IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i5p144-d1380839.html
   My bibliography  Save this article

Anticipating Job Market Demands—A Deep Learning Approach to Determining the Future Readiness of Professional Skills

Author

Listed:
  • Albert Weichselbraun

    (Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland
    webLyzard technology, Liechtensteinstrasse 41/26, 1090 Vienna, Austria)

  • Norman Süsstrunk

    (Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland)

  • Roger Waldvogel

    (Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland)

  • André Glatzl

    (Swiss Institute for Information Research, University of Applied Sciences of the Grisons, Pulvermühlestrasse 57, 7000 Chur, Switzerland)

  • Adrian M. P. Braşoveanu

    (Research Center of New Media Technology, Modul University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria
    Modul Technology, Am Kahlenberg 1, 1190 Vienna, Austria)

  • Arno Scharl

    (webLyzard technology, Liechtensteinstrasse 41/26, 1090 Vienna, Austria
    Research Center of New Media Technology, Modul University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria)

Abstract

Anticipating the demand for professional job market skills needs to consider trends such as automation, offshoring, and the emerging Gig economy, as they significantly impact the future readiness of skills. This article draws on the scientific literature, expert assessments, and deep learning to estimate two indicators of high relevance for a skill’s future readiness: its automatability and offshorability. Based on gold standard data, we evaluate the performance of Support Vector Machines (SVMs), Transformers, Large Language Models (LLMs), and a deep learning ensemble classifier for propagating expert and literature assessments on these indicators of yet unseen skills. The presented approach uses short bipartite skill labels that contain a skill topic (e.g., “Java”) and a corresponding verb (e.g., “programming”) to describe the skill. Classifiers thus need to base their judgments solely on these two input terms. Comprehensive experiments on skewed and balanced datasets show that, in this low-token setting, classifiers benefit from pre-training and fine-tuning and that increased classifier complexity does not yield further improvements.

Suggested Citation

  • Albert Weichselbraun & Norman Süsstrunk & Roger Waldvogel & André Glatzl & Adrian M. P. Braşoveanu & Arno Scharl, 2024. "Anticipating Job Market Demands—A Deep Learning Approach to Determining the Future Readiness of Professional Skills," Future Internet, MDPI, vol. 16(5), pages 1-19, April.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:5:p:144-:d:1380839
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/5/144/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/16/5/144/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:5:p:144-:d:1380839. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.