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Firms increasingly use ideas from online innovation communities to solve problems or to better address customer needs. However, in many cases the number of submitted ideas has exploded, it leads to an information overload that firms hardly can handle considering their limited cognitive resources. Therefore, we use the Elaboration Likelihood Model to distinguish between the quick and lean idea preselection process as a peripheral route of information processing and the subsequent idea review process as a central route of information processing. In our empirical study with a sample of more than 163,000 ideas collected from the Xiaomi MIUI community, we analyse influencing factors that increase the likelihood of ideas being preselected or reviewed. Results show that user status, user initiative contribution, and community recognition have a significantly positive influence on idea preselction, whereas user response contribution has no influence. Idea presentation characteristics have an inverted U-curve relationship with idea adoption. Community absorptive capacity has a moderate effect on the curvilinear relationship between idea description length and idea adoption.
In social networks or, more specifically, online communities on tech-products, opinion leaders are important sources of advice for other consumers in the adoption and diffusion of new products. However, possibilities for potential users to exert their influence on opinion leadership are ignored. This study determines whether and how lead users may serve as opinion leaders in social networks and advise other consumers in the adoption and diffusion of new products. Our survey with 308 users in the Xiaomi and Huawei communities suggests that higher lead userness is positively and significantly associated with the likelihood of opinion giving and passing. Product-possessing innovativeness has a higher impact compared with information-possessing innovativeness. Product involvement does not enhance the effect of information-possessing innovativeness. The findings provide a better understanding of the formation of opinion leadership in social networks for an accelerated diffusion of new products.