| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | July 9, 2015 |
| Latest Amendment Date: | April 7, 2016 |
| Award Number: | 1514283 |
| Award Instrument: | Standard Grant |
| Program Manager: |
William Bainbridge
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | July 1, 2015 |
| End Date: | June 30, 2020 (Estimated) |
| Total Intended Award Amount: | $525,555.00 |
| Total Awarded Amount to Date: | $533,555.00 |
| Funds Obligated to Date: |
FY 2016 = $8,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
360 HUNTINGTON AVE BOSTON MA US 02115-5005 (617)373-5600 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
MA US 02115-5005 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | HCC-Human-Centered Computing |
| Primary Program Source: |
01001617DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The goal of this research was to study the social and institutional structures that underlie creative production and success. This project made important contributions to our understanding of reputation and success in art, an area of human activity in which performance is difficult to quantify objectively. We demonstrate that networks of influence play a key role in determining artist?s access to resources and rewards. Our results codify the stratification of the art world, which limits access of artists to institutions that would be beneficial to their career. Artists who had access to prestigious institutions early in their careers, managed to sustain their careers and enjoyed life-long access to high-prestige venues. By contrast, artists that starting at the network periphery resulted had limited access to prestigious institutions and often abandoned careers in art entirely. We also contribute to knowledge on how individual artists and designers can learn through indirect interaction in online crowdsourcing communities. We show that individuals can learn in a competitive rather than a cooperative environment, in which they are exposed only to sparse and hard-to-interpret signals simply by studying the work of others. However, learning in such an environment is often difficult and can require significant initial investments before performance improves. This interdisciplinary research had broad impact by on our understanding of how institutional structures and human behavior influence learning and career trajectories of individuals working in creative fields. Insights from our work allow managers to design better crowdsourcing systems and can inform public policy to help level the playing field by quantifying barriers and the mechanism of access.
Last Modified: 10/06/2020
Modified by: Christoph Riedl
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