Related Diagrams

Related Diagrams

Related Diagrams

Experience cycle “in the large” and “in the small” Integrated experience across multiple scales: Apple as a case study. The fractal nature of the experience cycle

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Multiple touch-points across the life of a product

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Multiple touch-points in the store experience

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Multiple touch-points within the in-store purchase process

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Designers often speak of design as a process. Typically, design thinking leads to design making, which leads to artifacts. Yet the design process also leads to something more—to new knowledge. Thus, we might characterize designing as a form of learning.

Curiously, the converse is also true. We might characterize learning as a form of designing. That is, the process of observing, reflecting, and making (and iterating those steps) may aid learning. Several designers and teachers have recognized the link between designing and learning and are bringing designing into curricula not just in college but also in high school and even elementary school. See, for example, a recent New York Times article, “Putting New Tools in Students’ Hands” [1].

I acknowledge framing designing as learning (without providing further explanation) may be little more than trading one black-box process for another, but if we can find robust models of learning, they might prove useful in designing and might suggest ways to improve the design process.

The connection between designing and learning was brought into sharp focus for me last summer while editing an article by Maurício Manhães [2], who wrote, “Design and innovation are both knowledge creation processes” [3]. What struck me about Manhães’s article was that he introduced the SECI model of knowledge creation and explicitly applied it to analyzing and improving the design process. I was further struck by the similarity or even isomorphism of the SECI model and the analysis-synthesis bridge model described in this forum in the March + April 2008 issue [4].

Introducing the SECI model

The SECI model comes out of research in “knowledge management,” which is related to “organizational learning,” “business administration,” and “information systems.” SECI stands for socialization, externalization, combination, internalization—a model of knowledge creation proposed by Ikujiro Nonaka [5]. (It’s interesting to note that Nonaka received his MBA (1968) and Ph.D. (1972) from UC Berkeley, when West Churchman was teaching in the business school and offering seminars that included design-methods pioneers Horst Rittel and Christopher Alexander, who were on the faculty of the UCB College of Environmental Design. The problem of managing knowledge created in the design process is described by Horst Rittel in his work on Issues Based Information Systems (IBIS), which helped spawn an area of research in computer science known as design rationale [6].)

Nonaka sees ongoing knowledge creation as the source of continuous innovation and continuous innovation as the source of sustained competitive advantage. “When organizations innovate, they do not simply process information, from the outside in, in order to solve existing problems and adapt to a changing environment. They actually create new knowledge and information, from the inside out, in order to redefine both problems and solutions and, in the process, to re-create their environment.”

Nonaka considers knowledge “as a dynamic human process of justifying personal belief toward the ‘truth.’…This understanding emphasizes that knowledge is essentially related to human action….As a fundamental basis for the theory of organizational knowledge creation, we focus attention on the active, subjective nature of knowledge represented by such terms as commitment and belief that are deeply rooted in individuals’ value systems.” [7][Italics are from the original.]

“The basic argument is that knowledge creation is a synthesizing process through which an organization interacts with individuals and the environment to transcend emerging contradictions that the organization faces” [8].

The process moves from tacit knowledge to explicit knowledge and back. “Tacit knowledge is personal, context-specific, and therefore hard to formalize and communicate. Explicit or codified knowledge, on the other hand, refers to knowledge that is transmittable in formal, systematic language” [9]. Tacit knowledge tends to be specific to a context (available in a particular time and place), practical, routine, and procedural. Explicit knowledge can transcend a specific context (and is transferable to other times and places) and tends to be rationalizing, theoretical, and declarative.

Nonaka postulates four modes of “knowledge conversion that are created when tacit and explicit knowledge interact.”

• Socialization (tacit to tacit) “is the process of converting new tacit knowledge through shared experiences in day-to-day social interaction.”

• Externalization (tacit to explicit) is a process whereby “tacit knowledge is articulated into explicit knowledge…so that it can be shared by others to become the basis of new knowledge.”

• Combination (explicit to explicit) is a process whereby “explicit knowledge is collected from inside or outside the organization and then combined, edited, or processed to form more complex and systematic explicit knowledge…The new explicit knowledge is then disseminated among the members of the organization.”

• Internalization (explicit to tacit) is a process whereby “explicit knowledge created and shared throughout an organization is then converted into tacit knowledge by individuals…This stage can be understood as praxis, where knowledge is applied and used in practical situations and becomes the base for new routines.”

Successive iterations of the process form a spiral, with each loop amplifying the knowledge to a higher-level knowledge-creating entity; the process moves from individual to group to organization to community of organizations.

How the SECI Model Maps to the Analysis-Synthesis Bridge Model

The goal of this article is to introduce the SECI model to the design community. At the same time, this article also argues the SECI model and the analysis-synthesis bridge model are not just similar but also isomorphic. That is, they use different terms to describe essentially the same process. More precisely, the analysis-synthesis bridge model and related models [4] (Robinson model, Kumar innovation model, Kaiser/IDEO model, and Suri/IDEO model) are specific instances of the more general SECI model. (The Beer model and Alexander model are slightly different, though still roughly analogous. The 1966 Beer model is interesting in relation to SECI, as it describes the process of applying scientific models to managerial situations, a special form of knowledge creation.)

The analysis-synthesis bridge model describes a four-step design process. It begins with 1. directly observing a current situation, 2. reflecting on observations of the current situation to create a model representing essential elements, 3. reflecting on the model of the current situation to create a second model representing essential elements of an improved situation, and 4. instantiating the second model in a physical form or prototype. The process described by the analysis-synthesis bridge abstracts essential characteristics of both current and improved situations as a “scaffold” for moving from researching to making in the design process; using models as a bridge may be especially useful in complex areas of practice, such as software design, service design, and systems design, where the path from researching to making may often be unclear.

The four steps of the analysis-synthesis bridge model correspond to the four steps of the SECI model:

Step 1:
Observing the current situation is a form of socialization. Insight-gathering methods or problem-finding methods, such as ethnography, often rely on acquiring tacit knowledge through inhabiting a specific context and interacting with others in that context. Nonaka writes, “The key to acquiring tacit knowledge is experience. Without some form of shared experience, it is extremely difficult for one person to project her- or himself into another individual’s thinking process.”

Step 2:
Modeling the current situation is a form of externalization. Sharing one’s experience and insights with others, for example, writing an ethnography, requires abstracting and generalizing. Nonaka writes, “Externalization…is the quintessential knowledge-creation process in that tacit knowledge becomes explicit, taking the shapes of metaphors, analogies, concepts, hypotheses, or models.” He adds, “To make a hidden concept or mechanism explicit out of accumulated tacit knowledge, abduction, or retroduction is effective rather than induction or deduction.”

Step 3:
Modeling a better situation is a form of combination. A designer looks at aspects of what is and imagines combining them with other things that he or she has experienced or imagined. Nonaka writes that combination “synthesizes knowledge from many different sources in one context. The combination mode of knowledge conversion can also include the ‘breakdown’ of concepts. Breaking down a concept…also creates systemic, explicit knowledge.”

Step 4:
Instantiating a model is a form of internalization. Prototyping requires working out many details and determining many relationships, creating a new level of knowledge of the model on which the prototype was based. Nonaka writes, “Explicit knowledge, such as product concepts or manufacturing procedures, has to be actualized through action, practice, and reflection so that it can really become knowledge of one’s own.”

The bridge model is a specific instance of the SECI model.

Rotating the SECI model 90º counter-clockwise aligns it with the bridge model—so that they both “begin” in the lower left corner.

By rotating the SECI model, we can see that Socialize and Externalize tend to look backward at more known situations, and Combine and Internalize tend to look forward to less known situations.

Like the SECI model, the analysis-synthesis bridge model comprises four quadrants of a two-by-two matrix. In the SECI model, step 1 is in the upper left corner. In the analysis-synthesis bridge model, step 1 is in the lower left corner. Rotating the SECI model 90 degrees counter-clockwise aligns the two models. Nonaka does not label columns or rows in the SECI model. However, the analysis-synthesis bridge model labels the bottom row “descriptive/concrete” and the top row “interpretive/abstract.” The left column is “researching a current situation,” while the right column is “prototyping a future situation.” It’s not much of a stretch to apply these labels to the rows and columns of the SECI model.

The SECI model explicitly describes the iterative nature of the knowledge creation process by including a spiral. The analysis-synthesis bridge model does not refer to iteration directly, though the authors assume readers understand the design process as iterative. However, the Kaiser/IDEO model, which is isomorphic to the analysis-synthesis bridge model, includes a loop. And Kumar’s innovation model, which is also isomorphic, does explicitly include a spiral!

The SECI model is just one part of Nonaka’s theory of knowledge creation, which also comprises Ba and dialectic. Ba is a shared “place” or context—loosely bounded and evolving—that “enables a dialectic process among the actors.”

“A firm can be viewed as an organic configuration of various Ba, where people interact with each other and the environment based on the knowledge they have and the meaning they create.” This notion is similar to the Geogeghan and Pangaro notion that a firm is a collection of conversations for understanding, agreeing, acting, and learning [11]. Nonoka argues that “knowledge is created through the synthesis of the contradictions between the organization’s internal resources and the environment.” His notion of the dialectic spiral of synthesis of contradictions is similar to Rittel’s notion of designing as a process of reframing and argumentation.

While outside the scope of this article, Nonaka’s notion of Ba and his insistence on Ba and dialectic as parts of the knowledge-creation process suggest further opportunities for applying his work to designing.

Conclusion

Today, the practice of software and service design—indeed most design practice—is ad hoc, performed on an ‘as-needed’ basis and adapted to whatever context the designers encounter. Most design work still proceeds on an industrial-age model of ‘edition’ and project, in which design is ‘finished’—rather than on an information-age model of continuous improvement, multi-year beta, and organic growth, in which design is never finished. In the future, successful software and service organizations will recognize that software and service design are ongoing processes. Each design iteration and implementation leads to new knowledge. We need systems to identify, capture, and build on that knowledge in an ongoing process, if we are to develop a design practice appropriate for an information and services economy [10]. Applying the SECI model to designing is a step in the right direction.

If both the SECI model and the analysis-synthesis bridge model reasonably represent their subjects—learning (or knowledge creation) and designing—and if the models are isomorphic, then we may say that learning and designing are isomorphic, at least from one frame.

This conclusion has profound ramifications for both business practice and design practice. For business practice, it suggests that since knowledge creation is a central activity of the firm then designing is also a central activity of the firm. That is, designing is an important form of knowledge creation and thus the heart of value creation within the firm. For design practice, it suggests further study of the mechanisms of knowledge creation and knowledge management and their relation to traditional and emerging notions of designing. That is, learning is an important part of the design process, not just in design education and academic design discourse, but especially as design is practiced.

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Editor’s Note:
After a long career in systems engineering and design, John Rheinfrank died on July 4, 2004.

John’s Ph.D. dissertation explored what he called “organic systems theory,” or what’s now called “complex adaptive systems”—bridging multiple disciplines and theoretical frames (e.g., biology, computing, economics, psychology, and sociology). John spent most of his professional life applying principles derived from living systems to designing systems for people—from design languages that could serve as the foundation for a broad range of reprographic machines for Xerox, to personal information and communication appliances for Philips. In essence, he wanted us to design systems that are alive.

In the years since John’s death, complex systems have become deeply engrained in our everyday lives, from Facebook and Twitter to the interconnected financial systems that plunged us into the credit crisis. When John learned he was sick, he began working on a book on the relationship between design and systems. Sadly, he never finished, but some of his core ideas were preserved in a presentation on moving from static to adaptive worlds. John saw adaptive worlds as a new way to frame interaction design, which makes it an important topic for interactions. This presentation was his way of helping us make the leap from the present to the future he could already envision. Working from John’s presentation slides and a tape of his talk, we have summarized his ideas.

—Hugh Dubberly

The history of design is mostly the history of designing static worlds—objects, messages, and spaces that are fixed and invariant.

In a static world we are forced to adapt to the object. For instance, this chair is this height. It doesn’t matter if you’re short or tall—the height of the chair remains the same. Most chairs are still like this today. They are static objects. Your back and your butt must adjust to the chair. It is an object that we adapt to.

Along comes a new kind of chair. Not only can you turn in it, but you can also raise and lower it—or even tilt it to a position that is right for you. The designer’s role expanded from that of arbitrator of form to creator of resources for interacting with the chair. This meant the designer had a whole new range of representations to account for and choices to make. Because now that we can adjust the chair, we are more comfortable as we work.

Many early interaction design efforts focused on making things simple for naïve users. The idea was to take resources for decision-making away from people—automating features in cameras, for example—to reduce cognitive load or how much users would have to think about what they were doing. Reducing cognitive load often comes at a price: limiting choice and possibilities for expression. For example, point-and-shoot cameras with one wide-angle lens, fixed focal length, single f-stop, and single shutter speed. This approach should raise ethical concerns for designers, especially when it “de-skills” people.

In the early days of the photocopier, the machines would often fail. Problems as simple as a paper jam (identified by a cryptic “Error E31” on the display) would require calling a trained service professional to come on site to open and repair the machine. A team from design consultancy Fitch-Richardson Smith, led by John Rheinfrank, helped Xerox shift its cultural paradigm away from training service professionals to embedding information in the machines so that the machine users could fix problems themselves, quickly and effectively. In essence, the redesign provided information that helped people learn to use the machine as they used it, by offering a rich set of resources for managing the process (in this particular case, in the event of an equipment malfunction).

Adaptive Worlds

A new order of systems is emerging, that adapt to the worlds in which they play a part. Although the form they take varies widely from example to example, these systems all have in common some means for: 1. “perceiving” two or more states of the environment in which they are embedded; 2. creating, based on these perceptions, a “model” of the environment around them; and 3. adapting, based on this model, in a fashion to best meet the performance objectives of the system in the face of a changing environment. This need not be a one-shot event—it can occur continuously over time. For example, multilevel digital games (the system) have access to the score achieved by a player (perception of the environment), and, knowing the level at which that score was achieved, can assess the player’s skill level (creating a model) and adjust game difficulty in a way that keeps the player in the flow between boredom (this is too easy) and frustration (this is too difficult), which, ultimately, is the game designer’s goal (adapting to meet a performance objective).

It is not much of a stretch to go beyond this simple adaptive system loop to incorporate additional means of manipulating a system’s characteristics by the users engaging with that system (e.g., in our game example, consider the qualitative expansion afforded by Second Life). Dynamically co-constructed adaptive worlds give both creators and consumers the ability to design or improvise new activities that honor specific abilities as they emerge. In John’s words: “In this framework, we start to build worlds that collaboratively participate in the (co-evolution) of our individual and collective abilities. At the simplest level, we no longer are forced to adapt to the worlds in which we live, play, learn, or work. The worlds now shift to meet our abilities, to anticipate whatever they are or what we want them to be” [1].

John saw this “ability centered” framing of interaction as a way to enrich the user-centered notions that currently drive much of design. He felt that “user centered” focused on modeling explicit, articulated needs. Evaluating designs (usually specifications) against the articulated model was often seen as sufficient. With ability-centered design, the question is not What qualities of the user will allow them to perform this task in the easiest fashion? but rather, What are the latent, masked needs, the unobservable, inconceivable needs? In ability-centered design, functional prototyping and evaluation by end users are paramount.

What characterizes dynamically enabling adaptive worlds, and how can we even hope to design for them?

While static worlds are about objects and interactions, adaptive worlds are about flow and emergence. In a static world, objects are inflexible—they don’t have the ability to change or adapt built into them. In an adaptive world, objects and processes modify themselves based on information gleaned from people, either through sensing or explicit input. A service experience in an adaptive world would feel as though it had been custom-designed for every person. As each visitor entered the store, the environment would sense them and inform the staff of their preferences, their past purchases, etc.

Dynamically Enabling Flow

When John spoke of flow, he was referring to Mihály Csíkszentmihályi’s notion of flow [2]: a correspondence between you and what you’re doing—where the challenge you face matches your ability. You enter into a separate mental space, and you move in a different way. You’re in the “zone.”

Now, as we shift to co-constructed adaptive worlds [3], there are the beginnings of an adaptation between the things that fill my world and myself. There’s also a shift in learning from passive to active. Passive learning is the assumption that people are empty vessels for instructors to pour information into—instructors deliver content and students receive or digest [4].

Active learning involves not just hearing or reading facts, but also doing things that put them to use in a way that makes them real for the learner. Active learning requires a participatory learning environment where learners and instructors “play,” leverage their shared context, and co-construct knowledge in relation to each other and their experiences.

Advanced robots are just beginning to “learn” and adapt to the terrain around them, while constantly monitoring and adapting to mission-significant objectives like threats and the people they have to rescue. Another example of this new kind of system is financial tools geared toward consumers. These days, when you bring your money to a well-designed bank, the system evaluates your “life stream,” a constantly changing model of how you and people like you act in your progressing stages of life. So as you and your life change—for example, you buy a car or begin a family—bank services are reconfigured for you. PNC Bank’s Virtual Wallet, based on the money mind-set and financial lifestyle of Gen Y, takes on some of these qualities. Today the wallet helps people plan and save. The resources behind the wallet grow and change over the life of the user.

John believed “this dynamic can apply locally at the smallest scale and globally to the composite of forces that shape our lives. Until recently, we’ve done this coarsely, marginally, and at tremendous cost and over extraordinarily long time periods. We add handicap-access ramps to old buildings and design new buildings that seek to be barrier-free. The limitations of these world-shaping objects define what it means to be disabled. The objects—our designs—quite literally create the disability. This need not be so. The objects we can make today and tomorrow are no longer dumb in the exclusive light of our intelligence.”

What John called “emergent systems” are an ecology or community of these adaptive systems, in which elements in the system learn, adapt, and share the knowledge they gain about the world with other pieces of the system. An example of this type of system is Google. The system shares knowledge from Web search to Maps search to Images search, to help you find the thing you are looking for as easily as possible. Many organizations behave as emergent systems, for instance, the governing system of the Internet. Each node doesn’t hold enough power to sway the entire system, but as events arise, the standards and systems adapt to the emergent needs.

To explain the technology and trends that are enabling adaptive worlds, John mapped the elements across two axes. The horizontal axis is a continuum of materiality, from human being, thinking, and doing to machine being, thinking, and doing. The vertical axis represents the nature of the action you are undertaking, from understanding to doing. The elements on the bottom help you understand the system or the world better, while the elements on the top enable to you do things better. John provided four examples of trends that are moving toward complex adaptive systems.

Universal design is an example of a carbon-based emergent system. OXO created a line of kitchen tools specifically designed for the arthritic or the handicapped, but they ended up appealing to a much broader audience. People soon realized the OXO tools felt fantastic in the hand and made performing functions much easier and more enjoyable.

Ubiquitous computing is an example of a silicon-based trend. Ubiquitous computing is the increasingly embedded nature of sensors, processors, and networks in the physical objects that surround us, from medical equipment to our mobile phones.

A good example of assistive or augmentative technology is Dean Kamen’s iBOT-powered wheelchair. The unique technology and orientation was intended to enable those with severe mobility problems to ascend stairs. Anyone who uses this device daily will also mention the unintended appeal of the device: the mechanism can readily lift the user to be eye to eye with anyone they interact with.

Finally, an example of devices that help one understand better is the work done on Xerox machines. As noted earlier, the machine evolved into something that could teach users how to diagnose problems and quickly return the machine to a fully operational state.

John saw three different resources enabling adaptive systems. The first are the platforms for creating experiences, “the auto-catalytic foundation for co-constructing fluid, extensible interactions and meaningful relationships between people and hybrid physical/virtual worlds that matter to them.” The second is the people, places and things that contribute and benefit from the adaptive worlds, what John referred to as the material substrate. Last are the underlying elements that make up the networks within adaptive worlds, infrastructures that are self-organizing rather than guided by outside forces. At least one component from each of these clusters is required for any adaptive emergent system.

We now have the capacity to design and build objects that are active, semi-autonomous, evocative, emergent, mixed-initiative partners in the (re)formation of worlds that are magical by today’s standards.

Eric Schmidt, CEO of Google, recently suggested “people are not ready for the technology revolution that’s going to happen to them.” He was referring to the ubiquitous role of technology and the collection of data to enrich our interactions with the artificial. Many organizations, including Google, are under fire for provoking privacy concerns over the handling of their users’ data. The complex adaptive systems that are beginning to emerge are a testament to the benefits of systems that can learn and engage in a dialog.

What does this mean for interaction design? More broadly, what does this mean for the systems we will interact with in the future?

John, when speaking to a group of present and future designers, said: “Don’t be satisfied with my native abilities. Provide a setting in which my ability is extended… Help me reveal my potential.” He envisioned a world in which systems weren’t designed for specific interactions, but instead designed for the latent potential abilities that exist in everyone. John continued, “Let me feel that it’s alive. Don’t hide it from me. Don’t make it transparent.” Living systems are inherently fallible and magical: We make decisions that end up being mistakes or happy accidents. One of the qualities of biological systems is their ability to acknowledge and react to these events. John believed that complex adaptive systems should react the same way: They should evoke the same feeling of “alive.”

About the Authors

Shelley Evenson is a design manager at Microsoft leading a team that explores real-time communication products that engage and connect people in new ways to help them communicate and collaborate. Before Microsoft she was an associate professor teaching interaction design at Carnegie Mellon University. Evenson taught courses in designing conceptual models, interaction, and service design, and collaborated in projects with colleagues from the Tepper School of Business and the Human Computer Interaction Institute. She jump-started the study of service design in the U.S.—designing courses, energizing students, and hosting the first international conference on service design—Emergence. Before joining the faculty at Carnegie Mellon University, Evenson worked for more than 25 years in multidisciplinary consulting practices on a wide variety of design and development projects.

Justin Rheinfrank is an interaction designer at gravitytank, an innovation consulting firm in Chicago. He has helped visualize and define exciting new interaction and service concepts for organizations like Google, Samsung, Mayo Clinic, and NASA. Justin graduated from Carnegie Mellon University with Masters and Bachelors degrees in Human-Computer Interaction and a Bachelors in Industrial Design.

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A caricature of the popular conception of design vs. all other concepts.