Acta Psychol (Amst)
Indian Institute of Management Kozhikode, Kerela, 673570, India. Electronic address:
Published: February 2025
Artificial Intelligence is expected to be a value-adding intervention in HRM processes; however, there is still a large gap between its perception of value-addition and its actual utility. In this article, we utilize transaction cost and resource-based views to build a framework to assess the suitability and potential adoption of AI-based tools in specific HRM processes. AI-based tools add value when they streamline operations and bring efficiencies by automating repetitive tasks. The transaction cost view in our framework assists in assessing the value created. Also, the resource-based perspective assists in understanding how AI builds the strategic capabilities of HR. We first review articles that look at the current state of AI literature. We then apply our framework to four critical HRM processes - recruitment and selection, performance management, training and development, and compensation and benefits. Based on our framework, we then develop propositions. Our propositions act as a roadmap for the strategic adoption of AI tools, ensuring smoother integration with existing HR processes. Our study adds to the HRM literature by providing a structured analysis of AI's impact on HR processes. We conclude by highlighting emerging issues that need future attention from practitioners and scholars.
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http://dx.doi.org/10.1016/j.actpsy.2025.104816 | DOI Listing |
Mol Biol Rep
March 2025
Department of Chemistry, Faculty of Science, University of Kufa, Najaf, Iraq.
Background: End-stage chronic kidney disease (CKD) can lead to life-threatening complications and is caused primarily by CKD and cardiovascular issues. CKD is characterized by the inability of the kidneys to filter waste and excess fluids from the blood. This study investigated the associations of the genetic variants XRCC1 rs1799782 (C194T) and ERCC2/XPD rs25487 (Q399R) with CKD susceptibility in Iraqi patients and related biochemical changes.
View Article and Find Full Text PDFCamera based imaging is the most widely used technique for non-contact heart rate monitoring (HRM). However, it's robustness to motion artifacts and the absence of reliability when the patient's face is not in the camera's field of view still persists. This is often addressed with computationally heavy AI algorithms or hardware intensive multi-camera systems.
View Article and Find Full Text PDFActa Psychol (Amst)
February 2025
Indian Institute of Management Kozhikode, Kerela, 673570, India. Electronic address:
Artificial Intelligence is expected to be a value-adding intervention in HRM processes; however, there is still a large gap between its perception of value-addition and its actual utility. In this article, we utilize transaction cost and resource-based views to build a framework to assess the suitability and potential adoption of AI-based tools in specific HRM processes. AI-based tools add value when they streamline operations and bring efficiencies by automating repetitive tasks.
View Article and Find Full Text PDFMed Trop Sante Int
September 2024
Université Tunis El Manar, Institut Pasteur de Tunis, Laboratoire de recherche Parasitologie médicale, biotechnologie et biomolécules (LR 20-IPT-06), Tunisie.
Introduction: Colorectal cancer (CRC) is a major public health problem, including in Tunisia. It is classified as the second leading cause of cancer-related mortality on a global scale. The carcinogenesis process is multifactorial, mainly involving genetic and environmental factors.
View Article and Find Full Text PDFRes Social Adm Pharm
May 2025
Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, 00014, Helsinki, Finland; Tampere University Hospital, Hospital Pharmacy, Wellbeing Services County of Pirkanmaa, PL 272, 33101, Tampere, Finland. Electronic address:
Background: High-risk medicines (HRMs) are medicines that have a higher risk of causing severe consequences for the patient when used in error.
Objective: This study aimed to develop a Finnish High-risk Medicine Classification (FIN-RiskMeds) to support healthcare professionals in HRM risk management.
Methods: The development of FIN-RiskMeds was coordinated by the Finnish Medicines Agency (Fimea) using the Delphi consensus method.
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