When errors such as these surface, blaming seems to be a natural reflex in many organizations. Even those individuals who wish to learn from mistakes fall into naming culprits. Once we figure out who’s at fault, we then try to find out what is wrong with the supposed offenders. Only when we discover what is wrong with them do we feel we have grasped the problem. Clearly they are the problem, and changing or getting rid of them (or simply being angry at them) is the solution.

There’s a problem with this common scenario, however: Where there is blame, there is no learning. Where there is blame, open minds close, inquiry tends to cease, and the desire to understand the whole system diminishes. When people work in an atmosphere of blame, they naturally cover up their errors and hide their real concerns. And when energy goes into finger pointing, scapegoating, and denying responsibility, productivity suffers because the organization lacks information about the real state of affairs. It’s impossible to make good decisions with poor information.

In fact, blame costs money. When the vice president of marketing and the vice president of R&D are blaming each other for quality problems in product development, they can’t focus on working together to bring the best products to market. Their finger pointing results in lost sales potential.

Blame rarely enhances our understanding of our situation and often hampers effective problem solving. So how do we avoid the tendency to blame and create organizational environments where we turn less frequently to blame? Clarifying accountability is one option. This process of assigning responsibilities for a situation in advance can help create a culture of real learning.

Accountability comes from clear contracting, ongoing conversations, and an organizational commitment to support accountability rather than blame. The contracting focuses on tasks to be accomplished, roles to be taken, processes to be used, standards sought, and expected results. Periodic conversations over time review both explicit and tacit contracts in order to verify shared understanding. This communication becomes most useful when people are willing and able to discuss their common difficulties within a larger setting that values accountability.

The Differences Between Accountability and Blame

The dictionary helps clarify the differences between accountability and blame. To be accountable is “to be counted on or reckoned on.” To blame is “to find fault with, to censure, revile, reproach.” Accountability emphasizes keeping agreements and performing jobs in a respectful atmosphere; blaming is an emotional process that discredits the blamed.

A focus on accountability recognizes that everyone may make mistakes or fall short of commitments. Becoming aware of our own errors or shortfalls and viewing them as opportunities for learning and growth enable us to be more successful in the future. Accountability therefore creates conditions for ongoing, constructive conversations in which our awareness of current reality is sharpened and in which we work to seek root causes, understand the system better, and identify new actions and agreements. The qualities of accountability are respect, trust, inquiry, moderation, curiosity, and mutuality.

Blaming, on the other hand, is more than just a process of allocating fault. It is often a process of shaming others and searching for something wrong with them. Blaming provides an early and artificial solution to a complex problem. It provides a simplistic view of a complex reality: I know what the problem is, and you’re it. Blame thus makes inquiry difficult and reduces the chances of getting to the real root of a problem. Blame also generates fear and destroys trust. When we blame, we often believe that other people have bad intentions or lack ability. We tend to excuse our own actions, however, because we know firsthand the challenges we face. The qualities of blame are judgment, anger, fear, punishment, and self-righteousness.

The Organizational Consequences of Blame

Blame Slows Information Flow and Reduces Innovation. People sometimes use blame as a strategy to get others to take ownership of problems. But this approach often backfires because people begin to equate acknowledging mistakes and surfacing bad news with punishment. When this happens, two reinforcing sets of behaviors may emerge: one by managers who are ostensibly seeking information and then punishing those who bring bad news, and the other by groups of employees who hide information and try either to protect each other or to blame each other. People who feel compelled to protect themselves can’t admit mistakes-and therefore can’t learn from them. Under these conditions, individuals spend time denying problems rather than solving them, and

The Reinforcing Cycles of Blame

Blame causes fear, which increases cover-ups and reduces the flow of information. The lack of information hinders problem solving, creating more errors (R1). Fear also stifles risk taking and discourages innovation (R2).

As shown in “The Reinforcing Cycles of Blame,” blaming leads to fear, which increases cover-ups and reduces the flow of information by stopping productive conversation. The lack of timely and accurate information about an organization’s current reality hinders problem solving, leading to more errors and more blame (R1).

Blaming and the fear it generates also discourage innovation and creative solutions. Frightened people don’t take risks, which are essential for innovation. Lack of innovation, in turn, leads to an inability to solve problems effectively and an increase in errors (R2).

Blame “Shifts the Burden.” In a “Shifting the Burden” situation, a problem has multiple solutions. People often grab onto the most obvious, short-term fix rather than search for the fundamental source of the problem. The lack of a permanent, long-term solution reinforces the need for additional quick fixes. Blame is a fix that actually diverts the blamers’ attention away from long-term interpersonal or structural solutions to problems (see B1 in “The Addiction to Blame” on p. 3).  Although blame provides some immediate relief and a sense of having solved a problem (“It’s their fault”), it also erodes communication (R3) and shifts the focus even further from accountability (B2), the more fundamental solution.

Blaming can also be addictive, because it makes us feel powerful and keeps us from having to examine our own role in a situation. For example, Jim, a brewery manager, got word that things were slowing down on line 10, a new canning line. He left his office and headed to the plant floor. “Grady, you’ve got to get this line going. Get with it,” he told his line foreman. Grady replied, “Jim, you know those guys on the last shift always screw things up.”

This is a familiar conversation to both men. Each walks away thinking something is wrong with the other. Jim thinks, “That Grady, I give him responsibility and he just can’t get it together.” Grady thinks, “Why is he always on my case? Can’t he see this is a tough issue? He’s so simplistic and short-sighted.”

In this scenario, Jim can walk away feeling relieved because he knows what the problem is-Grady is a lousy supervisor and may need to be replaced. Grady, on the other hand, can blame Jim for being a shortsighted, run-the-plant-by-the-numbers manager. Both get some initial relief from blaming each other, but neither solves the ongoing problem.

Moving from Blame to Accountability

How, then, do we move from blame to accountability? Even within carefully designed systems, people may fail at their work. And even with a knowledge of system dynamics, we still often look for an individual’s failure as a way to explain a problem. One leverage point is to understand the organizational dynamics of blame as described above. There is also leverage in changing how we think about and conduct ourselves at work.

There are three levels of specific behavioral change in moving from blame to accountability-the individual level, the interpersonal level, and the group or organizational level. First, individuals must be willing to change their own thinking and feelings about blame. Second, people need to become skillful at making contracts with one another and holding each other accountable for results. Third, groups need to promote responsible and constructive conversations by developing norms for direct conflict resolution between individuals. These behavioral changes-and the use of systems thinking to focus on the structures involved and not the personalities-can help create a constructive organizational culture.

Individual Level

Below is a list of ways to start breaking the mental models we hold about blame. When you find yourself beginning to blame someone else for a chronic problem, refer to this list and to the sidebar “Distinctions Between Blame and Accountability” (see p. 4).

1. Remember that others are acting rationally from their own perspective. Given what they know, the pressures they are under, and the organizational structures that are influencing them, they are doing the best they can. Give others the benefit of the doubt.

2. Realize that you probably have a role in the situation.Your behavior may be influencing this person’s behavior and may be producing some unintended effects. Keep in mind that you will tend to justify your own actions and point of view and discount the other person’s perspective.

3. Remind yourself that judgment and criticism make it very difficult to see clearly. Judgments are mental models that limit the ability to take in new data. They tend to increase the likelihood of anger and make it difficult to learn. The following questions may help stretch your thinking and ease angry feelings. Ask yourself:

• What information am I missing that would help me understand this person’s behavior?
• How might this behavior make sense?
• What pressures is he or she under?
• What systems or structures might be influencing this behavior?

4. Use a systems thinking perspective to explore the pressures on the players involved. Notice that there are some larger forces at work that are probably having an impact on both of you. For example, when organizational goals, strategies, and values aren’t clear, groups will sometimes work toward different objectives. A group that values customer service over cost will conflict with a group that is trying to lower expenditures. Identify some key variables and their interrelationships, and ask, Is this situation an example of a vicious cycle, “Shifting the Burden,” or “Accidental Adversaries”?

5. Be willing to be held accountable. This means that, when an issue comes up, you are willing to consider whether you have lived up to your end of an agreement or expectation. Ask yourself:

• Did I have a role in this situation?
• Did I take some actions that seemed right at the time, but that had unintended consequences?

6.Work constructively with your anger. Sustained anger may point to personal issues that have been triggered by the current situation. Broken agreements, mistakes, and blame all have difficult associations for most people. However, in a learning environment, constructive resolution of conflict can also lead to significant personal growth. The guiding questions here are:

• What am I learning about myself in this situation?
• What does this remind me of?
• What new behaviors or thoughts does this situation call for that may be a stretch for me?

Interpersonal Level

Initial Contracting. At the beginning of any working relationship, it’s vital to come to some basic agreements defining the nature and scope of the work, specific and yet-to-be-defined tasks, deadlines and related outcomes, processes or methods to be used, interim checkpoints and expectations at those checkpoints, standards, and roles.

It’s also helpful to discuss what to do in the event of a misunderstanding, a lapse in communication, or a failure to keep an agreement. Imagine possible breakdowns and design a process for handling them. If breakdowns do occur, be prepared to remind others of the plan you had prepared.

When lapses do take place, they need to be brought to the collective attention as soon as possible. Misunderstandings and broken agreements often promote anger, frustration, and blame. Allowing unaddressed misunderstandings to fester can hamper productive conversations. By contrast, raising issues early can minimize escalation of problems.

Accountability Conversations. Once any project or working relationship is under way, it’s useful to check in periodically on the state of the partnership through accountability conversations. You may or may not have clear recollections of the initial contract regarding the task, roles, standards, processes, and expected results. Either way, it’s productive to establish or reestablish these agreements and explore what is working or not working as you take action together to create envisioned results.

Accountability conversations aren’t always easy. However, the skills they require can be applied and developed over time. Some of the basic tools of learning organizations come into play here-the ladder of inference, for example, can be used to create a conversation of inquiry rather than inquisition. The accountability conversation is also the perfect setting for practicing left-hand column skills to surface assumptions blocking honest and productive discourse. In addition, admitting the tendency to

Distinctions between blame and accountability

Here are steps for initiating an accountability conversation:

1. Find out whether the person you are working with is interested in seeing problems as learning opportunities. If so, when a problem occurs, include other people who are also interested in the situation. Other people’s perspectives can be helpful because often two people in conflict are actually mirroring the conflict of a larger system within the organization.

2. Create a setting that is conducive to learning.

• Allow plenty of time to address the issues.
• Reaffirm with each other that the goal is to learn, not blame.
• Establish confidentiality.
• Be truly open-minded.
• Listen hard to the other person’s perspective

3. Have a conversation in which the two (or more) of you

• Clarify your intention for the meeting.
• Identify the data and any assumptions or conclusions you have drawn based on that data.
• Identify the pressures each of you is experiencing in the situation.
• Identify any stated or unstated expectations. If implicit agreements were not jointly understood, this is a good time to clarify and reestablish shared agreements.
• Analyze the problem from a systems perspective. Clarify how your mutual beliefs and actions might be related and are perhaps reinforcing each other.
• Identify some new ways to address the problem.

Group Level

How people talk about one another in an organization affects the levels of accountability and trust. Often, because people are reluctant to discuss accountability issues directly, they go to a third party to relieve their discomfort and get support for their point of view. The complaint does not get resolved this way, however, although the person with the complaint gains some relief. Bringing a complaint to a third party to clarify a situation can be a much more productive alternative.

To see how this works, let’s take a situation where Tony is angry with Lee because Lee wasn’t fully supportive in a meeting. Tony complains to Robin that Lee is unreliable. Robin sympathizes with Tony and agrees that Lee is unreliable. Tony and Robin now feel closer because they share this point of view. Lee does not yet know that Tony has a complaint. Later, though, Robin, busy with other projects, puts off one of Tony’s requests. Now Tony complains about Robin to Lee, and Robin doesn’t get the necessary feedback. Over time, all of these relationships will erode.

What is the alternative to this kind of dysfunctional blaming and resentment? The solution is a deep commitment on the part of all these people to work through their reluctance to give and receive difficult feedback. In addition, they need to learn how to hold one another accountable in an ongoing way. Now, when Tony is angry with Lee and goes to Robin, the purpose is to get coaching on how to raise the issue with Lee, not to get Robin’s agreement on what is wrong with Lee. In addition, Robin’s role is to make sure that Tony follows through on raising the concern directly with Lee.

To resolve conflict directly:

1.Bring your complaints about someone else to a third person to get coaching on how to raise your concerns.
Valuable questions from the coach include:

• Tell me about the situation.
• What results do you want?
• What’s another way of explaining the other person’s actions?
• How might the other person describe the situation?
• What was your role in creating the situation?
• What requests or complaints do you need to bring to the other person?
• How will you state them in order to get the results you want?
• What do you think your learning is in this situation?

2. Raise your concerns directly with the other person. Reaffirm your commitment to maintaining a good working relationship and find a way to express your fundamental respect for the person. The ladder of inference can be a helpful tool for focusing on the problem. Start by identifying the data that is the source of your concern. Then spell out the assumptions you made as you observed the data and any feelings you have about the situation. Finally, articulate your requests for change. During the conversation, remind the other person that reviewing the concern is part of learning to work together better

3. Let the coach know what happened.

4. Outside of this framework, refrain from making negative comments about people

5. For listeners who frequently hear complaints about a third party and want to create a learning setting, it can be helpful to say something like: “I’d like to help, but only if you want to create a constructive situation. We can explore these questions; otherwise, I prefer not to listen to your complaints.”

Organizational Accountability: The IS Story

Systems thinking provides useful tools for surfacing and breaking reinforcing cycles of blame within an organization. In the story below, a group was able to use causal loop diagrams to help them move beyond blame and craft a constructive, long-term solution.

The Information Systems group of a manufacturing plant was meeting to discuss their lack of progress on a large project to overhaul the department. Initially, the IS group decided that top management’s actions caused the group’s ineffectiveness. The plant management team (PMT) kept adding projects to the group’s already full plate. Members of the PMT responded to “squeaky wheels” by giving otherwise low-priority projects the force of their support. Also, the PMT didn’t reinforce plant wide policies the IS group had developed. Most important, the team didn’t give group members the support they needed to stick to the IS overhaul they had committed to, and wouldn’t give them the budget to hire the additional staff they sorely needed.

But when the group mapped out their current situation in a causal loop diagram, they gained a new perspective on the problem. They found that the situation resembled a “Success to the Successful” story, in which two or more projects or groups compete for limited resources.

The diagram “Success to the Squeaky Wheel” shows how, in this case, the IS group’s attention to urgent requests diverted resources away from prioritized items. Because rewards for completing urgent requests were heightened, the urgent tasks continued to receive greater attention (R2).  At the same time, the rewards for and focus on prioritized tasks decreased (R1). Finally, as people realized that urgent requests received greater attention than prioritized items, the number of “squeaky wheels”-or people promoting their own agenda items to management-proliferated. This development was followed by an increase in management’s efforts to get action on those agenda items, which further promoted urgent items over prioritized ones (R3).

After examining the causal loop diagrams, the group realized that they had played a role in the stalled progress on the overhaul project. Although IS team members encouraged each other to blame the PMT, no one in the group had given the PMT feedback concerning the impact of their requests and lack of support.

Armed with a systems view, the group identified several actions they could take to shift these unproductive dynamics. They decided to tell the PMT that they recognized that the IS overhaul was a top priority for the plant as a whole. They would point out that they couldn’t make progress on the overhaul if they continued to respond to “squeaky wheels. “The group would also let the PMT know that when they received additional requests, they would ask:

• How much of a priority is this request for you?
• Are you aware that there is a tradeoff in priorities?

The group concluded that they would issue a memo to the PMT describing their priorities and soliciting the PMT’s support of those priorities. They would also request that the PMT clearly communicate the priorities to the rest of the plant. In the memo, they would indicate the tradeoffs they were making and identify how their choices would help the company as a whole. The group felt that, with the PMT’s support, they would have the authority to focus on the prioritized project instead of responding to urgent requests.

Conclusion

Developing accountability skills is challenging; it takes courage and the willingness to learn new ways of thinking and acting. So why is moving from blame to accountability worthwhile? Because blame is like sugar – it produces a brief boost and then a let-down. It doesn’t serve the system’s long-term needs and can actually prevent it from functioning effectively. On the other hand, developing accountability skills and habits on every level of your organization can be an important element in maintaining your organization’s long-term health.

Marilyn Paul, PhD, is an independent organizational consultant affiliated with Innovation Associates, an Arthur D. Little company. She has sixteen years of experience facilitating organizational change. One focus of her work is peer mentoring and capacity development.

The post Moving from Blame to Accountability appeared first on The Systems Thinker.

From my work with both business and education professionals over the last 15 years, I have come to believe that systems thinking’s steep learning curve is related to the fact that the discipline requires mastering a whole package of thinking skills.

STEPS IN THE SYSTEMS THINKING METHOD

Begin by specifying the problem you want to address. Then construct hypotheses to explain the problem and test them using models. Only when you have a sufficient understanding of the situation should you begin to implement change.

Much like the accomplished basketball player who is unaware of the many separate skills needed to execute a lay-up under game conditions – such as dribbling while running and without looking at the ball, timing and positioning the take-off, extending the ball toward the rim with one hand while avoiding the blocking efforts of defenders – veteran systems thinkers are unaware of the full set of thinking skills that they deploy while executing their craft. By identifying these separate competencies, both new hoop legends and systems thinking wannabes can practice each skill in isolation. This approach can help you master each of the skills before you try to put them all together in an actual game situation.

The Systems Thinking Method

Before exploring these critical thinking skills, it’s important to have a clear picture of the iterative, four-step process used in applying systems thinking (see “Steps in the Systems Thinking Method”). In using this approach, you first specify the problem or issue you wish to explore or resolve. You then begin to construct hypotheses to explain the problem and test them using models whether mental models, pencil and paper models, or computer simulation models. When you are content that you have developed a workable hypothesis, you can then communicate your new found clarity to others and begin to implement change.

When we use the term “models” in this article, we are referring to something that represents a specifically defined set of assumptions about how the world works. We start from a premise that all models are wrong because they are incomplete representations of reality, but that some models are more useful than others (they help us understand reality better than others).  There is a tendency in the business world, however, to view models (especially computer-based models) as “answer generators;” we plug in a bunch of numbers and get out a set of answers. From a systems thinking perspective, however, we view models more as “assumptions and theory testers” we formulate our understanding and then rigorously test it. The bottom line is that all models are only as good as the quality of the thinking that went into creating them. Systems thinking, and its ensemble of seven critical thinking skills, plays an important role in improving the quality of our thinking.

The Seven Critical Thinking Skills

As you undertake a systems thinking process, you will find that the use of certain skills predominates in each step. I believe there are at least seven separate but interdependent thinking skills that seasoned systems thinkers master. The seven unfold in the following sequence when you apply a systems thinking approach: Dynamic Thinking, System-as-Cause Thinking, Forest Thinking, Operational Thinking, Closed-Loop Thinking, Quantitative Thinking, and Scientific Thinking.

The first of these skills, Dynamic Thinking, helps you define the problem you want to tackle. The next two, System-as-Cause Thinking and Forest Thinking, are invaluable in helping you to determine what aspects of the problem to include, and how detailed to be in representing each. The fourth through sixth skills, Operational Thinking, Closed-Loop Thinking, and Quantitative Thinking, are vital for representing the hypotheses (or mental models) that you are going to test. The final skill, Scientific Thinking, is useful in testing your models.

Each of these critical thinking skills serves a different purpose and brings something unique to a systems thinking analysis. Let’s explore these skills, identify how you can develop them, and determine what their “non-systems thinking” counterparts (which dominate in traditional thinking) look like.

Dynamic Thinking: Dynamic Thinking is essential for framing a problem or issue in terms of a pattern of behavior over time. Dynamic Thinking contrasts with Static Thinking, which leads people to focus on particular events. Problems or issues that unfold over time as opposed to one-time occurrences are most suitable for a systems thinking approach.

You can strengthen your Dynamic Thinking skills by practicing constructing graphs of behavior overtime. For example, take the columns of data in your company’s annual report and graph a few of the key variables over time. Divide one key variable by another (such as revenue or profit by number of employees), and then graph the results. Or pick up today’s news-paper and scan the head-lines for any attention-grabbing events. Then think about how you might see those events as merely one interesting point in a variable’s overall trajectory over time. The next time someone suggests that doing this-and-that will fix such-and-such, ask, “Over what time frame? How long will it take? What will happen to key variables over time?”

System-as-Cause Thinking: Dynamic Thinking positions your issue as a pattern of behavior over time. The next step is to construct a model to explain how the behavior arises, and then suggest ways to improve that behavior. System-as-Cause Thinking can help you determine the extensive boundary of your model, that is, what to include in your model and what to leave out (see “Extensive and Intensive Model Boundaries”). From a System-as-Cause Thinking approach, you should include only the elements and inter-relationships that are within the control of managers in the system and are capable of generating the behavior you seek to explain.

By contrast, the more common System-as-Effect Thinking views behavior generated by a system as “driven” by external forces. This perspective can lead you to include more variables in your model than are really necessary. System-as-Cause Thinking thus focuses your model more sharply, because it places the responsibility for the behavior on those who manage the policies and plumbing of the system itself.

To develop System-as-Cause Thinking, try turning each “They did it” or “It’s their fault” you encounter into a “How could we have been responsible?” It is always possible to see a situation as caused by “outside forces.” But it is also always possible to ask, “What did we do to make ourselves vulnerable to those forces that we could not control?”

EXTENSIVE AND INTENSIVE MODEL BOUNDARIES

The extensive boundary is the breadth or scope of what’s included in the model. The intensive boundary is the depth or level of detail at which the items included in the model are represented.

Forest Thinking: In many organizations, people assume that to really know something, they must focus on the details. This assumption is reinforced by day-to-day existence—we experience life as a sequence of detailed events. We can also think of this as Tree-by-Tree Thinking. Models that we create by applying Tree-by-Tree Thinking tend to be large and overly detailed; their intensive boundaries run deep. In using such models, we would want to know whether that particular red truck broke down on Tuesday before noon, as opposed to being interested in how frequently, on average, trucks break down. Forest Thinking–inspired models, by contrast, group the details to give us an “on average” picture of the system. To hone your Forest Thinking skills, practice focusing on similarities rather than differences. For example, although everyone in your organization is unique, each also shares some characteristics with others. While some are highly motivated to perform and others are not, all have the potential to make a contribution. Regardless of the individual, realizing potential within an organization comes from the same generic structure. For example, what is the relationship among factors that tends to govern an individual’s motivation?

Operational Thinking :Operational Thinking tries to get at causality—how is behavior actually generated? This thinking skill contrasts with Correlational or Factors Thinking. Steven Covey’s The Seven Habits of Highly Effective People, one of the most popular nonfiction books of all time, is a product of Factors Thinking. So are the multitude of lists of “Critical Success Factors” or “Key Drivers of the Business” that decorate the office walls (and mental models) of so many senior executives. We like to think in terms of lists of factors that influence or drive some result.

There are several problems with mental models bearing such list structures, however. For one thing, lists do not explain how each causal factor actually works its magic. They merely imply that each factor “influences,” or is “correlated with,” the corresponding result. But influence or correlation is not the same as causality.

For example, if you use Factors Thinking to analyze what influences learning, you can easily come up with a whole “laundry list” of factors (see “Two Representations of the Learning Process”). But if you use Operational Thinking, you might depict learning as a process that coincides with the building of experience. Operational Thinking captures the nature of the learning process by describing its structure, while Factors Thinking merely enumerates a set of factors that in some way “influence” the process.

To develop your Operational Thinking skills, you need to work your way through various activities that define how a business works examine phenomena such as hiring, producing, learning, motivating, quitting, and setting price. In each case, ask, “What is the nature of the process at work?” as opposed to “What are all of the factors that influence the process?”

Closed-Loop Thinking :Imagine discussing your company’s profitability situation with some of your coworkers. In most companies, the group would likely list things such as product quality, leadership, or competition as influences on profitability (see “A Straight-Line vs. a Closed-Loop View of Causality”). This tendency to list factors stems from Straight-Line Thinking. The assumptions behind this way of thinking are 1) that causality runs only one way—from “this set of causes” to “that effect,” and 2) that each cause is independent of all other causes. In reality, however, as the closed-loop part of the illustration shows, the “effect” usually feeds back to influence one or more of the “causes,” and the causes themselves affect each other. Closed-Loop Thinking skills therefore lead you to see causality as an ongoing process, rather than a one-time event.
To sharpen your Closed-Loop Thinking skills, take any laundry list that you encounter and think through the ways in which the driven drives and in which the drivers drive each other. Instead of viewing one variable as the most important driver and another one as the second most important, seek to understand how the dominance among the variables might shift over time.

TWO REPRESENTATIONS OF THE LEARNING PROCESS

Factors Thinking merely enumerates a set of factors that in some way “influence” the learning process. Operational Thinking captures the nature of the learning process by describing its structure.

Quantitative Thinking: In this phrase, “quantitative” is not synonymous with “measurable.” The two terms are often confused in practice, perhaps because of the presumption in the Western scientific world that “to know, one must measure precisely.” Although Heisenberg’s Uncertainty Principle caused physicists to back off a bit in their quest for numerical exactitude, business folk continue unabated in their search for perfectly measured data. There are many instances of analysis getting bogged down because of an obsession with “getting the numbers right.” Measurement Thinking continues to dominate!

There are a whole lot of things, however, that we will never be able to measure very precisely. These include “squishy,” or “soft,” variables, such as motivation, self-esteem, commitment, and resistance to change. Many so-called “hard” variables are also difficult to measure accurately, given the speed of change and the delays and imperfections in information systems.

A STRAIGHT-LINE VS.A CLOSED-LOOP VIEW OF CAUSALITY

The assumptions behind Straight-Line Thinking are that causality runs only one way and that each cause is independent of all other causes. Closed-Loop Thinking shows that the “effect” usually feeds back to influence one or more of the “causes,” and the causes themselves affect each other.

But let’s return to our “squishy” variables. Would anyone want to argue that an employee’s self-esteem is irrelevant to her performance? Who would propose that commitment is unimportant to a company’s success? Although few of us would subscribe to either argument, things like self-esteem and commitment rarely make it into the spreadsheets and other analytical tools that we use to drive analysis. Why? Because such variables can’t be measured. However, they can be quantified. If zero means a total absence of commitment, 100 means being as committed as possible. Are these numbers arbitrary? Yes. But are they ambiguous? Absolutely not! If you want your model to shed light on how to increase strength of commitment as opposed to predicting what value commitment will take on in the third-quarter of 1997—you can include strength of commitment as a variable with no apologies. You can always quantify, though you can’t always measure.

To improve your Quantitative Thinking skills, take any analysis that your company has crunched through over the last year and ask what key “soft” variables were omitted, such as employee motivation. Then, ruminate about the possible implications of including them systems thinking gives you the power to ascribe full-citizen status to such variables. You’ll give up the ability to achieve perfect measurement. But if you’re honest, you’ll see that you never really had that anyway.

Scientific Thinking: The final systems thinking skill is Scientific Thinking. I call its opposite Proving Truth Thinking. To understand Scientific Thinking, it is important to acknowledge that progress in science is measured by the discarding of falsehoods. The current prevailing wisdom is always regarded as merely an “entertainable hypothesis,” just waiting to be thrown out the window. On the other hand, too many business models are unscientific; yet business leaders revere them as truth and defend them to the death. Analysts make unrelenting efforts to show that their models track history and therefore must be “true.”

Seasoned systems thinkers continually resist the pressure to “validate” their models (that is, prove truth) by tracking history. Instead, they work hard to become aware of the falsehoods in their models and to communicate these to their team or clients. “All models are wrong,”” said W. Edwards Deming. “Some models are useful.” Deming was a smart guy, and clearly a systems thinker.

In using Scientific Thinking, systems thinkers worry less about outfitting their models with exact numbers and instead focus on choosing numbers that are simple, easy to understand, and make sense relative to one another. Systems thinkers also pay lots of attention to robustness they torture-test their models to death! They want to know under what circumstances their model “breaks down.” They also want to know, does it break down in a realistic fashion? What are the limits to my confidence that this model will be useful?

The easiest way to sharpen your Scientific Thinking skills is to start with a computer model that is “in balance” and then shock it. For example, transfer 90% of the sales force into manufacturing. Set price at 10 times competitor price. Triple the customer base in an instant. Then see how the model performs. Not only will you learn a lot about the range of utility of the model, but you also will likely gain insight into the location of that most holy of grails: high-leverage intervention points.

A Divide and Conquer Strategy

As the success of Peter Senge’s The Fifth Discipline: The Art & Practice of the Learning Organization has shown, systems thinking is both sexy and seductive. But applying it effectively is not so easy. One reason for this difficulty is that the thinking skills needed to do so are many in number and stand in stark contrast to the skill set that most of us currently use when we grapple with business issues (see “Traditional Business Thinking vs. Systems Thinking Skills”).

By separating and examining the seven skills required to apply systems thinking effectively, you can practice them one at a time. If you master the individual skills first, you stand a much better chance of being able to put them together in a game situation. So, practice . . . then take it to the hoop!

“Barry Richmond is the managing director and founder of High Performance Systems, Inc. He has a PhD in system dynamics from the MIT Sloan School of Management, an MS from Case Western Reserve, and an MBA from Columbia University”

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What Does Systems Thinking Involve?

Why Use Systems Thinking?

Systems thinking expands the range of choices available for solving a problem by broadening our thinking and helping us articulate problems in new and different ways. At the same time, the principles of systems thinking make us aware that there are no perfect solutions; the choices we make will have an impact on other parts of the system. By anticipating the impact of each trade-off, we can minimize its severity or even use it to our own advantage. Systems thinking therefore allows us to make informed choices.
Systems thinking is also valuable for telling compelling stories that describe how a system works. For example, the practice of drawing causal loop diagrams forces a team to develop shared pictures, or stories, of a situation. The tools are effective vehicles for identifying, describing, and communicating your understanding of systems, particularly in groups.

When Should We Use Systems Thinking?

Problems that are ideal for a systems thinking intervention have the following characteristics:

• The issue is important.
• The problem is chronic, not a one-time event.
• The problem is familiar and has a known history.
• People have unsuccessfully tried to solve the problem before.

Where Should We Start?

When you begin to address an issue, avoid assigning blame (which is a common place for teams to start a discussion!). Instead, focus on items that people seem to be glossing over and try to arouse the group’s curiosity about the problem under discussion. To focus the conversation, ask, “What is it about this problem that we don’t understand?”

In addition, to get the full story out, emphasize the iceberg framework. Have the group describe the problem from all three angles: events, patterns, and structure (see “The Iceberg”).
Finally, we often assume that everyone has the same picture of the past or knows the same information. It’s therefore important to get different perspectives in order to make sure that all viewpoints are represented and that solutions are accepted by the people who need to implement them. When investigating a problem, involve people from various departments or functional areas; you may be surprised to learn how different their mental models are from yours.

How Do We Use Systems Thinking Tools?

Causal Loop Diagrams. First, remember that less is better. Start small and simple; add more elements to the story as necessary. Show the story in parts. The number of elements in a loop should be determined by the needs of the story and of the people using the diagram. A simple description might be enough to stimulate dialogue and provide a new way to see a problem. In other situations, you may need more loops to clarify the causal relationships you are surfacing.

Also keep in mind that people often think that a diagram has to incorporate all possible variables from a story; this is not necessarily true. In some cases, there are external elements that don’t change, change very slowly, or whose changes are irrelevant to the problem at hand. You can unnecessarily complicate things by including such details, especially those over which you have little or no control. Some of the most effective loops reveal connections or relationships between parts of the organization or system that the group may not have noticed before.
And last, don’t worry about whether a loop is “right”; instead, ask yourself whether the loop accurately reflects the story your group is trying to depict. Loops are shorthand descriptions of what we perceive as current reality; if they reflect that perspective, they are “right” enough.

THE ICEBERG

How Do We Know That We’ve “Got It”?

Here’s how you can tell you’ve gotten a handle on systems thinking:

• You’re asking different kinds of questions than you asked before.
• You’re hearing “catchphrases” that raise cautionary flags. For example, you find yourself refocusing the discussion when someone says, “The problem is we need more (sales staff, revenue).”
• You’re beginning to detect the archetypes and balancing and reinforcing processes in stories you hear or read.
• You’re surfacing mental models (both your own and those of others).
• You’re recognizing the leverage points for the classic systems stories.

Once you’ve started to use systems thinking for inquiry and diagnosis, you may want to move on to more complex ways to model systems-accumulator and flow diagrams, management flight simulators, or simulation software. Or you may find that adopting a systems thinking perspective and using causal loop diagrams provide enough insights to help you tackle problems. However you proceed, systems thinking will forever change the way you think about the world and approach issues. Keep in mind the tips we’ve listed here, and you’re on your way!

Michael Goodman is principal at Innovation Associates Organizational Learning

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Managers often don’t have the time, perspective, or framework to learn from the successes and failures of their problem-solving efforts. As a result, organizations fall into a recurring pattern of temporary relief followed by a return of the same problems. If people do attempt to learn from the past, they frequently find themselves ill-prepared to make sense of their own experience. Even in cases where the solutions produce lasting results, managers often lack an understanding of why these approaches succeeded.

A CORE THEORY OF SUCCESS

As the quality of relationships rises, the quality of thinking improves, leading to an increase in the quality of actions and results. Achieving high-quality results has a positive effect on the quality of relationships, creating a reinforcing engine of success.

Limitations of Traditional Approaches

When attempting to determine why an initiative succeeded, most managers talk in terms of the individual factors they believe were critical to the success. This propensity to focus on factors in isolation rather than seeing them as interrelated sets is part of what Barry Richmond refers to as “traditional business thinking” (“The ‘Thinking’ in Systems Thinking: How Can We Make It Easier to Master?” March 1997). Indeed, many companies formulate their thinking about success as lists of important attributes or competencies, without identifying the key ways in which the items are connected.

For example, companies often begin their efforts to improve their organizations by listing critical success factors. They identify a goal (for example, industry leadership) and then list the factors that management agrees are essential to achieving this goal (such as desirable products and services, ability to deliver). They then prioritize the items on the list and assign the top priorities to special teams. This list-based approach poses several problems. First, people frequently treat the factors separately, in a “divide and conquer” strategy. The danger here is that they may not properly consider important interactions among the different factors. Hence, a marketing department may not warn manufacturing and customer service about the potential impact of a major marketing campaign.

Another problem is that if management reduces the initial investments after a key success factor (KSF)has reached a certain desired level, the success may prove temporary. Often, when we have achieved a certain desired level with KSF1, we declare victory and shift resources over to KSF2. As we build up KSF2 and then KSF3, KSF1 starts to deteriorate because of lack of continued investments. So, we shift some resources back to KSF1 as we declare victory on KSF2 and KSF3.

Unless managers develop a theory of how these factors are interrelated in creating ongoing success (or failure), they cannot put the data from their experiences together in a way that serves as a guide for future actions. Unfortunately, most approaches to helping organizations solve persistent problems focus on applying other people’s theories and methods to the organization and not on developing a specific theory about the organization’s own operations. Systems thinking and organizational learning can offer tools and methods for companies to begin developing such theories and for putting them into action.

The Importance of Theory

Regrettably, the corporate world has little appreciation for the importance and power of theory. Many managers associate theory with universities and research institutions, which they view as too insulated from the real world. Hence, managers often dismiss theory as too academic and irrelevant to the pragmatic conduct of business. But the American Heritage Dictionary, Standard Edition, defines theory as “systematically organized knowledge applicable in a relatively wide variety of circumstances, especially a system of assumptions, accepted principles, and rules of procedure devised to analyze, predict, or otherwise explain the nature or behavior of a specified set of phenomena.” This definition clearly shows that there is nothing strictly academic about the concept of theory at all.

Using this definition of theory, we can say that creating a long-lived, successful organization means managers must develop systematically organized knowledge that represents the system of assumptions, accepted principles, and procedural rules they use to make sense of their past experience and to predict the future. In this sense, theory building is about developing a better understanding of our organizations and improving our capacity to predict the future. In other words, theory-building has everything to do with running a successful business.

We have to be cautious when we use the word “prediction,” because it tends to be used interchangeably with the word “forecast.” Forecasting attempts to provide a specific kind of prediction; however, it usually focuses on calculating specific numerical data that we expect to occur at some point in the future. The main criterion of success for forecasts is the accuracy of the result, not the accuracy of the assumptions or the methods used to produce it.

When we talk about predictions based on theory, however, we are more interested in the accuracy of the underlying assumptions and less in the numerical accuracy of the predicted result. Why? Because, in a complex world that is inherently unforecastable (a basic tenet in the emerging science of chaos), only understanding interrelationships can guide us in making the course corrections inevitably required in an environment of rapid and continual change. All good theories therefore help provide guidance by increasing our predictive power about the future

Theory-Building: Shifting from Factors to Loop

So, responsible leaders should ask themselves, “What good theories do we have that provide practical guidance for ensuring our organization’s future success?” The more clearly you can articulate your organization’s theories about what leads to success, the more deliberate you can be about investing in the elements that are critical to that success. From a systems thinking perspective, having a core theory of success means moving beyond identifying individual success factors to seeing the linkages that create the reinforcing engines of success within the organization.

For example, once we have a list of key success factors, we can take the next step of identifying how each KSF is connected to a reinforcing loop (see “Shifting to a Loop Perspective”). The key success loop (KSL) identified in our example shows that by increasing desirable products and services, we increase sales revenues and boost the amount of money available for investment. With more money to invest, we can draw more technical talent and produce even more desirable products and services (R loop).

Shifting our formulation of theories from factors to loops is important for several reasons. First, it forces us to think through the logical chain of causal forces that ensure that the KSF becomes self-sustaining. Second, it shifts our emphasis away from the factor itself to the broader set of interrelationships in which it is embedded. Third, by mapping each of our KSFs into Key Success Loops, we are more likely to see the interconnections among all the KSFs. This approach requires shifting our worldview from one that sees factors as the lowest unit of analysis to one that recognizes loops as the basic building blocks of organizational systems.

Theory as an Intervention Guide

Having an explicit theory of success allows an organization to continually test the impact of planned actions and assess whether these actions are having a net positive or negative effect on the company’s overall success. So what might a theory of success look like in a learning organization?

One such core theory of success would be based on the premise that as the quality of the relationships among people who work together increases high team spirit, mutual respect, and trust), the quality of thinking improves (consider more facets of an issue and share a greater number of different perspectives) (see “A Core Theory of Success,” p. 1).When the level of thinking is heightened, the quality of actions is also likely to improve (better planning, greater coordination, and higher commitment). In turn, the quality of results increases as well. Achieving high-quality results as a team generally has a positive effect on the quality of relationships, thus creating a virtuous cycle of better and better results.

The most important point about this kind of systemic theory is that success is not derived from any one of the individual variables that make up the loop, but rather from the loop itself. All of the variables are important for the theory to work properly, because if one of them isn’t functioning, there in forcing process doesn’t exist. If we believe that this loop describes a relevant theory of success for our organization, it forces us to pay attention to how all the variables are doing and how each is affecting the others in the loop.

As an example, we can use this Core Theory of Success to trace the implications of a common occurrence in corporations—top-down organizational efforts to get quick, short-term results. When results fall short of expectations, management often “helps” the people below by undertaking efforts intended to improve the bottom line immediately (see “Applying the Accelerator and the Brakes”). The “accelerator” (say, downsizing) works and improves the quality of results we are looking for (better profit picture). But those same action scan also serve as “brakes,” or unintended consequences that counteract any beneficial actions. These action scans destroy the quality of relationships by creating mistrust and low morale, and thus ultimately decrease the quality of results. The end result may be a lot of wasted energy with no real improvement in overall results.

Without having a core theory, we might simply focus on the “accelerator” aspect of the intervention and declare victory when the results improve in the short term. We wouldn’t necessarily connect the long term negative consequences of the “braking” action to the original intervention. When the results deteriorate again, the pressure to improve results increases. We might respond by repeating the same efforts that we believe worked so well the last time. By having the theory and the accompanying loop, on the other hand, we can see how the top-down efforts may have a negative impact and implement additional measures to counter balance that effect.

To illustrate how this generalized accelerator-and-brakes dynamic might play out in a specific situation, let’s look at an example. Curtis Nelson, president and CEO of Carlson Hospitality Worldwide (the parent company of Radisson Hotels), wrote in their company magazine: “Take care of your people, inspire them, allow them to grow to their full potential and evoke their personality, and they will reach a higher level of job satisfaction. That in turn inspires greater commitment, which leads to greater guest satisfaction.”

Although Nelson did not draw a loop in his article, he articulated in words his core theory of success for this hotel and cruise business (see “Hotel Core Success Loop”). The diagram shows that investments in people’s potential enhances job satisfaction, which builds commitment and translates into higher guest satisfaction and higher revenues. An increase in revenues means a rise in profits, which leads to more investments in people.

Now, suppose something unexpected happens to decrease profits, such as a rise in airfares that reduces business travel. Top management might respond by calling for cost-cutting measures to improve the profit picture. In the short term, profits are likely to rise – the intended result. However, an unintended consequence of enacting such measures may be substantial decreases in the company’s investment in its people, leading to a decrease in job satisfaction. This decrease in job satisfaction will reduce profits in the longer term, because employees will be less committed, causing a decline in customer satisfaction. Lower profits would then provoke another wave of cost cutting, repeating the accelerator and brakes dynamic. In this way, a one-time disturbance from the outside can trigger an internal response that keeps cycling for a long time.

Again, by articulating our core theory of success, we will be more likely to pay attention to both the short-term and the long-term consequences of our actions. In particular, our theory can prevent us from inadvertently undermining the very loop, we depend on for our success.

Of course, in a real company setting, a core theory of success is likely to involve many loops, not just one. The various loops will be interconnected in many ways, and their dynamic behavior will not always be intuitively obvious. Building and understanding such theories requires more than a one-time investment in creating a quick overview map (like the ones in this article); it requires a shift in mindset that values theory-building as a vital ongoing activity of the organization.

Managers as Researchers and Theory-Builders

But in order to survive and thrive in the emerging economic order, organizations must focus on producing

long-term, sustainable results. Managers at every level need a broader perspective-a theory-of how their organization can create and maintain success. Theory-building can no longer be seen as a separate activity from the practice of management; it must become an integral part of a manager’s job. Managers must take on new roles as researchers and theory builders, which will require investment in the development of new skills and capabilities (see “Applying the Disciplines of the Learning Organization”). Just as we currently depend on accountants and financial statements to help us manage our complex enterprises, there may come a time when we will depend on our theory-builders and organizational maps and models to navigate the turbulent waters of tomorrow’s business environment.

Daniel H. Kim is a co-founder of Pegasus Communications, Inc., and a co-founder of the MIT Center for Organizational Learning.

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As we will see, most organizations and people have mastered the ability to adapt to new situations and challenges. They can learn and improve, as long as the basic causes of their success do not change. But sometimes managers and enterprises become addicted to old ways of operating and making decisions, and thus fail to function well in a new environment. The result is decline. To understand the powerful dynamics that cause this turn of events, we must investigate the systems within which these organizations and managers operate.

Adaptation Versus Addiction

Adaptation and addiction differ in subtle ways. Adaptation takes place when we observe the symptoms of a problem and then take an action that counteracts the problem. Addiction occurs when we observe the symptoms of a problem and then take an action that suppresses the symptoms of the problem but makes the actual problem worse.

For example, lei’s say that you have just moved to a new area and find yourself spending a great deal of time alone. The number and quality of your social relationships are important indicators of the health of your personal system. Moving causes a decline in your system’s health when it leads to loneliness. An adaptive response to your loneliness could be to get involved with activities in your new community, to make connections with people at your new workplace, or to join a few clubs with members you find compatible.

The cause-effect mechanisms at work in this process are illustrated in the diagram “Adaptation” (p. 2). If the quality of your social life is important to you, then any change that causes loneliness in effect decreases the health of the system. After a delay, perceived health also goes down. When perceived health declines, the gap between perceived and desired health—between where you think your health is and where you want it to be—increases. So you take action to close the gap. In an adaptive system, the consequence of an action counteracts the problem and restores the health of the system. The whole process is a balancing loop that holds perceived health close to the level of health you actually desire.

But let’s look at another scenario, one of addiction. Say that when you find yourself feeling lonely, rather than trying to meet people, you have a few drinks. Drinking alcohol depresses the emotional center in your brain that causes you to experience loneliness. Thus, over the short term, the alcohol suppresses the symptoms of loneliness (sadness, self-pity, and so forth). But when you drink to an extreme, the quality of your social life deteriorates even further, making you even more lonely. So the action you took to ease the problem eventually only worsens it.

The cause-effect relations involved in addiction have two subtle differences from those associated with adaptation (see “Addiction” on p. 3). First, we take an action whose consequence raises our perceptions of the health of the system, but not the actual health. Second, our action actually damages the system’s real health.

Addiction, therefore, is a process by which an external problem can send us into a damaging cycle that quickly feeds on itself. Eventually, we don’t even need an external problem to spur us to take action; we simply generate our own internal problems through our addictive behavior—like someone who drinks salt water to quench his or her thirst.

Unfortunately, it is fairly easy to slide from adaptation into addiction, because it is usually difficult to measure the true health of any system. We often rely on symptoms—indirect measures—to determine the perceived health of the system. But information about symptoms typically comes to us only after a delay. The information also may contain deliberate biases or random errors. As a result, we take an action – that will eventually damage our true health, because the short-term symptoms cause us to feel better than we did before. A classic example of this pattern is smoking cigarettes, which can bring us immediate pleasure, but will also damage our health in the long run.

An Addictive Response in Organizations

Enterprises often become addicted to patterns of behavior that have brought them success in the past. They persist in pursuing policies that are no longer productive, until there is some sort of collapse within the organization, such as excessive outsourcing of technology until there is virtually no internal capacity left. This failure can happen when the feedback loops governing the firm’s success manifest a phenomenon called shifting dominance.

The “dominant” loop in a system is the one that principally controls the system’s behavior over a certain, often extended, period of time. When one loop dominates for a decade or more, a whole generation of managers, a set of control systems, and even a mythology grow up around the lever points that activate the loop governing the enterprise’s success; for example, “Marketing promotions are always the answer to a sales slump.” The company leaders see these lever points as the keys to their prosperity and act in ways that reinforce them.

But eventually, any loop will lose its dominance; another set of causal mechanisms will become more important. Then the usual lever points no longer lead to success, and the managers are left with a heritage of ineffective policies and irrelevant myths. At this point, the firm should drastically revise its policies. But often it simply redefines its measures of success so that the old policies still appear attractive. Why does this occur, and what can we do to prevent it from happening?

The Market Growth Model: Shifting Dominance at Ace Electronics

The concept of shifting dominance first became real to me in the 1960s when I encountered a model created by David Packer, an early member of the Industrial Dynamics Group at the Massachusetts Institute of Technology’s Sloan School of Management. Out of the group’s investigations evolved an elegant theory, later called the Market Growth Model, that illustrates the mechanics and importance of shifting dominance.

Packer and his colleagues applied system dynamics to a firm I will call Ace Electronics. In its earliest days, Ace had an enormously superior product. Its sales were limited only by the company’s capacity to market and sell the product. The dominant loop governing the firm’s profits was composed of its expanding sales force, growing orders and backlogs, swelling production capacity, and increasing deliveries (R4 in “Market Growth”). Because the budget for the marketing and sales department was a percentage of sales income, its budgets expanded, and the sales force grew even more. This loop produced rapid growth.

For a long time at Ace, the market growth loop was dominant, and a group of people who knew how to make this loop operate moved up through the firm’s ranks. However, eventually the dominance shifted within the system (B5 in “Market Growth”). The sales force booked far more orders than the factory could produce, so the order backlog started to increase. When the sales force could not promise timely delivery in a technologically sophisticated and rapidly changing market, its effectiveness in booking new orders declined. Sales began to drop. Before, expanding the sales force increased profits; now it cut into them.

When we find ourselves unknowingly caught up in a situation of shifting dominance, we often blame each other for our faltering fortunes. Ace is a perfect example of this phenomenon. We can imagine that the company leaders agonized over why the sales staff wasn’t as good as it used to be, what kind of new incentives were needed to whip the sales staff back into shape, and so forth. But in shifting dominance, the problem actually originates within the system in the form of an addiction to the old ways of doing things. Managers can push a sales force as hard as they like and still fail to revive sales—the system simply doesn’t respond to this kind of force when the control has shifted to a different loop.

As one particularly destructive result of Ace’s failure to adapt to change, the company eventually developed an addiction to a new, short-term “solution”: downsizing. Many companies fall into the trap of firing people in order to make the bottom line look good on the next quarterly report. Downsizing lowers costs and temporarily kicks up profits. But if it’s not done well—and often it isn’t—downsizing also drastically reduces the quality and size of the staff and dulls a company’s competitive edge. As its niche shrinks, the company has to fire even more people in order to boost its profits. The addictive cycle of downsizing takes over.

The Difficulties of Breaking Addiction

If the pitfalls of addiction seem so obvious, why is it so difficult to break out of addictive processes? There are three main forces that work against an individual or organization seeking to break the cycle of addiction.

The Pain of Withdrawal. One reason is that withdrawal is extremely painful. Remember that perceived health, which drives our actions in the addictive system, is affected by two factors: actual health and the consequences of the actions we take (see “Addiction” on p. 3). These addictive consequences progressively damage actual health, which means that we have to take more and more of the addictive action to offset the consequences. The process becomes a spiral of increasing use.

Codependency. When we get ourselves into the trap of addiction, it’s astonishing how the various components of the system work in collusion to sustain the addictive behavior. This subtle reorganization of the system to support the addictive action is called codependency and is another reason that breaking an addiction is so difficult.

Drifting Goals. Addiction has many forms. One interesting variant of the addictive structure occurs with the addition of a causal link that produces what is commonly known as “Drifting Goals.” If we don’t get what we want, we start to want what we get. When we lower our aspirations, the addiction causes progressive deterioration of our goal.

If we don’t get what we want, we start to want what we get.

For example, imagine a firm that borrows more and more in order to finance its operations. One symptom of a company’s health is its debt-equity ratio; there are industry standards that indicate the appropriate ratio of debt to equity in a healthy firm. When debt rises above this level, a company will undertake efforts to increase profitability or sell off assets to reduce debt. But if these efforts fail and the debt-equity ratio remains high, management may get used to the higher levels of debt and stop trying to reduce them. Spokespeople for the firm may even develop elaborate rationalizations indicating why the higher levels are acceptable. Of course, over the long term, high levels of debt can be fatal to an organization.

Understanding and Changing Systemic Structure

Addictive behaviors, with their self-perpetuating, destructive cycles, can seem particularly stubborn. But cycles of addiction can be broken, allowing us to respond more adaptively to situations of shifting dominance.

What is the key to breaking addictive responses in organizations? One place to begin is to familiarize ourselves with the laws of systemic behavior and learn to work with these laws (see The Fifth Discipline by Peter Senge). Most of the principles of systemic behavior apply directly to the process of addiction and contain the seeds of a solution (see “Moving Beyond Organizational Addictions” on p. 4).

The most effective way to combat organizational addiction is to learn to understand the system. When we do that, we can anticipate an imminent shift in dominance and prepare ourselves for it; in other words, we can design an adaptive instead of an addictive response. We can also identify opportunities to create new feedback loops that let us catalyze a desirable shift in dominance. But beware of spending too much time creating loops that aren’t going to dominate. The key is to make a change that will grip the system and take it down a different path.

To beat personal addictions, we often must place our trust in the potential of the system to change. Likewise, in organizations, if we build up confidence in a group’s ability to work together, to stay committed to each other, and to cope with problems in a way that will produce satisfactory results in the long run, we can get through withdrawal together. With the high turnover rates the business world is experiencing under downsizing, it has become harder for workers to place their faith in anyone or to adopt long-term perspectives. However, only trust can help an organization establish a sense of stability. Despite all the pressures to do otherwise, we must work to cultivate a culture of trust.

Herman Daly, a leader among economists in analyzing sustainable development, once made a statement that is profoundly applicable to the challenges discussed in this article: “The paths to sustainability are unknown, not because they’re hard to find, but because we never looked.” Let’s start looking for long-term solutions to organizational addictions.

Dennis Meadows is director of the Institute for Policy and Social Science at the University of New Hampshire. He directed the system dynamics graduate program at Dartmouth College for 16 years. He has written eight books that apply systems thinking to social and corporate issues.

Editorial support for this article was provided by the editorial staff and Joy Sobeck.

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The potential for measurement error is therefore an important modeling consideration. Traditional analytical approaches often leave out qualitative factors because they are hard to measure and reduce the precision of a model’s results. But the importance of measurement error diminishes when the investigative focus shifts from concern over the system’s current state to understanding the system’s behavior over time, which is often the purpose of a system dynamics model.

Measurement Error

In traditional business analysis, conceptual (pen-and-paper) models might contain a mix of quantitative and qualitative factors, but such models rarely advance past the theoretical stage. Traditional formal (computer) models usually either bury qualitative factors within more quantitative assumptions or leave them out entirely. As a result, organizations over the years have focused much more of their analytical attention on easily measurable “hard” factors (for example, production output, lines of code, or cash flow) than on “soft” variables (for example, employee morale, efficiency of code, or customer satisfaction). But these soft factors are important components of the system structure: They can strongly influence the performance of the system. So one of the key steps to understanding dynamic social systems is crafting and using simple but explicit and sensible measures for qualitative variables.

It is important to keep in mind that any measurement will contain some degree of error. For variables that have well-established units—feet, pounds, liters, volts—the measurement error is usually a function of the accuracy and precision of the measuring device. For example, if the smallest gradation on a ruler is 1/32nd of an inch, then any measurement taken with that tool may be off by as much as 1/64th of an inch. Whether this fundamental type of error is significant depends on what is being measured and the purpose of the measurement.

For soft variables that don’t have well-established units of measure, measurement error arises from two additional sources: the definition of a unit of measure and the creation of a measurement tool. For instance, when trying to measure customer satisfaction, we might invent a unit of measure, such as the customer satisfaction index. Through survey instruments, focus groups, and other tools, we could then come up with a number to represent our best estimate of current, actual customer satisfaction. Both of these steps introduce the potential for error, in addition to the fundamental error described above. And people often cite these additional opportunities for error as justification for excluding qualitative variables from a computer modeling effort.

The Dangers of Excluding Qualitative Variables

Although qualitative factors are generally more prone to measurement error than quantitative variables, we shouldn’t exclude them on that basis alone. How well a model recreates the system’s performance—and thereby the model’s usefulness—depends on much more than measurement precision.

Another perspective is that measurement error is an important but usually static source of error in models. For that reason, the measurement error in one time period does not affect (or has a limited effect on) the measurement error in the next. For instance, the measurement error associated with this month’s plant utilization, operating hours divided by total hours in the month, is not affected by last month’s measurement error, nor will it affect next month’s. Also, well-designed measurement standards are unbiased: They tend to overestimate values as often as they underestimate them. A static and unbiased measurement error will have a relatively small impact on the depiction of a system’s dynamic behavior (behavior over time). Leaving out a qualitative factor entirely, on the other hand, means potentially omitting an influential feedback loop, and thereby creating a dynamic source of model error. Dynamic errors, unlike measurement errors, compound over time, causing the model to lose validity very quickly.

For example, consider the variable “Employee Morale” (see “Interacting Hard and Soft Variables”). As profitability drops, management intervention increases. In response to greater management controls, employee morale decreases, leading to a decline in productivity, quality, and, ultimately, profitability. Leaving this variable out of the model ignores serious reinforcing processes and quickly leads to unacceptable levels of total model error. Can we precisely measure employee morale and its impact on related variables? No. Are the processes described real? Absolutely. Although we can worry about whether a system dynamics model, with all its qualitative factors, is generating precise output, one thing is certain: A model that leaves soft variables out entirely is definitely off the mark.

Quantitative Scales for Qualitative Variables

If qualitative factors are so important, how do we incorporate them into a computer model? Although most managers can give a ballpark estimate of qualitative variables such as market focus, they often feel uncomfortable about encoding this kind of estimate in a computer model. But for many qualitative factors, a ballpark estimate by an experienced management team is the only data available and is usually a pretty good reflection of the system’s state. At the very least, this data is important because it represents the mental models of the managers involved in the model-building process.

Indexed Variables. The most straightforward way to capture qualitative variables in a model is to create an indexed variable. To do this, we typically set the value of the variable equal to “1” at some given point in time, usually the start of the simulation. We can then identify the factors that affect the indexed variable and establish mathematical or graphical relationships to cause the indexed variable to change over time.

For example, suppose we create an index for customer satisfaction that has a value of “1” at the outset of the simulation. Notice that we are not attempting to say that the current level of satisfaction is high or low; we are simply establishing a starting point. Next, we determine that the ratio of customers to telephone representatives is a key driver of customer satisfaction. Third, we ask what ratio (everything else being equal) would maintain customer satisfaction at “1” and what would happen if the ratio changed? For instance, if the “steady state” ratio were 200 customers per telephone representative, the management team could estimate the impact of letting the ratio slip to 300: satisfaction might fall by 20 percent. When we run the model, we will then know whether satisfaction goes up, down, oscillates, or remains constant, based on the behavior of the indexed variable at any given time and its value relative to “1.”

Formulating an explicit equation or graphical relationship between a qualitative variable and its drivers provides a management team with the opportunity to share mental models about the business and to try to achieve a mutual understanding. Through modeling, differing assumptions about the strength of such relationships can be tested.

Assessing Models

Interacting Hard and Soft Variables

The qualitative variable “Employee Morale” Is a key component of these reinforcing loops. As profitability drops, management intervention increases. In response to greater management controls, employee morale decreases, leading to a decline in productivity, quality, and, ultimately, profitability.

If you are responsible for building and managing models, present the decision-maker with alternatives: the old way of model-building or a new way that captures the impact of qualitative variables. Use both kinds of models concurrently to explore the thinking and assumptions that went into each one, and to uncover new insights about the organization and business. See if you can move senior management from using modeling as a forecasting tool to using modeling as a way to test ideas, explore strategies, and learn how the system works.

If you are the decision-maker, ask tough questions about the assumptions going into the models you use, especially with respect to qualitative factors. For instance, ask whether employee morale and its impact on productivity are addressed in your business plan. If not, why not? Last but not least, figure out ways to free your team from the tyranny of quantitative spreadsheet thinking. Spreadsheets are an important and helpful tool, but today’s management teams need to have a variety of tools at their disposal and must know how to include qualitative factors in their thinking.

Gregory Hennessy Is co-founder and managing director of Dynamic Strategies, a collaboration of advisors working with clients to Improve organizational effectiveness, apply system dynamics, and develop organizational learning capabilities.

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What Is Organizational Anxiety?

Anxiety can be an insidious force: Not only does it sap energy levels and damage our health, it also eats away at job performance and stifles innovation and creativity. Like individuals, organizations can also suffer from symptoms of anxiety. Over the long run, anxiety can reduce an enterprise’s strategic adaptability and effectiveness.

In recent years, researchers have looked at anxiety from an intriguing new perspective. As they see it, the origin of anxiety is the struggle between life and death. This struggle that rages within individuals also takes place in work groups and organizations. Of course, organizations do not experience death in the same way that individuals do; however, they do face the very real possibility of financial or operational demise. Organizations can cease to exist through bankruptcy, takeover, mergers, and so forth. As a result, they experience their own brand of anxiety

Defenses Against Anxiety

Literature abounds on how work-groups and organizations try to cope with the destructive feelings of anxiety. According to one theory, some companies resort to a form of defense that combines three tactics called splitting, projection, and introjection that individuals often use to fend off anxiety. Splitting happens when we separate the “good” aspects of our lives from the “bad.” We then project “bad” qualities onto others and introject “good” qualities into ourselves. This tactic helps us to feel more in control of our panic, because we turn our attention to judging and trying to control others.

For example, an anxious manager might split good and bad by considering himself all-powerful (he introjects good into himself) while at the same time dismissing subordinates as unworthy (he projects bad onto others). Even worse, a manager in this frame of mind might be compelled to act on these projected feelings by punishing workers with extra work, impossible schedules, unreachable goals, and so forth. Companies tend to “institutionalize” this kind of behavior. Employees may quit and new ones may be hired, but the tough schedules and unattainable goals persist regardless of the individuals employed at any given time.

Groups or organizations that are leaderless can suffer more anxiety than most. For example, self-directed work teams may have difficulty making decisions if no leader steps forward. The team may become ineffective as it struggles to search for a leader, thus creating what can be paralyzing anxiety.

In these cases, the people involved often defend themselves against fearful emotions in three ways:

• Dependency. The group stops trying to solve its problems and instead waits for a “messiah” to save it.
• Pairing. Two individuals related to the group (for example, two group members, or one member and an outside consultant) combine to try to oust someone they consider a “bad” member.
• Flight/Fight. Group members blame all problems on an outside cause, or they pretend that no problem exists.

Defense mechanisms are neither good nor bad, and indeed can help protect us from emotional overload. But, the way these mechanisms are stitched together in an organization’s mental model can create the exact opposite of what the group wants and needs: Instead of reducing the anxiety, the behavior only worsens it. And mental models are notorious for leading to self-fulfilling prophecies: We see only what we expect to see, and then we act in ways that bring about results that confirm our assumptions.

When anxiety lodges itself in a company’s collective mental model of how things work, it will continue to perpetuate itself until the organization’s behavior changes to balance or reduce the increasing anxiety. For example, many organizations pride themselves on their “heroic” acts. When crises strike, creating high levels of anxiety, a few heroes step forward to “save the day.” The organization rewards the heroes. At the same time, by giving rewards, the organization inadvertently encourages the creation of future crises, which will lead to more anxiety and then to additional rewards for heroic action. This behavior is a perfect example of self-perpetuating anxiety.

Anxiety Amplified

Defensive actions can trigger reinforcing processes that serve to amplify and perpetuate anxiety. Here are examples of three reinforcing loops that can sustain or even worsen anxiety in organizations. Although these loops were created by a work group at a large company, they reflect dynamics experienced by many organizations.

The “Messiah” Loop. In this dynamic, if Anxiety about the organization’s performance intensifies, employees look for a “messiah”(Search for Savior). This search diminishes workers’ Accountability, in turn reducing their Perceived Ability to Succeed. The diminishing of workers’ self-esteem then leads to an increase in Anxiety.

The loop contains a bitter irony: The group searches for a savior to ease its anxiety, but waiting for a “messiah” only leads to more anxiety. The team could design a more fundamental, enduring solution to their anxiety by focusing instead on learning and performing. Sadly, however, the “quick fix” of seeking a savior diminishes the organization’s need for—and thus its ability to apply—a more fundamental solution.

THE “MESSIAH” LOOP

if Anxiety about the organization’s performance intensifies, employees Search for a Savior. This search diminishes workers’ Accountability, in turn reducing their Perceived Ability to Succeed and increasing Anxiety.

The “Manic Defense” Loop. In this reinforcing process, anxious managers project the organization’s problems onto their subordinates and then try to punish them. To justify this punishment, the managers focus obsessively on quantitative measurements, slavishly using them to control the action around them. Through this emphasis on metrics, the managers deplete the organization of the physical, financial, and—perhaps most important—psychological resources the team members need to succeed. All of this ultimately leads to even more intense anxiety.

In the “Manic Defense” loop, increased Anxiety leads to more Focus on Metrics, which in turn causes Resources Used for Measuring to go up. As Resources Used for measuring rises, Resources Available for Projects diminishes, which in turn increases Resources Requested. The diminished resource base for projects puts added pressure on those trying to complete projects. The project manager then requests more resources in order to complete the projects. As Resources Requested increases, Percentage of Resources Received is reduced because of the multiple demands on the system created by the additional resources requested for measurement.

This development further cuts into Perceived Ability to Succeed and ultimately heightens Anxiety.

The “Fight” Loop. We call the third reinforcing process the “Fight” loop because it captures the way anxiety sparks conflict within the team and encourages an aggressive desire to have one’s own viewpoints and decisions prevail. Increased Anxiety leads to increased Internal Competition, which leads to a greater Need to Be Right. Intensifying the Need to Be Right reduces the level of inquiry (Questions), which also lowers Understanding and increases the Resources Used for Making Decisions. More employee time and energy is needed to make decisions when there is little understanding of the issues facing the organization. The rest of the loop follows the “Manic Defense” loop, ultimately creating even more Anxiety.

Understanding Our Own Role

As we look at the three reinforcing loops, we can begin to see how team members themselves might create and intensify their own anxiety. Often, factors viewed as external causes for anxiety, such as perceptions of failure or layoffs, could really be internally driven. To surface these factors, we might ask, “Who is perceiving this failure—our own organization, stockholders, or customers?” If it is our own organization, we can begin to search for ways to change that perception. If we have suffered layoffs, could it be that our business is cyclical? If so, how is our organization perpetuating industry cycles? Many organizations aren’t aware of the role they play in perpetuating not just their own business cycles, but those of the entire industry.

Thus, often what an organization views as “not our problem” is just that. The organization tries to behave in a way that will produce positive results, but inadvertently creates undesired outcomes. This is an example of what Jay Forrester called “the counter-intuitive behavior of social systems.” Realizing that we often cause our own problems may be embarrassing, but it is also good news, for whatever we create in a system, we may be able to change if we gain insight into it.

In Search of Balancing Loops

The dynamics shown in the three loops present a grim image of the system of organizational anxiety. The picture is particularly discouraging because all the loops are reinforcing, creating a vicious cycle. But the picture does not have to remain grim: Reinforcing processes are not all necessarily bad. Just as the reinforcing loops in the diagrams can heighten anxiety exponentially, they can also reduce anxiety, if they are turned around to become virtuous cycles.

The lack of balancing loops is another important piece of information about the systems the diagrams depict. Without balancing loops, there are no processes in place for returning the system to equilibrium after a disturbance caused by the reinforcing loops. All three loops amplify the central variable—Anxiety—and no loops have been identified that keep it under control.

When drawing their system of anxiety, teams often neglect to build balancing loops into their models, perhaps because people tend to notice things that create rapid change (R loops) more than forces that keep things stable (B loops). Also, when addressing a specific problem, team members may focus on how their anxiety is worsening, not on how it might be alleviated.

Clearly, though, balancing loops have to exist in every organization; otherwise, the place would unravel toward anxiety-induced paralysis, anarchy, or some other extreme endpoint of a reinforcing process. Some sort of balancing dynamic often subtly works to keep the situation relatively under control. In fact, these hidden loops can create the sense of oscillating, persistent anxiety experienced by the staff.

Balancing loops that might control anxiety could include coping mechanisms such as open communication, flexible work hours, and personal leave time. Unfortunately, if the reinforcing loop around anxiety dominates the system, these coping mechanisms may never be able to balance out the increasing anxiety. Communication may open up and temporarily reduce anxiety, but then a sudden crisis may shut down communication and thereby increase anxiety again. This pattern causes the organization to ride the waves of anxiety time and again.

A team can also balance their anxiety by linking a new, outside force to Anxiety in a way that will ease feelings of fear rather than heighten them. If, for instance, the members of a group develop coping mechanisms in their private lives (loving families, close-knit communities, and so forth), they might be able to calm their collective anxiety, as shown in “Reversing Anxiety.” As the Use of Private-Life Coping Mechanisms increases, Anxiety and the Search for a Savior decrease. Account-ability and the group’s Perceived Ability to Succeed are then enhanced, leading to less of a need for reliance on the coping mechanisms.

REVERSING ANXIETY

As the Use of Private-Life Coping Mechanisms increases, Anxiety and the Search for a Savior decrease. Accountability and the group’s Perceived Ability to Succeed are enhanced, leading to less of a reliance on coping mechanism.

A Systemic Makeover

According to the field of System Dynamics, there are two main ways of actually changing a system: Shifting Loop Dominance or Direction, and Changing Loop Structure so as to alter the flow of feedback through the system. Here are some additional strategies for breaking the cycle of anxiety.

Shifting Loop Dominance or Direction. Often, the main loops in a system all lead to greater anxiety. For this reason, teams may want to explore how they can weaken those loops and reshape the system. For example, the “Messiah” loop could be reversed if team members gave up the search for a savior and instead enhanced their own empowerment and accountability. A team could weaken the “Manic Defense” loop by consciously reducing the focus on metrics. To do this, management could cut back on the number of metrics used, employ other ways of measuring the company’s performance, emphasize customer service over internal metrics, streamline bureaucracy, free up resources used for measuring, and so forth. Finally, a team could disarm the “Fight” loop by finding ways to reduce internal competition and the need to be right, by promoting inquiry skills (Questions), and by lessening resources used for making decisions.

Changing Loop Structure.We can actually reshape a systemic structure by incorporating new variables and links and removing others. By making these changes, we can alter the pathway by which feedback flows throughout the system. There are many possibilities for creating new links. In dealing with a system of organizational anxiety, one valuable addition might be the use of inquiry skills. Inquiry skills include methods of conversing that can overcome barriers to understanding and learning, whether the barriers are organizational or interpersonal. Thinkers such as Chris Argyris, David Bohm, and William Isaacs have written extensively about this set of skills. In the case of an anxious team, as the group gets more and more practice in using inquiry—and begins to achieve some success—it will learn to use these tools more readily in response to a surge in anxiety

Looking Ahead

Of course, a causal loop diagram is only an early step in the process of solving a systemic problem. Actually changing a systemic structure takes a lot more than just redrawing links. To reshape the way they do things, a group will need to think about what the links in its drawings mean.

For example, the more managers understand the anxiety-intensifying system that they’ve helped to create, the more motivated they may feel to restructure that especially irksome “Manic Defense” loop. Instead of projecting their anxiety onto “bad” subordinates, they could learn to recognize both the good and the bad in the way their organization operates. In a difficult but profoundly healthier process, the team members would examine things in a far more systemic way than the traditional short-term perspective on metrics allows, and would join together to do the hard work essential for improving their performance.

Making attitude changes isn’t simple or easy, and the team will need to dig even deeper to find the best leverage points for change. However, altering some key mental pictures of how things work is an organization’s best hope for pulling itself out of the anxiety-ridden system it has created. Talking about their anxiety system and drawing causal depictions of it can give a team vital insight into how they are creating their own problems.

The team might learn more at this stage if they also used a computer simulation model of their anxiety system. Modeling their system would require them to identify the things they think most strongly drive the loops, and it would give them a way to test the insights that they found while drawing the loops. In addition, modeling would make it easier for them to redesign the problematic structures in their system.

The group could then design interventions that apply pressure to any leverage points it identifies in the earlier steps. In many cases, the most powerful interventions would involve using new tools—particularly inquiry skills—for deepening the organization’s collective knowledge about itself. If all goes well, the team will grow less dependent on self-defeating defense mechanisms and rely increasingly on its own strengths, knowledge, skills, and resources.

An Example: ABC, Inc

A computer manufacturer, ABC, Inc., had suffered some significant business failures that generated a tremendous level of anxiety in the organization. Arguments over how to price products became the focus of people’s anxiety. The “old-timers” thought that the company should maintain its high prices to reflect its image as a pioneer in the industry and as a producer of high-quality products. On the other hand, the “newcomers” thought that customers were becoming more price sensitive because of the lower prices offered by ABC’s competitors.

The first step to resolving the impasse was to have both sides share their mental models of what was creating the anxiety over pricing. Using tools such as the ladder of inference, the groups discussed their own interpretations of the data they used to make pricing decisions. One manager reported, “Our data show that our best customers are more concerned about quality and are willing to pay the higher price.” Another stated, “All our customers care about is a low price. We are being destroyed by our competition.” Each side held tightly to its position and blamed the other group for undermining ABC’s success.

The groups then developed causal loops that captured the two perspectives. Through this process, they found that price was not the key issue; the real issue was defining what type of organization ABC would be in the future. Would ABC be an innovative producer of high-quality products, or would it be a mass producer of relatively high quality, but less innovative goods?

At this point, the company created a computer simulation to test the financial impact of the two scenarios. The simulation revealed that the innovative strategy would result in a loss of customers. However, by charging more per unit, the company could make up much of the lost revenue. Further investigation showed that customers who buy lower-priced products tend to demand more technical services, cutting further into revenues. This finding made the mass-production scenario less appealing in the long run.

By using causal loops and simulation in this way, ABC diffused the anxiety within the organization and took the focus away from blaming individuals for the company’s troubles. ABC was also able to make more informed decisions regarding its pricing and long-term business strategy.

Eradicating Anxiety

It is easy to view organizational anxiety as something that is out of a group’s—or anyone’s—control. But the discussion above shows how we can play a role in creating our own anxiety. Managers and employees often become trapped in mental models that only worsen their anxiety. Yet the team is far from helpless to control its behavior. We all possess the power to change our attitudes and behaviors in order to reshape dynamics that we ourselves have created. With this enhanced understanding, we can then take intelligent steps to manage or even eradicate anxiety and thereby enhance our effectiveness.

For references and further reading, please see Anxiety in the Workplace: Using Systems Thinking to Deepen Understanding (Pegasus Communications, 1998).

Janet M. Gould Wilkinson is director of the Organizations as Learning Systems project at the Massachusetts Institute of Technology.

John J. Voyer is associate professor of business administration and co-director of the MBA program at the School of Business, University of Southern Maine.

David N. Ford is an associate professor in the system dynamics program at the University of Bergen in Norway and a visiting professor in the School of the Management of Technology and Economics at the Chalmers University of Technology in Sweden.

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We regularly find ourselves stuck in futile conflicts over the choices we face. How can intelligent, committed people in the same organization be so divided? Could it be that both sides are right? If so, how does the conflict come about? And how can you and your team leverage the differences that exist among you? Help lies in understanding the structure of paradox.

Managing Paradox

In their study of organizational effectiveness, James Collins and Jerry Porras noted that a distinguishing characteristic of highly visionary companies is the capacity to manage paradox. These authors define such capacity as “the ability to embrace both extremes of a number of dimensions at the same time” (Built to Last, p. 44). This rare capability seems to allow successful companies to avoid falling into a pattern of values-based conflict, with parties becoming increasingly polarized around “either/or” choices.

Many decisions that groups face involve a choice between opposing values. Thus, when resolving a dispute, a team may feel the need to choose between the rights of an individual member and the well-being of the group as a whole.

Because the two choices are opposites, the group will take actions that support one value rather than the other. For example, in an emergency, people within a group may be expected either to “pitch in” and take actions that help the group as a whole, leaving aside their personal objectives for a time, or to complete their personal objectives first and help the team if they have spare time. Group norms and organizational reward systems tend to encourage one approach over the other.

In many cases, though, the values are interdependent. Over time, an organization requires both values to be healthy (Barry Johnson, Polarity Management). Thus, when any group is formed, a cycle begins between opposing values (see “Circularity of Values”). Initially, the team feels a strong need for one value, such as individualism. Team members then take actions that value individualism. If unchecked, however, individualism destroys the cohesiveness of the group. So, individualistic actions eventually create a need for actions that value community. Over the long run, this focus on community will in turn create a need for individualism, as group members lose their sense of personal identity. Some investigators of organizational culture refer to this movement between opposites as the “circularity of values“ (Hampden-Turner and Trompenaars, The Seven Cultures of Capitalism).

For example, Performance Management Associates (PMA), a small consulting company founded by Ralph and Sarah, had experienced consistently high demand for its services. Ralph was known for introducing leading-edge management concepts to organizations in need of change. He continually sought new ideas and built them into his consulting work. Ralph’s clients found his approach innovative and challenging.

Sarah, on the other hand, had long recognized that Ralph’s ideas were of limited value for businesses unless they could be further developed into systems and training products. By framing Ralph’s insights in ways that organizations could implement and use, Sarah helped clients institutionalize needed changes. She brought stability and quality control to PMA.

Encouraged by their company’s success, Ralph and Sarah hired two new consultants. With the support the additional staff would provide, Ralph planned to increase the pace of his innovative work, Barry and Frank, the newcomers, were excited by the prospect of further developing Ralph’s ideas.

CIRCULARITY OF VALUES

In this reinforcing loop, healthy circularity operates, as actions supporting one value create a need for its opposite.

However, at PMA, Barry and Frank felt they were approaching clients with half-formed products. Worse, Ralph kept coming up with new ideas even though the old ones still weren’t fully developed. Barry and Frank grew frustrated with their work. When they complained that life at PMA had gotten too chaotic, Ralph felt they didn’t understand the principles on which he and Sarah had built the business.

Thus, after years of success, PMA reached a state of crisis. The new consultants threatened to leave the organization. And several clients voiced their concern about the errors that sometimes crept into PMA’s administrative practices.

The introduction of new people at PMA disrupted what had been a healthy movement between the opposing values of innovation and product quality. At the same time, Ralph’s increased focus on innovation ultimately became detrimental to the organization as a whole. As we will see, PMA needed to learn how to manage opposing values, or paradox, in this new configuration.

Unconscious Assumptions

The movement between two opposites rarely happens smoothly. Often, the delay between actions that support one value and the growth in the need for its opposite leads to an unconscious overemphasis on the original value. For instance, when a group clearly sees a gain from actions valuing the individual, it tends to resolve subsequent dilemmas in favor of the individual. Over time, the team will find that it emphasizes individualism without consciously thinking about the alternative. In this way, the group creates an unconscious assumption that pursuing one value ahead of its opposite is the best way to act. Thus, individualism may become part of the group’s culture—its unconscious assumptions regarding the best way to act. This is represented in “Over-reliance on One Value” as the variable “Strength of Individualism,” which grows as a result of loop R2.

Another factor that causes imbalance between opposing values is that, as group members act in support of a value, they build their capacity to support that value (R3). An organization that has a history of valuing individualism is likely to have built up systems and skills that support individualism.

PMA was experiencing similar dynamics based on the values of “innovation” and “quality”: The company could invest in finding new products or in improving the quality of existing ones. Ralph found that his efforts to introduce innovative products brought gains in the form of satisfied customers. His capacity for generating further innovations also grew. Not surprisingly, his assumption about the benefits of innovation became embedded in PMA’s culture. This dynamic explains why Ralph, and PMA, pushed for more and more innovation in their work, despite the problems this created.

Finally, as mentioned earlier, “Actions Valuing Individualism” will eventually lead to a need for “Actions Valuing Community.” As the need to value community grows, the “Utility of Individualism” and the group’s gains from its actions valuing individualism also fall (B4). B4 thus acts as a limit to the growth that comes from the reinforcing processes in R2 and R3.

Conflicting Cultures

As at PMA, most organizations include groups with opposing values. Some focus on the gains to be made by sticking with the values that have brought them success in the past. Barry Johnson refers to such groups as “tradition-bearers.” As the need for the opposite value grows, other members of the organization act as “crusaders” for new values.

Tradition-bearers and crusaders within organizations conflict over values. As the strength of each group’s culture develops, so does the belief that the other’s values are wrong. At PMA, Ralph’s ever-growing belief in the value of innovation led him to reject calls for greater stability.

So far, the description of interdependent opposites has focused on the behavior of those in the organization holding the primary value (individualism in the diagram, or innovation in PMA’s case). Within most organizations, these dynamics will be mirrored by those holding the opposing value. It is easy for any group to look past the interdependence of the values that are in conflict. An organization has experienced gains based on its values and has made a commitment to those values by developing capacity around them. We often feel that if one value is good, its opposite must be bad (De Bono, I Am Right, You Are Wrong).

In addition, any group can readily find examples of the misuse of the opposing value. A value is misused when people continue to apply it past the point where it starts to undermine its opposite. Thus, extreme individualism destroys a group’s sense of community. Concern for community, taken too far, erodes individual freedom and opportunity.

This pattern allows people holding one value to categorize all those holding the opposite value as extremists who want to take the rejected value too far. So, for example, people who value individualism may label those with values that focus on community as “communists.” People who value community may label those with individualistic values as “anarchists.”

At PMA, Ralph could point to numerous examples of clients who suffered from their reluctance to adopt new ideas, and these cases intensified his reliance on innovation. Frank and Barry, on the other hand, saw plenty of clients who were unable to institutionalize change based on their work with PMA. In their view, these examples confirmed the need for higher levels of product quality at PMA.

Leverage

The circularity of values is a naturally occurring cycle in living systems; for instance, there is constant movement between inhaling and exhaling, exertion and relaxation, integration and differentiation. Healthy movement between these opposites is needed to sustain the system. Problems arise because of the unconscious over-reliance on one value at the expense of another. Conflict between groups within an organization is usually a tip-off that this unconscious process has begun.

Many people assume that solutions to problems caused by values-based conflict must involve power. Those crusading for a change in organizational practices feel that they should have more power in order to exert a greater influence. Tradition-bearers use the power they have to hold on to what they value. However, power-based strategies address only the symptoms of the structure of interdependent opposites; they resolve conflict by allowing one group to “win.” In the future, either the conflict will return because the need for the “losing” value remains, or the system will die. To expose the self-destructive nature of this power-based approach, Johnson encourages people to imagine the effect of treating breathing (inhaling and exhaling) as a conflict and having one side “win” at the expense of the other.

The back-and-forth dynamics within the structure tend to draw participants’ attention away from the healthy operation represented by the outside loop (see “Values-Based Conflict“). To gain leverage, participants need to become aware of the possibilities that emerge when the outside reinforcing loop is working well. Those functioning within the system should also become conscious of the interdependence of the opposing values. Dialogue can be a useful tool for surfacing the need for a circularity of movement between values.

People must be willing to move away from what they value in order to bring about the vision they desire—while trusting that the organization will eventually come back around the loop. They do not have to give up what they believe; they just have to live with a delay before their beliefs take center stage again. According to Robert Fritz, without awareness of this cycle, groups may oscillate between values rather than applying those most likely to bring about the greatest gains and highest leverage.

In Polarity Management, Johnson describes a simple yet powerful approach to attaining this leverage. His method involves charting both the upside and downside of each of the opposing values. This allows people to see and feel the need for movement between the values, determine the direction of movement currently needed, and establish how they will recognize the need to change emphasis. This approach is one way to achieve what Hampden-Turner describes as “reconciliation of values.”

At PMA, Sarah recognized the need to stop the values-based conflict. Her solution was to split the company into two divisions. At one division, Barry, Frank, and Sarah concentrate on customizing and running existing programs for clients, and on improving the quality of PMA’s services. At a separate location, Ralph develops new products. Once he develops a product, he passes it on to the other group, so that he can move on to new ideas.

The new structure allows everyone at PMA to appreciate all the values contained within the company. Ralph now sees the need for quality. Barry and Frank are increasingly innovative in their own work, building on a foundation of solid products. They often consult Ralph about ways to improve their practice. The reconciliation of values at PMA has had a beneficial impact on the company’s work with clients as well. Because client organizations also require this movement between innovation and quality, the company can offer them consulting services at whichever stage of the cycle the clients find themselves. As members of one division of PMA see their clients’ gains diminishing, they might refer them to the other division.

Interdependent Opposites and Organizational Learning

Research by Hampden-Turner and Trompenaars suggests that English speaking democracies (such as the U.S., U.K., Canada, Australia, and New Zealand) are characterized by:

• putting universal rules ahead of relationships
• putting individual rights ahead of community health
• dealing with complexity analytically as opposed to integratively
• awarding status on the basis of achievement rather than ascribing status on some other basis (for example, age or experience)

Each of these pairings follows the dynamics illustrated in “Values-Based Conflict”; for example, Universal Rules could be featured at the top of the diagram, with Relationships featured at the bottom. Because many groups and organizations in the West follow the pattern of valuing rules, individualism, analysis, and achievement, we could group all four of these values at the top and call them “Cluster A,” and then group relationships, community values, integration, and ascription at the bottom and call them “Cluster B.”

Organizational cultures within English-speaking democracies tend to overemphasize the Cluster A values. We can view the disciplines of organizational learning as a movement designed to compensate for this over-reliance. So, for instance, team learning emphasizes relationships and community ahead of managing or controlling individuals through the use of universal rules. Systems thinking encourages integrative thinking over analysis. Learning organizations may award status to members of the organizational community who share the community’s vision, rather than to those who achieve success according to analytically derived performance indicators.

Organizational learning practitioners thus take on the role of crusaders for values opposite to those unconsciously held by many in their organizations. Yet this crusading inevitably generates conflict with tradition-bearers. To support their crusade, practitioners may inadvertently enter into low-leverage, power-based strategies. They would do better to make the circularity of the values in contention visible to all, using the techniques described above.

Reconciliation

All groups face challenges involving opposing values. Indeed, the very nature of values and the structure of paradox lend themselves to conflict. Groups too easily see the benefit to be gained from their own values and a danger in pursuing values held by others.

The structure of paradox also encourages groups to pursue their traditional values until they experience crisis. But by definition, a crisis cannot be resolved by relying on the assumptions that originally got the organization into the situation (Mitroff et al., Framebreak). As we have seen, overemphasis on one value requires a shift to its opposite to undo harm that has been done. “Managing Opposing Values” provides examples of the results of either managing or mismanaging common pairs of opposing values. Only when these are managed well can an organization sustain itself over time.

Most diverse, complex organizations already possess the values required for building a sustained future. The challenge groups face is to reconcile these differing values—the same values that often generate the most heated conflict within the organization. Rather than experiencing differences in values as a struggle that immobilizes an organization, people should enjoy these differences as diversity that infuses the organization with vigor and variety.

Philip Ramsey is a lecturer in Training and Development and Organizational Learning at Massey University in New Zealand. He is the author of the Billlbonk series, a set of stories that teach systems thinking and organizational learning concepts.

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By following a systems thinking-based graphical facilitation process using hexagons, we can surface and focus on the issues that are most important in our organizations.This process can help shorten the amount of time it takes teams to move from ideas to action steps and can give us a more systemic view of the issues our organizations face.

The Hexagon Technique

The hexagon technique for brainstorming was created by Tony Hodgson and Gary Chicoine-Piper, British creativity and organizational development consultants. Unlike other brainstorming techniques, which focus on generating ideas, hexagons can also help us surface underlying assumptions. By writing people’s thoughts and ideas on hexagons and posting them in front of the entire team, we make those thoughts visible and separate the idea from the person. Because of their unique shape, hexagons can easily be grouped in a honeycomb structure; we can then move the hexagons into various configurations and combine ideas in different ways.

The following steps describe a brainstorming process using hexagons.

Unlike other brainstorming techniques, which focus on generating ideas, hexagons can also help us surface underlying assumptions.

Step One: Identifying Issues

Hexagon maps capture the event level—the world of who, what, when, where, and why. You want to start here, because you first have to connect with people’s understanding of their world. So begin by talking in everyday language. Surface team members’ concerns and mental models by asking questions such as: “What is really important to you?” “What issues do you feel are absolutely critical for success?” “What are your strongest opinions?” “What do you feel is holding us back?”

Summarize each response in three to six words and write it on a hexagon. Place the hexagons on the board in sequence, with one point at the top; don’t attempt to arrange the ideas at first. Number the hexagons sequentially for easy/reference. You can use color-coded hexagons (see “Color-Coding Hexagons”); for example, if you or someone else has a strong opinion, put it on a red hexagon.

But because your goal is to build a shared understanding, you have to go beyond simple brainstorming. You want to explore the reasoning—and the emotions—behind people’s statements. So, if someone says, “We need to raise prices,” you would ask, “Can you tell me more about that?” And they may respond, “If we don’t raise prices, this company is going to go under, because we won’t have enough revenues to support R&D!” The challenge is to capture of each person’s position in just a few words, so that the hexagon can serve as a vivid reminder for the entire group of that individual’s perspective. Separating an idea from the person who articulated it and creating a symbol for the idea that can be understood by the whole group is an important step in the process.

Step Two: Identifying Clusters

From this divergent step, move to convergence. Zero in on the key issues by asking, “Which hexagons seem to go together?” Find the hexagons that seem related, and then move them the same general area on the board. Once you have reviewed the positioning with the team, draw a circle around each grouping and give each cluster a name (see “Identifying Clusters”).

You may start by writing one title, and someone might say, “Let’s change it; that’s not quite right.” Remember, it’s an organic process through which the team starts to develop a common language. The titles of the most important clusters become the emergent agenda; these are the hot spots, according to the group.

Step Three: Identifying Variables

In this step, you begin to distinguish the most important trends and patterns of behavior over time within your organization. When you look for key variables, you’re really asking, “Can you tell me how something in this system might change over time?” Surface the key variables by asking a set of transition questions about the most important clusters on the board: “What do you really want?” “How would you know if you got it?” “Who are the key players?” “What do they want?” “How would they know if they got it?” “What are the key uncertainties?” You’re trying to shift to the systems point of view, and it takes some skill to navigate into that domain.

A variable is something that can increase or decrease over time. So, if you were concerned about the economy, you might say, “We might have a depression!” A depression, however, is not a variable; a variable is something that can go up or down, something you can measure. However, once you define a depression as eight consecutive quarters of declining GNP, you’ve found your variable. GNP is something that can go up or down. You can also identify “soft” variables— “morale” or “quality” can go up or down over time.

When identifying variables, choose brief, one- to two-word descriptions for each. Try not to use adverbs or lengthy adjectives. Write the variables on orange hexagons for easy identification.

IDENTIFYING CLUSTERS

After writing the issues on hexagons and numbering the hexagons sequentially for easy reference, cluster related issues. Draw a circle around each grouping and give each cluster a name. Then, surface the key variables by asking a set of transition questions about the most important clusters.

Next Steps

Once you’ve identified the trends and the variables, you’ll be amazed at how quickly you can move to a causal loop diagram by arranging the key variables. But for real breakthroughs, the maps must focus on the group’s most important problems and objectives. Otherwise, they can easily degenerate into meaningless “spaghetti” diagrams, or pictures of things that are connected but don’t necessarily have a cause-and-effect relationship. The steps outlined above should help your team make the transition from ideas to key variables.

Be sure to document your learning process for future reference. Take photographs of your hexagon maps, clusters, variables, and causal loops.

Beyond Hexagons

Using hexagons for brainstorming can be an engaging, dynamic way to move a group toward taking a systemic perspective. By allowing you to capture various viewpoints, hexagons provide a spring board for creative brainstorming and can help you elicit mental models. Finally, by helping you identify key issues, the hexagon technique enables teams to move from ideas to variables, and then on to causal loop and flow diagrams.

David Kreutzer is founder and president of GKA Incorporated, an international management consulting firm. The process described in this article is based on the initial steps of FASTBreak, a systems thinking-based facilitation methodology for moving from ideas to action designed by GKA Incorporated.

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Though Dynamic Thinking is one of the easiest of the systems thinking skills to master, it does not come naturally for most people. What seems more common is Static Thinking. For Static Thinkers, the starting point for understanding change is where they are right now; that is, the current state. These thinkers tend to see change as “jumping” from the current state to a future goal in a rather straightforward way. The historical trajectory leading up to the current state and the unfurling of the pathway from the current state to the future condition typically don’t garner much attention.

In the Pits

The trajectories indicate that there are several different ways to reach a current crisis point. Static Thinkers commonly project the path from “current crisis” to “future condition” as a straight line. Dynamic Thinkers chart paths that are longer and less linear, incorporating a “worse-before-better segment.

The Benefits of Dynamic Thinking

Why embrace Dynamic Thinking? Let’s look at some of the problems associated with the alternative and then see what opportunities Dynamic Thinking provides for improving performance.

In describing what ails their organizations, people tend to focus on the current crisis—profit margins are razor thin, the turnover rate is too high, customer satisfaction is in the pits. “Victory” is then defined as boosting profit margins to some higher level, lowering the turnover rate to a certain mark, or raising customer satisfaction to a particular degree. This type of focus, which is based in Static Thinking, has two basic problems.

The first problem is that the observation “customer satisfaction is in the pits” says nothing about the path it followed to get there. As the figure “In the Pits” illustrates, there are several different ways to reach a current crisis point.

If leaders and managers want to embark on a type of initiative that can successfully move a system from its current state to a desired future state, they must investigate the nature of the relationships that carried the system to where it is now (and may be holding it there!). Dynamic Thinking encourages people to use the historical trajectory for stimulating and guiding inquiry into the underlying relationships that produced it. The insights that stem from such an inquiry can help us design an initiative that successfully leverages the desired change in performance.

The second problem with Static Thinking is that the course of the “path forward” gets relatively little attention. As the illustration indicates, people commonly project the pathway from “current crisis” to “future condition” as a straight line, assuming that improvement will proceed at a steady pace in one direction. The assumption underlying such a projection is that improvement can be “engineered”—that the system is a “mechanism” and hence will passively accept change.

By contrast, those employing Dynamic Thinking skills carefully consider the shape and duration of the path forward. The assumption is “organization as organism”: The system will both adapt to and resist change. As a result, the paths forward charted by Dynamic Thinkers are typically longer and less linear than those traced by Static Thinkers. In particular, they often incorporate a “worse-before-better” segment—reflecting the idea that in order to improve a situation, you have to first invest something in the effort. Investing, in turn, usually implies enduring some sort of short-term “hit.”

Honing Dynamic Thinking with Reference Behavior Patterns

The most useful tool for honing Dynamic Thinking skills is the Reference Behavior Pattern (RBP), a kind of behavior over time graph. An RBP is a graph over time of the variable that best captures the issue or challenge of concern. Developing an RBP at the outset of any performance improvement or strategy design effort is one of the best ways to focus a group’s energy, while also encouraging a Dynamic Thinking perspective. Here are examples of how to use this tool most effectively.

Example 1: ‘World-Class” Teams. A group of senior managers from a hardware product group within a high-technology company was searching for a solution to performance problems in their group. In a meeting, they came to a consensus that the answer was to develop “world-class” teams. To explore this question, the group needed to address several other questions: How “world class” were the group’s teams at that moment? How had “world classness” changed over time? By how much did they think they could improve this variable and over what time frame? All of these questions fell flat as long as the group was unable to frame the challenge as a dynamic problem.

The question that got the managers thinking dynamically was: How would you know if you had world-class teams (that is, what performance indicators would characterize such teams)? This query led the group to identify a series of operational measures—like product-development cycle times, manufacturing defect rates, and so forth—that they could chart over time to reveal a historical trajectory, assign a current state, and use to imagine future trajectories. The insights gleaned from the RBPs enabled the team to think in non-abstract terms about initiatives they could implement to improve performance. Voila!

Example 2: Declining Revenues. The second example involves a group at a financial services company where the number of cardholders, amount of revenues, and number of transactions were all growing., Initially, RBPs of almost all the company’s key measures sloped upward. Things got interesting, though, when the group divided annual revenues by the number of cardholders. That curve rose for a few years, but then turned downward and continued to fall for the last five years. The decline of revenues per cardholder suggested that the company was gaining customers who felt less inclined to use their cards or who had little discretionary income—both signs of potential market saturation. This example indicates something else that’s important to remember in constructing RBPs. Often it is useful to focus on a relative rather than absolute performance indicator. “Dividing through” reveals relative changes that often stimulate insights.

These examples make it clear that the time axis plays a large role in the usefulness of RBPs. In constructing one of these graphs, therefore, think carefully about whether the issue in question is unfolding in minutes, weeks, or years. Electric utility people, for example, “live” with hour-to-hour load fluctuations and associated purchase price swings. But the long-term economic viability of a utility depends on capacity decisions that can play out with a yearly rhythm. It doesn’t make sense to cast an RBP in hours when you want to examine trends over a number of months or years! Paying close attention to the time units in an RBP is a great way to keep tactical and strategic aspects in proper perspective—and to generate vastly clearer insights about ways to improve performance.

A “Path Forward”

Dynamic Thinking, by focusing attention on historical trajectories, encourages you to look at underlying systemic relationships, and provides a first clue as to the nature of these relationships. This skill also guides attention to the shape and timing of the “path forward,” stimulating you to think about the many possible problems that may befall any change effort. By using Reference Behavior Pattern graphs, you can hone your Dynamic Thinking skills to a fine point. The new perspective that results from this kind of thinking can then help you develop high-leverage improvement initiatives.

Barry Richmond is the managing director and founder of High Performance Systems. Inc. He has a PhD In system dynamics from the MIT Sloan School of Management, an MS from Case Western Reserve, and an MBA from Columbia University.

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