Conference Proceeding Article
While mobile crowd-sourcing has become a game-changer for many urban operations, such as last mile logistics and municipal monitoring, we believe that the design of such crowdsourcing strategies must better accommodate the real-world behavioral preferences and characteristics of users. To provide a real-world testbed to study the impact of novel mobile crowd-sourcing strategies, we have designed, developed and experimented with a real-world mobile crowd-tasking platform on the SMU campus, called TA$Ker. We enhanced the TA$Ker platform to support several new features (e.g., task bundling, differential pricing and cheating analytics) and experimentally investigated these features via a two-month deployment of TA$Ker, involving 900 real users on the SMU campus who performed over 30,000 tasks. Our studies (i) show the benefits of bundling tasks as a combined package, (ii) reveal the effectiveness of differential pricing strategies and (iii) illustrate key aspects of cheating (false reporting) behavior observed among workers.
Crowd-sourcing, context-aware, empirical study, User behaviour
Artificial Intelligence and Robotics | Computer Sciences | Numerical Analysis and Scientific Computing
Intelligent Systems and Decision Analytics
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Heidelberg, Germany, September 12-16, 2016
City or Country
KANDAPPU, Thivya; JAIMAN, Nikita; DARATAN, Randy Tandriansyah; MISRA, Archan; CHENG, Shih-Fen; CHEN, Cen; LAU, Hoong Chuin; CHANDER, Deepthi; and DASGUPTA, Koustuv.
TASKer: Behavioral insights via campus-based experimental mobile crowd-sourcing. (2016). Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Heidelberg, Germany, September 12-16, 2016. 392-402. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3361
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