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Text mining of industry 4.0 job advertisements

Author

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  • Pejic-Bach, Mirjana
  • Bertoncel, Tine
  • Meško, Maja
  • Krstić, Živko

Abstract

Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job advertisements, which are often used as a channel for collecting relevant information about the required knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements, related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements. Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior level management positions and mainly came from the United States and Germany. Text mining analysis resulted in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production design and production control. The second group of job profiles was focused on more general knowledge areas, which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software. Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in Industry 4.0 organizations, would benefit from the results of our analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ininma:v:50:y:2020:i:c:p:416-431
    DOI: 10.1016/j.ijinfomgt.2019.07.014
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    Citations

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    Cited by:

    1. Papoutsoglou, Maria & Rigas, Emmanouil S. & Kapitsaki, Georgia M. & Angelis, Lefteris & Wachs, Johannes, 2022. "Online labour market analytics for the green economy: The case of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    2. Seyed Mohammad Ali Jafari & Ehsan Chitsaz, 2024. "Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market," Papers 2409.00658, arXiv.org.
    3. Bäck, Asta & Hajikhani, Arash & Jäger, Angela & Schubert, Torben & Suominen, Arho, 2022. "Return of the Solow-paradox in AI? AI-adoption and firm productivity," Papers in Innovation Studies 2022/1, Lund University, CIRCLE - Centre for Innovation Research.
    4. Tomasz Jałowiec & Henryk Wojtaszek, 2022. "Analysis of Directional Activities for Industry 4.0 in the Example of Poland and Germany," Sustainability, MDPI, vol. 14(7), pages 1-25, March.
    5. Spada, Irene & Chiarello, Filippo & Barandoni, Simone & Ruggi, Gianluca & Martini, Antonella & Fantoni, Gualtiero, 2022. "Are universities ready to deliver digital skills and competences? A text mining-based case study of marketing courses in Italy," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

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