Characterising exploratory search tasks: Evidence from different fields

Exploratory search is considered challenging for users due to its open-ended nature, and ambiguity of information needs. Clarifying exploratory searchers’ information needs is fundamental to design information environments that provide adaptive support. Since tasks are a representation of information needs, this study investigates the characteristics of exploratory tasks in different fields through multiple attributes to reveal exploratory searchers’ information needs. Repeated surveys were conducted in three representative fields: healthcare, artificial intelligence, and integrated circuit. Content analysis was used to identify 16 attributes. Learning and investigation differ in 14 attributes, and exploratory subtasks differ significantly in Source, Domain knowledge, Cognitive complexity and Search frequency . Different composition of user knowledge background leads to differences in two attributes of Motivation and Cognitive complexity , and difference in the correlation between Motivation and Interdependence . The findings help researchers design distinct exploratory tasks with domain characteristics, and help practitioners develop task- and domain-adaptive exploratory search systems.

Authors

Institutions

Publication Details

Journal
Journal of Information Science
Published
2025-04-15
DOI
https://doi.org/10.1177/01655515251330611
Citations
1
Primary Topic
Information Retrieval and Search Behavior
Type
article
Field-Weighted Citation Impact
3.25

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Characterising exploratory search tasks: Evidence from different fields

Jiangping Chen, Junbo Mao, Yaxi Liu, Xubu Ma et al.
1 citations
Journal of Information Science
Information Retrieval and Search Behavior
3.25
article

Characterising exploratory search tasks: Evidence from different fields

Jiangping Chen, Junbo Mao, Yaxi Liu, Xubu Ma, Chunxiu Qin, Huan He
article en
1 citations

Abstract

Exploratory search is considered challenging for users due to its open-ended nature, and ambiguity of information needs. Clarifying exploratory searchers’ information needs is fundamental to design information environments that provide adaptive support. Since tasks are a representation of information needs, this study investigates the characteristics of exploratory tasks in different fields through multiple attributes to reveal exploratory searchers’ information needs. Repeated surveys were conducted in three representative fields: healthcare, artificial intelligence, and integrated circuit. Content analysis was used to identify 16 attributes. Learning and investigation differ in 14 attributes, and exploratory subtasks differ significantly in Source, Domain knowledge, Cognitive complexity and Search frequency . Different composition of user knowledge background leads to differences in two attributes of Motivation and Cognitive complexity , and difference in the correlation between Motivation and Interdependence . The findings help researchers design distinct exploratory tasks with domain characteristics, and help practitioners develop task- and domain-adaptive exploratory search systems.

Journal of Information Science
University of North Texas (US), Xidian University (CN), Xiangtan University (CN)
National Social Science Fund of China
Openalex Percentile: Top 10%
Information Retrieval and Search Behavior
3.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Characterising exploratory search tasks: Evidence from different fields — Jiangping Chen, Junbo Mao, et al. · Journal of Information Science (2025) | TGRS Research Map | TGRS