This volume explores these questions and introduces the principles and practice of a quietly growing field: systems thinking. With roots in disciplines as varied as biology, cybernetics, and ecology, systems thinking provides a way of looking at how the world works that differs markedly from the traditional reductionistic, analytic view. Why is a systemic perspective an important complement to analytic thinking? One reason is that understanding how systems work – and how we play a role in them – lets us function more effectively and proactively within them. The more we understand systemic behavior, the more we can anticipate that behavior and work with systems (rather than being controlled by them) to shape the quality of our lives.

It’s been said that systems thinking is one of the key management competencies for the 21st century. As our world becomes ever more tightly interwoven globally and as the pace of change continues to increase, we will all need to become increasingly “system-wise.” This volume gives you the language and tools you need to start applying systems thinking principles and practices in your own organization.

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In the Knowledge Era, products and services will be composed of more intellect and less labor, material, and capital. The primary source of wealth creation will be the human imagination, something that cannot be managed using traditional methods. Thus, organizations will require a different set of operating assumptions for managing and monitoring their operations. Companies that create infrastructures to promote innovation and continual learning will flourish; those that do not respond effectively may find themselves struggling for survival.

Historical Perspective

From a historical perspective, industrialized Western societies have moved through two major eras that set the stage for the Knowledge Era (see “The Shifting Economic Base”). In the Agricultural Era, the main engines of wealth creation were land and labor. Organizations were primarily concerned with the production, movement, and storage of agricultural products within rural communities. Organizational structures were relatively simple, and land and labor were the key “raw materials.”

In the Industrial Era, labor continued to be important, but land’s contribution to wealth creation was surpassed by a new source—capital. Capital was required to fund the large purchases of plant and equipment, as well as to invest in research and development. Hierarchical structures, financial markets, production techniques (such as mass production), and scientific management philosophies were created to maximize the return on invested capital.

In what Savage calls the Knowledge Era, the traditional engines of wealth creation will be eclipsed by the importance of intellect. The sheer force of knowledge and knowledge creation in all its forms—know-how, know-who, know-what, know-why, know-where, and know-when—will dominate all other means for creating wealth. The shifts in managing for knowledge creation will be as profound as those experienced in the Industrial Era. In fact, the term “managing” may not even apply as companies experiment with new approaches for unleashing the potential and creative capability of all members of organization.

While the full impact of the Knowledge Era has yet to be seen, the shift has already begun. Consider these recent illustrations of how knowledge is becoming a basis for creating competitive advantage:

• Ryder Systems is considering selling its core business of truck leasing to concentrate on its knowledge-based logistics services.
• Software accounts for one-third of the total cost in a Minolta camera. This knowledge-based component makes the product easier to use and more valuable to the consumer.
• Microsoft has declared that its factories are “the human imagination.” Its stock sells at significantly over book value because of the market perception of its future earnings capability—its ability to innovate.
• IBM’s $3.5 billion purchase of Lotus Development included a substantial premium over book value owing to IBM’s belief in Lotus’ ability to continue to innovate and create exciting technology and groupware solutions.

As we enter the knowledge economy, intellectual components will be integrated into more products and services. This will require businesses to fundamentally rethink their past assumptions about management, and to build infrastructure geared toward capitalizing on the collective knowledge and learning capability of all members of the organization.

Managing the Learning Enterprise

What do we mean by “knowledge”? And how can companies create explicit processes for increasing knowledge and learning throughout the company? We define knowledge as the collective experience of the organization, which consists of the interaction between two primary elements: tacit and explicit knowledge.

Tacit knowledge includes things the organization knows, as well as those things it knows how to do but cannot express and codify. For example, one of the highest paid positions in a Dominos Pizza shop is the dough kneader. Attempts to proceduralize or codify how these “kneaders” perform their job has proved fruitless for Dominos. Therefore, the organization has instead focused on training new workers through apprenticeship programs, as well as retaining experienced “kneader” through higher salaries.

Explicit knowledge consists of the valuable information that can be expressed, communicated verbally, or codified through organizational artifacts such as knowledge repositories, policy manuals, user guides, visioning documents, etc. The process of learning through experience increases tacit knowledge, while codifying these experiences increases explicit knowledge. If we look at knowledge management from a structural perspective, the “learning rate” (of both tacit and explicit knowledge) is the inflow that increases overall organizational knowledge (see “Knowledge: A Structural View”). “Knowledge decay” (in the form of technology obsolescence or innovations that outdate the organization’s work) and “knowledge loss” (in the form of people leaving the organization or moving to other areas in the firm) are outflows that decrease the overall level of knowledge. Maximizing organizational knowledge, therefore, involves designing processes that in-crease the rates of learning and codification, and finding ways to decrease knowledge decay and loss.

But how can organizations design explicit knowledge-management processes? We have identified four specific areas of activity that can aid in this process:

1. Clearly articulate the purpose of creating organizational knowledge and how knowledge fits into the company’s overall business strategy.

2. Develop explicit knowledge and learning strategies that will enable the company to achieve its purpose.

3. Build organizational learning and knowledge-leveraging structures to implement the strategies.

4. Create feedback systems to measure the successes and shortcomings of the efforts, and provide data for continually modifying the strategies.

Together, these four elements make up an overall learning process—one that must be deliberately crafted with the same vigor as any key management strategy. Let’s briefly explore what this process looks like in more detail.

1. Clearly articulate the purpose of creating organizational knowledge.

In his book The Knowledge-Creating Company, Ikujiro Nonaka describes the Japanese view that “a company is not a machine but a living organism, and much like an individual, it can have a collective sense of identity and fundamental purpose. This is the organizational equivalent of self-knowledge—a shared understanding of what the company stands for, where it is going, and what world it wants to live in, and, most importantly, how it intends to make the world a reality.”

Without a clear understanding of how creating structures to improve learning and knowledge creation will provide value to customers and stake-holders, an organization’s efforts will be diffused and ineffective. A sense of a larger purpose creates the underlying impetus that will drive and sustain the process.

Developing this deep sense of purpose involves continuous conversations within the organization about what we want to achieve. It begins by creating a space in which these conversations can take place, which can continue throughout the next steps of the process. This awareness of the organization’s larger purpose, coupled with a clear understanding of current reality, provides the overall context for organizational learning.

2. Develop explicit knowledge and learning strategies.

Exploring the larger purpose of learning activities explains why an organization should develop a learning orientation; creating learning strategies focuses on how the organization will achieve its learning objectives. A company’s unique learning strategy will depend on the organization’s purpose and overall business strategy. A company that is committed to being “first to market” in order to generate high-margin sales from early adopters will have a dramatically different knowledge strategy than an organization that thrives on producing low-cost, mass-produced imitations.

Several frameworks exist for helping companies develop knowledge strategies. One such framework is the Organizational Learning Inventory (OLI), developed by Edwin Nevis, Anthony DiBella, and Janet Gould, who are affiliated with the MIT Center for Organizational Learning. The OLI was created through detailed studies of several companies in the U.S., Europe, and Asia to identify specific actions that promote organizational learning.

Knowledge: A Structural View

From a structural perspective, maximizing organizational knowledge Involves designing processes that Increase the learning rate, and finding ways to decrease knowledge decay and loss.

The cornerstone of the OLI is a set of 10 Facilitating Factors and seven Learning Orientations. Facilitating Factors are those activities or attitudes (such as environmental scanning and an experimental mindset) that promote or inhibit learning, while Learning Orientations describe stylistic differences in the ways companies approach learning (such as focusing on breakthrough thinking versus incremental improvements). By using the OLI to identify their overall learning system, companies can develop ways to manage their learning processes more explicitly.

Another framework is the Knowledge Management Assessment Tool (KMAT), created by Arthur Andersen and the American Productivity Quality Center. The KMAT was developed with input from 21 companies and has been used by more than 100 additional companies. It is used to help managers identify knowledge-management areas in their organization that require greater attention, as well as those knowledge-management practices in which the company excels (see “The KMAT Method”).

While both the KMAT and OLI can facilitate the process of designing a learning strategy, they approach it in different ways. The KMAT was originally developed as an external benchmarking tool. It allows participants to rate their performance on 24 emerging knowledge-management practices, as well as determine the importance of these practices to their organization. The OLI, on the other hand, looks at internal capabilities. It is designed to assess an organizational unit as a learning system—identifying the unique strengths and learning style of the work unit, and developing an organizational learning plan that capitalizes on those abilities. The OLI looks at the cultural side of organizational learning, and can be used very effectively as a framework for change management.

Both the OLI and the KMAT provide a structured process for clarifying an organization’s learning strategy, which will ultimately evolve beyond the confines of the tools themselves.

3. Build organizational learning and knowledge-leveraging structures.

Once the company has articulated its knowledge strategy, it can create action plans to close the gaps between the organization’s knowledge vision and its current performance. These action steps often involve building structures to promote effective knowledge creation and application.

For example, a successful oil and gas company was developing a new strategy to drive its future growth efforts, and the management team wanted to understand the systemic relationships between the different parts of the plan. One of the primary elements of the strategy was a shift in focus from growing through acquisitions to investing in exploration and production. However, they also believed that they needed to be the low-cost producer, and that they could only achieve this if they significantly reduced general and administrative expenses.

When they used causal loop diagrams to map the interrelationships among various elements of their strategy, they found that these two objectives were in conflict. In order to expand their exploration and production activities, they would have to increase the number of geologists and related staff to perform the increased work volume. But these salaries were considered part of general and administrative costs, and the pressure to become a low-cost producer would probably necessitate minimizing staff headcounts.

This conflict was resolved when the management team examined industry benchmarking data and found that they already had the lowest general and administrative costs in their peer group. Their margin was large enough that they could increase the staff headcount and still have excellent cost performance. The causal loop diagrams were then used to develop a simulation model that the management team used throughout the organization to create a greater understanding of how the shift in strategy would support the company’s long-term objectives.

Another client created a deliberate communication structure to provide a framework for validating and modifying key assumptions about the business. A cell structure was created for this 700-person organization, consisting of 50 cells of 10-20 employees each. Members of each cell came together in a series of workshops designed to allow them to share their insights about the organization and to learn from one another. The OLI was used to focus the process and engage employees in a meaningful conversation about the company’s particular learning style. Through this process, people found ways to capitalize on the firm’s learning strengths, and to leverage these capabilities throughout the organization.

A third company decided to begin its organizational learning strategy by introducing its employees to the basic concepts of organizational learning and leadership. As a symbol of its commitment to organizational learning, the company created a learning center.

The goal of the center’s staff is to expose the entire management team—approximately 2,000 people—to the disciplines of personal mastery, mental models, team learning, systems thinking, and shared vision. The concepts out-lined in The Fifth Discipline are providing the basic framework for this work. By raising the awareness of the theory and tools of organizational learning through-out the company, management hopes to leverage its core competencies into knowledge-based products and services that are valued by customers.

4. Create feedback systems to monitor progress.

Ongoing feedback on how well the organization is creating and implementing learning structures is critical to the successful implementation of any knowledge strategy. Key questions include:

• Did we get what we expected from building our infrastructure?
• Did we close the gaps identified in our strategy articulation process?
• What new elements do we need to consider in our strategy?
• How are we contributing to our inability to close the gaps identified?

Continual evaluation is crucial for keeping an organization’s learning process relevant in a changing business environment. Action strategies become less useful over time and must be continually revised and adjusted to meet emerging marker challenges.

Moving Forward

As we move into the turbulent and unpredictable Knowledge Era, we will need to engage in new and more meaningful ways in order to become effective at creating the results that we truly desire. Unraveling our old assumptions will take time, patience, and clear direction from every member of the organization. By reexamining our fundamental assumptions about learning and working together, we can begin to create deliberate and thoughtful structures for building upon the knowledge generated at all levels of the organization.

Rian M. Gorey and David R. Dobat are senior managers at Arthur Andersen Business Consulting, and are responsible for the Identification design, and delivery of Arthur Andersen’s Knowledge Services.

Editorial support for this article was provided by Colleen Lannon. Additional contributions were made by Tom Eisenbrook, Sam Israelit, and Lisa Kelley of Arthur Andersen.

Further reading:

Charles Savage, Fifth Generation Management (Boston: Butterworth-Heinemann) 1996.
Ikujro Nonaka and Hirotaka Takeuchi, The Knowledge-Creating Company (New York: Oxford University Press) 1995.

The Coming of the Knowledge-Based Business.” Stan Davis and Jim Botkin, Harvard Business Review, September/October 1994. Notes:

The discussion of tacit and explicit knowledge is based on Nonaka and Takeuchi, The Knowledge-Creating Company.

For more on the Organizational Learning Inventory tool, see “Charting a Corporate Learning Strategy” by Marilyn Darling and Gregory Hennessy (The Systems Thinker, December 1995/January 1996) and “Organizations as Learning Systems” by Janet Gould. Tony DiBella, and Ed Nevis (The Systems Thinker. October 1993).

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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.

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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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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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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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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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The answer is usually unspoken but real. Coaching is something I do to improve another person or team; it’s part of my job. Learning happens to me; it makes me feel vulnerable. Learning focuses on my weaknesses, pressuring me to change the way I think and behave. Besides, I’m a professional, with established competencies and knowledge. I’m paid to get results, not to learn.

Thus, managers’ most common response to the growing demand for corporations to become learning organizations is to scramble to be the teacher, not the taught—the coach, not the coached. But, to be an effective coach, an individual must understand the nature of learning. And to understand learning, a coach must be actively engaged in the learning process and personally familiar with the kinds of vulnerabilities and obstacles a learner experiences.

Developing Learning Capability

Learning, coaching, and building a learning culture are critical to the success of modern businesses. Because learning increases our ability to perform, the capacity to grow capability is becoming indistinguishable from the capacity to grow wealth. However, unacknowledged resistance to learning and coaching can make it difficult for us to realize the ideals of the learning organization.

As children, we were naturally engaged in learning in everything we did. Thus, as adults, we don’t really need to learn how to learn, as much as we need to remember what we once knew. We need to unlearn some of the attitudes and practices we picked up from our formal education that seriously undermine our natural appetite and inherent capability for learning.

The Inner Game approach (see “The Inner Game” on p. 2) is about unlearning the personal and cultural habits that interfere with our ability to learn and perform. The goal is simple, if not easy: to give ourselves and our team’s greater access to our innate abilities. The approach can be summarized in a simple formula:

Performance = Potential – Interference

“Potential” includes all of our capabilities—actualized or latent—as well as our ability to learn; “Interference” represents the ways that we undermine the fulfillment or expression of our own capacities.

Diminishing the Obstacles to Learning

We can achieve increased capacity for performance and learning either by actualizing potential or by decreasing interference—or by a combination of both. In my experience, the natural learning process—which is how we actualize potential—is gradual and ongoing. By contrast, reducing interference can have an immediate and far-reaching impact on learning and levels of performance. Thus, a successful model for skill development must take into account the phenomenon of interference.

But beware: The barriers to learning are often well guarded and may become even more entrenched when challenged. Coaches must generally be gentle in their approach to surfacing interference to learning and performance in an individual or team. Hints, suggestions, and indirect probing, though they may seem to take longer than a more direct approach, are usually more successful over the long run.

I learned a great deal about interference and how to help people work through it while coaching tennis and golf—two sports in which the obstacles to performance are difficult to disguise. And I have continued to find these sports excellent examples for exposing hidden obstacles to learning and performance. In addition, tennis and golf show the kinds of results that can occur when one succeeds in diminishing the impact of interference.

One of my favorite examples is what I call “the uh-oh experience.” A tennis ball is coming toward a player who thinks she has a weak backhand. As the ball approaches, she thinks.

“Here comes a probable mistake.” She tightens her muscles, steps back defensively as if to avoid the threat, then slashes jerkily at the ball. When this action results in either an error or an easy shot for the opponent, she confirms to herself, “I really do have a terrible backhand,” and unwittingly sets herself up for the same results on the next similar shot.

If a coach tried to correct each of the elements of the player’s stroke that were incorrect, it would take months of “learning.” However, if the coach worked at eliminating the player’s negative self-talk by focusing her attention instead on perceiving the details of the ball’s trajectory, most of the positive behavioral changes would take place without conscious effort. Working at changing a player’s perception instead of his or her behavior saves time and frustration for both student and coach.

Below is a partial list of obstacles to growing capability:

• The assumption that “I already know.”Professionals often feel that they must present the appearance of already knowing everything and already being perfectly competent. This is an obstacle to learning that young children do not share.
• The assumption that learning means remediation. For many people, the suggestion that they should learn means there is something wrong with them or their level of performance.
• Fear of being judged. We learn this early, through teachers and parents who used judgment as a means to control behavior and effort.
• Doubt. The uncertainty we feel when we face the unknown is a prerequisite for learning. Young children are not embarrassed by not knowing something. However, as we age, we are taught to feel stupid or incompetent if we lack knowledge or experience or are unable to perform up to expectations. We are especially vulnerable to this feeling when faced with the challenge of unlearning something. The prospect of acknowledging that we might have invested time and effort in a perspective that is no longer valid can seem especially threatening.
• Trying too hard to learn and to appear learned. This phenomenon is a derivative of fear and doubt, and leads to constricted potential and mistakes. Our errors then confirm ours self-doubt and bring about the very outcome that we feared.

Revealing the barriers to learning and performance can be an important first step in maximizing an individual’s or a team’s potential. To find the greatest leverage for reducing obstacles to learning in the workplace, I believe we should start with our definition of work itself. The way we see “work” has an impact on how we perceive everything we do in the workplace.

What Is Work?

If you ask executives the meaning of the word work, they focus on work as doing something—as accomplishing a goal, such as providing a product or service. In other words, to many people, work means performance. But definitions that equate work with performance can be limiting, especially in the current business environment.

Are there other results of work? When I ask executives this question, they generally offer responses that refer to two other distinct aspects of work. One is the domain of experience: How you feel while working is also a result of work. While working, people feel satisfaction, meaning, accomplishment, and challenge, as well as frustration, stress, anxiety, and boredom. Everyone at work experiences feelings that range from misery to fulfillment.

A second set of answers fall into the category of learning: While working, you can grow, develop know-how and skills, and improve your ability to communicate, plan, and strategize. Like performance and experience, learning is a universal and fundamentally human result of work—people of all ages, cultures, and levels of expertise are either learning and growing or stagnating and “devolving” while working. Adults can learn while working, just as children learn naturally while playing.

The Work Triangle

How are these fundamental results of work—performance, experience, and learning—related? They are unquestionably interdependent. If individuals aren’t learning, their performance will decline over time; if their predominant experience of work is boredom or stress, both learning and performance will suffer. These three results can be represented in a mutually supportive “Work Triangle,” with performance at the apex, and experience and learning at the base angles (see “The Work Triangle” on p. 3).

When I ask a group of executives, “Which of the three work results gains the greatest support and encouragement in your work environment?” their response is overwhelmingly, “Performance.” I then place my marking pen at the center of the Work Triangle and slowly draw a line toward the performance apex. “How much more priority is performance given over learning and enjoyment?” I ask. As the pen reaches the top of the triangle, a voice usually says, “Stop there.” In response, the majority chants, “Keep going,” until the line has gone past the apex and is several inches outside the triangle. There is a general chuckle and a sense of a common understanding of corporate priorities.

In the competitive world of business, it is easy to see why performance may be given priority over learning and experience. But what are the consequences of pursuing performance at the expense of learning and experience? In any but the shortest timeframe, the consequences are dire: performance itself will fall. And what will be management’s typical response? More pressure on performance, resulting in even less time and fewer resources directed toward learning or quality of experience.

How does the emphasis on performance play out in practice? Take your average sales manager who meets weekly with his sales representatives. The conversation usually focuses on performance issues, such as, how many calls did you make? What were the results of those calls in terms of sales? What are your plans for next week?

But what if the manager were committed to his own learning, as well as to his team’s development? He might also ask: What did you find out from customers that you didn’t know before—about their resistances, their needs, their perception of our products, how we compare to our competitors? How are different customers responding to our latest promotion? Did you gain any insights into your own selling skills? What is the competition doing? What are you interested in finding out next week? Did you learn anything that might help others on the team?

Our definition of work should include the worker’s experience and learning, as well as his or her performance. The real value of this redefinition of work is that it includes me as an individual. I directly and immediately benefit from the learning and experience components of the Work Triangle. The “Experience” side of the triangle reminds me that I can’t afford to neglect personal fulfillment during my working hours in the hope of enjoying myself only during vacation time or on weekends. I can never replace the hours of my life I spend at work, so I need to make the most of them.

The “Learning” side of the triangle reminds me that my future work prospects depend on the growth in in my capabilities. Even if I’m fired from my present job, I take with me what I have learned, which I can leverage into productive and valued performance elsewhere. When my customers, managers, teammates, and the surrounding culture pressure me for performance results, the Work Triangle helps me remember that the person producing those results is important, too. I neglect my own learning and quality of experience at great peril to myself as well as to my future levels of performance.

The Tunnel Vision of Performance Momentum

The definition of work that focuses strictly on performance results at the expense of learning and experience produces a kind of tunnel vision that prevents workers from being fully aware and focused. I call this state of unconsciousness “performance momentum.” At its worst, performance momentum is a series of actions an individual performs without true consciousness of how they relate to his or her most important priorities. Some call this mode of operation “fire-fighting.” Examples include getting so caught up in a game of tennis that you forget it is a game, or engaging in conversations that undermine a relationship for the sake of merely winning an argument. In short, performance momentum means getting caught up in an action to the extent that you forget the purpose of the action.

I don’t know of a more fundamental problem facing workers today. When individuals are caught up in performance momentum, they tend to forget not only important performance goals, but also their fundamental purpose as human beings. For example, my need to finish an article by the requested deadline obscures the reasons I chose to write the article in the first place, and dampens the natural enjoyment of expressing my thoughts and convictions. The person caught up in performance momentum neglects learning, growth, and the inherent quality of the work experience.

THE WORK TRIANGLE

The fundamental results of work—performance, experience, and learning—are interdependent. If individuals aren’t learning, their performance will decline over time; if their predominant experience of work is boredom or stress, both learning and performance will suffer.

The tunnel vision that results from performance momentum is difficult to escape when individuals are working in a team that confirms and enforces the focus on performance. Any activity that is not seen as driving directly toward the goal is viewed as suspect. However, when a team or individual sacrifices the learning and experience sides of the Work Triangle to performance momentum, long-term performance suffers. More important, however, the individual suffers. And because the individual constitutes the building block of the team, the team suffers as well.

Balancing the Work Triangle

A simple method for assessing the balance among the three elements in the Work Triangle is to evaluate the way an individual or team articulates performance goals in comparison with learning and experience goals. It is revealing that many employees, when asked about learning or experience goals, are vague and express less conviction than when discussing performance goals. Setting clear learning goals is a good way to begin rebalancing the Work Triangle.

However, the distinction between learning and performance is often blurred. Even individuals who have worked on plans for the development of their competencies often fall into the trap of expressing their learning goals in terms of performance; for example, “I want to learn to focus more on the customer”; “I want to learn to reach higher sales quotas”; and“ I’m working on learning how to get a promotion. ”The general rule for distinguishing between learning and performance goals is that learning can be viewed as a change that takes place within an individual, while performance takes place on the outside. Learning is an increased capacity to perform; performance is the evidence that the capacity exists.

A good way to focus on learning goals is through the acronym QUEST.

Q—qualities or attributes you might want to develop in yourself or others

U—increased understanding of the components of any person, situation, or system

E—development of expertise, knowledge, or skills

S—capacity for strategic, or systemic, thinking

T—capacity to optimize what you do with time

Teams and individuals can use QUEST to help form goals regarding what capabilities they want to develop. To be most effective, these objectives should support immediate performance goals but at the same time apply to many future performance challenges.

Coaching: A Conversation That Promotes Learning

When executives list the qualities, skills, and expertise they want from employees, they often list intangible attributes, such as creativity, accountability, sense of humor, team player, problem solver, and so on. So, how can you get the qualities and capabilities you want from people? The first response to this question is usually, “We have to do a better job in hiring.” Clearly, it is important to hire capable people. But the real question is how to build the capabilities in the people you have hired, and how to keep those qualities from diminishing.

Unfortunately, the tools of managing performance are not particularly useful for promoting or developing important qualities and core skills. And it is difficult to imagine a course that teaches the rudiments of initiative or cooperation. So what is left? The word I use for the capacity to promote such desired attributes is coaching.

Coaching is a way of being, listening, asking, and speaking that draws out and augments characteristics and potential that are already present in a person. An effective coaching relationship creates a safe and challenging environment in which learning can take place. Coaches know that an oak tree already exists within an acorn. They have seen the one grow into the other, over time and under the right conditions, and are committed to providing those conditions to the best of their abilities. Successful coaches continually learn how best to “farm” the potential they are given to nurture.

A primary role of the coach is to stop performance momentum by calling a time out and providing questions or perspective that can encourage learning. Actual learning happens through experience—taking actions, observing the results, and modifying subsequent actions. To turn a work experience into a learning experience, a particular mindset must be established beforehand. Establishing this perspective can be done through something I call a “set-up conversation,” which an individual can conduct alone through self-talk or with a coach. The set-up conversation helps make the learner aware of the possibilities that the imminent work experience could yield. In conducting one of these conversations, the coach asks questions that aid in focusing the individual’s or team’s attention.

At the end of a work experience, the coach and individual can hold a “debrief conversation.” During this interchange, they might “mine” the gold of what was learned and refine questions to take into the next work experience. In this way, experience itself becomes the teacher. The coach’s role becomes helping the learner as valuable questions of the “teacher” and interpret the answers.

Coaching is very different from what we are generally taught as managers or teachers. We cannot teach work teams and individuals how to grow capabilities—in the sense of the transference of information in a class-room environment. Nor can we build capabilities through managerial techniques—for example, requiring certain abilities and rewarding employees when they display them or punishing them when they don’t. Neither can we measure learning, because we can’t directly observe it. In sum, it is the learner alone who controls the process and perceives its benefits. Managers don’t even need to reward employees for learning—if learning indeed takes place, it will lead to improved performance. And employers generally award bonuses, raises, and promotions based on an increase in a worker’s performance results.

Employees and managers cannot afford to wait for their corporate cultures to become learning cultures. Workers benefit from an expanded definition of work that includes learning and experience goals, and therefore must make the commitment to achieve those objectives. But companies also benefit from this new perspective on work. Wise are the corporate leaders who recognize that redefining work in this way is a difficult task, but that the company and its shareholders also gain advantages from a balanced Work Triangle. The best managers will provide what support and resources they can to the effort, and will make it their mission to shape their workplace into an optimal learning environment. The payoff will be improved business results and a corporate culture that attracts employees who equally value growth in capabilities.

Tim Gallwey is credited with founding the field of sports psychology. His four best-selling books on The Inner Game have deeply influenced the worlds of business and sports. For the last 15years, Tim has spent most of his time working with companies that want to find a better way to implement change. This article is based on a working progress called The Inner Game of Work, to be published in 1998 by Random House.

Editorial support for this article was provided by Janice Molloy.

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To explore why improvement efforts so often fail to produce lasting results, my colleague Nelson Repenning and I, along with other members of the MIT System Dynamics Group, have studied a variety of improvement initiatives in large organizations. We have worked in close partnership with the managers from these firms and gratefully acknowledge their help. And what we’ve found is that many improvement efforts faker because managers mistakenly attribute the source of problems to the people in the system, not to the processes. Over time, managers receive feedback that seems to confirm their initial beliefs. We refer to this process as “superstitious learning,” because people develop strong, but false and often harmful, beliefs.

For example, the electronics division of a major global manufacturing firm carried out two initiatives that led to markedly different results. In the first, management was able to break free from the cycle of superstitious learning to achieve significant, lasting results. In the second, superstitious learning became increasingly entrenched, and the program fell far short of its potential. These examples can serve as lessons for all organizations as we strive for lasting improvement.

The MCT Initiative

The Manufacturing Cycle Time (MCT) initiative focused on the problem of long cycle times in the manufacturing process. A new general manufacturing manager reoriented the focus from labor and machine utilization to cycle time and value-added percentage (the fraction of the cycle time in which a feature or function is added to a part). He didn’t tell people what to do. Instead, he began by visiting plants. He would rummage through the bins of parts, pick one up, and ask why it had been in inventory for so long. And people were motivated to experiment with ways to measure and reduce cycle times.

The initiative was extremely successful. Over several years, the division reduced average cycle time from more than 15 days to less than a day. The quality of finished products improved, and manufacturing became more flexible. Revenue, profit, and cash flow all increased significantly. The huge reduction of work-in-process (WIP) inventory freed up so much floor space that the company was able to cancel the construction of two planned new plants, with additional savings of hundreds of millions of dollars.

The PDP Initiative

A second project, the Product Development Process (PDP) initiative, was launched in part because of the success of MCT. PDP was designed to improve the productivity of the development process. Implementation differed from the MCT effort; for PDP, a dedicated design team worked two years to benchmark best-in-class electronics producers and capture best practices within the organization.

The team produced a detailed, well-documented development process that focused on getting the engineers to follow procedure and use project management tools. In addition, the design team created the “Bookshelf,” a library of documented designs to enhance modularity and increase learning. A third concept was the “Wall of Innovation.” Engineers were encouraged to experiment with new technologies, but after a certain point in the design process—the Wall of Innovation—an engineer could go forward only with technologies that had been proven and posted to the Bookshelf.

Overall, PDP produced mixed results. The company estimates that it achieved about 80 percent of its goal of switching from traditional design tools to CAD/CAE/CAM tools. But the initiative was only 20 percent successful on its other objectives: the use of project management tools, the Bookshelf, and the Wall of Innovation.

Reducing Process Problems

These two examples present a paradox. The MCT effort was launched by a lower-level executive with almost no budget, a four-person staff, and no significant benchmarking. Yet it was highly successful. In the case of the PDP project, top management supported the effort, a cross-functional team designed the new processes, there was extensive benchmarking, and the design main had plenty of resources for training and internal marketing. Yet the initiative didn’t produce lasting results.

To understand this paradox and, more generally, why so many improvement programs don’t work, we developed a set of system dynamics models. The models can be tailored to specific settings such as manufacturing or product development.

A key concern in any process is the adequacy of net throughput, that is, the number of usable widgets, whether circuit boards or engineering drawings, that are produced by the process relative to the rate required (see “The Stocks and Flows of Defects and Process Problems” on p. 3). In general, when faced with a shortage of usable widgets, workers and managers have only two options: work harder or work smarter. Working harder involves expanding capacity, utilizing existing capacity more intensely, or reworking defective output. Each of these options boosts throughput, but only at a significant and recurring cost. The fundamental insight of the quality movement is that it is more effective to work smarter by eliminating the process problems that generate defects. Eliminating process problems, however, requires managers to train workers in improvement techniques and to provide them with time off from their normal duties. Most important, managers must give workers the freedom to experiment with new solutions and new ideas, knowing that many of these won’t work (see “Work Harder or Work Smarter?” on p. 3).

What Prevents Us from Working Smarter?

Clearly, the high leverage point for improvement is reducing process problems by working smarter—not working harder to compensate for low productivity. Eliminating process problems decreases the generation of new defects once and for all and frees up resources for investment in learning, creating a virtuous cycle of continuous improvement.

But even when we understand the leverage in working smarter, we seldom do it. There are several reasons. First, defects are easier to see and more tangible than process problems. In a manufacturing setting, for example, the defective products sit in full view on the factory floor. In contrast, process problems are often invisible. They exist in relationships, interactions, and activities.

Second, process improvement takes time, whereas defects are usually easily identified and can be quickly repaired or remade. Process improvement is also less certain than fixing defects. It requires a degree of faith. A competence in improvement has to be “grown” organically through improvisation and experimentation, but many people arc averse to uncertainty and ambiguity. As a result, we are tempted to work around the process problems and just fix the defects.

Finally our companies are conditioned to reward the heroes who put out the fires, not the folks who keep a crisis from occurring in the first place. As one interviewee in our study said, “Nobody ever gets credit for fixing problems that never happened.”

Biased Attributions

But there’s another, more insidious reason that we’re often unable to work smarter even when we know it’s the right thing to do. Suppose you’re a manager faced with inadequate throughput. You can solve the problem by having people work harder or work smarter. To decide how to proceed, you have to make a judgment about the cause of the low throughput. If you believe the system is generating defects because of process problems, then you’ll likely focus on working smarter. On the other hand, if you think that your workers are lazy or undisciplined, you’ll push them to work harder.

How do these judgments play out in the improvement setting? We see an operator at a machine, he or she performs an operation, and a defective widget pops out. Was the process or the operator at fault? Process problems are usually distant in space and time from the defects they create. They’re often not obvious. And the delay between the process problem and defect detection is long, variable, and often unobservable. Thus, it’s easy to conclude that the cause of low throughput is inadequate worker effort or insufficient discipline, rather than process problems. This attribution of a problem to the individuals in the system rather than to the system itself is so frequent and pervasive that psychologists have come to call it the “fundamental attribution error.”

Based on this misattribution, managers commonly increase production pressure to get more out of their workers. But while production pressure boosts throughput in the short run, it also has an undesirable long-term side effect. Workers under greater pressure to boost production have less time to spend on improvement and learning. They are less willing to conduct experiments that might temporarily reduce throughput. With less effort dedicated to improvement, fewer process problems are corrected. Defects increase. Throughput falls, and managers are forced to increase production pressure even more, creating a vicious cycle.

The Stocks and Flows of Defects and Process Problems

Net throughput of any process can be increased either by boosting gross throughput or by reducing the rate of defect generation. Defects arise from the stock of underlying process problems.

Work Harder or Work Smarter?

Managers have two options for increasing net throughput to meet the goal. They can Work Harder (B1) by adding capacity, increasing the work week pushing the work force to work harder, and so on. The other option is Work Smarter (B2) by training employees in improvement techniques and undertaking experimentation.

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These activities perhaps contain more optimism than realism. Learning is, at its heart, a complex and difficult process—a source of joy when it works, but a source of pain and tension when it does not. Learning forces us to fundamentally rethink the way we view the world—a process that is difficult in part because our cultural assumptions predispose us to take certain things for granted, rather than to re-examine them continually. Since learning and culture are so closely interrelated, it is incumbent upon us to understand more about the interaction of the two, and to identify, if possible, what elements of a culture might truly facilitate learning to learn.

Two Kinds of Learning

To understand the important role that organizational culture plays in learning, we need to first make a distinction between two types of learning: “adaptive” and “generative.” (The term “generative learning” comes from Peter Senge. However, the same process has been labeled by Chris Argyris and Donald Schön as “double-loop learning,” while Don Michael, Gregory Bateson, and others have identified it as “learning how to learn.”)

Adaptive learning is usually a fairly straightforward process. We identify a problem or a gap between where we are and where we want to be, and we set about to solve the problem and close the gap. Generative learning, on the other hand, comes into play when we discover that the identification of the problem or gap itself is contingent on learning new ways of perceiving and thinking about our problems (i.e., rethinking cultural assumptions and norms).

For example, from an adaptive point of view, we may decide that we have to replace corporate hierarchies with flat networks in order to reduce costs and increase coordination. From a generative point of view, however, we might instead begin by examining our mental models and considering how hierarchies and networks might be integrated into a more effective corporate design. From “either this or that” thinking, we might have to develop the capacity to think about “this and that.”

Two Kinds of Anxiety

The very process of identifying problems, seeing new possibilities, and changing the routines by which we adapt or cope requires rethinking and redesign, because we have to unlearn some things before new things can be learned. Thus, generative learning, by its very nature, asks us to question our mental models, our personal ways of thinking and acting, and our relationships with each other. This deep level of change can produce two kinds of anxiety.

The first is the fear of something new. Instability or unpredictability is uncomfortable and arouses anxiety— what I have called “Change Anxiety,” or the fear of changing—based on a fear of the unknown. Adaptive learning, whether it be in individuals, groups, or organizations, tends toward stability. We seek to institutionalize those things that work. Indeed, it is the stable routines and habits of thought and perception that we call “culture.” We seek novelty only when most of our surroundings are stable and under control.

However, learning how to learn may require deliberately seeking out unstable, less predictable, and possibly less meaningful situations. It may also require perpetual learning, which opens up the possibility of being continually subject to Change Anxiety. This is a situation most of us would prefer to avoid.

But if, as many people anticipate, the economic, political, technological, and sociocultural global environment will itself become more turbulent and unpredictable, then new problems will constantly emerge and past solutions will constantly become inadequate. This brings us to a second type of anxiety, which I call “Survival Anxiety”—the uncomfortable realization that in order to survive and thrive, we must change.

In order for learning to occur, somehow we must reach a psychological point where the fear of not learning (Survival Anxiety) is greater than the fear associated with entering the unknown and unpredictable (Change Anxiety).

As teachers, coaches, and managers, how then do we make sure that Survival Anxiety is greater than Change Anxiety? One method is to increase Survival Anxiety until the fear of not changing is so great that it overwhelms the fear of changing. We can do this by threatening the learner in various ways, or by providing strong incentives for learning. For example, if employees feel that they will not get promoted in the organization if they don’t use electronic mail or conduct their meetings with the latest groupware, it would seem logical that they would want to keep up with new technology.

However, humans don’t always do what logic dictates. If an employee’s Change Anxiety becomes too high, he or she may instead become defensive, misperceive the situation, deny reality, or rationalize his or her current behavior. Change agents often come up against this type of resistance to organizational change and retreat to the rationalization that “it’s simply human to resist change.”

Perhaps a more effective way to initiate change is to reduce Change Anxiety so that it is less than Survival Anxiety. We can do this by concentrating on making the learner feel more comfortable about the learning process, about trying new things, and about entering the perpetual unknown (see “Reducing Change Anxiety”).

Addressing the anxiety caused by learning and change is certainly a good way to begin the learning process, at least at the individual and small group levels. But how can we apply the generative learning process across various organizational boundaries and sustain the learning process over longer periods of time? This requires the creation of an organizational culture that supports perpetual learning at the individual, group, and organizational levels.

A Learning Culture

What would such a culture look like? Learning cultures share at least even basic elements:

1. A concern for people, which takes the form of an equal concern for all of the company’s stakeholders—customers, employees, suppliers, the community, and stockholders. No one group dominates management’s thinking because it is recognized that any one group can slow down or destroy the organization.
2. A belief that people can and will learn. It takes a certain amount of idealism about human nature to create a learning culture.
3. A shared belief that people have the capacity to change their environment, and that they ultimately make their own fate. If we believe that the world around us cannot be changed, what is the point of learning to learn?
4. Some amount of slack time available for generative learning, and enough diversity in the people, groups, and subcultures to provide creative alternatives. “Lean and mean” is not a good prescription for organizational learning.
5. A shared commitment to open and extensive communication. This does not mean that all channels in a fully connected network must be used all the time, but it does mean that such channels must be available and the organization must have spent time developing a common vocabulary so that communication can occur.
6. A shared commitment to learning to think systemically in terms of multiple forces, events being over-determined, short- and long-term consequences, feedback loops, and other systemic phenomena. Linear cause-and-effect thinking will prevent accurate diagnosis and, therefore, undermine learning.
7. Interdependent coordination and cooperation. As interdependence increases, the need for teamwork increases. Therefore, organizations must share a belief that teams can and will be effective, and that individualistic competition is not the answer to all questions.

Inhibitors to Learning

Culture is about shared mental models—shared ways of perceiving the world, sorting out that information, reacting to it, and ultimately understanding it. Therefore, in order to understand what prevents us from creating learning cultures, we need to explore the shared assumptions that act as inhibitors to learning. If we look at western (particularly U.S.) organizational and managerial cultures, there are several shared assumptions or myths that prevent organizations from developing the kind of learning culture I have described.

Generative learning, by its very nature, asks us to question our mental models, our personal ways of thinking and acting, and our relationships with each other.

Human history has left us with a legacy of patriarchy and hierarchy, and a myth of the “superiority” of our leaders based on the view of the leader as warrior and protector. This has created almost a state of “arrested development” in our organizations, in the sense that we have very limited models of how humans can and should relate to each other in organizational settings. The traditional hierarchical model is virtually the only one we have.

One consequence of this rigid model is that managers start with a self-image of needing to be completely in control—decisive, certain, and dominant. Neither the leader nor the follower wants the leader to be uncertain, to admit to not knowing or not being in control, or to embrace error rather than to defensively deny it. Of course, in reality leaders know that they do not have all of the answers, but few are willing to admit it. And since subordinates demand a public sense of certainty from their leaders, they reinforce this facade. Yet if organizational learning is to occur, leaders themselves must become learners, and in that process, begin to acknowledge their own vulnerability and uncertainty.

In the U.S., we have the additional cultural myth of “rugged individualism” that makes the lone problem-solver the hero. The interdependent, cooperative team player is not typically viewed as a “hero.” In fact, competition between organizational members is viewed as natural and desirable, as a way to identify talent (“the cream will rise to the top”). After all, if teamwork were more natural, would it be such a popular topic in organization development literature? For the most part, teamwork is viewed as a practical necessity, not an intrinsically desirable condition.

Another myth that has developed among managerial circles might be called the “divine rights of managers.” Management is believed to have certain prerogatives and obligations that are intrinsic and are, in a sense, the reward for having worked oneself up into the management ranks. The relatively young and egalitarian social structure of the U.S. exacerbates this problem by emphasizing achievement over formal status. We have no clear class structure that provides people with a clear position in society. Hence, they often rely instead on earned position, title, and visible status symbols (cars, homes, etc.) as a way of displaying rank. The competition based work hierarchy then ultimately becomes the main source of security and status, and higher level managers are expected to act in a more decisive and controlling manner to express that status.

Another barrier to learning is the fact that work roles and tasks are very compartmentalized in the U.S., and are separated from family and self-development concerns. These roles are expected to be treated in an emotionally neutral and objective manner, which makes it very hard to examine the pros and cons of organizational practices that put more emphasis on relationships and feelings. Even talking about anxiety in the workplace is often taboo. This creates an inherent dilemma: how can we effectively address learning-produced anxiety if we cannot discuss it?

Within the work context we have the further problem that task issues are always given primacy over relationship issues. Everyone pays lip service to the notion that people and relationships are important, but our society’s basic assumptions are that the real work of managers lies with quantitative data, money, and bottom lines. Within this framework, people can seem like nothing more than another resource to be “deployed” or “controlled.” If we have any doubts about the reality of this viewpoint, consider how many performance appraisal systems tend to reduce performance to numbers rather than deal with qualitative descriptions of performance and leadership potential.

In reality, leaders know that they do not have all of the answers, but few are willing to admit it. And since subordinates demand a public sense of certainty from their leaders, they reinforce this facade.

The bias toward viewing organizations in quantitative terms shows up most clearly in graduate schools of business, where the popularity of quantitative courses in finance, marketing, and production is much greater than qualitative courses in leadership, group dynamics, or communication. Associated with this myth that management is only about “hard” things is the focus on short time horizons. Driven by our current reporting systems, managers learn early on to pay closer attention to the short-term trends in their financial numbers than to the long-term morale or development of their employees. Creating an environment for learning is a long-range task, yet few managers feel that they have the luxury to plan for people and learning processes.

The combination of this task focus, preference for hard numbers, and short-run orientation all conspire to make systems thinking difficult. Systems are ultimately messy, and they cannot really be understood without taking a longer range point of view, as system dynamics has convincingly demonstrated.

Articulating the Challenge

Creating a learning culture from this set of assumptions is very difficult. It is one thing to specify what it will take for us to become effective learners; it is quite another thing to get there, given these strong cultural inhibitors. But the first and most necessary step is always a frank appraisal of reality. If we understand our cultural biases, we can either set out to overcome them slowly, or, better yet, figure out how to harness them for more effective learning.

But we first must acknowledge the difficulty of our task. Culture is about shared tacit ways of being. Because it operates outside of our awareness, we are often quite ignorant of the degree to which our culture influences us. Therefore, we cannot expect that we can just set about to create whatever culture we want, as if it were the same as creating espoused principles and values. Only shared successes in using a new way of thinking, perceiving, or valuing will create this new approach, and that takes time.

I believe one mechanism by which cultures change is to re-prioritize some of the shared assumptions that conflict with others. For example, as we discover that competition and rugged individualism fail in solving important problems, we will experiment more with other forms of organizing and coordinating. Initially we may do it only because it is pragmatically necessary. But gradually we will discover the power of relationships and teams to complete tasks more effectively and to improve learning. This “proactive pragmatism” will eventually force us to create a learning culture and, in that process, produce new and quite different 21st century organizations.

Edgar H. Schein is Sloan Fellows professor of management emeritus and a senior lecturer at the Sloan School of Management. He chairs the board of the MIT Center for Organizational Learning and is the author of numerous books on organization development.

Editorial support for this article was provided by Colleen Lannon.

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