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

jeny@essec.edu

This version, October 2017, do not quote without the permission of the authors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.