Description
Changes in Australian segment reporting standards over the last decade changed the
required disaggregation of segment information. The purpose of this paper is to investigate whether
increased disaggregation has implications for users’ confidence in decisions based on segment reports
and perceptions of segment reporting usefulness.
Accounting Research Journal
Forecasting confidence under segment reporting
J acqueline Birt Greg Shailer
Article information:
To cite this document:
J acqueline Birt Greg Shailer, (2011),"Forecasting confidence under segment reporting", Accounting
Research J ournal, Vol. 24 Iss 3 pp. 245 - 267
Permanent link to this document:http://dx.doi.org/10.1108/10309611111186993
Downloaded on: 24 January 2016, At: 21:14 (PT)
References: this document contains references to 37 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 1612 times since 2011*
Users who downloaded this article also downloaded:
M. Humayun Kabir, Fawzi Laswad, (2011),"Properties of net income and total comprehensive
income: New Zealand evidence", Accounting Research J ournal, Vol. 24 Iss 3 pp. 268-289 http://
dx.doi.org/10.1108/10309611111187000
Kevin Clarke, J ack Flanagan, Sharron O'Neill, (2011),"Winning ARC grants: comparing accounting with
other commerce-related disciplines", Accounting Research J ournal, Vol. 24 Iss 3 pp. 213-244 http://
dx.doi.org/10.1108/10309611111186984
Karol Marek Klimczak, Grzegorz Szafranski, (2013),"Coincident and forecast relevance of accounting
numbers", Accounting Research J ournal, Vol. 26 Iss 3 pp. 239-255http://dx.doi.org/10.1108/
ARJ -09-2012-0076
Access to this document was granted through an Emerald subscription provided by emerald-srm:115632 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Forecasting con?dence under
segment reporting
Jacqueline Birt
Department of Accounting & Finance, Monash University,
Cau?eld East, Australia, and
Greg Shailer
School of Accounting & Business Information Systems,
The Australian National University, Canberra, Australia
Abstract
Purpose – Changes in Australian segment reporting standards over the last decade changed the
required disaggregation of segment information. The purpose of this paper is to investigate whether
increased disaggregation has implications for users’ con?dence in decisions based on segment reports
and perceptions of segment reporting usefulness.
Design/methodology/approach – Using an experiment based on the differences between the
original AASB 1005 and the more detailed requirements of AASB 114, the authors test whether
segment report users’ con?dence in forecasting and their perceptions of segment report usefulness
differ between the different information sets provided under these standards.
Findings – It was found that the more disaggregated or ?ner reports based on AASB 114 provide
signi?cantly more con?dence to users, compared to the coarser segment reports based on the original
AASB 1005, but this is not associated with differences in segment report usefulness scores.
Research limitations/implications – The authors’ experiment is based on AASB 1005 and AASB
114 and the results cannot be generalized to differences with other reporting standards. Examination
of differences in recently released AASB 8 may reveal different implications for users’ con?dence and
perceptions of usefulness. More generally, other tests of usefulness are needed to con?rm whether
opinions of usefulness that are not con?rmed by decision-making practices provide a reliable basis for
determining usefulness.
Practical implications – By con?rming that decision makers’ con?dence can be increased by the
provision of ?ner information sets, the authors’ results have practical implications for accounting
standard setting.
Originality/value – By testing the impact of report differences on user decision con?dence, the
paper addresses a previously overlooked issue.
Keywords Australia, Accounting standards, Pro?t forecasting, Segment reporting, Fineness theorem,
Analysts, Experiment, Value relevance
Paper type Research paper
1. Introduction
There is substantial evidence that equity markets value segment data (Chen and Zhang,
2003; Berger and Hann, 2003) and this value relation is increasing with the amount of
data provided (Ettredge et al., 2005). Propositions as to why segment disclosures have
value largelyaccordwith the “?neness theorem” (Foster, 1975; Baldwin, 1984; Herrmann
and Thomas, 1997), which posits that a ?ner information structure is more valuable
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1030-9616.htm
The authors appreciate the helpful comments of the two anonymous reviewers and
Natalie Gallery.
Segment
reporting
245
Accounting Research Journal
Vol. 24 No. 3, 2011
pp. 245-267
qEmerald Group Publishing Limited
1030-9616
DOI 10.1108/10309611111186993
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
to a decision-maker than a coarser information structure (Marschak and Radner, 1971).
Analysts and investors endorse this view in relation to segment reporting, claiming it
improves analysis of the risk pro?les and growth of diversi?ed entities and allows better
integration of entity data with external data, and thus improves the accuracy of, and
con?dence in, earnings forecasts (Collins, 1976; Balakrishnan et al., 1990). Prior studies
con?rm the expectation of improved accuracy for earlier US segment reporting efforts
(Kinney, 1971; Collins, 1976; Baldwin, 1984; Balakrishnan et al., 1990) and the later SFAS
14 requirements (Lobo et al., 1998) but there has been little attention given to changes in
segment reporting regulation and the impact on forecasting con?dence. Maines et al.
(1997) examine perceptions of segment reporting reliability under SFAS 131, however
there is no comparable study of the segment reporting requirements under Australian
regulation. We argue that it is desirable to consider qualitative characteristics by report
users to better understand howdifferences in reports might affect users’ perceptions and
forecasting con?dence.
We address this knowledge gap by using two substantially different versions of
segment reports to test whether the provision of more detailed segment disclosures
improves earnings forecasting con?dence and the perceived usefulness of segment
reports. The results of our study offer potentially valuable feedback to standard setters
on the consequences of expanding segment disclosure details. This feedback is timely
because the AASB and the IASB recently issued AASB 8 (IFRS 8) that mandates the
use of the “management approach” to de?ning operating segments of the ?rm without
knowledge of how differences in disclosure details affects user con?dence[1].
As part of its IASB harmonisation program, the Australian Accounting Standards
Board issued a revised AASB1005 Segment Reporting in 2001, effective in 2002. In 2005,
this standard was replaced by AASB 114 Segment Reporting[2] as part of Australia’s
formal adoption of International Financial Reporting Standards (AIFRS). Compared to
the original AASB 1005, AASB 114 required substantially more segment data. It
required entities to choose whether their line of business (LOB) or geographic segments
were their primary segments, provide additional descriptive segment information, and
disclose at least nine segment items for the primary segments and three items for the
secondary segment. The original AASB 1005 required less disclosure in terms of
number of segments and explanatory details. Speci?cally, it mandated revenue,
pro?t and assets for both a ?rm’s geographic and industry segments, with little or no
supporting descriptive information. The original AASB 1005 also provided
management with more discretion regarding the de?nition of its reportable segments,
which was seen as facilitating reports based on vague, coarse or ambiguous segments
(Birt, 2008). Because AASB 114 provided a more disaggregated or ?ner view of a ?rm’s
results, compared to the original AASB1005, it is expected that users would perceive the
AASB 114 disclosures as more useful, thus increasing their con?dence in using the
reports. We investigate this conjecture in two complementary ways. We test whether
differences in the level of detail in segment disclosures are associated with:
(1) users’ con?dence levels for their earnings forecasts; and
(2) users’ perceptions of the usefulness of segment reports.
To do so, we use an experimental instrument based on the different levels of ?neness
associated with segment disclosure requirements of the original AASB 1005 and
AASB 114.
ARJ
24,3
246
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
We ?nd that users of more disaggregated reports based on AASB 114 have
signi?cantly more con?dence in their pro?t forecasts, compared to users of reports
based on the original AASB 1005. By revealing that decision makers’ con?dence can be
increased by the provision of ?ner information sets, this study contributes to the general
accounting literature concerned with the ?neness of information structures and users’
perceptions, and to the literature speci?cally concerned with the advantages and
disadvantages of different forms of segment reporting. Our ?ndings are timely feedback
to standard setters on the consequences of changing the levels of disaggregation of
segment disclosures, as the recently introduced AASB 8 Operating Segments again
substantially changes segment disclosure requirements.
2. Hypothesis development
The ?neness theorem posits that a ?ner information structure bene?ts the decision
maker. Information in set X is said to be as ?ne or ?ner than the information in set Y if
X is a sub-partition of Y (Demski, 1973). It follows that, if consolidated reports that
include segment disclosures provide ?ner information than consolidated reports
without segment disclosures, then including segment disclosures bene?ts report users.
It has been suggested that segment disclosures can improve the accuracy of and
con?dence in earnings forecasts (Collins, 1976; Balakrishnan et al., 1990). Consistent
with this suggestion, Mande and Ortman (2002) report that analysts perceive segment
data as useful in forecasting consolidated pro?ts. By extension, more detailed segment
disclosures should be more bene?cial than less detailed segment disclosures.
AASB 114 is ?ner than the original AASB 1005 to the extent that the additional
disclosure requirements of AASB 114 are a sub-partition of the original AASB 1005
requirements. The comparison of the original AASB 1005 and AASB 114 provided in
Table I shows AASB 114 does not strictly provide a sub-partitioning of the original
AASB 1005. AASB 114 was more restrictive and required additional disclosures for
some segments. In addition to distinguishing primary and secondary segments, it
required at least six additional disclosures for primary segments. However, while
requiring the additional disclosure of secondary segment capital additions, AASB
114 did not require the disclosure of secondary segment pro?t. This last factor means
AASB 114 is not necessarily a true sub-partition of the original AASB 1005 for every
company. Nonetheless, the sub-partitioning of primary segment data may be suf?cient
to bene?t users on average.
In relation to the increased explanatory details and number of segments in the USA
implementation of SFAS 131, Maines et al. (1997) report that the analysts perceived
segment reporting to be more reliable when there is congruence between internal and
external reporting as required by the SFAS 131. However, Bar-Yosef and Venezia (2004)
report that analysts’ forecast accuracy was not signi?cantly different under SFAS
131 disclosures compared to SFAS 14. These US results emphasise the importance of
distinguishing perceptions from realised outcomes. We adopt a more subtle distinction
here, where we distinguish perceived usefulness of reports and self-reported con?dence
in forecasts based on the reports. We are not concerned with differences in forecast
accuracy, which cannot be reliably tested in our experimental setting. Irrespective of
whether forecasts are different under the alternative information sets offered by the
original AASB 1005 and AASB 114, differences in con?dence in using the different
reports represent value to risk adverse users.
Segment
reporting
247
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The original AASB1005 was criticised for allowing too much discretion to management
that resulted in vague, ambiguous and coarse segment disclosures (Gavens, 1989;
Wines, 1997; Birt, 2008). AASB 114 mandated additional disclosures (such as segment
liabilities, segment depreciation and amortisation) and provided additional guidance to
management in determining their reportable segments. Reducing vagueness or
ambiguity in disclosures, may affect users’ perceptions of the usefulness of the
information, irrespective of the impact on forecasting accuracy. As described in the next
section, our experiment uses two sets of segment data, based on the original AASB1005
and AASB114, to test whether the different levels of detail affect users’ con?dence levels
when predicting pro?ts or their perceptions of segment report usefulness. We express
these objectives in two null-form hypotheses; for simplicity, we label the more
aggregated segment data based on the original AASB 1005 as coarse and the more
detailed segment data based on AASB 114 as ?ne:
Original AASB 1005 AASB 114
Reportable segments Reportable segments
The original standard required ?rms to identify
segments by industry. It de?ned a segment as a
distinguishable component engaged in providing
a product or service, or a collection of related
products or services, to customers external to the
reporting entity
The AASB 114 standard required ?rms to adopt a
management approach where segments are
identi?ed according to the information reported to
the CEO and the Board of Directors. If the internal
information reported to the CEO and the board is
not based on related products and services then
the next lowest level of internal management
reporting is used to identify the reportable
segments
The standard required entities to identify primary
and secondary segments, for which reporting
requirements differed
Determining the primary and secondary segments
The standard required a risk and returns
approach to identifying segments to re?ect how
an entity’s risks and returns are affected by
differences in the products and services it
provides or differences in the geographical areas
in which it operates
Required disclosures Required disclosures – primary segments
It required the same information for both industry
and geographical segments
1. Segment revenue
2. Segment result
1. Segment revenue 3. Carrying amount of segment assets
2. Segment pro?t
3. Segment assets
4. Acquisition of segments assets during the
period
5. Segment liabilities
6. Depreciation and amortisation
7. Other non-cash expenses
8. Share of associates pro?t and losses
9. Carrying amount of investment in associates
Required disclosures – secondary segments
1. Segment revenue
2. Segment assets
3. Capital additions
Table I.
Comparison of the
original AASB 1005 and
AASB 114 disclosures
ARJ
24,3
248
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
H1. There is no difference in users’ con?dence in their pro?t forecasts based on
the coarse segment data set compared to pro?t forecasts based on the ?ne
segment data.
H2. There is no difference in users’ perceived usefulness of the coarse segment
data compared to the ?ne segment data.
We have two purposes in testing H2. First, we are interested in users’ perceptions
per se. Second, we are interested in whether users’ perceptions are consistent with their
revealed measure of value, expressed as their self-assessed con?dence levels.
3. Research design
Our research objectives depend on observing how users of segment reports respond to
alternative forms of segment reporting in terms of con?dence and perceived
usefulness. These user responses cannot be observed in the market place and so we
rely on an experimental design in which we can manipulate the treatment of segment
reporting and solicit speci?c responses.
3.1 Experiment
We test the hypotheses using an experiment based on two versions of a case study that
differ only in the segment report, with one based on the original AASB 1005 and the
other based on AASB 114. The ?ctionalised case is based on the rescaled ?nancial
statements of a real corporation so that segment disclosures were not contrived[3]. The
two versions of the case materials were randomly assigned to ?nal year ?nance students
at an Australian university in the ?nal week of semester 2, 2006[4]. The details of the
alternate segment reports included in the experiment materials are shown in
Table II, whichclearly illustrates the different levels of detail inthe two forms of segment
reports. The other ?nancial statement components, common to both cases, are in the
Appendix.
Each participant completed a questionnaire, attached to their case, in which they
selected one of eight ranges for forecast consolidated pro?t (loss) for the next 12 months.
The eight options were losses in the ranges of $15,000 or more, $10,000-$14,999,
$5,000-$9,999, or 0-$4,999, or pro?ts before tax in the ranges of $1-$4,999, $5,000-$9,999,
$10,000-$14,999, and $15,000 or more. Participants then indicated their con?dence in
their selected forecast range by marking an interval point on a notionally continuous
scale ranging from 0 (not sure at all) to 100 (very sure). Participants also scored the
usefulness of six items in making their pro?t forecast: company background;
a discussion and analysis of the ?nancial statements; income statement; balance sheet;
statement of cash ?ows; and segment report. Respondents scored usefulness using a
?ve-point scale ranging from “strongly disagree” to “strongly agree” with respect to
each item’s usefulness.
The experiment did not intentionally test forecasting accuracy, and no
macroeconomic or comparative data, such as industry descriptions, statistics or
forecasts, were provided in the cases. Each respondent expressed his or her con?dence
level and perceived usefulness of their version of the segment report. Because each
respondent evaluated only one version of the segment report, we do not identify users’
preferences between the segment report options; however, this approach avoids
Segment
reporting
249
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
preferences for either option inducing exaggerated usefulness scores, which are more
likely to arise from users’ comparative rankings of alternative reports.
We collected age bands, sex and work experience bands for respondents as
potential controls. Prior research indicates that these respondent characteristics may
in?uence a variety of judgements. For example, research has shown that males exhibit
higher con?dence than females in areas of investment decision-making (Barber and
Odean, 2001). Males are more represented in the ?nance industry, spend more time
on investment decisions, invest in more risky assets and anticipate higher returns
than females (Yuh and Hanna, 1997). Lower con?dence levels of females may be
due to females basing their investment decisions on a more comprehensive group of
informational cues (Graham et al., 2002). Work experience has also been found to be a
signi?cant explanatory variable in describing differences in investment decisions
(Forbes and Kara, 2010; Menkhoff et al., 2010). Demographic characteristics such as job
tenure and years of work experience can impact on risk analysis (Kannadhasan and
Nandagopal, 2010). Age of the investor may also impact on the degree of con?dence in
investment decision-making. Research has shown that younger investors are less likely
to be overcon?dent (Menkhoff et al., 2010).
Property Car parks Investment Consolidated
Business segments 2004 2003 2004 2003 2004 2003 2004 2003
Case A: ?ne segment report under AASB 114
Segment revenue 67,969 59,111 37,637 15,770 4,939 10,930 110,545 85,811
Segment result 70 22,923 6,052 2,774 21,983 2,938 4,139 2,789
Share of net pro?t/loss of
equity investments – – – – – 75 – 75
Net pro?t/loss 4,139 2,864
Segment assets 14,698 17,863 67,594 68,316 11,115 22,446 93,407 108,625
Segment liabilities 7,288 8,090 19,886 37,677 9,752 8,697 36,926 54,464
Equity accounted
investments in segment
assets – – 19,135 – – 12 19,135 12
Acquisition of property,
plant and equipment 330 203 – – 21 1 351 204
Depreciation 197 203 35 – 136 166 368 369
Amortisation 1,378 1,307 – – – – 1,378 1,307
Business segments
The consolidated entity comprises the following main business segments based on the consolidated
entity’s management reporting system
(a) Car Parks. Operator of car parks
(b) Property. Property and project management and property development
(c) Investment. Equity securities portfolio and other investment activities
Geographical segments
The consolidated entity’s operations are located in Australia
Segment accounting policies
There are no signi?cant inter-segment sales
Case B: coarse segment report under the original AASB 1005
Segment revenue 67,969 59,111 37,637 15,770 4,939 10,930 110,545 85,811
Segment result 70 22,923 6,052 2,774 21,983 2,938 4,139 2,789
Segment assets 14,698 17,863 67,594 68,316 11,115 22,446 93,407 108,625
Table II.
Segment report
disclosures included in
the experiment materials
ARJ
24,3
250
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
3.2 Testing
We test each of the two hypotheses by regressing the appropriate
dependent variable against the respondents’ other responses and demographic
controls.
To test our con?dence hypothesis, H1, the appropriate dependent variable is the
con?dence measure provided by the respondent. To test our usefulness hypothesis, H2,
the appropriate dependent variable is the response selected on the ?ve-point scale to
identify a respondent’s opinion as to the usefulness of their version of the segment
report in forecasting pro?t.
The main test variable for both hypotheses is whether the responses (forecasting
con?dence and usefulness of the segment report) differ in relation to whether the
respondent’s case included the coarse segment report or the ?ne segment report. It is
arguable that respondents’ con?dence in their forecasts may positively re?ect their
views as to the usefulness of any of the various report sections. Therefore, we test for
whether the assessed usefulness of the segment report section is signi?cantly associated
with reported con?dence (H1) and control for assessed usefulness of the non-segment
report sections. Similarly, views regarding the usefulness of segment reports may re?ect
general attitudes to ?nancial reports and, therefore, we also test the relevance of the
assessed usefulness of the various other report sections in testing the usefulness of the
coarse segment report and the ?ne segment report.
We test all demographic controls variables obtained from the questionnaire, as
described in the results section.
We test our hypotheses using the following two models:
H1: Confidence ¼ b
0
þb
1
ðfine segment reportÞ þ b
j
ðcontrolsÞ
ð1Þ
H2: Segment report usefulness ¼ g
0
þg
1
ðfine segment reportÞ þ g
j
ðcontrolsÞ
ð2Þ
where the variables are de?ned as follows.
Response variables:
H1 con?dence = The respondent’s con?dence in his or her selected
forecast pro?t range, expressed as an integer between
0-100.
H2 segment report usefulness = The respondent’s assessed segment report
usefulness in relation to pro?t forecast,
measured on a ?ve-point Likert scale.
or:
scaled segment report usefulness ¼ segment report usefulness/mean report
usefulness;
where mean report usefulness ¼ (income statement usefulness þ balance sheet
usefulness þ cash ?owstatement usefulness)/3;
and
where the usefulness of each component was scored by the respondent using a
?ve-point Likert scale[5].
Segment
reporting
251
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Test (treatment) variable (H1 and H2):
Fine segment report ¼ 1 if the respondent received the ?ne segment report, and 0
otherwise.
Control variables:
Segment report usefulness or mean report usefulness (as de?ned above) is a
control variable for H1only.
Forecast error absolute ¼ difference between respondents forecast range
compared to the “correct” range.
Fine segment report £ forecast error ¼ the interaction between the treatment
?ne segment report and the imputed forecast error.
Male ¼ 1 if respondent is male, and 0 otherwise.
Current work experience ¼ 1 if a respondent reports current work experience in
accounting or ?nance, and 0 otherwise.
Prior work experience ¼ 1 if a respondent reports prior work experience in
accounting or ?nance, and 0 otherwise.
International ¼ 1 if a respondent is not an Australian permanent resident or
citizen, and 0 otherwise.
Fulltime ¼ 1 if a respondent is a fulltime student, and 0 otherwise.
Age ¼ an ordinal representation of the respondent’s age, taking the values 1, 2, 3
or 4 for ages ,19, 19-24, 25-30 or .30[6].
4. Results
4.1 Descriptive statistics and univariate associations
The two versions of the case study were distributed evenly but ad hoc to 144 students.
Participation was voluntary and some students chose to not participate or did not
complete the questionnaire. Complete responses were received for 64 cases of the ?ne
Characteristics Coarse segment report Fine segment report Total
Female 32 45 77
Male 27 19 46
Domestic student 24 18 42
International student 35 46 81
Part-time student 5 5 10
Full-time student 54 59 113
Current work experience 7 8 15
Prior work experience 9 12 21
No work experience 42 44 86
Age-band: #19 3 7 10
19-24 45 39 84
25-30 9 13 22
31 þ 2 5 7
59 64 123
Table III.
Respondent
characteristics
ARJ
24,3
252
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
segment report and 59 cases of the coarse segment report. Table III describes
respondents’ characteristics for each version of the case study. International students
and females are over-represented in the respondents using the ?ne segment report. With
the exception of forecast error, scaled segment report usefulness and mean report
usefulness, which are explained below, all variables are coded directly from the
responses.
Correlation matrices are reported in Table IVfor both Pearson correlations (Panel A)
and Spearman rank correlations (Panel B); as might be expected for tests of association
involving categorical data, the Pearson and Spearman correlations are near identical.
These correlations indicate an appropriate relation between age and work experience.
There is also an association between full-time candidature and age. We also note the
intuitively expected correlations between the perceived usefulness of the various
?nancial statement components.
Con?dence and segment report usefulness are positively but weakly correlated with
a coef?cient of 22 per cent, suggesting perceptions of usefulness are not strongly
informed by the revealed value. We note that con?dence is similarly correlated with
cash ?ow statement usefulness and mean report usefulness. For differences in our
response variables, con?dence and segment report usefulness, in relation to our test
variable (coarse versus ?ne segment report) and our categorical control variables for
respondents’ characteristics, we emphasise the differences in group means rather than
the correlation coef?cients, although they generally indicate similar conditions.
In assessing the signi?cance of the t-statistics for mean differences, we use two-tail
tests. We also report imputed forecast errors by coarse versus ?ne segment report and
by respondents’ characteristics in Table V. The only signi?cant mean difference for
any group comparison of forecast error in Table V is for the absolute value of forecast
error, grouped by respondents who received the ?ne segment report versus those who
received the coarse segment report; this mean difference is signi?cant at (t ¼ 1.89;
p ¼ 0.06)[7]. Therefore, we control for possible differences in this regard in our
regressions, together with the interaction of forecast errors and the treatment variable
when estimating models that include forecast error.
The difference in respondents’ mean con?dence between the coarse segment report
(57 per cent) and the ?ne segment report (62 per cent) is not signi?cant. When con?dence
is related to the demographic controls, there are marginal statistical differences in
relation to sex and work experience. The higher mean con?dence for males (68 per cent)
compared to females (55 per cent) is highly signi?cant (t ¼ 4.07; p , 0.01), and the
lower mean con?dence for respondents with no work experience, compared to other
respondents, is signi?cant (t ¼ 2.19; p ¼ 0.03) while respondents with prior work
experience have signi?cantly higher mean con?dence, compared to other respondents
(t ¼ 1.66; p ¼ 0.10).
We do not observe any signi?cant differences in the mean raw scores for segment
report usefulness with respect to the two versions of the segment report or between any
demographic groupings of the respondents. The mean scores for each of the ?nancial
reporting components included in the experiment instrument are similar, ranging from
3.5 to 4.1, and most do not signi?cantly differ by any grouping of the respondents. We
report the t-statistics for segment report usefulness, and note signi?cant differences in
means for international versus domestic students (t ¼ 2.51; p ¼ 0.01) and respondents
with prior work experience versus other respondents (t ¼ 1.97; p ¼ 0.05). Otherwise,
Segment
reporting
253
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
V
a
r
i
a
b
l
e
C
o
n
?
d
e
n
c
e
S
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
F
o
r
e
c
a
s
t
e
r
r
o
r
(
a
b
s
o
l
u
t
e
)
M
a
l
e
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
F
u
l
l
-
t
i
m
e
s
t
u
d
e
n
t
A
g
e
P
a
n
e
l
A
:
P
e
a
r
s
o
n
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
C
o
n
?
d
e
n
c
e
1
.
0
0
S
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
0
.
2
2
*
1
.
0
0
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
0
.
1
4
0
.
0
8
1
.
0
0
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
0
.
2
5
*
0
.
2
0
*
2
0
.
0
5
1
.
0
0
F
o
r
e
c
a
s
t
e
r
r
o
r
(
a
b
s
o
l
u
t
e
)
0
.
0
7
2
0
.
0
7
0
.
1
5
0
.
0
1
1
.
0
0
M
a
l
e
0
.
3
5
*
0
.
1
2
2
0
.
1
7
0
.
0
3
0
.
0
4
1
.
0
0
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
0
.
0
9
2
0
.
1
0
0
.
0
1
0
.
0
0
0
.
0
2
2
0
.
1
3
1
.
0
0
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
0
.
1
5
0
.
1
8
0
.
0
5
0
.
0
7
0
.
0
3
0
.
2
3
*
2
0
.
1
7
1
.
0
0
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
0
.
1
2
0
.
2
2
*
0
.
1
3
0
.
0
3
2
0
.
1
0
0
.
0
6
2
0
.
2
6
*
0
.
1
4
1
.
0
0
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
0
.
1
1
0
.
1
2
2
0
.
0
6
2
0
.
1
5
2
0
.
1
1
0
.
1
4
2
0
.
3
0
*
2
0
.
0
6
0
.
3
7
*
1
.
0
0
A
g
e
(
1
-
4
)
0
.
0
2
0
.
0
5
0
.
0
6
0
.
0
8
0
.
0
3
0
.
0
1
0
.
1
4
0
.
2
8
*
0
.
0
0
2
0
.
3
6
*
1
.
0
0
P
a
n
e
l
B
:
S
p
e
a
r
m
a
n
r
a
n
k
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
C
o
n
?
d
e
n
c
e
1
.
0
0
S
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
0
.
2
6
*
1
.
0
0
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
0
.
1
4
0
.
0
9
1
.
0
0
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
0
.
2
3
*
0
.
1
1
2
0
.
0
3
1
.
0
0
F
o
r
e
c
a
s
t
e
r
r
o
r
(
a
b
s
o
l
u
t
e
)
0
.
1
0
2
0
.
0
9
0
.
1
0
2
0
.
0
5
1
.
0
0
(
c
o
n
t
i
n
u
e
d
)
Table IV.
Correlation coef?cients
ARJ
24,3
254
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
V
a
r
i
a
b
l
e
C
o
n
?
d
e
n
c
e
S
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
F
o
r
e
c
a
s
t
e
r
r
o
r
(
a
b
s
o
l
u
t
e
)
M
a
l
e
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
F
u
l
l
-
t
i
m
e
s
t
u
d
e
n
t
A
g
e
M
a
l
e
0
.
3
4
*
0
.
0
9
2
0
.
1
7
0
.
0
1
0
.
0
3
1
.
0
0
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
0
.
1
0
2
0
.
1
1
0
.
0
1
2
0
.
0
1
2
0
.
0
2
2
0
.
1
3
1
.
0
0
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
0
.
1
5
0
.
1
8
*
0
.
0
5
0
.
0
8
2
0
.
0
1
0
.
2
3
*
2
0
.
1
7
1
.
0
0
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
0
.
0
9
0
.
1
9
*
0
.
1
3
0
.
0
2
2
0
.
1
5
0
.
0
6
2
0
.
2
6
*
0
.
1
4
1
.
0
0
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
0
.
0
4
0
.
1
0
2
0
.
0
6
2
0
.
1
6
2
0
.
0
7
0
.
1
4
2
0
.
3
0
*
2
0
.
0
6
0
.
3
7
*
1
.
0
0
A
g
e
0
.
0
4
0
.
0
7
0
.
0
5
0
.
1
0
2
0
.
0
1
0
.
0
2
0
.
1
0
0
.
3
6
*
0
.
0
5
2
0
.
3
2
*
1
.
0
0
N
o
t
e
s
:
*
I
n
d
i
c
a
t
e
s
s
i
g
n
i
?
c
a
n
t
a
t
t
h
e
5
p
e
r
c
e
n
t
l
e
v
e
l
;
c
o
n
?
d
e
n
c
e
¼
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
c
o
n
?
d
e
n
c
e
i
n
h
i
s
o
r
h
e
r
s
e
l
e
c
t
e
d
f
o
r
e
c
a
s
t
p
r
o
?
t
r
a
n
g
e
,
e
x
p
r
e
s
s
e
d
a
s
a
n
i
n
t
e
g
e
r
b
e
t
w
e
e
n
0
-
1
0
0
;
s
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
¼
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
a
s
s
e
s
s
e
d
u
s
e
f
u
l
n
e
s
s
o
f
t
h
e
s
e
g
m
e
n
t
r
e
p
o
r
t
,
a
s
s
e
s
s
e
d
o
n
a
?
v
e
-
p
o
i
n
t
s
c
a
l
e
;
?
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
¼
1
i
f
t
h
e
r
e
s
p
o
n
d
e
n
t
r
e
c
e
i
v
e
d
t
h
e
?
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
m
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
¼
(
i
n
c
o
m
e
s
t
a
t
e
m
e
n
t
u
s
e
f
u
l
n
e
s
s
þ
b
a
l
a
n
c
e
s
h
e
e
t
u
s
e
f
u
l
n
e
s
s
þ
c
a
s
h
?
o
w
s
t
a
t
e
m
e
n
t
u
s
e
f
u
l
n
e
s
s
)
/
3
;
f
o
r
e
c
a
s
t
e
r
r
o
r
a
b
s
o
l
u
t
e
¼
d
i
f
f
e
r
e
n
c
e
b
e
t
w
e
e
n
r
e
s
p
o
n
d
e
n
t
s
f
o
r
e
c
a
s
t
r
a
n
g
e
c
o
m
p
a
r
e
d
t
o
t
h
e
“
c
o
r
r
e
c
t
”
r
a
n
g
e
;
m
a
l
e
¼
1
i
f
r
e
s
p
o
n
d
e
n
t
i
s
m
a
l
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
c
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
r
e
p
o
r
t
s
c
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
i
n
a
c
c
o
u
n
t
i
n
g
o
r
?
n
a
n
c
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
p
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
r
e
p
o
r
t
s
p
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
i
n
a
c
c
o
u
n
t
i
n
g
o
r
?
n
a
n
c
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
i
n
t
e
r
n
a
t
i
o
n
a
l
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
i
s
n
o
t
a
n
A
u
s
t
r
a
l
i
a
n
p
e
r
m
a
n
e
n
t
r
e
s
i
d
e
n
t
o
r
c
i
t
i
z
e
n
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
f
u
l
l
t
i
m
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
i
s
a
f
u
l
l
t
i
m
e
s
t
u
d
e
n
t
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
a
g
e
¼
a
n
o
r
d
i
n
a
l
r
e
p
r
e
s
e
n
t
a
t
i
o
n
o
f
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
a
g
e
,
t
a
k
i
n
g
t
h
e
v
a
l
u
e
s
1
,
2
,
3
o
r
4
f
o
r
a
g
e
s
,
1
9
,
1
9
-
2
4
,
2
5
-
3
0
o
r
.
3
0
Table IV.
Segment
reporting
255
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
P
e
r
c
e
i
v
e
d
u
s
e
f
u
l
n
e
s
s
o
f
?
n
a
n
c
i
a
l
r
e
p
o
r
t
c
o
m
p
o
n
e
n
t
s
(
m
e
a
n
o
f
s
c
a
l
e
r
e
s
p
o
n
s
e
s
:
1
-
5
)
T
r
e
a
t
m
e
n
t
/
r
e
s
p
o
n
d
e
n
t
c
h
a
r
a
c
t
e
r
i
s
t
i
c
s
n
C
o
n
?
d
e
n
c
e
2
r
e
s
p
o
n
s
e
0
-
1
0
0
a
F
o
r
e
c
a
s
t
e
r
r
o
r
2
a
b
s
o
l
u
t
e
v
a
l
u
e
a
B
a
c
k
g
r
o
u
n
d
D
i
s
c
u
s
s
i
o
n
I
n
c
o
m
e
s
t
a
t
e
m
e
n
t
B
a
l
a
n
c
e
s
h
e
e
t
C
a
s
h
?
o
w
s
t
a
t
e
m
e
n
t
S
e
g
m
e
n
t
r
e
p
o
r
t
a
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
(
i
n
c
o
m
e
s
t
a
t
e
m
e
n
t
,
b
a
l
a
n
c
e
s
h
e
e
t
a
n
d
c
a
s
h
?
o
w
s
t
a
t
e
m
e
n
t
)
a
S
c
a
l
e
d
s
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
a
C
o
a
r
s
e
s
e
g
m
e
n
t
r
e
p
o
r
t
5
9
5
7
.
0
3
0
.
3
9
0
.
8
0
3
.
5
6
3
.
5
8
4
.
1
5
3
.
9
5
4
.
0
2
3
.
4
4
4
.
0
3
0
.
8
6
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
6
4
6
2
.
0
3
(
1
.
5
0
)
0
.
4
1
(
0
.
0
2
)
1
.
0
6
(
1
.
8
9
)
3
.
4
8
3
.
7
5
4
.
0
9
3
.
9
1
3
.
9
4
3
.
5
8
(
0
.
8
5
)
3
.
9
8
(
0
.
5
2
)
0
.
9
2
(
0
.
9
7
)
F
e
m
a
l
e
7
7
5
4
.
6
8
0
.
4
7
0
.
9
1
3
.
4
9
3
.
5
2
4
.
1
3
3
.
8
4
4
.
0
0
3
.
4
3
3
.
9
9
0
.
8
7
M
a
l
e
4
6
6
7
.
9
3
(
4
.
0
7
)
*
*
*
0
.
2
8
(
1
.
1
3
)
0
.
9
8
(
0
.
4
7
)
3
.
5
7
3
.
9
1
4
.
1
1
4
.
0
7
3
.
9
3
3
.
6
5
(
1
.
3
5
)
4
.
0
4
(
0
.
3
7
)
0
.
9
3
(
1
.
2
5
)
D
o
m
e
s
t
i
c
s
t
u
d
e
n
t
4
2
5
6
.
4
3
0
.
6
2
1
.
0
4
3
.
4
0
3
.
4
8
4
.
2
6
3
.
8
6
3
.
8
3
3
.
2
4
3
.
9
8
0
.
8
2
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
8
1
6
1
.
3
0
(
1
.
3
8
)
0
.
2
8
(
1
.
3
3
)
0
.
8
8
(
0
.
1
5
)
3
.
5
8
3
.
7
6
4
.
0
5
3
.
9
6
4
.
0
5
3
.
6
5
(
2
.
5
1
)
*
*
4
.
0
2
(
0
.
2
8
)
0
.
9
3
(
2
.
1
7
)
*
*
P
a
r
t
-
t
i
m
e
s
t
u
d
e
n
t
8
5
1
.
8
8
0
.
2
5
1
.
2
5
3
.
7
5
3
.
2
5
4
.
5
0
4
.
2
5
4
.
3
8
3
.
1
3
4
.
3
8
0
.
7
4
F
u
l
l
-
t
i
m
e
s
t
u
d
e
n
t
1
1
3
6
0
.
2
7
(
1
.
3
4
)
0
.
4
0
(
0
.
4
7
)
0
.
9
1
(
1
.
1
7
)
3
.
5
1
3
.
6
9
4
.
1
0
3
.
9
2
3
.
9
5
3
.
5
6
(
1
.
3
4
)
3
.
9
9
(
1
.
6
5
)
0
.
9
1
(
1
.
7
5
)
*
A
g
e
-
b
a
n
d
:
#
1
9
1
0
5
9
.
5
0
(
0
.
0
2
)
0
.
5
0
(
0
.
2
9
)
0
.
9
3
(
0
.
1
5
)
3
.
4
0
3
.
5
0
4
.
2
0
3
.
9
0
4
.
0
0
3
.
4
0
(
0
.
4
1
)
4
.
0
3
(
0
.
1
2
)
0
.
8
6
(
0
.
4
1
)
1
9
-
2
4
8
4
5
8
.
9
9
(
0
.
5
6
)
0
.
3
8
(
0
.
2
4
)
0
.
9
3
(
0
.
1
3
)
3
.
4
0
3
.
6
4
4
.
0
7
3
.
8
9
3
.
8
9
3
.
4
9
(
0
.
4
4
)
3
.
9
5
(
1
.
4
1
)
0
.
9
0
(
0
.
3
0
)
2
5
-
3
0
2
2
6
3
.
6
4
(
1
.
1
2
)
0
.
2
3
(
0
.
7
6
)
0
.
9
5
(
0
.
1
3
)
4
.
0
0
3
.
7
7
4
.
2
7
4
.
0
5
4
.
3
2
3
.
6
8
(
0
.
9
9
)
4
.
2
1
(
1
.
6
4
)
0
.
9
0
(
0
.
1
6
)
3
1
þ
7
5
5
.
0
0
(
0
.
6
8
)
1
.
0
0
(
1
.
4
2
)
1
.
0
0
(
0
.
6
9
)
3
.
5
7
3
.
8
3
4
.
1
4
4
.
0
0
3
.
8
6
3
.
4
3
(
0
.
2
5
)
4
.
0
0
(
0
.
0
4
)
0
.
8
5
(
0
.
3
9
)
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
1
5
6
4
.
0
0
(
0
.
9
7
)
0
.
5
3
(
0
.
4
8
)
1
.
0
7
(
0
.
6
9
)
3
.
6
0
3
.
4
7
4
.
1
3
3
.
9
3
3
.
9
3
3
.
2
7
(
0
.
7
1
)
4
.
0
0
(
0
.
0
5
)
0
.
8
4
(
0
.
8
6
)
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
2
1
6
5
.
7
1
(
1
.
6
6
)
*
0
.
0
5
(
1
.
5
3
)
1
.
0
0
(
0
.
4
1
)
3
.
9
0
4
.
0
5
4
.
1
4
3
.
9
5
4
.
2
4
3
.
8
6
(
1
.
9
7
)
*
*
4
.
1
1
(
0
.
9
2
)
0
.
9
6
(
1
.
3
1
)
N
o
w
o
r
k
e
x
p
e
r
i
e
n
c
e
8
6
5
7
.
2
7
(
2
.
1
9
)
*
*
0
.
4
5
(
0
.
8
0
)
0
.
9
0
(
0
.
8
5
)
3
.
4
2
3
.
6
0
4
.
1
2
3
.
9
2
3
.
9
2
3
.
4
7
(
0
.
8
9
)
3
.
9
8
(
0
.
6
4
)
0
.
8
8
(
0
.
5
4
)
O
v
e
r
a
l
l
1
2
3
5
9
.
6
3
0
.
4
0
0
.
9
3
3
.
5
2
3
.
6
6
4
.
1
2
3
.
9
3
3
.
9
8
3
.
5
1
4
.
0
1
0
.
8
9
N
o
t
e
s
:
*
,
*
*
a
n
d
*
*
*
i
n
d
i
c
a
t
e
s
i
g
n
i
?
c
a
n
t
a
t
t
h
e
1
0
,
5
a
n
d
1
p
e
r
c
e
n
t
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
a
t
-
s
t
a
t
i
s
t
i
c
s
f
o
r
m
e
a
n
d
i
f
f
e
r
e
n
c
e
s
a
r
e
i
n
p
a
r
e
n
t
h
e
s
e
s
a
n
d
a
s
s
u
m
e
e
q
u
a
l
v
a
r
i
a
n
c
e
s
Table V.
Response variables by
treatment and respondent
characteristics
ARJ
24,3
256
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
the apparent anchoring of usefulness perceptions suggests it would be dif?cult to
explain differences in the raw scores for segment report usefulness using the available
variables, or to distinguish between segment report usefulness scores independent of
other ?nancial report usefulness scores. To address this problem, we compute scaled
segment report usefulness by dividing each respondents segment report usefulness by
the mean perceived usefulness scores for the other components of the ?nancial
statements, thus[8]:
Scaled segment report usefulness ¼
Segment report usefulness
Mean report usefulness
ð3Þ
where
Mean report usefulness ¼
ðIncome statement usefulness þ balance sheet usefulness þ cash flowstatement usefulnessÞ
3
ð4Þ
The results for scaled segment report usefulness and mean report usefulness are also
included in the correlation matrix in Table IV and the comparative means in Table V.
There are no signi?cant differences between any group means for mean report
usefulness. For scaled segment report usefulness, the mean difference for international
versus domestic students remains evident (t ¼ 2.17; p ¼ 0.03) and there is a modest
difference in means between part-time and full-time students (t ¼ 1.75; p ¼ 0.08).
4.2 Results for hypothesis tests
Forecast con?dence (H1). Inestimatingthe relationbetween con?dence and?ne segment
report, we consider the potential signi?cant in?uence of all available controls. The
results are reported in Table VI. The treatment variable, ?ne segment report, is
signi?cant in all models (Model 1a: t ¼ 2.07, p ¼ 0.04; Model 1b: t ¼ 2.85, p ¼ 0.01;
Model 1c: t ¼ 2.47, p ¼ 0.02; Model 1d: t ¼ 3.05, p ,0.01). As discussed above,
respondents’ perceptions of the usefulness of the segment report, income statement,
balance sheet andcash?owstatement are all signi?cantly relatedso we test eachof these
usefulness measures (not reported) and mean report usefulness; no individual report
element was stronger than the mean report usefulness[9]. We report signi?cant results
for segment report usefulness in Models 1a and 1b (t ¼ 1.74; p ¼ 0.09) and (t ¼ 1.89;
p ¼ 0.06), respectively, and mean report usefulness in Models 1c and 1d (signi?cant at
p , 0.01 in both models). Thus, report usefulness is signi?cantly associated with
increased con?dence in all these models; the seemingly stronger results for mean report
usefulness suggest the respondents’ expressed con?dence is conditioned by the
perceived overall usefulness of ?nancial reports. Because expressed con?dence might be
a functionof bothperceived anddemonstrated usefulness, we also test the absolute value
of imputed forecast error, interacted with the treatment variable in Models 1b and 1c.
The positive coef?cients for Forecast error and the negative coef?cients for ?ne segment
report £ Forecast error are both signi?cant in both models, indicating that con?dence is
increasing with imputed error for the respondents using the coarse segment report
(original AASB 1005) but not for respondents using the ?ner segment. On the basis of
Models 1b and 1d, the difference in con?dence between users of the ?ner segment report
and the coarser original AASB 1005, where the ?ner information set is associated with
higher con?dence, is increased when imputed forecast error is taken into account.
Segment
reporting
257
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
M
o
d
e
l
1
a
M
o
d
e
l
1
b
M
o
d
e
l
1
c
M
o
d
e
l
1
d
V
a
r
i
a
b
l
e
b
t
-
s
t
a
t
.
b
t
-
s
t
a
t
.
b
t
-
s
t
a
t
.
b
t
-
s
t
a
t
.
T
e
s
t
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
6
.
5
1
2
.
0
7
*
*
1
4
.
5
4
2
.
8
5
*
*
7
.
5
4
2
.
4
7
*
*
1
5
.
1
2
3
.
0
5
*
*
C
o
n
t
r
o
l
s
S
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
3
.
1
2
1
.
7
4
*
3
.
3
7
1
.
8
9
*
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
7
.
7
6
3
.
3
3
*
*
7
.
6
7
3
.
3
2
*
*
F
o
r
e
c
a
s
t
e
r
r
o
r
(
a
b
s
o
l
u
t
e
)
7
.
6
5
2
.
1
3
*
*
6
.
9
1
1
.
9
9
*
*
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
£
F
o
r
e
c
a
s
t
e
r
r
o
r
–
–
2
9
.
1
8
2
2
.
1
0
*
*
2
8
.
5
7
2
2
.
0
2
*
*
M
a
l
e
1
3
.
5
0
4
.
0
4
*
*
1
3
.
9
4
4
.
1
9
*
*
1
3
.
6
5
4
.
2
3
*
*
1
4
.
1
4
4
.
4
0
*
*
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
1
1
.
6
9
2
.
3
0
*
*
1
3
.
5
7
2
.
6
7
*
*
1
2
.
0
1
2
.
4
5
*
*
1
3
.
7
5
2
.
7
9
*
*
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
3
.
5
0
0
.
7
8
4
.
7
7
1
.
0
6
3
.
8
8
0
.
9
0
5
.
1
5
1
.
1
9
I
n
t
e
r
n
a
t
i
o
n
a
l
2
.
1
3
0
.
5
8
2
.
2
3
0
.
6
2
2
.
1
0
0
.
6
0
2
.
2
5
0
.
6
5
F
u
l
l
t
i
m
e
7
.
1
5
0
.
9
6
6
.
0
2
0
.
8
1
1
1
.
8
6
1
.
6
3
1
4
.
7
5
1
.
4
8
A
g
e
2
0
.
5
3
2
0
.
2
1
2
1
.
3
8
2
0
.
5
3
2
0
.
4
1
2
0
.
1
6
2
1
.
1
9
2
0
.
4
7
C
o
n
s
t
a
n
t
3
1
.
3
2
2
.
7
5
*
*
2
6
.
4
6
2
.
2
7
*
*
5
.
8
0
0
.
4
1
2
.
6
0
0
.
1
8
n
1
2
3
1
2
3
1
2
3
1
2
3
F
-
s
t
a
t
i
s
t
i
c
4
.
1
4
*
*
3
.
8
8
*
*
5
.
4
0
*
*
4
.
8
5
*
*
A
d
j
u
s
t
e
d
R
2
0
.
1
7
0
.
1
9
0
.
2
2
0
.
2
4
N
o
t
e
s
:
*
a
n
d
*
*
i
n
d
i
c
a
t
e
s
i
g
n
i
?
c
a
n
t
a
t
t
h
e
1
0
a
n
d
5
p
e
r
c
e
n
t
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
c
o
n
?
d
e
n
c
e
¼
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
c
o
n
?
d
e
n
c
e
i
n
h
i
s
o
r
h
e
r
s
e
l
e
c
t
e
d
f
o
r
e
c
a
s
t
p
r
o
?
t
r
a
n
g
e
,
e
x
p
r
e
s
s
e
d
a
s
a
n
i
n
t
e
g
e
r
b
e
t
w
e
e
n
0
-
1
0
0
;
?
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
¼
1
i
f
t
h
e
r
e
s
p
o
n
d
e
n
t
r
e
c
e
i
v
e
d
t
h
e
?
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
s
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
¼
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
a
s
s
e
s
s
e
d
u
s
e
f
u
l
n
e
s
s
,
o
n
a
?
v
e
-
p
o
i
n
t
s
c
a
l
e
;
m
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
¼
(
i
n
c
o
m
e
s
t
a
t
e
m
e
n
t
u
s
e
f
u
l
n
e
s
s
þ
b
a
l
a
n
c
e
s
h
e
e
t
u
s
e
f
u
l
n
e
s
s
þ
c
a
s
h
?
o
w
s
t
a
t
e
m
e
n
t
u
s
e
f
u
l
n
e
s
s
)
/
3
;
f
o
r
e
c
a
s
t
e
r
r
o
r
a
b
s
o
l
u
t
e
¼
a
b
s
o
l
u
t
e
v
a
l
u
e
o
f
t
h
e
d
i
f
f
e
r
e
n
c
e
b
e
t
w
e
e
n
r
e
s
p
o
n
d
e
n
t
s
f
o
r
e
c
a
s
t
r
a
n
g
e
c
o
m
p
a
r
e
d
t
o
t
h
e
“
c
o
r
r
e
c
t
”
r
a
n
g
e
;
?
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
£
f
o
r
e
c
a
s
t
e
r
r
o
r
¼
t
h
e
i
n
t
e
r
a
c
t
i
o
n
b
e
t
w
e
e
n
t
h
e
t
r
e
a
t
m
e
n
t
;
m
a
l
e
¼
1
i
f
r
e
s
p
o
n
d
e
n
t
i
s
m
a
l
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
c
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
r
e
p
o
r
t
s
c
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
i
n
a
c
c
o
u
n
t
i
n
g
o
r
?
n
a
n
c
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
p
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
r
e
p
o
r
t
s
p
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
i
n
a
c
c
o
u
n
t
i
n
g
o
r
?
n
a
n
c
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
i
n
t
e
r
n
a
t
i
o
n
a
l
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
i
s
n
o
t
a
n
A
u
s
t
r
a
l
i
a
n
p
e
r
m
a
n
e
n
t
r
e
s
i
d
e
n
t
o
r
c
i
t
i
z
e
n
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
f
u
l
l
t
i
m
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
i
s
a
f
u
l
l
t
i
m
e
s
t
u
d
e
n
t
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
a
g
e
¼
a
n
o
r
d
i
n
a
l
r
e
p
r
e
s
e
n
t
a
t
i
o
n
o
f
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
a
g
e
,
t
a
k
i
n
g
t
h
e
v
a
l
u
e
s
1
,
2
,
3
o
r
4
f
o
r
a
g
e
s
,
1
9
,
1
9
-
2
4
,
2
5
-
3
0
o
r
.
3
0
Table VI.
OLS regressions for
con?dence (H1)
ARJ
24,3
258
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The control indicator variables male and current work experience (whether the
respondent was currently engaged in employment involving accounting or ?nance
functions) are also signi?cantly associated with increased con?dence in all models[10].
These associations are largely consistent with the differences in means reported in
Table V. No other control variables are signi?cant, and the reported results also hold for
parsimonious models that exclude non-signi?cant control variables (not reported).
The signi?cantly positive coef?cient for the treatment variable ?ne segment report
leads us to reject H1. On average, a respondent’s reported con?dence in the selected
forecast range is signi?cantly higher when the case used the statements based on the
?ne segment report, compared to a case based on the coarse segment report.
Usefulness (H2). The results for our tests of the perceived usefulness of the competing
versions of the segment report are reported in Table VII, where we regress segment
report usefulness on ?ne segment report. Because the response variable segment report
usefulness is unlikely to be scalar, we estimate Model 2a and 2b using an ordered logistic
regression. We do not ?nd any signi?cant relation between segment report usefulness
and ?ne segment report. Model 2b includes forecast error and its interaction with ?ne
segment report, which are not signi?cant. None of the demographic control variables are
signi?cant. Based on the results reported in Table VII, we cannot reject H2. We ?nd no
evidence of a difference in users’ perceived usefulness of the coarse segment data
compared to the ?ne segment data.
Segment report usefulness
Model 2a Model 2b
Variable b z-stat. b z-stat.
Test
Fine segment report 0.30 0.85 0.35 0.60
Controls
Forecast error (absolute) 20.14 20.30
Fine segment report £ forecast error 20.01 20.01
Male 0.22 0.58 0.25 0.67
Current work experience 20.35 20.62 20.35 20.60
Prior work experience 0.67 1.32 0.69 1.35
International 0.53 1.31 0.49 1.21
Fulltime 0.44 0.53 0.41 0.48
Age 0.10 0.34 0.09 0.30
n 123 123
Log likelihood 2149.56 2149.38
Pseudo R
2
0.03 0.03
Notes: Segment report usefulness ¼ the respondent’s assessed usefulness, on a ?ve-point scale; ?ne
segment report ¼ 1 if the respondent received the ?ne segment report, and 0 otherwise; forecast error
absolute ¼ absolute value of the difference between respondents forecast range compared to the
“correct” range; ?ne segment report £ forecast error ¼ the interaction between the treatment;
male ¼ 1 if respondent is male, and 0 otherwise; current work experience ¼ 1 if a respondent reports
current work experience in accounting or ?nance, and 0 otherwise; prior work experience ¼ 1 if a
respondent reports prior work experience in accounting or ?nance, and 0 otherwise; international ¼ 1
if a respondent is not an Australian permanent resident or citizen, and 0 otherwise; fulltime ¼ 1 if a
respondent is a fulltime student, and 0 otherwise; age ¼ an ordinal representation of the respondent’s
age, taking the values 1, 2, 3 or 4 for ages ,19, 19-24, 25-30 or .30
Table VII.
Ordered logistic
regressions for segment
report usefulness
hypothesis H2
Segment
reporting
259
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
As further testing, we replaced segment report usefulness with scaled segment
report usefulness in Models 2a and 2b and estimated the models using OLS. The
results were substantially the same as those reported for segment report usefulness with
no signi?cant variables. In ordered logistic regressions models for segment report
usefulness and OLS models for scaled segment report usefulness, we also replaced age
with dichotomous grouping variables for the age bands; these age band indicators are
not signi?cant in any model. We also added mean report usefulness to each of the
models, and also tested the interaction term ?ne segment report £ mean report
usefulness. We do not report the detailed results for these extended models because they
do not affect the tests of our hypotheses. The only signi?cant ?nding is mean report
usefulness is strongly positive in models for scaled segment report usefulness. Similar to
our results for con?dence in Model 1, the relation between mean report usefulness and
scaled segment report usefulness suggests that the respondents’ perceived usefulness of
the segment report is related to their attitude towards the usefulness of ?nancial reports
generally, but do not affect our main ?ndings.
5. Conclusion
The extant literature examining the role of segment reporting in investment judgments
is largely concerned with quantitative outcomes, emphasising market valuation and
forecast accuracy. By examining the impact of alternate segment reporting approaches
on users’ expressed con?dence in forecasts and the perceived usefulness of segment
reports, this study contributes to the general accounting literature concerned with the
?neness of information structures and users’ perceptions. We contribute to the
literature speci?cally concerned with the advantages and disadvantages of different
forms of segment reporting by revealing that decision makers’ con?dence can be
increased by the provision of ?ner information sets.
Application of the ?neness theoremsuggests the higher level of disaggregation under
AASB 114 should, yield higher value to users, compared to disclosures under the
original AASB 1005. However, attribution of increased ?neness is ambiguous because
AASB114 did not require disclosure of secondary segment pro?t that had been reported
for all segments under the original AASB1005. Inour experiment, users of the notionally
?ner segment report, on average, reported higher con?dence levels in their pro?t
forecasts compared to users of the coarser segment report. We suggest that increased
con?dence provides value to users. However, further research relating con?dence to
decision outcomes is necessary to improve our understanding of the value of increased
con?dence.
We ?nd no difference in the respondents’ perceptions of the usefulness of the two
forms of segment report tested here. It is plausible that attitudes towards segment
reports are heavily anchored by attitudes towards ?nancial reports in general, resulting
in little difference in perceived usefulness for the different levels of ?neness in segment
reports tested here. Our single-blind experimental design did not allow an individual
respondent to reveal preferences between disclosures under the two standards and we
cannot exclude the possibility that there are real differences in perceptions of usefulness
between different segment reporting approaches that might be revealed by a different
research design. Consequently, further research to reveal evidence of latent differences
in usefulness of report components, and especially different approaches to segment
reporting, is desirable. If our results hold across other tests of usefulness, it may indicate
ARJ
24,3
260
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
that opinions of usefulness that are not based on ranks or con?rmed by decision-making
practices do not provide a reliable basis for determining usefulness.
Subsequent to our experiment, AASB 8 Operating Segments was implemented in
2010.AASB 8 introduced the management approach to segment reporting previously
adopted in the USA in SFAS 131.AASB 8 requires ?rms to “look inside” their
organisation to how the organisation is managed in determining reportable segments.
This requirement may result in different segment reporting treatments for some
companies, with potential implications for users’ con?dence and perceptions of
usefulness; therefore, further study of differences in segment reporting in relation to
users’ behaviour and perceptions is warranted.
Notes
1. Australian reporting entities implemented AASB 8 Operating Segments from 2010.
2. The revised AASB 1005 and AASB 114 were almost identical in terms of application and
requirements. In this experiment, we compare the original AASB 1005 and AASB 114.
3. When the revised AASB 1005 was implemented (recall this was later reissued as AASB 114),
companies had to restate the comparative disclosures for the previous year that were under
the old standard to the form required under the new standard. This restatement provided the
two different segment reports for our test year. We ?ctionalised the name of the entity and
reduced the scaled of its ?nancial statements by a factor of 20 so that respondents could
more comfortably deal with whole numbers.
4. Ethics approval at the university level was obtained prior to administering this instrument
to students. The students were taking an advanced undergraduate investments course in a
?nance major. The course provides a practical knowledge of modern ?nancial markets,
traders and trading strategies while also covering the theoretical underpinnings, including
asset pricing and valuation, stock selection and market ef?ciency. The prerequisites for this
course include accounting, economics, statistics and ?nance courses. This educational
background suggests that the participants had the necessary skills to evaluate an
investment and forecast pro?t. The adequacy of students as proxies for real-world decision
makers has been considered previously. For example, marketing and advertising studies
have revealed no differences between managers and students as the respondents (Chang and
Ho, 2004) and Liyanarachchi and Milne (2004) provide evidence that students’ investment
decision-making compares well with those of practitioners.
5. Our basis for testing scaled segment report usefulness is a consequence of the correlations
between the usefulness of the ?nancial statement components, as explained in Section 4.1.
6. While the ordinal age variable is not consistent with the assumption of a scalar variable
assumed for OLS, it was suggested that it might reveal an age association that is not revealed
by regressing our response variables on the categorical age indicators We thank the editor for
this suggestion. We report correlations for both the ordinal and categorical age variables, and
we use categorical age variables to test for differences in means of our response variables. We
also test categorical age indicators in our further testing of the reported results.
7. In determining the forecast error, each respondent’s forecast range of pro?t/loss is compared
to the correct range of pro?t/loss.
8. This approach is generally based on the treatment for anchoring bias in survey responses in
Shailer et al. (2001). The treatment for anchoring bias in Shailer et al. (2001) was to divide
individuals’ responses by group (country) means to identify the extent to which individuals
deviated from their group norm. The scaling used here measures the extent to which an
Segment
reporting
261
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
individual’s perceived usefulness of the segment report deviates from their general (mean)
perception of the usefulness of the other accompanying ?nancial statements.
9. We also tested scaled segment report usefulness in place of segment report usefulness but this
was not signi?cant. We also tested composite measures based on extracted factors, means and
products of the usefulness scores for the income statement, balance sheet and cash ?ow
statement. None are superior to using mean report usefulness in Models 1c or 1d. A factor
analysis of the three variables ?elds a single factor with an eigen value of 1.3 an loadings
of 0.62, 0.78 and 0.59, respectively, that we also tested. The results for the numerous
exploratory regressions are not reported.
10. Work experience was also tested in relation to a combined variable for either prior or current
work experience. This combined variable is not signi?cant.
References
Balakrishnan, R., Harris, M. and Sen, T.S. (1990), “The predictive ability of geographic segment
disclosures”, Journal of Accounting Research, Vol. 28 No. 2, pp. 305-25.
Barber, B.M. and Odean, T. (2001), “Boys will be boys: gender, overcon?dence and common stock
investment”, Quarterly Journal of Economics, February, pp. 261-92.
Bar-Yosef, S. and Venezia, I. (2004), “Experimental study of implications of SFAS 131: the effects
of the new standard on the informativeness of segment reporting”, working paper, School
of Business Administration, The Hebrew University of Jerusalem, Jerusalem.
Berger, P.G. and Hann, R. (2003), “Segment pro?tability and the proprietary and agency costs of
disclosure”, Accounting Review, Vol. 82, pp. 869-906.
Birt, J.L. (2008), “Consequences of changing Australian segment reporting requirements”,
unpublished doctoral thesis, The Australian National University, Canberra.
Chang, C. and Ho, J.L. (2004), “Judgment and decision making in project continuation: a study of
students as surrogates for experienced managers”, Abacus, Vol. 40 No. 1, pp. 94-116.
Chen, P. and Zhang, G. (2003), “Heterogeneous investment opportunities in multiple-segment ?rms
andthe incremental value relevance of segment data”, AccountingReview, Vol. 78, pp. 397-428.
Collins, D.W. (1976), “Predicting earnings with sub-entity data: some further evidence”, Journal
of Accounting Research, Vol. 14 No. 2, pp. 306-24.
Demski, J. (1973), “The general impossibility of normative accounting standards”, Accounting
Review, Vol. 48 No. 4, pp. 718-23.
Ettredge, M.L., Kwon, S.Y., Smith, D.B. and Zarawon, P.A. (2005), “The impact of SFAS 131
business segment data on the market’s ability to anticipate future earnings”, Accounting
Review, Vol. 80 No. 4, pp. 773-804.
Forbes, J. and Kara, S.M. (2010), “Con?dence mediates how investment knowledge in?uences
investing self-ef?cacy”, Journal of Economic Psychology, Vol. 31, pp. 435-43.
Foster, G. (1978), “Financial Statement Analysis”, Prentice-Hall, Englewood Chess, NJ.
Gavens, J.J. (1989), “Segment reporting and the Australian conceptual framework: a survey of the
banking industry”, Accounting Forum, Vol. 24 No. 1, pp. 17-24.
Graham, J.F., Stendardi, E.J., Myers, J.K. and Graham, M.J. (2002), “Gender differences in
investment strategies: an information processing perspective”, International Journal of
Marketing, Vol. 20 No. 1, pp. 17-26.
Herrmann, D. and Thomas, W.B. (1997), “Geographic segment disclosures: theories, ?ndings, and
implications”, International Journal of Accounting, Vol. 32 No. 4, pp. 487-501.
ARJ
24,3
262
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Kannadhasan, M. and Nandagopal, R. (2010), “In?uence of decision makers’ characteristics on
risk analysis in strategic investment decisions”, Journal of Modern Accounting and
Auditing, Vol. 6 No. 4, pp. 38-44.
Kinney, W.R. (1971), “Predicting earnings: entity v sub-entity data”, Journal of Accounting
Research, Spring, pp. 112-36.
Liyanarachchi, G.A. and Milne, M.J. (2004), “Comparing the investment decisions of accounting
practitioners and students: an empirical study on the adequacy of student surrogates”,
Accounting Forum, Vol. 29 No. 1, pp. 1-15.
Lobo, G.J, Kwon, S.S and Ndubizu, G.A(1998), “The impact of SFAS No. 14 Segment information
on price variability and earnings forecast accuracy”, Journal of Business Finance &
Accounting, Vol. 25 No. 7/8, pp. 969-85.
Maines, L.A., McDaniel, L.S. and Harris, M.S. (1997), “Implications of proposed segment
reporting standards for ?nancial analysts’ investment judgments”, Journal of Accounting
Research, Vol. 35 No. 1, pp. 1-23.
Mande, V. and Ortman, R. (2002), “Are recent segment disclosures of Japanese ?rms useful?
Views of Japanese ?nancial analysts”, International Journal of Accounting, Vol. 3 No. 1,
pp. 27-46.
Marschak, J. Radner, R. (1972), “Economic Theory of Teams”, Yale University Press,
New Haven, CT.
Menkhoff, L., Schmeling, M. and Schmidt, U. (2010), “Overcon?dence, experience, and
professionalism: an experimental study”, Working Paper No. 1612, Kiel Institute for the
World Economy, Kiel.
Shailer, G., Willett, R.Y. and Wade, M. (2001), “Internationalisation of auditors perceptions of
litigation risk”, Managerial Auditing Journal, Vol. 2, pp. 87-102.
Wines, G. (1997), “Geographical segment identi?cation in Australia: further evidence of an
international reporting problem”, Accountability and Performance, Vol. 3 No. 1, pp. 85-104.
Yuh, Y. and Hanna, S. (1997), “The demand for risky assets in retirement portfolios”,
paper presented at the Annual Meeting of the Academy of Financial Services.
Further reading
Bodnar, G.M., Hwang, L.S. and Weintrop, J. (2003), “The value relevance of foreign income:
an Australian, Canadian, and British Comparison”, Journal of International Financial
Management and Accounting, Vol. 14 No. 3, pp. 171-93.
Edwards, P. and Smith, R.A. (1996), “Competitive disadvantage and voluntary disclosures:
the case of segmental reporting”, British Accounting Review, Vol. 28 No. 2, pp. 155-72.
Emmanuel, C.R. and Garrod, N. (2002), “On the relevance and comparability of segmental data”,
Abacus, Vol. 38 No. 2, pp. 215-32.
Kochanek, R.F. (1974), “Segmental ?nancial disclosure by diversi?ed ?rms and security prices”,
Accounting Review, Vol. 49 No. 2, pp. 245-58.
McDaniel, L.S. and Hand, J.R.M. (1996), “The value of experimental methods for practice-relevant
accounting research”, Contemporary Accounting Research, Vol. 13 No. 2, pp. 339-51.
Milne, M.J. and Chan, C.C. (1999), “Narrative corporate social disclosures: how much of a
difference do they make to investment decision-making?”, British Accounting Review,
Vol. 31 No. 4, pp. 439-57.
OECD (1990), Segmented Financial Information, Organisation for Economic Co-operation and
Development, Paris.
Segment
reporting
263
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Prencipe, A. (2004), “Proprietary costs and determinants of voluntary segment disclosure:
evidence from Italian listed companies”, European Accounting Review, Vol. 13 No. 2,
pp. 319-40.
Rennie, E.D. and Emmanuel, C.R. (1992), “Segmental disclosure practice: thirteen years on”,
Accounting & Business Research, pp. 151-9, Spring.
Roberts, C.B. (1989), “Forecasting earnings using geographical segment data: some UK
evidence”, Journal of International Financial Management and Accounting, Vol. 1 No. 2,
pp. 130-51.
Ronen, J. and Livnat, J. (1981), “Incentives for segment reporting”, Journal of Accounting
Research, Vol. 19 No. 2, pp. 459-81.
Appendix. Financial report components included in both experimental cases
Background to Schipper Ltd
Schipper Ltd is an Australian based company which was formed in 1988 and expanded rapidly
in the early 1990s. It has approximately 1,000 shareholders and is now a leading company
operating in its core activities of property development, car parking and investments.
The company identi?es and develops market opportunities in the property sector and is also
an established provider of car parking operating and management services where it operates an
established portfolio of leased and managed car parks in Australia. It also manages a modest
investment portfolio.
Schipper Ltd has a proven track record of project delivery, based on industry experience,
a well quali?ed and experienced development team and a strong network of contacts.
Discussion and analysis
The principal activities of Schipper Ltd during the ?nancial year comprised:
.
property development;
.
car parking operators; and
.
investment activities.
Schipper Ltd prepares its ?nancial statements on the basis of historical cost, applying generally
accepted accounting principles.
Income statement. Schipper Ltd consolidated operating pro?t after income tax for the 2004
?nancial year was $4,139 compared to the 2003 pro?t of $2,864 representing a 44.5 per cent or
$1,275 increase. This compared favourably to an operating loss of $6,241 in 2002. The
disappointing result in 2002 was the culmination of very dif?cult and competitive trading
conditions experienced by the parking division and also Schipper Ltd switching its property
development focus away from long-term projects with high risk pro?les.
Revenue from activities for Schipper Ltd of $110,500 was an increase of 28.8 per cent on the
previous year. The revenue re?ects strong contribution from the property division and improved
operating revenue from the car park division.
The sale of 13 Baker Gardens, Reid, Kingston Foreshore apartments and the Docklands
property, supported the property revenue increase. The result of a greater focus on revenue
improvement in each state coupled with the acquisition of a number of high turnover car parks.
The investment division experienced a decline in revenue.
Balance sheet. Total assets decreased by $15,200 or 14.0 per cent, the result of property sales.
As a result of the repayment of property borrowings during the year, total liabilities
decreased by $17,500 or 32.2 per cent.
The equity attributable to shareholders increased by $2,300 or 4.3 per cent re?ecting the
pro?t for the year of $4,100 and offset by $1,800 being the cost of the share buyback.
ARJ
24,3
264
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Statement of cash ?ows. The net operating cash ?ow of $13,433 includes receipt of monies
from property development and car park revenue which occurred during this period whilst, the
previous years net operating cash ?ow includes the receipt of monies from property
development and car park revenue completed during the period ending 30 June 2003 and 30 June
2002.
In addition, the payments for equity investments and loans granted resulted in a net investing
cash out?ow of $17,654.
As a result of the repayment of borrowings and payment of dividends during the year the
Group experienced a net ?nancing cash out?ow of $15,702.
2004 ($) 2003 ($) 2002 ($)
Income statement
Revenue 110,545 85,811 108,292
Cost of sales (property, investments) (30,627) (13,471) (65,875)
Expenses from ordinary activities:
Car park overheads (55,798) (51,801) (48,144)
Corporate overheads (3,519) (4,147) (3,225)
Employee expenses (11,569) (9,863) (10,841)
Other expenses from ordinary activities (3,172) (2,272) (1,947)
Borrowing costs (1,721) (1,318) (1,478)
Share of net pro?t (loss) of associates 0 75 (28)
Pro?t before income tax expense 4,139 2,864 (6,241)
Income tax expense 0 0 0
Net pro?t 4,139 2,864 (6,241)
Basic earnings per share (cents) 1.84 1.25 (2.73)
Statement of cash ?ows
Cash ?ows from operating activities
Receipts from customers 100,165 143,850 142,080
Payments to suppliers and employees (86,762) (109,438) (161,774)
Dividends received 84 97 47
Interest received 2,050 1,257 729
Borrowing costs paid (2,104) (1,807) (1,478)
Net cash provided by operating activities 13,433 33,959 (20,396)
Cash ?ows from investing activities
Payments for property, plant and equipment (328) (204) (661)
Proceeds from sale of property, plant and equipment 211 7
Payments for investments (16,265) (7,676) (1,096)
Proceeds from sale of/capital return of investments 3,534 9,123 1,962
Loans and deposits provided (4,595) (500) 0
Net cash provided by/(used in) investing activities (17,654) 954 (1,988)
Cash ?ows from ?nancing activities
Proceeds from borrowings 2,748 12,010 32,690
Repayment of borrowings (16,631) (39,846) (2,950)
Dividends paid (excluding buyback) (67,797) (56,322) 0
Payments for buyback of securities (1,819) (8) 0
Proceeds from share issues 0 26 0
Net cash provided by/(used in) ?nancing activities (15,702) (27,810) 29,740
Net increase/(decrease) in cash held (19,923) 7,103 7,356
Cash at beginning of year 24,429 17,326 9,970
Cash at end of year 4,506 24,429 17,326 Table AI.
Segment
reporting
265
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
B
a
l
a
n
c
e
s
h
e
e
t
2
0
0
4
2
0
0
3
2
0
0
2
C
u
r
r
e
n
t
a
s
s
e
t
s
C
a
s
h
a
s
s
e
t
s
4
,
5
0
6
2
4
,
4
2
9
1
8
,
0
1
8
R
e
c
e
i
v
a
b
l
e
s
1
0
,
0
7
9
7
,
0
3
9
6
8
,
3
4
6
O
t
h
e
r
?
n
a
n
c
i
a
l
a
s
s
e
t
s
1
,
1
5
5
4
,
0
7
9
3
,
4
7
9
I
n
v
e
n
t
o
r
i
e
s
2
1
,
9
3
8
4
4
,
2
4
3
4
2
,
6
2
2
O
t
h
e
r
2
,
8
0
8
2
,
3
3
0
2
,
4
8
9
T
o
t
a
l
c
u
r
r
e
n
t
a
s
s
e
t
s
4
0
,
4
8
6
8
2
,
1
2
0
1
3
4
,
9
5
4
N
o
n
-
c
u
r
r
e
n
t
a
s
s
e
t
s
R
e
c
e
i
v
a
b
l
e
s
2
,
1
7
7
5
0
0
0
I
n
v
e
s
t
m
e
n
t
s
1
9
,
1
3
5
1
2
3
2
6
O
t
h
e
r
?
n
a
n
c
i
a
l
a
s
s
e
t
s
5
,
6
9
8
4
,
2
5
5
6
,
1
6
3
P
r
o
p
e
r
t
y
,
p
l
a
n
t
a
n
d
e
q
u
i
p
m
e
n
t
8
9
0
9
0
8
1
,
2
0
9
I
n
v
e
n
t
o
r
i
e
s
2
5
,
0
2
1
2
0
,
8
3
0
2
0
,
4
0
1
T
o
t
a
l
n
o
n
-
c
u
r
r
e
n
t
a
s
s
e
t
s
5
2
,
9
2
1
2
6
,
5
0
5
2
8
,
0
9
9
T
o
t
a
l
a
s
s
e
t
s
9
3
,
4
0
7
1
0
8
,
6
2
5
1
6
3
,
0
5
3
C
u
r
r
e
n
t
l
i
a
b
i
l
i
t
i
e
s
P
a
y
a
b
l
e
s
5
,
9
1
0
1
1
,
3
6
4
3
6
,
8
3
0
I
n
t
e
r
e
s
t
b
e
a
r
i
n
g
1
8
,
4
6
4
3
0
,
6
9
3
5
1
,
6
7
8
P
r
o
v
i
s
i
o
n
s
6
7
4
3
7
8
2
0
0
O
t
h
e
r
5
7
2
4
5
3
,
8
2
0
T
o
t
a
l
c
u
r
r
e
n
t
l
i
a
b
i
l
i
t
i
e
s
2
5
,
1
0
5
4
2
,
6
8
0
9
2
,
5
2
8
N
o
n
-
c
u
r
r
e
n
t
l
i
a
b
i
l
i
t
i
e
s
P
a
y
a
b
l
e
s
1
,
3
0
7
1
,
3
0
7
1
,
3
0
7
I
n
t
e
r
e
s
t
b
e
a
r
i
n
g
l
i
a
b
i
l
i
t
i
e
s
9
,
8
8
5
5
,
0
7
9
1
2
,
6
2
2
P
r
o
v
i
s
i
o
n
s
6
2
9
4
4
8
3
7
5
O
t
h
e
r
0
4
,
9
5
0
4
,
9
5
0
T
o
t
a
l
n
o
n
-
c
u
r
r
e
n
t
l
i
a
b
i
l
i
t
i
e
s
1
1
,
8
2
1
1
1
,
7
8
4
1
9
,
2
5
4
T
o
t
a
l
l
i
a
b
i
l
i
t
i
e
s
3
6
,
9
2
6
5
4
,
4
6
4
1
1
1
,
7
8
2
N
e
t
a
s
s
e
t
s
5
6
,
4
8
1
5
4
,
1
6
1
5
1
,
2
7
1
E
q
u
i
t
y
C
o
n
t
r
i
b
u
t
e
d
e
q
u
i
t
y
3
8
9
,
8
3
7
3
9
1
,
6
5
6
3
9
1
,
6
3
0
R
e
s
e
r
v
e
s
3
,
1
0
5
3
,
1
0
5
3
,
1
0
5
A
c
c
u
m
u
l
a
t
e
d
l
o
s
s
e
s
(
3
3
6
,
4
6
1
)
(
3
4
0
,
6
0
0
)
(
3
4
3
,
4
6
4
)
T
o
t
a
l
E
q
u
i
t
y
5
6
,
4
8
1
5
4
,
1
6
1
5
1
,
2
7
1
Table AII.
ARJ
24,3
266
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
About the authors
Jacqueline Birt joined the Department of Accounting and Finance at Monash University in
July 2008. Previously she held positions at the Australian National University and the University
of Melbourne. Her research is in the area of ?nancial accounting and her PhD focused on segment
reporting and examined issues such as value relevance and voluntary segment disclosures. She
has published in journals such as the Australian Journal of Management, the Australian
Accounting Review and Accounting Education. Jacqueline Birt is the corresponding author and
can be contacted at: [email protected]
Greg Shailer is a Reader and Associate Professor at the Australian National University,
Canberra, where he leads the Corporations, Governance & Society Research Group. He is a
Fellow of CPA Australia and a member of the Governance Research Network and the European
Corporate Governance Institute. His research interests include multidisciplinary and institutional
aspects of corporate governance, the economics of organisation the regulation of corporations
and their disclosures, and the economics and regulation of auditing.
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
Segment
reporting
267
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This article has been cited by:
1. Nancy B. Nichols, Donna L. Street, Ann Tarca. 2013. The Impact of Segment Reporting Under the
IFRS 8 and SFAS 131 Management Approach: A Research Review. Journal of International Financial
Management & Accounting 24:10.1111/jifm.2013.24.issue-3, 261-312. [CrossRef]
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
doc_650341050.pdf
Changes in Australian segment reporting standards over the last decade changed the
required disaggregation of segment information. The purpose of this paper is to investigate whether
increased disaggregation has implications for users’ confidence in decisions based on segment reports
and perceptions of segment reporting usefulness.
Accounting Research Journal
Forecasting confidence under segment reporting
J acqueline Birt Greg Shailer
Article information:
To cite this document:
J acqueline Birt Greg Shailer, (2011),"Forecasting confidence under segment reporting", Accounting
Research J ournal, Vol. 24 Iss 3 pp. 245 - 267
Permanent link to this document:http://dx.doi.org/10.1108/10309611111186993
Downloaded on: 24 January 2016, At: 21:14 (PT)
References: this document contains references to 37 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 1612 times since 2011*
Users who downloaded this article also downloaded:
M. Humayun Kabir, Fawzi Laswad, (2011),"Properties of net income and total comprehensive
income: New Zealand evidence", Accounting Research J ournal, Vol. 24 Iss 3 pp. 268-289 http://
dx.doi.org/10.1108/10309611111187000
Kevin Clarke, J ack Flanagan, Sharron O'Neill, (2011),"Winning ARC grants: comparing accounting with
other commerce-related disciplines", Accounting Research J ournal, Vol. 24 Iss 3 pp. 213-244 http://
dx.doi.org/10.1108/10309611111186984
Karol Marek Klimczak, Grzegorz Szafranski, (2013),"Coincident and forecast relevance of accounting
numbers", Accounting Research J ournal, Vol. 26 Iss 3 pp. 239-255http://dx.doi.org/10.1108/
ARJ -09-2012-0076
Access to this document was granted through an Emerald subscription provided by emerald-srm:115632 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Forecasting con?dence under
segment reporting
Jacqueline Birt
Department of Accounting & Finance, Monash University,
Cau?eld East, Australia, and
Greg Shailer
School of Accounting & Business Information Systems,
The Australian National University, Canberra, Australia
Abstract
Purpose – Changes in Australian segment reporting standards over the last decade changed the
required disaggregation of segment information. The purpose of this paper is to investigate whether
increased disaggregation has implications for users’ con?dence in decisions based on segment reports
and perceptions of segment reporting usefulness.
Design/methodology/approach – Using an experiment based on the differences between the
original AASB 1005 and the more detailed requirements of AASB 114, the authors test whether
segment report users’ con?dence in forecasting and their perceptions of segment report usefulness
differ between the different information sets provided under these standards.
Findings – It was found that the more disaggregated or ?ner reports based on AASB 114 provide
signi?cantly more con?dence to users, compared to the coarser segment reports based on the original
AASB 1005, but this is not associated with differences in segment report usefulness scores.
Research limitations/implications – The authors’ experiment is based on AASB 1005 and AASB
114 and the results cannot be generalized to differences with other reporting standards. Examination
of differences in recently released AASB 8 may reveal different implications for users’ con?dence and
perceptions of usefulness. More generally, other tests of usefulness are needed to con?rm whether
opinions of usefulness that are not con?rmed by decision-making practices provide a reliable basis for
determining usefulness.
Practical implications – By con?rming that decision makers’ con?dence can be increased by the
provision of ?ner information sets, the authors’ results have practical implications for accounting
standard setting.
Originality/value – By testing the impact of report differences on user decision con?dence, the
paper addresses a previously overlooked issue.
Keywords Australia, Accounting standards, Pro?t forecasting, Segment reporting, Fineness theorem,
Analysts, Experiment, Value relevance
Paper type Research paper
1. Introduction
There is substantial evidence that equity markets value segment data (Chen and Zhang,
2003; Berger and Hann, 2003) and this value relation is increasing with the amount of
data provided (Ettredge et al., 2005). Propositions as to why segment disclosures have
value largelyaccordwith the “?neness theorem” (Foster, 1975; Baldwin, 1984; Herrmann
and Thomas, 1997), which posits that a ?ner information structure is more valuable
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1030-9616.htm
The authors appreciate the helpful comments of the two anonymous reviewers and
Natalie Gallery.
Segment
reporting
245
Accounting Research Journal
Vol. 24 No. 3, 2011
pp. 245-267
qEmerald Group Publishing Limited
1030-9616
DOI 10.1108/10309611111186993
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
to a decision-maker than a coarser information structure (Marschak and Radner, 1971).
Analysts and investors endorse this view in relation to segment reporting, claiming it
improves analysis of the risk pro?les and growth of diversi?ed entities and allows better
integration of entity data with external data, and thus improves the accuracy of, and
con?dence in, earnings forecasts (Collins, 1976; Balakrishnan et al., 1990). Prior studies
con?rm the expectation of improved accuracy for earlier US segment reporting efforts
(Kinney, 1971; Collins, 1976; Baldwin, 1984; Balakrishnan et al., 1990) and the later SFAS
14 requirements (Lobo et al., 1998) but there has been little attention given to changes in
segment reporting regulation and the impact on forecasting con?dence. Maines et al.
(1997) examine perceptions of segment reporting reliability under SFAS 131, however
there is no comparable study of the segment reporting requirements under Australian
regulation. We argue that it is desirable to consider qualitative characteristics by report
users to better understand howdifferences in reports might affect users’ perceptions and
forecasting con?dence.
We address this knowledge gap by using two substantially different versions of
segment reports to test whether the provision of more detailed segment disclosures
improves earnings forecasting con?dence and the perceived usefulness of segment
reports. The results of our study offer potentially valuable feedback to standard setters
on the consequences of expanding segment disclosure details. This feedback is timely
because the AASB and the IASB recently issued AASB 8 (IFRS 8) that mandates the
use of the “management approach” to de?ning operating segments of the ?rm without
knowledge of how differences in disclosure details affects user con?dence[1].
As part of its IASB harmonisation program, the Australian Accounting Standards
Board issued a revised AASB1005 Segment Reporting in 2001, effective in 2002. In 2005,
this standard was replaced by AASB 114 Segment Reporting[2] as part of Australia’s
formal adoption of International Financial Reporting Standards (AIFRS). Compared to
the original AASB 1005, AASB 114 required substantially more segment data. It
required entities to choose whether their line of business (LOB) or geographic segments
were their primary segments, provide additional descriptive segment information, and
disclose at least nine segment items for the primary segments and three items for the
secondary segment. The original AASB 1005 required less disclosure in terms of
number of segments and explanatory details. Speci?cally, it mandated revenue,
pro?t and assets for both a ?rm’s geographic and industry segments, with little or no
supporting descriptive information. The original AASB 1005 also provided
management with more discretion regarding the de?nition of its reportable segments,
which was seen as facilitating reports based on vague, coarse or ambiguous segments
(Birt, 2008). Because AASB 114 provided a more disaggregated or ?ner view of a ?rm’s
results, compared to the original AASB1005, it is expected that users would perceive the
AASB 114 disclosures as more useful, thus increasing their con?dence in using the
reports. We investigate this conjecture in two complementary ways. We test whether
differences in the level of detail in segment disclosures are associated with:
(1) users’ con?dence levels for their earnings forecasts; and
(2) users’ perceptions of the usefulness of segment reports.
To do so, we use an experimental instrument based on the different levels of ?neness
associated with segment disclosure requirements of the original AASB 1005 and
AASB 114.
ARJ
24,3
246
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
We ?nd that users of more disaggregated reports based on AASB 114 have
signi?cantly more con?dence in their pro?t forecasts, compared to users of reports
based on the original AASB 1005. By revealing that decision makers’ con?dence can be
increased by the provision of ?ner information sets, this study contributes to the general
accounting literature concerned with the ?neness of information structures and users’
perceptions, and to the literature speci?cally concerned with the advantages and
disadvantages of different forms of segment reporting. Our ?ndings are timely feedback
to standard setters on the consequences of changing the levels of disaggregation of
segment disclosures, as the recently introduced AASB 8 Operating Segments again
substantially changes segment disclosure requirements.
2. Hypothesis development
The ?neness theorem posits that a ?ner information structure bene?ts the decision
maker. Information in set X is said to be as ?ne or ?ner than the information in set Y if
X is a sub-partition of Y (Demski, 1973). It follows that, if consolidated reports that
include segment disclosures provide ?ner information than consolidated reports
without segment disclosures, then including segment disclosures bene?ts report users.
It has been suggested that segment disclosures can improve the accuracy of and
con?dence in earnings forecasts (Collins, 1976; Balakrishnan et al., 1990). Consistent
with this suggestion, Mande and Ortman (2002) report that analysts perceive segment
data as useful in forecasting consolidated pro?ts. By extension, more detailed segment
disclosures should be more bene?cial than less detailed segment disclosures.
AASB 114 is ?ner than the original AASB 1005 to the extent that the additional
disclosure requirements of AASB 114 are a sub-partition of the original AASB 1005
requirements. The comparison of the original AASB 1005 and AASB 114 provided in
Table I shows AASB 114 does not strictly provide a sub-partitioning of the original
AASB 1005. AASB 114 was more restrictive and required additional disclosures for
some segments. In addition to distinguishing primary and secondary segments, it
required at least six additional disclosures for primary segments. However, while
requiring the additional disclosure of secondary segment capital additions, AASB
114 did not require the disclosure of secondary segment pro?t. This last factor means
AASB 114 is not necessarily a true sub-partition of the original AASB 1005 for every
company. Nonetheless, the sub-partitioning of primary segment data may be suf?cient
to bene?t users on average.
In relation to the increased explanatory details and number of segments in the USA
implementation of SFAS 131, Maines et al. (1997) report that the analysts perceived
segment reporting to be more reliable when there is congruence between internal and
external reporting as required by the SFAS 131. However, Bar-Yosef and Venezia (2004)
report that analysts’ forecast accuracy was not signi?cantly different under SFAS
131 disclosures compared to SFAS 14. These US results emphasise the importance of
distinguishing perceptions from realised outcomes. We adopt a more subtle distinction
here, where we distinguish perceived usefulness of reports and self-reported con?dence
in forecasts based on the reports. We are not concerned with differences in forecast
accuracy, which cannot be reliably tested in our experimental setting. Irrespective of
whether forecasts are different under the alternative information sets offered by the
original AASB 1005 and AASB 114, differences in con?dence in using the different
reports represent value to risk adverse users.
Segment
reporting
247
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The original AASB1005 was criticised for allowing too much discretion to management
that resulted in vague, ambiguous and coarse segment disclosures (Gavens, 1989;
Wines, 1997; Birt, 2008). AASB 114 mandated additional disclosures (such as segment
liabilities, segment depreciation and amortisation) and provided additional guidance to
management in determining their reportable segments. Reducing vagueness or
ambiguity in disclosures, may affect users’ perceptions of the usefulness of the
information, irrespective of the impact on forecasting accuracy. As described in the next
section, our experiment uses two sets of segment data, based on the original AASB1005
and AASB114, to test whether the different levels of detail affect users’ con?dence levels
when predicting pro?ts or their perceptions of segment report usefulness. We express
these objectives in two null-form hypotheses; for simplicity, we label the more
aggregated segment data based on the original AASB 1005 as coarse and the more
detailed segment data based on AASB 114 as ?ne:
Original AASB 1005 AASB 114
Reportable segments Reportable segments
The original standard required ?rms to identify
segments by industry. It de?ned a segment as a
distinguishable component engaged in providing
a product or service, or a collection of related
products or services, to customers external to the
reporting entity
The AASB 114 standard required ?rms to adopt a
management approach where segments are
identi?ed according to the information reported to
the CEO and the Board of Directors. If the internal
information reported to the CEO and the board is
not based on related products and services then
the next lowest level of internal management
reporting is used to identify the reportable
segments
The standard required entities to identify primary
and secondary segments, for which reporting
requirements differed
Determining the primary and secondary segments
The standard required a risk and returns
approach to identifying segments to re?ect how
an entity’s risks and returns are affected by
differences in the products and services it
provides or differences in the geographical areas
in which it operates
Required disclosures Required disclosures – primary segments
It required the same information for both industry
and geographical segments
1. Segment revenue
2. Segment result
1. Segment revenue 3. Carrying amount of segment assets
2. Segment pro?t
3. Segment assets
4. Acquisition of segments assets during the
period
5. Segment liabilities
6. Depreciation and amortisation
7. Other non-cash expenses
8. Share of associates pro?t and losses
9. Carrying amount of investment in associates
Required disclosures – secondary segments
1. Segment revenue
2. Segment assets
3. Capital additions
Table I.
Comparison of the
original AASB 1005 and
AASB 114 disclosures
ARJ
24,3
248
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
H1. There is no difference in users’ con?dence in their pro?t forecasts based on
the coarse segment data set compared to pro?t forecasts based on the ?ne
segment data.
H2. There is no difference in users’ perceived usefulness of the coarse segment
data compared to the ?ne segment data.
We have two purposes in testing H2. First, we are interested in users’ perceptions
per se. Second, we are interested in whether users’ perceptions are consistent with their
revealed measure of value, expressed as their self-assessed con?dence levels.
3. Research design
Our research objectives depend on observing how users of segment reports respond to
alternative forms of segment reporting in terms of con?dence and perceived
usefulness. These user responses cannot be observed in the market place and so we
rely on an experimental design in which we can manipulate the treatment of segment
reporting and solicit speci?c responses.
3.1 Experiment
We test the hypotheses using an experiment based on two versions of a case study that
differ only in the segment report, with one based on the original AASB 1005 and the
other based on AASB 114. The ?ctionalised case is based on the rescaled ?nancial
statements of a real corporation so that segment disclosures were not contrived[3]. The
two versions of the case materials were randomly assigned to ?nal year ?nance students
at an Australian university in the ?nal week of semester 2, 2006[4]. The details of the
alternate segment reports included in the experiment materials are shown in
Table II, whichclearly illustrates the different levels of detail inthe two forms of segment
reports. The other ?nancial statement components, common to both cases, are in the
Appendix.
Each participant completed a questionnaire, attached to their case, in which they
selected one of eight ranges for forecast consolidated pro?t (loss) for the next 12 months.
The eight options were losses in the ranges of $15,000 or more, $10,000-$14,999,
$5,000-$9,999, or 0-$4,999, or pro?ts before tax in the ranges of $1-$4,999, $5,000-$9,999,
$10,000-$14,999, and $15,000 or more. Participants then indicated their con?dence in
their selected forecast range by marking an interval point on a notionally continuous
scale ranging from 0 (not sure at all) to 100 (very sure). Participants also scored the
usefulness of six items in making their pro?t forecast: company background;
a discussion and analysis of the ?nancial statements; income statement; balance sheet;
statement of cash ?ows; and segment report. Respondents scored usefulness using a
?ve-point scale ranging from “strongly disagree” to “strongly agree” with respect to
each item’s usefulness.
The experiment did not intentionally test forecasting accuracy, and no
macroeconomic or comparative data, such as industry descriptions, statistics or
forecasts, were provided in the cases. Each respondent expressed his or her con?dence
level and perceived usefulness of their version of the segment report. Because each
respondent evaluated only one version of the segment report, we do not identify users’
preferences between the segment report options; however, this approach avoids
Segment
reporting
249
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
preferences for either option inducing exaggerated usefulness scores, which are more
likely to arise from users’ comparative rankings of alternative reports.
We collected age bands, sex and work experience bands for respondents as
potential controls. Prior research indicates that these respondent characteristics may
in?uence a variety of judgements. For example, research has shown that males exhibit
higher con?dence than females in areas of investment decision-making (Barber and
Odean, 2001). Males are more represented in the ?nance industry, spend more time
on investment decisions, invest in more risky assets and anticipate higher returns
than females (Yuh and Hanna, 1997). Lower con?dence levels of females may be
due to females basing their investment decisions on a more comprehensive group of
informational cues (Graham et al., 2002). Work experience has also been found to be a
signi?cant explanatory variable in describing differences in investment decisions
(Forbes and Kara, 2010; Menkhoff et al., 2010). Demographic characteristics such as job
tenure and years of work experience can impact on risk analysis (Kannadhasan and
Nandagopal, 2010). Age of the investor may also impact on the degree of con?dence in
investment decision-making. Research has shown that younger investors are less likely
to be overcon?dent (Menkhoff et al., 2010).
Property Car parks Investment Consolidated
Business segments 2004 2003 2004 2003 2004 2003 2004 2003
Case A: ?ne segment report under AASB 114
Segment revenue 67,969 59,111 37,637 15,770 4,939 10,930 110,545 85,811
Segment result 70 22,923 6,052 2,774 21,983 2,938 4,139 2,789
Share of net pro?t/loss of
equity investments – – – – – 75 – 75
Net pro?t/loss 4,139 2,864
Segment assets 14,698 17,863 67,594 68,316 11,115 22,446 93,407 108,625
Segment liabilities 7,288 8,090 19,886 37,677 9,752 8,697 36,926 54,464
Equity accounted
investments in segment
assets – – 19,135 – – 12 19,135 12
Acquisition of property,
plant and equipment 330 203 – – 21 1 351 204
Depreciation 197 203 35 – 136 166 368 369
Amortisation 1,378 1,307 – – – – 1,378 1,307
Business segments
The consolidated entity comprises the following main business segments based on the consolidated
entity’s management reporting system
(a) Car Parks. Operator of car parks
(b) Property. Property and project management and property development
(c) Investment. Equity securities portfolio and other investment activities
Geographical segments
The consolidated entity’s operations are located in Australia
Segment accounting policies
There are no signi?cant inter-segment sales
Case B: coarse segment report under the original AASB 1005
Segment revenue 67,969 59,111 37,637 15,770 4,939 10,930 110,545 85,811
Segment result 70 22,923 6,052 2,774 21,983 2,938 4,139 2,789
Segment assets 14,698 17,863 67,594 68,316 11,115 22,446 93,407 108,625
Table II.
Segment report
disclosures included in
the experiment materials
ARJ
24,3
250
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
3.2 Testing
We test each of the two hypotheses by regressing the appropriate
dependent variable against the respondents’ other responses and demographic
controls.
To test our con?dence hypothesis, H1, the appropriate dependent variable is the
con?dence measure provided by the respondent. To test our usefulness hypothesis, H2,
the appropriate dependent variable is the response selected on the ?ve-point scale to
identify a respondent’s opinion as to the usefulness of their version of the segment
report in forecasting pro?t.
The main test variable for both hypotheses is whether the responses (forecasting
con?dence and usefulness of the segment report) differ in relation to whether the
respondent’s case included the coarse segment report or the ?ne segment report. It is
arguable that respondents’ con?dence in their forecasts may positively re?ect their
views as to the usefulness of any of the various report sections. Therefore, we test for
whether the assessed usefulness of the segment report section is signi?cantly associated
with reported con?dence (H1) and control for assessed usefulness of the non-segment
report sections. Similarly, views regarding the usefulness of segment reports may re?ect
general attitudes to ?nancial reports and, therefore, we also test the relevance of the
assessed usefulness of the various other report sections in testing the usefulness of the
coarse segment report and the ?ne segment report.
We test all demographic controls variables obtained from the questionnaire, as
described in the results section.
We test our hypotheses using the following two models:
H1: Confidence ¼ b
0
þb
1
ðfine segment reportÞ þ b
j
ðcontrolsÞ
ð1Þ
H2: Segment report usefulness ¼ g
0
þg
1
ðfine segment reportÞ þ g
j
ðcontrolsÞ
ð2Þ
where the variables are de?ned as follows.
Response variables:
H1 con?dence = The respondent’s con?dence in his or her selected
forecast pro?t range, expressed as an integer between
0-100.
H2 segment report usefulness = The respondent’s assessed segment report
usefulness in relation to pro?t forecast,
measured on a ?ve-point Likert scale.
or:
scaled segment report usefulness ¼ segment report usefulness/mean report
usefulness;
where mean report usefulness ¼ (income statement usefulness þ balance sheet
usefulness þ cash ?owstatement usefulness)/3;
and
where the usefulness of each component was scored by the respondent using a
?ve-point Likert scale[5].
Segment
reporting
251
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Test (treatment) variable (H1 and H2):
Fine segment report ¼ 1 if the respondent received the ?ne segment report, and 0
otherwise.
Control variables:
Segment report usefulness or mean report usefulness (as de?ned above) is a
control variable for H1only.
Forecast error absolute ¼ difference between respondents forecast range
compared to the “correct” range.
Fine segment report £ forecast error ¼ the interaction between the treatment
?ne segment report and the imputed forecast error.
Male ¼ 1 if respondent is male, and 0 otherwise.
Current work experience ¼ 1 if a respondent reports current work experience in
accounting or ?nance, and 0 otherwise.
Prior work experience ¼ 1 if a respondent reports prior work experience in
accounting or ?nance, and 0 otherwise.
International ¼ 1 if a respondent is not an Australian permanent resident or
citizen, and 0 otherwise.
Fulltime ¼ 1 if a respondent is a fulltime student, and 0 otherwise.
Age ¼ an ordinal representation of the respondent’s age, taking the values 1, 2, 3
or 4 for ages ,19, 19-24, 25-30 or .30[6].
4. Results
4.1 Descriptive statistics and univariate associations
The two versions of the case study were distributed evenly but ad hoc to 144 students.
Participation was voluntary and some students chose to not participate or did not
complete the questionnaire. Complete responses were received for 64 cases of the ?ne
Characteristics Coarse segment report Fine segment report Total
Female 32 45 77
Male 27 19 46
Domestic student 24 18 42
International student 35 46 81
Part-time student 5 5 10
Full-time student 54 59 113
Current work experience 7 8 15
Prior work experience 9 12 21
No work experience 42 44 86
Age-band: #19 3 7 10
19-24 45 39 84
25-30 9 13 22
31 þ 2 5 7
59 64 123
Table III.
Respondent
characteristics
ARJ
24,3
252
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
segment report and 59 cases of the coarse segment report. Table III describes
respondents’ characteristics for each version of the case study. International students
and females are over-represented in the respondents using the ?ne segment report. With
the exception of forecast error, scaled segment report usefulness and mean report
usefulness, which are explained below, all variables are coded directly from the
responses.
Correlation matrices are reported in Table IVfor both Pearson correlations (Panel A)
and Spearman rank correlations (Panel B); as might be expected for tests of association
involving categorical data, the Pearson and Spearman correlations are near identical.
These correlations indicate an appropriate relation between age and work experience.
There is also an association between full-time candidature and age. We also note the
intuitively expected correlations between the perceived usefulness of the various
?nancial statement components.
Con?dence and segment report usefulness are positively but weakly correlated with
a coef?cient of 22 per cent, suggesting perceptions of usefulness are not strongly
informed by the revealed value. We note that con?dence is similarly correlated with
cash ?ow statement usefulness and mean report usefulness. For differences in our
response variables, con?dence and segment report usefulness, in relation to our test
variable (coarse versus ?ne segment report) and our categorical control variables for
respondents’ characteristics, we emphasise the differences in group means rather than
the correlation coef?cients, although they generally indicate similar conditions.
In assessing the signi?cance of the t-statistics for mean differences, we use two-tail
tests. We also report imputed forecast errors by coarse versus ?ne segment report and
by respondents’ characteristics in Table V. The only signi?cant mean difference for
any group comparison of forecast error in Table V is for the absolute value of forecast
error, grouped by respondents who received the ?ne segment report versus those who
received the coarse segment report; this mean difference is signi?cant at (t ¼ 1.89;
p ¼ 0.06)[7]. Therefore, we control for possible differences in this regard in our
regressions, together with the interaction of forecast errors and the treatment variable
when estimating models that include forecast error.
The difference in respondents’ mean con?dence between the coarse segment report
(57 per cent) and the ?ne segment report (62 per cent) is not signi?cant. When con?dence
is related to the demographic controls, there are marginal statistical differences in
relation to sex and work experience. The higher mean con?dence for males (68 per cent)
compared to females (55 per cent) is highly signi?cant (t ¼ 4.07; p , 0.01), and the
lower mean con?dence for respondents with no work experience, compared to other
respondents, is signi?cant (t ¼ 2.19; p ¼ 0.03) while respondents with prior work
experience have signi?cantly higher mean con?dence, compared to other respondents
(t ¼ 1.66; p ¼ 0.10).
We do not observe any signi?cant differences in the mean raw scores for segment
report usefulness with respect to the two versions of the segment report or between any
demographic groupings of the respondents. The mean scores for each of the ?nancial
reporting components included in the experiment instrument are similar, ranging from
3.5 to 4.1, and most do not signi?cantly differ by any grouping of the respondents. We
report the t-statistics for segment report usefulness, and note signi?cant differences in
means for international versus domestic students (t ¼ 2.51; p ¼ 0.01) and respondents
with prior work experience versus other respondents (t ¼ 1.97; p ¼ 0.05). Otherwise,
Segment
reporting
253
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
V
a
r
i
a
b
l
e
C
o
n
?
d
e
n
c
e
S
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
F
o
r
e
c
a
s
t
e
r
r
o
r
(
a
b
s
o
l
u
t
e
)
M
a
l
e
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
F
u
l
l
-
t
i
m
e
s
t
u
d
e
n
t
A
g
e
P
a
n
e
l
A
:
P
e
a
r
s
o
n
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
C
o
n
?
d
e
n
c
e
1
.
0
0
S
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
0
.
2
2
*
1
.
0
0
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
0
.
1
4
0
.
0
8
1
.
0
0
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
0
.
2
5
*
0
.
2
0
*
2
0
.
0
5
1
.
0
0
F
o
r
e
c
a
s
t
e
r
r
o
r
(
a
b
s
o
l
u
t
e
)
0
.
0
7
2
0
.
0
7
0
.
1
5
0
.
0
1
1
.
0
0
M
a
l
e
0
.
3
5
*
0
.
1
2
2
0
.
1
7
0
.
0
3
0
.
0
4
1
.
0
0
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
0
.
0
9
2
0
.
1
0
0
.
0
1
0
.
0
0
0
.
0
2
2
0
.
1
3
1
.
0
0
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
0
.
1
5
0
.
1
8
0
.
0
5
0
.
0
7
0
.
0
3
0
.
2
3
*
2
0
.
1
7
1
.
0
0
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
0
.
1
2
0
.
2
2
*
0
.
1
3
0
.
0
3
2
0
.
1
0
0
.
0
6
2
0
.
2
6
*
0
.
1
4
1
.
0
0
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
0
.
1
1
0
.
1
2
2
0
.
0
6
2
0
.
1
5
2
0
.
1
1
0
.
1
4
2
0
.
3
0
*
2
0
.
0
6
0
.
3
7
*
1
.
0
0
A
g
e
(
1
-
4
)
0
.
0
2
0
.
0
5
0
.
0
6
0
.
0
8
0
.
0
3
0
.
0
1
0
.
1
4
0
.
2
8
*
0
.
0
0
2
0
.
3
6
*
1
.
0
0
P
a
n
e
l
B
:
S
p
e
a
r
m
a
n
r
a
n
k
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
C
o
n
?
d
e
n
c
e
1
.
0
0
S
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
0
.
2
6
*
1
.
0
0
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
0
.
1
4
0
.
0
9
1
.
0
0
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
0
.
2
3
*
0
.
1
1
2
0
.
0
3
1
.
0
0
F
o
r
e
c
a
s
t
e
r
r
o
r
(
a
b
s
o
l
u
t
e
)
0
.
1
0
2
0
.
0
9
0
.
1
0
2
0
.
0
5
1
.
0
0
(
c
o
n
t
i
n
u
e
d
)
Table IV.
Correlation coef?cients
ARJ
24,3
254
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
V
a
r
i
a
b
l
e
C
o
n
?
d
e
n
c
e
S
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
F
o
r
e
c
a
s
t
e
r
r
o
r
(
a
b
s
o
l
u
t
e
)
M
a
l
e
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
F
u
l
l
-
t
i
m
e
s
t
u
d
e
n
t
A
g
e
M
a
l
e
0
.
3
4
*
0
.
0
9
2
0
.
1
7
0
.
0
1
0
.
0
3
1
.
0
0
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
0
.
1
0
2
0
.
1
1
0
.
0
1
2
0
.
0
1
2
0
.
0
2
2
0
.
1
3
1
.
0
0
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
0
.
1
5
0
.
1
8
*
0
.
0
5
0
.
0
8
2
0
.
0
1
0
.
2
3
*
2
0
.
1
7
1
.
0
0
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
0
.
0
9
0
.
1
9
*
0
.
1
3
0
.
0
2
2
0
.
1
5
0
.
0
6
2
0
.
2
6
*
0
.
1
4
1
.
0
0
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
0
.
0
4
0
.
1
0
2
0
.
0
6
2
0
.
1
6
2
0
.
0
7
0
.
1
4
2
0
.
3
0
*
2
0
.
0
6
0
.
3
7
*
1
.
0
0
A
g
e
0
.
0
4
0
.
0
7
0
.
0
5
0
.
1
0
2
0
.
0
1
0
.
0
2
0
.
1
0
0
.
3
6
*
0
.
0
5
2
0
.
3
2
*
1
.
0
0
N
o
t
e
s
:
*
I
n
d
i
c
a
t
e
s
s
i
g
n
i
?
c
a
n
t
a
t
t
h
e
5
p
e
r
c
e
n
t
l
e
v
e
l
;
c
o
n
?
d
e
n
c
e
¼
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
c
o
n
?
d
e
n
c
e
i
n
h
i
s
o
r
h
e
r
s
e
l
e
c
t
e
d
f
o
r
e
c
a
s
t
p
r
o
?
t
r
a
n
g
e
,
e
x
p
r
e
s
s
e
d
a
s
a
n
i
n
t
e
g
e
r
b
e
t
w
e
e
n
0
-
1
0
0
;
s
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
¼
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
a
s
s
e
s
s
e
d
u
s
e
f
u
l
n
e
s
s
o
f
t
h
e
s
e
g
m
e
n
t
r
e
p
o
r
t
,
a
s
s
e
s
s
e
d
o
n
a
?
v
e
-
p
o
i
n
t
s
c
a
l
e
;
?
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
¼
1
i
f
t
h
e
r
e
s
p
o
n
d
e
n
t
r
e
c
e
i
v
e
d
t
h
e
?
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
m
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
¼
(
i
n
c
o
m
e
s
t
a
t
e
m
e
n
t
u
s
e
f
u
l
n
e
s
s
þ
b
a
l
a
n
c
e
s
h
e
e
t
u
s
e
f
u
l
n
e
s
s
þ
c
a
s
h
?
o
w
s
t
a
t
e
m
e
n
t
u
s
e
f
u
l
n
e
s
s
)
/
3
;
f
o
r
e
c
a
s
t
e
r
r
o
r
a
b
s
o
l
u
t
e
¼
d
i
f
f
e
r
e
n
c
e
b
e
t
w
e
e
n
r
e
s
p
o
n
d
e
n
t
s
f
o
r
e
c
a
s
t
r
a
n
g
e
c
o
m
p
a
r
e
d
t
o
t
h
e
“
c
o
r
r
e
c
t
”
r
a
n
g
e
;
m
a
l
e
¼
1
i
f
r
e
s
p
o
n
d
e
n
t
i
s
m
a
l
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
c
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
r
e
p
o
r
t
s
c
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
i
n
a
c
c
o
u
n
t
i
n
g
o
r
?
n
a
n
c
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
p
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
r
e
p
o
r
t
s
p
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
i
n
a
c
c
o
u
n
t
i
n
g
o
r
?
n
a
n
c
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
i
n
t
e
r
n
a
t
i
o
n
a
l
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
i
s
n
o
t
a
n
A
u
s
t
r
a
l
i
a
n
p
e
r
m
a
n
e
n
t
r
e
s
i
d
e
n
t
o
r
c
i
t
i
z
e
n
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
f
u
l
l
t
i
m
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
i
s
a
f
u
l
l
t
i
m
e
s
t
u
d
e
n
t
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
a
g
e
¼
a
n
o
r
d
i
n
a
l
r
e
p
r
e
s
e
n
t
a
t
i
o
n
o
f
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
a
g
e
,
t
a
k
i
n
g
t
h
e
v
a
l
u
e
s
1
,
2
,
3
o
r
4
f
o
r
a
g
e
s
,
1
9
,
1
9
-
2
4
,
2
5
-
3
0
o
r
.
3
0
Table IV.
Segment
reporting
255
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
P
e
r
c
e
i
v
e
d
u
s
e
f
u
l
n
e
s
s
o
f
?
n
a
n
c
i
a
l
r
e
p
o
r
t
c
o
m
p
o
n
e
n
t
s
(
m
e
a
n
o
f
s
c
a
l
e
r
e
s
p
o
n
s
e
s
:
1
-
5
)
T
r
e
a
t
m
e
n
t
/
r
e
s
p
o
n
d
e
n
t
c
h
a
r
a
c
t
e
r
i
s
t
i
c
s
n
C
o
n
?
d
e
n
c
e
2
r
e
s
p
o
n
s
e
0
-
1
0
0
a
F
o
r
e
c
a
s
t
e
r
r
o
r
2
a
b
s
o
l
u
t
e
v
a
l
u
e
a
B
a
c
k
g
r
o
u
n
d
D
i
s
c
u
s
s
i
o
n
I
n
c
o
m
e
s
t
a
t
e
m
e
n
t
B
a
l
a
n
c
e
s
h
e
e
t
C
a
s
h
?
o
w
s
t
a
t
e
m
e
n
t
S
e
g
m
e
n
t
r
e
p
o
r
t
a
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
(
i
n
c
o
m
e
s
t
a
t
e
m
e
n
t
,
b
a
l
a
n
c
e
s
h
e
e
t
a
n
d
c
a
s
h
?
o
w
s
t
a
t
e
m
e
n
t
)
a
S
c
a
l
e
d
s
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
a
C
o
a
r
s
e
s
e
g
m
e
n
t
r
e
p
o
r
t
5
9
5
7
.
0
3
0
.
3
9
0
.
8
0
3
.
5
6
3
.
5
8
4
.
1
5
3
.
9
5
4
.
0
2
3
.
4
4
4
.
0
3
0
.
8
6
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
6
4
6
2
.
0
3
(
1
.
5
0
)
0
.
4
1
(
0
.
0
2
)
1
.
0
6
(
1
.
8
9
)
3
.
4
8
3
.
7
5
4
.
0
9
3
.
9
1
3
.
9
4
3
.
5
8
(
0
.
8
5
)
3
.
9
8
(
0
.
5
2
)
0
.
9
2
(
0
.
9
7
)
F
e
m
a
l
e
7
7
5
4
.
6
8
0
.
4
7
0
.
9
1
3
.
4
9
3
.
5
2
4
.
1
3
3
.
8
4
4
.
0
0
3
.
4
3
3
.
9
9
0
.
8
7
M
a
l
e
4
6
6
7
.
9
3
(
4
.
0
7
)
*
*
*
0
.
2
8
(
1
.
1
3
)
0
.
9
8
(
0
.
4
7
)
3
.
5
7
3
.
9
1
4
.
1
1
4
.
0
7
3
.
9
3
3
.
6
5
(
1
.
3
5
)
4
.
0
4
(
0
.
3
7
)
0
.
9
3
(
1
.
2
5
)
D
o
m
e
s
t
i
c
s
t
u
d
e
n
t
4
2
5
6
.
4
3
0
.
6
2
1
.
0
4
3
.
4
0
3
.
4
8
4
.
2
6
3
.
8
6
3
.
8
3
3
.
2
4
3
.
9
8
0
.
8
2
I
n
t
e
r
n
a
t
i
o
n
a
l
s
t
u
d
e
n
t
8
1
6
1
.
3
0
(
1
.
3
8
)
0
.
2
8
(
1
.
3
3
)
0
.
8
8
(
0
.
1
5
)
3
.
5
8
3
.
7
6
4
.
0
5
3
.
9
6
4
.
0
5
3
.
6
5
(
2
.
5
1
)
*
*
4
.
0
2
(
0
.
2
8
)
0
.
9
3
(
2
.
1
7
)
*
*
P
a
r
t
-
t
i
m
e
s
t
u
d
e
n
t
8
5
1
.
8
8
0
.
2
5
1
.
2
5
3
.
7
5
3
.
2
5
4
.
5
0
4
.
2
5
4
.
3
8
3
.
1
3
4
.
3
8
0
.
7
4
F
u
l
l
-
t
i
m
e
s
t
u
d
e
n
t
1
1
3
6
0
.
2
7
(
1
.
3
4
)
0
.
4
0
(
0
.
4
7
)
0
.
9
1
(
1
.
1
7
)
3
.
5
1
3
.
6
9
4
.
1
0
3
.
9
2
3
.
9
5
3
.
5
6
(
1
.
3
4
)
3
.
9
9
(
1
.
6
5
)
0
.
9
1
(
1
.
7
5
)
*
A
g
e
-
b
a
n
d
:
#
1
9
1
0
5
9
.
5
0
(
0
.
0
2
)
0
.
5
0
(
0
.
2
9
)
0
.
9
3
(
0
.
1
5
)
3
.
4
0
3
.
5
0
4
.
2
0
3
.
9
0
4
.
0
0
3
.
4
0
(
0
.
4
1
)
4
.
0
3
(
0
.
1
2
)
0
.
8
6
(
0
.
4
1
)
1
9
-
2
4
8
4
5
8
.
9
9
(
0
.
5
6
)
0
.
3
8
(
0
.
2
4
)
0
.
9
3
(
0
.
1
3
)
3
.
4
0
3
.
6
4
4
.
0
7
3
.
8
9
3
.
8
9
3
.
4
9
(
0
.
4
4
)
3
.
9
5
(
1
.
4
1
)
0
.
9
0
(
0
.
3
0
)
2
5
-
3
0
2
2
6
3
.
6
4
(
1
.
1
2
)
0
.
2
3
(
0
.
7
6
)
0
.
9
5
(
0
.
1
3
)
4
.
0
0
3
.
7
7
4
.
2
7
4
.
0
5
4
.
3
2
3
.
6
8
(
0
.
9
9
)
4
.
2
1
(
1
.
6
4
)
0
.
9
0
(
0
.
1
6
)
3
1
þ
7
5
5
.
0
0
(
0
.
6
8
)
1
.
0
0
(
1
.
4
2
)
1
.
0
0
(
0
.
6
9
)
3
.
5
7
3
.
8
3
4
.
1
4
4
.
0
0
3
.
8
6
3
.
4
3
(
0
.
2
5
)
4
.
0
0
(
0
.
0
4
)
0
.
8
5
(
0
.
3
9
)
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
1
5
6
4
.
0
0
(
0
.
9
7
)
0
.
5
3
(
0
.
4
8
)
1
.
0
7
(
0
.
6
9
)
3
.
6
0
3
.
4
7
4
.
1
3
3
.
9
3
3
.
9
3
3
.
2
7
(
0
.
7
1
)
4
.
0
0
(
0
.
0
5
)
0
.
8
4
(
0
.
8
6
)
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
2
1
6
5
.
7
1
(
1
.
6
6
)
*
0
.
0
5
(
1
.
5
3
)
1
.
0
0
(
0
.
4
1
)
3
.
9
0
4
.
0
5
4
.
1
4
3
.
9
5
4
.
2
4
3
.
8
6
(
1
.
9
7
)
*
*
4
.
1
1
(
0
.
9
2
)
0
.
9
6
(
1
.
3
1
)
N
o
w
o
r
k
e
x
p
e
r
i
e
n
c
e
8
6
5
7
.
2
7
(
2
.
1
9
)
*
*
0
.
4
5
(
0
.
8
0
)
0
.
9
0
(
0
.
8
5
)
3
.
4
2
3
.
6
0
4
.
1
2
3
.
9
2
3
.
9
2
3
.
4
7
(
0
.
8
9
)
3
.
9
8
(
0
.
6
4
)
0
.
8
8
(
0
.
5
4
)
O
v
e
r
a
l
l
1
2
3
5
9
.
6
3
0
.
4
0
0
.
9
3
3
.
5
2
3
.
6
6
4
.
1
2
3
.
9
3
3
.
9
8
3
.
5
1
4
.
0
1
0
.
8
9
N
o
t
e
s
:
*
,
*
*
a
n
d
*
*
*
i
n
d
i
c
a
t
e
s
i
g
n
i
?
c
a
n
t
a
t
t
h
e
1
0
,
5
a
n
d
1
p
e
r
c
e
n
t
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
a
t
-
s
t
a
t
i
s
t
i
c
s
f
o
r
m
e
a
n
d
i
f
f
e
r
e
n
c
e
s
a
r
e
i
n
p
a
r
e
n
t
h
e
s
e
s
a
n
d
a
s
s
u
m
e
e
q
u
a
l
v
a
r
i
a
n
c
e
s
Table V.
Response variables by
treatment and respondent
characteristics
ARJ
24,3
256
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
the apparent anchoring of usefulness perceptions suggests it would be dif?cult to
explain differences in the raw scores for segment report usefulness using the available
variables, or to distinguish between segment report usefulness scores independent of
other ?nancial report usefulness scores. To address this problem, we compute scaled
segment report usefulness by dividing each respondents segment report usefulness by
the mean perceived usefulness scores for the other components of the ?nancial
statements, thus[8]:
Scaled segment report usefulness ¼
Segment report usefulness
Mean report usefulness
ð3Þ
where
Mean report usefulness ¼
ðIncome statement usefulness þ balance sheet usefulness þ cash flowstatement usefulnessÞ
3
ð4Þ
The results for scaled segment report usefulness and mean report usefulness are also
included in the correlation matrix in Table IV and the comparative means in Table V.
There are no signi?cant differences between any group means for mean report
usefulness. For scaled segment report usefulness, the mean difference for international
versus domestic students remains evident (t ¼ 2.17; p ¼ 0.03) and there is a modest
difference in means between part-time and full-time students (t ¼ 1.75; p ¼ 0.08).
4.2 Results for hypothesis tests
Forecast con?dence (H1). Inestimatingthe relationbetween con?dence and?ne segment
report, we consider the potential signi?cant in?uence of all available controls. The
results are reported in Table VI. The treatment variable, ?ne segment report, is
signi?cant in all models (Model 1a: t ¼ 2.07, p ¼ 0.04; Model 1b: t ¼ 2.85, p ¼ 0.01;
Model 1c: t ¼ 2.47, p ¼ 0.02; Model 1d: t ¼ 3.05, p ,0.01). As discussed above,
respondents’ perceptions of the usefulness of the segment report, income statement,
balance sheet andcash?owstatement are all signi?cantly relatedso we test eachof these
usefulness measures (not reported) and mean report usefulness; no individual report
element was stronger than the mean report usefulness[9]. We report signi?cant results
for segment report usefulness in Models 1a and 1b (t ¼ 1.74; p ¼ 0.09) and (t ¼ 1.89;
p ¼ 0.06), respectively, and mean report usefulness in Models 1c and 1d (signi?cant at
p , 0.01 in both models). Thus, report usefulness is signi?cantly associated with
increased con?dence in all these models; the seemingly stronger results for mean report
usefulness suggest the respondents’ expressed con?dence is conditioned by the
perceived overall usefulness of ?nancial reports. Because expressed con?dence might be
a functionof bothperceived anddemonstrated usefulness, we also test the absolute value
of imputed forecast error, interacted with the treatment variable in Models 1b and 1c.
The positive coef?cients for Forecast error and the negative coef?cients for ?ne segment
report £ Forecast error are both signi?cant in both models, indicating that con?dence is
increasing with imputed error for the respondents using the coarse segment report
(original AASB 1005) but not for respondents using the ?ner segment. On the basis of
Models 1b and 1d, the difference in con?dence between users of the ?ner segment report
and the coarser original AASB 1005, where the ?ner information set is associated with
higher con?dence, is increased when imputed forecast error is taken into account.
Segment
reporting
257
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
M
o
d
e
l
1
a
M
o
d
e
l
1
b
M
o
d
e
l
1
c
M
o
d
e
l
1
d
V
a
r
i
a
b
l
e
b
t
-
s
t
a
t
.
b
t
-
s
t
a
t
.
b
t
-
s
t
a
t
.
b
t
-
s
t
a
t
.
T
e
s
t
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
6
.
5
1
2
.
0
7
*
*
1
4
.
5
4
2
.
8
5
*
*
7
.
5
4
2
.
4
7
*
*
1
5
.
1
2
3
.
0
5
*
*
C
o
n
t
r
o
l
s
S
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
3
.
1
2
1
.
7
4
*
3
.
3
7
1
.
8
9
*
M
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
7
.
7
6
3
.
3
3
*
*
7
.
6
7
3
.
3
2
*
*
F
o
r
e
c
a
s
t
e
r
r
o
r
(
a
b
s
o
l
u
t
e
)
7
.
6
5
2
.
1
3
*
*
6
.
9
1
1
.
9
9
*
*
F
i
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
£
F
o
r
e
c
a
s
t
e
r
r
o
r
–
–
2
9
.
1
8
2
2
.
1
0
*
*
2
8
.
5
7
2
2
.
0
2
*
*
M
a
l
e
1
3
.
5
0
4
.
0
4
*
*
1
3
.
9
4
4
.
1
9
*
*
1
3
.
6
5
4
.
2
3
*
*
1
4
.
1
4
4
.
4
0
*
*
C
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
1
1
.
6
9
2
.
3
0
*
*
1
3
.
5
7
2
.
6
7
*
*
1
2
.
0
1
2
.
4
5
*
*
1
3
.
7
5
2
.
7
9
*
*
P
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
3
.
5
0
0
.
7
8
4
.
7
7
1
.
0
6
3
.
8
8
0
.
9
0
5
.
1
5
1
.
1
9
I
n
t
e
r
n
a
t
i
o
n
a
l
2
.
1
3
0
.
5
8
2
.
2
3
0
.
6
2
2
.
1
0
0
.
6
0
2
.
2
5
0
.
6
5
F
u
l
l
t
i
m
e
7
.
1
5
0
.
9
6
6
.
0
2
0
.
8
1
1
1
.
8
6
1
.
6
3
1
4
.
7
5
1
.
4
8
A
g
e
2
0
.
5
3
2
0
.
2
1
2
1
.
3
8
2
0
.
5
3
2
0
.
4
1
2
0
.
1
6
2
1
.
1
9
2
0
.
4
7
C
o
n
s
t
a
n
t
3
1
.
3
2
2
.
7
5
*
*
2
6
.
4
6
2
.
2
7
*
*
5
.
8
0
0
.
4
1
2
.
6
0
0
.
1
8
n
1
2
3
1
2
3
1
2
3
1
2
3
F
-
s
t
a
t
i
s
t
i
c
4
.
1
4
*
*
3
.
8
8
*
*
5
.
4
0
*
*
4
.
8
5
*
*
A
d
j
u
s
t
e
d
R
2
0
.
1
7
0
.
1
9
0
.
2
2
0
.
2
4
N
o
t
e
s
:
*
a
n
d
*
*
i
n
d
i
c
a
t
e
s
i
g
n
i
?
c
a
n
t
a
t
t
h
e
1
0
a
n
d
5
p
e
r
c
e
n
t
l
e
v
e
l
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
c
o
n
?
d
e
n
c
e
¼
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
c
o
n
?
d
e
n
c
e
i
n
h
i
s
o
r
h
e
r
s
e
l
e
c
t
e
d
f
o
r
e
c
a
s
t
p
r
o
?
t
r
a
n
g
e
,
e
x
p
r
e
s
s
e
d
a
s
a
n
i
n
t
e
g
e
r
b
e
t
w
e
e
n
0
-
1
0
0
;
?
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
¼
1
i
f
t
h
e
r
e
s
p
o
n
d
e
n
t
r
e
c
e
i
v
e
d
t
h
e
?
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
s
e
g
m
e
n
t
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
¼
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
a
s
s
e
s
s
e
d
u
s
e
f
u
l
n
e
s
s
,
o
n
a
?
v
e
-
p
o
i
n
t
s
c
a
l
e
;
m
e
a
n
r
e
p
o
r
t
u
s
e
f
u
l
n
e
s
s
¼
(
i
n
c
o
m
e
s
t
a
t
e
m
e
n
t
u
s
e
f
u
l
n
e
s
s
þ
b
a
l
a
n
c
e
s
h
e
e
t
u
s
e
f
u
l
n
e
s
s
þ
c
a
s
h
?
o
w
s
t
a
t
e
m
e
n
t
u
s
e
f
u
l
n
e
s
s
)
/
3
;
f
o
r
e
c
a
s
t
e
r
r
o
r
a
b
s
o
l
u
t
e
¼
a
b
s
o
l
u
t
e
v
a
l
u
e
o
f
t
h
e
d
i
f
f
e
r
e
n
c
e
b
e
t
w
e
e
n
r
e
s
p
o
n
d
e
n
t
s
f
o
r
e
c
a
s
t
r
a
n
g
e
c
o
m
p
a
r
e
d
t
o
t
h
e
“
c
o
r
r
e
c
t
”
r
a
n
g
e
;
?
n
e
s
e
g
m
e
n
t
r
e
p
o
r
t
£
f
o
r
e
c
a
s
t
e
r
r
o
r
¼
t
h
e
i
n
t
e
r
a
c
t
i
o
n
b
e
t
w
e
e
n
t
h
e
t
r
e
a
t
m
e
n
t
;
m
a
l
e
¼
1
i
f
r
e
s
p
o
n
d
e
n
t
i
s
m
a
l
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
c
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
r
e
p
o
r
t
s
c
u
r
r
e
n
t
w
o
r
k
e
x
p
e
r
i
e
n
c
e
i
n
a
c
c
o
u
n
t
i
n
g
o
r
?
n
a
n
c
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
p
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
r
e
p
o
r
t
s
p
r
i
o
r
w
o
r
k
e
x
p
e
r
i
e
n
c
e
i
n
a
c
c
o
u
n
t
i
n
g
o
r
?
n
a
n
c
e
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
i
n
t
e
r
n
a
t
i
o
n
a
l
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
i
s
n
o
t
a
n
A
u
s
t
r
a
l
i
a
n
p
e
r
m
a
n
e
n
t
r
e
s
i
d
e
n
t
o
r
c
i
t
i
z
e
n
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
f
u
l
l
t
i
m
e
¼
1
i
f
a
r
e
s
p
o
n
d
e
n
t
i
s
a
f
u
l
l
t
i
m
e
s
t
u
d
e
n
t
,
a
n
d
0
o
t
h
e
r
w
i
s
e
;
a
g
e
¼
a
n
o
r
d
i
n
a
l
r
e
p
r
e
s
e
n
t
a
t
i
o
n
o
f
t
h
e
r
e
s
p
o
n
d
e
n
t
’
s
a
g
e
,
t
a
k
i
n
g
t
h
e
v
a
l
u
e
s
1
,
2
,
3
o
r
4
f
o
r
a
g
e
s
,
1
9
,
1
9
-
2
4
,
2
5
-
3
0
o
r
.
3
0
Table VI.
OLS regressions for
con?dence (H1)
ARJ
24,3
258
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The control indicator variables male and current work experience (whether the
respondent was currently engaged in employment involving accounting or ?nance
functions) are also signi?cantly associated with increased con?dence in all models[10].
These associations are largely consistent with the differences in means reported in
Table V. No other control variables are signi?cant, and the reported results also hold for
parsimonious models that exclude non-signi?cant control variables (not reported).
The signi?cantly positive coef?cient for the treatment variable ?ne segment report
leads us to reject H1. On average, a respondent’s reported con?dence in the selected
forecast range is signi?cantly higher when the case used the statements based on the
?ne segment report, compared to a case based on the coarse segment report.
Usefulness (H2). The results for our tests of the perceived usefulness of the competing
versions of the segment report are reported in Table VII, where we regress segment
report usefulness on ?ne segment report. Because the response variable segment report
usefulness is unlikely to be scalar, we estimate Model 2a and 2b using an ordered logistic
regression. We do not ?nd any signi?cant relation between segment report usefulness
and ?ne segment report. Model 2b includes forecast error and its interaction with ?ne
segment report, which are not signi?cant. None of the demographic control variables are
signi?cant. Based on the results reported in Table VII, we cannot reject H2. We ?nd no
evidence of a difference in users’ perceived usefulness of the coarse segment data
compared to the ?ne segment data.
Segment report usefulness
Model 2a Model 2b
Variable b z-stat. b z-stat.
Test
Fine segment report 0.30 0.85 0.35 0.60
Controls
Forecast error (absolute) 20.14 20.30
Fine segment report £ forecast error 20.01 20.01
Male 0.22 0.58 0.25 0.67
Current work experience 20.35 20.62 20.35 20.60
Prior work experience 0.67 1.32 0.69 1.35
International 0.53 1.31 0.49 1.21
Fulltime 0.44 0.53 0.41 0.48
Age 0.10 0.34 0.09 0.30
n 123 123
Log likelihood 2149.56 2149.38
Pseudo R
2
0.03 0.03
Notes: Segment report usefulness ¼ the respondent’s assessed usefulness, on a ?ve-point scale; ?ne
segment report ¼ 1 if the respondent received the ?ne segment report, and 0 otherwise; forecast error
absolute ¼ absolute value of the difference between respondents forecast range compared to the
“correct” range; ?ne segment report £ forecast error ¼ the interaction between the treatment;
male ¼ 1 if respondent is male, and 0 otherwise; current work experience ¼ 1 if a respondent reports
current work experience in accounting or ?nance, and 0 otherwise; prior work experience ¼ 1 if a
respondent reports prior work experience in accounting or ?nance, and 0 otherwise; international ¼ 1
if a respondent is not an Australian permanent resident or citizen, and 0 otherwise; fulltime ¼ 1 if a
respondent is a fulltime student, and 0 otherwise; age ¼ an ordinal representation of the respondent’s
age, taking the values 1, 2, 3 or 4 for ages ,19, 19-24, 25-30 or .30
Table VII.
Ordered logistic
regressions for segment
report usefulness
hypothesis H2
Segment
reporting
259
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
As further testing, we replaced segment report usefulness with scaled segment
report usefulness in Models 2a and 2b and estimated the models using OLS. The
results were substantially the same as those reported for segment report usefulness with
no signi?cant variables. In ordered logistic regressions models for segment report
usefulness and OLS models for scaled segment report usefulness, we also replaced age
with dichotomous grouping variables for the age bands; these age band indicators are
not signi?cant in any model. We also added mean report usefulness to each of the
models, and also tested the interaction term ?ne segment report £ mean report
usefulness. We do not report the detailed results for these extended models because they
do not affect the tests of our hypotheses. The only signi?cant ?nding is mean report
usefulness is strongly positive in models for scaled segment report usefulness. Similar to
our results for con?dence in Model 1, the relation between mean report usefulness and
scaled segment report usefulness suggests that the respondents’ perceived usefulness of
the segment report is related to their attitude towards the usefulness of ?nancial reports
generally, but do not affect our main ?ndings.
5. Conclusion
The extant literature examining the role of segment reporting in investment judgments
is largely concerned with quantitative outcomes, emphasising market valuation and
forecast accuracy. By examining the impact of alternate segment reporting approaches
on users’ expressed con?dence in forecasts and the perceived usefulness of segment
reports, this study contributes to the general accounting literature concerned with the
?neness of information structures and users’ perceptions. We contribute to the
literature speci?cally concerned with the advantages and disadvantages of different
forms of segment reporting by revealing that decision makers’ con?dence can be
increased by the provision of ?ner information sets.
Application of the ?neness theoremsuggests the higher level of disaggregation under
AASB 114 should, yield higher value to users, compared to disclosures under the
original AASB 1005. However, attribution of increased ?neness is ambiguous because
AASB114 did not require disclosure of secondary segment pro?t that had been reported
for all segments under the original AASB1005. Inour experiment, users of the notionally
?ner segment report, on average, reported higher con?dence levels in their pro?t
forecasts compared to users of the coarser segment report. We suggest that increased
con?dence provides value to users. However, further research relating con?dence to
decision outcomes is necessary to improve our understanding of the value of increased
con?dence.
We ?nd no difference in the respondents’ perceptions of the usefulness of the two
forms of segment report tested here. It is plausible that attitudes towards segment
reports are heavily anchored by attitudes towards ?nancial reports in general, resulting
in little difference in perceived usefulness for the different levels of ?neness in segment
reports tested here. Our single-blind experimental design did not allow an individual
respondent to reveal preferences between disclosures under the two standards and we
cannot exclude the possibility that there are real differences in perceptions of usefulness
between different segment reporting approaches that might be revealed by a different
research design. Consequently, further research to reveal evidence of latent differences
in usefulness of report components, and especially different approaches to segment
reporting, is desirable. If our results hold across other tests of usefulness, it may indicate
ARJ
24,3
260
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
that opinions of usefulness that are not based on ranks or con?rmed by decision-making
practices do not provide a reliable basis for determining usefulness.
Subsequent to our experiment, AASB 8 Operating Segments was implemented in
2010.AASB 8 introduced the management approach to segment reporting previously
adopted in the USA in SFAS 131.AASB 8 requires ?rms to “look inside” their
organisation to how the organisation is managed in determining reportable segments.
This requirement may result in different segment reporting treatments for some
companies, with potential implications for users’ con?dence and perceptions of
usefulness; therefore, further study of differences in segment reporting in relation to
users’ behaviour and perceptions is warranted.
Notes
1. Australian reporting entities implemented AASB 8 Operating Segments from 2010.
2. The revised AASB 1005 and AASB 114 were almost identical in terms of application and
requirements. In this experiment, we compare the original AASB 1005 and AASB 114.
3. When the revised AASB 1005 was implemented (recall this was later reissued as AASB 114),
companies had to restate the comparative disclosures for the previous year that were under
the old standard to the form required under the new standard. This restatement provided the
two different segment reports for our test year. We ?ctionalised the name of the entity and
reduced the scaled of its ?nancial statements by a factor of 20 so that respondents could
more comfortably deal with whole numbers.
4. Ethics approval at the university level was obtained prior to administering this instrument
to students. The students were taking an advanced undergraduate investments course in a
?nance major. The course provides a practical knowledge of modern ?nancial markets,
traders and trading strategies while also covering the theoretical underpinnings, including
asset pricing and valuation, stock selection and market ef?ciency. The prerequisites for this
course include accounting, economics, statistics and ?nance courses. This educational
background suggests that the participants had the necessary skills to evaluate an
investment and forecast pro?t. The adequacy of students as proxies for real-world decision
makers has been considered previously. For example, marketing and advertising studies
have revealed no differences between managers and students as the respondents (Chang and
Ho, 2004) and Liyanarachchi and Milne (2004) provide evidence that students’ investment
decision-making compares well with those of practitioners.
5. Our basis for testing scaled segment report usefulness is a consequence of the correlations
between the usefulness of the ?nancial statement components, as explained in Section 4.1.
6. While the ordinal age variable is not consistent with the assumption of a scalar variable
assumed for OLS, it was suggested that it might reveal an age association that is not revealed
by regressing our response variables on the categorical age indicators We thank the editor for
this suggestion. We report correlations for both the ordinal and categorical age variables, and
we use categorical age variables to test for differences in means of our response variables. We
also test categorical age indicators in our further testing of the reported results.
7. In determining the forecast error, each respondent’s forecast range of pro?t/loss is compared
to the correct range of pro?t/loss.
8. This approach is generally based on the treatment for anchoring bias in survey responses in
Shailer et al. (2001). The treatment for anchoring bias in Shailer et al. (2001) was to divide
individuals’ responses by group (country) means to identify the extent to which individuals
deviated from their group norm. The scaling used here measures the extent to which an
Segment
reporting
261
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
individual’s perceived usefulness of the segment report deviates from their general (mean)
perception of the usefulness of the other accompanying ?nancial statements.
9. We also tested scaled segment report usefulness in place of segment report usefulness but this
was not signi?cant. We also tested composite measures based on extracted factors, means and
products of the usefulness scores for the income statement, balance sheet and cash ?ow
statement. None are superior to using mean report usefulness in Models 1c or 1d. A factor
analysis of the three variables ?elds a single factor with an eigen value of 1.3 an loadings
of 0.62, 0.78 and 0.59, respectively, that we also tested. The results for the numerous
exploratory regressions are not reported.
10. Work experience was also tested in relation to a combined variable for either prior or current
work experience. This combined variable is not signi?cant.
References
Balakrishnan, R., Harris, M. and Sen, T.S. (1990), “The predictive ability of geographic segment
disclosures”, Journal of Accounting Research, Vol. 28 No. 2, pp. 305-25.
Barber, B.M. and Odean, T. (2001), “Boys will be boys: gender, overcon?dence and common stock
investment”, Quarterly Journal of Economics, February, pp. 261-92.
Bar-Yosef, S. and Venezia, I. (2004), “Experimental study of implications of SFAS 131: the effects
of the new standard on the informativeness of segment reporting”, working paper, School
of Business Administration, The Hebrew University of Jerusalem, Jerusalem.
Berger, P.G. and Hann, R. (2003), “Segment pro?tability and the proprietary and agency costs of
disclosure”, Accounting Review, Vol. 82, pp. 869-906.
Birt, J.L. (2008), “Consequences of changing Australian segment reporting requirements”,
unpublished doctoral thesis, The Australian National University, Canberra.
Chang, C. and Ho, J.L. (2004), “Judgment and decision making in project continuation: a study of
students as surrogates for experienced managers”, Abacus, Vol. 40 No. 1, pp. 94-116.
Chen, P. and Zhang, G. (2003), “Heterogeneous investment opportunities in multiple-segment ?rms
andthe incremental value relevance of segment data”, AccountingReview, Vol. 78, pp. 397-428.
Collins, D.W. (1976), “Predicting earnings with sub-entity data: some further evidence”, Journal
of Accounting Research, Vol. 14 No. 2, pp. 306-24.
Demski, J. (1973), “The general impossibility of normative accounting standards”, Accounting
Review, Vol. 48 No. 4, pp. 718-23.
Ettredge, M.L., Kwon, S.Y., Smith, D.B. and Zarawon, P.A. (2005), “The impact of SFAS 131
business segment data on the market’s ability to anticipate future earnings”, Accounting
Review, Vol. 80 No. 4, pp. 773-804.
Forbes, J. and Kara, S.M. (2010), “Con?dence mediates how investment knowledge in?uences
investing self-ef?cacy”, Journal of Economic Psychology, Vol. 31, pp. 435-43.
Foster, G. (1978), “Financial Statement Analysis”, Prentice-Hall, Englewood Chess, NJ.
Gavens, J.J. (1989), “Segment reporting and the Australian conceptual framework: a survey of the
banking industry”, Accounting Forum, Vol. 24 No. 1, pp. 17-24.
Graham, J.F., Stendardi, E.J., Myers, J.K. and Graham, M.J. (2002), “Gender differences in
investment strategies: an information processing perspective”, International Journal of
Marketing, Vol. 20 No. 1, pp. 17-26.
Herrmann, D. and Thomas, W.B. (1997), “Geographic segment disclosures: theories, ?ndings, and
implications”, International Journal of Accounting, Vol. 32 No. 4, pp. 487-501.
ARJ
24,3
262
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Kannadhasan, M. and Nandagopal, R. (2010), “In?uence of decision makers’ characteristics on
risk analysis in strategic investment decisions”, Journal of Modern Accounting and
Auditing, Vol. 6 No. 4, pp. 38-44.
Kinney, W.R. (1971), “Predicting earnings: entity v sub-entity data”, Journal of Accounting
Research, Spring, pp. 112-36.
Liyanarachchi, G.A. and Milne, M.J. (2004), “Comparing the investment decisions of accounting
practitioners and students: an empirical study on the adequacy of student surrogates”,
Accounting Forum, Vol. 29 No. 1, pp. 1-15.
Lobo, G.J, Kwon, S.S and Ndubizu, G.A(1998), “The impact of SFAS No. 14 Segment information
on price variability and earnings forecast accuracy”, Journal of Business Finance &
Accounting, Vol. 25 No. 7/8, pp. 969-85.
Maines, L.A., McDaniel, L.S. and Harris, M.S. (1997), “Implications of proposed segment
reporting standards for ?nancial analysts’ investment judgments”, Journal of Accounting
Research, Vol. 35 No. 1, pp. 1-23.
Mande, V. and Ortman, R. (2002), “Are recent segment disclosures of Japanese ?rms useful?
Views of Japanese ?nancial analysts”, International Journal of Accounting, Vol. 3 No. 1,
pp. 27-46.
Marschak, J. Radner, R. (1972), “Economic Theory of Teams”, Yale University Press,
New Haven, CT.
Menkhoff, L., Schmeling, M. and Schmidt, U. (2010), “Overcon?dence, experience, and
professionalism: an experimental study”, Working Paper No. 1612, Kiel Institute for the
World Economy, Kiel.
Shailer, G., Willett, R.Y. and Wade, M. (2001), “Internationalisation of auditors perceptions of
litigation risk”, Managerial Auditing Journal, Vol. 2, pp. 87-102.
Wines, G. (1997), “Geographical segment identi?cation in Australia: further evidence of an
international reporting problem”, Accountability and Performance, Vol. 3 No. 1, pp. 85-104.
Yuh, Y. and Hanna, S. (1997), “The demand for risky assets in retirement portfolios”,
paper presented at the Annual Meeting of the Academy of Financial Services.
Further reading
Bodnar, G.M., Hwang, L.S. and Weintrop, J. (2003), “The value relevance of foreign income:
an Australian, Canadian, and British Comparison”, Journal of International Financial
Management and Accounting, Vol. 14 No. 3, pp. 171-93.
Edwards, P. and Smith, R.A. (1996), “Competitive disadvantage and voluntary disclosures:
the case of segmental reporting”, British Accounting Review, Vol. 28 No. 2, pp. 155-72.
Emmanuel, C.R. and Garrod, N. (2002), “On the relevance and comparability of segmental data”,
Abacus, Vol. 38 No. 2, pp. 215-32.
Kochanek, R.F. (1974), “Segmental ?nancial disclosure by diversi?ed ?rms and security prices”,
Accounting Review, Vol. 49 No. 2, pp. 245-58.
McDaniel, L.S. and Hand, J.R.M. (1996), “The value of experimental methods for practice-relevant
accounting research”, Contemporary Accounting Research, Vol. 13 No. 2, pp. 339-51.
Milne, M.J. and Chan, C.C. (1999), “Narrative corporate social disclosures: how much of a
difference do they make to investment decision-making?”, British Accounting Review,
Vol. 31 No. 4, pp. 439-57.
OECD (1990), Segmented Financial Information, Organisation for Economic Co-operation and
Development, Paris.
Segment
reporting
263
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Prencipe, A. (2004), “Proprietary costs and determinants of voluntary segment disclosure:
evidence from Italian listed companies”, European Accounting Review, Vol. 13 No. 2,
pp. 319-40.
Rennie, E.D. and Emmanuel, C.R. (1992), “Segmental disclosure practice: thirteen years on”,
Accounting & Business Research, pp. 151-9, Spring.
Roberts, C.B. (1989), “Forecasting earnings using geographical segment data: some UK
evidence”, Journal of International Financial Management and Accounting, Vol. 1 No. 2,
pp. 130-51.
Ronen, J. and Livnat, J. (1981), “Incentives for segment reporting”, Journal of Accounting
Research, Vol. 19 No. 2, pp. 459-81.
Appendix. Financial report components included in both experimental cases
Background to Schipper Ltd
Schipper Ltd is an Australian based company which was formed in 1988 and expanded rapidly
in the early 1990s. It has approximately 1,000 shareholders and is now a leading company
operating in its core activities of property development, car parking and investments.
The company identi?es and develops market opportunities in the property sector and is also
an established provider of car parking operating and management services where it operates an
established portfolio of leased and managed car parks in Australia. It also manages a modest
investment portfolio.
Schipper Ltd has a proven track record of project delivery, based on industry experience,
a well quali?ed and experienced development team and a strong network of contacts.
Discussion and analysis
The principal activities of Schipper Ltd during the ?nancial year comprised:
.
property development;
.
car parking operators; and
.
investment activities.
Schipper Ltd prepares its ?nancial statements on the basis of historical cost, applying generally
accepted accounting principles.
Income statement. Schipper Ltd consolidated operating pro?t after income tax for the 2004
?nancial year was $4,139 compared to the 2003 pro?t of $2,864 representing a 44.5 per cent or
$1,275 increase. This compared favourably to an operating loss of $6,241 in 2002. The
disappointing result in 2002 was the culmination of very dif?cult and competitive trading
conditions experienced by the parking division and also Schipper Ltd switching its property
development focus away from long-term projects with high risk pro?les.
Revenue from activities for Schipper Ltd of $110,500 was an increase of 28.8 per cent on the
previous year. The revenue re?ects strong contribution from the property division and improved
operating revenue from the car park division.
The sale of 13 Baker Gardens, Reid, Kingston Foreshore apartments and the Docklands
property, supported the property revenue increase. The result of a greater focus on revenue
improvement in each state coupled with the acquisition of a number of high turnover car parks.
The investment division experienced a decline in revenue.
Balance sheet. Total assets decreased by $15,200 or 14.0 per cent, the result of property sales.
As a result of the repayment of property borrowings during the year, total liabilities
decreased by $17,500 or 32.2 per cent.
The equity attributable to shareholders increased by $2,300 or 4.3 per cent re?ecting the
pro?t for the year of $4,100 and offset by $1,800 being the cost of the share buyback.
ARJ
24,3
264
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Statement of cash ?ows. The net operating cash ?ow of $13,433 includes receipt of monies
from property development and car park revenue which occurred during this period whilst, the
previous years net operating cash ?ow includes the receipt of monies from property
development and car park revenue completed during the period ending 30 June 2003 and 30 June
2002.
In addition, the payments for equity investments and loans granted resulted in a net investing
cash out?ow of $17,654.
As a result of the repayment of borrowings and payment of dividends during the year the
Group experienced a net ?nancing cash out?ow of $15,702.
2004 ($) 2003 ($) 2002 ($)
Income statement
Revenue 110,545 85,811 108,292
Cost of sales (property, investments) (30,627) (13,471) (65,875)
Expenses from ordinary activities:
Car park overheads (55,798) (51,801) (48,144)
Corporate overheads (3,519) (4,147) (3,225)
Employee expenses (11,569) (9,863) (10,841)
Other expenses from ordinary activities (3,172) (2,272) (1,947)
Borrowing costs (1,721) (1,318) (1,478)
Share of net pro?t (loss) of associates 0 75 (28)
Pro?t before income tax expense 4,139 2,864 (6,241)
Income tax expense 0 0 0
Net pro?t 4,139 2,864 (6,241)
Basic earnings per share (cents) 1.84 1.25 (2.73)
Statement of cash ?ows
Cash ?ows from operating activities
Receipts from customers 100,165 143,850 142,080
Payments to suppliers and employees (86,762) (109,438) (161,774)
Dividends received 84 97 47
Interest received 2,050 1,257 729
Borrowing costs paid (2,104) (1,807) (1,478)
Net cash provided by operating activities 13,433 33,959 (20,396)
Cash ?ows from investing activities
Payments for property, plant and equipment (328) (204) (661)
Proceeds from sale of property, plant and equipment 211 7
Payments for investments (16,265) (7,676) (1,096)
Proceeds from sale of/capital return of investments 3,534 9,123 1,962
Loans and deposits provided (4,595) (500) 0
Net cash provided by/(used in) investing activities (17,654) 954 (1,988)
Cash ?ows from ?nancing activities
Proceeds from borrowings 2,748 12,010 32,690
Repayment of borrowings (16,631) (39,846) (2,950)
Dividends paid (excluding buyback) (67,797) (56,322) 0
Payments for buyback of securities (1,819) (8) 0
Proceeds from share issues 0 26 0
Net cash provided by/(used in) ?nancing activities (15,702) (27,810) 29,740
Net increase/(decrease) in cash held (19,923) 7,103 7,356
Cash at beginning of year 24,429 17,326 9,970
Cash at end of year 4,506 24,429 17,326 Table AI.
Segment
reporting
265
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
B
a
l
a
n
c
e
s
h
e
e
t
2
0
0
4
2
0
0
3
2
0
0
2
C
u
r
r
e
n
t
a
s
s
e
t
s
C
a
s
h
a
s
s
e
t
s
4
,
5
0
6
2
4
,
4
2
9
1
8
,
0
1
8
R
e
c
e
i
v
a
b
l
e
s
1
0
,
0
7
9
7
,
0
3
9
6
8
,
3
4
6
O
t
h
e
r
?
n
a
n
c
i
a
l
a
s
s
e
t
s
1
,
1
5
5
4
,
0
7
9
3
,
4
7
9
I
n
v
e
n
t
o
r
i
e
s
2
1
,
9
3
8
4
4
,
2
4
3
4
2
,
6
2
2
O
t
h
e
r
2
,
8
0
8
2
,
3
3
0
2
,
4
8
9
T
o
t
a
l
c
u
r
r
e
n
t
a
s
s
e
t
s
4
0
,
4
8
6
8
2
,
1
2
0
1
3
4
,
9
5
4
N
o
n
-
c
u
r
r
e
n
t
a
s
s
e
t
s
R
e
c
e
i
v
a
b
l
e
s
2
,
1
7
7
5
0
0
0
I
n
v
e
s
t
m
e
n
t
s
1
9
,
1
3
5
1
2
3
2
6
O
t
h
e
r
?
n
a
n
c
i
a
l
a
s
s
e
t
s
5
,
6
9
8
4
,
2
5
5
6
,
1
6
3
P
r
o
p
e
r
t
y
,
p
l
a
n
t
a
n
d
e
q
u
i
p
m
e
n
t
8
9
0
9
0
8
1
,
2
0
9
I
n
v
e
n
t
o
r
i
e
s
2
5
,
0
2
1
2
0
,
8
3
0
2
0
,
4
0
1
T
o
t
a
l
n
o
n
-
c
u
r
r
e
n
t
a
s
s
e
t
s
5
2
,
9
2
1
2
6
,
5
0
5
2
8
,
0
9
9
T
o
t
a
l
a
s
s
e
t
s
9
3
,
4
0
7
1
0
8
,
6
2
5
1
6
3
,
0
5
3
C
u
r
r
e
n
t
l
i
a
b
i
l
i
t
i
e
s
P
a
y
a
b
l
e
s
5
,
9
1
0
1
1
,
3
6
4
3
6
,
8
3
0
I
n
t
e
r
e
s
t
b
e
a
r
i
n
g
1
8
,
4
6
4
3
0
,
6
9
3
5
1
,
6
7
8
P
r
o
v
i
s
i
o
n
s
6
7
4
3
7
8
2
0
0
O
t
h
e
r
5
7
2
4
5
3
,
8
2
0
T
o
t
a
l
c
u
r
r
e
n
t
l
i
a
b
i
l
i
t
i
e
s
2
5
,
1
0
5
4
2
,
6
8
0
9
2
,
5
2
8
N
o
n
-
c
u
r
r
e
n
t
l
i
a
b
i
l
i
t
i
e
s
P
a
y
a
b
l
e
s
1
,
3
0
7
1
,
3
0
7
1
,
3
0
7
I
n
t
e
r
e
s
t
b
e
a
r
i
n
g
l
i
a
b
i
l
i
t
i
e
s
9
,
8
8
5
5
,
0
7
9
1
2
,
6
2
2
P
r
o
v
i
s
i
o
n
s
6
2
9
4
4
8
3
7
5
O
t
h
e
r
0
4
,
9
5
0
4
,
9
5
0
T
o
t
a
l
n
o
n
-
c
u
r
r
e
n
t
l
i
a
b
i
l
i
t
i
e
s
1
1
,
8
2
1
1
1
,
7
8
4
1
9
,
2
5
4
T
o
t
a
l
l
i
a
b
i
l
i
t
i
e
s
3
6
,
9
2
6
5
4
,
4
6
4
1
1
1
,
7
8
2
N
e
t
a
s
s
e
t
s
5
6
,
4
8
1
5
4
,
1
6
1
5
1
,
2
7
1
E
q
u
i
t
y
C
o
n
t
r
i
b
u
t
e
d
e
q
u
i
t
y
3
8
9
,
8
3
7
3
9
1
,
6
5
6
3
9
1
,
6
3
0
R
e
s
e
r
v
e
s
3
,
1
0
5
3
,
1
0
5
3
,
1
0
5
A
c
c
u
m
u
l
a
t
e
d
l
o
s
s
e
s
(
3
3
6
,
4
6
1
)
(
3
4
0
,
6
0
0
)
(
3
4
3
,
4
6
4
)
T
o
t
a
l
E
q
u
i
t
y
5
6
,
4
8
1
5
4
,
1
6
1
5
1
,
2
7
1
Table AII.
ARJ
24,3
266
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
About the authors
Jacqueline Birt joined the Department of Accounting and Finance at Monash University in
July 2008. Previously she held positions at the Australian National University and the University
of Melbourne. Her research is in the area of ?nancial accounting and her PhD focused on segment
reporting and examined issues such as value relevance and voluntary segment disclosures. She
has published in journals such as the Australian Journal of Management, the Australian
Accounting Review and Accounting Education. Jacqueline Birt is the corresponding author and
can be contacted at: [email protected]
Greg Shailer is a Reader and Associate Professor at the Australian National University,
Canberra, where he leads the Corporations, Governance & Society Research Group. He is a
Fellow of CPA Australia and a member of the Governance Research Network and the European
Corporate Governance Institute. His research interests include multidisciplinary and institutional
aspects of corporate governance, the economics of organisation the regulation of corporations
and their disclosures, and the economics and regulation of auditing.
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
Segment
reporting
267
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This article has been cited by:
1. Nancy B. Nichols, Donna L. Street, Ann Tarca. 2013. The Impact of Segment Reporting Under the
IFRS 8 and SFAS 131 Management Approach: A Research Review. Journal of International Financial
Management & Accounting 24:10.1111/jifm.2013.24.issue-3, 261-312. [CrossRef]
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
4
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
doc_650341050.pdf