A critical review on analysis of human resource functions using AI technologies

Kaur, Gagandeep; Kaur, Rajwinder

doi:10.1063/5.0108980

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…AI algorithms can be trained to analyze performance data and eliminate any biases that may exist. This can help create a more objective and fair evaluation process, which can lead to better outcomes for all employees (Kaur & Kaur, 2022).…”

Section: Artificial Intelligence and Performance Managementmentioning

confidence: 99%

The Role of Artificial Intelligence in Managing the Diversity of Human Resources: A Proposed Research Model

2023

HNSJ

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The aim of this study is to examine the appropriate literature and emphasize the significance of taking into account the role of Artificial Intelligence (AI) in managing diversity in human resources. AI has the potential to revolutionize the way organizations manage diversity in their human resources. It can help identify biases in hiring, provide diversity training, monitor diversity metrics, and reduce bias in performance evaluations. The researchers reviewed various related literature and came up with propose framework/model that is predicting the role of AI in managing diversity in human resources. If the empirical validation of the model is successful, practitioners and other stakeholders will gain a valuable understanding of the crucial role played by AI, based on the findings.

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Section: Artificial Intelligence and Performance Managementmentioning

confidence: 99%

The Role of Artificial Intelligence in Managing the Diversity of Human Resources: A Proposed Research Model

2023

HNSJ

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“…Artificial intelligence (AI) is revolutionizing HR practices through automation, predictive analytics, and chatbots for employee support (Zel & Kongar, 2020). AI-powered tools can assist in talent acquisition, employee onboarding, performance management, and learning and development initiatives (Kaur & Kaur, 2022). Automation is another crucial aspect of digital transformation in HR, as it eliminates manual tasks and streamlines workflows.…”

Section: Digital Transformation In Hrmentioning

confidence: 99%

Adopting a Learning Organization Perspective for Improving Human Resources Service Quality During Digital Transformation

Hashim Alzadjali,

Jibolu Adedeji,

Mohammad Jamil Adwan

2023

Journal of Chinese Human Resources Management

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The study examined the relationship between adopting a learning organization perspective for improving HR service quality during digital transformation (d-HRM) and its impact on employee performance and HR service quality (QHRS). The analysis utilized a dataset with valid and reliable measurements. The d-HRM variable consisted of nine items, with satisfactory item loadings above 0.60, except for one item (d-HRM4) that was excluded due to a low loading value. The most influential item in forming the d-HRM variable was d-HRM7. The d-HRM variable demonstrated good internal consistency (Cronbach's Alpha=0.913), Composite Reliability (0.929), and Average Variance Extracted (0.621). The QHRS variable excluded two items (QHRS1 and QHRS2) and exhibited satisfactory reliability (Cronbach's Alpha=0.795), Composite Reliability (0.866), and AVE (0.618). The employee performance (EP) variable showed good internal consistency (Cronbach's Alpha=0.836), Composite Reliability (0.883), and AVE (0.603), with EP2 being the most influental item. The study found that 67.5% of the variance in employee performance and 61.2% of the variance in QHRS were determined by d-HRM. The remaining percentage suggest that there are other variables influencing both employee performance and HR service quality. No significant issues with multicollinearity were observed (VIF<10). The model met the eligibility requirements based on the Goodness of Fit test. The discriminant validity testing indicated that the overall validity of the variables exceeded the threshold of 0.60. The results supported the hypotheses, demonstrating significant positive influences of d-HRM on employee performance and QHRS through both direct and indirect relationships. The direct impact of d-HRM on QHRS was found to be the most influential. These findings contribute to understanding the role of a learning organization perspective in enhancing HR service quality during digital transformation and its impact on employee performance.

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“…By enabling workers to communicate and study together virtually, regardless of their physical locations, these technologies also facilitate collaborative learning. As a result, workers are more likely to collaborate, share expertise, and feel a feeling of belonging [16].…”

Section: Augmented Reality (Ar) and Virtual Reality (Vr) In Learningmentioning

confidence: 99%

Artificial Intelligence in Human Resource Management: Advancements, Implications and Future Prospects

Balakrishnan

et al. 2023

IJRITCC

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The present condition, challenges, and potential applications of artificial intelligence (AI) in human resource management (HRM) are all explored in this survey article. As an innovation, artificial intelligence (AI) has the potential to completely revolutionize several facets of human resource management (HRM). Examining the usage of AI-powered tools and systems in different HR processes, the present situation with AI in HRM is examined. These encompass learning and development, performance management, employee engagement, and recruiting. The use of AI algorithms and machine learning approaches to automate regular HR operations, analyze vast amounts of employee data, and provide insightful data to aid decision-making is addressed in this article. However, integrating AI into HRM also poses a number of difficulties that must be resolved. Bias, privacy issues, and transparency are just a few of the ethical and legal ramifications of using AI in decision-making processes that are discussed in this survey. The study emphasizes how accountability and fairness must be maintained in AI systems by responsible design, oversight, and periodic evaluation. With an emphasis on job displacement and workforce reorganization, the possible influence of AI on the human workforce is also explored. To effectively traverse this change, strategies including work role redefinition, employee up skilling, and establishing a collaborative atmosphere between humans and AI are suggested. The possible advantages and breakthroughs that AI might bring to HRM practices are highlighted as the future perspectives of AI in HRM are examined. As new applications for AI in HRM, sentiment analysis, predictive analytics, intelligent decision support, and personalized employee experiences are all highlighted. In order to fully realize the promise of AI in HRM, the study underlines the significance of data infrastructure, data governance frameworks, and a data-driven culture. Overall, this survey study offers an in-depth review of the existing situation, difficulties, and prospects for AI in HRM. It aggregates current information, identifies research gaps, and gives practitioners and scholars new perspectives on how AI will fundamentally alter the way HRM activities are carried out in the future.

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A critical review on analysis of human resource functions using AI technologies

Cited by 5 publications

References 25 publications

The Role of Artificial Intelligence in Managing the Diversity of Human Resources: A Proposed Research Model

The Role of Artificial Intelligence in Managing the Diversity of Human Resources: A Proposed Research Model

Adopting a Learning Organization Perspective for Improving Human Resources Service Quality During Digital Transformation

Artificial Intelligence in Human Resource Management: Advancements, Implications and Future Prospects

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