The literature review of foresight studies suggested that the majority of the formerly conducted studies were based on qualitative methods, while combining different approaches to foresight and summarizing experts’ opinions. The study of the scholars’ views on how to carry out a foresight study revealed how diverse their ideas were.

Various research methods are used in future studies, one of the most important of which is scenario planning, a technique which, by itself, consists of several steps during the implementation process. A panel of experts, Delphi, brainstorming, and environmental scanning are some other approaches involved in foresight and future studies. Most of the scenario analyses have been conventionally qualitative in nature, developing narratives for potential future states. However, scenarios are being increasingly quantified so as to further examine the likely impacts. These methods are combined and integrated during the implementation process. They are divided in different ways, one of the most common methods of which is classification based on the technology used in them. From such a perspective, these methods include the following approaches [9, 10]:

  • Numerical or quantitative methods, which are based on the past and present data. The most commonly applied methods are time series, simulation, and econometric models.

  • Pseudo-numerical or judgmental methods, which make a group of intermediate qualitative and quantitative approaches and involve quantification of mental judgments through a series of rules and definitions.

  • Qualitative methods, in which an expert’s opinion is the most reliable factor for drawing future perspectives. The experts express their views based on their evidence or expectations of the future. Some of the most critical sub-categories of these methods include brainstorming, SWOT, Delphi method, scenario planning, retrieval method, and key technologies.

Selection of foresight methods is a multi-factor process. Selecting a proper foresight approach depends on various factors, such as time, available financial resources, and predetermined objectives. The most important criteria for choosing an efficient foresight method are resources, especially money and time, the extent to which experts and stakeholders participate in a project, the need for different methods based on qualitative or quantitative data, suitability of the combination of methods for providing a mutual support, and process-oriented and outcome-oriented expectations we might have for that specific foresight project [11].

The literature review on foresight studies suggests that the majority of the formerly conducted research is based on qualitative methods that combine different approaches to practicing foresight by summarizing expert opinions.

In the study conducted by Popper, from among the varied qualitative methods, literature review, expert panels, and scenarios were shown to be the most utilized approaches taken by researchers, respectively. He listed the various methods used in foresight and identified the criteria for selecting an appropriate method for the problems as well [12].

Bootz et al. [13] categorized 4 types of approaches to foresight: decision support, strategic focus, mobilization, and change management. They examined the links between the French School of Foresight and organizational learning.

The study of the scholars’ views on how to carry out a foresight research revealed how diverse their ideas were. The most common and referenced foresight frameworks included Martin, Horton, Reger, Miles, Voros, Saritas, Santo, and Popper. Martin’s foresight process consisted of the 3 phases of “pre-foresight,” “foresight,” and “post-foresight.” The pre-foresight involved those steps and measures, which needed to be taken before initiation of the process, including the decision to start a project and preparatory activities [14]. The main stage of foresight involved designing a project, strategic analysis, agreement on feasible options, and dissemination of the project results. The post-foresight phase involved programming an efficient strategy for achieving goals, as well as choosing an approach to publish the results and determine the executives [6]. According to Horton’s framework [15], foresight was a 3-step process for expanding the range of possible future development options, including inputs, foresight, outputs, and activities. The information leading to the foundation of the foresight project was gathered from a variety of sources in the first stage. Various methods, such as environmental surveys, Delphi scopes, and systematic studies, were useful to be used here. This information was compared, summarized, and compressed by utilizing various methods, such as scenario planning, graphical comparisons, matrixes, and analysis of interactions. Then, the knowledge derived from the inputs was translated and interpreted. The translated knowledge became organizational perceptions. In the final stage, the perception obtained in the previous stage was evaluated and synchronized to form a commitment to be implemented in the organization. Reger [10] proposed a 7-stage framework for foresight, which included determining information needs, choosing a research area, and selecting information resources, as well as methods and tools, data collection, screening, analysis, interpretation of information, preparation of decisions, evaluation, decision-making, taking an action, and implementation. Miles and Keenan [16] also provided a framework for foresight, which, unlike most models that followed a hierarchical trajectory, had a repeatable process and involved a step-by-step update of results and processes during replication. Pre-foresight, project agent engagement, creation of the future vision, taking an action, and refreshment were the main stages of the foresight project. The framework proposed by Voros [7] involved the 4 vital elements of input, future, output, and strategy. Saritas et al. [17] presented a systematic framework for foresight based on the relationship between the context, content, and process of creating foresight in an organization. They believed that foresight was located and developed in an internal context or a combination of structures, such as internal processes, equipment and technologies, and behaviors, including culture, politics, skills, management, and external texture, incorporating social, technological, economic, ecological, and political systems. By content, they meant thematic areas and creation of the ideas related to those areas during the foresight process. According to Saritas et al. [17], content and texture had to be first identified during the process of foresight projects. This systematic framework was based on 5 activities, including understanding, combination, analysis, selection, and transformation of form and activity. Santo et al. [18] proposed a model, which could be thought of as a development on the works of Horton [15], Conway and Voros [19], and Voros [7]. According to their model, management of foresight activities took into account the 4 crucial stages of goal setting, topic selection, implementation, and decision-making.

Knowledge management has been studied by many researchers based on a futuristic view. Recent methods were found to be reviewed by Bootz et al. [20], who then assessed knowledge management in the field of foresight. Foresight quality could be enhanced by making a knowledge management network as a dynamic capability [21]. The concept of knowledge and its evolution in management, were evaluated by Coulet [22]. Future developments could be predicted by using knowledge management as a tool that played its role, along with the expert knowledge.

The method proposed in this article was one of the qualitative methods, which aimed at increasing the experts’ tacit knowledge and gaining their maximum knowledge by helping to remove the ambiguities of their linguistic variables and statements. In general, there were commonalities between the various approaches presented. It is noteworthy to state that this paper introduced a new framework that took into account the main stage of foresight, while relying on Martin’s map.

Fuzzy cognitive map (FCM)

Tolman was one of the leading cognitive psychologists, who considered cognitive variables for the first time [23]. Axelrod [24] introduced cognitive maps for presenting social science knowledge and a decision-making model in social and political systems [25]. In their diagrams of cognitive maps, the variables or concepts appeared as nodes and the edges played the role of relations between the variables. A cognitive map is drawn with various techniques, including the use of questionnaires to extract expert opinions and map the relationships between the variables, the use of content analysis to explore the relationships in the written texts, the use of quantitative data, and the process of in-depth interviews with different individuals and experts [26,27,28]. Kosko [29] presented fuzzy cognitive mapping (FCM) as an extension to the cognitive map with a unique potential to model causal and disruptive relationships between the weighted communications. A simple FCM is shown in Fig. 1.

Fig. 1

A simple fuzzy cognitive map

The main superiority of FCM on the cognitive map was defining the power of edges as the sticker of each edge, in which the relationship between the two variables of Ci and Cj was represented by wij. The power of correlation of relations was written in the form of linguistic variables in the interval of [0, 1] or [−1, +1]. The relationships between the variables based on the graph theory could be converted to the adjacency matrix or communication matrix in the form of E = [eij] and performed on that mathematical operation [4]. FCM is a method for demonstrating knowledge in systems with uncertainty, ambiguity, and complexity. This model has been used in various applications, such as decision-making, forecasting, strategic planning, engineering science, project management, investment analysis, medicine, and biology [30,31,32,33,34].

Real-world issues are usually faced with uncertainty and incompleteness. Uncertainty is based on inaccuracy, incompleteness, indeterminacy, judgments, obscurity in relation to the data, non-existence of the data on knowledge, and/or the stochastic nature of events. Individual cognitive maps derived from individual domains can be merged to form a social cognitive map. Accordingly, a complex adjacency matrix is developed, in which all the variables are formed in all the individual cognitive maps [25, 35]. One can use 2 strategic options development and analysis (SODA) approaches in his/her research topic to formulate cognitive social maps with soft operations:

  1. 1.

    The SODA I approach, which can be applied to integrate individual cognitive maps to help solve the problem using mathematical techniques on the edge weights and adjacency matrices.

  2. 2.

    Unlike the former one, the SODA II approach abandons individual cognitive maps and uses a group decision technology like Delphi to build cognitive maps directly with the help of the group.

The literature review [36] presented 2 methods for combining FCMs of different experts in the SODA I format:

  • The 1st method was based on the expert credibility weight, according to which an estimate of the weights of the different experts’ credits was first obtained to achieve the matrix of adjacency of the aggregated fuzzy cognitive map by weighting the adjacent matrices. The basis of this method was to calculate the Hamming distance between the inferences made by the different experts [37, 38].

  • The 2nd method was based on averaging multiple FCMs [25, 31, 38]. This method was more common for combining FCMs of the different experts. In this method, adjacent matrices were added and then divided by the number of experts to obtain the weights ([39]).


The concept of Z-number was intended to provide a basis for computation with numbers that were not reliable. Z-number was proposed by Zadeh [40] as a generalized version of the theory of uncertainty [41]. A Z-number is an ordered pair of fuzzy numbers denoted as Z = ((overset{sim }{mathrm{A}}overset{sim }{,mathrm{R}})). The 1st component, Ã, is a restriction on the values, indicating a real-valued uncertain variable, X. The 2nd component,(overset{sim }{mathrm{R}}), is a measure of reliability for the 1st component. Z-numbers can be used to model uncertain information in the real-world. For example, in risk analysis, loss of the 5th component severity is very low, with the confidence state of ‘very likely,’ which can be written as a Z-number as follows: Z = very low, very likely

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