Teenagers’ Destination Website Navigation. A Comparison Among Eye-Tracking, Web Analytics, and Self-declared Investigation

  • Edoardo Cantoni
  • Elena MarchioriEmail author
  • Lorenzo Cantoni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10290)


The aim of this study is to verify if teenagers’ actual navigation through webpages match with their self-declared preferences (in terms of tourist attractions), and if these preferences are in line with the official DMO data about most viewed pages. Particularly, self-declared attractions are confronted with the contents visualized during navigation, thus making possible to understand to what extent the exposure to certain themes influence preferences towards certain attractions. Results from this comparison suggest that contents that teenagers pay attention to during navigation are not always what they declare to prefer as tourist attraction.

In a second stage, a comparison with the official DMO data showing the most viewed pages is carried out in order to verify if there are any commonalities in terms of preferred attractions. Results show commonalities in terms of preferences: outdoor/sports and events/concerts are the preferred themes across all sources. But results also show discrepancies. In fact, at the same time, according to each type of approach used, the ranking of preferred themes changes. Therefore, results suggests that a multi-source approach helps to eliminate possible biases that may occur if only one approach is adopted.


Website navigation Eye-tracking Online behaviour Teenagers Web-analytics DMO 


  1. Gidlöf, K., Holmberg, N., Sandberg, H.: The use of eye-tracking and retrospective interviews to study teenagers’ exposure to online advertising. Vis. Commun. 11(3), 329–345 (2012)CrossRefGoogle Scholar
  2. Gidlöf, K., Wallin, A., Dewhurst, R., Holmqvist, K.: Using eye tracking to trace a cognitive process: gaze behaviour during decision making in a natural environment. J. Eye Mov. Res. 6(1), 3–14 (2013)Google Scholar
  3. Kaplan, M.: Teenage Online Shopping Trends. Accessed 20 June 2013
  4. Khan, K., Locatis, C.: Searching through cyberspace: the effects of link display and link density on information retrieval from hypertext on the World Wide Web. J. Am. Soc. Inf. Sci. 49(2), 176–182 (1998)CrossRefGoogle Scholar
  5. Lazonder, A.W., Biemans, H.J., Wopereis, I.G.: Differences between novice and experienced users in searching information on the World Wide Web. J. Am. Soc. Inf. Sci. 576–581 (2000)Google Scholar
  6. Loranger, H., Nielsen, J.: Teenage Usability: Designing Teen-Targeted Websites. Accessed 4 Feb 2013
  7. Marchiori, E., Cantoni, L.: Studying online contents navigation: a comparison between eye-tracking technique and self-reported investigation. In: Tussyadiah, I., Inversini, A. (eds.) Information and Communication Technologies in Tourism 2015, pp. 349–359. Springer, Cham (2015). doi: 10.1007/978-3-319-14343-9_26 Google Scholar
  8. Méndez, J.H.: Travel 2.0 tools: User behavior analysis and modelling. Special emphasis on advertising effectiveness through the eye-tracking methodology. University of Granada (2015)Google Scholar
  9. Nielsen, J., Pernice, K.: Eyetracking web usability. Pearson Education, Upper Saddle River (2010)Google Scholar
  10. Noone, B., Robson, S.K.: Using Eye Tracking to Obtain a Deeper Understanding of What Drives Online Hotel Choice. Cornell University (2014)Google Scholar
  11. Venkatraman, V., Payne, J., Huettel, S.A.: An overall probability of winning heuristic for complex risky decisions: Choice and eye fixation evidence. Organ. Behav. Hum. Decis. Process. 125(2), 73–87 (2014)CrossRefGoogle Scholar
  12. Wan Adnan, W.A., Hassan, W.N.H., Abdullah, N., Taslim, J.: Eye tracking analysis of user behavior in online social networks. In: Ozok, A.A., Zaphiris, P. (eds.) OCSC 2013. LNCS, vol. 8029, pp. 113–119. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-39371-6_13 CrossRefGoogle Scholar
  13. Wang, Y., Sparks, B.: An eye-tracking study of tourism photo stimuli: image characteristics and ethnicity. J. Travel Res. 55(5), 588–602 (2014)CrossRefGoogle Scholar
  14. Yang, Y., Pan, B., Song, H.: Predicting hotel demand using destination marketing organization’s web traffic data. J. Travel Res. 53(4), 433–447 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Edoardo Cantoni
    • 1
  • Elena Marchiori
    • 1
    Email author
  • Lorenzo Cantoni
    • 1
  1. 1.Faculty of Communication SciencesUniversità della Svizzera italianaLuganoSwitzerland

Personalised recommendations