Collaborative Research: University Commitment to Interdisciplinary Research: Scope, Causes, and Consequences

为了促进创新和解决社会面临的重要问题，跨学科研究（IDR），即整合两个或多个学科的观点，正在得到联邦和州资助机构的推动。然而，几乎没有证据表明大学在多大程度上参与了IDR，它们提供的机构承诺的类型，或者对科学研究的行为和大学的表现有什么影响。以前的研究考察了科学家个人对IDR的参与，但是他们忽略了当前项目研究的更大的制度背景。大学管理人员、资助机构和科学政策专家对该项目的结果感兴趣，因为他们决定如何投资研究，以及大学对IDR的承诺的哪些方面最有效地促进特定目标（例如，学术研究产出、商业产出和声望），使他们能够就投资重点做出明智的决定。本项目构建了一个全面的数据集，开发了多种衡量大学对IDR承诺的指标，并首次对其原因和后果进行了实证研究。该研究团队正在从多个来源收集所有（157所）美国顶尖研究型大学的数据，以创建一个独特的大学水平数据集。在开发了大学对IDR的承诺测度后，统计回归技术分为两个阶段。在分析的第一阶段，回归模型检验哪些因素促进了IDR的参与。可能的因素包括资源减少（例如，国家资助或大学排名下降）以及来自资助机构，大学管理人员和教师的追求IDR的压力和激励（例如，针对多学科团队的资助）。在分析的第二阶段，回归模型评估参与IDR如何影响大学和科学进步，特别是学术研究产出（如出版物和引用）、商业研究产出（如专利）和/或声望（如大学排名）。

To foster innovation and address important questions facing society, interdisciplinary research (IDR), which integrates perspectives from two or more disciplines, is being promoted by federal and state funding agencies. However there is little evidence about how well universities are engaging with IDR, the types of institutional commitments they provide, or what the consequences are for the conduct of scientific research and the performance of universities. Previous research examines individual scientists' engagement in IDR, but they miss the larger institutional context, which the current project studies. The results from this project are of interest to university administrators, funding agencies, and science policy experts as they decide how to invest in research and which aspects of university commitment to IDR are most effective for promoting specific goals (e.g., scholarly research output, commercial output, and prestige), allowing them to make informed decisions about where to focus their investments. This project constructs a comprehensive dataset, develops multiple measures of university commitment to IDR, and provides the first empirical study of its causes and consequences. The research team is compiling data on all (157) top U.S. research universities from multiple sources to create a unique university-level dataset. After developing measures of university commitment to IDR, statistical regression techniques are employed in two stages. In the first stage of analysis, regression models test which factors promote engagement with IDR. Possible factors include resource reductions (e.g., declines in state funding or university ranking) as well as pressures and incentives to pursue IDR that emanate from funding agencies, university administrators, and faculty (e.g., grants targeted to multidisciplinary teams). In the second stage of analysis, regression models assess how engagement with IDR affects universities and scientific progress, specifically scholarly research output (e.g. publications and citations), commercial research output (e.g., patents), and/or prestige (e.g., university rankings).