Date of Award

6-27-2025

Date Published

August 2025

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Information Science & Technology

Advisor(s)

Bei Yu

Keywords

Applied Natural Language Processing, Computational Social Science, Science of Science, Science Policy, Scientometrics

Abstract

Artificial intelligence (AI) has seen fast development in industry and academia. Striking advancements from industry companies have stunned the research field, inviting a fresh perspective on the relationship between industry and academia. Industry research teams have played a critical role by developing large-scale computational infrastructure, curating extensive datasets, and publishing impactful AI research. As a result, industry-led AI research has impacted both fundamental and applied AI, blurring the distinction between academic and industrial research. Concerns have arisen regarding the growing resource gap between industry research teams and academia research teams. While some AI research topics remain accessible, research in areas such as large language models (LLMs) necessitate more resources such as computational power and data access: resources largely concentrated among industry companies and a few top universities. This disparity raises critical questions about transparency, replicability, and inclusiveness in AI research, particularly regarding the role of academia in shaping the landscape of AI research. This dissertation addresses three key research questions: (1) What are the differences between industry and academia AI research in terms of impact and novelty? (2) How do research topics differ between resource-limited institutions and resource-rich institutions? (3) Has research from resource-rich institutions lowered or heightened the barriers for resource-limited researchers in AI? To explore these questions, three studies were conducted. Study 1 quantitatively examines the differences in impact, novelty, disruptiveness, and state-of-the-art status between industry and academic AI research over the last 25 years. The study reveals that articles published by teams consisting exclusively of industry researchers tend to get more attention, with a higher chance of being highly cited, and more likely to produce state-of-the-art models. In contrast, academia teams publish the bulk of AI research and tend to produce more atypical and citation-disruptive work. The respective impact-novelty advantages of industry and academia are robust to controls for subfield, team size, seniority, and prestige. We find that academia-industry collaborations produce the most impactful work overall but do not have the novelty and citation-disruptive level of academia teams. Study 2 surveys existing methodologies in citation context analysis to support Study 3. Study 3 investigates how research topics in AI have evolved and whether the resource-intensive nature of AI research has constrained topic and methodology selection for resource-limited researchers. Our findings reveal that resource-rich institutions are more likely to engage in research areas that are gaining popularity, while resource-limited institutions tend to work on topics that are declining in prominence. This suggests that resource constraints influence the feasibility of pursuing certain research directions. Moreover, by analyzing citation intent, our result indicates that despite significant industry-led advancements, research from well-funded teams has not necessarily lowered the barrier to entry but has, in some cases, made it more challenging for resource-limited researchers to contribute. The findings contribute to a broader understanding of the relationship between academia and industry, the resource gap, and the shifting landscape in AI research. We identify the unique and nearly irreplaceable characteristics that academia and industry have for the healthy progress of the academic field of AI. We provide empirical evidence on how resource-demanding research is shaping the field. These insights will support policymakers, academic institutions, and industry stakeholders in fostering a more equitable and transparent AI research ecosystem.

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