The original edition of this book summarized more than a decade of work on communications flow in science and engineering organizations, showing how human and organizational systems could be restructured to bring about improved productivity and better person-to-person contact. While many studies have been done since then, few of them invalidate the general conclusions and recommendations Allen offers. In a new preface he points out new developments, noting areas that need some modification, elaboration, or extension, and directing readers to the appropriate journal articles where the findings, are reported. The first three chapters provide an overview of the communication system in technology, present the author's research methods, and describe differences in the career paths and goals of engineers and scientists that cause special problems for organizations. The book then discusses how technological information is acquired by the R & D organization, shows how critical technical communication within the laboratory is for R & D performance, and originates the idea of the "gatekeeper," the person who links his or her organization to the world at large. Concluding chapters take up the influence of formal and informal organization and of architecture and office layouts on communication. Many of these ideas have been successfully incorporated by architects and managers in the design of new R & D facilities and complexes. Suggested Citation Handle: RePEc:mtp:titles:0262510278 Download reference as HTML Thomas J. Allen, 1984. "Managing the Flow of Technology: Technology Transfer and the Dissemination of Technological Information Within the R&D Organization," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262510278, November. More services and features MyIDEAS Follow serials, authors, keywords & more New papers by email Subscribe to new additions to RePEc Author registration Public profiles for Economics researchers MPRA Upload your paper to be listed on RePEc and IDEAS EconAcademics Blog aggregator for economics research Plagiarism Cases of plagiarism in Economics Rankings Various rankings of research in Economics & related fields RePEc Genealogy Who was a student of whom, using RePEc RePEc Biblio Curated articles & papers on various economics topics Job market papers RePEc working paper series dedicated to the job market Fantasy league Pretend you are at the helm of an economics department Services from the StL Fed Data, research, apps & more from the St. Louis Fed IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.
The rapid development of digital technology has a profound impact on the social economy, which is a significant sign that the society has entered into the digital age. Digital technology promotes the popularization and promotion of digital education while enabling education. Under this background, the European Union in 2020 issued the Digital Action Plan (2021-2027) at the end of September 2020, pointing out that the integration of digital technology and education is the general trend of the digital age. It is also pointed the development direction and priority actions for the digital transformation of education, and provides policy guidance for the digital transformation of vocational education. Based on the interpretation of the plan proposed by the European Union, and through the statistics and comparative analysis of the running data about vocational education in recent years, to discuss and combine with the digital education plan, the formation of high-quality and inclusive vocational education is in urgent need. Therefore, vocational education in the digital age need to constantly improve the digital capabilities of talents and promote the digital transformation of vocational education, so as to match the rapidly changing economics structure and the skill demand changed in the labor market.
Recent progress in artificial intelligence (AI)—a general purpose technology affecting many industries—has been focused on advances in machine learning, which we recast as a quality-adjusted drop in the price of prediction. How will this sharp drop in price impact society? Policy will influence the impact on two key dimensions: diffusion and consequences. First, in addition to subsidies and intellectual property (IP) policy that will influence the diffusion of AI in ways similar to their effect on other technologies, three policy categories—privacy, trade, and liability—may be uniquely salient in their influence on the diffusion patterns of AI. Second, labor and antitrust policies will influence the consequences of AI in terms of employment, inequality, and competition.
Martin Chalkley, Sakshi Mohan, Margherita Molaro
et al.
As health system modeling (HSM) advances to include more complete descriptions of the production of healthcare, it is important to establish a robust conceptual characterisation of the production process. For the Thanzi La Onse model in Malawi we have incorporated an approach to production that is based on a form of Leontief technology -- fixed input proportions. At first sight, this form of technology appears restrictive relative to the general conception of a production function employed in economics. In particular, the Leontief technology is associated with constant returns to scale, and level sets that are piecewise linear, both of which are highly restrictive properties. In this article we demonstrate that once incorporated into an all disease, agent-based model these properties are no longer present and the Leontief framework becomes a rich structure for describing healthcare production, and hence for examining the returns to health systems investments.
Minh Hieu Nguyen, Hien Thu Pham, Hiep Minh Ha
et al.
In today's fast-evolving business landscape, having insight into the technology stacks that organizations use is crucial for forging partnerships, uncovering market openings, and informing strategic choices. However, conventional technology mapping, which typically hinges on keyword searches, struggles with the sheer scale and variety of data available, often failing to capture nascent technologies. To overcome these hurdles, we present STARS (Semantic Technology and Retrieval System), a novel framework that harnesses Large Language Models (LLMs) and Sentence-BERT to pinpoint relevant technologies within unstructured content, build comprehensive company profiles, and rank each firm's technologies according to their operational importance. By integrating entity extraction with Chain-of-Thought prompting and employing semantic ranking, STARS provides a precise method for mapping corporate technology portfolios. Experimental results show that STARS markedly boosts retrieval accuracy, offering a versatile and high-performance solution for cross-industry technology mapping.
Which technologies are currently booming in power generation? The answer can clearly be seen in the trends in global electricity generation data. Analysing this data using the theory of diffusion of innovations reveals how photovoltaics and wind power are gaining ground worldwide. Other technologies - especially coal and nuclear power - are being displaced, with Germany playing a pioneering role.
Countries worldwide are increasingly focused on addressing the imbalance between the supply and demand for EV charging infrastructure, with the community-shared charging post (CSCP) co-construction project emerging as a promising solution. The broad participation and investment support of the residents are the keys to the success of the CSCP co-construction project. This study, grounded in the theory of planned behavior (TPB) from social psychology, incorporated factors such as community identity, perceived green value, economic benefit, uncivil behaviors, and perceived risk to construct a structural model explaining community residents’ intention to invest in the CSCP co-construction project. This research confirmed that (1) 85.73% of respondents expressed strong recognition of the CSCP co-construction project, with a mean recognition score of 5.56 out of a possible 7; (2) an individual’s social-related perceptions, including the subjective norms and community identity are the strongest determinant of the intention to invest in the CSCP co-construction project; (3) the willingness to invest in CSCP co-construction project differs significantly between the EV group and the non-EV group. Economic benefit was significant only for the non-EV group, while uncivil behaviors were significant only for the EV group. These results provide valuable guidelines for governments and corporations that are promoting or pursuing sharing community for the residents.
We address the challenge of acquiring real-world manipulation skills with a scalable framework. We hold the belief that identifying an appropriate prediction target capable of leveraging large-scale datasets is crucial for achieving efficient and universal learning. Therefore, we propose to utilize 3D flow, which represents the future trajectories of 3D points on objects of interest, as an ideal prediction target. To exploit scalable data resources, we turn our attention to human videos. We develop, for the first time, a language-conditioned 3D flow prediction model directly from large-scale RGBD human video datasets. Our predicted flow offers actionable guidance, thus facilitating zero-shot skill transfer in real-world scenarios. We deploy our method with a policy based on closed-loop flow prediction. Remarkably, without any in-domain finetuning, our method achieves an impressive 81\% success rate in zero-shot human-to-robot skill transfer, covering 18 tasks in 6 scenes. Our framework features the following benefits: (1) scalability: leveraging cross-embodiment data resources; (2) wide application: multiple object categories, including rigid, articulated, and soft bodies; (3) stable skill transfer: providing actionable guidance with a small inference domain-gap. Code, data, and supplementary materials are available https://general-flow.github.io
Gun Woo Warren Park, Payod Panda, Lev Tankelevitch
et al.
Effective meetings are effortful, but traditional videoconferencing systems offer little support for reducing this effort across the meeting lifecycle. Generative AI (GenAI) has the potential to radically redefine meetings by augmenting intentional meeting behaviors. CoExplorer, our novel adaptive meeting prototype, preemptively generates likely phases that meetings would undergo, tools that allow capturing attendees' thoughts before the meeting, and for each phase, window layouts, and appropriate applications and files. Using CoExplorer as a technology probe in a guided walkthrough, we studied its potential in a sample of participants from a global technology company. Our findings suggest that GenAI has the potential to help meetings stay on track and reduce workload, although concerns were raised about users' agency, trust, and possible disruption to traditional meeting norms. We discuss these concerns and their design implications for the development of GenAI meeting technology.
Climate change has become a global issue affecting all countries in the last decades. This phenomenon poses a concern to Indonesia as it is one of the climate change’s epicenters. Various studies have shown that climate change can harm multiple community activities, such as unstable agricultural production, decreased people’s health, and global warming. This study tried to model and analyze climate change topics discussed in the media. Finding hidden topics from texts can provide clues and information regarding public conversation surrounding climate change, such as public thoughts, perceptions, and readiness to mitigate the possible adverse effects of climate change. In order to identify hidden subjects from the corpus, this work modeled climate change issues in Indonesia using the latent Dirichlet allocation (LDA) algorithm to analyze texts from Indonesian media headlines. As many as 7,000 headline data from five online media were collected from 2017 to 2021 using web scraping techniques. The proposed approach produced eight topics related to climate change, which were determined by the highest coherence value of 0.560. Those topics were renewable energy, carbon emissions, environmental management, development economics, international cooperation, policy/regulation, rehabilitation, and disaster. Based on the results, the model could sufficiently describe the theme of discussion in society and photograph public thoughts and the government’s readiness in the form of policies and regulations in dealing with the climate change phenomenon.
Silvânia Alves Braga de Castro, André Carlos Silva
Abstract The modeling of mineral deposits has been improved over the years with the incorporation of mineralogical and metallurgical information obtained from drilling samples that make up the pillars for the construction of resource models. However, sampling data is being made available in large quantities, causing current databases to grow exponentially. The use of machine learning (ML) algorithms has been applied to deal with multidimensional data problems. Principal component analysis (PCA) is a multivariate analysis (MA) technique whose aim is to reduce the dimension of multivariate data. Studies show that results obtained with the reduction of variables were satisfactory in different areas of activity. The purpose of this article is to test variable selection criteria using PCA for geometallurgical data and to check the feasibility of the technique for simplifying variable types and defining typological domains.