Eye-Tracking Analysis of User Behavior and Performance in Web Search on Large and Small Screens

dc.contributor.authorKim, Jaewon
dc.contributor.authorThomas, Paul
dc.contributor.authorSankaranarayana, Ramesh S
dc.contributor.authorGedeon, Tamas (Tom)
dc.contributor.authorYoon, Hwan-Jin
dc.date.accessioned2015-12-10T22:53:26Z
dc.date.issued2015
dc.date.updated2021-03-07T07:17:05Z
dc.description.abstractIn recent years, searching the web on mobile devices has become enormously popular. Because mobile devices have relatively small screens and show fewer search results, search behavior with mobile devices may be different from that with desktops or laptops. Therefore, examining these differences may suggest better, more efficient designs for mobile search engines. In this experiment, we use eye tracking to explore user behavior and performance. We analyze web searches with 2 task types on 2 differently sized screens: one for a desktop and the other for a mobile device. In addition, we examine the relationships between search performance and several search behaviors to allow further investigation of the differences engendered by the screens. We found that users have more difficulty extracting information from search results pages on the smaller screens, although they exhibit less eye movement as a result of an infrequent use of the scroll function. However, in terms of search performance, our findings suggest that there is no significant difference between the 2 screens in time spent on search results pages and the accuracy of finding answers. This suggests several possible ideas for the presentation design of search results pages on small devices.
dc.identifier.issn2330-1643
dc.identifier.urihttp://hdl.handle.net/1885/59353
dc.publisherWiley Online Library
dc.sourceJournal of the Association for Information Science and Technology
dc.titleEye-Tracking Analysis of User Behavior and Performance in Web Search on Large and Small Screens
dc.typeJournal article
local.bibliographicCitation.issue3
local.bibliographicCitation.lastpage544
local.bibliographicCitation.startpage526
local.contributor.affiliationKim, Jaewon, College of Engineering and Computer Science, ANU
local.contributor.affiliationThomas, Paul, College of Engineering and Computer Science, ANU
local.contributor.affiliationSankaranarayana, Ramesh S, College of Engineering and Computer Science, ANU
local.contributor.affiliationGedeon, Tamas (Tom), College of Engineering and Computer Science, ANU
local.contributor.affiliationYoon, Hwan-Jin, College of Physical and Mathematical Sciences, ANU
local.contributor.authoruidKim, Jaewon, u4585422
local.contributor.authoruidThomas, Paul, u4161360
local.contributor.authoruidSankaranarayana, Ramesh S, u9508060
local.contributor.authoruidGedeon, Tamas (Tom), u4088783
local.contributor.authoruidYoon, Hwan-Jin, u3128131
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080500 - DISTRIBUTED COMPUTING
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4056230xPUB486
local.identifier.citationvolume66
local.identifier.doi10.1002/asi.23187
local.identifier.scopusID2-s2.0-84947272802
local.identifier.thomsonID000350100500008
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
01_Kim_Eye-Tracking_Analysis_of_User_2015.pdf
Size:
2.35 MB
Format:
Adobe Portable Document Format