Assessing the drivers of machine learning business value

Research output: Contribution to journalArticle

Abstract

Machine learning (ML) is expected to transform the business landscape in the near future completely. Hitherto, some successful ML case-stories have emerged. However, how organizations can derive business value (BV) from ML has not yet been substantiated. We assemble a conceptual model, grounded on the dynamic capabilities theory, to uncover key drivers of ML BV, in terms of financial and strategic performance. The proposed model was assessed by surveying 319 corporations. Our findings are that ML use, big data analytics maturity, platform maturity, top management support, and process complexity are, to some extent, drivers of ML BV. We also find that platform maturity has, to some degree, a moderator influence between ML use and ML BV, and between big data analytics maturity and ML BV. To the best of our knowledge, this is the first research to deliver such findings in the ML field.

Original languageEnglish
Pages (from-to)232-243
Number of pages12
JournalJournal of Business Research
Volume117
DOIs
Publication statusPublished - Sep 2020

Keywords

  • Business value
  • Competitive advantage
  • Dynamic capabilities theory
  • Machine learning

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