Article discussed: Schemmann, B., Herrmann, A. M., Chappin, M. M., & Heimeriks, G. J. (2016). Crowdsourcing ideas: Involving ordinary users in the ideation phase of new product development. Research Policy, 45(6), 1145-1154.

Introduction

Crowdsourcing ideas from customers is a contemporary phenomenon that has enjoyed widespread attention from both scholars and business practitioners. These events, albeit temporary or long-term, generate countless of ideas for the focal company, but only a handful are actually implemented. This is particularly true when ideas originate from ordinary users as opposed to experts, or lead users. It is thus pivotal to scrutinize this ordinary user group in order to improve efficiency of the yet expensive and time-consuming filtering process.

With this in mind, Schemmann et al. (2015) conducted a cross-sectional research in an effort to derive ideator and idea-related characteristics that could explain whether a crowdsourced idea from ordinary users is implemented. Based on prior studies and their analysis, they conclude that the following factors all positively relate to actual implementation:

• Attention paid to crowdsourced ideas of others (H2)
• Idea popularity (H3)
• Idea potential innovativeness (H4)

Synthesis of theory: value from juxtaposing user bases

The most interesting theoretical contribution of this paper stems from its focus on the ordinary user, contrasting most existing research, which emphasized the role of lead users. That is, whether ideator and idea-related characteristics can determine actual adoption by the organization. Motivation is found to result in more successful ideas due to individuals being stimulated to develop a qualitative strong idea (Bayus, 2013). However, no such finding is found to be significant within the current research. In addition to the ideator-based characteristic, idea-related characteristics also influence the eventual adoption of the idea. Whereas lead users are valued for their knowledge and expertise, ordinary users leverage an even more powerful mechanism called Wisdom of the Crowds (Surowiecki, 2004). The sheer number of ordinary users results in an extremely accurate prediction of whether an idea is likely to be successful or not. Furthermore, given that innovative ideas are positively related to implementation (Witell et al., 2011), we can infer that the increased diversity within ordinary users is likely to produce more novel ideas than lead users.

Such a juxtaposition of user base enables firms to tailor crowdsourcing initiatives to their respective objectives and move away from crowdsourcing failure rates that have been observed to be as high as 40% (Castellion & Markham, 2013). That is, firms can tweak two mechanisms that naturally emerge from this study’s insights. First, crowdsourcing participants can be limited to a certain user base. Secondly, crowdsourcing platforms can be designed to includes functions such as peer evaluations and idea visibility. The latter brings us to the study by Bockstedt et al. (2016) who distinguishes between blind and unblind innovation contests. Specifically, unblind contests are likely to enhance the positive relation of hypothesis 2 as the platform facilitates learning from others.

Methods used: weaknesses and improvements

The present study was conducted on a sample of 1456 ideas for which the company’s review process was completed. The data stems from an open idea call of a large beverage producer, which was publicly available. Although such a method bears generalizability issues, it can still be justified from a practical perspective. However, in addition to the limitations stated by the authors, another weakness relates to the codification of variables. That is, all variables are examined as dichotomous variables, with exception of ideator motivation. All hypotheses measured with dichotomous values are found to be significant, whereas the continuous variable used to test hypothesis 1 is rejected. Not only does such an outcome arouse suspicion, there is much room for improvement. As illustration, the independent variable attention paid to other ideas could also be coded as a continuous or count variable, measured by relatively time spent on other’s idea page or the number of other’s idea pages visited, respectively. Although arguments for both methods are evident, the authors could at least include such an empirical analysis as a robustness check. As a result, findings become more credible, increasing the likelihood of actual utilization when managing crowdsourcing platforms.

Conclusion

Thus, the present study presents insights into ideator and idea-related characteristics that determine idea implementation in long-term crowdsourcing initiatives. By analysis of ordinary users, the authors complement existing views with another relevant dimension that could help business practitioners manage difficult socio-technical dynamics inherent to crowdsourcing. The higher success rate of crowdsourcing is in turn, likely to spur innovation across a multitude of industries. Although such aspirations seem distant, achieving great feats is realized by taking one step at a time.

Bibliography

Bayus, B.L., 2013. Crowdsourcing new product ideas over time: an analysis of the Dell IdeaStorm Community. Manage. Sci. 59 (1), 226–244

Surowiecki, J., 2004. The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. Doubleday, New York, NY.

Witell, L., Löfgren, M., Gustafsson, A., 2011. Identifying ideas of attractive quality in the innovation process. TQM J. 23 (1), 87–99.

Castellion, G., Markham, S.K., 2013. Perspective: new product failure rates:influence of argumentum ad populum and self-interest. J. Prod. Innov. Manage.30 (5), 976–979.

Bockstedt, J., Druehl, C., & Mishra, A. (2016). Heterogeneous submission behavior and its implications for success in innovation contests with public submissions. Production and Operations Management, 25(7), 1157-1176.