Mapping the Digital Transformation of Reverse Logistics: A Multi-Level Taxonomy

Key stakeholders at the company level include workers, management, and business owners, all of whom are essential to the implementation of digitally enabled RL. A CSF frequently highlighted in the literature is the availability of skilled labor (e.g., Moroni et al., 2022), as the absence of such skills is a significant barrier to implementation (e.g., Pourmehdi et al., 2022). Automation technologies can partially alleviate workforce constraints by standardizing tasks such as negotiations and improving availability across production and service areas (e.g., Mastos et al., 2021). Moreover, RL itself has been shown to contribute to job creation in specific sectors such as the electrical and electronic equipment industry (Bressanelli et al., 2021).

Employee training programs are repeatedly emphasized as a prerequisite for equipping workers with the knowledge required for digital transformation and RL practices (e.g., Nikseresht et al., 2023). A lack of training is consistently cited as a barrier to implementation (e.g., Ambekar et al., 2022), and limited employee specialization can further inhibit the integration of RL practices (e.g., Chong et al., 2023). As a result, continuous knowledge management and upskilling must be prioritized to support digital RL processes (e.g., Santos and Proença, 2022). Technologies such as augmented and virtual reality have also been explored as effective tools for immersive, scalable training formats in the construction sector (Wijewickrama et al., 2021).

Managerial engagement is another critical enabler. The support of top management helps align strategic goals and facilitates the integration of digital tools into RL operations (e.g., Bag et al., 2021). On the other hand, insufficient executive commitment has been linked to stalled or failed implementation efforts (e.g., Trujillo-Gallego et al., 2021). Managers are expected to identify and apply context-appropriate digital technologies—for instance, smart prefabrication and 3D printing in construction (Elmualim et al., 2018), or AI-based pattern recognition and decision support tools in supply chain contexts (Abideen et al., 2021).

At the meso level, both customers and supply chain partners play pivotal roles. Consumer awareness of RL is essential for driving social acceptance and promoting CE principles (Khan et al., 2022). However, limited awareness remains a persistent barrier in many industries (e.g., Chong et al., 2023). Customer satisfaction and engagement are vital for building trust in RL systems (e.g., Kadaei et al., 2023), and active consumer participation has been linked to more successful implementation (Bressanelli et al., 2021). Despite this, reluctance or hesitancy from consumers can hinder adoption (e.g., Agnusdei et al., 2022).

Technological tools are increasingly being used to improve transparency and trust, particularly in e-commerce settings where blockchain, for example, can help prevent fraudulent returns (Difrancesco et al., 2023). Moreover, feedback mechanisms have been shown to reduce return rates and improve customer experiences (Bhatia et al., 2022). Engagement can also be supported through intermediary partners that facilitate returns and encourage the use of recyclable materials (Cui et al., 2021). RL can offer consumers direct benefits, such as access to higher-quality, longer-lasting products, and potential cost savings by enhanced detection of defective goods (Hsu et al., 2016). However, for tools like blockchain to be widely adopted, concerns around energy use and data security concerns need to be addressed (Naseem et al., 2023).

On the supply chain side, collaboration with partners is critical for successful RL implementation (e.g., Rasool et al., 2023). Conversely, the absence of collaboration has been consistently identified as a key barrier (e.g., Santos and Proença, 2022). Transparent communication across the supply chain helps maintain product quality and reliability (e.g., Bahiraei et al., 2015). Information-sharing platforms and I4.0 technologies—such as RFID, IoT, and blockchain—can enhance traceability and enable the creation of virtual supply chains, particularly for managing perishables and sustainability-related challenges (e.g., Fanta and Pretorius, 2022).

At the macro level, RL implementation is increasingly influenced by societal pressure, rising environmental consciousness, and broader sustainability concerns (e.g., de Campos et al., 2023). However, cultural norms and public unawareness—such as improper e-waste disposal practices—can act as substantial barriers to progress (Chan, 2007). Regional studies have demonstrated that limited environmental awareness can significantly slow down RL efforts, particularly in countries like Ghana (Chen et al., 2020), Colombia (Trujillo-Gallego et al., 2021), Brazil (Trevisan et al., 2023), and India (Ambekar et al., 2022). Moreover, cultural resistance to behavioral change has been highlighted as a challenge in certain contexts (Santos and Proença, 2022).

Forming strategic alliances with universities, research institutions, and NGOs has proven effective in accelerating RL practices across industries like construction and automotive (Santos and Proença, 2022; Wijewickrama et al., 2021). NGOs also play a crucial role in shaping public perception and promoting localized resource conservation initiatives (Kinally et al., 2022).

Embedding RL within corporate sustainability agendas is considered essential for advancing both RL and CE goals (e.g., Kerdpitak et al., 2020). The dynamic interaction between CE, RL, and supply chain management is illustrated in Figure 8. A central objective involves prolonging product lifecycles by optimizing waste reduction, material efficiency, and energy usage (e.g., Tseng et al., 2022). The application of R-strategies enables firms to minimize resource use while preserving product functionality (Morseletto, 2020; Potting et al., 2016). Nevertheless, concerns over potential cannibalization of primary sales through longer product lifecycles remain a challenge (Bressanelli et al., 2021).

Circular design approaches, prioritizing durability, repairability, and recyclability, are key to maintaining competitiveness while reducing environmental impact. Eco-design principles implemented early in the product development cycle support repair and longevity (e.g., Kinally et al., 2022). Sector-specific strategies such as Design for Deconstruction in construction (Elmualim et al., 2018) and Design for Recovery in manufacturing (Bhatia et al., 2022) further reinforce product lifecycle management. Efficient design thus emerges as a key enabler of RL, directly supporting more effective EoL strategies (e.g., Bouzon et al., 2020).

Ongoing innovation is crucial for improving both RL performance and eco-efficiency (García-Sánchez et al., 2019). Green innovation initiatives have also shown a positive correlation with financial outcomes, highlighting the multi-faceted value of sustainability (e.g., Bhatia et al., 2022).

A comprehensive understanding of resource systems is vital for optimizing resource use (Abideen et al., 2021). The incorporation of secondary materials into the production process has been demonstrated to have a dual benefit: it enhances the value of the product and reduces the dependence on finite resources (e.g., Wijewickrama et al., 2021). Tools based on knowledge systems can hereby help to identify untapped resource potential (Khan et al., 2022), while energy-saving technologies and sustainable packaging—such as reusable transport containers—offer environmental and operational gains despite higher initial costs (Yusuf et al., 2017).

The use of AI-driven EoL decision models supports optimal reuse, remanufacturing, and recycling routes (Oluleye et al., 2023b). Digital technologies are particularly effective in supporting R3–R7 implementation (Morseletto, 2020). Strategies like R2 (Reduce), R3 (Reuse), R8 (Recycle), and R9 (Recover) also contribute significantly to reducing waste and preventing improper disposal (Khan et al., 2022). Finally, sufficiency practices on both the production and consumption ends are essential to achieving broader sustainability objectives (Tseng et al., 2022).

RL-oriented product lifecycle management increasingly aspires to form an integrated product-process-environment enterprise, dynamically accounting for external environmental impacts and adjusting organizational functions to new sustainability demands (Michelini and Razzoli, 2008). Realizing this vision depends on effective collaboration across the supply chain, supported by robust network design, logistics integration, inventory strategies, and adaptive procurement systems. In the face of uncertainties—such as variable lead times or delivery delays—simulation models offer valuable decision-making support (Abideen et al., 2021).

At the core of this sub-dimension lies the development of integrative CE business models that rely on collaboration with supply chain partners. Such collaborative capabilities are essential to enabling digitally enhanced RL systems (e.g., Tseng et al., 2022). However, many organizations still face internal limitations in designing and implementing innovative business models, which significantly slows progress toward circular integration (Pourmehdi et al., 2022).

Well-established collaborative circular business models not only support sustainable competitive advantage but also stimulate entrepreneurial activities (e.g., Gharibi and Abdollahzadeh, 2021). However, the absence of competitive market dynamics can inhibit the pace and scope of circular innovation González-Torre et al. (2010).

Circular business models extend traditional frameworks by emphasizing post-sale strategies such as product take-back, reuse, refurbishment, and advanced RL system design (Abideen et al., 2021). Prioritizing the retention of asset value over time also supports the valuation of natural capital (Khan et al., 2022). Emerging technologies, particularly blockchain, further reinforce transparency by tracking hazardous materials or quality issues in returned products (Difrancesco et al., 2023). The shift toward service-based business models—such as “product-as-a-service” offerings—represents a significant evolution in lifecycle thinking. These models enable extended product longevity, higher resource efficiency, and new pathways for competitiveness and market differentiation (Bressanelli et al., 2021).

The growing scarcity of primary resources is a major catalyst for the advancement of RL practices. As access to virgin materials declines, firms are increasingly motivated to seek alternative solutions through recycling, reuse, and remanufacturing (e.g., Moroni et al., 2022). Rising disposal costs further reinforce the financial viability of these strategies, providing an additional economic incentive for RL development (e.g., Bouzon et al., 2020).

Despite these drivers, the consistent availability and accessibility of recycled or reused resources remain problematic. Several studies highlight the limited infrastructure, supply inconsistencies, and lack of consumer confidence as persistent barriers across sectors and regions (e.g., Lechner and Reimann, 2020). In Malaysia’s automotive industry, for example, the uncertainty surrounding secondary material supply and low consumer acceptance of used products significantly limit RL effectiveness (e.g., Chong et al., 2023). More broadly, the absence of robust markets for secondary goods continues to hinder circular transitions (Ambekar et al., 2022).

To overcome these systemic challenges, organizations must develop strategic capabilities to monitor, interpret, and adapt to market dynamics (e.g., Kerdpitak et al., 2020). This includes tracking price fluctuations, demand patterns, and policy shifts that influence the viability of circular practices (e.g., Moroni et al., 2022). However, volatile market conditions often act as inhibitors for the integration of digital technologies in RL, undermining visibility and responsiveness (e.g., Bressanelli et al., 2018).

Given these uncertainties, RL implementation requires robust resource and capability management strategies that can accommodate environmental volatility and evolving stakeholder relationships (Moroni et al., 2022). Building resilience in circular systems means not only securing secondary resources but also fostering adaptive capacities to manage complexity and reduce systemic risk (e.g., de Campos et al., 2023).

A clearly articulated corporate policy that explicitly defines RL goals, processes, and objectives is foundational for effective RL implementation. When RL is embedded within a company’s mission and strategic vision, it establishes direction, accountability, and resource prioritization (e.g., Kadaei et al., 2023). In contrast, misalignment or vagueness in strategic intent often emerges as a critical barrier to RL adoption (e.g., Ambekar et al., 2022).

Embedding RL into corporate policy increasingly requires the strategic integration of digital tools and eco-innovation initiatives (e.g., Hsu et al., 2016). A data-driven culture is essential to support this integration, enabling organizations to harness technologies for optimizing operations, reducing uncertainty, and enabling evidence-based decision-making (e.g., Bag et al., 2020). Without this cultural foundation, organizations often lack the agility and insight required for effective RL (e.g., de Campos et al., 2020).

Bag et al. (2020) identify data visualization capabilities as the most influential driver of strategic RL decisions, followed by strong analytics and data generation skills. For tactical decisions, the organizational data culture takes precedence, indicating the need for continuous investment in both technological infrastructure and employee competencies.

Access to funding, whether through internal capital allocations or external financial mechanisms, plays a decisive role in RL execution (Khan et al., 2022). However, the impact of financial support programs exhibits geographic disparities in the extent to which firms can access such resources. Companies operating in regions with strong governmental or institutional support are more likely to overcome investment-related barrier (Bag et al., 2020).

Effective RL implementation depends heavily on robust contractual frameworks that bind supply chain actors—including suppliers, manufacturers, retailers, and consumers—into mutually beneficial partnerships. Cross-sector agreements are vital for fostering integrative and symbiotic production systems, aligning stakeholders’ responsibilities across the product lifecycle (e.g., Ciliberto et al., 2021). However, contractual hesitations often stem from concerns over data security, intellectual property rights, and transparency, which have been identified as major barriers in the literature (e.g., Nikseresht et al., 2023).

NGOs and industry associations also play a crucial role in shaping the institutional landscape for digital RL. These actors facilitate the development of shared standards and norms by promoting best practices, lobbying for regulatory support, and advocating for measures such as minimum product lifespan regulations (e.g., Chen et al., 2020). Conversely, their absence or inactivity can act as a significant institutional void, limiting progress in RL adoption (e.g., Pratapa et al., 2022). Certifications for RL-based products, such as remanufactured or refurbished components, function as important enablers of market acceptance. These labels not only assure consumers of product quality and sustainability but also enhance brand credibility and justify premium pricing strategies (Moroni et al., 2022). However, the lack of standardized, credible certifications—or consumer distrust in existing schemes—undermines the perceived value of RL offerings, reducing the willingness to engage in circular consumption (Ambekar et al., 2022).

Government regulations, policies, and international agreements consistently emerge as critical enablers for digital RL. Legislative efforts that support CE adoption are central to RL implementation, particularly when they impose environmental obligations on EoL products (e.g., Bhatia et al., 2020). Among these, legislative mandates aimed at minimizing environmental impacts—such as producer responsibility regulations—are considered especially influential (Khan et al., 2022).

Beyond regulation, governments can shape market behavior through economic instruments such as subsidies, grants, and eco-taxes. These tools help offset investment risks and stimulate innovation in mechanized recycling, automated take-back systems, and other RL-enhancing technologies (e.g., Simonetto et al., 2022). Regulatory bans on outdated or non-energy-efficient technologies also help guide corporate behavior toward greener production and logistics models (Cui et al., 2021).

Despite their potential, many regions face critical gaps in governmental support and regulatory coherence. Weak or ambiguous policies, lack of funding, and absence of enforcement mechanisms continue to hinder RL development (e.g., González-Torre et al., 2010). Additionally, complex regulations surrounding I4.0 technologies—including blockchain and IoT—can impose legal uncertainty, further obstructing RL integration (Difrancesco et al., 2023; Naseem et al., 2023). Privacy legislation, particularly the European Union’s General Data Protection Regulation (GDPR), introduces another layer of complexity. The “right to be forgotten” clause presents an obstacle to long-term data retention necessary for RL optimization (Difrancesco et al., 2023). Simultaneously, the lack of government-led monitoring and auditing mechanisms undermines compliance and system effectiveness. Where such oversight exists, it has been shown to positively influence RL outcomes (Chen et al., 2020). Yet, where it does not, implementation efforts remain fragmented (Abideen et al., 2021).

The development, availability, and adaptability of internal infrastructure are widely recognized as critical prerequisites for effective RL implementation (e.g., Mastos et al., 2021).

Strategic investment in dedicated RL infrastructure—such as specialized return centers and processing facilities—is essential (Kadaei et al., 2023; Moroni et al., 2022). Conversely, the absence of such infrastructure constitutes a persistent barrier (e.g., Trevisan et al., 2023).

Technological systems, including decision-support tools and digital platforms, underpin RL operations by enhancing visibility, tracking, and coordination (e.g., Flygansvaer et al., 2019). Long-term benefits of these systems include cost efficiency, operational resilience, and enhanced value recovery (e.g., Gharibi and Abdollahzadeh, 2021). Yet, necessary initial investments can be resource-intensive and financially demanding (e.g., Dey et al., 2020).

Effective integration of RL with logistics, inventory, and distribution systems is necessary for efficiency and responsiveness (e.g., Oluleye et al., 2023a). However, poor planning and weak process coordination remain significant obstacles (e.g., Chong et al., 2023).

Proactive planning for return handling—such as shipping, storage, and technical support—further enhances profitability (Kadaei et al., 2023). For instance, de Campos et al. (2020) observed a positive correlation between RL capabilities and economic performance in Brazil, with transportation costs being particularly significant in the collection phase (Fanta and Pretorius, 2022). Because RL systems are often reactive and complex, dynamic process planning is essential. Over time, accumulated data allow organizations to improve demand forecasting, inventory management, and product quality control, thereby minimizing overproduction and rework (Simonetto et al., 2022). Emerging technologies like block-chain also enable real-time data sharing and enhance traceability in returns management (Difrancesco et al., 2023).

Dynamic organizational capabilities and proactive change management are equally important for adapting to RL demands (e.g., Tseng et al., 2019). This requires structured approaches to managing organizational change and developing new skill sets (Bag et al., 2021). Studies consistently report that resistance to change and inadequate dynamic capabilities hinder RL success (e.g., Liu et al., 2018).

Synchronizing RL with forward logistics operations—especially in terms of product consolidation—is crucial for end-to-end supply chain efficiency (e.g., Butt et al., 2023). However, coordination inefficiencies persist across many industries (e.g., Chong et al., 2023).

The adaptability of infrastructure to shifting internal and external conditions is essential for effective RL implementation (Khan et al., 2022). Collaboration among supply chain partners is key to developing an optimized, responsive network design that supports RL operations (e.g., Bag et al., 2021).

Core elements such as network architecture, inventory management, logistics integration, outsourcing strategies, and procurement models must be embedded within a flexible and scalable supply chain framework (Abideen et al., 2021). A resilient infrastructure enables the system to withstand disruptions and quickly adapt to change (Tseng et al., 2023). In contrast, rigid networks and poor adaptability present significant barriers to RL implementation (e.g., Goyal et al., 2018). Data privacy concerns must also be addressed to support transparent collaboration across stakeholders (Naseem et al., 2023).

The scalability of digital infrastructure—particularly through technologies like IoT-enabled digital twins—enhances dynamic route optimization and improves logistics efficiency (Abideen et al., 2021). More accurate tracking of material flows supports better decision-making and requires integrated planning and seamless data exchange (Simonetto et al., 2022). Equitable value creation across supply chain actors is also vital. All partners must perceive clear and fair benefits to maintain engagement and prevent imbalance or resistance (e.g., Ciliberto et al., 2021). Managing high transport and tracking costs in a just and transparent manner is a prerequisite for sustained collaboration (Yusuf et al., 2017).

The effective implementation of digital RL relies not only on private sector investment but also on robust public infrastructure and institutional support. Without an adequate public framework, even the most advanced internal systems struggle to function efficiently (Chen et al., 2020; Moroni et al., 2022). In many regions, however, low technological density and uneven development of digital infrastructure pose major barriers to RL adoption (Bressanelli et al., 2021). A lack of readiness for public investment further constrains the availability of critical facilities, such as recycling centers and remanufacturing plants (Trevisan et al., 2023). For example, research on electronic waste management in Ghana highlights the urgent need for government-backed environmental agencies with regional oversight and infrastructural capabilities (Chen et al., 2020). Similarly, the absence of regionally distributed remanufacturing and recycling hubs is a well-documented constraint on RL progress (Kadaei et al., 2023).

Access to efficient and sustainable transportation infrastructure is another essential component. Ideally, companies should be able to utilize various low-emission transport modes—such as electric vehicles, courier robots, and bicycles—to enhance RL efficiency and sustainability (e.g., Tseng et al., 2023). However, many regions lack the infrastructure to support such alternatives, leading to logistical bottlenecks and increased operational costs (e.g., Nikseresht et al. 2023).