Selected Publications

  • Riedl, C., Seidel, V. (2018). “Learning from Mixed Signals in Online Innovation Communities,” Organization Science, in press.
  • Riedl, C., et al. (2018). “Product Diffusion Through On-Demand Information-Seeking Behavior,” Journal of the Royal Society Interface, 15(139), 20170751.
  • Foley, M., Forber, P., Smead, R., Riedl, C. (2018). “Conflict and Convention in Dynamic Networks,” Journal of the Royal Society Interface, 15(140), 2017075.
  • Riedl, C., Woolley, A.W. (2017). “Teams vs. Crowds: A Field Test of the Relative Contribution of Incentives, Member Ability, and Emergent Collaboration to Crowd-Based Problem Solving Performance,” Academy of Management Discoveries, 3(4), 382-403.
  • Boudreau, K., Guinan, E., Lakhani, K., Riedl, C. (2016). “Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance and Resource Allocation in Science,” Management Science, 62(10), 2765-2783.
  • Blohm, I., Riedl, C., Füller, J., Leimeister, J. M. (2016). “Rate or Trade? Identifying Winning Ideas in Open Idea Sourcing,” Information Systems Research, 27(1), 27-48.

Selected Presentations

  • “Social Network Processes in Collaborative Decision-Making,” Conference on Opinion Dynamics, 13th June, 2016.
  • “Big Data and Financial Inclusion,” Rethinking Financial Inclusion, Harvard Kennedy School, Cambridge, MA, 19th April, 2016.
  • “Peer Production Networks, International Workshop on Network Theory,” Northwestern University, Evanston, 30th October, 2015.
  • “From Crowds to Collaborators: Self-Organization and Collaboration in Online Creative Teams”, Google Inc. (Human Computation Tech Talk, Mountain View, CA, 2014) and Facebook Inc. (Palo Alto, CA, 2014).
  • “Crowdsourcing for a Social Good: The Impact of Incentive Preferences on Consumers’ Creative Contributions to Social Innovation,” NASA Center of Excellence for Collaborative Innovation, June 2014.

Education

  • Ph.D. in Information Systems, Technische Universität München
  • M.Sc. in Information Systems, Technische Universität München
  • B.Sc. in Computer Science, Technische Universität München

Research & Teaching Interests

Professor Riedl holds interests in business analytics, data science, “Big Data,” and computational social science. He is also attracted to decision making by experts (peer-review) and non-experts (collective intelligence), as well as social media and online social networks, individual and team productivity, and the web as a platform for service innovation.

Services to the Profession

  • Member Editorial Review Board for Academy of Management Discoveries.
  • Core faculty, Network Science Institute.
  • Core faculty, Center for Digital Humanities & Computational Social Science (NUlab)
  • Visiting Fellow, Institute for Quantitative Social Science (IQSS), Harvard University

Awards & Recognition

  • National Science Foundation Grant (2015).
  • Young Investigator Program Award (YIP).
  • DRUID Best Paper Award (2016)
  • Joseph G. Riesman Research Professorship (D’Amore-McKim School of Business, 2016-2018)
  • Post-Doctoral Fellowship of the German Research Foundation (DFG; 2011-2013).