Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET)
Mawardi
Universitas Riau Kepulauan
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
THE IMPACT OF GENERATIVE AL ON EMPLOYEE PRODUCTIVITY AND JOB REDESIGN IN MODERN ORGANIZATIONS Mawardi; Dhenny Asmarazisa; Catur Fatchu Ukhriyawati
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 10 (2026): SEPTEMBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.23134869

Abstract

The rapid deployment of Generative Artificial Intelligence (GenAI) across enterprise environments represents a foundational shift in how work is structured, cognitive tasks are allocated, and individual productivity is realized. This critical review systematically synthesizes empirical and conceptual literature published between 2021 and 2026 to examine the multi-dimensional impact of GenAI on employee productivity and job redesign. Guided by Job Demands-Resources (JD-R) theory, Socio-Technical Systems (STS) theory, and the Task-Technology Fit (TTF) framework, this paper analyzes findings across 16 benchmark studies encompassing randomized field experiments, structural equation modeling, and qualitative case investigations. Results indicate that GenAI integration yields significant task-level productivity gains—ranging from 14% to 40% across professional domains—predominantly by automating routine cognitive tasks, reducing time-to-completion, and elevating output quality for lower-skilled workers. However, operationalizing these productivity gains requires comprehensive job redesign, characterized by cognitive task unbundling, skill reallocation toward prompt engineering and critical evaluation, and the mitigation of emerging techno-stressors. The review synthesizes core mechanisms into a structural path model and outlines a strategic HR governance framework to optimize human-AI collaboration.
Co-Authors Catur Fatchu Ukhriyawati Dhenny Asmarazisa