Disclosure of the Impacts of Adopting Australian Equivalents of International Financial Reporting Standards

8/11/2019 Disclosure of the Impacts of Adopting Australian Equivalents of International Financial Reporting Standards

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Accounting and Finance 48 (2008) 847870

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BlackwellPublishingLtdOxford,UKACFIAccountingand Finance0810-53911467-629xTheAuthorsJournalcompilation 2008 AFAANZXXX

ORIGINAL ARTICLES

P.D. PalmerP.D. Palmer

Disclosure of the impacts of adopting Australianequivalents of International Financial Reporting Standards

Philip D. Palmer

Flinders Business School, Flinders University, Adelaide, 5042, Australia

Abstract

This study investigates two disclosure variables (

Extent

and Quality

) in relation

to compliance with paragraph 4.1 (b) of AASB 1047 Disclosing the Impacts of

Adopting Australian Equivalents to International Financial Reporting Standards

.Using a sample of 150 Australian listed firms, I find that the extent and quality

of disclosure is influenced by firm size, leverage and auditor firm size, with the

latter variable being the most significant. In general, the results suggest that many

companies might have relied on sample disclosures provided by their auditors,

perhaps limiting both quality and intent. Additionally, the ultimate usefulness

of broad and imprecise standards might be questionable. Smaller companies

might also require more guidance and assistance with their preparation for the

adoption.

Key words

: Voluntary disclosure; Mandatory disclosure; Agency theory;

International accounting standards

JEL classification

: M41

doi

:

10.1111/j.1467-629x.2008.00262.x

1. Introduction

This paper investigates the quality and quantity of disclosures made in

compliance with AASB 1047 Disclosing the Impacts of Adopting Australian

Equivalents to International Financial Reporting Standards

(Australian Account-

ing Standards Board, 2004). As a means of keeping stakeholders informed of the

likely impact of adoption of Australian Equivalents of International Financial

The author would like to thank Peter Gerhardy, Bruce Gurd, Matthew Tilling, Carol Tilt,Bryan Howieson, Ian Zimmer (Deputy Editor) and an anonymous referee for their helpfulinput, comments and feedback on this paper.

Received 28 April 2006; accepted 20 December 2007 by Ian Zimmer (Deputy Editor)

.


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Reporting Standards (AIFRS), the AASB released AASB 1047. The standard

applied to annual and interim reporting periods ending on or after 30 June 2004

to first time adoption of AIFRS. AASB 1047 was of major significance as it

required companies to disclose their level of preparedness leading up to theadoption of AIFRS, and what they consider the impacts of adoption to be. The

objective of the standard was to ensure users of financial reports have information

about the impact of adoption, as well as information concerning how companies

are preparing for adoption.

The scope of the present study is limited to paragraph 4.1 (b) of AASB 1047,

which requires a narrative explanation of the key differences in accounting

policies that are expected to arise from adopting AIFRS. An explanation of how

the transition to AIFRS is being managed, required under paragraph 4.1 (a) of

AASB 1047, is therefore not considered in the current study.The transition to AIFRS has been perhaps the most significant event affecting

financial reporting in Australia for some time. The adoption of AIFRS is a once

in a generation change (PricewaterhouseCoopers, 2004); the biggest account-

ing disruption ever, eclipsing by far the introduction of the goods and services

tax in 2000 (Haswell and McKinnon, 2002, p. 9). Costs to companies in terms

of time and resources in preparing for the change were highly significant (Ham,

2002) and in some cases the cost of preparation was expected to be in the tens

of millions of dollars (Moullakis, 2004). It was also expected that changes to

reported profit might consequently affect the ability to pay dividends, generate

a need to revise profit incentive schemes and impact on loan covenants (Pound,

2004) as well as possible adverse share price reactions (Dodd and Sheehan,

2004, p. 66).

This study demonstrates the effectiveness of a particular regulatory policy

by showing the extent and quality of disclosures made in complying with the

requirements of the standard. Additionally, the exploratory focus on the quality

of disclosure demonstrates the role of AASB 1047 in helping users of financial

statements to cope with the change; that is, the quality of what is being disclosed

being just as important as the quantity of disclosure.

However, it is generally accepted that accounting firms, and particularly theBig Four firms, develop so-called boilerplate disclosures for clients to adopt

in response to major new or amended disclosure requirements (e.g. Ramsay

cited in Maiden, 2002). Sample AIFRS reports are available from some of the

Big Four auditors. If it is the case that companies are using boilerplate dis-

closures to comply with AASB 1047, then the disclosures might not provide an

accurate reflection of the impact of adopting AIFRS on the companies, or their

preparedness for adoption. Rather, the disclosures might, at least in part, reflect

what the audit firm perceives as the areas where impacts are likely to be greatest

and the minimum required to garner the Australian Securities and InvestmentsCommissions acceptance. The danger of this practice is that companies might

not fully comprehend the requirements of AIFRS or have fully investigated the

impact on the company, potentially leading to problems when adoption occurs.


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Additionally, some disclosures that have simply been reproduced might be irrele-

vant to the circumstances of the company (National Institute of Accountants, 2005).

These factors might have implications for the quality of disclosures made to

comply with AASB 1047 and could result in the users of financial reports,including the shareholders, being misled (National Institute of Accountants,

2005). Furthermore, smaller companies might have fewer resources in place to

prepare for the adoption of AIFRS; therefore, this makes it more difficult for

them to cope with the demands of the change. This might result in disclosures

of lower quality for smaller companies.

The study seeks to investigate these issues by considering what corporate

characteristics (size, auditor size, industry, profitability and leverage), if any,

appear to be not only related to the extent of disclosure, but also the quality of

disclosure. The remainder of the paper proceeds as follows. Section 2 providesthe theoretical background for the study and develops the hypotheses. Section 3

outlines the research methods used to test the hypotheses. Section 4 reports the

studys results. Section 5 concludes the study by discussing the implications of

the research findings, the potential limitations of the study and considering

future areas of research.

2. Prior literature and hypothesis development

Prior investigations (e.g. Ernst & Young, 2005; Jubb, 2005) of disclosures

made under AASB 1047 find that the most frequently cited expected accounting

policy differences because of adoption of AIFRS, in order of frequency, are:

AASB 112

Income Taxes

AASB 136

Impairment of Assets

AASB 2

Share-based Payment

AASB 132 and 139

Financial Instruments: Presentation and Disclosure

andFinancial Instruments: Recognition and Measurement

AASB 3

Business Combinations

(Jubb, 2005)

Additionally, Ernst & Young (2005) report that those accounting issuesexpected to have a high impact on profit or equity are:

Share-based payments

Impairment of assets

Income taxes

Defined benefit superannuation plans

The present study extends these preliminary surveys by examining the relation-

ship between extent and quality of disclosure and firm specific variables.

Previous disclosure studies have provided strong support for the relationshipbetween corporate characteristics and disclosure levels; however, the theoretical

basis for such a relationship is unclear (Wallace et al

., 1994, p. 44). Beattie (2005)

suggests that positive accounting theorists have sought to move on from


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explaining accounting policy choices to explaining voluntary disclosure choices.

Agency costs are frequently cited as an explanation of why companies might

disclose financial information (e.g. Chow and Wong-Boren, 1987; Hossain and

Adams, 1995) as such disclosures assist principals to monitor the activities oftheir agents (Jensen and Meckling, 1976). In the case of the adoption of AIFRS

the amount and nature of information released by each company is determined

by the company (agent) and not prescribed by AASB 1047. Therefore, the extent

and quality of disclosure are in effect voluntary and a means whereby agents

can minimize agency costs.

In the disclosure literature, many expressions such as adequate (Singhvi and

Desai, 1971), comprehensiveness (Wallace et al

., 1994) and depth (Naser et al

.,

2002) have been used to describe the quality of disclosure. However, in most

cases quality of disclosure was only used in the sense of measuring the numberof items disclosed.

Studies that investigate both the quantity and quality of disclosure base the

measurement of quality on the depth of information; that is, on consideration

of whether the disclosure improves a users understanding of the financial

statements (Wallace et al

., 1994). Quality is difficult to define and measure in

financial accounting information and quality in relation to narrative accounting

disclosures is complex, context-sensitive and subjective (Beattie et al

., 2004,

p. 229). The aspect of quality that is being investigated and measured in this study

is the perceived informativeness of the disclosure. Further detail regarding the

operationalizing of this measure of a quality score is detailed in Section 3.3.1.

Given the limited investigation of quality in any real sense in the prior litera-

ture, this part of the study is exploratory and, therefore, caution should be exer-

cised in interpreting the results.

2.1. Corporate size and disclosure

Corporate size is consistently found to be significantly and positively related

to the extent of disclosure (Lang and Lundholm, 1993; Clarkson et al

., 2003).Larger companies are more likely to have the resources in place to prepare for

an event such as the adoption of AIFRS (Ahmed and Nicholls, 1994; Hossain

and Adams, 1995) and are likely to have a higher level of internal reporting to

keep senior management informed of progress and, therefore, are likely to have

relevant information available (Owusu-Ansah, 1998). Jones and Higgins (2006)

report that larger firms tend to have greater knowledge of the expected financial

reporting impacts of adopting AIFRS, and are generally more advanced in the

implementation process than smaller firms. Additionally, larger companies are

likely to come under more scrutiny from financial analysts (Hossain and Adams,1995) and shareholders (Cooke, 1989) than smaller companies, leading to pressure

for better disclosure. Therefore, it is expected that larger firms will make more

disclosures and disclosures of better quality.


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H

1

: The extent of disclosure by companies complying with AASB 1047 is greater

for larger companies.

H

2

: The quality of disclosure by companies complying with AASB 1047 isgreater for larger companies.

2.2. Industry and disclosure

A number of studies investigate the relationship between a companys

industry membership and the extent of disclosure (e.g. Cerf, 1961; Owusu-

Ansah, 1998). Because of their unique features companies from a particular

industry group might have different disclosure levels compared to other

industries (Wallace et al

., 1994). Although this study is limited to disclosuresthat affect all firms in the sample (as detailed in Section 3.3.1) it is expected

that different industries will be impacted to a greater or lesser extent by the

adoption of AIFRS. Therefore, it is expected that there will be differing levels

of the extent and quality of disclosure.

H

3

: The extent of disclosure by companies complying with AASB 1047 differs

between industries.

H

4

: The quality of disclosure by companies complying with AASB 1047 differs

between industries.

2.3. Profitability and disclosure

The profitability of a company is also regularly included in disclosure studies

and hypothesized to be positively associated with the extent of a companys

disclosure (Inchausti, 1997; Owusu-Ansah, 1998). The adoption of AIFRS has

the potential to impact on the reported profits of Australian companies (Pound,

2004), and the majority of companies surveyed by Jones and Higgins (2006)

anticipate a negative impact from adoption of AIFRS and, therefore, potentialnegative impacts on share prices (Dodd and Sheehan, 2004). Furthermore,

where a negative impact on profitability is anticipated companies are found to

place higher importance on the issues surrounding adoption and how they could

communicate their continued underlying profitability to their shareholders (Jones

and Higgins, 2006). Therefore, it is expected that more profitable companies

will have a greater extent and quality of disclosure than less profitable firms.

H

5

: The extent of disclosure by companies complying with AASB 1047 is greater

for companies with higher profitability levels.

H

6

: The quality of disclosure by companies complying with AASB 1047 is

greater for companies with higher profitability levels.


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2.4. Leverage and disclosure

Several studies investigate the relationship between leverage (book value of

debt to shareholders equity or book value of debt to total assets) and disclosure,concluding that companies with a high level of leverage disclose more inform-

ation (Inchausti, 1997). This is because a company with a higher gearing level has

a greater obligation to satisfy the needs of its long-term creditors for information

(Wallace et al

., 1994). The adoption of AIFRS has the potential to impact on the

balance sheet (Goodwin and Ahmed, 2006), which might in turn impact debt

covenants with consequences for stakeholders (Ormrod and Taylor, 2004). There-

fore, it is expected that companies with greater levels of debt have a greater extent

and quality of disclosure to explain possible changes to their balance sheets.

H

7

: The extent of disclosure made to comply with AASB 1047 is higher for

companies with a greater proportion of debt in their capital structure.

H

8

: The quality of disclosure made to comply with AASB 1047 is higher for

companies with a greater proportion of debt in their capital structure.

2.5. Auditor size and disclosure

Companies with sound corporate governance are likely to be preparing for

the impending adoption of AIFRS and, therefore, are in a position to be able toprovide detailed disclosures regarding their adoption programme. Clarkson

et al

. (2003) argue that better corporate governance will positively influence the

extent of disclosure. External audits play a strong corporate governance role

and are instrumental in supporting transparent financial reporting (Ashbaugh

and Warfield, 2003). In this study auditor size is used to capture the corporate

governance aspect of the disclosures.

The use of larger auditors can be an indication of higher-quality audits and

enhanced credibility and financial accounting disclosures (Bushman et al

.,

2004). It has been suggested that the contents of annual reports are not only

audited but also influenced by auditors (Wallace et al

., 1994) and the larger and

better known the auditor, the greater influence they might be able to exercise

(Firth, 1979). Therefore, companies represented by the big international audi-

tors are likely to provide more detail in their annual reports than companies that

are not (Wallace et al

., 1994). Many companies are expected to rely heavily on

their auditors for advice regarding the adoption of AIFRS and that the larger the

firm the greater will be the involvement of the auditor (Jones and Higgins,

2006). Therefore, it is expected that there will be a positive relationship

between auditor size and the extent and quality of disclosure.

H

9

: The extent of disclosure made to comply with AASB 1047 is greater for

companies that use a larger (Big Four) audit firm than those that use a smaller

audit firm.


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H

10

: The quality of disclosure made to comply with AASB 1047 is greater for

companies that use a larger (Big Four) audit firm than those that use a smaller

audit firm.

3. Research design

3.1. Sample selection

As the study is concerned with company characteristics that might affect the

quantity and quality of disclosures made by Australian companies, the relevant

population of the study was all companies listed on the Australian Stock

Exchange (ASX). As the study was conducted in 2005 the study used those

entities listed as at 31 December 2004 with balance dates on or between 30

June 2004 and 31 December 2004. A final random sample of 150 companieswas selected.

1

3.2. Data source

The disclosures made to comply with AASB 1047 will be in narrative form

in the notes accompanying companies financial reports, accessed from the

Connect 4 Database.

3.3. Measurement of the variables

The dependent variable in the present study is the extent and quality of

disclosures. Because of the variability of disclosures, the present study focused

on one mandatory disclosure item and measured the extent and quality of that

disclosure. AASB 1047 disclosures have the advantage of being discretely and

easily identified and isolated in the notes of the annual report and, hence, the

extent of disclosure can be directly measured. Table 1 reports the number of

sentences devoted to each of the AASB Accounting Standards that are men-

tioned by companies in their disclosures made to comply with AASB 1047,

broken down by their Global Industry Classification Standard (GICS) two-digitindustry sector codes.

2

Table 1 reveals that the 150 companies making up the

sample used in this study devoted 2956 sentences to disclosures relating to

specific accounting standards.

1

A minimum final sample size of 150 was desired for testing individual predictors in stand-ard multiple regression (Tabachnick and Fidell, 2001). Trusts, companies without GICSclassification or sector codes, with negative equity, using international accounting stand-

ards, suspended or delisted or not reporting between the dates covered by the requirementsof AASB 1047 were not included in the sample.

2

GICS is a joint Standard & Poors/Morgan Stanley Capital International product that aimsto standardize industry classifications and definitions (Australian Stock Exchange, 2005).


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Table 1

Accounting standards referred to in AASB 1047 disclosures in annual reports of sample companies (by

number of sentences)

AASB Energy Materials Industrials

Consumer

discretionary

Consumer

staples

Health

care Financials

Inform

techno10 15 20 25 30 35 40 45

% % % % % % %

2a 13 8 113 11 58 14 48 13 23 17 58 22 30 12 36

3 0 0 47 4 64 15 62 17 10 7 24 9 15 6 14

6 34 21 131 12 0 0 0 0 0 0 0 0 0 0 10

101 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

108 0 0 14 1 0 0 1 0 0 0 0 0 3 1 3

112 a 26 16 181 17 88 21 59 16 22 16 39 14 59 24 43

116 0 0 30 3 22 5 11 3 0 0 7 3 1 0 0117 0 0 3 0 0 0 0 0 0 0 5 2 0 0 0

118 0 0 0 0 0 0 3 1 3 2 0 0 3 1 4

119 3 2 21 2 10 2 12 3 3 2 13 5 0 0 0

120 0 0 0 0 6 1 0 0 0 0 6 2 0 0 0

121 4 2 13 1 16 4 12 3 5 4 0 0 0 0 0

127 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0

128 0 0 4 0 1 0 10 3 0 0 0 0 0 0 0

131 5 3 0 0 0 0 0 0 0 0 0 0 0 0 0

132 1 1 1 0 3 1 0 0 0 0 0 0 0 0 2

136a 34 21 187 18 63 15 67 19 25 19 52 19 30 12 37

137 13 8 69 7 2 0 0 0 3 2 0 0 0 0 2138a 3 2 46 4 18 4 34 9 4 3 35 13 26 11 28

139a 26 16 195 18 74 17 42 12 37 27 30 11 70 28 28

140 0 0 0 0 0 0 0 0 0 0 0 0 8 3 0

162 100 1057 100 425 100 361 100 135 100 269 100 246 100 207

aIndicates the standards analysed in the current study.


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To draw valid conclusions about why firms might have disclosed more or less

than others, or have disclosures of a greater or lesser quality, the effect of

adopting AIFRS must be equal across the sample. Otherwise it might be the

case that some firms are more (less) effected by the adoption of AIFRS thanothers and, therefore, disclose more (less) as a result. Therefore, disclosures

investigated in this study are limited to those that affect all the firms in the

sample, highlighted in Table 1.

3

These results are similar to those reported in earlier studies considering

AASB 1047 disclosures (Ernst & Young, 2005; Jubb, 2005), which found

similar accounting issues were the most frequently cited.

The analysis in the current study considers the total extent of disclosures

as well as the quality of disclosures; that is, inferences are drawn from what

companies have disclosed in their notes. Therefore, the unit of analysis for thisstudy is sentences, which is the preferred unit of analysis if a meaning is to be

inferred (Gray et al

., 1995), with the total number of sentences giving the

extent of disclosure concerning the adoption of AIFRS by each company in the

study. Each sentence was given a qualitative score, based on the perceived

informativeness of the information disclosed. Scores were awarded from 1 to

4 based on a scale where the greater the specificity of the information, the

more useful it is deemed to be and, hence, of greater quality.

4

The rating

scheme used in this study is outlined in Table 2.

5

Having completed the rating of sentences, the qualitative score for each

company was then totalled.

6

3.3.1. Independent variables

Prior studies have adopted different measures of corporate size; however,

Ahmed and Nicholls (1994, p. 65) state that there is no overriding theoretical

reason to select one variable rather than another. The current study adopts total

3

Although Table 1 indicates no firms in the Telecommunication Services sector mentionAASB 139 as being relevant to them, this is more likely to be due to the small number offirms in that sector. AASB 139 was still included in the study because of its significantimpact on the other nine sectors.

4

A similar (four-point) scale is used by Clarkson et al

. (2003) when considering compa-nies voluntary disclosures concerning the year 2000 systems issue.

5

The coding rules and the coding of a sample of companies was reviewed by an independ-ent accounting researcher.

6

An initial version of the paper used a quality score per sentence by dividing the total qual-ity score by the number of sentences. In the present paper, the quality score used is the totalquality score, acknowledging that there is likely to be some interaction between the dimen-sions of extent and quality. Statistical results obtained under the alternate measure of qualitywere not materially different to those reported here.


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assets as a measure of corporate size.7The 150 companies included in the study

are classified according to Sector, being the broadest classification, to avoid

the problem of sparse cells. A 2-test was conducted to ensure the sample was

representative of the population. The results indicate that there is no statistical

difference between the sample and population with respect to industry classifica-

tion (2statistic = 5.5565, p= 0.783).

7Size has also been measured as net sales (e.g. Cooke, 1989) and market capitalization (e.g.Chow and Wong-Boren, 1987).

Table 2

Coding rules used as the basis for the qualitative score applied to each sentence of disclosure

Rating Criteria Examples

Ratings apply in relation to specific standards

1 Applies where the sentence identifies issues

specifically relevant to the company; for

example, detailing the existing accounting

policy or practice of the company, or detailing

the accounting policy or practice under the

new standard. However, no indication of the

impact of the change in accounting policy or

practice is mentioned in a sentence rated with

a score of 1.

In terms of pending AASB 136 Impairment

of Assets, the recoverable amount of an asset

will be determined as the higher of fair value

less costs to sell and value in use.

2 Indicates disclosure of some impact on thecompany without necessarily specifying what

that impact will be. This indicates that there

will be some change in policy or practice that

will impact on revenues, expenses, assets,

liabilities and/or equity, but the extent of

the impact has not yet been determined.

Additionally, sentences that stated there

might be some impact were included in

this rating.

Under AASB 2 Share Based Payments, thecompany will be required to determine the

fair value of options issued to employees as

remuneration and recognise an expense in the

Statement of Financial Performance.

For 3 or 4 the sentence must explicitly state that there will or will not be an impact

3 Applies where a sentence gives detailsabout the impact of adopting Australian

International Financial Reporting Standards

and gives an indication of the nature and

direction of the impact.

The recognition of the share-basedcompensation expense will decrease the

consolidated entitys profit in future.

4 Has the same criteria as 3, with the additional

requirement that the dollar value of the

expected change is provided. Sentences with

scores of 3 or 4 then give an indication of the

direction of the change.

The maximum deferred tax liability that might

be required to be recorded in relation to this is

approximately $A20 445 000.


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As the industry variable is categorical, dummy variables are used in the

regression analysis, with the Energy sector used as the reference group. 8Return

on equity (e.g. Inchausti, 1997) and Return on assets (e.g. Raffournier, 1995)

are used as measures of profitability in this study and Debt to assets (e.g.Alsaeed, 2005) and Debt to equity (e.g. Wallace et al., 1994) are used as a

measure of leverage. Auditoris classified as being either a member of the Big

Four (Deloitte Touche Tohmatsu, Ernst & Young, PricewaterhouseCoopers and

KPMG), or Other. A dummy variable (1, 0) is used in the regression analysis

where the dummy variable takes the value of 1 if the company is audited by one

of the Big Four firms.

3.4. Statistical tests

Both univariate and multivariate methods are used to test the hypotheses

developed above. Pearson productmoment correlation coefficients are used

to investigate the relationship between the explanatory variables, and an

independent-sample t-test is used to examine the relationship between industry

and the dependent variables. Between groups analysis of variance (anova) tests

are conducted to test differences in the medians of the dependent variables for

companies audited by different accounting firms. The multivariate test used in

this study is standard multiple regression.

4. Results

4.1. Descriptive statistics

Table 3 contains the descriptive statistics for the dependent variables (Panel A)

and the non-categorical independent variables (Panel B) defined in the previous

section.

The skewness and kurtosis coefficients of all the variables included in Table 3

indicate departures from normality, with the exception of Debt to assets, which

is only slightly positively skewed.9Additionally, the KolmogorovSmirnov one-sample test statistics for all of the variables, apart from Debt to assets, are all

significant, suggesting violation of the assumption of normality. To bring the

variables closer to normality for the purpose of the regression analysis trans-

formation of the variables was undertaken.

8The Industry Sectors represented in the study and their distributions are detailed in PanelC of Table 3.

9Foster (1986) suggests that a benchmark for suspecting positive and negative skewness isa skewness coefficient of greater than 0.50 or less than 0.50, respectively. Likewise, Foster(1986) suggests that a kurtosis coefficient of greater than 1.0 or less than 1.0 indicate aviolation from normality.


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Table 3

Descriptive statistics for the dependent variables extent and quality of disclosure and independent variables size, l

untransformed data

Panel A: Dependent

variables Panel B: Non-categor

Extent Quality Assets

Debt to

assets

Valid observations 150 150 150 150

Minimum 0 0 299 779 0.01

Maximum 48 61 10 286 400 000 0.95

Mean 11.54 15.52 412 801 296 0.3153Standard deviation 8.985 11.680 1 268 576 884 0.25080

Skewness 1.661 1.382 4.784 0.631

Kurtosis 3.595 2.381 28.077 0.487

KolmogorovSmirnov statistic 2.008 1.840 4.736 1.517

p-value (two-tailed) 0.001 0.002 0.000 0.020


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Panel C: Categorical independent variables

Industry sector

Frequency Percentage

Energy 9 6.0 Deloitte Touche Tohmatsu

Materials 49 32.7 PricewaterhouseCoopers

Industrials 19 12.7 KPMG

Consumer discretionary 18 12.0 Ernst & Young

Consumer staples 6 4.0 Other

Health care 12 8.0 Total

Financials 17 11.3

Information technology 13 8.7

Telecommunication 5 3.3

Utilities 2 1.3

Total 150 100.0

Extent is the number of sentences disclosed by a company. Quality is the quality score of the disclosures ba

Assetsis obtained from the annual report and used as a measure of the size of the company. Debt to assetsis the t

from the annual report and used as a measure of leverage). Debt to equityis the total liabilities divided by total eq

as a measure of leverage). Return on assets is profit divided by assets (obtained from the annual report and u

equityis profit divided by equity (obtained from the annual report and used as a measure of profitability).


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Table 4

Descriptive statistics for the dependent variablesExtentand Qualityof disclosure and non-categorical independe

transformed data

Panel A: Dependent

variables Panel B: Non-categ

Extent Quality Assets

Debt to

assets

De

eq

Valid observations 150 150 150 150

Minimum 0 0 5.48 0.11

Maximum 6.93 7.81 10.01 0.97

Mean 3.13 3.632 7.50 0.5079 0Standard deviation 1.33 1.53 0.9497 0.240 0.6

Skewness 0.101 0.012 0.591 0.023

Kurtosis 0.825 0.503 0.027 1.176

KolmogorovSmirnov statistic 1.037 0.878 1.044 1.415

p-value (two-tailed) 0.232 0.423 0.226 0.037

Extent is the number of sentences disclosed by a company (square root transformation applied). Quality is the

coding rules outlined in Table 3 (square root transformation applied). Assetsis obtained from the annual report and

(logarithmic transformation applied).Debt to assetsis total liabilities divided by total assets (obtained from the an

square root transformation applied).Debt to equityis total liabilities divided by total equity (obtained from the an

logarithmic transformation applied). Return on assetsis profit divided by assets (obtained from the annual reportand inverse transformation applied). Return on equityis profit divided by equity (obtained from the annual report

and inverse transformation applied).


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Tabachnick and Fidell (2001) suggest that consideration should be given to

transformation of variables in all situations unless there is some valid reason

not to; for example, difficulty of interpretation of the transformed variables.

Many of the disclosure studies considered in this study have applied logarithmicand square root transformations to the variables of interest (e.g. Ahmed and

Nicholls, 1994; Wallace et al., 1994; Hossain and Adams, 1995; Raffournier,

1995). Cohen and Cohen (1983) suggest that there might be more than one trans-

formation that will assist in correcting for failure of the assumption of normality.

In the present study, each variable was transformed using square root and log

transformations and negatively skewed variables were reflected before calcula-

tion of their inverse.10The transformed variable with the distribution that was

closest to normal was selected for inclusion in the parametric tests. Table 4 con-

tains the descriptive statistics for the transformed variables.As can be seen from Table 4 the departures from normality, reflected in the

skewness and kurtosis coefficients, is less for all of the transformed variables

than for the corresponding raw variables, except for Debt to assets, where the

skewness coefficient has decreased but the kurtosis coefficient has increased

over that of the raw variable. For most of the transformed variables, with the

exceptions of QualityandDebt to equity, some departure from normality is still

evident. However, applying a 0.05 level of significance in the Kolmogorov

Smirnov tests, the only variables for which the null hypothesis of normality is

rejected is Debt to assets and Return on assets, which have probability levels

associated with them of 0.037 and 0.043, respectively.

4.2. Univariate tests

Correlation analysis was used to test the relationship between the transformed

dependent variables, the transformed non-categorical independent variables and

the dichotomous categorical independent variables. Table 5 shows the relevant

Pearson productmoment correlation coefficients.

As can be seen from the first column of the table the correlation coefficients

between Extent and the independent non-categorical variables are significant11

10A variable is reflected by finding the largest score in the distribution and adding one to itto form a constant that is larger than any score in the distribution. A new variable is thencreated by subtracting each score from the constant. Therefore, a variable with negativeskewness is converted to one with positive skewness before transformation (Tabachnick andFidell, 2001). The reflection of a variable normally requires that the interpretation of it bereversed, or the variable re-reflected after transformation (Tabachnick and Fidell, 2001).

However, calculating the inverse of the variables after reflecting itself acts as a reflection;hence, no reversal of interpretation is required.

11 Cohen (1988) suggests that correlations of 0.100.29 are small, 0.300.49 are mediumand 0.501.0 are large.


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at the indicated levels,12are all positive, and of the hypothesized sign except for

one of the transformed measures of profitability. Using Cohens (1988) guide-

lines, the correlation between Extent and Size and Extent and Leverage is

medium, but small for Profitability. Therefore, it appears that some support forHypotheses 1 and 7 is provided by this test, that larger companies and com-

panies with more debt in their structure disclose more information. The test

appears to provide limited support for Hypotheses 5, that more profitable

companies disclose more information.

However, it is the correlation coefficient between Extent and the independent

categorical variable Auditor that is the most significant, providing support for

Hypothesis 9, that auditor size impacts on the extent of disclosure.

The correlation coefficients do not support the hypothesis relating to the

expected relationship between the dependent variable Extent and the explana-tory variable Industry.

While the dependent variable Extentmeasures the amount of disclosure, the

dependent variable Quality is the total informativeness score awarded to each

company. Table 5 also contains the Pearson productmoment correlation co-

efficients for the transformed dependent variable Qualityand the transformed

independent variables included in this study. As can be seen from the second

column of the table there is medium support (using Cohens (1988) guidelines)

for Hypothesis 2, that larger companies have a greater quality score and limited

support for Hypothesis 8, that companies with more debt in their structure have

a greater quality score. However, consistent with the extent result, it is the size

of the companys auditor that has the strongest relationship with the quality

of disclosure, providing support for Hypothesis 10. The correlation coefficients

do not support the hypothesis relating to the expected relationship between the

dependent variable Quality and the explanatory variables for profitability or

Industry.

Table 5 indicates that there is a medium (up to 0.560) correlation between

Assets and all of the other non-categorical independent variables, reflecting that

size probably captures most of other influences because of a high correlation

with many variables (Raffournier, 1995, p. 275). Additionally, as would beexpected, there is a significant correlation between Debt to assets and Debt to

equity and Return on assets and Return on equity. The first pair of variables

both measure leverage, and the latter pair measure profitability. The existence

of interaction between the independent variables indicates that multivariate

analysis is required to account for such relationships. Any single multivariate

analysis will only include one of the independent variables measuring leverage

and profitability.

12 Probability values of 0.050 or better are regarded as significant for the purpose of thisstudy.


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anova tests were conducted to further explore the impact of auditor size on

the extent and quality of disclosure. The results of the anova tests are pre-

sented in Panel A of Table 6.The results of the anova tests indicate that there is a statistically significant

difference at the p< 0.0001 level in Extentand Qualityscores for the different

groups of auditors. The effect size, calculated using eta squared was 0.257 for

Extent and 0.243 for Quality, which in Cohens (1988) terms are considered

large. Therefore, when taken as a group there is a significant difference in the

Extent and Quality scores between the Big Four and Other. The tests pro-

vide evidence to support Hypotheses 9 and 10, that on average, companies

audited by bigger auditors have higher Extentand Qualityscores.

Post-hoc comparisons using the Tukey HSD test indicate that for ExtentandQuality, the mean score for Other was only statistically different to that of

Deloitte Touche Tohmatsu and Ernst & Young. Panel B of Table 6 displays the

means and standard deviations for the auditors for both Extentand Quality.

Table 6

Investigation of the impact of auditor size on the extent and quality of disclosure and post-hoc

comparison of variations between the Big Four auditors

Panel B: Auditor mean and standard deviation for dependent variables Extentand Quality

Panel A: Analysis of variance test results

Sum of squares Degree of freedom Mean square F Probability

Extent

Between groups 67.75 4 16.938 12.538 0.000Within groups 195.88 145 1.351

Total 263.63 149Quality

Between groups 84.76 4 21.189 11.620 0.000Within groups 264.40 145 1.824Total 349.16 149

Auditor Number

Extent Quality

Mean Standard deviation Mean Standard deviation

Big FourDeloitte Touche Tohmatsu 18 3.84 1.38 4.45 1.51PricewaterhouseCoopers 19 2.97 0.76 3.39 0.92KPMG 18 3.05 1.16 3.53 1.36Ernst & Young 29 4.20 1.21 4.84 1.29

Other 66 2.52 1.16 2.96 1.41Total 150

Extentis the number of sentences disclosed by a company (square root transformation applied).

Qualityis the quality score of the disclosures based on the coding rules outlined in Table 3 (square root

transformation applied). Other is an auditor other than the Big Four.


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It is interesting to note that the rankings for the auditors are the same for

extent and quality of disclosure. One company could have a higher extent of

disclosure than another, but these additional words might not necessarily

improve a readers understanding (Wallace et al., 1994), but might just be pad-ding. The fact the rankings for extent and quality are the same indicates this is

not the case in this study, but the additional disclosures have potentially

improved the readers understanding of the impact of adoption of AIFRS. This

indicates that in disclosure studies of this type the quality of information being

disclosed is equally important as the extent of disclosure.

The relationship between the dependent variables and the categorical explan-

atory variable Industry was investigated using the independent-samples t-test.

The results indicate that it is not possible to reject the null hypothesis of no

difference in the extent of information disclosed across the different industrysectors; that is, the evidence does not support Hypothesis 3 or Hypothesis 4, that

the extent or quality of disclosure will be different for companies in different

industries.13

4.3. Multivariate tests

Multiple regression analysis was used for multivariate testing of the hypo-

theses. Each of the transformed dependent variables, Extent and Quality, was

regressed against the transformed independent variables of Size, Leverage and

Profitability. The dummy variable for Auditor and dummy variables for Industry

Sectors were also included. The results of these regressions are reported in

Table 7.14

4.3.1. Extent

The multiple regression model is highly significant (p0.001).15The coeffi-

cient of determination (adjusted R2) indicates that 19 per cent of the variation

in the dependent variable is explained by variation in the independent variables.

13The non-parametric alternative KruskalWallis test was conducted on the raw data, withthe results being consistent with the t-test results.

14Both models use the transformed variableReturn on assetsas the measure of profitabilityand Debt to assets as the measure of leverage. The regressions on both of the dependentvariables were re-estimated using the alternative measure for profitability of Return onequityand the alternative measure for leverage ofDebt to equity. The results of these addi-tional tests were not different in any significant way from the results reported in Table 7.

15 Both regression models reported were tested for heteroscedacity and multicollinearity.Neither was found to be a significant factor affecting the reliability of the results. ToleranceInflation Factors (VIF) are also reported in Table 7 with none being over 5, which Hair et al.(2003) suggest would be the maximum VIF value before multicollinearity becomes a factor.


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Table 7

Regression of size, leverage, profitability, industry and auditor on extent and quality of disclosure by

companies complying with AASB 1047

Explanatory variable Coefficient t-statistic Significance

Varianceinflation

factor

Panel A: Dependent variable: Extent

Constant 0.564 0.574

Assets 0.111 1.015 0.312 2.216

Debt to assets 0.233 2.260 0.025 1.951

Return on assets 0.110 0.137 0.891 1.250

Auditor 0.316 3.744 0.000 1.313

Materials 0.173 1.114 0.267 4.426

Industrials 0.064 0.510 0.611 2.901Consumer discretionary 0.021 0.168 0.867 2.771

Consumer staples 0.056 0.588 0.558 1.672

Health care 0.175 1.593 0.114 2.210

Financials 0.002 0.015 0.988 2.581

Information technology 0.081 0.718 0.474 2.350

Telecommunication services 0.006 0.061 0.951 1.523

Utilities 0.019 0.229 0.819 1.276

R2= 0.190;F= 3.686;p= 0.000; n= 150

Panel B: Dependent variable: Quality

Constant 1.077 0.284Assets 0.082 0.740 0.461 2.216

Debt to assets 0.201 1.922 0.057 1.951

Return on assets 0.032 0.385 0.701 1.250

Auditor 0.321 3.751 0.000 1.313

Materials 0.164 1.040 0.300 4.426

Industrials 0.069 0.539 0.591 2.901

Consumer discretionary 0.046 0.366 0.715 2.771

Consumer staples 0.056 0.576 0.566 1.672

Health care 0.210 1.890 0.061 2.210

Financials 0.012 0.101 0.920 2.581

Information technology 0.114 0.994 0.322 2.350Telecommunication services 0.035 0.379 0.706 1.523

Utilities 0.003 0.040 0.968 1.276

R2= 0.168;F= 3.307;p= 0.000; n= 150

Extentis the number of sentences disclosed by a company (square root transformation applied).

Qualityis the quality score of the disclosures based on the coding rules outlined in Table 3 (square root

transformation applied).Assetsis obtained from the annual report and used as a measure of the size of

the company (logarithmic transformation applied). Debt to assets is total liabilities divided by total

assets (obtained from the annual report and used as a measure of leverage; square root transformation

applied). Return on assets is profit divided by assets (obtained from the annual report and used as a

measure of profitability; reflect and inverse transformation applied). Auditoris a dummy variable (1 whena Big Four auditor is used (Deloitte Touche Tohmatsu, Ernst & Young, PricewaterhouseCoopers or

KPMG); 0 otherwise).Materials,Industrials, Consumer discretionary, Consumer staples,Health care,

Financials, Information technology, Telecommunication services and Utilities are dummy variables

representing industry sectors as detailed in Panel C of Table 5.


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The computed R2statistic indicates that the explanatory power of the model is

moderate, but is similar to those reported in the other disclosure studies. The

coefficients for Auditor and Leverage are statistically significant (p 0.001 and

p 0.05). Of the two statistically significant variables, Auditor (beta = 0.316) ismaking the strongest unique contribution to explaining the dependent variable,

when the variance explained by all other variables in the model is controlled for

(compared toDebt to assetswith a beta of 0.233). The results provide evidence

to support Hypotheses 7 and 9, that companies with more debt in their capital

structures and those audited by bigger auditors disclose more information.

4.3.2. Quality

The multiple regression model for Quality is highly significant (p 0.001).The coefficient of determination (adjustedR2) indicates that 16.8 per cent of the

variation in the dependent variable is explained by variation in the independent

variables, indicating that the explanatory power of the model is moderate. The

coefficient for Auditor is statistically significant (p 0.001) and of the variables

(beta = 0.321) is making the strongest unique contribution to explaining the

dependent variable, when the variance explained by all other variables in the

model is controlled for. The significance of the coefficient for Leverage is close

to acceptance (p= 0.057). The results provide evidence to support Hypothesis

10, that companies audited by bigger auditors have disclosures of a higher quality

and there is limited support for Hypothesis 8, that companies with more debt in

their capital structures have disclosures of a higher quality.

5. Conclusions

The present study is particularly important given the significance of the adop-

tion of AIFRS and the uncertainty surrounding adoption. Size tends to dominate

other variables in most disclosure studies investigating the relationship between

levels of disclosure and corporate characteristics. However, the present study

conducted in an Australian setting and using the introduction of AIFRS as anopportunity for investigating disclosure, finds that in this case the size of a com-

panys auditor is making the strongest contribution to the extent of disclosure.

The present study also explores the quality of disclosure and finds that,

consistent with the extent of disclosure, auditor size is making the strongest

contribution to the quality of disclosure; that is, the auditor effect is consistent

across both extent and quality of disclosure. It might be the uniqueness of the

disclosure being examined that partly explains the results of this study being

inconsistent with the results of previous disclosure studies that mainly find size

to be the most significant influence on the extent of disclosure. The resultssuggest that many companies might have relied extensively, if not solely, on

example disclosures provided by their auditors as a means of meeting the

requirements of AASB 1047.


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If it is the case that companies have relied on sample disclosures provided

by their auditor then the disclosures might not provide an accurate reflection of

the impact of adopting AIFRS on them, or their preparedness for adoption.

Furthermore, where the requirements of such a standard are broad and open tointerpretation, companies might be tempted to use boilerplate disclosures or

be inclined to disclose the minimum amount to meet the requirements of the

standard and satisfy regulators. Therefore, the information provided might be of

doubtful value to financial statement users; that is, it might not provide relevant

and reliable information which is of assistance in the decision-making of users.

The present study is subject to some limitations. First, the creation of a

disclosure index requiring the awarding of a quality score to each sentence, as

detailed in Section 3.3.1, involves a level of subjectivity. However, as outlined,

actions were taken to overcome this problem. Second, as Section 3.1 details,the number of companies in the sample does not allow the companies to be

classified into a more specific classification than the 10 GICS industry sectors.

This provides an opportunity for further investigation of the research question

utilizing a larger sample, allowing industry to be classified into more specific

categories than the 10 sectors used in the current study. An additional direction

for future research is to investigate the actual impact of the requirements of

specific standards on companies with a particular focus on those standards that

have been identified in Section 4.1 as potentially impacting most broadly across

the sample and those that are identified as having the largest potential impact on

the reported profits and retained earnings of companies. This research would

provide more detailed insights into the results of the current study.

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Documents

Disclosure of the Impacts of Adopting Australian Equivalents of International Financial Reporting Standards