The Impact of Career Planning and Self-Concept on Employability among University Students: Exploring the Mediating Role of Learning Attitudes

The Impact of Career Planning and Self-Concept on Employability among University Students: Exploring the Mediating Role of Learning Attitudes | 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 Research Article The Impact of Career Planning and Self-Concept on Employability among University Students: Exploring the Mediating Role of Learning Attitudes Yu Sun, Qian Liu, Xiaona Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7406818/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: With the rapid development of education, higher education in China has become increasingly accessible and widespread. The number of university graduates is rising every year, leading to a more competitive job market. As a result, enhancing college students’ employability has become an urgent issue. This study explores the mechanisms through which career planning and self-concept affect employability, and examines the mediating role of learning attitude, aiming to provide both theoretical and practical guidance for improving employability. Methods: Drawing on the Career EDGE model, this study used convenience sampling to survey 735 undergraduates from universities in Hebei Province, China. Data were collected via questionnaires and analyzed using SPSS Statistics 27 and AMOS. Structural Equation Modeling (SEM) was applied to test the effects of career planning, self-concept, and learning attitude on employability, and to verify the mediating role of learning attitude. Results: Career planning, self-concept, and learning attitude all showed significant positive effects on employability. Mediation analysis revealed that learning attitude mediated the relationship between career planning and employability, as well as between self-concept and employability. Conclusions: The findings demonstrate that career planning and self-concept positively influence employability, and that learning attitude serves as an important pathway in this process. Universities should assist students in developing clear career plans and building a positive self-concept to foster active learning attitudes, thereby enhancing their employability. Career planning Self-concept Learning attitude Employability Figures Figure 1 Figure 2 Introduction According to the 2022 China College Graduate Employment Report ‎[ 68 ] released by the Central People’s Government of the People’s Republic of China, the number of fresh graduates in 2022 exceeded 10 million for the first time, marking a record high in both total and incremental numbers. At the same time, a survey report by Zhaopin.com showed that the employment rate of college graduates in 2022 was 50.4%, representing a 6.5% decline compared with 2021‎[ 1 ]. With the continuing expansion of higher education, the number of college graduates is expected to keep increasing. Coupled with the downward pressure on economic growth, the intersection of a persistently high graduate population and ongoing economic restructuring is bound to create more complex employment challenges ‎[ 2 ]. In practice, structural mismatches have become a key issue: many graduates are not unable to find jobs, but rather unable to find satisfactory ones. This reflects the widespread problem of low employment quality‎[ 3 ]. Improving the quality of graduate employment depends on enhancing their employability‎[ 4 ]. Graduate employability has attracted increasing attention from scholars in the fields of higher education and human capital ‎[ 5 ]. Employment is a critical issue for the nation’s economy and people’s livelihoods, as it concerns both national stability and individuals’ pursuit of a better life ‎[ 1 ]. Employability is a key factor for maintaining sustainable employment in today’s unstable job market. It also reflects an individual’s flexibility-the ability to be adequately prepared to face the current complex and rapidly changing employment environment. Through learning in a professional field, individuals can acquire the skills needed to secure employment, maintain competitiveness, and continuously develop professional skills‎ [ 6 ]. According to the Career EDGE model of employability proposed by Dacre and Sewell‎[ 8 ], university students seeking to develop their employability must first enhance five core elements: career development learning, work and life experience, subject-specific knowledge and skills, generic skills, and emotional intelligence (first tier). They must then engage in ongoing reflection and evaluation of these experiences (second tier). This process promotes the development of higher-level attributes such as self-esteem, self-confidence, and self-efficacy, which are essential for building employability‎ [ 7 ]. Career planning refers to the process in which individuals set reasonable plans for their future careers to achieve their ideal goals, as well as the strategies and methods they adopt to realize these plans‎ [9, ‎10]. Career planning is not only part of the “career development learning” component in the Career EDGE model of employability, but also falls under the generic skills dimension, which includes planning, coordination, organization, and management abilities—the first tier in enhancing employability‎[ 8 ] (Dacre & Sewell, 2007). Research indicates that to improve their employability, university students should clearly define their career goals and continuously enrich and improve themselves with career planning as a guiding principle. This involves exploring their potential, enhancing professional qualities, and preparing for future career development to strengthen their competitiveness in the job market ‎[ 11 ]. Therefore, career planning plays a positive and significant role in promoting students’ employability‎ [ 70 ]. Self-concept refers to an individual’s overall view and perception of themselves, encompassing their beliefs, positions, and emotions, as well as their self-descriptions and self-definitions regarding physical, psychological, and social characteristics, and personal abilities ‎[ 13 ]. Within the Career EDGE model of employability, self-concept is part of the first-tier element of emotional intelligence and also falls under the reflection and evaluation stage. It plays an important role in the development of employability ‎[ 14 ]. Tentama and Abdillah ‎[ 15 ] found through student interviews that the most influential factors affecting employability are academic achievement and self-concept. Similarly, Kim et al. ‎[ 31 ] reported that having a strong and positive self-concept within a specific framework can enhance an individual’s employability, while a low self-concept tends to be associated with lower employability. Therefore, self-concept plays a vital role in improving employability ‎[ 16 ]. According to the Career EDGE model of employability, the second tier is reflection and evaluation. While it is important to provide students with opportunities to acquire the essential knowledge, skills, understanding, and attributes of the first tier, it is equally important to offer opportunities for reflection and evaluation of their learning experiences. Without such opportunities, students cannot fully assess how far they have progressed in developing their employability, nor can they identify the steps needed to further enhance it ‎[ 8 ]. Learning attitude refers to a learner’s positive or negative evaluations and feelings toward learning activities or environments, as well as their readiness or tendency to engage in active or passive learning behaviors ‎[ 17 ]. Learning attitude belongs to the second-tier “reflection and evaluation” component of the Career EDGE model. A positive learning attitude can effectively promote the employability of university students ‎[ 18 ]. During their studies, students should continuously reflect on and adjust themselves according to real circumstances. With an active learning attitude-thinking and learning proactively, and expanding their thinking and imagination-their future academic achievements and employability will be stronger [ 19 ]. Although research on college students’ employability has received increasing attention in academia, the mechanisms through which self-concept and career planning influence employability have not been fully explored, particularly with regard to systematic and in-depth examination of mediating pathways. Moreover, there is still a lack of theoretical and empirical studies on employability that align with China’s specific context‎[ 26 ]. Therefore, this study, grounded in the Career EDGE model of employability and situated within the Chinese educational context, investigates the interrelationships among career planning, self-concept, learning attitude, and employability, and further examines the potential mediating role of learning attitude. This research helps address gaps in the existing literature while testing the applicability of the Career EDGE model in China, providing a useful reference for strategies to enhance the employability of Chinese university students. Literature Review and Research Hypotheses The Impact of Career Planning on Employability According to the Career EDGE model of employability, career planning falls under the first-tier element of career development learning, as well as the generic skills of planning, coordination, organization, and management. These are the most fundamental factors for enhancing the employability of university students‎ [ 8 ]. Research has shown that effective career planning is essential for graduates to conduct successful job searches, achieve career satisfaction, and improve their employability-particularly when facing challenging and continuously changing work environments‎ [ 20 ].By engaging in career planning, students can gain a deeper and more comprehensive understanding of their professional pathways. In the context of current labor market demands, such planning helps them develop effective strategies, advance their career development, and achieve better career outcomes, thereby enhancing their employability ‎[ 21 ]. Gould ‎[ 9 ] equated career planning with goal setting‎[22, ‎23], noting that all employees engaged in career planning set specific and challenging career goals without exception. Based on goal-setting theory, employees formulate and implement career strategies to achieve these goals ‎[ 24 ]. The effective implementation of such strategies enables them to reach their ultimate career objectives, which in turn strengthens their employability ‎[ 28 ]. Career construction theory ‎[ 25 ] suggests that through career exploration, university students can reassess themselves rationally, strengthen the learning of relevant skills, and develop concrete strategies to achieve their goals, thereby enhancing their employability. Similarly, Guo ‎[ 26 ] argued that embedding career planning education throughout the entire undergraduate period—starting from the first year-can awaken students’ career awareness early, help them understand social and job skill requirements in advance, and enable them to plan career paths ahead of time. By purposefully and systematically preparing the professional skills and knowledge needed for future employment, students can develop correct employment concepts and improve their employability. Furthermore, Liu et al. ‎[ 27 ] confirmed that students’ career planning has a significant positive effect on their perceived employability. In other words, through a series of actions such as career exploration, career-related activities, and continuous evaluation and adjustment, students can formulate and refine their career plans, adopt a long-term perspective on job value, and cultivate a mindset for career development. This process encourages them to actively enhance the competitive skills required in the job market ‎[ 28 ]. Accordingly, the following hypothesis is proposed: H1: Career planning has a significant positive effect on the employability of university students. The impact of self-concept on employability According to the Career EDGE model of employability, in addition to career development learning and generic skills, emotional intelligence (EI) is also one of the fundamental first-tier elements for developing employability‎[ 8 ]. Emotional intelligence-also referred to as emotional quotient—refers to qualities related to emotion, affect, willpower, and resilience, as well as the ability to reason with emotions and use them to enhance thinking. It includes the capacity to accurately perceive emotions, to generate emotions that facilitate thought, to understand emotions and emotional knowledge, and to regulate emotions reflectively in ways that promote emotional and intellectual growth[‎29]. Emotional intelligence can be improved through reasoning, perception, and control of emotions, as well as understanding emotional experiences. It has a positive correlation with individual achievement—those with high emotional intelligence tend to be more self-motivated, achieve more, attain greater career success, build stronger interpersonal networks, and enjoy a healthier life compared to those with low emotional intelligence ‎[ 30 ]. Self-concept is a component of emotional intelligence within the Career EDGE model. Research indicates that in higher education, students’ self-concept is positively related to both academic achievement and future employability ‎[ 71 ]. In other words, individuals with a higher self-concept generally have higher employability, whereas those with lower self-concept tend to have lower employability‎ [ 15 ]. Kim et al. ‎[ 31 ], drawing on self-concept theory, demonstrated that self-concept can be a prerequisite for perceived employability. From the combined perspectives of self-concept theory and human capital theory, perceived employability can be understood more comprehensively. In addition, Dania et al. ‎[ 32 ] argued that self-concept is an important factor in the formation of employability within the school environment, and that the extent to which individuals act on their self-concept influences their future employability in the workplace‎[ 33 ]. Similarly, Rothwell et al. ‎[ 34 ] found that graduates’ employability is related to their self-concept as students. Ahmid and Abdullah ‎[ 66 ] also confirmed a highly significant positive effect of self-concept on students’ employability-that is, the stronger a student’s self-concept, the higher their employability. A positive self-concept can enhance employability because it enables individuals to adopt a proactive stance in the face of challenges, to better appreciate themselves, and to take feasible, positive steps toward securing employment, thereby improving their employability ‎[ 35 ]. Accordingly, the following hypothesis is proposed: H2: Self-concept has a significant positive effect on the employability of university students. The mediating effect of learning attitude between career planning and employability Learning attitude refers to a learner’s consistent and sustained psychological state and behavioral tendency toward the content they study, encompassing cognitive, emotional, and behavioral components‎[ 36 ]. Research has shown that learning attitude has a significant direct positive effect on employability. In other words, students with a positive learning attitude tend to develop stronger employability, whereas those with a negative learning attitude often demonstrate lower employability‎ [ 37 ]. Rothwell et al.‎[ 34 ], in exploring factors influencing employability, found that students’ learning attitudes can effectively predict certain aspects of their employability. Specifically, when students develop a positive learning attitude, their cognition and behavior extend beyond their studies and learning environment to their future work attitudes, thereby influencing their employability ‎[ 38 ]. Moreover, Dalrymple et al. ‎[ 69 ] conceptualized employability as a process rather than a fixed outcome, arguing that employability is not a stable state that can be indefinitely maintained, as it depends on various dynamic personal and environmental factors. For instance, employability may be strengthened or weakened by fluctuations in personal factors such as emotions and attitudes (including learning attitude)‎ [ 12 ]. Consistent with this, Fan and Pan‎ [ 18 ] emphasized that during their time at university, students should adjust their learning goals, attitudes, and methods in line with real-world circumstances to continuously enhance their professional knowledge and skills, which will in turn improve their future employability. According to the Career EDGE model of employability, career planning is part of the fundamental first-tier elements and is one of the key factors for enhancing employability [‎7, ‎8]. From the perspective of career psychological capital, Sepahvand et al.‎ [ 39 ] argued that career planning has a positive effect on career psychological capital-a positive psychological state that individuals demonstrate during their growth and development, which includes traits such as self-confidence, optimism, and positivity. A positive learning attitude is an important component of psychological capital; therefore, career planning can positively influence learning attitude. Furthermore, Monks et al.‎[ 40 ] suggested that providing students with career planning guidance, especially in the early stages of their university studies, helps them develop clear and effective career plans. By gaining a thorough understanding of their chosen major, identifying strengths and weaknesses, and clarifying learning goals and future development directions, students can learn more effectively, independently, and autonomously. This reduces aimlessness in their studies, fosters a more positive learning attitude during university, and ultimately enhances future employability. However, there is still a lack of clear empirical research verifying whether learning attitude mediates the relationship between career planning and employability. Based on the Career EDGE model, this study seeks to test the mediating effect of learning attitude, thereby addressing this empirical gap in the literature. Accordingly, the following hypotheses are proposed: H3: Career planning has a significant positive effect on the learning attitude of university students. H4: Learning attitude has a significant positive effect on the employability of university students. H5: Learning attitude mediates the relationship between career planning and employability among university students. The mediating effect of learning attitude between self-concept and employability According to the Career EDGE model of employability, self-concept is an important component of personality and a determinant of individual attitudes and behaviors ‎[ 41 ]. Individuals with a positive self-concept tend to feel confident, adopt a proactive stance toward challenges, and take feasible, constructive steps to prepare for success‎ [ 42 ]. Research has shown that self-concept has a significant positive effect on learning attitude; in other words, the stronger a university student’s self-concept, the more positive their learning attitude tends to be ‎[ 43 ]. Conversely, individuals with a negative self-concept are more likely to envy others, have poor control over their emotions, and perceive themselves as inferior or incompetent. This lack of self-confidence or hesitation in trying new things inevitably affects their learning attitude ‎[ 44 ]. Similarly, Metcalfe ‎[ 45 ] suggested that the more positive one’s self-concept, the more favorable the psychological representation and behavioral expression of learning attitude will be, as the values embedded in one’s self-concept shape both attitudes and behaviors. In this sense, a self-concept oriented toward positivity helps enhance students’ learning attitudes ‎[ 46 ]. Furthermore, Liu and Wang ‎[ 47 ] argued that self-concept influences students’ learning attitudes in such a way that those with stronger self-concepts, higher expectations for the future, and greater belief in their own abilities tend to have more positive learning attitudes. Conversely, students with weaker self-concepts often lack confidence in their learning abilities, doubt their capacity to master academic content, and therefore exhibit more negative learning attitudes. Research has found that university students’ career self-concept has a significant positive effect on their learning attitude. The clearer students perceive their career self-concept, the better their perceived learning attitude, leading to more proactive learning behaviors, enhanced learning outcomes, improved academic performance, and greater employability‎[ 48 ]. In addition, self-concept has a clear direct effect on academic performance, learning attitude, and employment outcomes, and it can also indirectly influence future employability through learning attitude ‎[ 49 ]. Specifically, the more positive a student’s self-concept, the higher their self-confidence, which in turn fosters a more active learning attitude. Such students approach learning with greater interest, are more likely to complete tasks successfully, achieve desirable academic outcomes, and enhance their employability‎[ 50 ]. Similarly, Guay et al.‎[ 51 ] suggested that students with a well-defined self-concept can see their true selves more clearly, better understand their values, interests, and abilities, and thus develop a more positive learning attitude, which translates into stronger employability. Although existing research has examined the relationship between self-concept and learning attitude, few studies have explored in depth the potential mediating role of learning attitude between self-concept and employability. Based on the Career EDGE model, this study further investigates how self-concept influences employability through learning attitude. Accordingly, the following hypotheses are proposed: H6: Self-concept has a significant positive effect on the learning attitude of university students. H7: Learning attitude mediates the relationship between self-concept and employability among university students. Research Method Research Framework This study is based on the Career EDGE model of employability and focuses on university students in Hebei Province, China. Using SPSS Statistics 27 and AMOS software, it examines the effects of career planning, self-concept, and learning attitude on employability, and tests the mediating role of learning attitude in the relationships between career planning and employability, as well as between self-concept and employability. The research framework is illustrated in Fig. 1 : Research participants and procedure Hebei Province plays an important role in the Beijing-Tianjin-Hebei coordinated development strategy, with the central government assigning distinct functional roles to each city and leveraging the unique advantages of local resources ‎[ 52 ]. The province has a relatively large number of higher education institutions distributed across a wide area, yet employment levels vary considerably among them ‎[ 53 ]. The coordinated development of Beijing-Tianjin-Hebei has significantly increased the supply of human resources, intensifying competition among universities in Hebei and increasing employment pressure. In response, the Hebei Provincial Party Committee and the provincial government have consistently adhered to the strategy of prioritizing employment, making every effort to maintain stability in the job market ‎[ 52 ]. At the same time, to enhance university students’ employability and address employment challenges, Hebei has introduced a series of policies specifically targeting graduate employment ‎[ 54 ]. Against this backdrop, this study selected university students in Hebei Province, China, as the research participants and conducted a questionnaire survey. A pilot survey was first conducted to test whether the scales used in this study were suitable for the sample. Using convenience sampling, 130 university students from one higher education institution were selected for the pilot test. The collected pilot questionnaires underwent reliability, validity, and item analyses, which informed the development of the final questionnaire. For the main survey, convenience sampling was again employed. Participants were drawn from five universities in Hebei Province, including two in Shijiazhuang and one each in Hengshui, Langfang, and Baoding, covering a balanced mix of comprehensive, normal, and science and engineering universities. To avoid overlap with the pilot sample, questionnaires were distributed via the Wenjuanxing online survey platform, where students completed the survey on-site by scanning a QR code. The platform was set to collect participants’ WeChat nickname, gender, and location to prevent duplicate responses. To ensure data quality, student affairs staff from each university were contacted to help organize the sampled students. These staff members explained the purpose and significance of the study to students in class, clarified the instructions for completing the questionnaire, and reminded them to answer carefully. Participation was voluntary, and completion of the questionnaire was taken as implied consent to participate in the research. According to Gorsuch ‎[ 55 ], the minimum sample size for a formal study should be at least ten times the total number of items in the scale. In this study, the four scales contained a total of 48 items, meaning that at least 480 valid responses were required. Considering the possibility of invalid responses during data collection, 800 questionnaires were distributed to university students from five higher education institutions in Hebei Province, with 160 questionnaires allocated to each institution. After excluding invalid responses, 735 valid questionnaires were obtained, yielding a valid response rate of 91.88%. The final sample met the above sampling standard and was therefore suitable for hypothesis testing and analysis. Among the respondents, 233 were male and 502 were female; 146 were first-year students, 129 were second-year students, 248 were third-year students, and 212 were fourth-year students. Research Instruments Career planning scale This study adopted the three-item version of Gould’ s ‎[ 9 ] Career Planning Scale as used by Koen et al. ‎[ 10 ], who applied it in educational settings. The scale includes items such as: “I have planned my career,” “I have a strategy to achieve my career goals,” and “I know what I need to do to achieve my career goals,” totaling three items. Responses were measured on a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Higher total scores indicate better career planning. Reliability analysis of the pilot test showed a Cronbach’s Alpha of .836, indicating good internal consistency. Confirmatory Factor Analysis (CFA) was conducted using the formal questionnaire, and the results indicated a saturated model. A saturated model refers to a hypothesized model in which the number of estimated parameters exactly equals the number of elements in the covariance matrix. In this case, all parameters have a unique solution, and both the chi-square value and the degrees of freedom are zero. Since the chi-square value is zero, the model is considered just-identified, meaning it will never be rejected and achieves a perfect fit with the data ‎[ 72 ]. Furthermore, all factor loadings were statistically significant at the p < .001 level, with standardized factor loadings ranging from .797 to .854, all exceeding the .500 threshold. The Composite Reliability (CR) of the latent construct was .869, meeting the recommended criterion of greater than .700 ‎[ 56 ]. The Average Variance Extracted (AVE) was .689, exceeding the recommended value of .500‎[ 57 ]. These results indicate that the Career Planning Scale demonstrates good convergent validity. Self-concept scale This study adopted the Self-Concept Scale developed by Hsiao ‎[ 49 ], which measures university students’ self-concept using items drawn from the Higher Education Database. The scale consists of six items. Responses were measured on a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”), with reverse-scored items coded accordingly. Reliability analysis of the pilot test yielded a Cronbach’s α of .886, indicating good internal consistency. CFA was conducted using the formal questionnaire to assess model fit and convergent validity. The results showed that χ²/df = 3.619, which is less than 5; GFI = .986 and AGFI = .966, both exceeding the .900 threshold; SRMR = .019 and RMSEA = .060, both below the .080 standard; NFI = .986, RFI = .976, CFI = .990, and IFI = .990, all greater than .900; PNFI = .591, above the .500 criterion; and CN = 382, greater than 200. These results indicate that the self-concept model has good model fit. In addition, all factor loadings were statistically significant at p < .001, with standardized factor loadings ranging from .701 to .854, all exceeding the .500 benchmark. The composite reliability (CR) of the latent construct was .893, surpassing the recommended value of .700 ‎[ 56 ]. The average variance extracted (AVE) was .583, above the .500 threshold ‎[ 57 ]. These findings indicate that the Self-Concept Scale demonstrates good convergent validity. Learning attitude scale This study adopted the revised Learning Attitude Scale developed by Xu et al.‎[ 48 ], which was adapted from the scales of Zhang‎[ 58 ] and Fennema and Sherman ‎[ 59 ]. The scale consists of three dimensions-cognitive attitude, affective attitude, and behavioral attitude-with a total of 27 items. Responses were measured on a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Higher total scores indicate a more positive learning attitude among university students. During the item analysis of the pilot questionnaire, items 15 to 18 did not meet the item analysis criteria and were therefore removed. The pilot test yielded a Cronbach’s α coefficient of .936, indicating high reliability. CFA was conducted using the formal questionnaire to assess model fit and convergent validity. The results showed that χ²/df = 2.333, less than the recommended threshold of 5; GFI = .947 and AGFI = .935, both above .900; SRMR = .030 and RMSEA = .043, both below .080; NFI = .942, RFI = .936, CFI = .966, and IFI = .966, all exceeding .900; PNFI = .846 and PGFI = .779, both greater than .500; and CN = 365, greater than 200. These results indicate that the learning attitude model has good model fit. All factor loadings were statistically significant at p < .001, with standardized factor loadings ranging from .679 to .823, all above the .500 benchmark. The CR values for the latent variables were: cognitive attitude = .912, affective attitude = .848, and behavioral attitude = .912, all exceeding the recommended criterion of .700 ‎[ 56 ]. The AVE values were: cognitive attitude = .535, affective attitude = .528, and behavioral attitude = .536, all above the .500 threshold ‎[ 57 ]. These results indicate that the Learning Attitude Scale demonstrates good convergent validity. Employability scale This study adopted the Employability Scale revised by Cheng and Dong [ 60 ], which consists of four factors: basic competence, social practice ability, professional ethics, and job-seeking ability, with a total of 16 items. Responses were measured on a five-point Likert scale ranging from 1 (“very poor”) to 5 (“very strong”). Higher total scores indicate stronger employability. The scale’s Cronbach’s α coefficient was .881, indicating good reliability. CFA was conducted using the formal questionnaire to assess model fit and convergent validity. The results showed that χ²/df = 3.300, less than the recommended threshold of 5; GFI = .951 and AGFI = .934, both above .900; SRMR = .032 and RMSEA = .056, both below .080; NFI = .947, RFI = .936, CFI = .962, and IFI = .962, all exceeding .900; PNFI = .789 and PGFI = .699, both greater than .500; and CN = 277, greater than 200. These indices indicate that the employability model has good model fit. All factor loadings were statistically significant at p < .001, with standardized factor loadings ranging from .689 to .872, all above the .500 benchmark. The CR values for the latent variables were: basic competence = .806, social practice ability = .896, professional ethics = .840, and job-seeking ability = .856, all exceeding the recommended criterion of .700 ‎[ 56 ]. The AVE values were: basic competence = .676, social practice ability = .552, professional ethics = .569, and job-seeking ability = .665, all above the .500 threshold ‎[ 57 ]. These results indicate that the Employability Scale demonstrates good convergent validity. Common method bias test Since this study’s questionnaire included more than two variables and all variables were answered by the same participants, there was a possibility of common method bias due to personal or psychological factors. To reduce this risk, the study followed the recommendation of Zhou and Long ‎[ 61 ] and applied Harman’s single-factor test through exploratory factor analysis to detect common method variance. All items from the formal questionnaire were entered into SPSS for factor analysis, and the variance explained by the first unrotated principal component was examined. If the variance explained by the first factor is less than 50%, it suggests that there is no serious common method bias. As shown in Table 1 , the analysis produced nine factors with eigenvalues greater than 1, and the first factor accounted for 31.499% of the variance, which is below the 50% threshold. Therefore, there is no significant common method variance in the formal questionnaire sample, and further data analysis can proceed. Table 1 Analysis of common method bias Factor Eigenvalue (≥ 1) Variance Explained (%) Cumulative Variance Explained (%) 1 15.120 31.499 31.499 2 4.368 9.099 40.598 3 2.567 5.347 45.946 4 2.237 4.660 50.606 5 1.622 3.380 53.986 6 1.551 3.232 57.217 7 1.534 3.197 60.414 8 1.185 2.470 62.883 9 1.063 2.214 65.098 Correlation analysis Pearson correlation analysis was conducted to examine the relationships among the variables. As shown in Table 2 , the correlation coefficients ranged from .361 to .520, and all correlations were significantly positive ( p < .001). This indicates that there are significant positive relationships among the variables. Furthermore, since none of the correlation coefficients exceeded .800, there is no concern of multicollinearity ‎[ 62 ]. Therefore, it is appropriate to proceed with the subsequent overall model validation analysis. Table 2 Results of correlation analysis Variable M SD Career Planning Self-Concept Learning Attitude Employability Career Planning 3.470 0.856 Self-Concept 3.732 0.804 .361*** Learning Attitude 3.660 0.561 .465*** .396*** Employability 3.747 0.527 .496*** .494*** .520*** --- Note. p < .001; M = mean; SD = standard deviation Path analysis of the overall model Based on the research hypotheses, a path analysis was conducted to examine the relationships among career planning, self-concept, learning attitude, and employability among university students. The overall model fit was first evaluated. Following Schumacher and Lomax‎ [ 63 ], this study assessed the model using three types of fit indices: absolute fit, parsimonious fit, and incremental fit. The evaluation criteria were as follows: χ²/df less than 5‎[ 63 ]; GFI, IFI, NFI, CFI, and TLI greater than .900 ‎[ 56 ]; and RMSEA less than .080 ‎[ 56 ]. As shown in Table 3 , the results indicate that χ²/df = 2.571, which is less than 5; GFI = .959 and AGFI = .944, both greater than .900; RMSEA = .046, less than .080; NFI = .956, RFI = .947, CFI = .973, and IFI = .973, all greater than .900; PNFI = .789 and PGFI = .698, both greater than .500; and CN = 356, exceeding 200. These results demonstrate that the model has a good overall fit. Table 3 Fit indices for the overall model Fit Category Index Criterion Value Model Fit Absolute Fit χ 2 /df .900 .959 Good Fit AGFI > .900 .944 Good Fit RMSEA .900 .956 Good Fit RFI > .900 .947 Good Fit CFI > .900 .973 Good Fit IFI > .900 .973 Good Fit Parsimonious Fit PNFI > .500 .789 Good Fit PGFI > .500 .698 Good Fit CN > 200 356 Good Fit Note. χ² = chi-square statistic; df = degrees of freedom; GFI = goodness-of-fit index; AGFI = adjusted goodness-of-fit index; RMSEA = root mean square error of approximation; NFI = normed fit index; RFI = relative fit index; CFI = comparative fit index; IFI = incremental fit index; PNFI = parsimonious normed fit index; PGFI = parsimonious goodness-of-fit index; CN = critical N. Direct effects As shown in Table 4 and Fig. 2 , career planning had a significant positive effect on employability, with a path coefficient of .293 ( p < .001) and a 95% confidence interval of [.199, .387], which does not include zero. Self-concept had a significant positive effect on employability, with a path coefficient of .339 ( p < .001) and a 95% confidence interval of [.253, .420], also excluding zero. Career planning had a significant positive effect on learning attitude, with a path coefficient of .478 ( p < .001) and a 95% confidence interval of [.392, .565]. Learning attitude had a significant positive effect on employability, with a path coefficient of .396 ( p < .001) and a 95% confidence interval of [.298, .498]. Self-concept also had a significant positive effect on learning attitude, with a path coefficient of .309 ( p < .001) and a 95% confidence interval of [.216, .398]. These results indicate that all five direct effects are significant, suggesting that career planning, self-concept, and learning attitude enhance university students’ employability, while career planning and self-concept also influence their learning attitude. Therefore, hypotheses H1, H2, H3, H4, and H6 are all supported. To further examine the mediating effect of learning attitude between career planning and employability, as well as between self-concept and employability, the bootstrap method was applied following the recommendations of Hayes ‎[ 64 ], with 5,000 resamples. Indirect effects As shown in Table 4 and Fig. 2 , the indirect effect of learning attitude between career planning and employability was .189 ( p < .001), with a 95% confidence interval of [.137, .258], which does not include zero. This indicates that the indirect effect is significant, suggesting that career planning can indirectly influence employability through learning attitude, thereby confirming the partial mediating effect of learning attitude. Thus, hypothesis H5 is supported. Similarly, the indirect effect of learning attitude between self-concept and employability was .122 ( p < .001), with a 95% confidence interval of [.080, .176], also excluding zero. This indicates that the indirect effect is significant, suggesting that self-concept can enhance employability through the strengthening of learning attitude, confirming the partial mediating effect of learning attitude. In other words, self-concept not only has a direct positive effect on employability, but may also indirectly influence employability via learning attitude. Therefore, hypothesis H7 is supported. Table 4 Bootstrap Test and Path Analysis Path Effect p 95% CI Direct Effects Career Planning → Employability .293 .000 [.199, .387] Self-Concept → Employability .339 .000 [.253, .420] Career Planning → Learning Attitude .478 .000 [.392, .565] Self-Concept → Learning Attitude .309 .000 [.216, .398] Learning Attitude → Employability .396 .000 [.298, .498] Indirect Effects Career Planning → Learning Attitude → Employability .189 .000 [.137, .258] Self-Concept → Learning Attitude → Employability .122 .000 [.080, .176] Total Effects Career Planning → Employability .482 .000 [.397, .565] Self-Concept → Employability .461 .000 [.372, .544] Note. Effect = standardized path coefficient; CI = confidence interval; LLCI = lower limit of the CI; ULCI = upper limit of the CI. All effects are significant at p < 0.001 based on 5,000 bootstrap samples. Discussion Career planning’s direct effect on employability The results indicate that career planning has a significant positive effect on the employability of university students. Thus, Hypothesis H1 is supported. This finding is consistent with previous studies [‎28, ‎65] suggesting that career planning plays an active role in enhancing students’ employability and is an important influencing factor. In other words, when university students develop specific career plans that align with their personal circumstances, they are better able to understand their career trajectories. Particularly in the current complex market environment, effective career planning can motivate students to formulate actionable implementation plans to advance their career goals, thereby improving their employability ‎[ 21 ]. Furthermore, career planning can enhance students’ self-awareness. Self-awareness requires students to use various methods-such as career interest tests, assessments of career values, and evaluations of professional skills-to gain a clearer understanding of themselves. This process helps students better define their future career development direction, choose occupations that match both their interests and capabilities, and make more rational career choices. It also reduces the influence of conformity and impulsive decision-making on future employment, thus improving employability‎[ 26 ]. In summary, career planning can positively predict employability. By engaging in career planning, students gain a clearer understanding of their career development goals, enhance their self-awareness, and make rational decisions, ultimately contributing to improved employability. Self-concept’s direct effect on employability The results show that self-concept has a significant positive effect on the employability of university students, confirming Hypothesis H2 . This finding is consistent with previous studies [‎15, ‎34, ‎66], indicating that a strong self-concept can contribute to the enhancement of employability. In other words, when university students possess a positive self-concept, it fosters greater confidence, self-efficacy, and a proactive approach to career development, all of which strengthen their employability. The reasons for this may be as follows. First, university students with a positive self-concept generally possess a strong sense of self-worth and self-efficacy, believing in their ability to secure an ideal job suited to their strengths. They are more likely to persevere in pursuing their goals despite setbacks, demonstrating strong motivation for employment. In contrast, students with a negative self-concept may face greater employment pressure, characterized by a lack of confidence and intrinsic motivation, which can lead to procrastination, negative emotions, and difficulty in facing future employment challenges, thereby lowering their employability ‎[ 35 ]. Second, students with a positive self-concept tend to have a clear understanding of themselves and their capabilities, and they are more likely to engage in proactive employment behaviors, such as actively acquiring new knowledge and skills and seeking job opportunities. Conversely, those with a negative self-concept may exhibit avoidant or passive job-seeking behaviors, lacking the confidence and courage to act on their abilities, which results in hesitation and passive responses, ultimately diminishing their employability. Third, students with a positive self-concept are generally better equipped to manage stress and overcome difficulties. They tend to have stronger self-awareness and emotional stability, which can reduce the anxiety caused by job-search pressures. In contrast, students with a negative self-concept may experience anxiety, low self-esteem, and timidity when facing employment difficulties, making it harder to cope effectively with future challenges, thus lowering their employability ‎[ 31 ]. In summary, self-concept influences students’ employment motivation, job-seeking behaviors, and psychological readiness, thereby affecting their employability. Therefore, enhancing university students’ employability requires giving due attention to the cultivation of their self-concept. The mediating effect of learning attitude between career planning and employability The findings show that learning attitude mediates the relationship between career planning and employability among university students. Hypotheses H3, H4, and H5 are supported. This result is consistent with the findings of Sepahvand et al.‎[ 39 ] (2023), indicating that career planning can indirectly influence employability through learning attitude. In other words, by formulating reasonable career plans, university students can strengthen their learning attitudes during their studies, which in turn enhances their employability. The reasons for this may be as follows. First, career planning helps students identify suitable career directions. When students have a clear sense of their career path, their learning interest and motivation are stimulated, leading to a more positive learning attitude throughout their university years. This enables them to purposefully develop the skills and qualities needed for future employment, thereby improving their employability ‎‎[ 67 ]. Second, career planning allows students to establish a correct outlook on life. By clarifying learning goals and developing a comprehensive plan suited to their personal circumstances, students are motivated to study diligently in order to realize their life plans and values, acquire more skills needed for the future, and ultimately enhance their employability. Third, through career planning, students can develop effective learning strategies, methods, and emotional regulation skills, thereby improving their learning character. This leads to a more active learning attitude, which promotes personal growth and strengthens employability ‎[ 21 ]. The mediating effect of learning attitude between self-concept and employability The results indicate that learning attitude mediates the relationship between self-concept and employability among university students. This finding is consistent with previous research ‎[43, ‎49] and supports Hypotheses H6 and H7 , suggesting that self-concept can indirectly influence employability through learning attitude. In other words, students’ self-concept shapes their learning attitude during university, which in turn affects their employability. The possible reasons are as follows. First, the more positive students’ self-concept, the greater their self-confidence. This leads to a more positive attitude toward their studies and campus life, such as curiosity about new knowledge, a strong desire to learn, and greater willingness to proactively acquire and master relevant skills and knowledge, thereby improving their employability ‎[ 50 ]. Second, when students have a clear self-concept, they are more certain about their choices, better able to see their true selves, and more aware of their values, interests, and abilities. This self-awareness fosters clear intrinsic motivation, which translates into maintaining a positive learning attitude. Such an attitude manifests in constructive learning behaviors that ultimately enhance employability ‎[ 51 ]. Third, by building a positive self-concept and strong core self-evaluations, students become more confident and proactive when facing employment challenges. This proactive mindset encourages them to persevere in their learning goals, working hard even when confronted with academic or life difficulties. This persistence and effort ultimately contribute to improving their employability and competitiveness. Conclusion and implications Conclusion Using SPSS and AMOS, this study surveyed Chinese university students to analyze the effects of career planning and self-concept on employability, with a particular focus on examining the mediating role of learning attitude. The main findings are as follows: Direct Effects Career planning, self-concept, and learning attitude all have significant direct effects on employability. In addition, career planning and self-concept were found to positively predict learning attitude. Indirect Effects This study further explored the mechanisms through which career planning and self-concept influence employability. The results reveal that career planning positively predicts employability indirectly through the mediating role of learning attitude. Similarly, self-concept also exerts an indirect positive effect on employability via learning attitude. Implications This study holds important theoretical and practical implications. Theoretical Implications: First, by integrating career planning, self-concept, learning attitude, and employability into a single theoretical model, this study reflects the relationships and underlying mechanisms among these variables. The findings provide a theoretical basis for enhancing university students’ employability and further enrich the body of theory related to each variable. In particular, learning attitude-conceptualized as a psychological disposition-was shown to mediate the effects of career planning and self-concept on employability. This finding not only offers new empirical support for the Career EDGE model of employability, but also provides a theoretical foundation for better understanding the internal mechanisms influencing students’ employability. Second, while employability has received increasing policy attention in China, research on university students’ employability by Chinese scholars began relatively late. There remains a lack of employability theories and empirical studies with strong local relevance, as well as an absence of broadly applicable theoretical frameworks. Building on the existing literature, this study explores the mechanisms through which career planning, self-concept, and learning attitude influence employability. The results help address existing research gaps and test the applicability of the Career EDGE model in the Chinese context, thereby filling an important theoretical void. Third, much of the current research on employability in China is theoretical in nature, with fewer empirical studies. Existing studies on influencing factors often rely on subjective judgments and lack empirical investigation and data support. By examining the mechanisms linking career planning, self-concept, and learning attitude to employability, this study supplements empirical research on Chinese university students’ employability. It also provides objective, data-driven evidence for understanding the factors that influence employability. Practical Implications: Based on the findings of this study, career planning, self-concept, and learning attitude all have significant positive effects on employability, providing feasible pathways to enhance university students’ employability. First, strengthen career planning education for university students. Universities should integrate career planning into general curricula, providing systematic guidance to help students plan their career paths in advance. This should follow the objective rules of students’ growth and development, tailoring guidance to different stages, levels, and individual characteristics. By aligning career design guidance with students’ developmental needs, institutions can lay a strong foundation for improving employability. Second, cultivate positive learning attitudes to form the intrinsic driving force of employability. Universities should create a positive learning atmosphere to stimulate students’ intrinsic motivation. This can be achieved by establishing learning communities, such as professional study groups, reading clubs, and research interest societies, to reduce feelings of isolation in learning. Additionally, providing students with guidance on effective learning strategies can help them gradually develop an attitude of proactive exploration, responsibility, and continuous growth, thereby laying a solid foundation for employability enhancement. Finally, build growth platforms to foster the development of a positive self-concept. Universities should adopt a student-centered approach, paying attention to students’ feelings, perceptions, and cognition, and promoting deeper self-understanding and reconstruction of self-concept. For example, offering platforms and contexts for learning and growth can allow students to construct interactions between self and society through group engagement, facilitating comprehensive self-development. Educators should pay particular attention to students’ inner worlds, listen to their voices, understand their needs, and help them build a positive self-concept. This includes enhancing students’ emotional regulation abilities and focusing on challenges they face, such as failure or setbacks, by guiding them to develop appropriate self-attribution skills. Furthermore, psychological training camps, group discussions, and other experiential activities can help students understand themselves from multiple perspectives and levels, improving self-awareness and, in turn, strengthening employability. Limitations and future research directions First, this study primarily employed a questionnaire survey method. While this method offers flexibility and convenience, the use of self-reported measures by students may introduce bias, as respondents may conceal their shortcomings during the survey process. As a result, the data may contain slight deviations. To gain deeper insights into the factors influencing employability, future studies could incorporate in-depth interviews with university students to better capture their genuine intentions and explore the multiple factors that affect employability. Second, the sample for this study was drawn from students at five universities in Hebei Province, China. This sampling approach has a certain degree of regional concentration and may not fully represent the national situation, although it does ensure that the findings are representative of Hebei Province. Future research could expand the geographic scope and coverage of the sample to improve the generalizability of the results. Third, while examining the factors influencing employability, this study focused primarily on the individual-level perceptions of students, which may carry a degree of subjectivity. Future research could integrate school-level, teacher-level, and individual-level factors, employing cross-level analytical methods to more comprehensively examine the determinants of employability. In addition, the recommendations and strategies proposed could be applied in university student management practices, allowing for practical evaluation of their effectiveness in enhancing employability and thereby broadening and deepening the scope of research. Fourth, the results indicate that career planning and self-concept have significant positive effects on employability. However, when learning attitude was included as a mediating variable, the path coefficients for the effects of career planning on employability and self-concept on employability decreased. This suggests that learning attitude plays a partial mediating role in both relationships and implies the possible existence of other mediating variables. Future studies could further investigate alternative mediators or examine potential moderating variables to refine and extend the research model. Declarations Ethics approval and consent to participate The researchers confirms that all research was performed in accordance with relevant guidelines/regulations applicable when human participants are involved (e.g., Declaration of Helsinki or similar). This study was approved by the office of the Dhurakij Pundit University Research Ethics Committee on Human (DPUREC), with ethical approval number (DPU_BSH 210367/2566). The written informed consent was obtained from all the participants Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This work was supported by the university-level scientific research project for high-level talents funded by the school of Hengshui University. Author Contribution Yu Sun conceptualized and designed the study. Yu Sun collected the data. 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Soc Psychol Educ. 2021;26:945–69. 10.1007/s11218-023-09778-y . Healy M, Hammer S, McIlveen P. Mapping graduate employability and career development in higher education research. Stud High Educ. 2022;47(4):799–811. 10.1080/03075079.2020.1804851 . Jaeger AJ. Job competencies and the curriculum: Emotional intelligence in graduate professional education. Res High Educt. 2003;44:615–39. 10.1023/A:1026119724265 . Wu ML. Structural equation modeling: AMOS operation and application. 2nd ed. Chongqing: Chongqing University; 2010. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7406818","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":519288445,"identity":"61c8e603-7abd-44b7-8e0b-15e063030a2b","order_by":0,"name":"Yu Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACNvaG9A8fDP7b8bM3EKmFj+fAM8YZFczJkj0HiNQiJ5H4jJnnDDPjhhkJxDqM53DaA942NmYDyccbbzDU2EQT1sLelm4g2cbDZy6dVmzBcCwtt4GwLWcSJAzbJJgtZ+eYSTA2HCZCi0T+B4nENgPGDTfPEK0lIU3iwJkExg03eIjVwnMg2bCh4gAwkIF+SSDGL/LtDYmP/xgcAEbl4Y03PtTYENaCDAwkEkhRDtFCqo5RMApGwSgYGQAAxs8/5QwMBgcAAAAASUVORK5CYII=","orcid":"","institution":"Hengshui University","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Sun","suffix":""},{"id":519288446,"identity":"4681f004-126b-4511-ac26-9cda3f69c26e","order_by":1,"name":"Qian Liu","email":"","orcid":"","institution":"Hengshui University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Liu","suffix":""},{"id":519288447,"identity":"d3154715-183d-4ca9-bbf3-0b84df790023","order_by":2,"name":"Xiaona Liu","email":"","orcid":"","institution":"Hengshui 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11:14:20","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181927,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7406818/v1/1d342feb290df5326fb91e3a.html"},{"id":92076862,"identity":"54aae06d-9bbc-4a0a-9d31-9f4e38319547","added_by":"auto","created_at":"2025-09-24 11:06:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52965,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Framework\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7406818/v1/c50e820f3c60c9e15998cad8.png"},{"id":92076863,"identity":"afada3fa-d2a2-4ab2-ba57-d08fb3447b8f","added_by":"auto","created_at":"2025-09-24 11:06:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108419,"visible":true,"origin":"","legend":"\u003cp\u003eMediation model path diagram; \u003cem\u003e\u003cstrong\u003eNote. \u003c/strong\u003e\u003c/em\u003e*** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001. L1 = cognitive attitude; L2 = affective attitude; L3 = behavioral attitude; E1 = basic competency; E2 = social practice competency; E3 = professional ethics; E4 = job-seeking competency.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7406818/v1/ced2a30aced53a919060b13d.png"},{"id":103402386,"identity":"fa429232-8043-4c63-84df-6cf904c96d33","added_by":"auto","created_at":"2026-02-25 09:28:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1305757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7406818/v1/1cbb449c-7a79-4172-b51c-8244cb6896b3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Career Planning and Self-Concept on Employability among University Students: Exploring the Mediating Role of Learning Attitudes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the 2022 China College Graduate Employment Report \u0026lrm;[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] released by the Central People\u0026rsquo;s Government of the People\u0026rsquo;s Republic of China, the number of fresh graduates in 2022 exceeded 10\u0026nbsp;million for the first time, marking a record high in both total and incremental numbers. At the same time, a survey report by Zhaopin.com showed that the employment rate of college graduates in 2022 was 50.4%, representing a 6.5% decline compared with 2021\u0026lrm;[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With the continuing expansion of higher education, the number of college graduates is expected to keep increasing. Coupled with the downward pressure on economic growth, the intersection of a persistently high graduate population and ongoing economic restructuring is bound to create more complex employment challenges \u0026lrm;[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In practice, structural mismatches have become a key issue: many graduates are not unable to find jobs, but rather unable to find satisfactory ones. This reflects the widespread problem of low employment quality\u0026lrm;[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Improving the quality of graduate employment depends on enhancing their employability\u0026lrm;[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Graduate employability has attracted increasing attention from scholars in the fields of higher education and human capital \u0026lrm;[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Employment is a critical issue for the nation\u0026rsquo;s economy and people\u0026rsquo;s livelihoods, as it concerns both national stability and individuals\u0026rsquo; pursuit of a better life \u0026lrm;[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEmployability is a key factor for maintaining sustainable employment in today\u0026rsquo;s unstable job market. It also reflects an individual\u0026rsquo;s flexibility-the ability to be adequately prepared to face the current complex and rapidly changing employment environment. Through learning in a professional field, individuals can acquire the skills needed to secure employment, maintain competitiveness, and continuously develop professional skills\u0026lrm; [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. According to the Career EDGE model of employability proposed by Dacre and Sewell\u0026lrm;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], university students seeking to develop their employability must first enhance five core elements: career development learning, work and life experience, subject-specific knowledge and skills, generic skills, and emotional intelligence (first tier). They must then engage in ongoing reflection and evaluation of these experiences (second tier). This process promotes the development of higher-level attributes such as self-esteem, self-confidence, and self-efficacy, which are essential for building employability\u0026lrm; [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCareer planning refers to the process in which individuals set reasonable plans for their future careers to achieve their ideal goals, as well as the strategies and methods they adopt to realize these plans\u0026lrm; [9, \u0026lrm;10]. Career planning is not only part of the \u0026ldquo;career development learning\u0026rdquo; component in the Career EDGE model of employability, but also falls under the generic skills dimension, which includes planning, coordination, organization, and management abilities\u0026mdash;the first tier in enhancing employability\u0026lrm;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] (Dacre \u0026amp; Sewell, 2007). Research indicates that to improve their employability, university students should clearly define their career goals and continuously enrich and improve themselves with career planning as a guiding principle. This involves exploring their potential, enhancing professional qualities, and preparing for future career development to strengthen their competitiveness in the job market \u0026lrm;[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, career planning plays a positive and significant role in promoting students\u0026rsquo; employability\u0026lrm; [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSelf-concept refers to an individual\u0026rsquo;s overall view and perception of themselves, encompassing their beliefs, positions, and emotions, as well as their self-descriptions and self-definitions regarding physical, psychological, and social characteristics, and personal abilities \u0026lrm;[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Within the Career EDGE model of employability, self-concept is part of the first-tier element of emotional intelligence and also falls under the reflection and evaluation stage. It plays an important role in the development of employability \u0026lrm;[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Tentama and Abdillah \u0026lrm;[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] found through student interviews that the most influential factors affecting employability are academic achievement and self-concept. Similarly, Kim et al. \u0026lrm;[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] reported that having a strong and positive self-concept within a specific framework can enhance an individual\u0026rsquo;s employability, while a low self-concept tends to be associated with lower employability. Therefore, self-concept plays a vital role in improving employability \u0026lrm;[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccording to the Career EDGE model of employability, the second tier is reflection and evaluation. While it is important to provide students with opportunities to acquire the essential knowledge, skills, understanding, and attributes of the first tier, it is equally important to offer opportunities for reflection and evaluation of their learning experiences. Without such opportunities, students cannot fully assess how far they have progressed in developing their employability, nor can they identify the steps needed to further enhance it \u0026lrm;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Learning attitude refers to a learner\u0026rsquo;s positive or negative evaluations and feelings toward learning activities or environments, as well as their readiness or tendency to engage in active or passive learning behaviors \u0026lrm;[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Learning attitude belongs to the second-tier \u0026ldquo;reflection and evaluation\u0026rdquo; component of the Career EDGE model. A positive learning attitude can effectively promote the employability of university students \u0026lrm;[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. During their studies, students should continuously reflect on and adjust themselves according to real circumstances. With an active learning attitude-thinking and learning proactively, and expanding their thinking and imagination-their future academic achievements and employability will be stronger [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough research on college students\u0026rsquo; employability has received increasing attention in academia, the mechanisms through which self-concept and career planning influence employability have not been fully explored, particularly with regard to systematic and in-depth examination of mediating pathways. Moreover, there is still a lack of theoretical and empirical studies on employability that align with China\u0026rsquo;s specific context\u0026lrm;[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, this study, grounded in the Career EDGE model of employability and situated within the Chinese educational context, investigates the interrelationships among career planning, self-concept, learning attitude, and employability, and further examines the potential mediating role of learning attitude. This research helps address gaps in the existing literature while testing the applicability of the Career EDGE model in China, providing a useful reference for strategies to enhance the employability of Chinese university students.\u003c/p\u003e"},{"header":"Literature Review and Research Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eThe Impact of Career Planning on Employability\u003c/h2\u003e\n\u003cp\u003eAccording to the Career EDGE model of employability, career planning falls under the first-tier element of career development learning, as well as the generic skills of planning, coordination, organization, and management. These are the most fundamental factors for enhancing the employability of university students\u0026lrm; [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research has shown that effective career planning is essential for graduates to conduct successful job searches, achieve career satisfaction, and improve their employability-particularly when facing challenging and continuously changing work environments\u0026lrm; [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].By engaging in career planning, students can gain a deeper and more comprehensive understanding of their professional pathways. In the context of current labor market demands, such planning helps them develop effective strategies, advance their career development, and achieve better career outcomes, thereby enhancing their employability \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Gould \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] equated career planning with goal setting\u0026lrm;[22, \u0026lrm;23], noting that all employees engaged in career planning set specific and challenging career goals without exception. Based on goal-setting theory, employees formulate and implement career strategies to achieve these goals \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The effective implementation of such strategies enables them to reach their ultimate career objectives, which in turn strengthens their employability \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eCareer construction theory \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] suggests that through career exploration, university students can reassess themselves rationally, strengthen the learning of relevant skills, and develop concrete strategies to achieve their goals, thereby enhancing their employability. Similarly, Guo \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] argued that embedding career planning education throughout the entire undergraduate period\u0026mdash;starting from the first year-can awaken students\u0026rsquo; career awareness early, help them understand social and job skill requirements in advance, and enable them to plan career paths ahead of time. By purposefully and systematically preparing the professional skills and knowledge needed for future employment, students can develop correct employment concepts and improve their employability. Furthermore, Liu et al. \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] confirmed that students\u0026rsquo; career planning has a significant positive effect on their perceived employability. In other words, through a series of actions such as career exploration, career-related activities, and continuous evaluation and adjustment, students can formulate and refine their career plans, adopt a long-term perspective on job value, and cultivate a mindset for career development. This process encourages them to actively enhance the competitive skills required in the job market \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. Accordingly, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003eH1: Career planning has a significant positive effect on the employability of university students.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eThe impact of self-concept on employability\u003c/h3\u003e\n\u003cp\u003eAccording to the Career EDGE model of employability, in addition to career development learning and generic skills, emotional intelligence (EI) is also one of the fundamental first-tier elements for developing employability\u0026lrm;[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Emotional intelligence-also referred to as emotional quotient\u0026mdash;refers to qualities related to emotion, affect, willpower, and resilience, as well as the ability to reason with emotions and use them to enhance thinking. It includes the capacity to accurately perceive emotions, to generate emotions that facilitate thought, to understand emotions and emotional knowledge, and to regulate emotions reflectively in ways that promote emotional and intellectual growth[\u0026lrm;29]. Emotional intelligence can be improved through reasoning, perception, and control of emotions, as well as understanding emotional experiences. It has a positive correlation with individual achievement\u0026mdash;those with high emotional intelligence tend to be more self-motivated, achieve more, attain greater career success, build stronger interpersonal networks, and enjoy a healthier life compared to those with low emotional intelligence \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. Self-concept is a component of emotional intelligence within the Career EDGE model. Research indicates that in higher education, students\u0026rsquo; self-concept is positively related to both academic achievement and future employability \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. In other words, individuals with a higher self-concept generally have higher employability, whereas those with lower self-concept tend to have lower employability\u0026lrm; [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eKim et al. \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], drawing on self-concept theory, demonstrated that self-concept can be a prerequisite for perceived employability. From the combined perspectives of self-concept theory and human capital theory, perceived employability can be understood more comprehensively. In addition, Dania et al. \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] argued that self-concept is an important factor in the formation of employability within the school environment, and that the extent to which individuals act on their self-concept influences their future employability in the workplace\u0026lrm;[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similarly, Rothwell et al. \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e] found that graduates\u0026rsquo; employability is related to their self-concept as students. Ahmid and Abdullah \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e] also confirmed a highly significant positive effect of self-concept on students\u0026rsquo; employability-that is, the stronger a student\u0026rsquo;s self-concept, the higher their employability. A positive self-concept can enhance employability because it enables individuals to adopt a proactive stance in the face of challenges, to better appreciate themselves, and to take feasible, positive steps toward securing employment, thereby improving their employability \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAccordingly, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u0026nbsp;\u003c/strong\u003eSelf-concept has a significant positive effect on the employability of university students.\u003c/p\u003e\n\u003ch3\u003eThe mediating effect of learning attitude between career planning and employability\u003c/h3\u003e\n\u003cp\u003eLearning attitude refers to a learner\u0026rsquo;s consistent and sustained psychological state and behavioral tendency toward the content they study, encompassing cognitive, emotional, and behavioral components\u0026lrm;[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Research has shown that learning attitude has a significant direct positive effect on employability. In other words, students with a positive learning attitude tend to develop stronger employability, whereas those with a negative learning attitude often demonstrate lower employability\u0026lrm; [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Rothwell et al.\u0026lrm;[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], in exploring factors influencing employability, found that students\u0026rsquo; learning attitudes can effectively predict certain aspects of their employability. Specifically, when students develop a positive learning attitude, their cognition and behavior extend beyond their studies and learning environment to their future work attitudes, thereby influencing their employability \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, Dalrymple et al. \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e] conceptualized employability as a process rather than a fixed outcome, arguing that employability is not a stable state that can be indefinitely maintained, as it depends on various dynamic personal and environmental factors. For instance, employability may be strengthened or weakened by fluctuations in personal factors such as emotions and attitudes (including learning attitude)\u0026lrm; [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Consistent with this, Fan and Pan\u0026lrm; [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] emphasized that during their time at university, students should adjust their learning goals, attitudes, and methods in line with real-world circumstances to continuously enhance their professional knowledge and skills, which will in turn improve their future employability.\u003c/p\u003e\n\u003cp\u003eAccording to the Career EDGE model of employability, career planning is part of the fundamental first-tier elements and is one of the key factors for enhancing employability [\u0026lrm;7, \u0026lrm;8]. From the perspective of career psychological capital, Sepahvand et al.\u0026lrm; [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] argued that career planning has a positive effect on career psychological capital-a positive psychological state that individuals demonstrate during their growth and development, which includes traits such as self-confidence, optimism, and positivity. A positive learning attitude is an important component of psychological capital; therefore, career planning can positively influence learning attitude. Furthermore, Monks et al.\u0026lrm;[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e] suggested that providing students with career planning guidance, especially in the early stages of their university studies, helps them develop clear and effective career plans. By gaining a thorough understanding of their chosen major, identifying strengths and weaknesses, and clarifying learning goals and future development directions, students can learn more effectively, independently, and autonomously. This reduces aimlessness in their studies, fosters a more positive learning attitude during university, and ultimately enhances future employability. However, there is still a lack of clear empirical research verifying whether learning attitude mediates the relationship between career planning and employability. Based on the Career EDGE model, this study seeks to test the mediating effect of learning attitude, thereby addressing this empirical gap in the literature.\u003c/p\u003e\n\u003cp\u003eAccordingly, the following hypotheses are proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3:\u0026nbsp;\u003c/strong\u003eCareer planning has a significant positive effect on the learning attitude of university students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4:\u0026nbsp;\u003c/strong\u003eLearning attitude has a significant positive effect on the employability of university students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5:\u0026nbsp;\u003c/strong\u003eLearning attitude mediates the relationship between career planning and employability among university students.\u003c/p\u003e\n\u003ch3\u003eThe mediating effect of learning attitude between self-concept and employability\u003c/h3\u003e\n\u003cp\u003eAccording to the Career EDGE model of employability, self-concept is an important component of personality and a determinant of individual attitudes and behaviors \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. Individuals with a positive self-concept tend to feel confident, adopt a proactive stance toward challenges, and take feasible, constructive steps to prepare for success\u0026lrm; [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. Research has shown that self-concept has a significant positive effect on learning attitude; in other words, the stronger a university student\u0026rsquo;s self-concept, the more positive their learning attitude tends to be \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. Conversely, individuals with a negative self-concept are more likely to envy others, have poor control over their emotions, and perceive themselves as inferior or incompetent. This lack of self-confidence or hesitation in trying new things inevitably affects their learning attitude \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. Similarly, Metcalfe \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e] suggested that the more positive one\u0026rsquo;s self-concept, the more favorable the psychological representation and behavioral expression of learning attitude will be, as the values embedded in one\u0026rsquo;s self-concept shape both attitudes and behaviors. In this sense, a self-concept oriented toward positivity helps enhance students\u0026rsquo; learning attitudes \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. Furthermore, Liu and Wang \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e] argued that self-concept influences students\u0026rsquo; learning attitudes in such a way that those with stronger self-concepts, higher expectations for the future, and greater belief in their own abilities tend to have more positive learning attitudes. Conversely, students with weaker self-concepts often lack confidence in their learning abilities, doubt their capacity to master academic content, and therefore exhibit more negative learning attitudes.\u003c/p\u003e\n\u003cp\u003eResearch has found that university students\u0026rsquo; career self-concept has a significant positive effect on their learning attitude. The clearer students perceive their career self-concept, the better their perceived learning attitude, leading to more proactive learning behaviors, enhanced learning outcomes, improved academic performance, and greater employability\u0026lrm;[\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. In addition, self-concept has a clear direct effect on academic performance, learning attitude, and employment outcomes, and it can also indirectly influence future employability through learning attitude \u0026lrm;[\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. Specifically, the more positive a student\u0026rsquo;s self-concept, the higher their self-confidence, which in turn fosters a more active learning attitude. Such students approach learning with greater interest, are more likely to complete tasks successfully, achieve desirable academic outcomes, and enhance their employability\u0026lrm;[\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]. Similarly, Guay et al.\u0026lrm;[\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e] suggested that students with a well-defined self-concept can see their true selves more clearly, better understand their values, interests, and abilities, and thus develop a more positive learning attitude, which translates into stronger employability. Although existing research has examined the relationship between self-concept and learning attitude, few studies have explored in depth the potential mediating role of learning attitude between self-concept and employability. Based on the Career EDGE model, this study further investigates how self-concept influences employability through learning attitude.\u003c/p\u003e\n\u003cp\u003eAccordingly, the following hypotheses are proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH6:\u0026nbsp;\u003c/strong\u003eSelf-concept has a significant positive effect on the learning attitude of university students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH7:\u0026nbsp;\u003c/strong\u003eLearning attitude mediates the relationship between self-concept and employability among university students.\u003c/p\u003e"},{"header":"Research Method","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eResearch Framework\u003c/h2\u003e\u003cp\u003eThis study is based on the Career EDGE model of employability and focuses on university students in Hebei Province, China. Using SPSS Statistics 27 and AMOS software, it examines the effects of career planning, self-concept, and learning attitude on employability, and tests the mediating role of learning attitude in the relationships between career planning and employability, as well as between self-concept and employability. The research framework is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResearch participants and procedure\u003c/h3\u003e\n\u003cp\u003eHebei Province plays an important role in the Beijing-Tianjin-Hebei coordinated development strategy, with the central government assigning distinct functional roles to each city and leveraging the unique advantages of local resources \u0026lrm;[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The province has a relatively large number of higher education institutions distributed across a wide area, yet employment levels vary considerably among them \u0026lrm;[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The coordinated development of Beijing-Tianjin-Hebei has significantly increased the supply of human resources, intensifying competition among universities in Hebei and increasing employment pressure. In response, the Hebei Provincial Party Committee and the provincial government have consistently adhered to the strategy of prioritizing employment, making every effort to maintain stability in the job market \u0026lrm;[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. At the same time, to enhance university students\u0026rsquo; employability and address employment challenges, Hebei has introduced a series of policies specifically targeting graduate employment \u0026lrm;[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Against this backdrop, this study selected university students in Hebei Province, China, as the research participants and conducted a questionnaire survey.\u003c/p\u003e\u003cp\u003eA pilot survey was first conducted to test whether the scales used in this study were suitable for the sample. Using convenience sampling, 130 university students from one higher education institution were selected for the pilot test. The collected pilot questionnaires underwent reliability, validity, and item analyses, which informed the development of the final questionnaire. For the main survey, convenience sampling was again employed. Participants were drawn from five universities in Hebei Province, including two in Shijiazhuang and one each in Hengshui, Langfang, and Baoding, covering a balanced mix of comprehensive, normal, and science and engineering universities. To avoid overlap with the pilot sample, questionnaires were distributed via the Wenjuanxing online survey platform, where students completed the survey on-site by scanning a QR code. The platform was set to collect participants\u0026rsquo; WeChat nickname, gender, and location to prevent duplicate responses. To ensure data quality, student affairs staff from each university were contacted to help organize the sampled students. These staff members explained the purpose and significance of the study to students in class, clarified the instructions for completing the questionnaire, and reminded them to answer carefully. Participation was voluntary, and completion of the questionnaire was taken as implied consent to participate in the research.\u003c/p\u003e\u003cp\u003eAccording to Gorsuch \u0026lrm;[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], the minimum sample size for a formal study should be at least ten times the total number of items in the scale. In this study, the four scales contained a total of 48 items, meaning that at least 480 valid responses were required. Considering the possibility of invalid responses during data collection, 800 questionnaires were distributed to university students from five higher education institutions in Hebei Province, with 160 questionnaires allocated to each institution. After excluding invalid responses, 735 valid questionnaires were obtained, yielding a valid response rate of 91.88%. The final sample met the above sampling standard and was therefore suitable for hypothesis testing and analysis. Among the respondents, 233 were male and 502 were female; 146 were first-year students, 129 were second-year students, 248 were third-year students, and 212 were fourth-year students.\u003c/p\u003e"},{"header":"Research Instruments","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCareer planning scale\u003c/h2\u003e\u003cp\u003eThis study adopted the three-item version of Gould\u0026rsquo; s \u0026lrm;[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Career Planning Scale as used by Koen et al. \u0026lrm;[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], who applied it in educational settings. The scale includes items such as: \u0026ldquo;I have planned my career,\u0026rdquo; \u0026ldquo;I have a strategy to achieve my career goals,\u0026rdquo; and \u0026ldquo;I know what I need to do to achieve my career goals,\u0026rdquo; totaling three items. Responses were measured on a five-point Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;). Higher total scores indicate better career planning. Reliability analysis of the pilot test showed a Cronbach\u0026rsquo;s Alpha of .836, indicating good internal consistency.\u003c/p\u003e\u003cp\u003eConfirmatory Factor Analysis (CFA) was conducted using the formal questionnaire, and the results indicated a saturated model. A saturated model refers to a hypothesized model in which the number of estimated parameters exactly equals the number of elements in the covariance matrix. In this case, all parameters have a unique solution, and both the chi-square value and the degrees of freedom are zero. Since the chi-square value is zero, the model is considered just-identified, meaning it will never be rejected and achieves a perfect fit with the data \u0026lrm;[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Furthermore, all factor loadings were statistically significant at the \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 level, with standardized factor loadings ranging from .797 to .854, all exceeding the .500 threshold. The Composite Reliability (CR) of the latent construct was .869, meeting the recommended criterion of greater than .700 \u0026lrm;[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The Average Variance Extracted (AVE) was .689, exceeding the recommended value of .500\u0026lrm;[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. These results indicate that the Career Planning Scale demonstrates good convergent validity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSelf-concept scale\u003c/h2\u003e\u003cp\u003eThis study adopted the Self-Concept Scale developed by Hsiao \u0026lrm;[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], which measures university students\u0026rsquo; self-concept using items drawn from the Higher Education Database. The scale consists of six items. Responses were measured on a five-point Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;), with reverse-scored items coded accordingly. Reliability analysis of the pilot test yielded a Cronbach\u0026rsquo;s α of .886, indicating good internal consistency.\u003c/p\u003e\u003cp\u003eCFA was conducted using the formal questionnaire to assess model fit and convergent validity. The results showed that χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.619, which is less than 5; GFI\u0026thinsp;=\u0026thinsp;.986 and AGFI\u0026thinsp;=\u0026thinsp;.966, both exceeding the .900 threshold; SRMR\u0026thinsp;=\u0026thinsp;.019 and RMSEA\u0026thinsp;=\u0026thinsp;.060, both below the .080 standard; NFI\u0026thinsp;=\u0026thinsp;.986, RFI\u0026thinsp;=\u0026thinsp;.976, CFI\u0026thinsp;=\u0026thinsp;.990, and IFI\u0026thinsp;=\u0026thinsp;.990, all greater than .900; PNFI\u0026thinsp;=\u0026thinsp;.591, above the .500 criterion; and CN\u0026thinsp;=\u0026thinsp;382, greater than 200. These results indicate that the self-concept model has good model fit. In addition, all factor loadings were statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, with standardized factor loadings ranging from .701 to .854, all exceeding the .500 benchmark. The composite reliability (CR) of the latent construct was .893, surpassing the recommended value of .700 \u0026lrm;[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The average variance extracted (AVE) was .583, above the .500 threshold \u0026lrm;[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. These findings indicate that the Self-Concept Scale demonstrates good convergent validity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLearning attitude scale\u003c/h2\u003e\u003cp\u003eThis study adopted the revised Learning Attitude Scale developed by Xu et al.\u0026lrm;[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which was adapted from the scales of Zhang\u0026lrm;[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] and Fennema and Sherman \u0026lrm;[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The scale consists of three dimensions-cognitive attitude, affective attitude, and behavioral attitude-with a total of 27 items. Responses were measured on a five-point Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;). Higher total scores indicate a more positive learning attitude among university students. During the item analysis of the pilot questionnaire, items 15 to 18 did not meet the item analysis criteria and were therefore removed. The pilot test yielded a Cronbach\u0026rsquo;s α coefficient of .936, indicating high reliability.\u003c/p\u003e\u003cp\u003eCFA was conducted using the formal questionnaire to assess model fit and convergent validity. The results showed that χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.333, less than the recommended threshold of 5; GFI\u0026thinsp;=\u0026thinsp;.947 and AGFI\u0026thinsp;=\u0026thinsp;.935, both above .900; SRMR\u0026thinsp;=\u0026thinsp;.030 and RMSEA\u0026thinsp;=\u0026thinsp;.043, both below .080; NFI\u0026thinsp;=\u0026thinsp;.942, RFI\u0026thinsp;=\u0026thinsp;.936, CFI\u0026thinsp;=\u0026thinsp;.966, and IFI\u0026thinsp;=\u0026thinsp;.966, all exceeding .900; PNFI\u0026thinsp;=\u0026thinsp;.846 and PGFI\u0026thinsp;=\u0026thinsp;.779, both greater than .500; and CN\u0026thinsp;=\u0026thinsp;365, greater than 200. These results indicate that the learning attitude model has good model fit. All factor loadings were statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, with standardized factor loadings ranging from .679 to .823, all above the .500 benchmark. The CR values for the latent variables were: cognitive attitude\u0026thinsp;=\u0026thinsp;.912, affective attitude\u0026thinsp;=\u0026thinsp;.848, and behavioral attitude\u0026thinsp;=\u0026thinsp;.912, all exceeding the recommended criterion of .700 \u0026lrm;[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The AVE values were: cognitive attitude\u0026thinsp;=\u0026thinsp;.535, affective attitude\u0026thinsp;=\u0026thinsp;.528, and behavioral attitude\u0026thinsp;=\u0026thinsp;.536, all above the .500 threshold \u0026lrm;[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. These results indicate that the Learning Attitude Scale demonstrates good convergent validity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEmployability scale\u003c/h2\u003e\u003cp\u003eThis study adopted the Employability Scale revised by Cheng and Dong [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], which consists of four factors: basic competence, social practice ability, professional ethics, and job-seeking ability, with a total of 16 items. Responses were measured on a five-point Likert scale ranging from 1 (\u0026ldquo;very poor\u0026rdquo;) to 5 (\u0026ldquo;very strong\u0026rdquo;). Higher total scores indicate stronger employability. The scale\u0026rsquo;s Cronbach\u0026rsquo;s α coefficient was .881, indicating good reliability.\u003c/p\u003e\u003cp\u003eCFA was conducted using the formal questionnaire to assess model fit and convergent validity. The results showed that χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.300, less than the recommended threshold of 5; GFI\u0026thinsp;=\u0026thinsp;.951 and AGFI\u0026thinsp;=\u0026thinsp;.934, both above .900; SRMR\u0026thinsp;=\u0026thinsp;.032 and RMSEA\u0026thinsp;=\u0026thinsp;.056, both below .080; NFI\u0026thinsp;=\u0026thinsp;.947, RFI\u0026thinsp;=\u0026thinsp;.936, CFI\u0026thinsp;=\u0026thinsp;.962, and IFI\u0026thinsp;=\u0026thinsp;.962, all exceeding .900; PNFI\u0026thinsp;=\u0026thinsp;.789 and PGFI\u0026thinsp;=\u0026thinsp;.699, both greater than .500; and CN\u0026thinsp;=\u0026thinsp;277, greater than 200. These indices indicate that the employability model has good model fit. All factor loadings were statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, with standardized factor loadings ranging from .689 to .872, all above the .500 benchmark. The CR values for the latent variables were: basic competence\u0026thinsp;=\u0026thinsp;.806, social practice ability\u0026thinsp;=\u0026thinsp;.896, professional ethics\u0026thinsp;=\u0026thinsp;.840, and job-seeking ability\u0026thinsp;=\u0026thinsp;.856, all exceeding the recommended criterion of .700 \u0026lrm;[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The AVE values were: basic competence\u0026thinsp;=\u0026thinsp;.676, social practice ability\u0026thinsp;=\u0026thinsp;.552, professional ethics\u0026thinsp;=\u0026thinsp;.569, and job-seeking ability\u0026thinsp;=\u0026thinsp;.665, all above the .500 threshold \u0026lrm;[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. These results indicate that the Employability Scale demonstrates good convergent validity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eCommon method bias test\u003c/h2\u003e\u003cp\u003eSince this study\u0026rsquo;s questionnaire included more than two variables and all variables were answered by the same participants, there was a possibility of common method bias due to personal or psychological factors. To reduce this risk, the study followed the recommendation of Zhou and Long \u0026lrm;[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and applied Harman\u0026rsquo;s single-factor test through exploratory factor analysis to detect common method variance. All items from the formal questionnaire were entered into SPSS for factor analysis, and the variance explained by the first unrotated principal component was examined. If the variance explained by the first factor is less than 50%, it suggests that there is no serious common method bias.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the analysis produced nine factors with eigenvalues greater than 1, and the first factor accounted for 31.499% of the variance, which is below the 50% threshold. Therefore, there is no significant common method variance in the formal questionnaire sample, and further data analysis can proceed.\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\u003eAnalysis of common method bias\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\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEigenvalue (\u0026ge;\u0026thinsp;1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariance Explained (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCumulative Variance Explained (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.499\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.598\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.883\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation analysis\u003c/h2\u003e\u003cp\u003ePearson correlation analysis was conducted to examine the relationships among the variables. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the correlation coefficients ranged from .361 to .520, and all correlations were significantly positive (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). This indicates that there are significant positive relationships among the variables. Furthermore, since none of the correlation coefficients exceeded .800, there is no concern of multicollinearity \u0026lrm;[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Therefore, it is appropriate to proceed with the subsequent overall model validation analysis.\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\u003eResults of correlation analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCareer Planning\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSelf-Concept\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLearning Attitude\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEmployability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCareer Planning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Concept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.361***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLearning Attitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.465***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.396***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.496***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.494***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.520***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e---\u003c/p\u003e\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 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; M\u0026thinsp;=\u0026thinsp;mean; SD\u0026thinsp;=\u0026thinsp;standard deviation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePath analysis of the overall model\u003c/h2\u003e\u003cp\u003eBased on the research hypotheses, a path analysis was conducted to examine the relationships among career planning, self-concept, learning attitude, and employability among university students. The overall model fit was first evaluated. Following Schumacher and Lomax\u0026lrm; [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], this study assessed the model using three types of fit indices: absolute fit, parsimonious fit, and incremental fit. The evaluation criteria were as follows: χ\u0026sup2;/df less than 5\u0026lrm;[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]; GFI, IFI, NFI, CFI, and TLI greater than .900 \u0026lrm;[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]; and RMSEA less than .080 \u0026lrm;[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the results indicate that χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.571, which is less than 5; GFI\u0026thinsp;=\u0026thinsp;.959 and AGFI\u0026thinsp;=\u0026thinsp;.944, both greater than .900; RMSEA\u0026thinsp;=\u0026thinsp;.046, less than .080; NFI\u0026thinsp;=\u0026thinsp;.956, RFI\u0026thinsp;=\u0026thinsp;.947, CFI\u0026thinsp;=\u0026thinsp;.973, and IFI\u0026thinsp;=\u0026thinsp;.973, all greater than .900; PNFI\u0026thinsp;=\u0026thinsp;.789 and PGFI\u0026thinsp;=\u0026thinsp;.698, both greater than .500; and CN\u0026thinsp;=\u0026thinsp;356, exceeding 200. These results demonstrate that the model has a good overall fit.\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\u003eFit indices for the overall model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFit Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCriterion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel Fit\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eAbsolute Fit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e/df\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eIncremental Fit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eParsimonious Fit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePNFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePGFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood Fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. χ\u0026sup2; = chi-square statistic; df\u0026thinsp;=\u0026thinsp;degrees of freedom; GFI\u0026thinsp;=\u0026thinsp;goodness-of-fit index; AGFI\u0026thinsp;=\u0026thinsp;adjusted goodness-of-fit index; RMSEA\u0026thinsp;=\u0026thinsp;root mean square error of approximation; NFI\u0026thinsp;=\u0026thinsp;normed fit index; RFI\u0026thinsp;=\u0026thinsp;relative fit index; CFI\u0026thinsp;=\u0026thinsp;comparative fit index; IFI\u0026thinsp;=\u0026thinsp;incremental fit index; PNFI\u0026thinsp;=\u0026thinsp;parsimonious normed fit index; PGFI\u0026thinsp;=\u0026thinsp;parsimonious goodness-of-fit index; CN\u0026thinsp;=\u0026thinsp;critical N.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eDirect effects\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, career planning had a significant positive effect on employability, with a path coefficient of .293 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a 95% confidence interval of [.199, .387], which does not include zero. Self-concept had a significant positive effect on employability, with a path coefficient of .339 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a 95% confidence interval of [.253, .420], also excluding zero. Career planning had a significant positive effect on learning attitude, with a path coefficient of .478 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a 95% confidence interval of [.392, .565]. Learning attitude had a significant positive effect on employability, with a path coefficient of .396 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a 95% confidence interval of [.298, .498]. Self-concept also had a significant positive effect on learning attitude, with a path coefficient of .309 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a 95% confidence interval of [.216, .398]. These results indicate that all five direct effects are significant, suggesting that career planning, self-concept, and learning attitude enhance university students\u0026rsquo; employability, while career planning and self-concept also influence their learning attitude. Therefore, hypotheses H1, H2, H3, H4, and H6 are all supported.\u003c/p\u003e\u003cp\u003eTo further examine the mediating effect of learning attitude between career planning and employability, as well as between self-concept and employability, the bootstrap method was applied following the recommendations of Hayes \u0026lrm;[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], with 5,000 resamples.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eIndirect effects\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the indirect effect of learning attitude between career planning and employability was .189 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with a 95% confidence interval of [.137, .258], which does not include zero. This indicates that the indirect effect is significant, suggesting that career planning can indirectly influence employability through learning attitude, thereby confirming the partial mediating effect of learning attitude. Thus, hypothesis \u003cb\u003eH5\u003c/b\u003e is supported. Similarly, the indirect effect of learning attitude between self-concept and employability was .122 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with a 95% confidence interval of [.080, .176], also excluding zero. This indicates that the indirect effect is significant, suggesting that self-concept can enhance employability through the strengthening of learning attitude, confirming the partial mediating effect of learning attitude. In other words, self-concept not only has a direct positive effect on employability, but may also indirectly influence employability via learning attitude. Therefore, hypothesis H7 is supported.\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\u003eBootstrap Test and Path 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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eDirect Effects\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCareer Planning \u0026rarr; Employability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[.199, .387]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Concept \u0026rarr; Employability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[.253, .420]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCareer Planning \u0026rarr; Learning Attitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[.392, .565]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Concept \u0026rarr; Learning Attitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[.216, .398]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLearning Attitude \u0026rarr; Employability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[.298, .498]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eIndirect Effects\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCareer Planning \u0026rarr; Learning Attitude \u0026rarr; Employability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[.137, .258]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Concept \u0026rarr; Learning Attitude \u0026rarr; Employability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[.080, .176]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTotal Effects\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCareer Planning \u0026rarr; Employability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[.397, .565]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Concept \u0026rarr; Employability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[.372, .544]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote.\u003c/b\u003e Effect\u0026thinsp;=\u0026thinsp;standardized path coefficient; CI\u0026thinsp;=\u0026thinsp;confidence interval; LLCI\u0026thinsp;=\u0026thinsp;lower limit of the CI; ULCI\u0026thinsp;=\u0026thinsp;upper limit of the CI. All effects are significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 based on 5,000 bootstrap samples.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eCareer planning\u0026rsquo;s direct effect on employability\u003c/h2\u003e\u003cp\u003eThe results indicate that career planning has a significant positive effect on the employability of university students. Thus, Hypothesis H1 is supported. This finding is consistent with previous studies [\u0026lrm;28, \u0026lrm;65] suggesting that career planning plays an active role in enhancing students\u0026rsquo; employability and is an important influencing factor. In other words, when university students develop specific career plans that align with their personal circumstances, they are better able to understand their career trajectories. Particularly in the current complex market environment, effective career planning can motivate students to formulate actionable implementation plans to advance their career goals, thereby improving their employability \u0026lrm;[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, career planning can enhance students\u0026rsquo; self-awareness. Self-awareness requires students to use various methods-such as career interest tests, assessments of career values, and evaluations of professional skills-to gain a clearer understanding of themselves. This process helps students better define their future career development direction, choose occupations that match both their interests and capabilities, and make more rational career choices. It also reduces the influence of conformity and impulsive decision-making on future employment, thus improving employability\u0026lrm;[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In summary, career planning can positively predict employability. By engaging in career planning, students gain a clearer understanding of their career development goals, enhance their self-awareness, and make rational decisions, ultimately contributing to improved employability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eSelf-concept\u0026rsquo;s direct effect on employability\u003c/h2\u003e\u003cp\u003eThe results show that self-concept has a significant positive effect on the employability of university students, confirming Hypothesis \u003cb\u003eH2\u003c/b\u003e. This finding is consistent with previous studies [\u0026lrm;15, \u0026lrm;34, \u0026lrm;66], indicating that a strong self-concept can contribute to the enhancement of employability. In other words, when university students possess a positive self-concept, it fosters greater confidence, self-efficacy, and a proactive approach to career development, all of which strengthen their employability.\u003c/p\u003e\u003cp\u003eThe reasons for this may be as follows. First, university students with a positive self-concept generally possess a strong sense of self-worth and self-efficacy, believing in their ability to secure an ideal job suited to their strengths. They are more likely to persevere in pursuing their goals despite setbacks, demonstrating strong motivation for employment. In contrast, students with a negative self-concept may face greater employment pressure, characterized by a lack of confidence and intrinsic motivation, which can lead to procrastination, negative emotions, and difficulty in facing future employment challenges, thereby lowering their employability \u0026lrm;[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Second, students with a positive self-concept tend to have a clear understanding of themselves and their capabilities, and they are more likely to engage in proactive employment behaviors, such as actively acquiring new knowledge and skills and seeking job opportunities. Conversely, those with a negative self-concept may exhibit avoidant or passive job-seeking behaviors, lacking the confidence and courage to act on their abilities, which results in hesitation and passive responses, ultimately diminishing their employability. Third, students with a positive self-concept are generally better equipped to manage stress and overcome difficulties. They tend to have stronger self-awareness and emotional stability, which can reduce the anxiety caused by job-search pressures. In contrast, students with a negative self-concept may experience anxiety, low self-esteem, and timidity when facing employment difficulties, making it harder to cope effectively with future challenges, thus lowering their employability \u0026lrm;[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, self-concept influences students\u0026rsquo; employment motivation, job-seeking behaviors, and psychological readiness, thereby affecting their employability. Therefore, enhancing university students\u0026rsquo; employability requires giving due attention to the cultivation of their self-concept.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eThe mediating effect of learning attitude between career planning and employability\u003c/h2\u003e\u003cp\u003eThe findings show that learning attitude mediates the relationship between career planning and employability among university students. Hypotheses H3, H4, and H5 are supported. This result is consistent with the findings of Sepahvand et al.\u0026lrm;[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] (2023), indicating that career planning can indirectly influence employability through learning attitude. In other words, by formulating reasonable career plans, university students can strengthen their learning attitudes during their studies, which in turn enhances their employability.\u003c/p\u003e\u003cp\u003eThe reasons for this may be as follows. First, career planning helps students identify suitable career directions. When students have a clear sense of their career path, their learning interest and motivation are stimulated, leading to a more positive learning attitude throughout their university years. This enables them to purposefully develop the skills and qualities needed for future employment, thereby improving their employability \u0026lrm;\u0026lrm;[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Second, career planning allows students to establish a correct outlook on life. By clarifying learning goals and developing a comprehensive plan suited to their personal circumstances, students are motivated to study diligently in order to realize their life plans and values, acquire more skills needed for the future, and ultimately enhance their employability. Third, through career planning, students can develop effective learning strategies, methods, and emotional regulation skills, thereby improving their learning character. This leads to a more active learning attitude, which promotes personal growth and strengthens employability \u0026lrm;[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eThe mediating effect of learning attitude between self-concept and employability\u003c/h2\u003e\u003cp\u003eThe results indicate that learning attitude mediates the relationship between self-concept and employability among university students. This finding is consistent with previous research \u0026lrm;[43, \u0026lrm;49] and supports Hypotheses \u003cb\u003eH6\u003c/b\u003e and \u003cb\u003eH7\u003c/b\u003e, suggesting that self-concept can indirectly influence employability through learning attitude. In other words, students\u0026rsquo; self-concept shapes their learning attitude during university, which in turn affects their employability.\u003c/p\u003e\u003cp\u003eThe possible reasons are as follows. First, the more positive students\u0026rsquo; self-concept, the greater their self-confidence. This leads to a more positive attitude toward their studies and campus life, such as curiosity about new knowledge, a strong desire to learn, and greater willingness to proactively acquire and master relevant skills and knowledge, thereby improving their employability \u0026lrm;[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Second, when students have a clear self-concept, they are more certain about their choices, better able to see their true selves, and more aware of their values, interests, and abilities. This self-awareness fosters clear intrinsic motivation, which translates into maintaining a positive learning attitude. Such an attitude manifests in constructive learning behaviors that ultimately enhance employability \u0026lrm;[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Third, by building a positive self-concept and strong core self-evaluations, students become more confident and proactive when facing employment challenges. This proactive mindset encourages them to persevere in their learning goals, working hard even when confronted with academic or life difficulties. This persistence and effort ultimately contribute to improving their employability and competitiveness.\u003c/p\u003e"},{"header":"Conclusion and implications","content":"\n\u003ch3\u003eConclusion\u003c/h3\u003e\n\u003cp\u003eUsing SPSS and AMOS, this study surveyed Chinese university students to analyze the effects of career planning and self-concept on employability, with a particular focus on examining the mediating role of learning attitude. The main findings are as follows:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDirect Effects\u003c/strong\u003e\u003cp\u003eCareer planning, self-concept, and learning attitude all have significant direct effects on employability. In addition, career planning and self-concept were found to positively predict learning attitude.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eIndirect Effects\u003c/strong\u003e\u003cp\u003eThis study further explored the mechanisms through which career planning and self-concept influence employability. The results reveal that career planning positively predicts employability indirectly through the mediating role of learning attitude. Similarly, self-concept also exerts an indirect positive effect on employability via learning attitude.\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003eImplications\u003c/h2\u003e\u003cp\u003eThis study holds important theoretical and practical implications.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003eTheoretical Implications:\u003c/h2\u003e\u003cp\u003eFirst, by integrating career planning, self-concept, learning attitude, and employability into a single theoretical model, this study reflects the relationships and underlying mechanisms among these variables. The findings provide a theoretical basis for enhancing university students\u0026rsquo; employability and further enrich the body of theory related to each variable. In particular, learning attitude-conceptualized as a psychological disposition-was shown to mediate the effects of career planning and self-concept on employability. This finding not only offers new empirical support for the Career EDGE model of employability, but also provides a theoretical foundation for better understanding the internal mechanisms influencing students\u0026rsquo; employability. Second, while employability has received increasing policy attention in China, research on university students\u0026rsquo; employability by Chinese scholars began relatively late. There remains a lack of employability theories and empirical studies with strong local relevance, as well as an absence of broadly applicable theoretical frameworks. Building on the existing literature, this study explores the mechanisms through which career planning, self-concept, and learning attitude influence employability. The results help address existing research gaps and test the applicability of the Career EDGE model in the Chinese context, thereby filling an important theoretical void. Third, much of the current research on employability in China is theoretical in nature, with fewer empirical studies. Existing studies on influencing factors often rely on subjective judgments and lack empirical investigation and data support. By examining the mechanisms linking career planning, self-concept, and learning attitude to employability, this study supplements empirical research on Chinese university students\u0026rsquo; employability. It also provides objective, data-driven evidence for understanding the factors that influence employability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003ePractical Implications:\u003c/h2\u003e\u003cp\u003eBased on the findings of this study, career planning, self-concept, and learning attitude all have significant positive effects on employability, providing feasible pathways to enhance university students\u0026rsquo; employability. First, strengthen career planning education for university students. Universities should integrate career planning into general curricula, providing systematic guidance to help students plan their career paths in advance. This should follow the objective rules of students\u0026rsquo; growth and development, tailoring guidance to different stages, levels, and individual characteristics. By aligning career design guidance with students\u0026rsquo; developmental needs, institutions can lay a strong foundation for improving employability. Second, cultivate positive learning attitudes to form the intrinsic driving force of employability. Universities should create a positive learning atmosphere to stimulate students\u0026rsquo; intrinsic motivation. This can be achieved by establishing learning communities, such as professional study groups, reading clubs, and research interest societies, to reduce feelings of isolation in learning. Additionally, providing students with guidance on effective learning strategies can help them gradually develop an attitude of proactive exploration, responsibility, and continuous growth, thereby laying a solid foundation for employability enhancement. Finally, build growth platforms to foster the development of a positive self-concept. Universities should adopt a student-centered approach, paying attention to students\u0026rsquo; feelings, perceptions, and cognition, and promoting deeper self-understanding and reconstruction of self-concept. For example, offering platforms and contexts for learning and growth can allow students to construct interactions between self and society through group engagement, facilitating comprehensive self-development. Educators should pay particular attention to students\u0026rsquo; inner worlds, listen to their voices, understand their needs, and help them build a positive self-concept. This includes enhancing students\u0026rsquo; emotional regulation abilities and focusing on challenges they face, such as failure or setbacks, by guiding them to develop appropriate self-attribution skills. Furthermore, psychological training camps, group discussions, and other experiential activities can help students understand themselves from multiple perspectives and levels, improving self-awareness and, in turn, strengthening employability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and future research directions\u003c/h2\u003e\u003cp\u003eFirst, this study primarily employed a questionnaire survey method. While this method offers flexibility and convenience, the use of self-reported measures by students may introduce bias, as respondents may conceal their shortcomings during the survey process. As a result, the data may contain slight deviations. To gain deeper insights into the factors influencing employability, future studies could incorporate in-depth interviews with university students to better capture their genuine intentions and explore the multiple factors that affect employability.\u003c/p\u003e\u003cp\u003eSecond, the sample for this study was drawn from students at five universities in Hebei Province, China. This sampling approach has a certain degree of regional concentration and may not fully represent the national situation, although it does ensure that the findings are representative of Hebei Province. Future research could expand the geographic scope and coverage of the sample to improve the generalizability of the results.\u003c/p\u003e\u003cp\u003eThird, while examining the factors influencing employability, this study focused primarily on the individual-level perceptions of students, which may carry a degree of subjectivity. Future research could integrate school-level, teacher-level, and individual-level factors, employing cross-level analytical methods to more comprehensively examine the determinants of employability. In addition, the recommendations and strategies proposed could be applied in university student management practices, allowing for practical evaluation of their effectiveness in enhancing employability and thereby broadening and deepening the scope of research.\u003c/p\u003e\u003cp\u003eFourth, the results indicate that career planning and self-concept have significant positive effects on employability. However, when learning attitude was included as a mediating variable, the path coefficients for the effects of career planning on employability and self-concept on employability decreased. This suggests that learning attitude plays a partial mediating role in both relationships and implies the possible existence of other mediating variables. Future studies could further investigate alternative mediators or examine potential moderating variables to refine and extend the research model.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThe researchers confirms that all research was performed in accordance with relevant guidelines/regulations applicable when human participants are involved (e.g., Declaration of Helsinki or similar). This study was approved by the office of the Dhurakij Pundit University Research Ethics Committee on Human (DPUREC), with ethical approval number (DPU_BSH 210367/2566). The written informed consent was obtained from all the participants\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the university-level scientific research project for high-level talents funded by the school of Hengshui University.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYu Sun conceptualized and designed the study. Yu Sun collected the data. Yu Sun and Xiaona Liu performed data analysis. Yu Sun drafted the manuscript. 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Chongqing: Chongqing University; 2010.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Career planning, Self-concept, Learning attitude, Employability","lastPublishedDoi":"10.21203/rs.3.rs-7406818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7406818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eWith the rapid development of education, higher education in China has become increasingly accessible and widespread. The number of university graduates is rising every year, leading to a more competitive job market. As a result, enhancing college students’ employability has become an urgent issue. This study explores the mechanisms through which career planning and self-concept affect employability, and examines the mediating role of learning attitude, aiming to provide both theoretical and practical guidance for improving employability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Drawing on the Career EDGE model, this study used convenience sampling to survey 735 undergraduates from universities in Hebei Province, China. Data were collected via questionnaires and analyzed using SPSS Statistics 27 and AMOS. Structural Equation Modeling (SEM) was applied to test the effects of career planning, self-concept, and learning attitude on employability, and to verify the mediating role of learning attitude.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCareer planning, self-concept, and learning attitude all showed significant positive effects on employability. Mediation analysis revealed that learning attitude mediated the relationship between career planning and employability, as well as between self-concept and employability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe findings demonstrate that career planning and self-concept positively influence employability, and that learning attitude serves as an important pathway in this process. Universities should assist students in developing clear career plans and building a positive self-concept to foster active learning attitudes, thereby enhancing their employability.\u003c/p\u003e","manuscriptTitle":"The Impact of Career Planning and Self-Concept on Employability among University Students: Exploring the Mediating Role of Learning Attitudes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 11:06:15","doi":"10.21203/rs.3.rs-7406818/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c65618c8-2984-4e8f-9fa6-c2874007680b","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T09:26:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 11:06:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7406818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7406818","identity":"rs-7406818","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}