دانلود مقاله کاربرد هوش مصنوعی در ارزیابی پتانسیل و استعداد یابی
خلاصه
1. معرفی
2. داده ها و روش ها
3. نتایج و بحث
4. نتیجه گیری
بیانیه مشارکت نویسنده
بیانیه تامین مالی
بیانیه در دسترس بودن داده ها
اعلامیه منافع رقابتی
منابع
Abstract
1. Introduction
2. Data and methods
3. Results and discussion
4. Conclusions
Author contribution statement
Funding statement
Data availability statement
Declaration of competing interest
References
چکیده
مطالعه ما بینش های ارزشمندی را در مورد رابطه بین هوش مصنوعی (AI) و مدیریت منابع انسانی (HRM) ارائه می دهد. ما سوگیری را به حداقل رسانده‌ایم و یافته‌های قابل اعتماد را با استفاده از مرور متون سیستماتیک و بیانیه PRISMA تضمین کرده‌ایم. ترکیب جامع ما از مطالعات موجود در این تحقیق، همراه با تجزیه و تحلیل کتاب‌سنجی مقالات، مجلات، نمایه‌ها، وابستگی‌های نویسندگان، نقل‌قول‌ها، هم‌روی کلمات کلیدی، و تجزیه و تحلیل هم‌نویسندگی، نتایج قوی ایجاد کرده است. بحث از یافته‌های ما بر حوزه‌های مهم مورد علاقه، مانند هوش مصنوعی و استعداد، تعصب هوش مصنوعی، اخلاق و قانون، و تأثیر آنها بر مدیریت منابع انسانی (HR) متمرکز است. تحقیقات ما به رسمیت شناختن اهمیت مدیریت استعداد توسط سازمان ها در دستیابی به مزیت رقابتی را برجسته می کند، زیرا مهارت های سطح بالاتر به طور فزاینده ای ضروری می شوند. اگرچه برخی از مدیران منابع انسانی از فناوری هوش مصنوعی برای جذب استعداد استفاده کرده اند، مطالعه ما نشان می دهد که هنوز جای پیشرفت وجود دارد. مطالعه ما مطابق با تحقیقات قبلی است که پتانسیل هوش مصنوعی برای متحول کردن مدیریت منابع انسانی و آینده کار را تایید می کند. یافته‌های ما بر نیاز مدیران منابع انسانی به فعال بودن در پذیرش فناوری و پر کردن شکاف‌های فنی، انسانی، اجتماعی و دولتی تأکید می‌کند. مطالعه ما به بدنه رو به رشد دانش مدیریت هوش مصنوعی و منابع انسانی کمک می کند و بینش ها و توصیه های ضروری را برای تحقیقات آینده ارائه می دهد. اهمیت مطالعه ما در تمرکز آن بر نقش منابع انسانی در ارتقای مزایای برنامه های کاربردی مبتنی بر هوش مصنوعی است، در نتیجه مجموعه بزرگتری از دانش از دیدگاه سازمانی ایجاد می شود.
Abstract
Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.
Introduction
The Peter Principle, as defined in the Peter Principle of Management [1], states that “every employee tends to rise to his level of incompetence”, meaning that the best employees are not always the best candidates for promotion. The Peter Principle states that promotion decisions often support the candidate's performance in current roles rather than necessarily their capacity to perform in long-term management roles. It also demonstrates that, even if an employee's tasks change, organisations continue to believe that the attributes that have made someone successful in the past will continue to contribute to their success in the future. According to Bersin & Chamorro-Premuzic [2], individuals are promoted based on past achievements.
Understanding the importance of hiring the right person has become a priority for many businesses [3]. While talent management encompasses various human resource strategies, Claussen et al. [4] argue that strategic talent management covers an organisation's identification of essential jobs and, later, the building of a talent pool to fill these positions. According to this definition, necessary position staffing is a critical component of talent management [4]. Organisations that efficiently manage their human capital are more likely to achieve outstanding performance and generate long-term competitive advantage [[5], [6], [7]]. A breakthrough McKinsey report published in 1997 [8] identified the “war for talent” as a strategic business problem and a significant driver of company success. Even when many people thought the struggle for talent was over, it was not. According to the authors [8], the battle for talent would continue for the next two decades due to persisting economic and societal causes, revealing that winning the fight for leadership talent requires much more than disruptive recruitment methods. It shows the pressing need for more about using time-tested concepts of attracting, developing, and keeping highly competent managers in daring new ways. Talent management ties directly to the notion of enterprise management and knowledge transfer. When seen through the lens of international corporations as a way of obtaining success, this issue takes on additional significance [7].
Conclusions
This comprehensive literature review aims to uncover the knowledge gaps in the application of AI for potential and talent identification within organisational settings. Our systematic and in-depth analysis of relevant studies and publications highlights the linkages between AI and HRM. It underscores the fact that research in this area is still in its early stages. Our findings, based on a robust methodology, reinforce and build upon the previous research that recognises the potential benefits of utilising AI in HR, offering valuable insights from an organisational perspective.RQ1)
How can AI models be used to evaluate employee potential? Organisations are recognising the critical role of talent management in gaining a competitive edge, particularly with the growing demand for higher-level skills in the knowledge-based economy. However, despite the advantages of using AI for talent acquisition, not all HR managers have adopted this technology. This is due to a lack of knowledge and skills in HR data analysis, limited organisational support, unavailability of structured information, and resource constraints. To address these gaps, HR managers must proactively acquire knowledge of AI and its benefits for HRM. They have a crucial role in bridging the technological, human, societal, and governmental divides and ensuring that AI effectively deploys while minimising risks and promoting favourable employee outcomes. There is a need for more research on implementing AI-based applications in HRM, and organisations must invest in developing their HR professionals to utilise AI technology effectively.