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jeasiq-1019
Cost of human resources from a behavioral perspective
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Purpose/objective:

1 - To explain the financial impact of the activities and areas of human resources management and the adoption of the methodology for estimating costs on the basis of conduct and statement of how to assess costs and benefits of human resource activities.

2 - Measuring  human capital, and its impact on the financial statements.

 

Design/methodology/approach:

 Concentrated dimensions of the research paper's lack of financial statements prepared by the organizations for information mandated human resource its components of the three (attraction - development - released), as well as the financial impact in the conduct of the organization will have the reader able to understand the pressure, the cost to the policies of human resource, as well as that understanding of awareness of behavioral financial issue in itself, should be addressed in order to find some kind of mental and intellectual relations about what that directs the behavior of the investor.

 

Findings:

The information about the cost of human resources, can increase the efficiency of the management staff, by helping to make sound decisions in the field of polarization, the selection and appointment, development, transport, upgrade, use, calendar, and bonuses related to them. Moreover, the financial statements based on cost (accounting) human capital, so that hurt the main purpose of disclosure and identification of related items.

 

Crossref
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Sat Feb 01 2014
Journal Name
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EXTENSION ADMINISTRATION PERFORMANCE UNDER QUALITY STANDARDS FROM THE PERSPECTIVE OF THE WORKING STAFF IN AGRICULTURAL EXTENSION AND VEGETABLES FARMERS OF CENTRAL GOVERNORATES IN IRAQ
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Sat Jun 01 2013
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Wed Jan 01 2025
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This study aims to conduct an exhaustive comparison between the performance of human translators and artificial intelligence-powered machine translation systems, specifically examining the top three systems: Spider-AI, Metacate, and DeepL. A variety of texts from distinct categories were evaluated to gain a profound understanding of the qualitative differences, as well as the strengths and weaknesses, between human and machine translations. The results demonstrated that human translation significantly outperforms machine translation, with larger gaps in literary texts and texts characterized by high linguistic complexity. However, the performance of machine translation systems, particularly DeepL, has improved and in some contexts

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