury purchasing experience. Luxury customers often expect individualized attention and assistance, and well-trained and knowledgeable anchors who can provide personalized recommendations, address customer inquiries, and offer a high level of service can contribute to a positive purchasing experience. Additionally, social interactions and peer influence can also impact luxury goods purchases. These interactions provide opportunities for customers to share their experiences, exchange opinions, and receive validation from others. Positive social interactions and the influence of peers can enhance customers' perceptions of the products, increase their confidence in purchasing, and create a sense of belonging to a luxury community. Second, in the case of consumers who have previously purchased luxury goods, the perceived value of luxury items did not mediate their purchase intention through live-streaming. However, consumer trust was found to have a significant impact on purchase intention. This indicates that these consumers already have established opinions and emotions towards luxury brands. Though live-streaming can convey perceived value, it may not necessarily increase their purchase intention. The risks and uncertainties associated with online luxury purchases can also hinder their decision-making. Therefore, establishing consumer trust through live-streaming can significantly increase purchase intention. On the other hand, for consumers who have no previous experience with luxury brands, live-streaming can enhance their perceived value of luxury items, leading to an increase in purchase behavior. Although consumer trust may not mediate their purchase intention, it can still be established through live-streaming. However, affordability also plays a role in their decision-making process. Finally, the interaction between the live-streaming servicescape and conspicuous consumption plays a certain role in regulating the perceived luxury value. This is particularly the case for consumers with luxury purchase experience, who can perceive the value of luxury goods with their conspicuous consumption motivation. However, the interaction between live-streaming servicescape and conspicuous consumption has no significant influence on purchase intention. Thus, in the online environment, conspicuous consumption is no longer a stimulus for consumers’ luxury purchasing. Theoretical contributions and managerial implications Studying luxury live-streaming service scenarios contributes to consumer behavior theory, social influence theory, technology adoption and consumer theory, marketing communication theory, and interactive media theory. On the one hand, it supplements servicescape theory and proposes novel measurement dimensions for the live-streaming servicescape, providing a new perspective on the measurement of emerging e-servicescapes and diversifying servicescape designs. Luxury live-streaming platforms facilitate social interactions among viewers, making it relevant to explore the applicability of social influence theory in the luxury domain. Studying luxury live-streaming involves examining mobile payment technologies, mobile shopping experiences, and mobile notifications, contributing to the understanding of the impact of technology on consumer behavior and decision-making processes. It provides valuable insights into the adoption and usage of mobile technologies in the luxury industry. Firstly, the physical factors such as the ambiance and aesthetics of the servicescape can play a significant role in luxury goods purchases. Luxurious and visually appealing environments can create a sense of exclusivity and enhance the perceived value of the products. For example, a high-end boutique with tasteful interior design, elegant lighting, and premium displays can create a luxurious atmosphere that aligns with the brand image, attracting customers and influencing their purchasing decisions. Secondly, the technical factors of the live-streaming servicescape, such as the usability, security, and mobile payment technology associated with live-streaming have significant effects on luxury purchases. Therefore, providing a user-friendly interface, ensuring a stable and secure live-streaming environment, and offering convenient mobile payment options enhance the overall purchasing experience, boost viewers' willingness to buy luxury products, and facilitate sales in the luxury industry. The user-friendly digital servicescape can provide a seamless and immersive online shopping experience, while also showcasing the brand's image and values. However, the social factors such as the level of personalized customer service provided by anchor within the servicescape can greatly impact luxury goods purchases only on the consumers with experience purchasing luxury goods. Luxury customers often expect individualized attention and assistance, and well-trained and knowledgeable anchors who can provide personalized recommendations, address customer inquiries, and offer a high level of service can contribute to a positive purchasing experience. Additionally, social interactions and peer influence can also impact luxury goods purchases. These interactions provide opportunities for customers to share their experiences, exchange opinions, and receive validation from others. Positive social interactions and the influence of peers can enhance customers' perceptions of the products, increase their confidence in purchasing, and create a sense of belonging to a luxury community. On the other hand, the essence of such an attempt to sell luxury goods by live-streaming is a kind of luxury democratization. Luxury firms can develop better positioning strategies for managing the luxury democratization challenge 33 . This in-depth study of the live-streaming marketing of luxury brands, by introducing the unique characteristics of luxury goods, extends the existing product categories in live-streaming research and provides references for luxury brands' digital operation and expansion. Researching luxury live-streaming services contributes to consumer behavior theory by providing empirical evidence and insights into consumer decision-making processes, motivations, and behavior patterns within the context of luxury live-streaming. It helps in understanding consumer demand for luxury products and the factors influencing their purchasing behavior during live-streaming sessions. Furthermore, this study provides practical recommendations for brands and platforms currently planning to invest in the live-streaming e-commerce industry. The most effective approach is to implement targeted and precise marketing strategies to consumers with or without prior luxury purchase experience. Firstly, for consumers with luxury purchase experience, the focus of branded online marketing should be to establish consumer trust in online platforms. To achieve this, brands can display real-life scenarios of physical stores or catwalk shows, create a soothing ambiance with music, improve interactions between live streamers and viewers, and select more comprehensive and mature platforms. Secondly, for consumers without prior luxury purchase experience, brands should concentrate on enhancing their understanding of luxury culture using live-streaming or other digital tools to increase the perceived luxury value. Finally, for both types of consumers, the role of conspicuous consumption motivation in luxury goods marketing is diminished in the live-streaming servicescape. Therefore, brands should prioritize the transmission of luxury culture and connotation rather than conspicuous value in their online marketing efforts. In contrast to traditional luxury marketing approaches, digital marketing strategies tend to de-emphasize conspicuous consumption's role. Furthermore, the impact of conspicuous consumption on purchase intention is not significant in live-streaming. Research indicates that Generation Z, which consists of digital natives, is less motivated by conspicuous consumption than other age groups. Therefore, businesses must prioritize updating the definition of luxury culture rather than making conspicuous consumption a central part of their marketing efforts. Limitations and future research Although this research has certain theoretical significance and offers practical value, it has some limitations that open several avenues for future research. Sample Representativeness: Research conducted on luxury live-streaming services may face challenges in obtaining a representative sample of luxury consumers. It can be difficult to access and recruit participants who actively engage in luxury live-streaming and make luxury purchases during such sessions. This limitation could impact the validity and applicability of the research findings. Influencer Bias: Luxury live-streaming often involves influential hosts or celebrities who endorse or promote luxury products. This influencer presence can introduce biases in consumer behavior and perceptions. Consumers may be influenced by the anchor's personal brand and opinions, making it challenging to isolate the effects of the live-streaming platform itself on consumer decision-making. Limited Long-term Effects Study: Luxury live-streaming research often focuses on immediate purchase behavior and short-term effects. Long-term effects, such as post-purchase satisfaction, brand loyalty, and repeat purchases, may be overlooked in the context of live-streaming studies. Understanding the sustainable impact of luxury live-streaming on consumer behavior requires longitudinal studies and follow-up research. Generalizability: The findings and insights derived from studying luxury live-streaming services may have limited generalizability due to the specific nature of the luxury industry and the unique characteristics of live-streaming platforms. The behaviors, preferences, and motivations of luxury consumers in a live-streaming context may not fully represent the broader population of luxury consumers or apply to different cultural contexts. The future of studying luxury live-streaming service scenarios holds immense potential: In the context of luxury brand live-streaming, servicescape encompasses the visual and auditory elements, including the set design, lighting, background music, and overall presentation of the live-streaming session. This research examines how the servicescape of luxury live-streaming influences consumers' perception of the luxury brand and their purchase intention. Researchers can explore the role of aesthetics, ambiance, and sensory elements in creating a desirable and immersive experience for consumers. As the field of data analytics continues to evolve, researchers can analyze the vast amount of data generated during luxury live-streaming sessions. By employing machine learning, artificial intelligence, and natural language processing, researchers can gain deeper insights into consumer behavior, sentiment analysis, and predictive modeling, enabling more accurate and targeted strategies for luxury brands. Luxury live-streaming platforms have the potential to offer personalized and customized experiences for individual viewers. Future research can focus on understanding consumer preferences, interests, and purchasing patterns to deliver tailored content, recommendations, and interactive features. This personalized approach can enhance consumer engagement, and satisfaction, and ultimately drive higher conversion rates. The integration of VR and AR technologies into luxury live-streaming holds significant promise. Researchers can explore the application of these immersive technologies to create virtual showrooms, try-on experiences, and interactive product demonstrations. By enhancing the sensory and experiential aspects of luxury live-streaming, VR and AR can provide consumers with a more realistic and engaging shopping experience. Conclusion The explosive growth of the live-streaming business but the cautious test of luxury marketers drives this research. The subtle mechanism of live-streaming servicescape on consumers with different backgrounds is an obstacle to whether to use live-streaming for luxury marketers, but there is still not enough theoretical explanation on how to solve this issue. Based on SOR theory, this paper proposed a comprehensive model, which includes consumer trust and value perception as mediator variables and conspicuous consumption as moderators. This paper observed the subtle influence of live-streaming servicescape on different entities. Our research showed that besides social factors, live-streaming servicescape has a positive impact on luxury goods purchase intention. This study enriches the theory of live-streaming servicescape and live-streaming commerce literature and provides insights into the digital marketing of luxury brands. Declarations Data Availability Statemen t: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Acknowledgments: We thank all study respondents and lab members for their sincere support. Author Contributions: Conflicts of Interest: The authors declare no conflicts of interest. Funding: References Sun, Y., Shao, X., Li, X., Guo, Y. & Nie, K. How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic commerce research and applications 37 , 100886 (2019). Li, Y., Li, X. & Cai, J. How attachment affects user stickiness on live streaming platforms: A socio-technical approach perspective. Journal of Retailing and Consumer Services 60 (2021). Zhang, M., Liu, Y., Wang, Y. & Zhao, L. How to retain customers: Understanding the role of trust in live streaming commerce with a socio-technical perspective. Computers in Human Behavior 127 , 107052 (2022). Chen, Y. H., Chen, M. C. & Keng, C. J. Measuring online live streaming of perceived servicescape: Scale development and validation on behavior outcome. Internet Research ahead-of-print (2020). Xu, X., Wu, J.-H. & Li, Q. What drives consumer shopping behavior in live streaming commerce? Journal of electronic commerce research 21 , 144-167 (2020). Bao, H., Li, B., Shen, J. & Hou, F. Repurchase intention in the Chinese e-marketplace: Roles of interactivity, trust and perceived effectiveness of e-commerce institutional mechanisms. Industrial Management & Data Systems 116 , 1759-1778 (2016). Hu, M. & Chaudhry, S. S. Enhancing consumer engagement in e-commerce live streaming via relational bonds. 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Wang, Y., Xu, A. J. & Zhang, Y. L’Art Pour l’Art: experiencing art reduces the desire for luxury goods. Journal of Consumer Research 49 , 786-810 (2023). Kapferer, J.-N. Why are we seduced by luxury brands? Journal of Brand Management 6 , 44-49 (1998). Sun, J. J., Bellezza, S. & Paharia, N. Buy less, buy luxury: Understanding and overcoming product durability neglect for sustainable consumption. Journal of Marketing 85 , 28-43 (2021). Christodoulides, G. & Wiedmann, K.-P. Guest editorial: a roadmap and future research agenda for luxury marketing and branding research. Journal of Product & Brand Management 31 , 341-350 (2022). Dion, D. & Borraz, S. Managing Status: How Luxury Brands Shape Class Subjectivities in the Service Encounter. Journal of marketing: A quarterly publication of the american marketing association , págs. 67-85 (2017). Albrecht, C. M., Backhaus, C., Gurzki, H. & Woisetschläger, D. M. Drivers of brand extension success: What really matters for luxury brands. Psychology & marketing 30 , 647-659 (2013). Zhang, J. Z., Chang, C.-W. & Neslin, S. A. How physical stores enhance customer value: The importance of product inspection depth. Journal of Marketing 86 , 166-185 (2022). Goor, D., Ordabayeva, N., Keinan, A. & Crener, S. The impostor syndrome from luxury consumption. Journal of Consumer Research 46 , 1031-1051 (2020). Dong, P. & Siu, Y. M. Servicescape elements, customer predispositions and service experience: The case of theme park visitors. Tourism Management 36 , 541-551 (2013). Harris, L. C. & Goode, M. M. H. Online servicescapes, trust, and purchase intentions. Journal of Services Marketing 24 , 230-243 (2010). Fengjun, L., Lu, M., Siyun, C. & Shen, D. The Impact of Network Celebrities' Information Source Characteristics on Purchase Intention. Chinese Journal of Management (2020). Simon Bründl; Matt, C. H., Thomas. Consumer Use of Social Live Streaming Services: The Influence of Co-Experience and Effectance on Enjoyment. (2017). Kolb, A. Y. & Kolb, D. A. Learning Styles and Learning Spaces: Enhancing Experiential Learning in Higher Education. Academy of Management Learning and Education 4 (2005). Baker, J., Grewal, D. & Parasuraman, A. The influence of store environment on quality inferences and store image. Journal of the Academy of Marketing Science 22 , 328-339 (1994). Song, J. H. & Zinkhan, G. M. Determinants of Perceived Web Site Interactivity. Journal of Marketing 72 , 99-113 (2008). Vigneron, F. & Johnson, L. W. in Advances in Luxury Brand Management (eds Jean-Noël Kapferer, Joachim Kernstock, Tim Oliver Brexendorf, & Shaun M. Powell) 199-234 (Springer International Publishing, 2017). Yu, P., Xing, Z. & Li, G. Research on the Determinants of Purchasing Intention in Online Shopping——From the Perspective of Trust and Perceived Risk. China Industrial Economics (2010). O'Cass, A. & Mcewen, H. Exploring consumer status and conspicuous consumption. Journal of Consumer Behaviour 4 (2010). Rosendo-Rios, V. & Shukla, P. When luxury democratizes: Exploring the effects of luxury democratization, hedonic value and instrumental self-presentation on traditional luxury consumers’ behavioral intentions. Journal of Business Research 155 (2023). https://doi.org:10.1016/j.jbusres.2022.113448 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4483569","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":312692131,"identity":"de0e2409-8527-456e-8d64-6d091d7e0aa3","order_by":0,"name":"Junying Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYHCCBCCWYGBgbzAD8RgbiNfCc4B4LVAgkUCkFoMbCc+kC9ss8uQjH297zMNgI7vhAPOzBwS0pEnPbJMoNrydVm7Mw5BmvOEAm7kBQS28bRKJG2fnmEnzMBxO3HCAh02COC0zz4C0/CdBy3wJHpCWA4S1SJ55kGzNc04icQNPWpnkHINk45mH2czwauE7npN4m6esLnF+++FtEm8q7GT7jjc/w6tF4QBPAgMjG9CFB8DuBGJmfOqBQL6BHaj2D4hBQOUoGAWjYBSMXAAAeaBHvn2aKS8AAAAASUVORK5CYII=","orcid":"","institution":"Donghua University","correspondingAuthor":true,"prefix":"","firstName":"Junying","middleName":"","lastName":"Yu","suffix":""},{"id":312692132,"identity":"0ee527cd-c6a8-4467-89b1-916107e80c7a","order_by":1,"name":"Jing Gao","email":"","orcid":"","institution":"Donghua University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Gao","suffix":""},{"id":312692133,"identity":"49172519-084a-4bb9-8ddf-82ec1baf44fa","order_by":2,"name":"Jiarui Guo","email":"","orcid":"","institution":"Donghua University","correspondingAuthor":false,"prefix":"","firstName":"Jiarui","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2024-05-27 08:51:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4483569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4483569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58308225,"identity":"a838a5b1-90ad-42d7-b063-31546fdd790c","added_by":"auto","created_at":"2024-06-13 18:47:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27604,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4483569/v1/83e102ccde9f8805d8d562ad.png"},{"id":72866523,"identity":"aa7c478d-12d3-4a87-b0f8-9b4a09b251bc","added_by":"auto","created_at":"2025-01-03 06:02:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":696417,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4483569/v1/c2029a46-7156-4d52-b10e-a48b43bd1383.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Consequences of Servicescape of Live-streaming on Luxury Goods Purchase Intention","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLive-streaming can provide consumers with richer information through real-time videos\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and has experienced a remarkable surge in recent years, revolutionizing the way products are sold and promoted\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Live-streaming has emerged as a new digital marketing mode\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e that seamlessly integrates live-streaming platforms with e-commerce, offering customers an instant and interactive servicescape \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Live-streaming e-commerce combines real-time interaction, authenticity, and social engagement\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, resulting in increased consumer trust, brand exposure, and sales conversion\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. As technology continues to advance, and consumer preferences evolve, live commerce is expected to further flourish, offering both brands and consumers new opportunities and a more immersive shopping experience\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Live-streaming e-commerce has emerged as a transformative force in the retail industry. The market for live-streaming e-commerce has been particularly gratifying for luxury brands seeking to transition to online retailing in China, especially during the economic winter brought on by the COVID-19 pandemic. In an attempt to harness data flows and realize profit transformation via live-streaming technology, luxury brands such as Louis Vuitton, Gucci, Chanel, and Herm\u0026egrave;s have made significant strides in online marketing and digitalization\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The adoption of live streaming by affordable luxury brands like Michael Kors, Coach, and Kate Spade has also attracted significant industry attention\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, it is still a challenge for luxury brands to unlock the power of live e-commerce, luxury brand marketers remain cautious in testing waters in this previously unexplored market, due to lacking in-depth understanding of the impact mechanism of the live-streaming servicescape \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLuxury brands are known for their creativity, uniqueness, artistry, accuracy, modernity, high quality, and premium\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These characteristics provide consumers with a sense of satisfaction for possessing exclusive goods and provide psychological satisfaction in the form of high status, high prestige, and social concern\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In a highly dynamic technological change background, new demands are placed on Luxury brands\u0026rsquo; marketing and communication leveraging digital and social media technologies and AI\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, although live-streaming e-commerce is becoming increasingly popular among luxury brands\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, the results of live-streaming by luxury brands remain mixed due to various uncertain factors concerning the adopted mode of live-streaming e-commerce. The perceived luxury value is usually inseparable from the servicescape of physical stores\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The value perception and trust building of luxury goods are often easier to achieve with a physical store experience\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. For instance, the excellent materials and exquisite craftsmanship of luxury goods are easier to examine in physical stores\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, where consumers can trust the brand. Physical luxury store service also plays an important role as the gatekeeper and instructor of the luxury class, which helps in building customer trust and loyalty. Online shopping for luxury goods poses higher risks and worries due to safety and trust issues, and the lack of product inspection\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, especially in live-streaming. Therefore, obtaining a thorough understanding of consumers' reactions to the live-streaming of luxury goods, as well as a grasp of the mediating effect mechanism of the perception and the trust of luxury goods value, is one of the most important challenges faced by luxury marketers.\u003c/p\u003e \u003cp\u003eTo delve into how the live-streaming of luxury goods influences consumer purchase intention, we utilize servicescape theory research. Servicescape refers to the environment in which customer and employee interactions occur, encompassing all tangible elements that facilitate service delivery \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. With the advent of internet technology, servicescape theory has been extended to online channels, giving rise to e-servicescape theory\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Researchers have studied the characteristics of live streamers, information sources\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, interactions, social presence \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and media richness of live streaming. However, given the complexity and evolving experience of the servicescape of live-streaming, and the different backgrounds of customers, it is important to determine how these factors influence purchase intention and the role of perceived value and consumer trust. Our study also draws on experiential learning theory (ELT)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, which suggests that customers without prior luxury purchase experience value the item's worth, while customers with such experience prioritize trust in live-streaming. This study adds to the servicescape theory and expands live-streaming research to the luxury goods category. It provides guidance and recommendations for designing the live-streaming servicescape for luxury brands. Furthermore, it presents a new framework that effectively utilizes digital technology to achieve targeted and distinctive marketing outcomes for various consumer segments. The findings of this research contribute to the understanding of the mechanisms that drive consumers' purchase intention of luxury goods in the live-streaming context. It provides valuable insights for luxury brands and marketers on how to optimize the servicescape, build consumer trust, and enhance perceived value to effectively engage consumers and drive their intention to purchase luxury goods in a live-streaming setting.\u003c/p\u003e \u003cp\u003eBased on SOR theory, a comprehensive theoretical model is constructed (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This framework represents three dimensions of the servicescape of live-streaming as the stimulus, consumer trust, and perceived value as organism evaluation, to pursue intention as a reaction. We formulated three hypotheses. The effects of the live-streaming servicescape on purchase intention are significant, and the purchase intention of consumers with prior luxury purchasing experience is more likely to be influenced by social factors(a), technical factors(b), and physical factors than that of consumers without such experience (H1). Consumer trust plays a more significant intermediary role between the live-streaming servicescape and purchase intention among consumers with prior luxury purchasing experience than those without it (H2). The impact of the live-streaming servicescape on purchase intention is mediated by the perceived luxury value among consumers without prior luxury purchase experience but not among consumers with such experience (H3). Conspicuous consumption serially moderates the effects of live-streaming servicescape on the perceived luxury value and purchase intention (H4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eScenario and Administration Procedure\u003c/h2\u003e\n \u003cp\u003eTo assess consumers\u0026apos; evaluation of perceived value and environmental trust in a live-streaming servicescape for luxury goods, along with their purchase intention, we utilized three scenario stimuli: situational recall, scenario display, and simulation methods. The questionnaire commenced with queries about a situational recall for live-streaming and an identification question to ascertain respondents\u0026apos; exposure to live-streaming sales. This screening process ensured that the samples possessed familiarity with the marketing approach, enabling us to obtain qualified participants with live-streaming experience. Subsequently, respondents were prompted to evaluate the social aspects of existing live-streaming tools based on their encounters. To precisely target the intended population and ensure respondents had genuine live-streaming experience, we included the question \u0026quot; Have you ever watched any live-streaming e-commerce?\u0026quot; to exclude individuals lacking exposure to live-streaming viewing. For luxury live-streaming, we presented a 20-second video extracted from a live stream on Taobao, featuring Helena, a French cosmetics brand. By utilizing a specific brand in the scenario, we aimed to ensure the video\u0026apos;s authenticity and brand presence. Following the video, respondents were instructed to evaluate the physical factors by observing another live stream featuring a luxury product. We chose cosmetics brands as our focus since we believed they possess a higher market penetration rate and are subject to less gender influence compared to clothing brands.\u003c/p\u003e\n \u003cp\u003eBefore measuring consumer purchase intention, we provided a comprehensive introduction to the featured cosmetics brands and products, including their respective prices. To gauge perceptions and purchasing behaviors related to luxury goods, we included the question \u0026quot;Have you ever purchased luxury goods, such as shoes, bags, or accessories costing more than 1,000 yuan?\u0026quot; in the questionnaire. This enabled us to differentiate between consumers with and without prior luxury purchase experience. Both groups were then asked to list the luxury brands they were familiar with or had purchased. The sample size of respondents with luxury purchase experience was approximately 6:4, facilitating an effective comparative study. Additionally, the questionnaire collected demographic information, encompassing gender, income level, age, geographic location, and educational background.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasurement Items\u003c/h2\u003e\n \u003cp\u003eThis research utilized questionnaires as a data collection method. To ensure the credibility and accuracy of the questionnaire data, various factors were incorporated from established scales, including physical factors \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, social factors \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, perceived luxury value \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, consumer trust \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, conspicuous consumption \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and purchase intention \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These factors were then tailored to fit the context of live-streaming e-commerce and combined with the item contents. All items were rated on a 7-level Likert scale (1 represents strongly disagree, while 7 represents strongly agree). Following the initial draft of the questionnaire, pretesting was conducted, and 90 groups of data were gathered, with 65 samples being valid. After analyzing reliability and validity, items with low reliability scores were adjusted.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eData collection and sample\u003c/h2\u003e\n \u003cp\u003eWe obtained samples from China to investigate the differences in attitudes towards luxury goods between consumers with and without prior luxury purchase experience during live-streaming. The study utilized a snowball sampling method to collect and compare the samples, each containing 300 data points. During the screening period, we sent well-informed invitation letters describing the objective and protocols of the study, and respondents who were willing to participate will fill in the next step questionnaire. After screening, we obtained 380 valid data points, with 232 samples having luxury purchase experience and 148 samples without. The questionnaire collection process was successful. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. presents the sample characteristics. The majority of respondents were between 18\u0026ndash;30 years old (67.9%), with men accounting for 30.5% (116 people) and women for 69.5% (264 people) of the total, aligning with the user characteristics of China\u0026apos;s live-streaming e-commerce.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic characteristics of the sample (N\u0026thinsp;=\u0026thinsp;380)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003echaracteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026thinsp;~\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026thinsp;~\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u0026thinsp;~\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efirst-tier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprovincial capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eelse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ejunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eundergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostgraduate and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eelse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eMonthly income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5000 RMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5001\u0026ndash;10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10001\u0026ndash;100000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;100001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eReliability, Validity test, and Common Method Variance Test\u003c/h2\u003e\n \u003cp\u003eThe results of the reliability and validity tests show that the Cronbach\u0026apos;s \u0026alpha; coefficients of all variables are greater than 0.7, indicating that the overall reliability of the questionnaire is very good. Moreover, the KMO values of all variables are greater than 0.6, indicating that the scale is suitable for factor analysis. After revising and checking the scales, the minimum factor load was calculated as 0.498, and the minimum cumulative variance explained was 63.60%, both of which were acceptable, as shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFactor analysis (N\u0026thinsp;=\u0026thinsp;380)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCronbach\u0026apos;s \u0026alpha;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKMO\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum factor load\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCumulative variance explained\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnique factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysical factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived luxury value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsumer trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConspicuous consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePurchase intention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTo address potential common method bias, Harman\u0026apos;s single-factor test was conducted to assess the presence of a single dominant factor that could account for the majority of the variance in the data. The test revealed that the first factor explained 36.02% of the total variance. However, this percentage did not exceed the commonly accepted threshold of 50%, indicating that common method bias was unlikely to be a significant concern in our study.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n \u003cp\u003eThe study was reviewed and approved by the Science and Technology Ethics Committee of Donghua University (No. DHUEC-GL-2024-07). \u0026nbsp;All methods were performed under the relevant guidelines and regulations and complied with the 1975 Helsinki Declaration on ethics in medical research. The study Informed consent was obtained from all subjects and/or their legal guardian(s). Before accessing the questionnaire, participants were informed about the purpose of this study, and that their participation was voluntary and confidential, with guaranteed anonymity and the option to withdraw at any time.\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAs expected, our analysis of the overall sample, including both the basic model and the model with control variables (See, Table 3.), revealed significant and positive effects of the servicescape on purchase intentions. This implies that the live-streaming servicescape positively influences purchase intentions, regardless of whether consumers have prior experience with luxury purchases (R2=0.589, F=179.75, p\u0026lt;0.001). Thus, our findings generally support hypothesis H1.\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eDV= PI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eTotal Sample\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eN=380\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel 2 with Controls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eB (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eB (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.151(.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.152(.362)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.277(.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.190\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.271(.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.074\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.431(.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.489\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.429(.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.394\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.301(.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.393\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.284(.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.965\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.003(.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEarning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.050(.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.055(.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.031(.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.081(.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e179.75\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e68.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 3. Three dimensions effect of servicescape on Purchase Intention\u003c/p\u003e\n\u003cp\u003eTo better comprehend the distinction between the two samples, we conducted an independent sample t-test of variables. As demonstrated in\u0026nbsp;Table 4\u003cstrong\u003e.\u003c/strong\u003e We have observed noteworthy differences in the effect of luxury goods purchase experience on the focal variables. These results are indicative of the fact that the impact of luxury goods purchasing experience is a crucial factor that needs to be taken into account when analyzing the influence of variables\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSample with prior luxury purchasing experience\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eN=232\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSample without prior luxury purchase experience\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eN=148\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eservicescape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.906\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.356\u0026plusmn;0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.853\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhysical factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.876\u0026plusmn;0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.318\u0026plusmn;0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.905\u0026plusmn;0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.346\u0026plusmn;0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.209\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConspicuous consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.606\u0026plusmn;1.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.655\u0026plusmn;1.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.936\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConsumer trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.74\u0026plusmn;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.104\u0026plusmn;1.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.604\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived luxury value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.801\u0026plusmn;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.164\u0026plusmn;0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.364\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePurchase intention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.864\u0026plusmn;0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.108\u0026plusmn;1.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.990\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p\u0026lt;0.05，\u0026nbsp;**p\u0026lt;0.01，\u0026nbsp;***p\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;4\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eComparison table of direct effect test\u003c/p\u003e\n\u003cp\u003eAdditionally, according to\u0026nbsp;Table 5., we found that within the sample of consumers with prior luxury purchasing experience, the physical, social, and technical factors of the live-streaming servicescape had significant positive effects on purchase intention (R2=0.705, F=184.67, p\u0026lt;0.001). In the sample without experience in luxury goods purchasing, the technical factors and physical also had significant positive effects on purchase intention (t=4.311, p\u0026lt;0.001; and t=2.194, p\u0026lt;0.05, respectively), while the social factor did not have a significant effect (t=0.931, p\u0026gt;0.05). These results confirm hypotheses H1a and H1c but reject H1b.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, when considering the regression analysis with control variables, the inclusion of these variables did not affect the significant effects of the three factors (physical, social, and technical) on purchase intention. Thus, the presence of control variables did not alter the impact of these factors on purchase intentions. In summary, our analysis of the overall sample, as well as the subgroups based on prior luxury purchasing experience, consistently demonstrates the significant influence of the physical, social, and technical factors within the live-streaming servicescape on purchase intentions. However, the social factor effect appears to be not significant for consumers without prior experience in luxury goods purchasing. These findings hold even when controlling for other variables, indicating the robustness of the observed effects.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"448\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eDV= PI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.857142857142854%\" colspan=\"2\"\u003e\n \u003cp\u003ewith prior luxury purchasing experience（N=232）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.857142857142854%\" colspan=\"2\"\u003e\n \u003cp\u003ewithout prior luxury purchase experience（N=148）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"42.857142857142854%\" colspan=\"2\"\u003e\n \u003cp\u003eBase Model with Controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.857142857142854%\" colspan=\"2\"\u003e\n \u003cp\u003eBase Model with Controls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eB (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eB (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e-.025（.388）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e1.123(.883)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e.385（.07）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5.497\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e.120(.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e.323（.067）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e4.820\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e.502(.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e3.953\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003ePF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e.296（.061）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e4.816\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e.247(.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e2.396\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e.060（.035）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e1.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e-.103(.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eEarning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e.018（.048）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e.003(.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e-.023（.038）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e-.122(.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-1.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e-.085（.071）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-1.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e.183(.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e-.001（.046）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e-.128(.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-1.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.857142857142854%\" colspan=\"2\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.857142857142854%\" colspan=\"2\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.857142857142854%\" colspan=\"2\"\u003e\n \u003cp\u003e70.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.857142857142854%\" colspan=\"2\"\u003e\n \u003cp\u003e11.83\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 5. Three dimensions effect of servicescape on Purchase Intention of two samples\u003c/p\u003e\n\u003cp\u003eWe utilized PROCESS Model 4, with 10,000 bootstrapped samples, to test for dual mediation. In the sample of individuals with luxury goods purchasing experience, the mediating effect of the perceived luxury value (B=0.07, SE=0.08, 95% CI=[-0.0953, 0.2261]) was found to be non-significant, while the mediating effect of consumer trust (B=0.28, SE=0.12, 95% CI= [0.0644, 0.5201]) was found to be significant. In the sample of individuals without prior luxury purchase experience, the mediating effect of the perceived luxury value (B=0.4025, SE=0.40, 95% CI=[0.1244, 0.6865]) was found to be significant, while the mediating effect of consumer trust (B=0.24, SE=0.24, 95% CI= [-0.0321, 0.5024]) was found to be non-significant. These results lend support to H2 and H3.\u003c/p\u003e\n\u003cp\u003eAccording to the PROCESS Model 8 conducted by Hayes in 2018, which involved 10,000 bootstrap samples, with the three factors (physical, social, and technical) as independent variables, perceived luxury value as the mediator, and purchase intention as the dependent variable, the interaction between the three factors and conspicuous consumption did not have a significant effect on the mediator variable (perceived luxury value) and purchase intention.\u003c/p\u003e\n\u003cp\u003eSpecifically, only in the sample of participants with prior luxury purchasing experience, the interaction between social factors and conspicuous consumption significantly influenced purchase intention(\u0026beta;=-.11, SE=.03, 95% [CI]=[-.1688, -.0589]). However, this interaction had a negative moderating effect. To better understand this interaction, we conducted a floodlight analysis to observe the range of conspicuous consumption for which the effect of anxiety on purchase intention was significant. There are no statistical significance transition points within the observed range of the moderator found using the Johnson-Neyman method.\u0026nbsp;As shown in\u0026nbsp;Table 6., we found that as\u0026nbsp;conspicuous consumption\u0026nbsp;becomes stronger,\u0026nbsp;the effect value gradually decreases.\u003c/p\u003e\n\u003cp\u003eThis suggests that in the presence of both social factors and conspicuous consumption, purchase intention may decrease. This could be because consumers in this sample place more importance on the social symbolic meaning of luxury goods rather than conspicuous consumption itself. It is important to note that although the interaction between the three factors and conspicuous consumption did not have a significant effect on the mediator variable (perceived luxury value), it does not imply that these factors do not influence purchase intention. They may still have direct or indirect effects on purchase intention through other pathways, separate from the mediator of perceived value. In conclusion, the interaction between social factors and conspicuous consumption has a significant effect on purchase intention, particularly in the sample of participants with prior luxury purchasing experience. However, this interaction has a negative moderating effect, indicating that purchase intention may decrease when both social factors and conspicuous consumption are present.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.1735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.1316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e8.9172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.9142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.4328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.2857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.1410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.1242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e9.1896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.3856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.5714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.1084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.1168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e9.4910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.3386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.8571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.0759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e9.8256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.2917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.1429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.0434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.1023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e10.1977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.2450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.4286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.0108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e10.6121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.1985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2.7143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.9783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e11.0734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.1524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e3.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.9458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e11.5855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.1066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e3.2857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.9132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e12.1494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.0614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e3.5714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e12.7610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.0167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e3.8571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e13.4056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.9729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e4.1429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e14.0500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.9300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e4.4286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e14.6327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.6777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e4.7143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e15.0562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.6524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e5.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e15.1944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.6249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.8112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e5.2857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.6855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e14.9297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.5950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e5.5714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.6530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e14.2142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.5625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e5.8571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.6205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e13.1111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.5272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.7137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e6.1429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.5879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e11.7689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.4895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.6864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e6.4286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.5554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e10.3515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.4497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.6611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e6.7143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.5229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e8.9818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.4081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.6376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e7.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.4903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e7.7277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.3653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e.6153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: Figures in boldface highlight Johnson\u0026ndash;Neyman points, identifying the range of hope for which the effect of anxiety on adoption intentions was significant.\u003c/p\u003e\n\u003cp\u003eTable 6. Conditional effect of focal predictor at values of the moderator:\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFirst, the live-streaming servicescape has a significant positive impact on Chinese consumers\u0026apos; purchase intentions for luxury goods. Firstly, the physical factors such as the ambiance and aesthetics of the servicescape can play a significant role in luxury goods purchases. Luxurious and visually appealing environments can create a sense of exclusivity and enhance the perceived value of the products. For example, a high-end boutique with tasteful interior design, elegant lighting, and premium displays can create a luxurious atmosphere that aligns with the brand image, attracting customers and influencing their purchasing decisions. However, social factors such as the level of personalized customer service provided by the anchor within the servicescape can greatly impact luxury goods purchases only on the consumers with prior luxury purchasing experience. Luxury customers often expect individualized attention and assistance, and well-trained and knowledgeable anchors who can provide personalized recommendations, address customer inquiries, and offer a high level of service can contribute to a positive purchasing experience. Additionally, social interactions and peer influence can also impact luxury goods purchases. These interactions provide opportunities for customers to share their experiences, exchange opinions, and receive validation from others. Positive social interactions and the influence of peers can enhance customers\u0026apos; perceptions of the products, increase their confidence in purchasing, and create a sense of belonging to a luxury community.\u003c/p\u003e\n\u003cp\u003eSecond, in the case of consumers who have previously purchased luxury goods, the perceived value of luxury items did not mediate their purchase intention through live-streaming. However, consumer trust was found to have a significant impact on purchase intention. This indicates that these consumers already have established opinions and emotions towards luxury brands. Though live-streaming can convey perceived value, it may not necessarily increase their purchase intention. The risks and uncertainties associated with online luxury purchases can also hinder their decision-making. Therefore, establishing consumer trust through live-streaming can significantly increase purchase intention. On the other hand, for consumers who have no previous experience with luxury brands, live-streaming can enhance their perceived value of luxury items, leading to an increase in purchase behavior. Although consumer trust may not mediate their purchase intention, it can still be established through live-streaming. However, affordability also plays a role in their decision-making process.\u003c/p\u003e\n\u003cp\u003eFinally, the interaction between the live-streaming servicescape and conspicuous consumption plays a certain role in regulating the perceived luxury value. This is particularly the case for consumers with luxury purchase experience, who can perceive the value of luxury goods with their conspicuous consumption motivation. However, the interaction between live-streaming servicescape and conspicuous consumption has no significant influence on purchase intention. Thus, in the online environment, conspicuous consumption is no longer a stimulus for consumers\u0026rsquo; luxury purchasing.\u003c/p\u003e\n\u003cp\u003eTheoretical contributions and managerial implications\u003c/p\u003e\n\u003cp\u003eStudying luxury live-streaming service scenarios contributes to consumer behavior theory, social influence theory, technology adoption and consumer theory, marketing communication theory, and interactive media theory. On the one hand, it supplements servicescape theory and proposes novel measurement dimensions for the live-streaming servicescape, providing a new perspective on the measurement of emerging e-servicescapes and diversifying servicescape designs. Luxury live-streaming platforms facilitate social interactions among viewers, making it relevant to explore the applicability of social influence theory in the luxury domain. Studying luxury live-streaming involves examining mobile payment technologies, mobile shopping experiences, and mobile notifications, contributing to the understanding of the impact of technology on consumer behavior and decision-making processes. It provides valuable insights into the adoption and usage of mobile technologies in the luxury industry.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirstly, the physical factors such as the ambiance and aesthetics of the servicescape can play a significant role in luxury goods purchases. Luxurious and visually appealing environments can create a sense of exclusivity and enhance the perceived value of the products. For example, a high-end boutique with tasteful interior design, elegant lighting, and premium displays can create a luxurious atmosphere that aligns with the brand image, attracting customers and influencing their purchasing decisions. Secondly, the technical factors of the live-streaming servicescape, such as the usability, security, and mobile payment technology associated with live-streaming have significant effects on luxury purchases. Therefore, providing a user-friendly interface, ensuring a stable and secure live-streaming environment, and offering convenient mobile payment options enhance the overall purchasing experience, boost viewers\u0026apos; willingness to buy luxury products, and facilitate sales in the luxury industry. The user-friendly digital servicescape can provide a seamless and immersive online shopping experience, while also showcasing the brand\u0026apos;s image and values. However, the social factors such as the level of personalized customer service provided by anchor within the servicescape can greatly impact luxury goods purchases only on the consumers with experience purchasing luxury goods. Luxury customers often expect individualized attention and assistance, and well-trained and knowledgeable anchors who can provide personalized recommendations, address customer inquiries, and offer a high level of service can contribute to a positive purchasing experience. Additionally, social interactions and peer influence can also impact luxury goods purchases. These interactions provide opportunities for customers to share their experiences, exchange opinions, and receive validation from others. Positive social interactions and the influence of peers can enhance customers\u0026apos; perceptions of the products, increase their confidence in purchasing, and create a sense of belonging to a luxury community.\u003c/p\u003e\n\u003cp\u003eOn the other hand, the essence of such an attempt to sell luxury goods by live-streaming is a kind of luxury democratization. Luxury firms can develop better positioning strategies for managing the luxury democratization challenge\u003ca href=\"#_ENREF_33\" title=\"Rosendo-Rios, 2023 #17\"\u003e\u003csup\u003e33\u003c/sup\u003e\u003c/a\u003e. This in-depth study of the live-streaming marketing of luxury brands, by introducing the unique characteristics of luxury goods, extends the existing product categories in live-streaming research and provides references for luxury brands\u0026apos; digital operation and expansion. Researching luxury live-streaming services contributes to consumer behavior theory by providing empirical evidence and insights into consumer decision-making processes, motivations, and behavior patterns within the context of luxury live-streaming. It helps in understanding consumer demand for luxury products and the factors influencing their purchasing behavior during live-streaming sessions.\u003c/p\u003e\n\u003cp\u003eFurthermore, this study provides practical recommendations for brands and platforms currently planning to invest in the live-streaming e-commerce industry. The most effective approach is to implement targeted and precise marketing strategies to consumers with or without prior luxury purchase experience. Firstly, for consumers with luxury purchase experience, the focus of branded online marketing should be to establish consumer trust in online platforms. To achieve this, brands can display real-life scenarios of physical stores or catwalk shows, create a soothing ambiance with music, improve interactions between live streamers and viewers, and select more comprehensive and mature platforms. Secondly, for consumers without prior luxury purchase experience, brands should concentrate on enhancing their understanding of luxury culture using live-streaming or other digital tools to increase the perceived luxury value. Finally, for both types of consumers, the role of conspicuous consumption motivation in luxury goods marketing is diminished in the live-streaming servicescape. Therefore, brands should prioritize the transmission of luxury culture and connotation rather than conspicuous value in their online marketing efforts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to traditional luxury marketing approaches, digital marketing strategies tend to de-emphasize conspicuous consumption\u0026apos;s role. Furthermore, the impact of conspicuous consumption on purchase intention is not significant in live-streaming. Research indicates that Generation Z, which consists of digital natives, is less motivated by conspicuous consumption than other age groups. Therefore, businesses must prioritize updating the definition of luxury culture rather than making conspicuous consumption a central part of their marketing efforts.\u003c/p\u003e\n\u003cp\u003eLimitations and future research\u003c/p\u003e\n\u003cp\u003eAlthough this research has certain theoretical significance and offers practical value, it has some limitations that open several avenues for future research. Sample Representativeness: Research conducted on luxury live-streaming services may face challenges in obtaining a representative sample of luxury consumers. It can be difficult to access and recruit participants who actively engage in luxury live-streaming and make luxury purchases during such sessions. This limitation could impact the validity and applicability of the research findings. Influencer Bias: Luxury live-streaming often involves influential hosts or celebrities who endorse or promote luxury products. This influencer presence can introduce biases in consumer behavior and perceptions. Consumers may be influenced by the anchor\u0026apos;s personal brand and opinions, making it challenging to isolate the effects of the live-streaming platform itself on consumer decision-making. Limited Long-term Effects Study: Luxury live-streaming research often focuses on immediate purchase behavior and short-term effects. Long-term effects, such as post-purchase satisfaction, brand loyalty, and repeat purchases, may be overlooked in the context of live-streaming studies. Understanding the sustainable impact of luxury live-streaming on consumer behavior requires longitudinal studies and follow-up research. Generalizability: The findings and insights derived from studying luxury live-streaming services may have limited generalizability due to the specific nature of the luxury industry and the unique characteristics of live-streaming platforms. The behaviors, preferences, and motivations of luxury consumers in a live-streaming context may not fully represent the broader population of luxury consumers or apply to different cultural contexts.\u003c/p\u003e\n\u003cp\u003eThe future of studying luxury live-streaming service scenarios holds immense potential: In the context of luxury brand live-streaming, servicescape encompasses the visual and auditory elements, including the set design, lighting, background music, and overall presentation of the live-streaming session. This research examines how the servicescape of luxury live-streaming influences consumers\u0026apos; perception of the luxury brand and their purchase intention. Researchers can explore the role of aesthetics, ambiance, and sensory elements in creating a desirable and immersive experience for consumers. As the field of data analytics continues to evolve, researchers can analyze the vast amount of data generated during luxury live-streaming sessions. By employing machine learning, artificial intelligence, and natural language processing, researchers can gain deeper insights into consumer behavior, sentiment analysis, and predictive modeling, enabling more accurate and targeted strategies for luxury brands. Luxury live-streaming platforms have the potential to offer personalized and customized experiences for individual viewers. Future research can focus on understanding consumer preferences, interests, and purchasing patterns to deliver tailored content, recommendations, and interactive features. This personalized approach can enhance consumer engagement, and satisfaction, and ultimately drive higher conversion rates. The integration of VR and AR technologies into luxury live-streaming holds significant promise. Researchers can explore the application of these immersive technologies to create virtual showrooms, try-on experiences, and interactive product demonstrations. By enhancing the sensory and experiential aspects of luxury live-streaming, VR and AR can provide consumers with a more realistic and engaging shopping experience.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe explosive growth of the live-streaming business but the cautious test of luxury marketers drives this research. The subtle mechanism of live-streaming servicescape on consumers with different backgrounds is an obstacle to whether to use live-streaming for luxury marketers, but there is still not enough theoretical explanation on how to solve this issue. Based on SOR theory, this paper proposed a comprehensive model, which includes consumer trust and value perception as mediator variables and conspicuous consumption as moderators. This paper observed the subtle influence of live-streaming servicescape on different entities. Our research showed that besides social factors, live-streaming servicescape has a positive impact on luxury goods purchase intention. This study enriches the theory of live-streaming servicescape and live-streaming commerce literature and provides insights into the digital marketing of luxury brands.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statemen\u003c/strong\u003et:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all study respondents and lab members for their sincere support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun, Y., Shao, X., Li, X., Guo, Y. \u0026amp; Nie, K. 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When luxury democratizes: Exploring the effects of luxury democratization, hedonic value and instrumental self-presentation on traditional luxury consumers\u0026rsquo; behavioral intentions. \u003cem\u003eJournal of Business Research\u003c/em\u003e\u003cstrong\u003e155\u003c/strong\u003e (2023). https://doi.org:10.1016/j.jbusres.2022.113448\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Live-streaming, Servicescape, Luxury brands, Conspicuous consumption, Consumer trust, Perceived value of luxury","lastPublishedDoi":"10.21203/rs.3.rs-4483569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4483569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the influence of the servicescape in live-streaming on consumer buying behavior regarding luxury goods. We examine the mediating role of perceived value and consumer trust, considering the prior luxury purchasing experience of consumers. Additionally, we explore the interplay between the live-streaming servicescape and conspicuous consumption in shaping the perceived luxury value. Our findings reveal intriguing results: Perceived value mediates the relationship between servicescape and purchase intention for consumers without prior luxury purchase experience, while consumer trust does not exhibit a mediating effect. Conversely, for individuals with prior luxury purchase experience, the mediating effect is reversed. Furthermore, we identify the interplay between the live-streaming servicescape and conspicuous consumption as a moderator of perceived value. These insights indicate that luxury brands can strategically enhance their live-streaming servicescape to cater to different consumer segments and strengthen their marketing endeavors. Overall, this study contributes valuable insights into the servicescape theory in the context of live-streaming.\u003c/p\u003e","manuscriptTitle":"The Consequences of Servicescape of Live-streaming on Luxury Goods Purchase Intention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 18:47:11","doi":"10.21203/rs.3.rs-4483569/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0bfc1dbf-736c-438d-836f-22f2f4def11e","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-03T05:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 18:47:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4483569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4483569","identity":"rs-4483569","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}