Ethnovention

EthnoventionTM is a term coined by Work Frontiers InternationalTM to describe our rapid, iterative method of studying the behavior of an organization or community using organizational ethnography and archaeology, and in the same cycle, introducing innovative interventions to enhance their behavior.

This cycle is repeated until the desired outcomes are reached. Thus the term, ethnoventionTM has sprung from a combination of the main elements of this approach: ethnography, intervention, and invention.

These quick EthnoventionTM cycles typically produce rapid improvements in the work practices of the community. This is because the community gains deep insights into their behaviors and is given the opportunity to reflect on them and change them themselves. This is much more effective than the traditional brute force method of change - being forced to implement changes designed by others without the participation or intimate knowledge of the affected community.

They also learn the new approach more quickly and deeply because they designed it and they get to keep experimenting with it until they find just the right fit for themselves.

An example of how this works is a recent knowledge audit conducted by Work Frontiers for a large non-profit organization. The desired outcomes of the knowledge audit were:

- Seek answers to the following questions:

• What knowledge do staff need to do their jobs?
• Where/whom do they get it from?
• How do they use it?
• What do they do with it when they’re finished?
• What enhances the flow & sharing of knowledge/information?
• What impedes it?

- Avoid rehashing & repeating the same old messages about what’s wrong with the organization
- Instead, focus on what works & what can realistically be done
- Seek to leverage the organization’s existing strengths & intellectual assets

Using EthnoventionTM , we sought to not only observe and record the knowledge behaviors of the organization staff. We also were there to influence them as soon and as much as possible, without inflating their expectations beyond the ability of the organization to meet them.

Two Work Frontiers ethnoventors spent about two and a half months interacting with most parts of the organization, studying its knowledge behaviors and artifacts. By immersing ourselves in the organization for this period, we were able to not only do objective data collection, but also to accumulate subjective impressions of the organization which helped us to understand the essence and root causes of the data rather than just the form of it.

Extraído de EthnoventionTM
A Powerful Tool for Organizational Reflection and Transformation
by Arian Ward, Wednesday, July 28, 1999

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Environment suitable for knowledge sharing

...knowledge is best facilitated, not managed. In fact, more time should be spent on the design of an environment conducive to sharing...and less on the formalized capture of organizational knowledge. The environment has the greatest value.

Some components needed in a well-crafted environment suitable for knowledge sharing:

• Informal, not structured
• Self-forming connections - let members of the environment decide how to interact
• Tool-rich - members should have many options for connecting and dialoguing.
• Simple/single starting point...but multiple branches/exits
• Diversity of participants - very critical...people tend to form into groups of similar people. Knowledge sharing and innovation require smashing together ideas of contradictory or unrelated nature
• Time - facilitation is best viewed as a small spark that grows into a roaring flame over time. Most managers seem to prefer explosions that die out quickly...
• Trust - knowledge sharing is about people. Safety and security (face to face or online) lead to trust. General community rules should value individual contributions and personalities.
• User-shaped - most KM initiatives begin with the mindset of building a house and then telling employees to move in an basically only hang up pictures. Instead, they should be given tools and supplies...and then allowed to create what they really need.
• Community feel - communities of practice have gained a reputation as being effective means of sharing knowledge...because we are most likely to share what we know with people we know.
• Capturing and searching - these staples of KM are still important. Newcomers should be able to observe the trials others have walked...and if done right, a KM system could link into persformance support systems...resulting in up to date resources for people...when they are needed.
  

Extraído de elearnspace

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Knowledge Sharing Environment

"Most people have a hard time dealing with the sheer number of e-mails they receive," says Andrew Mitchell, a research associate with London-based British Telecommunications E-Government Division. "We need solutions to store and summarize all this, and that will enable us to collaborate."

"I'm concerned that people don't know how to find what they need," says Mike Turillo, chief learning officer at KPMG International in Boston. "And when they finally find it, they don't know how to use it. We spend a lot of our time just trying to manage it."

To manage e-mail more efficiently and productively, both Mitchell and Turillo are testing new web-based software: Mitchell is trying out his own company's Knowledge Sharing Environment (KSE); at KPMG, Turillo is using Palo Alto, CA-based Tacit Knowledge Systems KnowledgeMail. In addition to helping people manage their e-mail, the software tracks a user's work, alerts users to important information and even notifies them that someone else in the company is doing similar work. This way, the software brings people (like A and B) together to share information and solve problems.

KnowledgeMail and KSE share similar technology, vernacular and features. KSE in particular can be traced directly to Yenta, software agent technology developed by Lenny Foner, a doctoral student at Massachusetts Institute of Technology in Cambridge, Mass.

Extraído de Feb. 15, 2000 Issue of CIO Magazine

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The Dark Side of Tacit Knowledge

Tacit knowledge by its very nature actually ‘emerges’ from the people’s heads.

The various mental processes that shape and construct certain knowledge are very difficult to comprehend. This sort of knowledge is a key behind exercising judgment in human decision-making and employing intuition or ‘gut-feeling’.

It is seen in experienced managers; because of their tacit knowledge and expertise based on this sort of knowledge, they are able to make better-informed and effective intuitive decisions. However, there is also a probability of these managers making a wrong judgment ending up in wrong decisions.

This paper was inspired by the authors’ experience when delivering presentations on knowledge management issues. In several cases members of the audience responded by observing that some tacit knowledge is inaccurate, incorrect or inappropriate.

Therefore, it is a possibility that the tacit knowledge that we are trying to capture may not be useful. Their objection seems valid when we find out various examples of big judgmental mistakes made by managers that risk and jeopardize a whole project. Through this short paper we acknowledge this fact and endeavor to explain the factors that affect the effectiveness of the tacit knowledge.

The paper also examines what can be done to make sure that tacit knowledge stays effective when captured and used in decision-making.

Extraído de Biases and Heuristics in Judgment and Decision Making: The Dark Side of Tacit Knowledge

Tayyab Maqsood, Andrew D. Finegan, Derek H. T. Walker

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Varieties of Tacit Knowledge

The distinction between tacit knowledge and explicit knowledge has sometimes been expressed in terms of knowing-how and knowing-that, respectively (Ryle 1949/1984, pp. 25-61), or in terms of a corresponding distinction between embodied knowledge and theoretical knowledge.

On this account knowing-how or embodied knowledge is characteristic of the expert, who acts, makes judgments, and so forth without explicitly reflecting on the principles or rules involved. The expert works without having a theory of his or her work; he or she just performs skillfully without deliberation or focused attention.

Knowing-that, by contrast, involves consciously accessible knowledge that can be articulated and is characteristic of the person learning a skill through explicit instruction, recitation of rules, attention to his or her movements, etc. While such declarative knowledge may be needed for the acquisition of skills, the argument goes, it no longer becomes necessary for the practice of those skills once the novice becomes an expert in exercising them, and indeed it does seem to be the case that, as Polanyi argued, when we acquire a skill, we acquire a corresponding understanding that defies articulation (Polanyi 1958/1974).

But the distinction between knowing-how and knowing-that breaks down upon examination. As Dretske has pointed out (Dretske 1988, p. 116), knowing-how involves more than just a certain technical or physical "know-how"; it also involves knowing how to obtain desired end-states, knowing what to do in order to obtain them, and knowing when to do it.

Implied in all this is that knowing how to perform action _A_ means knowing that certain things are the case regarding, for example, tools, the situation in which _A_-ing takes place, and so forth. If, as seems likely, this is the case, then knowing-how would seem to be closely bound up with, if not dependent on, some variety of knowing-that. Even if we are able to isolate the know-how that goes into _A_-ing, it isn't clear that the processes, involved -- which narrowly understood may amount to little more than automatized physical sequences or muscular reflexes -- count as cognitive in any interesting way.

Note, though, that in rejecting the distinction between knowing-how and knowing-that we are not thereby denying the existence of tacit knowledge per se; rather, we are denying its exclusive identification with procedural operations that may in the end have little to do with knowledge as such.

What is rejected is not the idea that skillful (or other) activities may rely on content states that are inaccessible to consciousness (or that conscious attention is not necessary for the exercise of a given skill), but rather the notion that a given behavior or performance stands as the proper criterion for possession of the tacit knowledge in question. Certainly there is no reason to suppose that the knowing-that which would seem to come into play even in expert performance cannot be tacit.

That an exhaustive equation of tacit knowledge with pretheoretical, skilled expertise cannot be maintained becomes particularly clear when we consider that one widely accepted paradigm of tacit knowledge is to be found in language competence (e.g., Chomsky 1986, pp. 263-273; 1980, pp. 69-70; 1972, pp. 103-104). In contrast to the variety of tacit knowledge described above, knowledge of language is not understood to constitute a skill, and thus to consist in a capacity to do something -- and consequently to have possession predicated on the appropriate behavioral criteria -- but rather is a properly cognitive capacity, and therefore defined in terms of mental states and structures that are not always or reliably manifested in behaviors or performances (Chomsky 1986, pp. 9-10; 1980, p. 48).

We might point to a third kind of tacit knowledge, which consists in what might be thought of as the presuppositions or stances many of our actions and behaviors commit us to.

Such stances are not occurrent beliefs, although they may be expressed as occurrent beliefs under the appropriate circumstances. Rather, they constitute a kind of cognitive background or disposition to believe that certain things are the case (cf Searle 1995, 1992, 1983; see also entry on The Background).

An example of this kind of tacit knowledge is that objects are rigid, a bit of knowledge few people ever bother to formulate, but which is evidenced in such basic everyday actions as sitting in a chair. Because such knowledge is expressible as a propositional content, it would seem to be a case of tacit knowing-that. (It is tacit knowledge of this sort that may ultimately explain the cognitive dimension at work in those cases held up as examples of embodied knowledge or knowing-how.) These tacit stances or presuppositions are perhaps best described as tacit beliefs or hypotheses that can be falsified under the appropriate conditions.

While the kinds of tacit knowledge underlying skills or expert performances on the one hand, and cognitive competences like knowledge of language on the other, appear to be domain-specific, this third type of tacit knowledge would appear to be more generally applicable. It seems to be the case that the cognitive content associated with tacit beliefs of this sort comes into play across a diverse set of activities and domains. Much, though by no means all, of the heterogeneous set of biological and cultural stances and capacities that Searle refers to as The Background (see entry on The Background) may be thought of as consisting in a generally applicable tacit knowledge of this sort.

Although the three conceptions of tacit knowledge outlined above differ from each other in significant ways, they do have one central feature in common, and that is the postulation of content states that are at once causally efficacious and inaccessible to (or not ordinarily accessed by) consciousness.

Extraído de tacit knowledge

Daniel Barbiero

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Five Challenges for the Next Five Years

1. Speed

The speed of business has changed a lot. We can buy almost anything on the web, and people expect to have the ability to buy things 24/7, 365 days a year because computers hum all the time. E-mail messages fill up overnight even after you deleted all messages the night before.

Business is where the buyer and seller meet. The way you do business is changing rapidly. Amazon.com is an example of this rapid movement. Capital moved away from Asia very fast when financial problems started there. The whole economy is moving faster, creating pressure on executives and employees; and decisions have to be made faster than before and globally. The speed mandates a large strategy to convey to the frontline employees on how to bring value, not just lip-service.

• KM has to deliver value, not just talk. For example, corporate yellow pages need to be developed, and communities need to move faster. Taxonomy became very important to deal with unstructured knowledge.
• KM can help Executives make better decisions, provide decision support for employees, social network support and taxonomy.

The best we can do for customers is to save them time. Business moving faster means allowing more decisions to be made at the lower level. We need intellectual working data on the desktops. We need to link to strategic imperative, which is customers' imperative

2. E-Commerce

E-Commerce is fuzzying (blurring) the boundaries. It's hard to know where one business ends and another begins. Before the fall of .com business, a survey was taken and showed that 1/3 predicted .com will prevail, 1/3 predicted that the traditional business will prevail, and 1/3 said "don't know." This year the internet commerce created $3 trillion.

E-commerce changes the fundamental ways to do business where sellers and buyers meet. It reduces error and provides real-time market data, which allows better use of the capital.

Dell can see the market real-time, so they can use that knowledge as the power to compete. When you buy something from the web, the seller knows where you came from: Google, Frederick, etc. -

This is tremendous knowledge for business. "Buyer and seller" are like conjoined twins and are interwoven. John Hagel calls this "Fuzzy Boundary of Business." Databases used to be guarded and were never shared with customers. Impact of KM can be seen in how Cisco helps customers configure their network and mediates the knowledge with their customers.

3. King Customer (Growing Power of Customer)

Automobile dealers are now faced with different negotiations with buyers because the buyers have more information about the dealer collected from the web. It became very hard to keep the price, and their profit margin was lowered from 11% to 6%.

Business has been taken over by consumers, such as decreasing telephone rates. Customer Power cannot be changed, but customer knowledge can be worked with. We have to offer customers better experiences. Therefore, Customer Relations Management becomes more important than ever before.

Customers do not like to be manipulated. You don't manage your wife. What you need to do is to make sure what you do is really valuable for customers. We need to identify unique and valuable selling.

CEO needs to ask "Why do people buy from us, not from others?" Three answers, a) cheaper, b) better quality, and c) we are innovative and responsive.

An organization needs to have cohesiveness, and how to be innovative and responsive depends on intellectual capital. We need to use intellectual assets where it matches.

Human capital is important when the talent is important, such as when you go to a certain restaurant because of the talent of the chef. When you go to McDonald's, it is predictive, cheap and fast with set recipes; this is Structural Capital McDonald's has. When you go to a restaurant because the waitresses are very friendly and you feel welcome, then you are going there for the Customer (Social) Capital. When you hire McKinsey as a consultant, you are buying their IQ, the talent - Human Capital.

When you hire Accenture, you are buying their formula, which is Structural Capital. When you are a customer for one bank, you have a long relationship with that bank - Customer (Social) Capital.

Any organization needs to have at the top level, at a minimum, one of 3 Intellectual Capitals - Human, Structural, and Customer. A beer company in Colorado is depending on structural capital and customer/social capital, when truck drivers for that beer company know to which bars to deliver beers and schmooze with workers at the bar. Customer Value proposition implies that you need to map your talent with those values.

4. Low Cost Worrier (Competition)

CEOs are struggling on how to deal with low-cost worriers, like Wal-Mart, Dell, Ameritrade, etc., that provide marginal quality service with low cost. To compete against low-cost worriers is to sell experience, such as Starbucks, to sell continuous innovation, to sell quality services, or become niche player. We need to find the competitive advantage.

5. Uncertainty on Decision Making

Management used to work with predictability since the scientific management started by Frederick Taylor. Then TQM promoted reduction of variances and control, "Execution" always matters.

We believed that people are rational. Now non-linearity and unpredictability are more common in the networked global environment. We talk about futility of forecasting, because you don't know how to plan under this environment where there exists ambiguity of outcome.

The relationship between cause and effect is obscure and the decision making model we used to use is not very helpful. We used to examine, evaluate, review options and decide--different kinds of decisions for different problems; There are four different kinds of problems (environment):

• a) Cause-effect well connected,
• b) Complicated problems,
• c) Complex Systems, and
• d) Chaos.

KM is currently helping with two problem areas. When there is a cause and effect relationship and this relation is knowable, then we can find an expert. KM can facilitate finding the expert. When we have a complicated problem, such as an airplane built by Boeing, if you disassemble the plane, you can re-assemble again because connections are fixed, knowable and predictable. KM can be useful here.

However, with complex systems, when you dissemble it and put it back together, they don't look the same. Different patterns emerge and communities are organic.

For example, an employee goes to another country to work for a year and returns to the same office. He/she will find that the organization is not the same any more. Even though it's the same office, the same people, but they don't use the same approach. We don't know how KM can help here.

Then there is Chaos, where there is no known cause and a random pattern. It's like you are lost on a mountain and are using a wrong map, then you will not be able to find a way out. It is important to know what kind of decision is needed. When you don't know the question, how do we know who needs to know what? It is a fundamental question for KM.

Thomas Stewart "Five Challenges for the Next Five Years"

Extraído de NINETEENTH KNOWLEDGE MANAGEMENT ROUNDTABLE CONVENED ON SEPTEMBER 24, 2004 AT MITRE WASHINGTON.

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Knowledge acquisition technique glossary

Card Sorting
Knowledge acquisition technique in which a collection of concepts (or other knowledge objects) are written on separate cards and sorted into piles by an expert in order to elicit properties (attributes and values).

Commentary
Knowledge acquisition technique in which the expert provides a running commentary of their own or another’s task performance. A valuable method for acquiring process knowledge. Includes various types such as self-reporting, imaginary self-reporting, self-retrospective, shadowing and retrospective shadowing.

Ladder
A ladder is a hierarchical (tree-like) network diagram. A ladder can comprise a single type of relationship throughout or have multiple relationships.
Important types of ladder include a taxonomy (aka concept ladder) which uses the "is a" relationship and a composition ladder that uses the "part of" relationship.

Laddering
Laddering is a knowledge acquisition technique that involves the construction, modification and validation of ladders. It is a valuable method for acquiring knowledge of concepts. For more information, click here.

Lessons Learnt Review
Knowledge Management technique whereby a structured group discussion takes place just after the end of a project to provide recommendations for future projects.

Peer Assist
Peer Assist is a knowledge management technique whereby a person (or team) experienced in a particular type of project helps a person (or team) unfamiliar with the same, or a similar type, of project.

Repertory Grid
Knowledge acquisition technique used to elicit properties (attributes and values) for a set of knowledge objects, rate them on a scale, and use cluster analysis to arrange and group similar properties and objects. Useful for acquiring detailed object knowledge and tacit knowledge. For more information, click here.

Extraído de Glossary

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Knowledge acquisition technique glossary

Card Sorting
Knowledge acquisition technique in which a collection of concepts (or other knowledge objects) are written on separate cards and sorted into piles by an expert in order to elicit properties (attributes and values).

Commentary
Knowledge acquisition technique in which the expert provides a running commentary of their own or another’s task performance. A valuable method for acquiring process knowledge. Includes various types such as self-reporting, imaginary self-reporting, self-retrospective, shadowing and retrospective shadowing.

Ladder
A ladder is a hierarchical (tree-like) network diagram. A ladder can comprise a single type of relationship throughout or have multiple relationships.
Important types of ladder include a taxonomy (aka concept ladder) which uses the "is a" relationship and a composition ladder that uses the "part of" relationship.

Laddering
Laddering is a knowledge acquisition technique that involves the construction, modification and validation of ladders. It is a valuable method for acquiring knowledge of concepts. For more information, click here.

Lessons Learnt Review
Knowledge Management technique whereby a structured group discussion takes place just after the end of a project to provide recommendations for future projects.

Peer Assist
Peer Assist is a knowledge management technique whereby a person (or team) experienced in a particular type of project helps a person (or team) unfamiliar with the same, or a similar type, of project.

Repertory Grid
Knowledge acquisition technique used to elicit properties (attributes and values) for a set of knowledge objects, rate them on a scale, and use cluster analysis to arrange and group similar properties and objects. Useful for acquiring detailed object knowledge and tacit knowledge. For more information, click here.

Extraído de Glossary

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DIKW - Russell Ackoff's view

According to Russell Ackoff, a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories: Data, Information, Knowledge, Understanding and Wisdom.

Ackoff adds another level i.e., understanding between knowledge and wisdom. He indicates that the first four categories relate to the past; they deal with what has been or what is known.

Only the fifth category, wisdom, deals with the future because it incorporates vision and design. With wisdom, people can create the future rather than just grasp the present and past.

But achieving wisdom isn't easy; people must move successively through the other categories. A further elaboration of Ackoff's definitions follows:

• Data... data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.
• Information... information is data that has been given meaning by way of relational connection. This "meaning" can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it.
• Knowledge... knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone "memorizes" information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge. For example, elementary school children memorize, or amass knowledge of, the "times table". They can tell you that "2 x 2 = 4" because they have amassed that knowledge (it being included in the times table). But when asked what is "1267 x 300", they can not respond correctly because that entry is not in their times table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level... understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.
• Understanding... understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between "learning" and "memorizing". People who have understanding can undertake useful actions because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.
• Wisdom... wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing. Unlike the previous four levels, it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answer period. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to possess wisdom. Wisdom is a uniquely human state, or as I see it, wisdom requires one to have a soul, for it resides as much in the heart as in the mind. And a soul is something machines will never possess (or perhaps I should reword that to say, a soul is something that, in general, will never possess a machine). It has been contended that the sequence is a bit less involved than described by Ackoff . It is understanding that support the transition from each stage to the next. Understanding is not a separate level of its own.

ACKOFF, Russell. From Data to Wisdom, Journal of Applies Systems Analysis, 16, 3-9, 1989.

Extraído da wikipedia

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Corporate Culture

What is Corporate Culture?

Culture refers to an organization's values, beliefs, and behaviors. In general, it is concerned with beliefs and values on the basis of which people interpret experiences and behave, individually and in groups.

Cultural statements become operationalized when executives articulate and publish the values of their firm which provide patterns for how employees should behave.

Firms with strong cultures achieve higher results because employees sustain focus both on what to do and how to do it.

Vadim Kotelnikov

Corporate Culture: Surface, Middle and Deepest Levels

• Surface Level: At this level, culture is both enacted and reinforced through visible appearances and behaviors, such as physical layouts, dress codes, organizational structure, company policies, procedures and programs, and attitudes.
• Middle Level: Here, culture is manifested through our beliefs and values.
• Deepest Level: At this level, culture is manifested through basic assumptions - our long-learned, automatic responses and established opinions.

By Edgar Schein

Extraído de Corporate Culture
Achieving Higher Results Through Sustaining Employees' Focus on What To Do and How to Do It
By Vadim Kotelnikov, Founder, Ten3 BUSINESS e-COACH – Innovation Unlimited!, 1000ventures.com

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Aquisição do Conhecimento

Knowledge acquisition includes the elicitation, collection, analysis, modelling and validation of knowledge for knowledge engineering and knowledge management projects.

Issues in Knowledge Acquisition
Some of the most important issues in knowledge acquisition are as follows:

• Most knowledge is in the heads of experts
• Experts have vast amounts of knowledge
• Experts have a lot of tacit knowledge
• They don't know all that they know and use
• Tacit knowledge is hard (impossible) to describe
• Experts are very busy and valuable people
• Each expert doesn't know everything
• Knowledge has a "shelf life"

Extraído de Knowledge Acquisition

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Typical Use of KA Techniques

How and when are the many techniques described above used in a knowledge acquisition project? To illustrate the general process, a simple method will be described. This method starts with the use of natural techniques, then moves to using more contrived techniques. It is summarised as follows.

1. Conduct an initial interview with the expert in order to (a) scope what knowledge is to be acquired, (b) determine what purpose the knowledge is to be put, (c) gain some understanding of key terminology, and (d) build a rapport with the expert. This interview (as with all session with experts) is recorded on either audiotape or videotape.
2. Transcribe the initial interview and analyse the resulting protocol. Create a concept ladder of the resulting knowledge to provide a broad representation of the knowledge in the domain.
3. Use the ladder to produce a set of questions which cover the essential issues across the domain and which serve the goals of the knowledge acquisition project.
4. Conduct a semi-structured interview with the expert using the pre-prepared questions to provide structure and focus.
5. Transcribe the semi-structured interview and analyse the resulting protocol for the knowledge types present. Typically these would be concepts, attributes, values, relationships, tasks and rules.
6. Represent these knowledge elements using the most appropriate knowledge models, e.g. ladders, grids, network diagrams, hypertext, etc. In addition, document anecdotes, illustrations and explanations in a structured manner using hypertext and template headings.
7. Use the resulting knowledge models and structured text with contrived techniques such as laddering, think aloud problem-solving, twenty questions and repertory grid to allow the expert to modify and expand on the knowledge already captured.
8. Repeat the analysis, model building and acquisition sessions until the expert and knowledge engineer are happy that the goals of the project have been realised.
9. Validate the knowledge acquired with other experts, and make modifications where necessary.

Extraído de Knowledge Acquisition

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Inteligência segundo Sternberg

Intelligence

The conventional view of intelligence is that it is some relatively stable attribute of individuals, which develops as an interaction between heredity and environment.

Conventional tests of intelligence and related abilities measure achievement that individuals should have mastered several years before.

Tests such as those of vocabulary, reading comprehension, verbal analogies, arithmetic problem solving, and the like are, in part all tests of achievement.

Even abstract reasoning tests measure achievement in dealing with geometric symbols, which are skills taught in Western schools.

(...)Developing expertise is defined here as the ongoing process of the acquisition and consolidation of a set of skills needed for a high level of mastery in one or more domains of life performance.

Extraído de "Practical Intelligence in everyday life" Robert J. Sternberg (p1)

Sternberg proposes three intelligences in human cognition.

• Analytical intelligence is the ability to analyze and evaluate ideas, solve problems and make decisions.
• Creative intelligence involves going beyond what is given to generate novel and interesting ideas.
• Practical intelligence is the ability that individuals use to find the best fit between themselves and the demands of the environment.

The three intelligences, or as he also calls them three abilities, comprise what Sternberg calls Successful Intelligence: "the integrated set of abilities needed to attain success in life, however an individuals defines it, within his or her sociocultural context."

Sternberg's attempts to establish the validity of practical intelligence as a construct have yielded significant empirical work and criticism. As such, it provides a window on the issues and ideas at the core of this debate.

Practical Intelligence

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Inteligência Prática

What is Practical Intelligence?

To do well in any everyday endeavor, whether the endeavor pertains to school, work, or play, requires practical intelligence.

Although intelligence as conventionally defined maybe useful in everyday life, practical intelligence is indispensable. It is the ability to adapt to, shape, and select every day environments.

Practical intelligence, like most abilities can be viewed as a form of developing expertise. Individuals who have developed the knowledge, skills, and abilities needed to succeed in a particular domain generally are characterized as experts.

Therefore, understanding expertise and how it develops provides a method of insight into practical intelligence.

So how do we come to characterize practical intelligence as a form of developing expertise? We begin with a distinction between viewing abilities as relatively stable attributes and viewing them as developing expertise.

Extraído de "Practical Intelligence in everyday life" Robert J. Sternberg (p1)

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Innovation Process: Diversion and Conversion of Ideas

"The process of innovation is a rhythm of search and selection, exploration and synthesis, cycles of divergent thinking followed by convergence".2

Divergence, or creative synthesis, is the interlocking of previously unrelated skills, or matrices of thought. The creation of such intellectual ferment is important to innovation - the more options offered, the more likely that an out-of-the-box perspective will be available for selection.

Just hearing a very different perspective challenges the mindset of others sufficiently that they will search beyond what initially appears to be an obvious solution.

This is a reason that intellectually heterogeneous cross-functional teams are more innovative than homogenous functional ones.

As soon as a sufficient choice of innovative ideas has been generated, a solution - convergence upon acceptable action - needs to be defined and agreed upon.

Confining the discussion here to managing the tacit dimensions of knowledge three types of tacit knowledge - overlapping specific, collective, and guiding - need to be managed.

2. "The Role of Tacit Knowledge in Group Innovation", Dorothy Leonard and Silvia Sensiper, 1998
Extraído de Managing Tacit Knowledge - a Tremendous Resource for Innovation
by Vadim Kotelnikov and Ten3 East-West

Veja também "Do gelo natural à indústria da refrigeração"

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Divulgar

Diffusion (anthropology)
From Wikipedia, the free encyclopedia
Jump to: navigation, search

The term diffusion is used in cultural anthropology to describe the spread of cultural items — such as ideas, styles, religions, technologies, etc. — between individuals, whether within a single culture or from one culture to another.

The diffusion of innovations within a single culture applies, for example, to the acceptance of new technological products like the wristwatch and the personal computer, foods like tomato sauce and California sushi, music styles like opera and bossa nova, dressing styles like the top hat and blue jeans, ideals like democracy or feminism, and so on.

Diffusion across cultures, too, is a well-attested and uncontroversial phenomenon. For example, the practice of agriculture is widely believed to have diffused from somewhere in the Middle East to all of Eurasia, less than 10,000 years ago, having been adopted by many pre-existing cultures.

Other established examples of diffusion include the spread of the war chariot and iron smelting in ancient times, and the use of cars and Western business suits in the 20th century.

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Transferir

Knowledge transfer
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Knowledge transfer in the fields of Organizational development and organizational learning, is the practical problem of getting a packet of knowledge from one part of the organization to another (or all other) parts of the organization.

It is considered to be more than just a communications problem. If it were merely that, then a memorandum, an e-mail or a meeting would accomplish the knowledge transfer.

Knowledge transfer is more complex because (1) knowledge resides in organizational members, tools, tasks, and their subnetworks (Argote & Ingram 2000) and (2) much knowledge in organizations is tacit or hard to articulate (Nonaka & Takeuchi 1995).

Argote & Ingram (1999) define knowledge transfer as "the process through which one unit (e.g., group, department, or division) is affected by the experience of another" (p 151). They further point out the transfer of organizational knowledge (i.e., routine or best practices) can be observed through changes in the knowledge or performance of recipient units.

The transfer of organizational knowledge, such as best practices, can be quite difficult to achieve (Szulanski 1996).

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Compartilhar

Sharing
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Sharing is the joint use of a resource. In its narrow sense, it refers to joint or alternating use of an inherently finite good, such as a common pasture or a timeshared residence. In a broader sense, it can also include the free granting of use rights to a good that is capable of being treated as a nonrival good, such as information. Still more loosely, "sharing" can actually mean giving something as an outright gift: for example, to "share" ones food really means to give some of it as a gift.

Sharing figures prominently in gift economies, but also can play a significant role in market economies, for example in car sharing.

The issue of handling shared resources figures prominently in computer science: for example time-sharing is an approach to interactive computing in which a single computer is used to provide apparently simultaneous interactive general-purpose computing to multiple users by sharing processor time.

Sharing is a key feature in the developing field of free software and open source software, with implications for economics. This is leading to a need to review licensing, patents and copyright, and to controversy in these areas, as well as new approaches like creative commons and the GPL.

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New Ways to Work

Let’s pause for a moment. Whereas tacit knowledge is the bedrock of craft, as firms gain experience with work under craft, additional learning occurs. This isn’t just craft workers becoming better craft workers.
A new type of knowledge, an important by-product of craft work, is generated: articulated knowledge. Leveraging articulated knowledge can propel a firm toward mass production.

Ignoring it (intentionally or not) means that a firm will remain in craft work. We see this pattern again and again on the right path.

One type of knowledge is the foundation for each of the four types of work. This foundation knowledge has to be managed for a firm to excel, be it in craft, mass production, process enhancement, or mass customization. Under each type of work, learning occurs and a by-product of work is generated: additional learning and knowledge, the key ingredients for firms taking the right path.

As firms capture and codify the by-product of articulated knowledge generated under craft, the story of the right path unfolds. We call the activities underlying the codification of articulated knowledge the development transformation, and it leads to mass production work.

Through development, a firm gleans, then begins to reuse the best ways to do the work. Development, in short, is a transformation that mines the rich veins if articulated knowledge and builds a machine like organization that mass-produces.

Using tools such as time and motion studies, process engineering, and automation, development captures the best approaches discovered in craft and applies them across the company.

Development instills discipline through processes, procedures, or automation so that work can be replicated anywhere, at any time, in any amount. Mass production is characterized by repeatable tasks, hierarchical control systems, functional structures, standardized routines and processes, automation, and division of labor.

Invented Here: Maximizing Your Organization’s Internal Growth and Profitability
Bart Victor
Andrew C. Boynton
harvard business school press
Boston, Massachusetts

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Tacit knowledge is far less tangible

Explicit knowledge is what you find in computer data banks, textbooks, and academic journals; once created, it is easily captured, distributed, and used by people other than the creator.

While explicit knowledge can create a competitive advantage- as in the case of a patent for a new technology- its half-life is increasingly brief. Once we know something can be done, much less time and money is required to imitate, reverse engineer, or clone it than was needed to demonstrate its initial viability.

For example, after Intel spent $1 billion and more than a year to create its 486 microprocessor, Cyrix was able to produce a clone in approximately 18 months for just $10 million.

A second type is tacit or implicit knowledge, which you may think of as personal and context-specific "know-how." Tacit knowledge is far less tangible and so deeply embedded into an organization's operating practices that it's often nearly invisible, being described broadly as just "the way we do things around here."

For individuaIs, it's often referred to as experience or intuition; in organizations, we often call it culture. What we refer to as intuition, though, are really bits of knowledge that we've gained and combined with other bits in ways that are not easily traced or described.

Tacit knowledge includes relationships, norms, values, and standard operating procedures. Because tacit knowledge is much harder to detail, copy, and distribute, it can be a sustainable source of competitive advantage.

Extraído de MEYER, Christopher. Relentless Growth: How Silicon Valley Innovation Strategies can work in your Business. New York: The Free Press, 1998.

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Publicly Defined Innovation Process

In any socio-technical system the people in the system work better when they understand how they fit into the system as a whole.

Road-mapping provides strategic aligning, a common language for innovation, and builds bridges between technologists and business managers within your corporation, and with your major suppliers and customers.

"When a firm lacks a publicly defined innovation process, everyone operates based on their own past experience and assumptions. Because these are likely to be different, task timing, deliverables, and interfaces won't match up."

• In successful medium and large companies, the innovation process is documented explicitly via maps and charts, and implicitly communicated by words and practice.
• In young companies, the innovation process is often a part of the firm's tacit knowledge base, and therefore it is invisible. To grow substantially, though, young firms must eventually make the core element of their innovation process explicit.

Extraído de Innovation Process Traditional and New Approaches

By Vadim Kotelnikov, Inventor & Founder, Ten3 BUSINESS e-COACH – Innovation Unlimited, 1000ventures.com

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Barriers to the Sharing of Tacit Knowledge

1. Hierarchies, when they implicitly assume wisdom accrues to those with the most impressive organizational titles
2. Strong preferences for analysis over intuition discouraging employees to offer ideas without "hard facts" to back it up
3. Penalties for failure discouraging experimentation
4. Strong preferences for a particular type of communication within working groups
5. Fear of failing to express the inexpressible when trying to convert tacit knowledge into explicit one
6. Inequality in status among the participants is a strong inhibitor for tacit knowledge sharing, especially when exacerbated by different frameworks for assessing information
7. Uneasiness of expressing emotional life experiences rather than intellectual disagreements
8. Distance, both physical separation and time

Extraído de Managing Tacit Knowledge

by Vadim Kotelnikov and Ten3 East-West

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Tipologia de Conhecimento Tácito

Emotional TK
Damasio points out how emotions involve learned associations that maintain organismic integrity through leading to patterns of action (flight, attack, and fancier ones) as well as to internal feelings on which we can reflect and act within limits.

Social conventions TK
Societies and cultures typically involve social conventions, for instance for conversational turn-taking, for how close you stand to people, for conventional greetings, e.g. shaking hands.

Unspoken on-the-fly understandings between people TK
Someone mentioned that people often reach tacit understandings through conversational TK. Let’s say A projects an expectation through the manner in which A makes a comment or asks a question, e.g. "Gosh, I didn’t realize it was almost six o’clock." B picks up on A’s implicit message—shouldn’t we be winding up—and follows through.

Expert chunking TK
Studies of expertise argue that experts chunk situations in their domains, recognizing large-scale patterns of significance, as in patterns of attack or defense in chess play. This allows them to encode complex situations parsimoniously in long-term memory. Thus, chess masters can memorize board layouts at a glance and play chess blindfolded. Similar phenomena have been documented in several domains.

Expert self-management TK
Bereiter and Scardamalia, as well as Eraut, pointed out that experts come to manage themselves well in their domains of expertise, allocating effort wisely, anticipating problems etc. This appears to involve TK.

Grammatical TK
This is a classic example of TK. We speak more or less grammatically in our mother tongues, we have a sense of what "sounds right," we can correct errors.

Extraído de Types of TK
D. Perkins, March 19, 2002

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The Tacimometer

"Hot" knowledge is the most explicit.

"Cold" knowledge is the most tacit.

Explicit as it gets

• Fully explicit (direct statements to others, oneself, written policies taken at face value, scientific laws, etc.)
  Entirely conscious but unspoken, for reasons of good grace, privacy (intentional unspoken understandings).
• Situated consciousness: we are aware of “the rules” or our moves in the very particular context as things come up, but hardly at all if asked out of the blue (often self-management principles, many social moves, some encoding chunks)
• Peripheral unfocused awareness—we know it’s there without examining or analyzing, just doing it (social rituals of greeting and departure).
• Mood and such that operate in the background, “coloring” our lives, although we may detect them if we pay attention (tension, etc., a la Damasio)
• Not conscious of what stimuli trigger what responses on an occasion (often emotional reactions, many other conditioned responses)
• Completely unconscious of practices, behaviors, rules—no direct conscious access, although we may surface them by examining our own conduct (grammar, how close you stand in different cultures, some encoding chunks)
• Embodied knowledge (the knowledge of physics, chemistry, mechanics embodied in biological structures: perhaps too tacit even to be called tacit knowledge).

Tacit as it gets

Extraído de The Tacimometer: Degrees of the Tacit

D. Perkins, May, 2002

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Knowledge Management and AI

Knowledge is not neatly packaged. It is difficult to extract, it is ephemeral and may only be approximately correct.

One of the chief barriers to the construction of expert systems is the difficulty of knowledge acquisition—and this barrier must also be overcome to achieve success in knowledge management. However, there is some good news. The problem may be more tractable in the new context.

Systems that support knowledge management typically do not try to solve a problem alone. Rather, they try to find the knowledge (best practices, lessons learned, tips, solutions to related problems, and so on) that assist people in developing their own solutions. Stated another way, the goal today is to help people solve problems.

Contrast this with the original goal of expert systems—to solve problems themselves at an expert level. This is not to say that the AI community should give up on the expert systems goal, but rather, that achieving the goal of giving powerful assistance to people as they solve problems is of great interest to the knowledge management community.

Extraído de The Road Ahead for Knowledge Management
An AI Perspective
Reid G. Smith and Adam Farquhar
AI MAGAZINE, WINTER 2000

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Tecnologia para a conversão do conhecimento

Knowledge Creation using Technology

A key enabler to the creation of all kinds of knowledge discussed above is technology.

The use of technology solutions for some elements of knowledge creation is not a recent phenomenon. Even before the availability of solutions such as Lotus Notes, which is the building block for many knowledge capture solutions today, intranets and networking technology were being used.

Collaboration and knowledge sharing solutions also arose from the development of on-line conferencing and forums. It is however important to realize that knowledge creation cannot be solely done using technology tools.

As was stated by Ackerman, in many respects the state of the art is such that many of the social aspects of work important in knowledge management cannot currently be addressed by technology. Ackerman refers to this situation as a “social technical gap.”

Decision support systems, executive information systems, data warehousing and mining systems and a host of other technologies have all been evaluated by Davenport & Prusak, and more recently Smith and Farquhar have discussed Artificial Intelligence, alluding to how all these solutions falls short in the process of knowledge creation.

Further, the different knowledge conversion processes, shown in Figure 5 below do not occur in isolation, are all equally important, simultaneous, and need common support.

The implementation of such a multi-dynamic approach to knowledge creation and management requires integrated IT systems & technologies. Some common examples of technologies used in each of these processes appear in Figure 5, and are then discussed in detail.

Figure 5: Technologies used in knowledge conversion processes.
(Source: Knowledge management technology, Marwick, A.D, IBM Systems Journal, Vol.40, No.4)

Extraído de Knowledge in Organizations Definition, Creation, and Harvesting
Smita Kothuri, May, 2002

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Knowledge Harvesting: A proprietary solution to knowledge creation

Even when knowledge transformation using various technologies is adopted and a context is provided to these processes, the essence of knowledge creation lies in a holistic approach. Knowledge harvesting is one such proprietary, comprehensive approach that is gaining wide acceptance.

Knowledge harvesting may be seen as a strategic solution to knowledge creation as it synthesizes the advantages of technology with the relevance of a context.

'Knowledge Harvesting is a proprietary suite of methodologies and technologies for efficiently capturing the implicit intuitive knowledge of top performers, converting that expertise into explicit knowledge, and transferring it to users.’[25] It is predominantly an exercise of making implicit knowledge explicit. Larry T Wilson and the group at Learner First developed it.

• Stage 1: Identification of knowledge - Identification involves mapping the organization’s key processes and the individuals who possess the best know-how.
  
• Stage 2: Elicitation of knowledge - Experts and activities are first identified; the experts are then asked to explain the activities.
  
• Stage 3: Capture of knowledge - The expertise of the top performers must be preserved to continue the success of the organization.
  
• Stage 4: Organization of knowledge - The knowledge captured from top performers must be arranged in a coherent or systematic form.
  
• Stage 5: Application of knowledge - The whole point of a knowledge management system comes down to later applications by individuals.
  
• Stage 6: Recording of knowledge - This sub-process records the learning that takes place with the user, causing the database of knowledge to grow.
  
• Stage 7: Sharing of knowledge - Knowledge that has been captured must be shared or its capture will be irrelevant and the effort and expense wasted.
  
• Stage 8: Evaluation of the knowledge creation process - Evaluation should be continuous so that the database can be kept up-to-date, relevant, and as small as possible.
  
• Stage 9: Improvement of the knowledge creation process - The improvement sub-process is the continuous betterment of the entire process.
  

Extraído de Knowledge in Organizations Definition, Creation, and Harvesting
Smita Kothuri, May, 2002

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Organizational forms and dominance of knowledge types

In table 1 the combinations of dominance of knowledge type and organizational form are presented. The level of analysis is the organizational process. More detailed determinations can be done for the constituting tasks within the organizational processes and for the individual actors.

The combinations are not the result of empirical research. They are the result of analytical reasoning and could be reformulated as hypotheses. I will llustrate the reasoning by shortly discussing the various combinations, from clan (ta +; c -; th -) to market (ta +; c +; th +).

A clan (ta +; c-; th -) consists of a limited group of actors that cooperate on the basis of trust, sometimes justified by family or very close friendship relations. Boisot (1995, p. 259) says that the term clan refers to a non-hierarchical group of limited size transacting on the basis of shared intangible knowledge and values.

These values are implicit and well-known by the members of the clan, but they are very difficult to formulate. Clans often are small and local, which means that different clans have different interpretations of what trust, loyalty, responsibility and obedience mean. If a clan is large, it normally consists of sub-clans, because of the nature of physical presence or proximity.

Table 1

Extráído de Knowledge Types and Organizational Forms in Knowledge Management
René JORNA

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The Many Meanings of Tacit Knowledge

Not one thing

TK is more like collegiate sports or flowering plants than suspension bridges. It’s a bundle concept.

Collegiate sports bundle under one label different kinds of things that have something in common—football, basketball, rowing, and so on. Flowering plants bundle under one label Elm trees, water lilies, and pitcher plants, although they all have share the fundamental process of capturing energy from sunlight.

Suspension bridges, on the other hand, are pretty much alike. Some are bigger, some smaller, some longer, some shorter, some with different materials, but they all look pretty much alike.

TK is a bundle concept for a simple reason. There are several different sense of “tacit,” for example tacit in the sense of entirely unconscious or in the sense of a tacit understanding (agreed upon in subtle ways without speaking) or in the sense of taken for granted without examination (we tacitly presumed that…).

Also, there are several different senses of “knowledge,” for instance knowledge in the sense of knowing facts about something, or know-how, or the knowledge embodied in habits. No wonder, then, that there are a number of different varieties of TK where what we’re examining is knowledge in one or another sense that is tacit in one or another sense.

Extraído de The many Meanings of Tacit Knowledge
D. Perkins, May, 2002

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The acquisition and activation of knowledge

The acquisition and activation of knowledge, both explicit and tacit, seems to help people succeed in their day-to-day endeavors. As such, it would seem to follow that it would be useful for educators. Thus it appears that educators ought to increase the explicit and tacit knowledge stores of their learners.

A great deal of research in education has addressed increasing knowledge in learners, but most of it so far has focused on the development of explicit knowledge. Tacit knowledge has been mostly overlooked. Does this mean that tacit knowledge cannot be taught?

The researchers of the Practical Intelligence for Schools (PIFS) research project suggest that tacit knowledge can be taught and learned (Gardner, et al., 1994; Sternberg, et al., 1990). However, there are some theoretical and practical arguments, particularly from the constructivist perspective, that suggest that tacit knowledge that has been learned through didactic means is not as useful as tacit knowledge that has been acquired through experience. Can these seemingly contradictory positions be integrated?

This article will examine three issues relating to the acquisition of tacit knowledge. The first part will summarize Sternberg's techniques for teaching tacit knowledge (Sternberg & Wagner, 1986; Sternberg, et al., 2000), especially as they were exemplified in the PIFS project.

The second part of this article will describe constructivism and look at how some interpretations of constructivism would question the robustness of the knowledge learned in PIFS. The final section will describe a potential "middle road" that integrates the two perspectives.

Extraído de

How can Tacit Knowledge and Intuition be Cultivated in Oneself and Others?

Scott Armstrong, September, 2001

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