Transferring Digital Twin Technology on Employee Skills: A Framework to Support Human Resources
Journal
Academy of Management Annual Meeting (AOM)
Type
conference paper
Date Issued
2023
Author(s)
Freise, Leonie Rebecca
;
Hupe, Anna
Research Team
IWi6
Abstract
Skill assessment has become increasingly important in recent years. The shortage of skilled workers has demonstrated the need to identify suitable candidates for jobs and to develop existing employees. However, human resources processes rely on resumes, references, or job certifications to assess their employees’ skills. A few years ago, the concept of a digital twin was introduced, which is a digital replica of a physical entity. While using blockchain, the digital twin technology offers tremendous potential for transfer to other contexts. In the context of these considerations, we present a framework that allows the technology of the digital twin to be combined with that of skill assessment to create an enhanced skill profile. With a job market signaling perspective, we integrate knowledge about skills, clarify the opportunities of assessment, and develop a framework for a digital skill twin. In doing so, we argue that the multi-source and multi-method approach of such a digital skill twin leads to increased validity, reliability, and standardization. We thereby contribute to theory and practice by developing a new framework that offers employees to know their strengths and development potential and organizations to assess their employees’ skills.
Language
English
Keywords
Digital Twin
Skill Assessment
Skill Profiles
Human Resources
HSG Classification
contribution to scientific community
Publisher place
Boston, Massachusetts, USA
Event Title
Academy of Management Annual Meeting (AOM)
Event Location
Boston, Massachusetts, USA
Event Date
04-08 Aug 2023
Subject(s)
Eprints ID
270131
File(s)![Thumbnail Image]()
open.access
Name
JML_1022.pdf
Size
397.13 KB
Format
Adobe PDF
Checksum (MD5)
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