Segmenting responders in online communities: an investigation on cue perceptions and disciplinary differences

Purpose When browsing online questions, users respond to those with their preferred question cues or specific characteristics of question expressions. Understanding different responders’ perceptions of their preferred questions can enhance the system’s ability to recommend questions to them. However, limited research has focused on segmenting responders based on their cue perceptions. This study leverages goal-framing theory to segment responders according to cue perceptions and profiles these segments based on their preferred topics and majors. Design/methodology/approach A survey was conducted with 208 responders. The STEM (Science, Technology, Engineering and Mathematics) and non-STEM dichotomy was used to classify responders’ preferred topics and majors. Cluster analysis was employed to segment the responders, and logistic regression was used to profile these segments. Findings This research identified three responder segments: Generalists, Specialists and Hobbyists. Generalists, who preferred financial topics but were less inclined toward STEM ones, were attracted to question cues emphasizing normative, gain and hedonic goals. Specialists preferred STEM questions drawn to cues emphasizing gain goals. Hobbyists, with non-STEM majors, were attracted to simple questions emphasizing hedonic goals. Originality/value This research innovatively connects question cues with goal-frames, enhancing the understanding of the goals that drive each responder segment to engage in problem-solving and highlighting the disciplinary differences among responders. The findings can assist librarians and information scientists in refining systems that assist users’ question generation, as well as enhancing question recommendation systems through the use of targeted question cues.

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Publication Details

Journal
Aslib Journal of Information Management
Published
2025-05-29
DOI
https://doi.org/10.1108/ajim-09-2024-0733
Citations
2
Primary Topic
Expert finding and Q&A systems
Type
article
Field-Weighted Citation Impact
6.49
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article

Segmenting responders in online communities: an investigation on cue perceptions and disciplinary differences

Qian Wu, Dion Hoe‐Lian Goh, Chei Sian Lee
2 citations
Aslib Journal of Information Management
Expert finding and Q&A systems
6.49
article

Segmenting responders in online communities: an investigation on cue perceptions and disciplinary differences

Qian Wu, Dion Hoe‐Lian Goh, Chei Sian Lee
article en
2 citations

Abstract

Purpose When browsing online questions, users respond to those with their preferred question cues or specific characteristics of question expressions. Understanding different responders’ perceptions of their preferred questions can enhance the system’s ability to recommend questions to them. However, limited research has focused on segmenting responders based on their cue perceptions. This study leverages goal-framing theory to segment responders according to cue perceptions and profiles these segments based on their preferred topics and majors. Design/methodology/approach A survey was conducted with 208 responders. The STEM (Science, Technology, Engineering and Mathematics) and non-STEM dichotomy was used to classify responders’ preferred topics and majors. Cluster analysis was employed to segment the responders, and logistic regression was used to profile these segments. Findings This research identified three responder segments: Generalists, Specialists and Hobbyists. Generalists, who preferred financial topics but were less inclined toward STEM ones, were attracted to question cues emphasizing normative, gain and hedonic goals. Specialists preferred STEM questions drawn to cues emphasizing gain goals. Hobbyists, with non-STEM majors, were attracted to simple questions emphasizing hedonic goals. Originality/value This research innovatively connects question cues with goal-frames, enhancing the understanding of the goals that drive each responder segment to engage in problem-solving and highlighting the disciplinary differences among responders. The findings can assist librarians and information scientists in refining systems that assist users’ question generation, as well as enhancing question recommendation systems through the use of targeted question cues.

Aslib Journal of Information Management
Nanyang Technological University (SG), Shanghai Jiao Tong University (CN)
Openalex Percentile: Top 4%
Expert finding and Q&A systems
6.49
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