Enabling Circular Decision Making for Productivity and Performance: The Pivotal Role of Data, Measurement, Assessment, and Disclosure
Over the past decade, the circular economy (CE) has gained increasing attention from academics, institutions, and industries as a paradigm capable of decoupling economic growth from resource depletion and environmental degradation (Kirchherr et al., 2017; De Angelis, 2018). When implemented effectively, circular strategies deliver measurable productivity and performance gains by prolonging resources (Blomsma and Brennan, 2017). More specifically, keeping materials, components and assets at their highest utility for longer reduces input intensity and scrap, lifts equipment and labour utilisation, and smooths flows via traceability, thereby increasing throughput, compressing lead times, lowering cost per unit and strengthening resilience (Halkos and Aslanidis, 2023). Despite its rising prominence, the transition towards circularity remains hindered by the inertia of linear thinking embedded in organisational routines, decision-making protocols, and institutional architectures (Greer et al., 2023; Lozano & Lozano, 2024). This immobility also reflects persistent obstacles in supply chains, where structural and cultural barriers hinder the integration of circular practices (Agarwal et al., 2023). Circularity, at its core, entails a fundamental reconfiguration of production and consumption models, one that transcends mere technological substitution and calls for systemic and transformative decision-making aligned with long-term sustainability goals (Diaz et al., 2021; Habiburrahman et al., 2025). Importantly, these transformations are tightly connected to performance outcomes, including operational efficiency, innovation capacity, resilience, and overall organisational and system-level performance.
This inertia is compounded by a widespread disconnect between information, measurement, and action. Decision-makers often lack timely, reliable, and actionable data about product life cycles, material flows, and system dynamics (Esposito et al., 2023). Existing circularity indicators are frequently fragmented and poorly aligned with practical decision-making needs (Cayzer et al., 2017; Saidani et al., 2019; Sassanelli et al., 2019; De Pascale et al., 2021; Muñoz et al. 2024). Assessment tools tend to provide static and one-dimensional evaluations, overlooking the complexity of trade-offs and interdependencies in real-world systems (Geisedorfer et al., 2021; Harris et al., 2021; de Koning et al., 2024). Moreover, the capacity to communicate circular performance effectively, both internally and externally, remains severely limited by the plethora of disclosure frameworks available (Opferkuch et al., 2023; L’Abate et al., 2024; Enciso‐Alfaro et al., 2024; Esposito et al., 2025). Accordingly, information asymmetries and weak data-sharing practices across actors and life-cycle stages exacerbate the uncertainty and complexity of circular choices (Jensen et al., 2023; Serna-Guerrero et al., 2022). This lack of alignment directly hinders the ability of organisations and systems to monitor, compare, and improve performance in terms of resource efficiency, sustainability outcomes, and competitiveness.
The urgency of addressing such challenges becomes even more compelling when considering the multi-level nature of CE transitions. Indeed, the CE can and should be enacted across micro (product decision-making units, firms, consumers), meso (eco-industrial parks, value chains), and macro (cities, regions, nations) levels (Kircherr et al., 207). Each of these levels presents specific decision-making contexts, actor constellations, and informational needs. For example, product-level design choices (micro) are shaped by supply chain constraints (meso), which in turn are embedded within regional policies and infrastructure (macro). A truly holistic understanding of circular decision-making, therefore, demands analytical approaches capable of integrating across levels, disciplines, and data types (Luthin et al., 2024). Such integration is essential not only for enabling circular strategies but also for achieving measurable performance improvements across efficiency, cost reduction, innovation, and environmental impact mitigation (Lamba et al., 2024).
Against this backdrop, Circular Decision-Making (CDM) has emerged as a key enabler of the CE transition (Palafox-Alcantar et al., 2020; Greer et al., 2023). CDM can be defined as a strategic and context-sensitive process through which organisations and institutions reorient their choices, priorities, and behaviours in alignment with the principles of circularity. It entails breaking away from path-dependent, linear logics and embracing a systemic perspective that integrates long-term thinking, interdependence among actors, and the co-evolution of economic, environmental, and social goals (ISO 59004; Eisenhardt and Zbaracki, 1992).
From a productivity and performance management perspective, CDM represents an opportunity to realign decision-making with organisational performance targets, ensuring that sustainability objectives also translate into tangible gains in productivity, competitiveness, and stakeholder value creation (Marín-Vinuesa et al., 2023). Accordingly, CDM requires a deep understanding of how decisions are informed, structured, and executed across systems, sectors, and scales within the CE (Yazdani et al., 2021; Coluccia et al., 2024).
To engage CDM, digital technologies and advanced analytics are increasingly hailed as potential enablers of such integration. Big Data Analytics and Artificial Intelligence (AI) tools have been shown to enhance firms’ capacity to generate actionable insights, fostering adaptive and data-driven decision-making in complex CE environments (Awan et al., 2021; Modgil et al., 2021; Agrawal et al., 2022; Mboli et al., 2022; Gupta et al., 2019; Raut et al., 2019; Deveci et al., 2024). Furthermore, digital product passports, as proposed in recent EU policy frameworks, promise to embed life-cycle information directly into product identities, thus equipping decision-makers across the value chain with the knowledge needed to enable reuse, remanufacturing, and recycling (Jensen et al., 2023; Fares et al., 2025). These tools not only improve decision quality but also provide the metrics and traceability required to monitor and enhance circular and sustainable performance outcomes both from a company and a supply chain perspective (Pham et al., 2024). From a productivity perspective, digital enablers can shorten decision cycles and smooth material and information flows. However, the dividend is neither automatic nor immediate: integration, interoperability, and data governance burdens can absorb capacity and introduce inefficiencies, while rebound effects may erode expected resource savings. These risks make rigorous productivity measurement essential to distinguish genuine improvements from accounting artefacts or cost shifting across tiers (Samadhiya et al., 2023). Furthermore, the role of disclosure and communication in shaping CE decisions cannot be overstated.
Transparency regarding circular performance serves both as a mechanism of accountability and as a strategic asset to enhance CDM (Barnabè & Nazir, 2021; Esposito et al., 2023; Opferkuch et al., 2023). By making circular performance visible and comparable, organisations are better equipped to demonstrate productivity gains, financial benefits, and non-financial performance improvements to stakeholders.
Scholars have discussed these topics at length in isolation, highlighting the relevance of measuring circularity, disclosing and communicating CE performance to enhance decision-making processes. Empirical studies on circular supply chain practices, for instance, demonstrate the importance of structured approaches to overcome complexity and foster performance and productivity- oriented decision-making (Saroha et al., 2022). However, there remains a compelling need to adopt a holistic perspective that reflects the systemic nature of circular transitions. In particular, there is a need to explicitly connect data, measurement, and disclosure with their impacts on productivity, organisational performance, and systemic outcomes within the CDM debate (Yin et al., 2023).
This Special Issue welcomes theoretical, methodological, and empirical contributions that illuminate how data, measurement, assessment, and disclosure operate not only as enablers of Circular Decision Making (CDM) but also as instruments of productivity and performance management. We particularly encourage interdisciplinary, systemic, and multi-level studies, as well as real-world applications of decision-support tools and frameworks across diverse contexts, with a focus on the productivity and performance implications of CDM. Specifically, it aims to evidence how CDM delivers verifiable gains in productivity, strengthens organisational competitiveness, and enhances performance management.
List of Topic Areas
- Decision Support Systems for CDM in Productivity and Performance Management: models, frameworks, and applications that embed circular criteria while improving productivity and performance outcomes at micro, meso and macro levels.
- Metrics and Performance Indicators: Development and comparison of indicator and metrics systems that couple circularity with productivity and performance measurement.
- Digitalisation and AI as Performance Enablers of CDM: examining how digital technologies enable data-driven, adaptive CDM, delivering measurable productivity gains alongside improvements in operational efficiency and sustainability performance.
- Disclosure and Communication for Performance Management: Design and evaluation of frameworks, practices, and innovations that enhance transparency, accountability, and stakeholder engagement in CE transitions, making circular performance decision-useful for managers and demonstrably supporting productivity improvement, competitiveness, and performance management.
Submission Information
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Journal Information: Scopus Journal Q1, H-index 83
Key Dates
Opening date for manuscript submissions: 1 May 2026
Closing date for manuscript submissions: 31 August 2026
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