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How do we elicit more user feedback in the social Q&A community? A consideration of the expertise-required question

Mi Zhou (Xi'an Jiaotong University, Xi'an, China)
Bo Meng (Xi'an Jiaotong University, Xi'an, China)
Weiguo Fan (Henry B Tippie College of Business, University of Iowa, Iowa City, Iowa, USA)

Information Technology & People

ISSN: 0959-3845

Article publication date: 22 May 2023

182

Abstract

Purpose

The current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&A communities and whether the expertise-required question influences the role of these factors on the feedback.

Design/methodology/approach

To understand the antecedents and consequences that influence the feedback received on answers to online community questions, the elaboration likelihood model (ELM) is applied in this study. The authors use web data crawling methods and a combination of quantitative analyses. The data for this study came from Zhihu; in total, 353,775 responses were obtained to 1,531 questions, ranging from 49 to 23,681 responses per question. Each answer received 0 to 113,892 likes and 0 to 6,250 comments.

Findings

The answers' cognitive and emotional components and the answerer's influence positively affect user feedback behavior. In addition, the expertise-required question moderates the effects of the answer's cognitive component and emotional component on the user feedback, moderating the effects of the answerer's influence on the user approval feedback.

Originality/value

This study builds upon a limited yet growing body of literature on a theme of great relevance to scholars, practitioners and social media users concerning the effects of the connotation of answers (i.e. their cognitive and emotional components) and the answerer's influence on user feedback (i.e. approval and collaborative feedback) in social Q&A communities. The authors further consider the moderating role of the domain expertise required by the question (expertise-required question). The ELM model is applied to explore the relationships between questions, answers and feedback. The findings of this study add a new perspective to the research on user feedback and have implications for the management of social Q&A communities.

Keywords

Acknowledgements

The research was supported by the National Natural Science Foundation of China (grant number 71872142).

Citation

Zhou, M., Meng, B. and Fan, W. (2023), "How do we elicit more user feedback in the social Q&A community? A consideration of the expertise-required question", Information Technology & People, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ITP-10-2022-0751

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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