Future competencies in human-machine interaction: An interdisciplinary approach for the anticipation of strategically relevant competencies in the field of automotive human-machine interaction

BACKGROUND: Digitalization and technological progress lead to an increasingly fast development of promising fields for action and new technologies whereas the time required to qualify employees for new activities and work content has remained largely the same. Organizations have to establish anticip...

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Bibliographic Details
Published in:Work (Reading, Mass.) Mass.), 2022-01, Vol.72 (4), p.1709-1725
Main Authors: Karwehl, Laura Johanna, Kauffeld, Simone
Format: Article
Language:English
Subjects:
Competitiveness
Data retrieval
Development strategies
Digitization
Human-computer interaction
Interdisciplinary aspects
New technology
Psychology
Resource development
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Summary:BACKGROUND: Digitalization and technological progress lead to an increasingly fast development of promising fields for action and new technologies whereas the time required to qualify employees for new activities and work content has remained largely the same. Organizations have to establish anticipative competence measures to secure their competitiveness. OBJECTIVES: Those developments suggest that a new approach to develop human resource development strategies is required. METHODS: This article describes the results of a competence survey that was developed in an interdisciplinary approach between organizational psychology and futurology and conducted in the field of automotive Human-Machine Interaction (HMI) research. The content of the questionnaire is based on a series of expert interviews focusing and a data-driven approach that scanned significant patents for competence demand data. RESULTS: The conducted ANOVAs show that both sources for data retrieval create relevant items even though experts from the conceptual field rate data-based items significantly less relevant than the other participants. Moreover, interview-based items lead to significantly more relevant ratings in methodological fields while data-driven items were rated significantly more relevant for the technological area. CONCLUSIONS: Even though there are some uncertainties to examine, the displayed approach seems promising for the derivation of more detailed and enriched future competency demands in technological fields.
ISSN:1051-9815
1875-9270
DOI:10.3233/WOR-211261