Analysis of User Intention to Use of AI-Assisted Customized Fashion Design Software in the Dimension of Interaction Design— —An empirical study in China based on an extended TAM model

Analysis of User Intention to Use of AI-Assisted Customized Fashion Design Software in the Dimension of Interaction Design— —An empirical study in China based on an extended TAM model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Analysis of User Intention to Use of AI-Assisted Customized Fashion Design Software in the Dimension of Interaction Design— —An empirical study in China based on an extended TAM model Erxuan Zeng, Rongbin Liu, Yuxue Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6149700/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As AI technology penetrates into customized fashion design, traditional TAM and UTAUT models have provided insights into the study of new technology acceptance, but the research on user intentions towards AI-assisted customized fashion design software requires the introduction of new variables.This study focuses on the impact of interaction design (including interaction behavior,interaction content, and interaction form) and user perception (including perceived usefulness, ease of use, personal innovation, and social influence) on user intentions.The construction and analysis of the new model show that interaction design elements indirectly affect users' intention to continue using through influencing mediating variables such as perceived ease of use and personal innovation. The impact of interaction content and interaction form is significant, while the impact of interaction behavior is relatively small.Personal innovation has a positive effect on the intention to continue use, with perceived usefulness being the most critical factor. The model in this study performs well in explaining user intentions to continue use, providing a theoretical basis for the design optimization of such AI-assisted design products, and has important guiding implications for development priorities and marketing strategies. Health sciences/Medical research Physical sciences/Engineering Technology Acceptance Model User Intention to Use AI-assisted Customized Fashion Design Software Interaction Design Figures Figure 1 Figure 2 1. Introduction With the rapid development of technology, AI is deeply penetrating various industries, fundamentally reshaping their operations and development(Leng et al., 2024 ). Artificial intelligence has brought revolutionary changes to the clothing design industry(Anwer et al., 2024 ), especially in the field of custom clothing, and its potential is huge(Wang et al., 2024). Yu Jiabei and Zhu Weiming argue that AI-assisted tools provide new opportunities for design efficiency, personalization and innovation( Yu and Zhu, 2024 ). AI-assisted customized fashion design software is subverting tradition and leading the industry into a new era. Technology Acceptance Model (TAM) has always been a key theory to understand users' acceptance of new technologies(Davis, 1989 ). While the Unified theory of Acceptance and Use of Technology (UTAUT) emphasizes the role of other factors that may help understanding users' acceptance of new technology,such as social influencing factors(Venkatesh et al., 2003 ). Moreover, the existing research lacks the research of user behavior in the context of AI-assisted custom design, and the exploration of interaction details and personal innovation is insufficient. This study will focus on the details of user interaction with AI-assisted customized fashion design software, and build a new model including TAM,social impact from UTAUT and additional variables, aiming to explore the key factors affecting user acceptance and provide theoretical basis and practical guidance for product optimization. This study not only deepens the theoretical understanding of user-AI product interaction, but also provides support for product design improvement, helps enterprises determine development priorities and marketing objectives, and improves competitiveness. 2. Model Construction and Hypothesis 2.1. Assumptions Based on TAM TAM was established by Davis in 1989. Based on the theory of rational behavior, TAM analyzes users' acceptance of new technologies(Davis, 1989 ). It consists of four internal variables - perceived usefulness, ease of use, attitude, and behavioral intent - plus the influence of external factors designed to explain or predict user adoption.Behavioral intention is the core of TAM, which is influenced by attitude, perceived usefulness and ease of use.Despite TAM focuses on functional aspects of technology, its limitations prompt scholars to integrate it with other models like the Information System Success Model(Yang et al., 2017 ) and the Expectation Confirmation Theory (ECT)(Tawafak et al., 2021 ) to address new phenomena from technological innovation. This integration is widely accepted for adapting to new environments. This study examines the intention to use AI-assisted customized fashion design software, acknowledging the influence of various interacting factors beyond technical functionality. Therefore, it will integrate TAM with additional theories to comprehensively explain this complexity, providing a robust theoretical foundation for further research. 2.1.1 Behavioral Intention to Use,BI. AI-assisted customized fashion software adoption intention serves as the core dependent variable in this study, representing users' intrinsic motivation to accept the software(Wu and Song, 2020 ). The research aims to explore users' willingness to adopt AI-assisted customized fashion software, uncovering the underlying psychological drivers. 2.1.2 Perceived Usefulness,PU. Perceived usefulness, defined as the degree to which users perceive a new information system to enhance their work efficiency(Wu and Song, 2020 ), specifically refers in this study to the benefits users believe the AI-assisted customized fashion design tool can bring, such as improving design efficiency and stimulating creativity.Therefore, the following reasonable hypothesis is proposed: H4: Perceived usefulness has a positive and significant impact on the intention to use AI-assisted customized fashion design software among users of AI-assisted customized fashion design software. 2.1.3 Perceived Ease of Use,PEU. Perceived ease of use, as defined in previous studies as the level of effort required for users to operate a new information system(Davis, 1989 ;Wang et al., 2021;Wang and Chen, 2018 ), specifically refers in this study to the effort level needed for users to master the AI-assisted customized fashion design tool. If users find the tool challenging to learn or use, their rating of perceived ease of use will decrease.Therefore, the following reasonable hypotheses are proposed: H5a: Perceived ease of use has a positive and significant impact on the perceived usefulness of users of AI-assisted customized fashion design software. H5b: Perceived ease of use has a positive and significant impact on the intention to use AI-assisted customized fashion design software among users of AI-assisted customized fashion design software. 2.2. Assumptions Based on UTAUT The UTAUT model(Venkatesh et al., 2003 ), provides a holistic explanation of information service adoption behavior, incorporating performance expectation, effort expectation, social influence, and facilitating conditions, with gender, age, experience, and voluntariness as moderators. Perceived usefulness and ease of use are pivotal in this model. Our study integrates the social influence component, examining the impact of peer recognition, organizational promotion, and industry trends on the acceptance of AI-assisted fashion design software. The research will delve into the mechanisms underlying social factors' influence on usage intention.Therefore, this study proposes the following reasonable hypothesis: H7: Social influence has a positive and significant impact on the intention to use AI-assisted customized fashion design software among users of AI-assisted customized fashion design software. 2.3. Personal Innovation and assumption Individual Creativity (PI), as defined by Agarwal R and Karahanna E.(Agarwal and Karahanna, 2000 )and Hwang Y. (Hwang, 2014 ), represents a user's consistent inclination to embrace new technologies. High innovation correlates with a more favorable attitude and increased intention to adopt such technologies, impacting the consistency of adoption across diverse scenarios(Xie and An, 2020). Users with high PI are more inclined to explore novel features, whereas those with low PI may exhibit skepticism, limiting their adoption(Lei and Xing, 2024). This research conceptualizes the individual creativity of AI-assisted fashion design tool users as their innovative spirit, which shapes their technology acceptance. Resistance to new technologies may lead to a diminished innovation score.Therefore, this study proposes the following reasonable hypothesis: H6: Individual creativity has a positive and significant impact on the intention to use AI-assisted customized fashion design software among users of AI-assisted customized fashion design software. 2.4. Product Interaction Design and Hypotheses. Xin Xiangyang proposed the five elements of interaction design: person, action, tool, purpose, and context(Xin, 2015), advocating a behavior-centric approach, distinct from traditional object-oriented design. He differentiated between functional and behavioral user interfaces, emphasizing that the latter places more importance on user experience(Xin, 2019). Interaction design aims to optimize the product system based on user behavioral logic, enhancing user experience, which is also crucial for AI-assisted custom clothing design software. Ma Guangtao and others categorized the elements of interaction design into three levels: Interaction form, Interaction behavior, and Interaction content(Ma and Fang, 2020), and considered Interaction behavior, Interaction content, and Interaction form to be the core(Bai and Xiao, 2021 ). These factors play a critical role in shaping the user experience and fostering the intention for ongoing engagement with software products. 2.4.1 Interaction Behaviour,IB. Interaction behavior is a key independent variable, including process, structure, and sequence of actions involved. In this study,it is defined as the steps required by the user to complete a specific task and the corresponding feedback.Therefore, this study proposes the following reasonable hypotheses: H1a: Interaction behavior has a positive impact on the perceived usefulness of users of AI-assisted customized fashion design software. H1b: Interaction behavior has a positive impact on the perceived ease of use of users of AI-assisted customized fashion design software. H1c: Interaction behavior has a positive impact on the individual creativity of users of AI-assisted customized fashion design software. H1d: Interaction behavior has a positive impact on the social influence of users of AI-assisted customized fashion design software. 2.4.2 Interaction Content,IC Interactive content refers to the information exchange between users and products, and is the logical structure supporting interaction design(Venkatesh et al., 2003 ;Guo, 2016;Bai and Xiao, 2021 ). In this study, it is defined as multimedia information, such as text and images, that users encounter in the course of product use.Therefore,this study proposes the following reasonable hypotheses: H2a: Interaction content has a positive impact on the perceived usefulness of users of AI-assisted customized fashion design software. H2b: Interaction content has a positive impact on the perceived ease of use of users of AI-assisted customized fashion design software. H2c: Interaction content has a positive impact on the individual creativity of users of AI-assisted customized fashion design software. H2d: Interaction content has a positive impact on the social influence of users of AI-assisted customized fashion design software. 2.4.3 Interaction Form,IF. Interactive forms refer to the expression modes involved in user interaction, including visual, tactile, auditory and other channels, as well as the integration of physical media and digital media(Xin, 2015). In this study, it is defined as a medium that transmits information during the use of a product.Therefore, this study proposes the following reasonable hypotheses: H3a: Interaction form has a positive impact on the perceived usefulness of users of AI-assisted customized fashion design software. H3b: Interaction form has a positive impact on the perceived ease of use of users of AI-assisted customized fashion design software. H3c: Interaction form has a positive impact on the individual creativity of users of AI-assisted customized fashion design software. H3d: Interaction form has a positive impact on the social influence of users of AI-assisted customized fashion design software. 2.5. The construction of an integrated theoretical model. Based on the above analysis, this study introduces a new assessment framework to examine the factors that affect users' willingness to use AI-assisted custom clothing design software. This framework combines "Perceived Usefulness", "Perceived Ease of Use" and "Behavioral Intention to Use" in the Technology Acceptance Model (TAM) as internal variables. At the same time, it adopts "Social Influence" in the Unified Theory of Acceptance and Use of Technology (UTAUT) as an internal variable, and also incorporates "Personal Innovation" unique to the use of AI-assisted design tools as an internal variable. In addition, in order to match the research's emphasis on interaction design, the model also includes interaction behavior, interaction and interaction form as external variables. The model structure is shown in Fig. 1 . 3. Data collection and analysis 3.1. Scale design The investigation into the long-term commitment to using AI-customized fashion design software remains nascent, with a dearth of maturity scales. In light of this, the author, leveraging real-world application contexts, has assessed the core functionalities of popular software and analyzed various factors, including cultural disparities. Through iterative consultations with MIS experts, an initial questionnaire was crafted. Drawing on established SEM research(Dawes, 2002 ;Brown, 2011 ), it was determined that larger scales generally exhibit superior reliability and validity. Consequently, a seven-point Likert scale was adopted, yielding a questionnaire with 8 potential variables and 40 items. Following the creation of the preliminary questionnaire, a pre-test was administered, yielding 62 data samples. Analysis prompted the exclusion of 12 items due to inadequate reliability, validity, or low factor loadings, and the inclusion of three new variables: gender, age, and occupation. This process culminated in a formal questionnaire of 31 items, with details and sources provided in Table 1 . Table 1 Question items and references Constructs Coding Items References Perceived usefulness (PU) PU1 Do you think AI-assisted customized fashion design can improve the efficiency of fashion design? Davis, 1989 PU2 Do you think AI-assisted customized fashion design is helpful in enhancing design innovation? PU3 In your opinion, is AI-assisted customized fashion design helpful in meeting customers' personalized needs? PU5 Can AI-assisted customized fashion design help shorten the cycle from design to production? Perceived ease of use (PEU) PEU1 Do you think it is easy to learn to use AI-assisted customized fashion design software? PEU3 Do you think the operation process of AI-assisted customized fashion design is concise and clear? PEU4 When using AI-assisted customized fashion design, do you rarely encounter technical problems? Personal Innovation(PI) PI1 Do you often try new design concepts and methods? Agarwal and Prasad, 1998 PI3 When facing new design challenges, are you willing to try new solutions even if they may involve risks? PI4 Do you think you have an innovative spirit in the field of fashion design? PI5 Will you actively explore new design ideas provided by AI tools? Social Influence(SI) SI1 Does the attitude of your peers or colleagues towards AI-assisted customized fashion design affect your willingness to use it? Venkatesh et al., 2003 SI2 Does your unit or school encourage the use of AI-assisted customized fashion design? SI3 Do you pay attention to the overall acceptance degree of AI-assisted customized fashion design in the fashion design industry? Interaction Behaviour(IB) IB2 Do you think AI-assisted customized fashion design software should provide real-time feedback to help you better perform design operations? The major functions of the pop AI-assisted customized fashion design product nowadays IB3 Do you hope to be able to interact and collaborate with other users in real time during the process of AI-assisted customized fashion design? IB5 During the design process, do you hope to receive more feedback on design suggestions? Interaction Content(IC) IC1 Do you hope to be able to use all the main functions of AI-assisted customized fashion design software? IC2 Do you hope that AI-assisted customized fashion design software can provide knowledge content on the corresponding cultural connotations of clothing? IC3 When using AI-assisted customized fashion design, do you hope that the software can provide relevant design cases for reference according to your input design requirements? IC4 Do you think AI-assisted customized fashion design software should provide knowledge content on clothing production processes? Interaction Form(IF) IF1 The interface (colors, pictures, display, etc.) of AI-assisted customized fashion design software is what I care about the most. IF2 Do you hope that the interface of AI-assisted customized fashion design software can be dynamically adjusted according to different design tasks? IF3 When using AI-assisted customized fashion design, do you hope that the software can provide multiple view modes to better view the design effect? Behavioral Intention to Use(BI) BI1 Do you have the intention to use AI-assisted customized fashion design in future fashion design work? Davis, 1989 BI2 If you have the opportunity, will you start using AI-assisted customized fashion design immediately? BI4 Will you recommend the use of AI-assisted customized fashion design to other peers or colleagues? BI5 Are you willing to pay an additional fee for AI-assisted customized fashion design? Basic information Your gender: (select One answer choice) A. Male B. Female Your age:(select One answer choice) A. 18–25 years old B. 26–35 years old C. 36–45 years old D. 46 years old and above Your occupation:(select One answer choice) A. Fashion designer B. Employee in the fashion industry (non-designer) C. Student (major in fashion design related fields) D. Others 3.2. Data collection The survey questionnaire was primarily distributed through the Questionnaire Star platform ( https://www.wjx.cn/ ), with the survey period set from September to October 2024. There were screening questions before the questionnaire to understand whether the respondents regularly use AI-assisted personalized fashion design products. Respondents who have not used the product were not required to continue answering. According to the backend statistics of Questionnaire Star, a total of 19 participants barely used AI-assisted custom clothing design products, accounting for 5.90% of the total sample. To ensure data quality, the platform's time recording function was enabled when distributing the questionnaire. As a result, 303 questionnaires (excluding 19 from non-users) were collected from the national fashion design industry and relevant academic figures. To ensure sample quality, the double criteria proposed by Wu, Vassileva, and Zhao(Wu and Chen, 2017 )were used to screen the questionnaires. First, based on pilot testing experience, the completion time for the questionnaire should be more than 260 seconds; questionnaires completed in less than 260 seconds were considered not seriously filled out and were invalid. Second, there were reverse questions in the questionnaire; if participants did not provide the expected opposite answers, the data was also invalid. After screening, 18 questionnaires were excluded, leaving 285 valid questionnaires for data analysis. In terms of sample size, Loehlin(Loehlin, 2004 ) conducted a statistical analysis of the sample sizes in 72 SEM papers and found the median to be 198; Barrett(Barrett, 2007 ) suggested that the sample size should exceed eight times the number of variables in the model but also noted that when the sample size exceeds 500, the maximum likelihood method may lead to chi-square inflation, affecting model fit. SEM expert Zhang Weihao recommended keeping the sample size within 500(Zhang Weihao, 2020 ). Thus, the sample size of this study meets academic standards. There were 285 valid questionnaires, sourced from 141 males and 144 females, with a balanced gender ratio. Age distribution was as follows: 66 people aged 18–25, 97 people aged 26–35, 74 people aged 36–45, and 46 people aged 46 and above. In terms of occupation, 40% were fashion designers, 35.09% were fashion industry professionals (non-designers), 22.46% were students majoring in fashion design, and 1.75% had other occupations. 3.3. Ethics declarations All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by Business College, Southwest University. Informed consent was obtained from all subjects and/or their legal guardian(s). 3.4. Reliability and validity analysis This study's data collection is sourced from a single channel, namely the personal perceptions and statements of the participants, a method that is susceptible to common method bias. To prevent artificial covariation between predictive and outcome variables due to consistent measurement environments, background, and question characteristics, the research team deliberately arranged questions of different variables on separate pages in the questionnaire to ensure that participants had ample rest time while filling out different pages, thereby mitigating the common method variance effect caused by the continuous use of the same scale(Shiau et al., 2019 ). Additionally, the research team used principal component analysis of the Harman single-factor test to detect common method bias. An exploratory factor analysis was performed on all items, extracting factors by principal component method and setting the number of factors to 1. The analysis revealed that the variance explanation rate of the first factor was 28.593%, which did not exceed 40%, indicating no common method bias. According to the indices of the Harman single-factor test, no significant common method bias effect was observed among variables(Shiau and Luo, 2012 ), and the results were within acceptable limits. Before verifying the measurement model, it is necessary to evaluate the reliability and validity of the questionnaire. The reliability evaluation was performed by Composite Reliability(CR) and Average Variance Extracted(AVE).Generally, a CR above 0.7 and an AVE above 0.5 are considered acceptable for the consistency among measurement items(Fornell and Larcker,1981). This study used Amos 28.0 software to calculate CR and AVE values, both of which exceeded 0.7 and 0.5 (see Table 2 ), indicating good internal consistency among the measurement items and reliability that meets the requirements. Table 2 Measurement model (convergent validity and reliability) Significance estimation Question reliability Constructs Items Unstd. S.E. z-Value p Std. SMC Component reliability Average Variance Extracted IB IB2 1.000 0.943 0.889 0.917 0.788 IB3 1.006 0.048 21.133 *** 0.856 0.733 IB5 0.951 0.045 21.357 *** 0.861 0.741 IC IC1 1.000 0.948 0.899 0.941 0.801 IC2 1.043 0.041 25.608 *** 0.889 0.790 IC3 0.933 0.04 23.123 *** 0.857 0.734 IC4 1.006 0.04 25.19 *** 0.884 0.781 IF IF1 1.000 0.967 0.935 0.935 0.828 IF2 0.953 0.039 24.556 *** 0.874 0.764 IF3 0.992 0.039 25.516 *** 0.886 0.785 PU PU1 1.000 0.976 0.953 0.945 0.81 PU2 0.942 0.035 26.843 *** 0.877 0.769 PU3 0.956 0.037 26.098 *** 0.87 0.757 PU5 0.961 0.036 26.427 *** 0.873 0.762 PEU PEU1 1.000 0.922 0.850 0.927 0.808 PEU3 1.055 0.047 22.303 *** 0.882 0.778 PEU4 1.049 0.046 22.874 *** 0.893 0.797 PI PI1 1.000 0.93 0.865 0.936 0.786 PI3 0.999 0.044 22.69 *** 0.868 0.753 PI4 0.929 0.041 22.768 *** 0.869 0.755 PI5 0.956 0.041 23.341 *** 0.878 0.771 SI SI1 1.000 0.921 0.848 0.925 0.804 SI2 1.067 0.046 23.41 *** 0.907 0.823 SI3 0.974 0.046 21.129 *** 0.861 0.741 BI BI1 1.000 0.942 0.887 0.957 0.846 BI2 0.998 0.036 27.536 *** 0.908 0.824 BI4 1.048 0.035 30.259 *** 0.933 0.870 BI5 0.952 0.036 26.392 *** 0.896 0.803 Note.***p < 0.001 Validity testing primarily observes the discriminant validity among variables. Discriminant validity refers to the low correlation and significant differences between latent variables, which can be assessed by comparing the square root of the Average Variance Extracted (AVE) with the correlation coefficients between variables. According to the criteria proposed by Fornell and Larcker (Fornell and Larcker,1981), if the correlation coefficient of a variable with other variables is less than the square root of its AVE, it indicates that the variable has good discriminative validity. As shown in Table 3 , the data in bold in the table represents the square root of AVE, greater than all the values in their respective columns.Therefore, the discriminant validity of the measurement model in this study is appropriate. Table 3 Analysis of discriminant validity. Discriminant validity Constructs AVE BI SI PI PEU PU IF IC IB BI 0.846 0.920 SI 0.804 0.427 0.897 PI 0.786 0.563 0.392 0.887 PEU 0.808 0.502 0.396 0.452 0.899 PU 0.81 0.554 0.445 0.454 0.396 0.9 IF 0.828 0.359 0.399 0.497 0.408 0.391 0.91 IC 0.801 0.388 0.376 0.466 0.381 0.41 0.247 0.895 IB 0.788 0.319 0.415 0.437 0.371 0.345 0.338 0.219 0.888 3.5. Model fitness test Since structural equation modeling (SEM) does not have a single powerful evaluation index like traditional analytical techniques such as analysis of variance (ANOVA) or regression analysis, the evaluation of its fit often requires a comparison between the sample covariance matrix and the theoretical model covariance matrix, which has led to the development of numerous model fit indices. However, not all fit indices need to be reported. Among the many indices used to measure the fit of structural equation models, the most commonly reported are: the minimum difference in chi-square (CMID), degrees of freedom (DF), normalized chi-square (CMID/DF), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), Tucker-Lewis index(TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (RMSR). Therefore, this study will also proceed with evaluation based on the aforementioned indices. There are no fixed standards for fit indices. For instance, while the chi-square value is preferred to be smaller, it can experience rapid inflation with increasing sample sizes, which affects its reference value, leading to the development of a series of chi-square-based indices. The standards for fitting indicators can also be affected by the specific content of the study, such as the difference in the criteria required for confirmatory and exploratory studies, which are usually lower than those for confirmatory studies; there are also differences in standards across different disciplines. Therefore, when assessing model fit, researchers often refer to suggestions provided by authoritative scholars in the field of structural equation modeling.Table 4 presents the index value results and recommended values for the model proposed in this study, and the comparative analysis suggests that the test of goodness-of-fit indices conforms to the recommended levels, indicating that the model has sufficient adaptability to the collected data. Table 4 Model fit indices. Indices Model indices values Standards Conclusion Standard sources CMID 345.852 The smaller the better DF 330 The smaller the better CMID/DF 1.048 0.8 acceptable;>0.9 excellent fit Excellent fit Bagozzi and Yi, 1988 AGFI 0.820 > 0.8 acceptable;>0.9 excellent fit Acceptable Scott, 1995 CFI 0.903 > 0.9 Excellent fit Bagozzi and Yi, 1988 TLI(NNFI) 0.998 > 0.9 Excellent fit Hair,Babin, etal.,2017 RMSEA 0.013 < 0.08 Acceptable Bagozzi and Yi, 1988 SRMR 0.0408 < 0.08 Excellent fit Hu and Bentler, 1998 3.6. Structural model validation Structural model validation was conducted using Amos 28.0 to calculate the path coefficients and the variance explained (R 2 ) by the variables collectively (Fig. 2 ). The model validation results indicate that all 17 hypotheses from H1a to H7 were supported(Table 5 ). Table 5 Results of model path analysis Assumptions Unstd. S.E. C.R. P Std.(β) R² Perceived Ease of Use<---Interaction Behaviour 0.215 0.057 3.791 *** 0.227 0.301 Perceived Ease of Use<---Interaction Content 0.231 0.049 4.707 *** 0.27 Perceived Ease of Use<---Interaction Form 0.22 0.049 4.53 *** 0.27 Social Influence<---Interaction Behaviour 0.273 0.058 4.72 *** 0.281 0.317 Social Influence<---Interaction Content 0.229 0.05 4.575 *** 0.26 Social Influence<---Interaction Form 0.207 0.05 4.181 *** 0.246 Personal Innovation<---Interaction Behaviour 0.239 0.051 4.729 *** 0.256 0.436 Personal Innovation<---Interaction Content 0.281 0.044 6.388 *** 0.333 Personal Innovation<---Interaction Form 0.267 0.044 6.145 *** 0.332 Perceived Usefulness<---Interaction Content 0.239 0.051 4.718 *** 0.273 0.313 Perceived Usefulness<---Interaction Behaviour 0.162 0.057 2.824 0.005 0.167 Perceived Usefulness<---Interaction Form 0.181 0.05 3.634 *** 0.217 Perceived Usefulness<---Perceived Ease of Use 0.141 0.066 2.126 0.033 0.138 Behavioral Intention to Use<---PU 0.299 0.054 5.519 *** 0.3 0.457 Behavioral Intention to Use<---Perceived Ease of Use 0.224 0.057 3.946 *** 0.22 Behavioral Intention to Use<---Personal Innovation 0.311 0.058 5.376 *** 0.301 Behavioral Intention to Use<---Social Influence 0.097 0.053 1.821 0.069 0.098 Note.***p < 0.001 The analysis presented in Table 5 demonstrates that Interaction Behavior (β = 0.227, p < 0.001), Interaction Content (β = 0.27, p < 0.001), and Interaction Form (β = 0.27, p < 0.001) all exert significant positive effects on users' Perceived Ease of Use for AI-assisted clothing customization products, confirming hypotheses H1b, H2b, and H3b. Additionally, Interaction Behavior (β = 0.281, p < 0.001), Interaction Content (β = 0.26, p < 0.001), and Interaction Form (β = 0.246, p < 0.001) show significant positive influences on Social Influence, supporting hypotheses H1d, H2d, and H3d. For Personal Innovation, Interaction Behavior (β = 0.256, p < 0.001), Interaction Content (β = 0.333, p < 0.001), and Interaction Form (β = 0.332, p < 0.001) are also significant positive predictors, thus validating hypotheses H1c, H2c, and H3c. On Perceived Usefulness, however, only Interaction Content (β = 0.273, p < 0.001) and Interaction Form (β = 0.217, p 0.001) and Perceived Ease of Use (β = 0.138, p > 0.001) do not demonstrate significant positive effects on Perceived Usefulness, thereby leading to the rejection of hypotheses H1a and H5a. Further findings indicate that Perceived Usefulness (β = 0.3, p < 0.001), Perceived Ease of Use (β = 0.22, p < 0.001), and Personal Innovation (β = 0.301, p 0.001) does not significantly affect Behavioral Intention to Use, leading to the rejection of hypothesis H7. Regarding explained variance, Interaction Behavior, Interaction Content, and Interaction Form account for 30.1% of the variance in Perceived Ease of Use. Together, these variables also explain 31.7% of the variance in Social Influence and 43.6% in Personal Innovation. Alongside Perceived Ease of Use, they explain 31.3% of the variance in Perceived Usefulness. Finally, Perceived Usefulness, Perceived Ease of Use, Personal Innovation, and Social Influence collectively explain 45.7% of the variance in Behavioral Intention to Use. 4. Discussion 4.1. The Differential Impact of Interaction Design Elements on Related Variables Amos analysis reveals the high correlation between the three elements of interaction design and user perception variables, explaining 30.1%, 43.6%, and 31.7% of the variance respectively. The path coefficient indicates that the impact of interaction content and interaction form on creativity is significantly higher than that of interaction behavior. 4.2. The Role of Personal Creativity in Driving Continual Use Intentions Research data clearly show that users' Personal Innovation (β = 0.301) has a direct and positive effect on their intention to continue using AI-assisted customization clothing design software. This fully demonstrates that in the context of AI-assisted customization clothing design, users have a strong desire for ample personalization space to fully exert their creativity. 4.3. Comparative Analysis of the Impact of Various Factors on Continual Use Intentions A deeper examination of the path coefficients reveals that Perceived Usefulness and Personal Innovation exert a stronger influence on users' intentions to continue using AI-assisted customization software for clothing design. These factors have a more substantial direct impact than Perceived Ease of Use and Social Influence, suggesting that users place higher value on functional satisfaction. Additionally, users motivated by innovation are more likely to engage in long-term use. These insights are essential for guiding software development priorities and for tailoring effective marketing strategies. 5. Conclusion 5.1. Identification of Core Factors This study focuses on AI-assisted software for customized fashion design, employing it as the primary research object. By integrating various theoretical frameworks and methodologies, we conduct thorough data analyses to confirm that Perceived Usefulness, Perceived Ease of Use, and Personal Innovation—core elements of the Technology Acceptance Model.They significantly influence users' intentions for sustained engagement. Especially Perceived Usefulness plays a more important role in influencing users' continual use intentions. Perceived Ease of Use, which concerns the convenience of users learning and using the software, has a significant impact on continual use intentions, but it is secondary to Perceived Usefulness. Personal Innovation reflects the innovative spirit of users in software use, and users with high creativity tend to use the software for a long time to achieve personalized design. 5.2. Indirect Impact of Interaction Design Elements This study demonstrates that Interaction Content,Interaction Behaviour, and Interaction Form exert indirect effects on users' intentions to continue using through variables like Perceived Ease of Use, Personal Innovation, and Social Influence, with Interaction Content and Interaction Form having a pronounced impact. Engaging, high-caliber Interaction Content, such as an extensive fashion material library and comprehensive design cases, offers users creative inspiration and resources, fostering software attachment and reliance. User-centered Interaction Form, characterized by an intuitive interface and versatile design tools, facilitates proficiency and strengthens user retention. Although Interaction Behaviour contributes, its influence is less significant compared to Interaction Content and Interaction Form. 5.3. Validation of Model Effectiveness This study deeply analyzes the model fit and reveals that the research model combining the Technology Acceptance Model (TAM),Use of Technology (UTAUT) with social influence factors and interaction design elements performs well in predicting users' continual use intentions for AI-assisted customized clothing design software. The integration of social factors (such as recommendations, recognition) and interaction design (interaction behaviour,interaction content,interaction form) effectively complements TAM, more comprehensively reflecting user intentions. This conclusion provides theoretical support for further research and optimization of AI-assisted clothing design software, promoting the development of theoretical and practical knowledge in the field. 6. Theoretical Contributions and Practical Significance 6.1. Theoretical Contributions This study is the first to fuse the social impact from UTAUT,interaction design elements and TAM to produce an effective research model for the AI-assisted fashion design software sector that influences user retention. The empirical analysis shows that perceived usefulness, perceived ease of use and individual innovation are the key factors driving user intention in this field, and perceived usefulness is established as the dominant factor. In addition, the study strongly supports social impact and interaction design elements as important enhancements to TAM's assessment of user retention for AI products, thereby broadening the usefulness of TAM and advancing the theoretical framework in the field. 6.2. Practical Significance This study provides a solid theoretical framework for promoting AI-assisted fashion design software. Developers should focus on improving product utility and user innovation, optimizing algorithms to improve design accuracy and efficiency. Initiatives such as creative communities are recommended to foster user creativity. To increase user engagement, operators should customize innovative experiences, update design resources, and provide personalized recommendations. Using VR and AR for design previews can further improve user retention. These strategies have an important guiding role in product development, function upgrading and marketing positioning of enterprises, which helps to meet user needs and ensure market competitiveness, thus promoting the sustainable and healthy development of the industry. 7. Limitations and Prospects 7.1. Research Limitations This study has some limitations. First, the scope of the research is relatively narrow, focusing mainly on users' intentions when using AI-assisted fashion design software, without delving into the more subtle psychological changes users experience when interacting with such products. Secondly, the source of samples is limited, and the samples are mainly concentrated in China, which is difficult to fully represent the user groups of different cultural backgrounds and industry fields around the world. 7.2. Future Prospects In view of the above limitations, the follow-up research should explore a variety of ways. The research content should use AI-assisted fashion design software to dig into the nuances of user psychology and improve design quality and innovation. Sample selection should be expanded geographically and across industries to include diverse cultural and professional backgrounds to obtain more representative data. This will enhance the universality and applicability of the research results, provide accurate user needs and behavioral insights for AI-customized fashion, and provide targeted theoretical and practical guidance for the development of the field. Declarations Author Contribution Zeng Erxuan designed the whole research and wrote the main manuscript text. Liu Rongbin helped conduct the survey. As a mentor to two undergraduate students,Feng Yuxue guided Zeng Erxuan and Liu Rongbin . All authors reviewed the manuscript. Acknowledgement Thank you to everyone who filled out the survey. Data Availability The data that support the findings of this research are available from the corresponding author upon reasonable request. References Leng, J. et al. Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges (Journal of Manufacturing Systems, 2024). Anwer, H., Ali, M. & Jamshaid, H. Applications of Artificial Intelligence in Textiles and Fashion[J] (Springer, 2024). WANG Jing, W. A. N. G., Xiaoyi, L. A. N., Cuiqin & XU Jiping. Application and prospect of information technology in personalized clothing customization[J]. J. Silk . 61 (1), 96–108 (2024). Jiabei, Y. U. & ZHU Weiming. The application of big data-driven generative AI in fashion design: Taking Midjourney as an example[J]. J. Silk . 61 (9), 20–27 (2024). Davis, F. D. Perceived usefulness, Perceived ease of use, and user acceptance of information technology[J]. MIS Q. , (13):319–340. (1989). Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. User acceptance of information technology: Toward a unified view. MIS Q. 27 (3), 425–478 (2003). Yang, M., Shao, Z., Liu, Q. & Liu, C. Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. Education Tech. Research Dev. 65 (5), 1195–1214 (2017). Tawafak, R. M. et al. A combined model for continuance intention e-learning system. Int. J. Interact. Mob. Technol. 15 (03), 113–129 (2021). Wu, J. J. & Song, S. Older Adults' Online Shopping Continuance Intentions: Applying the Technology Acceptance Model and the Theory of Planned Behavior[J]. Int. J. Hum Comput Interact. 37 (10), 938–948 (2020). Wang Shaofeng, T., Ahmed, Z., Lixin, Y. & Junfeng Do Playfulness and University Support Facilitate the Adoption of Online Education in a Crisis? COVID-19 as a Case Study Based on the Technology Acceptance Model[J]. Sustainability 13 (16), 1–16 (2021). Wang, C. H. & Chen, T. M. Incorporating data analytics into design science to predict user intentions to adopt smart TV with consideration of product features[J]. Comput. standards& Interfaces . 59 , 87–95 (2018). Agarwal, R. & Karahanna, E. Time Flies When You're Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage[J]. MIS Q. 24 (4), 665–694 (2000). Hwang, Y. User Experience and Personal Innovativeness: An Empirical Study on the Enterprise Resource Planning Systems[J]. Comput. Hum. Behav. 34 (5), 227–234 (2014). Xie Ruyu,An Liren. The Influence Mechanism of Innovation Characteristics on New Energy Vehicle Consumer's Adoption Intention: The Moderating Role of Individual Innovation[. J] Mod. ECONOMIC Sci. 42 (5), 113–121 (2020). Chengfeng, L. E. I. & Zhenjiang, X. I. N. G. AIGC Reshaping Behavior: Study on Influencing Factors of Intention to Continue Use[J]. Technoeconomics Manage. Res. , (06):152–158. (2024). XIN Xiangyang. Interaction Design: From Logic of Things to Logic of Behaviors[J] 58–62 (ZHUANGSHI, 2015). 1. XIN Xiangyang. From User Experience to Experience Design [J]. Packaging Eng. 40 (08), 60–67 (2019). Guangtao, M. A. & Qinpu, F. A. N. G. Research on the Emotion of Interaction Design Elements [J]. Design 33 (09), 103–105 (2020). Bai, Y. Q. & Xiao, J. J. The impact of cMOOC learners’ interaction on content production[C]. (2021). Interactive Learning Environments,(8). GUO Binye. Based on geriatric care of Rehabilitation Product Design Study[D] (North China University of Technology,, 2016). Bai, Y. Q. & Xiao, J. J. The impact of cMOOC learners’ interaction on content production[C]. Interactive Learning Environments, (8). (2021). Dawes, J. Five point vs. eleven point scales: Does it make a dif ference to data characteristics. Australasian J. Market Res. 10 (1), 1–17 (2002). Brown, J. D. Likert items and scales of measurement. Statistics 15 (1), 10–14 (2011). Agarwal, R. & Prasad, J. A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology [J]. Inform. Syst. Res. 9 (2), 204–215 (1998). Wu, B. & Chen, X. Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task tech nology fit (TTF) model. Comput. Hum. Behav. 67 , 221232 (2017). Loehlin, J. C. Latent variable models: An introduction to factor, path, and structural equation analysis (Lawrence Erlbaum Associates, 2004). Barrett, P. Structural equation modelling: Adjudging model fit. Pers. Indiv. Differ. 42 (5), 815–824 (2007). Shiau, W. L. & Luo, M. M. Factors affecting online group buy ing intention and satisfaction: A social exchange theory perspective. Comput. Hum. Behav. 28 (6), 2431–2444 (2012). Shiau, W. L., Sarstedt, M. & Hair, J. F. Internet research using partial least squares structural equation modeling (PLS-SEM). Internet Res. 29 (3), 398–406 (2019). Hair, J. F. Jr., Sarstedt, M., Hopkins, L., Kuppelwieser, G. & V Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. Eur. Bus. Rev. 26 (2), 106–121 (2014). Fornell, C. & Larcker, D. F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18 (1), 39–50 (1981). Hayduk, L. A. Structural equation modeling with LISREL: Essentials and advances (Jhu, 1987). Bagozzi, R. P. & Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 16 (1), 74–94 (1988). Scott, J. E. The measurement of information systems effective ness. ACM SIGMIS Database: The DATABASE for Advances. Inform. Syst. 26 (1), 43–61 (1995). Hu, L. & Bentler, P. M. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychol. Methods . 3 (4), 424–453 (1998). Zhang Weihao. Dancing with the Structural Equation Model: The Dawn Appears [M] (Xiamen University, 2020). Hair, J. F. Jr., Babin, B. J. & Krey, N. Covariance-based struc tural equation modeling in the Journal of Advertising: Review and recommendations. J. Advertising . 46 (1), 163–177 (2017). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6149700","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434792851,"identity":"abcd85a8-284a-4a16-a235-0d5b92ad0db1","order_by":0,"name":"Erxuan Zeng","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Erxuan","middleName":"","lastName":"Zeng","suffix":""},{"id":434792852,"identity":"d784f3de-6bd2-4dc7-bd72-1a62db83b811","order_by":1,"name":"Rongbin Liu","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Rongbin","middleName":"","lastName":"Liu","suffix":""},{"id":434792853,"identity":"ca56f3e8-e3df-4c63-afaf-4ead54784f93","order_by":2,"name":"Yuxue Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACNmaG9B8fKmzk+OUfMD4ACvDwEdLCx97wQHLGmTRjyYYEZgOQFjZCWuR4Dj6Q5m07lLjhQAKbBNhegg6TSE4wnHHmAFDL4WeVX3PsZNgYmB8+uoFXS1pCwoeKO8YzD7aZ3Zbdlgx0GJuxcQ5eLTkJB2eceSbbd5jB7LbkNmagFh42afxa8j8287YdZmw4xv6tWHJbPRFaeA4kMwO1KE44w2PG+HHbYSK0sDekMYIDeQZPsTTjtuM8bMwE/CLfzJDGAI5KCfaNH39uq7bnZ29++BifFhTAzAMmiVUOAow/SFE9CkbBKBgFIwYAALNUTLrXN2kRAAAAAElFTkSuQmCC","orcid":"","institution":"Southwest University","correspondingAuthor":true,"prefix":"","firstName":"Yuxue","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2025-03-04 00:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6149700/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6149700/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79840665,"identity":"01a5b828-bd44-4789-9e9b-41b2503004f1","added_by":"auto","created_at":"2025-04-03 12:34:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19797,"visible":true,"origin":"","legend":"\u003cp\u003eProposed research model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6149700/v1/5678c0eae20156631f8ac570.png"},{"id":79840668,"identity":"80ae9c59-f281-488e-8e5a-7305993ec0ba","added_by":"auto","created_at":"2025-04-03 12:34:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":261283,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of structural equation model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6149700/v1/e7b057d0162bcf2ff1e4d6ed.png"},{"id":88122619,"identity":"e85983f3-1d91-4e33-8d83-26fb8618bf07","added_by":"auto","created_at":"2025-08-01 16:09:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1738885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6149700/v1/98dd575c-cd06-4ab8-9d7e-c85ccf043881.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of User Intention to Use of AI-Assisted Customized Fashion Design Software in the Dimension of Interaction Design— —An empirical study in China based on an extended TAM model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the rapid development of technology, AI is deeply penetrating various industries, fundamentally reshaping their operations and development(Leng et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Artificial intelligence has brought revolutionary changes to the clothing design industry(Anwer et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), especially in the field of custom clothing, and its potential is huge(Wang et al., 2024). Yu Jiabei and Zhu Weiming argue that AI-assisted tools provide new opportunities for design efficiency, personalization and innovation(\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eYu and Zhu, 2024\u003c/span\u003e). AI-assisted customized fashion design software is subverting tradition and leading the industry into a new era.\u003c/p\u003e \u003cp\u003eTechnology Acceptance Model (TAM) has always been a key theory to understand users' acceptance of new technologies(Davis, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). While the Unified theory of Acceptance and Use of Technology (UTAUT) emphasizes the role of other factors that may help understanding users' acceptance of new technology,such as social influencing factors(Venkatesh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Moreover, the existing research lacks the research of user behavior in the context of AI-assisted custom design, and the exploration of interaction details and personal innovation is insufficient.\u003c/p\u003e \u003cp\u003eThis study will focus on the details of user interaction with AI-assisted customized fashion design software, and build a new model including TAM,social impact from UTAUT and additional variables, aiming to explore the key factors affecting user acceptance and provide theoretical basis and practical guidance for product optimization. This study not only deepens the theoretical understanding of user-AI product interaction, but also provides support for product design improvement, helps enterprises determine development priorities and marketing objectives, and improves competitiveness.\u003c/p\u003e"},{"header":"2. Model Construction and Hypothesis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Assumptions Based on TAM\u003c/h2\u003e \u003cp\u003eTAM was established by Davis in 1989. Based on the theory of rational behavior, TAM analyzes users' acceptance of new technologies(Davis, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). It consists of four internal variables - perceived usefulness, ease of use, attitude, and behavioral intent - plus the influence of external factors designed to explain or predict user adoption.Behavioral intention is the core of TAM, which is influenced by attitude, perceived usefulness and ease of use.Despite TAM focuses on functional aspects of technology, its limitations prompt scholars to integrate it with other models like the Information System Success Model(Yang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the Expectation Confirmation Theory (ECT)(Tawafak et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to address new phenomena from technological innovation. This integration is widely accepted for adapting to new environments. This study examines the intention to use AI-assisted customized fashion design software, acknowledging the influence of various interacting factors beyond technical functionality. Therefore, it will integrate TAM with additional theories to comprehensively explain this complexity, providing a robust theoretical foundation for further research.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Behavioral Intention to Use,BI.\u003c/h2\u003e \u003cp\u003eAI-assisted customized fashion software adoption intention serves as the core dependent variable in this study, representing users' intrinsic motivation to accept the software(Wu and Song, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The research aims to explore users' willingness to adopt AI-assisted customized fashion software, uncovering the underlying psychological drivers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Perceived Usefulness,PU.\u003c/h2\u003e \u003cp\u003ePerceived usefulness, defined as the degree to which users perceive a new information system to enhance their work efficiency(Wu and Song, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), specifically refers in this study to the benefits users believe the AI-assisted customized fashion design tool can bring, such as improving design efficiency and stimulating creativity.Therefore, the following reasonable hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH4: Perceived usefulness has a positive and significant impact on the intention to use AI-assisted customized fashion design software among users of AI-assisted customized fashion design software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Perceived Ease of Use,PEU.\u003c/h2\u003e \u003cp\u003ePerceived ease of use, as defined in previous studies as the level of effort required for users to operate a new information system(Davis, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e;Wang et al., 2021;Wang and Chen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), specifically refers in this study to the effort level needed for users to master the AI-assisted customized fashion design tool. If users find the tool challenging to learn or use, their rating of perceived ease of use will decrease.Therefore, the following reasonable hypotheses are proposed:\u003c/p\u003e \u003cp\u003eH5a: Perceived ease of use has a positive and significant impact on the perceived usefulness of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003cp\u003eH5b: Perceived ease of use has a positive and significant impact on the intention to use AI-assisted customized fashion design software among users of AI-assisted customized fashion design software.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Assumptions Based on UTAUT\u003c/h2\u003e \u003cp\u003eThe UTAUT model(Venkatesh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), provides a holistic explanation of information service adoption behavior, incorporating performance expectation, effort expectation, social influence, and facilitating conditions, with gender, age, experience, and voluntariness as moderators. Perceived usefulness and ease of use are pivotal in this model. Our study integrates the social influence component, examining the impact of peer recognition, organizational promotion, and industry trends on the acceptance of AI-assisted fashion design software. The research will delve into the mechanisms underlying social factors' influence on usage intention.Therefore, this study proposes the following reasonable hypothesis:\u003c/p\u003e \u003cp\u003eH7: Social influence has a positive and significant impact on the intention to use AI-assisted customized fashion design software among users of AI-assisted customized fashion design software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Personal Innovation and assumption\u003c/h2\u003e \u003cp\u003eIndividual Creativity (PI), as defined by Agarwal R and Karahanna E.(Agarwal and Karahanna, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e)and Hwang Y. (Hwang, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), represents a user's consistent inclination to embrace new technologies. High innovation correlates with a more favorable attitude and increased intention to adopt such technologies, impacting the consistency of adoption across diverse scenarios(Xie and An, 2020). Users with high PI are more inclined to explore novel features, whereas those with low PI may exhibit skepticism, limiting their adoption(Lei and Xing, 2024). This research conceptualizes the individual creativity of AI-assisted fashion design tool users as their innovative spirit, which shapes their technology acceptance. Resistance to new technologies may lead to a diminished innovation score.Therefore, this study proposes the following reasonable hypothesis:\u003c/p\u003e \u003cp\u003eH6: Individual creativity has a positive and significant impact on the intention to use AI-assisted customized fashion design software among users of AI-assisted customized fashion design software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Product Interaction Design and Hypotheses.\u003c/h2\u003e \u003cp\u003eXin Xiangyang proposed the five elements of interaction design: person, action, tool, purpose, and context(Xin, 2015), advocating a behavior-centric approach, distinct from traditional object-oriented design. He differentiated between functional and behavioral user interfaces, emphasizing that the latter places more importance on user experience(Xin, 2019). Interaction design aims to optimize the product system based on user behavioral logic, enhancing user experience, which is also crucial for AI-assisted custom clothing design software.\u003c/p\u003e \u003cp\u003eMa Guangtao and others categorized the elements of interaction design into three levels: Interaction form, Interaction behavior, and Interaction content(Ma and Fang, 2020), and considered Interaction behavior, Interaction content, and Interaction form to be the core(Bai and Xiao, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These factors play a critical role in shaping the user experience and fostering the intention for ongoing engagement with software products.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Interaction Behaviour,IB.\u003c/h2\u003e \u003cp\u003eInteraction behavior is a key independent variable, including process, structure, and sequence of actions involved. In this study,it is defined as the steps required by the user to complete a specific task and the corresponding feedback.Therefore, this study proposes the following reasonable hypotheses:\u003c/p\u003e \u003cp\u003eH1a: Interaction behavior has a positive impact on the perceived usefulness of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003cp\u003eH1b: Interaction behavior has a positive impact on the perceived ease of use of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003cp\u003eH1c: Interaction behavior has a positive impact on the individual creativity of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003cp\u003eH1d: Interaction behavior has a positive impact on the social influence of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Interaction Content,IC\u003c/h2\u003e \u003cp\u003eInteractive content refers to the information exchange between users and products, and is the logical structure supporting interaction design(Venkatesh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e;Guo, 2016;Bai and Xiao, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, it is defined as multimedia information, such as text and images, that users encounter in the course of product use.Therefore,this study proposes the following reasonable hypotheses:\u003c/p\u003e \u003cp\u003eH2a: Interaction content has a positive impact on the perceived usefulness of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003cp\u003eH2b: Interaction content has a positive impact on the perceived ease of use of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003cp\u003eH2c: Interaction content has a positive impact on the individual creativity of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003cp\u003eH2d: Interaction content has a positive impact on the social influence of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Interaction Form,IF.\u003c/h2\u003e \u003cp\u003eInteractive forms refer to the expression modes involved in user interaction, including visual, tactile, auditory and other channels, as well as the integration of physical media and digital media(Xin, 2015). In this study, it is defined as a medium that transmits information during the use of a product.Therefore, this study proposes the following reasonable hypotheses:\u003c/p\u003e \u003cp\u003eH3a: Interaction form has a positive impact on the perceived usefulness of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003cp\u003eH3b: Interaction form has a positive impact on the perceived ease of use of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003cp\u003eH3c: Interaction form has a positive impact on the individual creativity of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003cp\u003eH3d: Interaction form has a positive impact on the social influence of users of AI-assisted customized fashion design software.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5. The construction of an integrated theoretical model.\u003c/h2\u003e \u003cp\u003eBased on the above analysis, this study introduces a new assessment framework to examine the factors that affect users' willingness to use AI-assisted custom clothing design software. This framework combines \"Perceived Usefulness\", \"Perceived Ease of Use\" and \"Behavioral Intention to Use\" in the Technology Acceptance Model (TAM) as internal variables. At the same time, it adopts \"Social Influence\" in the Unified Theory of Acceptance and Use of Technology (UTAUT) as an internal variable, and also incorporates \"Personal Innovation\" unique to the use of AI-assisted design tools as an internal variable. In addition, in order to match the research's emphasis on interaction design, the model also includes interaction behavior, interaction and interaction form as external variables. The model structure is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data collection and analysis","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Scale design\u003c/h2\u003e \u003cp\u003eThe investigation into the long-term commitment to using AI-customized fashion design software remains nascent, with a dearth of maturity scales. In light of this, the author, leveraging real-world application contexts, has assessed the core functionalities of popular software and analyzed various factors, including cultural disparities. Through iterative consultations with MIS experts, an initial questionnaire was crafted. Drawing on established SEM research(Dawes, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e;Brown, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), it was determined that larger scales generally exhibit superior reliability and validity. Consequently, a seven-point Likert scale was adopted, yielding a questionnaire with 8 potential variables and 40 items.\u003c/p\u003e \u003cp\u003eFollowing the creation of the preliminary questionnaire, a pre-test was administered, yielding 62 data samples. Analysis prompted the exclusion of 12 items due to inadequate reliability, validity, or low factor loadings, and the inclusion of three new variables: gender, age, and occupation. This process culminated in a formal questionnaire of 31 items, with details and sources provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuestion items and references\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\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\u003ePerceived usefulness (PU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you think AI-assisted customized fashion design can improve the efficiency of fashion design?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eDavis, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you think AI-assisted customized fashion design is helpful in enhancing design innovation?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn your opinion, is AI-assisted customized fashion design helpful in meeting customers' personalized needs?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCan AI-assisted customized fashion design help shorten the cycle from design to production?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePerceived ease of use (PEU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you think it is easy to learn to use AI-assisted customized fashion design software?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you think the operation process of AI-assisted customized fashion design is concise and clear?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhen using AI-assisted customized fashion design, do you rarely encounter technical problems?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePersonal Innovation(PI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you often try new design concepts and methods?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAgarwal and Prasad, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1998\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhen facing new design challenges, are you willing to try new solutions even if they may involve risks?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you think you have an innovative spirit in the field of fashion design?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWill you actively explore new design ideas provided by AI tools?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSocial Influence(SI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes the attitude of your peers or colleagues towards AI-assisted customized fashion design affect your willingness to use it?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVenkatesh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes your unit or school encourage the use of AI-assisted customized fashion design?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you pay attention to the overall acceptance degree of AI-assisted customized fashion design in the fashion design industry?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInteraction Behaviour(IB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you think AI-assisted customized fashion design software should provide real-time feedback to help you better perform design operations?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eThe major functions of the pop AI-assisted customized fashion design product nowadays\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you hope to be able to interact and collaborate with other users in real time during the process of AI-assisted customized fashion design?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDuring the design process, do you hope to receive more feedback on design suggestions?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eInteraction Content(IC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you hope to be able to use all the main functions of AI-assisted customized fashion design software?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you hope that AI-assisted customized fashion design software can provide knowledge content on the corresponding cultural connotations of clothing?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhen using AI-assisted customized fashion design, do you hope that the software can provide relevant design cases for reference according to your input design requirements?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you think AI-assisted customized fashion design software should provide knowledge content on clothing production processes?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInteraction Form(IF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe interface (colors, pictures, display, etc.) of AI-assisted customized fashion design software is what I care about the most.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you hope that the interface of AI-assisted customized fashion design software can be dynamically adjusted according to different design tasks?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhen using AI-assisted customized fashion design, do you hope that the software can provide multiple view modes to better view the design effect?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBehavioral Intention to Use(BI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo you have the intention to use AI-assisted customized fashion design in future fashion design work?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDavis, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIf you have the opportunity, will you start using AI-assisted customized fashion design immediately?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWill you recommend the use of AI-assisted customized fashion design to other peers or colleagues?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAre you willing to pay an additional fee for AI-assisted customized fashion design?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBasic information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eYour gender: (select One answer choice) A. Male B. Female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eYour age:(select One answer choice) A. 18\u0026ndash;25 years old B. 26\u0026ndash;35 years old C. 36\u0026ndash;45 years old D. 46 years old and above\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eYour occupation:(select One answer choice) A. Fashion designer B. Employee in the fashion industry (non-designer) C. Student (major in fashion design related fields) D. Others\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=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Data collection\u003c/h2\u003e \u003cp\u003eThe survey questionnaire was primarily distributed through the Questionnaire Star platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn/\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with the survey period set from September to October 2024. There were screening questions before the questionnaire to understand whether the respondents regularly use AI-assisted personalized fashion design products. Respondents who have not used the product were not required to continue answering. According to the backend statistics of Questionnaire Star, a total of 19 participants barely used AI-assisted custom clothing design products, accounting for 5.90% of the total sample. To ensure data quality, the platform's time recording function was enabled when distributing the questionnaire. As a result, 303 questionnaires (excluding 19 from non-users) were collected from the national fashion design industry and relevant academic figures. To ensure sample quality, the double criteria proposed by Wu, Vassileva, and Zhao(Wu and Chen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)were used to screen the questionnaires. First, based on pilot testing experience, the completion time for the questionnaire should be more than 260 seconds; questionnaires completed in less than 260 seconds were considered not seriously filled out and were invalid. Second, there were reverse questions in the questionnaire; if participants did not provide the expected opposite answers, the data was also invalid. After screening, 18 questionnaires were excluded, leaving 285 valid questionnaires for data analysis.\u003c/p\u003e \u003cp\u003eIn terms of sample size, Loehlin(Loehlin, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) conducted a statistical analysis of the sample sizes in 72 SEM papers and found the median to be 198; Barrett(Barrett, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) suggested that the sample size should exceed eight times the number of variables in the model but also noted that when the sample size exceeds 500, the maximum likelihood method may lead to chi-square inflation, affecting model fit. SEM expert Zhang Weihao recommended keeping the sample size within 500(Zhang Weihao, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, the sample size of this study meets academic standards.\u003c/p\u003e \u003cp\u003eThere were 285 valid questionnaires, sourced from 141 males and 144 females, with a balanced gender ratio. Age distribution was as follows: 66 people aged 18\u0026ndash;25, 97 people aged 26\u0026ndash;35, 74 people aged 36\u0026ndash;45, and 46 people aged 46 and above. In terms of occupation, 40% were fashion designers, 35.09% were fashion industry professionals (non-designers), 22.46% were students majoring in fashion design, and 1.75% had other occupations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Ethics declarations\u003c/h2\u003e \u003cp\u003e All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by Business College, Southwest University. Informed consent was obtained from all subjects and/or their legal guardian(s).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Reliability and validity analysis\u003c/h2\u003e \u003cp\u003eThis study's data collection is sourced from a single channel, namely the personal perceptions and statements of the participants, a method that is susceptible to common method bias. To prevent artificial covariation between predictive and outcome variables due to consistent measurement environments, background, and question characteristics, the research team deliberately arranged questions of different variables on separate pages in the questionnaire to ensure that participants had ample rest time while filling out different pages, thereby mitigating the common method variance effect caused by the continuous use of the same scale(Shiau et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, the research team used principal component analysis of the Harman single-factor test to detect common method bias. An exploratory factor analysis was performed on all items, extracting factors by principal component method and setting the number of factors to 1. The analysis revealed that the variance explanation rate of the first factor was 28.593%, which did not exceed 40%, indicating no common method bias. According to the indices of the Harman single-factor test, no significant common method bias effect was observed among variables(Shiau and Luo, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the results were within acceptable limits.\u003c/p\u003e \u003cp\u003eBefore verifying the measurement model, it is necessary to evaluate the reliability and validity of the questionnaire. The reliability evaluation was performed by Composite Reliability(CR) and Average Variance Extracted(AVE).Generally, a CR above 0.7 and an AVE above 0.5 are considered acceptable for the consistency among measurement items(Fornell and Larcker,1981). This study used Amos 28.0 software to calculate CR and AVE values, both of which exceeded 0.7 and 0.5 (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating good internal consistency among the measurement items and reliability that meets the requirements.\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\u003eMeasurement model (convergent validity and reliability)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eSignificance estimation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eQuestion reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnstd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ez-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eComponent reliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAverage Variance Extracted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.055\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\u003e22.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote.***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValidity testing primarily observes the discriminant validity among variables. Discriminant validity refers to the low correlation and significant differences between latent variables, which can be assessed by comparing the square root of the Average Variance Extracted (AVE) with the correlation coefficients between variables. According to the criteria proposed by Fornell and Larcker (Fornell and Larcker,1981), if the correlation coefficient of a variable with other variables is less than the square root of its AVE, it indicates that the variable has good discriminative validity. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the data in bold in the table represents the square root of AVE, greater than all the values in their respective columns.Therefore, the discriminant validity of the measurement model in this study is appropriate.\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\u003eAnalysis of discriminant validity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e \u003cp\u003eDiscriminant validity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.920\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.897\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.887\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.899\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.895\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.888\u003c/b\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 \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Model fitness test\u003c/h2\u003e \u003cp\u003eSince structural equation modeling (SEM) does not have a single powerful evaluation index like traditional analytical techniques such as analysis of variance (ANOVA) or regression analysis, the evaluation of its fit often requires a comparison between the sample covariance matrix and the theoretical model covariance matrix, which has led to the development of numerous model fit indices. However, not all fit indices need to be reported. Among the many indices used to measure the fit of structural equation models, the most commonly reported are: the minimum difference in chi-square (CMID), degrees of freedom (DF), normalized chi-square (CMID/DF), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), Tucker-Lewis index(TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (RMSR). Therefore, this study will also proceed with evaluation based on the aforementioned indices.\u003c/p\u003e \u003cp\u003eThere are no fixed standards for fit indices. For instance, while the chi-square value is preferred to be smaller, it can experience rapid inflation with increasing sample sizes, which affects its reference value, leading to the development of a series of chi-square-based indices. The standards for fitting indicators can also be affected by the specific content of the study, such as the difference in the criteria required for confirmatory and exploratory studies, which are usually lower than those for confirmatory studies; there are also differences in standards across different disciplines. Therefore, when assessing model fit, researchers often refer to suggestions provided by authoritative scholars in the field of structural equation modeling.Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the index value results and recommended values for the model proposed in this study, and the comparative analysis suggests that the test of goodness-of-fit indices conforms to the recommended levels, indicating that the model has sufficient adaptability to the collected data.\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\u003eModel fit indices.\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\"\u003e \u003cp\u003eIndices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel indices values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandards\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard sources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e345.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe smaller the better\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe smaller the better\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMID/DF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHayduk, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1987\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.8 acceptable;\u0026gt;0.9 excellent fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBagozzi and Yi, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1988\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.8 acceptable;\u0026gt;0.9 excellent fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScott, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1995\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBagozzi and Yi, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1988\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI(NNFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHair,Babin, etal.,2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBagozzi and Yi, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1988\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHu and Bentler, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1998\u003c/span\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 \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Structural model validation\u003c/h2\u003e \u003cp\u003eStructural model validation was conducted using Amos 28.0 to calculate the path coefficients and the variance explained (R\u003csup\u003e2\u003c/sup\u003e) by the variables collectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The model validation results indicate that all 17 hypotheses from H1a to H7 were supported(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eResults of model path analysis\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssumptions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstd.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStd.(β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Ease of Use\u0026lt;---Interaction Behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Ease of Use\u0026lt;---Interaction Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Ease of Use\u0026lt;---Interaction Form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Influence\u0026lt;---Interaction Behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Influence\u0026lt;---Interaction Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Influence\u0026lt;---Interaction Form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal Innovation\u0026lt;---Interaction Behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal Innovation\u0026lt;---Interaction Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal Innovation\u0026lt;---Interaction Form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Usefulness\u0026lt;---Interaction Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Usefulness\u0026lt;---Interaction Behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Usefulness\u0026lt;---Interaction Form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Usefulness\u0026lt;---Perceived Ease of Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Intention to Use\u0026lt;---PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Intention to Use\u0026lt;---Perceived Ease of Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Intention to Use\u0026lt;---Personal Innovation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Intention to Use\u0026lt;---Social Influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote.***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrates that Interaction Behavior (β\u0026thinsp;=\u0026thinsp;0.227, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Interaction Content (β\u0026thinsp;=\u0026thinsp;0.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Interaction Form (β\u0026thinsp;=\u0026thinsp;0.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) all exert significant positive effects on users' Perceived Ease of Use for AI-assisted clothing customization products, confirming hypotheses H1b, H2b, and H3b.\u003c/p\u003e \u003cp\u003eAdditionally, Interaction Behavior (β\u0026thinsp;=\u0026thinsp;0.281, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Interaction Content (β\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Interaction Form (β\u0026thinsp;=\u0026thinsp;0.246, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) show significant positive influences on Social Influence, supporting hypotheses H1d, H2d, and H3d.\u003c/p\u003e \u003cp\u003eFor Personal Innovation, Interaction Behavior (β\u0026thinsp;=\u0026thinsp;0.256, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Interaction Content (β\u0026thinsp;=\u0026thinsp;0.333, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Interaction Form (β\u0026thinsp;=\u0026thinsp;0.332, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are also significant positive predictors, thus validating hypotheses H1c, H2c, and H3c.\u003c/p\u003e \u003cp\u003eOn Perceived Usefulness, however, only Interaction Content (β\u0026thinsp;=\u0026thinsp;0.273, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Interaction Form (β\u0026thinsp;=\u0026thinsp;0.217, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) show significant positive effects, leading to the acceptance of hypotheses H2a and H3a. In contrast, Interaction Behavior (β\u0026thinsp;=\u0026thinsp;0.167, p\u0026thinsp;\u0026gt;\u0026thinsp;0.001) and Perceived Ease of Use (β\u0026thinsp;=\u0026thinsp;0.138, p\u0026thinsp;\u0026gt;\u0026thinsp;0.001) do not demonstrate significant positive effects on Perceived Usefulness, thereby leading to the rejection of hypotheses H1a and H5a.\u003c/p\u003e \u003cp\u003eFurther findings indicate that Perceived Usefulness (β\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Perceived Ease of Use (β\u0026thinsp;=\u0026thinsp;0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Personal Innovation (β\u0026thinsp;=\u0026thinsp;0.301, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) positively influence Behavioral Intention to Use, supporting hypotheses H4, H5b, and H6. However, Social Influence (β\u0026thinsp;=\u0026thinsp;0.098, p\u0026thinsp;\u0026gt;\u0026thinsp;0.001) does not significantly affect Behavioral Intention to Use, leading to the rejection of hypothesis H7.\u003c/p\u003e \u003cp\u003eRegarding explained variance, Interaction Behavior, Interaction Content, and Interaction Form account for 30.1% of the variance in Perceived Ease of Use. Together, these variables also explain 31.7% of the variance in Social Influence and 43.6% in Personal Innovation. Alongside Perceived Ease of Use, they explain 31.3% of the variance in Perceived Usefulness. Finally, Perceived Usefulness, Perceived Ease of Use, Personal Innovation, and Social Influence collectively explain 45.7% of the variance in Behavioral Intention to Use.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1. The Differential Impact of Interaction Design Elements on Related Variables\u003c/h2\u003e \u003cp\u003eAmos analysis reveals the high correlation between the three elements of interaction design and user perception variables, explaining 30.1%, 43.6%, and 31.7% of the variance respectively. The path coefficient indicates that the impact of interaction content and interaction form on creativity is significantly higher than that of interaction behavior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2. The Role of Personal Creativity in Driving Continual Use Intentions\u003c/h2\u003e \u003cp\u003eResearch data clearly show that users' Personal Innovation (β\u0026thinsp;=\u0026thinsp;0.301) has a direct and positive effect on their intention to continue using AI-assisted customization clothing design software. This fully demonstrates that in the context of AI-assisted customization clothing design, users have a strong desire for ample personalization space to fully exert their creativity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Comparative Analysis of the Impact of Various Factors on Continual Use Intentions\u003c/h2\u003e \u003cp\u003eA deeper examination of the path coefficients reveals that Perceived Usefulness and Personal Innovation exert a stronger influence on users' intentions to continue using AI-assisted customization software for clothing design. These factors have a more substantial direct impact than Perceived Ease of Use and Social Influence, suggesting that users place higher value on functional satisfaction. Additionally, users motivated by innovation are more likely to engage in long-term use. These insights are essential for guiding software development priorities and for tailoring effective marketing strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Identification of Core Factors\u003c/h2\u003e \u003cp\u003eThis study focuses on AI-assisted software for customized fashion design, employing it as the primary research object. By integrating various theoretical frameworks and methodologies, we conduct thorough data analyses to confirm that Perceived Usefulness, Perceived Ease of Use, and Personal Innovation\u0026mdash;core elements of the Technology Acceptance Model.They significantly influence users' intentions for sustained engagement. Especially Perceived Usefulness plays a more important role in influencing users' continual use intentions. Perceived Ease of Use, which concerns the convenience of users learning and using the software, has a significant impact on continual use intentions, but it is secondary to Perceived Usefulness. Personal Innovation reflects the innovative spirit of users in software use, and users with high creativity tend to use the software for a long time to achieve personalized design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Indirect Impact of Interaction Design Elements\u003c/h2\u003e \u003cp\u003eThis study demonstrates that Interaction Content,Interaction Behaviour, and Interaction Form exert indirect effects on users' intentions to continue using through variables like Perceived Ease of Use, Personal Innovation, and Social Influence, with Interaction Content and Interaction Form having a pronounced impact. Engaging, high-caliber Interaction Content, such as an extensive fashion material library and comprehensive design cases, offers users creative inspiration and resources, fostering software attachment and reliance. User-centered Interaction Form, characterized by an intuitive interface and versatile design tools, facilitates proficiency and strengthens user retention. Although Interaction Behaviour contributes, its influence is less significant compared to Interaction Content and Interaction Form.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Validation of Model Effectiveness\u003c/h2\u003e \u003cp\u003eThis study deeply analyzes the model fit and reveals that the research model combining the Technology Acceptance Model (TAM),Use of Technology (UTAUT) with social influence factors and interaction design elements performs well in predicting users' continual use intentions for AI-assisted customized clothing design software. The integration of social factors (such as recommendations, recognition) and interaction design (interaction behaviour,interaction content,interaction form) effectively complements TAM, more comprehensively reflecting user intentions. This conclusion provides theoretical support for further research and optimization of AI-assisted clothing design software, promoting the development of theoretical and practical knowledge in the field.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Theoretical Contributions and Practical Significance","content":"\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Theoretical Contributions\u003c/h2\u003e \u003cp\u003eThis study is the first to fuse the social impact from UTAUT,interaction design elements and TAM to produce an effective research model for the AI-assisted fashion design software sector that influences user retention. The empirical analysis shows that perceived usefulness, perceived ease of use and individual innovation are the key factors driving user intention in this field, and perceived usefulness is established as the dominant factor. In addition, the study strongly supports social impact and interaction design elements as important enhancements to TAM's assessment of user retention for AI products, thereby broadening the usefulness of TAM and advancing the theoretical framework in the field.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Practical Significance\u003c/h2\u003e \u003cp\u003eThis study provides a solid theoretical framework for promoting AI-assisted fashion design software. Developers should focus on improving product utility and user innovation, optimizing algorithms to improve design accuracy and efficiency. Initiatives such as creative communities are recommended to foster user creativity. To increase user engagement, operators should customize innovative experiences, update design resources, and provide personalized recommendations. Using VR and AR for design previews can further improve user retention. These strategies have an important guiding role in product development, function upgrading and marketing positioning of enterprises, which helps to meet user needs and ensure market competitiveness, thus promoting the sustainable and healthy development of the industry.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Limitations and Prospects","content":"\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e7.1. Research Limitations\u003c/h2\u003e \u003cp\u003eThis study has some limitations. First, the scope of the research is relatively narrow, focusing mainly on users' intentions when using AI-assisted fashion design software, without delving into the more subtle psychological changes users experience when interacting with such products. Secondly, the source of samples is limited, and the samples are mainly concentrated in China, which is difficult to fully represent the user groups of different cultural backgrounds and industry fields around the world.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e7.2. Future Prospects\u003c/h2\u003e \u003cp\u003eIn view of the above limitations, the follow-up research should explore a variety of ways. The research content should use AI-assisted fashion design software to dig into the nuances of user psychology and improve design quality and innovation. Sample selection should be expanded geographically and across industries to include diverse cultural and professional backgrounds to obtain more representative data. This will enhance the universality and applicability of the research results, provide accurate user needs and behavioral insights for AI-customized fashion, and provide targeted theoretical and practical guidance for the development of the field.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZeng Erxuan designed the whole research and wrote the main manuscript text. Liu Rongbin helped conduct the survey. As a mentor to two undergraduate students,Feng Yuxue guided Zeng Erxuan and Liu Rongbin . All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThank you to everyone who filled out the survey.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this research are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLeng, J. et al. \u003cem\u003eUnlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges\u003c/em\u003e (Journal of Manufacturing Systems, 2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnwer, H., Ali, M. \u0026amp; Jamshaid, H. \u003cem\u003eApplications of Artificial Intelligence in Textiles and Fashion[J]\u003c/em\u003e (Springer, 2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG Jing, W. A. N. G., Xiaoyi, L. A. N., Cuiqin \u0026amp; XU Jiping. Application and prospect of information technology in personalized clothing customization[J]. \u003cem\u003eJ. Silk\u003c/em\u003e. \u003cb\u003e61\u003c/b\u003e (1), 96\u0026ndash;108 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiabei, Y. U. \u0026amp; ZHU Weiming. The application of big data-driven generative AI in fashion design: Taking Midjourney as an example[J]. \u003cem\u003eJ. Silk\u003c/em\u003e. \u003cb\u003e61\u003c/b\u003e (9), 20\u0026ndash;27 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, F. D. Perceived usefulness, Perceived ease of use, and user acceptance of information technology[J]. \u003cem\u003eMIS Q.\u003c/em\u003e, (13):319\u0026ndash;340. (1989).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh, V., Morris, M. G., Davis, G. B. \u0026amp; Davis, F. D. User acceptance of information technology: Toward a unified view. \u003cem\u003eMIS Q.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (3), 425\u0026ndash;478 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, M., Shao, Z., Liu, Q. \u0026amp; Liu, C. Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. \u003cem\u003eEducation Tech. Research Dev.\u003c/em\u003e \u003cb\u003e65\u003c/b\u003e (5), 1195\u0026ndash;1214 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTawafak, R. M. et al. A combined model for continuance intention e-learning system. \u003cem\u003eInt. J. Interact. Mob. Technol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (03), 113\u0026ndash;129 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, J. J. \u0026amp; Song, S. Older Adults' Online Shopping Continuance Intentions: Applying the Technology Acceptance Model and the Theory of Planned Behavior[J]. \u003cem\u003eInt. J. Hum Comput Interact.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e (10), 938\u0026ndash;948 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Shaofeng, T., Ahmed, Z., Lixin, Y. \u0026amp; Junfeng Do Playfulness and University Support Facilitate the Adoption of Online Education in a Crisis? COVID-19 as a Case Study Based on the Technology Acceptance Model[J]. \u003cem\u003eSustainability\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (16), 1\u0026ndash;16 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, C. H. \u0026amp; Chen, T. M. Incorporating data analytics into design science to predict user intentions to adopt smart TV with consideration of product features[J]. \u003cem\u003eComput. standards\u0026amp; Interfaces\u003c/em\u003e. \u003cb\u003e59\u003c/b\u003e, 87\u0026ndash;95 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgarwal, R. \u0026amp; Karahanna, E. Time Flies When You're Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage[J]. \u003cem\u003eMIS Q.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (4), 665\u0026ndash;694 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang, Y. User Experience and Personal Innovativeness: An Empirical Study on the Enterprise Resource Planning Systems[J]. \u003cem\u003eComput. Hum. Behav.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (5), 227\u0026ndash;234 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Ruyu,An Liren. The Influence Mechanism of Innovation Characteristics on New Energy Vehicle Consumer's Adoption Intention: The Moderating Role of Individual Innovation[. \u003cem\u003eJ] Mod. ECONOMIC Sci.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (5), 113\u0026ndash;121 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChengfeng, L. E. I. \u0026amp; Zhenjiang, X. I. N. G. AIGC Reshaping Behavior: Study on Influencing Factors of Intention to Continue Use[J]. \u003cem\u003eTechnoeconomics Manage. Res.\u003c/em\u003e, (06):152\u0026ndash;158. (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXIN Xiangyang. \u003cem\u003eInteraction Design: From Logic of Things to Logic of Behaviors[J]\u003c/em\u003e58\u0026ndash;62 (ZHUANGSHI, 2015). 1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXIN Xiangyang. From User Experience to Experience Design [J]. \u003cem\u003ePackaging Eng.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e (08), 60\u0026ndash;67 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuangtao, M. A. \u0026amp; Qinpu, F. A. N. G. Research on the Emotion of Interaction Design Elements [J]. \u003cem\u003eDesign\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e (09), 103\u0026ndash;105 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai, Y. Q. \u0026amp; Xiao, J. J. The impact of cMOOC learners\u0026rsquo; interaction on content production[C]. (2021). Interactive Learning Environments,(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUO Binye. \u003cem\u003eBased on geriatric care of Rehabilitation Product Design Study[D]\u003c/em\u003e (North China University of Technology,, 2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai, Y. Q. \u0026amp; Xiao, J. J. The impact of cMOOC learners\u0026rsquo; interaction on content production[C]. Interactive Learning Environments, (8). (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawes, J. Five point vs. eleven point scales: Does it make a dif ference to data characteristics. \u003cem\u003eAustralasian J. Market Res.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (1), 1\u0026ndash;17 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, J. D. Likert items and scales of measurement. \u003cem\u003eStatistics\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (1), 10\u0026ndash;14 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgarwal, R. \u0026amp; Prasad, J. A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology [J]. \u003cem\u003eInform. Syst. Res.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (2), 204\u0026ndash;215 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, B. \u0026amp; Chen, X. Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task tech nology fit (TTF) model. \u003cem\u003eComput. Hum. Behav.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e, 221232 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoehlin, J. C. \u003cem\u003eLatent variable models: An introduction to factor, path, and structural equation analysis\u003c/em\u003e (Lawrence Erlbaum Associates, 2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrett, P. Structural equation modelling: Adjudging model fit. \u003cem\u003ePers. Indiv. Differ.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (5), 815\u0026ndash;824 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiau, W. L. \u0026amp; Luo, M. M. Factors affecting online group buy ing intention and satisfaction: A social exchange theory perspective. \u003cem\u003eComput. Hum. Behav.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (6), 2431\u0026ndash;2444 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiau, W. L., Sarstedt, M. \u0026amp; Hair, J. F. Internet research using partial least squares structural equation modeling (PLS-SEM). \u003cem\u003eInternet Res.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (3), 398\u0026ndash;406 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair, J. F. Jr., Sarstedt, M., Hopkins, L., Kuppelwieser, G. \u0026amp; V Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. \u003cem\u003eEur. Bus. Rev.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (2), 106\u0026ndash;121 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFornell, C. \u0026amp; Larcker, D. F. Evaluating structural equation models with unobservable variables and measurement error. \u003cem\u003eJ. Mark. Res.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (1), 39\u0026ndash;50 (1981).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayduk, L. A. \u003cem\u003eStructural equation modeling with LISREL: Essentials and advances\u003c/em\u003e (Jhu, 1987).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagozzi, R. P. \u0026amp; Yi, Y. On the evaluation of structural equation models. \u003cem\u003eJ. Acad. Mark. Sci.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (1), 74\u0026ndash;94 (1988).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScott, J. E. The measurement of information systems effective ness. ACM SIGMIS Database: The DATABASE for Advances. \u003cem\u003eInform. Syst.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (1), 43\u0026ndash;61 (1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, L. \u0026amp; Bentler, P. M. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. \u003cem\u003ePsychol. Methods\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e (4), 424\u0026ndash;453 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Weihao. \u003cem\u003eDancing with the Structural Equation Model: The Dawn Appears [M]\u003c/em\u003e (Xiamen University, 2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair, J. F. Jr., Babin, B. J. \u0026amp; Krey, N. Covariance-based struc tural equation modeling in the Journal of Advertising: Review and recommendations. \u003cem\u003eJ. Advertising\u003c/em\u003e. \u003cb\u003e46\u003c/b\u003e (1), 163\u0026ndash;177 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Technology Acceptance Model, User Intention to Use, AI-assisted Customized Fashion Design Software, Interaction Design","lastPublishedDoi":"10.21203/rs.3.rs-6149700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6149700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs AI technology penetrates into customized fashion design, traditional TAM and UTAUT models have provided insights into the study of new technology acceptance, but the research on user intentions towards AI-assisted customized fashion design software requires the introduction of new variables.This study focuses on the impact of interaction design (including interaction behavior,interaction content, and interaction form) and user perception (including perceived usefulness, ease of use, personal innovation, and social influence) on user intentions.The construction and analysis of the new model show that interaction design elements indirectly affect users' intention to continue using through influencing mediating variables such as perceived ease of use and personal innovation. The impact of interaction content and interaction form is significant, while the impact of interaction behavior is relatively small.Personal innovation has a positive effect on the intention to continue use, with perceived usefulness being the most critical factor. The model in this study performs well in explaining user intentions to continue use, providing a theoretical basis for the design optimization of such AI-assisted design products, and has important guiding implications for development priorities and marketing strategies.\u003c/p\u003e","manuscriptTitle":"Analysis of User Intention to Use of AI-Assisted Customized Fashion Design Software in the Dimension of Interaction Design— —An empirical study in China based on an extended TAM model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 12:34:27","doi":"10.21203/rs.3.rs-6149700/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"887b1c32-1bf0-4195-a864-2da98c543595","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46296674,"name":"Health sciences/Medical research"},{"id":46296675,"name":"Physical sciences/Engineering"}],"tags":[],"updatedAt":"2025-08-01T16:08:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-03 12:34:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6149700","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6149700","identity":"rs-6149700","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}