题目:Enhancing Faculty Recruitment: Identifying High-Potential Academic Talent by Leveraging Resumes and Machine Learning
时间：2024年12月12日（周四）15:00-17:00
地点：浙江大学紫金港校区yl23411永利A423
主讲人：柯滨，新加坡国立大学商学院教授
主持人：王文明，yl23411永利百人计划研究员
主讲人简介：
柯滨（Bin KE），现任新加坡国立大学商学院会计系教授，教务长首席教授，亚洲会计研究中心主任。1999年在密歇根州立大学获得博士学位，曾任美国宾州州立大学和南洋理工大学大学商学院会计系教授。
柯滨教授在北美会计学术研究领域声誉卓著，是北美华人会计学者的杰出代表，获聘中国教育部“长江学者”讲座教授和上海市政府“上海千人计划”，并担任过北美华人会计教授会会长以及美国会计学会（AAA）会计研究杰出贡献奖评选委员会委员。柯滨教授的研究兴趣在于通过采用传统或跨学科研究方法来探索并解决当今日益复杂的商业问题，先后在国际会计学术领域的顶级期刊发表论文超过20篇，曾任“The Accounting Review”的Editor，并担任“Asian Pacific Journal of Accounting & Economics”的Associate Editor,以及“The Accounting Review”、“Journal of American Taxation Association”、“The International Journal of Accounting”等著名国际学术期刊的编辑委员会成员。
摘要：
Universities allocate significant resources each year to recruit new faculty from PhD programs, yet a substantial number of hires fail to achieve tenure, often due to insufficient research output. This study examines whether the faculty recruitment process can be optimized by using machine learning to identify candidates with higher research potential earlier in the hiring process. Focusing on the accounting rookie recruiting market, we develop a machine learning model leveraging readily available job application materials: (i) candidate resumes, (ii) the reputations of their educational institutions and dissertation advisors, and (iii) their dissertation work. Our results demonstrate that a machine learning model using these materials outperforms traditional human judgment in predicting research success. Notably, resumes are particularly strong predictors of future research performance, while the dissertation’s predictive value appears mixed.
