Open innovation contests that display all submitted ideas to participants are a popular way for firms to generate ideas. In such contest-based ideation, the authors show that seeing numerous competitive ideas of others harms, rather than stimulates, creative performance (Study 1). Others’ competitive prior ideas interfere with idea generation, as new ideas need to be differentiated from the preceding ones to be original. Exposure to an increasing number of prior ideas thus heightens individuals’ perceived constraints of expressing ideas and harms creative performance (Studies 2 and 3). Furthermore, creative performance monotonically reduces with an increasing number of prior ideas (Study 4). A final study demonstrates that showing only a limited number of ideas as well as grouping prior ideas offer actionable ways to reduce prior ideas’ harmful influence (Study 5). These results illustrate viable ways to improve contest-based ideation outcomes merely by changing how competitive prior ideas are presented.
In open innovation, firms increasingly rely on online consumer votes to evaluate ideas for new products and services. Votes can represent cost‐effective external information about idea quality that can inform and facilitate a firm's task of evaluating and screening of ideas at the early stages of the innovation process. Challenging this perception, we proposed that consumer votes provided in open innovation contests can be socially biased by reciprocal voting. On the basis of theories related to cooperation and social influence, we argued that both gregarious consumers (those who solicit social ties) and consumers who initiate direct reciprocity (those who vote for others) signal a willingness to cooperate that stimulates reciprocal voting from peers. We empirically investigated consumer voting behavior using a unique dataset with information obtained from actual open innovation contests in which consumers could submit their own ideas and see and vote for the ideas of others. We found that both gregariousness and the initiation of direct reciprocity positively influence votes received. Such cooperation pays off for consumers because firms indeed use votes to inform internal idea evaluations. We also found, however, that the votes an idea receives during an innovation contest cannot significantly explain its later revealed quality. Reciprocity may be an effective form of cooperation among consumers, but it has potentially negative implications for firms' evaluations. Our results also indicated that beyond reciprocity, consumers and firms value different types of ideas, which further differentiates their evaluations. Thus, firms should not only be aware of social biases in votes but also account for the diverging idea preferences of customers.
Micro-texts emerging from social media platforms have become an important source for research. Automatized classification and interpretation of such micro-texts is challenging. The problem is exaggerated if the number of texts is at a medium level, making it too small for effective machine learning, but too big to be efficiently analyzed solely by humans. We present a semi-supervised learning system for micro-text classification that combines machine learning techniques with the unmatched human ability for making demanding, i.e. nonlinear decisions based on sparse data. We compare our system with human performance and a predefined optimal classifier using a validated benchmark data-set.
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