Social capital scale and logic fuzzy an experiment to verify the pertinence of logic fuzzy in producing accurate results

INTERNATIONAL JOURNAL OF MANAGEMENT (IJM)
International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 5, Issue 10, October (2014), pp. 91-104 © IAEME
ISSN 0976-6502 (Print)
ISSN 0976-6510 (Online)
Volume 5, Issue 10, October (2014), pp. 91-104
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SOCIAL CAPITAL SCALE AND LOGIC FUZZY: AN
EXPERIMENT TO VERIFY THE PERTINENCE OF LOGIC
FUZZY IN PRODUCING ACCURATE RESULTS FROM
DATA OF A COMPLEX ORGANIZATIONAL REALITY
ANTONIO MARTINEZ FANDIÑO, DSc
Departamento de Ciências Administrativas e Contábeis (DCAC), Br 465, km 7, Seropédica/Rio de
Janeiro, UFRRJ, Brazil
MARIA AUGUSTA SOARES MACHADO, DSc
Departamento de Administração, Av. Presidente Wilson, 118 - Rio de Janeiro/Rio de Janeiro,
IBMEC, Brazil,
91
ABSTRACT
Organizational social capital represents an important asset for organizations' development,
and as a resource it needs to be measured accurately in order to be manageable. It is therefore critical
to have sensible instrument and methodology to capture and analyze it. One of this research's aims is
to test and demonstrate how fuzzy logic applied to social capital psychometric scale is a relevant
treatment to apprehend the organizational social capital. For this a Likert scale, developed based on
Nahapiet and Ghoshal theory, has been used to apprehend the mentioned reality, and afterwards the
logic fuzzy tools have been applied on the data through MATLAB software to prove how this
methodology can translate vague, paradoxical and complex data from individual perceptions into
reliable information. Samples from Brazilian and Portuguese organizations were used to test the
properties, pertinence and usability through the empirical analysis where the evidences have been
confirmed by previous researches regarding organizational area.
Keywords: Applicability, Fuzzy Logic, Social Capital, Scale.
1. INTRODUCTION
Social capital is an asset with singular characteristics because it is derived from the quality of
relations between individuals making up networks to acquire mutual benefits, which essentially
means people's perceptions. Considering that it is a qualitative interpretation, it demands a specific

measurement approach to get reliable results. The methods most used to do it are psychometric
scales, which in general, are analyzed and treated through statistical processes based on classical
logic operated by binary sets, giving less accurate information, because it encompasses only two
degrees, completely related or not related, for a linguistic variable which demands to be apprehended
through a metric ranging from totally true to totally false. This need put in the spotlight the fuzzy
logic concept, which is able to deal with vagueness phenomenon, degrees of certainty (truth), in the
case of this research expressed by linguistic values when people communicate their perceptions
about a certain reality [1].
It is also relevant to point out that its mathematical model allows the handling of incomplete
and imprecise information to a certain degree, making it a suitable tool to treat a measure resulting
from human perceptions which are inherently vague and ambiguous due to different points of view
and states of mind.
From this perspective, the research aim presented in this paper is to verify how fuzzy logic is
suitable to analyze a scale [2] designed for identifying and measuring gaps in and strengths of social
capital in organizations to make management decisions possible. For this, in the paper’s second
section the theoretical background which supports the construct definition is synthesized. In the third
section fuzzy logic's theoretical background and the benefits of its application are described. In the
fourth the methodology is unveiled, detailed in its procedures with high-performance interactive
software for numerical calculation and fourth-generation programming language - MATLAB. In the
fifth, the results are presented and analyzed, and lastly, the conclusions.
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2. SOCIAL CAPITAL
Since relationships have been understood as an asset in the creation of wealth through an
interconnected and organized labor network present in any economic production, scholars have been
analyzing it to understand its mechanisms, causes and consequences by means of different
knowledge areas.
The first works appeared in Sociology with Bourdieu [3] and Coleman [4], where cohesion in
social dynamics was pointed out as an important social resource to make it possible for individuals to
engage with each other to achieve common goals. The subsequent works incorporate different
standpoints in accordance with the theoretical backgrounds of each researcher. Therefore, as a
consequence of these different researchers' perspectives there is not yet a commonly accepted
definition for social capital [5]. The understanding extracted from the intersection of these different
theories can be synthesized as goodwill among individuals produced in their relationship networks
(work, family, friends, companions, etc.), that allows them to obtain profitable information; material
gains as well as emotional support [6, 7].
As social capital occurs from interactions among individuals, personally and/or in groups, it
means that it happens at the micro, meso and macro social levels. In this sense, social scientists have
identified two concepts of it. The first one is from a collective unity of analysis, a group attribute
where social cohesion is a main element in the context, in which it is operationalised through group-level
mechanisms like social control or collective socialization among others, creating shared values
such as religious beliefs or patriotism. It should be underlined that it is independent of any
individual network connection; it belongs to the group [8].
The second one is from individual unity of analysis, based on network theory [9]. In this the
resource belongs to individuals, being embedded in their social network; it is used by individuals for
their own benefit. As property, they can manage it according their own interests and goals.
As a consequence of these two concepts, scholars have broken social capital down into
different dimensions. One of the main conceptions is Bonding and Bridging. The first one is about
homogeneity and group identity, and their internal ties from a collective perspective, as well as their

strength to assure common goals. It creates trust and cooperative relations due to similarities of
collective beliefs between their members [6, 10].
Bridging is about individuals’ networks, in which relations are heterogeneous for social
identity and power [11]. It is a resource used by individuals through their external social ties within a
network to obtain advantages for their own benefit, e.g. connections to the establishment of a new
venture [12]. Thus, it is clearly related to the individual analysis perspective.
Another social capital analysis perspective, which emerged from individual and collective concepts,
is internal, inside a group; persons interacting having a sense of unity, sharing similar characteristics
that define a group’s boundaries, or external with other groups [13].
Social capital has been studied specifically as an organizational phenomenon, where social
dynamics occur due to economic interests; making members act in a specific way to achieve their
own goals, which are interrelated and somehow, in different ways, tied to the organizations’
objectives [14, 6]. Its Unity of analysis is a mix of both individual and collective perspectives [15].
This approach has been chosen as the theoretical background for the present research because it is
suitable to address the research’s object – organizational good will as an asset for organizations.
In this subject area, Nahapiet and Ghoshal [15] have defined a three dimensional theoretical
construct for organizational capital which encompasses those two mentioned perspectives named as:
structural, relational and cognitive. It should be highlighted, that these theorists state that the features
of theses dimensions are highly interrelated, making it hard to separate some of them in practice.
Structural refers to the network’s organizational structure, specifically the patterns of connections
between individuals. Its unity is formed by links that an individual has developed within an
organizational network, which can be directly or indirectly related between an actor and others. In
this sense, positioning inside the network is also important because it defines the level of influence
one actor has inside the structure, as well as the existence of clusters within it. It means that an
actor’s connections can be circumscribed in one cluster, limiting their level of influence; on the other
hand the ones who have connections between these clusters may have more power through
information capitalization, acting as a broker between these clusters [15; 16].
Lastly, dense networks mean a high level of information exchange that therefore benefits all
members, and as a consequence fosters the development of the two others dimensions [17].
Cognitive is based on sources, like narratives, that create shared knowledge, representations,
comprehension, and systems of shared symbols (i.e. language, codes and culture) which allows
actors a common understanding of information and to categorize them in the same parameters [18].
These components propagate shared values and goals, and it is worth drawing attention to the fact
that the higher the extent of shared goals is, the higher the commitment is to achieve group tasks and
outcomes, a critical asset to organizational performance [19].
Relational is focused on a pairs of actors; it is a dyadic relation focusing on the strength-of-ties
[20]. This strength is directly related to time of interactions, emotional intensity, mutual benefits
experienced, and closeness between people [20]. It is classified as strong and weak. Strong fosters
trust between individuals, propagating the transfer of important tacit knowledge due to confidence;
on the other hand weak propagates information diffusion due to a lack of commitment and intimacy
between actors [21, 22].
The main elements of that dimension are trust, norms, sanctions, duties and expectations.
Specifically in this kind of social structure, the trust that arises is a relational trust, embedded in the
organizational network. It happens through repeated interactions between two individuals in a certain
organizational structure which stimulate the growth of their reliability and positive expectations [6].
Summing up the relational dimension, like the other dimension, it also allows the setting of the
other dimensions as it builds the necessary conditions for relationships to exist.
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3. FUZZY SETS
The theory of Fuzzy Sets was developed by ZADEH [23]. In the 70's it began to be widely
adopted mainly in the areas of data classification, expert systems, decision analysis, robotics, pattern
recognition, time series forecasting and others.
For example, the phrase "high unemployment" can be interpreted in many different ways
depending on the situation. In Sweden, where the unemployment rate used to be 3%, any rate higher
than 5% can be interpreted as a high unemployment rate. In other countries, however, the
unemployment rate cannot be considered high unless it exceeds 10%. In terms of fuzzy logic, "high
unemployment", is called a subjective category.
In contrast to conventional probability theory, the theory of fuzzy sets uses language to
describe uncertainty in the real world. Thus it allows the use of linguistic expressions such as "Brazil
has a high level of unemployment," and assigns degrees of membership.
The degrees of membership values are intermediate between the values of true (0) and false (1) in
traditional Boolean logic. In fact, the Boolean logic is a particular case of fuzzy logic, the case in
which the degrees of relevance are the extremes. The degrees of membership are assigned through a
membership function.
Fuzzy Reasoning or approximate reasoning is an inference procedure that derives
conclusions from a set of fuzzy if-then rules of known facts.
The knowledge of the phenomenon is expressed through statements like: "if (a set condition
is satisfied) then (we can infer a set of consequences)" [24].
Fuzzy logic is based upon the theory of the Fuzzy Sets. This is a generalization of the
Traditional Sets theory to solve the paradoxes generated from the “true or false” classification of
Classical Logic. Traditionally, a logical proposition has two extremes, namely: either “completely
true” or “completely false”. Nevertheless, in the Fuzzy Logic, a premise ranges in the ‘true’ level
from 0 to 1, causing it to be partially true or partially false. Upon the implementation of the “true
level”, the Fuzzy sets theory expands the Traditional Sets theory. The groups are labeled qualitatively
(by using such linguistic terms as: high, warm, active, small, near etc.) and the elements of these sets
are characterized by varying the level of pertinence (a value that indicates the level at which an
element belongs in a set). For example, temperatures between 30° (thirty degrees) and 40° (forty
degrees) belong to the “high temperatures” set, although the 40° temperature has a higher level of
pertinence in this set [24].
In a way that is not well understood, humans have the capability to associate a level of
pertinence to a certain object without understanding consciously how to reach it. For example, it
would not be difficult for a student to assign a level to the teacher in the “good teachers” set. Such a
level is achieved immediately with no conscious analysis on the factors that influence such decision
[25].
The level of association is not probability. Basically, it is a measure of the compatibility
between the object and the concept represented by the Fuzzy Set. For instance, number 0.7 is the
compatibility of the 35° temperature with the definition of the Fuzzy Set for high temperatures. That
figure (0.7) is not the probability of 35° being a high temperature, for it is already defined as 35°
[25].
The conventional systems theory is based upon algebraic, differential or difference equations
(“crisp” mathematical models). For some types of systems, mathematical models can be obtained
such as the electromechanical models, since the laws of physics behind the process are well-understood
and well-defined. However, on a daily basis, we come across countless practical
problems, whereby an acceptable level of information required for the physical modeling to be made
becomes difficult to obtain. Moreover, such tasks are time-consuming and costly. These systems can
be found in the chemical and food-processing industries, in financial institutions, in biotechnology,

among other areas. A large part of such systems can only be obtained through the knowledge of
specialists who directly take part in the process under consideration. That knowledge, very often,
may be too vague or inaccurate to be expressed by mathematical models [26].
A fuzzy set A is characterized by the pair (X, ), where ‘X’ is the variable for the universe
being studied ( it being continuous or discrete), and is a function for which the image belongs to the
interval [0,1], where ‘1’ represents the concept of total pertinence and ‘0’ represents the concept of
non-pertinence.
For the discrete case, a fuzzy set ‘A’ could be:
A= 0 / -3 + 0, 25 / -2 + 0,50 / -1 + 1 / 0 + 0,85 / 1 + 0,50 / 2 + 0 / 3 (1)
Which is read like: ‘Variable X measures -3 with a pertinence level of zero’. The slashes used
are only good to separate the values of variable X from their respective levels of pertinence; symbols
‘+’ do not indicate addition, but union.
For the continuous case, a fuzzy set ‘A’ to express ‘HIGH’ could be:
= 0 X μ for X £ 1,70 (2)
95
X a
2 a
X
−
μ = for 1,70 £X £ 1,85 being ‘a‘ constant (2a)
X a
a
X
−
μ =
for £ 1,85 £X £ 2,00 being ‘a‘ constant (2b)
The intersection of two fuzzy sets A (X) and B(X) is given by:
A (X) Ç B (X) = min (μA(X), μB(X)) (3)
Where: μA, μB are the respective functions of pertinence of fuzzy sets A and B.
As an example, consider the following fuzzy sets:
A (X) = 0.4 / -7 + 0.8 / -6 + 0.6 / -5 (3a)
B (X) = 0.5 / -7 + 0.5 / -6 + 0.4 / -5 (3b)
The resulting fuzzy set is:
C (X) = 0.4 / -7 + 0.5 / -6 + 0.4 / -5 (3c)
The union of two fuzzy sets A(X) and B(X) is given by:
A (X) È B(X) = max (μA (X), μ B (X)) (4)
where: μA, μB are the respective functions of pertinence of fuzzy sets A and B.

96
For example, consider the following fuzzy sets:
A (X) = 0.8 / 1 + 0.1 / 2 + 0.5 / 5 (4a)
B (X) = 0.7 / 1 + 0.5 / 2 + 0.7 / 5 (4b)
The resulting fuzzy set is:
C (X) = 0.8 / 1 + 0.5 / 2 + 0.7 / 5 (4c)
The complement of a fuzzy set A(X) is given by:
( ) = 1 − μ ( )
A X X
A
(5)
where: is a function of pertinence of fuzzy set A.
For example, being the fuzzy set given by:
A (X) = 1 / 5 + 0.8 / 4 + 0.6 / 3, its complement is: (5a)
1 - A (X) = 0 / 5 + 0.2 / 4 + 0.4 / 3 (5b)
The concentration of a fuzzy set A(X) is a fuzzy set given by:
2 conc(A(X)) (X) A = μ (6)
The concentration of a fuzzy set is linguistically equivalent to the term VERY. Concentration
decreases fuzziness.
The expansion of a fuzzy set is a fuzzy set given by:
dil A X X A ( ( )) = μ ( )
(7)
The expansion of a fuzzy set is linguistically equivalent to the term MORE OR LESS.
Expansion increases fuzziness.
A fuzzy triangular number is a fuzzy set with a normalized function of pertinence. The
functions of pertinence of the fuzzy numbers can take different forms. Dispersion of the functions of
pertinence can be interpreted as a measure of error.
Fuzzy Logic uses Inference Systems based in fuzzy rules as:
Where the input fuzzy terms LOW, MEDIUM and HIGH are given by fig.1, and the output
fuzzy terms LOW, MEDIUM and HIGH are in fig. 2

Fig. 1: Example of fuzzy terms for inputs
Fig. 2: Example of fuzzy terms for outputs
With the usual t-norm, t-conorm, fuzzy inference and COG (Center Of Gravity)
defuzzification, we have a Mamdani fuzzy inference system that implements the desired output.
Obviously we could have chosen a TSK (Takagi-Sugeno-Kang) system, with a fuzzy modeling focus
on accuracy, replacing the consequent parts by linear equations [27].
To verify that the fuzzy inference system really does its job, we can make a little calculation
showing its output for some representative values of the input.
Suppose that the user adjusts the maximum allowed echo-request input rate to 100 pps
(packets per second). If at a given sampling interval, the actual rate is less than 25 pps, the
acceptance rate will be 100 %, resulting in the standard behavior (all packets will pass). If, during
another period, the incoming rate is 50 pps, the acceptance rate will be 50 % (one packet will pass
and the following be rejected, for example). When the incoming rate is 100 pps or higher, the
acceptance rate will be 0 %, meaning full blocking of incoming echo-request packets.
Such a control law seems sensible, taking into account that – according to the user’s
perception – a denial of service attack is in progress. Note that immediately after the end of attack
the standard behavior is restored and “authentic” traffic can flow again, without human intervention
[28, 29].
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98
4. METHODOLOGY
Initially, the selection of social capital and fuzzy logic was defined by their complementary
characteristics, where the first is created from the social relationships which happen at all levels of
any society, as described in the social capital referential, which means that it appears from the
chaotic nature of social complexity. And the second, a conceptual framework which is capable of
dealing with vagueness and uncertainty in social dynamics that naturally brings to light their core
contradictions and inconsistencies [30].
The research’s implementation, universe and sampling, were chosen according to the scale
purpose measurement; this identifies the qualities and conditions of connectivity among workers to
produce in a given organization. The instrument [2] has been designed to measure these conditions
specifically among skilled laborers, with at least secondary education completed, because they have
the capacity to create greater added value for organizations. In this research the sample was
composed from 21 different companies in Portugal, and 18 organizations in Brazil, as well as
professionals, who were taking masters courses, from both countries. They are from different
industrial, service and governmental organizations to represent broader collective aspects of the two
different societies in order to understand their labor relation and labor relationship perceptions at a
given point in time.
The data from the survey has been processed and analyzed in clusters according to Nahapiet
and Goshal’s social capital dimensions [15]. This approach was chosen because the research's
purpose is to demonstrate how fuzzy logic is suitable to give a more accurate outcome for
individuals’ perceptions based on linguistic statements (variables), not to carry out a detailed social
capital analysis for the two organizational groups, Brazilian and Portuguese. The empirical
experiment tests and demonstrates the levels of each groups’ perceptions cohesion about each
dimension, which means transforming all the respondents’ vague and somehow ambiguous ideas and
judgments into accurate mathematical terms to be processed, and thus giving a coherent and useful
result [30]. Moreover, this dimensions’ approach was chosen because it is a collective perspective
which evolves in the dynamics of social complexity; a chaotic pattern of behaviors, representing a
relevant group of conditions to apply the methodology to [30].
The measurement instrument adopted to compile the perceptions is the Likert bipolar scale,
measuring from total concordance to disagreement with a statement; adverbs of intensity have been
used in all labels and repeated in the same continuum in both sides of the scale: Completely Agree,
Mostly Agree, Slightly Agree, Neither Agree nor Disagree, Slightly Disagree, Mostly Disagree,
Completely Disagree, Not applicable. It should be highlighted that the ordinal/interval scale
implicitly catches the fuzziness of respondents about what the level of agreement or disagreement
means to them. This can be tackled through fuzzy triangular numbers.
At the end of data collection all responses were tabulated on a spreadsheet. At this stage, after
tabulation, the results are passed to MATLAB, a high-performance interactive software program
aimed at the numerical calculation and other functions, which receives as input parameters the
frequency of feedback from users (FOU) and calculates the Normalized data (Foun), (Fuzzy
Numbers) NNs, Fuzzy Number Average (NNM), the similarity between sets, as well as presenting
the results graphically to facilitate the analysis of results.
The following explanation of how MATLAB generates these results is given here to aid
understanding. After determining the frequency of reviews from users (FOU) the next step is to
normalize the data by dividing it by all more often frequency (Foun). Then we must find the Fuzzy
Number (NN) closest to the Foun. This number is obtained from the triangular fuzzy numbers that
have the same Foun mode using the formula.

NN (0|1)
NN (0|2)
NN (0|3)
NN (0|4)
0 2 4 6 8
Opinião
99
( ) ( )
A X B X
Ç
S A X B X È
( ), ( ) A X B X
( ) ( )
=
(8)
Triangular fuzzy numbers with their modes are shown ahead:
Fig.3: with zero mode
1
Pertinência
0
0 2 4 6 8
1
0
Opinião
Pertinência
NN (2|1)
NN (2|2)
NN (2|3)
NN (2|4)
0 2 4 6 8
1
0
Opinião
Pertinência
NN (4|1)
NN (4|2)
NN (4|3)
NN (4|4)
Fig. 4: with mode two Fig. 5: with mode four
0 2 4 6 8
1
0
Opinião
Pertinência
NN (6|1)
NN (6|2)
NN (6|3)
NN (6|4)
0 2 4 6 8
1
0
Opinião
Pertinência
NN (8|1)
NN (8|2)
NN (8|3)
NN (8|4)
Fig. 6: with mode six Fig. 7: with mode eight

Each question has its Foun and its NN. In addition, each metric has its Fuzzy Number Average
100
(NNM), which is found using the formula:
({ })
1 ( )

=
=
i k
i
n k A X
NNM X X
1...
1
...
μ
μ
(9)
All these data appear in the form of graphs that are generated for each response within each
metric. Two very important conclusions are noted from these graphs:
• The Mode that will determine the range in which the metric will be classified as follows: 0 -
Very Poor 2 - Poor 4 - Average, 6 - Good, 8 - Very Good;
• The amplitude will determine the dispersion of average users’ opinions. The amplitude is
related to trust, i.e. the lower the amplitude of the interval, the greater the confidence in the
data [24].
The amplitude will be represented as follows: 1 - Minimal 2 - Average 3 - High 4 - Very High;
5. RESULTS
Prior to the analysis, it is necessary to state that there is a variable acting on workers’
perceptions in both countries. This being the local socioeconomic conditions, meaning, the industry
and macro environment within which the organizations operate [31]. Although Brazil [32] was living
a prosperous economic moment during the research and Portugal [33, 34] was living the opposite a
crisis. It has not presented any relevant impact on the research subject, and because of this, it has not
been considered for analysis procedures.
The empirical application has shown how fuzzy logic is suitable to process psychometric
scales’ data, giving ascertained outcomes.
From the fuzzy modeling, the following results, presented in the three next tables, were obtained:
Table 1
Portugal (Cognitive) Brazil (Cognitive)
61 53
It can be seen that for cognitive dimension, Brazil´s sample has a great variability (3) and low
agreement (5). For Portugal's sample a low dispersion (1) with a great agreement (6) has been
obtained.
These results in the cognitive dimension reveal that in the Brazilian group there is an absence
of agreement with a bias to disagree about cognitive elements in the organizational environment,
although some respondents have it in their organizations. It shows that there are isolated occurrences,
not a collective tendency. This indicates in the collective a lack of conditions for creating a common
language, codes and culture to propagate shared values useful for organizations as a whole. This
dimension is a consequence of variables related to the country's cultural history, and it is confirmed
by researches in Brazil where the relations still are established as distant power relations between
employees and executives rooted in the beliefs of the slave-owning society's cultural tradition, where
the employees have a subjacent apartheid feeling [35].
In the Portuguese sample, the results point out to a strong concordance with existence of
shared knowledge, representations, comprehension and systems of shared symbols in the
organizations. There are factors in Portuguese society that can be associated with this behavior such

as prizing rules as well as being a collective society where people belong to groups that enable them
to form shared culture, codes and comprehension of organizational reality [36; 37; 38].
Table 2
Portugal (Structural) BRAZIL (Structural)
61 51
It can be seen that for structural dimension, Brazil´s sample has a low variability (1) and low
obtained.
Those outcomes clearly indicate in the Brazilian group an agreement about organizational
relationship design. The findings show that network ties and structures do not support interaction
which means that the workers do not have access to information resources and/or the support of their
colleagues. The organization’s network design does not foster the flow of information and
knowledge to make work activities easier. It is in accordance with patterns of individualism,
nepotism and personalism, where personal relationships are more important than competencies to
guarantee mutual personal favors identified in researches about Brazilian society [36, 39].
On the other hand, the Portugal sample has shown that its network connections allow the
participants get information from colleagues within the same network which reduces the effort, time
and investment needed to perform their tasks. It is corroborated in Lopes’ research, where he has
identified in collective Portuguese organizational pattern the tendency to create small relationship
work groups, in line with a high collectivism, allowing them to form teams easily [39].
Table 3
Portugal (Relational) BRAZIL (Relational)
61 51
It can be seen that for structural dimension, Brazil´s sample has a low variability (1) and low
obtained.
In the relational dimension the perceptions of both groups are the same as in the structural,
indicating a coherence between the results, as it is the structure that provide the means for
interactions to happen because it is through experiences that the relational elements appear as trust;
needing repeated interactions to blossom between participants. It is the same for cooperation norms
and obligations/expectations where the first is socially built by actors, and the second represents a
commitment or duty for future needs between the participants.
In the Brazilian sample there is a lack of belief about companionship from colleagues [39,
40], however in Portugal the findings show a more optimistic perspective; a good sense of
companionship among workers to perform their tasks, and enhance each other's work [37, 41].
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CONCLUSIONS
In this paper it has been shown through Brazilian and Portugal samples how fuzzy logic is
pertinent to deal with data generated from respondents’ varied and uncertain perceptions of a group,
as well as scarce information to generate a consistent collective understanding about socio-organizational
issues, corroborated by previous researches, as pointed out before. Nonetheless, there
are some case studies showing different results, which indicate that the subject and universes are
complex and need more researches to be comprehended. But the research’s goal has been fulfilled,

through the described experiment which demonstrates how fuzzy logic is useful as an analysis tool to
give results with significant accuracy levels.
Summing up the findings, for cognitive dimension the Brazilian group does not agree about
the organizational environment, even when some respondents have it in their organizations.
This dimension is related to the country's cultural history which is based on a slave-owning society's
cultural traditions, where the employees have a subjacent apartheid feeling. So, it can be said that for
Brazilians there are no common values to aggregate the organizations as a whole.
For the Portuguese group it is shown that they have a strong concordance so, they can contribute
with common things to the organizations.
For structural dimension, it can be seen that for the Brazilian group, the organization’s
network design does not foster the flow of information and knowledge to make easier the work
activities.
For the Portuguese group it can be said that its network connections allow the participants to
get information from colleagues within the same network reducing the effort, time and investment
needed to perform their tasks.
In the relational dimension the perceptions of Brazilian group are that there are not good
relations with colleagues; while the opposite is true in the Portuguese group. For future research,
further studies should be conducted about Brazilian and Portuguese Societies to investigate the
dimensions of social capital and its causes. As previously mentioned, it has to be highlighted, that
there is cultural diversity inside both countries that must be analyzed to have better understanding
about organizational relationships to propagate more effective human capital management.
102
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