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A measure of staying power: Is the persistence of emergent concepts more significantly influenced by technical domain or scale?

DOI:10.1007/s11192-017-2342-x
Authors:
Stephen Carley at Search Technology
Stephen Carley
  • Search Technology
Nils C. Newman at Maastricht University
Nils C. Newman
Alan L. Porter at Search Tecnology Inc.
Alan L. Porter
  • Search Tecnology Inc.
Jon G. Garner
Jon G. Garner
Jon G. Garner
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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Abstract

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.
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Carley, S. F., Newman, N.C., Porter, A.L., and Garner, J. (2017), A Measure of Staying Power: Is the
Persistence of Emergent Concepts More Significantly Influenced by Technical Domain or Scale?,
Scientometrics. http://rdcu.be/qfTB
10.1007/s11192-017-2342-x
... Xu et al. 2021) to more complex methods such as machine learning methods (Choi et al. 2021;S. Xu et al. 2021;Zhou et al. 2021) and hybrid methods (Ávila-Robinson and Miyazaki 2013;Carley et al. 2017;Q. Wang 2018). ...
... Q. Wang (2018) took novelty, relatively fast growth, coherence, and scientific impact as attributes of emergent research topics. Carley et al. (2017) also used novelty, growth, persistence, and community as attributes of emergence in their method. However, there is still room for improvement and adding new attributes of emergence. ...
... J. Garner et al. (2017) used a set of indicators to evaluate the emergence of the terms in terms of novelty, growth, community, and persistence, which was a combination of lexical and indicator methods; their method was called Emergence Score (EScore). Carley et al. (2017) used the same EScore method but evaluated the effects of scale and domain on the persistence of an emerging topic. 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. ...
Detecting Emerging Technologies in Artificial Intelligence Scientific Ecosystem Using an Indicator-based Model
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  • Oct 2022
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.
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... The Gartner Hype Cycle (Linden and Fenn 2003) and Schmoch (2007)'s double boom concept suggest that technologies are perceived as novel and grow, but then decline, and some re-emerge in a second wave based on a different set of factors including market acceptance (Raffaelli 2019) or technology breakthroughs (Schmoch 2007). Porter and Carley likewise found that emergent technologies do not follow a consistently upward path but have multiple cycles of decline and re-emergence over a 10-year period (Carley et al. (2017)). Novelty is unsatisfactory by itself because coming up with something new per se is necessary but not sufficient for ongoing impact. ...
... This section has shown that there is a conceptual discussion of what technology emergence means. There have been measures put forth to identify emerging technologies, including the Technology Emergence Indicator (Carley et al. 2017, Carley et al. 2018, Kwon et al. 2019, Liu and Porter 2020, Ranaei et al. 2020). This indicator method incorporates the characteristics of emerging technologies emphasized in these papers, such as novelty, growth, and persistence. ...
... This study used and extended the TEI algorithm (Carley et al. 2017) to understand the emergence of autonomous vehicle technologies over the three decades from 1991 to 2018. Our results showed that the emerging terms in the initial 10-year period included associated technologies designed for understanding the surrounding environment and path planning. ...
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Identifying emerging technologies has been of long-standing interest to many scholars and practitioners. Previous studies have introduced methods to capture the concept of emergence from bibliographic records, including the recently proposed Technology Emergence Indicator (Carley et al. 2018). This indicator method has shown to be applicable to various technological fields. However, the indicator uses a limited time window, which can overlook the potential long-term evolution of emerging technologies. Moreover, the existing method suffers from interpretability, because it can be difficult to understand the context in which identified emerging terms are used. In this paper, we propose an improved version of the Technology Emergence Indicator that addresses these issues. In doing so, we examine emerging topics within the field of autonomous vehicles technologies during the period of 1991-2018, guided by a proposition about the long-term diffusion of an emerging technology topic. The results show that different autonomous vehicle technology topics emerge during each of the three 10-year periods under analysis, including an initial period of understanding the surrounding environment and path planning, a second period marked by DARPA Grand Challenge motivated factors associated with the urban environment and communication technologies, and a third period relating to machine learning and object detection. This association with certain emerging technology topics in each decade is also characterized by different trajectories of continued or cyclical carryover across the decades. The results suggest a methodology that practitioners can use in examining research areas to understand which topics are likely to persist into the future.
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... How exactly one formulates the data search (Huang et al., 2015;Arora et al., 2013; can alter a tech domain's content significantly. Indicator sensitivity to shifts in content due to search variations, and over time (Carley et al., 2017), is important. For the contest we "look" in a research publication database (WoS), but recognize that signals of emergence might well be detected earlier in less structured sources on the internet (Carbonell et al., 2018)Participants had 10 days in April 2019, from provision of the test dataset to them until results were due to us. ...
... Our (Search Technology & STIP) approach to identifying emerging research topics within a domain is represented in Refs. (Carley et al., 2018;Porter et al., 2018a;Kwon et al., 2019;Carley et al., 2017), and (Shapira et al., 2017). It focuses solely on text analyses of topical fields of abstract record datasets. ...
Measuring tech emergence: A contest
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We conducted a contest to predict highly active research topics. Participants analyzed ten years of Web of Science abstract records in a target technological domain (synthetic biology) so as to indicate cutting edge sub-topics likely to be actively pursued in the following two years. We describe contest procedures and results provided by thirteen participating teams. Contestants used various topical and other fields in the abstract records; some augmented with external data. They applied at least 19 diverse methods in deriving emerging topics predicted to be actively researched in the coming two years. Besides topical text analyses, contestants variously brought to bear both backward and forward citation analyses, and network analyses, to help identify topics apt to be highly researched in the near future. This communal exercise on forecasting near-future research activity using a wide array of text analytic and other bibliometric tools provides a stimulating resource.
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... Three of the articles describe how to measure this feature. Carley et al. (2017;2018) argue that research which emerges again and again across multiple time periods outperforms research that only emerges once or twice. Thus, the enduring nature of these technologies lends more possibility to creating innovation. ...
... Thus, the enduring nature of these technologies lends more possibility to creating innovation. This indicator can be measured by imposing a minimum number of (consecutive) analysis years in which a term appears (Carley et al. 2017). We opted for 3 years. ...
A hybrid approach to detecting technological recombination based on text mining and patent network analysis
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  • Sep 2019
Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur’s theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn’s theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a field—its communities, its technologies, and their interactions—is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology holders and opportunities for future innovation through technological recombinations.
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... The techniques we employ to identify ETs have been used and validated in a number of previous studies (e.g. Carley et al., 2017;Garner et al., 2017;Carley et al., 2018) and our emergence indicator (catalogued below) is most compatible with the datasets used in our study, providing results in quantifiable format. ...
... A more thorough treatment of how we calculate emergent terms is provided by Carley et al. (2017;. ...
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Technological Convergence (TC) reflects developmental processes that overlap different technological fields. It holds promise to yield outcomes that exceed the sum of its subparts. Measuring emergence for a TC environment can inform innovation management. This paper suggests a novel approach to identify Emergent Topics (ETopics) of the TC environment within a target technology domain using patent information. A non-TC environment is constructed as a comparison group. First, TC is operationalized as a co-classification of a given patent into multiple 4-digit IPC codes (≥2-IPC). We take a set of patents and parse those into three sub-datasets based on the number of IPC codes assigned 1-IPC (Non-TC), 2-IPC and ≥3-IPC. Second, a method is applied to identify emergent terms (ETs) and calculate emergence score for each term in each sub-dataset. Finally, we cluster those ETs using Principal Components Analysis (PCA) to generate a factor map with ETopics. A convergent domain – 3D printing – is selected to present the illustrative results. Results affirm that for 3D printing, emergent topics in TC patents are distinctly different from those in non-TC patents. The number of ETs in the TC environment is increasing annually.
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... There are many different methods to detect emergence in any given field. One of the main methods is the slope of growth for the terms that have passed the criteria of growth, radical novelty, community, and persistence [32]. Another method uses the score of emergence based on the attributes of growth, novelty, scientific impact, and coherence [33]. ...
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... We select these to provide diversity in research fields, dataset size, and developmental stage. Carley et al. (2017)found that persistence and predictive utility of emergent topics could be influenced by both field (technical domain) and dataset size (scale). We believe that the present results offer reasonable generalizability based on the dataset diversities. ...
A 3-dimensional Analysis for Evaluating Technology Emergence Indicators
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Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study.
View
... With this assertion, it can be interpreted that continuity may be accepted as an important component while tracking emergence. Carley et al. (2017) analyzed continuity with persistence and illustrated that research with greater staying power was deserving of special attention, by using different datasets. ...
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