The digital layer: alternative data for regional and innovation studies

Abbasiharofteh, Milad; Krüger, Miriam; Kinne, Jan; Lenz, David; Resch, Bernd

doi:10.1080/17421772.2023.2193222

Cited by 8 publications

(2 citation statements)

References 57 publications

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“…In short, we establish a novel data source for monitoring the bioeconomy, overcoming several issues researchers and practitioners usually face when trying to measure bio-based economic activities. We test several hypotheses on the bioeconomy to validate our dataset and to demonstrate its applicability for future research, which is commonly done when introducing new methods or data to regional research (Abbasiharofteh et al 2023, Ozgun, Broekel 2022. We make an aggregated version of our dataset freely accessible for fellow researchers, enabling further analyses and contributions to regional bioeconomy studies.…”

Section: Introductionmentioning

confidence: 99%

Bioeconomy firms and where to find them

Kriesch,

Losacker

2024

REGION

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The bioeconomy represents a transformative approach to economic development and sustainability by harnessing biological resources and knowledge to produce goods, services, and energy while reducing dependence on non-renewable resources. In order to understand and support the bioeconomy, scholars and policymakers rely on an accurate measurement and monitoring of biobased economic activities. However, existing statistical frameworks and industry classifications often fall short in capturing the unique characteristics and complexities of the bioeconomy. This article addresses this challenge by developing a methodological approach for comprehensive measurement and mapping of biobased economic activities. We build a novel data set of bioeconomy firms in Germany using web-mining and machine learning techniques. This data set enables detailed analysis of biobased economic activities, providing valuable insights into the spatial organization of the bioeconomy. The paper demonstrates the applicability of the data set by testing several stylized facts about the bioeconomy. Our research contributes to a better understanding of the bioeconomy's regional impacts and offers a valuable resource for policymakers and researchers interested in understanding the geography of biobased economic activities. We make an aggregated version of the data set freely available online.

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Section: Introductionmentioning

confidence: 99%

Bioeconomy firms and where to find them

Kriesch,

Losacker

2024

REGION

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“…Kinne and Axenbeck utilized the hyperlink structure of corporate websites as an indicator for evaluating innovation, analyzing R&D collaborative activities among entities [11]. Youtie et al employed a web crawler to extract web text information from 30 German SMEs in the field of nanotechnology, using similarity analysis of the text content to identify the specific stage of innovation development and characterize the cognitive proximity between innovation subjects [12]. Gök et al employed keyword techniques to collect web text content for evaluating the R&D activities of 296 UK companies, comparing the results with patent and paper indicators and finding that website indicators were more accurate [13].…”

Section: Introductionmentioning

confidence: 99%

Innovation Networks of Science and Technology Firms: Evidence from China

Liu

Peng

Liu

et al. 2023

Land

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Examining and assessing the characteristics of innovation networks among science and technology firms at the city level is essential for comprehending the innovation patterns of cities and improving their competitiveness. Nevertheless, the majority of studies in this field solely rely on patent and paper data, neglecting the analysis of networks across diverse scales and dimensions. Websites offer a novel platform for companies to exhibit their products and services, and the utilization of hyperlink data better captures the dynamics of innovative cooperation. Thus, to attain a more realistic and precise comprehension of China’s technology enterprise cooperation networks, enhance the understanding of intra-city and cross-border cooperation within innovation networks, and offer more scientific guidance to cities in enhancing their innovation capabilities by investigating the factors influencing innovation scenarios and the mechanisms of their interactions, this study constructs an innovation network based on the hyperlink data extracted from Chinese science and technology enterprises’ websites in 2022. It explores the network’s inherent characteristics and spatial patterns across multiple dimensions and scales. Additionally, it employs GeoDetector to analyze the driving factors behind the heterogeneity of city quadrants across each dimension. The findings suggest the following: (1) Evident polarization of innovation capability exists, with a more pronounced differentiation of cities between high capability zones. (2) Contrary to the conventional notion of geographical proximity, cross-region website cooperation prevails, with cross-provincial cooperation being more prevalent than intra-provincial cross-city cooperation. (3) Enterprise cooperation tends to align with partners of similar scale, and small and medium-sized enterprises primarily engage in internal cooperation, primarily concentrated in second and third-tier cities. (4) Cities with high degree centrality and structure holes are primarily located in the construction areas of Chinese urban agglomerations, while those with low degree centrality and structure holes are situated near double-high cities. (5) The spatial heterogeneity of innovation networks across the four dimensions is primarily influenced by STI, while cooperation intensity and innovation capacity dimensions are strongly influenced by traffic capacity. The intra- and inter-city cooperation intensity dimensions are significantly impacted by administrative grade, and the enterprise scale and network location dimensions are most affected by the level of digital infrastructure.

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Driving Forces Behind Relational Knowledge Sourcing in Clusters: Single- and Multilevel Approaches

Abbasiharofteh,

Maghssudipour

2024

J Knowl Econ

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The digital layer: alternative data for regional and innovation studies

Cited by 8 publications

References 57 publications

Bioeconomy firms and where to find them

Bioeconomy firms and where to find them

Innovation Networks of Science and Technology Firms: Evidence from China

Driving Forces Behind Relational Knowledge Sourcing in Clusters: Single- and Multilevel Approaches

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