Decent work as a driver of employee productivity: A quantitative study in the Malaysian context

This study investigates the relationship between work attitude, opportunities in work, and the nature of the job as predictors of employee productivity within the Malaysian context. Employing a quantitative research approach, data were collected from 300 employees across various sectors using structured surveys. The data were analyzed through descriptive statistics, correlation analysis, and multiple regression to identify key factors influencing productivity. The findings reveal that all three variables significantly impact employee productivity, with the nature of the job emerging as the strongest predictor, followed by opportunities in work and work attitude. The study confirms the applicability of Herzberg’s Two-Factor Theory in explaining the role of both hygiene and motivation factors in shaping employee performance. Practical implications include recommendations for employers to foster positive work environments, invest in structured training programs, and align job roles with employee strengths and aspirations. Policymakers are urged to promote lifelong learning initiatives and provide incentives for organizations that prioritize employee development. This research contributes to the growing body of literature on workforce productivity and offers actionable insights for enhancing employee performance in Malaysia.

Authors

Institutions

Publication Details

Journal
International Journal of Innovative Research and Scientific Studies
Published
2025-05-26
DOI
https://doi.org/10.53894/ijirss.v8i3.7385
Primary Topic
Employment and Welfare Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Decent work as a driver of employee productivity: A quantitative study in the Malaysian context

Deshinta Arrova Dewi, M. Saad, Lee Harvey, Mashal Kasem Alqudah
International Journal of Innovative Research and Scientific Studies
Employment and Welfare Studies
article

Decent work as a driver of employee productivity: A quantitative study in the Malaysian context

Deshinta Arrova Dewi, M. Saad, Lee Harvey, Mashal Kasem Alqudah
article en

Abstract

This study investigates the relationship between work attitude, opportunities in work, and the nature of the job as predictors of employee productivity within the Malaysian context. Employing a quantitative research approach, data were collected from 300 employees across various sectors using structured surveys. The data were analyzed through descriptive statistics, correlation analysis, and multiple regression to identify key factors influencing productivity. The findings reveal that all three variables significantly impact employee productivity, with the nature of the job emerging as the strongest predictor, followed by opportunities in work and work attitude. The study confirms the applicability of Herzberg’s Two-Factor Theory in explaining the role of both hygiene and motivation factors in shaping employee performance. Practical implications include recommendations for employers to foster positive work environments, invest in structured training programs, and align job roles with employee strengths and aspirations. Policymakers are urged to promote lifelong learning initiatives and provide incentives for organizations that prioritize employee development. This research contributes to the growing body of literature on workforce productivity and offers actionable insights for enhancing employee performance in Malaysia.

International Journal of Innovative Research and Scientific StudiesVol. 8(3)
INTI International University (MY)
Openalex Percentile: Top 80%
Employment and Welfare Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.