Technostress and Remote Workload as Predictors of Job Burnout
Keywords:
Job burnout, Technostress, Remote workload, Remote work, Digital strain, Employee well-beingAbstract
This study aimed to examine the predictive roles of technostress and remote workload on job burnout among remote employees in Mexico. A correlational descriptive research design was employed with a sample of 380 remote workers selected based on the Krejcie and Morgan table. Standardized tools were used to measure the variables: the Maslach Burnout Inventory – General Survey (MBI-GS) for job burnout, the Technostress Creators Scale for technostress, and the Remote Work Overload Scale for remote workload. Data were analyzed using SPSS version 27. Pearson correlation coefficients were calculated to assess the bivariate relationships between the dependent and independent variables. Multiple linear regression analysis was then conducted to evaluate the combined predictive capacity of technostress and remote workload on job burnout. The results indicated that both technostress (r = .62, p < .01) and remote workload (r = .58, p < .01) had significant positive correlations with job burnout. The multiple regression model was significant, F(2, 377) = 123.36, p < .001, explaining 47% of the variance in burnout (R² = .47). Technostress (B = 0.41, β = .46, p < .001) and remote workload (B = 0.67, β = .33, p < .001) were both significant positive predictors of job burnout, with technostress having a stronger effect. The findings confirm that both technostress and remote workload significantly contribute to job burnout among remote workers, with technostress emerging as the stronger predictor. These results emphasize the need for organizations to implement strategies that reduce digital strain and manage workload effectively in remote work environments to prevent employee burnout.
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