Academic journal article The Journal of New Business Ideas & Trends

Predictive Crowding and Disruptive Innovation: How to Effectively Leverage Crowd Intelligence

Academic journal article The Journal of New Business Ideas & Trends

Predictive Crowding and Disruptive Innovation: How to Effectively Leverage Crowd Intelligence

Article excerpt

Introduction

The innovation process is undergoing significant disruption in many organizations (Pisano, 2015). Due to the speed of technological innovations and product lifecycles becoming shorter and shorter, companies are facing the need to speed up their innovation process (Enkel et al., 2009; Dahlander & Gann, 2010; Gassmann et al.,2010). Many companies nowadays start to use crowd intelligence to crowdsourcing to obtain ideas, feedback, and solutions to develop and initiate corporate activities (Diener & Piller, 2013; Gartner, 2013; Füller 2010) and to extend the existing knowledge base. Nevertheless, little is known about the use of crowds in the assessment of disruptive ideas (Christensen, 1997; Christensen & Raynor, 2003; Pisano, 2015) outside the current use of crowdsourcing in business design. Palacios et al. 2015 present five emerging research themes on crowdsourcing (1) problem solving (2) learning paradigms (3) open innovation program (4) new product development, and (5) collaborative initiative. Our research proposes an extension to their findings on the application of crowd intelligence, disruptive idea evaluation. Mollick & Randa (2015) suggest in their research on funding the arts that a crowd can play an important role in assessing innovative projects.

The approach to crowdsourcing using traditional processes and applications needs to be extended to understand the value of crowd-intelligence from both, a fuzzy front-end (search) and back-end perspective (capture), to the selection of crowd participants (Füller et al., 2014; Peisl et al., 2014). This approach shifts the focus from 'outside-in' innovation to 'inside-out' open innovation (Chesbrough, 2003; Chesbrough & Appleyard, 2007; Laursen, 2012), facilitated through predictive crowding, using the potential of (expert) crowds to access new ideas. Such use of predictive crowding to assess disruptive ideas goes beyond today's common applications of crowdsourcing, which are mainly related to incremental innovations. Yet, while crowdsourcing can contribute to incremental innovation, the assessment of disruptive ideas using crowd intelligence remains largely unanswered (Peisl et al., 2014).

This research contributes to the existing body of knowledge in two ways. A contribution to practice by investigating challenges organizations face when evaluating disruptive ideas outside their existing business model domain; and contribute to knowledge by extending crowdsourcing research beyond incremental ideation. Findings will provide new perspectives on applications of crowdsourcing and encourage further discussion on crowd definition and crowd selection for different purposes. A proposed way forward is suggested in how to start the use of predictive crowding to foster and manage disruptive innovation in organizations, and use the intelligence of the selected expert crowd (O'Connor & Ayers, 2005). For instance, the innovation process leader may contact a trusted innovation intermediary for tapping in to an expert crowd database, and selecting the crowd, based on competence profiles believed to deliver an assessment that can support the strategic decision making process at firm level, i.e. a predictive result (Hossain, 2012). Based on this information and subsequent decisions, the idea is taken through the innovation process inside the organization, or as a spin-off to a crowd-investing or crowdfunding platform, the latter may having been suggested by the expert crowd.

This paper is structured as follows. First, previous studies in the literature are analyzed to identify relevant research gaps. Next, the research methodology is outlined, followed by the main findings in relation to the postulated research questions, and discussion of the results. Finally, conclusions are drawn and areas for future research are identified.

Literature review

Chesbrough et al., (2006) argue that a lack of knowledge forces firms to use external sources to fill knowledge gaps that cannot be addressed internally. …

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