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Innovative classification of technology foresight methods/Nauja technologiju prognozavimo metodu klasifikacija.

1. Introduction

The complexity of social, technological, political and economic phenomena makes their anticipation necessary. The determinants of units' functioning, indispensable for choosing a working strategy and for competing should be created and reconstructed in advance (Pawlowski, K., Pawlowski, E. 2005; Skawinska 2007).

Traditional planning, modeling or forecasting of technological development should be strengthened by skills to use modern organizational approaches in the future management field. One of more important tools in this range is technology foresight programs, which integrate varied approaches: predictive, statistical, economic, technological, connected with management, etc. and which relate equally to the development of technology and economy, science and politics, social conditions and cultural aspects (Unido ... 2005; Okon-Hordynska 2006).

The article defines the concept of technology foresight and characterizes the factors affecting its research process identified by the author on the basis of both literature review as well as direct observation (author has so far actively participated in five foresight initiatives). The author has identified an extensive list of research methods which can be used in technology foresight projects. Considering that to date many foresight methods belong to several types, the author attempted to create his own, innovative classification, based on the cluster analysis, portraying groups of methods be characterized by similar features. In the opinion of the author these results can considerably facilitate the selection of individual methods in the procedure of building the foresight research process.

2. Essence of technology foresight

One of most popular definitions of foresight, was the one proposed in 1983 by Ben Martin and John Irvine (Martin 2010): Foresight is the process involved in systematically attempting to look into the long-term future of science, technology, economy and society with the aim of identifying the areas of strategic research and the emerging generic technologies likely to yield the greatest economic and social benefits.

Technology foresight is both the process and the tool, with the help of which the consensus of scholars, engineers, representatives of industry, workers of public administration as well as wide society is obtained.

D. Stout identified three following fundamental principles of foresight as a process (Stout 1995): (1) foresight is not foreseeing future, but creating it; (2) neither a single man, nor a single company it is able to create future alone; (3) new knowledge can benefit many, knowledge kept secret is worthless knowledge.

Foresight can be defined as a process of systematic inquiry into the future, the results of which affect the dynamic adjustment of the far-reaching options of the future to changing environments (Reger 2001). Foresight consists in understanding the future using the fundamental laws and factors of the observed structures and phenomena (Reid, Zyglidopoulos 2004).

In research of the future of complex issues a single scientific method will never be sufficient to achieve the aim of the research. The quality of the results of foresight research will depend on the skilful use of a set of methods which expand the possibility of anticipating the future. Methods used individually or in inappropriate combination reduce the optimum ability of identifying the future of possible technologies, therefore it is justifiable to search for a best combination of chosen methods. This can turn out the most essential working with enlarging the efficiency of foresight process (Slaughter 1998).

3. Factors influencing the technology foresight research process

A factor is one of causes producing the result (Polish Language Dictionary). For the design process, and the correct choice of suitable research methods, to be an effective process, indispensable are identification, profiling and indication of all factors influencing the technology foresight research process (Slaughter 2004).

On the basis of past studies the author has completed identification of factors which influence the technology foresight research process (Table 1).

Above mentioned factors and their proper selection are essential in the technology foresight research process. The author's own research shows that not taking all these factors into account foresight becomes a process which is non-systematic and incoherent, and is based solely on intuition and sometimes the inexperience and irresponsibility of practitioners and organizers.

4. Methods of technology foresight

As a result of the existence of foresight for some time such thing as research: basis (perspectives), paradigms and methods have been well developed. The interdisciplinary character of foresight causes that it uses many research methods. A method is a conscious and practical course of action leading to the achievement of an intended aim (Polish Language Dictionary).

One of the characteristics of foresight is the free selection of research methods used within the process. At present the list of methods and techniques which can be used is very extensive (Table 2) and still open.

There are important conditions when selecting appropriate methods. Literature highlights the three most important, and theese have been fulfilled by the author of this article (Alexandrova et al. 2007): (1) a most complete list of methods which can be used in foresight studies, including both popular methods as well as those less frequently used, (2) knowledge about the characteristics of each method, i.e., understanding the key features of each one (3) establishing a set of criteria that will allow the selection of appropriate methods and the rejection of other techniques.

Under the concept of MCDM lies a wide range of Multiple Criteria Decision Making Methods such as (Zavadskas, Turskis 2011; Gass 2005; Kildiene et al. 2011) MAUT, UTA, COPRAS, SAW, TOPSIS, Electre, Vikor, Promethee, Moora. The creators of the various techniques are, respectively, Keeney, R. L.; Jacquet-Lagreze, E. and Siskos, J.; Zavadskas, E. K.; Kaklauskas, A.; MacCrimon, K. R.; Hwang, C. L. and Yoon, K.S.; Roy, B.; Opricovic, S.; Brans, J. P.; Brauers, W. K. Other multicriteria methods such as AHP (developer: Saaty, T. L.), DEA, PRIME, SMART, prioritization, were extracted separately because of potential foresight research applications (Salo et al. 2003; Amanatidou 2008; Lee, H., Lee, Ch. 2008).

Despite such a wide range of conditions of foresight research, experts taking part in the realization of these types of programs agree with the opinion that a universal methodology of conducting them does not exist. On the one hand this situation is beneficial because those carrying out such programs are not hampered by strict guidelines. Meanwhile, on the other hand, lack of methodology conditioned by the above-mentioned factors can cause many difficulties since it is not known whether the chosen methods and techniques are optimal in the given situation.

5. Typology of technology foresight methods

The considerable freedom of choice in the selection of methods consisting the investigative apparatus of a foresight program (the project) decides about its elasticity as well as--when the choice made is accurate--the effectiveness of foresight as a predictive tool. Simultaneously the necessity of designing the course of the research process, depending on the needs and restricting conditions, can become a hindrance and forces the search of areas in range of which the systematization of accessible methods is possible, which facilitates synergistic composition of investigative methodology in a holistic framework.

In this chapter an attempt at typology of a set of methods characteristic to technology foresight has been made. According to the author this process can become the starting point to effective combining of individual methods into research methodologies structuring specific foresight undertakings.

Table 3 presents types of methods according to the following criteria: thinking about the future, type of data, method of data acquisition, kind of co-operation, working environment, reference to time, source of data, reference to technology, orientation, meaning for foresight, stage of research, character of results, essence of research, way of thinking, layer and way of inference. In the author's opinion the following profile can be helpful, on one side for novice foresight practitioners, and on the other, however, for experts dealing with technology foresight investigative methodology in a professional way as well as in the optimization process.

In explorative methods the present, from which follows the anticipation of a possible or probable future is the initial point. In case of normative methods, the activity of explorers is directed in the opposite direction. At the beginning a desirable vision is presented (created). Based on this the researchers define what should be done to reach such a state or to avoid it (Ludeke 2007). Methods based on uncertainty are those which accept unpredictability and are focused on management of critical and strategic changes, often based on tsunamis of change (May 2009).

During creating of the future, and especially in technology foresight programs, maintenance of the equilibrium in the use quantitative and qualitative methods is advisable. Qualitative methods are based on expert opinions (presumptions), while in quantitative methods numerical parameters characterizing the studied phenomenon or the object of investigation are defined. Qualitative methods should be used with very complex phenomena, trends which are difficult to numerically visualize unambiguously. Quantitative methods are mainly based on numerical representation of simple phenomena using mathematical models for this purpose. It is possible to distinguish so called indirect methods. With their help complex phenomenon can be represented using numerical data (Unido ... 2005; Gasparikova 2007).

The next set of methods, emphasized on account of the manner of data acquisition, are the heuristic methods--based on experts opinion (intuition), and analytic methods--based on accessible knowledge, evidence, statistics, etc. (Popper, Korte 2004).

Particularly important in foresight research is the distinction between bottom-up and top-down methods. Foresight practitioners often underline the validity of bottom-up research based on open participation of the widest group of stakeholders from different environments. In case of top-down research the main (and often the only) role is played by the experts (Popper, Korte 2004).

Making work environment into a criterion, technology foresight methods can be divided into two groups: those which use digital tools (computer programs, internet, etc.), and those which do not require the use of IT infrastructure (Popper, Korte 2004).

Foresight methods do not study only the future of a given phenomenon. Often, in order to define the desirable vision it is indispensable to reference the present or a past situation.

With the criterion of the source of data, methods using original data (gathered with interviews, for example) as well as method using secondary data (demographic data, epidemiological, etc.) are distinguished (Technology ... 2004).

The next type of methods, particularly useful in technology foresight, consists of methods in which technological aspect plays the leading role. In this group it is possible to distinguish methods describing factors which influence technology development and methods determining the influence of technology development on other aspects of life.

On account of orientation it is possible to single out methods focused on the product, as well as method focused on the process. Taking into consideration the character of foresight projects, methods from second group are more important in the hierarchy. Foresight should be a continuous process, having an iterative character, as it takes place, for example, in Japan.

Conducting of foresight projects on wider scale since the 80's allows for distinguishing of formal methods--checked and applied in many projects, as well as optional methods practiced less often (Popper et al. 2007).

An important type of methods, especially for novice foresight practitioners, can become type distinguished on account of the stage of research. Here, it is possible to distinguish 6 kinds of methods. The first group is the preliminary methods. The second group is the recruitment methods. With the help of these methods key shareholders of the foresight process are mobilized and engaged. The third group--the Generation methods are responsible for generating new knowledge through exploration, analysis and anticipation of the possible future. The fourth group of methods is action methods, shaping the future through strategic planning. The next to last group of methods in this category is final methods, identifying the final priorities of the research and development of technology. Considering the specifics of foresight projects it is possible to identify the last group in this category--the renewal methods, responsible for next iterations of the program (Popper et al. 2008b).

Considering the essence of the research (based on the so-called "foresight diamond") it is possible to distinguish methods which are based on: creativity, interaction, evidence, expertise. It would be unadvisable for well-chosen research methods to be dominated by one of the dimensions mentioned. Expertise links future possibilities with present scientific and technological challenges. Creativity often is an extreme image of the future. Cooperation facilitates the creation of one common (to all participants) vision of the future. Evidence is a good, real starting point for further research (Popper et al. 2007).

Considering the wide scale of technology foresight methods which can be used, the author has distinguished one more group of methods--based on the character of the results. In this type textual methods, graphical methods and verbal methods are distinguished.

Among perspective methods J. Voros distinguishes two types of methods: evolutionary methods and revolutionary methods. In the first group of methods the starting point is the present, which is followed by evolutionary, relatively stable, predictable and reliable development. These methods are distinguished by the following attributes: the evolution of extrapolation, regularity, smoothness, continuity, gradual growth. Revolutionary methods which focus on a distant, hard to foresee,, based on sudden events, future (most often possible and desirable), which is not necessarily connected with the present, are often characterized by a sudden, a distinct point of view. Methods from this group can be said to have the following attributes: revolution, break, separation, disruption, twist, sudden spike (Voros 2006).

In accordance to the criterion of the layer a mathematical method can be distinguished, which emphasizes the social aspect with an engineering approach and systemic methods. The methods of the last two groups have been applied for a long time and are strongly grounded in strategic management. Mathematical methods and those which emphasize the social aspect are not very common in future studies. Engineering methods relate to the future as a certain kind of a continuation of the present. Mathematical methods, in turn, are often based on complex adaptive systems. Systemic methods help to clarify many uncertainties (Aaltonen, Sanders 2006).

Inductive and deductive methods are distinguished on the basis of the way of infering. Inductive methods, developed mainly in experimental studies, also called methods from the particular to the general, are based on the formulation of claims on the basis of individual conditions of single repeatible observations. Deductive methods rely on the derivation of logical conclusions based on the opinions and recognized opinions and laws, from general to particular (Cempel 2002).

6. Cluster analysis of technology foresight methods

Since many foresight methods belong to several types the author attempted to develop his own classification, creating groups of methods characterized by similar features. This operation may be a significant contribution to the process of singling out groups of substitutive and complementary methods. This process can also considerably facilitate the selection of individual methods in the building of the foresight research process. Among other things, supported by the data from Tables 1 and 3, a list of features characterizing research methods was created. In the author's studies one of more popular taxonomy method--numerical taxonomy was used. Numerical taxonomy methods can be applied everywhere where it is possible to apply statistical methods i.e. in biology, medicine, sociology, economy. The notion of numerical taxonomy originates from biology sciences and was introduced by Caina and Harrisona, as a way of the calculation similarity between organisms based on all available features, without any preliminary valuation (Rutkowski 2000). In traditional English literature classic notions: "taxonomy", "numerical taxonomy" are used. In turn the term "cluster analysis" is universally used in American literature (Pociecha et al. 1988).

Cluster analysis consists of four basic stages (Sagan 2000):

1. Selection of variables and method of determining similarities between objects.

2. Selection of a method of subordinating objects into homogeneous groups.

3. Choice of number of clusters identified.

4. Interpretation and dividing of clusters obtained.

A binary system of coding the attributes was applied because it is unambiguous as well as the one most often used in numerical taxonomy. In an investigation the first stage is very important. Sixty five variables were chosen, which in the author's opinion correctly describe the objects assembled (research methods) and accurately refer to the goals of the analysis. These variables are:

1. Explorativity; 2. Rely on Uncertainty; 3. Normativity; 4. Qualitative character; 5. Indirect nature; 6. Qualitative Character; 7. Heuristic Character; 8. Analyticity; 9. Bottom-up Character; 10. Top-down Character; 11. Rely on Working in a Virtual Environment; 12. Rely on Working in the Real Environment; 13. Shaping the Future; 14. Analysing Present State; 15. Appeals to the Past; 16. Describing Factors Which Influence Technology Development; 17. Determining Influence of Technology Development for Other Aspects of Life; 18. Focus on the Product; 19. Focus on Process; 20. Formal Nature; 21. Optional Nature; 22. Preliminary Character; 23. Recruitment Character; 24. Refresher Character; 25. Graphic Character; 26. Text Character; 27. Verbal Nature; 28. Unambiguity (exclusion the free choice of usage of different ways and principles); 29. Expensive Character; 30. Requiring the Participation of a Numerous of Human Resources; 31. Time-consuming; 32. Objectivity; 33. Regularity; 34. Penetrative Character; 35. Rely on Creativity; 36. Rely on the Interaction; 37. Rely on Evidence; 38. Rely on Expertise; 39. Susceptibility; 40. Resistance to External Factors; 41. Resistance to Internal Factors; 42. Scanning Character; 43. Forecasting Character; 44. Creating Vision; 45. Planning Character; 46. Action Character; 47. Evolutionary Character; 48. Revolutionary Character; 49. Understanding Character; 50. Concerning Synthesis and Modeling; 51. Concerning the Analysis and Selection; 52. Mathematical Perspective; 53 Social Perspective; 54. Engineering Approach; 55. Systemic Thinking; 56. Technological Perspective; 57. Cognitive Perspective; 58. Inductive Character; 59. Deductive Character; 60. Reliance on Statistical Inference; 61. Using the Raw Data; 62. Using Secondary Data; 63. Generating Codified Results; 64. Complex Attitude; 65. Shot Scenario.

When it comes to the way of subordinating objects to homogeneous groups, the hierarchic approach was chosen. To create hierarchic diagrams the author used a module for cluster analysis of the STATISTICA 9 software. Cluster analysis is the typical method practiced in taxonometrics. When using two-stage features three types of distance are most often chosen: euclidean, squared euclidean and the city-block. In the present study research methods were divided into more homogeneous groups, using an agglomerate taxonomy method. The Ward's method (recognized as very effective in creating homogeneous clusters (Grabinski, Sokolowski 1984) was chosen, which is part of a group of procedures developed by G. M. Lance, W. T. Williams and J. H. Ward. This method attempts to minimize the sum of squares of any two clusters that can be formed at each step. This method is regarded as very efficient; however, it tends to create clusters of a small size (Nowak 1980). Distances between research methods were measured by using the city-block distance--"Manhattan". In the case of measuring the distance "Manhattan" and a binary system of coding the attributes, the distance between the two clusters presents the number of features by which one cluster differs from the other (Rutkowski 2000).

The results of the agglomerate taxonomy method are presented as a tree of connections (a diagram) in figure 1. Depending on the distance examined between objects it is possible to distinguish 10 homogeneous clusters of research methods.

Each class received an original name which characterizes their general nature (in terms of a combination of their features): I. Consultative; II. Creative; III. Prescriptive; IV. Multicriterial; V. Radar; VI. Simulation; VII. Diagnostic; VIII. Analytical; IX. Survey; X. Strategic. Table 4 presents a group of methods belonging to a particular class. These methods are listed in order of appearance in the diagram.

First class is a group of consultative methods, or those which involve the collection and analysis of opinions (usually expressed verbally) of the broadest range of stakeholders who are not necessarily experts in a given field. A method of this class may be a useful tool in every phase of foresight studies, with particular regard to the initial and final stages. Results of consultative methods are a strong indicator of a trend in the area investigated. One of the main advantages of these methods is their transparency and accessibility.

Second class brings together creative methods and those which in systematic way analyze, study, draft a vision of reality, while specifically relating to the consequences of this fact, e.g., economic, political, environmental, and descriptive roles potential stakeholders should play. These methods are characterized by the greatest, among all the classes, freedom, flexibility and spontaneity in understanding the examined phenomena. Most of the methods do not have strictly codified, scientific procedures. This class allows the emphasis of the validity of a forward-looking aspect, while at the same time expressing the complexity and multi-perspectivity of examined objects. These classes can also be described as revolutionary, because the final result of applied research approaches often break-, prevailing stereotypes of visions, proven patterns, methods and approaches--the picture of a future reality.

Prescriptive class is the third cluster isolated in the analysis process. It is a group of methods based on creativity and on defining the vision of development closely related to the anticipation of the future, which have a more formalized form, with less flexibility than the described above, the second class of creative methods. Prescriptive methods are addressed primarily to experts in a specific field, and permit one to look at the current situation through a prism of an imaginary future.


The fourth cluster is class of multicriterial methods. One of the main tasks of these methods is optimalization based on uncertainty (most often using a virtual environment) and the analysis and selection of a wide range of data concerning the current state. The methods of this group, often using expert opinion, allow the measuring of the mutual relationship between a large group of variables and criteria characterizing investigated objects.

Radar methods--the fifth group--focus on monitoring and analyzing, among others: bibliographical, Internet, and patent references, primarily related to technology as well as to other aspects, in order to detect important signals about the latest research findings, technological innovation, and any potential opportunities and threats.

The sixth, is a class of simulation methods. The most characteristic features of this cluster are: basis on work done in a virtual environment, focus on the process, high cost and time of investigation, thoroughness in the research, use of the properties of synthesis and modeling. Simulation methods allow the reflection of the behavior of a given model, which is a simplification of a selected fragment of reality. Methods facilitate the simulation and analysis of all possible variants of a complex system (e.g., economic, social) in the form of interactive links and combinations.

The seventh class consists of 12 diagnostic methods. These methods, rely on the different types of expertise, both subjective and objective, analyze, mostly in a systemic manner, identify, and assess the current status of the test object such as a region, characterizing the various factors and trends (political, legal, economic, social, technological, environmental, demographic, etc.) stimulating and inhibiting its (the object) development. The most characteristic features of this group are: time-consumption, cost, generating a codified text results, reliance on in-depth expertise, and resistance to external factors.

The eighth cluster is the most abundant cluster consisting of 17 analytical objects. Key characteristics of this group is time-consuming research methods focused on the product appeal to the objective future. The methods of this group allow (often using graphs, charts) the examination of developmental trends, of driving forces, variants of the changes, the structure of the reality being tested, of the public and potential stakeholders.

To the ninth survey class belong the so-called: diachronic and nomothetic review methods (resistant to external and internal factors) consisting of a time-consuming examination and evaluation of available evidence-based data, previous operations in the selected field of research and studies of space-time targeting the social systems.

The last, tenth, cluster is formed by strategic methods designing and analyzing complex objects. This class consists of evidence-based cognitive, insightful methods relating to the future of the object being analyzed. In the methods discussed the future image is divided into more detailed elements. The methods of this group are helpful in planning, scenario building, decision-making in solving complex decision problems and change management.

Based on cluster analysis a general conclusion can be drawn that each cluster forms a group of substitute methods relative to each other and complementary to the methods of other clusters. In such cases you should avoid the simultaneous use of all methods from one group, especially in reference to only one research context. In this case, the methods share similar information resources, and generate results in a similar manner.

The innovative classification presented in this section allowed us to find a common semantic ground of the methods belonging to a certain group which use a similar range of research. Innovative cluster division, because of the extensive foresight methodological environment, also allows to identify the characteristics of individual clusters, which should be remembered in the process of formulating a foresight research methodology, in a clearer way. The classification performed does not of course eliminate the ability of using several methods from the same group in one study. In certain circumstances it is even desirable. Analysis indicates, however, that the use of methods from only one group can impoverish these studies, by not taking into account many essential aspects with the same intensity.

7. Conclusions

In the opinion of the author, the typology and classification introduced in the article can possibly become helpful in activity directed at effective design of foresight technology research methodologies, influencing the accurate marking of far-reaching directions of development.

A general outline of a profile of individual types, as well as classification of technology foresight research methods, was introduced in the article, which creates a background for further investigation. Considering the considerable complexity of the approach to thinking about the future such as technology foresight and its continued evolution, the author's research can turn out to be indispensable for a systematic and rationally well-founded structuralization of foresight projects.

The current division into types (Table 3) is characterized by a great flexibility in the choice of foresight methods, which on one hand, it is highlighted as an advantage of foresight, and on the other hand can create many questions, especially for the new theorists of foresight. A wide range of research methods (Table 2) and their innovative classification (Fig. 1, Table 4) allows rational modification of a combination of research methods while retaining the essential feature of foresight--flexibility in choice research methods. Innovative classification allows for the extraction of essential features of homogeneous groups of methods, and at the same time for the discovering of unknown, until now, structure of the analyzed data.

The overall choice of methods should be subordinated to research objectives. When it comes to technology foresight research issues should be considered the problem. Such authors as M. Alexandrova, D. Marinova, D. Tchonkova, M. Keenan, R. Popper, A. Havas stress that foresight research methods should always be selected after establishing the objectives and never in the other way around (Alexandrova et al. 2007; Popper 2008a, b). Only after the identification of the phenomena, questions and hypotheses, and the relationship between them, we may begin the process of selecting methods, research tools, indicators. Selected methods should therefore give the fullest, most accurate and reasonable answers to the investigator's questions (Nowak 2006). In case of foresight, such functions can be carried out through different methods. It is therefore important to know the methods which may complement or substitute each other. It should be remembered that the objective is not the only determinant of the choice of a method or methods of the group. Other factors which were identified by the author's article (Table 1, Table 4) are also important.


Aaltonen, M.; Sanders, T. I. 2006. Identifying systems' new initial conditions as influence points for the future, Foresight 8(3): 28-35. doi:10.1108/14636680610668054

Alexandrova, M.; Marinova, D.; Tchonkova, D.; Keenan, M.; Popper, R.; Havas, A. 2007. Research Infrastructures Foresight. A practical guide for integrating foresight in research infrastructures policy formulation.

Amanatidou, E. 2008. Joint foresight: towards a mechanism for joint programming in Europe?, Foresight 10(6): 103-117. doi:10.1108/14636680810918603

Armstrong, J.; Armstrong, S. 2006. Findings from evidence-based forecasting: methods for reducing forecast error, International Journal of Forecasting 22: 583-598. doi:10.1016/j.ijforecast.2006.04.006

Bishop, P.; Hines, A.; Collins, T. 2007. The current state of scenario development: an overview of techniques, Foresight 9(1): 5-25. doi:10.1108/14636680710727516

Blueprints for Foresight Actions in the Regions: Upgrade Foresight Strategy and Actions to Assist Regions of Traditional Industry Towards a More Knowledge Based Community [online], [cited 5 January 2007]. Available from Internet: <>.

Cachia, R.; Compano, R.; Costa, O. 2007. Grasping the potential of online social networks for foresight, Technological Forecasting and Social Change 74: 1179-1203. doi:10.1016/j.techfore.2007.05.006

Cempel, C. 2002. Nowoczesne zagadnienia metodologii i filozofii hadan. Poznan. Available from Internet: <>.

Chamon, M.; Manasse, P.; Alessandro, P. 2007. Can we predict the next capital account crisis?, IMF Staff Papers 54: 270-305. doi:10.1057/palgrave.imfsp.9450012

Chrisidu-Budnik, A.; Korczak, J.; Pakula, A.; Supernat, J. 2005. Nauka organizacji i zarz?dzania. Kolonia Limited, Wroclaw.

Coates, J.; Durance, P.; Godet, M. 2010. Strategic foresight issue: introduction, Technological Forecasting and Social Change 77: 1423-1425. doi:10.1016/j.techfore.2010.08.001

Dolby, R. 1998. Niepewnosc wiedzy. Warszawa: Amber.

Gasparikova, J. 2007. Is new economic research more qualitative?, Ekonomicky Casopis 55(3): 287-296.

Gass, S. I. 2005. Model World: the great debate-MAUT versus AHP, Interfaces 35(4): 308-312. doi:10.1287/inte.1050.0152

Grabinski, T.; Sokolowski, A. 1984. Z badan nad efektywnoscia wybranych procedur taksonomicznych, ZN AE w Krakowie 181/1984: 63-79.

Grupp, H. H.; Linstone, A. 1999. National technology foresight activities around the globe resurrection and new paradigms, Technological Forecasting and Social Change 60: 85-94. doi:10.1016/S0040-1625(98)00039-0

Havas, A. 2008. Devising futures for universities in a multi-level structure: a methodological experiment, Technological Forecasting and Social Change 75: 558-582. doi:10.1016/j.techfore.2008.02.001

Jakuszewicz, I.; Kononiuk, A.; Magruk, A.; Nazarko, J. 2006. Inicjatywy foresight w Polsce i na swiecie, in Kieltyka, L. (red.). Multimedia w organizacjach gospodarczych i edukacji. Wyd. Difin, Warszawa.

Jasinski, L. J. 2007. Myslenieperspektywiczne. Uwarunkowania hadania przysztosci typu foresight. Instytut Nauk Ekonomicznych Polskiej Akademii Nauk, Warszawa.

Kildiene, S.; Kaklauskas, A.; Zavadskas, E. K. 2011. COPRAS based comparative analysis of the European country management capabilities within the construction sector in the time of crisis, Journal of Business Economics and Management 12(2): 417-434. doi:10.3846/16111699.2011.575190

Konnola, T.; Brummer, V.; Salo, A. 2007. Diversity in foresight: insights from the fostering of innovation ideas, Technological Forecasting and Social Change 74: 608-626. doi:10.1016/j.techfore.2006.11.003

Lee, H.; Lee, Ch. 2008. On the R&D priority setting in technology foresight: a DEA and ANP approach, International Journal of Innovation and Technology Management 5(2): 201-219. doi:10.1142/S0219877008001333

Ludeke, M. K. B. 2007. Bridging qualitative and quantitative methods in foresight, from Oracles to Dialogue, in Exploring New Ways to Explore the Future COST A22 Conference, Athens, July 9-11, 2007.

Magruk, A. 2005. Foresight--Nowa metoda prognozowania heurystycznego, in Kieltyka, L.; Nazarko, J. (red.). Technologie informatyczne iprognozowanie w zarz?dzaniu. Wybrane zagadnienia. Wyd. Politechniki Bialostockiej, Bialystok.

Martin, B. R. 2010. The origins of the concept of 'foresight' in science and technology: an insider's perspective, Technological Forecasting and Social Change 77: 1438-1447. doi:10.1016/j.techfore.2010.06.009

May, G. Selecting methods [online], [cited 23 June 2009]. Available from Internet: <>.

Nowak, E. 1980. Metody taksonomiczne w klasyfikacji ohiektow spoteczno-gospodarczych. PWE, Warszawa.

Nowak, S. 2006. Metodologia hadan spotecznych. Wydawnictwo Naukowe PWN, Warszawa.

Okon-Hordynska, E. 2006. Foresight--czyli jak okresla? priorytety rozwoju innowacji, in Szablowski, J. (red.). Zarzadzanie innowacjami--teoria i praktyka. Wyd. Wyzszej Szkoly Finansow i Zarzadzania w Bialymstoku oraz Balikesir Universitesi. Bialystok.

Pawlowski, K.; Pawlowski, E. 2005. Wspotczesne koncepcje zarzadzania a przedsiehiorstwo przysztosci [BBC], in Trzcielinski, S. (red.). Nowoczesne przedsiehiorstwo [abc]]. Politechnika Poznanska, Poznan, 30-39.

Pociecha, J.; Podolec, B.; Sokolowski, A.; Zajac, K. 1988. Metody taksonomiczne w hadaniach spoteczno-ekonomicznych. PWN, Warszawa.

Popper, R. 2008a. Foresight Methodology, in Georghiou, L.; Harper, C. J.; Keenan, M.; Miles, I.; Popper, R. (Eds.). The handbook of technology foresight: concepts and practice. Publisher: Edward Elgar.

Popper, R. 2008b. How are foresight methods selected?, Foresight 10(6): 62-89. doi:10.1108/14636680810918586

Popper, R. 2009. Selecting Foresight Methods [online], [cited 23 June 2009]. Available from Internet: <>.

Popper, R.; Korte, W. B. 2004. Xtreme Euforia: combining foresight methods, in EU-US Seminar: New Technology Foresight, Forecasting & Assessment Methods, Seville May 13-14, 2004.

Popper, R.; Keenan, M.; Butter, M. 2006. 2005 Mapping Report. The European Foresight Monitoring Network.

Popper, R.; Keenan, M.; Miles, I.; Butter, M.; Fuenta, G. S. 2007. Global Foresight Outlook 2007. The European Foresight Monitoring Network.

Reger, G. 2001. Technology foresight in companies: from an indicator to a Network and process perspective, Technology Analysis and Strategic Management 13(4): 533-553.

Reid, D. M.; Zyglidopoulos, S. C. 2004. Causes and consequences of the lack of strategic foresight in the decisions of multinational enterprises to enter China, Futures 36(2): 237-252.

Rohrbeck, R. 2007. Technology Scouting--a case study on the Deutsche Telekom Laboratories, in ISPIM-Asia Conference. India, New Delhi.

Rutkowski, P. 2000. Klasyfikacja rzedu orchidales w swietle analizy fenetycznej. Zastosowanie metod statystycznych w hadaniach naukowych. StatSoft Polska, Krakow.

Sagan, A. 2000. Przyktady zaawansowanych technik analitycznych w hadaniach marketingowych. StatSoft Polska, Krakow.

Salo, A.; Gustafsson, T.; Ramanathan, R. 2003. Multicriteria methods for technology foresight, Journal of Forecasting 22: 235-255. doi:10.1002/for.850

Santos, D. M.; Fellows Filho, L. 2007. The Role of Foresight Experience in the Promotion of Brazil's National Innovation System, Technology Foresight Summit 2007. Water Productivity in Industry, Budapest.

Schwarz, J. O. 2007. Business wargaming: developing foresight within a strategic simulation, Technology Analysis and Strategic Management 21(3): 291-305. doi:10.1080/09537320902750590

Shirai, Y. 2007. Introduction ofMitsubishi Research Institute, in Inc., STPISeminar at Taipei, 24 Octoher 2007.

Skawinska, E. (Ed.). 2007. Zarzadzanie przedsiehiorstwem. Wyd. Instytut Inzynierii Zarzadzania Politechniki Poznanskiej, Poznan.

Slaughter, R. A. 1998. Futures beyond dystopia, Futures 30(10): 993-1002. doi:10.1016/S0016-3287(98)00101-3

Slaughter, R. A. 2004. Road testinga new model at the Australian Foresight Institute, Futures 36: 837-852. doi:10.1016/j.futures.2004.01.010

Stownik Jezyka Polskiego PWN. Available from Internet: <>.

Stout, D. 1995. Technology foresight--a view from the front, Business Strategy Review 6: 1-16.

Technology Foresight for Organizers. 2003. Training Course for Black Sea Economic Cooperation Countries and the Newly Independent States, United Nations Industrial Development Organization. Turkey, Ankara.

Technology Futures Analysis Methods Working Group (TFAMWG) 2004. Technology futures analysis: Toward integration of the field and new methods, Technological Forecasting & Social Change 71: 287-303.

Tran, T. A.; Daim, T. 2008. A taxonomic review of methods and tools applied in technology assessment, Technological Forecasting & Social Change 75: 1396-1405.

Unido Technology Foresight Manual. 2005. Organizations and Methods. Unido, Vienna.

Voros, J. 2006. Introducing a classification framework for prospective methods, Foresight 8(2): 43-56. doi:10.1108/14636680610656174

Zavadskas, E. K.; Turskis, Z. 2011. Multiple criteria decision making (MCDM) methods in economics: an overview, Technological and Economic Development of Economy 17(2): 397-427. doi:10.3846/20294913.2011.593291

Andrzej Magruk

Management Faculty, Bialystok University of Technology, ul. Wiejska 45 A, 15-351 Bialystok, Poland


Received 10 November 2010; accepted 02 September 2011

Andrzej MAGRUK. Scientific and didactic worker of the Chair of Business Informatics and Logistics of the Management Faculty at Bialystok University of Technology. Scientific research interests: foresight methodology, E-business, Information & Communication Technologies in management. Member of the Polish Production Management Society, the Polish Statistical Association. Involved in the scientific work of the Polish National Foresight Programme as a member of the Support Group. Since 2009 he has appointed to the Steering Committee of the regional technology foresight programme--"NT FOR Podlaskie 2020. Regional strategy of nanotechnology development".
Table 1. Factors influencing the technology foresight research

Category                  Factors

Institutions realizing    public institutions; government; the
foresight                 academies of sciences; industrial
                          associations; firms

Range of area studied     individual technology; individual
                          discipline; wide fields; whole areas of
                          science and technique

Aims, tasks, the          determination economic priorities;
functions of foresight    building social consensus over some
                          issues; delimitation strategic economic

Levels                    supranational; subnational; national;
                          regional and local level; business

Meaning                   foresight as a product--foresight as a
                          process; formal--informal

Orientation               orientation on need; orientation on
                          problem; orientation on use

Approach to object of     professional analytical model; model of
investigations            social changes

Aspects                   technological; strategic; social;
                          cultural; political; economic; scientific;
                          consumer; etc.

Kind of possessed data    quantitative; qualitative; in digital
                          form; in printed form

Data source               literature; experts; own research,
                          universities; press; medias; scientific

Kind of stakeholders      scientists; businessman; politicians;

Work environment          scientific-business; virtual-real

Time                      horizon; project period

Objectives                policy development; networking, shared
                          visions, public discussion, future

Budget of project         high; low

Access to the data        quantitative--qualitative; low--wide

Legitimacy of a           low--medium--high--very high
combination of methods

References: The authors' study based on Magruk 2005; Okon-Hordynska
2006; Popper et al. 2006.

Table 2. Methods and techniques possible to use in technology
foresight programs

Action            Environmental     Morphological     STEEPV Analysis
Learning/         Scanning          Analysis

Agent Modeling    Essays            Multiple          Stochastic
                                    Perspectives      Forecasting

AHP               Expert Panels     Object            Structural
                                    Stimulation       Analysis

Alternative       Factor Analysis   Patent Analysis   Survey

Analogies         Failure Mode &    Polling           Sustainability
                  Effects An.                         Analysis

ANKOT             Force Field       PRIME             SWOT Analysis

Assumption        Future History    Prioritization    Synectics

Backcasting       Future Mapping    Probability       System Dynamics

Back-View         Futures           Relevance Trees   Technology Watch
Mirror Analysis   Biographies

Benchmarking      Futures Wheel     Requirement       Technological
                                    Analysis          Scanning

Bibliometrics     Genius            Retrospective     Technological
                  Forecasting       Analysis          Substitution

Brainstorming     Incasting         Rich Pictures     Technology

Business          Indicators/       Risk Analysis     Technology
Wargaming         (Time Series                        Barometer

Causal Layered    Input-Output      Role play         Technology
Analysis          Analysis          (Acting)          Mapping

Citizen Panels    Institutional     RPM (Robust       Technology
                  Analysis          Portfolio         Positioning

Classification    Interviews        Scenarios         Technology
Trees                                                 Roadmapping

Cluster           Issues            Science Fiction   Technology
Analysis          Management        Analysis          Scouting

Coates and        Key               Scientometrics    Theory of
Jarratt           Technologies                        Constraints

Conferences/      Lateral           S-Curve           Trend
Workshops         Thinking          Analysis          Extrapolation

Content           Literature        Sensitivity       Trend Impact
Analysis          Review            Analysis          Analysis

Correspondence    Long Wave         Shift-Share       Trial and Error
Analysis          Analysis          Analysis

Cost-Benefit      Macrohistory      SMART             TRIZ

Critical          MANOA             Social Impact     Visualization
Influence                           Assessment

Cross-Impact      MCDM              Social Networks   Voting
Analysis                            Analysis

DEA               Megatrend         Source Data       Weak Signals
                  Analysis          Analysis

DEGEST            Metaphors         Speculative       Web Research

Delphi            Migration         SRI Matrix        Wild Cards

Desk Research     Mindmapping       Stakeholder       Webometrics

Divergence        Modeling and      State Of the      Word Diamond
Mapping           Simulation        Future Index

References: Authors' study based on Armstrong, J. and Armstrong, S.
2006; Cachia et al. 2007; Chamon et al. 2007; Jasinski 2007; Konnola
et al. 2007; Popper et al. 2007; Popper and Korte 2004; Rohrbeck
2007; Santos and Fellows 2007; Schwarz 2007; Shirai 2007; Technology
Foresight ... 2003; Technology Futures ... 2004; Tran and Daim 2008.

Table 3. Typology of technology foresight research methods

Criterion                     Types of methods

thinking about the future     explorative (foreseeing)--based on
                              uncertainty (managing)--normative

type of data                  quantitative--indirect--qualitative

method of data acquisition    heuristic--analytic

kind of cooperation           bottom-up--top-down

working environment           based on virtual environment--based on
                              real environment

reference to time             shaping future--analyzing present
                              state--based on the past

source of data                using original data--using secondary

referencel to technology      describing factors which influence
                              technology development--determining
                              influence of technology development
                              for other aspects of life

orientation                   focused on product--focused on process

meaning for foresight         formal--alternatively

stage of research             preliminary--recruitment--generation--

character of results          textual--graphical--verbal

essence of research           based on creativity--interaction--

way of thinking               evolutionary--revolutionary

layer                         mathematical--social--engineering--

way of inference              inductive--deductive

References: The authors' study based on Aaltonen and Sanders 2006;
Bishop et al. 2007; Blueprints... 2007; Cachia et al. 2007;
Chrisidu-Budnik et al. 2005; Dolby 1998; Gasparikova 2007; Coates et
al. 2010; Grupp and Linstone 1999; Havas 2008; Jakuszewicz et al.
2006; Konnola et al. 2007; May 2009; Popper 2008a, 2009; Tran and
Daim 2008; Voros 2006.

Table 4. Classification of technology foresight research methods

Innovative         Methods belonging to each class

Consultative       Voting, Polling, Survey, Interviews, Expert
                   Panels, Essays, Conferences, Workshops, Citizen
                   Panels, Brainstorming

Creative           Wild Cards, Weak Signals, Mindmapping, Lateral
                   Thinking, Futures Wheel, Role Play, Business
                   Wargaming, Synectics, Speculative Writing,
                   Visualization, Metaphors, Assumption Reversal

Prescriptive       Relevance Trees, Morphological Analysis, Rich
                   Pictures, Divergence Mapping, Coates and
                   Jarratt, Future Mapping, Backcasting, SRI
                   Matrix, Science Fiction Analysis, Incasting,
                   Genius Forecasting, Futures Biographies, TRIZ,
                   Future History, Alternative History

Multicriterial     Key Technologies, Source Data Analysis,
                   Migration Anal., Shift-Share Anal., DEA, Factor
                   Anal., Correspondence Anal., Cluster Anal.,
                   Sensitivity Anal., AHP, Input-Output Anal.,
                   Priorization, SMART, PRIME, MCDM

Radar              Scientometrics, Webometrics, Patent Analysis,
                   Bibliometrics, Technological Substitution,
                   S-Curve Anal Technology Mapping, Analogies

Simulation         Probability Trees, Trend Extrapolation, Long
                   Wave Anal., Indicators, Stochastic Forecast,
                   Classification Trees, Modeling and Simulation,
                   System Dynamics, Agent Modeling

Diagnostic         Object Simulation, Force Field Anal., Word
                   Diamond, SWOT, STEEPVL, Institutional Anal.,
                   DEGEST, Trial&Error, Requirement Anal., Theory
                   of Constraint, Issue Management, ANKOT

Analytical         SOFI, Stakeholder Anal., Cross-Impact Anal.,
                   Trend Impact Anal., Structural Anal., Megatrend
                   Anal., Critical Influence Anal., Tech.
                   Barometer, Cost-Benefit Anal., Technology
                   Scouting, Technology Watch, Sustainability
                   Anal., Environmental Scanning, Content
                   Analysis, FMEA, Risk Anal., Benchmarking

Survey             Web Research, Desk Research, Tech. Assessment,
                   Social Network Anal., Literature Review,
                   Retrospective Analysis, Macrohistory, Back-View
                   Mirror Analysis

Strategic          Technology Roadmapping, Tech. Positioning,
                   Delphi, Scenarios, Social Impact Assessment,
                   RPM, Technological Scanning, Multiple
                   Perspectives Assessment, Causal Layered
                   Analysis, MANOA, Action Learning

References: The authors' study.
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