The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Crowdsourcing: A Review and Suggestions for Future Research
Open accessAbstract
As academic and practitioner studies on crowdsourcing have been building up since 2006, the subject itself has progressively gained in importance within the broad field of management. No systematic review on the topic has so far appeared in management journals, however; moreover, the field suffers from ambiguity in the topic's definition, which in turn has led to its largely unstructured evolution. The authors therefore investigate the existing body of knowledge on crowdsourcing systematically through a penetrating review in which the strengths and weakness of this literature stream are presented clearly and then future avenues of research are set out. The review is based on 121 scientific articles published between January 2006 and January 2015. The review recognizes that crowdsourcing is ingrained in two mainstream disciplines within the broader subject matter of innovation and management: (1) open innovation; and (2) co‐creation. The review, in addition, also touches on several issues covered in other theoretical streams: (3) information systems management; (4) organizational theory and design; (5) marketing; and (6) strategy. The authors adopt a process perspective, applying the ‘Input–Process–Output’ framework to interpret research on crowdsourcing within the broad lines of: (1) Input (Problem/Task); (2) Process (session management; problem management; knowledge management; technology); and (3) Outcome (solution/completed task; seekers’ benefits; solvers’ benefits). This framework provides a detailed description of how the topic has evolved over time, and suggestions concerning the future direction of research are proposed in the form of research questions that are valuable for both academics and managers.
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Cites
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A Reliable Weighting Scheme for the Aggregation of Crowd Intelligence to Detect Fake News
Tchakounté, F., Faissal, A., Atemkeng, M., & Ntyam, A. (2020) · Information · doi:10.3390/info11060319 · Cited by 23
Quantifying machine influence over human forecasters
Abeliuk, A., Benjamin, D. M., Morstatter, F., & Galstyan, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-72690-4 · Cited by 18
Sgoff, C., Acevedo, W., Paschalidi, Z., Ulbrich, S., Bauernschubert, E., Kratzsch, T., & Potthast, R. (2022) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4276 · Cited by 18
Metadata
- Authors
- Ghezzi, A., Gabelloni, D., Martini, A., & Natalicchio, A.
- Venue
- International Journal of Management Reviews
- Publisher
- Wiley
- Published
- 2017-01-19
- Volume
- 20
- Issue
- 2
- Pages
- 343-363
- ISSN
- 1460-8545 (ISSN-L)
- 1468-2370
- OpenAlex
- W2574988924
- Cited by
- 353
- References
- 7
- FWCI
- 36.82
- Type
- journal article
- Language
- en
- Open access
- green
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