Identifying R&D partners through Subject-Action-Object semantic analysis in a problem & solution pattern

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.     

Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis

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.     

A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies

The planning of technological research and development (R&D) is demanding in areas with many relationships between technologies. To support decision makers of a government organization with R&D planning in these areas, a methodology to make the technology impact more transparent is introduced. The method shows current technology impact and impact trends from the R&D of an organization's competitors and compares these to the technology impact and impact trends from the organization's own R&D. This way, relative strength, relative weakness, plus parity of the organization's R&D activities in technology pairs can be identified.A quantitative cross impact analysis (CIA) approach is used to estimate the impact across technologies. Our quantitative CIA approach contrasts to standard qualitative CIA approaches that estimate technology impact by means of literature surveys and expert interviews. In this paper, the impact is computed based on the R&D information regarding the respective organization on one hand, and based on patent data representative regarding R&D information of the organization's competitors on the other hand. As an illustration, the application field ‘defence’ is used, where many interrelations and interdependencies between defence-based technologies occur. Firstly, an R&D-based and patent-based Compared Cross Impact (CCI) among technologies is computed. Secondly, characteristics of the CCI are identified. Thirdly, the CCI data is presented as a network to show the overall structure and the complex relationships between the technologies. Finally, changes of the CCI are analyzed over time. The results show that the proposed methodology has the potential to generate useful insights for government organizations to help direct technology investments.     

Characterising a technology development at the stage of early emerging applications: nanomaterial-enhanced biosensors

We devise future-oriented technology analyses tools to investigate a technology at an interesting development stage of early emerging applications. At this stage, technologies show great potential with little established commercialisation. Future development pathways are highly uncertain and heavily dependent on contextual interactions. We apply R&D profiling, R&D-to-applications cross-charting, and technology delivery system modelling to help understand the phenomena that bear upon development prospects. We develop our approach through a two-tier case study: general treatment of nanomaterial-enhanced biosensors, followed by more specialised treatment of one subset of those. Results convey the importance of considering technological and social context factors together to understand likely innovation pathways.     

Exploring potential R&D collaborators with complementary technologies: The case of biosensors

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.     

Vacant technology forecasting using new Bayesian patent clustering

Vacant technology forecasting (VTF) is a technology forecasting approach to find technological needs for given industrial field in the future. It is important to know the future trend of developing technology for the R&D planning of a company and a country. In this paper, we propose a new Bayesian model for patent clustering. This is a VTF methodology based on patent data analysis. Our method is composed of Bayesian learning and ensemble method to construct the VTF model. To illustrate the practical way of the proposed methodology, we perform a case study of given technology domain using retrieved patent documents from patent databases in the world.     

Structural equation model for predicting technology commercialization success index (TCSI)

Expecting high return, many firms try to invest on R&D of new technology. However, critical loss of assets would occur, when a firm fails to commercialize the developed technology. It would be of interest to provide the ideal environment for commercialization from the R&D stage. In this study, we use a structural equation model (SEM) to forecast the technology commercialization success index (TCSI) in relation to technology developer, technology receiver, technology transfer center, and environmental factors. The proposed SEM is fitted based on partial least square (PLS) estimation procedure. Individual TCSI is then found following the approach used for American customer satisfaction index (ACSI) for various combinations of characteristics of the type of technology, technology receiver, and technology developer. We expect that the proposed approach for TCSI can be used as guidance for an ideal match of technology with technology developer and technology receiver.     

Why firm size matters: investigating the drivers of innovation and economic performance in New Zealand using the Business Operations Survey

The performance of the New Zealand (NZ) economy is something of an enigma. Although ranked one (of 144 countries) for four important ‘growth fundamentals’ NZ is ‘middle of the pack’ when it comes to economic growth, productivity and process innovation. Using four iterations (2005, 2007, 2009 and 2011) of the Business Operations Survey, this research seeks to shed some new light on this conundrum by using a multivariate probit regression (mvprobit) approach applied to pooled samples in excess of 22,000 unit record observations of NZ firms. The results suggest that factors including firm size, high perceived quality, investment/research and development (R&D) capability, major technology change, application of formal IP protection and new export markets are systematically and positively related to innovation; while many external issues, such as those related to geography, market structure, business environment, have little influence. At the firm level, innovations in NZ are highly dependent on the firms’ internal ability to develop new technologies and market demand. The (very small) size of firms does matter in NZ, which lacks a major ‘home market’ or a major trade block on its doorstep, such that ultimately, government may need to be involved to maintain a viable scale for domestic R&D.     

Mark-ups, economies of scale and the role of knowledge spillovers in OECD industries

Using R&D-based models of economic growth as a foundation, this paper argues that market-driven knowledge creation is necessarily linked as an engine of productivity growth to economies of scale and market-power. A cost function and factor demand model is applied to a cross-country industry data set to study market-power, economies of scale and the role of knowledge in an integrated approach. Empirical results reveal the presence of market-power and economies of scale in all of the industries investigated. R&D and spillovers explain some of the productivity growth observed. Spillovers are identified as an external source of economies of scale.     

Understanding the decision rules for 3D printing adoption

ABSTRACT

While 3D printing is a radically different manufacturing method based on advanced technologies, it has not been adopted to-date with as much alacrity as was originally expected. A variety of hidden rules may influence the broad adoption of this novel technology. The purpose of this study is to explore the decision rules for 3D printing adoption from an organisational perspective. Hybrid approaches are proposed to integrate the technology-organisation-environment (TOE) framework and the rough set theory (RST) in order to generate the decision rule and the determinants for 3D printing adoption. An empirical study on manufacturing enterprises in Taiwan is subsequently carried out, with the results revealing a considerable amount of meaningful information that may therefore be useful in the context of management.     

组织协同学习机制与整合优化方法研究

为回答通过何种组织学习形式提升组织学习效果的问题,系统梳理并分析现有组织学习相关成果存在的问题,结合协同机制原理构建组织协同学习模型。在此基础上,考虑到组织协同学习系统内部要素间存在复杂影响关系,借鉴ANP方法决策思想,提出从组织学习整体协同度方面整合优化组织协同学习系统复杂作用关系决策方法。结果表明,上述理论方法有效、可行,组织学习协同度能够较好地反映组织学习整体效果,对提升组织学习能力和创造企业竞争优势具有一定借鉴意义。     

Inbound open innovation in biopharmaceutical firms: unpacking the role of absorptive capacity

In this study, we extend recent attempts to clarify the role of antecedents of absorptive capacity in realising purposive inflows of external knowledge in the biopharmaceutical industry. By assuming that internal R&D resources contribute to absorptive capacity as a multidimensional concept, we propose a framework for the integrated analysis of the interactions between the different antecedents of absorptive capacity and inbound open innovation of biopharmaceutical firms. Our analysis relies on a set-theoretic approach and uses fuzzy-set qualitative comparative analysis (fsQCA) to study a set of selected Italian biopharmaceutical firms. The analysis reveals that biopharmaceutical companies with a diversified R&D business domain and an extended entrepreneurial scientific network are likely to adopt an inbound open innovation strategy.     

基于ANP的高校教师绩效考核实证研究

高校是现代化建设过程中人才培养的主要阵地,高校教师的绩效直接影响人才培养的质量.因此,研究如何科学的评价高校教师绩效是一项十分有意义的工作.高校教师的工作绩效考核会涉及到众多考核指标,且指标之间通常具有依赖与反馈关系.因此,论文引入ANP的方法构建具有网络层次的指标体系,有效解决了高校教师绩效考评过程中评价指标之间不完全独立的问题,并使用Super Decisions软件计算出网络层指标的最终权重,据此对多个教师的绩效水平进行了考核.通过本文所构建的高校教师缋效评价指标体系以及网络层次法所建立的网络模型,为高校教师绩效考核问题提供了一个有效的解决办法.实证研究的结果证明了该方法应用于高校教师缋效考核的科学性与可行性.     

An econometric model of production with endogenous improvement in energy efficiency, 1970-1995

The purpose of this paper is to introduce a modification of a standard four input production process where energy is used in an inefficient way due partly to unnecessary waste of energy. In this production process, R&D investment is an additional input in order to improve energy efficiency. It closes the gap between energy purchased and energy used effectively. The more is invested, the less is the waste of energy. With the cost and benefit of R&D investment incorporated in our model of the firm, we analyse the impact of an energy tax on R&D effort, on output and on the waste of energy. The model is implemented empirically by choosing a translog cost function and a set of first-order conditions, using data for the German chemical industry, 1970-1995. In a simulation study based on higher energy prices we found outsourcing as the consequent reaction of the firm- more material is used and less of energy, labour, and capital, given the unchanged output level. There is no indication of a double dividend in terms of environmental improvement as well as higher demand for labour on the industry level calling for a computable general equilibrium approach in order to answer this open question.     

The productivity effects of local R&D outsourcing: the moderating role of subsidiary mandate and internal R&D

This study examined the moderating effects of two kinds of internal R&D (parent R&D and subsidiary R&D) on the relationship between local R&D outsourcing and subsidiary productivity as internal R&D challenges the collaborative relationships in the local environment. Due to the specific feature of a subsidiary mandate, we further investigate whether two distinct kinds of internal R&D have different moderating effects between exploitation-oriented subsidiaries and exploration-oriented subsidiaries, based on a sample of longitudinal data set of 263 Taiwan-based manufacturing subsidiaries over the period 2006–2009. The empirical results show that for exploitation-oriented subsidiaries parent R&D plays a significant and negative moderating role on the impact of local R&D outsourcing based on local collaboration, while a positive moderating role is observed with parent R&D and subsidiary R&D for exploration-oriented subsidiaries.     

Identifying R&D partners for dye-sensitized solar cells: a multi-level patent portfolio-based approach

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.     

A dissensus based online Delphi approach: An explorative research tool

This paper presents an adapted Delphi methodology that is, contrary to the classical Delphi design is not aiming to minimize expert estimation variance, but to maximize the range of expert opinions inputted sequentially into an online system. After discussing the traditional Delphi approach and its dissensus based derivatives, the author opens the case for a dissensus Delphi based explorative research tool with special consideration of the Delphi aim, the expert sample and the Delphi design. The proposed online Delphi process is then presented conceptually. Next, the proposed tool is demonstrated based on a prototype, exploring the barrier factors to the adoption of mobile data services. A discussion on the theoretical design and practical R&D experience of the dissensus based online Delphi approach concludes the paper.     

Driving forces for research and development strategies: an empirical analysis based on firm-level panel data

This paper investigates empirically different ways to organize research and development (R&D) within Swiss firms. Based on a longitudinal data set comprising three cross-sections (1999, 2002, and 2005) of the Swiss Innovation Survey, four different types of R&D strategies could have been separated; firms combine in-house R&D with R&D co-operations (coop) or in-house R&D with external R&D (buy), or they conduct in-house R&D, external R&D, and R&D co-operations (mixed), or they exclusively rely on in-house R&D (make). It is the aim of this paper to understand what drive firms to go for different strategies. Based on econometric estimations controlling for correlations between the dependent variables and endogeneity among the independent variables, it was found that concepts related to the absorptive capacity, incoming spillovers, and appropriability, the importance of different knowledge sources, the competitive environment, costs, and skill aspects as well as technological uncertainty are essential factors to determine a firm's decision to choose a specific way to organize R&D.     

Participatory planning of industrial R&D activities

This paper summarizes and comments on the methodology actually employed in participatory planning of R&D activities for the iron and steel industry in Turkey. Through an organized collaboration with more than a hundred managers, engineers, experts, and planners: (1) technical, managerial, financial, and social problems that are obstacles to the achievement of predetermined goals for the industry are identified and defined; (2) these problems are formulated as R&D candidate projects; and (3) a set of R&D candidate projects are selected for implementation in the 1980s.