Empowered by Innovation: Unravelling Determinants of Idea Implementation in Open Innovation Platforms

Abstract

Companies use crowdsourcing to solve specific problems or to search for innovation. By using open innovation platforms, where community members propose ideas, companies can better serve customer needs. So far, it remains unclear which factors influence idea implementation in crowd sourcing context. With the research idea that we present here, we aim to get a better understanding of the success and failure of ideas by examining relationships between characteristics of ideators, characteristics of ideas and the likelihood of implementation. In order to test the methodological approach that we propose in this paper in which we investigate for business relevant innovativeness as well as sentiment based on text analytics, data including unstructured text was mined from Dell IdeaStorm using webcrawling and scraping techniques. Some relevant hypotheses that we define in this paper were confirmed on the Dell IdeaStorm dataset but in order to generalize our findings we want to apply to the Lego dataset in our current work in progress. Possible implications of our novel research idea can be used to fill theoretical gaps in marketing literature, help companies to better structure their search for innovation and for ideators to better understand factors contributing to successful idea generation.

Attracting solvers' participation in crowdsourcing contests: The role of linguistic signals in task descriptions

“…Second, our study developed the signalling theory by focusing on transmitting linguistic signals from employers to potential solvers. Although the information asymmetry in crowdsourcing applies to both sides, the employers and the solvers, in literature, the application of signal theory in the crowdsourcing context was often reflected in how the solvers send signals to the employer to increase the likelihood of winning the contest (Piazza et al, 2022; Situmeang et al, 2019) or improve bargaining power (Durward et al, 2016; Hong & Pavlou, 2012). Little literature systematically discusses how employers try to signal their intentions to potential solvers actively.…”

“…Although previous studies have fully explored crowdsourcing participation from three aspects: task design, individual characteristics, and environmental factors (Nov et al, 2009;Taylor & Joshi, 2019), only a few antecedents, such as length and the number of requirements have been considered from a task description perspective (Jiang et al, 2021;Walter & Back, 2011;Yang et al (2009) Second, our study developed the signalling theory by focusing on transmitting linguistic signals from employers to potential solvers. Although the information asymmetry in crowdsourcing applies to both sides, the employers and the solvers, in literature, the application of signal theory in the crowdsourcing context was often reflected in how the solvers send signals to the employer to increase the likelihood of winning the contest (Piazza et al, 2022;Situmeang et al, 2019) or improve bargaining power (Durward et al, 2016;Hong & Pavlou, 2012). Little literature systematically discusses how employers try to signal their intentions to potential solvers actively.…”