The Impact of Digital Intelligence Systems on Workplace Loneliness Based on K-Means Machine Learning
Year: 2025
Type: conference-paper
Study design: Observational Study
Abstract: In the era of digital transformation, digital intelligence systems have become indispensable tools in organizational management and human resource practices. These systems enhance efficiency and decision-making but simultaneously reshape patterns of workplace communication and social interaction. As face-to-face exchanges diminish, employees may experience a heightened sense of isolation, resulting in workplace loneliness. Addressing this emerging issue is essential for sustaining employee well-being and organizational resilience. This study employs a mixed-method approach combining grounded theory and the K-Means machine learning algorithm to examine how digital intelligence systems influence workplace loneliness and to uncover the underlying psychosocial mechanisms. Based on qualitative coding and quantitative clustering, the analysis identifies five core dimensions—adoption and efficiency, communication and tool support, emotional well-being, organizational support and inclusion, and social engagement and offline affinity—and reveals three distinct employee clusters with differing levels of digital adaptation, emotional response, and perceived support. The findings show that while digital intelligence systems enhance efficiency, transparency, and coordination, they may also weaken interpersonal bonds and expand psychological distance. By integrating grounded theory's interpretive depth with K-Means’ analytical precision, this research bridges qualitative insight and data-driven modeling, extending theoretical understanding of digital work and offering practical guidance for human–machine collaboration, system design, and organizational strategies that promote both technological advancement and employee well-being.
Author: Beibei Gui
Institution: Shanghai University
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7127567686