Abstract Purpose This study investigates the effects of concurrent multi-level policy implementation on university technology transfer performance. It clarifies the moderating roles of government financial support and regional research and development (R&D) conditions. It further examines heterogeneous policy effects across university disciplinary characteristics, geographical locations, regional technology intermediary density, and industrial structure. The findings offer a refined empirical perspective for evaluating multi-level policy effectiveness and optimizing university technology transfer practices. They also provide guidance for strengthening basic research capacity and better integrating of basic and applied research in universities. Design/methodology/approach Panel data from 61 universities directly under the Ministry of Education spanning 2011–2019 were analyzed. We employed the Difference-in-Differences (DID) method to empirically test the specific impact of the concurrent implementation of local and university-level policies on university technology transfer. Additionally, moderating effect models and grouped regression are adopted to explore the underlying mechanisms. Findings The empirical results show that the concurrent implementation of local and university-level policies exerts a significantly positive impact on university technology transfer. Second, government financial support and the regional R&D environment play positive moderating roles in the relationship between the joint-treatment indicator and university technology transfer. Third, the positive effect is more salient in specific subsamples, including science and engineering universities, comprehensive universities, universities located in the eastern region, and universities situated in areas with a high density of science and technology intermediaries and a high proportion of the tertiary industry. Research limitations This study has two major limitations. First, due to data accessibility, the research observation window is limited to 2011–2019. Although this period covers the critical pre- and post-revision stage of the Law on the Promotion of the Transformation of Scientific and Technological Achievements and enables the capture of short- and medium-term policy effects, the long-term evolutionary impacts of relevant policies remain to be further explored. Second, to ensure data integrity and sample representativeness, the research sample only covers universities directly affiliated with the Ministry of Education. Future studies can expand the sample scope to incorporate diverse types of higher education institutions to further generalize the existing empirical conclusions. Practical implications This study indicates that the effects of concurrent policy implementation exhibit significant regional and institutional heterogeneity, and that government financial support as well as the regional R&D environment positively moderate these effects. Based on this, in the future construction of multi-level policy systems and the optimization of the innovation ecosystem, it is necessary to balance the rigidity and flexibility of institutional design, improve policy efficiency, and facilitate the transformation of scientific and technological achievements in universities. Originality/Value This study contributes to the extant literature on policy-driven university technology transfer by systematically examining the mechanisms and pathways of the concurrent implementation of local and university-level policies. Unlike prior studies that focus on single-dimensional policy analysis, this paper differentiates between individual policy effects and concurrent policy implementation. It further introduces government financial support and regional R&D conditions as moderating variables to clarify the contingent mechanisms of policy implementation. Combined with heterogeneity analysis of universities’ disciplinary attributes and regional environmental characteristics, this study reveals the differentiated operational logic of concurrent policy implementation across diverse scenarios. The extended research framework enriches the theoretical boundary of policy effectiveness research in university technology transfer, providing empirical evidence for optimizing technology innovation and transformation governance systems.
Abstract Purpose This study aims to detect conditional outliers, which become anomalous only within a specified context, in mixed attribute data. It addresses contextual relevance, uncertainty and fuzziness that are often overlooked by conventional methods. Design/Methodology/Approach ICOD integrates fuzzy inclusion theory with fuzzy rough sets. An inclusion based membership function measures how closely each object’s fuzzy information granule conforms to a conditional subset. To avoid examining all attribute combinations, ICOD builds an attribute sequence and a nested attribute subset sequence for each object. Their membership degrees are combined with granule size weights to form a conditional outlier factor. The evaluation includes comparative experiments, parameter sensitivity analysis, ablation analysis, and Friedman and Nemenyi tests. Findings Experiments on 11 public datasets, comprising 22 contextual subsets, show that ICOD outperforms eight comparison methods on most subsets. It obtains the best results on 18 of the 22 subsets and statistically significant improvements over seven of the eight methods. Research Limitations Pairwise fuzzy relations require O (| AT || OB | 2 ) time and space, which may limit scalability on large datasets. Conditional subsets are constructed using domain informed filters, so robustness to alternative and data driven context construction strategies requires further study. Practical Implications ICOD enables domain experts to define relevant subgroups and identify deviations within them. Its unified treatment of heterogeneous attributes may support context dependent applications such as medical screening, transaction monitoring and fault detection while reducing information loss caused by preprocessing. Originality/Value ICOD characterizes anomalies through graded contextual conformity rather than global statistical deviation. Its value lies in combining fuzzy inclusion based membership, dynamically constructed object specific attribute and attribute subset sequences, and granule size weighting within a unified conditional outlier score that accounts for uncertainty.
Abstract Purpose This study aims to systematically review the core concepts, technical approaches, research themes, and tool ecosystems related to “academic innovation point extraction”, clarify the developmental trajectory of the field and identify current challenges and future directions. Design/methodology/approach A systematic literature review approach is adopted, supported by BERTopic-based hierarchical topic modeling to identify major research themes. Academic innovation points are categorized into explicit, implicit, and cross-document types, and a comparative analysis of the corresponding extraction methods and evaluation frameworks is conducted. Findings Academic innovation points exhibit complex and heterogeneous characteristics, including the coexistence of explicit and implicit expressions, multimodal information fusion, and cross-document evolution. Technological development has progressed from rule-based matching to deep learning integrated with semantic modeling, gradually forming a “semantic modeling–knowledge organization–intelligent service” chain. Substantial disciplinary differences are also observed in the structure, representation, and methodological treatment of academic innovation. Research limitations The analysis is based on the available literature and existing innovation point extraction studies, and therefore may not fully capture emerging approaches or rapidly evolving technological developments. In addition, differences in disciplinary contexts and evaluation practices may limit the generalizability of comparisons across research domains. Practical implications The study provides a systematic theoretical and methodological foundation for academic innovation point extraction and offers insights for improving cross-disciplinary knowledge circulation and academic evaluation. The identified extraction paradigms and technology-development trajectory can also inform the design of intelligent and scalable innovation knowledge service systems. Originality/value This study integrates conceptual classification, technological approaches, research themes, and tool ecosystems into a unified review framework for academic innovation point extraction. By distinguishing explicit, implicit, and cross-document innovation points and linking these forms to their corresponding extraction and evaluation approaches, the study provides a structured perspective for understanding the development and future evolution of innovation-oriented scholarly knowledge services.
Abstract Purpose Digital transformation (DT) is a configurational construct spanning technology, process, organizational, and strategic change, and valid measurement is a prerequisite for understanding it. Existing text-based methods fragment annual reports into keywords or isolated sentences, breaking the cross-sentence semantic structure that expresses the construct. This paper examines how textual representational granularity affects content validity in DT measurement, and proposes a complete-semantic measurement framework. Design/methodology/approach From 28,823 annual reports of Chinese listed manufacturers (2006–2023), we build a 1,000-report sample through structured sampling and design a three-stage complete-semantic framework (input, training-signal, and model-bearing). The framework is evaluated through comparative experiments, ablation, and sensitivity analysis, then applied to the full sample, with an existing sentence-level measure used for external validation. Findings Textual representational granularity shapes which forms of the construct enter measurement: a fragmented representation relies on explicit digital keywords and leaves early and implicit transformation unseen. Across the full sample, DT in Chinese manufacturing has widened in breadth and deepened in structure, shifting from localized exploration to cross-departmental integration. An existing sentence-level measure agrees on explicit, deep-stage transformation but diverges in the early stages that fragmentation struggles to capture. Research limitations The study focuses on Chinese manufacturing; its conclusions need testing in other industries, settings, and disclosure contexts, and on new model architectures and cross-construct tasks. Practical implications The framework requires little annotation, supports local deployment, and aligns with national standards, offering a reproducible tool for research, policy, and firm-level analysis. Originality/value The contribution lies not in using large language models (LLMs) for text classification, but in integrating the cross-sentence semantic structure on which a configurational construct depends. The paper brings textual representational granularity into content-validity assessment, extending construct validity from “item–construct domain” coverage to “textual representation–construct structure” alignment, and offering a testable lens for measuring complex organizational constructs from disclosure text.
Abstract The current approach to managing medical data separates security and governance into two distinct domains. This commentary argues that data security is a prerequisite for unlocking the value of data, and that data governance can enhance data quality and increase its value. These two aspects should be interdependent and mutually reinforcing.
