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The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

The online health community's success depends on doctors' active participation, so it is essential to understand the factors that affect doctors' knowledge contribution behavior in the online health communities. From the perspective of peer effect, this paper discusses the influence of focal doctors' peers on focal doctors' knowledge contribution behavior and the mechanism behind it. This paper aims to solve these problems.

Empirical data of 1,938 doctors were collected from a Chinese online health community, and propensity score matching and ordinary least squares were employed to verify the proposed theoretical model.

The results show that the presence of focal doctors' peers in online health communities has a positive effect on the knowledge contribution behavior of focal doctors, and the economic returns and social returns of focal doctors' peers have a significant mediating effect.

This paper discusses focal doctors' knowledge contribution behavior from the perspective of peer effect. It enhances the understanding of focal doctors' behavior in the online health communities by exploring the mediating role of their peers' economic and social returns. The results of this paper extend the research in the field of peer effect and online health and provide management implications and suggestions for online health platforms and doctors.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

Users' knowledge contribution behaviors are critical for online Q&A communities to thrive. Well-organized question threads in online Q&A communities enable users to clearly read existing answers and their evaluations before contributing. Based on the social comparison and peer influence literature, the authors examine peer influence on the informativeness of knowledge contributions in competitive settings. The authors also consider three levels of moderating factors concerning individuals' perception of competitiveness: question level, thread level and contributor level.

The authors collected data from one of the largest online Q&A communities in China. The hypotheses were validated using hierarchical linear models with cross-classified random effects. The generalized propensity score weighting method was employed for the robustness check.

The authors demonstrate the peer influence due to social comparison concerns among knowledge contribution behaviors in the same question thread. If more prior knowledge contributors choose to contribute long answers in the question thread, the subsequent contributions are more informative. This peer influence is stronger for factual questions and questions with higher popularity of answering but weaker in recommendation-type and well-answered questions and for contributors with higher social status.

This research provides a new cue of peer influence on online UGC contributions in competitive settings initiated by social comparison concerns. Additionally, the authors identify three levels of moderating factors (question level, thread level and contributor level) that are specific to online Q&A settings and are related to a contributor's perception of competitiveness, which affect the direct effect of peer influence on knowledge contributions. Rather than focus on motivation and quality evaluation, the authors concentrate on the specific content of online knowledge contributions. Peer influence here is not based on an actual acquaintance or a following relationship but on answering the same question. The authors also illustrate the competitive peer influence in subjective and personalized behaviors in online UGC communities.

In the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks.

This paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient.

The research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly.

The Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

The purpose of this paper is to draw on the social cognitive theory to identify the determinants of online knowledge community user continuance, which reflects a user’s continued use.

Based on the 271 valid responses collected from a survey, structural equation modelling was employed to examine the research model.

The results indicated that the cognitive factors of outcome expectation and the environmental factors of system quality and knowledge quality significantly affect a user’s continuance intention, which, in turn, affects continuance usage.

The results imply that service providers need to enhance community platforms and improve knowledge quality in order to retain users and facilitate their continuance.

Although previous research has examined online knowledge community user behaviour from multiple perspectives such as the social exchange theory and the motivational theory, it has seldom explored the relative effects of personal cognitions and environmental factors on user behaviour. This research fills the gap.

This article seeks to explore the micro‐dimensions of knowledge transfer partnerships (KTPs) with the aim of developing an appreciation of the personal interactions that facilitate the success of these university‐industry collaborations.

Empirical evidence concerning the operation of three KTPs, collected through interviews with the key partners and the review of relevant documentary material, is analysed through the lens of the communities of practice approach to situated learning.

The analysis of three case studies provides evidence to support the value of conceptualising the process of knowledge transfer between universities and industry as one of learning taking place within communities in which the development of mutual engagement, joint enterprise and shared repertoires play important roles facilitating successful collaborations. Moreover, the analysis highlights the significance of the boundary spanning roles of the KTP partners in facilitating the knowledge transfer process through engagement in both the university and industry communities.

By illuminating the dimensions of the inter‐personal interactions involved in the knowledge transfer process the CoPs analysis provides the foundations for recommendations to improve university‐industry KTPs, in particular, and, inter‐organisational knowledge transfer initiatives in general. A limitation of this research is its focus on the UK context. Moreover, given the exploratory nature of this study further research is required to verify the findings reported here.

The article makes an original empirical and conceptual contribution to understandings of university‐industry knowledge transfer collaborations.

The purpose of this paper is to propose the knowledge management (KM) model of the Thai “One Tambon One Product” (OTOP) community business by investigating the KM practices of the selected five‐star OTOP businesses of Thailand and identifying the KM factors that affect the success of their businesses.

A qualitative research method was used. The research units were three groups of Thai OTOP Champions with a score of five stars who have become successful in community income building, with national renown and ethnic representations. The research had three phases. Phase 1 was the study of KM practices of top OTOP businesses and factors affecting their successes of these businesses. Data were collected by in‐depth interviews, focus groups, and participatory observations. The informants were the OTOP businesses' administrators, operating staff, and members. Phase 2 involved data analysis and synthesis using content analysis. Phase 3 consisted of the development of a KM model for top OTOP businesses based on the results of Phase 1 and KM theories. The model was confirmed via a seminar technique and expert knowledge.

The findings lead to proposal of a KM model for top successful Thai OTOP businesses, with the following components: KM processes propelling the process of OTOP business management, and KM success factors including leadership, organizational cultures, man, and knowledge/intellect.

This research has developed a KM model for community business, which is based on the practices of successful national businesses. Therefore it can be applied for the management of any OTOP business in Thailand.

This paper describes the importance of strategic community creation as a new management style. It verifies that video‐based information networks utilizing information and multimedia technologies enhance the quality of competencies and knowledge possessed by strategic communities, and it also verifies, through case studies, these networks are valid as organizational learning support systems within the strategic communities. Innovations in the area of veterinary medicine utilizing video‐based information networks over the past four years in Japan are taken as examples. This paper describes how knowledge and competencies within strategic communities comprising “industry, government, and academia” are enhanced, how the new virtual methods of telemedicine and distance learning are incorporated into the business process, and how “concepts of regionally linked cooperative bodies” are realized.

This study aims to examine the social capital and technical determinants of knowledge adoption intentions in virtual communities and to explore the differences between posters and lurkers.

An online survey was conducted with a sample of 318 virtual community participants for a structural equation model test.

The structural and cognitive social capital, peer influence, and perceived usefulness positively contribute to virtual community participants' attitudes and intentions toward knowledge adoption. Lurkers' attitudes are more influenced by network ties, reciprocity norms, shared vision, and perceived usefulness, whereas posters are more affected by social trust and shared language.

The results produced in the Chinese context may not fully apply to other cultures.

Devising effective strategies to increase social capital and content value is crucial for fostering virtual community members' positive attitudes toward knowledge adoption.

This study is one of the first to combine and examine social capital and technology acceptance theories with regard to the knowledge adoption intentions of posters and lurkers in virtual communities.