Contained in:
Book Chapter

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,
+ Show More

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
  • Publication Year: 2021
  • Pages: 141-146
  • Content License: CC BY 4.0
  • © 2021 Author(s)

XML
  • Publication Year: 2021
  • Content License: CC BY 4.0
  • © 2021 Author(s)

Chapter Information

Chapter Title

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

Authors

Gianpaolo Zammarchi, Giulia Contu, Luca Frigau

Language

English

DOI

10.36253/978-88-5518-304-8.28

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

ASA 2021 Statistics and Information Systems for Policy Evaluation

Book Subtitle

Book of short papers of the opening conference

Editors

Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Publisher Name

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

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

245

Fulltext
downloads

303

Views

Export Citation

1,347

Open Access Books

in the Catalogue

2,262

Book Chapters

3,790,127

Fulltext
downloads

4,421

Authors

from 923 Research Institutions

of 65 Nations

65

scientific boards

from 348 Research Institutions

of 43 Nations

1,248

Referees

from 380 Research Institutions

of 38 Nations