Basic theories and methods of technological forecasting.

Table of contents

  • Definition

  • History

  • Theories of technological forecasting

  • Methods of technological forecasting

  • Commonly adopted methods of technology forecasting

  • Time Frame for Technology Forecasts

  • Benefits of Technological forecasting

  • References

Technology Foresight is a combination of creative thinking, expert views, and alternative scenarios to make a contribution to strategic planning. Technological Forecasting (TF) is concerned with the investigation of new trends, radically new technologies, and new forces that could arise from the interplay of factors such as new public concerns, national policies, and scientific discoveries. Many of these forces are beyond the control, influence, and knowledge of individual companies. 

The development of a technology forecast can be divided into three separate actions:

  • Framing the problem and defining the desired outcome of the forecast,

  • Gathering and analyzing the data using a variety of methodologies, and

  • Interpreting the results and assembling the forecast from the available information.

Technology forecasting has existed in one form or another for more than a century, but it was not until after World War II (WWII) that it began to evolve as a structured discipline. The motivation for this evolution was the U.S. government’s desire to identify technology areas that would have significant military importance.

Theories of technological forecasting

Neoclassical theories Technological change takes place in the form of shifts of the production function towards the origin.

Marxist theory

Karl marl perceived technology as not self-generating, but as a process directed by willful. Conscious, active people and moulded by useful forces. He held that technology changes the development of the productive forces which was the prime mover of history.

Schumpeter’s theory

Schumpeter's theory of development assigns a paramount role to the entrepreneur and innovations introduced by him in the process of economic development. According to Schumpeter, the process of production is marked by a combination of material and immaterial productive forces. The Schumpeterian production function can, therefore, be written as –

Q = ƒ [k, r, I, u, ν) …(1)

Where Q stands for the output, k for the Schumpeterian concept of “produced means of production”, r for natural resources, l for the employed labor force.

Evolutionary theory

This suggests a biological analogy to explain technological change. The Darwinian two-state process of mutation (invention) and selection (innovation) has been employed to understand the evolution of technology.

Market pull theory

Market Pull', refers to the need/requirement for a new product or a solution to a problem, which comes from the market place. The need is identified by potential customers or market research. A product or a range of products are developed, to solve the original need.

Technology push theory

Technology is defined as an autonomous or quasi-autonomous factor. It assumes a one-way casual determination approach ie from science to the economy.

Methods of technological forecasting


Commonly adopted methods of technology forecasting include the 

  • Delphi method

The Delphi technique is used where a consensus of expert opinion is required on the timing, probability, and identification of future technological goals or consumer needs and the factors likely to affect their achievement. It is best used in making long-term forecasts and revealing how new technologies and other factors could trigger discontinuities in technological trajectories. The choice of experts and the identification of their level and area of expertise are important; the structuring of the questions is even more important. Experts in non-technological fields can be included to ensure that trends in economic, social, and environmental fields are not overlooked.

  • Exploratory technique: The formal forecasting techniques are standard components that are described in many textbooks on forecasting techniques (see specific techniques). Specific techniques for forecasting fall into two main categories, exploratory and normative. Information about each technique is available in various references.

Exploratory techniques are primarily concerned with the analysis of historical data. Selected attributes such as functional performance, technical parameters, economic performance, etc. are plotted against time. Since it is usually assumed that progress is evolutionary and that technological progress is not random, it is possible to generate characteristic curves or patterns from the data, and from these patterns, forecasts can be made with varying degrees of certainty. However, changes do occur and the influence and impact of new or surprising factors must not be disregarded. Examples of relevant exploratory techniques are:



                            trend extrapolation

                            technology substitution

— all of which rely on a large amount of statistical data, which may or may not be available freely.

 Normative methods of technology forecasting: Normative techniques start by proposing a desired or possible states, such as the satisfaction of a market need or the achievement of technological development, and work backward from this to determine the steps necessary to reach the required outcome. The number of foreseeable paths of development from the present position to the objective could range from ‘none’, implying a completely new technology, to ‘several’. Each feasible path to the objective is analyzed for its relevance and difficulty. Examples of relevant normative techniques are:

                              relevance trees

                             morphological analysis

                             technology watch and technology monitoring

                              Delphi analysis

                             trend impact analysis

                              technology substitution.

Information needed for these techniques is likely to be more firm-specific than that needed for exploratory techniques. Technology-watch, in particular, needs a proactive role to help the organization identify and establish links with the most useful sources of information and opinion; typically these will be at the forefront of innovative activity.

  • Foresight techniques

The methods and systems used in foresight programs are drawn from the forecasting field, particularly technology forecasting. 

Intuitive thinking is used more in technology foresight than in technology forecasting and is used in a comprehensive and structured form. All intuitive thinking methods are relevant to foresight activities, but only a few of the exploratory and normative methods used in forecasting are applicable to foresight. Which exploratory or normative method to use, under different circumstances, will depend on the requirements of each specific study.

The use of ‘vision’ is a form of intuitive thinking. When companies formulate a business strategy the vision of key individuals can play an important part. The value of this kind of input is increasingly acknowledged.

It is unlikely that any single method on its own will meet the needs of a foresight program.

Other methods and techniques that can be used for foresighting include:

  • The general classes of cross-impact simulation, which try to develop a qualitative understanding of the structure and interrelationships of the situation.

  • Relevance trees, which investigate the dependence of technologies at one level to technologies at adjacent levels.

Both of these classes of methods provide some elements of semi-quantitative or judgmental analysis.

Patent analysis can be regarded as a specific foresight technique if the implications of the analysis are followed through.

Intuitive thinking is used in technology foresight in a comprehensive and structured form.

Time Frame for Technology Forecasts

Short Term: Short-term forecasts are those that focus on the near future (within 5 years of the present) to gain an understanding of the immediate world based on a reasonably clear picture of available technologies. Most of the critical underpinning elements are understood, and in a large fraction of cases, these forecasts support implementations. For example, a decision to invest in a semiconductor fabrication facility is based on a clear understanding of the technologies available within a short time frame.

Medium Term: Medium-term forecasts are for the intermediate future (typically within 5 to 10 years of the present) and can be characterized, albeit with some gaps in information, using a fairly well understood knowledge base of technology trends, environmental conditions, and competitive environments. These forecasts may emerge from ongoing research programs and can take into account the understandings of current investments in manufacturing facilities.
Long Term: Long-term forecasts are forecasts of the deep future. The deep future is characterized by great uncertainty in how current visions, signposts, and events will evolve, and the likelihood of unforeseen advances in technology and its applications. These forecasts are critical because they provide scenarios to help frame long-term strategic planning efforts and decisions and can assist in the development of a portfolio approach to long-term resource allocation. While long-term forecasts are by nature highly uncertain, they help decision-makers think about potential futures, strategic choices, and the ramifications of disruptive technologies.

Benefits of Technological forecasting

Forecasting and foresight extend and expand the benefits of near-term market intelligence and simultaneously stimulate learning and improvement practices. Forecasting and foresight studies try to shed light upon the nature, magnitude, probability, and timing of relevant scientific and technological developments.

Modern technological forecasting has only been utilized since the end of WWII. In the last 50 years, technology forecasts have helped decision-makers better understand potential technological developments and diffusion paths. 


Article shared by Ayesha J

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Graefe, Andreas, and Christof Weinhardt. 2008. Long-term forecasting with prediction markets—A field experiment on applicability and expert confidence. The Journal of Prediction Markets 2(2): 71-91.

Existing technology forecasting methods.

(All contents and reference stated by the author).


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