Blog

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16 Sep: Prompt Availability of Information Enables Efficient and Targeted Government Action in Crises
With the oil crisis, the bursting of the dot-com bubble, the financial and banking crisis, and the COVID-19 pandemic, the past fifty years have seen several economic shocks that often had severe consequences for the business landscape. Governments are often only able to provide targeted support and respond appropriately to such unexpected events with a significant time lag, as appropriate data is usually not available at the beginning of a crisis. AI-based analysis of corporate websites can remedy this situation by complementing traditional survey methods and providing reliable early forecasts in a timely manner. This is shown by studies conducted by ZEW Mannheim in cooperation with Justus Liebig University Giessen during the coronavirus crisis.
Digital Layer Germany Networks
20 Feb: Detecting and exploring company networks
Companies, like all economic players, are leaving behind digital traces of their activities to an increasing extent. The Digital Layer approach leverages these data sources and provides an alternative method for analyzing company networks. The integration of the Digital Layer approach into webAI provides our customers with a new tool for capturing and analyzing enterprise networks.
Deutschland webAI Marktanalyse
22 Oct: Up-to-date webAI market analyses on the coronavirus pandemic
The coronavirus pandemic hit the global economy hard in early 2020. Decision-makers in the fields of business and politics had to make far-reaching decisions based on often-incomplete data, especially at the beginning of the pandemic. Here, webAI developed by istari.ai offers a new approach for the comprehensive and daily updated analysis of highly dynamic market situations.

Press articles

Publications

Kinne J, Lenz D (2021) Predicting innovative firms using web mining and deep learning. PLOS ONE 16(4): e0249071.
Kinne, Jan und Janna Axenbeck (2020), Web Mining for Innovation Ecosystem Mapping: A Framework and a Large-scale Pilot Study, Scientometrics

Dania Eugenidis, David Lenz, Christoph Leser, Frauke Schleer-van Gellecom  und Peter Winker (2020), Text-mining basierte Analyse der Kapitalmarktreaktionen auf Ad-hoc-Mitteilungen, CORPORATE FINANCE, 09-10.

Kinne, Jan und David Lenz (2019), Predicting Innovative Firms Using Web Mining and Deep Learning, ZEW Discussion Paper No. 19-001, Mannheim.

Kinne, Jan und Resch Bernd (2018), Generating Big Spatial Data on Firm Innovation Activity from Text- Mined Firm Websites, GI_Forum 1, 8289.

Krüger, Miriam, Jan Kinne, David Lenz und Bernd Resch (2020), The Digital Layer: How Innovative Firms Relate on the Web, ZEW Discussion Paper No. 20-003, Mannheim.

Mirtsch, Mona, Jan Kinne und Knut Blind (2020), Exploring the Adoption of the International Information Security Management System Standard ISO/IEC 27001: A Web Mining-Based Analysis, IEEE Transactions on Engineering Management.

Rammer, Christian, Jan Kinne und Knut Blind (2019), Knowledge Proximity and Firm Innovation: A Microgeographic Analysis for Berlin, Urban Studies.

D. Lenz, C. Schulze, M. Guckert (2018),”Real-time Session-Based Recommendations using LSTM with neural Embeddings”Artificial Neural Networks and Machine Learning – ICANN 2018 | SpringerLink.

D. Lenz, P. Winker (2020), “Measuring the Diffusion of Innovations with Paragraph Vector Topic Models” PLOS ONE. 2020;15(1):1-18

Kinne, Jan, Miriam Krüger, David Lenz, Georg Licht und Peter Winker (2020), Corona-Pandemie betrifft Unternehmen unterschiedlich, Tagesaktuelle Webseiten-Analyse zur Reaktion von Unternehmen auf die Corona-Pandemie in Deutschland, ZEWKurzexpertise Nr. 20-05, Mannheim.

Kinne, Jan und Bernd Resch (2018), Analyzing and Predicting MicroLocation Patterns of Software Firms, ISPRS International Journal of GeoInformation 7, 1.

Kinne, Jan und Janna Axenbeck (2018), Web Mining of Firm Websites: A Framework for Web Scraping and a Pilot Study for Germany, ZEW Discussion Paper No. 18-033, Mannheim.