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25 Design Thinking Questions: What To Ask + Answer Examples

As Walter Isaacson, acclaimed biographer of creative genius Steve Jobs, emphasizes, “Innovation distinguishes between a leader and a follower.” The pursuit of innovation in business is what sets leaders apart; it’s the driving force behind the transformation of customer experiences. Every innovation, every groundbreaking product, and every revolutionary service begins with a question. That’s where the journey of design thinking questions and the power of asking comes into play.

Design Thinking isn’t just a methodology but a culture, and it’s been the driving force behind many remarkable creations. And what fuels this culture is all about asking the right questions.

Although formalized in the 21st century, design thinking has deep roots in history. In the 1950s, brilliant minds at Stanford University were already exploring new ways to enhance creative thinking. The goal was simple: breaking free from conventional problem-solving strategies. Let’s jump now to the 21st century, where design thinking has become a structured methodology at the heart of many renowned organizations’ strategies, such as Apple, Google, and Amazon.

What Are Design Thinking Questions?

The journey of design thinking is underpinned by a singular philosophy: to understand a problem truly, you must question it thoroughly and empathize with its challenges. This is where design thinking questions come into play.

Design thinking questions are open-ended, thought-provoking inquiries to understand a problem’s depths. These questions don’t just scratch the surface; they delve into the heart of the matter, searching for insights, ideas, and opportunities. The true power of these questions lies in their ability to cultivate empathy , unlock creativity , and catalyze innovative solutions.

We put together a table showcasing the elements of good design thinking questions:

These elements guide the formulation of effective design thinking questions essential for uncovering insights, sparking innovation, and solving complex problems through a human-centered approach.

What Are the Questions of Design Thinking and Their Use?

Behind design thinking, there’s a series of carefully crafted questions, each designed to guide problem-solvers through the journey of creativity and innovation. These questions serve several vital functions like:

• Empathy Building: They encourage the development of empathy for the end-users or the people affected by the problem you’re solving. These questions put you in their shoes to truly understand their needs and desires.
• Problem Definition: The right questions help you accurately define the problem you’re dealing with. You uncover hidden issues and complexities by questioning the situation from different angles.
• Ideation: Design thinking questions stimulate ideation. They fuel creativity, inspire innovative ideas, and help teams think outside the box.
• Solution Validation: Once you’ve generated ideas and developed solutions, questions become tools for validating your concepts. They help you ensure that the proposed solutions indeed address the problem.
• Continuous Improvement: Design thinking questions don’t stop with the first solution. They play a crucial role in ongoing evaluation, helping you continuously refine and enhance your offerings.

What Are the Most Important Points of Design Thinking?

To truly grasp the essence of design thinking questions, consider these vital principles that underpin the whole approach:

• User-Centric Approach: Design thinking fundamentally addresses the end-users’ needs and desires. Your questions should revolve around understanding them, their challenges, and their aspirations.
• Iterative Process: Design thinking isn’t a linear journey; it’s a continuous loop of understanding, ideating, prototyping, and testing. Questions guide you through these iterations.
• Problem Framing: Before diving into solutions, design thinking encourages an in-depth understanding of the problem itself. Your questions should focus on framing the issue from multiple perspectives.
• Collaboration: Design thinking is a collaborative effort. The questions foster teamwork, bringing together diverse skills and perspectives.
• Prototype Testing: Questions are tools for validating prototypes. The process includes creating a basic version of the solution and testing it to gather feedback, which is then incorporated into improvements.

In summary, design thinking is an innovation-driven approach that thrives on customer empathy , problem-solving, and continuous improvement, all facilitated by thought-provoking, open-ended questions.

Design Thinking Question Types

Throughout the design thinking process, specific types of questions serve as guiding stars, illuminating the path to innovation and customer-centric solutions:

• These questions go beyond the surface, delving into the heart of the matter: the people. They invite you to walk in your end-users or stakeholders’ shoes, to see the world through their eyes. When you ask empathizing questions, you’re on a quest to truly understand their needs, desires, challenges, and aspirations. It’s about peeling back the layers and getting to the core of human experiences. With empathizing questions, you unlock the profound insights needed to create solutions that genuinely resonate with people.
• In the realm of design thinking, defining the problem is an art form. These questions are like the skilled strokes of a painter’s brush, meticulously crafting the contours of the challenge at hand. They prompt you to consider the subtle details, the shades of the issue that might have gone unnoticed. With problem definition questions, you frame the challenge with precision, ensuring you’re targeting the right problem—no more, no less. They provide the scaffolding for your entire creative process.
• If empathy questions allow you to understand, ideation questions inspire you to dream to explore the uncharted territories of imagination. They’re your passport to a realm where possibilities are endless, and conventional thinking takes a back seat. These questions aren’t just about generating ideas; they’re about opening the doors to unbridled creativity. Ideation questions are open-ended, enticing you to challenge the status quo and venture into the territory of “thinking outside the box.” In this realm, groundbreaking ideas are born.
• You have ideas—bold, innovative, and possibly game-changing. But how do you know which ones have the potential to revolutionize your industry? That’s where validation questions come into play. They are the litmus test, the rigorous assessment that ensures your solutions are on target. Validation questions are the guardians of practicality, making certain that your ideas are not just impressive on paper but feasible in the real world. They help you confirm that the proposed solutions genuinely address the problem and, most importantly, the needs of your users.
• Once your solution is out in the wild, your journey doesn’t end; it transforms into an ongoing quest for refinement and enhancement. Iterative questions are the driving force behind this evolution. They encourage you to listen, learn, and adapt. With these questions, you delve into the feedback, data, and user experiences. You ask what’s working, what’s not, and most crucially, how you can make it better. Iterative questions are the engines of continuous improvement, enabling you to evolve your solutions harmoniously with the ever-changing landscape of customer needs and market dynamics.

With this arsenal of questions, design thinking becomes a powerful vehicle for innovation and transformation, propelling your organization to new heights of customer satisfaction and competitive success.

Design Thinking Success Examples

The impact of design thinking questions is most evident in the real-world examples of companies and organizations that have successfully employed this approach.

• Apple: One of the pioneers in using design thinking, Apple applies this philosophy from product design to the customer experience. They frequently ask empathizing questions like, “ How can we make the iPhone experience even more intuitive? “
• Google: Google’s work culture revolves around creative problem-solving. Their teams use ideation questions such as, “ What are new ways to simplify complex data access for users? “
• Amazon: Amazon applies design thinking to enhance its customer service and satisfaction. Questions like, “How can we make the customer’s online shopping experience more seamless and enjoyable? ” drive their innovation.
• IDEO: A global design consultancy, IDEO, is renowned for its design thinking expertise. They ask many problem definition questions to deeply understand various challenges before proposing solutions.

Free Template: 25 Design Thinking Questions (with Answer Examples)

Design thinking questions with example hypothetical answers:

Feel free to adapt these questions to your specific design thinking project and use them as a starting point for your journey into innovative problem-solving and product development.

Design Thinking Questions with QuestionPro

Integrating QuestionPro into your design thinking process can be a game-changer. Our suite of tools and solutions empowers you to formulate the right design thinking questions, collect valuable feedback, and convert insights into actionable strategies.

Whether you’re looking to enhance your product, service, or overall customer experience, our platform offers:

• Survey Design: Create custom surveys tailored to your design thinking needs with our intuitive survey builder.
• Feedback Collection: Gather feedback and responses effectively from diverse sources, from customers to employees.
• Data Analysis: Utilize advanced analytics to decipher the insights gained from your design thinking questions.
• Actionable Insights: Transform insights into actionable strategies for innovation and continuous improvement.

Design thinking questions are the compass guiding you through the intricate terrain of innovation. They empower you to understand, define, ideate, validate, and improve solutions.

When harnessed effectively, these questions can unlock a world of creativity and set your organization on a path to lasting success. So, embark on this journey with the right questions, and remember, innovation is just a question away.

LEARN MORE         FREE TRIAL

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Design Thinking in Practice: Research Methodology

January 10, 2021 2021-01-10

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Project Overview 

Over the last decade, we have seen design thinking gain popularity across industries. Nielsen Norman Group conducted a long-term research project to understand design thinking in practice. The research project included 3 studies involving more than 1000 participants and took place from 2018 to 2020: 

• Intercepts and interviews with 87 participants
• Digital survey with 1067 respondents
• In-depth case study at an institution practicing design thinking 

The primary goals of the project were to investigate the following:

• How do practitioners learn and use design thinking?
• How does design thinking provide value to individuals and organizations?
• What makes design thinking successful or unsuccessful? 

This description of what we did may be useful in helping you interpret our results and apply them to your own design-thinking practice. 

Project Findings

The findings from this research are shared in the following articles and videos:

• What Is Design Thinking, Really? (What Practitioners Say) (Article) 
• How UX Professionals Define Design Thinking in Practice (Video) 
• Design Thinking: The Learner’s Journey (Article)

In This Article:

Study 1: intercepts and interviews , study 2:  digital survey, study 3: case study .

In the first study we investigated how UX and design professionals define design thinking.  

This study consisted of 71 in-person intercepts in Washington DC, San Francisco, Boston, and North Carolina and 16 remote interviews over the phone and via video conferencing. These 87 participants were UX professionals from a diverse range of countries with varying roles and experience.

Intercepts consisted of two questions:

• What do think of when you hear the phrase “design thinking”?
• How would you define design thinking?

Interviews consisted of 10 questions, excluding demographic-related questions:

• What are the first words that come to mind when I say “design thinking”?
• Can you tell me more about [word they supplied in response to question 1]?
• How would you define design thinking? Why?
• What does it mean to practice design thinking?
• What are the positive or negative effects of design thinking?
• Products and services
• Clients/customers
• Using this scale, what is your experience using design thinking?
• Using this same scale, how successful has design thinking been in your experience?
• What could have been better?
• What is good about design thinking? What is bad about design thinking?

Our second study consisted of a qualitative digital survey that ran for two months and had 1067 professional respondents primarily from UX-related fields. The survey had 14 questions, excluding demographic-related questions. An alternative set of 4 questions was shown to those with little to no experience using design thinking.  

• Which of the following best describes your experience with design thinking?
• Where did you learn design thinking?  
• UX maturity 
• Frequency of crossteam collaboration 
• User-centered approach 
• Research-driven decision making
• How often do you, yourself, practice design thinking?
• In your own words, what does it mean to practice design thinking? 
• When do you use design thinking?
• What methods or exercises are used?
• In what situations is each one used and why?
• Which ones are done individually versus as a group?
• How is each exercise executed?
• Gives your organization a competitive advantage
• Drives innovation
• Fosters collaboration
• Provides structure to the organization
• Increases likelihood of success
• Please describe a situation where design thinking positively influenced your organization and why it was successful. 
• Please describe a situation where design thinking may have negatively influenced your organization and why it was negative. 
• Design thinking negatively affects efficiency.
• Design thinking requires a collaborative environment to work well.
• Anyone can learn and practice design thinking.
• Design thinking is rigid.
• Design thinking requires all involved to be human-centered.
• Design thinking takes a lot of time.
• Design thinking has low return on investment.
• Design thinking empowers personal growth.
• Design thinking grows interpersonal relationships.
• Design thinking improves organizational progress.

The 1067 survey participants had diverse backgrounds: they held varying roles across industries and were located across the globe. 94 responses were invalid, so we excluded them from our analysis.  

The majority of participants (33%) were UX designers, followed by UX researchers (13%) and UX consultants (12%). 

Of participants who responded “Other”, the most common response provided was an executive role (n=20). This included roles such as CEO, VP, director, founder, and “head of.” Other mentioned roles included service designer (n=17), manager (n=14), business designer or business analyst (n=11), and educator (including teacher, instructor, and curriculum designer) (n=11).

Geographically, we had respondents from 67 different countries. The majority of survey participants work in the United States (34%), followed by India (8%), United Kingdom (7%), and Canada (5%). 

Our survey participants also represented diverse industries, with the majority in software (22%) and finance or insurance (14%). 

Of participants who responded Other , the most common response provided was agency or consulting (n=26), followed by telecommunications (n=17), marketing (n=8), and tourism (n=7).

Our third and final study consisted of an in-person case study at a large, public ecommerce company. The case study involved 9 interviews with company employees, 6 observation sessions of design-thinking (or related) workshops, and an internal resource and literature audit. 

The interviews were 1-hour long and semistructured. Of the 8 participants, 3 were on the same team but had different roles: 1 UX designer, 1 product manager, and 1 engineer. The other 5 interviewees (3 design leaders and 2 UX designers) worked in different groups across the organization. Each participant completed the same digital survey from the second study prior to interviewing.    

In addition to interviews, we conducted 6 observation sessions: 3 design-thinking workshops, 2 meetings, and 1 lunch-and-learn. After the workshops, all participants were invited to fill out a survey about the workshop. The survey had 5 questions: 

• We achieved our goal of [x]. 
• The time and resources spent to conduct the workshop were worth it.
• What aspects were of greatest value to you, and why? 
• Where there any aspects you felt were not useful, and why?
• Will the workshop or its output impact any of your future work? If so, how?
• What is your role?

Lastly, we conducted a resource and literature audit of the company’s internal resources related to design thinking available to employees.  

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Ideas Made to Matter

Design thinking, explained

Rebecca Linke

Sep 14, 2017

What is design thinking?

Design thinking is an innovative problem-solving process rooted in a set of skills.The approach has been around for decades, but it only started gaining traction outside of the design community after the 2008 Harvard Business Review article [subscription required] titled “Design Thinking” by Tim Brown, CEO and president of design company IDEO.

Since then, the design thinking process has been applied to developing new products and services, and to a whole range of problems, from creating a business model for selling solar panels in Africa to the operation of Airbnb .

At a high level, the steps involved in the design thinking process are simple: first, fully understand the problem; second, explore a wide range of possible solutions; third, iterate extensively through prototyping and testing; and finally, implement through the customary deployment mechanisms. 

The skills associated with these steps help people apply creativity to effectively solve real-world problems better than they otherwise would. They can be readily learned, but take effort. For instance, when trying to understand a problem, setting aside your own preconceptions is vital, but it’s hard.

Creative brainstorming is necessary for developing possible solutions, but many people don’t do it particularly well. And throughout the process it is critical to engage in modeling, analysis, prototyping, and testing, and to really learn from these many iterations.

Once you master the skills central to the design thinking approach, they can be applied to solve problems in daily life and any industry.

Here’s what you need to know to get started.

Understand the problem 

The first step in design thinking is to understand the problem you are trying to solve before searching for solutions. Sometimes, the problem you need to address is not the one you originally set out to tackle.

“Most people don’t make much of an effort to explore the problem space before exploring the solution space,” said MIT Sloan professor Steve Eppinger. The mistake they make is to try and empathize, connecting the stated problem only to their own experiences. This falsely leads to the belief that you completely understand the situation. But the actual problem is always broader, more nuanced, or different than people originally assume.

Take the example of a meal delivery service in Holstebro, Denmark. When a team first began looking at the problem of poor nutrition and malnourishment among the elderly in the city, many of whom received meals from the service, it thought that simply updating the menu options would be a sufficient solution. But after closer observation, the team realized the scope of the problem was much larger , and that they would need to redesign the entire experience, not only for those receiving the meals, but for those preparing the meals as well. While the company changed almost everything about itself, including rebranding as The Good Kitchen, the most important change the company made when rethinking its business model was shifting how employees viewed themselves and their work. That, in turn, helped them create better meals (which were also drastically changed), yielding happier, better nourished customers.

Involve users

Imagine you are designing a new walker for rehabilitation patients and the elderly, but you have never used one. Could you fully understand what customers need? Certainly not, if you haven’t extensively observed and spoken with real customers. There is a reason that design thinking is often referred to as human-centered design.

“You have to immerse yourself in the problem,” Eppinger said.

How do you start to understand how to build a better walker? When a team from MIT’s Integrated Design and Management program together with the design firm Altitude took on that task, they met with walker users to interview them, observe them, and understand their experiences.  

“We center the design process on human beings by understanding their needs at the beginning, and then include them throughout the development and testing process,” Eppinger said.

Central to the design thinking process is prototyping and testing (more on that later) which allows designers to try, to fail, and to learn what works. Testing also involves customers, and that continued involvement provides essential user feedback on potential designs and use cases. If the MIT-Altitude team studying walkers had ended user involvement after its initial interviews, it would likely have ended up with a walker that didn’t work very well for customers. 

It is also important to interview and understand other stakeholders, like people selling the product, or those who are supporting the users throughout the product life cycle.

The second phase of design thinking is developing solutions to the problem (which you now fully understand). This begins with what most people know as brainstorming.

Hold nothing back during brainstorming sessions — except criticism. Infeasible ideas can generate useful solutions, but you’d never get there if you shoot down every impractical idea from the start.

“One of the key principles of brainstorming is to suspend judgment,” Eppinger said. “When we're exploring the solution space, we first broaden the search and generate lots of possibilities, including the wild and crazy ideas. Of course, the only way we're going to build on the wild and crazy ideas is if we consider them in the first place.”

That doesn’t mean you never judge the ideas, Eppinger said. That part comes later, in downselection. “But if we want 100 ideas to choose from, we can’t be very critical.”

In the case of The Good Kitchen, the kitchen employees were given new uniforms. Why? Uniforms don’t directly affect the competence of the cooks or the taste of the food.

But during interviews conducted with kitchen employees, designers realized that morale was low, in part because employees were bored preparing the same dishes over and over again, in part because they felt that others had a poor perception of them. The new, chef-style uniforms gave the cooks a greater sense of pride. It was only part of the solution, but if the idea had been rejected outright, or perhaps not even suggested, the company would have missed an important aspect of the solution.

Prototype and test. Repeat.

You’ve defined the problem. You’ve spoken to customers. You’ve brainstormed, come up with all sorts of ideas, and worked with your team to boil those ideas down to the ones you think may actually solve the problem you’ve defined.

“We don’t develop a good solution just by thinking about a list of ideas, bullet points and rough sketches,” Eppinger said. “We explore potential solutions through modeling and prototyping. We design, we build, we test, and repeat — this design iteration process is absolutely critical to effective design thinking.”

Repeating this loop of prototyping, testing, and gathering user feedback is crucial for making sure the design is right — that is, it works for customers, you can build it, and you can support it.

“After several iterations, we might get something that works, we validate it with real customers, and we often find that what we thought was a great solution is actually only just OK. But then we can make it a lot better through even just a few more iterations,” Eppinger said.

Implementation

The goal of all the steps that come before this is to have the best possible solution before you move into implementing the design. Your team will spend most of its time, its money, and its energy on this stage.

“Implementation involves detailed design, training, tooling, and ramping up. It is a huge amount of effort, so get it right before you expend that effort,” said Eppinger.

Design thinking isn’t just for “things.” If you are only applying the approach to physical products, you aren’t getting the most out of it. Design thinking can be applied to any problem that needs a creative solution. When Eppinger ran into a primary school educator who told him design thinking was big in his school, Eppinger thought he meant that they were teaching students the tenets of design thinking.

“It turns out they meant they were using design thinking in running their operations and improving the school programs. It’s being applied everywhere these days,” Eppinger said.

In another example from the education field, Peruvian entrepreneur Carlos Rodriguez-Pastor hired design consulting firm IDEO to redesign every aspect of the learning experience in a network of schools in Peru. The ultimate goal? To elevate Peru’s middle class.

As you’d expect, many large corporations have also adopted design thinking. IBM has adopted it at a company-wide level, training many of its nearly 400,000 employees in design thinking principles .

What can design thinking do for your business?

The impact of all the buzz around design thinking today is that people are realizing that “anybody who has a challenge that needs creative problem solving could benefit from this approach,” Eppinger said. That means that managers can use it, not only to design a new product or service, “but anytime they’ve got a challenge, a problem to solve.”

Applying design thinking techniques to business problems can help executives across industries rethink their product offerings, grow their markets, offer greater value to customers, or innovate and stay relevant. “I don’t know industries that can’t use design thinking,” said Eppinger.

Ready to go deeper?

Read “ The Designful Company ” by Marty Neumeier, a book that focuses on how businesses can benefit from design thinking, and “ Product Design and Development ,” co-authored by Eppinger, to better understand the detailed methods.

Register for an MIT Sloan Executive Education course:

Systematic Innovation of Products, Processes, and Services , a five-day course taught by Eppinger and other MIT professors.

• Leadership by Design: Innovation Process and Culture , a two-day course taught by MIT Integrated Design and Management director Matthew Kressy.
• Managing Complex Technical Projects , a two-day course taught by Eppinger.
• Apply for M astering Design Thinking , a 3-month online certificate course taught by Eppinger and MIT Sloan senior lecturers Renée Richardson Gosline and David Robertson.

Steve Eppinger is a professor of management science and innovation at MIT Sloan. He holds the General Motors Leaders for Global Operations Chair and has a PhD from MIT in engineering. He is the faculty co-director of MIT's System Design and Management program and Integrated Design and Management program, both master’s degrees joint between the MIT Sloan and Engineering schools. His research focuses on product development and technical project management, and has been applied to improving complex engineering processes in many industries.

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Kellogg School of Management at Northwestern University

Organizations Jul 1, 2020

How to do design thinking better, experts from kellogg and ideo explain the psychology behind this creative approach to problem solving..

This audio is powered by Spokn.

Leigh Thompson

David Schonthal

Christine Rösch

Design thinking has become tremendously popular. Businesses in every industry talk about ideating and iterating, a linguistic nod to the creative process made famous by design and consulting firm IDEO.

The design-thinking approach loosely follows a four-step process that involves observing a problem, reframing it, designing solutions, and testing them—all with the end goal of improving how humans experience a product or service.

But being familiar with this process and actually putting it into practice are very different things. “Sometimes people think they’re doing design thinking, but it’s really not,” says Leigh Thompson , a professor of management and organizations at Kellogg. “When you get it right, it’s really powerful.” Rather than blindly following the approach, she says, it can be helpful to understand the psychology behind it. And critically, social psychology also offers insight into specific ways to get more out of each step in the process. “The science is what explains the magic,” says David Schonthal , a clinical professor of strategy at Kellogg and director of the Levy Institute for Entrepreneurial Practice. He and Thompson recently published a paper on this topic and teach a course together on using creativity as a business tool. So why does design thinking work? And how can businesses effectively apply these principles themselves? Thompson and Schonthal explain.

1. Look for the gorilla.

The first step in the design-thinking process is to observe a situation and notice what is actually happening. This sounds straightforward. But Thompson points out that we are actually really bad at observing a situation and noticing what is actually happening—despite having a lot of confidence in our own abilities. Twenty years ago, researchers Christopher Chabris and Daniel Simons conducted a now-famous experiment in psychology . They showed participants a video of people playing basketball and instructed them to count how many times the players on a particular team passed the ball.

About 45 seconds into the video, a woman wearing a full-body gorilla costume walked across the screen. But a large number of participants didn’t notice this oddity at all. They were too focused on counting passes—an illustration of a phenomenon psychologists call inattentional blindness.

“Sometimes people think they’re doing design thinking, but it’s really not.” — Leigh Thompson

“People are very limited in what they’re able to perceive in their visual world when they’re focused on one thing,” Thompson explains. “Coupled with the fact that people believe themselves to be in the 99th percentile with regard to their perceptive abilities, that’s a dangerous combination.” So how can we get better at noticing things? As Thompson and Schonthal explain it, noticing is a cognitive strategy that can be broken down into three parts. First, observers must identify and abandon their cognitive scripts—the preexisting narratives that guide their understanding of situations. Next, they must learn inductively, making inferences based on limited information. And finally, they must find patterns in complex stimuli. This is why design thinkers must get out from behind their desks and observe a problem “in the wild,” as Schonthal puts it. Relying on people to self-report their habits is not enough. He points to an example of when a pharmaceutical company tasked IDEO with investigating its hypothesis that the packaging of its arthritis medication was too difficult for patients to open.

So the IDEO design team interviewed and—crucially—also observed patients who used the medication going about their daily routine. One elderly woman with arthritis said she had no trouble opening the packaging. But when the IDEO team asked her to actually show them how she did it, she took her pill bottle out of a drawer and put it on a meat slicer, then used the meat slicer to cut open the top of the container, because twisting the cap off herself was too painful. “One of the biggest takeaways from this example is to never take what people say they do at face value,” Schonthal says. “Actually seeing with your own eyes what is going on can immediately spark identification of unmet needs or better ways of solving a problem.”

2. Ask a question no one else is asking.

The second step in design thinking is framing and reframing. In this step, design thinkers look at a problem from multiple vantage points, trying on different lenses to determine the best approach to finding a solution. To better understand the importance of this process, Thompson and Schonthal highlight the work of economist Daniel Kahneman, who won the Nobel Prize for his research on cognitive framing. Kahneman showed that people make very different decisions depending on how those decisions are framed: specifically, whether they are focused on the possibility of gaining something—what design thinkers call a “promotion frame”—or by the possibility of not losing something—a “prevention frame.” Understanding a customer’s motivations for using a product or service are important for developing something that works for the customer. Take, for example, a project IDEO conducted on diabetes management for a healthcare company. IDEO’s team found that more traditional goals like losing weight and controlling blood sugar in order to avoid health problems (which activate a prevention frame) weren’t successful in actually motivating patients to make healthy changes. But setting social and emotional goals—like gaining the ability to walk a 5K or dance with your daughter at her wedding—activated a promotion mindset and actually motivated people to change.

“Prototypes are embodied questions. It’s not building something that you hope people will fall in love with as the final product.” — David Schonthal

Armed with this knowledge and new frame, IDEO was able to help the company move beyond creating a new medical device. Rather, IDEO helped them build a customizable app that instead solved for a different challenge: How do we help people with diabetes live their best lives? “With any really well-thought-out product, chances are the designers started by asking a different question or solving a different problem than all of their competitors,” Schonthal says.

3. Approach brainstorming with rigor.

The third step is to imagine and design—what Thompson and Schonthal describe as “the heart and soul” of the design-thinking process. Which is why, even more than the other steps, it’s crucial to understand the science behind successful ideation. At its core, brainstorming is about focusing on quantity over quality, building on one another’s ideas, and encouraging the most outlandish suggestions, all while avoiding criticism. And studies show that these principles, devised in the 1950s, remain effective today. Yet people regularly violate those rules, other studies show. They suggest too few ideas or criticize individuals rather than ideas. And criticizing the person who comes up with an idea can hurt further ideation. Thankfully, science also suggests some best practices. First, consider a smaller ideation group. As the number of people on a team increases, the productivity of ideas per person decreases . That’s because when people work in groups—like the traditional brainstorming session—they’re often inhibited by social demands like being polite and waiting their turn. Or consider using the brainwriting technique . In a brainwriting session, participants spend a set amount of time writing down as many ideas as they can, before a facilitator collects them all. This allows individuals to generate lots of ideas freely and without concern for criticism. One widely cited meta-analysis shows that brainwriting groups generated about two and a half times the volume of ideas generated by brainstorming groups—and a significantly greater percentage of their ideas were judged to be of higher quality.

“Most people don’t want to believe that group brainstorming is inferior to individual ideation, at least for a finite amount of time, because we feel good in groups. Groups are stimulating. They make us feel all warm and fuzzy,” Thompson explains. “But it isn’t necessarily the best way to have a creativity and innovation meeting.”

4. Fail faster. Learn sooner.

When it comes to actually building and testing solutions—the final step in design thinking—a successful designer must understand that failure is simply an expected part of the process and will ultimately make the work better. “The idea is to fail faster and learn sooner,” Thompson says. One key to this is ensuring that your group has a growth mindset . The term refers to the belief that ability and skill come through practice, not through innate talent. With this frame, failure becomes a way to learn, not proof of incompetency. For example, one study showed that participants who were prompted to have a growth mindset accomplished a complex task better and more enjoyably than participants who were prompted to have a “fixed” mindset. One way to foster a growth mindset, Thompson and Schonthal explain, is to use “How Might We” (HMW) questions that get design thinkers to push past constraints. “Groups that adopt a HMW focus are more likely to persist and be creative than those who don’t think about possibilities,” they write. Another way to reinforce the idea that you are simply experimenting—and thus that you are open to honest feedback—is to use low-fidelity materials to create prototypes. For example, to test different ways to redesign the long-haul flight experience, IDEO used materials found around the office. For one concept, the designers literally stacked office chairs to test the idea of “bunkbeds” on a plane. Airline executives who tried to lie in the chairs quickly rejected the concept—and IDEO moved on to the next idea. “Prototypes are embodied questions,” Schonthal says. “It’s not building something that you hope people will fall in love with as the final product.” At the end of the process, designers ultimately want to create something that people do fall in love with. A successful final product often seems intuitive—as if the idea sprang fully formed from the designer’s brain. But as Schonthal and Thompson’s research shows, a science-backed approach is critical to innovation. “Once you see a beautiful design, it seems obvious,” Thompson says. “But it’s really, really hard to figure out, ‘Now how does this get created to begin with?’”

J. Jay Gerber Professor of Dispute Resolution & Organizations; Professor of Management & Organizations; Director of Kellogg Team and Group Research Center; Professor of Psychology, Weinberg College of Arts & Sciences (Courtesy)

Clinical Professor of Strategy; Director of Entrepreneurship Programs at Kellogg; Faculty Director of the Zell Fellows Program; Director of the Levy Institute for Entrepreneurial Practice

About the Writer Jennifer Fisher is a freelance writer based in Chicago.

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Great Questions Lead to Great Design: A Guide to the Design-thinking Process

Great designers help teams and stakeholders make better decisions by using questions to identify opportunities, reveal underlying needs, and understand user context—all of which lead to better designs.

By Jorge Juan Perales

Jorge Juan has designed successful digital products for over a decade. He loves fast-moving teams and delivering great value to users.

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Great designers help teams and stakeholders make better decisions by using questions to identify opportunities, reveal underlying needs, and understand user context.

James Dyson, having been inspired by a centrifuge used to separate paint particles from the air, came up with the world’s first bagless vacuum cleaner in 1983 after famously going through 5,127 prototypes —the epitome of design thinking . He must have asked a lot of questions along the way…

Designers face tough problems every day—problems that require them to find design solutions that deal with business and technical constraints while also addressing user needs. At the same time, the urge to find solutions quickly shouldn’t preclude designers from thoroughly understanding the heart of the problem, as well as the user context, from the outset.

The critical “investigative phase” should not be bypassed—it is a vital component in the design-thinking process. It is where carefully formulated design thinking questions reveal themselves as a great way to approach a design problem even before designers start “designing.”

Questions are a genuine expression of our curiosity and interest in something. They are the means by which people seek meaning in the surrounding world and often trigger our willingness to explore.

When designers are faced with a problem, their brain is programmed to find a good enough solution right away and act upon it. However, it is important to note that those willing to deliver successful products and services must face the problems and build a deeper understanding of them in order to come up with valuable insights.

By knowing how questions on design work and how to use them cleverly, designers can unleash the potential of good questions to build understanding, trigger the imagination, and foster collaboration.

Why Designers Don’t Ask Questions

Designers typically operate in fast-moving environments which demand focusing on quick solutions and delivery . In that context, questions like “Why do we need to solve that problem?” or “How did you notice this problem?” which may lead to a better understanding of the underlying causes and needs, are seen as interruptions that slow down the process.

While quick wins are OK in some situations, designers also have the responsibility to help teams establish direction and not waste valuable resources working—no matter how fast—on the wrong problems.

Designers are like detectives; they need information from many different sources in order to resolve their cases. And what is a key skill that good detectives have? Asking smart questions that help them clarify the case, solve the puzzle and find the truth.

Why Don’t Designers Ask Questions as Often as They Should?

Some designers are afraid of annoying people . When someone presents a new idea or solution to the team, questions that reveal weaknesses or uncovered areas can make owners feel uncomfortable. They thought they had it all figured out, and suddenly, there’s an element of uncertainty introduced into the picture.

They realize there is more to think about than they had expected, so they look at the designer as an “annoyance.” Designers should make it clear that they are not there to annoy people or slow down the process unnecessarily but to help the team build better products; consequently, their feedback should be seen as a valuable contribution and a crucial part of a prudent design process.

A lot of people think of designers at an execution level —decisions are made by technology, business, and marketing teams while designers are there to simply execute commands. But designers also have the responsibility to expose the value of design at a strategic level.

Some designers lack the confidence and training —both to ask good questions and to do it in a way that clearly reveals their will to help and collaborate. As everything in life, asking good questions is a matter of training. The more you do it, the better you get at it. One of the purposes of this article is to provide designers with some ideas that will help them get started in the art of asking good questions.

Types of Effective Design Thinking Process Questions

A good question is the one that lets you obtain the type , quality , and quantity of information you need. In order to do so, designers have to decide both the type of questions they use and the way they formulate them.

Here are some basic but very effective design thinking prompts:

Open-ended questions encourage people to reflect and reveal what’s important for them. They allow people to freely expand on what is comfortable for them, rather than justifying their thoughts. Open-ended questions tend to explore possibilities, feelings, and the reasons why. Michael J. Marquardt, author of Leading with questions , describes some types of open-ended questions:

• Explorative questions force expansion on new points of view and uncovered areas. Have you thought of…?
• Affective questions reveal people’s feelings about something. How do you feel about…?
• Reflective questions encourage more elaboration. What do you think causes…?
• Probing questions invite a deeper examination. Can you describe how…?
• Analytical questions look for the roots of a problem. What are the causes of…?
• Clarifying questions help align and avoid misunderstandings. So, you mean that..?

Closed questions call for specific answers—usually yes or no—or they force the respondent to select an answer from a given set, or to agree or disagree with a statement. Closed questions tend to focus on facts—what, when, where—and are usually easy to answer. For example: “Where were you born? How many miles do you drive a month?”

The Anatomy of a Good Question

A good question doesn’t depend just on the type of question it is, but also on how you frame it. The form of a question is part of its function. Good questions should be framed under these principles:

Good questions should empower. Disempowering questions focus on why the person did not succeed, which puts that person in a defensive mode. Empowering questions are asked from trust—they get people to think and find their own answers, which transfers ownership and develops self-responsibility.

For example, when giving feedback, instead of just saying “I don’t think this would work,” you could ask, “What other options have you explored, and why did you choose this one?”

Good questions should challenge assumptions. They should help clarify the situation and cause individuals, teams, and organizations to explore the methods, processes, and conventions that drive their actions.

Good questions should cause the person to stretch. They should encourage reflection and help people go beyond the obvious. Good questions motivate people to take things to the next level. For example, when discussing with technology teams, instead of asking, “Can you do this?” you could ask, “Supposing this is the way to go, what would you need to have or eliminate in order to accomplish this?”

Good questions should encourage breakthrough thinking. Good questions open up new possibilities. They involve people in divergent thought processes that lead to new perspectives. For example, when designing a new login screen, instead of just asking, “How could we make the login process faster?” you could ask, “How could we deliver value to our users without them having to log in?”

The Setup for Good Questions

Even if you choose the right type of question and you frame it correctly, you need to set the stage in order for others to understand why you are asking questions and what for. Designers are not judges—they are facilitators that provide a context for the information to flow as part of the design thinking framework and help everyone make informed decisions.

Here is a process that helps accomplish that:

Adopt a learner mindset. Our mindset frames how we see the world. A learner is optimistic and seeks understanding as a way to guide their actions. Be curious, attentive, and receptive. You are not a judge, you are a designer who needs to investigate the problem more deeply in order to make decisions, so let people know that.

Find the right people to ask. Learn who can help you the most and be sure you can count on them: adapt to their schedule, look for the best moment to get them on board and engage them in your project.

Set the stage. Warm up. Provide context and get people to feel comfortable in order for them to be open and ready.

Ask your questions. Sometimes, you just want people to express their thoughts on something. Other times, you want to ask specific questions even if you know it will be unpleasant for them. If you really need answers to those, set the stage properly and ask them anyway.

Dig deeper. Ask follow-up questions in order to get deeper information and clarify that everyone understands the same thing. Use the power of silence—just keep silent, look people in the eye, and nod—so they can expand on their thoughts and ideas without interruption.

How Can Asking Good Questions Build Understanding?

Good questions challenge the status quo, forcing people to pay attention to what’s really going on. They help discover how things work, who’s involved, and how everything relates. Questions help create a clear map of the situation.

Find the root of the problem. Some designers focus on symptoms and simply provide solutions for them. Great designers focus on understanding the origin of those symptoms in order to make a good diagnosis.

Challenge assumptions. Individuals, teams, and organizations have their own habits and processes. Good questions help detect their biases and find new perspectives and points of view.

Understanding context. Designers use different mapping techniques in order to get a clear picture of how the whole system works. They use ethnography and empathy to understand people’s behaviors and mental models. Good questions help gain valuable insights and uncover social, economic, or cultural patterns that take place in a particular context.

Questioning Techniques That Build a Deeper Understanding

This method helps you get a deeper understanding of the root causes and underlying beliefs and motivations of people. It’s at the heart of a proper design thinking process. Sakichi Toyoda, one of the fathers of the Japanese industrial revolution, developed the technique in the 1930s. Here’s how to apply it:

• People don’t buy products in our online store. – Why?
• Because they don’t complete the purchase, they drop off. – Why?
• Because they tend to abandon the shopping cart. – Why?
• Because the cart is where we show shipping details and they think 10 days is too long. – Why?
• Because people buy our product as a gift to someone just a couple of days before the gifting date. 10 days is too long for shipping.

By question five, product designers most likely got closer to the root of the problem and shed light on new approaches to consider that weren’t necessarily the original, “assumed” problem. For a deeper description on the 5 Whys Method , visit Mindtools .

Who, What, Where, When, Why, and How

This is another framework that can be used in order to analyze and get a deeper understanding of the situation and context. Whenever you face a problem, asking these questions will help you get a clear view of the current situation, map critical pain points, and come up with possible ways of taking concrete action that will solve the problem:

• Who interferes with the process in the situation? Users, stakeholders, suppliers, clients, team…
• What elements compose the situation? Actions, behaviors, elements, tools…
• Where does it happen? Geographically, culturally, socially, economically…
• When does this occur? Past, present, future, situational context (when I’m in a rush), frequency…
• Why does this happen? Causes, constraints, needs, motivations…
• How is the situation created? Processes, metrics, results…

How Can Designers Trigger the Imagination by Asking Great Questions?

Great questions have the power to transport us to unimagined scenarios and transform the way we see reality. Questions like, “How would this be in 2050?” lead us to a mindset where our current constraints and biases are no longer valid, forcing us to operate under new paradigms.

When we reframe a situation with questions like, “What would happen if all humans were blind?” we are challenging the set of beliefs and values that we use when inferring meaning, so our view of the situation can change dramatically. When people see things from new perspectives, innovation happens.

Questioning Techniques That Can Trigger the Imagination

There are some question starters that will help you frame your questions in a way that encourages imagination and causes people to develop new perspectives:

• How would it be different if…?
• Suppose that…?
• What if we knew…?
• What would change if…?
• What other way could we…?

Design Question Examples that Foster Collaboration

Questions are also a good way to help teammates identify critical points in their designs and find stronger arguments for their decisions. Through intelligent and constructive feedback , the whole team can benefit from everyone’s point of view and area of expertise.

Instead of asking “Isn’t that interaction a bit awkward?” which could make people defensive, great designers ask questions like, “What were other options you considered, and why did you choose this one?” You’ll help people reflect on their work, explain the reasons why, and see questions as a gift.

Questions build respect and show interest in others’ feelings and thoughts. They help align team members , clarify goals, and give people a sense of responsibility and ownership.

Questions also improve self-awareness and develop better listening and greater understanding capabilities. When you ask your teammates questions, you learn about how they think, what they believe in, how they feel in certain situations, etc. It helps build solid links with the team.

Questioning Techniques That Foster Collaboration

As part of a design thinking exercise, there are some question starters that will help frame questions in a way that builds trust and encourages team collaboration:

• How do you feel about…?
• How would you describe…?
• How could we…?
• What help do we need in order to…?

The Design Thinking Process Using Great Questions

Questioning is a powerful tool that every designer should be able to use fluently. As part of a design thinking process, questions can help understand a situation and get valuable insights. They can also foster creativity and innovation within an organization, and can help teams align and unite.

Asking questions and letting the information flow is essential for growth as an individual and as an organization. But a questioning culture also requires an atmosphere of trust and responsibility, where everyone’s wisdom and capabilities are respected and promoted.

As a designer, ask questions and make sure everyone understands that they come from genuine curiosity and a desire to explore product design more deeply, with the aim of coming up with the best design solution.

Further Reading on the Toptal Blog:

• UI Design Best Practices and Common Mistakes
• Empty States: The Most Overlooked Aspect of UX
• Simplicity Is Key: Exploring Minimal Web Design
• Heuristic Principles for Mobile Interfaces
• Designing for Readability: A Guide to Web Typography (With Infographic)

Jorge Juan Perales

Madrid, Spain

Member since April 5, 2016

About the author

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Article contents

• Introduction
• What can we measure with design cognition?
• What can we measure with design physiology?
• What can we measure with design neurocognition?
• Summary, result correlation and post-processing
• Future work: exploring design thinking results to develop new models, new tools and new research questions
• Limitations

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A framework for studying design thinking through measuring designers’ minds, bodies and brains.

Published online by Cambridge University Press:  03 July 2020

This paper presents a framework for studying design thinking. Three paradigmatic approaches are described to measure design cognitive processes: design cognition, design physiology and design neurocognition. Specific tools and methods serve each paradigmatic approach. Design cognition is explored through protocol analysis, black-box experiments, surveys and interviews. Design physiology is measured with eye tracking, electrodermal activity, heart rate and emotion tracking. Design neurocognition is measured using electroencephalography, functional near infrared spectroscopy and functional magnetic resonance imaging. Illustrative examples are presented to describe the types of results each method provides about the characteristics of design thinking, such as design patterns, design reasoning, design creativity, design collaboration, the co-evolution of the problem solution space, or design analysis and evaluation. The triangulation of results from the three paradigmatic approaches to studying design thinking provides a synergistic foundation for the understanding of design cognitive processes. Results from such studies generate a source of feedback to designers, design educators and researchers in design science. New models, new tools and new research questions emerge from the integrated approach proposed and lay down future challenges in studying design thinking.

1 Introduction

In the past 50 years, design protocol studies have shaped the characteristics of design cognition research. Eastman’s seminal work on cognitive design processes (Eastman Reference Eastman 1969 , Reference Eastman and Moore 1970 ) paved the way for numerous protocol studies on design cognition (Hay et al. Reference Hay, Duffy, McTeague, Pidgeon, Vuletic and Grealy 2017 a , Reference Hay, Duffy, McTeague, Pidgeon, Vuletic and Grealy b ; Jiang & Yen Reference Jiang and Yen 2009 ). The protocol analysis methodology aims to study design cognition by analyzing verbal utterances of participants during a set experiment (Ericsson & Simon Reference Ericsson and Simon 1984 ; Van Someren, Barnard & Sandberg Reference Van Someren, Barnard and Sandberg 1994 ) or in situ. Recent studies focused on designers’ physiological signals such as eye movements, electrodermal activity (EDA; this corresponds to the activation of sweat glands in the skin) or heart rate variability (HRV; this is the variation in the interval between two heart beats) while performing a design task (Sun et al. Reference Sun, Xiang, Chai, Yang and Zhang 2014 ; Leinikka et al. Reference Leinikka, Huotilainen, Seitamaa-Hakkarainen and Mäkelä 2016 ; Yu & Gero Reference Yu, Gero, Rajagopalan and Andamon 2018 ). Techniques from neuroscience to analyze brain behavior during design thinking processes offer ways to better understand human behavior while designing (Alexiou et al. Reference Alexiou, Zamenopoulos, Johnson and Gilbert 2009 ; Shealy, Hu & Gero Reference Shealy, Hu and Gero 2018 ). The interest in using new methods from other research fields provides opportunities to better understand designers’ cognitive processes while designing.

The aim of this paper is to provide a framework for the study of the minds, the bodies and brains of designers while designing and to highlight new tools, new models and new research questions that emerge from such a framework. The framework is illustrated by relevant research studies, rather than an exhaustive literature review, in order to depict the potential of using the methods integrated within the framework. Other publications synthesize the large body of methods and results on advances in studying design through physiology and neurocognition. See Balters & Steinert ( Reference Balters and Steinert 2017 ) on monitoring emotion through neurophysiological tools; Seitamaa-Hakkarainen et al. ( Reference Seitamaa-Hakkarainen, Huotilainen, Mäkelä, Groth and Hakkarainen 2016 ) and Borgianni & Maccioni ( Reference Borgianni and Maccioni 2020 ) on neuroscience and design; and Pidgeon et al. ( Reference Pidgeon, Grealy, Duffy, Hay, McTeague, Vuletic, Coyle and Gilbert 2016 ) on neuroimaging and design creativity. Our aim encapsulates a descriptive framework to better understand the connection between the study of the minds, bodies and brains of designers while designing and to highlight new research challenges that will shape future work in design research.

We consider design thinking as the cognitive activity carried out by designers while they are designing. We categorize the research methods into three paradigmatic approaches for measuring design thinking activity through measuring design cognition (designers’ minds while designing), design physiology (designers’ bodies while designing) and design neurocognition (designers’ brains while designing). We will see that each paradigmatic approach provides different tools to measure distinct characteristics of the design thinking activity. The underlying aim of design physiology and design neurocognition is to inform design cognition by measuring some aspect of a designer’s physiology and neurophysiology while designing.

1.1 Design thinking

The richness and complexity of design tasks lead to a wide range of enquiries on how designers think while designing (Cross Reference Cross 2007 ). Design is often considered as a problem-solving activity (Alexander Reference Alexander 1964 ; Simon Reference Simon 1969 ) with the constraint that design problems, compared to other types of problems, contain a set of variables that are partly unknown. Called ill-defined (Simon Reference Simon 1973 ) or wicked (Rittel & Webber Reference Rittel and Webber 1973 ), design problems evolve along a temporal scale through the overall design activity development. Framing design problems is a prerequisite to proposing a design solution that will fit design requirements (Akin & Akin Reference Akin and Akin 1996 ). The formulation of a design solution gives feedback to designers (Schön Reference Schön 1992 ), which guides them in reframing the design situation in order to advance in their design process. Thus, design is considered to unfold through a co-evolution of the design problem and solution spaces (Maher & Poon Reference Maher and Poon 1996 ; Dorst & Cross Reference Dorst and Cross 2001 ). Design thinking calls for a variety of reasoning mechanisms. In a recent paper, Kannengiesser & Gero ( Reference Kannengiesser and Gero 2019 ) proposed a design thinking framework adapting Kahneman’s concept of thinking fast and slow (Kahneman Reference Kahneman 2011 ). With experience, designers learn to ‘think faster’ and will propose design solutions in an effortless and intuitive way. Fast design thinking becomes a shortcut compared to a longer, reasoned design approach. Multiple factors such as design expertise, education domain, as well as design tools used can affect design thinking patterns (Yu & Gero Reference Yu and Gero 2016 ). Design is a situated activity at a social level, implying that it is situated regarding the design context or the materials of the design situation, and at a personal level, meaning that it is situated in relation to a designer’s past experiences. These two levels of situatedness involve a large range of cognitive processes mobilized to tackle design problems.

1.2 Studying cognitive processes during design thinking

The foundational goal of design research is to gain a better understanding of designing in order to improve the design process, to produce tools for designers, to improve design pedagogy, and consequentially to improve the outcome of designing. Design cognition studies use direct (protocol analysis) and indirect (surveys, interviews and black-box experiments) measurements of designers’ cognitive behaviors while designing. Design physiology and design neurocognition studies provide information on designers’ physiological behaviors (from measuring eye movements, EDA and HRV) and brain behaviors (from non-invasive brain measurement techniques) that can be correlated with cognitive activities. Results from design studies provide feedback to designers and design educators on their practices, while doing design and teaching design. With a clearer comprehension of design thinking, we can develop design tools to assist designers in their tasks (Gero Reference Gero 1990 ), pedagogic tools to support a better design learning process and research tools to improve the efficiency and effectiveness of design research.

The combination of those three paradigmatic approaches, design cognition, design physiology and design neurocognition, creates empirically based knowledge that deepens our understanding of design thinking. The focus remains to study designers’ cognitive activity, which is described by the cognitive processes involved while design thinking. Design cognition studies provide a measurement of designers’ cognitive behaviors. Information on design physiology and design neurocognition provides an objective, indirect measure of design cognition and offers a synthetic approach to understanding human behavior while designing. Research in design cognition is the most mature of the three approaches as it started 50 years ago (Eastman Reference Eastman 1969 , Reference Eastman and Moore 1970 ). Research into design physiology and design neurocognition is increasing due to more accessible tools and more readily available software to analyze physiological and neurological data. The correlation among findings in design cognition, design physiology and design neurocognition is a major goal for future design thinking research and provides the foundations for the development of new models, new tools and new research questions in design thinking, all of which contribute to the understanding of design thinking.

1.3 Outline

In this paper, we present a framework, illustrated by examples, for how we can measure characteristics of design thinking with measures from the three paradigmatic approaches of design cognition, design physiology and design neurocognition. To elucidate how tools from each approach provide measurements of design thinking, we base our presentations on a non-exhaustive set of illustrative research studies. Through the illustrative body of research work we present, some characteristics of design thinking will be explored: design reasoning, processes and patterns; design fixation; design creativity; visual reasoning in design; the co-evolution of design spaces and design collaboration. In Section  2 , we discuss different approaches to studying design thinking with black-box experiments or protocol analysis. In Section  3 , we explore what we can measure with design physiology while Section  4 focuses on what we can measure with design neurocognition. Section  5 synthesizes results from the previous sections to draw an overview of methods and tools available to study design thinking and the type of results they provide. We also explore the correlation and post-processing of results from each paradigmatic approach. In Section  6 , we describe what feedback those results give to designers, educators and researchers; present the potential of new models and tools that can be derived from results; and highlight the emergence of new research questions.

2 What can we measure with design cognition?

Design cognition is the most developed of the three paradigmatic measurement approaches. Design cognition aims at measuring design reasoning, processes and patterns; divergent and convergent thinking; design fixation; design creativity; visual reasoning in design; design space co-evolution and design collaboration with design cognition tools, among others. Coley, Houseman & Roy ( Reference Coley, Houseman and Roy 2007 ) point out that several popular methods can be exploited to study design thinking in terms of cognitive behavior such as the think-aloud method within protocol analysis, observation of sketching behavior, ethnography and diary methods, to which we can add black-box experiments, retrospective interviews (Dorta, Beaudry-Marchand & Pierini Reference Dorta, Beaudry-Marchand, Pierini and Gero 2018 ) and surveys (Blizzard et al. Reference Blizzard, Klotz, Potvin, Hazari, Cribbs and Godwin 2015 ; Coleman et al. Reference Coleman, Shealy, Grohs and Godwin 2019 ). We can regroup these methods into three categories: social sciences methods; black-box experiments and protocol analysis as illustrated in Figure  1 . In the rest of the paper, we present our framework in the form of a diagram to allow for its easy comprehension.

Figure 1. Design cognition tools.

Social sciences methods are not considered further in the description of our framework. Black-box experiments focus on the outcomes of the design processes whereas protocol analysis aims at investigating cognitive actions during the design process. In the following, we will illustrate different methods to study design thinking based on cognitive science techniques through a set of illustrative examples to highlight to what end each methodology can be exploited. The results from those studies, although interesting, are not our main focus.

2.1 Black-box experiments: measuring the outcome of designing

In black-box experiments, multiple conditions are set to carry out a design task. One condition is the control and the other condition is the experiment. The outcome of the design task is measured for each condition. A comparative analysis can provide results on the effect of each experiment condition on the design outcome, from which a cognitive behavior can be inferred. For example, Purcell et al. ( Reference Purcell, Williams, Gero and Colbron 1993 ) focused on the design fixation effect during a design task for two different groups. The control group was given a written design brief, while the experiment group was given the same design brief with an illustrative example to show the level of detail expected in the design. The example in the illustration happened to be a possible solution. The fixation effect was measured by determining the similar details between design solutions proposed by designers and the illustrative example and comparing these details with those produced by the control group that had not seen the illustrative example.

2.2 Protocol analysis: measuring cognitive behavior

Protocol analysis, eliciting information from verbal utterances, is a common method to study design reasoning, processes and patterns (Ericsson & Simon Reference Ericsson and Simon 1984 ; Van Someren et al. Reference Van Someren, Barnard and Sandberg 1994 ). Used with different design frameworks, it provides a measurement of one or more designers’ cognitive behaviors. The development of a coding scheme to encode the protocols can be based on the data itself (grounded theory) or on a model defined a priori. Ericsson & Simon ( Reference Ericsson and Simon 1984 ) suggest running a pilot study in order to develop coding categories. A significant number of models and coding schemes have been exploited and developed to describe design cognitive processes (Hay et al. Reference Hay, Duffy, McTeague, Pidgeon, Vuletic and Grealy 2017 a , Reference Hay, Duffy, McTeague, Pidgeon, Vuletic and Grealy b ). For design thinking studies, a very small number of common coding schemes should be adopted (Gero Reference Gero, Dorst, Stewart, Staudinger, Paton and Dong 2010 ). Although mapping between coding schemes is sometimes possible, results from protocol analysis using different design thinking frameworks are often not commensurable, and as a consequence, the results from different experiments cannot be compared nor can the results from different experimenters, thus reducing their value. The richness of frameworks exploited to describe design processes is also a limit, and the use of a common framework and coding scheme is necessary in order to compare studies to others and provide a more synthetic description of design thinking.

In the following, we will reference research studies using different design frameworks but mainly examples based on protocols analyzed with the Function Behavior Structure (FBS) ontology (Gero Reference Gero 1990 ; Gero & Kannengiesser Reference Gero and Kannengiesser 2004 ). The FBS ontology was chosen given the coverage it has in terms of usage to study designing in different domains: architecture (Pauwels, Strobbe & De Meyer Reference Pauwels, Strobbe and De Meyer 2015 ), engineering (Masclet & Boujut Reference Masclet and Boujut 2010 ; Hamraz & Clarkson Reference Hamraz and Clarkson 2015 ), software design (Hofmeister et al. Reference Hofmeister, Kruchten, Nord, Obbink, Ran and America 2007 ) and systems design, where over 10,000 hours of designing in industry has been coded (Bott & Mesmer Reference Bott and Mesmer 2019 ). The two papers that outline the FBS ontology (Gero Reference Gero 1990 ; Gero & Kannengiesser Reference Gero and Kannengiesser 2004 ) have received over 3,500 citations between them (Google Scholar, accessed 15 January 2020). Multiple approaches to protocol analysis using a first-order coding (the most common form of coding), a second-order coding (two families of codes are used for a protocol), or linkography are described in the following sections. Combining protocol analysis coding results with statistical tools such as Markov models and correspondence analysis provides ways to produce an understanding of design reasoning, design creativity and the co-evolution of the design space.

Figure 2. Example of moving average of cognitive design effort spent on design issues over time (Neramballi, Sakao & Gero Reference Neramballi, Sakao, Gero, Eriksson and Paetzold 2019 ).

Figure 3. Percentage distributions of FBS design processes for 10 co-design protocols and for 9 architectural critiques (Milovanovic & Gero Reference Milovanovic and Gero 2020 ).

2.2.1 First-order coding

The protocol analysis method consists of analyzing verbal data generated by a single designer while designing (think-aloud or retrospective verbal transcripts) or a team of designers (conversational verbal transcripts) (Jiang & Yen Reference Jiang and Yen 2009 ) to determine cognitive actions while designing. Transcripts are then segmented and associated with a category from the defined coding scheme. Protocol analysis and FBS coding are used to represent the distribution of specific design issues and processes and their occurrence over time. Typical data generated by this analysis is each code’s quantitative distributions over time (Figure  2 ) and the distribution of design processes for several sessions (Figure  3 ).

Using protocol analysis and the FBS ontology, Kannengiesser & Gero ( Reference Kannengiesser and Gero 2019 ) explore the use of design thinking systems depending on the designers’ expertise. Design thinking demonstrates use of the dual process theory of thinking, elaborated in Kahneman’s System 1 or thinking fast and System 2 or thinking slow (Kahneman Reference Kahneman 2011 ). ‘Designing fast’ means designing in an intuitive, seamless way, which is associated with routine design, whereas ‘designing slow’ implies that the designer goes through a number of reasoning processes to tackle a design problem. Kannengiesser & Gero ( Reference Kannengiesser and Gero 2019 ) show that all student and professional designers across a number of protocol analysis studies exhibit both System 1 and System 2 thinking.

Other studies focus on design reasoning, or design collaboration with a first-order code. For example, Wolmarans ( Reference Wolmarans 2016 ) explored design reasoning with protocol analysis in terms of semantic gravity (navigation between abstraction and concretization in design thinking) and semantic density (level of integration of design disciplines in design thinking). Dong, Garbuio & Lovallo ( Reference Dong, Garbuio and Lovallo 2016 ) questioned the concept of generative sensing while designing as a form of abductive reasoning. Design team thinking is the focus of Valkenburg & Dorst’s ( Reference Valkenburg and Dorst 1998 ) analysis, which studies design framing in a collaborative environment. They based their analysis on three codes taken from Schön’s ( Reference Schön 1983 ) model of reflective practice (naming, moving and reflecting) to explore collective framing. Their approach provides a qualitative representation of design actions across time (Figure  4 ).

Figure 4. Example of a qualitative representation of cognitive states of design reflective practice of a design protocol coded with a first-order code (Valkenburg & Dorst Reference Valkenburg and Dorst 1998 ).

2.2.2 Second-order coding

A second-order coding adds another layer of coding to the original coded protocols, which provides the possibility of studying designers at other levels such as interacting with the others in co-design situations (Milovanovic & Gero Reference Milovanovic and Gero 2018 ; Gero & Milovanovic Reference Gero and Milovanovic 2019 ) or of integrating another coding scheme to assess team communication to supplement the study of design thinking processes (Darses et al. Reference Darses, Détienne, Falzon and Visser 2001 ; Stempfle & Badke-Schaub Reference Stempfle and Badke-Schaub 2002 ; Dorta et al. Reference Dorta, Kalay, Lesage and Pérez 2011 ). Second-order coding provides opportunities to both quantitatively and qualitatively explore the movement of a team’s focus on either the content of the design task (solution generation, analysis, evaluation, etc.) or on the collaborative processes (planning, decision, control, etc.) (Stempfle & Badke-Schaub Reference Stempfle and Badke-Schaub 2002 ); see Figure  5 .

Problem decomposition and recomposition is a common feature of many design processes. A second-order code that suits its analysis is the system–subsystem level (Gero & Mc Neill Reference Gero and Mc Neill 1998 ; Gero & Song Reference Gero and Song 2017 ). The defined levels are the system level, where designers focus on working on the design problem as a whole, the interaction level, where designers tackle interactions between design subsystems, and the subsystem level, where designers deal with detailing design subsystems. Problem decomposition and recomposition are defined by a change of level from the system level to the subsystem level for the decomposition process and inversely for the recomposition process. In their study, Gero & Song ( Reference Gero and Song 2017 ) found differences in the cognitive effort expended by designers in decomposing and recomposing design problems depending on their expertise (freshmen, seniors and professionals).

Another example of second-order coding is in the study of design creativity using an augmented FBS coding scheme, adding a ‘new’ and ‘surprising’ code to the FBS coding scheme (Gero & Kan Reference Gero and Kan 2016 ). The study explored the occurrences of new and surprising design issues to model a temporal trend of design creativity. The same augmented code was applied by Gero, Yu & Wells ( Reference Gero, Yu and Wells 2019 ) to study the effect of an engineering design capstone course on high school students’ abilities to design creatively. The aim of adding a second-order code to the original code is to provide a more granular analysis of the design activity, in this case to examine the creativity dimension of the design activity (Figure  6 ).

Figure 6. Cumulative distribution of FBS new design issues (Gero & Kan Reference Gero and Kan 2016 ).

A second-order code has been used to investigate the co-evolution of the design space (Jiang, Gero & Yen Reference Jiang, Gero, Yen and Gero 2014 ; Milovanovic & Gero Reference Milovanovic and Gero 2018 ).

2.3 Meta-level codes to generalize design thinking characteristics

Existing coding schemes can be used to generate a meta-level understanding by grouping codes into higher level categories. The use of a meta-level coding scheme provides a more general analysis of a design activity. The notion of co-evolution of the problem/solution space is a general concept defining the design activity (Maher & Poon Reference Maher and Poon 1996 ; Dorst & Cross Reference Dorst and Cross 2001 ) and can be studied by grouping FBS design issues into design problem related issues and design solution related issues (Jiang et al. Reference Jiang, Gero, Yen and Gero 2014 ; Milovanovic & Gero Reference Milovanovic and Gero 2018 ). Requirement (R), function (F) and expected behavior (Be) are part of the design problem space whereas structure (S) and behavior from structure (Bs) are within the design solution space. By grouping FBS codes into two meta-codes (problem and solution issues), we obtain a general time-based representation of the design activity expressing the cognitive focus of the activity.

Collaboration can also be studied by a meta-level coding scheme as exploited in Dorta et al. ’s ( Reference Dorta, Kalay, Lesage and Pérez 2011 ) analysis, which assesses patterns of co-ideation in design protocols. In their framework, seven types of design conversation are part of their coding scheme: referencing, naming, constraining, proposing, explaining, questioning and decision-making. The association of several of those verbal actions forms two types of co-design process, collaborative conversation and collaborative ideation, as illustrated in Figure  7 .

Figure 7. Identification of patterns of design collaboration based on a meta-level coding scheme analysis (Dorta et al. Reference Dorta, Kalay, Lesage and Pérez 2011 ).

2.4 Data analysis: various ways to exploit coded protocols

Information given by the coded protocols can be further analyzed using tools such as linkography, Markov modeling and correspondence analysis. Linkography analyzes semantic relationships between design segments (also called moves) (Goldschmidt Reference Goldschmidt 1990 , Reference Goldschmidt 2014 ). The most probable design process transitions (Kan & Gero Reference Kan, Gero, Dave, Li, Gu and Park 2010 ), reasoning (Gero & Peng Reference Gero and Peng 2009 ) or speaker transitions can be assessed by Markov models. Correspondence analysis highlights relationships between categories of protocols, such as design processes and participants or design processes and representation media.

Linkography to explore design reasoning structure

Linkography (Goldschmidt Reference Goldschmidt 1990 , Reference Goldschmidt 2014 ) aims at studying the relationships between design segments (moves), which are actions or steps, in the design process, and is based on design protocols. A linkograph represents the reasoning structure during a design session (Figure  8 ). It includes a horizontal axis, where the design segments are placed in chronological order. Two types of links are distinguished: forelinks, links going from one idea to another by moving forward in time, and backlinks, links going from one idea to another by moving back in time. A link is both a forelink and a backlink, depending on the reference move.

Figure 8. Example of a linkograph of a design critique (Goldschmidt, Hochman & Dafni Reference Goldschmidt, Hochman and Dafni 2010 ).

The Link Index is the number of links divided by the number of moves connected by these links. The higher the Link Index, the denser the number of links as compared to the number of moves. Critical Moves are moves with a high number of links and, according to Goldschmidt ( Reference Goldschmidt 1990 , Reference Goldschmidt 2014 ), account for the structure of the design activity. Results from the linkograph can be exploited in multiple ways to inform about the design activity, such as design entropy (Kan & Gero Reference Kan and Gero 2008 , Reference Kan and Gero 2017 ).

Markov models to explore patterns in the design activity

Transitions between design issues can be modeled with a first-order Markov model, which expresses the probability of transitioning from one state to another. Markov models normalize data and lose magnitude effects since they are based on percentage occurrences. The interest in using first-order Markov models is to reveal design patterns in the datasets collected from empirical data (Yu & Gero Reference Yu and Gero 2016 ; Kan & Gero Reference Kan and Gero 2017 ; Milovanovic & Gero Reference Milovanovic and Gero 2018 ) or participants’ design patterns in the case of a team design (Gero et al. Reference Gero, Kan, Jiang, Meboldt, Matthiesen, Badke-Schaub and Lohmeyer 2014 ); see Figure  9 . Using a similar tool, Stempfle & Badke-Schaub ( Reference Stempfle and Badke-Schaub 2002 ) explored transitions of team mental focus, either on content (solution generation, analysis, evaluation, etc.) or the collaborative processes engaged (planning, decision, control, etc.); see Figure  10 .

Figure 9. Transition diagram of design communication, showing probability of the next person being communicated with after an idea is expressed by one person (Gero et al. Reference Gero, Kan, Jiang, Meboldt, Matthiesen, Badke-Schaub and Lohmeyer 2014 ).

Figure 10. Transition diagram of team’s mental focus showing the probability of switching the focus between content and process (Stempfle & Badke-Schaub Reference Stempfle and Badke-Schaub 2002 ).

Correspondence analysis to highlight relative relationships between features of the design activity

Figure 11. Correspondence analysis of speakers’ interactions and design processes (Milovanovic & Gero Reference Milovanovic, Gero, Eriksson and Paetzold 2019 ).

3 What can we measure with design physiology?

The measurement of design physiology includes several signals that can be accessed from eye tracking, EDA, electrocardiogram (ECG) and emotion tracking (facial feature recognition); see Figure  12 . Physiology sensors are able to capture humans’ emotion reactivity in a design context (Balters & Steinert Reference Balters and Steinert 2017 ), and it is a relevant feature to analyze in order to deepen our understanding of design cognition. Here again, we introduce a set of illustrative studies to highlight how techniques and tools to measure physiological markers can provide information about design cognition processes.

Figure 12. Design physiology tools.

3.1 Eye movement: measuring design reasoning, comprehension and analysis

Eye-tracking devices and software capture eye movement and pupil dilation and provide an analysis of gaze points, fixation, saccades and tracking presented as heat maps, fixation sequences and areas of interest in different settings (Figure  13 ). Eye tracking can be screen-based, mobile see-through glasses or in virtual reality. In design, eye tracking has been used to analyze design reasoning (Yu & Gero Reference Yu, Gero, Rajagopalan and Andamon 2018 ), creativity (Sun et al. Reference Sun, Xiang, Chai, Yang and Zhang 2014 ) and design analysis (Matthiesen et al. Reference Matthiesen, Meboldt, Ruckpaul, Mussgnug, Lindemann, Srinivasan, Yong Se, Sang Won, Clarkson and Cascini 2013 ; Self Reference Self 2019 ). Studying eye movement gives insight into visual reasoning during a design task. Yu & Gero ( Reference Yu, Gero, Rajagopalan and Andamon 2018 ) looked at correlations between physiological and cognitive measurements of a design session in architecture. In this case study, architects used a parametric modeler to work on a high-rise building. The study shows that designers tend to focus more on facades than edges or corners of their design.

Figure 13. Example of gaze tracking (left) and heat map (right) (Self Reference Self 2019 ).

Eye-movement patterns can also be mapped onto creative moments. Sun et al. ( Reference Sun, Xiang, Chai, Yang and Zhang 2014 ) in an experiment showed a correlation between creative segments and eye movements while sketching. A creative segment is defined by the expression of an idea that offers a new goal, a new function or a new structure and followed by the documentation of that idea through sketches. During creative segments, designers have specific eye-movement characteristics: the fixations are longer and the pupils’ diameters dilate. A longer fixation on the sketches being drawn accounts for more effort on the perception of that new idea.

Matthiesen et al. ( Reference Matthiesen, Meboldt, Ruckpaul, Mussgnug, Lindemann, Srinivasan, Yong Se, Sang Won, Clarkson and Cascini 2013 ) used eye tracking to explore designer’s functional analysis of a drilling machine in four different representation types: on screen section, 3D model manipulation, paper section and a physical prototype. To study how designers analyze the functionality of an object, fixations on area of interests are monitored as well as scan paths and heat maps.

Self ( Reference Self 2019 ) looked at the impact of domain expertise on design communication with sketches, divided into four groups: idea sketch, study sketch, usability sketch and memory sketch. Industrial designers, engineers and managers were presented with four sketches, one of each type. Eye tracking was monitored in order to study points of interest for each group of experts. Fixation duration was captured to study attention and pupil dilation was analyzed to study cognitive load and comprehension. The study showed that industrial designers responded better to representations’ ambiguity compared to engineers and managers. They had an increased comprehension and a reduced challenge in understanding idea sketch and study sketch. The domain of expertise was found to be implicated in the capacity to comprehend sketches and interpret their meaning.

3.2 EDA: measuring learning in design and engagement in design tasks

The EDA and galvanic skin response (GSR) provide a measure of emotional arousal (joy, fear, stress), cognitive processing and attention (Dawson et al. Reference Dawson, Schell, Fillion, Cacioppo, Tassinary and Berntson 2012 ). The EDA occurs through the activation of sweat glands in the skin. The palmar and plantar surfaces are activated by behavioral triggers more than other parts of body’s thermoregulation. Therefore, variations in EDA measures in those areas suggest different cognitive states: emotional, arousal, information processing and/or attentional.

The EDA accounts for a strong emotion but the correlation with its valence (quality of emotion, positive or negative) is unclear. In their study, Villanueva et al. ( Reference Villanueva, Campbell, Raikes, Jones and Putney 2018 ) looked at engineering students’ emotions in different learning situations (passive and active learning) and workshop topics (problem statement, generation of ideas, selection of design solutions, prototyping, presenting design solution). This study showed an increase in EDA for active and collaborative learning compared to passive learning. The EDA measurements were coupled with an emotion self-report to qualify the type of emotions felt by students during each workshop to explore the correlation between EDA and emotional valence.

3.3 Heart Rate Variability with ECG: measuring mental stress and design creativity

The cardiovascular system can be affected by psychological factors such as stress (Berntson, Quigley & Lozano Reference Berntson, Quigley, Lozano, Cacioppo, Tassinary and Berntson 2012 ). Electrocardiography provides measures of electrical potential differences between two electrodes over time. A heart contraction is triggered by the depolarization (electrical impulse) of the sinoatrial node and the atrioventricular node. Those signals are recorded in the ECG, which represents one cardiac cycle.

Heart rate variability is a measure of variations in the interval between two heart beats and can be connected to mental stress (Nguyen & Zeng Reference Nguyen and Zeng 2014 ). The HRV can be analyzed with different methods such as time domain methods and frequency domain methods (Malik et al. Reference Malik, Bigger, Camm, Kleiger, Malliani, Moss and Schwartz 1996 ; Berntson et al. Reference Berntson, Quigley, Lozano, Cacioppo, Tassinary and Berntson 2012 ). Domain frequency methods provide an evaluation of the HRV spectrum over a time period. The spectrum is classified into different bands such as low frequency (LF) and high frequency (HF), which provide a measure of the LF/HF ratio that can be used to analyze mental stress (Nguyen & Zeng Reference Nguyen and Zeng 2014 ; Leinikka et al. Reference Leinikka, Huotilainen, Seitamaa-Hakkarainen and Mäkelä 2016 ).

Nguyen & Zeng ( Reference Nguyen and Zeng 2012 ) postulate that design creativity can be correlated with mental stress in the shape of an inverse U curve. Mental stress in design can be caused by the recursive nature of the design process and the uncertainty and unpredictability of the design outcome. According to those authors, too low or too high mental stress caused by the design task does not support creativity or performance. In their study, Nguyen & Zeng ( Reference Nguyen and Zeng 2014 ) analyzed designers’ mental stress, measured with HRV during an open-ended design problem such as designing a house that can fly. For the design task, designers used a graphic tablet that was recorded as well as the scene. The recorded drawings and annotations served as a basis to segment the video-recorder protocols based on changes in the designer’s actions. Each segment is associated with HRV measured by the LF/HF ratio, and the segments are then clustered in three levels of mental stress from low to high (Figure  14 ). From the study, it was found that over time, the mental stress did not differ significantly and that designers spend less time in high mental stress than in low and medium mental stress. Depending on the expertise and background knowledge of each designer, they show different patterns of mental stress. Leinikka et al. ( Reference Leinikka, Huotilainen, Seitamaa-Hakkarainen and Mäkelä 2016 ) carried out a study to analyze HRV in three different task situations: copying, designing and improvising, and with two different mediums of expression, drawing and modeling with clay. The study showed that the LF/HF HRV ratio was lower for designing and improvising tasks compared to the copying task, especially while drawing. The results from this study support that a larger amount of free cognitive capacity was available while designing and improvising.

Figure 14. Cluster of stress levels of designers during design tasks based on LF/HF ratio (Nguyen & Zeng Reference Nguyen and Zeng 2014 ).

3.4 Tracking emotions while designing

According to Balters & Steinert ( Reference Balters and Steinert 2017 ), the key to understanding human behavior is emotions, and they should be incorporated into the design process. Affective and emotion design is a developing field and aims at integrating a motivation to generate an emotion with the design object and at integrating future users’ emotions in the design process (Triberti et al. Reference Triberti, Chirico, La Rocca and Riva 2017 ). Monitoring emotions can also be used to better understand designers’ emotions while designing, and their potential effect on the design process. Ho & Siu ( Reference Ho and Siu 2012 ) proposed a model to better comprehend different aspects of emotion design through the definition of two key concepts: emotionalized design and emotional design. The first describes the introduction of the designer’s emotions into the design process and outcomes, and the second implies that designers include in their design a motivation to produce specific emotions on potential users. Here we give examples from studies of the designers’ own emotions during designing.

Studies on emotions often exploit a cognitive linguistic approach to measure emotions, although those methods are limited while considering design since emotional aspects of design are hard to express in lexical terms (Kim et al. Reference Kim, Bouchard, Ryu, Omhover and Aoussat 2012 ). Real-time monitoring of emotions with psycho-physiological methods suits the temporal characteristic of the design activity. As mentioned in Section  3.2 EDA gives a measure of a strong emotion but does not account for its valence (like or dislike feeling). Other techniques use facial coding to measure facial emotional expression. The Facial Action Coding System (FACS) developed by Ekman & Friesen ( Reference Ekman and Friesen 1976 ) is a standardized method to manually code video units with a set of described facial emotions. Automatic facial expression analysis has become more reliable and addresses the limits of time-intensive human coding with FACS. Algorithms such as AFFDEX and FACETS can detect and classify facial expressions based on psychological theories and statistical analysis (Stöckli et al. Reference Stöckli, Schulte-Mecklenbeck, Borer and Samson 2018 ). Body postures also inform designers’ emotions as explored by Behoora & Tucker ( Reference Behoora and Tucker 2015 ). In their study, they tested several machine learning algorithms to detect eight different postures that relate to design team interactions. The relevance of this approach is to correlate design team interactions, designers’ emotions and team performance.

Emotion processing and cognitive processing are known to be related although more research is needed to infer which particular types of cognitive processes influence particular channels of emotions (Ochsner & Gross Reference Ochsner and Gross 2005 ; Kim Reference Kim 2011 ). Therefore, studying emotions while designing with automatic facial expression analysis promises to increase our understanding of design processes. The Imotion software ( https://imotions.com/ ) integrates multiple biosensors such as eye tracking, EDA, electroencephalography (EEG) and facial expression analysis modules, which can be recorded simultaneously. Emotion changes across time can be measured with the AFFDEX module in the Imotion software as illustrated in Figure  15 (Abdellahi Reference Abdellahi 2020 ).

Figure 15. Example of emotion automatic recognition with the AFFDEX module with Imotion (Abdellahi Reference Abdellahi 2020 ).

4 What can we measure with design neurocognition?

Design neurocognition studies aim at exploring connections between cognitive processes and brain activity. Three tools provide data on brain activity (Seitamaa-Hakkarainen et al. Reference Seitamaa-Hakkarainen, Huotilainen, Mäkelä, Groth and Hakkarainen 2016 ; Shealy & Hu Reference Shealy, Hu, Mahalingam, Madras, Shealy and Gil 2017 ); see Figure  16 . The EEG measures the electrical activity of the brain with electrodes placed on the surface of the scalp. The EEG detects neural activity and communication via the identification of electrical current (neural connections) that represents brain activity. The temporal resolution of EEG is high, but its spatial resolution is low since it is difficult to separate the electrical activity when sensors are close to each other (surface or deeper in the brain) (Grohs et al. Reference Grohs, Shealy, Maczka, Hu, Panneton and Yang 2017 ). However, recently dense EEG with 256 electrodes has been developed (EGI 2020 ). Functional magnetic resonance imaging (fMRI) measures brain activity by detecting the blood oxygen level dependent (BOLD) changes connected to neuronal activity, with the assumption that when an area of the brain is used, the blood flow increases in that part of the brain. Compared to EEG, fMRI’s temporal resolution is low but its spatial resolution is very high in all three dimensions. Functional near infrared spectroscopy (fNIRS) measures the BOLD changes by analyzing light reflected from the brain when an infrared beam of light is shone into the brain. The light that reflects back to the sensor is due to a higher presence of blood oxygen in parts of the brain that are activated. The spatial resolution of fNIRS is low compared to fMRI and its temporal resolution is higher than fMRI but considerably lower than EEG. Brodmann areas define specific locations of the brain that are associated with a variety of cognitive functions (Brodmann Reference Brodmann and Garey 1909/2006 ). In general, the frontal lobe is connected to planning, judgement, decision-making, concentration, emotion and motor skills. Goel ( Reference Goel 2014 ) argues that distinct design cognitive processes relate to two distinct brain areas’ activation (left and right prefrontal cortex) and that the interactions between both are specific to design cognitive processes (Goel & Grafman Reference Goel and Grafman 2000 ).

Figure 16. Design neurocognition.

Design is a complex, high-order cognitive activity that relates to multiple cognitive processes such as visual processing and reasoning, decision-making, emotions and problem solving, among others. Moreover, design is situated and context related. Therefore, a wide range of brain areas can be activated during a design activity. Defining whether brain activation patterns and cognitive patterns are associated is central to research in design cognition, and methods in cognitive neuroscience can provide new measurements to enhance theories in design cognition (Shealy & Hu Reference Shealy, Hu, Mahalingam, Madras, Shealy and Gil 2017 ) and support methods for design education (Grohs et al. Reference Grohs, Shealy, Maczka, Hu, Panneton and Yang 2017 ).

4.1 EEG: measuring changes in brain behavior for different design tasks and expertise

The EEG method to record brain activity suits design thinking studies because of its high temporal resolution and usability, due to the availability of wireless headsets that allow free movement. Experiments studying design with EEG measurements can integrate sketching (Nguyen & Zeng Reference Nguyen and Zeng 2010 ; Vieira et al. Reference Vieira, Gero, Delmoral, Gattol, Fernanes and Fernandes 2019 b ) and modeling tasks (Kruk et al. Reference Kruk, Aravich, Deaver and deBeus 2014 ; Seitamaa-Hakkarainen et al. Reference Seitamaa-Hakkarainen, Huotilainen, Mäkelä, Groth and Hakkarainen 2016 ), which is not currently possible with fMRI monitoring (Alexiou et al. Reference Alexiou, Zamenopoulos, Johnson and Gilbert 2009 ).

Results from early EEG studies of designers are generally consistent with cognitive findings in design thinking research such as differences between problem-solving and open-ended design tasks (Vieira et al. Reference Vieira, Gero, Delmoral, Gattol, Fernandes, Parente and Fernandes 2019 a ), domain related divergences (Vieira et al. Reference Vieira, Gero, Delmoral, Gattol, Fernanes and Fernandes 2019 b ), the effect of expertise in problem solving (Göker Reference Göker 1997 ) and drawing (Belkofer, Van Hecke & Konopka Reference Belkofer, Van Hecke and Konopka 2014 ) and design reasoning defined by a sequence of design moves (Nguyen & Zeng Reference Nguyen and Zeng 2010 ; Nguyen, Nguyen & Zeng Reference Nguyen, Nguyen and Zeng 2015 , Reference Nguyen, Nguyen and Zeng 2019 ).

The EEG data is transformed in order to obtain different measures such as the transformed power of the sensor measurement (PoW) (Vieira et al. Reference Vieira, Gero, Delmoral, Gattol, Fernandes, Parente and Fernandes 2019 a , Reference Vieira, Gero, Delmoral, Gattol, Fernanes and Fernandes b ) calculated for each electrode, the task related power or TRP (Vieira et al. Reference Vieira, Gero, Delmoral, Gattol, Fernandes, Parente and Fernandes 2019 a , Reference Vieira, Gero, Delmoral, Gattol, Fernanes and Fernandes b ), which highlights differences in power between a rest or registration state and a specific task, or power spectral density of brain waves (Liu et al. Reference Liu, Nguyen, Zeng and Hamza 2016 ; Nguyen & Zeng Reference Nguyen and Zeng 2010 ). Each of the primary five components of brain wave frequencies – alpha, beta, delta, theta and gamma – is associated with cognitive states. Using a principal component analysis (PCA), Liu et al. ( Reference Liu, Nguyen, Zeng and Hamza 2016 ) explored the correlation between brain waves and design activities. According to this study, design activity correlates with the beta band (20–30 Hz) and the gamma band (30–50 Hz) of designers’ brain electrical activity.

Brodmann areas are connected to cognitive processes. Therefore, mapping Brodmann areas with brain activation during design informs us about cognitive design processes (Vieira et al. Reference Vieira, Gero, Delmoral, Gattol, Fernanes and Fernandes 2019 b ). In their study, Vieira et al. ( Reference Vieira, Gero, Delmoral, Gattol, Fernandes, Parente and Fernandes 2019 a ) also explore differences in brain activation of mechanical engineers for two different tasks: problem solving and designing. The brain areas activated differ depending on the task and also on the time during the task. The division of each session in deciles provides a temporal analysis of design neurocognition of mechanical engineers. The changes in brain area activation across time for the open-ended design task are shown in Figure  17 .

Figure 17. Variation in task PoW across time for mechanical engineers. The labels around the circle are the channel labels from a standard distribution of sensors, reflected around the center of the brain producing two hemispheres. (Vieira et al. Reference Vieira, Gero, Delmoral, Gattol, Fernanes and Fernandes 2019 b ).

Nguyen & Zeng ( Reference Nguyen and Zeng 2010 ) studied the correlation between power spectral density of brain waves and design segments related to the design activity of problem analysis, solution generation, solution evaluation and solution expression (drawing/writing). They developed a measure called transient microstate percentage that relates to the perceived hardness of design problems and that deciphers moments during the design session that are associated with different activities (Nguyen et al. Reference Nguyen, Nguyen and Zeng 2015 ).

4.2 fNIRS: measuring changes in brain behavior for different concept generation techniques

The fNIRS techniques allow designers to perform design tasks while mobile. This makes the method more suitable to study design thinking than fMRI when mobility is involved, while being able to monitor the activity with a higher spatial resolution than EEG. The fNIRS measurement involves a much less expensive device than fMRI. Design neurocognition studies using fNIRS explored concept generation using three different idea generation methods (Shealy et al. Reference Shealy, Hu and Gero 2018 ; Shealy & Gero Reference Shealy and Gero 2019 ) and analyzed differences in design cognition based on expertise (Shealy et al. Reference Shealy, Grohs, Hu, Maczka and Panneton 2017 ).

In their study, Shealy & Gero ( Reference Shealy and Gero 2019 ) compared brain activation in the prefrontal cortex of graduate engineering students during concept generation using differently structured techniques: TRIZ (structured), morphological analysis (semi-structured) and brainstorming (unstructured). Results show that each technique correlates with different patterns of activation of the left or the right hemisphere over the time of the task (Figure  18 ). More specifically, different subregions of the prefrontal cortex activate depending on the technique used.

Figure 18. Differences in activation of the prefrontal cortex during concept generation over time (10 deciles) for three different techniques: (a) brainstorming; (b) morphological analysis; (c) TRIZ (Shealy & Gero Reference Shealy and Gero 2019 ).

4.3 fMRI: measuring brain behavior in design reasoning

Experiments in design with fMRI have mainly focused on visual reasoning or analogical reasoning since movements are limited while in the fMRI scanner (Seitamaa-Hakkarainen et al. Reference Seitamaa-Hakkarainen, Huotilainen, Mäkelä, Groth and Hakkarainen 2016 ). Participants are often limited to clicking with a computer mouse without being able to sketch. In the experiment designed by Alexiou et al. ( Reference Alexiou, Zamenopoulos, Johnson and Gilbert 2009 ), participants are monitored while performing two different tasks: a problem-solving task and an open-ended design task. The task consisted of arranging a bedroom using a trackball mouse to interact with the projected layout. Differences in cognitive functions needed for the two tasks reflected in the distinct brain network activated for each task. According to Alexiou, Zamenopoulos & Gilbert ( Reference Alexiou, Zamenopoulos, Gilbert and Gero 2011 ), design thinking involves two layers of brain activity. The first one serves to construct an emotional and cognitive representation of the task and relates to the activation of the temporal, occipital and parietal areas of the brain. The second serves to monitor conflicts in different brain areas, constructs schemes of action and is related to the activation of the prefrontal cortex.

Goucher-Lambert, Moss & Cagan ( Reference Goucher-Lambert, Moss and Cagan 2019 ) looked at brain activation with fMRI during design ideation tasks while providing a support for analogical reasoning (words for the control group and images for the experimental group). The authors found distinct brain activation patterns for what they call inspired internal search (stimuli leading to analogies) and unsuccessful internal search (absence of stimuli); see Figure  19 . In the first case, designers make connections with retrieved concepts from memory to generate new ideas. In the second case, they tend to search the design problem space for insights, which is pointed in the data by an increase of activity in brain areas related to visual processing.

Figure 19. Mapping on a brain template of brain activation cluster for inspirational stimuli versus control with no stimuli for time locked response model (Goucher-Lambert et al. Reference Goucher-Lambert, Moss and Cagan 2019 ). The images here are used to show what the brain maps look like and are not meant to be read for results.

Some of the limitations in using fMRI to study design neurocognition have been ameliorated with new accessories. For example, recently developed drawing tablets that are not affected by a magnetic field can sit on the designer with visual feedback to a screen above them, and this provides new opportunities for the kinds of experiments that can be carried out.

5 Summary, result correlation and post-processing

5.1 synthesis of methods to study design thinking characteristics.

Each of the three paradigmatic approaches offers multiple ways to measure an activity related to design cognition processes such as design reasoning, design collaboration, design creativity, the co-evolution of design problem and solution space, design learning and differences in the design activity depending on the task, the medium used or design expertise (Table  1 ).

Table 1. Synthesis of methods to measure design thinking characteristics

5.2 Correlating and post-processing results

Three paradigmatic approaches were delineated in the previous sections, each providing different types of data on design thinking. By triangulating these heterogeneous data sources, we can improve our understanding of design thinking. Results from each type of study can be correlated or post-processed in order to provide new elements to describe designers’ cognitive processes (Figure  20 ).

Figure 20. Correlation and post-processing of design thinking analysis results.

5.2.1 Correlation: triangulating data

The correlation of results from different paradigms increases the overall understanding of the phenomenon being studied. Merging techniques to measure a task related to design has the potential to inform emotional evaluation of design products, a key aspect of feedback in designing that informs designers (Tomico et al. Reference Tomico, Mizutani, Levy, Takahiro, Cho, Marjanovic, Storga, Pavkovic and Bojcetic 2008 ; Kim et al. Reference Kim, Bouchard, Ryu, Omhover and Aoussat 2012 ), design creativity (Carroll & Latulipe Reference Carroll and Latulipe 2012 ), connection between emotions and CAD software for design tasks (Liu et al. Reference Liu, Ritchie, Lim, Kosmadoudi, Sivanathan and Sung 2014 ), or can provide new methods to segment design protocols (Nguyen et al. Reference Nguyen, Nguyen and Zeng 2015 , Reference Nguyen, Nguyen and Zeng 2019 ).

For example, Tomico et al. ( Reference Tomico, Mizutani, Levy, Takahiro, Cho, Marjanovic, Storga, Pavkovic and Bojcetic 2008 ) explored the comfortableness of a designed object (pencils) based on the measurement of participants’ emotions while evaluating the object, with a two-point EEG to monitor the fluctuation of alpha waves (design physiology) and a structured interview to gather subjective assessment of the object (design cognition). Similarly, Kim et al. ( Reference Kim, Bouchard, Ryu, Omhover and Aoussat 2012 ) explored the correlation of skin conductance response (SCR), self-assessment emotions and semantic descriptors to measure emotions related to the evaluation and perception of an industrial design object (vacuums). The EDA accounts for an emotional arousal but does not give a qualitative measure of the valence of the arousal. In Figure  21 (a), results from the SCR of designers reveal objects that induced a high arousal (N8, N6 and N4). Those products are described with a positive valence (N4) and as surprising (N6 and N8) in relation to the others based on the PCA of the self-assessment test (Figure  21 (b)).

Figure 21. (a) Results from designers’ SCR variations while observing the products, and (b) the PCA on the self-assessment test for emotions related to the products (Kim et al. Reference Kim, Bouchard, Ryu, Omhover and Aoussat 2012 ).

Liu et al. ( Reference Liu, Ritchie, Lim, Kosmadoudi, Sivanathan and Sung 2014 ) developed a model of four emotions (frustration, satisfaction, engagement, challenge) based on GSR, heart rate (HR) and EEG data measured during the experiment. The model they developed allowed the correlation of emotions with specific tasks during the CAD design session. The authors used a fuzzy model approach to define the emotions based on valence measured with EEG signals and arousal based on GSR and HR signals.

Nguyen et al. ( Reference Nguyen, Nguyen and Zeng 2015 , Reference Nguyen, Nguyen and Zeng 2019 ) pointed out the limits of manually segmented design protocols and explored how to use EEG data to perform that task. In their study, they compared manual segmentation to a set of algorithms that automatically segment the EEG dataset. Eight design sessions of up to 2-hour length were used for the analysis. The study showed that transient microstate algorithms worked well to describe the temporal aspects of design moves.

5.2.2 Post-processing: highlighting patterns

In their study, Goucher-Lambert & McComb ( Reference Goucher-Lambert and McComb 2019 ) provide an example of post-processing results of an fMRI using Hidden Markov Models to decipher brain activation patterns during ideation tasks. This technique uses machine learning algorithms to automatically infer cognitive states in fMRI datasets. Twelve states were analyzed over the 2-min sequence, where designers produced design solutions while in the MRI. Each of the 12 states represents average modes of brain activation during the solution generation sequence. The study explores differences between high performing designers and low performing designers in terms of the activations of each state over the time period.

6 Future work: exploring design thinking results to develop new models, new tools and new research questions

The set of results on design thinking obtained from all three paradigmatic approaches provides a source of feedback to designers, design educators and researchers in design science (Figure  22 ). This new knowledge has the potential to support the development of new models to describe design thinking, specifically design cognitive processes, not only through the lens of design cognition but also through the integration of all three paradigmatic approaches. New tools can be developed for designers, design educators and design researchers, which leads to the development of new research questions.

Figure 22. Feedback to researchers, educators and designers to develop new models, tools and research questions. This framework shows the relationships between the three measurement paradigms and the results that flow from them.

6.1 Feedback to designers

Designers face challenges in their work as demands for creativity, innovation, collaboration, efficiency and management are high. With a better comprehension of designers’ minds, bodies and brains, there is an opportunity to augment designers’ performance, self-reflection and self-regulation during the design process and facilitate designers’ collaboration and creativity. Feedback of design thinking research results to designers can be classified into three categories: feedback about the activity of designing, feedback about the designers themselves such as emotions that relate to a personal level and feedback about designers’ interactions when collaborating during co-designing.

Studies on design thinking bring a richer and more nuanced understanding about design cognition, which serves as a base for the development of design support and design augmentation tools. Design cognition, design physiology and neurocognition provide information about the designer’s mental and physical states while designing. While designing with an AI design tool, the designer’s physiological and neurocognitive states can give more information to the AI to better adapt to the design situation. Information can be given to the designer in real time about the design state.

At an individual level, we can draw a parallel between biofeedback to measure sports performance and biofeedback to measure a designer’s performance. For example, an Apple Watch can stimulate a person to take a walk to exercise or to sit down in order to rest based on their HR evaluation. The aims of research on biofeedback in real life situations, such as studying athletes’ performances, can be mapped onto a designer’s situation. Biofeedback to designers could be given in real time concerning their design processes to enhance divergent or convergent thinking, lateral or vertical transformation or to provide inspirational stimuli for idea generation (Goucher-Lambert et al. Reference Goucher-Lambert, Moss and Cagan 2019 ; Shealy et al. Reference Shealy, Gero, Milovanovic and Hu 2020 ). Emotional design takes into account designers’ emotions while designing. Designers might not be conscious of their emotions although they affect their decision-making processes while designing. Liu et al. ( Reference Liu, Ritchie, Lim, Kosmadoudi, Sivanathan and Sung 2014 ) developed a fuzzy model to monitor designers’ emotions based on neurophysiological signals. A real-time analysis of those signals could provide a direct feedback to designers on their cognitive state to enhance their design process, such as their design creativity.

In design collaboration, a part of the design activity is focused on team-oriented activities such as negotiating, planning and mutual understanding. Monitoring designers’ physiological, neurocognitive and emotion states can provide real-time feedback to the designers to enhance the collaboration. Appraisal, mental effort and stress can be captured through physiological data and be provided as real-time feedback to the design team while designing in order to address collaboration issues.

6.2 Feedback to design researchers

Researchers in design science are confronted by problems related to the feasibility of large-scale studies on design thinking. The cost of time and resources, as well as the access to professional designers, the intrusiveness of tools used to measure designers’ cognitive behaviors are hindrances to the development of wider experiments. Advances in design thinking studies can improve the efficiency of studying the design activity by developing new tools and hybrid methods. Protocol analysis gives a rich source of information of design cognitive processes but has limits. The method is time- and resource-consuming given that to ensure data reliability, each protocol has to be encoded several times by at least two coders. Therefore, studies often include only a small number of participants. Using physiological or neurocognitive data to study design protocols can be a more efficient way to analyze design situations. As explored by Nguyen et al. ( Reference Nguyen, Nguyen and Zeng 2015 , Reference Nguyen, Nguyen and Zeng 2019 ), EEG-based protocol segmentation is relevant to revealing design actions over time.

Another direction to explore concerns the automation of protocol analysis using speech recognition and machine learning using tools from natural language processing. Already coded protocols form a database for pattern classification that can be used by a machine learning algorithm to encode new datasets. To obtain a near- to real-time coding of the data, speech recognition could be added to the automated coding of the protocols.

6.3 Feedback to design educators

Teaching design includes particular challenges due to the nature of design thinking processes that are situated at social and personal levels. In an educational context, knowledge about students’ and tutors’ physiological and neurological behaviors during courses or studios can be used to better inform the effects of the pedagogic strategies used. Learning design in the studio implies social and collaborative learning, where tutors adapt their comments to students based on their perceived abilities. Obtaining data on students’ engagement and emotions during a course points out the effect of teaching methods on students (Villanueva et al. Reference Villanueva, Campbell, Raikes, Jones and Putney 2018 ). The design studio pedagogy is partly implicit, and tutors with professional backgrounds tend to teach without any pedagogical training. Using tools to measure physiological and neurological behaviors from students during such studio sessions can provide feedback on the effect of different pedagogic approaches to teaching design. New data obtained from these sources could provide a new perspective on different pedagogic strategies to teach design in the studio.

6.4 Developing new models, new tools and new research questions

The results from all three paradigmatic approaches to study design thinking provide feedback to researchers, educators and designers. Based on those results, new models of design thinking can be inferred or existing models modified if the results are not covered by existing models. New tools can be developed to support designing, and new research questions may emerge.

6.4.1 New models

Models of designing are grounded in either theory or the design cognition paradigm. A rich set of cognitive processes is identified as characteristics of design thinking such as problem framing, problem structuring, concept generation, visual reasoning and problem reframing to name a few (see Hay et al. Reference Hay, Duffy, McTeague, Pidgeon, Vuletic and Grealy 2017 a , Reference Hay, Duffy, McTeague, Pidgeon, Vuletic and Grealy b ). The exploration of mapping between cognitive processes and physiological and neurological measurements has the potential to generate objective knowledge about the designing mind (Cacioppo, Tassinary & Berntson Reference Cacioppo, Tassinary and Berntson 2012 ). It may be possible to determine whether designing is a set of unique mental activities or whether it is a unique combination of generic mental activities. New models may be required depending on the answer to such questions.

6.4.2 New tools

Results from physiological and neurophysiological studies on design activities depict design as an embodied activity (designing with brain, body and mind). Based on those results, tools to support designers can emerge. A correlation between design cognitive patterns and design neurocognitive patterns can serve as a starting point to develop brain–computer interfaces (BCI) for designers’ tools and software. The GUIs in current designing tools are limited, and BCI can provide an interface that may suit some designers’ tasks better (Esfahani & Sundararajan Reference Esfahani and Sundararajan 2012 ). For instance, designers using a BCI are already able to manipulate 3D objects (zoom in and out, rotate and scale) by thought alone; gaze monitoring with eye tracking can serve as a pointer and used to select objects, and gestures can be used to draw and model in VR or AR with devices.

Novel brain–design cognition interfaces have become possible through real-time feedback of brain signals to enhance a designer’s self-regulation capability, producing improvements in idea generation behaviors (Shealy et al. Reference Shealy, Gero, Milovanovic and Hu 2020 ). New user interfaces to communicate with design software are not the only perspective that opens up with new knowledge. Existing tools, using co-creative agents to support design creativity (Davis et al. Reference Davis, Popova, Sysoev, Hsiao, Zhang, Magerko, Colton, Ventura, Lavrač and Cook 2014 ), can be enhanced based on designers’ physiological and neurophysiological measurements. Collaborative AI design tools offer potential in enhancing designers’ performances, efficiency and creativity.

New tools from data science can be used to support researchers’ and educators’ data analysis (see Section  6.2 ) and enhance students’ learning experiences (see Section  6.3 ).

6.4.3 New research questions

Over the past 50 years, results from design thinking studies through the cognitive paradigm approach defined designing as an intentional activity, which evolves through time, based in its social situation that includes personal factors (designers’ experience and design learning), where the problem has to be defined, and is dependent on the design path taken. With a triangulation of the methods that have been presented, new research questions regarding the design activity itself emerge. We pointed out cognitive characteristics of designing, and we can question whether designing implies a unique association of common cognitive brain activation or a specific brain activation. Is there something fundamental in brain activations that defines designing? Cognitive methods to study design cognitive processes analyze design at the cognitive scale. Physiological and neurocognitive tools can measure design thinking at the microscale and provide opportunities to explore whether there are activities specific to designing that exists at this smaller scale.

In the previous section, we identified the emergence of new tools to accompany the design process. By acquiring increased knowledge of design thinking, we can develop more refined computational models of design activity based not only on design cognition but also on design physiology and design neurocognition. The integration of active computational agents into the design process provides a new kind of AI design tool. The BCI and ‘thinking caps’ are another direction to explore in terms of tool development based on results from design neurocognition studies. Design software (CAD, BIM) and design software interfaces (Graphical User Interface, Tangible User Interface) affect the design activity, which raises another question: What will be the effects of AI design tools and BCI on the design process and outcomes?

7 Limitations

The framework we presented covers past and ongoing design thinking research agendas and points to future areas of work. A limitation of this framework is that the measurements synthesized might not be sufficient to capture the complexity and diversity of designers’ thinking processes while designing. Moreover, there are inherent limits of experiments using the methodologies present to study design cognition, design physiology and design neurocognition. Laboratory measurements provide a better control over the experiment but suffer from a lack of realism available through in situ studies. To measure physiology and brain behavior, designers have to wear extra equipment, even during in situ experiments, that can affect their behaviors. Another common issue that we mentioned before is the scale of studies, often quite small due to the cost of collecting data, which limits the inference of any generalizations. In Section  6.2 , on the feedback to design researchers, we laid down future possibilities to address study scales and data collection limits.

8 Conclusion

In this paper, we have presented a framework for the measurement of design thinking based on three measurement paradigms: design cognition, design physiology and design neurocognition. Within each paradigm, we have outlined current methodological approaches that are used to study design thinking. Studying design thinking by measuring design cognition is well established and has a history of development over many decades. Studying design thinking by measuring design physiology and design neurocognition is still a novel approach that is gaining acceptance. These two approaches have become increasingly feasible as the cost of the measurement devices has dropped and the processing software has become more developed.

Utilizing the three paradigmatic approaches together provides a more articulated framework to explore the underlying cognitive, physiological and neurological patterns associated with design thinking. Through non-exhaustive illustrative research studies, we pointed out which characteristic of the design activity can be measured using techniques and tools from the cognitive, physiological or neurocognitive paradigm approach.

Results obtained from those three approaches to studying design thinking bring new challenges to design research. New models, new tools and new research questions emerge based on those results. By triangulating the data obtained, we are able to question existing design models, adapt those models and generate new ones describing design as an embodied activity that considers the designer’s mind, body and brain. New tools will be developed combining results from design studies and technologies and techniques from other research fields, such as brain–computer interfaces and brain–cognition interfaces along with AI and machine learning. Research questions stemming from an integrated approach to studying design thinking explore potential fundamental patterns of brain activation or physiological activity related to designing, as well as the effect of new tools to accompany the design activity and the relevance of new models to describe design thinking.

Acknowledgments

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

The support of the US National Science Foundation, NSF Grant Nos. CMMI-1161715, CMMI-1400466, CMMI-1762415, EEC-1463873 and EEC-1929896 and the US Defense Advanced Research Projects Agency, DARPA Grant No. BAA 07-21 is acknowledged.

Figure 2. Example of moving average of cognitive design effort spent on design issues over time (Neramballi, Sakao & Gero 2019).

Figure 3. Percentage distributions of FBS design processes for 10 co-design protocols and for 9 architectural critiques (Milovanovic & Gero 2020).

Figure 4. Example of a qualitative representation of cognitive states of design reflective practice of a design protocol coded with a first-order code (Valkenburg & Dorst 1998).

Figure 6. Cumulative distribution of FBS new design issues (Gero & Kan 2016).

Figure 7. Identification of patterns of design collaboration based on a meta-level coding scheme analysis (Dorta et al. 2011).

Figure 8. Example of a linkograph of a design critique (Goldschmidt, Hochman & Dafni 2010).

Figure 9. Transition diagram of design communication, showing probability of the next person being communicated with after an idea is expressed by one person (Gero et al. 2014).

Figure 10. Transition diagram of team’s mental focus showing the probability of switching the focus between content and process (Stempfle & Badke-Schaub 2002).

Figure 11. Correspondence analysis of speakers’ interactions and design processes (Milovanovic & Gero 2019).

Figure 13. Example of gaze tracking (left) and heat map (right) (Self 2019).

Figure 14. Cluster of stress levels of designers during design tasks based on LF/HF ratio (Nguyen & Zeng 2014).

Figure 15. Example of emotion automatic recognition with the AFFDEX module with Imotion (Abdellahi 2020).

Figure 17. Variation in task PoW across time for mechanical engineers. The labels around the circle are the channel labels from a standard distribution of sensors, reflected around the center of the brain producing two hemispheres. (Vieira et al. 2019 b ).

Figure 18. Differences in activation of the prefrontal cortex during concept generation over time (10 deciles) for three different techniques: (a) brainstorming; (b) morphological analysis; (c) TRIZ (Shealy & Gero 2019).

Figure 19. Mapping on a brain template of brain activation cluster for inspirational stimuli versus control with no stimuli for time locked response model (Goucher-Lambert et al. 2019). The images here are used to show what the brain maps look like and are not meant to be read for results.

Figure 21. (a) Results from designers’ SCR variations while observing the products, and (b) the PCA on the self-assessment test for emotions related to the products (Kim et al. 2012).

Five-minute design thinking prompts: questions to unpack problems

Hadassah Damien

UX Collective

As a design thinking facilitator and coach, a big part of my job is asking awesome questions .

Recently a colleague asked me if design thinking could be done in 5 minutes — an excellent question itself!

The answer is an absolute YES, and it got me thinking about which practices might fit this category. Quickly I realized: curious questions.

Design thinking has multiple methods for creating smart problem statements and design challenges, the ubiquitous “How Might We….” prompts you may see in the literature or on whiteboards. I value these deeply, but in this piece, I’m exploring the questions we try on before we get to the more polished ones: the messy, lighthearted, futuring , speculative or quick n dirty questions that spark the mind.

Below are a few examples of these design questions, ones I’ve used recently in the field with teams and individuals, which have to lead to some valuable insights.

But first, why do we leverage questions?

Sharp questions help people to adjust the focus on their problems, gain new frameworks for understanding them, and clarify their ideas on how they might address them. Questions can create a possibility space for innovation and needed change. They can bring people in, invite people to know themselves better, and set the stage for collaboration and deep empathy.

Questions are our friends when we work to innovate, change, or transform

— whether you work in team efforts like product development, service design, or program management, or individual pursuits like technical problem-solving in the stack or personal development.

Questions don’t DO the work, but they are critical to ensuring we’re doing better and more impactful things along the way: doing the right work.

Now, don’t get me wrong. The work of creative process itself does need time: ideation is the result of input which must be gathered, synthesis needs to be fit into context to develop meaning, teams or individuals need time to make and execute on decisions … in short, the work of applied creativity does need time to be, well, applied.

Insight is something that can happen quite quickly even as deep synthesis or implementation takes longer. This is why we enjoy memes, data visualizations, and other quick forms of information gathering. First thought, best thought? Sometimes.

This is where five-minute thinking prompts come into play.

Our “aha’s” come from new information as well as new ways of understanding old information. A reframing or a new facet to a problem space is hugely valuable — and can appear in moments. Especially in the case of giant, sticky, or complex problems we can design questions that free our regular thinking constraints and create massive possibility.

Here a few questions you can try to spark insight into your problems or innovation projects:

What would I try if it was okay to fail at fixing this [problem]? How can I rephrase this [complaint or problem] as a goal? Where does my thinking differ from others on the [topic or problem or project], and why? If there was nothing in my way to [do/fix/address] this thing, what would I do next? If I had only today to change this [issue], what would I do? How would my enemy/competitor deal with this? Why would or would not I do that? Say I’m explaining this to an old friend, what do I say? Imagine it’s five years from now, what happened [go for positive outcomes]?

I truly believe that innovation is available to everyone, and it requires only the curiosity to wonder how might things be different and the courage to fail forward in finding out the answer.

This is why I love working with teams and individuals on innovation and transformation projects whether they be technology services, governance, or personal ecosystems.

I’ll leave you with this question: How does curiosity fit into your creative applications of design thinking?

Written by Hadassah Damien

design strategist & facilitator // economics researcher @rffearlessmoney // progressive technologist // performer

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Assumptions & Questions

Everyone goes into a project with thoughts about what is really going on, or questions about what the real issues are. Assumptions & Questions are a great way to get your team or stakeholders to check their biases and identify areas that need deeper research.

Design Phase: Investigate Facilitator : 1  Activity Group:  4-8  Time:  30 Min  Materials:  Sticky notes and markers, Whiteboard Virtual Whiteboard

Before the Activity

Assemble the Team

• Have a clearly defined design team or stakeholder group that you’ll be working through the project with
• Make sure everyone is clear on the project scope and challenge.

Set up a whiteboard

• Designate two colors of sticky notes: one for Assumptions and one for Questions.
• Create a section of the board with a 4 quadrant chart, the X axis ranging from Certain to Uncertain and the Y axis ranging from Low Impact to High Impact.

During the Activity

Set Context and Goals

• Gather the group and provide a brief recap of the project scope and challenge.
• Let the group know which color sticky note is for Assumptions and which one is for Questions.

Begin Exercise 

• Instruct participants to write down as many of their Assumptions and Questions as they can in an open space on the whiteboard. This can usually be accomplished in 10 minutes.
• Once everyone has posted their thoughts, move on to the next step.

Identify Themes Themes can contain a mix of Assumptions and Questions, as long as they share a common thread.

• As groups of like ideas emerge, give titles to the clusters that are short, descriptive phrases of the themes.

Once all Assumptions and Questions have been themed, it’s time to identify where the team’s research efforts should be focused. Take approximately 5 minutes with the group to silently place the theme titles in the chart you prepared before the workshop. Base the theme placement on how certain the group is that the assumption or question is a known and how much of an impact it will have on the success of the project. With all themes charted, evaluate and discuss the placements as a group. There is no exact science here, we’re searching for the best placement based on our knowledge.

After the Activity

Evaluate the chart to determine how the team should proceed with design research. Any themes in the Low Impact/Certain quadrant can be left alone since the answer is clear and it won’t jeopardize the project. The themes in High Impact/Certain and Low Impact/Uncertain should get some attention to confirm or debunk the team’s assumptions or questions, but this is not where the bulk of the research should take place. The core of the team’s efforts should be placed in finding answers for the themes in the High Impact/Uncertain quadrant due to the ambiguous nature of the themes and their potential to make or break the project. Use this information to draft a research plan for the engagement and share the chart and plan with the team.

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Why Design Thinking Works

• Jeanne Liedtka

While we know a lot about practices that stimulate new ideas, innovation teams often struggle to apply them. Why? Because people’s biases and entrenched behaviors get in the way. In this article a Darden professor explains how design thinking helps people overcome this problem and unleash their creativity.

Though ostensibly geared to understanding and molding the experiences of customers, design thinking also profoundly reshapes the experiences of the innovators themselves. For example, immersive customer research helps them set aside their own views and recognize needs customers haven’t expressed. Carefully planned dialogues help teams build on their diverse ideas, not just negotiate compromises when differences arise. And experiments with new solutions reduce all stakeholders’ fear of change.

At every phase—customer discovery, idea generation, and testing—a clear structure makes people more comfortable trying new things, and processes increase collaboration. Because it combines practical tools and human insight, design thinking is a social technology —one that the author predicts will have an impact as large as an earlier social technology: total quality management.

It addresses the biases and behaviors that hamper innovation.

Idea in Brief

The problem.

While we know a lot about what practices stimulate new ideas and creative solutions, most innovation teams struggle to realize their benefits.

People’s intrinsic biases and behavioral habits inhibit the exercise of the imagination and protect unspoken assumptions about what will or will not work.

The Solution

Design thinking provides a structured process that helps innovators break free of counterproductive tendencies that thwart innovation. Like TQM, it is a social technology that blends practical tools with insights into human nature.

Occasionally, a new way of organizing work leads to extraordinary improvements. Total quality management did that in manufacturing in the 1980s by combining a set of tools—kanban cards, quality circles, and so on—with the insight that people on the shop floor could do much higher level work than they usually were asked to. That blend of tools and insight, applied to a work process, can be thought of as a social technology.

• JL Jeanne Liedtka is a professor of business administration at the University of Virginia’s Darden School of Business.

Partner Center

Design Thinking Research

Making Design Thinking Foundational

• © 2016
• Hasso Plattner 0 ,
• Christoph Meinel 1 ,
• Larry Leifer 2

Hasso Plattner Institute for Software Systems Engineering, Potsdam, Germany

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Center for Design Research (CDR), Stanford University, Stanford, USA

• Based on scientific evidence from the HPI Stanford
• Design Thinking Research Program Covers more than just best practice in design thinking and innovation
• Points out how design thinking can be used to innovate IT development

Part of the book series: Understanding Innovation (UNDINNO)

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Introduction

The Design Thinking Methodology at Work: Capturing and Understanding the Interplay of Methods and Techniques

Theoretical Foundations of Design Thinking

• Design thinking
• IT development
• Innovating creativity

Table of contents (16 chapters)

Front matter, manifesto: design thinking becomes foundational.

• Larry Leifer, Christoph Meinel

Introduction: The HPI-Stanford Design Thinking Research Program

• Claudia Koch, Christoph Meinel, Larry Leifer

Tools and Techniques for Improved Team Interaction

Globalized design thinking: bridging the gap between analog and digital for browser-based remote collaboration.

• Matthias Wenzel, Lutz Gericke, Christoph Thiele, Christoph Meinel

Diagnostics for Design Thinking Teams

• Neeraj Sonalkar, Ade Mabogunje, Gina Pai, Aparna Krishnan, Bernard Roth

Design Thinking Health: Telepresence for Remote Teams with Mobile Augmented Reality

• Lauren Aquino Shluzas, Gabriel Aldaz, Larry Leifer

Talkabout: Making Distance Matter with Small Groups in Massive Classes

• Chinmay Kulkarni, Julia Cambre, Yasmine Kotturi, Michael S. Bernstein, Scott Klemmer

Improving Design Thinking Through Collaborative Improvisation

• David Sirkin, Brian Mok, Stephen Yang, Rohan Maheshwari, Wendy Ju

Creativity and Creative Confidence

Designing a creativity assessment tool for the twenty-first century: preliminary results and insights from developing a design-thinking based assessment of creative capacity.

• Grace Hawthorne, Manish Saggar, Eve-Marie Quintin, Nick Bott, Eliza Keinitz, Ning Liu et al.

Innovation in Creative Environments: Understanding and Measuring the Influence of Spatial Effects on Design Thinking-Teams

• Claudia Nicolai, Marie Klooker, Dora Panayotova, Daniela Hüsam, Ulrich Weinberg

Building Blocks of the Maker Movement: Modularity Enhances Creative Confidence During Prototyping

• Joel Sadler, Lauren Shluzas, Paulo Blikstein, Riitta Katila

Measuring Design Thinking

Measuring the impact of design thinking.

• Jan Schmiedgen, Lea Spille, Eva Köppen, Holger Rhinow, Christoph Meinel

Developing Design Thinking Metrics as a Driver of Creative Innovation

• Adam Royalty, Bernard Roth

Documentation and Information Transfer in Design Thinking Processes

Experience and knowledge transfer through special topic coaching sessions.

• Franziska Häger, Thomas Kowark, Matthias Uflacker

Smart Documentation with Tele-Board MED

• Julia P. A. von Thienen, Anja Perlich, Johannes Eschrig, Christoph Meinel

Preserving Access to Previous System States in the Lively Kernel

• Lauritz Thamsen, Bastian Steinert, Robert Hirschfeld

Editors and Affiliations

Hasso Plattner, Christoph Meinel

Larry Leifer

Bibliographic Information

Book Title : Design Thinking Research

Book Subtitle : Making Design Thinking Foundational

Editors : Hasso Plattner, Christoph Meinel, Larry Leifer

Series Title : Understanding Innovation

DOI : https://doi.org/10.1007/978-3-319-19641-1

Publisher : Springer Cham

eBook Packages : Business and Management , Business and Management (R0)

Copyright Information : Springer International Publishing Switzerland 2016

Hardcover ISBN : 978-3-319-19640-4 Published: 19 August 2015

Softcover ISBN : 978-3-319-36772-9 Published: 22 October 2016

eBook ISBN : 978-3-319-19641-1 Published: 08 September 2015

Series ISSN : 2197-5752

Series E-ISSN : 2197-5760

Edition Number : 1

Number of Pages : VIII, 290

Number of Illustrations : 59 b/w illustrations, 68 illustrations in colour

Topics : IT in Business , Innovation/Technology Management , Software Engineering , Management of Computing and Information Systems , Media Management , Media Design

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Writing about Design

Principles and tips for design-oriented research.

How to define a research question or a design problem

Introduction.

Many texts state that identifying a good research question (or, equivalently, a design problem) is important for research. Wikipedia, for example, starts (as of writing this text, at least) with the following two sentences:

“A research question is ‘a question that a research project sets out to answer’. Choosing a research question is an essential element of both quantitative and qualitative research.” (Wikipedia, 2020)

However, finding a good research question (RQ) can be a painful experience. It may feel impossible to understand what are the criteria for a good RQ, how a good RQ can be found, and to notice when there are problems with some RQ candidate.

In this text, I will address the pains described above. I start by presenting a scenario of a project that has problems with its RQ. The analysis of that scenario allows me then to describe how to turn the situation described in the scenario for a better research or design project.

Scenario of a problematic project

Let us consider a scenario that you are starting a new research or design project. You have already an idea: your work will be related to communication with instant messaging (IM). Because you are a design-minded person, you are planning to design and develop a new IM feature: a possibility to send predefined replies on a mobile IM app. Your idea is that this feature will allow the user to communicate quickly with others in difficult situations where the they can only connect with others through their mobile phone. Your plan is to supply the mobile IM app with messages like “I’m late by 10 minutes but see you soon”, “I can’t answer back now but will do that later today”, and so on.

Therefore, your plan involves designing such an app, maybe first by sketching it and then illustrating its interaction with a prototyping software like Figma or Adobe XD. You may also decide to make your design functional by programming it and letting a selected number of participants to use it. These kinds of activities will let you demonstrate your skills as a designer-researcher.

Although predefined messages for a mobile IM app can be a topic of a great study, there are some problems with this project that require you to think more about it before you start. As the project is currently defined, it is difficult to provide convincing answers to these challenges:

• Challenge 1: Why would this be a relevant topic for research or design? Good studies address topics that may interest also other people than the author only. The current research topic, however, does not do that self-evidently yet: it lacks an explanation why it would make sense to equip mobile IM apps with predefined replies. There is only a guess that this could be useful in some situations, but this may not convince the reader about the ingenuity of this project.
• Challenge 2: How do you demonstrate that your solution is particularly good? For an outsider who will see the project’s outcome, it may not be clear why your final design would be the best one among the other possible designs. If you propose one interaction design for such a feature, what makes that a good one? In other words, the project lacks a yardstick by which its quality should be measured.
• Challenge 3: How does this project lead to learning or new knowledge? Even if you can show that the topic is relevant (point 1) and that the solution works well (2), the solution may feel too “particularized” – not usable in any other design context. This is an important matter in applied research fields like design and human–computer interaction, because these fields require some form of generalizability from their studies. Findings of a study should result in some kind of knowledge, such as skills, sensitivity to important matters, design solutions or patterns, etc. that could be used also at a later time in other projects, preferably by other people too.

All these problems relate to a problem that this study does not have a RQ yet . Identifying a good research question will help clarify all the above matters, as we will see below.

Adding a research question / design problem

RQs are of many kinds, and they are closely tied to the intended finding of the study: what contribution  should the study deliver. A contribution can be, for example, a solution to a problem or creation of novel information or knowledge. Novel information, in turn, can be a new theory, model or hypothesis, analysis that offers deeper understanding, identification of an unattended problem, description about poorly understood phenomenon, a new viewpoint, or many other things.

The researcher or thesis author usually has a lot of freedom in choosing the exact type of contribution that they want to make. This can feel difficult to the author: there may be no-one telling what they should study. In a way, in such a situation, the thesis/article author is the client of their own research: they both define what needs to be done, and then accomplish that work. Some starting points for narrowing down the space of possibilities is offered here.

Most importantly, the RQ needs to be focused on a topic that the author genuinely does not know, and which is important to find out on the path to the intended contribution. In our scenario about a mobile IM app’s predefined replies, there are currently too many alternatives for an intended contribution, and an outsider would not be able to know which one of them to expect:

• Demonstration that mobile IM apps will be better to use when they have this new feature.
• Report on the ways by which people would use the new feature, if their mobile IM apps would have such a feature.
• Requirements analysis for the specific design and detailed features by which the feature should be designed.
• Analysis of the situations where the feature would be most needed, and user groups who would most often be in such situations.

All of these are valid contributions, and the author can choose to focus on any one of them. This depends also on the author’s personal interests. This gives a possibility for formulating a RQ for the project. It is important to notice that each one of the possible contributions listed above calls for a different corresponding RQ:

RQ1: Do predefined replies in mobile IM apps improve their usability?

RQ2: How will users start using the predefined replies in mobile IM apps?

RQ3: How should the interaction in the IM app be designed, and what kind of predefined replies need to be offered to the users?

RQ4: When are predefined replies in IM apps needed?

This list of four RQs, matched with the four possible contributions, shows why the scenario presented in the beginning of this text was problematic. Only after asking these kinds of questions one is able to seek to answer to the earlier-presented three challenges in the end of the previous section. Also, each of the RQs needs a different research or design method, and its own kind of background research.

The choice and fine-tuning of the research question / design problem

Which one of the above RQs should our hypothetical researcher/designer choose? Lists of basic requisites for good RQs have been presented in many websites. They can help identify RQs that will still need refinement. Monash University offers the following kind of helpful list:

• Clear and focused.  In other words, the question should clearly state what the writer needs to do.
• Not too broad and not too narrow.  The question should have an appropriate scope. If the question is too broad it will not be possible to answer it thoroughly within the word limit. If it is too narrow you will not have enough to write about and you will struggle to develop a strong argument.
• Not too easy to answer.  For example, the question should require more than a simple yes or no answer.
• Not too difficult to answer.  You must be able to answer the question thoroughly within the given timeframe and word limit.
• Researchable.  You must have access to a suitable amount of quality research materials, such as academic books and refereed journal articles.
• Analytical rather than descriptive.  In other words, your research question should allow you to produce an analysis of an issue or problem rather than a simple description of it.

If a study meets the above criteria, it has a good chance of avoiding a problem of presenting a “non-contribution” : A laboriously produced finding that nonetheless does not provide new, interesting information. The points 3 and 6 above particularly guard against such studies: they warn the readers from focusing their efforts on something that is already known (3) and only describing what was done or what observations were made, instead of analysing them in more detail (6).

In fine-tuning a possible RQ, it is important to situate it to the right scope. The first possible RQ that comes to one’s mind is often too broad and needs to be narrowed. RQ4 above (“ When are predefined replies in IM apps most needed? ”), for example, is a very relevant question, but it is probably too broad.

Why is RQ4 too broad? The reason is that RQs are usually considered very literally. If you leave an aspect in your RQ unspecified, then it means that you intend that your RQ and your findings will be generalisable (i.e., applicable) to all the possible contexts and cases that your RQ can be applied to. Consider the following diagram:

With a question “ When are predefined replies in IM apps most needed?”, you are asking a question that covers both leisure-oriented and work-oriented IM apps which can be of very different kinds. Some of the IM apps are mobile-oriented (such as WhatsApp) and others are desktop-oriented (such as Slack or Teams). Unless you specify your RQ more narrowly, your findings should be applicable to all these kinds of apps. Also, RQ4 is unspecific also about the people that you are thinking as communication partners. It may be impossible for you to make a study so broad that it applies to all of these cases.

Therefore, a more manageable-sized scoping could be something like this:

RQ4 (version 2): In which away-from-desktop leisure life situations are predefined replies in IM apps most needed?

Furthermore, you can also narrow down your focus theoretically. In our example scenario, the researcher/designer can decide, for example, that they will consider predefined IM replies from the viewpoint of “face-work” in social interaction. By adopting this viewpoint, the researcher/designer can decide that they will design the IM’s replies with a goal that they help the user to maintain an active, positive image in the eyes of others. When they start designing the reply feature, they can now ask much more specific questions. For example: how could my design help a user in doing face-work in cases where they are in a hurry and can only send a short and blunt message to another person? How could the predefined replies help in situations where the users would not have time to answer but they know they should? Ultimately, would the predefined replies make it easier for users to do face-work in computer-mediated communications (CMC)?

You can therefore further specify RQ4 into this:

RQ4 (version 3): In which away-from-desktop leisure life situations are predefined replies in IM apps most needed when it is important to react quickly to arriving messages?

As you may notice, it is possible to scope the RQ too narrowly so that it starts to be close to absurd. But if that does not become a problem, the choice of methods (i.e., the research design ) becomes much easier to do.

The benefit of theoretically narrowed-down RQs (in this case, building on the concept of face-work in RQ4 version 3) have the benefit that they point you to useful background literature. Non-theoretical RQs (e.g., RQ4 version 2), in contrast, require that you identify the relevant literature more independently, relying on your own judgment. In the present case, you can base your thinking about IM apps’ on sociological research on interpersonal interaction and self-presentation (e.g., Goffman 1967) and its earlier applications to CMC (Nardi et al., 2000; Salovaara et al., 2011). Such a literature provides the starting points for deeper design considerations. Deeper considerations, in turn, increase the contribution of the research, and make it interesting for the readers.

As said, the first RQ that one comes to think of is not necessarily the best and final one. The RQ may need to be adapted (and also can be adapted) over the course of the research. In qualitative research this is very typical, and the same applies to exploratory design projects that proceed through small design experiments (i.e., through their own smaller RQs).

This text promised to address the pains that definition of a RQ or a design problem may pose for a student or a researcher. The main points of the answer may be summarized as follows:

• The search for a good RQ is a negotiation process between three objectives : what is personally motivating, what is realistically possible to do (e.g., that the work can be built on some earlier literature and there is a method that can answer to the RQ), and what motivates its relevance (i.e., can it lead to interesting findings).
• The search for a RQ or a design problem is a process and not a task that must be fixed immediately . It is, however, good to get started somewhere, since a RQ gives a lot of focus for future activities: what to read and what methods to choose, for example.

With the presentation of the scenario and its analysis, I sought to demonstrate why and how choosing an additional analytical viewpoint can be a useful strategy. With it, a project whose meaningfulness may be otherwise questionable for an outsider can become interesting when its underpinnings and assumptions are explicated. That helps ensure that the reader will appreciate the work that the author has done with their research.

In the problematization of the scenario, I presented the three challenges related to it. I can now offer possible answers to them, by highlighting why a RQ can serve as a tool for finding them:  

• Why would this be a relevant topic for research or design? Choice of a RQ often requires some amount of background research that helps the researcher/designer to understand how much about the problem has already been solved by others. This awareness helps shape the RQ to focus on a topic where information is not yet known and more information is needed for a high-quality outcome.
• How do you demonstrate that your solution is particularly good? By having a question, it is possible to analyse what are the right methods for answering it. The quality of executing these becomes then evaluatable. The focus on a particular question also will permit that the author compromises optimality in other, less central outcomes. For example, if smoothness of interaction is in the focus, then it is easy to explain why long-term robustness and durability of a prototype may not be critical.
• How does this project lead to learning or new knowledge? Presentation of the results or findings allows the researcher/design to devote their Discussion section (see the IMRaD article format ) to topics that would have been impossible to predict before the study. That will demonstrate that the project has generated novel understanding: it has generated knowledge that can be considered insightful.

If and when the researcher/designer pursues further in design and research, the experience of thinking about RQs and design problems accumulates. As one reads literature , the ability to consider different research questions becomes better too. Similarly, as one carries out projects with different RQs and problems, and notices how adjusting them along the way helps shape one’s work, the experience similarly grows. Eventually, one may even learn to enjoy the analytical process of identifying a good research question.

As a suggestion for further reading, Carsten Sørensen’s text  (2002) about writing and planning an article in information systems research field is a highly recommended one. It combines the question of choosing the RQ with the question on how to write a paper about it.

Goffman, E. (1967). On face-work: An analysis of ritual elements in social interaction. Psychiatry , 18 (3), 213–231.  https://doi.org/10.1080/00332747.1955.11023008

Nardi, B. A., Whittaker, S., & Bradner, E. (2000). Interaction and outeraction: Instant messaging in action. In Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW 2000) (pp. 79–88). New York, NY: ACM Press. https://doi.org/10.1145/358916.358975

Salovaara, A., Lindqvist, A., Hasu, T., & Häkkilä, J. (2011). The phone rings but the user doesn’t answer: unavailability in mobile communication. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI 2011) (pp. 503–512). New York, NY: ACM Press. https://doi.org/10.1145/2037373.2037448

Sørensen, C. (2002): This is Not an Article — Just Some Food for Thoughts on How to Write One. Working Paper. Department of Information Systems, The London School of Economics and Political Science. No. 121.

Wikipedia (2020). Research question. Retrieved from https://en.wikipedia.org/wiki/Research_question (30 November 2020).

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What Is the Design Thinking Process? The 5 Steps Complete Guide

If you’ve heard of Design Thinking, you may know that it’s an ideology concerned with solving complex problems in a creative, user-centric way. But what does Design Thinking look like in action? What does the process actually entail?

In this guide, we’ll tell you everything you need to know about the Design Thinking process —including where it comes from, why it’s so valuable, and what it’s used for. We’ll then explore the five stages of the Design Thinking process in detail.

We’ve divided our guide into the following sections. Feel free to skip ahead using the clickable menu!

• What is the Design Thinking process?
• What is the value of the Design Thinking process?
• How can I learn Design Thinking?

Ready to learn all about the Design Thinking process? Let’s go.

1. What is the Design Thinking process?

Before we can understand the Design Thinking process, it’s important to get to grips with the ideology behind it—that is, Design Thinking.

In simple terms, Design Thinking is a methodology that aims to tackle highly complex problems.

Complex problems—otherwise known as “wicked” problems— are those that are difficult to define and cannot be solved using standard methods and approaches. They are the opposite of “tame” problems, which can be solved by applying a tried-and-tested algorithm or logic. Let’s explore wicked vs. tame problems in more detail now.

Wicked vs. tame problems

Let’s imagine you’re holding a dinner party for six people. You’ve picked out a recipe for potato soup and you’ve bought all the necessary ingredients.

At the last minute, one of your guests asks if they can bring three friends along; you now need enough potato soup for nine people! Fortunately, this problem is easily solved—you’ll simply multiply the quantities of each ingredient on the recipe in order to make more soup.

This is an example of a tame problem. Based on what you know about cooking, and by applying some simple math, you are quickly able to find a solution. Wicked problems, on the other hand, have no known solution or algorithm. In fact, the more you try to solve a wicked problem, the more problems you expose!

Unlike our “tame” dinner party conundrum, wicked problems don’t have a final solution. Things like climate change, poverty, and world hunger are often-cited examples of wicked problems; they need to be tackled from multiple angles, and rather than looking for a single answer, they require a response that anticipates how the problem might evolve and mutate.

Wicked problems are everywhere in business, too.

Whether it’s reinventing an entire business model, trying to maintain your startup culture as the business grows, working out how to please a new customer group, or resolving conflict between different departments—none of these scenarios has a simple, tried-and-tested solution. They are complex, wicked problems that require Design Thinking !

Design Thinking fosters an outside-the-box approach, with a huge emphasis on creativity, innovation, and the needs of the user. The Design Thinking process is used to apply the Design Thinking ideology to real-world, wicked problems. It offers a solution-based approach to problem-solving.

Unlike problem-based thinking, which tends to fixate on obstacles and limitations, the Design Thinking process is all about outcomes. It provides a non-linear series of steps that you can follow to come up with innovative, actionable ideas.

You can learn more about solution-based vs. problem-based thinking in our comprehensive guide to Design Thinking .

Now we know what kinds of wicked problems we’re up against, let’s see what the Design Thinking process looks like in action.

The Design Thinking process in action

In the following video, design expert and mentor Camren Browne gives a beginner-friendly introduction to the design thinking process:

This list of five big organizations winning with Design Thinking explains how companies such as iBM, MassMutual, and Fidelity are “drawing on design thinking frameworks to jolt innovative ideas” and “drive bottom-line business outcomes.”

The Design Thinking workshop

One way to apply the Design Thinking process is through a Design Thinking workshop .

If you have a specific problem you want to tackle, a dedicated workshop will take you through each step of the Design Thinking process—from building empathy and defining the problem right through to prototyping and testing ideas—usually over the course of a few days or a week.

As a designer, you might invite your colleagues from other departments to harness a diversity of ideas. Design Thinking workshops aren’t just for designers, though; all teams can use and benefit from this creative approach to problem-solving.

Aside from dedicated workshops, Design Thinking can also be an embedded process—an overarching framework that informs how you make decisions and devise certain strategies.

Rather than going through the entire Design Thinking cycle in one sitting, you might choose to focus on just one element—such as getting to know your target audience (be it external customers or internal stakeholders) or conducting user tests.

In this sense, the Design Thinking process can be used to build a general culture that emphasizes putting the user first, collaborating in order to innovate, and testing early and often.

What is the goal of the Design Thinking process?

However you choose to implement the Design Thinking process, the goal is the same: to approach complex problems from a human perspective. The Design Thinking process fosters creativity, innovation, and user-centricity, helping you to come up with actionable solutions that are:

• Desirable for the user;
• Viable for business;
• Technologically feasible.

The Design Thinking process puts the needs and requirements of the user first. The first stage of the process is dedicated to building empathy with your target users and understanding their needs, expectations, and behaviors.

Next, you’ll focus on developing ideas quickly turned into prototypes and tested on real users. Inherent to the Design Thinking process is the early and frequent testing of your solutions; this way, you can gather feedback and make any necessary changes long before the product is developed.

In a nutshell: The Design Thinking process enables you to find innovative solutions to complex problems driven by the needs of the target user.

2. What are the 5 steps of the Design Thinking process?

The Design Thinking process can be divided into five key steps: Empathize, Define, Ideate, Prototype, and Test.

When considering the five steps of Design Thinking, it’s important to remember that it’s not a linear process. Although we talk about the process in terms of sequential steps, it’s a highly iterative loop. With each phase, you’ll make new discoveries that may require you to revisit the previous stages.

With that in mind, let’s consider the f ive key stages of the Design Thinking process in more detail.

1. Empathize

The Design Thinking process starts with empathy. To create desirable products and services, you need to understand who your users are and what they need. What are their expectations about the product you’re designing? What challenges and pain points do they face within this context?

During the empathize phase, you’ll spend time observing and engaging with real users (or people who represent your target group)—conducting interviews, seeing how they interact with an existing product, and generally paying attention to facial expressions and body language.

As the first step in the Design Thinking process, the empathize phase encourages you to set your assumptions aside. Armed with first-hand insights, you can design with real users in mind. That’s what Design Thinking is all about!

Learn more:

• What Is Empathy In Design Thinking?
• Storytelling for UX Design Teams

In the second stage of the Design Thinking process, you’ll define the user problem you want to solve. First, you’ll gather all of your findings from the empathize phase and start piecing them together. What common themes and patterns did you observe? What user needs and challenges consistently came up?

Once you’ve synthesized your findings, you’ll formulate what’s known as a problem statement . A problem statement—sometimes called a point of view (POV) statement—outlines the issue or challenge you seek to address.

As with anything in the Design Thinking process, the problem statement keeps the user in focus. Rather than framing your problem statement as a business goal, like “We need to increase gym membership among over-50s by 30%,” you’ll frame it from the user’s perspective: “Over-50s in London need flexible, affordable access to sports facilities to keep fit and healthy.”

By the end of the define phase, you will have a clear problem statement to guide you throughout the design process. This will form the basis of your ideas and potential solutions.

Learn more: How To Define A Problem Statement: Your Guide To The Second Step In The Design Thinking Process

The third stage in the Design Thinking process consists of ideation—or generating ideas. By this point, you know who your target users are and what they want from your product. You also have a clear problem statement that you’re hoping to solve. Now it’s time to come up with possible solutions.

The ideation phase is a judgment-free zone where the group is encouraged to venture away from the norm, explore new angles, and think outside the box. You’ll hold ideation sessions to generate as many ideas as possible—regardless of whether or not they’re feasible! For maximum creativity, ideation sessions are often held in unusual locations.

Throughout this stage of the Design Thinking process, you’ll continuously refer back to your problem statement. As you prepare to move on to the next phase, you’ll narrow it down to a few ideas, which you’ll later turn into prototypes to be tested on real users.

Learn more: What Is Ideation In Design Thinking? A Guide To The Most Important Ideation Techniques

4. Prototype

In the fourth stage of the Design Thinking process, you’ll turn your ideas from stage three into prototypes. A prototype is essentially a scaled-down version of a product or feature—be it a simple paper model or a more interactive digital representation.

The aim of the prototyping stage is to turn your ideas into something tangible which can be tested on real users. This is crucial in maintaining a user-centric approach, allowing you to gather feedback before you go ahead and develop the whole product. This ensures that the final design solves the user’s problem and is a delight to use!

Learn more: Step Four In The Design Thinking Process: Your Complete Introduction To Prototyping

T he fifth step in the Design Thinking process is dedicated to testing: putting your prototypes in front of real users and seeing how they get on. During the testing phase, you’ll observe your target users—or representative users—as they interact with your prototype. You’ll also gather feedback on how your users felt throughout the process.

The testing phase will quickly highlight any design flaws that must be addressed. Based on what you learn through user testing, you’ll go back and make improvements.

Remember: The Design Thinking process is iterative and non-linear. The results of the testing phase will often require you to revisit the empathize stage or run through a few more ideation sessions before you create that winning prototype.

Learn more: User Testing: A Guide To Step Five Of The Design Thinking Process

3. What is the value of the Design Thinking process?

We’ve touched upon the goal of Design Thinking and how it can be applied to real-world, wicked problems. Now, let’s consider the value that Design Thinking brings.

Here are just some of the benefits of the Design Thinking process:

• The Design Thinking process teaches people how to innovate and problem-solve: While most of us are programmed to solve problems that readily present themselves, we’re not necessarily inclined to look for problems. Design Thinking encourages creative problem-solving; it pushes you to redefine the problem space and seek out the challenge worth solving. This is especially useful in a business context—designing a competitive digital product, optimizing internal processes, or reinventing an entire business model.
• The Design Thinking process fosters teamwork and collaboration: As explained by the HPI Academy , “innovations and answers to complex questions are best generated in a heterogeneous team of five to six people.” The Design Thinking process brings multidisciplinary teams together, breaks down silos, and encourages people to collaborate and challenge their assumptions.
• The Design Thinking process offers a proven competitive advantage: Design-led companies have been shown to consistently outperform their competitors . As already mentioned, the aim of the Design Thinking process is to come up with solutions, products, or services that are desirable for the user, economically viable from a business perspective, and technologically feasible. This user-first approach, coupled with early and frequent testing, helps to minimize risk, drive customer engagement, and ultimately boost the bottom line.

So: Design Thinking is a tool for creativity, innovation, and problem-solving. Not only does it help designers to come up with ground-breaking products, but it also fosters a culture of innovation and user-centricity at every level of business.

Before we consider the five stages of the Design Thinking process, let’s take a look at where the Design Thinking process comes from.

4. How can I learn Design Thinking?

With all this talk of the Design Thinking process and just how valuable it is, you might be wondering how you can learn more about Design Thinking and eventually start applying it to your own work.

A good place to start is user experience (UX) design—creating user-friendly products and services that solve a real user need. Indeed, UX and Design Thinking often go hand-in-hand; many key principles and steps of the Design Thinking process are also critical to UX, such as building empathy through user research, creating prototypes, testing on real users, and continuously iterating.

Learning the essentials of UX will help you to understand better how Design Thinking fits into the development of real-world products and solutions. Our one-month course in UX Fundamentals provides a comprehensive introduction to Design Thinking and shows you how to apply the first stage of the Design Thinking process to a real-world problem.

We also recommend checking out this excellent collection of resources for getting started with Design Thinking provided by the d.school (Hasso Plattner Institute of Design at Stanford University). If you’d like to learn more about putting the Design Thinking process into context, you’ll find a comprehensive guide over on IDEO.com .

As we’ve seen, the Design Thinking process can be applied to all areas of business. It’s a tool that can be used by anyone in any department to foster innovation and find creative solutions to complex problems. Whether you’re a designer, a teacher, or a CEO, the Design Thinking process will transform the way you think, collaborate, and come up with ideas.

If you’d like to learn more about Design Thinking and user-centered design, check out the following articles:

• What Is User Experience (UX) Design? Everything You Need To Know To Get Started
• What Is Human-Centered Design? A Beginner’s Guide
• Why Empathy Matters As A User Experience Designer

1. What is the design thinking process?

The design thinking process is a problem-solving methodology used by designers to approach complex problems and find innovative solutions. It typically involves five stages: empathize, define, ideate, prototype, and test.

2. What is ideate in the design thinking process?

Ideate is a stage in the design thinking process where designers generate a large number of creative ideas and concepts in response to the problem statement developed during the previous stage.

3. During which stage of the design thinking process is a problem statement formed?

A problem statement is typically formed during the “define” stage of the design thinking process. This involves gathering insights from stakeholders and users to understand the problem that needs to be solved.

4. Which stage of the design thinking process involves learning about customers’ challenges?

The “empathize” stage of the design thinking process involves learning about customers’ challenges. This stage is focused on gaining a deep understanding of users’ needs, emotions, and behaviors in order to develop meaningful solutions.

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10 Insightful Design Thinking Frameworks: A Quick Overview

If you’ve just started to embark on your journey into the field of design thinking , you may have noticed different frameworks cropping up here and there. This is nothing to worry about—it’s simply the result of different people’s perceptions of the design thinking process. To help you get your head around these interpretations, we’ve prepared a useful summary of the most popular design thinking frameworks used by global design firms and national design agencies .

Design thinking means many things to many people—not only in its definition, but also in its practical implementation. A wide variety of design thinking frameworks and visualizations exist in the world today , and each typically contains between three and seven stages. Before we dive into these different frameworks, let’s look at a quick overview of the fundamental principles which form the basis behind all variations of the design thinking process.

Traits that are common across design thinking processes:

Starts with empathy . A deep focus on the humans involved will ensure you stay on track and follow the course of action most likely to bring about preferred solutions for individuals, business and society.

Reframes the problem or challenge at hand. This helps you gain new perspectives and explore different ways to think about the problem, and allows a more holistic approach towards reaching a preferred solution.

Initially employs divergent styles of thinking. This allows participants to generate and explore as many solutions as possible in an open , judgment-free ideation space.

Later employs convergent styles of thinking. This will allow your team to isolate, combine and refine potential solution streams out of your more mature ideas.

Creates and tests prototypes . Solutions which make it through the previous stages get tested further to remove any potential issues.

Iterates. You will revisit empathic frames of mind as you progress through the various stages and may redefine the challenge as new knowledge is gathered.

The process is all done in a collaborative, multidisciplinary team that leverages the experience and thinking styles of many folks to solve complex problems. It can feel quite chaotic at first, if you’re not used to it—however, if done correctly, it can result in emergent solutions that are desirable, feasible and viable.

Different implementation frameworks or models have different names and numbers of stages, but they all consist of the same principles and all involve points at which you will empathize , reframe, ideate, prototype and test. Let’s now take a quick look at 10 popular frameworks to further understand this innovative and revolutionary process.

1. The 5-Stage Design Thinking Process—d.school

First, let’s look at the 5-stage model that we will be following in this course.

The Stanford Design School (d.school), now known as the Hasso Plattner Institute of Design, initially taught design thinking via a simple but powerful 3-step process: Understand, Improve, Apply.

They have since built upon this, to formulate and openly share a famous 5-stage process which is widely used around the world, including here at the Interaction Design Foundation. The process they outlined is as follows:

The d.school also represents this 5-stage process through their hexagonal design thinking visualization. This ensures the stages are seen more as enablers or modes of thinking, rather than concrete linear steps.

The d.school’s model of design thinking consists of five iterative, non-linear phases: Empathize, Define, Ideate, Prototype and Test.

© Stanford d.School web, Public License. Source.

2. The Early Traditional Design Process—Herbert Simon

The earliest versions of the design thinking process still reflected the traditional design process . As design thinking evolved, however, deeper empathy, more collaboration and a multidisciplinary approach were thrown into the mix.

As Herbert Simon states in his 1969 seminal work The Sciences of the Artificial , the design process consists of the following seven stages: define , research , ideate , prototype , choose , implement and learn —and this has been the cornerstone of design processes ever since.

© Daniel Skrok and the Interaction Design Foundation, CC BY-NC-SA 3.0.

3. Head, Heart and Hand—AIGA

The American Institution of Graphic Arts (AIGA) states the value of modern design practice comes from designers’ unique blend of head, heart and hand. For example, design thinking participants wear many hats during the process and rely on their heads to solve complex problems. In the early stages, they also use their hearts to empathize and understand human needs and emotions . The particular gift of designers, however, is their ability to dive into practical creation by hand. The three combined create a holistic process which utilizes input from all of our faculties to be successful.

Designers have a unique blend of head, heart, and hand skills which combine to create holistic problem-solving abilities.

4. DeepDive™ Methodology—IDEO

The DeepDive™ technique was developed by IDEO as a way to rapidly immerse a group into a situation where they can effectively problem-solve and generate ideas. They expressed this variant of the design thinking process live on ABC Nightline back in the late ’90s.

An abridged version from the report about IDEO's DeepDive™ Methodology that was aired on ABC Nightline in the late '90s.

IDEO's DeepDive™ comprised the following steps:

© Daniel Skrok and Interaction Design Foundation, CC BY-NC-SA 3.0.

The DeepDive™ methodology was further documented and enhanced by Andy Boynton and Bill Fischer of the International Institute for Management Development (IMD) business school, and Deloitte Consulting then acquired the rights in 2006.

5. The 3-Stage Design Thinking Process—IDEO

IDEO uses a different process and, while it only has three stages, it covers pretty much the same ground as the other processes in this compilation.

IDEO’s 3-Stage Design Thinking Process consists of inspiration, ideation and implementation.

© IDEO, Public License.

The three stages are:

Inspire : The problem or opportunity inspires and motivates the search for a solution.

Ideate : A process of synthesis distills insights which can lead to solutions or opportunities for change.

Implement : The best ideas are turned into a concrete, fully conceived action plan.

IDEO also released a deck of IDEO Method Cards which cover the modes Learn , Look , Ask and Try —each with their own collection of methods for an entire innovation cycle.

6. Design Kit: The Human-Centered Design Toolkit—IDEO

IDEO has also developed contextualized toolkits, which repackage the design thinking process. One such iteration focuses on the social innovation setting in developing countries. For this context, the terminology needs to be simplified, made memorable and restructured for the typical challenges faced in those environments. The Human-Centered Design (HCD) Toolkit they developed for this purpose was re-interpreted as an acronym to mean hear , create and deliver.

IDEO’s 3-Stage Design Thinking Process was reinterpreted as Hear, Create, Deliver to coincide with the “HCD” acronym for Human-Centered Design.

Hear : Similar to early phases in other design thinking processes , the hear stage develops an empathic understanding of users, and defines the problem the team is trying to solve. It helps participants gain a solid foundation in the context of the problem and sufficiently reframe it to take on new perspectives.

Create : The create stage is concerned with exploration, experimentation and learning through making—similar to the ideate and prototype phases in d.school’s 5-stage approach. Potential areas of exploration are pinpointed, and those closest to the problem will be engaged with further to co-create solutions. This allows design teams to maintain the highest levels of empathy during early design phases and weed out any potential problematic assumptions made by designers who do not sufficiently understand the context.

Deliver : The deliver phase of the HCD process is centered around logistical implementation. It also aims to help overcome any obstacles which may exist when rolling out a solution within the required context. It is essential that solutions integrate into communities and bypass other roadblocks during implementation, and this stage will help participants achieve that.

7. The “Double Diamond” Design Process Model—Design Council

In the mid-2000s the British Design Council popularized the Double Diamond diagram, based on Béla H. Bánáthy’s 1996 “divergence-convergence” model. The Double Diamond diagram graphically represents a design thinking process. It highlights the divergent and convergent styles of thinking involved, and is broken down into four distinct phases:

Discover : The start of the project is based around an initial idea or inspiration, often gained from the identification of user needs .

Define : These user needs are interpreted and aligned with business objectives.

Develop : Design-led solutions are developed, iterated and tested.

Deliver : The end product or service is finalized and launched into the market.

The Double Diamond diagram from the Design Council helps to visualize the divergent and convergent stages of the design thinking process, and highlights the different modes of thinking that designers use.

8. Collective Action Toolkit (CAT) — Frog Design

Frog Design is an organization committed to social impact. They developed the Collective Action Toolkit (CAT) as a way to make the design process accessible to communities around the world—with the hope it will help them organize, collaborate and create solutions for the specific problems which affect their local area.

Frog’s Collective Action Toolkit process.

© Frog, Public License.

Frog’s CAT breaks the process down into six stages:

Clarify your goal : Agree on the problem you want to try and solve, as well as what goals you want to achieve.

Build your group : Bring people together in your community, identify their strengths and map out their commitment to your goals.

Seek new understanding : Ask questions, explore how people live and discover unmet needs to inform and inspire your group, and gain others’ perspectives.

Imagine new ideas : Come up with new solutions and decide what makes some of them more achievable than others.

Make something real : Test and experiment your better ideas and see what you discover.

Plan for action : Organize what each group member should do to reach your shared goals.

Frog make it clear these stages form a non-linear process, and you might have to revisit stages multiple times during a project—particularly the clarification stage.

9. Designing for Growth—Jeanne Liedtka & Tim Ogilvie

Jeanne Liedtka is a professor at the University of Virginia’s Darden School of Business, and Tim Ogilvie is CEO of innovation strategy consultancy firm Peer Insight. Both are experts in design thinking and strategic thinking, and their book, Designing for Growth , puts forward a unique spin on the design thinking journey. It reframes the terminology into a more inquisitive and intuitive set of four what questions:

What is ? Explore the current reality.

What if ? Envision alternative futures.

What wow s? Get users to help you make some tough choices.

What works ? Make the solution work in-market, and as a business.

“What if...?”—one of the most powerful phrases in the English language, and for good reason.

© Christine Prefontaine, CC BY-SA 2.0.

10. The LUMA System of Innovation—LUMA Institute

The LUMA System of Innovation process consists of looking, understanding and making.

© LUMA Institute, Public License.

The LUMA Institute is a global firm that teaches innovation and human-centered design. The team at LUMA have developed their own expression of the design thinking process which they have distilled into three key design skills: Looking , Understanding and Making.

They claim their system is flexible and versatile so it can be used for any type of problem, in any type of setting. The process unfolds through either a single set of activities or a combination of multiple methods—the latter being required for more complex challenges.

The Take Away

You could spend weeks exploring the many versions of the design thinking process which exist in the world today. Their differences and similarities are, in fact, celebrations of variety and non-conformity.

Now you’ve read the 10 most popular frameworks above, maybe you’ve decided on a favorite. Regardless of which approach you like the most, it’s important you peel away the steps and terminology and focus instead on its principles. At first sight, the design thinking process can seem mysterious, chaotic and, at times, complex. However, it's a discipline which will mature in you with direct practice. You will learn things in a practical manner and grow in confidence with each new experience of it. You may even be tempted to develop your own expression of these steps, modes and phases to suit a completely new context—that's part of the beauty of design thinking!

References & Where to Learn More

Herbert Simon, The Sciences of the Artificial , 1969.

Mike Morrison, Deep-Dive Brainstorming Technique – IDEO , 2018.

d.school, An Introduction to Design Thinking PROCESS GUIDE , 2010: https://web.stanford.edu/~mshanks/MichaelShanks/files/509554.pdf

David Clifford, Equity-Centered Design Framework .

IDEO, Design Kit: The Human-Centered Design Toolkit .

Jeanne Liedtka and Tim Ogilvie, Designing for Growth: A Design Thinking Tool Kit for Managers , 2011.

LUMA Institute, Our System .

Hero Image: © Teo Yu Siang and the Interaction Design Foundation, CC BY-NC-SA 3.0.

Design Thinking: The Ultimate Guide

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7 Types of Questions to Build Empathy for Design Thinking

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Building a sense of empathy with your audience is at the core of design thinking — as well as the very first step in the design process. . How else could you solve their problems unless you understand other people’s experiences, feelings, motivations, and struggles?

In this guide, we’ll cover:

• What is empathy in design thinking?
• Benefits of empathizing with people in design thinking
• The 7 types of questions to ask in the empathy stage
• A few templates to better empathize with your customers

Let’s get started!

What is the empathy stage of design thinking?

The ‘Empathize’ stage of design thinking lays the foundation for the following steps of design thinking by understanding all the human elements of the problem at hand. By empathizing with people, designers can embrace human-centered design methodology to create solutions that address their underlying problems, resulting in more meaningful and impactful outcomes.

Building empathy with the people or users experiencing the core problem can result in:

• Better, user-centered solutions
• Reduced biases and incorrect assumptions
• Improved problem-solving
• Better collaboration throughout the following steps of design thinking

The exact questions you need to ask during the Empathize stage can be wildly different based on the problem or empathy technique, but there is a common denominator — these questions should typically be open-ended and story-based to invite more observations and deeper insights.

7 types of questions to help build empathy for people 

1. introductory questions.

Empathic design goes beyond collecting facts like the age or location of the end user. So when you start your conversation, introductory questions that ask about specific instances can help you learn who your user really is and what they actually care about.

To put this in a real-world context, let’s take a look at Netflix. The company’s pivot to streaming is an excellent example of the design-thinking process, so let’s reimagine what those introductory questions might have been. 

• What was it like the last time you tried renting a DVD?
• Can you tell me what it’s like waiting a few days for the DVD to arrive in the mail?

A standard question like, “What was it like the last time you tried doing [the problem you’re addressing]?” can help you empathize with a user’s first-hand experience.

In a B2B space, you might ask additional questions like:

• What does a day at work look like?
• Can you tell me more about your role and responsibilities?
• How familiar are you with [a problem]?

2. Follow-up questions

The key to understanding customers and their pain points lies in a good follow-up question. You need to elicit stories instead of running through a checklist. An easy way to ask follow-up questions is to frame them as “Why” questions. While these can’t effectively be scripted beforehand, you can go beyond the typical “Why” questions. Here are three techniques you should consider:

• Rephrasing the question : If you feel like the interviewee is holding back, ask the same question you did before in a different way. For instance, “What does a day at work look like?” can be reframed as “How do you usually prioritize tasks during a typical day at work?”
• Link responses: Connect the dots between the answers. For instance, if a participant tells you about a current challenge they’re facing, ask questions like, “Is that like the time you had to do [solve a problem]?”  
• Dig into implications: When people give you a safe answer to a question, ask about the implications of their answers. For example, if you hear, “I’m always putting out fires,” you can ask, “What are the consequences of that in your workplace?”

Related: A step-by-step guide to identifying the real problem

3. Probing questions

Ask participants to elaborate on an answer with an example. Or ask them to explain something in detail. These open-ended questions are designed to encourage deep thought and go beyond what users are saying. 

• Can you tell me more about that?
• What led you to that conclusion?
• What were your original intentions?
• Why did you choose that option first?
• Can you give me an example of when this happened?

To become better at asking probing questions, practice active listening. It may be clear to a user why they behaved a certain way, but they may not be articulating it. If you don’t listen intently, you may miss out on some things that can help you get to the root of the issue.

Note: These in-depth questions can help solve one of the four most common challenges of design thinking .

4. Specifying questions

If you still sense you’re not getting closer to the truth, try asking questions that help you get specific details like, “How did it make you feel?” or “What did you do when [x] happened?”

But watch out for visual cues and body language. If a participant is non-verbally expressing their frustration when talking about a problem they had, asking how it made them feel will signal to them that you aren’t really paying attention.

You also don’t want to be too empathetic and ask, “Was that frustrating for you?” because that is a leading question that can introduce bias in the process. Instead, take note of their body language or demeanor and move on.

5. Direct questions

You can introduce topics directly that are either related to your project or based on something the interviewee said by asking direct questions. 

For instance, Instagram is likely trying to create a Twitter rival with its own text-based app. When conducting an empathy interview, it might ask participants something along the lines of: 

• How familiar are you with Twitter’s features and its competitors?
• Do you use Mastodon? How does it compare to Twitter?

It is vital to wait till the end to ask direct questions so participants have ample time to share their perspectives before you show your cards, and it prevents any undue influence. 

Related: Learn how IBM used Mural to scale a design culture globally

6. Indirect questions

If you don’t want to pose a question directly to customers, try an indirect approach. 

For example, if Instagram wanted to find out which Twitter features it should emulate, you could ask questions about other people’s experiences:

• Do you think people like the new For You tab on Twitter?
• How do you think users feel about Community Notes?

Since participants only share an interpretation of other users’ experiences, their inherent biases can creep in. So don’t treat subjective truths as facts. 

7. Interpreting questions

When you want to ensure you’ve understood a participant’s answer, you can ask interpreting questions.

For example, if a user has told you they’d never buy from a specific brand again, you might want to clarify if the decision was based on their shopping experience or the quality of the product or both. 

An example could be: “Am I right in understanding that the reason you’ll never shop again at [x] is because of the low quality?” or “Was it the poor customer service that put you off [x] brand?”

This line of questioning ensures you’re not misinterpreting what users are saying and connecting the wrong dots.

Need more question examples to ask your subjects? Check out these 25 brainstorming questions for generating better ideas .

The real purpose of empathy interviews 

The main goal of semi-structured empathy interviews or “chats” is to help designers identify user needs and behaviors — even those they’re unable to articulate or are unaware of. 

It’s also important not to take everything people say at face value. Recognize that there’s often a gap between what they say they do in a situation vs. what they actually do. You might have to shadow users as they go about their day and then ask questions to gain deeper context.

For instance, designers at Uber Eats follow partners on deliveries and sit in people’s homes as they order takeout to observe their design in use. This walk-a-mile immersion method helps them understand the real-world challenges that can’t be replicated in an office environment.

Note: because empathic design should serve real needs, designers will need to map out and challenge their assumptions . 

Treat empathy interview questions like a guide

There is no one-size-fits-all when it comes to an empathy interview. Although this semi-structured chat is popular among most design teams, some companies adopt a more structured approach or prefer observing customers in the wild instead. 

As long as you’re not trying to influence the outcome of the conversation — your questions remain neutral and non-binary — you’ll gain a deep understanding of your audience and design better products. 

Done with your user interviews? It’s time to place your observations in an empathy map . Use Mural’s empathy map template to share what you know about users with the rest of the team.

The Mural platform has the features, templates , and expertise teams and enterprises need to push innovative solutions forward without sacrificing creativity, bring out the best ideas from everybody, and fix how their teams collaborate.

Once you’ve empathized with your end-user or customer, you can move to the next stage of design thinking: Define .

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The Art of Asking Questions: A Design Thinking Workshop

Sarah Dzida

At our OpenIDEO Los Angeles March event, we did a Skills Deep Dive into the empathy and definition stages of design thinking. We focused on interview techniques and “how might we” statements. We had a great turnout — mostly because we welcomed members from NELAUX . They came energized and ready to jump into the night’s activities, and that’s how ideators and ideas spilled out of our host’s conference room to set up shop in four other rooms about the office. Our fearless leader led everyone through a full docket of activities, and everyone had a great time. I heard from many newbies that they planned to be back.

However at the event, everyone left with questions about questions. How do you ask them? What do you ask? How do you know to ask the right one? So for the April event, I decided to keep digging deeper into this very necessary problem-solving skill. And it’s also something I’ve been trying to figure out how to teach for awhile: the art of asking dynamic and substantial questions!

This is an important topic to me because I’ve come to consciously accept this as my superpower. It’s a skill I continually sharpen and has played a role in all my personal and professional successes. From teaching ESL in Japan, to interviewing subjects for freelance articles, to conceptualizing and developing books to building out entire product strategies, questions constantly buoy me toward positive achievement.

Written by Sarah Dzida

UX + content strategy consultant by day; creative writer by night. Check out my new hybrid memoir: Dearest Enemy! www.sarahdzida.com | www.dearest-enemy.com

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Design thinking: 7 questions to ask before you start

First it was Agile . Then it was DevOps . Now it’s design thinking (often in combination with DevOps) – the latest approach that aims to transform IT service delivery, user satisfaction, and business outcomes.

While relatively new to corporate IT, design thinking has been around for a while. With roots going back to Stanford University’s School of Engineering in the 1970’s and later proliferating in Silicon Valley product development, it’s a user-centric approach to developing new products and services. In the consumer world, design thinking inverts the traditional innovation method: Instead of developing a new product or service in isolation and then convincing customers to buy it, design thinking enables companies to create products and services based on an in-depth understanding of what customers want.

It’s easy to see the potential benefits for corporate IT: Instead of building a new system in isolation and throwing it over a wall, technology leaders can integrate the end-user perspective from the very start. In combination with more iterative DevOps/agile development processes and managerial styles, design thinking can create a broad array of positive results.

“Design thinking puts the user at the center of the universe and the services are designed and developed per the user,” says Yugal Joshi, vice president at management consultancy and research firm  Everest Group . “This user-centricity has been missing from the IT organization. The collaboration it can drive between IT, businesses, and end users can add significant value to the business in terms of employee engagement, productivity, cost of services, and revenue impact.”

But design thinking alone is not a silver bullet – and it won’t be right for every IT organization. Here are some questions IT leaders should ask before pursuing design thinking:

1. What’s the impetus for exploring design thinking?

“Design thinking is not an end in and of itself,” says Prashant Kelker, a partner in technology research and advisory firm  ISG’s  Digital Solutions Group. IT leaders who have found design thinking useful tend to frame it as an additional tool that the organization can employ to better understand end users and build solutions.

2. How will we define design thinking?

“Design thinking has multiple definitions and implementations, therefore choosing the preferred path and training users appropriately is necessary,” says Michael Cantor, CIO of  Park Place Technologies . To begin, CIOs might look to software vendors, IT service providers, or other third parties who have a design thinking methodology and partner with them to provide a framework and training.

Because design thinking isn’t terribly prescriptive, some CIOs can get frustrated with a certain specific design thinking approach “and throw the baby out with the bathwater assuming design thinking isn’t relevant for their model,” says Joshi. “[However], CIOs can always change the typically available design thinking approaches to suit their own environment.”

IT leaders should also make sure, once they have an approach in mind, “that the IT team has access to experienced mentors that will guide and set expectations for team members and stakeholders,” says Dean Pipes, CIO of unified communications and collaboration provider  TetraVX .

3. Can our organization embrace rapid testing, failure, and course correction?

“It’s risky to take on a new approach like this unless the culture is prepared for it,” says Pipes. “There needs to be a commitment to embracing the different cadences and lifecycle that comes with design thinking.”

IT organizations that have already adopted an agile framework or aspire to become more agile are good candidates for integrating design thinking. “While it won’t apply in all circumstances, a design thinking approach heavily supports the rapid prototype, test, and refactor agile pattern,” Cantor says. “It is incredibly useful in other development methodologies as well, but doesn’t necessarily align as cleanly as it does with Agile.”

At the same time, it’s important that everyone in the company understands the differences between – and benefits of – design thinking and agile, says Shawn Fields, VP of digital innovation with managed services provider  CompuCom .

In addition, adds Kelker, IT leaders should make sure that performance evaluations and incentive programs are aligned with the design thinking approach: Don’t punish the failures and risk-taking that are integral to the iterative learning that takes place.

4. Will your IT culture support the design thinking mindset?

As described by the well-known  design thinking shop IDEO , design thinking integrates “the needs of people, the possibilities of technology, and the requirements for business success.” Empathy with users or customers is the bedrock upon which design thinking is built.

Practitioners believe that fully understanding the experience of the user for whom you are designing – through observation, interaction, and immersion – is key to understanding what really matters to them.

“Corporate IT will be called on to have much stronger consulting skills as they co-design workflows in-house along with their business colleagues,” says Kelker.

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What Is Design Thinking & Why Is It Important?

• 18 Jan 2022

In an age when innovation is key to business success and growth, you’ve likely come across the term “design thinking.” Perhaps you’ve heard it mentioned by a senior leader as something that needs to be utilized more, or maybe you’ve seen it on a prospective employee's resume.

While design thinking is an ideology based on designers’ workflows for mapping out stages of design, its purpose is to provide all professionals with a standardized innovation process to develop creative solutions to problems—design-related or not.

Why is design thinking needed? Innovation is defined as a product, process, service, or business model featuring two critical characteristics: novel and useful. Yet, there’s no use in creating something new and novel if people won’t use it. Design thinking offers innovation the upgrade it needs to inspire meaningful and impactful solutions.

But what is design thinking, and how does it benefit working professionals?

What Is Design Thinking?

Design thinking is a mindset and approach to problem-solving and innovation anchored around human-centered design . While it can be traced back centuries—and perhaps even longer—it gained traction in the modern business world after Tim Brown, CEO and president of design company IDEO, published an article about it in the Harvard Business Review .

Design thinking is different from other innovation and ideation processes in that it’s solution-based and user-centric rather than problem-based. This means it focuses on the solution to a problem instead of the problem itself.

For example, if a team is struggling with transitioning to remote work, the design thinking methodology encourages them to consider how to increase employee engagement rather than focus on the problem (decreasing productivity).

The essence of design thinking is human-centric and user-specific. It’s about the person behind the problem and solution, and requires asking questions such as “Who will be using this product?” and “How will this solution impact the user?”

The first, and arguably most important, step of design thinking is building empathy with users. By understanding the person affected by a problem, you can find a more impactful solution. On top of empathy, design thinking is centered on observing product interaction, drawing conclusions based on research, and ensuring the user remains the focus of the final implementation.

The Four Phases of Innovation

So, what does design thinking entail? There are many models of design thinking that range from three to seven steps.

In the online course Design Thinking and Innovation , Harvard Business School Dean Srikant Datar leverages a four-phase innovation framework. The phases venture from concrete to abstract thinking and back again as the process loops, reverses, and repeats. This is an important balance because abstract thinking increases the likelihood that an idea will be novel. It’s essential, however, to anchor abstract ideas in concrete thinking to ensure the solution is valid and useful.

Here are the four phases for effective innovation and, by extension, design thinking.

The first phase is about narrowing down the focus of the design thinking process. It involves identifying the problem statement to come up with the best outcome. This is done through observation and taking the time to determine the problem and the roadblocks that prevented a solution in the past.

Various tools and frameworks are available—and often needed—to make concrete observations about users and facts gathered through research. Regardless of which tools are implemented, the key is to observe without assumptions or biased expectations.

Once findings from your observations are collected, the next step is to shape insights by framing those observations. This is where you can venture into the abstract by reframing the problem in the form of a statement or question.

Once the problem statement or question has been solidified—not finalized—the next step is ideation. You can use a tool such as systematic inventive thinking (SIT) in this stage, which is useful for creating an innovative process that can be replicated in the future.

The goal is to ultimately overcome cognitive fixedness and devise new and innovative ideas that solve the problems you identified. Continue to actively avoid assumptions and keep the user at the forefront of your mind during ideation sessions.

The third phase involves developing concepts by critiquing a range of possible solutions. This includes multiple rounds of prototyping, testing, and experimenting to answer critical questions about a concept’s viability.

Remember: This step isn’t about perfection, but rather, experimenting with different ideas and seeing which parts work and which don’t.

4. Implement

The fourth and final phase, implementation, is when the entire process comes together. As an extension of the develop phase, implementation starts with testing, reflecting on results, reiterating, and testing again. This may require going back to a prior phase to iterate and refine until you find a successful solution. Such an approach is recommended because design thinking is often a nonlinear, iterative process.

In this phase, don’t forget to share results with stakeholders and reflect on the innovation management strategies implemented during the design thinking process. Learning from experience is an innovation process and design thinking project all its own.

Check out the video about the design thinking process below, and subscribe to our YouTube channel for more explainer content!

Why Design Thinking Skills Matter

The main value of design thinking is that it offers a defined process for innovation. While trial and error is a good way to test and experiment what works and what doesn’t, it’s often time-consuming, expensive, and ultimately ineffective. On the other hand, following the concrete steps of design thinking is an efficient way to develop new, innovative solutions.

On top of a clear, defined process that enables strategic innovation, design thinking can have immensely positive outcomes for your career—in terms of both advancement and salary.

As of December 2021, the most common occupations requiring design thinking skills were:

• Marketing managers
• Industrial engineers
• Graphic designers
• Software developers
• General and operations managers
• Management analysts
• Personal service managers
• Architectural and engineering managers
• Computer and information systems managers

In addition, jobs that require design thinking statistically have higher salaries. Take a marketing manager position, for example. The median annual salary is $107,900. Marketing manager job postings that require design thinking skills, however, have a median annual salary of $133,900—a 24 percent increase.

Overall, businesses are looking for talent with design thinking skills. As of November 2021, there were 29,648 job postings in the United States advertising design thinking as a necessary skill—a 153 percent increase from November 2020, and a 637 percent increase from November 2017.

As businesses continue to recognize the need for design thinking and innovation, they’ll likely create more demand for employees with those skills.

Learning Design Thinking

Design thinking is an extension of innovation that allows you to design solutions for end users with a single problem statement in mind. It not only imparts valuable skills but can help advance your career.

It’s also a collaborative endeavor that can only be mastered through practice with peers. As Datar says in the introduction to Design Thinking and Innovation : “Just as with learning how to swim, the best way to practice is to jump in and try.”

If you want to learn design thinking, take an active role in your education. Start polls, problem-solving exercises, and debates with peers to get a taste of the process. It’s also important to seek out diverse viewpoints to prepare yourself for the business world.

In addition, if you’re considering adding design thinking to your skill set, think about your goals and why you want to learn about it. What else might you need to be successful?

You might consider developing your communication, innovation, leadership, research, and management skills, as those are often listed alongside design thinking in job postings and professional profiles.

You may also notice skills like agile methodology, user experience, and prototyping in job postings, along with non-design skills, such as product management, strategic planning, and new product development.

Is Design Thinking Right for You?

There are many ways to approach problem-solving and innovation. Design thinking is just one of them. While it’s beneficial to learn how others have approached problems and evaluate if you have the same tools at your disposal, it can be more important to chart your own course to deliver what users and customers truly need.

You can also pursue an online course or workshop that dives deeper into design thinking methodology. This can be a practical path if you want to improve your design thinking skills or require a more collaborative environment.

Are you ready to develop your design thinking skills? Explore our online course Design Thinking and Innovation to discover how to leverage fundamental design thinking principles and innovative problem-solving tools to address business challenges.

About the Author

What is design thinking?

Design and conquer: in years past, the word “design” might have conjured images of expensive handbags or glossy coffee table books. Now, your mind might go straight to business. Design and design thinking are buzzing in the business community more than ever. Until now, design has focused largely on how something looks; these days, it’s a dynamic idea used to describe how organizations can adjust their problem-solving approaches to respond to rapidly changing environments—and create maximum impact and shareholder value. Design is a journey and a destination. Design thinking is a core way of starting the journey and arriving at the right destination at the right time.

Simply put, “design thinking is a methodology that we use to solve complex problems , and it’s a way of using systemic reasoning and intuition to explore ideal future states,” says McKinsey partner Jennifer Kilian. Design thinking, she continues, is “the single biggest competitive advantage that you can have, if your customers are loyal to you—because if you solve for their needs first, you’ll always win.”

Get to know and directly engage with senior McKinsey experts on design thinking

Tjark Freundt is a senior partner in McKinsey’s Hamburg office, Tomas Nauclér is a senior partner in the Stockholm office, Daniel Swan is a senior partner in the Stamford office, Warren Teichner is a senior partner in the New York office, Bill Wiseman is a senior partner in the Seattle office, and Kai Vollhardt is a senior partner in the Munich office.

And good design is good business. Kilian’s claim is backed up with data: McKinsey Design’s 2018 Business value of design report  found that the best design performers increase their revenues  and investor returns at nearly twice the rate of their industry competitors. What’s more, over a ten-year period, design-led companies outperformed  the S&P 500 by 219 percent.

As you may have guessed by now, design thinking goes way beyond just the way something looks. And incorporating design thinking into your business is more than just creating a design studio and hiring designers. Design thinking means fundamentally changing how you develop your products, services, and, indeed, your organization itself.

Read on for a deep dive into the theory and practice of design thinking.

Learn more about McKinsey’s Design Practice , and check out McKinsey’s latest Business value of design report here .

How do companies build a design-driven company culture?

There’s more to succeeding in business than developing a great product or service that generates a financial return. Empathy and purpose are core business needs. Design thinking means putting customers, employees, and the planet at the center of problem solving.

McKinsey’s Design Practice has learned that design-led organizations start with design-driven cultures. Here are four steps  to building success through the power of design:

Understand your audience. Design-driven companies go beyond asking what customers and employees want, to truly understanding why they want it. Frequently, design-driven companies will turn to cultural anthropologists and ethnographers to drill down into how their customers use and experience products, including what motivates them and what turns them away.

Makeup retailer Sephora provides an example. When marketing leaders actually watched  shoppers using the Sephora website, they realized customers would frequently go to YouTube to watch videos of people using products before making a purchase. Using this information, the cosmetics retailer developed its own line of demonstration videos, keeping shoppers on the site and therefore more likely to make a purchase.

• Bring design to the executive table. This leader can be a chief design officer, a chief digital officer, or a chief marketing officer. Overall, this executive should be the best advocate for the company’s customers and employees, bringing the point of view of the people, the planet, and the company’s purpose into strategic business decisions. The design lead should also build bridges between multiple functions and stakeholders, bringing various groups into the design iteration process.
• Design in real time. To understand how and why people—both customers and employees—use processes, products, or services, organizations should develop a three-pronged design-thinking model that combines design, business strategy, and technology. This approach allows business leaders to spot trends, cocreate using feedback and data, prototype, validate, and build governance models for ongoing investment.

Act quickly. Good design depends on agility. That means getting a product to users quickly, then iterating based on customer feedback. In a design-driven culture, companies aren’t afraid to release products that aren’t quite perfect. Designers know there is no end to the design process. The power of design, instead, lies in the ability to adopt and adapt as needs change. When designers are embedded within teams, they are uniquely positioned to gather and digest feedback, which can lead to unexpected revelations. Ultimately, this approach creates more impactful and profitable results than following a prescribed path.

Consider Instagram. Having launched an initial product in 2010, Instagram’s founders paid attention to what the most popular features were: image sharing, commenting, and liking. They relaunched with a stripped-down version a few months later, resulting in 100,000 downloads in less than a week and over two million users in under two months —all without any strategic promotion.

Learn more about McKinsey’s Design Practice .

What’s the relationship between user-centered design and design thinking?

Both processes are design led. And they both emphasize listening to and deeply understanding users and continually gathering and implementing feedback to develop, refine, and improve a service.

Where they are different is scale. User-centered design focuses on improving a specific product or service . Design thinking takes a broader view  as a way to creatively address complex problems—whether for a start-up, a large organization, or society as a whole.

User-centered design is great for developing a fantastic product or service. In the past, a company could coast on a superior process or product for years before competitors caught up. But now, as digitization drives more frequent and faster disruptions, users demand a dynamic mix of product and service. Emphasis has shifted firmly away  from features and functions toward purpose, lifestyle, and simplicity of use.

Looking for direct answers to other complex questions?

McKinsey analysis has found that some industries—such as telecommunications, automotive, and consumer product companies— have already made strides toward combining product and service into a unified customer experience . Read on for concrete examples of how companies have applied design thinking to offer innovative—and lucrative—customer experiences.

Learn more about our Operations Practice .

What is the design-thinking process?

McKinsey analysis has shown that the design-thinking approach creates more value  than conventional approaches. The right design at the right price point spurs sustainability and resilience in a demonstrable way—a key driver of growth.

According to McKinsey’s Design  Practice, there are two key steps to the design-thinking process:

• Developing an understanding of behavior and needs that goes beyond what people are doing right now to what they will need in the future and how to deliver that. The best way to develop this understanding is to spend time with people.
• “Concepting,” iterating, and testing . First start with pen and paper, sketching out concepts. Then quickly put these into rough prototypes—with an emphasis on quickly. Get feedback, refine, and test again. As American chemist Linus Pauling said : “The way to get good ideas is to get lots of ideas and throw the bad ones away.”

What is D4VG versus DTV?

For more than a decade, manufacturers have used a design-to-value (DTV) model  to design and release products that have the features needed to be competitive at a low cost. During this time, DTV efforts were groundbreaking because they were based on data rather than experience. They also reached across functions, in contrast to the typical value-engineering approach.

The principles of DTV have evolved into design for value and growth (D4VG), a new way of creating products that provide exceptional customer experiences while driving both value and growth. Done right, D4VG efforts generate products with the features, form, and functionality that turn users into loyal fans .

D4VG products can cost more to build, but they can ultimately raise margins by delivering on a clear understanding of a product’s core brand attributes, insights into people’s motivations, and design thinking.

Learn more about our Consumer Packaged Goods Practice .

What is design for sustainability?

As consumers, companies, and regulators shift toward increased sustainability, design processes are coming under even more scrutiny. The challenge is that carbon-efficient production processes tend to be more complex and can require more carbon-intensive materials. The good news is that an increased focus on design for sustainability (DFS), especially at the research and development stage , can help mitigate some of these inefficiencies and ultimately create even more sustainable products.

For example, the transition from internal-combustion engines to electric-propulsion vehicles  has highlighted emissions-intensive automobile production processes. One study found that around 20 percent of the carbon generated by a diesel vehicle comes from its production . If the vehicle ran on only renewable energy, production emissions would account for 85 percent of the total. With more sustainable design, electric-vehicle (EV) manufacturers stand to reduce the lifetime emissions of their products significantly.

To achieve design for sustainability at scale, companies can address three interrelated elements at the R&D stage:

• rethinking the way their products use resources, adapting them to changing regulations, adopting principles of circularity, and making use of customer insights
• understanding and tracking emissions and cost impact of design decisions in support of sustainability goals
• fostering the right mindsets and capabilities to integrate sustainability into every product and design decision

What is ‘skinny design’?

Skinny design is a less theoretical aspect of design thinking. It’s a method whereby consumer goods companies reassess the overall box size of products by reducing the total cubic volume of the package. According to McKinsey analysis , this can improve overall business performance in the following ways:

• Top-line growth of 4 to 5 percent through improvements in shelf and warehouse holding power. The ability to fit more stock into warehouses ultimately translates to growth.
• Bottom-line growth of more than 10 percent . Packing more product into containers and trucks creates the largest savings. Other cost reductions can come from designing packaging to minimize the labor required and facilitate automation.
• Sustainability improvements associated with reductions in carbon emissions through less diesel fuel burned per unit. Material choices can also confer improvements to the overall footprint.

Read more about skinny design and how it can help maximize the volume of consumer products that make it onto shelves.

Learn more about McKinsey’s Operations Practice .

How can a company become a top design performer?

The average person’s standard for design is higher than ever. Good design is no longer just a nice-to-have for a company. Customers now have extremely high expectations for design, whether it’s customer service, instant access to information, or clever products that are also aesthetically relevant in the current culture.

McKinsey tracked the design practices of 300 publicly listed companies  over a five-year period in multiple countries. Advanced regression analysis of more than two million pieces of financial data and more than 100,000 design actions revealed 12 actions most correlated to improved financial performance. These were then clustered into the following four themes:

• Analytical leadership . For the best financial performers, design is a top management issue , and design performance is assessed with the same rigor these companies use to approach revenue and cost. The companies with the top financial returns have combined design and business leadership through bold, design-centric visions. These include a commitment to maintain a baseline level of customer understanding among all executives. The CEO of one of the world’s largest banks, for example, spends one day a month with the bank’s clients and encourages all members of the company’s C-suite to do the same.
• Cross-functional talent . Top-performing companies make user-centric design everyone’s responsibility, not a siloed function. Companies whose designers are embedded within cross-functional teams have better overall business performance . Further, the alignment of design metrics with functional business metrics (such as financial performance, user adoption rates, and satisfaction results) is also correlated to better business performance.
• Design with people, not for people . Design flourishes best, according to our research, in environments that encourage learning, testing, and iterating with users . These practices increase the odds of creating breakthrough products and services, while at the same time reducing the risk of costly missteps.
• User experience (UX) . Top-quartile companies embrace the full user experience  by taking a broad-based view of where design can make a difference. Design approaches like mapping customer journeys can lead to more inclusive and sustainable solutions.

What are some real-world examples of how design thinking can improve efficiency and user experience?

Understanding the theory of design thinking is one thing. Seeing it work in practice is something else. Here are some examples of how elegant design created value for customers, a company, and shareholders:

• Stockholm’s international airport, Arlanda, used design thinking to address its air-traffic-control problem. The goal was to create a system that would make air traffic safer and more effective. By understanding the tasks and challenges of the air-traffic controllers, then collaboratively working on prototypes and iterating based on feedback, a working group was able to design a new departure-sequencing tool  that helped air-traffic controllers do their jobs better. The new system greatly reduced the amount of time planes spent between leaving the terminal and being in the air, which in turn helped reduce fuel consumption.
• When Tesla creates its electric vehicles , the company closely considers not only aesthetics but also the overall driving experience .
• The consumer electronics industry has a long history of dramatic evolutions lead by design thinking. Since Apple debuted the iPhone in 2007, for example, each new generation has seen additional features, new customers, and lower costs—all driven by design-led value creation .

Learn more about our Consumer Packaged Goods  and Sustainability  Practices.

For a more in-depth exploration of these topics, see McKinsey’s Agile Organizations collection. Learn more about our Design Practice —and check out design-thinking-related job opportunities if you’re interested in working at McKinsey.

Articles referenced:

• “ Skinny design: Smaller is better ,” April 26, 2022, Dave Fedewa , Daniel Swan , Warren Teichner , and Bill Wiseman
• “ Product sustainability: Back to the drawing board ,” February 7, 2022, Stephan Fuchs, Stephan Mohr , Malin Orebäck, and Jan Rys
• “ Emerging from COVID-19: Australians embrace their values ,” May 11, 2020, Lloyd Colling, Rod Farmer , Jenny Child, Dan Feldman, and Jean-Baptiste Coumau
• “ The business value of design ,” McKinsey Quarterly , October 25, 2018, Benedict Sheppard , Hugo Sarrazin, Garen Kouyoumjian, and Fabricio Dore
• “ More than a feeling: Ten design practices to deliver business value ,” December 8, 2017, Benedict Sheppard , John Edson, and Garen Kouyoumjian
• “ Creating value through sustainable design ,” July 25, 2017, Sara Andersson, David Crafoord, and Tomas Nauclér
• “ The expanding role of design in creating an end-to-end customer experience ,” June 6, 2017, Raffaele Breschi, Tjark Freundt , Malin Orebäck, and Kai Vollhardt
• “ Design for value and growth in a new world ,” April 13, 2017, Ankur Agrawal , Mark Dziersk, Dave Subburaj, and Kieran West
• “ The power of design thinking ,” March 1, 2016, Jennifer Kilian , Hugo Sarrazin, and Barr Seitz
• “ Building a design-driven culture ,” September 1, 2015, Jennifer Kilian , Hugo Sarrazin, and Hyo Yeon

Want to know more about design thinking?

Related articles.

Skinny design: Smaller is better

The business value of design

More than a feeling: Ten design practices to deliver business value

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• Knowledge Base
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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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