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Mining mobile application usage data to understand travel planning for attending a large event

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  • Elena Not

    (Fondazione Bruno Kessler)

Abstract

Information and communication technology can play a crucial role in advertising large events and in making information available for the attendance experience to be attractive, easy to plan, pleasant and engaging, and to promote the other tourist attractions of the hosting place. Few studies have focused on understanding the role of mobile applications in supporting travellers’ information needs while attending an event onsite and during the preceding travel planning stage. Starting from a concrete case study, this paper discusses the utility of mining usage data collected by a mobile application to identify patterns of adoption and context-dependent usages (in time and space) that characterize different categories of large event attendees. The findings highlight the existence of classes of users with varied travel planning behaviour, ranging from users who start looking for practical information quite in advance, to users who look for information at the very last minute or just when arrived onsite. The outcomes of the study provide useful information and guidelines for designers and developers of information systems as well as for event organizers and tourism stakeholders. Suggestions include how to prepare information sources and adapt them to different classes of users, when to launch and advertise bespoke mobile services, what interaction aspects to trace to gather insights on visitors’ behaviour before and during the event. Benchmarking measures are proposed to evaluate the popularity of mobile applications for events. The research demonstrates the contribution that user behaviour analysis can provide to the field of electronic tourism management and marketing, for a deeper understanding of consumers’ behaviour and preferences that goes beyond standard analytics.

Suggested Citation

  • Elena Not, 2021. "Mining mobile application usage data to understand travel planning for attending a large event," Information Technology & Tourism, Springer, vol. 23(3), pages 291-325, September.
    • Handle: RePEc:spr:infott:v:23:y:2021:i:3:d:10.1007_s40558-021-00204-7
    DOI: 10.1007/s40558-021-00204-7
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    References listed on IDEAS

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    1. Plaza, Beatriz, 2011. "Google Analytics for measuring website performance," Tourism Management, Elsevier, vol. 32(3), pages 477-481.
    2. Elena Not & Adriano Venturini, 2013. "Discovering Functional Requirements and Usability Problems for a Mobile Tourism Guide through Context-Based Log Analysis," Springer Books, in: Lorenzo Cantoni & Zheng (Phil) Xiang (ed.), Information and Communication Technologies in Tourism 2013, edition 127, pages 12-23, Springer.
    3. Sergej Schmunk & Wolfram Höpken & Matthias Fuchs & Maria Lexhagen, 2013. "Sentiment Analysis: Extracting Decision-Relevant Knowledge from UGC," Springer Books, in: Zheng Xiang & Iis Tussyadiah (ed.), Information and Communication Technologies in Tourism 2014, edition 127, pages 253-265, Springer.
    4. Linus W. Dietz & Avradip Sen & Rinita Roy & Wolfgang Wörndl, 2020. "Mining trips from location-based social networks for clustering travelers and destinations," Information Technology & Tourism, Springer, vol. 22(1), pages 131-166, March.
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