Study on Knowledge Sharing Behavior of Users in Social Q&amp;amp;A Community

Gao, Shuli; Sun, Xiaofeng; Chen, Jianbin; Guo, Yanli

doi:10.4236/jssm.2020.134043

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…In the study of users' knowledge-sharing behavior in the Zhihu social question-and-answer community, Gao et al ( 15 ) argued that in the Zhihu community, self-display, user recognition, social learning, and other dimensions have a positive and significant effect on users’ knowledge-sharing behavior. These results align with the present study's findings that the respondents' participation is affected by the components of motivations (personal and social).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the study of user knowledge sharing behavior in SQA community (based on Zhihu’s social exchange theory) showed that in Zhihu community, self-display, user recognition and recreational learning have significant positive effects on user knowledge sharing behavior. In other words, individuals with more detailed personal information had a greater desire for self-display ( 15 ).…”

Section: Introductionmentioning

confidence: 99%

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Factors Affecting Respondents’ Strategies in Answering Queries Related to the Field of Health on Social Question Answering (SQA) Websites

Kheirabadi,

Mousavi Chelak,

Ziaei

et al. 2023

MJIRI

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Background The study of various aspects of information behavior has attracted the attention of many researchers. This study used the structural equation modeling method to identify factors affecting respondents’ strategies in answering health-related questions on social question answering (SQA) websites. Methods The study population in this quantitative-applied survey included all respondents answering health-related questions on national and international SQA websites, among whom 431 individuals were selected as the sample using SPSS SAMPLE POWER software and convenience sampling. The data were collected using the Respondents’ Motivations and Strategies Questionnaire and the Social Support Questionnaire. The items of these questionnaires are scored on a 5-point Likert scale. The conceptual research model was evaluated using the structural equation modeling method, and the collected data were analyzed in SPSS 26.0 and AMOS 24.0. Results The authors identified and analyzed the factors influencing respondents' strategies and the relationships between these factors. Motivations, social support, sex, age, income, level of education, amount of activity per week, and response time are effective on response strategies with factor loadings of 0.61, 0.56, 0.50, 0.53, 0.31, 0.66, 0.53, and 0.65, respectively. The variable determination coefficient of response strategies in the structural equation model is reported to be 0.55 and significant. Finally, response strategies can be predicted based on the independent variables. Conclusion In order to enhance response strategies, it is important to promote effective response behaviors, as determined by the components that influence response strategies. The quality of related online services, such as expert question-answering and digital reference services, can be improved with the help of the present findings.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Factors Affecting Respondents’ Strategies in Answering Queries Related to the Field of Health on Social Question Answering (SQA) Websites

Kheirabadi,

Mousavi Chelak,

Ziaei

et al. 2023

MJIRI

View full text Add to dashboard Cite

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Transforming knowledge-sharing intentions into behaviour: the role of ambidextrous knowledge-sharing activities on mobile platforms

Lu,

Chen,

Lin

2024

Knowledge Management Research & Practice

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Recurrence quantification analysis of Q&A behavior from the perspective of explicit and tacit knowledge – an empirical study based on Zhihu's hashtags

Feng

Wang

2021

AJIM

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PurposeThe purpose of this research is to investigate the time structure characteristics of collaborative knowledge production behaviors in Q&A (question-and-answer) communities for explicit and tacit knowledge, and systematically investigate the supply side and the demand side of knowledge production.Design/methodology/approachTaking Zhihu as the research object, using the methods of recurrence plot and recurrence quantification analysis, this paper analyzes the recursive characteristics of the motion trajectories of the three behavioral sequences of questioning, answering, and discussion, qualitatively and quantitatively analyzing the generation and evolution mechanism of explicit and tacit knowledge.FindingsThe results show that compared with the demand-side behavior sequence, the supply-side behavior sequence exhibits higher stability, complexity and periodicity. Compared with the tacit knowledge topics, the demand-side behavior sequence of the explicit knowledge topics shows stronger nonlinearity, and the supply-side behavior sequence shows lower complexity.Originality/valueThe research conclusions provide preliminary evidence for the effectiveness of the recurrence plot method in distinguishing different types of knowledge production behaviors and have important application value for the “crowdsourcing” knowledge generation and identification under the knowledge economy and the sustainable development of the socialized question-and-answer community.

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Study on Knowledge Sharing Behavior of Users in Social Q&A Community

Cited by 3 publications

References 23 publications

Factors Affecting Respondents’ Strategies in Answering Queries Related to the Field of Health on Social Question Answering (SQA) Websites

Factors Affecting Respondents’ Strategies in Answering Queries Related to the Field of Health on Social Question Answering (SQA) Websites

Transforming knowledge-sharing intentions into behaviour: the role of ambidextrous knowledge-sharing activities on mobile platforms

Recurrence quantification analysis of Q&A behavior from the perspective of explicit and tacit knowledge – an empirical study based on Zhihu's hashtags

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