AI-ENHANCED PERFORMANCE MANAGEMENT: TRANSFORMING APPRAISALS IN MODERN HRM

Clicks: 1
ID: 312349
2025
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #367 of 395 articles by views in Social Sciences & Humanity Research Review

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 395 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Background: New technologies in human resource management (HRM) have come in the form of integrated performance management systems that reinvent appraisals by incorporating Artificial Intelligence (AI). Nevertheless, the awareness of its feasibility as well as the issues in the application design remain important.Objective: This research contribution shall examine how AI-driven systems affect performance and appraisal outcomes for employees with an assessment of the positive and negative effects it has within organizations.Methods: In this study, a quantitative cross-sectional approach was administratively used to administer a structured questionnaire to 355 respondents from different sectors. Independent variables were thus AI-powered analytics, bias reduction, and continuing monitoring, the mediating variable was the AI-driven feedback mechanisms and the dependent variable was the employee performance. Descriptive measures that were used include Normality tests for reliability and Cronbach Alpha plus all Inferential measures.Results: It emerged that the majority of the participants supported the use of AI systems having a high percentage of “Agree’ and ‘Strongly’ agree’. However, the results revealed serious degrees of departure from normality also, and the internal consistency of the instrument which is assessed by Cronbach’s Alpha came to a mere 0.043. Standard deviation indicates that the aperture in certain aspects of the AI systems and related equipment is acceptable to the participants, but the effectiveness of the systems depends on the context they are implemented.Conclusion: Appraisal improvements are made possible through the integration of AI into performance management systems and come with considerable potential if implemented well, evaluated frequently, and relevant to organizational requirements. Further understanding of their methodological and contextual drawbacks described in the present study will be instrumental in unlocking their full capability and increasing their long-term efficiency.
Reference Key
imported_1776691850_69e62a8a05ea4 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Amin Ul Haider, Kamila Mariam Iftikhar, Dr. Fauzia Imtiaz
Journal Social Sciences & Humanity Research Review
Year 2025
DOI
DOI not found
URL
https://sshrr.online/index.php/4/article/view/90
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.