11524: Persuading Academics to Engage with Industry: A Linguistic Approach Utilizing Machine Learning (session 26144)

Tuesday, July 29, 2025

12:00 - 13:30 CEST (GMT+2)

Location: Bella Center: Hall B- B3-m1

Session Type: Paper Session
Orientation: Research
Shareable link: https://cdmcd.co/4Rp4Yz

Author(s)

DJ

David Johnson

Durham U. Business School, United Kingdom
JA

Jingning Ao

Morgan State U., United States
SL

Sunkee Lee

Carnegie Mellon U. - Tepper School of Business, United States
AB

Adam J. Bock

U. of Wisconsin, Madison, United States
VS

Viktor Schlegel

ASUS Intelligent Cloud Services

Academic-industry engagement, crucial for facilitating the development of university-centered entrepreneurial ecosystems (UCEEs), hinges on the persuading language used in contract research proposals submitted by firms. Successful contract research proposals, when accepted by academics, can drive academic entrepreneurship. Yet, the role of proposal narratives remains undertheorized and understudied. To understand the persuasive power of language and its influence on academic-industry engagement activities, we employ Machine Learning (ML). This study discovers key linguistic features related to successful proposals and predicts engagement success with 83% accuracy. These findings offer new linguistic insights into academic-industry engagement, informing theory, practice, and policy in UCEEs. Additionally, our ML techniques enrich discussions on ML implementation in academic entrepreneurship, and pave the way for future research to leverage ML’s potential for pattern recognition and outcome prediction in large volumes of data.

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