RESUMO
Online brand communities (OBCs) could benefit firms in many usages, ranging from collecting consumers' suggestions or advice to interacting with community members directly and transparently. Creating a positive emotional atmosphere is essential for such communities' healthy development as its boosts the continuous involvement of each member. However, the dynamic cross-influences and evolution of emotions in OBCs have not been fully explored, which was the research gap this paper tried to fill. Based on emotional contagion theory, this study identifies three sources of textual sentiment through machine learning methods in OBCs: member's posts, other members' feedback, and the focal firm's official feedback. This study further tested the dynamic emotional contagion process among these sources on valence (mean) and volatility (dispersion), namely how they affected each other. Data was collected from the MIUI forum, a large forum launched by Xiaomi corporate on August 1, 2011, which contained 17,622 posts and 99,426 feedback. Results showed that: (1) in the emotional contagion process, there existed differences in the influence of emotional valence and volatility from different sources; (2) all emotional interactions were temporary and mostly lasted no more than three days; (3) the most significant contributor of each sources' emotion was itself, which could be explained by lagged effect; (4) the valence of focal firm's emotion (focal firm's official feedback) was the second contributor of the valence of member's emotion (member's posts) and other members' emotion (other members' feedback). Three sources of emotion in OBCs and emotional valence/volatility should be considered when firms try to guide the emotional changes in such communities. Furthermore, firms could proactively influence members' emotions by carefully designing the feedback to members' posts. Besides, since all interactions are temporary, firms need to engage in online communities frequently, like consistently offering feedback.
RESUMO
In virtual brand communities, users and firms continuously use different or similar linguistic styles to communicate with each other. Existing literature has demonstrated that the linguistic style matching (LSM) between the coming users' posts [user-generated content (UGC)] and existing firms' content will influence users' behavior, like promoting users to release more posts. However, little research has been conducted to analyze how firms' feedbacking behaviors influence LSM. To fill the gap, this paper uses Python to measure the LSM between 69,463 posts from 9,777 users and existing firms' generated content in the MIUI community and examines the impact of firms' feedbacks on this LSM. The results show that the firms' feedbacks frequency increased the LSM, but the firms' feedbacks text length decreased the LSM. In addition, users' textual sentiment and the published text length moderate the impact of firms' feedbacks (e.g., frequency, text length) on LSM. Specifically, the users' textual sentiment valence increases the positive effect of firms' feedbacks frequency and weakens the negative effect of firms' feedbacks text length on LSM. The users' produced content text length reduced the positive effect of firms' feedbacks frequency and offset the negative effect of the firms' feedbacks text length on LSM. Further, the effects above are significant for the relatively active users but not for the inactive ones. Based on communication accommodation theory, this paper investigates the impact of firms' feedbacks frequency and text length on subsequent users' posting behaviors, providing an essential reference for guiding firms' virtual brand community management.
RESUMO
In online communities, such as Twitter, Facebook, or Reddit, millions of pieces of contents are generated by users every day, and these user-generated contents (UGCs) show a great variety of topics discussed that make the online community vivid and attractive. However, the reasons why UGCs show great variety and how a firm can influence this variety was unknown, which had been an obstacle to understanding and managing UGCs' variety. This study fills these two gaps based on variety-seeking theory and topic modeling, which is a technique in machine learning. We extract, quantitatively, the topic of the UGCs using topic modeling and divide UGCs into two types: single topic and multiple topics. The user's tendency to choose the type of UGC is used to measure variety-seeking behavior. We found that users have an intrinsic preference for variety when producing UGCs; the more single topic UGCs were produced in the past, the higher the probability of producing multiple topics UGC and the lower the probability of producing single topic UGC would be in the next, and vice versa. Furthermore, we discussed the effect of language/linguistic style matching (LSM) between firm feedbacks and UGCs on users' variety-seeking tendencies in UGCs' production. This study makes three contributions: (1) broadening variety-seeking theory to new behavior, that is content production behavior, and the results demonstrated that people would show a variety-seeking behavior in producing UGCs. (2) a new feasible method to measure the variety of UGCs by using topic modeling to extract the topics of UGCs and then measure the variety-seeking behavior in producing UGCs by analyzing the choice between single topic and multiple topics. (3) guidance for the firm to alter LSM of feedbacks to influence the variety of UGCs.