Unleashing the Potential of Crowd Work: The Need for a Post-Taylorism Crowdsourcing Model
Abstract
Paid crowdsourcing connects task requesters to a globalized, skilled workforce that is available 24/7. In doing so, this new labor model promises not only to complete work faster and more efficiently than any previous approach but also to harness the best of our collective capacities. Nevertheless, for almost a decade now, crowdsourcing has been limited to addressing rather straightforward and simple tasks. Large-scale innovation, creativity, and wicked problem-solving are still largely out of the crowd’s reach. In this opinion paper, we argue that existing crowdsourcing practices bear significant resemblance to the management paradigm of Taylorism. Although criticized and often abandoned by modern organizations, Taylorism principles are prevalent in many crowdsourcing platforms, which employ practices such as the forceful decomposition of all tasks regardless of their knowledge nature and the disallowing of worker interactions, which diminish worker motivation and performance. We argue that a shift toward post-Taylorism is necessary to enable the crowd address at scale the complex problems that form the backbone of today’s knowledge economy. Drawing from recent literature, we highlight four design rules that can help make this shift, namely, endorsing social crowd networks, encouraging teamwork, scaffolding ownership of one’s work within the crowd, and leveraging algorithm-guided worker self-coordination.
Downloads
References
Alkhatib, A., Bernstein, M. S. & Levi, M. (2017). Examining crowd work and gig work through the historical lens of piecework. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 4599–4616). ACM, Denver Colorado USA. doi: 10.1145/3025453.3025974
Aloisi, A., Commoditized Workers. Case Study Research on Labour Law Issues Arising from a Set of ‘On-Demand/Gig Economy’ Platforms (May 1, 2016). Comparative Labor Law & Policy Journal, 37(3), 2016, Available at SSRN: http://dx.doi.org/10.2139/ssrn.2637485
Ballardini, R. M., Lindman, J. & Ituarte, I. F. (2016). Co-creation, commercialization and intellectual property-challenges with 3D printing. European Journal of Law and Technology, 7(3), 1–37.
Basu Roy, S., Lykourentzou, I., Thirumuruganathan, S., Amer-Yahia, S. & Das, G. (2014). Optimization in knowledge-intensive crowdsourcing. ArXiv E-Prints, arXiv:1401.1302.
Berg, J. (2016). Income security in the on-demand economy: Findings and policy lessons from a survey of crowdworkers. Comparative Labor Law and Policy Journal, 37(3), 506–543.
Cherry, M. A. (2009). Working for (virtually) minimum wage: Applying the fair labor standards act in cyberspace. Pacific McGeorge School of Law Research Paper. Retrieved from https://ssrn.com/abstract=1499823
Chesbrough, H. (2003) Open Innovation: The New Imperative for Creating and Profiting, from Technology. Harvard Business School Press, Boston.
Brabham, D. C. (2008). Crowdsourcing as a model for problem solving. Convergence, 14(1), 75–90. doi: 10.1177/1354856507084420
Deng, X., Joshi, K. D. & Galliers, R. D. (2016). The duality of empowerment and marginalization in microtask crowdsourcing: Giving voice to the less powerful through value sensitive design. MIS Quarterly, 40(2), 279–302. doi: 10.25300/MISQ/2016/40.2.01
Derksen, M. (2014). Turning men into machines? Scientific management, industrial psychology, and the ‘human factor’. Journal of the History of the Behavioral Sciences, 50(2), 148–165. doi: 10.1002/jhbs.21650
Ekbia, H. R. & Nardi, B. A. (2017). Heteromation, and other stories of computing and capitalism. MIT Press.
El Maarry, K., Milland, K. & Balke, W. (2018). A fair share of the work? The evolving ecosystem of crowd workers. In H. Akkermans, K. Fontaine, & I. Vermeulen (Eds.), Proceedings of the 10th ACM conference on web science (pp. 145–152). Association for Computing Machinery. doi: 10.1145/3201064.3201074
Felin, T. & Zenger, T. R. (2014). Closed or open innovation? Problem solving and the governance choice. Research Policy, 43(5), 914–925. doi: 10.1016/j.respol.2013.09.006
Fixson, S. K. & Marion, T. J. (2016). A case study of crowdsourcing gone wrong. Harvard Business Review, December 15. Retrieved from https://hbr.org/2016/12/a-case-study-of-crowdsourcing-gone-wrong
Florisson, R. & Mandl, I. (2018). Platform work: Types and implications for work and employment – Literature review. Working paper WPEF18004 Eurofound, Dublin.
Gillespie, T. (2010). The politics of ‘platforms’. New Media & Society, 12(3), 347–364. doi: 10.1177/1461444809342738
Gray, M., Shoaib, S., Kulkarni, D. & Suri, S. (2016). The crowd is a collaborative network. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing (pp. 134–147). ACM, San Francisco California USA. doi: 10.1145/2818048.2819942
Grewal-Carr, V. & Bates, C. (2016). The three billion Enterprise crowdsourcing and the growing fragmentation of work. Deloitte LLP. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/de/Documents/Innovation/us-cons-enterprise-crowdsourcing-and-growing-fragmentation-of-work%20(3).pdf
Grier, D. A. (2013). Crowdsourcing for dummies. John Wiley & Sons.
Irani, L. (2015). The cultural work of microwork. New Media & Society, 17(5), 720–739. doi: 10.1177/1461444813511926
Kankanhalli, A., Tan, B. C. Y. & Wei, K. (2005). Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS Quarterly, 29(1), 113–143. doi: 10.2307/25148670
Khan, J., Lykourentzou, I., Papangelis, K. & Markopoulos, P. (2019). Macro-task crowdsourcing: Engaging the crowds to address complex problems. Springer Human Computer Interaction Series. Springer Nature Switzerland.
Kim, S. & Robert, L. P. (2019). Crowdsourcing coordination: A review and research agenda for crowdsourcing coordination used for macro-tasks. In J. Khan, I. Lykourentzou, K. Papangelis, & P. Markopoulos (Eds.), Macro-task crowdsourcing: Engaging the crowds to address complex problems (pp. 17–43). Springer Human Computer Interaction Series. Springer, Cham, New York USA. doi: 10.1007/978-3-030-12334-5_2
Lykourentzou, I., Antoniou, A., Naudet, Y. & Dow, S. P. (2016). Personality matters: Balancing for personality types leads to better outcomes for crowd teams. In D. Gergle & M. R. R. Morris (Eds.), Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 260–273). Association for Computing Machinery. doi: 10.1145/2818048.2819979
Lykourentzou, I., Khan, V. J., Papangelis, K. & Markopoulos, P. (2019). Macrotask crowdsourcing: An integrated definition. In V.–J. Khan, K. Papangelis, I. Lykourentzou & P. Markopoulos (Eds.), Macrotask crowdsourcing (pp. 1–13). Springer.
Lykourentzou, I., Kraut, R. E. & Dow, S. P. (2017). Team dating leads to better online ad hoc collaborations. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 2330–2343). ACM, Portland Oregon USA. doi: 10.1145/2998181.2998322.
Majchrzak, A. & Malhotra, A. (2013). Towards an information systems perspective and research agenda on crowdsourcing for innovation. Journal of Strategic Information Systems, 22(4), 257–268. doi: 10.1016/j.jsis.2013.07.004
Mention, A. L., Barlatier, P.-J. & Josserand, E. (2019). Using social media to leverage and develop dynamic capabilities for innovation. Technological Forecasting and Social Change, 144, 242–250. doi: 10.1016/j.techfore.2019.03.003
Milland, K. (2016). A mechanical turk worker’s perspective. Journal of Media Ethics, 31(4), 263–264. doi: 10.1080/23736992.2016.1228813
Morris, M. R., Bigham, J. P., Brewer, R., Bragg, J., Kulkarni, A., Li, J. & Savage, S. (2017). Subcontracting Microwork. In G. Mark & S. Fussell (Eds.), CHI ‘17: Proceedings of the 2017 CHI Conference on human factors in computing systems (pp. 1867–1876). Association for Computing Machinery. doi: 10.1145/3025453.3025687
Robert, L. P., Dennis, A. R. & Ahuja, M. K. (2008). Social capital and knowledge integration in digitally enabled teams. Information Systems Research, 19(3), 314–334. doi: 10.1287/isre.1080.0177
Robert, L. P. (2019). Crowdsourcing controls: A review and research agenda for crowdsourcing controls used for macro-tasks. In J. Khan, I. Lykourentzou, K. Papangelis, & P. Markopoulos (Eds.), Macro-task crowdsourcing: Engaging the crowds to address complex problems (pp. 45–126). Springer Human Computer Interaction Series. doi: 10.1007/978-3-030-12334-5_3
Salehi, N. & Bernstein, M. S. (2018). Hive: Collective design through network rotation. Proceedings of the ACM on Human-Computer Interaction, 2, 1–26. doi: 10.1145/3274420
Seiner, J. (2017). Tailoring class actions to the on-demand economy. Ohio State Law Journal, 78(1), 21–71.
Silberman, M. S., Tomlinson, B., LaPlante, R., Ross, J., Irani, L. & Zaldivar, A. (2018). Responsible research with crowds: Pay crowd-workers at least minimum wage. CACM, 61(3), 39–41. doi: 10.1145/3180492
Taylor, F. W. (1911). The principles of scientific management. Harper & Row.
Taylor, J. & Joshi, K. (2018). How IT leaders can benefit from the digital crowdsourcing workforce. MIS Quarterly Executive, 17(4), 281–295. doi: 10.17705/2msqe.00002
Valentine, M. A., Retelny, D., To, A., Rahmati, N., Doshi, T. & Bernstein, M. S. (2017). Flash organizations: Crowdsourcing complex work by structuring crowds as organizations. In G. Mark & S. Fussell (Eds.), Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 3523–3537). Association for Computing Machinery. doi: 10.1145/3025453.3025811
Van Alstyne, M. W., Di Fiore, A. & Schneider, S. (2017). 4 mistakes that kill crowdsourcing efforts. Harvard Business Review, July 21. Retrieved from https://hbr.org/2017/07/4-mistakes-that-kill-crowdsourcing-efforts
Wood, A. J., Graham, M., Lehdonvirta, V. & Hjorth, I. (2019). Networked but commodified: The (dis)embeddedness of digital labour in the gig economy. Sociology, 53(5), 931–950. doi: 10.1177/0038038519828906
Woolley, J., Madsen, T. L. & Sarangee, K. (2015). Crowdsourcing or expertsourcing: Building and engaging online communities for innovation? In Conference Paper Presented at DRUID15, (pp. 15–17). 15–17 June 2015, Rome.
Ye, T., You, S. & Robert, L. (2017). When does more money work? Examining the role of perceived fairness in pay on the performance quality of crowdworkers. In Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 327–336. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14876
Copyright (c) 2021 Ioanna Lykourentzou, Lionel P. Robert Jr., Pierre-Jean Barlatier
![Creative Commons License](http://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors retain copyright of their work, with first publication rights granted to the AIMS.