A Crowdsourcing Task Allocation Mechanism for Hybrid Worker Context Based on Skill Level Updating
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9640-7_2
Reference15 articles.
1. Tong, Y., She, J., Ding, B., et al.: Online mobile micro-task allocation in spatial crowdsourcing. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 49–60. IEEE (2016)
2. Tarable, A., Nordio, A., Leonardi, E., et al.: The importance of being earnest in crowdsourcing systems.In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 2821–2829. IEEE (2015)
3. Jiang, L., Wagner, C., Nardi, B.: Not just in it for the money: a qualitative investigation of workers’ perceived benefits of micro-task crowdsourcing.In: 2015 48th Hawaii International Conference on System Sciences, pp. 773–782. IEEE (2015)
4. Miller, G.J.: Stakeholder roles in artificial intelligence projects. Proj. Leadersh. Soc. 3, 100068 (2022)
5. Acar, O.A.: Motivations and solution appropriateness in crowdsourcing challenges for innovation. Res. Policy 48(8), 103716 (2019)
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