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Breaking down the 5 decision-making models

Decisions, decisions, decisions: 5 decision-making models.

Making effective decisions is a critical leadership quality . However, settling on the best course of action is often easier said than done. When instinct and reasoning alone aren't enough to pinpoint the best decision out of your available options, it can often be helpful to utilize a decision-making model.

A decision-making model works by walking you through the decision-making process — and there are several such models available for you to choose from.

To help you improve your problem-solving abilities and make better decisions, let's take a look at five proven decision-making models and when you should use them.

Defining decision-making models

Decision-making models are frameworks designed to help you analyze possible solutions to a problem so that you can make the best possible decision. Because different decision-making models take different approaches to this goal, it's important to match the model with your unique situation and leadership style.

Given that only 20% of team members say that their organization excels at decision-making, most organizations and team leaders have a lot of room to improve in this area. If you want to improve your decision-making approach, mastering the five decision-making models is a great place to start.

The 5 main decision-making models

There are five main decision-making models designed to help leaders analyze relevant information and make optimal decisions.

Once again, each of these models takes a unique approach to decision-making, so it is important to choose the model that will work best for you and your unique situation. With that said, let's take an in-depth look at each model and the situations where each one is most applicable.

1) Rational decision-making model

The rational decision-making model involves identifying the criteria that will have the biggest impact on your decision's outcome and then evaluating possible alternatives against those criteria. The steps of the rational decision-making model are:

• Step #1) Define the problem: You'll want to start by identifying the issue you are trying to solve or the goal you are trying to achieve with your decision.
• Step #2) Define criteria: The next step is to define the criteria you are looking for in your decision. For instance, if you are deciding on a new car, you might be looking for criteria such as space, fuel efficiency, and safety.
• Step #3) Weight your criteria: If all of the criteria you define are equally important to you, then you can skip this step. If some factors are more important, you will want to assign a numerical value to your criteria based on how important each factor is.
• Step #4) Generate alternatives: Having defined and weighted the criteria you are looking for, it's time to brainstorm ideas and develop a few alternatives that meet your criteria.
• Step #5) Evaluate your alternatives: For each possible solution you come up with, you should evaluate it against your criteria, giving extra consideration to the criteria you weighted more heavily.
• Step #6) Choose the best alternative: After evaluating all possible alternatives, select the option that best matches your weighted criteria.
• Step #7) Implement the decision: The next to last step in the rational decision-making model is simply putting your decision into practice.
• Step #8) Evaluate your results: It's essential to evaluate your results anytime you make a decision. Looking at your decision from a retrospective point of view can help you decide if you should use the same decision-making process in the future.

When to use this model

The rational decision-making model is best employed when you have numerous options to consider and plenty of time to evaluate them. One example of a scenario where this model might prove useful is choosing a new hire from a pool of candidates.

2) Bounded rationality decision-making model

Sometimes, taking action quickly and choosing a "good enough" option is better than getting bogged down in searching for the best possible solution. The bounded rationality decision-making model dictates that you should limit your options to a manageable set and then choose the first option that meets your criteria rather than conducting an exhaustive analysis of each one. Going with the first option that meets your minimum threshold of requirements is a process known as "satisficing." While this may not be the best process for every decision, a willingness to satisfice can prove valuable when time constraints limit you.

The bounded rationality decision-making model is best employed when time is of the essence. It's the best model to use when inaction is more costly than not making the best decision. For example, suppose your company has encountered an issue causing extended downtime. In that case, you may want to use the bounded rationality decision-making model to quickly identify the first acceptable solution since every minute wasted is costly.

3) Vroom-Yetton decision-making model

The Vroom-Yetton decision-making model presents seven "yes or no" questions for a decision-maker to answer followed by five decision-making styles for them to choose from. It's the most complex decision-making model on our list, requiring decision-makers to utilize a decision tree to arrive at the right decision-making style based on their answers to the model's questions.

Check out this helpful resource for a complete breakdown of the Vroom-Yetton decision-making model and a copy of the decision tree template you will need to use.

The Vroom-Yetton decision-making model was specifically designed for collaborative decision-making and is best employed when you involve multiple team members in the decision-making process. In fact, one of the main objectives of this model is to determine how much weight should be given to the input from a leader's subordinates.

4) Intuitive decision-making model

Have you ever heard that it's often best to go with your gut? While making decisions based only on instinct may not seem like the best idea to those who prefer a more careful and logical approach, there are plenty of instances where going with your gut is the best way forward.

For example, if you don't have much information to consider, instinct may be the only tool for finding the best solution that you have available. Likewise, trusting your instinct can often yield the best results in cases where you are already deeply experienced with the matter at hand since nothing hones instinct better than experience.

The intuitive decision-making model probably shouldn't be the first model you turn to when you need to make a decision, but there are instances where it can be useful. We've mentioned a couple already, including cases where there isn't enough information for you to make a more informed decision and instances where your own experience is more reliable than the available information.

The intuitive decision-making model can also be useful in cases where you don't have a lot of time and need to make a decision quickly.

5) The recognition primed model

The recognition primed model is similar to the intuitive decision-making model in that it relies heavily on the decision-maker's experience and instinct. However, the recognition primed model is a little more structured than intuitive decision-making and includes the following steps:

• Step #1) Analyze available information to identify possible solutions: The first step in the recognition primed model is to brainstorm possible solutions based on your available information.
• Step #2) Run scenarios through your head: For each possible solution, run the scenario through your head and see how it plays out.
• Step #3) Make a decision: The recognition primed model dictates that the solution that leads to the best possible outcome when you visualize it in your mind is the solution that you should choose.

Like the intuitive decision-making model, the recognition primed model works best in instances where:

• You don't have a lot of information available.
• You trust your instinct and experience.
• Time constraints are a factor.

With that said, using this model effectively does require a certain degree of creativity and imagination since you will have to visualize the outcome of each possible solution.

A note on decision-making biases

Anytime you are faced with an important decision, it is essential not to let biases get in your way. Biases might be rooted in prior experiences, but that doesn't inherently mean that they are grounded in facts. In many cases, avoiding biases is also key to making an ethical decision since biases can sometimes cause you to mistreat certain people and their ideas.

Understanding the different biases

Preventing biases from getting in the way of your decision-making skills starts with identifying the types of biases you need to be aware of, including:

• Confirmation bias: Confirmation bias entails favoring or focusing on information that confirms your pre-existing beliefs and ignoring information that runs counter to those beliefs. While it's important to trust your own experience and beliefs, you don't want to subconsciously favor information just because it aligns with what you already believe to be true.
• Availability bias: Information that is easily accessible in your memory often gets undue weight, and this is known as availability bias. One example of availability bias is overestimating the likelihood of an event just because you can remember a similar event happening to you in the past.
• Survivorship bias: Survivorship bias entails focusing only on the solutions that have generated success in the past. While it's important to consider past results, ignoring possible solutions just because they are unproven will place unnecessary constraints on your decision-making process.
• Anchoring bias: Anchoring bias is the tendency to "anchor" yourself to the first piece of information you learn. Information should not get extra weight just because you have known about it for longer, and new information can be equally important to consider.
• Halo effect: The halo effect occurs when positive experiences with or impressions of one aspect of a possible solution cause you to view the entire solution positively. Rather than being blinded by the positives, seek out and consider the negatives as well.

Define your decision-making process with Range

A lot goes into making good decisions, and the decision-making models we've covered in this article can serve as excellent tools for helping you find the best possible solution to any challenge.

No matter which model you go with, communication, collaboration, and organization are key to making good decisions.

With Range, leaders and team members alike are able to effortlessly organize their ideas, communicate back and forth, share important information, and make collaborative decisions. If you want to get started using powerful team management software to organize your decision-making process, sign up for Range today.

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7 important steps in the decision making process

The decision making process is a method of gathering information, assessing alternatives, and making a final choice with the goal of making the best decision possible. In this article, we detail the step-by-step process on how to make a good decision and explain different decision making methodologies.

We make decisions every day. Take the bus to work or call a car? Chocolate or vanilla ice cream? Whole milk or two percent?

There's an entire process that goes into making those tiny decisions, and while these are simple, easy choices, how do we end up making more challenging decisions? 

At work, decisions aren't as simple as choosing what kind of milk you want in your latte in the morning. That’s why understanding the decision making process is so important. 

What is the decision making process?

The decision making process is the method of gathering information, assessing alternatives, and, ultimately, making a final choice. 

Decision-making tools for agile businesses

In this ebook, learn how to equip employees to make better decisions—so your business can pivot, adapt, and tackle challenges more effectively than your competition.

The 7 steps of the decision making process

Step 1: identify the decision that needs to be made.

When you're identifying the decision, ask yourself a few questions: 

What is the problem that needs to be solved?

What is the goal you plan to achieve by implementing this decision?

How will you measure success?

These questions are all common goal setting techniques that will ultimately help you come up with possible solutions. When the problem is clearly defined, you then have more information to come up with the best decision to solve the problem.

Step 2: Gather relevant information

​Gathering information related to the decision being made is an important step to making an informed decision. Does your team have any historical data as it relates to this issue? Has anybody attempted to solve this problem before?

It's also important to look for information outside of your team or company. Effective decision making requires information from many different sources. Find external resources, whether it’s doing market research, working with a consultant, or talking with colleagues at a different company who have relevant experience. Gathering information helps your team identify different solutions to your problem.

Step 3: Identify alternative solutions

This step requires you to look for many different solutions for the problem at hand. Finding more than one possible alternative is important when it comes to business decision-making, because different stakeholders may have different needs depending on their role. For example, if a company is looking for a work management tool, the design team may have different needs than a development team. Choosing only one solution right off the bat might not be the right course of action. 

Step 4: Weigh the evidence

This is when you take all of the different solutions you’ve come up with and analyze how they would address your initial problem. Your team begins identifying the pros and cons of each option, and eliminating alternatives from those choices.

There are a few common ways your team can analyze and weigh the evidence of options:

Pros and cons list

SWOT analysis

Decision matrix

Step 5: Choose among the alternatives

The next step is to make your final decision. Consider all of the information you've collected and how this decision may affect each stakeholder. 

Sometimes the right decision is not one of the alternatives, but a blend of a few different alternatives. Effective decision-making involves creative problem solving and thinking out of the box, so don't limit you or your teams to clear-cut options.

One of the key values at Asana is to reject false tradeoffs. Choosing just one decision can mean losing benefits in others. If you can, try and find options that go beyond just the alternatives presented.

Step 6: Take action

Once the final decision maker gives the green light, it's time to put the solution into action. Take the time to create an implementation plan so that your team is on the same page for next steps. Then it’s time to put your plan into action and monitor progress to determine whether or not this decision was a good one. 

Step 7: Review your decision and its impact (both good and bad)

Once you’ve made a decision, you can monitor the success metrics you outlined in step 1. This is how you determine whether or not this solution meets your team's criteria of success.

Here are a few questions to consider when reviewing your decision:

Did it solve the problem your team identified in step 1? 

Did this decision impact your team in a positive or negative way?

Which stakeholders benefited from this decision? Which stakeholders were impacted negatively?

If this solution was not the best alternative, your team might benefit from using an iterative form of project management. This enables your team to quickly adapt to changes, and make the best decisions with the resources they have. 

Types of decision making models

While most decision making models revolve around the same seven steps, here are a few different methodologies to help you make a good decision.

​Rational decision making models

This type of decision making model is the most common type that you'll see. It's logical and sequential. The seven steps listed above are an example of the rational decision making model. 

When your decision has a big impact on your team and you need to maximize outcomes, this is the type of decision making process you should use. It requires you to consider a wide range of viewpoints with little bias so you can make the best decision possible. 

Intuitive decision making models

This type of decision making model is dictated not by information or data, but by gut instincts. This form of decision making requires previous experience and pattern recognition to form strong instincts.

This type of decision making is often made by decision makers who have a lot of experience with similar kinds of problems. They have already had proven success with the solution they're looking to implement. 

Creative decision making model

The creative decision making model involves collecting information and insights about a problem and coming up with potential ideas for a solution, similar to the rational decision making model. 

The difference here is that instead of identifying the pros and cons of each alternative, the decision maker enters a period in which they try not to actively think about the solution at all. The goal is to have their subconscious take over and lead them to the right decision, similar to the intuitive decision making model. 

This situation is best used in an iterative process so that teams can test their solutions and adapt as things change.

Track key decisions with a work management tool

Tracking key decisions can be challenging when not documented correctly. Learn more about how a work management tool like Asana can help your team track key decisions, collaborate with teammates, and stay on top of progress all in one place.

Related resources

Listening to understand: How to practice active listening (with examples)

How executives and individual contributors differ when it comes to AI

Fiedler’s Contingency Theory: Why leadership isn’t uniform

What is management by objectives (MBO)?

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

7.2 Decision-making Models

Decision-making models.

In this section, we are going to discuss different decision-making models designed to understand and evaluate the effectiveness of nonprogrammed decisions. We will cover four decision-making approaches, starting with the rational decision-making model, moving to the bounded rationality decision-making model, the intuitive decision-making model, and ending with the creative decision-making model. The importance of making good decisions relates to our ability to manage our emotional intelligence to make sure we make the right decisions. These models will help us make better decisions, which results in better human relations.

In this Section:

• Rational Decision-making
• Bounded Rationality

Intuitive Decision-making

Creative decision-making, rational decision-making.

The rational decision-making model describes a series of steps that decision makers should consider if their goal is to maximize the quality of their outcomes. In other words, if you want to make sure that you make the best choice, going through the formal steps of the rational decision-making model may make sense.

Let’s Focus: Rational Decision-making Model

Buying your first car.

Let’s imagine that your old, clunky car has broken down, and you have enough money saved for a substantial down payment on a new car. It will be the first major purchase of your life, and you want to make the right choice.

The first step, therefore, has already been completed—we know that you want to buy a new car. Next, in step 2, you’ll need to decide which factors are important to you. How many passengers do you want to accommodate? How important is fuel economy to you? Is safety a major concern? You only have a certain amount of money saved, and you don’t want to take on too much debt, so price range is an important factor as well. If you know you want to have room for at least five adults, get at least twenty miles per gallon, drive a car with a strong safety rating, not spend more than $22,000 on the purchase, and like how it looks, you have identified the decision criteria . All the potential options for purchasing your car will be evaluated against these criteria.

Before we can move too much further, you need to decide how important each factor is to your decision in step 3. If each is equally important, then there is no need to weigh them, but if you know that price and mpg are key factors, you might weigh them heavily and keep the other criteria with medium importance. Step 4 requires you to generate all alternatives  about your options. Then, in step 5, you need to use this information to evaluate each alternative against the criteria you have established. You choose the best alternative (step 6), and then you would go out and buy your new car (step 7).

Of course, the outcome of this decision will influence the next decision made. That is where step 8 comes in. For example, if you purchase a car and have nothing but problems with it, you will be less likely to consider the same make and model when purchasing a car the next time.

While decision makers can get off track during any of these steps, research shows that searching for alternatives in the fourth step can be the most challenging and often leads to failure. In fact, one researcher found that no alternative generation occurred in 85 percent of the decisions he studied (Nutt, 1994). Conversely, successful managers know what they want at the outset of the decision-making process, set objectives for others to respond to, carry out an unrestricted search for solutions, get key people to participate, and avoid using their power to push their perspective (Nutt, 1998).

The rational decision-making model has important lessons for decision makers. First, when making a decision, you may want to make sure that you establish your decision criteria before you search for alternatives. This would prevent you from liking one option too much and setting your criteria accordingly. For example, let’s say you started browsing cars online before you generated your decision criteria. You may come across a car that you feel reflects your sense of style and you develop an emotional bond with the car. Then, because of your love for the particular car, you may say to yourself that the fuel economy of the car and the innovative braking system are the most important criteria. After purchasing it, you may realize that the car is too small for your friends to ride in the back seat, which was something you should have thought about. Setting criteria before you search for alternatives may prevent you from making such mistakes. Another advantage of the rational model is that it urges decision makers to generate all alternatives instead of only a few. By generating a large number of alternatives that cover a wide range of possibilities, you are unlikely to make a more effective decision that does not require sacrificing one criterion for the sake of another.

Despite all its benefits, you may have noticed that this decision-making model involves a number of unrealistic assumptions as well. It assumes that people completely understand the decision to be made, that they know all their available choices, that they have no perceptual biases, and that they want to make optimal decisions. Nobel Prize–winning economist Herbert Simon observed that while the rational decision-making model may be a helpful device in aiding decision makers when working through problems, it doesn’t represent how decisions are frequently made within organizations. In fact, Simon argued that it didn’t even come close.

Think about how you make important decisions in your life. It is likely that you rarely sit down and complete all eight of the steps in the rational decision-making model. For example, this model proposed that we should search for all possible alternatives before making a decision, but that process is time consuming, and individuals are often under time pressure to make decisions. Moreover, even if we had access to all the information that was available, it could be challenging to compare the pros and cons of each alternative and rank them according to our preferences. Anyone who has recently purchased a new laptop computer or cell phone can attest to the challenge of sorting through the different strengths and limitations of each brand and model and arriving at the solution that best meets particular needs. In fact, the availability of too much information can lead to analysis paralysis , in which more and more time is spent on gathering information and thinking about it, but no decisions actually get made. A senior executive at Hewlett-Packard Development Company LP admits that his company suffered from this spiral of analyzing things for too long to the point where data gathering led to “not making decisions, instead of us making decisions (Zell et al., 2007). Moreover, you may not always be interested in reaching an optimal decision. For example, if you are looking to purchase a house, you may be willing and able to invest a great deal of time and energy to find your dream house, but if you are only looking for an apartment to rent for the academic year, you may be willing to take the first one that meets your criteria of being clean, close to campus, and within your price range.

Bounded Rationality: Making “Good Enough” Decisions

The bounded rationality model of decision making recognizes the limitations of our decision-making processes. According to this model, individuals knowingly limit their options to a manageable set and choose the first acceptable alternative without conducting an exhaustive search for alternatives. An important part of the bounded rationality approach is the tendency to satisfice (a term coined by Herbert Simon from satisfy and suffice), which refers to accepting the first alternative that meets your minimum criteria.

For example, many college graduates do not conduct a national or international search for potential job openings. Instead, they focus their search on a limited geographic area, and they tend to accept the first offer in their chosen area, even if it may not be the ideal job situation. Satisficing is similar to rational decision making. The main difference is that rather than choosing the best option and maximizing the potential outcome, the decision maker saves cognitive time and effort by accepting the first alternative that meets the minimum threshold.

The  intuitive decision-making model has emerged as an alternative to other decision making processes. This model refers to arriving at decisions without conscious reasoning. A total of 89 percent of managers surveyed admitted to using intuition to make decisions at least sometimes and 59 percent said they used intuition often (Burke & Miller, 1999). Managers make decisions under challenging circumstances, including time pressures, constraints, a great deal of uncertainty, changing conditions, and highly visible and high-stakes outcomes. Thus, it makes sense that they would not have the time to use the rational decision-making model. Yet when CEOs, financial analysts, and health care workers are asked about the critical decisions they make, seldom do they attribute success to luck. To an outside observer, it may seem like they are making guesses as to the course of action to take, but it turns out that experts systematically make decisions using a different model than was earlier suspected. Research on life-or-death decisions made by fire chiefs, pilots, and nurses finds that experts do not choose among a list of well thought out alternatives. They don’t decide between two or three options and choose the best one. Instead, they consider only one option at a time. The intuitive decision-making model argues that in a given situation, experts making decisions scan the environment for cues to recognize patterns (Breen, 2000; Klein, 2003; Salas & Klein, 2001). Once a pattern is recognized, they can play a potential course of action through to its outcome based on their prior experience. Thanks to training, experience, and knowledge, these decision makers have an idea of how well a given solution may work. If they run through the mental model and find that the solution will not work, they alter the solution before setting it into action. If it still is not deemed a workable solution, it is discarded as an option, and a new idea is tested until a workable solution is found. Once a viable course of action is identified, the decision maker puts the solution into motion. The key point is that only one choice is considered at a time. Novices are not able to make effective decisions this way, because they do not have enough prior experience to draw upon.

In addition to the rational decision making, bounded rationality, and intuitive decision-making models, creative decision making is a vital part of being an effective decision maker. Creativity is the generation of new, imaginative ideas. With the flattening of organizations and intense competition among companies, individuals and organizations are driven to be creative in decisions ranging from cutting costs to generating new ways of doing business. Please note that, while creativity is the first step in the innovation process, creativity and innovation are not the same thing. Innovation begins with creative ideas, but it also involves realistic planning and follow-through. Innovations such as 3M’s Clearview Window Tinting grow out of a creative decision-making process about what may or may not work to solve real-world problems.

The five steps to creative decision making are similar to the previous decision-making models in some keys ways. All the models include problem identification , which is the step in which the need for problem solving becomes apparent. If you do not recognize that you have a problem, it is impossible to solve it. Immersion is the step in which the decision maker consciously thinks about the problem and gathers information. A key to success in creative decision making is having or acquiring expertise in the area being studied. Then, incubation occurs. During incubation, the individual sets the problem aside and does not think about it for a while. At this time, the brain is actually working on the problem unconsciously. Then comes illumination , or the insight moment when the solution to the problem becomes apparent to the person, sometimes when it is least expected. This sudden insight is the “eureka” moment, similar to what happened to the ancient Greek inventor Archimedes, who found a solution to the problem he was working on while taking a bath. Finally, the verification and application stage happens when the decision maker consciously verifies the feasibility of the solution and implements the decision.

Here’s an Example

A NASA scientist describes his decision-making process leading to a creative outcome as follows: He had been trying to figure out a better way to de-ice planes to make the process faster and safer.

After recognizing the problem, he immersed himself in the literature to understand all the options, and he worked on the problem for months trying to figure out a solution. It was not until he was sitting outside a McDonald’s restaurant with his grandchildren that it dawned on him.

The golden arches of the M of the McDonald’s logo inspired his solution—he would design the de-icer as a series of Ms. This represented the illumination stage. After he tested and verified his creative solution, he was done with that problem, except to reflect on the outcome and process.

How do you know if your decision process is creative?

Researchers focus on three factors to evaluate the level of creativity in the decision-making process. Fluency refers to the number of ideas a person is able to generate. Flexibility refers to how different the ideas are from one another. If you are able to generate several distinct solutions to a problem, your decision-making process is high on flexibility.  Originality refers to how unique a person’s ideas are.

Some experts (Amabile, 1999; Amabile et al., 1996; Ford et al., 2000; Tierney et al., 1999; Woodman et al., 1993) have proposed that creativity occurs as an interaction among three factors: people’s personality traits (openness to experience, risk taking), their attributes (expertise, imagination, motivation), and the situational context (encouragement from others, time pressure, physical structures). For example, research shows that individuals who are open to experience, less conscientious, more self-accepting, and more impulsive tend to be more creative (Feist, 1998).

Consider This

Ideas for enhancing creativity in groups.

Some ideas for enhancing creativity in groups include:

Team Composition

• Diversify your team  to give them more inputs to build on and more opportunities to create functional conflict while avoiding personal conflict.
• Change group membership  to stimulate new ideas and new interaction patterns.
• Leaderless teams  can allow teams freedom to create without trying to please anyone up front.

Team Process

• Engage in brainstorming  to generate ideas. Remember to set a high goal for the number of ideas the group should come up with, encourage wild ideas, and take brainwriting breaks.
• Use the nominal group technique (see  Tools and Techniques for Making Better Decisions  below) in person or electronically  to avoid some common group process pitfalls. Consider anonymous feedback as well.
• Use analogies  to envision problems and solutions.
• Challenge teams  so that they are engaged but not overwhelmed.
• Let people decide how to achieve goals  rather than telling them what goals to achieve.
• Support and celebrate creativity  even when it leads to a mistake. Be sure to set up processes to learn from mistakes as well.
• Role model  creative behavior.
• Institute organizational memory  so that individuals do not spend time on routine tasks.
• Build a physical space conducive to creativity  that is playful and humorous—this is a place where ideas can thrive.
• Incorporate creative behavior  into the performance appraisal process.

Sources: Adapted from ideas in Amabile, T. M. (1998). How to kill creativity. Harvard Business Review, 77-87. Gundry, L. K., Kickul, J. R., & Prather, C. W. (1994). Building the creative organization. Organizational Dynamics, 22(4), 22–37. ; Keith, N., & Frese, M. (2008). Effectiveness of error management training: A meta-analysis. Journal of Applied Psychology, 93(1), 59–69.; Pearsall, M. J., Ellis, A. P. J., & Evans, J. M. (2008). Unlocking the effects of gender faultlines on team creativity: Is activation the key? Journal of Applied Psychology, 93(1), 225–234.; Thompson, L. (2003). Improving the creativity of organizational work groups. Academy of Management Executive, 17, 96-109.

Enhancing Creative Decision-making

There are many techniques available that enhance and improve creativity. Linus Pauling (2008), the Nobel Prize winner who popularized the idea that vitamin C could help strengthen the immune system, said, “The best way to have a good idea is to have a lot of ideas.” One popular method of generating ideas is to use brainstorming. Brainstorming is a group process of generating ideas that follow a set of guidelines, including no criticism of ideas during the brainstorming process, the idea that no suggestion is too crazy, and building on other ideas (piggybacking). Research shows that the quantity of ideas actually leads to better idea quality in the end, so setting high idea quotas,  in which the group must reach a set number of ideas before they are done, is recommended to avoid process loss and maximize the effectiveness of brainstorming. Another unique aspect of brainstorming is that since the variety of backgrounds and approaches give the group more to draw upon, the more people are included in the process, the better the decision outcome will be. A variation of brainstorming is wildstorming , in which the group focuses on ideas that are impossible and then imagines what would need to happen to make them possible (Scott et al., 2004).

One example of a creative decision making model is the Edward Debono model. The  Edward Debono’s model of the Six Thinking Hats  provides us with a different way of thinking about the way we make decisions. The six hats provide us with perspectives from six different perspectives. Similar to the rational decision making model discussed earlier, this model uses hats to represent the steps we need to follow in order to make good decisions. For example, the white hat helps us look at the facts of the situation. The red hat helps us look at the emotional aspect of the problem or solution. The black hat helps us to look at the negatives of the solution, while the yellow hat helps us think about the positives of the solution. The green hat allows us to come up with potential solutions or courses of action, while the blue hat helps us manage the process of making the decision.

Let’s Focus  

Andi and the 6 hats: an example.

Consider the opening scenario where Andi is considering which job to accept. If she were using the six hats model, first she would look at the facts—that is, the aspects of each job offer (white hat). Then, she would look at how she feel (red hat) about each job. Next, she would look at the downsides of each job (black hat). Then, she would look at the positives of each job (yellow hat). Next, she would use the green hat to look at the job offers from a creative way and look at potential of choosing one job over another. Finally, the blue hat would cause Andi to make sure she used all hats to make a decision and, based on the data, would go ahead and make the best choice.

Which Model is Best?

Now that we’ve reviewed all four types of models for non-programmed decisions, you may be wondering: which model is best to use when I make a decision? As we’ve learned, each of these models has potential advantages and disadvantages. Figure 7.6 below offers some suggestions as to when each model is helpful based on the time/information available, the importance of the decision, and the clarity of the problem/solutions.

Table 7.2 Which decision-making model should I use?

Let’s Review

• In this section we learned about four models for nonprogrammed decision-making: rational, bounded rationality, intuitive, and creative decision making.
• Each of these can be useful, depending on the circumstances and the problem that needs to be solved.
• Have you used the rational decision-making model to make a decision? What was the context? How well did the model work?
• Share an example of a decision in which you used satisficing. Were you happy with the outcome? Why or why not? When would you be most likely to engage in satisficing?
• Do you think intuition is respected as a decision-making style? Do you think it should be? Why or why not?

This section is adapted from:

Chapter 8: Make Good Decisions   in  Human Relations  by Saylor Academy under a  Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License

Amabile, T. M. (1988). A model of creativity and innovation in organizations. In B. M. Staw & L. L. Cummings (Eds.),  Research in organizational behavior (vol. 10, pp. 123–67). JAI Press.

Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the work environment for creativity. Academy of Management Journal ,  39 , 1154–84.

Breen, B. (2000, August). What’s your intuition?  Fast Company , 290.

Burke, L. A., & Miller, M. K. (1999). Taking the mystery out of intuitive decision making. Academy of Management Executive ,  13 , 91–98.

Feist, G. J. (1998). A meta-analysis of personality in scientific and artistic creativity. Personality and Social Psychology Review ,  2 , 290–309.

Ford, C. M., & Gioia, D. A. (2000). Factors influencing creativity in the domain of managerial decision making.  Journal of Management ,  26 , 705–32

Klein, G. (2003). Intuition at work . Doubleday.

Pauling, L. (n.d.). The best way to have good ideas. What Quote. Retrieved May 1, 2008, from http://www.whatquote.com/quotes/linus-pauling/250801-the-best-way-to-have.htm

Scott, G., Leritz, S, G , & Mumford, M. D. (2004). The effectiveness of creativity training: A quantitative review. Creativity Research Journal ,  16 , 361–88.

Nutt, P. C. (1994). Types of organizational decision processes.  Administrative Science Quarterly ,  29 , 414–550.

Nutt, P. C. (1998). Surprising but true: Half the decisions in organizations fail.  Academy of Management Executive ,  13 , 75–90.

Salas, E., & Klein, G. (2001). Linking expertise and naturalistic decision making . Lawrence Erlbaum Associates.

Tierney, P., Farmer, S. M., & Graen, G. B. (1999). An examination of leadership and employee creativity: The relevance of traits and relationships.  Personnel Psychology ,  52 , 591–620.

Woodman, R. W., Sawyer, J. E., & Griffin, R. W. (1993). Toward a theory of organizational creativity.  Academy of Management Review ,  18 , 293–321.

Zell, D. M., Glassman, A. M., & Duron, S. A. (2007). Strategic management in turbulent times: The short and glorious history of accelerated decision making at Hewlett-Packard. Organizational Dynamics ,  36 , 93–104.

Psychology, Communication, and the Canadian Workplace Copyright © 2022 by Laura Westmaas, BA, MSc is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Examination of Ethical Decision-Making Models Across Disciplines: Common Elements and Application to the Field of Behavior Analysis

Victoria d. suarez.

1 Endicott College, Beverly, MA USA

Videsha Marya

2 Village Autism Center, Marietta, GA USA

Mary Jane Weiss

3 Behavioral Health Center of Excellence, Los Angeles, CA USA

Human service practitioners from varying fields make ethical decisions daily. At some point during their careers, many behavior analysts may face ethical decisions outside the range of their previous education, training, and professional experiences. To help practitioners make better decisions, researchers have published ethical decision-making models; however, it is unknown the extent to which published models recommend similar behaviors. Thus, we systematically reviewed and analyzed ethical decision-making models from published peer-reviewed articles in behavior analysis and related allied health professions. We identified 55 ethical decision-making models across 60 peer-reviewed articles, seven primary professions (e.g., medicine, psychology), and 22 subfields (e.g., dentistry, family medicine). Through consensus-based analysis, we identified nine behaviors commonly recommended across the set of reviewed ethical decision-making models with almost all ( n = 52) models arranging the recommended behaviors sequentially and less than half ( n = 23) including a problem-solving approach. All nine ethical decision-making steps clustered around the ethical decision-making steps in the Ethics Code for Behavior Analysts published by the Behavior Analyst Certification Board ( 2020 ) suggesting broad professional consensus for the behaviors likely involved in ethical decision making.

Ethical decision making is operant behavior involving a behavior chain of complex responses (Marya et al., 2022 ). As behavior analysts, we make difficult ethical decisions daily. Behavior analysts are typically taught to respond to ethical scenarios via vignettes or descriptions of real-world ethical dilemmas (e.g., Bailey & Burch, 2016 ; Sush & Najdowski, 2019 ). However, the variability in ethical dilemmas that behavior analysts contact can be extensive and often contains contextual information not included in past training. Such contextual variables (e.g., impact of and on stakeholders, organizational variables, perspective of the funding source) might alter one’s course of action. Ethical decision-making models can equip behavior analysts with the needed tools to navigate varied and complex dilemmas. Thus, behavior analysts can benefit from models that allow an analysis of contextual variables because those variables often impact solutions.

Ethical conduct of board certified behavior analysts is governed by the Behavior Analyst Certification Board (BACB) ethical codes. Since its inception, the BACB has disseminated three major codes— Guidelines for Responsible Conduct for Behavior Analysts (BACB, 2004 , 2010 ), the Professional and Ethical Compliance Code for Behavior Analysts (BACB, 2014 ), and most recently the Ethics Code for Behavior Analysts (BACB, 2020 ). Although versions prior to 2020 outlined specific ethical obligations and provided a framework and reference for considering paths of action when confronted with ethical challenges, no ethical decision-making tool was embedded until the most recent Code iteration.

Within applied behavior analysis (ABA), several ethical decision-making models have been published to guide behavior analysts to make optimal decisions (BACB, 2020 ; Bailey & Burch, 2013 , 2022 ; Brodhead, 2015 ; Brodhead, Quigley, & Wilczynski, 2018 ; Newhouse-Oisten et al., 2017 ; Rosenberg & Schwartz, 2019 ; Sush & Najdowski, 2019 ). These models unanimously share the common goal of providing readers with a systematic approach to ethical decision making, yet include unique elements that provide varying contextual recommendations. Some models offer a generalizable approach affording wider applicability to a variety of ethical situations (BACB, 2020 ; Bailey & Burch, 2013 , 2016 , 2022 ; Brodhead et al., 2018 ; Rosenberg & Schwartz, 2019 ; Sush & Najdowski, 2019 ), and other models provide guidance to navigate specific ethical situations (Brodhead, 2015 ; Newhouse-Oisten et al., 2017 ). Moreover, some models incorporate a problem-solving approach wherein multiple behaviors are considered along with their possible outcomes to aid decision making in ethical contexts (Rosenberg & Schwartz, 2019 ).

Existing models within the behavior analytic literature have all emerged in the last 7 years and offer a discipline-specific approach. However, many other allied disciplines (e.g., medicine, psychology) have published literature offering models for ethical decision making for a longer period than the field of behavior analysis. Recently, there have been calls to action where behavior analysts have been looking to and learning from related professions (LaFrance et al., 2019 ; Miller et al., 2019 ; Pritchett et al., 2021 ; Taylor et al., 2019 ; Wright, 2019 ). Learning from other disciplines may help the field of behavior analysis rule out ineffective approaches or derive novel effective solutions more quickly.

The purpose of this systematic literature review was to conduct a descriptive analysis of ethical decision-making models across behavior analysis and allied disciplines. This literature review aimed to identify similarities and differences in approaches to ethical decision making that could inform future ethical decision-making models and aid the development of ethical decision-making skills in behavior analysts.

Inclusion Criteria

Articles included in this systematic review met the following three criteria: published in peer-reviewed journals through June 2020, written in English, and the title or abstract included keywords from the search (described below). We began the review in July 2020 and completed it in August 2021.

Search Procedure

We conducted a systematic review of the literature on ethical decision-making models for the fields of applied behavior analysis, education, medicine, occupational therapy, psychology, social work, and speech language pathology using the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher, Liberati, Tetzlaff, Altman, & Prisma Group, 2009 ). We chose these fields because of their similarities to behavior analysis’ mission in serving vulnerable populations. The following procedures were completed in accordance with the PRISMA guidelines: (1) potential articles meeting inclusion criteria were identified; (2) the identified articles were comprehensively screened; (3) the eligibility of each article was evaluated across dependent measures; and (4) the included articles were analyzed.

The first and second authors completed primary database searches using PsycINFO and PubMed. The keywords used to identify potential articles to be included in this analysis were: applied behavior analysis, clinical psychology, counseling psychology, decision mak*, educat*, ethic*, model, medicine, nursing, occupational therapy, speech and language*, and social work. In particular, the key words “ethic*”, “decision mak*”, and “model” were used in combination with the terms “applied behavior analysis,” or “clinical psychology,” or “counseling psychology,” or “medicine,” or “nursing,” or “occupational therapy,” or “speech,” or “language.”

The initial PsycINFO and PubMed searches yielded 635 articles. Of these, 46 were duplicates. The titles and abstracts of the remaining 589 articles were read by the first and second authors to evaluate the inclusion of keywords. Full-text articles were retrieved for studies that included the words ethics or ethical , decision making , or model in their abstracts or titles ( n = 249). Of these, a total of 173 articles were selected for full-text review.

The articles selected for full-text review ( n = 173) were read in their entirety to evaluate whether they met these criteria: (1) included humans as the population of interest; (2) mentioned decision making; (3) mentioned ethics; (4) provided at least three identifiable steps to be followed as a part of a model in either a text or figure format; and (5) the provided model addressed how to respond to ethical dilemmas. The first and second authors scored each of the 173 articles across the aforementioned criteria to determine whether they would be included in the final analysis. Articles ( n = 27) for which it was unclear whether they met any of the criteria were coded as needing additional review, and the third and fourth authors completed an additional full-text review to determine whether they would be included in the final analysis. A total of 126 articles were removed for not meeting all five of the criteria. Thus, 47 articles remained to be included in the analysis.

Next, the first and second authors conducted a manual search (i.e., identification through other sources) of the references ( n = 1,354) for the remaining 47 articles. The screening criteria for this search was identical to the initial screening in which the title and abstract were searched for the inclusion of the words ethics or ethical , decision making , and model . Seventy-nine additional articles were identified through this process. Of these 79 articles, 16 were identified as duplicates from the initial PsycINFO and PubMed searches. Twelve articles were inaccessible to us online or through available library loans and were thus excluded. A list of these articles is not included in this article but is available upon request. Upon reviewing the full text of the remaining 51 articles, 26 additional articles met eligibility to be included in the analysis. In sum, a total of 60 articles met all inclusion criteria and were included.

Interrater reliability was scored using a consensus-based approach. In particular, all four authors collaboratively scored each of the models across the various measures described in the section below. If there was disagreement on scoring at any point, the authors collaboratively reviewed the model using figures provided within the article and any available text describing the model until consensus in scoring was reached.

Dependent Measures

Articles that met criteria for inclusion were evaluated across four dependent measures. First, we evaluated the steps included within the models from each article. Second, we categorized the model by the professional discipline or field of study. Third, we evaluated whether the model author presented the model in a specific order or sequence (i.e., linear or sequential model). Lastly, we scored whether the model included a problem-solving approach. We provide greater detail on each of these dependent measures below.

Decision-Making Steps

The models from each article were evaluated across nine steps (Table ​ (Table1). 1 ). These steps were developed during the process of data synthesis. We read the included articles and identified common themes based on their prevalence in the examined literature. Next, we began classifying articles by the inclusion of these steps, indicating whether each article contained each of the identified steps. Then, we began tracking additional steps that appeared in articles. If those steps appeared in multiple articles, we added them as official steps in the analysis. When this was done, all previously coded articles were recoded for these additional steps. For the purpose of the current review, we identified the following nine components of ethical decision making: (1) ethical radar; (2) urgent detour; (3) pinpoint the problem; (4) information gathering; (5) available options/behaviors; (6) ranking and weighing; (7) analysis; (8) implementation; and (9) follow-up. Details on scoring criteria for each of these steps can be found in Appendix Table ​ Table4. 4 . We scored models included in each article as either including or not including the steps listed above. This was done by using the text description of the model, if provided, or the figure representation of the model if descriptive text was not included.

Steps from the Decision-Making Model from the Ethics Code for Behavior Analysts ( 2020 ) and from the Current Literature Review

*Step 4 of the BACB model aligns with components from Step 6 of current literature review.

Decision-making Steps

Field of Study

The field of study of each article was recorded (e.g., psychology). Where possible, we also included a secondary field of study (e.g., school psychology). The primary field of study of the article was determined based on the journal that it was published in and the intended audience of the article. Secondary fields of study were coded to further gather information about the specific subfield. For example, if the article was published in a psychology journal and the audience of the article was specifically school psychologists.

Problem Solving

Models within each article were scored as including a problem-solving component or approach if the model author(s) guided the model users to identify two or more possible solutions and likely outcomes or consequences to the possible solutions. Models that did not include more than one possible solution and did not anticipate outcomes to solutions were scored as not including a problem-solving component.

Linear or Sequential

We coded whether the proposed model was linear or sequential in nature. That is, the model author(s) indicated that steps in the model followed a certain order or sequence wherein each preceding step in the model was to be considered prior to moving on to subsequent steps. If a model was not linear or sequential, this was also recorded.

Number of Models

A total of 55 ethical decision-making models across 60 peer-reviewed journal articles were analyzed. Models included in more than one article were counted as duplicates, and papers that included more than one model resulted in each unique model being coded.

Table ​ Table2 2 shows the number of models that included each of the nine steps. None of the steps were present in all models and the step that was included in the greatest number of models was ranking and weighing information ( n = 51; 93%). After ranking and weighing information, the steps found in the most-to-least number of models were: affected parties and available options/behaviors ( n = 49; 89%); reference other codes of ethics (e.g., personal, religious, organizational; n = 44; 80%); analysis ( n = 43; 78%), reference of professional codes ( n = 40; 73%); case specific information ( n = 38; 69%); implementation and pinpoint the problem (29 models each; 52%); follow up ( n = 26; 47%); ethical radar ( n = 21; 38%); urgent detour ( n = 16; 29%); and, information gathering ( n = 11; 20%).

Steps Included in Each Model

Figure ​ Figure1 1 shows a stacked bar chart of the primary and secondary fields of the ethical decision-making models. Medicine dominated the resulting set of models, followed by psychology, education, business, then child and youth care and organizational behavior management (OBM). Nevertheless, 23 different subspecialties were represented in the secondary field of the ethical decision-making models.

Stacked-Bar Graph Showing the Number of Ethical Decision-Making Models Based on the Primary and Secondary Literatures from which It Came

Table ​ Table3 3 presents a list of the synthesized models and their respective fields of study. The most common field of study across the 55 models was medicine ( n = 34; 62%). Seventeen of the models from medicine were specific to the subfield of nursing (50%) and three were specific to the subfield of psychiatry (9%). Of the remaining models from the field of medicine, one each was specific to critical care (3%), dentistry (3%), emergency medicine (3%), geriatrics (3%), internal medicine (3%), and oncology (3%). The remaining models from the field of medicine were coded as “general medicine” because they did not indicate a specific subfield.

Field of Study of Included Models

Thirteen models were specific to the field of psychology (24%). Four of the psychology specific models were from the subfield of counseling (31%) and two were specific to the subfield of school psychology (15%). Other specified psychology subfields included coaching ( n = 1; 8%), industrial/organizational psychology ( n = 1; 8%), pediatric psychology ( n = 1; 8%), and psychobiography ( n = 1; 8%). The remaining models were coded as “general psychology” because they did not indicate a specific subfield.

Three models were specific to the field of education (5%). Two of these were specific to the subfield of teaching (67%) and one was specific to the subfield of administration and leadership (33%). Two models were specific to the field of business (4%); one of these was specific to the subfield of management (50%) and the other to the subfield of leadership (50%). One model was specific to the field of child and youth care (2%), one was specific to engineering (2%), and one was specific to OBM (2%).

Figure ​ Figure2 2 shows the number of models that contained a problem-solving approach. A total of 23 models included a problem-solving approach (42%) and 32 did not (58%). Most of the models with a problem-solving component came from medicine ( n = 15; 65%), followed by psychology ( n = 7; 30%), and engineering ( n = 1; 43%). No models from the fields of business, education, or OBM included a problem-solving component.

Bar Graph Showing the Number of Decision-Making Models with and without a Problem-Solving Component, and Models that were Sequential or Nonsequential

Figure ​ Figure2 2 also shows the number of models that were sequential. A total of 52 models were linear or sequential in nature (95%), whereas 3 were not (5%). Most of the models that were sequential came from medicine ( n = 32; 62%), followed by psychology ( n = 14; 27%), education ( n = 3; 58%), business ( n = 2; 4%), engineering ( n = 1; 2%), and child and youth care ( n = 1; 2%).

The goal of this literature review was to identify and analyze published ethical decision-making models in behavior analysis and allied disciplines to determine consistency in recommended approaches. We examined 55 ethical decision-making models to collect data on what recommended steps were included and what approaches were most frequently emphasized. Three general themes within ethical decision-making models arose from our analysis. These include: (1) What steps were included within models; (2) Whether the steps were sequential (i.e., a behavior chain); and (3) Whether the entire process could be labeled as problem solving (i.e., Szabo, 2020 ). We discuss each of these findings in turn.

Behaviors Involved in Ethical Decision Making

The first main finding surrounds the variability in recommended steps of ethical decision making across models. We found that each of the nine steps coded appeared in an average (arithmetic mean) of 58% of the articles (range: 20%–93%). This suggests that some consistency exists in what behaviors various scholars recommend practitioners should engage in when faced with an ethical decision. However, the wide variability in how frequently each behavior appeared also highlights that ABA practitioners would benefit from researchers clarifying at least three important characteristics of ethical decision-making models. These are: (1) What behaviors are necessary and sufficient to make an optimal ethical decision in ABA contexts (i.e., component analysis)? (2) What are the conditions under which specific steps are and are not needed (i.e., conditional discrimination analysis)? (3) Is there an optimal functional result of ethical decision making that is more important than the specific topographies a practitioner uses to contact that outcome (i.e., functional analysis; see Cox, 2021 )? Practitioners and researchers may begin to explore some of these questions when engaging in ethical decision making.

More than half of the articles examined emphasized the need for consulting ethical codes. It is interesting that more ethical models recommended practitioners reference codes of ethics from outside their discipline ( n = 44; 80% of models; e.g., personal, religious, organizational) than their own discipline’s code of ethics ( n = 40; 73%). To our knowledge, the conflict between personal and professional codes of ethics is an underexplored topic in the ABA literature. Nevertheless, the slightly greater emphasis on other codes of ethics in addition to one’s own discipline suggests this might be an important area where practitioners could use guidance. Also, the field of ABA would likely benefit from future research and scholarship surrounding the conditions and functional outcomes of ethical decisions where personal and professional values conflict.

It is important to mention that our review was done prior to the publication of the BACB’s ( 2020 ) ethical decision-making model. The BACB’s model was published in the analysis and writing stage of this review. Our findings suggest a robust literature spanning 40+ years, 60+ articles, and 50+ models all clustered around similar ethical decision-making steps published by the BACB. Perhaps most intriguing is that we identified the nine steps from our review prior to the publication of the BACB’s model, and no previous models had incorporated all nine ethical decision-making steps until the BACB published their decision model (BACB, 2020). Practicing behavior analysts would benefit from future component analyses, conditional discrimination analyses, functional analyses, and empirical support surrounding the BACB’s ethical decision-making model.

Our analysis also suggests that behavior analysts and allied professionals approach ethical decision making similarly. Given the complexity of ethical decision making and the shared types of dilemmas human service professionals contact, some convergence is expected. However, there are many reasons that two professionals from different disciplines may come into disagreement (Boivin et al., 2021 ; Bowman et al., 2021 ; Cox, 2019 ; Gasiewski et al., 2021 ). Having familiar systems with empirical support for how to navigate ethical dilemmas might improve the likelihood that a positive resolution occurs. Further, such interprofessional similarities in ethical decision-making processes allows future interdisciplinary dialogue to focus more on specific areas of agreement because what and how information will be used to make a decision is already agreed upon.

Behavior Chains and Behavior Topography

We found that 95% of the ethical decision-making models could be described as a behavior chain (e.g., Catania, 2013 ). Framing ethical decision making as a behavior chain might be useful as it highlights the interrelated and sequential nature of ethical decision making. That is, completing one step in an ethical decision-making behavior chain leads to a context wherein the next response in the chain is more likely to contact reinforcement. For example, until you have gathered all relevant information about how the decision will affect all relevant parties, your ranking and weighing of information seems less likely to lead to the best outcome. That said, the temporally delayed nature of behaviors and consequences involved in ethical decision making is different than how behavior chains have been studied in laboratory settings (e.g., Baum, 2017 ; Cox, 2021 ; Slocum & Tiger, 2011 ). Future research will likely be needed to better understand the effects of temporal relations on behavior chains and thus determine what approach best provides a behavioral description of ethical decision making.

It is interesting that the order in which steps were proposed differed across models. We are unaware of any research that compares the effectiveness of different sequential ethical decision-making models to understand whether the order of behaviors recommended as a chain are more or less useful. Nevertheless, future research that identifies the extent to which rigid sequences of behaviors need to occur to optimize decision making would be helpful for the field of ABA. Such information would likely improve behavior analytic training programs and prove useful for clinical directors, ethics committee chairs, case supervisors (e.g., BCBAs), and direct staff (e.g., RBTs).

Ethical Decision Making as Problem Solving

Recent attention has been given to the common-sense problem-solving approach (Szabo, 2020 ), which we used to score models within the current analysis. This problem-solving approach may offer great utility and is observed across various fields (e.g., cognitive psychology; Szabo, 2020 ). Within behavior analysis, this problem-solving approach has increasingly been applied to teach complex skills (e.g., Suarez et al., 2021 ). Our review involves an interesting extension of this analysis to ethical decision making and indicates the steps of the models may also point to additional precurrent behaviors or mediating strategies that could prove to be important elements of the behavioral chain.

We found that 42% of the ethical decision-making models could be described as including problem solving (e.g., Kieta et al., 2019 ). Framing ethical decision making as involving problem solving is advantageous because of the existing empirical literature on how to teach problem-solving skills and recognition of the importance of verbal stimuli and verbal behavior (e.g., Kieta et al., 2019 ). However, this also might have the drawbacks of adding complexity and less empirical support specific from the behavior analytic literature on describing, predicting, and controlling problem solving. This suggests that there are either components of ethical decision making outside of problem solving or that there are components of problem solving that might be missing from current decision-making models. Future research using concept analysis (e.g., Layng, 2019 ) combined with laboratory experiments may help clarify which of the above scenarios is more likely (or if there’s an unknown third!).

We also found that 58% of the ethical decision-making models could not be described as including problem solving. We are unaware of any research that has directly compared the effectiveness of ethical decision-making models with and without problem-solving components. Nevertheless, a practically useful set of empirical questions might identify the conditions under which ethical decision-making models with and without problem-solving components are more helpful for practitioners. Behavior analytic training programs subsequently could teach fluency toward ethical decision making via problem solving under some conditions and ethical decision making without problem solving under other conditions.

Limitations and Final Thoughts

The current study included several limitations. One limitation centers on the procedures used for rater agreement. Article ratings were completed in a group format and by consensus among the authors. It is possible that reactivity to other members of the group affected overall ratings (e.g., Asch, 1956 ). It is also possible that the search terms we used failed to capture relevant ethical decision-making models or that additional search terms would have led to different results. Further, we also restricted our inclusion criteria to specific human service fields allied to ABA. Thus, it is possible that a more comprehensive search of ethical decision-making models across more varied professions would lead to different outcomes. Finally, we did not include ethical decision-making models published in books mainly due to access issues and a typical lack of peer-review for books. Regardless, these limitations may provide greater support for our primary findings that the existing variability in ethical decision-making steps and overall lack of empirical support suggest this area is ripe for future research.

The development of an ethical decision-making skill set is vital for behavior analysts and for other human service providers. Dilemmas present as complex circumstances, with specific and unique contextual variations that require nuanced assessment. The process of training behavior analysts to meet these demands is daunting. There is a need to identify strategies for navigating dilemmas and for making ethical decisions. Allied professions and behavior analysis have identified steps in this process. Many of these models use problem-solving techniques. The BACB’s Decision Making Model overlaps substantially with existing literature across professions, and uses a problem-solving, sequential approach. These results are especially interesting as we had completed identifying the decision-making steps scored in the current article before the BACB model was released. It seems that the field has built a model that is entirely aligned with and built upon this interprofessional database. It will be important to empirically evaluate this new model. It will also be important to explore other decision-making approaches, to compare models, and to (potentially) match models to the contextual variables embedded in the presenting dilemma. The field of behavior analysis has, at times, been insular, and this has been a source of internal and external criticism. However, this review of the literature supports the substantial overlap across fields and provides concrete hope for mutually beneficial interdisciplinary collaboration. So, although decision-making models can be field-specific, ethical dilemmas appear to be universal and so are the intended outcomes. As behavior analysis tackles this complex skill set, it is important to learn from colleagues in allied disciplines, examine the component skills likely to be crucial to the development of this behavioral repertoire, and develop procedures for measuring, teaching, and training clinicians to methodically approach ethical dilemmas.

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

No funding was received to assist with the preparation of this manuscript.

Declarations

The authors do not have any potential conflicts of interest to disclose and have no relevant financial or nonfinancial interests to disclose.

No human participants were involved in this research, and therefore informed consent was not obtained.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

All articles with an asterisk indicate the final articles included in the review

• Asch SE. Studies of independence and conformity: I. A minority of one against a unanimous majority. Psychological Monographs: General & Applied. 1956; 70 (9):1–70. doi: 10.1037/h0093718. [ CrossRef ] [ Google Scholar ]
• Bailey, J., & Burch, M. (2013). Ethics for behavior analysts  (2nd expanded ed).
• Bailey J, Burch M. Ethics for behavior analysts . 3. Routledge; 2016. [ Google Scholar ]
• Bailey J, Burch M. Ethics for behavior analysts . 4. Routledge; 2022. [ Google Scholar ]
• Baum WM. On Human Nature . Academic Press; 2017. Behavior analysis, Darwinian evolutionary processes, and the diversity of human behavior; pp. 397–415. [ Google Scholar ]
• Behavior Analyst Certification Board. (BACB, 2004, 2010). Guidelines for responsible conduct for behavior analysts. https://www.bacb.com/wp-content/uploads/2020/09/2010-Disciplinary-Standards_.pdf
• Behavior Analyst Certification Board. (BACB, 2014). Professional and ethical compliance code for behavior analysts . https://www.bacb.com/wp-content/uploads/2022/05/BACB-Compliance-Code-10-8-15watermark.pdf
• Behavior Analyst Certification Board. (BACB, 2020). Ethics code for behavior analysts . https://bacb.com/wp-content/ethics-code-for-behavior-analysts/
• Boccio DE. Does use of a decision-making model improve the quality of school psychologists’ ethical decisions? Ethics & Behavior. 2021; 31 (2):119–135. doi: 10.1080/10508422.2020.1715802. [ CrossRef ] [ Google Scholar ]
• Boivin, N., Ruane, J., Quigley, S. P., Harper, J., & Weiss, M. J. (2021). Interdisciplinary collaboration training: An example of a preservice training series. Behavior Analysis in Practice, 1–14 . 10.1007/s40617-021-00561-z [ PMC free article ] [ PubMed ]
• Bolmsjö IÅ, Edberg AK, Sandman L. Everyday ethical problems in dementia care: A teleological model. Nursing Ethics. 2006; 13 (4):340–359. doi: 10.1191/0969733006ne890oa. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bolmsjö IÅ, Sandman L, Andersson E. Everyday ethics in the care of elderly people. Nursing Ethics. 2006; 13 (3):249–263. doi: 10.1191/0969733006ne875oa. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bommer M, Gratto C, Gravander J, Tuttle M. A behavioral model of ethical and unethical decision making. Journal of Business Ethics. 1987; 6 (4):265–280. doi: 10.1007/BF00382936. [ CrossRef ] [ Google Scholar ]
• Bowman KS, Suarez VD, Weiss MJ. Standards for interprofessional collaboration in the treatment of individuals with autism. Behavior Analysis in Practice. 2021; 14 :1191–1208. doi: 10.1007/s40617-021-00560-0. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brodhead MT. Maintaining professional relationships in an interdisciplinary setting: Strategies for navigating nonbehavioral treatment recommendations for individuals with autism. Behavior Analysis in Practice. 2015; 8 (1):70–78. doi: 10.1007/s40617-015-0042-7. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brodhead MT, Quigley SP, Wilczynski SM. A call for discussion about scope of competence in behavior analysis. Behavior Analysis in Practice. 2018; 11 (4):424–435. doi: 10.1007/s40617-018-00303-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Candee D, Puka B. An analytic approach to resolving problems in medical ethics. Journal of Medical Ethics. 1984; 10 (2):61–70. doi: 10.1136/jme.10.2.61. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cassells JM, Gaul AL. An ethical assessment framework for nursing practice. Maryland Nurse. 1998; 17 (1):9–21. [ Google Scholar ]
• *Cassells, J. M., Jenkins, J., Lea, D. H., Calzone, K., & Johnson, E. (2003). An ethical assessment framework for addressing global genetic issues in clinical practice. Oncology Nursing Forum, 30 (3): 383–392. Oncology Nursing Society. [ PubMed ]
• Catania, A. C. (2013). Learning (5 th ed.). Sloan. ISBN 10: 1-59739-023-7
• Christensen PJ. An ethical framework for nursing service administration. Advances in Nursing Science. 1988; 10 (3):46–55. doi: 10.1097/00012272-198804000-00006. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cottone RR. A social constructivism model of ethical decision making in counseling. Journal of Counseling & Development. 2001; 79 (1):39–45. doi: 10.1002/j.1556-6676.2001.tb01941.x. [ CrossRef ] [ Google Scholar ]
• Cottone RR. Displacing the psychology of the individual in ethical decision-making: The social constructivism model. Canadian Journal of Counselling & Psychotherapy. 2004; 38 (1):5–9. [ Google Scholar ]
• Cottone RR, Claus RE. Ethical decision-making models: A Review of the literature. Journal of Counseling & Development. 2000; 78 (3):275–283. doi: 10.1002/j.1556-6676.2000.tb01908.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cox DJ. Ethical considerations in interdisciplinary treatments. In: Rieske RD, editor. Handbook of interdisciplinary treatments for autism spectrum disorder . Springer; 2019. pp. 49–61. [ Google Scholar ]
• Cox DJ. Descriptive and normative ethical behavior appear to be functionally distinct. Journal of Applied Behavior Analysis. 2021; 54 (1):168–191. doi: 10.1002/jaba.761. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• DeWolf MS. Ethical decision-making. Seminars in Oncology Nursing. 1989; 5 (2):77–81. doi: 10.1016/0749-2081(89)90063-6. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• du Preez E, Goedeke S. Second order ethical decision-making in counselling psychology: Theory, practice and process. New Zealand Journal of Psychology. 2013; 42 (3):44–49. [ Google Scholar ]
• Duff M, Passmore J. Ethics in coaching: An ethical decision making framework for coaching psychologists. International Coaching Psychology Review. 2010; 5 (2):140–151. doi: 10.53841/bpsicpr.2010.5.2.140. [ CrossRef ] [ Google Scholar ]
• Eberlein L. Introducing ethics to beginning psychologists: A problem-solving approach. Professional Psychology: Research & Practice. 1987; 18 (4):353–359. doi: 10.1037/0735-7028.18.4.353. [ CrossRef ] [ Google Scholar ]
• Ehrich LC, Kimber M, Millwater J, Cranston N. Ethical dilemmas: A model to understand teacher practice. Teachers & Teaching: Theory & Practice. 2011; 17 (2):173–185. doi: 10.1080/13540602.2011.539794. [ CrossRef ] [ Google Scholar ]
• Fan LC. Decision-making models for handling ethical dilemmas. Proceedings of the ICE-Municipal Engineer. 2003; 156 :229–234. doi: 10.1680/muen.2003.156.4.229. [ CrossRef ] [ Google Scholar ]
• Ferrell BR, Eberts MT, McCaffery M, Grant M. Clinical decision making and pain. Cancer Nursing. 1991; 14 (6):289–297. doi: 10.1097/00002820-199112000-00002. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• *Forester-Miller, H., & Davis, T. E. (1996). A practitioner's guide to ethical decision making . American Counseling Association. https://www.counseling.org/docs/ethics/practitioners_guide.pdf?sfvrsn=2
• Garfat T, Ricks F. Self-driven ethical decision-making: A model for child and youth care workers. Child & Youth Care Forum. 1995; 24 (6):393–404. doi: 10.1007/BF02128530. [ CrossRef ] [ Google Scholar ]
• Gasiewski K, Weiss MJ, Leaf JB, Labowitz J. Collaboration between behavior analysts and occupational therapists in autism service provision: Bridging the gap. Behavior Analysis in Practice. 2021; 14 (4):1209–1222. doi: 10.1007/s40617-021-00619-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Green J, Walker K. A contingency model for ethical decision-making by educational leaders. International Journal of Educational Leadership Preparation. 2009; 4 (4):1–10. [ Google Scholar ]
• Greipp ME. Ethical decision making and mandatory reporting in cases of suspected child abuse. Journal of Pediatric Health Care. 1997; 11 (6):258–265. doi: 10.1016/S0891-5245(97)90081-X. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grundstein-Amado R. An integrative model of clinical-ethical decision making. Theoretical Medicine. 1991; 12 (2):157–170. doi: 10.1007/BF00489796. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grundstein-Amado R. Ethical decision-making processes used by healthcare providers. Journal of Advanced Nursing. 1993; 18 (11):1701–1709. doi: 10.1046/j.1365-2648.1993.18111701.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Haddad AM. Ethical considerations in home care of the oncology patient. Seminars in Oncology Nursing. 1996; 12 (3):226–230. doi: 10.1016/S0749-2081(96)80040-4. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Harasym PH, Tsai TC, Munshi FM. Is problem-based learning an ideal format for developing ethical decision skills? Kaohsiung Journal of Medical Sciences. 2013; 29 (10):523–529. doi: 10.1016/j.kjms.2013.05.005. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hayes JR. Consultation-liaison psychiatry and clinical ethics: A model for consultation and teaching. General Hospital Psychiatry. 1986; 8 (6):415–418. doi: 10.1016/0163-8343(86)90022-8. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Heyler SG, Armenakis AA, Walker AG, Collier DY. A qualitative study investigating the ethical decision making process: A proposed model. The Leadership Quarterly. 2016; 27 (5):788–801. doi: 10.1016/j.leaqua.2016.05.003. [ CrossRef ] [ Google Scholar ]
• Hill M, Glaser K, Harden J. A feminist model for ethical decision making. Women & Therapy. 1998; 21 (3):101–121. doi: 10.1300/J015v21n03_10. [ CrossRef ] [ Google Scholar ]
• Hough MC. Learning, decisions and transformation in critical care nursing practice. Nursing Ethics. 2008; 15 (3):322–331. doi: 10.1177/0969733007088430. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hughes KK, Dvorak EM. The use of decision analysis to examine ethical decision making by critical care nurses. Heart & Lung. 1997; 26 (3):238–251. doi: 10.1016/S0147-9563(97)90061-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hundert EM. A model for ethical problem solving in medicine, with practical applications. Focus. 2003; 144 (4):839–435. doi: 10.1176/foc.1.4.427. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Johnsen DC, Flick K, Butali A, Cunningham-Ford MA, Holloway JA, Mahrous A, Marchini L, Clancy JM. Two critical thinking models—Probing questions and conceptualization—Adding 4 skill sets to the teacher's armamentarium. Journal of Dental Education. 2020; 84 (7):733–741. doi: 10.1002/jdd.12177. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Johnson RL, Liu J, Burgess Y. A model for making decisions about ethical dilemmas in student assessment. Journal of Moral Education. 2017; 46 (2):212–229. doi: 10.1080/03057240.2017.1313725. [ CrossRef ] [ Google Scholar ]
• Jones TM. Ethical decision making by individuals in organizations: An issue-contingent model. Academy of Management Review. 1991; 16 (2):366–395. doi: 10.5465/amr.1991.4278958. [ CrossRef ] [ Google Scholar ]
• Kaldjian LC, Weir RF, Duffy TP. A clinician’s approach to clinical ethical reasoning. Journal of General Internal Medicine. 2005; 20 (3):306–311. doi: 10.1111/j.1525-1497.2005.40204.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kanoti GA. Ethics and medical-ethical decisions. Critical Care Clinics. 1986; 2 (1):3–12. doi: 10.1016/S0749-0704(18)30620-1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kieta AR, Cihon TM, Abdel-Jalil A. Problem solving from a behavioral perspective: Implications for behavior analysts and educators. Journal of Behavioral Education. 2019; 28 (2):275–300. doi: 10.1007/s10864-018-9296-9. [ CrossRef ] [ Google Scholar ]
• Kirsch NR. Ethical decision making: Application of a problem-solving model. Topics in Geriatric Rehabilitation. 2009; 25 (4):282–291. doi: 10.1097/TGR.0b013e3181bdd6d8. [ CrossRef ] [ Google Scholar ]
• LaFrance DL, Weiss MJ, Kazemi E, Gerenser J, Dobres J. Multidisciplinary teaming: Enhancing collaboration through increased understanding. Behavior Analysis in Practice. 2019; 12 (3):709–726. doi: 10.1007/s40617-019-00331-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Laletas S. Ethical decision making for professional school counsellors: Use of practice-based models in secondary school settings. British Journal of Guidance & Counselling. 2018; 47 (3):283–291. doi: 10.1080/03069885.2018.1474341. [ CrossRef ] [ Google Scholar ]
• Layng TJ. Tutorial: Understanding concepts: Implications for behavior analysts and educators. Perspectives on Behavior Science. 2019; 42 (2):345–363. doi: 10.1007/s40614-018-00188-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Liang B, Chung A, Diamonti AJ, Douyon CM, Gordon JR, Joyner ED, Meerkins TM, Rene MK, Seinkiewicz SA, Weber AE, Wilson ES. Ethical social justice: Do the ends justify the means? Journal of Community & Applied Social Psychology. 2017; 27 (4):298–311. doi: 10.1002/casp.2323. [ CrossRef ] [ Google Scholar ]
• Macpherson I, Roqué MV, Segarra I. Moral dilemmas involving anthropological and ethical dimensions in healthcare curriculum. Nursing Ethics. 2020; 27 (5):1238–1249. doi: 10.1177/0969733020914382. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Marya VG, Suarez VD, Cox DJ. Ethical decision-making and evidenced-based practices. In: Fisher WW, Piazza CC, Roane HS, editors. Handbook of applied behavior analysis interventions for autism . Springer; 2022. pp. 47–70. [ Google Scholar ]
• Newhouse-Oisten MK, Peck KM, Conway AA, Frieder JE. Ethical considerations for interdisciplinary collaboration with prescribing professionals. Behavior Analysis in Practice. 2017; 10 (2):145–153. doi: 10.1007/s40617-017-0184-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Marco CA, Lu DW, Stettner E, Sokolove PE, Ufberg JW, Noeller TP. Ethics curriculum for emergency medicine graduate medical education. Journal of Emergency Medicine. 2011; 40 (5):550–556. doi: 10.1016/j.jemermed.2010.05.076. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Miller KL, Re Cruz A, Ala'i-Rosales S. Inherent tensions and possibilities: Behavior analysis and cultural responsiveness. Behavior & Social Issues. 2019; 28 (1):16–36. doi: 10.1007/s42822-019-00010-1. [ CrossRef ] [ Google Scholar ]
• Moher D, Liberati A, Tetzlaff J, Altman DG, Prisma Group Reprint—preferred reporting items for systematic reviewsand meta-analyses: the PRISMA statement. Physical Therapy. 2009; 89 (9):873–880. doi: 10.1093/ptj/89.9.873. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Murphy MA, Murphy J. Making ethical decisions—Systematically. Nursing. 1976; 6 (5):CG13–CG14. doi: 10.1016/j.jemermed.2010.05.076. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nekhlyudov L, Braddock CH., III An approach to enhance communication about screening mammography in primary care. Journal of Women's Health. 2009; 18 (9):1403–1412. doi: 10.1089/jwh.2008.1184. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Park EJ. An integrated ethical decision-making model for nurses. Nursing Ethics. 2012; 19 (1):139–159. doi: 10.1177/0969733011413491. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Park EJ. The development and implications of a case-based computer program to train ethical decision-making. Nursing Ethics. 2013; 20 (8):943–956. doi: 10.1177/0969733013484489. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Park EJ, Park M. Effectiveness of a case-based computer program on students’ ethical decision making. Journal of Nursing Education. 2015; 54 (11):633–640. doi: 10.3928/01484834-20151016-04. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Phillips S. Ethical decision-making when caring for the noncompliant patient. Journal of Infusion Nursing. 2006; 29 (5):266–271. doi: 10.1097/00129804-200609000-00005. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ponterotto JG, Reynolds JD. Ethical and legal considerations in psychobiography. American Psychologist. 2017; 72 (5):446–458. doi: 10.1037/amp0000047. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pritchett, M., Ala’i-Rosales, S., Cruz, A. R., & Cihon, T. M. (2021). Social justice is the spirit and aim of an applied science of human behavior: Moving from colonial to participatory research practices. Behavior Analysis in Practice, 1–19 . 10.1007/s40617-021-00591-7 [ PMC free article ] [ PubMed ]
• Rosenberg NE, Schwartz IS. Guidance or compliance: What makes an ethical behavior analyst? Behavior Analysis in Practice. 2019; 12 (2):473–482. doi: 10.1007/s40617-018-00287-5. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schaffer MA, Cameron ME, Tatley EB. The value, be, do ethical decision-making model: Balancing students’ needs in school nursing. Journal of School Nursing. 2000; 16 (5):44–49. doi: 10.1177/105984050001600507. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schneider GW, Snell L. CARE: An approach for teaching ethics in medicine. Social Science & Medicine. 2000; 51 (10):1563–1567. doi: 10.1016/S0277-9536(00)00054-X. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shahidullah JD, Hostutler CA, Forman SG. Ethical considerations in medication-related roles for pediatric primary care psychologists. Clinical Practice in Pediatric Psychology. 2019; 7 (4):405. doi: 10.1037/cpp0000285. [ CrossRef ] [ Google Scholar ]
• Siegler M. Decision-making strategy for clinical-ethical problems in medicine. Archives of Internal Medicine. 1982; 142 (12):2178–2179. doi: 10.1001/archinte.1982.00340250144021. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sileo FJ, Kopala M. An A-B-C-D-E worksheet for promoting beneficence when considering ethical issues. Counseling & Values. 1993; 37 (2):89–95. doi: 10.1002/j.2161-007X.1993.tb00800.x. [ CrossRef ] [ Google Scholar ]
• Slocum SK, Tiger JH. An assessment of the efficiency of and child preference for forward and backward chaining. Journal of Applied Behavior Analysis. 2011; 44 (4):793–805. doi: 10.1901/jaba.2011.44-793. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Soskolne CL. Ethical decision-making in epidemiology: The case study approach. Journal of Clinical Epidemiology. 1991; 44 (1):125–130. doi: 10.1016/0895-4356(91)90187-E. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Suarez, V. D., Najdowski, A. C., Tarbox, J., Moon, E., St Clair, M., & Farag, P. (2021). Teaching individuals with autism problem-solving skills for resolving social conflicts. Behavior Analysis in Practice, 1–14 . 10.1007/s40617-021-00643-y [ PMC free article ] [ PubMed ]
• Sullivan PA, Brown T. Common-sense ethics in administrative decision making. Part II. Proactive steps. Journal of Nursing Administration. 1991; 21 (11):57–61. doi: 10.1097/00005110-199111000-00013. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sush, D., & Najdowski, A. C. (2019). A workbook of ethical case scenarios in applied behavior analysis . Academic Press.
• Szabo, T. (2020). Problem solving. In M. Fryling, R. Rehfeldt, J. Tarbox, & L. Hayes (Eds.), Applied behavior analysis of language and cognition: Core concepts and principles for practitioners. Context Press.
• Taylor BA, LeBlanc LA, Nosik MR. Compassionate care in behavior analytic treatment: Can outcomes be enhanced by attending to relationships with caregivers? Behavior Analysis in Practice. 2019; 12 (3):654–666. doi: 10.1007/s40617-018-00289-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Toren O, Wagner N. Applying an ethical decision-making tool to a nurse management dilemma. Nursing Ethics. 2010; 17 (3):393–402. doi: 10.1177/0969733009355106. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tsai TC, Harasym PH. A medical ethical reasoning model and its contributions to medical education. Medical Education. 2010; 44 (9):864–873. doi: 10.1111/j.1365-2923.2010.03722.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tunzi M, Ventres W. Family medicine ethics: An integrative approach. Family Medicine. 2018; 50 (8):583–588. doi: 10.22454/FamMed.2018.821666. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tymchuk AJ. Guidelines for ethical decision making. Canadian Psychology/Psychologie Canadienne. 1986; 27 (1):36. doi: 10.1037/h0079866. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wright PI. Cultural humility in the practice of applied behavior analysis. Behavior Analysis in Practice. 2019; 12 :805–809. doi: 10.1007/s40617-019-00343-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zeni TA, Buckley MR, Mumford MD, Griffith JA. Making “sense” of ethical decision making. The Leadership Quarterly. 2016; 27 (6):838–855. doi: 10.1016/j.leaqua.2016.09.002. [ CrossRef ] [ Google Scholar ]

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Effective Decision Making Techniques for Every Situation

In this guide, we will explore effective decision-making techniques to help you make better decisions in various aspects of your life. Whether you’re facing personal dilemmas, professional challenges, or ethical quandaries, these techniques will provide you with the tools you need to navigate through any situation with confidence and clarity.

What is a Decision Making Technique?

A decision-making technique is a method or approach used to help people make better decisions. These techniques provide step-by-step processes or tools to consider options, weigh their pros and cons, and choose the best course of action. Examples include listing the advantages and disadvantages of each option, visualizing potential outcomes, or prioritizing based on key factors. These techniques help people make clearer, more informed decisions in different situations, like personal choices or business strategies.

6 Decision Making Techniques for Better Decisions

Here are 6 decision-making techniques that can be applied in various contexts, including personal decision-making, professional decision-making, strategic planning, problem-solving, and more. These techniques help individuals and organizations make better choices by providing structured approaches to analyze options, mitigate risks, and achieve desired outcomes.

​​1. Pugh Matrix

What it is : The Pugh Matrix, also known as the Decision Matrix, is a structured technique for comparing multiple alternatives against a set of criteria. It helps objectively evaluate options by assigning scores based on predefined criteria.

How to use it in decision-making :

• Identify the decision to be made and the alternatives available.
• Determine the criteria for evaluation, such as cost, time, quality, etc.
• Assign weights to each criterion based on its importance.
• Compare each alternative against the criteria and assign scores.
• Calculate the total scores for each alternative to determine the best option.
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Brainstorming

What it is : Brainstorming is a creative technique used to generate a large number of ideas or solutions to a problem in a short amount of time. It encourages free thinking and idea generation without criticism.

How to use it in decision making :

• Gather a group of participants with diverse backgrounds and perspectives.
• Clearly define the problem or decision to be addressed.
• Set a time limit and encourage participants to generate as many ideas as possible.
• Record all ideas without judgment or evaluation.
• After brainstorming, evaluate and refine the ideas generated to identify potential solutions.

The Heuristic Method

What it is : The heuristic method involves using practical rules or shortcuts to make decisions quickly, often in situations with limited information or time.

• Identify the decision to be made and any constraints or limitations.
• Use heuristics, or mental shortcuts, to simplify the decision-making process .
• Examples of heuristics include the “satisficing” approach (choosing the first option that meets the minimum criteria), the “availability heuristic” (relying on readily available information), or the “anchoring and adjustment heuristic” (starting with an initial estimate and adjusting based on new information).

Tiered Voting

What it is : Tiered voting is a decision-making technique where participants vote on options in multiple rounds, with the lowest-ranking options eliminated in each round until a consensus is reached.

• Present the options to be voted on to the participants.
• In the first round of voting, each participant ranks the options from best to worst.
• Eliminate the options with the lowest rankings and proceed to the next round of voting.
• Repeat the process until only one option remains, or until a predetermined threshold for consensus is reached.

SWOT Analysis

What it is : SWOT Analysis is a strategic planning tool used to identify the Strengths, Weaknesses, Opportunities, and Threats of a decision, project, or organization.

• Identify the decision or project to be analyzed.
• List the internal Strengths and Weaknesses, such as resources, capabilities, or limitations.
• Identify external Opportunities and Threats, such as market trends, competition, or regulatory changes.
• Analyze the SWOT factors to inform decision-making and develop strategies to capitalize on strengths, address weaknesses, exploit opportunities, and mitigate threats.

Game Theory

What it is : Game Theory is a mathematical framework used to analyze decision-making in situations where the outcomes depend on the choices of multiple parties, or “players.”

• Identify the decision or interaction involving multiple parties with conflicting interests.
• Define the players, their available strategies, and the possible outcomes.
• Use mathematical models to analyze the potential strategies and outcomes, considering factors such as payoff, risk, and utility.
• Determine the optimal strategy for each player, considering the potential responses of others, to achieve the best possible outcome or equilibrium.

What it is : Scenario planning is a technique used to make decisions in the face of uncertainty about the future. It involves creating multiple plausible future scenarios and analyzing their potential impact on the decision at hand.

• Identify the decision to be made and any uncertainties or future factors that could influence the outcome.
• Develop multiple scenarios, each depicting a different plausible future based on various combinations of key uncertainties.
• Evaluate each scenario’s potential impact on the decision, considering factors such as risks, opportunities, and challenges.
• Assess the robustness of the decision under each scenario and identify strategies to mitigate risks or capitalize on opportunities.
• Make the decision based on an understanding of how it would perform across different possible futures.

Priority Matrix

What it is : A Priority Matrix, also known as an Eisenhower Matrix or Urgent-Important Matrix, is a tool used to prioritize tasks or decisions based on their urgency and importance. It helps individuals or teams focus their efforts on the most critical tasks or decisions, thereby improving productivity and effectiveness.

• Identify the decisions or tasks that need to be prioritized.
• Create a prioritization grid with four quadrants: Urgent and Important, Important but Not Urgent, Urgent but Not Important, and Neither Urgent nor Important.
• Place each decision or task into one of the quadrants based on its level of urgency and importance.
• Focus on addressing tasks in the Urgent and Important quadrant first, as they require immediate attention.
• Delegate or schedule tasks in the Important but Not Urgent quadrant for later action, to prevent them from becoming urgent.
• Consider whether tasks in the Urgent but Not Important quadrant can be delegated or deferred, as they may distract from more critical priorities.
• Minimize or eliminate tasks in the Neither Urgent nor Important quadrant, as they contribute little value to achieving goals.

Effective decision making is a skill that can be honed through practice and awareness. By understanding the decision-making process, utilizing proven decision making techniques, and adapting to different contexts, you can navigate through life’s challenges with confidence and clarity. Remember, every decision you make shapes your future, so choose wisely.

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Amanda Athuraliya is the communication specialist/content writer at Creately, online diagramming and collaboration tool. She is an avid reader, a budding writer and a passionate researcher who loves to write about all kinds of topics.

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5 time tested mental models to help you become a better change leader.

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In today's fast-paced environment, effective change leadership is essential. However, navigating the complexities of transformation can be daunting. This is where mental models come into play. Mental models are cognitive frameworks that help us comprehend and interpret our surroundings. For change leaders, these models can serve as valuable tools, enabling clearer thinking and better decision-making. Let's explore five time-tested mental models that can elevate your change leadership abilities.

First Principles Thinking

First Principles Thinking is about deconstructing complex problems into their most basic elements. It allows leaders to identify the core truths and build solutions from the ground up.

Why is this relevant to leading change? Because change often involves complex challenges with many moving parts. By breaking down these challenges to their fundamental components, you can uncover innovative solutions and avoid getting bogged down by assumptions and conventions. It's like stripping down a machine to its essential parts to understand how it works and how it can be improved.

How can you practice First Principles Thinking? Start by questioning every assumption related to the change you're leading. Ask yourself, "What do I know for sure?" and "Why is this important?" Challenge the status quo and encourage your team to do the same. When faced with a problem, try to reconstruct it from scratch, focusing on the fundamental truths rather than accepted practices.

Second Order Thinking

Second Order Thinking involves considering the long-term consequences of decisions, not just the immediate outcomes. It's about thinking several steps ahead.

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Why is this mental model crucial for change leaders? Because any significant change will have ripple effects throughout an organization. These effects aren't always immediately apparent and failing to anticipate them can lead to unforeseen challenges. By adopting Second Order Thinking, you can better prepare for and mitigate potential negative impacts, ensuring a smoother transition.

To incorporate Second Order Thinking into your decision-making, always ask, "And then what?" Consider the cascading effects of your actions. For example, if you're implementing a new technology, think beyond its immediate benefits and consider how it might affect workflow, employee morale and customer experience in the long run. This holistic approach will help you make more informed and sustainable decisions.

Inversion Technique

The Inversion Technique is about thinking backward to move forward. Instead of asking how to achieve a goal, you consider what might prevent you from achieving it.

Why is inversion relevant to leading change? Because identifying potential obstacles and pitfalls in advance can help you avoid them. It forces you to look at the change process from a different angle, revealing blind spots and helping you develop more robust strategies.

To practice the Inversion Technique, start by envisioning the worst-case scenarios. Ask yourself, "What could go wrong?" and "What would failure look like?" Once you have a clear picture of potential pitfalls, you can take proactive steps to address them. This negative visualization can be a powerful tool for risk management and contingency planning, ensuring you're prepared for any eventuality.

The Pareto Principle

The Pareto Principle, also known as the 80/20 rule, suggests that 80% of outcomes result from 20% of efforts. It's about focusing on what truly matters.

Why should change leaders care about the Pareto Principle? Because in any change initiative, resources are limited. By identifying and concentrating on the critical few factors that will have the most significant impact, you can maximize efficiency and effectiveness. It's about working smarter, not harder.

To apply the Pareto Principle, start by analyzing your change initiative to identify the key drivers of success. Ask yourself, "What are the 20% of activities that will yield 80% of the results?" Focus your energy and resources on these high-impact areas. This targeted approach will help you achieve more with less, driving meaningful progress without overextending your team.

The Map is not Your Territory

This mental model reminds us that our perceptions and representations of reality are just that—representations. They are not the reality itself.

Why is this distinction important for leading change? Because leaders often rely on data, reports, and plans to guide their decisions. While these tools are valuable, they can never fully capture the complexity and nuances of real-world situations. Recognizing this limitation helps you stay adaptable and responsive to actual conditions as they unfold.

To practice this mental model, remain open-minded and flexible. Regularly validate your assumptions and plans against the real-world outcomes. Engage with your team and stakeholders to gather diverse perspectives and feedback. The goal is to adapt and iterate based on what you learn, not to rigidly follow a plan that might become outdated or incomplete.

These models provide frameworks for navigating the intricacies of transformation, helping you to break down complex problems, foresee long-term impacts, identify potential obstacles, focus on high-impact activities and remain adaptable to real-world conditions. Embrace these mental models as part of your change leadership toolkit and you'll be well-equipped to lead your organization through successful change.

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If we’re all so busy, why isn’t anything getting done?

Have you ever asked why it’s so difficult to get things done in business today—despite seemingly endless meetings and emails? Why it takes so long to make decisions—and even then not necessarily the right ones? You’re not the first to think there must be a better way. Many organizations address these problems by redesigning boxes and lines: who does what and who reports to whom. This exercise tends to focus almost obsessively on vertical command relationships and rarely solves for what, in our experience, is the underlying disease: the poor design and execution of collaborative interactions.

About the authors

This article is a collaborative effort by Aaron De Smet , Caitlin Hewes, Mengwei Luo, J.R. Maxwell , and Patrick Simon , representing views from McKinsey’s People & Organizational Performance Practice.

In our efforts to connect across our organizations, we’re drowning in real-time virtual interaction technology, from Zoom to Slack to Teams, plus group texting, WeChat, WhatsApp, and everything in between. There’s seemingly no excuse to not collaborate. The problem? Interacting is easier than ever, but true, productive, value-creating collaboration is not. And what’s more, where engagement is occurring, its quality is deteriorating. This wastes valuable resources, because every minute spent on a low-value interaction eats into time that could be used for important, creative, and powerful activities.

It’s no wonder a recent McKinsey survey  found 80 percent of executives were considering or already implementing changes in meeting structure and cadence in response to the evolution in how people work due to the COVID-19 pandemic. Indeed, most executives say they frequently find themselves spending way too much time on pointless interactions that drain their energy and produce information overload.

Most executives say they frequently find themselves spending way too much time on pointless interactions.

Three critical collaborative interactions

What can be done? We’ve found it’s possible to quickly improve collaborative interactions by categorizing them by type and making a few shifts accordingly. We’ve observed three broad categories of collaborative interactions (exhibit):

• Decision making, including complex or uncertain decisions (for example, investment decisions) and cross-cutting routine decisions (such as quarterly business reviews)
• Creative solutions and coordination, including innovation sessions (for example, developing new products) and routine working sessions (such as daily check-ins)
• Information sharing, including one-way communication (video, for instance) and two-way communication (such as town halls with Q&As)

Below we describe the key shifts required to improve each category of collaborative interaction, as well as tools you can use to pinpoint problems in the moment and take corrective action.

Decision making: Determining decision rights

When you’re told you’re “responsible” for a decision, does that mean you get to decide? What if you’re told you’re “accountable”? Do you cast the deciding vote, or does the person responsible? What about those who must be “consulted”? Sometimes they are told their input will be reflected in the final answer—can they veto a decision if they feel their input was not fully considered?

It’s no wonder one of the key factors for fast, high-quality decisions is to clarify exactly who makes them. Consider a success story at a renewable-energy company. To foster accountability and transparency, the company developed a 30-minute “role card” conversation for managers to have with their direct reports. As part of this conversation, managers explicitly laid out the decision rights and accountability metrics for each direct report. The result? Role clarity enabled easier navigation for employees, sped up decision making, and resulted in decisions that were much more customer focused.

How to define decision rights

We recommend a simple yet comprehensive approach for defining decision rights. We call it DARE, which stands for deciders, advisers, recommenders, and executors:

Deciders are the only ones with a vote (unlike the RACI model, which helps determine who is responsible, accountable, consulted, and informed). If the deciders get stuck, they should jointly agree on how to escalate the decision or figure out a way to move the process along, even if it means agreeing to “disagree and commit.”

Advisers have input and help shape the decision. They have an outsize voice in setting the context of the decision and have a big stake in its outcome—for example, it may affect their profit-and-loss statements—but they don’t get a vote.

Recommenders conduct the analyses, explore the alternatives, illuminate the pros and cons, and ultimately recommend a course of action to advisers and deciders. They see the day-to-day implications of the decision but also have no vote. Best-in-class recommenders offer multiple options and sometimes invite others to suggest more if doing so may lead to better outcomes. A common mistake of recommenders, though, is coming in with only one recommendation (often the status quo) and trying to convince everyone it’s the best path forward. In general, the more recommenders, the better the process—but not in the decision meeting itself.

Executers don’t give input but are deeply involved in implementing the decision. For speed, clarity, and alignment, executers need to be in the room when the decision is made so they can ask clarifying questions and spot flaws that might hinder implementation. Notably, the number of executers doesn’t necessarily depend on the importance of the decision. An M&A decision, for example, might have just two executors: the CFO and a business-unit head.

To make this shift, ensure everyone is crystal clear about who has a voice but no vote or veto. Our research indicates while it is often helpful to involve more people in decision making, not all of them should be deciders—in many cases, just one individual should be the decider (see sidebar “How to define decision rights”). Don’t underestimate the difficulty of implementing this. It often goes against our risk-averse instinct to ensure everyone is “happy” with a decision, particularly our superiors and major stakeholders. Executing and sustaining this change takes real courage and leadership.

Creative solutions and coordination: Open innovation

Routine working sessions are fairly straightforward. What many organizations struggle with is finding innovative ways to identify and drive toward solutions. How often do you tell your teams what to do versus empowering them to come up with solutions? While they may solve the immediate need to “get stuff done,” bureaucracies and micromanagement are a recipe for disaster. They slow down the organizational response to the market and customers, prevent leaders from focusing on strategic priorities, and harm employee engagement. Our research suggests  key success factors in winning organizations are empowering employees  and spending more time on high-quality coaching interactions.

How microenterprises empower employees to drive innovative solutions

Haier, a Chinese appliance maker, created more than 4,000 microenterprises (MEs) that share common approaches but operate independently. Haier has three types of microenterprises:

• Market-facing MEs have roots in Haier’s legacy appliance business, reinvented for today’s customer-centric, web-enabled world. They are expected to grow revenue and profit ten times faster than the industry average.
• Incubating MEs focus on emerging markets such as e-gaming or wrapping new business models around familiar products. They currently account for more than 10 percent of Haier’s market capitalization.
• “Node” MEs sell market-facing ME products and services such as design, manufacturing, and human-resources support.

Take Haier. The Chinese appliance maker divided itself into more than 4,000 microenterprises with ten to 15 employees each, organized in an open ecosystem of users, inventors, and partners (see sidebar “How microenterprises empower employees to drive innovative solutions”). This shift turned employees into energetic entrepreneurs who were directly accountable for customers. Haier’s microenterprises are free to form and evolve with little central direction, but they share the same approach to target setting, internal contracting, and cross-unit coordination. Empowering employees to drive innovative solutions has taken the company from innovation-phobic to entrepreneurial at scale. Since 2015, revenue from Haier Smart Home, the company’s listed home-appliance business, has grown by more than 18 percent a year, topping 209 billion renminbi ($32 billion) in 2020. The company has also made a string of acquisitions, including the 2016 purchase of GE Appliances, with new ventures creating more than $2 billion in market value.

Empowering others doesn’t mean leaving them alone. Successful empowerment, counterintuitively, doesn’t mean leaving employees alone. Empowerment requires leaders to give employees both the tools and the right level of guidance and involvement. Leaders should play what we call the coach role: coaches don’t tell people what to do but instead provide guidance and guardrails and ensure accountability, while stepping back and allowing others to come up with solutions.

Haier was able to use a variety of tools—including objectives and key results (OKRs) and common problem statements—to foster an agile way of working across the enterprise that focuses innovative organizational energy on the most important topics. Not all companies can do this, and some will never be ready for enterprise agility. But every organization can take steps to improve the speed and quality of decisions made by empowered individuals.

Managers who are great coaches, for example, have typically benefited from years of investment by mentors, sponsors, and organizations. We think all organizations should do more to improve the coaching skills of managers and help them to create the space and time to coach teams, as opposed to filling out reports, presenting in meetings, and other activities that take time away from driving impact through the work of their teams.

But while great coaches take time to develop, something as simple as a daily stand-up or check-in can drive horizontal connectivity, creating the space for teams to understand what others are doing and where they need help to drive work forward without having to specifically task anyone in a hierarchical way. You may also consider how you are driving a focus on outcomes over activities on a near-term and long-term basis. Whether it’s OKRs or something else, how is your organization proactively communicating a focus on impact and results over tasks and activities? What do you measure? How is it tracked? How is the performance of your people and your teams managed against it? Over what time horizons?

The importance of psychological safety. As you start this journey, be sure to take a close look at psychological safety. If employees don’t feel psychologically safe, it will be nearly impossible for leaders and managers to break through disempowering behaviors like constant escalation, hiding problems or risks, and being afraid to ask questions—no matter how skilled they are as coaches.

Employers should be on the lookout for common problems indicating that significant challenges to psychological safety lurk underneath the surface. Consider asking yourself and your teams questions to test the degree of psychological safety you have cultivated: Do employees have space to bring up concerns or dissent? Do they feel that if they make a mistake it will be held against them? Do they feel they can take risks or ask for help? Do they feel others may undermine them? Do employees feel valued for their unique skills and talents? If the answer to any of these is not a clear-cut “yes,” the organization likely has room for improvement on psychological safety and relatedness as a foundation to high-quality interactions within and between teams.

Information sharing: Fit-for-purpose interactions

Do any of these scenarios sound familiar? You spend a significant amount of time in meetings every day but feel like nothing has been accomplished. You jump from one meeting to another and don’t get to think on your own until 7 p.m. You wonder why you need to attend a series of meetings where the same materials are presented over and over again. You’re exhausted.

An increasing number of organizations have begun to realize the urgency of driving ruthless meeting efficiency and of questioning whether meetings are truly required at all to share information. Live interactions can be useful for information sharing, particularly when there is an interpretive lens required to understand the information, when that information is particularly sensitive, or when leaders want to ensure there’s ample time to process it and ask questions. That said, most of us would say that most meetings are not particularly useful and often don’t accomplish their intended objective.

We have observed that many companies are moving to shorter meetings (15 to 30 minutes) rather than the standard default of one-hour meetings in an effort to drive focus and productivity. For example, Netflix launched a redesign effort to drastically improve meeting efficiency, resulting in a tightly controlled meeting protocol. Meetings cannot go beyond 30 minutes. Meetings for one-way information sharing must be canceled in favor of other mechanisms such as a memo, podcast, or vlog. Two-way information sharing during meetings is limited by having attendees review materials in advance, replacing presentations with Q&As. Early data show Netflix has been able to reduce the number of meetings by more than 65 percent, and more than 85 percent of employees favor the approach.

Making meeting time a scarce resource is another strategy organizations are using to improve the quality of information sharing and other types of interactions occurring in a meeting setting. Some companies have implemented no-meeting days. In Japan, Microsoft’s “Work Life Choice Challenge” adopted a four-day workweek, reduced the time employees spend in meetings—and boosted productivity by 40 percent. 1 Bill Chappell, “4-day workweek boosted workers’ productivity by 40%, Microsoft Japan says,” NPR, November 4, 2019, npr.org. Similarly, Shopify uses “No Meeting Wednesdays” to enable employees to devote time to projects they are passionate about and to promote creative thinking. 2 Amy Elisa Jackson, “Feedback & meeting-free Wednesdays: How Shopify beats the competition,” Glassdoor, December 5, 2018, glassdoor.com. And Moveline’s product team dedicates every Tuesday to “Maker Day,” an opportunity to create and solve complex problems without the distraction of meetings. 3 Rebecca Greenfield, “Why your office needs a maker day,” Fast Company , April 17, 2014, fastcompany.com.

Finally, no meeting could be considered well scoped without considering who should participate, as there are real financial and transaction costs to meeting participation. Leaders should treat time spent in meetings as seriously as companies treat financial capital. Every leader in every organization should ask the following questions before attending any meeting: What’s this meeting for? What’s my role? Can I shorten this meeting by limiting live information sharing and focusing on discussion and decision making? We encourage you to excuse yourself from meetings if you don’t have a role in influencing the outcome and to instead get a quick update over email. If you are not essential, the meeting will still be successful (possibly more so!) without your presence. Try it and see what happens.

High-quality, focused interactions can improve productivity, speed, and innovation within any organization—and drive better business performance. We hope the above insights have inspired you to try some new techniques to improve the effectiveness and efficiency of collaboration within your organization.

Aaron De Smet is a senior partner in McKinsey’s New Jersey office; Caitlin Hewes is a consultant in the Atlanta office; Mengwei Luo is an associate partner in the New York office; J.R. Maxwell is a partner in the Washington, DC, office; and Patrick Simon is a partner in the Munich office.

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