Intellectual Capital: design science research supporting a novel IC
framework
Intellectual Capital: design science rese=
arch
supporting a novel IC framework
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ABSTRACT
Intellectual Capital provide=
s an
alternative to conventional accounting, tackling the hurdle of dealing with
intangible assets. Over the past few decades, various measurement methods h=
ave
been developed. However, these methods are primarily tailored to specific t=
ypes
of companies and are often unsuitable for micro and small businesses. Based=
on
this context, the objective of this study is to propose a novel framework to
assist readers, academics, and managers identifying the most suitable method
for measuring Intellectual Capital by articulating the following traits:
purpose, economic sector, and business size. This study adopts an
exploratory-descriptive approach with a qualitative methodology, employing =
a Systematic
Literature Review, Content Analysis, and Design Science to achieve its
objectives. To determine the goals and methods of measuring Intellectual
Capital, this study employed content analysis in the mixed category, with
descriptors defined and adjusted as the research progressed. As a result, s=
even
purposes, fifty-eight methods for measuring Intellectual Capital, four econ=
omic
sectors, and five business sizes were identified, categorized, and incorpor=
ated
into the proposed framework. Thus, the novel framework proposed by this stu=
dy
is primarily intended to guide stakeholders through the various possibiliti=
es
for measuring and disseminating Intellectual Capital across corporations,
cities, and even nations. As a final recommendation for future research,
applying the framework to real-world situations in both the public and priv=
ate
sectors is encouraged.
RESUMO
O Capi= tal Intelectual proporciona uma alternativa à contabilidade tradicional ao lidar com ativos intangíveis. Nos últimos anos, foram desenvolvidos diversos méto= dos de mensuração, porém, a maioria é adequada apenas para tipos específicos de empresas, sendo geralmente inadequados para micro e pequenas empresas. Com = base no exposto, este estudo tem como objetivo propor um novo modelo que ajude leitores, acadêmicos e gestores a encontrar o método mais apropriado para m= edir o Capital Intelectual, considerando os seguintes aspectos: propósito, setor econômico e porte empresarial. Este estudo é caracterizado por sua natureza exploratória-descritiva, abordagem qualitativa e pela utilização da Revisão Sistemática da Literatura, Análise de Conteúdo e Design Science para alcançar o objetivo proposto. A análise de conteúdo na categoria mista foi empregada para determinar os objetivos e métodos de mensuração do Capital Intelectual, com os descritores sendo definidos e ajustados ao longo da pesquisa. Foram identificados e categorizados sete propósitos, cinquenta e = oito métodos de mensuração do Capital Intelectual, quatro setores econômicos e c= inco portes empresariais, que foram articulados no framework proposto por este estudo. Dessa forma, o novo modelo visa orientar as partes interessadas sobre as diversas possibilidades de mensuração e disseminação do Capital Intelectual em empresas, cidades e até mesmo países. Como recomendação para futuras pesquisas, sugere-se a aplicação do framework em situações r= eais nos setores público e privado.
Palavras-chave: capital intelectu=
al. propósito.
mensuração. porte empresarial. setor econômico.
Recebido em 04/07/2024. Aprovado em 13/08/2= 024. Avaliado pelo sistema double blind = peer review. Publicado conforme normas da APA.
https://doi.org/10.22279/navus.v14.196=
3
1 I=
NTRODUCTION
Intellectual Capital (IC) emerges as an
approach oriented toward intangible assets, such as knowledge, patents,
trademarks, customers, and distribution channels. It represents an alternat=
ive
to traditional accounting, which historically focuses on tangible assets su=
ch
as machinery and physical facilities (Stewart, 1997; Edvinsson & Malone,
1997; Roos et al., 1997; Bontis, 1998; Guthrie, 2001; Cikrikci & Dastan,
2002; Bozzolan et al., 2003; Ricceri, 2008; Curado, 2008; Denicolai et al.,
2015; Morris, 2015).
In line with the above, the relevance of =
the
field of Intellectual Capital (IC) is reinforced by the prominence of the
knowledge economy, in which companies increasingly exhibit a growing volume=
of
intangible assets relative to tangible ones. Over the years, various
measurement methods have been developed in response to the pervasiveness and
growing importance of intangible assets. However, not all methods are suita=
ble
for companies of different sizes. For instance, the Skandia Navigator™
(Edvinsson & Malone, 1997) appears impractical when applied to micro,
small, and even medium-sized enterprises, as its model comprises 164 metric=
s,
with 91 focused on intellectual aspects and 73 on traditional ones.
Nonetheless, it is important to note that many organizations still lack
standardized administrative procedures to effectively address accounting ne=
eds,
particularly in relation to IC.
Furthermore, according to Marr et al. (20=
03), an
organization must first determine its purpose regarding IC before selecting=
a
specific method or report. For instance, a public report may be more
appropriate if the organization intends to showcase its IC to stakeholders.=
In
contrast, dynamic measurement methods might be better if the goal is to dri=
ve
sales, purchases, or mergers.
Besides the orientation between the
organization's purposes and measurement methods, some approaches may still =
be
unattainable due to other features, such as the economic sector in which the
company operates or its size, as exemplified earlier. Thus, a multidimensio=
nal
relationship delineates the entanglement of method, purpose, economic secto=
r,
and business size. Based on these constructs, this research seeks to deline=
ate
a framework that allows organizations to decide the most relevant method for
their IC accounting needs.
As Eccles et al. (2002, p. 127) aptly sta=
te,
"What is easy to measure is not important, and what is important is not
easily measured." This statement underscores the challenge of measurin=
g IC
and the crucial task of selecting the proper method. Accordingly, this rese=
arch
aims to provide a novel framework for determining the most suitable method =
and
equip readers, managers, and academics with practical knowledge and a tool =
that
can be applied in real-world scenarios considering variables like purpose,
sector, and size of organizations.
Based on the preceding, this research is
structured as follows: Section 1 introduces the study, followed by Section =
2,
which outlines the theoretical foundations that support it. Section 3 detai=
ls
the methodological procedures adopted, while Section 4 presents the analysi=
s of
the results obtained from data collection. Finally, Section 5 provides the
conclusion, summarizing the key findings, offering suggestions for future
research, and discussing the limitations encountered during the study.
2 THEORETICAL BACKGROUND
Capital, in the corporate context, refers=
to
any asset that has the ability to generate future cash flows. As a result, =
tangible
assets are an integral part of the common categories of assets, which inclu=
de
physical and financial items. Companies periodically disclose the value of
these assets, which are readily accessible in their balance sheet and finan=
cial
records (Sherif & Elsayed, 2016). From another perspective, intangible
assets such as information, knowledge, workforce skills, and organizational=
structure
are the cornerstone of the knowledge economy and are increasingly important=
in
determining corporate value and profits (Werlang et al., 2019; Smith et al.,
2020; Santos & Silva, 2020).
Thus, the existence of intangible assets,=
such
as IC, has been recognized for years within organizations. Still, these ass=
ets
were not considered significant in the past, as the resources deemed releva=
nt
for profit generation were primarily tangible. However, over the last three
decades, global economies have witnessed a significant shift in focus. The
knowledge economy has driven this change from traditional tangible resource=
s to
technology-intensive sectors (Guthrie, 2001; Gamerschlag & Möller, 2009=
). Several
recent studies have emphasized the importance of raising awareness among
individuals and organizations regarding the significance of IC in value
creation (Marr and Chatzkel, 2004; Petty and Guthrie, 2000; Tan et al., 200=
5).
In reality, the Knowledge Economy has gra=
dually
shifted intellectual resources to the center of the debate, revealing the
limitations of the traditional ones (i.e., physical and financial resources=
) as
essential contributors to corporate wealth development (Chen et al., 2005).
Currently, IC is considered a critical factor that influences performance,
competitiveness, success, value creation, and the long-term survival of
organizations. Although numerous studies over the past decades have emphasi=
zed
the importance of IC and proposed approaches to address it, measuring IC
remains a significant challenge for many organizations and their managers (=
Kogut
& Zander, 1992; Bierly & Chakrabarti, 1996; Brennan & Connell,
2000; Bontis & Fitz-enz, 2002; Cronje & Moolman, 2013; Bontis et al=
.,
2015; Xu & Wang, 2018).
The literature on intangible assets featu=
res
significant contributions from scholars across various fields. Remarkably, =
Karl
Erik Sveiby has emerged as a prominent figure in this research area, provid=
ing
relevant contributions to IC research. According to Sveiby (2010), this fie=
ld
of study has produced a plethora of methods and theories over the years.
However, it has also brought with it the dilemma of measuring social phenom=
ena
with scientific precision. As a result, the degree of uncertainty inherent =
in
measuring and determining the lifecycle of intangible assets makes it diffi=
cult
to define a single consistent proposal that serves to accomplish such inten=
t.
Moreover, empirical studies have sought to
identify not only methods but also the underlying purposes for measuring IC=
. Findings
indicate a lack of a clear definition of the goals for measuring this
intangible asset, leading to a limited understanding of organizational
structures. Accordingly, decision-making around IC management is often
characterized by confusion and uncertainty among administrators. This sugge=
sts
that, despite numerous models and tools for IC management available in the
literature, identifying their purpose and operationalizing them in practice
remains uneasy (Carlucci & Kujansivu, 2014). To improve the understandi=
ng
of IC measuring, Marr et al. (2003) regarded purpose as a crucial criterion=
for
evaluating IC, thereby establishing foundational elements for its determina=
tion
(Table 1).
Table 1
Purposes for Measuring Intellectual Capita=
l.
|
Purpose |
Author(s) |
Description |
|
Di Vaio et al., 2020; Salvi et al., 2020ª ; Marr et al., 2003 |
Corporate strategy is determined by analyzing the decision-making
process, which provides a clear understanding of the organization's
objectives and purposes. It defines what the firm intends to give to its
shareholders, employees, consumers, and other stakeholders. Recent studie=
s on
the measurement of IC have shown that gaining a clear grasp of business
strategy helps to identify and manage risks more effectively, leading to
improved allocation of corporate resources. |
|
|
Alfiero et al., 2021; Marr et al., 2003; Kaplan and Norton,1996 |
Successful strategy implementation requires continuous evaluation =
and
the integration of learning into its cycle. Additionally, investors close=
ly
relate the process of operationalizing strategy to the quality and adequa=
cy
of information about intellectual assets. |
|
|
Assisting in diversification and expansion decisions |
Vitolla et al., 2020 ; Marr et al., 2003 |
Strategic partnerships, joint ventures, mergers, and acquisitions =
are
practical ways through which organizations achieve inorganic growth. In t=
his
context, ensuring that non-financial information is consistent, relevant,
reliable, and comparable over time and across companies is mandatory. |
|
Support compensations |
Kelchevskaya et al., 2021; Marr et al., 2003 |
Creativity, personnel training, expertise, research and developmen=
t,
and customer satisfaction are quickly becoming inputs for corporate value
creation. These factors support financial compensations for talents and
personal skills and sustain financial returns for investors in business v=
alue
generation. |
|
Recognizing stakeholders' significant influence over company resou=
rce
management fosters better interaction with them, decreasing information
asymmetries and positively impacting corporate reputation. |
Also relevant to IC accounting is the fac=
t that
the modern economy comprises a complex intertwining of economic activities.=
It
consists of the relationship in the production of all goods and services
intended for society's needs (Tomiato et al., 2010). However, due to its
magnitude and inherent complexity, individually accounting for each operati=
on
becomes unfeasible. Therefore, to achieve accounting objectives, the economy
must be divided into interconnected sectors based on the similarities and c=
ore
nature of activities (Tomiato et al., 2010).
Following the approach of other authors, =
this
research classifies economic sectors as the product trading sector (industr=
y),
where there is the sale of self-produced products; the merchandise trading
sector, which refers to the sale of products acquired from third parties; t=
he
service trading sector, which trades services performed by contract or task
(Rodrik, 2016; Silva et al., 2016; De Almeida et al., 2013; Oreiro & Fe=
ijó,
2010; Kupfer, 2009); and the public sector (Pereira, 1989), whose primary
function is to protect public assets.
In accordance with the above, selecting t=
he
purpose and measurement method for IC appears to first require an understan=
ding
of the economic sector and the company's size. Still, regarding the company=
's
size, in contrast to other countries where the number of employees and
operational revenue primarily moderate its definition (Meyer et al., 2020; =
Kweh
et al., 2021), understanding and defining a company's size remains somewhat
confusing, at least in Brazil.
=
In the
literature, there is a diversity of criteria adopted to classify companies,
including number of employees, revenue, sector of activity, profit, net wor=
th,
and fixed assets. Depending on the purpose, other criteria or even multiple
criteria may be applied (Martins et al., 2016). It is noted that quantitati=
ve
criteria tend to be more commonly used due to their ease of definition,
collection, manipulation, measurement, and parameter definition (Leone &
Leone, 2012). In Brazil, there is a lack of standardization in classificati=
ons
regarding company size, as the Federal Government, companies, agencies,
institutes, scholars, and funding agencies use different classification mod=
els
to meet their research objectives.
The Brazilian Micro and Small Business Su=
pport
Service (SEBRAE, 2010) adopts the number of workers and economic activity
sector as criteria for classifying the size of firms. The Brazilian Develop=
ment
Bank (BNDES, 2013) defines the company's annual revenue and economic segmen=
t to
which the company belongs as a criterion. Additionally, the Brazilian Insti=
tute
of Geography and Statistics (IBGE) and the Ministry of Labor and Employment
(MTE) also use the data of employed people and the sector of activity as cr=
iteria
to characterize the size of the company (Dias, 2012). The company's gross
annual revenue establishes its size for the National Health Surveillance Ag=
ency
(ANVISA) (Secretaria da Receita Federal, 2005). Finally, for the Ministry of
Development, Industry, and Foreign Trade (MDIC), the number of employees,
exported value, and sector of activity in the defined period determine the
company's size.
In 1996, Nick Bontis, when discussing the
importance of selecting methods to measure IC, pointed highlighted the chal=
lenges
posed by the vast array of options, diverse benefits, and multiple purposes=
in
finding the ideal method for measuring intangible assets. Supporting this
perspective, Tóth and Kövesi (2008) stated the following:
In reality, there is no method that can be applied broadly and
universally, but there are a series of methods and tools that are effective=
in
specific situations and for specific types of corporations. Furthermore, mo=
st
experts disagree with the identification of a single common denominator (p.=
3).
Recognizing that not only the purpose, bu=
t also
the company size and economic sector, influence the choice of method for
measuring IC (Užienė & Stankutė, 2015), this research has made
every effort to understand the current dynamics and propose a framework that
articulates these variables (purpose, economic sector, company size, and
methods), either simultaneously or individually.
The intent to develop a novel IC framewor=
k is
based on the perceived shortcomings of academic theory in establishing a sh=
ared
language for conceptualizing IC, the lack of clarity among companies in
defining objectives for measuring this asset, and, most importantly, the
absence of integration between monetary and non-monetary models for measuri=
ng
IC. The formation of this understanding has been the subject of scholarly
debate for several decades (Sveiby, 2010; Guthrie et al., 2012; Smith et al=
.,
2020).
=
3 METHODS
In this work,
research is comprehended as a methodical investigation, primarily aimed at
generating or refining ideas and, occasionally, solving problems (Gough et =
al.,
2012). Accordingly, this study employs the Design Science (DS) methodology
using the Design Science Research (DSR) framework to effectively align with
these objectives. To apply DS principles in practice and guarantee the
execution of thorough investigations that incorporate these concepts, it is
essential first to evaluate a suitable research methodology for this
implementation (Hevner et al., 2004; Manson, 2006).
Research adopt=
ing DS
is not limited to exploring, describing, or explaining problems, but also w=
ith
unfolding frameworks that contribute to better human performance, whether in
society or organizations. In this sense, prescribing the solution or design=
ing
a system generates knowledge with relevance and rigor (Dresch et al., 2015;
Hevner et al., 2004). Therefore, due to the limitations of traditional
scientific methods in constructing software, frameworks, and technological
systems, the approach used in this study follows the precepts of DSR (Figure
1). This methodology aims to structure the development of artifacts as a me=
ans
to produce epistemological scientific knowledge.
Figure 1
Design Science Research framework consider=
ed
in this work.
Note: Adapted from Hevner et al., 2004, p.80.
The success of=
the
DSR approach to crafting meaningful artifacts (e.g., IC frameworks) hinges
firstly on the researchers' meticulous understanding of the environment and=
the
selection of relevant problems or opportunities (the relevance cycle).
Simultaneously, the method rigor (the rigor cycle) is ensured through the
efficient use of theoretical foundations of knowledge and research. The
artifact’s engineering (the design cycle), aligned with the other cycles, is
responsible for building and validating the solution, as well as moderating=
the
relationship between the other cycles, ensuring that the process is repeate=
d as
many times as required (Hevner et al., 2004).
This research =
is
exploratory, motivated by the indispensability of an initial understanding =
of
the problem under study. At the same time, its descriptive characteristic s=
eeks
to deepen the detailed presentation of the investigated phenomenon (Perovan=
o,
2016). A qualitative methodological approach is adopted to identify causal
relationships, predictions, and generalizations of results (Hoepfl, 1997). =
It
also allows an interpretive exploration of the subject of interest, promoti=
ng a
more in-depth analysis (Mascarenhas, 2012).
For the definition of population and sa=
mple,
the former is determined by the extent of research related to IC, while the
latter consists of studies aligned with the objectives of this research,
encompassing articles that address the purposes for measuring IC, covering =
the
diversity of economic sectors in question. The chosen data collection metho=
d,
the Systematic Literature Review (SLR), is a comprehensive tool that identi=
fies
the need for review, evaluates study quality, and presents findings. This c=
hoice
is based on the SLR's suitability for a comprehensive evaluation and
interpretation of all relevant and available research related to a specific
research question, topic area, or phenomenon of interest (Kitchenham, 2004)=
.
As a result, data collection stems from =
an SLR
planned to be conducted on the Scopus and Web of Science (WoS) databases. T=
he
selection of these databases is aligned with Falagas et al. (2008), who sta=
te
that the SCOPUS database covers research from 1966 onwards and indexes 12,8=
50
journals, and Guz and Rushchitsky (2009), who indicate that the WoS database
comprises about 10,000 journals and consists of seven distinct citation
databases. These are considered relevant compared to other databases, as
reported by the same authors.
For data analysis, Bardin's Content Anal=
ysis
method (1977) was used, which involves the "[...] analysis of
communications aiming to obtain, by systematic and objective procedures of
messages content description, indicators (quantitative or not) that allow t=
he
inference of knowledge" (Bardin, 1977, p. 42). Applying this method
requires defining categories, which relies on the investigation of content
segments from the original text for subsequent ordering, categorization, and
frequency counting. This study will employ a mixed category, recognizing th=
at
the current understanding based on existing evidence may face adjustments
throughout the research's evolutionary process.
4 RESULTS AND DISCUSSIONS
Firstly, it is=
worth
noting that this research primarily focuses on the impact of intangible ass=
ets
on business value, as well as the inherent difficulties in capturing,
quantifying, and disclosing the performance and value of IC in companies.
Observations suggest that evaluating intangible assets is a complicated tas=
k,
mainly due to constraints in data availability, uncertainties, and the abse=
nce
of impartiality and verifiability of information (Bandeira & Andrade,
2018). Consequently, professionals and scholars have raised doubts about the
accuracy and effectiveness of the measurement frameworks used in recent yea=
rs
due to the limits observed in many existing approaches.
However, given=
that
accounting IC is crucial for the growth of businesses and scientific
advancements, its complexity should not deter firms and scientists from
studying it. Hence, this study endeavors to elucidate the objectives behind=
the
measurement of IC by micro, small, medium, and big firms across different
economic sectors. Here, it is believed that by identifying the purpose of I=
C,
one can obtain a more effective experience in selecting measurement methods
that are more suitable for different economic sectors and organizational si=
zes.
When discussin=
g the
importance of measuring IC, Marr (2008, p. 4) stated: "To positively
impact their future value, organizations need a better understanding of
Intellectual Capital and its latest tools available to identify, measure, a=
nd
report this important driver of corporate value." Corroborating this v=
iew,
Sveiby (2010, p. 1), one of the leading researchers on IC theory, was
categorical: "Rarely is the question: why measure intangibles? asked. =
The
answer is not self-explanatory. Intangibles are difficult and expensive to
measure, and the results are uncertain, so the reason better be good."
Therefore, in response to Bernard Marr and Karl-Erik Sveiby's call, this
research aims to support the construction of a framework that articulates t=
he
variables of purpose, economic sector, business size, and measuring methods=
in
order to better guide readers, academics, and managers through the still
challenging accounting of IC.
The research
protocols, as detailed in Table 2, were meticulously implemented to investi=
gate
the purposes and methods of IC measurement. In order to ascertain the purpo=
ses
of measuring IC, a comprehensive collection of 1,231 scientific studies,
spanning the years 1998 to 2022, was gathered. Similarly, a thorough
investigation of the methods used for measurement was conducted, resulting =
in
the identification of 677 scientific studies published between 1995 and 202=
2. Together,
these studies amount to a total of 1,889 scientific articles. Both systemat=
ic
literature reviews (SLRs) included an extraction stage, during which
publications were reviewed in their entirety and followed specific criteria=
to
ensure the quality of data extraction, thereby providing a thorough apprais=
al
of the available literature on the topics of interest.
Table 2
Result of protocol application.
|
Stage |
Procedure |
Quantity Purposes |
Quantity. Methods |
||||||||
|
Criterion 1 - Not containing descriptors in keywords |
Criterion 2 - Not discussing purposes of measuring IC |
Criterion 3 - Not being a scientific article |
Criterion 4 - Duplicates |
Criterion 5 - Unavailable for download |
Partial Result |
Extraction (quality assessment) |
Extraction of objective results from studies: Introduction,
theoretical framework, methodological procedures, analysis and discussion=
of
results, and conclusion. |
Final Result |
Guidelines (Hevner et al., 2004) |
Description of
the guideline (Hevner et al=
.,
2004; Dresch et al., 2015) |
Approach in t=
his
research (The Author)<= o:p> |
|
Research using DSR should produce a viable artifact in the form of=
a
construct, model, method, or instantiation. |
The artifact will be the framework aimed at articulating the varia=
bles
economic sector, company size, purposes for measuring IC, and methods of
measuring IC. |
||||||||||
|
The objective of DSR is to develop solutions that solve important
(relevant) problems for organizations. |
The IC theory lacks models to guide users in choosing IC measureme=
nt
methods. Therefore, the proposed solution aims not only to aggregate meth=
ods
for measuring IC but also to articulate variables (methods, purpose, comp=
any
size, and economic sector) that influence the choice of the best option f=
or
organizations. |
||||||||||
|
Methods of evaluation should be employed to demonstrate the utilit=
y,
quality, and effectiveness of the artifact. According to Hevner et al.
(2004), one of these five types of evaluation methods can be used:
analytical, experimental, test, descriptive, and observational. |
In this research, the descriptive evaluation method was chosen, wh=
ich
can be articulated in two ways: · &nb=
sp;
Informed
argument: Using information from knowledge bases (e.g., relevant research=
) to
construct a compelling argument about the utility of the artifact. · &nb=
sp;
Scenarios:
Building detailed scenarios around the artifact to demonstrate its utilit=
y. |
||||||||||
|
The design principles should be clear and verifiable, either by ad=
ding
to the current knowledge base or by applying knowledge in new ways to
existing ones. Research conducted through DSR should provide contribution=
s in
the specific areas of the developed artifacts. |
The present research adds knowledge in several aspects: · &nb=
sp;
Updating
the list of IC measurement methods proposed by Sveiby (2010); · &nb=
sp;
Including
two new purposes for IC measurement, added to Marr et al.'s 2003 list; · &nb=
sp;
Classifying
the economic sector and company size, essential for the development of the
artifact; · &nb=
sp;
Generalizing
the solution to the class of problems; · &nb=
sp;
Introducing
new knowledge that can be applied in similar situations; · &nb=
sp;
Immersing
the researcher in the construction of the artifact evaluation method; · &nb= sp; Designing the original construction of the artifact in spreadsheets, among others.<= o:p> |
||||||||||
|
Rigorous methods must be applied in research utilizing DSR, both in
the construction and evaluation of artifacts. |
In constructing the framework for measuring IC, the rigor of the
protocol (Kitchenham, 2004) applied in Systematic Literature Reviews (SLR=
s)
was used, resulting in the consolidation of the articulated variables
(methods, purpose, company size, and economic sector). |
||||||||||
|
6- Project as a research process |
One should seek to design an effective artifact that utilizes
available means to achieve the desired results, while respecting the rule=
s of
the problem environment. |
This article aims to deliver a framework that addresses the challe=
nge
faced by readers, academics, and managers in the search for IC measurement
methods that best suit each organizational reality. |
|||||||||
|
Research using DSR should be presented to audiences in both the
technology and management fields. |
Given that this article is based on a doctoral thesis, the simulations conducted by the researcher and the subsequent
evaluation by the professors present on the thesis defense committee aime=
d to
validate the artifact. However, there is a recognized need for validation
with managers and expansion within the academic community. |
Note: Adapted from Hevner et al., 2004, p. 83.
Regarding the above, it=
is
observed that the framework developed is both applicable and capable of
generalization; premises of DS that involve the efficient use of theoretical
foundations, knowledge base, and research procedures. However, the success =
of this
research depends concomitantly on the researcher's ability to select releva=
nt
procedures to construct the framework and on the selection of acceptable
methods to justify this proposal. The proposal for developing the framework=
is
based not only on the academic community's failure to define a common langu=
age
for conceptualizing IC and on companies' lack of understanding in determini=
ng
their purposes, but, primarily, on the lack of consolidation of IC measurem=
ent
models.
Thus, this research
presents a novel framework for measuring IC in both public and private
organizations. Developed through a systematic review of 80 scientific artic=
les
focused on IC measurement methods, the framework aids in selecting appropri=
ate
methodologies based on specific needs. So, the proposed framework addresses=
the
problem of selecting suitable IC measurement methods by integrating various
variables (IC measurement purpose, business size, economic sector, IC
measurement category, IC method and IC measurement class) (Figure 2) and
offering a user-friendly solution for readers, managers, and academics alik=
e.
As a
result, this final delivery addresses and rectifies one of the major
deficiencies observed in the IC literature: the lack of a framework that
assists users in choosing IC measurement methods for specific purposes,
economic sectors, and business sizes. This outcome acknowledges that “[...]
there is no method that can be applied broadly and universally, but there a=
re a
series of methods and tools that are effective in specific situations and f=
or
specific types of corporations”` (Tóth & Kövesi, 2008, p. 3). Thus, the
framework is available at the link https://zenodo.org/records/11061996/files/FRAMEWORK%20FOR%20CI%20MEA=
SURING.xlsx?download=3D1. For
optimal use, it is recommended to download the file.
Figure 2
Proposed Framework for IC Measuring.
Note:=
Research
data (2023).
The proposed framework
records a total of 35 studies addressing business size, containing various
methods, purposes, and economic sectors. In proposing an integrated model f=
or
measuring IC in small and medium enterprises, Montequín et al. (2006) clari=
fy
that transitioning to a company that efficiently manages all aspects of
knowledge is not simple, particularly for businesses of this size. In line =
with
this, observing the evolution of studies in small and medium firms, especia=
lly
over the past decade, is noteworthy. This academic growth can be attributed=
to
the development of the knowledge economy and the recognition that these
companies play a crucial role in national economic development by providing
substantial employment, social infrastructure, and an increasing contributi=
on
to Gross Domestic Product (GDP) (Hina et al., 2020; Matos et al., 2020;
Khalique et al., 2018; Montequín et al., 2006).
Regarding medium-large =
and
large companies, most of the research is monetary in nature and directed
towards the industry and service trade. These studies highlight sustainable
growth (Zhang & Wang, 2022; Xu et al., 2020), the generation of additio=
nal
value (Xu et al., 2022; Mohammad & Bujang, 2019; Yao et al., 2019;
Silvestri & Veltri, 2014), and the improvement of financial performance=
of
companies (Obeidat et al., 2021; Yousaf, 2021; Zhu et al., 2020; Phusavat et
al., 2011) as the major differentiators for measuring IC.
An important aspect
identified by this research is the growing use of IC measurement methods in=
the
public sector. Secundo et al. (2017) propose a method focused on public
universities, which presents a strategic approach segmented into maturity
stages. Zeng et al. (2021) analyze the contribution of IC to the economic
growth of cities, associating IC with individuals, families, groups, and
communities. The performance of health organizations within the Italian pub=
lic
healthcare system is the focus of the study by Alfiero et al. (2021). The
proposal by Fazlagic and Szczepankiewicz (2018) introduces the concept of a
"knowledge city" and uses four dimensions of IC (human capital,
structural capital, relational capital, and renewal and development capital=
) to
measure IC in counties.
Furthermore, Marr et al.
(2003) considered purpose a fundamental basis for measuring IC. Thus, when
analyzing the articulation of purposes for IC measurement, 38 studies were
identified as formulating and executing strategy. The strategic aspect is c=
ross-cutting
in research, permeating various sectors, sizes, methods, and categories of =
IC
measurement. However, studies by Wudhikarn and Pongpatcharatorntep (2022),
Garafiev and Garafieva (2021), Mohd Ariff et al. (2016), and Gogan (2014)
consolidate the Balanced Scorecard (BSC) (Kaplan & Norton, 1992) as a
strategic management method for IC. However, the development and improvemen=
t of
methods in the "scorecard methods" category demonstrate that the =
BSC,
despite being consolidated, does not suit all business sizes.
Regarding the aspect
related to influence on behavior, 39 studies are concerned with aiding dive=
rsification
and expansion decisions. At the same time, 33 studies address the basis for
compensations, whether in the form of returns to investors or employees. Am=
ong
the studies focusing on strategic decision-making, notable works include the
article by Wang et al. (2021), which investigates the impact of investment
decisions in information technology on Industry 4.0. Similarly, the study by
Matos et al. (2020) addresses a wide range of variables aimed at assisting
decision-making by strategic managers, while Garcia et al. (2018) analyze t=
he
determining factors for decision-making related to Knowledge Management and
Intellectual Capital.
Furthermore, when
addressing the relationship with stakeholders, Nupap et al.'s (2016) resear=
ch
points out communication as an important pillar for adequately developing t=
he
organizational environment. However, the asymmetry of published information=
and
the lack of standardization and regulation in IC reports make their
correspondence among varying publics a chimera. In an attempt to reduce this
gap, the research by Matos et al. (2020), Heryana et al. (2020), and Bogdan=
et
al. (2017) strive to find consensus in this regard, as evidence shows that =
the
degree of disclosure of annual IC reports directly relates to organizational
performance.
Lastly, the purposes
"innovate" and "measure the wealth of the public sector"
were added by this research to the other purposes found by Marr et al. (200=
3).
However, it is known that they still require acceptance and consolidation by
the academic community. Despite this, their use in specific IC measurement
methods highlights their contemporary importance. Thus, innovation emerges =
not
as a trend but as a necessity for the sustainable development of organizati=
ons.
Furthermore, in the studies by Amran et al. (2021), Zhu et al. (2020), Burt=
on
et al. (2013), and González-Loureiro and Dorrego (2012), innovation is trea=
ted
as a variable in IC measurement, given its relevance.
In regard to the public
sector, Fazlagic and Szczepankiewicz's (2018) research proposes an original
concept for measuring IC in counties and introduces the dimension "ren=
ewal
and development capital" as a measurement variable. Additionally, Neva=
do
Peña et al. (2017) present a model that incorporates knowledge sources in
various domains (human resources, infrastructure efficiency, mobility,
accessibility, business, image, quality of life, tourism, innovation, and
environmental sustainability), enabling the smart and sustainable growth of
cities.
Finally, it is importan=
t to
highlight that this analysis was designed to provide the reader with a
comprehensive overview of the variety and development of IC measurement,
aligning with the primary goal of this research. Nearly all the studies
examined establish a correlation between IC, value creation, competitive
advantages, and wealth generation. These studies cover various business siz=
es
and sectors of the economy, including the public sector. This approach
underscores the strategic significance of IC.
5 CONCLUSION
One of the most importa=
nt
issues observed during the development of this work concerns the fact that
users of IC measurement methods themselves often struggle to understand the
motivation for their application (i.e., what problem they want to solve). T=
he
debate among researchers about the role of intangible assets in fostering
sustainable competitive advantages in organizations is ongoing. An addition=
al
concern in the IC research field is the desire among academics to standardi=
ze
techniques for measuring intangible assets, which could potentially prevent
organizations from revealing their unique competitive advantages.
Furthermore, setting
standards for intangibles is problematic, mainly due to the absence of spec=
ific
laws or recognized criteria for evaluating these assets. Thus, disregarding=
the
dependence of IC measurement on the uniqueness of organizational strategy, =
as
well as the diversity of forms of these intangible assets, seems unreasonab=
le.
Notably, the existing
conceptual challenge in the field of IC research has spurred this study, wh=
ich
aims to identify the methods and purposes of IC measurement. This research =
has
also sought to understand the theoretical foundations that support scientif=
ic
exploration in this context. Moreover, it has sparked interest in examining=
the
relationship between methods and purposes and their application in different
economic sectors and business sizes.
To meet the requirements
established for this research, a total of 1,889 scientific articles were
collected and analyzed. Each document was subjected to rigorous content
analysis following the appropriate systematic literature review (SLR) proto=
col To distinguish the purposes for measuring IC, 73 s=
tudies
were selected for inclusion in the final scope. Meanwhile, 80 documents
remained in the final selection of studies for identifying IC measurement
methods, all of which adhered to the same rigorous protocol.
The full reading of the=
se
articles not only brought the foundation of purposes for measuring IC propo=
sed
by Bernard Marr and collaborators but also added two new purposes to the
previous scope. As a result, 7 purposes (1: aiding in strategy formulation;=
2:
facilitating strategy execution; 3: assisting in diversification and expans=
ion
of decision-making; 4: supporting compensation; 5: guiding communication to
stakeholders; 6: innovating; 7: measuring public sector wealth) began to gu=
ide
the efforts of individuals embarking on an IC measurement initiative.
Concerning the search for IC measurement methods, 58 IC measurement methods
were identified, with 50 representing new
findings that should be added to Sveiby’s 2010 list (Sveby, 2010). Thus, a
scope of 92 IC measurement methods is now available to readers, academics, =
and
managers.
In developing the
framework, identifying a relevant theoretical foundation proved essential. =
This
research employs Design Science (DS) methodology within the Design Science
Research (DSR) process from the outset. However, in the studies analyzed, no
models in the IC literature were found to have been developed using DS and =
DSR.
Nevertheless, the framework’s design, aimed at bridging theory and practice,
successfully captured the reality structure and transformed it into a useful
representation as a meaningful tool, thereby reinforcing the initial
methodological choice.
Throughout this researc=
h,
as conceptual understanding deepened, it became increasingly clear that the
debate surrounding the use of intangible assets remains a major issue for
academics and managers. Even today, the absence of a universal definition f=
or
IC not only reflects the magnitude of the challenges established by this wo=
rk
but also makes IC measurement susceptible to manipulation and direction
according to the interests of researchers and managers. The amount of 58 mo=
dels
and 7 purposes for measuring IC, in addition to the 4 economic sectors and =
the
5 company sizes, underscores the importance of this research within the fie=
ld
of IC.
Considering the breadth=
of
this research, the challenge of integrating numerous variables into a single
framework becomes evident. This framework aims to provide readers, academic=
s,
and managers with a new perspective on available IC measurement methods, as
well as guide them toward relevant studies on the subject. However, the fin=
al
delivery of this research is not complete without users of the framework
understanding its application and recognizing its importance.
Nevertheless, one of the
limitations observed in the development of the framework is related to its
evaluation and validation. The final version of the artifact was informally
reviewed by the author, a few researchers, and potential users. In this sen=
se,
assessing the framework´s use by a more significant number of readers,
academics, and public and private managers could reveal opportunities for
improvement.
Finally, as a suggestion
for future research, it is recommended to apply the framework in real-life
situations in both the public and private sectors. Evaluating the model acr=
oss
various company sizes will also benefit the artifact's development.
Furthermore, maintaining rigor in the use of RSLs in future research is
suggested, facilitating the identification of new purposes and new methods =
for
measuring IC and promoting the updating and maturation of this research fie=
ld.
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Vinícius Figueire= do de Faria; Dárlinton Barbosa Feres Carvalho; Fábio Corrêa; Leandro Cearenço = Lima; Renata de Souza França
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IS=
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2237-4558 • Navus
• Florianópolis •
SC • v. 14 • p. 01-27
• jan./dez. 2024 |
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ISSN 2237-4558
•
Navus •
Florianópolis • SC |
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