TY - GEN
T1 - TASKer
T2 - 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
AU - Kandappu, Thivya
AU - Jaiman, Nikita
AU - Tandriansyah, Randy
AU - Misra, Archan
AU - Cheng, Shih Fen
AU - Chen, Cen
AU - Lau, Hoong Chuin
AU - Chander, Deepthi
AU - Dasgupta, Koustuv
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/9/12
Y1 - 2016/9/12
N2 - 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$Ker, involving 900 real users on the SMU campus who performed over 30,000 tasks. Our studies (i) show the benefits of bundling tasks as a combined package, (ii) reveal the effectiveness of differential pricing strategies and (iii) illustrate key aspects of cheating (false reporting) behavior observed among workers.
AB - While mobile crowd-sourcing has become a game-changer for many urban operations, such as last mile logistics and municipal monitoring, we believe that the design of such crowdsourcing strategies must better accommodate the real-world behavioral preferences and characteristics of users. To provide a real-world testbed to study the impact of novel mobile crowd-sourcing strategies, we have designed, developed and experimented with a real-world mobile crowd-tasking platform on the SMU campus, called TA$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.
KW - Crowd-sourcing, context-aware, empirical study
KW - User behaviour
UR - https://www.scopus.com/pages/publications/84991491954
U2 - 10.1145/2971648.2971690
DO - 10.1145/2971648.2971690
M3 - 会议稿件
AN - SCOPUS:84991491954
T3 - UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
SP - 392
EP - 402
BT - UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PB - Association for Computing Machinery, Inc
Y2 - 12 September 2016 through 16 September 2016
ER -