Applying knowledge capital models in the Lisbon Stock
Exchange
APRIL 2002
Paulo Cardoso do Amaral
Universidade Católica Portuguesa
Palma de Cima 1600 Lisboa, Portugal
José Maria Pedro
Instituto de Estudos Superiores de Contabilidade
Rua Brancamp, 9 - Lisboa, Portugal
Abstract
This
work considers the application of
knowledge capital models in the Portuguese competitive environment. The
Intangible Calculated Value (ICV) and Market to Book (DiffMtoB) models were
chosen among a list of 25 models identified. We present a summary of the knowledge
capital model analysis and show why the two models were chosen due to its
applicability to the Stock Exchange data. Using all available data,
encompassing 139 firms for a period of 9 years. Also, by comparing the outcome
of the two knowledge capital models, we have found evidence of complementary
results. We discuss the theoretical foundations of the two models to conclude
why they are complementary. Finally, we put forward a new measure of the
knowledge capital. This new model gets two effects: The Market Effect and The
Performance Effect combined to express the value of Knowledge Capital
1. Introduction
Intangible assets are a most important
management aspect for the organizations of the Information Society. These
assets are often overlooked but need to be managed at least as rigorously as
their tangible counterparts. Several authors [KAPLAN & NORTON, 1996;
EDVINSSON, 1997; SVEIBY, 1998; STEWART, 1997] have revealed the hidden value of
intangible asstes. We are interested in collecting empirical evidence to
understand better the value of intangible assets in organizations, in the form
of Knowledge Capital. This understanding may itself be valuable to better
perform knowledge management [NONAKA & TAKEUCHI, 1995].
Information management, knowledge
management and organizational learning are currently part of the management
science agenda [DAVENPORT & PRUSAK, 1998]. Stewart [STEWART, 1997] and
Edvinsson [EDVINSSON, 1997] consider Knowledge Capital a most important asset.
Many authors have put forward the
importance of knowledge for organizations, e.g.:
·
The
pace of change enforced by the globalization effect overdues organizational
knowledge faster than ever [DAVIS & MEYER, 1999].
·
Competition
is becoming ever more costumer centric [KOTLER, 1996] and highly depends on
knowledge about costumer behavior and needs.
·
Successful
globalization depends on the need to know how to adapt locally according to
each particular culture [NEEF, 1999].
·
Increasing
perceived value on-line on the Internet builds upon new and more flexible
organizational structures [MINTZEBERG & HEYDEN, 1999] where knowledge
reveals itself in the new coordination and control structures.
·
Information
technology supports communication and virtual interaction. Communication
undoubtedly seeds knowledge in organizations [CARNCROSS, 1997].
·
Large
organizations need to manage their human resources well because knowledge is
created and resides in people’s heads. To leverage innovation, learning and
knowledge creation, organizations need to “unlock
the mystery of tacit knowledge and release the power of innovation” [KROG
& ICHIJO & NONAKA, 2000].
Knowledge is an intangible asset
whose availability is not reduced by its consumption and is thus a public good.
Knowledge has to be used to create new knowledge.
Knowledge lives in people's heads.
Each time anyone uses its own knowledge, acquires experience simultaneously,
thus enhancing its own personal knowledge stock. The same happens when
knowledge is used by a group of people, that is, new knowledge is created
though collective experience. Because knowledge stock of organizations has an
enormous value creating potential this intangible asset can be reffered as
Knowledge Capital [KROG & ROOS, 1995].
The importance of Knowledge Capital
for organizations in the new competitive environment is unquestionable.
Edvinsson and Malone [EDVINSSON & MALONE - 1997] argue that "an economy that cannot measure its
value adequately cannot distribute with objectivity its resources nor reward
its citizens adequately". Therefore, we strongly believe that in order
to acquire insight on the effective knowledge management practices there is a
desperate need to identify and measure the value sources of intangible assets.
Understanding
what knowledge means is a quest with thousands of years of history. The
knowledge theory is known to be born in ancient Greece. Aristotle, Locke,
Thorndike, Skinner and others developed an empirical perspective of what
knowledge is, through the relationship between the subject and the object
itself in the act of knowing. Krog and Roos [KROG & ROOS, 1995] put forward
the assumptions of this epistemological path:
q
Reality is just a
representation of the truth (each individual has its own personal
interpretation of reality)
q
Individuals are transparent regarding
information (human senses are always prepared for reception)
q
We can retain and process information
q
We can reason
q
We can make decisions with incomplete
information (heuristics)
Every
individual learns in the context of an organization, within its community, much
like social entities. Learning requires communication and interaction. We thus
need to understand the organizational dimension of knowledge. Knowledge does
have value creating potential, but the current knowledge measurement models
focus on mainly results. Measuring results is not necessarily wrong (e.g., we still
use exams to measure student knowledge, by observing its application) but is
nevertheless an indirect measurement.
The current work applies models for intellectual capital measurement in
the Lisbon Stock Exchange. We selected two models to measure indirectly the
value of intangible assets (e.g., financial results, organizational structures,
intellectual property and relationship with customers) among a list of
twenty-five models identified during our research.
Our objective is to quantify as objectively as possible the production
of knowledge value using a representative sample of the Portuguese economy. We
believe that a good measure of organizational value is given by the market
evaluation performed with 139 organizations representing all sectors for a
period 9 years.
The rest of this work is organized as follows:
- In
the next section, we refer 25 knowledge capital models classified in 6
different categories.
- In
Section 3, we explain why the chosen models are the Intangible Calculated Value (ICV) and Difference Market to Book
(DiffMtoB), regarding our objectives of their applicability using the
Lisbon Stock Exchange data available
- In
Section 4 we discuss the theoretical foundations of both models to show
why they are complementar, and put forward the MPM model that results from
the combination of the ICV and DiffMtoB models.
- Finally,
in Section five we draw the conclusions upon the application of the MPM
model using the universe of 139 organizations in the Lisbon Stock Exchange
during a period of 9 years:
- (i)
Sectors and companies are classified according to the following criteria:
best, worst and undefined.
- (ii)
Service industries have more Knowledge Capital
- (iii) Future tendencies are presented
2. Short Overview of Knowledge Capital
Models
This section overvues the 25
knowledge capital measurement models and presents the Intangible Calculated
Value (ICV) and Market to Book (DiffMtoB) models which were selected, as
presented in this section, according to the objectives of their applicability to
the Lisbon Stock Exchange.
A common definition of Knowledge
Capital is still missing [BROOKING, 1996; EDVINSSON, 1997]. Each model measures
Knowledge Capital according to the value sources defined by each author. The
models can be grouped in the following cathegories (see
annex II):
- Market Value Models: developed from accounting variables and
from the market value of the organization. They usually admit that the
difference between the market value and book value represents the value of
the knowledge capital;
- Performance and Knowledge Management Models: these models intend to
measure knowledge capital through the performance of knowledge management
functions inside the organization, or the knowledge transfer inside the
company;
- Strategy Models: these models measure how the company accomplishes strategic goals
and the critical success factors;
- Customer Capital Models - these models measure variables
associated with external stakeholders;
- Structural Capital Models – these models measure internal
efficiency, and organizational structure;
- Human Capital Models – these models measure competencies and the
ability to create value through innovation.
Identifying with precision the value sources
within organizations is an outstanding difficulty in measuring Knowledge
Capital. We propose to identify and measure at the same time several indirect
facets of the same reality.
For the purpose of the current work, the following criteria would match
the usability of data available in the Lisbon Stock Exchange:
q
Value source measurement, directly or
indirectly, according to author's definition;
q
A measurement pattern;
q
A process of calculation;
q Objectivity;
q
Extensive knowledge capital source
identification;
q
A reporting structure;
q
Data availability.
Some
models are very interesting, because they use a large number of quantitative
and qualitative variables. Each model has a set of relevant characteristics, as
you can see in annex VI, but each
propertie should be evaluated in order to fulfill our purpose of measuring
Knowledge Capital using Lisbon Stock Exchange data.
The first
two categories are more suitable to our purposes because they can produce a
financial number as result of calculation; they are more studied; they have
more models to chose; and they cover two very important sources of information:
market, industry and company.
2.1. The Intangible Calculated Value
(ICV) and the Market-to-Book (DiffMtoB) model
Market Value
Models are interesting because they objectively quantify the financial outcome
that results from the Knowledge Capital. A second model focusing the internal
performance of the organization complements the first external view. Strategic
measures would enhance the accuracy of Knowledge Capital measurement but state
of the art of strategic Knowledge Capital models do not meet the criteria
stated in the previous section, and are thus inadequate for our purposes.
Besides, where are the data sources to measure the strategic Knowledge Capital?
Combining both market and
performance models provides a reasonable measure of the organizational
Knowledge Capital. Together, they reveal at the same time the investor’s
preferences along and the organization's performance in its competitive
environment. These are indirect measures of Knowledge Capital because their
calculation is done upon financial results and the value of the firm’s public
shares.
The chosen models for the current
work are the Intangible Calculated Value (ICV) and Market to Book (DiffMtoB).
Both models use data audited by Portuguese IRS and by the Lisbon Stock
Exchange, so they are, in our view, reliable enough.
The Intangible Calculated
Value (ICV) - NCI Research
The ICV relies upon the idea that the value of Intangible Assets is
equivalent to its ability to beat competitors with similar tangible assets.
The steps of ICV calculation are the
following [STEWART, 1997]:
Step 1.
“Calculate average pre-tax earnings for three years
Step 2.
Get the average year-end tangible assets for three years from the balance
sheet;
Step 3.
Divide earnings by assets to get the return on assets;
Step 4.
For the same three years, find the industry’s average ROA;
Step 5.
Calculate de “excess
return”. Multiply
the industry-average ROA by the company’s average tangible assets. Now subtract
that from this
company’s pre-tax earnings.
Step 6.
Pay Uncle Sam. Calculate the three-year-average income tax rate, and
multiply this by the excess return. This is the premium attributable to
intangible assets;
Step 7.
Calculate the net present value of the premium. You do this by dividing
the Premium by an appropriate percentage, such us the company cost of capital”.
The net present value of the premium
is the Intangible Calculated Value.
Difference (DiffMtoB) or
Ratio (M2B) “Market-to-Book” model
The value of a given firm is given by the value
of all its public shares, i.e.,
Value of Market = (share price x total number
of shares in the market).
DifMtoB = [Intellectual Capital][1] = (market value[2] – book equity[3])
This is a fairly simple yet powerful
model due to the use of real market evaluation. In case of positive results,
the firm is more valuable than the prudent accounting evaluation practice and
represents the firm's Knowledge Capital measure.
3. The MPM Model
In this section we discuss the theoretical foundations of the ICV and
DiffMtoB models and we put forward the MPM model as a measure of the knowledge intensity that combines the former models.
3.1. ICV model
analisys
Thomas Stewart introduces the ICV model [STEWART, 1997] referring that
the IRS in the EUA uses it in order to measure the firm's intangible value.
The ICV model uses information from the firm's
accounting information systems as well as the available data from the firm's
industry. The importance of data reliability is paramount. We are comparing in
this case, internal data representing the return on assets with the average
sector's return on assets. This assures we are measuring real organizational
performance in its competitive environment.
When the ICV result is positive, we can assume
that the organization performs better than average in its sector. We can state
that this model captures the performance
effect of the organization and we can thus assume that this results from
the presence of Knowledge Capital inside the organization. It is an indirect
measure, but it is nevertheless valuable.
Two organizations with similar tangible assets in the same market differ
in their knowledge stock and in its use. Each one possesses different
individual and collective experiences, culture, skills, structure, processes
and management, as well as resulting different performance figures. ICV is a
measure of the firm's intangible assets through this above average Performance
Effect.
3.2. DiffMtoB model
analysis
The DiffMtoB model is fairly simple to use. It is perhaps the most
straightforward indirect measure of Knowledge Capital. This model reveals
investor preferences based on their future expectation in face of all available
information on both the firm and its competitive environment. We can assume
that this model captures the Market
Effect and quantifies in this regard the presence of Knowledge Capital in
the firm.
It is our belief that the data used
to compute this model is credible due to its origin on the accounting system
and the market's finantial system.
3.3. The MPM Model
The results of DiffMtoB and ICV models are not necessarily the same
because they measure different indirect outcomes. The first relies on
information that matters to shareholders whereas the second uses market information
that relates organizational performance.
Each model thus provides a different
perspective of the same reality, as depicted in figure 1.
Figure 1 - The complementarities of the ICV
and DifMtoB models
By combining the two models we get a richer view of intangible assets,
seen both from a market perspective (shareholders) and an internal performance
perspective (industry).
Nevertheless, our aim in using both models has to guarantee its combined
applicability:
q
The
models are applied with data reflecting an average of three years because
average gives more stability to the results.
q
The combined
result considers the same weight for each model.
Therefore, by combining the two
models, we will measure both internal and external views of Knowledge Capital.
The end result is the MPM (Model of Performance and Market) as follows (see annex IV):
MPM=a*[Performance]+b*[Market], with (a+b)=1
Or
MPM=a*[ICV]+b*[DifMtoB], with (a+b)=1
We should have some precautions with parameters a and b
when applying the MPM model. There is some research work to do on these
parameters. Until this point of our work we can give some advise on a
and b boundaries, because we should use a differente combination
of a and b for each industry:
q
If
Stock Market doesn’t work properly, set b=0;
q
If
competition is limited in industry, set a=0;
q
If
Stock Market and industry competition are working properly you can set a=b=0.5;
4. The MPM in action
This section summarizes the application of the MPM model to a universe
of 139 firms in the Lisbon Stock Exchange over a period of 9 years (1991 a 1999[4]). The data set is fairly complete
(not just a sample) and considers all major firms in all sectors in Portugal.
We will start to present the industry results for the year 1999 (an average
evolution of Knowledge Capital during the period 9 years) and, finally, the
future tendencies drawn from the application of a second-degree polynomial
algorithm.
4.1. Knowledge Capital by company
The MPM model calculates indirectly
the Knowledge Capital of each particular firm. We cannot add up the outcome of
several different firms because the net result would combine negative and
positive values thus hiding the real value of a model that reveals Knowledge
Capital on an individual basis.
Table 1 shows that until 1998 the
number of firms with negative Knowledge Capital is greater than the number of
firms with positive Knowledge Capital. Positive Knowledge Capital, as given by
the MPM model, means that the firm's performance is above average in its sector
and that the market recognizes its value to be greater than what is recorded in
the accounting system. Therefore, even if the average MtoB ratio is positive
for most years (except 1991, 1992 and 1995) the MPM model only revelals more
firms with positive Knowledge Capital in 1999. The MPM model is conservative.
Table 1 - Percentage of companies
with positive knowledge capital
|
Year |
Frequency of positive values
of Knowledge Capital |
|
1993 |
35% |
|
1994 |
37% |
|
1995 |
38% |
|
1996 |
46% |
|
1997 |
45% |
|
1998 |
49% |
|
1999 |
59% |
Source:
Data from Lisbon Stock Exchange
To easily obtain a relative position
of each firm within its sector, and be able to select the best and the worst
firms (in terms of Knowledge Capital), we use the following selection criteria:
Selection Criteria for
the MPM model
TYPE Description
Best: Positive
Knowledge Capital at least during 4 consecutive years
Worst: Negative
Knowledge Capital at least during 4 consecutive years
Undefined The
Rest
Using the above criteria, the results for 1999
are the following:
Best
companies 36%
Worst
companies 44%
Undefined
companies 20%
Using this classification, only one third of the firms in the Lisbon
Stock Exchange are well evaluated in 1999 regarding their Knowledge Capital.
Using the MPM model we know exactly which ones have a positive Knowledge
Capital measure.
4.2. Industry Results for the year 1999
We can have the complete view of the
best and worst industries by combining the end results from all firms in all sectors.
The results per sector in 1999 are presented in Annex III.
Using the criteria defined above
(subsection 4.1) we have identified the best and the worst industries, as
follows:
The best industries in knowledge capital:
Real estate
Commerce and Distribution
Telecommunications and Information Technologies
Metallurgic Industries
Chemical Industries
Electricity
The worse industries in knowledge capital:
Metallic Products and Machinery
Textile Industries
Food and Beverage
Mineral and non-Metallic Products
Restaurants, and Leisure
Transportation and Storage
The unstable industries in capital knowledge:
Sports and Culture
Construction
Paper and Graphic
Arts
Insurance and Pension
Funds
Financial
Intermediation
We can conclude that only one third of the
industries are among the best. Once again, using the MPM model we know which
ones are better prepared to deal with the competitive enrironment of the
information society. It would be interesting to know the relative weight of
each group of industries in Lisbon Stock Exchange to get a quantitative
picture.
4.3. Future tendencies
Using a second-degree polynomial algorithm
we may predict the industry attractiveness in the years to come. The results
put forward sectors with a positive trend, as follows:
Figure 2 - Trend of evolution of the knowledge capital for
each industry
|
Industry |
Tendency |
R2 |
|
Real estate |
Increasing |
0,917 |
|
Retail and Distribution |
Increasing |
0,968 |
|
Construction |
Increasing |
0,380 |
|
Telecommunications and Information Technology |
Increasing |
0,888 |
|
Metallic Products and Machinery |
Increasing |
0,654 |
|
Food and Beverage |
Increasing |
0,263 |
|
Paper and Graphic Arts |
Increasing |
0,350 |
|
Financial Intermediation |
Increasing |
0,404 |
|
Electricity |
Increasing |
0,722 |
|
Mineral Products |
Increasing |
0,880 |
|
Restaurants and Leisure |
Increasing |
0,912 |
|
Insurance and Pension Funds |
Increasing |
0,959 |
|
Sports and Culture |
Decreasing |
0,074 |
|
Textile |
Decreasing |
0,679 |
|
Metallurgic industries |
Decreasing |
0,878 |
|
Chemical Industries |
Decreasing |
0,399 |
|
Transportation and Storage |
Decreasing |
0,880 |
Source: data from Lisbon Stock
Exchange
5.
Conclusion
Models for Knowledge Capital
measurement are still in its infancy. Until today at least 25 different models
have been proposed which can be grouped in six cathegories:
q
Market
Value Models;
q
Performance
and Knowledge Management Models;
q
Stategy
Models;
q
Customer
Capital Models;
q
Sructural
Capital Models and
q
Human
Capital Models.
Choosing
the right model is a most important decision, which should be carefully well
thought-out according to the strategic objectives of its application, so that
the outcome turns out objective and valuable.
In order to measure knowledge
capital using the Lisbon Stock Exchange data, we defined a selection criterion
that leads the identification of the ICV and the DiffMtoB models. We put
forward a new model - Market Performance Model (MPM), resulting from the
combination of these two models.
Firms in the service sector show
better results in Knowledge Capital. This outcome is intuitive because the
other sectors are in fact more capital intensive whereas the service sector
employs more people and is, by definition, more knowledge intensive. Intangible
assets in the service sector are thus expected to have a greater share in the
total firm's value. Our measurements using the MPM model confirm this
hypothesis.
The MPM model provides insight
regarding intangible assets. The top performers establish a baseline for
organizational performance comparison over time. However, the direct comparison
of absolute results between firms is meaningless for organizations that are not
similar in shape. For example, a large firm may reveal a relative small weight
in Knowledge Capital although possessing a large absolute base of intangible
assets. The comparison should therefore be made, as shown, regarding relative
values[5].
5.1 Future research
Our research should proceed in the
sense of incorporating more knowledge sources in the MPM model (even if
indirectly measured).
The market value of organizations is composed
by tangible and intangible assets, as presented for example in annex I. Pursuing the idea of a global
knowledge measurement procedure, we aim to develop an equation with three main
complementary perspectives, as considered elsewhere on an individual basis
[Kaplan & Norton, 1996; Edvinsson, 1997; Sveiby, 1998; Stewart, 1997]
CC=a(potential)+b(structural)+c(environmental)[6]
The combination of these three components
pretends to extend the measurement of intangible sources as follows:
q Potential or human – this contribution includes human resources as the real and living
knowledge sources; intelectual capital is a visible outcome of this potential,
and is, at the same time, the inovation thrust for new knowledge creation.
q Structural footmarks - footmarks are left by people through decisions, compromise solutions,
procedures and processes that influence organizational behavior. These structural
footmarks are the ones left by knowledge management processes, business
processes and the relation between internal and external resources.
q Environmental or Market - the environmental contribution considers the allies strategic
aliances and joint ventures established to better attain the strategic
objectives.
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