Evaluating CoLiDeS + Pic: The Role of Relevance of Pictures in User Navigation Behaviour

Autor/inn/en	Karanam, Saraschandra; van Oostendorp, Herre; Indurkhya, Bipin
Titel	Evaluating CoLiDeS + Pic: The Role of Relevance of Pictures in User Navigation Behaviour
Quelle	In: Behaviour & Information Technology, 31 (2012) 1, S.31-40 (10 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0144-929X
DOI	10.1080/0144929X.2011.606335
Schlagwörter	Semantics; Hypermedia; Semiotics; Visual Stimuli; Models; Cognitive Processes; Internet; Computer Software; Behavior; Task Analysis; Validity; Prediction; Evaluation Methods; Usability; Web Sites; Multiple Regression Analysis; Information Technology; Foreign Countries; Higher Education; India + Suchen Sie Ihr Suchwort? Semantik; Semiotik; Analogiemodell; Cognitive process; Kognitiver Prozess; Aufgabenanalyse; Gültigkeit; Vorhersage; Web-Design; Informationstechnologie; Ausland; Hochschulbildung; Hochschulsystem; Hochschulwesen; Indien
Abstract	CoLiDeS + Pic is a cognitive model of web-navigation that incorporates semantic information from pictures into CoLiDeS. In our earlier research, we have demonstrated that by incorporating semantic information from pictures, CoLiDeS + Pic can predict the hyperlinks on the shortest path more frequently, and also with greater information scent, compared to earlier cognitive models of web-navigation like CoLiDeS that relied only on textual information from hyperlinks. In this article, we investigate the following research questions. First, would the increase in information scent have an impact on the actual user navigation behaviour? Second, do users actually follow the navigation path predicted by CoLiDeS + Pic? In other words, would CoLiDeS + Pic predict actual user navigation behaviour more accurately than CoLiDeS? We investigate these questions by varying the relevance of pictures on a web page and studying the impact of varying relevance on the user navigation patterns. We found that under the highly relevant picture condition, users were more accurate and took less time to finish their tasks. Also, under the highly relevant picture condition, CoLiDeS + Pic predicts significantly greater number of actual user clicks. There was no significant difference in model predictions between the lowly relevant picture condition and no-picture condition. These results validate the predictions made by CoLiDeS + Pic. (Contains 5 figures and 3 tables.) (As Provided).
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Update	2017/4/10