A method for improving ideas selection in crowdsourcing

This paper presents an early-stage application of the design science research (DSR) method to obtain a new idea selection approach, which uses clustering to filter ideas while taking into account the seeker’s goals and the learning dynamics. Most of previous researches has considered the idea provider as main subject of analysis. Instead, we focus on the bounded rationality of the solution seeker. Seekers often estimate that the main cost of an idea challenge comes from the reward itself; yet, making mistake in the selection process and picking the wrong idea might result in the seeker wasting time and money. Thus, we argue that the research contribution can be classified as “exaptation”, a known solution to a new problem. To do so, we consider crowdsourcing as the search for new sources of innovation or solutions for challenges faced by an organization. Thus, our kernel knowledge comes from the notion of learning in idea competitions that are “distant” (that is looking also outside its established boundaries), investigating its effects on seekers. Moreover, our suggested method extends the use of two solutions for brainstorming: (a) chainstorming, where participants have to use ideas from a previous brainstorming to solve a new problem and (b) cheatstorming, where participants can’t use new ideas but only the ones from previous brainstorming.