disclosure on intangible

8/2/2019 Disclosure on Intangible

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The Extent of Disclosure on Intangibles in Annual

Reports

by

Susanne Arvidsson

Paper presented at the 4th

annual SNEE congress in Mlle, 20-23 May, 2003.

Department of Business Administration

Lund UniversityPO Box 7080

220 07 Lund, Sweden

Phoneno. +46-46-222 79 81

E-mail: [email protected]

May not be copied, reproduced or quoted without permission from the author


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Abstract

The purpose of this paper is to analyse the extent of

disclosure on intangibles in annual reports and to

identify company related factors, which explain the

extent of disclosure. The focus is both on the extent of

overall disclosure on intangibles and on the extent of

disclosure related to five categories of intangibles,

i.e. Human, Relational, Organisational, R&D and

Environ/Social.

The methodology underlying this study is a comprehensive

analysis of the extent of disclosure on intangibles in

annual reports. The disclosure study focuses on 36 annual

reports made by Danish, Finnish, Norwegian and Swedish

companies belonging to the pharmaceutical, biotechnology

or health care equipment & supplies industries during

1999.

The present study provides evidence that the management

teams use the opportunity to voluntarily supply outsiderswith information on intangibles. The extent of disclosure

on intangibles in annual reports is, however, not

overwhelming. Only half of the items in the checklist is

on average disclosed in the annual reports. R&D is the

category, which the knowledge-intense companies disclose

most information on. Like R&D, information related to

relationships with suppliers, customers and partners also

appears to be deemed highly relevant when the management

teams design their disclosures on intangibles. Social and

environmental disclosure does not appear to be

prioritised. The disclosure scores also reveal that the

management teams do not seem to follow up the old adage

the employees are our most valuable asset with

information related to this high-valued asset.

Four hypotheses were posed to test the relationship

between company related factors and the extent of

disclosure on intangibles. Company size was found to be

positively related to the extent of disclosure on

intangibles. The regression result did not lend support

to the hypothesis that high leveraged companies disclose


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more information on intangibles than low leveraged

companies do. The hypothesis that Swedish companies

disclose more on intangibles than companies from the

other Nordic countries do was confirmed in the

regression. Inconsistent with earlier disclosure studies,internationally listed companies were not found to

disclose more information on intangibles than only-

domestically listed companies.


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The Extent of Disclosure on Intangibles in

Annual Reports

1.1 Introduction

During the last decades the number of knowledge-intense companies, e.g.

biotechnology, informationtechnology and telecommunication companies, haveincreased. The most striking difference between these companies and traditional

companies, e.g. manufacturing and forest companies, is that the knowledge-intense

companies to a greater extent base their competitive strength and, thus, their value

creation on intangibles (Holland, 2002; Lev, 2001; Sullivan and Sullivan, 2000;

Sveiby, 1997; Wallman, 1996). According to a study conducted by the Brookings

Institution (Blair and Kochan, 2000) 83 percent of corporate value was in 1978 due to

tangible assets and 17 percent was due to intangible assets. In 1998 the proportions

were almost reversed, i.e. 69 percent of corporate value stemmed from intangible

assets and only 31 percent was associated with tangible assets. Considering the nature

of todays corporate value-creation process an important task for a management team

is to communicate information related to intangibles and their role in the companys

value-creation process.

Holland (2002) concludes that the major changes in the corporate value-creation

process have resulted in both companies and actors on the capital market

acknowledging the relevance of disclosures on intangibles. Several studies have

confirmed that actors on the capital market in addition to financial information require

more information on intangibles (Holland, 2001; Ernst and Young Center for Business

Innovation, 1997; Mavrinac and Siesfeld, 1997; Eccles and Mavrinac, 1995). Bukh,

Gormsen, Mouritsen and Nielsen (2002) conclude after analysing the information

content in IPO prospectuses that the disclosure on intangibles has increasedsubstantially between 1990 and 2001. There is, however, an oft-stated concern that the

disclosure on intangibles has not kept pace with the capital markets demand for

increased information (Johanson, Mrtensson and Skoog, 2001; Hoegh-Krohn and

Knivsfl, 2000; Wallman, 1995). Consequently, the shift in the nature of value

creation is argued to render difficulties for the valuation of knowledge-intense

companies (Lev, 2001; Sullivan and Sullivan, 2000; Chan, Lakonishok and

Sougiannis, 1999).

More informative disclosures are found to result in an overall more effective

allocation of capital due to reduced information asymmetry, decreased bid-ask


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spreads, increased stock liquidity,a lower average cost of both equity and debt capital

and, consequently, better investment decisions (Botosan and Plumlee, 2002;

Richardson and Welker, 2001; Healy, Hutton and Palepu, 1999; Sengupta, 1998;

Botosan, 1997; Lang and Lundholm, 1996; Welker, 1995). These findings along with

the rising demand, from the users of financial statements, for more information onintangibles have resulted in an intense activity in accounting bodies, at both national

and international level, directed at examining if and how the boundaries of financial

statements can be extended to also incorporate information on intangibles (see, e.g.

FASB, 2001a,b, IAS No. 38; AICPA, 1994). Although more or less meritorious

efforts, the problems with defining, classifying and valuing intangibles prolong the

process. Even though accounting methods do not explicitly prescribe companies to

disclose information on intangibles, the management team can compensate the lack of

information on intangibles in financial statements by voluntarily disclose this type of

information (FASB, 2001a). IAS No. 1 (Presentation of Financial Statements. rev.

1997) paragraph 8c implicitly encourages companies to include information onintangibles, which are paramount to the value creation but unrecognised in the balance

sheet.

The purpose of this paper is to analyse the extent of disclosure on intangibles in

annual reports and to identify company related factors, which explain the extent of

disclosure. The focus is both on the extent of overall disclosure on intangibles and on

the extent of disclosure related to five categories of intangibles, i.e. Human,

Relational, Organisational, R&D and Environ/Social. Thus, the present study aims at

making a contribution to the research frontier focused at understanding how

information suppliers (i.e. management teams) communicate intangibles in annual

reports. A secondary purpose is to determine if the five categories measure a commonentity, i.e. intangibles. Therefore, the structure of the disclosure scores is also

examined in the paper.

The study is motivated for four reasons. First, due to the importance intangibles are

emphasised to play in the value-creation process (Holland, 2002; Sullivan and

Sullivan, 2000) and the findings of reduced information asymmetry and a lower cost

of capital following more informative disclosures (e.g. Richardson and Welker, 2001;

Sengupta, 1998; Botosan, 1997), it is relevant to examine if and how the management

teams use the opportunity to voluntarily supply outsiders with information on

intangibles, in order to pave the way for a better understanding of what creates value

in the company. Although there is a body of empirical studies examining the

disclosure in annual reports (see, e.g. Ahmed and Courtis, 1999; Marston and Shrives,

1991), there is a lack of studies focused on the disclosure on intangibles. Second, the

information process surrounding the valuation of companies involves a demand side,

i.e. the actors on the capital market and a supply side, i.e. the management teams. The

present study provides us with useful information concerning the emphasis the supply-

side places on intangibles. Third, establishing the extent of overall disclosure on

intangibles in annual reports, as well as which categories of intangibles the disclosure

is focused on, should be relevant input for management teams when they design their

disclosure on intangibles. Fourth, the result of the study should be valuable in the


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accounting societys current work with preparing rules and policies concerning how

soft information related to intangibles should be disclosed in financial statements.

The methodology underlying this study is a comprehensive analysis of the extent of

disclosure on intangibles in annual reports. The disclosure study focuses on 36 annualreports made by Danish, Finnish, Norwegian and Swedish companies belonging to the

pharmaceutical, biotechnology or health care equipment & supplies industries during

1999. The reason to focus on annual reports is motivated by the argument that this

document is a good proxy for the level of voluntary disclosure a company provides

across all different forms of disclosure (Botosan, 1997).1The argument is supported

by Gelb (2002) and Lang and Lundholm (1993) who find the disclosure level in

annual reports to be positively correlated with the extent of disclosure provided via

other types of communication. The reason to focus on knowledge-intense companies

is motivated by these companies heavy reliance on intangibles in their value-creation

process (see, e.g. Holland, 2002). The choice to include companies from all the Nordiccountries enables an analysis of the potential existence of country specific disclosure

styles (see discussion related to hypothesis 3 in section 3.3).

The paper has the following disposition: The next section provides the reader with the

theoretical and empirical foundation upon which the study rests. The hypotheses to be

tested in the study are posed in section 1.3 along with a discussion on their theoretical

and empirical basis. Thereafter, in section 1.4, the reader is presented with a detailed

report on the research design and empirical methodology. In section 1.5, the empirical

results from the analyses of the annual reports are presented. The paper ends with a

discussion on the results and some concluding remarks along with suggestions for

future research.

1.2 Theoretical and empirical foundation

1.2.1 Information asymmetry

Since information asymmetry exists between the insiders of a company, i.e. the

management team and the outsiders of a company, i.e. the shareholders, voluntary

disclosure can be seen as originated from a principal-agent problem (Jensen andMeckling, 1976). Thus, to reduce the information asymmetry a company can choose

to disclose voluntary information that exceeds mandatory disclosure regulations

(Tasker, 1998).2

The information gap between insiders and outsiders is argued to be

especially wide when knowledge-intense companies are involved (Aboody and Lev,

1Considering the adoption by the Securities and Exchange Commission of new rules (SEC, 2000,

Regulation, FD, effective as of October 2000) against selective disclosure of significant information,

it can be expected that the importance of the annual report as a disclosure medium is going to

increase.2

See Marston and Shrives (1991) for a discussion on the differences between voluntary and required

(i.e. mandatory) disclosure.


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1999; Barth, Kasznik and McNichols, 1999). This is due to the importance intangibles

play for the value creation in knowledge-intense companies combined with the

difficulties outsiders are faced with trying to acquire and interpret information on

intangibles.

Barth, Kasznik and McNichols (1999) conclude that analyst coverage is much higher

for knowledge-intense companies than for traditional companies. They explain their

findings with the difficulties financial analysts encounter when they try to acquire and

interpret information related to intangibles. These difficulties result in more time

having to be spent on covering knowledge-intense companies. Analogous with these

findings, Amir, Lev and Sougiannis (1999) find that financial analysts contribution to

valuation is largest in high-tech industries characterised by a large proportion of

intangibles. These findings are explained by the fact that the informativeness of

financial statements is especially low in these industries and that financial analysts,

therefore, play an important role in acquiring the information needed for valuationdirectly from these companies management teams. Several studies have confirmed a

decreased value relevance of financial statements (Brown, Lo and Lys, 1999; Lev and

Zarowin, 1999). The results of these studies are often taken as an evidence of an

inadequacy of todays accounting methods to capture the whole value of knowledge-

intense companies (Hall, 2001; Barth, Kasznik and McNichols, 1999; Hall, 1993).

Sullivan and Sullivan (2000:328) argue that:

Traditional accounting methods, which were created to account for tangible assets, are

inadequate for valuing companies whose assets are largely intangible.

Thus, the presence of information asymmetry between the insiders and outsiders of acompany especially significant for knowledge-intense companies risks impairing

the efficient allocation of capital due to larger bid-ask spreads, higher average cost of

capital and more illiquid capital markets (FASB, 2001a; Diamond and Verrecchia,

1991).

1.2.2 Cost-benefit analysis

Although more informative disclosures are found to result in a more effective

allocation of capital due to reduced information asymmetry (see, e.g. Botosan and

Plumlee, 2002; Sengupta, 1998; Welker, 1995) perfectly informative disclosures will

probably never be achieved. There is a non-negligible trade-off between supplying

and withholding information, i.e. the choice between exceeding mandatory disclosure

regulations by disclosing voluntary information or only disclosing mandatory

information. Thus, designing a disclosure strategy calls for performing a cost-benefit

analysis.3 Considering the company and its owners, a lower average cost of capital,

more liquid capital markets with smaller bid-ask spreads resulting in smoother

valuation, better investment decisions, enhanced credibility and improved investor

relations are argued to be the prime benefits stemming from enhanced disclosure (see,

3See Elliott and Jacobson (1994) for a comprehensive discussion on costs and benefits related to

business information disclosure.


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e.g. FASB, 2001a; Holland, 1997; Lang and Lundholm, 1996;Elliott and Jacobson,

1994).

Proprietary costs is put forward as the chief factor on the cost side (Wallman, 1996;

Gray, Meek and Roberts, 1995). Proprietary costs arise when a company disclosesinformation, which is sensitive and may result in competitive disadvantage due to

increased competition or government regulations. Johanson, Mrtensson and Skoog

(2001) find that companies emphasise that their extent of disclosure on intangibles is

decided by their business-protection policy. According to Elliott and Jacobson (1994)

there are three types of information, which might create competitive disadvantage: (1)

information about technological and managerial innovations, (2) strategies, plans and

tactics and (3) information about operations. Holland (1997) concludes from his case

study that companies regard information on corporate innovations to be particularly

sensitive in a disadvantage perspective. Analogous with Hollands (1997) findings,

Meek, Roberts and Gray (1995) argue that companies with substantial R&D activities(e.g. chemical companies) are likely to be more sensitive about disclosing information

than companies in other industries are. Besides proprietary costs, information

production costs associated with gathering, processing and disseminating information

appear on the cost side.

1.2.3 Earlier empirical studies

There is a body of empirical studies analysing the disclosure in annual reports (see,

e.g. Ahmed and Courtis, 1999; Marston and Shrives, 1991). The focus in these

disclosure studies varies from only considering voluntary information (Adrem, 1999;

Gray, Meek and Roberts, 1995; Chow and Wong-Boren, 1987) to wider perspective

where both voluntary and mandatory information are considered (Inchausti, 1997;

Choi, 1973; Singhvi and Desai, 1971). Some studies examine the extent of disclosure

in one specific country (Bukh, Gormsen, Mouritsen and Nielsen, 2002; Inchausti,

1997; Hossain, Perera and Rahman, 1995; Cooke, 1989b), while other studies

compare the extent of disclosure in different countries (Gray, Meek and Roberts,

1995; Barrett, 1976). In common to all disclosure studies is that they share the notion

that informative disclosures are useful for the investment-decision process (Inchausti,

1997) a notion, which the present study also rests upon.

Following Cookes (1989b) argument it is assumed in the present study that acompany has found the benefits to exceed the costs when voluntary disclosure on

intangibles are made. Although disclosure studies usually focus on the extent of

voluntary disclosure (e.g. Gray, Meek and Roberts, 1995; Hossain, Perera and

Rahman, 1995; Cooke, 1989b), there is a lack of studies, which have used a checklist

consisting of items exclusively related to intangibles. In an analysis of IPO

prospectuses, Bukh, Gormsen, Mourtisen and Nielsen (2002) conclude that the

disclosure on intangibles has increased substantially between 1990 and 2000.

Williams (2001:201)4

arrives at the same conclusion when he analyses annual reports

4 Williams (2001:192) analyses annual reports over the period 1996-2000.


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from 31 U.K. companies listed on FTSE 100. In Bukh, Gormsen, Mourtisen and

Nielsens (2002) study the most comprehensive disclosures on intangibles are found

in the IPO prospectuses of knowledge-intense companies. This is argued to support

the notion that companies with a large proportion of intangibles, as compared to

companies with a small proportion of intangibles, are more prone to discloseinformation on intangibles in order to reduce the information gap and, thus, decrease

the information asymmetry present between insiders and outsiders. This notion is

empirically supported by Gelb (2002) who finds that companies with higher levels of

intangibles are the ones, which to a greater extent rely on voluntary disclosure.

Thus, the present study differs from prior disclosure studies in that: firstly, it examines

the extent of disclosure on intangibles, instead of, as usually, examining the overall

extent of voluntary disclosure, secondly, the disclosure on intangibles is broken down

into five different categories of intangibles, thirdly, the potential existence of country

specific disclosure styles is examined by including companies from all the Nordiccountries, fourthly, instead of including companies from miscellaneous industries the

study exclusively focuses on companies belonging to knowledge-intense industries.

1.3 Determinants of the extent of disclosure on

intangibles

To examinewhat determines the extent of disclosure on intangibles, four hypotheses

are posed. The hypotheses are based on four company related factors, which

considering the sample structure, the literature on intangibles and earlier disclosurestudies are relevant for inclusion in the regressions. Each of the four company related

factors, which are tested as determinants of the extent of disclosure on intangibles are

discussed in relation to them being a demand-driven determinant, i.e. demand from

actors on the capital market drives the extent of disclosure or a supply-driven

determinant, i.e. characteristics of the company drive the extent of disclosure. Figure

1.1 presents an outline of how the extent of disclosure on intangibles is both demand

and supply driven. Thus, the extent of disclosure is the result of pull and push forces,

i.e. the actors on the capital market pull information from the company, while the

companypushes information on to the capital market.

Figure 1.1 How demand and supply drive the extent of disclosureSupply-driven

Push

Demand-driven

Pull

Capitalmarket

Company


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Company related factor 1: Company size

Many different arguments have been proposed as reasons for why company size is

found to be positively related to the extent of disclosure. Agency theory (Jensen and

Meckling, 1976) is probably the most commonly used approach for deriving the sizehypothesis. The underlying notion is that agency costs increase with company size and

that more extensive disclosures is a way to reduce information asymmetry between the

management team and the companys owners and, thereby, reduce agency costs (see

Marston and Shrives, 1996). Cooke (1989b) can be seen to elaborate further on the

reduced information asymmetry-approach when he argues that larger companies

have more complex business structures (e.g. a large number of business divisions,

several product lines and global sales) and that this explains why they are prone to

have extensive disclosures. Thus, companies with complex structures simply have

more information to disclose. Meek, Roberts and Gray (1995) put forward lower

information production costs as yet another potential explanation to why larger

companies disclose more information in their annual reports than smaller companiesdo. Considering the theoretical origin of the size hypothesis, size could be argued to

be both a demand- and a supply-driven determinant. While the objective to reduce

agency costs and information asymmetry primarily is demand driven, the complexity

of a companys business structure, as well as its information-production costs are

supply driven. In view of the theoretical arguments underlying the size hypothesis, the

following hypothesis is posed:

Hypothesis 1: Larger companies disclose more on intangibles than smaller companies

do.

Considering earlier disclosure studies, company size5 appears to be the most

frequently tested determinant of the extent of disclosure. Williams (2001) did not find

a relationship between size and the extent of disclosure on intangibles. The results of

studies where the extent of overall6 disclosure has been examined do, however,

strongly suggest that size is positively related to disclosure extent (see, e.g. Inchausti,

1997; Hossain, Perera and Rahman, 1995; Cooke, 1989b, 1989c; Chow and Wong-

Boren, 1987; Singhvi and Desai, 1971).7

Company related factor 2: Leverage

Following the argument that agency costs increase with leverage (Jensen andMeckling, 1976), companies with high leverage are expected to disclose more

5Company size has been operationalised in several different ways, e.g. total assets (Cooke, 1989c;

Singhvi and Desai, 1971), logarithm of assets (Inchausti, 1997; Hossain, Perera and Rahman, 1995),

total sales (Adrem, 1999; Meek, Roberts and Gray, 1995), logarithm of sales (Inchausti, 1997),

market value of equity plus book value of debt (Chow and Wang-Boren, 1987) and number of

shareholders (Cooke, 1989c).6

The term overall disclosure is used to define disclosure, which is not exclusively focused on

intangibles.7

See Ahmed and Courtis (1999) and Marston and Shrives (1996) for comprehensive reviews of the

results of studies, which have tested company size as a determinant of the extent of disclosure.


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information than companies with low leverage. Their propensity to increase their

disclosure is driven by a wish to reduce information asymmetry and, thereby, reduce

agency costs. Due to increased financial risk, the demand from e.g. lenders,

shareholders, authorities and employees, for disclosure increases with a companys

leverage. All of these parties have some sort of claim on the company, e.g. a loan,invested capital or salaries/pensions. With high leverage, the probability of the

company getting into financial distress increases, which risks the value of the claims.

Demanding more extensive disclosures is a way for external parties to assess a

companys financial risk. Thus, leverage could be argued to primarily be a demand-

driven determinant. The theoretical arguments underlying the leverage hypothesis are

relevant to test and the following hypothesis is posed:

Hypothesis 2: High leveraged companies disclose more on intangibles than low

leveraged companies do.

Considering earlier disclosure studies the support for a positive relationship betweenthe extent of disclosure and leverage is weak. While Inchausti (1997) and Chow and

Wong-Boren, (1987) find no relationship between leverage and the extent of overall

disclosure, Hossain, Perera and Rahman (1995) find a weak positive relationship. A

weak positive relationship has also been found between leverage and the extent of

disclosure on intangibles (Williams, 2001).

Company related factor 3: Country affiliation

Swedish companies are regarded as precursors when it comes to disclose information

on intangibles (Bukh, Larsen and Mourtisen, 2001; FASB, 2001b). The relatively longtradition Swedish companies have with disclosing information on intangibles in

annual reports could mean that their disclosures, with respect to intangibles, are more

extensive than the disclosures made by companies in the other Nordic countries are.

Since the focus on intangibles has a relatively long tradition in Sweden, Swedish

companies could be expected to have been exposed to a large demand for disclosure

on intangibles, which has influenced the emphasis they place on intangibles in their

disclosures. Thus, country affiliation could be argued to primarily be a demand-driven

determinant. Following the above line of reasoning, the following hypothesis is posed:

Hypothesis 3: Swedish companies disclose more on intangibles than companies from

the other Nordic countries do.

The relationship between country affiliation Sweden and the extent of disclosure on

intangibles has not been tested in earlier disclosure studies.

Company related factor 4: Listing status

Like the size and leverage hypotheses, the listing-status hypothesis is often derived

from agency theory (Jensen and Meckling, 1976).For example, Cooke (1989c) argues

that monitoring costs are higher for companies with multiple quotations due to the fact

that these companies usually have a greater number of shareholders and that increased


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disclosure is one way to reduce monitoring costs and, thus, minimise agency

problems. The notions that internationally listed companies, as compared to only-

domestically listed, are exposed to more extensive listing requirements (Cooke,

1989c; Singhvi and Desai, 1971) and additional capital-market pressure (Meek,

Roberts and Gray, 1995; Cooke, 1989b) have also been used as support for the listing-status hypothesis. It could be argued that a company does not have to be

internationally listed to be exposed to extensive capital-market pressure. A company

with a large share of foreign owners could be expected to be equally exposed to

capital-market pressure. Testing listing status as a determinant does, however,

include the disclosure pressure stemming from listing requirements.8 Taken together,

listing status could be argued to primarily be a demand-driven determinant. The

listing-status hypothesis originates from listing requirements and capital-market

pressure, which both exercise a demand for disclosure. Since the sample included in

the present study consists of both internationally and only-domestically listed

companies, the following hypothesis is posed:

Hypothesis 4: Internationally listed companies disclose more on intangibles than only-

domestically listed companies do.

Reviewing the results of earlier studies reveals that international listing is positively

related to the extent of overall disclosure (see, e.g. Inchausti, 1997; Hossain, Perera

and Rahman, 1995; Cooke9, 1989b; 1989c). A positive, however, inconclusive

relationship is found between listing status and the extent of disclosure on intangibles

(Williams, 2001).

1.4 Research design and empirical methodology

1.4.1 Selection criteria for companies

The disclosure study focuses on annual reports made by Nordic companies belonging

to knowledge-intense industries. In order for a company to be included in the study,

the following selection criteria had to be fulfilled:

Danish, Finnish, Icelandic, Norwegian or Swedish companies belonging to thepharmaceutical, biotechnology or health care equipment & supplies industries.1011

8Reviewing my sample shows that companies, which are internationally listed have a larger share of

foreign owners than companies only-domestically listed have. Thus, since the two determinants are

positively correlated, testing share of foreign owners as a determinant of extent of disclosure on

intangibles would probably yield similar results as testing listing status.9

In Cooke (1989b; 1989c) both listed and unlisted companies have been included in the samples. His

findings show that companies, which are internationally listed have more extensive disclosures than

only-domestically listed companies have. Furthermore, only-domestically listed companies have

more extensive disclosures than unlisted companies have.10 Annual reports from four sub-Nordic companies have been included in the study.


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Listed on either of the Nordic countries Stock Exchanges during 199912.

The selection process resulted in 36 companies (19 Swedish, 11 Danish, 4 Norwegian

and 2 Finnish). There were no Icelandic companies, which fulfilled the imposedselection criteria. See Appendix 1 for a list of the 36 companies from which annual

reports13

were included in the study.

1.4.2 Disclosure checklist

When it comes to analyse the extent of voluntary disclosure, previous research has

shown that a disclosure index is a useful research instrument (Marston and Shrives,

1991). A disclosure index is based on a disclosure checklist, which includes a number

of different items. The number of items included in a disclosure checklist varies

substantially from one study to another. For example, Barret (1976) used a checklist

of 17 items, while Cooke (1989a) included 224 items in his checklist.

To develop the checklist used in the present study an explorative qualitative approach

was applied. A review of the literature on intangibles was conducted to examine,

which different categories of intangibles are most frequently discussed (see

Arvidsson, 2002). Then, in order to distinguish all of the different categories of

intangibles, briefly or elaborately, discussed in the annual reports, the annual reports

were read and re-read twice. The analysis identified that the annual reports disclosed

information related to 5 categories of intangibles. The analysis also distinguished a

number of different items, which were discussed in relation to each of the 5

categories. The next step in developing the checklist, involved a review of checklistsused in earlier disclosure studies.14

The checklists, which were most influential on the

design of the checklist used in the present study were the ones used in Bukh,

Gormsen, Mouritsen and Nielsen (2002), Adrem (1999) and Gray, Meek and Roberts

(1995). The final version of the disclosure checklist includes 81 items related to

intangibles, which are categorised into 5 categories (see Appendix 2). Thus, by

11The companies industry codes have been collected either from the Stock Exchanges GICS codes or

the Stock Exchanges own coding systems. The Copenhagen Stock Exchange, the Oslo Stock

Exchange and the Stockholm Stock Exchange all use GICS (i.e. Global Industry Classification

Standard) codes to classify stocks into different industries. The GICS coding system is developed by

MorganStanley Capital International Inc. and Standard & Poors. The Helsinki Stock Exchange andthe Reykjavik Stock Exchange use their own coding systems.

12At the time of data collection, the latest annual reports available were the once covering the financial

year 1999. Four of the included annual reports cover the split financial year 1998/1999.13

The analysis primarily focused on the English language version of the 1999 annual reports. However,

2 of the 36 analysed annual reports were only published in a non-English language version (i.e. two

annual reports were only published in Swedish). The choice to focus on the English language version

of the reports was intentional since it reduces the potential of a conceptual confusion originated from

an analysis including reports written in different languages. A conceptual confusion would risk

affecting the stringency of the results from the analysis.14

The following studies were reviewed in the process of designing the checklist: Bukh, Gormsen,

Mouritsen and Nielsen, 2002; Adrem, 1999; Inchausti, 1997; Gray, Meek and Roberts, 1995;

Hossain, Perera and Rahman, 1995; Meek, Roberts and Gray, 1995; Cooke, 1989a; 1989b; Chow

and Wong-Boren, 1987; Barrett, 1976; Choi, 1973; Singhvi and Desai, 1971.


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reviewing the literature on intangibles, reading annual reports and examining earlier

disclosure checklists, the checklist was developed to be valid in the analysis of the

extent of disclosure on intangibles in annual reports.

The disclosure checklist, see Figure 1.2, has a hierarchical structure with two levels,i.e. the aggregated level represented by the total disclosure score and the level below

represented by the five categories. In earlier dated disclosure studies it has been most

common to focus the analysis of disclosure to one (aggregated) level, i.e. the total

disclosure score (Chow and Wong-Boren, 1987; Barrett, 1976). However, a

hierarchical structure enables a more refined analysis of the extent of disclosure. Gray,

Meek and Robert (1995) introduced a hierarchical structure of their disclosure

checklist, which enabled them to conduct an analysis on two levels. Adrem (1999)

refined the analysis further when he used a three-level structure in his checklist. The

notion underlying the use of a hierarchical structure is that the decision relevance of

information varies by type and that the variables affecting the choice of disclosureextent also may vary by information type (Meek, Roberts and Gray, 1995).

Figure 1.2 The structure of the disclosure checklist

Although each of the five categories measures a common entity, i.e. intangibles, they

represent five distinct categories of intangibles. The categories and their items have

been carefully reviewed to minimise the probability of double counting.15 The first

level of the disclosure checklist is represented by the five categories, which are

labelled Human, Relational, Organisational, R&D and Environ/Social,

respectively. The category Human consists of items focused on information related

to board members, directors of the management team and employees. Items related to

recruitment policy and competence development program are also included in the

category Human. Relational focuses on information related to a companys

relationships with, e.g. partners, suppliers, distributors, customers and the public. The

category Organisational- consists of items focused on information on knowledgesharing, IT, organisational routines and processes. R&D includes items, which focus

on information concerning, e.g. R&D operations, R&D projects, product portfolio and

patents. The final category is labelled Environ/Social and consists of items related to

a companys policy and agenda for engaging in ethical, environment friendly and

socially responsible actions as well as the outcome of these efforts.

15In order to determine if the checklist consists of five distinctcategories withseparate items, the

checklist was also reviewed by two independent researchers. Their conclusions were that the

categories are related, however, distinct and that the probability of double counting was negligible.

Total DisclosureScore

(81 items)

Human(28 items)

Relational

(16 items)

Organisational

(11 items)

R&D

(15 items)

Environ/Social

(11 items)


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On the second level of the disclosure checklist, all disclosure items originated from

the five categories are aggregated to a total disclosure score. Thus, the total disclosure

score is a measure of the extent of overall disclosure on intangibles in each of the

annual reports.

1.4.3 Scoring procedure16

The disclosure checklist was used to examine the content of the entire annual report. If

the report disclosed information on an item it was assigned 1, otherwise 0. Companies

were not penalised if they did not disclose information on an item, which was

irrelevant with respect to their business activities (see Adrem, 1999, Hossain, Perera

and Rahman, 1995; Cooke, 1989b). To assess if an item was relevant or irrelevant to a

particular company the annual report was studied in detail before the scoring

procedure was initiated.17

Thus, the disclosure score for each annual report is additive and unweighted. Using an

unweighted scoring technique assumes that each item is of equal importance. By using

this technique the subjectivity otherwise involved in assigning weights to the different

items when user preferences are unknown is reduced (Adrem, 1999; Gray, Meek and

Roberts, 1995). Due to the critique against using a weighted scoring technique, an

additive and unweighted scoring technique has been most commonly used in earlier

disclosure studies (see, e.g. Inchausti, 1997; Gray, Meek and Roberts, 1995; Cooke,

1989b). Courtis (1996) goes one step further and concludes that an unweighted

scoring technique has become the norm in this type of studies.

For each of the annual reports, the disclosure score was calculated as the number of

items disclosed in the report divided with the total number of items relevant to the

particular company, which the report covers:

Dj =

=

jn

i j

ji

n

d

1

whereDj is total disclosure score for companyj, dijis disclosure item i, which is 1 if

the item is disclosed and 0 otherwise and nj is the total number of items relevant for

companyj, which the report covers.

16To ensure the reliability of the study, i.e. that it is apt for replication the disclosure checklist

underlying the analysis is presented in its full version in Appendix 2 along with a list of the sample

companies (Appendix 1). Although the 81 items are carefully specified, practical problems do arise

in the scoring procedure. To mitigate these problems detailed scoring instructions and comments on

the actual score related to each item have been drawn up. These scoring instructions and comments

can be requested from the author.17

For example, if a company only has R&D projects in pre-clinical stages it was not penalised for not

disclosing information on items related to product portfolio.


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1.4.4. Analysis of reliability and structure of the disclosure

scores

To test the internal consistency of the disclosure scores (SPSS, 1994), the reliability

test Cronbachs alpha (Cronbach, 1951)was used. Cronbachs alpha, measures howwell a set of items, in this case the five categories, measures a common entity, i.e.

intangibles (SPSS, 1994). The test is based on the average correlation among items

within a test (Nunnally and Bernstein, 1994). Thus, the logic behind the test is that if

the inter-correlations among the items are high the items measure the same underlying

construct. A Cronbachs alpha coefficient of 0.60-0.70 or higher indicates that there is

an internal consistency in the disclosure scores.18 The following formula is used to

calculate Cronbachs alpha:

rN

rN

*)1(1

*

+

=

where N is equal to the number of items and ris the average inter-item correlation

among these items.

1.4.5 Regression analysis

To examine what determines the extent of disclosure on intangibles, fourhypotheses

were posed in section 3.3. The four company related factors underlying the hypotheses

are: Company size, Leverage, Country affiliation: Sweden and Listing status. The four

hypotheses were tested using OLS regressions.19

To analyse if and how a companys extent of overall disclosure on intangibles is

related to the company related factors, the total disclosure score was used as

dependent variable in the multivariate regression.20

Following Meek, Roberts and

Grays (1995) argument that the factors affecting the choice of disclosure extent may

vary by information type, the hierarchical structure of the checklist was used to

examine if and how the extent of disclosure related to each of the five categories (i.e.

Human, Relational, Organisational, R&D and Environ/Social) were affected by the

company related factors. Thus, five additional multivariate regressions were run where

each of the five categories disclosure scores was used as dependent variable. The

same four company related factors were used as independent variables in all themultivariate regressions. The choice to use the same independent variables for all

different types of information is the common approach used in earlier disclosure

studies (see, e.g. Adrem, 1999; Meek, Gray and Roberts, 1995).

18According to Sureshchandar, Rajendran and Anantharaman (2002), a Cronbachs alpha of 0.70 and

above testifies strong scale reliability. Liouville and Bayad (1998) use the threshold 0.60 to

determine strong scale reliability.19

In the regression analysis the regressions are run under the assumption of a super population of

Nordic knowledge-intense companies over time.20 The disclosure scores are assumed to disclose interval scale properties.


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1.4.6 Operationalisation of company related factors

The company related factors, which were used as independent variables in the

regressions, were operationalised as follows:

Company size (SIZE) was operationalised as the logarithm of total assets in 1999.21

Leverage (LEV) was measured as the ratio of total liabilities to equity in 1999.22

Country affiliation: Sweden (D1Country) was indicated by a dummy variable, which

took the value 1 if the company was Swedish and 0 otherwise. Listing status(D2Listing)

was indicated by a dummy variable, which took the value 1 if the company was

internationally listed and 0 if it was only listed on the domestic market.

1.5 Empirical results

In this section, the empirical results from the analyses of the annual reports are

presented. The section starts with a presentation of the results from the disclosure

scoring. Then the reliability and structure of the disclosure scores are examined.

Finally, the disclosure scores are analysed with OLS regressions in order to test the

four hypotheses.

1.5.1 Disclosure scores

Table 1.1 summarises the results of the disclosure scores for the full sample and for

the four sub-samples Swedish companies, Danish companies, Norwegian companiesand Finnish companies, respectively.

21The logarithm of total sales was also tested as a size variable. There was, however, no significant

difference in the results of the regressions when the logarithm of total assets was replaced with the

logarithm of total sales.22

Considering that securitiesed debt normally involves stricter information requirements than regular

debt, securitiesed debt, e.g. bond issues could be expected to be more related to the extent of

disclosure on intangibles than leverage operationalised as total liabilities to equity. Reviewing my

sample, however, shows that very few of the companies have securitiesed debt, why such an analysis

would be of no consequence.


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Table 1.1 Summary of disclosure scores

Human Relational Organisational R&D Environ/Social Total

Swedish companies (N=19)

Highest 0,929 0,875 1,000 1,000 0,636 0,875

Lowest 0,308 0,250 0,200 0,545 0,000 0,328

Average 0,495 0,632 0,512 0,789 0,243 0,546

Median 0,500 0,667 0,400 0,769 0,182 0,514

Danish companies (N=11)

Highest 0,500 0,750 0,727 0,923 0,818 0,639

Lowest 0,154 0,222 0,000 0,500 0,000 0,288

Average 0,287 0,519 0,421 0,664 0,338 0,425

Median 0,259 0,500 0,455 0,667 0,222 0,425

Norwegian companies (N=4)

Highest 0,536 0,769 0,636 0,929 0,818 0,667

Lowest 0,111 0,500 0,100 0,786 0,111 0,377

Average 0,330 0,645 0,423 0,875 0,343 0,509

Median 0,336 0,656 0,477 0,893 0,222 0,496

Finnish companies (N=2)

Highest 0,630 0,750 0,700 0,818 0,222 0,644

Lowest 0,407 0,583 0,545 0,800 0,222 0,500

Average 0,519 0,667 0,623 0,809 0,222 0,572

Median 0,519 0,667 0,623 0,809 0,222 0,572

Full sample (N=36)

Highest 0,929 0,875 1,000 1,000 0,818 0,875

Lowest 0,111 0,222 0,000 0,500 0,000 0,288

Average 0,415 0,601 0,481 0,761 0,282 0,506

Median 0,415 0,620 0,477 0,760 0,222 0,503

1.5.1.1 Disclosure scores: Full sample

Considering the full sample, the companies disclose on average 50.6 percent of the

items in the checklist. To determine if this is a high or low disclosure percentage, the

results were compared to the disclosure scores in Bukh, Gormsen, Mouritsen and

Nielsen (2002). Although the Danish study focuses on IPO prospectuses and the

present study focuses on annual reports, the comparison of the two studies disclosure

percentages should provide relevant information about the disclosure scores. The

comparison focused on 37 items, which are included in both the present checklist and

the disclosure checklist used by Bukh, Gormsen, Mouritsen and Nielsen (2002) on 68

Danish IPO prospectuses. The companies included in the present study do on average

disclose 50.4 percent of the 37 items. In the Danish study, the companies, which

belong to both knowledge-intense (pharmaceutical, research, IT and technology) and

traditional industries (trade and production), disclose on average 24.9 percent of the


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37 items. Separating out companies in the Danish study, which belong to the

pharmaceutical and research23

industries, gives a disclosure percentage of 35.9. The

comparison shows that the disclosure percentage for the present sample is

significantly higher than the percentages reported in Bukh, Gormsen, Mouritsen and

Nielsen (2002). Thus, the comparison results in to indications.First, knowledge-intense companies appear to disclose more on intangibles than traditional companies

do. Second, Danish knowledge-intense companies appear to disclose less on

intangibles relative Nordic knowledge-intense companies do.

The category, which the companies disclose definitely most information on is R&D

where 76.1 percent of the items on average is accounted for in the annual reports. This

result is predictable since the companies have their affiliation in knowledge-intense

industries where R&D is the predominant business activity. The result is in line with

the study conducted by Bukh, Gormsen, Mouritsen and Nielsen (2002) in which they

found pharmaceutical and research companies to be the ones, which are best atdisclosing information related to R&D.

Relational is the category, which the companies disclose second most information on.

Reviewing the disclosure scores in Table 1.1 shows that 60.1 percent of the items

related to a companys relationships with, e.g. other companies, suppliers and

customers on average is accounted for in the annual reports.

The companies do not appear to prioritise disclosure on items related to the

Organisational category. Neither does employee-related information appear to be high

up on the management teams disclosure agenda. Thus, the scoring results indicate

that companies are not very good at following up the old adage the employees areour most valuable asset with disclosure related to their employees.

The results of the disclosure scores reveal that the companies disclose least

information on items related to environmental and social responsibility. Considering

the environmental trend, which resulted in an increase in companies environmental

reporting in the beginning of the 1990th (see, e.g. Ljungdahl, 1999), the result is

somewhat unexpected. The result is, however, consistent with Gray, Javad, Power and

Sinclair (2001) who conclude, after analysing the content in annual reports from 31

U.K. companies listed on FTSE 100, that the extent of social and environmental

disclosure is relatively small.

1.5.1.2 Disclosure scores: Sub-samples

Both in the full sample and in the Swedish and Finnish sub-samples are R&D the

category with the highest disclosure scores followed by Relational, Organisational,

Human and Environ/Social. The ordering is the same in the Danish and Norwegian

sub-samples except for Human being the category with the lowest average disclosure

23The research category in Bukh, Gormsen, Mouritsen and Nielsen (2002) includes companies with

businesses in biotechnology and health care.


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scores and Environ/Social being the category with the second lowest average

disclosure scores.

It should be noted that the Finnish sub-sample only consists of two companies.24

These two companies are large in size and one of them is foreign-listed, whichfollowing the accuracy of the hypotheses should imply that they are good at disclosing

information on intangibles. Thus, the interpretation of the results of the Finnish sub-

sample should take this into consideration. Reviewing Table 1.1 reveals that the

Finnish sub-sample is exhibiting the highest total average disclosure scores followed

by the Swedish, Norwegian and Danish sub-samples. Also in the categories Human,

Relational and Organisational do the Finnish companies disclose most information.

Although they appear to master the disclosure technique, they are the ones with the

lowest scores in the Environ/Social category.

The Danish sub-sample is not only the one, which has the lowest total average scorebut it also positions itself as the sub-sample, which discloses least information related

to the Human, Relational, Organisational and R&D categories. These results

contradict the argument that Danish companies should have informative disclosures

on intangibles (see Bukh, Rosenkrands Johansen, Garca Meca and Mourtisen, 2002).

1.5.2 Reliability and structure of the disclosure scores

A calculation of Cronbachs alpha for all the five categories resulted in a coefficient of

0.68. Considering the rule of 0.60-0.70 or higher, the result is somewhat inconclusive

and the coefficient might indicate that the disclosure scores have a mild tendency

towards a multidimensional structure.

To examine the structure of the disclosure score a factor analysis was conducted. The

Kaiser-Meyer-Olkin measure (KMO) was calculated to determine how adequate the

sample is for a factor analysis. For the present sample the value of KMO is 0.60,

which indicates a mediocre sampling adequacy (SPSS, 1993). Thus, the KMO

suggests that the results of the factor analysis might be indistinct. Considering the

value of the Cronbachs alpha-coefficient it is, however, relevant to proceed with the

analysis of the disclosure scores.

Table 1.2 Correlation matrix

Human Relational Organisational R&D Environ/Social

Human 1,000

Relational 0,416 1,000

Organisational 0,483 0,566 1,000

R&D 0,335 0,348 -0,025 1,000

Environ/Social 0,189 0,330 0,543 -0,090 1,000

24The choice to include the two Finnish companies in the study is motivated by the fact that they are

contributing with valuable information related to the company factors (i.e. company size, leverage,

country affiliation and listing status), which are analysed in the regressions.


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Table 1.3 Unrotated and rotated factor matrix

Unrotateda

Rotateda,b

Factor Factor Cummunalities

1 2 1 2Human 0,716 0,307 0,391 0,674 0,607

Relational 0,807 0,163 0,550 0,613 0,678

Organisational 0,836 -0,337 0,872 0,229 0,813

R&D 0,330 0,840 -0,238 0,871 0,815

Environ/Social 0,613 -0,565 -0,829 -0,086 0,695

Eigenvalue 2,349 1,260 1,960 1,649

% of variance 47,0 25,2 39,2 33,0

a) Extraction method: Principal component analysis

b) Rotation method: Varimax with Kaiser normalisation

The factor analysis on the disclosure scores of the five categories resulted in two

factors25, which together explain 72.2 percent of total variance. The unrotated factor

matrix (see Table 1.3) reveals that the four categories Human, Relational,

Organisational and Environ/Social are strongly correlated to factor 1, while R&D only

exhibits a weak correlation with factor 1. Instead, R&D is the category, which exhibits

the strongest correlation with factor 2. The results are a bit blurred when a rotated

factor analysis is run (see Table 1.3). R&D is still the category with the strongest

correlation with factor 2. Organisational and Environ/Social exhibits a stronger

correlation with factor 1 and a weaker correlation with factor 2. However, in the

rotated factor analysis Human and Relational both exhibit a weaker correlation withfactor 1 and a stronger correlation with factor 2, which makes the results less distinct.

Although the results of the factor analysis are somewhat indistinct there is an

underlying trend in the results implying that the R&D category is causing the mild

tendency towards a multidimensional structure in the disclosure scores.

Thus, to further examine the structure in the results of the disclosure scores a cluster

analysis, based on Wards method, was conducted. The analysis reveals that there are

three distinct clusters. The clusters are neither country-specific nor is their

distinctiveness related to company related factors. Instead the distinctiveness of the

clusters is related to disclosure styles. A prediction value of 92 percent was obtained

when a step-wise discriminant analysis was run. When prior probabilities werecomputed from group sizes, 13 of the 15 companies in cluster 1 were correctly

classified, 7 of the 8 companies in cluster 2 were correctly classified and all of the

companies in cluster 3 were correctly classified. Table 1.4presents the descriptive

data for the three clusters regarding their disclosure scores in the five categories.

25The number of factors was chosen according to the Kaiser-Guttman rule, which states that a factor

should have an eigenvalue exceeding 1 (see Nunnally and Bernstein, 1994).


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Table 1.4 Summary of cluster analysis

Human Relational Organisational R&D Environ/Social

Cluster 1 (N=15)

Highest 0,607 0,813 0,636 1,000 0,818

Lowest 0,154 0,462 0,100 0,636 0,000

Average 0,408 0,649 0,423 0,845 0,227

Median 0,423 0,625 0,400 0,857 0,182

Cluster 2 (N=8)

Highest 0,500 0,500 0,455 0,923 0,222

Lowest 0,111 0,222 0,000 0,545 0,000

Average 0,278 0,398 0,207 0,707 0,068

Median 0,290 0,441 0,200 0,667 0,000

Cluster 3 (N=13)

Highest 0,929 0,875 1,000 1,000 0,818

Lowest 0,231 0,500 0,455 0,500 0,182

Average 0,506 0,670 0,715 0,699 0,477

Median 0,500 0,667 0,727 0,692 0,545

Full sample (N=36)

Highest 0,929 0,875 1,000 1,000 0,818

Lowest 0,111 0,222 0,000 0,500 0,000

Average 0,415 0,601 0,481 0,761 0,282

Median 0,415 0,620 0,477 0,760 0,222

Companies in cluster 1 are the ones, which are best at disclosing information related

to category R&D. Considering the other four categories, these companies disclose

around average compared to full sample average. Cluster 2 is characterised by

companies, which are well below average when it comes to disclose information.

Their deficiency in disclosing information applies to all five categories. It can,

however, be noted that the category, which they are best at disclosing information on

is R&D were they are only moderately below average. Compared to the other two

clusters, companies in cluster 3 are characterised as being the ones, which are best at

disclosing information related to the four categories Human, Relational,Organisational and Environ/Social. For all of these categories they disclose above

average (i.e. Relational) or well above average (i.e. Human, Organisational and

Environ/Social). Also in cluster 3, R&D is the category, which stands out. This cluster

includes those companies, which are worst at disclosing information related to R&D.26

Thus, the results from the cluster analysis indicate that the difference in disclosure

styles is especially apparent when the R&D category is concerned. Furthermore,

26The term around average is used to define a deviation from full sample average with less than 20

percent. Well below/above average is used to define a deviation from full sample average with

more than 20 percent.


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Cronbachs alpha is improved from 0.68 to 0.73 when it is calculated for all categories

except R&D. This further strengthens the notion that the R&D category might be

causing the mild tendency of a multidimensional structure in the disclosure scores.

1.5.3 Factors influencing the disclosure scores

The correlations between the independent variables are presented in Table 1.5. The

table shows that company size is significantly correlated with listing status. No other

significant correlations appear between the independent variables. Thus, studying the

correlation matrix does not imply that there should be any problem of isolating the

influence of the different independent variables in the regression analysis.

Table 1.5 Correlation between the independent variables

SIZE LEV DCountry DListing

SIZE 1

LEV 0,328 1

DCountry -0,114 -0,065 1

DListing 0,434** 0,214 -0,047 1

** Correlation is significant at the 0.01 significant level (2-tailed)

Table 1.6presents the results from the multivariate regressions where total disclosure

score and the score from each of the five categories, respectively are used as

dependent variables.


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Table 1.6 Summary of results from multivariate analyses

The multivariate regressions27

are run as:

SCOREi = + 1SIZE+ 2LEV+ 3D1Country+4D2Listing + i

where SCOREi is company is total score and its score for each of the five categories, respectively (i.e.Human, Relational, Organisational, R&D and Environ/Social) and the independent variables are: SIZE

= logarithm of total assets in 1999 for company i, LEV= company is ratio of total liabilities to book

equity in 1999, D1Country = dummy variable, 1 if company i is Swedish and 0 otherwise and D2Listing=

dummy variable, 1 if company i is internationally listed and 0 if it is only domestically listed.

The regressions are run with White-adjusted standard errors28

Independent variables

Dependent

variable

Intercept SIZE LEV D1Country D2Listing Adjusted R2

Total score 0,1730*

(1,513)

0,0214**

(2,541)

-0,0276

(-0,768)

0,0888**

(2,525)

0,0380

(0,875)

0,187

Human 0,3070**

(1,973)

0,0034

(0,302)

-0,0469

(-1,111)

0,1580***

(3,278)

0,0623

(1,203)

0,172

Relational 0,2187*

(1,337)

0,0262**

(2,342)

-0,0442

(-1,044)

0,0738*

(1,613)

0,0409

(1,092)

0,127

Organisational -0,4926**

(-1,950)

0,0677***

(3,979)

-0,0292

(-0,747)

0,0996*

(1,476)

-0,0209

(-0,230)

0,314

R&D 0,9581***

(7,169)

-0,0164**

(-1,767)

-0,0436

(-0,982)

0,0554

(1,284)

0,1310**

(2,460)

0,099

Environ/Social -0,6346**(-2,568)

0,0670***(3,647)

0,0661(0,945)

-0,0546(-0,767)

-0,1307(-1,169)

0,274

Note: The t-values are presented in the parentheses

*** Significant at the 1% level for one-tailed tests** Significant at the 5% level for one-tailed tests

* Significant at the 10% level for one-tailed tests

N = 3529

Multivariate regression: Total Score

The result of the multivariate regression, where Total score was dependent variable, is

presented on the first row of Table 1.6. Reviewing the result shows that the

coefficients of SIZE and D1Country are positive and significantly different from zero.This strengthens the support for hypothesis 1 and 3, which assumed company size and

country affiliation Sweden to be positively related to the extent of disclosure on

intangibles. The coefficients ofLEV and D2Listing are both insignificant and do,

thereby, not lend any support to hypotheses 2 and 4, i.e. that high leveraged

27Univariate regressions were also run. The results were consistent with the results from the

multivariate regressions.28

To control for the presence of heteroskedasticity, the White-adjusted standard errors were calculated

(White, 1980). The White-adjusted standard errors did not affect the significance of the results.29Due to negative equity, one of the 36 companies had to be excluded from the regressions.


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companies and internationally listed companies disclose more on intangibles than low

leveraged and only-domestically listed companies do.

The evidence that larger companies disclose more on intangibles than smaller

companies do is not consistent with the result in Williamss (2001) study onintangibles where no relationship was found. The result is, however, consistent with

earlier disclosure studies, which have examined the effect size has on the extent of

overall disclosure (Hossain, Perera and Rahman, 1995; Cooke, 1989b;1989c; Chow

and Wong-Boren, 1987).

For some of the years in the period 1996-2000, Williams (2001) found leverage to be

positively related to the extent of disclosure on intangibles. In the present study,

leverage does not appear to be influential. Also this result is, however, in line with

earlier studies, where the relationship between leverage and overall disclosure extent

has been non-existent or weak (Inchausti, 1997; Hossain, Perera and Rahman, 1995;Chow and Wong-Boren, 1987).

The argument that Swedish companies are precursors when it comes to disclose

information on intangibles (Bukh, Larsen and Mourtisen, 2001; FASB, 2001b) is

confirmed in the present study.

Although listing status is found to be positively related both to the extent of disclosure

on intangibles (Williams, 2001) and to the extent of overall disclosure (Inchausti,

1997; Hoasain, Perera and Rahman, 1995; Cooke, 1989b;1989c), the result from the

multivariate regression does not indicate that there is a relationship between listing

status and the extent of disclosure on intangibles. Considering that Sweden is arguedto be the place where disclosures on intangibles are originated from, a possible

explanation could be that the demand for disclosures on intangibles is greater in the

Nordic countries than in the U.K. and in the U.S. and that this makes the listing-status

hypothesis less relevant when Nordic countries and disclosures on intangibles are

concerned.

Multivariate regression: Human

D1Country is the only coefficient, which is significantly different from zero when the

multivariate regression is run with Human as dependent variable. The coefficient is

positive and significant on the 1% significant level. Thus, the result suggests thatSwedish companies disclose more information on items related to board members, the

management team and employees than companies in the other Nordic countries do.

An explanation to the result might be that human resource costing and accounting

(Grjer and Johanson, 1996), has a long tradition in Sweden and that this has

influenced companies to place a greater emphasis on employee-related disclosure.

Multivariate regression: Relational

The result of the multivariate regression run with Relationalas dependent variable

(row 3 of Table 1.6) is similar to the result when Total score was dependent variable,

i.e. the coefficients ofSIZEandD1Country are positive and significantly different from


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zero.D1Country is, however, only significant on the 10% significant level as compared

to the 5% level in the total score regression. Thus, large companies and Swedish

companies appear to have more extensive disclosures on items related to a companys

relationships with its, e.g. owners, partners, suppliers and customers than smaller

companies and non-Swedish companies have. The size effect might be explained bylarger companies having more ongoing projects, collaboration agreements and

alliances with external parties than smaller companies have. Consequently, they have

more information to disclose on items related to the Relational category.

Multivariate regression: Organisational

The SIZE-coefficient is significantly positive on the 1% significant level when the

multivariate regression is run with Organisational as dependent variable. D1Country is

the only other coefficient, which is significantly different from zero. The dummy

variable is, however, only significant on the 10% significant level. Thus, like with the

regressions run with Total score and Relational, respectively as dependent variable,SIZEandD1Country are the only two coefficients significantly different from zero. The

result suggests that large companies disclose more on items in the Organisational

category than smaller companies do. This result could probably be expected since

large companies tend to have a complex organisational structure with many different

divisions located in different countries or even on different continents. To be able to

run this type of company it is vital to have clear policies on organisational routines

and processes as well as systems for communication and knowledge sharing. Thus,

larger companies may disclose more on items in the Organisational category simply

because organisational routines, processes and systems are more developed than they

are in smaller companies with organisational structures less complex.

Multivariate regression: R&D

Considering the regression result, the extent of disclosure on R&D appears to be

positively related to a companys listing status. The coefficientD2Listing, which is

significant on the 5% significant level, indicates that an internationally listed company

discloses more information related to R&D than a company only listed on its domestic

stock exchange does. The result also suggests that there is a significant and negative

relationship between company size and disclosure on R&D.30 The listing effect might

be explained by the fact that companies, which are internationally listed are likely to

issue informative disclosures on their R&D-activities since they are not so well known

in the country where they are foreign-listed as they are in their home country. Smallercompanies might have informative disclosures on their R&D-activities for the same

reason, i.e. a small companys business activities are usually not so well known as the

business activities of a large company, which is more visible in the media. Thus, this

might explain why smaller companies are more prone to disclose information on their

R&D activities in annual reports.

30To check the robustness of the results a univariate regression was run with R&D as the dependent

variable and SIZEas the independent variable. There was no significant difference in the results,

which implies that the results are robust.


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Multivariate regression: Environ/Social

The multivariate regression shows that the extent of disclosure on items related to the

Environ/Social category is positively related to company size. The size coefficient,

which is the only coefficient significantly different from zero, is significant on the 1%significant level. This result is consistent with earlier studies, which have found

company size to be positively related to the extent of social and environmental

disclosure (Gray, Javad, Power and Sinclair, 2001; Adams, Hill and Roberts, 1998;

Hackston and Milne, 1996; Trotman and Bradley, 1981). The result can be explained

with larger companies being more scrutinised and watched by the media, the

government and pressure groups than smaller companies are. The annual report might

be regarded an important media to use for communicating that they are conducting

businesses in compliance with the societys environmental, social and ethical code.

Thus, Environ/Social disclosure might be seen as a way for large companies to

achieve an environmental, social and ethical certificate.

1.6 Discussion and concluding remarks

The present study provides evidence that the management teams use the opportunity

to voluntarily supply outsiders with information on intangibles. The extent of

disclosure on intangibles in annual reports is, however, not overwhelming. Although

the value-creation process in knowledge-intense companies is argued to rely heavily

on intangibles only half of the items (50.6 percent) in the checklist is on average

disclosed in the annual reports. To determine if the disclosure percentage was high or

low, the results were compared to the disclosure scores in Bukh, Gormsen, Mouritsenand Nielsen (2002). The comparison focused on 37 items, which were included in

both studies checklists. The results of the comparison showed that the disclosure

percentage for the present sample (50.4) is significantly higher than the percentages

reported in Bukh, Gormsen, Mouritsen and Nielsen (2002). The disclosure percentage

(50.4) was not only higher than the disclosure percentage for the Danish total sample

(24.9), which includes both knowledge-intense and traditional companies, but also

higher than the Danish sub-sample (35.9), which exclusively includes companies

belonging to the pharmaceutical and research industries. Thus, the results of the

comparison indicate that the disclosure percentage in the present study is relatively

high. Furthermore, the results lend credence to the notion that companies belonging to

knowledge-intense industries disclose more on intangibles than traditional companies

do.

Four hypotheses were posed to test the relationship between company related factors,

i.e. size, leverage, country affiliation and listing-status, and the extent of disclosure on

intangibles.31

Consistent with the hypothesis, company size was found to be positively

related to the extent of disclosure on intangibles. This result is not consistent with

Williams (2001:200) who found no relationship between size and extent of disclosure

31The present section refers to the results of the multivariate regression, which were run with Total

score as dependent variable.


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on intangibles. The result is, however, in line with earlier studies, which have

examined the size effect on the extent of overall disclosure (Hossain, Perera and

Rahman, 1995; Cooke, 1989b;1989c; Chow and Wong-Boren, 1987). The regression

result did not lend support to the hypothesis that high leveraged companies disclose

more information on intangibles than low leveraged companies do. Also this result isinconsistent with Williamss (2001) findings and consistent with the findings in earlier

studies on the extent of overall disclosure (Inchausti, 1997; Hossain, Perera and

Rahman, 1995; Chow and Wong-Boren, 1987). The hypothesis that Swedish

companies disclose more on intangibles than companies from the other Nordic

countries do was confirmed in the regression. This may in part be explained by

Swedens relatively long tradition with disclosing voluntary information on

intangibles. A tradition, which with justice it seems has earned them the epithet

precursors. Inconsistent with both the hypothesis and with earlier disclosure studies,

internationally listed companies were not found to disclose more information on

intangibles than only-domestically listed companies. Following the notion thatdisclosures on intangibles are originated from Sweden, a possible explanation could

be that the demand for disclosures on intangibles is greater in the Nordic countries

than in the U.K. and in the U.S. Thus, the listing-status hypothesis might be less

relevant when Nordic countries and disclosures on intangibles are concerned.

Also in the regressions, which were run with each of the five categories disclosure

scores as dependent variable, did size and country affiliation Sweden appear to be the

two independent variables, which are most related to the extent of disclosure in the

five categories of intangibles. This lends further support to the hypotheses stating that

larger companies and companies from Sweden disclose more on intangibles than

smaller and non-Swedish companies do irrespective of if the overall disclosure onintangibles or a specific category of intangibles is at focus.

The disclosure scores revealed that R&D is the category, which the knowledge-intense

companies disclose most information on in their annual reports. Considering that

R&D is the predominant business activity in companies belonging to high-tech

industries, the result lends support to the notion that industry membership exerts an

influence on a companys disclosure focus (Meek, Roberts and Gray, 1995). Their

argument that companies with substantial R&D activities are likely to be more

sensitive about disclosing information than companies in other industries are is,

however, refuted in the present study. Instead the result is in line with Lang and

Lundholms (1993) argument that R&D-intense companies are prone to disclose

information on R&D to reduce the information asymmetry between the management

team and the investors. Like R&D, information related to relationships with suppliers,

customers and partners also appears to be deemed highly relevant when the

management teams design their disclosures on intangibles. The result is probably a

direct outcome of the increasing trend in knowledge-intense industries towards

engaging in collaboration agreements with, e.g. other companies, suppliers and

customers. Thus, with the increasing prevalence of collaborative activities comes a

motive for companies to supply more information related to their collaborations, e.g.

choice of partners, purpose and effect of the collaboration. This motive is likely to be

intensified by a greater demand from the actors on the capital market for detailed


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information on the companies collaboration activities. Social and environmental

disclosure does not appear to be prioritised. Although the result is consistent with

earlier studies (Gray, Javad, Power and Sinclair, 2001) it is somewhat unexpected

considering the prevailing social and environmental trend. The disclosure scores also

reveal that the management teams do not seem to follow up the old adage theemployees are our most valuable asset with information related to this high-valued

asset.

The descriptive analysis of the disclosure scores indicates that there are differences,

both in type and extent of disclosure on intangibles, between the companies in the four

Nordic countries. The Finnish companies have the most extensive disclosures on

intangibles followed by the Swedish, Norwegian and Danish companies. While the

Finnish companies are the ones with the highest average scores in the categories

Human, Relational and Organisational, the Danish companies are the ones with the

lowest average scores in all categories except Environ/Social. The disclosure scoresrevealed that all of the four Nordic sub-samples disclose most information related to

the R&D category followed by the categories Relational and Organisational. Human

and Environ/Social appear to be the less prioritised disclosure categories.

When the internal consistency of the disclosure scores was tested there appeared to be

a mild tendency towards a multidimensional structure. Further analyses indicated that

the R&D category might be causing the mild tendency of a multidimensional structure

in the disclosure scores. This pattern was also present in the results of the cluster

analysis. The cluster analysis revealed that the companies position themselves in three

distinct clusters, which exhibit different disclosure styles. The clusters are neither

country-specific nor is their distinctiveness related to company related factors.Companies in cluster 1 are the ones, which are best at disclosing information on R&D.

Considering the other four categories of intangibles they disclose about average

compared to full sample. Cluster 2 is characterised by companies, which are well

below average when it comes to disclose information on intangibles. Their deficiency

applies to all categories. Their disclosure on R&D is, though, only moderately below

average. Companies in cluster 3 are the ones, which are best at disclosing information

related to the categories Human, Relational, Organisational and Environ/Social. They

are, however, the ones with the worst disclosures on R&D.

After conducting the present study, some suggestions concerning the design of future

studies have arisen. First, considering that the interest in intangibles has gradually

increased since the beginning of the 1990th

, it would be relvant to conduct a

longitudinal study to examine if and how the extent of disclosure on intangibles in

annual reports has changed over time. Second, to determine if a companys extent and

style of disclosure are industry specific, the present checklist could be used on

companies belonging to other industries than knowledge-intense. Third, the checklist

could be used on other types of documents. Since the information process surrounding

the valuation of companies involves both a demand side, i.e. the actors on the capital

market and a supply side, i.e. the management teams, it would be relevant to also

examine the emphasis the demand side places on intangibles. Especially relevant

would be to examine if the disclosure in analyst reports focuses on the same categories


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of intangibles as the annual reports were found do. This would give a clue to which

categories of intangibles financial analysts regard as important when they make

company valuations. Besides being relevant for deciding if there is an information gap

between the demand side and the supply side concerning intangibles, the result of this

type of study could be useful to management teams when they design their disclosureson intangibles.Fourth, although the choice to use the same independent variables for

all different types of information is the common approach used in earlier disclosure

studies, it would be relevant for future studies to include additional factors, which

could be thought of as explaining the extent of disclosure in each of the five

categories. For example, number of collaborations to explain disclosure extent in

Relational and number of ISO-certificate to explain disclosure extent in

Environ/Social.


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