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Emergence scoring to identify frontier R&D topics and key players
Abstract
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.
... They consider as emerging technologies a word or a words' combination appearing for the first time in the text, achieving remarkable results with generic keywords and keyphrases. Other studies involve text mining from patent for identifying emerging technologies (Ranaei et al., 2020;Zhou et al., 2020;Porter et al., 2019;Jang et al., 2021;Sarica et al., 2020) or exploring the convergence phenomena among technological fields (Gustafsson et al., 2015;Song et al., 2017). At the state-of-the-art, text mining techniques for technological analysis focus on generic terms and not on specific ones for investigating the technological change. ...
... For example, Ranaei et al. (2020) analyse the emergence of technologies using three text-based approaches: tf-idf metrics for capturing technological changes, LDA for evaluating the emerging topics, textbased score developed by Porter et al. (2019) and Carley et al. (2018). The authors found that the three different methods provide somewhat distinct perspectives improving the understanding of technological change. ...
... The technology extractor may be used in conjunction with the current methods for the fostering of emerging technologies. In their seminal work, Porter et al. (2019) use text mining and a R&D emergence indicator based on four criteria for mapping the pathways of emerging technologies: novelty, persistence, growth and community. However, the method proposed by the authors recognizes as technologies also terms that may not be defined as such. ...
Identifying technologies is a key element for mapping a domain and its evolution. It allows managers and decision makers to anticipate trends for an accurate forecast and effective foresight. Researchers and practitioners are taking advantage of the rapid growth of the publicly accessible sources to map technological domains. Among these sources, patents are the widest technical open access database used in the literature and in practice.
Nowadays, Natural Language Processing (NLP) techniques enable new methods for the analysis of patent texts. Among these techniques, in this paper we explore the use of Named Entity Recognition (NER) with the purpose to identify the technologies mentioned in patents' text. We compare three different NER methods, gazetteer-based, rule-based and deep learning-based (e.g. BERT), measuring their performances in terms of precision, recall and computational time.
We test the approaches on 1600 patents from four assorted IPC classes as case studies. Our NER systems collected over 4500 fine-grained technologies, achieving the best results thanks to the combination of the three methodologies. The proposed method overcomes the literature thanks to the ability to filter generic technological terms. Our study delineates a valid technology identification tool that can be integrated in any text analysis pipeline to support academics and companies in investigating a technological domain.
... Carley et al. (2018) elaborated on the EScore method more thoroughly and implemented it on a dye-sensitized solar cells (DSSCs) dataset and found the emergent terms, authors, and affiliations in this field. Porter et al. (2019) implemented the EScore method and revised it to identify the emerging terms and key players, as well as high-priority research papers and patents. Ranaei et al. (2020) evaluated and compared this method to other methods to see the strengths and weaknesses of the EScore method. ...
... Term clumping is a semi-automated process of cleaning, consolidating, and clustering the terms (Zhang et al. 2014); It has been used by some papers in the process of emergence detection (Carley et al. , 2018Garner et al. 2017;Huang et al. 2021;Liu and Porter 2020;Porter et al. 2019;Ranaei et al. 2020). Most of the previous studies saw the keyword extraction step as part of the term clumping, and they did not pay enough attention to the keyword extraction step or its different methods. ...
... Porter and Detampel (1995) used growth in the number of keywords in publication titles and abstracts as an indicator of emergence. After that, many studies used the growth in the number of records including a topic as an emergence indicator of that topic (Carley et al. , 2018Garner et al. 2017;Porter et al. 2019;Q. Wang 2018). ...
Early identification of emergent topics is of eminent importance due to their potential impacts on society. There are many methods for detecting emerging terms and topics, all with advantages and drawbacks. However, there is no consensus about the attributes and indicators of emergence. In this study, we evaluate emerging topic detection in the field of artificial intelligence using a new method to evaluate emergence. We also introduce two new attributes of collaboration and technological impact which can help us use both paper and patent information simultaneously. Our results confirm that the proposed new method can successfully identify the emerging topics in the period of the study. Moreover, this new method can provide us with the score of each attribute and a final emergence score, which enable us to rank the emerging topics with their emergence scores and each attribute score.
... Identifying emerging themes is closely associated with the emergence concept discussed in science and technology innovation (Porter et al., 2019). This study utilizes this concept to objectively identify the topics within ROA in PV that garner significant research interest. ...
... After identifying the emerging descriptors, the Louvain clustering algorithm extracts the relevant themes. The benchmarks for these parameters draw from Garner et al. [23], and Porter et al. [24]. ...
Solar photovoltaic (PV) projects are pivotal in addressing climate change and fostering a sustainable energy future. However, the complex landscape of renewable energy investments, characterized by high upfront costs, market uncertainties, and evolving technologies, demands innovative evaluation methods. The Real Options Approach has emerged as a powerful tool, offering strategic flexibility in decision-making under uncertainty. This paper comprehensively analyzes the application of real options for evaluating solar photovoltaic projects in 2008–2023. Analysis of document descriptors (author keywords, index keywords, and noun phrases extracted from titles and abstracts) reveals that the dominant research topics in the last ten years (2014–2023) include investment optimization, strategic analysis, energy policy, optimization of energy generation and investments in wind energy. These descriptors are used to analyze the evolution of research interests on a two-year basis and reveal the yearly evolution of the research topics. Finally, the concept of emergence is used to unveil emerging research trends, providing valuable insights for researchers and practitioners in the renewable energy sector. Ultimately, this work contributes to a deeper understanding of how real options analysis empowers decision-makers to make informed choices in advancing clean and sustainable energy solutions.
... In addition, we draw on key conceptual aspects of "tech emergence" to identify a set of requirements and modes of research acceleration from Rotolo et al. (2015). Here, we use abstract records instead of IARPA-preferred full text and adapt thresholds to meet the criteria of term novelty, persistence, community, and growth Porter et al., 2018). We also combine four trend measures to detect accelerating usage. ...
... . Exploring long COVID research topics . . Emerging LC topics Our "Tech Emergence Indicators" were introduced in Section 2; their rationale and construction are described by Carley et al. (2018) and Porter et al. (2018). In brief, a set of topical terms are prepared. ...
... • Sub-step 3.2.1. Analysis of patents time distribution, through the emergence score index (Porter et al., 2019) and the comparison with the S-curve (Mao et al., 2017). o The emergence score index allows discriminating the most promising technologies within a patent pool in which several technologies are claimed if their patents have a particular pattern of increasing publication over time. ...
... In this way data can be sorted based on the emergence score index associated with the patents from which they were extracted. The hypothesis is that these data are more meaningful because they refer to potentially more emergent solutions/components and with more chances to be developed and disseminated in the future (Porter et al., 2019). The final result of the analysis will therefore be influenced by the weights assigned to the data in the inventory. ...
Prospective life cycle assessment (LCA) was introduced with the goal to evaluate the environmental sustainability of eco-design solutions (i.e., ideas, prototypes, immature products, emerging technologies) prospectively rather than existing products, at the present time, as in traditional LCA. The main difference lies in the inventory, which is foreground and is based solely on the extraction of data from prospective documents, including patents, although this task, is tricky and can make the final result uncertain. This study proposes a systematic method to collect all the flows about a specific function of the product lifecycle from patent literature for building the foreground inventory of prospective LCA, ensuring comparability, data quality and scale-up. This was done by studying the intersections between patent analysis techniques and LCA requirements for reducing the uncertainty, prescribed by ISO 14040, ISO 14044, Pedigree Matrix and Data Quality Indicators for Life Cycle Inventory Data. The application of the proposed method to a case study related to the production of titanium powders using an innovative process revealed its main advantages in collecting patents and extracting data. Patent search recall and precision are increased. Patents are filtered by seeking a trade-off to ensure time consistency and avoid anomalous fluctuations in the data resulting from predatory patenting strategies. Data reliability and significance are controlled. Results can be expressed without levelling them around the average value, but adding time evolution and forecasting considerations. For example, the global warming potential (GWP) of the innovative process is 1.5 % lower than the GWP of the current process, considering the average patent data of the last 10 years. In addition, this value showed a 1 % increase for each year.
... In addition, we draw on key conceptual aspects of "tech emergence" to identify a set of requirements and modes of research acceleration from Rotolo et al. (2015). Here, we use abstract records instead of IARPA-preferred full text and adapt thresholds to meet the criteria of term novelty, persistence, community, and growth Porter et al., 2018). We also combine four trend measures to detect accelerating usage. ...
... . Exploring long COVID research topics . . Emerging LC topics Our "Tech Emergence Indicators" were introduced in Section 2; their rationale and construction are described by Carley et al. (2018) and Porter et al. (2018). In brief, a set of topical terms are prepared. ...
While the COVID-19 pandemic morphs into less malignant forms, the virus has spawned a series of poorly understood, post-infection symptoms with staggering ramifications, i. e., long COVID (LC). This bibliometric study profiles the rapidly growing LC research domain [5,243 articles from PubMed and Web of Science (WoS)] to make its knowledge content more accessible. The article addresses What? Where? Who? and When? questions. A 13-topic Concept Grid presents bottom-up topic clusters. We break out those topics with other data fields, including disciplinary concentrations, topical details, and information on research “players” (countries, institutions, and authors) engaging in those topics. We provide access to results via a Dashboard website. We find a strongly growing, multidisciplinary LC research domain. That domain appears tightly connected based on shared research knowledge. However, we also observe notable concentrations of research activity in different disciplines. Data trends over 3 years of LC research suggest heightened attention to psychological and neurodegenerative symptoms, fatigue, and pulmonary involvement.
... For the empirical setting, we choose Nano-Enabled Drug Delivery (NEDD) papers as the research publications on nanomedicine because NEDD is one of the prominent subdomains of the nanomedicine research fields (De Jong & Borm, 2008). As a comparison group, we use synthetic biology (SynBio) papers for several empirical conveniences which will be illustrated in section 3. We measure the degree to which the scientific discovery in a research paper relates to emerging technological topics within the field by using the emergence score algorithm (Carley, Newman, Porter, & Garner, 2018;Porter, Garner, Carley, & Newman, 2019). ...
... Our analysis begins with estimating the average impact of the FDA's release of the report in 2007 on the patent citation accrued to a NEDD paper compared to a SynBio paper by using the Difference-in-Differences (DiD) approach. To examine if the sign and size of the impact differ by the degree to which the discovery in a research paper associates with emerging technological topics, we measure the degree to which a research paper contains emerging technological terms within the field of the paper (i.e., NEDD or SynBio), by using the recently developed emerging score algorithm (Carley et al., 2018;Porter et al., 2019). By using the text data in the title and abstract of a corpus of research papers in each research domain, this algorithm allows one to extract emerging terms and quantify the extent to which each of the extracted terms represents technological emergence within the field (i.e., emergence score). ...
This study investigates the effect of regulatory uncertainty on the translation of scientific discovery on emerging research topics to technical applications in science-driven industry. Our empirical analysis using the case of the US Federal Drug and Food Administration’s release of the report on the regulatory approach to nanomedicine in 2007 shows that; (1) the regulatory uncertainty decelerated the translation of nanomedicine research to technical applications, (2) this effect was particular for the nanomedicine research on emerging topics in the field. Our further analysis suggests that the effect of the regulatory uncertainty originated from the suppressed business activities in the field where the regulatory uncertainty presents. Our study elaborates on how regulatory authority actions shape the innovation process by shedding light on the impact of regulatory uncertainty on the development of technical applications of an emerging scientific area.
... Therefore, many efforts are now being made to identify the research trends in different scientific areas in various regions around the world via bibliometric, scientometric, webometric, visualization technologies, social network analysis and other such analytical methods (Huang and Chang, 2014, Dantu et al., 2021, Han et al., 2022. One of the widely used analyses in research and development scoring is the bibliographic analysis using different indicators like publication number, publication year, citation number, and keywords similarities etc., (Porter et al., 2019, Xu et al., 2021. In this view, the present study was specifically designed for the bibliometric analyses of the neuroscience research trend(s) in SA. ...
... Growth does not always positively influence impact. In early-stage technologies, small bases amplify base effects, leading to potentially negative correlations (Porter et al., 2019). High growth rates often reflect proportional accumulation rather than enhanced influence and may result from novelty-driven growth (Castaldi et al., 2015;Rosiello & Maleki, 2021). ...
Introduction. Impact, as an intrinsic attribute of emerging technologies, plays a crucial role in their identification and understanding. Traditional citation metrics, while reflective of impact magnitude, fail to explain its nature. Method. Addressing this deficiency, this study categorizes the impact of emerging technologies into internal and external field, as well as impacts on basic versus applied development. With a focus on digital medical technology, this research investigates these four distinct impact patterns and their determinants. Using ordinary least squares (OLS) regression analysis on patent records from the Derwent innovations index (DII), the study examines how temporal novelty, innovation novelty, growth, and uncertainty influence both the magnitude and patterns of technological impact. Analysis. Analysis shows a U-shaped relationship between temporal novelty and impact, indicating that older technologies gain influence through cumulative effects, while emerging technologies attract attention early on due to their pronounced novelty. Additionally, innovation novelty positively correlates with impact, underscoring its critical role in the effectiveness of emerging technologies. In contrast, growth rates and uncertainty demonstrate inconsistent effects. Conclusions. These insights offer valuable guidance for policymakers, investors, and R&D managers in strategically advancing the development and deployment of emerging technologies.
... This line of text mining methods has great implications for research topics and trends analysis. It also makes its way into emerging topic identification and prediction [20]. In this study, we utilized text mining to explore key concepts and topics that highlight the content of scholarly literature in urology. ...
... Numerous experts and scholars in the field of bibliometrics have contributed to the discovery of emerging technologies. For example, the Foresight and Understanding of Science Expositions (FUSE) project, funded by the Intelligence Advanced Research Projects Activity (IARPA) in 2011, aimed to identify emerging topics from scientific, technological, and patent literature (McKeown et al., 2016;Porter et al., 2019). Conversely, some scholars have focused on emerging technology forecasting in the medical-device industry in their study (AlSumait et al., 2009). ...
Purpose
Nanomedicine has significant potential to revolutionize biomedicine and healthcare through innovations in diagnostics, therapeutics, and regenerative medicine. This study aims to develop a novel framework that integrates advanced natural language processing, noise-free topic modeling, and multidimensional bibliometrics to systematically identify emerging nanomedicine technology topics from scientific literature.
Design/methodology/approach
The framework involves collecting full-text articles from PubMed Central and nanomedicine-related metrics from the Web of Science for the period 2013–2023. A fine-tuned BERT model is employed to extract key informative sentences. Noiseless Latent Dirichlet Allocation (NLDA) is applied to model interpretable topics from the cleaned corpus. Additionally, we develop and apply metrics for novelty, innovation, growth, impact, and intensity to quantify the emergence of novel technological topics.
Findings
By applying this methodology to nanomedical publications, we identify an increasing emphasis on research aligned with global health priorities, particularly inflammation and biomaterial interactions in disease research. This methodology provides deeper insights through full-text analysis and leading to a more robust discovery of emerging technologies.
Research limitations
One limitation of this study is its reliance on the existing scientific literature, which may introduce publication biases and language constraints. Additionally, manual annotation of the dataset, while thorough, is subject to subjectivity and can be time-consuming. Future research could address these limitations by incorporating more diverse data sources, and automating the annotation process.
Practical implications
The methodology presented can be adapted to explore emerging technologies in other scientific domains. It allows for tailored assessment criteria based on specific contexts and objectives, enabling more precise analysis and decision-making in various fields.
Originality/value
This study offers a comprehensive framework for identifying emerging technologies in nanomedicine, combining theoretical insights and practical applications. Its potential for adaptation across scientific disciplines enhances its value for future research and decision-making in technology discovery.
... One example is the Loglet function (Meyer et al., 1999), which allows for the decomposition of a growth or diffusion curve into several S-shaped logistic components, representing consecutive waves of innovation. Furthermore, scholars may consider using alternative measures of maturity, such as citation network analysis (Ogawa and Kajikawa, 2015) or other indicators of technological emergence (Porter et al., 2019), to provide a more nuanced view of the quality and impact of patents. Doing so will help improve our understanding of how environmental innovations emerge and grow. ...
Patents are one of the most widely used tools to analyze environmental technologies. Organizations such as the World Intellectual Property Organization and OECD have developed search strategies to retrieve green patents based on their patent classification. These classifications divide patents into clusters, which are aligned with different sustainability goals. In this paper, we take advantage of this to analyze the distribution of patents across 1.221 patent classes within six clusters defined by OECD's ENV-TECH classification. We also assess the maturity stage of each patent class by fitting two commonly used S-curve models, namely logistic and Gompertz. We find that (a) most patent classes are still in a relatively early stage of the technology life cycle and (b) considerable heterogeneity exists in the distribution of patents, both within and across clusters. We discuss the methodological implications of our results and provide recommendations for scholars, drawing on green patent analyses, to conduct future work on environmental technologies.
... Textual analysis of Twitter has been adopted in literature using various methods for analysing emerging technologies. Some methods are based on bibliometric data, such as citation analysis (Small et al., 2014), whereas others are based on textual data (Porter et al., 2019;Ranaei et al., 2020), such as patents, scientific paper, and social media. The analysis of textual data with NLP outperforms bibliometric analysis (Arts et al., 2021), and Twitter has been confirmed to be a reliable source to explore trends in product development (Ozcan et al., 2021). ...
This study delves into the evolving role of generative Large Language Models (LLMs). We develop a data-driven approach to collect and analyse tasks that users are asking to generative LLMs. Thanks to the focus on tasks this paper contributes to give a quantitative and granular understanding of the potential influence of LLMs in different business areas. Utilizing a dataset comprising over 3.8 million tweets, we identify and cluster 31,747 unique tasks, with a specific case study on ChatGPT. To reach this goal, the proposed method combines two Natural Language Processing (NLP) Techniques, Named Entity Recognition (NER) and BERTopic. The combination makes it possible to collect granular tasks of LLMs (NER) and clusters them in business areas (BERTopic). Our findings reveal a wide spectrum of applications, from programming assistance to creative content generation, highlighting LLM's versatility. The analysis highlighted six emerging areas of application for ChatGPT: human resources, programming, social media, office automation, search engines, education. The study also examines the implications of these findings for innovation management, proposing a research agenda to explore the intersection of the identified areas, with four stages of the innovation process: idea generation, screening/idea selection, development, and diffusion/sales/marketing.
... Es necesario subrayar que la I+D es el proceso mediante el cual las organizaciones buscan mejorar productos, servicios o procesos a través de la investigación y la aplicación de nuevos conocimientos (Chen et al., 2018). Las tecnologías emergentes han redefinido este proceso al proporcionar a las empresas nuevas herramientas y enfoques para abordar problemas y oportunidades de manera más eficiente y efectiva (Lin et al., 2019;Porter et al., 2019). La I+D impulsada por estas tecnologías potencia la capacidad de las organizaciones para abordar desafíos y oportunidades de mejor manera, apoyando la innovación y la eficiencia. ...
La investigación y desarrollo (I+D) es esencial en la búsqueda de la solución de desafíos globales. Conscientes de esta importancia y de otras ventajas asociadas, las empresas han desarrollado estrategias que contemplen I+D en sus operaciones. Para abordar las tendencias en este ámbito, se realizó una revisión sistemática y un análisis cienciométrico en Scopus de trabajos publicados entre 2018 y 2022. Se identificaron tres tipos de tendencias: consolidadas, intermedias y emergentes; así como tres subtemas flamantes de investigación en I+D empresarial: tecnologías emergentes, innovación abierta y patentes. El estudio revela la importancia de las tecnologías emergentes en la I+D empresarial y destaca la necesidad de abordar los desafíos éticos y legales que plantean. La estrategia de innovación abierta se presenta como valiosa para aquellas empresas que buscan colaboración externa, mientras que la protección de patentes se mantiene fundamental para salvaguardar las inversiones y preservar la ventaja competitiva. Se sugiere que las organizaciones consideren la combinación de patentes con otros mecanismos de propiedad intelectual para extender la protección de sus innovaciones.
... The literature of intellectual property representations, such as trademarks and trademarks, is extremely common and is therefore often used in the BI review (Lee and Lee, 2017; Yoon & Kim, 2012). In BI science, however, professional/academic publishing evidence is used reasonably commonly (Porter, Garner, et al. 2018;Shiau, Dwivedi, et al. 2018). In comparison, web-based data involves social networks (e.g. ...
Obviously, social networking voices (VoC) for analysts able to perform customer-driven business intelligence (BI) analysis have emerged as quality evidence. However, to the best of the understanding of scholars, there is still a shortage of study that deals with such impressive content sources and deals with different accessible data from the BI science viewpoint (e.g., social media, intellectual property). This analysis has therefore been aimed at evaluating the applicability of social network data in BI research and systematically reviewing the primary research papers in this area. This research contrasts social network data in terms of data quality, processing, updating capability and framework, with other accessible data (e.g. grey literature, public service data), which is decided through a cautious discussion with experts. Then, the research collected 57 papers from the Web of Science (WoS) website centered on social networking, and three questions on details, methodology, and findings have been examined with a view to unraveling the field of analysis. The results are to educate current researchers regarding potential research recommendations, encourage entrants to gain insight into the overall analysis process of social media data, and offer practitioners environmentally friendly approaches to social media analysis.
... All the considered patents were evaluated by associating them with four bibliometric indexes taken from the literature: Innovation index (Iindex) (Míguez et al., 2020), Emergence Score index (ES-index) (Porter et al., 2019), Independent Claims index (IC-index) (e.g. Altuntas et al., 2020) and Technology Cycle Time index (TCT-index) (e.g. ...
To stop the dispersion of microplastics in the ecosystem, many technologies for collecting them were designed, tested and developed in the last period. However, a complete and exhaustive comparison of these technologies to guide in the choice and/or in the development of the most suitable appropriate one is missing in the literature. This study investigates the presence of some known technological trends, deriving from the TRIZ (Russian acronym for "Theory of Inventive Problem Solving") in the behaviour (i.e. the operating principle) of these technologies. To do this, a systematic methodology was followed, which has a general value and consists in analysing the patents relating to these technologies through various bibliometric indexes (i.e. Innovation index, Emergence Score index, Independent Claims index and Technology Cycle Time index). In general, the obtained results did not reveal a clearly identifiable ranking of the behaviour which was unanimously confirmed by all the considered bibliometric indexes. In addition, the average of the scores of the different indexes associated with the different behaviours equalized their differences. However, these results are mainly due to the markedly different evaluations obtained by the Emergence Score index compared to those of the other indexes. From the comparison of the results with the evolutionary trends, it emerged that the operative zone reduction trend was the most confirmed, while the technical system dematerialization was the least confirmed by the bibliometric analysis of all the indexes. In particular, the ranking of the behaviours provided by the Innovation index best confirmed all the evolutionary trends, while that of the Emergence Score index was the worst. In conclusion, this study confirmed the adherence of the development that technologies for collecting microplastics are following to the evolutionary trends through bibliometric analysis: this sequence places magnetic technologies in first place, followed by chemical, fluid dynamics, dynamic mechanics and static mechanics. The analysis of the performances declared in the patents substantially confirms this result.
... All the considered patents were evaluated by associating them with four bibliometric indexes taken from the literature: Innovation index (Iindex) (Míguez et al., 2020), Emergence Score index (ES-index) (Porter et al., 2019), Independent Claims index (IC-index) (e.g. Altuntas et al., 2020) and Technology Cycle Time index (TCT-index) (e.g. ...
... This abstract perspective can help designers, engineers and the R&D managers to generate novel ideas. Patent analysis is largely used for analyzing technological evolution (iii) (Daim et al., 2006), and in particular for identifying emerging technologies (Porter et al., 2019), convergence phenomena (Giordano et al., 2021), novelty (Gerken and Moehrle, 2012) or performing a technological roadmapping (Jeong and Yoon, 2015). The resolution of a contradiction, and so the boosting of the technology's ideality is a sign of technological change in a given domain (Porter and Cunningham, 2004). ...
Patents are the main means for disclosing an invention. These documents encompass many steps of the inventive process starting with the definition of the problem to be solved and ending with the identification of a solution. In this study we focus on three fundamental concepts of the inventive process: (A) technical problems; (B) solutions; and (C) advantageous effects of the invention, which, based on the WIPO guidelines, any patent should include.We propose a system based on Natural Language Processing (NLP) pipeline that uses transformer language models to identify technical problems, solutions and advantageous effects from patents. We use a training dataset composed of 480,000 patents sentences contained in sections manually labeled by inventors or attorneys.Our model reaches a F1 score of 90%. The model is evaluated on a random set of patents to assess its deployability in a real world scenario.The proposed model can be used as a novel tool for prior art mapping, novel ideas generation and technological evolution identification and can help to disclose valuable information hidden in patent documents.
... The attributes of the technology emergence are defined as radical novelty, fast growth, coherence, prominent impact, uncertainty and ambiguity . Following studies (Carley et al., 2018;Porter et al., 2019) also adopted these attributes in their papers. The attributes such as novelty, prominent impact, uncertainty are immanent characteristics for all periodic stages of the emergence of novel technology. ...
The emergence of new technologies is an important and highly researched field. This paper identifies characteristics of the early stage of technology emergence by analyzing the initial patents in five different technologies (Optical information storage, optical information transmission, genome sequencing, 3D printing and magnetic resonance imaging). From the science and technology push perspective, the characteristics such as influence of initial technological knowledge, linkage to scientific knowledge, and actors of technological activity are identified in the emerging stage of the new technology. The main findings of the research are firstly that technological and scientific knowledge are strongly linked in the emerging stage as the initial patents in all five technologies cite scientific papers more highly than do patents that support the ongoing development of the technologies. Second, the initial patents during the emerging stage do not play a significant role in the overall technological knowledge flow over time. Finally, the proportion of patents held by non-firm entities in the early stage of technology emergence is relatively larger than that in the other stages in four out of five technological domains. The results provide some important hypotheses about the interaction of scientific results and technological invention that have not previously been offered and that can be further researched as data sources improve.
... Understanding technology convergence trends helps to: improve the radical innovation level of enterprises from a technical perspective; enhance experts' accurate judgement of the future direction of technology research and development; and help enterprise managers to grasp industry development patterns and develop response strategies. As a result, measuring the law of technology convergence has received extensive attention from scholars (Porter, Garner, Carley, & Newman, 2019). The existing research results on technology convergence can be summarized as mainly including the following three research topics: 1) Technological philosophy. ...
Technology convergence, characterized by the blurring of boundaries between technologies, is an inevitable trend in the industry development. Measuring technology convergence trends can assist experts to adjust industry structures and develop reasonable R&D strategies. However, existing technology convergence measurement methods focus more on qualitative descriptions of the overall industry evolution and therefore lack quantitative methods to reveal the details of technology changeover. So, a synthetical technology convergence measurement analysis method and corresponding decision support system is proposed. The proposed method is capable of capturing both macro and quantitative features. From a macro perspective, the technology readiness level is determined based on the improved Gompertz model. From a quantitative measurement perspective, indicators of technical breadth and technical depth that describe the correlation of cross-domain technologies and the difficulty of mutual co-occurrence are proposed. In order to improve the efficiency of data processing and analysis, a decision support system has been developed based on the proposed method for the Derwent database. The proposed method is applied to measure the technology convergence law in the seismic isolation industry, to prove its effectiveness and advantages. The proposed method can provide enterprise managers with strategic judgment on the industry development status and effectively support expert decision-making.
... Li [94] proposed the use of keyword aggregation across topics to identify ERTs by analysing dispersed risk signals, which proved to be a timely and efficient method. Carley et al. [95] and Porter et al. [96] advanced an indicator set that operationalised the four aspects discussed by Rotolo et al. [63]. ...
This article uses the characteristics of citation curves in emerging research topics (ERTs) and combines them with the ERTs’ knowledge bases to draw conclusions by comparing their development patterns. The goal of this study is to enrich the toolset for predicting breakthroughs in scientific research. A set of multidimensional and practical bibliometric indicators is used to identify ERTs, to further identify the knowledge bases of ERTs and construct citation curves for both ERTs and their knowledge bases. The development trends of the citation curves of ERTs and their knowledge bases in different time periods are compared and analysed from two dimensions: knowledge transition and continuous growth. We use the field of stem cell research to test our method. Based on the outcome of the analysis, we can assess the breakthrough potential of ERTs. The stratification, transition and recent changes of the citation curve can be used as a basis for analysing and assessing the ERTs’ breakthrough potential. The combination of different citation diffusion patterns of ERTs and their knowledge bases can improve the effectiveness of identifying ERTs that can become breakthrough innovations.
... A more topic-driven approach to collecting external data is the analysis of scientific literature. As numerous authors point out, professional and academic publication data can provide useful information because they contain emerging technologies, latest trends and best methodologies [1,28,67,68]. Moreover, the periodic character of scientific publications (e.g. ...
Background:
Semantic analyses of patents have been used for years to unlock technical knowledge. Nevertheless, information retrievable from patents remains widely unconsidered when making strategic decisions, when recruiting candidates or deciding which qualifications to offer to employees in technological fields.
Objectives:
This paper provides an approach to evaluate whether competencies and competence demands in technological fields can be derived from patents and if this process can be automated to a certain extent.
Methods:
A sample of significant patents is analyzed with regard to comprised competence data via semantic structures like n-gram and Subject--Action-Object (SAO) analysis. The retrieved data is cleansed and matched semantically to inventor competencies from social career networks and checked for similarities.
Results:
A social career network profile analysis of significant inventors revealed a total of 570 competencies that were matched with the results of the n-gram and SAO analysis. Overall, 15%of the extracted social career network competence data were covered through extracted n-grams (87 out of 570 terms), while the SAO analysis showed a match rate of 18.8%, covering 107 terms.
Conclusions:
The outlined approach suggests a partly automatable process of promising character to identify technological competence demands in patents.
... In addition, the feasibility and utility of the proposed approach cannot be easily confirmed since it provides potential business opportunities with the potential for new value creation although they are not yet monopolised or saturated. As a remedy, taking into account that the number of relevant players (i.e., communities) that increase activity in a business area is positively correlated with emergingness (Porter et al., 2019;Moehrle and Caferoglu, 2019), we performed a t-test to statistically compare the average values of the number of applicants in the two different sets of business areas (emerging area vs pre-emerging area). Here, pre-emerging area was used as a comparison group, because it is most likely to develop into emerging area in the future, but not yet to have emergingness characteristics now. ...
Emerging business areas are early indicators of potential business opportunities, which are considered key to formulating new business strategies and envisioning near-future business environments. However, existing methods for analysing business opportunities solely depend on the opinion and knowledge of experts, which are time-consuming and labour-intensive. In academia, recent years have witnessed a significant increase in attempts to identify emerging business areas as near-future business opportunities with data-driven approaches. Although successful innovation requires sources of novelty, how to measure the novelty of business areas has barely been investigated in the literature. As a solution, we propose an approach to identifying emerging business areas with high novelty with a systematic process and quantitative outcomes. At the heart of the proposed approach is the composite use of the language model and local outlier factor (LOF). The meaning of business opportunities become more explicit by identifying emerging business areas composed of novel goods and services, with implications for the business operation stage. Finally, business opportunity maps are developed based on recency and visibility values, thereby investigating the implications as business opportunities. A case study of the trademarks related to scientific apparatus is presented to illustrate the proposed approach. The systematic process and quantitative results are expected to be employed in practice as a complementary tool, serving as a cornerstone for analysing business opportunities using trademarks.
Objective: The main objective of this article is to diagnose the state-of-the-art technology for hydrogen (H2) production from algae to assess competitiveness in the renewable fuels market. Theoretical Framework: Actions to decarbonize the energy matrix are part of the Kyoto Protocol and the Paris Agreement to control climate change, without negatively impacting energy security. H2 has become one of the competitive energy sources for decarbonization actions and one of the ways to control the increase in global temperature. Algae are potential renewable sources of competitive H2 due to their rapid growth, metabolic versatility and sulfur-free biomass. Method: A patentometric study was carried out based on patent documents published between 2013 and 2023, mined in the software VantagePoint to calculate the Emergency Score. Results and Discussion: The retrieved patent documents highlighted bioremediation processes in biorefineries and chemical and physical processes to separate H2 from other elements as emerging technological routes. Investments in these routes associated with genetic engineering of algae and better bioreactor design are possible combinations that will generate competitive advantage. Research Implications: Even if algae are not yet viable at the present time, the diversification of sources encourages the execution of new projects to evaluate biomass and develop industrial processes. The issues highlighted are actions that will potentially strengthen socio-environmental responsibility initiatives, associated with the concept of circular economy. Originality/Value: This study contributes to the literature by indicating paths for the development of technological solutions for hydrogen production from algae biomass, becoming another alternative aimed at energy security.
Retail analytics has become a cornerstone of modern retail strategy, fueled by rapid technological innovations and the digital transformation of traditional retail environments. This paper reviews 563 documents indexed in Scopus, critically evaluating current trends and emerging challenges in retail analytics. The analysis is structured around four key dimensions: artificial intelligence and machine learning, big data and data analytics, data-driven retail, and data-driven decision-making. By synthesizing the literature, this study identifies strategic opportunities and future research directions essential for leveraging retail analytics to enhance customer experience, optimize operations, and sustain competitive advantage in physical and digital retail landscapes. The findings aim to guide researchers, data scientists, and industry professionals in addressing practical and theoretical challenges in this evolving field.
Technological innovation serves as the catalyst for the shift towards circular practices. Technologies not only address technical challenges, facilitating the transition to a more circular economy, but they also enhance business efficiency and profitability. Furthermore, they promote inclusivity and create job opportunities, ultimately yielding positive societal impacts. The research in this area tends to focus on digital technologies, neglecting other technological areas. Moreover, it heavily relies on literature reviews and expert opinions, potentially introducing biases. In this article we investigate the technological landscape of the circular economy through Natural Language Processing (NLP), examining key technologies used in this sector and the primary challenges in managing these technologies. The methodology is applied to more than 45,000 scientific publications and aims to extract technologies in the text of scientific articles with NLP. The findings of our analysis reveal a strong emphasis on emerging digital, life cycle assessment and biomaterials technologies. Furthermore, we identified seven distinct technological domains within the CE field. Finally, we provide advantages and problems arising in the adoption and implementation of these technologies in an industrial context.
Federal RePORTER, a recently retired federally funded research and development (R&D) grant database, contained a vast amount of information on federally funded R&D and was utilized by researchers, citizens, and policymakers alike to uncover insights. In this report, we provide a classification of research topics contained within Federal RePORTER project abstracts, as well as trends in these topics over time, using natural language processing (NLP) and machine learning techniques. In collaboration with the National Center for Science and Engineering Statistics (NCSES), we examined how the topics and their trends change as a result of the number of topics produced by the model. In addition, we utilized information retrieval techniques to find theme-related topics and their trends over time. This is realized through two case-studies, the first using the theme of pandemics and the second using the theme of coronavirus.
In this study, employing the IEEE Xplore database as the data source, articles on different topics (keywords) and their usage data generated from January 2011 to December 2020 were collected and analyzed. The study examined the temporal relationships between these usage data and publication counts at the topic level via Granger causality analysis. The study found that almost 80% of the topics exhibit significant usage-publication interactions from a time-series perspective, with varying time lag lengths depending on the direction of the Granger causality results. Topics that present bidirectional Granger causality show longer time lag lengths than those exhibiting unidirectional causality. Additionally, the study found that the direction of the unidirectional Granger causality was influenced by the significance of a topic. Topics with a greater preference for article usage as the Granger cause of publication counts were deemed more important. The findings’ reliability was confirmed by varying the maximum lag period. This study provides strong support for using usage data to identify hot topics of research.
The novel generative Artificial Intelligence (AI) developed by OpenAI, i.e., ChatGPT, rised a great interest in both scientific and business contexts. This new wave of technological advancement typically produces deep transformation in the workplace, requiring new skills. However, none of the studies in literature provide quantitative analysis and measures on the impact of ChatGPT on human skills. To address this gap, we collected a database of 616,073 tweets about ChatGPT, and used Natural Language Processing techniques to identify the tasks users requested ChatGPT to perform, and the sentiment related to these tasks. Then, we compared these tasks with a standard taxonomy of skills (i.e., ESCO) using BERT. The results of the study underline that ChatGPT impacts 185 different skills. Moreover, we proposed a model to represent the interaction of the user and ChatGPT, useful to define four skills which are emerging for using this new technology.
Identifying potentially disruptive technologies is challenging but important for innovators. Existing research based on tech mining approach pays much attention to technological change, but less attention to characterizing its disruptive process and effects. This research suggested a novel perspective to help understand and identify potentially disruptive technology that displace the mainstream technology (termed as “alternative disruption”) by modelling it as a process in which alternative technologies compete against incumbent technology. Accordingly, we proposed a systematic framework to identify this type of technological disruptors by quantitatively characterizing its disruptive process and effects. To illustrate the alternative features, uniquely, the framework is solutions-focused that answer a same technological problem with the mainstream one, in which Subject-action-object (SAO) semantic analysis and community detection algorithm are used to mine and cluster the found solutions into groups as candidate technologies, including mainstream technologies and potentially alternative ones. Incorporating disruptive characteristics of technological advance, technology applicability and market niche, the alternative one with a highest comprehensively competitive position against mainstream technologies remains as the most disruptive potential. Finally, the case of cancer treatments verifies the feasibility and effectiveness of this framework. Also, this proposed framework can provide quantitative information for decision making in promising technologies deployment and resource allocation.
The escalating issue of water pollution has become a worldwide issue that has captured the attention of numerous scientists. Molecularly imprinted polymers (MIPs) have emerged as adaptable materials with exceptional attributes, including easy synthesis, low cost, remarkable durability, long life, and accessibility. These attributes have motivated researchers to develop novel materials based on MIPs to tackle hazardous contaminants in environmental matrices. The purpose of this paper was to conduct a bibliometric analysis on MIPs’ publications, in order to shed light on the developments and focus points of the field. The selected publications were obtained from Scopus database and subjected to a filtering process, resulting in 11,131 relevant publications. The analysis revealed that the leading publication source (journal) is Biosensors and Bioelectronics ; the mostly employed keywords are solid-phase extraction, electrochemical sensor, and molecular recognition; and the top contributing countries are China, Iran, and the USA. The Latent Dirichlet Allocation (LDA) algorithm was used for extracting thematic axes from the textual content of the publications. The results of the LDA model showcase that the topic of synthesis and performance of MIPs for environmental applications can be considered as the most dominant topic with a share value of 72.71%. From the analysis, it can be concluded that MIPs are a cross-disciplinary research field.
In the last decade, many artificial intelligence (AI) techniques have been used to solve various problems in sustainable energy (SE). Consequently, an increasing volume of research has been devoted to this topic, making it difficult for researchers to keep abreast of its developments. This paper analyzes 18,715 articles—about AI techniques used for SE—indexed in Scopus and published from 2013 to 2022, which were retrieved and selected following a novel iterative methodology. Besides calculating basic bibliometric indicators, we used clustering techniques and a co-occurrence analysis of author keywords to discover and characterize dominant themes in the literature. As a result, we found eight dominant themes in SE (solar energy, smart grids and microgrids, fuel cells, hydrogen, electric vehicles, biofuels, wind energy, and energy planning) and nine dominant techniques in AI (genetic algorithms, support vector machines, particle swarm optimization, differential evolution, classical neural networks, fuzzy logic controllers, reinforcement learning, deep learning, and multi-objective optimization). Each dominant theme is discussed in detail, highlighting the most relevant work and contributions. Finally, we identified the AI techniques most widely used in each SE area to solve its specific problems.
Technology convergence (TC), in which technological innovation occurs by integrating at least two or more existing technologies into hybrid technologies, has been a growing innovation pattern in recent decades. Facing substantial and accelerated technological changes, it is important for technology-based firms to identify TC patterns in pursuing competitive strengths. However, most studies do not consider multi-TC with technical emergence. This research thus develops an anticipation methodology based on the network theory and association rule mining (ARM) for early identification of multi-TC patterns associated with emerging technology classes from patent data in the smart health industry. First, a technological knowledge flow network is built for subsequent analyses using smart health related patent data. Then, core-periphery analysis is used to identify peripheral technology classes which may become influential in the future. Several technical emergence indicators are developed to evaluate emergence scores of identified peripheral technology classes. Finally, ARM is used to discover intriguing multi-TC associated with emerging technology classes. We conclude that the research results are useful for R&D managers for early discovery of emerging multi-TC patterns to explore potential technological opportunities.
Patents are the main means for disclosing an invention. These documents encompass many steps of the inventive process starting with the definition of the problem to be solved and ending with the identification of a solution. In this study we focus on three fundamental concepts of the inventive process: (A) technical problems; (B) solutions; and (C) advantageous effects of the invention, which, based on the WIPO guidelines, any patent should include. We propose a system based on Natural Language Processing (NLP) pipeline that uses transformer language models to identify technical problems, solutions and advantageous effects from patents. We use a training dataset composed of 480,000 patents sentences contained in sections manually labelled by inventors or attorneys. Our model reaches a F1 score of 90%. The model is evaluated on a random set of patents to assess its deployability in a real-world scenario. The proposed model can be used as a novel tool for prior art mapping, novel ideas generation and technological evolution identification and can help to disclose valuable information hidden in patent documents.
Quantum communication is a key to information security, which has a wide range of applications in military, financial, and social development. Understanding the technological emergence in advance would contribute to gain full socio-economic benefits and reducing risk. This research attempts to explore the emergence of quantum communication from the bibliometric perspective. By analyzing the patent from the Derwent Innovation Index, this research analyzes the development phase, the technology emergence, the main contributors’ network, the distribution of regions, and technology categories of quantum communication. The results show that quantum communication is in the growth phase according to the theory of the technology life cycle. China, the United States, and Japan are the leading countries. This research also identifies the main patent assignees and their collaboration networks. By introducing the technological emergence indicators, this research identifies four emerging topics in quantum communication and analyzes the layout of main patent assignees within these topics. This research would contribute to the endeavour of scientific communities toward the development of quantum communication.
Citations are used for research evaluation, and it is therefore important to know which factors influence or associate with citation impact of articles. Several citation factors have been studied in the literature. In this study we propose a new factor, topic growth, that no previous study has studied empirically. The growth rate of topics may influence future citation counts because a high growth in a topic means there are more publications citing previous publications in that topic. We construct topics using community detection in a citation network and use a two-part regression model to study the association between topic growth and citation counts in eight broad disciplines. The first part of the model uses quantile regression to estimate the effect of growth ratio on citation counts for publications with more than three citations. The second part of the model uses logistic regression to model the influence of the explanatory variables on the probability of being lowly cited versus being modestly or highly cited. Both models control for three variables that may distort the association between the topic growth and citations: journal impact, number of references, and number of authors. The regression model clearly shows that publications in fast-growing topics have a citation advantage compared to publications in slow-growing or declining topics in all of the eight disciplines. Using citation indicators for research evaluation may give incentives for researchers to publish in fast-growing topics, but they may cause research to be less diversified. The results have also some implications for citation normalization.
The detection of emerging trends is of great interest to many stakeholders such as government and industry. Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. However, these approaches inevitably have time delay problems. For the reason that leader papers of "emerging topics" share the similar characters with the "cover papers", this study present a novel approach to translate the "emerging topics" detection to "cover paper" prediction. By using "AdaBoost model" and topic model, we construct a machine learning framework to imitate the top journal (chief) editor's judgement to select cover paper from material science. The results of our prediction were validated by consulting with field experts. This approach was also suitable for the Nature, Science, and Cell journals.
To what extent is military technology innovation emergent? This study answers this question by applying an emergence detection algorithm to roughly 300,000 technical terms extracted from military technology patents granted from 1980 to 2019. Emergence – instances of sudden and rapid growth of a technical term within the military patent corpus – is found to vary greatly over time. Military technology innovation during the period of 1996-2008 is found to be highly emergent. This period was found to be characterized by high organization-type diversity; non-traditional vendors, traditional defense contractors, large civilian-facing firms, and individuals generated military patents containing many novel emergent technical terms. However, in recent years, military technology innovation has exhibited markedly less emergence. The period of low emergence is characterized by reduced contributions by non-traditional vendors, defense prime contractors, and individual inventors to military patents containing emergent terms. These observations suggest that policies attempting to ensure a healthy defense innovation ecosystem should seek organization-type diversity and may benefit from employing promotion strategies targeted at distinct organization types.
Purpose: The present study aims to present the findings of research conducted in Iran and the world regarding the most important approaches and methods proposed to predict the trend of scientific production in the future.
Methodology: The present study was conducted using the scoping review method. For this purpose, related researches were searched in international databases in English and domestic databases in Persian without considering time limit. In this regard, the JBI Institute evaluation tool was used to assess the quality of selected resources. Finally, 117 effects were analyzed.
Findings: A review of studies showed that the most common approach used to identify emerging topics and predict future research trends was the scientometric approach. However, in recent years, due to the limitations of quantitative analysis and scientometric methods to determine the direction of future research, and with the increase in the volume of scientific production and the problems resulting from the analysis of large volumes of data, advances in computer technology and word processing tools. Text mining and machine learning approaches have been used to identify emerging areas and predict future research trends due to their high power in big data analysis along with traditional scientometric methods. The disciplines of basic sciences and engineering have paid more attention to the future study of scientific productions than other fields and have used the articles published in journals more than other sources in order to achieve their goals.
Conclusion: Different models and approaches have been proposed by researchers to follow the evolution of scientific products in the future, but it seems that a combination of quantitative and qualitative approaches is needed to make accurate and reliable predictions and overcome the limitations of each of these methods. Be used simultaneously. In addition, utilizing expert opinions can be considered as a complement to scientometric analysis to predict the future. Having a future research approach in scientific policy-making and research management can play an effective role in charting the prospects of scientific development and provide the possibility of policy-making and planning for the future for researchers and stake-holders in various fields.
Countries, research institutions, and scholars are interested in identifying and promoting high-impact and transformative scientific research. This paper presents a novel set of text- and citation-based metrics that can be used to identify high-impact and transformative works. The 11 metrics can be grouped into seven types: Radical-Generative, Radical-Destructive, Risky, Multidisciplinary, Wide Impact, Growing Impact, and Impact (overall). The metrics are exemplified, validated, and compared using a set of 10,778,696 MEDLINE articles matched to the Science Citation Index ExpandedTM. Articles are grouped into six 5-year periods (spanning 1983–2012) using publication year and into 6,159 fields constructed using comparable MeSH terms, with which each article is tagged. The analysis is conducted at the level of a field-period pair, of which 15,051 have articles and are used in this study. A factor analysis shows that transformativeness and impact are positively related (ρ = .402), but represent distinct phenomena. Looking at the subcomponents of transformativeness, there is no evidence that transformative work is adopted slowly or that the generation of important new concepts coincides with the obsolescence of existing concepts. We also find that the generation of important new concepts and highly cited work is more risky. Finally, supporting the validity of our metrics, we show that work that draws on a wider range of research fields is used more widely.
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.
“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.
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.
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.
Novelty and salience of the research topics are vital for the competitiveness of research institutions and the development of science and technology. In this study, two novel weighting methods were proposed to differentiate the emergence and salience of research topics. A methodology was constructed to measure and visualize the contributions of research institutions to emerging themes and salient ones. The methods were illustrated with the data of ninety Chinese and American Library and Information Science research institutions collected from the Engineering Compendex and China National Knowledge Infrastructure databases between 2001 and 2012. The contributions of the investigated research institutions to the emerging themes and salient ones were calculated and visualized with the Treemap technique. The institutions were further ranked by their contributions and categorized into four types. The findings can help research institutions evaluate novelty and salience of their research topics, discover research fronts and hotspots and promote their research development.
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.
In this paper we develop and use mapping tools to investigate emerging technological fields by studying the dynamics of expectations, agenda building and early networks. In our approach, expectations describe shared beliefs with regard to prospective entities and positions. Agendas are sets of priorities present to guide the actors in their work. The structure that arises as a result of the actions and interactions of actors is the emerging network. For emerging technologies these processes are susceptible to change and the technological paths that may arise are still easy to influence. We propose that not only looking at expectation dynamics, but also including agenda setting and networks dynamics is essential in order to successfully capture the complexities of the emergence of technological paths. A major challenge for this work lies in unveiling the socio-technical dynamics leading to path emergence. For this purpose we investigate the phenomena of irreversibilities that emerge during the ongoing interactions of researchers, institutes, policy makers and firms. With these aspects in mind, we will use a broadened view of expectation dynamics in order to arrive at an improved understanding of the building blocks of path emergence. We illustrate our approach with a case study of Lab-on-a-chip technology for medical and pharmaceutical applications.
There is considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective. Yet as an area of study, emerging technologies lacks key foundational elements, namely a consensus on what classifies a technology as ’emergent’ and strong research designs that operationalize central theoretical concepts. The present paper aims to fill this gap by developing a definition of ’emerging technologies’ and linking this conceptual effort with the development of a framework for the operationalisation of technological emergence. The definition is developed by combining a basic understanding of the term and in particular the concept of ’emergence’ with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. The framework for operationalising emerging technologies is then elaborated on the basis of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indicators and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence.
Unlabelled:
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.
The identification of emerging topics is of current interest to decision makers in both
government and industry. Although many case studies present retrospective analyses of
emerging topics, few studies actually nominate emerging topics for consideration by decision makers. We present a novel approach to identifying emerging topics in science and technology. Two large scale models of the scientific literature, one based on direct citation, and the other based on co-citation, are combined to nominate emerging topics using a difference function that rewards clusters that are new and growing rapidly. The top 25 emergent topics are identified for each year 2007 through 2010. These topics are classified and characterized in various ways in order to understand the motive forces behind their emergence, whether scientific discovery, technological innovation, or exogenous events. Topics are evaluated by searching for recent major awards associated with the topic or its key researchers. The evidence presented suggests that the methodology nominates a viable list of emerging topics suitable for inspection by decision makers.
Bibliometric analysis of publication metadata is an important tool for investigating emerging fields of technology. However, the application of field definitions to define an emerging technology is complicated by ongoing and at times rapid change in the underlying technology itself. There is limited prior work on adapting the bibliometric definitions of emerging technologies as these technologies change over time. The paper addresses this gap. We draw on the example of the modular keyword nanotechnology search strategy developed at Georgia Institute of Technology in 2006. This search approach has seen extensive use in analyzing emerging trends in nanotechnology research and innovation. Yet with the growth of the nanotechnology field, novel materials, particles, technologies, and tools have appeared. We report on the process and results of reviewing and updating this nanotechnology search strategy. By employing structured text-mining software to profile keyword terms, and by soliciting input from domain experts, we identify new nanotechnology-related keywords. We retroactively apply the revised evolutionary lexical query to 20 years of publication data and analyze the results. Our findings indicate that the updated search approach offers an incremental improvement over the original strategy in terms of recall and precision. Additionally, the updated strategy reveals the importance for nanotechnology of several emerging cited-subject categories, particularly in the biomedical sciences, suggesting a further extension of the nanotechnology knowledge domain. The implications of the work for applying bibliometric definitions to emerging technologies are discussed.
The global science, technology and innovation (STI) system is characterized by some researchers as a complex adaptive system (e.g. Heimricks, 2009). Scientific and technological progress entails both evolutionary and revolutionary change, with a high degree of non-linearity and unpredictability. In the terms of complexity science, this progress is emergentit defies a reductionist approach to characterizing the phenomenon. We discuss the properties associated with emergent behavior (Goldstein, 1999; Deguet et al., 2002), and apply them to an integrative framework for describing activities in scientific research and technological development. We further elaborate on this framework to suggest the developments and events in the global STI system which may be hallmarks of technical emergence, defined as the set of properties and phenomena observed in the development of a concept with potential scientific and technological significance. The conceptual framework provides a useful tool for focusing further research on specific dynamics of scientific and technological activity.
The ability to delineate the boundaries of an emerging technology is central to obtaining an understanding of the technology’s
research paths and commercialization prospects. Nowhere is this more relevant than in the case of nanotechnology (hereafter
identified as “nano”) given its current rapid growth and multidisciplinary nature. (Under the rubric of nanotechnology, we
also include nanoscience and nanoengineering.) Past efforts have utilized several strategies, including simple term search
for the prefix nano, complex lexical and citation-based approaches, and bootstrapping techniques. This research introduces
a modularized Boolean approach to defining nanotechnology which has been applied to several research and patenting databases.
We explain our approach to downloading and cleaning data, and report initial results. Comparisons of this approach with other
nanotechnology search formulations are presented. Implications for search strategy development and profiling of the nanotechnology
field are discussed.
This paper provides an overview of emerging information and communication technologies (ICTs) that can be expected to become
socially relevant in the next 10 to 15 years. It describes the results of a dual discourse analysis of publiccations on emerging
ICTs. Sources were, on the one hand, government/policy publications and, on the other, publications by research institutions.
This discourse analysis led to the identification of 11 emerging ICTs. For each of these ICTs, defining features were collected.
In order to gain a larger scale understanding, the defining features were then regrouped to assess which likely effect they
might have on the relationship between humans and their environment. These features are then interpreted and investigated
with regard to what they betray about the implied assumptions about individuals, society and technology. The paper ends by
critically reflecting the chosen approach and asking how this research can help us develop technology in desirable ways.
KeywordsEmerging ICT–methodology–social consequences
Emering generic technologies seen set to make a revahtionary impact
on the economy and society. However, success in developing such technologies
depends uon advances in science. Confrnted with increasing global
economic competition, policy-makers and scientists are grappling
with the problem of how to select themost promising research areas
and emerging technologies on which to target resources and, hence,
derwe the greatest benefits. This bapaer analyzes the experiences
of Japan, the US, the Netherlands, Gennany, Australia, New Zealarul
and the UK in using foresight to help in selecting and exploiting
research that is likely to yeld longer-term economic and severl bendfits.
It puts forward a model of the foresight process for identifing research
areas and technoloies of strategic importance, and also analyzes
why some foresight exercises have proved more successful than other.
It concludes by drawing an analogy between models of innovation and
foresight.
This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science – research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature – an evolving network of scientific publications cited by research front concepts. Kleinberg’s burst detection algorithm is adapted to identify emergent research front concepts. Freeman’s betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are: 1) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, 2) the value of a co-citation cluster is explicitly interpreted in terms of research front concepts and 3) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981-2004) and terrorism (1990-2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.
Dimensions ID: pub.1001131784
Detecting emerging research topics is essential, not only for research agencies but also for individual researchers. Previous studies have created various bibliographic indicators for the identification of emerging research topics. However, as indicated by Rotolo et al. (2015), the most serious problems are the lack of an acknowledged definition of emergence and incomplete elaboration of the linkages between the definitions that are used and the indicators that are created. With these issues in mind, this study first adjusts the definition of an emerging technology that Rotolo et al. (2015) have proposed in order to accommodate the analysis. Next, a set of criteria for the identification of emerging topics is proposed according to the adjusted definition and attributes of emergence. By the use of two sets of parameter values, several emerging research topics are identified. Finally, evaluation tests are conducted by demonstration of the proposed approach and comparison with previous studies. The strength of the present methodology lies in the fact that it is fully transparent, straightforward, and flexible.
Published in 1991, the first edition of Forecasting and Management of Technology was one of the leading handful of books to deal with the topic of forecasting of technology and technology management as this discipline was emerging. The new, revised edition of this book will build on this knowledge in the context of business organizations that now place a greater emphasis on technology to stay on the cutting edge of development. The scope of this edition has broadened to include management of technology content that is relevant to now to executives in organizations while updating and strengthening the technology forecasting and analysis content that the first edition is reputed for. Updated by the original author team, plus new author Scott Cunningham, the book takes into account what the authors see as the innovations to technology management in the last 17 years: the Internet; the greater focus on group decision-making including process management and mechanism design; and desktop software that has transformed the analytical capabilities of technology managers. Included in this book will be 5 case studies from various industries that show how technology management is applied in the real world.
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Decision makers considering policy or strategy related to the development of emerging technologies expect high quality data on the support for different technological options in order to track trends and allocate resources. A natural starting point would be R&D funding statistics. This paper explores the limitations of such aggregated data in relation to the substance and quantification of funding for emerging technologies. Using biotechnology as an illustrative case, we test the utility of a novel taxonomy to demonstrate the endemic weaknesses in the availability and quality of data from public and private sources. Using the same taxonomy, we consider the extent to which tech-mining presents an alternative, or potentially complementary, way to determine support for emerging technologies using proxy measures such as patents and scientific publications. We find that using proxy measures provides additional visibility of technological emergence and suggest these are a useful complement to financial data.
The notion of ‘core documents’, first introduced in the context of co-citation analysis and later re-introduced for bibliographic coupling and extended to hybrid approaches, refers to the representation of the core of a document set according to given criteria. In the present study, core documents are used for the identification of new emerging topics. The proposed method proceeds from independent clustering of disciplines in different time windows. Cross-citations between core documents and clusters in different periods are used to detect new, exceptionally growing clusters or clusters with changing topics. Three paradigmatic types of new, emerging topics are distinguished. Methodology is illustrated using the example of four ISI subject categories selected from the life sciences, applied sciences and the social sciences.
How can national capabilities to develop emerging technologies be measured? We use INSPEC and EI Compendex class codes to examine 33 countries' research and development activity. We select candidate emerging technologies based on the Rand Corporation's categories. We screen these to tally those that show strong recent, and increasing, R&D publication rates. The resulting measures show strong convergence; indeed, their lack of divergence is unsettling. Our measures suggest that China now stands forth as an ‘emerging technology’ research power comparable to Germany, the UK, and France. A number of other nations evidence a striking lack of R&D activity, posing questions about their longer-range high-tech competitiveness. Copyright , Beech Tree Publishing.
As the strategic importance of understanding changes in technology for successful business of most firms increases, the ability to analyze and monitor the current stage and history of technology is reckoned as a critical asset both for gaining competitive advantage and identifying promising niches. Patent citation networks have been widely used for systematic and empirical analysis of technology development. Understanding of technology's detailed changes in large patent citation networks, however, is difficult to achieve because of these networks' large and complex structures. To overcome this problem, we suggest an algorithm that identifies patent development paths from a large patent citation network by evaluating the weight of citations between patents. We then apply this algorithm to flash memory patents in an empirical study. Our algorithm is a new methodology that can be used to analyze the dynamic and complex structure of individual technologies.
We present an approach to efficiently generate effective intelligence on emerging technologies. This approach draws on monitoring and bibliometrics to mine the wealth of information available in major public electronic databases. The approach uses new software to expedite secondary analyses of database searches on topics of interest. We illustrate the range of information profiles possible by examining research and development (R&D) publications and patents pertaining to electronics assembly and, more specifically, to multichip module development.
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.
The concept of emergence is used in a variety of scientific fields and disciplines to denote an even wider variety of phenomena. This has resulted in a plethora of perspectives and treatments. The diversity of phenomena going under the moniker of emergence raises the question of what these phenomena have in common and whether different types of emergence can be distinguished on general grounds. Since emergence is recognised in so many different fields as a relevant concept, it would be useful to have a general conceptual framework that allows a treatment of emergence without explicit reference to the specific underlying mechanism.This article aims to provide a general framework in which emergence can be addressed. For this a new concept, the conjugate, is introduced and the place of the observer relative to the system is given a renewed appreciation. Since the framework abstracts from the underlying interactions, emergence in different types of systems can be compared and discussed on equal terms. Reframing emergence in this way leads to a typology of emergences that is both illuminating and integrativeIn the elaboration the concepts and different types of emergence are discussed and compared with other treatments and typologies of emergence in the literature. Examples from various fields are given to illustrate the theoretical discourse. Furthermore applications of the concepts and typology to social systems, evolution, and co-evolution in ecosystems will be given It will be concluded that many apparently different views and treatments of emergence can be restated and compared within the framework presented in this article.
Classifying journals or publications into research areas is an essential
element of many bibliometric analyses. Classification usually takes place at
the level of journals, where the Web of Science subject categories are the most
popular classification system. However, journal-level classification systems
have two important limitations: They offer only a limited amount of detail, and
they have difficulties with multidisciplinary journals. To avoid these
limitations, we introduce a new methodology for constructing classification
systems at the level of individual publications. In the proposed methodology,
publications are clustered into research areas based on citation relations. The
methodology is able to deal with very large numbers of publications. We present
an application in which a classification system is produced that includes
almost ten million publications. Based on an extensive analysis of this
classification system, we discuss the strengths and the limitations of the
proposed methodology. Important strengths are the transparency and relative
simplicity of the methodology and its fairly modest computing and memory
requirements. The main limitation of the methodology is its exclusive reliance
on direct citation relations between publications. The accuracy of the
methodology can probably be increased by also taking into account other types
of relations, for instance based on bibliographic coupling.
Patents constitute an up-to-date source of competitive intelligence in technological development; thus, patent analysis has
been a vital tool for identifying technological trends. Patent citation analysis is easy to use, but fundamentally has two
main limitations: (1) new patents tend to be less cited than old ones and may miss citations to contemporary patents; (2)
citation-based analysis cannot be used for patents in databases which do not require citations. Naturally, citation-based
analysis tends to underestimate the importance of new patents and may not work in rapidly-evolving industries in which technology
life-cycles are shortening and new inventions are increasingly patented world-wide. As a remedy, this paper proposes a patent
network based on semantic patent analysis using subject-action-object (SAO) structures. SAO structures represent the explicit
relationships among components used in a patent, and are considered to represent key concepts of the patent or the expertise
of the inventor. Based on the internal similarities between patents, the patent network provides the up-to-date status of
a given technology. Furthermore, this paper suggests new indices to identify the technological importance of patents, the
characteristics of patent clusters, and the technological capabilities of competitors. The proposed method is illustrated
using patents related to synthesis of carbon nanotubes. We expect that the proposed procedure and analysis will be incorporated
into technology planning processes to assist experts such as researchers and R&D policy makers in rapidly-evolving industries.
Carbon nanotube field emission display (CNT-FED) represents both emerging application of nanotechnology and revolutionary
invention of display. Therefore, it is an important subject to monitor the states and trends of CNT-FED technology before
the next stage of development. The present paper uses patent bibliometric analysis and patent network analysis to monitor
the technological trends in the field of CNT-FED. These results firstly reveal the different aspects of patenting activities
in the field of CNT-FED. Then, patent network analysis indicates the developing tendency of worldwide FED production based
on the synthesis of CNT materials. Furthermore, key technologies of three clusters can be identified as the depositing CNT
on substrate, coating phosphor on screen and assembling process for whole device. Finally, emitter material is taken for the
key factor in R&D work to improve the efficacy in CNT-FED technology.
This contribution deepens the feasibility issues of building state-of-the-art patent indicators with historical patent documents
available in electronic form from the German Patent Office since the introduction of the Patent Law for the German Empire
in 1877. The paper is divided into two parts: a methodological discussion and a case study on the chemical sector in Germany.
The development of the technology sector defined matches remarkably well with stylised facts that institutional analysis in
the chemical sector have provided us with so far. Moreover, the possibility of varying the level of aggregation in the analysis
of technological areas discloses empirical evidence for the path-dependent development in the chemical sector after the advent
of the organic chemistry and its application in the chemical synthesis of dye stuffs. Our findings enhance institutional and
historical contributions about technological change in the chemical sector and suggest new research questions for innovation
studies.
Contenido: 1. Qué es la minería tecnológica: Innovación tecnológica y la necesidad de la minería tecnológica; Cómo trabaja la minería tecnológica; Qué puede hacer por usted la minería tecnológica; Ejemplo de resultados; Qué buscar con la minería tecnológica; 2. Hacer minería tecnológica: Encontrar las fuentes correctas; Formulación de la pregunta correcta; Obtención de datos; Análisis básico; Análisis avanzado; Análisis de tendencias; Análisis de patentes; Generación y presentación de indicadores de innovación; Administración del proceso de minería tecnológica; Medición de los resultados de la minería tecnológica; Ejemplo del proceso.
Due to uncertainties of several aspects of emerging health technologies, there is a need to anticipate these developments early. A first step would be to gather information and develop future visions about the technology. This paper introduces metaphor analysis as a novel way to do this. Specifically, we study the future of pharmacogenomics by comparing this technology with orphan drugs, which are more established and often act as a model with comparable (economic, research organisation, etc.) characteristics. The analysis consists of describing the dominant metaphors used and structurally exploring (dis)similarities between pharmacogenomics and orphan drugs developments. This comparison leads to lessons that can be learnt for the emerging pharmacogenomics future. We carried out a comprehensive literature review, extracting metaphors in a structured way from different areas of the drug research and development pipeline. The paper argues that (1) there are many similarities between orphan drugs and pharmacogenomics, especially in terms of registration, and social and economic impacts; (2) pharmacogenomics developments are regarded both as a future 'poison' and a 'chance', whereas orphan drugs are seen as a 'gift', and at the same time as a large 'problem'; and (3) metaphor analysis proves to be a tool for creating prospective images of pharmacogenomics and other emerging technologies.
In this chapter we describe several systems that detect emerging trends in textual data. Some of the systems are semi-automatic, requiring user input to begin processing, others are fully-automatic, producing output from the input corpus without guidance. For each Emerging Trend Detection (ETD) system we describe components including linguistic and statistical features, learning algorithms, training and test set generation, visualization and evaluation. We also provide a brief overview of several commercial products with capabilities for detecting trends in textual data, followed by an industrial viewpoint describing the importance of trend detection tools, and an overview of how such tools are used.
Bursty and Hierarchical Structure in Streams
- J Kleinberg
Kleinberg, J., 2002. Bursty and Hierarchical Structure in Streams. Proceedings of the 8th
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (91-101),
Edmonton, Alberta. ACM Press, Canada.
Identifying technological emergence using text mining and machine learning
- S Ranaei
- A Suominen
- A Porter
- S Carley
Ranaei, S., Suominen, A., Porter, A., Carley, S., 2018. Identifying technological emergence using text mining and machine learning. Technol. Forecast. Soc. Chang (in
process).
Newman is the President of Search Technology. Mr. Newman has a Bachelor of Mechanical Engineering and an MS in Technology and Science Policy from Georgia Tech. He is currently pursuing a PhD in Economics from
- C Nils
Nils C. Newman is the President of Search Technology. Mr. Newman has a Bachelor of
Mechanical Engineering and an MS in Technology and Science Policy from Georgia Tech.
He is currently pursuing a PhD in Economics from MERIT at the University of Maastricht
in the Netherlands.
Technological Forecasting & Social Change xxx (xxxx) xxx-xxx
- A L Porter
A.L. Porter et al.
Technological Forecasting & Social Change xxx (xxxx) xxx-xxx
Identifying technological emergence using text mining and machine learning
- Ranaei