Doctoral Dissertation Research in Science of Science and Innovation Policy: Understanding the Impact of Sources of Inspiration in Creative Design: The Role of Conceptual Distance

创新从根本上说是从一个好主意开始的。但好主意从何而来呢？许多研究表明，创新的突破往往受到过去经验的启发：一个人在世界上与之互动过的东西和想法。然而，同样可以激励创新的经历也可能会通过专注于以前不成功的解决方案来限制或损害创新。这个项目测试指导与灵感来源互动的原则，以最大限度地增加它们的好处，最大限度地减少它们的陷阱。特别是，它侧重于来源的概念距离的作用。以下问题是这项工作的重点：1)好主意是主要建立在与问题密切相关的来源上(例如，建立在现有回收努力的基础上，以解决人们扔掉电子产品的问题)，还是最经常受到来自遥远相关领域的来源的启发(例如，设计尼龙搭扣时受到毛刺附着的启发)？2)当考虑多个来源时，是应该努力确保来源彼此相似(即，深入探索一个方向)，还是应该考虑不同的来源？智力价值创新研究人员和实践者已经为这些问题制定了建议，但这些建议的科学证据并不完整，因为这些建议基于少量的案例研究，或者涉及玩具问题的解决速度比现实世界的问题快得多。该项目通过分析发布在网上众包创新平台上的数千种复杂创新挑战的解决方案(例如，增加选举的可及性、振兴陷入困境的城市地区)，扩大和改进了了解创新来源的证据基础，这些解决方案要求贡献者发布想法来源。为了分析这些证据，它结合了自动处理文本内容的计算机算法和从数千个想法中提取含义的统计算法。由专家对想法的质量和新颖性进行编码，然后使用算法统计检验来源距离和来源多样性对想法成功的关系。更广泛的影响这项工作的结果将对创新的科学理论产生直接影响，并与创造新创新的系统设计有关(例如，在线众包创新平台、创造力支持系统和方法)。此外，这项工作中采用的新方法以及产生的数据将与其他创新研究人员共享，以促进对创新如何发生的科学进行更多和更好的研究。归根结底，这项工作有助于建立一个坚实的科学研究基础，支持创新者(现有的和有抱负的)和政策制定者努力解决当今世界面临的最紧迫和最困难的问题。

Innovation fundamentally begins with a good idea. But where do good ideas come from? Much research suggests that innovative breakthroughs are often inspired by past experience: things and ideas that one has interacted with in the world. However, the same experiences that can inspire innovation can also can constrain or harm innovation through focus on previously unsuccessful solutions. This project tests principles for guiding interactions with sources of inspiration to maximize their benefits and minimize their pitfalls. In particular, it focuses on the role of conceptual distance of sources. The following questions are the focus of this work: 1) Are good ideas built mainly on sources that are closely related to the problem (e.g., building on existing recycling efforts to address the problem of people throwing away electronics), or are they most often inspired by sources that are from distantly related domains (e.g., being inspired by how burrs cling to a dog's fur when designing Velcro)? 2) When considering multiple sources, should one try to ensure that the sources are similar to each other (i.e., deeply exploring one direction), or should one consider diverse sources? Intellectual MeritInnovation researchers and practitioners have formulated recommendations for these questions, but the scientific evidence for these recommendations is incomplete because it is based on small numbers of case studies or involved toy problems solved much more quickly than real-world problems. This project expands and improves the evidence base for understanding the sources of innovation by analyzing thousands of solutions to complex innovation challenges (e.g., increasing accessibility in elections, revitalizing struggling urban areas) posted on an online crowd-sourced innovation platform that required contributors to post sources of ideas. To analyze this evidence, it combines computer algorithms for automatically processing the content of text along with statistical algorithms for extracting meaning across thousands of ideas. The quality of the ideas are coded by experts for quality and novelty, and then the algorithms are used to statistically test the relationship between both source distance and source variety on ideation success. Broader ImpactsThe results of this work will have immediate implications for scientific theories of innovation, and are relevant to the design of systems for creating new innovations (e.g., online crowd-sourced innovation platforms, creativity support systems and methods). Further, the novel methods employed in this work, as well as the data generated, will be shared with other innovation researchers to facilitate more and better investigations into the science of how innovation happens. Ultimately, this work contributes to a solid scientific research base that can support innovators (both existing and aspiring) and policy makers in their efforts to address the most pressing and difficult problems facing the world today.