An interdisciplinary review of AI and HRM: Challenges and future directions

Human Resource Management Review, Journal Year: 2022, Volume and Issue: 33(1), P. 100924 - 100924

Published: June 16, 2022

Language: Английский

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Emotion AI at Work: Implications for Workplace Surveillance, Emotional Labor, and Emotional Privacy

Published: April 19, 2023

Workplaces are increasingly adopting emotion AI, promising benefits to organizations. However, little is known about the perceptions and experiences of workers subject AI in workplace. Our interview study with (n=15) US adult addresses this gap, finding that (1) participants viewed as a deep privacy violation over workers' sensitive emotional information; (2) may function enforce compliance labor expectations, engage mechanism preserve their emotions; (3) be exposed wide range harms consequence Findings reveal need recognize define an individual right what we introduce privacy, well raise important research policy questions on how protect within beyond

Language: Английский

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Voice artificial intelligence service failure and customer complaint behavior: The mediation effect of customer emotion

Electronic Commerce Research and Applications, Journal Year: 2023, Volume and Issue: 59, P. 101261 - 101261

Published: March 27, 2023

Language: Английский

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The dark side of AI-enabled HRM on employees based on AI algorithmic features

Journal of Organizational Change Management, Journal Year: 2023, Volume and Issue: 36(7), P. 1222 - 1241

Published: Nov. 23, 2023

Purpose AI is an emerging tool in HRM practices that has drawn increasing attention from researchers and practitioners. While there little doubt AI-enabled exerts positive effects, it also triggers negative influences. Gaining a better understanding of the dark side holds great significance for managerial implementation enriching related theoretical research. Design/methodology/approach In this study, authors conducted systematic review published literature field HRM. The enabled to critically analyze, synthesize profile existing research on covered topics using transparent easily reproducible procedures. Findings used algorithmic features (comprehensiveness, instantaneity opacity) as main focus elaborate effects Drawing inconsistent literature, distinguished between two concepts comprehensiveness: comprehensive analysis data collection. differentiated into instantaneous intervention interaction. Opacity was delineated: hard-to-understand hard-to-observe. For each feature, study connected organizational behavior theory elaborated potential mechanism HRM's employees. Originality/value Building upon identified secondary dimensions features, behind This elaboration establishes robust foundation advancing AI-enable Furthermore, discuss future directions.

Language: Английский

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Howdy, Robo-Partner: exploring artificial companionship and its stress-alleviating potential for service employees

Journal of service management, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 18, 2024

Purpose The emergence of new generations artificial intelligence (AI), such as ChatGPT or Copilot has brought about a wave innovation in the service workplace. These robotic agents can serve companions, helping employees cope with work-related stress. This research introduces concept “artificial companionship,” which explains how function partners assisting to fulfill their job responsibilities and maintain mental well-being. Design/methodology/approach uses mixed methods approach grounded social support theory from psychology management develop conceptual framework for stress-alleviating implications companionship. A qualitative employee survey is conducted justify relevance propositions. Findings delineates It highlights four distinct roles that AI play companionship – instrumental, informative, caring, intimate. Building on this foundation, presents series propositions elucidate potential mitigating stress among employees. Practical Firms should consider aligning types demands inherent employees’ better reinforce resilience sustainment overcoming challenges. Originality/value perspective through lens theory. extends current understanding human-robot collaboration workspaces derives set guide future investigations.

Language: Английский

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Utilizing MFCCs and TEO-MFCCs to Classify Stress in Females Using SSNNA

IIUM Engineering Journal, Journal Year: 2025, Volume and Issue: 26(1), P. 324 - 335

Published: Jan. 10, 2025

All individuals are susceptible to experiencing stress in their everyday lives. Nevertheless, has a greater influence on females due both biological and environmental factors. This study utilized female speeches detect classify no women. Using speech, composed of non-invasive non-intrusive approaches, helps identify better females. A comparative analysis was conducted between Mel-frequency Cepstral Coefficients (MFCCs) Teager Energy Operator- MFCCs (TEO-MFCCs) determine the best speech feature for classifying emotions associated with no-stress conditions voices. With assistance Stress Speech Neural Network Architecture (SSNNA), an improved accuracy 93.9% achieved. research showed that enhanced higher-frequency components stressed distinguishing classes. shows SSNNA achieved high 14 voices, confirming its ability function independently speaker identity. ABSTRAK: Semua individu terdedah kepada stres dalam kehidupan seharian mereka. Walau bagaimanapun, memberi pengaruh yang lebih besar terhadap wanita akibat faktor biologi dan persekitaran. Kajian ini menggunakan ucapan untuk mengesan mengklasifikasikan tiada kalangan wanita. Penggunaan ucapan, merupakan pendekatan tidak invasif mengganggu, membantu mengenal pasti tekanan dengan baik Analisis perbandingan telah dijalankan antara Operator-MFCCs (TEO-MFCCs). Tujuannya adalah menentukan ciri terbaik bagi emosi berkaitan keadaan suara Dengan bantuan metrik prestasi tinggi ketepatan dicapai. Penyelidikan menunjukkan bahawa meningkatkan komponen frekuensi stres, secara efektif membezakan kelas stres. mencapai wanita, mengesahkan ia berfungsi bebas daripada identiti penutur.

Language: Английский

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Artificial Intelligence or Human Service, Which Customer Service Failure Is More Unforgivable? A Counterfactual Thinking Perspective

Psychology and Marketing, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

ABSTRACT With the continuous development and progress of Artificial Intelligence (AI) technology, intelligent customer service stands on tip wind waves AI. This rapid industry has made comparison collision between AI artificial a hot topic. paper proposes model exploring how failure human personnel influences satisfaction differently, with mediation variable counterfactual thinking moderation variables psychological distance empathy. Four studies using experimental design were conducted. Study 1 ( N = 80) investigates whether finding that can lead to higher than service. 2 demonstrates effect thinking, produces lower failure. 3 200) in process influencing satisfaction. 4 illustrates empathy from perspective uncanny valley effect. These findings provide evidence for research service, guidance improvement industry.

Language: Английский

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From humans to algorithms: A sociotechnical framework of workplace surveillance

Digital Business, Journal Year: 2025, Volume and Issue: unknown, P. 100120 - 100120

Published: April 1, 2025

Language: Английский

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Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

Annals of Operations Research, Journal Year: 2022, Volume and Issue: 339(1-2), P. 163 - 183

Published: Jan. 30, 2022

Language: Английский

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Exploring Opportunities for Artificial Intelligence in Organization Development

Human Resource Development Review, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 21, 2024

The purpose of this research was to examine the utilization artificial intelligence (AI) in organization development (OD) through a comprehensive review existing literature. We also propose potential avenues for future on AI OD. conducted systematic literature 68 studies OD based Cummings and Worley’s four categories (i.e., human process, resource, strategic change, technostructural interventions). first summarized analyzed key information about how is implemented contexts, then examined underlying theories or theoretical frameworks utilized focusing AI. application OD, ethical concerns, recommendations practice using paper concludes with discussion implications practice.

Language: Английский

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