Competences for Digital Transformation in Companies – An Analysis of Job Advertisements in Germany

Autor(en): Ernst, Frieda
Ghofrani, Mana
Sahrmann, Carlo
Schwegmann, Paul
Brink, Henning
Paul, Fynn-Hendrik
Herausgeber: Jallouli, R.
Bach Tobji, M.A.
Belkhir, M.
Soares, A.M.
Casais, B.
Stichwörter: 'current; Competence; Competence sets; competences; Computing communication; Content analysis; Data mining; Digital technologies; digital transformation; Labour market; Literature reviews; model; Personnel; Text-mining; Websites
Erscheinungsdatum: 2023
Herausgeber: Springer Science and Business Media Deutschland GmbH
Journal: Lecture Notes in Business Information Processing
Volumen: 485 LNBIP
Startseite: 81 – 96
Zusammenfassung: 
Digital transformation (DT) offers major improvement of a business by significantly changing its characteristics through the combination of information, computing, communication, and connectivity technologies by using new digital technologies. To take advantage of these opportunities, certain competences are required. However, not all of the required competences can be fulfilled by the current employees. To fill these gaps, companies are looking for employees with different competence sets on employment websites. Most studies analyzing competences for DT are conducting interviews, questionnaires, or performing literature reviews which disregards the actual requirements of companies looking for employees to conquer DT. To better understand what competences companies are looking for in the labor market, we conducted a content analysis of job advertisements on StepStone. Using text mining, data mining, and a subsequent manual analysis of 3,138 job advertisements posted on a relevant employment website, we were able to derive 23 different competences relevant to DT. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Beschreibung: 
Cited by: 0; Conference name: The 8th International Conference on Digital Economy, ICDEc 2023; Conference date: 2 May 2023 through 4 May 2023; Conference code: 301909
ISBN: 9783031427879
ISSN: 1865-1348
DOI: 10.1007/978-3-031-42788-6_6
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174442388&doi=10.1007%2f978-3-031-42788-6_6&partnerID=40&md5=efc0a21221e60e3b9bc2e4d7684a0f0f

Zur Langanzeige

Seitenaufrufe

3
Letzte Woche
1
Letzter Monat
1
geprüft am 14.05.2024

Google ScholarTM

Prüfen

Altmetric