Why Big Data Can Make Creative Destruction More Creative – But Less Destructive

Pehr-Johan Norbäck and Lars Persson

No 1454, Working Paper Series from Research Institute of Industrial Economics

Abstract: The application of machine learning (ML) to big data has become increasingly important. We propose a model where firms have access to the same ML, but incumbents have access to historical data. We show that big data raises entrepreneurial barriers making the creative destruction process less destructive (less business-stealing) if the entrepreneur has weak access to the incumbent’s data. It is also shown that this induces entrepreneurs to take on more risk and be more creative. Policies making data generally available may therefore be suboptimal. Supporting entrepreneurs’ access to ML might be preferable since it stimulates creative entrepreneurship.

Keywords: Machine Learning; Big Data; Creative Destruction; Entrepreneurship; Operational Data (search for similar items in EconPapers)
JEL-codes: L10 L20 M13 O30 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2023-02-22
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ent, nep-ino, nep-mic, nep-sbm and nep-tid
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:iuiwop:1454

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