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Our Research on Comparing Idea-Sharing Versus Unmindcuffing the Crowd

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Unleashing the Crowd

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Abstract

We describe our field research in which we compare the rated innovativeness of crowds who were exposed to the Idea-Sharing process against “unmindcuffed” crowds. To just collect the data, we had to embark on a painstaking five-year effort of data collection and analysis because all third-party vendors were using the Idea-Sharing process. We describe details about how we ran the 20 crowdsourcing events in the field with partner organizations, making changes to third-party software to create the condition of unmindcuffing the crowd. We randomly assigned events to either the Idea-Sharing or the Unmindcuffed condition. We had executives rate the ideas for their novelty, implementability, and competitive advantage. We found that the unmindcuffed crowds produce more innovative solutions than the more traditionally used Idea-Sharing process!

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Notes

  1. 1.

    IBM Innovation Jams doesn’t constrain the crowd exactly as in Table 2.1 because they offer to the crowd a wicked question such as helping Royal Bank of Canada articulate a purpose, vision, values, and priorities for a new competitive era but their discussions are highly moderated, heavy involvement of senior management, and not anonymous, which leaves the crowd not constrained to the Idea-Sharing process, but still constrained. In our final chapter when we discuss future Research, we suggest that future Research should begin to assess the degree to which different variations of less constrained instructions affect innovativeness of ideas generated. Additionally, Open Ideo offers the crowd wicked problems but their instructions segment discussions by funnel model stages having the organization to select which ideas they want the crowd to focus on, so we could not use their crowdsourcing events as data. See Bjelland, O. M., & Wood, R. C. (2008). An inside view of IBM’s “Innovation Jam”. MIT Sloan Management Review, 50(1), 32.

  2. 2.

    See table comparing different types of crowdsourcing by their knowledge-sharing properties in Majchrzak, A. and Malhotra, A. (2016). Effect of Knowledge-sharing Trajectories on Innovative Outcomes in Temporary Online Crowds, Information Systems Research, 27(4), 685–703.

  3. 3.

    Information about HyperloopTT can be found on their website and in Majchrzak, A., Griffith, T., Reez, D., Alexy, O. (2018) Organizations Designed for Grand Challenges: Generative Dilemmas and Implications for Organization Design Theory. Academy of Management Discoveries, 4(4), 472–496.

  4. 4.

    Malhotra, A., Majchrzak, A., Kesebi, L., Looram, S. (2017) Developing Innovative Solutions Through Internal Crowdsourcing, Sloan Management Review, 58(4), 73–79. Malhotra et al. MIT Sloan Management Review, 2017.

  5. 5.

    Malhotra, A., and Majchrzak, A. (in press). Engaging Customer Care Employees in Internal Collaborative Crowdsourcing: Managing the Inherent Tensions and Associated Challenges. Human Resource Management.

  6. 6.

    Malhotra, A. Majchrzak, A., and Niemiec, R. (2017) Using Public Crowds for Open Strategy Formulation: Mitigating the Risks of Knowledge Representation Gaps, Long Range Planning, 50(3), 397–410.

  7. 7.

    Shadish, W. R., Clark, M. H., and Steiner, P. M. (2008). Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments. Journal of the American Statistical Association 103(484): 1334–1344.

  8. 8.

    Hutter, K., Hautz, J., Füller, J., Mueller, J., & Matzler, K. (2011). Communitition: The tension between competition and collaboration in community-based design contests. Creativity and innovation management, 20(1), 3–21. Luo, X., Slotegraaf, R. J., & Pan, X. (2006). Cross-functional “coopetition”: The simultaneous role of cooperation and competition within firms. Journal of Marketing, 70(2), 67–80; Kaufmann, N., Schulze, T., & Veit, D. (2011, August). More than fun and money: Worker Motivation in Crowdsourcing-A Study on Mechanical Turk. AMCIS, 11: 1–11; Leimeister, J. M., Huber, M., Bretschneider, U., & Krcmar, H. (2009). Leveraging crowdsourcing: activation-supporting components for IT-based ideas competition. Journal of management information systems, 26(1), 197–224.; Rogstadius, J., Kostakos, V., Kittur, A., Smus, B., Laredo, J., & Vukovic, M. (2011). An assessment of intrinsic and extrinsic motivation on task performance in crowdsourcing markets. ICWSM, 11, 17–21; Zheng, H., Li, D., & Hou, W. (2011). Task design, motivation, and participation in crowdsourcing contests. International Journal of Electronic Commerce, 15(4), 57–88.

  9. 9.

    For scholarship describing the value of passion over extrinsic motivation, see: Alam, S. L., & Campbell, J. (2017). Temporal Motivations of Volunteers to Participate in Cultural Crowdsourcing Work. Information Systems Research, 28(4), 744–759; Feng, J., Zhang, Y., Liu, X., Zhang, L., & Han, X. (2018). Just the right amount of ethics inspires creativity: a cross-level investigation of ethical leadership, intrinsic motivation, and employee creativity. Journal of Business Ethics, 153(3), 645–658; Lee, J., & Seo, D. (2016). Crowdsourcing not all sourced by the crowd: An observation on the behavior of Wikipedia participants. Technovation, 55, 14–21.

  10. 10.

    Faraj, S., Jarvenpaa, S. L., & Majchrzak, A. (2011). Knowledge collaboration in online communities. Organization Science, 22(5), 1224–1239; Malinen, S. (2015). Understanding user participation in online communities: A systematic literature review of empirical studies. Computers in Human Behavior, 46, 228–238; Massa, F. G. (2017). Guardians of the Internet: Building and Sustaining the Anonymous Online Community. Organization Studies, 38(7), 959–988.

  11. 11.

    Berg, J. 2016. Balancing on the creative high-wire: Forecasting the success of novel ideas in organizations. Administrative Science Quarterly, 61: 433–468.

  12. 12.

    Nembhard, I. M., & Edmondson, A. C. (2006). Making it safe: The effects of leader inclusiveness and professional status on psychological safety and improvement efforts in health care teams. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 27(7), 941–966.

  13. 13.

    Dennis, A. R., Wixom, B. H., & Vandenberg, R. J. (2001). Understanding fit and appropriation effects in group support systems via meta-analysis. MIS quarterly, 25(2), 167–193; McFarland, L. A., & Ployhart, R. E. (2015). Social media: A contextual framework to guide Research and practice. Journal of Applied Psychology, 100(6), 1653.

  14. 14.

    Dennis, A., & Williams, M. (2003). Electronic brainstorming. Group creativity: Innovation through collaboration, 160–178.

  15. 15.

    Tsoukas, H. (2009). A dialogical approach to the creation of new knowledge in organizations. Organization Science, 20(6), 941–957; Fuller, J., Hutter, K., Hautz, J., and Matzler, K. 2014. User roles and contributions in innovation-contest communities. Journal of Management Information Systems, 31(1), 273–307.

  16. 16.

    Pinsonneault, A., & Heppel, N. (1997). Anonymity in group support systems Research: A new conceptualization, measure, and contingency framework. Journal of Management Information Systems, 14(3), 89–108.

  17. 17.

    Jeppesen, L. B., & Lakhani, K. R. (2010). Marginality and problem-solving effectiveness in broadcast shar. Organization Science, 21(5), 1016–1033; Teplitskiy, Misha, Hardeep Ranu, Gary Gray, Michael Menietti, Eva Guinan, and Karim R. Lakhani. “Do Experts Listen to Other Experts? Field Experimental Evidence from Scientific Peer Review.” Harvard Business School Working Paper, No. 19–107, April 2019.

  18. 18.

    Von Hippel, E. (2005). Democratizing Innovation. MIT Press; von Hippel, E. (2016). Free Innovation, MIT Press.

  19. 19.

    Tsoukas, H. (2009). A dialogical approach to the creation of new knowledge in organizations. Organization Science, 20(6), 941–957.

  20. 20.

    Hackman, J. R., & Kaplan, R. E. (1974). Interventions into group process: An approach to improving the effectiveness of groups. Decision Sciences, 5(3), 459–480.

  21. 21.

    Nunamaker Jr., J. F., Applegate, L. M., & Konsynski, B. R. (1987). Facilitating group creativity: Experience with a group decision support system. Journal of Management Information Systems, 3(4), 5–19; Pinsonneault, A., & Heppel, N. (1997). Anonymity in group support systems Research: A new conceptualization, measure, and contingency framework. Journal of Management Information Systems, 14(3), 89–108; Rains, S. A., & Scott, C. R. (2007). To identify or not to identify: A theoretical model of receiver responses to anonymous communication. Communication Theory, 17(1), 61–91.

  22. 22.

    Bailey, D. E., Leonardi, P. M., & Barley, S. R. (2012). The lure of the virtual. Organization Science, 23(5), 1485–1504; Treem, J. W., & Leonardi, P. M. (2013). Social media use in organizations: Exploring the affordances of visibility, editability, persistence, and association. Annals of the International Communication Association, 36(1), 143–189. Leonardi, P. M. (2014). Social media, knowledge sharing, and innovation: Toward a theory of communication visibility. Information systems Research, 25(4), 796–816.

  23. 23.

    Nicolini, D., Mengis, J., & Swan, J. (2012). Understanding the role of objects in cross-disciplinary collaboration. Organization Science, 23(3): 612–629.

  24. 24.

    Baralou, E., & Tsoukas, H. (2015). How is new organizational knowledge created in a virtual context? An ethnographic study. Organization Studies, 1–28.

  25. 25.

    McFarland, L. A., & Ployhart, R. E. (2015). Social media: A contextual framework to guide Research and practice. Journal of Applied Psychology, 100(6), 1653.

  26. 26.

    Baralou, E., & Tsoukas, H. (2015). How is new organizational knowledge created in a virtual context? An ethnographic study. Organization Studies, 1–28.

  27. 27.

    Bailey, D. E., Leonardi, P. M., & Barley, S. R. (2012). The lure of the virtual. Organization Science, 23(5), 1485–1504; Baralou, E., & Tsoukas, H. (2015). How is new organizational knowledge created in a virtual context? An ethnographic study. Organization Studies, 1–28; Goffman, E. (1959). The Presentation of Self in Everyday Life. Doubleday, New York.

  28. 28.

    Baralou, E., & Tsoukas, H. (2015). How is new organizational knowledge created in a virtual context? An ethnographic study. Organization Studies, 1–28.

  29. 29.

    Piezunka, H. & Dahlander, L. (2015). Distant shar, narrow attention: How crowding alters organization’s filtering of suggestions in crowdsourcing. Academy of Management Journal, 58(3), 856–880.

  30. 30.

    Berg, J. (2016). Balancing on the creative high-wire: Forecasting the success of novel ideas in organizations. Administrative Science Quarterly, 61: 433–468; Mueller, J., Melwani, S., Loewenstein, J., & Deal, J. J. (2018). Reframing the decision-makers’ dilemma: Towards a social context model of creative idea recognition. Academy of Management Journal, 61(1), 94–110.

  31. 31.

    Amabile, Teresa M. Creativity in context: Update to the social psychology of creativity. Hachette, UK, 1996. This procedure is commonly done for crowdsourcing ideas in which experts are asked to assess for each idea its user-value, producibility, and originality: Mack, T., & Landau, C. (2015). Winners, losers, and deniers: Self-selection in crowd innovation contests and the roles of motivation, creativity, and skills. Journal of Engineering and Technology Management, 37, 52–64; Magnusson, P.R., 2009. Exploring the contributions of involving ordinary users in ideation of technology-based services. J. Prod. Innov. Manag. 26, 578–593; Kristensson, P., Magnusson, P.R. (2010). Tuning users’ innovativeness during ideation. Creat. Innov. Manag. 19, 147–159. One way in which our procedure differed from common practice is that we asked each executive to judge each idea independently, giving us an innovativeness rating of all the ideas. Common practice picks the top ten ideas only, turning a continuous outcome variable into a binomial, losing important variability needed for analysis.

  32. 32.

    Malhotra, A., Majchrzak, A., Kesebi, L., Looram, S. (2017). Developing Innovative Solutions Through Internal Crowdsourcing, Sloan Management Review, 58(4), 73–79.

  33. 33.

    Symonds, M. R., Moussalli, A. (2011). A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behavioral Ecology and Sociobiology, 65(1), 13–21.

  34. 34.

    Abbott, A. (2001). Time matters: On theory and method. University of Chicago Press.

  35. 35.

    Mächler, M., and Bühlmann, P. (2004). “Variable Length Markov Chains: Methodology, Computing, and Software,” Journal of Computational and Graphical Statistics, 13(2), 435–455.

  36. 36.

    Van Der Aalst, Wil M. and Ter Hofstede, Arthur H. and Kiepuszewski, Bartosz and Barros, Alistair P. (2003). Workflow Patterns. Distributed and Parallel Databases, 14(1), 5–51.

  37. 37.

    Majchrzak, A. and Malhotra, A. (2016). Effect of Knowledge-sharing Trajectories on Innovative Outcomes in Temporary Online Crowds, Information Systems Research, 27(4), 685–703.

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Authors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Ann Majchrzak

  2. University of North Carolina, Chapel Hill, NC, USA

    Arvind Malhotra

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  1. Ann Majchrzak
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Correspondence to Ann Majchrzak .

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Majchrzak, A., Malhotra, A. (2020). Our Research on Comparing Idea-Sharing Versus Unmindcuffing the Crowd. In: Unleashing the Crowd. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-25557-2_2

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