Predicting U.S. jet fighter aircraft introductions from 1944 to 1982: A dogfight between regression and TFDEA

Since its inception in 2001, technology forecasting using data envelopment analysis (TFDEA) has been used with a number of applications. This paper presents a formal comparison of TFDEA with a previously published application from Technological Forecasting and Social Change by Joseph Martino. Using the data and multiple-regression model from Martino, we compare results obtained from TFDEA to those previously published. Both techniques predict the first flights of fighter jets introduced between 1960 and 1982 by using first-flight data of aircraft introduced between 1944 and 1960. TFDEA was found to better predict the first-flight dates than the multiple-regression forecast. These results indicate that TFDEA may be a powerful new technique for predicting complex technological trends and time to market for new products.     

Forecasting the electric transformation in transportation: the role of battery technology performance

In an effort to encourage consumers to purchase electric vehicles (EVs), the government has been funding battery research to solve some of these problems. This paper presents a study using technology forecasting using data envelopment analysis (TFDEA) to forecast future battery performance characteristics. The results were compared against the performance goals established by the US Department of Energy (DOE). We find that the foreseen progress of EV battery performance will be insufficient to meet the DOE projected goals for the range that EVs can travel before running out of power. Therefore, a new battery technology must be developed because the incremental improvements in current battery technologies leave EVs considerably short of the DOE performance specification for longer trip ranges.     

Forecasting emerging technologies: Use of bibliometrics and patent analysis

It is rather difficult to forecast emerging technologies as there is no historical data available. In such cases, the use of bibliometrics and patent analysis have provided useful data. This paper presents the forecasts for three emerging technology areas by integrating the use of bibliometrics and patent analysis into well-known technology forecasting tools such as scenario planning, growth curves and analogies. System dynamics is also used to be able to model the dynamic ecosystem of the technologies and their diffusion. Technologies being forecasted are fuel cell, food safety and optical storage technologies. Results from these three applications help us to validate the proposed methods as appropriate tools to forecast emerging technologies.     

Assessing and forecasting technology dynamics in smartphones: a TFDEA approach

Over the last 20 years, the smartphone technologies at the device level have undergone tremendous change. This paper puts forward a framework to characterise, assess and forecast the smartphone technologies at the device level. The study assesses and forecasts the technological advancement observed in smartphones using technology forecasting using data envelopment analysis with an objective to evaluate the technological rate of change in the device. A quarterly data set comprising 31 quarters from 2007 to 2014 was analysed for smartphone releases in a particular price range. For the validation purpose, the analysis was designed to set the point of forecasting somewhere in 2012 using the data set between 2007 and 2012, so as to forecast the technologies thereon till 2014. The results indicate that the rate of technological change in smartphones is accelerating.     

DELPHI Findings about Internet Systems Problems, with Implications for Other Technologies

This article first reports the results of an on-line DELPHI Questionnaire study of the management problems of Internet/Web systems development. The study results indicate two "most important' clusters of problems: those that are concerned with strategy-related issues, and those that are concerned with data integrity and privacy issues. The first group is generally rated as being more important than the second. The article briefly discusses the overall results of the study. Thereafter the article is devoted to the "strategy' category of problems, with attention to the findings of other studies, including six prior similar-methodology DELPHI studies involving problems of managing technologies in other technology realms. Strategy problems are ranked among the most important technology management problems in all of the fields of these studies. A broad industry and academic effort to deal with the strategy and planning problem seems necessary to establish strategy theory and best practices for planning that are useful across many technology fields.     

Factor analysis and structural equation model for patent analysis: a case study of Apple’s technology

In this study, we analyse patent data for technology analysis (TA) because patents are rich in information on developed technology. The results of TA can be used to perform more efficient research and development (R&D) planning. Most companies are trying to develop new and innovative technologies to improve their competitive positions. Research involving TA has been introduced in a variety of fields. Most of the published research analysed original variables related to a target technology. However, it is necessary to analyse the latent variables as well as the original variables included in the technology to achieve a better TA model. Therefore, we propose a factor analysis and a structural equation model for patent analysis. In addition, we use Apple’s patents to determine the target technology. In our case study, we analyse Apple’s technologies by latent variables. Our case study shows how the proposed model is applied to Apple’s R&D planning.     

Environmental technology foresight: New horizons for technology management

Decision-making in corporate technology management and government technology policy is increasingly influenced by the environmental impact of technologies. Technology foresight (TF) and environmental impact assessment (EIA) are analyzed with regard to the roles they can play in developing long-term strategies and policies for reducing the environmental impact of technologies. The methods for TF and EIA are quite well developed, but remain within a tradition of rational decision-making. However, recent studies of tecnology management and innovation have shown that technology development can be explained only to a certain degree by rational decision-making. EIA is usually presented as an objective and scientific method, based on normative underpinnings that usually remain hidden. Thus, systematic information-providing instruments such as TF and EIA can play only a limited role. We propose a procedure for environmental technology foresight. The case is made that environmental technology management will be most helped by a 'constructive' approach in order to stimulate incorporation of long-term environmental objectives.     

Delphi as a tool for identifying future appropriate technologies in India

The concept of appropriate technologies with particular reference to developing countries like India is discussed. It is suggested that appropriate technologies should: (1) include the entire range of technologies from very primitive to very modern, (2) be available when required, and (3) have the maximum impact on the largest fraction of the poorest sections of the population, especially through increasing employment. The problem of identifying appropriate technologies that would meet these criteria has also been discussed. In this article we have attempted to use the Delphi technique of technology forecasting for identifying future appropriate technologies in the areas of food, health, and energy using respondents in India. Subsequently, we have analyzed our results in terms of the criteria previously assigned to appropriate technologies and have thereby evaluated our experiment.It is concluded that the Delphi is an adequate first step in identification of such technologies; however, it must be followed by more rigorous techniques (possibly quantitative) for assigning further priorities within the technologies identified by the Delphi. This will help in arriving at suitable policy decisions and allocation of funds to a few of the most important areas.Finally, two alternate scenarios have been presented (using the Delphi results as well as our own perceptions and observations of the Indian society), depending on whether appropriate technologies and policies are adopted.     

Multivariate multiple regression modelling for technology analysis

Technology analysis is important for technology management areas such as research and development strategy and new product development. So many studies on technology analysis have been used across a diverse array of fields. Most of these were based on patent analysis, which analyses patent documents using text mining and statistics. The studies on conventional patent analyses constructed models consisting of various independent variables (technologies) and one dependent variable. But in reality, we have to consider a model that includes several dependent variables at the same time, because most technologies influence each other. In this paper, we propose a methodology for patent analysis that reflects the various response technologies simultaneously. We perform multivariate multiple regression modelling in order to efficiently conduct our technology analysis. To show how our modelling can be applied to realistic context, we carry out a case study using the patent documents related to three-dimensional printing technology.     

Forecasting the semiconductor industry cycles by bootstrap prediction intervals

In recent years, there has been a recognition that point forecasts of the semiconductor industry sales may often be of limited value. There is substantial interest for a policy maker or an individual investor in knowing the degree of uncertainty that attaches to the point forecast before deciding whether to increase production of semiconductors or purchase a particular share from the semiconductor stock market. In this article, I first obtain the bootstrap prediction intervals of the global semiconductor industry cycles by a vector autoregressive (VAR) model using monthly US data consisting of four macroeconomic and seven industry-level variables with 92 observations. The 24-step-ahead out-of-sample forecasts from various VAR setups are used for comparison. The empirical result shows that the proposed 11-variable VAR model with the appropriate lag length captures the cyclical behaviour of the industry and outperforms other VAR models in terms of both point forecast and prediction interval.     

A theory of classification and evolution of technologies within a Generalised Darwinism

This paper here proposes a theory of classification and evolution of technology based on taxonomic characteristic of interaction between technologies that is an under-studied field of research in economics of technical change and management of technology. The proposed classification of technologies, in a broad analogy with evolutionary ecology of parasites, within a theoretical framework of Generalised Darwinism, is: (1) parasitic technologies, (2) commensal technologies, (3) mutualistic technologies, (4) symbiotic technologies. This theory here suggests the property of mutual benefaction from interaction between different technologies and the theorem of not independence of any technology to explain and predict characteristics and evolutionary pathways of technologies over time. Overall, then, this study may be useful for bringing a new perspective in economics of innovation to categorise and analyse the interaction between technologies that can be a ground work for development of more sophisticated concepts to explain and predict the evolution of technology and generalise aspects of technological change in human society.     

基于K-means与技术生命周期的技术预见方法研究——以水体净化技术为例

预见水体净化技术发展趋势有助于实现“美丽中国”建设目标。已有技术预见方法缺乏定量客观依据,相关评判指标也不够全面。从Innography数据库检索2008-2019年发布的3552个污水处理技术专利,运用多维标度分析和K均值聚类法,基于专利静态指标分析专利技术发展潜力,采用技术生命周期分析法从动态视角判断每类技术的发展前景。研究发现：①污水与污染物双重回收可持续性技术在多个静态评价指标方面均优于其它技术,且处于从引入期到发展期的过渡阶段,具有较大的发展空间;②以去除特定污染物为目标的功能单一技术已被淘汰,市场开始应用污水处理原理不同的多种技术联合处理方式。据此,提出企业应选择污染物回收率高、二次污染物排放少、整体“净效益”为正的污水处理技术,并采取新旧技术联合处理方式降低企业采用新技术的转换成本。     

A model for forecasting the substitution of one technology for another

Most technological changes can be described as a substitution of one material, process or product for another. Each such substitution, if successful, normally tends to follow an S-shaped (or “logistic”) curve: that is, it starts slowly as initial problems and resistances have to be overcome; then it proceeds more rapidly as the competition between the new and the old technology grows keener and the new technology gains an advantage; and finally, as the market for the new technology approaches saturation, the pace of substitution slows down. Sometimes, when the process is completed, the old technology continues to retain some specialized portion of the total market (i.e., a sub-market) for which it is particularly well adapted. In forecasting the course and speed of the substitution process especially when it has already begun and partially taken place, the simplest approach is to project a function having the appropriate S-shaped curve, using historical data to determine the free parameters of the function. While useful, especially where data are not available for a more sophisticated study, the simple curve-fitting techniques fail to take into account several important factors that affect economic and management decisions on the part of producers and intermediate users (as well as “final” consumers) and thereby influence the course which the substitution process is likely to take. To overcome this limitation, a simulation model has been developed at IR&T which allows some of these factors to be evaluated and incorporated explicitly and quantitatively. The model is described and its application is illustrated in the case of the substitution of plastic for glass in bottles. It is most applicable where the competing technologies are rather precisely defined, where a good deal of current technical and economic data are available, and where an in-depth analysis is desired. Because this particular forecast was made before the sudden precipitous increase in petroleum prices, which upsets the price relationships assumed in the forecast, there is discussion of the vulnerability of forecasts to political and other contra-economic developments.     

Forecasting house prices for the four census regions and the aggregate US economy in a data-rich environment

This article considers the ability of large-scale (involving 145 fundamental variables) time-series models, estimated by dynamic factor analysis and Bayesian shrinkage, to forecast real house price growth rates of the four US census regions and the aggregate US economy. Besides the standard Minnesota prior, we also use additional priors that constrain the sum of coefficients of the VAR models. We compare 1- to 24-months-ahead forecasts of the large-scale models over an out-of-sample horizon of 1995:01–2009:03, based on an in-sample of 1968:02–1994:12, relative to a random walk model, a small-scale VAR model comprising just the five real house price growth rates and a medium-scale VAR model containing 36 of the 145 fundamental variables besides the five real house price growth rates. In addition to the forecast comparison exercise across small-, medium- and large-scale models, we also look at the ability of the ‘optimal’ model (i.e. the model that produces the minimum average mean squared forecast error) for a specific region in predicting ex ante real house prices (in levels) over the period of 2009:04 till 2012:02. Factor-based models (classical or Bayesian) perform the best for the North East, Mid-West, West census regions and the aggregate US economy and equally well to a small-scale VAR for the South region. The ‘optimal’ factor models also tend to predict the downward trend in the data when we conduct an ex ante forecasting exercise. Our results highlight the importance of information content in large number of fundamentals in predicting house prices accurately.     

Economic growth and financial development in Asian countries: A bootstrap panel Granger causality analysis

This study using Kónya (2006) [Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling 23, 978–992.] method of bootstrap panel Granger causality analysis, which considers the issues of cross-sectional dependency and slope heterogeneity among countries investigated simultaneously, analyzes the causality between financial development and economic growth among ten Asian countries surveyed during period 1980 to 2007. We find that the direction of causality between financial development and economic growth is sensitive to the financial development variables used in the ten Asian countries examined in this work. Moreover, our findings support the supply-leading hypothesis, as many financial development variables lead economic growth in some of the ten Asian countries surveyed, especially in China.     

基于CiteSpace的技术预测研究的可视化分析

基于科学知识图谱软件CiteSpace,对从中国知网(CNKI)数据库中获取的技术预测科学文献,从文献维度、内容维度等角度进行可视化共现及聚类分析,探索技术预测研究现状及发展趋势。相关分析表明:①技术预测合作研究主要限于学科领域内部;②技术预测研究方法主要包括TRIZ理论、文献计量、文本挖掘和知识图谱等,呈现出从定性向定量、从数字化向知识化发展的特征;③技术预测研究方向主要包括新兴技术主题识别、技术机会识别、颠覆性技术预测、技术态势探索等,呈现出深化实证应用的发展趋势。     

Medical technology and the production of health care

This article investigates the factors that determine differences across OECD countries in health outcomes, using data on life expectancy at age 65, over the period 1960 to 2007. We estimate a production function where life expectancy depends on health and social spending, lifestyle variables, and medical innovation. Our first set of regressions include a set of observed medical technologies by country. Our second set of regressions proxy technology using a spatial process. This article also tests whether in the long-run countries tend to achieve similar levels of health outcomes. Our results show that health spending has a significant and mild effect on health outcomes, even after controlling for medical innovation. However, its short-run adjustments do not seem to have an impact on health care productivity. Spatial spill overs in life expectancy are significant and point to the existence of interdependence across countries in technology adoption. Furthermore, nations with initial low levels of life expectancy tend to catch up with those with longer-lived populations.     

Residential grid storage technology battles: a multi-criteria analysis using BWM

This article focuses on the battle for dominance between various battery technologies in the residential grid storage market (< 10?KWh) in the context of residential energy systems and the related home energy management systems. We focus on five major battery technologies that are available in the market (lithium-based batteries, lead-based batteries, flow batteries, nickel-based batteries, and sodium-based batteries). Based on a literature review and expert interviews, we study the factors for technology success in the residential grid storage market. By applying the best worst method (BWM), we assign the relative importance to the factors and predict which technology will have the highest chance of achieving success. We compare this to the technology that now has the highest market share and conclude that BWM is a useful method to indicate technology dominance in this market.