Task allocation interface design and personalization in gamified participatory sensing for tourism

TL;DR

This study investigates how interface design and personality influence data collection efficiency in gamified participatory sensing for tourism, finding that map-based interfaces gather more data overall, while chat-based ones target high-demand spots, with user type affecting contributions.

Contribution

It introduces and compares two interface designs for gamified participatory sensing and analyzes how user personality impacts data contribution and interface preference.

Findings

Map-based interface collects more data overall.

Chat-based interface targets high-demand information spots.

User personality influences contribution levels and interface preferences.

Abstract

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.