How Does Art Website Image Innovation Influence Purchase Intention from the Perspective of Co-presence? —A Dual Mediation Model

How Does Art Website Image Innovation Influence Purchase Intention from the Perspective of Co-presence? —A Dual Mediation Model | 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 How Does Art Website Image Innovation Influence Purchase Intention from the Perspective of Co-presence? —A Dual Mediation Model peng xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7566219/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract As digital technology has been profoundly reshaping art transaction settings, traditional e-commerce theory has difficulty in interpreting cultural and high-involvement consumption aspects of art e-commerce. Using co-presence (CP) theory and the S-O-R model, to explore how innovative art website images influence purchase intentions through perceived value and risk. A six - dimension art website - image scale was constructed, and structural equation modeling was employed to analyze data from 421 art enthusiasts. The results indicate that CP - driven virtual co-presence experiences (such as VR gallery crowd - behavior visualization) enhance the positive connection between website - image innovation and purchase intention. Perceived value and risk have asymmetric mediation effects: value promotes purchase intention for mid - low - priced art through aesthetic resonance, while risk does so for high - priced art because of group risk - sharing. Website - image innovation reconstructs the risk - value assessment system through digital presence, transforming the absence in physical - space into premiums - based on group trust, thereby reshaping the pricing - power distribution in the art - finance market . Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Art website image Perceived value Perceived risk Co-presence Figures Figure 1 Figure 2 Introduction The saying goes,"In chaotic times, gold is the refuge; in peaceful times, art is the pursuit." With the continuous deep penetration of internet technology and the structural changes in the digital economy, the global art trading market is undergoing a paradigm shift from offline to online. According to the 51st statistical report by CNNIC in 2025, by December 2024, China's internet user population exceeded 1.23 billion, with an internet penetration rate of 86.5%, and mobile phone users accounted for 98.2%. This has laid the user foundation for the large - scale development of art e - commerce. Meanwhile, the global online art trading market has witnessed explosive growth. According to the data from the "2025 Hiscox Global Online Art Trade Report" and the "Art Basel & UBS Annual Report on the Global Art Market," in 2025, the online trading volume exceeded 45.0 billion US dollars, accounting for 37.5% of the global art trading total. This represents a 12 - percentage - point increase compared to 2024 and is significantly higher than the 11.32 billion US dollars (38% share) in 2023. The trading volume has nearly tripled. The Chinese art market has been particularly outstanding, with its online trading volume reaching 54.0 billion US dollars in 2025, accounting for 45% of the global share and maintaining its top global position consecutively. In the post - pandemic era, the online process has continued to accelerate. Data from international leading auction houses has confirmed this trend. In 2025, the total online - only auction turnover of Sotheby's, Christie's, and Phillips reached 32.0 billion US dollars, which is 20 times the figure of the same period in 2019 (1.53 billion US dollars). It is predicted that with the growing popularity of blockchain tracing technology and immersive virtual galleries, the global online art trading scale is expected to exceed 40.0 billion US dollars in 2024. Among these, the Asia - Pacific region, especially China, will reconstruct the global landscape with a growth contribution of 68%. This is because of the unique momentum created by the activation of private art collections through legislation and innovation in art finance in China, which has promoted the development of the art market. Literature Review Art Website Image Dimensions under Co-presence Art websites are core virtual shopping platforms.Their innovation, which spans visual optimization, interactive enhancement, and diverse content presentation, aims to elevate brand image, user appeal, and market competitiveness 1 . Co-presence (CP) reflects the degree of perceiving others' virtual presence 2 and the significance of virtual interpersonal relationships in online settings 3,4 .Numerous factors shape website image,such as visuals, colors, ads, and salespeople 5 .Classic models classify online store images into design aesthetics, information quality, product assurance, and safety 6 . The reliability, usability, and usefulness of websites significantly impact buyers' purchase intentions 7 . For instance, "product allocation" and "ranking functions" enhance shopping efficiency and reduce search costs 8 . "Functional convenience," "price transparency," and "product variety" are key elements of user - friendly websites, directly influencing user attitudes and repurchase intentions 9 . This perspective has been extended in promotional strategy research: "Free shipping policies," "real-time logistics tracking," and "dynamic discounts" (such as shopping cart promotions and limited-time offers for new products) enhance the attractiveness of online stores by reducing decision-making resistance, thereby driving sales conversion 5 .Trust in website images relies on multi - dimensional safeguards. "User - friendly interfaces" (e.g., clear navigation), "transparent privacy statements," and "secure payment signs" form the basic trust framework 10 . "Fulfillment certainty" (e.g., precise delivery times) and "after - sales responsiveness" (e.g., fast refunds) mitigate perceived risks, especially in high - value categories like luxury goods 11 . Art products, having economic and investment attributes, serves as a value - display method for the nouveau riche to showcase status or address identity anxiety 12 . As rational economic agents, art buyers' intentions are influenced by price, brand, store information, trust 13 , risk, satisfaction, service, and value recognition. Consumer Perceived Risk and Value in Art E-commerce In the art e-commerce context, perceived value is composed of emotional value (aesthetic resonance), social value (status symbol), functional value (investment return), and hedonic value (virtual exhibition experience) 14,15 . Given the high involvement nature of art, value perception depends more on credibility cues such as authoritative certification and collector reputation, rather than the traditional "value for money" logic 16 . The risks in online art transactions are distinct, with financial risk (valuation deviation), social risk (aesthetic recognition pressure), functional risk (transport damage), and institutional risk (policy compliance) all being factors 17 . Different from regular products, high perceived risk can increase scarcity perception and stimulate the purchasing intention among high - net - worth users 18 . This paradox reveals the limitations of traditional linear risk - intention models and emphasizes the necessity of integrating digital technologies like VR inspection and smart contracts to update the theoretical framework 19 . With the penetration of AI large models into permeate daily life and the evolution of global shopping methods, co-presence has increased the expectations for participation in online shopping. Currently, however, research on online perceived risk and value often draws on traditional methods, with limited attention given to art purchases. Given the rapid growth of online art buying, this study aims to answer two key questions: Are the risk and value dimensions of online art the same as those of regular online products? How can we effectively reduce the high perceived risk in online art purchases? A Study on Consumer Co-presence and Purchase Intention in E-commerce Based on S-O-R Theory The "S-O-R" theory,deeply rooted in human behavioral psychology, serves as a crucial theoretical foundation in consumer behavior and marketing studies. Building upon on the TRA and TRM models and incorporating co-presence theory, this study applies concepts of website image, perceived value, and perceived risk into the online art market. It aims to explore the relationships among these factors and provide valuable insights into online art consumer behavior. While extensive research exists on website image and purchase intention, three primary limitations persist. First, existing studies predominantly focus on standardized e-commerce products, thereby overlooking the unique cultural asset attributes of art and the specific requirements for website image design. Critically, they fail to address the core value of "co-presence" in art appreciation and trading. That is, the immersive experience of real - time interaction and co-presence among users, creators, collectors, and other consumers in virtual space, which is vital for fostering emotional connection.Second, the analysis of perceived value and risk continues to be largely based on offline scenarios. New interactive models such as live - stream appraisal (e.g., real - time Q&A between experts and collectors) and NFT authentication (e.g., visualizing the creation process of digital art) have not been adequately examined for there co-presence responses.Third, existing models have yet to incorporate big-data-driven dynamic user profiling (e.g., predicting users’ aesthetic preferences). Furthermore, the dynamic generation mechanism of co-presence (e.g., scenario adaptation based on real-time user interaction data) remains unintegrated into current analytical frameworks.. In response, This study develops an "S-O-R + dual - mediation" theoretical model of art website purchase intention, emphasizing "co-presence" as a key mediating variable. The aim is to reveal how innovative art website images (such as virtual curatorial spaces and real - time interactive appraisal functions) influence consumer decisions by enhancing users' co-presence experience with art, creators, and the community within a digital context. This research also seeks to elucidate the underlying impact mechanisms between unobservable variables and to delineate the specific path through which art website image innovation influences consumers' purchase intentions. Theoretical Framework This study, building upon Eroglu’s research on website image and the Stimulus-Organism-Response (S-O-R) theory, refines the theoretical model. The online art shopping environment, serving as a “stimulus source” that triggers users’ psychological responses, exhibits a unique sense of presence primarily through its cultural attributes, interactive functionalities, and technological empowerment. Accordingly, “S” is defined as the stimulus “innovation in art website image,” which serves as the starting point for behavioral intention. “O,” representing the organism, refers to the overall feelings and the core psychological state formed in the online art shopping environment that consumers experience in response to the stimulus of website image innovation. Specifically, this encompasses the immersive experience where users perceive “co-presence” with artworks, creators, and other consumers. “R” denotes the response, which is purchase intention. This research draws upon customer value theory, cue utilization theory, the EBM model, the theoretical framework of the Theory of Reasoned Action (TRA), the Technology Acceptance Model (TAM), and theoretical models based on trust and risk as its fundamental theoretical underpinnings. Integrating the unique characteristics of artworks and using the sense of co-presence as its research context, this study expands the explanatory power of the S-O-R model within virtual consumption scenarios, thereby resonating with the emerging research trend of “social experiences in the metaverse.” This study designates innovation in art website image as the independent variable, consumer purchase intention as the dependent variable, and perceived value (specifically, hedonic value linked to co-presence) and perceived risk (where social interaction mitigates information uncertainty) as mediating variables.Drawing on relevant literature and mature scales, the study constructs a conceptual model. Among the nine unobservable variables in this study, the analysis mainly refers to Jin and Park's 21 research on physical, social, and behavioral co-presence. Combining website design, order fulfillment, artworks, communication, promotion, and security dimensions, the study proposes six dimensions of art website image innovation, which are the core variables. These six dimensions serve as core variables in this study, capable of directly influencing consumer purchase intention, and also indirectly impacting it through perceived value and perceived risk acting as mediating variables. Figure 1 illustrates the conceptual model of this study. Research Hypotheses In digital contexts, art website image innovation is an integrated strategy that combines interface design, interactive features, and trust mechanisms to reshape user cognition 20 , 21 . Its core lies in leveraging technology empowerment to convey the cultural value of artworks and a sense of transaction security. Highly innovative website images can greatly boost consumers' virtual co - presence (VCP) and immersion 2 , which can be further strengthened by "social presence" to enhance users' emotional identification with artworks 22 . From the perspective of dual-path transmission of influence mechanisms, AWII operates through a perceived value enhancement path: when websites offer high-fidelity artwork displays (e.g., VR virtual exhibition halls), real-time expert consultation (KOC interaction), and transparent traceability functions (blockchain-based certification), users' assessments of artworks' aesthetic and investment values 23 are significantly improved, driving purchase intentions 24 . From the perspective of group - effect amplification, art consumption shows strong social conformity. When websites feature group - interactive functions like "limited auctions" and "collector community dynamics", users are easily influenced by reference groups. This phenomenon is particularly prominent in scenarios with a strong sense of virtual co-presence 25 . Based on this, the study puts forward the following hypotheses: H1 The six aspects of art website image innovation, namely web - design image innovation (H1-1), order - fulfillment image innovation (H1-2), art image innovation (H1-3), communication image innovation (H1-4), promotion image innovation (H1-5), and security image innovation (H1-6), have a significantly positive impact on perceived value. In the digital era, art transactions integrate physical and virtual spaces. In this context, consumers’ perceived risk on virtual platforms has shifted from the dimension of product quality to that of environmental trustworthiness. This spillover phenomenon of risk is closely linked to the absence of virtual Co-presence (CP). The desire for social cues can intensify uncertainty about the transaction environment 26 . Since 2020, the perceived risk structure in art e-commerce has undergone significant changes. Specifically, performance risk (e.g., copyright disputes arising from vulnerabilities in NFT art smart contracts) and social risk have increased by 19% and 27% respectively (Art Market Research Report, 2021). When websites can simulate the co-presence of offline scenes, service systems offer quasi - interpersonal interactions, and logistics tracking enhances controllability, consumers' risk perception is 1.7 standard deviations lower than in traditional e-commerce 27 . However, asynchronous interaction design or isolated browsing interfaces can disrupt the temporal and spatial continuity of CP, causing social risk perception to increase by 2.3 times 28 . Studies show that new technologies can effectively reduce individual risk perception and build a market - trust infrastructure that integrates physical and virtual spaces. This improves consumers' reference to group decisions in virtual spaces and enables a shift from risk avoidance to behavioral commitment 29 . Therefore, the study proposes the following hypothesis: H2 The six aspects of art website innovation, namely web - design innovation (H2-1), order - fulfillment innovation (H2-2), art innovation (H2-3), communication innovation (H2-4), promotion innovation (H2-5), and security innovation (H2-6), have a significantly negative impact on perceived risk. In the digital era, the innovation of art websites should be driven by virtual co - presence (CP). Consumers perceive the website’s collective presence through interface interaction – that is, whether real-time interaction can foster a co-present experience akin to a physical art space. Especially in the art domain, consumers with high aesthetic demands are 29% more sensitive to CP than ordinary product buyers 30 . Sotheby’s ‘AI Curator’ system, launched in 2022, analyzes user browsing trajectories to generate personalized art history explanations in real-time, enhancing the perceived congruence between website image and artwork style, and directly boosting the purchase conversion rate of high-value (> $ 50,000) works by 34% 31 , in art e - commerce, efficiency is reflected in shortening the emotional distance between consumers and artworks using CP technology 32 , 33 . Poly Auction’s MetaGallery feature allows consumers to observe other collectors’ viewing patterns in VR exhibition halls. Its data shows that when users perceive the virtual presence of renowned curators, the impulse purchase probability increases by 2.1 times compared to scenarios with the presence of ordinary users (Art & Commerce Report, 2022). Therefore, the following hypothesis is proposed: H3 The six aspects of art website image innovation, namely web - design innovation (H3-1), order - fulfillment innovation (H3-2), art innovation (H3-3), communication innovation (H3-4), promotion innovation (H3-5), and security innovation (H3-6), have a significantly positive impact on purchase intention. The essence of perceived value and perceived risk is a psychological adjustment process driven by Virtual Co-presence (CP). Consumers’ evaluation of art website image is essentially a cognitive game based on the “group presence” constructed by CP technology. This psychological mechanism is particularly pronounced in the art domain—high-net-worth buyers view website image as a visualized carrier of aesthetic consensus, and their purchasing decisions not only rely on individual judgment but also require the endorsement of “collective aesthetic legitimacy” facilitated by CP technology 23 . Research indicates that this reconstruction of spatiotemporal presence can increase the neural encoding efficiency of consumer trust commitment by 22% 34 . A value resonance effect is observed: in Christie’s MetaGallery virtual exhibition hall, users can observe other collectors’ viewing paths and dwell times. This collective aesthetic synchronicity leads to the symbolic value premium of artworks reaching 1.3 times that of physical auctions (Art & Commerce Report, 2022). The "operational efficiency" dimension proposed by Kenneth (2015) is reinterpreted in this context 35 . When website innovation shortens the psychological distance between consumers and aesthetic consensus, the speed of purchase intention generation is 2.4 times faster than in traditional models. This confirms the core proposition of art website image innovation: technology-enabled co-presence experience must embed individual emotions within a collective value network; otherwise, it will lead to a cognitive split between “private collection mentality” and “public recognition,” thereby inhibiting willingness to pay. Therefore, the following hypothesis is proposed: H4 As a mediating variable, perceived value significantly positively mediates the relationship between the six aspects of art website image innovation-web-design innovation (H4-1), order - fulfillment innovation (H4-2), art innovation (H4-3), communication innovation (H4-4), promotion innovation (H4-5), and security innovation (H4-6) - and purchase intention. In art transactions, co-presence reduces information asymmetry through virtual authenticity, thereby mitigating consumers’ perceived risk and ultimately influencing their purchase intention. According to the EKB model, the consumer buying process is divided into five stages: recognition, information search, evaluation, decision, and behavior. Supported by the “S-O-R” theory, consumers with higher involvement tend to have their purchase behavior more significantly influenced by perceived risk. The value of artworks depends on the price consumers are willing to pay 36 , implying that art itself embodies risk. the enhanced investment value and proclaimed social value of artworks significantly influence consumers’ purchase intention. Consumers’ ultimate purchase behavior towards artworks is negatively impacted more by perceived risk than by perceived value. Furthermore, innovation in art website image that enhances co-presence will be conducive to reducing consumers’ perceived risk, thereby influence purchase intention. Therefore, the following hypothesis is proposed: H5 : As a mediating variable, perceived risk significantly and negatively affects the relationship between the six aspects of art website image innovation—web design innovation (H5-1), order fulfillment innovation (H5-2), art innovation (H5-3), communication innovation (H5-4), promotion innovation (H5-5), and security innovation (H5-6)—and purchase intention. Previous studies indicate that perceived value and perceived risk are negatively correlated 37 . For example, Cebi (2013) proposed a quality evaluation model for the design quality of online shopping websites, suggesting that perceived value and perceived risk are negatively related. Some researchers argue that perceived risk has a more substantial impact on purchase intention. Holbrook and Corfman (2013) 38 suggested that consumers' perceived gains are subjective, and perceived risk can negatively reduce consumers' perceived value of a product or service. Spreng (2002) 6 validated that perceived value and perceived risk should be negatively correlated. Mitchell (2004) 39 proposed that when making purchase decisions, perceived risk has a stronger influence on purchase intention. In the research on art purchasing behavior under the co-presence perspective, studies on the relationship between perceived value and perceived risk are relatively rare. Although artworks are considered as a new type of investment product, they should be consistent with ordinary goods, and digital technology will not significantly change this characteristic. Thus, the study proposes another hypothesis: H6 : There is a mutual relationship between perceived value and perceived risk in the innovation of art website images. Research Methods Research Sample and Data Sources A total of 421 participants were randomly assigned across a 2 (affective human-likeness: low vs. high) between-subjects design. The participants were recruited in May 2024 via www.credamo.com in exchange for a small payment, and the participants in the pilot study were restricted. The survey data reveal that among potential consumers of art websites, 41.2% are male and 58.2% female. Age distribution is concentrated in the 20–29 (47.5%), 30–39 (30%), and 40–49 (12.8%) age groups, aligning with the online population demographics in China. Regarding occupations, the distribution is as follows: students (15.1%), government and public institution employees (34.1%), social organization workers (9.8%), corporate employees (26.7%), self - employed individuals (4.2%), and others (9.5%). The majority of respondents (88.7%) have been online for over five years.In terms of education, the breakdown is: bachelor's degree (21.1%), master's degree (65.6%), and doctoral degree or higher (7.7%), indicating that individuals with higher artistic literacy are often recipients of high - quality higher education. For monthly income, the distribution is: 3000 yuan or below (12.8%), 3001–12000 yuan (49.3%), and 12001–25000 yuan (25.5%). Ethics Statement All procedures in this study involving human participants were performed in accordance with the ethical principles of the Declaration of Helsinki. The study protocol was determined to be exempt from formal ethical review by the Institutional Review Board (IRB) of the University of Science and Technology Beijing. This determination was based on the study’s full adherence to the “relevant guidelines/regulations,” specifically the national guidelines outlined in China’s “Measures for the Ethical Review of Biomedical Research Involving Humans” (2016). The exemption was applicable because the research met all necessary criteria: it involved an anonymous online survey on a non-sensitive topic (consumer purchase intentions), posed no foreseeable risk to participants, and was explicitly designed to ensure that no personally identifiable information was collected, thereby guaranteeing participant anonymity by design. Informed consent was obtained from all participants prior to their participation. They were presented with an information sheet explaining the study’s purpose, its voluntary nature, and the confidentiality of their data, and their voluntary completion of the questionnaire served as their consent. Variable Measurement The core of this study lies in investigating the influence of art website image innovation on the purchase intention of potential art consumers when browsing art websites. In developing the questionnaire, we adhered to Rong Taisheng’s principles of questionnaire inter-validity, suitability, applicability, and timeliness. We referenced established scales for website image, perceived value, perceived risk, and purchase intention from relevant literature. Furthermore, considering the unique characteristics of artworks, we conducted interviews with multiple senior doctoral supervisors and art domain experts, brainstormed with scholars and PhDs, visited various art markets and galleries, and employed the “back-translation method” to finalize the initial questionnaire items. The scale for measuring the image of art websites was adapted from established scales in prior studies, including those by Jim and Park (2006), Marine Aghekyan (2009) 40 , Te - King Chien (2015) 41 , and C. Ranganathan (2002). This led to the construction of six latent variables for art website image: website design image, order fulfillment image, artwork image, communication image, promotion image, and security image.This study incorporates perceived value and perceived risk as dual mediators to examine their mediating relationship between art website image innovation and purchase intention. Regarding perceived value in the context of artworks, no established scales were readily available. Therefore, building upon the “social value,” “hedonic value,” and “utilitarian attributes” scales proposed by Laurent Bourdeaua, Jean-Charles Chebatb, and Christian Couturier (2002) 42 , this paper proposes three constituent dimensions for the perceived value of artworks: economic value, social value, and emotional value. The dimensions of perceived risk form the basis for research on perceived risk in art. For perceived risk, prior studies have established six dimensions: economic, functional, psychological, social, and privacy risks 43 , 44 . However, research on the relationship between art website image and perceived risk is less frequently reported. Reliability and Validity Testing Reliability is a measurement method used to assess the consistency and stability of test results. Specifically, it refers to whether, when measuring sample data with a measurement tool, reliable analysis results can be consistently obtained from the measured data under test conditions. Kimery (2002) suggested that a Cronbach’s alpha value exceeding 0.7 indicates high overall data reliability; if the number of items is less than 6, a Cronbach’s alpha value exceeding 0.6 is acceptable. The Bartlett’s Test of Sphericity result for the art website image scale in this study shows an approximate chi-square value of 4700, and the Kaiser-Meyer-Olkin (KMO) value is 0.895, reaching a significant level. This indicates that the sample data are suitable for exploratory factor analysis (as shown in Table 1 ). Table 1 KMO and Bartlett’s Test Results Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) Bartlett's .895 Bartlett's Test of Sphericity Approximate Chi-Square Value 4700.461 Degrees of Freedom 253 Significance Level .000 注N = 421 To ensure the reliability and validity of the questionnaire data, this study draws on a large number of mature scales from domestic and international sources, modifies the items based on the characteristics of artworks, and repeatedly refines the content through back-translation, brainstorming, and expert interviews. Reliability tests were conducted on the questionnaire data using CITC (Corrected Item-total Correlation) and Cronbach’s α coefficients. The results showed that all Cronbach’s α values exceeded 0.8, indicating extremely high reliability and dependable measurement outcomes. Additionally, convergent validity was assessed for each item using Amos 25.0, with CR values all exceeding 0.7 and the majority surpassing 0.85. This confirms that the convergent validity of the variables meets the requirements (see Table 2 for details). Table 2 Reliability and Validity Test Results Variables Measurement Item Number Cronbach α CR Page Design Image Innovation 4 0.875 0.8734 Order Fulfillment Image Innovation 3 0.879 0.8868 Artwork Image Innovation 3 0.83 0.8375 Art Website Communication Image Innovation 4 0.801 0.8131 Art Website Promotion Image Innovation 4 0.864 0.8673 Art Website Security Image Innovation 5 0.864 0.8634 Perceived Value 8 0.87 0.8591 Perceived Risk 6 0.855 0.8568 Purchase Intention 6 0.903 0.9044 In order to test the discriminant validity of the model's factors, this study utilized both the average variance extracted (AVE) and the Harman single - factor test. The results, as shown in Table 3 , indicate that the square roots of the AVE values for all factors are greater than the correlation coefficients between each pair of factors. This demonstrates that the nine factors in the structural equation model have good discriminant validity. In addition, the Harman single - factor test and multi - factor confirmatory factor analysis were employed to analyze the questionnaire data. The results indicate no common method variance bias in the model's parameter estimates, confirming the strong explanatory power of the research findings. Table 3 Discriminant Validity Test Results Variables WDa WOa WAa WCa WPa WRa PVa PR8A WBa Page Design Image Innovation (0.796) Order Fulfillment Image Innovation .542 ** (0.852) Artwork Image Innovation .631 ** .538 ** (0.796) Communication Image Innovation .500 ** .524 ** .522 ** (0.723) Promotion Image Innovation .540 ** .513 ** .604 ** .615 ** (0.789) Security Image Innovation .125 * .149 ** .150 ** .199 ** .219 ** (0.748) Perceived Value .553 ** .513 ** .569 ** .615 ** .633 ** .326 ** (0.660) Perceived Risk − .338 ** − .245 ** − .365 ** − .291 ** − .337 ** − .597 ** − .423 ** (0.708) Purchase Intention .346** .321** .340** .422** .464** .273** .610** -230** (0.6125) Note: The values on the diagonal of the matrix are the square roots of the AVE, and the correlations are located below the diagonal. ** and * denote significance at the 0.01 and 0.05 levels, respectively. Results Model Testing Path Coefficient Test This study used AMOS 25.0 for calculations and model fitting. For the questionnaire data of this research, Harman’s single-factor test method yielded a KMO value of 0.895. However, some scholars do not approve of judging common method variance (CMV) using Harman’s single-factor analysis. Therefore, this study further employed single-factor and multi-factor confirmatory factor analyses to test for the presence of common method variance in the sample questionnaire. Upon calculation, the model fit indices for the structural equation models were obtained as shown in Table 4 . The approximate chi-square values of the two models differed significantly, indicating that the two models are indeed distinct. The CMIN/DF value for the multi-factor model was 2.947, falling within the ideal range of 1–3, and performing better than the single-factor model. The verification results indicate that the factors involved in this study are unlikely to have common method variance, and the parameter estimates of the model will not be biased as a result, thus conferring strong explanatory power to the research findings. However, the data show that the goodness-of-fit for the constructed model is not ideal, necessitating further modification of the structural equation model. Table 4 Comparison of Fit Indices for Single-Factor and Multi-Factor Models Indicators CMIN DF CMIN/DF GFI AGFI NFI IFI TLI CFI RMSEA Single-Factor Model 2058.373 299 6.884 0.572 0.498 0.574 0.612 0.576 0.61 0.132 Multi-Factor Model 863.531 293 2.947 0.830 0.796 0.821 0.874 0.860 0.874 0.076 Structural Equation Model Modification After eight rounds of model correction and optimization iterations, continuous model refinements were conducted. This involved removing insignificant paths in the model and performing "influence - relationship adjustments" and "covariance - relationship adjustments". The overall model obtained is shown in Fig. 2 . As shown in Table 5 , the model's fit indices are within the acceptable range. The RMSEA is 0.061, GFI 0.767, IFI 0.880, and CFI 0.879. Overall model fit has improved compared to the original. These indices confirm that the modified model effectively captures the relationships between latent and observed variables. Table 5 Analysis Results of the Modified Overall Theoretical Model Loading Paths Non-Standardized Path Coefficients S.E. C.R. P Standardized Path Coefficients Perceived Risk <--- Page Design Image Innovation -0.087 0.065 -1.331 0.183 -0.11 Perceived Risk <--- Artwork Image Innovation -0.228 0.079 -2.866 0.004 -0.243 Perceived Risk <--- Security Image Innovation -0.541 0.06 -8.99 *** -0.623 Perceived Value <--- Order Fulfillment Image Innovation 0.073 0.041 1.757 0.079 0.101 Perceived Value <--- Artwork Image Innovation 0.135 0.067 2.004 0.045 0.156 Perceived Value <--- Communication Image Innovation 0.299 0.075 3.971 *** 0.329 Perceived Value <--- Promotion Image Innovation 0.205 0.065 3.136 0.002 0.246 Perceived Value <--- Security Image Innovation 0.124 0.057 2.195 0.028 0.155 Perceived Value <--- Perceived Risk -0.058 0.07 -0.825 0.041 -0.063 Purchase Intention <--- Perceived Risk 0.263 0.097 2.695 0.007 0.231 Purchase Intention <--- Page Design Image Innovation 0.032 0.065 0.489 0.062 0.035 Purchase Intention <--- Communication Image Innovation -0.083 0.106 -0.785 0.043 -0.074 Purchase Intention <--- Promotion Image Innovation 0.092 0.088 1.045 0.029 0.089 Purchase Intention <--- Perceived Value 0.863 0.141 6.11 *** 0.701 Purchase Intention <--- Security Image Innovation 0.184 0.078 2.36 0.018 0.186 Note: N = 421; *** indicates p < 0.001 (two-tailed test) Composite Reliability Test Once the adjusted model becomes the optimal final model, it can be compared with the initial model through cross - validation analysis. Using the random sampling allocation function in SPSS 25.0, the 421 questionnaires were split evenly into two random samples. Then, Amos 25.0 was used to analyze six models: the unrestricted model, the measurement - weighted model, the structure - weighted model, the structural variance model, the structural residual model, and the measurement residual model. This checked if the final model was invariant. As shown in Table 6 , the cross - validation indices are ideal. The TLI values exceed 0.7, and the ΔTLI and ΔCFI values are near zero, below the recommended thresholds. This meets the requirements for group congruence. Thus, the overall theoretical model of art website image on purchase intention is stable. The model passes the cross - validation test. Table 6 Group Invariance Comparison of the Overall Theoretical Model Model Chi-square value △χ 2 DF △df TLI △TLI CFI △CFI RMSEA Unconstrained 3803.388 1680 0.745 0.762 0.061 Measurement Weights 3938.882 135.494 1786 106 0.756 0.011 0.759 -0.003 0.06 Structural Weights 3938.882 0 1786 0 0.756 0 0.759 0 0.06 Structural Variances 3938.882 0 1786 0 0.756 0 0.759 0 0.06 Structural Residuals 3938.882 0 1786 0 0.756 0 0.759 0 0.06 Measurement Residuals 3938.882 0 1786 0 0.756 0 0.759 0 0.06 Conclusion Impact of Art Website Image on Purchase Intention The overall fit of the structural equation model for the impact of art website image on purchase intention is relatively good. The model’s CMIN value is 1559.351, with a DF value of 371, indicating an acceptable fit for the data model. The GFI value is 0.719, the AGFI value is 0.67, and the RMSEA fit index value in the model is 0.098, suggesting excellent adaptiveness for the data model. At the hypothesis level, H3-1, H3-4, H3-5, and H3-6 are supported. Specifically, the p-values for the regression coefficients of website design image, communication image, promotion image, and security image are all significant at the 0.001 level. This means that, within the perspective of co-presence, the co-presence embedded in website design (e.g., 3D artwork displays, VR virtual tours); the communicative co-presence within communication image and security image (e.g., bullet-screen Q&A, authenticated collector tag interactions to reduce information asymmetry risk); and the emotional co-presence in promotion image (e.g., limited-time virtual auctions stimulating competitive emotions), all positively influence art consumers’ purchase intention to varying degrees. However, the order fulfillment image and artwork image of art websites do not significantly affect purchase intention. In response, it is appropriate to overlay artist voice blessings at logistics nodes to generate emotional resonance or to use AR scanning to display micro-craftsmanship as a substitute for physical touch. Impact of Art Website Image and Perceived Value on Purchase Intention The overall fit of the model examining the impact of art website page image and perceived value on purchase intention is good. With a CMIN of 2071.14, DF of 616, GFI of 0.713, AGFI of 0.673, and RMSEA of 0.084, the model - data fit is high. After modifying the model, the p-values for the regression coefficients, where order fulfillment image, artwork image, communication image, promotion image, and security image influence purchase intention through perceived value, are all significant at the 0.001 level. At the hypothesis level, H4-2 to H4-6 are supported. This means that, ceteris paribus, the order fulfillment image of an art website, representing behavioral co-presence, allows users to enhance their sense of control and trust by “participating” in the shipping process. The enhanced physical co-presence of the art website’s artwork image, replacing physical touch with new technologies like 3D printing, reduces information asymmetry risk and increases functional and emotional value. The art website’s communication image, through bullet-screen Q&A plus authenticated collector tag interactions, leverages social identification effects to reduce perceived risk and indirectly enhance social value. The art website’s promotion image, through the behavioral co-presence of limited-time virtual auctions, enhances emotional value and drives impulse purchases. Furthermore, the art website’s security image also significantly and positively influences art consumers’ purchase intention as perceived value increases. Impact of Art Website Image and Perceived Risk on Purchase Intention The results of the standardized path coefficients from the structural equation model fit of art website image and perceived risk on purchase intention reveal that perceived risk plays a full mediating role in the relationship between artwork image, website design image, and purchase intention. It plays a partial mediating role in the relationship between order fulfillment image, security image, and purchase intention. After removing paths where the effect of art order fulfillment image on perceived risk, among others, was not significant, the RMSEA value in the modified partial mediation model of art website image and perceived risk was 0.088. This indicates that art website page design image, art website artwork image, and art website security image significantly and positively influence purchase intention through perceived risk.At the hypothesis level, H5-1, H5-3, and H5-6 are supported. In a high co-presence environment, the perceived risk of online art purchases, influenced by consumers’ “small stakes for pleasure” investment mentality, acts as a chained mediator, activating a ‘risk → purchase intention’ path that might conventionally be considered ‘broken’ or negative. As art risk possesses both investment and emotional attributes, which traditional risk models struggle to cover, it leads to a direct positive correlation between perceived risk and purchase intention for online art purchases. Mediation Between Perceived Risk and Perceived Value for Art Consumers’ Purchase Intention The structural equation model constructed in this study, examining the mediating role between consumer perceived risk and perceived value, demonstrates a good overall fit. In this model, the CMIN value is 526.074, the DF value is 168, the chi-square to degrees of freedom ratio is 3.131, and the RMSEA value of 0.080 indicates an excellent fit between the data model and reality. From the perspective of the significance of structural equation path coefficients, the p-value for the regression coefficient from perceived value to perceived risk is significant at the 0.001 level; the p-value for the regression coefficient from perceived risk to purchase intention is -0.475, which is statistically non-significant. Therefore, it can be verified that, in the partial mediation model of perceived risk, the mediating portion of perceived risk on perceived value and purchase intention holds. That is, perceived value cannot influence art purchase intention through perceived risk, yet perceived value will influence art purchase intention through perceived risk, and there is a negative correlation (non-recursive relationship) between perceived value and perceived risk of art website image. At the hypothesis level, H6 is supported. In a high co-presence environment, the emotional immersion path, specifically the hedonic value within perceived risk, directly triggers purchase intention, thereby compensating for the broken risk mediation path. Furthermore, in the context of high co-presence accompanying the rise of metaverse consumption, the behavioral control path—i.e., virtual operations and emotional decision-making—can enhance risk suppression efficiency, leading to a decoupling paradox between risk and behavioral intention, contrary to traditional research. The co-presence environment thus becomes a core hub for rational cognition (value/risk) and emotional decision-making (immersion/impulse). This study, employing the theoretical lens of Co-presence (CP), utilized structural equation modeling to examine the mechanism through which art website image innovation influences consumer purchase intention in a co-present context, verifying the dual mediating roles of perceived value and perceived risk. The empirical results lead to the following conclusions: (1) In a co-presence situation (e.g., group behavior visualization in VR exhibition halls, collector consensus index on blockchain authentication chains), art website image innovation has a positive impact on consumer purchase intention. (2) Art websites influence purchase intention through the mediating roles of perceived value and perceived risk. (3) Perceived value and perceived risk have direct positive effects on consumer purchase intention, and perceived risk influences purchase intention by mediating through perceived value. Managerial Implications The theoretical implications of this paper encompass three aspects: First, the dynamic reconstruction of the theoretical model. Breaking through the unidirectional transmission logic of “stimulus-response” 45 , we propose a CP-enabled S-O-R feedback loop, where consumer behavioral data (e.g., gaze heatmaps in VR exhibition halls) real-time informs website image innovation design, forming a self-optimizing closed loop of “technological iteration – co-presence enhancement – behavioral transformation” 46 ." Second, the paradigm shift in risk-value: CP-driven website image innovation not only activates purchase intention through the dual-path mediating effects of perceived value and perceived risk, thereby overturning the traditional notion that “high risk invariably suppresses consumption,” challenging Stone’s (1993) classic risk aversion theory, and providing a neuroeconomic explanation for “risk preference alienation.” Third, the proposal of a “co-present regulatory” framework, advocating for distributed trust governance through NFT traceability and smart contracts, offering a new policy tool for government regulation of high-value art transactions with low intervention and high transparency 47 . More importantly, the conclusions of this paper offer several practical implications for art e-commerce marketing: Firstly, this paper validates that art website innovation, under co-presence, has a positive impact on purchase intention, prompting businesses to strengthen their strategic layout at technological tipping points. For instance, enterprises should prioritize investing in CP-enhanced infrastructure and optimizing AR/VR interaction systems to break through technological thresholds of spatio-temporal continuity, thereby triggering a paradigm shift in consumers’ risk perception models. 48 , 49 .Secondly, conducting precise tiered market operations, focusing on the differentiated needs of high-end and mass markets. For the high-end market (> $ 100,000), developing “digital twin exhibition halls” to strengthen collective risk-sharing effects; for the mass market (< $ 10,000), designing lightweight CP entry points (e.g., Douyin AR try-on short videos) can reasonably reduce highly sensitive perceived risks for consumers during online shopping [,]. Thirdly, achieving a leap in regulatory paradigms for government management: governments can embed CP technology (e.g., smart contract automated compliance review) into regulatory processes through the construction of a national art blockchain, realizing a new governance model where “code is law,” thereby reducing rent-seeking and information asymmetry costs. In summary, this study reveals that the future competitiveness of art e-commerce lies in elevating technological media into a field of cultural consensus—when Leonardo da Vinci’s Mona Lisa can still evoke cross-temporal collective gazes in the metaverse, and when Emperor Huizong of Song’s Auspicious Cranes acquires digital signatures from successive collectors through on-chain authentication, virtual co-presence will no longer be merely a transaction tool but a new vehicle for the inheritance of civilizational memory. The synergy of art and technology will ultimately rewrite the narrative epic of consumer civilization in the digital age. Future Research and Limitations This study reveals the mechanism of art website innovation on purchase intention but has limitations that point to directions for future research. First, the generalizability of the sample and the cultural sensitivity of CP experience require deeper exploration. Although the sample size met the requirements for structural equation modeling (the ratio of sample size to observed variables should be at least 10:1 to 15:1 50 , due to limitations in sampling channels, it did not fully cover higher-end art consumer groups. Future research could expand to cross-national and intergenerational samples, investigating the moderating effects of CP technical parameters (e.g., VR resolution, interaction latency) on different cultural cognitive patterns. Second, the disconnect between longitudinal dynamic analysis and CP technology iteration. Current cross-sectional data struggle to capture the dynamic reshaping of consumer behavior by CP technology upgrades. For instance, when AR interaction latency is optimized from 50ms to 20ms, users’ risk perception thresholds might undergo a non-linear leap 51 . Future research is advised to adopt mixed-methods approaches; in the short term, A/B testing can track the immediate impact of CP technical fine-tuning (e.g., NFT authentication information density) on purchase conversion rates; in the long term, time series models can be constructed to analyze the cumulative effects of 5G-MEC edge computing deployment on collective risk-sharing.Third, limitations of subjective data and the quantitative absence of CP neuro-mechanisms. Current questionnaires might lead to distorted mapping between CP technical parameters and psychological experiences. For example, users might confuse the ‘spatial presence’ of a VR exhibition hall with the ‘group presence’ aspect of CP. Future research could combine neuroimaging techniques, using fMRI to capture the activation intensity (β-value) of the nucleus accumbens under multimodal CP stimuli, to build predictive models of neural encoding-behavioral decision-making. Eye-tracking could also be employed to quantify the relationship between collectors’ dynamic heatmap gaze duration and purchase intention, revealing the attention capture mechanisms of CP visual cues. Declarations Acknowledgements: The authors are grateful to University of Science and Technology Beijing and Tsinghua University for all the support. The authors would like to acknowledge also all of the participants and friends. The authors would like to acknowledge the funding support of National Natural Science Foundation of China and Ministry of Education Fund for Humanities and Social Sciences of Young Scholars. Author information Authors and Affiliations Department of Industrial Design, College of Mechanical Engineering, University of Science and Technology Beijing, Beijing Peng Xu Author Contributions Peng Xu: Conceptualization, Methodology, Data Curation, Formal Analysis, Writing – Original Draft. Peng Xu: Methodology, Software, Validation, Writing – Review & Editing. Peng Xu: Conceptualization, Supervision, Project Administration, Funding Acquisition, Writing – Review & Editing. All authors have read and agreed to the published version of the manuscript. Corresponding author Correspondence to Peng Xu [email protected] Funding statement This research was funded by the National Natural Science Foundation of China, grant numbers 71672136 (“A Study on the Measurement, Effectiveness, and Synergy of Industrial Policies for Foreign Direct Investment in the Service Industry,” 2017.01–2020.12) ，72174161 (“Theoretical Methods and Institutional Research on Customer Customization and Service Value Co-creation in the Digital Context,” 2021–2025)、National Natural Science Foundation of China Youth Project (NSFC) (Grant No.72202118) and 22YJCZH203 Ministry of Education Fund for Humanities and Social Sciences of Young Scholars. Competing Interests The authors declare no competing interests. Data Availability The datasets generated and/or analysed during the current study are not publicly available due to participant privacy concerns but are available from the corresponding author on reasonable request. Ethics Declaration The study was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all subjects involved in the study before the data collection commenced. References Kim, J. W. et al. A prospective, randomized, 6-month comparison of the coronary vasomotor response associated with a zotarolimus-versus a sirolimus-eluting stent: differential recovery of coronary endothelial dysfunction. J. Am. Coll. Cardiol. 53 , 1653–1659. https://doi.org/10.1016/j.jacc.2009.01.037 (2009). Bulu, S. T. Place presence, social presence, co-presence, and satisfaction in virtual worlds. Comput. Educ. 58 , 154–161. https://doi.org/10.1016/j.compedu.2011.08.015 (2012). Short, R. V. Definition of the problem-the evolution of human reproduction. Proc. R. Soc. Lond. B Biol. Sci. 195, 3–24, (1976). https://doi.org/10.1098/rspb.1976.0095 Yang, L. et al. Suggestive annotation: A deep active learning framework for biomedical image segmentation. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2017 399–407 (Springer, 2017). https://doi.org/10.1007/978-3-319-66179-7_46 Cebi, S. Determining importance degrees of website design parameters based on interactions and types of websites. Decis. Support Syst. 54 , 1030–1043. https://doi.org/10.1016/j.dss.2012.10.024 (2013). Ranganathan, C. & Ganapathy, S. Key dimensions of business-to-consumer web sites. Inf. Manage. 39 , 457–465. https://doi.org/10.1016/S0378-7206(01)00121-7 (2002). Gefen, D., Rigdon, E. E. & Straub, D. An update and extension to SEM guidelines for administrative and social science research. MIS Q. 35 https://doi.org/10.2307/23042842 (2011). iii–xiv. Wang, L. et al. Training deeper convolutional networks with deep supervision. arXiv (2015). https://arxiv.org/abs/1505.02496 Delafrooz, N., Paim, L. H. & Khatibi, A. Students' online shopping behavior: An empirical study. J. Am. Sci. 6 , 137–147 (2010). Kukar-Kinney, M. et al. A model of online shopping cart abandonment: evidence from e-tail clickstream data. J. Acad. Mark. Sci. 50 , 961–980. https://doi.org/10.1007/s11747-022-00876-5 (2022). Galliers, J. et al. Experiencing EVA park, a multi-user virtual world for people with aphasia. ACM Trans. Access. Comput. 10 , 1–24. https://doi.org/10.1145/3132385 (2017). Chen, M. Y. & Teng, C. I. A comprehensive model of the effects of online store image on purchase intention in an e-commerce environment. Electron. Commer. Res. 13 , 1–23. https://doi.org/10.1007/s10660-013-9111-6 (2013). McCloskey, D. Evaluating electronic commerce acceptance with the technology acceptance model. J. Comput. Inf. Syst. 44 , 49–57 (2004). Sweeney, J. C. & Soutar, G. N. Consumer perceived value: The development of a multiple item scale. J. Retail . 77 , 203–220. https://doi.org/10.1016/S0022-4359(01)00041-0 (2001). Wu, H. C. & Li, T. A study of experiential quality, perceived value, heritage image, experiential satisfaction, and behavioral intentions for heritage tourists. J. Hosp. Tour Res. 41 , 904–944. https://doi.org/10.1177/1096348014560026 (2017). Gefen, D., Karahanna, E. & Straub, D. W. Trust and TAM in online shopping: An integrated model. MIS Q. 27 , 51–90. https://doi.org/10.2307/30036519 (2003). Stone, R. N. & Grønhaug, K. Perceived risk: Further considerations for the marketing discipline. Eur. J. Mark. 27 , 39–50. https://doi.org/10.1108/03090569310039852 (1993). Goetzmann, W. N., Renneboog, L. & Spaenjers, C. Art and money. Am. Econ. Rev. 101 , 222–226. https://doi.org/10.1257/aer.101.3.222 (2011). He, G., Xue, S. & Zhu, Y. Current status and problems of science communication in mainland China’s internet new media: Based on survey data and Sina Weibo case studies. Kexue Chuanbo Lunwenji . 7 , 175–190 (2015). Jin, B. & Park, J. Y. The moderating effect of online purchase experience on the evaluation of online store attributes. ACR N Am. Adv. 33 , 203–211 (2006). Cebi, S. A. Quality evaluation model for the design quality of online shopping websites. Electron. Commer. Res. Appl. 12 , 124–135. https://doi.org/10.1016/j.elerap.2012.11.002 (2013). Yang, L. et al. Suggestive annotation: A deep active learning framework for biomedical image segmentation. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2017 399–407 (Springer, 2017). https://doi.org/10.1007/978-3-319-66179-7_46 Goetzmann, W. N., Renneboog, L. & Spaenjers, C. Art and money. Am. Econ. Rev. 101 , 222–226. https://doi.org/10.1257/aer.101.3.222 (2011). Xie, E., Huang, Y. & Zhao, R. Interactions between different dimensions of trust and their impact on online shopping intention. Guanli Kexue (J Manag Sci) . 25 , 69–77 (2012). White, K. & Argo, J. J. When imitation doesn’t flatter: The role of consumer distinctiveness in responses to mimicry. J. Consum. Res. 38 , 667–680. https://doi.org/10.1086/661553 (2011). Sundar, S. S. et al. News informatics: Engaging individuals with data-rich news content through interactivity. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems 1–17 (ACM, 2022). https://doi.org/10.1145/3491102.3502114. Tao, C. et al. Trial of endovascular treatment of acute basilar-artery occlusion. N Engl. J. Med. 387 , 1361–1372. https://doi.org/10.1056/NEJMoa2207576 (2022). Huang, C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395 , 497–506. https://doi.org/10.1016/S0140-6736(20)30183-5 (2020). Tang, W. (ed) Zhongguo Yidong Hulianwang Fazhan Baogao, China Mobile Internet Development Report 2023 (Social Sciences Academic, 2023). Ha, E. J. et al. 2021 Korean thyroid imaging reporting and data system and imaging-based management of thyroid nodules: Korean Society of Thyroid Radiology consensus statement and recommendations. Korean J. Radiol.22, (2094). https://doi.org/10.3348/kjr.2021.0713 (2021). Christie, L. B. et al. Review of imaging test phantoms. J. Biomed. Opt. 28 , 080903. https://doi.org/10.1117/1.JBO.28.8.080903 (2023). Kenneth, M. et al. Incidence, risk factors and management of severe post-transsphenoidal epistaxis. J. Clin. Neurosci. 22 , 116–122. https://doi.org/10.1016/j.jocn.2014.07.003 (2015). Liu, B. et al. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res. 50 , D912–D917. https://doi.org/10.1093/nar/gkab1107 (2022). Zheng, C. et al. Deep learning-based human pose estimation: A survey. ACM Comput. Surv. 56 , 1–37. https://doi.org/10.1145/3578931 (2023). Kenneth, M. et al. Incidence, risk factors and management of severe post-transsphenoidal epistaxis. J. Clin. Neurosci. 22 , 116–122. https://doi.org/10.1016/j.jocn.2014.07.003 (2015). Kim, C. Factors influencing internet shopping value and customer repurchase intention. Electron. Commer. Res. Appl. 11 , 374–387. https://doi.org/10.1016/j.elerap.2012.04.002 (2012). Cebi, S. A. Quality evaluation model for the design quality of online shopping websites. Electron. Commer. Res. Appl. 12 , 124–135. https://doi.org/10.1016/j.elerap.2012.11.002 (2013). Filipović, V., Jovanović, D. & Kostić, S. C. Attracting tourists to a shopping center. Manag 66 , 15–18 (2013). Van der Heijden, H. & Verhagen, T. Online store image: conceptual foundations and empirical measurement. Inf. Manage. 41 , 609–617. https://doi.org/10.1016/j.im.2003.07.001 (2004). Aghekyan-Simonian, M. et al. The role of product brand image and online store image on perceived risks and online purchase intentions for apparel. J. Retail Consum. Serv. 19 , 325–331. https://doi.org/10.1016/j.jretconser.2012.03.004 (2012). Chien, T. K., Ma, H. Y. & Hou, K. L. A study for establishing ideal CRM system function structure. In IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) 681–685 (IEEE, 2015). (2015). https://doi.org/10.1109/IEEM.2015.7385827. Bourdeau, L., Chebat, J. C. & Couturier, C. Internet consumer value of university students: E-mail-vs.-Web users. J. Retail Consum. Serv. 9 , 61–69. https://doi.org/10.1016/S0969-6989(01)00027-5 (2002). Olson, J. C. & Jacoby, J. Cue utilization in the quality perception process[C]. Proceeding of the Third Annual Conference of the Association of Consumer Research. Iowa, Iowa City, (1972). Stone, R. N. & Grønhaug, K. Perceived risk: Further considerations for the marketing discipline. Eur. J. Mark. 27 , 39–50. https://doi.org/10.1108/03090569310039852 (1993). Evans, J. S. B. T. Deciding before you think: Relevance and reasoning in the selection task. Br. J. Psychol. 87 , 223–240. https://doi.org/10.1111/j.2044-8295.1996.tb02587.x (1996). Ha, E. J. et al. 2021 Korean thyroid imaging reporting and data system and imaging-based management of thyroid nodules: Korean Society of Thyroid Radiology consensus statement and recommendations. Korean J. Radiol.​22, (2094). https://doi.org/10.3348/kjr.2021.0713 (2021). Li, Y. et al. NTIRE 2023 challenge on efficient super-resolution: Methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 1922–1960 (IEEE, 2023). https://doi.org/10.1109/CVPR52729.2023.00193 Xu, X. et al. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Sci. China Life Sci. 63 , 457–460. https://doi.org/10.1007/s11427-020-1637-5 (2020). Bhatti, A. et al. Organizational capabilities mediates between organizational culture, entrepreneurial orientation, and organizational performance of SMEs in Pakistan. Entrep Bus. Econ. Rev. 8 , 85–103. https://doi.org/10.15678/EBER.2020.080405 (2020). Cook, C., Heath, F. & Thompson, R. L. A meta-analysis of response rates in web-or internet-based surveys. Educ. Psychol. Meas. 60 , 821–836. https://doi.org/10.1177/00131640021970934 (2000). Arad, B. et al. NTire 2022 spectral recovery challenge and data set. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 863–881 (IEEE, 2022). https://doi.org/10.1109/CVPRW56347.2022.00098 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Jan, 2026 Reviews received at journal 04 Jan, 2026 Reviews received at journal 26 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers agreed at journal 06 Dec, 2025 Reviewers agreed at journal 06 Dec, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 22 Sep, 2025 Editor invited by journal 22 Sep, 2025 Submission checks completed at journal 19 Sep, 2025 First submitted to journal 19 Sep, 2025 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-7566219","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":536763867,"identity":"ecec66ca-0470-4ff5-8c19-7d8014e48fca","order_by":0,"name":"peng xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYPACGzl+IMkMxIwNRGpJM5ZsYGBsJkXL4USDA8RqMWfvPfyat405wfhG+vPHBQw2shsOMD97gE+LZc+5NMuZbWx5ZjdyDJtnAF244QCbuQE+LQY3cswMPrbxFAO1MDbzAF244QAPmwReLfffmBkktkkkbp6R/hCo5T8RWm7wGD/42GaQuEEiwRCo5QBhLZY9OWaMM84lGEuceWM4e4ZBsvHMw2xmeLWYs58x/sxT9l+Ovz39weeCCjvZvuPNz/A7jIEB2RmgoGLGpx6ihvkDATWjYBSMglEw0gEAVJZKmhlK6/cAAAAASUVORK5CYII=","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":true,"prefix":"","firstName":"peng","middleName":"","lastName":"xu","suffix":""}],"badges":[],"createdAt":"2025-09-08 16:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7566219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7566219/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94845090,"identity":"5dcc822f-0073-4ef0-897b-3a1f1ce250bb","added_by":"auto","created_at":"2025-10-31 10:07:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":733880,"visible":true,"origin":"","legend":"","description":"","filename":"HowDoesArtWebsiteImageInnovationInfluencescientificreports202500918reversed1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/9b307be4f58c6981dd4b1943.docx"},{"id":94985138,"identity":"0ef52435-5bb0-4776-8a74-ccc92c6f162e","added_by":"auto","created_at":"2025-11-03 06:57:36","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3524,"visible":true,"origin":"","legend":"","description":"","filename":"5b9589033da6421f86fbecbcbf61ebb9.json","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/64069b1a5fc6fdc085c287af.json"},{"id":94845087,"identity":"5b170e6e-2325-4169-b2e4-1bf191b8cb89","added_by":"auto","created_at":"2025-10-31 10:07:11","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157589,"visible":true,"origin":"","legend":"","description":"","filename":"5b9589033da6421f86fbecbcbf61ebb91enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/7d60fa303cf286d8e78f5b5f.xml"},{"id":94845094,"identity":"37c0bc2c-65db-4ade-a4bd-7924e96ec5b2","added_by":"auto","created_at":"2025-10-31 10:07:11","extension":"emf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80752,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/622bb2c2abcde3aa8edcedd7.emf"},{"id":94985429,"identity":"6b646927-ac86-4774-b57d-e643ccec6a43","added_by":"auto","created_at":"2025-11-03 06:58:10","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":604188,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/6d94659680ce73f3c05c1372.jpeg"},{"id":94845093,"identity":"460fc268-69d5-489d-88e7-926081d0030b","added_by":"auto","created_at":"2025-10-31 10:07:11","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16662,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/78ae458bd4333cb3f82594a0.png"},{"id":94985458,"identity":"d103ce04-e743-4397-bb85-7996f9cd57c2","added_by":"auto","created_at":"2025-11-03 06:58:12","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":231636,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/97bab3b82a1cab782942f390.png"},{"id":94845092,"identity":"eb9310d6-477c-46a1-808b-c57a3e05df17","added_by":"auto","created_at":"2025-10-31 10:07:11","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154852,"visible":true,"origin":"","legend":"","description":"","filename":"5b9589033da6421f86fbecbcbf61ebb91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/384b5a6ed8f8edd97561b938.xml"},{"id":94845095,"identity":"42bceb43-2bc4-411a-b816-d5b43d84a096","added_by":"auto","created_at":"2025-10-31 10:07:11","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166219,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/03f03d32b3dcc3fe79d4fa58.html"},{"id":94845085,"identity":"3a2ad2c0-7570-446c-9d37-9522086c9f94","added_by":"auto","created_at":"2025-10-31 10:07:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53330,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Conceptual Model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/e6250a8e2639b5b09c42b855.png"},{"id":94985356,"identity":"6749a7bb-7026-444a-b933-1a6bf0217a68","added_by":"auto","created_at":"2025-11-03 06:58:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":307357,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized Path Diagram of the Final Model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/a2a36946cd3447c3cffc303d.png"},{"id":94990354,"identity":"53f15493-7c16-4ca2-b13c-815a8f3d3993","added_by":"auto","created_at":"2025-11-03 07:16:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1514025,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7566219/v1/4839e193-bbc4-49de-8911-7d72f0404241.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Does Art Website Image Innovation Influence Purchase Intention from the Perspective of Co-presence? —A Dual Mediation Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe saying goes,\"In chaotic times, gold is the refuge; in peaceful times, art is the pursuit.\" With the continuous deep penetration of internet technology and the structural changes in the digital economy, the global art trading market is undergoing a paradigm shift from offline to online. According to the 51st statistical report by CNNIC in 2025, by December 2024, China's internet user population exceeded 1.23\u0026nbsp;billion, with an internet penetration rate of 86.5%, and mobile phone users accounted for 98.2%. This has laid the user foundation for the large - scale development of art e - commerce. Meanwhile, the global online art trading market has witnessed explosive growth. According to the data from the \"2025 Hiscox Global Online Art Trade Report\" and the \"Art Basel \u0026amp; UBS Annual Report on the Global Art Market,\" in 2025, the online trading volume exceeded 45.0\u0026nbsp;billion US dollars, accounting for 37.5% of the global art trading total. This represents a 12 - percentage - point increase compared to 2024 and is significantly higher than the 11.32\u0026nbsp;billion US dollars (38% share) in 2023. The trading volume has nearly tripled. The Chinese art market has been particularly outstanding, with its online trading volume reaching 54.0\u0026nbsp;billion US dollars in 2025, accounting for 45% of the global share and maintaining its top global position consecutively.\u003c/p\u003e\u003cp\u003eIn the post - pandemic era, the online process has continued to accelerate. Data from international leading auction houses has confirmed this trend. In 2025, the total online - only auction turnover of Sotheby's, Christie's, and Phillips reached 32.0\u0026nbsp;billion US dollars, which is 20 times the figure of the same period in 2019 (1.53\u0026nbsp;billion US dollars). It is predicted that with the growing popularity of blockchain tracing technology and immersive virtual galleries, the global online art trading scale is expected to exceed 40.0\u0026nbsp;billion US dollars in 2024. Among these, the Asia - Pacific region, especially China, will reconstruct the global landscape with a growth contribution of 68%. This is because of the unique momentum created by the activation of private art collections through legislation and innovation in art finance in China, which has promoted the development of the art market.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003e\u003cstrong\u003eArt Website Image Dimensions under Co-presence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArt websites are core virtual shopping platforms.Their innovation, which spans visual optimization, interactive enhancement, and diverse content presentation, aims to elevate brand image, user appeal, and market competitiveness\u003csup\u003e1\u003c/sup\u003e. Co-presence (CP) reflects the degree of perceiving others\u0026apos; virtual presence\u003csup\u003e2\u003c/sup\u003e and the significance of virtual interpersonal relationships in online settings\u003csup\u003e3,4\u003c/sup\u003e.Numerous factors shape website image,such as visuals, colors, ads, and salespeople\u003csup\u003e5\u003c/sup\u003e.Classic models classify online store images into design aesthetics, information quality, product assurance, and safety\u003csup\u003e6\u003c/sup\u003e. The reliability, usability, and usefulness of websites significantly impact buyers\u0026apos; purchase intentions\u003csup\u003e7\u003c/sup\u003e. For instance, \u0026quot;product allocation\u0026quot; and \u0026quot;ranking functions\u0026quot; enhance shopping efficiency and reduce search costs\u003csup\u003e8\u003c/sup\u003e. \u0026quot;Functional convenience,\u0026quot; \u0026quot;price transparency,\u0026quot; and \u0026quot;product variety\u0026quot; are key elements of user - friendly websites, directly influencing user attitudes and repurchase intentions\u003csup\u003e9\u003c/sup\u003e. This perspective has been extended in promotional strategy research: \u0026quot;Free shipping policies,\u0026quot; \u0026quot;real-time logistics tracking,\u0026quot; and \u0026quot;dynamic discounts\u0026quot; (such as shopping cart promotions and limited-time offers for new products) enhance the attractiveness of online stores by reducing decision-making resistance, thereby driving sales conversion\u003csup\u003e5\u003c/sup\u003e.Trust in website images relies on multi - dimensional safeguards. \u0026quot;User - friendly interfaces\u0026quot; (e.g., clear navigation), \u0026quot;transparent privacy statements,\u0026quot; and \u0026quot;secure payment signs\u0026quot; form the basic trust framework\u003csup\u003e10\u003c/sup\u003e. \u0026quot;Fulfillment certainty\u0026quot; (e.g., precise delivery times) and \u0026quot;after - sales responsiveness\u0026quot; (e.g., fast refunds) mitigate perceived risks, especially in high - value categories like luxury goods\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eArt products, having economic and investment attributes, serves as a value - display method for the nouveau riche to showcase status or address identity anxiety\u003csup\u003e12\u003c/sup\u003e. As rational economic agents, art buyers\u0026apos; intentions are influenced by price, brand, store information, trust\u003csup\u003e13\u003c/sup\u003e, risk, satisfaction, service, and value recognition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsumer Perceived Risk and Value in Art E-commerce\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the art e-commerce context, perceived value is composed of emotional value (aesthetic resonance), social value (status symbol), functional value (investment return), and hedonic value (virtual exhibition experience)\u003csup\u003e14,15\u003c/sup\u003e. Given the high involvement nature of art, value perception depends more on credibility cues such as authoritative certification and collector reputation, rather than the traditional \u0026quot;value for money\u0026quot; logic\u003csup\u003e16\u003c/sup\u003e. The risks in online art transactions are distinct, with financial risk (valuation deviation), social risk (aesthetic recognition pressure), functional risk (transport damage), and institutional risk (policy compliance) all being factors\u003csup\u003e17\u003c/sup\u003e. Different from regular products, high perceived risk can increase scarcity perception and stimulate the purchasing intention among high - net - worth users\u003csup\u003e18\u003c/sup\u003e. This paradox reveals the limitations of traditional linear risk - intention models and emphasizes the necessity of integrating digital technologies like VR inspection and smart contracts to update the theoretical framework\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWith the penetration of AI large models into permeate daily life and the evolution of global shopping methods, co-presence has increased the expectations for participation in online shopping. Currently, however, research on online perceived risk and value often draws on traditional methods, with limited attention given to art purchases. Given the rapid growth of online art buying, this study aims to answer two key questions: Are the risk and value dimensions of online art the same as those of \u0026nbsp;regular online products? How can we effectively reduce the high perceived risk in online art purchases?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA Study on Consumer Co-presence and Purchase Intention in E-commerce Based on S-O-R Theory\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;S-O-R\u0026quot; theory,deeply rooted in human behavioral psychology, \u0026nbsp;serves as a crucial theoretical foundation in consumer behavior and marketing studies. Building upon on the TRA and TRM models and incorporating co-presence theory, this study applies concepts of website image, perceived value, and perceived risk into the online art market. It aims to explore the relationships among these factors and provide valuable insights into online art consumer behavior.\u003c/p\u003e\n\u003cp\u003eWhile extensive research exists on website image and purchase intention, three primary limitations persist. First, existing studies predominantly focus on standardized e-commerce products, thereby overlooking the unique cultural asset attributes of art and the specific requirements for website image design. Critically, they fail to address the core value of \u0026quot;co-presence\u0026quot; in art appreciation and trading. That is, the immersive experience of real - time interaction and co-presence among users, creators, collectors, and other consumers in virtual space, which is vital for fostering emotional connection.Second, the analysis of perceived value and risk continues to be largely based on offline scenarios. New interactive models such as live - stream appraisal (e.g., real - time Q\u0026amp;A between experts and collectors) and NFT authentication (e.g., visualizing the creation process of digital art) have not been adequately examined for there co-presence responses.Third, existing models have yet to incorporate big-data-driven dynamic user profiling (e.g., predicting users\u0026rsquo; aesthetic preferences). Furthermore, the dynamic generation mechanism of co-presence (e.g., scenario adaptation based on real-time user interaction data) remains unintegrated into current analytical frameworks..\u003c/p\u003e\n\u003cp\u003eIn response, This study develops an \u0026quot;S-O-R + dual - mediation\u0026quot; theoretical model of art website purchase intention, emphasizing \u0026quot;co-presence\u0026quot; as a key mediating variable. The aim is to reveal how innovative art website images (such as virtual curatorial spaces and real - time interactive appraisal functions) influence consumer decisions by enhancing users\u0026apos; co-presence experience with art, creators, and the community within a digital context. This research also seeks to elucidate the underlying impact mechanisms between unobservable variables and to delineate the specific path through which art website image innovation influences consumers\u0026apos; purchase intentions.\u003c/p\u003e\n\u003ch3\u003eTheoretical Framework\u003c/h3\u003e\n\u003cp\u003eThis study, building upon Eroglu\u0026rsquo;s research on website image and the Stimulus-Organism-Response (S-O-R) theory, refines the theoretical model. The online art shopping environment, serving as a \u0026ldquo;stimulus source\u0026rdquo; that triggers users\u0026rsquo; psychological responses, exhibits a unique sense of presence primarily through its cultural attributes, interactive functionalities, and technological empowerment. Accordingly, \u0026ldquo;S\u0026rdquo; is defined as the stimulus \u0026ldquo;innovation in art website image,\u0026rdquo; which serves as the starting point for behavioral intention. \u0026ldquo;O,\u0026rdquo; representing the organism, refers to the overall feelings and the core psychological state formed in the online art shopping environment that consumers experience in response to the stimulus of website image innovation. Specifically, this encompasses the immersive experience where users perceive \u0026ldquo;co-presence\u0026rdquo; with artworks, creators, and other consumers. \u0026ldquo;R\u0026rdquo; denotes the response, which is purchase intention. This research draws upon customer value theory, cue utilization theory, the EBM model, the theoretical framework of the Theory of Reasoned Action (TRA), the Technology Acceptance Model (TAM), and theoretical models based on trust and risk as its fundamental theoretical underpinnings. Integrating the unique characteristics of artworks and using the sense of co-presence as its research context, this study expands the explanatory power of the S-O-R model within virtual consumption scenarios, thereby resonating with the emerging research trend of \u0026ldquo;social experiences in the metaverse.\u0026rdquo; This study designates innovation in art website image as the independent variable, consumer purchase intention as the dependent variable, and perceived value (specifically, hedonic value linked to co-presence) and perceived risk (where social interaction mitigates information uncertainty) as mediating variables.Drawing on relevant literature and mature scales, the study constructs a conceptual model. Among the nine unobservable variables in this study, the analysis mainly refers to Jin and Park's\u003csup\u003e21\u003c/sup\u003e research on physical, social, and behavioral co-presence. Combining website design, order fulfillment, artworks, communication, promotion, and security dimensions, the study proposes six dimensions of art website image innovation, which are the core variables. These six dimensions serve as core variables in this study, capable of directly influencing consumer purchase intention, and also indirectly impacting it through perceived value and perceived risk acting as mediating variables. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the conceptual model of this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eResearch Hypotheses\u003c/h3\u003e\n\u003cp\u003eIn digital contexts, art website image innovation is an integrated strategy that combines interface design, interactive features, and trust mechanisms to reshape user cognition\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Its core lies in leveraging technology empowerment to convey the cultural value of artworks and a sense of transaction security. Highly innovative website images can greatly boost consumers' virtual co - presence (VCP) and immersion\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, which can be further strengthened by \"social presence\" to enhance users' emotional identification with artworks\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFrom the perspective of dual-path transmission of influence mechanisms, AWII operates through a perceived value enhancement path: when websites offer high-fidelity artwork displays (e.g., VR virtual exhibition halls), real-time expert consultation (KOC interaction), and transparent traceability functions (blockchain-based certification), users' assessments of artworks' aesthetic and investment values\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e are significantly improved, driving purchase intentions\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFrom the perspective of group - effect amplification, art consumption shows strong social conformity. When websites feature group - interactive functions like \"limited auctions\" and \"collector community dynamics\", users are easily influenced by reference groups. This phenomenon is particularly prominent in scenarios with a strong sense of virtual co-presence\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBased on this, the study puts forward the following hypotheses:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cp\u003eThe six aspects of art website image innovation, namely web - design image innovation (H1-1), order - fulfillment image innovation (H1-2), art image innovation (H1-3), communication image innovation (H1-4), promotion image innovation (H1-5), and security image innovation (H1-6), have a significantly positive impact on perceived value.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eIn the digital era, art transactions integrate physical and virtual spaces. In this context, consumers\u0026rsquo; perceived risk on virtual platforms has shifted from the dimension of product quality to that of environmental trustworthiness. This spillover phenomenon of risk is closely linked to the absence of virtual Co-presence (CP). The desire for social cues can intensify uncertainty about the transaction environment\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Since 2020, the perceived risk structure in art e-commerce has undergone significant changes. Specifically, performance risk (e.g., copyright disputes arising from vulnerabilities in NFT art smart contracts) and social risk have increased by 19% and 27% respectively (Art Market Research Report, 2021).\u003c/p\u003e\u003cp\u003eWhen websites can simulate the co-presence of offline scenes, service systems offer quasi - interpersonal interactions, and logistics tracking enhances controllability, consumers' risk perception is 1.7 standard deviations lower than in traditional e-commerce\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, asynchronous interaction design or isolated browsing interfaces can disrupt the temporal and spatial continuity of CP, causing social risk perception to increase by 2.3 times\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Studies show that new technologies can effectively reduce individual risk perception and build a market - trust infrastructure that integrates physical and virtual spaces. This improves consumers' reference to group decisions in virtual spaces and enables a shift from risk avoidance to behavioral commitment\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTherefore, the study proposes the following hypothesis:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003cp\u003eThe six aspects of art website innovation, namely web - design innovation (H2-1), order - fulfillment innovation (H2-2), art innovation (H2-3), communication innovation (H2-4), promotion innovation (H2-5), and security innovation (H2-6), have a significantly negative impact on perceived risk.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eIn the digital era, the innovation of art websites should be driven by virtual co - presence (CP). Consumers perceive the website\u0026rsquo;s collective presence through interface interaction \u0026ndash; that is, whether real-time interaction can foster a co-present experience akin to a physical art space. Especially in the art domain, consumers with high aesthetic demands are 29% more sensitive to CP than ordinary product buyers\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Sotheby\u0026rsquo;s \u0026lsquo;AI Curator\u0026rsquo; system, launched in 2022, analyzes user browsing trajectories to generate personalized art history explanations in real-time, enhancing the perceived congruence between website image and artwork style, and directly boosting the purchase conversion rate of high-value (\u0026gt;\u003cspan\u003e$\u003c/span\u003e50,000) works by 34%\u003csup\u003e31\u003c/sup\u003e, in art e - commerce, efficiency is reflected in shortening the emotional distance between consumers and artworks using CP technology\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Poly Auction\u0026rsquo;s MetaGallery feature allows consumers to observe other collectors\u0026rsquo; viewing patterns in VR exhibition halls. Its data shows that when users perceive the virtual presence of renowned curators, the impulse purchase probability increases by 2.1 times compared to scenarios with the presence of ordinary users (Art \u0026amp; Commerce Report, 2022).\u003c/p\u003e\u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003cp\u003eThe six aspects of art website image innovation, namely web - design innovation (H3-1), order - fulfillment innovation (H3-2), art innovation (H3-3), communication innovation (H3-4), promotion innovation (H3-5), and security innovation (H3-6), have a significantly positive impact on purchase intention.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe essence of perceived value and perceived risk is a psychological adjustment process driven by Virtual Co-presence (CP). Consumers\u0026rsquo; evaluation of art website image is essentially a cognitive game based on the \u0026ldquo;group presence\u0026rdquo; constructed by CP technology. This psychological mechanism is particularly pronounced in the art domain\u0026mdash;high-net-worth buyers view website image as a visualized carrier of aesthetic consensus, and their purchasing decisions not only rely on individual judgment but also require the endorsement of \u0026ldquo;collective aesthetic legitimacy\u0026rdquo; facilitated by CP technology\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Research indicates that this reconstruction of spatiotemporal presence can increase the neural encoding efficiency of consumer trust commitment by 22%\u003csup\u003e34\u003c/sup\u003e. A value resonance effect is observed: in Christie\u0026rsquo;s MetaGallery virtual exhibition hall, users can observe other collectors\u0026rsquo; viewing paths and dwell times. This collective aesthetic synchronicity leads to the symbolic value premium of artworks reaching 1.3 times that of physical auctions (Art \u0026amp; Commerce Report, 2022).\u003c/p\u003e\u003cp\u003eThe \"operational efficiency\" dimension proposed by Kenneth (2015) is reinterpreted in this context\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. When website innovation shortens the psychological distance between consumers and aesthetic consensus, the speed of purchase intention generation is 2.4 times faster than in traditional models. This confirms the core proposition of art website image innovation: technology-enabled co-presence experience must embed individual emotions within a collective value network; otherwise, it will lead to a cognitive split between \u0026ldquo;private collection mentality\u0026rdquo; and \u0026ldquo;public recognition,\u0026rdquo; thereby inhibiting willingness to pay.\u003c/p\u003e\u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003cp\u003eAs a mediating variable, perceived value significantly positively mediates the relationship between the six aspects of art website image innovation-web-design innovation (H4-1), order - fulfillment innovation (H4-2), art innovation (H4-3), communication innovation (H4-4), promotion innovation (H4-5), and security innovation (H4-6) - and purchase intention.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eIn art transactions, co-presence reduces information asymmetry through virtual authenticity, thereby mitigating consumers\u0026rsquo; perceived risk and ultimately influencing their purchase intention. According to the EKB model, the consumer buying process is divided into five stages: recognition, information search, evaluation, decision, and behavior. Supported by the \u0026ldquo;S-O-R\u0026rdquo; theory, consumers with higher involvement tend to have their purchase behavior more significantly influenced by perceived risk. The value of artworks depends on the price consumers are willing to pay\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, implying that art itself embodies risk. the enhanced investment value and proclaimed social value of artworks significantly influence consumers\u0026rsquo; purchase intention. Consumers\u0026rsquo; ultimate purchase behavior towards artworks is negatively impacted more by perceived risk than by perceived value. Furthermore, innovation in art website image that enhances co-presence will be conducive to reducing consumers\u0026rsquo; perceived risk, thereby influence purchase intention.\u003c/p\u003e\u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003e\u003cb\u003eH5\u003c/b\u003e: As a mediating variable, perceived risk significantly and negatively affects the relationship between the six aspects of art website image innovation\u0026mdash;web design innovation (H5-1), order fulfillment innovation (H5-2), art innovation (H5-3), communication innovation (H5-4), promotion innovation (H5-5), and security innovation (H5-6)\u0026mdash;and purchase intention.\u003c/p\u003e\u003cp\u003ePrevious studies indicate that perceived value and perceived risk are negatively correlated\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. For example, Cebi (2013) proposed a quality evaluation model for the design quality of online shopping websites, suggesting that perceived value and perceived risk are negatively related. Some researchers argue that perceived risk has a more substantial impact on purchase intention. Holbrook and Corfman (2013)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e suggested that consumers' perceived gains are subjective, and perceived risk can negatively reduce consumers' perceived value of a product or service. Spreng (2002)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e validated that perceived value and perceived risk should be negatively correlated. Mitchell (2004)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e proposed that when making purchase decisions, perceived risk has a stronger influence on purchase intention. In the research on art purchasing behavior under the co-presence perspective, studies on the relationship between perceived value and perceived risk are relatively rare. Although artworks are considered as a new type of investment product, they should be consistent with ordinary goods, and digital technology will not significantly change this characteristic.\u003c/p\u003e\u003cp\u003eThus, the study proposes another hypothesis:\u003c/p\u003e\u003cp\u003e\u003cb\u003eH6\u003c/b\u003e: There is a mutual relationship between perceived value and perceived risk in the innovation of art website images.\u003c/p\u003e"},{"header":"Research Methods","content":"\u003cp\u003e\u003cb\u003eResearch Sample and Data Sources\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 421 participants were randomly assigned across a 2 (affective human-likeness: low vs. high) between-subjects design. The participants were recruited in May 2024 via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.credamo.com\u003c/span\u003e\u003cspan address=\"http://www.credamo.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e in exchange for a small payment, and the participants in the pilot study were restricted.\u003c/p\u003e\u003cp\u003eThe survey data reveal that among potential consumers of art websites, 41.2% are male and 58.2% female. Age distribution is concentrated in the 20\u0026ndash;29 (47.5%), 30\u0026ndash;39 (30%), and 40\u0026ndash;49 (12.8%) age groups, aligning with the online population demographics in China. Regarding occupations, the distribution is as follows: students (15.1%), government and public institution employees (34.1%), social organization workers (9.8%), corporate employees (26.7%), self - employed individuals (4.2%), and others (9.5%). The majority of respondents (88.7%) have been online for over five years.In terms of education, the breakdown is: bachelor's degree (21.1%), master's degree (65.6%), and doctoral degree or higher (7.7%), indicating that individuals with higher artistic literacy are often recipients of high - quality higher education. For monthly income, the distribution is: 3000 yuan or below (12.8%), 3001\u0026ndash;12000 yuan (49.3%), and 12001\u0026ndash;25000 yuan (25.5%).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthics Statement\u003c/b\u003e\u003c/p\u003e\u003cp\u003e All procedures in this study involving human participants were performed in accordance with the ethical principles of the Declaration of Helsinki. The study protocol was determined to be exempt from formal ethical review by the Institutional Review Board (IRB) of the University of Science and Technology Beijing. This determination was based on the study\u0026rsquo;s full adherence to the \u0026ldquo;relevant guidelines/regulations,\u0026rdquo; specifically the national guidelines outlined in China\u0026rsquo;s \u0026ldquo;Measures for the Ethical Review of Biomedical Research Involving Humans\u0026rdquo; (2016).\u003c/p\u003e\u003cp\u003eThe exemption was applicable because the research met all necessary criteria: it involved an anonymous online survey on a non-sensitive topic (consumer purchase intentions), posed no foreseeable risk to participants, and was explicitly designed to ensure that no personally identifiable information was collected, thereby guaranteeing participant anonymity by design. Informed consent was obtained from all participants prior to their participation. They were presented with an information sheet explaining the study\u0026rsquo;s purpose, its voluntary nature, and the confidentiality of their data, and their voluntary completion of the questionnaire served as their consent.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariable Measurement\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe core of this study lies in investigating the influence of art website image innovation on the purchase intention of potential art consumers when browsing art websites. In developing the questionnaire, we adhered to Rong Taisheng\u0026rsquo;s principles of questionnaire inter-validity, suitability, applicability, and timeliness. We referenced established scales for website image, perceived value, perceived risk, and purchase intention from relevant literature. Furthermore, considering the unique characteristics of artworks, we conducted interviews with multiple senior doctoral supervisors and art domain experts, brainstormed with scholars and PhDs, visited various art markets and galleries, and employed the \u0026ldquo;back-translation method\u0026rdquo; to finalize the initial questionnaire items.\u003c/p\u003e\u003cp\u003eThe scale for measuring the image of art websites was adapted from established scales in prior studies, including those by Jim and Park (2006), Marine Aghekyan (2009)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, Te - King Chien (2015)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, and C. Ranganathan (2002). This led to the construction of six latent variables for art website image: website design image, order fulfillment image, artwork image, communication image, promotion image, and security image.This study incorporates perceived value and perceived risk as dual mediators to examine their mediating relationship between art website image innovation and purchase intention. Regarding perceived value in the context of artworks, no established scales were readily available. Therefore, building upon the \u0026ldquo;social value,\u0026rdquo; \u0026ldquo;hedonic value,\u0026rdquo; and \u0026ldquo;utilitarian attributes\u0026rdquo; scales proposed by Laurent Bourdeaua, Jean-Charles Chebatb, and Christian Couturier (2002)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, this paper proposes three constituent dimensions for the perceived value of artworks: economic value, social value, and emotional value. The dimensions of perceived risk form the basis for research on perceived risk in art. For perceived risk, prior studies have established six dimensions: economic, functional, psychological, social, and privacy risks\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. However, research on the relationship between art website image and perceived risk is less frequently reported.\u003c/p\u003e\u003cp\u003e\u003cb\u003eReliability and Validity Testing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eReliability is a measurement method used to assess the consistency and stability of test results. Specifically, it refers to whether, when measuring sample data with a measurement tool, reliable analysis results can be consistently obtained from the measured data under test conditions. Kimery (2002) suggested that a Cronbach\u0026rsquo;s alpha value exceeding 0.7 indicates high overall data reliability; if the number of items is less than 6, a Cronbach\u0026rsquo;s alpha value exceeding 0.6 is acceptable. The Bartlett\u0026rsquo;s Test of Sphericity result for the art website image scale in this study shows an approximate chi-square value of 4700, and the Kaiser-Meyer-Olkin (KMO) value is 0.895, reaching a significant level. This indicates that the sample data are suitable for exploratory factor analysis (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKMO and Bartlett\u0026rsquo;s Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eKaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) Bartlett's\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.895\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBartlett's Test of Sphericity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApproximate Chi-Square Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4700.461\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDegrees of Freedom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e253\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignificance Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e注N\u0026thinsp;=\u0026thinsp;421\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo ensure the reliability and validity of the questionnaire data, this study draws on a large number of mature scales from domestic and international sources, modifies the items based on the characteristics of artworks, and repeatedly refines the content through back-translation, brainstorming, and expert interviews. Reliability tests were conducted on the questionnaire data using CITC (Corrected Item-total Correlation) and Cronbach\u0026rsquo;s α coefficients. The results showed that all Cronbach\u0026rsquo;s α values exceeded 0.8, indicating extremely high reliability and dependable measurement outcomes. Additionally, convergent validity was assessed for each item using Amos 25.0, with CR values all exceeding 0.7 and the majority surpassing 0.85. This confirms that the convergent validity of the variables meets the requirements (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReliability and Validity Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeasurement Item Number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCronbach α\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePage Design Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8734\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrder Fulfillment Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8868\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArtwork Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8375\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArt Website Communication Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArt Website Promotion Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8673\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArt Website Security Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8591\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8568\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.9044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn order to test the discriminant validity of the model's factors, this study utilized both the average variance extracted (AVE) and the Harman single - factor test. The results, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, indicate that the square roots of the AVE values for all factors are greater than the correlation coefficients between each pair of factors. This demonstrates that the nine factors in the structural equation model have good discriminant validity. In addition, the Harman single - factor test and multi - factor confirmatory factor analysis were employed to analyze the questionnaire data. The results indicate no common method variance bias in the model's parameter estimates, confirming the strong explanatory power of the research findings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiscriminant Validity Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWDa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWOa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWAa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWCa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWPa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWRa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePVa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePR8A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eWBa\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePage Design Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e(0.796)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrder Fulfillment Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.542\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.852)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArtwork Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.631\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.538\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e(0.796)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunication Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.500\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.524\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.522\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e(0.723)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePromotion Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.540\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.513\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.604\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.615\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(0.789)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecurity Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.125\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.149\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.150\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.199\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.219\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e(0.748)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.553\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.513\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.569\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.615\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.633\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.326\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e(0.660)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.338\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.245\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.365\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.291\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.337\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.597\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.423\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0.708)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.346**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.321**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.340**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.422**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.464**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.273**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.610**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-230**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e(0.6125)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: The values on the diagonal of the matrix are the square roots of the AVE, and the correlations are located below the diagonal. ** and * denote significance at the 0.01 and 0.05 levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eModel Testing\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePath Coefficient Test\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study used AMOS 25.0 for calculations and model fitting. For the questionnaire data of this research, Harman\u0026rsquo;s single-factor test method yielded a KMO value of 0.895. However, some scholars do not approve of judging common method variance (CMV) using Harman\u0026rsquo;s single-factor analysis. Therefore, this study further employed single-factor and multi-factor confirmatory factor analyses to test for the presence of common method variance in the sample questionnaire. Upon calculation, the model fit indices for the structural equation models were obtained as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The approximate chi-square values of the two models differed significantly, indicating that the two models are indeed distinct. The CMIN/DF value for the multi-factor model was 2.947, falling within the ideal range of 1\u0026ndash;3, and performing better than the single-factor model. The verification results indicate that the factors involved in this study are unlikely to have common method variance, and the parameter estimates of the model will not be biased as a result, thus conferring strong explanatory power to the research findings. However, the data show that the goodness-of-fit for the constructed model is not ideal, necessitating further modification of the structural equation model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Fit Indices for Single-Factor and Multi-Factor Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCMIN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCMIN/DF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAGFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eIFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle-Factor Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2058.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMulti-Factor Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e863.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStructural Equation Model Modification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter eight rounds of model correction and optimization iterations, continuous model refinements were conducted. This involved removing insignificant paths in the model and performing \"influence - relationship adjustments\" and \"covariance - relationship adjustments\". The overall model obtained is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the model's fit indices are within the acceptable range. The RMSEA is 0.061, GFI 0.767, IFI 0.880, and CFI 0.879. Overall model fit has improved compared to the original. These indices confirm that the modified model effectively captures the relationships between latent and observed variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis Results of the Modified Overall Theoretical Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eLoading Paths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-Standardized Path Coefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eC.R.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStandardized Path Coefficients\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePage Design Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArtwork Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-2.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.243\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSecurity Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-8.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOrder Fulfillment Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArtwork Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCommunication Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePromotion Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSecurity Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerceived Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerceived Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePage Design Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCommunication Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePromotion Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerceived Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.701\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase Intention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSecurity Image Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: N\u0026thinsp;=\u0026thinsp;421; *** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (two-tailed test)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComposite Reliability Test\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOnce the adjusted model becomes the optimal final model, it can be compared with the initial model through cross - validation analysis. Using the random sampling allocation function in SPSS 25.0, the 421 questionnaires were split evenly into two random samples. Then, Amos 25.0 was used to analyze six models: the unrestricted model, the measurement - weighted model, the structure - weighted model, the structural variance model, the structural residual model, and the measurement residual model. This checked if the final model was invariant.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the cross - validation indices are ideal. The TLI values exceed 0.7, and the ΔTLI and ΔCFI values are near zero, below the recommended thresholds. This meets the requirements for group congruence. Thus, the overall theoretical model of art website image on purchase intention is stable. The model passes the cross - validation test.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGroup Invariance Comparison of the Overall Theoretical Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChi-square value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e△χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e△df\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e△TLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e△CFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnconstrained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3803.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasurement Weights\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3938.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStructural Weights\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3938.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStructural Variances\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3938.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStructural Residuals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3938.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasurement Residuals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3938.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u003cb\u003eImpact of Art Website Image on Purchase Intention\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe overall fit of the structural equation model for the impact of art website image on purchase intention is relatively good. The model\u0026rsquo;s CMIN value is 1559.351, with a DF value of 371, indicating an acceptable fit for the data model. The GFI value is 0.719, the AGFI value is 0.67, and the RMSEA fit index value in the model is 0.098, suggesting excellent adaptiveness for the data model. At the hypothesis level, H3-1, H3-4, H3-5, and H3-6 are supported. Specifically, the p-values for the regression coefficients of website design image, communication image, promotion image, and security image are all significant at the 0.001 level. This means that, within the perspective of co-presence, the co-presence embedded in website design (e.g., 3D artwork displays, VR virtual tours); the communicative co-presence within communication image and security image (e.g., bullet-screen Q\u0026amp;A, authenticated collector tag interactions to reduce information asymmetry risk); and the emotional co-presence in promotion image (e.g., limited-time virtual auctions stimulating competitive emotions), all positively influence art consumers\u0026rsquo; purchase intention to varying degrees. However, the order fulfillment image and artwork image of art websites do not significantly affect purchase intention. In response, it is appropriate to overlay artist voice blessings at logistics nodes to generate emotional resonance or to use AR scanning to display micro-craftsmanship as a substitute for physical touch.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImpact of Art Website Image and Perceived Value on Purchase Intention\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe overall fit of the model examining the impact of art website page image and perceived value on purchase intention is good. With a CMIN of 2071.14, DF of 616, GFI of 0.713, AGFI of 0.673, and RMSEA of 0.084, the model - data fit is high. After modifying the model, the p-values for the regression coefficients, where order fulfillment image, artwork image, communication image, promotion image, and security image influence purchase intention through perceived value, are all significant at the 0.001 level. At the hypothesis level, H4-2 to H4-6 are supported. This means that, ceteris paribus, the order fulfillment image of an art website, representing behavioral co-presence, allows users to enhance their sense of control and trust by \u0026ldquo;participating\u0026rdquo; in the shipping process. The enhanced physical co-presence of the art website\u0026rsquo;s artwork image, replacing physical touch with new technologies like 3D printing, reduces information asymmetry risk and increases functional and emotional value. The art website\u0026rsquo;s communication image, through bullet-screen Q\u0026amp;A plus authenticated collector tag interactions, leverages social identification effects to reduce perceived risk and indirectly enhance social value. The art website\u0026rsquo;s promotion image, through the behavioral co-presence of limited-time virtual auctions, enhances emotional value and drives impulse purchases. Furthermore, the art website\u0026rsquo;s security image also significantly and positively influences art consumers\u0026rsquo; purchase intention as perceived value increases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImpact of Art Website Image and Perceived Risk on Purchase Intention\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe results of the standardized path coefficients from the structural equation model fit of art website image and perceived risk on purchase intention reveal that perceived risk plays a full mediating role in the relationship between artwork image, website design image, and purchase intention. It plays a partial mediating role in the relationship between order fulfillment image, security image, and purchase intention. After removing paths where the effect of art order fulfillment image on perceived risk, among others, was not significant, the RMSEA value in the modified partial mediation model of art website image and perceived risk was 0.088. This indicates that art website page design image, art website artwork image, and art website security image significantly and positively influence purchase intention through perceived risk.At the hypothesis level, H5-1, H5-3, and H5-6 are supported. In a high co-presence environment, the perceived risk of online art purchases, influenced by consumers\u0026rsquo; \u0026ldquo;small stakes for pleasure\u0026rdquo; investment mentality, acts as a chained mediator, activating a \u0026lsquo;risk \u0026rarr; purchase intention\u0026rsquo; path that might conventionally be considered \u0026lsquo;broken\u0026rsquo; or negative. As art risk possesses both investment and emotional attributes, which traditional risk models struggle to cover, it leads to a direct positive correlation between perceived risk and purchase intention for online art purchases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMediation Between Perceived Risk and Perceived Value for Art Consumers\u0026rsquo; Purchase Intention\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe structural equation model constructed in this study, examining the mediating role between consumer perceived risk and perceived value, demonstrates a good overall fit. In this model, the CMIN value is 526.074, the DF value is 168, the chi-square to degrees of freedom ratio is 3.131, and the RMSEA value of 0.080 indicates an excellent fit between the data model and reality. From the perspective of the significance of structural equation path coefficients, the p-value for the regression coefficient from perceived value to perceived risk is significant at the 0.001 level; the p-value for the regression coefficient from perceived risk to purchase intention is -0.475, which is statistically non-significant. Therefore, it can be verified that, in the partial mediation model of perceived risk, the mediating portion of perceived risk on perceived value and purchase intention holds. That is, perceived value cannot influence art purchase intention through perceived risk, yet perceived value will influence art purchase intention through perceived risk, and there is a negative correlation (non-recursive relationship) between perceived value and perceived risk of art website image. At the hypothesis level, H6 is supported. In a high co-presence environment, the emotional immersion path, specifically the hedonic value within perceived risk, directly triggers purchase intention, thereby compensating for the broken risk mediation path. Furthermore, in the context of high co-presence accompanying the rise of metaverse consumption, the behavioral control path\u0026mdash;i.e., virtual operations and emotional decision-making\u0026mdash;can enhance risk suppression efficiency, leading to a decoupling paradox between risk and behavioral intention, contrary to traditional research. The co-presence environment thus becomes a core hub for rational cognition (value/risk) and emotional decision-making (immersion/impulse).\u003c/p\u003e\u003cp\u003eThis study, employing the theoretical lens of Co-presence (CP), utilized structural equation modeling to examine the mechanism through which art website image innovation influences consumer purchase intention in a co-present context, verifying the dual mediating roles of perceived value and perceived risk. The empirical results lead to the following conclusions: (1) In a co-presence situation (e.g., group behavior visualization in VR exhibition halls, collector consensus index on blockchain authentication chains), art website image innovation has a positive impact on consumer purchase intention. (2) Art websites influence purchase intention through the mediating roles of perceived value and perceived risk. (3) Perceived value and perceived risk have direct positive effects on consumer purchase intention, and perceived risk influences purchase intention by mediating through perceived value.\u003c/p\u003e\n\u003ch3\u003eManagerial Implications\u003c/h3\u003e\n\u003cp\u003eThe theoretical implications of this paper encompass three aspects: First, the dynamic reconstruction of the theoretical model. Breaking through the unidirectional transmission logic of \u0026ldquo;stimulus-response\u0026rdquo;\u003csup\u003e45\u003c/sup\u003e, we propose a CP-enabled S-O-R feedback loop, where consumer behavioral data (e.g., gaze heatmaps in VR exhibition halls) real-time informs website image innovation design, forming a self-optimizing closed loop of \u0026ldquo;technological iteration \u0026ndash; co-presence enhancement \u0026ndash; behavioral transformation\u0026rdquo; \u003csup\u003e46\u003c/sup\u003e.\" Second, the paradigm shift in risk-value: CP-driven website image innovation not only activates purchase intention through the dual-path mediating effects of perceived value and perceived risk, thereby overturning the traditional notion that \u0026ldquo;high risk invariably suppresses consumption,\u0026rdquo; challenging Stone\u0026rsquo;s (1993) classic risk aversion theory, and providing a neuroeconomic explanation for \u0026ldquo;risk preference alienation.\u0026rdquo; Third, the proposal of a \u0026ldquo;co-present regulatory\u0026rdquo; framework, advocating for distributed trust governance through NFT traceability and smart contracts, offering a new policy tool for government regulation of high-value art transactions with low intervention and high transparency\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMore importantly, the conclusions of this paper offer several practical implications for art e-commerce marketing: Firstly, this paper validates that art website innovation, under co-presence, has a positive impact on purchase intention, prompting businesses to strengthen their strategic layout at technological tipping points. For instance, enterprises should prioritize investing in CP-enhanced infrastructure and optimizing AR/VR interaction systems to break through technological thresholds of spatio-temporal continuity, thereby triggering a paradigm shift in consumers\u0026rsquo; risk perception models.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.Secondly, conducting precise tiered market operations, focusing on the differentiated needs of high-end and mass markets. For the high-end market (\u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000), developing \u0026ldquo;digital twin exhibition halls\u0026rdquo; to strengthen collective risk-sharing effects; for the mass market (\u0026lt;\u003cspan\u003e$\u003c/span\u003e10,000), designing lightweight CP entry points (e.g., Douyin AR try-on short videos) can reasonably reduce highly sensitive perceived risks for consumers during online shopping [,]. Thirdly, achieving a leap in regulatory paradigms for government management: governments can embed CP technology (e.g., smart contract automated compliance review) into regulatory processes through the construction of a national art blockchain, realizing a new governance model where \u0026ldquo;code is law,\u0026rdquo; thereby reducing rent-seeking and information asymmetry costs.\u003c/p\u003e\u003cp\u003eIn summary, this study reveals that the future competitiveness of art e-commerce lies in elevating technological media into a field of cultural consensus\u0026mdash;when Leonardo da Vinci\u0026rsquo;s Mona Lisa can still evoke cross-temporal collective gazes in the metaverse, and when Emperor Huizong of Song\u0026rsquo;s Auspicious Cranes acquires digital signatures from successive collectors through on-chain authentication, virtual co-presence will no longer be merely a transaction tool but a new vehicle for the inheritance of civilizational memory. The synergy of art and technology will ultimately rewrite the narrative epic of consumer civilization in the digital age.\u003c/p\u003e\n\u003ch3\u003eFuture Research and Limitations\u003c/h3\u003e\n\u003cp\u003eThis study reveals the mechanism of art website innovation on purchase intention but has limitations that point to directions for future research. First, the generalizability of the sample and the cultural sensitivity of CP experience require deeper exploration. Although the sample size met the requirements for structural equation modeling (the ratio of sample size to observed variables should be at least 10:1 to 15:1\u003csup\u003e50\u003c/sup\u003e, due to limitations in sampling channels, it did not fully cover higher-end art consumer groups. Future research could expand to cross-national and intergenerational samples, investigating the moderating effects of CP technical parameters (e.g., VR resolution, interaction latency) on different cultural cognitive patterns. Second, the disconnect between longitudinal dynamic analysis and CP technology iteration. Current cross-sectional data struggle to capture the dynamic reshaping of consumer behavior by CP technology upgrades. For instance, when AR interaction latency is optimized from 50ms to 20ms, users\u0026rsquo; risk perception thresholds might undergo a non-linear leap \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Future research is advised to adopt mixed-methods approaches; in the short term, A/B testing can track the immediate impact of CP technical fine-tuning (e.g., NFT authentication information density) on purchase conversion rates; in the long term, time series models can be constructed to analyze the cumulative effects of 5G-MEC edge computing deployment on collective risk-sharing.Third, limitations of subjective data and the quantitative absence of CP neuro-mechanisms. Current questionnaires might lead to distorted mapping between CP technical parameters and psychological experiences. For example, users might confuse the \u0026lsquo;spatial presence\u0026rsquo; of a VR exhibition hall with the \u0026lsquo;group presence\u0026rsquo; aspect of CP. Future research could combine neuroimaging techniques, using fMRI to capture the activation intensity (β-value) of the nucleus accumbens under multimodal CP stimuli, to build predictive models of neural encoding-behavioral decision-making. Eye-tracking could also be employed to quantify the relationship between collectors\u0026rsquo; dynamic heatmap gaze duration and purchase intention, revealing the attention capture mechanisms of CP visual cues.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to University of Science and Technology Beijing and Tsinghua University for all the support. The authors would like to acknowledge also all of the participants and friends. The authors would like to acknowledge the funding support of National Natural Science Foundation of China and Ministry of Education Fund for Humanities and Social Sciences of Young Scholars.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Industrial Design, College of Mechanical Engineering, University of Science and Technology Beijing, Beijing\u003c/p\u003e\n\u003cp\u003ePeng Xu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeng Xu: Conceptualization, Methodology, Data Curation, Formal Analysis, Writing – Original Draft.\u003c/p\u003e\n\u003cp\u003ePeng Xu: Methodology, Software, Validation, Writing – Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003ePeng Xu: Conceptualization, Supervision, Project Administration, Funding Acquisition, Writing – Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Peng Xu [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China, grant numbers 71672136 (“A Study on the Measurement, Effectiveness, and Synergy of Industrial Policies for Foreign Direct Investment in the Service Industry,” 2017.01–2020.12)\u0026nbsp;，72174161 (“Theoretical Methods and Institutional Research on Customer Customization and Service Value Co-creation in the Digital Context,” 2021–2025)、National Natural Science Foundation of China Youth Project (NSFC) (Grant No.72202118) and 22YJCZH203 Ministry of Education Fund for Humanities and Social Sciences of Young Scholars.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to participant privacy concerns but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all subjects involved in the study before the data collection commenced.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKim, J. W. et al. A prospective, randomized, 6-month comparison of the coronary vasomotor response associated with a zotarolimus-versus a sirolimus-eluting stent: differential recovery of coronary endothelial dysfunction. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 1653\u0026ndash;1659. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacc.2009.01.037\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2009.01.037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBulu, S. T. Place presence, social presence, co-presence, and satisfaction in virtual worlds. \u003cem\u003eComput. Educ.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e, 154\u0026ndash;161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compedu.2011.08.015\u003c/span\u003e\u003cspan address=\"10.1016/j.compedu.2011.08.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShort, R. V. Definition of the problem-the evolution of human reproduction. Proc. R. Soc. Lond. B Biol. Sci. 195, 3\u0026ndash;24, (1976). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rspb.1976.0095\u003c/span\u003e\u003cspan address=\"10.1098/rspb.1976.0095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, L. et al. Suggestive annotation: A deep active learning framework for biomedical image segmentation. In Medical Image Computing and Computer Assisted Intervention \u0026ndash; MICCAI 2017 399\u0026ndash;407 (Springer, 2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-66179-7_46\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-66179-7_46\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCebi, S. Determining importance degrees of website design parameters based on interactions and types of websites. \u003cem\u003eDecis. Support Syst.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e, 1030\u0026ndash;1043. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dss.2012.10.024\u003c/span\u003e\u003cspan address=\"10.1016/j.dss.2012.10.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRanganathan, C. \u0026amp; Ganapathy, S. Key dimensions of business-to-consumer web sites. \u003cem\u003eInf. Manage.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e, 457\u0026ndash;465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0378-7206(01)00121-7\u003c/span\u003e\u003cspan address=\"10.1016/S0378-7206(01)00121-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGefen, D., Rigdon, E. E. \u0026amp; Straub, D. An update and extension to SEM guidelines for administrative and social science research. \u003cem\u003eMIS Q.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/23042842\u003c/span\u003e\u003cspan address=\"10.2307/23042842\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011). iii\u0026ndash;xiv.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, L. et al. Training deeper convolutional networks with deep supervision. \u003cem\u003earXiv\u003c/em\u003e (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arxiv.org/abs/1505.02496\u003c/span\u003e\u003cspan address=\"https://arxiv.org/abs/1505.02496\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelafrooz, N., Paim, L. H. \u0026amp; Khatibi, A. Students' online shopping behavior: An empirical study. \u003cem\u003eJ. Am. Sci.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 137\u0026ndash;147 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKukar-Kinney, M. et al. A model of online shopping cart abandonment: evidence from e-tail clickstream data. \u003cem\u003eJ. Acad. Mark. Sci.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 961\u0026ndash;980. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11747-022-00876-5\u003c/span\u003e\u003cspan address=\"10.1007/s11747-022-00876-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalliers, J. et al. Experiencing EVA park, a multi-user virtual world for people with aphasia. \u003cem\u003eACM Trans. Access. Comput.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3132385\u003c/span\u003e\u003cspan address=\"10.1145/3132385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, M. Y. \u0026amp; Teng, C. I. A comprehensive model of the effects of online store image on purchase intention in an e-commerce environment. \u003cem\u003eElectron. Commer. Res.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 1\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10660-013-9111-6\u003c/span\u003e\u003cspan address=\"10.1007/s10660-013-9111-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCloskey, D. Evaluating electronic commerce acceptance with the technology acceptance model. \u003cem\u003eJ. Comput. Inf. Syst.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, 49\u0026ndash;57 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSweeney, J. C. \u0026amp; Soutar, G. N. Consumer perceived value: The development of a multiple item scale. \u003cem\u003eJ. Retail\u003c/em\u003e. \u003cb\u003e77\u003c/b\u003e, 203\u0026ndash;220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0022-4359(01)00041-0\u003c/span\u003e\u003cspan address=\"10.1016/S0022-4359(01)00041-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu, H. C. \u0026amp; Li, T. A study of experiential quality, perceived value, heritage image, experiential satisfaction, and behavioral intentions for heritage tourists. \u003cem\u003eJ. Hosp. Tour Res.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 904\u0026ndash;944. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1096348014560026\u003c/span\u003e\u003cspan address=\"10.1177/1096348014560026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGefen, D., Karahanna, E. \u0026amp; Straub, D. W. Trust and TAM in online shopping: An integrated model. \u003cem\u003eMIS Q.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 51\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/30036519\u003c/span\u003e\u003cspan address=\"10.2307/30036519\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStone, R. N. \u0026amp; Gr\u0026oslash;nhaug, K. Perceived risk: Further considerations for the marketing discipline. \u003cem\u003eEur. J. Mark.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 39\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/03090569310039852\u003c/span\u003e\u003cspan address=\"10.1108/03090569310039852\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1993).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoetzmann, W. N., Renneboog, L. \u0026amp; Spaenjers, C. Art and money. \u003cem\u003eAm. Econ. Rev.\u003c/em\u003e \u003cb\u003e101\u003c/b\u003e, 222\u0026ndash;226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1257/aer.101.3.222\u003c/span\u003e\u003cspan address=\"10.1257/aer.101.3.222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe, G., Xue, S. \u0026amp; Zhu, Y. Current status and problems of science communication in mainland China\u0026rsquo;s internet new media: Based on survey data and Sina Weibo case studies. \u003cem\u003eKexue Chuanbo Lunwenji\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, 175\u0026ndash;190 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin, B. \u0026amp; Park, J. Y. The moderating effect of online purchase experience on the evaluation of online store attributes. \u003cem\u003eACR N Am. Adv.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 203\u0026ndash;211 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCebi, S. A. Quality evaluation model for the design quality of online shopping websites. \u003cem\u003eElectron. Commer. Res. Appl.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 124\u0026ndash;135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.elerap.2012.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.elerap.2012.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, L. et al. Suggestive annotation: A deep active learning framework for biomedical image segmentation. In Medical Image Computing and Computer Assisted Intervention \u0026ndash; MICCAI 2017 399\u0026ndash;407 (Springer, 2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-66179-7_46\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-66179-7_46\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoetzmann, W. N., Renneboog, L. \u0026amp; Spaenjers, C. Art and money. \u003cem\u003eAm. Econ. Rev.\u003c/em\u003e \u003cb\u003e101\u003c/b\u003e, 222\u0026ndash;226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1257/aer.101.3.222\u003c/span\u003e\u003cspan address=\"10.1257/aer.101.3.222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie, E., Huang, Y. \u0026amp; Zhao, R. Interactions between different dimensions of trust and their impact on online shopping intention. \u003cem\u003eGuanli Kexue (J Manag Sci)\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e, 69\u0026ndash;77 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhite, K. \u0026amp; Argo, J. J. When imitation doesn\u0026rsquo;t flatter: The role of consumer distinctiveness in responses to mimicry. \u003cem\u003eJ. Consum. Res.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 667\u0026ndash;680. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/661553\u003c/span\u003e\u003cspan address=\"10.1086/661553\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSundar, S. S. et al. News informatics: Engaging individuals with data-rich news content through interactivity. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems 1\u0026ndash;17 (ACM, 2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3491102.3502114.\u003c/span\u003e\u003cspan address=\"10.1145/3491102.3502114.\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTao, C. et al. Trial of endovascular treatment of acute basilar-artery occlusion. \u003cem\u003eN Engl. J. Med.\u003c/em\u003e \u003cb\u003e387\u003c/b\u003e, 1361\u0026ndash;1372. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa2207576\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2207576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e395\u003c/b\u003e, 497\u0026ndash;506. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(20)30183-5\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30183-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang, W. (ed) \u003cem\u003eZhongguo Yidong Hulianwang Fazhan Baogao, China Mobile Internet Development Report 2023\u003c/em\u003e (Social Sciences Academic, 2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHa, E. J. et al. 2021 Korean thyroid imaging reporting and data system and imaging-based management of thyroid nodules: Korean Society of Thyroid Radiology consensus statement and recommendations. Korean J. Radiol.22, (2094). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3348/kjr.2021.0713\u003c/span\u003e\u003cspan address=\"10.3348/kjr.2021.0713\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChristie, L. B. et al. Review of imaging test phantoms. \u003cem\u003eJ. Biomed. Opt.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 080903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1117/1.JBO.28.8.080903\u003c/span\u003e\u003cspan address=\"10.1117/1.JBO.28.8.080903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKenneth, M. et al. Incidence, risk factors and management of severe post-transsphenoidal epistaxis. \u003cem\u003eJ. Clin. Neurosci.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 116\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jocn.2014.07.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jocn.2014.07.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, B. et al. VFDB 2022: a general classification scheme for bacterial virulence factors. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, D912\u0026ndash;D917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkab1107\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkab1107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng, C. et al. Deep learning-based human pose estimation: A survey. \u003cem\u003eACM Comput. Surv.\u003c/em\u003e \u003cb\u003e56\u003c/b\u003e, 1\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3578931\u003c/span\u003e\u003cspan address=\"10.1145/3578931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKenneth, M. et al. Incidence, risk factors and management of severe post-transsphenoidal epistaxis. \u003cem\u003eJ. Clin. Neurosci.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 116\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jocn.2014.07.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jocn.2014.07.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim, C. Factors influencing internet shopping value and customer repurchase intention. \u003cem\u003eElectron. Commer. Res. Appl.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 374\u0026ndash;387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.elerap.2012.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.elerap.2012.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCebi, S. A. Quality evaluation model for the design quality of online shopping websites. \u003cem\u003eElectron. Commer. Res. Appl.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 124\u0026ndash;135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.elerap.2012.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.elerap.2012.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFilipović, V., Jovanović, D. \u0026amp; Kostić, S. C. Attracting tourists to a shopping center. \u003cem\u003eManag\u003c/em\u003e \u003cb\u003e66\u003c/b\u003e, 15\u0026ndash;18 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan der Heijden, H. \u0026amp; Verhagen, T. Online store image: conceptual foundations and empirical measurement. \u003cem\u003eInf. Manage.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 609\u0026ndash;617. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.im.2003.07.001\u003c/span\u003e\u003cspan address=\"10.1016/j.im.2003.07.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAghekyan-Simonian, M. et al. The role of product brand image and online store image on perceived risks and online purchase intentions for apparel. \u003cem\u003eJ. Retail Consum. Serv.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 325\u0026ndash;331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jretconser.2012.03.004\u003c/span\u003e\u003cspan address=\"10.1016/j.jretconser.2012.03.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChien, T. K., Ma, H. Y. \u0026amp; Hou, K. L. A study for establishing ideal CRM system function structure. In IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) 681\u0026ndash;685 (IEEE, 2015). (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/IEEM.2015.7385827.\u003c/span\u003e\u003cspan address=\"10.1109/IEEM.2015.7385827.\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBourdeau, L., Chebat, J. C. \u0026amp; Couturier, C. Internet consumer value of university students: E-mail-vs.-Web users. \u003cem\u003eJ. Retail Consum. Serv.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 61\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0969-6989(01)00027-5\u003c/span\u003e\u003cspan address=\"10.1016/S0969-6989(01)00027-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlson, J. C. \u0026amp; Jacoby, J. Cue utilization in the quality perception process[C]. Proceeding of the Third Annual Conference of the Association of Consumer Research. Iowa, Iowa City, (1972).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStone, R. N. \u0026amp; Gr\u0026oslash;nhaug, K. Perceived risk: Further considerations for the marketing discipline. \u003cem\u003eEur. J. Mark.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 39\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/03090569310039852\u003c/span\u003e\u003cspan address=\"10.1108/03090569310039852\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1993).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEvans, J. S. B. T. Deciding before you think: Relevance and reasoning in the selection task. \u003cem\u003eBr. J. Psychol.\u003c/em\u003e \u003cb\u003e87\u003c/b\u003e, 223\u0026ndash;240. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2044-8295.1996.tb02587.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2044-8295.1996.tb02587.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1996).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHa, E. J. et al. 2021 Korean thyroid imaging reporting and data system and imaging-based management of thyroid nodules: Korean Society of Thyroid Radiology consensus statement and recommendations. Korean J. Radiol.​22, (2094). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3348/kjr.2021.0713\u003c/span\u003e\u003cspan address=\"10.3348/kjr.2021.0713\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, Y. et al. NTIRE 2023 challenge on efficient super-resolution: Methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 1922\u0026ndash;1960 (IEEE, 2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/CVPR52729.2023.00193\u003c/span\u003e\u003cspan address=\"10.1109/CVPR52729.2023.00193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, X. et al. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. \u003cem\u003eSci. China Life Sci.\u003c/em\u003e \u003cb\u003e63\u003c/b\u003e, 457\u0026ndash;460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11427-020-1637-5\u003c/span\u003e\u003cspan address=\"10.1007/s11427-020-1637-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhatti, A. et al. Organizational capabilities mediates between organizational culture, entrepreneurial orientation, and organizational performance of SMEs in Pakistan. \u003cem\u003eEntrep Bus. Econ. Rev.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 85\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15678/EBER.2020.080405\u003c/span\u003e\u003cspan address=\"10.15678/EBER.2020.080405\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook, C., Heath, F. \u0026amp; Thompson, R. L. A meta-analysis of response rates in web-or internet-based surveys. \u003cem\u003eEduc. Psychol. Meas.\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 821\u0026ndash;836. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/00131640021970934\u003c/span\u003e\u003cspan address=\"10.1177/00131640021970934\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArad, B. et al. NTire 2022 spectral recovery challenge and data set. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 863\u0026ndash;881 (IEEE, 2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/CVPRW56347.2022.00098\u003c/span\u003e\u003cspan address=\"10.1109/CVPRW56347.2022.00098\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Art website image, Perceived value, Perceived risk, Co-presence","lastPublishedDoi":"10.21203/rs.3.rs-7566219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7566219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs digital technology has been profoundly reshaping art transaction settings, traditional e-commerce theory has difficulty in interpreting cultural and high-involvement consumption aspects of art e-commerce. Using co-presence (CP) theory and the S-O-R model, to explore how innovative art website images influence purchase intentions through perceived value and risk. A six - dimension art website - image scale was constructed, and structural equation modeling was employed to analyze data from 421 art enthusiasts. The results indicate that CP - driven virtual co-presence experiences (such as VR gallery crowd - behavior visualization) enhance the positive connection between website - image innovation and purchase intention. Perceived value and risk have asymmetric mediation effects: value promotes purchase intention for mid - low - priced art through aesthetic resonance, while risk does so for high - priced art because of group risk - sharing. Website - image innovation reconstructs the risk - value assessment system through digital presence, transforming the absence in physical - space into premiums - based on group trust, thereby reshaping the pricing - power distribution in the art - finance market .\u003c/p\u003e","manuscriptTitle":"How Does Art Website Image Innovation Influence Purchase Intention from the Perspective of Co-presence? —A Dual Mediation Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 10:07:06","doi":"10.21203/rs.3.rs-7566219/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-20T03:26:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T04:08:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-26T15:04:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313553386390306248977982495018508792367","date":"2025-12-11T14:19:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18173860753896383973809825556231864123","date":"2025-12-06T19:22:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88237215394871234809953092650173461845","date":"2025-12-06T12:56:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T06:00:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T13:23:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-22T12:03:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-19T08:27:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-19T08:20:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"74190e24-857c-4e61-b037-17a3b56728c5","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57076117,"name":"Business and commerce/Business and management"},{"id":57076118,"name":"Social science/Business and management"},{"id":57076119,"name":"Business and commerce/Information systems and information technology"}],"tags":[],"updatedAt":"2026-02-27T15:56:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 10:07:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7566219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7566219","identity":"rs-7566219","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}