Key process, management & organizational interactions

XV.1 January + February 2008
Page: 28
Digital Citation

COVER STORYToward a model of innovation


Authors:
Hugh Dubberly

For the past few years, innovation has been a big topic in conversation about business management. A small industry fuels that conversation with articles, books, and conferences.

Designers, too, are involved. Prominent product-design firms offer workshops and other services promising innovation. Leading design schools promote “design thinking” as a path to innovation.

But despite all the conversation, there is little consensus on what innovation is and how to achieve it.

The current conversation about innovation is similar to an earlier conversation about quality. As recently as the late 1980s, quality was something businesses actively sought but had trouble defining. Today, statistical process control, TQM, Kaizen, and Six-Sigma management are common tools in businesses around the world.

As businesses have become good at managing quality, quality has become a sort of commodity—“table stakes,” necessary but not sufficient to ensure success. When everyone offers quality, quality no longer stands out. Businesses must look elsewhere for differentiation. The next arena for competition has become innovation.

The question is: Can innovation be “tamed,” as quality was?

A key step in taming quality was proposed by Walter Shewhart and Edward Deming’s process model [1]. Their quality cycle is now widely taught and has become an important part of the quality canon. But innovation has no corresponding model.

Can we reach consensus on such a model for innovation?

One step may be to propose models for discussion.

Last year Lance Carlson, president of the Alberta College of Art and Design (ACAD), initiated a project (through ACAD’s Institute for the Creative Process) to create a “concept map” of innovation. The Institute worked with ACAD faculty, Dubberly Design Office, Paul Pangaro, and Nathan Felde to develop a series of models and published one as a poster.

This article describes the published model and illustrates its development.

Concept Maps

This model of innovation takes the form of a concept map. “A concept map is a schematic device for representing a set of concept meanings embedded in a framework of propositions [4].” In a concept map, nodes and links form a web of meaning, a semantic mesh. Nodes are nouns. Links are verbs. A noun-verb-noun sequence forms a proposition, a sentence. Concept maps are similar to entity-relationship diagrams and entailment meshes, though less constrained and less rigorous.

This concept map uses text direction and arrows to indicate reading direction. Type size indicates importance and hierarchy. Colored backgrounds join related terms.

Creating concept maps involves trade-offs. Adding terms provides detail and may clarify intent, but more terms mean more links, increasing the reader’s effort.

Concept maps differ from traditional texts by making links explicit, creating multiple pathways. People often ask, “Where should I start reading?” You can start anywhere. Concept maps have no real starting point; they are webs. Still, like any model, concept maps benefit from explanation. They can be explained by telling a story. Conversely, telling a story paints a picture; it creates a model in the mind of the listener.

Reading the Map

The map is built on the idea that innovation is about the evolution of paradigms.

In contrast to innovation processes, quality processes typically work within existing paradigms. Quality is largely about improving efficiency, whereas innovation is largely about improving effectiveness. Improving quality is decreasing defects. Defects can be measured, progress monitored, quality managed.

Business Week design editor Bruce Nussbaum asserts, “You can’t Six Sigma your way to high-impact innovation [5].” Although some Six-Sigma advocates disagree, Nussbaum points out a fundamental difference between managing quality and managing innovation. Innovation is not getting better at playing the same game; it’s changing the rules and changing the game. Innovation is not working harder; it’s working smarter.

Chris Conley, head of the product design program at IIT’s Institute of Design, suggests a slightly different frame. He contrasts innovation with operations. He observes, “Most businesses organize for operation, not innovation [6].” Organizations by their nature are conservative: They maintain a way of doing business, a way of living, a way of using language. They conserve convention.

Vertical axis: The innovation cycle. The map situates innovation between two conventions. An innovation replaces an earlier convention and, in time, becomes a new convention. It is a cycle—a process in which insight inspires change and creates value.

We rarely recognize innovation while it’s happening. Instead, innovation is often a label applied after the fact, when the results are clear and the new convention has been established.

The process begins when external pressure or internal decay disturbs the relation between a community and its context or environment, a relationship maintained by some convention. The original convention no longer “fits.” Perhaps the context has changed, or the community, or even the convention. Someone notices the lack of fit. It causes stress and increases bio-cost. It creates enough friction, enough pain, to force its way into people’s consciousness.

Perception of misfit almost simultaneously gives rise to proposals for change, for reframing. It creates the opportunity for insight.

Insights move forward only when shared, articulated, prototyped. Sharing is a test: Does the insight resonate with others? Proposals for change compete for attention. Most are ignored and fade away. The changes that survive are by definition ones the community finds effective. They spread because they increase fit, because they create value.

The map suggests a cycle moving from fit through misfit and back again. The vertical axis loops back on itself, reflecting the cycle.

The yellow loops: the role of feedback. Of course, innovation processes are rarely linear. The map includes several feedback loops, suggesting the role of iteration and the recursive nature of the process. At a basic level, innovation involves experimentation, making something new and testing it. To some extent, the process may be trial and error. The process may lead to new insights. Or it may prompt reframing of goals, consideration of new approaches, new generative metaphors. Success also leads to change: new beliefs, actions, and artifacts.

In turn, these lead to second-order change. Innovation in one place affects related conventions and may reduce their fit, hastening further innovation.

Ethnography and other research techniques can help identify opportunities for innovation. Design methods can increase the speed of generating and testing new ideas. But new ideas are still subject to natural selection (or natural destruction) in the marketplace or political process.

Variety: a regulator. The map posits variety as a regulator of innovation. Variety is a measure of information [3]. Here, it is the language available to an individual or community. Language enables conversation; conversation enables agreement; agreement enables action. At the same time, language constrains action, because language limits what can be discussed and agreed.

Pressure to increase efficiency creates pressure to reduce variety, as maintaining less variety requires less effort or saves time. Reducing variety decreases the number of options a community can discuss. Conversely, increasing variety increases the number of options that can be discussed—increasing the likelihood of insight. (In practice, an increase in variety may be required for some insights to be found.) A community seeking to increase variety must integrate individuals who can increase the community’s language, provide new points of view, draw on additional types of experience, foster new conversations, and provoke action [7].

Horizontal axis: The importance of individuals. The map posits individuals as drivers of innovation—and the source of insight. But to succeed, individuals must participate in a community, where they contribute variety.

Individuals who drive innovation also have a sense of what is not known but necessary for progress, and they understand how to find it. Individuals who drive innovation also seem to possess a healthy measure of optimism. They are motivated by the value that innovation creates (which need not be monetary).

Innovation remains messy, even dangerous. Luck and chance—being at the right place at the right time—still play a role.

Like the vertical axis, the horizontal axis also folds back on itself.

An invitation to interaction. The story above describes one path through major points on the map, but the map offers multiple paths and invites closer reading.

While this model is not a recipe, it hints at ways in which we might increase the probability of innovation. But more important, it invites further thinking.

Computer scientist Alan Kay has noted, “We do most of our thinking with models [8].” They are “boundary objects,” enabling discourse between communities of practice [9]. This is what makes models powerful.

The poster includes an invitation to react and participate in improving this model of innovation. Just as quality is founded on the feedback loop of “plan-do-check-act” and feedback loops are necessary for successful innovation, we seek your insights and feedback as well.

The team’s hope is for this model to spur thinking and discussion—interaction among readers. We hope it leads to other, more useful models.

References

1. Shewhart, W. Statistical Method from the Viewpoint of Quality Control, Washington, D.C.: Graduate School of the Department of Agriculture, 1939.

2. Schumpeter, J. Capitalism, Socialism and Democracy. New York: Harper & Brothers, 1942.

3. Ashby, W. R. An Introduction to Cybernetics. London: Chapman & Hall, Ltd., 1957.

4. Novak, J. D., and D. B. Gowan Learning How to Learn. New York and Cambridge: Cambridge University Press, 1984.

5. Nussbaum, B. “The Empathy Economy.” Business Week, 8 March 2005.

6. Conley, C. “Building a Creative Culture,” a presentation, Denver, Colo.: AIGA Image Space Object Conference, 2007.

7. Esmonde, P. Notes on the Role of Leadership and Language in Regenerating Organizations. Menlo Park, Calif.: Sun Microsystems, 2002.

8. Kay, A. From an interview in the video, “Project 2000.” Cupertino, Calif.: Apple, 1988.

9. Star, S. L. and J. R. Griesemer “Institutional Ecology, ‘Translations,’ and Boundary Objects: Amateurs and Professionals in Berkeley’s Museum of Vertebrate Zoology, 1907—1939.” Social Studies of Science 19, no.3 (1989): 387-420.

Author

Hugh Dubberly
Dubberly Design Office
hugh@dubberly.com

About the Author

Hugh Dubberly manages a consultancy focused on making services and software easier to use through interaction design and information design. As vice president he was responsible for design and production of Netscape’s Web services. He was at Apple for 10 years where he managed graphic design and corporate identity and co-created the Knowledge Navigator series of videos. Dubberly also founded an interactive media department at Art Center and has taught at San Jose State, IIT/ID, and Stanford.

Figures

F1Figure 1. PDCA Quality CycleIn 1939 mathematician Walter Shewhart published Statistical Method from the Viewpoint of Quality Control, in which he introduced the PDCA quality cycle. Edward Deming worked with Shewhart at Bell Laboratories and later popularized the quality cycle, especially in Japan.

F2Figure 2. Model-Story CycleExplaining a model involves telling a story, navigating a path through the model. Similarly, telling a story builds a model of actors and their relationships in the mind of the listener.

UF1Figure. A Model of Innovation, March 2007. Dubberly Design Office prepared this 27-by-38-inch concept map as a project of the Institute for Creative Process at the Alberta College of Art and Design (ACAD). Written and designed by Hugh Dubberly, Nathan Felde, and Paul Pangaro, additional design by Sean Durham and Ryan Reposar. Research by Satoko Kakihara and ACAD faculty Chris Frey, Wayne Giles, and Darlene Lee. The model is a direct product of interactions among the team, but it is also the indirect product of interactions with several others who shared their insights with the authors, including Robin Bahr, Chris Conley, Peter Esmonde, Shelley Evenson, Michael Geoghegan, Fred Murrell, and Rick Robinson. To download Hugh’s model as a full-size, printable PDF file, please visit: http://interactions.acm.og/content/XV/dubberly.pdf

UF2Figure. Separating the Model into Components

UF3Figure. Twelve sketches developed during the design process. More than 50 were printed at full size for discussion. The sketches are arranged in chronological order.

UF4Figure. A series of sketches developed by Nathan Felde in chronological order.

Sidebar: Another View

“Innovation” has frustrated me for some time. Does “innovation” mean “new idea,” “invention,” “design concept,” “product revision,” or “game-changing revolution on the order of general relativity?”

Making a concept map is a good way to decide what we mean. In the process of collaborating to build this map, I felt that coming to the core entailment—“innovation is an insight that inspires change and creates value”—was an insight of its own about innovation. I sensed that if this insight countered the dilution of meaning and inspired a change in use of the term, it would create value. An innovation about innovation. But, as with any innovation, saying it does not make it so—it actually has to change a convention, and for the better. (“Value” means “positive value.”)

There was a point where that core entailment was lost in revision, one of many twists and turns in the process. This shows that the process of innovation can be fragile. Perhaps because I was a participant, I feel the story of making the map is as interesting as the outcome. Reviewing the spreads reprinted here retells some of that story; flipping through 50-plus full-size prototypes retells it fortissimo. What neither tells is the tug-of-views across cities, threads of email, and fields of Post-it notes. One key argument was: What parts of the process of innovation are messy, unpredictable, ineffable, mystical, magical, intuitive? The more that innovation is those things, the less we can help the process and make a deliberate innovation; at one extreme, that phrase becomes an oxymoron. Conversely, what parts of innovation are predictable, likely, improvable, or even deterministic? We certainly resist the idea that the source of inspiration, the source of hypotheses, can be fully known, reduced to algorithm.

While we explored those questions, I learned that bringing about innovation, in addition to requiring creativity, requires stubbornness. Without stubbornness, obsessiveness even, why would an individual rage against the lock-in of current convention—spend all that time in the patent office and on trains, in thought experiments outside of prior language in order to see anew? So, this is the unpredictable part: getting to the moment of genuine insight, when a new means to solve a problem (a new metaphor for framing the problem-solution) breaks the lock-in of convention. This is the inventor’s phase of innovation.

Yet innovation requires a second form of obsessiveness: Inspired by the possibility of bringing value, there must be drive to do something with the inventor’s insight. This role can be called “the innovator,” and often a different person plays it. Propelled by the demonstration of possibility, the innovator moves from insight to demonstration to fruition—to creating value.

Is it inevitable that, once invented, an insight with real potential brings about valuable change? It would seem so, though timelines and paths are not predictable. The innovator’s phase seems more understand-able, plan-able, work-able from experience. These are the aspects we can better understand, and foster, and improve.—Paul Pangaro

About the Author

Paul Pangaro is the CTO at CyberneticLifestyles.com in New York City where he consults at the intersection of product strategy, marketing, and organizational dynamics. He is recognized as an authority on search and related conversational impedences in human-machine interaction, and on entailment meshes, a highly rigorous framework for representing knowledge. He was CTO of several start-ups, including Idealab’s Snap.com, and was also the senior director and distinguished market strategist at Sun Microsystems. Paul has also taught at Stanford University.

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