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Despite the demonstrated importance of customer sentiment in social media for outcomes such as purchase behavior and of firms’ increasing use of customer engagement initiatives, surprisingly few studies have investigated firms’ ability to influence the sentiment of customers’ digital engagement. Many firms track buyers’ offline interactions, design online content to coincide with customers’ experiences, and face varied performance during events, enabling the modification of marketer-generated content to correspond to the event outcomes. This study examines the role of firms’ social media engagement initiatives surrounding customers’ experiential interaction events in influencing the sentiment of customers’ digital engagement. Results indicate that marketers can influence the sentiment of customers’ digital engagement beyond their performance during customers’ interactions, and for unfavorable event outcomes, informational marketer-generated content, more so than emotional content, can enhance customer sentiment. This study also highlights sentiment’s role as a leading indicator for customer lifetime value.

Crowdsourcing ideas from consumers can enrich idea input in new product development.After a decade of initiatives (e.g., Starbucks' MyStarbucksIdea, Dell's IdeaStorm), the implications of crowdsourcing for idea generation are well understood, but challenges remain in dealing with the large volume of rapidly-generated ideas produced in crowdsourcing communities. This study proposes a model that can assist managers in efficiently processing crowdsourced ideas by identifying the aspects of ideas that are most predictive of future implementation and identifies three sources of information available for an idea: its content, the contributor proposing it, and the crowd's feedback on the idea (the "3Cs"). These information sources differ in their time of availability (content/contributor information is available immediately; crowd feedback accumulates over time) and in the extent to which they comprise structured or unstructured data. This study draws from prior research to operationalize variables corresponding to the 3Cs and develops a new measure to quantify an idea's distinctiveness. Applying automated information retrieval methods (latent semantic indexing) and testing several linear methods (linear discriminant analysis, regularized logistic 2 regression) and nonlinear machine-learning algorithms (stochastic adaptive boosting, random forests), this article identifies the variables that are most useful towards predicting idea implementation in a crowdsourcing community for an IT product (Mendeley). Our results indicate that consideration of content and contributor information improves ranking performance between 22.6% and 26.0% over random idea selection, and that adding crowdrelated information further improves performance by up to 48.1%. Crowd feedback is the best predictor of idea implementation, followed by idea content and distinctiveness, and the contributor's past idea-generation experience. Firms are advised to implement two idea selection support systems: one to rank new ideas in real time based on content and contributor experience, and another that integrates the crowd's idea evaluation after it has had sufficient time to provide feedback.Keywords: idea selection, crowdsourcing, idea selection support system, innovation, realtime analysis, machine learning Practitioner Points When using automated idea screening that incorporates crowd feedback as an initial step for subsequent human evaluation, practitioners should utilize nonlinear machine learning algorithms because these outperform classical linear methods. Ranking ideas in real time is a viable option, but waiting for the wisdom of the crowd is desirable. Therefore, firms should implement two idea selection support systems: one real-time system that can immediately rank new ideas based on content and contributor experience; and an additional one that integrates the crowd's idea evaluation after sufficient time for feedback. The two systems are complementary and 3 can be used simultaneously. When ranking ideas in real time, managers should...

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