Crowd Science 2018: HICSS Mini-Track
IT-mediated Crowds are being implemented for multifarious purposes, using multifarious
techniques. In this minitrack we seek to coalesce a specific and enduring community of IS and
IS-related researchers focused on the study of IT-mediated crowds as a phenomenon.
Our aim is to harness, and thus focus, the currently very broad inter-disciplinary study of IT-
mediated Crowds within the IS discipline proper, to incite a sharing of results and a cross-
pollination of ideas among researchers currently looking at IT-mediated Crowds from IS, I-
School, HCI, Computer Science, Marketing, Education, Natural Sciences, Communication, and
Technology Innovation perspectives.
In the purview of this mini-track, IT-mediated crowd phenomena include:
Crowd Finance (Crowdfunding, Blockchains, Digital Ledgers, etc)
Open Innovation/Competition platforms
Social Media for resource creation
Wikis & Wikipedia
Big Data from crowds
Participatory Sensing (Crowdsensing)
Spatial Crowdsourcing (the Sharing & Gig Economy)
Situated/Geo-fenced/IoT Crowdsourcing/VR crowds
IT-mediated Collective Intelligence
We encourage new empirical and theoretical submissions from social, economic, technical, and
organizational scholars, investigating these phenomena in a variety of contexts, including:
Entrepreneurship/User Innovation/Creative Consumers
Institutional & Strategic perspectives
International Business & Development perspectives
Particular questions/topics of interest include:
Human computation, micro-tasking and virtual labour markets
Crowdsourced contests, their design and efficacy
Gamification in IT-mediated crowds
IT-mediated crowds and law/intellectual property
IT-mediated crowds for invention and commercialization
Business models of IT-mediated crowd companies and startups
The economics of IT-mediated crowds
The knowledge dynamics of IT-mediated crowds
IT-mediated crowds and 3D printing
Wearables & Sensors in, and as crowds
IT-mediated crowds and machine learning
The role of Bots/AI in IT-mediated crowds
Measuring IT-mediated crowds and outcomes
Formal models/computational models/simulations
IT-mediated crowd platforms
IT-mediated crowds & Common pool resources
Varieties of Crowd Capital
IT-mediated crowds and Industry/competitive dynamics
Crowd-labor movements and labor dynamics
Expert, non-expert, and mixed Crowds
Knowledge management in, and through, IT-mediated crowds
Double-sided markets/electronic markets/platforms
As track co-chairs, we endeavour to coalesce a set of compelling talks, provide developmental
paper reviews, and special issues stemming from the track, focused on one or more of the
areas mentioned here.
In the last two years, we’re delighted that we’ve been able to welcome eight substantial
contributions to the Crowd Science program, which as a whole cross disciplinary boundaries,
employ a variety of methodologies, and mark important new avenues in the field. These
contributions, as well as a bibliography of what we consider to be fundamental Crowd Science
research, are listed below.
Papers are due June 15 2017. We look forward to your submission!
Lulea University of Technology
Afuah, A., & Tucci, C.L. (2012). Crowdsourcing as a solution to distant search. Academy of Management
Review. 37(3), 355-375.
Aitamurto, T. (2016). Collective Intelligence in Law Reforms: When the Logic of the Crowds and the
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Sciences, vol. 49, pp. 5-8. 2016.
Archak, N., & Sundararajan, A. (2009). Optimal Design of Crowdsourcing Contests. In ICIS 2009
Arrow, K.J, R. Forsythe, M. Gorham, R. Hahn, R. Hanson, J.O. Ledyard, S. Levmore, R. Litan, P. Milgrom,
F.D. Nelson, G.R. Neumann, M. Ottaviani, T.C. Schelling, R.J. Shiller, V.L. Smith, E. Snowberg, C.R.
Sunstein, P.C. Tetlock, P.E. Tetlock, H.R. Varian, J. Wolfers, & E. Zitzewitz. (2008). The promise of
prediction markets. Science. 320(5878), 877-878.
Bayus, B.L. (2012). Crowdsourcing New Product Ideas over Time: An Analysis of the Dell IdeaStorm
Community. Management Science. 59(1), 226-244.
Belleflamme, P., Lambert, T., & Schwienbacher, A. (2013). Crowdfunding: Tapping the right crowd.
Journal of Business Venturing. 29(5), 585-609.
Bernstein, M.S., Brandt, J., Miller, R.C., & Karger, D.R. (2011). Crowds in two seconds: Enabling realtime
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Bernstein, M.S. (2013). Crowd-powered systems. Electrical Engineering and Computer Science.
Cambridge, Massachusetts. Massachusetts Institute of Technology Ph.D. Dissertation.
Blohm, I., Bretschneider, U., Leimeister, J.M., & Krcmar, H. (2011). Does collaboration among
participants lead to better ideas in IT-based idea competitions? An empirical investigation. International
Journal of Networking and Virtual Organisations. 9(2), 106-122.
Bott, M., Gigler, B.-S., & Young, G. (2014). The Role of Crowdsourcing for Better Governance in Fragile
State Contexts. International Bank for Reconstruction and Development/The World Bank.
Brabham, D.C. (2008). Crowdsourcing as a model for problem solving. Convergence. 14(1), 75-90.
Brabham, D.C. (2012). The Effectiveness of Crowdsourcing Public Participation in a Planning Context.
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Brabham, D.C. (2013). Using Crowdsourcing in Government. IBM Center for the Business of
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consequences of contribution patterns in crowd-funded markets. Information Systems Research. 24(3),
Chesbrough, H.W, (2003). Open Innovation: The new imperative for creating and profiting from
technology. Boston: Harvard Business School Press.
Chua, R.Y.J., Roth, Y., & Lemoine, J.F. (2015). The Impact of Culture on Creativity How Cultural Tightness
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Crowston, K., & Prestopnik, N. (2013). Motivation and Data Quality in a Citizen Science Game: A Design
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Cullina, E., Conboy, K. & Morgan, L. (2016). Choosing the Right Crowd: An Iterative Process for Crowd
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De Vreede, T., Nguyen, C., De Vreede, G. J., Boughzala, I., Oh, O., & Reiter-Palmon, R. (2013). A
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Difallah, D.E., Catasta, M., Demartini, G., Ipeirotis, P.G., & Cudré-Mauroux, P. (2015). The dynamics of
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Dron, J., & Anderson, T. (2014). Teaching Crowds: Learning and Social Media. Edmonton, AB:AU Press,
Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected
world. Cambridge University Press.
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Jeppesen, L.B., & Lakhani, K.R. (2010). Marginality and Problem-Solving Effectiveness in Broadcast
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Kamar, E., S. Hacker, & E. Horvitz, (2012). Combining human and machine intelligence in large-scale
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Kietzmann, J., & Canhoto, A. (2013). Bittersweet! Understanding and managing electronic word of
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