Diversity, networks, and innovation: A text analytic approach to measuring expertise diversity
Diversity, networks, and innovation: A text analytic approach to measuring expertise diversity
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DOI：
10.1017/nws.2022.34
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发表时间：
2022-12
期刊：
影响因子：
1.7
通讯作者：
Alina Lungeanu;Ryan Whalen;Y. J. Wu;Leslie A. DeChurch;N. Contractor
中科院分区：
文献类型：
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作者：
Alina Lungeanu;Ryan Whalen;Y. J. Wu;Leslie A. DeChurch;N. Contractor
尽管不同的专业知识在帮助解决困难的跨学科问题的重要性，衡量它是具有挑战性的，往往依赖于代理措施和推定的相关性的实际知识和经验。为了应对这一挑战，我们提出了一个基于文本的措施，使用研究人员的先前工作，以估计他们的实质性的专业知识。这些专业知识的估计，然后用来衡量团队层面的专业知识的多样性，通过确定成员的先验知识和技能的相似性或不相似性。使用这一措施对280万团队发明的专利授予美国专利局，我们显示的证据表明，随着时间的推移，跨团队规模的专业知识的多样性的趋势，以及其与团队的创新产出的质量和影响的关系。
Abstract Despite the importance of diverse expertise in helping solve difficult interdisciplinary problems, measuring it is challenging and often relies on proxy measures and presumptive correlates of actual knowledge and experience. To address this challenge, we propose a text-based measure that uses researcher’s prior work to estimate their substantive expertise. These expertise estimates are then used to measure team-level expertise diversity by determining similarity or dissimilarity in members’ prior knowledge and skills. Using this measure on 2.8 million team invented patents granted by the US Patent Office, we show evidence of trends in expertise diversity over time and across team sizes, as well as its relationship with the quality and impact of a team’s innovation output.