| The "enterprise ecosystem" based on the trinity of "social media—users—enterprises" is forming in the era of big data.User big data has become a vital source of value co-creation in the enterprise ecosystems and the key for enterprises to obtain competitive advantages.However,many enterprises are still faced with challenges such as low-level user participation and stagnant participative state.To understand how to motivate more user participation,this research studies the influence mechanism of social interactions on user knowledge seeking and knowledge contribution behaviors in the firm-hosted online community.Based on the systematic review of existing research,this research takes friend relationships and review relationships into consideration and focuses on three research questions: How do friends and network position affect user knowledge seeking and knowledge contribution behaviors? How do informational feedback and emotional feedback in received reviews motivate user knowledge contributions? How do multisource interactions affect user participative state transition? The paper collects data from a firm-hosted online community.Social network analysis,quantitative content analysis and machine learning clustering algorithm are used to measure the variables.Then econometric regression models are used to conduct the following empirical studies.First,understanding the influence of friends and network position on user knowledge-seeking and knowledge contribution behaviors.We studied the influence of peers and network position on user participation based on the longitudinal data of 2,192 regular users in a firm-hosted online community.Drawing upon social capital theory,we found that an individual's quantity of friends' participation is positively related to his or her participation.The expert friends' degree of participation negatively affects user participation.We especially found that network centrality which depicts an individual's relative position in the network significantly moderates the effects of expert friends' degree of participation.Interestingly,closeness centrality and betweenness centrality exert different moderating effects on the effects of expert friends.This study identifies the influence of friend behavior and network location on user participation,and provides insights for community managers to design the friendship mechanism.Second,identifying the influence of informational feedback and emotional feedback in reviews on user knowledge contribution behaviors.Drawing upon social support theory,we studied the influence of informational feedback and emotional feedback based on the the post,reply and comment data of 2324 expert users in a firm-hosted online community.The empirical research revealed that argument quality and source credibility as informational support positively affect user knowledge contributions in both initiated posts and replies.For emotional factors,emotional approval was proven to have significant influence on knowledge contributions in both initiated posts and replies.We found emotional consistency had no influence on either type of knowledge contribution.We also showed that there was an inverted-U shaped relationship between emotional difference and posting knowledge contribution.This study contributes to the literature on user participation by identifying the influence of emotional factors,and provide suggestions for community managers to design effective review feedback mechanism.Third,understanding the influence of multi-source social information on user participative state transitions.Based on social information processing theory,we identified the social information as context information from the firm community,context information from peers and emotional information from the user.This study tracked the participation of 690 regular users and identified their participative states as high,medium or low.We studied the hazard ratio of the influence factors.We got the following results.First,we found that a user's positive emotion toward product can increase the probability of his or her state transition from low to medium,and state transition from medium to high.A user's positive emotion toward other users can decrease the probability of his or her state transition from low to medium,and state transition from medium to high.Moreover,the influence of positive emotion toward other users on medium-high transition will gradually increase over time.Second,the influence of social image varies with time.Social image can reduce the probability of low-medium transition of newcomers.However,after a period of time,social image inversely increase the probability of low-medium transition of users.Social image shows no influence on medium-high state transition.Third,reviewers' feedback can increase the probability of medium-high transition.Moreover,reviewers' recognition can increase the probability of low-medium transition.This research contributes to the literature on user participation by understanding user participation dynamics.It also provides insights for community managers to set up user incentive mechanisms that "vary from person to person" and "adapt to changing circumstances".The findings of this paper extends the literature on user participation by revealing the influence of social interactions on user participation behaviors and dynamics.They can also provide suggestions and guidance for managers of firm-hosted online communities to build effective incentive mechanisms to motivate user participation. |
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