Strategic partnership and its effect on external learning of technology descendants

We examine how strategic partnership affects external learning of technology descendants from emerging markets under the context of Taiwan??s flat panel display industry. The study takes patent citation as a trail of knowledge flow, and incorporates 1,726 pairs relations of the cited and citing firms. Our empirical evidence shows positive pattern of external learning through strategic technology partnership. After controlling the quality factor of the knowledge, technology descendants do learn more from their alliance partners than other non-allied firms; particularly, trading type of partnerships characterized by the asymmetric relations appears to bring more impact. Furthermore, a focused approach in extrapolating knowledge from strategic partners seems to be the dominant practice.     

Beyond the joint-venture: knowledge sourcing in Chinese automotive events

Abstract

This article analyses how firms use events and trade fairs for external knowledge sourcing, which barriers emerge and how event organisers strategically mediate and influence those processes. The research setting focuses on two major automotive events in Shanghai, highlighting that knowledge sourcing in these events do complement other types of knowledge accessed in permanent ‘sites’ and organisational configurations, such as in clusters and through joint-ventures. Firms use automotive events to access buzz, to monitor other firms and to explore options for new collaborations. Yet, it is also argued that a focus on existing relations, the defensive strategies deployed by lead firms and the intrinsic complexity of exhibited technologies hinder the process of knowledge sourcing that is influenced by event organisers’ content, matchmaking and access policies.     

Industrial Knowledge Bases as Drivers of Open Innovation?

The article presents an analysis of a large-scale survey with the aim of understanding differences in the open, interactive and distributed nature of external innovation relations amongst firms belonging to different industrial knowledge bases. The thesis is that the source of critical innovation relevant knowledge differs between industrial knowledge bases, making the character and the need of openness contingent on these specificities. Accordingly, we anticipate that we will observe systematic variations in how industries access and combine innovation-related external knowledge. In our analyses we attempt to address a gap in the literature by examining how industrial knowledge bases affect the recombination of knowledge by analysing the different extents, forms (formal and informal) and geography of inbound open innovation. The article illustrates that features and structures of inbound open innovation align, to a large extent, with the industries' knowledge bases and that there is a interplay between an industry's knowledge base, the internal organisation of innovation processes and the channels and geography of inbound open innovation.     

Knowledge Composition and Its Influence on New Product Development Performance in the Big Data Environment

Product innovation is regarded as a primary means for enterprises to maintain their competitive advantage. Knowledge transfer is a major way that enterprises access knowledge from the external environment for new product innovation. Knowledge transfer may face the risk of infringement of the intellectual property rights of other enterprises and the termination of licensing agreements by the knowledge source. Enterprises must develop independent innovation knowledge at the same time they profit from knowledge transfers. Therefore, new product development by an enterprise usually consists of three types of new knowledge: big data knowledge transferred from big data knowledge providers, private knowledge transferred from other enterprises, and new knowledge developed independently by an enterprise in the big data environment. To find what the influences of different types of knowledge are on new product development (NPD) performance, a model is presented that maximizes the expected NPD performance. The results show that the greater the weight of independent innovation knowledge, the greater the performance of NPD. Enterprises tend to transfer knowledge from the external environment when the research and development (R&D) investment is much higher, and enterprises will speed up independent innovation when independent innovation knowledge is expected to bring a larger market share. The model can help enterprises to determine knowledge composition, the scale of R&D investment and predict the performance of NPD.     

The mutual information of university-industry-government relations: An indicator of the Triple Helix dynamics

University-industry-government relations provide a networked infrastructure for knowledge-based innovation systems. This infrastructure organizes the dynamic fluxes locally and the knowledge base remains emergent given these conditions. Whereas the relations between the institutions can be measured as variables, the interacting fluxes generate a probabilistic entropy. The mutual information among the three institutional dimensions provides us with an indicator of this entropy. When this indicator is negative, self-organization can be expected. The self-organizing dynamic may temporarily be stabilized in the overlay of communications among the carrying agencies. The various dynamics of Triple Helix relations at the global and national levels, in different databases, and in different regions of the world, are distinguished by applying this indicator to scientometric and webometric data.     

Does distance hinder the collaboration between Australian universities in the humanities,arts and social sciences?

Australia is a vast country with an average distance of 1911 km between its eight state capital cities. The quantitative impact of this distance on collaboration practices between Australian universities and between different types of Australian universities has not been examined previously and hence our knowledge about the spatial distribution effects, if any, on collaboration practices and opportunities is very limited. The aim of the study reported here was therefore to analyse the effect of distance on the collaboration activities of humanities, arts and social science scholars in Australia, using co-authorship as a proxy for collaboration. In order to do this, gravity models were developed to determine the distance effects on external collaboration between universities in relation to geographic region and institutional alliance of 25 Australian universities. Although distance was found to have a weak impact on external collaboration, the strength of the research publishing record within a university (internal collaboration) was found to be an important factor in determining external collaboration activity levels. This finding would suggest that increasing internal collaboration within universities could be an effective strategy to encourage external collaboration between universities. This strategy becomes even more effective for universities that are further away from each other. Establishing a hierarchical structure of different types of universities within a region can optimise the location advantage in the region to encourage knowledge exchange within that region. The stronger network could also attract more collaboration between networks.     

Betweenness and diversity in journal citation networks as measures of interdisciplinarity—A tribute to Eugene Garfield

Journals were central to Eugene Garfield’s research interests. Among other things, journals are considered as units of analysis for bibliographic databases such as the Web of Science and Scopus. In addition to providing a basis for disciplinary classifications of journals, journal citation patterns span networks across boundaries to variable extents. Using betweenness centrality (BC) and diversity, we elaborate on the question of how to distinguish and rank journals in terms of interdisciplinarity. Interdisciplinarity, however, is difficult to operationalize in the absence of an operational definition of disciplines; the diversity of a unit of analysis is sample-dependent. BC can be considered as a measure of multi-disciplinarity. Diversity of co-citation in a citing document has been considered as an indicator of knowledge integration, but an author can also generate trans-disciplinary—that is, non-disciplined—variation by citing sources from other disciplines. Diversity in the bibliographic coupling among citing documents can analogously be considered as diffusion  or differentiation of knowledge across disciplines. Because the citation networks in the cited direction reflect both structure and variation, diversity in this direction is perhaps the best available measure of interdisciplinarity at the journal level. Furthermore, diversity is based on a summation and can therefore be decomposed; differences among (sub)sets can be tested for statistical significance. In the appendix, a general-purpose routine for measuring diversity in networks is provided.     

模糊神经网络在钛合金研究领域中的应用

模糊神经网络具有较强的非线性问题处理能力,可直接处理"不确定性"结构化知识,已成为材料科学研究领域中重要的使用技术。综述了模糊神经网络在钛合金材料化学成分设计与优化、材料成分与性能关系、微观组织定量研究等领域的应用情况,指出将该方法应用于钛合金材料研究领域,不仅可精确地描述材料成分-工艺-组织-性能之间高度复杂非线性关系,而且能够应用到材料加工的智能控制以及性能预测等领域。     

Relations among science indicators or more generally among anything one might wish to count about texts

In a previous paper a static model for the relations among science indicators was discussed. From the perspective of science dynamics, we are interested not in relations among variables or indicators, but in the prediction of an event, given comparable events about which we already have knowledge. The quality of the prediction can be measured by the expected information valueI of the message, which converts thea priori probabilities of the events stored in the knowledge base into thea posteriori probabilities of the event. The possibility of predicting in terms of specified variables with hindsight, gives a quantitative measure for testing hypotheses concerning the reconstruction of scientific developments. Some implications for the construction of artificial intelligence using textual archives, as a knowledge base will be discussed.     

多高层钢筋混凝土房屋抗震初步设计专家系统

本文介绍一个多高层钢筋混凝土房屋初步设计知识库专家系统-RCBAD。该系统利用面向对象程序设计技术在微机上实现。它能够对几种常见结构类型的多层与高层RC房屋作满足抗震规范TJ11-78要求的结构初步设计;并进一步利用外部分析程序进行动力时程计算来校验抗震性能。系统采用层次式分块知识库结构,可扩充功能完善为一个工程实用系统。     

A Knowledge-Enriched and Span-Based Network for Joint Entity and Relation Extraction

The joint extraction of entities and their relations from certain texts plays a significant role in most natural language processes. For entity and relation extraction in a specific domain, we propose a hybrid neural framework consisting of two parts: a span-based model and a graph-based model. The span-based model can tackle overlapping problems compared with BILOU methods, whereas the graph-based model treats relation prediction as graph classification. Our main contribution is to incorporate external lexical and syntactic knowledge of a specific domain, such as domain dictionaries and dependency structures from texts, into end-to-end neural models. We conducted extensive experiments on a Chinese military entity and relation extraction corpus. The results show that the proposed framework outperforms the baselines with better performance in terms of entity and relation prediction. The proposed method provides insight into problems with the joint extraction of entities and their relations.     

《锦绣万花谷》与宋代风物文化的关系

《锦绣万花谷》是一部宋代私人编纂的供百姓案头翻阅的日用类书,与官方编纂的带有政治目的的类书不同,书中以词条类目的形式为时人辑录了古人生活常识,同时也记录了与宋人日常生活相关的知识,其内容更贴近于百姓对生活日常的理解。《锦绣万花谷》对于研究宋人的思想、文明和知识是非常重要的。《锦绣万花谷》展示了宋代的市井生活,可以看作是描写宋代社会文化的引子,具体表现在经济关系、社会关系、价值观念、民风民俗等方方面面,特别点在于追寻宋代传统文化及其在器物外化上的文化根源。     

Relationship between impact energy and design parameters of glass bottles

Glass containers are relatively heavy. Also, glass is fragile, and breakage sometimes occurs during handling and transport. Therefore, glass bottles must be constructed to achieve maximum strength at minimum weight (wall thickness). To our knowledge, about 85% of all breakage is caused by external impact. The purpose of the present paper is to study the relationship between the shape and thickness of glass bottles and the impact loads they can resist by using linear multivariate statistical/mathematical regression (or calibration) techniques (UN-SCRAMBLER), in order to compute minimum required thickness of the bottle as a function of impact strength and vice versa. The study was based on 10 different types of bottles. Moreover, we have concentrated on measurements related to the heel of the bottle. The set of bottles used are described as follows: returnable, round body, straight side wall and without metal oxide coating. All the bottles were given a standard abusement before the impact tests. The following conclusions were reached:

• i there are strong relations between glass thickness and the resistance to external impact (as expected);
• ii multivariate calibration gave much better results than using only one variable at a time
• iii the predictive ability is not good enough (accuracy of ±10–15%). But provides useful information that would be difficult to obtain by other methods

(iii) .     

An extension of the Cottey conduction model

By postulating that the scattering from sources other than external surfaces can be described by a Mayadas-Shatzkes effective relaxation time τ_g and then by using a Cottey relation to describe the external surface scattering it is possible to derive analytical expressions for the conductivity, the temperature coefficient of the resistivity and the strain coefficient of the resistivity of thin polycrystalline or monocrystalline metallic films.These expressions constitute more adequate relations than the equations previously proposed by Dobierzewska-Mozrzymas and Warkusz because they take into account the effects that thickness and grain boundaries have on the resistivity and the strain coefficient of the resistivity.     

Interactive knowledge exchanges under complex social relations: A simulation model of a developing country cluster

This is a model of knowledge exchange by informal interaction among agents in a low technology cluster. The paper studies these knowledge exchanges in an environment of complex social relations. This study tests whether the small-world network structure is the most favorable for knowledge exchanges in these environments, and explores the influence of social relations and network distance on magnitude and equity of knowledge diffused. The results show that, when knowledge exchanges are undertaken in environments of complex social relations, a small-world network structure may still be the best network structure facilitating the highest performance, but it is not the best in terms of the most equitable knowledge distribution. The results also confirms that the highest and most equitable knowledge distribution is achieved when there is perfect affinity among the agents. These results contribute to the existing series of studies on efficient network structures for knowledge diffusion, and on the broader literature on the social forces shaping learning and knowledge diffusion.     

Development and application of a keyword-based knowledge map for effective R&D planning

With the growing recognition of the importance of knowledge creation, knowledge maps are being regarded as a critical tool for successful knowledge management. However, the various methods of developing knowledge maps mostly depend on unsystematic processes and the judgment of domain experts with a wide range of untapped information. Thus, this research aims to propose a new approach to generate knowledge maps by mining document databases that have hardly been examined, thereby enabling an automatic development process and the extraction of significant implications from the maps. To this end, the accepted research proposal database of the Korea Research Foundation (KRF), which includes a huge knowledge repository of research, is investigated for inducing a keyword-based knowledge map. During the developmental process, text mining plays an important role in extracting meaningful information from documents, and network analysis is applied to visualize the relations between research categories and measure the value of network indices. Five types of knowledge maps (core R&D map, R&D trend map, R&D concentration map, R&D relation map, and R&D cluster map) are developed to explore the main research themes, monitor research trends, discover relations between R&D areas, regions, and universities, and derive clusters of research categories. The results can be used to establish a policy to support promising R&D areas and devise a long-term research plan.     

Stratification among Journals in Management Research: A Bibliometric Study of Interaction between European and American Journals

Tho key features of science are its rapid growth and its continuous differentiation. The establishment of new journals can be seen as an expression of both growth and differentiation. In this study of the network among management journals, the focus is on forms of differentiation, i.e., the relationship between stratification and specialization in a network of journals. The question asked in this study is whether the different position of American and European journals corresponds with different levels of specialization. A tendency toward such a structuration of the journal network would indicate an interregional integration of management research. Articles published in six of the most influential American and European journals covering the period from 1981 to 1998 have been downloaded. The findings in this study indicate that even though European journals formed a periphery in relation to the American journals in terms of clearly asymmetrical exchange relations, it was the European journals that seemed to be more comprehensive in scope. The tendency during the investigated period indicated differentiation in terms of segmentation rather than specialization.     

Operational design of a supply chain system using the Taguchi method,response surface methodology,simulation, and optimization

Managing a supply chain to meet an organization's objectives is a challenge to many firms. It involves collaboration in multiple dimensions, such as cooperation, information sharing, and capacity planning. In this research, we focus on identifying the ‘best’ operating conditions for a supply chain. We propose a hybrid approach that incorporates simulation, Taguchi techniques, and response surface methodology to examine the interactions among the factors, and to search for the combination of factor levels throughout the supply chain to achieve the ‘optimal’ performance. This study makes it possible for firms to understand the dynamic relations among various factors, and provides guidelines for management to minimize the impact of demand uncertainty on the performance of the supply chain. The results help the manufacturer determine the proper plant capacity and adopt the right level of delayed differentiation strategy for its products. We also quantify the potential gains of cooperation among different members of the supply chain. Using such knowledge, a manufacturer can develop an appropriate incentive plan to motivate the retailers and suppliers to collaborate, and to realize the potential of the entire supply chain.     

Extension of quadrature orthogonal signal corrected two-dimensional (QOSC 2D) correlation spectroscopy I: principal component analysis based QOSC 2D

The present study proposes a new quadrature orthogonal signal correlation (QOSC) filtering method based on principal component analysis (PCA). The external perturbation variable vector typically used in the QOSC operation is replaced with a matrix consisting of the spectral data principal components (PCs) and their quadrature counterparts obtained by using the discrete Hilbert-Noda transformation. Thus, QOSC operation can be carried out for a dataset without the explicit knowledge of the external variables information. The PCA-based QOSC filtering can be most effectively applied to two-dimensional (2D) correlation analysis. The performance of this filtering operation on the simulated spectra data set with the interference of strong random noise demonstrated that the PCA-based QOSC filtering not only eliminates the influence of signals that are unrelated to the final target but also preserves the out-of-phase information in the data matrix essential for asynchronous correlation analysis. The result of 2D correlation analysis has also demonstrated that essentially only one principal component is necessary for PCA-based QOSC to perform well. Although the present PCA-based QOSC filtering scheme is not as powerful as that based on the explicit knowledge of the external variable vector, it still can significantly improve the quality of 2D correlation spectra and enables OSC 2D to deal with the problems of losing the quadrature (or out-of-phase) information. In particular, it opens a way to perform QOSC for the spectral dataset without external variables information. The proposed approach should have wide applications in 2D correlation analysis of spectra driven by multiplicative effects in complicated systems in biological, pharmaceutical, and agriculture fields, and so on, where the explicit nature of the external perturbation cannot always be known.     

Relations between tooth enamel dose and organ doses for electron spin resonance dosimetry against external photon exposure

An analysis of doses to tooth enamel and to organs was carried out to develop a method that can predict the organ doses and the effective dose by electron spin resonance (ESR) dosimetry using tooth samples for external photon exposure. Absorbed dose to tooth enamel and organ doses were obtained by Monte Carlo calculations using the EGS4 code in combination with a mathematical human model with a newly defined teeth part. The calculations gave quantitative relations between tooth enamel dose and organ doses for some cases of external photon exposure. It was also found that tooth enamel dose depends more significantly on energy of incident photons than the other organ dose or the effective dose. The obtained data are to be useful for the assessment of individual dose in past exposure events by the ESR dosimetry using tooth enamel.