No "One-size fits all": Towards a principled approach for incentives in mobile crowdsourcing
John P. Rula*, Vishnu Navda†, Fabián E. Bustamante*, Ranjita Bhagwan† and Saikat Guha†.
(*) Northwestern University
(†) Microsoft Research India
In Proc. of the Fifteenth Workshop on Mobile Computing Systems and Applications (HotMobile), February 2014.
EECS Department
Northwestern University
Evanston, IL 60201, USA
Abstract
We are becoming increasingly aware that the effectiveness of mobile crowdsourcing systems critically depends on the whims of their human participants, impacting everything from participant engagement to their compliance with the crowdsourced tasks.
In response, a number of such systems have started to incorpo- rate different incentive features aimed at a wide range of goals that span from improving participation levels, to extending the systems’ coverage, and enhancing the quality of the collected data. Despite the many related efforts, the inclusion of incentives in crowdsourced systems has so far been mostly ad-hoc, treating incentives as a wild-card response fitted for any occasion and goal.
Using data from a large, 2-day experiment with 96 participants at a corporate conference, we present an analysis of the impact of two incentive structures on the recruitment, compliance and user effort of a basic mobile crowdsourced service. We build on these preliminary results to argue for a principled approach for selecting incentive and incentive structures to match the variety of requirements of mobile crowdsourcing applications and discuss key issues in working toward that goal.
