As workplace technology evolves rapidly, technostressors may intensify job insecurity and underemployment, particularly among ethnic minority workers who already face social dilemmas such as discrimination (Kinitz et al., 2025). This study investigated the associations between five technostressors (techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty) and adverse outcomes of job insecurity and underemployment. We tested minority status as a moderator to determine whether these relationships are stronger among racial and ethnic minority employees. A cross-sectional online survey of employed U.S. workers (N = 538) was recruited on Prolific and assessed technostress (Ragu-Nathan et al., 2008), perceived job insecurity (Oldham et al., 1986), and underemployment (Allan et al., 2017). Multiple regression in RStudio was used to test the hypothesized relationships. For job insecurity, we only found significant main effects for techno-overload, techno-uncertainty, and techno-insecurity, while techno-invasion and techno-complexity were nonsignificant. Moreover, the effects of techno-uncertainty on job insecurity were stronger for minority employees compared to non-minority employees. For underemployment, significant main effects were found for minority status, techno-invasion, and techno-insecurity. The only significant interaction was techno-overload and minority status, such that techno-overload predicted higher underemployment among minority workers, but not among non-minorities. These findings highlight the need for organizations to reduce techno-overload and techno-uncertainty for minority employees through interventions, culturally accessible training, and supportive policies, while also addressing underemployment disparities experienced by ethnic minority workers.

Authors:
Phillip Nguyen, Auburn University, United States
Michelle Ngoc-Trinh Nguyen, Auburn University, United States
Gwendolyn Paige Watson, Auburn University, United States

About the Presenter(s)
Phillip Nguyen, Graduate Data Analyst, occupational health psychology, and artificial intelligence, Master's Thesis: The Double-Edged Sword of AI for Employee Recovery: Understanding Its Dual Impact Through Competence Satisfaction and Frustration.

Connect on Linkedin
https://www.linkedin.com/in/-phillipnguyen/

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