The growing interest of the scientific literature regarding purchase behavior, circular economy and new business models has generated the need, as well as the opportunity, for a comprehensive review and categorization of the state of the existing research carried out so far. The present study aims at reconciling the wide but fragmented literature dealing with the purchase intention of recycled products. An integrative theoretical framework, able to combine several constructs, perspectives, and theories discussed to date on the topic, is proposed. Such framework represents a further step toward a comprehensive understanding of behavioral theories and constructs, which need to be understood to design effective business models for the circular economy. This effort could be highly valuable both for scholars interested in the topic—as the integrative framework could assist them in theorizing additional effects—and for firms’ managers—who can understand, more in depth, the drivers of the consumers’ purchasing process and act accordingly.

In this paper, we introduce the themes addressed and the approaches used in the Special Issue entitled “Sustainable Product Lifecycle: The Role of ICT”. Specifically, by offering multiple perspectives of analysis, this work increases our comprehension and understanding of the role of information and communications technologies (ICTs) in enhancing sustainable product lifecycle.

This study examines how technological innovation and sustainability jointly reshape contemporary public administration by integrating digital transformation with public value creation. Using a mixed-method approach, we compile a Scopus-based bibliographic dataset and conduct descriptive and network analyses on 199 articles to map publication trends, methodological patterns, and core keyword clusters. We then perform an in-depth qualitative content analysis of 83 papers, coding public sector domains, actors, technological innovations, and sustainability dimensions. Findings highlight a shift from early e-government, centered on administrative efficiency, toward a paradigm of “sustainable digital governance”, where AI, IoT, blockchain and data analytics drive the twin digital–green transition. Five conceptual clusters and several application domains show that public value increasingly emerges within collaborative ecosystems involving administrations, firms, universities, citizens and digital platforms. The study offers an integrated overview of this evolving field and clarifies technology’s role as an enabling factor in sustainable governance. Building on the review results, we propose the Sustainable Public Innovation Ecosystem (SPIE) framework, which links systemic enablers (technological and sustainability innovation) governance efficiency and sustainable public value through ecosystem dynamics and governance mechanisms. It also outlines a future research agenda on hybrid actors ethical and regulatory issues, and approaches to measuring sustainable public value, providing guidance for scholars and policymakers designing digitally enabled and sustainability-oriented public reforms.

The growing environmental impact of textile consumption has intensified the need for efficient post-consumer waste collection systems capable of supporting circular economy transitions. Designing effective logistics systems for textile waste is therefore crucial, and their proper dimensioning requires accurate forecasts of collected volumes. However, textile waste flows are highly heterogeneous and strongly influenced by behavioural factors, making reliable forecasting particularly challenging. This study investigates whether urban textile waste collection can be effectively predicted by combining stable bin-level heterogeneity with time-varying socio-spatial and behavioural indicators. Using panel data generated by a hybrid simulation model for the municipality of Parma, we implemented a fixed-effects econometric framework and compared its performance with traditional benchmarks, including seasonal means and Holt–Winters exponential smoothing. The results demonstrate that incorporating structural heterogeneity across collection points, together with behaviour-related dynamics, enhances prediction accuracy and significantly outperforms traditional univariate time-series approaches, both at the aggregate level (R² ≈ 0.81) and at the bin level (MAE ≈ 25). These findings also support the robustness and generalizability of the proposed panel-data econometric framework, which shows strong potential for application in other urban settings characterized by similar structural and behavioural features.