The First International Conference on IoT in Urban Space

Research Article

Quo vadis?: Persuasive computing using real time queue information

  • @INPROCEEDINGS{10.4108/icst.urb-iot.2014.257204,
        author={Wouter Meys and Maarten Groen},
        title={Quo vadis?: Persuasive computing using real time queue information},
        proceedings={The First International Conference on IoT in Urban Space},
        publisher={ACM},
        proceedings_a={URB-IOT},
        year={2014},
        month={11},
        keywords={persuasive computing measuring queue length sensor iot planning behaviour},
        doi={10.4108/icst.urb-iot.2014.257204}
    }
    
  • Wouter Meys
    Maarten Groen
    Year: 2014
    Quo vadis?: Persuasive computing using real time queue information
    URB-IOT
    ICST
    DOI: 10.4108/icst.urb-iot.2014.257204
Wouter Meys1,*, Maarten Groen1
  • 1: Amsterdam University of Applied Science
*Contact email: w.t.meys@hva.nl

Abstract

By presenting tourists with real-time information an increase in efficiency and satisfaction of their day planning can be achieved. At the same time, real-time information services can offer the municipality the opportunity to spread the tourists throughout the city centre. An important factor for success is if we can influence tourist day planning. Therefore we studied how tourists could be persuaded to change their planning with real-time information services. This was done by providing the tourists with real-time sensor data about the queue length at the Van Gogh museum in Amsterdam. Two groups of tourists were interviewed about an application that was able to show the queue length at the museum. One group of tourists was interviewed while in the process of planning their day, and one group was interviewed while they were waiting in the queue. Results showed that the information about the queue length and information to visit alternative tourist attractions were trusted by both of the groups. Furthermore, the tourists were very inclined to change their route and plans for that day based on the queue length.