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| SciBERT-based Measurement of Knowledge Intersection in Scientific Collaboration and Its Causal Effects on Sustained Research Output |
| Feng Xiaodong, Huang Yuhang |
| School of Information Management, Sun Yat-sen University, Guangzhou 510006 |
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Abstract With the increasing complexity and integration of scientific research problems, scientific collaboration and interdisciplinary research have intensified. Knowledge intersection represents a process of interaction, integration, and innovation across different fields, which can lead to significant scientific achievements. Breaking through the perspective of disciplinary granularity and static presentation in existing interdisciplinary research, this study explores the measurement of fine-grained knowledge intersection between research subjects and collaborators with respect to the accumulation of dynamic knowledge, experience, and continuous scientific research output. We constructed a measurement model to quantify the intensity of knowledge intersection in research collaborations using text-mining approaches based on a bidirectional encoder representations from transformers (BERT) model trained on scientific text (SciBERT) to represent the knowledge concept or sentence in a manuscript. Using scientific publication data from the Information Systems discipline as a case study, we generated panel data and applied the Generalized Propensity Score Matching method to conduct a causal effect analysis. The differential impacts of knowledge intersection on research questions and methodology were further explored. These findings indicate that the overall knowledge intersection in research collaboration has an inverted U-shaped effect on subsequent research output. Notably, knowledge intersection in research methodologies exerts a more substantial influence on the continuous research outputs of subjects than research questions.
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Received: 23 October 2024
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