Coordinamento Servizi Informatici Bibliotecari di Ateneo
	Università degli Studi di Lecce

S.MICHAEL MALINCONICO
From Information Management to Knowledge Management
[versione italiana]

• Introduction
• Data, Information, Knowledge
• Explicit Knowledge and Tacit Knowledge
• Managing Knowledge Environments and Communication
• Knowledge Work
• Knowledge Work & Physical Work
• Knowledge Components of Blue Collar Work
• Work Teams
• Virtual Knowledge Teams (VKTs)
• Accessing Benefits of Knowledge Management
• Metrics Proposed
• Conclusion

   Introduction
As a consequence of the industrial revolution the developed world has enjoyed a level of prosperity — and, for good or ill, control of its environment — unimagined in the millennia that preceded it.  Throughout most of history the vast majority of people engaged in agriculture.  Beginning in the latter half of the 18^th century, people in rapidly increasing numbers turned to occupations in manufacture, and industry.  Industry rapidly became the principal generator of wealth.  Several studies have shown that early in the 20^th century, manufacturing, or industry, was the principal employer of people in the US.[1]^,[2] —  It is safe to assume that the same is true of the rest of the developed world. —  Yet, the industrial revolution and the industrial economy that it spawned had a surprisingly short reign — scarcely two centuries.  In the latter half of the 20^th century, the basis of the developed world’s economy underwent a profound change.  The majority of people ceased to be employed manufacturing goods.  By the mid-1970s they were instead engaged in activities that created or transformed information.  Thus, it can be said that we live in an information age and that our economy is an information economy.  It now appears that the information economy may have had an even shorter reign than the industrial economy.  In less than half a century the focus has once again changed:  this time from information to knowledge.

The difference is not merely semantic.  The change marks an important difference in the way work is done.  In an information-based environment, one attempts to capture as much factual data about the world as possible and to endow machines with the particular skills of human experts — or much more often with the meager abilities of human drones and the patience of metal and plastic.  Thus, seemingly intelligent systems are used to keep track of inventory and finances with precision and reliability that humans cannot possibly equal, computer assisted design programs develop highly detailed blueprints — free of computational errors — for structures as simple as a kitchen or as complex as a transatlantic aircraft, computer assisted manufacturing programs direct machines to produce complex parts that are usually more precise than those produced by human machinists, publishing programs layout attractive pages nearly as well as the best printers, information retrieval systems answer certain classes of questions by recalling information previously supplied by human experts, etc.   However, most enterprises are realizing that innovation and creative problem solving are the real bases for competitive advantage.  Information systems are neither creative nor do they support creativity particularly well.  Thus, many enterprises are turning their attention to creating learning organizations and to empowering their employees so they can be creative.

Many enlightened organizations and managers understand this intuitively.  An increasing number of organizations have established positions such as Chief Knowledge Officer or Chief Learning Officer and a management discipline has emerged, knowledge management, KM.  By 1997 more than 100 of the Fortune 500 companies — the 500 companies identified each year by Fortune magazine as the most successful companies in the US — had already created a position that in name or in responsibility is a Chief Knowledge Officer. [3]  However, the managers who champion these programs lack objective measures that they can use to judge the efficacy or relative efficacy of programs or to justify them to upper management and to shareholders.  Clearly the problem is one of metrics.  It is relatively easy to identify the costs associated with knowledge management programs, but it is not so easy to derive measures of their contribution to an organization’s goals.  Though there has been some progress in this regard, the matter is still badly in need of resolution.  It might well be a problem that might occupy the interest and attention of applied mathematicians.  We shall return to the problem of metrics later.  For now let us examine what knowledge management is.

   Data, Information, Knowledge

Regrettably, these distinctions are not as precise as many writers would have us believe.  The distinctions are not so simple, nor can they be arranged simply as discrete points in a one-dimensional space.  The differences often depend on context.  Thus, what may be information in one context is only data in another.  For example, someone might argue that additional information regarding the demographics of the regions is needed before we have actionable information, or knowledge.  Likewise, one could argue that the difference between a good and a bad decision is a consequence of inadequate information.  Fortunately, we need not concern ourselves with all of these fine distinctions.  Nonetheless, we will want to make a distinction between two different types of knowledge, explicit knowledge and tacit knowledge.

   Explicit Knowledge and Tacit Knowledge

Ikujiro Nonaka, one of the seminal writers on knowledge management, offers an instructive illustration of the transformation of tacit to explicit knowledge to new tacit knowledge. Matsushita, a major Japanese manufacturing company, initiated a program to design a bread-making machine for home use. Matsushita’s engineers created some prototype machines that made bread that was judged to be mediocre.  Some of the engineers knew that the Osaka International hotel had a bread chef that was reputed to make excellent bread.  Ikuko Tanaka, one of the engineers on the project volunteered to apprentice herself to the chef to learn the secrets of making excellent bread.  After several months of working with the chef, Tanaka discovered that the difference between the prototype bread machines and the chef was the way the chef stretched out the bread dough.  Tanaka brought this new insight back to the engineering group, which added some fins to the machine to stretch the dough.  The result was greatly improved bread.  In fact, the bread machine became one of Matsushita’s best selling products.  The result was more than an excellent product and a commercial success.  It resulted in a cultural change within the company.  The engineers internalized the idea that excellent products lead to commercial success.  Consequently, one can assume that the next time the group is faced with a similar situation attempts to promote compromises would very likely be met with stern resistance.

There are four possible knowledge transformations:

·        Tacit ® Tacit

·        e.g., Tanaka’s apprenticeship.

·        Explicit ® Explicit

·        Not illustrated in the foregoing.

·        Transformations of explicit knowledge to other forms of explicit knowledge.  These transformations do not usually create new knowledge.

·        Tacit ® Explicit

·        e.g., when Tanaka articulated the specifications to the other engineers.

·        Explicit ® Tacit

·        e.g., the explicit knowledge shared & internalized when the connection between product quality and its success was noted and it altered the culture of the engineering group.

   Managing Knowledge Environments and Communication

Communication infrastructures and database technologies can be instrumental in supporting knowledge management programs.  Communication systems, such as Intranets, allow people within organizations to communicate and share knowledge regardless of their physical separation.  Readily accessible databases of skills and abilities, sometimes known as organizational yellow pages, permit individuals with a problem to solve easily to locate others within their organization who have previously solved similar problems or who have the expertise and skills needed to solve it.  Readily accessible databases of internal and external best practices have already proven their worth in many organizations.  For example, in 1994 Tom Engibous, president, of Texas Instruments’s (TI’s) Semiconductor Group, noted that there was a marked difference in the yields of TI’s various wafer fabrication plants.  He reasoned that if all could be brought up to the level of the best, TI would achieve significant gains.  He directed the fabrication plant managers to create a free plant by identifying and adopting the best practices employed at Texas Instruments’s 13 wafer plants.   The plant managers using various communication technologies and face-to-face meetings achieved, as directed, 500 million $USD increased productivity.  The cost of a new plant was estimated to be between 500 million and 1 billion $USD.[8]  Identifying and collecting information about the best practices within an organization or an industry and recording that information in a database is now a standard knowledge management technique, known as benchmarking.  Texas Instruments did not originate this practice.  Arthur Andersen had been doing it in an organized way since 1992.

In general, knowledge management relies more on management initiatives and cultural change than technological infrastructure.  For example, managers can create and foster a culture in which knowledge is shared rather than hoarded for personal advantage.  They can reward those who reuse knowledge instead of recreating it.  In most organizations it is recognized knowledge is power.  Thus, individuals learn to hoard information and knowledge.  One of the major objectives of knowledge management is to change that culture by recognizing individuals that proactively share knowledge.  For example, since the mid-1990s Price Waterhouse has added knowledge sharing to its performance appraisal system.  Employees must be able to produce evidence that they have engaged in knowledge sharing activities such as coaching, mentoring, tutoring, publishing, presentations, etc.[9]  Managers can also encourage the exchange and sharing of knowledge by facilitating communication among members of the organizations, e.g., by creating working environments such as open office layouts, comfortable break rooms that encourage informal communication, and opportunities for members of the organization to socialize, etc.

Nonetheless, technological infrastructure still plays an essential role.  Adequate communication infrastructures are particularly important in today’s globally dispersed organizations.  Increasingly people who work in such organizations find themselves working in virtual environments with colleagues half a world and a dozen time zones away.  Thus, for many organizations connectivity has of necessity become virtually ubiquitous.  Knowledge workers and virtual work groups have special characteristics and needs.  Desktop access in an integrated manner to internal and external information resources is essential for them to be able to do their jobs effectively.  Providing and maintaining these facilities is an important function of knowledge management.

Thus, knowledge managers must be skilled in both sociology and technology.  They must understand the dynamics that govern how people behave, learn, and interact with each other; and what motivates them to contribute to a collective activity.  They must also be conversant with the latest technologies for communication and information handling.  Finally, they must be able to combine these very different kinds of knowledge to solve problems and to exploit opportunities.

   Knowledge Work

The economy of the developed world is of necessity dominated by knowledge work.  It is at a serious competitive disadvantage with respect to purely physical work.  Its wages are too high.  Consequently, products and services produced by businesses in the developed world, if they are to compete successfully for markets, must contain a significant knowledge component.  For example, assembling Nike sneakers involves primarily physical work.  On the other hand, designing and marketing them is knowledge work.

Teaching others is the most obvious form of knowledge work.  Education is already big business and is growing as a commercial enterprise.  Education now competes with medical care as the biggest single industry in the US.  Private investment in on-line universities and adult education is already significant and growing.  One of the biggest investors is Michael Milken, the financier who was jailed for his dealings in junk bonds in the mid-1980s.  Junk bonds contributed to the collapse of numerous savings and loan associations in the US.  Milken has joined with Larry Elison, Chief Executive of Oracle Corporation to form Knowledge Universe, which is promoted as a “cradle-to-grave learning company.” Knowledge Universe was already earning revenues of 1.2 billion $US in 1999.[10]^,[11]

Colleges and universities in the US already offer an estimated 90,000 distance learning courses. Many established universities that want to expand their reach without the expense of expanding their physical facilities are finding Web-based course offerings very attractive.  Many have already spun off for-profit Web ventures such as Columbia University's Morningside Ventures or New York University’s NYU Online. In addition, there are hundreds of smaller academic and commercial ventures.[12]

Even traditional, campus-based education has long been an attractive export for some countries.  When international students attend a university in another country and pay tuition that constitutes a foreign product bought by the students’ home countries, which contributes to the relative balance of trade between the two countries.  In fact, one of the areas in which the US has a healthy, positive trade balance is education.  Educational exports (9.6 Billion $US) erased 10% of the 1999 US trade deficit (86.2 Billion $US).[13]  Since the Internet knows no national boundaries, there is a global market for online course offerings.  Because of the contribution such services can make to national economies, we can expect that developed countries will vigorously seek to exploit this opportunity.

It is difficult to overstate the importance of continuing education and knowledge acquisition.  Arno Penzias, Nobel laureate, best described the situation in a commencement address in 1995.  He said, “I think today we are the first generation in human history where knowledge is going to be obsolete, not just once during our careers but several times.”[14]

   Knowledge Work & Physical Work

We can contrast the two kinds of work by considering how they differ with respect to some key characteristics:  core tasks, critical skills, work processes, work outcome, and type of knowledge employed.  However, we should first note that all work consists of multiple tasks and that the differences among various kinds of work are more a matter of degree than clear distinctions.[15]

As we shall see the differences in the characteristics of physical work and knowledge work are more often a matter of degree rather than sharp differentiation.  However, we can identify core tasks that differentiate knowledge work from physical work.  The core task of physical work involves doing while the core task of knowledge work is thinking.  All work requires that those who do it possess critical skills in order to be proficient at it.  The critical skills for physical work are, not surprisingly, physical, while the critical skills for physical work are, of course, mental.  The nature of the processes used is another distinguishing feature of the kinds of work.  Physical work generally employs liner processes.  Procedures are usually predefined and their individual steps are performed sequentially.  Knowledge work, on the other hand, often involves unpredictable processes that generally defy simple description, e.g., the design of a new product.  The outcome of physical work is usually some tangible product.  The outcome of knowledge work is far more difficult to describe.  It is usually information.  Information that reduces uncertainty, that answers questions, that provides a sense of direction, etc.  In short, knowledge work usually produces more knowledge.  That is what makes it so valuable and why we are so concerned about its characteristics.  Finally, we can contrast physical work and knowledge work with respect to the type of knowledge they each employ.  Physical work generally utilizes applied knowledge, e.g., procedural knowledge, “this is how you operate particular tools,” “this is how the tools are used to make X.”  Knowledge work also relies on applied knowledge, but knowledge workers must often create new knowledge to solve the particular problem he or she is working on.

In sum, knowledge work, when it is successful, results in additional knowledge.  The process of creating that new knowledge is often unpredictable and very costly in terms of effort expended and time consumed.  Thus, businesses that rely on knowledge workers are very keen on finding ways to make them more productive.  They would, if at all possible, like to avoid expending again the often substantial effort that is required to recreate knowledge that had been previously created within the organization.  Since these firms are usually in businesses where the products of the efforts of their knowledge workers determines their competitive success in the marketplace, they are most receptive to programs that increase the quantity and quality of the knowledge their employees create — in many cases that knowledge remains locked in the brain of one or another of their employees, in some cases it is shared by work groups, and sometimes it is externalized and can be captured in a database.

The most obvious instances of the foregoing are found in consulting companies, firms that specialize in selling knowledge and expertise.   In fact, knowledge management has its roots in work done by consulting firms such as Arthur Andersen, Coopers & Lybrand, Ernst & Young, McKinsey, Price Waterhouse, etc., especially Athur Andersen.

In the early 1990s, Arthur Andersen began creating a Global Best Practices (GBP) database, a record of the best business practices in various industries.  The database was reproduced on CD-ROM and made available to AA’s more than 40,000 professional consultants in the field.  This ever-growing body of knowledge was used to help Arthur Andersen’s clients improve the performance of their businesses.  The Global Best Practices database was a remarkably versatile tool.  It contained:

·        Best practices information

·        Best company profiles

·        Relevant Arthur Andersen engagement experience

·        Top 10 studies and articles

·        World class performance measures

·        Diagnostic tools

·        Customizable presentations

·        Process definitions and directory of internal experts

·        Best control practices

·        Tax implications[16]

In short, it was a superb marketing tool for a consulting company.  By using to the Global Best Practices database consultants were spared the effort of reproducing the same research for each of their customers and were able to assemble professional looking presentations for prospective customers with very little effort.  This allowed them to complete assignments, and solicit engagements, sooner and with less effort, which they could devote to the client’s special needs.

By 1996, it became impractical to maintain the timeliness of the CD-ROM-based GBP.  In additions, its user interface was found to be a barrier preventing users from making the best use of its rich knowledge base.  Consequently, Arthur Andersen’s Business Consulting Group created a new, Intranet-based tool, KnowledgeSpace.[17]  Andersen’s KnowledgeSpace includes a publicly accessible tour that demonstrates its capabilities.[18]  KnowledgeSpace, also called Knowledge Xpress, has been described as one of the most ambitious knowledge management projects.[19]

   Knowledge Components of Blue Collar Work

“The technologies here have been increasing at an incredible rate.  You just about need a technical master’s degree to operate the machinery we use to make engines today.”[20]

This is borne out by the amount and nature of training that Cummins plant employees receive.  Each employee is required to undergo nearly 300 hours of training, including 72 hours in mathematics, 37 hours in statistics, and 56 hours in process and product technology.[21]

   Work Teams

In traditional hierarchical organization order and consistency is achieved by management direction, departmentalization, formalized rules and procedures.  Quality control is assured through audits.  Employee roles tend to be highly specialized.  Self-directed work teams, on the other hand, operate with minimal external control.  They rely instead on access to information and communication to coordinate and control their activities.  Appropriate knowledge management interventions can enhance the productivity of work teams, the quality of their work and their ability to govern themselves.  For example, a number of major manufacturers have substituted information systems for hierarchical controls.  Workers in a General Electric dishwasher plant see a real-time display of the most frequent defects that hour.  A Hewlett Packard printed circuit manufacturing facility uses a control panel display of key production data that is never more than 15 minutes old.  They allow equipment operators and engineers to identify problems and to take corrective action before they cause serious disruption.  An electronic scoreboard that all plant employees can see displays quality and productivity data.  The display allows everyone working in the plant to know in real-time if the number of engines being produced is sufficient to meet production goals.[22]

Self-directed work teams rely on training and work redesign rather than post hoc inspection and rework to control defects.  Since teams are responsible for complete activities, they tend to create multi-skilled members.  Learning, communication and access to information are essential for the success of team-based organizations.

The foregoing considerations apply in general to both to teams engaged in physical and knowledge work, though there are some differences.  However, these differences do not materially affect this discussion, so we will ignore them.

   Virtual Knowledge Teams (VKTs)

The economies and productivity gains are often considerable and sufficient to offset the additional infrastructure and other associated costs.  For example, IBM provides some of its sales representatives with notebook computers, software, two home phone lines, a fax machine, remote printer, pager, and cellular telephone at a cost of approximately 8,000 $US/individual.  IBM earns a handsome return on this investment.  It is estimated that it saves 50% in real estate costs for the office space to accommodate these individuals.[23]

This creates a particular challenge for knowledge managers.  First, they need to ensure that the technological infrastructure necessary to support mobile, ad hoc work teams and their members is implemented and maintained.  They must also be aware of, and minimize, the negative social consequences that might undermine otherwise effective technological solutions.  It is particularly difficult to foster personal contacts that lead to exchange of knowledge among member of physically dispersed work teams.  Knowledge managers need to be particularly creative in order to address this problem.

Hewlett Packard (HP) employs a team of elite individuals known as the Strategic Alignment Services (SAS) Team.  The SAS team consists of specialists in organizational design and change management, systems modeling, business analysis and strategy, etc., whose responsibility is helping groups anywhere in HP’s worldwide organization work most effectively.  They are a group of highly skilled, internal consultants.  Their place of work is wherever their expertise may be needed.  They really have no need for a fixed location. Hewlett Packard equips each of their homes with a fully functioning office. Everyone in the group is completely equipped to be mobile with most of the office equipment they would have at home.[24]  Nonetheless, Hewlett Packard maintains office space for them, though they seldom use it, because a shared location fosters a common identity.  Admittedly, this is an unusual circumstance.[25]

A more practical and affordable solution was implemented at one of IBM’s facilities.  Office space is provided for members of a knowledge work team that frequently work in the field or at home.  There is only enough office space for about half of the team at any one time.  Office space consists of unassigned cubicles that have telephones and other office equipment and jacks where team members can connect their portable computers to the IBM’s data network.  Individuals use whatever cubicle is free when they are resident at the facility.  In order to personalize the workspace, each person is allotted nearly one square meter on a long wall along one side of the cubicle complex that can be used for personal effects such as photographs, certificates, artwork created by the team members’ children.[26]

Organizations are also employing the opposite approach:  assembling virtual work teams.  That is, individuals from different locations, disparate units within the organization or even other organizations, may use modern communication facilities to come together as a work team without leaving their principal place of work.  Implementing and supporting the tools necessary for such teams to operate effectively is a major challenge for knowledge managers and general managers alike.  But, even greater are the social challenges.  Team members from different functional units may assume different approaches to problems.   This is at once an advantage and a disadvantage; e.g., someone from a manufacturing or service division may have a perspective on product design different from that of someone from engineering, finance or sales.  Likewise, design teams can anticipate and address problems if they also include representatives of present or future customers, or, as Boeing did when it included representatives of the US Federal Aviation Agency on the design team for its 777 aircraft, representatives of regulatory agencies.[27]  The different viewpoints should lead to a better product design, but first communication gaps must be closed.  Because of the multinational nature of many contemporary organizations, virtual work teams may frequently include individuals from different countries and different cultures.  Further compounding these problems, the teams must frequently work across many time zones.

Clearly, supporting virtual knowledge work teams presents major challenges.  For example, the teams must be able to function across multiple technical specialties, multiple cultures, multiple languages, multiple time zones, and multiple organizational allegiances.  Challenges such as these were once rare, but are now commonplace.  Furthermore, teams assemble and disband in a short time.  Members of knowledge work teams are usually specialists.  Their expertise may be needed to solve several different problems; hence, they may simultaneously be members of multiple teams.  Thus, we find virtually the antithesis of the characteristics that foster success for work teams:  collocation, homogeneity and consistent membership.

Yet, in spite of these formidable problems, virtual knowledge work teams are the best or only approach in an increasing number of instances.  Many new projects involve yet undeveloped technologies.  Thus, they require very large and risky investments.  Joint ventures are a means for obtaining the necessary resources and distributing the attendant risk.  The necessary knowledge and expertise are rarely present in a single location; sometimes it is not even available in a single company.  By including broad participation in the development stage, organizations can help to ensure widespread commitment, which will facilitate later implementation or acceptance of the development effort.  Finally, physical collocation of the experts needed to carry out a project is simply impossible or logistically impractical.  Many companies in a broad range of industries are using virtual knowledge work teams for product development, e.g., Hewlett-Packard, Intel, IBM, Delco, and Weyerhauser, to name a few.[28]

   Assessing Benefits of Knowledge Management

It might be instructive to examine this hidden value for some well-known companies.  If divide the market value of a company, i.e., the total value of all its outstanding shares, by the value of its tangible assets, we arrive at something called the Tobin ratio, or the ratio of market to book value.  Book value is the sum that investors could hope to receive if they sold all a company’s assets and paid all of its creditors.  If we calculate the market to book value for the Microsoft Corporation as of May, 2000, we get 8.57.  That is Microsoft is worth nearly 9 times as much as its tangible assets to its investors.  Likewise, if we compute the same ratio for Intel, we find its value to be 10.93.  In both cases this reflects the belief that investors have that these high tech companies will continue to produce products that will be in demand and sell well.  In other words, some 90% of what investors are buying are intangible assets.  Admittedly, this may be due in part to the glamour of the stock and irrational forces in the market, but a similar pattern is present if we consider other companies.

Figure 1

For example, the ratio for General Motors is 2.41; reflecting, I would assert, the significant knowledge value that goes into automobile design.  If we turn our attention to an industry in which technology and knowledge assets play a lesser role, US railroads, the ratio for the CFX railroad is 0.89.  In other words, the holders of shares in CFX could, in principle, elect to sell off the railroad’s assets and receive more than their shares are currently worth.  Turning finally, to an industry that has notably failed to invest in technology, steel making, the market to book ratio for Bethlehem Steel is 0.49, i.e., its investors could, again, in principle only, elect to sell off its assets and receive more than twice what their shares are worth.

It is precisely a firm’s intangible assets, particularly its knowledge assets, that knowledge managers seek to grow or maximize.  Thus, we need a metric that can be used to judge the success of the interventions that knowledge managers make.  There are no generally recognized metrics.  Those that do exist are often closely bound up with an organization’s specific goals — which is as it should be.  For example, Buckman laboratories, one of the early adopters and a promoter of knowledge management, uses the percentage of sales from products less than 5 years old as a measure of the effectiveness of its knowledge management interventions.  Buckman laboratories manufacturers specialty chemical products and provides consulting advice to help solve complex industrial problems.  Its management believes that solving changing customer problems is the key to its competitive advantage, hence, this measure is appropriate for it.  Buckman introduced a communication system, K’Netix, in 1992.  In the 4 years prior to the implementation of K’Netix sales of products less than 5 years old accounted for 22% of sales.  In the 4 years following these products accounted for nearly 33% of sales.  Thus, Buckman is able to conclude that its investment in K’Netix was justified.[29]

   Metrics Proposed

In 1992 Robert Kaplan of the Harvard Business School wrote what has become classic paper in which he proposed using a Balanced Scorecard that includes a small, manageable number of selected operational and financial measures.[30]  Kaplan argues that financial measures depict past performance, while operational measures depict future performance.  Operational measures include customer satisfaction, state of internal processes, development and delivery time, rate of on-time delivery, repeat business, employee turnover, etc.  Operational measures complement financial measures.  Kaplan recommends that specific operational measures should be based on a company’s strategy.  A balanced scorecard is meant to addresses four questions:

·        How do customers see the enterprise?

·        Customer’s perspective

·        What must we excel at?

·        Internal perspective

·        Can the enterprise improve and learn?

·        Innovation and learning perspective

·        How Does Enterprise Look to Shareholders?

·        Financial Perspective

Another approach is that proposed by Baruch Lev, Baruch Lev, Professor of Accounting and Finance at New York University's School of Business.  He proposes a measure of Intellectual capital.  This parameter can be used to judge the efficacy of new programs and initiatives.  Lev’s scheme is called the knowledge capital scoreboard.  The value of a firm’s Intellectual capital is computed by first determining what it should have earned from its financial and tangible assets.  There are standard sources for the expected rates of return for these assets for various industries.  This expected return is then subtracted from the actual after-tax returns the firm earned.  The next does not is not universally accepted.  The remainder is divided by the discount rate (expected rate of return) for knowledge assets.  Since no one knows what knowledge assets are, let their expected rate of return, Lev uses a proxy.  His proxy is the average after-tax expected return on equity in three knowledge-intensive industries:  software, bio-technology and pharmaceuticals.  There is a standard published reference that supplies these data (Ibbotsen & Associates, Cost of equity).  In 1999 this rate was 10.5%.  Not surprisingly, Lev’s approach is not without critics.  Figure 2 is an example of the knowledge capital scoreboard approach applied to Merck’s financial data for 197.

Knowledge Capital Scoreboard

Merck and Co. 1997

Figure 2

Lev’s knowledge capital scoreboard is relatively simple to apply and to interpret.  Kaplan’s balanced scorecard involves multiple dimension, but every attempt is made to limit the number of measures that are included in the scoreboard.  The best developed measures are those that Leif Edvinsson Of Skandia insurance in Sweden and his colleagues have developed.  Edvinsson’s model attempts to capture the full richness of intellectual capital, but at the cost of some complexity.  We will only sketch the model.  Time does not permit dealing with it in depth.

Edvinsson’s attempts to deal with intellectual capital at Skandia insurance, where he works, began as a one-page addendum to the 1993 Skandia annual report.  It began as a simple narrative statement of the state of Skandia’s intellectual capital.  Since 1994 it has become a complete report on Skandia’s intellectual capital, published as a supplement to the formal annual report.  Edvinsson’s scheme attempts to capture the flows and transformations of intellectual capital.  He and his colleagues Ross and Dragonetti contend that flows among different forms of IC must be managed as much as other stocks.

Though Edvinsson’s model is relatively complex, it does have theoretical elegance.  He begins by dividing a firm’s assets into Financial Capital and Intellectual Capital.  He then divides intellectual capital into Human Capital, i.e., people and their skills, and Structural Capital, i.e., what’s left when people go home, patents, proprietary processes, procedures, databases, organizational relationships, etc.  Measures of human capital are Competence, Attitudes, and Intellectual Agility .  Indicators of competence include percentage of employees with advanced degrees, IT literacy, hours of training/employee, average length of employment, etc.  Examples of attitude indicators are:  time spent debriefing, time spent by senior staff explaining strategy, leadership index, motivation index, etc.  Intellectual agility is the ability to apply knowledge in new situations and the ability to transfer knowledge from one context to another.  Indicators of intellectual agility are savings from employee suggestions that are implemented, new solutions and products suggested by employees, background variety index, company diversification index, etc.

The second component of Intellectual Capital is structural capital.  Measures of structural capital are Relationships, Organization, and Renewal and Development.  Examples of indicators for relationships are:  percentage of the firm’s suppliers or customers business which the firm represents, length of relationships, partner satisfaction index, customer retention, etc.  Indicators used to assess contribution a firm’s organization makes to structural capital include administrative expenses/total revenue, revenues from patents, processes completed without error, cycle/process time, etc.  The final, and perhaps the most important measure of structural capital is renewal and development; indicators include % of business from new products, training expenses/employee, hours spent on training/employee, renewal expenses/operating expenses, new patents filed, etc.[31]

Edvinsson has devised a list of 30 indicators that Scandia’s unit managers use to report the status of the company’s intellectual capital.[32]  Each of Scandia’s operating units has identified from Edvinsson’s list the five intellectual capital items that are most relevant and critical for its success.  From these sets of the five most relevant and critical items, a set of three major intellectual capital measures, was developed.  These are customer capital ratios, human capital ratios, and structural capital ratios.  Unit managers periodically report using these measures.  Changes in these parameters are analyzed for their effect on profitability.  Based on this analysis, Scandia’s unit managers are able to focus attention on the intellectual capital factors that need attention and that should be changed in order to improve the performance of the company.[33]

   Conclusion

With a heightened appreciation of the importance of knowledge to the success of enterprises comes a heightened aversion to recreating intellectual work that has already been done either inside or outside of an organization.  Thus, many organizations have instituted formal programs in which best practices are identified and made available for adoption elsewhere.

Many knowledge workers are expected to do most of their work, or at least significant parts of it, at places other than a fixed office location.  Yet these same individuals if they are to grow professionally and contribute to the corpus of organizational knowledge, need to have excellent, unimpeded communication with the rest of the organization.  This presents yet another challenge for modern management.  The nature of modern organizations further complicates the matter of supporting knowledge workers in their work.  Many organizations are physically dispersed and often enter into joint ventures with a variety of other partners.  Thus, they often must assemble teams of individual specialists who must work together to complete assignments in spite of differences in location, culture, language, and organizational allegiance.

Thus, there is an ever-growing appreciation that knowledge is an asset that must be husbanded and managed.  The special needs of knowledge workers are also getting increased attention resulting in large-scale investments in knowledge sharing infrastructures.

It is clear that companies in knowledge-intensive businesses possess assets that traditional accounting methods fail to recognize.  These companies are often valued as much as 10 times the net worth of their physical assets.  To explain this discrepancy economists, beginning with John Kenneth Galbraith, have identified something called Intellectual Capital.  Measuring Intellectual capital, charting its growth and justifying the often very large investments in technological infrastructure and human resources is a major, new challenge.  This, and the challenges associated with creating the organizational structures and technological infrastructures should occupy the attention of many different individuals, managers, information scientists, communication scientists, engineers, social scientists, etc., for some time to come.

The challenge is very great.  It will take the efforts of a great many individuals to

That is, they have realized that knowledge is an asset that must be managed just li Work in the developed world is increasingly knowledge work.  Even activities that are essentially physical include increasingly large components of knowledge work.  The ability to create, grow and exploit knowledge has become a critical competitive factor in the post-industrial age.

  [1] Fritz Machlup, The Production and Distribution of Knowledge in the United States (Princeton, NJ: Princeton University Press, 1962).
  [2] Marc Uri Porat, The Information Economy: Sources and Methods for Measuring the Primary Information Sector (Washington, D.C.: U.S. Department of Commerce, Office of Telecommunications, 1977); and Marc Uri Porat, The Information Economy: Definition and Measurement (Washington, D.C.: U.S. Department of Commerce, Office of Telecommunications, 1977).

  [3] Thomas A. Stewart, Is this job really necessary? Fortune (12 January 1997): 154.

  [4] Quoted in Ikujiro Nonaka, The knowledge creating company, In:  Harvard business review on knowledge management, Boston, Massachusetts:  Harvard Business Review, 1998, p.27.

  [5] Carla O’Dell and C.Jackson Grayson, Jr. with Nilly Essaides, If only we knew what we know:  the transfer of internal knowledge and best practices, New York, Free Press, 1998.

  [6] Ibid, ix.

  [7] Ibid.

  [8] Ibid., pp. 61-2.

  [9] Ibid., p. 83.

  [10] James W. Michaels and Dirk Smillie, Webucation, Forbes, 165 (11)(15 May 2000):  92.

  [11] Michael Milken, comeback king, The economist, 350 (8112)(27 March 1999):  34.

  [12] Michaels and Smillie, Loc. Cit.

  [13] Douglas B. Weinberg, U.S. international transactions, first quarter 2000, Survey of current business,80(7)(July 2000):  79-124.

  [14] Quoted in: Kimball Fisher and Mareen Duncan Fisher, The distributed mind:  achieving high performance through the collective intelligence of knowledge work teams, New York:  AMACOM, 1998, p. 174.

  [15] Fisher and Fisher, Op. Cit., pp. 8-25.

  [16] Wendi Bukowitz, In the know, CIO magazine, 15 April 1996, http://www.cio.com/archive/041596_ins_content.html.

  [17] Microsoft Corp., Press release, Arthur Andersen: Intranet solutions case study, http://www.microsoft.com/technet/Analpln/Cs/authand.asp.

  [18] KnowledgeSpace, http://www.knowledgespace.com/splash/

  [19] Thomas H. Davenport and Laurence Prusak, Working knowledge:  how organizations manage what they know, Boston, Massachusetts:  Harvard Business School Press, 1998, p.121.

  [20] Ibid., p. 33.

  [21] Ibid.

  [22] Fisher and Fisher, Op. Cit., p. 176.

  [23] Ibid., p. 132.

  [24] Ibid., p. 75.

  [25] Ibid., p. 152.

  [26] Ibid.

  [27] Ibid., pp. 37-8.

  [28] Ibid., p. 134.

  [29] O’Dell and Grayson, Op. Cit., pp. 132-4.

  [30] Robert S. Kaplan and David P. Norton, The balanced scorecard:  measures that drive performance, Harvard business review, 70 (1) (Jan/Feb 1992): 71-79.

  [31] Johan Roos, Göran Roos, Leif Edvinsson, and Carlo Dragonetti, Intellectual capital:  navigating the new business landscape, New York:  New York University Press, 1998, pp. 28-58.

 [32] Michael S. Malone, New metrics for a new age, http://www.forbes.com/asap/97/0407/040.htm.