Re-FLIGHT Model: ESG-Based Strategic Communication to Mitigate Layoffs Issues in the National Aviation Industry
Bibliographic record
Abstract
A global wave of layoffs has swept across industries such as technology (Google, Amazon, Meta, Microsoft), media (News Corp, BBC), manufacturing, automotive, banking, and higher education. The World Economic Forum (2025) projects that 41% of global companies—including those in aviation—plan workforce reductions within five years, driven by economic slowdown, geopolitical uncertainty, cost pressures, and AI adoption. The aviation sector has been severely affected: Boeing cut 17,000 jobs, Airbus 2,500, and airlines like Southwest and WestJet have also reduced staff. In Indonesia, Lion Air, AirAsia, and Garuda Indonesia collectively laid off thousands of workers between 2020–2021. Though mass layoffs have since slowed, rising costs and fragile finances signal renewed risks that could threaten public welfare and national stability. This study examines how ESG-based strategic communication can mitigate layoff risks in Indonesia’s aviation industry. It proposes the Re-FLIGHT Model—anchored on six pillars: Reskilling, Fair Labour, Innovation, Growth, Human-Centered, and Transition—as a framework for proactive, ethical, and sustainable communication. Integrating ESG principles into corporate and regulatory strategies, the model promotes inclusive dialogue, responsible leadership, and resilience, positioning ESG as a foundation for social transformation in the aviation sector. The World Economic Forum 2025 reports that 41% of global companies project workforce reductions within five years, including the aviation industry. This continuing wave of layoffs in 2025 is driven by global economic slowdown, uncertain geopolitics, corporate restructuring, operational cost pressures, and the implementation of new technologies (AI), which shift workforce demands. The aviation industry, both global and national, has also faced layoffs. Aircraft manufacturers like Boeing reduced 17,000 employees, while Airbus cut 2,500 from its defense and space division. Global airlines followed: Southwest Airlines (USA) laid off 1,750 staff in February 2025; WestJet (Canada) laid off 6,900 employees in 2022. In Indonesia, Lion Group laid off 2,600 employees in 2020; AirAsia laid off or furloughed 882 staff in 2020. Garuda Indonesia Group laid off 2,400 employees (including pilots) between January 2020 and November 2021. These layoffs are a lingering consequence of the COVID-19 pandemic, digital transformation, post-pandemic efficiency, and macroeconomic pressures. Though Indonesia has not experienced another mass layoff wave, signs of stress are evident in rising operational costs, unrecouped international routes, and airline financial vulnerabilities. The potential for further layoffs in Indonesia’s aviation industry poses significant risks to public and national interests. Layoffs here could have systemic impacts on public welfare and national stability. This study explores how ESG-based strategic communication can mitigate layoff risks in Indonesia’s aviation sector. It emphasizes planned crisis communication, multistakeholder dialogue, and human-centered public narratives to build institutional trust and resilience. Through six key pillars—Reskilling, Fair Labour, Innovation, Growth, Human-Centered, and Transition—the study analyzes how strategic communication, leadership, organizational narrative, and public governance can create a more inclusive and adaptive employment ecosystem. The Re-FLIGHT Model integrates ESG values into organizational communication strategies and introduces an innovative communication framework grounded in sustainability, social justice, and inclusive governance. It can be adopted by regulators and national airlines as an anticipatory, proactive, fair, strategic, and sustainable workforce crisis communication model—broadening ESG perspectives in aviation as a driver of meaningful social transformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".