Constraints, mechanisms, and strategies for industry-education integration in vocational education: An empirical study

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 1 / 18 OPEN ACCESS Citation: Yu B, Li R, Zhang S, Mao P, Xu C, Shi J, et al. (2025) Constraints, mechanisms, and strategies for industry-education integration in vocational education: An empirical study. PLoS One 20(12): e0339158. https://doi.org/10.1371/journal.pone.0339158 Editor: Han Lin, Nanjing Audit University, CHINA Received: August 29, 2025 Accepted: November 29, 2025 Published: December 18, 2025 Copyright: © 2025 Yu et al . This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data availability statement: All relevant data are within the paper and its Supporting information files. Funding: We declare that this research received no additional external funding beyond the sup- port from the projects (the Education Science Planning Project of Shenzhen [grant number RESEARCH ARTICLE Constraints, mechanisms, and strategies for industry-education integration in vocational education: An empirical study Bo Yu1, Ruipu Li 2*, Siyuan Zhang3, Peng Mao4, Chun Xu2, Jing Shi1, Gang Wu1 1 School of Construction Engineering, Shenzhen Polytechnic University, Shenzhen, China, 2 Quality Assurance Center, Shenzhen Polytechnic University, Shenzhen, China, 3 School of Construction Engineering, Guangzhou Vocational University of Science and Technology, Guangzhou, China, 4 College

of Civil Engineering, Nanjing Forestry University, Nanjing, China * liruipu@szpu.edu.cn Abstract Identifying the constraints hindering industry-education integration (IEI) in vocational education is a critical prerequisite for promoting its high-quality development. How- ever, existing research has yet to establish a systematic solution to this core issue. Therefore, this study takes Shenzhen, China, as a case study and adopts an empiri- cal research approach to construct an analytical framework of “factor identification-mechanism analysis-strategy formulation,” aiming to thoroughly inves- tigate the key constraints on IEI development in vocational education. First, based on a literature review and questionnaire survey, the study identifies 12 critical con- straining factors of IEI. Subsequently, through expert interviews and Interpretative Structural Modelling (ISM), it reveals the underlying mechanisms of these constraints, classifying the 12 factors into five hierarchical levels. These factors form 16 constraint pathways, with inadequate policies, weak foundations for school-enterprise collabora- tion, insufficient institutional commitment, and ineffective communication mechanisms identified as critical bottom-level factors. These indirectly influence intermediate factors such as hardware/software conditions, alignment of talent cultivation philos- ophies, education-market demand matching, collaboration channel efficiency, and stakeholder interest fulfillment. Consequently, they diminish corporate engagement

enthusiasm, blur responsibility-rights boundaries between stakeholders, and dampen faculty participation motivation, ultimately impeding IEI progress. Finally, the study proposes targeted countermeasures, emphasizing tripartite collaboration among the government, vocational institutions, and enterprises. Key strategies include strength- ening legal safeguards, consolidating industry-education collaboration foundations, and proactively fulfilling educational responsibilities. The findings resolve the dilem- mas constraining IEI development, providing policymakers with evidence-based ref- erences and offering practical guidance for deeper collaboration between vocational

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 2 / 18 institutions and enterprises. This study holds significant theoretical and practical value for advancing high-quality vocational education. 1. Introduction With the rapid development of globalization and technology, industrial structure and economic forms are undergoing profound changes [1,2]. Traditional education models can no longer meet the demand for highly skilled talent in modern society, especially against the backdrop of economic transformation and upgrading, making vocational education increasingly important [3,4]. In China, IEI has been elevated to a national strategic height: it is explicitly positioned as a core measure in the “National Vocational Education Reform Implementation Plan” (2019) to address the mismatch between talent supply and industrial demand, and further emphasized in the “14th Five-Year Plan for Vocational Education Development” (2021–2025) as a key path- way to build a skills-based society and support high-quality economic development. Against this macro backdrop, deepening IEI has become not only a requirement for vocational education reform but also a critical support for China’s industrial upgrad- ing and sustainable economic growth. As an important avenue for cultivating skilled technical talent, vocational education plays a crucial role in supporting social and economic development [5,6]. Industry-education integration (IEI), as a key strategic measure to promote vocational education, refers to the deep cooperation between

education and industry, aiming to align educational content closely with industry needs to enhance educational quality and ensure precise talent development [7]. Currently, IEI has become a comprehensive institutional arrangement for vocational education reform and talent resource development. However, since IEI is a complex system project involving multiple stakeholders (such as the government, universities, and enterprises), its development faces numerous challenges [8]. These include insufficient policy support, an unreasonable management system, underdeveloped IEI platforms, imperfect resource allocation mechanisms, and low levels of talent sharing between universities and enterprises [9–11]. Under these constraints, the role of the government and social organizations in the integration process of vocational education is limited, and the channels for university-enterprise collaboration are not smooth enough. Furthermore, the conflict between the public welfare nature of universities and the profit-oriented demands of enterprises often leads to disputes over property rights, profit distribution, and responsibility allocation, which severely hinders the effective implementation of voca- tional education and IEI [12–14]. Therefore, it is necessary to explore the reasons restricting the development of vocational education and IEI at the current stage and propose targeted and effective countermeasures to promote the high-quality develop- ment of IEI in vocational education. Current research on IEI in vocational education has garnered significant academic

attention. A systematic review of existing studies reveals that scholarly focus has primarily centered on integration models, evaluation systems, talent cultivation, and yb24026]; the Foundation for University- Industry Collaborative Education Program, PRC [grant number 2023122865621]; the Shenzhen Polytechnic University Fund [grant number 7023310184]; the Research Projects of Department of Education of Guangdong Province [grant number 2023WCXTD037]; and the Guangdong Provincial Higher Vocational Education Teaching Reform Research and Practice Project [grant number 2023JG355]). Competing interests: The authors have declared that no competing interests exist.

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 3 / 18 influencing factors. Regarding integration models, extant research has examined Germany’s “dual system,” America’s “cooperative education,” and Australia’s “training package” approaches [15–18]. Some scholars have attempted to trans- plant these models to other contexts [19,20]. For instance, Hong investigated the applicability of Germany’s dual system in China, finding direct adoption problematic without contextual adaptation and innovation [21]. Consequently, researchers have proposed context-specific integration models accounting for regional and industrial particularities. Notable examples include Li et al.’s framework for China’s water conservancy sector, featuring physical hybrid, biological grafting, chemical compounding, and ecological symbiosis models, demonstrating the necessity of localized model development [22]. Regarding evaluation systems, existing research indicates that developed countries generally emphasize the assess- ment of IEI, albeit with varying evaluation approaches and requirements across nations [7]. Germany employs a dual eval- uation mechanism where vocational colleges develop quality rating scales combining internal and external assessments [23]. The United States prioritizes effective articulation between vocational education and industrial sectors, mandating state-level development of evaluation plans and performance indicators [24]. In Japan, quality evaluation constitutes a

statutory obligation for vocational institutions, implemented through combined self-evaluation and external review pro- cesses [25]. Furthermore, scholars have developed diverse evaluation frameworks using distinct methodologies: Chen et al. established an urban IEI index system by applying modified fuzzy linguistic approaches based on the Context- Input-Process-Product model [26]; Gong constructed a performance evaluation model grounded in coupling coordination theory, incorporating the Structure-Conduct-Performance paradigm from industrial organization theory [8]; while Qi and Feng proposed an effectiveness evaluation model utilizing backpropagation neural networks [27]. Regarding talent cultivation, existing research has primarily focused on addressing the question of “how to develop high-quality vocational talents through IEI.” These studies have proposed recommendations and strategies to enhance talent cultivation quality, mainly from perspectives of curriculum systems, teaching models, and instructional methods [28]. For instance, Laine et al. argued that vocational institutions should leverage their unique strengths to establish talent cultivation curriculum systems, continuously enrich teaching models, and adopt diversified pedagogical approaches within IEI [29]. Whittle and Hutchinson emphasized the need to prioritize the coordination between social development and voca- tional education advancement, suggesting that vocational colleges should design hierarchical curriculum systems, imple-

ment reasonable assessment methods for internship programs, and balance student engagement with corporate interests to stimulate enterprise motivation and ensure talent cultivation quality [30]. Furthermore, discipline-specific investigations have been conducted, such as Ren et al.’s exploration of objectives and models for geology talent cultivation, which iden- tified directions for geological education reform and proposed improvements through new training programs and innova- tions in teaching content and methodologies [31]. Regarding influencing factors, scholars have investigated key determinants affecting the implementation effectiveness of IEI. For instance, Pan and Zhang contend that the ultimate outcomes of vocational education integration depend not on isolated factors but rather on the synergistic interplay of multiple elements, including institutional capacity of vocational colleges, government intervention, and corporate engagement [32]. Thune emphasizes that successful integration hinges on four critical dimensions: goal-aligned partnerships, robust collaboration mechanisms, conflict-resolution communication competencies, and sustainable funding [33]. Furthermore, given that integration should be grounded in stakeholder needs, some studies adopt specific stakeholder perspectives to examine participation willingness and its determinants. A case in point is Zheng et al., who surveyed 184 hydrogen energy enterprises to analyze how resource benefits, economic costs,

and policy subsidies influence corporate engagement willingness [34]. Their findings reveal that universities’ provision of high-quality resources significantly enhances corporate participation propensity. Current research has effectively promoted the development of IEI in vocational education to some extent, but there are still certain limitations. First, many scholars primarily focus on specific issues that need to be addressed during the imple- mentation of IEI, such as which model to adopt or what kind of evaluation system to construct. However, few scholars have conducted systematic and in-depth research on the difficulties existing in the development of IEI, resulting in a lack

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 4 / 18 of corresponding solutions and measures. Second, although some scholars have conducted research on the challenges in the development of IEI in vocational education, these studies mainly propose countermeasures from a macro perspec- tive based on specific cases. Due to the lack of in-depth analysis of the underlying causes restricting the development of IEI, the proposed countermeasures may deviate from actual situations, potentially leading to counterproductive outcomes. Therefore, it is necessary to explore the factors and mechanisms restricting the development of IEI in vocational educa- tion in greater depth, in order to propose targeted countermeasures that will help overcome the current challenges in the development of IEI. To address this identified research gap, this study selects Shenzhen, China as an empirical case to explore the con- straining factors and underlying mechanisms hindering the integration of vocational education and industry, while proposing effective countermeasures. Shenzhen serves as an exemplary research context due to its dual identity as both a global manufacturing hub and a national pilot city for educational reform. As a national pilot, Shenzhen has formulated a multi- tiered policy framework for vocational education IEI, anchored by documents such as Shenzhen Three-Year Action Plan for High-Quality Development of Technical and Vocational Education (2023–2025), the draft Shenzhen Special Economic Zone

Vocational Education Regulations, Shenzhen Work Plan for Improving the Lifelong Vocational Skills Training System, and Shenzhen Measures for Vocational Skills Training Subsidies. Specifically, the Three-Year Action Plan explicitly mandates the construction of 10 industry colleges with benchmark enterprises and 20 model industry-education integration training bases by 2025, while stipulating that enterprises participating in collaboration are eligible for fiscal, financial, and tax incentives. The draft Vocational Education Regulations further clarifies the legal obligations and incentive mechanisms for enterprise partic- ipation in IEI, and the Training Subsidies Measures provides tangible financial support for enterprises engaged in vocational skills training, forming a policy system covering institutional guarantees, resource input, and benefit incentives. In addition, Shenzhen hosts a concentration of high-tech industries (e.g., Huawei, Tencent) and vocational institutions, presenting a microcosm of both tensions and synergies in workforce supply-demand dynamics. The city’s rapid industrial upgrading and explicit policy mandates necessitate close industry-education collaboration [35]. However, persistent challenges, including curriculum misalignment with Industry 4.0 skill requirements and uneven enterprise participation – mirror systemic issues commonly faced by emerging economies. Consequently, using Shenzhen as an empirical case offers transferable insights for other similar emerging economies that aim to enhance vocational education-industry integration.

To achieve the research objectives, this study first employed a literature review and questionnaire surveys to identify the constraining factors affecting the development of IEI in vocational education. Then, by using expert interviews and Interpretative Structural Modelling (ISM), the study elucidated the mechanisms that restrict the development of IEI and reveals specific pathways of constraint. Finally, response strategies to promote the integrated development of industry- education collaboration in vocational education were proposed from three key stakeholders: the government, vocational institutions, and enterprises. 2. Research process and methods Since existing research has not yet systematically explored the constraining factors and mechanisms of IEI, this study draws upon research methodologies concerning influencing factors and mechanisms from other related fields to fill this gap [36–38]. Through synthesis, their research processes can be categorized into three distinct phases: (1) identifying the potential constraining factors, (2) determining the critical constraining factors, and (3) analyzing the constraining mecha- nisms. Consequently, this study adopts the same three-phase research framework, wherein the research methods used at each stage, along with the corresponding outcomes, are illustrated in Fig 1. 2.1. Identifying the potential constraining factors The literature review is widely acknowledged as an effective method for identifying potential factors and has been extensively

utilized in contemporary research [37,39–41]. This study also employs a literature review to identify the potential factors that

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 5 / 18 constrain the development of IEI in vocational education. Specifically, the study draws on research findings related to IEI, sourced from major databases such as ScienceDirect and Web of Science, to establish a comprehensive foundation for the review. Initially, relevant literature was collected using keywords such as “industry-education integration,” “integration of industry and education,” “school-enterprise cooperation,” and “school-enterprise collaboration.” The selected literature was systematically reviewed, resulting in the identification of 13 potential factors that hinder the development of IEI. These factors include: the completeness of policies, the willingness of enterprises to cooperate, the alignment of talent cultivation quality, the level of attention given by universities, the degree of teacher involvement, the extent of student engagement, the ade- quacy of hardware and software facilities, the differences in talent cultivation philosophies, the achievement of school- enterprise mutual interests, the clarity of school-enterprise responsibilities and rights, the effectiveness of school-enterprise communication, the closeness of prior university-enterprise collaboration, and the fluidity of school-enterprise cooperation channels. The definitions and sources of these potential constraining factors are provided in Table 1. 2.2. Determining the critical constraining factors

Determining the critical constraining factors is essential for unraveling the underlying mechanisms that impede the devel- opment of IEI. A questionnaire survey, as a widely used method for identifying critical factors in contemporary research [37,51,52], was consequently adopted in this study. Specifically, the survey focuses on stakeholders who are closely involved in the integration of vocational education and industry, including government agencies, vocational educational institutions, and enterprises, all based in Shenzhen, China. The questionnaire comprises two sections. The first section collects fundamental demographic data regarding the respondents, including gender, age, education level, profession, years of work experience, and their understanding their comprehension of IEI within the context of vocational education. This section aims to ensure that the selection of respondents is both scientifically valid and representative. The second section comprises items designed to measure the degree of constraint imposed by the identified potential constraining factors, as detailed in Table 2. A total of 13 items are included, each corresponding to one of the 13 previously identified potential constraining factors. Furthermore, this study utilizes a five-point Likert scale to assess the level of constraint associated with each factor. Respondents are asked to rate each item on a scale from “1” to “5,” corresponding to “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree,” respectively. The survey was conducted from

October 2023 to February 2024, utilizing both online and offline methods for distribution and data collection. In total, 624 questionnaires were collected, with 534 valid responses. Fig 1. The research process and methods. https://doi.org/10.1371/journal.pone.0339158.g001

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 6 / 18 Table 1. Definitions and sources of potential constraining factors. No. Potential constraining factors Definitions Sources 1 The completeness of policies The degree of perfection in policies, regulations, standards, and norms related to IEI. [34,42,43] 2 The willingness of enterprises to cooperate Enterprises’ willingness to engage in IEI with higher education institutions. [13,34] 3 The alignment of talent cultivation quality The degree of alignment between the quality of talent cultivation in higher educa- tion institutions and enterprise employment requirements. [42,44] 4 The level of attention given by universities The degree of emphasis higher education institutions place on implementing IEI. [32] 5 The degree of teacher involvement The willingness and enthusiasm of faculty members to participate in IEI. [34] 6 The extent of student engagement The willingness and enthusiasm of students to participate in IEI. [27,30] 7 The adequacy of hardware and software facilities The degree of perfection in software/hardware facilities (e.g., faculty, training venues, equipment) during IEI. [7,14] 8 The differences in talent cultivation philosophies The degree of difference between higher education institutions and enterprises regarding vocational education talent cultivation concepts. [45] 9 The achievement of school-enterprise mutual interests The degree to which the interests and expectations of higher education institu- tions and enterprises are achieved through IEI. [13,34,46]

10 The clarity of school-enterprise responsi- bilities and rights The degree of clarity in the rights, responsibilities, and obligations of higher edu- cation institutions and enterprises in IEI. [12,13,24] 11 The effectiveness of school-enterprise communication The degree of effectiveness in communication and interaction between higher education institutions and enterprises during IEI. [42,47,48] 12 The closeness of prior university- enterprise collaboration The degree of closeness in collaboration between higher education institutions and enterprises prior to implementing IEI. [49] 13 The fluidity of school-enterprise cooperation channels The degree of smoothness in collaboration channels between higher education institutions and enterprises. [49,50] https://doi.org/10.1371/journal.pone.0339158.t001 Table 2. Measurement items for potential constraining factors. No. Potential constraining factors Measurement items 1 The completeness of policies I believe that the inadequacy of policies related to IEI will constrain its development. 2 The willingness of enterprises to cooperate I believe that the lack of strong willingness from enterprises to engage in IEI will constrain its development. 3 The alignment of talent cultivation quality I believe that the failure of talent cultivation quality to meet the employment requirements of enterprises will constrain the development of IEI. 4 The level of attention given by universities I believe that the insufficient attention given by universities to IEI will constrain its development.

5 The degree of teacher involvement I believe that the low level of teacher participation in IEI will constrain its development. 6 The extent of student engagement I believe that the low level of student participation in IEI will constrain its development. 7 The adequacy of hardware and software facilities I believe that the lack of hardware and software facilities, such as faculty, training sites, and equipment, will constrain the development of IEI. 8 The differences in talent cultivation philosophies I believe that the differences in talent cultivation philosophies between universities and enterprises will constrain the development of IEI. 9 The achievement of school- enterprise mutual interests I believe that the failure to meet mutual expectations regarding interests between universities and enterprises will constrain the development of IEI. 10 The clarity of school-enterprise responsibilities and rights I believe that the lack of clarity regarding the responsibilities and rights of universities and enterprises will constrain the development of IEI. 11 The effectiveness of school- enterprise communication I believe that the lack of effective communication between universities and enterprises will constrain the development of IEI. 12 The closeness of prior university- enterprise collaboration I believe that the weak foundation of early-stage cooperation between universities and enterprises will constrain the development of IEI. 13 The fluidity of school-enterprise cooperation channels

I believe that the insufficient smoothness of cooperation channels between universities and enterprises will constrain the development of IEI. https://doi.org/10.1371/journal.pone.0339158.t002

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 7 / 18 To ensure the accuracy of the survey results and drawing upon existing research findings [38], this study identifies potential constraining factors as critical constraining factors if they are rated at 4 or higher by more than 80% of respon- dents. The survey results are shown in S1 Text. The findings reveal that, with the exception of the factor “The extent of student engagement,” all other 12 potential constraining factors were rated 4 or higher by 80% of the respondents. There- fore, these 12 factors are identified as critical constraining factors for the development of IEI in vocational education. 2.3. Analyzing the constraining mechanisms The analysis of constraining mechanisms constitutes the most critical research phase, as it is fundamental for formulating targeted strategies to effectively promote IEI in vocational education. This study employed a comprehensive methodolog- ical approach combining expert interviews with ISM to systematically examine the interrelationships among constraining factors and identify key constraining pathways. This dual-method approach enables us to elucidate the underlying mecha- nisms that constrain the development of IEI in vocational education. 2.3.1. Expert interview. This study employed expert interviews to determine the interrelationships among various constraining factors. To ensure the validity of the investigation and the accuracy of results, this study invited 30 experts

from government agencies, vocational institutions, and enterprises who possess profound insights into IEI in vocational education and have substantial research and practical experience in this field. The sample reached theoretical saturation when no new findings emerged after interviewing the 21st participant and subsequent interviewees. The criteria for judging theoretical saturation in this study were defined based on three core principles, consistent with established qualitative research standards [37,38]: (1) No new insights on factor interrelationships. After the 21st interview, subsequent experts did not propose additional types of influence relationships between the 12 constraining factors (e.g., no new direct/indirect impact pathways beyond those identified in earlier interviews); (2) Consistency in key judgments. The degree of agreement on critical factor relationships (e.g., the influence of “policy completeness” on “school attention level”) stabilized at over 80% (matching the study’s pre-established inter-rater agreement threshold) and no contradictory judgments on core mechanisms emerged; (3) Sufficiency of explanatory logic. All rationales for factor relationships provided by later interviewees were repetitions of arguments from earlier participants, with no new theoretical or practical justifications for constraint mechanisms added. The interview process followed a structured protocol: First, participants were briefed on the research background and

objectives, followed by an introduction to the 12 pre-identified constraining factors. Subsequently, experts completed a standardized documentation to assess the interrelationships among these factors. Finally, based on their documented responses, in-depth interviews were conducted to verify the identified constraining factors and explore the underlying rationales for these relationships. The face-to-face interviews were conducted between March and May 2024. S2 Text presents the demographic char- acteristics of the interviewees. Recognizing potential inter-rater discrepancies, this study established an 80% agreement threshold – a widely adopted benchmark in existing studies – to validate the findings [38]. Only relationships endorsed by over 80% of respondents were considered statistically significant, thereby ensuring methodological rigor and result reliability. 2.3.2. ISM. ISM is a systematic analytical approach that primarily utilizes expert knowledge to examine the interrelationships among various elements within a system [53]. By employing directed graphs, matrices, and computer programming techniques, ISM converts complex and ambiguous relationships among system elements into a clear, intuitive, and hierarchical structural model, thereby enhancing its readability and reference value [54]. This method is particularly suitable for investigating multi-factor interaction mechanisms [55,56]. Therefore, this study employs ISM

relationships among system elements, encompassing both direct and indirect effects. This matrix is derived through Boolean algebraic operations performed on the structural self-interaction matrix, with its computational implementation realized through MATLAB programming. (4) Implementing level division. Hierarchical partitioning was conducted by using the reachability matrix to determine the tier of each factor within the system. The computational procedure consisted of four main steps: (a) Identifying connected components: This task aims to group elements that have direct or indirect influence relation- ships into independent sub-systems. It helps eliminate isolated elements (if any) that have no connection to the core constraint system, ensuring the subsequent analysis focuses only on mutually related factors that truly affect IEI development. Fig 2. Implementation steps of ISM. https://doi.org/10.1371/journal.pone.0339158.g002

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 9 / 18 (b) Performing condensation operations: This step simplifies the complex network of element relationships by merging elements within the same connected component into a single “condensed node” if they have identical reachabil- ity and antecedent sets. Its purpose is to reduce redundant calculations and clarify the hierarchical structure by removing duplicate or highly overlapping element relationships. (c) Executing edge reduction: This involves removing transitive edges (i.e., indirect influence relationships that can be inferred through intermediate elements) from the network. It streamlines the relationship structure, avoiding over-complication of the model and making the direct causal links between elements more prominent. (d) Conducting level partitioning. Specifically, for each factor, the reachability set, antecedent set, and intersection set were computed from the reachability matrix, which facilitated the assignment of tiers based on predefined rules (i.e., an element is assigned to a level if its reachability set intersects with its antecedent set exactly at the reach- ability set itself). (5) Developing the ISM-based hierarchical framework. The final hierarchical framework was established by integrating level division results with the reachability matrix, thereby ensuring a systematic and comprehensive structure. Rep- resented through nodes (denoting elements) and directed edges (indicating influence relationships), the framework

adheres to the principle of simplification by removing redundant transitive paths. This visualization enhances model clarity and facilitates intuitive understanding of systemic structures. 3. Results and discussion 3.1. The structural self-interaction matrix Based on the interview results, the structural self-interaction matrix established in this study is shown in Table 3. “V” indicates that the constraint Si in the row had a direct impact on the constraint Sj in the corresponding column, such as “S1 (The completeness of policies)” had a direct impact on “S4 (The level of attention given by universities)”; “A” means that the constraint Sj in the column had a direct impact on the constraint Si in the corresponding row, such as “S3 (The alignment of talent cultivation quality)” had a direct impact on “S2 (The willingness of enterprises to cooperate)”; and “O” Table 3. The structural self-interaction matrix. Constraint factorsa S12 S11 S10 S9 S8 S7 S6 S5 S4 S3 S2 S1 O O O V O O O O V O O S2 A A A O A A O O O A S3 O A A O O A A O O S4 V O O O O O V V S5 O A A O O O O S6 O O O O O O S7 O O A O O S8 O O A O S9 O O A S10 V A S11 V aS1 (The completeness of policies), S2 (The willingness of enterprises to cooperate), S3 (The alignment of talent cultivation quality), S4 (The level of atten- tion given by universities), S5 (The degree of teacher involvement), S6 (The adequacy of hardware and software facilities), S7 (The differences in talent

cultivation philosophies), S8 (The achievement of school-enterprise mutual interests), S9 (The clarity of school-enterprise responsibilities and rights), S10 (The effectiveness of school-enterprise communication), S11 (The closeness of prior university-enterprise collaboration), S12 (The fluidity of school- enterprise cooperation channels). https://doi.org/10.1371/journal.pone.0339158.t003

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 10 / 18 denotes that there was no direct impact between the constraint Si in the row and the constraint Sj in the corresponding col- umn, such as “S1 (The completeness of policies)” and “S11 (The closeness of prior university-enterprise collaboration)”. 3.2. The reachability matrix The reachability matrix, calculated based on the structural self-interaction matrix in this study, is shown in Table 4. In the reachability matrix, “1” indicates that the constraint Si in the row directly or indirectly influences the constraint Sj in the corresponding column, such as “S1 (The completeness of policies)” had affected “S2 (The willingness of enterprises to cooperate)”, “S3 (The alignment of talent cultivation quality)”, “S4 (The level of attention given by universities)”, “S5 (The degree of teacher involvement)”, “S6 (The adequacy of hardware and software facilities)”, “S9 (The clarity of school- enterprise responsibilities and rights)”, and “S12 (The fluidity of school-enterprise cooperation channels)”; “0” indicates that the constraint Si in the row does not directly or indirectly influence the constraint Sj in the corresponding column, such as “S1 (The completeness of policies)” had not affected “S7 (The differences in talent cultivation philosophies)”, “S8 (The achievement of school-enterprise mutual interests)”, “S10 (The effectiveness of school-enterprise communication)”, and “S11 (The closeness of prior university-enterprise collaboration)”. 3.3. The level partition

Based on the reachability matrix, this study systematically calculated the reachability, previous, and intersec- tion sets of each constraint, and determined the level partition of each constraint according to the calculation rule L1={Si|R(Si)∩Q(Si)=R(Si), i = 0,1,2,…, k}, as shown in Table 5. The results indicated that the 12 constraints are divided into five levels. Specifically, the constraints “S2 (The willingness of enterprises to cooperate),” “S5 (The degree of teacher involvement),” and “S9 (The clarity of school-enterprise responsibilities and rights)” were classified into Level I; “S3 (The alignment of talent cultivation quality),” “S8 (The achievement of school-enterprise mutual interests),” and “S12 (The fluidity of school-enterprise cooperation channels)” were located at Level II; “S6 (The adequacy of hardware and software facil- ities)” and “S7 (The differences in talent cultivation philosophies)” belong to Level III; “S4 (The level of attention given by Table 4. The reachability matrix. Constraint factors S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 Driving powera S1 1 1 1 1 1 1 0 0 1 0 0 1 8 S2 0 1 0 0 0 0 0 0 0 0 0 0 1 S3 0 1 1 0 0 0 0 0 0 0 0 0 2 S4 0 1 1 1 1 1 0 0 0 0 0 1 6 S5 0 0 0 0 1 0 0 0 0 0 0 0 1 S6 0 1 1 0 0 1 0 0 0 0 0 0 3 S7 0 1 1 0 0 0 1 0 0 0 0 0 3 S8 0 1 0 0 0 0 0 1 0 0 0 0 2 S9 0 0 0 0 0 0 0 0 1 0 0 0 1 S10 0 1 1 0 1 0 1 1 1 1 0 1 8 S11 0 1 1 0 1 0 1 1 1 1 1 1 9

S12 0 1 0 0 0 0 0 0 0 0 0 1 2 Dependence powerb 1 10 7 2 5 3 3 3 4 2 1 5 46 aDriving force refers to the extent to which a constraint influences other constraints. The greater the driving force, the more constraints it influences. bDependence power refers to the extent to which a constraint is influenced by other constraints. The greater the dependence power, the more con- straints it is influenced by. https://doi.org/10.1371/journal.pone.0339158.t004

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 11 / 18 universities)” and “S10 (The effectiveness of school-enterprise communication)” were at Level IV; “S1 (The completeness of policies)” and “S11 (The closeness of prior university-enterprise collaboration)” were placed in Level V. 3.4. The ISM-based hierarchical framework As shown in Fig 3, the ISM-based hierarchical framework was established based on the mutual influencing relationship between the constraints and the level partition. In this framework, the constraints at Level I were called the surface fac- tors, which were “S2 (The willingness of enterprises to cooperate),” “S5 (The degree of teacher involvement),” and “S9 (The clarity of school-enterprise responsibilities and rights)”; The 5 constraints were located at Level II and Level III, which were called the intermediate factors, i.e., “S3 (The alignment of talent cultivation quality),” “S8 (The achievement of school- enterprise mutual interests),” “S12 (The fluidity of school-enterprise cooperation channels),” “S6 (The adequacy of hardware Table 5. The level partition (iterations 1 to 5 combined). Constraint factors Reachability set Antecedent set Intersection set Level S1 S1, S2, S3, S4, S5, S6, S9, S12 S1 S1 V S2 S2 S1, S2, S3, S4, S6, S7, S8, S10, S11, S12 S2 I S3 S2, S3 S1, S3, S4, S6, S7, S10, S11 S3 II S4 S2, S3, S4, S5, S6, S12 S1, S4 S4 IV S5 S5 S1, S4, S5, S10, S11 S5 I S6 S2, S3, S6 S1, S4, S6. S6 III S7 S2, S3, S7 S7, S10, S11 S7 III S8 S2, S8 S8, S10, S11 S8 II

S9 S9 S1, S9, S10, S11 S9 I S10 S2, S3, S5, S7, S8, S9, S10, S12 S10, S11 S10 IV S11 S2, S3, S5, S7, S8, S9, S10, S11, S12 S11 S11 V S12 S2, S12 S1, S4, S10, S11, S12 S12 II https://doi.org/10.1371/journal.pone.0339158.t005 Fig 3. The ISM-based hierarchical framework. https://doi.org/10.1371/journal.pone.0339158.g003

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 12 / 18 and software facilities)” and “S7 (The differences in talent cultivation philosophies)”; The constraints at Level IV and Level V were called the bottom factors, which were “S4 (The level of attention given by universities),” “S10 (The effectiveness of school-enterprise communication),” “S1 (The completeness of policies)” and “S11 (The closeness of prior university- enterprise collaboration)”. 3.5. Constraining mechanisms The mechanisms constraining the development of IEI in vocational education were identified through the ISM-based hier- archical framework and structural self-interaction matrix, as illustrated in Fig 4. The results indicate that the ineffective implementation is primarily attributed to enterprises’ weak willingness to par- ticipate, ambiguous delineation of responsibilities, rights, and obligations between schools and enterprises during col- laboration, and insufficient teacher engagement. These direct factors are further shaped by various intermediate- and bottom-level factors, which exert either direct or indirect influences. Specifically, the critical constraining factors include inadequate policies and regulations for IEI, a weak foundation for school-enterprise collaboration, insufficient institutional commitment from vocational colleges, and ineffective communication during implementation. These bottom-level factors directly or indirectly impact the adequacy of software/hardware facilities (e.g., teaching staff, training venues, equipment),

Fig 4. The constraint mechanism of IEI development in vocational education. https://doi.org/10.1371/journal.pone.0339158.g004

PLOS One | https://doi.org/10.1371/journal.pone.0339158 December 18, 2025 13 / 18 the misalignment between educational philosophies and industry needs, the discrepancy between graduate competencies and employer requirements, the effectiveness of collaboration channels, and the fulfillment of mutual benefits and expec- tations. As intermediate-level factors, they further constrain development by decreasing enterprise willingness, obscuring clarity in responsibilities/rights/obligations, and undermining teacher motivation in the IEI process. Through a systematic analysis of the interrelationships among constraining factors, 16 constraining pathways inhibiting the development of IEI in vocational education were identified, as detailed in Table 6. The root causes analysis reveals that two bottom-level factors – the completeness of policies and the strength of prior university-enterprise collaboration – predominantly influence other mediating factors, ultimately constraining the progress of IEI. Regarding the completeness of policies, four distinct constraint pathways were established: (1) The existing incom- pleteness of policies governing IEI leads to unclear definitions of responsibilities, rights, and obligations between voca- tional institutions and enterprises; (2) These inadequacies significantly weaken institutional emphasis on integration implementation, consequently reducing faculty engagement; (3) Insufficient policy support adversely affects the estab-

lishment of effective school-enterprise collaboration channels; and (4) Policy gaps impede the development of essential supporting infrastructure and resources. Specifically, incomplete policies often lack clear funding provisions for vocational institutions to upgrade their training equipment or build industry-aligned training facilities. They also fail to set standards for corporate contributions of hardware, such as equipment donations or on-site training space. This leads to chronically underfunded hardware infrastructure. Notably, ineffective collaboration channels directly undermine corporate participa- tion willingness, while inadequate infrastructure indirectly impacts enterprise engagement by weakening the alignment between institutional talent cultivation outcomes and industry workforce requirements. Collectively, both insufficient enter- prise willingness and low faculty motivation directly hinder the effective implementation of IEI. Regarding the closeness of prior university-enterprise collaboration, a total of 12 constraining pathways were identified. Among these, two pathways hinder the development of IEI in vocational education by diminishing teachers’ motivation for engagement. Specifically, the lack of a robust foundation for prior collaboration between universities and enterprises directly deminishes teachers’ motivation to participate in IEI. Additionally, it indirectly affects teachers’ enthusiasm by impairing the effectiveness of communication and interaction between universities and enterprises. Furthermore, nine

pathways constrain the development of IEI by reducing enterprises’ willingness to collaborate. Specifically, a weak foun- dation of prior collaboration directly diminishes enterprises’ motivation to engage in IEI. It also indirectly lowers their willingness by reducing the smoothness of collaboration channels, the alignment between the quality of talent cultivation in universities and enterprises’ employment requirements, and the effectiveness of university-enterprise communication. Notably, the effectiveness of communication further indirectly influences enterprises’ willingness by directly affecting the differences in talent cultivation philosophies between universities and enterprises, as well as the fulfillment of mutual inter- ests and expectations in IEI. Ultimately, these factors impede the implementation of IEI in vocational education. In addi- tion, one pathway constrains the development of IEI by affecting the delineation of responsibilities, rights, and obligations between universities and enterprises. Specifically, a weak foundation of prior collaboration weakens the effectiveness of communication, and the lack of effective interaction hinders the clarification of each party’s responsibilities, rights, and obligations in the implementation process of IEI, thereby undermining its effective advancement. 4. Strategies for promoting IEI To promote IEI development in vocational education, this study proposes targeted strategies for the government, institu- tions, and enterprises based on the identified constraint mechanisms.

4.1. Government: Strengthening institutional safeguards The findings reveal that inadequate policies are a critical constraint on IEI. Therefore, the government should provide policy guidance and legal frameworks to standardize and promote its development. First, the responsibilities, rights, and