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Identifying the development trends of emerging technologies with disruptive potential as early as possible is crucial for enterprise research and development (R&D) investment planning and government R&D strategic planning. Many researchers have used academic papers and patent data to identify trends in emerging technologies, although they rarely have made use of web news data focused on emerging technologies. Web news contains a large amount of social awareness data that represents the public's sense of and response to emerging technologies, and the social awareness data is of great significance for identifying the development trends of emerging technologies. Therefore, this article presented a research framework to identify the development trends of emerging technologies using patent analysis and web news data mining. In the research framework, first, we have used patent analysis to analyze the evolution topics of these emerging technologies, and have applied the topic model to study the changing patterns of publics’ topics of concern regarding emerging technologies contained in web news. Second, we have made a comparison analysis of the results of patent analysis and the public’ sense of and response to emerging technologies contained in web news. Then, we have applied an improved sentiment analysis to study the changing patterns of publics’ expectations for emerging technologies contained in web news. Finally, by studying the changes in publics’ topics of concern and publics’ expectations for emerging technologies over time, we have identified the development trends of emerging technologies. The perovskite solar cells technology is used as a case study to analyze the effectiveness and feasibility of the framework. This article provides a new research perspective to identify technology trends, and contribute to the technology forecasting methodology.

Despite being a strong stimulus for the invention of new alternatives, morphology analysis (MA) suffers the limitations of being a nonquantitative, vague, and static methodology. As a consequence, the MA outcomes typically do not provide practical technology opportunities. This paper, therefore, proposes a new hybrid approach that enhances the performance of MA by combining it with conjoint analysis (CA) and citation analysis of patent information. First, keywords are extracted from patent documents using text mining, and the morphology of existing patents is identified by these keywords. Alternatives for new technology development from among the emerging technologies are presented by combining the valuable levels of each attribute in a morphology matrix predefined by domain experts. Then, configurations of new technology are suggested in order of priority using CA, and the technological feasibility of each new configuration is subsequently investigated. The technological competitiveness of a company can be analyzed by a newly suggested index, ldquotechnology share,rdquo which is analogous to the concept of market share in traditional CA. The proposed MA-CA hybrid process is illustrated with a case example of patent information from the thin film transistor-liquid crystal display (TFT-LCD) patent database.

Errors in requirements specifications have been identified as a major contributor to costly software project failures. It would be highly beneficial if information systems developers could verify requirements by predicting workplace acceptance of a new system based on user evaluations of its specifications measured during the earliest stages of the development project, ideally before building a working prototype. However, conventional wisdom among system developers asserts that prospective users must have direct hands-on experience with at least a working prototype of a new system before they can provide assessments that accurately reflect future usage behavior after workplace implementation. The present research demonstrates that this assumption is only partially true. Specifically, it is true that stable and predictive assessments of a system's perceived ease of use should be based on direct behavioral experience using the system. However, stable and behaviorally predictive measures of perceived usefulness can be captured from target users who have received information about a system's functionality, but have not had direct hands-on usage experience. This distinction is key because, compared to ease of use, usefulness is generally much more strongly linked to future usage intentions and behaviors in the workplace. Two longitudinal field experiments show that preprototype usefulness measures can closely approximate hands-on based usefulness measures, and are significantly predictive of usage intentions and behavior up to six months after workplace implementation. The present findings open the door toward research on how user acceptance testing may be done much earlier in the system development process than has traditionally been the case. Such preprototype user acceptance tests have greater informational value than their postprototype counterparts because they are captured when only a relatively small proportion of project costs have been incurred and there is greater flexibility to modify a new system's design attributes. Implications are discussed for future research to confirm the robustness of the present findings and to better understand the practical potential and limitations of preprototype user acceptance testing.

While many scholars of organizational innovations have examined characteristics of innovations such as relative advantage and complexity and how they facilitate the adoption of an innovation by organizations, others have used mathematical models to fit diffusion patterns. In this study, the authors attempt to integrate these two areas of inquiry and explore the possibilities to predict diffusion patterns based on characteristics of the innovation and the adopting entities. Based on a cross-sectional sample of 313 large American firms, 20 information technology (IT) innovations were examined and their diffusion patterns assessed with respect to models that espoused internal and external influence. The mixed influence model (Bass model) was chosen as a robust common representation for the set of diffusion patterns. However, the external influence as represented by the coefficient of innovation was found to be extremely small and the internal influence dominates for all innovations. The other two parameters of the model, the saturation level and the coefficient of imitation, which represents internal influence, were then used to perform a cluster analysis. Five clusters of technologies emerged, and the potential relationships between their innovation characteristics and diffusion patterns were explored. Rigorous examination of these potential relationships by future researchers may result in practical methods for predicting patterns of IT innovation diffusion based on innovation and technology characteristics.

Technology investment models usually assume that a known number of technologies will appear in the future, though their appearance, cost, and revenue parameters may be uncertain. In practice, however, it is not always clear how many future technologies or periods need to be considered. We introduce the concept of a technology horizon, which we define as the minimum number of future technologies and future periods that need to be considered to guarantee that the optimal decision would not change even if additional technologies or periods worth of data were to be considered. We also allow the costs and revenue of existing technologies in our model to vary depending on the latest technology introduced. Further, we allow for implementation costs, which enables us to model situations where it may cost more to leapfrog technologies than if one were to buy intervening technologies. We then provide a fast algorithm to identify such technology horizons.

The proliferation, assumptions, motivation, and behavior of various substitution models of the technological diffusion process are explored. The underlying notion is that such an understanding helps the model user to choose the most appropriate model for the situation. The authors discuss the development, motivation, and assumptions of various deterministic and binary substitution models and compare them on the basis of their three mathematical characteristics. It is shown that the study of the interrelationships between the models is useful in narrowing the choice. The behavior of the models is studied through an illustration of diffusion of innovative oxygen-steel technology in Spain and in Japan. >

It is noted that a key issue in engineering management is how technological forecasting affects marketing and planning decisions. Because published market forecasts influence both supplier and customer decision making, there is a need to understand the factors influencing these forecasts. Completing the management cycle requires analysis of the results and recommendations to change the original plan. In this case, a post-audit of the forecasts was made to determine past performance. Recommendations were made to standardize the terminology so that future market forecasts could be better understood, analyzed, and post-audited. A comprehensive literature search of publicly available forecasts for the United States fiber optic communications market was made. The research indicated that two major trends within the fiber optic communications industry are expected to develop. The two major trends are continued growth and the shift of emphasis from long-haul to short-haul systems. It is concluded that growth will be driven by cost effectiveness. >

A major problem of firms around the world is the development of new technology and the adaptation of the firm to technological change. Increasingly, we must find ways to sharpen our ability to do technological forecasting and to translate these forecasts into strategies that enable the organization to maximize its opportunities for gain from these technologies. This approach means that more attention must be paid to the problems of organizational adaptation as part of the problem of managing technology within the firm. We need to look at the problems of adaptation and develop the set of necessary conditions for management to make a transition to a new technology in a way that will help the firm achieve its profit goals. These problems are difficult ones and require an interdisciplinary approach. In general, we see the need for those interested in the problems of research management and the management of technology to broaden their skills and their interests. We must move more strongly into the general management area and grapple with the problems of organizational behavior as well as the more traditional problems of research and technology management. >

New models for describing the time pattern of the spread of innovations through a population are presented. In general, most existing temporal models of innovation diffusion are binomial in the sense that they tacitly assume that the population can be divided, without loss of generality, into adopters and potential adopters. This paper discusses the limitations of the binomial models and presents the development and applications of a model where the potential adopter population is dynamic in nature. The possibility is considered of developing an analytical framework to study innovation diffusion processes where the population had polynomial dimensions-polynomial in the sense that it comprises members who are adopters, rejecters, disapprovers, and uncommitted. Some stochastic models to complement the most commonly used deterministic binomial models were developed. A few case studies are included.

When a technological innovation, a new technology or a new technological product is introduced in an economic sector, a market, a country, or in the international environment, the technology manager, based on the already observed data, needs, from the early stages of the diffusion process, to make reliable predictions for the future trends. In practice, forecasts are frequently based on the logistic or on the various sigmoid-form models after an estimation of the upper limit, a case which cannot always be met in the early stages of the diffusion. This work tries to improve the predictions based on early and middle stage data by introducing two new innovation diffusion models. Both models arise from the generalized rational models (GRM) and may be useful for early and middle stage predictions because they do not include a parameter expressing the upper limit. The properties of these models are studied and the methodology of the used nonlinear regression analysis techniques is discussed. Two applications of the proposed models on time-series data selected from the technological innovation diffusion area indicate the usefulness of these models. Finally an extension of one of the models and a comparative application follows.

The technology decomposition theory is proposed for technologies that exhibit rapid technical change. This theory is intended to support technology forecasting by means of definition of the technical change in technology constituents. Technology decomposition shows that rapid technical change is mainly accomplished by improvements in the manufacturing process. The improvements are the result of the design of new production equipment. Measurements of technical change are used to derive a technology constituent value. It is proposed that the derived constituent value is the maximum achievable. This leads to implications for the management of technology in general and, specifically, for the management of product engineering, if the product depends on one or more rapidly changing technologies. >

A dynamic and spatial model of technology transfer is presented, in relatively simple mathematical form, which incorporates time and potential technological distance as a level parameter. The existing models of technological change at a particular location (that is, the technological substitution models) are shown to be special cases of the developed time-level model. Transfer of computer technology is used as an illustrative example. Analysis of the model results indicate that the developed time-level model of technology transfer can be used as a guide in determining the optimal partners for most effective technology transfer.

Actions taken in one company in order to speed the transition of R&D results to commercial use are described and evaluated. A useful analysis framework taken from technological forecasting is presented. Conclusions drawn from this analysis are used as a basis for a systematic realignment work, comprising changes in formal organization, communication channels, hiring practices, and introduction of systematic R&D methods.

Experience curves have for some time provided a conceptual framework for forecasting unit costs of new products or production activities. On a practical level, however, statistical estimation of experience curves involves a number of subtle model specification issues which if ignored can render estimated curves totally inappropriate. Several of these issues are explored in a heuristic setting. A major finding of the analysis undertaken is that many estimates of experience curves are estimates of models seriously flawed due to specification errors. First, price experience curves and the problems encountered in estimating these functions due to missing variables are treated. Then, attention is focused on cost experience curves. It is argued that estimates of cost experience curves are generally biased due to both missing variables and missing equations; the latter is usually termed simultaneous equation bias.

Reports a study of 54 organizations, 30 of which were nonservice firms. The purpose of the study was to evaluate various aspects of environmental uncertainty and their impacts on innovativeness (new products and processes). In a recursive, causal path model of the nonservice sample of firms, it was found significantly (p

China needs to enhance its sources of capital, accept competition at individual and enterprise levels, and reduce governmental influence in the management of technological processes. In addition, shifting scientific engineering norms to share information, more effective reward mechanisms for innovation, better staff support, and upgrading management capabilities would encourage technological development. On balance, these weaknesses are outweighed by the strong national commitment to technology-based economic development and the demonstrated economic accomplishments of the past decade. Barring the unlikely resurgence of radical leftist forces, the authors predict that China will significantly impact global markets for technology-intensive products by the year 2000. >

This paper describes a new approach to corporate technological performance assessment, based on patent citation analysis. We define this technological performance assessment as “the use of patent counting, clustering, and citation analysis in the evaluation of corporate, industry-wide, and national technological activity.” After reviewing the state-of-the-art of patent citation analysis, we discuss five different ways of formatting large-scale patent data for performance assessment. These formats are: 1) patent lists, with citing and cited patents, 2) laboratory impact tables, 3) corporate technology profiles, 4) corporate linkage maps, and 5) patent citation network diagrams. The technological performance assessements to which these different formats may be applied include: 1) corporate activity measurement and competitor analysis, 2) anti-trust, 3) technological forecasting and long-range planning, 4) R&D productivity measurement, 5) company linkages and corporate technological integration, 6) strategic targeting, especially for merger and acquisition purposes, 7) international trade and technology flow analysis, and 8) market and license identification.

A `real' forecast of technological developments in farm tractors is presented here. The methods of Delphi and cross-impact analysis are used and briefly described along with some criticism in the background of the experience gained from their application. In addition, some purely methodological findings are noted.

Technological change is discussed in the context of systems cost analysis in two ways. First, it is a specific objective of research and development (R&D) programs and is a direct determinant of R&D cost. Methods of measuring technological change are evaluated and an example of an R&D cost equation is set forth. Second, technological change underlies the relationships between design/performance characteristics and procurement costs. For many systems, the year-to-year change is highly significant, and cost analyses which overlook this phenomenon can be in serious error. Examples are discussed.

Technical innovation is a concept proposed in 1939 by economist Joseph Schumpeter. Initially ignored, it is now considered the key to improving high-tech productivity. Most writers agree on a definition which identifies the start as a novel creative step, and the end as the time of commercial availability or general use. There appear to be two basic categories of technical innovation: the first stems from research or exploration, starts with some kind of breakthrough, and requires a long period to bring to market. The second is the result of a new planned effort and usually reaches the market in a fraction of the time required for a research innovation.

The Marine Engineering Laboratory (MEL), the Annapolis Division, Naval Ship R&D Center, has developed a procedure for the development of long-range R&D planning particularly aimed at the logical objective determination of the requirements imposed on new ship procurement by the Navy mission. Starting from a broad concept suggested by Naval Ship Systems Command (NAVSHIPSYSCOM), MEL has developed the structure of a procedure which, when completed and validated, will provide a logical, objective determination of future requirements and a standardized format for Ship System Formulation. The structure consists of mission objectives, vehicle types, specific techniques and components, as well as forecasts of the functional capabilities techniques and components.

This Research Abstract describes a recently published book on technological forecasting. It includes an abbreviated version of the Preface of the book, and the Table of Contents.

Editor's Note: We are pleased to present, as a service to our readers, the preface and bibliographic reference portions of a forthcoming anthology on R&D management. The new book, edited by Marvin Cetron and Joel Goldhar, is entitled “The science of managing organized technology” and will be published in the spring of 1970 by Gordon and Breach, New York. It will offer a very comprehensive review of the literature relevant to R&D management, as can be seen by the table of contents. The Book Reviews and “To Dig Deeper” Sections should prove useful for practitioners as well as students of R&D management. — A. H. Rubenstein

Technological forecasting is becoming a widespread activity in business and the military. The actual accomplishment of the forecasting task is increasing in methodological sophistication. This article presents many practical problems and pitfalls encountered in planning and preparing technological forecasts. The authors examine the factors to be considered in the initial stages of implementing forecasts, the validity of previous forecasts, summarize recent advances, point out new hazards, evaluate the forecasting technique, and include some realistic suggestions for improved technological forecasts.

Government and industry are developing many new ways to forecast future technical developments, but the payoff comes when these projections are incorporated as part of the R&D planning process. This is done on two levels — when deciding on future work in a specific development project and when assigning priorities to the overall R&D effort. Systems being developed in industry and in the federal government are able to integrate technological forecasts with data on future needs, probabilities of success, and potential funding levels. The computerized result is a complete ranking of all on-going and potential projects according to their overall worth. But care must be taken to ensure that the computer printout retains its role as a servant and not a ruler of managers. One of many normative (goal-oriented) technological forecasting techniques currently being examined will be discussed in this paper. It is hoped that this technique will serve two interrelated purposes: 1) to explore the structure of project-selection decision problems in the context of the information and organization environment of the R&D manager and 2) to explore characteristics of the R&D process that are relevant to the design and implementation of management system for planning and controlling resource allocation among various R&D projects.

An examination by the authors of the complexity of business decision making led to the conclusion that an organized information gathering and analysis mechanism is necessary to cope with this complexity. In this paper a proposed structure of the decision-making process is presented in the context of an R&D organization engaged in many different technical programs and projects. The foundation of this structure is an effective management team. On this foundation rest the main supports of the structure, which include a model representing the R&D process, a technological forecasting scheme to provide an indication of future direction, the project proposing and planning activities to provide the technical information needed for decision making, a set of quantitized judgments to which analytic tools can be applied, an algorithm for optimum resource allocation, and finally, a hybrid decision-making technique. An experiment has been performed with this method and the result, through not reported here, was very encouraging.

This paper discusses a simple and logical method for using Navy Exploratory Development Goals (EDG) and the Navy Technological Forecast (NTF) for the allocation of resources to technology to meet future requirements. First, the military worth of the various EDG in meeting Navy objectives is determined. Second, the utility of the various technologies to the EDG is determined by systems design and scientific/engineering offices. The military worth and technology utilization values are then combined into an Index of Worth value indicating the worth of the various technologies in meeting Navy objectives. A mathematical model is suggested as a future refinement.