Virtual Hiring and Training Processes in the Metaverse: Remote Work Apps, Sensory Algorithmic Devices, and Decision Intelligence and Modeling
Virtual Hiring and Training Processes in the Metaverse: Remote Work Apps, Sensory Algorithmic Devices, and Decision Intelligence and Modeling
Author(s): Katarina Frajtova Michalikova, Petr Šuleř, Rachel RobinsonSubject(s): Labor relations, Management and complex organizations
Published by: Addleton Academic Publishers
Keywords: virtual; hiring; training; metaverse; remote work app; decision making;
Summary/Abstract: Despite the relevance of virtual hiring and training processes in the metaverse, only limited research has been conducted on this topic. In this article, we cumulate previous research findings indicating that augmented analytics and virtual connectivity tools, predictive modeling, and digital twin technologies assist virtual meetings and employee engagement metrics. We contribute to the literature on remote work apps, sensory algorithmic devices, and decision intelligence and modeling by showing that virtual avatars as remote workforce can deploy data mining techniques, metaverse-related technologies, and collaboration tools to optimize performance parameters. Throughout March 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “metaverse” + “virtual hiring and training processes,” “remote work apps,” “sensory algorithmic devices,” and “decision intelligence and modeling.” As we inspected research published in 2022, only 80 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 12, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, Distiller SR, and MMAT.
Journal: Psychosociological Issues in Human Resource Management
- Issue Year: 10/2022
- Issue No: 1
- Page Range: 50-63
- Page Count: 14
- Language: English
- Content File-PDF