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Abstract

Technology positions of firms may determine their competitive advantages and innovation capabilities. While a tangible understanding of technology positions can inform competitive intelligence, they are heterogeneous, intangible and difficult to analyze. We introduce a data-driven network visualization and analysis methodology to assess and compare the technology positions of firms for competitive intelligence analytics based on patent data. This article demonstrates the methodology via comparative analyses of multiple firms for strategic insights on innovation and competition.

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... To facilitate the search and retrieval of patent documents and technical terms from different technology fields, we utilize the cloud- analysis [65] and to analyze the degrees of competition among different firms [66]. ...
... Patent documents may provide rich design details for systems, products, and processes but requires more time to read and efforts for comprehension that may cause fixation [13,68]. A map of technology fields may avoid ideation fixation by broadening the search and provide rapid inspiration for design directions, but the resulting ideas are macro-level and not specific enough for implementation [66,69]. Our system synthesizes the provisions of rapid design stimulation from concept terms, nuanced and systematic design stimulation from patent documents, and macro-level inspiration for design directions from the field nodes in the total technology space map during the computer-aided ideation process. ...
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Data-driven conceptual design methods and tools aim to inspire human ideation for new design concepts by providing external inspirational stimuli. In prior studies, the stimuli have been limited in terms of coverage, granularity, and retrieval guidance. Here, we present a knowledge-based expert system that provides design stimuli across the semantic, document and field levels simultaneously from all fields of engineering and technology and that follows creativity theories to guide the retrieval and use of stimuli according to the knowledge distance. The system is centered on the use of a network of all technology fields in the patent classification system, to store and organize the world's cumulative data on the technological knowledge, concepts and solutions in the total patent database according to statistically-estimated knowledge distance between technology fields. In turn, knowledge distance guides the network-based exploration and retrieval of inspirational stimuli for inferences across near and far fields to generate new design ideas by analogy and combination. With two case studies, we showcase the effectiveness of using the system to explore and retrieve multilevel inspirational stimuli and generate new design ideas for both problem solving and open-ended innovation. These case studies also demonstrate the computer-aided ideation process, which is data-driven, computationally augmented, theoretically grounded, visually inspiring, and rapid.
... While Google Maps is used to position buildings (geographical objects), exploring neighborhoods, and determining directions to far locations in the physical space, InnoGPS allows for positioning technologies (and related companies and persons), exploring neighborhoods and determining directions to far locations in the technology space. An early version of InnoGPS was used to position companies on a map according to their patent records for diversification analysis [66] and to analyze the degrees of competition among different firms [67]. ...
... Patent documents may provide rich design details for systems, products, and processes but requires more time to read and efforts for comprehension that may cause fixation [13,69]. A map of technology fields may avoid ideation fixation by broadening the search and provide rapid inspiration for design directions, but the resulting ideas are macro-level and not specific enough for implementation [67,70]. Our system synthesizes the provisions of rapid design stimulation from concept terms, nuanced and systematic design stimulation from patent documents, and macro-level inspiration for design directions from the field nodes in the total technology space map during the computer-aided ideation process. ...
Article
Data-driven conceptual design methods and tools aim to inspire human ideation for new design concepts by providing external inspirational stimuli. In prior studies, the stimuli have been limited in terms of coverage, granularity, and retrieval guidance. Here, we present a knowledge-based expert system that provides design stimuli across the semantic, document and field levels simultaneously from all fields of engineering and technology and that follows creativity theories to guide the retrieval and use of stimuli according to the knowledge distance. The system is centered on the use of a network of all technology fields in the patent classification system, to store and organize the world’s cumulative data on the technological knowledge, concepts and solutions in the total patent database according to statistically-estimated knowledge distance between technology fields. In turn, knowledge distance guides the network-based exploration and retrieval of inspirational stimuli for inferences across near and far fields to generate new design ideas by analogy and combination. With two case studies, we showcase the effectiveness of using the system to explore and retrieve multilevel inspirational stimuli and generate new design ideas for both problem solving and open-ended innovation. These case studies also demonstrate the computer-aided ideation process, which is data-driven, computationally augmented, theoretically grounded, visually inspiring, and rapid.
... Literature suggests that assignees and inventors often diversify their portfolios by exploring technological domains that are less distant from the home domain (Alstott, Triulzi, Yan, & Luo, 2017;Sarica, Yan, & Luo, 2020). Based on this premise, for an assignee or an inventor, the knowledge proximity from the home domain could represent the likelihood of entering a target domain. ...
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Knowledge proximity refers to the strength of association between any two entities in a structural form that embodies certain aspects of a knowledge base. In this work, we operationalize knowledge proximity within the context of the US Patent Database (knowledge base) using a knowledge graph (structural form) named PatNet built using patent metadata, including citations, inventors, assignees, and domain classifications. Using several graph embedding models (e.g., TransE, RESCAL), we obtain the embeddings of entities and relations that constitute PatNet. The cosine similarity between the corresponding (or transformed) embeddings entities denotes the knowledge proximity between these. We evaluate the plausibility of these embeddings across different models in predicting target entities. We also evaluate the meaningfulness of knowledge proximity to explain the domain expansion profiles of inventors and assignees. We then apply the embeddings of the best-preferred model to associate homogeneous (e.g., patent-patent) and heterogeneous (e.g., inventor-assignee) pairs of entities.
... Song and Luo [24] and Song et al. [23] extracted functional words/phrases from patent data to build function networks for data-driven design. Technology space maps [38][39][40][41] were created by analyzing patent data (InnoGPS 2 ) and were then used to explore design opportunities and directions. Wand and Chen [42] analyzed the relations between patents Table 1 Network-based data-driven design approaches mining design information to support design ideation. ...
Article
The rapid growth of data and the requirement of designers to track massive data to obtain design stimuli have posed challenges to conceptual design, thereby promoting the development of data-driven design. Concept networks precisely capture design information from a large volume of unstructured and heterogeneous textual data and saliently decrease time and labor cost for designers to read texts, which creates new opportunities for developing a smart product design system. To advance data-driven design, this study proposes the novel function-structure concept network (FSCN) construction method, which combines sentence parsing and word/phrase extraction to integrate functional and structural information. Furthermore, a network analysis method is proposed to explore design information associations that contain both explicit and implicit associations together and thereby recommend them simultaneously to designers as inspirational stimuli to support design ideation. This approach can enhance designers' capabilities to build associations between design information, conceive new design ideas during conceptual design, and increase creativity for solving design problems. The proposed FSCN construction and analysis method can be used as an auxiliary tool to visualize associations among design information so as to inspire idea generation in the early stage of conceptual design. An illustrative example was used to validate the practicability of the proposed methodology. The code of the proposed method is available at https://github.com/KWflyer/FSCN.
... Network metrics have provided a medium to derive useful design-related insights from the structure of the graphs, and various layout methods have provided ways of representing the design-related data in an easily comprehensible way (Lim et al., 2016;. For example, network visualizations have been utilized to represent the whole technology space to support innovation and competitive intelligence (Luo et al., , 2018Sarica, Yan, et al., 2020), show the relations between components and subsystems to evalute designs (He and Luo, 2017;Pasqual and De Weck, 2012;Sosa et al., 2007) and inform design decisions (Kim and Kim, 2012;Sosa et al., 2007), discover the patterns of design activities (Alstott et al., 2017;Cash et al., 2014;Cash and Štorga, 2015), reveal the structure of design document repositories to guide retrievals (Fu et al., 2013;Luo et al., 2021), and represent mind maps (Camburn, Arlitt, et al., 2020;Camburn, He, et al., 2020) and concept networks (Chen et al., 2019;Chen and Krishnamurthy, 2020;Liu et al., 2020;Sarica et al., 2019Sarica et al., , 2021Shi et al., 2017;Song, Evans, et al., 2020;Souili et al., 2015) for design ideation uses. On the other hand, a few studies explored other visualization methods such as word-clouds (He, Camburn, Liu, et al., 2019; based on design description texts. ...
Conference Paper
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Engineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of the technical design descriptions and the unfamiliarity to the domain make it hard for engineers to comprehend the function, behavior, and structure of a design. To help engineers quickly understand a complex technical design description new to them, one approach is to represent it as a network graph of the design-related entities and their relations as an abstract summary of the design. While graph or network visualizations are widely adopted in the engineering design literature, the challenge remains in retrieving the design entities and deriving their relations. In this paper, we propose a network mapping method that is powered by Technology Semantic Network (TechNet). Through a case study, we showcase how TechNet’s unique characteristic of being trained on a large technology-related data source advantages itself over common-sense knowledge bases, such as WordNet and ConceptNet, for design knowledge representation.
... Abad-Segura et al. (2020) applied multiple co-occurrence analysis to analyze digital education trends. Sarica et al. (2020) used multiple coexistence analysis to patent-mine a firm's technology development in order to analyze competing firms' superior technologies. Multiple co-presentation analysis can more comprehensively, systematically, and deeply excavate the interrelationships between data, and its application to the field of research technology layout is more conducive to discovering multiple, intersecting, and potential relationships (Leydesdorff 2010). ...
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Technology prediction is an important technique to help new energy vehicle (NEV) firms keep market advantage and sustainable development. Under fierce competition in the new energy industry, there is an urgent necessity for innovative technology prediction method to effectively identify core and frontier technologies for NEV firms. Among the various methods of technology prediction, one of the most frequently used methods is to make technology prediction from patent data. This paper synthesizes the frequent pattern growth (FP-growth) algorithm and input-output analysis to construct a new technology prediction method based on the knowledge flow perspective, takes the data of NEV patent family in 1989–2018 the Derwent patent database as a sample, divides the data according to the 5-year standard, and uses the method to identify the core and frontier technologies in the NEV field during different periods. Furthermore, the multiple co-occurrence method applies to analyze the technology layout and evolution patterns in China’s NEV field. The results show that the technology prediction method proposed in this paper can effectively identify core and frontier technologies to achieve NEV technology prediction.
... Network metrics have provided a medium to derive useful design-related insights from the structure of the graphs, and various layout methods have provided ways of representing the design-related data in an easily comprehensible way [22,23]. For example, network visualizations have been utilized to represent the whole technology space to support innovation and competitive intelligence [24,25,26], show the relations between components and subsystems to evalute designs [27,28,29] and inform design decisions [22,29,30], discover the patterns of design activities [31,32], reveal the structure of design document repositories to guide retrievals [4], and represent mind maps [33,34] and concept networks [21,35,36,37,38,39] for design ideation uses. On the other hand, a few studies explored other visualization methods such as word-clouds [40,41] based on design description texts. ...
Preprint
Full-text available
Engineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of the technical design descriptions and the unfamiliarity to the domain make it hard for engineers to comprehend the function, behavior, and structure of a design. To help engineers quickly understand a complex technical design description new to them, one approach is to represent it as a network graph of the design-related entities and their relations as an abstract summary of the design. While graph or network visualizations are widely adopted in the engineering design literature, the challenge remains in retrieving the design entities and deriving their relations. In this paper, we propose a network mapping method that is powered by Technology Semantic Network (TechNet). Through a case study, we showcase how TechNet's unique characteristic of being trained on a large technology-related data source advantages itself over common-sense knowledge bases, such as WordNet and ConceptNet, for design knowledge representation.
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The present study aims to better understand how and to what extent the different dimensions of Big Data can offer insights on technology evolution. By using a patent analytics perspective, in this paper, we introduce a novel approach based on co-words analysis using the abstracts of 170,279 European patents in the Internet of Things (IoT) field published from 2011 to 2019. In so doing, we map and visualize an industry’s technology structure, development, and trends, as well as disentangle the IoT technology conceptual structure, highlighting its core and boundary concepts. This is the first study that applies a decomposition framework to clarify the determinants of IoT inventions, showing relevant changes in the focus of IoT technology overtime. By shedding light on the evolutionary dynamics of the field, this research offers a valuable contribution to the technology innovation literature.
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This study examines the topological characteristics of interfirm collaboration networks (CNs) in the global electronics industry. Our results show that high-performing firms exhibit significant relational CN power, manage CNs that follow a power-law shape degree distribution, are predominantly horizontally integrated with low geographic complexity, and maintain a balanced exploration-exploitation collaboration relationship portfolio. We complement our topological analysis with graphical visualizations of each of these CNs over three timeframes (2004-06; 2007-09; 2010-12). Theoretically, we demonstrate the association of topological CN characteristics with high-performance of firms. Methodologically, our study defines and implements a data-driven analyses and visualization of CNs in high clockspeed industries. Our study makes important managerial contributions to the systemic design, engineering, and management of CNs.
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Since the strategies of firms for protecting their innovations could vary between patents and trade secrets according to the characteristics of industries, patent analysis might not always be appropriate for forecasting technological trends in industries. This paper aims to identify relevant industries where patent information can be effectively utilized to scrutinize the trends and effects of technological activities. To this end, first, sectoral differences in patenting activities are explored by analysing the community innovation survey (CIS) data. Second, the applicability of patent trend analysis for technological forecasting is examined in each industry through the S-curve fitting process with patent data. Finally, correlation analysis between R&D data (R&D investment and loyalty income) and patent application data is performed to demonstrate the explanatory power of patent information in R&D management, by investigating the relationship between the inputs and outputs of a R&D system. The results of this paper will help support a strategic process for using patent analysis to envisage future trends and comprehend diverse characteristics of a technology.
Article
Rigby D. L. Technological relatedness and knowledge space: entry and exit of US cities from patent classes, Regional Studies. US patent and citation data are used to measure technological relatedness between major patent classes in the United States Patent and Trademark Office (USPTO). The technological relatedness measures, constructed as the probability that a patent in class j will cite a patent in class i, form the links of a knowledge network. Changes in this knowledge network are examined from 1975 to 2005. Evolution of the patent knowledge base within US metropolitan areas is tracked by combining the knowledge network with annual patent data for each city. Entries and exits of cities from patent classes are linked to local and non-local measures of technological relatedness.
Article
Empirical technology analyses need not take months; they can be done in minutes. One can thereby take advantage of wide availability of rich science and technology publication and patent abstract databases to better inform technology management. To do so requires developing templates of innovation indicators to answer standard questions. Then, one can automate routines to generate composite information representations (“one-pagers”) that address the issues at hand, the way that the target users want.
Article
We use patent data from the worldwide semiconductor industry from 1984 to 1994 to study the effect of the structure of organizational knowledge bases, or the patterns of coupling between their elements of technical knowledge, on the usefulness of inventions and knowledge-base malleability. We argue that organizational variations in coupling patterns between knowledge elements can be reflected in a spectrum of knowledge-base structures—varying from fully decomposable (the knowledge base is composed of distinct clusters of knowledge elements coupled together with no significant ties between clusters) through nearly decomposable (knowledge clusters are discernable but are connected through cross-cluster couplings) to non-decomposable (no knowledge clusters emerge, as the couplings are pervasively distributed)—and that organizations may differ in the way they use their knowledge because of variations in their knowledge-base structure, rather than because of differences in the knowledge elements themselves. Results show that a nearly decomposable knowledge base increases the usefulness of the inventions generated from it, as measured by patent citations, and also the knowledge base's malleability or capacity for change.
Article
This paper presents a new global patent map that represents all technological categories, and a method to locate patent data of individual organizations and technological fields on the global map. This overlay map technique can support competitive intelligence and policy decision-making. The global patent map is based on similarities in citing-to-cited relationships between categories of the International Patent Classification (IPC) of European Patent Office (EPO) patents from 2000 to 2006. This patent dataset, extracted from the PatStat database, includes 760,000 patent records in more than 400 IPC categories. The paper overlays nanotechnology-related patenting activities of two companies and two different nanotechnology subfields on the global patent map. The exercise shows the potential of patent overlay maps to visualize technological areas and potentially support decision-making. Furthermore, this study shows that IPC categories that are similar to one another based on citing-to-cited patterns (and thus are close in the global patent map) are not necessarily in the same hierarchical IPC branch, thus revealing new relationships between technologies that are classified as pertaining to different (and sometimes distant) subject areas in the IPC scheme.
Article
Examines the correlation between the exploration of new possibilities and the exploitation of old certainties in organizational learning. Also discusses the difficulty in balancing resource management between gaining new information about alternatives to improve future returns (i.e., exploration) and using information currently available to improve present returns (i.e., exploitation). Two models which evaluate the formation and use of knowledge in organizations are developed. The first is a model of mutual learning in a closed system having fixed organizational membership and stability. The second is a model which considers the ways in which competitive advantage is affected by knowledge accumulation. The analysis indicates that the choice to rapidly develop exploitation over exploration might be effective in the short term, but is potentially detrimental to the firm in the long term. (SFL)
Article
Schumpeter first reviews the basic economic concepts that describe the recurring economic processes of a commercially organized state in which private property, division of labor, and free competition prevail. These constitute what Schumpeter calls "the circular flow of economic life," such as consumption, factors and means of production, labor, value, prices, cost, exchange, money as a circulating medium, and exchange value of money. The principal focus of the book is advancing the idea that change (economic development) is the key to explaining the features of a modern economy. Schumpeter emphasizes that his work deals with economic dynamics or economic development, not with theories of equilibrium or "circular flow" of a static economy, which have formed the basis of traditional economics. Interest, profit, productive interest, and business fluctuations, capital, credit, and entrepreneurs can better be explained by reference to processes of development. A static economy would know no productive interest, which has its source in the profits that arise from the process of development (successful execution of new combinations). The principal changes in a dynamic economy are due to technical innovations in the production process. Schumpeter elaborates on the role of credit in economic development; credit expansion affects the distribution of income and capital formation. Bank credit detaches productive resources from their place in circular flow to new productive combinations and innovations. Capitalism inherently depends upon economic progress, development, innovation, and expansive activity, which would be suppressed by inflexible monetary policy. The essence of development consists in the introduction of innovations into the system of production. This period of incorporation or adsorption is a period of readjustment, which is the essence of depression. Both profits of booms and losses from depression are part of the process of development. There is a distinction between the processes of creating a new productive apparatus and the process of merely operating it once it is created. Development is effected by the entrepreneur, who guides the diversion of the factors of production into new combinations for better use; by recasting the productive process, including the introduction of new machinery, and producing products at less expense, the entrepreneur creates a surplus, which he claims as profit. The entrepreneur requires capital, which is found in the money market, and for which the entrepreneur pays interest. The entrepreneur creates a model for others to follow, and the appearance of numerous new entrepreneurs causes depressions as the system struggles to achieve a new equilibrium. The entrepreneurial profit then vanishes in the vortex of competition; the stage is set for new combinations. Risk is not part of the entrepreneurial function; risk falls on the provider of capital. (TNM)
Article
A large body of work argues that scientific research increases the rate of technological advance, and with it economic growth. The precise mechanism through which science accelerates the rate of invention, however, remains an open question. Conceptualizing invention as a combinatorial search process, this paper argues that science alters inventors' search processes, by leading them more directly to useful combinations, eliminating fruitless paths of research, and motivating them to continue even in the face of negative feedback. These mechanisms prove most useful when inventors attempt to combine highly coupled components; therefore, the value of scientific research to invention varies systematically across applications. Empirical analyses of patent data support this thesis. Copyright © 2004 John Wiley & Sons, Ltd.
Article
While the course of technological change is widely accepted to be highly uncertain and unpredictable, little work has identified or studied the ultimate sources and causes of that uncertainty. This paper proposes that purely technological uncertainty derives from inventors' search processes with unfamiliar components and component combinations. Experimentation with new components and new combinations leads to less useful inventions on average, but it also implies an increase in the variability that can result in both failure and breakthrough. Negative binomial count and dispersion models with patent citation data demonstrate that new combinations are indeed more variable. In contrast to predictions, however, the reuse of components has a nonmonotonic and eventually positive effect on variability.
Article
We use bibliometric (in particular patent-based) methods and techniques to develop a cartography of technology. Two types of maps are presented: co-word maps and co-classification maps. Both types of maps have been constructed for the entire domain of technology (the macro-level), i.e. the ensemble of all fields of technology in their mutual relations. Time series clearly illustrates the changing relations between the major clusters of technology, and in particular the changing role of fields which act as a “bridge” between clusters, or as a (declining or emerging) centre of technological activities within a specific cluster. Maps visualize relations between fields of technology. In order to have measures of the relative strength of these relations, we develop the concept of affinity between fields. A special feature of our macro-maps concerns the role of Japan in technology.
Article
Studies of technological change constitute a field of growing importance and sophistication. In this paper we contribute to the discussion with a methodological reflection and application of multi-stage patent citation analysis for the measurement of inventive progress. Investigating specific patterns of patent citation data, we conclude that single-stage citation analysis cannot reveal technological paths or lineages. Therefore, one should also make use of indirect citations and bibliographical coupling. To measure aspects of cumulative inventive progress, we develop a “shared specialization measure” of patent families. We relate this measure to an expert rating of the technological value added in the field of variable valve actuation for internal combustion engines. In sum, the study presents promising evidence for multi-stage patent citation analysis in order to explain aspects of technological change.
Article
Obtaining sufficient competitive intelligence is a critical factor in helping business managers gain and maintain competitive advantages. Patent data is an important source of competitive intelligence that enterprises can use to gain a strategic advantage. Under existing approaches, to detect changes in patent trends, business managers must rely on patent analysts to compare two patent analysis charts of different time periods. The discovery of change of trends currently still needs laborious human efforts and no efficient computer-based approaches are available for helping this task. In this paper, we propose a patent trend change mining (PTCM) approach that can identify changes in patent trends without the need for specialist knowledge. The proposed approach consists of steps including patent collection, patent indicator calculation, and change detection. In change detection phase the approach firstly excavate rules between two different time periods, comparing them to determine the trend changes. These trend changes are then classified into four categories of change, evaluated with change degree and ranked by their change degree as the output information to be referred by decision makers. We apply the PTCM approach to Taiwan’s semiconductor industry to discover changes in four types of patent trends: the R&D activities of a company, the R&D activities of the industry, company activities in the industry and industry activities generally. The proposed approach generates competitive intelligence to help managers develop appropriate business strategies.
Article
Competitive Intelligence (CI) aims to monitor a firm's external environment for information relevant to its decision-making process. As an excellent information source, the Internet provides significant opportunities for CI professionals as well as the problem of information overload. Internet search engines have been widely used to facilitate information search on the Internet. However, many problems hinder their effective use in CI research. In this paper, we introduce the Competitive Intelligence Spider, or CI Spider, designed to address some of the problems associated with using Internet search engines in the context of competitive intelligence. CI Spider performs real-time collection of Web pages from sites specified by the user and applies indexing and categorization analysis on the documents collected, thus providing the user with an up-to-date, comprehensive view of the Web sites of user interest. In this paper, we report on the design of the CI Spider system and on a user study of CI Spider, which compares CI Spider with two other alternative focused information gathering methods: Lycos search constrained by Internet domain, and manual within-site browsing and searching. Our study indicates that CI Spider has better precision and recall rate than Lycos. CI Spider also outperforms both Lycos and within-site browsing and searching with respect to ease of use. We conclude that there exists strong evidence in support of the potentially significant value of applying the CI Spider approach in CI applications.
Article
Patent data have been widely used in research to characterize firms’ locations in technological or knowledge space, as well as the proximities among firms. Researchers have measured firms’ technological or knowledge proximities with a variety of measures based on patent data, including Euclidean distances (using the technological classifications listed on patents), and overlap in cited patents. Often research has employed only the first listed patent classification in measures of proximities. We explore the effects of using the first listed patent class as well as other methods to measure proximities. We point out that measures of proximity based on small numbers of patents are imprecisely measured random variables. Measures computed on samples with few patents or a single patent class generate both biased and imprecise measures of proximity. We discuss the implications of this for typical research questions employing measures of proximity, and explore the effects of larger sample sizes and coarser patent class breakdowns in mitigating these problems. Where possible, we suggest that researchers increase their sample sizes by aggregating years or using all of the listed patent classes on a patent, rather than just the first.
Article
The procedures and the nature of “technologies” are suggested to be broadly similar to those which characterize “science”. In particular, there appear to be “technological paradigms” (or research programmes) performing a similar role to “scientific paradigms” (or research programmes). The model tries to account for both continuous changes and discontinuities in technological innovation. Continuous changes are often related to progress along a technological trajectory defined by a technological paradigm, while discontinuities are associated with the emergence of a new paradigm. One-directional explanations of the innovative process, and in particular those assuming “the market” as the prime mover, are inadequate to explain the emergence of new technological paradigms. The origin of the latter stems from the interplay between scientific advances, economic factors, institutional variables, and unsolved difficulties on established technological paths. The model tries to establish a sufficiently general framework which accounts for all these factors and to define the process of selection of new technological paradigms among a greater set of notionally possible ones.The history of a technology is contextual to the history of the industrial structures associated with that technology. The emergence of a new paradigm is often related to new “schumpeterian” companies, while its establishment often shows also a process of oligopolistic stabilization.
Article
Based on the assumption that intensity and structure are the most important dimensions of a firm's technological network, the authors identity seven different types of technology-oriented network configurations. Drawing upon a database of 321 high-tech companies, they show that innovation success is significantly correlated with a firm's technological network. Product and process innovations are shown to demand different types of network configurations.
Article
Intensified technology convergence, increasing relatedness between technological fields, is a mega-trend in 21st century science and technology. However, scientometrics has been unsuccessful in identifying this techno-economic paradigm change. To address the limitations and validity problems of conventional measures of technology convergence, we introduce a multi-dimensional contingency table representation of technological field co-occurrence and a relatedness measure based on the Mantel–Haenszel common log odds ratio. We used Korean patent data to compare previous and proposed methods. Results show that the proposed method can increase understanding of the techno-economic paradigm change because it reveals significant changes in technological relatedness over time.
Article
Competitive Intelligence is one of the key factors for enterprise risk management and decision support. However, the functions of Competitive Intelligence are often greatly restricted by the lack of sufficient information sources about the competitors. With the emergence of Web 2.0, the large numbers of customer-generated product reviews often contain information about competitors and have become a new source of mining Competitive Intelligence. In this study, we proposed a novel graphical model to extract and visualize comparative relations between products from customer reviews, with the interdependencies among relations taken into consideration, to help enterprises discover potential risks and further design new products and marketing strategies. Our experiments on a corpus of Amazon customer reviews show that our proposed method can extract comparative relations more accurately than the benchmark methods. Furthermore, this study opens a door to analyzing the rich consumer-generated data for enterprise risk management.