Measuring the digitalisation of firms: A novel text mining approach

Janna Axenbeck and Patrick Breithaupt

No 22-065, ZEW Discussion Papers from ZEW - Leibniz Centre for European Economic Research

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.

Keywords: web-mining; text as data; machine learning; digitalisation (search for similar items in EconPapers)
JEL-codes: C53 C81 O30 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-bec, nep-big, nep-cmp, nep-cse and nep-sbm
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:zewdip:22065

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