web-mining , text as data , machine learning , digitalisation
Abstract:
Due to the omnipresence of digital technologies in the economy, measuring firm digitalisation is of high importance. However, current indicators show several shortcomings, e.g., they lack timeliness and regional granularity. In this study, we show that advances in text mining and comprehensive firm website content can be leveraged to generate real-time and large-scale estimates of firm digitalisation. We use a transfer learning approach to capture the latent definition of digitalisation. For this purpose, we train a random forest regression model on labeled German newspaper articles and apply it on firm’s website content. The predictions are used as a continuous indicator for firm digitalisation. Plausibility checks confirm the link to established digitalisation indicators at the firm and sectoral level as well as for firm size classes and regions. Lastly, we illustrate the indicator’s potential for giving timely answers to pressing economic issues by analysing the link between digitalisation and firm resilience during the Covid-19 shock.
Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.
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