The Mediating Role of Big Data Analysis Applications in the Relationship between Knowledge Management Competence and Quality Performance in Vocational Education Institutions

Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17346190 171 ISRG PUBLISHERS Abbreviated Key Title: Isrg J Econ Bus Manag ISSN: 2584-0916 (Online) Journal homepage: https://isrgpublishers.com/isrgjebm/ Volume – III Issue - V (September-October) 2025 Frequency: Bimonthly The Mediating Role of Big Data Analysis Applications in the Relationship between Knowledge Management Competence and Quality Performance in Vocational Education Institutions Sun Weijuan1*, Cheok Mui Yee2 1, 2 Universiti Tun Abdul Razak (UNIRAZAK), Malaysia 1WEIHAI VOCATIONAL COLLEGE | Received: 07.10.2025 | Accepted: 11.10.2025 | Published: 14.10.2025 *Corresponding author: Sun Weijuan Universiti Tun Abdul Razak (UNIRAZAK), Malaysia Abstract This study investigates how Big Data Analysis Applications (BDAA) mediate the relationship between Knowledge Management Technical Competence (KMTC) and Quality Performance (QP) in vocational education institutions amid ongoing digital transformation. Grounded in the Knowledge-Based View (KBV) and the Information System Success Model (ISSM), a quantitative research design was employed using survey data from 412 educators and administrators across Chinese vocational universities and colleges, analyzed through Structural Equation Modeling (SEM). The results reveal that KMTC positively influences both BDAA and QP, while BDAA exerts a significant direct impact on QP and partially mediates the KMTC–QP relationship, confirming the knowledge–analytics–performance (KAP) mechanism. These findings demonstrate that knowledge management competence enhances institutional quality primarily through the effective application of data analytics. The study extends KBV and ISSM by integrating analytics as a mediating mechanism within the educational management context and provides practical insights for vocational institutions seeking to strengthen data governance, analytics capability, and quality assurance systems in pursuit of sustainable digital transformation. Keywords: Knowledge Management Technical Competence ； Big Data Analysis Applications ； Quality Performance ； Vocational Education. Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17346190 172 1. Introduction In the era of global digital transformation, the integration of knowledge management and data analytics has become a driving force behind organizational competitiveness, innovation, and quality assurance (Cadden et al., 2023). Vocational education institutions, as vital contributors to national talent development and industrial upgrading, are undergoing rapid changes in their management and operation models due to the proliferation of big data technologies. New phenomena—such as data-driven decisionmaking, smart campus systems, and AI-based quality monitoring— are reshaping how institutions ensure service quality and institutional performance. However, beneath these transformations lies a critical question: how can vocational education institutions effectively translate their knowledge management and technical capabilities into measurable quality outcomes through data analytics? Therefore, exploring the mechanism linking knowledge management technical competence, big data analysis applications, and quality performance is both a necessary and urgent task in the context of digital education reform. Existing studies have examined the relationship between knowledge management and organizational performance from various theoretical perspectives, such as the Knowledge-Based View (KBV), Dynamic Capabilities Theory, and Information System Success Model (ISSM). Scholars have employed quantitative methods—including structural equation modeling and regression analysis—to explore how knowledge creation, sharing, and utilization affect innovation, efficiency, and quality improvement across manufacturing, service, and education sectors. The findings generally confirm that effective knowledge management enhances performance through technological support and data-driven decision-making(Al-Alawi & Al-Rashidi, 2024). However, several research gaps remain. First, few studies have empirically tested how knowledge management technical competence directly facilitates the application of big data analytics, especially within educational organizations. Second, the mediating role of big data analysis applications between knowledge management and quality performance has not been systematically validated. Third, prior research in the field of vocational education primarily focuses on teaching quality and employability outcomes, but lacks a managerial perspective integrating digital capability and quality management mechanisms. Based on these observations, this study investigates the mediating role of Big Data Analysis Applications (BDAA) in the relationship between Knowledge Management Technical Competence (KMTC) and Quality Performance (QP) within vocational education institutions. Specifically, it aims to answer the following questions: (1) How does knowledge management technical competence promote the adoption of big data analytics? (2) How do big data analysis applications enhance institutional quality performance? and (3) Does big data analytics act as a mediator transforming knowledge competence into quality outcomes? To address these questions, this research adopts a quantitative design using structural equation modeling (SEM) based on survey data collected from vocational educators and administrators. The findings are expected to enrich the theoretical integration of KBV and ISSM by elucidating the digital mechanism of quality enhancement, and to provide practical insights for vocational institutions seeking to build data-driven quality management systems. 2. Literature Review 2.1 Knowledge Management Technical Competence Knowledge management (KM) has long been regarded as a strategic asset for organizations seeking sustainable competitive advantage. Recent studies emphasize that the mere possession of knowledge resources is not enough; firms must develop technical competence in KM—specifically, the ability to acquire, store, integrate, and utilize knowledge effectively through technological systems and human–technology integration. Technical KM competence includes not only soft elements like organizational culture and incentives but also hard competencies such as system infrastructure, data architecture, taxonomy, knowledge indexing, and the embedding of knowledge through digital tools and systems. Research has shown that technological competence, when integrated with KM practices, significantly enhances sustainable organizational performance (Hussain, Amed, & Qureshi, 2024). In educational contexts, knowledge management is often split into technological systems (such as software platforms, AI-driven tools, and databases) and information systems (such as process models and communication flows) to support faculty, administrative staff, and knowledge workers—particularly within evolving ―smart campus‖ architectures that integrate big data, cloud computing, and IoT to enhance decision-making and learning efficiency (Ma, 2023). These systems work together to improve knowledge creation, sharing, and utilization in universities and educational institutions, enabling more adaptive and intelligent learning environments..However, empirical studies specifically examining knowledge management technical competence in vocational institutions remain scarce. Most extant literature focuses on knowledge sharing, knowledge creation, or knowledge transfer broadly, without distinguishing the technical infrastructure or systems competence dimension. 2.2 Big Data Analysis Applications and Organizational (or Educational) Performance The rise of big data analytics (BDA) has opened a new frontier for bridging knowledge management (KM) and organizational performance. Scholars increasingly view BDA capabilities and applications as key enablers of data-driven decision-making, operational efficiency, and innovation(Makhloufi et al., 2023). For example, Aljehani et al. (2024) demonstrate that organizations leveraging BDA to foster green innovation and effective KM practices can significantly enhance performance, with both green innovation and KM acting as mediators between BDA and performance outcomes (Aljehani et al., 2024). In the education sector, there is growing interest in how big data and AI technologies influence teacher effectiveness, student learning, and institutional outcomes. For instance, a study of Chinese private higher vocational colleges found that adoption of big data analytics and AI had a direct positive effect on teacher performance, particularly in areas such as instructional effectiveness and professional development, based on a survey of 750 teachers (Sun & Song, 2023). Similarly, Chowdhury et al. (2023) highlight how big data analytics is transforming higher education in Bangladesh by enabling improved decision-making, predictive modeling for student outcomes, and enhancing institutional quality (Chowdhury et al., 2023). These studies confirm that educational institutions are beginning to adopt data analytics for quality improvement, though the underlying mechanisms and contextual factors remain underexplored (Elam, 2024). Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17346190 173 2.3 Mediating Mechanisms and Research Gaps While many studies confirm a positive relationship between knowledge management or big data analytics and performance, the mechanistic pathway—how exactly technical competence in KM translates into improved performance via analytics—has received less empirical scrutiny. The Information Systems Success Model (ISSM), recently applied in university settings, suggests that system quality, information quality, and user satisfaction can drive net benefits and decision-making effectiveness, especially when integrated into analytics frameworks (Reascos-Garrido et al., 2025). However, few studies have empirically tested this kind of mediation in educational or vocational institutions. Some studies in organizational contexts examine mediation by business model innovation (Jenkison et al., 2024) or green innovation (Aljehani et al., 2024), but these are domain-specific and not necessarily focused on KM technical competence.In the higher education domain, a recent study ―Adoption of Big Data Analytics and Its Impact on Organizational Performance in Higher Education Mediated by Knowledge Management‖ begins to probe related mediating processes, but focuses more on general KM rather than the technical competence dimension. Therefore, this study has identified three research gaps: Gap 1: Lack of empirical research isolating the technical competence dimension of knowledge management (versus general KM) and examining its direct effect on big data analytics adoption in educational institutions. Gap 2: Insufficient validation of mediation role of big data analysis applications between KM competence and quality/performance outcomes. Gap 3: Limited studies in vocational education / educational institutions context combining KM, BDA, and performance (especially quality performance) into a unified empirical model. 2.4 Conceptual Framework Figure 1：Research Framework Diagram 3. Research Design 3.1 Research Approach This study employs a quantitative, cross-sectional research design to examine how Big Data Analysis Applications (BDAA) mediate the relationship between Knowledge Management Technical Competence (KMTC) and Quality Performance (QP) in vocational education institutions. Given the objective of testing causal relationships among latent variables, the survey method was selected as the primary data collection strategy. Structural Equation Modeling (SEM) using SPSS and AMOS was adopted to evaluate the measurement and structural models. 3.2 Population and Sampling The study targeted teachers, academic managers, and IT administrators from vocational colleges and vocational universities across China who are actively involved in quality management and digital transformation initiatives. A stratified random sampling method was employed to ensure representation across institutional types (college/university) and regions (East, Central, West China). A total of 500 questionnaires were distributed electronically via institutional networks, yielding 412 valid responses after excluding incomplete or inconsistent data. This sample size satisfies the recommended ratio of 10 respondents per estimated parameter for SEM. 3.3 Measurement Instruments All constructs in this study were measured using validated multiitem scales adapted from prior research. Each item was rated on a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), reflecting the respondents’ level of agreement with each statement. The measurement items were slightly modified to suit the context of vocational education institutions while retaining the original conceptual integrity of the constructs. The theoretical foundations and dimensional structures of the variables are summarized in Table 1. Table 1：Theoretical Foundations and Dimensional Structure of the Study Constructs Construct Theoretical Dimensions KMTC (Knowledge Management Technical Competence) Knowledge Management Theory； Knowledge-Based View Knowledge acquisition, storage, integration, utilization BDAA (Big Data Analysis Applications) Information System Success Model； Technology Acceptance Model Data integration, predictive analytics, decision support QP (Quality Performance) Total Quality Management (TQM) Theory；Organizational Performance Theory Product/service quality, process optimization, stakeholder satisfaction Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17346190 174 3.4 Data Analysis Process The data analysis process was carried out in three stages to ensure rigor and validity. First, preliminary data screening was performed using SPSS 27.0 to detect missing values, outliers, and assess normality. Descriptive statistics, including means, standard deviations, and frequency distributions, were computed to summarize respondents’ demographic characteristics and confirm data suitability for subsequent analysis. Next, the measurement model was assessed to evaluate reliability and validity. Internal consistency was examined through Cronbach’s α and Composite Reliability (CR), both exceeding the recommended threshold of 0.70. Convergent validity was confirmed when the Average Variance Extracted (AVE) values exceeded 0.50, while discriminant validity was established using the Fornell–Larcker criterion and the HTMT ratio (< 0.85). Model fit was evaluated through Confirmatory Factor Analysis (CFA) with acceptable indices (χ²/df < 3, CFI ≥ 0.90, TLI ≥ 0.90, RMSEA ≤ 0.08). Finally, the structural model was tested using AMOS 26.0 to verify the hypothesized relationships among KMTC, BDAA, and QP. The bootstrapping method (5,000 resamples) with 95% confidence intervals was applied to examine the mediating role of BDAA. This multi-step procedure ensured the reliability, validity, and robustness of the empirical results. 4. Data Analysis and Results 4.1 Descriptive Statistics A total of 412 valid responses were collected from teachers, administrators, and IT staff working in vocational education institutions across China. The demographic characteristics of the respondents are summarized in Table 2. The sample consisted of 223 males (54.1%) and 189 females (45.9%), indicating a relatively balanced gender distribution. In terms of professional position, 245 respondents (59.5%) were fulltime lecturers or instructors, 112 (27.2%) were middle-level administrators (such as department heads or program coordinators), and 55 (13.3%) held senior administrative or managerial positions. Regarding years of professional experience, 118 participants (28.6%) had less than 5 years of experience, 167 (40.5%) had between 6 and 10 years, and 127 (30.8%) had more than 10 years of working experience in vocational education. With respect to educational qualification, 296 respondents (71.8%) held a bachelor’s degree, 96 (23.3%) held a master’s degree, and 20 (4.9%) possessed a doctoral degree. The respondents represented a broad regional distribution, including eastern (36.9%), central (33.0%), and western (30.1%) provinces of China. This diverse composition provides a comprehensive view of vocational institutions across different developmental contexts. Table 2：Demographic Profile of Respondents (N = 412) Variable Category Frequency Percentage (%) Gender Male 223 54.1 Female 189 45.9 Position Lecturer/Instructor 245 59.5 Mid-level Administrator 112 27.2 Senior Administrator 55 13.3 Years of Experience < 5 years 118 28.6 6–10 years 167 40.5 > 10 years 127 30.8 Educational Qualification Bachelor’s Degree 296 71.8 Master’s Degree 96 23.3 Doctoral Degree 20 4.9 Region Eastern China 152 36.9 Central China 136 33 Western China 124 30.1 4.2 Reliability and Validity Analysis To ensure the accuracy and internal consistency of the measurement model, both reliability and validity tests were conducted using SPSS 27.0 and AMOS 26.0. Internal consistency reliability was first examined through Cronbach’s alpha (α) and Composite Reliability (CR) values for each construct. All Cronbach’s α values ranged from 0.88 to 0.91, and CR values ranged from 0.90 to 0.93, exceeding the commonly accepted threshold of 0.70, thus indicating excellent internal reliability. Convergent validity was then evaluated by calculating the Average Variance Extracted (AVE). As shown in Table 3, AVE values for all constructs were above 0.70, surpassing the recommended minimum of 0.50 (Fornell & Larcker, 1981). This suggests that the observed items strongly represent their respective latent constructs. Furthermore, factor loadings for all items ranged between 0.73 and 0.89, demonstrating satisfactory item reliability and confirming that each item contributed meaningfully to its construct. Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17346190 175 Table 3. Reliability and Validity Statistics of the Measurement Model Construct Cronbach’s α CR AVE Factor Loading Range KMTC 0.89 0.91 0.72 0.74–0.88 BDAA 0.91 0.93 0.76 0.78–0.89 QP 0.88 0.9 0.7 0.73–0.87 4.3 Model Fit and Hypothesis Testing The structural model was evaluated using Structural Equation Modeling (SEM) with AMOS 26.0 to examine the hypothesized relationships among Knowledge Management Technical Competence (KMTC), Big Data Analysis Applications (BDAA), and Quality Performance (QP). Prior to hypothesis testing, the model’s overall fit was assessed to ensure that the empirical data adequately represented the proposed conceptual framework. As presented in Table 4, the model demonstrated a satisfactory level of goodness-of-fit according to widely accepted statistical benchmarks (Hair et al., 2019). Specifically, the ratio of chi-square to degrees of freedom (χ²/df = 2.47) was below the recommended threshold of 3.00, indicating acceptable model parsimony. The Comparative Fit Index (CFI = 0.94), Tucker–Lewis Index (TLI = 0.92), and Goodness-of-Fit Index (GFI = 0.91) all exceeded the cutoff value of 0.90, while the Root Mean Square Error of Approximation (RMSEA = 0.059) was below 0.08. These indices collectively confirm that the proposed model fits the observed data well. Table 4. Model Fit Indices of the Structural Model Fit Index Recommended Threshold Observed Value Model Evaluation χ²/df < 3.00 2.47 Good fit CFI ≥ 0.90 0.94 Good fit TLI ≥ 0.90 0.92 Good fit GFI ≥ 0.90 0.91 Acceptable fit RMSEA ≤ 0.08 0.059 Good fit Following model validation, hypothesis testing was conducted to examine the direct and indirect relationships among the constructs. The results of the structural path analysis are summarized in Table 5. All three hypothesized relationships were found to be statistically significant at the 0.05 level or better. The path from KMTC to BDAA was strong and positive (β = 0.73, p < 0.001), confirming that institutions with higher levels of knowledge management technical competence tend to adopt and utilize big data analytics more effectively (H1 supported). The path from BDAA to QP was also significant (β = 0.62, p < 0.001), indicating that effective application of big data analytics enhances institutional quality performance (H2 supported). The direct path from KMTC to QP remained positive but weaker (β = 0.21, p = 0.032), suggesting a partial mediation effect of BDAA (H3 partially supported). To further confirm the mediating role of BDAA, a bootstrapping analysis with 5,000 resamples was conducted using bias-corrected 95% confidence intervals. The indirect effect of KMTC on QP through BDAA was significant (β = 0.45, p < 0.001), as the confidence interval did not include zero, supporting the hypothesized mediation mechanism. These results suggest that big data analysis applications play a critical mediating role in transforming knowledge management competence into measurable quality outcomes in vocational education institutions. Table 5. Structural Path Coefficients and Hypothesis Testing Results Hypothesis Path Standardized β p-value Result H1 KMTC → BDAA 0.73*** < 0.001 Supported H2 BDAA → QP 0.62*** < 0.001 Supported H3 KMTC → QP (Direct) 0.21* 0.032 Partially Supported KMTC → BDAA → QP (Indirect) 0.45* < 0.001 Supported (Mediation) Note：***p < 0.001, **p < 0.01, *p < 0.05. Standardized coefficients are reported. Bootstrap resampling (5,000 iterations) confirmed the significance of indirect effects at the 95% confidence level. The results confirm that Knowledge Management Technical Competence (KMTC) serves as a key driver of Big Data Analysis Applications (BDAA), which in turn significantly enhances Quality Performance (QP). The partial mediation effect indicates that while technical competence directly influences quality performance, the use of big data analytics amplifies this effect by enabling data-driven decision-making, process optimization, and continuous improvement. These findings align with the Knowledge-Based View (KBV) and Information System Success Model (ISSM), reinforcing the argument that integrating knowledge capabilities with data analytics is essential for Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17346190 176 achieving sustainable quality performance in vocational education institutions. 4.4 Mediation Analysis and Discussion To further verify the mediating mechanism proposed in this study, the bootstrapping procedure with 5,000 resamples was employed to examine the indirect effect of Knowledge Management Technical Competence (KMTC) on Quality Performance (QP) through Big Data Analysis Applications (BDAA). The bias-corrected 95% confidence interval of the indirect effect did not include zero (β = 0.45, p < 0.001), confirming that BDAA significantly mediates the relationship between KMTC and QP. The magnitude of the indirect effect was more substantial than that of the direct path (β = 0.21, p = 0.032), suggesting a partial mediation effect. This finding indicates that while knowledge management competence directly contributes to quality performance, a considerable portion of its impact is transmitted through the effective application of big data analytics. 5. Discussion 5.1 Discussion of Key Findings The empirical results of this study provide strong support for the hypothesized model linking Knowledge Management Technical Competence (KMTC), Big Data Analysis Applications (BDAA), and Quality Performance (QP). In alignment with the KnowledgeBased View (KBV), the positive direct effect from KMTC to BDAA (β = 0.73, p < 0.001) demonstrates that vocational institutions possessing robust technical infrastructure and effective knowledge management systems are more likely to adopt and integrate data analytics into their processes. This finding resonates with systematic research conclusions—big data analytics application (particularly when underpinned by robust knowledge management infrastructure) correlates positively with innovation performance and operational excellence (Makhloufi et al., 2023). The significant path from BDAA to QP (β = 0.62, p < 0.001) indicates that the adoption and application of big data analytics meaningfully contributes to institutional quality improvements— including service consistency, process optimization, and stakeholder satisfaction. This echoes findings in educational settings that integrating analytics in decision-making and operations enhances institutional outcomes (Fan et al., 2024). Crucially, the mediation analysis confirms that BDAA partially mediates the relationship between KMTC and QP: the indirect effect (β = 0.45, p < 0.001) is substantive, while the direct effect (β = 0.21, p = 0.032) remains significant but attenuated. This indicates that knowledge management technical competence contributes to quality not just directly (through better coordination, organizational learning, and process control), but more powerfully by enabling data-driven pathways. In effect, BDAA serves as a mechanistic bridge that converts technical knowledge assets into quality gains—a result consistent with the logic of the Information System Success Model (ISSM) when extended into the analytics domain. 5.2 Theoretical Implications This study contributes to theory in several ways. First, it extends KBV by integrating analytics as a mediating mechanism, showing that technical knowledge capacity alone is insufficient—without analytics, knowledge remains latent rather than transformative. In this regard, the ―knowledge–analytics–performance‖ (KAP) chain provided here offers a novel operational pathway for knowledge to drive institutional outcomes in the digital era. Second, this research validates and extends the ISSM framework within a vocational education context by placing BDAA at the center of converting system and information quality into net benefits (quality performance). As recent studies in higher education information systems have shown, integrating analytics functionalities in learning management systems and decision support modules strengthens the explanatory power of ISSM. Third, focusing on vocational education—which is often underexamined in digital education research—this study demonstrates that the same knowledge-analytics-performance logic applies beyond business or higher education settings. It enriches the cross-domain applicability of KM and analytics theories, and underscores that institutions regardless of sector must integrate knowledge systems with data capabilities for sustainable quality outcomes. 5.3 Practical Implications From a managerial standpoint, the results suggest that vocational education institutions should treat knowledge management infrastructure and analytics capability as twin pillars of digital governance and quality systems. First, investments should target interoperable data and knowledge platforms (e.g. unified knowledge bases, data repositories, analytics workbenches) to reduce fragmentation and facilitate cross-sectional data flows. Second, institutions should elevate analytics literacy and data competency among staff and faculty. Training programs should center on data interpretation, predictive modeling, and visualization to bridge the gap between raw data and meaningful decisions. In educational quality assurance contexts, analytics engines are increasingly being used to monitor course effectiveness, student progression, and operational risks. Third, fostering interdepartmental collaboration is essential. Data engineers, curriculum designers, and quality assurance units must co-operate to translate analytic outputs into actionable interventions. Embedding analytics in curricular review, teacher evaluation, and student support loops can create a continuous quality feedback mechanism. 5.4 Policy Implications and Future Research Directions At the policy level, educational authorities should encourage and support the institutionalization of the knowledge–analytics–quality chain in vocational sectors. This could entail funding incentives for analytics infrastructure, establishing interoperability standards, and regulating data governance and ethics frameworks. Policies aligned with national digital education strategies should emphasize analytics-driven quality assurance as a benchmark of institutional modernization. For future research, scholars are encouraged to pursue longitudinal or mixed-method studies to capture the dynamic evolution of the KAP chain over time. Comparative studies across educational levels (vocational vs. higher education) may reveal contextual moderations. Incorporating moderating variables (such as institutional culture, leadership support, or innovation climate) and objective outcome metrics (e.g., accreditation data, graduation rates) would strengthen causal claims and external validity. 6. Conclusion This study investigated how Big Data Analysis Applications (BDAA) mediate the relationship between Knowledge Management Technical Competence (KMTC) and Quality Performance (QP) within vocational education institutions Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17346190 177 undergoing digital transformation. Drawing upon the KnowledgeBased View (KBV) and the Information System Success Model (ISSM), the research provided empirical evidence for a knowledge–analytics–performance (KAP) mechanism, demonstrating that the transformation of technical knowledge capabilities into measurable quality outcomes depends largely on the effective application of big data analytics. The findings revealed three critical insights. First, institutions with higher KMTC levels—characterized by strong capabilities in knowledge acquisition, storage, integration, and utilization—are significantly more likely to implement data analytics effectively. Second, BDAA directly enhances quality performance by supporting evidence-based decision-making, process optimization, and stakeholder satisfaction. Third, the mediating role of BDAA indicates that knowledge management alone does not guarantee improved quality; instead, data-driven analytics serve as a catalyst that converts knowledge resources into actionable improvements in organizational quality. Theoretically, this study contributes to extending KBV into the data analytics domain, providing a concrete pathway through which knowledge resources are operationalized via digital tools. It also reinforces the applicability of the ISSM in educational contexts by emphasizing system quality and information use as precursors to institutional performance. Practically, the results highlight the necessity for vocational institutions to invest in interoperable data infrastructures, strengthen staff analytics capabilities, and institutionalize data governance frameworks that facilitate cross-departmental collaboration and continuous improvement. Despite its contributions, this research is not without limitations. The cross-sectional design restricts causal inference, and data collection was limited to Chinese vocational institutions, which may constrain generalizability. Future research could employ longitudinal or comparative cross-national designs to examine the evolution of the KMTC–BDAA–QP mechanism over time and across diverse educational systems. Additionally, incorporating moderating variables such as institutional culture, leadership support, or innovation climate could deepen understanding of contextual influences on this mechanism. 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