Floccus of control: will it ever be possible to place a monetary value on intangible assets in a reliable, meaningful way? Well, possibly. Garry Carnegie and Lucia Lima Rodrigues propose a valuation technique that uses fuzzy logic within an enterprise governance framework.

Intangible assets are widely recognised as either the key drivers of commercial value--eg, brand names--or the main generators of new streams of value--eg, R&D. But the meaningful financial reporting of the monetary value of these assets, including goodwill, is as problematic now as it has ever been.

Traditional accounting practice has tended to accept that the historical costs applied to intangibles may be capitalised as assets in financial statements. On the other hand, internally generated goodwill is not to be recognised as an asset for reporting purposes. Other internally generated intangibles may be recognised in the accounts, providing that they can be reliably measured, if at all.

These measurement problems mean that many of the most important value drivers or creators in any enterprise are left off the balance sheet. It's well understood that conventional statements of financial position do not constitute an attempt to depict the total market capitalisation of the ordinary shares of publicly listed companies. Fuzzy logic offers a way to handle the subjectivity inherent in placing financial values on key intangible resources--for internal decision-making and evaluation purposes only.

Managers are coming under increasing pressure to combine value-based management techniques for enhancing performance with balanced scorecard methods of performance measurement, a recent attempt by CIMA and the International Federation of Accountants (IFAC) to encapsulate the latest thinking in both corporate governance--ie, conformance--and business governance--ie, performance--has been described as enterprise governance (1). A key part of enterprise governance in a plc is to conduct regular reviews of its strategic position. For completeness, this requires assessments of changes in the monetary values of the business's main "value stimulants". We are not suggesting for one moment that intangibles of all types, whether purchased or not, should be valued for recognition in conventional financial reports, but their monetary values can be estimated for strategic management accounting purposes. We are proposing an intangible assets monetarisation system using fuzzy logic as a test method. This should help to make the CIMA Strategic Scorecard work, especially the "strategic position" element (2).

The term "strategic assets" describes key resources that are critical to the company's present position and success in the long term. Examples of such resources include an outstanding workforce, a commanding market share or instant brand recognition. As is widely accepted, the market "recognises these soft assets and, to widely varying degrees of accuracy, factors them into projections of cash flow and then prices shares accordingly" (3).

These key resources are seen as generally hard to obtain, secure or develop, and difficult for other firms to emulate quickly. They may also be described as a unique combination of capabilities or competencies. Companies that hold resources of this kind will be able to sustain at least a short-term edge over their competitors. Such advantages are reflected in abnormal returns or, in other words, the difference between a company's market capitalisation and its book value, excluding net book values for intangible assets, if any.

Lotfi Zadeh, professor of electrical engineering at the University of California, introduced fuzzy set theory, also known as fuzzy logic, in 1965 as a way to deal with the problem of ambiguity in decision processes (4). The theory has since found many applications in diverse fields. A quarter of a century later, Awni Zebda, now professor of accounting at Texas A&M University, proposed using fuzzy logic to model ambiguity in accounting (5).

Fuzzy logic is a powerful problem-solving method. It provides a remarkably simple way to draw conclusions from ambiguous or vague information. In a sense, fuzzy logic resembles human decision-making in its ability to work from approximate data and find precise solutions. It recognises that not all things can be classified into mutually exclusive groups and allows items to be members of fuzzy sets to varying degrees.

The main difference between fuzzy logic and Boolean (standard) logic is that possible values range from 0 all the way up to 1, and not only 0 and 1. This means that the former system allows for gradual membership of the characteristic function, while the latter covers the membership in a binary manner. For example, consider the fuzzy set of "old houses". Where is the dividing line between new houses and old houses? Is a house constructed in 1988 an old house? If not, is a house built in 1962, or even in 1955, an old house? The assessment is subjective and lies, of course, in the eyes of the beholder.

Although these kinds of views are fuzzy, there are ways to give rational values to intuitive assessments of individual elements of a fuzzy set. In other words, we translate from human fuzziness to numbers that can be manipulated by a computer. We do so by assigning assessments of conditions a value ranging from 0 to 1. For instance, the fuzzy, truth value of the statement "this house is old" may be set at 0.7 if the particular house was built 50 years ago. So the fuzzy set "old houses" could be defined as "houses more than 10 years old", while the older the house, the larger its degree of membership becomes--ie, towards a value of 1.

Unlike classical logic, which requires a deep understanding of a system, exact equations, and precise numeric values, a fuzzy logic expert system allows for the expression of knowledge with subjective concepts such as "very small" or "bright red". Words such as "important" "strong" and "difficult" are all fuzzy variables that are based not on precise measurements but on expressions of opinion. A fuzzy variable becomes a linguistic variable when we add descriptive words such as "somewhat", "fairly" or "very". Linguistic variables can be related to each other with conditional "if, then" statements. Take the following example: "If the intangible resource is very difficult to imitate, then the residual income will grow very quickly."

The concept of a linguistic variable gives us a way to characterise phenomena too complex to be described in conventional quantitative terms. Unlike statistical estimators, fuzzy systems estimate a function without a mathematical formula that gives a description of how outputs depend on inputs. Such systems store fuzzy associations in terms of rules, which must be provided by an expert. Conditional and unconditional propositions can be used. Rules of inference are developed to describe the interdependence among variables. A fuzzy algorithm is a procedure made up of statements relating linguistic variables.

Given a descriptive list of company's intangibles, strategic management accountants are then able to depict, as monetary estimates, the respective fuzzy contributions of each listed intangible resource to the total market value of the company at a given date, recognising those intangibles that contribute in greater proportions than others to the earning of abnormal returns. The key questions involved in implementing this process are depicted in the diagrams.

This approach, while simplified for explanatory purposes, requires the identification of the respective contributions to the company's abnormal returns of each key intangible asset listed. For example, 30 per cent may be related to a highly skilled and committed group of employees, 20 per cent may be linked to a renowned brand name, 25 per cent may be associated with a prime location for the business and so on.

Such figures are, of course, not auditable and are nothing more than approximations under the framework of assumptions we are using. Nevertheless, basing business performance evaluations exclusively on traditional, historical indicators is no longer enough. The use of fuzzy logic in enhancing performance management systems is therefore becoming increasingly important as a way to monitor a company's critical factors and, more broadly, to cheek the vitality of a company's key value-adding drivers.

CIMA and IFAC recommend that the two dimensions of enterprise governance--conformance and performance--should be managed carefully in balance. This holistic approach reflects the dual role of board directors as monitors and strategists. It also acknowledges the inherent tensions between governance and value creation. The periodic evaluation of prized intangibles will help directors to focus on the key factors that move their companies forward as part of assessing the "strategic position" under enterprise governance. Importantly, directors are likely |o become better informed about the determinants of value creation in their companies. This should make them better equipped to assess the vitality of such value stimulants in the interests of enhancing accountability and performance--as the enterprise governance concept aptly advances.

REFERENCES

(1) Enterprise Governance: Getting the Balance Right, CIMA and IFAC, February 2004 (www.cimaglobal.com/downloads/ enterprise_governance.pdf).

(2) "Up the right tree", Financial Management, March 2004.

(3) R Eccles, R Herz, M Keegan and D Phillips, The Value Reporting Revolution, John Wiley & Sons, 2001.

(4) L Zadeh, "Fuzzy sets", Information and Control, Vol 8, No 3, 1965.

(5) A Zebda, "The problem of ambiguity and vagueness in accounting", Behavioural Research in Accounting, Vol 3, 1991.

Garry Carnegie ([e-mail]) is a professor in the School of Enterprise at Melbourne University Private 

Lucia Lima Rodrigues ([e-mail]) is a professor in the School of Economics and Management at the University of Minho, Portugal