Recognition and Disclosure of Intangible Assets – A Meta-Analysis Review
and Framework
Anne JENY*
ESSEC Business School, Cergy-Pontoise, France
Rucsandra MOLDOVAN
John Molson School of Business, Concordia University, Montréal, Québec, Canada
Abstract
We review over one hundred recent empirical archival papers on internally-developed
intangible assets. The knowledge economy based on intangible and intellectual capital
demands a re-examination of the accounting treatment for intangibles. We use a two-
dimensional matrix framework to organize our review; the first dimension is the recognition
of intangible-related amounts, either in the balance sheet or the income statement, versus
disclosure of such information in the notes to financial statements or other corporate
documents; the actors that are part of the financial reporting environment represent the
second dimension. Where the number of papers permits, we summarize the findings using
meta-analysis. The framework and meta-analyses allow us to highlight a consensus in the
empirical results and point out numerous avenues for future research in this area.
Keywords: Intangible assets; Research and development; Recognition; Capitalization;
Disclosure; Meta-analysis
*Corresponding Author:
Ave Bernard Hirsch, B.P. 50105, Cergy-Pontoise Cedex, 95021, FRANCE
Tél.: + 331 34432803
Fax: + 331 34432811
This version, October 2017, do not quote without the permission of the authors
Aknowledgment: We thank Jin Jiang for excellent research assistance, Ann Gallon for her editing work and
participants at the European Accounting Association Annual Meeting 2016 Maastricht, The Netherlands and the
Association Francophone de Comptabilité 2016 Toulouse, France conferences for helpful comments. Anne Jeny
acknowledges the financial support from the Research Center of ESSEC Business School.
2
Recognition and Disclosure of Intangible Assets – A Meta-Analysis Review
and Framework
Abstract
We review over one hundred recent empirical archival papers on internally-developed
intangible assets. The knowledge economy based on intangible and intellectual capital
demands a re-examination of the accounting treatment for intangibles. We use a two-
dimensional matrix framework to organize our review; the first dimension is the recognition
of intangible-related amounts, either in the balance sheet or the income statement, versus
disclosure of such information in the notes to financial statements or other corporate
documents; the actors that are part of the financial reporting environment represent the
second dimension. Where the number of papers permits, we summarize the findings using
meta-analysis. The framework and meta-analyses allow us to highlight a consensus in the
empirical results and point out numerous avenues for future research in this area.
Keywords: Intangible assets; Research and development; Recognition; Capitalization;
Disclosure; Meta-analysis
3
Recognition and Disclosure of Intangible Assets – A Meta-Analysis Review and
Framework
1. Introduction
The so-called “knowledge economy” (OECD, 1996) and the new Internet-based business
models that are being developed make it imperative to better understand intangible assets and
to examine the role that accounting and financial reporting could or should play in this
newly-created context.1 The purpose of this survey is (1) to review prior empirical accounting
research on intangible assets in order to organize and synthesize the research findings on this
topic, and (2) to identify areas and questions of interest where empirical research on
intangible assets would be most useful to address issues raised by current economic and
business developments. We use a meta-analysis as this enables us to generalize the nature of
the dependent and independent variables included in earlier studies, and to evaluate whether
the results of a set of studies represent similar phenomena. This study aims to contribute to
the ongoing debate on recognition versus disclosure of intangible assets.
We first organize the review according to a two-dimensional framework based on the
recognition versus disclosure of intangible assets debate (first dimension) and the point of
view of the actors involved, i.e. standard-setters, preparers, auditors, and users of financial
statements (second dimension). Our first dimension is founded on the reasoning that in the
case of intangible assets, for which the recognition rules are relatively strict and arguably not
aligned with the needs of the knowledge economy, “disclosures can bridge the gap between a
firm’s financial statement numbers and its underlying business fundamentals” (Merkley,
2014). Disclosure on intangible assets encompasses required disclosures under IAS 38
1 “Uber, the world’s largest taxi company, owns no vehicles. Facebook, the world’s most popular media owner,
creates no content. Alibaba, the most valuable retailer, has no inventory. And Airbnb, the world’s largest
accommodation provider, owns no real estate. Something interesting is happening” (Goodwin, 2015).
4
Intangible assets and voluntary disclosures in the notes to financial statements (permitted by
IAS 38) or in other public corporate documents.
The second dimension, with its focus on the parties involved in one way or another
with firms’ financial statements, allows us to classify the literature on the views held by each
of these parties on the issue of recognition versus disclosure of intangible assets, and to
highlight a number of research questions that, if explored, could shed more light on the role
of financial reporting as it concerns intangible assets.
We conduct a comprehensive keyword search on the widely-used research article
databases provided by EBSCO and ProQuest to identify the relevant papers to review. Our
survey includes 102 papers that focus on the accounting treatment and reporting of intangible
assets other than goodwill published in accounting and finance journals, and a small number
of relevant recent working papers made public on SSRN. We then use meta-analyses to
summarize prior findings by testing the variables measuring intangible assets, their
determinants and their consequences as identified in prior studies.
Financial reporting of intangible assets is the subject of a debate between advocates
for an increase in mandatory disclosures and broader recognition of internally-developed
intangible assets (Cañibano, Garcia-Ayuso, & Sanchez, 2000; Lev, 2008), and defenders of
the present rules which rely mainly on voluntary disclosures with limited recognition of
intangible assets (Penman, 2009; Skinner, 2008a, 2008b). At the heart of this debate is the
nature of intangibles, which occupy a space “at the center of an information gap that arises
from the forward-looking and uncertain nature of economic activity” (Wyatt, 2008).
Advocates for additional mandatory disclosures claim that financial statements fail to reflect
key intangible resources; consequently, outsiders do not have sufficient information and this
negatively affects investments in such assets (Cañibano et al., 2000; Lev, 2001; Nakamura,
1999). Persistent under-investment could lead to severe adverse consequences, impairing
5
long-term growth potential for companies and economies. Actors on this side of the debate
also argue that financial statements are less relevant for capital markets nowadays than in the
past, and point to the persistent decrease in the book-to-market ratio and earnings response
coefficients over time (Brown, Lo, & Lys, 1999; Chang, 1999; Lev & Zarowin, 1999).
On the other side of the debate, promoters of the status quo argue that the capital
markets fulfil their function of providing financial resources to intangible-intensive firms and
that such companies do not appear to under-invest in intangibles (Skinner, 2008b). From a
valuation standpoint, their argument is that as intangibles generate wealth, revenue streams
will eventually flow through the income statement, allowing financial statement users to infer
the value of these assets (Penman, 2009). They make the case that more recognition or
disclosures are not necessary for market participants to assess intangibles’ implications for
enterprise values. The gap between book and market values could also be an indication that
firms relying on intangible assets, i.e., with a low base of recognized assets, benefit from a
high market value. They also point out that mandatory recognition of intangibles has inherent
reliability issues, as measurement problems are typically exacerbated for these assets:
intangibles are synergistic, many are not separately saleable as their value depends on other
assets, and they are not actively traded on a secondary market (Basu & Waymire, 2008).
Any changes to the accounting treatment of intangibles would eventually have to
come from the standard-setters, with implications for all actors in the financial reporting
environment: regulators and institutions, auditors, and financial statement users, mainly
investors and financial analysts. In order to understand the implications of the recognition
versus disclosure debate on intangible assets, it is useful to situate it in the broader context of
the general debate on recognition versus disclosure that stretches well beyond intangible
assets.
6
Preparers’ perspectives on recognition versus disclosure seem to be influenced by
capital market pressures and perception of this dichotomy (Clor-Proell & Maines, 2014).2
This result suggests that a circular relationship exists between users and preparers’
perceptions of recognition versus disclosure. Although preparers are the actors initially
establishing reliability for financial information (Clor-Proell & Maines, 2014), they
anticipate, and assume, that users expect lower reliability in disclosures.
Disclosure on intangible assets mostly, however, relates to the second point made by
Schipper (2007) (i.e., items that could never be recognized) due to the intrinsic, uncertain
nature of intangible assets which makes them hard to measure reliably. In this situation, the
purpose of disclosure is to (1) increase predictive ability, (2) provide information to undo un-
comparable accounting or create an alternative treatment, and (3) reduce uncertainty
(Schipper, 2007). In general, prior empirical disclosure literature suggests that more
disclosure is good for users.3 However, there is also evidence that too much disclosure, i.e.,
disclosure overload (EFRAG, 2012), may overwhelm users (e.g., André, Filip, & Moldovan,
2016; Lehavy, Li, & Merkley, 2011). Therefore, the recognition versus disclosure debate on
intangibles is overlaid by the issues of how much disclosure of intangible assets users find
useful, and whether disclosure should be mandatory or left entirely up to management, as is
the case under the current financial reporting standards.
Examining recognition and disclosure in the context of intangible assets also allows
us to contribute to the discussions on the role of financial statements, which the IASB has
been revising as part of its Disclosure Initiative. Before any arguments concerning the failure
of the current accounting model (e.g., Lev, 2008), the role of financial statements in today’s
2 Clor-Proell & Maines (2014) conduct an experiment with financial managers over the recognition versus
disclosure of contingent liabilities and find that managers in public companies put in more effort and are less
strategically biased under recognition than disclosure. At the same time, cognitive effort and bias are unaffected
by this choice for managers in private firms. 3 See Healy & Palepu (2001) for an extensive literature review on empirical disclosures studies.
7
knowledge economy should first be clarified. Skinner (2008b) maintains that as long as
intangible-intensive firms are able to attract financing even though the accounts do not
recognize many of their intangibles, then there are no problems with accounting for
intangibles, and therefore no problems with the current accounting model.
We apply a number of limits to simplify and streamline our survey. First, we restrict
our focus to all intangible assets except goodwill. Similar to Skinner (2008b), we consider
that recognition and measurement of goodwill relates to accounting for business
combinations rather than strictly intangibles.4 Compared to previous reviews of the literature
on intangible assets (e.g., Wyatt, 2008), we take a broader perspective on financial reporting
for intangibles, considering both the recognition intangible asset amounts (by capitalization
or expensing), and disclosure of information about intangible assets. Second, our survey only
includes papers that have been published in accounting journals or focus mainly on
accounting for intangibles. Intangible assets, particularly R&D investments, are studied by
papers in a varied range of areas, from biotechnology advancements to strategic and
operations management. The point of interest to us, however, is the way companies account
for and externally report these investments. Therefore, we limit the survey to accounting
papers that touch upon the accounting treatment of intangible assets. Third, we only consider
empirical papers (archival or experimental), without including analytical research in this
area.5 Focusing on empirical research allows us to discuss the depth of the reporting
environment and the role of other stakeholders in the reporting decision. Lastly, we limit our
4 “Accounting standard-setters have also devoted a great deal of attention to accounting for goodwill, which is a
topic that I leave aside because it is largely separable from the discussion in many of the proposals on
intangibles accounting and because its recognition and measurement is related to accounting for business
combinations, which I see as taking the discussion too far afield. I would note though that a loose definition of
goodwill - as the excess of a business' economic value over its book value - is taken by commentators as
evidence of the failure of the current accounting model to correctly recognize intangibles” (Skinner, 2008b). 5 We believe that modelling-based research necessarily assumes away a lot of the complexities of the
environment in which managers decide how to account for intangible assets (Beyer, Cohen, Lys, & Walther,
2010).
8
survey’s main focus to recently-published and working papers, so as to complement existing
literature reviews in this area (e.g., Cañibano et al., 2000; Wyatt, 2008; Zéghal & Maaloul,
2011).
Our survey contributes to future research in several ways. First, our discussion of
recognition versus disclosure of internally-developed intangible assets emphasizes a number
of areas where more research could shed additional light and advance understanding. Some
of the fundamental questions that still remain unanswered are: the role of auditors, the impact
of IAS 38 on firms' behavior, international comparisons of intangible asset accounting
treatment and their consequences.
Second, by aggregating and summarizing the evidence on recognition and disclosure
of intangible assets, we contribute to the ongoing debate between actors asking for more
recognition and disclosure related to intangible assets (e.g., Lev, 2008) and actors who
believe the status quo is perfectly adequate for the accounting treatment of intangibles to
achieve its stated purpose (e.g., Skinner, 2008a,b). Empirical evidence seems to favor
recognition of certain internally-developed intangibles (development costs and brands) as
assets, even though this could be a channel for earnings management. But the same is true of
any accounting choice, and once again we are faced with a trade-off between reliability and
relevance in accounting information.
Third, this review contributes input for the standard-setters’ work on a disclosure
framework as part of the Disclosure Initiative. At a time when the current accounting model
is regarded by some (Lev, 2008) as insufficient and inconsonant with the knowledge-based
business models, the IASB is revisiting some of the conceptual underpinnings of the financial
statements. Our literature survey is relevant because it highlights the attitudes of most of the
actors involved in the financial reporting environment vis-à-vis the main issue that could
make financial statements less useful in today’s world.
9
We continue by describing our organizing framework for analyzing the empirical
accounting literature on intangible assets and the meta-analysis methodology in section 2.
Section 3 summarizes the papers relating to standard-setters and auditors. Section 4 reviews
the papers on preparers, and section 5 the papers on financial statement users. Section 6
concludes and discusses avenues for future research.
2. Organizing framework and research methodology
2.1 Framework for organizing the literature on intangible assets
We organize the literature review along two dimensions. The first dimension concerns
the accounting treatment of internally-developed intangible assets from a recognition versus
disclosure perspective.6 The tension between recognition and disclosure arises from the
perceived differential in reliability and the question of whether users actually read and
understand disclosures. In the context of intangible assets, where recognition rules are
relatively strict, and perhaps out of step with the knowledge economy, “disclosures can
bridge the gap between a firm’s financial statement numbers and its underlying business
fundamentals” (Merkley, 2014).
The second dimension of the framework is represented by stakeholders who have
some interest in the matter of intangible asset recognition and disclosure: (1) standard-setters
and regulators, (2) preparers of financial information, (3) financial statement users (investors,
financial analysts and creditors) and (4) auditors. Examining the literature from the
perspective of each player reveals that some areas have been well researched while other
areas have not yet been fully explored, and draws attention to different interests related to
intangible assets that are not yet clearly understood (e.g. potential biases, strategic decisions).
6 Recognition refers to the number recognized on the face of financial statements, either as an asset on the
balance sheet or as expense on the income statement. Disclosure refers to the narrative or numerical information
provided in the notes to financial statements, other parts of the annual report, and other public corporate
documents (Schipper, 2007).
10
[FIGURE 1 ABOUT HERE]
As economies and the business world evolve towards a more intangible nature, the
spotlight is cast on the parties involved in the financial reporting process and environment,
and the facilitating or debilitating role they play in the provision of relevant financial
information. Both recognition and disclosure of information related to intangible assets result
from interaction between at least four actors. Preparers refer to the accounting standards and
principles they abide by, and weigh up the costs and benefits of disclosing more information.
Standard-setters must make sure their standards can withstand the challenges of “the business
world of tomorrow” without becoming entirely obsolete so that new standards must be issued
whenever a change occurs (Tokar, 2015).7 Investors, creditors, and financial analysts demand
decision-useful information from preparers and engage with standard-setters to ensure their
information needs are met. Auditors need to keep up with the ever-changing business
environment while simultaneously balancing the requirements of the applicable standards
with their duty towards shareholders and their independence of the audited company’s
management. The interactions are complex and the actors’ sometimes conflicting incentives
lead to awkward situations, or situations that put the least powerful party at a disadvantage.
Understanding these actors’ stakes in accounting for intangible assets, a relevant topic to the
new business models of the digitalized economy, places the accounting community in a better
position to tackle the accounting for intangible assets.
For each category of stakeholder, we discuss the results of the meta-analyses,
provided enough studies are available for that category. For standard-setters and auditors, the
number of studies is limited and meta-analysis is not suitable, and so those categories’
attitude towards intangible assets is discussed in a traditional literature review format.
7 In order to begin to address these challenges, the IASB, for example, conducts meetings with practitioners and
academics on “help shape the future of financial reporting” (https://www.icas.com/events/help-shape-the-future-
of-financial-reporting last accessed on November 29, 2015).
11
2.2 Meta-analysis methodology
Meta-analysis is a statistical technique for summarizing quantitative empirical studies.
It provides a comprehensive way of analyzing a relationship between two variables that has
been examined in at least two prior studies, and a coherent way of making inferences from
the findings of several studies, thereby overcoming some of the shortcomings of narrative
literature reviews.8 Trotman & Wood (1991) emphasize that meta-analysis “leads to more
valid inferences about the knowledge of a set of studies than can be derived from a narrative
literature review.” We analyze the literature on intangible assets using meta-analysis when
the number of published articles is sufficient. Where the same sample is analyzed more than
once, we use the main result in order to ensure sample independence9.
Following prior accounting literature reviews that used meta-analysis (e.g., Hay,
Knechel, & Wong, 2006; Khlif & Chalmers, 2015) and considering the available information
included in our sample of studies, we use the Stouffer Combined test (Wolf, 1986). This
technique uses individual z-stats or converts individual p-values to z-scores and computes an
overall Z-statistic that can be used to test the direction and significance of an effect for the
relationship between two variables. We compute the Z-statistic using the Lipták-Stouffer
method (Lipták, 1958; Stouffer, DeVinney, & Suchmen, 1949) also known as the weighted
Z-test, that weights each z-score based on the sample size n from which it is derived. The
overall Z is then converted into an overall p-value which will be used to assess its
significance.
8 Our aim is to conduct a comprehensive review of the intangible asset literature; hence we use meta-analysis in
an exploratory manner, without developing any ex-ante hypotheses. 9 One study could report several results, usually one for the main research question/hypothesis and two, three
others for secondary research questions/hypotheses (e.g., interaction results). We use only one result (usually the
one for the first hypothesis) from each study included in the literature review.
12
𝑍 =∑ 𝑛𝑖 × 𝑍𝑖𝑘𝑖=1
√∑ 𝑛𝑖2𝑘
𝑖=1
(1)
Rosenthal’s effect size formula uses Z to provide a measure of the overall correlation
between the two variables subject to the meta-analysis (Rosenthal, 1991) and is computed as
follows.
𝐸𝑆(𝑟) =𝑍
√𝑁
(2)
where N is the total sample.
We use the file drawer test to assess the robustness of our meta-analyses. First, we
compute Rosenthal’s Fail-Safe N (FSN) (Rosenthal, 1979) to determine the number of
studies with non-significant results needed to reverse conclusions about a significant
association with a 95% confidence level (Khlif & Chalmers, 2015).
𝐹𝑆𝑁 =𝑘2 × 𝑍2
2.706− 𝑘
(3)
where k is the number of studies included in the analysis. The benchmark for FSN is the
critical number of studies, FSNC. If FSN is larger than FSNC, then the effect is robust.
𝐹𝑆𝑁𝐶 = 5 × 𝑘 + 10 (4)
For each meta-analysis, we also test the homogeneity of the studies by computing a
chi-square statistic with k-1 degrees of freedom. The null hypothesis is that the samples
included in the meta-analysis are homogeneous. If the null hypothesis is corroborated, then
the variation in individual effects is due to statistical errors rather than moderating factors.
𝜒2𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 =∑(𝑧𝑠𝑐𝑜𝑟𝑒𝑖 −∑𝑧𝑠𝑐𝑜𝑟𝑒𝑖
𝑛𝑖)2
(5)
We conduct two sensitivity tests. First, since our main results are based on studies
with geographically-diverse samples which could contribute to the heterogeneity of
individual effects, we run a test that excludes all non-U.S. samples. Second, we test the
13
sensitivity to outliers of the effects uncovered, by eliminating the observation with the
highest z-score (i.e., the highest individual effect) for each meta-analysis.
2.3 Papers reviewed and sample of papers used in the meta-analysis
We begin our endeavor of reviewing the literature on intangible assets by collecting
all the papers related to this topic. We conduct a keyword search on EBSCO and ProQuest,
the two largest published-article databases, the search is limited to journals in accounting and
finance, and on SSRN for the most recent working papers on this topic. The keywords used
are “intangible”, “intangible asset”, “research and development”, “R&D”, “intellectual
capital”, “IAS 38”, “software development” and associated forms of these words. The search
query included the title, abstract, and keywords of published articles in these databases. We
also check the list of references in previous literature reviews on intangibles, to include any
relevant papers that may not have appeared in our database search. We further limit the
results by keeping only papers using an empirical archival research method and non-goodwill
related papers. The search yielded 102 different relevant papers that we include in our
review, of which 3 are working papers. Table 1 provides an overview of all these papers
organized in our review framework discussed above. Out of the 102 papers we review, 69
papers study the recognition of intangible assets (68%) and 33 examine the disclosure of
intangibles (32%). The vast majority of the papers take the point of view of financial
statement users (58 papers, 57%) and preparers (38 papers, 37%). Very few examine the role
of auditors (4 papers) or standard setters (2 papers).
[TABLE 1 ABOUT HERE]
The sample used for meta-analyses is further restricted by the research methodology
(i.e., empirical archival studies only) and by the published status of the paper. Following
Habib (2013), we include only published papers in the sample for meta-analysis, since
14
unpublished manuscripts have not yet received the vetting of the review process and not all
are publicly available, which induces sample selection bias. Another 19 papers look at
intangible assets in specific settings, which impedes our classification of the variables used
(i.e., we cannot classify those papers in a meaningful way given our framework), and 12
papers examine variable pairs that are not examined in other papers (i.e., only one paper per
variable pair). After these exclusions (Table 2, Panel A), the restricted sample of papers used
for meta-analyses contains 63 papers corresponding to 164 observations at the paper-variable
pair level.
Table 2, Panel B presents the distribution of this restricted sample by publication.
There are 25 papers (40%) published in high-quality journals (i.e., The Accounting Review,
Journal of Accounting Research, Journal of Accounting and Economics, Contemporary
Accounting Research and Review of Accounting Studies).10
Table 2, Panel C presents the
sample distribution by the country or geographic region from which the study sample is
drawn. Most studies examine U.S. companies (44 papers, 67%), followed by the United
Kingdom (8 papers, 12%), Australia (4 papers, 6%) and France (2 papers, 3%).
[TABLE 2 ABOUT HERE]
3. Insights from standard-setters and auditors on intangible assets11
3.1 Standard-setters’ stance on intangible asset recognition and disclosure
Recognition and disclosure of internally-developed intangible assets differs under
IFRS and U.S. GAAP, but both sets of standards struggle with the question of how to
incorporate the economic properties of intangible assets into the financial reporting system
(Powell, 2003).
10
According to usual academic journal rankings. 11
The number of papers published on the views of standard setters and auditors on intangibles is low (2 papers
and 4 papers, respectively), hence we cannot apply the meta-analysis methodology; we discuss their insights
separately based on our organizing framework.
15
IAS 38 Intangible assets requires most internally-developed intangible assets, such as
customer lists, trademarks, brands, mastheads, etc., to be expensed. Since their cost cannot be
distinguished from the normal cost of doing business (IAS 38 par. 16), reliable measurement
for these assets is difficult. For R&D projects, however, the standard distinguishes between
costs incurred in the research phase and costs incurred in the development phase. While the
distinction involves considerable judgment, the general discriminating principle is the
probability of future economic benefits. Since the research phase has highly uncertain
outcomes, the standard mandates expensing of research costs. The development phase,
however, is an application phase to advance the project to a ready-for-use or sale state, at
which point future economic benefits are probable. Development costs must also meet six
recognition criteria before being capitalized. The identification phase along with the six
recognition criteria for development costs constitute a high recognition threshold which
means that, although companies applying IFRS capitalize some of the development costs,
most R&D costs are expensed.
The disclosure requirements in IAS 38 mainly concern the accounting policies for
recognized classes of intangible assets. Required disclosures also include the amount of
expensed R&D expenditure during the period. Without actually making it mandatory, the
standard encourages disclosures about the fully amortized intangible assets still in use and a
description of the significant intangible assets controlled by the entity but not recognized as
assets because they did not meet the recognition criteria (IAS 38 par. 128).
Under U.S. GAAP, the accounting treatment for internally-developed intangibles is
conservative and requires immediate expensing. However, recognition of purchased
intangibles is allowed (Ciftci & Darrough, 2015). In the early 2000s, the FASB worked on a
project related to “Disclosure of information about intangible assets not recognized in the
financial statements” intended to expand note disclosure on internally-developed intangible
16
assets (FASB, 2001). In the AAA comment letter on this project, Skinner et al. (2003) note
that “voluntary disclosure of intangibles information is not widespread” suggesting that the
costs of measuring intangibles and proprietary costs outweigh the benefits of disclosure.12
Chen, Gavious and Lev (2015) show, however, that under the IAS 38 capitalization of
development costs requirement, Israeli companies that switched from U.S. GAAP to IFRS
disclosed more R&D-related information than previously, and more than companies that
continue to apply U.S. GAAP. This finding indicates that when the information is available,
managers are more likely to disclose it, which further suggests that the cost of producing this
information is probably the highest hurdle against disclosure.
The IASB’s Disclosure Initiative Project includes a review of disclosure requirements
in the existing financial reporting standards. Although a redraft of the disclosure requirements
in IAS 38 is not yet available, if IAS 16 Property, plant and equipment is any indication, the
IASB is likely to require more details about the business model as related to the particular
item being disclosed and the risks associated with that item for the entity, on top of the
disclosures of measurement basis and changes during the year already included in most
standards (IASB, 2015).
Any upcoming changes related to disclosure requirements in IAS 38 could be
carefully used to answer questions related to the usefulness of such disclosure and the costs
incurred in providing it. Changes in U.S. accounting standards related to intangible assets are
relatively old.13
However, the switch to IFRS in 2005 in the EU and worldwide is a fruitful
event to exploit for examining preparers and investors’ reaction to the change. Stolowy,
Haller and Klockhaus (2001) detail the differences between French and German GAAP and
IAS 38 and note that, for example, capitalization of internally-generated brands was possible
12
The FASB removed this project from its technical agenda in 2004 stating that any action on this topic will be
taken jointly with the IASB. 13
SFAS 2 “Accounting for research and development costs” was issued in 1974. SFAS 86 “Accounting for the
costs of computer software to be sold, leased, or otherwise marketed” was issued in 1985.
17
under French GAAP, and that allocation to brands of the difference arising on first
consolidation was a widely-used practice in France before the IFRS adoption in 2005. Future
research could, for example, use such differences to examine the relevance of IAS 38
compared to local GAAP.
Nixon (1997) surveyed senior UK accountants for their views on the treatment of
R&D expenditure. While most respondents prefer to expense all R&D costs immediately
given that the ex ante benefits are too uncertain, there is a strong consensus that the ex post
benefits of R&D expenditure are positive. Two other perspectives emerge: (1) disclosure is
much more important than the accounting treatment of R&D expenditure and (2) financial
statements are not viewed as the primary channel of communication ons R&D. These
perceptions suggest that regulators need to move beyond a narrow focus on the technical
issues related to intangibles, and consider the role of financial statements in the wider
communication process that occurs between companies and users.
3.2 External auditors
Very few papers have studied the role of auditors in intangible assets’ recognition and
disclosure. In the U.S. context of R&D expensing, Godfrey and Hamilton (2005) find that
more R&D-intensive firms are more likely to choose auditors who specialize in auditing
R&D contracts. Additionally, R&D-intensive firms tend to appoint top-tier auditors. The
results are particularly strong for small firms where auditor choice is not constrained by the
need to appoint a top-tier auditor to ensure the auditor’s financial independence of the client.
Tutticci, Krishnan and Percy (2007) and Krishnan and Wang (2014) examine
questions related to auditing and R&D capitalization. Using an Australian sample, Tutticci et
al. (2007) find that that external monitoring by a Big 5 auditor and the Australian Security
Commission decreases managers’ tendency to capitalize R&D costs. Furthermore, a well-
18
known or “brand-name” auditor leads to a stronger relationship between capitalized R&D
and stock returns, consistent with the high audit quality associated with brand-name auditors.
Complementing these findings, using a U.S. sample over the period 2004-2009, Krishnan and
Wang (2014) find that audit fees are smaller for companies that capitalize software
development costs, after controlling for traditional measures of client risk. This result is
consistent with the idea that capitalized software development costs are informative about
audit risk.
4. Preparers
4.1 Meta-analysis results
Table 3 presents the results of the meta-analyses conducted on studies that examine
variables related to preparers and intangible assets. Table 3, Panel A lists the 17 papers
included that relate to preparers, either examining the consequences of recognition or
disclosure of intangible assets or examining the determinants of decisions related to
recognition or disclosure of intangible assets.
Table 3, Panel B presents the results of the meta-analyses. For the question of the
consequences of recognition or disclosure of intangible assets, we generally find significant
positive associations between future firm profitability and variables that proxy for internally-
generated intangible assets, specifically advertising expenses (Z=8.875, p-value<0.01),
intangible assets (Z=3.813, p-value<0.01), capitalized R&D (Z=14.890, p-value<0.01), total
R&D expenditure (Z=4.441, p-value<0.01), and R&D expense (Z=9.427, p-value<0.01). The
weakest result is for the association between recognized intangible assets and future
profitability, which could be overturned by 19 file-drawer studies reporting insignificant or
opposite results (critical Fail-Safe N is 20 studies). The associations between future firm
profitability and, respectively, R&D expense and capitalized R&D are the strongest, with
19
file-drawer results of 1307 studies versus critical Fail-Safe N of 30 studies and 2094 studies
versus critical Fail-Safe N of 50 studies. The chi-square statistic is significant for results on
future profitability and advertising expense, capitalized R&D, R&D expenditure, and R&D
expense, indicating heterogeneity among the studies included in tests of those relationships.
However, since the number of studies (k) on which the meta-analysis results are based is
small (i.e., ranging between 2 studies for advertising expense and 8 for R&D expense),
splitting the samples further to search for moderating variables is not feasible. Therefore,
while caution is necessary regarding the heterogeneity of the samples, we believe that our
results are still informative about the intangible assets literature at an aggregate level.
Regarding the determinants of recognition or disclosure of intangible assets, the meta-
analysis provides more mixed results. Firms’ investment in research and development (i.e.,
Indicator variable for R&D-intensive) is strongly positively associated with the amount of
cash held (Z=40.572, p-value<0.01), the amount of dividends issued (Z=3.027, p-value<0.01)
and the firm’s indebtedness (i.e., Leverage, Z=90.020, p-value<0.01). The Fail-Safe N is well
above the critical FSNC for each of these associations. The associations between an indicator
variable for capitalizers of R&D and leverage, profitability and R&D expenditure,
respectively are negative but not significant.
Papers that look beyond the primary financial statements use manually collected data,
or more recently computerized techniques, to measure the quantity and other characteristics
of intangible-related disclosures. Since the disclosure requirements in existing accounting
standards are limited, essentially all intangible-related disclosure is voluntary, regardless of
its location (i.e., in the notes or in other corporate documents).14
On the question of the
determinants of disclosure of intangibles from the preparer’s perspective, we were able to
14
Skinner (2008b) expresses skepticism related to the efficacy of mandated disclosure on intangibles due to the
lack of easy standardization, lack of verifiability, and high proprietary costs which could potentially lead to non-
compliance.
20
obtain meta-analysis results for a number of variables such as analyst following, cross-listing
status, general disclosure policy, leverage, market-to-book ratio, number of patents, firm
profitability, R&D expense and stock price. However, the associations brought to light are
either not significant, or weak based on the file-drawer test, most likely due to the fact that
only two studies contributed to these results.
We conduct two sensitivity tests. First, we remove all non-U.S. studies and repeat the
meta-analyses (Table 3, Panel C). This gives a lower number of results since some of the
variable pairs (e.g., variables related to R&D capitalization) are tested only in non-U.S.
settings. None of the results discussed above changes. Second, for each pair of variables, we
eliminate the observation with the highest empirical association (i.e., the maximum
coefficient) to ensure that our results are not driven by outliers. We therefore lose all results
that were based on any two studies. The few differences compared to the results discussed
above are the following: (1) the meta-associations between future profitability and capitalized
R&D and R&D expenditure, respectively, become negative but non-significant; and (2) the
meta-associations between the R&D-intensive indicator variable and dividends and leverage,
respectively, also become non-significant. The meta-analysis results that remain strongly
significant are the positive associations between future firm profitability and R&D expense
and between cash and the indicator variable for R&D-intensive firms.
[TABLE 3 ABOUT HERE]
4.2 Review of papers not included in the meta-analyses
4.2.1 Recognition of intangible assets – determinants and consequences for preparers
The accounting treatment of intangibles seems to affect managers’ decision-making.
In an experimental setting, Seybert (2010) finds that managers responsible for initiating an
R&D project are more likely to overinvest when R&D is capitalized, and reputation concerns
21
enhance this overinvestment. Furthermore, when R&D is capitalized, experienced executives
anticipate overinvestment and expect project abandonment to have a stronger negative impact
on the responsible manager’s reputation and future prospects at their firm. Jones (2011)
provides corroborating empirical evidence from Australia by evaluating the merits of
capitalization from a bankruptcy and default risk perspective. His results indicate that failing
firms capitalize intangible assets more aggressively over the long term, but particularly in the
years leading up to firm failure, and voluntary capitalization has strong discriminating and
predictive power in a firm failure model.
Jones (2011) also finds that managers’ propensity to capitalize intangible assets is
strongly associated with earnings management, particularly among failing firms. A number of
other studies provide supporting evidence in this respect. For example, Markarian, Pozza and
Prencipe (2008) show that Italian companies tend to use cost capitalization for earnings-
smoothing purposes. Ciftci (2010) documents a decline in the quality of earnings in the
software industry after the adoption of SFAS No. 86 that requires capitalization of software
development costs, whereas no such decline is observed in other high-tech industries.
Furthermore, among capitalizers, firms with a large increase in software capital have lower
earnings quality, and in the software industry the quality of earnings is greater for expensers
than for capitalizers.
Managers’ earnings management incentives translate not only into capitalization
versus expensing decisions, but into practical business decisions. For example, Perry and
Grinaker (1994) take a US sample to investigate how far earnings management goals appear
to affect the pattern of R&D activity. The results of this study are consistent with the
hypothesis that managers adjust R&D expenditures to meet earnings expectations. Also using
U.S. data, Kothari, Laguerre and Leone (2002) compare the relative contributions of current
investments in R&D and property, plant and equipment to future earnings variability, and
22
show that R&D investment generates future benefits that are far more uncertain than benefits
from investments in property, plant and equipment. Johnston (2012), however, concludes that
the environmental component of R&D expenditures contributes significantly less to the
variability of future earnings than the residual component.
Our review uncovered one paper that examines the tax incentives associated with
R&D investment. Klassen, Pittman and Reed (2004) investigate the impact of tax incentives
and financial constraints on corporate R&D expenditure decisions by comparing R&D
expenditures in the United States and Canada. They document positive relationships between
tax credit incentives and R&D spending, consistent with companies responding to tax
incentives as though they are not financially constrained. The Canadian credit system induces
an average $1.30 of additional R&D spending per dollar of taxes forgone while the U.S.
system induces an average $2.96 of additional spending.
4.2.2 Intangible-related disclosures – determinants and consequences for preparers
Interviews conducted in the late 1990s with managers from Canadian technology-
based companies and financial analysts reveal that both managers and analysts tend to prefer
to communicate R&D information through disclosures rather than by capitalizing
development costs (Entwistle, 1999). In-depth case study analysis of three UK and three
Swedish companies from the pharmaceutical industry reveals that these companies
consistently provided voluntary disclosures on their R&D activities over the period 1984-
1998 (Gray & Skogsvik, 2004). The consistency in disclosure reveals the importance
managers attach to these disclosures.
Simpson (2008) explores a change in U.S. regulations, which required mandatory
disclosure of advertising outlays up to 1994, then made them voluntary afterwards. This
study finds that in the pre-1994 period, companies were less likely to continue disclosing if
23
their own disclosure benefitted their competitors (e.g., in terms of market valuation or future
profitability) and more likely if their disclosure benefitted themselves.
The biotechnology sector is particularly relevant for intangible asset research. Biotech
companies are fast innovators and the barriers to entry are low, leading to significant
information asymmetries between managers and investors (Cerbioni & Parbonetti, 2007;
Guo, Lev, & Zhou, 2004). Cerbioni and Parbonetti (2007) investigate whether corporate
governance features of European biotechnology firms are related to intellectual capital
disclosures in the MD&A by collecting information on the number of items disclosed, the
categories of information disclosed (internal, external, human capital-related), time-horizon
(historical vs. forward-looking) and the type of news (positive vs. negative information).15
Good corporate governance features are generally positively associated with the number of
items disclosed, but the relationship with particular disclosure characteristics are weaker and
sporadic, i.e., the number of independent directors shows a positive association with
disclosure of internal information; CEO duality is negatively related to forward-looking
intellectual capital disclosure. Guo et al. (2004) focus on IPO prospectuses of U.S. biotech
companies to investigate the role of competitive costs in the extent of disclosure about
product developments.16
Situations that give the company an advantage over potential
competitors lead to increases in the extent of product disclosure: the more advanced the stage
of product development, the lower the risk that competitors will catch up, and the higher the
disclosure level; protection by patent availability reduces imitation, and allows firms to
increase disclosures; the presence of venture capitalists ensures that resources are available to
take action against threatening competitors. Additionally, as predicted by signaling models,
15
Cerbioni and Parbonetti (2007) define intellectual capital broadly as the number of research projects, patent
exclusivity and protectionism, research collaborations, education of workers, know-how etc. 16
Disclosure can play an essential role in mitigating information asymmetry between firms and potential
investors, which at the time of IPO is at its peak; however, IPO disclosure about product developments may also
provide precious information to competitors. (Guo et al., 2004).
24
the ownership percentage retained by pre-IPO owners is negatively related to the extent of
disclosure.
Three recent papers examine managers’ presentation choices with respect to R&D
expenses – whether presented as a separately-identified item, or aggregated with other
expense categories such that the amount of R&D expense is “invisible”. Koh and Reeb
(2015) report results from multiple tests that compare R&D expense disclosure with patents
obtained by firms and missing R&D before and after a forced change in auditor (for former
Arthur Andersen clients). These results indicate that missing R&D data is a discretionary
reporting choice. Koh, Reeb and Wald (2015) investigate the determinants of missing R&D.
Their preliminary results suggest that industry profitability, litigation risk, patent innovation,
and volatility are related to missing R&D expenses for companies with patent applications,
confirming the strategic intent of missing R&D expenses. Furthermore, using the staggered
application of regulation at the state level in the U.S. and changes in CEO type as
identification strategy, Koh, Reeb, & Zhao (2015) are able to show that confident CEOs are
more likely than cautious CEOs to present R&D expenses separately in the income statement.
5. Financial statement users
5.1. Meta-analysis results
Table 4 presents the results of the meta-analysis conducted on studies that investigate
the intangible recognition issue in the light of its impact for financial statement users.
Table 4, Panel A lists the 46 papers included in this meta-analysis, which relate to three
different categories of financial statement users: financial analysts (9 papers), bondholders (2
papers) and investors (35 papers). The papers cover three main areas: (1) the value relevance
of recognized intangible assets (in the balance sheet or the income statement), (2) investors’
25
understanding of recognized intangible assets, and (3) the impact of intangible assets
recognition on earnings quality (conservatism, earnings-response coefficient, underpricing).
Table 4, Panel B presents the results of the meta-analysis by category of financial
statement user, i.e. analysts, bondholders and investors.
Some papers have specifically looked at financial analysts’ reactions to recognition
and disclosures on intangible assets. For this category of financial statement users, there are
contrasting results between analysts’ forecast accuracy, forecast dispersion and following.
We find a significant, positive association between analysts’ earnings forecast accuracy and
variables that measure internally-generated intangible assets that are not recognized in the
balance sheet, specifically advertising expenses (Z=1.989, p-value<0.05), intangible assets
(Z=3.033, p-value<0.01) and R&D expenses (Z=13.537, p-value<0.01). However, a negative
and non-significant association is found between analysts’ earnings forecast accuracy and the
disclosure score. The strongest results are for intangible assets and R&D expenses, with
respectively file-drawer results of 28 studies versus critical Fail-Safe N of 25 studies and
2432 studies versus critical Fail-Safe N of 40 studies. The Chi-square statistic is significant
for results on intangible assets and R&D expenses, indicating heterogeneity among the
studies included in tests of those relationships. However, since the number of studies (k) on
which the meta-analysis results are based is small (ranging from 3 to 6), splitting the samples
further to search for moderating variables is not feasible. Regarding the results for analysts’
earnings forecasts dispersion or analyst following, none of the independent variables
(disclosure score, intangible assets or R&D expense) is significant.
Very few studies consider the role of the accounting treatment of intangibles for
creditors’ decision-making (Eberhart, Maxwell, & Siddique, 2008; Shi, 2003). For this
second category of financial statement users, which we call “bondholders”, the meta-analysis
26
results show an absence of any significant relationship between bond ratings or bond risk
premiums and R&D expenses.
Researchers have tried to demonstrate that investments in R&D and advertising lead
to higher earnings, and consequently are positively associated with firm value. Some studies
have shown a positive association between future profitability and investments in advertising
(L. K. C. Chan, Lakonishok, & Sougiannis, 2001) and R&D (Lev & Sougiannis, 1999). Other
studies have examined the relevance of capitalization of R&D in the light of its recognition
by the financial markets (Aboody & Lev, 1998; Callimaci & Landry, 2004; Cazavan-Jeny &
Jeanjean, 2006; Zhao, 2002). Our third category of financial statement users, investors, has
been the most extensively studied, and the meta-analysis results are more consistent. The
relationship between market-to-book ratios and R&D expenditures (Z=26.413, p-value<0.01)
or R&D expense (Z=3.686, p-value<0.01) is strongly significant and positive. The Fail-
Safe N is above the critical FSNc for the association with R&D expenditures, but not for
R&D expense. It seems that the total R&D investment effort has a real positive impact on
firms’ growth opportunities (proxied by their market-to-book). Value relevance studies
investigating the association between internally-generated intangible assets (capitalized or
non-capitalized) and share prices consistently show positive significant results. Share prices
are positively and significantly associated with advertising expenses, brand value, goodwill,
intangible assets, R&D capitalized and R&D expense (Z ranges from 2.891 to 10.728, p-
value<0.01). The Fail-Safe N is well above the FSNC for each of these associations. The
results on stock returns are less consistent since the associations are significantly positive
only for Brand value, Disclosure score, R&D capitalized and R&D expense, being negative
and non-significant for advertising expense and indicator variable for capitalizers. Finally, the
relationship between bid-ask spread and disclosure score is negative and non-significant.
27
We conduct two sensitivity analyses. First, we remove all non-U.S. studies and rerun
the meta-analysis (Table 4, Panel C). This gives a lower number of results, since some of the
variable pairs are tested only in non-U.S. settings. The results discussed above are
unchanged. Second, for each pair of variables, we eliminate the observation with the highest
empirical association to ensure that our results are not driven by outliers. We therefore lose
all results that were based on any two studies. The few differences are the following: (1) the
meta-association between analysts’ earnings forecast accuracy and intangible assets becomes
negative but non-significant; (2) the meta-association between share price and capitalized
R&D becomes negative but also non-significant as well. The meta-analysis results that
remain strongly significant are the positive association between analysts’ earnings forecast
accuracy and R&D expense, the positive association between share price and advertising
expenses, intangible assets and R&D expenses, and the positive association between stock
returns and R&D capitalized and R&D expense.
[TABLE 4 ABOUT HERE]
5.2 Review of papers not included in the meta-analysis
5.2.1 Investors and the recognition of intangibles
In the stream of literature on the value relevance of recognized intangible assets,
empirical studies have sought to develop estimators of R&D capital, which is generally
estimated by a regression of operating profit on R&D expenses (Lev & Sougiannis, 1999).17
This methodology assumes that R&D growth, the probability of success and amortization
rates are constant for all firms in the economy or for all firms in a given sector at a certain
period. To overcome this limitation, Zarowin (1999) and Ballester, Garcia-Ayuso, & Livnat
(2003) adopt an alternative approach, estimating R&D capital on the basis of time-series
17
These studies were carried out in a U.S. environment, where capitalization of R&D expenses is prohibited.
28
analyses. These two studies find significant differences in capitalization and amortization
rates for R&D between firms, implying that cross-sectional studies could be affected by a
significant bias problem. Using the time-series approach to estimate capitalized R&D,
meanwhile, implies that the parameters for capitalization and amortization are constant over
time for each firm. These results are interesting, but have limitations, as they are based on
estimates rather than the figures actually reported to investors.
The recognition of identified intangibles also involves valuation issues. The existing
accounting model fails to recognize many knowledge-based intangibles. This raises some
concerns about investors’ ability to value intangible-intensive companies whose fundamental
values are largely dependent on knowledge and technology, potentially affecting these
companies’ ability to raise capital. Kimbrough (2007) studies the consequences of FAS 141
on the informativeness of purchase price allocation. He examines the relationship between
the relative price paid to acquire the target (consideration paid divided by the acquirer’s
market value), and cumulative abnormal returns upon disclosure of the PPA. Kimbrough
(2007) finds that investors react positively when the PPA results in high levels of separately
identified intangibles, and negatively when high levels of goodwill are recognized. He argues
that goodwill is a composite asset with several components (e.g., going concern goodwill,
external synergies and overpayment) that are hard to disentangle, and is relatively less
informative to market participants than specific intangible assets.
5.2.2 Investors and disclosure of intangibles
R&D investment is the indicator most commonly used to measure innovation, but it
does have some serious drawbacks. The R&D variable is not primarily a measure of output
but rather a measure of input, and cannot therefore capture changes in the efficiency of the
innovation process. R&D can also be a long process, and investors are likely to attribute a
29
different value to the firm depending on the level of progress in the innovation process
(Pinches, Narayanan, & Kelm, 1996).
Amir and Lev (1996) focus on 14 U.S. companies in the cellular communications
industry over 10 years (1984-1993) to examine the incremental value-relevance of
nonfinancial information over financial information. The cellular communications industry is
one example of an intensive research and scientific-based technological industry where R&D
costs are expensed under U.S. GAAP. Analyses in this setting show that earnings have little,
if any, value relevance, but that dislosures in annual reports of nonfinancial information that
that helps investors assess firm growth and market penetration complement the accounting
numbers and are highly value-relevant.18
Taking the research examining firm-customer relationships a step further, Livne,
Simpson and Talmor (2011) examine whether customer acquisition costs, customer retention,
and usage are important factors for firm performance and valuation.19
The setting is the
mature wireless communication industry in the U.S. and Canada between 1997 and 2004, and
the study covers a total of 26 companies, for which the World Cellular Information Service
(WCIS) publishes extensive statistics. According to Simpson and Talmor, the WCIS obtains
the data by directly contacting the companies, from company reports, news feeds, industry
press, conferences and exhibitions. It is therefore safe to assume that the capital markets also
have access to this data. The main results show that customer acquisition costs are positively
related with future profits and current market values, but not with future revenues, suggesting
that such costs contribute future economic benefits through cost savings. The results further
suggest that customer retention and network usage are mediators between customer
18
Population coverage and number of subscribers. 19
According to Livne et al. (2011), customer acquisition costs include handset subsidies, marketing, advertising,
and administrative costs, dealer commissions and bonuses, SIM card cost, credit check cost, share of fixed costs
etc.; customer retention is the negative of the churn rate; usage is defined as minutes of (voice) use, excluding
test messaging.
30
acquisition costs and benefit generation. Unlike Amir and Lev (1996), Livne et al. (2011)
find that fundamental accounting variables such as book value and operating profit are value-
relevant in their sample period.
Ittner and Larcker (1998) use customer satisfaction measures as a proxy for the
overall intangible value of a company, and test whether these measures are predictors of
future client retention and business unit operating performance (revenues, expenses, profit
margin, number of new clients), and whether they can explain stock market value. The
correlation of customer satisfaction with client- and business unit-level outcomes is generally
positive, but often non-linear, with a decrease at high satisfaction levels. The customer
satisfaction index is strongly and positively related to market value, suggesting that this
measure provides insight into the value that investors perceive for a company that is not
reflected in firms’ accounting book value. An extension to this paper (Ittner and Larcker,
1998) could also take into account the determinants of customer satisfaction, including the
investments that companies make to develop customer satisfaction but are expensed rather
than capitalized on the balance sheet. Focusing on 7 companies in the U.S. airline industry
between 1988 and 1996 and using third-party disclosure by analysts and newspapers, Behn
and Riley (1999) find that customer satisfaction, along with other non-financial metrics of
activity, are contemporaneously associated with operating income, revenues, and expenses,
and are also predictive of future financial performance. Overall, the evidence summarized
above suggests that certain corporate investments that are not recognized as intangible assets
under current accounting standards are good indicators of future financial performance, and
are also reflected in market capitalizations. In the same airline industry setting and using the
same sample, Riley, Pearson and Trompeter (2003) find that customer satisfaction and
nonfinancial metrics of airline activity are value-relevant for quarterly stock returns above
and beyond accounting numbers such as earnings per share and abnormal earnings per share.
31
For a relatively large sample of U.S. companies (388 companies over the period 1985-
1995), Deng, Lev and Narin (1999) show that information on how a company’s patents are
used, i.e., citation intensity, and the level of innovation that went into obtaining the patent,
i.e., the patent’s link to basic scientific research (previously uncited), and the median age of
the patents cited in the firm’s patents, is positively related to future stock returns and market-
to-book values. This result suggests that such non-financial information is reflective of the
value of the company’s R&D expenditure.
Espinosa, Gietzmann and Raonic (2009) use the setting created by European biotech
and pharmaceutical companies cross-listed in the U.S. in order to examine (1) how U.S.
institutional investors respond to disclosure of non-financial information by foreign
companies and (2) whether this response is conditional on the company adopting IFRS or
U.S. GAAP, or continuing to apply national GAAP. Their main model regresses the market
value of U.S. institutional holdings in each of the 57 European companies in the sample on a
set of news flow items collected from Factiva (i.e., contract agreements, licensing
agreements, share capital financing, management moves, new product approvals, official
trials and tests, acquisitions and mergers, joint ventures, intellectual property, patents,
earnings and earnings projections). Results suggest that US institutional investors’ holdings
are higher for European companies providing enhanced nonfinancial disclosure. Moreover,
the results are stronger for companies that continue to apply national GAAP. The authors
interpret these results as evidence that instead of switching to a higher standard of financial
reporting that provides only limited improvement in monitoring ability for intangible-
intensive industries (i.e., where most of the market value reflects non-accounting
information), companies can attract U.S. investors by increasing their nonfinancial
disclosures.
32
6. Conclusions and future research
We review the relatively recent empirical archival literature on internally-developed
intangible assets, focusing on the actors involved in the financial reporting environment. By
using meta-analyses to summarize the effects uncovered in this literature, we contribute to
the ongoing debate on recognition versus disclosure of intangible assets. A number of
observations and potential future research questions arise.
First, the majority of studies reviewed deal with recognition of intangibles in the
accounts, either as an asset on the balance sheet or as an expense on the income statement.
There are fewer studies on disclosure of intangibles, although the criteria for accounting
recognition is so strict (especially in the U.S.) that if managers of intangible-intensive firms
want or need to reduce the information asymmetry relative to outsiders, voluntary disclosure
about intangibles is the only solution. Second, we observe that most papers examine the U.S.
setting. The European and international settings could yield additional insights into the role
of recognition and disclosure of intangibles, given the differences in standards and the fact
that capitalization of development costs is allowed by IAS 38. Taking into consideration the
various types of listing (i.e., foreign listed companies, domestic companies, companies cross-
listed in the U.S. but applying IFRS etc.), future research could address questions related to
country-level influences (i.e., investor protection, legal tradition, enforcement etc.) on the
recognition and disclosure of intangible assets.
Regarding recognition of intangibles, one unanswered question relates to the value-
relevance of capitalized development costs and expensed research costs, as required by IAS
38. Under IFRS, capitalization of R&D expenses is mandatory when the project is profitable
(IAS 38). It would be interesting to study the value-relevance of such capitalization for
European listed companies, based on their financial statements published since 2005.
33
Regarding disclosure of intangible-related information, there are multiple avenues of
research to follow up the studies reviewed here. For example, researchers could look at
initiatives by the standard-setters in response to the emergence of new business models. As
mentioned above, the IASB plans to review the disclosure requirements in all its existing
standards, but the FASB does not seem inclined to re-activate the disclosure of intangible
assets project abandoned in 2004. Will these decisions put European companies at a
disadvantage compared to their U.S. competitors due to increased mandatory disclosures?
Action by the regulators (i.e., the SEC and ESMA) with respect to the intangible assets
standards could reveal the sensitive areas of disclosure that companies try to avoid. Dowdell
& Press (2004), for example, discuss the enforcement actions by the SEC and firms’
subsequent restatements of purchased intangibles.
The European setting is particularly interesting, since it is characterized by low
litigation risk and a movement towards enhanced corporate transparency (Roach, 2013). One
legislative development that could prove interesting is the Trade Secrets Directive adopted by
the European Parliament in 2015.20
This directive harmonizes EU countries’ previously
varying national trade secret laws and enhances the channels for legal action open to
companies that are the victims of corporate espionage and violations of trade secrets.
Many papers that look at non-financial intangibles-related disclosure use commercial
databases (e.g., ACNielsen Media for advertising expenditure). It is not clear to what extent
investors (especially retail investors) have access to and use that data, regardless of whether
the information provided proxies for more disclosure by the firm or for some other,
unobservable firm characteristic. The question thus arises: who are the investors in R&D-
intensive firms (i.e., institutional or individual investors)? Researchers could also focus on
20
http://www.lexology.com/library/detail.aspx?g=8c809937-1729-4873-b878-0d2033c6c6e9 last accessed on
November 5, 2016
34
certain Internet-based industries where intangibles are prevalent, such as the entertainment
industry. What do managers of companies in these sectors disclose? What questions about
intangibles do analysts ask the managers during conference calls? What intangible-related
variables are value-relevant for these companies? How does intangibles’ disclosure affect the
costs and benefits of disclosure? Looking at the IPO prospectus intangible-related disclosures
by “unicorn” tech companies could shed light on the current relationship between intangibles,
information asymmetry, and the uncommonly-high market valuation for such companies
(Fan, 2015).
Intangible-related disclosure is essential for financial analysts covering R&D-
intensive firms. Some of the papers reviewed above use the discussion in analysts’ reports of
firms’ intangibles as anecdotal evidence to support earnings forecast and recommendation
analyses (Xu, Magnan, & André, 2007). Future research could more directly examine
analysts’ reports on this topic to assess analysts’ assessments of intangibles disclosure (for
example, remarks about the quantity of disclosure).
The role of auditors in the intangible assets recognition debate is fundamental, since
they are central actors for improving the reliability of financial reporting. Surprisingly, very
few papers so far have investigated the auditors’ role in this debate.
Overall, we believe that academic research on accounting for intangible assets has
considerable potential to inform standard setters and practitioners as they navigate their way
through the “knowledge economy.”
35
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Figure 1. Organizing framework
Standard
setters/
Regulators
Preparers
Auditors
Financial
statement users
Treatment of intangibles– recognition and measurement vs. disclosure
- Standard requirements, regulatory environment, comparison across sets of standards (US
GAAP vs IFRS)
- Changes in Conceptual Framework, e.g., focus on neutrality – what they (should) mean
for intangible assets recognition
- Disclosure Framework Initiative – any references to intangibles disclosure in the
Exposure Draft?
Determinants: firm-level corporate governance, stage in the life of the firm (e.g., IPO,
SEO), business model, product market competition, strategic bias/earnings management,
current earnings performance, lobbying activities, tax incentives, compensation etc.
Consequences (feedback loop): future earnings performance/profitability/growth,
investment efficiency...
- Attitude related to (or role of auditors for) R&D recognition vs. disclosure;
- Audit risk, audit fees, industry specialization, audit quality
- Do investors understand the recognized vs. disclosed amounts (i.e., processing of info)?
- Economic/market consequences (value-relevance)
- Financial analysts – earnings forecast accuracy, dispersion, recommendation, views
expressed in analyst reports etc.
- Peers – information transfers due to intangible asset recognition/disclosure?
- Bond holders, credit market consequences
41
Table 1. All papers reviewed organized in our framework
Interested party Treatment of intangibles
Recognition Disclosure
Standard setters / Regulators
2
Powell, European Accounting Review, 2003
1
#Chen, Gavious and Lev, Working Paper, 2015
1
Preparers
Determinants
Perry and Grinaker, Accounting Horizons, 1994
Luft and Shields, The Accounting Review, 2001
Muller, Journal of Accounting and Economics, 1999
Klassen, Pittman and Reed, Contemporary Accounting Research, 2004
4
Consequences of IA recognized as an asset
Healy, Myers and Howe, Journal of Accounting Research, 2002
Mohd, The Accounting Review, 2005
Wyatt, The Accounting Review, 2005
Ahmed and Falk, Journal of Accounting and Public Policy, 2006 #Ritter and Wells, Accounting and Finance, 2006
Anagnostopoulou and Levis, International Journal of Accounting, 2008
Markarian, Pozza and Prencipe, International Journal of Accounting, 2008
Oswald, Journal of Business Finance & Accounting, 2008
Seybert, The Accounting Review, 2010
Ciftci, European Accounting Review, 2010
Jones, Accounting Horizons, 2011
Cazavan-Jeny, Jeanjean and Joos, Journal of Accounting and Public Policy,
2011
12
Consequences of IA recognized as an expense
Abdel-khalik, The Accounting Review, 1975
Bah and Dumontier, Journal of Business Finance and Accounting, 2001
Kothari, Laguerre and Leone, Review of Accounting Studies, 2002
Ho, Xu and Yap, Accounting and Finance, 2004
Amir, Guan and Livne, Journal of Business, Finance & Accounting, 2007
Brown and Kimbrough, Review of Accounting Studies, 2011
Ciftci and Cready, Journal of Accounting and Economics, 2011
Pandit, Wasley and Zach, Journal of Accounting, Auditing and Finance,
2011
Johnston, Journal of Accounting and Public Policy, 2012
Shust, Journal of Accounting, Auditing & Finance, 2015
Determinants
Entwistle, Accounting Horizons, 1999
Gelb, Journal of Business Finance & Accounting, 2002
Gray and Skogsvik, European Accounting Review, 2004
Guo, Lev and Zhou, Journal of Accounting Research,
2004
Cerbioni and Parbonetti, European Accounting Review,
2007 #Jones, Contemporary Accounting Research, 2007
Simpson, Journal of Accounting, Auditing, and Finance,
2008
Kang and Gray, International Journal of Accounting,
2011
Weiss, Falk and Zion, Accounting and Finance, 2013
Koh and Reeb, Journal of Accounting and Economics,
2015
Koh, Reeb and Wald, Working Paper, 2015
Koh, Reeb and Zhao, Working Paper, 2015
12
Both determinants and consequences for the firm
Merkley, The Accounting Review, 2014
1
42
38
9
25
13
Auditors
4
Godfrey and Hamilton, Contemporary Accounting Research, 2005
Tutticci, Krishnan and Percy, Journal of International Accounting
Research, 2007
Krishnan and Wang, Accounting Horizons, 2014
3
Nixon, European Accounting Review, 1997
1
Financial Statement Users –
Investors
Value relevance of recognized IA (in the BS or the IS)
Hirschey and Weygandt, Journal of Accounting Research, 1985
Sougiannis, The Accounting Review, 1994
Lev and Sougiannis, Journal of Accounting and Economics, 1996
Green, Stark and Thomas, Journal of Business Finance & Accounting,
1996
Aboody and Lev, Journal of Accounting Research, 1998
Barth, Clement, Foster and Kasznik, Review of Accounting Studies, 1998
Lev and Sougiannis, Journal of Business Finance & Accounting, 1999
Chan, Lakonishok and Sougiannis, Journal of Finance, 2001
Zhao, Journal of International Financial Management and Accounting,
2002
Ballester, Garcia-Ayuso and Livnat, European Accounting Review, 2003
Callimaci and Landry, Canadian Accounting Perspectives, 2004
Han and Manry, International Journal of Accounting, 2004
Kallapur and Kwan, The Accounting Review, 2004
Cazavan-Jeny and Jeanjean, European Accounting Review, 2006 #Ritter and Wells, Accounting and Finance, 2006
Franzen and Radhakrishnan, Journal of Accounting and Public Policy,
2009
Gu and Li, Journal of Accounting, Auditing and Finance, 2010
Ciftci, Darrough and Mashruwala, European Accounting Review, 2014
Ciftci and Darrough, Journal of Business, Finance & Accounting, 2015
Yu, Wang and Chang, Review of Quantitative Finance and Accounting,
2014
20
Do investors understand the recognized intangibles (in BS or IS)?
Bublitz and Ettredge, The Accounting Review, 1989
Chan, Martin and Kensinger, Journal of Financial Economics, 1990
Boone and Raman, Journal of Accounting and Public Policy, 2001
Chambers, Jennings and Thompson, Review of Accounting Studies, 2002
Disclosure of non-financial information
Amir and Lev, Journal of Accounting and Economics,
1996
Ittner & Larcker, Journal of Accounting Research, 1998
Behn and Riley, Journal of Accounting, Auditing and
Finance, 1999
Deng, Lev and Narin, Financial Analysts Journal, 1999
Hirschey, Richardson and Scholz, Review of
Quantitative Finance and Accounting, 2001
Abdel-khalik, European Accounting Review, 2003
Rajgopal, Venkatachalam and Kotha, Journal of
Accounting Research, 2003
Riley, Pearson and Trompeter, Journal of Accounting and
Public Policy, 2003
Ely, Simko and Thomas, Journal of Accounting,
Auditing and Finance, 2003
Hirschey and Richardson, Journal of Empirical Finance,
2004
Shortridge, Journal of Business Finance & Accounting,
2004
Xu, Magnan and André, Contemporary Accounting
Research, 2007
Espinosa, Gietzmann and Raonic, European Accounting
Review, 2009
Shah, Stark and Akbar, International Journal of
Accounting, 2009
Livne, Simpson and Talmor, Journal of Business Finance
& Accounting, 2011
Ciftci, Lev and Radhakrishnan, Journal of Accounting,
Auditing and Finance, 2011 #Chen, Gavious and Lev, Working Paper, 2015
43
48
Boone and Raman, Journal of Accounting, Auditing and Finance, 2004
Kimbrough, The Accounting Review, 2007
Ali, Ciftci and Cready, Journal of Business Finance & Accounting, 2012
Donelson and Resutek, Review of Accounting Studies, 2012
8
Earnings quality: Earnings conservatism, ERC, underpricing
Lev, Sarath and Sougiannis, Contemporary Accounting Research, 2005
Guo, Lev and Shi, Journal of Business Finance & Accounting, 2006
Oswald and Zarowin, European Accounting Review, 2007
Givoly and Shi, Journal of Accounting, Auditing and Finance, 2008
4
32
16
Financial Statement Users –
Financial Analysts
8
Barth, Kasznik and McNichols, Journal of Accounting Research, 2001
Barron, Byard, Kile and Riedl, Journal of Accounting Research, 2002
Gu and Wang, Journal of Business, Finance & Accounting, 2005
Matolcsy and Wyatt, Accounting and Finance, 2006
Anagnostopoulou, Journal of International Financial Management and
Accounting, 2010
Palmon and Yezegel, Contemporary Accounting Research, 2012
6
García-meca, Parra, Larrán, & Martínez, European
Accounting Review, 2005 #Jones, Contemporary Accounting Research, 2007
2
Financial Statement Users –
Bondholders / Creditors
2
Shi, Journal of Accounting and Economics, 2003
Eberhart, Maxwell and Siddique, Journal of Accounting Research, 2008
2
Total 102 69 33
This table provides an overview of all the papers that we review organized and classified in our framework. We review 102 distinct papers. Papers that are classified into two
boxes are preceded by #.
44
Table 2. Composition of the sample of studies included in the meta-analyses
Panel A: Sample selection for meta-analyses
Number of
studies Percent
Papers reviewed 102
(-) Papers using other than empirical archival quantitative
research methods -5
(-) Unpublished papers -3
Initial sample of papers considered for meta-analyses 94 100%
(-) Cannot classify dependent or independent variable -19
(-) Only one study to examine the relation between two
variables -12
Final sample of papers for meta-analyses 63 67.02%
This table describes the sample selection for the studies included in the meta-analyses. The final sample of
studies represents 158 observations at the paper-variable pair level.
Panel B: Sample by journal
Journal name and abbreviation Frequency Percent
Accounting and Finance (AF) 3 4.76
Canadian Accounting Perspectives (CAP) 1 1.59
Contemporary Accounting Research (CAR) 3 4.76
European Accounting Review (EAR) 4 6.35
Financial Management (FM) 1 1.59
Journal of Accounting, Auditing and Finance (JAAF) 7 11.11
Journal of Accounting and Economics (JAE) 4 6.35
Journal of Accounting and Public Policy (JAPP) 3 4.76
Journal of Accounting Research (JAR) 9 14.29
Journal of Business Finance & Accounting (JBFA) 9 14.29
Journal of Empirical Finance (JEF) 1 1.59
Journal of Financial Economics (JFE) 1 1.59
Journal of International Accounting Research (JIAR) 1 1.59
Journal of International Financial Management and Accounting
(JIFMA) 1 1.59
Review of Accounting Studies (RAS) 4 6.35
Review of Quantitative Finance and Accounting (RQFA) 2 3.17
The Accounting Review (TAR) 5 7.94
The International Journal of Accounting (TIJA) 4 6.35
Total 63 100%
Papers in high-quality journals 25 40%
This table presents the distribution of the final sample of papers included in the meta-analyses by publication
journal. Journals in boldface font are considered high-quality.
45
Panel C: Sample by country
Country/Region Frequency Percentage
Australia 4 6.06%
Canada 1 1.52%
Continental Europe 1 1.52%
France 2 3.03%
International (emerging) 1 1.52%
International (including U.S.) 1 1.52%
Japan 1 1.52%
South Korea 1 1.52%
Spain 1 1.52%
Taiwan 1 1.52%
United Kingdom 8 12.12%
United States 44 66.67%
Total 66 100%
This table presents the distribution of the final sample of papers included in the meta-analyses by country or
region from which the sample of companies is drawn. Bah & Dumontier (2001) is counted four times since their
sample contains observations from four countries and they conduct the analyses per country.
46
Table 3. Meta-analyses of studies related to intangible assets and preparers
Panel A: List of papers
Authors
Publication
Year Journal Country Sample period
Sample
size
Preparers – Consequences of the recognition of or disclosure related to intangible assets
Ahmed and Falk 2006 JAPP Australia 1992 - 1999 1172
Amir, Guan, and Livne 2007 JBFA U.S. 1972 - 2002 37263
Anagnostopoulou and Levis 2008 TIJA UK 1990 - 2003 15488
Brown and Kimbrough 2011 RAS U.S. 1980 - 2006 119436
Cazavan-Jeny, Jeanjean, and Joos 2011 JAPP France 1992 - 2001 1060
Ciftci and Cready 2011 JAE U.S. 1975 - 2003 122636
Pandit, Wasley, and Zach 2011 JAAF U.S. 1972 - 2000 20391
Ritter and Wells 2006 AF Australia 1979 - 1997 1078
Shust 2015 JAAF U.S. 1988 - 2010 77003
Sougiannis 1994 TAR U.S. 1975 - 1985 66
Weiss, Falk, and Zion 2013 AF U.S. 1990 - 2005 528
Preparers – Determinants of recognition of or disclosure related to intangible assets
Bah and Dumontier 2001 JBFA Continental Europe 1996 - 1996 204
Japan 1996 - 1996 353
UK 1996 - 1996 233
U.S. 1996 - 1996 1069
Cazavan-Jeny, Jeanjean, and Joos 2011 JAPP France 1992 - 2001 1060
Garcia-Meca, Parra, Larran, and Martinez 2005 EAR Spain 2000 - 2001 257
Gelb 2002 JBFA U.S. 1981 - 1993 710
Guo, Lev, and Zhou 2004 JAR U.S. 1995 - 1997 265
Jones 2007 CAR U.S. 1997 - 1997 119
Kang and Gray 2011 TIJA International (emerging) 2002 - 2002 181
Merkley 2014 TAR U.S. 1996 - 2007 22482
Muller 1999 JAE UK 1988 - 1996 66
Oswald 2008 JBFA UK 1996 - 2004 3229
This table provides the list of papers included in the meta-analyses of studies related to intangible assets and
preparers.
47
Panel B: Results of the meta-analyses of studies related to intangible assets and preparers
Dependent variable
Independent
variable
Sample
(N)
Number
studies
(k)
Effect
size Z-statistic p-value Sig FSN FSNC 2 Sig
2
Preparers - Consequences
Discretionary
accruals R&D expense 84594 2 -0.012 -3.376 1.000 n.s 15 20 0.00 n.s
Future cash flows R&D expense 27982 2 0.001 0.173 0.431 n.s n/a 20 51.92 ***
Future profitability Advertising expense 37329 2 0.046 8.875 0.000 *** 114 20 389.04 ***
Intangible assets 120514 2 0.011 3.813 0.000 *** 19 20 2.65 n.s
R&D capitalized 40023 4 0.074 14.890 0.000 *** 1307 30 175.71 ***
R&D expenditure 17720 3 0.033 4.441 0.000 *** 63 25 22.98 ***
R&D expense 308971 8 0.017 9.427 0.000 *** 2094 50 321.53 ***
Preparers - Determinants
Disclosure score Analysts following 22601 2 0.016 2.395 0.008 *** 6 20 1.01 n.s
Cross-listing 438 2 0.090 1.876 0.030 ** 3 20 0.17 n.s
Disclosure policy 22601 2 0.009 1.283 0.100 * 0 20 0.30 n.s
Leverage 438 2 -0.089 -1.870 0.969 n.s 3 20 2.95 *
MTB 22920 3 -0.038 -5.778 1.000 n.s 108 25 36.79 ***
Number of patents 384 2 0.022 0.440 0.330 n.s n/a 20 0.25 n.s
Profitability 22739 2 -0.013 -1.989 0.977 n.s 4 20 2.22 n.s
R&D expense 829 2 -0.073 -2.116 0.983 n.s 5 20 5.80 **
Share Price 438 2 0.115 2.402 0.008 *** 7 20 0.23 n.s
Indicator Variable for
Capitalizers Leverage 4355 3 -0.040 -2.661 0.996 n.s 21 25 6.83 **
Profitability 4289 2 -0.028 -1.819 0.966 n.s 3 20 6.41 **
R&D expenditure 4289 2 -0.036 -2.377 0.991 n.s 6 20 1.61 n.s
Indicator Variable for
R&D-intensive Cash 1859 4 0.941 40.572 0.000 *** 9729 30 477.25 ***
Dividends 1859 4 0.070 3.027 0.001 *** 50 30 69.56 ***
Leverage 1859 4 2.088 90.020 0.000 *** 47911 30 7196.98 ***
48
This table presents the results of the meta-analyses of variables related to intangible assets and preparers. Sample (N) is the total number of observations added up across the
studies that examine a pair of variables. Number of studies (k) refers to the number of independent samples (i.e., papers). FSN is Rosenthal’s Fail Safe N and FSNC is the
critical number of studies in the file drawer. For each meta-analysis (i.e., variable pair), the 2 column indicates the chi-square statistic with k-1 degrees of freedom for testing
whether the empirical correlations are homogeneous. Statistical significance is indicated as follows: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.1; n.s. denotes p-
value not significant at conventional levels.
49
Panel C: Sensitivity test – U.S. samples only
Dependent variable
Independent
variable
Sample
(N)
Number
studies
(k)
Effect
size
Z-
statistic
p-
value Sig FSN FSNC 2
Sig
2
Preparers – Consequences of the recognition of or disclosure related to intangible assets
Discretionary accruals R&D expense 84594 2 -0.012 -3.376 1.000 n.s 15 20 0.00 n.s
Future cash flows R&D expense 27982 2 0.001 0.173 0.431 n.s n/a 20 51.92 ***
Future profitability Advertising expense 37329 2 0.046 8.875 0.000 *** 114 20 389.04 ***
R&D capitalized 37791 2 0.077 14.965 0.000 *** 329 20 45.80 ***
R&D expense 307911 7 0.017 9.434 0.000 *** 1605 45 262.97 ***
Preparers – Determinants of recognition or disclosure related to intangible assets
Disclosure score Analysts following 22601 2 0.016 2.395 0.008 *** 6 20 1.01 n.s
Disclosure policy 22601 2 0.009 1.283 0.100 * 0 20 0.30 n.s
Number of patents 384 2 0.022 0.440 0.330 n.s n/a 20 0.25 n.s
R&D expense 829 2 -0.073 -2.116 0.983 n.s 5 20 5.80 **
This table presents the results of a sensitivity test of the meta-analyses of studies related to intangible assets and preparers that excludes all non-U.S. samples. Sample (N) is
the total number of observations added up across the studies that examine a pair of variables. Number of studies (k) refers to the number of independent samples (i.e., papers).
FSN is Rosenthal’s Fail Safe N and FSNC is the critical number of studies in the file drawer. For each meta-analysis (i.e., variable pair), the 2 column indicates the chi-square
statistic with k-1 degrees of freedom for testing whether the empirical correlations are homogeneous. Statistical significance is indicated as follows: *** p-value < 0.01; ** p-
value < 0.05; * p-value < 0.1; n.s. denotes p-value not significant at conventional levels.
50
Panel D: Sensitivity test – eliminate the largest empirical correlation
Dependent variable
Independent
variable
Sample
(N)
Number
studies
(k)
Effect
size
Z-
statistic p-value Sig FSN FSNC 2
Sig
2
Preparers – Consequences of the recognition of or disclosure related to intangible assets
Future profitability R&D capitalized 2760 3 -0.015 -0.791 0.786 n.s n/a 25 7.75 **
R&D
expenditure 2232 2 -0.036 -1.701 0.956 n.s 2 20 0.39 n.s
R&D expense 271708 7 0.010 5.196 0.000 *** 482 45 29.63 ***
Preparers – Determinants of recognition or disclosure related to intangible assets
Disclosure score MTB 22739 2 -0.038 -5.799 1.000 n.s 48 20 17.12 ***
Indicator Variable
for Capitalizers Leverage 4289 2 -0.041 -2.685 0.996 n.s 9 20 0.32 n.s
Indicator Variable
for R&D-intensive
Cash 790 3 1.022 28.716 0.000 *** 2740 25 264.05 ***
Dividends 790 3 -0.151 -4.243 1.000 n.s 57 25 19.37 ***
Leverage 790 3 0.042 1.189 0.117 n.s 2 25 26.29 ***
This table presents the results of a sensitivity test of the meta-analyses on variables related to preparers that eliminates the highest empirical correlation. Sample (N) is the
total number of observations added up across the studies that examine a pair of variables. Number of studies (k) refers to the number of independent samples (i.e., papers).
FSN is Rosenthal’s Fail Safe N and FSNC is the critical number of studies in the file drawer. For each meta-analysis (i.e., variable pair), the 2 column indicates the chi-square
statistic with k-1 degrees of freedom for testing whether the empirical correlations are homogeneous. Statistical significance is indicated as follows: *** p-value < 0.01; ** p-
value < 0.05; * p-value < 0.1; n.s. denotes p-value not significant at conventional levels.
51
Table 4. Meta-analyses of studies related to intangible assets and financial statement users
Panel A: List of papers
Authors
Publication
Year Journal Country
Sample
period
Sample
size
Financial Statement Users - Analysts
Barron, Byard, Kile, and Riedl 2002 JAR U.S. 1986 - 1998 1103
Barth, Kasznik, and McNichols 2001 JAR U.S. 1983 - 1994 10631
Chambers, Jennings, and Thompson II 2002 RAS U.S. 1979 - 1998 89419
Ciftci, Lev and Radhakrishnan 2011 JAAF U.S. 1979 - 1997 7591
Gu and Wang 2005 JBFA U.S. 1981 - 1998 6167
Jones 2007 CAR U.S. 1997 - 1997 119
Matolcsy and Waytt 2006 AF U.S. 1990 - 1997 421
Merkley 2014 TAR U.S. 1996 - 2007 22482
Rajgopal, Venkatachalam, and Kotha 2003 JAR U.S. 1999 - 2000 434
Financial Statement Users - Bondholders
Eberhart, Maxwell, and Siddique 2008 JAR U.S. 1990 - 1998 72
Shi 2003 JAE U.S. 1991 - 1994 81
Financial Statement Users - Investors
Aboody and Lev 1998 JAR U.S. 1987 - 1995 778
Ahmed and Falk 2006 JAPP Australia 1992 - 1999 1172
Ali, Ciftici, and Cready 2012 JBFA U.S. 1975 - 2006 38853
Barth and Clinch 1998 JAR Australia 1991 - 1995 1750
Barth, Clement, Foster, and Kasznik 1998 RAS U.S. 1991 - 1996 595
Boone and Raman 2004 JAAF U.S. 1994 - 1997 52
Brown and Kimbrough 2011 RAS U.S. 1980 - 2006 119436
Bulitz and Ettredge 1989 TAR U.S. 1974 - 1983 2832
Callimaci and Landry 2004 CAP Canada 1997 - 1999 109
Cazavan-Jeny and Jeanjean 2006 EAR France 1993 - 2002 770
Chambers, Jennings, and Thompson II 2002 RAS U.S. 1979 - 1998 89419
Chan, Martin, and Kensinger 1990 JFE U.S. 1979 - 1985 79
Chauvin and Hirschey 1993 FM U.S. 1988 - 1990 4653
Ciftci and Cready 2011 JAE U.S. 1975 - 2003 122636
Ciftci, Darrough, and Mashruwala 2014 EAR U.S. 1975 - 2007 171894
Donelson and Resutek 2012 RAS U.S. 1973 - 2008 56145
Ely and Waymire 1999 JAR U.S. 1927 - 1927 146
Ely, Simko, and Thomas 2003 JAAF U.S. 1988 - 1998 193
Franzen and Radhakrishnan 2009 JAPP U.S. 1982 - 2002 47167
Givoly and Shi 2008 JAAF U.S. 1986 - 1998 390
Green, Stark, and Thomas 1996 JBFA UK 1990 - 1992 230
Gu and Li 2010 JAAF U.S. 1995 - 2004 4966
Guo, Lev, and Zhou 2004 JAR U.S. 1995 - 1997 265
Han and Manry 2004 TIJA South Korea 1988 - 1998 3191
Hirschey and Richardson 2004 JEF U.S. 1989 - 1995 1720
Hirschey and Weygandt 1985 JAR U.S. 1977 - 1977 390
Hirschey, Richardson, and Scholz 2001 RQFA U.S. 1989 - 1995 1290
Kallapur and Kwan 2004 TAR UK 1984 - 1998 232
Lev and Sougiannis 1996 JAE U.S. 1975 - 1991 3000
Lev and Sougiannis 1999 JBFA U.S. 1975 - 1989 1200
Merkley 2014 TAR U.S. 1996 - 2007 22482
Oswald and Zarowin 2007 EAR UK 1991 - 1999 1002
Palmon and Yezegel 2012 CAR U.S. 1993 - 2004 8620
52
Ritter and Wells 2006 AF Australia 1979 - 1997 1078
Shah, Stark, and Akbar 2009 TIJA UK 1990 - 1998 9752
Shevlin 1991 TAR U.S. 1980 - 1985 145
Shortridge 2004 JBFA U.S. 1985 - 1996 172
Shust 2015 JAAF U.S. 1988 - 2010 77003
Sougiannis 1994 TAR U.S. 1975 - 1985 66
Tutticci, Krishnan, and Percy 2007 JIAR Australia 1992 - 2002 386
Xu, Magnan, and André 2007 CAR U.S. 1998 - 2004 1232
Yu, Wang, and Chang 2015 RQFA Taiwan 2003 - 2006 751
Zhao 2002 JIFMA International
(including U.S.)
1990 - 1999 13029
This table provides the list of papers included in the meta-analyses of studies related to intangible assets and
financial statement users.
Panel B: Results of meta-analyses of studies related to intangible assets and financial statement users
Dependent variable
Independent
variable
Sample
(N)
Number
studies
(k)
Effect
size Z-statistic p-value Sig FSN FSNC 2 Sig
2
Financial Statement Users - Analysts
Analyst earnings
forecast accuracy Advertising expense 7270 2 0.023 1.989 0.023 ** 4 20 0.53 n.s
Disclosure score 22601 2 -0.014 -2.148 0.984 n.s 5 20 0.77 n.s
Intangible assets 7691 3 0.035 3.033 0.001 *** 28 25 17.28 ***
R&D expense 104833 6 0.042 13.537 0.000 *** 2432 40 205.08 ***
Analyst earnings
forecast dispersion Disclosure score 22601 2 -0.013 -1.979 0.976 n.s 4 20 2.20 n.s
Intangible assets 1524 2 -0.057 -2.222 0.987 n.s 5 20 0.19 n.s
R&D expense 8813 3 -0.006 -0.579 0.719 n.s n/a 25 22.69 ***
Analysts following Intangible assets 11052 2 -0.027 -2.829 0.998 n.s 10 20 11.96 ***
Financial Statement Users - Bondholders
Bond rating R&D expense 153 2 -0.191 -2.357 0.991 n.s 6 20 1.86 n.s
Bond risk Premium R&D expense 153 2 -0.307 -3.792 1.000 n.s 19 20 0.29 n.s
Financial Statement Users - Investor
Bid-ask Spread Disclosure score 22747 2 -0.022 -3.262 0.999 n.s 14 20 0.98 n.s
MTB R&D expenditure 981 2 0.843 26.413 0.000 *** 1029 20 3229.27 ***
R&D expense 1356 2 0.100 3.686 0.000 *** 18 20 7.55 ***
Share Price Advertising expense 14795 3 0.088 10.728 0.000 *** 380 25 102.46 ***
Brand value 827 2 0.193 5.564 0.000 *** 44 20 10.31 ***
Intangible assets 3752 4 0.151 9.226 0.000 *** 499 30 29.14 ***
R&D capitalized 19526 7 0.021 2.964 0.002 *** 152 45 34.07 ***
R&D expense 195524 14 0.007 2.891 0.002 *** 591 80 580.49 ***
Stock Return Advertising expense 10989 3 -0.048 -4.989 1.000 n.s 80 25 143.21 ***
Brand value 827 2 0.220 6.319 0.000 *** 57 20 2.86 *
Disclosure score 22747 2 0.016 2.380 0.009 *** 6 20 8.41 ***
54
Indicator Variable
for Capitalizers 1778 3 -0.059 -2.475 0.993 n.s 17 25 10.78 ***
R&D capitalized 9298 7 0.115 11.110 0.000 *** 2228 45 111.26 ***
R&D expense 574316 18 0.012 9.277 0.000 ***
1028
7 100 274.87 ***
This table provides the results of the meta-analyses of studies related to intangible assets and financial statement users. Sample (N) is the total number of observations added
up across the studies that examine a pair of variables. Number of studies (k) refers to the number of independent samples (i.e., papers). FSN is Rosenthal’s Fail Safe N and
FSNC is the critical number of studies in the file drawer. For each meta-analysis (i.e., variable pair), the 2 column indicates the chi-square statistic with k-1 degrees of
freedom for testing whether the empirical correlations are homogeneous. Statistical significance is indicated as follows: *** p-value < 0.01; ** p-value < 0.05; * p-value <
0.1; n.s. denotes p-value not significant at conventional levels.
55
Panel C: Sensitivity test – U.S. samples only
Dependent variable
Independent
variable
Sample
(N)
Number
studies
(k)
Effect
size
Z-
statistic
p-
value Sig FSN FSNC 2
Sig
2
Financial Statement Users - Analysts
Analyst earnings
forecast accuracy Advertising expense 7270 2 0.023 1.989 0.023 ** 4 20 0.53 n.s
Disclosure score 22601 2 -0.014 -2.148 0.984 n.s 5 20 0.77 n.s
Intangible assets 7691 3 0.035 3.033 0.001 *** 28 25 17.28 ***
R&D expense 104833 6 0.042 13.537 0.000 *** 2432 40 205.08 ***
Analyst earnings
forecast dispersion Disclosure score 22601 2 -0.013 -1.979 0.976 n.s 4 20 2.21 n.s
Intangible assets 1524 2 -0.057 -2.222 0.987 n.s 5 20 0.19 n.s
R&D expense 8813 3 -0.006 -0.579 0.719 n.s n/a 25 22.69 ***
Analysts following Intangible assets 11052 2 -0.027 -2.829 0.998 n.s 10 20 11.96 ***
Financial Statement Users - Bondholders
Bond rating R&D expense 153 2 -0.191 -2.357 0.991 n.s 6 20 1.86 n.s
Bond risk Premium R&D expense 153 2 -0.307 -3.792 1.000 n.s 19 20 0.29 n.s
Financial Statement Users - Investors
Bid-ask Spread Disclosure score 22747 2 -0.022 -3.262 0.999 n.s 14 20 0.98 n.s
MTB R&D expense 1356 2 0.100 3.686 0.000 *** 18 20 7.55 ***
Share Price Advertising expense 5043 2 0.250 17.782 0.000 *** 465 20 29.42 ***
Intangible assets 924 2 0.073 2.231 0.013 ** 5 20 9.62 ***
R&D expense 183833 10 0.005 2.336 0.010 *** 192 60 485.36 ***
Stock Return Advertising expense 7798 2 -0.018 -1.633 0.949 n.s 2 20 155.95 ***
Disclosure score 22747 2 0.016 2.380 0.009 *** 6 20 8.41 ***
R&D capitalized 4168 3 0.085 5.512 0.000 *** 98 25 29.93 ***
R&D expense 569186 14 0.012 9.232 0.000 *** 6159 80 232.50 ***
This table presents the results of a sensitivity test of the meta-analyses of studies related to intangible assets and financial statement users that excludes all non-U.S. samples.
Sample (N) is the total number of observations added up across the studies that examine a pair of variables. Number of studies (k) refers to the number of independent
56
samples (i.e., papers). FSN is Rosenthal’s Fail Safe N and FSNC is the critical number of studies in the file drawer. For each meta-analysis (i.e., variable pair), the 2 column
indicates the chi-square statistic with k-1 degrees of freedom for testing whether the empirical correlations are homogeneous. Statistical significance is indicated as follows:
*** p-value < 0.01; ** p-value < 0.05; * p-value < 0.1; n.s. denotes p-value not significant at conventional levels.
57
Panel D: Sensitivity test – eliminate the largest empirical correlation
Dependent variable
Independent
variable
Sample
(N)
Number
studies
(k)
Effect
size
Z-
statistic
p-
value Sig FSN FSNC 2
Sig
2
Financial Statement Users - Analysts
Analyst earnings
forecast accuracy
Intangible assets 1524 2 -0.056 -2.202 0.986 n.s 5 20 0.27 n.s
R&D expense 15414 5 0.019 2.345 0.010 *** 46 35 45.19 ***
Analyst earnings
forecast dispersion R&D expense 7710 2 -0.015 -1.358 0.913 n.s 1 20 3.78 *
Financial Statement Users - Investors
Share Price Advertising expense 10142 2 0.036 3.665 0.000 *** 18 20 7.33 ***
Intangible assets 2674 3 0.121 6.250 0.000 *** 127 25 12.54 ***
R&D capitalized 6497 6 -0.005 -0.383 0.649 n.s n/a 40 22.10 ***
R&D expense 190871 13 0.005 2.389 0.008 *** 344 75 317.22 ***
Stock Return Advertising expense 6023 2 -0.170 -13.161 1.000 n.s 254 20 9.16 ***
Indicator Variable
for Capitalizers 1392 2 -0.082 -3.041 0.999 n.s 12 20 1.07 n.s
R&D capitalized 6107 6 0.069 5.373 0.000 *** 378 40 49.72 ***
R&D expense 451680 17 0.006 4.292 0.000 *** 1950 95 214.54 ***
This table provides the results of a sensitivity test of the meta-analyses of studies related to intangible assets and financial statement users that eliminates the highest empirical
correlation. Sample (N) is the total number of observations added up across the studies that examine a pair of variables. Number of studies (k) refers to the number of
independent samples (i.e., papers). FSN is Rosenthal’s Fail Safe N and FSNC is the critical number of studies in the file drawer. For each meta-analysis (i.e., variable pair), the
2 column indicates the chi-square statistic with k-1 degrees of freedom for testing whether the empirical correlations are homogeneous. Statistical significance is indicated as
follows: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.1; n.s. denotes p-value not significant at conventional levels.