Evidence map›Paper›PMID 38903456›Full record

SynthesisFrontiers in psychology2024

The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges.

Justine Dima, Marie-Hélène Gilbert, Julie Dextras-Gauthier, Laurent Giraud

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Justine DimaSchool of Engineering and Management Vaud, HES-SO, Yverdon-les-Bains, Switzerland.
Marie-Hélène GilbertDepartment of Management, Faculty of Business Administration, Laval University, Quebec, QC, Canada.
Julie Dextras-GauthierDepartment of Management, Faculty of Business Administration, Laval University, Quebec, QC, Canada.
Laurent GiraudIREGE, IAE Savoie Mont Blanc, Savoie Mont Blanc University, Annecy, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study analyzes the existing academic literature to identify the effects of artificial intelligence (AI) on human resource (HR) activities, highlighting both opportunities and associated challenges, and on the roles of employees, line managers, and HR professionals, collectively referred to as the HR triad. Methods: We employed the scoping review method to capture and synthesize relevant academic literature in the AI-human resource management (HRM) field, examining 27 years of research (43 peer-reviewed articles are included). Results: Based on the results, we propose an integrative framework that outlines the five primary effects of AI on HR activities: task automation, optimized HR data use, augmentation of human capabilities, work context redesign, and transformation of the social and relational aspects of work. We also detail the opportunities and challenges associated with each of these effects and the changes in the roles of the HR triad. Discussion: This research contributes to the ongoing debate on AI-augmented HRM by discussing the theoretical contributions and managerial implications of our findings, along with avenues for future research. By considering the most recent studies on the topic, this scoping review sheds light on the effects of AI on the roles of the HR triad, enabling these key stakeholders to better prepare for this technological change. The findings can inform future academic research, organizations using or considering the application of AI in HRM, and policymakers. This is particularly timely, given the growing adoption of AI in HRM activities.

Indexed as

artificial intelligenceHR activitiesHR digitalizationHR techHR technologyhuman resource (HR)human resource management (HRM)scoping review

Identifiers

PMID38903456
PMCPMC11188403

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.