Artificial Intelligence (AI) in Human Resource Management (HRM)

Management and industrial engineering, Journal Year: 2024, Volume and Issue: unknown, P. 37 - 70

Published: Jan. 1, 2024

Language: Английский

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Artificial intelligence for international business: Its use, challenges, and suggestions for future research and practice

Thunderbird International Business Review, Journal Year: 2024, Volume and Issue: 66(2), P. 185 - 200

Published: Feb. 9, 2024

Abstract The emergence of artificial intelligence (AI) has transformed global business, aiding operational efficiency and innovation. It utilizes machine learning big data analytics, driving predictive market trends strategic decision‐making. However, despite the rising discussion accessibility AI tools, understanding its impact on international business remains limited. This article explores AI's potential in strategies, practices, activities. To address this aim, we reviewed 37 articles existing literature to critically explore within context business. More specifically, explored how can be applied innovation approaches selection, entry modes, foreign exchange, human resource management, supply chains, managing across cultures, more topics. necessitated changes workplace configurations need for organizational employee adjustments response technology. As a result foregoing issues integration our analysis provided an exploratory around use, challenges, managerial implications, suggested areas requiring future studies.

Language: Английский

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Interactive effects of AI awareness and change-oriented leadership on employee-AI collaboration: The role of approach and avoidance motivation

Tourism Management, Journal Year: 2024, Volume and Issue: 105, P. 104966 - 104966

Published: May 20, 2024

Language: Английский

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

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

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Effect of leadership styles on turnover intention among staff nurses in private hospitals: the moderating effect of perceived organizational support

BMC Health Services Research, Journal Year: 2024, Volume and Issue: 24(1)

Published: Feb. 14, 2024

Leadership styles have often been proven to support employees in performing their duties better and with more efficiency while enabling them extended organizational tenures. Staff nurses are an essential resource of hospitals ensure proper administration quality patient health care. The study aims determine how transformational authentic leadership affect the staff nurses' turnover intention private hospitals. In addition, it also finds moderating effect perceived support. An explanatory quantitative research design a cross-sectional investigation stratified sampling strategy was used for study. Data from 296 eight chosen Kingdom Bahrain were gathered using questionnaire 24 items. Smart-PLS employed conduct PLS-SEM (partial least squares structural equation modeling) measure direct indirect effects. result indicates that transformational, significantly negatively intention. confirms negative between positive Managers should concentrate on style avoid its impact By considering human practices such as communication training strategies cope intention, organizations can enhance employee engagement, improve job satisfaction, foster stable productive work environment. present revealed adverse within by examining association styles. made significant contribution existing literature delving into focusing study's findings shed light intricate relationship turnover, providing valuable insights both scholars practitioners field. collect data ensured absence standard method variance. enhanced social dominance theory (SDT) moderates

Language: Английский

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Barriers and Enablers of AI Adoption in Human Resource Management: A Critical Analysis of Organizational and Technological Factors

Information, Journal Year: 2025, Volume and Issue: 16(1), P. 51 - 51

Published: Jan. 15, 2025

This paper examines the key factors recognized as transformative in field of human resource management (HRM) and explores their influence on global adoption artificial intelligence (AI). While AI holds significant promise for enhancing HRM efficiency, employee engagement, Decision Making, its implementation presents a range organizational, technical, ethical challenges that organizations worldwide must navigate. Change aversion, data security worries, integration expenses are major roadblocks, but strong digital leadership, company culture, advancements NLP machine learning enablers. complex analysis questions common perception only disruptive by delving into relationship between power dynamics, corporate technology infrastructures. In this paper, we bring together research from several fields to help scholars practitioners understand nuances HRM, with an emphasis importance inclusive methods frameworks.

Language: Английский

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Embedding transparency in artificial intelligence machine learning models: managerial implications on predicting and explaining employee turnover

The International Journal of Human Resource Management, Journal Year: 2022, Volume and Issue: 34(14), P. 2732 - 2764

Published: April 27, 2022

Employee turnover (ET) is a major issue faced by firms in all business sectors. Artificial intelligence (AI) machine learning (ML) prediction models can help to classify the likelihood of employees voluntarily departing from employment using historical employee datasets. However, output responses generated these AI-based ML lack transparency and interpretability, making it difficult for HR managers understand rationale behind AI predictions. If do not how why are based on input datasets, unlikely augment data-driven decision-making bring value organisations. The main purpose this article demonstrate capability Local Interpretable Model-Agnostic Explanations (LIME) technique intuitively explain ET predictions given dataset managers. From theoretical perspective, we contribute International Human Resource Management literature presenting conceptual review algorithmic then discussing its significance sustain competitive advantage principles resource-based view theory. We also offer transparent implementation framework LIME which will provide useful guide increase explainability models, therefore mitigate trust issues decision-making.

Language: Английский

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Managing the dark side of digitalization in the future of work: A fuzzy TISM approach

Journal of Innovation & Knowledge, Journal Year: 2022, Volume and Issue: 7(4), P. 100275 - 100275

Published: Sept. 23, 2022

The rise of new-age technologies has spurred a new industrial revolution, resulting in digital transformation the way we work. global COVID-19 pandemic further accelerated digitalization While many positive outcomes, its darker side should be proactively managed, not neglected. In this regard, paper aims to identify and investigate human resource (HR) practices that can enable employees manage challenges caused by future work (FoW). To do so, employ fuzzy total interpretive structural modeling (TISM) on survey data acquired from senior professionals with HR responsibilities ascertain influence managing dark FoW. doing showcase (i) promote work-life balance, (ii) democratization technologies, (iii) employee empowerment, (iv) entrepreneurial behavior, (v) reskilling for mastery, (vi) wellbeing FoW, thereby advancing theory practice

Language: Английский

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The effect of algorithmic management and workers’ coping behavior: An exploratory qualitative research of Chinese food-delivery platform

Tourism Management, Journal Year: 2022, Volume and Issue: 96, P. 104716 - 104716

Published: Dec. 30, 2022

Language: Английский

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The impacts of artificial intelligence on managerial skills

Journal of Decision System, Journal Year: 2022, Volume and Issue: 32(3), P. 566 - 599

Published: June 13, 2022

Artificial Intelligence (AI) in organisations may change ways of working and disrupt occupations, including managerial ones. Yet, the literature lacks information about how skills will be affected by implementation AI within organisations. To investigate this topic, a thematic content analysis was performed on data collected from qualitative semi-structured interviews with 40 experts. These first results were then confirmed through descriptive statistics 103 other experts who also ranked to developed order priority. Our final show that most are likely augmented AI, while only few them replaced (information gathering simple decision-making) or remain unaffected (leadership imagination). study updates existing technical non-technical taxonomies needed keep pace AI. It contributes development AI-Human Resource Management interface.

Language: Английский

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