1,720,962 research outputs found
Knowledge and technology transfer in German universities : an exploratory study leveraging information from university websites
No full textDespite the multidimensionality of knowledge and technology transfer (KTT), existing quantitative evaluations predominantly focus on a limited range of channels, often resulting from the availability of data rather than theoretical considerations. To address this gap, we leverage unstructured data from university websites to develop indicators that provide a comprehensive view of universities’ KTT activities. Applying large-language-transformers to scraped websites of German universities, we identify five distinct KTT dimensions: engagement in collaborative research consortia with industry, technical consulting activities, start-up activities, the development of regional tech transfer hubs, as well as technology transfer offices. Based on exploratory regression analyses, we find that the intensity of the type of KTT activities varies with the regional environment as well as university characteristics. Moreover, our regressions unveil a synergistic relationship between KTT and basic research, where this finding again seems to depend strongly on the type of KTT activity
Measuring the causal economic effects of scientific research: Evidence from the staggered foundation of the SENAI Innovation Institutes in Brazil
No full textHow to estimate the economic returns of public science is a longstanding but equally challenging topic in quantitative science studies. In this paper, we exploit the staggered foundation of the SENAI Innovation Institutes (ISI) in Brazil since 2012, to estimate their effects on GDP using a difference-in-differences (DiD) approach. Building on historical and institutional insights from interviews on the foundation process, we unravel the conditions under which the parallel trends assumption is likely to hold. Our analysis reveals that these institutes significantly contribute to GDP per capita, with an average treatment effect of 985 BRL (approximately €160). Moreover, by relying on detailed project-level data, we were able to show that the effects come almost exclusively from genuine research projects and not from the provision of scientific services, such as metrology. Finally, tentative calculations suggest that the SENAI institutes may account for about 0.66% of Brazil's overall GDP, emphasising the importance of applied science in regional economic development and providing insights into effective collaboration between research and industry
Measuring the causal economic effects of scientific research : evidence from the staggered foundation of the SENAI innovation institutes in Brazil
No full textHow to estimate the economic returns of public science is a longstanding but equally challenging topic in quantitative science studies. In this paper, we exploit the staggered foundation of the SENAI Innovation Institutes (ISI) in Brazil since 2012 to estimate their effects on GDP using a difference-in-differences (DiD) approach. Building on historical and institutional insights from interviews on the foundation process, we unravel the conditions under which the parallel trends assumption is likely to hold. Our analysis reveals that these institutes significantly contribute to GDP per capita, with an average treatment effect of 985 BRL (approximately €160). Moreover, by relying on detailed project-level data, we were able to show that the effects come almost exclusively from genuine research projects and not from the provision of scientific services, such as metrology. Finally, tentative calculations suggest that the SENAI ISI institutes may account for about 0.66 % of Brazil's overall GDP, emphasising the importance of applied science in regional economic development and providing insights into effective collaboration between research and industry
Data-driven innovators – An empirical analysis of data-driven SMEs and start-ups
Get PDFIn today's fast-paced technological landscape, the adoption of Big Data Analytics (BDA) goes beyond incremental improvements in productivity and efficiency, enabling the creation of new products, services, and even business models, known as Data-Driven Innovations (DDI). As technology evolves rapidly, reshaping industries and our daily lives, businesses must adapt to survive and innovate to thrive. Entrepreneurs, in particular, have a vast horizon of possibilities to explore through data, opening avenues for new ventures. However, the literature in the field has pointed to a lack of empirical evidence on the actual realization of these possibilities, the so-called ‘deployment gap’. Also, regarding established firms, there is a recurrent call for longitudinal analysis to understand the dynamics of BDA adoption and firm performance.
Motivated by these challenges, this study employs a data-driven methodology that integrates data science techniques, like web scraping, natural language processing (NLP), and neural topic modeling (BERTopic), to provide large-scale empirical evidence on the realization of the DDI, focusing on understanding the firms behind them. The objectives range from identifying data-driven firms using website text to analyzing the determinants of adoption, firm performance dynamics, and emerging business models in startups.
The study starts by focusing on German knowledge-intensive SMEs and identifying factors influencing BDA adoption, following the Technology-Organization-Environment (TOE) Framework. The findings show that larger, younger firms with international ownership are more likely to adopt BDA technologies, and this adoption is positively associated with innovation indicators such as patents and trademarks. The second study, grounded on Resource-Based-View (RBV), extends the analysis by exploring the timing of DDI deployment and its impact on firm performance over time using panel data. The results show that early adoption confers performance gains, particularly in technology-intensive sectors, but these gains tend to decrease as the technology becomes more widespread.
The third study shifts the focus to the global start-up ecosystem, analyzing emerging data-driven business models (DDBMs) by examining the value propositions of start-ups across various sectors. Using neural topic modeling, the research identifies key trends and patterns in DDBMs, confirming the increasing emphasis on AI and data science as central themes. The study also tracks the evolution of these trends over time, identifying a shift towards more specialized technological areas within start-ups' value propositions.
The empirical findings contribute to the broader discussion on BDA technologies, innovation, and their influence on firm performance. They offer insights not only to researchers conducting qualitative and theoretical studies but also to practitioners and policymakers involved in technology adoption and entrepreneurship. Methodologically, this work contributes to innovation studies by applying advanced data science techniques to analyze large-scale, unstructured data. These methods introduce a novel approach to uncovering patterns and insights that traditional methods may overlook, thereby advancing the study of digital innovations
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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