Does visual attention help? Towards better understanding and predicting users’ good abandonment behavior in mobile search

Author:

Wu Dan,Zhang ShutianORCID

Abstract

PurposeGood abandonment behavior refers to users obtaining direct answers via search engine results pages (SERPs) without clicking any search result, which occurs commonly in mobile search. This study aims to better understand users' good abandonment behavior and perception, and then construct a good abandonment prediction model for mobile search with improved performance.Design/methodology/approachIn this study, an in situ user mobile search experiment (N = 43) and a crowdsourcing survey (N = 1,379) were conducted. Good abandonment behavior was analyzed from a quantitative perspective, exploring users' search behavior characteristics from four aspects: session and query, SERPs, gestures and eye-tracking data.FindingsUsers show less engagement with SERPs in good abandonment, spending less time and using fewer gestures, and they pay more visual attention to answer-like results. It was also found that good abandonment behavior is often related to users' perceived difficulty of the searching tasks and trustworthiness in the search engine. A good abandonment prediction model in mobile search was constructed with a high accuracy (97.14%).Originality/valueThis study is the first to explore eye-tracking characteristics of users' good abandonment behavior in mobile search, and to explore users' perception of their good abandonment behavior. Visual attention features are introduced into good abandonment prediction in mobile search for the first time and proved to be important predictors in the proposed model.

Publisher

Emerald

Subject

Library and Information Sciences,Information Systems

Reference35 articles.

1. Patterns of search result examination: query to first action,2019

2. Evaluating mobile web search performance by taking good abandonment into account,2014

3. Relating eye-tracking measures with changes in knowledge on search tasks,2018

4. Relevance prediction from eye-movements using semi-interpretable convolutional neural networks,2020

5. Query abandonment prediction with recurrent neural models of mouse cursor movements,2020

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