Abstract

Digital fatigue is no longer merely a transient phenomenon; it has become a structural threat to mental health, job satisfaction, and the productivity of the entire organization.

Research Objective: The present study aimed to design and explain the Interpretive Structural Modeling (ISM) model of digital fatigue in government organizations.

Research Design; Research Methodology: This study is applied in nature and uses a mixed-methods approach, carried out through qualitative content analysis and quantitative modeling. The statistical population included senior managers and human resource experts in government organizations in Khorramabad. Using purposive sampling, 20 of them were selected as experts to participate in semi-structured interviews and complete matrix questionnaires. Qualitative data were analyzed using MAXQDA 2020, and quantitative data for structural modeling were analyzed using MATLAB. The validity and reliability of the instruments were confirmed through content, theoretical, intra-coder, and inter-coder methods.

Research Findings: The findings showed that digital fatigue is formed through a complex interaction among components such as “cognitive load caused by virtual meetings,” “digital multitasking,” and “work-life interference.” The modeling results indicated that interventions at the level of strategic management and setting work boundaries are the key pathway for reducing the destructive effects of digital fatigue in government organizations. Four main contexts—including social networks, messengers and email, online meetings and virtual training, and the digital work environment and remote work—have the greatest share in creating and exacerbating this fatigue.

Conclusion: This study can help managers create healthier digital work environments by redesigning communication protocols.

Research Innovation: The main innovation of this study can be explained at two levels: First, the use of Interpretive Structural Modeling (ISM) instead of traditional regression methods, which makes it possible to manage uncertainty in expert opinions and model complex and nonlinear relationships.