CHS: Medium: Collaborative Research: Understanding Online Creative Collaboration over Multidimensional Networks

这是对基于互联网的协作的结构和动态的研究。该项目寻求对多维网络配置如何塑造众包系统和在线社区内价值创造过程的成功的开创性见解。这项研究还提供了新的计算社会科学方法来理论化和研究社会结构和影响在技术中介的沟通和合作过程中的作用。这些发现将为那些对优化开源软件开发、学术项目和商业等领域的各种形式的合作感兴趣的领导者提供决策依据。系统设计者将能够识别人际关系动态，并开发新的功能，以便进行意见聚合和有效合作。此外，这项研究将告诉管理者如何最好地使用众包解决方案来支持创新和营销战略，包括点对点营销，以将在线社区内的活动转化为销售。这项研究将分析数字跟踪数据，从而能够以前所未有的规模研究人口层面的人类互动。了解这种相互作用对于预测我们的社会、经济和政治生活以及系统设计的影响至关重要。这种互动的一个场所是众包系统--社会技术系统，通过它，由不同和分散的个人组成的在线社区动态地协调工作和关系。许多众包系统不仅产生创造性的内容，还包含丰富的协作和评估社区，在这些社区中，创造性内容的创建者和采用者之间以及通过诸如从属关系、沟通、亲和力和购买等重叠关系与构件进行交互。这些关系构成了多维网络，并在多个层面上创造了结构。实证研究尚未检验众包中的多维网络如何实现有效的大规模协作。这些数据来自两个截然不同的来源，因此提供了在一系列以创造为导向的在线社区之间进行比较的机会。一个是创意服装设计的众包平台和电子商务网站，另一个是参与者基于废旧材料创造创新设计的平台。这个项目将分析在线社区活动和线下购买行为。这些数据提供了一个独特的机会，可以了解推动同龄人影响和意见形成的社会互动的重叠结构，以及这种在线活动的线下经济后果。这项研究通过(1)分析这些社会技术系统中人际和社会技术互动的多维网络结构，(2)模拟成功如何反馈到价值创造过程并促进学习，以及(3)开发方法来预测在这些背景下产生的创意产品的经济成功，从而为文献做出贡献。各种计算和统计方法的应用和整合将有助于开发可扩展到其他形式的数据密集型查询的技术资源，从而为更广泛的科学研究界带来巨大的红利。这包括关于综合分类和预测方法的最佳做法的文件；培训学生进行大规模数据分析的课程；以及开发新的理论方法以了解网络-人类系统的多维基础。

This is a study of the structure and dynamics of Internet-based collaboration. The project seeks groundbreaking insights into how multidimensional network configurations shape the success of value-creation processes within crowdsourcing systems and online communities. The research also offers new computational social science approaches to theorizing and researching the roles of social structure and influence within technology-mediated communication and cooperation processes. The findings will inform decisions of leaders interested in optimizing all forms of collaboration in fields such as open-source software development, academic projects, and business. System designers will be able to identify interpersonal dynamics and develop new features for opinion aggregation and effective collaboration. In addition, the research will inform managers on how best to use crowdsourcing solutions to support innovation and marketing strategies including peer-to-peer marketing to translate activity within online communities into sales.This research will analyze digital trace data that enable studies of population-level human interaction on an unprecedented scale. Understanding such interaction is crucial for anticipating impacts in our social, economic, and political lives as well as for system design. One site of such interaction is crowdsourcing systems - socio-technical systems through which online communities comprised of diverse and distributed individuals dynamically coordinate work and relationships. Many crowdsourcing systems not only generate creative content but also contain a rich community of collaboration and evaluation in which creators and adopters of creative content interact among themselves and with artifacts through overlapping relationships such as affiliation, communication, affinity, and purchasing. These relationships constitute multidimensional networks and create structures at multiple levels. Empirical studies have yet to examine how multidimensional networks in crowdsourcing enable effective large-scale collaboration. The data derive from two distinctly different sources, thus providing opportunities for comparison across a range of online creation-oriented communities. One is a crowdsourcing platform and ecommerce website for creative garment design, and the other is a platform for participants to create innovative designs based on scrap materials. This project will analyze both online community activity and offline purchasing behavior. The data provide a unique opportunity to understand overlapping structures of social interaction driving peer influence and opinion formation as well as the offline economic consequences of this online activity. This study contributes to the literature by (1) analyzing multidimensional network structures of interpersonal and socio-technical interactions within these socio-technical systems, (2) modeling how success feeds back into value-creation processes and facilitates learning, and (3) developing methods to predict the economic success of creative products generated in these contexts. The application and integration of various computational and statistical approaches will provide significant dividends to the broader scientific research community by contributing to the development of technical resources that can be extended to other forms of data-intensive inquiry. This includes documentation about best practices for integrating methods for classification and prediction; courses to train students to perform large-scale data analysis; and developing new theoretical approaches for understanding the multidimensional foundations of cyber-human systems.