nep-knm New Economics Papers
on Knowledge Management and Knowledge Economy
Issue of 2023‒02‒06
four papers chosen by
Laura Nicola-Gavrila
Centrul European de Studii Manageriale în Administrarea Afacerilor

  1. The importance of access to knowledge for technological progress in the Industrial Revolution By Erik Hornung; Julius Koschnick; Francesco Cinnirella
  2. Initiation of knowledge and technology transfer from academia to industry: Opportunity recognition and transfer channel choice By Matthias Huegel; Philip Doerr; Martin Kalthaus
  3. Knowledge spillovers from clean and emerging technologies in the UK By Martin, Ralf; Verhoeven, Dennis
  4. Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance By Guijin Son; Hanwool Lee; Nahyeon Kang; Moonjeong Hahm

  1. By: Erik Hornung (University of Cologne); Julius Koschnick (London School of Economics); Francesco Cinnirella (University of Bergamo)
    Abstract: Sustained technological progress was at the heart of the Industrial Revolution. This column argues that access to knowledge was crucial for innovation and technological diffusion during this period. Inventors and entrepreneurs needed access to useful knowledge to generate new ideas and continue innovating. Such access was provided by the ‘economic societies’ – associations of individuals interested in improving the local economy. These societies became drivers of knowledge diffusion and innovation.
    Date: 2022–12
  2. By: Matthias Huegel (Friedrich Schiller University Jena, Department of Economics); Philip Doerr (Friedrich Schiller University Jena, Department of Economics); Martin Kalthaus (Friedrich Schiller University Jena, Department of Economics)
    Abstract: The transfer of knowledge and technology from academia to industry is usually understood as a process. While previous research focuses on phenomena along the process and its outcomes, the starting point of the process – the initiation of a transfer activity – remains unstudied. We provide first empirical insights on the initiation of the transfer process and conceptualize this initiation as a simultaneous recognition of a transfer opportunity and the choice of a transfer channel. We focus on Science-Industry collaboration, Intellectual Property Rights and spin-off creation as relevant channels. We use survey data from 1, 149 scientists from the German state of Thuringia and utilize seemingly unrelated regressions to account for selection and multiple channel choices in our econometric approach. Our results show a positive relationship between scientists’ probability to recognize a transfer opportunity and different kinds of prior knowledge. Contrary to our expectation, scientific quality reduces the likelihood of recognizing a transfer opportunity. For the choice of the transfer channel, the results show a positive relationship between choosing the spin-off channel and risk willingness, as well as basic research. Applied research increases the likelihood to choose Intellectual Property Rights as a channel. Furthermore, role models are positively associated with these two channels.
    Keywords: Transfer Process, Transfer Initiation, Opportunity Recognition, Transfer Channel, Science-Industry Collaboration, Intellectual Property Right, Academic Spin-off
    JEL: L26 O31 O33 O34
    Date: 2023–01–18
  3. By: Martin, Ralf; Verhoeven, Dennis
    Abstract: The UK government has committed to increase R&D support for clean technologies in an effort to meet its net-zero target by 2050. The opportunity cost of such programs crucially depends on the value of knowledge spillovers that accrue from clean relative to other (emerging) technologies. Using patent information to measure the value of direct and indirect knowledge spillovers, we derive estimates for the expected economic returns of subsidising a particular technology field. Our method allows comparing fields by the returns a hypothetical additional subsidy would have generated within the UK or globally. Clean technologies are top-ranked in terms of within-UK returns, with Tidal and Offshore Wind showing particularly high returns. In terms of global returns, emerging technologies such as Wireless, as well as Electrical Engineering outperform Clean by a small margin. We also find that cross-border knowledge spillovers are important for all technology fields, with global return rates over ten times larger than within-UK ones. In sum, our results suggest that the opportunity cost of R&D support programs for clean innovation in the UK is low at worst.
    Keywords: innovation; knowledge spillovers; clean technology; innovation policy; patent data
    JEL: R14 J01 J1
    Date: 2022–03–02
  4. By: Guijin Son; Hanwool Lee; Nahyeon Kang; Moonjeong Hahm
    Abstract: Extraction of sentiment signals from news text, stock message boards, and business reports, for stock movement prediction, has been a rising field of interest in finance. Building upon past literature, the most recent works attempt to better capture sentiment from sentences with complex syntactic structures by introducing aspect-level sentiment classification (ASC). Despite the growing interest, however, fine-grained sentiment analysis has not been fully explored in non-English literature due to the shortage of annotated finance-specific data. Accordingly, it is necessary for non-English languages to leverage datasets and pre-trained language models (PLM) of different domains, languages, and tasks to best their performance. To facilitate finance-specific ASC research in the Korean language, we build KorFinASC, a Korean aspect-level sentiment classification dataset for finance consisting of 12, 613 human-annotated samples, and explore methods of intermediate transfer learning. Our experiments indicate that past research has been ignorant towards the potentially wrong knowledge of financial entities encoded during the training phase, which has overestimated the predictive power of PLMs. In our work, we use the term "non-stationary knowledge'' to refer to information that was previously correct but is likely to change, and present "TGT-Masking'', a novel masking pattern to restrict PLMs from speculating knowledge of the kind. Finally, through a series of transfer learning with TGT-Masking applied we improve 22.63% of classification accuracy compared to standalone models on KorFinASC.
    Date: 2023–01

This nep-knm issue is ©2023 by Laura Nicola-Gavrila. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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