FRAMEWORK PAPER 00-01

ASSESSING KNOWLEDGE ASSETS: A REVIEW OF THE MODELS USED TO MEASURE INTELLECTUAL CAPITAL

Dr. Nick Bontis Michael G. DeGroote School of Business McMaster University Queen’s University at Kingston, April 2000

Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

This paper was commissioned by the Queen’s Management Research Centre for Knowledge-Based Enterprises and is part of a Framework Series established to organize existing research and identify key research issues for future investigation. The series can be found at http://www.business.queensu.ca/kbe or by contacting the KBE Centre at 613-533-3088.

THE FRAMEWORK SERIES: Measuring Knowledge Assets, 00-01, Nick Bontis This paper provides an overview of the literature pertaining to the measurement of knowledge assets, including intellectual capital. Bontis builds a framework to classify research in this area and identifies key challenges and opportunities for future work. The Knowledge Based Economy, 00-02, Richard Harris This paper reviews the concept of a knowledge-based economy and the role of knowledge and technology in driving productivity and economic growth. The Knowledge-Based Enterprise, 00-06, Sandy Staples and Jim McKeen, Staples and McKeen set out to build a research framework in directing the KBE Centre to understand all of the research areas that collectively inform the management of knowledge-based enterprises. Knowledge Management Systems, 00-04, Brent Gallupe This paper examines approaches used to guide thinking with respect to the role of knowledge management systems. Knowledge Work and Knowledge Worker,00-03, Julian Barling and Kevin Kelloway This paper reviews the literature to establish a research framework to guide future investigations into key HR issues surrounding knowledge-based enterprises including motivation and reward. Knowledge Exchange Between Firms, 00-05, Douglas Reid Reid’s paper reviews the current status of knowledge exchange between firms and integrates key themes from disciplines such as strategic management, international business and the study of networks.

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ABSTRACT: This paper reviews the literature pertaining to the measurement of knowledge assets including intellectual capital. Since knowledge assets are at the crux of sustainable competitive advantage, the burgeoning field of intellectual capital is an exciting area for both researchers and practitioners. Unfortunately, the measurement of such intangible assets is difficult. A variety of models have surfaced in an attempt to measure IC and this paper aims to highlight their strengths, weaknesses and operationalizations. Author Information: Dr. Nick Bontis, Ph.D. Michael G. DeGroote School of Business, McMaster University 1280 Main Street West, MGD #207 Hamilton, Canada L8S 4M4 Tel: (905) 525-9140 x23918, [email protected]

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TABLE OF CONTENTS

1.

Skandia Navigator ....................................................................................................... 8

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IC-Index...................................................................................................................... 12

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Technology Broker.................................................................................................... 15

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Intangible Asset Monitor........................................................................................... 17

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Knowcorp.................................................................................................................... 21

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Tobin’s Q .................................................................................................................... 26

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MVA and EVA .......................................................................................................... 28

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Citation-Weighted Patents......................................................................................... 29

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Balance Score Card ................................................................................................... 31

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Human Resource Accounting .................................................................................... 33

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Research Agenda………………………………………………………………….…31

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Conclusion…………………………………………………………………………...33

Copyright  Dr. Nick Bontis 2000. Version Mar-22-2000. All rights reserved. This paper is open for comment and is submitted on behalf of the Queen’s Management Research Centre for Knowledge-Based Enterprises. No part of this work may be reproduced without the permission of the author. The author would like to acknowledge the significant research contribution that Rosemary Park (PhD Candidate, McMaster University) made in this paper.

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INTRODUCTION By the year 2010, all of the world’s knowledge will double every 11 hours.

codified

Dr. Nick Bontis, Ph.D. March 5, 1999 – Johannesburg, South Africa Keynote Speech, African KM Summit

Individuals are being bombarded by knowledge. The exponential velocity with which this rate of bombardment is increasing is unfathomable. Although society as a whole will benefit from increased technology due to this ubiquitous bombardment, the average business manager may not be prepared to take advantage of this knowledge-intensive world. The popular use of the terms in the following list hint at the increased importance knowledge assets have in organizations: intellectual capital, knowledge capital, knowledge organizations, learning organizations, organizational learning, information age, knowledge era, information assets, intangible assets, intangible management, hidden value, and human capital. These terms and others are part of a new lexicon describing new forms of economic value. They are descriptors belonging to a paradigm where sustainable competitive advantage is tied to individual workers’ and organizational knowledge. Reliance on productive tangible assets such as “raw materials, fixed capital, and even managerial knowledge” no longer account for investments made and wealth created by new and prospering companies (OECD, 1996, p.15). Instead, leveraging knowledge is the key reason attributed to corporate success stories such as the tremendous ‘overvaluation’ of high-tech and Internet companies (Standfield, 1999). The adoption of this new lexicon, concepts, and explanations has been swift and far-reaching. The notions of intellectual capital were first advanced by economist John Kenneth Galbraith who wrote the following to fellow economist Michal Kalecki in 1969: I wonder if you realise how much those of us the world around have owed to the intellectual capital you have provided over these last decades (cited in Hudson, 1993, p.15). Intellectual capital was further expounded upon by management guru Peter Drucker (1993) in his description of post-capitalist society. By the end of the 1990s, references to intellectual capital in contemporary business publications were commonplace (Bontis, 1999). Intellectual capital management became the domain of the so-called CKO or Chief Knowledge Officer (Bontis, 2000). Stewart, in his ground-breaking cover-story in Fortune Magazine, is credited with providing the main impetus for a new world of intellectual capitalists (Stewart, 1991). Endorsements by highly respected scholars such as Dr. Baruch Lev (from New York University) and Dr. Tom Davenport (from Boston Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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University) coupled with practitioner icons such as Leif Edvinsson (formerly of Skandia) and Hubert Saint-Onge (formerly of CIBC) help to round out the academic and practitioner love affair with this phenomenon. Perhaps the most impressive evidence suggesting a transition in thinking about a new structure and process supporting a company’s productive assets is in the inclusion of intellectual capital as a strategic performance measure. In 1998, Arthur Andersen conducted an international survey of the measurement of intellectual capital. A total of 368 companies from a pool of 2,350 (15% response rate) European, North American, and Asian organizations responded to direct mail surveys. The survey revealed some interesting results. First, the majority of respondents believed that IC (intellectual capital) reporting would increase. Second, about three-quarters of the respondents already tracked two or more non-financial metrics. Third, most agreed that knowledge measurement would improve organizational performance. Fourth, roughly half believed that what was learned from the process of measuring IC was as important as information received from the measures. Finally, while researchers admitted that the respondents may have represented a biased sample of “pro-IC” organizations, they concluded that IC would likely not be included on financial balance sheets any time in the near future. External reporting of IC would be done on a voluntary disclosure basis and that IC measurement would be more useful as an internal management tool than as an external communication to shareholders or investors. Similar research results have been found elsewhere. A 1998 study by Waterhouse and Svendsen of 65 CEOs and 49 Directors of Boards of large Canadian companies showed that IC disclosure was rated as a key strategic issue and should be regularly reported to boards. This results was reported as often as other non-financial strategic measures relating to internal operating efficiencies such as innovation capacity, product quality, customer relations, and investor relations. Other strategic issues involving investor relations, partner relations, community relations and environment, health and safety were reported less often. Yet, of the nine strategic non-financial measures rated highly, CEOs and Directors expressed least satisfaction with their IC measures. Huseman and Goodman (1999) also examined IC accounting as it related to human capital in 202 of the largest 1,500 companies in the US. A small minority (i.e., 15%) had systems that attempted to quantify human capital as it is typically defined in the IC literature, and only 35% of senior HR respondents thought they would have an HC accounting system in the future. However, the large majority of companies were, in fact, actively collecting information about employees. This included 66% of all respondents who reported that they had programs or systems in place that tried to capture knowledge, skills, and best practices. The frustration expressed by the three aforementioned studies regarding IC measurement is interesting. It suggests a period in time when tangible measures of intangible assets of intellectual capital are wanted but early renditions have proven unworthy. This is fascinating because it is occurring in tandem with continuing high levels of satisfaction expressed by CEOs and Directors regarding traditional financial measures such as profit and loss statements and capital expenditure reports (Waterhouse and Svendsen, Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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1998). Yet these traditional measures themselves are now generally acknowledged as inadequate. It is indeed ironic that the reason for their inadequacy is because of the same competitive forces that have given rise to the need for IC measures. Perhaps, IC measures are recognized as necessary but are unsatisfying due to the embryonic stage of their development. David Moore, research director for the CICA (Canadian Institute for Chartered Accountants) states: Financial performance measures derived from information in financial statements or other financial sources have been used by publicly listed companies for many years. They highlight specific aspects of a company’s profitability, solvency, liquidity, productivity or market strength. Such performance measures, are however based on historical and transaction based information that does not take into account changes in values or internally generated intangibles. There is the growing view that financial performance measures by themselves are inadequate for strategic decision making. They need to be supplemented or even to some extent, replaced [italics the author] by non-financial measures that cover such matters as, for example, customer satisfaction and operating efficiency. (Waterhouse and Svendsen, 1998, p. v)

Is a Paradigm Shift Occurring? Brooking (1996) attributes the shift in thinking to information-age technology, the media, and communications which have provided tools with enormous intangible benefits to organizations. Standfield (1999) believes the obvious impact of intangibles such as knowledge technology, and intellectual property, has made executives feel they need to factor in such intangibles, and lose confidence in their decision-making ability based on traditional tangible data. Yet to move from historical understandings of financial value based on accepted assumptions and concepts developed over 500 years, to the identification of a new structure of assets is not an easy task. Some practitioners and scholars have labelled the process a paradigm shift. Is it? Kuhn (1962) hypothesized that a scientific paradigm will change if sufficient anomalies appear in core concepts, methodologies, and/or research findings. He also believed in the logical possibility of scientific revolutions versus more gradual evolutions when a paradigm (thesis) is undermined by the discovery of an antithesis (anomalies) sufficient to create a revolution and synthesis (new paradigm) (Scott, Mitchell, and Birnbaum, 1981). A new model whether introduced in theoretical or applied realms, must contain superior philosophical, conceptual and empirical elements before the model will replace any existing set of ideas. Moreover, like most transformational change efforts, model development likely will proceed through a relatively orderly series of change steps. First, there must be an awareness that something is amiss. Old methods will be used to try to explain new information. A general dissatisfaction must be created around flaws in existing procedures and explanations. Alternative solutions based on new conceptualizations likely already being advanced, Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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must be subject to testing and promoted as having the potential of providing superior understanding. These solutions must represent a whole new way of doing business to qualify as a true paradigm shift. Furthermore, it is important for respected champions to advocate a change. Finally, those solutions found superior to others must be supported while others must be discarded – at least for the time being (French, 1992). There is ample evidence that at least the first few change steps to value new forms of economic wealth have occurred. In some large measure, we have also moved into the final two stages as respected practitioners and scholars with impressive credentials line up to acknowledge that traditional measures are insufficient, and promote the use of alternative financial performance models. What about the critical step? Currently, measuring knowledge assets is in an experimental phase where a myriad of possible solutions (i.e., new concepts, definitions, criteria, and operational measures) are being promoted and tried. Some researchers are stating that intangible measures are now into their third generation (Standfield, 1999). However, field research such as that reported above by Arthur Andersen (1998), Waterhouse & Svendsen (1998) and Huseman & Goodman (1999) suggests that for a statement such as Standfield’s to be more than presupposition, a much more systematic account of relations among variables and their outcomes contained in this new paradigm is necessary. If our understanding of paradigms is correct and useful (i.e., that “order out of chaos” can be acquired through the rational acquisition of knowledge), we will be able to continue and proceed through this critical step. The solve this challenge requires a multi-layered structure of ideas. At its foundation are philosophical assumptions, or as Lincoln and Gibba (1985) describe them “metaphysical truths” of what we think but cannot prove. These assumptions in turn support presuppositions or an orientation regarding how beliefs are to be logically organized and defended or else remain as beliefs. Commitments are then made as to a research design and accepted measurement. Finally, a paradigm requires actual findings from measured variables to confirm observed and expected events. Of all business models advanced to date, it would be truly ironic if the IC (intellectual capital) model could not be tested for its defensibility as a new paradigm. Reviewing Measurement of Knowledge Assets The purpose of this paper is to summarize what is currently known about assessing knowledge assets through trends and features of current IC measurement models. The paper is divided into 10 sections which introduce and describe the main models currently in use. Each section reviews the assumptions of the measurement model and describes its main conceptualizations. The review concludes with a summary of each model plus a framework that attempts to position each model along the dimensions of “usage universality” and “quantitative objectivity”.

1.

SKANDIA NAVIGATOR

Skandia is considered the first large company to have made a truly coherent effort at measuring knowledge assets (Bontis, 1996; Huseman and Goodman, 1999). Skandia first developed its IC report internally in 1985, and became the first company to issue an IC addendum accompanying its traditional Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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financial report to shareholders in 1994. Other companies including Dow Chemical’s initiatives in valuing its R&D and patent process have relied extensively on Skandia’s multi-dimensional conceptualization of organizational value. Leif Edvinsson, the chief architect behind Skandia’s initiatives developed a dynamic and holistic IC reporting model called the Navigator with five areas of focus: financial, customer, process, renewal and development, and human capital. This new accounting taxonomy sought to identify the roots of a company’s value by measuring hidden dynamic factors that underlie “the visible company of buildings and products” (Edvinsson and Malone, 1997, p.11). According to Skandia’s model the hidden factors of human and structural capital when added together comprise intellectual capital. Human Capital is defined as the combined knowledge, skill, innovativeness, and ability of the company’s individual employees to meet the task at hand. It also includes the company’s values, culture, and philosophy. Human capital cannot be owned by the company. Structural Capital is the hardware, software, databases, organizational structure, patents, trademarks, and everything else of organizational capability that supports those employees’ productivity - in other words, everything that gets left behind at the office when employees go home. Structural capital also provides customer capital, the relationships developed with key customers. Unlike human capital, structural capital can be owned and thereby traded. Intellectual Capital equals the sum of human and structural capital. According to Edvinsson and Malone (1997), IC encompasses the applied experience, organizational technology, customer relationships and professional skills that provide Skandia with a competitive advantage in the market In sum, Skandia’s value scheme contains both financial and non-financial building blocks that combine to estimate the company’s market value shown below. This conceptualization achieved a balance for Skandia in trying to represent both financial and non-financial reporting, uncovering and visualizing its intellectual capital, tying its strategic vision to the company’s core competencies reflecting knowledgesharing technology and knowledge assets beyond intellectual property, and reflecting better its market value. Edvinsson and Malone (1997) argue that IC represents such a fundamentally new way of looking at organizational value that it will never be confined to playing an adjunct role to traditional accounting. They also assert that the presence and value of intangible assets is capable of accounting for the significant widening gap between companies’ valuing of enterprises stated in corporate balance sheets and investors’ assessment of those values.

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SKANDIA’S VALUE SCHEME Total Value

Financial Capital

Intellectual Capital

Human Capital

Competence

Attitude

Structural Capital

Intellectual Agility

Relationship

Organisation

Renewal and Development

Operationalization The Skandia IC report uses up to 91 new IC metrics plus 73 traditional metrics to measure the five areas of focus making up the Navigator model. Edvinsson and Malone (1997) acknowledge that various indices may be redundant or of varying importance. Yet in trying to use their experience to create a universal IC report, they still recommend 112 metrics. The following table summarizes some of these metrics:

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SAMPLE OF SKANDIA IC M EASURES

Financial Focus

Customer Focus

Process Focus Renewal and Development Focus Human Focus

• • • • • • • • • • • • • • •

revenues / employee ($) revenues from new customers / total revenue ($) profits resulting from new business operations ($) days spent visiting customers (#) ratio of sales contacts to sales closed (%) number of customers gained versus lost (%) PCs / employee (#) IT capacity – CPU (#) processing time (#) satisfied employee index (#) training expense / administrative expense (%) average age of patents (#) managers with advanced degrees (%) annual turnover of staff (%) leadership index (%)

The 112 indices use direct counts, dollar amounts, percentages and even survey results. Edvinsson and Malone (1997) encourage direct counts to be compared with other direct counts to produce ratios or be transformed into money leaving only two types of measurement. Monetary measures are combine using a pre-determined weighting to produce an overall IC value (C) for the organization. Percentages, that can be considered measures of incompleteness, can be combined to produce the coefficient of IC efficiency (i) that captures the organization’s “velocity, position, and direction” (p. 184). An organization’s IC represents a multiplicative function of the two sums, C and i. Organizational Intellectual Capital = i C When trying to come up with a monetary value of an organization’s IC, Edvinsson and Malone (1997) recommend reducing the number of indices available to create a more parsimonious measure. They note that Navigator’s five “focuses” have 36 monetary measures that cross-reference each other. They also recommend multiplying out the denominators in those that are ratios e.g., “value added/employee”, and excluding from a final list any redundancies and entries that are found on the traditional balance sheet Their examination leaves them with 21 indices which they believe can act as IC measurements for a fiscal year. The second coefficient of IC efficiency (i) is what Edvinsson and Malone call the “truth detector” of their equation. While the absolute (C) variable “emphasizes an organization’s commitment to the future, the efficiency (i) variable grounds those claims in present performance” (p.186). The two authors take from the general report only percentages and ratios, “once more cull out redundancies and apply some subjective judgement” to arrive at 9 indices of an organization’s IC efficiency. Edvinsson and Malone Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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(1997) then choose to combine the nine percentage measures into a single percentage (i.e., determine the average of the indices in an effort to represent how effectively the organization is currently using its IC). By giving equal weight to each index, the equation assumes that a complete breakdown of one part of the nine organization’s operations would diminish the coefficient by just over 12%. Whether or not organizations decide to create a monetary value for their IC, the two authors are sufficiently confident in their 112 indices that they believe they can be used not only by for-profit businesses in many different sectors, but also non-profit organizations including all levels of government, the military, charitable organizations, etc. Strengths and Weaknesses Most researchers agree that Skandia’s considerable efforts to create a taxonomy to measure a company’s intangible assets has emboldened others to look beyond traditional assumptions of what creates value for organizations. Skandia’s model is particularly impressive in recognizing the role of customer capital in creating value for an organization and how the very nature of customer relationships has changed. For example, Edvinsson and Malone (1997) offer five very specific indicators of customer capital: customer type, duration, role, support, and success as evidence of the important role played by customers in creating value for organizations. Skandia also provides a broad coverage of organizational structural and process factors with its focus on process and renewal and development contributions to organizational value that has not been attempted before.

Lynn (1998) points out that Skandia assigns no dollar value to its IC, but uses proxy measures of IC to track trends in the assumed value added. Roos, Roos, Dragonetti, and Edvinsson (1997) looked at the assumptions underlying three of Skandia’s metrics and were able to offer plausible alternative interpretations about what each metric might represent for an organization. As a result, they concluded that every company would need to possess a unique understanding of which intangible assets were truly valuable for the organization to choose which assumption was most valid and identify appropriate metrics. Moreover, given the requirement to create unique standards for their metrics, Roos and his colleagues felt that generic standards for measuring IC among companies or across industries likely would be slow in coming. They also emphasized that because Skandia follows a balance sheet approach when measuring its intangible assets, it offers only a snapshot in time and cannot represent dynamic flows of an organization. Finally, Huseman and Goodman (1999) note that Skandia’s inclusion of Structural Capital variables that include computer PCs, etc. as creators of true value can be criticized because it presumes that employees showing up for work and sitting in front of their computers end up investing knowledge into that computer that translates into the company’s competitive advantage. Yet for that to occur, data given to the employee must be transformed into information, and that information converted into added-value knowledge which is rarely automatic.

2.

IC-INDEX

The IC-Index is an example of “second generation” practices that attempt to consolidate all the different individual indicators into a single index, and to correlate the changes in intellectual capital with changes in Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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the market (Roos, Roos, Dragonetti and Edvinsson, (1997). According to the authors, second generation practices still seek “to improve the visualization of the value-creating processes of the company so that they can be managed comprehensively [but] in effect create a bottom-line for IC. This synthesis allows managers to assess the IC situation of a company holistically, whereas the first generation practices give information only on the single components of intellectual capital” (p. 80). A summary index further provides an immediate improvement to having long lists of individual indicators because it requires companies to understand the priorities and relationships that exist between their different measures. Operationalization The notion of an IC-Index was first advanced by Goran Roos and his colleagues at Intellectual Capital Services Ltd. and was first used by Skandia in its 1997 IC supplement to the annual report. Since Skandia’s adoption, the logic of an IC-Index has been endorsed and implemented by many other practitioners. According to Roos, Roos, Dragonetti and Edvinsson, (1997), the IC-Index has several distinct features: • • • • •

it is an idiosyncratic measure it focuses on the monitoring of the dynamics of IC it is capable of taking into account performance from prior periods it sheds light on a company different from an external view typically based on an examination of physical assets it is a self-correcting index in that if performance of the IC-Index does not reflect changes of the market value of the company, then the choice of capital forms, weights, and/or indicators is flawed.

The IC-Index is context specific because it permits boundaries to be placed around the measurement of intellectual capital. While the concept of IC can include all intangible resources and their flows (i.e., any factor that contributes to the value generating process that do not come from a company’s physical or monetary assets), Bontis et al. (1999) support restricting the IC conceptual definition used to create an IC-Index to those company intangible processes that are more or less under the control of the company itself. An idiosyncratic measure then also permits any IC metric to have maximum relevance for an organization. Roos et al. (1997) propose that the specific measurement of company IC forms, weightings, and indicators can be decided by knowing the company’s strategy, characteristics of the particular business of the company, and its day-to-day operations. Their table reproduced below shows how they believe each influence can logically be used to decide an IC-Index selection of IC forms, weights, and indicators. To give an example, Roos et al. (1997) suggest that company strategy and those IC forms which help the company achieve its strategic goals should be the guiding factor in deciding which IC structural or human capital form to emphasize in an index. Moreover, the main consideration for selecting weights assigned the IC forms should be the relative importance each capital form has in the particular business

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of the company. And finally, knowledge of a company’s day-to-day operations should be known in order to know which specific indicators to choose. Bontis et al. (1999) suggest that a process model can help create an IC measurement system and especially the selection of the right indicators. To do this, they refer to the “value scheme” (see section on Skandia Navigator) that describes the sources of company value coming from intellectual capital. Bontis and his colleagues believe that once a company has a clear idea about its identity and strategy, it should use its long-term goals to identify two sets of variables: one set comprising its value-creating path (i.e., those IC categories that really drive company value creation); and the other set that can act as performance measurements. This second set is made up of key success factors (KSF) that can describe more than one company, and indicators which reflect a company’s characteristics more closely. Information from the two steps are then to be joined leading to the creation of an IC system. Unfortunately, although the authors state that information from the two sets should be joined together to create the IC measurement system, they do not explain whether each category has its own measurement, and how such measures duplicate or offer unique variance from that contributed by the second set of KSF and indicators.

Strengths and Weaknesses An IC-Index is very much context specific and is therefore is limited in its universality among companies. Definitions, strategic prioritizing, choice of indicators, etc. all make comparisons of any absolute ICIndex summary value calculated for different companies or over time by one company meaningless. In addition, because only proxy measures are taken of IC stock, all metrics are dimensionless, ordinal numbers (Roos et al. 1997). As a result, the value of an IC-Index continues to lie in its measurement of changes in IC stocks i.e., IC flow. This stock-flow perspective is quite powerful for researchers since they can examine firms as organizational learning systems which try to minimize stock-flow misalignment (Bontis, Crossan and Hulland, 2001). Bontis et al. (1999) suggest changes in an IC-Index reflect changes in the underlying IC elements, that in turn signal changes in the underlying drivers of future earnings potential. They conclude: A company that improved its IC-Index by 50 per cent is invariably doing better than another that improved the same measure ‘only’ by 25 per cent. The nature of IC and its increasing returns also eliminate any concern about the starting point of the two companies. In fact, companies with higher starting IC levels would probably increase their IC performance more easily, contrary to common logic (p. 399). Like most other measures of tangible assets, an IC-Index does depend on value judgements, in the choice of weights, indicators, and even the assumption that IC is present and important in company operations. Although this charge of subjectivity can also be made of certain traditional accounting methods and assumptions, Roos et al. (1997) argue that at least IC measurement and especially a consolidated measure such as the IC-Index, makes a larger part of the organization visible and open to valuation. On a final note, because the IC-Index takes past performance into account, it is subject to Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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“one-off special events” which can have a strong influence on moving the index up or down for some years after the event. On the other hand, the IC-Index allows mangers to “finally understand the effects a particular strategy has on the IC of a company and compare two alternatives to understand which one is preferable from an IC point of view” (p. 92).

3.

TECHNOLOGY BROKER

Brooking (1996) makes a practical contribution to IC measurement by offering three measurement models to help calculate the dollar value of IC as identified through the Technology Broker’s IC audit. Brooking defines IC as the combined amalgam of these four components: market assets, human-centred assets, intellectual property assets and infrastructure assets. Market assets equal the potential an organization has due to market-related intangibles such as brands, customers, repeat business, backlog, distribution channels, contracts and agreements such as licensing and franchises. Human-centred assets are the collective expertise, creative and problem-solving capability, leadership, entrepreneurial and managerial skills embodied by employees of the organization. Intellectual property assets contain the legal mechanism for protecting many corporate assets, and infrastructure assets including know-how, trade secrets, copyright, patent and various design rights, trade and service marks. Finally, infrastructure assets equal those technologies, methodologies and processes which enable the organization to function including corporate culture, methodologies for assessing risk, methods of managing a sales force, financial structure, databases of information on the market or customers, and communication systems. Operationalization Brooking begins the diagnostic process by having the organization answer twenty questions that make up the IC indicator. The results of this test suggest that the less a company is able to answer in the affirmative to the 20 questions, the more it needs to focus on strengthening it’s intellectual capital. SAMPLE OF 5 IC INDICATOR QUESTIONS • • • • •

In my company every employee knows his job and how it contributes to corporate goals. In my company we evaluate ROI on R&D. In my company we know the value of our brands. In my company there is a mechanism to capture employees’ recommendations to improve any aspect of the business. In my company we understand the innovation process and encourage all employees to participate within it.

Each component of Brooking’s IC model is then examined via a number of specific audit questionnaires that ask questions specific to those variables thought to contribute to that asset category. For example, to identify the hidden value due to Market-Related intangibles, Brooking asks 15 Brand audit, 14 Customer audit, 7 Name audit, 5 Backlog audit, and 6 Collaboration audit questions. Intellectual Property intangible assets are identified by 9 Patent audit, 6 Copyright audit, 3 Design audit, and 4 Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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Trade-Secret audit questions. Human-Centred hidden assets are identified by 5 audit questions about Employee Education, 5 Vocational audit, 12 Work-related Knowledge audit, 8 Occupational Assessment audit, 8 Work-related Competency audit, 10 Corporate Learning audit, and 3 Humancentred Asset Management audit questions. Lastly, Infrastructure hidden assets are evaluated by 6 Management Philosophy audit, 4 Corporate Culture audit, 31 Corporate Culture Collaboration audit, 7 Information Technology Systems audit, 6 Database audit, and 4 IT Manager audit questions. In total, the Technology Broker IC Audit is comprised of 178 questions. SAMPLE OF 20 IC AUDIT QUESTIONS • • • • • • • • • • • • • • • • • • • •

What is the annual cost of protecting this brand? What is the potential for repeat business with our customers? What does your company name mean to the financial community and investors? What is the optimum backlog for your company? How does your company track and identify opportunities to collaborate with partners? To what extent are the patents owned by your company optimally exploited? What copyrights owned by your company are of value? Would a design right give your company a competitive advantage in some area? Where are trade secret agreements kept in your company? Does your company give any advice or counselling to employees on educational issues? How do your employees know when it is time to learn new vocational skills? On what special knowledge does your company depend to operate? How is information generated from personality tests used in your company? How are work related competencies planned for the future? What is the average length of time that knowledge in your company is current and useful? Is the management philosophy an asset or a liability? Is the culture conducive to achieving corporate goals? What is the ratio of employee to PCs in your company? Are databases able to be queried to satisfy the user’s need? What use is made of e-mail, Internet and the WWW in your company?

Brooking proposes that the value an organization place on its IC is wholly dependent upon the goals of the organization and the state of the market As such, any valuation is organization-specific and limited in time (Lynn, 1998). Once an organization completes its IC Technology Broker audit, Brooking offers three methods to calculate a dollar value for the IC identified by the audit: • • •

the cost approach, which is based on assessment of replacement cost of the asset; the market approach, which uses market comparables to assess value; and the income approach, which assesses the income-producing capability of the asset (i.e., the NPV of its net cash benefits)

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Strengths and Weaknesses The Technology Broker approach has been lauded for offering a tool box for organizations to assign value to IC. Lynn (1998) suggests that Brooking has created an IC audit that itself represents an intellectual asset for organizations. Moreover, her active marketing of the instrument and its conceptual basis has served to help others identify, value, and leverage the IC in their organizations. The main weakness in these items is that there is a considerable leap that must be made from the qualitative results of the questionnaire to actual dollar values for these assets. For example, using replacement cost implies that a cost figure actually represents value and that notwithstanding their unique value in creating competitive advantage, a “replacement” value can actually be determined for such intangible items as management systems or brands. A market-based valuation suffers from a lack of efficient market-based prices for many elements of IC. Finally, the income-based model suffers from subjectivity of estimations and uncertainties inherent in the cash-flow model. Almost all of the items in the IC audit can be converted into Likert-type based scales which may help organizations assign quantitative values to qualitative questions. For example, the second sample question above can be reworded as follows: “We are confident in the potential for repeat business with our customers”. Multiple respondents in the organization can now answer this question on a scale from 1 (strongly disagree) to 7 (strongly agree). The results will yield a richer (quantitative) description of this item. There are also many similarities between the Technology Broker IC audit questions which are subjective in nature and Skandia’s IC measures which are objective in measure. For example, both models look at the number of PCs per employee as a proxy for structural capital or infrastructure assets.

4.

INTANGIBLE ASSET MONITOR

Karl-Erik Sveiby (1997) believes that difficulties in measuring intangible assets can be overcome. He foresees an intangible model as clearly understood as that of an organization’s book value equal to tangible assets minus visible debt. Sveiby asserts that key to such a system is having a coherent conceptual framework. But to do this, Sveiby argues that money has to stop being used as a proxy for human effort. A 500-year-old system of accounting must make way for a system of non-financial knowledge flows and intangible assets that use new proxies. Sveiby proposes a conceptual framework based on three families of intangible assets: external structure (brands, customer and supplier relations); internal structure (the organization: management, legal structure, manual systems, attitudes, R&D, software); and individual competence (education, experience). While efficiency of the internal structure or “operational efficiency” of an organization has historically been part of most traditional accounting measurement, the other two intangible assets in his model are not. Sveiby believes that the problem with using measures of these two assets is not that they are difficult to design, rather their outcomes seem difficult to interpret as they correlate with changes in business performance. Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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First, Sveiby recommends replacing the traditional accounting framework with a new framework that contains a knowledge perspective. Within this framework, he argues that both non-financial measures to measure intangible assets, and financial measures to measure visible equity can be jointly used to provide a complete indication of financial success and shareholder value.

SEEING INTANGIBLE ASSETS Visible Equity

Intangible Assets (Stock Price Premium)

(book value)

External Structure

Tangible assets minus visible debt.

(brands, customer and supplier relations)

Internal Structure (management, legal structure, manual systems, R&D, software)

Individual Competence (education, experience)

According to Sveiby, the purpose of measuring these three indicators of intangible assets is to provide management control. To do this, the first preliminary step is to identify who will be interested in the results. In an external presentation, a company needs to describe itself as accurately as possible to stakeholders, customers, creditors, and shareholders so that these external agents can assess the quality of its management and whether the company is likely to be a reliable supplier or a dependable creditor. External parties are usually interested in a company’s position versus changes and flows, given that external accounts are provided only at relatively lengthy intervals. They also need to assess risk. Finally, the presentation’s form is important given their lesser familiarity with how the business works. As a result, Sveiby recommends that management information given external parties about a company’s intangible assets should include key indicators and explanatory text given that it is not possible to compile a full balance sheet that expresses in monetary terms every intangible asset Moreover, in the process of redefining what it is to be measured, new contributors of data will likely include these outside parties. Sveiby believes that companies should be prepared to pay for this assistance. Internal measurement on the other hand is undertaken for management which needs to know as much as possible about the company so that it can monitor its progress and take corrective action when needed. It in fact becomes a management information system. With business today in a constant state of flux, Sveiby suggests that management information should emphasize flow, trends, change, and control figures. He believes that managers are more likely concerned with the speed with which intangible assets are measured than with accuracy. Notwithstanding this acknowledgement that business cycles have shortened, it is interesting that Sveiby recommends that the measurement of intangible assets should include at least three measurement cycles in order evaluate results, and repeated yearly.

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Operationalization In his conceptual model, Sveiby identifies three measurement indicators: growth and renewal i.e., change, efficiency, and stability for each of the three intangible assets. He recommends managers select one or two variables indicative of each indicator similar to those developed in the example of his Intangible Assets Monitor model shown below.

SAMPLE M EASURES FOR INTANGIBLE ASSETS External Structure Growth & Renewal

Efficiency

• organic volume growth • growth in market share • satisfied customers • quality index • profit per customer • sales per employee

Internal Structure • investments in IT • time devoted to R&D • attitude index of personnel toward managers, culture, customers • proportion of support staff • sales per support staff

Competence of People • share of sales from competenceenhancing customers • growth in average professional experience • competence turnover • change in added value per employee • change in proportion of employee

In essence, the Intangible Assets Monitor is “a presentation format that displays a number of relevant indicators in a simple fashion” (Sveiby, 1997, p. 197). The choice of indicators depends on the company’s strategy but should include only a few of the measurement indicators for each intangible asset with the most important areas needing to be covered those of growth and renewal, efficiency, and stability. The IAM can be integrated into the management information system. And lastly, it should not exceed one page in length but should be accompanied by a number of comments. The second step in designing a measurement system for intangible assets is to classify all employee groups within one of the two categories: professional and support staff. Professionals are those who plan, produce, process, or present the product or solutions, and who are all directly involved in client work. They are the only employees considered when assessing the third intangible asset: competence of personnel. All other employees whose work seeks to preserve, maintain, and develop the internal rather than external structure e.g., those who work in accounting, administration, reception, etc., while essential to a firm’s long-term viability, contribute to an organization’s internal structure and should be measured under that category. Where employees perform a variety of duties, the time spent working for clients is assigned professional, with the rest charged to the internal structure. As such, time is an important variable to record in knowledge organizations. Outside experts and suppliers, although essential contributors to production in many companies, are not classed as employees i.e., professionals in Sveiby’s model. Rather, they are considered under the external structure as an important element in the external networks that a knowledge company builds to support the process of knowledge conversion. Indeed, where independent contractors may be so important to an organization that the Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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organization becomes virtual; i.e., “ it ceases to be possible to see where the competence of the organization ends and that of its suppliers begin.” (Sveiby, 1997, p.166). Sveiby lists specific indices for each of his three growth and renewal, efficiency, and stability measurement indicators used to assess each category of intangible assets of a knowledge organization. To measure professional competence intangible assets, the indices include: • • •

growth/renewal: number of years in the profession, education level, training and education costs, grading of executives, professional turnover, competence-enhancing customers efficiency: proportion of professionals in the company, the leverage effect of professionals, valueadded per professional stability: average age, seniority, relative pay position, professional turnover rate

To measure internal structure intangible assets, the indices include: • • •

growth/renewal: investment in the internal structure, investment in information processing systems, customers contributing to internal structure efficiency: proportion of support staff, sales per support person, values and attitude measurements stability: age of the organization, support staff turnover, the rookie ratio

To measure external structure intangible assets, the indices include: • • •

growth/renewal: profitability per customer, organic growth efficiency: the satisfied customer index, win/loss index, sales per customer stability: proportion of big customers, age structure, devoted customers ratio, frequency of repeat orders

Strengths and Weaknesses Celemi, a Swedish company selling software and consulting services, has been measuring and monitoring its knowledge assets for several years by following IC growth through non-financial models and non-financial indicators. Although the Celemi endeavours to measure growth of its intellectual network, it does not assign a financial value to it. However, in the same 1998 Celemi report, there is an attempt to provide a ValueAdded Statement outlining key indicators that they measured including: Value added % of sales, Profit capacity % sales, Return on equity capacity after tax, value added per employee and value added per expert. Both Sveiby and Celemi assume financial outcomes are somehow related, and by leveraging IC correctly, financial outcomes will follow suit. According to Lynn (1998), this idea of “innate value creation” has been argued before with JIT, diversity management, etc. but only when organization culture supports it such as case in Celemi. It has not worked for many North American companies when they tried to institute JIT, quality circles, etc. without appropriate support of financial feedback Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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systems. Lynn argues for most organizations, making a business case means creating ties to financial results. And she suspects that only those companies which can emulate Celemi’s culture, will also be able to emulate its highly successful reporting system on IC. Finally, Sveiby has developed an executive training module called the TANGO Simulation which is intended to help senior managers understand how to account for IC using similar measures that he has developed in his IAM model (Bontis and Girardi, 2000).

5.

KNOWCORP

Ken Standfield (1999) proposes that there are two main ways to measure knowledge assets: subjectively and objectively. Subjective measures are dependent on company strategy and are used by management to help run their businesses more effectively. Standfield reports that those firms wishing to improve internal management and strategic performance, choose first or second generation measures such as those found in the Skandia or Celemi models that aim to describe human (e.g., education level of employees) and structural capital (e.g., # of PCs per employee). A second way is to benchmark performance objectively and independent of strategy. Standfield calls these measures third generation since they are used by investors and shareholders to make better investment decisions. He has developed several of these models including Knowcorp® Intangible Accounting, Intangible Cost Analysis, and Intangible Risk Management. These models aim to communicate results externally and are positioned in much the same way as Stern Stewart’s EVATM (Economic Value Analysis). Standfield argues that he can offer a valid, objective measure of IC because intangible assets are not really intangible at all. They are simply potential tangible costs with potential tangible benefits that have cause and effect sequences for which there is not yet documented proof of their value. As a result, Standfield has created a longer timeframe for assessing value and one that forces the organization to estimate the future. In his models, he invokes the notion of mutual co-dependence which acknowledges every staff members’ contribution to the generation of revenue. As such, he asserts that exclusively auditing revenue-generating divisions and ignoring associated-support divisions leads to an incorrect assessment of knowledge assets within a firm. To illustrate the timeframe and type of intangible behaviour he is referring to, he offers this example: An event at time 0 triggers a cost at time 1, a benefit at time 2, a cost at time 3, a cost at time 4, etc. If the overall effect is a positive dollar amount, then an intangible revenue is created. If the overall effect is a negative dollar amount, then an intangible cost is created. For example, an understaffed organization that retrenches staff to reduce tangible costs (e.g., wages, payroll) will face workflow accumulation as existing staff will not be capable of meeting current demand. Such an organization will witness falling sales and decreasing customer satisfaction as current workloads cannot be satisfied. Depending on the level of understaffing, staff stress may lead initially to decreased quality (i.e., cutting corners), to increased sick leave and absenteeism, and to increased Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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turnover. As the firm’s ability to create free cash flow decreases, the market will decrease the value of the firm in the market (Standfield, 1999) To illustrate further, Standfield makes the distinction between traditional and information age accounting. Traditionally, accounting relied on two equations that only uncovered the nature and extent of a firm’s tangible operating structure:

A + L = E (Assets + Liabilities = Equity) R - E = P (Revenue - Expenses = Profit) However, the information age allows organizations to leverage knowledge, time, and technology creating sizeable gains and significant market share at very little cost. This leads to a new equation reflecting such realities:

(T x E) - O = OE ([Organizational Time x Operational Effectiveness Revenue] - Overheads = Operational Effectiveness) In Standfield’s intangible accounting model, T represents an aggregate amount of productive time staff use to generate revenue each year with productive time being defined as actual work minus holidays, sick leave, absenteeism, skill/knowledge overheads. For example, if there are 1,500 staff working for 1,000 hours each year, then the firm has 1.5 million hours of organizational time or T. The essential point to note is that workers may be paid for 2,000 hours of work (i.e., 40 hours/week for 50 weeks), yet only actually work 50% or less of this time due to holidays, absenteeism, human interaction time, etc. E represents organizational effectiveness, or productivity. E is quite generalizable since it can be used by non-profit sector management (e.g., police departments, health departments, etc) as well as traditional profit sector management (e.g., insurance companies, banks, software firms, etc). Assume that 1,500 staff contribute an average of $125 each hour in effectiveness (or productivity) to the organization. Thus, $187.5 million (1,500 staff x 1,000 hours x $125/hr). of organizational value has been created O represents the firm's tangible cost structure. Hence, wages, advertising, marketing, selling, administrative, rental costs, etc. all find their way in the firm’s O figure. Assume the firm has overheads of $117.5 million, then the firm’s operational effectiveness is $70 million. The above formula is simply an adjusted Revenue (T x E) less Expenses (O) formula. According to Standfield, the $70 million is practically the same as the profit calculated by conventional measures. The only difference lies in the concept of "non-employee contributed revenue" (NECR: this is gross revenue that staff do not have to apply productive time to acquire, i.e., investments). If the above firm had investments of $5 million, then non-employee contributed revenue would be $5 million and this Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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would need to be added back to the $70 million to arrive at the accounting profit for the firm of $75 million. The focus here is to state the exact human capital efficiency of the firm. If we aggregate the impacts of non-time based activities into the equation, we will overestimate productivity – hence, they are excluded. Basically, any revenue that makes its way into the firm without any expenditure of time by staff is non-employee contribute revenue. This means that the profit figure of $70 million represents the employee contributed profit (ECP) of the firm. The accounting profit (AP) is employee contributed profit (ECP) + non-employee contributed revenue (NECR), that is: AP = ECP + NECR. Using this formula as a foundation for measuring intangibles, Standfield then proposes a process for auditing a firm’s knowledge assets. Knowcorp Audit The conceptual model used to support Standfield’s audit process is shown below and contains three stages. If it is assumed that firms are knowledge-based, Stage I of the Knowcorp Audit represents a supply-side analysis of production inputs. A major supply of these production inputs is the knowledge possessed by individual staff members and new information that is supplied to them. Supplier risk acknowledges that the potential knowledge available to the firm (denoted as information capital) and the actual knowledge available to the firm (denoted as knowledge capital) can be eroded and devalued if the supply of information or knowledge is corrupted, interrupted or strategically sabotaged (Standfield, 1999). Stage II entails the processing side of the business where the organization leverages its supply of information and knowledge against the firm’s infrastructure (structural capital) and employees (human capital). According to Standfield, a firm will seek to increase the knowledge velocity within the firm by capturing premium knowledge. Stage III is then the output phase of the process where the market (customers) appraise the firm’s efforts and reward (profit) or penalize (loss) its knowledge processing abilities. Standfield believes that revenue generation is dependent on a sub-component of a firm’s IC, that of market communications capital (i.e., the percentage of the target market that know about the companies goods and services). Courtesy of the Internet, market communications capital is considered relatively inexpensive to build. Stage III also includes a firm’s level of current production in future technologies (i.e., derived demand) that will have a significant impact on a firm’s sustainable competitive advantage. Finally, customer capital is the “natural outcome” of all processes leading to this stage. Nonetheless, Customer Capital can be eroded if there are “Competitive Loopholes” in the business units operational policies resulting from a company failing to change with changing markets. At a deeper level, the firm’s Strategy Capital measures management’s ability to effectively and profitably operate the business. If the firm is slow to innovate or change it can be out-performed by rivals.

KNOWCORP IC AUDIT

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Knowcorp’s Tools In order to help organizations succeed through the KM/IC audit, Standfield introduces three tools: the TI Index, Intangible Cost Analysis and Intangible Risk Management. The TI Index seeks to show how management decisions create or destroy value for an organization. The TI Index takes these decisions and summarizes “a significant number of transactions” [decisions] into a single number. If this number is greater than 1, then value creation is occurring. If the number is below 1, then the decision is value-destroying and should be avoided. Value enhancement or destruction can occur at two levels – the tangible level (cost reduction, process efficiency, etc.) and the long-term, or intangible level (product quality, customer satisfaction, market share, etc.) The focus of the TI index is on intangible measurement. In his model, Standfield arbitrarily defines a current intangible as a tangible cost or benefit that has a high probability of occurring within 12 months, and non-current intangible as likely to occur outside 12 months. In this way, the Index allows an organization to estimate intangibles on an ongoing basis, plus uncover exactly how intangibles are financially impacting your organization. To identify the impact of intangibles, Knowcorp’s Intangible Cost Analysis uses the traditional accounting framework such that for every tangible accounting category, there is a parallel intangible category. Thus, there are intangible categories of assets, liabilities, capital, expenses, revenue and profit/loss captured in a Balance Sheet and Profit and Loss Statement containing such intangibles. Standfield calls these categories indicators of an intangible operating structure that interacts with other intangibles and tangibles. In calculating intangible costs especially as they relate to IC, Standfield identifies three assumptions that need to be factored into an intangible valuation: •

The Law of Inflow Apportionment states that wages should not be used as the only base of valuation for employees’ cost and contribution. Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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• •

The Law of Business Focus states that businesses exist only to make sustained returns to owners, this requires a business to maintain operating efficiency i.e. achievement of an optimal internal employment level for the “Law” to remain valid. Law of Time Valuation states that when employees are being paid – even thought they are engaged in non-productive activities – a correct valuation of an Information Age cost should be which considers the potential decrease in revenue generating ability of that employee.

Finally, Standfield’s Intangible Risk Management combines all theory and practice provided for in Knowcorp’s conceptual model and instruments to conduct intangible risk management. As seen below, risk, information & knowledge, and IC management contribute to intangible accounting and intangible cost analysis that in turn leads to intangible risk management. Standfield (1999) lists several examples of risk categories he examines based on the model, including information, knowledge, time, human, structural, derived demand, market communications, customer, and strategy risks. For example, information risk is defined as not having the correct information at the right place at the right time. Knowledge risk is the risk of employees exhibiting knowledge deficiencies. Moreover, Standfield believes each organization has parallel intangible cost centres that can include these risk categories such as an information cost-centre, knowledge cost-centre, time cost-centre, human cost-centre, etc. For example, a time cost-centre considers intangible, or long-run costs associated with mismanaging the firm’s time capital. A human cost-centre includes intangible costs associated with mismanaging the organization’s human capital.

Strengths and Weaknesses Knowcorp’s methodology for adjusting the traditional method of accounting by adjusting for humanfocused activities is only as robust as the underlying accounting methodology used by the company itself. Nevertheless, the tangible and intangible dichotomy of parallel statements has intuitive appeal. The real strength in the Knowcorp model is its ability to discern the value of training and development. Generally, most of the $55.3 billion annual investment in training and development in U.S. organizations is paid for without one solid indication of what rate of return was received (ASTD, 1996). Human resource managers tend to recruit the best and brightest employees and then invest in training them for the sole purpose of increasing their organization’s human capital. All of this is done with the hope that such investment will boost business performance. Plott argues, “companies that invest more heavily on workplace learning are more successful, more profitable and more highly valued on Wall Street” (1998: 8). Superior individual knowledge allows a firm to train its workforce more effectively and devise a more productive system of organization (Spender, 1994, Døving, 1996). The Knowcorp model helps answer the question of whether or not a training expenditure is of value. The following example is provided. Assume that during a training session, an employee is asked to read and understand a 500 page document (e.g., a new procedures manual). Assume that this would take on average 5.5 days. This time constitutes information leave. The wage cost associated with analysing the information, at $25 per hour, would be $1,042 per employee. Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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When employees are training, productive time is temporarily suspended. This suspension causes employees to undergo “information leave” – where they temporarily cannot perform productive tasks – even though they may be physically present in the work environment. Most tasks create periods of information leave mixed with periods of productivity. As every interruption requires revision of “just read” information, analysis actually takes longer than is stated here. As the major cost of information analysis is the productivity cost and not the non-productive wage allocation, it is essential to incorporate this amount as well. A minimum security ratio of 3 to 1 is used. This means that for every $1 of wages paid to an employee, that employee must directly, or indirectly, contribute $3 to productivity. The first dollar is used to “payback” the employee's wage, the second dollar contributes to the firm’s cost structure (e.g., advertising, rent, light, power, debt servicing, marketing, printing, etc) and the third dollar provides a return to stock holders, owners and investors. Firms with a security ratio below the 3 to 1 level run the risk of financial disaster as they have an unacceptably high risk of being (or becoming) insolvent. Assuming the minimum security ratio of 3 to 1, the productivity cost for each employee becomes $3,125 (3 x $1,042). Therefore, the overall cost of analysing the document is $4,167 per employee ($3,125 + $1,042). To “pay-back” the total information cost, the individual must earn $4,167 of additional revenue as a direct result of applying his/her new knowledge. To generate a 100% knowledge profit, the individual must generate $8,334 in additional revenue. The Knowcorp Costing Statement assists in clarifying the time, cost and Compression components of information activities. The concept of knowledge profit, developed by Standfield, is a critical aspect of information utilization in the Information Age. If the organization cannot leverage the new knowledge attained by the sample employee, the organization will add $4,167 of information expenses to its cost structure without any corresponding gain. However, if the new knowledge allows employees to increase after-analysis productivity, the organization has the potential to generate a knowledge profit. The knowledge profit is the rate of excess return on knowledge investment and relates to the increase in after-analysis productivity above the total information cost. In this example, the total information cost is $4,167, however, Standfield argues that firms should not break-even, but generate a minimum 100% knowledge profit by structuring activities that proactively take advantage of training investment.

6.

TOBIN’S Q

Although initially not intended by Tobin as a measure of IC, Stewart (1997), Bontis (1999) and others including Federal Reserve chairman Alan Greenspan, believe that Tobin’s q can act as a proxy for an organization’s IC. Tobin’s q was originally created as a method for predicting corporate investment decisions independent of macroeconomic factors such as interest rates. Tobin’s reasoning was based on the premise that a company is unlikely to buy more of an asset when that asset is worth less than its Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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replacement cost. Conversely , a company is likely to invest if an asset is worth more than its replacement costs. Tobin’s q ratio is calculated by dividing the company’s market value by the replacement or book value of its assets. This ratio is an improvement over other market-to-book ratios that infer intellectual capital as being the difference between a company’s market value and its book equity i.e., everything left in market value after fixed assets are accounted for. Stewart (1997) in his critique of these latter marketto-book ratios points out that the volatility of stock market prices, often outside the control of a company’s management, and that directly affect the market value of a company’s intellectual capital means that IC calculated in this way is not a robust measure. Second, Stewart argues that there’s evidence to show that both book and market value are usually understated. Third, Stewart wonders just what one is to make of conclusions that state a company has x dollars in intangible assets. The use of differences between book and market values still can have meaning, however, if attention is paid to the actual ratio between the two values. Ratios allow for comparisons to be made between companies, with a larger industry average, or over time. While not a precise indicator, Stewart (1997) does believe that ratios permit managers and investors to gauge how a company is doing compared to its competitors. The attractiveness of Tobin’s q, therefore, is that it has the advantage of a market-to-book ratio while also saying something about the effect of diminishing returns. Stewart (1997, p.226) states: When q is very high (say 2: an asset is worth twice its replacement cost) the company is getting extraordinary returns on that class of asset and not feeling the bite from diminishing returns. In this sense, q is a measure of what economists call “monopoly rents” i.e., a company’s ability to get unusually high profits because it’s got something that no one else has. That’s not a bad definition of the manifest power of intellectual capital: You and your competitors have similar fixed assets, but one of you has something uniquely its own - people, systems, customers - that allows it to make money. In sum, Stewart believes Tobin’s q is considered applicable for not just individual assets but for company assets as a whole. It is considered capable of neutralizing different depreciation policies, and is most informative when companies are compared over a period of several years. The weakness of this measures is that it has no rich descriptive capability. It is not intended to actively describe the actual IC components of a company but, rather, it attempts to provide the user with an objective measure of its marketable value. It is very clear that there are enormous differences in this ratio that have nothing to do with intangibles and one has to be very careful in making inferences with this measure. For example, industries with old assets (e.g., chemicals) will appear to have very large Tobin’s q, but that is reflective of the historical nature of the way in which the assets are valued on the books. Similarly, industries where there are lots Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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of take-overs will show strange results (some high and some low). This is due to the revaluing of the book value of assets and the tendency to overvalue assets in take-overs. Hence, simple inferences about knowledge assets and IC, while trendy, may be confounded.

7.

MVA AND EVA

Economic Value Added (EVATM) was introduced by Stern Stewart as a comprehensive performance measure that uses the variables of capital budgeting, financial planning, goal setting, performance measurement, shareholder communication, and incentive compensation to properly account for all ways in which corporate value can be added or lost (Bontis, Jacobsen, Dragonetti, and Roos, 1999). Bontis et al. describe EVA as providing “a common language and benchmark for managers to discuss valuecreation [and because] it is blessed with widespread acceptance in the financial community, can increase the legitimacy of a company in the eyes of financial markets, as a valuable measure of corporate valuecreation or destruction over a given period” (p.394). According to Strassman (1999), economic value added is the net result of all managerial activities. EVA is intended to offer improvements to the market value added (MVATM) calculation. MVA represents the spread between the cash that a firm’s investors have put into the business since the start up of the company and the present value of the cash that they could get out of it by selling their shares. By maximizing this spread, corporate managers maximize the wealth of the company’s shareholders relative to other uses of capital (Bontis et al., 1999). According to Bontis et al. (1999), MVA can represent the market’s assessment of the net present value of a company’s current and contemplated capital investment projects. As such, MVA is a “significant summary assessment of corporate performance” (p.395). However, a key disadvantage with MVA is that gains and losses accruing from historic activities are aggregated on a one-to-one basis with last year’s results plus today’s moods as they are shown in market price. As a result, a company with a successful history will keep on showing positive and high MVA even when current or future prospects are bleak and unrewarding. Operationalization EVA only concentrates on changes in MVA occurring from new projects to account for the spread between market value and total capital. It accomplishes this by emphasizing maximizing incremental earnings over capital costs. To have a positive EVA, therefore, a company’s rate of return on capital must exceed its required rate of return. Bontis et al. (1999) define EVA as “the difference between net sales and the sum of operating expenses, taxes and capital charges where capital charges are calculated as the weighted average cost of capital multiplied by the total capital invested. In practice, EVA is increased if the weighted average cost of capital is less than the return on net assets, and vice versa.” (p.395). Its equation is given below: Net sales - operating expenses - taxes - capital charges = EVA

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Bontis et al. (1999) further liken EVA to an accounting concept introduced much earlier, that of residual income (RI). RI represents the value remaining after a company’s stockholders and all other providers of capital have been compensated. The sole distinction the authors make between EVA and RI is that EVA has simply been paid more attention. Given its positive reception, some writers have suggested that EVA can be used as a surrogate measure for the stock of intellectual capital if it can be assumed that effective management of knowledge assets increases EVA. Strengths and Weaknesses EVA is a financial measurement system that seeks to properly account for many important factors and their trade-offs involved when creating value. Yet in terms of its use as a surrogate measure of IC, Bontis et al (1999) note that if EVA is used, it implies that no specific measures of intangible assets are needed. Moreover, managers are no better off understanding exactly what are the company’s intangible resources or their specific contribution. Such a “black box” approach to accounting blocks any real effort to validate the value of or manage a company’s IC. Strassman (1999) does say that EVA represents something that defies the laws of conservation energy which state that output of any system can never be greater than its input. Delivering a positive EVA therefore comes from an act of creativity that is coming from an intangible. Put another way, Strassman (1999) believes that “if EVA is the interest earned from an accumulation of knowledge residing within the firm, then the value of this principal can be calculated by dividing the EVA by the price one pays for such capital” (p.4). Bontis et al. (1999) state that EVA uses 164 different areas of performance adjustment to solve problems such as trying to account for these intangibles and long-term investments that lack a high degree of certainty. However, the very fact that the model contains 164 adjustments suggests that managers will have to engage in a trade-off between complexity, accuracy, and ease. Given the very great likelihood that managers will likely pick and choose from this larger list, it runs the risk of making comparisons of EVA values difficult if not meaningless between companies or over time. Three other limitations in the calculations used to create EVA include that the use of book assets relies on historical costs which give little indication of current market or replacement value; that empirical research has not shown EVA as a better predictor of stock price or its variation (Dodd & Chen, 1997); and that the starting point for EVA analysis is that companies should be run in the interest of shareholders inclusively. In sum, the EVA performance measure as it being applied to quantifying the value of intangible assets may not be appropriate.

8.

CITATION-WEIGHTED PATENTS

According to Bontis (1996), Dow Chemical has been at the forefront in using patents as proxies for practical IC measurement. Former Director of Intellectual Asset Management at Dow, Gordon Petrash, implemented a six-step process for managing intellectual assets that includes: •

defining the role of knowledge in the business; Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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• • • • •

assessing the competition’s strategies and knowledge assets; classifying the company’s portfolio of knowledge assets; evaluating the value of those assets to keep, develop, sell, or abandon; investing in areas where gaps have been found; and assembling the new knowledge portfolio and repeat ad infinitum.

Dow Chemical instituted this IC initiative at the same time as it was reorganizing and delayering its organization to facilitate critical communication links. According to Lynn (1998), these organizational changes and concern for knowledge sharing and teamwork represented a cultural revolution for an oversized Dow that had developed knowledge silos and minimal exchanges of knowledge between various parts of its organization. Worse, “the idea of selling ideas discovered at Dow or developing ideas not invented at Dow (the not-invented-here syndrome) or even collaborating to develop in-house or outside ideas were foreign concepts.” (p. 31). A significant component of Dow’s initial management of intellectual assets has been its review of patent maintenance within R&D to create objective, major cost savings for the firm (Lynn, 1998). The Dow model estimates a “technology factor” to identify the impact of R&D efforts that lead to the creation of intellectual property and uses indicators such as R&D expense per sales dollar, number of patents, income per R&D expense, cost of patent maintenance per sales dollar, and project lie-cycle cost per sales dollar. The “patent evaluation process” is a team-based effort where members from R&D and marketing interact directly with production to decide on the viability of undertaking and/or continuing the research process. The team may review one indicator or sets of indicators for longer than a year in order to decide whether the intellectual property is valuable. It also triggers management action to investigate whether the intellectual property might have value for someone else i.e. sell the idea, or whether it should be abandoned and written off, like other unproductive assets. Dow started with patents as an obvious and important example of intellectual assets in order to make IC visible to the organization. Patents can be readily understood to be indicators of intellectual property. Traditional accounting methods assign value to patents, but only in terms of the cost to obtain the patent, and not the cost of the R&D leading to the patent, nor the potential for marketability if put into production, nor any legal considerations about the patent. Objectively measuring and monitoring patents using multiple indicators within Dow’s “technology factor”, has made this intangible asset become meaningful. It also has the benefit of more thoroughly incorporating the bottom-line impact of R&D efforts. In addition, the Dow patent evaluation process can measure the internal operations that created the intellectual property, and can be benchmarked against other companies in the industry or compared to industry averages. In 1996, Dow produced its first public IC report as a supplement to its annual report, comparable to Skandia’s. In separate work, Hall, Jaffe and Tratzenberg (1999) make the distinction between patents and their citations as evidence of technological output and information flow. Using the financial market valuation of firms that own the patents, they found higher market valuations due mainly to firms with highly cited patents per R&D dollar spent. Hall et al. interpret their findings to suggest that citation-weighted patents can act as a better measure of innovative output than pure patent counts. Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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Citation-weighted patents also did as good a job as R&D in explaining the market valued firms relative to their book value, mostly because the explanatory power of R&D declined when patent citations were included in the regression. Hall et al. interpreted the unique variance added by citations as success in innovative activity or success in appropriating returns to such activity. Indeed, using a firm’s average citations per patent revealed that citation rate had a substantial effect on market value beyond that due to R&D or patenting behaviour. That is, an increase of one citation per market was associated with a 3-4% increase in market value at the firm level.

9.

BALANCE SCORE CARD

In 1992, Kaplan and Norton questioned whether accounting measures that relied on short-term financial indicators were allowing managers to monitor new strategies and innovative processes (Huseman & Goodman, 1999). Deficiencies in the traditional accounting approach had become particularly noticeable for enterprises as they moved into 21st century and were needing more extensive data to be tracked. Moreover, intangible corporate assets not found in a company’s financial statements were becoming highly significant factors (Roos et al., 1997). Kaplan and Norton advocated that new measurement goals were required to reflect these newer organizational goals and processes. In the process, a quantitative shift in measurement to non-financial measures also had to occur (Roos et al., 1997). Eccles identified the same requirement for monitoring non-financial measures in his highly influential 1991 Harvard Business Review article. The key feature of Kaplan and Norton’s “Balanced Scorecard” is that it highlights a “new measurement architecture” based on the strategy of the company (Roos et al., 1997). The Balanced Scorecard is a measurement accounting system more than a specific instrument because it is driven by organizational strategy. It requires an organization to have its own rationale and method for tracking the creation and flow of Intellectual Capital in the organization (Huseman & Goodman, 1999). Its operationalized measures are to reflect key strategic factors found in the organization’s general mission statement (Roos et al., 1997). To be effective as a decision-making tool, it requires the organization to know its goals, the organizational context, and be sufficiently dynamic to reflect a time dimension (Huseman & Goodman, 1999). In sum, the IC accounting system created by the organization is highly idiosyncratic using measures of IC defined by the company itself (Huseman & Goodman, 1999). It is significant that Kaplan and Norton do not advocate a generic accounting system for IC. Their argument is that is that a generic system risks missing information that can only be given meaning once an organization’s goals, processes, and context are understood. Stewart cited by H&G is less certain, but does argue that set accounting formulas to capture IC are premature for “a field that is too new for cookbooks” (Stewart, 1991, p.144). Operationalization Kaplan and Norton’s balanced scorecard represents four perspectives:

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• • • •

a financial perspective that includes traditional accounting measures yet uses selectively chosen measures designed to fit the different parts of the company and their respective Strategic Business Unit strategy. a customer perspective that “groups measures relating to the identification of target groups for the company’s products in addition to marketing-focused measures of customer satisfaction, retention, etc.” (Bontis et al., 1999, p.396). an internal business perspective that “includes all processes related to the realization of products and services to satisfy the customers’ needs … drawing heavily from the concept of the learning chain” (Bontis et al., 1999, p.396). a learning and growth perspective that includes all measures relating to employees and systems the company has in place to facilitate learning and knowledge diffusion.

According to Kaplan and Norton, an organization’s Balanced Scorecard (BSC) that gives a thorough treatment of the four dimensions of an organization’s operations and goals becomes capable of showing financial investors the true value of a company. Ironically, however, Roos et al. (1997) suggest the extensive coverage and relevance of the scorecard’s integrated information cannot be used. It would reveal too much of a company’s strategy. To build a BSC around the four perspectives, the organization’s top management, as a team, have to reinterpret the existing organizational vision, or long-term strategy “through the lenses of the four perspectives” (Bontis, et al., 1999, p396). Key success factors for each perspective should then be apparent and can be translated into critical measures. All measures should in turn be linked through a cause and effect chain culminating in a relationship with financial results. To achieve a meaningful balance of the four perspectives, Roos et al. (1997) suggest that specific measures chosen for each perspective should satisfy three conditions. Measures should: • • •

be affected by actions of the unit, and only the unit in question be consistent with short-term and long-term company goals, and be reliable.

To illustrate the importance of these criteria being used, Roos et al. (1997) believe the three dimensions underlying balanced scorecard measures are useful standards for both traditional financial and their balanced accounting systems. Their comparison of the two systems using such dimensions suggests the following: BSC VERSUS TRADITIONAL FINANCIAL M EASURES

Reliability Ease of use Comprehensiveness Time and Effort to develop

Traditional Financial Measures High High Low Low

Balanced Score Card Medium Low High High

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Comparability

Medium

Low

One method suggested by Bontis et al. (1999) to confirm the correct implementation of a strategy is to examine actual financial results obtained within the time frame projected. If results are not as predicted, assumptions made about hypothesized cause and effect relations, and the time lags forecast should be checked. Strengths and Weaknesses The BSC approach has two obvious strengths. First, building a BSC requires managers to carry out four activities in a recursive and spiralling i.e., incremental fashion: • • • •

communication and networks with other managers by achieving a strategic alignment of the objectives of the whole organization; business planning by managing targets, coordinating initiatives and budget planning; feedback and learning by updating plans, strategies, and the BSC; and translating the vision by clarifying the mission and long-term strategy to all constituents inside the organization.

Second, Bontis et al. (1999) are particularly impressed by the creation of a sophisticated measurement system using the BSC that becomes, in essence, a management system. That is, creation of a BSC signals a willingness by management to keep track of multiple management dimensions in a systematic way. One criticism of the BSC is that it can be relatively rigid (Bontis et al., 1999). For example, the four perspectives drive the identification of key success factors. Very possibly, some KSFs (if not most) are cross-perspective, impacting simultaneously more than one dimension of the intangible assets of the company. Trying to identify KSFs using one perspective at a time assumes a structural versus process interpretation and may overlook some important KSFs. Similarly, the perspectives themselves can be limiting. While Kaplan and Norton suggest additional perspectives are possible, the BSC approach treats them as exhaustive. An additional criticism has been that consideration of the external environment is limited to customers.

10.

HUMAN RESOURCE ACCOUNTING

In 1964, Hermanson argued that human assets could be measured and valued. Researchers who accepted this challenge during the 1960’s and 70’s created Human Resource Accounting measures largely adopted from economic models and traditional accounting methods to assess employees’ economic value (Bontis, Jacobsen, Dragonetti, and Roos (1999). Cost models considered the historical, acquisition, replacement or opportunity cost of human assets. HR value models combined non-monetary behavioural with monetary economic value models. Monetary emphasis models calculated discount estimates of future earnings or wages. According to Bontis et al., these HRA models in their simplest form attempted to calculate the contribution that human assets make to firms by capitalizing salary expenditures. For example, instead of typically classifying total wages as an expense Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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on the income statement, a discounted flow of total wages would be classified in the asset section of the balance sheet by considering different factors such as average length of tenure per employee, and average increase in wages per year all discounted back to year one. HRA failed to garner widespread support in part because the methodology required many assumptions, failed to include knowledge-creating activities beyond those understood to occur in training classrooms, and potentially treated employees as possessions subject to cynical manipulation (Roos et al., 1997). Lindell (1996) also identified some HRA models containing non-sensical assumptions, some having been empirically disproven, and still others suffering from internal inconsistency. In sum, the HRA has been particularly difficult to validate given its many assumptions, has suffered from subjectivity, and has lacked reliability in that measures could not be audited with any assurance (Bontis et al., 1999). Although Bontis et al. (1999) acknowledge that HRA problems have always been known and pose the question of whether human assets can be valued in the conventional sense, they do suggest three uses of HRA information: • • •

11.

as part of the official audited reporting of results to external users of the firm’s financial data (e.g., creditors, investors, government, regulatory authorities); as internal feedback to organization members on the accomplishment of strategic goals; and as a starting point to develop future plans and strategy by recognizing the core competencies inherent in the intellectual capital resident in the organization.

RESEARCH AGENDA

As the concluding section to this review, it would be useful to emphasize what we have learned so far in this field. Interestingly, many IC models have similar constructs and measures that are merely labelled differently. For example, human capital (Skandia Navigator) is also called human-centred assets (Technology Broker) and competence of personnel (Intangible Asset Monitor). This re-labelling of similar conceptualizations can be construed as both positive and negative for the field of IC measurement. On a positive note, it shows that researchers are narrowing their frameworks and focusing on important concepts that are consistent across perspectives. However, since the field is still in its embryonic stage, no one is willing to give up their own nomenclature and build off each other’s work. Perhaps, a change for the better will occur as this field further develops and the desire for more valid and generalizable measures emerges. It is important for the development of this field in the nearterm to build on each researcher’s work so that a common set of definitions can be used. Another challenge with IC research thus far has been that it has been primarily of the anecdotal variety. Most researchers have conducted case-based reviews of organizations who have established intellectual capital initiatives already. Other researchers have merely documented the metrics that have been developed by Skandia and others without advancing or testing them. A way to overcome this challenge is for researchers to pursue more empirical research. Using survey data, Bontis (1998) has already shown a very strong and positive relationship between Likert-type measures of intellectual capital and business performance in a pilot study. The explanatory power of the final specified model was highly Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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significant and substantive (R2 = 56.0%, p-value < 0.001). In order to make any significant claims, IC researchers must now move from perceptual measures in isolated cases to large scale studies with objective measures. This task is daunting since the challenges are enormous but the potential benefits are far-reaching across many management disciplines including accounting, human resources, finance, training and development, and strategy to name a few. Such a grand vision for IC research requires some realistic goals. Unfortunately, the pursuit of measuring IC assets objectively is a noble but difficult one. Top executives in large U.S. and Canadian businesses agree that new intellectual capital measures are required to help manage knowledge assets. Stivers et al. (1998) surveyed 253 companies among the U.S. Fortune 500 and Canadian Post 300 in their study of nonfinancial measure usage. Results showed that even though 63 per cent of the sample felt that measuring innovation was important, only 14 per cent were actually measuring it, and only 10 per cent were actually using the measures for strategy development. Stivers and her colleagues argue that these results show a significant measurement-use gap. However, the evidence presented at many conferences on the topic show that this gap is decreasing. What was once a small club of practitioners has grown into a community of thousands and thousands of organizations around the world who are developing and experimenting with new IC measurement techniques. A realistic goal for this field is to continue to document such activity and to share measurement practices across industries in order to take full advantage of the innovation that is already taking place. Given the challenges of: i) trying to measure an intangible construct; ii) putting forth nascent efforts to conceptualize an IC domain; iii) establishing bi-directional cause-effect relationships, and iv) maintaining a reliance on the use of proxy variables, it should not be surprising that different companies’ IC management systems contain any number of unconnected and unproved individual indicators. Göran Roos and his colleagues also note that companies in their search for individual proxies of IC tend to produce long lists of multiple indicators. Typically, the indicators are weighted equally. Put together, companies can end up with a measurement system that is awkward and complex, and may contain invalid measures. Moreover, these indicators are likely expressed in qualitative and quantitative units, that attempt to represent any number of diverse elements of an organization’s functioning, range in their degree of specificity and focus i.e., from the individual employee to macro-organization relationships, and potentially represent either linear or non-linear relationships. To complicate matters further, IC measurement may attempt to capture not only forms of individual IC resources using a balance sheet approach, but also changes in these stocks of capital i.e., the flows or transformation of intellectual capital into financial capital and vice versa (Roos et al, 1997). Another realistic goal for IC researchers is to pursue international research settings so as to remove oneself from the anglophonic bias of the field’s initial growth. The goal of international research would be to show that the relationship between intellectual capital and performance can be generalized to other countries and industries. In a recent study of Malaysian managers, Bontis, Chua and Richardson (2000) showed that many of the hypotheses tested in previously Anglophonic settings of intellectual capital also held true. This speaks well for the generalizability of intellectual capital research across a variety of national and industry settings. Professor Ante Pulic and his colleagues in Austria are also finding

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evidence of generalizable results across Europe. The lesson here is that there exists a growing amount of IC research that is not necessarily published in English. Furthermore, a training tool that is gaining widespread use by senior managers and CKOs (Chief Knowledge Officers) alike (Bontis, 2000; Mitchell and Bontis, 2000) is the TANGO Simulation developed by Karl-Erik Sveiby. Research has shown that this simulation game significantly improves the perceived importance of developing intellectual capital metrics among its participants (Bontis and Girardi, 2000) and could prove to be quite useful in further enhancing the intellectual capital management skills of knowledge managers.

12.

CONCLUSION

Intangible assets have a substantial implication for financing a knowledge organization’s vision. While visible financing is usually simple to calculate consisting of equity, short-term and a few long-term loans, it is more difficult for knowledge organizations because of a lack of tangible collateral. Attempts to measure intangible assets have included treating employees as balance sheet items and measured in dollars, and using financial variables e.g., discounting a person’s output during a lifetime, costing out sick leaves or personnel turnover to create personnel accounting calculations for managers’ use. Unfortunately, these efforts to create human resources costing and accounting systems have not considered the full range of intangible assets that can exist, nor have they been particularly useful as management information systems monitoring the daily progress of business. They have tended to adopt a manufacturing or industrial perspective. Yet service companies now account for two-thirds of the employment in the industrialized world. Even more compelling, the wealth of knowledge-intensive organizations is now surpassing the manufacturing sector in most global economies.

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REFERENCES ASTD. (1996). “Expenditures on Employer-Provided Training”, American Society of Training and Development, July. Bontis, Nick, Dragonetti, N. C., Jacobsen, K., and Roos, G. (1999). The Knowledge Toolbox: A review of the tools available to measure and manage intangible resources. European Management Journal, 4 (August), 391- 401. Bontis, Nick and John Girardi. (2000). “Teaching Knowledge Management and Intellectual Capital Lessons: An empirical examination of the TANGO simulation”, International Journal of Technology Management, forthcoming. Bontis, Nick, Chua, W. and S. Richardson. (2000) “Intellectual Capital and the Nature of Business in Malaysia”, Journal of Intellectual Capital, forthcoming. Bontis, Nick, Crossan, M. and J. Hulland. (2001) “Managing an Organizational Learning System by Aligning Stocks and Flows”, Journal of Management Studies, forthcoming. Bontis, Nick. (1996). “There’s a price on your head: Managing intellectual capital strategically,” Business Quarterly, Summer. Bontis, Nick. (1998). “Intellectual Capital: An Exploratory Study that Develops Measures and Models,” Management Decision, 36, 2, 63-76. Bontis, Nick. (1999). “Managing an Organizational Learning System by Aligning Stocks and Flows of Knowledge: An empirical examination of intellectual capital, knowledge management and business performance”, PhD dissertation, London, Canada: Ivey School of Business, University of Western Ontario. Bontis, Nick. (1999). “Managing Organizational Knowledge by Diagnosing Intellectual Capital: Framing and Advancing the State of the Field”, International Journal of Technology Management, 18, 5/6/7/8. Bontis, Nick. (2000). “CKO Wanted – Evangelical Skills Necessary: A review of the Chief Knowledge Officer position”, Knowledge and Process Management, 7, 4, in press. Boudreau, J.W., and Ramstad, P. (1997). Measuring Intellectual Capital: Learning from financial history. Human Resource Management. 1-30. Boudreau, J.W., and Ramstad, P. (1998). Human Resource Metrics: Can measures be strategic? Working Paper 98-10. Cornell University, New York. Brooking, A. (1996). Intellectual Capital: Core Assets for the Third Millennium Enterprise. Thomson Business Press, London, England. Chan, Y.C.L., and Lynn, B.E. (1991). Performance evaluation and the analytic hierarchy process. Journal of Management Accounting Research, 3 (Fall), 57-87. Døving, E. (1996). “In the Image of Man: Organizational Action, Competence and Learning”, In D. Grant and C. Oswick (Eds.), Metaphors and Organizations, London: Sage. Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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Drucker, P.F. (1993). Post-Capitalist Society. HarperCollins, New York. Eccles, R.G. (1991). The Performance Measurement Manifesto. Harvard Business Review, JanFeb, 131-137. Edvinsson, L., and Malone, M.S. (1997). Intellectual Capital: Realizing Your Company’s True Value by Finding its Hidden Brainpower. HarperBusiness, New York. French, W.L., and Bell, C.H. (1995). Organization Development: Behavioral Science Interventions for Organization Development. Prentice Hall, Englewood Cliffs. Hudson, W. (1993). Intellectual Capital: How to build it, enhance it, use it, New York: John Wiley & Sons. Kaplan, R.S. and D.P. Norton (1992). “The Balanced Scorecard Measures that Drive Performance”, Harvard Business Review, January-February, 71-79. Kuhn, T.S. (1962). The Structure of Scientific Revolutions. University of Chicago Press, Chicago. Lev, B. (1999). Seeing is Believing - A Better Approach to Estimating Knowledge Capital. CFO Magazine, (cited in Strassman internet article but no issue or page nos. given) Lincoln, Y.S. and Gubba, E.G. (1985). Naturalistic Inquiry. Sage, Beverly Hills. Lynn, L.E. (1998). The Management of Intellectual Capital: The issues and the practice. Management Accounting Issues Paper 16 Management Accounting Practices Handbook. Society of Management Accountants of Canada, Hamilton, Ontario. Management Accounting. (1997). What is EVA and How Can it Help Your Company? Management Accounting, November. McConville, D. (1994). All about EVA. Industry Week, 18 April. Plott, C. (1998). “Learning is linked to profitability”, Corporate University, July/August. Roos, J., Roos, G., Dragonetti, N.C., and Edvinsson, L. (1997). Intellectual Capital: Navigating in the New Business Landscape. Macmillan, Houndsmills, Basingtoke. Scott, T.H., Mitchell, T.R., and Birnbaum, P.H. (1981). Organization Theory: A Structural and Behavioral Analysis (4th ed.). Richard D. Irwin Inc., Homewood, Ill. Spender, J.-C. (1994). “Organizational knowledge, collective practice and Penrose rents”, International Business Review, 3, 4. Standfield, K. (1999). Knowcorp, http://www.knowcorp.com. October 10, 1999. Stewart, Thomas A. (1997). Intellectual Capital: The New Wealth of Organizations. Doubleday/Currency, New York. Stewart, Thomas A. (1991). “Brainpower: How Intellectual Capital is Becoming America’s Most Valuable Asset”. FORTUNE. June 3, 1991. pp.44-60. Stivers, B., Covin, J., Green Hall, N. and Smalt, S. (1998). ‘How nonfinancial performance measures are used’, Management Accounting, February. Queen’s Management Research Centre for Knowledge-Based Enterprises http://www.business.queensu.ca/kbe

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Strassman, P.A. (1999). The Value of Knowledge Capital http://www.strassmann.com. October 23, 1999. Svieby, K.E. (1997). The New Organizational Wealth: Managing and Measuring Knowledgebased Assets. Barrett-Kohler Publishers, Inc., San Francisco. Waterhouse, and Svendsen, (1999). (not certain if this is its’ title Measuring What People Know.)

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SUMMARY OF IC MEASUREMENT MODELS

PURPOSE: KEY POINT: POPULATION: ORIGIN: PRINCIPAL:

PURPOSE: KEY POINT: POPULATION: ORIGIN: PRINCIPAL:

PURPOSE: KEY POINT: POPULATION: ORIGIN: PRINCIPAL:

PURPOSE: KEY POINT: POPULATION: ORIGIN: PRINCIPAL:

SKANDIA NAVIGATOR To provide management with a taxonomy for classifying an organization’s knowledge assets and a series of metrics to measure them. Intellectual capital consists of human and structural capital. Considered generic to all sizes of for-profit and not-for-profit organizations Reports first issued internally in 1985, published as IC addendum in 1994 Leif Edvinsson, former Corporate Director of Intellectual Capital at Skandia

IC-INDEX To develop and apply a summary index of consolidated measures of intellectual capital. Combine similar type first generation IC measures in order to see trends. Generic to all organizations. 1995 Goran Roos, Principal of Intellectual Capital Services Ltd., London, UK

TECHNOLOGY BROKER To help companies locate valuable hidden intangible assets, and assign a dollar value to them using cost, market, and/or income approaches. IC consists of market, human-centred, IP and infrastructure assets. Enterprises which require a sophisticated work-force which relies on expertise and technology more than manual labour. 1996 Annie Brooking, founder and managing director of The Technology Broker

INTANGIBLE ASSET M ONITOR An IC performance measurement reporting system that uses an HR, and information systems rather than financial perspective. Intangible assets consist of external structure, internal structure and competence of personel. Organizations wanting to transform and become smart organizations. 1986 Karl-Erik Sveiby, Queensland, Australia

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KNOWCORP PURPOSE: KEY POINT: POPULATION: ORIGIN: PRINCIPAL:

To assist decision makers devise a correctly weighted decision making approach where short-term goals can be weighed against long-term consequences. Consideration of time as an opportunity cost to acquiring new knowledge. All organizations with training and development expenditures. 1991 Ken Standfield, Principal of Knowcorp, Australia

TOBIN’S Q PURPOSE: KEY POINT: POPULATION: ORIGIN: PRINCIPAL:

To understand an organization’s value of investments in technology and human capital. Market value divided by replacement cost. Publicly-traded companies where market value can be easily determined. 1960s James Tobin, Yale University, Nobel prize in Economics in 1981

MVA and EVA PURPOSE: KEY POINT: POPULATION: ORIGIN: PRINCIPAL:

Uses adjustment variables to properly account for all ways in which corporate value can be added or lost. EVA is increased if the weighted average cost of capital is less than the return on net assets, and vice versa. For-profit organizations. 1980s Stern Stewart and Co., New York

CITATION-WEIGHTED PATENTS PURPOSE: Estimates the importance of an organization’s technologies. Management of patents allow organizations to make decisions regarding the viability KEY POINT: of undertaking/continuing R&D. POPULATION: Scientific-based organizations. ORIGIN: mid 1960s, F.M. Scherer brought attention of patents to American Economists PRINCIPAL: Government offices (e.g., US Patent Office at http://www.uspto.gov)

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BALANCED SCORE CARD PURPOSE: KEY POINT: POPULATION: ORIGIN: PRINCIPAL:

A multi-dimensional, intangible asset accounting system designed to guide management decisions. BSC represents four perspectives: financial, customer, internal business, learning & growth Generic to all organizations. 1992 Robert Kaplan, Harvard; David Norton, Renaissance Strategy Group

HUMAN RESOURCE ACCOUNTING

PURPOSE: KEY POINT: POPULATION: ORIGIN: PRINCIPAL:

Employees’ economic cost and value can be measured and budgeted using traditional accounting methods in order to provide input to managerial and financial decisions. Employee salaries should be capitalized as assets and not treated as expenses. Generic to all organizations. 1960s R. Hermanson; Michigan State University

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