Enhancing Social Sustainability in Automotive Supply Chains: A Framework for Effective Grievance Mechanisms
Bibliographic record
Abstract
Purpose: The automotive industry operates within complex, globalized supply chains characterized by multi-tiered structures and extensive outsourcing, often lacking visibility and accountability for potential human rights violations. Increasingly, regulatory frameworks place explicit obligations on original equipment manufacturers (OEMs), including the requirement to install an effective grievance mechanism along the supply chain. This paper explores how grievance mechanisms can be integrated into automotive supply chain practices to strengthen social sustainability. Methodology: A qualitative approach was employed, and expert interviews were conducted with a diverse group of stakeholders from the automotive industry, non-governmental organizations (NGOs), regulatory bodies, suppliers, and unions to provide a well-rounded view of grievance mechanisms in supply chains. The data was analyzed by employing Kuckartz’s qualitative content analysis with MAXQDA software to systematically code and identify key themes critical for an effective grievance framework. Findings: This paper proposes a practical framework for OEMs to address human rights and ethical issues across global networks. It offers a common structure adaptable to industry players, emphasizing accessibility, confidentiality, and trust. It also recommends combining OEM-specific mechanisms with an industry-wide collaboration platform to standardize processes, share best practices, and enable collective action. The study findings support that grievance mechanisms play a crucial role in social sustainability by providing workers with secure channels to report violations. Integrating grievance data into OEMs’ risk assessments enhances proactive risk mitigation. Unique Contribution to Theory, Practice and Policy: This research uniquely addresses the gap in academic literature and practice related to social sustainability in supply chains, particularly in grievance mechanisms. In practice, the automotive industry faces fragmented and inconsistent implementation of grievance mechanisms, with the absence of a standardized framework. This paper fills these gaps by developing a comprehensive, industry-specific grievance mechanism framework that ensures accessibility, consistency, and effectiveness across all supply chain tiers.
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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 | 0.051 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.013 | 0.066 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".