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
1
Letzter Monat
1
1
geprüft am 14.05.2024