BIG DATA INTEGRATION IN ANALYZING EFFICIENCY-DRIVEN LAYOFFS IN INDONESIA: A STATISTICAL MODELING APPROACH
Abstract
This study examines efficiency-driven mass layoffs in Indonesia, particularly in the post-COVID-19 period, when efficiency discourse has increasingly been used to justify workforce reductions. The study addresses a gap in the literature by integrating Critical Discourse Analysis (CDA), Discourse Network Analysis (DNA), and Structural Equation Modeling (SEM) within a single analytical framework. This integration constitutes the main novelty of the study, as previous research has generally examined layoffs from separate legal, economic, or organizational perspectives rather than combining discursive, relational, and causal dimensions. Data were collected from 30 documents consisting of news reports, policy-related texts, and public discourse materials identified through internet-based searches, complemented by Focus Group Discussions with affected employees, HR personnel, and policy experts, as well as a survey of 100 respondents conducted over one week using snowball sampling. The results show that governance and regulation, as well as technology and innovation, significantly influence efficiency, which in turn significantly increases the likelihood of mass layoffs. Efficiency also mediates the relationship between governance, technology, and layoffs. The study contributes by offering an integrated empirical framework for understanding how layoff decisions are shaped not only by organizational considerations but also by discourse and policy environments. Practically, the findings suggest the need for layoff management policies that emphasize regulatory transparency, accountability, legal certainty, and social protection for workers. A limitation of this study is its reliance on a relatively limited qualitative document set and non-probability survey sampling, which may restrict generalizability. Nevertheless, the findings provide useful evidence for designing more balanced efficiency policies that protect both organizational sustainability and worker welfare.
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