Employee Technostress: Analyzing the Influence of Sociodemographic Characteristics on Techno-Stressors and Their Consequences

Abstract

Technostress has garnered increasing interest in recent times due to the pervasive role of technology and its impact on well-being and overall quality of life. Understanding and addressing technostress is crucial to promoting healthier relationships with technology, mitigating its negative effects, and developing strategies to manage the challenges it poses. This article presents a set of sociodemographic characteristics, explores their relationships with different dimensions of technostress (i.e., techno-stressors), and analyzes the consequences of these techno-stressors. The aim of this research is threefold: first, we examine how the sociodemographic characteristics of gender, age, living arrangement, education level, work experience, tenure, organization size, and organization type relate to techno-stressors, namely techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty. Second, we identify technostress profiles based on their levels of technostress across these five dimensions. Third, we analyze the impact of the selected sociodemographic variables in predicting cluster membership and examine differences in role overload, role conflict, life satisfaction, and work–family conflict across the identified clusters. Data were collected cross-sectionally from a convenience sample of 1187 Chilean workers and analyzed using R Studio for Windows. Descriptive statistics, cluster analysis, and multinomial logistic regression were performed. Our results show that certain sociodemographic variables have a significant impact on specific dimensions of technostress. We also identified three distinct clusters based on their technostress levels (high, moderate, and low). Cluster 1 emerged as the profile with the lowest levels across all dimensions, cluster 2 displayed moderate levels, and cluster 3 exhibited the highest levels. Multinomial logistic regression also revealed that living alone, education level, tenure, and organization size were all significant predictors of technostress cluster membership. There were also significant differences among the three clusters in terms of the consequences of technostress. Specifically, individuals in the high technostress cluster reported higher levels of role overload and role conflict, lower life satisfaction, and greater work–family conflict. Future research avenues and implications for mental health and well-being are discussed at the end of the article.