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In a competitive business environment, an early understanding of the dynamics of technological change is crucial to help policymakers and managers make better-informed decisions. Bibliometric analyses help in studying trends and technological evolution. Tech mining (text analyses of science and technology information resources) enhances Bibliometric analyses. However, more often than not, such analyses focus on a specific technological area, and mainly result in incremental advance forecasts. An analysis of the interconnected dynamics of technology change warrants new approaches for identifying technology emergence, technological substitution, and the influences of vital socioeconomic forces. This paper introduces a unique combination that applies a tech mining and semantic TRIZ as a case study to Dye-Sensitized Solar Cell (DSSC) technology. This methodological combination brings broader insights to the emergence of DSSC in conjunction with related technologies that affect its progress, enriching the associated technological progression's empirical characterization.

Accurately evaluating opportunities in new and emerging science and technologies is a growing concern. This study proposes an integrated framework for identifying a range of potential innovation pathways and commercial applications for solid lipid nanoparticles – one particularly promising contender within the field of nano-enabled drug delivery. Several text mining techniques – term clumping, SAO technique, and net effect analysis – as well as technology roadmapping, are combined with expert judgment to identify the main areas of RD however, the “time-to-market” is much shorter for cosmetics than it is for pharmaceuticals. The most significant contributions of this paper have been highlighted as follows. One innovation is extracting the intelligence from three kinds of data sources after in-depth considering their characteristics and matching with the features of different technology development stages to identify innovative research topics. The second one is combining SAO technique with net effect analysis to identify what the evolutionary links between research topics are, and then to use TRM to visualize the evolution of the main areas of R&D over time.

Indicators of technological emergence promise valuable intelligence to those determining R&D priorities. We present an implemented algorithm to calculate emergence scores for topical terms from abstract record sets. We offer a family of emergence indicators deriving from those scores. Primary emergence indicators identify “hot topic” terms. We then use those to generate secondary indicators that reflect organizations, countries, or authors especially active at frontiers in a target R&D domain. We also flag abstract records (papers or patents) rich in emergent technology content, and we score research fields on relative degree of emergence. This paper presents illustrative results for example topics – Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data.

Many fields are managing better by incorporating advanced data analytics—e.g., even sports such as baseball. We illustrate with a novel set of indicators that distinguish cutting edge R&D activity to provide competitive technical intelligence on “who's doing what, where, and when.” Our aim is to stimulate your thinking about what data analytics could help you achieve your technology management goals more effectively.

As one of the most impactful emerging technologies, big data analytics and its related applications are powering the development of information technologies and are significantly shaping thinking and behavior in today's interconnected world. Exploring the technological evolution of big data research is an effective way to enhance technology management and create value for research and development strategies for both government and industry. This paper uses a learning-enhanced bibliometric study to discover interactions in big data research by detecting and visualizing its evolutionary pathways. Concentrating on a set of 5840 articles derived from Web of Science covering the period between 2000 and 2015, text mining and bibliometric techniques are combined to profile the hotspots in big data research and its core constituents. A learning process is used to enhance the ability to identify the interactive relationships between topics in sequential time slices, revealing technological evolution and death. The outputs include a landscape of interactions within big data research from 2000 to 2015 with a detailed map of the evolutionary pathways of specific technologies. Empirical insights for related studies in science policy, innovation management, and entrepreneurship are also provided.

Technological Convergence (TC) reflects developmental processes that overlap different technological fields. It holds promise to yield outcomes that exceed the sum of its subparts. Measuring emergence for a TC environment can inform innovation management. This paper suggests a novel approach to identify Emergent Topics (ETopics) of the TC environment within a target technology domain using patent information. A non-TC environment is constructed as a comparison group. First, TC is operationalized as a co-classification of a given patent into multiple 4-digit IPC codes (≥2-IPC). We take a set of patents and parse those into three sub-datasets based on the number of IPC codes assigned 1-IPC (Non-TC), 2-IPC and ≥3-IPC. Second, a method is applied to identify emergent terms (ETs) and calculate emergence score for each term in each sub-dataset. Finally, we cluster those ETs using Principal Components Analysis (PCA) to generate a factor map with ETopics. A convergent domain – 3D printing – is selected to present the illustrative results. Results affirm that for 3D printing, emergent topics in TC patents are distinctly different from those in non-TC patents. The number of ETs in the TC environment is increasing annually.

Clinical translation of scientific discoveries from bench to bedside is typically a challenging process with sporadic progress along its trajectory. Analyzing R&D can provide key intelligence on advancing biomedical innovation in target domains of interest. In this study, we explore the feasibility of using a streamlined tech mining approach for identification of translational indicators and potential opportunities, using observable markers extracted from selected research literature. We apply this strategy to analyze a set of 23,982 PubMed records that involved gold nanostructures (GNSs) research. Nine indicators are generated to assess what different GNSs research activities had achieved and to predict where GNSs research will likely go. We believe such analysis can provide useful translation intelligence for researchers, funding agencies, and pharmaceutical and biotech companies.

[EN] Technological Forecasting and Social Change (TF&SC) is a leading international journal that publishes major advances related to technological forecasting and future studies. The journal was launched in 1969 and in 2019 celebrated its 50th anniversary. To celebrate 50 years of outstanding contributions, this study presents a bibliometric analysis of TF&SC publications and patterns of citations within TF&SC in terms of authors, institutions and countries. The analysis relies on the Web of Science Core Collection database for bibliographic content and Visualization of Similarities viewer software for mapping of bibliometric data. Our analysis identifies leading authors, universities and countries that produce publications in TF&SC. This study also applies bibliometric analysis of co-citations and bibliographic coupling. Results suggest that authors and publications originating in the USA and the Netherlands are particularly influential. However, the journal is becoming more geographically diverse. Mapping of co-citations and bibliographic coupling suggests that work published in TF&SC is represented by several heterogeneous clusters.

International audience; While there is a general recognition that breakthrough innovation is non-linear and requires an alignment betweenproducers (supply) and users (demand), there is still a need for strategic intelligence about the emergingsupply chains of new technological innovations. This technology delivery system (TDS) is an updated form of theTDS model and provides a promising chain-link approach to the supply side of innovation. Building on earlyresearch into supply-side TDS studies, we present a systematic approach to building a TDS model that includesfour phases: (1) identifying the macroeconomic and policy environment, including market competition, financialinvestment, and industrial policy; (2) specifying the key public and private institutions; (3) addressing the coretechnical complements and their owners, then tracing their interactions through information linkages andtechnology transfers; and (4) depicting the market prospects and evaluating the potential profound influences ontechnological change and social developments. Our TDS methodology is illustrated using the field of BigData & Analytics (“BDA”).

© 2016 Elsevier Inc. The number and extent of current Science, Technology & Innovation topics are changing all the time, and their induced accumulative innovation, or even disruptive revolution, will heavily influence the whole of society in the near future. By addressing and predicting these changes, this paper proposes an analytic method to (1) cluster associated terms and phrases to constitute meaningful technological topics and their interactions, and (2) identify changing topical emphases. Our results are carried forward to present mechanisms that forecast prospective developments using Technology Roadmapping, combining qualitative and quantitative methodologies. An empirical case study of Awards data from the United States National Science Foundation, Division of Computer and Communication Foundation, is performed to demonstrate the proposed method. The resulting knowledge may hold interest for R&D management and science policy in practice.

Technological Forecasting and Social Change (TF&SC) celebrates its fiftieth anniversary this year. The anniversary represents an appropriate time for an introspective analysis of the journal's history and impact. This study presents a bibliometric analysis of TF&SC in terms of how often TF&SC is cited by other journals (citation outflow), how often other journals are cited by TF&SC (citation inflow), citations by Web of Science and SCImago disciplinary categories, most-cited articles in TF&SC, co-citation of journals, and co-occurrence of author keywords. Analysis is conducted by using the Web of Science (WOS) database and Visualization of Similarities (VOS) viewer software. The incoming versus outgoing citation patterns identified here suggest an asymmetry in the knowledge flows of TF&SC. Papers published in TF&SC have increasingly cited knowledge from journals in Technology and Innovation Management (TIM), Engineering, and Decision Sciences, but the journal impacts a different set of disciplinary categories such as Energy, Environmental Sciences, and Social Sciences. From 1969–2018, Innovation, Foresight and Forecasting feature as the most popular keywords. Focus on topics such as Patents/Patent Analysis, Climate Change, Sustainability, and Energy seems to have intensified in the last decade. Findings suggest that focus on two countries of interest, India and China, is emerging in research published in TF&SC. Different regions of the world can be expected to place differential emphasis on various topics based on their socioeconomic-technological environments. The journal needs to be receptive to this diversity of perspectives from a growing community of scholars worldwide.

We conducted a contest to predict highly active research topics. Participants analyzed ten years of Web of Science abstract records in a target technological domain (synthetic biology) so as to indicate cutting edge sub-topics likely to be actively pursued in the following two years. We describe contest procedures and results provided by thirteen participating teams. Contestants used various topical and other fields in the abstract records; some augmented with external data. They applied at least 19 diverse methods in deriving emerging topics predicted to be actively researched in the coming two years. Besides topical text analyses, contestants variously brought to bear both backward and forward citation analyses, and network analyses, to help identify topics apt to be highly researched in the near future. This communal exercise on forecasting near-future research activity using a wide array of text analytic and other bibliometric tools provides a stimulating resource.

Identification of technology development trends is essential for supporting decision-makers in forecasting and identifying related innovation activities as well the industrial growth. In difference of the traditional technology development trends based on SAO, which usually predicting the trend by finding key problems and technologies used to resolve them, the method proposed in this paper allows to identify what industry goal can be achieved by the interaction between the problems and technologies, the detailed paths to achieve it and the key countries which can encourage the trend by engaging technological innovation scenarios. This method builds a technology roadmapping (TRM) with seven layers based on Subject-Action-Object (SAO) analysis, which is composed by material, technology, influencing factor, component, product, goal and application area. It is a combination of vertical and horizontal TRM and has the advantages of both of them. Comparing with the existing technology roadmapping, this method indicates not only the development paths of the key technologies but also those of them that are most opportune to resolve the specific problem and achieve the particular industry goal. Furthermore, with the intention to identify the key countries supporting the technological development, this method sets three indicators acting as the discriminating standards. To demonstrate the effectiveness of the method, this paper presents a case study of literature related to dye-sensitized solar cells. The result reveals the industry goal and trends in the field of DSSCs, in addition, it can identify the detail approach of the trends and the key countries to support it.

“Disruptive technology & disruptive innovation” have been of scholarly interest for years, but there is still a need to better understand the nature of disruptions and their relationship to emerging technology processes. This paper pursues these issues by analyzing the interplay of technological emergence, disruption, and innovation. Applying bibliometric methods, the paper explores the conceptual foundations, themes, and research communities within these research domains. The results highlight the multiple theoretical foundations of research around technological change processes, disruption, and emergence. These differences among the domains invite conceptual cross-fertilization and consideration of interdisciplinary approaches to technological (and commercial) emergence.

Tech Mining seeks to extract intelligence from Science, Technology & Innovation information record sets on a subject of interest. A key set of Tech Mining interests concerns which R&D activities are addressed in the publication and patent abstract records under study. This paper presents six “term clumping” steps that can clean and consolidate topical content in such text sources. It examines how each step changes the content, potentially to facilitate extraction of usable intelligence as the end goal. We illustrate for an emerging technology, dye-sensitized solar cells. In this case we were able to reduce some 90,980 terms & phrases to more user-friendly sets through the clumping steps as one indicator of success. The resulting phrases are better suited to contributing usable technical intelligence than the original results. We engaged seven persons knowledgeable about dye-sensitized solar cells (DSSCs) to assess the resulting content. These empirical results advanced the development of a semi-automated term clumping process that can enable extraction of topical content intelligence.

We explore the contention that the seminal US National Academies consensus report, How People Learn (HPL), played a major role in bridging the flow of knowledge from Cognitive Science to Education. Our paper yielded four important results: First, HPL is, on a number of bibliometric measures, an unusually interdisciplinary work. Focusing on the fields of particular interest here, our citation analysis shows the Education, Cognitive Science, and Border field (e.g., Educational Psychology, Learning Sciences, and Learning Technology and Human-Computer Interaction) literatures all to have been major influences on it. Second, we found HPL to be unusually highly cited – and by publications from an unusually diverse set of disciplines. Beyond Education, Cognitive Science, and Border field publications, HPL was also relatively highly cited by publications in Medical/Health-related, Engineering, and other Discipline-Based Education Research fields. Third, undermining the claim that HPL served as a gateway to the Cognitive Science literature, we found Education articles citing HPL not to be more likely to have Cognitive Science as a major influence than are Education articles more generally, as indicated by their cited references. Finally, the Education publications that cited HPL were far more likely to refer to concepts in HPL that were already prevalent in the Education literature rather than to concepts from Cognitive Science. Conversely, the Cognitive Science publications that cited HPL were more apt to refer to concepts already in the Cognitive Science literature. Taken together, these results are a caution that, even for a highly regarded multidisciplinary work cited widely by publications from multiple disciplines, its direct influence could be largely disciplinary. Implications for the policy goals of fostering interdisciplinary research and the role of National Academies consensus reports are discussed.

This study empirically examines the association between the extent of emerging technological ideas in a scientific publication and its future scientific impact measured by number of citations. We analyze metadata of scientific publications in three scientific domains: Nano-Enabled Drug Delivery, Synthetic Biology, and Autonomous Vehicles. By employing a bibliometric indicator for identifying and quantifying emerging technological ideas – as derived terms from the titles and abstracts – we measure the extent to which the publication contains emerging technological ideas in each domain. Then, we statistically estimate the size and statistical significance of the relationship between the publication-level technological emergence score and the normalized number of citations accruing to the publication. Our analysis shows that the degree to which a paper contains technologically emerging ideas is positively and strongly associated with its future citation impact in each of the three domains. An additional analysis demonstrates that this relationship holds for citations from other publications, both in the same field as, and in different fields from, the scientific domain of the focal publication. A series of tests for validation further support our argument that the greater the extent to which scientific knowledge (a paper) contains emerging ideas, the bigger its scientific impact. Implications for academic researchers, research policymakers, and firms are discussed.

Seemingly endless new technologies are emerging. Mapping out Science and Technology (S&T) planning correctly on the national level would help innovation shareholders remain current on technological development trends and gain an advantageous position among the fierce future competition of the global market. Thus, formulating effective S&T planning is significant for a nation, especially for new and emerging technologies. This paper proposes an industry S&T planning framework. Different from previous frameworks, this methodology's dynamic is directed in four dimensions (nation, technology, industry, risks and impacts), tries to find the key elements in a specific technology area, and aims to aid in national S&T planning. China's solar cell industry is employed as the case study.

To estimate the future development of one technology and make decisions whether to invest in it or not, one needs to know the current stage of its technology life cycle (TLC). The dominant approach to analysing TLC uses the S-curve to observe patent applications over time. But using the patent application counts alone to represent the development of technology oversimplifies the situation. In this paper, we build a model to calculate the TLC for an object technology based on multiple patent-related indicators. The model includes the following steps: first, we focus on devising and assessing patent-based TLC indicators. Then we choose some technologies (training technologies) with identified life cycle stages, and finally compare the indicator features in training technologies with the indicator values in an object technology (test technology) using a nearest neighbour classifier, which is widely used in pattern recognition to measure the technology life cycle stage of the object technology. Such study can be used in management practice to enable technology observers to determine the current life cycle stage of a particular technology of interest and make their R&D strategy accordingly.

This paper explores how to extract empirical knowledge from R&D, patent, and business literature compilations to help compose an innovation system model. It adapts the key elements and dynamics of ‘technology delivery system’ modeling to a given Newly Emerging Science & Technology. We present a 10-step analytical approach to help characterize the technology, gauge its state of development, and depict the socio-technical system institutions and actors. We apply this to the case of Dye-Sensitized Solar Cells (DSSCs). A new ‘cross-charting’ method appears effective at associating novel technology-enabled capabilities to gain functional advantages, and to link those functions to potential applications. The resulting systems model can help private and public sector decision makers grasp key structures and processes, and how these can be tuned to enhance the prospects of successful innovation.

Nanobiomedical science is a promising area in the application of nanotechnology. This paper profiles a group of 21 leading scientists in nanobiomedicine based on high publication rate and high citations. Comparisons with other researchers indicate that the leaders publish more in high impact journals and collaborate more extensively (team science). They reside most heavily in the United States and Western Europe. We compare their research publications using multiple indicators–Integration, Specialization and a Multidisciplinary Index. Relations among interdisciplinarity indicators generated support a three-factor model based on principles of diversity. We locate this research among the disciplines using science overlay mapping. Key term analyses, based on keywords and on natural language processing help profile the research emphases of these leading researchers. Such results could serve as leading indicators to help identify directions of future nanobio development.

Open Innovation presses the case for timely and thorough intelligence concerning research and development activities conducted outside one’s organization. To take advantage of this wealth of R&D, one needs to establish a systematic “tech mining” process. We propose a 5-stage framework that extends literature review into research profiling and pattern recognition to answer posed technology management questions. Ultimately one can even discover new knowledge by screening research databases. Once one determines the value in mining external R&D, tough issues remain to be overcome. Technology management has developed a culture that relies more on intuition than on evidence. Changing that culture and implementing effective technical intelligence capabilities is worth the effort. P&G's reported gains in innovation call attention to the huge payoff potential.

Technology Policy and Assessment Center, Georgia Institute of Technology, Atlanta,GA 30332–0345, USA. alan.porter@isye.gatech.eduNanotechnology-enhanced thin-film solar cells constitute promising solar energy solutions andan important emerging application of nanotechnology. This paper profiles the researchpatterns via ‘tech mining’ to capture key technological attributes, leading actors and networks.We compare the leading countries, and key organizations, in terms of R&D quantity, impactand diversity. We find that India is a leader in this field, which is a little surprising. India andChina show strong trends of relative increase in both research activity and impact. OneGerman organization appears as especially productive and the central node in Germany’sresearch network, which contrasts with the diffused network of the United States. Interna-tional collaboration patterns also vary, with China particularly showing much less interna-tional cooperation than others. Some countries appear to share interests, but they do not showmuch cooperation – e.g., China with Japan. Research profiling, as illustrated here, can help anR&D manager or policy-maker locate one’s intended research activity among existingendeavors, to determine how attractive the opportunities are. Such depictions can also helpidentify collaboration opportunities and potentially attractive partners.

This paper compares the positions of national nanotechnology development efforts based on analyses of patenting from 1994 to 2005. Searching Derwent world patent index files, 19,351 unique patents are collected based on a composite search algorithm. These abstract records are categorized multiple ways — by top patent assignees, by International Patent Classifications, and through content analyses of the “Use” subfield. We classify the R&D activities by using a 3-stage, life cycle, value chain of nano-raw materials, nanointermediates, and nano-products. Profiles of Japanese, American (US), and European (German) emphases show notable differences in concentration and value chain niche. Such characterizations offer significant research management and policy implications.

An organization's knowledge gained through technical conference attendance is generally isolated to the individual(s) attending the event. The aggregate corporate knowledge is extremely limited, unless the organization institutes a process to document and transfer that knowledge to the organization. Even if such a process exists, the knowledge gains are limited to the experiences and communication skills of the individuals attending the conference. Many conference proceedings are now published and provided to attendees in electronic format, such as on CD-ROM and/or published on the internet, such as IEEE conference proceedings listed at . These proceedings provide a rich repository that can be mined. Paper abstract compilations reflect "hot topics," as defined by the researchers in the field, and delineate the technical approaches being applied. R&D profiling can more fully exploit recorded conference proceedings' research to enhance corporate knowledge. This paper illustrates the potential in profiling conference proceedings through use of WebQL information retrieval and TechOasis (VantagePoint) text mining software. It shows how tracking research patterns and changes over a sequence of conferences can illuminate R&D trends, map dominant issues, and spotlight key research organizations.

Reflect back briefly on what was considered good manufacturing management circa 1960 and what we expect today. Prior to the quality movement, manufacturing operations were based heavily on intuition. Experienced shop floor personnel knew their machines and generated a certain level of efficiency and quality-few suspected that anything better was possible. Then came the quality movement-collecting performance data along with statistical analyses enabled supervisors to discern much finer performance tolerances. And, lo and behold, production processes improved dramatically, leading to far fewer flaws in automobiles, integrated circuits or whatever. And that provided tremendous competitive advantage to the Japanese and others who implemented successful quality management (1). Managers in most fields are exploiting data to enhance performance. Finance, logistics, marketing, and sales depend heavily upon empirically based knowledge. Even sports management is being made over. The headline in a Sporting News article reads: "Playing the numbers game: now more than ever, the sports world is looking to statistics for performance-enhancing insight, fueling the quest to devise perfect predictors of success" (2). The article discusses the expanding use of empirical indicators across all the major sports. This notion gained popular notoriety with Moneyball-the tale of how Billy Beane, as general manager of baseball's Oakland A's, gets more bang for his limited bucks by using a variety of telling player statistics (3). For instance, CERA-catcher's earned run average-incorporates a catcher's talent for handling pitchers together with other defensive skills. Data convey valuable intelligence. How about R&D management? I assert that research managers still reach decisions based largely on intuition. For example, the cornerstone in allocating federal research funding by the National Science Foundation and the National Institutes of Health is peer review. When the government confronts difficult science and technology ("S&T") issues, it turns to the National Academies (Science, Engineering, and the Institute of Medicine) for eminent expertise. This use of expert opinion is certainly laudable, particularly when compared to the alternative of uninformed, arbitrary choices. But, the point of this article is that we can do much better by incorporating a richer base of empirical information into our R&D management processes. The data and the tools to analyze them are available now. It's time to augment expertise with empirical knowledge. In so doing, we should be able to enhance our effectiveness at least as much as has been proven possible in manufacturing and other arenas. I see payoffs in R&D management from the introduction of carefully targeted mining of R&D information resources. These range over the spectrum of R&D and innovation processes. The researcher contemplating a new project can position this work against what others are doing to identify novel approaches, locate potential collaborators, and exploit the most promising opportunities. The portfolio manager can optimize resource allocation to target the timeliest opportunities and take full advantage of his or her assets. The intellectual property manager, new product developer, and other technology managers can similarly enhance their positioning and payoffs (4). Managers who comprehend the research landscape, backed up by explicit information, have a major competitive advantage over counterparts driving solely on intuition. Let's consider how exploiting R&D information resources can make a difference now. Tech Mining Concepts The analogy to production and sports management understates the challenge. Empirical knowledge in those domains derives mainly from numerical data. For S&T, the data are a mix of numerical and text. "Tech Mining" is my shorthand label for text mining of technical information resources (5). It depends upon an understanding of what matters in technological innovation processes, text mining tools to get at the pertinent intelligence, and effective delivery of findings to users. …

International audience; Understanding the evolution and emergence of technology domains remains a challenge, particularly so for potentially breakthrough technologies. Though it is well recognized that emergence of new fields is complex and uncertain, to make decisions amidst such uncertainty, one needs to mobilize various sources of intelligence to identify known–knowns and known–unknowns to be able to choose appropriate strategies and policies. This competitive technical intelligence cannot rely on simple trend analyses because breakthrough technologies have little past to inform such trends, and positing the directions of evolution is challenging. Neither do qualitative tools, embracing the complexities, provide all the solutions, since transparent and repeatable techniques need to be employed to create best practices and evaluate the intelligence that comes from such exercises. In this paper, we present a hybrid roadmapping technique that draws on a number of approaches and integrates them into a multi-level approach (individual activities, industry evolutions and broader global changes) that can be applied to breakthrough technologies. We describe this approach in deeper detail through a case study on dye-sensitized solar cells. Our contribution to this special issue is to showcase the technique as part of a family of approaches that are emerging around the world to inform strategy and policy.

International audience; “Global technological competitiveness” is widely acknowledged, but the challenge is to go beyond this recognition to develop empirical indicators of important transitions. These may concern particular technologies, the competitive position of particular organizations, or national/regional shifts. For decades, the US has been the world leader in biomedical technologies, with attendant implications for organizational priorities in terms of R&D location and market targeting. Recent years have seen a tremendous acceleration in Asian research in most domains, including biomedical, particularly visible in China. This paper investigates comparative patterns between the US and China in a promising emerging area of biotechnology — Nano-Enhanced Drug Delivery. It then explores indicators of, and implications for, future transitions at the national level — an approach we label “Forecasting Innovation Pathways.”

Empirical technology analyses need not take months; they can be done in minutes. One can thereby take advantage of wide availability of rich science and technology publication and patent abstract databases to better inform technology management. To do so requires developing templates of innovation indicators to answer standard questions. Then, one can automate routines to generate composite information representations (“one-pagers”) that address the issues at hand, the way that the target users want.

Many forms of analyzing future technology and its consequences coexist, for example, technology intelligence, forecasting, roadmapping, assessment, and foresight. All of these techniques fit into a field we call technology futures analysis (TFA). These methods have matured rather separately, with little interchange and sharing of information on methods and processes. There is a range of experience in the use of all of these, but changes in the technologies in which these methods are used-from industrial to information and molecular-make it necessary to reconsider the TFA methods. New methods need to be explored to take advantage of information resources and new approaches to complex systems. Examination of the processes sheds light on ways to improve the usefulness of TFA to a variety of potential users, from corporate managers to national policy makers. Sharing perspectives among the several TFA forms and introducing new approaches from other fields should advance TFA methods and processes to better inform technology management as well as science and research policy. (C) 2003 Elsevier Inc. All rights reserved.

“Innovation indicators” strive to track the maturation of an emerging technology to help forecast its prospective development. One rich source of information is the changing content of discourse of R&D, as the technology progresses. We analyze the content of research paper abstracts obtained by searching large databases on a given topic. We then map the evolution of that topic's emphasis areas. The present research seeks to validate a process that creates factors (clusters) based on term usage in technical papers. Three composite quality measures—cohesion, entropy, and F measure—are computed. Using these measures, we create standard factor groupings that optimize the composite term sets and facilitate comparisons of the R&D emphasis areas (i.e., clusters) over time. The conceptual foundation for this approach lies in the presumption that domain knowledge expands and becomes more application specific in nature as a technology matures. We hypothesize implications for this knowledge expansion in terms of the three factor measures, then observe these empirically for the case of a particular technology—autonomous navigation. These metrics can provide indicators of technological maturation.

Empirical technology forecasting (TF) is not well utilized in technology management. Three factors could enhance managerial utilization: capability to exploit huge volumes of available information, ways to do so very quickly, and informative representations that help manage emerging technologies. This paper reports on efforts to address these three factors via partially automated processes to generate helpful knowledge from text quickly and graphically. We first illustrate a process to generate a family of technology maps that help convey emphases, players, and patterns in the development of a target technology. Second, we exemplify the generation of particular “innovation indicators” that measure particular facets of R&D activity to relate these to technological maturation, contextual influences, and market potential. Both technology mapping and innovation indicators rely upon searches in huge, easily accessible, abstract databases and text mining software. We augment these through “macros” (programming scripts) that automatically sequence the necessary steps to generate particular desired information products. These analytical findings can be tailored to the needs of particular technology managers.

Technological forecasting is now poised to respond to the emerging needs of private and public sector organizations in the highly competitive global environment. The history of the subject and its variant forms, including impact assessment, national foresight studies, roadmapping, and competitive technological intelligence, shows how it has responded to changing institutional motivations. Renewed focus on innovation, attention to science-based opportunities, and broad social and political factors will bring renewed attention to technological forecasting in industry, government, and academia. Promising new tools are anticipated, borrowing variously from fields such as political science, computer science, scientometrics, innovation management, and complexity science.

Introduction In inviting these perspectives, Hal Linstone wrote that technology forecasting (TF) seems to have peaked around 1970 with a decline in methodological advance thereafter. That stimulated me to conduct a bibliometric analysis of TF activity over the past dozen years. Results support Hal’s contention, but also point toward “next wave” possibilities. Before digging into the data, it is worth considering what is different today from the earlier “heydey” periods for TF. In the 1950s and 1960s, TF was driven by military competition with the Soviet Union. TF was initiated primarily as a tool to help anticipate military technology needs and to help plan and prioritize R&D and systems development. Many of these technologies had long development times (for example, anti-ballistic missile program, new fighter planes). Today, military technology development has been supplanted by commercial technological competition as the primary motivator for TF. Competitive technological intelligence—a remarkable growth area of the 1990s—conjures images of companies, more than intelligence agencies, most actively trying to find out “who’s doing what.” This interest in tracking and forecasting others’ technology development has been heightened by tendencies to constrain corporate R&D to more immediate and more applied targets. In addition, both industry and government (even in the United States) evidence interest in trying to plan, prioritize, and evaluate their R&D programs, placing a premium on information about relative prospects and performance metrics. National technology foresight initiatives and industry or product technology road maps are important emerging forms of TF [30]. The defining characteristic of today’s technology is that we are in the Information Age. While cases could be made for various emerging technologies (for example, advanced materials, biotech) as flowering, it is clearly IT—information technology—that defines our era. That poses a challenge for TF in that typical IT life cycles are much shorter than military systems technology life cycles of 40 years ago. In addition to targeting rather different technological targets these days, TF draws on different resources. The availability of electronic information in several forms pro

A Delphi study projects a series of critical changes in electronics manufacturing over the coming quarter-century. These changes imply a need for early planning to adapt production processes to altered materials, integrated products, environmentally conscious processes, different applications, and a markedly different workforce.

This article presents some of the major results obtained during the third phase of a continuing research effort to develop and implement national indicators of competitiveness in high technology industries. The first phase, begun in 1987, developed a conceptual model of the processes by which industrializing nations gain access to external technology and technical information, absorb that technology/information effectively, and institutionalize a science-based development and manufacturing capability leading to export-led growth in high technology products. Four “input” or leading indicators of a nation's future capacity (15-year time horizons) to compete in international markets in high technology products were developed, as were three “output” indicators of a nation's current international competitiveness. During this first phase, the seven indicators were applied to data for twenty countries representing a range of regions and extent of industrialization. The second phase used 1990 data on an expanded set of countries to examine in detail the indicators' reliability and validity. The third phase of indicators work (1992–1995): (1) developed seven indicators whose definitions were recommended in our 1991 final report to the Science Indicators Unit of the National Science Foundation, and (2) collected the necessary data (1993) and applied them to generate a set of indicators for 28 countries using these recommended formulations. This article focuses on the input indicators for the 28 countries and compares these 1993 results with those from 1990. We discuss the implications of these results for technology-based development theory and for development policy. A separate, companion article published elsewhere presents the output indicator results.

International audience; "New" and "Emerging Science" and "Technologies" ("NESTs") have tremendous innovation potential. However this must be weighed against enormous uncertainties caused by many unknowns. The authors of this paper offer a framework to analyze NESTs to help ascertain likely innovation pathways. We have devised a 10-step framework based on extensive Future-oriented Technology Analyses ("FTA") experience, enriched by in-depth case analyses. In the paper, we describe our analytical activities in two case studies. The nanobiosensor experience is contrasted with that of deep brain stimulation in relative quantitative and qualitative emphases. We close the paper by reflecting on this systematic FTA framework for emerging science and technologies, for its intended goal, that is to support decision making.

A new expert opinion technique is developed and applied to estimate the impact of information technologies on clerical work. EFTE stands for estimate, feedback, talk, estimate—the sequence of steps involved in this variant on the Delphi approach. EFTE offers advantages in obtaining expert opinions on complex tasks where social interaction poses little problem.

This paper develops microcomputer-based R and D project selection and project evaluation models. The models use Lotus 1-2-3 as a vehicle to provide practical research management support suitable for a developing country. The models are readily adaptable for various R and D management applications.