  The collection of spatiotemporal tourism information is important in smart
tourism and user-generated contents are perceived as reliable information.
Participatory sensing is a useful method for collecting such data, and the
active contribution of users is an important aspect for continuous and
efficient data collection. This study has focused on the impact of task
allocation interface design and individual personality on data collection
efficiency and their contribution in gamified participatory sensing for
tourism. We have designed two types of interfaces: a map-based with active
selection and a chat-based with passive selection. Moreover, different levels
of elaborateness and indirectness have been introduced into the chat-based
interface. We have employed the Gamification User Types Hexad framework to
identify the differences in the contributions and interface preferences of
different user types. The results of our tourism experiment with 108
participants show that the map-based interface collects more data, while the
chat-based interface collects data for spots with higher information demand. We
also found that the contribution to sensing behavior and interface preference
differed depending on the individual user type.
