Intellectual capital disclosure (ICD)

Intellectual capital disclosure(ICD)

A comparison of Italy and the UK

Saverio BozzolanUniversity of Padova, Padova, Italy

Philip O’ReganUniversity of Limerick, Limerick, Ireland, and

Federica RicceriUniversity of Padova, Padova, Italy

Abstract

Purpose – To explore the hypothesis that differences in intellectual capital disclosure (ICD) practicescan be explained, if in part, by industrial sector (traditional; knowledge intensive) and nationality oforigin (Italy; UK).

Design/methodology/approach – Content analysis of the annual reports of two reasonablymatched samples of both high-technology and traditional non-financial firms in Italy and the UK.Univariate and multivariate analyses are then used to test the hypothesis proposed.

Findings – Size and industrial sector are found to be predictors of levels of ICD; the hypothesisrelating nationality of origin to ICD is not supported.

Research limitations/implications – The main limitation relates to sample size due to the onerousnature of this form of research. Further research following this matched-sample methodology shouldattempt to maximise sample sizes allowing for the incorporation of more specific nationally of originfactors.

Practical implications – Owing to the increasing importance of intangibles and intellectual capital,how these are reported is of interest to a large range of stakeholders. There is, as yet, no universallyaccepted form, or indeed regulation, of ICD.

Originality/value – The matched-sample methodology on international ICD comparison expandson extant approaches.

Keywords Intellectual capital, Industrial services, Italy, United Kingdom

Paper type Research paper

1. IntroductionA primary objective of corporate disclosure is to satisfy the information needs of users ina manner that enables both decision-making and accountability (Grojer and Johanson,1998; Guthrie and Petty, 2000a; Sveiby, 2001; Eccles et al., 2001; Verrecchia, 2001;

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Earlier versions of this paper have been presented at McMaster World Congress 2004, FinancialReporting and Business Communication Conference 2004 (Cardiff Business School), IrishAccounting and Finance Association Conference 2005.

The authors appreciate the helpful comments on various versions of this paper by JamesGuthrie, Vivien Beattie (discussant), Sergio Beretta, David O’Donnell and Birgitta Olson (editor).Saverio Bozzolan and Federica Ricceri gratefully acknowledge the financial support provided bythe University of Padova (Progetto di Ricerca di Ateneo 2003).

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Journal of Human Resource Costing &AccountingVol. 10 No. 2, 2006pp. 92-113q Emerald Group Publishing Limited1401-338XDOI 10.1108/14013380610703111

Watson et al., 2002; Firer and Williams, 2003). Viewed as critical in the functioning ofcapital markets, both theoretical and empirical studies (Healy and Palepu, 2001; Healyet al., 1999) confirm this role: reduction in the costs of equity (Botosan, 1997; Botosan andPlumlee, 2002) and debt (Sengupta, 1998); improvement in share performance not relatedto current and expected earnings (Healy et al., 1999); and higher correlation betweenstock price and future earnings compared to companies with low disclosure levels (Gelband Zarowin, 2000).

The traditional financial reporting model is inadequate in meeting the informationneeds of users (Francis and Schipper, 1999) as its usefulness, measured by theassociation between accounting data and capital market values, has decreasedsubstantially over the past 20 years (Lev and Zarowin, 1999). The increasingcomplexity of business strategies, operations and regulations makes it difficult tointerpret financial information on its own without some accompanying explanations(Marston and Shrives, 1991a, b). Communicating long-term value generatingcapabilities should be reinforced by increasing the amount of information disclosedwith regard to a firm’s perspectives on future performance and on the sustainability ofits current value creation drivers (Watson et al., 2002).

In this context, the use of the “story” is central to improving disclosure and someform of narrative should form a central element in public disclosures (Holland, 2004).Narrative information is an important means not only of clarifying and validatingquantitative financial measures but is particularly important in disclosing informationabout critical success factors, related performance indicators (Mouritsen et al., 2001)and those value creation drivers not represented in financial statements (Lev andZarowin, 1999).

Intellectual capital (IC) and intangible assets in general pose real challenges forgovernments, regulators and firms (Mouritsen et al., 2001; O’Regan et al., 2001). A keychallenge is the need to identify theoretical and practical solutions to the recognition,measurement and reporting of intangible assets, processes and potentials notheretofore tracked by traditional accounting metrics (Abeysekera, 2006; Ducharme,1998; Guthrie and Petty, 2000a; van der Meer-Kooistra and Zijlstra, 2001) – hence theemergence of intellectual capital disclosure (ICD) theory and practice within which thispaper is situated.

Information asymmetries are driven by factors such as the intensity of demand forinformation by users (private and institutional investors, financial intermediaries, etc.),the intensity of IC resources considered strategic in the realisation of the firm’sbusiness model or in sustaining competitive advantage, the characteristics of financialmarkets as sources of financial resources as well as the competition between firms inraising such financial resources, and the requirements of different societal legalsystems and regulatory bodies on the nature of reporting practices (La Porta et al.,1997; Lev and Zarowin, 1999; Ball et al., 2000). The resulting information asymmetriessignificantly impact investors’ risk perceptions and compromise the capacity ofknowledge intensive companies to generate funding (van der Meer-Kooistra andZijlstra, 2001; Dutch Ministry of Economic Affairs, 1998).

In addressing such complexity, this paper compares ICD of 30 matched firms inItaly and in the UK. The methodology is content analysis of the annual reports of these60 firms followed by univariate and multivariate analysis. The argument presented isthat differences in ICD practices can be explained, if in part, by industrial sector

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(traditional; knowledge intensive) and by nationality of origin (Italy; UK). The first ICDcomparison explored here relates to its type and the second to its extent. Italy and theUK are deemed appropriate to this exploration of cross-national ICD variation becauseof their respective characteristics:

. Italy has a civil/code legal system whereas the UK is the common law exemplar.

. The Italian stock market is relatively small whereas the London Stock Exchange(LSE) is one of the largest in the world.

. Italian listed companies are characterised by few institutional investors incontrast to their significant presence in the UK.

. UK regulatory and professional bodies place increasing emphasis on the type ofinformation requested by the market and what companies should disclose,whereas their Italian equivalents have yet to do so.

The remainder of the paper is structured as follows. Section 2 offers an overview on theemergent ICD phenomenon and discusses some prior empirical research. The possibleinfluences of corporate and national level or societal characteristics on disclosurepractices in general is explored in Section 3 leading to the proposal of the two mainhypothesis. This is followed by a discussion on the unique methodology employed inSection 4. The empirical findings are then presented and briefly discussed in Section 5.The paper concludes with a discussion of some implications of the findings, thelimitations of the study and some suggestions for future work from the methodologicalperspective adopted here.

2. Developments in intellectual capital disclosureIC and intangible assets pose complex information asymmetry challenges forgovernments, regulators, practitioners and academics. Different national solutions tothe recognition, measurement and reporting of those “soft assets” not tracked byfinancial accounting increases the information asymmetries among investors andbetween investors and managers thereby compromising the capacity of companies toattract funds. Action by governments and regulators to promote greater corporate ICDhas been identified as one critical initiative that would enable firms to monitorperformance and better address such information asymmetries (Blair and Wallman,2000).

In this context, several standard setters and professional bodies have attempted tofoster improved business reporting by adopting a user focus, i.e. by investigating theinformation needs of investors and other stakeholders (AICPA, 1994). One branch ofthis work has focused on the use of various non-financial, forward-looking disclosuressuch as benchmarks, narrative disclosures and other indicators as a means ofrecognising and measuring such resources (IFAC, 1998; FASB, 2001). While recentdecades have seen some gradual convergence in accounting practice and disclosurecultures, scope still exists for considerable variation across national boundaries(Vanstraelen et al., 2003) and at corporate levels, particularly in the area of voluntarydisclosures. Factors such as national business culture and legal origin continue to be ofinterest to accounting researchers investigating variations in disclosure practicesacross national and regional boundaries (Healy and Palepu, 2001).

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The Financial Reporting Committee of the Institute of Chartered Accountants inEngland and Wales (ICAEW) issued a series of discussion papers on human and ICaimed at helping management to make key aspects of a company’s capabilities moretransparent to investors (ICAEW, 2000a, b). Predicated on the notion that it is necessaryto supplement traditional performance measures with narrative disclosure andindicative measures of future potential, these have encouraged enhanced disclosureabout key business risks and how these risks are managed and measured (IASC, 1998;FASB, 2001). In broadly similar vein, recent initiatives in relation to the introduction ofan equivalent of the US Management Discussion and Analysis (MD&A) suggest anincreasing awareness of the importance of supplementary narrative information ontrends and factors underlying an entity’s development, performance and position.

In a European context, the effects of differences in reporting practices may havebeen mediated, to some extent, by various EU Directives, dual-market listings andthe gradual adoption of global accounting standards (Gonzalo and Gallizo, 1992). InJune 2000, for example, the European Commission determined that all EU listedcompanies should adopt International Financial Reporting Standards (IFRS) by 2005 inpreparing their consolidated accounts. In October 2005, the International AccountingStandards Board (IASB) issued a discussion paper on the “Management Commentary”,a report that supplements and complements financial information, providing insightinto an entity’s performance. Similarly, IAS 38 “Intangible Assets” provides for somedisclosure of IC elements in the annual report. At the same time, national disclosurerequirements have been increasing significantly as various regulatory, statutory andgovernance initiatives have sought to ensure greater transparency and accountabilityin response to financial scandals. Thus, the characteristics of the information requestedby market regulatory bodies might differ across countries while the presence andimportance of institutional investors would be expected to impact the level andnature of information disclosed (Bushman and Smith, 2001). Notwithstanding allthese recent developments “most of [a firm’s] IC resources remain undisclosed”(van der Meer-Kooistra and Zijlstra, 2001, p. 457).

3. Possible influences on intellectual capital disclosure: corporate andnationalWhat influences disclosure and ICD? This question is addressed here at two levels;corporate/industry level and national level. Ahmed and Courtis (1999) in theirmeta-analysis of 29 studies on corporate disclosure practices identify size, audit firmsize, capital intensity, profitability, foreign listing, internationality, and ownershipstructure as key variables of interest. The empirical evidence has sometimes shown aclear positive relationship (size; capital intensity; foreign listing; internationality;ownership structure) and sometimes a mixed relationship (leverage, audit firm size,profitability) with levels of disclosure.

Also, Subbarao and Zeghal (1997) in an international comparison of human capitalinformation disclosure in annual reports, highlighted that companies in differentcountries differed in their disclosure behaviour.

With specific reference to the ICD literature, several studies have investigated thequantity and nature of information voluntarily reported in various countries.For instance, ICD has been analysed within Australia (Guthrie and Petty, 2000b),Sweden (Olsson, 2001), Ireland (Brennan, 2001), Canada (Bontis, 2003), Italy


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(Bozzolan et al., 2003), Spain (Meca et al., 2003), Hong Kong (Guthrie et al., 2006) andSri Lanka (Abeysekera and Guthrie, 2004, 2005). These studies have mainly focussedon the content and amount of ICD.

Within the ICD literature, some prior studies have considered, at a country level,various corporate characteristics in explaining the extent of disclosure (Bozzolan et al.,2003; Meca et al., 2003). Even allowing for different data sources (annual reports; andpresentations prepared for analysts’ meetings), these studies find size and industry to besignificant explanatory variables. This result is consistent with evidence providedwithin the corporate disclosure literature (Ahmed and Courtis, 1999). Moreover, in theSpanish study, profitability, listing status, and leverage were found to be significantexplanatory variables. There are risks in disclosing information in the business worldand the consequent pressures to keep a firm’s IC resources undisclosed or to voluntarilydisclose them are, we argue, driven by industry sector. Each sector has its own uniqueintangible asset base, business model and core competitive resources (Holland, 2004).For pragmatic reasons we cannot test all corporate level variables here but we expectthat knowledge intensive firms will report greater ICD than more traditional firms due totheir perceived greater reliance on intangible assets. This leads to our first hypothesis:

H1. The level of ICD (measured as the percentage of disclosures about internal,external, and human capital) is influenced by industry type (traditional vsknowledge intensive)

Moving from the corporate level, the idea that different countries may be characterisedby different reporting behaviours is not new (Ahmed and Courtis, 1999). Dye (1985b;1986) demonstrates that voluntary disclosure is affected by disclosure requirements.International accounting literature identifies macro- or national-level influences suchas culture (Jaggi and Low, 2000) and legal systems (Hope, 2003), historicaldevelopments, government regulation, professional influence and taxation cultures(Adhikari et al., 1998; Meek and Gray, 1989; Ball et al., 2000) as explanatory variableson disclosure practices. Zarzeski (1996), for instance, finds that cultural values, as wellas market forces, have a significant impact on financial disclosure practices in sevenindustrialised countries. Consequently, the role of these macro- or national-levelvariables continues to be of interest to accounting researchers investigating variationsin disclosure practices across national and regional boundaries (Ahmed and Courtis,1999; Healy and Palepu, 2001; Vanstraelen et al., 2003).

In several studies, legal origin has been identified as a critical issue partly because itconstitutes the underlying or “primitive” (Hope, 2003, p. 222) factor that most impactsothers (Gray, 1988; Gray and Roberts, 1989; Ball et al., 2000; La Porta et al., 1997). Inparticular, a country’s classification as one whose accounting culture has emergedfrom either a common/canon law or civil/code law context – often used as a proxy forpolitical influence on accounting (Ball et al., 2000) – has been shown to significantlyreflect disclosure policy (La Porta et al., 1997; Ball et al., 2000; Hope, 2003) includingvoluntary disclosure (Vanstraelen et al., 2003). For example, in the accounting systemsof common law countries, accounting income is disclosed in a timelier fashion than incode law countries (Ball et al., 2000).

The argument most commonly summoned to explain the impact of these common/code legal system origins derives from the agency perspective (Ball et al., 2000;Hope, 2003). In common law countries the predominant governance structure, based on

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capital markets and the presence of numerous investors, means that firms deal withtheir shareholders at “arms length”. This produces a high demand for information from“anonymous” investors at a distance (Ball et al., 2000). In this context, the legal systemhas developed structures and procedures that protect the information requirements ofinstitutional investors and other external stakeholders (Adhikari et al., 1998; Gernonand Meek, 2001). In code law countries, where there is a greater level of insider (Hope,2003) and crossover (Ball et al. 2000) ownership by entities such as banks, there is moreimmediate access to information either directly from management or throughparticipation at board level (Jaggi and Low, 2000; Hope, 2003). Compared to commonlaw countries, there is less demand for public disclosure and accounting disclosure ismore likely to be influenced by the payout preferences of stakeholders (Ball et al., 2000).

Ownership concentration across countries varies inversely with the quality of acountry’s accounting disclosures (La Porta et al., 1997; Kothari, 2001; Bushman andSmith, 2001).

Demand, and, therefore, supply of quality information will be high if corporations are bestdescribed as owned by widely dispersed, individually atomistic shareholders. High-qualityinvestor protection laws, good enforcement of these laws, and a common-law legal systemcollectively are conducive to diffusely owned corporations (Kothari, 2001, p. 90).

In these circumstances, disclosures by companies with a diffuse ownership, i.e. those ina common law country (such as the UK), will be expected to be greater (Kothari, 2001;Hope, 2003).

From this discussion, we expect that nationality of origin will be related to the levelof ICD as firms in common law countries (UK) are expected to disclose more than firmslocated in civil law countries (Italy). This leads us to propose the second hypothesis ofthis paper as follows:

H2. The level of ICD is influenced by nationality (UK levels will be higher thanItalian levels)

4. Research methodologyWe conducted cross sectional analyses on the association between the dispersion of thethree dimensions (external; internal; human) of ICD and industry (H1); and between thequantity of ICD and country of origin (H2). We obtained disclosure data throughapplying content analysis methodology to a sample of 30 firms listed on the MilanStock Exchange and 30 firms listed on the London Stock Exchange. Independent andcontrol variables were computed from Datastream Database, firm annual reports, andmarket data. Financial data of Italian companies were reported in Euro and UKcompanies in Sterling Pounds; all data were converted to Euro using the exchange rateat the closing date of the annual report considered in the analysis.

4.1 Dependent variablesThe research design is based on a measure of two characteristics of ICD (thepercentage of disclosures among its three categories and the extent of disclosures).We adopt a self-constructed measure simply because public disclosure scores orindices are not available on IC. We are, however, aware of the problems associated withthis type of disclosure measure (Healy and Palepu, 2001). This relies on the confidencethat disclosure measures truly capture what is intended. Even though some relevant


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limitations have been pointed out in the literature concerning replication of themeasurement process (reliability) and the ability of the suggested measures to trulycapture what is intended (validity) (Abeysekera, 2006; Healy and Palepu, 2001),self-constructed disclosure measures are still widely adopted and have proved to be avaluable research tool (Marston and Shrives, 1991a, b; Beattie and Thompson, 2005).

The data source considered in ICD analysis here is the annual report for 2001 for the60 companies concerned in Italy and the UK. Annual reports are chosen because theyare considered the most important source of information and the disclosure level inannual reports is positively correlated with the amount of disclosure information tothe market and to stakeholders using other media (Lang and Lundholm, 1993).

The method used to analyse ICD is content analysis (Guthrie et al., 2004) and thephrase is chosen as the recording unit. To identify and classify IC information, theframework proposed by Guthrie et al. (1999) and also applied by Bozzolan et al. (2003)and Guthrie et al. (2003) is adopted (Table I). Human capital is defined as theknowledge, competence, skill and experience that reside with employees and that gowith them on their person when they return home. Internal (structural) capital is theknowledge that stays within the firm even if employees who contribute to generatingthis knowledge leave the company. It is the knowledge captured in data bases, inorganisational procedures, and very often codified in managerial routines andprocesses. External (relational) capital is composed of the resources linked to theexternal relationships of the firm with suppliers, customers, partners, and competitors.

The first dependent variable measures differences in ICD amongst the firms. Itrefers to the distance between the firm’s percentage of ICD in each category and themedian computed over all firms. Three measures of distance from the median arecalculated as the absolute value of the difference between the observed percentage ofeach firm’s disclosure and the median disclosure behaviour. Each measure represents“how different” the percentage of ICD is in that item in relation to the “medianbehaviour” of the sample analysed (diff_int, diff_ext, diff_hc, respectively, for internal,external and human capital). The overall difference (diff_all) between firm disclosurebehaviour and median disclosure behaviour is computed by averaging the distancesobtained in the three IC categories. Therefore, if the variable diff_all equals 0, thismeans that the company discloses IC information according to the most commondisclosure behaviour of the sample; on the other hand, the more the variable diff_allmoves away from 0, the more the company’s disclosure behaviour diverges from themost diffused disclosure behaviour of the sample. The second dependent variable (icd)measures the extent of ICD. Previous literature differentiates alternative measures of

1. Internal structure (structural) 2. External structure (relational) 3. Human capitalIntellectual property 2.a Brands 3.a Know-how1.a Patents 2.b Customers 3.b Education1.b Copyrights 2.c Customer loyalty 3.c Employees1.c Trademarks 2.d Distribution channels 3.d Work related knowledgeInfrastructure assets 2.e Business collaboration 3.e Work related competence1.d Corporate culture 2.f Research collaborations1.e Management processes 2.g Financial contacts1.f Information systems 2.h Licensing agreements1.g Networking systems 2.i Franchising agreements1.h Research projects

Table I.The IC framework

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the extent of disclosure between disclosure indices and disclosure scores (Marston andShrives, 1991a, b). Usually, a disclosure index is computed using as denominator thetotal number of different relevant categories in which disclosures can be conducted(maximum potential score for a firm) and as numerator the number of categories inwhich a firm discloses at least one information item (actual score awarded to the firm).Consequently, the disclosure index varies between 0 and 1 and corresponds to thepercentage of categories in which a company offers at least one information item.Disclosure score measures the absolute number of information items communicated byattributing a score to each sentence/phrase in relation to the presence or the absence ofthe researched characteristic. Disclosure scores can be weighted or un-weighted. Forthe un-weighted disclosure score, the scoring procedure attributes 0 for non-disclosureand 1 for disclosure. Disclosure scores can be weighted, in order to appreciate not onlythe quantity of disclosure but also its quality, according to the importance attributedby different classes of users (Hooks et al., 2002), or the type of measure (qualitative vsquantitative) associated with the information disclosed (Botosan, 1997) or the extent ofdisclosure (Robb et al., 2001). In this study, the qnt variable is a weighted disclosurescore: each sentence is coded as no information (with a score of 0), as qualitativeinformation (with a score of 1), and as quantitative information (with a score of 2). Forinstance, the sentence, “The firm’s customers are drawn from a wide geographicalarea.” scores “1” under the sub-heading “customers” within “external capital.” Similaritems of information repeated in the annual report are only counted once.

A key issue with self-constructed disclosure measures is reliability. Three types ofreliability have been identified in the content analysis literature (Krippendorf, 1980):stability refers to the level to which a coding process is invariant over time,reproducibility deals with the assessment of coding errors when multiple coders areinvolved (inter-coder reliability) and accuracy compares the results of reliabilityobtained with a predefined standard. Because stability is a weak measure ofreliability and standards already exist, the most frequently reported measureof reliability is inter-coder reliability (Beattie and Thompson, 2005).

To verify inter-coders reliability, the three authors conducted coding after acoordination phase where a set of coding rules had been prepared and discussed.Afterwards each researcher proceeded with a separate coding of a sub-sample ofannual reports. Following this first phase, questionable points were discussed and newcoding rules introduced, either being better specified or rewritten. To quantify the levelof inter-coders reliability, the Scott’s p measure of reliability (Krippendorf, 1980) wascalculated and the result obtained (76 percent) at the category level is within the range(over 70 percent) considered acceptable in disclosure studies (Guthrie and Mathews,1985, p. 261; Boyatzis, 1998: 156).

4.2 Independent variables4.2.1 Size. Jensen and Meckling (1976) and Leftwich et al. (1981) suggest that largefirms have higher agency costs. Moreover, they are more visible than smaller firms andas a result are more exposed to public interest and political costs (Watts andZimmerman, 1986). Large firms are likely to be more complex and subject to greaterdemands for information from external sources (Singhvi and Desai, 1971). The largerthe firm the more likely it will be to have sophisticated information systems capable ofproducing data for internal and external reporting (Cooke, 1989).


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4.2.2 Leverage. Jensen and Meckling (1976) suggest that firms with high leveragesustain high monitoring costs and these can be reduced by a high level of disclosure.Chow and Wong-Boren (1987) claim that financial leverage effects voluntary disclosureby influencing the magnitude of agency costs; a more heavily leveraged firm has agreater need to satisfy the demand for information by its long-term creditors.

4.2.3 Ownership structure. Managers of firms whose ownership is diffuse have anincentive to disclose more information because it is a way of reducingmonitoring/agency costs (Raffournier, 1995). At the same time, the market demandsfor disclosure is lower for firms with a significant presence of insiders, especially ifthese insiders are from banks or institutional investors. Insiders reduce agency costsand, as a consequence, the incentives for managers to disclose information.

4.2.4 Industry. The technological and market constraints exerted by a competitiveindustrial environment deeply influence business models and the sources of sectoralcompetitive advantage. Accordingly, the literature provides complementaryinterpretation keys for explaining the industry effect on corporate disclosure. First,proprietary costs vary according to industry (Verrecchia, 1983). Second, firms arepressed to disclose industry-related information in their annual reports (Cooke, 1992)by external investors who need information on a company’s relative position in anindustry in order to assess company value (Dye, 1985a; Lev and Zarowin, 1999).Disclosure within an industry may also be shaped by the behaviour of a dominantcompany (Cooke, 1992). Furthermore, historical reasons may also be responsible forbandwagon effects (Cooke, 1989) or the international exposure of a particular industrymight affect the extent of disclosure (Raffournier, 1995).

4.2.5 Profitability. Signalling theory suggests that profitable firms are more likely todisclose more information to the market to differentiate themselves from poorerperformers. Singhvi and Desai (1971) argue that higher profitability represents anincentive to managers to disclose in order to increase the confidence of investors; Langand Lundholm (1993) contend, however, that this is true only when informationasymmetries are high. Empirical results about the effects of profitability on disclosure,however, remain contentious (Ahmed and Courtis, 1999).

4.3 MeasurementThis section describes how the variables are measured:

. Size is measured as the logarithm of the firm’s sales taken from Datastream. UKdata were translated to Euro at the exchange rate at the closing date of the annualreport. Sales was chosen because it is less affected by variation in accountingprinciples than the other most common financial measure of size (i.e. total assets)and it does not depend on the financial market as market capitalisation does.

. Country of origin is indicated using a 0,1 dummy to classify firms, respectively,into Italy and the UK.

. Firms are also classified according to traditional or knowledge intensive industryusing a 0,1 dummy. Knowledge intensive industries include internet applicationprovision, biotechnology, entertainment, IT distribution, high tech manufacturing,media, retail, software, systems integration, telecommunications, and web services.Traditional industries include sectors such as food, automobiles, chemicals,

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construction, electronics, manufacturing, oil, utilities, textile/clothing, andtourism/leisure.

. Profitability is measured by Return on Investment, computed by dividingoperating profit by invested capital, both drawn from Datastream.

. Leverage is calculated as the firm’s financial long-term debt to equity ratio, dataalso obtained from Datastream.

. Ownership structure is measured by the percentage of share capital owned byunknown shareholders, defined as those who possess less than 2 percent, forItalian firms, or 3 percent, for UK firms (substantial investors) of share capital.This difference derives from the availability of data obtained from the Milan andLondon Stock Exchanges.

4.4 Sample designThe main logic behind the sample design was to avoid bias in selection as much aspossible. The procedure used to draw the sample was based on random stratifiedsampling. First, all Italian listed firms were subdivided in two strata: traditional andknowledge intensive sectors. We identified knowledge intensive industries accordingto the definition of the Italian Stock Exchange: knowledge based sectors are those thatcompose the Nuovo Mercato, the Italian equivalent of the US NASDAQ and the UKTechMARK. Second, Italian firms were ordered by size and, following Camffermanand Cooke (2002), in order to avoid any issues related to capital market liquidity andefficiency, sales was used as the measure of size. Subsequently, Italian firms wereallocated to deciles strata according to this dimension and, finally, a random sample of30 firms was drawn: 20 firms from traditional sectors and ten from knowledge basedsectors.

All UK companies were allocated to deciles strata based on sales and then allocatedto traditional and knowledge intensive sectors based on the market in which they arelisted: TECHmark for knowledge based companies and MainMarket for traditionalcompanies. To construct a sample that permits comparison between Italian and UKfirms, we did a pairwise match. To determine the final sample, 20 traditional UKcompanies and the ten knowledge based UK companies were matched with Italiantraditional and knowledge based companies using relative size. We define relative sizein relation to the deciles strata based on sales: Italian and UK companies were,therefore, reasonably matched not only by industrial sector but also by relative size inthe financial market (Table II).

5. Data analysis and main findingsThe first stage of the analysis is to investigate the quantity and type of ICD. Table IIIpresents summary descriptive statistics on ICD.

On average, companies disclose 44.26 elements of IC information with a statisticallydifferent (t-statistic ¼ 22.81, p-value ¼ 0.0067) amount between traditional(35 elements) and knowledge intensive firms (63 elements). This difference can betraced in the main to ICD on external structure and not to internal structure and humancapital (Table IV). The difference in the extent of ICD between traditional andknowledge intensive firms is consistent with previous studies on ICD (Bozzolan et al.,2003) and is usually explained by the fact that knowledge based companies in most


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Name Log (sales) Sector

ItalyLa Gaiana 6.252 LPBonifica 6.684 LPAereoporto firenze 7.409 LPFilatura Pollone 7.410 LPStayer 7.612 LPRichard Ginori 7.702 LPGabetti 7.821 LPGarboli 7.892 LPTarghetti 8.108 LPCSP international 8.213 LPJuventus 8.239 LPCementir 8.356 LPMarangoni 8.398 LPAmplifon 8.557 LPAutostrade 8.623 LPDanieli 8.662 LPDalmine 8.994 LPAEM 9.046 LPMarzotto 9.245 LPFiat 10.763 LPNovuspharma 6.204 HPVitaminic 6.650 HPEL. En. 7.445 HPArt’e 7.591 HPDigital Bros 7.624 HPCad.it 7.785 HPPrima ind 8.049 HPEngineering 8.326 HPTiscali 8.789 HPEsprinet 8.904 HPUKGrosvenor 6.318 LPRonson 7.158 LPCaffe Nero 7.619 LPDensitron 7.667 LPShaftesbury 7.723 LPDerwent 7.876 LPVictrex 8.061 LPMaiden 8.107 LPBrixton 8.139 LPIFX Power 8.144 LPRoyal Doulton 8.424 LPLow & Bonar 8.439 LPRoxboro 8.447 LPDart 8.492 LPBodycote 8.884 LPSpectris 8.939 LPMayflower 9.015 LPKelda 9.107 LP

(continued )Table II.Matched-sample

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cases are characterised by new and more high risk business models in which ICconstitutes a key driver in the value creation process.

Looking at ICD by country of origin, Italian companies disclose on average 46 ICDitems while UK companies disclose 42. In comparing ICD between countriesstatistically significant differences do not emerge. If the total sample is split both bycountry of origin and industry it should also be noted that the average disclosure bothof the knowledge based (62 Italy, 64 UK) and traditional companies (38 Italy, 32 UK) isquite similar (due to the limited sub-sample size statistical tests are not performed).

Name Log (sales) Sector

Hunting Oil 9.219 LPCoats 9.311 LPAcambis 7.158 HPMicrogen 7.526 HPAxis Shield 7.838 HPAxon 7.838 HPSherwood 7.952 HPTorex 8.325 HPITNET 8.450 HPCelltech 8.686 HPSage Group 8.889 HPColt 9.161 HP Table II.

PercentilesMean Std. dev. 25 50 75

Panel A Overall disclosure 35.05 38.15 13 23 45Internal structure (percent) 0.286 0.188 0.176 0.274 0.355External structure (percent) 0.488 0.235 0.421 0.501 0.627Human capital (percent) 0.226 0.202 0.075 0.148 0.25

Panel B Overall disclosure 62.7 30.84 47 58 82Internal structure (percent) 0.271 0.222 0.127 0.168 0.424External structure (percent) 0.584 0.204 0.435 0.673 0.729Human capital (percent) 0.145 0.112 0.699 0.121 0.166

Panel C Overall disclosure 42.5 34.53 13 37 58Internal structure (percent) 0.261 0.185 0.153 0.221 0.35External structure (percent) 0.563 0.178 0.451 0.557 0.703Human capital (percent) 0.175 0.158 0.081 0.134 0.231

Panel D Overall disclosure 46.33 41.63 15 36 69Internal structure (percent) 0.301 0.212 0.156 0.257 0.5External structure (percent) 0.477 0.265 0.324 0.476 0.673Human capital (percent) 0.187 0.199 0.051 0.141 0.258

Total sample Overall disclosure 44.267 37.96 14 37 60Internal structure (percent) 0.281 0.198 0.155 0.231 0.381External structure (percent) 0.521 0.228 0.422 0.524 0.696Human capital (percent) 0.198 0.178 0.077 0.134 0.234

Notes: Panel A: traditional firms; panel B: knowledge based firms; panel C: UK firms; panel D: Italianfirms

Table III.Descriptive statistics


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With regard to the relative importance of the three categories of ICD (Table V), thefocus on external structure ranks highest (56.30 percent), then internal structure(26.58 percent) and finally human capital (17.12 percent). This pattern is broadlyconsistent for both countries. However, some differences emerge if we look at ICDbetween knowledge based and traditional companies[1].

Focusing on external capital disclosure (Table VI), ranking profiles are broadlysimilar for the two countries: information about customers is similar in Italy and UK;brands rank fourth in Italy and second in UK; and business collaborations rank secondin Italy, and third in UK. Others include customer loyalty, financial contacts, licensingagreements, franchising agreements and research collaboration. Internal ICD isconcentrated on research projects (particularly high for Italy), management processes,unique market knowledge, information systems and patents. Disclosure on humancapital is similar for both Italian and UK companies related to employees, theireducation and their work related competencies.

Table VII contains descriptive statistics while Table VIII provides a correlationanalysis of the dependent, independent and control variables. Correlation analysisreports that the extent of ICD (icd) is significantly correlated with size/sales (0.5153)and industry (0.3462). ICD is not significantly correlated with any other controlvariable. Moving to the variables used to measure how different ICD is among firms(diff_int, diff_ext, diff_hc, respectively, for internal, external and human capital anddiff_all for the overall difference), all are negatively correlated with size. This meansthat larger firms show a more similar pattern of ICD between the three IC categories

df Diff Std. Err. t p-value

Panel A vs panel B Overall disclosure 58 227.65 9.837 22.8106 0.0067Internal structure 58 25.05 3.921 21.2877 0.2030External structure 58 220.325 5.7918 23.5093 0.0009Human capital 58 22.275 2.2534 20.8978 0.3730

Panel C vs panel D Overall disclosure 58 3.5333 9.8758 0.3578 0.7218Internal structure 58 2.8333 3.7314 0.7593 0.4507External structure 58 21.06 6.0087 20.2663 0.791Human capital 58 2.3 2.3866 0.9637 0.3392

Notes: Panel A: traditional firms; panel B: knowledge based firms; panel C: UK firms; panel D: Italianfirms

Table IV.Two sample t-test

Italian firms UK firms

Internal capital (percent) Traditional firms 28.07 29.72 28.90Knowledge based firms 29.43 19.09 24.26

28.75 24.41 26.58External capital (percent) Traditional firms 50.13 52.99 51.56

Knowledge based firms 54.63 67.45 61.0452.38 60.22 56.30

Human capital (percent) Traditional firms 21.80 17.30 19.55Knowledge based firms 15.93 13.46 14.70

18.87 15.38 17.12

Table V.Distribution of ICelements

JHRCA10,2

104

than smaller firms. These correlations are statistically significant for all pairs exceptthe diff_int variable that measures the difference of disclosure behaviour amongcompanies in relation to internal capital.

The results of the OLS multiple regressions are reported in Table IX. In relation toH1, which states that the level of ICD (measured as the percentage of disclosures aboutinternal, external, and human capital) depends on industry type (traditional vsknowledge intensive), multivariate analysis does not reject it. In relation to model A1(dependent variable diff_all), among the control variables, only size is statisticallysignificant with a negative association with the dependent variable diff_all; implyingthat for larger companies disclosure behaviour is more similar. From the regressionmodel a negative relation is found here between industry (ind) and the distancebetween the firm disclosure behaviour and the median disclosure behaviour (diff_all)showing that industry is associated to different IC disclosure behaviour. As thedummy variable ind assumes value 0 for traditional firms and 1 for knowledge basedfirms, the regression model shows that ICD is more different from the median for firmsbelonging to knowledge based sectors than for firms in traditional sectors. It implies

Italy UKN Mean Percent Rank N Mean Percent Rank

External capitalBrands 106 3.53 14.72 4 247 8.23 32.16 2Customers 226 7.53 31.39 1 278 9.27 36.20 1Customer loyalty 12 0.40 1.67 8 25 0.83 3.26 6Distribution channels 124 4.13 17.22 3 49 1.63 6.38 5Business collaborations 146 4.87 20.28 2 96 3.20 12.50 3Research collaborations 25 0.83 3.47 7 0 0.00 0.00 9Financial contacts 26 0.87 3.61 6 55 1.83 7.16 4Licensing agreements 51 1.70 7.08 5 17 0.57 2.21 7Franchising agreements 4 0.13 0.56 9 1 0.03 0.13 8Total 720 24 100 768 25.60 100Internal capitalPatents 19 0.63 4.80 5 83 2.77 26.69 1Copyrights 4 0.13 1.01 8 0 0.00 0.00Trademarks 15 0.50 3.79 6 25 0.83 8.04 5Management philosophy 0 0.00 0.00 1 0.03 0.32 8Corporate culture 9 0.30 2.27 7 18 0.60 5.79 6Management processes 91 3.03 22.98 2 18 0.60 5.79 6Information systems 34 1.13 8.59 4 56 1.87 18.01 2Networking systems 0 0.00 0.00 8 0.27 2.57 7Research projects 184 6.13 46.46 1 48 1.60 15.43 4Unique market knowledge 40 1.33 10.10 3 54 1.80 17.36 3Total 396 13.2 100 311 10.37 100Human capitalKnow how 6 0.20 2.26 5 0 0.00 0.00Education 46 1.53 17.36 2 21 0.70 10.71 3Employees 160 5.33 60.38 1 127 4.23 64.80 1Work related knowledge 29 0.97 10.94 3 9 0.30 4.59 4Work related competencies 24 0.80 9.06 4 39 1.30 19.90 2Total 265 8.83 100 196 6.53 100

Table VI.IC disclosure in detail


105

that knowledge based firms show more heterogeneity in their disclosures among thethree IC categories than traditional firms. Looking at the dimensions of ICD theregression models (A2, A3, A4) show that industry is significantly associated only withthe external capital dimension but not with the internal or human capital dimensions.Based on this evidence we claim qualified support for H1 on the influence of industry(traditional; knowledge intensive) on disclosure behaviour on ICD.

In relation to H2 (Table VII: model B), we reject the hypothesis that UK firmsdisclose more information on IC than Italian firms. Quite unexpectedly we foundevidence that firms in common law countries (UK) do not disclose more than firmslocated in civil law countries (Italy) and that nationality of origin is not related to thelevel of ICD.

6. ConclusionThis paper set out to compare ICD practices of 30 reasonably matched firms in Italyand 30 in the UK. The methodology of content analysis of the annual reports wasapplied to these 60 firms followed by univariate and multivariate analysis. Theargument presented was that differences in ICD practices could be explained, if in part,by industrial sector (traditional; knowledge intensive: H1) and by nationality of origin(Italy; UK: H2). We claim modest support for H1 on the influence of industry type onICD. A secondary finding here relates to the influence of firm size on ICD, whichsupports the extant literature in this area.

Contra our expectations, we find no support for H2 on the influence of country oforigin on ICD; in fact, the findings at face value suggest the opposite. Italy and the UKwere deemed appropriate because of their respective characteristics:

. civil/code vs common law legal system;

. small vs large stock market;

. differences in ownership structures; and

. the influence of regulatory and professional bodies.

Variable Obs Mean Std. dev. Min Max

icd 60 44.267 37.965 0.000 193.000Diff_int 60 0.093 0.081 0.001 0.411Diff_ext 60 0.106 0.089 0.001 0.336Diff_hc 60 0.076 0.106 0.000 0.531Diff_all 60 0.092 0.068 0.027 0.296size 60 8.138 0.828 6.204 10.763own 60 0.583 0.497 0.000 1.000lev 60 0.464 0.291 20.514 1.008prof 60 0.034 0.125 20.483 0.358

Notes: icd ¼ the extent of ICD; diff_int ¼ difference between the observed percentage of internalcapital disclosure and the median; diff_ext ¼ difference between the observed percentage of externalcapital disclosure and the median; diff_hum ¼ difference between the observed percentage of humancapital disclosure and the median; diff_all ¼ average of diff_int; diff_ext and diff_hum; size ¼ naturallogarithm of sales; own ¼ percentage of share capital owned by unknown shareholders;lev ¼ long-term financial debts divided by net equity; prof ¼ operating profit divided by investedcapital

Table VII.Descriptive statistics

JHRCA10,2

106

Icd

Int

(per

cen

t)E

xt

(per

cen

t)H

um

(per

cen

t)D

iff_

int

Dif

f_ex

tD

iff_

hu

mD

iff_

all

Cou

ntr

yS

ize

Ind

Ow

nL

evP

rof

Icd

20.

0912

0.20

702

0.05

282

0.16

772

0.35

672

0.24

972

0.35

342

0.04

690.

5143

0.34

620.

1317

0.11

330.

0251

Int

(per

cen

t)0.

4881

20.

5435

20.

2825

0.58

230.

0456

20.

1626

0.16

812

0.10

032

0.09

892

0.03

710.

0615

0.03

942

0.16

42E

xt

(per

cen

t)0.

1126

0.00

002

0.45

982

0.49

412

0.43

652

0.48

782

0.64

330.

1888

0.18

500.

2000

0.10

090.

1929

0.02

16H

um

(per

cen

t)0.

6886

0.02

880.

0002

0.10

980.

2612

0.78

970.

4828

20.

0360

0.03

982

0.14

832

0.08

632

0.16

510.

1601

Dif

f_in

t0.

2004

0.00

000.

0001

0.40

340.

3670

0.00

730.

5645

20.

0783

20.

1057

20.

1155

20.

0033

0.05

662

0.04

14D

iff_

ext

0.00

520.

7296

0.00

050.

0438

0.00

390.

5356

0.86

832

0.33

752

0.27

152

0.16

002

0.16

080.

0682

20.

0138

Dif

f_h

um

0.05

430.

2146

0.00

010.

0000

0.95

590.

0000

0.75

942

0.09

312

0.19

782

0.18

062

0.06

822

0.11

560.

0863

Dif

f_al

l0.

0056

0.19

920.

0000

0.00

010.

0000

0.00

000.

0000

20.

2274

20.

2642

20.

2105

20.

1072

20.

0080

0.02

25C

oun

try

0.72

180.

4457

0.14

850.

7845

0.55

190.

0084

0.47

930.

0806

0.11

280.

0000

0.23

662

0.20

150.

0450

Siz

e0.

0000

0.45

230.

1571

0.76

290.

4216

0.03

590.

1299

0.04

130.

3907

20.

1536

0.22

040.

4154

0.31

92In

d0.

0067

0.77

830.

1255

0.25

820.

3797

0.22

210.

1673

0.10

651.

0000

0.24

142

0.04

782

0.16

362

0.22

82O

wn

0.31

580.

6406

0.44

290.

5119

0.97

990.

2196

0.60

450.

4148

0.06

870.

0906

0.71

680.

0961

0.08

70L

ev0.

3887

0.76

480.

1398

0.20

740.

6677

0.60

460.

3792

0.95

180.

1227

0.00

100.

2116

0.46

500.

1400

Pro

f0.

8490

0.21

000.

8698

0.22

180.

7536

0.91

650.

5121

0.86

460.

7327

0.01

290.

0795

0.50

840.

2859

Notes:

Cor

rela

tion

sar

ein

the

up

per

sid

e;p-

val

ues

(2-t

aile

d)

are

inth

elo

wer

sid

e.V

aria

ble

s:ic

d¼

the

exte

nt

ofIC

D;i

nt

per

cen

t¼

the

per

cen

tag

eof

ICd

iscl

osu

res

onin

tern

alca

pit

al;

ext

per

cen

t¼

the

per

cen

tag

eof

ICd

iscl

osu

res

onex

tern

alca

pit

al;

hu

mp

erce

nt¼

the

per

cen

tag

eof

ICd

iscl

osu

res

onh

um

anca

pit

al;

dif

f_in

t¼

dif

fere

nce

bet

wee

nth

eob

serv

edp

erce

nta

ge

ofin

tern

alca

pit

ald

iscl

osu

rean

dth

em

edia

n;

dif

f_ex

t¼

dif

fere

nce

bet

wee

nth

eob

serv

edp

erce

nta

ge

ofex

tern

alca

pit

ald

iscl

osu

rean

dth

em

edia

n;d

iff_

hu

m¼

dif

fere

nce

bet

wee

nth

eob

serv

edp

erce

nta

ge

ofh

um

anca

pit

ald

iscl

osu

rean

dth

em

edia

n;d

iff_

all¼

aver

age

ofd

iff_

int;

dif

f_ex

tan

dd

iff_

hu

m;c

oun

try¼

du

mm

yv

aria

ble

(0¼

Ital

y;1

¼U

K);

size

¼n

atu

ral

log

arit

hm

ofsa

les;

ind¼

du

mm

yv

aria

ble

(0¼

trad

itio

nal

firm

s;1¼

kn

owle

dg

eb

ased

firm

s);

own¼

per

cen

tag

eof

shar

eca

pit

alow

ned

by

un

kn

own

shar

ehol

der

s;le

v¼

lon

g-t

erm

fin

anci

ald

ebts

div

ided

by

net

equ

ity

;p

rof¼

oper

atin

gp

rofi

td

ivid

edb

yin

ves

ted

cap

ital

Table VIII.Correlation table


107

In particular, the fact that Italian companies disclose on average the same amount of ICinformation as UK companies is inconsistent with theories of efficiencies in civil/codeand common/canon law countries discussed above. The research questions hereremain open to further study.

This study is one of the first to investigate ICD in an international comparativecontext; that said, it has some obvious limitations. First and foremost, the sample sizeis small. This is due to the onerous nature of the content analysis methodologyemployed and future work should, if possible, attempt to maximise sample size. Largersample sizes in future work using the methods introduced here and perhaps expandingfrom cross-sectional to longitudinal would have greater statistical power in attemptingto explore the country of origin effects that we believe do exist in the ICD field. Contentanalysis itself is subject to its own inherent limitations, but at this stage of ICdevelopment in general it remains an appropriate tool for further research in ICD.

In conclusion, it is apparent that companies in Italy and the UK voluntarily disclosesubstantial amounts of information about their IC, particularly on external capital. Themain findings confirm the explanatory power of size and, in part, industrial sector inthe ICD field. The empirical evidence supports the idea that not only the extent, butalso the type of ICD is driven by size and industry with the main focus on externalcapital disclosure. The industry effect on the extent of disclosure was also suggestedby other empirical studies (Bozzolan et al., 2003; Meca et al., 2003; Olsson, 2004).

Model A1 A2 A3 A4 BDependent variable Diff_all Diff_int Diff_ext Diff_hum Icd

Constant 0.9618 0.2125 0.4081 0.3412 2199.4990.001 * * * 1.80 * 3.51 * * * 2.30 * * 2 4.90 * * *

Size 20.0773 20.015 20.343 20.2792 29.94832 2.14 * * 2 0.96 2 2.24 * * 2 1.42 5.56 * * *

Lev 0.0271 0.0254 0.0353 20.0338 215.37430.27 0.59 0.83 2 0.62 2 1.03

Prof 0.1197 20.0231 0.0337 0.1091 221.59250.55 2 0.25 0.36 0.92 2 0.66

Own 20.0063 0.0048 20.0089 20.0022 4.72152 0.12 0.21 2 0.40 2 0.08 0.59

Country 20.737 20.0077 20.0338 20.0189 211.72322 1.37 2 0.33 2 1.45 2 0.65 2 1.46

Ind 20.1008 20.0223 20.0471 20.0446 33.06912 2.01 * * 2 0.94 2 2.06 * * 2 1.49 4.03 * * *

Obs 60 60 60 60 60R 2 0.1976 0.0416 0.2222 0.1103 0.4797Prob . F 0.0433 0.8862 0.0319 0.3772 0.0000

Notes: Variables: icd ¼ the extent of ICD; diff_int ¼ difference between the observed percentage ofinternal capital disclosure and the median; diff_ext ¼ difference between the observed percentage ofexternal capital disclosure and the median; diff_hum ¼ difference between the observed percentage ofhuman capital disclosure and the median; country ¼ dummy variable (0 ¼ Italy; 1 ¼ UK);size ¼ natural logarithm of sales; ind ¼ dummy variable (0 ¼ traditional firms; 1 ¼ knowledgebased firms); own ¼ percentage of share capital owned by unknown shareholders; lev ¼ long-termfinancial debts divided by net equity; prof ¼ operating profit divided by invested capital

Table IX.Regression models

JHRCA10,2

108

In relation to the type of disclosure studies based on multi-industry samples highlightthe dominance of the disclosure of external capital information (Bozzolan et al., 2003;Meca et al., 2003); on the contrary, other studies considering specific industries suggestthat in some cases disclosure patterns may be different. For instance Olsson (2004) ininvestigating ICD in Swedish retail industry, highlights a skewing towardsinformation on internal capital. In relation to the size effect results are consistentwith previous analyses of single countries that demonstrate a positive and significantrelationship between ICD and size (Bozzolan et al., 2003; Meca et al., 2003), and on sizeand disclosure in general (Ahmed and Courtis, 1999; Amber et al. 2001).

Note

1. We deal with this particular point referring to H1.

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Further reading

Wooldridge, J.M. (2003), Introductory Econometrics: A Modern Approach, Mason, Thomson.

Corresponding authorFederica Ricceri can be contacted at: [email protected]


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