Design Thinking vs. Cognitive Biases: 10 Biases for Innovation to Overcome

Cognitive biases make most of us poor experimenters — plus we live in organizational cultures (and often have individual mindsets) that punish what we see as “failure” — so innovators find it hard to see the failure of an idea as evidence of learning rather than having made a mistake. There are multiple layers to how Design Thinking addresses these issues. Let’s start by stepping back to look at the well-researched topic of human error and how it interferes with making good choices.

We want to call out 10 specific biases1  that cause especially serious problems for innovators. Design Thinking’s ability to fight these common biases accounts for its ability to help us test our ideas successfully.

Category 1 Biases

The source of error lies within us and how we see the world.

1. Projection Bias: This is a tendency to project the present into the future, resulting in predictions that tend to overestimate the extent to which the future will resemble the present. This projection of an innovator’s past interferes with imagining a new future and impedes both seeing novel ideas and accurately assessing their likelihood of success.
2. Hot/Cold Gap: An innovator’s emotional state, whether hot (highly emotional) or cold (unemotional), has been demonstrated to influence assessment of the potential value of an idea, leading them to either under- or overvalue them in the present, impeding the accuracy of their prediction of how others (even themselves) will react in the future, when their emotional state is different.
3. Egocentric Empathy Gap: This bias causes innovators to consistently overestimate the similarity between what they value and what others value, and to project their own thoughts and preferences onto others. This leads to the creation of new ideas that they value but those they are designing for do not.
4. Selective Perception/Functional Fixedness: Here, innovators overestimate the effect of one factor at the expense of others, overreacting to specific stimuli and ignoring others, resulting in a more narrow set of ideas.

How Design Thinking Helps:

• We collect deeper data: By immersing ourselves as vividly as possible in someone else’s experience, we reduce reliance on our own experiences as the primary source of information.
• We see different perspectives: By using diverse teams, we expose ourselves to the way others think, helping us surface our own hidden biases.

Category 2 Biases

The source of error lies with those we are designing for.

5. Say/Do Gap: Innovators often try to minimize the impact of their own biases by asking users what they want. Unfortunately, users are often unable to accurately describe their own current behavior, much less provide reliable data on their deeper needs and wants, resulting in the “say/do” gap between what they say now and what they will do later. Research demonstrates that consumers are not reliable predictors of their own purchase behavior for any type of goods studied.
6. Response Bias: The “say/do” gap is made worse, in some situations, by users’ tendency to tell us either what they think we want to hear (no one wants to hurt our feelings!) or things that make them look good and are socially acceptable (of course we floss twice a day!). This is particularly damaging to managing innovation efficiency because it often results in false positives — users tell us they will buy (but they won’t). This causes us to invest in ideas that will ultimately fail.

How Design Thinking Helps:

• We ask open-ended questions: Ethnographic conversations uncover hidden needs unlocked through probing and focusing on experiences and journeys.
• We focus on behaviors, not intentions: Design Thinking teaches us to value observation and to focus our questions based on actions, not attitudes and opinions (which releases users from potentially feeling judged).
• We leverage immersive tools: Storyboards and journey maps make new ideas tangible and allow potential users to see real strengths and pitfalls in solutions.

Category 3 Biases

The sources of error relate to how we handle the information we collect.

7. The Availability Bias: This causes innovators to undervalue ideas that are harder for them to imagine. Since the familiarity of an idea is likely to be inversely related to its novelty, this leads to a preference for more incremental solutions.
8. Planning Fallacy: When innovators do create new ideas, they often see an overly rosy future, characterized by overly optimistic predictions about how well it will be received, resulting in the “planning fallacy.” They only rarely include considerations of failures.
9. The Endowment Effect: Innovators’ attachment to early ideas makes giving up their current solution more painful than the pleasure of getting a new and improved one.
10. Hypothesis Confirmation Bias: In what is perhaps the most commonly discussed bias, innovators search for facts that support their favored solutions and find it hard to even recognize data that disconfirms them. Interestingly, even when a decision-maker’s bias is revealed to them, they often fail to correct it.

How Design Thinking Helps:

• We treat everything as a hypothesis to be tested: We actively seek disconfirming data and try to surface our assumptions during prototyping.
• We explore many options: By testing multiple solutions simultaneously, Design Thinking helps us invest less in any given option, making it easier to let go of our “babies.”
• We co-create: We delay accepting early compromises and use collaboration across diverse perspectives to build higher-order solutions.

But individuals are not the only ones to suffer biases that aggravate error. Organizations are also replete with them — particularly around challenges to be customer-centric rather than organization-centric and the way they view the failure of ideas during experimentation as incompetence (rather than learning).

Research has now demonstrated quite conclusively how design done well shapes organizational cultures in significant ways that make them both more learning-oriented and more user-centric.2

Excerpted from Experiencing Design: The Innovator’s Journey by Jeanne Liedtka, Karen Hold and Jessica Eldridge, Copyright © 2021 Columbia University Press. Used by arrangement with the publisher. All rights reserved. 

• 1For those interested in learning more about Design Thinking and cognitive bias, see Jeanne Liedtka, “Linking Design Thinking With Innovation Outcomes Through Cognitive Bias Reduction,” Journal of Product Innovation Management 32, No. 6 (2015): 925–938.
• 2Kimberly D. Elsbach and Ileana Stigliani, “Design Thinking and Organizational Culture: A Review and Framework for Future Research,” Journal of Management 44, No. 6 (2018): 2274–2306.