In a competitive business environment, an early understanding of the dynamics of technological change is crucial to help policymakers and managers make better-informed decisions. Bibliometric analyses help in studying trends and technological evolution. Tech mining (text analyses of science and technology information resources) enhances Bibliometric analyses. However, more often than not, such analyses focus on a specific technological area, and mainly result in incremental advance forecasts. An analysis of the interconnected dynamics of technology change warrants new approaches for identifying technology emergence, technological substitution, and the influences of vital socioeconomic forces. This paper introduces a unique combination that applies a tech mining and semantic TRIZ as a case study to Dye-Sensitized Solar Cell (DSSC) technology. This methodological combination brings broader insights to the emergence of DSSC in conjunction with related technologies that affect its progress, enriching the associated technological progression’s empirical characterization.
- Techmining-semantic TRIZ helps to understand the competition influence among technologies.
- The understanding of the architectural design, the system, helps to clearly understand the role of the different components.
- Understanding the components’ role in the system helps to guide the techmining analysis and to understand the different trends.
- Using the S-AO and not the SAO problem solving, the present work is able to find other competing or not, technologies that help to understand if that will support the emergence of the original or the competing technology.
- This cross-tech-components have different role in other architectures. Perovskites, enhance silicon solar cells efficiency.
https://doi.org/10.1016/j.techfore.2021.120826 or download FULL-TEXT
Author(s):J.M. Vicente-Gomila, M.A. Artacho-Ramírez, Ma Ting, A.L.Porter
Organization(s):Universitat Politècnica de València, Beijing Institute of Technology, Search Technology
Source: Technological Forecasting and Social Change
Uncovering the driving forces, strategic landscapes, and evolutionary mechanisms of China’s research systems is attracting rising interest around the globe. One such interest is to understand the problem-solving patterns in China’s research systems now and in the future. Targeting a set of high-quality research articles published by Chinese researchers between 2009 and 2018, and indexed in the Essential Science Indicators database, we developed an intelligent bibliometrics-based methodology for identifying the problem-solving patterns from scientific documents. Specifically, science overlay maps incorporating link prediction were used to profile China’s disciplinary interactions and predict potential cross-disciplinary innovation at a macro level. We proposed a function incorporating word embedding techniques to represent subjects, actions, and objects (SAO) retrieved from combined titles and abstracts into vectors and constructed a tri-layer SAO network to visualize SAOs and their semantic relationships. Then, at a micro level, we developed network analytics for identifying problems and solutions from the SAO network, and recommending potential solutions for existing problems. Empirical insights derived from this study provide clues to understand China’s research strengths and the science policies beneath them, along with the key research problems and solutions Chinese researchers are focusing on now and might pursue in the future.
FULL-TEXT available at https://www.mitpressjournals.org/doi/pdf/10.1162/qss_a_00100
Author(s): Yi Zhang, Mengjia Wu, Zhengyin Hu, Robert Ward, Xue Zhang, Alan Porter
Organization(s): University of Technology Sydney, Chengdu Library and Information Centre (CAS), Georgia Institute of Technology
Source: Quantitative Science Studies
Knowledge base construction (KBC) aims to populate knowledge bases with high-quality information from unstructured data but how to effectively conduct KBC from scientific documents with limited preknowledge is still elusive. This paper proposes a KBC framework by applying computational intelligent techniques through the integration of intelligent bibliometrics—e.g., co-occurrence analysis is used for profiling research topics/domains and identifying key players, and recommending potential collaborators based on the incorporation of a link prediction approach; an approach of scientific evolutionary pathways is exploited to trace the evolution of research topics; and a search engine incorporating with fuzzy logics, word embedding, and genetic algorithm is developed for knowledge searching and ranking. Aiming to examine and demonstrate the reliability of the proposed framework, a case of gene-related cardiovascular diseases is selected, and a knowledge base is constructed, with the validation of domain experts.
For FULL-TEXT https://doi.org/10.2991/ijcis.d.200728.001
Author(s): Yi Zhang, Mengjia Wu, Hua Lin, Steven Tipper, Mark Grosser, Guangquan Zhang, Jie Lu
Organization(s): University of Technology Sydney, 23 Strands
Source: International Journal of Computational Intelligence Systems
This study aims at identifying potential industry-University-research institution collaborations partners (IURC) efficaciously and analyzes the conditions and dynamics in the IURC process, based on knowledge potential and the knowledge spillover theory. Furthermore, a new identification method is constructed that takes into account the law of scientific research cooperation and economic factors. The method utilizes multisource data, combining bibliometric and econometrics analyses to achieve the network core of the existing collaboration network, and institution competitiveness in the innovation chain. Empirical analysis of the genetic engineering vaccine field shows that throughout the distribution characteristics of creative technologies from different institutions, the analysis based on the innovation chain can identify more complementarities between institutions. Compared to previous studies, this study emulates the real conditions of IURC. The rule of technological innovation can be better revealed, potential partners of IURC can be more easily identified, and the conclusion has a higher value in consultation. In particular, diverse informative indices can assist researchers in deriving appropriate partners for research and development cooperation.
Author(s): Haiyun Xu, Kun Dong, Ling Wei, Chao Wang, Shu Fang
Organization(s): Chengdu Documentation and Information Center, CAS; University of Chinese Academy of Sciences
Source: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Business intelligence enables enterprises to make effective and good quality business decisions. In the knowledge economy, patents are seen as strategic assets for companies as they provide a competitive advantage and at the same time ensure the freedom to operate and form the basis for new alliances. Publication or disclosure of intellectual property (IP) strategy based on patent filings is rarely available in the public domain. Because of this, the only way to understand IP strategy is to look at patent filings, analyze them and, based on the trends, deduce strategy. This paper tries to uncover IP strategies of five US and Indian IT companies by analyzing their patent filings. Gathering business intelligence via means of patent analytics can be used to understand the strategies used by companies in advocating their patent portfolio and aligning their business needs with patenting activities. This study reveals that the Indian companies are far behind in protecting their IPs, although they are now on course correction and have started aggressively protecting their inventions. It is also observed that the rival companies in the study are not directly competing with each other in the same technological domain. Different patent filing strategies are used by firms to gain a competitive advantage. Companies make use of disclosure as strategy or try to cover many aspects of a technology in a single patent, thereby signaling their dominance in a technological area and at the same time as they add information.
Author(s): Shabib-Ahmed Shaikh, Tarun Kumar Singhal
Organization(s): Symbiosis International University (SIU), Symbiosis Centre for Management Studies
Source: Journal of Intelligence Studies in Business
The purpose of this paper is to identify the dimensions of organizational health with the help of existing literature and focus group discussion on organizational health. The study also tries to categorize various antecedents and consequences of organizational health.
Literature review was conducted with limited search word on organizational health using databases like Emerald, Ebsco and Science direct. Focus group discussions were performed at Central Salt and Marine Chemicals Research Institute and National Metallurgical Laboratory – laboratories of Council of Scientific and Industrial Research, an Indian R&D organization. A total of 29 male and 6 female respondents participated in the focus group discussion.
The results showed that various dimensions of organizational health which were found using focus group discussions were in congruence with the literature reviewed on organizational health. The findings of focus group discussion also listed the antecedents and consequences of organizational health in an R&D organization.
The literature presented conflicting views on organizational health construct. The focus group discussion provided clarity on the dimensions of organizational health. An empirical research can be done on organizational health considering dimensions identified during the focus group discussion.
Author(s): Anupama Singh, Sumi Jha
Organization(s): National Institute of Industrial Engineering
Source: Industrial and Commercial Training
Electromobility (e-mobility) is applicable to issues from sustainable transportation to revolutionary driving behaviour. The wide-ranging influence of this concept calls for a shift toward an internationalization of e-mobility research in developed and developing countries alike. Germany and China, as the major exporters and volume producers in the automotive industry, have established the goal of becoming market leaders in e-mobility by 2020. Compared to China, Germany, as a forerunner in the field of e-mobility, is unexpectedly lagging behind in both the sale volume of electric vehicles (EVs) and the share of international publications. Since 2006, China has been the second largest single “producer” of EV-related published research, trailing only the United States. However, the technological capabilities—applying science to real-world issues—seem to be under-represented in these publications. This paper explores structural differences in e-mobility research landscapes and examines possible contextual explanations for the differences between Germany and China. The study involves a detailed comparison of articles sourced from the two countries, beginning with a broad overview of recent research and ending with a short content analysis of the statement concerning current progress and practical challenges for e-mobility development in Germany and China. The conclusion reached is that both countries have explored topics related to EV modes, batteries, energy management and the smart grid; however, specific terms of interest have evolved differently in the two countries. Compared with China, Germany has not achieved a rapid increase in the number of international publications but has still accumulated a vast reservoir of scientific talents and technological resources through the scientific collaboration between academia and industry. Universities, as the main loci of scientific research in China, have actively engaged in international cooperation, addressing problems with no apparent differences from those addressed in Germany. The authors’ views relative to the development of e-mobility in the two countries vary greatly from group to group, indicating that differences should be considered in both the pattern of knowledge production and the research context.
Organization: Humboldt-Universität zu Berlin
As technology competition among enterprises become more intense, technical crisis occurs in enterprises, such as technological substitution and technology divulgence. Thus, it is necessary to warn enterprises of those technical crises that can be called technology early warning. As patent data contains much technology information, it becomes an efficient source to analyse technology. This paper proposes a technology early warning model based on patent data to help enterprises execute technology early warning from the perspective of its technology status. To do so, we set ten indicators from four aspects to evaluate the enterprise’s technology status at first, calculate the index of enterprise’s technical crisis with AHP, and then propose five early warning levels. China Petroleum & Chemical Corporation (Sinopec Group) and the China National Petroleum Corporation (CNPC) are taken as comparative case studies.
Author(s): Ying Guo, Ganlu Sun, Lili Zhang, Fan Yang, Junfang Guo, Lin Ma
Organization(s): Beijing Institute of Technology
Source: International Journal of Technology Management
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.
Author(s): Haiyun Xu, Chao Wang, Kun Dong, Rui Luo, Zenghui Yue, Hongshen Pang
Source: Journal of Data and Information Science
Accurately evaluating opportunities in new and emerging science and technologies is a growing concern. This study proposes an integrated framework for identifying a range of potential innovation pathways and commercial applications for solid lipid nanoparticles – one particularly promising contender within the field of nano-enabled drug delivery. Several text mining techniques – term clumping, SAO technique, and net effect analysis – as well as technology roadmapping, are combined with expert judgment to identify the main areas of R&D in this field, and to track their evolution over time. Through analysis, data from multiple sources, including research publications, patents, and commercial press, reveal possible future applications and commercialization opportunities for this emerging technology. We find that research is moving away from materials and delivery outcomes toward clinical applications. The most promising markets are pharmaceuticals and cosmetics; however, the “time-to-market” is much shorter for cosmetics than it is for pharmaceuticals.
The most significant contributions of this paper have been highlighted as follows. One innovation is extracting the intelligence from three kinds of data sources after in-depth considering their characteristics and matching with the features of different technology development stages to identify innovative research topics. The second one is combining SAO technique with net effect analysis to identify what the evolutionary links between research topics are, and then to use TRM to visualize the evolution of the main areas of R&D over time.
Author(s):Xiao Zhou, Lu Huang, Alan Porter, Jose M.Vicente-Gomila
Organization(s): Xidian University, Beijing Institute or Technology
Source: Technological Forecasting and Social Change