Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Organisasi Dan Manajemen BISMA (Bisnis dan Manajemen)
Sunu Widianto
Universitas Padjadjaran
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
RETRACTED: Sustainable human resource management: A transformation perspective of human resource management functions through optimised artificial intelligence Arif Furqon Nugraha Adz Zikri; Sunu Widianto; Rita Komaladewi
BISMA (Bisnis dan Manajemen) Vol. 16 No. 2 (2024)
Publisher : Universitas Negeri Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/bisma.v16n2.p167-189

Abstract

We would like to formally announce the retraction of the article “Sustainable Human Resource Management: A Transformation Perspective of Human Resource Management Functions Through Optimized Artificial Intelligence,” published in Volume 16, Issue 2, May 2024. This retraction arises from the discovery that the article was also published in the Jurnal Aplikasi Bisnis dan Manajemen, violating our policy regarding duplicate publication. As a result, the document associated with this article, including its content and any supplementary materials, has been retracted from the journal BISMA (Bisnis dan Manajemen). It is, therefore, essential to take appropriate steps to eliminate all references to this article.
Work from Home Enhance Individual Productivity? A Predictive Analytics Using Machine Learning Towards Well-Being, Work -Life Balance, Technological and Organizational Support Amy Mardhatillah; Irfan Aulia Syaiful; Sunu Widianto; Mimi Fitriana
Jurnal Organisasi dan Manajemen Vol. 21 No. 2 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jom.v21i2.9833.2025

Abstract

Purpose – This study aims to identify the main predictors of employee productivity among individuals who work from home. The research explores how well-being, work-life balance, focus, and organizational as well as technological support influence employees’ ability to perform effectively in a remote work setting. Methodology – Data were collected from 461 employees across various organizations. The study applied predictive analytics through several machine learning methods, including random forest and K-nearest neighbor algorithms, to test models related to individual productivity, well-being, work-life balance, organizational, and technological support. Findings – The results show that employee well-being, satisfaction, and work-life balance are the strongest predictors of productivity during work from home. The second strongest predictor is the ability to stay focused, followed by the ability to complete tasks on time and satisfaction with work-life balance. Technological support was found to be a necessary precondition to enhance productivity. Originality – This study contributes to the growing literature on remote work by integrating individual, organizational, and technological factors into a comprehensive predictive model that explains employee productivity in work-from-home settings.
Co-Authors Amy Mardhatillah Arif Furqon Nugraha Adz Zikri Irfan Aulia Syaiful Mimi Fitriana Rita Komaladewi