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Exploring Novelty Differences between Industry and Academia: A Knowledge Entity-centric Perspective

arXiv 2603.19319 ↗First posted 16 Mar 2026cs.DL cs.CL cs.CYcited by 0

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Abstract (arXiv)

Academia and industry each possess distinct advantages in advancing technological progress. Academia's core mission is to promote open dissemination of research results and drive disciplinary progress. The industry values knowledge appropriability and core competitiveness, yet actively engages in open practices like academic conferences and platform sharing, creating a knowledge strategy paradox. Highly novel and publicly accessible knowledge serves as the driving force behind technological advancement. However, it remains unclear whether industry or academia can produce more novel research outcomes. Some studies argue that academia tends to generate more novel ideas, while others suggest that industry researchers are more likely to drive breakthroughs. Previous studies have been limited by data sources and inconsistent measures of novelty. To address these gaps, this study conducts an analysis using four types of fine-grained knowledge entities (Method, Tool, Dataset, Metric), calculates semantic distances between entities within a unified semantic space to quantify novelty, and achieves comparability of novelty across different types of literature. Then, a regression model is constructed to analyze the differences in publication novelty between industry and academia. The results indicate that academia demonstrates higher novelty outputs, which is particularly evident in patents. At the entity level, both academia and industry emphasize method-driven advancements in papers, while industry holds a unique advantage in datasets. Additionally, academia-industry collaboration has a limited effect on enhancing the novelty of research papers, but it helps to enhance the novelty of patents. We release our data and associated codes at https://github.com/tinierZhao/entity_novelty.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 62 references that are in the map. First posted on arXiv 16 Mar 2026.

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References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2024The Nature of NLP: Analyzing Contributions in NLP Paperscited by 12
  3. 2020Pandemics are catalysts of scientific novelty: Evidence from COVID-19cited by 6
  4. 2021Team Power Dynamics and Team Impact: New Perspectives on Scientific Collaboration using Career Age as a Proxy for Team Powercited by 5
  5. 2019The Rise of Multiple Institutional Affiliations in Academiacited by 4
  6. 2024The complementary contributions of academia and industry to AI researchcited by 4
  7. 2022Expertise diversity of teams predicts originality and long-term impact in science and technologycited by 1
  8. 2026Quantifying the Knowledge Proximity Between Academic and Industry Research: An Entity and Semantic Perspectivecited by 1

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