Abstract
Crowdsourcing is increasingly gaining attention as one of the most promising forms of large-scale dynamic collective work. However current crowdsourcing approaches do not offer guarantees often demanded by consumers, for example regarding minimum quality, maximum cost or job accomplishment time. The problem appears to have a greater impact in corporate environments because in this case the above-mentioned performance guarantees directly affect its viability against competition. Guided crowdsourcing can be an alternative to overcome these issues. Guided crowdsourcing refers to the use of Artificial Intelligence methods to coordinate workers in crowdsourcing settings, in order to ensure collective performance goals such as quality, cost or time. In this paper, we investigate its potential and examine it on an evaluation setting tailored for intra and inter-corporate environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Archak, N.: Optimal design of crowdsourcing contests. In: ICIS 2009 Proceedings, vol. 200(512), p. 16 (2009)
Balog, K., Azzopardi, L., de Rijke, M.: Formal models for expert finding in enterprise corpora. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, pp. 43–50. ACM, New York (2006)
Bernstein, M.S., Karger, D.R., Miller, R.C., Brandt, J.: Analytic methods for optimizing realtime crowdsourcing. CoRR, abs/1204.2995 (2012)
Doan, A., Ramakrishnan, R., Halevy, A.Y.: Crowdsourcing systems on the world-wide web. Commun. ACM 54(4), 86–96 (2011)
Ghosh, A., Hummel, P.: Implementing optimal outcomes in social computing: a game-theoretic approach. In: Proceedings of the 21st International Conference on World Wide Web, WWW 2012, pp. 539–548. ACM, New York (2012)
Ipeirotis, P.G.: Analyzing the amazon mechanical turk marketplace. XRDS 17(2), 16–21 (2010)
Ipeirotis, P.G., Horton, J.J.: The need for standardization in crowdsourcing. In: CHI 2011 Crowdsource Workshop, pp. 1–4 (2011)
Khazankin, R., Schall, D., Dustdar, S.: Predicting QoS in scheduled crowdsourcing. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 460–472. Springer, Heidelberg (2012)
Kittur, A.: Crowdsourcing, collaboration and creativity. XRDS 17(2), 22–26 (2010)
Liu, X., Lu, M., Ooi, B.C., Shen, Y., Wu, S., Zhang, M.: Cdas: a crowdsourcing data analytics system. Proc. VLDB Endow. 5(10), 1040–1051 (2012)
Lykourentzou, I., Papadaki, K., Vergados, D.J., Polemi, D., Loumos, V.: Corpwiki: A self-regulating wiki to promote corporate collective intelligence through expert peer matching. Inf. Sci. 180(1), 18–38 (2010)
Poetz, M.K., Schreier, M.: The value of crowdsourcing: Can users really compete with professionals in generating new product ideas? Journal of Product Innovation Management 29(2), 245–256 (2012)
Psaier, H., Skopik, F., Schall, D., Dustdar, S.: Resource and agreement management in dynamic crowdcomputing environments. In: 2011 15th IEEE International Enterprise Distributed Object Computing Conference (EDOC), pp. 193–202 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lykourentzou, I., Vergados, D.J., Papadaki, K., Naudet, Y. (2013). Guided Crowdsourcing for Collective Work Coordination in Corporate Environments. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-40495-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40494-8
Online ISBN: 978-3-642-40495-5
eBook Packages: Computer ScienceComputer Science (R0)