Study on the Impact of AI Integration on Large-scale Sports Event Platform Supply Chain Information Transmission Efficiency -- A Hybrid SEM–fsQCA Approach Considering Network Structure Mediation

Study on the Impact of AI Integration on Large-scale Sports Event Platform Supply Chain Information Transmission Efficiency -- A Hybrid SEM–fsQCA Approach Considering Network Structure Mediation | 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 Study on the Impact of AI Integration on Large-scale Sports Event Platform Supply Chain Information Transmission Efficiency -- A Hybrid SEM–fsQCA Approach Considering Network Structure Mediation Qianlan Chen, Siyi Mao, Guiping Zhu, Chao Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8728300/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In the rapidly changing market environment, supply chain management faces multiple challenges such as globalization and technological advancement. Especially for large-scale sports events, it is necessary to meet the massive, centralized and diversified needs efficiently, accurately and reliably in a short time window. How to make the relevant supply chain resources more effective for information interaction and work collaboration has become an increasingly concerned issue. The application of artificial intelligence (AI) has significantly transformed the mode of supply chain management. Drawing on social network theory and information ecology theory, this study explores the mechanism of AI in large-scale sports event platform supply chains (defined as a networked collaborative ecosystem connecting multiple subjects via digital platforms) and its impact on information transmission and interaction effects. To address the research gap, we adopt a mixed-method approach combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA): SEM is used to test the linear causal relationships and mediating effects between variables, while fsQCA identifies the nonlinear configuration paths of high information transmission efficiency. Based on 316 valid questionnaires from large-scale sports event platform supply chain enterprises, the results show that: (1) AI embedding directly and positively improves information transmission efficiency (standardized coefficient β = 0.21, p < 0.05); (2) network density (mediation effect ratio = 18.6%), relationship strength (32.2%), and supply chain length (27.9%) play partial mediating roles, while network centrality has no significant mediating effect; (3) three types of trigger modes (tight collaboration, intelligent interconnection, network connection) and four configuration paths for high information transmission efficiency are identified. Theoretically, this study supplements the theoretical mechanism of AI and network structure synergistically influencing large-scale sports event supply chain information transmission. Practically, it proposes optimization strategies from four aspects (AI application, network structure optimization, supply chain length simplification, and relationship strength enhancement), providing targeted references for improving the management level of large-scale sports event supply chains. Humanities/Complex networks Social science/Complex networks Physical sciences/Mathematics and computing AI large-scale sports event platform supply chain information transmission network structure factors influencing transmission efficiency Figures Figure 1 Figure 2 Figure 3 1. Introduction In the context of the development of the internet and digital economy, supply chain digitization has become a central strategic issue for enterprises. At the national level, policies such as coordinating digital transformation and building cross-border collaborative innovation systems are driving the evolution of supply chains from traditional linear structures to networked ecosystems (GARTNER, 2021) [ 1 ] . Large-scale sports events ( Olympic Games, World Cup, Formula One Series Grand Prix, Tennis Series Grand Prix, etc. ), as extreme scenes of demand outbreak, extremely short timeliness, and multi-party collaboration, have become a ' stress test field ' for testing the digital capabilities of the supply chain. With the update and iteration of digital technology, large-scale sports events have gradually changed from simple competitive competitions to complex projects for massive data generation and exchange. If there is no timely and efficient communication and interaction between the main bodies of the supply chain of sports events, it will form an ' information island ', thus affecting the smooth development of the event. In this new wave of technological revolution, AI, as the core of next-generation information technology, leverages capabilities like deep learning and real-time data processing. It not only optimizes internal corporate processes but also reshapes collaboration models and the logic of information exchange among supply chain enterprises. AI has become a key driver for breaking down “information silos” and enhancing supply chain resilience (ALICKE et al., 2021) [ 2 ] . In the context of large-scale sports events, AI significantly compresses the ' golden 4 hours ' response window by predicting audience traffic, scheduling franchised goods, and optimizing catering replenishment, which has become the core technology to ensure the event experience and urban operation. While existing research has confirmed AI's enabling role in supply chain information sharing and organizational structures, two limitations persist: First, most studies focus solely on AI's technical empowerment, lacking in-depth exploration of the interactive mechanisms between AI and platform supply chain structural characteristics (e.g., network density, supply chain length). Second, traditional studies predominantly employ linear methods like regression analysis or SEM, which struggle to capture the impact of multifactorial nonlinear combinations on information transmission efficiency (DILDA et al., 2021) [ 3 ] . Within platform supply chains, sports event organization forms complex networks with multiple actors and nodes. Information transmission efficiency is influenced by the synergistic effects of AI integration, network structure, cooperative relationships, and other factors. This calls for moving beyond linear thinking in order to uncover multiple causal pathways. Especially in the supply chain of large-scale sports events platform, the characteristics of temporary network, government-led and sudden change of audience demand make the efficiency of information transmission face higher uncertainty. It is urgent to break through linear thinking and reveal multiple causal paths. Based on this, this paper combines social network theory and information ecology theory to develop a theoretical framework for 'AI embedding, network structure, and information transmission efficiency.' It uses SEM to examine both direct and mediating effects among the variables, while using fsQCA to identify the combinations of conditions that lead to high information transmission efficiency. Finally, it proposes targeted optimization strategies for large-scale sports events, offering theoretical support and practical solutions for upgrading the information transmission mechanism in large-scale sports event platform supply chains. 2. Literature Review 2.1 Studies on AI Embedding Since the concept of AI was first introduced in 1956, its theoretical scope has continuously expanded alongside technological advancements. Ontologically, AI is an integrated system combining hardware, algorithms, and technical methodologies, with its core aim being the simulation of human intelligence (Niculescu, M. F. et al., 2018) [ 4 ] . Functionally, AI possesses progressive cognitive abilities that include information perception, reasoning, and autonomous decision-making (Toorajipour, R. et al., 2021) [ 5 ] . In practice, AI automates complex tasks and transforms them into efficient execution pathways, with widespread applications in scenarios such as supply chain demand forecasting and risk early warning (DORA et al., 2022) [ 6 ] . In the supply chain domain, AI applications follow a "technology-structure-efficiency" transmission logic: Companies such as Huawei and Amazon have deployed AI strategies to promote vertical coordination and horizontal network expansion within their supply chains, dismantling traditional linear structures and creating ecosystems centered around intelligent platforms (Ozcan, P., & Hannah, D., 2020) [ 7 ] . Academically, GARTNER (2021) identifies AI as one of the eight key trends in supply chains, enhancing transparency and decision-making responsiveness [ 1 ] . However, Helmberg, C. et al. (2022) also highlight that current AI technologies struggle to achieve full-process automation in supply chains due to bottlenecks such as technological heterogeneity and data security concerns [ 8 ] . Regarding information transmission, TOORAJIPOUR et al. (2021) found that artificial neural networks (ANN) can uncover hidden patterns in large datasets, alleviating the bullwhip effect [ 5 ] . Dora, M. et al. (2022) further indicate that AI improves information efficiency by enhancing supply chain transparency and traceability, although its effectiveness depends on technological infrastructure and data privacy protection mechanisms [ 6 ] . 2.2 Studies on Platform Supply Chain Network Structure The theory of supply chain structure has evolved through a progression from “linear chains to supply networks to platform ecosystems.” Early research focused on the linear upstream-downstream division of labor (Lambert, D. M., 1998) [ 9 ] . As industrial complexity increased, the concept of “supply networks” emerged, defined as stable, multi-organizational structures formed through long-term corporate collaboration (Roseira, C. et al., 2010) [ 10 ] . Platform supply chains, as an emerging form, are viewed by Jiang, J. et al. (2016) as reshaping governance models through disruptive technologies [ 11 ] , while Li, L. et al. (2016) emphasize their realization of economies of scale through network effects [ 12 ] .Network characteristics research: Lin and Shaw (1998) first proposed the “supply chain network” framework, conceptualizing it as a collaborative system of autonomous business entities [ 13 ] ; Chopra and Meindl (2010) further emphasized its demand-oriented attributes [ 14 ] . Social network theory provides analytical tools: Caldarelli and Catanzaro (2004) introduced metrics like network size and density [ 15 ] ; Lv, D. et al. (2020) expanded these with dynamic indicators such as average shortest path and clustering coefficient, forming a dual-dimensional “whole-individual” analysis system [ 16 ] .Supply chain length research: As a core complexity metric, Porter (1985) defined it from a value chain perspective as the value-adding process “from the supplier's supplier to the customer's customer” [ 17 ] ; Gereffi and Henderson (1994) emphasized physical distance and coordination levels within the global value chain framework [ 18 ] ; Boehm, C. E. (2019) focused on “time length,” i.e., the cycle from product design to delivery [ 19 ] . Quantitative methods are categorized into three levels: enterprise (number of suppliers, production stages), industry (input-output “average number of handoffs”), and global (upstream/downstream depth) (Wang et al., 2016) [ 20 ] .Relationship strength research: Uzzi (1997) highlighted from a social network perspective that strong relationships are characterized by reciprocal trust and information-sharing mechanisms [ 21 ] ; Williamson (1985), grounded in transaction cost theory, introduced the concept of “relationship-specific investments” [ 22 ] ; Dyer and Singh (1998) regarded relationship strength as the core driver of knowledge sharing and collaborative innovation [ 23 ] . Measurement methods include subjective scales (trust, commitment), behavioral indicators (collaboration duration, investment amount), and network analysis techniques (transaction frequency, centrality) (Gulati & Sytch, 2007) [ 24 ] . 2.3. Studies on Supply Chain of Large-scale Sports Events The global influence of large-scale sports events continues to grow, and its one-time, super-peak, multi-node, short-cycle supply chain characteristics have attracted academic attention. Qiao, L. (2006) applied the supply chain theory to the logistics management of the Beijing Olympic Games, and focused on the construction and performance evaluation of the supply chain system of the Beijing Olympic Games [ 30 ] ; Zhang, C. X. (2008) creatively applied the SCOR model to the theoretical analysis of Olympic logistics, and combined with the characteristics of Olympic logistics process and operation mode, analyzed and constructed the Olympic logistics SCOR model from the four levels of overall planning, core process layer, configuration layer and decomposition layer, and creatively selected the appropriate evaluation index from the perspective of core process layer to construct the performance evaluation system [ 29 ] . Wang, Q. T. et al. (2013) pointed out that the logistics of large-scale sports events has the dual pressures of concentrated demand outbreak and extremely short window period, and proposed to reduce information lag by sharing logistics information platform [ 25 ] . Yang, T. (2010) defined the basic model of the supply chain of the Chinese basketball professional league, the core enterprise, the popular analysis and other conceptual connotations, focusing on the supply chain of the basketball professional league athletes supply management, supply chain relationship management, supply chain audience demand management and so on [ 28 ] . Yu, H. W. et al. (2010) pointed out that the existence of supply chain risk of sports events seriously affects the normal holding of sports events, and it is necessary to manage its risk. Through research, it is concluded that option financial derivatives can be effectively applied to the field of supply chain risk management of sports events as a risk management tool [ 26 ] . Chen, X. D. (2012) put forward the connotation, system structure, type, influencing factors and operation mechanism of the supply chain of competitive sports performance products in China [ 27 ] . China Journal of Logistics and Purchasing (2023) reported that the Hangzhou Asian Games Logistics Center deployed intelligent equipment such as unmanned forklifts, inventory drones, wearable exoskeletons, and connected to the event logistics digital twin system to realize the whole process visualization of 30 days before the competition, 4 hours in the competition, and 48 hours after the competition. It provides real event scene data support for the framework of ' AI-embedding-network structure-information efficiency ' in this paper. The research on the supply chain of large-scale sports events has moved from the early logistics scheduling to the green, resilient and digital multi-dimensional perspective. However, there is still a lack of quantitative test on the interaction mechanism between AI embedding and network structure, and there is no systematic answer to the question of " what kind of combination of conditions can achieve high information transmission efficiency during the competition. " This study is a supplement and expansion of the gap. 2.4. Studies on Supply Chain Information Efficiency Definition and Role of Information Transmission Efficiency: Lee (2000) proposed the “bullwhip effect,” which revealed the distortion in demand information transmission and indicated that information sharing could reduce inventory costs and improve response speed [ 31 ] . Lambert (1998) found that corporate confidentiality needs hinder information exchange [ 9 ] , while Chen (2006) suggested optimizing information sharing through risk contracts and compensation mechanisms [ 32 ] . In platform contexts, Zhang, X. et al. (2024) analyzed quality information disclosure decisions on third-party shared manufacturing platforms [ 33 ] , and Zhou, S. (2022) examined the role of information sharing in low-carbon supply chains [ 34 ] . Research on Influencing Factors: From a technological perspective, Huang et al. (2005) confirmed that the Internet of Things (IoT) enhances information efficiency, though technical heterogeneity may neutralize its benefits [ 35 ] ; Saberi et al. (2019) found that blockchain smart contracts reduce information verification time [ 36 ] . On the institutional level, Sun, B. et al. (2009) identified proactive government information disclosure as a key driver [ 37 ] , while Chen et al. (2013) proposed a “policy signal transmission model,” emphasizing the impact of clear regulatory policies [ 38 ] . At the organizational level, Liu, Y. (2012) found that non-standardized interfaces caused 32% information distortion [ 39 ] , and Dyer and Chu (2003) confirmed a positive correlation between trust levels and information sharing frequency (a one-standard-deviation increase in trust boosted sharing frequency by 22%) [ 40 ] . Based on domestic and international literature, existing studies have explored AI's role in driving innovation within supply chains, mostly focusing on technological empowerment. However, research on AI’s deeper functions within large-scale sports event platform supply chains, particularly regarding information transmission mechanisms, remains limited. The integration of AI with large-scale sports event platform supply chains is still in its early stages, suggesting significant research potential. When it comes to supply chain information transmission mechanisms, scholars adopt various perspectives. Some examine transmission strategies from the perspective of power structures, often modeling supply chains as linear chains. In the digital age, however, supply chains are shifting from traditional linear structures to relational networks involving multiple equally powerful entities. Thus, methods based on power structures and linear supply chains lack applicability, necessitating a more comprehensive, objective perspective that transcends linear constraints. Given the enhanced AI integration within large-scale sports event platform supply chains and its influence on structural features and information transmission modes, this study considers the objective structural forms of large-scale sports event platform supply chains to explore their information transmission mechanisms, highlighting both innovation and necessity. 3. Research Methods 3.1 Structural Equation Modeling Methodology Structural Equation Modeling (SEM) is a widely used linear statistical modeling technique in economics, psychology, sociology, management, and other fields (Guo, Z., 1999) [ 41 ] . It is employed to test hypothesized relationships between variables, with its core focus on exploring covariate relationships through covariance structure analysis to explain the variance among variables. Consequently, it is also referred to as covariance structure analysis (Hou, J. et al., 2004) [ 42 ] . Methodologically, SEM overcomes the limitations of traditional analytical approaches by allowing flexible specification of relational pathways between variables based on theoretical assumptions, rather than requiring rigid predefined models. This makes it suitable for both exploratory research and theoretical validation. It comprises two components: measurement equations and structural equations (Anderson J C, 1988) [ 43 ] . Measurement equations characterize the relationships between latent variables and their observed indicators, reflecting the validity of measurement instruments; structural equations describe causal relationships among latent variables, revealing intrinsic connections between theoretical constructs. SEM data analysis involves a dynamic modification process, widely applied across three model types: First, validation models compare observed data with model predictions to assess theoretical validity; if the fit is poor, alternative models must be tested. Second, model selection involves establishing multiple candidate models and comparing them using information criteria such as AIC or BIC. When fit levels are comparable, the model with fewer parameters and simpler structure is chosen to prevent overfitting. Third, model generation begins by proposing a basic model and checking its fit. After analyzing deficiencies, modifications are made, and the revised model is validated using samples to ultimately determine the optimal model. 4. Research Hypotheses 4.1. AI Embeddings, Network Density, and Information Transmission Efficiency Network density reflects the closeness of connections between nodes. Peter and Noshir (2003) define it as "the ratio of actual connections to the maximum possible connections." [ 44 ] AI facilitates the creation of secure sharing platforms through blockchain and supports real-time collaboration through cloud computing, thereby enhancing supply chain connectivity (Collins et al., 2006) [ 45 ] . High-density networks strengthen mutual trust among enterprises and accelerate information flow (Uzzi, 1997) [ 21 ] . Based on the above analysis, this study proposes the following hypotheses: H1: Enterprise AI embedding improves information transmission efficiency in large-scale sports event platform supply chains. H2: Enterprise AI embedding enhances network density among enterprises in large-scale sports event platform supply chains. H3: Network density positively influences information transmission efficiency in large-scale sports event platform supply chains. 4.2. AI Embeddings, Network Centrality, and Information Transmission Efficiency Network centrality measures a node's core position within a network, encompassing measures such as degree centrality and betweenness centrality (Friedkin, 1991) [ 46 ] . AI improves operational efficiency and innovation capacity within enterprises, thus enhancing their resource aggregation and intermediary roles in supply chains (Batjargal, 2000) [ 47 ] . Central nodes play a crucial role in controlling information flow, thereby improving transmission efficiency (Giuliani, 2005) [ 48 ] . Based on this, the following research hypotheses are proposed: H4: Enterprise AI embedding significantly increases the network centrality of large-scale sports event platform supply chain enterprises. H5: Network centrality positively influences information transmission efficiency within large-scale sports event platform supply chains. 4.3. AI Embedding, Supply Chain Length, and Information Transmission Efficiency Supply chain length refers to the number of stages or links from raw materials to end consumers (Boehm, C. E., 2019) [ 19 ] . AI shortens processes by utilizing intelligent scheduling and automating transactions, thereby reducing intermediate stages (Saberi et al., 2019) [ 36 ] ; excessively long supply chains are prone to information delays and distortions (Lee, 2000) [ 31 ] . Based on this, the following research hypotheses are proposed: H6: Enterprise AI embedding significantly reduces supply chain length for large-scale sports event platform supply chain enterprises. H7: Supply chain length and information transmission efficiency within large-scale sports event platform supply chains exhibit an inverse relationship. 4.4. AI Embedding, Relationship Strength, and Information Transmission Efficiency Relationship strength reflects the closeness of cooperation between enterprises (Granovetter, 1973) [ 49 ] . AI enhances transparency through data integration, optimizes collaboration tools, and strengthens mutual trust (Reagans, 2003) [ 50 ] ; strong relationships facilitate precise information transmission and reduce opportunistic behavior (Dyer & Singh, 1998) [ 23 ] . Based on this, the following research hypotheses are proposed: H8: Corporate AI embedding significantly increases relationship strength among enterprises in large-scale sports event platform supply chains. H9: Relationship strength positively influences information transmission efficiency in large-scale sports event platform supply chains. According to these hypotheses, the theoretical model of factors affecting information transmission efficiency in large-scale sports event platform supply chains is presented in Fig. 1 . 5. Research Design 5.1. Variable Measurement This paper categorizes the variables into exogenous and endogenous variables. By definition, AI embedding is an exogenous variable, while information transmission efficiency is an endogenous variable. At the same time, network density, network centrality, network scope, supply chain length, and relationship strength serve as both endogenous variables for AI embedding and exogenous variables for information transmission efficiency. The measurement framework for AI embedding is custom-designed based on IBM's Enterprise AI Maturity Model [ 51 ] . The measurement of network density is based on Qi, J. (2007) scale [ 52 ] ; network centrality measurement draws from Batjargal (2000), Giuliani (2005), and Friedkin, N. E. (1991). The definition of supply chain length in this study involves designing measurement items independently, considering the number of raw material suppliers, the number of intermediate processing stages in product manufacturing, and the number of distribution channels. The measurement of relationship strength follows scales from Burt (1992) [ 53 ] , Reagans (2003), and Granovetter (1973). The measurement of information transmission efficiency uses Lou, C. (2007) scale to design measurement items [ 54 ] . Regarding AI embedding, measurements are based on IBM's Enterprise AI Maturity Model, covering five items (XA1-XA5) related to business performance enhancement, technical capabilities, and employee willingness to use AI. For network density, the measurement follows Qi, J. (2007) scale, with four items (MA1-MA4) assessing collaboration frequency and information acquisition. For network centrality, the integrated scales of Batjargal (2000) and Giuliani (2005) are used, with five items (MB1-MB5) focusing on resource aggregation and bridging roles. For supply chain length, the measurement includes five items (MC1-MC5) related to the number of raw material suppliers, intermediate processing stages, and other related factors. Regarding relationship strength, five items (MD1-MD5) are designed, measuring coordination and mutual trust, based on the work of Burt (1992), Reagans (2003), and Granovetter (1973). Lastly, for information transmission efficiency, the scale by Lou, C. (2007) is adapted, with five items (YA1-YA5) measuring timeliness, security, and other factors. 5.2. Questionnaire Design and Data Collection Based on established scales, the measurement items were adjusted to align with the characteristics of large-scale sports event platform supply chains, using a 7-point Likert scale (1 = 'Strongly Disagree', 7 = 'Strongly Agree'). The research object is the employees ( grass-roots to senior ) of the supply chain enterprises of the sports event platform, covering the roles of equipment manufacturers, franchised commodity providers, catering service providers, official logistics providers, security and medical material suppliers, and event technical service providers. After revisions based on a pilot survey (50 responses), the questionnaire was formally distributed. A total of 359 responses were collected, with 43 logically inconsistent responses excluded, resulting in 316 valid responses—a response rate of 88.02%. The enterprise industry types collected in this questionnaire include competition equipment manufacturing, event franchise commodity manufacturing, cold chain catering, intelligent logistics, security equipment, medical consumables, digital technology services, etc. Among them, the proportion of franchise commodity manufacturing and cold chain catering reached 31.329% and 25.949% respectively, accounting for the largest proportion. Regarding enterprise type, the samples are all core or first-level cooperative enterprises in the supply chain of the event platform, and the distribution is relatively average. Among them, official logistics and franchised commodity suppliers account for the largest proportion, up to 22.785%. Furthermore, the surveyed group of front-line operators of sporting events was predominantly aged between 21 and 50, accounting for 64.241% of the sample. As those directly engaged in race materials scheduling, audience service, cold chain transportation, security materials distribution and other key processes, they provided firsthand insights into timing information response, use of AI tools, frequency of collaboration. Their feedback is vital for understanding actual operation status of event supply chain, ensuring the data is reasonably representative. 5.3. Reliability and Validity Testing 5.3.1. Reliability Testing of Data Currently, the Cronbach's α coefficient method is the most widely used approach for assessing reliability. It evaluates the reliability of measurement tools by calculating the degree of consistency among items within a scale. The following reliability test results were obtained using SPSS Pro in this study: Table 1 Reliability Test of Variables Variable Cronbach’s α AVE CR AI Embedding 0.939 0.758 0.940 Network Density 0.913 0.725 0.913 Network Centrality 0.908 0.716 0.924 Supply Chain Length 0.952 0.801 0.953 Relationship Strength 0.935 0.744 0.935 Information Transmission Efficiency 0.941 0.763 0.941 Overall Reliability 0.899 - - As shown in Table 1 , the Cronbach's α reliability coefficients for all variables exceed 0.9. This indicates that the scale's content is well-constructed and demonstrates high internal consistency (Feng, D., 2018) [ 55 ] . Moreover, all AVE (Average Variance Extracted) values exceed the 0.5 threshold, with the AVE values for all variables, except for 'Network Scope,' surpassing 0.7. This suggests that the latent variables exhibit strong convergent validity and minimal measurement bias. In addition, the composite reliability (CR) for all variables meets the 0.5 threshold, with most exceeding 0.9. This confirms that the scale's reliability is within an acceptable range (Li, K., & Jiang, J., 2025) [ 56 ] . 5.3.2. Data Validity Testing The scales used in this study primarily draw on established measurement tools from prior research, ensuring content validity. Construct validity is an important dimension for assessing the quality of measurement tools, as it reflects whether the instrument accurately captures the structural relationships among constructs in the theoretical model. In empirical research, the KMO test and Bartlett's sphericity test are commonly employed methods for evaluating construct validity. The KMO test assesses the suitability of factor analysis by analyzing partial correlations between indicators. Its value ranges from 0 to 1, with a KMO value exceeding 0.8 generally indicating strong correlations between indicators and higher suitability for factor analysis (Bi, C. et al., 2025) [ 57 ] . Bartlett's sphericity test assesses the correlations among indicators within variables, with the null hypothesis being that indicators are independent. Data are suitable for factor analysis only when the null hypothesis is rejected, meaning that the significance level (Sig value) should be less than 0.01 (Gong, H. et al., 2025) [ 58 ] . The KMO and Bartlett's sphericity test results for each variable in this study are as follows: Table 2 KMO and Bartlett's Sphericity Test Results for Variables 变量 KMO Sig Bartlett's Sphericity Test df AI Embedding 0.909 0.000 1346.068 10 Network Density 0.855 0.000 855.431 6 Network Centrality 0.886 0.000 1181.899 10 Supply Chain Length 0.916 0.000 1578.302 10 Relationship Strength 0.904 0.000 1280.432 10 Information Transmission Efficiency 0.911 0.000 1369.557 10 Overall validity 0.977 0.000 9668.483 406 Table 2 indicates that network density (KMO = 0.855), network centrality (KMO = 0.886), and other variables (KMO > 0.9) all meet the conditions for factor analysis. Bartlett's sphericity test rejected the null hypothesis (Sig < 0.01), confirming the suitability of all variables for factor analysis (Gong, H. et al., 2025). In confirmatory factor analysis (CFA), the model with 6 factors, 29 observed variables, and a sample size of 316 met the requirements. Most factor loadings were > 0.8, with fit indices CFI, NFI, and NNFI > 0.9, RMSEA < 0.1, and CMIN/DF < 3, indicating good convergent validity for the scales. (The GFI and RMR values for network density and centrality slightly fell short, but this did not affect the overall validity.) (Yang, G., 2025) [ 59 ] .Table 3 presents the results of the confirmatory factor analysis (CFA), which was used to test the convergent validity of the measurement model. Table 3 Confirmatory Factor Analysis Results CMIN/DF GFI RMSEA RMR CFI NFI NNFI Criteria for Judgment 0.9 < 0.10 0.9 > 0.9 > 0.9 AI Embedding 1.497 0.995 0.04 0.025 0.998 0.995 0.996 Network Density 0.999 0.00 0.011 1.001 0.999 1.003 0.999 Network Centrality 0.993 0.043 0.038 0.998 0.993 0.995 0.993 Supply Chain Length 1.229 0.996 0.027 0.017 0.999 0.996 0.999 Relationship Strength 2.112 0.992 0.059 0.03 0.996 0.992 0.991 Information Transmission Efficiency 0.950 0.997 0.00 0.019 1.00 0.997 1.00 Overall 1.822 0.934 0.051 0.172 0.969 0.934 0.965 Discriminant validity assessment is a crucial step in verifying the quality of measurement instruments. This study examined discriminant validity by observing whether the square root of the average variance extracted (AVE) for each latent variable exceeded the correlation coefficients between that variable and all other variables. As shown in the data analysis results in Table 4 , the main diagonal elements (i.e., the square roots of AVE) were generally larger than the non-diagonal elements in the same row or column (correlation coefficients between variables), indicating that the measured variables possess good discriminant validity. Table 4 Correlation Coefficients Among Variables and Square Roots of Average Variance Extracted (AVE) AI Embedding Network Density Network Centrality Supply Chain Length Relationship Strength Information Transmission Efficiency AI Embedding 0.871 Network Density 0.650 0.851 Network Centrality 0.544 0.694 0.846 Supply Chain Length -0.762 -0.721 -0.635 0.895 Relationship Strength 0.710 0.694 0.584 -0.844 0.863 Information Transmission Efficiency 0.796 0.770 0.625 -0.867 0.854 0.873 Note: The numbers on the diagonal represent the square root of the AVE for each factor. 5.4. Structural Equation Model Testing 5.4.1. Structural Equation Model Construction This study employed AMOS 28.0 software to construct a structural equation model comprising six latent variables and 29 observed variables. In the model diagram, ellipses represent latent variables, rectangles denote observed variables, and small circles indicate measurement error terms. Model analysis results indicate that all observed variables' factor loadings exceeded the 0.50 threshold, with most indicators achieving loadings above 0.8, demonstrating the measurement tool's strong convergent validity. Path coefficient estimates fell within the theoretical range of (-1, 1), further validating the scale design's rationality. Model fit indices revealed that the structural equation model adequately fitted the sample data. Figure 2 presents the path diagram of the initial structural equation model, illustrating the theoretical relationships among AI embedding, network density, network centrality, supply chain length, relationship strength, and information transmission efficiency, along with their measurement variables. 5.4.2. Path Results and Hypothesis Testing AMOS 28.0 path coefficients (Table 8 ) indicate that only the 'network centrality → information transmission efficiency' path is not significant (P = 0.412), thus failing to validate H5. All other paths are significant at the 1% or 5% level, confirming H1-H4 and H6-H9. The specific empirical results are analyzed as follows: AI embedding positively influences information transmission efficiency (β = 0.21, p < 0.05, supporting H1), network density (β = 0.767, p < 0.001, supporting H2), network centrality (β = 0.604, p < 0.001, supporting H4), relationship strength (β = 0.825, p < 0.001, supporting H8), and negatively impacts supply chain length (β = -0.866, p < 0.001, supporting H6). Network density (β = 0.222, p < 0.001, supporting H3) and relationship strength (β = 0.356, p < 0.001, supporting H9) positively influence information transmission efficiency, while supply chain length negatively affects it (β = -0.294, p < 0.001, supporting H7). Network centrality has no significant effect on information transmission efficiency (β = -0.026, p = 0.412, H5 not supported). Table 5 lists the standardized coefficients of each hypothesized path in the structural equation model and their significance test results. Table 5 Hypothesis Path Coefficients Factor (Latent Variable) → Analyzed Item (Observed Variable) Standardized Coefficient Standard error Z P Hypothesis Verification Results AI Embedding → Information Transmission Efficiency 0.21 0.087 2.375 0.018** Support AI Embedding → Network Density 0.767 0.058 14.005 0.000*** Support Network Density → Information Transmission Efficiency 0.222 0.04 5.179 0.000*** Support AI Embedding → Network Centrality 0.604 0.048 8.119 0.000*** Support Network Centrality → Information Transmission Efficiency -0.026 0.047 -0.821 0.412 Not supported AI Embedding → Supply Chain Length -0.866 0.052 -17.16 0.000*** Support Supply Chain Length → Information Transmission Efficiency -0.294 0.054 -5.202 0.000*** Support AI Embedding → Relationship Strength 0.825 0.056 15.424 0.000*** Support Relationship Strength → Information Transmission Efficiency 0.356 0.047 7.018 0.000*** Support Note: ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively. 5.4.3. Mediating Effect Testing Given the potential indirect effects between AI embedding and large-scale sports event supply chain information transmission efficiency on platforms, this subsection will examine the mediating effects. As shown in the conceptual model of this study, there are four mediating pathways: "AI Embedding → Network Density → Information Transmission Efficiency," "AI Embedding → Network Centrality → Information Transmission Efficiency," "AI Embedding → Supply Chain Length → Information Transmission Efficiency," and "AI Embedding → Relationship Strength → Information Transmission Efficiency." The results from testing using the Bootstrap function in AMOS 28.0 are as follows: Table 6 Test of Mediating Effects Path effect SE S.E. P 95%CI LB UB Overall effect 0.832 0.063 0.001*** 0.721 0.962 AI Embedding→Network Density→Information Transmission Efficiency Direct effect 0.676 0.083 0.002*** 0.513 0.850 Indirect effects 0.155 0.056 0.002*** 0.066 0.290 AI Embedding→Network Centrality→Information Transmission Efficiency Direct effect 0.846 0.067 0.001*** 0.724 0.983 Indirect effects -0.014 0.021 0.500 -0.058 0.030 AI Embedding→Supply Chain Length→Information Transmission Efficiency Direct effect 0.600 0.121 0.003*** 0.361 0.818 Indirect effects 0.232 0.121 0.016** 0.034 0.501 AI Embedding→Relationship Strength→Information Transmission Efficiency Direct effect 0.564 0.111 0.002*** 0.338 0.776 Indirect effects 0.268 0.086 0.001*** 0.124 0.465 Table 6 shows that the total effect of AI embedding on information transmission efficiency is 0.832, significant at the 0.001 level, indicating that AI embedding has a significant positive impact on information transmission efficiency. Among the four pathways: ① “AI Embedding → Network Density → Information Transmission Efficiency” has a direct effect of 0.676 and an indirect effect of 0.155 (both significant at the 1% level). Network density partially mediates this relationship, accounting for 18.6% of the total effect. ② “AI Embedding → Network Centrality → Information Transmission Efficiency”: Direct effect 0.846 (significant at 1%), indirect effect − 0.014 (p = 0.500, not significant). Network centrality does not mediate the relationship; ③ “AI Embedding → Supply Chain Length → Information Transmission Efficiency”: Direct effect 0.600 (significant at 1%), indirect effect 0.232 (significant at 5%). Supply chain length partially mediates the relationship, accounting for 27.9% of the total effect; ④ “AI Embedding → Relationship Strength → Information Transmission Efficiency”: Direct effect 0.564, indirect effect 0.268 (both significant at 1%). Relationship strength partially mediates the effect, accounting for 32.2% of the total effect. In summary, AI embedding has a significant positive impact on improving information transmission efficiency in large-scale sports event platform supply chains, primarily achieved through both direct and indirect effects (such as increasing network density, shortening supply chain length, and enhancing relationship strength). However, network centrality did not play a significant mediating role in this process. 5.4.4. Evaluation of Structural Equation Model Fit EM fit evaluation aims to validate the model's effectiveness in describing the data and research hypotheses, assess model applicability, determine the significance of variable relationships, and identify any necessary modifications. Common indicators include CMIN/DF, GFI, RMR, RMSEA, NFI, CFI, and NNFI. Evaluation of the unmodified model (Table 7 ) shows: CMIN/DF = 2.492 ( 0.9), indicating a high proportion of observed variance is explained; RMSEA = 0.069 ( 0.05), suggesting some room for improvement, but requiring consideration alongside other indices; CFI = 0.943, NFI = 0.909, NNFI = 0.937 (all > 0.9), indicating a significantly improved model fit compared to the baseline model, with an overall good fit. Considering the above, most indicators fall within acceptable ranges. Therefore, the unmodified structural equation model in this study is considered to have an acceptable fit. Table 7 Model Fitting Indices Before Correction Indicator CMIN/DF GFI RMSEA RMR CFI NFI NNFI Criteria for Judgment 0.9 < 0.10 0.9 > 0.9 > 0.9 Value 2.492 0.909 0.069 2.286 0.943 0.909 0.937 Based on the results of the path hypothesis testing, Hypothesis H5 was not validated, indicating that no significant positive relationship exists between network centrality and information transmission efficiency. Thus, after removing the latent variable of network centrality, the revised structural equation model is as follows. Figure 3 is the diagram of the revised structural equation model, reconstructed after excluding the non-significant "network centrality" path. This model is more concise and has better fit, highlighting the influence mechanisms of AI embedding, network density, supply chain length, and relationship strength on information transmission efficiency. As shown in Table 8 , the revised structural equation model exhibits all indicators approaching the ideal ranges except for the GFI, which slightly deviates from the ideal standard value. Specifically, the chi-square degrees of freedom ratio (CMIN/DF), root mean square error of approximation (RMSEA), and root mean square residual (RMR) all decreased compared to their values prior to modification, indicating greater proximity to the ideal state. The remaining indices showed slight increases, gradually approaching 1. Overall, these results suggest that the revised model demonstrates a fit closer to the ideal state and superior to that of the model before modification. Table 8 Revised Model Fitting Indices Indicator CMIN/DF GFI RMSEA RMR CFI NFI NNFI Criteria for Judgment 0.9 < 0.10 0.9 > 0.9 > 0.9 Value 1.974 0.880 0.056 0.169 0.970 0.941 0.966 5.5. Configuration Analysis SEM has identified AI embeddings, network density, supply chain length, and relationship strength as key antecedent variables for information transmission efficiency in large-scale sports event platform supply chains. However, these variables do not influence efficiency independently; rather, their effects are driven by multifaceted interactions. Traditional statistical methods (e.g., regression, SEM) can reveal direct relationships between variables but assume linear independence of causality, making it difficult to capture complex interactions. Large-scale sports event platform supply chains are multifaceted complex systems where variable combinations may influence efficiency through nonlinear or interactive pathways, limiting traditional methods' ability to address multiple causal paths. Fuzzy Set Qualitative Comparative Analysis (fsQCA), proposed by Ragin (2006), effectively addresses such complex causal relationships. Integrating set theory, Boolean algebra, and fuzzy logic, it is suitable for small-to-medium sample sizes (20–100). By analyzing the set membership relationships between condition and outcome variables, fsQCA identifies necessary or sufficient combinations of conditions that lead to outcomes. 5.5.1. Data Calibration First, to facilitate subsequent operations and descriptions, the four condition variables are assigned codes: AI embedding as X, network density as MA, supply chain length as MC, and relationship strength as MD. The outcome variable, information transmission efficiency, is designated as Y. The data from the 7-point Likert scale is mapped to the interval [0,1], with the 25th, 50th, and 75th percentiles representing non-membership, cross-membership, and full membership points, respectively (Ragin, 2006) [ 60 ] . The calibrated variables and their respective anchor points are detailed in Table 9 below: Table 9 Set and Calibration Point Statistics Pre-calibration variables Calibrated Variable Threshold Fully subordinate intersection Completely unaffiliated Prerequisite X FX 6.2 5.8 5.4 MA FMA 6.25 6.0 5.5 MC FMC 2.4 1.8 1.6 MD FMD 6.2 5.8 5.15 Outcome variable Y FY 6.2 5.8 5.2 5.5.2. Necessity Analysis As shown in Table 10 , the consistency of all condition variables (AI embedding, network density, supply chain length, relationship strength) is < 0.9 (with FX = 0.772 and FMD = 0.728 under high information transmission efficiency). No single condition is indispensable; the combination of conditions requires analysis (Zhang, H. et al., 2025) [ 61 ] . Table 10 Necessity Condition Analysis Results High information transmission efficiency Non-high information transmission efficiency Consistency Coverage Consistency Coverage FX 0.771722 0.765481 0.394066 0.360684 ~FX 0.355471 0.388664 0.743775 0.750406 FMA 0.671849 0.822442 0.302700 0.341924 ~FMA 0.462421 0.418157 0.842811 0.703260 FMC 0.440467 0.449169 0.737604 0.694069 ~FMC 0.699996 0.743000 0.414620 0.406095 FMD 0.727705 0.750949 0.402606 0.383371 ~FMD 0.402457 0.421994 0.738454 0.714487 5.5.3. Configuration Analysis To ensure the reliability of condition combinations, this study set a consistency threshold of 0.8 and a frequency threshold of 3 (Rihoux B, 2009) [ 62 ] . Table 11 shows that the consistency of each configuration exceeds 0.75, with an overall model consistency of 0.762 (above the theoretical standard of 0.75) and an overall coverage rate of 0.827 (explaining over 82% of the samples). Based on the core pathways and logical mechanisms, the four types of configurations with high information transmission efficiency are categorized as: Tightly Collaborative, Intelligent Interconnected, and Network-Linked. Table 11 Analysis of configuration results Configuration Close Collaboration Type Smart Connected Network-connected S1 S2 S3 S4 FX ⬤ ⬤ FMA ⬤ ⬤ FMC ⮾ ⚫ ⚫ FMD ⬤ Consistency 0.821 0.856 0.773 0.845 Coverage 0.601 0.610 0.320 0.274 Net Coverage Rate 0.116 0.077 0.047 0.018 Overall consistency 0.762 Overall Coverage 0.827 Note: ⚫ and ⬤ indicate the condition exists; ⮾ and ⮾ indicate the condition does not exist; blank indicates the condition has dual possibilities of existence and non-existence. ⬤ and ⮾ are core conditions, while ⚫ and ⮾ are peripheral conditions. 5.5.4. Analysis of Results (1) Tightly Collaborative Model The configuration path is characterized by “high relationship intensity * low supply chain length,” with both variables serving as core conditions. This indicates that establishing robust and trusting cooperative relationships enables rapid information flow, thereby enhancing the overall flexibility and responsiveness of the supply chain. High relationship intensity refers to the close ties formed between relevant enterprises through long-term collaboration, frequent interaction, and mutual dependence. Low supply chain length streamlines the structure by eliminating unnecessary intermediaries and processes, ensuring information and materials reach their destinations swiftly and efficiently. This tightly collaborative model also facilitates trust-building among large-scale sports event supply chain nodes. Collaborative efforts extend beyond transactional levels to encompass information sharing, technological cooperation, and risk-sharing. Such comprehensive partnerships further solidify strategic alliances between enterprises, fostering stronger overall competitive advantages in fiercely competitive markets. (2) Smart Interconnected Model The configuration paths are “High AI Embedding * High Network Density” and “High AI Embedding * High Supply Chain Length.” High AI Embedding serves as the core condition, highlighting the pivotal role of advanced technologies in enhancing information processing capabilities and strengthening decision support. With continuous advancements in artificial intelligence, enterprises significantly boost the efficiency of information screening and transmission through intelligent analysis and automation. AI's analytical capabilities overcome historical delays and misjudgments caused by information silos, ensuring real-time communication across all links for swift and precise responses. High network density further facilitates this information flow, enabling every participant to access required real-time data within minutes, thereby enhancing overall supply chain flexibility. Conversely, in scenarios characterized by “high AI integration * long supply chains,” geographically dispersed operations prolong information transmission times, thereby slowing response speeds. High AI integration leverages intelligent algorithms to monitor the entire supply chain's operational status in real time, identifying potential risks and bottlenecks to minimize losses from information delays. AI's early warning capabilities enable businesses to swiftly adjust operation schedules and logistics arrangements, ensuring the supply chain maintains high efficiency even amid change. (3) Network-Connected Type The configuration path is “High Network Density * High Supply Chain Length,” with high network density as the core condition and high supply chain length as the peripheral condition. High network density enables rapid information transmission across multiple nodes, covering a broader range of participants. This significantly enhances information liquidity and accessibility, ensuring tight connections among all supply chain stakeholders and fostering a dense information exchange environment. Even when facing extended supply chains, the dense network structure accelerates information dissemination through multiple pathways. The broad network coverage enables information to reach diverse stakeholders more extensively, ensuring every link receives relevant data and instructions promptly. This diversity in information transmission not only enhances accessibility but also facilitates more effective collaboration and coordination among participants in supply chain management, thereby boosting overall flexibility and responsiveness. 6. Optimization Strategies for Large-scale Sports Event Platform Supply Chain Information Transmission Mechanisms 6.1. Optimization Strategies Based on Enterprise AI Embedding 6.1.1 Expanding the Application Scope of AI Technologies Research confirms that widespread AI adoption can significantly enhance the network density, centrality, and relationship strength of enterprises within large-scale sports event platform supply chains, thereby optimizing supply chain management and operational efficiency. In the context of large-scale sports events, enterprises should extend AI to the full cycle of ' audience peak prediction-dynamic replenishment during the game-quick post-game clearance '. For example, at the 2024 Paris Olympic Games, artificial intelligence is used to optimize the schedule, resource allocation and logistics management of the event. Through big data analysis, the flow density is predicted to ensure the efficient and orderly conduct of various activities. By integrating AI to analyze supply chain data and update information in real time, transmission efficiency improves, enabling businesses to respond flexibly to complex environments. Implementing AI-powered intelligent scheduling systems in logistics, inventory management, and other areas achieves optimal resource allocation while eliminating information delays and efficiency losses. Enterprises must drive comprehensive AI integration, leveraging deep learning and machine learning for real-time data processing and demand forecasting to reduce response times and optimize information flow. 6.1.2. Focus on AI-Driven Supply Chain Collaboration AI-driven supply chain collaboration breaks down information silos to achieve real-time, effective interaction. For large-scale sports events, it is suggested that the organizing committee should force the construction of ' event AI collaborative middle stage ', so that suppliers such as franchised goods, catering, logistics and security can be accessed within 48 hours to achieve ' golden 4 hours ' response. Building cross-enterprise information sharing platforms serves as the foundation. AI can automatically adjust communication frequency and content based on participants' needs, adapting to environmental changes while preventing information overload and asymmetry. By leveraging AI to integrate data, upstream and downstream enterprises can synchronize critical information—including inventory levels, demand forecasts, and transportation arrangements—in real time, thereby enhancing response speed and accuracy. 6.2. Optimization Strategies Based on Network Characteristics Enterprises should strengthen ties with all supply chain participants to build a tightly integrated collaborative network, enhancing information transmission efficiency and response speed while boosting the overall competitiveness and flexibility of the supply chain. Large-scale sports events can rely on the government 's ' event brain ' to quickly increase network density - for example, the Hangzhou Asian Games through the ' Asian Games nail ' so that both event officials, partners or volunteers, venue workers, etc., can freely access the event venues within his authority, and tens of thousands of collaborators Smooth and unimpeded communication. Simultaneously, digital tools should be leveraged to establish cross-enterprise supply chain collaboration platforms, breaking down information barriers and accelerating data flow. Furthermore, establishing standardized information exchange protocols ensures data compatibility, while developing intelligent decision support systems improves response accuracy and timeliness. Implementing dynamic risk assessment mechanisms enhances the network's resilience. These mutually reinforcing measures collectively elevate the overall efficiency of the supply chain network, delivering comprehensive solutions. 6.3. Optimization Strategies Based on Supply Chain Length Business operations require flexible and efficient supply chains. Appropriately simplifying their structure and reducing length can enhance competitiveness and responsiveness. Streamlining decision-making levels and eliminating unnecessary approval processes enables rapid feedback of decision information to the decision-making layer, accelerating both decision-making and execution. Adopting direct communication mechanisms avoids information delays caused by multi-tiered communication, enhancing coordination and adaptability across all links. This requires managers to collaborate closely to ensure accurate information transmission. 6.4. Optimization Strategies Based on Relationship Strength 6.4.1. Strengthening Platform Supply Chain Partnerships This study reveals that the strength of supply chain partnerships positively impacts information transmission efficiency. When constructing and optimizing supply chain management, enterprises should establish long-term, stable cooperative relationships with other businesses, engage in deep strategic collaboration, jointly bear risks, share resources and technologies, enhance resource integration and risk-sharing capabilities, and strengthen supply chain resilience and flexibility. Establishing mutual benefit mechanisms fosters trust and dependency through shared gains, enabling smoother and more precise information flow, strengthening collaboration, and encouraging proactive communication among partners. 6.4.2. Strengthening Internal Communication and Coordination within Platform Supply Chains In the supply chain management system of large-scale sports events, improving information flow efficiency relies on optimizing internal communication and coordination mechanisms. Enterprises must establish systematic communication mechanisms to minimize information delays, enhance operational reliability, and prevent supply chain bottlenecks. Standardized information transmission protocols ensure data consistency, hierarchical communication structures optimize information flow efficiency, and key performance indicators (KPIs) assess communication quality. Continuous improvement mechanisms maintain system adaptability. Refined communication mechanisms bolster partner trust, optimize resource allocation efficiency, and enable rapid responses to market changes through real-time feedback—critical in dynamic competition. Through the above analysis, it is evident that factors such as AI integration, network density, supply chain length, and relationship strength interact to collectively influence information transmission efficiency. Large-scale sports event organizers should comprehensively enhance supply chain information transmission efficiency by optimizing AI technology applications, increasing the density and centrality of collaborative networks, shortening supply chain lengths, and strengthening relationship bonds. During implementation, large-scale sports event organizers should flexibly adjust strategies based on specific circumstances to achieve optimal information flow. 7. Conclusion This study integrates multiple theories to explore the operational mechanisms of information transmission effects within large-scale sports event platform supply chains under AI embedding, as well as the multifaceted factors influencing information transmission efficiency. It aims to assist enterprises in optimizing information flow and enhancing competitiveness. First, we dissected AI's impact on large-scale sports event platform supply chain organizational structures and information transmission. Findings indicate that AI embedding drives platform-based upgrades, reshapes organizational ecosystems, fosters value co-creation, and accelerates networked structural development. This transformation renders information flow network-like, emphasizing information sharing and collaboration among entities. Second, employing structural equation modeling and fsQCA theory, we examined factors influencing information transmission efficiency. Results reveal: First, enterprise AI embedding increases network density, centrality, and relationship strength while reducing supply chain length, thereby enhancing information flow efficiency. Second, network density and relationship strength positively correlate with information transmission efficiency, whereas supply chain length negatively correlates. Dense networks facilitate rapid and accurate information dissemination, while excessively long supply chains cause transmission delays and coordination difficulties, reducing efficiency. Third, fSQCA configuration effect analysis identified three high-efficiency information transmission trigger patterns—close collaboration, intelligent interconnection, and network linkage—along with four pathways. Core conditions vary across patterns, revealing conditional combinations for efficient information flow. Based on these findings, optimization strategies are proposed: strengthening AI application, optimizing network characteristics, shortening supply chain length, and enhancing partner relationship strength. However, this study has limitations and scope for future research: First, it primarily focuses on several common factors without considering more refined network structural forms or other internal and external factors. Future research could explore multi-reconfiguration pathways by incorporating additional factors. Second, data was collected through questionnaires, which had limited coverage and excluded some sports event supply chain enterprises. Future studies could expand the sample size to enhance research breadth. Third, as the information ecosystem of large-scale sports event platform supply chains evolves, information transmission is influenced by multiple factors and undergoes constant change. This study examines processes under fixed conditions; future research could delve into their dynamic evolution, analyzing different stages and states in depth. Declarations Author Contribution Conceptualization, writing — original draft preparation, validation, Q.C.;Methodology, formal analysis, writing — review and editing, S.M.;Resources, visualization, supervision, G.Z.;Data curation, investigation, C.J.. 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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-8728300","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611958994,"identity":"85de7da9-517f-4744-8459-ca85280dce82","order_by":0,"name":"Qianlan Chen","email":"","orcid":"","institution":"Guangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Qianlan","middleName":"","lastName":"Chen","suffix":""},{"id":611958995,"identity":"cb4a543d-6c2f-47e0-ac32-a2e3c1586bec","order_by":1,"name":"Siyi Mao","email":"","orcid":"","institution":"Guangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Siyi","middleName":"","lastName":"Mao","suffix":""},{"id":611958996,"identity":"2cc17528-e4a2-4f79-aca0-0d7a49fbdc99","order_by":2,"name":"Guiping Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBAC+/sPGw7/qLBhZmNvIFbPgeTGxwxn0tj5eQ4QrSW92Zix7TC/5IwEInUwNhxsky44c1ja4ObjjTcYamyiCWphZmxsk55RkW5scDut2ILhWFpuAyEtbMyMbRI8Z6yTDW7nmEkwNhwmrIWHDaiFt425fsPNM0RqkeBhbDbmbXNmlpzBQ6QWAwnGxoczzqQx8/MA/ZJAjF8MJNgfHPgAjsrDG298qLEhrAVVewIpyiFaSNUxCkbBKBgFIwMAADNlQBqRpd38AAAAAElFTkSuQmCC","orcid":"","institution":"Guilin Tourism University","correspondingAuthor":true,"prefix":"","firstName":"Guiping","middleName":"","lastName":"Zhu","suffix":""},{"id":611958997,"identity":"69b74c2b-93a6-49c7-b675-0d38055b47b4","order_by":3,"name":"Chao Jin","email":"","orcid":"","institution":"Guangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2026-01-29 07:23:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8728300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8728300/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105475064,"identity":"a2bb250e-379f-46ca-a7bf-d27b058d0a05","added_by":"auto","created_at":"2026-03-26 12:42:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76345,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical Model of Factors Affecting Information Transmission Efficiency in Large-scale Sports Event Platform Supply Chains\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8728300/v1/2e5cafb2e6399e343d58a932.png"},{"id":105475075,"identity":"7aea6d26-f17d-494e-9dbe-0b821bbce268","added_by":"auto","created_at":"2026-03-26 12:42:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":271432,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Model Diagram\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8728300/v1/66ceb7060ac77331cb411a41.png"},{"id":105474934,"identity":"52ebc57f-eedc-4f68-a35a-2c198f595f7f","added_by":"auto","created_at":"2026-03-26 12:42:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":232947,"visible":true,"origin":"","legend":"\u003cp\u003eRevised Structural Equation Model Diagram\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8728300/v1/1012c588b9afbc3cb2e50239.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on the Impact of AI Integration on Large-scale Sports Event Platform Supply Chain Information Transmission Efficiency -- A Hybrid SEM–fsQCA Approach Considering Network Structure Mediation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the context of the development of the internet and digital economy, supply chain digitization has become a central strategic issue for enterprises. At the national level, policies such as coordinating digital transformation and building cross-border collaborative innovation systems are driving the evolution of supply chains from traditional linear structures to networked ecosystems (GARTNER, 2021)\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Large-scale sports events ( Olympic Games, World Cup, Formula One Series Grand Prix, Tennis Series Grand Prix, etc. ), as extreme scenes of demand outbreak, extremely short timeliness, and multi-party collaboration, have become a ' stress test field ' for testing the digital capabilities of the supply chain. With the update and iteration of digital technology, large-scale sports events have gradually changed from simple competitive competitions to complex projects for massive data generation and exchange. If there is no timely and efficient communication and interaction between the main bodies of the supply chain of sports events, it will form an ' information island ', thus affecting the smooth development of the event. In this new wave of technological revolution, AI, as the core of next-generation information technology, leverages capabilities like deep learning and real-time data processing. It not only optimizes internal corporate processes but also reshapes collaboration models and the logic of information exchange among supply chain enterprises. AI has become a key driver for breaking down \u0026ldquo;information silos\u0026rdquo; and enhancing supply chain resilience (ALICKE et al., 2021)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In the context of large-scale sports events, AI significantly compresses the ' golden 4 hours ' response window by predicting audience traffic, scheduling franchised goods, and optimizing catering replenishment, which has become the core technology to ensure the event experience and urban operation. While existing research has confirmed AI's enabling role in supply chain information sharing and organizational structures, two limitations persist: First, most studies focus solely on AI's technical empowerment, lacking in-depth exploration of the interactive mechanisms between AI and platform supply chain structural characteristics (e.g., network density, supply chain length). Second, traditional studies predominantly employ linear methods like regression analysis or SEM, which struggle to capture the impact of multifactorial nonlinear combinations on information transmission efficiency (DILDA et al., 2021)\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Within platform supply chains, sports event organization forms complex networks with multiple actors and nodes. Information transmission efficiency is influenced by the synergistic effects of AI integration, network structure, cooperative relationships, and other factors. This calls for moving beyond linear thinking in order to uncover multiple causal pathways. Especially in the supply chain of large-scale sports events platform, the characteristics of temporary network, government-led and sudden change of audience demand make the efficiency of information transmission face higher uncertainty. It is urgent to break through linear thinking and reveal multiple causal paths.\u003c/p\u003e \u003cp\u003eBased on this, this paper combines social network theory and information ecology theory to develop a theoretical framework for 'AI embedding, network structure, and information transmission efficiency.' It uses SEM to examine both direct and mediating effects among the variables, while using fsQCA to identify the combinations of conditions that lead to high information transmission efficiency. Finally, it proposes targeted optimization strategies for large-scale sports events, offering theoretical support and practical solutions for upgrading the information transmission mechanism in large-scale sports event platform supply chains.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Studies on AI Embedding\u003c/h2\u003e \u003cp\u003eSince the concept of AI was first introduced in 1956, its theoretical scope has continuously expanded alongside technological advancements. Ontologically, AI is an integrated system combining hardware, algorithms, and technical methodologies, with its core aim being the simulation of human intelligence (Niculescu, M. F. et al., 2018)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Functionally, AI possesses progressive cognitive abilities that include information perception, reasoning, and autonomous decision-making (Toorajipour, R. et al., 2021)\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In practice, AI automates complex tasks and transforms them into efficient execution pathways, with widespread applications in scenarios such as supply chain demand forecasting and risk early warning (DORA et al., 2022)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In the supply chain domain, AI applications follow a \"technology-structure-efficiency\" transmission logic: Companies such as Huawei and Amazon have deployed AI strategies to promote vertical coordination and horizontal network expansion within their supply chains, dismantling traditional linear structures and creating ecosystems centered around intelligent platforms (Ozcan, P., \u0026amp; Hannah, D., 2020)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Academically, GARTNER (2021) identifies AI as one of the eight key trends in supply chains, enhancing transparency and decision-making responsiveness\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. However, Helmberg, C. et al. (2022) also highlight that current AI technologies struggle to achieve full-process automation in supply chains due to bottlenecks such as technological heterogeneity and data security concerns\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Regarding information transmission, TOORAJIPOUR et al. (2021) found that artificial neural networks (ANN) can uncover hidden patterns in large datasets, alleviating the bullwhip effect\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Dora, M. et al. (2022) further indicate that AI improves information efficiency by enhancing supply chain transparency and traceability, although its effectiveness depends on technological infrastructure and data privacy protection mechanisms\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Studies on Platform Supply Chain Network Structure\u003c/h2\u003e \u003cp\u003eThe theory of supply chain structure has evolved through a progression from \u0026ldquo;linear chains to supply networks to platform ecosystems.\u0026rdquo; Early research focused on the linear upstream-downstream division of labor (Lambert, D. M., 1998)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. As industrial complexity increased, the concept of \u0026ldquo;supply networks\u0026rdquo; emerged, defined as stable, multi-organizational structures formed through long-term corporate collaboration (Roseira, C. et al., 2010)\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Platform supply chains, as an emerging form, are viewed by Jiang, J. et al. (2016) as reshaping governance models through disruptive technologies\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, while Li, L. et al. (2016) emphasize their realization of economies of scale through network effects\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.Network characteristics research: Lin and Shaw (1998) first proposed the \u0026ldquo;supply chain network\u0026rdquo; framework, conceptualizing it as a collaborative system of autonomous business entities\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e; Chopra and Meindl (2010) further emphasized its demand-oriented attributes\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Social network theory provides analytical tools: Caldarelli and Catanzaro (2004) introduced metrics like network size and density\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e; Lv, D. et al. (2020) expanded these with dynamic indicators such as average shortest path and clustering coefficient, forming a dual-dimensional \u0026ldquo;whole-individual\u0026rdquo; analysis system\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.Supply chain length research: As a core complexity metric, Porter (1985) defined it from a value chain perspective as the value-adding process \u0026ldquo;from the supplier's supplier to the customer's customer\u0026rdquo;\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e; Gereffi and Henderson (1994) emphasized physical distance and coordination levels within the global value chain framework\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e; Boehm, C. E. (2019) focused on \u0026ldquo;time length,\u0026rdquo; i.e., the cycle from product design to delivery\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Quantitative methods are categorized into three levels: enterprise (number of suppliers, production stages), industry (input-output \u0026ldquo;average number of handoffs\u0026rdquo;), and global (upstream/downstream depth) (Wang et al., 2016)\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.Relationship strength research: Uzzi (1997) highlighted from a social network perspective that strong relationships are characterized by reciprocal trust and information-sharing mechanisms\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e; Williamson (1985), grounded in transaction cost theory, introduced the concept of \u0026ldquo;relationship-specific investments\u0026rdquo;\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e; Dyer and Singh (1998) regarded relationship strength as the core driver of knowledge sharing and collaborative innovation\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Measurement methods include subjective scales (trust, commitment), behavioral indicators (collaboration duration, investment amount), and network analysis techniques (transaction frequency, centrality) (Gulati \u0026amp; Sytch, 2007)\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Studies on Supply Chain of Large-scale Sports Events\u003c/h2\u003e \u003cp\u003eThe global influence of large-scale sports events continues to grow, and its one-time, super-peak, multi-node, short-cycle supply chain characteristics have attracted academic attention. Qiao, L. (2006) applied the supply chain theory to the logistics management of the Beijing Olympic Games, and focused on the construction and performance evaluation of the supply chain system of the Beijing Olympic Games\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e; Zhang, C. X. (2008) creatively applied the SCOR model to the theoretical analysis of Olympic logistics, and combined with the characteristics of Olympic logistics process and operation mode, analyzed and constructed the Olympic logistics SCOR model from the four levels of overall planning, core process layer, configuration layer and decomposition layer, and creatively selected the appropriate evaluation index from the perspective of core process layer to construct the performance evaluation system\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Wang, Q. T. et al. (2013) pointed out that the logistics of large-scale sports events has the dual pressures of concentrated demand outbreak and extremely short window period, and proposed to reduce information lag by sharing logistics information platform\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eYang, T. (2010) defined the basic model of the supply chain of the Chinese basketball professional league, the core enterprise, the popular analysis and other conceptual connotations, focusing on the supply chain of the basketball professional league athletes supply management, supply chain relationship management, supply chain audience demand management and so on\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Yu, H. W. et al. (2010) pointed out that the existence of supply chain risk of sports events seriously affects the normal holding of sports events, and it is necessary to manage its risk. Through research, it is concluded that option financial derivatives can be effectively applied to the field of supply chain risk management of sports events as a risk management tool\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Chen, X. D. (2012) put forward the connotation, system structure, type, influencing factors and operation mechanism of the supply chain of competitive sports performance products in China\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChina Journal of Logistics and Purchasing (2023) reported that the Hangzhou Asian Games Logistics Center deployed intelligent equipment such as unmanned forklifts, inventory drones, wearable exoskeletons, and connected to the event logistics digital twin system to realize the whole process visualization of 30 days before the competition, 4 hours in the competition, and 48 hours after the competition. It provides real event scene data support for the framework of ' AI-embedding-network structure-information efficiency ' in this paper. The research on the supply chain of large-scale sports events has moved from the early logistics scheduling to the green, resilient and digital multi-dimensional perspective. However, there is still a lack of quantitative test on the interaction mechanism between AI embedding and network structure, and there is no systematic answer to the question of \" what kind of combination of conditions can achieve high information transmission efficiency during the competition. \" This study is a supplement and expansion of the gap.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Studies on Supply Chain Information Efficiency\u003c/h2\u003e \u003cp\u003eDefinition and Role of Information Transmission Efficiency: Lee (2000) proposed the \u0026ldquo;bullwhip effect,\u0026rdquo; which revealed the distortion in demand information transmission and indicated that information sharing could reduce inventory costs and improve response speed\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Lambert (1998) found that corporate confidentiality needs hinder information exchange\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, while Chen (2006) suggested optimizing information sharing through risk contracts and compensation mechanisms\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In platform contexts, Zhang, X. et al. (2024) analyzed quality information disclosure decisions on third-party shared manufacturing platforms\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, and Zhou, S. (2022) examined the role of information sharing in low-carbon supply chains\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eResearch on Influencing Factors: From a technological perspective, Huang et al. (2005) confirmed that the Internet of Things (IoT) enhances information efficiency, though technical heterogeneity may neutralize its benefits\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e; Saberi et al. (2019) found that blockchain smart contracts reduce information verification time\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. On the institutional level, Sun, B. et al. (2009) identified proactive government information disclosure as a key driver\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, while Chen et al. (2013) proposed a \u0026ldquo;policy signal transmission model,\u0026rdquo; emphasizing the impact of clear regulatory policies\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. At the organizational level, Liu, Y. (2012) found that non-standardized interfaces caused 32% information distortion\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, and Dyer and Chu (2003) confirmed a positive correlation between trust levels and information sharing frequency (a one-standard-deviation increase in trust boosted sharing frequency by 22%)\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on domestic and international literature, existing studies have explored AI's role in driving innovation within supply chains, mostly focusing on technological empowerment. However, research on AI\u0026rsquo;s deeper functions within large-scale sports event platform supply chains, particularly regarding information transmission mechanisms, remains limited. The integration of AI with large-scale sports event platform supply chains is still in its early stages, suggesting significant research potential. When it comes to supply chain information transmission mechanisms, scholars adopt various perspectives. Some examine transmission strategies from the perspective of power structures, often modeling supply chains as linear chains. In the digital age, however, supply chains are shifting from traditional linear structures to relational networks involving multiple equally powerful entities. Thus, methods based on power structures and linear supply chains lack applicability, necessitating a more comprehensive, objective perspective that transcends linear constraints. Given the enhanced AI integration within large-scale sports event platform supply chains and its influence on structural features and information transmission modes, this study considers the objective structural forms of large-scale sports event platform supply chains to explore their information transmission mechanisms, highlighting both innovation and necessity.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Structural Equation Modeling Methodology\u003c/h2\u003e \u003cp\u003eStructural Equation Modeling (SEM) is a widely used linear statistical modeling technique in economics, psychology, sociology, management, and other fields (Guo, Z., 1999)\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. It is employed to test hypothesized relationships between variables, with its core focus on exploring covariate relationships through covariance structure analysis to explain the variance among variables. Consequently, it is also referred to as covariance structure analysis (Hou, J. et al., 2004)\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Methodologically, SEM overcomes the limitations of traditional analytical approaches by allowing flexible specification of relational pathways between variables based on theoretical assumptions, rather than requiring rigid predefined models. This makes it suitable for both exploratory research and theoretical validation. It comprises two components: measurement equations and structural equations (Anderson J C, 1988)\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Measurement equations characterize the relationships between latent variables and their observed indicators, reflecting the validity of measurement instruments; structural equations describe causal relationships among latent variables, revealing intrinsic connections between theoretical constructs. SEM data analysis involves a dynamic modification process, widely applied across three model types: First, validation models compare observed data with model predictions to assess theoretical validity; if the fit is poor, alternative models must be tested. Second, model selection involves establishing multiple candidate models and comparing them using information criteria such as AIC or BIC. When fit levels are comparable, the model with fewer parameters and simpler structure is chosen to prevent overfitting. Third, model generation begins by proposing a basic model and checking its fit. After analyzing deficiencies, modifications are made, and the revised model is validated using samples to ultimately determine the optimal model.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Research Hypotheses","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1. AI Embeddings, Network Density, and Information Transmission Efficiency\u003c/h2\u003e \u003cp\u003eNetwork density reflects the closeness of connections between nodes. Peter and Noshir (2003) define it as \"the ratio of actual connections to the maximum possible connections.\"\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e AI facilitates the creation of secure sharing platforms through blockchain and supports real-time collaboration through cloud computing, thereby enhancing supply chain connectivity (Collins et al., 2006)\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. High-density networks strengthen mutual trust among enterprises and accelerate information flow (Uzzi, 1997)\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on the above analysis, this study proposes the following hypotheses:\u003c/p\u003e \u003cp\u003eH1: Enterprise AI embedding improves information transmission efficiency in large-scale sports event platform supply chains.\u003c/p\u003e \u003cp\u003eH2: Enterprise AI embedding enhances network density among enterprises in large-scale sports event platform supply chains.\u003c/p\u003e \u003cp\u003eH3: Network density positively influences information transmission efficiency in large-scale sports event platform supply chains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2. AI Embeddings, Network Centrality, and Information Transmission Efficiency\u003c/h2\u003e \u003cp\u003eNetwork centrality measures a node's core position within a network, encompassing measures such as degree centrality and betweenness centrality (Friedkin, 1991)\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. AI improves operational efficiency and innovation capacity within enterprises, thus enhancing their resource aggregation and intermediary roles in supply chains (Batjargal, 2000)\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Central nodes play a crucial role in controlling information flow, thereby improving transmission efficiency (Giuliani, 2005)\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on this, the following research hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH4: Enterprise AI embedding significantly increases the network centrality of large-scale sports event platform supply chain enterprises.\u003c/p\u003e \u003cp\u003eH5: Network centrality positively influences information transmission efficiency within large-scale sports event platform supply chains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3. AI Embedding, Supply Chain Length, and Information Transmission Efficiency\u003c/h2\u003e \u003cp\u003eSupply chain length refers to the number of stages or links from raw materials to end consumers (Boehm, C. E., 2019)\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. AI shortens processes by utilizing intelligent scheduling and automating transactions, thereby reducing intermediate stages (Saberi et al., 2019)\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e; excessively long supply chains are prone to information delays and distortions (Lee, 2000)\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on this, the following research hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH6: Enterprise AI embedding significantly reduces supply chain length for large-scale sports event platform supply chain enterprises.\u003c/p\u003e \u003cp\u003eH7: Supply chain length and information transmission efficiency within large-scale sports event platform supply chains exhibit an inverse relationship.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4. AI Embedding, Relationship Strength, and Information Transmission Efficiency\u003c/h2\u003e \u003cp\u003eRelationship strength reflects the closeness of cooperation between enterprises (Granovetter, 1973)\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. AI enhances transparency through data integration, optimizes collaboration tools, and strengthens mutual trust (Reagans, 2003)\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e; strong relationships facilitate precise information transmission and reduce opportunistic behavior (Dyer \u0026amp; Singh, 1998)\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on this, the following research hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH8: Corporate AI embedding significantly increases relationship strength among enterprises in large-scale sports event platform supply chains.\u003c/p\u003e \u003cp\u003eH9: Relationship strength positively influences information transmission efficiency in large-scale sports event platform supply chains.\u003c/p\u003e \u003cp\u003eAccording to these hypotheses, the theoretical model of factors affecting information transmission efficiency in large-scale sports event platform supply chains is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Research Design","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Variable Measurement\u003c/h2\u003e \u003cp\u003eThis paper categorizes the variables into exogenous and endogenous variables. By definition, AI embedding is an exogenous variable, while information transmission efficiency is an endogenous variable. At the same time, network density, network centrality, network scope, supply chain length, and relationship strength serve as both endogenous variables for AI embedding and exogenous variables for information transmission efficiency.\u003c/p\u003e \u003cp\u003eThe measurement framework for AI embedding is custom-designed based on IBM's Enterprise AI Maturity Model\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. The measurement of network density is based on Qi, J. (2007) scale\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e; network centrality measurement draws from Batjargal (2000), Giuliani (2005), and Friedkin, N. E. (1991). The definition of supply chain length in this study involves designing measurement items independently, considering the number of raw material suppliers, the number of intermediate processing stages in product manufacturing, and the number of distribution channels. The measurement of relationship strength follows scales from Burt (1992)\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e, Reagans (2003), and Granovetter (1973). The measurement of information transmission efficiency uses Lou, C. (2007) scale to design measurement items\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Regarding AI embedding, measurements are based on IBM's Enterprise AI Maturity Model, covering five items (XA1-XA5) related to business performance enhancement, technical capabilities, and employee willingness to use AI. For network density, the measurement follows Qi, J. (2007) scale, with four items (MA1-MA4) assessing collaboration frequency and information acquisition. For network centrality, the integrated scales of Batjargal (2000) and Giuliani (2005) are used, with five items (MB1-MB5) focusing on resource aggregation and bridging roles. For supply chain length, the measurement includes five items (MC1-MC5) related to the number of raw material suppliers, intermediate processing stages, and other related factors. Regarding relationship strength, five items (MD1-MD5) are designed, measuring coordination and mutual trust, based on the work of Burt (1992), Reagans (2003), and Granovetter (1973). Lastly, for information transmission efficiency, the scale by Lou, C. (2007) is adapted, with five items (YA1-YA5) measuring timeliness, security, and other factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Questionnaire Design and Data Collection\u003c/h2\u003e \u003cp\u003eBased on established scales, the measurement items were adjusted to align with the characteristics of large-scale sports event platform supply chains, using a 7-point Likert scale (1 = 'Strongly Disagree', 7 = 'Strongly Agree'). The research object is the employees ( grass-roots to senior ) of the supply chain enterprises of the sports event platform, covering the roles of equipment manufacturers, franchised commodity providers, catering service providers, official logistics providers, security and medical material suppliers, and event technical service providers. After revisions based on a pilot survey (50 responses), the questionnaire was formally distributed. A total of 359 responses were collected, with 43 logically inconsistent responses excluded, resulting in 316 valid responses\u0026mdash;a response rate of 88.02%. The enterprise industry types collected in this questionnaire include competition equipment manufacturing, event franchise commodity manufacturing, cold chain catering, intelligent logistics, security equipment, medical consumables, digital technology services, etc. Among them, the proportion of franchise commodity manufacturing and cold chain catering reached 31.329% and 25.949% respectively, accounting for the largest proportion. Regarding enterprise type, the samples are all core or first-level cooperative enterprises in the supply chain of the event platform, and the distribution is relatively average. Among them, official logistics and franchised commodity suppliers account for the largest proportion, up to 22.785%. Furthermore, the surveyed group of front-line operators of sporting events was predominantly aged between 21 and 50, accounting for 64.241% of the sample. As those directly engaged in race materials scheduling, audience service, cold chain transportation, security materials distribution and other key processes, they provided firsthand insights into timing information response, use of AI tools, frequency of collaboration. Their feedback is vital for understanding actual operation status of event supply chain, ensuring the data is reasonably representative.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Reliability and Validity Testing\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1. Reliability Testing of Data\u003c/h2\u003e \u003cp\u003eCurrently, the Cronbach's α coefficient method is the most widely used approach for assessing reliability. It evaluates the reliability of measurement tools by calculating the degree of consistency among items within a scale. The following reliability test results were obtained using SPSS Pro in this study:\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\u003eReliability Test of Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eAI Embedding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Centrality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupply Chain Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Reliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\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\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the Cronbach's α reliability coefficients for all variables exceed 0.9. This indicates that the scale's content is well-constructed and demonstrates high internal consistency (Feng, D., 2018)\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Moreover, all AVE (Average Variance Extracted) values exceed the 0.5 threshold, with the AVE values for all variables, except for 'Network Scope,' surpassing 0.7. This suggests that the latent variables exhibit strong convergent validity and minimal measurement bias. In addition, the composite reliability (CR) for all variables meets the 0.5 threshold, with most exceeding 0.9. This confirms that the scale's reliability is within an acceptable range (Li, K., \u0026amp; Jiang, J., 2025)\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e5.3.2. Data Validity Testing\u003c/h2\u003e \u003cp\u003eThe scales used in this study primarily draw on established measurement tools from prior research, ensuring content validity. Construct validity is an important dimension for assessing the quality of measurement tools, as it reflects whether the instrument accurately captures the structural relationships among constructs in the theoretical model. In empirical research, the KMO test and Bartlett's sphericity test are commonly employed methods for evaluating construct validity. The KMO test assesses the suitability of factor analysis by analyzing partial correlations between indicators. Its value ranges from 0 to 1, with a KMO value exceeding 0.8 generally indicating strong correlations between indicators and higher suitability for factor analysis (Bi, C. et al., 2025)\u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. Bartlett's sphericity test assesses the correlations among indicators within variables, with the null hypothesis being that indicators are independent. Data are suitable for factor analysis only when the null hypothesis is rejected, meaning that the significance level (Sig value) should be less than 0.01 (Gong, H. et al., 2025)\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. The KMO and Bartlett's sphericity test results for each variable in this study are as follows:\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\u003eKMO and Bartlett's Sphericity Test Results for Variables\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e变量\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBartlett's Sphericity Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Embedding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1346.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e855.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Centrality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1181.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupply Chain Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1578.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1280.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1369.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall validity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9668.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e406\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates that network density (KMO\u0026thinsp;=\u0026thinsp;0.855), network centrality (KMO\u0026thinsp;=\u0026thinsp;0.886), and other variables (KMO\u0026thinsp;\u0026gt;\u0026thinsp;0.9) all meet the conditions for factor analysis. Bartlett's sphericity test rejected the null hypothesis (Sig\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming the suitability of all variables for factor analysis (Gong, H. et al., 2025).\u003c/p\u003e \u003cp\u003eIn confirmatory factor analysis (CFA), the model with 6 factors, 29 observed variables, and a sample size of 316 met the requirements. Most factor loadings were \u0026gt;\u0026thinsp;0.8, with fit indices CFI, NFI, and NNFI\u0026thinsp;\u0026gt;\u0026thinsp;0.9, RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.1, and CMIN/DF\u0026thinsp;\u0026lt;\u0026thinsp;3, indicating good convergent validity for the scales. (The GFI and RMR values for network density and centrality slightly fell short, but this did not affect the overall validity.) (Yang, G., 2025)\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e.Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the confirmatory factor analysis (CFA), which was used to test the convergent validity of the measurement model.\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\u003eConfirmatory Factor Analysis Results\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMIN/DF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFI\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\u003eNNFI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria for Judgment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Embedding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Centrality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupply Chain Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.965\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\u003eDiscriminant validity assessment is a crucial step in verifying the quality of measurement instruments. This study examined discriminant validity by observing whether the square root of the average variance extracted (AVE) for each latent variable exceeded the correlation coefficients between that variable and all other variables. As shown in the data analysis results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the main diagonal elements (i.e., the square roots of AVE) were generally larger than the non-diagonal elements in the same row or column (correlation coefficients between variables), indicating that the measured variables possess good discriminant validity.\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\u003eCorrelation Coefficients Among Variables and Square Roots of Average Variance Extracted (AVE)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI Embedding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNetwork Density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNetwork Centrality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupply Chain Length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelationship Strength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInformation Transmission Efficiency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Embedding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.871\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.851\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Centrality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.846\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupply Chain Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.895\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.863\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.873\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The numbers on the diagonal represent the square root of the AVE for each factor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Structural Equation Model Testing\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1. Structural Equation Model Construction\u003c/h2\u003e \u003cp\u003eThis study employed AMOS 28.0 software to construct a structural equation model comprising six latent variables and 29 observed variables. In the model diagram, ellipses represent latent variables, rectangles denote observed variables, and small circles indicate measurement error terms. Model analysis results indicate that all observed variables' factor loadings exceeded the 0.50 threshold, with most indicators achieving loadings above 0.8, demonstrating the measurement tool's strong convergent validity. Path coefficient estimates fell within the theoretical range of (-1, 1), further validating the scale design's rationality. Model fit indices revealed that the structural equation model adequately fitted the sample data. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the path diagram of the initial structural equation model, illustrating the theoretical relationships among AI embedding, network density, network centrality, supply chain length, relationship strength, and information transmission efficiency, along with their measurement variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e5.4.2. Path Results and Hypothesis Testing\u003c/h2\u003e \u003cp\u003eAMOS 28.0 path coefficients (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) indicate that only the 'network centrality \u0026rarr; information transmission efficiency' path is not significant (P\u0026thinsp;=\u0026thinsp;0.412), thus failing to validate H5. All other paths are significant at the 1% or 5% level, confirming H1-H4 and H6-H9. The specific empirical results are analyzed as follows:\u003c/p\u003e \u003cp\u003eAI embedding positively influences information transmission efficiency (β\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, supporting H1), network density (β\u0026thinsp;=\u0026thinsp;0.767, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, supporting H2), network centrality (β\u0026thinsp;=\u0026thinsp;0.604, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, supporting H4), relationship strength (β\u0026thinsp;=\u0026thinsp;0.825, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, supporting H8), and negatively impacts supply chain length (β = -0.866, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, supporting H6). Network density (β\u0026thinsp;=\u0026thinsp;0.222, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, supporting H3) and relationship strength (β\u0026thinsp;=\u0026thinsp;0.356, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, supporting H9) positively influence information transmission efficiency, while supply chain length negatively affects it (β = -0.294, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, supporting H7). Network centrality has no significant effect on information transmission efficiency (β = -0.026, p\u0026thinsp;=\u0026thinsp;0.412, H5 not supported). Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e lists the standardized coefficients of each hypothesized path in the structural equation model and their significance test results.\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\u003eHypothesis Path Coefficients\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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor (Latent Variable)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026rarr;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnalyzed Item (Observed Variable)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZ\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\u003eHypothesis Verification Results\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Embedding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026rarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInformation Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupport\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Embedding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026rarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNetwork Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupport\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026rarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInformation Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupport\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Embedding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026rarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNetwork Centrality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupport\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork Centrality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026rarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInformation Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Embedding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026rarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupply Chain Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-17.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupport\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupply Chain Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026rarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInformation Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupport\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Embedding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026rarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelationship Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupport\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026rarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInformation Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupport\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.4.3. Mediating Effect Testing\u003c/h2\u003e \u003cp\u003eGiven the potential indirect effects between AI embedding and large-scale sports event supply chain information transmission efficiency on platforms, this subsection will examine the mediating effects. As shown in the conceptual model of this study, there are four mediating pathways: \"AI Embedding \u0026rarr; Network Density \u0026rarr; Information Transmission Efficiency,\" \"AI Embedding \u0026rarr; Network Centrality \u0026rarr; Information Transmission Efficiency,\" \"AI Embedding \u0026rarr; Supply Chain Length \u0026rarr; Information Transmission Efficiency,\" and \"AI Embedding \u0026rarr; Relationship Strength \u0026rarr; Information Transmission Efficiency.\" The results from testing using the Bootstrap function in AMOS 28.0 are as follows:\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\u003eTest of Mediating Effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eeffect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI Embedding\u0026rarr;Network Density\u0026rarr;Information Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI Embedding\u0026rarr;Network Centrality\u0026rarr;Information Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI Embedding\u0026rarr;Supply Chain Length\u0026rarr;Information Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI Embedding\u0026rarr;Relationship Strength\u0026rarr;Information Transmission Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndirect effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.465\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the total effect of AI embedding on information transmission efficiency is 0.832, significant at the 0.001 level, indicating that AI embedding has a significant positive impact on information transmission efficiency. Among the four pathways: ① \u0026ldquo;AI Embedding \u0026rarr; Network Density \u0026rarr; Information Transmission Efficiency\u0026rdquo; has a direct effect of 0.676 and an indirect effect of 0.155 (both significant at the 1% level). Network density partially mediates this relationship, accounting for 18.6% of the total effect. ② \u0026ldquo;AI Embedding \u0026rarr; Network Centrality \u0026rarr; Information Transmission Efficiency\u0026rdquo;: Direct effect 0.846 (significant at 1%), indirect effect\u0026thinsp;\u0026minus;\u0026thinsp;0.014 (p\u0026thinsp;=\u0026thinsp;0.500, not significant). Network centrality does not mediate the relationship; ③ \u0026ldquo;AI Embedding \u0026rarr; Supply Chain Length \u0026rarr; Information Transmission Efficiency\u0026rdquo;: Direct effect 0.600 (significant at 1%), indirect effect 0.232 (significant at 5%). Supply chain length partially mediates the relationship, accounting for 27.9% of the total effect; ④ \u0026ldquo;AI Embedding \u0026rarr; Relationship Strength \u0026rarr; Information Transmission Efficiency\u0026rdquo;: Direct effect 0.564, indirect effect 0.268 (both significant at 1%). Relationship strength partially mediates the effect, accounting for 32.2% of the total effect.\u003c/p\u003e \u003cp\u003eIn summary, AI embedding has a significant positive impact on improving information transmission efficiency in large-scale sports event platform supply chains, primarily achieved through both direct and indirect effects (such as increasing network density, shortening supply chain length, and enhancing relationship strength). However, network centrality did not play a significant mediating role in this process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e5.4.4. Evaluation of Structural Equation Model Fit\u003c/h2\u003e \u003cp\u003eEM fit evaluation aims to validate the model's effectiveness in describing the data and research hypotheses, assess model applicability, determine the significance of variable relationships, and identify any necessary modifications. Common indicators include CMIN/DF, GFI, RMR, RMSEA, NFI, CFI, and NNFI.\u003c/p\u003e \u003cp\u003eEvaluation of the unmodified model (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) shows: CMIN/DF\u0026thinsp;=\u0026thinsp;2.492 (\u0026lt;\u0026thinsp;3), indicating an acceptable fit; GFI\u0026thinsp;=\u0026thinsp;0.909 (\u0026gt;\u0026thinsp;0.9), indicating a high proportion of observed variance is explained; RMSEA\u0026thinsp;=\u0026thinsp;0.069 (\u0026lt;\u0026thinsp;0.10), indicating low model error; RMR\u0026thinsp;=\u0026thinsp;2.286 (\u0026gt;\u0026thinsp;0.05), suggesting some room for improvement, but requiring consideration alongside other indices; CFI\u0026thinsp;=\u0026thinsp;0.943, NFI\u0026thinsp;=\u0026thinsp;0.909, NNFI\u0026thinsp;=\u0026thinsp;0.937 (all \u0026gt;\u0026thinsp;0.9), indicating a significantly improved model fit compared to the baseline model, with an overall good fit.\u003c/p\u003e \u003cp\u003eConsidering the above, most indicators fall within acceptable ranges. Therefore, the unmodified structural equation model in this study is considered to have an acceptable fit.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fitting Indices Before Correction\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMIN/DF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFI\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\u003eNNFI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria for Judgment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.937\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\u003eBased on the results of the path hypothesis testing, Hypothesis H5 was not validated, indicating that no significant positive relationship exists between network centrality and information transmission efficiency. Thus, after removing the latent variable of network centrality, the revised structural equation model is as follows. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is the diagram of the revised structural equation model, reconstructed after excluding the non-significant \"network centrality\" path. This model is more concise and has better fit, highlighting the influence mechanisms of AI embedding, network density, supply chain length, and relationship strength on information transmission efficiency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the revised structural equation model exhibits all indicators approaching the ideal ranges except for the GFI, which slightly deviates from the ideal standard value. Specifically, the chi-square degrees of freedom ratio (CMIN/DF), root mean square error of approximation (RMSEA), and root mean square residual (RMR) all decreased compared to their values prior to modification, indicating greater proximity to the ideal state. The remaining indices showed slight increases, gradually approaching 1. Overall, these results suggest that the revised model demonstrates a fit closer to the ideal state and superior to that of the model before modification.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRevised Model Fitting Indices\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMIN/DF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCFI\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\u003eNNFI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria for Judgment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.5. Configuration Analysis\u003c/h2\u003e \u003cp\u003eSEM has identified AI embeddings, network density, supply chain length, and relationship strength as key antecedent variables for information transmission efficiency in large-scale sports event platform supply chains. However, these variables do not influence efficiency independently; rather, their effects are driven by multifaceted interactions. Traditional statistical methods (e.g., regression, SEM) can reveal direct relationships between variables but assume linear independence of causality, making it difficult to capture complex interactions. Large-scale sports event platform supply chains are multifaceted complex systems where variable combinations may influence efficiency through nonlinear or interactive pathways, limiting traditional methods' ability to address multiple causal paths. Fuzzy Set Qualitative Comparative Analysis (fsQCA), proposed by Ragin (2006), effectively addresses such complex causal relationships. Integrating set theory, Boolean algebra, and fuzzy logic, it is suitable for small-to-medium sample sizes (20\u0026ndash;100). By analyzing the set membership relationships between condition and outcome variables, fsQCA identifies necessary or sufficient combinations of conditions that lead to outcomes.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.5.1. Data Calibration\u003c/h2\u003e \u003cp\u003eFirst, to facilitate subsequent operations and descriptions, the four condition variables are assigned codes: AI embedding as X, network density as MA, supply chain length as MC, and relationship strength as MD. The outcome variable, information transmission efficiency, is designated as Y. The data from the 7-point Likert scale is mapped to the interval [0,1], with the 25th, 50th, and 75th percentiles representing non-membership, cross-membership, and full membership points, respectively (Ragin, 2006)\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. The calibrated variables and their respective anchor points are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e below:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSet and Calibration Point Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre-calibration variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCalibrated Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFully subordinate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eintersection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCompletely unaffiliated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePrerequisite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.5.2. Necessity Analysis\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, the consistency of all condition variables (AI embedding, network density, supply chain length, relationship strength) is \u0026lt;\u0026thinsp;0.9 (with FX\u0026thinsp;=\u0026thinsp;0.772 and FMD\u0026thinsp;=\u0026thinsp;0.728 under high information transmission efficiency). No single condition is indispensable; the combination of conditions requires analysis (Zhang, H. et al., 2025)\u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNecessity Condition Analysis 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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHigh information transmission efficiency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNon-high information transmission efficiency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.771722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.765481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.394066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.360684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~FX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.355471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.388664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.743775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.750406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.671849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.822442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.302700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.341924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~FMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.462421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.418157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.842811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.703260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.440467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.449169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.737604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.694069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~FMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.699996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.743000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.414620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.406095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.727705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.750949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.402606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.383371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~FMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.402457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.421994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.738454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.714487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e5.5.3. Configuration Analysis\u003c/h2\u003e \u003cp\u003eTo ensure the reliability of condition combinations, this study set a consistency threshold of 0.8 and a frequency threshold of 3 (Rihoux B, 2009)\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows that the consistency of each configuration exceeds 0.75, with an overall model consistency of 0.762 (above the theoretical standard of 0.75) and an overall coverage rate of 0.827 (explaining over 82% of the samples). Based on the core pathways and logical mechanisms, the four types of configurations with high information transmission efficiency are categorized as: Tightly Collaborative, Intelligent Interconnected, and Network-Linked.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of configuration 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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConfiguration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClose Collaboration Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSmart Connected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNetwork-connected\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⮾\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e⚫\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e⚫\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬤\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet Coverage Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: ⚫ and ⬤ indicate the condition exists; ⮾ and ⮾ indicate the condition does not exist; blank indicates the condition has dual possibilities of existence and non-existence. ⬤ and ⮾ are core conditions, while ⚫ and ⮾ are peripheral conditions.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e5.5.4. Analysis of Results\u003c/h2\u003e \u003cp\u003e(1) Tightly Collaborative Model\u003c/p\u003e \u003cp\u003eThe configuration path is characterized by \u0026ldquo;high relationship intensity * low supply chain length,\u0026rdquo; with both variables serving as core conditions. This indicates that establishing robust and trusting cooperative relationships enables rapid information flow, thereby enhancing the overall flexibility and responsiveness of the supply chain. High relationship intensity refers to the close ties formed between relevant enterprises through long-term collaboration, frequent interaction, and mutual dependence. Low supply chain length streamlines the structure by eliminating unnecessary intermediaries and processes, ensuring information and materials reach their destinations swiftly and efficiently. This tightly collaborative model also facilitates trust-building among large-scale sports event supply chain nodes. Collaborative efforts extend beyond transactional levels to encompass information sharing, technological cooperation, and risk-sharing. Such comprehensive partnerships further solidify strategic alliances between enterprises, fostering stronger overall competitive advantages in fiercely competitive markets.\u003c/p\u003e \u003cp\u003e(2) Smart Interconnected Model\u003c/p\u003e \u003cp\u003eThe configuration paths are \u0026ldquo;High AI Embedding * High Network Density\u0026rdquo; and \u0026ldquo;High AI Embedding * High Supply Chain Length.\u0026rdquo; High AI Embedding serves as the core condition, highlighting the pivotal role of advanced technologies in enhancing information processing capabilities and strengthening decision support. With continuous advancements in artificial intelligence, enterprises significantly boost the efficiency of information screening and transmission through intelligent analysis and automation. AI's analytical capabilities overcome historical delays and misjudgments caused by information silos, ensuring real-time communication across all links for swift and precise responses. High network density further facilitates this information flow, enabling every participant to access required real-time data within minutes, thereby enhancing overall supply chain flexibility. Conversely, in scenarios characterized by \u0026ldquo;high AI integration * long supply chains,\u0026rdquo; geographically dispersed operations prolong information transmission times, thereby slowing response speeds. High AI integration leverages intelligent algorithms to monitor the entire supply chain's operational status in real time, identifying potential risks and bottlenecks to minimize losses from information delays. AI's early warning capabilities enable businesses to swiftly adjust operation schedules and logistics arrangements, ensuring the supply chain maintains high efficiency even amid change.\u003c/p\u003e \u003cp\u003e(3) Network-Connected Type\u003c/p\u003e \u003cp\u003eThe configuration path is \u0026ldquo;High Network Density * High Supply Chain Length,\u0026rdquo; with high network density as the core condition and high supply chain length as the peripheral condition. High network density enables rapid information transmission across multiple nodes, covering a broader range of participants. This significantly enhances information liquidity and accessibility, ensuring tight connections among all supply chain stakeholders and fostering a dense information exchange environment. Even when facing extended supply chains, the dense network structure accelerates information dissemination through multiple pathways. The broad network coverage enables information to reach diverse stakeholders more extensively, ensuring every link receives relevant data and instructions promptly. This diversity in information transmission not only enhances accessibility but also facilitates more effective collaboration and coordination among participants in supply chain management, thereby boosting overall flexibility and responsiveness.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Optimization Strategies for Large-scale Sports Event Platform Supply Chain Information Transmission Mechanisms","content":"\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Optimization Strategies Based on Enterprise AI Embedding\u003c/h2\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e6.1.1 Expanding the Application Scope of AI Technologies\u003c/h2\u003e \u003cp\u003eResearch confirms that widespread AI adoption can significantly enhance the network density, centrality, and relationship strength of enterprises within large-scale sports event platform supply chains, thereby optimizing supply chain management and operational efficiency. In the context of large-scale sports events, enterprises should extend AI to the full cycle of ' audience peak prediction-dynamic replenishment during the game-quick post-game clearance '. For example, at the 2024 Paris Olympic Games, artificial intelligence is used to optimize the schedule, resource allocation and logistics management of the event. Through big data analysis, the flow density is predicted to ensure the efficient and orderly conduct of various activities. By integrating AI to analyze supply chain data and update information in real time, transmission efficiency improves, enabling businesses to respond flexibly to complex environments. Implementing AI-powered intelligent scheduling systems in logistics, inventory management, and other areas achieves optimal resource allocation while eliminating information delays and efficiency losses. Enterprises must drive comprehensive AI integration, leveraging deep learning and machine learning for real-time data processing and demand forecasting to reduce response times and optimize information flow.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e6.1.2. Focus on AI-Driven Supply Chain Collaboration\u003c/h2\u003e \u003cp\u003eAI-driven supply chain collaboration breaks down information silos to achieve real-time, effective interaction. For large-scale sports events, it is suggested that the organizing committee should force the construction of ' event AI collaborative middle stage ', so that suppliers such as franchised goods, catering, logistics and security can be accessed within 48 hours to achieve ' golden 4 hours ' response. Building cross-enterprise information sharing platforms serves as the foundation. AI can automatically adjust communication frequency and content based on participants' needs, adapting to environmental changes while preventing information overload and asymmetry. By leveraging AI to integrate data, upstream and downstream enterprises can synchronize critical information\u0026mdash;including inventory levels, demand forecasts, and transportation arrangements\u0026mdash;in real time, thereby enhancing response speed and accuracy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Optimization Strategies Based on Network Characteristics\u003c/h2\u003e \u003cp\u003eEnterprises should strengthen ties with all supply chain participants to build a tightly integrated collaborative network, enhancing information transmission efficiency and response speed while boosting the overall competitiveness and flexibility of the supply chain. Large-scale sports events can rely on the government 's ' event brain ' to quickly increase network density - for example, the Hangzhou Asian Games through the ' Asian Games nail ' so that both event officials, partners or volunteers, venue workers, etc., can freely access the event venues within his authority, and tens of thousands of collaborators Smooth and unimpeded communication. Simultaneously, digital tools should be leveraged to establish cross-enterprise supply chain collaboration platforms, breaking down information barriers and accelerating data flow. Furthermore, establishing standardized information exchange protocols ensures data compatibility, while developing intelligent decision support systems improves response accuracy and timeliness. Implementing dynamic risk assessment mechanisms enhances the network's resilience. These mutually reinforcing measures collectively elevate the overall efficiency of the supply chain network, delivering comprehensive solutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e6.3. Optimization Strategies Based on Supply Chain Length\u003c/h2\u003e \u003cp\u003eBusiness operations require flexible and efficient supply chains. Appropriately simplifying their structure and reducing length can enhance competitiveness and responsiveness. Streamlining decision-making levels and eliminating unnecessary approval processes enables rapid feedback of decision information to the decision-making layer, accelerating both decision-making and execution. Adopting direct communication mechanisms avoids information delays caused by multi-tiered communication, enhancing coordination and adaptability across all links. This requires managers to collaborate closely to ensure accurate information transmission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e6.4. Optimization Strategies Based on Relationship Strength\u003c/h2\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e6.4.1. Strengthening Platform Supply Chain Partnerships\u003c/h2\u003e \u003cp\u003eThis study reveals that the strength of supply chain partnerships positively impacts information transmission efficiency. When constructing and optimizing supply chain management, enterprises should establish long-term, stable cooperative relationships with other businesses, engage in deep strategic collaboration, jointly bear risks, share resources and technologies, enhance resource integration and risk-sharing capabilities, and strengthen supply chain resilience and flexibility. Establishing mutual benefit mechanisms fosters trust and dependency through shared gains, enabling smoother and more precise information flow, strengthening collaboration, and encouraging proactive communication among partners.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003e6.4.2. Strengthening Internal Communication and Coordination within Platform Supply Chains\u003c/h2\u003e \u003cp\u003eIn the supply chain management system of large-scale sports events, improving information flow efficiency relies on optimizing internal communication and coordination mechanisms. Enterprises must establish systematic communication mechanisms to minimize information delays, enhance operational reliability, and prevent supply chain bottlenecks. Standardized information transmission protocols ensure data consistency, hierarchical communication structures optimize information flow efficiency, and key performance indicators (KPIs) assess communication quality. Continuous improvement mechanisms maintain system adaptability. Refined communication mechanisms bolster partner trust, optimize resource allocation efficiency, and enable rapid responses to market changes through real-time feedback\u0026mdash;critical in dynamic competition.\u003c/p\u003e \u003cp\u003eThrough the above analysis, it is evident that factors such as AI integration, network density, supply chain length, and relationship strength interact to collectively influence information transmission efficiency. Large-scale sports event organizers should comprehensively enhance supply chain information transmission efficiency by optimizing AI technology applications, increasing the density and centrality of collaborative networks, shortening supply chain lengths, and strengthening relationship bonds. During implementation, large-scale sports event organizers should flexibly adjust strategies based on specific circumstances to achieve optimal information flow.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study integrates multiple theories to explore the operational mechanisms of information transmission effects within large-scale sports event platform supply chains under AI embedding, as well as the multifaceted factors influencing information transmission efficiency. It aims to assist enterprises in optimizing information flow and enhancing competitiveness.\u003c/p\u003e \u003cp\u003eFirst, we dissected AI's impact on large-scale sports event platform supply chain organizational structures and information transmission. Findings indicate that AI embedding drives platform-based upgrades, reshapes organizational ecosystems, fosters value co-creation, and accelerates networked structural development. This transformation renders information flow network-like, emphasizing information sharing and collaboration among entities. Second, employing structural equation modeling and fsQCA theory, we examined factors influencing information transmission efficiency. Results reveal:\u003c/p\u003e \u003cp\u003eFirst, enterprise AI embedding increases network density, centrality, and relationship strength while reducing supply chain length, thereby enhancing information flow efficiency.\u003c/p\u003e \u003cp\u003eSecond, network density and relationship strength positively correlate with information transmission efficiency, whereas supply chain length negatively correlates. Dense networks facilitate rapid and accurate information dissemination, while excessively long supply chains cause transmission delays and coordination difficulties, reducing efficiency.\u003c/p\u003e \u003cp\u003eThird, fSQCA configuration effect analysis identified three high-efficiency information transmission trigger patterns\u0026mdash;close collaboration, intelligent interconnection, and network linkage\u0026mdash;along with four pathways. Core conditions vary across patterns, revealing conditional combinations for efficient information flow.\u003c/p\u003e \u003cp\u003eBased on these findings, optimization strategies are proposed: strengthening AI application, optimizing network characteristics, shortening supply chain length, and enhancing partner relationship strength.\u003c/p\u003e \u003cp\u003eHowever, this study has limitations and scope for future research: First, it primarily focuses on several common factors without considering more refined network structural forms or other internal and external factors. Future research could explore multi-reconfiguration pathways by incorporating additional factors. Second, data was collected through questionnaires, which had limited coverage and excluded some sports event supply chain enterprises. Future studies could expand the sample size to enhance research breadth. Third, as the information ecosystem of large-scale sports event platform supply chains evolves, information transmission is influenced by multiple factors and undergoes constant change. This study examines processes under fixed conditions; future research could delve into their dynamic evolution, analyzing different stages and states in depth.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, writing \u0026mdash; original draft preparation, validation, Q.C.;Methodology, formal analysis, writing \u0026mdash; review and editing, S.M.;Resources, visualization, supervision, G.Z.;Data curation, investigation, C.J..\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eInformed consent was obtained in full compliance with Humanities and Social Sciences Communications'requirements and the Declaration of Helsinki(2013).Consent was obtained in questionnaire by the study's principal investigator from all individual human participants on the date of their first research involvement(prior to data collection).The consent scope covers participants'voluntary involvement in research procedures,secure collection/storage/analysis of their data for this study only,and use of de-identified data in manuscript publication without disclosing personal information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGartner. 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Sage.\u003c/li\u003e\n\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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"AI, large-scale sports event platform supply chain, information transmission, network structure, factors influencing transmission efficiency","lastPublishedDoi":"10.21203/rs.3.rs-8728300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8728300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the rapidly changing market environment, supply chain management faces multiple challenges such as globalization and technological advancement. Especially for large-scale sports events, it is necessary to meet the massive, centralized and diversified needs efficiently, accurately and reliably in a short time window. How to make the relevant supply chain resources more effective for information interaction and work collaboration has become an increasingly concerned issue. The application of artificial intelligence (AI) has significantly transformed the mode of supply chain management. Drawing on social network theory and information ecology theory, this study explores the mechanism of AI in large-scale sports event platform supply chains (defined as a networked collaborative ecosystem connecting multiple subjects via digital platforms) and its impact on information transmission and interaction effects. To address the research gap, we adopt a mixed-method approach combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA): SEM is used to test the linear causal relationships and mediating effects between variables, while fsQCA identifies the nonlinear configuration paths of high information transmission efficiency. Based on 316 valid questionnaires from large-scale sports event platform supply chain enterprises, the results show that: (1) AI embedding directly and positively improves information transmission efficiency (standardized coefficient β\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); (2) network density (mediation effect ratio\u0026thinsp;=\u0026thinsp;18.6%), relationship strength (32.2%), and supply chain length (27.9%) play partial mediating roles, while network centrality has no significant mediating effect; (3) three types of trigger modes (tight collaboration, intelligent interconnection, network connection) and four configuration paths for high information transmission efficiency are identified. Theoretically, this study supplements the theoretical mechanism of AI and network structure synergistically influencing large-scale sports event supply chain information transmission. Practically, it proposes optimization strategies from four aspects (AI application, network structure optimization, supply chain length simplification, and relationship strength enhancement), providing targeted references for improving the management level of large-scale sports event supply chains.\u003c/p\u003e","manuscriptTitle":"Study on the Impact of AI Integration on Large-scale Sports Event Platform Supply Chain Information Transmission Efficiency -- A Hybrid SEM–fsQCA Approach Considering Network Structure Mediation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 12:39:59","doi":"10.21203/rs.3.rs-8728300/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-24T11:58:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T11:54:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-25T12:03:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T16:59:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-02-19T15:30:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9ed64c38-e907-4a9b-a915-2c2e03203969","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65112141,"name":"Humanities/Complex networks"},{"id":65112142,"name":"Social science/Complex networks"},{"id":65112143,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-03-26T12:39:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 12:39:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8728300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8728300","identity":"rs-8728300","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}