Innovative classification of methods of the Future-oriented Technology Analysis

Abstract

In the era characterized by significant dynamics of the environment traditional methods of anticipating the future, assuming the immutability of the factors affecting the forecasted phenomenon, may be in the long term ineffective. The modern approach of predicting the future of technology, taking into account the multidimensionality of the environment, is, among other things, the Future-Oriented Technology Analysis (FTA). Designing the FTA research procedure is a complex process, both in organizational and methodological terms. The catalogue of methods that can be used in this process is extensive and constantly open. However, in the source literature the rules for the selection of methods appropriate for the type of research were not specified. The ways of combining methods in the research process were also missing. The main aim of this article was to present the author’s classification of methods of future-oriented technology analysis and indicate the possibilities of its application. In the text, using statistical methods and artificial neural networks, the classification of methods with the potential of exploitation in prospective technology analysis was carried out. Each of the received classes was analysed, the characteristics of particular groups of methods were selected, and authorial names characterizing the given classes were chosen. According to the author, the application of the proposed classification of methods of future-oriented technology analysis facilitates the design of the FTA research process. It will contribute to the systematization and standardization of the manner of selection of research methods. It will also allow for the selection of complementary methods.

Full text

Copyright © 2016 Vilnius Gediminas Technical University (VGTU) Press
http://www.tandfonline.com/TTED
TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY
ISSN 20294913 / eISSN 2029-4921
2016 Volume 22(4): 574–597
doi:10.3846/20294913.2016.1197164
Corresponding author Katarzyna Halicka
E-mail: k.halicka@pb.edu.pl
INNOVATIVE CLASSIFICATION OF METHODS OF
THE FUTURE-ORIENTED TECHNOLOGY ANALYSIS
Katarzyna HALICKA
Bialystok University of Technology, Wiejska 45A str., 15-351 Bialystok, Poland
Received 23 October 2015; accepted 28 February 2016
Abstract. In the era characterized by signicant dynamics of the environment traditional meth-
ods of anticipating the future, assuming the immutability of the factors aecting the forecasted
phenomenon, may be in the long term ineective. e modern approach of predicting the future
of technology, taking into account the multidimensionality of the environment, is, among other
things, the Future-Oriented Technology Analysis (FTA). Designing the FTA research procedure is
a complex process, both in organizational and methodological terms. e catalogue of methods that
can be used in this process is extensive and constantly open. However, in the source literature the
rules for the selection of methods appropriate for the type of research were not specied. e ways
of combining methods in the research process were also missing. e main aim of this article was
to present the author’s classication of methods of future-oriented technology analysis and indicate
the possibilities of its application. In the text, using statistical methods and articial neural networks,
the classication of methods with the potential of exploitation in prospective technology analysis
was carried out. Each of the received classes was analysed, the characteristics of particular groups of
methods were selected, and authorial names characterizing the given classes were chosen. According
to the author, the application of the proposed classication of methods of future-oriented technology
analysis facilitates the design of the FTA research process. It will contribute to the systematization
and standardization of the manner of selection of research methods. It will also allow for the selec-
tion of complementary methods.
Keywords: technology analysis, technology foresight, technology assessment, technology forecast-
ing, classication, Future-oriented Technology Analysis, articial neural networks.
JEL Classication: C38, C45, C44, O31, O32, O33, E27.
Introduction
e dynamic development of the industry, in the conditions of globalization and strong
competition, determines the use of new, innovative, more ecient and economically viable
technologies. One of the important factors giving evidence of the competitive businesses

Technological and Economic Development of Economy, 2016, 22(4): 574–597 575
are the technical and technological resources owned by them, including knowledge and
innovation. In the situation of the increasing demand for innovative technologies and a
broad technology trading market, the issue of in-depth analysis of technology is gaining
in importance. It is an essential element of technology management and is used, inter
alia, in: (i) the exploitation of technology owned by the company; (ii) the acquisition of
new technologies that would improve the competitiveness of the enterprise or prevent its
deterioration; (iii) exchange of the already applied technology and introduction of new
technologies in its place.
An in-depth analysis of the technology is difficult, because of the costs, the complex-
ity of the problem and above all the pace of technological change on the global market.
The analysis of technology requires the possession of appropriate resources of knowledge
that is dispersed, and also concerns many aspects of technological development. During
the analysis of technology the tools and skills allowing for carrying out the substantive as-
sessment of the technical characteristics and the properties of technologies are useful. The
knowledge about current trends in technology is also necessary. According to the current
trends, technology analysis should take into account the economic, technical, social, as well
as environmental factors. It is therefore necessary to use specific systems and tools, thanks
to which the investment in research and development, the infrastructure and the qualifi-
cations of the staff will be tailored to the current, as well as future market and industrial
needs (Ejdys et al. 2015). Those prerequisites justify the use of appropriate – future-orient-
ed – methods of technology analysis. The Future-oriented Technology Analysis approach
belongs to such tools. The FTA enables the investigation of the interaction of technology
with the environment and the identification of factors affecting the development of the
technology, but also the indication of the effects of the impact of technology on the envi-
ronment. It supports the determination of directions of a given technology development in
the long term perspective. It also enables the specification of the level of maturity of a given
technology and the identification of obsolete technologies. It facilitates the identification
of emerging new technologies.
The FTA term was first used in 2004 in the title of a seminar on New Horizons and Chal-
lenges for Future-Oriented Technology Analysis: New Technology Foresight, Forecasting and
Assessment organized by the Institute for Prospective Technological Studies (IPTS). Then,
the FTA concept was defined as the so-called “umbrella” that covered a number of different
methods of technology analysis in the field of technology foresight, technology forecasting
and technology assessment (Cagnin et al. 2008). According to Saritas et al. (2014) FTA
explains a broad range of future-looking activities involving foresight, forecasting, futures,
and technology assessment among the others. However, in the opinion of Boden, John-
ston, Scapolo (2012) FTA facilitates decision-making and coordination of future activities,
especially in the fields of science, technology and innovation as well as politics. On the
other hand, some researchers such as H. Haegeman, F. Scapolo and C. Cagnin, A. Havas
and O. Saritas claimed that the future-oriented technology is a common term denominat-
ing a collection of different tools that can be used to study and understand the future of
technologies from different methodological perspectives (Haegeman et al. 2013; Cagnin
et al. 2013). Over time, FTA started to be treated as a kind of future management concept.

576 K. Halicka. Innovative classication of methods of the Future-oriented Technology Analysis
This approach began to develop in two parallel trends: technological and decision-mak-
ing. In the first, methods and tools necessary to analyse, assess, predict the development of
technologies (Huang et al. 2012b) as well as manage their future were used (Cheng et al.
2008). In the second, FTA began to be treated as a tool for policy-making, a tool used by
policymakers (Cagnin et al. 2011; Carabias-Huetter, Haegeman 2013; Georghiou, Harper
2013; Kim et al. 2010; Kwakkel, Pruyt 2013; Marinho, Cagnin 2014; Weber et al. 2012). The
research conducted by the author is located in the first mainstream, the technology main-
stream. When reviewing the database of scientific IEEE publications and Web of Science,
as well as websites it can be noted that FTA is most commonly used to analyse emerging
technologies (Robinson, Propp 2008) specifically related to such areas as: nanotechnology
(Alencar et al. 2007; Ma et al. 2014; Huang et al. 2011; Schaper-Rinkel 2013), micro and na-
noelectronics (Gesche et al. 2012; Huang et al. 2012a; Markus, Mentzer 2014; Moore et al.
2014; Robinson et al. 2013) and renewable energy sources (Guo et al. 2012, 2011; Halicka
2011; Halicka et al. 2015; Nygren et al. 2015).
The abovementioned definitions – regardless of the study trend – are generic and do
not reflect the nature of the FTA approach. Therefore, the author proposes her own defini-
tion of FTA as a process, the main objective of which is to predict the future of technology
through evaluation and detailed analysis (scanning) of its current state and identification of
strategic factors of its development in the future. Designing the process of future-oriented
technology analysis is a difficult, multi-stage project, providing various relevant informa-
tion on the analysed technologies at every stage. This information may relate to the tech-
nology itself, but also to the factors affecting a particular technology and its development.
They may determine both the impact of the environment on technology and technology
on the environment. They can also be considered in different time perspectives, i.e. may
relate to the past, present and future of the technology. According to the author, the FTA
process is carried out through the following functions:
1. Collecting information on the purpose and scope of technology analysis.
2. Collecting and organizing information on technology.
3. Processing information associated with the current development of technology.
4. Processing and generating new information on the current state of technology.
5. Collecting information on the impact of the environment on technology and technol-
ogy on the environment.
6. Transmission of the acquired information on technology.
7. Collecting information on the factors aecting the development of technology.
8. Generating new information concerning the development of the technology.
9. Interpreting and using the obtained information.
Due to diverse functions implemented during the future-oriented technology analysis,
there is no single best method with the use of which a research problem can be solved. It
is necessary to use several different methods. However, a set of methods that can be used
in FTA is comprehensive (Amanatidou et al. 2009; Georghiou et al. 2011; Loveridge, Sari-
tas 2012). These methods may be used in different ways, depending on the features and
context of the analyses (Halicka 2015; Hamarat et al. 2013; Shin et al. 2013). Some of these

Technological and Economic Development of Economy, 2016, 22(4): 574–597 577
methods are similar to each other and can be used interchangebly (Damrongchai et al.
2010). However, the source literature presents neither the principles of selection of appro-
priate research methods, nor the ways to combine them. It is, therefore, important to col-
lect, analyse and classify the methods with the potential of use in the FTA. Classes obtained
in such a manner – integrating research methods possible to use in a prospective analysis
of technology – will help to facilitate and standardize the whole process of designing FTA.
The above-mentioned proposals for the design of the FTA process justify the purpose-
fulness of undertaking a research task, which consists, inter alia, in the development of
methodology for classification of methods of future-oriented technology analysis.
1. e current classication of methods used in FTA– a literature review
FTA is a process that uses a variety of methods allowing for a detailed characterization and
systematic analysis of technology as well as identication and presentation of its develop-
ment paths. According to Marinelli etal. (2014) the future-oriented technology analysis
is particularly useful when technologies are costly but essential for the development of a
country, region or company. is approach combines forecasting technology (TF) technol-
ogy assessment (TA) and technology foresight.
Technological forecasting is a kind of technology development prediction, allowing for
studying the changes in technology, presenting its development path or functional capabili-
ties (Ayres 1969; Nazarko 1993). Technological forecasting is primarily based on data from
the past and generally refers to the near future (time horizon – up to several years) (Cuhls
2003; Cunningham, Kwakkel 2011; Makridakis, Wheelwright 1978; Nazarko 2011). On the
other hand, to standardize the definitions available in the literature, technology assessment
can be defined as the measurement of specific technologies and their consequences from
the point of view of the social, economic and environmental criteria (De Piante Henriksen
1997; Mazurkiewicz et al. 2015; Musango 2012). According to A. E. Gudanowska (2013,
2014a) it is an assessment and analysis of the impact of the existing technologies on the
society. However, in the opinion of B. Martin (2001) and J. Nazarko (Nazarko et al. 2011,
2012; Nazarko 2013) technological foresight is aimed at activities enabling the creation of
the future, allows to predict both future characteristics of new technologies and the pe-
riod of their appearance. Technology assessment, forecasting its development and foresight
studies form the basis of FTA; hence, these approaches show great consistency in terms of
methodology. However, taking into account the studies conducted by Andersen, Alkærsig
(2014) and Mikova, Sokolova (2014), these approaches are not the same. Each of these
projects plays completely separate role and very often they complement one another.
Methods used in the future-oriented technology analysis are derived from both the so-
cial sciences (Eerola, Miles 2011), as well as technical sciences (Halicka 2014; Idier 2000).
They are often modified for the purpose of far-reaching analyses of technology develop-
ment. A set of methods that can be used in FTA is open (May 1996). Selection of appropri-
ate methods for the future-oriented technology analysis – with such a vast catalogue – es-
pecially for a novice researcher, can be a big challenge. Organising and classifying methods
with similar properties into classes seems helpful. Initially, the author has reviewed the

578 K. Halicka. Innovative classication of methods of the Future-oriented Technology Analysis
existing classification of the research methods of future. Table 1 presents a summary of
selected classifications of methods used in all areas of FTA.
When analysing Table 1 in detail, it can be noticed that classifications available in the
literature order the methods only in terms of methods’ nature, and do not take into account
the multi-stage nature of FTA, and above all, do not take into account different – impor-
tant – functions performed in the course of a future-oriented technology analysis.
Table 1. Selected examples of common classications of research methods of future
Area
e number
of classes
and methods
Names of classes (the number
of methods in each class)
e author of the
classication
forecasting 3 classes
21 methods
Correlative (5); Direct (9);
Structural (7 )
A.T. Roper etal.
(Roper etal. 2011)
forecasting 5 klas
20 methods
Extrapolator (4); Pattern Analyst (4);
Goal Analyst (4); Counter-Puncher (4);
Intuitor (4)
J. H. Vanston (1995)
forecasting 3 classes
31 methods
Subjective assessment methods (4);
Exploratory methods (20);
Normative approaches (7)
Somnath Mishra,
S.G. Deshmukh,
Prem Vrat (Ayres
1969; Makridakis,
Wheelwright 1978;
Mishra etal. 2002)
technology
assessment
9 classes
70 methods
Economic Analysis (14); Decision analysis (7);
Externalities/impact analysis (6); Information
monitoring (4); Market analysis (6); Risk
assessment (5);
Systems engineering/analysis (8);
Technical performance assessment (12);
Technology forecasting (8)
T.A. Tran (Tran,
Daim 2008), De Piante
Henriksen (1997)
foresight 4 classes
13 methods
Identifying Issues (3); Extrapolative
Approaches (4); Creative Approaches (4);
Prioritization (2)
I. Miles, M. Keenan
(Unido 2005)
foresight 3 classes
40 methods
Foreseeing (10); Managing (14);
Creating (16)
G.H. May (1996)
foresight 3 classes
44 methods
Quantitative (11); Qualitative (22);
Semi-quantitative (11)
Popper (Georghiou
etal. 2008)
foresight 10 classes
117 methods
Consultative (10); Creative (12);
Prescriptive (15); Multicriterial (15);
Radar (8); Simulation (9); Diagnostic (12);
Analytical (17); Survey (8); Strategic (11)
A. Magruk (2011)
FTA 13 classes
53 methods
Creativity (3); Monitoring and intelligence
methods (2); Descriptive (4); Matrices (3);
Statistical methods (2); Trend analysis (4);
Expert opinion (4); Modeling and simulation
(6); Logical/cause analysis (9); Roadmapping
(4); Scenarios (5);
Valuation (5); Modications (2)
A.L. Porter, F. Scapolo
(Cagnin etal. 2008)
Source: own elaboration.

Technological and Economic Development of Economy, 2016, 22(4): 574–597 579
One of the first classifications of methods used for technological forecasting was pro-
posed by A. L. Porter, T. W. Mason, F. A. Rossini, J. Banks (Roper et al. 2011). They have
identified three classes: correlative, direct, and structural. Methods within the classes di-
rectly measure the functional capacity or some other relevant characteristic of the technol-
ogy. In turn, correlative methods relate the development of technology to the growth or
change of one or more elements in the same context or in contexts regarded as analogous.
Methods belonging to structural category analyse in detail the cause-and-effect relation-
ships that effect growth.
In contrast, Somnath Mishra, S. G. Deshmukh, Prem Vit (2002) reviewed the methods
most commonly used for forecasting. They identified 31 methods and grouped them into
three classes: subjective assessment methods, exploratory methods of technological fore-
casting, and normative approaches to technological forecasting.
One of the first classifications of methods used to foresight was developed by I. Miles
and M. Keenan. They selected four classes. The first class refers to methods of scanning and
defining a general framework for research. The other one includes methods using both a
statistical approach (e.g. trend extrapolation), and being based on expert opinion (e.g. the
Delphi method). Methods from the class three are characterized by flexibility and spon-
taneity in experiencing the analysed phenomena (Vanston 1995). They are used mostly to
develop a vision of the studied reality. The fourth class includes methods whose purpose
is to identify priorities of the development of technology.
A common classification of foresight methods is the so-called foresight diamond – the
division developed by R. Popper. He distinguished three classes: quantitative, indirect and
qualitative methods, considered in 4 dimensions: creativity – synergy – the facts – exper-
tise. Creative feature is demonstrated by methods characterized by ingenuity and creative
inventiveness. Expertise is an opinion, specialised examination carried out by experts. Syn-
ergy enables the creation of a common – for all participants – vision of the future. The facts
are helpful in understanding the current state of the studied area of research. Qualitative
methods are often based on the opinions of a particular group of people (UNIDO 2005). In
turn, with the use of quantitative methods, numerical parameters characterizing the studied
phenomenon or the object of study are defined. In contrast, the class of indirect methods
uses both qualitative and quantitative methods. For example, the opinions of experts can
be analysed using statistical models.
An interesting classification was presented by A. Magruk (2011). He identified 117
methods with the potential of use for foresight research and grouped them into 10 classes.
Methods from the consultative class enable collecting and analysing opinions of a wide
range of stakeholders on the study area and the factors associated with it. Class of creative
methods is based on spontaneity and flexibility, it facilitates the creation of a vision of
the researched items. Methods from the normative class are connected to anticipating the
future, and they primarily consist in defining the vision of development. Methods of the
multi-criterial class enables measuring the relationship between a group of variables and
the criteria characterizing the researched items. Methods from the radar class facilitate
monitoring, detecting and analysing important signals about the latest research and tech-
nological discoveries, potential innovations that could be related to the researched item.

580 K. Halicka. Innovative classication of methods of the Future-oriented Technology Analysis
The class of simulation methods is made up of analytical tools, using the expertise and
the characteristics of synthesis and modelling. Methods of the diagnostic class allow for
an assessment of the current state of the researched object as well as management of the
development of the researched object. Analytical methods refer to the study of development
trends, driving forces, variants of change, the structure of the researched reality, society as
well as potential stakeholders. The class of review methods allows for the diagnosis and
evaluation of data relating to past operations, results and the space-time studies. Methods
from the strategic class facilitate planning, scenario building, solving complex decision-
making problems and change management (Magruk 2015).
F. Scapolo and A. L. Porter (Cagnin et al. 2008) were the first – and so far the only –
authors, who gathered and systematized FTA methods. They have designated 53 methods
that may be used in a future-oriented technology analysis, and then classified them into
13 classes (methods families): creative approaches, monitoring & intelligence, descriptive,
matrices, statistical analyses, trend analyses, expert opinion, modelling & simulation, logi-
cal/causal analyses, roadmapping, scenarios, valuing/decision-aiding/economic analyses,
combinations.
The largest – consisting of 9 methods – is the class called logical/causal analyses. This
family consists of analytical methods, determining factors affecting the economic indi-
cator under examination and the scale of impact of individual factors on the deviation
resulting from previous comparisons. Another large class is modelling and simulation.
Tools from this class include primarily quantitative methods allowing for the creation of
the model and identification of actions related to the creation of the future strategy of the
researched subject. In contrast, the methods from the scenarios class allow for the construc-
tion of a future vision of the phenomenon, or the possible aspects of the future. In turn,
the valuing/decision support/economic analysis class consists of 5 methods comprising
the optimization, analysis and selection of numerous data on the status quo. Methods of
the descriptive class characterise the technological sphere and present the latest scientific,
technological and innovative achievements. The class of matrixes is formed by 3 methods
combining intuitive and analytical element. They are used for analysing the future states of
the researched systems on the basis of the identified mutual interactions between variables
(forces, trends, events) occurring in the studied systems. Methods of the trend analyses
family allow for the analysis of trends and potential factors that could affect the develop-
ment of technology. Whereas the expert opinion class creates methods involving the col-
lection and analysis of the views of a wide range of stakeholders engaged in the research,
experts in the field. Methods of the creative approach class are characterized by freedom,
flexibility and spontaneity in understanding of the studied phenomena. The smallest class
is represented by statistical analyses and monitoring and intelligence. Methods belonging
to the first-class determine correlation, probability and consequences of the event. On the
other hand, the methods of another class take into account, inter alia, scanning of both
the environment and technology, and include the identification of opportunities and risks
associated with a given technology.
According to the author, F. Scapolo and A. L. Porter’s classification does not cover all the
possible and necessary tools for thorough technology analysis. The set of methods selected

Technological and Economic Development of Economy, 2016, 22(4): 574–597 581
by F. Scapolo A. L. Porter lack above all the tools enabling identification, assessment of the
factors that influence the development of technology. There are also no tools to evaluate the
state of technology, its technological maturity, and technological possibilities. In contrast,
scenario methods constitute a large class. According to the author, methods identified by
F. Scapolo and A. L. Porter are to a greater extent associated with the decision-making
trend rather than technology. Given the foregoing, it is noted that it is necessary to develop
a methodology for classification of FTA methods which takes into account complementa-
tion of the methods catalogue identified by F. Scapolo and A. L. Porter.
The detailed methodology of classification of methods of future-oriented technology
analysis is shown in Figure 1. The research process was supported by the following research
methods: critical analysis of literature, logical analysis and design, examination of the docu-
ments, mini-Delphi, statistical methods and artificial intelligence.
2. Identication methods with potential use in the technological FTA current
Given the denition and the function of FTA, the author has identied the methods used
to evaluate technologies in technological forecasting, technological foresight, as well as in
the future-oriented technology analysis based on literature review and direct observation.
Subsequently, each of these methods has been examined in detail in terms of its use in the
FTA technological stream. Table 2 shows the 90 nally selected methods.
Fig.1. Methodology for the classication of methods of future-oriented technology analysis
STEP I:
Identication of methods with
the potential of use in the FTA
technological trend
STEP III:
Qualication of methods
• Web of Science Base, IEEE
•Websites connected with FTA,
foresight, TA and forecasting
Catalogue of
90 methods
STEP II:
Evaluation of methods
e tool: mini-Delphi method
Criterion: informative function
of the FTA methods
Tools:
(1 Taxonomic Agglomeration
method – the Ward’s method
(2) Kohonen neural networks
STEP IV:
Analysis of the methods within
the class
Classes of
methods
Features and class
names

582 K. Halicka. Innovative classication of methods of the Future-oriented Technology Analysis
Table 2. Methods with potential use in the FTA technological stream
Name of the
method
Name of the
method
Name of the
method
Name of the
method
Name of the
method
factor analysis
(Georghiou
etal. 2008;
Magruk2011;
Saritas etal. 2014)
source data
analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
analysis of long-
term (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
analysis of the
action (Cagnin
etal. 2008)
institutional
analysis (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
stakeholder
analysis (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
correspondence
analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
life cycle costing
(Gieraszewska,
Romanowska
2014)
cost-benet
analysis (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Musango
2012; Saritas etal.
2014)
life cycle analyses
S-curve analysis
(Georghiou etal.
2008; Magruk
2011; Musango
2012; Saritas etal.
2014)
megatrend
analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
morphological
analysis (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
patent analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
force eld analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
benchmarking
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
requirements
analysis (Cagnin
etal. 2008)
FMEA
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
retrospective
analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
risk analysis
(Cagnin etal.
2008; Georghiou
etal. 2008;
Magruk 2011;
Saritas etal.
2014)
scientometrics
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
cluster analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
STEEPVL
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014; Nazarko
etal. 2011)
structural analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014; Nazarko
etal. 2011)
time series
analysis (Nazarko
2004)
webometrics
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
trend and impact
analysis (Cagnin
etal. 2008)
sensitivity analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
content analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
sustainability
analysis (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
ANKOT
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
desk research
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
technology
barometer
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
bibliometrics
(Cagnin etal.
2008; Georghiou
etal. 2008;
Magruk 2011;
Saritas etal. 2014)
brainstorming
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
delphi (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)

Technological and Economic Development of Economy, 2016, 22(4): 574–597 583
Name of the
method
Name of the
method
Name of the
method
Name of the
method
Name of the
method
classication trees
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
trees references
(Cagnin etal.
2008; Georghiou
etal. 2008;
Magruk 2011;
Saritas etal. 2014)
probability trees
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
diusion of
technology
(Cagnin etal.
2008)
net present value
(Gieraszewska,
Romanowska
2014)
wild cards
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
trend
extrapolation
(Georghiou etal.
2008; Magruk
2011; Musango
2012; Saritas etal.
2014)
production
function
(Gieraszewska,
Romanowska
2014)
key technologies
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
circle the future
(Cagnin etal.
2008; Georghiou
etal. 2008;
Magruk 2011;
Saritas etal. 2014)
correlations
(Cagnin etal.
2008; Musango
2012)
cross-impact
analysis (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
ranking lists
(prioritisation)
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
macrohistory
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
MANOA
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
technology
mapping (Cagnin
etal. 2008;
Gudanowska
2014)
technology
roadmapping
(Cagnin etal.
2008; Nazarko
etal. 2011)
Data Envelopment
Analysis (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
strategic position
and action
evaluation
(Gieraszewska,
Romanowska
2014)
portfolio methods
(Gieraszewska,
Romanowska
2014)
technology
readiness levels
(Nazarko etal.
2011)
agent-based
modeling (Cagnin
etal. 2008)
modelling and
simulation
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
robust portfolio
modeling
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
input-output
modeling (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
design thinking
(Saritas etal.
2014)
technological
observation
(Cagnin etal.
2008)
life cycle
assessment
(Gieraszewska,
Romanowska
2014)
assessment of the
impact on society
(Musango 2012)
expert panel
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
analytic hierarchy
process (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Musango
2012; Saritas etal.
2014)
analog forecasting
(Cagnin etal.
2008; Georghiou
etal. 2008;
Magruk 2011;
Saritas etal. 2014)
stochastic
forecasting
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
backcasting
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
simple multi-
attribute ranking
technique
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
Continue of Table 2

584 K. Halicka. Innovative classication of methods of the Future-oriented Technology Analysis
Name of the
method
Name of the
method
Name of the
method
Name of the
method
Name of the
method
literature review
(Cagnin etal.
2008; Georghiou
etal. 2008;
Magruk 2011;
Saritas etal. 2014)
scenarios (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
social networks
analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
environmental
scanning
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
technological
scanning
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
weak signals
(Cagnin etal.
2008; Georghiou
etal. 2008;
Magruk 2011;
Saritas etal. 2014)
survey (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
State of the Future
Index (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
SWOT (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
(MPA) multi
perspective
approach (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
tech mining
(Cagnin etal.
2008; Georghiou
etal. 2008;
Magruk 2011;
Saritas etal. 2014)
theory of
inventive problem
solving (Cagnin
etal. 2008;
Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
causal layered
analysis
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
future workshops
(Cagnin etal.
2008; Georghiou
etal. 2008;
Magruk 2011;
Saritas etal. 2014)
IRR– internal
rate of return
(Gieraszewska,
Romanowska
2014)
visions of the
future (Cagnin
etal. 2008)
PI– protability
index
complex adaptive
systems (Cagnin
etal. 2008)
focus group
(Cagnin etal.
2008)
technology
scouting
(Georghiou etal.
2008; Magruk
2011; Saritas etal.
2014)
Source: own elaboration.
The set of methods that can be used for the future-oriented technology analysis contin-
ues to be extensive. Therefore, it seems essential to find ways to facilitate and systematize
the process of selection of research methods with potential use in FTA. Given the various
functions performed during the process of FTA, it is necessary to evaluate and organize
these methods in terms of their use for the implementation (realization) of the following
functions: (1) collecting information on the purpose and scope of the analysis; (2) collect-
ing and collating information on technologies; (3) processing information about the past
of the technologies; (4) processing and generating new information on the current state
of technology; (5) gathering information on the impact of the environment on technology
and technology on the environment; (6) transmission of the acquired information; (7) col-
lecting information on the factors affecting the development of technology; (8) generating
new information concerning the development of a particular technology; (9) interpreting
and using the obtained information.
Methods with potential use in FTA have been evaluated, due to their informative func-
tion, by a group of experts. The mini-Delphi method was used for the evaluation of the
End of Table 2

Technological and Economic Development of Economy, 2016, 22(4): 574–597 585
methods. This method usually consists of a preparatory stage, the measurement of variables
with the use of a questionnaire in two or more rounds, the analysis and implementation of
the results after the completion of the study. The mini-Delphi method may take the form
of direct talks, interviews, meetings or seminars, as well as interactions with experts via the
Internet (Nazarko 2013). In this study, the experts were contacted by means of electronic
communication. The research process of the Delphic proceedings is shown in Figure 2.
The study involved 12 experts. The experts have been selected purposefully, taking into
account their knowledge and experience in the field of future studies. Subsequently, the
method assessment questionnaire has been prepared in electronic form. The questionnaire
had the shape of a matrix with dimensions of 90×9, whose rows were the names of the
methods with potential use in the future-oriented technology analysis and the columns
represented the functions implemented in the process of FTA, (Fig. 3). In addition, the au-
thor has developed a set of abstracts – basic information about the methods with potential
use in FTA.
Subsequently, the developed Delphi questionnaire, along with information about the
methods was distributed among the experts. The experts had at their disposal a four-step
assessment scale and determined the extent to which a particular method performs the
FTA function, where: 0 – unsuitability of a method for the realization of the FTA function;
Fig.2. Research process using the mini-Delphi method
Fig.3. Scheme of the questionnaire for the assessment of methods with the potential use in FTA
Group of experts
Group of experts
Recruitment of experts
e design of the electronic version of the
questionnaire for the evaluation of methods with
potential use in FTA
Sending out questionnaires of method assessment
Sending out questionnaires of method assessment
along with the results from the 1 round
Production of results
Completion of the questionnaire in the 1 round
2
round
Completion of the questionnaire in the 2 round
1
round
Initial
phase
Final
phase
Group of experts
Author
Function 1 Function 2 Function 3 … Function 9
Method 1
Method 2
Method 3
…
Method 90

586 K. Halicka. Innovative classication of methods of the Future-oriented Technology Analysis
1 – low applicability of the method for the realization of the FTA function; 2 – average ap-
plicability of the method for the realization of the FTA function; 3 – high applicability of
the method for the realization of the FTA function.
The collected results were analysed in detail. Subsequently, the questionnaire for the
evaluation of methods with potential use in FTA, together with the developed results from
round 1, was again sent to the same experts. In the next round of the study, the experts
completed the same questionnaire, while having the opportunity to familiarize themselves
with the aggregated results from the first round of the study. They were able to compare
their own positions with the opinion of the group, and – after analysing the arguments –
could change their mind. The questionnaires received from the second round of the study
have been re-examined. Eventually, a matrix of the implementation (realization) of the
function of the FTA process through various methods has been obtained. To develop the
final matrix, the dominant of the expert assessments has been adopted. Subsequently, with
the use of the statistical methods and Kohonen artificial neural networks, these methods
have been classified.
3. Results of the classication of methods
e process of classication of methods was carried out in two steps, both the statistical
methods (cluster analysis method), and the methods of articial intelligence (Kohonen
networks) were used. For the determination of the number of classes the Ward’s method
of agglomeration with Euclidean distances is used, the result of which is a dendodram
(diagram of clusters). e number of method clusters with the potential use in FTA was
determined on the basis of the analysis of the chart of the course of agglomeration, as well
as dendodram analysis. e chart of the course of agglomeration shows distances between
clusters at the time of their bonding and its analysis makes it possible to nd the intersec-
tions of the tree diagram and thus determine the number of classes (Fig.4).
When analysing Figure 4 it can be seen that the first clear increase (leap) in agglomera-
tion distance occurs at the level of about 320. It is a place, where multiple clusters formed
at the same bonding distance (Jarocka 2015). This distance has been marked on the chart
(Fig. 5) of the Ward’s method. The point of intersection of the tree diagram determines
the number of classes.
When analysing Figure 5 it was observed that 7 clusters can be identified. The meth-
ods of forming these classes were analysed and it was found that they do not always form
substantially coherent classes. Therefore, to determine the methods in particular classes,
because of the excellent classification abilities, artificial neural networks – Kohonen net-
works were used (Jamroz, Niedoba 2015).
An important advantage of neural networks is the fact that they allow the presentation
of non-linearities and to resolve problems, for which it is difficult to precisely define the
cause-and-effect relationships. They are effective in situations in which there are no simple
rules for classification (Halicka 2011). According to R. Tadeusiewicz (Dudek-Dyduch et al.
2009), a Kohonen network can detect relationships that would be overlooked if the tradi-
tional statistical grouping method was used. Kohonen neural networks are non-model sys-

Technological and Economic Development of Economy, 2016, 22(4): 574–597 587
Fig.4. Chart of the course of agglomeration
Fig.5. Diagram of clusters of future oriented technology method
e chart of distances between bonds relative to the stages of bonding
Euclidean distance
0 9 18 27 36 45 54 63 72 81 90
Stage
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
Distances between bonds
Bond ing dis tance
Tree diagram
Ward’s method
Euclidean distance
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
Bonding distance
1 2 3 4 5 6 7
tems, they recognize the relationship between the studied individuals without any a priori
assumptions as to their type, structure (Sulkava et al. 2015). This approach is different from
statistical surveys, in which it is necessary to initially formulate a hypothesis, determine
the research sample and select the methods of their verification (Mohebi, Bagirov 2015).

588 K. Halicka. Innovative classication of methods of the Future-oriented Technology Analysis
In addition, neural networks are capable of identifying clusters with any spatial structure,
are insensitive to the presence of non-standard units and are insensitive to the presence
of a significant number of units not forming clusters (Sousa et al. 2015). Taking into ac-
count the mentioned advantages of the network and also taking into account the research
problem, the Kohonen networks seem to be the right tool for the classification of methods
with potential use in FTA.
The Kohenen network is referred to as the Self-Organizing Map (SOM), or Self-Orga-
nizing Feature Map (SOFM). The network aims to create such a structure, which would
best replicate the interrelations between the input vectors. In the Kohonen network, indi-
vidual neurons identify and recognize the individual clusters of data. Kohonen network
has two layers: an input layer and an output layer (in the literature also known as competi-
tive or Kohonen layer). The number of neurons in the input layer is unequivocal with the
number of diagnostic features. In the undertaken research problem, the usefulness of the
method for the implementation (realization) of a particular FTA function was adopted
as a diagnostic feature, according to which the division of the methods was performed.
During the study 9 functions were selected. In turn, the number of neurons in the output
layer is determined by the number of homogeneous clusters. The analysis of the chart and
diagram of the course of agglomeration made possible to identify 7 homogeneous clusters.
Therefore, it was finally possible to construct a neural network consisting of 9 neurons in
the input layer and 7 neurons in the output layer.
The developed Kohonen neural network has undergone learning and ultimately in the
first class twelve methods were found, five methods in the second, and in subsequent class-
es, respectively: eight, eighteen, nine, twenty-two, and in the final cluster sixteen methods
can be distinguished.
Afterwards, each of the classes was analysed in detail. The main characteristics of each
of the classes were determined. Subsequently, authorial names for each of the classes were
proposed. Detailed information on the classes, methods and the activation of each method
are shown in Table 3.
First class, accumulation, consists of 12 methods primarily enabling the collection of
information on technologies, based on literature databases, patent databases, reports, web-
sites, radio and television. These methods also make it possible to: (1) organize, select data
on the technology, its characteristics, determinants, possibilities of application, costs; (2)
assess the state (advancement) of development works on new technologies; (3) acquire the
knowledge and skills necessary to conduct research on technologies; (4) identify potential
partners, competitors, suppliers and recipients of technology. As a result of these methods,
collective knowledge bases concerning a particular technology are developed and presented
in a synthetic manner, rankings and comparisons reflecting the interdependencies between
several variables are generated.
Another class, creation, is complementary to the previous class. It consists of 5 methods
for the accumulation of knowledge about technology, factors influencing its development,
and the environment, based on expert knowledge. On the basis of knowledge, skills, and
the experience of experts, new, often still undisseminated knowledge. Using the methods
from this class, it is possible to: (1) estimate the probability, outcome, and time of the oc-

Technological and Economic Development of Economy, 2016, 22(4): 574–597 589
Table 3. Authorial classes together with the FTA research methods belonging to them
Class Main features of the
classes Methods
I
accumulation
12 methods
collection of
information
source data analysis, patent analysis, webometrics, content
analysis, desk research, technology mapping, Technology
Readiness Levels TRL, literature review, technology
scouting, tech mining, scientometrics, bibliometrics
II
creation
5 methods
generation of new
knowledge
Delphi, key technologies, expert panel, eory of
Inventive Problem Solving, focus group
III
retrospection
8 methods
analysis of historical
data in order to
identify trends
analysis of long-term, life cycle analyses, S-curve analysis,
benchmarking, retrospective analysis, time series analysis,
ANKOT, trend extrapolation, macrohistory
IV
exploration
18 methods
analysis of
technologies from
dierent perspectives:
social, technological,
economic, ecological,
political, values, legal
force eld analysis, FMEA, STEEPVL, structural analysis,
brainstorming, cross-impact analysis, strategic position
and action evaluation, portfolio methods, agent-based
modelling, modelling and simulation, input-output
modelling, assessment of the impact on society, survey,
SWOT, causal layered analysis, future workshops, social
networks analysis, environmental scanning
V
quantication
9 methods
an estimate of the
costs associated
to the lifecycle of
technologies
life cycle costing, cost-benet analysis, sensitivity analysis,
Net Present Value– NPV, production function, DEA,
life cycle assessment, internal rate of return, protability
index
VI
selection
22 methods
identication,
classication, ranking
of analysed objects
(stimuli aecting
the development of
technology and the
analysed technologies)
factor analysis, analysis of the action, institutional
analysis, correspondence analysis, risk analysis, cluster
analysis, sustainability analysis, classication trees,
probability trees, diusion of technology, wild cards,
correlations, ranking lists, robust portfolio modelling,
analytic hierarchy process, stochastic forecasting,
simple multi-attribute ranking technique, weak signals,
technological observation, stakeholder analysis (citizen
panel), morphological analysis, technological scanning
VII
projection
16 methods
presentation of the
development paths of
technologies; analysis
of trends and potential
events that may aect
the trajectory of
the development of
technologies
requirements analysis, technology barometer, trees
references, circle the future (futures wheel), MANOA,
technology road mapping, design thinking, analogue
forecasting, backcasting, scenarios, estimatingmulti
perspective, visions of the future, complex adaptive
systems, megatrend analysis, trend and impact analysis,
state of the future index
Source: own elaboration.
currence of future events; (2) identify research directions that have the potential for devel-
opment in the future; (3) determine the priority directions of technological development;
(4) separate technologies, which to the highest degree contribute to the development of the
examined area; (5) search for completely new solutions to complex problems related to the
analysed technology; (6) develop new technological solutions.
The third class – retrospection – consists of 8 methods enabling the analysis of past
events describing the causes and mechanisms of historical changes in order to understand

590 K. Halicka. Innovative classication of methods of the Future-oriented Technology Analysis
the potential structure of the future. These methods allow for: (1) the analysis of the past
in order to construct a better future; (2) identification and study of development trends
in the economy, technology and society; (3) the examination of the market age of each
product of a company or any technology used in it, and consequently to rationally plan
the product portfolio and the costs associated with the introduction and creation of new
products and technologies.
Fourth class – exploration – contains 18 methods for the examination of the environ-
ment and the analysis of the interior of the analysed object, and focusing on observation,
testing, monitoring and systematic description of the technological, socio-cultural, eco-
logical and economic context of the technology being tested. These methods allow for: (1)
the analysis of external environmental factors (social, technological, economic, ecological,
political, values, legal) affecting the development of the technology being tested; (2) pre-
dicting the probability of occurrence of future states of the analysed systems, based on the
identified interactions between variables (forces, trends, events) occurring in the studied
systems. Often, these methods make use of the group, creative work of a team.
Another class – quantification – consists of 9 methods for the identification and evalu-
ation of all costs associated with the life cycle of technology. Using these methods makes
it possible to: (1) compare the total expected costs with the total expected benefits of use,
production of a given technology; (2) assess the effects that a given technology would have
on the environment during its whole life; (3) determining the cost-effectiveness threshold
of the use and production of a given technology at varying levels of force of factors affect-
ing them.
The most numerous class – selection – consists of 22 methods for the identification,
evaluation, classification and ranking of the examined objects. They can be used in the
context of the key factors for influencing the future of a given technology, as well as in
the context of the elements or components of technology. If during the FTA several tech-
nologies are analysed, the methods from this class will help in the assessment, ranking,
and selection of technologies. These methods also make it possible to: (1) predict the oc-
currence of events affecting the innovative paths of development of the technology under
assessment; (2) study the relationship between the factors influencing the development of
technology; (3) make decisions in the context of significant, forward-looking technologies.
The last class – projection – includes methods for presenting the direct or indirect
future of technology and the related changes and trends in selected areas. Using these
methods makes it possible to: (1) shape the vision, formulate alternative scenarios for the
development of a particular technology; (2) present technological development in the long
term from various perspectives simultaneously: technical, organizational, social, environ-
mental, economic, personal and others; detect, characterize and analyse important devel-
opment trends (persisting for a long time) in the surveyed areas with a global reach, and
their impact on the society.

Technological and Economic Development of Economy, 2016, 22(4): 574–597 591
4. Analysis of the results of research
Analysing the resulting classes it can be seen that the collection, organization, and pre-
sentation of information related to the current state of the analysed technology will be
possible with the use of the Class I methods (accumulation). For the processing of the
acquired information on the current state of technology, as well as their presentation, the
methods of retrospection are used (Class III). In contrast, to generate new information
on the current state of technology and the cost of its application, Class V methods can be
used (quantication). To collect information on the environment and factors aecting the
development of technology, Class IV methods can be useful (exploration). e collection
and generation of new information related to the future development of the analysed tech-
nology will be facilitated by Class VI tools (selection). In contrast, interpreting and using
the acquired information on the development of the analysed technology will be enabled
by the use of Class VII methods (projection). In turn, Class II methods (creation) are used
for the performance of most of the FTA functions. e methods in this class are primarily
useful in the generation of new information, but also are needed to gather information on
a particular technology, its impact on the environment and the factors that determine the
development of technology.
The proprietary classification presented in an article allowed to find common semantic
ground for the methods belonging to a particular class. The new classification, given the
rich methodical environment of the future-oriented technology analysis, also allows a more
clear way to identify the characteristics of individual clusters that should be considered
during the FTA design process and the formulation of the research methodology. The con-
ducted study has allowed to reduce information overload and to establish the relationship
between the studied methods.
It should be noted that the current division into classes is characterized by considerable
freedom in the selection of methods, which can cause many ambiguities, especially for
inexperienced researchers. In addition, the previous classification covers either only a few
classes, referring to the important, but only general characteristics (over-simplifying the
classification criteria, and thus the principle of selection of methods), or a small number
of methods.
Some classification approaches (e.g. classification of foresight methods according to
Popper (Georghiou et al. 2008), FTA classification (Cagnin et al. 2008), and classification
of forecasting according to Porter (Roper et al. 2011)) are very popular in the literature on
the subject, but in the opinion of the author, have their limitations. These classifications
cover only a few dimensions.
Summing up the obtained results it should be stated that the undertaken subject is
innovative in nature and its development will have practical application. It can become a
source of helpful tips in the long-term process of technology management. The problem of
the classification of methods used in the future-oriented technology analysis, undertaken
in the article, constituted a considerable research challenge, both because of the attempt to
approach the subject in an innovative way, an because of the existing – according to the au-
thor – conceptual and methodological chaos and insufficient theoretical basis in the litera-
ture on the subject and in practice concerning the classification of the future test methods.

592 K. Halicka. Innovative classication of methods of the Future-oriented Technology Analysis
Novum in the research undertaken within the framework of the article should be con-
sidered in three areas: the identification of methods for the potential use in FTA (i), the
selection of a criterion (ii), and the tools for the classification of these methods (iii). The
author supplemented the set of methods applicable in the future-oriented technology analy-
sis. In addition, she classified the methods in terms of their use in the implementation
of individual FTA functions. So far, in the literature on the subject, the future-oriented
technology analysis methods with regard to their usefulness in carrying out particular
functions of this process have not been evaluated or organised. What is more, the process
of classification of the future methods did not involve the artificial intelligence methods,
which according to the author have a high potential for classification.
Conclusions
Currently– in an era characterized by signicant dynamics of the environment– careful
consideration, or even planning the future development of technology is gaining in im-
portance. e tool allowing the presentation of a wide approach to the future of selected
technologies, developed taking into account the knowledge and experience in that area is
the future-oriented technology analysis. is process facilitates the integration of science
and technology with business practice, and the identication of opportunities in the eld
of development of new technologies. It also allows the coordinated development of the
technological potential along with the scenarios of market or sector development.
The lack of clear guidelines, both in Polish and foreign literature, limited the fully
correct and effective use of the future-oriented technology analysis in the study of evolu-
tion of technology. The article presents the methods that can be used for future-oriented
technology analysis. The text presents an original classification of the identified methods
with potential use in FTA.
For the conducted future-oriented technology analysis to be fully fair, one should look
at the research problem in a systemic way. According to the author, the ability to classify
the FTA methods may support their complementary selection during the design of the
predictive process, without limiting the flexibility of these studies at the same time.
The problem undertaken in the article is important, therefore, work in this area will be
continued by the author. Further research will focus on the development of an algorithm,
a method of joining and selecting methods from individual classes.
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Katarzyna HALICKA. PhD, since 2008 is an assistant professor at the Faculty of Management and
Finance at the Bialystok University of Technology. Deputy Head of the Department of Business Infor-
matics and Logistics and an editor of the logistics management section of the Economics and Manage-
ment Journal. Author of about 60 scientic articles. Research interests: forecasting, foresight studies,
technology management, methods of articial intelligence.