Innovative Bias
/Human bias is not an unknown concept. It is the foundation of how people interpret the same information in multiple ways, due to singular or multiple influences. These biases often influence innovation but how can they be overcome?
When the last time you admitted you were biased? Unlikely that it was very recently. Each human mind is biased. Minute nuances how problems are viewed and solved, leading to multiple solutions to a singular task – which is great for a creative process. However, some influences can lead to faltering innovation initiatives, costing companies time and money. There are several biases impacting innovation but below are a few biases that teams should keep an eye out for while planning and executing innovation efforts.
Mere Exposure Effect
This refers to the tendency to select and prefer something only for the reason of being familiar with it. Also known as the familiarity principle, it translates into individuals opting for an option because it is known, often requiring into minimal cognitive effort in the decision-making process. This goes against the innovative process, which is designed to create unique and differentiated products and services.
Backfire Effect
This could humorously be termed as the Ostrich effect. Backfire Effect is the phenomenon when someone strengthens their beliefs as a reaction to having their viewpoint challenged. In a dynamic innovation environment, strengthening misconceived or preconceived notions has no place as it impacts both, the creativity of the team while also results in lost time. A challenging environment can often result in new and revolutionary ideas, only if people are willing to look beyond their own.
Ambiguity Bias
Ambiguity can either result in excitement or fear. Ambiguity bias occurs when individuals are prone to take actions where there is a known outcome. To explain it better, consider people opting for fixed interest rates when research shows that variable interest rates can save money over time. To be a successful innovator, it is crucial (among other things) to take path less taken, especially when the destination is unknown.
Confirmation Bias
People search for and interpret information that reinforces existing beliefs. At times, individuals also recall specific elements relevant to their beliefs when presented with a large amount of information. This impacts the decision process, which works against the concept of innovation. Not interpreting data in unique ways can instantly put companies on the back-foot.
Functional Fixedness
Functional fixedness results in an individual viewing an extensively used object solely in the capacity of which it has been traditionally used. Examples that highlight the overcoming of this bias include something as simple as using a coin to tighten a screw or using a water pump as an engine for a bicycle. Viewing objects as they have always been used severely limits a team’s ability to think out of the box.
Framing Effect
The framing of or viewpoint of an individual when studying information can result in different conclusions according to how it is presented, either in positive or negative context. For example, 5% fat in a product versus it being 95% fat-free or keeping $20 out of $50 versus losing $30. When defining an innovation strategy, the framing of the objective needs to be designed in a way that results in an accurate interpretation.
Context Effect
Situational and environmental factors have a deep impact on how innovation is perceived or for that matter, even approached. Stressful situations may not result in differentiated solutions, while potentially resulting in the failure of a truly unique proposition. This is important to understand as teams working at a solution for long periods of time with little respite can often not perform at their peak, impacting productivity.
Hyperbolic Discounting
This bias highlights a strong preference for near-term wins over long term successes, constraining future choices. In an economic sense, hyperbolic discounting can result in individuals opting out of long-term saving plans but choosing to save towards objectives achievable in the near future. The impact of this on innovation can result in short-term solutions that may not be a fit for the longer-term big picture, either for organization or consumer.
Bandwagon Effect
The bandwagon effect is probably the most common bias in innovation – the tendency to do something simply because others are doing the same thing, i.e. jumping on the bandwagon. Innovation hubs, accelerators and incubators are emerging all over the world, across companies. However, one could question the viability of these initiatives and their actual purpose – to deliver value or to be at par with a company’s closest competitors with no real differentiated results.
Strategies to Overcome Bias
These biases are inherent in individuals and therefore companies and teams. However deep their impact on an innovation strategy, these biases can be overcome or at the very least, minimized to ensure the integrity of a strategic initiative.
External facilitators can provide a fresh perspective to a jaded approach to a specific industry while also ensuring that an innovation team is not viewing their ideas through rose colored lenses. Flat and lean collaboration by breaking down organizational silos will encourage open channels where multiple perspectives are accounted for prior to taking important decisions. Granular level observation of initiatives will ensure that every facet of a strategy is unbiased and testable with actual market insights. The implementation of SMART solutions (Specific, Measurable, Actionable, Realistic and Time-Bound) can also help prepare uniform guidelines without being clouded by individual influences. Teams should make sure their solutions answer the what, why, where and who while aligning with organizational objectives. Any innovation plan should have in place definitive milestones with go/ no go points clarified upfront, preventing initiatives from going too far ahead before cancelling them.
Lastly, rely on data, data and more data. Empirical evidence gives the teams the opportunity to innovation from an empirical perspective, not just emotional. This is what can help design a non-biased innovation culture.