By Oguz A. Acar
Everyone loves an innovative idea, until they do not. According to Oguz Acar, “Idea novelty promotes acceptance up to a point; however, when an idea is too unfamiliar its chances of being accepted drop significantly.” How then do you conquer the biases inimical to innovation? Read on to find out.
Open innovation is often viewed as the panacea to all sorts of innovation challenges. And this is for a good reason. Building on innovation partners’ novel, diverse and complementary perspectives should help organisations to find breakthroughs in their innovative endeavours. Yet, open innovation initiatives often fail to live up to these promises.
Consider the initiative of the Verband Deutscher Maschinen- und Anlagenbau (VDMA)—a mechanical engineering industry association representing over 3000 European companies. The association launched an open, global call for solutions to several unresolved challenges that are faced by its members1. Despite receiving a wide range of feasible and promising solutions, VDMA’s members did not adopt any of these solutions at all—not a single one!
Why do so many open innovation initiatives return underwhelming results? My research on open innovation over the past decade provides an answer: Organisations often fail to recognise the potential of exceptional ideas from external innovation partners because they are prone to idea evaluation biases. Specifically, I identify three implicit biases that systematically derail open innovation initiatives: positivity, familiarity, and proximity biases.
We know from past psychology, communications and marketing research that the affective content of a message—especially the extent to which it contains negative vs. positive information— influences the reactions of message recipients. This research mostly points to a negativity bias across many domains2; people tend to give greater weight to negative information than neutral or positive information.
However, my research3 with Johanna Brunneder, shows that the opposite is true in the case of open innovation. Drawing on a study of 257 ideas in a crowdsourcing platform, we found that the proportion of positive words contained in an idea predicts idea implementation. In other words, idea evaluators focus their attention primarily on positive words and are drawn to ideas that are expressed positively, shying away from ideas that are expressed negatively. Importantly, these results remain intact when we control for independent ratings of neutralised versions of ideas (i.e., when emotional words were replaced with neutral words in such a way that the essence of ideas was preserved), and for the number of comments and likes those ideas have received in the platform. This further confirms that innovation decisions are sidetracked by the linguistic style of ideas above and beyond their actual innovative potential.
Most companies engage in open innovation to find ideas that are new to them. As such, it is plausible to expect them to embrace ideas that offer truly fresh perspectives.
My research4, however, shows that this is not really the case. In a recent study, I explored how the unfamiliarity of ideas impacts their acceptance based on an analysis of 1000 ideas from a crowdsourcing platform owned by a global consumer electronics company. The results show an interesting pattern; the relationship between the novelty of an idea and its odds of being implemented takes an inverted U-shape. That is, idea novelty promotes acceptance up to a point; however, when an idea is too unfamiliar, its chances of being accepted drop significantly. In other words, the evaluators were biased against highly innovative ideas; they were only open to accepting moderately novel and relatively familiar ones.
Most open innovation endeavours suffer from a general not-invented-here syndrome5; employees are inherently biased against external ideas. Innovation crowdsourcing is no exception despite the convincing evidence6 showing that external ideas could outperform internally generated ones.
My interactions and interviews with many open innovators suggest that the bias towards external ideas is more nuanced. Specifically, I observe that this bias is minimal when employees’ perceived expertise distance with the external parties is moderate. When this distance is small employees perceive the outsiders as a threat to their identity and job security. However, once the expertise distance is perceived to be large, employees start to question the outsiders’ credibility in terms of being able to fully understand and effectively solve innovation problems.
Debiasing innovation decisions
How could managers debias innovation decisions and make the most of their innovation crowdsourcing efforts? A necessary first step in overcoming any implicit bias is its diagnosis.
Once diagnosed, the next step is designing appropriate interventions. Awareness interventions (e.g., seminars, videos, etc.) to inform evaluators about their potential biases hold great promise. For example, a simple training video about common cognitive biases was found to significantly reduce these biases7 even after two months.
Nudging evaluators to engage in deliberate reasoning8 before idea assessment would also help prevent the activation of these biases. The key here is to slow evaluators down right before making decisions. A set of clear idea evaluation criteria, for example, would prompt evaluators to be more mindful and careful in their assessment. The effectiveness of this could be further enhanced by asking evaluators to explicitly justify their decisions based on the criteria.
Another promising way to debias innovation decisions is putting together diverse teams to evaluate ideas9 instead of relying on individual evaluators or non-diverse teams. These teams could also include a devil’s advocate10 who is tasked to confront biased evaluations.
Finally, managers could target the underlying drivers of these biases. For example, promoting tolerance for uncertainty might bring about a more open mindset towards unfamiliar ideas. Likewise, assuring employees about their job security might reduce biases towards external ideas.
All in all, open innovation offers tremendous potential to tackle various innovation problems companies are facing today. Recognising and minimising implicit biases of idea evaluators could hold the key to fully unlocking this innovation potential.
About the Author
Oguz A. Acar is a Professor at King’s Business School, King’s College London and a Research Affiliate at Harvard University’s Laboratory for Innovation Science. His research is on behavioural innovation—it draws on behavioural science to understand the creation, evaluation and adoption of innovative outputs.
- Hannen, J., Antons, D., Piller, F., Salge, T. O., Coltman, T., & Devinney, T. M. (2019). Containing the Not-Invented-Here Syndrome in external knowledge absorption and open innovation: The role of indirect countermeasures. Research Policy, 48(9), 103822.
- Rozin, P., & Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5(4), 296-320.
- Brunneder, J., & Acar, O. A. (2019) The Emotional Fallacy: How Positive Emotions Bias Experts’ Implementation Decisions in Crowdsourcing Ideas. Proceedings of the European Marketing Academy.
- Acar O.A. (2022). The bias against radical innovation, The European Business Review
- Brunneder, J., Acar, O. A., Deichmann, D., & Sarwal, T. (2020). A new model for crowdsourcing innovation. Harvard Business Review.
- Nishikawa, H., Schreier, M., & Ogawa, S. (2013). User-generated versus designer-generated products: A performance assessment at Muji. International Journal of Research in Marketing, 30(2), 160-167.
- Morewedge, C. K., Yoon, H., Scopelliti, I., Symborski, C. W., Korris, J. H., & Kassam, K. S. (2015). Debiasing decisions: Improved decision making with a single training intervention. Policy Insights from the Behavioral and Brain Sciences, 2(1), 129-140.
- Kahneman, D. (2003). A perspective on judgment and choice: mapping bounded rationality. American Psychologist, 58(9), 697.
- Criscuolo, P., Dahlander, L., Grohsjean, T., & Salter, A. (2017). Evaluating novelty: The role of panels in the selection of R&D projects. Academy of Management Journal, 60(2), 433-460.
- McKinsey “The business logic in debiasing” (2017), available at: https://www.mckinsey.com/business-functions/risk-and-resilience/our-insights/the-business-logic-in-debiasing