=Paper=
{{Paper
|id=Vol-2732/20200411
|storemode=property
|title=Neuro-Genetic Hybrid System for Management of Organizational
Development Measures
|pdfUrl=https://ceur-ws.org/Vol-2732/20200411.pdf
|volume=Vol-2732
|authors=Olena Skrynnyk,Tetyana Vasilyeva
|dblpUrl=https://dblp.org/rec/conf/icteri/SkrynnykV20
}}
==Neuro-Genetic Hybrid System for Management of Organizational
Development Measures==
https://ceur-ws.org/Vol-2732/20200411.pdf
Neuro-Genetic Hybrid System for Management of
Organizational Development Measures
Skrynnyk Olena1[0000-0001-8300-6616] and Vasilyeva Tetyana2[0000-0003-0635-7978]
1 modis, Stuttgart, Germany
Sumy State University, Sumy, Ukraine
skrynnykolena@googlemail.com
2 Balatskyi Academic and Research institute of Finance, Economy and Management, Sumy
State University, Sumy, Ukraine
tavasilyeva@fem.sumdu.edu.ua
Abstract. Current practical experience in measuring the effectiveness of organ-
izational development activities is largely based on the evaluation of surveys. In
this paper we present an approach based on an artificial neural network with el-
ements of a fuzzy approach and a genetic algorithm to control organizational
development. Based on genetic algorithms, the organizational development
measures are initiated, selected, combined or mutated with the goal of finding
the best possible solution for each concrete case. Since many variables have the
uncertain set of their values, the use of a hybrid neuro-fuzzy mechanism makes
it possible to analyze the behavioral components up to the combinations of
needs and thereby select the appropriate organizational development measures.
The system is designed to ensure the long-term effectiveness of organizational
development measures. We supplement the previously known measures of or-
ganizational development with technology-based in order to increase the degree
of automation in practice. This article is intended as an orientation for other sci-
entists who are researching the same topic and are interested in the current state
of the art, as well as for companies who want to ensure compliance with inter-
nal company rules using digital tools.
Keywords: neuro-genetic hybrid system, organizational development, fuzzy
logic.
1 Introduction
Organizational development is a long-term continuous, planned process of optimizing
attitudes and behaviors of organization members to achieve organizational goals. This
process requires tremendous methodological knowledge of the participants and the
commitment to change. Since changes in the state of the object of organizational de-
velopment are often not clearly measurable over time, the genetic application with
elements of fuzzy logic is particularly beneficial. Several already published studies
offer approaches for management organizational change in general [1, 6, 8, 16, 18],
employee performance [4], behavior [2, 15] using the technologies of artificial intelli-
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
gence. These indicate high-quality approaches, some of which could also be applied
to goals defined by us, but do not cover the entire range of the problem. Based on
artificial intelligence, we have developed a model with three modules for organiza-
tional development. The first module is used to diagnose and record the current state
of the organization, analyze the received data, and determine long-term measures for
organizational development. The second module gradually monitors the results of the
implemented measures, introduces and implements corrections. The third module has
the main function of managing the system. The functions of the first two modules
were realized through hybrid neural networks, partly with fuzzy weights. We apply
the genetic algorithm to determine the behavior of individual (especially immaterial)
multi-level variables. The reason for using the neurofuzzy system is on the one hand
partially non-linear dependencies of the variables (their weighting), and on the other
hand we implement the genetic approach to reach the system's ability for learning and
adaptation. Although organizational development is primarily concerned with the
behavior of organization members, the variables we measure are more than directly
related to people. In this article, we limit our scope to the neuro-genetic hybrid sys-
tem and present an example of just one functionality of the system that serves as the
basis for in-depth behavioral correction.
2 Methodology
Neuro-genetic hybrid systems are mainly used for complex systems that, for example,
map human behavior (subsystem investigated by us). These have a multilevel ap-
proach to capture, analyze and predict various processes or to offer a solution for a
specific case [10-12, 14, 17, 19]. In our system for management of organizational
development, we consider several subsystems, ranging from corporate performance
and standards system to group behavior or individual motivation. In general, the neu-
ro-genetic hybrid system works according to the following principle (Fig. 1):
• Genetic algorithm: the current element population receives three types of sequen-
tial rules of genetic operations to form the next generation of elements. A distinc-
tion is made between selection, crossover and mutation (Fig. 2). According to the
first rule, a parent element from which the child elements follow is defined. The
second rule defines the parent pairs that will create the respective children. The
third rule determines the random changes to each parent element for the later crea-
tion of the child elements. Artificial neural network: The child elements enter the
artificial neural network model as an input variable. In the artificial neural network
model, the input data is analyzed and the option is outputted.
• Decoded strings from the current population enter the fitness calculation with the
prediction results.
• The optimum variables are determined from the fitness evaluation.
• In case of non-conformity (data no longer correct or sufficient), the procedure is
repeated.
Fig. 1. General functional principle of neuro-genetic hybrid system
Fig. 2. Types of rules for the definition of next generation from current population
Since the majority of the variables are linguistic variables with multiple meanings,
we use fuzzy logic to describe the relations in the system.
3 Results
The primary goal of organizational development is to ensure the long-term evolution-
ary improvement of the human factor as an organizational component to achieve or-
ganizational goals i. a. by influencing behavior. The individual is a member of a
group within an organization and is therefore considered in connection with other
group members (the individual is the lowest level in the organizational chain). At the
same time, the group is a part of the department/area/the whole organization (the
middle level in the organization chain). Thus, within the development of the organiza-
tion, the group is also considered a member of the organization with all its connec-
tions. Since the form of the organization and the number of levels vary, we break the
chain of organization after the third, the highest level - the organization itself (the
departments, units and divisions are excluded, as they are considered as organizations
within organizations). Consequently, the organizational development measures are
focused on the single elements of the organizational system, their internal and exter-
nal relationships, and the organization as a whole. Since the planned improvements
are intended to be irreversible, the organizational development measures per se have
the learning character. Most known methods of organizational development are lim-
ited to such motivation and behavior controls, that influence employees and groups
directly or indirectly live, from print or digital media (meetings, workshops, employee
information, leaflets, intranet contributions, etc.). Our approach refers to the control
of motivation and behavior by providing timely actual information and assistance,
where wrong behavior or work performance is excluded. This can be achieved
through the digital assistant systems.
Since our organizational system is very complex, we have established several sub-
systems with complex structures [9, 18]. The neuro-genetic hybrid system, which is
designed as a heuristic algorithm for searching the solution for optimization and mod-
eling by selecting and combining of variables. In this case the neural network search-
es the potential solutions of multi-level fuzzy sets for further use by the genetic algo-
rithm. The genetic algorithm consists of initialization, selection, cross-over and muta-
tion. Fig. 3 shows the principle of the system. In the context of organizational devel-
opment, certain measures are performed, for example, informing employees about the
new corporate values. After a certain period of time, the employee or group of em-
ployees shows behavior that does not correspond to the expected behavior. In the
system, the behavior is split up into corresponding components. These are analyzed in
steps and new corrective measures are offered. The employee or group of employees
executes the measures. If the second measure is better than the first one, it is selected
as one of the most effective measures in the measure pool. The next step is to improve
the behavior of the employee or group of employees. To do so, the measures from the
measure pool are combined or modified. If the behavior is not successful after the
implementation of new measures, the process starts again.
Fig. 3. Basic principle of the developed neuro-genetic hybrid system (example - mutation)
Input variables are collected in two ways: through video, audio or text recording
mechanisms (self-developed, based on Microsoft tools) and from the connected per-
formance measurement systems. In the first way e.g., emotions and modus operandi
are recorded, in the second way e.g. work productivity and error rate are measured.
The incoming information is analyzed in a fragmented way. For example, facial ex-
pressions with different voice positions or gestures are interpreted differently.
Furthermore, an example for the application of the neuro-genetic hybrid subsystem
is presented.
The company is positioning itself as diversity-oriented. Cosmopolitanism and ac-
ceptability belong to the organizational values. During a conversation between em-
ployees in a working group, our system several times detects racist context (unac-
ceptable words) that is offensive to human dignity. This resists one of the organiza-
tional goals, the stabilization of organization-compliant behavior of employees (in this
case focused on behavior with colleagues and superiors, employee as part of the com-
pany, individual performance for overall goal).
The conversation is recognized as an emotional act. The variables of the act are the
type of activity (conversation, by voice recognition), quality (in this case unsatisfacto-
ry, because of the recognized context), duration (in this case medium (2 < x < 30
minutes)), and iterations (in this case multiple), see Table 1. In this case, certain con-
text components (unacceptable words) are recognized as hints. The hints serve as
markers for variable values and indicate the allowed limits. The captured emotion is
analyzed as happiness (through few iterations of smiles by face and voice recogni-
tion). Such behavior is declared as neutral conversation with unacceptable words.
Table 1. Act variables
type of activity quality duration iterations
conversation exemplary brief one
monitoring activity desirable medium few
writing activity good long multiple
manual activity satisfying very long combination
coordinating activity unsatisfying
specific activity bad
In the case described, we refer to a certain type of activity. In other cases, for exam-
ple, when the performance data of the person (speed and quality of the assembly, skill
level of a working step) is collected externally, other variables will be input into the
system. The goal of our system is to evolutionize the activity in small steps and to
achieve the learning effects by applying appropriate measures. In other words, we
intend the gradual implementation of the measures, not only to avoid unacceptable
activities, but also to direct the underlying motives and needs to the benefit of the
company. Here the fitness value correlates with the desired state of the act. Therefore,
the first population refers to the quality, duration, iterations on the one hand, and emo-
tion on the other hand.
The variables of the current acts flow into the neural network. This is necessary to
select the optimal measures in the neural network by defining the corresponding mo-
tives and needs. The motivation and accordingly the needs are mainly derived from
the type of activity, its quality and the hints. Kotlyarov [7], Petrenko and Taba-
harnyuk [13] in their model of motivational space for organizational education, guid-
ed by Draker's theory, propose a three-dimensional vector space (expediency, result,
effect) to describe the motivational strategy of an organization, group and individual.
We have partially adopted and expanded their approaches.
Special attention should be given to the phases of the motivation need cycle, as
these are directly related to the motivation optimum and therefore activate the motiva-
tion behavior subsystem [5, 20]. In the described scenario, the person is in the phase
of actualization of the need, which is combined with an increase in emotional tension,
a feeling of lack, a desire to do something, a desire for activity that is not directed. In
this case, the measures proposed and applied by our system must correct the behavior
of the person according to the organizational values and change the phase from the
need-motivational cycle, either in the direction of the search phase or in the direction
of the latent phase.
Thanks to the neural network, the system learns to manage special complex prob-
lems. The main layers refer to behavior components, motivation and needs.
• The behavior in our model is represented as a set of activities with defined vectors
of acts and emotions. The act, in turn, is defined by the function of weighted mo-
tives. As a result, scalars of actions can acquire positive and negative integer and
fractional values:
𝑝∈ ℚ; ; (1)
• Weighting G is a complex function of dependence of key indicators, such as the
value of expected result, target density, resources spent, external oppressive or
binding factors, opportunities, etc. on their correlation ratio. These determinants re-
flect the views of H. Heckhausen's theories as well as those of J. V. Brem and E.
Heckhausen. A. Self [3] and depend on activity type (are defined individually).
Since this model investigates not only personal but also environmental factors, they
are considered as an indicator of the influence on the force of the motive.
Weighting takes the form of a vector of positive scalars of integers or fractional
numbers:
𝑔∈ ℚ; 𝐺 = {𝑔1...𝑔𝑛}; (2)
• Motive M is a function of need: the total number of appropriately prioritized needs
reproduces the motive vector. Since a motive is not always positive, its individual
scalars can be negative fractional numbers (c is a need dimension function). The
mathematical content of a motif is expressed as follows:
М ∈ ℚ; 𝑀 = {𝑚1...𝑚𝑛}; mn = (3)
• Need a in mathematical context is a positive integer. Needs that define a motive are
represented as vector A:
𝑎 ∈ ℕ; 𝐴= {𝑎1 … 𝑎𝑛} (4)
Our system is based on the combined approach of motives and needs. Selection of
needs for motivation combinations is based on the theories of Maslow, McClelland,
Alderfer and Herzberg. In the described case, the act is based on the motivations of
identification, authority, prosocial motivation and consequently the fundamental
needs of self-affirmation, acknowledgment, authority and security with corresponding
degree of involvement. The degree of involvement shows how deeply the need is
present in the motivation.
The concrete IF THEN rules for the motivation-need relationship are shown in Ta-
ble 2 (IF “need 1” = “degree of influence x” AND ”need n” = “degree of influence y”
THEN “motivation 1” AND “motivation n”). In most cases, the behaviour is due to
the combination of several motivations and therefore depends on multiple needs.
Table 2. Overview of dependencies in motivation-need relation on employee level
motivation
pprocedurally sub-
self-development
self-affirmation
achievements
identification
affiliation
prosocial
authority
stantive
need
self- ••••• ••• •• •••• •••• •••• ••••• •
affirmation
acknowl- ••• ••••• •••• ••••• ••• •• • ••••
edgment
respect ••• •••• •••• ••••• •• •• • •••
identification •••• •• •••• ••• ••••• •• •••• ••
affiliation • •••• ••••• •• •• •• • •••••
development •• • • •••• ••••• ••••• •••• •
authority • ••• ••• ••••• • • • •
achievement •••• • • •••• ••••• ••••• •••• •
involvement • ••• •••• •• •• • • •••••
security • • •• •••• ••• ••• • ••
physiologi- • • • ••• •• •• • •
cal
• - no or very weak involvement
•• - weak involvement
••• - medium involvement
•••• - strong involvement
••••• - very strong involvement (dominant need)
Each level of neural network has its componets, with the generalized form
(Помилка! Джерело посилання не знайдено..):
─ The abstract element E (in our case need/motivation/act etc.) has the following
form:
(5)
─ Therefore, the rule becomes the general form:
(6)
─ and the output is accordingly:
(7)
─ The total system output is expressed by the formula:
(8)
Fig. 4. Part of the artificial neural fuzzy network for an element level (e.g. motivation)
In general, individual layers can be described as follows:
1. Initial layer: The outputs of the nodes are degrees in which the given inputs satisfy
the functions associated with these nodes.
2. Rule layer: Each node calculates the intensity of the rule. All nodes are marked
with T and can be selected to simulate logical AND.
3. Normalization layer: Each node normalizes the intensity of the rule:
(9)
4. Neuron output layer: Neuron output is the product of normalized rule intensity and
individual rule output:
(10)
5. Total output layer: Single output neuron calculate the network output:
(11)
The neural network consists of such elements, where the output of one level corre-
sponds to the input of the other level. In case one of the variables does not occur, it is
still recorded with minimum value.
In the case of usage of unacceptable words, different measures are implemented
one after the other, in the order of information - warning - sanction. In this case, the
first step is general information (as a voice reminder or on the display screen): "In this
company such phrases are not being used". Next is "Please use the following words
instead of (unacceptable words)...". The employee is subsequently warned of the fol-
lowing "Any unacceptable words will be punished by (certain measure)". If these
measures do not work, the sanction will follow. At the same time, measures are being
taken to adopt new behavior patterns in order to achieve the organizational goal of
long-term stabilization of employee behavior.
The following system can not only be applied to commercial and public organiza-
tions, but can also be used for integration projects of diverse groups.
4 Conclusion and Discussion
The system we describe should only be seen as part of the overall organizational de-
velopment system, which cannot be described within an article because of its com-
plexity. The whole system offers the monitoring of organizational development at all
levels of the company and therefore provides continuous improvement.
The main strength of neuro-genetic hybrid systems with fuzzy neurons and rules is
that they are universal approximators. Nevertheless, this method also has disad-
vantages in the implementation of organizational development, such as very long
processing time and uncertain convergence. Furthermore, the limitations of the sys-
tem proposed by us include the complexity, high data volume and preparation effort
on the organization side, as the system depth and organizational development
measures have to be created individually by each company.
The main motivation for the use of such systems is the timely integration of appro-
priate measures in order to achieve the organizational goal in an optimal way.
I would like to express a special thanks to the two unknown reviewers who have
high-lighted the open issues and allowed me to formulate the article in a more com-
prehensive way.
Special thanks to Oleksandr Marchuk for his professional support during the de-
velopment of the theme.
The survey was supported by the Ministry of Education and Science of Ukraine
and performed the results of the project “Modeling and forecasting of the socio-
economic-political road map of reforms in Ukraine for the transition to a sustainable
growth model” (registration number 0118U003569).
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