Learning environment, school–enterprise cooperation, and student motivation as predictors of employment competitiveness among undergraduates in application-oriented universities

Abstract

Employment competitiveness is widely regarded as a key indicator of higher education effectiveness, particularly in application-oriented universities where graduate employability affects institutional reputation and social recognition. Grounded in Situated Cognition Theory, Synergy Theory, Self-Determination Theory, and Human Capital Theory, this study develops an integrated framework to examine how the learning environment, school–enterprise cooperation, and student motivation jointly affect undergraduate employment competitiveness. Using a stratified random sample of 956 undergraduates from a private application-oriented university in China, standardized questionnaires were administered, and data were analyzed through Pearson correlation, multiple regression, and Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that all three factors are positively correlated with employment competitiveness. When considered simultaneously, school–enterprise cooperation shows the strongest effect, followed by students' motivation, while the learning environment does not exhibit a significant direct effect. However, it influences employability indirectly through students' motivation and participation in school–enterprise cooperation. These findings highlight the critical role of university–industry collaboration and students' motivation in enhancing employability. They also provide insight into the mechanisms linking educational and psychological factors to students' transition into the labor market.

1 Introduction

In today's increasingly complex and rapidly evolving labor market, employment competitiveness has become a strategic imperative for both individuals pursuing long-term career success and organizations aiming to strengthen their market position. Modern employers demand not only technical proficiency but also strong motivation and resilience to effectively manage organizational changes and drive innovation (Vázquez-Rodríguez et al., 2025). At the same time, graduates are expected to demonstrate employability-related competencies that meet the expectations of contemporary workplaces and align with the ongoing transformation of higher education (Hisa et al., 2024; Ragunathan, 2023). Viewed in an integrated manner, employability represents a key intersection between individual career development, institutional effectiveness and broader economic advancement. Consequently, it has gained prominence in educational and policy discussions, drawing growing attention from governments, employers, higher education institutions, and students to bridge the gap between applied talent cultivation and the ever-changing demands of the real economy.

Previous research has highlighted the critical influence of the learning environment, school–enterprise cooperation and student motivation on the development of employment readiness (Fitriyani et al., 2025; Yang and Mohamad Nasr, 2025). Nevertheless, there remains a lack of comprehensive empirical studies examining the combined effects of these factors on student employability, especially within the context of private higher education institutions in China. Previous empirical studies done by Zhang et al. (2022) and Chen et al. (2025) linking institutional factors with school-enterprise cooperation and student employability but very few studies combine the three factors (learning environment, school-enterprise cooperation and student motivation). Integrative analysis remains rare and further evidence that more systematic, integrative research is needed.

This study contributes to Educational Psychology by developing an integrated theoretical framework that connects learning environment, school–enterprise cooperation, and students' motivation in explaining employment competitiveness. By drawing on multiple theoretical perspectives, including Situated Cognition Theory, Synergy Theory, Self-Determination Theory, and Human Capital Theory, this research moves beyond single-factor approaches and clarifies how psychological and institutional processes operate together in practice-oriented higher education.

In contrast to previous studies that have focused primarily on traditional academic universities, this study examines employment competitiveness in private, practice-oriented institutions, thereby extending existing research into underexplored educational contexts and providing a more context-specific perspective on employability development. Using PLS-SEM, this study further examines the interrelationships among learning environment, school–enterprise cooperation, students' motivation, and provides empirical evidence on the dynamic mechanisms underlying students' transition from higher education to employment. Rather than treating these factors as isolated predictors, the present analysis emphasizes their interactive and process-based nature, contributing to a more nuanced understanding of employability development.

Against this background, this study examines how the learning environment, school–enterprise cooperation, and student motivation collectively influence undergraduate employment competitiveness in application-oriented universities. By integrating multiple theoretical perspectives, the study seeks to provide a more comprehensive understanding of the mechanisms through which educational and psychological factors shape employability.

1.1 Problem statement

In many Chinese universities, particularly non-elite and private institutions, the learning environment remains predominantly lecture-based, exam-oriented and insufficiently responsive to the evolving demands of the digital and knowledge economy (Zhang, 2023). A persistent disconnect exists between curriculum content and the practical skill sets required in contemporary workplaces. Despite increased digitalization, online tools are frequently used as content delivery platforms rather than for fostering interaction, creativity or real-world problem solving (Hung et al., 2024). However, although there is growing recognition of the value of modern, student-centered, and industry-relevant learning environments, many Chinese universities—particularly those classified as private or application-oriented—continue to rely on traditional lecture-based teaching, outdated curricular structures, and poorly integrated digital tools (Lan et al., 2024). Consequently, students are often exposed to a predominantly theoretical and passive learning experience that lacks alignment with the practical requirements of contemporary industries (Ni et al., 2025). By contrast, an ideal learning environment should be immersive, adaptable, and technologically advanced, enabling students to participate in experiential learning, engage in authentic problem-solving, and collaborate across disciplines under the joint guidance of academic instructors and industry professionals (Huang et al., 2025; Shin and Idang, 2025; Shin et al., 2025).

Although school–enterprise cooperation is officially endorsed in China as a critical policy initiative to improve graduates' employability, its implementation in practice often falls short in terms of depth, scale, and long-term sustainability. In many cases, partnerships between universities and enterprises remain superficial, lacking clearly articulated goals, robust quality assurance frameworks, and sustained institutional commitment. Enterprises frequently demonstrate limited motivation or capacity to engage meaningfully in academic collaboration, resulting in training programs that are inadequately aligned with actual workplace demands. Additionally, curricular content is often disconnected from industrial contexts, while students are provided with minimal exposure to real-world case studies, industry mentorship or hands-on project experience. These issues hinder students' acquisition of market-relevant skills and contribute to a persistent gap between academic training and employer expectations (Shao et al., 2023; Xu and Sun, 2021).

A considerable number of students exhibit a lack of intrinsic motivation and inadequate career planning abilities, leading to low academic engagement and passive learning behaviors (Cao et al., 2025). Recent empirical studies done by Cao and Han (2024). demonstrate that many Chinese university students, especially those in non-elite or vocational institutions, show low intrinsic motivation, with learning largely driven by external forces like grades, family pressure, or societal expectations, rather than personal ambition or career planning. Although the critical role of academic motivation in shaping students’ employability has been widely recognized, a significant gap remains between the actual motivational profiles of university students and the levels required for sustained career development. In many cases, students' motivation is predominantly extrinsic in nature. Recent research indicates that a large proportion of Chinese university students remain primarily driven by external rewards (e.g., grades, parental expectations) rather than intrinsic interest or career-oriented goals (Zhang, 2024). While intrinsic motivation and achievement goals have been found to correlate strongly with employability-enhancing behaviors such as persistence, self-regulation and communication, these motivational traits are not adequately supported by institutional structures, resulting in a failure to translate academic effort into real-world employment readiness (Petruzziello, 2022).

1.2 Research objectives

1.3 Research questions

1.4 Hypotheses of the study

• H1: Based on Situated Cognition Theory, the learning environment has a positive effect on undergraduates' employment competitiveness.

• H2: Based on Synergy Theory, school–enterprise cooperation has a positive effect on undergraduates' employment competitiveness.

• H3: Based on Self-Determination Theory, student motivation has a positive effect on undergraduates' employment competitiveness.

• H4: The influence of the learning environment on employment competitiveness is mediated by students' engagement in school–enterprise cooperation and their motivational resources.

• H5: School–enterprise cooperation and student motivation jointly account for differential contributions to employment competitiveness, with student motivation serving as a proximal psychological driver.

2 Literature reviews

2.1 Learning environment and employment competitiveness of undergraduates

Situated Cognition Theory provides a robust conceptual foundation for analyzing and designing effective learning environments. Central to the theory is the idea that learning is inseparable from the context and activity in which it occurs (Brown et al., 1989). From this perspective, an effective learning environment should be authentic, learning tasks should reflect real-world professional or workplace contexts, enabling students to engage in activities that mirror those of their future careers (Kenaphoom and Niyomves, 2023; Liu et al., 2022). Therefore, a well-structured and supportive learning environment plays a crucial role in cultivating competencies essential for employability, such as communication, collaboration, adaptability, and problem-solving. For instance, positive classroom environments promote deeper learning and foster cognitive and socio-emotional growth, which in turn enhance students' professional potential (Derakhshan et al., 2024).

The learning environment influences not only students' academic outcomes but also their development of employability-related competencies. Within this environment, experiential learning components such as internships and workplace exposure are particularly important for preparing students for professional careers. For example, Chukwuedo and Ementa (2022) found that participation in internships and workplace learning can strengthen students' self-efficacy and confidence regarding their future employment. Similarly, Lhalloubi et al. (2024) argued that experiential learning contributes to the development of essential soft skills, including communication, leadership, and teamwork, which are widely valued by employers. However, the benefits of experiential learning are not guaranteed. The extent to which these experiences enhance employability often depends on how well workplace practice is integrated with academic reflection. When such experiences are poorly structured, they may not necessarily lead to meaningful improvements in students' employability.

A supportive learning environment plays an important role in shaping students' academic experiences and professional development. In application-oriented universities, such environments often emphasize the integration of theoretical learning with practical experience, thereby creating opportunities for students to engage in industry-related activities and school–enterprise collaboration (Imjai et al., 2025). Hoidn and Klemenčič (2021) report that positive classroom environments promote deeper learning and foster cognitive and socio-emotional growth, which in turn enhance students' professional potential. However, it is important to note that much of this literature relies on proximal indicators such as perceived readiness or confidence rather than objective employment outcomes.

At the same time, the learning environment can influence students' motivational resources. Studies in educational psychology suggest that supportive classroom environments contribute to the development of intrinsic motivation, perceived competence, and active learning engagement (Deci and Ryan, 2000; Dweck, 2006; Reeve, 2012). When students experience a positive learning environment, they are more likely to develop stronger learning motivation and take initiative in seeking practical experiences that enhance their professional skills.

The relationship between the learning environment and employment competitiveness may operate indirectly through several mediating mechanisms. Institutional conditions can shape the development of employability-related attributes by influencing how students interact with learning opportunities. For example, recent studies indicate that learning environments may enhance employability-related competencies through intermediate factors such as digital adaptation skills (Imjai et al., 2025). In this regard, the learning environment functions primarily as a contextual condition that supports students in translating academic experiences into competencies relevant to the labor market.

Although numerous studies highlight the importance of the learning environment in developing employability-related competencies, recent evidence indicates that its effect may often be indirect. For example, Edy and Rifqi (2020) found that the learning environment had limited direct influence on students’ employability skills, with its effects largely mediated by learning motivation and academic performance, underscoring the role of psychological and achievement-related factors. Similarly, Selvaratnam (2021) noted that direct links between physical learning spaces and employability outcomes remain underexplored, suggesting that environmental design needs to be complemented by effective pedagogical approaches and purposeful learning activities to produce measurable improvements in employability.

Subsequent empirical studies have produced mixed evidence concerning the direct influence of the learning environment on employability. Some research reports positive associations with skills such as communication, teamwork, and problem-solving, whereas other studies find non-significant effects or suggest that these relationships are mediated by factors like career-development programs, internships, or school–enterprise cooperation (Chigbu and Nekhwevha, 2022; Decius et al., 2024; Jackson, 2018). Collectively, these findings imply that the learning environment may operate more as a facilitating or contextual factor rather than a standalone determinant of employability.

Furthermore, participation in school–enterprise cooperation activities provides students with valuable opportunities to apply academic knowledge in real-world contexts and develop practical competencies required in the labor market. At the same time, a supportive learning environment may encourage students to participate more actively in these collaborative activities while also fostering stronger motivational resources. Through these mechanisms, students are more likely to translate academic experiences into career-related competencies that contribute to their employment competitiveness. Therefore, it is reasonable to expect that the influence of the learning environment on employment competitiveness may operate indirectly through students' engagement in school–enterprise cooperation and their motivational resources.

2.2 School–enterprise cooperation and employment competitiveness of undergraduates

School–enterprise cooperation (SEC) has gained prominence as a strategic approach to mitigating increasing employment pressures in higher education, particularly within application-oriented universities. In the Chinese context, where the number of university graduates has surpassed 11 million annually in 2023, according to the Ministry of Education of the People's Republic of China. Higher education institutions are encountering escalating challenges in securing satisfactory graduate employment outcomes. At the same time, employers continue to report persistent skill mismatches and deficiencies among newly recruited graduates in the development of strong professional identities (Sá and Serpa, 2018).

The application of Synergy Theory to school–enterprise cooperation (SEC) offers a compelling explanation for the transformative potential of collaborative educational models. In this synergistic framework, universities contribute foundational strengths, including disciplinary knowledge, research capacity, student talent, and academic rigor, while enterprises offer applied experience, cutting-edge technologies, industry mentors, and current labor market intelligence. Through structured partnerships, these entities engage in co-designed curricula, joint supervision of internships, collaborative research and development (R&D) projects, and dual-track talent cultivation (Cricchio and Di, 2025; Xin and Ahmad, 2024). Synergy Theory thus provides a systemic perspective that helps institutions move beyond transactional partnerships toward sustainable, strategic alliances that reinforce both educational and industrial innovation systems.

One of the key advantages of school–enterprise cooperation (SEC) lies in its potential to effectively bridge the disconnect between academic instruction and the evolving demands of industry. By integrating real-world learning experiences into university curricula, such as internships, cooperative education initiatives, and project-based learning, students are afforded meaningful opportunities to translate theoretical knowledge into practical application. This facilitates the development of workplace competencies such as critical thinking, time management, teamwork, and adaptability to fulfill industry requirement (Chen and Li, 2025; Liu et al., 2025; Watters et al., 2016; Zhang and Chen, 2023). By systematically integrating employer feedback into course development and instructional practices, universities are better positioned to align academic content with evolving labor market demands. For students, this alignment fosters more informed career choices and deeper professional identity formation for market readiness (Chen and Li, 2025; Liu et al., 2025).

Moreover, school–enterprise cooperation facilitates the development of students' tacit knowledge, an essential yet often overlooked component of effective workplace performance that is typically absent from conventional academic instruction. Tacit knowledge, acquired through direct involvement in professional contexts, enhances students' capacity to navigate complex and unpredictable work environments. Research indicates that such early exposure enables smoother school-to-work transitions and strengthens employment competitiveness (Brown, 2021; Eichhorst and Rinne, 2024).

Beyond the direct benefits to students, school–enterprise cooperation enhances institutional adaptability, advances broader educational policy objectives, and contributes to the development of regional innovation ecosystems (Zhuang and Zhou, 2023). Universities improve their public reputation and industry ties, while employers gain early access to skilled graduates and fulfill corporate social responsibility commitments.

Although school–enterprise cooperation offers concrete institutional pathways for enhancing students' employment competitiveness, its effectiveness ultimately relies on students' active participation and engagement. This highlights the importance of students' motivational resources in determining whether industry-based learning opportunities are fully utilized and translated into employability advantages.

2.3 Students' motivation and employment competitiveness of undergraduates

Motivation as a Driver of Employability-Oriented Behaviors. Students who exhibit high levels of motivation, particularly autonomous forms such as intrinsic motivation, identified regulation and integrated regulation, are significantly more likely to engage in employability-enhancing behaviors. These include proactively seeking skill development opportunities, participating in internships, getting themselves in extracurricular projects, and actively pursuing career guidance (Li et al., 2022; Yang et al., 2025). Such proactive engagement fosters the development of transferable competencies, including adaptability, critical thinking, resilience, and initiative—core dimensions of employability.

Self-Determination Theory (SDT), developed by Deci and Ryan (1972, 1985) proposes a comprehensive model of motivation built on three basic psychological needs that foster intrinsic motivation, engagement and well-being. Research grounded in Self-Determination Theory (SDT) indicates that students with a strong sense of autonomy and competence are more likely to develop a clearly articulated “future work self.” This self-concept, in turn, drives goal-directed career behaviors and supports the formulation of effective employability strategies (Li et al., 2023). Intrinsic motivation, learning driven by interest and personal relevance, leads to deeper engagement, higher effort and better learning outcomes (Di Domenico and Ryan, 2017). In contrast, controlled forms of motivation, which stem from external pressures or obligations, may result in short-term compliance but typically fail to support the persistence, creativity or proactive behaviors required for success in competitive employment contexts (Lisá et al., 2023; Parker et al., 2010).

Achievement motivation and self-efficacy serve as critical mediating variables in the relationship between student motivation and employability. Students with a strong sense of competence, reflected in their belief in their own capabilities, are more likely to actively pursue job opportunities, undertake challenging tasks and perform effectively in recruitment processes such as interviews and assessments. A strong achievement motivation, shaped by self-belief and internal drive, is linked to long-term career ambition and labor market success (Mahfud et al., 2024). Grounded in Self-Determination Theory (SDT) and achievement motivation frameworks (Deci and Ryan, 2000), the inclusion of motivational constructs, self-efficacy and career planning indicators in the study's analytical framework offers valuable insight into the mechanisms through which certain students achieve more successful transitions into the labor market.

Taken together, existing studies suggest that student motivation functions as a critical psychological driver linking educational contexts and institutional arrangements to employment outcomes. However, less is known about how learning environment, school–enterprise cooperation, and student motivation operate simultaneously within a single analytical framework, particularly in application-oriented higher education settings.

2.4 Theoretical integration and conceptual framework

Although previous studies have examined the effects of the learning environment, school–enterprise cooperation, and student motivation on employment competitiveness, most have considered these factors separately. Their combined influence, particularly from a theoretical perspective, remains underexplored. To address this gap, this study develops an integrated framework linking each factor to its theoretical foundation—situated cognition theory, synergy theory, and self-determination theory—and examines their joint contribution to employment competitiveness. Human capital theory is further used to explain how these factors collectively support employability development. The proposed framework is illustrated in Figure 1.

Figure 1

As shown in Figure 1, this study builds a conceptual framework based primarily on Situated Cognition Theory, Synergy Theory, and Self-Determination Theory, with Human Capital Theory used to interpret employment outcomes.

From the perspective of Situated Cognition Theory, learning is shaped by specific social, institutional, and professional contexts. The learning environment provides the conditions for students to engage in authentic, practice-oriented activities, but its influence on employment competitiveness is likely indirect, operating through institutional arrangements and students' active involvement in learning experiences.

School–enterprise cooperation reflects Synergy Theory, emphasizing coordinated collaboration among academic and industry actors. By combining academic resources with industry expertise through curriculum design, joint supervision, and work-based learning, these partnerships help students acquire job-relevant skills and professional experience, thereby enhancing employability.

At the individual level, Self-Determination Theory highlights how motivation links contextual and institutional factors to employment outcomes. When students' basic needs for autonomy, competence, and relatedness are supported, they are more likely to develop intrinsic motivation, confidence in their abilities, and proactive career behaviors, all of which contribute to readiness for competitive employment.

Finally, Human Capital Theory helps explain the significance of these experiences: the knowledge, skills, and practical experiences acquired through supportive learning environments, school–enterprise integration mechanisms, and active engagement constitute human capital, which in turn underpins students' employment competitiveness. Positioning Human Capital Theory in this way allows the framework to remain focused and coherent while still accounting for the key outcomes of interest.

3 Research method

3.1 Study design

This study employed a quantitative approach, using a cross-sectional survey to collect data from undergraduate students at Qingdao Hengxing University, China. Data were gathered through structured online questionnaires incorporating standardized Likert-scale instruments. To examine the relationships among the learning environment, school–enterprise cooperation, student motivation, and employment competitiveness, Pearson correlation and multiple linear regression were conducted for preliminary analyses, followed by Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed structural relationships.

3.2 Instrument of the study

The study instrument consisted of a structured questionnaire developed to measure four core constructs: learning environment, school–enterprise cooperation, student motivation, and employment competitiveness. All items were assessed using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), reflecting the degree of participants’ agreement with each statement.

To ensure the questionnaire was appropriate for the context, some items with similar content were removed, and several items were slightly revised to better capture the instructional practices and learning environments of Chinese application-oriented universities. The questionnaire was translated into Chinese using a translation and back-translation procedure to maintain both linguistic accuracy and conceptual consistency (Chen and Shin, 2025). Before conducting the main survey, the instrument was carefully examined by experts in higher education and educational measurement to ensure clarity and cultural suitability.

The Learning Environment was measured using an adapted version of the What Is Happening in This Class? (WIHIC) questionnaire developed by Fraser (2023), which has been widely used to assess classroom learning environments across educational contexts. The original WIHIC instrument comprises seven dimensions: student cohesiveness (SC), teacher support (TS), involvement (INVO), investigation (INVE), task orientation (TO), cooperation (COOP), and equity (EQU). For the purposes of this study, a 23-item version representing the seven subscales was adopted and contextualized for application-oriented higher education.

The School–Enterprise Cooperation Questionnaire was measured using an adapted version of the scale originally developed by Xu Xiaoying in 2011 and further applied in subsequent research (Chunying, 2020). The instrument comprises four dimensions: cooperation behavior in schools(MCBS), cooperation behavior in enterprises (MCBBE), students' psychological capital (MPC), and students' employability (MEVS). The final version used in this study includes 19 items across these subscales.

The Students' Motivation Questionnaire in this study was adapted from the Student Psychological Needs Scale developed by Goldman et al. (2017) and originally grounded in Gottfried's (1985) work on academic motivation. The instrument includes 15 items organized into five dimensions: autonomy(AUTO), competence(COMP), relatedness with classmates(RC), relatedness with instructors(RI), and intrinsic motivation(IM).

The Employment Competitiveness Questionnaire was measured using an adapted version of the framework proposed by Zhang et al. (2022). The instrument consists of 24 items and covers eight dimensions: professional knowledge and skills (PKAS), learning ability (LA), strain ability (SA), communication ability (CA), practical capability (PCA), teamwork competence (TA), information acquisition ability (IAA), and career planning (CP).

3.3 Study population

This study focuses on undergraduate students enrolled in private, application-oriented universities in Shandong Province, a region where local educational policy has explicitly prioritized the development of application-oriented institutions as part of broader higher education reform. The Shandong High-Level Application-Oriented University Construction Implementation Plan outlines goals to strengthen applied talent cultivation through mechanisms such as school–enterprise collaboration and industry–education integration.

Nationally, private universities form a substantial segment of China's higher education ecosystem, with approximately 27% of students enrolled in private institutions, highlighting their significant contribution to expanding access and meeting diverse educational demands. Compared with public universities, private institutions predominantly rely on non-governmental funding and often orient programs toward market and employment demands.

According to Cayuela et al. (2018), quantitative research rarely involves data collection from the entire population, largely because of the logistical complexity associated with large, dispersed populations. Grounded in Sampling Theory and Generalizability Theory (Cronbach et al., 1972), the selected samples is considered a methodologically sound approach, capable of producing results that are generalizable to the target population.

To enhance practicality and minimize the influence of extraneous variables, this study adopts a focused sampling approach by selecting a single institution, Qingdao Hengxing University, as the research site. This strategy contributes to improving the internal validity of the study by maintaining consistency in contextual variables across participants (Creswell, 2008). Moreover, the institution's geographically favorable location facilitates efficient data collection and reduces the likelihood of sampling imbalances that may arise in multi-institutional research designs (Etikan et al., 2016).

The study sample comprised third- and fourth-year undergraduate students from Hengxing University of Qingdao, Shandong Province, with a total population of 11,327 students. In this study, a stratified random sampling approach was employed to ensure that students from different faculties were adequately represented. The total population comprised approximately 3,100 undergraduate students, including about 1,200 from the Science and Technology faculty, nearly 1,000 from the Humanities and Social Sciences faculty, and about 900 from the Art faculty. The required minimum sample size was determined using Cochran (1977) formula for large populations.

Based on the proportional stratified sampling principle (Krejcie and Morgan, 1970), the sample was distributed according to each faculty's student population. Participants were then randomly selected from each faculty to complete the survey. A total of 1,281 questionnaires were returned. Among these, 54 questionnaires were excluded due to the answer time being less than 80 s. After data screening, questionnaires with extreme or identical responses (e.g., all items rated as “1” and “5”) were excluded to enhance data validity and normality.

The final dataset included student responses from the faculties of Science and Technology, Humanities and Social Sciences, and Art, resulting in a total of 956 valid samples (effective rate = 75%). The sampling proportions (31%–36%) remained well balanced across disciplines, providing strong representativeness and sufficient statistical power for regression and correlation analyses (Cohen, 2016; Hair et al., 2009). The final sample of 956 participants not only meets but surpasses the recommended thresholds, ensuring sufficient statistical power and reliable estimation for multivariate analyses in this study.

3.4 Pilot study

The questionnaires were first evaluated by two experts in educational research to ensure clarity, cultural appropriateness, and alignment with the study objectives. A pilot study was conducted at Qingdao Hengxing University, China, from March 26 to April 8, 2025, with 156 undergraduate students excluded from the main sample. The questionnaire items were reviewed for content clarity and contextual relevance. Following these revisions, the internal consistency of each scale was reassessed using Cronbach's alpha coefficients (Tavakol and Dennick, 2011). Internal consistency was satisfactory for all scales: Learning Environment (α = 0.865), School–Enterprise Cooperation (α = 0.839), Students' Motivation (α = 0.841), and Employment Competitiveness (α = 0.863), exceeding the conventional threshold of 0.70 (Nunnally and Bernstein, 1994). Exploratory factor analysis confirmed construct validity, with KMO values above 0.70 and significant Bartlett's tests (p < .01) for all scales, indicating suitability for factor extraction. These results demonstrate that the instruments are reliable and valid for use in the main study.

The pilot data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Internal consistency reliability was assessed using Cronbach's alpha and composite reliability (CR), with all values exceeding the recommended threshold of 0.70. Convergent validity was examined through indicator loadings and average variance extracted (AVE), with loadings above 0.60 and AVE values above 0.50. Discriminant validity was evaluated using the Fornell–Larcker criterion and further confirmed through the heterotrait–monotrait ratio (HTMT). The results indicated that the measurement model demonstrated satisfactory reliability and validity, supporting its use in the subsequent main analysis.

4 Findings

In the present study, the sample comprised 956 undergraduate students in their third or fourth year of study at Qingdao Hengxing University. Table 1 presents the distribution of students by gender and faculty.

Among the 956 participants, 464 were male (48.54%) and 492 were female (51.46%), indicating a relatively balanced gender distribution. Regarding faculty affiliation, 326 students (34.10%) were from Science and Technology, 318 (33.26%) from Humanities and Social Sciences, and 312 (32.64%) from Arts, showing that students from different academic backgrounds were almost equally represented in the study.

4.1 Normality test

In the main phase of the study, a larger sample size of 956 participants was utilized to assess the normality of the data distribution. To ensure a comprehensive evaluation, both skewness and kurtosis statistics were examined in conjunction with the Kolmogorov–Smirnov and Shapiro–Wilk tests.

As presented in Table 2, the Kolmogorov–Smirnov (K–S) test yielded statistically significant results (p < .001) for all four variables—learning environment, school–enterprise cooperation, students' motivation, and employment competitiveness—indicating that none of the distributions satisfy the assumption of normality.

Both the Kolmogorov–Smirnov and Shapiro–Wilk tests yielded statistically significant results (p < .05) for all measured variables, indicating deviations from normality. Nevertheless, as noted by Matore and Khairani (2020), in the context of educational and social science research, distributions with skewness and kurtosis values between −2.0 and +2.0 are typically regarded as sufficiently normal. As reported in Table 3, the skewness and kurtosis values for all variables fell within this acceptable range, thereby supporting the assumption of approximate normality despite the formal test results.

Although the results of the normality tests indicate statistically significant deviations, such findings are commonly observed in large samples due to the sensitivity of formal tests. In applied educational research, skewness and kurtosis are considered more robust indicators of distribution shape (George and Mallery, 2019). Given that all variables fall within the acceptable thresholds (±2.0), the assumption of approximate normality is deemed reasonable. Therefore, the use of parametric statistical techniques—including simple linear regression, multiple regression, and hierarchical regression—is justified in the context of this study (Tabachnick and Fidell, 2019).

4.2 Multicollinearity test

To assess the assumption of multicollinearity, this study examined the tolerance and Variance Inflation Factor (VIF) values associated with each independent variable. As recommended by Hair et al. (2014), multicollinearity is considered absent when the tolerance value exceeds 0.10 and the corresponding VIF is below the threshold of 10. These criteria are widely accepted in social science research as indicators of acceptable levels of intercorrelation among predictors. Table 4 summarizes the tolerance and VIF statistics for all independent variables included in the regression model. The results indicate that none of the predictor variables violate the multicollinearity assumption, thereby validating the appropriateness of including all predictors in the subsequent regression analyses.

As shown in Table 4, the tolerance values for the three predictors ranged from 0.848 to 0.906, with VIF values between 1.104 and 1.179. All of these statistics remain well within the commonly recommended thresholds (Tolerance < 0.20 or VIF > 5). These results suggest that multicollinearity is not present among the independent variables included in the model. The satisfaction of this assumption affirms the appropriateness of employing parametric statistical techniques, including simple linear regression, multiple regression, and hierarchical multiple regression, in the subsequent analytical procedures.

4.3 Psychometric properties of constructs

Table 5 presents the main characteristics of the study constructs, including their means and standard deviations, measures of reliability (Cronbach's α and CR), and assessments of validity (KMO and Bartlett tests, factor loadings, and AVE).

Descriptive statistics were calculated to examine the general characteristics of the main research variables. On average, students perceived the Learning Environment positively (M = 3.598, SD = 0.619). School–Enterprise Cooperation received a similarly favorable evaluation, with a mean of 3.697 (SD = 0.628). Student Motivation achieved the highest average among all constructs (M = 3.801, SD = 0.540), reflecting strong engagement and goal orientation. Employment Competitiveness was also rated moderately high (M = 3.731, SD = 0.583), suggesting a relatively positive self-assessment of employability. Overall, all variables exceeded the midpoint of the measurement scale, indicating that both institutional support and individual motivational factors were generally perceived positively, providing a solid foundation for subsequent correlation and structural model analyses.

The Cronbach's alpha coefficients for the four measurement scales employed in this study provide evidence of their internal consistency. Specifically, the reliability coefficients for the Learning Environment (0.899), School–Enterprise Cooperation (0.889), Student Motivation (0.845), and Employment Competitiveness (0.889) scales all substantially exceed the commonly accepted threshold of 0.70 (DeVellis and Thorpe, 2021). These results indicate a high level of internal reliability, supporting the use of these instruments in subsequent statistical analyses.

The standardized factor loadings for all measurement constructs indicate satisfactory item representation across the study variables. For the Learning Environment construct, loadings range from 0.438 to 0.727, while those for School–Enterprise Cooperation fall between 0.408 and 0.676. The indicators for Student Motivation exhibit loadings from 0.415 to 0.559, and those for Employment Competitiveness range from 0.412 to 0.649. All values exceed the recommended threshold of 0.40, suggesting that each item adequately captures the variance of its respective construct and providing evidence of acceptable convergent validity for the measurement model.

All constructs demonstrated satisfactory internal consistency, with composite reliability (CR) between 0.885 and 0.917, well above the recommended threshold of 0.70. Convergent validity was also supported, as the average variance extracted (AVE) values ranged from 0.586 to 0.688, exceeding the minimum criterion of 0.50 (Hair et al., 2021). These findings indicate that the measurement model possesses adequate reliability and convergent validity.

4.4 Relationship between learning environment, school–enterprise cooperation, and students' motivation toward the employment competitiveness of students in Qingdao Hengxing University, China

Table 6 presents the results of the Pearson correlation analysis conducted on a sample of 956 undergraduates, revealing positive correlations between the independent variables and the employment competitiveness of students. Specifically, the school–enterprise cooperation revealed a strong positive correlation with the employment competitiveness of students (r=0.712, p<0.01), indicating a statistically significant and strong relationship. In contrast, both learning environment and students' motivation showed small yet statistically significant positive correlations with the employment competitiveness of students, with correlation coefficients of r = 0.276 (p < 0.01) and r = 0.278 (p < 0.01), respectively.

The results indicate that all three independent variables, learning environment, school–enterprise cooperation, and students’ motivation, are significantly associated with students' employment competitiveness. Among them, school–enterprise cooperation exhibits the strongest correlation, considerably higher than that of the learning environment and students' motivation. Therefore, the hypotheses H1, H2, and H3 are supported, confirming that each variable demonstrates a statistically significant relationship with undergraduate students' level of employment competitiveness.

4.5 The influence of learning environment, school–enterprise cooperation, and students' motivation on the employment competitiveness of students in Qingdao Hengxing University, China

A multiple linear regression analysis was employed to investigate the predictive influence of the three independent variables—learning environment, school–enterprise cooperation, and students’ motivation—on the dependent variable, namely the employment competitiveness of students. All three predictor variables were entered into the regression model and evaluated at a significance level of p < 0.05. As shown in Table 7, school–enterprise cooperation (β = 0.681, t = 27.924, p < .001) and students' motivation (β = 0.106, t = 4.476, p < .001) exert statistically significant positive effects on students' employment competitiveness. In contrast, the learning environment (β = 0.012, t = 0.478, p = .633) does not demonstrate a significant predictive effect. Among the three independent variables, school–enterprise cooperation emerges as the most influential factor, highlighting its critical role in enhancing students' employability through practice-oriented learning and institutional collaboration.

As presented in Table 7, the standardized beta coefficients (β) and corresponding t-values reveal distinct levels of influence among the three predictor variables. While the learning environment shows a positive but statistically insignificant effect on students' employment competitiveness, a one-unit increase in the Learning Environment corresponds to a 0.012 unit increase in Employment Competitiveness, its contribution appears limited in explaining variations in employability outcomes. In contrast, students' motivation exerts a significant positive influence, with a one-unit increase predicting a 0.106 unit rise in Employment Competitiveness, indicating a modest effect. Notably, school–enterprise cooperation demonstrates the strongest predictive power: a one-unit increase is associated with a substantial 0.681 unit improvement in employment competitiveness. Collectively, these findings highlight that institutional collaboration and motivational factors substantially contribute to students' employment readiness, whereas the learning environment alone does not yield a significant predictive effect in this model.

The multiple linear regression analysis revealed that the three predictor constructs—learning environment, school–enterprise cooperation, and students' motivation—jointly accounted for 51.8% of the variance in employment competitiveness of students (R² = 0.518). The adjusted coefficient of determination (Adjusted R² = 0.516) indicates that the model demonstrates a high level of explanatory power. This suggests that the model's fit remains robust and is not the result of overfitting. The standard error of the estimate is 0.219, reflecting a relatively small average deviation between the observed and predicted values, and thus indicating a high degree of predictive precision.

Based on the regression results, both school–enterprise cooperation and students' motivation exhibit statistically significant and positive effects on employment competitiveness (p < .05). In contrast, the learning environment does not show a statistically significant effect on employment competitiveness (p = .633), suggesting that its direct contribution is limited within the current model. Overall, the findings indicate that institutional collaboration and student motivation are important predictors of employability outcomes, whereas the direct effect of the learning environment appears nonsignificant.

The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a bootstrapping procedure to estimate path coefficients, significance levels, and the explanatory power of the model. The results of the structural model assessment, including standardized path coefficients and explained variance, are presented in Figure 2 and Tables 8–11.

Figure 2

To examine the potential presence of common method variance (CMV), Harman's single-factor test was performed. All measurement items were entered into an exploratory factor analysis using an unrotated principal component approach. The results indicate that the first factor explains 17.68% of the total variance, which is below the commonly suggested threshold of 50% (Podsakoff et al., 2003). This result suggests that common method bias is unlikely to be a serious issue in the present study.

Discriminant validity was examined using the HTMT criterion. As shown in Table 8, all HTMT values were below the recommended threshold of 0.90, indicating that the constructs were empirically distinct and that discriminant validity was established (Henseler et al., 2015).

The structural model was assessed by examining collinearity (VIF), effect sizes (f²), explanatory power (R²), and predictive relevance (Q²). Collinearity among predictor constructs was examined using variance inflation factor (VIF). As shown in Table 9, all VIF values were well below the threshold of 3.3, indicating no serious multicollinearity issues among the independent variables in the proposed model, suggesting good model stability and reliability.

The structural model was further evaluated in terms of effect size (f²), explanatory power (R²), and predictive relevance (Q²). The effect size results showed that school–enterprise cooperation had a very strong effect on employment competitiveness (f² = 0.923), while learning environment had a moderate effect on school–enterprise cooperation (f² = 0.153). In contrast, the effects of learning environment (f² = 0.000) and student motivation (f² = 0.010) on employment competitiveness were negligible. The coefficient of determination (R²) indicated that the model explained 54.0% of the variance in employment competitiveness, suggesting a moderate level of explanatory power. The R² values for school–enterprise cooperation (0.133) and student motivation (0.056) were relatively lower, reflecting that these constructs may also be influenced by additional factors not included in the present model. In addition, all Q² values were greater than zero, confirming the predictive relevance of the model.

Based on the initial model, model refinement was conducted by examining the factor loadings of the measurement items. Following the recommendation of Hair et al. (1998), indicators with factor loadings below 0.60 should be considered for removal. The analysis showed that the item EQU under the learning environment construct (factor loading = 0.597), as well as three indicators under the employment competitiveness construct—CPA (0.311), TA (0.558), and IAA (0.458)—all fell below the recommended threshold of 0.60, indicating insufficient explanatory power for their respective latent constructs. These indicators were therefore removed from the model.

After eliminating these items, the model was re-estimated. The revised model, shown in Figure 2, includes four latent constructs and twenty observed variables. Compared with the initial specification, the overall model fit improved noticeably after the modification.

The structural relationships among the constructs were examined using the bootstrapping procedure in PLS-SEM. As shown in Table 10, the direct effect of the learning environment on employment competitiveness is not statistically significant (β = 0.013, t = 0.492, p = 0.623). However, the learning environment shows significant positive effects on both school–enterprise cooperation (β = 0.364, t = 10.668, p < 0.001) and student motivation (β = 0.237, t = 7.317, p < 0.001). These findings suggest that the learning environment does not directly predict employment competitiveness. With regard to the predictors of employment competitiveness, school–enterprise cooperation demonstrates a strong positive effect (β = 0.710, t = 24.155, p < 0.001), while student motivation also shows a significant but comparatively smaller effect (β = 0.071, t = 3.242, p = 0.001). School–enterprise cooperation emerges as the dominant predictor of employment competitiveness, whereas student motivation shows a smaller yet statistically significant contribution. Accordingly, H5 is partially supported.

To examine the mediating roles, we applied a bootstrapping procedure within the PLS-SEM model. As shown in Table 11, the indirect effect of the learning environment on employment competitiveness through student motivation is statistically significant (β = 0.017, t = 2.876, p = 0.004). The 95% confidence interval ranges from 0.007 to 0.030, indicating a significant mediating effect. And then, school–enterprise cooperation also mediates the relationship between the learning environment and employment competitiveness. The indirect effect through school–enterprise cooperation is substantially stronger (β = 0.259, t = 9.438, p < 0.001), with a 95% confidence interval of [0.205, 0.311].

These results suggest that both student motivation and school–enterprise cooperation serve as mediators in the relationship between the learning environment and employment competitiveness, although the mediating role of school–enterprise cooperation appears to be considerably stronger. Accordingly, H4 is supported.

5 Discussion and conclusion

5.1 Relationship between learning environment and employment competitiveness

The Pearson correlation analysis showed a weak but significant positive relationship between the learning environment and employment competitiveness (r = .276, p < .001). However, the PLS-SEM results indicate that the learning environment does not have a significant direct effect on employment competitiveness, but influences it indirectly through school–enterprise cooperation and student motivation.

5.1.1 Theoretical interpretation

From a theoretical standpoint, these findings offer valuable insights for both Situated Cognition Theory and employability frameworks based on Human Capital Theory. According to Situated Cognition Theory, a learning environment provides the context in which knowledge can be constructed; however, developing employability-related competencies depends largely on students' active participation in authentic practices and social interactions. This suggests that the learning environment acts more as a supporting context that enables students' skill development rather than directly generating employment competitiveness.

From a Human Capital Theory perspective, employability should be viewed not merely as the accumulation of knowledge and skills, but also as the capacity to apply and leverage these resources effectively in meaningful contexts. In this sense, the learning environment acts as a supportive context that strengthens the relevance and transferability of human capital, rather than serving as a direct driver of employment competitiveness.

Overall, the learning environment serves as a supportive context that facilitates skill development rather than directly driving employment competitiveness. These insights highlight the importance of considering contextual, motivational, and experiential factors when modeling employment competitiveness in higher education.

5.1.2 Exploration of underlying mechanisms

In the PLS-SEM model, according to our analysis, the direct effect of the learning environment on employment competitiveness was not significant (β = 0.013, t = 0.492, p = .623), suggesting that a supportive learning environment alone does not automatically translate into higher employability. However, the learning environment showed a strong positive and significant effect on school–enterprise cooperation (β = 0.364, t = 10.668, p < .001) and a moderate positive and significant effect on student motivation (β = 0.237, t = 7.317, p < .001). This suggests that better learning environments can facilitate collaborative activities between universities and enterprises while also enhancing students' motivational resources.

In the mediation analysis, the indirect effect of learning environment on employment competitiveness through school–enterprise cooperation was substantial (β = 0.259, 95% CI [0.205, 0.311]), whereas the indirect effect on student motivation was smaller but still significant (β = 0.017, 95% CI [0.007, 0.030]). These findings indicate that the learning environment primarily contributes to employability by promoting school–enterprise cooperation, while its influence through student motivation, though statistically significant, plays a more limited role. That highlights the importance of experiential and motivational pathways in translating environmental support into enhanced employment competitiveness.

The learning environment, as a macro-level contextual factor, typically does not directly enhance students' capabilities. Instead, it operates indirectly by influencing students' motivational states and providing opportunities for engagement. In other words, a supportive learning environment can stimulate students' intrinsic motivation and offer authentic contexts for applying knowledge, which in turn directly shape employability. Even with excellent facilities, if students lack motivation or if opportunities for meaningful school–enterprise collaboration are absent, the learning environment alone provides no direct “lever” to enhance employment competitiveness.

Employment competitiveness can be considered a form of “practical wisdom,” which cannot be acquired merely through classroom instruction. Environmental improvements must be converted into intrinsic learning drive, prompting students to engage proactively, and must leverage school–enterprise cooperation as a bridge to real industry projects. In this sense, the learning environment functions as both a “catalyst” and an “incubator,” and its value depends on activating internal motivation and external experiential opportunities. These mechanisms explain why the learning environment contributes indirectly—rather than directly—to students' employability.

5.1.3 Empirical comparison and practical implications

The findings of this study indicate that the learning environment has a modest yet significant positive association with students' employability. This is generally consistent with prior research suggesting that supportive and inclusive learning environments can enhance student engagement, collaboration, and intrinsic motivation, thereby contributing to the development of employability-related competencies (Hoidn and Kärkkäinen, 2021). In addition, learning environments that encourage persistence, constructive feedback, and collaborative problem-solving may help students develop adaptive mindsets and resilience, which are increasingly valued in contemporary workplaces (Dweck, 2006).

However, the present study further reveals that the learning environment does not exert a significant direct effect on employment competitiveness when other factors are considered simultaneously. This finding is in line with studies suggesting that the influence of the learning environment is often indirect rather than immediate (Edy and Rifqi, 2020). In application-oriented universities, greater emphasis is placed on practical training and industry engagement, where school–enterprise cooperation provides more direct exposure to workplace demands. As a result, the effect of general learning conditions may be overshadowed when more practice-oriented factors are included in the model.

Furthermore, as highlighted by Selvaratnam (2021), the effectiveness of learning environments depends on how they are enacted through appropriate pedagogical strategies and meaningful learning activities. Without active student participation and sufficient motivational support, the perceived quality of the learning environment may not readily translate into employability outcomes. Taken together, these findings suggest that the learning environment functions more as an enabling condition that supports employability development indirectly, rather than as a direct determinant of employment competitiveness.

Given the modest positive correlation between the learning environment and students' perceived employability, several practical implications can be drawn for higher education institutions. First, universities can move beyond simulated exercises to create authentic learning environment, where campus-based activities closely reflect the production processes, quality standards, and management practices of partner enterprises. Such alignment allows students to experience professional workflows and industry norms within the learning environment. Second, universities should establish mechanisms that connect learning environments with workplace contexts, for example, through remote observation systems or virtual practice platforms. These approaches enable students to engage with real-world tasks, observe professional operations, and apply theoretical knowledge in practice before entering the workforce. Third, optimizing physical and collaborative learning spaces can foster team-based, project-oriented learning. Flexible classrooms equipped for group work, presentations, and interactive discussions encourage students to develop transferable skills such as communication, problem-solving, and collaboration, which are crucial for career readiness. Collectively, these strategies emphasize the integration of contextual, experiential, and collaborative elements to create learning environments that meaningfully support employability. According to Choi-Lundberg et al. (2024), highlighting the importance of optimizing the learning environment requires a systematic and strategic approach that aligns pedagogical quality with labor market expectations, thereby comprehensively enhancing students’ career preparedness and competitiveness.

5.2 Relationship between school–enterprise cooperation and employment competitiveness

The Pearson correlation analysis revealed a strong positive relationship between school–enterprise cooperation and employment competitiveness (r = .712, p < .001). The PLS-SEM results further show that school–enterprise cooperation has the strongest positive effect on employment competitiveness among all predictors (β = 0.710, f² = 0.923).

5.2.1 Theoretical interpretation

From a theoretical perspective, the strong positive relationship observed between school–enterprise cooperation and students' employment competitiveness provides important insights for Synergy Theory and Human Capital Theory. Synergy Theory posits that collaborative systems can generate outcomes that exceed the additive effects of individual actors. The present findings empirically support this proposition by showing that structured and meaningful school–enterprise cooperation creates synergistic value in employability development. Rather than functioning merely as an extension of academic instruction, cooperation mechanisms such as curriculum co-design, joint supervision, and authentic industry-based tasks integrate institutional resources in ways that strengthen students' readiness for the labor market.

While traditional human capital perspectives emphasize knowledge acquisition, the strong association observed in this study suggests that school–enterprise cooperation enhances the quality, contextualization, and transferability of human capital. Through sustained engagement with industry partners, students gain access to tacit knowledge, professional norms, and real-world problem-solving experiences that are difficult to obtain through classroom-based learning alone. These contextualized learning experiences allow students to translate academic knowledge into practical competence, thereby strengthening the employability value of human capital rather than simply increasing the amount of knowledge students possess.

Taken together, these findings position school–enterprise cooperation as a powerful mechanism for enhancing employment competitiveness. By integrating insights from Synergy Theory and Human Capital Theory, the results highlight how collaborative educational arrangements can strengthen employability by connecting academic learning with industry practice and facilitating the practical application of students' competencies.

5.2.2 Exploration of underlying mechanisms

The PLS-SEM results indicate that school–enterprise cooperation plays a central role in the mechanism underlying employment competitiveness. Among all predictors, the path from school–enterprise cooperation to employment competitiveness shows the strongest and most significant effect (β = 0.710, f² = 0.923). This suggests that participation in cooperative activities with enterprises directly contributes to the development of employability-related competencies.

From a mechanism perspective, school–enterprise cooperation functions as an important channel through which students translate academic learning into practical capability. Unlike classroom-based learning, cooperative programs with enterprises typically involve internships, project collaboration, and exposure to real industrial environments. These experiences enable students to apply theoretical knowledge to practical tasks, develop professional skills, and become familiar with workplace expectations. Such experiential learning processes directly enhance students' employment competitiveness. School–enterprise cooperation also provides students with opportunities to connect with the external labor market. Through engagement with industry partners, students may gradually accumulate valuable social capital, such as professional networks, recommendation opportunities, and internal referrals. These resources can offer practical support in the job search process and contribute to the development of students' employment competitiveness.

5.2.3 Empirical comparison and practical implications

Students who engage in more meaningful and structured forms of industry collaboration perceive themselves as better prepared for the labor market. This empirical result is largely consistent with the findings of previous research conducted in comparable educational contexts. School–enterprise cooperation (SEC), as a strategic response to mounting employment pressures in higher education, effectively bridges the gap between academic instruction and industry requirements (Chen and Li, 2025; Liu et al., 2025; Zhang and Chen, 2023). By embedding real-world experiences into curricula—through internships, cooperative education programs and project-based learning, students develop opportunities to apply theoretical knowledge in practice, thereby enhancing critical thinking, time management, teamwork and adaptability. Furthermore, SEC facilitates the acquisition of tacit knowledge, often absent from formal academic training but essential for functioning in complex and unpredictable work environments (Brown, 2021; Sá and Serpa, 2018). Moreover, the SEC contributes to curriculum relevance and innovation. Incorporating employer feedback into course design ensures better alignment between academic content and labor market needs, supporting informed career choices and the development of strong professional identities (Sá and Serpa, 2018).

The strong predictive effect of school–enterprise cooperation also provides several practical implications for higher education institutions seeking to strengthen students' employment competitiveness. First, universities should establish long-term and mutually beneficial partnerships with industry stakeholders. Enterprise participation should not be limited to internships but should extend to the design of talent cultivation programs, curriculum co-design, joint supervision of projects, and the provision of skills-oriented training. Through such collaboration, teaching standards can be more closely aligned with industry requirements. This approach enables students to acquire not only technical skills but also contextual knowledge about industry practices, thereby strengthening their readiness for the labor market (Liu et al., 2025).

Second, the structure and quality of school–enterprise cooperation should be supported by clearer evaluation mechanisms. Universities can introduce performance assessment systems that examine the effectiveness of cooperative activities, with measurable learning outcomes linked to indicators of employability development. Such evaluation frameworks help ensure that collaboration with enterprises produces tangible learning benefits rather than remaining symbolic or short-term partnerships (Chen and Li, 2025).

Third, policymakers and educational institutions should encourage the development of scalable models of school–enterprise collaboration, particularly in applied disciplines. Universities and enterprises may jointly establish industry–education–research platforms to promote collaborative research and technological innovation. Through such partnerships, research projects can feed back into teaching activities, enabling the sharing of resources and facilitating mutually beneficial development between universities and industry partners. Governments may also consider providing incentives for companies that actively participate in talent cultivation, thereby encouraging stronger industry engagement in higher education. In this context, school–enterprise cooperation should be regarded as an important strategic component of employability-oriented higher education reform.

5.3 Relationship between students' motivation and employment competitiveness

The Pearson correlation analysis revealed a small but statistically significant positive relationship between students' motivation and employment competitiveness (r = .278, p < .001). The PLS-SEM results further show that student motivation has a positive and statistically significant effect on employment competitiveness (β = 0.071, p = 0.001), although the effect size is relatively small (f² = 0.01).

5.3.1 Theoretical interpretation

From a theoretical perspective, the modest yet statistically significant relationship between students' motivation and employment competitiveness provides important insights for Self-Determination Theory (SDT) and employability-related frameworks grounded in Human Capital Theory. SDT posits that individuals’ motivation quality, particularly intrinsic motivation and internalized forms of extrinsic motivation, plays a critical role in sustaining engagement, performance, and personal development. The present findings support this premise, demonstrating that motivated students tend to report higher perceived employability, reinforcing the relevance of SDT in explaining employability outcomes.

At the same time, students' motivation can be conceptualized as a distal factor in employability development. While it provides the foundational drive that encourages students to engage with learning and practical opportunities, its effect is smaller in magnitude compared with proximal factors such as school–enterprise cooperation. Motivation enables students to participate actively in projects and internships, enhancing the application of knowledge and skills in real-world contexts.

From the perspective of Human Capital Theory, motivation influences employability not merely through the accumulation of skills but through the depth of learning, persistence in skill acquisition, and the effective transfer of competencies to professional settings. Highly motivated students are more likely to engage in continuous learning, adapt to changing career demands, and translate theoretical knowledge into practical competence, thus enhancing the long-term value of their human capital.

5.3.2 Exploration of underlying mechanisms

In the PLS-SEM model, the results indicate that student motivation has a positive and statistically significant effect on employment competitiveness (β = 0.071, p = 0.001). Students with stronger motivation tend to participate more actively in learning activities, seek opportunities to develop relevant skills, and invest greater effort in preparing for their future careers. These forms of engagement may gradually contribute to the development of competencies valued in the labor market. At the same time, the relatively small effect size (f² = 0.01) suggests that motivation alone is unlikely to substantially shape employment competitiveness. Employability development generally depends on multiple factors, including institutional support, practical training opportunities, and exposure to workplace contexts.

From a theoretical perspective, motivation can be viewed as a relatively distal factor that primarily influences students' engagement, persistence, and depth of learning. In contrast, employment competitiveness represents a complex and more distal outcome shaped by multiple contextual and experiential factors. By comparison, school–enterprise cooperation functions as a more proximal factor that is closely connected to labor market outcomes. Through participation in cooperative programs, students gain access to industry experience, professional networks, and employment-related signals that are directly relevant to employers' expectations.

In addition, school–enterprise cooperation may partially compensate for differences in students' motivational levels. Even students with relatively lower intrinsic motivation may enhance their employment competitiveness through successful engagement with industry partners, exposure to professional role models, and the development of practical competencies during cooperative experiences. In this sense, institutional opportunities provided by school–enterprise collaboration can complement students' internal motivational resources in shaping employability outcomes. As a result, this helps explain why the direct effect of motivation appears relatively small in the model.

5.3.3 Empirical comparison and practical implications

The analysis revealed a modest but statistically significant link between student motivation and perceived employment competitiveness. This supports earlier findings that autonomous forms of motivation, including intrinsic motivation and internalized regulation, encourage proactive engagement in skill-building, internships, and career-oriented projects (Li et al., 2022; Yang et al., 2025). Students with higher motivation tend to develop transferable competencies, such as critical thinking, adaptability, and initiative, which are central to employability (Di Domenico and Ryan, 2017).

However, compared with these prior studies, the predictive strength of motivation in the current study is relatively modest. This finding aligns with research suggesting that motivation often exerts its effects indirectly, through mediating factors such as learning engagement, achievement motivation, and participation in authentic learning experiences (Edy and Rifqi, 2020; Selvaratnam, 2021). In the context of our study, the presence of proximal factors—particularly school–enterprise cooperation—may absorb part of the variance in employment competitiveness, thereby reducing the apparent direct effect of student motivation. Additionally, while intrinsic motivation provides the internal drive for sustained engagement, controlled or externally regulated motivation may dilute its observable impact in the labor market, consistent with Parker et al. (2010) and Lisá et al. (2023).

Considering the statistically significant yet relatively weak positive correlation between student motivation and employment competitiveness, it is imperative for higher education institutions to implement systematic measures: First, courses and learning experiences should be redesigned to foster competence through project-based learning, modular skill ladders, and elective options that provide tangible mastery, self-directed progress, and personal responsibility. Second, motivation should be linked to authentic school–enterprise experiences, including structured internships, enterprise challenges, and reflection sessions, allowing students to validate and amplify their motivation through practical achievements. Third, support systems should be optimized to build a sense of belonging. This can include cross-grade learning communities, developmental mentorship programs involving faculty, alumni, and industry partners, as well as growth-oriented evaluation mechanisms that encourage sustained engagement and collaborative learning. Fourth, learning motivation can be elevated into career purpose by connecting academic content to long-term professional goals through seminars, alumni sharing, and career-oriented discussions, helping students internalize the relevance of their studies to self-actualization and professional identity. Finally, these strategies collectively ensure that intrinsic motivation is effectively harnessed and translated into observable employability outcomes, complementing proximal factors such as school–enterprise cooperation.

5.4 Predictive influence of learning environment, school–enterprise cooperation, and students' motivation on employment competitiveness

These results are largely consistent with previous empirical research and align with theoretical perspectives emphasizing the centrality of collaborative and motivational factors in fostering students' employability. School–enterprise cooperation exhibits a statistically significant effect on employment competitiveness; its standardized coefficient suggests it contributes the most among the three predictors. Extensive empirical research has affirmed the positive contribution of university–industry collaboration to students' employability. It constitutes a strategic educational approach that systematically links theoretical learning with professional practice, reinforcing students' long-term career readiness and competitiveness (Zhang and Chen, 2023). Collectively, these studies suggest that well-integrated school–enterprise cooperation not only bridges the gap between academic training and industry needs but also directly contributes to the development of students' employability and long-term career adaptability. School–enterprise cooperation represents a proximal institutional mechanism that directly connects educational processes with labor market demands, which may explain its dominant predictive strength in the structural model.

Students' motivation has a modest yet significant effect on employment competitiveness, ranking second among the three predictors in the model. This suggests that motivation remains an important contributor to employability outcomes. Students with higher intrinsic motivation tend to pursue meaningful learning experiences, invest greater effort, and persist in challenging tasks, which in turn enhance their readiness for employment (Di Domenico and Ryan, 2017). Conceptually, student motivation can be regarded as an individual-level psychological resource that mediates the effect of environmental conditions on employability-related behaviors.

Compared with the other two predictors, the learning environment did not show a statistically significant effect on employment competitiveness, suggesting that its direct contribution to employability is limited. Rather, the learning environment appears to function as a distal contextual factor, whose impact on employment competitiveness is primarily indirect. It is likely mediated through more proximal mechanisms, such as students' motivation and engagement in school–enterprise cooperation programs, which translate the educational context into concrete employability outcomes.

The present study found no significant association between the learning environment and employment competitiveness. This may be because more proximal factors, such as school–enterprise cooperation and practical experiences, play a stronger role in shaping employability in the current sample, reducing the observable direct effect of the learning environment. Similarly, Batterley et al. (2025) found no notable differences in employability skill development across different learning environments. While the statistical effect of the learning environment appears limited in this model, it may still exert influence indirectly through students' motivation and school–enterprise cooperation. Its pedagogical and developmental significance, therefore, remains both theoretically and practically relevant.

6 Conclusion

This study examined the effects of the learning environment, school–enterprise cooperation, and students' motivation on employment competitiveness among undergraduates in application-oriented universities in China. The results show that school–enterprise cooperation plays the most prominent role in predicting employment competitiveness, followed by students' motivation. By contrast, the learning environment does not display a significant direct effect. Instead, its influence appears to operate indirectly through students' motivation and school–enterprise cooperation. Taken together, these findings suggest that graduates' employment competitiveness is shaped by the combined influence of institutional opportunities and students' own motivational resources within application-oriented higher education.

Beyond the empirical results, this study also contributes to the theoretical understanding of employment competitiveness in application-oriented higher education. Drawing on Situated Cognition Theory, Synergy Theory, and Self-Determination Theory, the study provides an integrated perspective for explaining how contextual conditions, institutional collaboration, and students' motivational processes jointly shape employment competitiveness. From a broader perspective, these findings also resonate with Human Capital Theory by illustrating how educational experiences and learning engagement contribute to the development and application of human capital. The findings further clarify the differentiated roles of key predictors within this framework. School–enterprise cooperation plays a particularly prominent role by connecting educational activities with industry contexts and facilitating the practical application of knowledge and skills. Students' motivation functions as an internal psychological driver that encourages learning engagement and career preparation. In contrast, the learning environment exerts its influence mainly through indirect pathways rather than through a strong direct effect. By revealing these distinct yet interconnected mechanisms, the study extends existing employability research that often focuses on single explanatory factors.

Despite these contributions, several limitations should be noted. The cross-sectional design limits causal inference, and self-reported data may be influenced by subjective perceptions or social desirability, though measures such as anonymous responses, expert-reviewed items, and Harman's single-factor test were employed to reduce potential bias. In addition, the sample was drawn from a single application-oriented university in China, which may restrict the generalizability of the findings. While the specific effect sizes may vary, the mechanisms identified, School–enterprise cooperation and students' motivation, show strong associations with employment competitiveness. The learning environment contributes indirectly through its influence on students' motivation and participation in school–enterprise collaboration. While effect sizes may vary across institutions, these mechanisms are expected to operate in other application-oriented or similar higher education contexts. Future research could examine whether these mechanisms operate similarly in public, research-intensive, or international universities, adopt longitudinal designs to capture dynamic development, and employ multi-level or cross-national studies to test the robustness of the proposed framework across different educational and cultural settings.

References

• 1

  BatterleyM.LimniouM.MasonG.BodeC. (2025). How learning environments affect university students’ employability skills development: students and staff views. Educ. Sci.16 (1), 41. 10.3390/educsci16010041

  • CrossRef
  • Google Scholar

• 2

  BrownJ. L. (2021) University students’ dispositional employability and career adaptive behaviour. (Doctoral dissertation). University of Southern Queensland, Australia.

  • Google Scholar

• 3

  BrownJ. S.CollinsA.DuguidP. (1989). Situated cognition and the culture of learning. Educ. Res.18 (1), 32–42. 10.3102/0013189X018001032

  • CrossRef
  • Google Scholar

• 4

  CaoC.TaddeseE. T.GebresilaseB. M.ShoroukA.BerhanuK. Z. (2025). The influence of learning motivation on academic performance in Chinese vocational college students: a self-determination theory perspective. Int. J. Innov. Res. Sci. Stud.8 (6), 3297–3309. 10.53894/ijirss.v8i6.10339

  • CrossRef
  • Google Scholar

• 5

  CaoX.HanY. (2024). The effect of Chinese vocational college students’ perception of feedback on online learning engagement: academic self-efficacy and test anxiety as mediating variables. Front. Psychol.15, 1326746. 10.3389/fpsyg.2024.1326746

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6

  CayuelaH.RougemontQ.PrunierJ. G.MooreJ. S.ClobertJ.BesnardA.et al (2018). Demographic and genetic approaches to study dispersal in wild animal populations: a methodological review. Mol. Ecol.27 (20), 3976–4010. 10.1111/mec.14848

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  ChenJ.LinL.GaiX.ZhangY. (2025). Bridging the gap: an empirical study on learning motivation, pathways, and outcomes in China’s 3+4 integrated vocational-bachelor education program. Front. Psychol.16, 1565323. 10.3389/fpsyg.2025.1565323

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8

  ChenX.LiM. (2025). Research on strategies for improving the employment ability of vocational college graduates from the perspective of school–enterprise cooperation. Front. Educ. Res.4 (3), 115–123. 10.47365/fer.v4i3.18667

  • CrossRef
  • Google Scholar

• 9

  ChenL.ShinC. (2025). Assessing questionnaire validity and reliability on learning environment, school-enterprise cooperation, motivation, and employment competitiveness. Malays. J. Soc. Sci. Hum.10 (9), e003544. 10.47405/mjssh.v10i9.3544

  • CrossRef
  • Google Scholar

• 10

  ChigbuB. I.NekhwevhaF. H. (2022). Academic-faculty environment and graduate employability: variation of work-readiness perceptions. Heliyon8 (3), e09086. 10.1016/j.heliyon.2022.e09086

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  Choi-LundbergD.DouglasT.BirdM.-L.ColemanB.GreenwoodM.MartinR.et al (2024). Employability learning and teaching research: a twenty year structured narrative review. J. Univ. Teach. Learn. Pract.21 (5), 1–38. 10.53761/g8mryt07

  • CrossRef
  • Google Scholar

• 12

  ChukwuedoS. O.EmentaC. N. (2022). Students’ work placement learning and employability nexus: reflections from experiential learning and social cognitive career theories. Ind. High. Educ.36 (6), 742–755. 10.1177/09504222221099198

  • CrossRef
  • Google Scholar

• 13

  ChunyingM. (2020). Mediating role of psychological capital in relationships between school-enterprise cooperation and employment of students higher vocational education institutions. Rev. Psicol. Deporte (J. Sport Psychol.)29 (4), 135–148.

  • Google Scholar

• 14

  CochranW. G. (1977). Sampling Techniques, 3rd Edn.New York, NY: John Wiley & Sons.

  • Google Scholar

• 15

  CohenJ. (2016). A power primer. Psychol. Bull.112 (1), 155–159. 10.1037/0033-2909.112.1.155

  • CrossRef
  • Google Scholar

• 16

  CreswellJ. W. (2008). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, 3rd Edn.Boston, MA: Pearson.

  • Google Scholar

• 17

  CricchioJ.Di MininA. (2025). The rise of open innovation in Chinese academia: a systematic review of the literature. R&D Manag.55 (3), 899–928. 10.1111/radm.12730

  • CrossRef
  • Google Scholar

• 18

  CronbachL. J.GleserG. C.NandaH.RajaratnamN. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. New York, NY: John Wiley & Sons.

  • Google Scholar

• 19

  DeciE. L.RyanR. M. (1972). Intrinsic motivation and internalization of external rewards. Psychol. Rev.79 (1), 48–58. 10.1037/h0030735

  • CrossRef
  • Google Scholar

• 20

  DeciE. L.RyanR. M. (1985). Intrinsic Motivation and Self-determination in Human Behavior. New York, NY: Plenum Press.

  • Pubmed Abstract
  • Google Scholar

• 21

  DeciE. L.RyanR. M. (2000). The “what” and “why” of goal pursuits: human needs and the self-determination of behavior. Psychol. Inq.11 (4), 227–268. 10.1207/S15327965PLI1104_01

  • CrossRef
  • Google Scholar

• 22

  DeciusJ.KnappsteinM.KlugK. (2024). Which way of learning benefits your career? The role of different forms of work-related learning for different types of perceived employability. Eur. J. Work Organ. Psychol.33 (1), 24–39. 10.1080/1359432X.2023.2191846

  • CrossRef
  • Google Scholar

• 23

  DerakhshanA.DavariS. M.KarimipourH. (2024). Interconnected factors in EFL engagement: classroom climate, growth mindset, and achievement goals. Front. Psychol.15, 1353360. 10.3389/fpsyg.2024.1353360

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24

  DeVellisR. F.ThorpeC. T. (2021). Scale Development: Theory and Applications, 5th Edn.Thousand Oaks, CA: Sage Publications.

  • Google Scholar

• 25

  Di DomenicoS. I.RyanR. M. (2017). The emerging neuroscience of intrinsic motivation: a new frontier in self-determination research. Front. Hum. Neurosci.11, 145. 10.3389/fnhum.2017.00145

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  DweckC. S. (2006). Mindset: The New Psychology of Success. New York, NY: Random House.

  • Google Scholar

• 27

  EdyD. L.RifqiM. R. (2020). The effect of learning environment and learning motivation for vocational college’s employability skills. J. Phys. Conf. Ser.1511 (1), 012122. 10.1088/1742-6596/1511/1/012122

  • CrossRef
  • Google Scholar

• 28

  EichhorstW.RinneU. (2024). “School-to-work transitions and early career developments,” in Handbook on Labour Markets in Transition, eds. G. Bosch, S. Lehndorff, and J. Rubery (Cheltenham: Edward Elgar Publishing), 371–388.

  • Google Scholar

• 29

  EtikanI.MusaS. A.AlkassimR. S. (2016). Comparison of convenience sampling and purposive sampling. Am. J. Theor. Appl. Stat.5 (1), 1–4. 10.11648/j.ajtas.20160501.11

  • CrossRef
  • Google Scholar

• 30

  FitriyaniA.WahyudiW.KurniasihD. (2025). The effect of soft skills and self-efficacy on fresh graduate job readiness: mediation of work motivation. J. Manaj. Kepemimp. Supervisi Pendid.,10 (1), 331–346. 10.31851/jmksp.v10i1.18159

  • CrossRef
  • Google Scholar

• 31

  FraserB. J. (2023). The evolution of the field of learning environments research. Educ. Sci.13 (3), 257. 10.3390/educsci13030257

  • CrossRef
  • Google Scholar

• 32

  GeorgeD.MalleryP. (2019). IBM SPSS Statistics 25 Step by Step: A Simple Guide and Reference, 15th Edn.New York, NY: Routledge.

  • Google Scholar

• 33

  GoldmanZ. W.GoodboyA. K.WeberK. (2017). College students’ psychological needs and intrinsic motivation to learn: an examination of self-determination theory. Commun. Q.65 (2), 167–191. 10.1080/01463373.2016.1215338

  • CrossRef
  • Google Scholar

• 34

  GottfriedA. E. (1985). Academic intrinsic motivation in elementary and junior high school students. J. Educ. Psychol.77 (6), 631–645. 10.1037/0022-0663.77.6.631

  • CrossRef
  • Google Scholar

• 35

  HairJ. F.AndersonR. E.TathamR. L.BlackW. C. (1998). Multivariate Data Analysis, 5th Edn. Upper Saddle River, NJ: Prentice-Hall.

  • Google Scholar

• 36

  HairJ. F.BlackW. C.BabinB. J.AndersonR. E. (2009). Multivariate Data Analysis, 7th Edn.Upper Saddle River, NJ: Pearson Prentice Hall.

  • Google Scholar

• 37

  HairJ. F.BlackW. C.BabinB. J.AndersonR. E. (2014). Multivariate Data Analysis. 7th Edn Pearson new international edition. Harlow, Essex: Pearson Education.

  • Google Scholar

• 38

  Hair JrJ. F.HultG. T. M.RingleC. M.SarstedtM.DanksN. P.RayS. (2021). “Evaluation of reflective measurement models,” in Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook, (Cham: Springer International Publishing), 75–90.

  • Google Scholar

• 39

  HenselerJ.RingleC. M.SarstedtM. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci.43, 115–135. 10.1007/s11747-014-0403-8

  • CrossRef
  • Google Scholar

• 40

  HisaA.MohiddinF.SusantoH. (2024). The impact of youth leadership program on perceived employability skills: evidence from Brunei Darussalam. Asian Acad. Manag. J.29 (2), 133–164. 10.21315/aamj2024.29.2.6

  • CrossRef
  • Google Scholar

• 41

  HoidnS.KárkkáinenK. (2014). Promoting Skills for Innovation in Higher Education: A Literature Review on the Effectiveness of Problem-based Learning and of Teaching Behaviours (OECD Education Working Papers No. 100). Paris: OECD Publishing. 10.1787/5k3tsj67l226-en

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42

  HoidnS.KlemenčičM. (eds.). (2021). The Routledge International Handbook of Student-centered Learning and Teaching in Higher Education.Abingdon, UK:Routledge.

  • Google Scholar

• 43

  HuangX.HuY.ChenL. (2025, July). A three-stage exercise-oriented problem-based learning model for double-weak students in applied undergraduate education. Front. Educ.10, 1584982. 10.3389/feduc.2025.1584982

  • CrossRef
  • Google Scholar

• 44

  HungC.-T.WuS.-E.ChenY.-H.SoongC.-Y.ChiangC.-P.WangW.-M. (2024). The evaluation of synchronous and asynchronous online learning: student experience, learning outcomes, and cognitive load. BMC. Med. Educ.24, 326. 10.1186/s12909-024-05311-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  ImjaiN.ChansamranS.SungthongS.UsmanB.AujirapongpanS. (2025). Developing employability digital competencies of Thai Gen Z business students: the role and matter of digital learning environments and digital adaptation skills. Int. J. Manag. Educ.23 (3), 101219.

  • Google Scholar

• 46

  JacksonD. (2018). Developing graduate career readiness in Australia: shifting from extra-curricular internships to work-integrated learning. Int. J. Work-Integr. Learn.19 (1), 23–35.

  • Google Scholar

• 47

  KenaphoomS.NiyomvesB. (2023). Enhancing employability through work-integrated learning: an in-depth analysis of strategies, challenges and impacts on graduates’ career readiness. Asian Educ. Learn. Rev.1 (2), 27–47. 10.14456/aelr.2023.8

  • CrossRef
  • Google Scholar

• 48

  KrejcieR. V.MorganD. W. (1970). Determining sample size for research activities. Educ. Psychol. Meas.30 (3), 607–610. 10.1177/001316447003000308

  • CrossRef
  • Google Scholar

• 49

  LanH.BaileyR.TanW. H. (2024). Assessing the digital competence of in-service university educators in China: a systematic literature review. Heliyon10 (16), e35675. 10.1016/j.heliyon.2024.e35675

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50

  LhalloubiJ.RhomariL.IbnchahidF. (2024). The influence of experiential learning on the perceived employability of engineering students in their final year: the role of soft skills. Moroc. J. Quant. Qual. Res.6 (4), 49132. 10.48379/IMIST.PRSM/mjqr-v6i4.49132

  • CrossRef
  • Google Scholar

• 51

  LiH.NgoH. Y.ChuiH. (2023). The impact of future work self on perceived employability and career distress. Aust. J. Career Dev.32 (1), 5–13. 10.1177/10384162221140338

  • CrossRef
  • Google Scholar

• 52

  LiX.PuR.PhakdeephirotN. (2022). The influence of achievement motivation on college students’ employability: a chain mediation analysis of self-efficacy and academic performance. Front. Psychol.13, 972910. 10.3389/fpsyg.2022.972910

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  LisáE.SokolováL.JablonickáP.KardelisováL. (2023). Motivation to succeed is not enough: motivated students need to know how to plan/organize their steps on their way to success. Front. Psychol.14, 1119409. 10.3389/fpsyg.2023.1119409

  • CrossRef
  • Google Scholar

• 54

  LiuL.SaeedM. A.AbdelrasheedN. S. G.ShakibaeiG.KhafagaA. F. (2022). Perspectives of EFL learners and teachers on self-efficacy and academic achievement: the role of gender, culture and learning environment. Front. Psychol.13, 996736. 10.3389/fpsyg.2022.996736

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55

  LiuY.ZhangH.WangJ. (2025). Impact of industry–education integration on employment quality in higher vocational colleges. Educ. Sci.15 (10), 1316. 10.3390/educsci15101316

  • CrossRef
  • Google Scholar

• 56

  MahfudT.HidayatR. A.ThaibD.KholifahN.SariA. I. (2024). Let’s be an entrepreneur through education! the role of entrepreneurial attitude orientation and psychological capital among university students. Glob. Bus. Finance Rev.29 (8), 91–106. 10.17549/gbfr.2024.29.8.91

  • CrossRef
  • Google Scholar

• 57

  MatoreE. M.KhairaniA. Z. (2020). The pattern of skewness and kurtosis using mean score and logit in measuring adversity quotient (AQ) for normality testing. Int. J. Future Gener. Commun. Netw.13 (1), 688–702.

  • Google Scholar

• 58

  NiL. B.LiangL.HaibaoC. (2025). Learning management systems and pedagogical innovation: evidence from academic staff in China. Int. J. Res. Innov. Appl. Sci.10 (6), 1209–1214. 10.51584/IJRIAS.2025.10060090

  • CrossRef
  • Google Scholar

• 59

  NunnallyJ. C.BernsteinI. H. (1994). Psychometric Theory, 3rd Edn.New York, NY: McGraw-Hill.

  • Google Scholar

• 60

  ParkerS. K.BindlU. K.StraussK. (2010). Making things happen: a model of proactive motivation. J. Manage.36 (4), 827–856. 10.1177/0149206310363732

  • CrossRef
  • Google Scholar

• 61

  PetruzzielloG. (2022) Exploring perceived employability of university students and graduates. (Master’s thesis/Doctoral dissertation).

  • Google Scholar

• 62

  PodsakoffP. M.MacKenzieS. B.LeeJ. Y.PodsakoffN. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol.88 (5), 879.

  • Pubmed Abstract
  • Google Scholar

• 63

  RagunathanC. D. (2023) Work integrated learning in higher education: enhancing workplace readiness and graduate employability in Malaysia. (Doctoral dissertation). Victoria University, Melbourne, Australia.

  • Google Scholar

• 64

  ReeveJ. (2012). “A self-determination theory perspective on student engagement,” in Handbook of Research on Student Engagement, eds. S. L. Christenson, A. L. Reschly, and C. Wylie (Boston, MA: Springer US), 149–172.

  • Google Scholar

• 65

  SáM. J.SerpaS. (2018). Transversal competences: their importance and learning processes by higher education students. Educ. Sci.8 (3), 126.

  • Google Scholar

• 66

  SelvaratnamR. (2021). The link between learning spaces and employability outcomes. J. Learn. Spaces10 (2):48–53.

  • Google Scholar

• 67

  ShaoJ.GaoC.ZhouL. (2023). Current situation and construction of innovation mechanism of school-enterprise cooperation and collaborative education in higher vocational education based on machine learning. Adult High. Educ.5, 37–51.

  • Google Scholar

• 68

  ShinC.IdangJ. (2025). I-shape by shape: real-world STEM learning in Malaysia. Child. Educ.101 (3), 50–53. 10.1080/00094056.2025.2486917

  • CrossRef
  • Google Scholar

• 69

  ShinC.NiL. B.LeeR.ChinY. F. (2025). Making a global connection: learning about animals to learn about the world. Child Educ.101 (5), 56–59. 10.1080/00094056.2025.2552628

  • CrossRef
  • Google Scholar

• 70

  TabachnickB. G.FidellL. S. (2019). Using Multivariate Statistics, 7th Edn.Boston, MA: Pearson.

  • Google Scholar

• 71

  TavakolM.DennickR. (2011). Making sense of cronbach’s alpha. Int. J. Med. Educ.2, 53–55. 10.5116/ijme.4dfb.8dfd

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72

  Vázquez-RodríguezA.Quiroga-CarrilloA.García-ÁlvarezJ.Sáez-GambínD. (2025). Soft skills and the corporate social dimension: the perspective of university graduate employers. Educ. Res. Policy Pract.24, 1–29. 10.1007/s10671-025-09395-w

  • CrossRef
  • Google Scholar

• 73

  WattersJ.PillayH.FlynnM. (2016). Industry-school Partnerships: A Strategy to Enhance Education and Training Opportunities. Brisbane: Queensland University of Technology.

  • Google Scholar

• 74

  XinZ.AhmadS. B. B. S. (2024, October). “University-Enterprise collaboration in China: a thematic,” in Seminar Kebangsaan Majlis Dekan Pendidikan Universiti Awam 2024, 248.

  • Google Scholar

• 75

  XuL.SunQ. (2021). Digital skills gap in Chinese higher education: a curriculum reform perspective. Educ. Inf. Technol.26 (2), 1341–1357. 10.1007/s10639-020-10446-1

  • CrossRef
  • Google Scholar

• 76

  YangL.Mohamad NasrN. (2025). Research on the path of cultivating career development and employability of vocational school students under the school–enterprise collaboration model. Int. J. Acad. Res. Bus. Soc.15 (12), 2310–2321. 10.6007/IJARBSS/v15-i12/27135

  • CrossRef
  • Google Scholar

• 77

  YangZ.ShinC.SharifS. B. (2025). Classroom engagement in Chinese private colleges: the role of teacher-student interactions and peer relationships. Human. Soc. Sci. Lett.13 (3), 1130–1144. 10.18488/73.v13i3.4390

  • CrossRef
  • Google Scholar

• 78

  ZhangJ. (2023). Academic self-formation through problem-based learning: a case study of Chinese undergraduate students’ participation in the Dachuang programme. (MSc thesis). University of Oxford. 10.5287/ora-n1nkedvag

  • CrossRef
  • Google Scholar

• 79

  ZhangW. (2024) Goals and motivation: a study investigating the well-being of higher vocational education students in China. (Doctoral dissertation). University of Surrey, Guildford, UK.

  • Google Scholar

• 80

  ZhangY.-C.ZhangY.XiongX.-L.LiuJ.-B.ZhaiR.-B. (2022). An empirical study on the improvement of college students’ employability based on university factors. Front. Psychol.13, 793492. 10.3389/fpsyg.2022.793492

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81

  ZhangY.ChenX. (2023). Empirical analysis of university–industry collaboration in postgraduate education: a case study of Chinese universities of applied sciences. Sustainability15 (7), 6252. 10.3390/su15076252

  • CrossRef
  • Google Scholar

• 82

  ZhuangT.ZhouH. (2023). Developing a synergistic approach to engineering education: china’s national policies on university–industry educational collaboration.Asia Pac. Educ. Rev.24 (1), 145–165. 10.1007/s12564-022-09743-y

  • CrossRef
  • Google Scholar