Community managers often struggle to ensure the viability of innovation communities (IC) due to their big data characteristics and inferior member participation, which result in minimal activity and low-quality input. In response to a recent call in the innovation literature for new approaches to dealing with the challenges of big data, we propose an IC-management strategy that relies on extracting linguistic-style cues from community posts to identify future inferior member participation. When future destructive IC behavior is signaled, the moderator can effectively select the correct member for corrective treatment to prevent negative community impact. This article uses text mining to extract self-interest-oriented and positive emotional writing style cues from 39,387 posts written by 1611 members of 10 ICs. Two multilevel regression models deliver novel insights into the relationship between these linguistic cues and the likelihood of inferior community participation (quantity and quality). First, a community member's use of a positive emotional writing style signals less inferior participation quantity and quality in the future. Second, a moderator's use of a self-interest-oriented writing style suggests more inferior participation quality, while a self-interest-oriented community indicates less inferior participation quality. Third, community managers should work to build a positive-emotion-driven community, as such communities experience constructive member participation. This article shows that community managers who struggle with their IC must realize that in addition to what people say, how they say it gives insights into the IC's viability. We conclude our study by revealing the theoretical and managerial implications for IC management and community moderators. Practitioner PointsThis study leverages the big data context of innovation communities by proposing a new, effective community management approach that relies on HLM modeling and text analysis to proactively identify members likely to exhibit inferior participation.Self-interest-oriented and positive emotional writing style cues of the member, moderator and the community are presented as signals for inferior member participation.This article presents concrete community management practices that help guarantee the innovation community viability.

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In their ongoing search for competitive advantage, firms increasingly leverage online innovation communities (ICs). The viability of these ICs may be jeopardized by big data environments and inferior member participation. Therefore, community managers must address poor member participation, together with the data-rich environment. This study examines the viability of a proactive motivational email campaign to reduce inferior member participation using uplift modeling; it also explores optimal treatment characteristics, including message scope (untargeted versus targeted), message content (hedonic, cognitive, and social message), and member profile (self-interest-oriented and positive emotional writing style). The findings indicate that marketing decision makers should use proactive, targeted emails with cognitive motivational elements to mitigate inferior levels of member participation. These findings have important implications for innovation scholars and community managers.