Crowdsourcing Coordination: A Review and Research Agenda for Crowdsourcing Coordination Used for Macro-tasks

  • Sangmi KimEmail author
  • Lionel P. Robert Jr.
Part of the Human–Computer Interaction Series book series (HCIS)


Crowdsourcing has become a widely accepted approach to leveraging the skills and expertise of others to accomplish work. Despite the potential of crowdsourcing to tackle complex problems, it has often been used to address simple micro-tasks. To tackle more complex macro-tasks, more attention is needed to better comprehend crowd coordination. Crowd coordination is defined as the synchronization of crowd workers in an attempt to direct and align their efforts in pursuit of a shared goal. The goal of this chapter is to advance our understanding of crowd coordination to tackle complex macro-tasks. To accomplish this, we have three objectives. First, we review popular theories of coordination. Second, we examine the current approaches to crowd coordination in the HCI and CSCW literature. Finally, the chapter identifies shortcomings in the literature and proposes a research agenda directed at advancing our understanding of crowd coordination needed to address complex macro-tasks.



This book chapter was supported in part by the National Science Foundation [grant CHS-1617820].


  1. Adler, P. S., Kwon, S. W., & Heckscher, C. (2008). Perspective—professional work: The emergence of collaborative community. Organization Science, 19(2), 359–376.CrossRefGoogle Scholar
  2. Alavi, M., & Tiwana, A. (2002). Knowledge integration in virtual teams: The potential role of KMS. Journal of the American Society for Information Science and Technology, 53(12), 1029–1037.CrossRefGoogle Scholar
  3. Anderson, E. W., Potter, K. C., Matzen, L. E., Shepherd, J. F., Preston, G. A., & Silva, C. T. (2011). A user study of visualization effectiveness using EEG and cognitive load. In Computer graphics forum (Vol. 30, No. 3, pp. 791–800). Oxford, UK: Blackwell Publishing Ltd.Google Scholar
  4. Andres, H. P., & Zmud, R. W. (2002). A contingency approach to software project coordination. Journal of Management Information Systems, 18(3), 41–70.CrossRefGoogle Scholar
  5. Austin, J. R. (2003). Transactive memory in organizational groups: The effects of content, consensus, specialization, and accuracy on group performance. Journal of Applied Psychology, 88(5), 866–878.CrossRefGoogle Scholar
  6. Bechky, B. A. (2003). Sharing meaning across occupational communities: The transformation of understanding on a production floor. Organization Science, 14(3), 312–330.CrossRefGoogle Scholar
  7. Bechky, B. A. (2006). Gaffers, gofers, and grips: Role-based coordination in temporary organizations. Organization Science, 17(1), 3–21.CrossRefGoogle Scholar
  8. Bolici, F., Howison, J., & Crowston, K. (2009). Coordination without discussion? Socio-technical congruence and stigmergy in free and open source software projects. Paper presented at the International Conference on Software Engineering, Vancouver, BC, Canada. Retrieved from
  9. Bolici, F., Howison, J., & Crowston, K. (2016). Stigmergic coordination in FLOSS development teams: Integrating explicit and implicit mechanisms. Cognitive Systems Research, 38, 14–22.CrossRefGoogle Scholar
  10. Brandon, D. P., & Hollingshead, A. B. (2004). Transactive memory systems in organizations: Matching tasks, expertise, and people. Organization Science, 15(6), 633–644.CrossRefGoogle Scholar
  11. Choi, S. Y., Lee, H., & Yoo, Y. (2010). The impact of information technology and transactive memory systems on knowledge sharing, application, and team performance: A field study. MIS Quarterly, 34(4), 855–870.CrossRefGoogle Scholar
  12. Crowston, K. (1994). A taxonomy of organisational dependencies and coordination mechanisms. MIT Center for Coordination Science Working Paper. Massachusetts Institute of Technology, August 1994.Google Scholar
  13. Crowston, K., Howison, J., & Rubleske, J. (2006). Coordination theory: A ten year retrospective. In P. Zhang & D. Galletta (Eds.), Human-computer interaction in management information systems—foundations (pp. 120–138). Armonk, NY: M. E. Sharpe Inc.Google Scholar
  14. Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32–64.CrossRefGoogle Scholar
  15. Faraj, S., & Xiao, Y. (2006). Coordination in fast-response organizations. Management Science, 52(8), 1155–1169.CrossRefGoogle Scholar
  16. Feldman, M. S. (2000). Organizational routines as a source of continuous change. Organization Science, 11(6), 611–629.CrossRefGoogle Scholar
  17. Gittell, J. H. (2002). Coordinating mechanisms in care provider groups: Relational coordination as a mediator and input uncertainty as a moderator of performance effects. Management Science, 48(11), 1408–1426.CrossRefGoogle Scholar
  18. Gittell, J. H. (2006). Relational coordination: Coordinating work through relationships of shared goals, shared knowledge and mutual respect. In O. Kyriakidou & M. F. Özbilgin (Eds.), Relational perspectives in organizational studies: A research companion (pp. 74–94). Cheltenham, UK: Edward Elgar Publishers.Google Scholar
  19. Gittell, J. H. (2011). New directions for relational coordination theory. In K. S. Cameron & G. M. Spreitzer (Eds.), The Oxford handbook of positive organizational scholarship (pp. 400–411). New York, NY: Oxford University Press.Google Scholar
  20. Gittell, J. H. (2016). Transforming relationships for high performance: The power of relational coordination. Palo Alto, CA: Stanford University Press.Google Scholar
  21. Haas, D., Ansel, J., Gu, L., & Marcus, A. (2015). Argonaut: Macrotask crowdsourcing for complex data processing. Proceedings of the VLDB Endowment, 8(12), 1642–1653.CrossRefGoogle Scholar
  22. Heylighen, F. (2015). Stigmergy as a universal coordination mechanism: Components, varieties and applications. In T. Lewis & L. Marsh (Eds.), Human stigmergy: Theoretical developments and new applications. New York, NY: Springer.Google Scholar
  23. Heylighen, F. (2016). Stigmergy as a universal coordination mechanism I: Definition and components. Cognitive Systems Research, 38, 4–13.CrossRefGoogle Scholar
  24. Holland, O., & Melhuish, C. (1999). Stigmergy, self-organization, and sorting in collective robotics. Artificial Life, 5(2), 173–202.CrossRefGoogle Scholar
  25. Howe, J. (2006). The rise of crowdsourcing. Wired, 14(6), 1–4.Google Scholar
  26. Jehn, K. A. (1997). A quantitative analysis of conflict types and dimensions in organizational groups. Administrative Science Quarterly, 42(3), 530–557.CrossRefGoogle Scholar
  27. Jung, J. Y., & Mellers, B. A. (2016). American attitudes toward nudges. Judgment & Decision Making, 11(1), 62–74.Google Scholar
  28. Kankanhalli, A., Tan, B. C., & Wei, K. K. (2006). Conflict and performance in global virtual teams. Journal of Management Information Systems, 23(3), 237–274.CrossRefGoogle Scholar
  29. Kaur, H., Williams, A. C., Thompson, A. L., Lasecki, W. S., Iqbal, S. T., & Teevan, J. (2018). Creating better action plans for writing tasks via vocabulary-based planning. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 86.Google Scholar
  30. Khuong, A., Gautrais, J., Perna, A., Sbaï, C., Combe, M., Kuntz, P., et al. (2016). Stigmergic construction and topochemical information shape ant nest architecture. Proceedings of the National Academy of Sciences, 113(5), 1303–1308.CrossRefGoogle Scholar
  31. Kim, J., Sterman, S., Cohen, A. A. B., & Bernstein, M. S. (2017). Mechanical novel: Crowdsourcing complex work through reflection and revision. In Proceedings of the 2017 ACM Conference on Computer-supported Cooperative Work and Social Computing (pp. 233–245). New York, NY: ACM.Google Scholar
  32. Kittur, A., Smus, B., Khamkar, S., & Kraut, R. E. (2011). Crowdforge: Crowdsourcing complex work. In Proceedings of the 24th annual ACM symposium on User Interface Software and Technology (pp. 43–52). New York, NY: ACM.Google Scholar
  33. Kulkarni, A., Can, M., & Hartmann, B. (2012). Collaboratively crowdsourcing workflows with Turkomatic. In Proceedings of the ACM 2012 Conference on Computer-supported Cooperative Work (pp. 1003–1012). New York, NY: ACM.Google Scholar
  34. Lave, J. (1991). Situating learning in communities of practice. Perspectives on Socially Shared Cognition, 2, 63–82.CrossRefGoogle Scholar
  35. Lave, J. (2009). The practice of learning. In K. Illeris (Ed.), Contemporary learning theories (pp. 200–208). London, UK: Routledge.Google Scholar
  36. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
  37. Lewis, K. (2003). Measuring transactive memory systems in the field: Scale development and validation. Journal of Applied Psychology, 88, 587–604.CrossRefGoogle Scholar
  38. Malone, T. W., & Crowston, K. (1994). The interdisciplinary study of coordination. ACM Computing Surveys (CSUR), 26(1), 87–119.CrossRefGoogle Scholar
  39. Marques-Quinteiro, P., Curral, L., Passos, A. M., & Lewis, K. (2013). And now what do we do? The role of transactive memory systems and task coordination in action teams. Group Dynamics: Theory, Research, and Practice, 17(3), 194–206.CrossRefGoogle Scholar
  40. Montoya-Weiss, M. M., Massey, A. P., & Song, M. (2001). Getting it together: Temporal coordination and conflict management in global virtual teams. Academy of Management Journal, 44(6), 1251–1262.Google Scholar
  41. Moreland, R. L. (1999). Transactive memory: Learning who knows what in work groups and organizations. In L. L. Thompson, J. M. Levine, & D. M. Messick (Eds.), Shared cognition in organizations: The management of knowledge (pp. 3–31). Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
  42. Munson, S. A., Kervin, K., & Robert Jr., L. P. (2014). Monitoring email to indicate project team performance and mutual attraction. In Proceedings of the 17th ACM Conference on Computer-supported Cooperative Work & Social Computing (pp. 542–549). New York, NY: ACM.Google Scholar
  43. Okhuysen, G. A., & Bechky, B. A. (2009). Coordination in organizations: An integrative perspective. In J. P. Walsh & A. P. Brief (Eds.), Academy of management annals (Vol. 3, pp. 463–502). Essex, UK: Routledge.Google Scholar
  44. Paul, S., Seetharaman, P., Samarah, I., & Mykytyn, P. P. (2004). Impact of heterogeneity and collaborative conflict management style on the performance of synchronous global virtual teams. Information & Management, 41(3), 303–321.CrossRefGoogle Scholar
  45. Ren, Y., & Argote, L. (2011). Transactive memory systems 1985–2010: An integrative framework of key dimensions, antecedents, and consequences. Academy of Management Annals, 5(1), 189–229.CrossRefGoogle Scholar
  46. Retelny, D., Bernstein, M. S., & Valentine, M. A. (2017). No workflow can ever be enough: How crowdsourcing workflows constrain complex work. Proceedings of the ACM on Human-Computer Interaction, 1(CSCW), 89.Google Scholar
  47. Retelny, D., Robaszkiewicz, S., To, A., Lasecki, W. S., Patel, J., Rahmati, N.,… Bernstein, M. S. (2014). Expert crowdsourcing with flash teams. In Proceedings of the 27th annual ACM Symposium on User Interface Software and Technology (pp. 75–85). New York, NY: ACM.Google Scholar
  48. Rezgui, A., & Crowston, K. (2018). Stigmergic coordination in Wikipedia. In Proceedings of the 14th International Symposium on Open Collaboration (pp. 1–12). Paris, France: ACM Press.Google Scholar
  49. Robert, L. P. (2016). Far but near or near but far?: The effects of perceived distance on the relationship between geographic dispersion and perceived diversity. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 2461–2473). New York, NY: ACM.Google Scholar
  50. Robert, L. P., Dennis, A. R., & Ahuja, M. (2008). Social capital and knowledge integration in digitally enabled teams. Information Systems Research, 19(3), 314–334.
  51. Robert, L. P., Dennis, A. R., & Ahuja, M. (2018). Differences are different: Examining the effects of communication media on the impacts of racial and gender diversity in decision-making teams. Information Systems Research, 29(3), 525–545. Scholar
  52. Robert, L. P., & Romero, D. M. (2015). Crowd size, diversity and performance. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1379–1382). New York, NY: ACM.Google Scholar
  53. Robert, L. P., Jr., & Romero, D. M. (2017). The influence of diversity and experience on the effects of crowd size. Journal of the Association for Information Science and Technology, 68(2), 321–332.CrossRefGoogle Scholar
  54. Salehi, N., McCabe, M., Valentine, M., & Bernstein, M. S. (2017). Huddler: Convening stable and familiar crowd teams despite unpredictable availability. In Proceedings of the 20th ACM Conference on Computer-supported Cooperative Work & Social Computing (CSCW’17). New York, NY: ACM.Google Scholar
  55. Schmitz, H., & Lykourentzou, I. (2018). Online sequencing of non-decomposable macrotasks in expert crowdsourcing. ACM Transactions on Social Computing, 1(1), 1–34.CrossRefGoogle Scholar
  56. Strode, D. E., Huff, S. L., Hope, B., & Link, S. (2012). Coordination in co-located agile software development projects. Journal of Systems and Software, 85(6), 1222–1238.CrossRefGoogle Scholar
  57. Suchman, L. (1987). Plans and situated actions: The problem of human–machine communication. Cambridge, MA: Cambridge University Press.Google Scholar
  58. Teevan, J., Iqbal, S. T., & Von Veh, C. (2016). Supporting collaborative writing with microtasks. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 2657–2668). New York, NY: ACM.Google Scholar
  59. Valckenaers, P., Kollingbaum, M., & Van Brussel, H. (2004). Multi-agent coordination and control using stigmergy. Computers in Industry, 53(1), 75–96.zbMATHCrossRefGoogle Scholar
  60. Valentine, M. A., & Edmondson, A. C. (2014). Team scaffolds: How mesolevel structures enable role-based coordination in temporary groups. Organization Science, 26(2), 405–422.CrossRefGoogle Scholar
  61. Valentine, M. A., Retelny, D., To, A., Rahmati, N., Doshi, T., & Bernstein, M. S. (2017, May). Flash organizations: Crowdsourcing complex work by structuring crowds as organizations. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 3523–3537). New York, NY: ACM.Google Scholar
  62. Wegner, D. M. (1987). Transactive memory: A contemporary analysis of the group mind. In B. Mullen & G. R. Geothals (Eds.), Theories of group behavior (pp. 185–208). New York, NY: Springer.CrossRefGoogle Scholar
  63. Windeler, J., Maruping, L., Robert, L. P., & Riemenschneider, C. (2015). E-identity, conflict and shared understanding in distributed teams. Journal of the Association for Information Systems, 16(7), 608–645.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Michigan School of InformationAnn ArborUSA

Personalised recommendations