Artificial Intelligence-Driven Talent Management System: Exploring the Risks and Options for Constructing a Theoretical Foundation

Faqihi, Ali; Miah, Shah Jahan

doi:10.3390/jrfm16010031

Cited by 15 publications

(3 citation statements)

References 67 publications

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“…By implementing AIdriven chatbots, the company achieved seamless and personalized onboarding experiences for new hires. The case demonstrates how technology not only streamlined administrative tasks but also enhanced the overall employee experience, setting the tone for a positive and productive tenure (Faqihi & Shah, 2023).…”

Section: Excellence Unveiled: Case Studies In E-human Resourcesmentioning

confidence: 97%

Transforming Talent Management: The Journey to E-Human Resources Excellence

Tuli

2023

Asian bus. rev.

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This study offers a comprehensive exploration of the dynamic evolution within human resources (HR) and the transformative journey of talent management in the digital era. Beginning with an examination of foundational talent management principles, the narrative traces the shift from traditional HR frameworks to the contemporary landscape of E-Human Resources (E-HR) excellence. The study emphasizes the role of digital technologies as catalysts for this evolution, showcasing their impact on optimizing recruitment, performance management, and employee engagement. Throughout, HR innovation emerges as a driving force, highlighting how novel approaches and technologies are reshaping the HR landscape. This initiative underscores the pursuit of excellence in talent management within the E-HR paradigm, outlining strategies and best practices for organizations to navigate and thrive in the complexities of the modern workforce. In essence, this exploration encapsulates the ongoing narrative of innovation and excellence in the transformative journey of talent management within the digital era, providing valuable insights for HR professionals, organizational leaders, and policymakers navigating the evolving HR landscape.

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Section: Excellence Unveiled: Case Studies In E-human Resourcesmentioning

confidence: 97%

Transforming Talent Management: The Journey to E-Human Resources Excellence

Tuli

2023

Asian bus. rev.

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“…Future studies should not only assess the effectiveness of methods like the Improved Context-aware Data Fusion (ICDF) but also critically examine their long-term sustainability impacts, focusing on ethical data management and the potential risks associated with data-centric HR practices [124,125]. A critical evaluation of ethical challenges and bias in AI-driven HR systems is imperative, shedding light on the sustainability of these technologies in practice [126,127].…”

Section: Emerging Trends and Future Research Directionsmentioning

confidence: 99%

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

Asfahani

2024

Discov Sustain

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

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“…An intelligent talent recommendation algorithm for college students in preparation for the future job market is an essential tool designed to assist students in identifying and developing skills and competencies that align with the evolving demands of the workforce [12]. This algorithm leverages a combination of data analysis, machine learning, and predictive modeling techniques to provide personalized recommendations tailored to each student's academic background, career aspirations, and industry trends [13].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Talent Recommendation Algorithm for College Students for the Future Job Market

Leilei Dong

2024

jes

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An intelligent talent recommendation algorithm is a sophisticated system that utilizes advanced data analysis techniques and machine learning algorithms to match individuals with suitable job opportunities or educational programs. By analyzing a wide range of data points such as skills, qualifications, work experience, and preferences, these algorithms can generate personalized recommendations tailored to each individual's unique profile and career goals. Moreover, intelligent talent recommendation algorithms continuously learn and improve over time, refining their recommendations based on user feedback and performance data. This paper presents an intelligent talent recommendation algorithm tailored for college students preparing for the future job market, utilizing Ranking Hidden Chain Deep Learning (RHC-DL). The algorithm aims to provide personalized recommendations by analysing students' skills, qualifications, interests, and career aspirations. Through simulated experiments and empirical validations, the efficacy of the RHC-DL-enhanced recommendation algorithm is evaluated. Results demonstrate significant improvements in recommendation accuracy and relevance compared to traditional methods. For instance, students using the RHC-DL algorithm reported a 40% increase in job offer acceptance rates and a 30% improvement in job satisfaction levels. Additionally, the algorithm adapts and learns from user interactions, continuously refining its recommendations based on real-time feedback.

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Artificial Intelligence-Driven Talent Management System: Exploring the Risks and Options for Constructing a Theoretical Foundation

Cited by 15 publications

References 67 publications

Transforming Talent Management: The Journey to E-Human Resources Excellence

Transforming Talent Management: The Journey to E-Human Resources Excellence

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

Intelligent Talent Recommendation Algorithm for College Students for the Future Job Market

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