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The technological convergence network (TCN) is an effective method to identify the advancement of technology convergence. However, the previous TCN investigations are limited to a single level of International Patent Classification (IPC) rather than different IPC hierarchies, which can only provide decision support for policy makers with one dimension instead of various ones. In this article, we propose a new approach to construct TCNs across different IPC hierarchies based on technology coclassification analysis, and further identify key technology fields by employing the indicator of betweenness centrality in the TCNs from any IPC hierarchy. This article makes two important contributions. First, theoretically, our article is to contribute to understanding the advancement of technological convergence from various IPC hierarchies, rather than a single IPC level. Second, methodologically, the new approach we propose can benefit decision makers serving at various levels of technology management agencies. We conclude possible implications and future directions.

Over the past two decades, the discovery of CRISPR-Cas immune systems and the repurposing of their effector nucleases as biotechnological tools have revolutionized genome editing. The corresponding work has been captured by 90,000 authors representing 7,600 affiliations in 126 countries, who have published more than 19,000 papers spanning medicine, agriculture, and biotechnology. Here, we use tech mining and an integrated bibliometric and networks framework to investigate the CRISPR literature over three time periods. The analysis identified seminal papers, leading authors, influential journals, and rising applications and topics interconnected through collaborative networks. A core set of foundational topics gave rise to diverging avenues of research and applications, reflecting a bona fide disruptive emerging technology. This analysis illustrates how bibliometrics can identify key factors, decipher rising trends, and untangle emerging applications and technologies that dynamically shape a morphing field, and provides insights into the trajectory of genome editing.

Efforts to involve data science in policy analysis can be traced back decades but transforming analytic findings into decisions is still far from straightforward task. Data-driven decision-making requires understanding approaches, practices, and research results from many disciplines, which makes it interesting to investigate whether data science and policy analysis are moving in parallel or whether their pathways have intersected. Our investigation, from a bibliometric perspective, is driven by a comprehensive set of research questions, and we have designed an intelligent bibliometric framework that includes a series of traditional bibliometric approaches and a novel method of charting the evolutionary pathways of scientific innovation, which is used to identify predecessor-descendant relationships in technological topics. Our investigation reveals that data science and policy analysis have intersecting lines, and it can foresee that a cross-disciplinary direction in which policy analysis interacting with data science has become an emergent area in both communities. However, equipped with advanced data analytic techniques, data scientists are moving faster and further than policy analysts. The empirical insights derived from our research should be beneficial to academic researchers and journal editors in related research communities, as well as policy-makers in research institutions and funding agencies.

IEEE Technological innovation is a dynamic process that spans the life cycle of an idea, from scientific research to production. Within this process, there are often a few key innovations that significantly impact a technology's development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this article, we present a framework for identifying the technology's main evolutionary pathway. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Finally, we apply integrated approaches of main path analysis (MPA)—namely global MPA and global key-route main analysis—for extracting technological trajectories at different technological stages. We illustrate this approach with dye-sensitized solar cells (DSSCs), a low-cost solar cell belonging to the group of thin-film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision makers manage the technological stages of their innovation processes more effectively.

Uncovering the driving forces, strategic landscapes, and evolutionary mechanisms of China’s research systems is attracting rising interest around the globe. One topic of interest is to understand the problem-solving patterns in China’s research systems now and in the future. Targeting a set of high-quality research articles published by Chinese researchers between 2009 and 2018, and indexed in the Essential Science Indicators database, we developed an intelligent bibliometrics-based methodology for identifying the problem-solving patterns from scientific documents. Specifically, science overlay maps incorporating link prediction were used to profile China’s disciplinary interactions and predict potential cross-disciplinary innovation at a macro level. We proposed a function incorporating word embedding techniques to represent subjects, actions, and objects (SAO) retrieved from combined titles and abstracts into vectors and constructed a tri-layer SAO network to visualize SAOs and their semantic relationships. Then, at a micro level, we developed network analytics for identifying problems and solutions from the SAO network, and recommending potential solutions for existing problems. Empirical insights derived from this study provide clues to understand China’s research strengths and the science policies underlying them, along with the key research problems and solutions that Chinese researchers are focusing on now and might pursue in the future.

Technology emergence has become a hot topic in RD thus, they serve to predict highly active R&D foci within the technical domain under study.

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.

Scientometric methods have long been used to identify technological trajectories, but we have seldom seen reproducible methods that allow for the identification of a technological emergence in a set of documents. This study evaluates the use of three different reproducible approaches for identifying the emergence of technological novelties in scientific publications. The selected approaches are term counting technique, the emergence score (EScore) and Latent Dirichlet Allocation (LDA). We found that the methods provide somewhat distinct perspectives on technological. The term count based method identifies detailed emergence patterns. EScore is a complex bibliometric indicator that provides a holistic view of emergence by considering several parameters, namely term frequency, size, and origin of the research community. LDA traces emergence at the thematic level and provides insights on the linkages between emerging research topics. The results suggest that term counting produces results practical for operational purposes, while LDA offers insight at a strategic level.

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.

In this paper we seek to better understand the relationship between forward diversity in the Cognitive Science and Educational Research literature, as well as what we call Border fields (i.e. those fields which exist at the intersection of Cognitive Science and Education Research). We find a clear and convincing relationship between forward and backward diversity in the datasets we study. Among all available explanatory variables, Integration scores claim the strongest correlation in terms of their ability to account for forward diversity. When comparing results from this study to benchmark results from a prior study (using the same indicators) the datasets in this study show a tendency to be both more integrative and diffuse.

Purpose The ability to identify the scholarship of individual authors is essential for performance evaluation. A number of factors hinder this endeavor. Common and similarly spelled surnames make it difficult to isolate the scholarship of individual authors indexed on large databases. Variations in name spelling of individual scholars further complicates matters. Common family names in scientific powerhouses like China make it problematic to distinguish between authors possessing ubiquitous and/or anglicized surnames (as well as the same or similar first names). The assignment of unique author identifiers provides a major step toward resolving these difficulties. We maintain, however, that in and of themselves, author identifiers are not sufficient to fully address the author uncertainty problem. In this study we build on the author identifier approach by considering commonalities in fielded data between authors containing the same surname and first initial of their first name. We illustrate our approach using three case studies. Design/methodology/approach The approach we advance in this study is based on commonalities among fielded data in search results. We cast a broad initial net—i.e., a Web of Science (WOS) search for a given author’s last name, followed by a comma, followed by the first initial of his or her first name (e.g., a search for ‘John Doe’ would assume the form: ‘Doe, J’). Results for this search typically contain all of the scholarship legitimately belonging to this author in the given database (i.e., all of his or her true positives), along with a large amount of noise, or scholarship not belonging to this author (i.e., a large number of false positives). From this corpus we proceed to iteratively weed out false positives and retain true positives. Author identifiers provide a good starting point—e.g., if ‘Doe, J’ and ‘Doe, John’ share the same author identifier, this would be sufficient for us to conclude these are one and the same individual. We find email addresses similarly adequate—e.g., if two author names which share the same surname and same first initial have an email address in common, we conclude these authors are the same person. Author identifier and email address data is not always available, however. When this occurs, other fields are used to address the author uncertainty problem. Commonalities among author data other than unique identifiers and email addresses is less conclusive for name consolidation purposes. For example, if ‘Doe, John’ and ‘Doe, J’ have an affiliation in common, do we conclude that these names belong the same person? They may or may not; affiliations have employed two or more faculty members sharing the same last and first initial. Similarly, it’s conceivable that two individuals with the same last name and first initial publish in the same journal, publish with the same co-authors, and/or cite the same references. Should we then ignore commonalities among these fields and conclude they’re too imprecise for name consolidation purposes? It is our position that such commonalities are indeed valuable for addressing the author uncertainty problem, but more so when used in combination. Our approach makes use of automation as well as manual inspection, relying initially on author identifiers, then commonalities among fielded data other than author identifiers, and finally manual verification. To achieve name consolidation independent of author identifier matches, we have developed a procedure that is used with bibliometric software called VantagePoint (see www.thevantagepoint.com) While the application of our technique does not exclusively depend on VantagePoint, it is the software we find most efficient in this study. The script we developed to implement this procedure is designed to implement our name disambiguation procedure in a way that significantly reduces manual effort on the user’s part. Those who seek to replicate our procedure independent of VantagePoint can do so by manually following the method we outline, but we note that the manual application of our procedure takes a significant amount of time and effort, especially when working with larger datasets. Our script begins by prompting the user for a surname and a first initial (for any author of interest). It then prompts the user to select a WOS field on which to consolidate author names. After this the user is prompted to point to the name of the authors field, and finally asked to identify a specific author name (referred to by the script as the primary author) within this field whom the user knows to be a true positive (a suggested approach is to point to an author name associated with one of the records that has the author’s ORCID iD or email address attached to it). The script proceeds to identify and combine all author names sharing the primary author’s surname and first initial of his or her first name who share commonalities in the WOS field on which the user was prompted to consolidate author names. This typically results in significant reduction in the initial dataset size. After the procedure completes the user is usually left with a much smaller (and more manageable) dataset to manually inspect (and/or apply additional name disambiguation techniques to). Research limitations Match field coverage can be an issue. When field coverage is paltry dataset reduction is not as significant, which results in more manual inspection on the user’s part. Our procedure doesn’t lend itself to scholars who have had a legal family name change (after marriage, for example). Moreover, the technique we advance is (sometimes, but not always) likely to have a difficult time dealing with scholars who have changed careers or fields dramatically, as well as scholars whose work is highly interdisciplinary. Practical implications The procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research, especially when the name under consideration is a more common family name. It is more effective when match field coverage is high and a number of match fields exist. Originality/value Once again, the procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research. It combines preexisting with more recent approaches, harnessing the benefits of both. Findings Our study applies the name disambiguation procedure we advance to three case studies. Ideal match fields are not the same for each of our case studies. We find that match field effectiveness is in large part a function of field coverage. Comparing original dataset size, the timeframe analyzed for each case study is not the same, nor are the subject areas in which they publish. Our procedure is more effective when applied to our third case study, both in terms of list reduction and 100% retention of true positives. We attribute this to excellent match field coverage, and especially in more specific match fields, as well as having a more modest/manageable number of publications. While machine learning is considered authoritative by many, we do not see it as practical or replicable. The procedure advanced herein is both practical, replicable and relatively user friendly. It might be categorized into a space between ORCID and machine learning. Machine learning approaches typically look for commonalities among citation data, which is not always available, structured or easy to work with. The procedure we advance is intended to be applied across numerous fields in a dataset of interest (e.g. emails, coauthors, affiliations, etc.), resulting in multiple rounds of reduction. Results indicate that effective match fields include author identifiers, emails, source titles, co-authors and ISSNs. While the script we present is not likely to result in a dataset consisting solely of true positives (at least for more common surnames), it does significantly reduce manual effort on the user’s part. Dataset reduction (after our procedure is applied) is in large part a function of (a) field availability and (b) field coverage.

The unprecedented, explosive growth of the COVID-19 domain presents challenges to researchers to keep up with research knowledge within the domain. This article profiles this research to help make that knowledge more accessible via overviews and novel categorizations. We provide websites offering means for researchers to probe more deeply to address specific questions. We further probe and reassemble COVID-19 topical content to address research issues concerning topical evolution and emphases on tactical vs. strategic approaches to mitigate this pandemic and reduce future viral threats. Data suggest that heightened attention to strategic, immunological factors is warranted. Connecting with and transferring in research knowledge from outside the COVID-19 domain demand a viable COVID-19 knowledge model. This study provides complementary topical categorizations to facilitate such modeling to inform future Literature-Based Discovery endeavors.

Topic extraction presents challenges for the bibliometric community, and its performance still depends on human intervention and its practical areas. This paper proposes a novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data. The experimental results of a comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-means, principal component analysis, and topic models) on two bibliometric datasets demonstrate its effectiveness across either a relatively broad range of disciplines or a given domain. An empirical study on bibliometric topic extraction from articles published by three top-tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental evidence of the method’s ability on topic extraction. Additionally, this empirical analysis reveals insights into both overlapping and diverse research interests among the three journals that would benefit journal publishers, editorial boards, and research communities.

A new bibliometric technique enables one to distinguish high emergence topical content. This technique can be applied to sets of research publication abstracts reflecting a given technical domain (...

Purpose This paper presents findings of a quasi-experimental assessment to gauge the research productivity and degree of interdisciplinarity of research center outputs. Of special interest, we share an enriched visualization of research co-authoring patterns. Design/methodology/approach We compile publications by 45 researchers in each of 1) the iUTAH project, which we consider here to be analogous to a “research center,” 2) CG1— a comparison group of participants in two other Utah environmental research centers, and 3) CG2—a comparison group of Utah university environmental researchers not associated with a research center. We draw bibliometric data from Web of Science and from Google Scholar. We gather publications for a period before iUTAH had been established (2010–2012) and a period after (2014–2016). We compare these research outputs in terms of publications and citations thereto. We also measure interdisciplinarity using Integration scoring and generate science overlay maps to locate the research publications across disciplines. Findings We find that participation in the iUTAH project appears to increase research outputs (publications in the After period) and increase research citation rates relative to the comparison group researchers (although CG1 research remains most cited, as it was in the Before period). Most notably, participation in iUTAH markedly increases co-authoring among researchers—in general; and for junior, as well as senior, faculty; for men and women: across organizations; and across disciplines. Research limitations The quasi-experimental design necessarily generates suggestive, not definitively causal, findings because of the imperfect controls. Practical implications This study demonstrates a viable approach for research assessment of a center or program for which random assignment of control groups is not possible. It illustrates use of bibliometric indicators to inform R&D program management. Originality/value New visualizations of researcher collaboration provide compelling comparisons of the extent and nature of social networking among target cohorts.

Developing useful intelligence on scientific and technological emergence challenges those who would manage R&D portfolios, assess research programs, or manage innovation. Recently, the U.S. Intelligence Advanced Research Projects Activity Foresight and Understanding from Scientific Exposition Program has explored means to detect emergence via text analyses. We have been involved in positing conceptual bases for emergence, framing candidate indicators, and devising implementations. We now present a software script to generate a family of Emergence Indicators for a topic of interest. This paper offers some background, then discusses the development of this script through iterative rounds of testing, and then offers example findings. Results point to promising and actionable intelligence for R&D decision-makers.

In an accurate and timely manner, capturing the risk signals for a specific emerging technology from academic publications is important to facilitate risk governance and to reduce the potential negative impact on socioeconomic systems. In the past decade, three-dimensional printing (3D printing) has become a promising emerging technology. To identify the relevant research on risk analysis for 3D printing, “term clumping” on “risk analysis” is explored using a quantitative method, and an integrated framework for risk identification is proposed with regard to 3D printing. This method involves a variation of TF*IDF and several new metrics for a Boolean query of the literature. The empirical results for the risk analysis studies of 3D printing show that, to date, very little attention has been paid to the relevant topics. However, although the risk signals of 3D printing are still weak and dispersed in many different categories, the potential threats to human health, cyber-security, and the environment are revealed in some facets. This enables initiation of strategies for anticipatory governance, involving science and technology policies and regulations.

Purpose The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps. Design/methodology/approach We use the combined set of 2015 Journal Citation Reports for the Science Citation Index (n of journals = 8,778) and the Social Sciences Citation Index (n = 3,212) for a total of 11,365 journals. The set of Web of Science Categories in the Science Citation Index and the Social Sciences Citation Index increased from 224 in 2010 to 227 in 2015. Using dedicated software, a matrix of 227 × 227 cells is generated on the basis of whole-number citation counting. We normalize this matrix using the cosine function. We first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. A routine for making overlays on the basis of the map (“wc15.exe”) is available at http://www.leydesdorff.net/wc15/index.htm. Findings Findings appear in the form of visuals throughout the manuscript. In Figures 1–9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies. Research limitations As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science. Practical implications Visualization of science overlay maps is now more accurate and true to the 2015 Journal Citation Reports than was the case with the previous version of the routine advanced in our paper. Originality/value The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent Journal Citation Reports.

The ability to accurately identify scholarly authors is central to bibliometric analysis. Efforts to disambiguate author names using algorithms or national or societal registries become less effective with increases in the number of publications from China and other nations where shared and similar names are prevalent. This work analyzes the adoption and integration of an open source, cross-national identification system, the Open Researcher and Contributor ID system (ORCID), in Web of Science metadata. Results at the article level show greater adoption, to date, of the ORCID identifier in Europe as compared with Asia and the US. Focusing analysis on individual highly cited researchers with the shared Chinese surname “Wang,” results indicate limitations in the adoption of ORCID. The mechanisms for integrating ORCID identifiers into articles also come into question in an analysis of co-authors of one particular highly cited researcher who have varying percentages of articles with ORCID identifiers attached. These results suggest that systematic variations in adoption and integration of ORCID into publication metadata should be considered in any bibliometric analysis based on it.

This study advances a four-part indicator for technical emergence. While doing so it focuses on a particular class of emergent concepts--those which display the ability to repeatedly maintain an emergent status over multiple time periods. The authors refer to this quality as staying power and argue that those concepts which maintain this ability are deserving of greater attention. The case study we consider consists of 15 subdatatsets within the dye-sensitized solar cell framework. In this study the authors consider the impact technical domain and scale have on the behavior of persistently emergent concepts and test which of these has a greater influence.

© 2017, Akadémiai Kiadó, Budapest, Hungary. Whether it be for countries to improve the ability to undertake independent innovation or for enterprises to enhance their international competitiveness, tracing historical progression and forecasting future trends of technology evolution is essential for formulating technology strategies and policies. In this paper, we apply co-classification analysis to reveal the technical evolution process of a certain technical field, use co-word analysis to extract implicit or unknown patterns and topics, and employ main path analysis to discover significant clues about technology hotspots and development prospects. We illustrate this hybrid approach with 3D printing, referring to various technologies and processes used to synthesize a three-dimensional object. Results show how our method offers technical insights and traces technology evolution pathways, and then helps decision-makers guide technology development.

Increasingly complex competitive environments drive corporations in almost all industries to conduct omnibearing innovation activities to enhance their technological innovation capability and international competitiveness. Against this background, we propose subject---action---object (SAO) based morphological analysis to identify technology opportunities by detecting prioritized combinations within the morphology matrix. SAO structures emphasize the key concepts with provision of diverse technology information based on semantic relationships. The combination of SAO semantic structures can support the establishment of matrix, which consists of two dimensions: compositions and properties of technology. Later, novel indicators are used to evaluate the subsequent technological feasibility of each new configuration under a customized analysis and prior combinations aided by a high score can be identified. We apply this method to the case of dye-sensitized solar cells (DSSCs) in patents documents. The approach holds promise to strengthen information support systems for commercial enterprises in technical innovation and market innovation activities. We believe the analysis can be adapted well to fit other technologies, especially in their emerging stage.

© 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.

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.

The quantitative study of science, technology and innovation (ST&I science, technology, and innovation (STI)) has experienced significant growth with advancements in disciplines such as mathematics, computer science and information sciences. From the early studies utilizing the statistics method, graph theory, to citations or co-authorship, the state of the art in quantitative methods leverages natural language processing and machine learning. However, there is no unified methodological approach within the research community or a comprehensive understanding of how to exploit text-mining potentials to address ST&I research objectives. Therefore, this chapter intends to present the state of the art of text mining within the framework of ST&I. The major contribution of the chapter is twofold; first, it provides a review of the literature on how text mining extended the quantitative methods applied in ST&I and highlights major methodological challenges. Second, it discusses two hands-on detailed case studies on how to implement the text analytics routine.

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.

Bibliometric and "tech mining" studies depend on a crucial foundation--the search strategy used to retrieve relevant research publication records. Database searches for emerging technologies can be problematic in many respects, for example the rapid evolution of terminology, the use of common phraseology, or the extent of "legacy technology" terminology. Searching on such legacy terms may or may not pick up R&D pertaining to the emerging technology of interest. A challenge is to assess the relevance of legacy terminology in building an effective search model. Common-usage phraseology additionally confounds certain domains in which broader managerial, public interest, or other considerations are prominent. In contrast, searching for highly technical topics is relatively straightforward. In setting forth to analyze "Big Data," we confront all three challenges--emerging terminology, common usage phrasing, and intersecting legacy technologies. In response, we have devised a systematic methodology to help identify research relating to Big Data. This methodology uses complementary search approaches, starting with a Boolean search model and subsequently employs contingency term sets to further refine the selection. The four search approaches considered are: (1) core lexical query, (2) expanded lexical query, (3) specialized journal search, and (4) cited reference analysis. Of special note here is the use of a "Hit-Ratio" that helps distinguish Big Data elements from less relevant legacy technology terms. We believe that such a systematic search development positions us to do meaningful analyses of Big Data research patterns, connections, and trajectories. Moreover, we suggest that such a systematic search approach can help formulate more replicable searches with high recall and satisfactory precision for other emerging technology studies.

EDITOR'S SUMMARY Focusing on graphene, a material made from carbon atoms, researchers constructed a map illustrating the material in the context of scientific developments and patent activity over time. Two basemaps reflecting general science and patent activity were created, in addition to two overlay maps reflecting scientific publication and patenting activities specific to graphene. The science and patent overlay maps together display how the material was discovered and used between 2000 and 2013. The maps reveal 222 Web of Science categories and 466 International Patent Classification categories and identify 18 macro-disciplines and 35 macro-patent categories. Graphene research is shown to focus on material sciences and closely related fields such as engineering and physical chemistry, while graphene inventions cover technologies from catalysis and semiconductors to the pharmaceutical arena. The node size, density of relationships and colors of the map give additional information on research and development efforts and relationships among subset topics. The process employed text mining, visualization and graphics software tools in addition to a mapping kit available from the authors.

In the present study, we aim to quantitatively investigate and compare the intellectual landscapes of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) research between 2000 and 2014. The overall intellectual structure of these fields is illustrated by emerging trends of bursting keywords and thematic concentrations of co-cited references. This study is based on two sets of bibliographic records retrieved from the Web of Science database. The SWCNTs dataset contains 18,700 original research and review articles. The MWCNTs dataset, consisting of 23,584 records, is also collected from the database. We find that both domains have scrutinized chemical concepts which underlie the properties of the materials. Recent thematic trends show that MWCNTs research focuses on the improvement of the material while SWCNTs research lays more emphasis on their applications. In conclusion, it is argued that SWCNTs and MWCNTs have co-evolved. At the same time, both fields are distinctively diverging with their own scientific concerns.

© 2017 PICMET. Newly Emerging Science and Technologies (NESTs) bring numerous innovation opportunities and challenges. At the same time, the highly uncertain dynamics of NESTs pose special challenges to traditional technology forecasting tools. Dye-Sensitized solar cells (DSSCs), a promising third-generation photovoltaic technology, can add functionality and lower costs, enhancing the value proposition of solar power generation in the early years of the 21st century. We have analyzed DSSCs R&D activity patterns and trends through a series of studies in the past several years, and come to realize the validation analyses is useful to improve the Science, Technology & Innovation (ST&I) policy processes. Yet, rarely do we revisit forecasts or projections to ascertain how well they fared. One of our aim is to check accuracy, to gain some sense of how valid those studies were and whether they proved useful to others in some ways. Additionally, we want to assess the degree to which such future-oriented analyses did or did not make good use of available information. Moreover, we seek indications of what information is key, and how Future-oriented technology analyses (FTA) processes can better utilize such information.

© 2017 PICMET. As one of the most representative emerging technologies, big data analytics and its related applications are rapidly leading 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.

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, then we use those to generate secondary indicators that reflect organizations, countries, or authors especially active at research frontiers in a target domain. We also flag abstract records (papers or patents) rich in emergent technology content, and we score technological fields on relative degree of emergence. We show illustrative results for example topics -- Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data.

Clinical translation of technological discoveries from bench to bedside has been a slow and incremental process. Capturing early events in technology development can provide key insights into the nature of biomedical innovation and its bottlenecks. The sheer volume of the available information, however, presents a significant barrier to a systematic assessment of current capabilities. Nanomedicine, which deals with medical applications of nanotechnology, perfectly exemplifies the challenges facing translational research. In this study, we have explored the feasibility of using a streamlined tech mining approach for identification of translational innovation pathways using observable markers found in research literature. The framework contains three sections: 1) extraction of feature terms from titles and abstracts; 2) tagging research articles with translational stages and application markers; and 3) analysis of topical changes, translational phases, and innovation pathways. We applied this strategy to analyze a set of 23,982 PubMed records that involved gold nanostructures (GNSs), which have been extensively studied in a wide range of biomedical applications. The studies were classified based on their intended clinical application, research field, disease, and translational stage. Our results have identified a significant increase in GNSs studies in the areas of cancer, therapeutic applications, and animal testing. Additionally, the tags along with feature terms were used to build innovation pathway maps for three types of biomedical applications: treatment, in vitro detection, or imaging. The framework described in this paper can be useful for academic researchers, funding agencies, as well as pharmaceutical and medical device companies to facilitate assessment of translational readiness and future research planning

Nano-enabled drug delivery (NEDD) systems are rapidly emerging as a key area for nanotechnology application. Understanding the status and developmental prospects of this area around the world is important to determine research priorities, and to evaluate and direct progress. Global research publication and patent databases provide a reservoir of information that can be tapped to provide intelligence for such needs. Here, we present a process to allow for extraction of NEDD-related information from these databases by involving topical experts. This process incorporates in-depth analysis of NEDD literature review papers to identify key subsystems and major topics . We then use these to structure global analysis of NEDD research topical trends and collaborative patterns, inform future innovation directions. From the Clinical Editor This paper describes the process of how to derive nano-enabled drug delivery-related information from global research and patent databases in an effort to perform comprehensive global analysis of research trends and directions, along with collaborative patterns.

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.

Research that integrates the social and natural sciences is vital to address many societal challenges, yet is difficult to arrange, conduct, and disseminate. This paper compares diffusion of the research supported by a unique U.S. National Science Foundation program on Human and Social Dynamics ("HSD") with a matched group of heavily cited papers. We offer a measure of the distance of cites between the Web of Science Category ("WoSC") in which a publication appears and the WoSC of the journal citing it, and find that HSD publications are cited more distantly than are comparison publications. We provide another measure--citation velocity--finding that HSD publications are cited with similar lag times as are the comparison papers. These basic citation distance and velocity measures enrich analyses of research knowledge diffusion patterns.

We are developing indicators for the emergence of science and technology (S&T) topics. To do so, we extract information from various S&T information resources. This paper compares alternative ways of consolidating messy sets of key terms [e.g., using Natural Language Processing on abstracts and titles, together with various keyword sets]. Our process includes combinations of stopword removal, fuzzy term matching, association rules, and term commonality weighting. We compare topic modeling to Principal Components Analysis for a test set of 4104 abstract records on Dye-Sensitized Solar Cells. Results suggest potential to enhance understanding regarding technological topics to help track technological emergence.

In recent years, the Triple Helix model has identified feasible approaches to measuring relations among universities, industries, and governments. Results have been extended to different databases, regions, and perspectives. This paper explores how bibliometrics and text mining can inform Triple Helix analyses. It engages Competitive Technical Intelligence concepts and methods for studies of Newly Emerging Science & Technology (NEST) in support of technology management and policy. A semantic TRIZ approach is used to assess NEST innovation patterns by associating topics (using noun phrases to address subjects and objects) and actions (via verbs). We then classify these innovation patterns by the dominant categories of origination: Academy, Industry, or Government. We then use TRIZ tags and benchmarks to locate NEST progress using Technology Roadmapping. Triple Helix inferences can then be related to the visualized patterns. We demonstrate these analyses via a case study for dye-sensitized solar cells. © 2013 Akadémiai Kiadó, Budapest, Hungary.

Technology roadmapping offers a flexible instrument to portray development status in support of technology forecasting and assessment. This paper integrates bibliometrics with qualitative methodologies and visualisation techniques to construct a hybrid model for composing technology roadmaps. The mapping arrays details on the evolution of the technology under study and contributes to understanding the macro-technology development status. We generate a global technology roadmap for electric vehicles to demonstrate the approach in an empirical study. © 2013 Copyright Taylor and Francis Group, LLC.

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.

Visual analytics has been increasingly used to help to better grasp the complexity and evolution of scientific and technological activities over time, across science and technological areas and in organisations. This chapter presents general insights into some important fields of expertise such as mapping, network analysis and visual analytics applied to patent information retrieval and analysis. We also present a new global patent map and overlay technique and illustrative examples of its application. The concluding remarks offer considerations for future patent analysis and visualisation.

Innovations around "Big Data" can be characterized in terms of rapid technology development and deployment dynamics. For this purpose, combining "tech mining" (extraction of usable intelligence) from publication and patent databases with tech mining of business-related databases can elucidate activities and interests of business communities regarding Big Data innovation pathways. In this paper, we focus on commercially oriented databases -- ABI/INFORM as a source from which to extract business intents. We select the database to help gauge "hot topics" in industry with regard to Big Data. Our results show that certain types of firms can be clustered into thematic groups relating to Big Data discussions and activities. In the paper we demonstrate that such analyses can illuminate themes being pursued by businesses. Like social media analyses, this text mining can provide useful intelligence to inform more in-depth investigation mobilizing other data sources and techniques.

Technology assessment is a systematic examination of the effects on or of new developments such as technologies, processes, policies, organizations, and so on. In this paper, we present a systematic method for technology assessments a part of the suite of tools for Forecasting Innovation Pathways (FIP). We explore means to combine tech mining tools with human intelligence in several idea exchange rounds to uncover potential secondary effects, and array them in terms of likelihood and magnitude. Big Data is studied as the case study. This is on-going research. We are currently on the 2nd round of state 2. Technology assessment is a necessary component of FIP. It identifies areas in which significant impacts may occur, their likelihood, and their significance. The forecaster must evaluate these impacts, consider measures to enhance or inhibit them, and factor them into the planning process for developing the technology.