Cited by Mapping the impacts of neural networks on human resource management research: a bibliometric analysis

Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT DOI

Pawan Budhwar, Soumyadeb Chowdhury, Geoffrey Wood

et al.

Human Resource Management Journal, Journal Year: 2023, Volume and Issue: 33(3), P. 606 - 659

Published: July 1, 2023

Abstract ChatGPT and its variants that use generative artificial intelligence (AI) models have rapidly become a focal point in academic media discussions about their potential benefits drawbacks across various sectors of the economy, democracy, society, environment. It remains unclear whether these technologies result job displacement or creation, if they merely shift human labour by generating new, potentially trivial practically irrelevant, information decisions. According to CEO ChatGPT, impact this new family AI technology could be as big “the printing press”, with significant implications for employment, stakeholder relationships, business models, research, full consequences are largely undiscovered uncertain. The introduction more advanced potent tools market, following launch has ramped up “AI arms race”, creating continuing uncertainty workers, expanding applications, while heightening risks related well‐being, bias, misinformation, context insensitivity, privacy issues, ethical dilemmas, security. Given developments, perspectives editorial offers collection research pathways extend HRM scholarship realm AI. In doing so, discussion synthesizes literature on AI, connecting it aspects processes, practices, outcomes, thereby contributing shaping future research.

Language: Английский

Citations

403

Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace DOI

Ali Fenwick, Gábor Molnár,

Piper Frangos

et al.

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 6

Published: Jan. 15, 2024

The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional data-driven. In age AI, role HRM professionals organizations continues evolve. Artificial intelligence (AI) is transforming many practices throughout creating system process efficiencies, performing advanced data analysis, contributing value creation organization. A growing body evidence highlights benefits AI brings field HRM. Despite increased interest AI-HRM scholarship, focus on human-AI interaction at work AI-based technologies for limited fragmented. Moreover, lack considerations tech design deployment can hamper digital transformation efforts. This paper provides a contemporary forward-looking perspective strategic human-centric plays within as becomes integrated workplace. Spanning three distinct phases integration (technocratic, integrated, fully-embedded), it examines technical, human, ethical challenges each phase suggestions how overcome them using approach. Our importance evolving AI-driven organization roadmap bring humans machines closer together

Language: Английский

Citations

AI in knowledge sharing, which ethical challenges are raised in decision-making processes for organisations? DOI

Mojtaba Rezaei,

Marco Pironti,

Roberto Quaglia

et al.

Management Decision, Journal Year: 2024, Volume and Issue: unknown

Published: April 24, 2024

Purpose This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices their implications for decision-making (DM) processes within organisations. Design/methodology/approach The employs a mixed-methods approach, beginning comprehensive literature review extract background information on AI KS potential challenges. Subsequently, confirmatory factor analysis (CFA) is conducted using data collected from individuals employed business settings validate identified impact DM processes. Findings findings reveal that related privacy protection, bias fairness transparency explainability are particularly significant DM. Moreover, accountability responsibility of employment also show relatively high coefficients, highlighting importance process. In contrast, such as intellectual property ownership, algorithmic manipulation global governance regulation found be less central Originality/value research contributes ongoing discourse knowledge management (KM) By providing insights recommendations researchers, managers policymakers, emphasises need holistic collaborative approach harness benefits technologies whilst mitigating risks.

Language: Английский

Citations

Integrating AI in organizations for value creation through Human-AI teaming: A dynamic-capabilities approach DOI

Cristina Simón, Elena Revilla, María Jesús Sáenz

et al.

Journal of Business Research, Journal Year: 2024, Volume and Issue: 182, P. 114783 - 114783

Published: June 27, 2024

Language: Английский

Citations

Fusing talent horizons: the transformative role of data integration in modern talent management DOI

Ahmed M. Asfahani

Discover Sustainability, Journal Year: 2024, Volume and Issue: 5(1)

Published: March 7, 2024

Abstract This study elucidates the transformative influence of data integration on talent management in context evolving technological paradigms, with a specific focus sustainable practices human resources. Historically anchored societal norms and organizational culture, has transitioned from traditional methodologies to harnessing diverse sources, shift that enhances HR strategies. By employing narrative literature review, research traces trajectory emphasizing juxtaposition structured unstructured data. The digital transformation is explored, not only highlighting evolution Human Resource Information Systems (HRIS) but also underscoring their role promoting workforce management. advanced technologies such as machine learning natural language processing examined, reflecting impact efficiency ecological aspects practices. paper underscores imperative balancing data-driven strategies quintessential element provides concrete examples demonstrating this balance action for practitioners scholars

Language: Английский

Citations

Utilizing machine learning to predict employee turnover in high-stress sectors DOI

Kudirat Bukola Adeusi,

Prisca Amajuoyi,

Lucky Bamidele Benjami

et al.

International Journal of Management & Entrepreneurship Research, Journal Year: 2024, Volume and Issue: 6(5), P. 1702 - 1732

Published: May 21, 2024

This study investigates the application of machine learning techniques to predict employee turnover in high-stress sectors. The primary objective is enhance retention strategies by accurately identifying potential risks. research utilizes a comprehensive dataset comprising various factors, including demographics, job satisfaction, performance metrics, and stress levels. Multiple algorithms, such as logistic regression, decision trees, random forests, neural networks, are employed build predictive models. methodology involves data preprocessing, feature selection, model training, evaluation. Cross-validation hyper parameter tuning performed ensure robustness accuracy each algorithm assessed using metrics accuracy, precision, recall, area under receiver operating characteristic curve (AUC-ROC). Key findings reveal that models can effectively turnover, with forests networks demonstrating superior performance. Significant predictors include levels, ratings. concludes integrating into human resource practices provide valuable insights for preemptive interventions, ultimately reducing rates environments. Future should explore integration real-time deep further accuracy. Additionally, ethical implications HR decisions warrant careful consideration fairness transparency. Keywords: Machine Learning (ML), Employee Turnover, Predictive Analytics, Human Resources (HR), High-Stress Sectors, Decision Trees, Random Forests, Extreme Gradient Boosting (XGBoost), Personalized Retention Strategies, Business Intelligence (BI) Tools, Data Quality, Ethical Considerations, Privacy, Natural Language Processing (NLP), Deep Learning, Real-time Analysis, Engagement, Work-Life Balance, Organizational Performance, Data-Driven Insights.

Language: Английский

Citations

Generative AI inspiration and hotel recommendation acceptance: Does anxiety over lack of transparency matter? DOI

GuoQiong Ivanka Huang, IpKin Anthony Wong, Chen Zhang

et al.

International Journal of Hospitality Management, Journal Year: 2025, Volume and Issue: 126, P. 104112 - 104112

Published: Jan. 18, 2025

Language: Английский

Citations

The Machine Learning-Based Task Automation Framework for Human Resource Management in MNC Companies DOI

Suchitra Deviprasad,

N. Madhumithaa,

I. Walter Vikas

et al.

Published: Dec. 18, 2023

Recently, machine learning-based task automation framework have been gaining attention in human resource management of Multi-National Companies (MNCs). Task helps MNCs to automate repetitive HR tasks, analyse data quickly and accurately, forecast workforce, recognize employees. are now beginning use ML algorithms combination with Artificial Intelligence (AI) streamline the processes. Most large-scale operations decentralized organization structures which put additional pressure on teams carry out intricate tedious manual To ease process, ML-based facilitates leverage power AI perform tasks a more effective efficient manner. The utilizes bots can simulate all processes such as recruitment, time attendance, tracking employee records, scheduling calendar, office administration tasks. predictive analytics identify trends, patterns, behaviour, anomalies, important insights from large volumes structured unstructured data.

Language: Английский

Citations

A methodological and theoretical framework for implementing explainable artificial intelligence (XAI) in business applications DOI