The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Crowdsourcing as a Model for Problem Solving: An Introduction and Cases
Abstract
Crowdsourcing is an online, distributed problem-solving and production model that has emerged in recent years. Notable examples of the model include Threadless, iStockphoto, InnoCentive, the Goldcorp Challenge, and user-generated advertising contests. This article provides an introduction to crowdsourcing, both its theoretical grounding and exemplar cases, taking care to distinguish crowdsourcing from open source production. This article also explores the possibilities for the model, its potential to exploit a crowd of innovators, and its potential for use beyond forprofit sectors. Finally, this article proposes an agenda for research into crowdsourcing.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Uses and Gratifications Research
Katz, E., Blumler, J. G., & Gurevitch, M. (1973) · Public Opinion Quarterly · doi:10.1086/268109 · Cited by 4,012
Muniz, A. M., & O'Guinn, T. C. (2001) · Journal of Consumer Research · doi:10.1086/319618 · Cited by 3,998
Lakhani, K. R., & Wolf, R. G. (2003) · doi:10.2139/ssrn.443040 · Cited by 1,531
Cited by
A literature and practice review to develop sustainable business model archetypes
Bocken, N. M. P., Short, S. W., Rana, P., & Evans, S. (2014) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2013.11.039 · Cited by 4,366
A brief introduction to weakly supervised learning
Zhou, Z.-H. (2018) · National Science Review · doi:10.1093/nsr/nwx106 · Cited by 1,899
Manifesto of computational social science
Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G., Kertesz, J., Loreto, V., Moat, S., Nadal, J. P., Sanchez, A., et al. (2012) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2012-01697-8 · Cited by 426
Stupid tutoring systems, intelligent humans
Baker, R. S. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-016-0105-0 · Cited by 425
Social media: A contextual framework to guide research and practice.
McFarland, L. A., & Ployhart, R. E. (2015) · Journal of Applied Psychology · doi:10.1037/a0039244 · Cited by 414
Rumor Identification in Microblogging Systems Based on Users’ Behavior
Liang, G., He, W., Xu, C., Chen, L., & Zeng, J. (2015) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2016.2517458 · Cited by 169
Public deliberation on government-managed social media: A study on Weibo users in China
Medaglia, R., & Zhu, D. (2017) · Government Information Quarterly · doi:10.1016/j.giq.2017.05.003 · Cited by 114
Bockstedt, J., Druehl, C., & Mishra, A. (2016) · Production and Operations Management · doi:10.1111/poms.12552 · Cited by 111
Factors affecting participation of solvers in crowdsourcing: an empirical study from China
Shao, B., Shi, L., Xu, B., & Liu, L. (2012) · Electronic Markets · doi:10.1007/s12525-012-0093-3 · Cited by 97
Goals and Tasks: Two Typologies of Citizen Science Projects
Wiggins, A., & Crowston, K. (2012) · 2012 45th Hawaii International Conference on System Sciences · doi:10.1109/hicss.2012.295 · Cited by 93
Women (Still) Ask For Less: Gender Differences in Hourly Rate in an Online Labor Marketplace
Foong, E., Vincent, N., Hecht, B., & Gerber, E. M. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274322 · Cited by 80
(2012) · Cited by 68
Understanding Human-Machine Networks: A Cross-Disciplinary Survey
Tsvetkova, M., Yasseri, T., Meyer, E. T., Pickering, J. B., Engen, V., Walland, P., Lüders, M., Følstad, A., & Bravos, G. (2018) · ACM Computing Surveys · doi:10.1145/3039868 · Cited by 67
Performance evaluation and creativity: Balancing originality and usefulness
Ostermaier, A., & Uhl, M. (2020) · Journal of Behavioral and Experimental Economics · doi:10.1016/j.socec.2020.101552 · Cited by 16
ThinkWrite: Design Interventions for Empowering User Deliberation in Online Petition
Kim, J., Kim, C., & Nam, K.-Y. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3519644 · Cited by 10
Metadata
- Authors
- Brabham, D. C.
- Venue
- Convergence: The International Journal of Research into New Media Technologies
- Publisher
- SAGE Publications
- Published
- 2008-02
- Volume
- 14
- Issue
- 1
- Pages
- 75-90
- ISSN
- 1354-8565 (ISSN-L)
- 1748-7382
- OpenAlex
- W2129444086
- Cited by
- 1,943
- References
- 3
- FWCI
- 160.97
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
