Idea selection and adoption by users – a process model in an online innovation community
Nan Wang, Victor Tiberius, Xiangxiang Chen, Alexander Brem, Fei Yu
Abstract
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.