We begin by assuming that cultures are created or emerge for a reason. Culture is a means to an end, not the end in itself. Great leaders begin by clearly defining and communicating why their organizations exist and create a culture that can achieve this purpose.
We propose that the power of organizational culture lies in its ability to create a collective intelligence that can acquire and apply knowledge and skills at a rate far exceeding that of any individual. This power derives from the organization’s shared tacit knowledge, collective explicit knowledge, acceptable behavior patterns, and the structure of the underlying social network that contains the organization’s social capital. A culture of innovation is the means by which great leaders create a collective intelligence capable of attaining the organization’s higher purpose in any environment.
We differentiate between an innovative culture and a “culture of innovation”, the latter being characterized by a collective intelligence that willingly exposes itself to new paradigms, challenges its existing paradigms and, thereby, continuously expands the range and volume of knowledge and skills it can acquire and apply.
This leads to a definition of innovation as the acquisition and application of knowledge and skills that change behaviors and create value in a culture. This definition acknowledges that perception of an innovation and its value are relative. One culture’s valuable innovation might be the demise of another. Second, the definition recognizes that innovations change behavior patterns, affecting relationships which in turn changes the underlying social network of an organization or society, and thus its social capital.
Further, the resulting model provides a means to identify practical actions leadership can affect to transform an organization’s culture to better compete in a world of complexity and accelerating change. It provides a “systems approach” to ensure the transformation effort will be coherent, complete and successful. Finally, the model is highly measurable providing for all four generations of innovation metrics defined by the President’s Council on Competitiveness.
The WHY of Culture
We begin with Simon Sinek’s simple but elegant model of leadership based on his concept of “The Golden Circle”. As Sinek explains every organization understands WHAT they do, their product or service. They differentiate themselves by HOW they do it, this is their “secret sauce” or competitive advantage. But very few organization’s clearly articulate WHY they do what they do. The WHY is the organization’s purpose or cause, its reason to exist.
Figure 1: The Golden Circle
The WHY is enduring, the WHY unifies, motivates and engenders loyalty and trust. Sinek claims that great leaders start with WHY and this determines their uncanny ability to attract a cult-like following and succeed year after year regardless of changes in their environment.
Apple is a great example. Founded in 1976 as “Apple Computer Inc.” it defined itself narrowly as a personal computer company. In 1997 it changed its name to “Apple Inc.” and launched its new slogan “Think different.” It launched a series of television ads featuring images of people who changed the world from Albert Einstein to Martin Luther King. The ads ended with the line “The people who think they are crazy enough to change the world are the ones who do”, followed by the Apple logo and the slogan “Think different.”
Throughout Jobs’ tenure at Apple he positioned its reason for existence, its WHY, as the defender of, and home for those who are different, who don’t want to be part of the establishment, that think of themselves as fighting the “Borg”. This cognitive model has attracted extremely creative employees and enabled Apple’s journey from computers, to iPods, iPhones, iPads, iTunes and the App Store. It has served them as they transitioned from a closed proprietary operating system to the iTunes and App Store platforms that leverage thousands of non-Apple programmers creating apps that now number more than one million, generating over $15 billion for developers since its launch in 2008 and an estimated $1 billion for Apple in the fourth quarter of 2013 alone. The WHAT and HOW may have changed dramatically from the days of the first Macintosh but Apple’s WHY has endured.
A more recent example is Google. Google’s WHY is “…to organize the world’s information and make it universally accessible and useful.” While Google’s original competitive advantage, their “technical HOW”, might have been the “page rank algorithm” which produced better Web search results than the competition, they have moved far beyond that. Google’s WHY has served them well even as the methods and types of information they make available has changed significantly.
A military example was the David Taylor Research Center. Rodney Carlisle chronicled the hundred year history from 1898 to 1998 of one of the Navy’s premier laboratories. Created at a time when shipbuilding was an art, the David Taylor Research Center’s overarching WHY was “…to bring science and engineering to the design of the U.S. fleet” and thereby “…create a more effective instrument of foreign policy.” The David Taylor Research Center moniker was proudly displayed on buildings and ID badges. This WHY served the organization from the time of the Monitor to the modern nuclear Navy during which the relevant domains of science and engineering varied over an enormous spectrum.
According to Carlisle it was also a time of expansionist ideology and the cultural paradigm was shifting from looking at the oceans as “protective moats” to the “highways of commerce”. Carlisle notes that Mahan had put forth the then widely accepted theory that “… a nation’s greatness would flow from a strong maritime commerce and a strong navy to protect that commerce.” This popular belief coupled with the David Taylor Research Center’s WHY broadened its appeal and enabled the center to compete with national labs, prestigious universities and industry to attract some of the best scientists in the world.
One last example is the National Aeronautics and Space Administration (NASA) founded in July of 1958. NASA’s WHY for over half a century has been to “pioneer the future in space exploration, scientific discovery and aeronautics research”. Their WHAT has changed from launching earth-orbit satellites to putting a man on the moon, the space shuttle, and now exploring the far reaches of space.
So how does culture relate to The Golden Circle? We propose that the HOW, the “secret sauce” for great organizations that stand the test of time, is their culture. Yes, on the surface you could have analyzed any of the examples above at any point in time and argued that a certain set of technologies or business relationships constituted the secret sauce at that time, but such arguments fall flat when you look over decades. Only a culture that can repeatedly fulfill their WHY, producing the next great WHAT, will keep an organization at the top for sustained periods of time.
Figure 2: The Golden Circle and Culture
The significance for leadership is that cultural transformation must begin with the WHY, the reason for the organization to exist, its purpose, cause or mission. It might be tempting to start with the organization’s vision and strategy but while this may result in building a culture that is optimal for a specific envisioned future it might equally result in a culture unresponsive to a different future. Only by starting with WHY can leadership build a culture characterized by a collective intelligence that is deep, agile and capable of succeeding in a volatile and unpredictable world.
Definition of Culture
The dictionary defines culture as:
The arts and other manifestations of human intellectual achievement regarded collectively.
In 1871 anthropologist Edward Tylor published “Primitive Culture” in which he defined culture as:
”…the full range of learned human behavior patterns.”
Geert using a software metaphor defines culture as:
“…the collective programming of the mind that distinguishes the members of one group or category of people from others.” And notes that “…culture is a socially constructed attribute of organizations that serves as the social glue binding an organization together.”
Adamsky defines culture in a military context as:
“…a set of shared formal and informal beliefs, assumptions, and modes of behavior, derived from common experiences and accepted narratives (both oral and written), that shape collective identity and relationships to other groups, and which influence and sometimes determine appropriate ends and means for achieving security objectives.”
Cameron and Quinn note that a review of existing literature produced over one hundred fifty definitions of culture. Their own definition is:
“…the concept of culture refers to the taken-for-granted values, underlying assumptions, expectations, and definitions that characterize organizations and their members.”
They further define their model of culture as a four level pyramid starting at the bottom with the unobservable “Implicit Assumptions” and proceeding to the more observable “Conscious Contracts and Norms”, “Artifacts”, and “Explicit Behaviors”.
While these are all acceptable definitions they are insufficient for our purposes. We need a model of culture that can be used to guide the actions of leadership and produce measurable results.
Culture of Collective Intelligence
The figure below diagrams the model of culture as a collective intelligence that we will develop in the following sections of this paper.
Figure 3: Culture of Collective Intelligence
The power of organizational culture lies in its ability to create a collective intelligence that can acquire and apply knowledge and skills at a rate that far exceeds that of the individual. We will show that the collective intelligence of an organization arises from its shared tacit knowledge, collective explicit knowledge, and the acceptable behavior patterns that determine the structure of its underlying social network and ultimately the organization’s social capital, “the stock of active connections among people: the trust, mutual understanding, and shared values and behaviors that bind the members of human networks and communities and make cooperative action possible”.
Shared Tacit Knowledge
Our starting point for this new model of culture is the theory of knowledge developed by Polanyi and Nonaka, and Kuhn’s seminal work on the structure of scientific revolutions.
In the Polanyi/Nonaka theory there are two types of knowledge: tacit and explicit. Tacit knowledge is the knowledge contained in one’s mind. Explicit knowledge is tacit knowledge that has been codified in a form that can be accumulated, stored, shared among and used by the members of the organization.
According to Nonaka tacit knowledge can be decomposed into cognitive and technical tacit knowledge. Cognitive tacit knowledge is comprised of the metaphors, analogies and models we use to understand our environment. Technical tacit knowledge is comprised of the skills, craft and knowhow we apply to interact with our environment.
While our mental models will determine if we will react to a change in our environment it is our skills, craft and knowhow that will determine the form of that reaction. A carpenter and a mason may both have a mental model that prompts them to react to the environment by building shelter, however the type of shelter each builds will be influenced by their technical tacit knowledge.
Kuhn explored the role of tacit knowledge in scientific communities. He explains that a community of scientists follow a given paradigm describing the world which guides the questions they ask and the answers they will find acceptable. Their shared tacit knowledge, the paradigm, is “what the members of a community share, and, conversely, a scientific community consists of men who share a paradigm.” So it is the shared tacit knowledge of a community that helps define its culture.
In the fiftieth anniversary edition Kuhn clarifies the definition of paradigm to include: symbolic generalizations (models), analogies and metaphors, values (e.g. predictability), and shared exemplars of acceptable problem/solution sets. Kuhn’s “paradigm” maps closely to Polanyi/Nonaka’s tacit knowledge.
Kuhn made another important point, words have different meanings in different scientific cultures. For example, “mass” and “energy” in a classic Newtonian scientific culture can never be equated with each other as they are in the relativistic paradigm E=mc2. In the Newtonian culture a scientist would not even ask about the equivalence of mass and energy.
As a more general example, in Spanish the word “bridge” (el puente) is a masculine noun and the words most frequently associated with it in Spanish cultures are: strong, dangerous, long, sturdy, big and towering. In German the word “bridge” (die brucke) is a feminine noun and the words most frequently associated with it in Germanic cultures are: fragile, elegant, beautiful, peaceful, slender and pretty. The same object, two cultures with two totally different sets of associations. Thus we will add “language” to the shared cognitive tacit knowledge of a culture.
Finally, Kuhn notes the importance of shared values. For his scientific paradigms Kuhn notes quantitative predictions are preferable to qualitative ones, and simplicity, self-consistency, plausibility, and compatibility where possible with other theories, are important values.
Non-scientific cultures are also guided by a set of values. For example, the original values of our American culture were perhaps best captured in the lines from Emma Lazarus’ poem emblazoned on a plaque inside the lower level of the Statue of Liberty:
“Give me your tired, your poor,
Your huddled masses yearning to breathe free,
The wretched refuse of your teeming shore.
Send these, the homeless, tempest-tossed, to me:
I lift my lamp beside the golden door.”
Which proclaims how American culture values freedom and opportunity for all.
Each member of an organization will bring a set of tacit knowledge that relates to their background, education and role in the organization. The tacit knowledge of a researcher will differ from that of an accountant or lawyer, however, it is the shared tacit knowledge of all members of an organization that contributes to the organization’s culture.
As an example consider the power of a shared metaphor or logo. If a shared element of tacit knowledge is embodied in a logo like IBM’s “Think” or Apple’s “Think different” it will help to develop a culture that values thinking. In contrast, if a metaphor like the Navy’s “stay in your swim lane” is shared across an organization it will discourage thinking outside of a narrowly defined lane and hinder collaboration. The power of metaphors lies in the associations they both stimulate and repress in people’s minds, thus influencing their behaviors and affecting the culture.
Collective Explicit Knowledge
As noted above, explicit knowledge is tacit knowledge that has been codified in a form that can be accumulated, stored, shared among and used by the members of the organization. This can be as simple as a hand written manuscript or as complex as a body of legal or financial policy. But it need not be codified in only the written word, tacit knowledge can be captured in technology as simple as a hammer and nail or as complex as a nanotechnology laboratory. In fact, technology is the means by which a society or organization embodies the deep tacit knowledge of the few in a form that can be used by the many to enable behaviors and create value. From a cultural perspective the best embodiments enable the greatest number of users to create value with a minimum of prior knowledge.
Policy is a major mechanism by which leadership shapes the collective explicit knowledge of an organization. Policy, properly written, embodies the values that leadership wishes to promote. For example, the Navy’s Working Capital Funds (WCF) policy values behaviors that are customer-centric, behaviors focused on the needs of the customer. Some feel this was a reaction in the early 1990’s to block funding, which valued advancing the frontiers of science over a focus on current customer requirements. Both values, customer focus and advancing the frontiers of science, are laudable values for research and development organizations. The historical focus of our military on advancing the frontiers of science produced most of the key technologies that are the basis for today’s economy, and the recent strong focus on customer needs has rapidly produced solutions to problems in the proliferation of conflicts.
Most policies are written very broadly because they need to be applicable in many different situations over a long period of time. Practice is how current management interprets policy. Practice defines the daily behaviors we deem acceptable to implement a policy. For example, in some Navy labs each individual engineer or scientist competes with every other engineer or scientist for WCF funds. This can lead to dysfunctional behavior such as not collaborating or sharing knowledge for fear it will advantage the other person seeking the same funds.
As a second example of well-intended policy the practice of which may be less than optimal consider laboratory equipment purchased to pursue a solution for a current customer need. In some Navy labs this equipment remains the exclusive property of the group that won the WCF funds. The reason often given is the schedules are so tight the group cannot afford the risk of another group sharing the equipment, consuming cycles, and perhaps contaminating results. These are very rational arguments from a customer-centric perspective. However, the unintended consequences are duplication of equipment and stifling of collaboration.
As a counter example consider NRL’s nanotechnology lab run by Dr. Snow. The lab has NISE 219 funding that NRL scientists can compete for, however, Dr. Snow’s policy and practice is that no proposal will be considered unless it is sponsored by a collection of scientists from multiple NRL organizations. Further, the equipment in the nanotechnology lab is for use by all NRL scientists. This policy and practice is likely to increase collaboration within NRL and grow its underlying social network.
Acceptable Behavior Patterns
The organization’s collective explicit knowledge enables its members to apply their tacit knowledge in a way that can provide a distinct competitive advantage. For example, the one of a kind wave tank of NSWCCD has historically enabled Carderock engineers and scientists to design and test hull configurations that no one else can. Similarly, NAWCAD’s simulation tools and flight test ranges have enabled it to achieve advances in unmanned aircraft unmatched by others. In both organizations their collective explicit knowledge enabled “design and test” behaviors that resulted in a competitive advantage for our Navy.
Thus the organization’s shared tacit knowledge and collective explicit knowledge determine the behavior patterns exhibited by an organization. The behavior patterns that are characteristic of the organization’s culture.
The importance of behaviors cannot be over emphasized. Our actions towards others determine our social relationships. Our language and business literature are full of quotes that speak to this like Mark Twain’s: “Actions speak louder than words, but not nearly as often”, “practice what you preach”, and the business guidance that leadership must “walk the walk” and not just “talk the talk”. To put our model in such linguistic terms the shared tacit knowledge is “thinking the thought”, the collective explicit knowledge is “talking the talk” and the acceptable behaviors patterns are “walking the walk”. To be an effective culture the thought, talk, and walk must be consistent.
Underlying Social Network
The three elements of shared tacit knowledge, collective explicit knowledge, and acceptable behavior patterns cover many of the sociological and anthropological definitions of culture. But for our purposes we will include one additional element, the organization’s underlying social network.
To understand the role of social networks in culture we need to first look at Cameron and Quinn’s competing values framework (CVF) and Ronfeldt’s theory of societal evolution. The CVF characterizes the culture of an organization along two orthogonal axes. One axis measures the organization’s propensity for control versus flexibility, the other its degree of internal versus external focus. This results in four cultural archetypes as depicted below, the Clan, Command & Control, Market, and Adhocratic.
Figure 4: CVF Cultural Archetypes
Along each diagonal there are opposing archetypes. For example, in the lower left the organization values control and an internal focus while in the upper right the organization values flexibility and reaching out beyond its boundaries. Cameron and Quinn point out that in reality few if any organizations fit completely into any of the archetypes, instead organizational cultures are a blend of the four archetypes. In fact you would expect that subunits of an organization might lean more or less to one archetype or another. A research and development group might be more adhocratic while manufacturing organizations might produce optimal results with a command and control culture.
Ronfeldt arrived at a very similar conclusion about societal evolution where he posits that societies progress through organizational forms beginning with tribal, progressing to institutional, then market and finally what he calls networked (Cameron’s adhocracy). He claims that each successive form incorporates the previous forms, restricting the earlier forms to what they do best, and extending the society’s capacity for solving ever more complex problems by the addition of the newer forms. For example, as a society evolves from a collection of independent tribes to an institutional form, multiple tribes are incorporated into and coordinated through a set of hierarchical relationships to deliver a solution that is beyond any one tribe. The institutional organization coordinates the research, development, manufacturing, marketing and sales “tribes” to deliver a product or service more complex than anything a single tribe can produce.
Ronfeldt makes another very important claim. He claims that the emergence of the more sophisticated forms of society parallel the rise and use of more robust forms of communication. The institutional form could not arise before the advent of the printing press, the market form was made possible by telephony and television, and the adhocracy that is just now emerging is made possible by the Internet, smart phones and social media.
Technologies will change with time and organizations may or may not adopt them so we think it is more meaningful to view the evolution of forms in terms of the topology of the dialogues practiced between members. As the form of dialogue between members progresses from primarily one-to-one, to a-few-to-many, and finally many-to-many, and transitions from an internal to external focus, you enable respectively the communication characteristics of the clan, institution, market and adhocratic forms. Ronfeldt notes that historical evidence indicates each successive societal form is capable of solving more complex problems. He does not give any supporting logic for why this is so but our model will show that it is related to the society’s increasing collective intelligence.
We will now look deeper at the underlying social network. The basic building block of social networks is the triad, three people that stand in relation to each other. We will use simple network theory to understand how leadership can design policies that will grow the organization’s social network and increase the density of connections, and thus dialogues, between members of the network.
Figure 5: Triads and Social Networks
To simplify things we will ignore the subtleties of real human relationships and only allow a positive (+) or negative (-) relationship between any two members of a triad. With this constraint, network theory will tell us there are four possible triadic configurations, two of them stable and two unstable.
The stable “Common Enemy” form is used by business, political and military leaders worldwide to “rally the troops” against the opposition. Think Apple versus IBM, the United States versus Soviet Union, or the political left versus the right. By identifying a common enemy leadership sets the boundary of the organizations’ social network, in general members of the organization’s network do not “fraternize with the enemy”, i.e. they do not engage with the enemy in the tacit/explicit dialogue. In designing policy leadership needs to balance the value of the motivation produced by the common enemy form with the value of the knowledge that the organization will not be able to access in the “enemy’s” network.
In the Deal with the Devil form all three relationships are negative. This form usually leads to two of the opponents teaming up to defeat the third only to return to an adversarial relationship again. Unfortunately this is the structure of much of the politics of the Middle East today. Policies that pit everyone against everyone else in the organization do not lead to a stable growing social network and thus diminish collective intelligence.
In the Lovers Triangle two members of the triad compete for the favors of the third knowing only one can win. This may seem like normal healthy competition, for example two organizations competing for a contract, and it is, if we are talking about two separate organizations. However, if policy creates the Lovers Triangle within a single organization it will destroy the underlying social network. As an example prior to IBM’s decline in 1991 the company had a practice of lifetime employment. Employees were encouraged to have their spouses and children join IBM creating a familial culture. Every IBM researcher would willingly share their findings with every other researcher, after all they were all members of the same family. When the Board of Directors brought in outside leadership they instituted a policy of ranking all employees and year after year terminated the bottom ten percent until the costs reached the desired level. While this may have been financially sound it had a chilling effect on the sharing of knowledge with each researcher afraid they might give away the insight that would contribute to the other researcher being ranked higher. IBM had unwittingly implemented a policy based on the Lovers Triangle.
The final triadic form is the stable Friends are Friends. This form leads to growth and stability of the underlying social network. But how could policy foster this triadic form? One example comes from the Naval Research Laboratory (NRL). Within NRL is the Nanotechnology Lab which has a collection of sophisticated tools and funding resources. The director of the laboratory implemented a policy that would only consider proposals to use the lab if they were coauthored by two or more NRL groups. This policy fosters Friends are Friends formations and successful experiments conducted in the Nanotechnology lab not only develop valuable knowledge, they foster social relationships that live on, creating a cumulative effect that increases the density of connections in NRL’s network, facilitating more tacit/explicit dialogues and increasing their collective intelligence. It is actually possible to measure this in the network of inventors revealed by the patents issued to Navy labs. Of the six Navy labs we examined NRL had the greatest connected component.
By way of counter example the Navy’s implementation of Working Capital Funding (WCF) with fund raising responsibility placed at the level of the individual researcher is an example of policy that creates a destructive Lovers Triangle. While the Navy cannot abandon WCF it could learn something from NRL and modify the implementation of WCF to increase Friends of Friends formation.
Google X, the secretive Google laboratory where they pursue their “moon shot” projects, has a policy that allows any employee to leave their current manager and go to work for another. This may seem extreme but in the end bad managers find themselves alone and leave the company, only those that can create a cohesive Friends are Friends network survive.
Even something as simple as equipment ownership policy can make a difference. Equipment and laboratory facilities are part of an organization’s collective explicit knowledge, with emphasis on collective. Policy that creates facility and equipment “silos” is not only financially unsound it eliminates the opportunity to form the social bonds that are created when people use a common facility like NRL’s Nanotechnology Lab. Social bonds that facilitate the tacit/explicit dialogues essential to the collective intelligence.
Thus, network theory provides another tool for policy formation. As demonstrated in our own studies and others there are many forms of the organization’s collective explicit knowledge such as publications, patents, funding documents and email traffic that can be used to quantify and visualize the organization’s underlying social network. This provides a valuable tool to measure the effectiveness of new policy and make changes where appropriate. The more means we have of visualizing the underlying social network the better we can measure the effectiveness of policy.
We will now formally define collective intelligence. One definition of intelligence is an individual’s ability to acquire and apply knowledge and skills. By analogy we will define “collective intelligence” as:
Collective Intelligence is the organization’s collective ability to acquire and apply knowledge and skills.
Nonaka claims the fundamental process by which this happens is the tacit/explicit dialogue. A dialogue in which one person makes their tacit knowledge explicit to another person who, if they internalize and justify it as a “true belief”, makes it a part of their tacit knowledge. This collective ability is related to the number of possible tacit/explicit dialogues between members. Given a collection of people in a social network who are willing to engage in dialogue the number of possible different combinations of people and thus dialogues is governed by Reed’s law. With two people there is only one possible dialogue, with four there are eleven, with eight there are two hundred forty-seven. In general Reed’s law states:
Number of possible dialogues = 2N-N-1, where N is the number of people
Thus the number of potential dialogues grows quickly with the size of the social network, this is the power of collective intelligence.
But “potential” is the key word. It is impossible for any individual to conduct meaningful bidirectional dialogue with more than a handful of people at any given time. Studies show that even people who have thousands of “friends” on social media only conduct frequent bidirectional dialogues with a handful of those friends. But by Reed’s law the size of the network involved in the tacit/explicit dialogue need not be more than a handful for the potential collective intelligence to exceed individual intelligence by orders of magnitude. Herein lies the power of a culture that supports the growth of a robust social network and facilitates the tacit/explicit dialogue.
There is one more factor to consider. A limitation of a clan or tribe is that all members will have primarily the same tacit knowledge. While the clan’s collective intelligence will allow it to generate knowledge quicker relative to the clan’s unifying paradigm it is highly unlikely they will ever experience in the words of Toffler a “paradigm shift”. Paradigm shifts require the clan to be exposed to other paradigms. This is part of the power of the institutional form that unites multiple clans with different paradigms. It can generate a greater volume and a broader range of knowledge. But the institution never achieves the level of a market form or adhocracy because the internally focused command and control hierarchy restricts cross paradigm dialogue to a small subset of all potential dialogues. The message for leadership is that not only do we need to build a robust social network and facilitate dialogue, we must promote dialogue between communities that believe in different paradigms. Koestler called this the “bisociation” process which he claimed is the root of all creativity. A bisociative dialogue is one in which participants are forced to juxtapose two normally disparate planes of thought resulting in creative tension that produces an in insight.
Since we are interested in creating a collective intelligence that increases our capacity for innovation we need to look at policies affecting the bisociative form of the tacit/explicit dialogue. Policy that encourages dialogues that juxtapose normally orthogonal paradigms. Attendance at conferences with a wide spectrum of participants from different organizations and scientific or technical domains is a good policy to encourage bisociative dialogue. Hosting distinguished speakers that have unusual models of the world is another. More generally, policies that provide for the information technology infrastructure that facilitates the many-to-many dialogue (social media, MMOWGLI, etc.) increases the probability of bisociations leading to novel insights. Unfortunately these are the types of policies that are eliminated or restricted when budgets get tight.
Culture of Innovation
Not all cultures of collective intelligence are created equal. At one extreme end are cultures that cling to a single unchanging paradigm, at the other end are cultures that constantly challenge their own paradigms looking for yet another paradigm that better predicts and explains the world, changing behaviors and creating more value.
This leads us to offer a definition for one special type of culture, a “culture of innovation”.
A society or organization that possesses a culture of innovation is characterized by a collective intelligence that willingly exposes itself to new paradigms, challenges its existing paradigms and, thereby, continuously expands the range and volume of knowledge and skills it can acquire and apply.
In other words a culture of innovation is constantly innovating its own culture.
As a counter example consider the culture of Kodak. Kodak was definitely an innovative culture that kept acquiring and applying new knowledge and skills relative to a film-based paradigm for photography. Ironically Kodak also patented some of the earliest inventions for digital photography, however, they were not a culture of innovation because they could not challenge the film-based photography paradigm that dominated their culture.
This also leads us to a definition of innovation. Like culture there are probably over one hundred definitions in the literature, the operational definition we have selected is:
Innovation is the acquisition and application of knowledge and skills that changes behaviors and creates value in a culture.
This definition acknowledges that that perception of an innovation and its value are relative to the culture. One culture’s valuable innovation might be the demise of another culture. For example, the snowmobile was a highly valued innovation in many cultures, however, when it was introduced to the Skolt Lapps it decimated their culture resulting in rampant unemployment and lifetime dependence on the Finish government.
Finally, the definition recognizes that innovations change behavior patterns, sometimes for the good, sometimes not. Changed behavior patterns affect relationships which in turn changes the underlying social network of an organization or society leading to a change in the organization’s social capital.
Designing a Cultural Transformation
The model of culture as a collective intelligence can be used as a framework to guide cultural transformation.
Figure 6: Framework for Cultural Transformation
We suggest that the starting point is defining the organization’s WHY. This is not as simple as it sounds. It is far too easy to plug in a sentence from the current mission, vision or strategy documents. The WHY is more enduring than the latest strategic plan. The WHY outlives changes in the environment that force rethinking of strategy.
Beyond the WHY there is no prescribed order for traversing the four elements of the model. It is possible to begin with any element, the key is to touch all four elements to ensure completeness.
For example some organizations might be most comfortable identifying the desired behavior patterns. Once a desired behavior has been identified the group might jump to the underlying social network and ask what kind of stable or unstable triadic relationships would this behavior create? If the group determines that the behavior would create Friends-are-Friends or Common-Enemy triads that would be good to grow and/or motivate the underlying social network. If it can be reasoned that the behavior might create Lovers-Triangle or Deal-with-the-Devil triads the group might want to rethink the desirability of such behavior patterns.
Assuming the desired acceptable behavior will grow and/or motivate the organization’s social network the group might next ask what collective explicit knowledge does the behavior require? For example, what effect will existing policies have on promoting or inhibiting the behavior pattern? Do financial, human resource, reward and recognition, facilities, contracting, legal, training, and information technology policies and practices support the behavior? If not is it the policy or the practice that is the problem? Often it is the practice, the narrow interpretation of a policy that is to blame. If it is, can the practice be changed within the bounds of the policy? If it is the policy, can it be changed? Once the existing policies and practices have been reviewed the group should ask if a new policy or practice is necessary to support the desired behavior, if it is, the group should engage a team of experts and stakeholders in designing and piloting the new policy or practice.
But policy is but one form of collective explicit knowledge. The group should also consider what technologies will be required to support the desired behavior. Does it require special equipment, facilities or information technology?
Finally, the group would need to consider the shared tacit knowledge that supports the behavior. What language do we need to use, which metaphors and analogies are most closely associated with the desired behavior? Is there a mental model for the behavior and a set of values that might help us measure it? Can we use these metrics to assess the cultural transformation?
The last element is the technical tacit knowledge required by the behavior. What skills competency and knowhow must our organization possess to execute the desired behavior patterns? If the organization is lacking in this way the group might revisit the human resource and training policies to acquire and or grow the required skills.
One of our objectives was to produce a model of culture that was both robust and measurable. We think we have accomplished this. Some of the measurements are logical yes/no metrics, for example, have we defined the desired behaviors, do we have policies and practices to support the behavior, etc.? The acceptable behavior patterns themselves have been measured by anthropologists and sociologists for a long time using survey mechanisms and longitudinal studies. More recently the underlying social network is also yielding to visualization and measurement. We have used social network analysis tools to visualize networks of inventors and authors. Others have used similar tools on email traffic and funds flows. Once mapped network science can be applied to reveal a host of quantifiable and revealing characteristics. For example, with invention networks based on patent data we have been able to identify the existence of an Innovation Backbone™, a small collection of inventors, usually less than ten percent of the inventing population, that span the entire network holding it together like a primate’s backbone and facilitating the transmission of ideas across the network. We have been able to measure a networks ability to propagate new knowledge by using an epidemiological analogy and calculating the largest connected component.
In fact the model of culture as a collective intelligence enables us to use all four generations of innovation metrics that were identified by the National Innovation Initiative of the President’s Council on Competitiveness and shown in Table 1.
Table 1: Evolution of Innovation Metrics
In summary the model of culture as collective intelligence will not answer the questions for you, rather it will ensure that an organization systematically asks a complete and robust set of questions that will increase the probability of a successful cultural transformation.
Innovation Business Partners, Inc.
Copy Right 14 November 2014
 “Start with Why: How Great Leaders Inspire Everyone to Take Action”, by Simon Sinek.
 See Google Company at https://www.google.com/about/company/
 “Where the Fleet Begins – A History of the David Taylor Research Center”, by Rodney Carlisle, National Historical Center, Department of the Navy, Washington 1998.
 “The Influence of Sea Power Upon History 1660-1783”, by Alfred Mahan.
 Google online dictionary.
 Wikipedia http://en.wikipedia.org/wiki/Culture
 “Cultures and Organizations: Software of the Mind” 3rd edition, Geert et al
 “The Culture of Military Innovation: The Impact of Cultural Factors on the Revolution in Military Affairs” by Adamsky
 “Diagnosing and Changing Organizational Culture: Based on the Competing Values Framework”, by Cameron and Quinn.
 “In Good Company – How Social Capital Makes Organizations Work” by Don Cohen and Laurence Prusak, Harvard Business School Press, 2001.
 “The Tacit Dimension”, by Michael Polanyi.
 “Managing Flow – A Process Theory of the Knowledge-Based Firm” and “The Knowledge-Generating Company” by Ikujiro Nonaka et al.
 “The Structure of Scientific Revolutions – 50th Anniversary Edition”, by Thomas Kuhn.
 National Public Radio, Shakespeare Had Roses All Wrong, April 6, 2009
 See Title 10 – Armed Forces – U.S.C. 2208 Working-capital funds
 “Diagnosing and Changing Organizational Culture: Based on the Competing Values Framework”, by Kim S. Cameron and Robert E. Quinn.
 “Tribes, Institutions, Markets, Networks: A Framework About Societal Evolution” by David Ronfeldt of RAND Corporation.
 “Analysis of Innovation Backbone Inventor Interviews – Network Theory of Creativity & Innovation Project” 8/3/2013 and “Carderock Innovation Survey – Final Report” 11/15/2013 studies by Innovation Business Partners
 Google online dictionary.
 See Wikipedia for a discussion of Reed’s Law http://en.wikipedia.org/wiki/Reed’s_law. Also see the criticism of it and the concept of Dunbar’s Number http://en.wikipedia.org/wiki/Dunbar%27s_Number. In this model we use Reed’s Law in a very restrictive sense that should satisfy Dunbar’s criticism.
 “Networks, Crowds and Markets – Reasoning about a Highly Connected World”, by Easley and Kleinberg, see Chapter 3.4 Tie Strength, Social Media and Passive Engagement.
 “Future Shock”, by Alvin Toffler, Random House LLC, 1990.
 “The Act of Creation”, by Arthur Koestler, 1964.
 “Diffusion of Innovations”, Everett Rogers, 1962.
Bee Hive Image by David Goehring. This image was cropped, the original can be found at https://www.flickr.com/photos/carbonnyc/7179913993