Artificial Intelligence in HRM

doi:10.4018/978-1-7998-8497-2.ch001

Paper

Artificial Intelligence in HRM

Published 2022 · E. Sipahi, Erkin Artantaş

Handbook of Research on Innovative Management Using AI in Industry 5.0

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Abstract

Artificial intelligence's ability to enhance the applicant and employee involvement by automating routine, low-value responsibilities, and freeing up time to concentrate on the more planned, innovative work that teams need and want to do has been a burning topic in the research world for years. The technology may lead to improved recruitment, performance evaluations, training, and career management approaches. This literature review looks at artificial intelligence in HRM in terms of recruitment, performance measurement, training and coaching, and career management operations. It allows HR departments to enhance the applicant and employee experience by automating low-value, routine activities, allowing resources to concentrate on more strategic, disruptive work.

Literature Review

Study Snapshot

Artificial intelligence in HRM can enhance applicant and employee involvement by automating routine activities, freeing up time for strategic, disruptive work.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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Paper

Artificial Intelligence in HRM

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References

Citations

What Drives The Diffusion of AI Recruitment Systems in Swiss HRM? The Importance of Technological Expertise, Innovative Climate, Competitive Pressure, Employees’ Expectations and Contextual Factors

Technological expertise, innovative climate, competitive pressure, and employee expectations drive the diffusion of AI recruitment systems in Swiss HRM, with public-sector organizations more reluctant but this can be moderated by an innovative climate and HR involvement in strategic direction.

Machine Learning Based Method for Deciding Internal Value of Talent

Machine learning techniques can help managers make more equitable and least biased salary decisions, enhancing employee motivation.

Multi-echelon and multi-period supply chain management network design considering different importance for customers management using a novel meta-heuristic algorithm

The Seeker Evolutionary Algorithm (SEA) effectively optimizes multi-echelon and multi-period supply chains by considering customer importance, providing high-quality solutions and acceptable speed of operation.

Exploring the Relationship between Abusive Management, Self-Efficacy and Organizational Performance in the Context of Human–Machine Interaction Technology and Artificial Intelligence with the Effect of Ergonomics

Abusive management and self-efficacy directly impact enterprise performance and employee satisfaction in AI-based human-machine interaction technology contexts.