مرور نظام‌مند مدیریت منابع انسانی هوشمند داده محور
محورهای موضوعی : بهره وری و توانمندسازی نیروی انسانیمصطفی طغیانی پزوه 1 , محمدرضا دلوی 2 , رسول آقا داوود 3
1 - دانشجوی دکتری، رشته مدیریت دولتی -مدیریت تطبیقی و توسعه ، دانشگاه آزاد اسلامی واحد دهاقان ، دهاقان، ایران.
2 - محمدرضا دلوی دانشیار، گروه مدیریت، واحد دهاقان، دانشگاه آزاد اسلامی، دهاقان، ایران.
3 - استادیار، گروه مدیریت، دانشگاه آزاد اسلامی دهاقان، دهاقان، ایران.
کلید واژه: مدیریت منابع انسانی, مدیریت هوشمند, داده محور,
چکیده مقاله :
زمینه و هدف: مدیریت منابع انسانی هوشمند، رویکردی نوین است از ترکیب دو عنصر کلیدی یعنی مدیریت منابع انسانی و تحلیل داده‌ها برای بهبود فرآیندها و تصمیم‌گیری‌ها، که به سازمان‌ها کمک می کند تا داده‌های خود را به شکلی هوشمندانه تبدیل کند. هدف پژوهش، مرور نظامند مدیریت منابع انسانی هوشمند داده محور در دانشگاه های آزاد و دولتی می باشد. روش بررسی: این پژوهش ازنظر هدف بنیادی و از روش فرا تحلیل استفاده شد. جامعه مورد بررسی شامل تحقیقات انجام‌شده در مورد موضوع در پایگاه‌های معتبر علمی بود که در مجموع 33 منبع مرتبط با ویژگی های مورد نظرشناسایی شدند. از روش کدگذاری باز، محوری و انتخابی برای طراحی الگو استفاده شد. یافته ها: مولفه‌های الگوی نهایی شامل 15 مضمون بود از جمله: رهبری هوشمند، تامین منابع استراتژیک و سرمایه گذاری هدفمند، معماری سازمانی مناسب برای نظام های آموزش و جذب و نگهداشت. نتیجه گیری: به منظور تحقق مدیریت منابع انسانی هوشمند داده محور، تمرکز بر مولفه های شناسایی شده و عملیاتی کردن آنها در دانشگاه ها از اهمیت بالایی برخوردار است.
Background and purpose: Intelligent human resource management is a new approach of combining two key elements namely human resource management and data analysis to improve processes and decisions, which helps organizations to transform their data in an intelligent way. The purpose of this research is to review the intelligent data-oriented human resources management system in open and public universities. Research method: This research was based on the fundamental goal and the meta-analysis method was used. The investigated community included the research conducted on the subject in reliable scientific databases, which identified a total of 33 sources related to the desired characteristics. Open, central and selective coding methods were used to design the model. Findings: The components of the final model included 15 themes, including: intelligent leadership, provision of strategic resources and targeted investment, suitable organizational architecture for training and recruitment and retention systems. Conclusion: In order to realize intelligent data-driven human resource management, focusing on the identified components and operationalizing them in universities is of great importance.
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