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Why do users transition from knowledge seeking behavior to knowledge contribution behavior? Evidence from social question-and-answer communities

Year: 2025

Type: article

Abstract: This study investigates the mechanisms driving users' transitions from knowledge seeking to knowledge contribution behaviors in social question-and-answer communities, with the goal of advancing theoretical understanding and promoting the sustainable development of these platforms. Grounded in motivation theory and trust theory, this study develops and tests a research model using covariance-based structural equation modeling and bootstrap method with 452 valid samples. The results reveal that knowledge seeking behavior positively influences both intrinsic motivations (altruism, self-efficacy, emotional empathy) and extrinsic motivations (platform reward, social need, group identity), with all motivations except emotional empathy significantly promoting knowledge contribution behavior. Furthermore, intrinsic (altruism, self-efficacy) and extrinsic motivations (platform reward, social need, group identity) partially mediate the relationship between knowledge seeking and contribution behaviors. Moderation analysis indicates that platform trust and interpersonal trust strengthen this relationship, with interpersonal trust having a stronger effect. Notably, moderation analysis indicates that platform trust and interpersonal trust strengthen this relationship, with interpersonal trust exhibiting a stronger effect. By integrating motivation theory and trust theory, this study provides a unified framework to examine the transition from knowledge seeking to contribution behaviors from theoretical, behavioral, and contextual perspectives. The findings offer practical implications for social question-and-answer community managers, including strategies to diversify reward systems, enhance users' self-efficacy and group identity, foster meaningful social interactions, and establish multi-level trust mechanisms to promote long-term platform engagement and growth.

Source: Acta Psychologica

DOI: 10.1016/j.actpsy.2025.106072

Authors: Zhongyang Xu, Zhiqian Meng, Xingshang Yu, Qinze Mi, Lei Pei

Institutions: Nanjing University, University of Science and Technology Liaoning, Sichuan University, Hangzhou Dianzi University

Language: English

Topic: Expert finding and Q&A systems

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,670

Funder: National Social Science Fund of China