Description
Whether it’s for altruistic reasons, personal gains, or third party’s interests, users are influenced by different kinds of motivations when making use of mobile geo-crowdsourcing applications (geoCAs). These reasons, extrinsic and/or intrinsic, must be factored in when evaluating the use intention of these applications and how effective they are. A functional geoCA, particularly if designed for Volunteered Geographic Information (VGI), is the one that persuades and engages its users, by accounting for their diversity of needs across a period of time. This paper explores a number of proven and novel motivational factors destined for the preservation and collection of Intangible Cultural Heritage (ICH) through geoCAs. By providing an overview of personalisation research and digital behaviour interventions for geo-crowdsoured ICH, the paper examines the most relevant usability and trigger factors for different crowd users, supported by a range of technology-based principles. In addition, we present the case of StoryBee, a mobile geoCA designed for “crafting stories” by collecting and sharing users’ generated content based on their location and favourite places. We conclude with an open-ended discussion about the ongoing challenges and opportunities arising from the deployment of geoCAs for ICH.
Date of creation, presentation, or exhibit
Summer 7-12-2020
Document Type
Conference Proceeding
Department, Program, or Center
School of Interactive Games and Media (GCCIS)
Recommended Citation
Federica Lucia Vinella, Ioanna Lykourentzou, and Konstantinos Papangelis. 2020. Motivational Principles and Personalisation Needs for GeoCrowdsourced Intangible Cultural Heritage Mobile Applications. In Adjunct Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’20 Adjunct), July 14–17, 2020, Genoa, Italy. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3386392.3399284
Campus
RIT – Main Campus
Comments
© ACM 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Adjunct Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’20 Adjunct), http://dx.doi.org/ 10.1145/3386392.3399284