TASKer: Behavioral insights via campus-based experimental mobile crowd-sourcing

• Main Authors: KANDAPPU, Thivya, JAIMAN, Nikita, DARATAN, Randy Tandriansyah, MISRA, Archan, CHENG, Shih-Fen, CHEN, Cen, LAU, Hoong Chuin, CHANDER, Deepthi, DASGUPTA, Koustuv
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Crowd-sourcing; context-aware; empirical study; User behaviour; Artificial Intelligence and Robotics; Computer Sciences; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/3361; https://ink.library.smu.edu.sg/context/sis_research/article/4363/viewcontent/TASKER_ubicomp16.pdf
• Institution: Singapore Management University

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.