Cognitive Determinants and Technological Adoption: Exam-ining Behavioral Intention Mechanisms in Artisanal Design Practices Mediated by AI-Generated Content

Cognitive Determinants and Technological Adoption: Exam-ining Behavioral Intention Mechanisms in Artisanal Design Practices Mediated by AI-Generated Content | 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 Cognitive Determinants and Technological Adoption: Exam-ining Behavioral Intention Mechanisms in Artisanal Design Practices Mediated by AI-Generated Content Weiping He, Xiwen Zeng, Liang Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7362510/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Artificial Intelligence-Generated Content (AIGC) is a rapidly evolving technology with significant computational and creative potential, offering new possibilities in design, aesthetics, and process innovation. However, traditional artisans view AIGC as a potential threat to the authenticity and value of their work. Research on artisans' acceptance of AIGC is limited. This study aims to identify the key factors influencing artisans’ willingness to adopt AIGC-assisted design and develop a validated measurement scale and evaluation model. A mixed-methods approach combining qualitative and quantitative research was used, including user interviews and literature review. Exploratory factor analysis and multiple regression analysis identified six key factors affecting adoption: serendipity for design, artisanal epistemology, productivity enhancement, and technical usability. Simulated craft authenticity and epistemic openness did not show direct effects. The study highlights the need for technology integration that respects artisans' cognitive frameworks and creative traditions, advocating for a shift from technological neutrality to cultural adaptation and collaborative co-creation. Business and commerce/Business and management Social science/Business and management Humanities/Cultural and media studies Social science/Cultural and media studies Business and commerce/Information systems and information technology Humanities/Philosophy Social science/Science technology and society Artisans AIGC Examining Behavioral Intention Serendipity for Design Artisanal Epis-temology Productivity Enhancement Technical Usability Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction A craftsman typically refers to an individual who integrates both design conception and hands-on production capabilities, independently creating artistic works or producing them with the support of assistants or apprentices (Bach 1922). These practitioners are committed to uniting practical techniques with artistic expression, striving for a seamless synthesis of functionality and aesthetic value (Bergadaà 2008). Often regarded as custodians of tradition, culture, and principles of sustainable development, craftsmen embody a professional ethos that transcends mere technical skill (Sennett 2008). This ethos is most evident in their deep understanding and skillful manipulation of materials, tools, and production processes (Manfredi Latilla et al. 2019). The relationship between the craftsman's professional spirit and their technical knowledge forms a dialectical dynamic that warrants close scholarly attention. This interplay not only shapes individual craftsmanship but also plays a pivotal role in the evolution of the artisan community as a whole (Kunneman 2013). In the context of rapid technological advancement, however, artisans often face challenges in actively adapting their practices. The transformation of traditional craft models is frequently reactive rather than proactive, and many handicraft industries have declined due to limited adaptability to emerging technologies (Schiffer and Skibo 1987, Gourinchas et al. 2020, Überbacher, Brozzi, and Matt 2020). Although some scholars argue that the integration of new technologies does not necessarily disrupt the internal logic of traditional craftsmanship—and may, in fact, foster its long-term sustainability (Wenji, Rongrong, and Li 2022, Zhang et al. 2023, Rao and Gopi 2016)—the conditions under which such integration is embraced remain underexplored. Consequently, understanding artisans’ willingness to adopt and engage with new technologies in times of technological transformation has become an increasingly important area of inquiry. AIGC is an artificial intelligence–based technology that autonomously produces content by leveraging generative models trained on large-scale datasets (Wu et al. 2023). With its powerful computational and creative capabilities, AIGC has the potential to support and enhance various aspects of artisanal practice, including manual production techniques (Shimin et al.), stylistic modeling references (ldzwan bin Ismail and Huang), and models of craftsmanship development (Karimova et al. 2024). However, the distinct cultural and emotional dimensions inherent in artisanal work have led many craftsmen to question whether these qualities can be authentically replicated by artificial intelligence systems such as AIGC (Zhu et al. 2024, Torres et al. 2025). Some artisans perceive AIGC not as a tool for innovation, but as a threat to the authenticity and intrinsic value of handcrafted objects. From this perspective, the integration of automated technologies may undermine the uniqueness and cultural significance of traditional craftsmanship (Chauhan 2020, Song and Education 2022). As a result, tensions have emerged between proponents of AIGC and members of the artisan community. Nonetheless, the adoption of new production technologies has been a recurring feature of historical development (David 2000). It is therefore likely that, over time, AIGC will become more widely accepted within the artisan sector (Lou and Innovation 2023, Burden 2022). Despite this inevitability, empirical research examining artisans’ attitudes toward and acceptance of AIGC remains extremely limited. This lack of evidence-based understanding hinders meaningful engagement with the underlying tensions between emerging technologies and traditional cultural practices, and constrains the development of effective integration strategies. Artisans’ perceptions of, and willingness to engage with, AI-Generated Content (AIGC) represent a complex and interrelated set of attitudes. While existing literature has touched upon related issues, focused investigations specifically addressing artisans as a distinct group remain scarce. Prior studies have primarily examined AIGC’s role in assisting particular stages of technical workflows, such as those performed by process engineers (Wang, Dong, and Technology 2023, Liu and Xu 2024, Wen et al. 2024), or have subsumed artisans within broader categories such as designers (WU and WANG 2024, Pan et al. 2024). Although there are overlaps in the creative practices of artisans and designers, the methodologies, value systems, and cognitive approaches that define artisanal work differ significantly from those found in modern design professions (Junaidy and Nagai 2013, Lozano 1990). Moreover, current evaluation instruments—often derived from generalized technology acceptance models—lack empirical validation in the context of artisanal practice. As such, their applicability is limited, and their findings may be influenced by subjective assumptions. To address these gaps, the present study draws on established methodologies employed by Wang, Deng, and Jiang (2023) and Xing and Jiang (2024), combining qualitative approaches (e.g., user interviews, open-ended questionnaires, and literature review) with quantitative techniques such as factor analysis and linear regression. This mixed-methods approach aims to systematically identify the key factors influencing artisans’ adoption of AIGC-assisted design, and to analyze the relationships between these factors and users’ behavioral intentions. The study further seeks to develop a targeted evaluation scale and behavioral intention model tailored to the artisan context. In doing so, it contributes to the theoretical understanding of technology adoption among traditional cultural practitioners and offers a solid empirical foundation for informing the integration of AIGC into artisanal domains. In conclusion, the core objectives of this study are twofold: First, through the integration of multiple research methods, including literature review, user research, and data analysis, this study identifies the key factors influencing artisans' adoption of AIGC-assisted design. Based on these findings, a user experience evaluation scale is developed. Second, by employing linear regression analysis, the study examines the relationships and underlying logic between each influencing factor and artisans’ willingness to adopt AIGC, ultimately constructing a corresponding behavioral model. Building on these results, the study offers recommendations for the future development of traditional handicrafts, focusing on the integration of AIGC tools in design and creation processes, and the role of new technologies in shaping the evolution of artisanal practices. 2 Methods 2.1 Research process Based on the preceding discussion, this research will be structured into four main sections, with the research process illustrated in Figure 1. The first section focuses on the development of the evaluation scale. In this phase, the study will collect artisans' evaluations of AIGC-assisted design through a combination of literature review and user interviews. The feedback will be synthesized and reviewed by industry experts to ensure its relevance and accuracy. Ultimately, a behavioral intention evaluation scale will be constructed to assess artisans' willingness to use AIGC in the design process. The second section involves the identification of key impact factors. In this phase, the evaluation scale developed in the first section will be distributed widely as a questionnaire. The validity of the scale will be tested through reliability analysis, exploratory factor analysis, and confirmatory factor analysis. This will allow the identification of the primary factors influencing artisans' willingness to adopt AIGC. Building upon the literature review and observed variables within each factor, the study will name each factor in the context of traditional handicrafts and AIGC tools, further delineating the specific dimensions that shape users' willingness to engage with the technology. The third section is the construction of the behavioral model. In this phase, the study will explore the relationships between the identified factors and users' willingness to adopt AIGC. During the questionnaire distribution phase of the second section, a well-established "intention to use" evaluation scale will be introduced. This will allow for the collection of user evaluations in conjunction with the evaluation scale from the first section. Afterward, EFA and CFA will be conducted once again to ensure strong discriminant validity between the "willingness to use" factor and the previously identified factors, and to verify that the observed variables within each factor demonstrate satisfactory convergence. Following these validations, linear regression analysis will be employed to examine the influence of each factor on artisans' willingness to use AIGC. Based on these results, a behavioral intention model for artisans' adoption of AIGC-assisted design will be constructed. The fourth section will focus on the discussion of the results. Here, the study will integrate the findings from the behavioral model developed in the third section to analyze and interpret the relationships between each factor and the willingness to use. Drawing from these insights, the study will propose design strategies and management recommendations for optimizing AIGC tools to better assist artisans in the design and creation process. 2.2 Data sources 2.2.1 User interview strategy 2.2.1.1 Participants As the foundation for selecting the research subjects, this study focuses on the group of Chinese craftsmen, whose representativeness and research significance can be demonstrated from several key dimensions. Firstly, Chinese craftsmanship is represented by a well-established and influential community that mirrors similar groups in other countries or regions (Hang and Guo 2006). From an industrial perspective, the Chinese artisan sector has made substantial economic and social contributions. Statistical data reveals a consistent growth trend in the operating income of large-scale enterprises within the arts and crafts industry, with an annual growth rate of 14.26% and a profit increase of 18.64%. The total annual output value of this industry is approximately 412.8 billion US dollars, supporting over 6.5 million direct employees and an additional 13 million workers in related sectors. This has fostered the development of a highly scalable professional group with significant economic impact (WuYaNan 2024.06). Secondly, in terms of international market performance, traditional Chinese craftsmanship occupies a prominent position in global trade. For example, the export value of decorative wood products from January to August 2024 reached 1.937 billion US dollars, underscoring the competitive strength of Chinese craftsmanship in the international market (zhiyanzixun 2024.11). This economic influence has further catalyzed the growth of related industries, such as tourism (Hedin 2024). Additionally, from a cultural perspective, Chinese craftsmanship holds substantial cultural significance and has a broad regional impact. The cultural exchanges between China and Japan provide a clear illustration of this influence, as the artisan traditions of both countries share considerable commonalities in terms of skill transmission and professional ethics. This cultural connection can be traced to the cross-domain diffusion of Confucianism (Yanagi and Leach 1989, Xu 2013). In the contemporary context, the fusion of Chinese craft aesthetics with international design principles has given rise to new cultural forms, particularly in the realms of luxury handcrafted goods and traditional handicrafts (Zhang 2024, Ji and Sirisuk 2024). The size and cultural influence of the Chinese artisan community, as a critical component of the global artisan landscape, makes it a key subject of study. Exploring the willingness of Chinese artisans to adopt AIGC-assisted design and identifying the factors influencing their acceptance will provide valuable theoretical insights into the technological adaptability of artisan groups, not only in Asia but also on a global scale in the AIGC era. 2.2.1.2 Research strategy In the user research phase, this study will employ a structured interview approach to gather user evaluation data. This method was selected based on existing studies demonstrating that structured interviews can significantly improve the reliability of data sources (Segal et al. 2006). The primary interview questions will focus on two key areas: (1) What features do respondents believe would encourage them to use AIGC-assisted design tools for handicraft design? (At least three features will be required.) (2) What factors do respondents think would deter them from using AIGC-assisted design tools for handicraft design? (At least three factors should be listed.). All participants were fully informed of the study's purpose and procedures and provided their consent by signing an informed consent form. With the participants' consent, the user interviews were fully audio-recorded, and key points were also documented in writing. Three research members participated in each interview, with one serving as the lead interviewer and the other two responsible for recording the interview and asking follow-up or clarifying questions based on the discussion. A total of 111 artisans who had experience using AIGC tools were interviewed for this study, significantly exceeding the sample size standards recommended by Raita (2012) and Hartling (Hartling et al. 2017). The sample included 71 females and 40 males, representing a diverse range of educational backgrounds and types of handicrafts. As such, the sample offers a degree of generalizability. The basic demographic information of the respondents is presented in Table 1. The studies involving human participants were reviewed and approved by the Academic Ethics Committee of Changzhou Textile and Apparel Vocational College (GVTG/PP/20250805). The participants provided their written informed consent to participate in this study. The studies were conducted in accordance with the local legislation guidelines and institutional requirements. Table 1. Basic information of respondents during the user interview stage Name Option Frequency Percentage (%) Gender man 40 36.04 woman 71 63.96 Age 18-20 4 3.60 21-30 59 53.15 31-40 40 36.04 41-50 6 5.41 51-60 2 1.80 Educational background High school/technical secondary school 2 1.80 Junior college 5 4.50 Undergraduate college 69 62.16 Master 31 27.93 Doctor 4 3.60 Handicraft type Weaving and tie-making 16 14.41 Sculpture 7 6.31 Tool and equipment manufacturing 17 15.32 Furniture 8 7.21 Metal smelting and processing 6 5.41 Engraving and painting 3 2.70 Ceramic production 20 18.02 Production of stationery 7 6.31 Lacquer coating technique 4 3.60 Weaving and dyeing techniques 15 13.51 Special processes and others 8 7.21 Total 111 100.00 2.2.2 Literature retrieval strategy To minimize data bias and the potential for skewed evaluations from a single source, this study also incorporates a comprehensive review of existing literature on artisans' use of AIGC to complement the current research. The research methodology employed is a holistic approach for data collection and selection. The first step involves reviewing references from the Web of Science Core Collection (WoS CC), a reputable and reliable source of high-quality citation data. In the second step, the objective is to gather relevant records from academic journals across multiple databases pertinent to the research topic, adhering to specific inclusion criteria. A search was conducted within the WoS CC subject area using the following search terms: (((((TS=(handicraft)) OR TS=(craft)) AND TS=(AI) AND DT=(Article OR Review) AND LA=(English) AND PY=(2025). This search resulted in the retrieval of 170 relevant documents. In the third step, the top thirty articles most relevant to the search criteria were selected based on their relevance, forming the basis for the literature review conducted in this study. 2.3 Data Collection Process During the data analysis phase of the user interviews, a total of 625 valid evaluations were collected. Subsequently, five graduate students, with no vested interest in the thesis, were invited to review and summarize the results of the user research. These summaries were then evaluated by two university professors with expertise in the relevant field, who provided feedback and suggestions for revision. The process continued until both the graduate students and professors reached consensus on the final summary. Ultimately, 28 observed variables were identified and summarized from the user interview stage. In the literature review phase, this study manually extracted relevant expressions regarding the experience characteristics of artisans using AIGC-assisted design from the 30 retrieved papers, ultimately identifying 33 valid evaluations. Subsequently, five graduate students with no vested interest in the thesis were invited to summarize the findings. Once the summaries were completed, two industry experts were engaged to review and assess the results. The process continued until all five graduate students and the two experts reached a consensus with no objections to the final summary. In total, 20 observed variables were identified during the literature review phase. Upon completion of the review, the aggregated content from the user interviews and literature review was combined and utilized as the observed variables for the usage intention evaluation scale to be developed in this study. A total of 28 observed variables were identified after summarization. To ensure consistency in the description of each variable, all negatively evaluated variables were rephrased as positive evaluations. Additionally, each observed variable was encoded using the format "Q+ number." The final results are presented in Table 2. To facilitate a deeper exploration of the underlying logic and the relationships between influencing factors and users' willingness to adopt AIGC-assisted design, this study employed a well-established "willingness to use" evaluation scale. This scale has proven validity in measuring users' behavioral intentions and attitudes and has been widely referenced in prior research. The specific content of the questionnaire is presented in Table 3. Additionally, to align the scale with the focus of this study, adjustments were made to its content, specifically replacing the original research subject with the AIGC tool, thereby ensuring that the questionnaire better reflects the research objectives. Table 2 Artisans use the AIGC-assisted design Behavior Intention Evaluation Scale Number Observed Variable Frequency Source Q1 Enhance design efficiency 93 Literature Review(Yadav and Rena , Yadav and Tripathi 2025, Zhang et al. 2025) and User Survey Q2 Stimulate inspiration and foster creativity 72 Literature Review(Zhang et al. 2025, Danry et al. 2025, Bao et al. 2025) and User Survey Q3 The output exhibits meticulous craftsmanship 59 Literature Review(Zhang et al. 2025) and User Survey Q4 The produced works demonstrate a high degree of precision 44 Literature Review(Zhang et al. 2025) and User Survey Q5 User-friendly interface 43 Literature Review(Yadav and Tripathi 2025, Danry et al. 2025, Bao et al. 2025) and User Survey Q6 The produced works exhibit a handcrafted texture 39 Literature Review(Bao et al. 2025) and User Survey Q7 The produced works exhibit clear differentiation from existing products 32 User Survey Q8 Guided production process 30 Literature Review(Zhang et al. 2025, Bao et al. 2025) and User Survey Q9 The produced works reflect personal style and originality 28 User Survey Q10 Ease of use 26 Literature Review(Zhang et al. 2025, Danry et al. 2025, Bao et al. 2025) and User Survey Q11 Customizable according to user requirements 23 User Survey Q12 AIGC tools effectively interpret user instructions 22 Literature Review(Vartiainen et al. 2025) and User Survey Q13 The produced works demonstrate a high degree of feasibility 21 Literature Review(Bao et al. 2025) and User Survey Q14 Provide creative references and inspiration 20 Literature Review(Zhang et al. 2025, Danry et al. 2025) and User Survey Q15 The produced works embody humanistic values 18 Literature Review(Bao et al. 2025) and User Survey Q16 The produced works avoid copyright and ownership disputes 12 Literature Review(Bao et al. 2025) and User Survey Q17 Offer creative ideas and suggestions 10 Literature Review(Zhang et al. 2025, Danry et al. 2025) and User Survey Q18 Low learning curve for AIGC tools 10 Literature Review(Bao et al. 2025) and User Survey Q19 Assist artisans throughout the production process 9 Literature Review(Pan et al. 2025) and User Survey Q20 The produced works are not constrained by existing training data 9 User Survey Q21 Generate novel ideas and creativity 8 Literature Review(Bao et al. 2025, Hevi et al. 2025) and User Survey Q22 Stable software performance 8 Literature Review(Bao et al. 2025) and User Survey Q23 Enhance the artistic quality of the output 4 User Survey Q24 Streamline the production process 4 Literature Review(Zhang et al. 2025, Pan et al. 2025) and User Survey Q25 The design process mirrors traditional handicraft techniques 4 User Survey Q26 Low training costs for AIGC tools 4 User Survey Q27 Facilitate the mass production of handicraft products 3 User Survey Q28 Reduce the complexity of creation 3 Literature Review(Yadav and Rena , Danry et al. 2025) and User Survey Table 3 Evaluation Scale for Intention to Use Latent Variable Observed Variable Literature Source Intention to Use, ITU I intend to utilize AIGC tools in the near future. (Huang and Qian 2021) I am inclined to adopt AIGC tools. I would advocate for the adoption of AIGC tools by others. 2.3.1.1 The user testing stage of the evaluation scale After finalizing the initial evaluation scale, this study integrated the summarized evaluation items with an established usage intention scale to create the final AIGC usage intention evaluation scale. The questionnaire was then distributed online using a seven-point Likert scale to gather user evaluations. Following usability assessments on multiple online survey platforms, the study opted to use "Credamo," a widely recognized Chinese platform, for data collection. Although some studies have noted potential issues with the reliability and quality of data from this platform (Zhang and Computing 2024), it remains a suitable choice due to its cost-effectiveness, reliability, and overall data quality. Given the sensitivity of the research topic, all responses were collected anonymously. Since the target sample consisted of Chinese artisans, the original questionnaire was administered in Chinese and later translated into English for analysis. To ensure sample homogeneity and objectivity, clear sociodemographic inclusion criteria were established (Abdelmoety et al. 2022), requiring that all respondents be Chinese residents aged 18 or older. The questionnaire survey consists of four sections. The first section is the Informed Consent Form, which provides detailed information about the research objectives and procedures. Respondents can proceed with the questionnaire only after selecting the option "I am aware of and agree to participate in the research." The second section includes the Conditional Screening Question, which asks, "Have you used AIGC tools in the process of handicraft creation?" Respondents who select "Yes" may continue with the questionnaire, while those who select "No" will be automatically excluded from the survey. This ensures the authenticity and reliability of the collected data. The third section gathers basic demographic information, including gender, age, the type of handicraft practiced, educational background, and other relevant details. The fourth section assesses user evaluations using the proposed scale. The content of this section is presented in Table 3. A seven-point Likert scale is employed, where "1" indicates "strongly disagree" and "7" indicates "strongly agree."。 After distributing the questionnaire on a large scale, a total of 597 responses were collected. Upon review, invalid responses—such as those with excessively short completion times, duplicate entries, or missing answers—were excluded. This resulted in 474 valid responses, yielding an effective response rate of 79%. The sample size was sufficient to meet the requirements for both factor analysis and linear regression (Price 1993, Comrey and Lee 1992, Dai et al. 2020, Hair Jr et al. 2021). The demographic information of the respondents is presented in Table 4. Table 4. Basic Information Table of Respondents in the User Testing Phase name option Frequency Percentage (%) Gender man 244 51.48 woman 230 48.52 Age 18-20 31 6.54 21-30 282 59.49 31-40 126 26.58 41-50 30 6.33 51-60 5 1.05 Educational back-ground High school/technical secondary school 1 0.21 Junior college 2 0.42 Undergraduate college 24 5.06 Master 60 12.66 Doctor 310 65.40 Weaving and tie-making 65 13.71 Sculpture 12 2.53 Handicraft Type Tool and equipment manufacturing 50 10.55 Furniture 52 10.97 Metal smelting and processing 70 14.77 Engraving and painting 59 12.45 Ceramic production 25 5.27 Production of stationery 30 6.33 Lacquer coating technique 84 17.72 Weaving and dyeing techniques 19 4.01 Special processes and others 7 1.48 man 44 9.28 woman 34 7.17 Total 474 100.00 2.3.2 Certainty assessment After collecting the questionnaires, this study aimed to analyze the specific dimensions of the AIGC-assisted design behavior intention evaluation scale for artisans. First, exploratory factor analysis (EFA) was conducted using SPSS 26.0 software on the 474 valid questionnaires. The results revealed that the 28 observed variables were clustered into six factors: serendipity for design (including observed variables Q2, Q11, Q14, Q17, Q19, Q21, Q23), artisanal epistemology (including observed variables Q4, Q7, Q12, Q13, Q16, Q22), simulated craft authenticity (including observed variables Q3, Q6, Q9, Q15, Q22), productivity enhancement (including observed variables Q8, Q24, Q27, Q28), epistemic openness (including observed variables Q18, Q20, Q26), and technical usability (including observed variables Q1, Q5, Q10). Each factor demonstrated high reliability (Graham-Rowe et al. 2012). Subsequently, confirmatory factor analysis (CFA) was performed using Amos 26.0 software to validate the exploratory factor analysis results. The findings showed good discriminant validity among the factors, with each factor strongly correlating with its corresponding observed variables. These results indicate that the user experience dimensions identified in this study, which influence artisans' intention to use AIGC-assisted design, fully meet the required standards. Based on these findings, this study conducts a linear regression analysis to explore the relationships between the identified factors and users' willingness to use. First, exploratory factor analysis was performed to assess the discriminant validity between "willingness to use" and the user experience factors. The results indicate that each factor demonstrates strong internal consistency among its observed variables, satisfying the criteria necessary for conducting linear regression analysis. Subsequently, the linear regression analysis revealed that serendipity for design, artisanal epistemology, productivity enhancement, and technical usability have a direct positive impact on the willingness to use. In contrast, simulated craft authenticity and epistemic openness do not exhibit a direct influence on the willingness to use. Overall, the research data and findings align with the study's expectations. 3 Results 3.1 Evaluate the analysis results of the scale 3.1.1 Exploratory factor analysis This study first employed exploratory factor analysis to examine the user evaluation data presented in Table 2, identifying the main factors influencing artisans' use of AIGC-assisted design. The research findings are summarized in Table 5. After importing the data into SPSS 26.0 software, six factors with eigenvalues greater than 1 were extracted. The results of the Bartlett’s test of sphericity showed p = 0.000 ( 0.6), indicating that the data are suitable for factor analysis (Nunnally 1994). Moreover, the factor loadings and communalities of the observed variables for each factor were all greater than 0.4, and each observed variable was strongly associated with only one factor. These results suggest significant distinctions among the observed variables in different factors, and strong correlations among the variables within a single factor. Therefore, the exploratory factor analysis results are robust and meet the required standards (Kaiser 1958). Additionally, a reliability analysis was conducted, revealing that the Cronbach's alpha for each factor exceeded 0.7, signifying high internal consistency and reliability of the data used in this study. These findings underscore the credibility of the results (Eisinga, Te Grotenhuis, and Pelzer 2013). Table 5. Exploratory factor analysis of the evaluation scale Observed Variable Factor Loading Coefficients Communality Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Q2 0.773 0.139 -0.032 0.097 0.061 0.146 0.652 Q11 0.726 0.157 0.070 0.091 0.046 0.149 0.589 Q14 0.667 0.085 0.145 0.219 0.074 0.194 0.564 Q17 0.746 0.139 0.038 0.197 0.063 0.023 0.621 Q19 0.636 0.101 0.083 0.253 0.033 0.125 0.502 Q21 0.695 0.209 0.055 0.068 0.149 0.178 0.589 Q23 0.691 0.040 0.200 0.234 -0.015 0.186 0.608 Q4 0.065 0.744 0.199 0.073 0.105 0.127 0.630 Q7 0.156 0.754 0.221 -0.035 -0.015 0.024 0.644 Q12 0.142 0.733 0.155 0.021 0.189 0.075 0.623 Q13 0.119 0.679 0.197 0.174 0.112 0.019 0.556 Q16 0.234 0.753 0.094 0.079 0.064 0.015 0.641 Q22 0.095 0.571 0.291 0.218 0.158 0.130 0.509 Q15 0.008 0.175 0.705 0.094 0.125 0.061 0.556 Q17 0.059 0.202 0.755 0.054 0.118 -0.043 0.633 Q19 0.051 0.249 0.694 -0.030 0.164 0.106 0.585 Q22 0.163 0.143 0.765 0.007 0.156 0.065 0.660 Q27 0.128 0.199 0.692 0.059 0.210 0.061 0.586 Q8 0.313 0.063 0.098 0.625 0.019 0.171 0.532 Q24 0.236 0.180 0.001 0.771 0.053 0.057 0.688 Q27 0.147 -0.010 0.032 0.766 0.118 0.203 0.664 Q28 0.245 0.149 0.049 0.762 0.036 0.054 0.670 Q18 0.032 0.072 0.298 0.068 0.830 -0.034 0.790 Q20 0.168 0.306 0.332 0.071 0.644 0.053 0.654 Q26 0.135 0.198 0.236 0.100 0.817 0.050 0.794 Q1 0.247 0.120 0.012 0.131 0.031 0.795 0.726 Q5 0.305 0.214 0.064 0.121 0.019 0.741 0.707 Q10 0.262 -0.037 0.148 0.222 0.003 0.718 0.657 Before Rotation Eigenvalue 8.448 3.552 1.767 1.497 1.301 1.066 Variance Explained (%) 30.172 12.686 6.310 5.346 4.648 3.808 After Rotation Eigenvalue 4.172 3.592 3.229 2.588 2.043 2.008 Variance Explained (%) 14.899 12.827 11.531 9.244 7.297 7.171 Cronbach α 0.876 0.856 0.833 0.804 0.816 0.771 KMO and Bartlett's Test KMO 0.917 Bartlett's Sphericity Test 0.000 3.1.2 Confirmatory factor analysis To further assess the correlation among the observed variables within each factor and the discriminant validity among the factors, this study conducted a confirmatory factor analysis (CFA) on the data. The results of this analysis are presented in Table 6. The findings show that the standardized loadings of the observed variables within each factor are all greater than 0.5, the average variance extracted (AVE) for each factor exceeds 0.5, and the composite reliability (CR) is greater than 0.6. These results indicate a strong correspondence between each factor and its associated observed variables, as well as a high degree of aggregation among the observed variables (Muilenburg and Berge 2005, Shevlin and Miles 1998, Ahmad, Zulkurnain, and Khairushalimi 2016, Fornell and Larcker 1981). Additionally, the results of the AVE square root tests demonstrate that the square root of the AVE for each factor is greater than the correlation between that factor and the other factors, confirming the presence of good discriminant validity among the six factors, as shown in Table 7. Collectively, these analyses confirm that the user intention evaluation scale developed in this study fully meets the necessary criteria. Table 6. Confirmatory factor analysis of the evaluation scale Factor Observed Variable Coef. Std. Error z p Std. Estimate AVE CR Factor 1 Q2 1.000 - - - 0.733 0.503 0.876 Q11 0.958 0.065 14.822 0.000 0.709 Q14 0.924 0.062 14.904 0.000 0.713 Q17 0.965 0.064 15.030 0.000 0.719 Q19 0.872 0.064 13.689 0.000 0.656 Q21 0.968 0.066 14.735 0.000 0.705 Q23 1.029 0.068 15.139 0.000 0.724 Factor 2 Q4 1.000 - - - 0.728 0.500 0.857 Q7 0.948 0.065 14.511 0.000 0.714 Q12 0.952 0.064 14.908 0.000 0.734 Q13 0.854 0.061 13.968 0.000 0.687 Q16 0.952 0.064 14.760 0.000 0.727 Q22 0.823 0.062 13.248 0.000 0.651 Factor 3 Q3 1.000 - - - 0.653 0.501 0.834 Q6 1.146 0.089 12.888 0.000 0.713 Q9 1.176 0.092 12.774 0.000 0.705 Q15 1.263 0.095 13.343 0.000 0.747 Q25 1.209 0.093 12.961 0.000 0.719 Factor 4 Q8 1.000 - - - 0.648 0.511 0.806 Q24 1.220 0.094 12.925 0.000 0.758 Q27 1.175 0.096 12.207 0.000 0.697 Q28 1.191 0.093 12.823 0.000 0.749 Factor 5 Q18 1.000 - - - 0.779 0.604 0.820 Q20 0.861 0.057 15.179 0.000 0.732 Q26 0.982 0.059 16.505 0.000 0.818 Factor 6 Q1 1.000 - - - 0.736 0.535 0.775 Q5 1.082 0.077 13.967 0.000 0.781 Q10 0.991 0.078 12.691 0.000 0.672 Table 7. Discriminant Validity Analysis of the Evaluation Scale Factor Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 1 0.709 Factor 2 0.397 0.707 Factor 3 0.275 0.517 0.708 Factor 4 0.530 0.297 0.188 0.715 Factor 5 0.283 0.443 0.563 0.237 0.777 Factor 6 0.544 0.290 0.222 0.431 0.162 0.731 Note: The diagonal lines with gray background patterns represent the square root values of AVE 3.1.3 Factor naming The aforementioned analysis confirms that the six factors identified through factor analysis fully meet the research criteria. Based on the content of the observed variables within each factor and in conjunction with extensive literature review, this study assigns specific names to each factor in order to further define the dimensions of each influencing factor. The final factor naming results are presented in Table 8. Factor 1 comprises seven observed variables: Q2, Q11, Q14, Q17, Q19, Q21, and Q23. This factor primarily represents the role of artificial intelligence-generated content (AIGC) technology in enhancing creative efficiency, artistry, and expressive diversity by offering inspiration, suggestions, structured feedback, and personalized support during the creative process (Wang, Hong, and He 2024). AIGC has the capacity to inspire artisans with "unexpected but reasonable" ideas by generating vast amounts of images or text, particularly in the early stages of conceptualization (Shimin et al.). By incorporating user input—such as style preferences, material constraints, and cultural elements—AIGC can learn individual preferences and tailor outputs, providing highly personalized design recommendations for handicrafts (Liu and Xu 2024). Scholars have suggested that AI functions as a conversational collaborator, emphasizing that AIGC, through iterative generation and text-based interaction, guides creators to expand their design horizons. The images or sketches produced by AIGC can serve not only as objects for imitation but also as sources of inspiration, encouraging artisans to innovate by modifying materials and process structures. This aligns with the concept of "Serendipity for Design," as proposed by Halvorsen (2016), which highlights the nonlinear output mechanism of AIGC that enables users to discover novel insights through "unexpected but interesting" results. Based on this research and the insights from the literature, this study has named Factor 1 "Serendipity for Design," referring specifically to the unexpected inspiration that artisans may encounter during the creative process when using AIGC. Factor 2 includes five observed variables: Q4, Q7, Q12, Q13, Q16, and Q22. When evaluating generative artificial intelligence tools, the artisan community primarily focuses on the effectiveness and uniqueness of the outputs (Garcia 2024). Additionally, the tool's ability to comprehend the artisan's instructions and the stability of its operation reflect users' perceptions of the system's capabilities, which in turn influence their willingness to adopt it (Cusumano 1992). The feasibility of the generated output—whether it can be practically implemented—is a key criterion in evaluating quality within the context of AIGC applications (Zhu et al. 2025). Moreover, users demonstrate sensitivity to the legal risks associated with copyright infringement of AI-generated content, which has been shown to significantly hinder the adoption of such technologies (Depoorter 2008). Artisanal epistemology refers to the deep attention and meticulous control artisans apply during the process of knowledge production, encompassing aspects such as precision, originality, verifiability, feasibility, and accountability (Lehmann 2012). This concept emphasizes that creators not only possess technical expertise but also uphold a "craftsman's sense of cognitive responsibility," prioritizing credible creation over mere content generation (Smith 2018, Brinkmann, Tanggaard, and education 2010). This concept aptly captures the content reflected in these five observed variables. Consequently, this study has named Factor 2 "Artisanal Epistemology." Factor 3 encompasses five observed variables: Q3, Q6, Q9, Q15, and Q22. Handicraft creators typically invest considerable emotional energy into their work, placing a significant emphasis on the unique expression of their individual style (Hoyte and Research 2019). Unlike ordinary users, their acceptance of AIGC (Artificial Intelligence Generated Content) technology is not primarily driven by functional attributes such as efficiency or ease of use. Instead, it hinges on the technology's ability to generate content that embodies humanistic depth and unique expression (Song et al. 2025, Bardzell, Rosner, and Bardzell 2012). When the outputs of AIGC demonstrate exceptional craftsmanship, with attention to detail and cultural significance, they are not perceived as a threat to traditional methods. Rather, they may be regarded as an auxiliary tool to inspire creativity and expand the boundaries of artistic creation (Shimin et al. , Zhu, Wang, and Ji). In the context of AIGC gradually making its way into the realm of craft creation, the willingness of traditional craftsmen to embrace the technology is contingent upon its ability to replicate the intrinsic values and aesthetic judgments inherent in their work (Pan et al. 2025). The concept of "software craftsmanship" captures the creator's perception when evaluating the output of digital tools—namely, whether it closely resembles the real craftsmanship experience in terms of precision, handcrafted texture, individual expression, cultural depth, and operational stability (McBreen 2002). This notion integrates multiple theoretical frameworks, including the realism of craftsmanship, human-machine collaborative creation, cultural adaptability, and perceptual authenticity. It highlights the creator's holistic evaluation of the personalization, cultural embedding, and controllability evident in the system's outputs (Thwaites 2018, Lebanon and El-Geish 2018). Thus, in this study, Factor 3 is designated as "Simulated Craft Authenticity" to capture the key acceptance mechanism influenced by the high emotional investment and expressive needs of the handicraft community in relation to AIGC technology. Factor 4 encompasses four observed variables: Q8, Q24, Q27, and Q28. In handicraft design, optimizing the creative process is a critical factor in enhancing both design efficiency and production capacity. Streamlining the design process allows the handicraft community to focus more on creativity and artistic expression by reducing the complexity of design steps and increasing the level of design automation. This, in turn, improves work efficiency and reduces cognitive load (Burden 2022). Some scholars argue that with the aid of AIGC (Artificial Intelligence Generated Content), artisans can achieve mass production while maintaining the uniqueness of handcrafted products. This approach not only addresses market demands for large-scale production but also reduces production costs, thereby increasing the competitiveness of handicraft products in the marketplace (Shen, Lin, and Lin 2025). Additionally, AIGC's technical support can alleviate the challenges associated with creation by assisting artisans in completing highly technical and repetitive tasks, thereby enhancing their ability to focus on more complex aspects of design (Tao et al. 2025). These factors provide a solid theoretical foundation for artisans to leverage AIGC in their manual operations. The concept of "Productivity Enhancement" particularly highlights how technology can foster economic and productivity growth by improving work efficiency, simplifying processes, and automating operations (Nallusamy and Adil Ahamed 2017, Sayer and Campbell 2002). In the context of handicraft design, AIGC’s role in simplifying and automating the creative process not only boosts design efficiency but also liberates artisans from technical and repetitive tasks, allowing them to dedicate more time to creativity and artistic expression. This serves as a clear manifestation of productivity improvement. Therefore, this study designates Factor 4 as "Productivity Enhancement." Factor 5 encompasses three observed variables: Q18, Q20, and Q26. In handicraft creation, the accessibility of tools and the operational threshold are crucial factors influencing artisans' willingness to adopt them. A gentler learning curve can significantly reduce cognitive load, enabling artisans to focus on creative expression and artistic realization without having to invest excessive energy in adapting to emerging technologies (George, Baskar, and Srikaanth 2024). Additionally, if the training process for AIGC tools involves low resource costs, it can expand their accessibility to small-scale workshops and individual creators. This lowers economic and technical barriers, thereby enhancing the universality and inclusivity of these tools (Anderson and Sciences 2006, Hardy III et al. 2017). More importantly, when the generative capacity of AIGC is no longer constrained by existing training data and can be flexibly derived based on user input, it provides artisans with greater creative freedom. This flexibility allows them to preserve their unique style and cultural expression while benefiting from technological intervention (Lou and Innovation 2023, LIU, Bezuhla, and Design 2024). Together, these factors form the foundation for AIGC tools to achieve a low threshold, high flexibility, and cultural adaptability in craft creation (Bao et al. 2025, Song and Bai 2025). The concept of "Epistemic Openness" emphasizes the cognitive accessibility of the technical system—that is, whether users can engage with the system based on their own knowledge structure, expression habits, and cultural experiences, enabling creative transfer and reconfiguration within the system (Martin 2001, John 2018). In handicraft creation, the controllability of learning costs, the adjustability of generation logic, and the flexibility of resource input are essential for artisans to internalize AIGC as a valuable creative support tool (Wu et al. 2025, Wang et al. 2024). Therefore, in this study, Factor 5 is named "Epistemic Openness." Factor 6 encompasses three observed variables: Q1, Q5, and Q10. These variables collectively reflect the ease of use of AIGC technology during its application by artisans. The user-friendliness of technology is a critical determinant of its acceptance. The extent to which the design of AIGC technology meets the needs of artisans and is intuitive and accessible significantly influences their willingness to adopt it (Fan and Jiang 2024). Moreover, the simplicity of operation—an essential factor for technology acceptance—can lower barriers to usage, making it easier for users to learn and apply new technologies (Menon and Shilpa 2023). At the same time, the convenience of the technology is crucial for enhancing work efficiency and facilitating the creative process, which is particularly important for artisans during their design work (Kofler and Walder 2024). Therefore, these factors effectively capture the ease of use of AIGC technology, highlighting that its simplicity, user-friendliness, and convenience are pivotal in influencing artisans' willingness to incorporate AIGC into their design processes. This aligns with the concept of "Technical Usability" proposed by Lu et al. (2022) . Consequently, this set of factors is termed "Technical Usability." Table 8. Factor naming result Factor Name Observed Variable Detailed Description Definition Factor 1 Serendipity for Design Q2 Stimulate inspiration and foster creativity AIGC technology encompasses a range of functions that enhance creative efficiency, artistic expression, and diversity. It achieves this by offering inspiration, suggestions, structured feedback, and personalized support to individuals engaged in creative work. Q11 Customizable according to user requirements Q14 Provide creative references and inspiration Q17 Offer creative ideas and suggestions Q19 Assist artisans throughout the production process Q21 Generate novel ideas and creativity Q23 Enhance the artistic quality of the output Factor 2 Artisanal Epistemology Q4 The produced works demonstrate a high degree of precision The effectiveness and originality of the output generated with the assistance of artificial intelligence tools. Q7 The produced works exhibit clear differentiation from existing products Q12 AIGC tools effectively interpret user instructions Q13 The produced works demonstrate a high degree of feasibility Q16 The produced works avoid copyright and ownership disputes Q22 Stable software performance Factor 3 Simulated Craft Authenticity Q3 The output exhibits meticulous craftsmanship When the output generated by AIGC reflects a high level of craftsmanship and cultural significance. Q6 The produced works exhibit a handcrafted texture Q9 The produced works reflect personal style and originality Q15 The produced works embody humanistic values Q25 The design process mirrors traditional handicraft techniques Factor 4 Productivity Enhancement Q8 Guided production process The simplification and automation of the creative process facilitated by AIGC technology. Q24 Streamline the production process Q27 Facilitate the mass production of handicraft products Q28 Reduce the complexity of creation Factor 5 Epistemic Openness Q18 Low training costs for AIGC tools The low entry barrier, high flexibility, and cultural adaptability of AIGC tools in the context of craft creation. Q20 The produced works are not constrained by existing training data Q26 Low training costs for AIGC tools Factor 6 Technical Usability Q1 Enhance design efficiency The design of AIGC technology aligns with the needs of artisans and is both intuitive and user-friendly. Q5 User-friendly interface Q10 Ease of use 3.2 Analysis results of the behavioral model 3.2.1 Hypothesis put forward This study aims to apply linear regression analysis to explore the relationship between various influencing factors and artisans' willingness to use AIGC. The objective is to develop a behavioral model that explains the willingness of artisans to adopt AIGC technology. Prior to conducting the formal data analysis, this research proposes the following hypotheses based on the influencing factors identified in Section 3.1, as illustrated in Figure 3: H1: Exercise Safety Assurance positively affects artisans' behavioral intention to use AIGC-assisted design. H2: Physical Activity Tracking positively affects artisans' behavioral intention to use AIGC-assisted design. H3: Emotional Social Support positively affects artisans' behavioral intention to use AIGC-assisted design. H4: Dialogue Support positively affects artisans' behavioral intention to use AIGC-assisted design. H5: Epistemic Openness positively affect artisans' behavioral intention to use AIGC-assisted design. H6: Technical Usability positively affect artisans' behavioral intention to use AIGC-assisted design. For the purpose of analysis, in addition to the six influencing factors previously identified, this study introduces an established "willingness to use" dimension to capture the artisan group's evaluation of their intention to adopt AIGC. To ensure that the inclusion of the "willingness to use" dimension does not interfere with the established dimensions of the evaluation scale, this study will integrate the six identified factors with the "willingness to use" dimension prior to conducting the formal linear regression analysis. Furthermore, exploratory factor analysis and confirmatory factor analysis will be performed to ensure strong discriminant validity among the six factors and to verify that the observed variables within each factor exhibit adequate convergent validity. 3.2.2 Exploratory factor analysis of behavioral models This study employed exploratory factor analysis to assess whether the inclusion of the "willingness to use" dimension would influence the results of the evaluation scale developed in this research. The results, presented in Table 9, indicate that the seven factors are effectively distinguishable. The observed variables within each factor are not influenced by other factors, and the integrity of the constructed evaluation scale remains intact. Additionally, the reliability of each factor exceeds 0.7, demonstrating strong internal consistency and reliability. Table 9 Exploratory Factor Analysis of the Behavioral Model Observed Variable Factor Loading Coefficients Communality Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 ITU Q2 0.767 0.138 -0.031 0.092 0.059 0.142 0.113 0.652 Q11 0.731 0.164 0.067 0.106 0.045 0.159 0.018 0.604 Q14 0.653 0.077 0.150 0.201 0.071 0.178 0.175 0.563 Q17 0.738 0.136 0.038 0.189 0.064 0.020 0.114 0.618 Q19 0.617 0.086 0.092 0.222 0.028 0.095 0.233 0.509 Q21 0.689 0.207 0.058 0.061 0.147 0.165 0.123 0.588 Q23 0.681 0.037 0.196 0.226 -0.008 0.189 0.111 0.603 Q4 0.047 0.728 0.211 0.040 0.099 0.092 0.206 0.638 Q7 0.154 0.752 0.226 -0.038 -0.018 0.018 0.046 0.645 Q12 0.139 0.734 0.155 0.022 0.190 0.078 0.047 0.627 Q13 0.120 0.686 0.190 0.188 0.117 0.040 -0.007 0.572 Q16 0.231 0.756 0.091 0.085 0.067 0.024 0.035 0.647 Q22 0.086 0.563 0.301 0.201 0.148 0.107 0.135 0.507 Q3 0.008 0.172 0.708 0.094 0.120 0.064 0.009 0.558 Q6 0.060 0.201 0.755 0.057 0.117 -0.036 -0.012 0.632 Q9 0.039 0.238 0.698 -0.049 0.161 0.088 0.111 0.594 Q15 0.162 0.138 0.770 0.002 0.150 0.050 0.049 0.665 Q25 0.132 0.204 0.687 0.072 0.212 0.073 -0.022 0.586 Q8 0.309 0.069 0.090 0.631 0.021 0.189 0.079 0.548 Q24 0.217 0.166 0.011 0.741 0.048 0.022 0.236 0.682 Q27 0.144 -0.006 0.028 0.769 0.121 0.208 0.078 0.677 Q28 0.233 0.141 0.053 0.745 0.034 0.036 0.175 0.665 Q18 0.027 0.071 0.300 0.063 0.831 -0.032 0.032 0.793 Q20 0.164 0.304 0.335 0.066 0.642 0.048 0.061 0.654 Q26 0.134 0.200 0.241 0.098 0.812 0.046 0.042 0.789 Q1 0.231 0.112 0.018 0.106 0.027 0.769 0.213 0.715 Q5 0.289 0.204 0.072 0.095 0.012 0.711 0.224 0.695 Q10 0.264 -0.025 0.137 0.240 0.010 0.744 0.007 0.701 ITU1 0.335 0.144 0.037 0.235 0.001 0.223 0.566 0.559 ITU2 0.211 0.077 0.025 0.183 0.095 0.191 0.786 0.747 ITU3 0.357 0.188 0.071 0.276 0.025 0.088 0.664 0.693 Before Rotation Eigenvalue 9.318 3.790 1.772 1.526 1.318 1.091 0.916 Variance Explained (%) 30.057 12.225 5.716 4.924 4.251 3.518 2.956 After Rotation Eigenvalue 4.336 3.609 3.260 2.660 2.037 2.030 1.799 Variance Explained (%) 13.988 11.642 10.515 8.579 6.571 6.547 5.805 Cronbach α 0.876 0.856 0.833 0.804 0.816 0.771 0.752 KMO and Bartlett's Test KMO 0.926 Bartlett's Sphericity Test 0.000 3.2.3 Confirmatory factor analysis of behavioral models This study employed confirmatory factor analysis to examine the degree of aggregation among the observed variables within each factor, following the inclusion of the "willingness to use" dimension, as well as the discriminant validity among the factors. The analysis results, presented in Tables 10 and 11, show that the standardized factor loadings for all observed variables exceed 0.5. Additionally, the average variance extracted values for each factor are greater than 0.36, the composite reliability values exceed 0.6, and the square roots of the AVE for each factor are greater than the correlation coefficients between that factor and the other factors. These findings indicate that each factor exhibits strong discriminant validity, and there is a high degree of aggregation among the observed variables within each factor. Therefore, this study is now ready to proceed with linear regression analysis. Table 10. Confirmatory Factor Analysis of the Behavioral Model Factor Observed Variable Coef. Std. Error z p Std. Estimate AVE CR Factor 1 Q2 1.000 - - - 0.732 0.503 0.876 Q6 0.953 0.065 14.757 0.000 0.705 Q7 0.928 0.062 14.988 0.000 0.716 Q8 0.965 0.064 15.051 0.000 0.719 Q9 0.879 0.064 13.825 0.000 0.661 Q10 0.968 0.066 14.763 0.000 0.705 Q11 1.028 0.068 15.147 0.000 0.723 Factor 2 Q16 1.000 - - - 0.730 0.500 0.857 Q18 0.943 0.065 14.546 0.000 0.713 Q20 0.948 0.063 14.964 0.000 0.733 Q21 0.850 0.061 14.000 0.000 0.685 Q23 0.947 0.064 14.806 0.000 0.726 Q26 0.821 0.062 13.322 0.000 0.652 Factor 3 Q15 1.000 - - - 0.653 0.501 0.834 Q17 1.146 0.089 12.905 0.000 0.714 Q19 1.173 0.092 12.766 0.000 0.704 Q22 1.262 0.095 13.348 0.000 0.746 Q27 1.208 0.093 12.971 0.000 0.719 Factor 4 Q4 1.000 - - - 0.648 0.510 0.806 Q12 1.228 0.094 13.103 0.000 0.763 Q13 1.168 0.095 12.247 0.000 0.693 Q14 1.192 0.092 12.946 0.000 0.749 Factor 5 Q24 1.000 - - - 0.779 0.604 0.820 Q25 0.861 0.057 15.179 0.000 0.732 Q28 0.982 0.059 16.504 0.000 0.818 Factor 6 Q1 1.000 - - - 0.740 0.535 0.774 Q3 1.083 0.076 14.305 0.000 0.786 Q5 0.973 0.077 12.716 0.000 0.664 ITU ITU1 1.000 - - - 0.675 0.504 0.753 ITU2 1.073 0.086 12.549 0.000 0.692 ITU3 1.162 0.086 13.450 0.000 0.761 Table 11 Discriminant Validity Analysis of the Behavioral Model Factor Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 ITU Factor 1 0.709 Factor 2 0.397 0.707 Factor 3 0.275 0.517 0.708 Factor 4 0.530 0.297 0.188 0.714 Factor 5 0.283 0.443 0.563 0.237 0.777 Factor 6 0.544 0.290 0.222 0.431 0.162 0.731 ITU 0.597 0.367 0.195 0.556 0.218 0.512 0.710 3.2.4 Linear regression analysis Building on the theoretical assumptions presented in Section 3.2.1, this study further examined the relationships among the factors through linear regression analysis. The results of this analysis are shown in Table 12. Factors 1 to 6 were treated as independent variables, with "willingness to use" as the dependent variable. The analysis revealed that, except for H3 and H5, the p-values for the other hypothesized relationships were all below 0.05. This indicates that H1, H2, H4, and H6 are supported, while H3 and H5 are not. Based on these findings, as well as the earlier discussions on factor naming, this study developed a behavioral intention model for process engineers using AIGC to assist in design, as depicted in Figure 4. Table 12 Linear Regression Analysis Hypothesis X Y Unstandardized Coefficients Standardized Coefficients t p Collinearity Diagnosis Result B Std. Error Beta VIF Tolerance H1 Factor 1 ITU 0.337 0.050 0.301 6.699 0.000** 1.800 0.556 Supported H2 Factor 2 0.127 0.040 0.133 3.167 0.002** 1.572 0.636 Supported H3 Factor 3 -0.048 0.037 -0.057 -1.318 0.188 1.698 0.589 Rejected H4 Factor 4 0.278 0.040 0.280 6.884 0.000** 1.474 0.678 Supported H5 Factor 5 0.005 0.028 0.008 0.183 0.855 1.569 0.638 Rejected H6 Factor 6 0.190 0.039 0.201 4.890 0.000** 1.501 0.666 Supported 4 Discussion This study systematically identifies and analyzes the key factors influencing artisans' adoption of AIGC technology for auxiliary design, addressing the current gap in the literature regarding the technology adoption mechanisms of traditional craft communities. The findings indicate that performance expectations, social influence, and ease of use of the tools have a significant positive impact on artisans' behavioral intention to use AIGC technology. Conversely, technology anxiety, perceived risk, and cultural identity conflicts serve as major barriers to technology acceptance. These results not only reflect the challenges faced by the artisan community in the digital era but also offer strategic insights for the cross-generational integration and cultural preservation of artisans and the art industry. Firstly, traditional technology adoption models generally focus on efficiency improvements and ease of use as the primary drivers for user acceptance of new tools (Taherdoost 2018, Dillon and Morris 1996). However, this framework proves limited when applied to creative practitioners such as artisans. For this group, tool performance is not solely determined by task completion speed or output quantity; rather, it is more importantly measured by how effectively tools support the realization of artistic concepts and cultural expressions (De Munck 2019, Betjemann 2008). Handicraft creation is inherently more than a process of material manipulation; it is also a means of conveying emotions and embodying value systems (Pöllänen 2013). Consequently, an artisan's acceptance of a tool often hinges on whether it can accurately express their personal style, cultural symbols, and craftsmanship traditions (Ferreira et al. 2019). In this context, the value of AIGC technology does not lie in its degree of automation or its potential to replace human labor, but rather in its ability to expand the creative space and stimulate design ideas (Van der Zande et al. 2019, Pan et al. 2025, Gao et al. 2025). This preference reflects the inclination of artisans to view AIGC not as a replacement or leader, but as an assistant in the creative process (Lou and Innovation 2023). This perspective is consistent with recent academic trends emphasizing collaborative intelligent tools (Liu, Huang, and Review 2025). A common misconception in promoting AIGC tools to artisans is to position them as more efficient alternatives (Wu et al. 2023). This narrative often triggers resistance, as it overlooks the psychological characteristics of creators, who are particularly sensitive to creative control and autonomy in expression (Lin, Yang, and Sun 2024). In contrast, a more persuasive approach would involve framing AIGC as a creative extender or collaborative partner, emphasizing its supportive and flexible role in the creative process (Zi-yang and Studies 2024). By promoting a co-creativity communication framework, rather than one centered on human-machine competition, this approach not only mitigates artisans' resistance to technological intervention but also enhances their sense of identity and willingness to adopt the technology (Wang et al.). Thus, the promotion of AIGC should shift from a singular focus on technical effectiveness to a more nuanced, multidimensional perspective that emphasizes creative support. This shift will facilitate the natural integration of technology into the creative process and provide important theoretical and practical pathways for building a human-centered model of human-machine collaboration. Secondly, despite the significant advancements of AIGC technology in areas such as image generation and language processing, its adoption in the field of handicrafts, where skill inheritance is central, still encounters substantial cultural resistance. Unlike the technical discomfort that general users experience due to unfamiliarity with the operation, artisans' resistance to AIGC is more deeply rooted in a conflict between identity recognition and value systems (Yuan et al. 2025, Lin, Yang, and Sun 2024). Traditional craftsmanship has long been viewed as a concentrated expression of individual cultivation, emotional investment, and the accumulation of time (Liu 2025). In this context, craftsmanship is not merely a functional skill but a mode of existence and an aesthetic stance embodied through "making by hand" (Nimkulrat 2012). The algorithmic generation logic underlying AIGC, to some extent, diminishes the expressive space of "ingenious craftsmanship" and the "visible touch" of handwork, which is often seen as a negation of the creative value inherent in traditional handcraft. This resistance is not an instinctive rejection of technology itself but a response to the potential cultural changes that such technology might provoke (Zhu et al. 2025, Avlonitou and Papadaki 2025). Moreover, the rise of technological anxiety is closely tied to the blurring of boundaries around the identity of creators. In traditional handicraft models, the creator is both the laborer and the bearer of the work's conceptual meaning (Roy and Sarkar 2025). AIGC's intervention disrupts the classic framework of "creation = labor + thought," rendering the question of authorship and the work's irreplaceability more ambiguous. When certain creative tasks are automated by algorithms, questions arise about whether the work retains its authorial essence, both psychologically for creators and within the market framework (do Nascimento and Venturelli 2025, Sarkar 2023). Thus, the promotion strategy for AIGC in the handicraft sector should not merely highlight its technical advantages, such as efficiency, convenience, or cost reduction. Instead, it must clearly define AIGC's supportive role and outline the boundaries of the technology's influence, emphasizing the priority of human agency. In other words, the narrative surrounding AIGC should avoid positioning it as a substitute for human creators and instead frame it as a creative support system—expanding expressive possibilities and enhancing design without undermining the creator's authority. This approach, grounded in the non-substitutive nature of culture, not only helps reduce resistance to technology among artisans but also creates a new point of convergence for integrating traditional value systems with digital technology (Li 2022, Gong, Jiang, and Liang 2022). In this regard, the true challenge for AIGC lies not in improving its functional performance but in its ability to be embedded within the existing cultural context, where it can be viewed as a tool of assistance rather than a threat. Thirdly, in addition to emotional anxiety, artisans often express structural concerns when engaging with AIGC tools, specifically regarding the perceived risks associated with these technologies. These risks arise from the creator's cognitive uncertainty regarding the system's operational logic and the boundaries of control (Ostrom 1980, Neal 2017). The "black box" nature of generative technology has disrupted the traditional "operation - feedback - correction" cycle, making it difficult for creators to avoid decision-making blind spots and ambiguous expression when confronted with generated outputs (Lehtimäki 2024). When issues such as copyright ownership or the definition of creative participation become unclear, it may undermine artisans' legitimate status and emotional attachment within the cultural market (Geiger 2015, Rahmatian 2011). To mitigate the perceived risks of AIGC in the handicraft community, it is necessary to go beyond improving technical performance or user training. A more fundamental solution involves establishing mechanisms for controllability and cognitive transparency at the system design level (Ge, Wang, and Wang, Liu et al. 2023, Patama 2025). Specifically, AIGC can enhance the understandability of the generation process and restore visibility into the decision chain by establishing a logical mapping between prompt inputs and generated outputs. Allowing users to independently adjust the integration ratio of manual style and algorithmic style would enable a dynamic balance between traditional craftsmanship and new technologies. Additionally, clearly defining the structure and responsibility distribution in human-machine collaboration is essential to ensuring the creator's dominant position in authorship, participation, and market discourse. Such mechanisms not only bolster users' confidence and psychological security in operating the tools but also provide critical support for safeguarding the expression rights and subjectivity of traditional craftsmanship and culture within the digital context. In essence, controllability is not only a matter of technology usability but also a prerequisite for cultural acceptance and value recognition. Fourth, social influencing factors have shown a significant positive effect on artisans' behavioral intention to adopt AIGC tools, indicating that technology adoption within this group is not solely based on individual decisions but also reflects a collective response mechanism. However, unlike institutional pressures within organizations or the internalization of norms in educational settings, the social influence within the handicraft artisan community manifests primarily as a horizontal recognition grounded in trust based on practical experience (Stinchfield et al. 2013, Gamble 2004). The adoption behavior of this group is largely shaped by the exemplary experiences of others (Li, Li, and Kou 2022). This feedback-driven influence mechanism forms a chain of trust: when a respected artisan leader successfully introduces a new tool and achieves positive outcomes, other artisans are more likely to regard the tool as a viable and acceptable creative medium (Epstein 1998, Veckie and Veckie 2021). Thus, promoting AIGC within the handicraft community requires moving beyond traditional, broad communication strategies aimed at the general public. Instead, a more targeted, community-embedded communication approach is necessary. Specifically, emphasis should be placed on identifying and engaging key members within local traditional craft communities, craft associations, and master-apprentice systems—particularly those with significant influence and credibility within the local craft ecosystem. These bridging figures can not only provide practical endorsement for the rationality of new technologies but also help reduce emotional resistance among grassroots artisans who may feel apprehensive about the unfamiliarity and perceived lack of control over these technologies. Furthermore, the social impact on technology adoption extends beyond mere verbal recommendations or cognitive persuasion. It must be translated into action intentions through tangible, perceptible experience scenarios (Ding 2024). To this end, it is recommended that future promotional efforts include "AI + traditional craftsmanship" integration displays or "human-machine co-creation workshops." These activities, through highly interactive and immersive methods, would allow the technology to take on a concrete, visible, and operable form. Such events not only help mitigate the psychological distance created by the abstraction of technology but also facilitate a shift in identity from "viewers" to "testers" and, ultimately, to "adopters" (Radermecker, Loots, and Management 2025, Martins et al. 2020, Zabulis et al. 2023). Fifth, while AIGC technology has experienced significant expansion in image and text creation in recent years, the handicraft community, particularly older practitioners, still faces a considerable technical threshold when adapting to new technologies (Chacur et al. 2024). This threshold not only presents operational challenges but also directly influences their motivation to use the tools and their trust in the technology (Che and Hashim, Shafi et al. 2024). Such frustrating experiences often lead to negative judgments about the entire system in a short period, resulting in a lasting rejection of the technology (Barley and Orr 1997). In the early stages of adopting AIGC, this phenomenon can significantly reduce users' acceptance and the likelihood of continued usage. Particularly in creative activities, the opacity and lack of control in the tools are more likely to be perceived as a loss of creative power, rather than simply a learning barrier (Bilton 2015). Therefore, when designing AIGC systems for the handicraft community, it is essential to lower the technical threshold and adopt an interaction concept that is de-engineered. Specifically, scenario-based templates should be provided, allowing artisans to generate outputs based on specific styles, functions, or uses, thus reducing the need for abstract prompt messages. Additionally, voice input and natural language guidance can be integrated through voice recognition and intelligent prompt systems, enabling users to interact with the system in a manner similar to daily communication, which lowers the threshold for text-based input. The system should automatically analyze the style of the user's existing works and apply it to the output, achieving personalized matching and enhancing the user's sense of ownership and control. Furthermore, real-time feedback at the image level should be provided for each operation, with the option to rollback, adjust, or recreate results, ensuring that interruptions in the creative process do not lead to irreversible damage. These design features not only help increase artisans' confidence in using the tools and support their creative rhythm but, more importantly, help restore their belief in "creative dominance" at a psychological level. This approach is not only an engineering requirement for enhancing tool usability but also a response to the core expectations within handicraft culture regarding the integrity of skill control and expression. In conclusion, the potential of AIGC technology lies not in replacing craftsmanship, but in extending and enhancing it. In designing pathways for integration, it is essential to fully respect the creative logic and cultural practices of artisans, and to develop a guiding strategy that focuses on technological empowerment while prioritizing psychological alignment. Artisans are not adversaries of technology; rather, they are defenders and reconstructors of culture in its evolution. The key factor influencing a craftsman's adoption of AIGC is not simply the tool itself, but whether it can be effectively integrated into the logic of craftsmanship, cultural values, and the artisan's sense of self-identity. Looking ahead, the design and promotion of AIGC for the handicraft community must shift from neutralization to cultural adaptation, from user-centeredness to collaborative creation, and from technological efficiency to emotional resonance. The goal should be to build bridges between technology and people, between efficiency and emotion, and between tradition and the future. This is not merely about the use of a tool, but about the continuation and innovation of a way of life and a knowledge system. 5 Conclusion This study provides a comprehensive examination of the behavioral intentions of artisans in using AIGC-assisted design tools, along with the key factors that influence their adoption. A systematic hybrid research approach was employed, integrating qualitative analysis with quantitative data, to explore the acceptance and usage intentions of artisans towards emerging technologies. The goal was to address the existing gap in academic research regarding the technological adaptation mechanisms of artisan communities and to offer theoretical insights into how traditional handicraft groups respond to digital technological changes. The findings reveal several critical factors that influence artisans' use of AIGC tools, including tool effectiveness, the cultural and artistic value of the works, ease of use, and social impact. Specifically, performance expectations and tool ease of use were identified as significant positive drivers of behavioral intentions, while technical anxiety, perceived risks, and cultural identity conflicts emerged as major barriers to technology acceptance. Further analysis shows that, when adopting new technologies, traditional craft groups not only focus on whether the tools can enhance work efficiency but also place great emphasis on their ability to support personalized creation and cultural expression. The construction of behavioral models for AIGC-assisted design indicates that artisans are more likely to view AIGC as a collaborative tool in the creative process rather than a replacement for traditional craftsmanship. The primary concerns of artisans center on how AIGC tools can expand creative possibilities, inspire innovation, and enhance the artistic quality of their designs, rather than solely improving production efficiency. Therefore, the promotion of AIGC tools should emphasize their advantages in creative collaboration and innovation, rather than positioning them solely as efficiency-enhancing instruments. Additionally, the study highlights the cultural resistance artisans face when accepting AIGC tools, particularly regarding identity recognition and the preservation of traditional craftsmanship values. The "black box" nature of the technology, coupled with concerns over copyright ownership and creative control, has led to caution within the artisan community. To mitigate this resistance, the research underscores the importance of ensuring technological controllability and transparency, as well as clearly defining the boundaries between technology and the roles of creators. These factors are essential for reducing resistance and fostering greater acceptance of AIGC tools. In conclusion, this study offers valuable practical recommendations for promoting AIGC tools in the handicraft design sector, emphasizing the need for cultural adaptability and a personalized user experience in the promotion process. Future research could further explore how to facilitate the deeper integration and widespread adoption of technology within traditional craftsmanship through targeted community activities and demonstration projects. Although this study is pioneering in developing assessment tools and behavioral models to analyze the intention of artisan groups to adopt AIGC technology and its direct influencing factors, two key limitations in the research design must be acknowledged. First, the cultural specificity of the sample may limit the generalizability of the findings. This study focuses on Chinese handicraft practitioners, whose cognitive frameworks are inevitably shaped by the norms of traditional Chinese craft culture. While some findings may reflect cross-cultural commonalities, the acceptance of AIGC technology among artisan groups in different countries is likely to exhibit structural differences. Future research should adopt a multi-country comparative paradigm. By systematically comparing technology adoption behaviors across various countries, this approach can not only identify universal mechanisms with cultural resilience but also uncover context-specific differences, thereby enhancing the cultural adaptability and generalizability of the theoretical model. Second, the validity of the measurement tools warrants attention. The current user experience evaluation system is primarily based on self-reported data. Although the methodology has been strengthened through literature analysis and in-depth interviews, the inherent cognitive biases in subjective statements may still affect the accuracy of the results. Future research could incorporate multimodal data collection strategies, such as integrating objective behavior indicators like usage logs and behavior tracking techniques. This would create a cross-validation mechanism for both subjective and objective data, helping to overcome the methodological limitations of relying on a single data source and significantly improving the validity and reliability of the assessment scale. Building on these considerations, future research should expand in three key areas: first, extending the geographical scope of cross-border comparative studies while incorporating cultural distance as a moderating variable; second, enriching the measurement dimensions by exploring mediating factors such as technology application scenarios and the influence of social norms; and third, adopting a longitudinal design to track the evolving technology adoption behaviors of artisan groups over time. These improvements will contribute to a more comprehensive theoretical framework and provide practical guidance for the innovative application of AIGC technology in the global handicraft sector. Abbreviations Artificial Intelligence-Generated Content Declarations Author Contributions: For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “Conceptualization, W.P. and X.W.; methodology, W.P.; software, W.P.; validation, X.W. and LQ.; writing—original draft preparation, W.P.; writing—review and editing, W.P.; visualization, W.P.; supervision, W.P.; Conflicts of Interest: The authors declare no conflicts of interest. Funding Declaration : This research did not receive any financial assistance from institutions. It was entirely funded voluntarily by the authors to collect user intention information. 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Zi-yang, HU %J Journal of Literature, and Art Studies. 2024. "AIGC related context: A new communication culture for human." 14 (10):921-931. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 03 Oct, 2025 Reviews received at journal 02 Oct, 2025 Reviews received at journal 25 Sep, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers invited by journal 22 Sep, 2025 Editor assigned by journal 18 Sep, 2025 Editor invited by journal 04 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 03 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":86238,"visible":true,"origin":"","legend":"\u003cp\u003eresearch procedure\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7362510/v1/94de398c469c809a86f6c81a.png"},{"id":92708182,"identity":"511452b5-c02e-4897-81ce-5baa568d3027","added_by":"auto","created_at":"2025-10-03 10:22:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":331633,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation scale construction process\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7362510/v1/414d293fbd03a1b62b58401d.png"},{"id":92708179,"identity":"c88bdc07-15ce-4ba2-8119-288fb1d2d4a4","added_by":"auto","created_at":"2025-10-03 10:22:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105458,"visible":true,"origin":"","legend":"\u003cp\u003eHypothetical Model of Users’ Intention to Use\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7362510/v1/e2457ca4d9902f5fca0261a6.png"},{"id":92708906,"identity":"9eb66a68-4d84-4e6d-adea-91c359532f47","added_by":"auto","created_at":"2025-10-03 10:30:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":127166,"visible":true,"origin":"","legend":"\u003cp\u003eArtisans use AIGC to assist in designing behavioral intention models\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7362510/v1/b66749ada7ad5c1828bc647a.png"},{"id":92709902,"identity":"f3629099-ac83-4122-becb-4e0ad04144ff","added_by":"auto","created_at":"2025-10-03 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These practitioners are committed to uniting practical techniques with artistic expression, striving for a seamless synthesis of functionality and aesthetic value (Bergada\u0026agrave; 2008). Often regarded as custodians of tradition, culture, and principles of sustainable development, craftsmen embody a professional ethos that transcends mere technical skill (Sennett 2008). This ethos is most evident in their deep understanding and skillful manipulation of materials, tools, and production processes (Manfredi Latilla et al. 2019). The relationship between the craftsman's professional spirit and their technical knowledge forms a dialectical dynamic that warrants close scholarly attention. This interplay not only shapes individual craftsmanship but also plays a pivotal role in the evolution of the artisan community as a whole (Kunneman 2013). In the context of rapid technological advancement, however, artisans often face challenges in actively adapting their practices. The transformation of traditional craft models is frequently reactive rather than proactive, and many handicraft industries have declined due to limited adaptability to emerging technologies (Schiffer and Skibo 1987, Gourinchas et al. 2020, \u0026Uuml;berbacher, Brozzi, and Matt 2020). Although some scholars argue that the integration of new technologies does not necessarily disrupt the internal logic of traditional craftsmanship\u0026mdash;and may, in fact, foster its long-term sustainability (Wenji, Rongrong, and Li 2022, Zhang et al. 2023, Rao and Gopi 2016)\u0026mdash;the conditions under which such integration is embraced remain underexplored. Consequently, understanding artisans\u0026rsquo; willingness to adopt and engage with new technologies in times of technological transformation has become an increasingly important area of inquiry.\u003c/p\u003e\u003cp\u003eAIGC is an artificial intelligence\u0026ndash;based technology that autonomously produces content by leveraging generative models trained on large-scale datasets (Wu et al. 2023). With its powerful computational and creative capabilities, AIGC has the potential to support and enhance various aspects of artisanal practice, including manual production techniques (Shimin et al.), stylistic modeling references (ldzwan bin Ismail and Huang), and models of craftsmanship development (Karimova et al. 2024). However, the distinct cultural and emotional dimensions inherent in artisanal work have led many craftsmen to question whether these qualities can be authentically replicated by artificial intelligence systems such as AIGC (Zhu et al. 2024, Torres et al. 2025). Some artisans perceive AIGC not as a tool for innovation, but as a threat to the authenticity and intrinsic value of handcrafted objects. From this perspective, the integration of automated technologies may undermine the uniqueness and cultural significance of traditional craftsmanship (Chauhan 2020, Song and Education 2022). As a result, tensions have emerged between proponents of AIGC and members of the artisan community. Nonetheless, the adoption of new production technologies has been a recurring feature of historical development (David 2000). It is therefore likely that, over time, AIGC will become more widely accepted within the artisan sector (Lou and Innovation 2023, Burden 2022). Despite this inevitability, empirical research examining artisans\u0026rsquo; attitudes toward and acceptance of AIGC remains extremely limited. This lack of evidence-based understanding hinders meaningful engagement with the underlying tensions between emerging technologies and traditional cultural practices, and constrains the development of effective integration strategies.\u003c/p\u003e\u003cp\u003eArtisans\u0026rsquo; perceptions of, and willingness to engage with, AI-Generated Content (AIGC) represent a complex and interrelated set of attitudes. While existing literature has touched upon related issues, focused investigations specifically addressing artisans as a distinct group remain scarce. Prior studies have primarily examined AIGC\u0026rsquo;s role in assisting particular stages of technical workflows, such as those performed by process engineers (Wang, Dong, and Technology 2023, Liu and Xu 2024, Wen et al. 2024), or have subsumed artisans within broader categories such as designers (WU and WANG 2024, Pan et al. 2024). Although there are overlaps in the creative practices of artisans and designers, the methodologies, value systems, and cognitive approaches that define artisanal work differ significantly from those found in modern design professions (Junaidy and Nagai 2013, Lozano 1990). Moreover, current evaluation instruments\u0026mdash;often derived from generalized technology acceptance models\u0026mdash;lack empirical validation in the context of artisanal practice. As such, their applicability is limited, and their findings may be influenced by subjective assumptions. To address these gaps, the present study draws on established methodologies employed by Wang, Deng, and Jiang (2023) and Xing and Jiang (2024), combining qualitative approaches (e.g., user interviews, open-ended questionnaires, and literature review) with quantitative techniques such as factor analysis and linear regression. This mixed-methods approach aims to systematically identify the key factors influencing artisans\u0026rsquo; adoption of AIGC-assisted design, and to analyze the relationships between these factors and users\u0026rsquo; behavioral intentions. The study further seeks to develop a targeted evaluation scale and behavioral intention model tailored to the artisan context. In doing so, it contributes to the theoretical understanding of technology adoption among traditional cultural practitioners and offers a solid empirical foundation for informing the integration of AIGC into artisanal domains.\u003c/p\u003e\u003cp\u003eIn conclusion, the core objectives of this study are twofold:\u003c/p\u003e\u003cp\u003e First, through the integration of multiple research methods, including literature review, user research, and data analysis, this study identifies the key factors influencing artisans' adoption of AIGC-assisted design. Based on these findings, a user experience evaluation scale is developed.\u003c/p\u003e\u003cp\u003eSecond, by employing linear regression analysis, the study examines the relationships and underlying logic between each influencing factor and artisans\u0026rsquo; willingness to adopt AIGC, ultimately constructing a corresponding behavioral model. Building on these results, the study offers recommendations for the future development of traditional handicrafts, focusing on the integration of AIGC tools in design and creation processes, and the role of new technologies in shaping the evolution of artisanal practices.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003ch2\u003e2.1 Research process\u003c/h2\u003e\n\u003cp\u003eBased on the preceding discussion, this research will be structured into four main sections, with the research process illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe first section focuses on the development of the evaluation scale. In this phase, the study will collect artisans\u0026apos; evaluations of AIGC-assisted design through a combination of literature review and user interviews. The feedback will be synthesized and reviewed by industry experts to ensure its relevance and accuracy. Ultimately, a behavioral intention evaluation scale will be constructed to assess artisans\u0026apos; willingness to use AIGC in the design process.\u003c/p\u003e\n\u003cp\u003eThe second section involves the identification of key impact factors. In this phase, the evaluation scale developed in the first section will be distributed widely as a questionnaire. The validity of the scale will be tested through reliability analysis, exploratory factor analysis, and confirmatory factor analysis. This will allow the identification of the primary factors influencing artisans\u0026apos; willingness to adopt AIGC. Building upon the literature review and observed variables within each factor, the study will name each factor in the context of traditional handicrafts and AIGC tools, further delineating the specific dimensions that shape users\u0026apos; willingness to engage with the technology.\u003c/p\u003e\n\u003cp\u003eThe third section is the construction of the behavioral model. In this phase, the study will explore the relationships between the identified factors and users\u0026apos; willingness to adopt AIGC. During the questionnaire distribution phase of the second section, a well-established \u0026quot;intention to use\u0026quot; evaluation scale will be introduced. This will allow for the collection of user evaluations in conjunction with the evaluation scale from the first section. Afterward, EFA and CFA will be conducted once again to ensure strong discriminant validity between the \u0026quot;willingness to use\u0026quot; factor and the previously identified factors, and to verify that the observed variables within each factor demonstrate satisfactory convergence. Following these validations, linear regression analysis will be employed to examine the influence of each factor on artisans\u0026apos; willingness to use AIGC. Based on these results, a behavioral intention model for artisans\u0026apos; adoption of AIGC-assisted design will be constructed.\u003c/p\u003e\n\u003cp\u003eThe fourth section will focus on the discussion of the results. Here, the study will integrate the findings from the behavioral model developed in the third section to analyze and interpret the relationships between each factor and the willingness to use. Drawing from these insights, the study will propose design strategies and management recommendations for optimizing AIGC tools to better assist artisans in the design and creation process.\u003c/p\u003e\n\u003ch2\u003e2.2 Data sources\u003c/h2\u003e\n\u003ch3\u003e2.2.1\u0026nbsp;\u0026nbsp;User interview strategy\u003c/h3\u003e\n\u003ch4\u003e2.2.1.1\u0026nbsp;Participants\u003c/h4\u003e\n\u003cp\u003eAs the foundation for selecting the research subjects, this study focuses on the group of Chinese craftsmen, whose representativeness and research significance can be demonstrated from several key dimensions.\u003c/p\u003e\n\u003cp\u003eFirstly, Chinese craftsmanship is represented by a well-established and influential community that mirrors similar groups in other countries or regions (Hang and Guo 2006). From an industrial perspective, the Chinese artisan sector has made substantial economic and social contributions. Statistical data reveals a consistent growth trend in the operating income of large-scale enterprises within the arts and crafts industry, with an annual growth rate of 14.26% and a profit increase of 18.64%. The total annual output value of this industry is approximately 412.8 billion US dollars, supporting over 6.5 million direct employees and an additional 13 million workers in related sectors. This has fostered the development of a highly scalable professional group with significant economic impact (WuYaNan 2024.06).\u003c/p\u003e\n\u003cp\u003eSecondly, in terms of international market performance, traditional Chinese craftsmanship occupies a prominent position in global trade. For example, the export value of decorative wood products from January to August 2024 reached 1.937 billion US dollars, underscoring the competitive strength of Chinese craftsmanship in the international market (zhiyanzixun 2024.11). This economic influence has further catalyzed the growth of related industries, such as tourism (Hedin 2024).\u003c/p\u003e\n\u003cp\u003eAdditionally, from a cultural perspective, Chinese craftsmanship holds substantial cultural significance and has a broad regional impact. The cultural exchanges between China and Japan provide a clear illustration of this influence, as the artisan traditions of both countries share considerable commonalities in terms of skill transmission and professional ethics. This cultural connection can be traced to the cross-domain diffusion of Confucianism (Yanagi and Leach 1989, Xu 2013). In the contemporary context, the fusion of Chinese craft aesthetics with international design principles has given rise to new cultural forms, particularly in the realms of luxury handcrafted goods and traditional handicrafts (Zhang 2024, Ji and Sirisuk 2024).\u003c/p\u003e\n\u003cp\u003eThe size and cultural influence of the Chinese artisan community, as a critical component of the global artisan landscape, makes it a key subject of study. Exploring the willingness of Chinese artisans to adopt AIGC-assisted design and identifying the factors influencing their acceptance will provide valuable theoretical insights into the technological adaptability of artisan groups, not only in Asia but also on a global scale in the AIGC era.\u003c/p\u003e\n\u003ch4\u003e2.2.1.2\u0026nbsp;Research strategy\u003c/h4\u003e\n\u003cp\u003eIn the user research phase, this study will employ a structured interview approach to gather user evaluation data. This method was selected based on existing studies demonstrating that structured interviews can significantly improve the reliability of data sources (Segal et al. 2006). The primary interview questions will focus on two key areas: (1) What features do respondents believe would encourage them to use AIGC-assisted design tools for handicraft design? (At least three features will be required.) (2) What factors do respondents think would deter them from using AIGC-assisted design tools for handicraft design? (At least three factors should be listed.). All participants were fully informed of the study\u0026apos;s purpose and procedures and provided their consent by signing an informed consent form. With the participants\u0026apos; consent, the user interviews were fully audio-recorded, and key points were also documented in writing. Three research members participated in each interview, with one serving as the lead interviewer and the other two responsible for recording the interview and asking follow-up or clarifying questions based on the discussion. A total of 111 artisans who had experience using AIGC tools were interviewed for this study, significantly exceeding the sample size standards recommended by Raita (2012) and Hartling (Hartling et al. 2017). The sample included 71 females and 40 males, representing a diverse range of educational backgrounds and types of handicrafts. As such, the sample offers a degree of generalizability. The basic demographic information of the respondents is presented in Table 1.\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the\u0026nbsp;Academic Ethics Committee of\u0026nbsp;Changzhou Textile and Apparel Vocational College (GVTG/PP/20250805). The participants provided their written informed consent to participate in this study. The studies were conducted in accordance with the local legislation guidelines and institutional requirements.\u003c/p\u003e\n\u003cp\u003eTable 1. Basic information of respondents during the user interview stage\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eOption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003ePercentage\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 121px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e36.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ewoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e63.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 121px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e18-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e21-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e53.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e31-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e36.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e41-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e51-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 121px;\"\u003e\n \u003cp\u003eEducational background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eHigh school/technical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eJunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eUndergraduate college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e62.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eMaster\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e27.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eDoctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 121px;\"\u003e\n \u003cp\u003eHandicraft type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eWeaving and tie-making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e14.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eSculpture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eTool and equipment manufacturing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e15.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eFurniture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eMetal smelting and processing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eEngraving and painting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eCeramic production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e18.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eProduction of stationery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eLacquer coating technique\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eWeaving and dyeing techniques\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eSpecial processes and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 318px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e2.2.2\u0026nbsp;\u0026nbsp;Literature retrieval strategy\u003c/h3\u003e\n\u003cp\u003eTo minimize data bias and the potential for skewed evaluations from a single source, this study also incorporates a comprehensive review of existing literature on artisans\u0026apos; use of AIGC to complement the current research. The research methodology employed is a holistic approach for data collection and selection.\u003c/p\u003e\n\u003cp\u003eThe first step involves reviewing references from the Web of Science Core Collection (WoS CC), a reputable and reliable source of high-quality citation data. In the second step, the objective is to gather relevant records from academic journals across multiple databases pertinent to the research topic, adhering to specific inclusion criteria. A search was conducted within the WoS CC subject area using the following search terms: (((((TS=(handicraft)) OR TS=(craft)) AND TS=(AI) AND DT=(Article OR Review) AND LA=(English) AND PY=(2025). This search resulted in the retrieval of 170 relevant documents. In the third step, the top thirty articles most relevant to the search criteria were selected based on their relevance, forming the basis for the literature review conducted in this study.\u003c/p\u003e\n\u003ch2\u003e2.3 Data Collection Process\u003c/h2\u003e\n\u003cp\u003eDuring the data analysis phase of the user interviews, a total of 625 valid evaluations were collected. Subsequently, five graduate students, with no vested interest in the thesis, were invited to review and summarize the results of the user research. These summaries were then evaluated by two university professors with expertise in the relevant field, who provided feedback and suggestions for revision. The process continued until both the graduate students and professors reached consensus on the final summary. Ultimately, 28 observed variables were identified and summarized from the user interview stage.\u003c/p\u003e\n\u003cp\u003eIn the literature review phase, this study manually extracted relevant expressions regarding the experience characteristics of artisans using AIGC-assisted design from the 30 retrieved papers, ultimately identifying 33 valid evaluations. Subsequently, five graduate students with no vested interest in the thesis were invited to summarize the findings. Once the summaries were completed, two industry experts were engaged to review and assess the results. The process continued until all five graduate students and the two experts reached a consensus with no objections to the final summary. In total, 20 observed variables were identified during the literature review phase.\u003c/p\u003e\n\u003cp\u003eUpon completion of the review, the aggregated content from the user interviews and literature review was combined and utilized as the observed variables for the usage intention evaluation scale to be developed in this study. A total of 28 observed variables were identified after summarization. To ensure consistency in the description of each variable, all negatively evaluated variables were rephrased as positive evaluations. Additionally, each observed variable was encoded using the format \u0026quot;Q+ number.\u0026quot; The final results are presented in Table 2.\u003c/p\u003e\n\u003cp\u003eTo facilitate a deeper exploration of the underlying logic and the relationships between influencing factors and users\u0026apos; willingness to adopt AIGC-assisted design, this study employed a well-established \u0026quot;willingness to use\u0026quot; evaluation scale. This scale has proven validity in measuring users\u0026apos; behavioral intentions and attitudes and has been widely referenced in prior research. The specific content of the questionnaire is presented in Table 3. Additionally, to align the scale with the focus of this study, adjustments were made to its content, specifically replacing the original research subject with the AIGC tool, thereby ensuring that the questionnaire better reflects the research objectives.\u003c/p\u003e\n\u003cp\u003eTable 2 Artisans use the AIGC-assisted design Behavior Intention Evaluation Scale\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eObserved Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eEnhance design efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Yadav and Rena , Yadav and Tripathi 2025, Zhang et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eStimulate inspiration and foster creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Zhang et al. 2025, Danry et al. 2025, Bao et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eThe output exhibits meticulous craftsmanship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Zhang et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eThe produced works demonstrate a high degree of precision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Zhang et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eUser-friendly interface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Yadav and Tripathi 2025, Danry et al. 2025, Bao et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eThe produced works exhibit a handcrafted texture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Bao et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eThe produced works exhibit clear differentiation from existing products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eUser Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eGuided production process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Zhang et al. 2025, Bao et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eThe produced works reflect personal style and originality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eUser Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eEase of use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Zhang et al. 2025, Danry et al. 2025, Bao et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eCustomizable according to user requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eUser Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eAIGC tools effectively interpret user instructions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Vartiainen et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eThe produced works demonstrate a high degree of feasibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Bao et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eProvide creative references and inspiration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Zhang et al. 2025, Danry et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eThe produced works embody humanistic values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Bao et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eThe produced works avoid copyright and ownership disputes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Bao et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eOffer creative ideas and suggestions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Zhang et al. 2025, Danry et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eLow learning curve for AIGC tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Bao et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eAssist artisans throughout the production process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Pan et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eThe produced works are not constrained by existing training data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eUser Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eGenerate novel ideas and creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Bao et al. 2025, Hevi et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eStable software performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Bao et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eEnhance the artistic quality of the output\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eUser Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eStreamline the production process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Zhang et al. 2025, Pan et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eThe design process mirrors traditional handicraft techniques\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eUser Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eLow training costs for AIGC tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eUser Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eFacilitate the mass production of handicraft products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eUser Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003eReduce the complexity of creation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003eLiterature Review(Yadav and Rena , Danry et al. 2025) and User Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 Evaluation Scale for Intention to Use\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eLatent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eObserved Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eLiterature Source\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 20px;\"\u003e\n \u003cp\u003eIntention to Use, ITU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eI intend to utilize AIGC tools in the near future.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(Huang and Qian 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eI am inclined to adopt AIGC tools.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eI would advocate for the adoption of AIGC tools by others.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch4\u003e2.3.1.1\u0026nbsp;The user testing stage of the evaluation scale\u003c/h4\u003e\n\u003cp\u003eAfter finalizing the initial evaluation scale, this study integrated the summarized evaluation items with an established usage intention scale to create the final AIGC usage intention evaluation scale. The questionnaire was then distributed online using a seven-point Likert scale to gather user evaluations. Following usability assessments on multiple online survey platforms, the study opted to use \u0026quot;Credamo,\u0026quot; a widely recognized Chinese platform, for data collection. Although some studies have noted potential issues with the reliability and quality of data from this platform (Zhang and Computing 2024), it remains a suitable choice due to its cost-effectiveness, reliability, and overall data quality. Given the sensitivity of the research topic, all responses were collected anonymously. Since the target sample consisted of Chinese artisans, the original questionnaire was administered in Chinese and later translated into English for analysis. To ensure sample homogeneity and objectivity, clear sociodemographic inclusion criteria were established (Abdelmoety et al. 2022), requiring that all respondents be Chinese residents aged 18 or older.\u003c/p\u003e\n\u003cp\u003eThe questionnaire survey consists of four sections.\u003c/p\u003e\n\u003cp\u003eThe first section is the Informed Consent Form, which provides detailed information about the research objectives and procedures. Respondents can proceed with the questionnaire only after selecting the option \u0026quot;I am aware of and agree to participate in the research.\u0026quot;\u003c/p\u003e\n\u003cp\u003eThe second section includes the Conditional Screening Question, which asks, \u0026quot;Have you used AIGC tools in the process of handicraft creation?\u0026quot; Respondents who select \u0026quot;Yes\u0026quot; may continue with the questionnaire, while those who select \u0026quot;No\u0026quot; will be automatically excluded from the survey. This ensures the authenticity and reliability of the collected data.\u003c/p\u003e\n\u003cp\u003eThe third section gathers basic demographic information, including gender, age, the type of handicraft practiced, educational background, and other relevant details.\u003c/p\u003e\n\u003cp\u003eThe fourth section assesses user evaluations using the proposed scale. The content of this section is presented in Table 3. A seven-point Likert scale is employed, where \u0026quot;1\u0026quot; indicates \u0026quot;strongly disagree\u0026quot; and \u0026quot;7\u0026quot; indicates \u0026quot;strongly agree.\u0026quot;。\u003c/p\u003e\n\u003cp\u003eAfter distributing the questionnaire on a large scale, a total of 597 responses were collected. Upon review, invalid responses\u0026mdash;such as those with excessively short completion times, duplicate entries, or missing answers\u0026mdash;were excluded. This resulted in 474 valid responses, yielding an effective response rate of 79%. The sample size was sufficient to meet the requirements for both factor analysis and linear regression (Price 1993, Comrey and Lee 1992, Dai et al. 2020, Hair Jr et al. 2021). The demographic information of the respondents is presented in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4. Basic Information Table of Respondents in the User Testing Phase\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003ename\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003ePercentage\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e51.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003ewoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e48.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 117px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e18-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e6.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e21-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e59.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e31-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e26.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e41-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e51-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 117px;\"\u003e\n \u003cp\u003eEducational back-ground\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eHigh school/technical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eJunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eUndergraduate college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eMaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e12.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eDoctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e65.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eWeaving and tie-making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e13.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eSculpture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 117px;\"\u003e\n \u003cp\u003eHandicraft Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eTool and equipment manufacturing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e10.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eFurniture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e10.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eMetal smelting and processing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e14.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eEngraving and painting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e12.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eCeramic production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eProduction of stationery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eLacquer coating technique\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e17.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eWeaving and dyeing techniques\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eSpecial processes and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003ewoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 307px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e2.3.2\u0026nbsp;\u0026nbsp;Certainty assessment\u003c/h3\u003e\n\u003cp\u003eAfter collecting the questionnaires, this study aimed to analyze the specific dimensions of the AIGC-assisted design behavior intention evaluation scale for artisans. First, exploratory factor analysis (EFA) was conducted using SPSS 26.0 software on the 474 valid questionnaires. The results revealed that the 28 observed variables were clustered into six factors: serendipity for design (including observed variables Q2, Q11, Q14, Q17, Q19, Q21, Q23), artisanal epistemology (including observed variables Q4, Q7, Q12, Q13, Q16, Q22), simulated craft authenticity (including observed variables Q3, Q6, Q9, Q15, Q22), productivity enhancement (including observed variables Q8, Q24, Q27, Q28), epistemic openness (including observed variables Q18, Q20, Q26), and technical usability (including observed variables Q1, Q5, Q10). Each factor demonstrated high reliability (Graham-Rowe et al. 2012). Subsequently, confirmatory factor analysis (CFA) was performed using Amos 26.0 software to validate the exploratory factor analysis results. The findings showed good discriminant validity among the factors, with each factor strongly correlating with its corresponding observed variables. These results indicate that the user experience dimensions identified in this study, which influence artisans\u0026apos; intention to use AIGC-assisted design, fully meet the required standards.\u003c/p\u003e\n\u003cp\u003eBased on these findings, this study conducts a linear regression analysis to explore the relationships between the identified factors and users\u0026apos; willingness to use. First, exploratory factor analysis was performed to assess the discriminant validity between \u0026quot;willingness to use\u0026quot; and the user experience factors. The results indicate that each factor demonstrates strong internal consistency among its observed variables, satisfying the criteria necessary for conducting linear regression analysis. Subsequently, the linear regression analysis revealed that serendipity for design, artisanal epistemology, productivity enhancement, and technical usability have a direct positive impact on the willingness to use. In contrast, simulated craft authenticity and epistemic openness do not exhibit a direct influence on the willingness to use. Overall, the research data and findings align with the study\u0026apos;s expectations.\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1 Evaluate the analysis results of the scale\u003c/h2\u003e\n\u003ch3\u003e3.1.1\u0026nbsp;\u0026nbsp;Exploratory factor analysis\u003c/h3\u003e\n\u003cp\u003eThis study first employed exploratory factor analysis to examine the user evaluation data presented in Table 2, identifying the main factors influencing artisans\u0026apos; use of AIGC-assisted design. The research findings are summarized in Table 5. After importing the data into SPSS 26.0 software, six factors with eigenvalues greater than 1 were extracted. The results of the Bartlett\u0026rsquo;s test of sphericity showed p = 0.000 (\u0026lt; 0.05), and the Kaiser-Meyer-Olkin (KMO) measure was 0.918 (\u0026gt; 0.6), indicating that the data are suitable for factor analysis (Nunnally 1994). Moreover, the factor loadings and communalities of the observed variables for each factor were all greater than 0.4, and each observed variable was strongly associated with only one factor. These results suggest significant distinctions among the observed variables in different factors, and strong correlations among the variables within a single factor. Therefore, the exploratory factor analysis results are robust and meet the required standards (Kaiser 1958). Additionally, a reliability analysis was conducted, revealing that the Cronbach\u0026apos;s alpha for each factor exceeded 0.7, signifying high internal consistency and reliability of the data used in this study. These findings underscore the credibility of the results (Eisinga, Te Grotenhuis, and Pelzer 2013).\u003c/p\u003e\n\u003cp\u003eTable 5. Exploratory factor analysis of the evaluation scale\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003eObserved Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 71px;\"\u003e\n \u003cp\u003eFactor Loading Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eCommunality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n 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style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n 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\u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBefore Rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eEigenvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003eVariance Explained (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e30.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e5.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e4.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAfter Rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eEigenvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e4.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003eVariance Explained (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e11.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e9.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eCronbach \u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 100px;\"\u003e\n \u003cp\u003eKMO and Bartlett\u0026apos;s Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eKMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eBartlett\u0026apos;s Sphericity Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e3.1.2\u0026nbsp;\u0026nbsp;Confirmatory factor analysis\u003c/h3\u003e\n\u003cp\u003eTo further assess the correlation among the observed variables within each factor and the discriminant validity among the factors, this study conducted a confirmatory factor analysis (CFA) on the data. The results of this analysis are presented in Table 6. The findings show that the standardized loadings of the observed variables within each factor are all greater than 0.5, the average variance extracted (AVE) for each factor exceeds 0.5, and the composite reliability (CR) is greater than 0.6. These results indicate a strong correspondence between each factor and its associated observed variables, as well as a high degree of aggregation among the observed variables (Muilenburg and Berge 2005, Shevlin and Miles 1998, Ahmad, Zulkurnain, and Khairushalimi 2016, Fornell and Larcker 1981). Additionally, the results of the AVE square root tests demonstrate that the square root of the AVE for each factor is greater than the correlation between that factor and the other factors, confirming the presence of good discriminant validity among the six factors, as shown in Table 7. Collectively, these analyses confirm that the user intention evaluation scale developed in this study fully meets the necessary criteria.\u003c/p\u003e\n\u003cp\u003eTable 6. Confirmatory factor analysis of the evaluation scale\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eObserved Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eStd. Estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e15.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e15.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e15.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e16.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7. Discriminant Validity Analysis of the Evaluation Scale\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFactor 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNote: The diagonal lines with gray background patterns represent the square root values of AVE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e3.1.3\u0026nbsp;\u0026nbsp;Factor naming\u003c/h3\u003e\n\u003cp\u003eThe aforementioned analysis confirms that the six factors identified through factor analysis fully meet the research criteria. Based on the content of the observed variables within each factor and in conjunction with extensive literature review, this study assigns specific names to each factor in order to further define the dimensions of each influencing factor. The final factor naming results are presented in Table 8.\u003c/p\u003e\n\u003cp\u003eFactor 1 comprises seven observed variables: Q2, Q11, Q14, Q17, Q19, Q21, and Q23. This factor primarily represents the role of artificial intelligence-generated content (AIGC) technology in enhancing creative efficiency, artistry, and expressive diversity by offering inspiration, suggestions, structured feedback, and personalized support during the creative process (Wang, Hong, and He 2024). AIGC has the capacity to inspire artisans with \u0026quot;unexpected but reasonable\u0026quot; ideas by generating vast amounts of images or text, particularly in the early stages of conceptualization (Shimin et al.). By incorporating user input\u0026mdash;such as style preferences, material constraints, and cultural elements\u0026mdash;AIGC can learn individual preferences and tailor outputs, providing highly personalized design recommendations for handicrafts (Liu and Xu 2024). Scholars have suggested that AI functions as a conversational collaborator, emphasizing that AIGC, through iterative generation and text-based interaction, guides creators to expand their design horizons. The images or sketches produced by AIGC can serve not only as objects for imitation but also as sources of inspiration, encouraging artisans to innovate by modifying materials and process structures. This aligns with the concept of \u0026quot;Serendipity for Design,\u0026quot; as proposed by Halvorsen (2016), which highlights the nonlinear output mechanism of AIGC that enables users to discover novel insights through \u0026quot;unexpected but interesting\u0026quot; results. Based on this research and the insights from the literature, this study has named Factor 1 \u0026quot;Serendipity for Design,\u0026quot; referring specifically to the unexpected inspiration that artisans may encounter during the creative process when using AIGC.\u003c/p\u003e\n\u003cp\u003eFactor 2 includes five observed variables: Q4, Q7, Q12, Q13, Q16, and Q22. When evaluating generative artificial intelligence tools, the artisan community primarily focuses on the effectiveness and uniqueness of the outputs (Garcia 2024). Additionally, the tool\u0026apos;s ability to comprehend the artisan\u0026apos;s instructions and the stability of its operation reflect users\u0026apos; perceptions of the system\u0026apos;s capabilities, which in turn influence their willingness to adopt it (Cusumano 1992). The feasibility of the generated output\u0026mdash;whether it can be practically implemented\u0026mdash;is a key criterion in evaluating quality within the context of AIGC applications (Zhu et al. 2025). Moreover, users demonstrate sensitivity to the legal risks associated with copyright infringement of AI-generated content, which has been shown to significantly hinder the adoption of such technologies (Depoorter 2008). Artisanal epistemology refers to the deep attention and meticulous control artisans apply during the process of knowledge production, encompassing aspects such as precision, originality, verifiability, feasibility, and accountability (Lehmann 2012). This concept emphasizes that creators not only possess technical expertise but also uphold a \u0026quot;craftsman\u0026apos;s sense of cognitive responsibility,\u0026quot; prioritizing credible creation over mere content generation (Smith 2018, Brinkmann, Tanggaard, and education 2010). This concept aptly captures the content reflected in these five observed variables. Consequently, this study has named Factor 2 \u0026quot;Artisanal Epistemology.\u0026quot;\u003c/p\u003e\n\u003cp\u003eFactor 3 encompasses five observed variables: Q3, Q6, Q9, Q15, and Q22. Handicraft creators typically invest considerable emotional energy into their work, placing a significant emphasis on the unique expression of their individual style (Hoyte and Research 2019). Unlike ordinary users, their acceptance of AIGC (Artificial Intelligence Generated Content) technology is not primarily driven by functional attributes such as efficiency or ease of use. Instead, it hinges on the technology\u0026apos;s ability to generate content that embodies humanistic depth and unique expression (Song et al. 2025, Bardzell, Rosner, and Bardzell 2012). When the outputs of AIGC demonstrate exceptional craftsmanship, with attention to detail and cultural significance, they are not perceived as a threat to traditional methods. Rather, they may be regarded as an auxiliary tool to inspire creativity and expand the boundaries of artistic creation (Shimin et al. , Zhu, Wang, and Ji). In the context of AIGC gradually making its way into the realm of craft creation, the willingness of traditional craftsmen to embrace the technology is contingent upon its ability to replicate the intrinsic values and aesthetic judgments inherent in their work (Pan et al. 2025). The concept of \u0026quot;software craftsmanship\u0026quot; captures the creator\u0026apos;s perception when evaluating the output of digital tools\u0026mdash;namely, whether it closely resembles the real craftsmanship experience in terms of precision, handcrafted texture, individual expression, cultural depth, and operational stability (McBreen 2002). This notion integrates multiple theoretical frameworks, including the realism of craftsmanship, human-machine collaborative creation, cultural adaptability, and perceptual authenticity. It highlights the creator\u0026apos;s holistic evaluation of the personalization, cultural embedding, and controllability evident in the system\u0026apos;s outputs (Thwaites 2018, Lebanon and El-Geish 2018). Thus, in this study, Factor 3 is designated as \u0026quot;Simulated Craft Authenticity\u0026quot; to capture the key acceptance mechanism influenced by the high emotional investment and expressive needs of the handicraft community in relation to AIGC technology.\u003c/p\u003e\n\u003cp\u003eFactor 4 encompasses four observed variables: Q8, Q24, Q27, and Q28. In handicraft design, optimizing the creative process is a critical factor in enhancing both design efficiency and production capacity. Streamlining the design process allows the handicraft community to focus more on creativity and artistic expression by reducing the complexity of design steps and increasing the level of design automation. This, in turn, improves work efficiency and reduces cognitive load (Burden 2022). Some scholars argue that with the aid of AIGC (Artificial Intelligence Generated Content), artisans can achieve mass production while maintaining the uniqueness of handcrafted products. This approach not only addresses market demands for large-scale production but also reduces production costs, thereby increasing the competitiveness of handicraft products in the marketplace (Shen, Lin, and Lin 2025). Additionally, AIGC\u0026apos;s technical support can alleviate the challenges associated with creation by assisting artisans in completing highly technical and repetitive tasks, thereby enhancing their ability to focus on more complex aspects of design (Tao et al. 2025). These factors provide a solid theoretical foundation for artisans to leverage AIGC in their manual operations. The concept of \u0026quot;Productivity Enhancement\u0026quot; particularly highlights how technology can foster economic and productivity growth by improving work efficiency, simplifying processes, and automating operations (Nallusamy and Adil Ahamed 2017, Sayer and Campbell 2002). In the context of handicraft design, AIGC\u0026rsquo;s role in simplifying and automating the creative process not only boosts design efficiency but also liberates artisans from technical and repetitive tasks, allowing them to dedicate more time to creativity and artistic expression. This serves as a clear manifestation of productivity improvement. Therefore, this study designates Factor 4 as \u0026quot;Productivity Enhancement.\u0026quot;\u003c/p\u003e\n\u003cp\u003eFactor 5 encompasses three observed variables: Q18, Q20, and Q26. In handicraft creation, the accessibility of tools and the operational threshold are crucial factors influencing artisans\u0026apos; willingness to adopt them. A gentler learning curve can significantly reduce cognitive load, enabling artisans to focus on creative expression and artistic realization without having to invest excessive energy in adapting to emerging technologies (George, Baskar, and Srikaanth 2024). Additionally, if the training process for AIGC tools involves low resource costs, it can expand their accessibility to small-scale workshops and individual creators. This lowers economic and technical barriers, thereby enhancing the universality and inclusivity of these tools (Anderson and Sciences 2006, Hardy III et al. 2017). More importantly, when the generative capacity of AIGC is no longer constrained by existing training data and can be flexibly derived based on user input, it provides artisans with greater creative freedom. This flexibility allows them to preserve their unique style and cultural expression while benefiting from technological intervention (Lou and Innovation 2023, LIU, Bezuhla, and Design 2024). Together, these factors form the foundation for AIGC tools to achieve a low threshold, high flexibility, and cultural adaptability in craft creation (Bao et al. 2025, Song and Bai 2025). The concept of \u0026quot;Epistemic Openness\u0026quot; emphasizes the cognitive accessibility of the technical system\u0026mdash;that is, whether users can engage with the system based on their own knowledge structure, expression habits, and cultural experiences, enabling creative transfer and reconfiguration within the system (Martin 2001, John 2018). In handicraft creation, the controllability of learning costs, the adjustability of generation logic, and the flexibility of resource input are essential for artisans to internalize AIGC as a valuable creative support tool (Wu et al. 2025, Wang et al. 2024). Therefore, in this study, Factor 5 is named \u0026quot;Epistemic Openness.\u0026quot;\u003c/p\u003e\n\u003cp\u003eFactor 6 encompasses three observed variables: Q1, Q5, and Q10. These variables collectively reflect the ease of use of AIGC technology during its application by artisans. The user-friendliness of technology is a critical determinant of its acceptance. The extent to which the design of AIGC technology meets the needs of artisans and is intuitive and accessible significantly influences their willingness to adopt it (Fan and Jiang 2024). Moreover, the simplicity of operation\u0026mdash;an essential factor for technology acceptance\u0026mdash;can lower barriers to usage, making it easier for users to learn and apply new technologies (Menon and Shilpa 2023). At the same time, the convenience of the technology is crucial for enhancing work efficiency and facilitating the creative process, which is particularly important for artisans during their design work (Kofler and Walder 2024). Therefore, these factors effectively capture the ease of use of AIGC technology, highlighting that its simplicity, user-friendliness, and convenience are pivotal in influencing artisans\u0026apos; willingness to incorporate AIGC into their design processes. This aligns with the concept of \u0026quot;Technical Usability\u0026quot; proposed by Lu et al. (2022) . Consequently, this set of factors is termed \u0026quot;Technical Usability.\u0026quot;\u003c/p\u003e\n\u003cp\u003eTable 8. Factor naming result\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eObserved Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eDetailed Description\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSerendipity for Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eStimulate inspiration and foster creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAIGC\u0026nbsp;technology encompasses a range of functions that enhance creative efficiency, artistic expression, and diversity. It achieves this by offering inspiration, suggestions, structured feedback, and personalized support to individuals engaged in creative work.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eCustomizable according to user requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eProvide creative references and inspiration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eOffer creative ideas and suggestions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eAssist artisans throughout the production process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eGenerate novel ideas and creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eEnhance the artistic quality of the output\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 96px;\"\u003e\n \u003cp\u003eArtisanal Epistemology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eThe produced works demonstrate a high degree of precision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 180px;\"\u003e\n \u003cp\u003eThe effectiveness and originality of the output generated with the assistance of artificial intelligence tools.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eThe produced works exhibit clear differentiation from existing products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eAIGC tools effectively interpret user instructions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eThe produced works demonstrate a high degree of feasibility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eThe produced works avoid copyright and ownership disputes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eStable software performance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSimulated Craft Authenticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eThe output exhibits meticulous craftsmanship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 180px;\"\u003e\n \u003cp\u003eWhen the output generated by AIGC reflects a high level of craftsmanship and cultural significance.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eThe produced works exhibit a handcrafted texture\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eThe produced works reflect personal style and originality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eThe produced works embody humanistic values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eThe design process mirrors traditional handicraft techniques\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFactor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 96px;\"\u003e\n \u003cp\u003eProductivity Enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eGuided production process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 180px;\"\u003e\n \u003cp\u003eThe simplification and automation of the creative process facilitated by AIGC technology.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eStreamline the production process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eFacilitate the mass production of handicraft products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eReduce the complexity of creation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFactor 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eEpistemic Openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eLow training costs for AIGC tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 180px;\"\u003e\n \u003cp\u003eThe low entry barrier, high flexibility, and cultural adaptability of AIGC tools in the context of craft creation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eThe produced works are not constrained by existing training data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eLow training costs for AIGC tools\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFactor 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eTechnical Usability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eEnhance design efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 180px;\"\u003e\n \u003cp\u003eThe design of AIGC technology aligns with the needs of artisans and is both intuitive and user-friendly.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eUser-friendly interface\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eQ10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003eEase of use\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Analysis results of the behavioral model\u003c/h2\u003e\n\u003ch3\u003e3.2.1\u0026nbsp;\u0026nbsp;Hypothesis put forward\u003c/h3\u003e\n\u003cp\u003eThis study aims to apply linear regression analysis to explore the relationship between various influencing factors and artisans\u0026apos; willingness to use AIGC. The objective is to develop a behavioral model that explains the willingness of artisans to adopt AIGC technology. Prior to conducting the formal data analysis, this research proposes the following hypotheses based on the influencing factors identified in Section 3.1, as illustrated in Figure 3:\u003c/p\u003e\n\u003cp\u003eH1: Exercise Safety Assurance positively affects artisans\u0026apos; behavioral intention to use AIGC-assisted design.\u003c/p\u003e\n\u003cp\u003eH2: Physical Activity Tracking positively affects artisans\u0026apos; behavioral intention to use AIGC-assisted design.\u003c/p\u003e\n\u003cp\u003eH3: Emotional Social Support positively affects artisans\u0026apos; behavioral intention to use AIGC-assisted design.\u003c/p\u003e\n\u003cp\u003eH4: Dialogue Support positively affects artisans\u0026apos; behavioral intention to use AIGC-assisted design.\u003c/p\u003e\n\u003cp\u003eH5: Epistemic Openness positively affect artisans\u0026apos; behavioral intention to use AIGC-assisted design.\u003c/p\u003e\n\u003cp\u003eH6: Technical Usability positively affect artisans\u0026apos; behavioral intention to use AIGC-assisted design.\u003c/p\u003e\n\u003cp\u003eFor the purpose of analysis, in addition to the six influencing factors previously identified, this study introduces an established \u0026quot;willingness to use\u0026quot; dimension to capture the artisan group\u0026apos;s evaluation of their intention to adopt AIGC. To ensure that the inclusion of the \u0026quot;willingness to use\u0026quot; dimension does not interfere with the established dimensions of the evaluation scale, this study will integrate the six identified factors with the \u0026quot;willingness to use\u0026quot; dimension prior to conducting the formal linear regression analysis. Furthermore, exploratory factor analysis and confirmatory factor analysis will be performed to ensure strong discriminant validity among the six factors and to verify that the observed variables within each factor exhibit adequate convergent validity.\u003c/p\u003e\n\u003ch3\u003e3.2.2\u0026nbsp;\u0026nbsp;Exploratory factor analysis of behavioral models\u003c/h3\u003e\n\u003cp\u003eThis study employed exploratory factor analysis to assess whether the inclusion of the \u0026quot;willingness to use\u0026quot; dimension would influence the results of the evaluation scale developed in this research. The results, presented in Table 9, indicate that the seven factors are effectively distinguishable. The observed variables within each factor are not influenced by other factors, and the integrity of the constructed evaluation scale remains intact. Additionally, the reliability of each factor exceeds 0.7, demonstrating strong internal consistency and reliability.\u003c/p\u003e\n\u003cp\u003eTable 9 Exploratory Factor Analysis of the Behavioral Model\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003eObserved Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFactor Loading Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eCommunality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eITU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n 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style=\"width: 10px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n 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style=\"width: 10px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n 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style=\"width: 10px;\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n 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\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eITU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eITU2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eITU3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBefore Rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eEigenvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e9.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eVariance Explained (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e30.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e12.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e5.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 100px;\"\u003e\n \u003cp\u003eAfter\u0026nbsp;Rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eEigenvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003eVariance Explained (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e13.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e11.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e10.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e8.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e6.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e6.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e5.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eCronbach \u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 100px;\"\u003e\n \u003cp\u003eKMO and Bartlett\u0026apos;s Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eKMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eBartlett\u0026apos;s Sphericity Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e3.2.3\u0026nbsp;\u0026nbsp;Confirmatory factor analysis of behavioral models\u003c/h3\u003e\n\u003cp\u003eThis study employed confirmatory factor analysis to examine the degree of aggregation among the observed variables within each factor, following the inclusion of the \u0026quot;willingness to use\u0026quot; dimension, as well as the discriminant validity among the factors. The analysis results, presented in Tables 10 and 11, show that the standardized factor loadings for all observed variables exceed 0.5. Additionally, the average variance extracted values for each factor are greater than 0.36, the composite reliability values exceed 0.6, and the square roots of the AVE for each factor are greater than the correlation coefficients between that factor and the other factors. These findings indicate that each factor exhibits strong discriminant validity, and there is a high degree of aggregation among the observed variables within each factor. Therefore, this study is now ready to proceed with linear regression analysis.\u003c/p\u003e\n\u003cp\u003eTable 10. Confirmatory Factor Analysis of the Behavioral Model\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eObserved Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eStd. Estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e15.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e15.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e15.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e16.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003eFactor 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003eITU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eITU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eITU2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eITU3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 11 Discriminant Validity Analysis of the Behavioral Model\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eITU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eFactor 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eITU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e3.2.4\u0026nbsp;\u0026nbsp;Linear regression analysis\u003c/h3\u003e\n\u003cp\u003eBuilding on the theoretical assumptions presented in Section 3.2.1, this study further examined the relationships among the factors through linear regression analysis. The results of this analysis are shown in Table 12. Factors 1 to 6 were treated as independent variables, with \u0026quot;willingness to use\u0026quot; as the dependent variable. The analysis revealed that, except for H3 and H5, the p-values for the other hypothesized relationships were all below 0.05. This indicates that H1, H2, H4, and H6 are supported, while H3 and H5 are not. Based on these findings, as well as the earlier discussions on factor naming, this study developed a behavioral intention model for process engineers using AIGC to assist in design, as depicted in Figure 4.\u003c/p\u003e\n\u003cp\u003eTable 12 Linear Regression Analysis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eHypothesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003eCollinearity Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003eBeta\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eTolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 8px;\"\u003e\n \u003cp\u003eITU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e6.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e-1.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eRejected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eFactor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e6.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eH5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eFactor 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eRejected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eH6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eFactor 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e4.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study systematically identifies and analyzes the key factors influencing artisans' adoption of AIGC technology for auxiliary design, addressing the current gap in the literature regarding the technology adoption mechanisms of traditional craft communities. The findings indicate that performance expectations, social influence, and ease of use of the tools have a significant positive impact on artisans' behavioral intention to use AIGC technology. Conversely, technology anxiety, perceived risk, and cultural identity conflicts serve as major barriers to technology acceptance. These results not only reflect the challenges faced by the artisan community in the digital era but also offer strategic insights for the cross-generational integration and cultural preservation of artisans and the art industry.\u003c/p\u003e\u003cp\u003eFirstly, traditional technology adoption models generally focus on efficiency improvements and ease of use as the primary drivers for user acceptance of new tools (Taherdoost 2018, Dillon and Morris 1996). However, this framework proves limited when applied to creative practitioners such as artisans. For this group, tool performance is not solely determined by task completion speed or output quantity; rather, it is more importantly measured by how effectively tools support the realization of artistic concepts and cultural expressions (De Munck 2019, Betjemann 2008). Handicraft creation is inherently more than a process of material manipulation; it is also a means of conveying emotions and embodying value systems (P\u0026ouml;ll\u0026auml;nen 2013). Consequently, an artisan's acceptance of a tool often hinges on whether it can accurately express their personal style, cultural symbols, and craftsmanship traditions (Ferreira et al. 2019). In this context, the value of AIGC technology does not lie in its degree of automation or its potential to replace human labor, but rather in its ability to expand the creative space and stimulate design ideas (Van der Zande et al. 2019, Pan et al. 2025, Gao et al. 2025). This preference reflects the inclination of artisans to view AIGC not as a replacement or leader, but as an assistant in the creative process (Lou and Innovation 2023). This perspective is consistent with recent academic trends emphasizing collaborative intelligent tools (Liu, Huang, and Review 2025). A common misconception in promoting AIGC tools to artisans is to position them as more efficient alternatives (Wu et al. 2023). This narrative often triggers resistance, as it overlooks the psychological characteristics of creators, who are particularly sensitive to creative control and autonomy in expression (Lin, Yang, and Sun 2024). In contrast, a more persuasive approach would involve framing AIGC as a creative extender or collaborative partner, emphasizing its supportive and flexible role in the creative process (Zi-yang and Studies 2024). By promoting a co-creativity communication framework, rather than one centered on human-machine competition, this approach not only mitigates artisans' resistance to technological intervention but also enhances their sense of identity and willingness to adopt the technology (Wang et al.). Thus, the promotion of AIGC should shift from a singular focus on technical effectiveness to a more nuanced, multidimensional perspective that emphasizes creative support. This shift will facilitate the natural integration of technology into the creative process and provide important theoretical and practical pathways for building a human-centered model of human-machine collaboration.\u003c/p\u003e\u003cp\u003eSecondly, despite the significant advancements of AIGC technology in areas such as image generation and language processing, its adoption in the field of handicrafts, where skill inheritance is central, still encounters substantial cultural resistance. Unlike the technical discomfort that general users experience due to unfamiliarity with the operation, artisans' resistance to AIGC is more deeply rooted in a conflict between identity recognition and value systems (Yuan et al. 2025, Lin, Yang, and Sun 2024). Traditional craftsmanship has long been viewed as a concentrated expression of individual cultivation, emotional investment, and the accumulation of time (Liu 2025). In this context, craftsmanship is not merely a functional skill but a mode of existence and an aesthetic stance embodied through \"making by hand\" (Nimkulrat 2012). The algorithmic generation logic underlying AIGC, to some extent, diminishes the expressive space of \"ingenious craftsmanship\" and the \"visible touch\" of handwork, which is often seen as a negation of the creative value inherent in traditional handcraft. This resistance is not an instinctive rejection of technology itself but a response to the potential cultural changes that such technology might provoke (Zhu et al. 2025, Avlonitou and Papadaki 2025). Moreover, the rise of technological anxiety is closely tied to the blurring of boundaries around the identity of creators. In traditional handicraft models, the creator is both the laborer and the bearer of the work's conceptual meaning (Roy and Sarkar 2025). AIGC's intervention disrupts the classic framework of \"creation\u0026thinsp;=\u0026thinsp;labor\u0026thinsp;+\u0026thinsp;thought,\" rendering the question of authorship and the work's irreplaceability more ambiguous. When certain creative tasks are automated by algorithms, questions arise about whether the work retains its authorial essence, both psychologically for creators and within the market framework (do Nascimento and Venturelli 2025, Sarkar 2023). Thus, the promotion strategy for AIGC in the handicraft sector should not merely highlight its technical advantages, such as efficiency, convenience, or cost reduction. Instead, it must clearly define AIGC's supportive role and outline the boundaries of the technology's influence, emphasizing the priority of human agency. In other words, the narrative surrounding AIGC should avoid positioning it as a substitute for human creators and instead frame it as a creative support system\u0026mdash;expanding expressive possibilities and enhancing design without undermining the creator's authority. This approach, grounded in the non-substitutive nature of culture, not only helps reduce resistance to technology among artisans but also creates a new point of convergence for integrating traditional value systems with digital technology (Li 2022, Gong, Jiang, and Liang 2022). In this regard, the true challenge for AIGC lies not in improving its functional performance but in its ability to be embedded within the existing cultural context, where it can be viewed as a tool of assistance rather than a threat.\u003c/p\u003e\u003cp\u003eThirdly, in addition to emotional anxiety, artisans often express structural concerns when engaging with AIGC tools, specifically regarding the perceived risks associated with these technologies. These risks arise from the creator's cognitive uncertainty regarding the system's operational logic and the boundaries of control (Ostrom 1980, Neal 2017). The \"black box\" nature of generative technology has disrupted the traditional \"operation - feedback - correction\" cycle, making it difficult for creators to avoid decision-making blind spots and ambiguous expression when confronted with generated outputs (Lehtim\u0026auml;ki 2024). When issues such as copyright ownership or the definition of creative participation become unclear, it may undermine artisans' legitimate status and emotional attachment within the cultural market (Geiger 2015, Rahmatian 2011). To mitigate the perceived risks of AIGC in the handicraft community, it is necessary to go beyond improving technical performance or user training. A more fundamental solution involves establishing mechanisms for controllability and cognitive transparency at the system design level (Ge, Wang, and Wang, Liu et al. 2023, Patama 2025). Specifically, AIGC can enhance the understandability of the generation process and restore visibility into the decision chain by establishing a logical mapping between prompt inputs and generated outputs. Allowing users to independently adjust the integration ratio of manual style and algorithmic style would enable a dynamic balance between traditional craftsmanship and new technologies. Additionally, clearly defining the structure and responsibility distribution in human-machine collaboration is essential to ensuring the creator's dominant position in authorship, participation, and market discourse. Such mechanisms not only bolster users' confidence and psychological security in operating the tools but also provide critical support for safeguarding the expression rights and subjectivity of traditional craftsmanship and culture within the digital context. In essence, controllability is not only a matter of technology usability but also a prerequisite for cultural acceptance and value recognition.\u003c/p\u003e\u003cp\u003eFourth, social influencing factors have shown a significant positive effect on artisans' behavioral intention to adopt AIGC tools, indicating that technology adoption within this group is not solely based on individual decisions but also reflects a collective response mechanism. However, unlike institutional pressures within organizations or the internalization of norms in educational settings, the social influence within the handicraft artisan community manifests primarily as a horizontal recognition grounded in trust based on practical experience (Stinchfield et al. 2013, Gamble 2004). The adoption behavior of this group is largely shaped by the exemplary experiences of others (Li, Li, and Kou 2022). This feedback-driven influence mechanism forms a chain of trust: when a respected artisan leader successfully introduces a new tool and achieves positive outcomes, other artisans are more likely to regard the tool as a viable and acceptable creative medium (Epstein 1998, Veckie and Veckie 2021). Thus, promoting AIGC within the handicraft community requires moving beyond traditional, broad communication strategies aimed at the general public. Instead, a more targeted, community-embedded communication approach is necessary. Specifically, emphasis should be placed on identifying and engaging key members within local traditional craft communities, craft associations, and master-apprentice systems\u0026mdash;particularly those with significant influence and credibility within the local craft ecosystem. These bridging figures can not only provide practical endorsement for the rationality of new technologies but also help reduce emotional resistance among grassroots artisans who may feel apprehensive about the unfamiliarity and perceived lack of control over these technologies. Furthermore, the social impact on technology adoption extends beyond mere verbal recommendations or cognitive persuasion. It must be translated into action intentions through tangible, perceptible experience scenarios (Ding 2024). To this end, it is recommended that future promotional efforts include \"AI\u0026thinsp;+\u0026thinsp;traditional craftsmanship\" integration displays or \"human-machine co-creation workshops.\" These activities, through highly interactive and immersive methods, would allow the technology to take on a concrete, visible, and operable form. Such events not only help mitigate the psychological distance created by the abstraction of technology but also facilitate a shift in identity from \"viewers\" to \"testers\" and, ultimately, to \"adopters\" (Radermecker, Loots, and Management 2025, Martins et al. 2020, Zabulis et al. 2023).\u003c/p\u003e\u003cp\u003eFifth, while AIGC technology has experienced significant expansion in image and text creation in recent years, the handicraft community, particularly older practitioners, still faces a considerable technical threshold when adapting to new technologies (Chacur et al. 2024). This threshold not only presents operational challenges but also directly influences their motivation to use the tools and their trust in the technology (Che and Hashim, Shafi et al. 2024). Such frustrating experiences often lead to negative judgments about the entire system in a short period, resulting in a lasting rejection of the technology (Barley and Orr 1997). In the early stages of adopting AIGC, this phenomenon can significantly reduce users' acceptance and the likelihood of continued usage. Particularly in creative activities, the opacity and lack of control in the tools are more likely to be perceived as a loss of creative power, rather than simply a learning barrier (Bilton 2015). Therefore, when designing AIGC systems for the handicraft community, it is essential to lower the technical threshold and adopt an interaction concept that is de-engineered. Specifically, scenario-based templates should be provided, allowing artisans to generate outputs based on specific styles, functions, or uses, thus reducing the need for abstract prompt messages. Additionally, voice input and natural language guidance can be integrated through voice recognition and intelligent prompt systems, enabling users to interact with the system in a manner similar to daily communication, which lowers the threshold for text-based input. The system should automatically analyze the style of the user's existing works and apply it to the output, achieving personalized matching and enhancing the user's sense of ownership and control. Furthermore, real-time feedback at the image level should be provided for each operation, with the option to rollback, adjust, or recreate results, ensuring that interruptions in the creative process do not lead to irreversible damage. These design features not only help increase artisans' confidence in using the tools and support their creative rhythm but, more importantly, help restore their belief in \"creative dominance\" at a psychological level. This approach is not only an engineering requirement for enhancing tool usability but also a response to the core expectations within handicraft culture regarding the integrity of skill control and expression.\u003c/p\u003e\u003cp\u003eIn conclusion, the potential of AIGC technology lies not in replacing craftsmanship, but in extending and enhancing it. In designing pathways for integration, it is essential to fully respect the creative logic and cultural practices of artisans, and to develop a guiding strategy that focuses on technological empowerment while prioritizing psychological alignment. Artisans are not adversaries of technology; rather, they are defenders and reconstructors of culture in its evolution. The key factor influencing a craftsman's adoption of AIGC is not simply the tool itself, but whether it can be effectively integrated into the logic of craftsmanship, cultural values, and the artisan's sense of self-identity. Looking ahead, the design and promotion of AIGC for the handicraft community must shift from neutralization to cultural adaptation, from user-centeredness to collaborative creation, and from technological efficiency to emotional resonance. The goal should be to build bridges between technology and people, between efficiency and emotion, and between tradition and the future. This is not merely about the use of a tool, but about the continuation and innovation of a way of life and a knowledge system.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study provides a comprehensive examination of the behavioral intentions of artisans in using AIGC-assisted design tools, along with the key factors that influence their adoption. A systematic hybrid research approach was employed, integrating qualitative analysis with quantitative data, to explore the acceptance and usage intentions of artisans towards emerging technologies. The goal was to address the existing gap in academic research regarding the technological adaptation mechanisms of artisan communities and to offer theoretical insights into how traditional handicraft groups respond to digital technological changes. The findings reveal several critical factors that influence artisans' use of AIGC tools, including tool effectiveness, the cultural and artistic value of the works, ease of use, and social impact. Specifically, performance expectations and tool ease of use were identified as significant positive drivers of behavioral intentions, while technical anxiety, perceived risks, and cultural identity conflicts emerged as major barriers to technology acceptance. Further analysis shows that, when adopting new technologies, traditional craft groups not only focus on whether the tools can enhance work efficiency but also place great emphasis on their ability to support personalized creation and cultural expression. The construction of behavioral models for AIGC-assisted design indicates that artisans are more likely to view AIGC as a collaborative tool in the creative process rather than a replacement for traditional craftsmanship. The primary concerns of artisans center on how AIGC tools can expand creative possibilities, inspire innovation, and enhance the artistic quality of their designs, rather than solely improving production efficiency. Therefore, the promotion of AIGC tools should emphasize their advantages in creative collaboration and innovation, rather than positioning them solely as efficiency-enhancing instruments. Additionally, the study highlights the cultural resistance artisans face when accepting AIGC tools, particularly regarding identity recognition and the preservation of traditional craftsmanship values. The \"black box\" nature of the technology, coupled with concerns over copyright ownership and creative control, has led to caution within the artisan community. To mitigate this resistance, the research underscores the importance of ensuring technological controllability and transparency, as well as clearly defining the boundaries between technology and the roles of creators. These factors are essential for reducing resistance and fostering greater acceptance of AIGC tools. In conclusion, this study offers valuable practical recommendations for promoting AIGC tools in the handicraft design sector, emphasizing the need for cultural adaptability and a personalized user experience in the promotion process. Future research could further explore how to facilitate the deeper integration and widespread adoption of technology within traditional craftsmanship through targeted community activities and demonstration projects.\u003c/p\u003e\u003cp\u003eAlthough this study is pioneering in developing assessment tools and behavioral models to analyze the intention of artisan groups to adopt AIGC technology and its direct influencing factors, two key limitations in the research design must be acknowledged. First, the cultural specificity of the sample may limit the generalizability of the findings. This study focuses on Chinese handicraft practitioners, whose cognitive frameworks are inevitably shaped by the norms of traditional Chinese craft culture. While some findings may reflect cross-cultural commonalities, the acceptance of AIGC technology among artisan groups in different countries is likely to exhibit structural differences. Future research should adopt a multi-country comparative paradigm. By systematically comparing technology adoption behaviors across various countries, this approach can not only identify universal mechanisms with cultural resilience but also uncover context-specific differences, thereby enhancing the cultural adaptability and generalizability of the theoretical model. Second, the validity of the measurement tools warrants attention. The current user experience evaluation system is primarily based on self-reported data. Although the methodology has been strengthened through literature analysis and in-depth interviews, the inherent cognitive biases in subjective statements may still affect the accuracy of the results. Future research could incorporate multimodal data collection strategies, such as integrating objective behavior indicators like usage logs and behavior tracking techniques. This would create a cross-validation mechanism for both subjective and objective data, helping to overcome the methodological limitations of relying on a single data source and significantly improving the validity and reliability of the assessment scale. Building on these considerations, future research should expand in three key areas: first, extending the geographical scope of cross-border comparative studies while incorporating cultural distance as a moderating variable; second, enriching the measurement dimensions by exploring mediating factors such as technology application scenarios and the influence of social norms; and third, adopting a longitudinal design to track the evolving technology adoption behaviors of artisan groups over time. These improvements will contribute to a more comprehensive theoretical framework and provide practical guidance for the innovative application of AIGC technology in the global handicraft sector.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eArtificial Intelligence-Generated Content\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “Conceptualization, W.P. and X.W.; methodology, W.P.; software, W.P.; validation, X.W.\u0026nbsp;and LQ.; writing—original draft preparation, W.P.; writing—review and editing, W.P.; visualization,\u0026nbsp;W.P.; supervision, W.P.;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThis research did not receive any financial assistance from institutions. It was entirely funded voluntarily by the authors to collect user intention information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analysed during the current study are not publicly available due to \u0026nbsp; concerns regarding participant anonymity, but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":" References","content":"\u003col\u003e\n \u003cli\u003e\u0026nbsp;Abdelmoety, Ziad Hassan, Sameh Aboul-Dahab, Gomaa %J Journal of Retailing Agag, and Consumer Services. 2022. \u0026quot;A cross cultural investigation of retailers commitment to CSR and customer citizenship behaviour: The role of ethical standard and value relevance.\u0026quot; \u0026nbsp; 64:102796.\u003c/li\u003e\n \u003cli\u003eAhmad, Sabri, Nazleen Nur Ain Zulkurnain, and Fatin Izzati Khairushalimi. 2016. \u0026quot;Assessing the validity and reliability of a measurement model in Structural Equation Modeling (SEM).\u0026quot; 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Springer.\u003c/li\u003e\n \u003cli\u003eZhang, Lufang, Yue Wang, Zhichuan Tang, Xia Liu, and Moran %J Sustainability Zhang. 2023. \u0026quot;A virtual experience system of bamboo weaving for sustainable research on intangible cultural heritage based on VR technology.\u0026quot; \u0026nbsp; 15 (4):3134.\u003c/li\u003e\n \u003cli\u003eZhang, Shunan, Xin Xie, Desheng Lyu, and Yunfeng %J International Journal of Human\u0026ndash;Computer Interaction Shu. 2025. \u0026quot;KiteMR: An Interactive Mixed Reality System for Preserving and Experiencing Traditional Chinese Kite Craftsmanship.\u0026quot;1-22.\u003c/li\u003e\n \u003cli\u003eZhang, Sichen %J Journal of Big Data, and Computing. 2024. \u0026quot;How AI Literacy Affects the Intention to Use AIGC: An Empirical TAM-Based Study.\u0026quot; \u0026nbsp; 2 (3):169.\u003c/li\u003e\n \u003cli\u003ezhiyanzixun. 2024.11. \u0026quot;China\u0026apos;s export volume and export value of wood products for domestic or decorative use were 590,000 tonnes and US$1,937 million, respectively, in January-August 2024.\u0026quot; zhiyanzixun.\u003c/li\u003e\n \u003cli\u003eZhu, Sijin, Zheng Wang, Yuan Zhuang, Yuyang Jiang, Mengyao Guo, Xiaolin Zhang, Ze %J Telematics Gao, and Informatics Reports. 2024. \u0026quot;Exploring the impact of ChatGPT on art creation and collaboration: Benefits, challenges and ethical implications.\u0026quot; \u0026nbsp; 14:100138.\u003c/li\u003e\n \u003cli\u003eZhu, Yingfei, Weixing Wang, and Weifeng %J Available at SSRN 5312869 Ji. \u0026quot;Aigc-Driven Integration of Shape Grammars and Entropy-Weighted Topsis for Product Design in Guizhou Miao Embroidery.\u0026quot;\u003c/li\u003e\n \u003cli\u003eZhu, Zijian, Tao Yu, Yijing Wang, and Junping %J IEEE Access Xu. 2025. \u0026quot;Revolutionizing Design Content with AIGC: User-Centered Challenges, Opportunities, and Workflow Evolution.\u0026quot;\u003c/li\u003e\n \u003cli\u003eZi-yang, HU %J Journal of Literature, and Art Studies. 2024. \u0026quot;AIGC related context: A new communication culture for human.\u0026quot; \u0026nbsp; \u0026nbsp;14 (10):921-931.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artisans, AIGC, Examining Behavioral Intention, Serendipity for Design, Artisanal Epis-temology, Productivity Enhancement, Technical Usability","lastPublishedDoi":"10.21203/rs.3.rs-7362510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7362510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial Intelligence-Generated Content (AIGC) is a rapidly evolving technology with significant computational and creative potential, offering new possibilities in design, aesthetics, and process innovation. However, traditional artisans view AIGC as a potential threat to the authenticity and value of their work. Research on artisans' acceptance of AIGC is limited. This study aims to identify the key factors influencing artisans\u0026rsquo; willingness to adopt AIGC-assisted design and develop a validated measurement scale and evaluation model. A mixed-methods approach combining qualitative and quantitative research was used, including user interviews and literature review. Exploratory factor analysis and multiple regression analysis identified six key factors affecting adoption: serendipity for design, artisanal epistemology, productivity enhancement, and technical usability. Simulated craft authenticity and epistemic openness did not show direct effects. The study highlights the need for technology integration that respects artisans' cognitive frameworks and creative traditions, advocating for a shift from technological neutrality to cultural adaptation and collaborative co-creation.\u003c/p\u003e","manuscriptTitle":"Cognitive Determinants and Technological Adoption: Exam-ining Behavioral Intention Mechanisms in Artisanal Design Practices Mediated by AI-Generated Content","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 10:22:19","doi":"10.21203/rs.3.rs-7362510/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-03T07:16:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T12:37:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T13:30:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T13:48:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202667839907152461851372862410264203174","date":"2025-09-23T13:45:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330313452580659488763245879319056592781","date":"2025-09-22T10:40:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112267280109929513869397312464451917334","date":"2025-09-22T08:48:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-22T08:44:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T07:03:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T06:34:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T05:21:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-03T05:16:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be4c7f1e-6a5c-471e-a1a1-ff284e61075d","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":55673084,"name":"Business and commerce/Business and management"},{"id":55673085,"name":"Social science/Business and management"},{"id":55673086,"name":"Humanities/Cultural and media studies"},{"id":55673087,"name":"Social science/Cultural and media studies"},{"id":55673088,"name":"Business and commerce/Information systems and information technology"},{"id":55673089,"name":"Humanities/Philosophy"},{"id":55673090,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-05-18T05:09:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 10:22:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7362510","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7362510","identity":"rs-7362510","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}