Journal of Artificial Intelligence and Digital Business
Vol. 5 No. 2 (2026): Mei-Juli

Job Stress and Job Satisfaction Effects on Counterproductive Work Behavior among Employees

Ardy Wiratama (Universitas Selamat Sri)
Zefi Nafira (Universitas Selamat Sri)
Sherli Junianingrum (Universitas Selamat Sri)



Article Info

Publish Date
31 Jul 2026

Abstract

This study examines the effects of job stress and job satisfaction on counterproductive work behavior (CWB) among employees of Company X in Pemalang. A quantitative associative design was applied to test the partial and simultaneous relationships among the variables. All 115 employees were included through saturated sampling, and data were collected using a five-point Likert-scale questionnaire. The data were analyzed with SPSS through validity and reliability testing, classical assumption testing, multiple linear regression, t-tests, and an F-test. The results confirmed that the regression model met the assumptions of normality, multicollinearity, and heteroskedasticity. The simultaneous test produced an F-value of 46.839 with a significance level of 0.000, indicating that job stress and job satisfaction jointly influenced CWB. Partially, job stress had a positive and significant effect on CWB, with a regression coefficient of 0.455, a t-value of 4.755, and a significance level of 0.000. Job satisfaction also had a significant effect, with a regression coefficient of 0.345, a t-value of 3.514, and a significance level of 0.001. Job stress showed the stronger standardized contribution to the model. These findings emphasize that employee behavior is shaped by both perceived work pressure and evaluations of the work environment. Organizations should therefore manage workloads, clarify roles, strengthen supervisory support, and implement transparent and equitable reward practices to reduce counterproductive behavior.

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Journal Info

Abbrev

RIGGS

Publisher

Subject

Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

Description

Journal of Artificial Intelligence and Digital Business (RIGGS) is published by the Department of Digital Business, Universitas Pahlawan Tuanku Tambusai in helping academics, researchers, and practitioners to disseminate their research results. RIGGS is a blind peer-reviewed journal dedicated to ...