{"title":"Scholar's Career Switch from Academia to Industry: Mining and Analysis from AMiner","authors":"Zhou Shao , Sha Yuan , Yinyu Jin , Yongli Wang","doi":"10.1016/j.bdr.2024.100441","DOIUrl":null,"url":null,"abstract":"<div><p>The phenomenon of scholars switching their careers from academia to industry has become more prevalent nowadays. This paper proposes a combination approach of bibliometrics analysis and data mining to study the phenomenon from the perspective of Science of Science (SciSci). Based on the proposed methods, this paper first provides an overview of frequent companies and frequent universities as well as the exponentially increasing number of scholars under the scenario. And then, this study uncovers the excessively single patterns in South Korean scholars switches using frequent pattern mining from their papers. This paper studies the knowledge and technology transfer (KTT) and the research change of scholars by using the language model, the result of which illustrates that the research difference between industry and academia gradually decreases and reaches a steady state in recent years. In exploring the driving factors of the phenomenon, deep preliminary cooperation may be an essential reason, and the career switches will not promote the published amounts of papers but may benefit its academic influence. This study should, therefore, be of value to researchers wishing to study the academia-industry career switches more intensely.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"36 ","pages":"Article 100441"},"PeriodicalIF":3.5000,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data Research","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214579624000170","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The phenomenon of scholars switching their careers from academia to industry has become more prevalent nowadays. This paper proposes a combination approach of bibliometrics analysis and data mining to study the phenomenon from the perspective of Science of Science (SciSci). Based on the proposed methods, this paper first provides an overview of frequent companies and frequent universities as well as the exponentially increasing number of scholars under the scenario. And then, this study uncovers the excessively single patterns in South Korean scholars switches using frequent pattern mining from their papers. This paper studies the knowledge and technology transfer (KTT) and the research change of scholars by using the language model, the result of which illustrates that the research difference between industry and academia gradually decreases and reaches a steady state in recent years. In exploring the driving factors of the phenomenon, deep preliminary cooperation may be an essential reason, and the career switches will not promote the published amounts of papers but may benefit its academic influence. This study should, therefore, be of value to researchers wishing to study the academia-industry career switches more intensely.
期刊介绍:
The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic.
The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.