Unconscious Physiological Effects of Search Latency on Users and Their Click Behaviour

Barreda-Ángeles, Miguel; Arapakis, Ioannis; Bai, Xiao; Cambazoğlu, B. Barla; Pereda-Baños, Alexandre

doi:10.1145/2766462.2767719

Cited by 22 publications

(19 citation statements)

References 24 publications

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“…Moreover, the positive affect experienced at post-task was found to be significantly higher for the fast SE, indicating a potential positive bias towards the latter. These findings appear to be consistent with what is reported in [5], where the authors did not establish any significant effects of response latency on users. However, we note that in [5], the search site condition was fixed and the authors did not perform the same brand comparison across different commercial search sites as we report in the current study.…”

Section: Discussionsupporting

confidence: 92%

“…On the other hand, the reported FA within each group did not change significantly across the latency values. This finding appears to be analogous with what is reported in [5], where the FA scale revealed a pattern which, although not statistically significant, suggests a reduced engagement as the latency increases to higher values.…”

Section: Discussionsupporting

confidence: 89%

“…In [5], the authors conducted a controlled user study to reveal the physiological effects of response latency on users. The reported results indicate that the latency effects are present even at small increases in response latency.…”

Section: Query Response Latencymentioning

confidence: 99%

“…They complemented their user study by a query log analysis similar to ours. The user studies conducted in our work differ from those in [5] in that our work focuses on the perceivable effects of increasing response latency, while the latter work investigatesunconscious latency effects that may still effect user behaviour.…”

Section: Query Response Latencymentioning

confidence: 99%

See 3 more Smart Citations

Understanding and Leveraging the Impact of Response Latency on User Behaviour in Web Search

Bai

Arapakis

Cambazoğlu

et al. 2017

ACM Trans. Inf. Syst.

Self Cite

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The interplay between the response latency of web search systems and users’ search experience has only recently started to attract research attention, despite the important implications of response latency on monetisation of such systems. In this work, we carry out two complementary studies to investigate the impact of response latency on users’ searching behaviour in web search engines. We first conduct a controlled user study to investigate the sensitivity of users to increasing delays in response latency. This study shows that the users of a fast search system are more sensitive to delays than the users of a slow search system. Moreover, the study finds that users are more likely to notice the response latency delays beyond a certain latency threshold, their search experience potentially being affected. We then analyse a large number of search queries obtained from Yahoo Web Search to investigate the impact of response latency on users’ click behaviour. This analysis demonstrates the significant change in click behaviour as the response latency increases. We also find that certain user, context, and query attributes play a role in the way increasing response latency affects the click behaviour. To demonstrate a possible use case for our findings, we devise a machine-learning framework that leverages the latency impact, together with other features, to predict whether a user will issue any clicks on web search results. As a further extension of this use case, we investigate whether this machine-learning framework can be exploited to help search engines reduce their energy consumption during query processing.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 89%

Section: Query Response Latencymentioning

confidence: 99%

Section: Query Response Latencymentioning

confidence: 99%

See 2 more Smart Citations

Understanding and Leveraging the Impact of Response Latency on User Behaviour in Web Search

Bai

Arapakis

Cambazoğlu

et al. 2017

ACM Trans. Inf. Syst.

Self Cite

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“…Third, we need to design experiments that lead to quantitative results about a technique's impact on QoE. In some studies [5,6], participants are shown a page load in person in a controlled environment and give qualitative feedback to interviewers. Clearly, this approach neither scales nor yields quantitative results.…”

Section: Introductionmentioning

confidence: 99%

Eyeorg

Varvello

Blackburn

Naylor

et al. 2016

Proceedings of the 12th International on Conference on Emerging Networking EXperiments and Technologies

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Tremendous effort has gone into the ongoing battle to make webpages load faster. This effort has culminated in new protocols (QUIC, SPDY, and HTTP/2) as well as novel content delivery mechanisms. In addition, companies like Google and SpeedCurve investigated how to measure "page load time" (PLT) in a way that captures human perception. In this paper we present Eyeorg [12], a platform for crowdsourcing web quality of experience measurements. Eyeorg overcomes the scaling and automation challenges of recruiting users and collecting consistent user-perceived quality measurements. We validate Eyeorg's capabilities via a set of 100 trusted participants. Next, we showcase its functionalities via three measurement campaigns, each involving 1,000 paid participants, to 1) study the quality of several PLT metrics, 2) compare HTTP/1.1 and HTTP/2 performance, and 3) assess the impact of online advertisements and ad blockers on user experience. We find that commonly used, and even novel and sophisticated PLT metrics fail to represent actual human perception of PLT, that the performance gains from HTTP/2 are imperceivable in some circumstances, and that not all ad blockers are created equal.

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Investigating the role of eye movements and physiological signals in search satisfaction prediction using geometric analysis

Liu

Tsai

et al. 2019

Asso for Info Science & Tech

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Two general challenges faced by data analysis are the existence of noise and the extraction of meaningful information from collected data. In this study, we used a multiscale framework to reduce the effects caused by noise and to extract explainable geometric properties to characterize finite metric spaces. We conducted lab experiments that integrated the use of eye-tracking, electrodermal activity (EDA), and user logs to explore users' information-seeking behaviors on search engine result pages (SERPs). Experimental results of 1,590 search queries showed that the proposed strategies effectively predicted query-level user satisfaction using EDA and eye-tracking data. The bootstrap analysis showed that combining EDA and eye-tracking data with user behavior data extracted from user logs led to a significantly better linear model fit than using user behavior data alone. Furthermore, cross-user and cross-task This article is protected by copyright. All rights reserved. This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/asi.24240 validations showed that our methods can be generalized to different search engine users performing different preassigned tasks.

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Unconscious Physiological Effects of Search Latency on Users and Their Click Behaviour

Cited by 22 publications

References 24 publications

Understanding and Leveraging the Impact of Response Latency on User Behaviour in Web Search

Understanding and Leveraging the Impact of Response Latency on User Behaviour in Web Search

Eyeorg

Investigating the role of eye movements and physiological signals in search satisfaction prediction using geometric analysis

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