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Identifying R&D partners through Subject-Action-Object semantic analysis in a problem & solution pattern
Abstract
Today’s companies still rely heavily on expert knowledge rather than quantitative data with a systematic approach to effectively identify and choose Research and Development (R&D) partners. It is advantageous to identify and select potential R&D partners using a Problem & Solution (P&S) pattern. This paper presents a novel process for identifying R&D partners on the basis of solution similarities that assist technology managers in understanding the relationships between research targets. First, we choose a thematic dataset that contains problems and quantitative data with relative topic terms. Then, we extract Subject-Action-Object semantic structures in a P&S pattern from the dataset, and identify various solutions to a technical problem, with each as a subject. In addition, we provide correlation mapping to visualise the text characters and identify R&D partners. Finally, we validate the proposed method through a case study of the dye-sensitized solar cells sector.
... For example, if an organisation's motivation is based on sharing the costs of R&D activities or to strengthen its existing technological capacities, the organisation with a similar research background is the more suitable choice. While, if the organisation wants to learn new technologies or fill a technological vacuum, the organisations with complementary technologies are more suitable (Wang et al., 2017). Creating a generally applicable partner selection method that meets the needs of all organisations is difficult. ...
... Many methods have been proposed to identify partner candidates, such as technological complementarity in products consisting of multidisciplinary technologies (Wang, 2012), designing 14 indices to guide strategic partner selection (Geum et al., 2013), using morphology analysis and a generative topology map (Yoon and Song, 2014), using bibliographic coupling analysis and latent semantic analysis (Park et al., 2015) and solution similarities (Wang et al., 2017). These systematic processes for potential R&D partner identification have made some remarkable advancement compared to the conventional word of mouth-based methods. ...
... Yoon and Song (2014) constructed a systematic process to explore proper partners by using morphology analysis and a generative topology map. Park et al. (2015) also proposed a systematic framework for R&D collaborator exploration using bibliographic coupling analysis and latent semantic analysis. Wang et al. (2017) presented a novel process for identifying R&D partners on the basis of solution similarities that assist technology managers in understanding the relationships between research targets. ...
This paper proposes a systematic process to identify potential research and development (R&D) partners from a technological perspective based on subject-action-object (SAO) semantic analysis. Improvements to traditional methods are made by combining the SAO structure map and the collaboration network analysis. The SAO structure map reveals the technological development trends, organizations research contributions, and their research experiences in the field, which are the factors that indicate an organizations R&D capabilities. Furthermore, we explore the organizations collaboration statuses through collaborative network analysis and their collaborative publications, which make it easier to identify the organizations sense of cooperation. Potential R&D partners are identified by examining the organizations R&D capabilities and sense of cooperation. An exploratory study is conducted on dye-sensitized solar cells (DSSCs). The proposed method provides useful information for organizations (firms, institutions, universities, etc.) to identify potential R&D partners or make cooperation related policies.
... Second, since simply using keywords may limit the accuracy of the analysis, chunk-level analysis (for example, SAO analysis) has been developed (Gerken and Moehrle 2012). The main feature of SAO analysis is that it extracts a key concept rather than a key word, and this concept can describe the function of an invention (Wang et al. 2017). When an SAO structure (for example, A-improve-B) is extracted from the patent text, the subject item (A) can represent a solution, while the action-object items (improve-B) can indicate a problem (Park et al. 2013a, b). ...
... Purpose of analysis Moehrle et al. (2005) Providing a basis for human resource management during the R&D process Yoon and Kim (2011a, b) Identifying technology opportunities by detecting outlier patents Choi et al. (2012) Supporting the development of a technology tree Choi et al. (2012) Supporting the construction of technology roadmaps Yoon and Kim (2012a, b) Identifying trends and the evolution of the concept of technology forecasting Park et al. (2013a, b) Identifying promising patents based on the concept of TRIZ evolutionary trends Yoon et al. (2013) Developing a dynamic patent map to visualize technology competition trends Wang et al. (2017) Supporting the construction of technology roadmaps Guo et al. (2016) Forecasting technology and innovation based on a modified morphology analysis 1 3 is important to take into consideration the expected effect (and purpose) of a technology when structuring patent information to identify technology opportunities. This study also aims to methodologically contribute to patent analysis by improving the performance of the SAO method in terms of the accuracy of information extraction. ...
A patent is regarded as one of the most reliable data sources to investigate such opportunities and has been analyzed in numerous ways. The recent trend of patent analysis has focused on the unstructured part of patent information to extract detailed technological information. In particular, information regarding the purpose or effect of technology, which can be pulled from the unstructured part of patent information, is expected to offer useful insights into expanding its application to other areas. Some previous attempts have been made to systematically use this information to identify new technology opportunities, partly due to difficulties in analyzing the unstructured text data in patent documents. To overcome the limitations of previous studies, this study aims to develop a new method, namely Subject–Action–Object–others (SAOx), which enables an in-depth examination of the purpose and effect of the technology in an efficient manner by analyzing “for” and “to” phrases as well as gerund forms for an object element. We also introduce 39 engineering parameters of TRIZ and technology-designative terms of patent documents to define SAO sets and improve information accuracy. The proposed method is applied to human–machine interaction technologies to understand technology trends and explore technology opportunities based on topic modeling. Methodologically, the research findings contribute to patent engineering by extending the range of information extracted from patent information. Practically, the proposed approach will support corporate decision making in R&D investment by providing comprehensive information regarding the purpose or effect of technology in a structured form, fully extracted from patent documents.
... Many factors influence the assessment of the feasibility of a developed learning media. These factors include design attractiveness, product resilience, development strategy, and realm needs (Wang et al., 2017). This simple monopoly learning media packs very complex material from several integrated lesson content to be more interesting and simpler. ...
The low learning outcomes of fifth-grade students are caused by the lack of thematic learning media. Therefore, this study aimed at developing simple monopoly learning media on the subtheme of animal movement organs. The design of this study was development research based on ADDIE model. The collected data in this study were qualitative and quantitative data. The subjects in this study were a material expert, a media expert, an instructional design expert, and a learning practitioner. The data were collected through questionnaire. Then, the collected data were analyzed using descriptive qualitative and descriptive quantitate method. The results of this study show that the simple monopoly learning media developed is valid based on a review of material experts, reviews of learning media experts, and reviews of learning design experts with very good qualifications. Meanwhile, this learning media has good qualification to be used in thematic learning on the subtheme of animal movement organs. It is based on the result of teacher’s response to the developed media. It can be concluded that the monopoly learning media is suitable to in the thematic learning specially for learning animal movement organs. The implication of the result of this study is that it can help students be actively involved in meaningful and fun learning.
... Authors in [54] attempted to extract and analyze SAO structures to detect patent infringement. Authors in [55] focused on the identification of rapidly evolving technological trends, and authors in [56] proposed a method to recommend research and development candidates by extracting the SAO structure from problem-solution patterns of patent information. However, a few studies have described the relationships between problems and solutions extracted from papers. ...
Science and technology activities can be considered problem-solving activities, and scientific papers and patent publications can be viewed as providing explicit knowledge gained from the problem-solving of academia and industry respectively. However, even in the same field, the approach to the same problem is not consistent between a paper and the patented technology. The creation of information silos in science and technology generates inefficiency in human intellectual production. Therefore, this study examines whether insights from technical problems can be shared with academics to solve scientific problems. We propose a concept to link the problems between these two domains using a linguistic approach for knowledge discovery that connects science and technology. We extracted scientific papers from the Association for Computational Linguistics dataset, and patent literature from the Derwent Innovation platform. From these, pairs of problem defining sentences were identified and extracted using an attention-based language model. For example, we were able to extract examples of issues that do not necessarily arise from scientific papers, such as annotation difficulties in the analysis of social network data, but can be hinted at by patented techniques prior to the paper. These results suggest that scientific problems and industrial solutions can provide mutual insight. This knowledge discovery approach is recommended not only for benefiting corporate activities but also for grasping research trends.
... The potential R&D collaboration partners were visualized in the form of a patent-assignee-level map based on the technological similarity between patents using network analyses. Wang et al. (2017) identified R&D partners through subject-action-object (SAO) semantic analyses in a problem and solution pattern and combination term clumping. Xu et al. (2015Xu et al. ( , 2016 used multiple indicators to synthetically assess the new IURC situation and identify cooperation partners for collaboration. ...
Purpose
This study aims at identifying potential industry-university-research collaboration (IURC) partners effectively and analyzes the conditions and dynamics in the IURC process based on innovation chain theory.
Design/methodology/approach
The method utilizes multisource data, combining bibliometric and econometrics analyses to capture the core network of the existing collaboration networks and institution competitiveness in the innovation chain. Furthermore, a new identification method is constructed that takes into account the law of scientific research cooperation and economic factors.
Findings
Empirical analysis of the genetic engineering vaccine field shows that through the distribution characteristics of creative technologies from different institutions, the analysis based on the innovation chain can identify the more complementary capacities among organizations.
Research limitations
In this study, the overall approach is shaped by the theoretical concept of an innovation chain, a linear innovation model with specific types or stages of innovation activities in each phase of the chain, and may, thus, overlook important feedback mechanisms in the innovation process.
Practical implications
Industry-university-research institution collaborations are extremely important in promoting the dissemination of innovative knowledge, enhancing the quality of innovation products, and facilitating the transformation of scientific achievements.
Originality/value
Compared to previous studies, this study emulates the real conditions of IURC. Thus, the rule of technological innovation can be better revealed, the potential partners of IURC can be identified more readily, and the conclusion has more value.
... Secondly, because of the wide range of possible verbs that can be used, the traditional SAO analysis can be complex. Although some studies have tried to minimize the complexity of SAO by summarizing key verbs or use only key technological terms to reduce this variety (e.g., Choi et al., 2013;Park, Ree, et al., 2013;Wang et al., 2017), the TRT analysis, which uses a limited number of clearly defined prepositions to identify the relationships between technological terms, is likely to reduce complexity further and to be simpler to use, more reliable and consistent than the SAO analysis for analyzing technological structures. ...
Patents are one of the most reliable sources of technology intelligence, and the true value of patent analysis stems from its capability of describing the content of technology based on the relationships between keywords. To date a number of techniques for analyzing the information contained in patent documents that focus on the relationships between keywords have been suggested. However, a drawback of the existing keyword approaches is that they cannot yet determine the types of relationships between the keywords. This study proposes a novel approach based on preposition semantic analysis network which overcomes the limitations of the existing keywords-based network analysis and demonstrates its potential through an application. A preposition is a word that defines the relationship between two neighboring words, and, in the case of patents, prepositions aid in revealing the relationships between keywords related to technologies. To demonstrate the approach, patents regarding an electric vehicle were employed. 13 prepositions were identified which could be used to define 5 relationships between neighboring technological terms: “inclusion (utilization),” “objective (purpose),” “effect,” “process,” and “likeness.” The proposed approach is expected to improve the usability of keyword-based patent analyses and support more elaborate studies on patent documents.
Purpose
Based on the theory of performance feedback, this study aims to explore the theoretical relationship between performance shortfalls and the financialization of non-financial enterprises. It further analyzes the moderating effect of economic policy uncertainty (EPU) and organizational redundant resources.
Design/methodology/approach
Multiple regression analysis is used on 16,555 initial samples of 2,658 Chinese A-share issuing enterprises from 2007 to 2019 to empirically test the relationship between performance shortfalls and the financialization of non-financial enterprises, and an instrumental variables-generalized moments estimation model is also used to verify the robustness of the results.
Findings
The results reveal that the greater the performance gap below the aspiration level, the higher the degree of enterprise financialization. Moreover, EPU strengthens the relationship between performance shortfalls and financialization, whereas organizational redundant resources weaken the relationship between performance shortfalls and financialization.
Practical implications
Decision-makers should determine the aspirated performance level of enterprises to make investment decisions that are most conducive to the long-term development of enterprises. Each enterprise should establish scientific management evaluation and supervision systems to avoid financial investment behaviors that place too much emphasis on short-term performance.
Originality/value
This study finds that financialization is one of the reactions when performance of enterprises is lower than the aspiration level, thus expanding the functional dimensions of performance feedback and supplementing the research on the influencing factors of enterprise financialization. The results also reveal information about situational factors, helping identify the boundary conditions through which performance below aspirations affects enterprise financialization.
Using a sample of listed firms from 2011 to 2019 in Chinese A-share markets, this paper examine the consistency of strategic information disclosure and strategic behavior change, and it's impact on firm's future growth and the cost of capital. Using the expected strategic adjustment as a proxy of strategic information disclosure, we find that expected strategic adjustment is positively associated with actual strategic changes, and such positive association is strengthened for the firms with state-owned nature, more redundant resources, defensive strategy, lower economic policy uncertainty and higher value. Furthermore, we find that the consistency of talks and actions is associated with a higher firm growth. If talk more and do less, firms will face a higher cost of capital. In brief, this paper provides an indicator of strategic information disclosure to predict corporate strategic behavior for Chinese listed firms. More importantly, we suggest that the management of firm's talk-do relation is meaningful to firm growth, and the market will negatively respond to the deviation of “talk more but do less”.
Selecting the right collaboration partner is one of the most important contributors to success in collaborative innovation. Accordingly, numerous methods for selecting an appropriate partner have been developed to guide would-be collaborators in their search. Most rely on bibliographic information, which may be easier for that data is readily available and relatively normalized. However, with the benefit of today’s text mining and fusion techniques, it is possible to mine the content of papers and patents so as to result in far more nuanced and advantageous choices. In this article, we explore how to select partners for collaborative innovation by combining the characteristics of the authors of paper and patent documents as well as their content. Drawing on existing research, we developed a systematic framework that relies on topic analysis and link prediction. With a corpus of papers and patents assembled, the framework extracts correlated scientific and technological topics followed by a list of author institutions and a list of patentees. These organisations are parsed and evaluated using two indicators of innovation—capability and openness—to result in two separate ranked lists. Two integrated collaboration networks that include both author institutions and patentees are then built, and a link prediction method identifies missing links with a high likelihood of fruitful cooperation. A case study on hepatitis C virus research shows that the ranking procedure and the link prediction method can be used either together or separately to effectively identify collaborative innovation partners. Our results provide significant quantitative evidence for policymakers who are looking to foster cooperation between research institutions and/or high-tech enterprises. Our research may also serve as the basis for further in-depth research on collaborative innovation, R&D cooperation, and link prediction theories and methods.
Text analysis on technology has recently been progressing from the level of words to semantic relations between words. However, existing research methods, such as Subject-Action-Object, have focused on specific purposes or analytical techniques. There is an insufficient amount of fundamental study on what types of semantic relations in technical information need to be analysed to provide meaningful information. At the same time, in the field of NLP, the deep learning-based semantic relation model has been establishing as useful for specific tasks. However, there is a limit to applying the NLP model itself for technical analysis because it does not consider the characteristics of the textual information about technology. Therefore, this study proposes a deep learning-based semantic relation model for technology information analysis. First, meaningful types of semantic relations are derived from the text information about technology. By analysing the F-term classification code, which is a multi-dimensional technology hierarchy with descriptions, a technology semantic labelled dataset is constructed. Finally, we develop a classification model that analyses the semantic relations of technology based on the sentence embedding model. This study contributes to the construction of a deep learning model by developing a meaningful type in the analysis of technical information and constructing a technical text dataset with labels. The result of semantic technology relations can also be utilized as a high-quality source for various applications on technology analysis, such as technology tree and technology roadmap. In other words, it has the advantage of being able to provide generalizable technical information that is not dependent on a specific analysis purpose.
Expert finding systems try to alleviate the information overload problem and recommend experts who can satisfy users’ needs. They support researchers to find research collaborators automatically. The main challenge of current expert finding systems is that they retrieve experts based on the content of their documents but ignore the human interaction perspective. The human interaction perspective comprises the factors that influence collaborator selection decisions in real life. This study aimed to develop a collaborator selection model for expert finding systems in research universities. This model includes human capital, social capital, and cultural capital factors that influence collaborator selection. The researchers integrated the Scientific and Technical Human Capital (STHC) model and Social Capital Theory to determine these factors. The authors conducted a survey comprising 349 researchers from Malaysian research universities to validate the research hypotheses. A partial least squares structural equation model (PLS-SEM) was employed to analyze all the survey data. The empirical results revealed that the significant factors that influence collaborator selection in the research universities context were cognitive accessibility, reliability, relevance, commitment, physical accessibility, cultural experiences, complementary skills, and research experience. Surprisingly, the results revealed that network ties, relational accessibility, and reputation were insignificant factors for collaborator selection. This study proposed a research model for collaborator selection in the research universities context and provided several recommendations for the policymakers and practitioners. The model will help to provide sufficient criteria to select academic research collaborator in universities and can be used by expert finding systems designers, researchers, collaborators, and universities.
Applied to search, question answering, and semantic web of close-or-open domain, knowledge graph (KG) is known for its incompleteness subject to the rapid knowledge growing pace. Inspired by the agricultural grafting technology to fruit variety, this paper proposes a heuristic knowledge grafting strategy (HGS) for manufacturing knowledge graph (MKG) named KnowTree extending and completion with natural language processing (NLP) mining engineering cases document. Based on similarity analysis, firstly the grafting related definitions and mechanisms (completeness, relatedness, connectivity and reutilization) are defined. Then, focused on the four mechanisms, HGS takes a pair same engineering documents as input. KnowWords is built as a collection of KnowScion, and each scion is mined from engineering documents based on the SAO structure network, whose importance is evaluated by SAORank counting the in-out degree of centrality. On another hand, the KnowRoot system is designed based on the extended P&S ontology model to characterize the structure of abstract document into four sub-space of knowledge: know-what (problem), know-why (context), know-how (solution) and know-with (result), where a pre-trained language representation model K-BERT is used to classify the KnowScion candidates into the designed KnowRoot system with a fine-tuning classification task. In the knowledge grafting process, the connection unit is constructed based on the extracted domain knowledge triples of the K-BERT model, where the head element of a triple is from the KnowScion candidate set KnowWords satisfying the threshold value, the tail element is from the domain MKG to be extended, and a connection factor is used to evaluate the relationship of union combination. To the goal of knowledge reuse, the path based reasoning rules are designed for KnowTree reutilization. Finally, take the latest engineering case abstract (ECA) in whitegoods domain as resources, a case study is conducted to validate the proposed HGS strategy.
This article presents an improved method of measuring technology similarity by introducing a subject-action-object (SAO) analysis that uses the feature weights of semantic structure and professional vocabulary to measure technology similarity in the medical field. First, the SAO semantic structures are extracted and cleaned; then the structures related to technology are identified using a semantic network of the unified medical language system (UMLS). Second, the similarity between the SAO semantic structures is evaluated using semantic information from the Metathesaurus of the UMLS. Third, the feature weights of the SAO semantic structure are introduced to represent the importance of the patentees’ technology features. Finally, using the SAO and weight information, each patentee's vector is constructed to measure the technology complementarity between different patentees. This study conducts empirical research on Alzheimer's disease. The results indicate that the propose method for measuring technology similarity enables finer distinctions with more reliable outcomes than the traditional methods that are based on keywords and international patent classification.
The role of text mining based on technological documents such as patents is important in the research field of technology intelligence for technology R&D planning. In addition, WordNet, an English-based lexical database, is widely used for pre-processing text data such as word lemmatization and synonym search. However, technological vocabulary information is complex and specific, and WordNet’s ability to analyze technological information is limited in its reflecting technological features. Thus, to improve the text mining performance of technological information, this study proposes a methodology for designing a TechWord-based lexical database that is based on the lexical characteristics of technological words that are differentiated from general words. To do this, we define TechWord, a technology lexical information, and construct a TechSynset, a synonym set between TechWords. First, through dependency parsing between words, TechWord, a unit word that describes a technology, is structured and identifies nouns and verbs. The importance of connectivity is investigated by a network centrality index analysis based on the dependency relations of words. Subsequently, to search for synonyms suitable for the target technology domain, a TechSynset is constructed through synset information, with an additional analysis that calculates cosine similarity based on a word embedding vector. Applying the proposed methodology to the actual technology-related information analysis, we collect patent data on the technological fields of the automotive field, and present the results of the TechWord and TechSynset. This study improves technological information-based text mining by structuring the word-to-word link information in technological documents based on an automated process.
Finding new industry partners poses a challenge to many public research organizations. This article explores how statistical classification can support partner selection at the example of the Fraunhofer Society in Germany, Europe's largest public organization for applied research. We use internal cooperation data and feature sets based on unstructured data, i.e., text and industry codes, both of which describe business activities of firms. An important advantage of this data is that it is available for most companies in Germany, even small and medium enterprises, which allows for an almost complete screening of the market, in contrast to using other data sources, e.g., patents. In addition, we also include economic variables linked to firms, as turnover, number of employees/managers and firm age. We report the performance of various classification techniques such as logistic regression, support vector machines, and random forests in our dataset for diverse combinations of feature sets. Results show that simple methods with fewer parameters remain competitive in comparison to complex ones. Overall, the performance of most classifiers is high enough to support the decision process of finding new industry partners for public research.
There is a great and urgent necessity for appropriate data and methods to help organisations select appropriate R&D (Research and Development) partners. In response, this study presents a structured framework for identifying and selecting the R&D partners with the greatest potential to meet a given technological need. The formal methods in the framework include systematic information collection, patent portfolio analysis, technological similarity screening, and semantic evaluation that views subject-action-object (SAO) sentence structures as problem-solution patterns. The results of an empirical evaluation, with dye-sensitive solar cells as the case, demonstrate that using this framework can reveal more appropriate R&D partners for technological needs (TNS) in less time and with less cost than methods based solely on one criterion.
Because of the flexibility and complexity of Newly Emerging Science and Technologies (NESTs), traditional statistical analysis fails to capture technology evolution in detail. Tracking technology evolution pathways supports industrial, governmental, and academic decisions to guide future development trends. Patents are one of the most important NESTs data sources and are pertinent to developmental paths. This paper draws upon text analyses, augmented by expert knowledge, to identify key NESTs sub-domains and component technologies. We then complement those analyses with patent citation analysis to help track developmental progressions. We identify key sub-domain patents, associated with particular component technology trajectories, then extract pivotal patents via citation analysis. We compose evolutionary pathways by combining citation and topical intelligence obtained through term clumping. We demonstrate our approach with empirical analysis of dye-sensitized solar cells (DSSCs), as an example of a promising NESTs, contributing to the remarkable growth in the renewable energy industry. The systematic approach we proposed not only offers a macro-perspective covering technology development levels and future trends, but also makes R&D information accessible for micro-level probes as needed. We work to uncover developmental trends and to compile mentions of possible applications, and this study informs NESTs management by spotting prime opportunities for innovation.
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.
Purpose
– The purpose of this paper is to propose a systematic approach to exploring potential partners for open innovation in order to facilitate the process of technological collaboration.
Design/methodology/approach
– The proposed approach utilizes patent information that is considered the most effective data to investigate innovation activities by applying morphology analysis (MA) and generative topology map (GTM) to the process to identify the configurations of technologies and visualize the collected patent information. In particular, diverse informative indices can assist researchers in deriving appropriate partners for technological cooperation.
Findings
– The hybrid approach that combines MA, GTM, and indices is useful to discover technological opportunity, identify necessary technologies and explore potential partners. The systematic process can overcome the limitations of existing approaches that depend on the insights and strategic decisions of top management, reflect a superficial relationship among firms and mostly fail to consider both theoretical and practical approaches to retrieve potential partners.
Practical implications
– The results of this paper will help practitioners, academic researchers, and policy makers link a pair of partners who can maximize the synergy of collaboration in a systematic manner. The proposed approach is illustrated to show the validity of the process with a case of light emitting diode (LED) technology. In addition, the social cost for collaboration can be reduced by applying the proposed approach in open innovation.
Originality/value
– This paper tackles the important issue of exploring appropriate partners by applying a systematic approach that utilizes text mining, MA and GTM because existing research mostly deals with ad-hoc processes. The proposed approach utilizes multiple methodologies of data mining and technology forecasting to derive promising technology areas in a systematic manner and then employs critical indices to explore proper partners. Thus, this approach considers both quantitative and qualitative methodologies to suggest a sophisticated tool for successful open innovation. Moreover, a real case – LED case was illustrated to demonstrate that the proposed approach can be employed to implement technological cooperation.
In the never-ending search for ways of gaining a sustainable competitive advantage, global corporations have increasingly turned to the use of strategic alliances as a method of international expansion. Unfortunately, many firms have been unpleasantly surprised by the outcomes of their strategic alliance relationships. In this paper, we draw upon previous theoretical and empirical work to develop a framework for analysing the likely success of international strategic alliances. Dubbed ‘the 4 Cs’, we show how this analytic framework can assist firms in their efforts to avoid picking the wrong alliance partners.
Nowadays, the development of emerging technology has become a double-edged sword in the scientific world. It can not only bring lots of innovation to society, but may also cause some terrible consequences due to its unknown factors. International collaboration may be able to reduce risks, which means a lot to the exploration of the emerging technology. Taking dye-sensitized solar cells (DSSCs) as an example, this paper examines the rapid growth of Chinese DSSCs research and the rise of collaboration between China and other countries/region. We use bibliometric and social network analysis methods to explore the patterns of scientific collaboration at country, institution and individual levels using data from the Science Citation Index. Examining overall trends shows that China has increased her position in DSSCs around the world. Furthermore, by focusing on the individual level, we find that the most influential authors tend to have fixed co-author networks and author name order, which is something worth considering. We use co-author analysis software independently developed to check three kinds of fixed co-author networks to explore author contributions, influence, and Author Activity Index rank in collaboration networks and use the rank we calculated to further explain author contributions in the networks. Results show that Chinese-X (e.g., Chinese-American) authors have pushed the collaboration between country and country and almost every kind of small network has a top author in it to gather others together. The modified author activity index rank list may reflect real research level. Author collaboration patterns have been impacted by the kinds of their institutions to some degree. These results can undoubtedly promote the international collaboration and the innovation process in the similar emerging technology fields.
Competitive Technical Intelligence (CTI) addresses the landscape of both opportunities and competition for
emerging technologies, as the boom of Newly Emerging Science & Technology (NEST) – characterized by a
challenging combination of great uncertainty and great potential – has become a significant feature of the
globalized world. We have been focusing on the construction of a “NEST Competitive Intelligence”
methodology that blends bibliometric and text mining methods to explore key technological system components,
current R&D emphases, and key players for a particular NEST. This paper emphasizes the semantic TRIZ
approach as a useful tool to process “Term Clumping” results to retrieve “Problem & Solution (P&S)” patterns,
and apply them to Technology Roadmapping (TRM). We attempt to extend our approach into NEST
Competitive Intelligence studies by using both inductive and purposive bibliometric approaches. Finally, an
empirical study for Dye-Sensitized Solar Cells (DSSCs) is used to demonstrate these analyses.
This paper examines interfirm knowledge transfers within strategic alliances. Using a new measure of changes in alliance partners' technological capabilities, based on the citation patterns of their patent portfolios, we analyze changes in the extent to which partner firms' technological resources ‘overlap’ as a result of alliance participation. This measure allows us to test hypotheses from the literature on interfirm knowledge transfer in alliances, with interesting results: we find support for some elements of this ‘received wisdom’—equity arrangements promote greater knowledge transfer, and ‘absorptive capacity’ helps explain the extent of technological capability transfer, at least in some alliances. But the results also suggest limits to the ‘capabilities acquisition’ view of strategic alliances. Consistent with the argument that alliance activity can promote increased specialization, we find that the capabilities of partner firms become more divergent in a substantial subset of alliances.
A persistent theme in the academic literature on technological innovation is that incumbent enterprises have great difficulty crossing the abyss created by a radical technological innovation. It is argued that incumbents go into decline, while new entrants rise to market dominance by exploiting the new technology. While many incumbent organizations do decline when markets embrace radical new technological innovations, this tendency is not universal. There are outliers in any population, and much can be learned from examining this group. Herein we identify a number of factors that help to explain incumbent performance in markets shaken by a radical technological innovation.
A central part of the innovation process concerns the way firms go about organizing search for new ideas that have commercial potential. New models of innovation have suggested that many innovative firms have changed the way they search for new ideas, adopting open search strategies that involve the use of a wide range of external actors and sources to help them achieve and sustain innovation. Using a large-scale sample of industrial firms, this paper links search strategy to innovative performance, finding that searching widely and deeply is curvilinearly (taking an inverted U-shape) related to performance. Copyright © 2005 John Wiley & Sons, Ltd.
Natural Language Processing techniques for text-mining and information retrieval are finding application in the analysis of
many kinds of documentation, from technical documentation to World Wide Web. Particularly, Functional Analysis techniques
are based on the extraction of the interactions between the entities described in the document: these interactions are expressed
as Subject-Action-Object (SAO) triples (obtainable using a suitable syntactic parser) which represent a concept in its most
synthesizing form. In this work, the techniques developed for a functional analysis of patents and their implementation in
the PAT-Analyzer tool are presented. The same technique has been properly tailored and applied to the analysis of software
requirements documents. Current work in the direction of the development of a SAO-based Content Analysis of technical documentation
is presented.
The resource-based view of the firm, with its focus on firm-specific `capabilities', has attracted considerable attention in recent work by management scholars, but has not sparked much empirical analysis. This paper relies on the resource-based view to examine partner choice in interfirm collaborations, emphasizing the role of partners' technological capabilities. Patent citation data are used to measure `technological overlap' between firms before and after alliance formation. Our results provide support for the resource-based view of the firm. Partner selection can be predicted by measures of technological overlap and, once formed, alliances appear to affect firms' technological portfolios in ways predicted by the resource-based view.
Tech Mining supports management of technology (MOT) decision processes. To this end, we set out 13 MOT issues that lead into 39 MOT questions. We then array some 200 candidate empirical measures and more elaborate “innovation indicators” to address those MOT issues and questions. These indicators are grounded in understanding of technological innovation processes so as to track technology life cycle, innovation context, and market prospects. The chapter presents the expert opinion approaches that complement empirical tech mining. Representation of indicators raises challenges in matching user style preferences and appropriate visualizations and delivery modes. We offer “one-pagers” as technology information products that compile information to answer a particular MOT question. This chapter also considers how scripting can expedite analyses and how results can be integrated into business decision systems.
The aim of the present research is to provide a new systematic methodology to explore potential
Research and Development (R&D) collaboration partners using patent information. The
potential R&D collaboration partners are visualised as a patent assignee level map based on
technological similarity between patents by using the network analysis. The proposed framework
utilises two analytic methods to measure technological similarity. The first method,
bibliographic coupling analysis, measures technological similarity based on the citation relationship
using patent bibliographic information. Second, latent semantic analysis is utilised
based on semantic similarity using patent textual information. The fuel cell membrane electrode
assembly technology field is selected and applied to illustrate the proposed methodology.
The proposed approach allows firms, universities, research institutes and governments to
identify potential R&D collaborators as a systematic decision-making support tool.
The present paper tries to show that the current state of the art in syntactics and semantics, in computer systems based on the theory of inventive problem solving known as TRIZ, may help in the task of literature based discovery. With a structured and logic cause linkage between concepts, LBD could be faster and with less expert involvement at the beginning of the LBD process. The author tries to demonstrate the concept with two different problems: the hearing and balance problem known as Meniere’s disease, and to some of the current problems in the lithium air batteries for electric vehicles. By using open literature based discovery from An to Bn and from Bn to Cn, and with the logic relationships of real causes and effects approach, the author finds several relative new concepts such as vitamin A. Other concepts as niacin or fish oil, are also found, as potential to help in the Meniere’s disease .. Secondly, using such procedure the author is able to find patents from disparate domain of expertise, as patents about odour control or metal casting.
This paper aims to show how the information contained in patent documents can be used to identify basic and specific technological proximities between firms and therefore a potential research and development (R&D) partner. More generally, it looks at patents as a strategic tool that can be used for concluding cooperative R&D agreements (CRDA). The approach begins by looking at the state of the art on the role of technological proximity in CRDAs. This review clearly raises the problem of measuring technological proximity, which needs to be gauged at a two-fold level: general and specific. Then a dual method based on patent portfolios for analyzing the profiles of different potential partners is described along with an example of its application. Concretely, the exploratory study proposed here is based on an analysis of the patent portfolios of 14 French listed biotechnology companies and those of their main R&D partners. The analysis of 5,603 patents filed by the focal firms and their partners shows how the approach can be used to identify compatible partners that are more or less technologically matched.
Tech Mining seeks to extract intelligence from Science, Technology & Innovation information record sets on a subject of interest. A key set of Tech Mining interests concerns which R&D activities are addressed in the publication and patent abstract records under study. This paper presents six “term clumping” steps that can clean and consolidate topical content in such text sources. It examines how each step changes the content, potentially to facilitate extraction of usable intelligence as the end goal. We illustrate for an emerging technology, dye-sensitized solar cells. In this case we were able to reduce some 90,980 terms & phrases to more user-friendly sets through the clumping steps as one indicator of success. The resulting phrases are better suited to contributing usable technical intelligence than the original results. We engaged seven persons knowledgeable about dye-sensitized solar cells (DSSCs) to assess the resulting content. These empirical results advanced the development of a semi-automated term clumping process that can enable extraction of topical content intelligence.
Patent maps showing competition trends in technological development can provide valuable input for decision support on research and development (R&D) strategies. By introducing semantic patent analysis with advantages in representing technological objectives and structures, this paper constructs dynamic patent maps to show technological competition trends and describes the strategic functions of the dynamic maps. The proposed maps are based on subject-action-object (SAO) structures that are syntactically ordered sentences extracted using the natural language processing of the patent text; the structures of a patent encode the key findings of the invention and expertise of its inventors. Therefore, this paper introduces a method of constructing dynamic patent maps using SAO-based content analysis of patents and presents several types of dynamic patent maps by combining patent bibliographic information and patent mapping and clustering techniques. Building on the maps, this paper provides further analyses to identify technological areas in which patents have not been granted (“patent vacuums”), areas in which many patents have actively appeared (“technological hot spots”), R&D overlap of technological competitors, and characteristics of patent clusters. The proposed analyses of dynamic patent maps are illustrated using patents related to the synthesis of carbon nanotubes. We expect that the proposed method will aid experts in understanding technological competition trends in the process of formulating R&D strategies.
Given that in terms of technology novel inventions are crucial factors for companies; this article contributes to the identification of inventions of high novelty in patent data. As companies are confronted with an information overflow, and having patents reviewed by experts is a time-consuming task, we introduce a new approach to the identification of inventions of high novelty: a specific form of semantic patent analysis. Subsequent to the introduction of the concept of novelty in patents, the classical method of semantic patent analysis will be adapted to support novelty measurement. By means of a case study from the automotive industry, we corroborate that semantic patent analysis is able to outperform available methods for the identification of inventions of high novelty. Accordingly, semantic patent information possesses the potential to enhance technology monitoring while reducing both costs and uncertainty in the identification of inventions of high novelty.
A technology tree (TechTree) is a branching diagram that expresses relationships among product components, technologies, or functions of a technology in a specific technology area. A TechTree identifies strategic core technologies and is a useful tool to support decision making in a given market environment for organizations with specified capabilities. However, existing TechTrees generally overemphasize qualitative and expert-dependent knowledge rather than incorporating quantitative and objective information. In addition, the traditional process of developing a TechTree requires vast amounts of information, which costs considerably in terms of time, and cannot provide integrated information from a variety of technological perspectives simultaneously. To remedy these problems, this research presents a text mining approach based on Subject–Action–Object (SAO) structures; this approach develops a TechTree by extracting and analyzing SAO structures from patent documents. The extracted SAO structures are categorized by similarities, and are identified by the type of technological implications. To demonstrate the feasibility of the proposed approach, we developed a TechTree regarding Proton Exchange Fuel Cell technology.
Function-Oriented Search (FOS) has been proposed as a tool for use in searching patent databases to find existing solutions to new problems. To implement FOS effectively, a well-structured Function-based Technology Database (FTDB) is required. An FTDB is a data repository of technology information represented as “function”. To implement an FTDB, four features should be addressed: continual data updating, limited area searching, function generalization, and semantics handling. In this paper, we consider these features to suggest a fact-oriented ontological approach to implementing an FTDB by Subject–Action–Object (SAO)-based function modeling of patents. The proposed approach uses fact-oriented ontology modeling of SAO structures extracted from patent documents, and implements an FTDB, which is an SAO-based patent retrieval system to support FOS. We also verify the feasibility of the proposed approach to by using it to conduct case studies of patent retrieval.
The study provides a framework for exploring potential R&D collaborators with technological complementarity in products consisting of multidisciplinary technologies. This framework is proper when firms have insufficient information on who may possess the desired complementary technologies. The proposed framework applies two exploratory methods to patent information. The first method, association analysis, mines the interaction between different technologies for the studied products, and produces results that are useful to understanding the complementarity of various technologies. The proposed framework then uses nonlinear principal components analysis to determine the relationship among integrated technologies, specific technology fields, and patentees. The proposed method allows firms to identify patentees with complementary technologies and locate potential R&D collaborators. This study uses an empirical case from the biosensor industry to illustrate how to identify potential R&D collaborators.
In many innovation projects firms search for external knowledge sources since they cannot rely solely on their own R&D efforts. This study of technology alliances argues that collaborations with external partners affect the innovation output of firms not only directly but also indirectly by influencing the efficiency of internal R&D. Using a sample of firms in electrical and electronic equipment, empirical evidence identifies heterogeneous interaction between alliances and internal R&D efforts dependent on the type of partner. While collaboration with partners in related industries is more beneficial to a firm's internal R&D for producing innovations in comparison to alliances with partners in the same industry, collaboration with partners in unrelated fields decreases the efficiency of internal R&D efforts.
Non-hierarchical regional production networks are the vision of a virtual enterprise model which is investigated in a collaborative research center at Chemnitz University of Technology. In the center of interest is the development of a virtual enterprise model which is based on small performance units—the so-called competence cells. The general intention of this model is to improve the competitiveness of small and medium-sized enterprises, which results from the enterprise-spanning cooperation of the participants. Thus, the concentration on the own core competences is supported and the market power is contemporaneously increased by the help of the network. The possibly automated selection of the partners is one of the major problems in virtual enterprises. Therefore, we introduce a method for choosing the most capable competence cells from a pool of potential competence cells. The selected competence cells fulfil the tasks of a value chain particularly well. Within our approach, this problem will be solved using Ant Colony Optimization. Additionally, social facts will also be considered in order to select the suitable competence cells for the network.
I argue that the linkage-formation propensity of firms is explained by simultaneously examining both inducement and opportunity factors. Drawing upon resource-based and social network theory literatures I identify three forms of accumulated capital— technical, commercial, and social—that can affect a firm's inducements and opportunities to form linkages. Firms possessing these capital stocks enjoy advantages in linkages formation. However, firms lacking these accumulated resources can still form linkages if they generate a radical technological break- through. Thus, I identify paths to linkage formation for leading as well as peripheral firms. I test these arguments with longitudinal data on technical collaborative linkages in the global chemicals industry. Copyright © 2000 John Wiley & Sons, Ltd.
ABSTRACT ,This paper describes a business model that is growing in prevalence and that carries novel implications: the development,of general-purpose technologies for licensing to downstream specialists. In the archetypical format, these general-purpose technologies are constructed to enable subsequent downstream,applications but are flexible enough,to accommodate,differences in the strategies of downstream licensees. This business model has implications for industry structure, organizational capabilities and even the content and context for the upstream science. 2
This article describes a business model that is growing in prevalence and that carries novel implications: the development of general-purpose technologies for licensing to downstream specialists. In their archetypical format, these general-purpose technologies are constructed in ways that can be employed by different potential downstream licensees, and can accommodate their different strategies. This strengthens the hand of innovative firms in the rising markets for knowledge-based assets, and can be expected to improve their ability to capture a greater share of the value their technology creates. The innovation of business model designed for licensing such technologies will have unpredictable, but inevitable, consequences for industry structure and organizational capabilities, as well as for the content and context for the upstream science.
Purpose
The purpose of this paper is to establish a mechanism for partner selection via adapting relative weights of criteria according to the priority of motivations for establishing strategic alliance.
Design/methodology/approach
The analytic network process (ANP) approach derived from the idea of the Markov chain is employed to deal with this dynamic situation and to establish a partner selection mechanism. With this approach, the priority of motivations and the relative importance of criteria are determined simultaneously.
Findings
Although choosing an appropriate partner is an important variable influencing success of alliance, attempts to identify a universal list of criteria and their corresponding relative importance which enterprises should employ when seeking a proper partner would be futile since the objectives of forging alliances vary depending on specific motivations. Based on this iterative review approach proposed in this paper, a proper weight setting for these criteria is available and will comply with the original motivation for establishing the strategic alliance. This is essential for selecting an appropriate partner for establishing an alliance that matches the original motivation.
Research limitations/implications
The limitation of this research is the neglect of the possible inner dependence among criteria and sub‐criteria, although that can be coped with by choosing them properly.
Practical implications
The content of motivations and criteria as well as their priority and weightings may vary with different kinds of alliances or situations. The partner evaluation and selection mechanism proposed in this paper can meet different situations by adapting the relative weights of criteria and attributes according to the relationship between the criteria and motivations for every particular situation, thus enabling decision‐makers to think more comprehensively before conducting a selection process. If the priority of the motivations obtained from the mechanism is consistent with that set initially, the relative weights of these criteria can then be employed to evaluate the candidate partners in the selection mechanism. If it is not, the decision maker should reconsider the weighting process or measure again the relative weights for the criteria before conducting the evaluation and selection processes to avoid selecting an inappropriate partner that runs contrary to the original motivations.
Originality/value
The emphasis is on the interdependence between motivations and criteria for partner selection. This paper systematically deals with the interdependence of these two factors. Based on this iterative review approach proposed in this paper, a proper weight setting for these criteria is available and will comply with the original motivation for establishing the strategic alliance.
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It is widely recopnized that many different opes and forms of knowledge contribute to technology development. Formal codified knowledge, tacit knowledge, informal knowledge and cultural knowledge have all recently been addressed. However, one other particular form of knowledge—the contribution of knowledge/information embodied in the working context—has not been directly or explicitly addressed to the same extent. Tet this form of knowledge—‘contingent knowledge’—it is argued, plays a crucial but under-appreciated role in technology development and innovation. In this paper, the concept of contingent knowledge is further explicated and illustrated by means of examples, and the strategic and practical implications are drawn out.
THE large-scale use of photovoltaic devices for electricity generation is prohibitively expensive at present: generation from existing commercial devices costs about ten times more than conventional methods1. Here we describe a photovoltaic cell, created from low-to medium-purity materials through low-cost processes, which exhibits a commercially realistic energy-conversion efficiency. The device is based on a 10-µm-thick, optically transparent film of titanium dioxide particles a few nanometres in size, coated with a monolayer of a charge-transfer dye to sensitize the film for light harvesting. Because of the high surface area of the semiconductor film and the ideal spectral characteristics of the dye, the device harvests a high proportion of the incident solar energy flux (46%) and shows exceptionally high efficiencies for the conversion of incident photons to electrical current (more than 80%). The overall light-to-electric energy conversion yield is 7.1-7.9% in simulated solar light and 12% in diffuse daylight. The large current densities (greater than 12 mA cm-2) and exceptional stability (sustaining at least five million turnovers without decomposition), as well as the low cost, make practical applications feasible.
This paper investigates the adoption of Open Innovation in the bio-pharmaceutical industry, studying through which organisational modes it is put into practice and how these modes are interwoven with the different phases of drug discovery and development process. Two rounds of interviews with industry experts were carried out to develop a model describing the adoption of Open Innovation by bio-pharmaceutical companies. This framework was then applied to an extensive and longitudinal empirical basis, which includes data about the adoption of Open Innovation by the top 20 worldwide industry players, in the time period 2000–2007. The paper provides a thorough discussion of how bio-pharmaceutical firms have used different organisational modes (i.e. licensing agreements, non-equity alliance, purchase and supply of technical and scientific services) to enter into relationship with different types of partners (i.e. large pharmaceutical companies, product biotech firms, platform biotech firms and universities) with the aim to acquire (Inbound Open Innovation) or commercially exploit (Outbound Open Innovation) technologies and knowledge. The implications of the study for Open Innovation research and possible avenues for future investigation are discussed at length in the paper.
Purpose
This paper aims to provide a practical model usable by organizations to help form agile virtual enterprises. The model helps to integrate a variety of factors, tangible and intangible, strategic and operational, for decision‐making purposes.
Design/methodology/approach
A comprehensive development of factors is determined from the literature and an analytical network process (ANP) methodology is introduced for decision model development. An illustrative example is presented.
Findings
The results provide a robust model that will aid decision makers and agile virtual enterprise brokers form partnerships within these organizational structures.
Research limitations/implications
The paper introduces a conceptual model with an illustrative validating example. A practical application and reapplication of the model are required to further validate the model. ANP can require significant managerial input for its application, potentially causing fatigue for decision makers.
Practical implications
Practical implications include a partner selection tool and framework for decision makers. The model may be easily tweaked by the elimination or addition of decision factors and their relationships.
Originality/value
The paper is useful to practitioners and organizations seeking to manage partnership formation of agile virtual enterprises, an emerging organizational form. This work expands the number of factors and interrelationships among these factors that no other model has explicitly addressed for the agile virtual enterprise formation situation.
This study examines how different governance modes for external business development activities and venture relatedness affect a firm's innovative performance. Building on studies which have suggested that interorganizational relationships enhance the innovative performance of firms, we propose that governance modes and venture relatedness interact in their effect on innovative performance. Analyzing a panel of the largest firms in four information and communication technology sectors, we find that degree of relatedness for corporate venture capital investments, alliances, joint ventures, and acquisitions influences their impact on innovative performance.
In this paper, we are going to present a method for detecting the risks of patent infringement by evaluating similarities between patent documents on the basis of semantic patent analysis. This approach enables the user to visualize similarities in the contents on a semantic patent map by means of multi-dimensional scaling. The effectiveness of the semantic patent map has already been demonstrated by Dressler (2006) with regard to patents of seal technology, in which documents are commonly kept short and the extracted contents are concise. This paper will open out to the field of biotechnology, where patents can easily comprise several hundreds of pages. The method presented here conveys an interdisciplinary approach and combines computer-aided natural language processing with domain-specific expertise of biochemical processes. This is illustrated by an authentic case of infringement involving two manufacturers of DNA chips. Our experiment will show how the infringement case is visualized on a patent map based on semantic patent analysis. This experiment can be compared with the search for a needle in a haystack, the two competitive patents representing significantly conflicting needles. From an approximate number of 4,000 patents in the current US Class 435/6, a set of patents was selected that included the needles mentioned. This paper will point out how such mutual interference can be detected by way of semantic patent analysis, and what advice may be given to R&D managers who are faced with the risk of patent infringement.
Much of the prior research on interorganizational learning has focused on the role of absorptive capacity, a firm's ability to value, assimilate, and utilize new external knowledge. However, this definition of the construct suggests that a firm has an equal capacity to learn from all other organizations. We reconceptualize the firm-level construct absorptive capacity as a learning dyad-level construct, relative absorptive capacity. One firm's ability to learn from another firm is argued to depend on the similarity of both firms' (1) knowledge bases, (2) organizational structures and compensation policies, and (3) dominant logics. We then test the model using a sample of pharmaceutical–biotechnology R&D alliances. As predicted, the similarity of the partners' basic knowledge, lower management formalization, research centralization, compensation practices, and research communities were positively related to interorganizational learning. The relative absorptive capacity measures are also shown to have greater explanatory power than the established measure of absorptive capacity, R&D spending. © 1998 John Wiley & Sons, Ltd.
Profiles of inventors' technological competence are a valuable source of information for decision-making in research and development (R&D) management, e.g. concerning inventor assessment, human resource development and R&D team-building. In the following exposition, a new method of inventor profiling will be put forward, which is based in particular on semantic patent analysis and multidimensional scaling. First, in the course of semantic patent analysis, specialized software, equipped with a natural language processor, reads the patent text transferring the contents into a subject–action–object–format (SAO). The extracted SAO structures are then used to create similarity matrices for patents or patent sets, respectively, according to a specific similarity value. Subsequently, an inventor competence map can be produced by means of multidimensional scaling.The benefits of this method for R&D-related issues in human resource management will be illustrated by the example of a German mechanical engineering company. Two distinct types of profiles were generated and tested: (i) the profile of a single key inventor and (ii) a profile of key inventor sets. The single key inventor profile gives information on the range of competence, i.e. the homogeneity or heterogeneity of a certain inventor's competences, providing far more detailed insights than resorting to bibliographic data like international patent classification (IPC) classes or citations, whereas the latter kind of profile establishes the position of a certain key inventor in relation to others, helping to highlight specific groups of inventors and their domains. These results are clearly apt to support human resource management.
In this paper, we use data from the Carnegie Mellon Survey on industrial R&D to evaluate for the U.S. manufacturing sector the influence of "public"(i.e., university and government R&D lab) research on industrial R&D, the role that public research plays in industrial R&D, and the pathways through which that effect is exercised. We find that public research is critical to industrial R&D in a small number of industries and importantly affects industrial R&D across much of the manufacturing sector. Contrary to the notion that university research largely generates new ideas for industrial R&D projects, the survey responses demonstrate that public research both suggests new R&D projects and contributes to the completion of existing projects in roughly equal measure overall. The results also indicate that the key channels through which university research impacts industrial R&D include published papers and reports, public conferences and meetings, informal information exchange, and consulting. We also finnd that, after controlling for industry, the influence of public research on industrial R&D is disproportionately greater for larger firms as well as start-ups.
This study establishes a mechanism for partner selection that emphasizes the relation of criteria and motivation. Since the motivations for establishing strategic alliances follow different enterprises’ needs, attempting to identify universal criteria weights that enterprises should employ when seeking a proper partner are not productive. Consequently, the weighting process for criteria must consider the intensity of motivations for establishing the alliance. When evaluating companies with closer levels of performance, the approach of pair-wise comparison is more suitable than the direct scoring method. Considering the strategic level, most comparisons may be vague and linguistic variables defined as fuzzy numbers are applied to this situation. The calculation procedure for the weighting and evaluation processes under a vague environment is proposed and validated by using an illustrative example.
This paper examines the nature of the search process firms go through in identifying partners for technological cooperation, and, in particular, the extent to which systematic information collection on potential partners is likely to enhance the choice of satisfactory partners. The results, based on 118 Dutch companies, suggest that only few companies have formal procedures to find technology partners, and that they tend to depend on industry contacts for information. A company's pro-activeness and experience in finding partners were found to have a positive influence on the final selection of an appropriate partner. This was also true for an extensive evaluation, when preceded by intensive search. Direct top management involvement and company size, however, were negatively correlated with successful partner selection. Finally, the results showed that companies were overall less successful in identifying potential partners in related areas of technology, but more successful in finding appropriate partners that cover unrelated technologies.
The decision processes surrounding outsourcing are complicated by the very nature of uncertainty involved in the outsourcing process and by poor vendor management. In this study, we focus on vendor selection, one of the two basic issues of vendor management in outsourcing. Due to the limitation of the classic one-stage vendor selection model, we propose a two-stage vendor selection research framework in outsourcing. The first stage is a trial phase that helps the client to find the best match between the vendor and the outsourced project. In the second stage, the client employs the chosen vendor for the full implementation of the project. We formulate this selection decision under the two-stage framework as a combinatorial optimization model. We analyze the complexity of the problem and develop a solution procedure to find the exact optimal solution. By applying this model to numerical case studies, we demonstrate that benefit to adopt two-stage process to the vendor depends on information improvement in the first stage and the client's ability to adapt to updated knowledge. We also argue that the selection of vendors for the first stage testing is more about creating a good vendor portfolio than simply picking the frontrunners.
This study examines the post-M&A innovative performance of acquiring firms in four major high-tech sectors. Non-technological M&As appear to have a negative impact on the acquiring firm's post-M&A innovative performance. With respect to technological M&As, a large relative size of the acquired knowledge base reduces the innovative performance of the acquiring firm. The absolute size of the acquired knowledge base only has a positive effect during the first couple of years after which the effect turns around and we see a negative effect on the innovative performance of the acquiring firm. The relatedness between the acquired and acquiring firms’ knowledge bases has a curvilinear impact on the acquiring firm's innovative performance. This indicates that companies should target M&A ‘partners’ that are neither too unrelated nor too similar in terms of their knowledge base.
Using firm level data, this paper explores the determinants of R&D cooperation. It focuses on the impact of information flows or spillovers on R&D cooperation, but also explores the role of the traditionally considered factors (firm size, cost and risk sharing, and complementarities). The estimation methods used allow testing the endogeneity for the explanatory variables, which in other papers are assumed to be endogenous a priori. I find that the choice of an appropriate “structure” of endogeneity has important consequences for the estimates: only in this case do cost-risk sharing and complementarities have the expected positive effect. I also find that the overall picture of the importance of the explanatory variables depends on the estimation method. In this sense, two-step procedures overestimate the importance of spillovers. With a more efficient procedure, I find that cost-risk sharing is the most important determinant of R&D cooperation in Spain. Finally, the overall results on the importance of spillovers are consistent with the existing literature, but I find that a greater level of legal protection in the industry has a negative effect on R&D cooperation.
The paper proposes a new approach to create a patent classification system to replace the IPC or UPC system for conducting patent analysis and management. The new approach is based on co-citation analysis of bibliometrics. The traditional approach for management of patents, which is based on either the IPC or UPC, is too general to meet the needs of specific industries. In addition, some patents are placed in incorrect categories, making it difficult for enterprises to carry out R&D planning, technology positioning, patent strategy-making and technology forecasting. Therefore, it is essential to develop a patent classification system that is adaptive to the characteristics of a specific industry. The analysis of this approach is divided into three phases. Phase I selects appropriate databases to conduct patent searches according to the subject and objective of this study and then select basic patents. Phase II uses the co-cited frequency of the basic patent pairs to assess their similarity. Phase III uses factor analysis to establish a classification system and assess the efficiency of the proposed approach. The main contribution of this approach is to develop a patent classification system based on patent similarities to assist patent manager in understanding the basic patents for a specific industry, the relationships among categories of technologies and the evolution of a technology category.
The main objective of this paper is to present a new fuzzy approach to partnership selection in the formation of virtual enterprises. The phases of the virtual enterprise life cycle are briefly described and it is shown that the partnership selection is a key factor in the formation of such complex organisations. It is justified that the partnership selection process should be formulated as a multiple criteria decision-making problem under uncertainty. A new fuzzy programming method is proposed for assessment of uncertain weights of partnership selection criteria and uncertain scores of alternative partners, in the basic framework of the Analytic Hierarchy Process. The proposed fuzzy prioritisation method uses interval pairwise comparison judgements rather than exact numerical values of the comparison ratios and transforms the initial prioritisation problem into a linear program. The method can derive priorities from inconsistent interval comparison matrices, thus eliminating the drawbacks of the existing interval prioritisation methods. Moreover, the method generalises the known prioritisation methods, since it can be used for deriving priorities from exact, interval or mixed comparison matrices, regardless of their consistency. A numerical example, illustrating the application of this method to partnership selection process is given.