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Preparing for the future of work: a novel data-driven approach for the identification of future skills

Author

Listed:
  • Julia Brasse

    (University of Ulm)

  • Maximilian Förster

    (University of Ulm)

  • Philipp Hühn

    (University of Ulm)

  • Julia Klier

    (University of Regensburg)

  • Mathias Klier

    (University of Ulm)

  • Lars Moestue

    (University of Ulm)

Abstract

The future of work is changing rapidly as result of fast technological developments, decarbonization and social upheavals. Thus, employees need a new skillset to be successful in the future workforce. However, current approaches for the identification of future skills are either based on s small sample of expert opinions or the result of researchers interpreting the results of data-driven approaches and thus not meaningful for the stakeholders. Against this background, we propose a novel process for the identification of future skills incorporating a data-driven approach with expert interviews. This enables identifying future skills that are comprehensive and representative for a whole industry and region as well as meaningful for the stakeholders. We demonstrate the applicability and utility of our process by means of a case study, where we identify 33 future skills for the manufacturing industry in Baden-Wuerttemberg, Germany. Our work contributes to the identification of comprehensive and representative future skills (for whole industries).

Suggested Citation

  • Julia Brasse & Maximilian Förster & Philipp Hühn & Julia Klier & Mathias Klier & Lars Moestue, 2024. "Preparing for the future of work: a novel data-driven approach for the identification of future skills," Journal of Business Economics, Springer, vol. 94(3), pages 467-500, April.
    • Handle: RePEc:spr:jbecon:v:94:y:2024:i:3:d:10.1007_s11573-023-01169-1
    DOI: 10.1007/s11573-023-01169-1
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    References listed on IDEAS

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    1. Pejic-Bach, Mirjana & Bertoncel, Tine & Meško, Maja & Krstić, Živko, 2020. "Text mining of industry 4.0 job advertisements," International Journal of Information Management, Elsevier, vol. 50(C), pages 416-431.
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    Keywords

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    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • M00 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General - - - General

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