Design Thinking is an approach to discovering problems and opportunities by understanding people’s behavior and experiences, generating potential solutions, and learning through repeated prototyping and testing. It combines a range of methods for developing and improving products, services, business processes, and organizational systems.
The word design may suggest the creation of attractive products or visual appearances. However, Design Thinking is not limited to aesthetics. It can be applied to product usability, service improvement, business process redesign, organizational development, and the exploration of new business opportunities.
Consider a company developing a new business application. In a conventional development process, managers might define the required functions and ask engineers to build the system accordingly.
However, observing actual users may reveal activities that managers had not anticipated, including exceptional procedures and informal workarounds that are not documented in official workflows.
By investigating users’ behavior and experiences, the development team may discover problems that were not included in the original requirements.
The distinctive feature of Design Thinking is not simply asking users what they want and then providing it. Rather, it seeks to discover problems and opportunities that users themselves may not have clearly recognized, and to deepen understanding by testing possible solutions in concrete forms.
However, Design Thinking does not guarantee innovative products or successful solutions. Observation, interpretation, ideation, prototyping, and testing all have limitations. Appropriate technical expertise and other forms of investigation remain essential.
Is Design Thinking a Single Method?
Design Thinking is not a single technique like Brainstorming or a Cause-and-Effect Diagram. It is a broader approach combining human-centered design, observational research, problem structuring, idea generation, prototyping, and evaluation.
The study of how designers approach problems has a long history.
In The Sciences of the Artificial (1969), Herbert A. Simon examined design as an activity concerned with transforming existing situations into preferred ones.
Richard Buchanan’s influential 1992 article, “Wicked Problems in Design Thinking,” explored the relationship between design and complex problems whose definitions are not clearly established.
Alongside these academic developments, practitioners increasingly applied methods associated with professional design to product development, business innovation, and organizational challenges.
David Kelley and Tim Brown of IDEO played important roles in popularizing Design Thinking. Stanford University’s d.school, formally the Hasso Plattner Institute of Design, also contributed significantly to its teaching and dissemination.
However, Design Thinking should not be understood as a method invented by a single individual. It has developed through interactions among design practice, design research, cognitive science, social research, management, and related fields.
Nor is there one universally accepted Design Thinking procedure. Representative models share common characteristics, but they differ in how they organize activities and describe the design process.
The Five Activities of Design Thinking
One widely recognized explanation of Design Thinking is the five-mode framework associated with Stanford’s d.school.
| Activity | Main Purpose | Typical Methods |
|---|---|---|
| Empathize | Understand users’ experiences and circumstances | Observation, interviews, contextual research |
| Define | Clarify the problem worth addressing | Synthesizing research findings, reframing problems |
| Ideate | Explore alternative solutions | Brainstorming, analogy, combining ideas |
| Prototype | Represent ideas concretely | Physical models, interface mockups, service scenarios |
| Test | Learn from user behavior and responses | Usability testing, trials, observation, comparison |
These categories are useful for understanding the activities involved in Design Thinking.
However, actual projects do not necessarily follow this sequence.
Testing a prototype may reveal that the original problem definition was inappropriate. Developing an idea may expose gaps in the team’s understanding and lead to further user research.
The d.school also emphasizes that the five modes should not be treated as a single fixed design process.
What matters is not completing activities in a prescribed order but using what is learned from each activity to improve subsequent decisions.
1. Empathize — Understanding Users’ Experiences
Design Thinking emphasizes understanding problems from the perspectives of the people who experience them.
For example, when users are asked, “What features do you need?”, their answers may reflect existing products and familiar ways of working.
However, people may have become accustomed to inconveniences and no longer consciously recognize them as problems.
They may also propose features that would not necessarily solve their underlying difficulties.
Observing what people actually do can therefore reveal problems that interviews alone might miss.
Suppose an employee using a business application repeatedly copies information displayed on a screen onto paper.
The employee may regard this as a normal part of the job.
An observer, however, might hypothesize that the employee needs to copy the information because the application cannot display several relevant pieces of information simultaneously.
Such observations can lead to problem definitions that differ considerably from the user’s initial requests.
Empathy Is Not the Same as Accurate Observation
In Design Thinking, empathy refers to an effort to understand the circumstances and experiences of other people.
However, empathizing with someone does not automatically produce an accurate understanding of their situation.
For example, suppose an older user struggles to operate a device.
Concluding that “older people are not comfortable with digital technology” is an interpretation, not an observation.
The actual problem might involve unclear screen labels, complicated procedures, missing information, or entirely different factors.
It is therefore important to distinguish observed facts from hypotheses intended to explain those facts.
Rather than automatically treating users’ statements as definitive answers, designers should examine behavior, operating circumstances, and relevant constraints while reconsidering their interpretations.
Representative Research Methods
Several methods can be used to understand users, including interviews, field observations, contextual inquiry, and diary studies.
Interviews can reveal users’ experiences, opinions, and explanations.
Field observations may uncover habitual actions, operational constraints, or informal procedures that users do not consciously recognize or mention.
Customer Journey Maps can also be used to organize the sequence of interactions between users and products or services.
For example, a journey might include information gathering before purchase, inquiries, purchasing, implementation, daily use, and support during problems.
The purpose is to examine what happens at each stage and identify possible difficulties.
However, findings from a small number of interviews or observations do not necessarily apply to all users.
Researchers should consider how participants were selected and whether important user groups or situations remain unobserved.
2. Define — Reframing the Problem
After collecting information, the next task is to clarify which problem should be addressed.
This activity is central to Design Thinking.
Suppose a development team’s original assignment is to improve the usability of a product’s operating interface.
User research may reveal, however, that the most significant difficulty is not routine operation but the inability to locate necessary information when an unexpected problem occurs.
The problem might then be reframed from:
“Improve the operating interface.”
to:
“Enable users to find the information they need and make appropriate decisions quickly when abnormal conditions occur.”
Changing the problem definition changes the range of solutions worth considering.
Instead of focusing only on larger buttons or simpler menus, the team might explore context-sensitive information displays, guided procedures, or improved access to troubleshooting information.
How Might We Questions
Design Thinking frequently uses questions beginning with “How Might We …?” to express problems as opportunities for exploration.
For example:
“How might we help maintenance technicians identify the information they need quickly when equipment abnormalities occur?”
This type of question avoids prescribing a particular solution in advance.
If the assignment were instead defined as “Develop a smartphone application for maintenance technicians,” solutions that do not involve a smartphone application might be excluded.
A broader question allows the team to consider changes to existing equipment displays, operating procedures, information management, and other possibilities.
However, a problem statement should not be so broad that it becomes impossible to investigate.
The intended users, operating circumstances, and desired outcomes must still be sufficiently clear.
Effective problem framing involves adjusting the scope of the question as understanding develops.
3. Ideate — Expanding the Range of Possible Solutions
Once a problem has been formulated, the next activity is to explore possible solutions.
Rather than immediately selecting the first plausible idea, the team considers alternatives from different perspectives.
Techniques such as Brainstorming, SCAMPER, analogy, and Morphological Analysis can support this activity.
For example, to improve access to information during equipment failures, a team might consider:
- Automatically displaying relevant information according to the type of abnormality.
- Linking equipment alarms directly to appropriate maintenance procedures.
- Connecting current equipment conditions with historical failure records.
- Providing voice-based access to operating instructions.
- Reconsidering which information maintenance technicians actually need.
However, generating a large number of ideas does not guarantee that a valuable solution will emerge.
Candidates must eventually be evaluated in terms of user benefits, technical feasibility, implementation costs, operational constraints, and other relevant considerations.
Divergent and Convergent Thinking
Design Thinking often emphasizes alternating between divergent and convergent thinking.
Divergent thinking expands the range of possible problem definitions or solutions.
Convergent thinking compares, organizes, and selects candidates for further consideration.
However, selecting the most visually attractive idea or the one receiving the most votes does not necessarily lead to the best outcome.
A technologically appealing solution may be incompatible with safety requirements.
Conversely, a modest change to an operating procedure may be more effective than an expensive technological solution.
Expanding the range of possibilities and evaluating those possibilities are different activities. Both require appropriate methods and judgment.
4. Prototype — Making Ideas Concrete to Learn from Them
Prototyping involves representing an idea in a concrete form so that its meaning, usability, or feasibility can be examined.
A prototype does not necessarily need to be a functioning product.
Paper interface sketches, simple physical models, service scenarios, and simulated business procedures can all serve as prototypes.
For example, when evaluating a new business application interface, developers do not necessarily need to build a functioning application immediately.
They might begin with paper sketches and ask prospective users to perform representative tasks.
This can reveal whether users understand the interface, can locate necessary information, and can follow the intended operating sequence.
The value of prototyping is not merely that it allows a near-final product to be produced quickly.
Its greater value may lie in revealing problems that were not recognized before an idea was tested concretely.
A Prototype Is a Tool for Investigating Hypotheses
Suppose the team proposes the following hypothesis:
“Displaying all relevant information on one screen will reduce the time users need to locate information during equipment abnormalities.”
A prototype can be used to investigate this hypothesis.
Users might perform representative tasks while researchers measure the time required to find information or the frequency of incorrect selections.
The results may reveal that placing too much information on one screen actually makes important details harder to find.
In that case, the team should reconsider the screen design or the underlying hypothesis.
Prototyping is not intended simply to demonstrate that a proposed idea is correct.
A prototype is a tool for asking new questions about users, operating conditions, and the constraints of a proposed solution.
5. Test — Learning from User Behavior and Results
Testing involves allowing users to interact with prototypes and examining the outcomes.
For example, when evaluating a new interface, merely asking users whether they find it easy to use may provide insufficient information.
It may be more useful to ask users to perform realistic tasks and observe whether they succeed, where they hesitate, and what kinds of errors occur.
Users may report that an interface is easy to use even when it takes them a considerable amount of time to complete tasks.
Subjective assessments and observed behavior should therefore be distinguished.
Qualitative and Quantitative Testing
During early prototyping, detailed observations of a small number of users may reveal important problems.
However, quantitative measurements may be needed to determine how widely those problems occur or how much a proposed change improves performance.
Measures might include task completion time, task success rate, error frequency, and the number of support inquiries.
Nevertheless, findings from a small usability test cannot automatically be generalized to all potential users.
Differences in experience, operating environments, tasks, and user characteristics must be considered.
Testing may also reveal that the original problem definition or understanding of the users needs to be revised.
This is why Design Thinking is better understood as an iterative approach than as a linear sequence ending with testing.
The Double Diamond — Exploring Both Problems and Solutions
Another widely recognized model associated with Design Thinking is the Double Diamond, developed by the UK’s Design Council.
The model represents two broad areas of exploration: understanding the problem and developing the solution.
Each involves divergence and convergence.
| Stage | Role | Main Activities |
|---|---|---|
| Discover | Explore possible problems broadly | Research, observation, information gathering |
| Define | Identify the problem to address | Synthesis, problem framing |
| Develop | Explore alternative solutions | Ideation, prototyping, comparison, improvement |
| Deliver | Develop and implement an appropriate solution | Testing, selection, implementation, evaluation |
The first diamond represents the process of questioning and expanding the original understanding of a problem before identifying an appropriate focus.
The second diamond represents the exploration, development, and evaluation of potential solutions.
This model illustrates that both problem definition and solution development require divergent and convergent thinking.
However, the Double Diamond does not prescribe a sequence that every project must follow without deviation.
For example, developing a prototype may reveal that the problem was originally defined incorrectly, requiring a return to earlier investigation.
The Design Council’s broader Framework for Innovation also emphasizes principles including human-centered thinking, collaboration, communication, and iteration.
Three Perspectives for Evaluating Design Solutions
IDEO has emphasized the importance of combining desirability, feasibility, and viability when developing solutions.
| Perspective | Main Question |
|---|---|
| Desirability | Does the solution address an important problem or need for people? |
| Feasibility | Can the solution be realized with the available or developable technologies and capabilities? |
| Viability | Can the solution be sustained under relevant economic, organizational, and operational conditions? |
For example, users may strongly desire a function that cannot currently be implemented with available technology.
Alternatively, a technically feasible solution may be too expensive to operate sustainably.
A product with attractive business potential may also fail if it does not address a problem that users consider important.
Although Design Thinking emphasizes human-centered inquiry, successful development cannot be evaluated through user preferences alone.
Furthermore, these three perspectives are not entirely independent.
Technological limitations may affect how users behave, while changes in usage patterns may alter operating costs and business feasibility.
Practical Example — Improving Responses to Manufacturing Equipment Failures
Consider a hypothetical application of Design Thinking to the development of an equipment maintenance support system.
At a manufacturing plant, maintenance technicians require considerable time to investigate equipment abnormalities and restore normal operation.
Management initially assumes that the maintenance application’s interface is difficult to use.
Investigating Users
Observation of maintenance work reveals that technicians consult not only the application’s operating screens but also paper manuals, historical failure reports, and separate information systems.
Experienced technicians can often determine which information is relevant from their knowledge of equipment behavior.
Less experienced technicians, however, may struggle to identify which records should be examined.
These observations suggest that the problem may involve more than interface usability.
Reframing the Problem
The team reformulates the problem as follows:
“When equipment abnormalities occur, maintenance technicians cannot quickly connect current equipment conditions with relevant historical information and troubleshooting procedures.”
This definition suggests possible solutions beyond simply improving existing screens.
Candidates might include context-sensitive information displays, improved retrieval of historical failure records, or assistance with identifying appropriate maintenance procedures.
Generating Solutions
The team explores several approaches.
One involves linking alarm codes to relevant technical documents.
Another uses equipment conditions to identify historical cases with similar characteristics.
A third provides guidance for checking and recording troubleshooting steps.
The team compares different approaches rather than committing immediately to one solution.
Learning Through Prototypes
A simple interface prototype is developed, and maintenance technicians perform simulated troubleshooting tasks.
The prototype may make relevant information easier to find.
However, testing might reveal that displaying too much information at once increases the risk of overlooking important alarms.
The team may also discover that some forms of automated guidance help inexperienced technicians but provide little benefit to experienced users.
These findings can lead to changes in the interface, the target user groups, or the underlying problem definition.
Quantitative Evaluation
The team can measure task completion time, the time required to locate necessary information, the frequency of consulting incorrect documents, and the success rate of simulated maintenance tasks.
Alternative interfaces can then be compared.
However, if the system will be used in safety-critical operations, successful simulated tests are not sufficient to justify immediate deployment.
The system must also be examined for possible failures, missing information, and hazardous operating conditions.
This example illustrates how user observations can lead to a revised problem definition, how prototypes can support hypothesis testing, and how quantitative analysis can contribute to evaluating improvements.
The Relationship Between Problem Structuring and Design Thinking
Design Thinking is closely related to Problem Structuring.
Both approaches recognize that the initially stated problem may not be the problem that should ultimately be addressed.
However, their areas of emphasis differ.
Problem Structuring focuses on clarifying the components of a problem, relationships among factors, stakeholder perspectives, objectives, constraints, and possible causal structures.
Design Thinking places particular emphasis on understanding users’ behavior and experiences, reframing problems, generating possible solutions, and learning through prototyping.
Design Thinking can therefore be understood as an approach that includes elements of problem structuring while extending into solution generation and implementation.
However, not all forms of Problem Structuring fall within Design Thinking.
For example, problems involving equipment reliability or complex production planning may depend primarily on physical constraints and mathematical models rather than user interviews.
Similarly, public policy and organizational problems may require consideration of competing interests and values among multiple stakeholder groups, rather than the perspective of a single category of users.
The appropriate approach depends on the problem.
In many situations, Problem Structuring and Design Thinking can be used together.
Design Thinking and the Integration of Qualitative and Quantitative Analysis
Design Thinking is often associated with qualitative methods such as interviews and observation.
However, it is not inherently opposed to quantitative analysis.
For example, detailed observation may reveal a previously unrecognized user problem.
Usage logs or surveys can then help estimate how frequently that problem occurs across a broader population.
Similarly, several interface prototypes can be developed from qualitative research findings and evaluated quantitatively using task completion times or error rates.
Design of Experiments (DOE) may also be used to investigate how interface components or information presentation methods affect user behavior.
However, improved numerical performance does not necessarily mean that users’ actual needs have been better addressed.
For example, an interface change might reduce task completion time by eliminating a verification step, while also increasing the risk of serious errors.
A single performance indicator should not be treated as a complete measure of success.
The intended purpose, actual operating conditions, and possible consequences of the intervention must also be considered.
Design Thinking can therefore involve not only movement from qualitative understanding to quantitative testing but also the revision of problem definitions in response to measured results.
Design Thinking and Creative Thinking
Design Thinking connects problem discovery with the generation of possible solutions.
However, it does not provide a unique or exclusive principle for creative ideation.
Brainstorming, analogy, SCAMPER, TRIZ, and other techniques can be incorporated into Design Thinking projects.
For example, user observations may reveal a need to support decisions that were previously made entirely by human operators.
This discovery could lead to an exploration of automated decision-support technologies used in other industries.
In such a case, user research provides an opportunity to identify the problem, but developing a technically innovative solution requires engineering knowledge and potentially cross-domain exploration.
Conversely, technological discoveries may precede the identification of particular applications.
A new material, sensing method, or computational technology may enable products and services that users had not previously imagined.
Such technology-driven innovation does not necessarily begin with user research.
Design Thinking provides a valuable human-centered perspective on problem discovery, but it is not the only possible starting point for innovation.
Limitations and Criticisms of Design Thinking
Design Thinking has become widely used in product and service development. However, questions remain about its definitions, effectiveness, and appropriate scope.
For example, Lucy Kimbell’s 2011 article, “Rethinking Design Thinking,” critically examines attempts to describe Design Thinking as a universal method while paying insufficient attention to the diversity of actual design practices and their social contexts.
Furthermore, because researchers and practitioners define and implement Design Thinking in different ways, it can be difficult to determine which aspects of the approach produce particular outcomes.
Several limitations deserve attention.
Empathy Does Not Guarantee Accurate Understanding
Empathy can encourage attention to users’ experiences, but it does not eliminate errors of observation or interpretation.
Researchers may project their assumptions onto users or overlook relevant conditions.
It is therefore important to distinguish facts from interpretations and conduct additional investigations when necessary.
Users’ Requests Are Not Necessarily Appropriate Solutions
Users can provide valuable information about the problems they experience.
However, they may not possess the technical expertise needed to identify the best solutions.
For example, a user may request additional buttons, while simplifying the overall operating procedure might be more effective.
Respecting users’ experiences is different from translating every user request directly into a design requirement.
Small Studies Do Not Necessarily Represent All Users
Interviews and usability testing with a small number of participants can reveal specific problems.
However, those problems may not occur in the same way across the entire user population.
Usage frequency, experience, operating environment, and cultural background may influence user behavior.
Generalizing findings may require additional sampling, quantitative research, or testing across different contexts.
Short Workshops Cannot Necessarily Solve Complex Problems
Design Thinking is sometimes practiced through short workshops.
However, completing observation, problem definition, ideation, prototyping, and testing activities within a limited period does not mean that the problem has been adequately understood.
Following a prescribed sequence is not the same as conducting a rigorous investigation.
A workshop can also create an impression of progress even when the resulting ideas have not been critically examined.
Technical and Economic Feasibility Require Separate Evaluation
An idea may be desirable to users but technically infeasible.
Even a technically feasible solution may encounter constraints involving cost, regulation, safety, maintenance, or organizational capabilities.
In manufacturing, healthcare, transportation, and infrastructure, user satisfaction alone cannot justify implementation.
Appropriate engineering verification and safety evaluation are essential.
User Interests Do Not Always Align with Broader Social Interests
The expression human-centered raises the question of which people are being considered.
A service that is convenient for its direct users may increase the workload of employees responsible for delivering it.
Likewise, a product that satisfies a particular customer group may create environmental burdens or negative consequences for other stakeholders.
Design decisions should therefore consider not only immediate users but also other people and systems affected by the proposed solution.
How Should the Effectiveness of Design Thinking Be Evaluated?
When evaluating Design Thinking, the number of ideas generated or the satisfaction of workshop participants is not sufficient evidence of effectiveness.
Different kinds of outcomes should be examined separately.
| Evaluation Area | Possible Evaluation Methods |
|---|---|
| Improved problem understanding | Identification of previously unrecognized needs, problems, or constraints |
| Quality of proposed solutions | Relevance to important user problems |
| Usability | Task success rates, completion times, error rates |
| Technical feasibility | Prototyping, engineering tests, design verification |
| Business outcomes | Adoption, continued use, costs, revenue |
| Organizational outcomes | Cross-functional understanding, decision quality, development progress |
These outcomes are related but not interchangeable.
For example, a deeper understanding of user problems does not necessarily lead to increased product sales.
Likewise, an increase in sales does not establish that Design Thinking caused the improvement. Market conditions, sales initiatives, and other factors may also have contributed.
Research evaluating Design Thinking continues to develop, but differences in definitions, implementation methods, and outcome measures make universal claims of effectiveness difficult to justify.
Design Thinking in the AI Era
Large language models and other AI technologies can support many activities associated with Design Thinking.
For example, AI systems can extract recurring concepts and characteristics from interview transcripts and open-ended responses.
They can help classify research findings and generate initial drafts of Customer Journey Maps or problem structures.
AI can also suggest alternative problem definitions, generate potential solutions, and assist with interface prototyping.
However, automating Design Thinking activities does not automatically produce a correct understanding of users.
Suppose an organization asks an LLM to generate “typical customer problems” without conducting actual user research.
The resulting descriptions may be plausible and consistent with general patterns, but there is no guarantee that they accurately represent the intended users or product.
The same caution applies to AI-generated personas.
A persona is a representation used to organize characteristics of a user group. However, a detailed fictional description is not evidence that the represented characteristics are valid.
Without an empirical basis, an AI-generated persona remains a hypothetical representation rather than a research finding.
When using AI in Design Thinking, it is therefore important to distinguish:
- Facts established through observation or research
- Interpretations developed from those facts
- Hypotheses proposed by AI
- Results obtained through actual testing
AI-generated suggestions may also steer exploration toward familiar and conventional solutions because they reflect patterns commonly represented in available information.
Researchers should therefore avoid relying exclusively on AI-generated ideas and continue investigating real users, technical possibilities, and alternative problem definitions.
Design Thinking in ThinkNavi
ThinkNavi treats Design Thinking as a comprehensive approach that begins with understanding people’s experiences, proceeds through problem reframing and solution generation, and uses testing to deepen understanding.
Related individual methods include Brainstorming, the KJ Method, analogy, SCAMPER, Problem Structuring, Concept Maps, Decision Matrices, and Design of Experiments.
For example, findings from user research can be organized using the KJ Method, allowing different interpretations of the problem to be considered.
Once the problem has been formulated, Brainstorming or analogy may be used to generate possible solutions.
Decision Matrices can help compare alternatives, while prototypes and experiments can provide evidence about their effectiveness.
For ThinkNavi Free, possible tools include worksheets and interactive methods for organizing research findings, developing problem statements, comparing ideas, and defining evaluation criteria.
Potential LLM-assisted functions include extracting characteristics from observation records, proposing alternative problem definitions, generating possible solutions, and connecting hypotheses with verification results.
These are directions for future development and do not imply that all such functions are currently available.
The essential point in understanding Design Thinking is not to perform five activities in the correct order.
Nor does simply adopting the principle of human-centered design guarantee new knowledge or successful solutions.
The practical value of Design Thinking lies in changing how we understand a problem through observation, discovering new facts by testing concrete solutions, and revising both the problem definition and the proposed solution in response to what we learn.
Related Topics
- Problem Structuring — Breaking Down Problems, Understanding Relationships, and Finding Paths to Solutions
- Brainstorming — Generating More Ideas by Suspending Judgment
- SCAMPER | Generating New Ideas Through Seven Perspectives
- Analogy | Transferring Relationships Across Domains
- The KJ Method | Building Meaningful Structures from Fragmented Information
- Concept Maps | Expressing Relations Between Concepts
- Systems Thinking | Interdependence and Change Over Time
- Induction, Deduction, and Abduction — Discovering Patterns, Drawing Conclusions, and Generating New Hypothese
- The Scientific Method and Falsifiability — Generating Hypotheses and Testing Them Critically
- Qualitative and Quantitative Thinking — Two Perspectives on Understanding the World and Their Integration
- Design of Experiments (DOE) — Investigating Multiple Factors and Their Interactions Through Systematic Experimentation
- Decision Matrices
- Analytic Hierarchy Process (AHP)
- Root Cause Analysis (RCA) — Understanding Why Problems Occur and Preventing Recurrence
- Concept Exploration
References and Further Reading
- Tim Brown, “Design Thinking,” Harvard Business Review, June 2008.
- Tim Brown, Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation, HarperBusiness, 2009.
- Herbert A. Simon, The Sciences of the Artificial, MIT Press, 1969.
- Richard Buchanan, “Wicked Problems in Design Thinking,” Design Issues, 8(2), 1992, pp. 5–21.
- Lucy Kimbell, “Rethinking Design Thinking: Part I,” Design and Culture, 3(3), 2011, pp. 285–306.
- Pietro Micheli et al., “Doing Design Thinking: Conceptual Review, Synthesis, and Research Agenda,” Journal of Product Innovation Management, 36(2), 2019, pp. 124–148.
- Philip Cash, Jaap Daalhuizen, and Paul Hekkert, “Evaluating the Efficacy and Effectiveness of Design Methods: A Systematic Review and Assessment Framework,” Design Studies, 88, 2023, 101204.
- Stanford d.school — Design Thinking Bootleg
- Stanford d.school — Let’s Stop Talking About THE Design Process
- IDEO — The Design Thinking Process
- IDEO — What Is Design Thinking?
- Design Council — Framework for Innovation
- Design Council — History of the Double Diamond