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 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