Italian annual intellectual capital disclosure An empirical analysis

The Emerald Research Register for this journal is available at http://www.emeraldinsight.com/researchregister The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/1469-1930.htm Italian annual intellectual capital disclosure An empirical analysis Saverio Bozzolan, Francesco Favotto and Federica Ricceri Department of Economics, University of Padova, Padova, Italy Keywords Intellectual capital, Disclosure, Stakeholders Abstract In recent years a world-wide debate has emerged on the future of business reporting. There is growing agreement that traditional financial reporting is inadequate in meeting with the information needs of stakeholders, particularly in a knowledge economy characterised by a rapidly emerging emphasis on intellectual capital (IC). This study examines voluntary intellectual capital disclosure (ICD) provided by listed Italian companies in annual reports from the year 2001. The study aims to answer two research questions namely: what is the amount and content of ICD; and what are the factors that influence different voluntary reporting behaviours. In relation to amount and content of information disclosed, the results are consistent with previous ICD studies showing extensive disclosure of external capital (in particular about customers ). Regarding the factors that can explain different voluntary reporting practices, findings suggest that industry and size are not important in determining the content of information disclosed, however, as found in social and environmental disclosure (SED) studies, these factors are relevant in explaining the amount of information disclosed. In summary, this paper highlights the ICD practices of Italian listed companies by examining their annual reports, and compares these results with a number of previous national studies. Introduction In recent years there has been increasing dissatisfaction with traditional financial reporting and its ability to provide stakeholders with sufficient information on a company s ability to create wealth. This becomes particularly apparent in a knowledge economy which is characterised by technological advances and a rapidly increasing emphasis on IC (Stewart, 2001). For instance, PriceWaterhouseCoopers conducted a series of surveys on the type of information investors need[1] (Eccles et al., 2001). Among the ten information types considered most important to investors, only three were financial (cash flow, earnings, gross margin), and only one of these (earnings) was subject to strict regulation. Of the remaining seven, two were derived from internal company data (strategic directions and competitive landscape) and the remaining five may be considered as intangibles [2] (market growth, quality/experience of the management team, market size and market share, speed to market). The 14 information types considered of medium importance The authors wish to thank James Guthrie and the two anonymous reviewers for their valuable comments. The funding provided by the University of Padova (Project CPDA017911) is also gratefully acknowledged. Italian annual ICD 543 Journal of Intellectual Capital Vol. 4 No. 4, 2003 pp. 543-558 q MCB UP Limited 1469-1930 DOI 10.1108/14691930310504554

JIC 4,4 544 were sorted by the authors into three categories: customers (distribution channels, brand equity/visibility, and customer turnover rates); employees (intellectual capital, employee retention rate, and revenue per employee); and innovation (revenue from new products, new products success rate, R&D expenditure, and product development cycle). It is interesting to note that almost all of the information perceived as important by investors and analysts can be classified as IC. Moreover, findings suggest that the majority of these information types are in fact not disclosed by managers, thus creating an information gap. Within accounting literature, recent studies have focused on the informative role of external reporting for the effective functioning of capital markets (Healey and Palepu, 2001). The positive effects of disclosure have been shown by Botosan (1997) to reduce the cost of equity; by Sengupta (1998) to reduce the cost of debts; by Healey et al. (1999) to increase share performance, not related to current and expected earnings; and by Gelb and Zarowin (2000) to yield a higher stock-price correlation with future earnings when compared to companies with low disclosure levels. Other contributions have emphasised the increasing relevance of narrative reporting, as well as the declining value of information conveyed within financial statements (Breton and Taffler, 2001; Francis and Shipper, 1999). Lev and Zarowin (1999), using statistical association between accounting data and capital market values, have demonstrated the decreasing usefulness of accounting-based information over the last 20 years. Standard-setters have tried to promote improved reporting systems. The Jenkins report (AICPA, 1994), proposes a framework for voluntary disclosure based on the information needs of investors and creditors. The report presents a wider range of information types sorted into five categories: (1) financial and non financial data; (2) the management s analysis of financial and non financial data; (3) forward looking information; (4) information about managers and stakeholders; and (5) background about the company. A recent empirical analysis of disclosure practices outside financial statements (FASB, 2001) adds the IC dimension to the five categories of the Jenkins report. Wallman (1995, 1996) has proposed a framework which orders variables from those that fully meet accounting recognition criteria (financial statements figures) to those that do not, or only partially meet accounting recognition criteria (research and development, forward looking information, customer satisfaction, risk measures and IC). Accounting scholars are using these and similar frameworks to determine which information companies are voluntary disclosing and what factors can explain different reporting behaviours. There are a number of studies that

provide evidence that industry and size are two main factors in explaining reporting practices of companies, for example SED studies (for a review see, Mathews, 1997; Gray, 2002). Focusing on ICD, there are studies investigating the amount and content of the information voluntarily reported in Australia, Sri Lanka and Ireland (Guthrie and Petty, 2000b; Abeysekera, 2000; Brennan, 2001) however, to date, no such studies have been made of Italy. Moreover, there has been little investigation into the factors that can explain different voluntary IC reporting behaviours (Williams, 2001). This paper replicates and extends Guthrie and Petty (2000b), which, in analysing Australian disclosure practices, provides a methodology for identifying and reporting IC in annual reports. This paper differs from Guthrie and Petty (2000b) firstly because it uses a sample of Italian listed companies, and subsequently the results are anticipated to differ due to the unique characteristics of Italian listed companies. Second, the paper will examine the factors influencing ICD by using the technique of regression analysis. Furthermore, this study would like to pose the following two research questions regarding voluntary ICD (i.e. those disclosures not explicitly required by professional standards) provided by Italian listed companies to external stakeholders. First, what is the amount and content of ICD? Second, what factors can explain the observed differences in voluntary disclosure patterns? The analysis of ICD is particularly interesting in the Italian context because, since 1999 several government initiatives have been in place to promote organisations to invest in IC, particularly in R&D projects. Within this context we would expect organisations to have increased their attention to the importance of IC, and consequently, placed a greater emphasis on ICD. Moreover, a feature of Italian listed companies is the scarce presence of institutional investors within their ownership structures, as well as the low percentage of shares traded on the financial market. These features could, in accordance with the agency theory, see the amount of disclosure reduce as managers do not have any strong incentives to convince stakeholders of the company s optimal performance. Equally, according to the signalling theory, managers do not have the necessity to signal to the market that they are creating hidden IC resources for. The paper is structured as follows: Section 2 provides a brief overview of prior studies on ICD in annual reports. Section 3 focuses on the research methodology; Section 4 summarises the results of the analysis; and, Section 5 provides some conclusions and discusses the directions of future research. Italian annual ICD 545 Annual reporting of intellectual capital The IC disclosure (ICD) has traditionally been connected with its financial dimension: information on research and development, software, marketing and

JIC 4,4 546 training, are presented in company reports as an explanation of accounting figures. This traditional approach does not allow the identification of new intangibles (Guthrie and Petty, 2000a) such as staff competencies, customer relationships, administrative systems, database and decision support systems, and so forth, which are the new levers of value in knowledge organisations. Wayne (2001) states that the problem facing the disclosure of these new intangibles is that they cannot be recognised in financial statements as they do not meet the accounting definition of an asset[3]. This failure to report intangibles due to the limitations of traditional accounting has seen an emerging interest amongst stakeholders to seek out non-financial information, especially soft assets (IC), through which the long-term value-generating ability of a company might be ascertained (Robb et al., 2001). The identification of new intangibles and subsequent creation of a monitoring system for management and disclosure purposes was the main aim of the Konrad Group[4] (Sveiby, 1988). A result of their work was the intangible asset monitor (IAM) (Sveiby, 1997), one of the best known models for understanding and reporting on IC. This model identifies three main categories of IC: external (customer related) capital; internal (structural) capital; and human capital. Other models widely used are the business navigator or skandia value scheme (Edvinsson and Malone, 1997), the balanced scorecard (Kaplan and Norton, 1992; 1996; 2000), and the intellectual capital accounts (DATI, 1998). Even if from a theoretical point of view scholars invariably agree on the identification of these three IC categories (Edvinsson and Malone, 1997; Sveiby, 1997), however the question remains: are companies actually making use of these categories? Guthrie and Petty (2000a), referring to annual reporting practices in Australia, focus on the ICD of the 19 largest (by market capitalisation) listed companies. They use the content analysis method and a modified version of Sveiby s framework to classify the narrative information. The three dimensions of the intellectual capital framework (internal capital, external capital and human capital), along with their categories (nine for internal capital, nine for external capital and six for human capital), are coded with a value of 0 if the variable is not in the annual report, 1 if the variable is presented in a discursive way, 2 if the information is quantitative, and 3 if the information is evaluated in dollars. The content analysis provides a descriptive picture of the frequency of reporting of these specific attributes among different industries. The Australian findings suggested that, even though there seems to be an awareness of the importance of intellectual capital, the reporting practices are far from systematic. There is no established and mutually agreed framework for IC reporting within Australian companies and, moreover, there seems to be a lot of empty rhetoric surrounding the notion of measuring, valuing, and reporting IC (Guthrie and Petty, 2000b, p. 246). This is further supported by the claim that nearly every instance regarding IC was expressed in a

discursive way rather than in numerical terms (Guthrie and Petty, 2000b, p. 247). Brennan (2001), replicating Guthrie and Petty s (2000b) study, investigated the reporting practices of 11 knowledge-based companies listed in the Irish Stock Exchange. She found that the Irish context was similar to the Australian, in that there seemed to be no framework in use when it came to the reporting of IC in annual reports, and companies were found in general to express their evaluations of IC in qualitative terms. As Guthrie and Petty (2000b) observed, these findings outline the difficulties that companies are facing in managing, measuring and reporting soft assets. Social accounting literature (for a review see Mathews, 1997; Gray, 2002) provides evidence regarding the factors that influence the different disclosure practices observed between companies. It has been found that size and industry are the two main factors in explaining different reporting behaviours. Although a systematic examination of the relationship between size, industry and the content of disclosure has not yet been provided, it is commonly accepted that larger companies or companies belonging to particular industries are more inclined to a more thorough disclosure. The second research question of this study aims to identify the factors that can explain the observed differences in voluntary ICD patterns, but as mentioned little research has been done on this area. Williams (2001) links ICD with a measure of IC performance. Findings of his study highlight a negative association between IC performance, measured by the VAICe, and the level of disclosure. The latter is reduced when IC performance is higher. This is probably due to the fact that in such cases management may perceive that high IC performance levels could provide a signal to competitors and to those wishing to enter the market of possible value creating opportunities. To maintain any competitive advantage it has, a firm could reduce the Intellectual Capital disclosure levels in an effort not to signal competitors and others as to where potential opportunities may lie (Williams, 2001, p. 201). In summary, the above brief literature review has identified that there are no surveys of the amount and content of ICD in Italian listed companies, although such surveys exist in a number of other countries. Further, there has been little study into the factors that can explain the different levels of IC disclosure between companies. Italian annual ICD 547 The research methodology This section explains the methodological approach used by the study. The first subsection describes the data source and how the sample was drawn. The second subsection focuses on the technique used for the analysis of narratives, and includes a discussion of the framework, recording unit, disclosure index and results of the reliability tests. The third subsection reports on the regression model used and provides a brief explanation of its variables and measures.

JIC 4,4 548 Sample selection and data source This study focuses on a sample of 30 organisations chosen from the non-financial companies listed in the Italian Stock Exchange (as at 31 December 2001). The complete list consisted of 201 organisations. The sample selection was not a simple random procedure, as stated previously, research has demonstrated that disclosure is positively correlated with size and type of industry (Mathews, 1997; Gray, 2002). A stratified sampling procedure was adopted: the first stratum was the market where the companies were listed. Samples were randomly chosen from two groups. The first group was those companies listed in the Nuovo Mercato (42 companies belonging to high tech industries such as internet providers, biotechnology, entrainment, Internet, IT distribution, high-tech manufacturing, media, retail, software, system integration and telecommunication, Web services). The second group was of those companies listed in the Ordinario, Star and Blue Chips (159 companies belonging to traditional industries and including food, automobile, chemical, building, electronics, manufacturing, media, oil, utilities, textiles and clothing, tourism and leisure). Within each group, the listed companies were stratified using two variables, industry and sales, as a proxy for the size. The final sample was then drawn using a systematic procedure. The sources of the data were the 2001 annual reports. Annual reports were chosen for two reasons (Lang and Lundholm, 1993). Firstly, because they are considered an important source of company information by external users such as stakeholders; and secondly, the disclosure level in annual reports is positively correlated with the amount of corporate information communicated to the market and to stakeholders using other media. Techniques for the analysis of narratives The method used to analyse the ICD was content analysis: a research technique for making replicable and valid inferences from data according to their context (Krippendorff, 1980, p. 21). The application of the method consisted of different phases (Krippendorff, 1980; Weber, 1985): the choice of the framework used to classify information; the definition of the recording unit; the coding; and the assessment of the level of reliability achieved. To classify the gathered information, we used the framework tested by Guthrie and Petty (2000b), which we slightly modified considering the FASB (2001) project results. The framework is illustrated in Table I. The IC framework consists of three categories: (1) Internal structure: this consists of the two main elements of intellectual property and infrastructure assets. The first is related to the IC elements that are protected by law (patents, copyrights, and trademarks), and the second refers to the IC elements that can be created within the company or acquired from the outside (corporate culture, management processes,

1. Internal structure (structural) 2. External structure (relational) 3. Human capital Intellectual property 2.a brands 3.a Know-how 1.a patents 2.b customers 3.b education 1.b copyrights 2.c customer loyalty 3.c employees 1.c trade-marks 2.d distribution channels 3.d work-related knowledge Infrastructure assets 2.e business collaboration 3.e work-related competence 1.d corporate culture 2.f research collaborations 1.e management processes 2.g financial contacts 1.f information systems 2.h licensing agreements 1.g networking systems 2.i franchising agreements 1.h research projects Italian annual ICD 549 Table I. The IC framework information systems, networking systems). Within this category the research projects element were added to take into account innovations that are, or are going to be, developed by the company. (2) External structure: this relates to the relationship of the company with different external stakeholders, and includes elements such as customers, distribution channels, business collaborations, franchising agreements, and so forth. (3) Human capital: this refers to human resources and includes general features such as education, work related knowledge and competencies, and other characteristics (e.g. average age, turnover) that are grouped under the employee element. Sentences were chosen as the recording unit to overcome problems related to the use of words or portions of pages, that seems to add unnecessary unreliability. Milne and Adler (1999, p. 243) state: As a basis for coding sentences are far more reliable than any other unit of analysis... Individual words have no meaning to provide a sound basis for coding social and environmental disclosures without a sentence or sentences for context. Likewise laying a plastic grid sheet over a body of test and trying to code the contents of each grid square would result in meaningless measures. Each sentence was coded as follows: with a score of 0, if providing no information; with a score of 1 if providing qualitative information; or, with a score of 2 if providing quantitative information. This differs from the Guthrie and Petty (2000b) study in which a 0-4 scoring index was applied[5]. If the same information was repeated in the report, we only considered this information once. The amount of disclosure was measured by counting the frequency at both the category and element levels. An overall index was given to a company in relation to the total amount of the information disclosed, and moreover, disclosure indexes were also calculated for each category. A major concern in using the content analysis method is its reliability. Krippendorff (1980) identifies three types of reliability: accuracy, reproducibility

JIC 4,4 550 and stability. In this paper we refer to these three dimensions of reliability and use them to assess our findings. First, accuracy is ensured by the use of two coders (two of the authors) and the following defined coding procedure:. Explanatory notes on the content of each category-item and examples of sentences to be coded in each category-item were prepared and discussed before the start of the analysis.. Five annual reports were analysed simultaneously by the two coders in order to identify the potential differences between the coders and to standardise the coding classification.. Each annual report was subdivided into units of analysis (sentences) by one coder; and each coder codified 15 annual reports. Second, the reproducibility of the content analysis was assessed by the Krippendorff alpha which, after the second round of coding, resulted in 0.876 at the category level and 0.771 at the element level, highlighting a degree of agreement above the minimum limit of acceptance (Milne and Adler, 1999). Third, the stability of the content analysis was verified by coding the annual report a week later in a second round of coding, this was then analysed and yielded a result of 0.938. The model Previous studies have highlighted the relevance of industry and size in determining the amount of social and environmental disclosure (Mathews, 1997; Gray, 2002). To investigate whether these variables are relevant in explaining the amount of ICD (both overall and at category level), we estimated, using OLS regression, the following general equation: Disclosure amount ¼ f ðindustry; sizeþ Industry. The demand for ICD is greater for companies that operate in industries where the variability of the future is higher and the ability to forecast results is more difficult. This is especially the case high tech industries, and as a result high tech companies are seen to invest heavily in intellectual capital (such as human resources, knowledge, brand, customer loyalty programs, and so forth). Managers in such industries are more likely to disclose additional information to stakeholders. This hypothesis is consistent with empirical evidence about the relevance of industry in determining the level of SED (Hackstone and Milne, 1996; Robb et al., 2001). A way to consider industry within the voluntary disclosure literature is to identify high profile and low profile industries (Patten, 1991; Roberts, 1992). In this study the listing segments of the Italian Stock Exchange are used as the variable to group the sample companies: the ones listed in the New Market (Nuovo Mercato) are considered high profile whilst the companies listed in other segments (Ordinario, Star, Mib30) are considered low profile. The variable

used (INDTYPE) is a dummy (1 for high profile companies and 0 otherwise). A positive association between industry and the amount of ICD is expected. Size. The main assumption underlying the inclusion of this variable in the model is that larger companies undertake more activities, and usually have different business units which may have different critical success factors and different long term value-creating potential (Hackstone and Milne, 1996). This means that more information needs to be disclosed to provide stakeholders with a complete picture of the company. The empirical literature provides strong evidence of the effect of size (SIZE) on the amount of voluntary disclosure (Beattie et al., 2002; Robb et al., 2001; Hackstone and Milne, 1996; Gray et al., 1995; Healey and Palepu, 1994). In these studies different measures have been proposed as a proxy for size: sales, total assets, number of employees; and Kimberly (1976) highlighted a strong correlation between them. In this paper, the following measures are used: market capitalisation, sales and total assets. We expect a positive association between size and the amount of ICD. The next section presents the results of the content analysis and subsequent regression model. It also provides useful insights for a comparison between the Italian, Australian (Guthrie and Petty, 2000b) and Irish (Brennan, 2001) ICD practices. Italian annual ICD 551 The empirical analysis: results and discussion The current section is structured as follows. The first subsection focuses on the findings of the descriptive analysis, which refers to the amount and the content of disclosure. The second subsection briefly summarises the results of the regression model, which investigates the role of industry and size in explaining the amount and the content type of ICD. Findings of content analysis on the amount and type of disclosure The descriptive statistics of indices referring to both overall and category disclosure are reported in Table II. The descriptive analysis focuses on the amount and content of the information disclosed. N Overall index St Mean Dev. External structure Internal structure Human capital St St St Mean Dev. % Mean Dev. % Mean Dev. % High profile companies 10 84 18.47 40 15.87 48 27 17.85 21 17 7.60 9 Low profile companies 20 34 33.18 17 17.70 50 9 10.40 28 7 10.45 22 Total 30 51 38 25 20.09 49 15 15.33 30 11 10.64 21 Table II. Descriptive statistics

JIC 4,4 552 With regard to the amount of disclosure, results show that the average number of IC elements disclosed is 51. This figure suggests that Italian companies, on average, are aware of the importance of IC. Concerning the content of disclosure, the findings indicate that most of the information reported (49 per cent) is related to external structure; 30 per cent is related to internal structure and the remaining 21 per cent concerns human capital. The small percentage represented by human capital disclosure might be explained by the argument that, although managers would like to offer additional relevant or useful information to the public, they are concerned about the risk of such information being used by competitors. As Williams (2001) points out, such disclosures may attract unwanted attention. Therefore, even if there are sufficient arguments to convince managers of the necessity of disclosing information about the company s intangible assets, it is feared that disclosure could have a negative effect on the company itself, especially if the company has a strong IC base. Even if a company has a poor IC base, disclosure may carry risks, though of a different sort. On the one hand, disclosure may signal that one of the company s priorities is to invest financial resources into producing internally or acquiring, when possible, such assets. Or adversely, disclosure from a company with a poor IC base may have negative effects on its reputation in the capital market, as it may be perceived as an unsuccessful actor in the knowledge economy. In analysing categories, several features emerge, and this information is summarised in Table II. It was found that almost all disclosure regarding internal structure was made by only two companies, and refers to infrastructure assets. As expected, the most disclosed item, both at category (51 per cent) and overall (15.3 per cent) level, is the one related to research projects; 29 per cent of the information about internal structure is related to management processes; and 17 per cent is about information systems. Information relating to intellectual property was not frequently disclosed except in two cases: a pharmaceutical company (information disclosed as patents) and a car company (information disclosed as trade marks). In the case of all other companies the information provided on this topic is insignificant. Information about external structure refers mainly to four elements: customers (35 per cent), distribution channels (18 per cent); business collaborations (16 per cent); and brands (13 per cent). All the companies that provided disclosure on external structure presented information in at least three of these elements. Research collaborations and financial contacts accounted for 5 and 6 per cent of the external structure disclosure, respectively, but 90 per cent of the items of these elements was disclosed by a single company (the pharmaceutical one). In relation to human capital, six companies did not provide any information. The element that was most disclosed related to employees (turnover, average

age, etc.) with 57 per cent of the total human capital disclosure. The remaining disclosure is distributed among work related knowledge (17 per cent), work related competencies (11 per cent), and employees education (13 per cent). Findings of the regression model on factors that can explain different reporting behaviours To examine if industry has a statistically significant effect on the amount and the content of intellectual capital disclosure, two different tests were performed. As Table III shows, the two sample t-test for the means of disclosure indices provides support for the hypothesis that industry affects the amount of disclosure: namely, high profile companies disclose more information than low profile ones both at an overall and category level. However, what is interesting is that, even if high profile companies show a higher level of disclosure, they disclose in the same way as low profile companies do. The association between industry and the distribution of information disclosed in each category was tested using the Chi-square (Chi-square with 22 degrees of freedom equals 0.924; p-value ¼ 0.6299). The Chi-square result indicates that the content of ICD is not statistically related with industry. To investigate the multiple effects of size and industry on the amount of ICD in the Italian Stock Market, a multiple regression analysis at both overall and category level was conducted. The results are shown in Table IV. The model interprets ICD patterns quite well. The results show that industry and size have the highest explanatory potential at the overall level, that is, without the dependant variables of industry and size (adjusted R 2 of 0.635). The adjusted R 2 for the regression indicates that the control variables (industry type and size) are a major reason for the variation in ICD. Note that the coefficient of all control variables have their expected sign, and that all control variables have coefficients that are statistically significant at the one per cent level. However, when single categories are taken into account, the significance of industry and size as explanatory factors is considerably reduced. (the lowest explanatory factor reached is related to internal structure, with an adjusted R 2 of 0.326). Among the measures used to represent company size (market capitalisation, total assets, and sales), the natural log of sales is statistically significant (except in the model of internal structure) with a positive effect on the level of disclosure at the overall as well as the category level. Italian annual ICD 553 Two sample t-test for the means Overall index Internal structure External structure Human capital t-statistic p t-statistic p t-statistic p t-statistic p Equal 2 4.568, 0.0001** 2 3.281 0.0028** 2 3.451 0.0018** 2 2.721 0.0111* Notes: *significant at the 5 per cent level; **significant at the 1 per cent level Table III. Results of industry effect on amount and content of disclosure in each category

JIC 4,4 554 Dependent variable: Estimate St. err. t p-value Overall index Independent variables Intercept 2122.64 36.84 2 3.33 0.0025 ** Size 8.35 1.93 4.31 0.0002 ** Industry 60.89 9.13 6.67, 0.0001 ** Model summary R 2 0.6608 Adjusted R 2 0.6356 F-statistic 26.30 p-value of F-statistic, 0.0001 ** External structure Independent variables Intercept 271.39 22.11 2 3.23 0.0033 ** Size 4.70 1.16 4.04 0.0004 ** Industry 28.02 5.48 5.11, 0.0001 ** Model summary R 2 0.5633 Adjusted R 2 0.5309 F-statistic 17.41 p-value of F-statistic, 0.001 Internal structure Independent variables Intercept 24.52 21.51 2 0.21 0.84 Size 0.76 1.13 0.67 0.51 Industry 17.67 5.33 3.32 0.0026 ** Model summary R 2 0.2895 Adjusted R 2 0.3268 F-statistic 5.50 p-value of F-statistic 0.0099 ** Table IV. Multiple regression results for overall index and for each category Human capital Independent variables Intercept 239.62 12.83 2 3.09 0.0046 ** Size 2.49 0.68 3.70 0.001 ** Industry 12.86 3.18 4.04 0.004 ** Model summary R 2 0.4748 Adjusted R 2 0.4359 F-statistic 12.20 p-value of F-statistic 0.002 **

The central significance of industry is evident in each model. In explaining the variations in intellectual capital disclosure both at the overall and category level, the different models show that high profile companies disclose, ceteris paribus, 61 items more than low profile companies if the overall index is considered, 28 items when the dependent variable is external structure, 18 items when considering internal structure, and 13 items when the dependent variable is human capital. It is clear that higher levels of disclosure can help to reduce the variability of future performance as perceived by external investors, as well as risk and, therefore, the cost of capital. As a consequence, higher level of disclosure seems to be necessary where and when a higher variability of future results is expected. The relevance of industry and size in determining disclosure practices leads to an interesting comparison with the results of Guthrie and Petty (2000b), concerning the amount of disclosure. Despite the use of a slightly different coding system[5], Italian companies were found to disclose more IC information on average than Australian companies (see Table V). This result was unexpected as the sample analysed in G&P study includes 19 of the 20 largest Australian listed companies. One possible explanation of this difference in reporting practices can be due to the recent Italian government initiatives (see Section 1) that may have lead to an increased attention to IC dimensions and, as a consequence, to a greater emphasis on ICD. Another explanation may be related to the difference in the year end of the annual reports considered (2001 vs 1998), in this three year period we have witnessed a sharply increasing consciousness of the importance of IC drivers on company performance. Moreover, the industry type of companies analysed should be considered. We explicitly referred to companies listed on the Nuovo Mercato (New Market) where the components of IC are structurally higher. Italian annual ICD 555 Concluding remarks This paper has analysed the ICD practices of Italian companies and has provided an analysis of the main factors explaining these reporting patterns. Using consistent sampling and measurement methods, we have examined the amount and content of disclosure provided in annual reports to stakeholders Guthrie and Petty (2000b) Average number of IC attributes reported per company 51 8.9 Minimum number of IC attributes reported per company 0 2 Maximum number of IC attributes reported per company 113 17 Table V. IC attributes reported per company

JIC 4,4 556 and the main factors that can explain the observed differences in voluntary reporting behaviours between companies. With regard to the first research question, which asks about the amount and content of ICD, some conclusions may be reached based on the findings reported above. Disclosure by Italian companies mainly occurs with regard to external structure (with particular attention to customers, distribution channels, business collaboration and brands). This finding is not comparable with Australian voluntary reporting practices (Guthrie and Petty, 2000b) while it is comparable with the Irish one (Brennan, 2001). With regard to the second research question of the paper, industry and size seem to be relevant factors in explaining the differences in reporting behaviour amongst Italian companies. This result is consistent with the majority of previous studies on SED (see Mathews, 1997; Gray, 2002). Results suggest some considerable differences in the amount of ICD in annual reports between companies belonging to high profile industries and those belonging to low profile industries. However, interestingly, it has been found that high profile and low profile companies disclose the same type of information. Limitations of the paper and future research directions This study is still exploratory in nature, as it is the first attempt to investigate Italian IC reporting practices, and further work needs to be done in several ways. As the sample used is quite small, the first step would be to use a larger sample in order to allow the inclusion in the regression model of other independent variables (i.e. risk, ownership, and percentage of shares traded in capital markets), particularly in the Italian context, may be relevant in explaining the amount and the content of ICD. A second step would be to extend the analysis on a longitudinal basis aiming to monitor the progress and development of IC reporting practices. These research directions will provide a more complete picture of annual reporting ICD practices in Italy. Notes 1. The respondents (belonging not only to US [??] but also to 13 European and Asian countries) ranked information in three categories according to their relevance. 2. In this study we refer to IC and intangibles as interchangeable concepts. 3. Wayne (2001) provides a comparison between FASB and IASC frameworks and accounting principles; the study points out the lack of correlation between the cost incurred and the value of future benefits. This makes financial information on intangibles, and in particular cost based ones, inadequate as measures of their value. 4. The Konrad Group was formed by managers of Swedish knowledge-intensive companies during the mid 1980s. 5. The use of such coding system reduces the results in terms of the amount of disclosure when, ceteris paribus, financial measures are provided.

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