Conference Presentation
Reference
The determinants of Internet financial reporting by Swiss companies
RAFFOURNIER, Bernard
RAFFOURNIER, Bernard. The determinants of Internet financial reporting by Swiss companies.
In: Annual congress of the European Accounting Association, Seville (Spain), 2003
Available at:
http://archive-ouverte.unige.ch/unige:47255
Disclaimer: layout of this document may differ from the published version.
26th Congress of the European Accounting Association, Seville (Spain) 2003
THE DETERMINANTS OF INTERNET FINANCIAL REPORTING BY SWISS COMPANIES
*Bernard RAFFOURNIER University of Geneva bernard.raffournier@hec.unige.ch
- Abstract -
This paper investigates factors that have an impact on Internet financial reporting. The websites of 147 Swiss listed companies have been examined to measure the extent and quality of information disclosed, and the resulting indexes related to several firm characteristics (firm size, ownership diffusion, international visibility, profitability and industry membership).
Although firm size was positively associated with both measures, the findings suggest that the determinants of quantity and quality of disclosure are different, in as much as ownership diffusion and international visibility had an impact on one index only. No influence was found for industry membership, except that insurance companies were characterised by low values on both indexes.
Key-words: Internet, financial reporting, voluntary disclosure
Address for correspondence:
Bernard Raffournier
HEC – University of Geneva 40, bd du Pont d'Arve
1211 Genève 4 Switzerland
* This research was sponsored by the Swiss National Fund (grant 12-61703.00). The author gratefully acknowledges his research assistants, Entela Lula and Pierre Vallier, for their help in data collection.
THE DETERMINANTS OF INTERNET FINANCIAL REPORTING BY SWISS COMPANIES
1. INTRODUCTION
The widespread diffusion of the Internet in all parts of the world has given rise to an abundant literature on the use of the Internet for financial reporting. As early as 1997, Lymer (1997) showed that 92% of the 50 largest British companies had a website and that 65% used it for financial disclosure. Similar results were found in Finland (Lymer and Tallberg, 1997), Sweden (Hedlin, 1999), Germany (Deller et al., 1999; Marston and Polei, 2002), Austria (Pirchegger and Wagenhofer, 1999), Spain (Gowthorpe and Amat, 1999) and the USA (Asbaugh et al., 1999; Deller et al., 1999). As noted by Xiao et al. (2002) most of these studies are descriptive. They provide a description of the content of websites but do not explain variations among firms in the use of the Internet for external financial reporting.
The purpose of this paper is to extend prior research by focusing on factors that may have an impact on the extent and quality of financial disclosure on the Internet. Several hypotheses are formulated and tested on a sample of 147 Swiss listed companies. Next section provides a review of the literature on the determinants of Internet financial reporting. It is followed by a description of the research hypotheses (section 3) and the methodology used (section 4).
Empirical results are presented in section 5 and concluding comments in the last section.
2. PRIOR RESEARCH
Craven and Marston (1999) were among the first to investigate relationships between Internet financial reporting (IFR) and company characteristics. More precisely, they tested the
influence of size and industry membership on the presence of financial accounting
information on the website of 206 large UK companies. A similar research was conducted in the US by Asbaugh et al. (1999). This study, based on a sample of 290 US companies
identified by the Association for Investment Management and Research (AIMR)1, relates the use of Internet financial reporting to size, profitability, disclosure reputation (as measured by the AIMR disclosure score) and the percentage of firm's shares held by individual
shareholders. According to Asbaugh et al. (1999) a firm is defined as practicing IFR when it provides in its website either a comprehensive set of financial statements, a link to its annual report elsewhere on the Internet, or a link to the EDGAR database2. In New-Zealand, Oyelere et al. (2003) have recently investigated the association between IFR and several firms
characteristics such as size, profitability, leverage, internationalisation, ownership diffusion and industry membership. Their definition of IFR is wider than that of Asbaugh et al. (1999) since provision of financial highlights extracted from financial statements (including partial and/or summarized financial statements) is sufficient to be classified as practicing Internet financial reporting.
In all previous studies, IFR activity was measured by a dummy variable whose value was 1 when the website had financial content and 0 otherwise. Some authors have gone further by developing measures of the extent of Internet financial reporting. Ettredge et al. (2002) for example calculated two separate scores, one for data already included in SEC filings, the
1 AIMR is a US association of financial analysts. One of its committees periodically reviews and evaluates the corporate reporting practices of a group of publicly traded companies.
2 EDGAR is the database created by the US Security Exchange Commission (SEC) to collect and diffuse financial information provided by companies whose securities are traded on a US market.
other one for voluntary disclosures. Each measure was then related to several company characteristics, such as size, profitability or the need for new external equity capital.
Pirchegger and Wagenhofer (1999) used a large list of criteria to evaluate websites of Austrian companies. These items were selected to capture four dimensions of information provided: content, timeliness, technology, and user support. The resulting score was then related to company size and ownership diffusion. The problem is that all criteria were included in a single measure, which makes its interpretation difficult.
Debreceny et al. (2002) examined websites of 660 large companies in 22 countries. These sites were analysed using two scores aimed at measuring separately the content and presentation of financial information. Each score, ranging from 0 to 3 or 4, was associated with firm characteristics and environmental variables representing the level of required financial disclosure and Internet penetration in the country. Marston and Polei (2002) also used separate scores to look for determinants of IFR in Germany. Their scores were more detailed than those of Debreceny et al. (2002) since they resulted from an in-depth analysis of websites based on 51 items measuring various dimensions of IFR. They were related to explanatory variables such as firm size, profitability, ownership structure, systematic risk or foreign listing status.
3. HYPOTHESES
The literature on voluntary disclosure combined with an analysis of the advantages of the Internet for financial reporting suggest that five firm characteristics may have an impact on the extent and quality of financial disclosure on websites.
Size
There is a general agreement that a positive relationship between the size of a company and its extent of disclosure is to be expected. Several reasons have been advanced in support of this assumption. First, more disclosure may be needed for large companies because of their complexity. Secondly, disclosing detailed information is relatively less costly for large firms because they are assumed to produce this information already for internal purpose. Thirdly, because information disclosed by themselves is the primary source of information for their competitors, small firms should be reluctant to make a full disclosure of their activities. It can also be assumed that large firms are more sensitive to political costs and, consequently, will disclose more in order to allay public criticism or government intervention in their affairs.
Finally, given that the cost of information diffusion on the Internet is probably unrelated to firm size (Pirchegger and Wagenhofer, 1999), the benefits of Internet financial reporting are likely to be increasing with size. This lead us to the following hypothesis:
H1: The extent (quality) of financial disclosure on websites is positively related to firm size.
This hypothesis is validated by most studies on voluntary disclosure3, as well as by all those that specifically addressed Internet financial reporting (Asbaugh et al., 1999; Craven and Marston, 1999; Debreceny et al., 2002; Ettredge et al., 2002; Marston and Polei, 2002;
Oyelere et al., 2003).
Ownership diffusion
According to agency theory, the separation of ownership and control generates costs resulting from conflicting interests between management and shareholders. Managers have thus an incentive to engage in bonding activities to reassure shareholders that they are acting in a manner consistent with shareholders' interests. Providing extended information may be seen
3 For a review of these studies, see Ahmed and Courtis (1999).
as a bonding activity since it contributes to limit wealth transfers by making them more apparent. Given that deviations from wealth-maximising behaviour are more likely in companies where managers hold a small part of stock, these firms should disclose more information than those controlled by large shareholders. To the extent that Internet allows companies to provide users with more comprehensive, in-depth, and timely information than that included in traditional financial statements (Oyelere et al., 2003), Internet should be particular useful to companies whose ownership is diffuse. The resulting hypothesis is:
H2 : The extent (quality) of financial information on websites is positively related to ownership diffusion.
This influence of ownership structure was tested by Marston and Polei (2002) and Oyelere et al. (2003). Both found evidence consistent with this hypothesis, contrary to Asbaugh et al.
(1999) who report no relationship between Internet financial reporting and the percentage of stock held by individual investors.
International visibility
Companies whose securities are traded on foreign markets should disclose more information than those which are listed on their domestic market only, because they must comply with the requirements of several stock exchanges. For example, foreign companies listed on the New York Stock Exchange must provide a reconciliation to US GAAP in addition to their financial statements. Dispersion of ownership across countries also gives rise to geographical
information asymmetry which can be reduced by additional disclosure. Because it allows instantaneous dissemination of information throughout the world, the Internet is particularly appropriate to reduce such information asymmetries. This lead us to conclude that multi-listed companies should make a larger use of Internet financial reporting. Holding a foreign
subsidiary may even be sufficient to create pressure for additional disclosure. Given that
foreign investments are often financed locally, the parent company is obliged to take into account the information needs of local investors and thus to increase its extent of disclosure.
Finally, the hypothesis is thus:
H3: The extent (quality) of financial disclosure on websites is positively related to international visibility.
This hypothesis is not supported by empirical evidence. Neither Marston and Polei (2002) in Germany, nor Oyelere et al. (2003) in New-Zealand, found a significant influence of foreign listing on the extent of IFR. Debreceny et al. (2002) even report a negative relationship between these two variables.
Profitability
The rationale for an influence of profitability on voluntary disclosure is obvious. When the rate of return is high, managers are motivated to disclose detailed information in order to support the continuance of their position and remuneration. Inversely, when the rate of return is low, they should disclose less in order to conceal the reasons for losses or declining profits.
Signalling theory also suggests that profitable firms should be incited to disclose more in order to distinguish themselves from less successful firms and consequently raise capital at better conditions. Craven and Marston (1999) also argue that reporting on the Internet may be a signal of high quality because it implies that the firm is modern and up to date with the latest technology. This leads to the following hypothesis:
H4: The extent (quality) of financial disclosure on websites is positively related to profitability.
This hypothesis is not supported by empirical evidence since no study found a significant relationship between profitability and Internet financial reporting, neither in the US (Asbaugh
et al., 1999; Ettredge et al., 2002) nor elsewhere (Marston and Polei, 2002; Oyelere et al., 2003).
Industry
Some companies need to be more visible on the Internet because their products apply to a large public, they are doing E. commerce, or because their activity is in relation with the web (data processing, telecoms). Firms in high-tech industries may also view their website as a window they can use to show their technical expertise and ability to utilise the latest technological facilities. Debreceny et al. (2002) also argue that the Internet can allow for frequent disclosures on rapid changes in the technological and business environment of high- tech companies. A relationship between industry classification and the comprehensiveness of websites can thus be expected, which allows us to formulate the following hypothesis:
H5 : The extent (quality) of financial disclosure on websites is related to industry classification.
Craven and Marston (1999) found no relationship between industry type and Internet financial reporting but this hypothesis is validated by Ettredge et al. (2001), Oyelere et al. (2003), and partially by Debreceny et al. (2002) who found a positive relationship between IFR intensity and the level of technology employed by the firm. Asbaugh et al. (1999) also report variations in the use of Internet for financial reporting across industries but they did not test whether these differences were significant.
4. METHODOLOGY
The sample
The sample is representative of non-financial Swiss listed companies. It was constituted as described in table 1.
- Insert table 1 about here -
At the end of 2000, there were 272 Swiss companies listed on the Swiss Stock Exchange (SWX), including 27 banks, 29 financial companies and 20 investment firms. These latter categories were excluded for two reasons. First, it would be difficult, for these firms, to distinguish what part of financial information is aimed at investors and what part is only describing operating activities. Secondly, several banks make a large use of the Internet, due to the development of on-line banking. Their inclusion in the sample would probably have biased the results. Out of the remaining 196 companies, 8 had no websites and 34 had a website but did not use it for financial purpose. Finally, 7 additional companies were excluded because they were not in the Worldscope databank from which most data were extracted. The resulting final sample is thus composed of 147 companies.
The websites of these companies were located using several sources (Swiss Stock Guide, a yearly publication of Finanz und Wirtschaft, Worldscope databank, annual reports, etc.). Their content was analysed between March and August 2001, using a worksheet aimed at
measuring the extent and quality of information disclosed. This worksheet identifies the presence of financial statements, key-figures, market data, press releases, etc. It also notes qualitative characteristics of information disclosed, in particular their format (HTML, PDF, Excel).
The disclosure and quality indexes
To measure the extent of disclosure, points were attributed to each website according to the presence of specific documents or information. A schedule was used, the detail of which is given in table 2. For every site, the maximum number of points which could be obtained was 10. Points were multiplied by 10 to arrive at a score (QI) ranging from 0 (no financial
disclosure) to 100 (disclosure of all expected information).
- Insert table 2 about here -
The same methodology was used to measure disclosure quality. Information was considered of high quality when it was easy to find (financial information grouped into an "investor relations" section, presence of a search engine, etc.) or when the company used interactivity and multimedia facilities offered by the Internet (possibility to register on a recipient list for financial information, or to send e-mails to persons in charge of investor relations, presence of audio or video documents on the site, etc.). Points were thus attributed to each site using a schedule based on these criteria (table 2). To avoid penalising companies with partial disclosure, quality assessment was limited to information really disclosed. The maximum number of points a site could obtain was thus different from a company to another. It ranged from 5 for the least disclosing companies to 12 for firms with full disclosure. The quality index QI was calculated by dividing points obtained by this maximum number and
multiplying the resulting quotient by 100 in order to obtain a score ranging from 0 to 100.
5. RESULTS
The determinants of disclosure quantity
Table 3 reports the results obtained by regressing the disclosure index DI with each
independent scale variable, and those of variance analyses conducted with nominal factors.
- Insert table 3 about here -
The influence of size is clearly established since all size variables have the predicted sign and are significant, especially LSALES which by its own explains more than 16% of total
variance. There is also a strong association between DI and ownership diffusion. LNS (logarithm of the number of shareholders) is even the highest significant variable with an adjusted R2 of 19.8%. This value should nevertheless be interpreted with prudence given the relatively small number of observations (67). International visibility is measured by two variables: FORSAL (percentage of foreign sales) and FORLIST (a binary variable for foreign listing). Both exhibit a positive and strong association with the disclosure index. In contrast, the influence of profitability is not validated since neither AVROE nor AVMR are significant at usual levels. The hypothesis of an association between the extent of disclosure and industry membership is also clearly rejected.
Two types of multivariate analyses have been conducted. Given that some variables are numeric and others are nominal, a covariance analysis was run, with LSALES, FRFLOAT, AVROE as scale variables, and FORLIST and IND as factors. The results are summarised in table 4. Again, the influence of size is clearly established, LSALES being the most significant variable. There is also a significant association between the extent of disclosure and
FRFLOAT, a proxy for ownership diffusion. In contrast, the hypotheses of an influence of
international visibility, profitability and industry membership are rejected. On the overall, the model explains 24.4% of the total variance.
- Insert table 4 about here -
The absence of significant relationship between IND and DI indicates that industry membership is not, on the overall, a determinant of disclosure intensity. But this does not mean there is no difference across industries. Some industries may be characterised by a larger or a smaller extent of disclosure than others. To explore these differences, it was necessary to treat each segment as a separate variable. Accordingly, a regression was run, with a dummy variable for each industry. Its results confirm those of the covariance analysis.
Size and ownership diffusion are significant at usual levels, but not international visibility and profitability. With regard to industry, the hypothesis of an association between business activity and the level of disclosure is validated for insurance companies which appear as disclosing less than firms in other segments, but no significant relationship was found for other industry variables. Incidentally, replacing IND by individual dummy variables results in a small increase of adjusted R2 which moves from 24.4% to 27.8%.
The conclusion which can be drawn from these multivariate analyses is that firms
characterised by a high level of financial disclosure on the Internet are larger, have an higher ownership diffusion and are not insurance companies.
The determinants of disclosure quality
The results of univariate analyses (table 5) show a strong and positive relationship between firm size and disclosure quality. The influence of international visibility also is clearly established, especially when this dimension is measured by the percentage of foreign sales (FORSAL), which by itself explains about 12% of the QI variance. For ownership diffusion, the evidence is mixed, in as much as there are considerable differences in the level of
significance among variables. The same for profitability, which is significant when measured by ROE but not when market return is used. Contrary to what was observed with the
disclosure index, the global industry variable is significant at usual levels.
- Insert table 5 about here -
Multivariate analyses confirm the influence of two variables: international visibility (as measured by the percentage of foreign sales) and size (table 6). By contrast, neither
profitability nor ownership diffusion exhibit a significant relationship with disclosure quality.
The influence of industry membership is not significant when measured globally (IND).
Nevertheless, insurance companies and, to a lesser extent, service firms are characterised by a lower level of disclosure quality.
- Insert table 6 about here -
Multivariate analyses lead to the conclusion that firms with high quality financial information on the Internet are more internationally diversified, larger and are not insurance or service companies.
6. CONCLUSION
The purpose of this research was to identify factors that have an impact on Internet financial reporting. The websites of 147 Swiss listed companies have been examined to measure the extent and quality of information disclosed, and the resulting indexes related to firm
characteristics. Although firm size was positively associated with both measures, the results suggest that the determinants of quantity and quality of disclosure are different, in as much as ownership diffusion and international visibility has an impact on one index only. No influence
was found for industry membership, except that insurance companies are characterised by low values on both indexes.
REFERENCES
Ahmed K. and J.K. Courtis (1999), "Associations between corporate characteristics and disclosure levels in annual reports: A meta-analysis", The British Accounting Review, vol. 31, pp. 35-61.
Asbaugh H., K.M. Johnstone and T.D. Warfield (1999), "Corporate reporting on the Internet", Accounting Horizons, vol. 13, pp. 241-257.
Craven B.M. and C.L. Marston (1999), "Financial reporting on the Internet by leading UK companies", The European Accounting Review, vol. 8, pp. 321-333.
Deller D., M. Stubenrath and C. Weber (1999), "A survey on the use of the Internet for investor relations in the USA, the UK and Germany", The European Accounting Review, vol.
8, pp. 351-364.
Debreceny R., G.L. Gray and A. Rahman (2002), "The determinants of Internet financial reporting", Journal of Accounting and Public Policy, vol. 21, pp. 371-394.
Ettredge M., V.J. Richardson and S. Scholz (2001), "The presentation of financial information at corporate web sites", International Journal of Accounting Information Systems, vol. 2, pp.
149-168.
Ettredge M., V.J. Richardson and S. Scholz (2002), "Dissemination of information for investors at corporate web sites", Journal of Accounting and Public Policy, vol. 21, pp. 357- 369.
Gowthorpe C. and O. Amat (1999), "External reporting of accounting and financial
information via the Internet in Spain", The European Accounting Review, vol. 8, pp. 365-371.
Hedlin P. (1999), "The Internet as a vehicle for investor information: the Swedish case", The European Accounting Review, vol. 8, pp. 373-381.
Lymer A. (1997). "The use of the Internet for corporate reporting – A discussion of the issues and survey of current usage in the UK", working paper, University of Birmingham.
Lymer A. and A. Tallberg (1997), "Corporate reporting on the Internet – A survey and commentary on the use of the WWW in corporate reporting in the UK and Finland", working paper, University of Birmingham, UK.
Marston C. and A. Polei (2002), "Corporate reporting on the Internet by German companies", paper presented at the congress of the European Accounting Association, Copenhagen, 2002.
Oyelere P., F. Laswad and R. Fisher (2003), "Determinants of Internet financial reporting by New Zealand companies", Journal of International Financial Management and Accounting, vol. 14, pp. 26-63.
Pirchegger B. and A. Wagenhofer (1999), "Financial information on the Internet: a survey of the homepages of Austrian companies", The European Accounting Review, vol. 8, pp. 383- 395.
Table 1. Constitution of the sample
Number
Swiss listed companies at end 2000 272
Less:
- Banks -27
- Financial services -29
- Investment funds -20
- 196
- Companies with no website -8
- Companies with no financial information on their website -34
- Companies not included in Worldscope databank -7
Final sample 147
Table 2. Calculation of the disclosure and quality indexes
Points obtainable Points obtained Disclosure index DI: presence of:
- last year full annual report 2 ……..
- annual reports of several years 1 ……..
- simplified annual reports 1 ……..
- half-year reports 1 ……..
- key-figures 1 ……..
- data on stock prices 1 ……..
- press releases 1 ……..
- documents presented at conferences 1 ……..
- reports of financial analysts 1 ……..
Number of points 10 x
Disclosure index DI = 10 x Quality index QI:
For all sites:
- financial information in English 1 ……..
- presence of an "investor relations" section 1 ……..
- financial agenda (important dates) 1 ……..
- possibility of sending e-mails to persons in charge
of investor relations 1 ……..
- presence of a search engine 1 ……..
For sites with financial statements:
- documents in Excel format 2 ……..
- documents in PDF 1 ……..
For sites with data on stock prices :
- presence of charts 1 ……..
- possibility to obtain customized charts 1 ……..
For sites with press releases :
- possibility of registration on a recipient list 1 ……..
For sites with documents presented to conferences :
- presence of audio or video documents 1 ……..
Number of points maximum 12 y
Quality index QI = 100 y / maximum number of points
Table 3. Disclosure index - Results of univariate analyses Regressions
Variables Sign N t Signif. Adj. R2
LSAL + 147 5.476 0.000 0.166
SALES + 147 3.651 0.000 0.078
TA + 147 3.130 0.002 0.057
Size
MV + 147 2.370 0.019 0.031
LNS + 67 3.662 0.001 0.198
FF + 139 3.093 0.002 0.058
NS + 67 2.938 0.005 0.104
Ownership diffusion
OC + 141 2.214 0.028 0.027
International visibility FSAL + 113 2.850 0.005 0.060
ROE + 131 1.064 0.290 0.001
Profitability MR + 116 1.057 0.293 0.001
Variance analyses
Variables N Mean Std dev. F Signif.
FL = 1 24 65.0 20.9
International visibility FL = 0 109 52.4 16.7 7.653 0.006
Chemical 11 49.1 15.1
Building 6 61.7 9.8
Electrical 12 62.5 19.1
Food 9 52.2 20.5
Health 12 60.8 21.5
Industrial 19 51.6 20.1
Insurance 7 44.3 30.0
Machinery 18 58.9 19.7
Media 5 56.0 20.7
Retailers 5 64.0 13.4
Services 16 53.1 26.8
Techno 20 55.5 14.0
Industry
Utilities 7 41.4 24.8
0.986 0.465
Legend :
LSAL = Log. of sales; TA = Total assets; MV = Market value of shares; LNS = Log. of number of shareholders; FF = Free float (as estimated by Finanz und Wirtschaft); NS = Number of shareholders; OC = 100 – total percentage of capital held by known shareholders;
FSAL = Percentage of foreign sales; ROE = Average ROE on 3 years; MR = Average market return on 3 years; FL (Foreign listing) = 1 if the company is listed on a foreign stock market,
= 0 otherwise
Table 4. Disclosure index – Results of multivariate analyses Covariance analysis (N = 124)
Variable F Signif. Adj. R2
Size LSAL 30.181 0.000
Ownership diffusion FF 4.508 0.036
International visibility FL 2.028 0.158
Profitability ROE 0.695 0.407
Industry IND 0.884 0.565
FL*IND 0.529 0.866
Intercept 1.790 0.184
0.244
Multiple regression (N = 124)
Model : DI = Constant + α1 LSAL + α2 FF + α3 ROE + α4 FL + α5 Building + α6 Chemical + α7 Electrical + α8 Food + α9 Health + α10 Industrial + α11 Insurance + α12 Machinery + α13 Media + α14 Retailers + α15 Services + α16 Techno + α17 Utilities
Variables Sign t Signif. F
(signif.) Adj. R2
Size LSAL + 5.525 0.000
Ownership diffusion FF + 2.138 0.035
International visibility FL - -1.260 0.210
Profitability ROE + 0.616 0.539
Insurance - -2.578 0.011
Chemical - -1.416 0.160
Electrical + 0.965 0.337
Media - -0.810 0.420
Food - -0.720 0.473
Retailers + 0.717 0.475
Techno + 0.590 0.556
Services + 0.471 0.639
Building - -0.448 0.655
Industrial - -0.359 0.720
Utilities - -0.258 0.797
Industry
Health - -0.046 0.963
3.957
(0.000) 0.278
Legend :
LSAL = Log. of sales; FF = Free float (as estimated by Finanz und Wirtschaft); FL (Foreign listing) = 1 if the company is listed on a foreign stock market, = 0 otherwise; ROE = Average ROE on 3 years; IND (Industry) = categorical variable
Table 5. Quality index - Results of univariate analyses Regressions
Variables N Sign t Signif. Adj. R2
LSAL 147 + 2.751 0.007 0.043
MV 147 + 2.551 0.012 0.036
SALES 147 + 2.167 0.032 0.025
Size
TA 147 + 1.928 0.056 0.018
FF 139 + 2.870 0.005 0.050
LNS 67 + 1.971 0.053 0.042
OC 141 + 1.774 0.078 0.015
Ownership diffusion
NS 67 + 1.170 0.246 0.006
International visibility FSAL 113 + 4.135 0.000 0.126
ROE 131 + 2.650 0.009 0.044
Profitability MR 116 + 1.045 0.298 0.001
Variance analyses
Variables N Mean Std dev. F Signif.
FL = 1 24 60.7 16.9
International visibility FL = 0 109 51.5 19.4 4.585 0.034
Chemical 11 55.3 12.9
Building 6 60.2 23.4
Electrical 12 51.7 20.0
Food 9 48.0 15.1
Health 12 61.3 19.7
Industrials 19 56.3 15.9
Insurance 7 33.9 31.1
Machinery 18 60.2 15.3
Media 5 49.8 22.5
Retailers 5 55.6 19.1
Services 16 42.6 20.5
Techno 20 59.6 12.0
Industry
Utilities 7 44.3 16.4
2.143 0.018
Legend :
LSAL = Log. of sales; TA = Total assets; MV = Market value of shares; LNS = Log. of number of shareholders; FF = Free float (as estimated by Finanz und Wirtschaft); NS = Number of shareholders; OC = 100 – total percentage of capital held by known shareholders;
FSAL = Percentage of foreign sales; ROE = Average ROE on 3 years; MR = Average market return on 3 years; FL (Foreign listing) = 1 if the company is listed on a foreign stock market,
= 0 otherwise
Table 6. Quality index – Results of multivariate analyses Covariance analysis (N = 104)
Variable F Signif. Adj. R2
International visibility FSAL 7.194 0.009
Size LSAL 4.790 0.031
Industry IND 1.513 0.135
Profitability ROE 1.515 0.222
Ownership diffusion FF 0.894 0.347
Intercept 5.006 0.028
0.272
Multiple regression (N = 104)
Model : DI = Constant + α1 LSAL + α2 FF + α3 FSAL + α4 ROE + α5 Building + α6 Chemical + α7 Electrical + α8 Food + α9 Health + α10 Industrial + α11 Insurance + α12 Machinery + α13 Media + α14 Retailers + α15 Services + α16 Techno + α17 Utilities
Variables Sign t Signif. F
(signif.) Adj. R2 International visibility FSAL + 2.682 0.009
Size LSAL + 2.188 0.031
Profitability ROE + 1.231 0.222
Ownership diffusion FF + 0.946 0.347
Insurance - -2.691 0.009
Services - -1.790 0.082
Food - -1.211 0.229
Chemical - -1.047 0.298
Electrical - -1.021 0.310
Techno + 0.972 0.334
Retailers + 0.698 0.487
Utilities - -0.276 0.784
Media - -0.251 0.802
Building + 0.086 0.932
Health - -0.050 0.960
Industry
Industrial + 0.031 0.976
3.408
(0.000) 0.272
Legend :
FSAL = Percentage of foreign sales; LSAL = Log. of sales; IND (Industry) = categorical variable; ROE = Average ROE on 3 years; FF = Free float (as estimated by Finanz und Wirtschaft)
