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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.

In the hybrid knowledge-sharing platform where paid and nonpaid (“free”) knowledge activities coexist, users’ free knowledge contribution may be influenced by financial factors. From the perspective of opportunity cost, this study investigates the direct effect of how the amount of monetary income from users’ contribution to paid knowledge activities influences their free knowledge contribution behavior in the future. Further, this study aims to verify the interaction effect of financial and nonfinancial factors (i.e. the experience of free knowledge contribution and social recognition) on free knowledge contribution.

Objective data was collected from a hybrid knowledge-sharing platform in China and then analyzed by using zero-inflated negative binomial regression model.

Results show that the amount of monetary income that knowledge suppliers gain from paid knowledge contribution negatively influences their free knowledge contribution. Experience of free knowledge contribution strengthens the negatively main effect, while social recognition has the weakening moderating role.

Although some studies have explored and verified the positive spillover effect of financial incentives on free knowledge contribution, the quantity dimension is ignored. This study examines the hindering influence of the quantity of monetary income from the perspective of opportunity cost. By taking the characteristic of knowledge suppliers and platforms as moderators, this study deepens the understanding of the influence of monetary income on free knowledge contribution in the hybrid knowledge-sharing platform.

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.

The research aims to explore the influence mechanism of peer feedback and users' knowledge contribution behavior. This study draws on the social identity theory and considers social identity as a mediating factor into the research framework.

This paper collected users' activity data of 142,191 ideas submitted by 76,647 users from the MIUI community between October 2010 and May 2018 via Python software, and data were processed using Stata 16.0.

The results indicate that knowledge feedback and social feedback positively influence users' knowledge contribution (quantity and quality), respectively. User's cognitive identity positively mediates the relationship between peer feedback and knowledge contribution behavior, affective identity positively mediates the relationship between peer feedback and knowledge contribution behavior, while evaluative identity positively mediates the relationship between peer feedback and knowledge contribution quality, but there is no mediating effect between peer feedback and knowledge contribution quantity.

This study advances knowledge management by highlighting peer feedback on online innovation communities. By demonstrating the significant mediating effect of social identity, this study empirically clarifies the relationships of peer feedback (knowledge feedback and social feedback) to specific dimensions of knowledge contribution, thereby providing managerial guidance to the online innovation community on incentivizing and managing user interaction to foster the innovation development of firms.

The purpose of this study is to examine the impact of social media affordances, specifically social engagement and social endorsement, on knowledge contribution in online Q&A communities. Building on self-determination theory, this research seeks to tackle the issue of under-provision of knowledge in these communities.

The study employs a sample collected from a popular social Q&A community in China and uses linear panel data models along with multiple robustness checks to test the research model.

The findings reveal that both social engagement and social endorsement have a positive effect on users' knowledge contribution to the online Q&A community. However, the impact of social engagement is mitigated by social endorsement.

This paper makes a valuable contribution to the field by filling the research gap on the role of social engagement behaviors and their interaction with social endorsement in online Q&A communities. The results provide insights into how social media affordances can be leveraged to enhance knowledge contribution in these communities.

This paper aims to explore the effects of normative and hedonic motivations on continuous knowledge contribution, and how past contribution experience moderates the effects of the motivations on continuous knowledge contribution.

Based on goal-framing theory, the present study proposes a comprehensive theoretical model by integrating normative and hedonic motivations, past contribution experience and continuous knowledge contribution. The data for virtual community members' activities were collected using the Python Scrapy crawler. Logit regression was used to validate the integrative model.

The results show that both normative motivation (reflected by generalized reciprocity and social learning) and hedonic motivation (reflected by peer recognition and online attractiveness) are positively associated with continuous knowledge contribution. Moreover, these effects are found to be significantly influenced by members' past knowledge contribution experience. Specifically, the results suggest that past knowledge contribution experience undermines the influence of generalized reciprocity on continuous knowledge contribution but strengthens the effect of peer recognition and online attractiveness.

Although the emerging literature on continuous knowledge contribution mainly focuses on motivations as antecedents that promote continuous knowledge contribution, most of these studies assume that the relationship between motivating mechanisms and continuous knowledge contribution does not change over time. The study is one of the initial studies to examine whether and how the influence of multiple motivations evolves relative to levels of past contribution experience.

Social loafing in knowledge contribution (namely, knowledge contribution loafing [KCL]) usually happens in group context, especially in the mobile collaboration tasks. KCL shows dynamic features over time. However, most previous studies are based on static assumption, that is, KCL will not change over time. This paper aims to reveal the dynamics of KCL in mobile collaboration and analyze how network centrality influences KCL states considering the current loafing state.

This study is based on empirical study design. Real mobile collaboration behavioral data related to knowledge contribution were collected to investigate the dynamic relationship between network centrality and KCL. In total, 4,127 chat contents were collected through Slack (a mobile collaboration APP). The text data were first analyzed using the text analysis method and then analyzed by a machine learning method called hidden Markov model.

First, the results reveal the inner structure of KCL, showing that it has three states (low, medium and high). Second, it is found that network centrality positively influences individuals involved in medium and high loafing state, while it has a negative influence on individuals with low loafing state.

The limitations are related to the single machine learning method and no subdivision of social network. First, this paper only uses one kind of text classification model (TF-IDF) to divide chat contents, which may not be superior to other classification models. This paper considers the eigenvector centrality, and not further divides the social network into advice network and expressive network.

This study helps companies infer tendency of different KCL and dynamically re-organize a mobile collaborative team for better knowledge contribution.

First, previous studies based on static assumptions regarding KCL as static and the relationship between loafing reducing mechanisms and team members KCL does not change over time. This study relaxes static assumptions and allows KCL to change during the process of collaboration. Second, this study allows the impact of network centrality to be different when members are in different KCL states.

This paper aims to investigate how to promote two types of knowledge contribution tasks. The authors focus on the role of supervisor and coworker support on motivation, and their effects on two different contribution tasks. Motivating employees to contribute knowledge is quite challenging. While previous studies have tried to understand how to promote knowledge contribution, few have differentiated between knowledge contribution tasks.

Information technology support was chosen as the context of this study, and data were collected from system administrators within a Fortune 500 company via a web-based survey.

Results show the differential effects of two forms of motivation on different contribution tasks, and supervisor support is positively associated with intrinsic motivation. Specifically, while intrinsic motivation is positively associated with challenging knowledge contribution, external motivation is positively related to mundane knowledge contribution and negatively related to challenging knowledge contribution.

This study contributes to the current literature by providing a deeper theoretical understanding of knowledge contribution tasks, and contributes to practice by offering suggestions on how to better motivate employees within organizations and promote different knowledge contribution tasks.

The purpose of this paper is to investigate the effect of reward type on knowledge contribution behavior. Individual knowledge contribution, which determines the effectiveness of information systems, benefits the organization at the cost of individual advantage as knowledge is usually considered highly private or even a source of individual prestige. Therefore, organizations provide rewards to compensate for their contributors’ knowledge loss. Surprisingly, some scholars report a positive relationship between reward and knowledge contribution, while others find this relationship to be insignificant or even negative. Based on regulatory focus theory, this study proposes and tests that such inconsistencies result from disparity between reward type and knowledge contribution measures.

A between-group laboratory experiment with 144 undergraduate student is designed and hierarchical regression is applied to test the hypotheses.

An incremental reward (additional reward for attaining outstanding achievements) aroused individual promotion focus, leading to an increase in self-perceived knowledge contribution (self-reported) and knowledge contribution quantity (experiment observers rated), but a decrease in knowledge contribution quality (peer rated). However, a decremental reward (deducted for errors) primed individual prevention focus, leading to an increase in self-perceived knowledge contribution (self-reported) and knowledge contribution quality (peer rated), but a decrease in knowledge contribution quantity (experiment observers rated).

The findings help explain why previous empirical results on the reward-knowledge contribution relationship were inconsistent and add to extant literature by introducing a new theoretical perspective for understanding motivation in knowledge management research.

In light of the recent increase in the scholarly attention given to meaningful engagement with gamified information systems (IS), this research explores the definition and measurement of meaningful engagement as well as its role in predicting employees’ knowledge contributions via gamified knowledge management systems (KMSs).

The authors conducted two empirical studies. Study 1 develops a measure of meaningful engagement and evaluates its validity and reliability. Drawing on the literature on user engagement and work gamification theory, Study 2 places meaningful engagement in a nomological network and assesses the construct’s utility for predicting the quantity and quality of knowledge contributions via a gamified KMS.

The results show that meaningful engagement encompasses five specific dimensions: intense involvement, sense of meaning, self-discovery, pursuit of excellence, and personal expressiveness. The results also indicate that fostering meaningful engagement, which goes beyond hedonic and instrumental engagement, is essential to enhance the quality and quantity of knowledge contribution.

This research contributes to the literature on gamification by drawing scholarly attention to meaningful engagement as a parsimonious yet powerful construct that complements the notions of hedonic and instrumental engagement with KMSs. Although previous studies have highlighted the significance of meaningful engagement with gamified IS, little effort has been made to develop a scale to measure meaningful engagement. The scale the authors have developed will help researchers precisely measure users’ meaningful engagement and systematically examine its role in gamified systems compared to that of other forms of engagement. The study also has practical implications, as the results can inform future design strategies to enable the successful implementation of gamified KMSs that facilitate knowledge contribution in the workplace.

The development of new constructs is the starting point for theoretical development. This research responds to the call to conceptualize meaningful engagement with gamified IS.