Digital product passports can convert sustainability into competitive value in fashion and cosmetics by enabling traceability, reuse and cross‑firm knowledge exchange; their benefits materialize only when technical capabilities are matched with organizational readiness, consumer engagement and interoperable standards, otherwise coordination failures and lock‑in risks persist.

Main Finding

Digital Product Passports (DPPs) function as a socio-technical, cognitive infrastructure: when DPP technical capabilities are aligned with organizational readiness and consumer engagement, they materially support circularity (raw-material reuse), supply‑chain resilience, and cross‑firm knowledge exchange—turning sustainability from a compliance burden into a source of innovation and value in fashion and cosmetics.

Key Points

• DPPs enhance transparency and traceability across complex supply chains, enabling material circularity and more resilient sourcing decisions.
• DPPs facilitate knowledge sharing and open innovation (OI) across firms, embedding sustainability and knowledge management into operational practice.
• Two consumer segments were identified:
  • Aware consumers: environmentally attuned and receptive to digital innovation and sustainability information.
  • Unaware consumers: prioritize immediate, tangible benefits (price, convenience) over sustainability information.
• Successful DPP adoption requires matching technical functionalities (data granularity, interoperability, user interfaces) with firm-level readiness and strategies to engage different consumer segments.
• Conceptual contribution: DPPs should be seen as both technical data platforms and participatory tools that enable collaborative value creation and responsible consumption (supports SDG 12).
• Practical implication: DPP deployment must combine standards, governance, and user-centric design to unlock circular-economy benefits.

Data & Methods

• Context: Italian fashion and cosmetics industries.
• Design: Mixed-methods empirical study using two online surveys targeting stakeholders/consumers in the two sectors.
• Quantitative methods:
  • K‑means cluster analysis to segment consumers (aware vs unaware).
  • Principal component analysis (PCA) to identify underlying dimensions of DPP functionalities and sustainability practices.
  • Logistic regression to identify drivers of DPP adoption and the relationships among DPP features, organizational practices, and consumer profiles.
• Outcome: segmentation of consumers, identification of adoption drivers, and mapping of interrelationships among technical, organizational, and consumer factors.
• Note: sample sizes and survey instrument details are not provided in the summary.

Implications for AI Economics

• Data as economic asset: DPPs generate high-quality, structured product and lifecycle data that are non-rivalrous and highly reusable—raising firm-level incentives to invest in AI models (forecasting, optimization, provenance verification) that exploit this data to capture value across production, secondary markets, and services.
• Market design & platform economics:
  • DPP ecosystems resemble multi-sided platforms (producers, recyclers, consumers, certifiers). Network effects (more participants → more valuable DPP data) can create winner-take-most dynamics unless standards and interoperability are enforced.
  • Proprietary vs open DPP data regimes will shape competition: closed data can lead to lock-in and market power; open standards can spur broader innovation but may reduce short-term rent extraction.
• Adoption externalities and coordination failures:
  • Benefits of DPPs accrue systemically (e.g., improved circularity), so private incentives to adopt may be insufficient—policy interventions, subsidies, or consortium governance can correct underinvestment and coordination failures.
• Role of AI/ML:
  • Use cases: provenance verification (ML-based anomaly detection and image/text verification), predictive maintenance for durable goods, demand forecasting for closed-loop logistics, personalized sustainability nudges, automated compliance reporting.
  • Technical needs: labeled, interoperable DPP datasets; methods for domain adaptation and causal inference to evaluate policy/intervention effects.
• Consumer heterogeneity & targeted interventions:
  • Segmentation (aware vs unaware) implies different AI-driven approaches: targeted personalization and recommender systems for aware consumers; default, nudging, and tangible-benefit signals for unaware consumers.
  • Beware algorithmic bias: training data skew (e.g., overrepresenting “aware” users) can distort targeting, reducing welfare for less-engaged groups.
• Governance, privacy & fairness:
  • DPPs raise privacy and surveillance risks if personal data are linked to product use. Economic regulation should incentivize privacy-preserving analytics (federated learning, differential privacy) and data minimality.
  • Transparency and explainability requirements for AI decisions that affect reuse/repair markets are important to maintain consumer trust and avoid exclusionary practices.
• Policy and measurement:
  • Standardized data schemas and interoperable protocols reduce transaction costs and increase returns on AI investments; public-good components (shared taxonomies, open benchmarks) will accelerate innovation.
  • Need for economic evaluation frameworks: quantify welfare gains from circularity, reduced externalities, and efficiency improvements versus costs from governance, privacy safeguards, and potential market consolidation.
• Research directions for AI economics:
  • Empirical causal studies on how DPP+AI interventions change recycling rates, second‑hand market prices, and firm investment in circular processes.
  • Modeling strategic firm behavior around proprietary vs shared DPP data and the implications for competition and innovation.
  • Designing incentive mechanisms (data trusts, revenue-sharing, certification markets) to align firm incentives with social welfare from circularity.

Actionable recommendations (high-level): - Promote interoperable DPP standards and open data layers for core provenance and material attributes to reduce lock-in and enable third‑party AI innovation. - Support privacy-preserving methods and clear governance to maintain trust while enabling ML applications. - Design AI-driven consumer interfaces tailored to identified segments: informative transparency for aware consumers; simple, benefit-oriented defaults and nudges for unaware consumers. - Use public funding or consortia to overcome coordination failures in early-stage DPP ecosystem formation so that positive network effects can materialize without excessive market power concentration.