Document Type

Article

Publication Title

ISRG Journal of Humanities and Cultural Studies

Abstract

Fine jewelry retailers must reconcile uncertain demand, high-value inventory, complex material histories, and expectations of responsible sourcing. This article develops a responsible artificial intelligence framework that connects demand forecasting, provenance evidence, and governance within retail planning. A conceptual framework-development approach informed by design-science research combines a targeted literature update through October 9, 2026, with descriptive analysis of published market statistics and an external retail forecasting benchmark. The analysis distinguishes original design propositions from empirical findings reported by other researchers; no retailer-level experiment or environmental impact assessment was conducted. Three sourced bar charts illustrate changing gold jewelry consumption and the methodological relevance of inventory-based model evaluation. The resulting architecture integrates data inputs, predictive analytics, provenance and traceability, ethical governance, and human-reviewed decisions with feedback. Five design principles and five testable propositions specify how forecasts can inform assortment, replenishment, allocation, and sourcing while preserving evidence quality and accountability. The framework shifts evaluation from forecast accuracy alone toward decision quality under explicit operational and provenance constraints. It also separates inventory efficiency, sourcing documentation, and environmental outcomes, preventing improvements in one category from being misrepresented as proof of another. Managerial implications include staged implementation, evidence-qualified sourcing rules, meaningful review authority, and documented overrides. Future validation should compare complete decision policies, preserve temporal information boundaries, measure calibrated reliance, and assess environmental consequences independently. The article contributes a sector-specific architecture and evaluation agenda for accountable retail intelligence without claiming that the integrated framework has already demonstrated operational or sustainability benefits across fine jewelry businesses and markets.

Research Highlights

  • Core Objective: The study develops a responsible artificial intelligence framework connecting demand forecasting, provenance evidence, and governance within retail planning to reconcile uncertain demand, high-value inventory, complex material histories, and expectations of responsible sourcing.

  • Methodology & Corpus: The research utilizes a conceptual framework-development approach informed by design-science research, combining a targeted literature update through October 9, 2026, with descriptive analysis of published market statistics and an external retail forecasting benchmark, explicitly conducting no retailer-level experiment or environmental impact assessment.

  • Consolidated Findings: The resulting architecture integrates data inputs, predictive analytics, provenance and traceability, ethical governance, and human-reviewed decisions with feedback; the framework establishes that separating inventory efficiency, sourcing documentation, and environmental outcomes prevents improvements in one category from being misrepresented as proof of another; descriptive evidence indicates global gold jewelry consumption fell from 1,866.9 tonnes in 2024 to 1,542.3 tonnes in 2025 while demand value increased 18%, justifying the separation of volume, value, and provenance measures.

  • Practical Implications: Managers are advised to adopt a staged implementation approach beginning with a narrow, consequential decision, establishing evidence-qualified sourcing rules, implementing meaningful human review authority, documenting overrides, and utilizing dashboards that expose the differences among forecast, recommendation, and approved action.

Publication Date

10-2026

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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