Article Two – AI Diffusion Monitoring among S&P500 Companies: Empirical Results and Methodoligical Advancements


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Article Two – AI Diffusion Monitoring among S&P500 Companies: Empirical Results and Methodoligical Advancements
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The digital transformation of firms plays an increasingly important role in the economy and society. However, limited access to data on firm-level digital intensity is an impediment to advancement of multiple research projects concerned with firm digitalization. To alleviate this challenge, this paper proposes a method for estimating firm-level digital intensity based on other more readily available firm-level data and reference data on digitalization, which is available on sector-level. The proposed method utilizes firm-level revenue breakdown by sector to estimate sector revenue-weighted digital intensity scores, which lead to classification of firms into low, medium and high digital intensity groups. The output from the proposed method can be directly used in research concerned with firm digitalization and investigating this multifaceted phenomenon. Results from the application of the proposed method to an illustrative sample of large US and non-US firms (2000 observations in total) indicate that firm-level digital intensity can be efficiently estimated for large samples using data commonly available to researchers. 

Aalto University
Department of Industrial Engineering and Management (TUTA)
Maarintie 8, 02150 Espoo
Finland

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