How AI Tools Usage Affects Chinese Students’ Key Competencies for Sustainability in Higher Education Context: PLS-SEM and fsQCA Method

How AI Tools Usage Affects Chinese Students’ Key Competencies for Sustainability in Higher Education Context: PLS-SEM and fsQCA Method | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article How AI Tools Usage Affects Chinese Students’ Key Competencies for Sustainability in Higher Education Context: PLS-SEM and fsQCA Method Mengyao He, Fengjuan Zhang, Lei Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9169805/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The use of artificial intelligence (AI) tools has become a regular part of university students’ learning. Many students depend on AI for writing, searching for information, and solving problems. However, it is still not clear how this use affects the development of key competencies for sustainability (KCS). These competencies are essential to higher education’s mission in achieving the United Nations 2030 Agenda, as they help students think critically, act responsibly, and deal with complex challenges in society. This study explores how Chinese university students’ interactions with AI tools relate to the cultivation of KCS. Based on 393 completed surveys, the dataset was processed through Partial Least Squares–Structural Equation Modeling (PLS-SEM) and further examined using Fuzzy-set Qualitative Comparative Analysis (fsQCA). The PLS-SEM results indicate that perceived personalization (PP), subjective norm (SN), learning relevance (LR), perceived ease of use (PEOU), and perceived usefulness (PU) influence students’ behavioral intention to use (BIU) and actual use of (AU) AI tools, which subsequently promote the development of KCS. The fsQCA findings identify several distinct configurations leading to high KCS, suggesting that competency growth can result from multiple interacting conditions rather than a single causal factor. These results suggest that universities should focus not only on providing access to AI tools but also on helping students integrate them meaningfully into academic learning, encouraging reflective, responsible, and goal-oriented use that supports education for sustainable development. Social science/Education Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Social science/Science technology and society Education for Sustainable Development (ESD) Sustainability Competencies AI use TAM2 Higher Education Figures Figure 1 Figure 2 Figure 3 1. Introduction Education for Sustainable Development (ESD) represents a continually evolving framework that redefines the role of education in fostering responsibility across generations for creating a sustainable future (UNESCO, 2002 ). Recognized as a central mechanism for advancing the Sustainable Development Goals (SDGs) (UNESCO, 2017 ), ESD emphasizes education’s contribution to global sustainability (UNESCO, 2015 ). Following the United Nations (UN) General Assembly’s Resolution 57/254, which announced 2005–2014 as the Decade of Education for Sustainable Development, the field has gained prominence as a major focus of sustainability research. The initiative identified education’s foundational role in promoting sustainability, aligning closely with Chap. 36 of Agenda 21 which covers education, awareness, and training. It was first introduced during the 1992 UN Conference on Environment and Development held in Rio de Janeiro (Imara & Altinay, 2021 ). Within educational contexts, competencies are regarded as a central framework that shapes the way teaching and learning contribute to the advancement of sustainable development (UNECE, 2011 ; Wals, 2010 ; Wiek et al., 2011 ). ESD aims to cultivate awareness and competencies connected to the sustainable development goals (SDGs). The sustainability key competencies equip individuals to handle the multifaceted challenges of contemporary society and play a crucial role in advancing the achievement of the SDGs. These competencies enable citizens to understand the interconnectedness of the SDGs, fostering a holistic view of the 2030 Agenda for Sustainable Development (Lichtenthaler, 2023 ; Redman & Wiek, 2021 ). Studies in the field suggest that various theoretical models have been introduced to define the essential competencies underpinning ESD (Rieckmann, 2012 ; UNESCO, 2017 ; Wiek et al., 2011 , 2015 ). UNESCO’s framework for sustainability emphasizes eight key cross-cutting competencies include systemic thinking, anticipatory, normative, strategic, collaboration, critical thinking, self-awareness and integrated problem solving competency that enable individuals to act consciously towards the global challenges (Annelin & Boström, 2023 ; Günther et al., 2024 ). The framework builds upon previous research and frameworks, notably those by Wiek et al. ( 2011 ), de Haan ( 2010 ), Rieckmann ( 2012 ) and others to enhance competencies in sustainability education. Brundiers et al., ( 2021 ) further developed these competencies, emphasizing their relevance in contemporary educational settings. This framework is significant as it not only aligns with the SDGs but also engages a broader audience beyond academia, enhancing its applicability and relevance in various educational contexts (Galkute et al., 2023 ). Artificial intelligence (AI) is now deeply embedded in everyday life, influencing multiple fields of activity and supporting the development of human skills and capacities. Its applications range from healthcare to logistics, demonstrating its versatility and potential to drive innovation (Crompton & Burke, 2023 ; Singh, 2024 ). As AI develops further, it is transforming the ways people and institution’s function, fostering greater efficiency and more informed decision-making across diverse contexts (Crompton et al., 2020 ). The incorporation of AI into the educational domain has markedly influenced instructional practices and learning experiences, enhancing efficiency and outcomes. AI technologies facilitate personalized learning experiences, improve administrative efficiency, and foster pedagogical innovation (Lee, 2025 ; Timotheou et al., 2023 ). The application of AI in higher education has expanded rapidly over the past five years (Ouyang et al., 2022 ), accompanied by the emergence of numerous new digital tools. Previous studies(Chen et al., 2020a ; Crompton et al., 2020 ; Crompton & Song, 2021 ) have highlighted how AI can support both teaching and learning processes in university contexts. Reported advantages include adapting instruction to meet diverse learner needs, offering timely and personalized feedback, supporting assessment design, and forecasting student achievement (Dever et al., 2020 ; Gupta, 2024 ; Verdú et al., 2017 ). Existing studies offer important perspectives on how AI can be meaningfully incorporated into teaching and learning practices within higher education. It is widely acknowledged that technological progress, particularly in AI, holds significant promise for addressing sustainability challenges. Such technologies can connect multiple dimensions of sustainability, optimize the use of resources, stimulate innovation, strengthen cooperation among stakeholders, and support long-term strategic planning for sustainable development (Haefner et al., 2021 ; Tripathi et al., 2024 ). AI has the potential to significantly advance SDGs in education by addressing various challenges and enhancing opportunities for diverse populations (Dever et al., 2020 ). AI technologies can address barriers to ESD by enabling personalized learning, providing real-time feedback, and facilitating data-driven evaluations, thus enhancing access to quality resources and improving teacher training within adaptive learning frameworks (Singh et al., 2024 ). Professional training initiatives can help teachers acquire the competencies required to incorporate AI tools into their instruction, thereby enhancing the overall quality and effectiveness of learning outcomes (Leong et al., 2025 ). Learning platforms powered by AI can greatly expand access to quality education for learners in remote or disadvantaged areas by delivering interactive and personalized materials that reflect local circumstances and support region-specific environmental goals. These platforms leverage advanced technologies to personalize learning experiences, improve engagement, and reduce educational disparities (Avalekar et al., 2025 ; Sharma, 2024 ). The integration of artificial intelligence in education (AIEd) is not only transforming traditional learning environments but also promoting sustainable development and educational equity. Furthermore, AI tools are increasingly valued for enabling educators to teach complex sustainability concepts more efficiently while allowing accurate monitoring of student performance and clearer identification of specific areas requiring improvement. (Alshahrani, 2023 ; Kamruzzaman et al., 2023 ; C.-C. Lin et al., 2023 ). Although the transformative potential of AI tools usage in promoting ESD has been proved by scholars, there remains a notable lack of empirical investigation into the intrinsic psychological and behavioral mechanisms that underpin this process. Current studies have predominantly emphasized the positive outcomes of AI in ESD, but the critical question of how and why these effects occur still requires further investigation, specifically through what internal structural pathways AI-enabled learning influences the development of students’ key competencies for sustainability (KCS). The Technology Acceptance Model (TAM) indicates that users’ perceptions of usefulness and ease of use play a crucial role in determining their willingness to adopt new technologies such as AI tools (Davis, 1989 ). The TAM has been extensively employed to investigate how individuals accept and utilize AI-based tools in a wide range of fields. The Extended Technology Acceptance Model (TAM2) is an evolution of the original TAM, designed to better understand the factors influencing technology adoption (Altawalbeh, 2023 ). There exists a gap to systematically examined the impact of AI tools usage on Chinese students’ KCS within TAM2. To address this gap, the present study proposes and tests a structural equation model based on TAM2, examining how AI tools usage influences Chinese students’ KCS. 2. Literature Review and Hypotheses 2.1 Theoretical Foundation: The Extended Technology Acceptance Model (TAM 2) The Technology Acceptance Model (TAM), formulated by Davis ( 1989 ), provides a parsimonious and powerful framework for explaining technology adoption through two core beliefs: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). These beliefs are posited to directly shape a user’s Behavioral Intention to Use (BIU) technology, which in turn drives Actual Use (AU) behavior. However, the original model’s limited scope, particularly its omission of social and contextual influences, prompted significant extensions (Legris et al., 2003 ; Venkatesh et al., 2003; Venkatesh & Davis, 2000 ). The most influential of these is the Extended TAM (TAM2), developed by Venkatesh and Davis ( 2000 ) through a series of longitudinal field studies. TAM 2 incorporates both Social Influence Processes (such as Subjective Norm) and Cognitive Instrumental Processes (including Job Relevance, Output Quality, and Result Demonstrability) as direct determinants of PU. A critical finding from their work is that the effect of Subjective Norm (SN) on intention is moderated by experience and voluntariness of use. These important qualifications are essential for understanding adoption in complex environments like universities (Ursavaş et al., 2025 ). TAM serves as a foundational theory for understanding adoption of AI tools in learning. For instance, studies have confirmed its relevance in contexts such as the use of AI-based intelligent tutoring systems (e.g., Chiu et al., 2023 ), the adoption of AI applications for language learning (e.g., Hu and Gong, 2025 ), and the acceptance of AI tools by university students (e.g., Zhu et al., 2025 ). These studies consistently demonstrate that the core constructs of PU and PEOU are pivotal in explaining students’ BIU toward AI technologies. 2.1.1 Subjective Norm and Behavioral Intention to Use Subjective Norm (SN) refers to an individual’s perception of social expectations from important referents regarding whether they should engage in a particular behavior (Han et al., 2010 ). In this study, SN is defined as the degree to which Chinese university students perceive that people who matter to them expect or encourage the use of AI tools. Liu ( 2025 ) examined the influence of SN on behavioral intention through an online survey conducted with 504 university teachers selected by random sampling. The findings revealed that SN had a strong and statistically significant positive relationship with the behavioral intention to use (BIU). Maleknia et al. ( 2025 ) indicated that SN functions as an important pathway through which social expectations shape individuals’ behavioral intentions and demonstrated that SN plays a pivotal mediating role in connecting external social influences with behavioral intention. Accordingly, if Chinese university students perceive encouragement from people who are important to them, they are more likely to believe that AI tools will be beneficial for their study and in turn be willing to use them. Based on this rationale, the following hypotheses was proposed: H1 SN could positively influence BIU. 2.1.2 Subjective Norm, Learning Relevance and Perceived Usefulness Research on technology acceptance in education shows that subjective norm and task relevance strongly affect PU. This study targets Chinese university students, therefore learning relevance (LR) is used as a relevant construct. Huang et al. ( 2020 ) applied the extended Technology Acceptance Model to explore the factors that affect Chinese university students’ intentions to use Internet-based learning technologies. The study found that subjective norm had a significant positive effect on their PU of such technologies. Social pressure from peers or teachers can enhance perceived usefulness, especially when technology use is encouraged or demonstrated by others (Venkatesh & Davis, 2000 ). Similarly, when a tool closely supports learning goals, referred to as learning relevance (LR) (Cheung & Vogel, 2013 ), it is more likely to be seen as useful. Therefore, we hypothesize: H2 SN could positively influence PU. H3 LR could positively influence PU. 2.1.3 Perceived Ease of Use, Perceived Usefulness and Behavioral Intention to Use Perceived ease of use is a well-established predictor of both PE and BIU in educational contexts (Davis, 1989 ). When students find an AI tool easy to interact with, they are more likely to regard it as useful and to form intentions to use it. This relationship has been confirmed in studies on e-learning systems and digital platforms (Granić & Marangunić, 2019 ). Ma et al. ( 2025 ) explore the determinants of AI tool adoption by assessing how PU and PEOU influence BIU among users. The findings showed that PEOU significantly affect both PU and BIU. Drawing on the existing literature, the following hypotheses were developed. H4 PEOU could positively influence PU. H5 PEOU could positively influence BIU. 2.1.4 Perceived Usefulness and Behavioral Intention to Use The positive effect of perceived usefulness on behavioral intention is a cornerstone of TAM. In settings where academic performance is emphasized, students are more likely to adopt tools that they believe can improve their learning or help them study more efficiently (Šumak et al., 2011 ). Based on TAM,Wu et al. ( 2024 ) investigated the key predictors of behavioral intention to use AI among 464 Chinese university students majoring in English as a foreign language. Mediation analysis showed that PU exerted a significant positive effect on students’ behavioral intention through their attitudes toward AI.Wang ( 2025 ) confirmed business management students’ behavioral intention to adopt AI tools by considering factors such as AI self-efficacy, AI anxiety, perceived usefulness, curiosity, enjoyment, and perceived control. The findings indicate that PU influence BIU. These lead to the following hypotheses: H6 PU could positively influence BIU. 2.1.5 Behavioral Intention to Use and Actual Use Behavioral intention to use is often recognized as the most direct influence on actual technology use (Venkatesh et al., 2003). The relationship between BIU and AU of AI technologies is a critical area of study, particularly in educational contexts. Studies (Garcia et al., 2025 ; Li, 2023a , 2023b ; Zheng et al., 2024 ) indicate that BIU of AI significantly predicts AU, influenced by factors such as PU, PEOU, and motivational constructs. When students express a clear intention to use an AI tool, they are more likely to engage with it in practice if external conditions allow (Soliman et al., 2025 ). Accordingly, we hypothesize: H7 BIU could positively influence AU. 2.2 The Role of Perceived Personalization in AI Technology Acceptance TAM2 offers a valuable foundation for studying technology adoption. However, its original constructs fail to reflect a defining quality of AI-enabled learning: the capacity to personalize instruction. AI-driven personalized learning systems adapt educational content to fit each learner’s skills, pace, and preferences (Jayanthi R et al., 2025 ). Therefore, this study adds Perceived Personalization (PP) as a factor that influences both PEOU and PE. This addition helps clarify how AI-specific characteristics shape student evaluations of the technology. The concept of PP has its roots in the broader study of technology adaptation and user-centered design. While early work on personalization emerged in marketing research (e.g., (Brusilovsky, 1996 )), its application to educational technology gained prominence with the rise of intelligent tutoring systems and adaptive learning platforms. Acosta-Enriquez et al. ( 2024 )d rekci & Çelik (2024) demonstrate that university students’ attitudes toward AI tools are shaped by their perceptions of personalization and digital literacy. These perceptions influence their intent to use AI in academic activities, highlighting the role of PP in fostering acceptance of AI. Wang et al. ( 2025 ) expand the understanding of AI acceptance in language learning by incorporating emotional factors and intrinsic motivation, suggesting that positive emotions related to personalized AI interactions can enhance acceptance and engagement. PP and PEOU highlight their significant roles in shaping user acceptance and behavioral intentions across various technological contexts. Some studies (Jo, 2025 ; Kang & Namkung, 2019 ; C.-P. Lin et al., 2008 )proved that PP had significantly affected PEOU. Jo ( 2025 )extends the TAM by integrating these GAI-specific variables to assess their impact on PEOU. The findings indicated that personalization also positively influences PEOU, indicating its crucial role in GAI adoption.Yadegaridehkordi et al. ( 2019 ) expanded TAM by adding mobility, collaboration, and personalization as external variables. They proved that personalization significantly influences PEOU and has insignificant impacts on PU. Guang and Xueliang ( 2025 )obtained promising results which revealed the positive influence of personalization on PEOU and PU. Based on these studies, the following hypotheses were proposed. H8 PP could positively influence PEOU. H9 PP could positively influence PU. 2.3 From AI Use to Key Competencies for Sustainability The cognitive impact of AI use on students varies significantly based on engagement level. Shallow interactions may hinder competency gains, while deep, strategic use fosters enhanced thinking, decision-making, and problem-solving abilities, emphasizing the importance of responsible AI utilization (Fredricks et al., 2004 ; Pooja, 2024 ). This study uses TAM2 as a theoretical basis, implicitly captures a form of intentional use. Students are posited to use AI not out of compulsion, but because they perceive it as useful and easy to integrate into their learning processes. This perceived utility, especially when coupled with personalized feedback, creates the conditions for the sustained and purposeful engagement necessary for competency development. Lozano et al. (2017) used Principal Component Analysis (PCA) to classify the various competencies for sustainability into three dimensions (shown as Table 1 ). This classification was based on the loading results of several specific capabilities in the empirical data. Accordingly, the construct was formed by three dimensions: Extrospective-social competencies, reflecting abilities for collaboration, critical thinking, and normative judgment. Introspective-personal competencies, centering on self-awareness. Systemic thinking, anticipatory thinking, strategic action and integrated problem-solving are included in Cogitative-processual competencies. AI tools can support the development of systemic thinking skills in educational settings by providing accurate and helpful responses to prompts. Osman ( 2023 ) explores an AI-supported program that combined project-based learning with the Moodle platform. The findings showed that, compared with traditional instruction, this approach improved university students’ academic performance and strengthened their systemic thinking skills, illustrating the practical value of AI tools in higher education. Gebreegziabher et al. ( 2023 ) present an interactive AI tool that synthesizes rules based on user annotations in real-time. Its human-AI collaborative approach exemplifies how explainable and flexible AI systems can support qualitative coding tasks, promoting anticipatory understanding through interactive rule synthesis. AI tools can personalize learning experiences, adapting sustainability education to diverse cultural contexts. By addressing unique learner needs, these tools enhance strategic competency , fostering deeper understanding and engagement, ultimately contributing to a more sustainable and equitable global society (Ghosh, 2025 ; Yingsoon et al., 2025 ).Štuikys et al. ( 2025 ) point that AI tools can enhance integrated problem solving competency by promoting critical thinking , creativity, and conceptual understanding in STEM education. Their structured integration supports personalized, inquiry-based learning, essential for developing skills necessary for sustainability and addressing future challenges. Asrifan et al. ( 2025 )explores the integration of AI and sustainability in education, highlighting its potential to enhance student engagement, critical thinking , and problem-solving skills, while promoting environmental consciousness and social accountability. Henriksen et al. ( 2025 ) provides a critical thematic literature review and present that AI offers promising applications for enhancing self-awareness , empathy, and social skills through tools like real-time emotional feedback and personalized learning experiences.Tian & Zheng ( 2025 ) emphasized the educational potential of AI in fostering students’ higher-order competencies, particularly the 4C skills (critical thinking, communication, collaboration, and creativity). Their study offers meaningful implications for future studies and practical initiatives that seek to employ AI tools to strengthen these competencies by identifying the conditions that optimize their educational impact. Similarly,Abulibdeh et al. ( 2024 ) critically analyzed the integration of AI tools, with particular attention to ChatGPT, in the context of ESD. By providing insights and strategies, their paper emphasized the enhancement of learning experiences and normative competency in learners for sustainable development. From the above studies, the actual use of AI tools has a positive impact on each of the students’ sustainability competency. This study hypothesizes that using AI in learning builds KCS. By offloading lower-order tasks (Sweller, 2011 ), AI allows students to engage in the higher-order thinking these competencies require. The feedback of the technology also forces critical engagement and self-reflection (Hattie & Timperley, 2007 ), directly cultivating competencies like critical thinking and systems thought as defined by UNESCO ( 2017 ). Therefore, this study proposed the following hypotheses: H10 AU could positively affect KCS Table 1 Sustainability competences’ groups Group Sustainability competence Extrospective-social Collaboration (Tian & Zheng, 2025 ); Critical thinking (Asrifan et al., 2025 ; Štuikys et al., 2025 ; Tian & Zheng, 2025 ); Normative (Abulibdeh et al., 2024 ) Introspective-personal Self-awareness (Henriksen et al., 2025 ) Cogitative-processual Systemic thinking (Osman, 2023 ); Anticipatory (Gebreegziabher et al., 2023 ); Strategic (Ghosh, 2025 ; Yingsoon et al., 2025 ); Integrated problem solving (Asrifan et al., 2025 ; Štuikys et al., 2025 ) 3. Methods 3.1 Research Instrument This study adopted a questionnaire as the research instrument which consisted of two parts. The first section collected demographic data such as respondents’ gender, age, academic year, and field of study. The second part consists of questionnaire questions to verify the hypothesis of this study. The items collected from several relevant studies mainly focus on eight constructs. The items of Perceived Personalization (PP) are adapted from Higgins et al. ( 2018 ) and Sheng et al. ( 2008 ) and some items are slightly modified from the previous studies to meet this study’s objectives. The modified version of some items adapted from To and Tang ( 2019 ), Teo and Van Schalk ( 2009 ), Fan and Wang ( 2023 ) and Falebita and Kok ( 2025 ) are used for six constructs of this study: Subjective Norm (SN), Learning Relevance (LR), Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Behavioral Intention to Use (BIU) and Actual Use (AU). The questionnaire content was further refined through the addition and removal of specific items to ensure that each construct was accurately represented. This process helped confirm that all questions were contextually appropriate and meaningful for the target population. The items of Key Competencies for Sustainability (KCS) are organized according to the framework proposed by Lozano et al. (2017). A total of 3 items for KCS were then developed based on the foundational work of UNESCO ( 2017 ) and subsequent empirical study (G. Liu & Ma, 2024 ). All 28 items were measured using a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Each construct’s items were reviewed by subject-matter experts and refined through several rounds of revision to ensure clarity and relevance. The detailed questionnaire items and corresponding reference sources are provided in Appendix 1 . 3.2 Data Collection and Participants A total of 482 questionnaires were distributed to universities through the online platform “ Wenjuanxing” (Professional Version). To expand the sample size, the study also applied a snowball sampling approach, allowing participants to invite additional respondents from their networks. To ensure participant privacy, the data was collected anonymously. After excluding 89 invalid questionnaires (response time too long or too short, too many consecutive answers and selected as not having used AI tools), 393 valid questionnaires were collected. The participants in this study were university students (undergraduate and postgraduate) from Zhejiang and Shanxi provinces in China. There was a total of 393 participants and all participants had experience using AI tools. The demographic profile of the participants was summarized in Table 2 . The sample was characterized by a gender distribution of 40.2% males and 59.8% females. In terms of age, most participants were young adults, with the 19–21 age group being the largest (38.9%), followed by the 16–18 age group (32.1%). Most respondents were undergraduate students (84.8%), with freshmen making up 62.8% of the sample. Their fields of study were mainly in the Natural Sciences (49.1%) and Social Sciences (46.8%). Most participants reported regular use of AI tools, including 54.3% who used them “often” and 6.6% who used them “very often.” Table 2 Demographic characteristics Categories Item Frequency Percentage Gender Male 158 40.2% Female 235 59.8% Age 16–18 126 32.1% 19–21 153 38.9% 22–24 60 15.3% > 24 54 13.7% Grade Freshman 247 62.8% Sophomore 52 13.2% Junior 16 4.2% Senior 18 4.6% Postgraduate 60 15.3% Major Natural Sciences 193 49.1% Social Sciences 184 46.8% Others 16 4.1% The frequency of using AI tools Rarely 21 5.3% Occasionally 133 33.8% Often 213 54.3% Very often 26 6.6% 3.4 Data Analysis First, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the data and evaluate the proposed hypotheses. PLS-SEM has become increasingly popular in empirical research because it can effectively handle complex structural models, perform well with relatively small samples, and does not impose strict assumptions about data normality. This method is particularly advantageous in fields like educational technology and public sector research, where traditional methods may falter due to the complexity of relationships and the nature of data (Demir & Uşak, 2025 ; J. Hair & Alamer, 2022 ). The PLS-SEM analysis is carried out in two stages (shown as Fig. 1 ) (Hair et al., 2013 ). The measurement model, which specifies the relationships between observed indicators and their corresponding latent constructs, was examined in the initial stage to confirm the reliability and validity of the operationalized variables. Specifically, Confirmatory Composite Analysis (CCA) was conducted for this purpose. In addition, Cronbach’s Alpha (CA), factor loadings, Average Variance Extracted (AVE), Composite Reliability (CR), and the Fornell-Larcker criterion for discriminant validity were employed. These procedures ensured that the observed variables accurately represented the underlying theoretical constructs they were designed to measure (Hair et al., 2019 ). In the second stage, the structural model which specifies the relationships among the latent constructs was assessed to test the study’s hypotheses and examine the effects between variables. This analysis made it possible to evaluate the direction, significance, and strength of the paths connecting the constructs, thereby providing empirical evidence for confirming or rejecting the proposed hypotheses (Sarstedt et al., 2017 ). The theoretical framework and hypotheses were tested using SmartPLS version 4.1.1.4. Additionally, Fuzzy-set Qualitative Comparative Analysis (fsQCA) was employed to better understand the relationships between these factors. FsQCA is a methodology that combines case-oriented and variable-oriented analysis, allowing researchers to examine causal complexities through set membership calibration, emphasizing configurational relationships among conditions rather than independent variable effects on outcomes (Mendel, 2013 ). The fsQCA analysis, as an asymmetric approach, enables a deeper understanding of the non-linear and complex relationships that exist among the variables (Prentice, 2019 ). FsQCA employs Boolean algebra to analyze how combinations of causal conditions relate to specific outcomes, allowing for the identification of multiple pathways to success (Fainshmidt, 2020 ). The software fsQCA 4.0 was used in this study to examine the configurations between the different latent constructs in the research model (Fig. 2 ). 4. Results 4.1 PLS-SEM Analysis 4.1.1 Evaluation of Measurement Model The validated dataset was analyzed for reliability and validity to assess the performance of the measurement model. The reliability of the latent constructs was examined using both Composite Reliability (CR) and Cronbach’s Alpha (CA) (Hair et al., 2019 ). The latent constructs were deemed reliable when their CR and Cronbach’s Alpha CA values exceeded the threshold of 0.70 (Fornell & Larcker, 1981 ; Hair & Alamer, 2022 ), which indicates acceptable internal consistency across all constructs. As presented in Table 3 , all values exceeded the recommended threshold of 0.70, with CR ranging from 0.838 to 0.902 and CA ranging from 0.712 to 0.854. Convergent validity was further evaluated using the Average Variance Extracted (AVE) and standardized factor loadings. The AVE values ranged from 0.569 to 0.696, all above the threshold of 0.50 recommended by Fornell and Larcker ( 1981 ), which means that more than half of the variance in the observed indicators is captured by the latent constructs. Furthermore, all factor loadings were greater than 0.70 (see Fig. 2 ), which confirms that each indicator strongly represents its corresponding construct (Chin, 1998 ). Table 3 Result of confirmatory factor analysis Construct CA CR AVE PP 0.801 0.869 0.625 SN 0.765 0.864 0.679 LR 0.739 0.852 0.657 PEOU 0.750 0.840 0.569 PU 0.854 0.902 0.696 BIU 0.786 0.862 0.610 AU 0.744 0.854 0.662 KCS 0.712 0.838 0.634 Following the discriminant validity criterion proposed by Fornell and Larcker ( 1981 ), the AVE for each construct should exceed the squared correlations or the square roots of the AVEs of the other constructs. As displayed in Table 4 , the diagonal values were higher than those in the corresponding rows and columns, indicating that discriminant validity has been satisfactorily established for the study data. Table 4 Discriminant validity for the measurement model AU AU BIU KCS LR PEOU PP PU SN 0.814 BIU 0.690 0.781 KCS 0.561 0.504 0.796 LR 0.475 0.494 0.289 0.811 PEOU 0.560 0.717 0.449 0.481 0.754 PP 0.396 0.448 0.354 0.436 0.560 0.790 PU 0.512 0.620 0.460 0.496 0.687 0.551 0.834 SN 0.447 0.480 0.251 0.368 0.419 0.516 0.489 0.824 4.1.2 Evaluation of Structural Model The structural model was evaluated for collinearity, path coefficients, coefficient of determination ( \(\:{R}^{2}\) ), and effect sizes ( \(\:{f}^{2}\) ). As shown in Table 5 , all variance inflation factor (VIF) values range from 1.000 to 2.089 below the conservative threshold of 3.3 (Hair et al., 2019 ). This result indicates that collinearity is not a concern in the model. Bootstrapping procedure was applied with 5000 subsamples to examine the path coefficients, their significance levels, and t-values. The analysis showed that the strongest predictor of BIU was PEOU with a path coefficient of β = 0.524 (p < 0.001), followed by PU (β = 0.175, p < 0.001) and SN (β = 0.175, p < 0.001). These findings provide strong support for hypotheses H5, H6, and H1. Regarding the factors influencing PU, PEOU had the strongest positive effect (β = 0.468, p < 0.001), followed by SN (β = 0.167, p < 0.001), LR (β = 0.149, p < 0.01), and PP (β = 0.138, p < 0.05). These findings provide support for hypotheses H4, H2, H3, and H9. The results also show that PP had a significant impact on PEOU (β = 0.560, p < 0.001), supporting hypotheses H8. The path from BIU to AU was particularly strong (β = 0.690, p < 0.001) and the result support to hypothesis H7. Finally, AU had a notable effect on KCS (β = 0.561, p < 0.001), confirming hypotheses H10. The model’s predictive power was verified through the coefficient of determination (R²). As presented in Fig. 3 , the value for BIU (0.568) and PU (0.553) reflects moderate to substantial predictive accuracy based on the criteria suggested by Hair et al. ( 2019 ) and Chin et al. ( 2008 ). AU (0.476) and KCS (0.315) exhibit moderate explanatory power and PEOU (0.313) represents an acceptable level for behavioral studies. To further evaluate the strength of the relationships, the effect size (f²) was examined. The values of f² 0.35, 0.15, and 0.02 represent significant, moderate, and minor impacts (Cohen, 2013 ). The link between BIU and AU shows a large effect, highlighting its central role in the model. The strong influence also appeared between AU and KCS. The effects of PEOU on both PU and BIU fall within the medium impact, suggesting meaningful contributions of these paths. Although some other relationships were smaller in magnitude, all proposed links were statistically significant, supporting the robustness of the structural model. Out-of-sample predictive power of this model was evaluated using the PLSpredict procedure with 10 folds. All constructs demonstrated positive Q² values, ranging from 0.101 (KCS) to 0.383 (PU). According to the guidelines established by Hair et al. ( 2019 ) and Shmueli et al. ( 2019 ), any positive Q² predict value indicates that the model possesses predictive relevance. Values above 0.25 are generally interpreted as indicating medium predictive relevance, and those exceeding 0.50 suggest a large level of predictive accuracy. In this study, BIU (0.274), PEOU (0.303) and PU (0.383) reached the medium range. Although KCS (0.101) presented a smaller Q² value, its positive result still indicates that the model holds predictive relevance for this construct (Geisser, 1974 ; Stone, 1976 ). Overall, these findings suggest that the proposed model not only captures the relationships among variables but is also capable of providing meaningful out-of-sample predictions. Table 5 Hypotheses testing for the overall model Hypotheses Paths \(\:\beta\:\) VIF T values P values \(\:{f}^{2}\) Result H1 SN -> BIU 0.175 1.337 4.349 0.000 0.053 Supported H2 SN -> PU 0.167 1.438 3.324 0.001 0.043 Supported H3 LR -> PU 0.149 1.399 2.689 0.007 0.036 Supported H4 PEOU -> PU 0.468 1.649 8.354 0.000 0.298 Supported H5 PEOU -> BIU 0.524 1.927 11.365 0.000 0.330 Supported H6 PU -> BIU 0.175 2.089 3.223 0.001 0.034 Supported H7 BIU -> AU 0.690 1.000 17.694 0.000 0.909 Supported H8 PP -> PEOU 0.560 1.000 11.495 0.000 0.456 Supported H9 PP -> PU 0.138 1.742 2.277 0.023 0.024 Supported H10 AU -> KCS 0.561 1.000 12.406 0.000 0.459 Supported 4.2 FsQCA Analysis 4.2.1 Calibration To apply the collected survey data to fsQCA analysis, the original values needed to be transformed into fuzzy sets, assigning each variable a membership score between 0 and 1. A value of 1 indicates full membership in the set, 0 indicates full non-membership, and 0.5 represents the crossover point, indicating that a case is neither fully in nor fully out of the set (Ragin, 2009 ). The 5-point Likert scale was used for data collection in this study, the direct calibration method was employed to convert the original scores. Direct calibration relies on three anchor points representing full membership, the crossover point, and full non-membership (Fainshmidt et al., 2019 ). Considering that Likert-scale data may be skewed toward one extreme, using the midpoint alone as the crossover point could be problematic. Therefore, following Fainshmidt et al. ( 2019 ) recommendation to combine theoretical understanding with empirical data, the 75th percentile, mean, and 25th percentile of each variable were set as the thresholds for full membership, the crossover point, and full non-membership, respectively. This approach allows for adjusting asymmetries across variables and ensures that the calibration reflects the relative positions of cases within the sample. 4.2.2 Necessity analysis of a single antecedent condition Before conducting the sufficiency analysis, a necessary-condition test was conducted to examine whether any single antecedent condition must always be present for the outcome to occur. In fsQCA, a condition is considered necessary if the outcome never occurs in its absence, which is evaluated through consistency and coverage (Ragin, 2009 ; Rihoux & Ragin, 2009 ). The consistency of 0.90 is commonly used to identify a necessary condition (Greckhamer et al., 2018 ). In this study, all calibrated antecedent conditions were tested for both presence and absence in relation to the outcome. The results in Table 6 show that none of the conditions reached a consistency of 0.90, and their coverage was not high enough to be considered important on their own. This means that no single factor is necessary for the outcome, suggesting that the result comes from a mix of factors working together. Table 6 Analysis of necessary conditions KCS ~ KCS Consistency Coverage Consistency Coverage PP 0.758 0.775 0.611 0.642 ~ PP 0.650 0.619 0.786 0.770 SN 0.751 0.691 0.661 0.625 ~ SN 0.593 0.630 0.674 0.735 LR 0.749 0.724 0.674 0.670 ~ LR 0.658 0.663 0.722 0.748 PEOU 0.693 0.754 0.582 0.652 ~ PEOU 0.681 0.613 0.781 0.723 PU 0.754 0.788 0.596 0.640 ~ PU 0.655 0.612 0.802 0.770 BIU 0.800 0.782 0.613 0.616 ~ BIU 0.607 0.604 0.783 0.801 AU 0.804 0.772 0.577 0.570 ~ AU 0.552 0.560 0.770 0.802 4.2.3 Adequacy analysis of condition configuration Following the necessity analysis, which indicated that no single condition was necessary for Key Competencies for Sustainability (KCS), this study proceeded to identify sufficient configurations of conditions through fsQCA. The construction of the truth table represented the next critical step. In this process, a frequency threshold of 3, a raw consistency cutoff of 0.8, and a proportional reduction in inconsistency (PRI) cutoff of 0.7 were established to optimally balance consistency and coverage, following established methodological practice (Pappas & Woodside, 2021 ; Patala et al., 2021 ). The fsQCA software simplifies the truth table data using Boolean algebra minimization, allowing the generation of complex, intermediate, and parsimonious solutions. Consequently, six distinct configurations leading to high BI were identified. The analysis identified six distinct configurations sufficient for achieving KCS. The overall solution coverage was 0.617, indicating that these six pathways collectively explain 61.7% of the cases exhibiting high sustainability competencies. The overall solution consistency was 0.896, confirming that these configurations are highly reliable sufficient conditions for the outcome (Rihoux & Ragin, 2009 ). The specific configurations are presented in Table 7 and interpreted below. Across the six configurations, AU appeared in every pathway as either a core or marginal condition. This shows that students’ direct use of AI tools is closely related to higher levels of KCS. Several configurations showed the influence of PP and SN. Solutions 3, 4, and 5 contained both PP and SN. This means that when students feel that AI tools match their learning needs and when they receive encouragement from others, the outcome of high KCS is enhanced, even when some other conditions are not present. In contrast, LR was absent in Solutions 2, 5, and 6. This means that high KCS can still appear even when students do not strongly view AI tools as directly related to course content. In these cases, other conditions such as PP, PEOU, or AU play a more important role. Solutions 1, 3, and 6 showed the combined presence of PEOU, PU, and BIU. These configurations suggest that positive judgments about AI tools and the intention to use them often appear together with higher KCS, especially when students also use the tools in practice. Among all pathways, Solution 4 had the highest consistency value (0.988) and also had a relatively high raw coverage (0.558). This configuration contained PP, SN, PEOU, BIU, and AU. This combination shows a strong pattern in which ease of use, personalization, social expectations, and actual usage appear together with high KCS. Table 7 Sufficient configurations Causal conditions Solution 1 2 3 4 5 6 PP ● ● ● ● SN ● ● ● LR ● ⊗ ⊗ ⊗ PEOU ● ● ● ● PU ● ● BIU ● ● ● ● ● AU ● ● ● ● ● ● Consistency 0.863 0.885 0.906 0.988 0.908 0.918 Raw coverage 0.541 0.508 0.572 0.558 0.366 0.383 Unique coverage 0.016 0.004 0.012 0.006 0.006 0.009 Solution consistency 0.896 Solution coverage 0.617 Note : ● denotes the presence of a condition, ⊗ denotes its absence, and a blank cell indicates that the condition is not relevant. A large symbol represents a core condition, whereas a small symbol represents a peripheral condition. 4.2.4 Robustness tests To confirm the stability of the fsQCA results, several robustness tests were conducted. First, the consistency threshold was adjusted from 0.80 to 0.85. The main configurations remained and the core conditions did not change after the adjustment, which shows that the findings are not sensitive to small variations in the consistency level. Second, the frequency threshold was increased by one case. The purpose was to test whether the configurations would shift when low-frequency cases were excluded. The results closely matched the original solutions. The key conditions in the main pathways remained unchanged, and the consistency and coverage stayed at similar levels. Third, the analysis was repeated using an alternative calibration procedure with slightly different crossover points. The aim was to check whether the outcome depended on a specific calibration choice. The recalibrated results produced configurations that matched the original structure. The same groups of conditions appeared as either core or marginal, and the key pathways to high KCS remained stable. These checks indicate that the findings of the fsQCA analysis are reliable under different threshold settings and calibration decisions. 5. Discussion 5.1 Discussion of the PLS-SEM Findings 5.1.1 Subjective Norm (H1, H2) The PLS-SEM findings show that SN exerts significant positive effects both on BIU and on PU. This pathway highlights the important role of social influence in shaping both what students believe the AI tools will do for them (i.e., its usefulness) and whether they intend to use it. The Theory of Planned Behavior (TPB) where SN is one of the factors of behavioral intention also support the positive and significant effect of SN on BIU (Ajzen, 1991 ). In contexts of AI use, this suggested that individuals’ perceptions that important others (e.g., instructors, peers, supervisors) expect them to use the AI tools will increase their intention to use it. Previous meta-analyses of technology acceptance confirm that SN often contributes to BIU, though effect sizes vary by context (Marikyan et al., 2023 ). The positive relationship between SN and PU shows that when instructors or classmates show a positive attitude toward AI tools, students are likely to take these views as an indication of the tools’ effectiveness in learning (Delcker et al., 2024 ). In the Chinese higher education environment, the role of SN is especially strong as the culture places high importance on conformity and respect for authority (Liu & Guo, 2023 ). Students usually follow the attitudes and behaviors expected by their teachers and peers, especially when these expectations show support from the institution or society for using technology (Edmunds et al., 2012 ). This explains why students who perceive strong social encouragement toward AI-based learning tools are more likely to consider these tools beneficial and necessary for academic success. Studies by Al-Nuaimi e t al. (2021) and Zhou et al. ( 2023 ) also found that in learning environments that emphasize cooperation, encouragement from teachers and peers can enhance both perceived usefulness and students’ intention to use digital tools. 5.1.2 Learning Relevance (H3) The analysis shows that LR has a strong positive effect on PU. This means that when students feel that AI tools are directly connected to their learning needs and course objectives, they are more likely to believe these tools are helpful for their academic progress. The sense of relevance makes the use of AI tools purposeful rather than optional, and this perception strengthens their belief that such tools can improve learning outcomes. Learning relevance is one of the key factors that shape students’ evaluation of educational technologies. Students valued AI learning tools more when these tools can clearly support their academic goals and self-regulated learning behaviors (Rets et al., 2021 ). The relevance of AI tools to students’ subject content strongly influenced their perception of usefulness and their motivation to continue using them (Ucha, 2023 ). University students often follow structured curricula and aim to meet specific academic standards. When an AI tool is perceived as directly helping them understand key concepts or improve performance, they are more willing to view it as valuable. Therefore, highlighting the connection between AI tools and students’ concrete learning goals is critical for increasing their perception of usefulness. 5.1.3 Perceived Ease of Use (H4, H5) The results indicate that PEOU has a significant positive effect on both PU and BIU. This suggests that when students perceive the AI tools as easy to use, they often consider it helpful and intend to incorporate it into their studies. Students’ perception of ease reduces the barrier of effort and supports their judgment that the tool can support outcomes, supporting the findings of Lin and Yu ( 2023 ). In addition, when students believe that the fact using the AI tools will not require large amounts of time or effort also contributes directly to their intention to use AI tools (Widiar et al., 2023 ). In a higher education context, the effects of PEOU are especially important. While university students commonly use AI to support academic tasks, they may avoid complicated tools that require significant effort, despite their potential benefits, due to concerns about usability and the time constraints they face (Triyanto & Handayani, 2025). This finding shows that educators and developers need to pay as much attention to usability as to functionality. 5.1.4 Perceived Usefulness (H6) The PLS-SEM results show that PU has a strong positive effect on students’ BIU. This suggests that when students believe AI tools can help them learn more effectively or complete tasks with better results, they tend to develop a stronger willingness to use them. Students’ perception of the usefulness of AI-based learning applications was one of the main predictors of their intention to use them (Ayanwale & Molefi, 2024 ). Students’ recognition of AI writing tools’ perceived usefulness significantly influences their attitudes, suggesting that highlighting these tools’ benefits can enhance motivation and adoption among university students in completing coursework and improving learning quality (Malmous & Zaidoune, 2024 ). These findings indicate that educators should not only introduce AI tools but also guide students to see their academic value. 5.1.5 Behavioral Intention to Use (H7) The model results indicate that BIU significantly predicts AU of AI tools among Chinese higher-education students. When students form the intention to use a tool, the chances increase that they will engage with it in their learning practices (Sergeeva et al., 2025 ; Strzelecki, 2024 ). A meta-analytic review based on UTAUT2 conducted by Zheng et al. ( 2025 ) also found that BIU is the key influencing factor of use behavior in education settings. In this study, the strong relationship between BIU and AU shows that when students see an AI tool as useful and easy to use, their intention often leads to actual use. This finding is consistent with the TAM and its extensions, which regards intention as the direct step before behavior. BIU alone may not lead to actual technology use, particularly when contextual variables are absent (Doleck et al., 2018 ). Students may need technical help, encouragement from teachers, or examples of how the tools can be applied before they start using them regularly. 5.1.6 Perceived Personalization (H8, H9) The results indicate that PP plays a meaningful role in PEOU and PU of AI tools. Students who felt that the AI system responded to their preferences or adjusted to their learning progress reported that the tools were easier to handle and more helpful in their study (Chen et al., 2020b ; Yaseen et al., 2025 ). This suggests that personalization shapes both the sense of effort and the sense of value attached to AI use. For university students, particularly where the use of new technology is still growing, feeling that an AI system fits their way of learning is often more important than how convenient it is to use. While standard interfaces are sufficient for basic use, students respond more positively when AI systems notice their study habits and provide suitable adjustments or feedback. This responsiveness makes the AI tools more relevant, engaging, and supportive of individual learning needs (Naseer & Khawaja, 2025 ; Slimi et al., 2025 ). These findings point that usability and usefulness in educational technology are not just technical features. They are deeply shaped by how personally the system engages each user. Personalization is increasingly recognized as a foundational requirement, not only an optional enhancement for genuine ease of use and perceived learning value. 5.1.7 Actual Use (H10) The model shows that AU of AI tools have a positive effect on students’ KCS. When students move from intention to sustained use, they practice tasks that call for systemic thinking, anticipatory reasoning, collaboration, and problem-solving. This practice supports growth in the competencies described in recent updates to the KCS literature(Brundiers et al., 2021 ). Current work in sustainability education points to pedagogies that require students to use digital tools while engaging real problems. Studies report that project-based courses and technology-supported activities raise students’ SDG knowledge and strengthen competence development, which is consistent with the effect observed here for AU on KCS (Espino-Díaz- et al., 2025). Research on personalized and interactive digital learning further shows that when students actively work with the AI tools, they build transferable skills linked to KCS (Lozano et al., 2022 ). These results fit with the competence-based approach now common in higher education for sustainable development. Programs that ask students to apply tools to open-ended tasks are more likely to develop the targeted competencies than programs that keep learning at the level of exposure only. 5.2 Discussion of the fsQCA Findings The fsQCA results provide a detailed view of how different combinations of factors lead to higher levels of key competencies for sustainability. While the PLS-SEM analysis identified direct linear effects between constructs, the configurational approach shows that several different paths can produce the same outcome. This means that students can reach high KCS through different mixes of personal, social, and technological factors. AU appears in all configurations, showing that frequent and meaningful use of AI tools is closely linked to competence development. This is consistent with the PLS-SEM result that AU directly affects KCS, but the fsQCA findings further show that AU interacts with other conditions such as PP, SN, and PEOU. Students develop stronger competencies when they use AI tools in contexts that feel adaptive, socially supported, and easy to manage. Several configurations include both PP and SN, suggesting that when students experience a sense of personalization and receive encouragement from others, they are more engaged and reflective in their learning. Supportive and adaptive learning environments enhance motivation and lead to deeper understanding (Kember et al., 2010 ; Zourmpakis et al., 2023 ). This shows that the social and personal aspects of AI-supported learning work together to strengthen outcomes. Other configurations highlight the effects of PU, PEOU, and BIU. When students find AI tools useful, simple to use, and intend to apply them, their engagement becomes more consistent and leads to better learning results. This pattern also appeared in the PLS-SEM model, but the fsQCA findings show that these factors need to appear together rather than act separately (Alkhawaja et al., 2022 ; Panergayo & Aliazas, 2021 ; Tawafak et al., 2023 ). The presence and absence of LR across configurations show that the same outcome can occur under different conditions. In some cases, high KCS appears even when students do not see a strong link between AI tools and course content. This means that other factors, such as personalization and AU, can make up for low relevance (Sayed et al., 2023 ). The result illustrates the idea that educational effects are not symmetrical and that different combinations of factors can work in multiple ways to reach similar results (Ragin, 2009 ). The fsQCA results complement the PLS-SEM findings by showing how the conditions work together rather than acting alone. The linear model explains the separate effects of each factor on KCS, while the configurational results show the ways in which these factors appear in combination. The findings suggest that the development of key competencies for sustainability depends on how students perceive and use AI tools in practice. It is influenced by their evaluation of the tools’ usefulness, the simplicity of operation, and the level of personalization they experience. 6. Conclusion, Implications and Limitations This study examined how the use of AI tools influences Chinese university students’ key competencies for sustainability by combining PLS-SEM and fsQCA methods. The structural model showed that perceived personalization, subjective norm, learning relevance, perceived ease of use, and perceived usefulness jointly affect students’ behavioral intention and actual use of AI tools, which in turn improves KCS. The configurational results complemented these findings by showing that high levels of KCS can arise from several different combinations of conditions rather than a single dominant path. Across these configurations, actual use consistently appeared as a central condition, confirming that frequent and meaningful engagement with AI tools plays an important role in competence development. These two methods provide both a linear and a configurational understanding of how perceptions, intentions, and behaviors interact in improving students’ KCS in the context of higher education. These findings have value for both theory and practice. They extend existing studies on AI use in higher education by showing that the growth of sustainability competencies depends on how students engage with technology and how they connect it to learning objectives. The results broaden the TAM by applying it to sustainability, showing that ease of use, usefulness, and personalization form a linked process that leads to stronger intention and more frequent use. For teachers, these findings suggest that promoting active use of AI tools involves more than teaching technical skills. Guidance should focus on helping students relate the functions of AI tools to their academic work and understand how these tools can support critical and reflective thinking. Universities can also create teaching policies that support responsible and purposeful use of AI, encouraging students to apply the technology in ways that strengthen learning quality and awareness of sustainable development. Several limitations should be mentioned while this study offers valuable findings. The data were collected from a sample of Chinese universities, and the results may not represent other national or institutional contexts. The study relied on self-reported measures, which describe students’ perceptions rather than their actual performance. Future work could apply longitudinal or experimental designs to track changes in sustainable competencies development and verify the causal direction of the observed relationships. It would also be useful to examine how institutional environments, teaching practices, and disciplinary differences influence the use of AI tools in higher education. Extending this study to different regions and educational systems could provide a broader view of how AI-based learning contributes to education for sustainable development. Declarations Competing interests The author(s) declared no potential conflicts of interest with respect to the publication of this article, and/or authorship. Ethics Approval Our study was approved by the Ethics Review Committee of the School of Education, Zhejiang University of Technology (Approval number: 2026042101) on 21st April. The study was conducted in accordance with the ethical principles outlined in the Helsinki Declaration. Informed Consent Not applicable. Informed consent was obtained verbally from all individual participants before taking part in the questionnaire survey conducted from January to April 2026. The anonymity and confidentiality of the participants were guaranteed, and participation was entirely voluntary. All participants have also given their consent to the potential publication of this study. Fundings No funding was received for this study. Author Contribution Authors’ contributions:Author H: conceptualization, methodology, original writing, data collection and data analysis. Author Z: data collection, reviewing, supervision, and editing. Author W: conceptualization, supervision, and methodology. 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LR LR1 In my learning, use of AI tools is frequent To and Tang ( 2019 ) LR2 In my learning, usage of AI tools is relevant. LR3 In my learning, use of AI tools is important. PEOU PEOU1 I find it easy to access AI tools. Fan and Wang, ( 2023 ); Falebita and Kok, ( 2025 ) PEOU2 I find it easy to learn how to use AI tools. PEOU3 I find it easy to understand the content provided by AI tools. PEOU4 I find AI tools to be flexible to interact with. PU PU1 I think AI tools make my learning easy. Fan and Wang, ( 2023 ); Falebita and Kok, ( 2025 ) PU2 I think AI tools can assist me in learning more efficiently. PU3 I find AI tools useful in my learning tasks. PU4 I think AI tools can help me improve the quality of my learning. BIU BIU1 I am willing to spend time and effort to learn how to use AI tools better. Fan and Wang, ( 2023 ); Falebita and Kok, ( 2025 ) BIU2 I plan to use AI tools for my learning needs in the future. BIU3 I expect to use AI tools frequently in the future for learning purposes. BIU4 I think it is necessary to use AI tools in assisting learning. AU AU1 I rely on AI tools to help me complete a variety of tasks. Fan and Wang, ( 2023 ); Falebita and Kok, ( 2025 ) AU2 I invest time and effort in learning and using AI tools. AU3 I am constantly exploring new AI tools and incorporating them in completing various tasks. KCS KCS1 AI tools help me better collaborate with others in learning. Liu and Ma ( 2024 ); UNESCO ( 2017 ) KCS2 Using AI tools improves my ability to integrate different perspectives to solve complex sustainability problems. KCS3 Using AI tools in learning makes me more aware of my responsibilities to society and the environment. Additional Declarations No competing interests reported. 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Zhang","email":"data:image/png;base64,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","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Fengjuan","middleName":"","lastName":"Zhang","suffix":""},{"id":641481362,"identity":"cbcbd55c-6c33-48b9-9e22-6055c402f495","order_by":2,"name":"Lei Wu","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-03-19 13:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9169805/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9169805/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109465712,"identity":"1d624fcb-bcee-47fb-93eb-35c9921423bc","added_by":"auto","created_at":"2026-05-18 11:59:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87253,"visible":true,"origin":"","legend":"\u003cp\u003eProcedure of PLS-SEM (Hair et al., 2013)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9169805/v1/2b2fe6f44e9679d155759453.png"},{"id":109759334,"identity":"996ff00e-053d-4e08-b261-4e01577c6ace","added_by":"auto","created_at":"2026-05-22 07:26:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51550,"visible":true,"origin":"","legend":"\u003cp\u003eThe Hypothesized Research Model\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9169805/v1/89b98a6be40c4c427332b17f.png"},{"id":109760451,"identity":"24a8f26e-32ae-4055-893e-10d822a402dc","added_by":"auto","created_at":"2026-05-22 07:28:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204444,"visible":true,"origin":"","legend":"\u003cp\u003eStructural model\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9169805/v1/25c58d09ae41621108b179ca.png"},{"id":109799714,"identity":"566286e3-e268-4da3-b95b-022196d84d17","added_by":"auto","created_at":"2026-05-22 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11:59:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":134747,"visible":true,"origin":"","legend":"","description":"","filename":"Questionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9169805/v1/b67d8daa5862b74331e00e0c.pdf"},{"id":109799708,"identity":"354f8251-f814-4735-9430-87afc37f9bce","added_by":"auto","created_at":"2026-05-22 15:33:23","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":60923,"visible":true,"origin":"","legend":"","description":"","filename":"DataRaw.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9169805/v1/fe5d8ebac196537b081c284b.xlsx"},{"id":109759611,"identity":"43d35354-ec2f-4d0a-8b63-8050d4fb7086","added_by":"auto","created_at":"2026-05-22 07:27:26","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":79276,"visible":true,"origin":"","legend":"","description":"","filename":"DataProcessed.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9169805/v1/eeab68158c1924809e53475d.xlsx"},{"id":109760655,"identity":"b712d4f1-572a-4728-a634-bb086bbaba8a","added_by":"auto","created_at":"2026-05-22 07:28:57","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":28588,"visible":true,"origin":"","legend":"","description":"","filename":"DatafsQCA.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9169805/v1/289e684753e8715b76b30d86.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"How AI Tools Usage Affects Chinese Students’ Key Competencies for Sustainability in Higher Education Context: PLS-SEM and fsQCA Method","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEducation for Sustainable Development (ESD) represents a continually evolving framework that redefines the role of education in fostering responsibility across generations for creating a sustainable future (UNESCO, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Recognized as a central mechanism for advancing the Sustainable Development Goals (SDGs) (UNESCO, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), ESD emphasizes education\u0026rsquo;s contribution to global sustainability (UNESCO, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Following the United Nations (UN) General Assembly\u0026rsquo;s Resolution 57/254, which announced 2005\u0026ndash;2014 as the Decade of Education for Sustainable Development, the field has gained prominence as a major focus of sustainability research. The initiative identified education\u0026rsquo;s foundational role in promoting sustainability, aligning closely with Chap.\u0026nbsp;36 of Agenda 21 which covers education, awareness, and training. It was first introduced during the 1992 UN Conference on Environment and Development held in Rio de Janeiro (Imara \u0026amp; Altinay, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin educational contexts, competencies are regarded as a central framework that shapes the way teaching and learning contribute to the advancement of sustainable development (UNECE, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wals, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wiek et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). ESD aims to cultivate awareness and competencies connected to the sustainable development goals (SDGs). The sustainability key competencies equip individuals to handle the multifaceted challenges of contemporary society and play a crucial role in advancing the achievement of the SDGs. These competencies enable citizens to understand the interconnectedness of the SDGs, fostering a holistic view of the 2030 Agenda for Sustainable Development (Lichtenthaler, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Redman \u0026amp; Wiek, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies in the field suggest that various theoretical models have been introduced to define the essential competencies underpinning ESD (Rieckmann, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wiek et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). UNESCO\u0026rsquo;s framework for sustainability emphasizes eight key cross-cutting competencies include systemic thinking, anticipatory, normative, strategic, collaboration, critical thinking, self-awareness and integrated problem solving competency that enable individuals to act consciously towards the global challenges (Annelin \u0026amp; Bostr\u0026ouml;m, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; G\u0026uuml;nther et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The framework builds upon previous research and frameworks, notably those by Wiek et al. (\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), de Haan (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Rieckmann (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and others to enhance competencies in sustainability education. Brundiers et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) further developed these competencies, emphasizing their relevance in contemporary educational settings. This framework is significant as it not only aligns with the SDGs but also engages a broader audience beyond academia, enhancing its applicability and relevance in various educational contexts (Galkute et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eArtificial intelligence (AI) is now deeply embedded in everyday life, influencing multiple fields of activity and supporting the development of human skills and capacities. Its applications range from healthcare to logistics, demonstrating its versatility and potential to drive innovation (Crompton \u0026amp; Burke, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Singh, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As AI develops further, it is transforming the ways people and institution\u0026rsquo;s function, fostering greater efficiency and more informed decision-making across diverse contexts (Crompton et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The incorporation of AI into the educational domain has markedly influenced instructional practices and learning experiences, enhancing efficiency and outcomes. AI technologies facilitate personalized learning experiences, improve administrative efficiency, and foster pedagogical innovation (Lee, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Timotheou et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The application of AI in higher education has expanded rapidly over the past five years (Ouyang et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), accompanied by the emergence of numerous new digital tools. Previous studies(Chen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Crompton et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Crompton \u0026amp; Song, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have highlighted how AI can support both teaching and learning processes in university contexts. Reported advantages include adapting instruction to meet diverse learner needs, offering timely and personalized feedback, supporting assessment design, and forecasting student achievement (Dever et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gupta, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Verd\u0026uacute; et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Existing studies offer important perspectives on how AI can be meaningfully incorporated into teaching and learning practices within higher education.\u003c/p\u003e \u003cp\u003eIt is widely acknowledged that technological progress, particularly in AI, holds significant promise for addressing sustainability challenges. Such technologies can connect multiple dimensions of sustainability, optimize the use of resources, stimulate innovation, strengthen cooperation among stakeholders, and support long-term strategic planning for sustainable development (Haefner et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tripathi et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). AI has the potential to significantly advance SDGs in education by addressing various challenges and enhancing opportunities for diverse populations (Dever et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AI technologies can address barriers to ESD by enabling personalized learning, providing real-time feedback, and facilitating data-driven evaluations, thus enhancing access to quality resources and improving teacher training within adaptive learning frameworks (Singh et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Professional training initiatives can help teachers acquire the competencies required to incorporate AI tools into their instruction, thereby enhancing the overall quality and effectiveness of learning outcomes (Leong et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Learning platforms powered by AI can greatly expand access to quality education for learners in remote or disadvantaged areas by delivering interactive and personalized materials that reflect local circumstances and support region-specific environmental goals. These platforms leverage advanced technologies to personalize learning experiences, improve engagement, and reduce educational disparities (Avalekar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sharma, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The integration of artificial intelligence in education (AIEd) is not only transforming traditional learning environments but also promoting sustainable development and educational equity. Furthermore, AI tools are increasingly valued for enabling educators to teach complex sustainability concepts more efficiently while allowing accurate monitoring of student performance and clearer identification of specific areas requiring improvement. (Alshahrani, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kamruzzaman et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; C.-C. Lin et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the transformative potential of AI tools usage in promoting ESD has been proved by scholars, there remains a notable lack of empirical investigation into the intrinsic psychological and behavioral mechanisms that underpin this process. Current studies have predominantly emphasized the positive outcomes of AI in ESD, but the critical question of how and why these effects occur still requires further investigation, specifically through what internal structural pathways AI-enabled learning influences the development of students\u0026rsquo; key competencies for sustainability (KCS). The Technology Acceptance Model (TAM) indicates that users\u0026rsquo; perceptions of usefulness and ease of use play a crucial role in determining their willingness to adopt new technologies such as AI tools (Davis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). The TAM has been extensively employed to investigate how individuals accept and utilize AI-based tools in a wide range of fields. The Extended Technology Acceptance Model (TAM2) is an evolution of the original TAM, designed to better understand the factors influencing technology adoption (Altawalbeh, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). There exists a gap to systematically examined the impact of AI tools usage on Chinese students\u0026rsquo; KCS within TAM2. To address this gap, the present study proposes and tests a structural equation model based on TAM2, examining how AI tools usage influences Chinese students\u0026rsquo; KCS.\u003c/p\u003e"},{"header":"2. Literature Review and Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Foundation: The Extended Technology Acceptance Model (TAM 2)\u003c/h2\u003e \u003cp\u003eThe Technology Acceptance Model (TAM), formulated by Davis (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), provides a parsimonious and powerful framework for explaining technology adoption through two core beliefs: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). These beliefs are posited to directly shape a user\u0026rsquo;s Behavioral Intention to Use (BIU) technology, which in turn drives Actual Use (AU) behavior. However, the original model\u0026rsquo;s limited scope, particularly its omission of social and contextual influences, prompted significant extensions (Legris et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Venkatesh et al., 2003; Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The most influential of these is the Extended TAM (TAM2), developed by Venkatesh and Davis (\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) through a series of longitudinal field studies. TAM 2 incorporates both Social Influence Processes (such as Subjective Norm) and Cognitive Instrumental Processes (including Job Relevance, Output Quality, and Result Demonstrability) as direct determinants of PU. A critical finding from their work is that the effect of Subjective Norm (SN) on intention is moderated by experience and voluntariness of use. These important qualifications are essential for understanding adoption in complex environments like universities (Ursavaş et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTAM serves as a foundational theory for understanding adoption of AI tools in learning. For instance, studies have confirmed its relevance in contexts such as the use of AI-based intelligent tutoring systems (e.g., Chiu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the adoption of AI applications for language learning (e.g., Hu and Gong, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and the acceptance of AI tools by university students (e.g., Zhu et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These studies consistently demonstrate that the core constructs of PU and PEOU are pivotal in explaining students\u0026rsquo; BIU toward AI technologies.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Subjective Norm and Behavioral Intention to Use\u003c/h2\u003e \u003cp\u003eSubjective Norm (SN) refers to an individual\u0026rsquo;s perception of social expectations from important referents regarding whether they should engage in a particular behavior (Han et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this study, SN is defined as the degree to which Chinese university students perceive that people who matter to them expect or encourage the use of AI tools. Liu (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) examined the influence of SN on behavioral intention through an online survey conducted with 504 university teachers selected by random sampling. The findings revealed that SN had a strong and statistically significant positive relationship with the behavioral intention to use (BIU). Maleknia et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) indicated that SN functions as an important pathway through which social expectations shape individuals\u0026rsquo; behavioral intentions and demonstrated that SN plays a pivotal mediating role in connecting external social influences with behavioral intention. Accordingly, if Chinese university students perceive encouragement from people who are important to them, they are more likely to believe that AI tools will be beneficial for their study and in turn be willing to use them. Based on this rationale, the following hypotheses was proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eSN could positively influence BIU.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Subjective Norm, Learning Relevance and Perceived Usefulness\u003c/h2\u003e \u003cp\u003eResearch on technology acceptance in education shows that subjective norm and task relevance strongly affect PU. This study targets Chinese university students, therefore learning relevance (LR) is used as a relevant construct. Huang et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) applied the extended Technology Acceptance Model to explore the factors that affect Chinese university students\u0026rsquo; intentions to use Internet-based learning technologies. The study found that subjective norm had a significant positive effect on their PU of such technologies. Social pressure from peers or teachers can enhance perceived usefulness, especially when technology use is encouraged or demonstrated by others (Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Similarly, when a tool closely supports learning goals, referred to as learning relevance (LR) (Cheung \u0026amp; Vogel, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), it is more likely to be seen as useful. Therefore, we hypothesize:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eSN could positively influence PU.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eLR could positively influence PU.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Perceived Ease of Use, Perceived Usefulness and Behavioral Intention to Use\u003c/h2\u003e \u003cp\u003ePerceived ease of use is a well-established predictor of both PE and BIU in educational contexts (Davis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). When students find an AI tool easy to interact with, they are more likely to regard it as useful and to form intentions to use it. This relationship has been confirmed in studies on e-learning systems and digital platforms (Granić \u0026amp; Marangunić, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Ma et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) explore the determinants of AI tool adoption by assessing how PU and PEOU influence BIU among users. The findings showed that PEOU significantly affect both PU and BIU. Drawing on the existing literature, the following hypotheses were developed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003ePEOU could positively influence PU.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5\u003c/strong\u003e \u003cp\u003ePEOU could positively influence BIU.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4 Perceived Usefulness and Behavioral Intention to Use\u003c/h2\u003e \u003cp\u003eThe positive effect of perceived usefulness on behavioral intention is a cornerstone of TAM. In settings where academic performance is emphasized, students are more likely to adopt tools that they believe can improve their learning or help them study more efficiently (Šumak et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Based on TAM,Wu et al. (\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) investigated the key predictors of behavioral intention to use AI among 464 Chinese university students majoring in English as a foreign language. Mediation analysis showed that PU exerted a significant positive effect on students\u0026rsquo; behavioral intention through their attitudes toward AI.Wang (\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) confirmed business management students\u0026rsquo; behavioral intention to adopt AI tools by considering factors such as AI self-efficacy, AI anxiety, perceived usefulness, curiosity, enjoyment, and perceived control. The findings indicate that PU influence BIU. These lead to the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH6\u003c/strong\u003e \u003cp\u003ePU could positively influence BIU.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.5 Behavioral Intention to Use and Actual Use\u003c/h2\u003e \u003cp\u003eBehavioral intention to use is often recognized as the most direct influence on actual technology use (Venkatesh et al., 2003). The relationship between BIU and AU of AI technologies is a critical area of study, particularly in educational contexts. Studies (Garcia et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Li, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) indicate that BIU of AI significantly predicts AU, influenced by factors such as PU, PEOU, and motivational constructs. When students express a clear intention to use an AI tool, they are more likely to engage with it in practice if external conditions allow (Soliman et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Accordingly, we hypothesize:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH7\u003c/strong\u003e \u003cp\u003eBIU could positively influence AU.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.2 The Role of Perceived Personalization in AI Technology Acceptance\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTAM2 offers a valuable foundation for studying technology adoption. However, its original constructs fail to reflect a defining quality of AI-enabled learning: the capacity to personalize instruction. AI-driven personalized learning systems adapt educational content to fit each learner\u0026rsquo;s skills, pace, and preferences (Jayanthi R et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, this study adds Perceived Personalization (PP) as a factor that influences both PEOU and PE. This addition helps clarify how AI-specific characteristics shape student evaluations of the technology.\u003c/p\u003e \u003cp\u003eThe concept of PP has its roots in the broader study of technology adaptation and user-centered design. While early work on personalization emerged in marketing research (e.g., (Brusilovsky, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1996\u003c/span\u003e)), its application to educational technology gained prominence with the rise of intelligent tutoring systems and adaptive learning platforms. Acosta-Enriquez et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)d rekci \u0026amp; \u0026Ccedil;elik (2024) demonstrate that university students\u0026rsquo; attitudes toward AI tools are shaped by their perceptions of personalization and digital literacy. These perceptions influence their intent to use AI in academic activities, highlighting the role of PP in fostering acceptance of AI. Wang et al. (\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) expand the understanding of AI acceptance in language learning by incorporating emotional factors and intrinsic motivation, suggesting that positive emotions related to personalized AI interactions can enhance acceptance and engagement.\u003c/p\u003e \u003cp\u003ePP and PEOU highlight their significant roles in shaping user acceptance and behavioral intentions across various technological contexts. Some studies (Jo, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kang \u0026amp; Namkung, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; C.-P. Lin et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)proved that PP had significantly affected PEOU. Jo (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)extends the TAM by integrating these GAI-specific variables to assess their impact on PEOU. The findings indicated that personalization also positively influences PEOU, indicating its crucial role in GAI adoption.Yadegaridehkordi et al. (\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) expanded TAM by adding mobility, collaboration, and personalization as external variables. They proved that personalization significantly influences PEOU and has insignificant impacts on PU. Guang and Xueliang (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)obtained promising results which revealed the positive influence of personalization on PEOU and PU. Based on these studies, the following hypotheses were proposed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH8\u003c/strong\u003e \u003cp\u003ePP could positively influence PEOU.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH9\u003c/strong\u003e \u003cp\u003ePP could positively influence PU.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 From AI Use to Key Competencies for Sustainability\u003c/h2\u003e \u003cp\u003eThe cognitive impact of AI use on students varies significantly based on engagement level. Shallow interactions may hinder competency gains, while deep, strategic use fosters enhanced thinking, decision-making, and problem-solving abilities, emphasizing the importance of responsible AI utilization (Fredricks et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Pooja, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study uses TAM2 as a theoretical basis, implicitly captures a form of intentional use. Students are posited to use AI not out of compulsion, but because they perceive it as useful and easy to integrate into their learning processes. This perceived utility, especially when coupled with personalized feedback, creates the conditions for the sustained and purposeful engagement necessary for competency development.\u003c/p\u003e \u003cp\u003eLozano et al. (2017) used Principal Component Analysis (PCA) to classify the various competencies for sustainability into three dimensions (shown as Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This classification was based on the loading results of several specific capabilities in the empirical data. Accordingly, the construct was formed by three dimensions: Extrospective-social competencies, reflecting abilities for collaboration, critical thinking, and normative judgment. Introspective-personal competencies, centering on self-awareness. Systemic thinking, anticipatory thinking, strategic action and integrated problem-solving are included in Cogitative-processual competencies.\u003c/p\u003e \u003cp\u003eAI tools can support the development of systemic thinking skills in educational settings by providing accurate and helpful responses to prompts. Osman (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explores an AI-supported program that combined project-based learning with the Moodle platform. The findings showed that, compared with traditional instruction, this approach improved university students\u0026rsquo; academic performance and strengthened their \u003cem\u003esystemic thinking\u003c/em\u003e skills, illustrating the practical value of AI tools in higher education.\u003c/p\u003e \u003cp\u003eGebreegziabher et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) present an interactive AI tool that synthesizes rules based on user annotations in real-time. Its human-AI collaborative approach exemplifies how explainable and flexible AI systems can support qualitative coding tasks, promoting \u003cem\u003eanticipatory\u003c/em\u003e understanding through interactive rule synthesis. AI tools can personalize learning experiences, adapting sustainability education to diverse cultural contexts. By addressing unique learner needs, these tools enhance \u003cem\u003estrategic competency\u003c/em\u003e, fostering deeper understanding and engagement, ultimately contributing to a more sustainable and equitable global society (Ghosh, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yingsoon et al., \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).Štuikys et al. (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) point that AI tools can enhance \u003cem\u003eintegrated problem solving competency\u003c/em\u003e by promoting \u003cem\u003ecritical thinking\u003c/em\u003e, creativity, and conceptual understanding in STEM education. Their structured integration supports personalized, inquiry-based learning, essential for developing skills necessary for sustainability and addressing future challenges. Asrifan et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)explores the integration of AI and sustainability in education, highlighting its potential to enhance student engagement, \u003cem\u003ecritical thinking\u003c/em\u003e, and \u003cem\u003eproblem-solving\u003c/em\u003e skills, while promoting environmental consciousness and social accountability.\u003c/p\u003e \u003cp\u003eHenriksen et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) provides a critical thematic literature review and present that AI offers promising applications for enhancing \u003cem\u003eself-awareness\u003c/em\u003e, empathy, and social skills through tools like real-time emotional feedback and personalized learning experiences.Tian \u0026amp; Zheng (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) emphasized the educational potential of AI in fostering students\u0026rsquo; higher-order competencies, particularly the 4C skills (critical thinking, communication, collaboration, and creativity). Their study offers meaningful implications for future studies and practical initiatives that seek to employ AI tools to strengthen these competencies by identifying the conditions that optimize their educational impact. Similarly,Abulibdeh et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) critically analyzed the integration of AI tools, with particular attention to ChatGPT, in the context of ESD. By providing insights and strategies, their paper emphasized the enhancement of learning experiences and normative competency in learners for sustainable development.\u003c/p\u003e \u003cp\u003eFrom the above studies, the actual use of AI tools has a positive impact on each of the students\u0026rsquo; sustainability competency. This study hypothesizes that using AI in learning builds KCS. By offloading lower-order tasks (Sweller, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), AI allows students to engage in the higher-order thinking these competencies require. The feedback of the technology also forces critical engagement and self-reflection (Hattie \u0026amp; Timperley, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), directly cultivating competencies like critical thinking and systems thought as defined by UNESCO (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, this study proposed the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH10\u003c/strong\u003e \u003cp\u003eAU could positively affect KCS\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSustainability competences\u0026rsquo; groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSustainability competence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtrospective-social\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollaboration (Tian \u0026amp; Zheng, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Critical thinking (Asrifan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Štuikys et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tian \u0026amp; Zheng, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Normative (Abulibdeh et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrospective-personal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-awareness (Henriksen et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCogitative-processual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystemic thinking (Osman, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Anticipatory (Gebreegziabher et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Strategic (Ghosh, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yingsoon et al., \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Integrated problem solving (Asrifan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Štuikys et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Instrument\u003c/h2\u003e \u003cp\u003eThis study adopted a questionnaire as the research instrument which consisted of two parts. The first section collected demographic data such as respondents\u0026rsquo; gender, age, academic year, and field of study. The second part consists of questionnaire questions to verify the hypothesis of this study.\u003c/p\u003e \u003cp\u003eThe items collected from several relevant studies mainly focus on eight constructs. The items of Perceived Personalization (PP) are adapted from Higgins et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Sheng et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and some items are slightly modified from the previous studies to meet this study\u0026rsquo;s objectives. The modified version of some items adapted from To and Tang (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Teo and Van Schalk (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Fan and Wang (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Falebita and Kok (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) are used for six constructs of this study: Subjective Norm (SN), Learning Relevance (LR), Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Behavioral Intention to Use (BIU) and Actual Use (AU). The questionnaire content was further refined through the addition and removal of specific items to ensure that each construct was accurately represented. This process helped confirm that all questions were contextually appropriate and meaningful for the target population. The items of Key Competencies for Sustainability (KCS) are organized according to the framework proposed by Lozano et al. (2017). A total of 3 items for KCS were then developed based on the foundational work of UNESCO (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and subsequent empirical study (G. Liu \u0026amp; Ma, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll 28 items were measured using a five-point Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;). Each construct\u0026rsquo;s items were reviewed by subject-matter experts and refined through several rounds of revision to ensure clarity and relevance. The detailed questionnaire items and corresponding reference sources are provided in \u003cspan refid=\"Sec35\" class=\"InternalRef\"\u003eAppendix 1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data Collection and Participants\u003c/h2\u003e \u003cp\u003eA total of 482 questionnaires were distributed to universities through the online platform \u0026ldquo;\u003cem\u003eWenjuanxing\u0026rdquo;\u003c/em\u003e (Professional Version). To expand the sample size, the study also applied a snowball sampling approach, allowing participants to invite additional respondents from their networks. To ensure participant privacy, the data was collected anonymously. After excluding 89 invalid questionnaires (response time too long or too short, too many consecutive answers and selected as not having used AI tools), 393 valid questionnaires were collected.\u003c/p\u003e \u003cp\u003eThe participants in this study were university students (undergraduate and postgraduate) from Zhejiang and Shanxi provinces in China. There was a total of 393 participants and all participants had experience using AI tools. The demographic profile of the participants was summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The sample was characterized by a gender distribution of 40.2% males and 59.8% females. In terms of age, most participants were young adults, with the 19\u0026ndash;21 age group being the largest (38.9%), followed by the 16\u0026ndash;18 age group (32.1%). Most respondents were undergraduate students (84.8%), with freshmen making up 62.8% of the sample. Their fields of study were mainly in the Natural Sciences (49.1%) and Social Sciences (46.8%). Most participants reported regular use of AI tools, including 54.3% who used them \u0026ldquo;often\u0026rdquo; and 6.6% who used them \u0026ldquo;very often.\u0026rdquo;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreshman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSophomore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMajor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eThe frequency of using AI tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Data Analysis\u003c/h2\u003e \u003cp\u003eFirst, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the data and evaluate the proposed hypotheses. PLS-SEM has become increasingly popular in empirical research because it can effectively handle complex structural models, perform well with relatively small samples, and does not impose strict assumptions about data normality. This method is particularly advantageous in fields like educational technology and public sector research, where traditional methods may falter due to the complexity of relationships and the nature of data (Demir \u0026amp; Uşak, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; J. Hair \u0026amp; Alamer, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The PLS-SEM analysis is carried out in two stages (shown as Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Hair et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The measurement model, which specifies the relationships between observed indicators and their corresponding latent constructs, was examined in the initial stage to confirm the reliability and validity of the operationalized variables. Specifically, Confirmatory Composite Analysis (CCA) was conducted for this purpose. In addition, Cronbach\u0026rsquo;s Alpha (CA), factor loadings, Average Variance Extracted (AVE), Composite Reliability (CR), and the Fornell-Larcker criterion for discriminant validity were employed. These procedures ensured that the observed variables accurately represented the underlying theoretical constructs they were designed to measure (Hair et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the second stage, the structural model which specifies the relationships among the latent constructs was assessed to test the study\u0026rsquo;s hypotheses and examine the effects between variables. This analysis made it possible to evaluate the direction, significance, and strength of the paths connecting the constructs, thereby providing empirical evidence for confirming or rejecting the proposed hypotheses (Sarstedt et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The theoretical framework and hypotheses were tested using SmartPLS version 4.1.1.4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, Fuzzy-set Qualitative Comparative Analysis (fsQCA) was employed to better understand the relationships between these factors. FsQCA is a methodology that combines case-oriented and variable-oriented analysis, allowing researchers to examine causal complexities through set membership calibration, emphasizing configurational relationships among conditions rather than independent variable effects on outcomes (Mendel, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The fsQCA analysis, as an asymmetric approach, enables a deeper understanding of the non-linear and complex relationships that exist among the variables (Prentice, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). FsQCA employs Boolean algebra to analyze how combinations of causal conditions relate to specific outcomes, allowing for the identification of multiple pathways to success (Fainshmidt, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The software fsQCA 4.0 was used in this study to examine the configurations between the different latent constructs in the research model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 PLS-SEM Analysis\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Evaluation of Measurement Model\u003c/h2\u003e \u003cp\u003eThe validated dataset was analyzed for reliability and validity to assess the performance of the measurement model. The reliability of the latent constructs was examined using both Composite Reliability (CR) and Cronbach\u0026rsquo;s Alpha (CA) (Hair et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The latent constructs were deemed reliable when their CR and Cronbach\u0026rsquo;s Alpha CA values exceeded the threshold of 0.70 (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Hair \u0026amp; Alamer, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which indicates acceptable internal consistency across all constructs. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all values exceeded the recommended threshold of 0.70, with CR ranging from 0.838 to 0.902 and CA ranging from 0.712 to 0.854. Convergent validity was further evaluated using the Average Variance Extracted (AVE) and standardized factor loadings. The AVE values ranged from 0.569 to 0.696, all above the threshold of 0.50 recommended by Fornell and Larcker (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), which means that more than half of the variance in the observed indicators is captured by the latent constructs. Furthermore, all factor loadings were greater than 0.70 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which confirms that each indicator strongly represents its corresponding construct (Chin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of confirmatory factor analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFollowing the discriminant validity criterion proposed by Fornell and Larcker (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), the AVE for each construct should exceed the squared correlations or the square roots of the AVEs of the other constructs. As displayed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the diagonal values were higher than those in the corresponding rows and columns, indicating that discriminant validity has been satisfactorily established for the study data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity for the measurement model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKCS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.814\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.781\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.796\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.811\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.754\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.790\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.834\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.824\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Evaluation of Structural Model\u003c/h2\u003e \u003cp\u003eThe structural model was evaluated for collinearity, path coefficients, coefficient of determination (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e), and effect sizes (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}^{2}\\)\u003c/span\u003e\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, all variance inflation factor (VIF) values range from 1.000 to 2.089 below the conservative threshold of 3.3 (Hair et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This result indicates that collinearity is not a concern in the model. Bootstrapping procedure was applied with 5000 subsamples to examine the path coefficients, their significance levels, and t-values. The analysis showed that the strongest predictor of BIU was PEOU with a path coefficient of β\u0026thinsp;=\u0026thinsp;0.524 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by PU (β\u0026thinsp;=\u0026thinsp;0.175, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SN (β\u0026thinsp;=\u0026thinsp;0.175, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings provide strong support for hypotheses H5, H6, and H1. Regarding the factors influencing PU, PEOU had the strongest positive effect (β\u0026thinsp;=\u0026thinsp;0.468, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by SN (β\u0026thinsp;=\u0026thinsp;0.167, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LR (β\u0026thinsp;=\u0026thinsp;0.149, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and PP (β\u0026thinsp;=\u0026thinsp;0.138, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings provide support for hypotheses H4, H2, H3, and H9. The results also show that PP had a significant impact on PEOU (β\u0026thinsp;=\u0026thinsp;0.560, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting hypotheses H8. The path from BIU to AU was particularly strong (β\u0026thinsp;=\u0026thinsp;0.690, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the result support to hypothesis H7. Finally, AU had a notable effect on KCS (β\u0026thinsp;=\u0026thinsp;0.561, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming hypotheses H10.\u003c/p\u003e \u003cp\u003eThe model\u0026rsquo;s predictive power was verified through the coefficient of determination (R\u0026sup2;). As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the value for BIU (0.568) and PU (0.553) reflects moderate to substantial predictive accuracy based on the criteria suggested by Hair et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Chin et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). AU (0.476) and KCS (0.315) exhibit moderate explanatory power and PEOU (0.313) represents an acceptable level for behavioral studies. To further evaluate the strength of the relationships, the effect size (f\u0026sup2;) was examined. The values of f\u0026sup2; 0.35, 0.15, and 0.02 represent significant, moderate, and minor impacts (Cohen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The link between BIU and AU shows a large effect, highlighting its central role in the model. The strong influence also appeared between AU and KCS. The effects of PEOU on both PU and BIU fall within the medium impact, suggesting meaningful contributions of these paths. Although some other relationships were smaller in magnitude, all proposed links were statistically significant, supporting the robustness of the structural model.\u003c/p\u003e \u003cp\u003eOut-of-sample predictive power of this model was evaluated using the PLSpredict procedure with 10 folds. All constructs demonstrated positive Q\u0026sup2; values, ranging from 0.101 (KCS) to 0.383 (PU). According to the guidelines established by Hair et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Shmueli et al. (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), any positive Q\u0026sup2; predict value indicates that the model possesses predictive relevance. Values above 0.25 are generally interpreted as indicating medium predictive relevance, and those exceeding 0.50 suggest a large level of predictive accuracy. In this study, BIU (0.274), PEOU (0.303) and PU (0.383) reached the medium range. Although KCS (0.101) presented a smaller Q\u0026sup2; value, its positive result still indicates that the model holds predictive relevance for this construct (Geisser, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Stone, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Overall, these findings suggest that the proposed model not only captures the relationships among variables but is also capable of providing meaningful out-of-sample predictions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypotheses testing for the overall model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN -\u0026gt; BIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN -\u0026gt; PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLR -\u0026gt; PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU -\u0026gt; PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU -\u0026gt; BIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU -\u0026gt; BIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIU -\u0026gt; AU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP -\u0026gt; PEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP -\u0026gt; PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAU -\u0026gt; KCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 FsQCA Analysis\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Calibration\u003c/h2\u003e \u003cp\u003eTo apply the collected survey data to fsQCA analysis, the original values needed to be transformed into fuzzy sets, assigning each variable a membership score between 0 and 1. A value of 1 indicates full membership in the set, 0 indicates full non-membership, and 0.5 represents the crossover point, indicating that a case is neither fully in nor fully out of the set (Ragin, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The 5-point Likert scale was used for data collection in this study, the direct calibration method was employed to convert the original scores. Direct calibration relies on three anchor points representing full membership, the crossover point, and full non-membership (Fainshmidt et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Considering that Likert-scale data may be skewed toward one extreme, using the midpoint alone as the crossover point could be problematic. Therefore, following Fainshmidt et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) recommendation to combine theoretical understanding with empirical data, the 75th percentile, mean, and 25th percentile of each variable were set as the thresholds for full membership, the crossover point, and full non-membership, respectively. This approach allows for adjusting asymmetries across variables and ensures that the calibration reflects the relative positions of cases within the sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Necessity analysis of a single antecedent condition\u003c/h2\u003e \u003cp\u003eBefore conducting the sufficiency analysis, a necessary-condition test was conducted to examine whether any single antecedent condition must always be present for the outcome to occur. In fsQCA, a condition is considered necessary if the outcome never occurs in its absence, which is evaluated through consistency and coverage (Ragin, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rihoux \u0026amp; Ragin, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The consistency of 0.90 is commonly used to identify a necessary condition (Greckhamer et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this study, all calibrated antecedent conditions were tested for both presence and absence in relation to the outcome. The results in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show that none of the conditions reached a consistency of 0.90, and their coverage was not high enough to be considered important on their own. This means that no single factor is necessary for the outcome, suggesting that the result comes from a mix of factors working together.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of necessary conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eKCS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e~ KCS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~ PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~ SN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~ LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~ PEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~ PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~ BIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~ AU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Adequacy analysis of condition configuration\u003c/h2\u003e \u003cp\u003eFollowing the necessity analysis, which indicated that no single condition was necessary for Key Competencies for Sustainability (KCS), this study proceeded to identify sufficient configurations of conditions through fsQCA. The construction of the truth table represented the next critical step. In this process, a frequency threshold of 3, a raw consistency cutoff of 0.8, and a proportional reduction in inconsistency (PRI) cutoff of 0.7 were established to optimally balance consistency and coverage, following established methodological practice (Pappas \u0026amp; Woodside, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Patala et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The fsQCA software simplifies the truth table data using Boolean algebra minimization, allowing the generation of complex, intermediate, and parsimonious solutions. Consequently, six distinct configurations leading to high BI were identified.\u003c/p\u003e \u003cp\u003eThe analysis identified six distinct configurations sufficient for achieving KCS. The overall solution coverage was 0.617, indicating that these six pathways collectively explain 61.7% of the cases exhibiting high sustainability competencies. The overall solution consistency was 0.896, confirming that these configurations are highly reliable sufficient conditions for the outcome (Rihoux \u0026amp; Ragin, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The specific configurations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and interpreted below.\u003c/p\u003e \u003cp\u003eAcross the six configurations, AU appeared in every pathway as either a core or marginal condition. This shows that students\u0026rsquo; direct use of AI tools is closely related to higher levels of KCS. Several configurations showed the influence of PP and SN. Solutions 3, 4, and 5 contained both PP and SN. This means that when students feel that AI tools match their learning needs and when they receive encouragement from others, the outcome of high KCS is enhanced, even when some other conditions are not present. In contrast, LR was absent in Solutions 2, 5, and 6. This means that high KCS can still appear even when students do not strongly view AI tools as directly related to course content. In these cases, other conditions such as PP, PEOU, or AU play a more important role.\u003c/p\u003e \u003cp\u003eSolutions 1, 3, and 6 showed the combined presence of PEOU, PU, and BIU. These configurations suggest that positive judgments about AI tools and the intention to use them often appear together with higher KCS, especially when students also use the tools in practice. Among all pathways, Solution 4 had the highest consistency value (0.988) and also had a relatively high raw coverage (0.558). This configuration contained PP, SN, PEOU, BIU, and AU. This combination shows a strong pattern in which ease of use, personalization, social expectations, and actual usage appear together with high KCS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSufficient configurations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCausal conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolution consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolution coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: ● denotes the presence of a condition, \u0026otimes; denotes its absence, and a blank cell indicates that the condition is not relevant. A large symbol represents a core condition, whereas a small symbol represents a peripheral condition.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.2.4 Robustness tests\u003c/h2\u003e \u003cp\u003eTo confirm the stability of the fsQCA results, several robustness tests were conducted. First, the consistency threshold was adjusted from 0.80 to 0.85. The main configurations remained and the core conditions did not change after the adjustment, which shows that the findings are not sensitive to small variations in the consistency level. Second, the frequency threshold was increased by one case. The purpose was to test whether the configurations would shift when low-frequency cases were excluded. The results closely matched the original solutions. The key conditions in the main pathways remained unchanged, and the consistency and coverage stayed at similar levels. Third, the analysis was repeated using an alternative calibration procedure with slightly different crossover points. The aim was to check whether the outcome depended on a specific calibration choice. The recalibrated results produced configurations that matched the original structure. The same groups of conditions appeared as either core or marginal, and the key pathways to high KCS remained stable. These checks indicate that the findings of the fsQCA analysis are reliable under different threshold settings and calibration decisions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Discussion of the PLS-SEM Findings\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1 Subjective Norm (H1, H2)\u003c/h2\u003e \u003cp\u003eThe PLS-SEM findings show that SN exerts significant positive effects both on BIU and on PU. This pathway highlights the important role of social influence in shaping both what students believe the AI tools will do for them (i.e., its usefulness) and whether they intend to use it. The Theory of Planned Behavior (TPB) where SN is one of the factors of behavioral intention also support the positive and significant effect of SN on BIU (Ajzen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). In contexts of AI use, this suggested that individuals\u0026rsquo; perceptions that important others (e.g., instructors, peers, supervisors) expect them to use the AI tools will increase their intention to use it. Previous meta-analyses of technology acceptance confirm that SN often contributes to BIU, though effect sizes vary by context (Marikyan et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The positive relationship between SN and PU shows that when instructors or classmates show a positive attitude toward AI tools, students are likely to take these views as an indication of the tools\u0026rsquo; effectiveness in learning (Delcker et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the Chinese higher education environment, the role of SN is especially strong as the culture places high importance on conformity and respect for authority (Liu \u0026amp; Guo, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Students usually follow the attitudes and behaviors expected by their teachers and peers, especially when these expectations show support from the institution or society for using technology (Edmunds et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This explains why students who perceive strong social encouragement toward AI-based learning tools are more likely to consider these tools beneficial and necessary for academic success. Studies by Al-Nuaimi e t al. (2021) and Zhou et al. (\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also found that in learning environments that emphasize cooperation, encouragement from teachers and peers can enhance both perceived usefulness and students\u0026rsquo; intention to use digital tools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2 Learning Relevance (H3)\u003c/h2\u003e \u003cp\u003eThe analysis shows that LR has a strong positive effect on PU. This means that when students feel that AI tools are directly connected to their learning needs and course objectives, they are more likely to believe these tools are helpful for their academic progress. The sense of relevance makes the use of AI tools purposeful rather than optional, and this perception strengthens their belief that such tools can improve learning outcomes. Learning relevance is one of the key factors that shape students\u0026rsquo; evaluation of educational technologies. Students valued AI learning tools more when these tools can clearly support their academic goals and self-regulated learning behaviors (Rets et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The relevance of AI tools to students\u0026rsquo; subject content strongly influenced their perception of usefulness and their motivation to continue using them (Ucha, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). University students often follow structured curricula and aim to meet specific academic standards. When an AI tool is perceived as directly helping them understand key concepts or improve performance, they are more willing to view it as valuable. Therefore, highlighting the connection between AI tools and students\u0026rsquo; concrete learning goals is critical for increasing their perception of usefulness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3 Perceived Ease of Use (H4, H5)\u003c/h2\u003e \u003cp\u003eThe results indicate that PEOU has a significant positive effect on both PU and BIU. This suggests that when students perceive the AI tools as easy to use, they often consider it helpful and intend to incorporate it into their studies. Students\u0026rsquo; perception of ease reduces the barrier of effort and supports their judgment that the tool can support outcomes, supporting the findings of Lin and Yu (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, when students believe that the fact using the AI tools will not require large amounts of time or effort also contributes directly to their intention to use AI tools (Widiar et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In a higher education context, the effects of PEOU are especially important. While university students commonly use AI to support academic tasks, they may avoid complicated tools that require significant effort, despite their potential benefits, due to concerns about usability and the time constraints they face (Triyanto \u0026amp; Handayani, 2025). This finding shows that educators and developers need to pay as much attention to usability as to functionality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e5.1.4 Perceived Usefulness (H6)\u003c/h2\u003e \u003cp\u003eThe PLS-SEM results show that PU has a strong positive effect on students\u0026rsquo; BIU. This suggests that when students believe AI tools can help them learn more effectively or complete tasks with better results, they tend to develop a stronger willingness to use them. Students\u0026rsquo; perception of the usefulness of AI-based learning applications was one of the main predictors of their intention to use them (Ayanwale \u0026amp; Molefi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Students\u0026rsquo; recognition of AI writing tools\u0026rsquo; perceived usefulness significantly influences their attitudes, suggesting that highlighting these tools\u0026rsquo; benefits can enhance motivation and adoption among university students in completing coursework and improving learning quality (Malmous \u0026amp; Zaidoune, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings indicate that educators should not only introduce AI tools but also guide students to see their academic value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.1.5 Behavioral Intention to Use (H7)\u003c/h2\u003e \u003cp\u003eThe model results indicate that BIU significantly predicts AU of AI tools among Chinese higher-education students. When students form the intention to use a tool, the chances increase that they will engage with it in their learning practices (Sergeeva et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Strzelecki, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A meta-analytic review based on UTAUT2 conducted by Zheng et al. (\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) also found that BIU is the key influencing factor of use behavior in education settings. In this study, the strong relationship between BIU and AU shows that when students see an AI tool as useful and easy to use, their intention often leads to actual use. This finding is consistent with the TAM and its extensions, which regards intention as the direct step before behavior. BIU alone may not lead to actual technology use, particularly when contextual variables are absent (Doleck et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Students may need technical help, encouragement from teachers, or examples of how the tools can be applied before they start using them regularly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e5.1.6 Perceived Personalization (H8, H9)\u003c/h2\u003e \u003cp\u003eThe results indicate that PP plays a meaningful role in PEOU and PU of AI tools. Students who felt that the AI system responded to their preferences or adjusted to their learning progress reported that the tools were easier to handle and more helpful in their study (Chen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Yaseen et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This suggests that personalization shapes both the sense of effort and the sense of value attached to AI use. For university students, particularly where the use of new technology is still growing, feeling that an AI system fits their way of learning is often more important than how convenient it is to use. While standard interfaces are sufficient for basic use, students respond more positively when AI systems notice their study habits and provide suitable adjustments or feedback. This responsiveness makes the AI tools more relevant, engaging, and supportive of individual learning needs (Naseer \u0026amp; Khawaja, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Slimi et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings point that usability and usefulness in educational technology are not just technical features. They are deeply shaped by how personally the system engages each user. Personalization is increasingly recognized as a foundational requirement, not only an optional enhancement for genuine ease of use and perceived learning value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e5.1.7 Actual Use (H10)\u003c/h2\u003e \u003cp\u003eThe model shows that AU of AI tools have a positive effect on students\u0026rsquo; KCS. When students move from intention to sustained use, they practice tasks that call for systemic thinking, anticipatory reasoning, collaboration, and problem-solving. This practice supports growth in the competencies described in recent updates to the KCS literature(Brundiers et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Current work in sustainability education points to pedagogies that require students to use digital tools while engaging real problems. Studies report that project-based courses and technology-supported activities raise students\u0026rsquo; SDG knowledge and strengthen competence development, which is consistent with the effect observed here for AU on KCS (Espino-D\u0026iacute;az- et al., 2025). Research on personalized and interactive digital learning further shows that when students actively work with the AI tools, they build transferable skills linked to KCS (Lozano et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results fit with the competence-based approach now common in higher education for sustainable development. Programs that ask students to apply tools to open-ended tasks are more likely to develop the targeted competencies than programs that keep learning at the level of exposure only.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Discussion of the fsQCA Findings\u003c/h2\u003e \u003cp\u003eThe fsQCA results provide a detailed view of how different combinations of factors lead to higher levels of key competencies for sustainability. While the PLS-SEM analysis identified direct linear effects between constructs, the configurational approach shows that several different paths can produce the same outcome. This means that students can reach high KCS through different mixes of personal, social, and technological factors.\u003c/p\u003e \u003cp\u003eAU appears in all configurations, showing that frequent and meaningful use of AI tools is closely linked to competence development. This is consistent with the PLS-SEM result that AU directly affects KCS, but the fsQCA findings further show that AU interacts with other conditions such as PP, SN, and PEOU. Students develop stronger competencies when they use AI tools in contexts that feel adaptive, socially supported, and easy to manage. Several configurations include both PP and SN, suggesting that when students experience a sense of personalization and receive encouragement from others, they are more engaged and reflective in their learning. Supportive and adaptive learning environments enhance motivation and lead to deeper understanding (Kember et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zourmpakis et al., \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This shows that the social and personal aspects of AI-supported learning work together to strengthen outcomes.\u003c/p\u003e \u003cp\u003eOther configurations highlight the effects of PU, PEOU, and BIU. When students find AI tools useful, simple to use, and intend to apply them, their engagement becomes more consistent and leads to better learning results. This pattern also appeared in the PLS-SEM model, but the fsQCA findings show that these factors need to appear together rather than act separately (Alkhawaja et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Panergayo \u0026amp; Aliazas, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tawafak et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The presence and absence of LR across configurations show that the same outcome can occur under different conditions. In some cases, high KCS appears even when students do not see a strong link between AI tools and course content. This means that other factors, such as personalization and AU, can make up for low relevance (Sayed et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The result illustrates the idea that educational effects are not symmetrical and that different combinations of factors can work in multiple ways to reach similar results (Ragin, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fsQCA results complement the PLS-SEM findings by showing how the conditions work together rather than acting alone. The linear model explains the separate effects of each factor on KCS, while the configurational results show the ways in which these factors appear in combination. The findings suggest that the development of key competencies for sustainability depends on how students perceive and use AI tools in practice. It is influenced by their evaluation of the tools\u0026rsquo; usefulness, the simplicity of operation, and the level of personalization they experience.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion, Implications and Limitations","content":"\u003cp\u003eThis study examined how the use of AI tools influences Chinese university students\u0026rsquo; key competencies for sustainability by combining PLS-SEM and fsQCA methods. The structural model showed that perceived personalization, subjective norm, learning relevance, perceived ease of use, and perceived usefulness jointly affect students\u0026rsquo; behavioral intention and actual use of AI tools, which in turn improves KCS. The configurational results complemented these findings by showing that high levels of KCS can arise from several different combinations of conditions rather than a single dominant path. Across these configurations, actual use consistently appeared as a central condition, confirming that frequent and meaningful engagement with AI tools plays an important role in competence development. These two methods provide both a linear and a configurational understanding of how perceptions, intentions, and behaviors interact in improving students\u0026rsquo; KCS in the context of higher education.\u003c/p\u003e \u003cp\u003eThese findings have value for both theory and practice. They extend existing studies on AI use in higher education by showing that the growth of sustainability competencies depends on how students engage with technology and how they connect it to learning objectives. The results broaden the TAM by applying it to sustainability, showing that ease of use, usefulness, and personalization form a linked process that leads to stronger intention and more frequent use. For teachers, these findings suggest that promoting active use of AI tools involves more than teaching technical skills. Guidance should focus on helping students relate the functions of AI tools to their academic work and understand how these tools can support critical and reflective thinking. Universities can also create teaching policies that support responsible and purposeful use of AI, encouraging students to apply the technology in ways that strengthen learning quality and awareness of sustainable development.\u003c/p\u003e \u003cp\u003eSeveral limitations should be mentioned while this study offers valuable findings. The data were collected from a sample of Chinese universities, and the results may not represent other national or institutional contexts. The study relied on self-reported measures, which describe students\u0026rsquo; perceptions rather than their actual performance. Future work could apply longitudinal or experimental designs to track changes in sustainable competencies development and verify the causal direction of the observed relationships. It would also be useful to examine how institutional environments, teaching practices, and disciplinary differences influence the use of AI tools in higher education. Extending this study to different regions and educational systems could provide a broader view of how AI-based learning contributes to education for sustainable development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the publication of this article, and/or authorship.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Approval\u003c/h2\u003e \u003cp\u003eOur study was approved by the Ethics Review Committee of the School of Education, Zhejiang University of Technology (Approval number: 2026042101) on 21st April. The study was conducted in accordance with the ethical principles outlined in the Helsinki Declaration.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e \u003cp\u003e \u003cb\u003eNot applicable.\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003ewas obtained verbally from all individual participants before taking part in the questionnaire survey conducted from January to April 2026. The anonymity and confidentiality of the participants were guaranteed, and participation was entirely voluntary. All participants have also given their consent to the potential publication of this study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFundings\u003c/h2\u003e \u003cp\u003eNo funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors\u0026rsquo; contributions:Author H: conceptualization, methodology, original writing, data collection and data analysis. Author Z: data collection, reviewing, supervision, and editing. Author W: conceptualization, supervision, and methodology.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData availability statement:The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.Adding research funding statements: No funding was received for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbulibdeh A, Zaidan E, Abulibdeh R (2024) Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. 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Exploring the acceptance of generative artificial intelligence-assisted learning and design creation among students in art design specialties: based on the extended TAM model. \u003cem\u003eEducation and Information Technologies\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(13), 18651\u0026ndash;18678. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-025-13551-3\u003c/span\u003e\u003cspan address=\"10.1007/s10639-025-13551-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZourmpakis A-I, Kalogiannakis M, Papadakis S (2023) Adaptive Gamification in Science Education: An Analysis of the Impact of Implementation and Adapted Game Elements on Students\u0026rsquo; Motivation. Computers 12(7):143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/computers12070143\u003c/span\u003e\u003cspan address=\"10.3390/computers12070143\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Appendix 1","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe AI tools used in learning match my personal interests.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSheng et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2008\u003c/span\u003e);\u003c/p\u003e \u003cp\u003eHiggins et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe AI tools used in learning offer personalized services based on my preferences.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe AI tools offer customized learning paths or options for me.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe AI tools used in learning match my needs.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeople who influenced my study think I should use AI tools in learning.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTeo and Van Schalk (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeople whose opinions I value encourage me to use AI tools in learning.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeople who are important to me support me to use AI tools in learning.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn my learning, use of AI tools is frequent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTo and Tang (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn my learning, usage of AI tools is relevant.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn my learning, use of AI tools is important.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI find it easy to access AI tools.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFan and Wang, (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e);\u003c/p\u003e \u003cp\u003eFalebita and Kok, (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI find it easy to learn how to use AI tools.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI find it easy to understand the content provided by AI tools.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI find AI tools to be flexible to interact with.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI think AI tools make my learning easy.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFan and Wang, (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e);\u003c/p\u003e \u003cp\u003eFalebita and Kok, (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI think AI tools can assist me in learning more efficiently.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI find AI tools useful in my learning tasks.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI think AI tools can help me improve the quality of my learning.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI am willing to spend time and effort to learn how to use AI tools better.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFan and Wang, (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e);\u003c/p\u003e \u003cp\u003eFalebita and Kok, (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI plan to use AI tools for my learning needs in the future.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI expect to use AI tools frequently in the future for learning purposes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI think it is necessary to use AI tools in assisting learning.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI rely on AI tools to help me complete a variety of tasks.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFan and Wang, (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e);\u003c/p\u003e \u003cp\u003eFalebita and Kok, (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI invest time and effort in learning and using AI tools.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI am constantly exploring new AI tools and incorporating them in completing various tasks.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eKCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKCS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI tools help me better collaborate with others in learning.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLiu and Ma (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e);\u003c/p\u003e \u003cp\u003eUNESCO (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKCS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsing AI tools improves my ability to integrate different perspectives to solve complex\u0026nbsp;sustainability\u0026nbsp;problems.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKCS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsing AI tools in learning makes me more aware of my responsibilities to society and the environment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Education for Sustainable Development (ESD), Sustainability Competencies, AI use, TAM2, Higher Education","lastPublishedDoi":"10.21203/rs.3.rs-9169805/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9169805/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe use of artificial intelligence (AI) tools has become a regular part of university students\u0026rsquo; learning. Many students depend on AI for writing, searching for information, and solving problems. However, it is still not clear how this use affects the development of key competencies for sustainability (KCS). These competencies are essential to higher education\u0026rsquo;s mission in achieving the United Nations 2030 Agenda, as they help students think critically, act responsibly, and deal with complex challenges in society. This study explores how Chinese university students\u0026rsquo; interactions with AI tools relate to the cultivation of KCS. Based on 393 completed surveys, the dataset was processed through Partial Least Squares\u0026ndash;Structural Equation Modeling (PLS-SEM) and further examined using Fuzzy-set Qualitative Comparative Analysis (fsQCA). The PLS-SEM results indicate that perceived personalization (PP), subjective norm (SN), learning relevance (LR), perceived ease of use (PEOU), and perceived usefulness (PU) influence students\u0026rsquo; behavioral intention to use (BIU) and actual use of (AU) AI tools, which subsequently promote the development of KCS. The fsQCA findings identify several distinct configurations leading to high KCS, suggesting that competency growth can result from multiple interacting conditions rather than a single causal factor. These results suggest that universities should focus not only on providing access to AI tools but also on helping students integrate them meaningfully into academic learning, encouraging reflective, responsible, and goal-oriented use that supports education for sustainable development.\u003c/p\u003e","manuscriptTitle":"How AI Tools Usage Affects Chinese Students’ Key Competencies for Sustainability in Higher Education Context: PLS-SEM and fsQCA Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 11:59:04","doi":"10.21203/rs.3.rs-9169805/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-10T01:55:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34413920763855251719708427864054148671","date":"2026-05-08T05:10:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144117744605014590783041857085627553120","date":"2026-05-07T11:23:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167847038267566891952330855341495841194","date":"2026-05-07T10:36:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28983900209736252849652661521515760689","date":"2026-05-07T09:36:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T09:28:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T09:25:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-06T18:40:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-06T18:12:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-05-06T13:18:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ba3d6026-c142-4258-a92a-a9b329033c31","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-10T01:55:59+00:00","index":42,"fulltext":""},{"type":"reviewerAgreed","content":"34413920763855251719708427864054148671","date":"2026-05-08T05:10:38+00:00","index":39,"fulltext":""},{"type":"reviewerAgreed","content":"144117744605014590783041857085627553120","date":"2026-05-07T11:23:48+00:00","index":38,"fulltext":""},{"type":"reviewerAgreed","content":"167847038267566891952330855341495841194","date":"2026-05-07T10:36:34+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"28983900209736252849652661521515760689","date":"2026-05-07T09:36:26+00:00","index":36,"fulltext":""},{"type":"reviewersInvited","content":"17","date":"2026-05-07T09:28:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T09:25:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-06T18:40:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-06T18:12:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-05-06T13:18:28+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":68235962,"name":"Social science/Education"},{"id":68235963,"name":"Business and commerce/Information systems and information technology"},{"id":68235964,"name":"Physical sciences/Mathematics and computing"},{"id":68235965,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-05-18T11:59:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 11:59:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9169805","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9169805","identity":"rs-9169805","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}