But the importance of service quality is not limited to service industries like banking, insurance, or package delivery. As the global environment grows fiercer every day, manufacturing companies are realizing the importance of focusing on the quality of their services, not just their products.

“You can’t touch or feel a service, nor can you inspect it after the service is completed. At the point of ‘delivery,’ everything that is needed to provide the service must converge in order to provide the customer with high quality.”

“Moments of Truth”

Improving service quality promises a myriad of benefits. It costs far less to keep a customer than to win a new one, for example, and perceived high-quality service firms can often charge up to 10% more for their products than competitors. The question is how to do it.

In the rush to reap the benefits of improving service quality, companies may be too quick to borrow from past experience in manufacturing. But there are important differences that make improving service quality much more elusive.

One obvious difference between products and services is that one is tangible while the other is not. You can’t touch or feel a service, nor can you inspect it after the service is completed. At the point of “delivery,” everything that is needed to provide the service must converge in order to provide the customer with high quality. Each of those interactions are the “moments of truth” which determine whether you are seen as a high-quality service operation or not, according to Jan Carlzon in his highly-acclaimed book, Moments of Truth.

Quality, as seen by the customer, is determined by each moment-of-truth encounter with frontline personnel. The net benefit of millions of dollars worth of capital equipment, buildings, salaries, etc. that a company has assembled will be judged, in large measure, by the quality of those interactions. Those moments of truth are numerous, ephemeral and difficult, if not impossible, to measure. And yet, the long-term reputation and success of a service company is largely riding on them. This suggests that many companies are investing far too little in their front-line personnel. How many moments of truth are actually “moments of despair” for their customers?

Just-in-Time vs. Just-in-Case

For some companies, providing high quality service means creating a just-in-time (JIT) operation, where all the necessary ingredients converge at the point of delivery exactly when it is needed. In a JIT production system, inventories are kept to a minimum throughout the factory by making sure there is just enough inventory at each step of the process to supply the next batch. But it is dangerous to carry the JIT philosophy too far in the service arena.

In manufacturing, the product has already been designed; all that remains is to run the production line as smoothly as possible. Variances in the production line can then be control-charted and maintained. In a service setting, however, front-line personnel have to be ready to produce a service whose design is not fully complete until they interact with the customer. Unlike the manufacturing setting, customers often introduce variances that cannot be controlled in advance. Having adequate capacity online is critical to providing high quality service. In a JIT production system, if the production line goes down, the down-time does not affect the quality of the next product off the line. If there are enough buffer stocks of finished goods, the customer won’t even experience any difference in delivery. If you are under capacity in a service setting, however, there is no way to make it up in real time with “buffered” service time. In addition, it is virtually impossible to “recall” a poorly delivered service. A flight that arrives two hours late and causes people to miss a meeting cannot be changed. A package that is delivered too late for a speech is simply too late. The capacity has to be online and available precisely when the customer requests it. This suggests that, unlike JIT, one may need to plan in terms of Just-in-Case service capacity—that is, service capacity should be weighted more towards peak volume than average volume.

Complexity Line Model

Complexity Line Model

Work can be divided into simple and complex tasks. If the intrinsic needs of the customer is a 50-50 mix, there is a quality gap if any of the quality indicators differs from that mix. For example, Quality Goal assumes a 6040 mix, so a Gap 1 exists.

The Complexity Line Model offers a way of looking at service quality in terms of four different capacity requirements: voice of the customer, the quality goal, working quality standard, and actual quality. In the Complexity Line Model, all service work is viewed either as simple (processing) work or complex (technical) work. In reality, there are many gradations, but for ease of use we will work with the two categories. Simple work means things that can be handled by an entry-level person. Complex work, on the other hand, requires a lot more experience and skill. In general, complex work also requires more time.

Suppose we are managing a customer service call center. Ideally, we should be staffed to match the exact needs of the customer as shown on the Voice of the Customer line (see “Complexity Line Model” diagram). Suppose that the intrinsic needs of the customer calls coming into our center are split 50-50 between simple and complex. That is, on any given day, 50% of the calls are routine. The other 50% are complex and require more understanding about the business.

Gap 1: Understanding the Voice of the Customer. In reality, we never know exactly what the customer needs. The customers themselves may not fully know what they need. The quality goal line represents our current understanding of the customer’s needs. For example, we may be staffed and prepared to handle a call volume that we believe is 60% simple and 40% complex. Gap 1 represents the difference between what the customer actually needs and what we think the customer needs. In this case, 10% of the customers will not receive proper service. Reducing this gap requires an investment in understanding what the voice of the customer is.

Gap 2: Understanding the Voice of the Process. The Working Quality Standard line may be different from the quality goal if the service capacity in place is not sufficient to provide the stated quality goal. In this case, say the people doing the work are capable of handling a call volume where 70% of the work is simple, and 30% is complex. Gap 2 represents our lack of understanding of what our current system is capable of (voice of the process) relative to what we are asking it to do (quality goal). In this case, we fall short of our own goal by another 10%.

Gap 3: Managing Customer-Generated Variance. The Actual Quality line represents the day-to-day moments of truth in which the customer actually experiences our service quality. If we are staffed to meet a 70-30 complexity mix, and the volume of calls stays relatively constant, customers will experience quality at the working quality standard level. Suppose, however, that incoming call volume suddenly jumps by 20%. What will the pressure in the system do to actual quality? The only way to serve a larger number of customers with the same number of people and skill mix is to reassign complex work as simple. The work can then be given to less experienced staff (“I know Joe’s only 2 weeks on the job, but I think he can handle this one”) or we can treat it as simple and spend less time on it (“I don’t think they need to know about all the other options…”).

So now work is handled as if it is 80% simple, 20% complex, which represents the actual quality. Gap 3 represents the daily adjustments that have to be made when customer volume and special requests exceed the capacity established by the working quality standard. Although our working quality standard has not changed, actual quality has grown worse. If this becomes a frequent occurrence, the quality standard can be pulled downward toward actual quality. As the quality standard adjusts to a lower level, actual quality can get pulled down still further the next time the call volume exceeds the already lowered capacity (see “Quality Erosion over Time” diagram).

Drifting Goals Structure

The dynamics of service quality can be captured in a “Drifting Goals” archetype (B1 and B2 in “Managing All the Quality Gaps”). A gap between the quality goal and working quality standard can be closed in one of two ways—lowering the goal (B1) or raising the standard (B2). Lowering the goal is easy and quick; raising the standard takes time and investment (see “Drifting Goals: The Boiled Frog Syndrome,” Toolbox, October 1990).

Focusing on the needs of the customer can help balance the pressure to reduce the quality goal. The voice of the customer increases Gap 1 which increases the pressure to raise the quality goal (B3). In this model, we see that the art of setting quality goals requires balancing the voice of the customer with the voice of the process. In terms of TQM, this means continually trying to identify the intrinsic needs of the customer (voice of the customer) and understand the systems and processes enough (voice of the process) to design them to be in line with those needs.

Managing All the Quality Gaps

Achieving service quality excellence means managing all three gaps to set quality goals that are sensitive to the voice of the customer and the capabilities of the current system.

Maintaining the working quality standard without losing ground requires managing the gap between actual quality and working quality standard. A high-quality operation should have adequate capacity to handle the majority of the variance it encounters and should keep actual quality within a narrow band around the working quality standard. An operation that is out of control would have a wildly-fluctuating and persistent gap.

Managing All the Quality Gaps

Achieving service quality excellence means managing all three gaps simultaneously. Focusing exclusively on the customer and making your quality goals aligned with their needs will reduce Gap 1, but it will only make Gap 2 worse. If you raise the quality goal without investing in the requisite training, personnel and systems, employees will see it as nothing more than banner-waving and go on with business as usual.

If you focus exclusively on reducing Gap 2, however, you may encounter tremendous pressure in the system to close the gap by lowering the goal. The history of the quality standard can often provide compelling evidence that the quality goal is out of line with the “real” system, and lead to “Drifting Goals.” Focusing exclusively on eliminating Gap 3 will create an identical tendency for the standard to float with actual quality.

The customer’s experience of quality is determined by the sum of all three gaps. The challenge for service companies (as well as manufacturing firms with service operations) is to develop the ability to identify and eliminate all three gaps even as the voice of the customer continually changes. It requires investing in service capacity ahead of the current requirements in order to be able to treat each moment of truth with the quality that the customer intrinsically needs or wants. The Complexity Line Model is based on the work of Bob Bergin and Gerri Prusko at Hanover Insurance Co. (Worcester, MA). The author has developed and used a Service Quality Management computer simulator to provide practice fields for managers to understand the complexity line concepts. The software runs only on Macintosh computers. If you wish to acquire a copy, please write to Daniel II. Kim, MIT Organizational Learning Center, MO-294, 1 Amherst St., Cambridge, MA 02139.

Further reading: “Now Quality Means Service Too,” Fortune. Apri122, 1991; Jan Carlzon, Moments of Truth (New York: Harper & Row, 1987).

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According to project manager Janet Gould, the research will address three basic questions:

• What does it mean to be competent in systems thinking?
• What skills must people acquire in order to become competent in systems thinking?
• What additional skills are necessary to become an active facilitator of systems thinking within an organization?

Much of the initial work in the project has been devoted to defining what should be included in a list of systems thinking competencies. The diagram at the right shows a proposed framework for addressing the issue. Deciding what specific skills fall under each matrix cell is a crucial aspect of the research and will re-main fluid for some time. Even the current definition of the axes is a tentative selection.

Although dialog about the course content continues, a few formats for delivery of the course have been suggested. One possibility is to conduct an intensive, five-day course which would immerse the participants in the principles of systems thinking. Such an experience, explains Tom Grimes of Hanover Insurance Company, a sponsoring company, might help participants retain the lessons from the course. “We have such a capacity to think linearly in our lives,” he explains, “that it’s going to take a major learning experience to turn it around.”

Another possible format would be to have three days of instruction followed by “refresher courses” held every few months. In between, participants would keep logs describing how systems thinking is affecting their work. The iterative process could continue for a year or more, notes Gould.

Regardless of its final format, an essential element of the course will be team learning. Groups of people from the same division of a company will be encouraged to go through the course together and to continue using the skills they have learned back in the workplace. Explains Gould, “We think the learning might last longer with this method, because the participants would be working with a group of people with whom they can continue talking about systems thinking, rather than being isolated.”

Initially, the content of the course will be modeled after two courses already available—the MIT Summer Session, a week-long introduction to systems thinking, and a five-day course designed by consultant David Kreutzer for his clients. Both courses teach participants simple feedback loops and how to build simple computer models of complex systems. A limitation of both course designs, however, is that the skills covered do not fully address all the cells of the research matrix.

Grimes hopes to address four main objectives with the course:

1) To raise awareness of the limits and some of the potential dangers of linear thinking.

2) To use systems thinking as a way of identifying the assumptions we make underlying our decisions.

3) To develop a common language for talking about systemic issues.

4) To critique and expand our view of reality without getting into issues of personality or emotionality.

A prototype course should be ready in a few months. At that point, the researchers will begin to implement it in four or five participating companies. But Gould emphasizes that the course design is only part of the research project. “We also need to know from a research standpoint whether this course is going to do anything for a company. Are people actually learning what we’re expecting?”

…an essential element of the course will be team learning.

In order to evaluate the course, participants will answer questionnaires that test how well they have assimilated key concepts. Not only will their answers help the researchers gauge the success of the course, but Gould notes that the participants will also be able to track their own progress.

Internal facilitators will also play a crucial role in implementing systems thinking in a company. “Essentially you need to build up internal expertise in systems thinking,” explains Dan Simpson, Director of Planning at The Clorox Company. “Without that internal expertise, it’s unlikely any new thinking mentality will infiltrate the organization very well.” Simpson adds that a separate, more intensive course may be necessary to train the facilitators who will continue the systems thinking learning process inside their companies. “These people will continually make the translation from what is an academic field of study into operational action inside an organization.”

Despite the questions on how well the course might implement systems thinking in companies, there is no doubt among the course planners that systems thinking is a valuable tool for organizations. As Simpson describes it, “Systems thinking helps practicing managers begin to think through the ‘ripple effects’ of their decisions. It’s often not clear when you make a decision as a practicing manager in one area that there are interactions with other areas, intended or not. Systems thinking offers a way to control—or at least consciously manage—the ripple effect, as opposed to just letting things happen.”

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

A CORE THEORY OF SUCCESS

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

Limitations of Traditional Approaches

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

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

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

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

The Importance of Theory

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

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

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

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

Theory-Building: Shifting from Factors to Loop

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

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

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

Theory as an Intervention Guide

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

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

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

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

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

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

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

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

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

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

Managers as Researchers and Theory-Builders

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

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

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

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

Adaptation Versus Addiction

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

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

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

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

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

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

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

An Addictive Response in Organizations

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

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

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

The Market Growth Model: Shifting Dominance at Ace Electronics

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

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

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

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

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

The Difficulties of Breaking Addiction

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

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

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

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

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

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

Understanding and Changing Systemic Structure

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

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

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

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

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

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

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

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

Measurement Error

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

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

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

The Dangers of Excluding Qualitative Variables

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

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

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

Quantitative Scales for Qualitative Variables

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

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

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

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

Assessing Models

Interacting Hard and Soft Variables

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

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

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

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

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

Managing Paradox

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

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

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

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

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

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

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

CIRCULARITY OF VALUES

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

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

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

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

Unconscious Assumptions

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

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

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

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

Conflicting Cultures

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

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

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

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

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

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

Leverage

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

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

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

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

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

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

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

Interdependent Opposites and Organizational Learning

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

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

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

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

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

Reconciliation

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

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

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

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

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But if we are not going to define “leanness” in financial terms, how should we define it? I believe we need to expand our thinking to include the application of employee competency to achieve leanness. I strongly believe that people are a company’s only long-term competitive advantage. As such, we should view them as assets and resources to be developed, rather than as line-item expenses to be controlled. By taking this approach, we might discover value-added activities that would enable us to keep employees on the payroll even during tight times.

Business Process

Within Harley-Davidson’s motorcycle operations, we are trying to establish a business process that will accommodate such thinking. At the top of our business process diagram is an umbrella that sets the context for our work (see “Business Process: Setting the Context”). Under this umbrella, we identify the values, issues, and stakeholders that are the basis for our vision statement, which is to be “a leader of continuous improvement in the quality of mutually beneficial relationships with all of our stakeholders.” We measure our progress in achieving that vision against the following statement: “The key to our success is to balance stakeholder interests through empowered employees focused on value-added activities.”

These statements could be viewed as esoteric rhetoric. But we hope they will operate instead as a guiding light toward effective leanness. If we adopt this view, then we can start to utilize the workforce as a resource, creating an environment in which all employees seek to apply their competencies to value-added activities that can result in employment security.

In this context, “employment security” is dramatically different from conventionally stated job security. In employment security, the employee’s focus is on ensuring that the company survives, while job security centers on ensuring that he or she continues to do the same thing day in and day out. If all employees focus on creating employment security by providing value-added activities (in conjunction with others with complementary competencies), the result will be a lean organization. In addition, their work will generate additional resources to develop the company further, enabling the company to become more externally and future oriented.

The Role of Financial Measurement

Defining leanness in terms of value-added activities does not eliminate the need for financial measures. In order for the company to survive over the long term, it must be financially viable, and all of the employees must understand this. A primary measure of employee effectiveness is the company’s financial results. Those results come only when employees deliver value-added activities that are recognized as such by the customers. Therefore, if customers are not purchasing our products, it is up to all employees to seek ways to apply their competencies toward creating new products or services that will ensure the long-term financial viability of the company. If this strategy is not recognized and adopted by all employees, it is likely that leanness will have to be associated with meanness in the form of down-sizing efforts that have cost reduction as their only objective.

In order to survive, Harley-Davidson had to experience such a downsizing. In 1982, we reduced our workforce by 40%. It was not an easy decision, but we did it as humanely as possible—far more humanely than our bankers thought necessary. However, this approach put us in good stead with the people inside the company, because they knew that we were in crisis and they put forth the extra effort to help the company recover.

While that result sounds great, I can’t help but speculate that if we had consciously worked on having all employees focus on value-added activities, the solutions to our problems could have been identified much earlier. By redefining our approach to leanness, we are hopefully putting ourselves on the right path to prevent a recurrence of that difficult experience.

Rich Teerlink is president and chief executive officer of Harley-Davidson. Inc.

Reprinted with permission from Collective Intelligence (Vol. 1 . No.1) May 1995. ©MIT Center for Organizational Learning. All rights reserved.

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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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