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What Is Creative Problem-Solving & Why Is It Important?

Business team using creative problem-solving

  • 01 Feb 2022

One of the biggest hindrances to innovation is complacency—it can be more comfortable to do what you know than venture into the unknown. Business leaders can overcome this barrier by mobilizing creative team members and providing space to innovate.

There are several tools you can use to encourage creativity in the workplace. Creative problem-solving is one of them, which facilitates the development of innovative solutions to difficult problems.

Here’s an overview of creative problem-solving and why it’s important in business.

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What Is Creative Problem-Solving?

Research is necessary when solving a problem. But there are situations where a problem’s specific cause is difficult to pinpoint. This can occur when there’s not enough time to narrow down the problem’s source or there are differing opinions about its root cause.

In such cases, you can use creative problem-solving , which allows you to explore potential solutions regardless of whether a problem has been defined.

Creative problem-solving is less structured than other innovation processes and encourages exploring open-ended solutions. It also focuses on developing new perspectives and fostering creativity in the workplace . Its benefits include:

  • Finding creative solutions to complex problems : User research can insufficiently illustrate a situation’s complexity. While other innovation processes rely on this information, creative problem-solving can yield solutions without it.
  • Adapting to change : Business is constantly changing, and business leaders need to adapt. Creative problem-solving helps overcome unforeseen challenges and find solutions to unconventional problems.
  • Fueling innovation and growth : In addition to solutions, creative problem-solving can spark innovative ideas that drive company growth. These ideas can lead to new product lines, services, or a modified operations structure that improves efficiency.

Design Thinking and Innovation | Uncover creative solutions to your business problems | Learn More

Creative problem-solving is traditionally based on the following key principles :

1. Balance Divergent and Convergent Thinking

Creative problem-solving uses two primary tools to find solutions: divergence and convergence. Divergence generates ideas in response to a problem, while convergence narrows them down to a shortlist. It balances these two practices and turns ideas into concrete solutions.

2. Reframe Problems as Questions

By framing problems as questions, you shift from focusing on obstacles to solutions. This provides the freedom to brainstorm potential ideas.

3. Defer Judgment of Ideas

When brainstorming, it can be natural to reject or accept ideas right away. Yet, immediate judgments interfere with the idea generation process. Even ideas that seem implausible can turn into outstanding innovations upon further exploration and development.

4. Focus on "Yes, And" Instead of "No, But"

Using negative words like "no" discourages creative thinking. Instead, use positive language to build and maintain an environment that fosters the development of creative and innovative ideas.

Creative Problem-Solving and Design Thinking

Whereas creative problem-solving facilitates developing innovative ideas through a less structured workflow, design thinking takes a far more organized approach.

Design thinking is a human-centered, solutions-based process that fosters the ideation and development of solutions. In the online course Design Thinking and Innovation , Harvard Business School Dean Srikant Datar leverages a four-phase framework to explain design thinking.

The four stages are:

The four stages of design thinking: clarify, ideate, develop, and implement

  • Clarify: The clarification stage allows you to empathize with the user and identify problems. Observations and insights are informed by thorough research. Findings are then reframed as problem statements or questions.
  • Ideate: Ideation is the process of coming up with innovative ideas. The divergence of ideas involved with creative problem-solving is a major focus.
  • Develop: In the development stage, ideas evolve into experiments and tests. Ideas converge and are explored through prototyping and open critique.
  • Implement: Implementation involves continuing to test and experiment to refine the solution and encourage its adoption.

Creative problem-solving primarily operates in the ideate phase of design thinking but can be applied to others. This is because design thinking is an iterative process that moves between the stages as ideas are generated and pursued. This is normal and encouraged, as innovation requires exploring multiple ideas.

Creative Problem-Solving Tools

While there are many useful tools in the creative problem-solving process, here are three you should know:

Creating a Problem Story

One way to innovate is by creating a story about a problem to understand how it affects users and what solutions best fit their needs. Here are the steps you need to take to use this tool properly.

1. Identify a UDP

Create a problem story to identify the undesired phenomena (UDP). For example, consider a company that produces printers that overheat. In this case, the UDP is "our printers overheat."

2. Move Forward in Time

To move forward in time, ask: “Why is this a problem?” For example, minor damage could be one result of the machines overheating. In more extreme cases, printers may catch fire. Don't be afraid to create multiple problem stories if you think of more than one UDP.

3. Move Backward in Time

To move backward in time, ask: “What caused this UDP?” If you can't identify the root problem, think about what typically causes the UDP to occur. For the overheating printers, overuse could be a cause.

Following the three-step framework above helps illustrate a clear problem story:

  • The printer is overused.
  • The printer overheats.
  • The printer breaks down.

You can extend the problem story in either direction if you think of additional cause-and-effect relationships.

4. Break the Chains

By this point, you’ll have multiple UDP storylines. Take two that are similar and focus on breaking the chains connecting them. This can be accomplished through inversion or neutralization.

  • Inversion: Inversion changes the relationship between two UDPs so the cause is the same but the effect is the opposite. For example, if the UDP is "the more X happens, the more likely Y is to happen," inversion changes the equation to "the more X happens, the less likely Y is to happen." Using the printer example, inversion would consider: "What if the more a printer is used, the less likely it’s going to overheat?" Innovation requires an open mind. Just because a solution initially seems unlikely doesn't mean it can't be pursued further or spark additional ideas.
  • Neutralization: Neutralization completely eliminates the cause-and-effect relationship between X and Y. This changes the above equation to "the more or less X happens has no effect on Y." In the case of the printers, neutralization would rephrase the relationship to "the more or less a printer is used has no effect on whether it overheats."

Even if creating a problem story doesn't provide a solution, it can offer useful context to users’ problems and additional ideas to be explored. Given that divergence is one of the fundamental practices of creative problem-solving, it’s a good idea to incorporate it into each tool you use.


Brainstorming is a tool that can be highly effective when guided by the iterative qualities of the design thinking process. It involves openly discussing and debating ideas and topics in a group setting. This facilitates idea generation and exploration as different team members consider the same concept from multiple perspectives.

Hosting brainstorming sessions can result in problems, such as groupthink or social loafing. To combat this, leverage a three-step brainstorming method involving divergence and convergence :

  • Have each group member come up with as many ideas as possible and write them down to ensure the brainstorming session is productive.
  • Continue the divergence of ideas by collectively sharing and exploring each idea as a group. The goal is to create a setting where new ideas are inspired by open discussion.
  • Begin the convergence of ideas by narrowing them down to a few explorable options. There’s no "right number of ideas." Don't be afraid to consider exploring all of them, as long as you have the resources to do so.

Alternate Worlds

The alternate worlds tool is an empathetic approach to creative problem-solving. It encourages you to consider how someone in another world would approach your situation.

For example, if you’re concerned that the printers you produce overheat and catch fire, consider how a different industry would approach the problem. How would an automotive expert solve it? How would a firefighter?

Be creative as you consider and research alternate worlds. The purpose is not to nail down a solution right away but to continue the ideation process through diverging and exploring ideas.

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Continue Developing Your Skills

Whether you’re an entrepreneur, marketer, or business leader, learning the ropes of design thinking can be an effective way to build your skills and foster creativity and innovation in any setting.

If you're ready to develop your design thinking and creative problem-solving skills, explore Design Thinking and Innovation , one of our online entrepreneurship and innovation courses. If you aren't sure which course is the right fit, download our free course flowchart to determine which best aligns with your goals.

definition of creative problem solving

About the Author

  • Board of Directors

Creative Education Foundation

What is CPS?

Cps = c reative p roblem s olving, cps is a proven method for approaching a problem or a challenge in an imaginative and innovative way. it helps you redefine the problems and opportunities you face, come up with new, innovative responses and solutions, and then take action..

definition of creative problem solving

Why does CPS work?

CPS begins with two assumptions:

  • Everyone is creative in some way.
  • Creative skills can be learned and enhanced.

Osborn noted there are two distinct kinds of thinking that are essential to being creative:

Divergent thinking.

Brainstorming is often misunderstood as the entire Creative Problem Solving process.   Brainstorming is the divergent thinking phase of the CPS process.   It is not simply a group of people in a meeting coming up with ideas in a disorganized fashion. Brainstorming at its core is generating lots of ideas.  Divergence allows us to state and move beyond obvious ideas to breakthrough ideas. (Fun Fact: Alex Osborn, founder of CEF, coined the term “brainstorm.” Osborn was the “O” from the ad agency BBDO.)

Convergent Thinking

Convergent thinking applies criteria to brainstormed ideas so that those ideas can become actionable innovations.  Divergence provides the raw material that pushes beyond every day thinking, and convergence tools help us screen, select, evaluate, and refine ideas, while retaining novelty and newness.

To drive a car, you need both the gas and the brake.

But you cannot use the gas and brake pedals at the same time — you use them alternately to make the car go. Think of the gas pedal as Divergence , and the brake pedal as Convergence . Used together you move forward to a new destination.

Each of us use divergent and convergent thinking daily, intuitively. CPS is a deliberate process that allows you to harness your natural creative ability and apply it purposefully to problems, challenges, and opportunities.

definition of creative problem solving

The CPS Process

Based on the osborn-parnes process, the cps model uses plain language and recent research., the basic structure is comprised of four stages with a total of six explicit process steps. , each step uses divergent and convergent thinking..

definition of creative problem solving

Learner’s Model based on work of G.J. Puccio, M. Mance, M.C. Murdock, B. Miller, J. Vehar, R. Firestien, S. Thurber, & D. Nielsen (2011)

Explore the Vision.   Identify the goal, wish, or challenge.

Gather Data.   Describe and generate data to enable a clear understanding of the challenge.

Formulate Challenges. Sharpen awareness of the challenge and create challenge questions that invite solutions.

Explore Ideas. Generate ideas that answer the challenge questions.

Formulate Solutions. To move from ideas to solutions. Evaluate, strengthen, and select solutions for best “fit.”

Formulate a Plan.  Explore acceptance and identify resources and actions that will support implementation of the selected solution(s).

Explore Ideas. Generate ideas that answer the challenge question

Core Principles of Creative Problem Solving

  • Everyone is creative.
  • Divergent and Convergent Thinking Must be Balanced.  Keys to creativity are learning ways to identify and balance expanding and contracting thinking (done separately), and knowing  when  to practice them.
  • Ask Problems as Questions.  Solutions are more readily invited and developed when  challenges and problems are restated as open-ended questions  with multiple possibilities. Such questions generate lots of rich information, while closed-ended questions tend to elicit confirmation or denial. Statements tend to generate limited or no response at all.
  • Defer or Suspend Judgment.  As Osborn learned in his early work on brainstorming, the  instantaneous judgment in response to an idea shuts down idea generation . There is an appropriate and necessary time to apply judgement when converging.
  • Focus on “Yes, and” rather than “No, but.”  When generating information and ideas, language matters.  “Yes, and…” allows continuation and expansion , which is necessary in certain stages of CPS. The use of the word “but” – preceded by “yes” or “no” – closes down conversation, negating everything that has come before it.
  • Idea Generation: What is Creative Problem Solving?

definition of creative problem solving

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definition of creative problem solving

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Creative Problem Solving (CPS) is a key idea generation technique. Currently, though better service quality is important, it is not enough on its own. Without combining it with innovation and creativity, one cannot expect to achieve lasting success at the international level. Reading this article, you’ll learn these aspects about CPS: 1) definition , 2) Osborn-Parnes CPS , 3) stages and models of creative problem solving (CPS) , and 4) some techniques .

In simple words, Creative Problem Solving may be defined as a problem solving technique that addresses a challenge or problem in a creative manner. The solution is creative because it is not obvious. To meet the criteria for solving a problem in a creative manner, the solution should resolve the declared problem in an original manner with the solution being reached independently. This idea generation strategy usually incorporates a team approach. This is owing to the fact that people inside the workplace are allowed to engage in the process of change in their search for creative solutions.

Coming to the more specific use of the term, Creative Problem Solving refers to the trademark Osborn-Parnes (CPS) process of creatively solving problems. The process was crafted by Dr. Sidney J. Parnes and Alex Osborn in the 1950s. The difference between this process and other CPS strategies is that there is utilization of both convergent and divergent thinking in the course of each process step, and not only when coming up with ideas to fix the problem. Each step starts with divergent thinking, an extensive search for multiple alternatives. After this comes convergent thinking that involves evaluating and selecting. This strategy is taught at the Creative Problem Solving Institute, the International Center for Studies in Creativity, and the CREA conference. It is particularly recognized as an important influence on the Productive Thinking Model.


When describing the Osborne-Parnes process of Creative Problem Solving, one can think of no less than four models. Here, three are discussed.

In the linear model, each of the six stages of the Creative Problem Solving process is represented by a diamond shape. This shape signifies first, generating or diverging options, followed by a selection of a refreshed focus and then, moving on. Thinking was in straight lines, moving just one step at a time for the sake of maintaining order, channeling freedom. This model came out in the 1970s.

In the 1990s, the diamond shapes changed into connected bubbles representing attitude shifts towards directed and meaningful connectedness. Channeled freedom gets wider birth. There are three unique stages in the bubble model. Visually, this indicates authorization to enter not solely at the first stage (as was the case in the 1970s model), but at any stage of the process. The linear model has diamond shapes with smoother edges, and there are arrows to give directions. The three bubbles in the bubble model let you know exactly what you should do.

3. Systemic

The Thinking Skills Model is a system with many entry points determined by the task at hand (center hub) or situation. The construction in this model is in agreement with our current web-like interrelated view of the world. It depicts the distinctive core of each stage by renaming. While the bubble and accordion (diamond) CPS models offer rational, logical approaches to CPS, providing an overt course of action, this model tells you what happens. It outlines the three key phases and charts the thinking processes utilized for each. You can see the diamonds remain, the three key focus points join in fluid colors with the beginning point varying with the situational requirement.


There are six stages in the Osborn-Parnes process of creative-problem solving. Here, the six stages are described with two examples of questions for some of the stages, to stimulate your thinking.

1. Objective Finding

Pinpointing the challenge or goal and delineating your preferred output is the basis of the CPS strategy. At times, people pay no heed to certain essential aspects about the problem or take something for granted to solve it rapidly. This causes an obscurement of the thought process, and the person fails to take note of the big picture. Delineating the goal or objective provides a lucid idea pertaining to the problem that facilitates the investigation of various possible solutions to it.

Questions : What bottlenecks or barriers exist? What is it that you wish to be better organized?

2. Fact Finding

Collecting information pertaining to the problem and associated data is essential for comprehending the problem. At this stage, make a list of key details such as what and who is involved, assumptions and perceptions, viewpoints of interested parties, feelings and facts, and so on so that you may begin the process of crafting ideas.

Questions : Who should be or is already involved? Why doesn’t/does it happen?

3. Problem Finding

Using the problem objective and gathered data as a basis, determine possible challenges that may come about and the possible opportunities that are present inside of it. This would assist you with concentrating on the problem. It is so simple to move your attention away from the aim and to come up with answers to the incorrect problems.

Questions : What is the actual problem? What is the key objective?

4. Idea Finding

Reusing a solution when we come across a problem that we possibly encountered before, is a very easy process. Our mind detects ‘conceptual blocks’ that comprise hurdles such as commitment, complacency, compression, and constancy. These hinder us from thinking creatively and developing fresh concepts or ideas. Thus, it is essential to investigate, brainstorm and determine as many probable solutions as you can.

5. Solution Finding

After you’ve done with coming up with new ideas and noting down probable solutions in list fashion, assess them to determine whether they meet your specification for success and can be executed. Improvise, reinforce and select the best idea. Make sure that the solutions are not only creative, but also useful. At times, will power is the sole solution.

Questions : Will it work? Are the technology and materials available?

6. Acceptance Finding

You have selected the best probable solution that is both actionable and satisfies the requirements for success. The next thing to do is to plan your steps for action by lucidly describing responsibilities and determining the best method to utilize the available resources. The calls for action that you put out should be comprehended by all associated with the problem solving process so that it becomes an accepted solution.


1. synectics.

Synectics is usually classified as a Creative Problem-Solving (CPS) Technique along with Brainstorming and Lateral Thinking. This problem solving methodology inspires thought processes that the subject might not be aware of. The credit for developing the technique that had its beginnings in the 1950s in the Arthur D. Little Invention Design Unit goes to George M. Prince and William J.J. Gordon.

The process was gathered from tape recorded (starting with audio with video coming later) meetings, assessment of the outcomes, and experiments with other methods of coping with the barriers to achievements, in the meeting.

The term “Synectics” has its origins from the Greek language and means the combining of different and supposedly irrelevant elements. Though Synectics is a trademarked name, it has turned into a standard word for delineating Creative Problem Solving that takes place in groups. This idea generation technique approaches problem solving and creativity in a rational manner.

In Gordon’s opinion, Synectics research has to do with three key assumptions:

  • It is possible to describe and teach the creative process;
  • Invention processes in science and arts are analogous and propelled by the same “psychic” processes;
  • Creativity at the level of individual and group is analogous.

In short, if people comprehend the working of creativity, they can improve their ability to be creative.

2. TRIZ methodology

TRIZ (or TIPS – Theory of Inventive Problem Solving) was created by Genrich Altshuller and his coworkers. It is a Russian method of problem solving. This strategy is meant to cultivate the creation of patentable inventions. However, the technique is also helpful for developing non-product solutions.

In the beginning, following the invention of bulletproof glass, a trade off happened. Though the glass would prevent the bullet from entering, the former would crack to such an extent that the vision of the pilot or driver behind the glass would be obscured. TRIZ has a considerable list of principles for settling trade offs. In this particular case, the pertinent principle was segmentation for which the solution was to create a huge pane of glass from smaller panes. This was to ensure that the cracks were limited to the one small pane. If you are capable of articulating your trade off, the chances are high that TRIZ has methods to triumph over it that have proved successful with respect to other problems.

3. Brainstorming

Brainstorming is an individual or group activity by which attempts are made to determine a conclusion for a particular problem by collecting a list of ideas that its members spontaneously contributed. Alex Faickney Osborn popularized the term in Applied Imagination, a 1953 book.

4. Mindmapping

This creativity technique both reframes the situation and cultivates creativity. A mind map is a representation of concepts and ideas in a graphical manner. This visual thinking tool assists with structuring information, assisting with better analysis, synthesis, comprehension, recall and engendering of new ideas. The power of the mind map is traceable to its simplicity.

5. Reversal of problem

This approach is about coming up with ideas to solve problems by way of a different/opposite perspective (turning it around: upside-down, inside-out or back to front).

6. Look beyond something’s common function

Split an object into all its individual parts. If you have a description suggesting a function (just like the function of a prong is transporting electricity), describe it in a more generic manner by way of shape, size and the make-up of the material (such as rectangular, flat, small piece of metal). If you call an item an electric plug’s prong, the description may conceal the fact that the item could also turn into a screwdriver if required.

Here’s an example of looking beyond a thing’s common function: Imagine that the passengers of the luxury liner Titanic had considered the iceberg to be a huge floating surface instead of an object that hits ships. If they had thought so, perhaps many lives could have been saved by using the ship as a lifeboat because the iceberg would not sink.

7. Lateral thinking

Lateral thinking is a manner of thinking that looks for a solution to an obstinate issue through unorthodox elements or methods that would usually be disregarded by logical thinking. To be more precise, “lateral thinking” may be defined as a way to solve problems by a creative or indirect approach, utilizing reasoning that may not be obvious straight away or incorporating ideas that cannot be gathered by utilizing only conventional step-by-step logic. The term was coined by Edward de Bono , a foremost creativity practitioner, in 1967. De Bono created two different models pertaining to creativity thinking namely “parallel thinking” and lateral thinking. The creativity practitioner created the two models over many years with “Mechanism of the Mind” – his book, coming out in print in 1969.

Parallel thinking has to do with pondering over an issue in a single state of mind at a time as against confusing ourselves by attempting to process several issues differently in a single go. Coming back to lateral thinking, the concept makes you realize that coming up with breakthrough ideas doesn’t necessarily have to spring from a shotgun effort or luck. The method provides a systematic and most importantly, deliberate process for which the outcome is innovative thinking.

Creative thinking is no talent but rather, a learnable skill. It empowers those who adopt it by strengthening their natural abilities, which enhances innovation and creativity, which in turn leads to a boost in efficiency and profit.

Challenge, alternatives, and provocation and movement are three examples of lateral thinking techniques.

The basis for SCAMPER is the belief that everything new is a variation of something already in existence. SCAMPER is an acronym, and each letter indicates a different method by which the person can toy around with the features of whatsoever it is that is challenging him to come out with new ideas. The letters and their full forms are as follows:

S  = S ubstitute

C  = C ombine

A  = A dapt

M  = M agnify

P  = P ut to Other Uses

E  = E liminate (alternative is Minify)

R  = R earrange (alternative is Reverse)

To utilize the SCAMPER technique, start by stating the problem you wish to solve or the thought you wish to develop. This thought/idea can be anything: a product, process or service you wish to improve, a challenge in business, or other problem. Once you have identified the challenge, you need to come up with questions. Utilize the SCAMPER checklist for guidance. Here’s a sample:

S : What to substitute in my process of selling?

C : How do I blend selling with other activities?

A : What to copy or adapt the selling process of another person or company?

M : What do I put more weight on or magnify when selling?

P : What other uses can I put my selling to?

E : What do I eliminate or make easier in my process of selling?

R : How do I change, reverse or reorder my manner of selling?

With the help of these questions, you are pushed to a different viewpoint with respect to your problem and ultimately come up with original solutions.

Whether at business or in your personal life, Creative Problem Solving can help you see aspects and solutions that you may never have realized when you only permitted your mind to move the conventional path. So embrace it!

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  • Creative Problem Solving

Glossary of Meeting Terms

What is creative problem solving.

Creative Problem Solving (CPS) is a method that attempts to approach a problem or a challenge in an innovative way. The process helps redefine problems and opportunities to come up with new responses and solutions.

There are many variations on the basic Creative Problem Solving process, some of which work nicely in group meetings. The simplest form of the process includes these steps:

  • Clarify (the objectives, the problem, the facts, the opportunity)
  • Generate Ideas (come up with possible solutions or approaches)
  • Solve (develop ideas into solutions or experiments)
  • Implement (create a plan and secure commitment to next steps)
  • Creative Problem Solving: Finding Innovative Solutions to Challenges ~ on MindTools
  • 10 Examples of Creative Problem Solving ~by John Spacey
  • Idea Generation: What is Creative Problem Solving? ~ by Anastasia for Cleverism

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The Basics of Creative Problem Solving – CPS

definition of creative problem solving

By: Jeffrey Baumgartner

Creative problem solving isn’t just brainstorming, although that’s what many people may associate it with. It’s actually a well-defined process that can help you from problem definition to implementing solutions, according to Jeffrey Baumgartner.

Creative ideas do not suddenly appear in people’s minds for no apparent reason. Rather, they are the result of trying to solve a specific problem or to achieve a particular goal. Albert Einstein’s theories of relativity were not sudden inspirations. Rather they were the result of a huge amount of mental problem solving trying to close a discrepancy between the laws of physics and the laws of electromagnetism as they were understood at the time.

Albert Einstein, Leonardo da Vinci, Thomas Edison and other creative geniuses have always worked in the same way. They do not wait for creative ideas to strike them. Rather they focus on trying to solve a clearly stated, at least in their minds, problem. This is just like important TED talks to ideate for business innovation specifically discussed to get a better solution for existing problems.

This approach has been formalized as Creative Problem Solving (CPS). CPS is a simple process that involves breaking down a problem to understand it, generating ideas to solve the problem and evaluating those ideas to find the most effective solutions. Highly creative people tend to follow this process in their heads, without thinking about it. Less naturally creative people simply have to learn to use this very simple process.

A 7-step CPS framework

Although creative problem solving has been around as long as humans have been thinking creatively and solving problems, it was first formalised as a process by Alex Osborn, who invented traditional brainstorming, and Sidney Parnes. Their Creative Problem Solving Process (CPSP) has been taught at the International Center for Studies in Creativity at Buffalo College in Buffalo, New York since the 1950s.

However, there are numerous different approaches to CPS. Mine is more focused on innovation (that is the implementation of the most promising ideas). It involves seven straightforward steps.

How to Turn Crowdsourced Ideas Into Business Proposals

In October 2020, Pact launched AfrIdea, a regional innovation program supported by the U.S. Department of State. This was geared towards unlocking the potential of West African entrepreneurs, social activists, and developers in uncovering solutions to post-COVID challenges. Through a contest, training, idea-a-thon and follow-on funding, they sought to activate a network of young entrepreneurs and innovators from Guinea, Mali, Senegal, and Togo to source and grow innovative solutions. Learn their seven-stage process in the AfrIdea case study.

Get the Case Study

  • Clarify and identify the problem
  • Research the problem
  • Formulate creative challenges
  • Generate ideas
  • Combine and evaluate the ideas
  • Draw up an action plan
  • Do it! (implement the ideas)

Let us look at each step more closely:

1. Clarify and identify the problem

Arguably the single most important step of CPS is identifying your real problem or goal. This may seem easy, but very often, what we believe to be the problem is not the real problem or goal. For instance, you may feel you need a new job. However, if you break down your problem and analyse what you are really looking for, it may transpire that the actual issue is that your income does not cover your costs of living. In this case, the solution may be a new job, but it might also be to re-arrange your expenses or to seek a pay rise from your existing employer.

Five whys: A powerful problem-definition technique

The best way to clarify the problem and understand the underlying issues is to ask yourself – or better still, ask a friend or family member to ask you – a series of questions about your problem in order to clarify the true issues behind the problem. The first question to ask is simply: “why is this a problem?” or “why do I wish to achieve this goal?” Once you have answered that, ask yourself “why else?” four more times.

For instance, you might feel you want to overcome your shyness. So, you ask yourself why and you answer: “because I am lonely”. Then ask yourself “Why else?” four times. You answer: “Because I do not know many people in this new city where I live”, “Because I find it hard to meet people”, “Because I am doing many activities alone” and “Because I would like to do activities with other people who share my interests”. This last “why else” is clearly more of the issue than reducing shyness. Indeed, if you had focused your creative energy on solving your shyness issue, you would not have actually solved the real problem. On the other hand, if you focused your creative energy on finding people with whom to share activities, you would be happier without ever having to address the shyness issue.

More questions you can ask to help clearly define the problem

In addition, you can further clarify your problem by asking questions like: “What do I really wish to accomplish?”, “What is preventing me from solving this problem/achieving the goal?”, “How do I envision myself in six months/one year/five years [choose most relevant time span] as a result of solving this problem?” and “Are my friends dealing with similar problems? If so, how are they coping?”

By the time you have answered all these questions, you should have a very clear idea of what your problem or real goal is.

Set criteria for judging potential solutions

The final step is to decide what criteria you will eventually use to evaluate or judge the ideas. Are there budget limitations, timeframe or other restrictions that will affect whether or not you can go ahead with an idea? What will you want to have accomplished with the ideas? What do you wish to avoid when you implement these ideas? Think about it and make a list of three to five evaluation criteria. Then put the list aside. You will not need it for a while.

2. Research the problem

The next step in CPS is to research the problem in order to get a better understanding of it. Depending on the nature of the problem, you may need to do a great deal of research or very little. The best place to start these days is with your favourite search engine. But do not neglect good old fashioned sources of information and opinion. Libraries are fantastic for in-depth information that is easier to read than computer screens. Friends, colleagues and family can also provide thoughts on many issues. Fora on sites like LinkedIn and elsewhere are ideal for asking questions. There’s nothing an expert enjoys more than imparting her knowledge. Take advantage of that. But always try to get feedback from several people to ensure you get well-rounded information.

3. Formulate one or more creative challenges

By now, you should be clear on the real issues behind your problems or goals. The next step is to turn these issues into creative challenges. A creative challenge is basically a simple question framed to encourage suggestions or ideas. In English, a challenge typically starts with “In what ways might I [or we]…?” or “How might I…?” or “How could I…?”

Creative challenges should be simple, concise and focus on a single issue. For example: “How might I improve my Chinese language skills and find a job in Shanghai?” is two completely separate challenges. Trying to generate ideas that solve both challenges will be difficult and, as a result, will stifle idea generation. So separate these into two challenges: “How might I improve my Chinese language skills?” and “How might I find a job in Shanghai?” Then attack each challenge individually. Once you have ideas for both, you may find a logical approach to solving both problems in a coordinated way. Or you might find that there is not a coordinated way and each problem must be tackled separately.

Creative challenges should not include evaluation criteria. For example: “How might I find a more challenging job that is better paying and situated close to my home?” If you put criteria in the challenge, you will limit your creative thinking. So simply ask: “How might a I find a more challenging job?” and after generating ideas, you can use the criteria to identify the ideas with the greatest potential.

4. Generate ideas

Finally, we come to the part most people associate with brainstorming and creative problem solving: idea generation. And you probably know how this works. Take only one creative challenge. Give yourself some quiet time and try to generate at least 50 ideas that may or may not solve the challenge. You can do this alone or you can invite some friends or family members to help you.

Irrespective of your idea generation approach, write your ideas on a document. You can simply write them down in linear fashion, write them down on a mind map, enter them onto a computer document (such as Microsoft Word or OpenOffice) or use a specialized software for idea generation. The method you use is not so important. What is important is that you follow these rules:

Write down every idea that comes to mind. Even if the idea is ludicrous, stupid or fails to solve the challenge, write it down. Most people are their own worst critics and by squelching their own ideas, make themselves less creative. So write everything down. NO EXCEPTIONS!

If other people are also involved, insure that no one criticizes anyone else’s ideas in any way. This is called squelching, because even the tiniest amount of criticism can discourage everyone in the group for sharing their more creative ideas. Even a sigh or the rolling of eyes can be critical. Squelching must be avoided!

If you are working alone, don’t stop until you’ve reached your target of 50 (or more) ideas. If you are working with other people, set a time limit like 15 or 20 minutes. Once you have reached this time limit, compare ideas and make a grand list that includes them all. Then ask everyone if the have some new ideas. Most likely people will be inspired by others’ ideas and add more to the list.

If you find you are not generating sufficient ideas, give yourself some inspiration. A classic trick is to open a book or dictionary and pick out a random word. Then generate ideas that somehow incorporate this word. You might also ask yourself what other people whom you know; such as your grandmother, your partner, a friend or a character on you favourite TV show, might suggest.

Brainstorming does not need to occur at your desk. Take a trip somewhere for new inspiration. Find a nice place in a beautiful park. Sit down in a coffee shop on a crowded street corner. You can even walk and generate ideas.

In addition, if you browse the web for brainstorming and idea generation, you will find lots of creative ideas on how to generate creative ideas!

One last note: If you are not in a hurry, wait until the next day and then try to generate another 25 ideas; ideally do this in the morning. Research has shown that our minds work on creative challenges while we sleep. Your initial idea generation session has been good exercise and has certainly generated some great ideas. But it will probably also inspire your unconscious mind to generate some ideas while you sleep. Don’t lose them!

5. Combine and evaluate ideas

After you have written down all of your ideas, take a break. It might just be an hour. It might be a day or more. Then go through the ideas. Related ideas can be combined together to form big ideas (or idea clusters).

Then, using the criteria you devised earlier, choose all of the ideas that broadly meet those criteria. This is important. If you focus only on the “best” ideas or your favorite ideas, the chances are you will choose the less creative ones! Nevertheless, feel free to include your favorite ideas in the initial list of ideas.

Now get out that list of criteria you made earlier and go through each idea more carefully. Consider how well it meets each criterion and give it a rating of 0 to 5 points, with five indicating a perfect match. If an idea falls short of a criterion, think about why this is so. Is there a way that it can be improved in order to increase its score? If so, make a note. Once you are finished, all of the ideas will have an evaluation score. Those ideas with the highest score best meet your criteria. They may not be your best ideas or your favorite ideas, but they are most likely to best solve your problem or enable you to achieve your goal.

Depending on the nature of the challenge and the winning ideas, you may be ready to jump right in and implement your ideas. In other cases, ideas may need to be developed further. With complex ideas, a simple evaluation may not be enough. You may need to do a SWOT (strengths, weaknesses, opportunities and threats) analysis or discuss the idea with others who will be affected by it. If the idea is business related, you may need to do a business case, market research, build a prototype or a combination of all of these.

Also, keep in mind that you do not need to limit yourself to one winning idea. Often you can implement several ideas in order to solve your challenge.

6. Draw up an action plan

At this point, you have some great ideas. However, a lot of people have trouble motivating themselves to take the next step. Creative ideas may mean big changes or taking risks. Some of us love change and risk. Others are scared by it. Draw up an action plan with the simple steps you need to take in order to implement your ideas. Ideas that involve a lot work to implement can be particularly intimidating. Breaking their implementation down into a series of readily accomplished tasks makes these ideas easier to cope with and implement.

This is the simplest step of all. Take your action plan and implement your idea. And if the situation veers away from your action plan steps, don’t worry. Rewrite your action plan!

CPS and innovation

Any effective innovation initiative or process will use CPS at the front end. Our innovation process does so. TRIZ  also uses elements of CPS. Any effective and sustainable idea management system or ideation activity will be based on CPS.

Systems  and methods that do not use CPS or use it badly, on the other hand, tend not to be sustainable and fail early on. Suggestion schemes in which employees or the public are invited to submit any idea whatsoever are effectively asking users of the system to determine a problem and then offer a solution. This will result not only in many ideas, but many different problems, most of which will not be relevant to your strategic needs. Worse, having to evaluate every idea in the context of its implied problem – which may not be clear – is a nightmare from a resource point of view.

Systems and methods which are based on CPS, but in which creative challenges are poorly defined, also deliver poor results either because users do not understand the challenge or the problem is poorly understood and the resulting challenge stimulates ideas which in themselves are good, but which are not actually solutions to the true problem.

That said, CPS is a conceptually simple process – but critical to any innovation process. If you do not use it already, familiarize yourself with the process and start using it. You will find it does wonders for your innovativeness.

By Jeffrey Baumgartner

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What Is Creative Problem Solving and Why Is It Important?

definition of creative problem solving

Numerous studies, including ones from the US Department of Education , World Economic Forum , and Bloomberg indicate that tomorrow’s jobs will demand “creative problem solving skills.” But what exactly are creative problem solving skills? And are they being taught effectively to the next generation who will face competition for jobs from automation? To learn more about creative problem solving in the classroom, Adobe conducted a new study to understand how educators and policymakers think about creative problem solving skills, how critical these skills are to future jobs, and how they are currently being nurtured in schools today.

We asked educators and policymakers to talk to us about creative problem solving based upon the following definition: “Creative Problem Solving is the process of redefining problems and opportunities, coming up with new, innovative responses and solutions, and then taking action.” We wanted to know how skills like independent learning, learning through success or failure, and working with diverse teams are critical to a students’ ability to succeed in the future workforce.

What we discovered was extremely illuminating. Three quarters of the educators surveyed believe that students need to develop these skills to protect their futures, as the professions that require creative problem solving are less likely to be impacted by automation. However, it isn’t just job-protection where creative problem solving makes a difference. Almost 90 percent of respondents believe students who excel at creative problem solving will have higher-earning job opportunities in the future, and 85 percent agreed that these same skills are in high demand by today’s employers for senior-level and higher-paying careers.

definition of creative problem solving

Knowing that 90 percent of educators believe creative problem solving should be integrated across all curricula, and that policymakers are in vehement agreement, it’s reasonable to assume that schools are already providing opportunities for students to develop these skills. Alarmingly though, this critical skillset is not emphasized enough in schools today due to the barriers educators face – from tight budgets and lack of resources to outdated testing requirements. Coupled with the fact that more than half of educators explain that they do not have the training or knowledge to help students develop creative problem solving skills, the challenge that educators and students face is vast.

Adobe believes that we need to support educators who are teaching creative problem solving, get technology into the hands of schools and students, and inspire young people to create. While technology alone is not the answer, it plays a key role. That is why Adobe is working to update its licensing models, so students – including those under the age of 13, consistent with U.S children’s privacy regulations – can access Creative Cloud in the classroom and at home using just their school I.D. to log in. This will reap benefits for the users, as the educators surveyed who use Creative Cloud in the classroom report that their students are more prepared for the jobs of the future .

Adobe is also constantly developing new storytelling tools like Spark, so students can easily create high quality, visually compelling reports, research papers, posters, writing assignments, presentations and so much more. Lastly, Adobe recognizes that it is critical to challenge students and encourage them to create and to have a positive social impact. That is why we created Project 1324 , which works with emerging creatives and leading youth arts organizations around the world to showcase artists who create the art and change they want to see in their communities.

To read the full study findings, and to learn more about how Adobe is working to get much-needed technology into the hands of students and educators, support educators in teaching creative problem solving skills, and inspire students to create, please visit Creative Problem Solving .

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Creative problem solving: basics, techniques, activities

Why is creative problem solving so important.

Problem-solving is a part of almost every person's daily life at home and in the workplace. Creative problem solving helps us understand our environment, identify the things we want or need to change, and find a solution to improve the environment's performance.

Creative problem solving is essential for individuals and organizations because it helps us control what's happening in our environment.

Humans have learned to observe the environment and identify risks that may lead to specific outcomes in the future. Anticipating is helpful not only for fixing broken things but also for influencing the performance of items.

Creative problem solving is not just about fixing broken things; it's about innovating and creating something new. Observing and analyzing the environment, we identify opportunities for new ideas that will improve our environment in the future.

The 7-step creative problem-solving process

The creative problem-solving process usually consists of seven steps.

1. Define the problem.

The very first step in the CPS process is understanding the problem itself. You may think that it's the most natural step, but sometimes what we consider a problem is not a problem. We are very often mistaken about the real issue and misunderstood them. You need to analyze the situation. Otherwise, the wrong question will bring your CPS process in the wrong direction. Take the time to understand the problem and clear up any doubts or confusion.

2. Research the problem.

Once you identify the problem, you need to gather all possible data to find the best workable solution. Use various data sources for research. Start with collecting data from search engines, but don't forget about traditional sources like libraries. You can also ask your friends or colleagues who can share additional thoughts on your issue. Asking questions on forums is a good option, too.

3. Make challenge questions.

After you've researched the problem and collected all the necessary details about it, formulate challenge questions. They should encourage you to generate ideas and be short and focused only on one issue. You may start your challenge questions with "How might I…?" or "In what way could I…?" Then try to answer them.

4. Generate ideas.

Now you are ready to brainstorm ideas. Here it is the stage where the creativity starts. You must note each idea you brainstorm, even if it seems crazy, not inefficient from your first point of view. You can fix your thoughts on a sheet of paper or use any up-to-date tools developed for these needs.

5. Test and review the ideas.

Then you need to evaluate your ideas and choose the one you believe is the perfect solution. Think whether the possible solutions are workable and implementing them will solve the problem. If the result doesn't fix the issue, test the next idea. Repeat your tests until the best solution is found.

6. Create an action plan.

Once you've found the perfect solution, you need to work out the implementation steps. Think about what you need to implement the solution and how it will take.

7. Implement the plan.

Now it's time to implement your solution and resolve the issue.

Top 5 Easy creative thinking techniques to use at work

1. brainstorming.

Brainstorming is one of the most glaring CPS techniques, and it's beneficial. You can practice it in a group or individually.

Define the problem you need to resolve and take notes of every idea you generate. Don't judge your thoughts, even if you think they are strange. After you create a list of ideas, let your colleagues vote for the best idea.

2. Drawing techniques

It's very convenient to visualize concepts and ideas by drawing techniques such as mind mapping or creating concept maps. They are used for organizing thoughts and building connections between ideas. These techniques have a lot in common, but still, they have some differences.

When starting a mind map, you need to put the key concept in the center and add new connections. You can discover as many joints as you can.

Concept maps represent the structure of knowledge stored in our minds about a particular topic. One of the key characteristics of a concept map is its hierarchical structure, which means placing specific concepts under more general ones.

3. SWOT Analysis

The SWOT technique is used during the strategic planning stage before the actual brainstorming of ideas. It helps you identify strengths, weaknesses, opportunities, and threats of your project, idea, or business. Once you analyze these characteristics, you are ready to generate possible solutions to your problem.

4. Random words

This technique is one of the simplest to use for generating ideas. It's often applied by people who need to create a new product, for example. You need to prepare a list of random words, expressions, or stories and put them on the desk or board or write them down on a large sheet of paper.

Once you have a list of random words, you should think of associations with them and analyze how they work with the problem. Since our brain is good at making connections, the associations will stimulate brainstorming of new ideas.

5. Storyboarding

This CPS method is popular because it tells a story visually. This technique is based on a step-creation process. Follow this instruction to see the storyboarding process in progress:

  • Set a problem and write down the steps you need to reach your goal.
  • Put the actions in the right order.
  • Make sub-steps for some steps if necessary. This will help you see the process in detail.
  • Evaluate your moves and try to identify problems in it. It's necessary for predicting possible negative scenarios.

7 Ways to improve your creative problem-solving skills

1. play brain games.

It's considered that brain games are an excellent way to stimulate human brain function. They develop a lot of thinking skills that are crucial for creative problem-solving.

You can solve puzzles or play math games, for example. These activities will bring you many benefits, including strong logical, critical, and analytical thinking skills.

If you are keen on playing fun math games and solving complicated logic tasks, try LogicLike online.

We created 3500+ puzzles, mathematical games, and brain exercises. Our website and mobile app, developed for adults and kids, help to make pastime more productive just in one place.

2. Practice asking questions

Reasoning stimulates you to generate new ideas and solutions. To make the CPS process more accessible, ask questions about different things. By developing curiosity, you get more information that broadens your background. The more you know about a specific topic, the more solutions you will be able to generate. Make it your useful habit to ask questions. You can research on your own. Alternatively, you can ask someone who is an expert in the field. Anyway, this will help you improve your CPS skills.

3. Challenge yourself with new opportunities

After you've gained a certain level of creativity, you shouldn't stop developing your skills. Try something new, and don't be afraid of challenging yourself with more complicated methods and techniques. Don't use the same tools and solutions for similar problems. Learn from your experience and make another step to move to the next level.

4. Master your expertise

If you want to keep on generating creative ideas, you need to master your skills in the industry you are working in. The better you understand your industry vertical, the more comfortable you identify problems, find connections between them, and create actionable solutions.

Once you are satisfied with your professional life, you shouldn't stop learning new things and get additional knowledge in your field. It's vital if you want to be creative both in professional and daily life. Broaden your background to brainstorm more innovative solutions.

5. Develop persistence

If you understand why you go through this CPS challenge and why you need to come up with a resolution to your problem, you are more motivated to go through the obstacles you face. By doing this, you develop persistence that enables you to move forward toward a goal.

Practice persistence in daily routine or at work. For example, you can minimize the time you need to implement your action plan. Alternatively, some problems require a long-term period to accomplish a goal. That's why you need to follow the steps or try different solutions until you find what works for solving your problem. Don't forget about the reason why you need to find a solution to motivate yourself to be persistent.

6. Improve emotional intelligence

Empathy is a critical element of emotional intelligence. It means that you can view the issues from the perspective of other people. By practicing compassion, you can understand your colleagues that work on the project together with you. Understanding will help you implement the solutions that are beneficial for you and others.

7. Use a thinking strategy

You are mistaken if you think that creative thinking is an unstructured process. Any thinking process is a multi-step procedure, and creative thinking isn't an exclusion. Always follow a particular strategy framework while finding a solution. It will make your thinking activity more efficient and result-oriented.

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Article • 10 min read

The Simplex Process

A robust and creative problem-solving tool.

By the Mind Tools Content Team

Imagine that you and your team are tasked with eliminating bottlenecks in your organization's billing process. Suppliers are angry, managers are frustrated, and the problem is costing the company money.

But, try as you might, you just can't pinpoint what's wrong, and the fixes that you've tried so far haven't worked.

Here's where the Simplex Process, now known as Simplexity Thinking, could help. This powerful tool enables you to identify and deal with problems creatively and effectively. It takes you through an eight-step process, from identifying the problem to implementing a solution.

In this article, we'll explain what Simplexity Thinking is, and describe how to use each stage.

Click here to view a transcript of this video.

What Is Simplexity Thinking?

The Simplex Process was created by management and creativity specialist Min Basadur, and was popularized in his 1995 book, " The Power of Innovation ."

The process is made up of eight steps, grouped into three stages: Problem Formulation, Solution Formulation and Solution Implementation. It is a versatile tool that can be used in organizations of all sizes, and for almost any type of problem.

Basadur has developed and refined the Process since the original publication of his book. Figure 1, below explains the most recent version.

Figure 1. Follow eight steps to solve a problem by using Simplexity Thinking.

definition of creative problem solving

Reproduced with permission from Dr Min Basadur. See Basadur Applied Creativity for more information on Simplex and Simplexity Thinking. From " The Power of Innovation: How to Make Innovation a Way of Life & How to Put Creative Solutions to Work ," by Min Basadur. Copyright © 1995 and 2002.

How to Use the Process

Let's look at the eight steps in more detail, below.

1. Problem Finding

Often, the most difficult part of any problem-solving exercise is finding the right issue to tackle. So, this is the first step to carry out. Problems may be obvious but, if they're not, you can identify them by using “trigger questions” such as:

  • What do our customers want us to improve? What are they complaining about?
  • What could they be doing better if we were to help them?
  • What small problems do we have that could grow into bigger ones?
  • What slows down our work or makes it more difficult? How can we improve quality?
  • What are our competitors doing that we could do?
  • What is frustrating and irritating to our team?

You can also consider issues that may arise in the future.

For example, think about how you expect markets and customers to change over the next few years. There could be problems as your organization expands. Social, political or legal changes may affect it, too. See our article, PEST Analysis for more on this.

It's also worth exploring possible problems from the perspective of different "actors" in the situation. This is where techniques such as the CATWOE checklist are helpful.

You may not have enough information to define your problem precisely, even after asking plenty of questions. But don't worry about this until you reach Step 3!

2. Fact Finding

The next stage is to research the problem as fully as possible.

Start by analyzing the data you have to see whether the problem really does exist. Then, establish whether the benefits of solving the problem will be worth the effort and resources that you'll need to spend.

Be clear which processes, components, services or technologies you want to use, and explore any solutions that others have already tried.

Next, work out how different people perceive the situation, explore your customers' needs in more detail, and investigate your competitors' best ideas.

3. Problem Definition

Identify the problem at the right level. For example, if you ask questions about it in terms that are too broad, then you'll never have enough resources to answer them effectively. If, however, your questions are too narrow, you may end up fixing the symptoms of a problem, rather than the problem itself. Our article, The Problem Definition Process , explores this issue.

Min Basadur suggests asking "Why?" to broaden your definition of the problem, and "What's stopping you?" to narrow it.

Let's say your system has difficulty maintaining stock levels in your warehouse. Start by asking, "Why is the system not doing its job properly?" The answer might lead you to ask a broader question, such as, "Why are we asking the system to do something that it's not good at?"

A "What's stopping you?" question here could give you the answer, "We don't know enough about the capabilities of the system we're using." In this way you may realize that you're not actually looking to fix a malfunctioning part, but to get the warehouse to use the system correctly, or to introduce a new system that is a better fit.

Big problems are often made up of many smaller ones. In the Problem Definition stage you can use a technique like Drill Down to break the problem down to its component parts. You can also use the 5 Whys Technique , Cause and Effect Analysis and Root Cause Analysis to help you get to the root of a problem.

Negative thinking can affect the Problem Definition stage. You or your team might start using phrases such as "We can't," or "We don't," or "This costs too much." Shift the focus toward creating a solution by addressing objections with the phrase "How might we...?".

4. Idea Finding

Generate as many problem-solving ideas as possible.

Ways of doing this range from asking other people for their opinions, through programmed creativity tools such as Creative Problem Solving and lateral-thinking techniques, to brainstorming. You should also look at the problem from other perspectives .

Don't evaluate or criticize ideas during this stage. Instead, just concentrate on generating ideas. Remember, impractical ideas can often trigger good ones!

5. Evaluation and Selection

Once you have generated a number of possible solutions to your problem, you need to select the best one.

The best solution may be obvious. If it's not, then consider the criteria that you'll use to select the best idea. Our articles on Decision Making Techniques explore a wide range of methods for doing this.

Once you've selected an idea, develop it as far as possible . You then need to evaluate it. Common sense is more important than ego here: be objective, and consider each course of action on its merits.

If your idea doesn't offer a big enough benefit, either see whether you can generate more ideas, or restart the process. (You can waste years of your life developing creative ideas that no-one wants!)

6. Action Planning

When you've picked an idea, and you're confident that it's worthwhile, it's time to start planning its implementation.

Developing Action Plans is a good way to manage simple projects. Action plans lay out the who, what, when, where, why, and how of delivering the work.

For larger projects, it's worth using formal project management techniques . These enable you to deliver projects efficiently, successfully, and within a realistic timeframe.

7. Gaining Acceptance

Until this stage you may have been working on your own, or with just a small team. Now you have to sell your solution to the people you need support from. These people may include your boss, investors, and any other stakeholders involved with the project.

When you're selling your idea, you'll have to address not only the practicalities, but also other factors, such as internal politics and fear of change. Your goal should be to foster both a sense of ownership among the stakeholders, and an understanding of the benefits they will derive from what you're doing.

Also, think about change management in cases where implementation is likely to affect several people or groups of people. Understanding this will help you to make sure that your project gains support.

After the creativity and preparation comes action.

This is where your careful work and planning pays off. Again, if you're implementing a large-scale change or project, brushing up on your change-management skills can help you to implement the process smoothly.

When the action is underway, return to Stage 1, Problem Finding, to continue developing your idea. You can also adopt the principles of the Kaizen model of continuous improvement to refine your project.

Simplexity Thinking is a powerful approach to creative problem-solving. It is suitable for projects and organizations of almost any scale.

The process follows an eight-step cycle. When you've completed each step, you can start it again to find and solve another problem. This encourages a culture of continuous improvement.

The eight steps in the process are:

  • Problem Finding.
  • Fact Finding.
  • Problem Definition.
  • Idea Finding.
  • Evaluation and Selection.
  • Action Planning.
  • Gaining Acceptance.

This process can foster intense creativity: by moving through these steps you give yourself the best chance of solving the most significant problems with the best solutions available.

Basadur, M. (2002). ' The Power of Innovation .' London: Pitman Publishing.

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Original research article, creative problem solving as overcoming a misunderstanding.

definition of creative problem solving

  • Department of Psychology, University of Milano-Bicocca, Milan, Italy

Solving or attempting to solve problems is the typical and, hence, general function of thought. A theory of problem solving must first explain how the problem is constituted, and then how the solution happens, but also how it happens that it is not solved; it must explain the correct answer and with the same means the failure. The identification of the way in which the problem is formatted should help to understand how the solution of the problems happens, but even before that, the source of the difficulty. Sometimes the difficulty lies in the calculation, the number of operations to be performed, and the quantity of data to be processed and remembered. There are, however, other problems – the insight problems – in which the difficulty does not lie so much in the complexity of the calculations, but in one or more critical points that are susceptible to misinterpretation , incompatible with the solution. In our view, the way of thinking involved in insight problem solving is very close to the process involved in the understanding of an utterance, when a misunderstanding occurs. In this case, a more appropriate meaning has to be selected to resolve the misunderstanding (the “impasse”), the default interpretation (the “fixation”) has to be dropped in order to “restructure.” to grasp another meaning which appears more relevant to the context and the speaker’s intention (the “aim of the task”). In this article we support our view with experimental evidence, focusing on how a misunderstanding is formed. We have studied a paradigmatic insight problem, an apparent trivial arithmetical task, the Ties problem. We also reviewed other classical insight problems, reconsidering in particular one of the most intriguing one, which at first sight appears impossible to solve, the Study Window problem. By identifying the problem knots that alter the aim of the task, the reformulation technique has made it possible to eliminate misunderstanding, without changing the mathematical nature of the problem. With the experimental versions of the problems exposed we have obtained a significant increase in correct answers. Studying how an insight problem is formed, and not just how it is solved, may well become an important topic in education. We focus on undergraduate students’ strategies and their errors while solving problems, and the specific cognitive processes involved in misunderstanding, which are crucial to better exploit what could be beneficial to reach the solution and to teach how to improve the ability to solve problems.


“A problem arises when a living creature has a goal but does not know how this goal is to be reached. Whenever one cannot go from the given situation to the desired situation simply by action, then there has to be recourse to thinking. (…) Such thinking has the task of devising some action which may mediate between the existing and the desired situations.” ( Duncker, 1945 , p. 1). We agree with Duncker’s general description of every situation we call a problem: the problem solving activity takes a central role in the general function of thought, if not even identifies with it.

So far, psychologists have been mainly interested in the solution and the solvers. But the formation of the problem remained in the shadows.

Let’s consider for example the two fundamental theoretical approaches to the study of problem solving. “What questions should a theory of problem solving answer? First, it should predict the performance of a problem solver handling specified tasks. It should explain how human problem solving takes place: what processes are used, and what mechanisms perform these processes.” ( Newell et al., 1958 , p. 151). In turn, authors of different orientations indicate as central in their research “How does the solution arise from the problem situation? In what ways is the solution of a problem attained?” ( Duncker, 1945 , p. 1) or that of what happens when you solve a problem, when you suddenly see the point ( Wertheimer, 1959 ). It is obvious, and it was inevitable, that the formation of the problem would remain in the shadows.

A theory of problem solving must first explain how the problem is constituted, and then how the solution happens, but also how it happens that it is not solved; it must explain the correct answer and with the same means the failure. The identification of the way in which the problem is constituted – the formation of the problem – and the awareness that this moment is decisive for everything that follows imply that failures are considered in a new way, the study of which should help to understand how the solution of the problems happens, but even before that, the source of the difficulty.

Sometimes the difficulty lies in the calculation, the number of operations to be performed, and the quantity of data to be processed and remembered. Take the well-known problems studied by Simon, Crypto-arithmetic task, for example, or the Cannibals and Missionaries problem ( Simon, 1979 ). The difficulty in these problems lies in the complexity of the calculation which characterizes them. But, the text and the request of the problem is univocally understood by the experimenter and by the participant in both the explicit ( said )and implicit ( implied ) parts. 1 As Simon says, “Subjects do not initially choose deliberately among problem representations, but almost always adopt the representation suggested by the verbal problem statement” ( Kaplan and Simon, 1990 , p. 376). The verbal problem statement determines a problem representation, implicit presuppositions of which are shared by both.

There are, however, other problems where the usual (generalized) interpretation of the text of the problem (and/or the associated figure) prevents and does not allow a solution to be found, so that we are soon faced with an impasse. We’ll call this kind of problems insight problems . “In these cases, where the complexity of the calculations does not play a relevant part in the difficulty of the problem, a misunderstanding would appear to be a more appropriate abstract model than the labyrinth” ( Mosconi, 2016 , p. 356). Insight problems do not arise from a fortuitous misunderstanding, but from a deliberate violation of Gricean conversational rules, since the implicit layer of the discourse (the implied ) is not shared both by experimenter and participant. Take for example the problem of how to remove a one-hundred dollar bill without causing a pyramid balanced atop the bill to topple: “A giant inverted steel pyramid is perfectly balanced on its point. Any movement of the pyramid will cause it to topple over. Underneath the pyramid is a $100 bill. How would you remove the bill without disturbing the pyramid?” ( Schooler et al., 1993 , p. 183). The solution is burn or tear the dollar bill but people assume that the 100 dollar bill must not be damaged, but contrary to his assumption, this is in fact the solution. Obviously this is not a trivial error of understanding between the two parties, but rather a misunderstanding due to social conventions, and dictated by conversational rules. It is the essential condition for the forming of the problem and the experimenter has played on the very fact that the condition was not explicitly stated (see also Bulbrook, 1932 ).

When insight problems are used in research, it could be said that the researcher sets a trap, more or less intentionally, inducing an interpretation that appears to be pertinent to the data and to the text; this interpretation is adopted more or less automatically because it has been validated by use but the default interpretation does not support understanding, and misunderstanding is inevitable; as a result, sooner or later we come up against an impasse. The theory of misunderstanding is supported by experimental evidence obtained by Mosconi in his research on insight problem solving ( Mosconi, 1990 ), and by Bagassi and Macchi on problem solving, decision making and probabilistic reasoning ( Bagassi and Macchi, 2006 , 2016 ; Macchi and Bagassi, 2012 , 2014 , 2015 , 2020 ; Macchi, 1995 , 2000 ; Mosconi and Macchi, 2001 ; Politzer and Macchi, 2000 ).

The implication of the focus on problem forming for education is remarkable: everything we say generates a communicative and therefore interpretative context, which is given by cultural and social assumptions, default interpretations, and attribution of intention to the speaker. Since the text of the problem is expressed in natural language, it is affected, it shares the characteristics of the language itself. Natural language is ambiguous in itself, differently from specialized languages (i.e., logical and statistical ones), which presuppose a univocal, unambiguous interpretation. The understanding of what a speaker means requires a disambiguation process centered on the intention attribution.

Restructuring as Reinterpreting

Traditionally, according to the Gestaltists, finding the solution to an insight problem is an example of “productive thought.” In addition to the reproductive activities of thought, there are processes which create, “produce” that which does not yet exist. It is characterized by a switch in direction which occurs together with the transformation of the problem or a change in our understanding of an essential relationship. The famous “aha!” experience of genuine insight accompanies this change in representation, or restructuring. As Wertheimer says: “… Solution becomes possible only when the central features of the problem are clearly recognized, and paths to a possible approach emerge. Irrelevant features must be stripped away, core features must become salient, and some representation must be developed that accurately reflects how various parts of the problem fit together; relevant relations among parts, and between parts and whole, must be understood, must make sense” ( Wertheimer, 1985 , p. 23).

The restructuring process circumscribed by the Gestaltists to the representation of the perceptual stimulus is actually a general feature of every human cognitive activity and, in particular, of communicative interaction, which allows the understanding, the attribution of meaning, thus extending to the solution of verbal insight problems. In this sense, restructuring becomes a process of reinterpretation.

We are able to get out of the impasse by neglecting the default interpretation and looking for another one that is more pertinent to the situation and which helps us grasp the meaning that matches both the context and the speaker’s intention; this requires continuous adjustments until all makes sense.

In our perspective, this interpretative function is a characteristic inherent to all reasoning processes and is an adaptive characteristic of the human cognitive system in general ( Levinson, 1995 , 2013 ; Macchi and Bagassi, 2019 ; Mercier and Sperber, 2011 ; Sperber and Wilson, 1986/1995 ; Tomasello, 2009 ). It guarantees cognitive economy when meanings and relations are familiar, permitting recognition in a “blink of an eye.” This same process becomes much more arduous when meanings and relations are unfamiliar, obliging us to face the novel. When this happens, we have to come to terms with the fact that the usual, default interpretation will not work, and this is a necessary condition for exploring other ways of interpreting the situation. A restless, conscious and unconscious search for other possible relations between the parts and the whole ensues until everything falls into place and nothing is left unexplained, with an interpretative heuristic-type process. Indeed, the solution restructuring – is a re -interpretation of the relationship between the data and the aim of the task, a search for the appropriate meaning carried out at a deeper level, not by automaticity. If this is true, then a disambiguant reformulation of the problem that eliminates the trap into which the subject has fallen, should produce restructuring and the way to the solution.

Insight Problem Solving as the Overcoming of a Misunderstanding: The Effect of Reformulation

In this article we support our view with experimental evidence, focusing on how a misunderstanding is formed, and how a pragmatic reformulation of the problem, more relevant to the aim of the task, allows the text of the problem to be interpreted in accordance with the solution.

We consider two paradigmatic insight problems, the intriguing Study Window problem, which at first sight appears impossible to solve, and an apparent trivial arithmetical task, the Ties problem ( Mosconi and D’Urso, 1974 ).

The Study Window problem

The study window measures 1 m in height and 1 m wide. The owner decides to enlarge it and calls in a workman. He instructs the man to double the area of the window without changing its shape and so that it still measures 1 m by 1 m. The workman carried out the commission. How did he do it?

This problem was investigated in a previous study ( Macchi and Bagassi, 2015 ). For all the participants the problem appeared impossible to solve, and nobody actually solved it. The explanation we gave for the difficulty was the following: “The information provided regarding the dimensions brings a square form to mind. The problem solver interprets the window to be a square 1 m high by 1 m wide, resting on one side. Furthermore, the problem states “without changing its shape,” intending geometric shape of the two windows (square, independently of the orientation of the window), while the problem solver interprets this as meaning the phenomenic shape of the two windows (two squares with the same orthogonal orientation)” ( Macchi and Bagassi, 2015 , p. 156). And this is where the difficulty of the problem lies, in the mental representation of the window and the concurrent interpretation of the text of the problem. Actually, spatial orientation is a decisive factor in the perception of forms. “Two identical shapes seen from different orientations take on a different phenomenic identity” ( Mach, 1914 ).

The solution is to be found in a square (geometric form) that “rests” on one of its angles, thus becoming a rhombus (phenomenic form). Now the dimensions given are those of the two diagonals of the represented rhombus (ABCD).


Figure 1. The study window problem solution.

The “inverted” version of the problem gave less trouble:

[…] The owner decides to make it smaller and calls in a workman. He instructs the man to halve the area of the window […].


Figure 2. The inverted version.

With this version, 30% of the participants solved the problem ( n = 30). They started from the representation of the orthogonal square (ABCD) and looked for the solution within the square, trying to respect the required height and width of the window, and inevitably changing the orientation of the internal square. This time the height and width are the diagonals, rather than the side (base and height) of the square.

Eventually, in another version (the “orientation” version) it was explicit that orientation was not a mandatory attribute of the shape, and this time 66% of the participants found the solution immediately ( n = 30). This confirms the hypothesis that an inappropriate representation of the relation between the orthogonal orientation of the square and its geometric shape is the origin of the misunderstanding .

The “orientation” version:

A study window measures 1 m in height and 1 m wide. The owner decides to make it smaller and calls in a workman. He instructs the man to halve the area of the window: the workman can change the orientation of the window, but not its shape and in such a way that it still measures one meter by one meter. The workman carries out the commission. How did he do it?

While with the Study window problem the subjects who do not arrive at the solution, and who are the totality, know they are wrong, with the problem we are now going to examine, the Ties problem, those who are wrong do not realize it at all and the solution they propose is experienced as the correct solution.

The Ties Problem ( Mosconi and D’Urso, 1974 )

Peter and John have the same number of ties.

Peter gives John five of his ties.

How many ties does John have now more than Peter?

We believe that the seemingly trivial problem is actually the result of the simultaneous activation and mutual interference of complex cognitive processes that prevent its solution.

The problem has been submitted to 50 undergraduate students of the Humanities Faculty of the University of Milano-Bicocca. The participants were tested individually and were randomly assigned to three groups: control version ( n = 50), experimental version 2 ( n = 20), and experimental version 3 ( n = 23). All groups were tested in Italian. Each participant was randomly assigned to one of the conditions and received a form containing only one version of the two assigned problems. There was no time limit. They were invited to think aloud and their spontaneous justifications were recorded and then transcribed.

The correct answer is obviously “ten,” but it must not be so obvious if it is given by only one third of the subjects (32%), while the remaining two thirds give the wrong answer “five,” which is so dominant.

If we consider the text of the problem from the point of view of the information explicitly transmitted ( said ), we have that it only theoretically provides the necessary information to reach the solution and precisely that: (a) the number of ties initially owned by P. and J. is equal, (b) P. gives J. five of his ties. However, the subjects are wrong. What emerges, however, from the spontaneous justifications given by the subjects who give the wrong answer is that they see only the increase of J. and not the consequent loss of P. by five ties. We report two typical justifications: “P. gives five of his to J., J. has five more ties than P., the five P. gave him” and also “They started from the same number of ties, so if P. gives J. five ties, J. should have five more than P.”

Slightly different from the previous ones is the following recurrent answer, in which the participants also consider the decrease of P. as well as the increase of J.: “I see five ties at stake, which are the ones that move,” or also “There are these five ties that go from one to the other, so one has five ties less and the other has five more,” reaching however the conclusion similar to the previous one that “J. has five ties more, because the other gave them to him.” 2

Almost always the participants who answer “five” use a numerical example to justify the answer given or to find a solution to the problem, after some unsuccessful attempts. It is paradoxical how many of these participants accept that the problem has two solutions, one “five ties” obtained by reasoning without considering a concrete number of initial ties, owned by P. and J., the other “ten ties” obtained by using a numerical example. So, for example, we read in the protocol of a participant who, after having answered “five more ties,” using a numerical example, finds “ten” of difference between the ties of P. and those of J.: “Well! I think the “five” is still more and more exact; for me this one has five more, period and that’s it.” “Making the concrete example: “ten” – he chases another subject on an abstract level. I would be more inclined to another formula, to five.”

About half of the subjects who give the answer “five,” in fact, at first refuse to answer because “we don’t know the initial number and therefore we can’t know how many ties J. has more than P.,” or at the most they answer: “J. has five ties more, P. five less, more we can’t know, because a data is missing.”

Even before this difficulty, so to speak, operational, the text of the problem is difficult because in it the quantity relative to the decrease of P. remains implicit (−5). The resulting misunderstanding is that if the quantity transferred is five ties, the resulting difference is only five ties: if the ties that P. gives to J. are five, how can J. have 10 ties more than P.?

So the difficulty of the problem lies in the discrepancy between the quantity transferred and the bidirectional effect that this quantity determines with its displacement. Resolving implies a restructuring of the sentence: “Peter gives John five of his ties (and therefore he loses five).” And this is precisely the reasoning carried out by those subjects who give the right answer “ten.”

We have therefore formulated a new version in which a pair of verbs should make explicit the loss of P.:

Peter loses five of his ties and John takes them.

However, the results obtained with this version, submitted to 20 other subjects, substantially confirm the results obtained with the original version: the correct answers are 17% (3/20) and the wrong ones 75% (15/20). From a Chi-square test (χ 2 = 2,088 p = 0.148) it results no significant difference between the two versions.

If we go to read the spontaneous justifications, we find that the subjects who give the answer “five” motivate it in a similar way to the subjects of the original version. So, for example: “P. loses five, J. gets them, so J. has five ties more than P.”

The decrease of P. is still not perceived, and the discrepancy between the lost amount of ties and the double effect that this quantity determines with its displacement persists.

Therefore, a new version has been realized in which the amount of ties lost by P. has nothing to do with J’s acquisition of five ties, the two amounts of ties are different and then they are perceived as decoupled, so as to neutralize the perceptual-conceptual factor underlying it.

Peter loses five of his ties and John buys five new ones.

It was submitted to 23 participants. Of them, 17 (74%) gave the answer “ten” and only 3 (13%) the answer “five.” There was a significant difference (χ 2 = 16,104 p = 0.000) between the results obtained using the present experimental version and the results from the control version. The participants who give the correct solution “ten” mostly motivate their answer as follows: “P. loses five and therefore J. has also those five that P. lost; he buys another five, there are ten,” declaring that he “added to the five that P. had lost the five that J. had bought.” The effectiveness of the experimental manipulation adopted is confirmed. 3

The satisfactory results obtained with this version cannot be attributed to the use of two different verbs, which proved to be ineffective (see version 2), but to the splitting, and consequent differentiation (J. has in addition five new ties), of the two quantities.

This time, the increase of J. and the decrease of P. are grasped as simultaneous and distinct and their combined effect is not identified with one or the other, but is equal to the sum of +5 and −5 in absolute terms.

The hypothesis regarding the effect of reformulation has also been confirmed in classical insight problems such as the Square and the Parallelogram ( Wertheimer, 1925 ), the Pigs in a Pen ( Schooler et al., 1993 ), the Bat & Ball ( Frederick, 2005 ) in recent studies ( Macchi and Bagassi, 2012 , 2015 ) which showed a dramatic increase in the number of solutions.

In their original version these problems are true brain teasers, and the majority of participants in these studies needed them to be reformulated in order to reach the solution. In Appendix B we present in detail the results obtained (see Table 1 ). Below we report, for each problem, the text of the original version in comparison with the reformulated experimental version.


Table 1. Percentages of correct solutions with reformulated experimental versions.

Square and Parallelogram Problem ( Wertheimer, 1925 )

Given that AB = a and AG = b, find the sum of the areas of square ABCD and parallelogram EBGD ( Figures 3 , 4 ).


Figure 3. The square and parallelogram problem.


Figure 4. Solution.

Experimental Version

Given that AB = a and AG = b , find the sum of the areas of the two partially overlapping figures .

Pigs in a Pen Problem ( Schooler et al., 1993 )

Nine pigs are kept in a square pen . Build two more square enclosures that would put each pig in a pen by itself ( Figures 5 , 6 ).


Figure 5. The pigs in a pen problem.


Figure 6. Solution.

Nine pigs are kept in a square pen. Build two more squares that would put each pig in a by itself .

Bat and Ball Problem ( Frederick, 2005 )

A bat and a ball cost $1.10 in total. The bat costs $ 1.00 more than the ball. How much does the ball cost? ___cents.

A bat and a ball cost $1.10 in total. The bat costs $ 1.00 more than the ball. Find the cost of the bat and of the ball .

Once the problem knots that alter the aim of the task have been identified, the reformulation technique can be a valid didactic tool, as it allows to reveal the misunderstanding and to eliminate it without changing the mathematical nature of the problem. The training to creativity would consist in this sense in training to have interpretative keys different from the usual, when the difficulty cannot be addressed through computational techniques.

Closing Thoughts

By identifying the misunderstanding in problem solving, the reformulation technique has made it possible to eliminate the problem knots, without changing the mathematical nature of the problem. With the experimental reformulated versions of paradigmatic problems, both apparent trivial tasks or brain teasers have obtained a significant increase in correct answers.

Studying how an insight problem is formed, and not just how it is solved, may well become an important topic in education. We focus on undergraduate students’ strategies and their errors while solving problems, and the specific cognitive processes involved in misunderstanding, which are crucial to better exploit what could be beneficial to reach the solution and to teach how to improve the ability to solve problems.

Without violating the need for the necessary rigor of a demonstration, for example, it is possible to organize the problem-demonstration discourse according to a different criterion, precisely by favoring the psychological needs of the subject to whom the explanation discourse is addressed, taking care to organize the explanation with regard to the way his mind works, to what can favor its comprehension and facilitate its memory.

On the other hand, one of the criteria traditionally followed by mathematicians in constructing, for example, demonstrations, or at least in explaining them, is to never make any statement that is not supported by the elements provided above. In essence, in the course of the demonstration nothing is anticipated, and indeed it happens frequently that the propositions directly relevant and relevant to the development of the reasoning (for example, the steps of a geometric demonstration) are preceded by digressions intended to introduce and deal with the elements that legitimize them. As a consequence of such an expositive formalism, the recipient of the speech (the student) often finds himself in the situation of being led to the final conclusion a bit like a blind man who, even though he knows the goal, does not see the way, but can only control step by step the road he is walking along and with difficulty becomes aware of the itinerary.

The text of every problem, if formulated in natural language, has a psychorhetoric dimension, in the sense that in every speech, that is in the production and reception of every speech, there are aspects related to the way the mind works – and therefore psychological and rhetorical – that are decisive for comprehensibility, expressive adequacy and communicative effectiveness. It is precisely to these aspects that we refer to when we talk about the psychorhetoric dimension. Rhetoric, from the point of view of the broadcaster, has studied discourse in relation to the recipient, and therefore to its acceptability, comprehensibility and effectiveness, so that we can say that rhetoric has studied discourse “psychologically.”

Adopting this perspective, the commonplace that the rhetorical dimension only concerns the common discourse, i.e., the discourse that concerns debatable issues, and not the scientific discourse (logical-mathematical-demonstrative), which would be exempt from it, is falling away. The matter dealt with, the truth of what is actually said, is not sufficient to guarantee comprehension.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

LM and MB devised the project, developed the theory, carried out the experiment and wrote the manuscript. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

  • ^ The theoretical framework assumed here is Paul Grice’s theory of communication (1975) based on the existence in communication of the explicit layer ( said ) and of the implicit ( implied ), so that the recognition of the communicative intention of the speaker by the interlocutor is crucial for comprehension.
  • ^ A participant who after having given the solution “five” corrects himself in “ten” explains the first answer as follows: “it is more immediate, in my opinion, to see the real five ties that are moved, because they are five things that are moved; then as a more immediate answer is ‘five,’ because it is something more real, less mathematical.”
  • ^ The factor indicated is certainly the main responsible for the answer “five,” but not the only one (see the Appendix for a pragmatic analysis of the text).
  • ^ Versions and results of the problems exposed are already published in Macchi e Bagassi 2012, 2014, 2015.

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Pragmatic analysis of the problematic loci of the Ties problem, which emerged from the spontaneous verbalizations of the participants:

- “the same number of ties”

This expression is understood as a neutral information, a kind of base or sliding plane on which the transfer of the five ties takes place and, in fact, these subjects motivate their answer “five” with: “there is this transfer of five ties from P. to J. ….”

- “5 more, 5 less”

We frequently resort to similar expressions in situations where, if I have five units more than another, the other has five less than me and the difference between us is five.

Consider, for example, the case of the years: say that J. is five years older than P. means to say that P. is five years younger than J. and that the difference in years between the two is five, not ten.

In comparisons, we evaluate the difference with something used as a term of reference, for example the age of P., which serves as a basis, the benchmark, precisely.

- “he gives”

The verb “to give” conveys the concept of the growth of the recipient, not the decrease of the giver, therefore, contributes to the crystallization of the “same number,” preventing to grasp the decrease of P.

Appendix B 4

Given that AB = a and AG = b, find the sum of the areas of square ABCD and parallelogram EBGD .

Typically, problem solvers find the problem difficult and fail to see that a is also the altitude of parallelogram EBGD. They tend to calculate its area with onerous and futile methods, while the solution can be reached with a smart method, consisting of restructuring the entire given shape into two partially overlapping triangles ABG and ECD. The sum of their areas is 2 x a b /2 = a b . Moreover, by shifting one of the triangles so that DE coincides with GB, the answer is “ a b ,” which is the area of the resultant rectangle. Referring to a square and a parallelogram fixes a favored interpretation of the perceptive stimuli, according to those principles of perceptive organization thoroughly studied by the Gestalt Theory. It firmly sets the calculation of the area on the sum of the two specific shapes dealt with in the text, while, the problem actually requires calculation of the area of the shape, however organized, as the sum of two triangles rectangles, or the area of only one rectangle, as well as the sum of square and parallelogram. Hence, the process of restructuring is quite difficult.

To test our hypotheses we formulated an experimental version:

In this formulation of the problem, the text does not impose constraints on the interpretation/organization of the figure, and the spontaneous, default interpretation is no longer fixed. Instead of asking for “the areas of square and parallelogram,” the problem asks for the areas of “the two partially overlapping figures.” We predicted that the experimental version would allow the subjects to see and consider the two triangles also.

Actually, we found that 80% of the participants (28 out of 35) gave a correct answer, and most of them (21 out of 28) gave the smart “two triangles” solution. In the control version, on the other hand, only 19% (9 out of 47) gave the correct response, and of these only two gave the “two triangles” solution.

The findings were replicated in the “Pigs in a pen” problem:

Nine pigs are kept in a square pen . Build two more square enclosures that would put each pig in a pen by itself.

The difficulty of this problem lies in the interpretation of the request, nine pigs each individually enclosed in a square pen, having only two more square enclosures. This interpretation is supported by the favored, orthogonal reference scheme, with which we represent the square. This privileged organization, according to our hypothesis, is fixed by the text which transmits the implicature that the pens in which the piglets are individually isolated must be square in shape too. The function of enclosure wrongfully implies the concept of a square. The task, on the contrary, only requires to pen each pig.

Once again, we created an experimental version by reformulating the problem, eliminating the word “enclosure” and the phrase “in a pen.” The implicit inference that the pen is necessarily square is not drawn.

The experimental version yielded 87% correct answers (20 out of 23), while the control version yielded only 38% correct answers (8 out of 25).

The formulation of the experimental versions was more relevant to the aim of the task, and allowed the perceptual stimuli to be interpreted in accordance with the solution.

The relevance of text and the re-interpretation of perceptual stimuli, goal oriented to the aim of the task, were worked out in unison in an interrelated interpretative “game.”

We further investigated the interpretative activity of thinking, by studying the “Bat and ball” problem, which is part of the CRT. Correct performance is usually considered to be evidence of reflective cognitive ability (correlated with high IQ scores), versus intuitive, erroneous answers to the problem ( Frederick, 2005 ).

Bat and Ball problem

A bat and a ball cost $1.10 in total. The bat costs $ 1.00 more than the ball. How much does the ball cost?___cents

Of course the answer which immediately comes to mind is 10 cents, which is incorrect as, in this case, the difference between $ 1.00 and 10 cents is only 90 cents, not $1.00 as the problem stipulates. The correct response is 5 cents.

Number physiognomics and the plausibility of the cost are traditionally considered responsible for this kind of error ( Frederick, 2005 ; Kahneman, 2003 ).

These factors aside, we argue that if the rhetoric structure of the text is analyzed, the question as formulated concerns only the ball, implying that the cost of the bat is already known. The question gives the key to the interpretation of what has been said in each problem and, generally speaking, in every discourse. Given data, therefore, is interpreted in the light of the question. Hence, “The bat costs $ 1.00 more than” becomes “The bat costs $ 1.00,” by leaving out “more than.”

According to our hypothesis, independently of the different cognitive styles, erroneous responses could be the effect of the rhetorical structure of the text, where the question is not adequate to the aim of the task. Consequently, we predicted that if the question were to be reformulated to become more relevant, the subjects would find it easier to grasp the correct response. In the light of our perspective, the cognitive abilities involved in the correct response were also reinterpreted. Consequently, we reformulated the text as follows in order to eliminate this misleading inference:

This time we predicted an increase in the number of correct answers. The difference in the percentages of correct solutions was significant: in the experimental version 90% of the participants gave a correct answer (28 out of 31), and only 10% (2 out of 20) answered correctly in the control condition.

The simple reformulation of the question, which expresses the real aim of the task (to find the cost of both items), does not favor the “short circuit” of considering the cost of the bat as already known (“$1,” by leaving out part of the phrase “more than”).

It still remains to be verified if those subjects who gave the correct response in the control version have a higher level of cognitive reflexive ability compared to the “intuitive” respondents. This has been the general interpretation given in the literature to the difference in performance.

We think it is a matter of a particular kind of reflexive ability, due to which the task is interpreted in the light of the context and not abstracting from it. The difficulty which the problem implicates does not so much involve a high level of abstract reasoning ability as high levels of pragmatic competence, which disambiguates the text. So much so that, intervening only on the pragmatic level, keeping numbers physiognomics and maintaining the plausible costs identical, the problem becomes a trivial arithmetical task.

Keywords : creative problem solving, insight, misunderstanding, pragmatics, language and thought

Citation: Bagassi M and Macchi L (2020) Creative Problem Solving as Overcoming a Misunderstanding. Front. Educ. 5:538202. doi: 10.3389/feduc.2020.538202

Received: 26 February 2020; Accepted: 29 October 2020; Published: 03 December 2020.

Reviewed by:

Copyright © 2020 Bagassi and Macchi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Laura Macchi, [email protected]

This article is part of the Research Topic

Psychology and Mathematics Education

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Problem Redefinition

  • Roni Reiter-Palmon 2 &
  • Vignesh R. Murugavel 3  
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This entry examines the process of problem redefinition. Problem definition is discussed as it fits into the larger creative problem-solving process. Specifically, the distinctions between problem redefinition and problem definition are detailed. A formal definition of the problem redefinition process is formed from these qualifications. Theoretical and empirical works on redefining problems to produce creative solutions are examined to better understand the utility of the redefinition process. Literature from organizational science, social and cognitive psychology, and design thinking is reviewed to elucidatethe problem redefinition process. Both individual-level and group-level problem redefinition processes are considered. A brief discussion of research on goal change is provided to further describe how and why problem redefinition occurs. Finally, the role of the possible is discussed to capture the essence of problem redefinition.

  • Creative problem solving
  • Creative process
  • Problem definition
  • Problem redefinition

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Reiter-Palmon, R., Murugavel, V.R. (2022). Problem Redefinition. In: The Palgrave Encyclopedia of the Possible. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-98390-5_185-1

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Creativity and Ethics: The Relationship of Creative and Ethical Problem-Solving

Students of creativity have long been interested in the relationship between creativity and deviant behaviors such as criminality, mental disease, and unethical behavior. In the present study we wished to examine the relationship between creative thinking skills and ethical decision-making among scientists. Accordingly, 258 doctoral students in the health, biological, and social sciences were asked to complete a measure of creative processing skills (e.g., problem definition, conceptual combination, idea generation) and a measure of ethical decision-making examining four domains, data management, study conduct, professional practices, and business practices. It was found that ethical decision-making in all four of these areas was related to creative problem-solving processes with late cycle processes (e.g., idea generation and solution monitoring) proving particularly important. The implications of these findings for understanding the relationship between creative and deviant thought are discussed.

Creative ideas are held to be reflected in original, and useful, problem solutions ( Ghiselin, 1963 ; Mumford & Gustafson, 1988 ). The originality apparent in creative ideas involves a departure from normative behavior ( Stokes & Balsam, 2003 ). This observation, in turn, has led many scholars to ask whether creativity is related to other forms of deviant behavior. For example, although controversial ( Ramey & Weisberg, 2004 ), Jamison (1993) has argued that creativity may be linked to bipolar disorder. Similarly, Sass and Schuldberg (2001) have suggested that creativity may be linked to schizophrenia. Other scholars ( Brower, 1999 ; Eisenman, 1999 ) have provided evidence indicating that at least certain forms of creative thought may be linked to criminal behavior – a form of deviance. Still other investigations have examined the relationship between creativity and moral, or ethical, behavior ( Runco & Nemiro, 2003 ) noting that the relationship obtained between measures of moral reasoning and creative thought have been inconsistent ( Andreani & Pagnin, 1993 ).

In the present effort we examined the relationship between creative thinking processes ( Mumford, Mobley, Uhlman, Reiter-Palmon, & Doares, 1991 ) and ethical decision-making ( Mumford et al., 2006 ) in a specific domain ( Baer, 2003 ). More specifically, we wished to examine the relationship between creative thought and ethical decision-making among scientists. Our interest in scientists was based, in part, on the fact that creative thought is considered critical to eminent achievement in this arena ( Feist & Gorman, 1998 ; Mumford et al., 2005 ). And, in part, our interest was based on the fact that ethical conduct is considered a critical aspect of performance in the sciences ( Steneck, 2006 ). Although scientists do not always display ethical conduct, scientists are expected to adhere to ethical codes of conduct in doing their work ( Hamner & Organ, 1978 ; McCabe and Pavela, 1998 ), and scientists are routinely trained in ethical conduct ( National Institute of Medicine, 2002 ).

Ethical Conduct

Traditionally, unethical conduct in the sciences was held to be reflected in fabrication, falsification, and plagiarism ( Koenig, 2001 ). These actions, while clearly ethical issues ( Marshall, 1996 ), do not necessarily cover all forms of ethical conduct relevant to performance in the sciences (Steneck, 2004 ; 2006 ). For example, ethical issues are brought to fore by conflicts of interest arising from scientists’ involvement in multiple related ventures ( Campbell, Louis, & Blumenthal, 1998 ). Similarly, assignment of authorship on publications broaches ethical concerns with regard to both inappropriate allocation of authorship and failure to award authorship to those that have actually contributed to the work ( Macrina, 2000 ).

Although authorship misallocation and conflicts of interest are less severe than fabrication, falsification, and plagiarism, they may occur with greater frequency. Some support for this conclusion has been provided in a recent study by Martinson, Anderson, and de Vries (2005) . They conducted a survey study examining scientists’ exposure to incidents of unethical behavior. They found that a large proportion, more than 30%, had been exposed to incidents of misconduct in the year prior to the survey. Moreover, many of the ethical breeches scientists reported having been exposed to reflected less severe forms of misconduct (e.g., authorship misallocation) than fabrication, falsification, and plagiarism.

Recognition of the variety of ethical issues arising in the sciences has led to attempts to formulate viable taxonomies examining ethical conduct in the sciences. In one effort intended to address this issue, Helton-Fauth et al. (2003) reviewed codes of conduct published by professions (e.g., American Psychological Association, American Medical Association, American Society for Cell Biology) in the health, biological, and social sciences. This review led to the identification of 17 dimensions of ethical behavior organized into four broader areas, namely data management (data massaging, publication practices), study conduct (institutional review board, informed consent, confidentiality protection, protection of human participants, protection of animal subjects), professional practices (objectivity in evaluating work, recognition of expertise, protection of intellectual property, adherence to professional commitments, protection of public welfare and the environment, exploitation of staff and /or collaborators), and business practices (conflicts of interest, deceptive bid and contract practices, inappropriate use of physical resources, inappropriate management practices). The evidence compiled by Helton-Fauth et al. (2003) indicate that these dimensions, while of varying importance across fields, can account for most incidents of misconduct. Moreover, Kligyte, Marcy, Sevier, Godfrey, and Mumford (2008) have provided evidence for the generality of these dimensions to other scientific fields such as meteorology and computer science.

This taxonomy, and other related taxonomies, of course, provides a basis for assessing ethical conduct ( Fleishman & Quaintance, 1984 ). However, ethical behavior with respect to these dimensions might be assessed using any of a number of techniques (e.g., self-reports, behavioral observations). As O’Fallon and Butterfield (2005) noted, however, the relative infrequency of unethical conduct, and the social consequences of such conduct, have led many investigators to assess ethical conduct through ethical decision-making measures.

Recently, Mumford et al. (2006) developed a set of ethical decision-making measures intended to provide measures of these dimensions. These ethical decision-making measures were developed using a low-fidelity simulation approach ( Motowidlo, Dunnette, & Carter, 1990 ). Here, a general real-world scenario was presented where participants were asked to assume the role of the principal actor. Subsequently, events unfolding from this scenario were presented where each event had implications for one of the 17 dimensions included in the taxonomy of ethical behavior. For each event, participants were asked to select the best two, of six to twelve, response options where response options varied with respect to ethical implications on a given dimension.

When scores on these dimensions were aggregated to provide measures of data management, study conduct, professional practices, and business practices, the resulting scale scores were found to be moderately related to intelligence ( r = .19), negatively related to narcissism and cynicism ( r = −.18), but unrelated to social desirability ( r = −.01) in a sample of 156 doctoral students working in health, biological, and social science fields. More centrally, it was found that exposure to unethical events in the course of these doctoral students’ day-to-day work was negatively related to ethical decisions ( r = −.45). Moreover, scores on these ethical decision-making measures were positively related to the severity of the punishments awarded for ethical violations by doctoral students when working on an incident review panel ( r = .47). Thus, it appears that this measure of ethical decision-making evidenced some construct validity as a measure of ethical behavior among younger scientists.

Creativity and Ethics

Of course, the central question underlying the present study was how ethical decision-making would be related to creativity among scientists. One model that might be used to account for this relationship has been provided by Ludwig ( 1995 ; 1998 ). He (1995; 1998) was most directly concerned with the relationship between “madness”, poor mental health, and eminent achievement, historically notable achievement, in the arts and sciences. He argued that the sciences, in contrast to the arts, emphasize formal, objective modes of creative problem-solving. When creative thought is based on this formal objective mode of thinking, the relationship between poor mental health and creativity is held to be undermined. In fact, Ludwig ( 1995 ; 1998 ) provides support for this theory by showing not only that the incidence of mental health problems is lower for successful scientists than artists, but that it is lower for artists employing a formal as opposed to emotive style.

Although Ludwig’s ( 1995 ; 1998 ) work speaks most directly to eminent achievement and madness, it does have relevance for understanding the relationship between creativity and ethics among scientists. Although creative scientists sometimes display irrational behavior, creative work in the sciences depends on systematic theorizing accompanied by systematic, potentially replicable, tests of this theory ( Feist & Gorman, 1998 ; Tweney, 1992 ). The formal, systematic nature of scientific thought, in turn, implies that scientific creativity occurs within a rule bound system – rule bound systems in both conceptual and methodological terms. These rules, or optimized courses of action for theory development and testing, are, of course, subject to change ( Kuhn, 1970 ). However, creative achievement in the sciences often appears to require the ability both to recognize these rules and manipulate theory, and tests, within the rule system applying at that particular point in time. Thus, creative work in the sciences requires both convergent and divergent thinking within a system of constraints ( Zuckerman, 1977 ).

The ability to recognize and apply rules when working through creative problems in the sciences implies that one would, for two reasons, expect a positive relationship between scientific creativity and ethical decision-making. First, because scientists work in a rule bound world, one would expect that they would be particularly attentive to rule systems bearing on their work ( Ericsson & Charness, 1994 ). This attentiveness to work rules should, given the ethical context in which scientific work occurs, encourage scientists to attend to relevant ethical rules. Second, scientists manipulate and reason within the rule system applying to their particular work domain. Accordingly, one would expect that scientists, incoming younger scientists such as doctoral students, would be skilled in working creatively within these rule systems to give rise to a positive relationship between creativity and ethical decision-making. These observations led to our first hypothesis:

Hypothesis One: Creative thinking skills will be positively related to ethical decision-making among doctoral students in the sciences.

Of course, ethical decisions, particularly ethical decisions in complex and ambiguous settings, do not arise in a vacuum. Recently, Mumford ( Kligyte et al., 2008 ; Mumford et al., in press ) proposed a model of how people think about ethical decisions. In this model, it is held that ethical decision-making is based on interpersonal and professional sensemaking ( Drazin, Glynn, & Kazanjian, 1999 ; Weick, 1995 ). In sensemaking, people attempt to create a mental model for understanding the ethical issue at hand with people using this model to forecast the likely outcomes of actions in emotionally charged and ambiguous interpersonal situations. This sensemaking model of ethical decision-making, in turn, implies that effective ethical decision-making will depend on multiple strategic processing operations such as recognizing circumstances, dealing with emotions, questioning judgment, self-reflection, anticipating consequences, and considering others. Kligyte et al. (2008) and Mumford et al. (in press) showed that training interventions intended to improve application of these strategies leads to improved ethical decision-making. Other work by Mumford et al. (2006) has shown that effective application of these strategies is also strongly positively related to ethical decision-making ( r = .40).

The importance of these strategies, however, suggests a second way creative thinking might contribute to ethical decision-making. More specifically, creative thinking skills might contribute to more effective execution of each of these strategies. For example, creative thinking may allow people to forecast a larger range of outcomes or construct the ethical problem at hand from the perspective of others. The more effective application of these ethical decision-making strategies brought about by creative thinking skills should lead to more ethical decisions among scientists, including doctoral students beginning their career in the sciences. These observations, in turn, led to our second hypothesis:

Hypothesis Two

Creative thinking skills will be positively related to strategies held to contribute to ethical decision-making among doctoral students in the sciences.

With regard to these first two hypotheses, however, it should be clear that we have formulated hypotheses with respect to creative thinking skills as a general phenomenon. However, over the years a number of models of the cognitive processing operations underlying creative though have been proposed (e.g., Hennessey & Amabile, 1988 ; Isaksen & Parres, 1985 ; Merrifield, Guilford, Christensen, & Frick, 1962 ; Osborn, 1953 ; Sternberg, 1986 ). In a review of these process models, Mumford et al. (1991) identified eight core processing activities involved in creative thought – problem definition, information gathering, concept selection, conceptual combination, idea generation, idea evaluation, implementation planning, and monitoring. In fact, subsequent work by Mumford and his colleagues (e.g., Lonergan, Scott, & Mumford, 2005; Mumford, Supinski, Baughman, Costanza, & Threlfall, 1997 ; Osburn & Mumford, 2006 ; Scott, Lonergan, & Mumford, 2005 ) has led Brophy (1998) and Lubart (2001) to conclude this model represents the best available description of creative thinking processes.

Although all of these processes, in conjunction with knowledge ( Hunter, Bedell-Avers, Ligon, Hunsicker, & Mumford, 2008 ; Weisberg, 2006 ), exert unique effects on creative problem-solving, these processes represent distinct entities. Mumford (2001) distinguished these processes with respect to early cycle processing activities involving knowledge generation (i.e., problem definition, information gathering, concept selection, and conceptual combination) and late cycle processing activities involving product production (i.e., idea generation, idea evaluation, implementation planning, monitoring). Late cycle processes differ from early cycle processes in that process execution is contextualized to take into account real-world considerations. Thus, Finke, Ward, and Smith (1992) found that idea generation was enhanced by asking people to consider potential applications of new understandings emerging from conceptual combination. Similarly, Lonergan et al. (2005) showed that idea evaluation improves when people employ a compensatory approach seeking to offset real-world deficiencies in new ideas.

This contextualization of late cycle processing activities is significant because it has implications for the kind of creative processing skills that would influence strategy execution and ethical decision-making. Eisenman (1999) contrasted more and less creative prisoners to norms of three creative tests – singing, dancing, and storytelling based on the Thematic Apperception Test. It was found that creative prisoners obtained higher scores than non-creative peers on singing and dancing but not storytelling – presumably due to the external constraints imposed on creativity by storytelling requirements ( Eisenman, 1999 ). Ethical decisions, of course, imply that decisions must be made to take into account the real-world consequences of actions. This observation, in turn, implies that late cycle processing activities will exert stronger effects on ethical decisions, and the strategies employed in making these decisions, than early cycle processing activities. One would expect these relationships to hold for both experienced scientists and doctoral students just starting their careers in the sciences. Hence, our final two hypotheses:

Hypothesis Three

Late cycle creative processing activities will be more strongly, and positively, related to ethical decision-making among doctoral students in the sciences than early cycle creative processing activities.

Hypothesis Four

Late cycle creative processing activities will be more strongly, and positively, related to strategies underlying ethical decision-making among doctoral students in the sciences than early cycle creative processing activities.


The sample used to test these hypotheses consisted of 258 doctoral students attending a large research university in the southwest. The 98 men and 151 women (9 unreported) recruited to participate in this study had a minimum of 4 months experience working at the university and a maximum of 60 months experience. Sample members were recruited from programs awarding doctoral degrees in the biological (40%), health (27%), and social sciences (33%). All programs required independent research for award of a doctorate. On average, sample members were 28 years old with 61% of the sample being composed of majority group members and 39% of the sample being composed of minority group members. A typical sample member had completed 17 years of education prior to admission into their relevant doctoral program. Although 45% of the sample was employed in non-research (primarily teaching) positions, 55% were employed as full time research assistants. All sample members, however, reported being actively involved in one or more research projects, and most go on to active careers in research in either applied or academic settings.

General Procedures

The present study was conducted as a part of a larger, federally funded, initiative concerned with research integrity. The university provided names, department assignments, email addresses, and telephone numbers of all doctoral students attending the university in 2005 and 2006. A three stage recruitment process was used to recruit the doctoral students who agreed to participate in this study. First, flyers announcing the study, and that $100.00 would be provided as compensation for participation, were placed in the mailboxes of doctoral students. Second, one phone call was made to each doctoral student to encourage participation. Finally, each doctoral student was sent up to four email requests to solicit participation.

In all stages of this recruitment process, it was noted that the study was concerned with research integrity. More specifically, the study was described as examining the effects of educational experience on integrity and problem-solving. Those students who agreed to participate in this study were asked to schedule a time when they could complete a four hour battery of paper-and-pencil measures. Students were asked to read and complete each measure under conditions where no time pressure was induced. Once the doctoral students had completed these measures, they were debriefed.

The battery of paper-and-pencil measures the doctoral students were asked to complete included a number of inventories. First, students were asked to complete a background information form. Second, they were asked to describe the work they were doing and events that had occurred in the course of doing this work. Third, they were asked to assume the role of an institutional review board member and assign penalties for ethical breeches. Fourth, they were asked to complete a battery of individual differences measures intended to provide covariate controls. Fifth, and finally, participants were asked to complete a professional, field relevant, problem-solving measure. It is of note that this field specific problem-solving measure was structured such that people were asked to make ethical decisions and engage in creative problem-solving vis--vis issues that might be encountered in their day-to-day work. This measure was administered as a problem-solving measure after the review board task, which expressly focused on ethics, to minimize demand characteristics. Thus, participants saw the ethical decision-making measures as a work performance measure.

The first two control measures examined cognitive abilities that might influence peoples’ problem-solving ( Vincent, Decker, & Mumford, 2002 ). More specifically, people were asked to complete a 30-item verbal reasoning measure drawn from the Employee Aptitude Survey ( Ruch & Ruch, 1980 ) along with a 5-item consequences test ( Merrifield, Guilford, Christensen, & Frick, 1962 ) intended to provide a measure of divergent thinking. The consequences test was scored for fluency given its use as a control. Both these measures yielded split-half reliability coefficients above .80. Evidence bearing on their construct validity may be obtained by consulting Ruch and Ruch (1980) and Merrifield, Guilford, Christensen, and Frick (1962) .

In addition to these cognitive measures, participants were asked to complete two sets of personality measures. The first set of measures examined general dispositional characteristics that might be relevant to creativity or ethics. Accordingly, participants were asked to complete John, Donahue, and Kentle’s (1991) behavioral self-report inventory to provide measures of agreeableness, extraversion, conscientiousness, neuroticism, and openness – NEO dimensions ( McCrae & Costa, 1987 ). Additionally, participants were asked to complete Paulhus’s (1984) behavioral self-report measure of socially desirable responding. All the scales included in these inventories produced internal consistency coefficients above .70. Paulhus’s (1984) and John, Donahue, and Kentle (1991) have provided evidence bearing on the construct validity of their instruments.

The second set of personality measures, again all behavioral self-report inventories, examined personality characteristics that have been linked to integrity and ethical decision-making. Thus, participants were asked to complete Emmon’s (1987) measure of narcissism and Wrightsman’s (1974) measure of cynicism. Finally, based on Fromm’s (1973) observations concerning the impact of uncertainty on ethical breeches, participants were asked to complete Taylor’s (1953) Manifest Anxiety Scale. Again, these scales all produced internal consistency coefficients in the low .70s. Evidence bearing on the construct validity of these scales has been provided by Emmons (1987) , Taylor (1953) , and Wrightsman (1974) .

Ethical Decision-Making

The principle criterion measure of concern in the present study was the measure of ethical decision-making developed by Mumford et al. (2006) . Development of this measure was based on Helton-Fauth et al.’s (2003) taxonomy describing the major behavioral dimensions included in ethical conduct in the sciences. More specifically, this measure was intended to provide an assessment of the four broader dimensions, data management, study conduct, professional practices, and business practices, based on expression of specific dimensions subsumed under these broader rubrics, such as the objectivity in evaluating work and protection of intellectual property – dimensions subsumed under professional practices.

Development of the ethical decision-making measure began with a review of ethics websites (e.g., On-line Ethics Center, American Psychological Association) to identify work-oriented cases that might be used to assess decision-making with respect to one or more of the 17 dimensions identified by Helton-Fauth et al. (2003) . This review led to the identification of 45 cases in each of the three fields under consideration – biological, health, and social sciences. These cases were then reviewed, by three psychologists, with respect to their ability to meet three criteria: 1) relevance to day-to-day work, 2) both ethical and technical issues involved, and 3) potentially challenging decisions across a range of expertise. These criteria led to selection of the 10 to 15 cases applying in a given field that would be used to develop the measures of ethical decision-making.

Development of the ethical decision-making measure involved two stages – context preparation and item development. Context generation began with rewriting of the case into a short one or two paragraph scenario. Next, a panel of three psychologists, and a subject matter expert, generated a list of 8 to 12 events that might occur within this scenario under the constraint that half these events were to have only technical implications and half of the events were to have ethical implications for one of the 17 dimensions. Panel members were asked to review these events, the basis for item development, and select the two technical and two ethical events most likely to occur in this scenario. Panel members were then asked to take the two best ethical, and the two best technical, events and organize them into a flow of action within the scenario.

The two ethical events identified in each scenario provided the basis for developing the measure of ethical decision-making. For each of these events, 6 to 12 potential responses were generated by three psychologists for each ethical event. One third of these responses were to reflect highly ethical responses, one third moderately ethical responses, and one third poor ethical responses. These response options were based on professional codes of conduct. All response options generated were reviewed by a panel of three psychologists, all with more than seven years experience working in the area of scientific ethics, with respect to the responses proposed scoring (high, moderate, poor), the relevance of the responses to a hypothesized dimensions (e.g., data massaging), and clarity. On average, 2 to 3 events were formulated for each of the 17 dimensions of ethical conduct applying to a given field – biological, health, and social sciences. Separate measures were formulated for each field. Figure 1 illustrates the ethical decision-making questions administered to doctoral students in the social sciences.

Moss is a researcher in the laboratory of Dr. Abrams, a well-known researcher in the field of economics. Moss is trying to develop a model to predict performance of stocks in the technology sector, but she is having difficulty analyzing and selecting trends to include in the model. She enlists the help of Reynolds, another experiences researcher working on a similar topic. With Reynolds’s help, Moss eventually analyzes and identifies some key trends working them into a testable model. She also discusses some of her other research ideas with Reynolds. Two weeks later, Moss comes across a grant proposal developed by Reynolds and Abrams. She sees that it includes ideas very similar to those she discussed with Reynolds. She takes the matter to Abrams, who declines to get involved, saying that the two researchers should work it out on their own.

  • Fire Reynolds from the lab on the grounds of academic misconduct
  • Leave Reynolds as first author on the proposal since he wrote up the ideas
  • Remove Reynolds from the proposal team, and offer Moss the position if she allows her ideas to be used
  • Ask Moss to join the grant team, placing her as third author on the proposal if she allows her ideas to be used
  • Acknowledge Moss in the grant proposal because the ideas were hers originally
  • Apologize to Moss and indicate that the proposal must go out as is to meet the deadline
  • Remove Moss’s ideas from the proposal and try to rework it before the deadline

In responding to these questions, participants were asked to read through the scenario and assume the role of the primary actor in the scenario. After they had read through an event, they were to select the two response options that they believed would most likely resolve the issue broached by the event. Responses selected were scored as high (3), moderate (2), and low (1). The average of the two responses provided a participant’s score for the event. The average of these scores was then obtained for all questions bearing on a given dimension of ethical conduct. The average of scores across dimensions subsumed under the four general rubrics of ethical conduct, data management, study conduct, professional practices, and business practices, provided the final measures of ethical conduct applied in the present study.

The average, across field, split-half reliability obtained for scores on the data management, study conduct, professional practice, and business practices dimensions was .74. Evidence bearing on the construct validity of these scales has been provided by Mumford and colleagues (2006) who noted that 1) the scales measuring these four dimensions of ethical decision-making evidenced an interpretable pattern of relationships (e.g., data management and professional practices were strongly related ( r = .57) while data management and study conduct ( r = .22) displayed a weaker relationship), 2) these scales yielded an interpretable pattern of relationships with relevant individual difference measures (e.g., proving to be negatively related to cynicism and narcissism), 3) these scales were uncorrelated with social desirability, 4) these scales were negatively related to career events held to influence ethical conduct, and 5) these scales were positively related to the severity of punishment awarded for incidents of misconduct.

Cognitive Strategies

Prior work on ethical decision-making by Kligyte et al. (2008) and Mumford et al. (in press) led to the identification of seven cognitive strategies that might contribute to ethical decision-making: 1) recognition of circumstances, 2) seeking help, 3) questioning one’s judgment, 4) anticipating consequences, 5) dealing with emotions, 6) analysis of personal motivations, and 7) consideration of the effects of one’s actions on others ( Butterfield, Treviño, & Weaver, 2000 ; Street, Douglas, Geiger, & Martinko, 2001 ; Yaniv & Kleinberger, 2000 ).

To develop measures of these strategies, operational definitions of each strategy were formulated. A panel of four psychologists, all psychology doctoral students, was presented with these operational definitions and the way in which application of each strategy manifested itself in ethical decisions. Following this 20 hour training program, the judges were asked to read through each scenario, the associated ethical events, and the response options that might be provided for these events. Judges were then asked to rate the extent to which each response option would emerge from application of each of these cognitive strategies using a 7-point Likert scale (1 = Low, 7 = High).

The interrater agreement coefficient obtained for these ratings of strategy application was .91. Scores on each strategy were obtained by weighting each response based on the judges’ average rating and then multiplying these weights by each response selected to obtain the average strategy score across all selected responses. Evidence bearing on the validity of these strategy scores has been provided by Mumford et al. (2006) and Mumford et al. (in press) . The findings obtained in these studies indicate not only that execution of these strategies is positively related to ethical decision-making ( r = .40), but that effective educational interventions with regard to ethics result in improvements of peoples skill in executing these strategies.

Creative Thinking Skills

The measure of creative thinking skills applied in the present study was based on the model of creative thinking processes developed by Mumford et al. (1991) . This model holds that creative thought involves 8 core processing activities: 1) problem definition, 2) information gathering, 3) concept selection, 4) conceptual combination, 5) idea generation, 6) idea evaluation, 7) implementation planning, and 8) monitoring. Prior studies by Lonergan et al. (2004) , Mumford et al. (1997) , Osburn and Mumford (2006) , and Scott et al. (2005) have provided evidence indicating the influence of effective execution of these processing activities on creative thought.

In the present effort, measures of these creative processing activities were based on the technical events following scenarios being used to measure ethical decision-making. As noted earlier, half the events following a given scenario had ethical implications while the remaining half of the events had technical implications. All technical events were written to call for the production of novel, potentially useful, solutions to the technical problem broached by the event, and this could be said to reflect creative thought ( Mumford & Gustafson, 1988 ). Four psychologists wrote those questions concerning technical events with respect to the 8 processes identified by Mumford and colleagues (1991) . Prior to writing these technical event questions, three psychologists, again all doctoral students in industrial and organizational psychology, were provided with a 20 hour training program describing each process and how it was reflected in technical work in the field. The judges, and a subject matter expert, were asked to generate 4 to 8 events in each field that would fit with the relevant scenario and reflect application of the relevant process.

The measurement of these processes occurred using a variation on the procedures suggested by Runco, Dow, and Smith (2006) . More specifically, once events calling for application of a given process had been generated, the three judges, and a subject matter expert, were asked to generate potentially usable response options that might be used in resolving the technical issues broached by this event. Response options were to be generated under the constraints that 1) one-quarter of the options were to reflect responses of high quality and high originality, 2) one-quarter of the options were to reflect responses of high originality but low quality, 3) one-quarter of the options were to reflect responses of low originality but high quality, and 4) one-quarter of the options were to reflect responses of low quality and low originality.

Again, participants were asked to read through the scenario and assume the role of the principle actor. After reading through the description of a technical event, they were asked to select the two options they believed would most likely resolve the issue broached by the event. The options selected were given scores of 3 (high quality, high originality), 2 (high quality, low originality or low quality, high originality), or 1 (low quality, low originality) with average scores being obtained for the two options selected. These scores were then averaged over all events bearing on the application of a given process to obtain process application scores within the three fields of health, biological, and social sciences. Figure 2 provides an illustration of 2 creative problem-solving items, for idea generation and conceptual combination, for social scientists.

Baron works in a non-profit research center set up as part of a 500 square-mile wildlife reserve. Researchers in this lab study how wild animals respond to regular contact with humans. The National Parks and Recreations Service has funded three of the center’s research projects examining the impact of human-animal contact on reproductive behavior in different small mammal species. These projects were developed jointly and were funded together because similarities in ecosystem variables and observation methodologies will enable some level of comparison of results across species. Baron is the principal investigator for one of these projects.

Idea Generation

  • Decrease the amount of animals in the study by half
  • Replace half of the research team with minimum wage workers to assist in the treatment
  • Split the teams into smaller groups where only two group members must be present per animal session
  • Assign specific jobs to each team member to reduce excess manpower on mundane tasks
  • Shorten the timeframe on the contract in order to reduce overhead costs and shift that money onto the payment of more researchers
  • Each week, randomly split the teams into groups of three, and assign each group to oversee either the control group or the treatment group
  • Assign only one person per group to record behaviors of the control group as opposed to all members splitting the responsibilities
  • Train current researchers to multi-task experimental sessions so that each researcher is in charge of inducing the experimental manipulation as well as recording responses

Conceptual Combination

  • Drop the control animals from the study and shift all researcher attention to the “treatment groups” for a cross-species comparison
  • Train researchers to be more efficient in their daily animal care tasks and shift the extra time over to participating in more experimental sessions
  • Train minimum wage workers to be more efficient in their daily animal care tasks and multi-tasking the experimental sessions so they have more responsibilities
  • Replace some members of the research team with minimum-wage workers while training them to complete specific auxiliary tasks for the researchers
  • Train researchers to multi-task experimental sessions while rotating them through experimental groups
  • Divide researcher groups into “experimental” and “data management” groups so that each more work can be done faster and the contract can be shortened
  • Decrease the amount of animals in the control group and assign only one researcher to oversee these animals
  • Assign researchers specific jobs to during experimental sessions while reducing number of researchers that must be present at each session

The average split-half reliability coefficient obtained across the three fields for scores on these 8 process dimensions was .71. Some initial evidence bearing on the construct validity of these measures was obtained by examining their convergent and divergent validity. Thus, conceptual combination was found to be positively related to idea generation ( r = .29) and idea evaluation ( r = .15) but not problem definition ( r = .07). Problem definition, however, was found to be positively related to implementation planning ( r = .19). Taken together, these relationships provide some initial evidence for the construct validity of these measures.

Initially, scores on the measures of creative processing were correlated with the measures of ethical decision-making. Subsequently, scores on the four ethical decision-making dimensions, data management, study conduct, professional practices, and business practices, were regressed on the creative processing dimensions. It is of note that each decision-making dimension was then treated as a separate criterion based on prior studies indicating that they demonstrate different patterns of relationships with respect to certain predictors ( Mumford et al., 2006 ). These analyses were then repeated after adding individual differences control measures as the first block of predictors. This same set of analyses was then replicated using the strategy measures as the criterion variables of interest.

Table 1 presents the correlations, and associated significance levels, of the ethical decision-making measures with the measures of creative processing skills. As may be seen, two critical creative processing skills, specifically conceptual combination ( r = .17) and idea generation ( r = .26), were positively related to ethical decisions involving data management, study conduct, professional practices, and business practices. In addition, solution monitoring ( r = .19) was positively related to these four dimensions of ethical decision-making. Thus, it appears that creative thinking skills are related to ethical behavior, at least as it is reflected in this kind of low-fidelity simulation.

Correlations of Creative Thinking and Ethical Decision-Making Measures

Note . M = Mean, SD = Standard Deviation,

In this regard, however, it is important to bear in mind the other dimensions of creative thought were related to specific dimensions of ethical decision-making. For example, idea evaluation ( r = .17) was found to be positively related to ethical decision-making with regard to professional practices, perhaps reflecting the external evaluative aspects of professional behavior. Similarly, implementation planning ( r = .25) was found to be positively related to study conduct, a relationship that may reflect the importance of planning in conducting research projects. More surprising were the findings that information gathering ( r = −.30) and concept selection ( r = −.14) were negatively related to ethical decisions with respect to study conduct. Although these relationships may reflect the self-protective tendencies evidenced by people engaging in unethical conduct ( Fromm, 1973 ), they do suggest that there may be a complex pattern of relationships between creative thinking skills and ethical decision-making.

Table 2 presents the results obtained in the regression analyses when scores on the measures of creative processes were used to account for ethical decision-making both with and without taking into account the individual differences control variables. Significant ( p ≤ .05) multiple correlations of .34, .51, .37, and .39 were obtained when only the creative thinking skills were used to predict ethical decision-making. When the creative thinking skills were used added to the block of individual differences measures, significant ( p ≤ .05) gains in prediction were obtained with multiple correlations of .45, .57, .46, and .45 being observed for the data management, study conduct, professional practices, and business practices. In keeping with the observations of Mumford et al. (2006) intelligence, vis--vis study conduct (β = .21), professional practices (β = .24), and business practices (β = .14), and cynicism, vis-à-vis data management (β = −.16) and professional practices (β = −.15), produced the strongest relationships.

Regression of Ethical Decision-Making on Creative Thinking Measures with and without Controls

Note . β 1 = Standardized regression weight (no controls), β 2 = Standardized regression weight (with controls),

More centrally, when relationships among the creative thinking skills were taken into account, it was found that idea generation, both with and without controls taken into account, produced significant ( p ≤ .05) regression weights for data management (β 1 = .23, β 2 = .23), study conduct (β 1 = .18, β 2 = .18), professional practices (β 1 = .25, β 2 = .24), and business practices (β 1 = .28, β 2 = .30). In addition, solution monitoring produced significant ( p ≤ .05) regression weights with respect to ethical decisions involving data management (β 1 = .12), study conduct (β 1 = .22, β 2 = .17), professional practices (β 1 = .15), and business practices (β 1 = .15, β 2 = .12). Again, in the case of study conduct, information gathering (β 1 = −.24, β 2 = −.22) and concept selection (β 1 = −.14, β 2 = −.14) produced significant ( p ≤ .05) negative relationships. Implementation planning (β 1 = .18, β 2 = .15) produced a significant ( p ≤ .05) positive relationship. Even bearing these relationships in mind, however, it appears that two late cycle processing skills ( Mumford et al., 1991 ), idea generation and solution monitoring, are the aspects of creative thought most strongly, and consistently, related to ethical behavior.

Ethical Decision Strategies

Table 3 presents the correlations, and accompanying significance levels, of the strategies held to contribute to ethical decision-making with the measures of creative thinking skills. As may be seen, idea generation ( r = .30) and solution monitoring ( r = .22) were consistently positively correlated with application of these strategies. Moreover, both idea evaluation and implementation planning yielded significant ( p ≤ .05) relationships with select strategies. More specifically, idea evaluation was positively related to seeking help ( r = .13), questioning judgment ( r = .23), dealing with emotions ( r = .22), and analyzing personal motivations ( r = .24) – all strategies where a skeptical evaluative approach would prove beneficial. Similarly, implementation planning was positively related to recognition of circumstances ( r = .15) and anticipating consequences ( r = .18), both strategies integral to planning ( Mumford, Schultz, & Osburn, 2002 ). In keeping with this interpretation, implementation planning was found to be negatively related to seeking help ( r = −.18). Thus, it appears that late cycle creative thinking skills were related to ethical decision-making strategies.

Correlations of Creative Thinking and Ethical Strategies Measures

Among early cycle processes, conceptual combination, problem definition, and information gathering produced the strongest relationships. Conceptual combination was positively related to recognition of circumstances ( r = .25), seeking help ( r = .16), anticipating consequences ( r = .22) and consideration of the effects of actions on others ( r = .21). Because all these strategies require bringing other considerations to bear on ethical problems it was not surprising that they were related to conceptual combination. Problem definition, however, produced negative relationships with seeking help ( r = −.23), questioning judgment ( r = −.20), dealing with emotions ( r = −.15), and analysis of personal motivations ( r = −.21) – a pattern of findings suggesting that a firm grasp of the problem may undermine more sophisticated analyses of ethical issues. Finally, information gathering was positively related to questioning judgment ( r = .18), dealing with emotions ( r = .20), and analysis of personal motivations ( r = .14) – all strategies that would benefit from information gathering.

Table 4 presents the results obtained when the ethical decision-making strategies were regressed on the creative processing skills. In all cases, the multiple correlations obtained from the processing skills measures were significant ( p ≤ .05) and sizeable ( R = .48) when no controls were applied. When the control measures were entered as the first block of predictors, again intelligence (β = .30), cynicism (β = −.17), and openness (β = .17) produced the largest regression weights. More centrally, addition of the creative thinking processes resulted in significant ( p ≤ .05) gains in prediction with an average multiple correlation of .58 being obtained. Thus, creative processing skills do seem related to application of ethical decision-making strategies among doctoral students.

Regression of Ethical Strategies Measures on Creative Thinking Measures with and without Covariates

The regression weights, however, indicated that two processing skills, idea generation and solution monitoring, were consistently strongly related to application of these ethical decision-making strategies. Idea generation produced an average regression weight of .30 when no controls were applied and an average regression weight of .29 when controls were entered first. Similarly, solution monitoring yielded significant ( p ≤ .05) regression weights for 6 of the 7 ethical decision-making strategies with the average regression weights obtained when controls were not, and were, included being .26 and .19. Thus, it appears idea generation and solution monitoring are related not only to ethical decision-making but also the strategies held to underlie these decisions.

In keeping with the findings obtained in the correlational analyses, implementation planning was found to be negatively related to seeking help (β = −.19), questioning judgment (β = −.13), and analysis of personal motivations (β = −.14). Although idea evaluation was positively related to questioning judgment (β = .13), dealing with emotions (β = .12), and analysis of personal motivations (β = .14) when controls were not applied, these relationships did not reach significance when the controls were entered first.

The relationships produced by the early cycle processing skills with ethical decision-making strategies were substantially weaker than those produced by the late cycle processing skills. However, it was found that conceptual combination was positively related to recognition of circumstances in both the control and no control regressions (β = .13) while it was positively related to anticipating consequences (β = .12) when controls were not considered. Similarly, information gathering was positively related to dealing with emotions (β = .13) in both regressions and was positively related to questioning judgment (β = .11) only when the controls were not considered. In contrast, concept selection was negatively related (β = −.12) to consideration of the effects of actions on others – a finding that may reflect the negative effects of abstraction on ethical cognition ( Fromm, 1973 ). Finally, in keeping with the correlational findings, problem definition was found to be negatively related to seeking help (β = −.16), in both analyses, and to analysis of personal motivations (β = −.12) when controls were taken into account. Despite the existence of these relationships, however, it seems that the late cycle processes produced a stronger, more consistent, pattern of relationships with ethical decision-making strategies and ethical decisions.

Before turning to the broader conclusions flowing from the present study, certain limitations should be noted. To begin, we have not examined all aspects of creativity and creative thought that might conceivably be related to ethics. For example, certain aspects of creative motivation such as flow ( Csikszentmihalyi, 1999 ) or certain aspects of divergent thinking ( Andreani & Pagnin, 1993 ; Runco & Nemiro, 2003 ) that might also be related to ethical behavior and ethical decision-making have not been examined. Instead, in the present study creative capacities were assessed with reference to the process model of creative thought proposed by Mumford et al. (1991) . Although substantial evidence is available for this particular model of creative thought ( Lubart, 2001 ; Scott et al., 2005 ), it should be recognized that it is only one model of creative thought (e.g., Sternberg, 1988 ), and the present study focuses solely on these cognitive aspects of creativity.

Moreover, it should be recognized that we have examined only one form of ethical conduct. More specifically, we have examined ethical conduct with respect to ethical decision-making using a series of field-specific, low-fidelity, simulations ( Motowidlo, Dunnette, & Carter, 1990 ). Although ethical decision-making measures, especially low-fidelity work simulations, are commonly used as a low impact mechanism of assessing ethical behavior, and, clearly ethical decision-making is a precursor to overt ethical breeches ( O’Fallon & Butterfield, 2005 ), it is also true that the present study has not examined overt incidents of misconduct. Moreover, we have not examined how situational attributes might shape these incidents of misconduct ( James, Clark, & Cropanzano, 1999 ).

Along related lines, the use of low-fidelity simulation measures allowed us to examine only one form of ethical misconduct. These simulations require active, conscious, processing. Accordingly, the results obtained in this study do not speak to ethical mistakes arising from unintentional breeches. Nonetheless, it should be noted that unintentional breeches do occur, although they were not examined in the present study.

It should also be recognized that we examined the relationship between creative thinking skills and ethical decision-making at a particular point in scientists’ careers. With regard to career stage, we have examined the relationship between creative thinking and ethical decision-making among doctoral students. Doctoral students are at the beginning of their careers in the sciences ( Zuckerman, 1977 ). By the same token, these initial experiences set the groundwork for subsequent work. And, more centrally, all these students were actively involved in research. Nevertheless, the question remains as to whether these findings can be extended to more experienced professionals.

Finally, it should be recognized that the present study focused on scientists, health, biological, and social scientists, to help establish the generality of our conclusions in this regard. As Ludwig ( 1995 ; 1998 ) has pointed out, there is reason to suspect that deviance might not be linked to creativity in fields emphasizing formal thought, such as the sciences. What should be recognized in this regard, however, is that caution is called for in generalizing our findings to other forms of creative work, such as the arts ( Feist, 1999 ).

One must, of course, bear these limitations in mind when interpreting the findings obtained in the present study. Nonetheless, our findings lead to one clear cut conclusion with regard to the relationship between ethical decision-making and creative thought. More specifically, creative thinking skills are positively related to ethical decision-making among doctoral students in the sciences. As noted above, because the sciences emphasize formal thought and adherence to replicable procedures, it is not surprising that creative thinking skills would be linked to ethical conduct (Ludwig, 1995 ; 1998 ).

By the same token, however, the practical importance of this finding should not be underestimated. People remember salient events ( Mumford et al., 2002 ). Not only are ethical breeches salient, their salience increases when these breeches are committed by world class creative scientists. Reporting of these events, and their salience in our minds, has led to an assumption that creative thinking may act to undermine ethical conduct. The results obtained in the present study, however, bring to question the truth of this urban legend, at least in the case of scientists – specifically doctoral students beginning their careers in the sciences. Indeed, our findings indicate that creative thinking skills are positively related to ethical decision-making, and, in fact, creative thinking seemed to promote ethical decisions in multiple areas where scientists must make ethical decisions. Hence sizable, and significant, multiple correlations were obtained when creative thinking skills were used to predict ethical decisions concerning data management, study conduct, professional practices, and business practices.

The strong, consistent relationships observed between creative thinking and ethical decision-making in the present study are such that they bring to fore the question why weak, inconsistent, results have been obtained in prior studies ( Andreani & Pagnin, 1993 ; Runco & Nemiro, 2003 ). What should be remembered here, however, is that in prior studies assessments of ethics, and creative thinking skills, were based on general, non-domain specific measures. Thus, the relationship between creative thought and ethics may be stronger when with field specific skills rather than when general, cross-field, capacities are examined.

One reason these field specific effects might arise is evident in the way people make ethical decisions. Ethical decisions involve cognition in a complex, high stakes, ambiguous setting where a premium is placed on the interpersonal and personal sensemaking ( Mumford et al., in press ). A number of cognitive strategies, such as recognizing circumstances, dealing with emotions, and anticipating consequences of actions for others, all appear to influence ethical decision-making. The findings obtained in the present study indicate that creative thinking skills contribute to more effective ethical decision-making because creative thinking skills, at least among doctoral students in the sciences, are associated with more effective strategic processing.

Of course, this argument might be questioned on two bases. First, is there evidence available indicating that execution of these strategies, in fact, contribute to ethical decisions? The studies conducted by Mumford et al. (in press) , examining the effects of strategy training on ethical decision-making, and Mumford et al. (2006) , examining the relationship between application of these strategies and ethical decision-making, indicate that these strategies are, in fact, a powerful influence on ethical decisions.

Second, do other explanatory systems exist that might account for these relationships? Of course, it is impossible to rule out every alternative explanation in any study. Nonetheless, the findings obtained in the regression analyses indicated that the relationship of creative thinking skills to ethical decision-making and the strategies contributing to these decisions held when general cognitive capacities (e.g., intelligence), general personality characteristics (e.g., openness), and personality characteristics (e.g., cynicism) expressly linked to ethical decision-making were taken into account. Thus, it seems plausible to argue that, at least among young scientists (doctoral students), creative thinking skills contribute to more effective application of ethical decision-making strategies which, in turn, contribute to better ethical decision-making.

These observations, of course, pertain to two of our initial hypotheses – confirming both these hypotheses. Our two remaining hypotheses, however, referred to the influence of late versus early cycle ( Mumford, 2001 ) creative processing skills on ethical decision-making and the strategies people employ when making these decisions. The findings obtained in the correlational and regression analyses indicated that late cycle processing skills – idea generation, idea evaluation, implementation planning, and solution monitoring – made a stronger contribution to prediction of both ethical decision-making, and ethical decision-making strategies, than early cycle creative thinking skills such as problem definition, information gathering, concept selection, and conceptual combination.

Although these findings confirm our remaining two hypotheses, they also broach a broader question. Exactly how do these late cycle processes exert a positive influence on the ethical decision-making of scientists? In part, an answer to this question lies in the two dimensions of creative thought that produced strong, consistent relationships. More specifically, in both the correlational and regression analyses, idea generation and solution monitoring were found to be related to both the various types of ethical decisions under consideration and the strategies applied in making these decisions.

The effects of solution monitoring on the ethical thinking of doctoral students in the sciences can be interpreted in a relatively straightforward fashion. More specifically, a concern with the effects of ones’ actions, an aspect of solution monitoring, may well lead to a concern with the effects of ones’ actions on others. This concern would give rise to more intensive and extensive analysis of the social implications of action, thus contributing to better strategic processing with regard to ethical decisions and hence better ethical decisions.

The effects of idea generation on ethical decision-making and the strategies applied in making these decisions might, at first glance, appear more surprising. Presumably a wide array of ideas, including unethical ideas, might be generated by creative people. In contrast to this traditional view of idea generation, Finke, Ward, and Smith (1992) have argued that idea generation is an exploratory activity where people examine the applications and implications of new understandings created through conceptual combination ( Baughman & Mumford, 1995 ). Exploration of the implications and applications of new understandings, however, implies that context must be taken into account in idea generation. Consideration of these contextual issues, coupled with generation of ideas to address these issues, provides one plausible explanation as to why idea generation would also be positively related to ethical thought among younger scientists.

Although these findings indicate that late cycle creative thinking skills are positive influences on creative thought, it should also be recognized that early cycle creative thinking skills were weakly related, and in some cases negatively related, to ethical decision-making and strategies held to contribute to these decisions. This finding is of some importance because it suggests that failure of consistent effects to emerge in prior studies examining the relationship between deviance and creativity may not be solely a function of field, for example science versus the arts (Ludwig, 1995 ; 1998 ), but also the specific creative thinking skills being examined in these studies ( Eisenman, 1999 ). Hopefully, the present study will provide impetus to future studies more expressly delineating exactly what aspects of creative thought are being examined in studies of deviance and creativity.


We would like to thank Jason Hill, Ginamarie Scott-Ligon, Whitney Helton-Fauth, and Blaine Gaddis for their contributions to the present effort. The data collection was supported, in part, by the National Institutes of Health, National Center for Research Resources, General Clinical Center Research Grant (M01RR-14467). This work was conducted under the auspices of a grant from the National Institutes of Health and the Office of Research Integrity (5R01-NS049535-02), Michael D. Mumford, Principal Investigator.

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