Collaborative Research: DAT and MOD: The Division of Innovative Labor

创新正变得越来越开放，通常涉及多个组织的创新劳动分工（DoIL）。 经济中的创新劳动分工被认为是创新过程中以下步骤在企业和其他组织之间的分配：1）研究/创意产生; 2）开发; 3）商业化。 尽管它的重要性，很少有广泛的，系统的证据的程度或性质的DoIL，也没有对企业业绩或技术进步的速度的影响。 该项目通过调查制造业和选定服务业的20，000多家公司，收集了美国DoIL的第一批系统数据。 收集的数据量化了DoIL的关键特征。 它还为开发和实证检验DoIL模型及其对创新绩效的影响提供了基础。 该项目解决了一些重要问题，如与老牌企业相比，初创企业在多大程度上是新想法的产生者，大学作为产业创新源泉的重要性，以及企业利用外部知识投入提高创新绩效的程度。最后，该研究通过生成捕获DoIL的正式模型并对这些模型进行实证测试来推进创新经济学。更广泛的影响：该项目是第一个收集创新活动数据的国家努力，超越了研发和专利。它是科学和创新政策科学所需的数据基础设施的重要补充。 通过生成关于创新活动的新数据，创建新的度量标准，并开发创新过程的新模型，该研究还为公共政策和企业战略提供了信息。 这项研究应有助于审议知识产权政策和标准政策以及可能影响技术市场和DoIL增长的合作研究和开发协议。 通过强调小公司、初创公司和大学在创新过程中的作用，研究结果还应有助于为联邦对大学研究和新公司组建的支持提供信息。 该项目的研究结果还将有助于联邦政府不断努力改进其工业研发和创新数据的收集工作。 这项研究还有助于创新的管理，突出了回报校外知识采购，或形成战略联盟，以开发或商业化的新技术。

Innovation is becoming more open, often involving multiple organizations in a division of innovative labor (DoIL). The division of innovative labor in the economy is conceived of as the distribution across firms and other organizations of the following steps in the innovation process: 1) research/idea generation; 2) development; and 3) commercialization. Despite its importance, there is little broad-based, systematic evidence on the extent or nature of the DoIL, nor on its impact on firm performance or the rate of technical advance. The project collects the first systematic data on the DoIL for the United States by surveying over 20,000 firms in manufacturing and selected service industries,. The collected data quantifies key features of the DoIL. It also provides the basis for developing and empirically testing models of the DoIL and its effects on innovative performance. The project addresses a number of important questions, such as the extent to which startups, as compared to established firms, are generators of new ideas, the importance of universities as sources of industrial innovation, and the extent to which firms' use of external knowledge inputs increases their innovative performance. Finally, the research advances the economics of innovation by generating formal models that capture the DoIL and testing those models empirically.Broader Impacts: This project is the first national effort to collect data on innovative activity generally, going beyond R&D and patenting. It is an important addition to the data infrastructure required for a science of science and innovation policy. By generating new data on innovative activity, creating new metrics, and developing new models of the innovation process, the research also informs both public policy and firm strategy. The research should contribute to deliberations on intellectual property policy and policies on standards and cooperative research and development agreements that potentially affect the growth of technology markets and the DoIL. By highlighting the role of small firms, startups, and universities in the innovation process, the findings should also help inform federal support to university research and new firm formation. The project findings should also contribute to the federal government's ongoing efforts to improve its collection of data on industrial R&D and innovation. The research also contributes to the management of innovation by highlighting the returns to extramural knowledge sourcing, or strategic alliances formed to develop or commercialize new technologies.