Approaches to KM:


There is a growing recognition in the business community about the importance of knowledge as a critical resource for organizations. Traditionally, this resource has not been treated with the degree of systematic, deliberate, or explicit effort devoted to managing human, material, and financial resources. But in the coming years, the firm that leaves knowledge to its own devices may be putting itself in sever jeopardy. More and more practitioners and researchers believe that knowledge resources matter more than the conventionally tended resources (material, labor, capital) and must be managed explicitly, not left to fend for itself.

Knowledge management can be defined as a method to simplify and improve the process of sharing, distributing, creating, capturing and understanding knowledge in a company. Knowledge management is description, organization, sharing and development of knowledge in a firm. Knowledge management is managing knowledge-intensive activities in a company. Knowledge management refers to identifying and leveraging the collective knowledge in a company to help the company compete. Knowledge management is a method for achieving corporate goals, by collecting, creating and synthesizing and sharing information, insights, reflections, thoughts and experience. Knowledge management is a discipline focused on systematic and innovative methods, practices, and tools for managing the generation, acquisition, exchange, protection, distribution, and utilization of knowledge, intellectual capital and intangible assets.

The purpose of knowledge management is to help companies create, share and use knowledge more effectively. Effective knowledge management causes fewer errors, less work, more independence in time and space for knowledge workers, fewer questions, and better decisions, less reinventing of wheels, improved customer relations, improved service and improved profitability. Knowledge management is purported to increase both innovation and responsiveness. The recent interest in organizational knowledge has prompted the issue of managing knowledge to the organization’s benefit.

Earl (2001) developed taxonomy for knowledge management that he labeled schools of knowledge management. Each school was proposed as an ideal type. No claims were made that any new school outperforms others. Each represents a particular orientation r perspective. The schools are not mutually exclusive.

Earl’s (2001) taxonomy is applied to classify a number of approaches to knowledge management. This classification of approaches is based on an overall match to each ideal type in terms of school of knowledge management. Three relevant schools are labeled the economic school, the organizational school and the strategic school. The economic school has a focus of income, in which the aim is to exploit knowledge assets. The organizational school has a focus of networks, in which the aim is knowledge pooling. The strategic school has a focus of competitive advantage, in which the aim is to identify, exploit and explore knowledge capabilities.

The Economic School

According to Earl (2001), the economics school is explicitly concerned with both protecting and exploiting a firm’s knowledge or intellectual assets to produce revenue streams (or rent). It is concerned with managing knowledge as an asset, in which knowledge or intellectual assets include patents, trademarks, copyrights and know-how. Intellectual property could be another means of describing the object being managed. This school is more concerned with exploitation of knowledge and less concerned with exploration. One critical success factor in this school appears to be the development of a specialist team or function to aggressively manage knowledge property through intellectual capital

accounting, intellectual capital management and creation of effective and efficient knowledge marketplace. Otherwise it is too easily forgotten.

Intellectual Capital Accounting

According to Roslender and Fincham (2001). Intellectual capital is currently the focus of significant discussion and enquiry across the management disciplines and beyond. This reflects the recognition that intellectual capital provides a crucial source of value for the contemporary business enterprise. It is a source that requires careful management if it is to fulfill its maximum potential.

In the case of those businesses whose shares are publicly quoted, the success with which organizations manage their intellectual capital is increasingly mirrored in their market values, values that are often many times the book values of enterprises. Bridging the gap between these two values provides one motivation for seeking to account for intellectual capital.

Another motivation for seeking to account for intellectual capital is the need to manage intellectual capital successfully. Given the importance of managing intellectual capital successfully, accounting is being challenged to develop new approaches to performance measurement that capture the quality of management evident in the context of intellectual capital.

Stewart (1997) has suggested several tools for measuring intellectual capital. Value is defined by the buyer, not the seller. A company, therefore, is worth what the stock market says: price per share x total number of shares outstanding = market value; what the company as a whole is worth. One measure of intellectual capital is the difference between its market value and its book equity. The assumptions is that everything left in the market value after accounting for the fixed assets must be intangible assets. If Microsoft is worth 100 billion dollars, and its book value is 10 billion dollars, then its intellectual capital is 90 billion dollars.

Three components of intellectual capital can be identified. Human capital is the first component, consisting of the know-how, capabilities, skills and expertise of human members of an organization. Relational capital is the second component, consisting of any connection that people outside the organization have with it, together with customer loyalty, market share, the level of backorders, and so forth. Structural capital embraces the remaining component of intellectual capital, including both systems and networks, and cultures and values, together with elements of intellectual property such as patents, copyrights, trademarks, and so forth.

To begin intellectual capital accounting necessitates an acceptance that it is possible to include within the same financial statement objective measures of value, as in the case of tangible assets for which there are historical expenditures. Intangible assets such as goodwill are already problematic in accounting. For example, in the UK, only purchased goodwill can be reported in the accounts of the business that acquires it.

If goodwill continues to prove problematic for financial accounting and reporting, intellectual capital as the new goodwill serves to multiply the difficulties involved. Intellectual capital assumes many more forms than does goodwill, and while both concepts are ultimately open-ended, several years of thinking about intellectual capital have confirmed its greater breadth and depth. One consequence of this, according to Roslender and Fincham (2001), is that we might now think in terms of degrees of intangibility, so that while brands, patents and know-how still count as intangible assets, customer data, distribution channels and employee qualification profiles are more intangible. Off the scale are such assets as employee commitment, organizational culture and corporate values, yet it is just such assets that ensure that some businesses exhibit impressive market-to-book value rations.

The market-to-book value ratio is sometimes used to indicate the value of intellectual capital in an organization. Three decades ago, the market-to-book value ratio was close to one in most businesses. Today, this ratio has grown to four on average. Microsoft is an extreme example. The book value of the company was 11 billion dollars in 1997, while the market value was 200 billion. This give a market-tobook value ration of 20. Afuah and Tucci (2003) argue that this ratio is caused by intellectual capital.

A number of approaches to valuing knowledge assets exist. Reliable approaches require a common language to discuss the underlying value of an organization’s knowledge assets. The knowledge-value

added methodology seems to conform to this reinforcement as one of the more robust approaches. The knowledge-value-added (KVA) methodology as described by Housel and Bell (2001) addresses a need long recognized by executives and managers by showing how to leverage and measure the knowledge resident I employees, information technology, and core processes. KVA analysis produces a return-onknowledge (ROK) ratio to estimate the value added by given knowledge assets, regardless of where they are located.

The essence of KVA is that knowledge utilized incorporate core processes is translated into numerical form. This translation allows allocation of revenue in proportion to the value added by the knowledge as well as the cost to use that knowledge.

Balance Including Intellectual Capital in a Business Organization (this example developed by Egil Sandvik using Invisible Balance Sheet in Sveiby’s toolkit:

Tangible assets 25,000,000 Human capital 20,000,000 Relational capital 25,000,000 Structural capital 30,000,000 Balance Sheet Material values 15,000,000 Immaterial values 75,000,000 Debt 10,000,000
Assets 100,000,000 Liabilities 100,000,000

Tracking the conversion of knowledge into value while measuring its bottom-line impacts enables managers to increase the productivity of these critical assets. Housel and Bell (2001) present the following example.

The example begins with an average person who needs to learn how to produce all the outputs of a given company. In a very real sense, then, her adding processes including selling, marketing, producing, accounting for, financing, servicing, and maintaining. It is these core processes that add value while converting inputs into outputs that generate the company’s revenue.

Knowledge Value Added (KVA):

KVA provides a methodology for allocating revenue and cost to a company’s core processes based on the amount of change each produces. Significantly, the knowledge required to make these changes is a convenient way to describe the conversion process.

We define knowledge in a particular way here: It is the know-how required to produce process outputs. This kind of knowledge is proportionate to the time it takes to learn it. Learning time has been found to be a quick and convenient way to measure the amount of knowledge contained in any given process. This understanding can be put to test with the example. In a widget company, there is one person, the owner, who makes and sells widgets. This person knows all there is to know in order to make and sell widgets for $1. The owner’s sales production knowledge can be used as a surrogate for the dollar of revenue generated by the owner’s application of the core process knowledge. And we can determine how long it would take the widget company owner to transfer all the necessary sales and production knowledge to a new owner. Further, we can use these learning times to allocate to dollar of revenue between the sales and production processes.

In Housel and Bell’s (2001) example, it is assumed that it takes 100 hours for the new owner to learn the processes, with 70 hours spent learning how to make the widget and 30 hours learning how to sell it. This would indicate that 70 percent of the knowledge and value added was contained in the production process and 30 percent in the sales process. It would follow that #0.70 of the revenue would be allocated to production knowledge and $0.30 to sales knowledge.

All that would be left to do in this example would be to determine how much it costs to use the sales and production knowledge, and then we would have a ratio of knowledge value added to knowledge utilization cost. In other words, we can measure return on knowledge (ROK). For the sake of argument, it is assumed that the total cost to sell and produce a widget was $0.50 : $0.25 for sales and $0.25 for production. The basic approach here is to find out how much it costs to use the sales and production knowledge. In this case the cost is directly tied to how long the new owner spends performing each

process. As it turns out, in this case, the new owner spends the same amount of time to do both and, therefore, the cost to use the knowledge of each process is the same.

Based on our estimates for distribution of revenue and cost, we would generate an estimate of ROK. We would conclude that the production process is a more productive use of the knowledge asset (ROK = 0.70/0.25 = 280%) than the sales process (ROK = 0.30/0.25 = 120%).

VN:F [1.9.14_1148]
Rating: 0.0/10 (0 votes cast)
VN:F [1.9.14_1148]
Rating: 0 (from 0 votes)