User idea implementation in open innovation communities: Evidence from a new product development crowdsourcing community

Abstract

In collaborative crowdsourcing communities for open innovation, users generate and submit ideas as idea co‐creators. Firms then select and implement valuable ideas for new product development. Despite the popularity and success of these open innovation communities, relatively little is known about the factors that determine the implementation of the user‐generated ideas. Based on research on individual creativity, we propose a conceptual model integrating users' previous experience, idea presentation characteristics and feedback valence to explain the likelihood of idea implementation. We validate our research model with a panel data analysis of 43 550 ideas submitted by 16 360 users in the MIUI new product development community hosted by Xiaomi, a large electronics manufacturing company in China. We find an inverted U‐shaped relationship between users' past successful experience and idea implementation. Furthermore, the length of ideas is positively associated with the likelihood of idea implementation. There is also an inverted U‐shaped relationship between supporting evidence and idea implementation. Finally, we demonstrate the negative effect of positive feedback and the positive effect of negative feedback on idea implementation. These findings offer rich insights to understand the phenomenon of open innovation better. Theoretical and practical implications are discussed.

Open Innovation Community for University–Industry Knowledge Transfer: A Colombian Case

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“…Different authors have approached knowledge as a resource for organizations [53]; in this context, the concept of community has gained relevance, going from occupational communities [53] through learning communities [54,55], communities of practice [14,27,56,57], to open innovation communities, which focus on the generation of added value [31,32].…”

Digital social innovation: An overview and research framework

“…Social intermediation (Kistruck et al, 2013;Shalini et al, 2021); market linkages (Hota et al, 2019;Kistruck et al, 2013); knowledge creation and transfer (Chliova & Ringov, 2017;Inkpen & Tsang, 2005); mission drift (Bhatt, 2021;Bhatt et al, 2019;Grimes et al, 2019Grimes et al, , 2020Klein et al, 2020;Ometto et al, 2019) Scaling Open innovation (Feller et al, 2011;Madon & Schoemaker, 2021;Parthiban et al, 2021;Yun et al, 2017) Crowdsourcing (Liu et al, 2020;Schlagwein et al, 2019;Taylor & Joshi, 2019) Paradox/tension (Kibere, 2016;Mahrer & Krimmer, 2005;McLennan, 2016;Poole & Van de Ven, 1989;Rajão & Marcolino, 2016;Smith & Lewis, 2011) Collective action (Ghobadi & Clegg, 2015;Leong, Faik, et al, 2020;Saebø et al, 2020;Thapa et al, 2012;Young, 2018;Young et al, 2019;Zheng & Yu, 2016); social movements (Ghobadi & Clegg, 2015;Leong, Faik, et al, 2020;McKenna, 2020;Miranda et al, 2016;Young et al, 2019) Scaling deep Does long-term engagement of DSIrs develop capabilities in local communities? If so, how?…”

“…Technology-savvy companies have built platforms or utilities exploiting internet-based infrastructure to increase the pace of innovation. These digital innovations led to a paradigm shift in how technology is leveraged to generate value (Parthiban et al, 2021), as exemplified by the sharing economy (Qureshi et al, 2021a), collaborative consumption (Shalini et al, 2021), gig work and crowdsourcing (Deng et al, 2016;Liu et al, 2020). However, societal outcomes of these digital innovations are at best mixed, as highlighted in privacy and security concerns (Newell & Marabelli, 2015), surveillance (Zuboff, 2019), oppressive and unethical outcomes of algorithms (Kane et al, 2021), and discrimination and exploitation in sharing economy platforms (Attri & Bapuji, 2021;Van Doorn, 2017).…”

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

“…Sentiment analysis is limited to classifying text into positive, neutral and negative. For example, 43 550 product ideas' feedback valences were categorized into positive and negative [42]. What was lost, or at least left outside of the article, was more detailed information on the nature and common denominators of positive or negative feedback.…”