Cognitive mapping the topic of technological forecasting

Cognitive mapping the topic of technological forecasting

Joseph Kim-keung Ho

Independent Trainer

Hong Kong, China

Abstract: The topic of technological forecasting in the subject of Technology Management is complex. By making use of the cognitive mapping technique to conduct a brief literature review on the technological forecasting topic, the writer renders a systemic image on the topic of technological forecasting. The result of the study, in the form of a cognitive map on technological forecasting, should be useful to those who are interested in the topics of cognitive mapping, literature review and technological forecasting.

Key words: Technological forecasting, cognitive mapping, literature review

Introduction

As a topic in Technology Management, technological forecasting is complex. It is thus useful to employ some learning tool to conduct its study, notably for literature review purpose. For a teacher in research methods, systems thinking and management, the writer is specifically interested in finding out how the cognitive mapping technique can be employed to go through a literature review on  technological forecasting. This literature review exercise is taken up and reported in this article.

On the cognitive mapping exercise for literature review

Literature review is an important intellectual learning exercise, and not just for doing final year dissertation projects for tertiary education students. On these two topics of intellectual learning and literature review, the writer has compiled some e-learning resources. They are the Managerial intellectual learning Facebook page and the Literature on literature review Facebook page. Conducting literature review with the cognitive mapping technique is not novel in the cognitive mapping literature, see Eden and Simpson (1989), Eden, Jones and Sims (1983), Open University (n.d) and the Literature on cognitive mapping Facebook page. In this article, the specific steps involved in the cognitive mapping exercise are as follows:

Step 1: gather some main points from a number of academic journal articles on Technological forecasting. This result in the production of a table (Table 1) with the main points and associated references.

Step 2: consolidate  the main points from Table 1 to come up with a table listing the cognitive map variables (re: Table 2).

Step 3: link up the cognitive  map variables in a plausible way to produce a cognitive map (re: Figure 1) on the topic under review.

The next section applies these three steps to produce a cognitive map on technological forecasting.

Descriptions of cognitive map variables on the technological forecasting topic

From the reading of some academic articles on Technological forecasting, a number of main points (e.g., viewpoints, concepts and empirical findings) were gathered by the  writer. They are shown in Table 1 with explicit referencing on the points.

Table 1: Main points from the technological forecasting literature and referencing

Main points from the technological forecasting literature	Referencing
Point 1: "TECHNOLOGY forecasting (TF) anticipates the direction and rate of technology change, and thus, facilitates the decision-making process in such managerial issues such as priority setting, resource allocation, and risk reduction for technology development. Therefore, TF can satisfy both public and private needs".	Yoon, B. and Y. Park. 2007. "Development of New Technology Forecasting Algorithm: Hybrid  Approach for Morphology Analysis and Conjoint Analysis of Patent Information" IEEE Transactions on Engineering Management 54(3) August: 588-599.
Point 2: "The government requires [technology forecasting] TF to progress the public agendas in the face of increasing rates of technological change and under budgetary constraints [1]. Intensive economic competition among businesses means it is inevitable that TF is employed in prioritizing R&D projects, and creating strategic alliances such as licensing and joint ventures".	Yoon, B. and Y. Park. 2007. "Development of New Technology Forecasting Algorithm: Hybrid  Approach for Morphology Analysis and Conjoint Analysis of Patent Information" IEEE Transactions on Engineering Management 54(3) August: 588-599.
Point 3: "Forecasting the future of a technology is an intractable task beset with hazards such as the uncertainty and unreliability of data, and the complexity of real world feedback [3]. Numerous TF methods have been developed to reduce uncertainty and support the decision-making process. Although there are manifold forecasting methods, several surveys have reported that trend extrapolation and Delphi, a structured group communication process for developing consensus from an expert group, are the most widely adopted for practical usage".	Yoon, B. and Y. Park. 2007. "Development of New Technology Forecasting Algorithm: Hybrid  Approach for Morphology Analysis and Conjoint Analysis of Patent Information" IEEE Transactions on Engineering Management 54(3) August: 588-599.
Point 4: "Technological forecasting deals primarily with the fairly long term and seeks to determine what technology will likely be available and what the influence of important technological developments and innovations will be".	Jeon, Y., K.R. Hyun and C.W.J. Granger. 2004. "Long-term technological forecasting" Telektronikk 4: 3-12.
Point 5: "Using technological forecasting to predict events ten years into the future is a particularly difficult task; even five years ahead is complicated enough. Most of the commonly used forecasting techniques would be irrelevant in attempting such a task".	Jeon, Y., K.R. Hyun and C.W.J. Granger. 2004. "Long-term technological forecasting" Telektronikk 4: 3-12.
Point 6: "The common perception in the area of long-term technological forecasting is that countries tend to follow a specific and predictable path with regard to the various stages of growth".	Jeon, Y., K.R. Hyun and C.W.J. Granger. 2004. "Long-term technological forecasting" Telektronikk 4: 3-12.
Point 7: "No matter how well established and technical the field of technological forecasting becomes in its development, it can never become a purely technical or scientific concern. There will always remain at the heart of forecasting a basic philosophical element which can never be completely removed".	Mitroff, I.I. and M. Turoff. 1973. "Technological Forecasting and Assessment: Science and/or Mythology?" Technological Forecasting and Social Change 5: 113-134.
Point 8: "It should be borne in mind as we proceed that the question of concern is not how we can know the future with “perfect certainty,” for put in this form the answer is clearly that we can’t. But then neither can we know all there is to know about the present with “perfect certainty.” The real question is what can we know of the future, and, even more to the point, how we can justify what we think we know. It is on this very issue that the difference between Inquiring Systems arises".	Mitroff, I.I. and M. Turoff. 1973. "Technological Forecasting and Assessment: Science and/or Mythology?" Technological Forecasting and Social Change 5: 113-134.
Point 9: "Technology forecasting experts agree that models should be used in combination [1,2]. With complex consumer technologies there are usually several organization factors—political, cultural, etc—that influence the rate of diffusion for a commercial technology. Technical trend analyses alone usually cannot incorporate the organizational and political scenarios that will influence future technologies".	Daim, T.U., G. Rueda, H. Martin and P. Gerdsri. 2006. "Forecasting emerging technologies: Use of bibliometrics and patent analysis" Technological Forecasting & Social Change 73, Elsevier: 981-1012.
Point 10: "Traditional system dynamic models used in technology forecasting incorporate historical data for calibration and validation. Models here also integrate the use of scenarios, bibliometrics, and patent trend analysis. System dynamics models have typically not been viewed as appropriate forecasting tools but are used primarily to uncover feedback loops and how factors interrelate for strategic analysis [1]. By incorporating scenario planning and the use of bibliometrics, patent analysis and growth curve model it is our belief that these models can also become a useful decision-making tool".	Daim, T.U., G. Rueda, H. Martin and P. Gerdsri. 2006. "Forecasting emerging technologies: Use of bibliometrics and patent analysis" Technological Forecasting & Social Change 73, Elsevier: 981-1012.
Point 11: "In the post-cold-war scenario, technology has emerged as the most potent weapon for economic growth. The paradigm in developed countries has shifted to sophisticated technologies, based on new models of economical and social structures. This has led to cut-throat competition amongst blocks of countries and technical alliances amongst rivals hitherto unheard of, based on mutual strength essentially to emerge as a technological power with hopes of economic gains. In this context, it has become a national exercise, in some advanced countries, to carry out long-term technology forecasting both at the national level and organizational level, through various models, which brings out in a focused manner the relevant future technologies depending on the needs of the society and culture".	Chakravarti, A.K., B. Vasanta, A.S.A. Krishnan and R.K. Dubash. 1998. "Modified Delphi Methodology for Technology Forecasting: Case Study of Electronics and Information Technology in India" Technological Forecasting and Social Change 58, Elsevier: 155-165.
Point 12: "While formulating a model for technology forecasting to address the technology development paradigm, developing economies such as India have to be aware of the technological changes that are going to occur and the appropriateness of the new technology that has already been harnessed in the developed countries".	Chakravarti, A.K., B. Vasanta, A.S.A. Krishnan and R.K. Dubash. 1998. "Modified Delphi Methodology for Technology Forecasting: Case Study of Electronics and Information Technology in India" Technological Forecasting and Social Change 58, Elsevier: 155-165.
Point  13: "The Science and Technology Agency embarked on technological forecasting in early 1970 and has since been conducting a regular technological forecast survey, about every five years, with the results of the latest survey, the sixth, released in June 1997. Each survey projects 30 years into the future, and the Delphi method has been used throughout".	Kuwahara, T. 1999. "Technology Forecasting Activities in Japan" Technological Forecasting and Social Change 60, Elsevier: 5-14.
Point 14: "Technological forecasting—its purpose, methods, terminology, and uses—will be shaped in the future, as in the past, by the needs of corporations and government agencies. These have a continual pressing need to anticipate and cope with the direction and rate of technological change. The future of technological forecasting will also depend on the views of the public and their elected representatives about technological progress, economic competition, and the government’s role in technological development".	Coates, V., M. Farooque, R. Klavans, K. Lapid, H.A. Linstone, C. Pistorius and A. Porter.  2001. "On the Future of Technological Forecasting" Technological Forecasting and Social Change 67, Elsevier: 1-17.
Point 15: "...“technological forecasting” (TF) includes several new forms—for example, national foresight studies, roadmapping, and competitive technological intelligence—that have evolved to meet the changing demands of user institutions. It also encompasses technology assessment (TA) or social impact analysis, which emphasizes the downstream effects of technology’s invention, innovation, and evolution".	Coates, V., M. Farooque, R. Klavans, K. Lapid, H.A. Linstone, C. Pistorius and A. Porter.  2001. "On the Future of Technological Forecasting" Technological Forecasting and Social Change 67, Elsevier: 1-17.
Point 16: "From its beginnings over a half-century ago, the development of TF [technological forecasting] has responded to ever-changing organizational needs, in both the private and public sectors. From the late 1940s through the mid-1970s both quantitative and qualitative methods were developed, refined, and used; in many cases they were independently developed at different times and given different names in several organizations".	Coates, V., M. Farooque, R. Klavans, K. Lapid, H.A. Linstone, C. Pistorius and A. Porter.  2001. "On the Future of Technological Forecasting" Technological Forecasting and Social Change 67, Elsevier: 1-17.
Point 17: "Technology forecasting (TFC) itself is not new. Out of curiosity, people have long forecast future lives, science and technology (S&T) and other areas".	Eto, H. 2003. "The suitability of technology forecasting/foresight methods for decision systems and strategy: A Japanese view" Technological Forecasting & Social Change 70, Elsevier: 231-249.
Point 18: "As the S&T [science and technology] strategy of Japan was to follow that of advanced countries, those who worked on theories about S&T were suspected as criticising the policy and were often arrested. In fact, the famous S&T philosophers, Tosaka and Miki, died in prison in 1945, respectively, 1 week before and 6 weeks after the end of World War II".	Eto, H. 2003. "The suitability of technology forecasting/foresight methods for decision systems and strategy: A Japanese view" Technological Forecasting & Social Change 70, Elsevier: 231-249.
Point 19: "TFC [technology forecasting] itself is universal and has been practised in many countries (e.g., recently in France [70]). Among many countries, Japan is marked by its regular practice of large-scale Delphi studies by the government over the last three decades. Further, its decision system is known as a combination of bottom–up and decentralised modes [71,72] with expertise autonomy".	Eto, H. 2003. "The suitability of technology forecasting/foresight methods for decision systems and strategy: A Japanese view" Technological Forecasting & Social Change 70, Elsevier: 231-249.

With a set of main points collected, the writer produces a set of cognitive map variables. These variables are informed by the set of main points from Table 1. These variables are presented in Table 2.

Table 2: Cognitive map variables based on Table 1

Cognitive map variables	Literature review points
Variable 1: Drivers of interest in technological forecasting	Point 11: "In the post-cold-war scenario, technology has emerged as the most potent weapon for economic growth. The paradigm in developed countries has shifted to sophisticated technologies, based on new models of economical and social structures. This has led to cut-throat competition amongst blocks of countries and technical alliances amongst rivals hitherto unheard of, based on mutual strength essentially to emerge as a technological power with hopes of economic gains. In this context, it has become a national exercise, in some advanced countries, to carry out long-term technology forecasting both at the national level and organizational level, through various models, which brings out in a focused manner the relevant future technologies depending on the needs of the society and culture". Point  13: "The Science and Technology Agency embarked on technological forecasting in early 1970 and has since been conducting a regular technological forecast survey, about every five years, with the results of the latest survey, the sixth, released in June 1997. Each survey projects 30 years into the future, and the Delphi method has been used throughout". Point 14: "Technological forecasting—its purpose, methods, terminology, and uses—will be shaped in the future, as in the past, by the needs of corporations and government agencies. These have a continual pressing need to anticipate and cope with the direction and rate of technological change. The future of technological forecasting will also depend on the views of the public and their elected representatives about technological progress, economic competition, and the government’s role in technological development". Point 16: "From its beginnings over a half-century ago, the development of TF [technological forecasting] has responded to ever-changing organizational needs, in both the private and public sectors. From the late 1940s through the mid-1970s both quantitative and qualitative methods were developed, refined, and used; in many cases they were independently developed at different times and given different names in several organizations". Point 17: "Technology forecasting (TFC) itself is not new. Out of curiosity, people have long forecast future lives, science and technology (S&T) and other areas". Point 19: "TFC [technology forecasting] itself is universal and has been practised in many countries (e.g., recently in France [70]). Among many countries, Japan is marked by its regular practice of large-scale Delphi studies by the government over the last three decades. Further, its decision system is known as a combination of bottom–up and decentralised modes [71,72] with expertise autonomy".
Variable 2: Improve intellectual understanding of technological forecasting	Point 1: "TECHNOLOGY forecasting (TF) anticipates the direction and rate of technology change, and thus, facilitates the decision-making process in such managerial issues such as priority setting, resource allocation, and risk reduction for technology development. Therefore, TF can satisfy both public and private needs". Point 4: "Technological forecasting deals primarily with the fairly long term and seeks to determine what technology will likely be available and what the influence of important technological developments and innovations will be". Point 7: "No matter how well established and technical the field of technological forecasting becomes in its development, it can never become a purely technical or scientific concern. There will always remain at the heart of forecasting a basic philosophical element which can never be completely removed". Point 8: "It should be borne in mind as we proceed that the question of concern is not how we can know the future with “perfect certainty,” for put in this form the answer is clearly that we can’t. But then neither can we know all there is to know about the present with “perfect certainty.” The real question is what can we know of the future, and, even more to the point, how we can justify what we think we know. It is on this very issue that the difference between Inquiring Systems arises".
Variable 3: Effective technological forecasting practices	Point 2: "The government requires [technology forecasting] TF to progress the public agendas in the face of increasing rates of technological change and under budgetary constraints [1]. Intensive economic competition among businesses means it is inevitable that TF is employed in prioritizing R&D projects, and creating strategic alliances such as licensing and joint ventures". Point 5: "Using technological forecasting to predict events ten years into the future is a particularly difficult task; even five years ahead is complicated enough. Most of the commonly used forecasting techniques would be irrelevant in attempting such a task". Point 9: "Technology forecasting experts agree that models should be used in combination [1,2]. With complex consumer technologies there are usually several organization factors—political, cultural, etc—that influence the rate of diffusion for a commercial technology. Technical trend analyses alone usually cannot incorporate the organizational and political scenarios that will influence future technologies". Point 10: "Traditional system dynamic models used in technology forecasting incorporate historical data for calibration and validation. Models here also integrate the use of scenarios, bibliometrics, and patent trend analysis. System dynamics models have typically not been viewed as appropriate forecasting tools but are used primarily to uncover feedback loops and how factors interrelate for strategic analysis [1]. By incorporating scenario planning and the use of bibliometrics, patent analysis and growth curve model it is our belief that these models can also become a useful decision-making tool". Point 12: "While formulating a model for technology forecasting to address the technology development paradigm, developing economies such as India have to be aware of the technological changes that are going to occur and the appropriateness of the new technology that has already been harnessed in the developed countries". Point 15: "...“technological forecasting” (TF) includes several new forms—for example, national foresight studies, roadmapping, and competitive technological intelligence—that have evolved to meet the changing demands of user institutions. It also encompasses technology assessment (TA) or social impact analysis, which emphasizes the downstream effects of technology’s invention, innovation, and evolution".
Variable 4: Learn from technological forecasting practices	Point 3: "Forecasting the future of a technology is an intractable task beset with hazards such as the uncertainty and unreliability of data, and the complexity of real world feedback [3]. Numerous TF methods have been developed to reduce uncertainty and support the decision-making process. Although there are manifold forecasting methods, several surveys have reported that trend extrapolation and Delphi, a structured group communication process for developing consensus from an expert group, are the most widely adopted for practical usage". Point 6: "The common perception in the area of long-term technological forecasting is that countries tend to follow a specific and predictable path with regard to the various stages of growth". Point 18: "As the S&T [science and technology] strategy of Japan was to follow that of advanced countries, those who worked on theories about S&T were suspected as criticising the policy and were often arrested. In fact, the famous S&T philosophers, Tosaka and Miki, died in prison in 1945, respectively, 1 week before and 6 weeks after the end of World War II".

The next step is to relate the cognitive map variables to make up a cognitive map on technological forecasting. The cognitive map and its explanation are presented in the next section.

A cognitive map on technological forecasting and its interpretation

By relating the four variables identified in Table 2, the writer comes up with a cognitive map on technological forecasting, as shown in Figure 1.

These cognitive  map variables, four of them altogether, are related to constitute a systemic image of technological forecasting. The links in the cognitive map (re: Figure 1) indicate direction of influences between variables. The + sign shows that an increase in one variable leads to an increase in another variable while a -ve sign tells us that in increase in one variable leads to a decrease in another variable.  If there no signs shown on the arrows, that means the influences can be positive or negative.  For further information on technological forecasting, readers are referred to the Literature on technological forecasting Facebook page.

Concluding remarks

The cognitive mapping exercise captures in one diagram some of the main variables involved in technological forecasting. The resultant cognitive map promotes an exploratory way to study technological forecasting in a holistic tone. The experience of the cognitive mapping exercise is that it can be a quick, efficient and entertaining way to explore a complex topic such as technological forecasting in Technology Management. Finally, readers who are interested in cognitive mapping should also find the article informative on this mapping topic.

Bibliography

1.      Chakravarti, A.K., B. Vasanta, A.S.A. Krishnan and R.K. Dubash. 1998. "Modified Delphi Methodology for Technology Forecasting: Case Study of Electronics and Information Technology in India" Technological Forecasting and Social Change 58, Elsevier: 155-165.

2.      Coates, V., M. Farooque, R. Klavans, K. Lapid, H.A. Linstone, C. Pistorius and A. Porter.  2001. "On the Future of Technological Forecasting" Technological Forecasting and Social Change 67, Elsevier: 1-17.

3.      Daim, T.U., G. Rueda, H. Martin and P. Gerdsri. 2006. "Forecasting emerging technologies: Use of bibliometrics and patent analysis" Technological Forecasting & Social Change 73, Elsevier: 981-1012.

4.      Eden, C. and P. Simpson. 1989. "SODA and cognitive mapping in practice", pp. 43-70, in Rosenhead, J. (editor) Rational Analysis for a Problematic World, Wiley, Chichester.

5.      Eden, C., C. Jones and D. Sims. 1983. Messing about in Problems: An informal structured approach to their identification and management, Pergamon Press, Oxford.

6.      Eto, H. 2003. "The suitability of technology forecasting/foresight methods for decision systems and strategy: A Japanese view" Technological Forecasting & Social Change 70, Elsevier: 231-249.

7.      Jeon, Y., K.R. Hyun and C.W.J. Granger. 2004. "Long-term technological forecasting" Telektronikk 4: 3-12.

8.      Kuwahara, T. 1999. "Technology Forecasting Activities in Japan" Technological Forecasting and Social Change 60, Elsevier: 5-14.

9.      Literature on cognitive mapping Facebook page, maintained by Joseph, K.K. Ho (url address: https://www.facebook.com/Literature-on-cognitive-mapping-800894476751355/).

10. Literature on literature review Facebook page, maintained by Joseph, K.K. Ho (url address: https://www.facebook.com/literature.literaturereview/).

11. Literature on technological forecasting Facebook page, maintained by Joseph, K.K. Ho (url address: https://www.facebook.com/Literature-on-technological-forecasting-1209380532522479/).

12. Managerial intellectual learning Facebook page, maintained by Joseph, K.K. Ho (url address: https://www.facebook.com/managerial.intellectual.learning/).

13. Mitroff, I.I. and M. Turoff. 1973. "Technological Forecasting and Assessment: Science and/or Mythology?" Technological Forecasting and Social Change 5: 113-134.

14. Open University. n.d. "Sign graph" Systems Thinking and Practice (T552): Diagramming, Open University, U.K. (url address: http://systems.open.ac.uk/materials/T552/) [visited at April 10, 2017].

15. Yoon, B. and Y. Park. 2007. "Development of New Technology Forecasting Algorithm: Hybrid  Approach for Morphology Analysis and Conjoint Analysis of Patent Information" IEEE Transactions on Engineering Management 54(3) August: 588-599.