Research topic
I want to find research articles about how search query time for an online search system (like Google or Google scholar, for example) affects user experience and usage. I want empirical studies that measure changes in user behavior.
Report
Detailed summary
The search confirmed that increased search query time negatively impacts user experience and behavior, with users showing heightened sensitivity to delays beyond certain latency thresholds, leading to changes in click behavior, reduced engagement, and satisfaction [1, 2, 6].
Key Findings:User Sensitivity and Behavioral Impact:
- Users are significantly sensitive to response latency, with behavior changing notably when delays surpass specific thresholds (around 200-500 ms reported) [1, 2].
- Increases in query response time are associated with negative shifts in user actions, such as decreased session lengths and click-through rates [1, 2, 5].
Thresholds and Engagement:
- Multiple studies have established that user satisfaction degrades noticeably beyond latency thresholds, with users abandoning slower systems for faster alternatives [2, 3, 6].
- Physiological responses such as frustration and cognitive load increases are reported even with minor delay increments [6, 8].
Empirical Methods:
- Controlled user studies and large-scale log analyses provide robust insights into how response time directly affects user behaviors across varied contexts, including mobile and desktop environments [1, 2, 8].
These findings highlight the critical importance of maintaining low latency in online search systems to sustain user engagement and satisfaction, while pointing to opportunities for further research into adaptive systems and user-specific tolerance thresholds.
Categories of papers
Precisely Relevant Studies on Query Time
- Studies that empirically investigate the impact of search query time on user behavior in online search systems, specifically measuring changes in metrics like click behavior and engagement.
- References: [1, 2, 4, 5, 6, 8]
- Details:
- [1, 2, 4] Conduct controlled user studies examining user sensitivity to response latency and analyze Yahoo search query logs to reveal behavioral changes.
- [5] Investigates query response delays using Information Foraging Theory, highlighting interaction changes due to delays.
- [6] Analyzes physiological and emotional responses to latency, emphasizing changes in click behavior.
- [8] Explores differences in user behavior and tolerance in mobile searches compared to desktop.
- Focus on understanding user tolerance to different levels of latency and the impact on preferences for search systems.
- References: [3, 7, 13]
- Details:
- [3] Compares user preferences between fast and slow search engines, identifying specific latency thresholds affecting choice.
- [7, 13] Investigate willingness to wait within "slow search" systems, examining behavior modification over time for quality trade-offs.
- Studies that explore the cognitive load and psychological effects of delayed search query times.
- References: [6, 8, 10]
- Details:
- [6] Uses physiological experiments to show attentional shifts and emotional effects due to latency.
- [8] Looks at mobile-specific emotional responses, reporting user frustration at higher latency durations.
- [10] Studies emotional reactions under time constraints and system delays, measuring impact on satisfaction and task difficulty.
- Research applying theoretical frameworks like Information Foraging and Search Economic Theories to explain user behavior under response latency.
- References: [5, 9]
- Details:
- [5] Employs theories to hypothesize and test behaviors under delays, noting differences from expected theoretical predictions.
- [9] Explores decision-making impacts under delays, outlining theories about user effort minimization during search.
- Studies leveraging query logs to understand real-world user behavior in response to latency.
- References: [1, 2, 6]
- Details:
- [1, 2] Analyze Yahoo Web Search logs, correlating latency increases with changes in click behavior and search engagement.
- [6] Conducts a large-scale analysis of click behavior changes using web query logs from a major search engine.
Timeline and citation network
Timeline and Development of Ideas
Early Foundations (2000-2008):
- Initial studies, such as by Hoxmeier and Dicesare (2000) [16], begin exploring the impact of system response time on user satisfaction, noting dissatisfaction with increased response time. This set the stage for identifying thresholds of acceptability.
- Brutlag et al. (2008) [3] expand by empirically comparing user preferences for fast vs. slow search engine latencies, establishing more concrete insights into user sensitivity and behavior changes, such as abandonment of slower systems.
Advancing Methodologies and Theories (2014-2017):
- Arapakis et al. (2014) [2] and Bai et al. (2017) [1] contribute significantly through controlled user studies and log analyses, illustrating the thresholds of latency that affect user satisfaction and click behavior.
- Maxwell and Azzopardi (2014) [5] introduce Information Foraging and Search Economic Theories to examine search behavior under latency, providing deeper theoretical frameworks.
Exploring Cognitive and Emotional Effects (2015-2016):
- Barreda-Ángeles et al. (2015) [6] delve into physiological and emotional responses to latency, uncovering nuanced attentional and emotional shifts even with minor delays.
- Research in 2016 [10][15] extends to assessing time constraints and delays, diversifying focus to cognitive and task performance impacts under structured conditions.
Nuanced Contextual Analyses (2016-2021):
- Studies like Burton and Collins-Thompson (2016) [7] and Arapakis et al. (2021) [8] refine understanding by focusing on user adaptability in slow search scenarios and mobile vs. desktop latency tolerances, highlighting situational variances in user behavior.
Ioannis Arapakis and Collaborators:
- A significant cluster of research led by Arapakis, with contributions to understanding both theoretical and applied aspects of response latency on user behavior in search systems through various studies [2][4][8].
David Maxwell and Leif Azzopardi:
- This team provided foundational theoretical contributions using Information Foraging Theory and Search Economic Theory to elucidate user behavior dynamics under latency influence [5][15].
These researchers and their collaborative works have consistently advanced domain knowledge by integrating empirical findings with theoretical models, shaping current understanding and prompting further inquiry into adaptive search systems and behavior insights.
Discovery progress: more than 90% complete (~9-25 papers found)
So far, I've closely analyzed 520 of the most promising papers, and I've found
~9-25
papers with relevant abstracts, which is probably
more than 90%
of all that exist.
To get this estimate, we do a statistical analysis of the discovery process.