Contenuto in:
Capitolo

Using eye-tracking to evaluate the viewing behavior on tourist landscapes

  • Gianpaolo Zammarchi
  • Giulia Contu
  • Luca Frigau

Every tourist website employs images to attract potential tourists. In particular, destination tourism websites use environmental images, such as landscapes, to attract the attention of tourists and to address their purchase choice. Nowadays the effectiveness of these tools has been enhanced by the use of eye-tracking technology. That allows measuring the exact eye position during the visualization of images, texts, or other visual stimuli. Consequently, eye-tracking data can be processed to obtain quantitative measures of viewing behavior that can be analyzed for several purposes in many fields such as to cluster consumers, to improve the effectiveness of a website and for neuroscience studies. This work is aimed to use eye-tracking technology to investigate user behavior according to different types of images (e.g. natural landscapes, city landscapes). Specifically, we compare different statistical descriptive tools with supervised and unsupervised models. Furthermore, we discuss the effectiveness of their results and their capacity to provide satisfactory and interpretable solutions that can be used by decision-makers.

  • Keywords:
  • Tourism,
  • Eye-tracking,
  • Fixations,
+ Mostra di più

Gianpaolo Zammarchi

University of Cagliari, Italy - ORCID: 0000-0002-9733-380X

Giulia Contu

University of Cagliari, Italy - ORCID: 0000-0001-9750-1896

Luca Frigau

University of Cagliari, Italy - ORCID: 0000-0002-6316-4040

  1. Busswell, G.T. (1935). How people look at pictures: A study of the psychology of perception in art. University of Chicago Press, Chicago, (US).
  2. Dupont, L., Antrop, M., Van Eetvelde, V. (2013). Eye-tracking analysis in landscape perception research: influence of photograph properties and landscape characteristics. Landscape Research, 39(4), pp. 1-18.
  3. Garín-Muñoz, T., Amaral, T. (2011). Internet Usage for Travel and Tourism. The Case of Spain. Tourism Economics, 17, pp. 1071-1085.
  4. Itti, L. (2004). Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Transactions on Image Processing, 13, pp. 1304–1318.
  5. Jiménez-García, M., Ruiz-Chico, J., Peña-Sánchez, A.R. (2020). Landscape and Tourism: Evolution of Research Topics. Land, 9(12), pp. 1-17.
  6. Judd, T., Ehinger, K., Durand, F., Torralba, A. (2009). Learning to Predict where Humans Look. ICCV 2009.
  7. Li, Q., Huang, Z., Christianson, K. (2016). Visual attention toward tourism photographs with text: An eyetracking Study. Tourism Management, 54, pp. 243-258.
  8. Mannan, S.K., Ruddock, K.H., Wooding, D.S. (1996). The relationship between the locations of spatial features and those of fixations made during visual examination of briefly presented images. Spatial Vision, 10, pp. 165–188.
  9. Parkhurst, D.J., Niebur, E. (2003). Scene content selected by active vision. Spatial Vision, 16, pp. 125–154.
  10. R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, (AT). URL https://www.R- project.org/.
  11. Ruhanen, L.M., McLennan, C.-L. J., Moyle, B.D. (2013). Strategic issues in the Australian tourism industry: A 10-year analysis of national strategies and plans. Asia Pacific Journal of Tourism Research, 18(3), pp. 220–240.
  12. Scott, N., Zhang, R., Le, D., Moyle, B. (2019) A review of eye-tracking research in tourism. Current Issues in Tourism, 22(10), pp. 1244-1261.
  13. Scrucca, L., Fop, M., Murphy, T.B., Raftery, A.E. (2016). mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), pp. 289– 317.
  14. Venables, W.N., Ripley, B.D. (2002). Modern Applied Statistics with S, Fourth edition. Springer, New York, (US). ISBN 0-387-95457-0.
  15. Wang, Y., Sparks, B.A. (2016). An Eye-Tracking Study of Tourism Photo Stimuli: Image Characteristics and Ethnicity. Journal of Travel Research, 55(5), pp. 588-602.
  16. Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, New York, (US).
  17. WTTC, GLOBAL ECONOMIC IMPACT & TRENDS 2020. https://wttc.org/Research/Economic- Impact/moduleId/1445/itemId/91/controller/DownloadRequest/action/QuickDownload (12/20).
PDF
  • Anno di pubblicazione: 2021
  • Pagine: 141-146

XML
  • Anno di pubblicazione: 2021

Informazioni sul capitolo

Titolo del capitolo

Using eye-tracking to evaluate the viewing behavior on tourist landscapes

Autori

Gianpaolo Zammarchi, Giulia Contu, Luca Frigau

Lingua

English

DOI

10.36253/978-88-5518-304-8.28

Opera sottoposta a peer review

Anno di pubblicazione

2021

Copyright

© 2021 Author(s)

Licenza d'uso

CC BY 4.0

Licenza dei metadati

CC0 1.0

Informazioni bibliografiche

Titolo del libro

ASA 2021 Statistics and Information Systems for Policy Evaluation

Sottotitolo del libro

Book of short papers of the opening conference

Curatori

Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci

Opera sottoposta a peer review

Anno di pubblicazione

2021

Copyright

© 2021 Author(s)

Licenza d'uso

CC BY 4.0

Licenza dei metadati

CC0 1.0

Editore

Firenze University Press

DOI

10.36253/978-88-5518-304-8

eISBN (pdf)

978-88-5518-304-8

eISBN (xml)

978-88-5518-305-5

Collana

Proceedings e report

ISSN della collana

2704-601X

e-ISSN della collana

2704-5846

269

Download dei libri

381

Visualizzazioni

Salva la citazione

1.388

Libri in accesso aperto

in catalogo

2.597

Capitoli di Libri

4.205.799

Download dei libri

4.979

Autori

da 1067 Istituzioni e centri di ricerca

di 66 Nazioni

70

scientific boards

da 375 Istituzioni e centri di ricerca

di 43 Nazioni

1.304

I referee

da 397 Istituzioni e centri di ricerca

di 38 Nazioni