Thu 02.18.16
Thu 02.18.16
Thu 02.18.16
Thu 02.18.16
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.
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.
