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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2307.03195 |
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| _version_ | 1866918034413715456 |
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| author | Qin, Chuan Zhang, Le Cheng, Yihang Zha, Rui Shen, Dazhong Zhang, Qi Chen, Xi Sun, Ying Zhu, Chen Zhu, Hengshu Xiong, Hui |
| author_facet | Qin, Chuan Zhang, Le Cheng, Yihang Zha, Rui Shen, Dazhong Zhang, Qi Chen, Xi Sun, Ying Zhu, Chen Zhu, Hengshu Xiong, Hui |
| contents | In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_03195 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics Qin, Chuan Zhang, Le Cheng, Yihang Zha, Rui Shen, Dazhong Zhang, Qi Chen, Xi Sun, Ying Zhu, Chen Zhu, Hengshu Xiong, Hui Computers and Society Artificial Intelligence In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics. |
| title | A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2307.03195 |