Authentic versus synthetic: An investigation of the influences of study settings and task configurations on search behaviors

Abstract

In information seeking and retrieval research, researchers often collect data about users' behaviors to predict task characteristics and personalize information for users. The reliability of user behavior may be directly influenced by data collection methods. This article reports on a mixed-methods study examining the impact of study setting (laboratory setting vs. remote setting) and task authenticity (authentic task vs. simulated task) on users' online browsing and searching behaviors. Thirty-six undergraduate participants finished one lab session and one remote session in which they completed one authentic and one simulated task. Using log data collected from 144 task sessions, this study demonstrates that the synthetic lab study setting and simulated tasks had significant influences mostly on behaviors related to content pages (e.g., page dwell time, number of pages visited per task). Meanwhile, first-query behaviors were less affected by study settings or task authenticity than whole-session behaviors, indicating the reliability of using first-query behaviors in task prediction. Qualitative interviews reveal why users were influenced. This study addresses methodological limitations in existing research and provides new insights and implications for researchers who collect online user search behavioral data.

A reference dependence approach to enhancing early prediction of session behavior and satisfaction

“…authentic task vs. simulated task) and search environment (e.g. controlled lab, naturalistic setting) may affect the way in which users' reference points and expectations affect their search decision-making [4,12,28]. With more empirical evidences on the role and impact of reference dependence effects in searching, researchers will be able to build computationally solid and behaviorally realistic user models for developing and evaluating user-oriented intelligent search systems.…”

Leveraging user interaction signals and task state information in adaptively optimizing usefulness-oriented search sessions

“…Current search technologies and digital libraries still face plenty of challenges when applied in addressing complex search tasks that trigger multi-round interactions between users and information (e.g., finding useful information for applying for PhD programs or deciding investment portfolios) [8,35,36,44]. Part of the complexity of this problem is reflected in the cognitive variation, transitions of sub-goals, as well as significant behavioral changes during the process of search interactions.…”