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.
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Availability of data and material
The log data used for the research presented in the paper is owned by the two ICT companies Suggesto and Interline (Trento, Italy), which developed the information systems considered for the case study and provided access for research purposes. Availability of data is subject to their consent and to compliance with the General Data Protection Regulation in force in Italy.
In Italy, the military service was compulsory until 2004. From 2005 the service is voluntary only.
The SEO (Search Engine Optimization) strategy includes decisions on how to organize a web site and its contents and keywords to optimize the way the site is ranked in the result list of web search engines.
https://www.elastic.co/ (accessed 20 August 2020).
https://www.elastic.co/kibana (accessed 20 August 2020).
The bounce rate is the percentage of site visits that are single-page sessions, with the visitor leaving without viewing a second page.
The Kruskal–Wallis test is a non-parametric substitute of ANOVA for data not normally distributed.
Also here the Kruskal–Wallis test was used in place of ANOVA since the data was not normally distributed, as indicated by the skewness and kurtosis check.
Adomavicius G, Mobasher B, Ricci F, Tuzhilin A (2011) Context-aware recommender systems. AI Mag 32(3):67–80. https://doi.org/10.1609/aimag.v32i3.2364
Ardissono L, Kuflik T, Petrelli D (2012) Personalization in cultural heritage: the road travelled and the one ahead. User Model User-Adapted Interact 22(1):73–99. https://doi.org/10.1007/s11257-011-9104-x
Baggio R, Scaglione M (2017) Strategic Visitor Flows (SVF) Analysis using mobile data. In: Schegg R, Stangl B (eds) Information and communication technologies in tourism 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-51168-9_11
Baltrunas L, Ludwig B, Peer S, Ricci F (2012) Context relevance assessment and exploitation in mobile recommender systems. Pers Ubiquitous Comput 16(5):507–526. https://doi.org/10.1007/s00779-011-0417-x
Buhalis D, O’Connor P (2005) Information communication technology revolutionizing tourism. Tour Recreat Res 30(3):7–16. https://doi.org/10.1080/02508281.2005.11081482
Chatzidimitris T, Gavalas D, Kasapakis V, Konstantopoulos C, Kypriadis D, Pantziou G, Zaroliagis C (2020) A location history-aware recommender system for smart retail environments. Pers Ubiquitous Comput 1–12
Clifton B (2012) Advanced web metrics with google analytics, 3rd edn. Wiley Publishing
Dietz LW, Sen A, Roy R, Wörndl W (2020) Mining trips from location-based social networks for clustering travelers and destinations. Inf Technol Tour 22(1):131–166
Diriye A, White R, Buscher G, Dumais S (2012) Leaving so soon? Understanding and predicting web search abandonment rationales. In: Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM ’12). Association for Computing Machinery, New York, NY, USA, pp 1025–1034. https://doi.org/10.1145/2396761.2398399
Dolnicar S (2008) Market segmentation in tourism. In: Woodside AG, Martin D (eds) Tourism management: analysis, behaviour and strategy. CAB International, Cambridge, pp 129–150
Driscoll B (2015) Sentiment analysis and the literary festival audience. Continuum 29(6):861–873. https://doi.org/10.1080/10304312.2015.1040729
Economou M, Meintani N (2011) Promising beginning? Evaluating museum mobile phone apps. In: Ciolfi L, Scott K, Barbieri S (eds) Rethinking technology in museums 2011. Emerging experiences, 26–27 May 2011, Limerick, Ireland
European Union (2016) Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)
Falk J (2016) Identity and the museum visitor experience. Routledge
Fang Y-M, Lin C (2019) The usability testing of VR interface for tourism apps. Appl Sci 9(16):3215. https://doi.org/10.3390/app9163215
Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39
Gao S, Krogstie J, Gransæther PA (2008) Mobile services acceptance model. In: Proceedings—2008 international conference on convergence and hybrid information technology, ICHIT 2008, pp 446–453. https://doi.org/10.1109/ICHIT.2008.252
Gavalas D, Kasapakis V, Konstantopoulos C, Mastakas K, Pantziou G (2013) A survey on mobile tourism recommender systems. In 2013 third international conference on communications and information technology (ICCIT). IEEE, pp 131–135
Getz D (2008) Event tourism: definition, evolution, and research. Tour Manag 29(3):403–428. https://doi.org/10.1016/j.tourman.2007.07.017
Grün C, Werthner H, Pröll B, Retschitzegger W, Schwinger W (2008) Assisting tourists on the move—an evaluation of mobile tourist guides. In: Proceedings of the 7th international conference on mobile business, pp 171–180. https://doi.org/10.1109/ICMB.2008.28
Hoehle H, Venkatesh V (2015) Mobile application usability: conceptualization and instrument development. MIS Q 39(2):435–472. https://doi.org/10.25300/MISQ/2015/39.2.08
Jackson M, White G, White M G (2001) Developing a tourist personality typology. In: CAUTHE 2001: capitalising on research; Proceedings of the 11th Australian tourism and hospitality research conference. University of Canberra Press, p 177
Jannach D, Zanker M (2020) Interactive and context-aware systems in tourism. In: Xiang Z, Fuchs M, Gretzel U, Höpken W (eds) Handbook of e-tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_125-1
Jurek A, Mulvenna MD, Bi Y (2015) Improved lexicon-based sentiment analysis for social media analytics. Secur Inf. https://doi.org/10.1186/s13388-015-0024-x
Kellner L, Egger R (2016) Tracking tourist spatial-temporal behavior in urban places, a methodological overview and GPS case study. In: Inversini A, Schegg R (eds) Information and communication technologies in tourism 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-28231-2_35
Kenteris K, Gavalas D, Economou D (2011) Electronic mobile guides: a survey. Pers Ubiquitous Comput 15(1):97–111. https://doi.org/10.1007/s00779-010-0295-7
Kim HW, Lee HL, Choi SJ (2011) An exploratory study on the determinants of mobile application purchase. J Soc e-Bus Stud. https://doi.org/10.7838/jsebs.2011.16.4.173
Korkut S, Mele E, Cantoni L (2021) User experience and usability: the case of augmented reality. In: Xiang Z, Fuchs M, Gretzel U, Höpken W (eds) Handbook of e-tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_62-1
Kushlev K, Proulx J, Dunn EW (2016) “Silence Your Phones”: smartphone notifications increase inattention and hyperactivity symptoms. In: Proceedings of the 2016 CHI conference on human factors in computing systems (CHI ’16). Association for Computing Machinery, New York, NY, USA, pp 1011–1020. https://doi.org/10.1145/2858036.2858359
Lai K (2018) Influence of event image on destination image: The case of the 2008 Beijing Olympic Games. J Destin Mark Manag 7(2018):153–163. https://doi.org/10.1016/j.jdmm.2016.09.007
Leiva L, Böhmer M, Gehring S, Krüger A (2012) Back to the app: the costs of mobile application interruptions. In: Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services (MobileHCI ’12). Association for Computing Machinery, New York, NY, USA, pp 291–294. https://doi.org/10.1145/2371574.2371617
Leng Y, Noriega A, Pentland AS, Winder I, Lutz N, Alonso L (2016) Analysis of tourism dynamics and special events through mobile phone metadata. In: Proceedings of data for good exchange (D4GX), New York, NY
Li J, Xu L, Tang L, Wang S, Li L (2018) Big data in tourism research: a literature review. Tour Manag 68:301–323
Libaque-Sáenz CF, Wong SF, Chang Y, Bravo ER (2021) The effect of fair information practices and data collection methods on privacy-related behaviors: a study of Mobile apps. Inf Manag 58(1):2021
Littledata (2020) What is the average bounce rate from mobile Google search for Travel websites? https://www.littledata.io/average/bounce-rate-from-mobile-Google-search/Travel-websites. Accessed 2 July 2020
Liu B (2006) Web data mining: exploring hyperlinks, contents, and usage data (data-centric systems and applications). Springer, Berlin
Localytics (2019) 25% of users abandon apps after one use. http://info.localytics.com/blog/25-of-users-abandon-apps-after-one-use. Accessed 2 July 2020
Localytics (2020) 2019 Mobile app benchmark report to inform your 2020 strategy. https://www.localytics.com/lp/2019-mobile-app-benchmark-report-to-inform-your-2020-strategy/. Accessed 2 July 2020
Luxford A, Dickinson J (2015) The role of mobile applications in the consumer experience at music festivals. Event Manag. https://doi.org/10.3727/152599515X14229071392909
Maeng HY, Jang HY, Li JM (2016) A critical review of the motivational factors for festival attendance based on meta-analysis. Tour Manag Perspect 17(2016):16–25. https://doi.org/10.1016/j.tmp.2015.10.003
Massimo D, Ricci F (2019) Clustering users’ POIs visit trajectories for next-POI recommendation. In: Pesonen J, Neidhardt J (eds) Information and communication technologies in tourism 2019. Springer, Cham
Massimo D, Ricci F (2020) Enhancing travel experience leveraging on-line and off-line users’ behaviour data. In: Proceedings of the 25th international conference on intelligent user interfaces companion (IUI ’20). Association for Computing Machinery, New York, NY, USA, pp 65–66
McGookin D, Tahiroğlu K, Vaittinen T, Kytö M, Monastero B, Vasquez JC (2019) Investigating tangential access for location-based digital cultural heritage applications. Int J Hum Comput Stud 122:196–210
McKinsey & Company and World Travel & Tourism Council (2017) Coping with success. Managing overcrowding in tourism destinations
Miah SJ, Vu HQ, Gammack J, McGrath M (2017) A big data analytics method for tourist behaviour analysis. Inf Manag 54(6):771–785
Miluniec A, Swacha J (2020) Museum apps investigated: availability, content and popularity. E-Rev Tour Res (eRTR) 17(5):2020
Nielsen J (2012) Usability 101: introduction to usability. https://www.nngroup.com/articles/usability-101-introduction-to-usability/. Accessed 17th Mar 2021
Not E (2019) Studying the information seeking preferences of participants to a large event. In: Proceedings of the 13th biannual conference of the Italian SIGCHI chapter: designing the next interaction (CHItaly '19). Association for computing machinery, New York, NY, USA, Article 17, 1–8. https://doi.org/10.1145/3351995.3352050
Not E, Venturini A (2011) The unexploited benefits of travel planning functionalities: a case study of automatic qualitative market analysis. E-Review of Tourism Research (eRTR). Special Section: ENTER 2011
Not E, Venturini A (2013) Discovering functional requirements and usability problems for a mobile tourism guide through context-based log analysis. In: Cantoni L, Xiang Z (eds) Information and communication technologies in tourism 2013. Springer, Berlin, pp 12–23
Pentland A (2009) Reality mining of mobile communications: toward a new deal on data. In: The Global Information Technology Report 2008–2009, 2009 World Economic Forum
Pitman A, Zanker M, Fuchs M, Lexhagen M (2010) Web usage mining in tourism—a query term analysis and clustering approach. In: Gretzel U, Law R, Fuchs M (eds) Information and communication technologies in tourism 2010. Springer, Vienna, pp 393–403
Plaza B (2011) Google analytics for measuring website performance. Tour Manag 32(3):477–481
Ranjith S, Paul PV (2020). A survey on recent recommendation systems for the tourism industry. In: Accelerating knowledge sharing, creativity, and innovation through business tourism, pp 205–237. IGI Global
Ricci F (2002) Travel recommender systems. IEEE Intell Syst 17(6):55–57
Ricci F (2011) Mobile recommender systems. Int J Inf Technol Tour 12(3):205–231
Ricci F (2020) Recommender systems in tourism. In: Xiang Z, Fuchs M, Gretzel U, Höpken W (eds) Handbook of e-tourism. Springer, Cham
Roxin A-M, Gaber J, Wack M, Nait-Sidi-Moh A (2007) Survey of wireless geolocation techniques. In: 2007 IEEE Globecom Workshops, Washington, DC
Schmunk S, Höpken W, Fuchs M, Lexhagen M (2013) Sentiment analysis: extracting decision-relevant knowledge from UGC. In: Xiang Z, Tussyadiah I (eds) Information and communication technologies in tourism 2014. Springer International Publishing, Cham, pp 253–265
Schwinger W, Grün C, Pröll B, Retschitzegger W (2009) Context-awareness in mobile tourist guides. In: Khalil I (ed) Handbook of research on mobile multimedia, 2nd edn. IGI Global, pp 534–552. https://doi.org/10.4018/978-1-60566-046-2.ch037
Semrad KJ, Rivera M (2018) Advancing the 5E’s in festival experience for the Gen Y framework in the context of eWOM. J Destin Mark Manag 7(2018):58–67. https://doi.org/10.1016/j.jdmm.2016.08.003
Sigg S, Lagerspetz E, Peltonen E, Nurmi P, Tarkoma S (2019) Exploiting usage to predict instantaneous app popularity: trend filters and retention rates. ACM Trans Web 13(2):Article 13. https://doi.org/10.1145/3199677
Stangl B, Ukpabi DC, Park S (2020) Augmented reality applications: the impact of usability and emotional perceptions on tourists’ app experiences. In Neidhardt, Julia; Wörndl, Wolfgang (eds) Information and communication technologies in tourism 2020: Proceedings of the international conference in Surrey, United Kingdom, January 08–10, 2020. Cham: Springer, 181–191. https://doi.org/10.1007/978-3-030-36737-4_15
Swart K, George R, Cassar J, Sneyd C (2018) The 2014 FIFAWorld Cup™: Tourists’ satisfaction levels and likelihood of repeat visitation to Rio de Janeiro. J Destin Mark Manag 8(2018):102–113. https://doi.org/10.1016/j.jdmm.2017.01.001
Swart MPN, Sotiriadis MD, Engelbrecht WH (2019) Investigating the intentions of tourism providers and trade exhibition visitors to use technology: a technology acceptance model approach. Acta Commercii 19(1):1–11
Swierenga SJ, Propst DB, Ismirle J, Figlan C, Coursaris CK (2014) Mobile design usability guidelines for outdoor recreation and tourism. In: Nah FFH (eds) HCI in business. HCIB 2014. Lecture notes in computer science, vol 8527. Springer, Cham. https://doi.org/10.1007/978-3-319-07293-7_36
Tay SW, The PS, Payne SJ (2021) Reasoning about privacy in mobile application install decisions: risk perception and framing. Int J Hum Comput Stud 145
Van Winkle C, Bueddefeld J (2020) Information and communication technology in event management. In: Xiang Z, Fuchs M, Gretzel U, Höpken W (eds) Handbook of e-tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_86-1
Van Winkle CM, Bueddefeld JN, Halpenny E, MacKay KJ (2019) The unified theory of acceptance and use of technology 2: understanding mobile device use at festivals. Leis Stud 38(5):634–650
Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478
Wang Z, He SY, Leung Y (2018) Applying mobile phone data to travel behaviour research: a literature review. Trav Behav Soc 11:141–155. https://doi.org/10.1016/j.tbs.2017.02.005(ISSN 2214-367X)
Witten I, Frank E (2005) Data Mining. Morgan Kaufmann, Practical Machine Learning Tools and Techniques, Second Edition
Wörndl W, Herzog D (2020) Mobile applications for e-tourism. In: Xiang Z, Fuchs M, Gretzel U, Höpken W (eds) Handbook of e-tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_17-1
Zarmpou T, Saprikis V, Markos A, Vlachopoulou M (2012) Modeling users’ acceptance of mobile services. Electron Commer Res 12:225–248
Zhang D, Adipat B (2005) Challenges, methodologies, and issues in the usability testing of mobile applications. Int J Hum Comput Interact 18(3):293–308
The research described in this paper was partially funded by the Suggesto Marketspace and Destinazione 4.0 projects. Suggesto Marketspace (2016-2018) was funded by the Autonomous Province of Trento under the work programme for industrial research (art. 5, L.P. n.6/1999). Destinazione 4.0 (2018–2019) was funded by the Autonomous Province of Trento under the FESR 2014-2020 work programme. We thank the two ICT companies Suggesto and Interline involved in the projects who kindly provided access to the log data and provided feedback on the research work.
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Not, E. Mining mobile application usage data to understand travel planning for attending a large event. Inf Technol Tourism (2021). https://doi.org/10.1007/s40558-021-00204-7
- Mobile applications
- Data mining
- Interaction analysis
- Travel planning
- Large events