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Technology Analysis & Strategic
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The impact of intangibles on firm growth
Stefano Denicolaia, Enrico Cotta Ramusinoa & Francesco Sottia
a University of Pavia, Pavia, Italy
Published online: 26 Sep 2014.
To cite this article: Stefano Denicolai, Enrico Cotta Ramusino & Francesco Sotti (2014): The
impact of intangibles on firm growth, Technology Analysis & Strategic Management, DOI:
10.1080/09537325.2014.959484
To link to this article: http://dx.doi.org/10.1080/09537325.2014.959484
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Technology Analysis & Strategic Management, 2014
http://dx.doi.org/10.1080/09537325.2014.959484
The impact of intangibles on firm growth
Stefano Denicolai∗, Enrico Cotta Ramusino and Francesco Sotti
University of Pavia, Pavia, Italy
This study investigates and measures the impact of intangibles on firm growth. We distinguish
between internally and externally generated intangible assets and analyse the role played by
firm size, measuring if it can alter the relationship between intangibles and performance. In
doing so, we combine the resource-based view of the firm – as a cornerstone for this sur-
vey – with accounting principles. In particular, we focus on intangible ‘assets’ recorded in
firms’ books according to international accounting standards. The empirical analysis explores
a proprietary database of 294 listed companies headquartered in Europe. Findings confirm that
intangibles are crucial in fostering firm performance, show that this effect varies with firm size
and that an additional boost is created by externally generated intangibles.
Keywords: resource-based view; intangible assets; externally generated intangible; firm
size
1. Introduction: how much intangibles really matter?
In the contemporary economy, competition is more and more driven by the development and
accumulation of intangible assets – such as technological knowledge, brand, reputation, and cus-
tomer base – crucial resources that may make competitive advantage unique and inimitable.
Interestingly, companies are increasingly considering a wider spectrum of sources in order to
increase their portfolio of resources: as internal development seems to be no longer enough,
technology sourcing and other inbound flows of intangibles have become key practices for
managers.
Mainstream literature recognises the relevance of current developments: a plethora of schol-
ars have explored the topic by discussing why and how both internally and externally generated
intangibles feed the competitive advantage of firms (Hall 1992; Peteraf 1993; Teece 1998). Sur-
prisingly, there are few surveys aimed at measuring these dynamics, or – when available – they
are too focused on knowledge assets only (e.g. patents), mostly ignoring other important types
of intangibles. Furthermore, quantitative surveys in the field are often affected by response bias
due to the adoption of subjective metrics such as Likert scales (Bontis 2001; Chareonsuk and
Chansa-ngavej 2010). Even though the strategic relevance of intangibles is confirmed by a num-
ber of theoretical arguments, we know only a little regarding how much this kind of resource
really matters, and under what conditions this effect might be better exploited.
∗Corresponding author. Email: stefano.denicolai@unipv.it
© 2014 Taylor & Francis
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2S. Denicolai et al.
According to this premise, this study investigates and measures the role of intangible assets on
firm performance through a quantitative approach, distinguishing between internally and exter-
nally generated assets. This survey also analyses the role played by firm size, measuring if and
how it can alter the relationship between intangibles and performance.
Hall (1992) distinguishes between assets – such as trademarks, patents, copyrights, and reg-
istered designs – which meet the criteria of accounting rationale, and skills, or competencies –
such as tacit knowledge or corporate culture. This paper focuses on the first type, namely those
intangible ‘assets’ which are entered in the accounting records of firms according to interna-
tional accounting principles. We recognise that this choice has some limitations: only a subset
of these investments can be capitalised, while ‘soft’ resources are also relevant for competi-
tive advantage. Nevertheless – as mentioned above – the literature in the field shows not only a
large consensus from a theoretical standpoint, but also a poor empirical validation regarding its
key assumptions, due to a scarcity of quantitative analyses and some controversial findings. We
need surveys focused on specific aspects and rooted on objective measurements to make a step
forward. Moreover, we think that the integration of strategic management pillars and account-
ing metrics into the same theoretical framework supports a better understanding of managerial
implications, thus reducing the distance between academics and practitioners. The pros and cons
of this approach are discussed later.
The empirical analysis explores a sample of 294 listed firms headquartered in the UK,
Germany, France, and Italy. The need for a distinction between internally and externally gen-
erated intangibles led us to collect primary data and develop a proprietary database due to the
absence of such information from available sources such as Facsets, Orbis, and Community
Innovation Survey (CIS).
Our analysis is structured as follows: In Section 2, we introduce the theoretical framework as
well as four research hypotheses regarding the role of internally and externally generated intan-
gibles in affecting firm growth. We also introduce firm’s size as a moderating variable. Section 3
describes the sample, the data-set, the definition of firm performance, and explanatory variables.
In this section, we motivate the adoption of accounting measures, comparing our approach with
those prevailing in existing managerial studies. Section 4 shows the main findings of empirical
analysis and discusses the research propositions. In Section 5, we conclude, drawing some the-
oretical and managerial implications from our work and highlighting possible extensions of this
study.
2. Theoretical framework and hypothesis development: intangible assets, external
sourcing, and the role of firm size
The importance of intangible resources has been underlined by several authors (Gerpott, Thomas,
and Hoffmann 2008; Hussi and Ahonen 2002). Nevertheless, academic scholars are affected,
first, by the absence of a shared and accepted definition of ‘intangible asset’ (Andriessen 2004;
Bontis et al. 1999; Mølbjerg-Jørgensen 2006; Sveiby 1997) and, second, by the adoption of het-
erogeneous taxonomies used by both researchers and practitioners (Bontis 2001). The divergent
research path followed by the strategic management stream on one side and accounting litera-
ture on the other has a role in explaining the lack of common terminology. This study aims at
integrating these two areas of research.
To achieve this objective, we define ‘Intangible asset’ – for the sake of brevity, also Intangibles
– as a non-financial resource without physical substance that supports both primary and sup-
port activities of the firm, potentially enhancing its core-competences and competitive advantage
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Impact of intangibles on firm growth 3
(Blair and Wallman 2001; Epstein and Mirza 2005; Onyeiwu 2003). This definition highlights
their strategic role and is consistent with international accounting standards.1The remaining part
of this section develops four research hypotheses concerning the role of this type of resource in
affecting the firm growth.
2.1. Intangible assets as a driver of competitive advantage
The resource based view (RBV) stream holds that differences in firm performance are basically
the consequence of differences in a firm’s endowment of resources, especially intangible ones
(Rumelt 1984). It stresses the importance of internal firm-specific factors, more than industry-
specific ones. Strategic resources support the organisation in developing sustainable advantage
since they are rare, if not unique (Barney 1991; Dierickx and Cool 1989). They foster the
appropriability of competitive advantage since they are difficult to imitate and hard to substi-
tute through other resources (Brown and Kimbrough 2011; Teece 2006). Intangible assets boost
firm performance, especially in the medium-long run (Edvinsson and Malone 1997). Indeed,
the development and accumulation of such resources require time. Second, while investment in
physical assets often shows diminishing marginal returns, investment in intangible assets and
human resources is characterised by increasing returns over time (Onyeiwu 2003). Furthermore,
intangible assets support the appropriability of the competitive advantage (Jacobides, Knudsen,
and Augier 2006).
After the seminal articles in the RBV stream, further studies developed the topic. In the last
decades, a large number of scholars have confirmed the positive role of intangibles as a critical
success factor, especially by focusing on a specific type of assets, such as Brand (O’Cass and
Weerawardena 2010;Urde2009), Knowledge (Blumentritt and Johnston 1999; Teece 1998),
Human resources (Wright, Dunford, and Snell 2001), Organisational routines (Becker 2004;
Leonardbarton 1992), and Customer base (Coltman 2007; Rapp, Trainor, and Agnihotri 2010).
The majority of extant research in the field consists of conceptual articles (Chareonsuk and
Chansa-ngavej 2008; Kristandl and Bontis 2007; Teece 1998) or case study analyses (Hall 1993;
Huarng and Yu 2011; Stanworth et al. 2004), while we need more quantitative studies to measure
and validate the positive role of intangibles (Pike, Roos, and Marr 2005). Our survey addresses
this research gap. The remaining part of this section discusses the outcomes of prior quantitative
surveys and draws four research hypotheses that will be empirically tested in Section 4. In line
with the aim of this study, the theoretical framework relies on the strategic management view
(Hall 1992; Rumelt 1984; Rumelt, Schendel, and Teece 1991).
As mentioned above, only a limited number of quantitative surveys explicitly address the
impact of intangible assets on firm performance. The surveys of Chareonsuk and Chansa-Ngavej
(2008,2010) confirm the positive relationship and add that, to sustain long-term growth, intan-
gibles must be carefully monitored. It highlights the need for accounting practices in enabling
the strategic potential of this kind of asset. However, empirical outcomes come from subjective
opinions collected through questionnaires.
Onyeiwu (2003) studied a sample of 50 companies through a binary-logit estimation proce-
dure. Findings suggest that companies which spend more on intangible assets and marketing
activities develop superior core competencies. Nevertheless, the author finds controversial
outcomes in the case of some kinds of intangibles, such as technological knowledge (e.g.
patents).
Chen et al. (2005) used data drawn from Taiwanese listed companies and Pulic’s Value Added
Intellectual Coefficient (VAICe) as the efficiency measure of intellectual capital. This survey
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4S. Denicolai et al.
shows that this component of the intangibles’ portfolio has a positive impact on market value
and financial performance, but does not focus on the sources of intangible assets. Empirical
analyses have also suggested that companies with a high portion of intangibles to total assets –
in contrast with capital- and labour-based firms – show a relatively better ability to cope with
the turbulence of the contemporary scenario (Bell, Crick, and Young 2004; Powell and Snellman
2004). The rise of intangible assets in size and contribution to corporate growth over the last
decades is also noticed in companies’ annual reports (Gu and Wang 2005).
Hence, according to this literature review, we first posit the following research hypothesis:
Hypothesis 1: Intangible Intensity is positively associated with firm performance.
We focus on a ‘ratio’ measure, more than on stocks. The aim is to investigate the structure of
the resource portfolio, and to mitigate the effect of firm size (Carmeli and Tishler 2004; Deni-
colai et al. 2014; Srivastava 2014). Thus we define ‘Intangible Intensity’ as the total value of
Intangibles to the total value of non-current assets. The focus on ‘non-current assets’ makes it
possible to consider only strategic assets which are held by firms in the medium and long run.
2.2. Internally vs. externally generated intangibles
The literature also emphasises the difference between internally and externally generated intan-
gibles (Stolwijk et al. 2012). The global economy and turbulent technological evolution have
pushed towards the decentralisation of knowledge sourcing, and the diversification of the brand
portfolio. As such, this strategic pathway cannot be fully internally pursued due to its complexity
and the huge related investments. Thus, companies open up their business model and consider
the option to access and internalise intangibles developed by independent organisations (Ches-
brough 2003; Huang 2011). This solution offers a number of advantages. The company usually
acquires an external intangible when the latter has already expressed its potential, or if its value
is confirmed by evidence, thus reducing the risk of the investment (Granstrand et al. 1992). For
the same reason, by leveraging externally generated intangibles, the company reduces the time
lag between investment (expenditure) and returns.
This orientation is more and more widespread and takes place in many ways: strategic
alliances, business acquisitions aimed at accessing a strategic asset (e.g. a well-known trade-
mark), direct acquisition of a specific asset through a market transaction (e.g. a patent), licensing
agreement, and so on. Prior research confirmed a relationship among external intangibles – espe-
cially knowledge – innovation and firm performance (Jones, Lanctot, and Teegen 2001; Taylor
and Lowe 1997; Tsai and Wang 2007). Nevertheless, some scholars argue that the topic is quite
controversial and call for further investigation (Stolwijk et al. 2012). Hence, we posit a second
research hypothesis as follows:
Hypothesis 2: The orientation towards externally generated intangibles is positively associated with
firm performance.
2.3. The impact of intangibles and firm size
The literature also investigates whether intangibles are sensitive to organisational conditions,
which may enhance or mitigate their effect on performance (Tsai and Wang 2007; Vega-Jurado,
Gutierrez-Gracia, and Fernandez-De-Lucio 2009). Prior research discussed a number of variables
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Impact of intangibles on firm growth 5
which can moderate the relationship between intangibles and performance, such as innova-
tion capability (Gomez and Vargas 2012), technology life cycle (Stolwijk et al. 2012), tangible
resources and technological slack (Bueno et al. 2010), foreign direct investment (Jiang et al.
2011), and corporate responsibility (Surroca, Tribo, and Waddock 2010), firm age (Dehlen et al.
2014), strategic orientation (Hagen et al. 2012; Troilo, De Luca, and Atuahene-Gima 2014), top
management traits and behaviour (Li et al. 2011), etc. In particular, the literature highlights the
positive role of absorptive capacity, which positively moderates the decision to acquire external
intangibles, especially when the motivation to acquire is low, or financial resources are limited
(Ruth, Iyer, and Sharp 2013).
Surprisingly, we have not found surveys aimed at investigating firm size as a moderating
variable; in extant research, size is considered just as a control variable, not as a key driver
(Chareonsuk and Chansa-Ngavej 2010). We surmise that firm size is a fundamental aspect
in moderating the relationship between intangible intensity and firm performance. SMEs are
expected to be more sensitive to the role of intangibles because of the relative weight of critical
assets in their business life cycle. Larger organisations also benefit from intangible assets, but
their performance tends to depend on a broader spectrum of factors (Elsayed 2006; Gopalakr-
ishnan and Bierly 2006). Thus, we investigate if the effect of intangible intensity on firm
performance varies according to firm size. Prior research shows controversial findings regard-
ing this standpoint. Some scholars argue that larger firms can take advantage of economies of
scale and therefore more effectively turn intangible assets into competitive advantage (Jiang et
al. 2011). By contrast, a large body of literature neglects this assumption and conversely posits
that small enterprises are more able to exploit the ‘boost effect’ derived from intangible assets.
The typical high-potential SME develops its competitive advantage on high-value intangible
resources, while often it is poor in terms of physical and financial assets (Maranto-Vargas and
Rangel 2007; Thorpe et al. 2005). By contrast, on average, the large-sized company shows a
more diversified portfolio of resources. Moreover, the boost produced by intangibles tends to be
more evident in young and small firms since – because of their state – they have more chance
to grow, and faster, than the already established big companies. We believe that a better under-
standing of this aspect could reveal relevant implications for both managers and policy-makers.
Thus, we posit the following hypothesis:
Hypothesis 3a: Firm size negatively moderates the impact of intangible intensity on firm perfor-
mance.
Firm size could, in principle, even affect the successful acquisition of external assets. In other
words, we surmise that the impact of externally generated intangibles on firm performance varies
across small, medium, and large firms. In particular, small and medium enterprises may have
limited resources for technology acquisitions, but they are relatively more likely to benefit from
externally generated intangibles when this happens (Anderson and Eshima 2013). Successful
SMEs counterbalance their lack of financial resources through superior capabilities and flexibil-
ity in searching/introducing intangibles from outside (Kotlar et al. 2013). It is a sort of ‘survival
need’ which can make the difference between successful companies and organisations that stay
small, or fail (Cho and Yu 2000; Il Park and Ghauri 2011). Big companies, on the contrary, show
relatively more capacity in terms of internal development and are less dependent on external
players (Hennart 1991). Moreover, large organisations tend to be more path-dependent to inter-
nal processes (Nerkar and Paruchuri 2005) as well as more affected by the so-called Not Invented
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6S. Denicolai et al.
PERFORMANCE
(firm growth
2010-2013)
CONTROL VARIABLES
a) Industry
b) Country
c) Firm Size
EXTERNALLY GENERATED
INTANGIBLE RARIO (EGIR)
INTANGIBLE INTENSITY
FIRM SIZE
Figure 1. Research model.
Here Syndrome (Chesbrough and Crowther 2006). Hence, we assume a negative moderating
effect, and we posit a final research hypothesis:
Hypothesis 3b: Firm size negatively moderates the effect of externally generated intangibles on firm
performance.
In a nutshell, the first two hypotheses investigate the impact of Intangibles on firm perfor-
mance, by distinguishing between the whole portfolio (Hp1) and the specific impact of externally
generated assets (Hp2). Hypotheses 3a and 3b study the role of firm size as a moderating vari-
able that might reinforce, or mitigate, these dynamics. Figure 1summarises the whole research
model.
3. Methodology and variables
3.1. Sample and source of data
We developed a proprietary database composed of secondary data and consistent with the pur-
pose of our survey. This approach differentiates our study from others which rely on information
collected through surveys. The latter has, of course, its advantages, since it permits the collection
of data which are consistent with any kind of pre-defined research model. On the other side, the
reliability of information collected through questionnaires is entirely based on the commitment
of responding firms.
We decided to gather quantitative data from annual reports, an approach that is both objective,
being based on official information, and practical, as it would have been difficult to use ques-
tionnaires to a huge amount of precise information from a large sample of firms. We are aware
that our approach also has, of course, some limitations as financial reports do not contain infor-
mation about the whole portfolio of intangibles available for the firm, but only those recorded
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Impact of intangibles on firm growth 7
Table 1. Composition of the sample in terms of countries.
Country No. of companies Percentage
UK 153 49.35
Germany 81 26.13
France 54 17.42
Italy 22 7.10
Total 310 100.00
Table 2. Composition of the sample in terms of industries.
Industry No. of companies Percentage
Industrial goods and services 126 40.65
Technology 93 30.00
Health care 32 10.32
Other 59 19.03
Total 310 100.00
Table 3. Composition of the sample in terms of firm size (number of
employees).
Number of employees No. of companies Percentage
Less than 50 25 8.06
51–250 65 20.97
251–2500 112 36.13
More than 2500 108 34.84
Total 310 100.00
according to accounting standards. We only looked at European listed companies and consoli-
dated financial statements, all adopting the IAS/IFRS since 2005, thus guaranteeing relevance,
materiality, comparability, verifiability, faithfulness, and as a consequence, homogeneous, and
consistent data.
Data about relevant aspects of intangibles were collected partly from raw figures drawn from
the income statement and balance sheet, partly combining these two schemes and partly from the
notes of disclosure.
We decided to focus on companies listed in the financial markets of the four major EU
economies: UK,2Germany,3France,4and Italy,5representing together more than half of the
European Union’s GDP. We first developed a preliminary analysis of all the companies (2178)
listed on the mentioned exchanges at the date we started our research (2011), in order to identify
those that could be used for our purposes. We then focused on firms with consolidated reports, as
these are perfectly comparable among countries. Finally, we selected only companies showing
a clear-cut distinction between ‘internally generated intangible assets’ and ‘externally generated
intangible assets’. This left us with a sample of 310 companies: 153 in the UK, 54 in France, 81
in Germany, and 22 in Italy. Tables 1–3show some descriptive statistics. The sample is similar
in country composition to the full population of listed companies in the above-mentioned stock
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8S. Denicolai et al.
exchanges. Due to some missing values and outliers, the final sample for regression analysis con-
sists of 294 observations. Though it is a limited sample in absolute terms, it is well representative
of European listed firms.
3.2. Dependent variable
In this section, we describe the variables used in our research model, coherently with our research
hypotheses.
Turnover growth (GROW) is, in our research scheme, the measure of a firm’s success and
the dependent variable in our regression analysis. We measured it, according to IAS 18, §7,
through total revenues generated by the group during the fiscal year. In this study, we assume
turnover’s compounded average growth rate (CAGR) over the 2008–2010 period as proxy for
firm performance. Like other metrics, this option has some limitations. In particular, it can favour
SMEs and new ventures which – due to their nature – might have more growth potential due to
the fact they start from a smaller base compared with big companies. However, sales growth is
a more stable indicator compared with profitability, which shows high volatility and is highly
industry-specific. Third, we decided to focus on this indicator coherently with the approach used
in a number of managerial studies, where growth is widely referred to as a proxy for firm success
(Bruton and Rubanik 2002; Coad 2010).
3.3. Independent variables
The first independent variable is ‘Intangible Intensity’ (INT), defined as ‘intangible assets’ scaled
by ‘total non-current assets’. Total intangible assets is the net (of amortisation and impairment
losses) book value of all intangible assets, excluding goodwill, including acquired and internally
generated.
This value is recorded in the firm’s accounts on the basis of effective requirements derived
from IAS 38 (intangible assets) and IFRS 3 (business combinations). In particular, IAS 38 is
very strict in defining, identifying, measuring, and recognising intangible assets. Given the lack
of physical substance, the IAS/IFRS framework sets precise requirements an asset must meet in
order to be recorded in a firm’s balance sheet.
According to IAS 38, assets in this category are intangible resources such as scientific or
technical knowledge, design and implementation of new processes or systems, licences, intel-
lectual property, market knowledge, and trademarks (including brand names and publishing
titles).6However, an item from this list is defined as an intangible asset and it is recognised in
the financial statement if, and only if, it meets the following three criteria: identifiability, control
over a resource, and existence of future economic benefits (IAS 38, §10). After being recognised,
an intangible asset may be included in the balance sheet if and only if:
•it is probable that the expected future economic benefits attributable to the asset will flow to
the entity and
•the cost of the asset can be measured reliably (IAS 38, §21).
Undoubtedly, financial statements analysed provide reliable data and information, but we can-
not exclude that there could be intangible resources that are not recognised in the balance sheet
even if they are useful for the entity.
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Impact of intangibles on firm growth 9
Total non-current assets include the net book value of all those assets classified in the balance
sheet as non-current as they are not expected to be realised in the entity’s normal operating cycle
or within 12 months from the end of the reporting period.
Assets are measured at the cost of acquisition (or generation) minus accumulated amortisation
and depreciation, or at fair value if it can be measured reliably.
From the balance sheet, we have drawn the net total accumulated value of intangibles – both
internally and externally generated – excluding goodwill, at the end of the fiscal year. As the
IAS/IFRS framework does not impose the distinction between the two categories of intangibles,
relevant data about this aspect have been collected through the analysis of the notes of disclosure.
The second key variable in this study is the externally generated ratio (EXT), measured by the
amount of Externally generated intangibles to Internally plus Externally generated intangibles
(total intangibles).
Internally generated intangible assets derive from internal activities performed by the firm
during the year in question. According to IFRS, firms do not recognise internally generated
goodwill and other items such as brands, mastheads, publishing titles, or customer lists. IAS 38
recognises the difficulties in identifying whether an internally generated intangible asset meets
the criteria for recognition. §51 highlights the problems of assessing an internally generated
asset’s future benefits and the problem of reliable cost allocation. The IFRS consequently intro-
duce a set of additional requirements for the recognition and initial measurement of internally
generated intangible assets. If the intangible asset fulfils all recognition criteria, its cost, mea-
sured as the sum of expenditures incurred from the date when the intangible asset first met all
the recognition criteria, will be capitalised. These costs include ‘all directly attributable costs
necessary to create, produce, and prepare the asset to be capable of operating in the man-
ner intended by management’ as for instance, cost of materials, costs of employee benefits,
cost to register legal rights, amortisation of patents, and licences that are used to generate the
intangible asset. In short, internally generated intangibles are the following: licences and fran-
chises, copyrights, patents and other industrial property rights, service and operating rights,
recipes, formulae, models, designs, prototypes, and intangible assets under development. (IAS
39, § 119)
Externally acquired intangible assets include all acquired intangibles, excluding goodwill,
making no difference between the different ways of acquisition (separate acquisition or business
combinations).
According to IFRS, separately acquired intangible assets by definition satisfy criteria for
recognition, based on the assumption that the price paid reflects the expectation of obtaining
future benefits and that the cost of obtaining the asset is easily identifiable.
Some categories of intangible assets such as customer lists, brands, and similar items may
therefore be recognised and included in the balance sheet if acquired in a separate transaction or
in a business combination, while this is not the case when they are internally generated. Exter-
nally acquired intangible assets thus include know-how assets that accounting principles do not
allow to be capitalised when generated internally.
The above description is aimed at clarifying that the quantitative approach we followed has,
of course, some limitations. First, we must note that accounting principles do not, by definition,
give a full picture of the whole stock of intangible resources at work within the firm, for the
benefit of the firm. There are ‘soft’ resources which do not appear but are nevertheless relevant
to a firm’s success, as in the case of employee commitment and ability, tacit knowledge, reputa-
tion, relationships with stakeholders, and, as mentioned above, there are cases in which intangible
resources may emerge only as a consequence of external acquisition, while they do not emerge at
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10 S. Denicolai et al.
all in companies which do not engage in these transactions. In summary, we adopted an approach
based on consistent financial information that allows quantitative analysis based on comparabil-
ity among firms and a solid framework guaranteed by the international accounting principles
framework, aware that other approaches exist, with their pros and cons.
We used the logarithm of turnover in 2010 as a measure of firm size (SIZE) in order to investi-
gate the moderating effect between intangibles and firm growth. Finally, we also included some
control variables, namely industry dummies and country dummies.
4. Regression analysis and discussion of findings
Table 4shows some preliminary data about descriptive statistics and correlations. The Intangible
Intensity (INT) is little right-skewed since the majority of companies (88% of the sample) have
less than 50% of intangibles in their resources portfolio, but the sample covers almost the full
range between 0% and 100%. The orientation towards externally generated intangibles (EXT)
is very well balanced: the variable range is fully covered, while the mean is close to the middle
(46.77%). The Pearson correlations between the variables are fairly small, thus the likelihood of
a multicollinearity problem in the regression analysis is low.
An Ordinary Least Squares (OLS) regression using STATA 12 has been developed to investi-
gate the above-defined research hypotheses. Table 5outlines the results of this analysis. Model
1 includes only control variables, among which we include ‘firm size’ (SIZE), considered in
Hypotheses 3a and 3b. It is considered as a moderating variable. Industry and country dummy
variables are also included in ‘Model 1’. The explanatory power is low (R2=0.065), thus sug-
gesting that sector, country, and size have a secondary role in explaining firm growth for the
companies in our sample. In particular, none of the country dummies is statistically signifi-
cant; nevertheless, we retain them in all subsequent regressions to capture any country-specific
effects. ‘Health Care’ is the only significant coefficient among the industry dummies. This is not
surprising as it reflects the positive trends in biotech and pharma industries in recent years.
Interestingly, even firm size is non-significant and this result partially contrasts with studies
which assume that size is a proxy for the resource portfolio owned by a company (Dhanaraj
and Beamish 2003). Conversely, the fact that control variables have little merit in explaining
firm performance suggests the need for more discussion about the explanatory variables used in
verifying our research hypotheses.
Model 2 considers the link between Intangible Intensity (INT) and firm performance
(GROW). The result is statistically significant and improves the explanatory power of the model
Table 4. Descriptive statistics and correlation coefficients.
Minimum Maximum Mean Std. dev SIZEaINT EXT
TURNOVER (mile) 17 109,100 3214 11,638
INT 0.00% 96.92% 23.79% 21.934 −0.3398*
EXT 0.00% 100.00% 46.77% 35.922 0.2665*−0.2339*
GROW −28.85% 64.96% 2.93% 0.143 −0.1553*0.2809*0.0219
aSIZE =log (TURNOVER).
*Correlation is significant at the 0.01 level.
Note: Italic values refer to correlations among key variables.
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Impact of intangibles on firm growth 11
Table 5. Regression analysis.
Model 1 Model 2 Model 3 Model 4
C_UK 0.0086 (0.725) −0.0073 (0.761) 0.005 (0.843) 0.0131 (0.599)
C_GE 0.0017 (0.947) −0.01 (0.694) 0.001 (0.969) 0.0153 (0.564)
C_IT −0.0495 (0.169) −0.0554 (0.115) . −0.0538 (0.125) −0.0469 (0.178)
I_IND −0.0267 (0.25) −0.012 (0.598) −0.0061 (0.791) −0.0082 (0.719)
I_TECH −0.0031 (0.901) −0.0098 (0.693) −0.0039 (0.875) −0.0046 (0.853)
I_HC 0.0628 (0.057)* 0.0624 (0.053)* 0.0652 (0.043)** 0.0767 (0.017)**
SIZE −0.0141 (0.101) −0.0074 (0.384) −0.0084 (0.331) 0.0181 (0.159)
INT 0.1663 (0.000)*** 0.1761 (0.000)*** 0.1113 (0.019)**
EXT 0.044 (0.079)* 0.0435 (0.079)*
INT_size −0.0977 (0.006)***
_cons 0.1517 (0.072)* 0.065 (0.443) 0.0361 (0.676) −0.1836 (0.117)
No. of obs 294 294 293 293
Prob >F 0.0068 0.0000 0.0000 0.0000
R20.0653 0.1161 0.1258 0.1488
Adj R20.0425 0.0913 0.0980 0.1186
Root MSE 0.1408 0.1372 0.1369 0.1353
Mean VIF 1.74 1.70 1.70 2.12
*Regression coefficient is significant at the 10% level.
**Regression coefficient is significant at the 5% level.
***Regression coefficient is significant at the 1% level.
(R2=0.116). The coefficient of INT is positive, thus supporting Hypothesis 1: Intangible Inten-
sity is positively associated with performance, meaning that – according to our data – having
a significant portion of intangible assets within the stock of resources is a positive aspect for
firm’s growth. Interestingly, the coefficient of the constant decreases dramatically, thus offering
a further confirmation regarding the reliability of the Intangible Intensity variable.
We then extended our analysis by considering the orientation towards externally generated
intangibles (EXT) in Model 3. The explanatory power is further improved (R2=0.126), the
new coefficient is strongly significant and positive, and the constant coefficient is decreased by
half. These findings support Hypothesis 2 and confirm that a strategy grounded on inbound flows
of intangibles – through acquisitions and/or business combinations – is associated with better
performance in terms of growth. Interestingly, the coefficient INT grows slightly in Model 3,
thus supporting the mutual, positive interplay between intangible intensity and the orientation
towards externally generated intangibles in affecting firm performance. In both cases, firm size –
verified in terms of its direct impact – emerges as a non-significant element across the regression
models.
One may think that large organisations are in a better position to gain from externally gen-
erated intangibles due to the availability of financial resources, while this option may not be
possible for many SMEs (Bayona-Saez, Cruz-Cazares, and Garcia-Marco 2013; Chudnovsky,
Lopez, and Pupato 2006). We thus investigated, in Model ‘4’, whether firm size (included in the
regression as ‘INT_size’7) plays an indirect role, moderating the effect of intangible intensity
(both internal and external). Interestingly, the coefficient is significant, negative and supports
the further improvement of the model (R2=0.149). Thus, Hypothesis 3a is also supported:
the intangible boost works differently in SMEs and large-sized companies. Our findings sug-
gest the positive effect of intangibles is amplified in medium and, especially, small firms, while
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12 S. Denicolai et al.
Table 6. Average firm growth: INT and firm size.
SIZE (turnover)
<28.7 mil
(<q1)
28.7 mil −
941.3 mil
(q1 −q3) >941.3 mil
(>q3) Total
Intangible
intensity HIGH (over
mean) 9.51% 6.25% 1.75% 6.92%
LOW (below
mean) 6.13% −1.11% 1.84% 0.75%
Total 8.28% 1.16% 1.82% 2.93% (whole sample)
this impact decreases as firm size increases. After all, even the internal R&D activity is very
expensive, and the acquisition of external intangibles is a relatively cheaper and less risky
option. Our findings are consistent with the dominant stream of literature in the field (Ches-
brough 2003; D’angelo et al. 2013; Fernhaber, Mcdougall-Covin, and Shepherd 2009; Tsai and
Wang 2008). The sample consisting of listed firms suggests that almost all these companies
are in a position to pursue some technology acquisitions. The regression analysis concerning
Hypothesis 3b does not show a reliable outcome: the coefficient (EXT_size) is statistically
non-significant (0.992), while the improvement of the model is almost zero. For this reason,
we do not report these results in Table 4. Hypothesis 3b is rejected: firm’s size does not alter
the positive impact of externally generated intangibles on firm performance, which tends to
remain stable in both SMEs and large-sized firms. Our explanation is twofold. First, both cat-
egories of firms are in a position to benefit from the acquisition of external intangibles: while
a large firm can leverage superior capabilities and more financial resources (Granstrand et al.
1992), SMEs can be more focused, flexible and speedy in selecting external intangibles and
putting them to work (Chesbrough and Crowther 2006; Cho and Yu 2000; Narula 2004). Sec-
ond, acquiring external intangibles is an explicit investment decision, implying rationality in
valuation: the firm, whatever its size, is expected to accurately analyse the value of this invest-
ment to efficiently allocate financial resource (Pablo, Sitkin, and Jemison 1996). The coefficients
of all explanatory variables remain stable across models, thus reinforcing the reliability of our
findings.
In Table 6, we provide further descriptive statistics. The cells show the average firm growth in
the period 2008–2010 by making a split between low and high degrees of Intangible Intensity, as
well as among small, medium, and large-sized companies. Due to the nature of the sample (listed
companies), we use the first and third quartile of turnover distribution as thresholds for firm size,
instead of the mainstream definitions of SMEs. All combinations among these two variables are
reported in the Table. The data confirm the above findings.
Finally, Table 7shows three robustness checks (Columns 2–4), compared to the main set
of findings (Column 1). Basically, we repeated the regression analysis using both adjusted and
different dependent variables. First, in Column 2 we managed the outliers through the winsoris-
ing procedure (Hawkins 1993).8Second, in Column 3 we transformed the dependent variable
GROW into a dummy variable9and ran a Logit regression. Finally, in Column 4 we introduced a
different performance indicator, namely the growth of non-current assets (Asset Growth).10 The
results shown in Table 7overall confirm the reliability of our findings, especially with regards to
the positive role of Intangible Intensity and Externally Generated Intangibles.
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Impact of intangibles on firm growth 13
Table 7. Robustness checks.
Dependent
variable 1. Turnover growth
(main indicator)
2. Turnover growth
(outliers set to
extremes) 3. Turnover growth
as dummy variable 4. Asset growth
Type of
regression OLS OLS logit OLS
C_UK 0.0131 (0.599) 0.009 (0.021) 0.182 (0.387) −0.012 (0.661)
C_GE 0.0153 (0.564) 0.012 (0.022) −0.082 (0.412) 0 (1)
C_IT −0.0469 (0.178) −0.046 (0.029) −1.027 (0.602)* −0.06 (0.131)
I_IND −0.0082 (0.719) 0.006 (0.735) 0.199 (0.584) 0.017 (0.507)
I_TECH −0.0046 (0.853) 0.017 (0.423) 0.234 (0.551) 0.024 (0.376)
I_HC 0.0767 (0.017)** 0.076 (0.006)*** 0.987 (0.054)* 0.023 (0.509)
SIZE 0.0181 (0.159) 0.015 (0.149) 0 (0.997) 0.024 (0.064)*
INT 0.1113 (0.019)** 0.115 (0.005)*** 2.76 (0.000)*** 0.117 (0.03)**
EXT 0.0435 (0.079)* 0.045 (0.034)** 0.847 (0.031)** 0.047 (0.087)*
INT_size −0.0977 (0.006)*** −0.05 (0.098)* 0.258 (0.655) −0.062 (0.093)*
_cons −0.1836 (0.117) −0.181 (0.071)* −1.53 (0.411) −0.223 (0.063)*
No. of obs 293 293 293 293
Prob >F 0.0000 0.0000 0.0007 0.0205
R20.1488 0.1272 0.0711
Pseudo R20.0765
5. Conclusions
The aim of this study is to measure the impact of intangibles – distinguishing between internally
and externally generated ones – on firm performance, the latter measured in terms of growth. The
distinction between the two mentioned categories of intangible resources is relevant from a theo-
retical point of view as it gives an idea of the different alternatives available in the accumulation
of crucial assets. Moreover, the assessment of different types of intangibles and the introduction
into the research model of a moderating variable – firm size – serve the purpose of making a step
forward in the field, considering the controversial findings noticed in extant literature, especially
in terms of quantitative evidence. As a consequence, we have been compelled to build a propri-
etary database, given that in those currently used for empirical analysis, such information is not
available.
From a theoretical point of view, our contribution may prove interesting as the methodol-
ogy we used creates a link between managerial studies, focused on the strategic relevance of
intangibles in building competitive advantage, and accounting studies, mainly focused on how
intangibles must be identified, measured, and reported in financial statements. To this end, we
used the IAS/IFRS definition of intangibles – that is, the one used by European listed companies
to report their results – aware that this approach has clear advantages and some limitations. On
the one side, we have the advantage of relying on objective financial information, a fundamental
basis for comparison and quantitative measurement. It makes it possible to address a notable
research gap since only a limited number of studies about intangibles have been supported by
quantitative surveys and reliable data. On the other, we know that only some categories of intan-
gibles may be recorded in a firm’s account, while others are not coherent with the principles of
financial reporting.
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14 S. Denicolai et al.
Our findings confirm the theoretical cornerstones of the RBV and introduce novel evidence.
First, our results support the idea that intangibles are crucial in fostering firm performance. Sec-
ond, we find that externally generated intangibles additionally boost performance. Third, our
findings show that firm size does not influence per se performance. However, the impact of intan-
gibles on firm growth varies depending on firm size, slowly decreasing as the company grows.
Even if intangibles remain crucial for large organisations, it is sensible to assume that they may
radically change the future of a small firm, while big companies are relatively more dependent
on a higher number of strategic factors, both internal and external.
Managerial implications are relevant. Findings suggest that ‘type of assets and composition
of portfolio’ are more important that ‘having a large stock of resources’, even though different
organisational structures (i.e. small vs. big companies) require different configurations of the
resource portfolio (i.e. intangible intensity). In short, no matter how large the asset portfolio
is, you must have the strategic resources. The sample period of this study coincides with the
global economic and financial crisis; we think that our outcomes are interesting since they further
support the positive role of such investments, which boost performance even in hard times.
The cornerstones of this study may have limitations. First the focus on listed companies –
which are mainly big firms – limits the validity of our findings to this particular kind of organiza-
tion, whilst a similar survey on unlisted SMEs would be desirable. Second, this investigation is
limited to intangible assets which are reported in company financial reports (patents, trademarks
and so on). This approach, by construction, excludes other categories of intangible resources
which may be relevant for firm performance. Third, longitudinal analyses are needed to confirm
the causal relations among the variables. Nevertheless, we think that working on objective data
makes it possible to take a step forward in the strategic management field, where the analysis of
intangibles is often pursued by using questionable metrics.
Funding
Stefano Denicolai gratefully acknowledges financial support from Cariplo Foundation International Recruitment Call:
The internationalisation of Italian firms: the role of intangibles, managerial resources, and corporate governance.
Notes
1. According to the ‘Financial Accounting Standards Board Accounting Standard Codification 350 (ASC 350)’, an
intangible is an asset, other than a financial asset, that lacks physical substance. The ‘International Accounting
Standard 38’ defines an intangible asset as an identifiable non-monetary asset without physical substance.
2. London Stock Exchange plus Alternative Investment Market.
3. Xetra.
4. Euronext Paris.
5. Mercato Telematico Azionario – Italian stock exchange.
6. Common examples of items encompassed by these broad headings are computer software, patents, copyrights,
motion picture films, customer lists, mortgage servicing rights, fishing licences, import quotas, franchises, customer
or supplier relationships, customer loyalty, market share, and marketing rights (IAS 38, §9).
7. INT_size =INT*log(turnover).
8. According to the winsorising procedure, we calculated the upper and lower limits by adding and subtracting the
standard deviation (0.143) to the GROW mean (0.0293). The upper limit is 0.169, while the lower one is −0.111.
We maintained the observations within these thresholds – 221 out 293 observations, meaning the 75.4% of the whole
sample – while we replaced with limit values the ones which exceed them, in order to manage the outliers and, at
the same time, keep the total number of observations.
9. We used the mean as a threshold between worse performers (set to 0) and best performers (set to 1).
10. ‘Asset growth’ has been operationalised as the log-difference between asset stocks in years 2010 and 2008. The
correlation between turnover growth and asset growth is positive, but relatively low (0.2928).
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Impact of intangibles on firm growth 15
Notes on contributors
Stefano Denicolai is Assistant Professor of ‘Innovation Management’ at the University of Pavia (Italy), where he is also
Head of Double Degree Programs and International Activities. His research interests include the strategic management
of intangibles, the economic optimisation of open innovation dynamics, and the international entrepreneurship. He is
author of books and papers in the field, including in journals such as International Business Review,Journal of World
Business,andTourism Management.
Enrico Cotta Ramusino is a Full Professor of Economics and Business Management at the Economics Faculty at the
University of Pavia. He previously taught at the Universities of Perugia and Insubria (Varese). He is Vice President of
the Consortium of Postgraduate Studies in Corporate Economics, in Pavia. He is also a member of the board of directors
of the Maria Corti Foundation. He is the administrator of various Banks and Finance companies and has also written
numerous scientific publications on Strategy, Finance, and Enterprise Valuation.
Francesco Sotti, PhD, is Assistant Professor in Accounting at the University of Pavia. He is also Chartered accountant
and Statutory auditor. He is an author of books and paper in the accounting field and concerning the measurement of
intangible assets.
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