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...

For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Steven Hoornaert and Dirk Van den PoelDepartment of Marketing, Ghent University, Ghent, Belgium Abstract Purpose -The purpose of this paper is to study consumer engagement as a dynamic, iterative process in the context of TV shows. A theoretical framework involving the central constructs of brand actions, customer engagement behaviors (CEBs), and consumption is proposed. Brand actions of TV shows include advertising and firm-generated content (FGC) on social media. CEBs include volume, sentiment, and richness of user-generated content (UGC) on social media. Consumption comprises live and time-shifted TV viewing. Design/methodology/approach -The authors study 31 new TV shows introduced in 2015. Consistent with the ecosystem framework, a simultaneous system of equations approach is adopted to analyze data from a US Cable TV provider, Kantar Media, and Twitter. Findings -The findings show that advertising efforts initiated by the TV show have a positive effect on time-shifted viewing, but a negative effect on live viewing; tweets posted by the TV show (FGC) have a negative effect on time-shifted viewing, but no effect on live viewing; and negative sentiment from tweets posted by viewers (UGC) reduces time-shifted viewing, but increases live viewing. Originality/value -Content creators and TV networks are faced with the daunting challenge of retaining their audiences in a media-fragmented world. Whereas most studies on engagement have focused on static firm-customer relationships, this study examines engagement from a dynamic, multi-agent perspective by studying interrelationships among brand actions, CEBs, and consumption over time. Accordingly, this study can help brands to quantify the effectiveness of their engagement efforts in terms of encouraging CEBs and eliciting specific TV consumption behaviors.

No abstract

No abstract