Digital platforms for circular economy: Empirical development of a taxonomy and archetypes

Petrik, Dimitri; Hiller, Simon; Morar, Dominik Article — Published Version Digital platforms for circular economy: Empirical development of a taxonomy and archetypes Electronic Markets Provided in Cooperation with: Springer Nature Suggested Citation: Petrik, Dimitri; Hiller, Simon; Morar, Dominik (2025) : Digital platforms for circular economy: Empirical development of a taxonomy and archetypes, Electronic Markets, ISSN 1422-8890, Springer, Berlin, Heidelberg, Vol. 35, Iss. 1, https://doi.org/10.1007/s12525-025-00792-w This Version is available at: https://hdl.handle.net/10419/323628 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Electronic Markets (2025) 35:60 https://doi.org/10.1007/s12525-025-00792-w RESEARCH PAPER Digital platforms forcircular economy: Empirical development ofataxonomy andarchetypes DimitriPetrik1 · SimonHiller2· DominikMorar2 Received: 17 November 2024 / Accepted: 8 May 2025 © The Author(s) 2025 Abstract Digital platforms hold promise to leverage the transition from a linear to a circular economy (CE), as they have already disrupted value-creation mechanisms and formed dynamic ecosystems in various domains. However, the solution space for CE platforms is vast and remains under-researched, challenging platform providers to find purposeful platform configurations and industrial companies to choose the right platform on the market. To support informed platform design decisions and uncover the existing CE platform configurations, we develop a taxonomy from a systematic literature review and an empirical dataset of 129 cases of CE platforms. The taxonomy provides a holistic view of the solution space, advancing our understanding of key configuration options. The taxonomy allows us to perform cluster analysis and derive six CE platform archetypes. These findings advance the theoretical knowledge indicating how platforms can support CE and help decisionmakers design CE platforms or identify suitable CE platforms for their own circular activities. Keywords Circular economy platforms· Marketplaces for circular economy· Taxonomy· Archetypes· Cluster analysis Introduction Population growth and economic prosperity are driving global sustainability pressures by depleting the Earth’s resources (Krausmann etal., 2009). The circular economy (CE) is a critical paradigm for decoupling value creation from finite resources (Lüdeke‐Freund etal., 2019). CE proposes different approaches to move away from linear ‘takemake-waste’ value streams, such as slowing them down, narrowing them, closing them, or opening them up to new application scenarios or additional life cycles (Bocken etal., 2016). This shift requires systemic changes beyond the boundaries of individual organizations (Geisendorf & Pietrulla, 2018; Irani & Sharif, 2018; Korhonen etal., 2018; Manninen etal., 2018). Consequently, a successful realization of a CE is considered a sociotechnical challenge that affects the business processes of all actors in the value creation system (Aarikka-Stenroos etal., 2021). To overcome this challenge, research sees the necessity to bundle inter-organizational value creation in circular ecosystems (Chertow, 2007; Kanda etal., 2021). Ecosystems act as an alignment structure for a multilateral network of actors that need to interact with each other to materialize a focal value proposition (Adner, 2017). Similarly, the joint approach in circular ecosystems aims to reduce resource consumption and launch circular business models (CBMs) through collaboration (Kanda etal., 2021; Parida etal., 2019; Potting etal., 2017). Digital platforms enhance collaboration as digital hubs, providing a valuecreation architecture that establishes a “hub and spoke” structure. In this structure, companies are connected as distributed nodes to a platform, which acts as a central hub. Therefore, in ecosystems that employ digital platforms, value streams and associated transactions increasingly shift to the platform, including those that support circular activities (Blackburn etal., 2023; Jacobides etal., 2018). Existing research suggests that digital platforms can take on the role of meta-organizations for orchestrating circular value creation (Blackburn etal., 2023). Initial case studies illustrate how a digital platform for circular Responsible Editor: Martin Adam * Dimitri Petrik dimitri.petr[email protected]art.de 1 Graduate School ofExcellence Advances Manufacturing Engineering (GSaME), University ofStuttgart, Nobelstr. 12, 70569Stuttgart, Germany 2 Ferdinand Steinbeis Institute, Bildungscampus 9, 74076Heilbronn, Germany Electronic Markets (2025) 35:60 60 Page 2 of 25 value creation can be set up in certain domains and which processes the platforms take over (Hirota etal., 2022, 2023). From the perspective of incumbents, there are initial insights into how such companies need to transform themselves in order to operate a digital platform and commit to the ecosystem based on it (Budde etal., 2024; Parida etal., 2019). Existing research still barely covers the versatility of digital platforms, well-documented in other platform-mediated domains (Arnold etal., 2022; Duparc etal., 2022), and likely applicable to the setting of CE (Henry etal., 2020; Lüdeke‐Freund etal., 2019). Despite the empowering role that digital platforms can play in the realization of CE (Alt, 2020; Kirchherr etal., 2023a, 2023b), previous research has mainly examined the platform assessment based on environmental, social, and governance (ESG) criteria, as well as the design of platforms for ESG without specifically addressing CE (Li etal., 2023; Plugge etal.; Ryu etal., 2024). As a result, the solution space for the features of digital platforms and their configuration patterns for leveraging the CE remains poorly understood (Zeiss etal., 2021). As evidenced in other platform-mediated domains, sustaining digital platforms is far from trivial due to necessary business model changes and the complexity of the inter-organizational orchestration of value on the platform (Parker etal., 2017; van Alstyne etal., 2016). Hence, we argue that the existing lack of knowledge hinders research on strategies for the successful launch and establishment of CE platforms. Acknowledging the challenges of establishing digital platform in business-to-business settings (Mosch etal., 2023; Pauli etal., 2021) and the significant risks of failed platform launches (Jesus etal., 2024; Pidun etal., 2022), we conclude that without a structural analysis of the value propositions unlocked by digital platforms for CE, successful configurations of CE platforms cannot be understood well. Previous research has identified initial governance mechanisms and practices to transform a traditional business model into a platform-based business model to fulfill the CE principles (Blackburn etal., 2023; Budde etal., 2024), but a holistic view of the digital platform solution space for CE is lacking. On the other hand, existing morphological analyses do not yet focus specifically on digital platforms (Lüdeke‐Freund etal., 2019). This calls for further systematization of platform characteristics relevant to CE and the associated value propositions to demarcate the solution space of CE platforms. Guided by this research gap, this study aims to systematize the knowledge on the configurations of CE platform characteristics and to illustrate the existing CE platform archetypes. Therefore, we ask: RQ1: How can digital platforms for CE be classified according to key dimensions and characteristics? RQ2: What are the archetypes of CE platforms? To answer these research questions, this study employs a two-phase research design. In the first phase, the study synthesizes the existing scientific literature on CE platforms and an empirical dataset of 129 CE platforms to identify relevant CE platform characteristics through a morphological analysis. These characteristics are organized in a taxonomy because taxonomies are recognized as classification artifacts suitable for organizing knowledge about a certain phenomenon and helping systematize emergent research fields (Kundisch etal., 2022; Schoormann etal., 2023). In the second phase, we perform a cluster analysis of the empirical data sample based on the taxonomy to derive six archetypes of CE platforms. This also serves as an evaluation of the taxonomy by applying it to real-world cases. We additionally evaluate the taxonomy in 16 interviews. In conclusion, the taxonomy provides a structured view of the different platform characteristics in the context of CE and contributes to the descriptive knowledge in the hitherto insufficiently researched research field of platform characteristics relevant to the CE paradigm. The archetypes help practitioners make informed decisions about configuring or finding the right platform to support the circular transformation of their business and their network. Related work In order to outline related concepts and previous work relevant to our study, this section introduces the notion of CE and explains why data is so important to its realization. It then presents the notion of digital platforms, explains how they can support the embracement of CE, and provides an overview of the current state of research. The circular economy The CE paradigm has emerged as a response to environmental constraints fundamentally challenging the consumption of finite resources (Durán-Romero etal., 2020). Despite possible rebound effects, CE is seen as an effective approach to sustainable value creation that minimizes resource input and waste (Barros etal., 2021; Zink & Geyer, 2017). It draws on a rich history of ideas from industrial ecologists, notably drawing on the concepts such as the spaceman economy, the industrial ecology, or the performance economy (Kalmykova etal., 2018). The spaceman economy views planet Earth as a spaceship, a closed system with limited resources (Boulding, 1966). According to the industrial ecology, industrial systems are understood as biological ecosystems, in which waste is transformed into a resource, following natural cycles (Graedel, 1996). Meanwhile, the performance economy postulates that it is necessary to focus on product performance rather than product ownership to improve resource efficiency Electronic Markets (2025) 35:60 Page 3 of 25 60 (Stahel, 2016). In the recent academic discourse, the CE has been further developed and conceptualized as a distinct academic discipline, with proposals on how to enrich its theoretical foundations (Kirchherr etal., 2023a, 2023b; Mignacca etal., 2025). As CE has been researched for several decades, there are many definitions, and this fragmentation has led to a lack of consensus on what CE is (Homrich etal., 2018). To promote the diffusion of the CE, the Ellen MacArthur Foundation, a non-profit organization, emphasizes three core principles: reduction of waste and pollution, keep products and materials in use as long as possible, and regeneration of natural systems (MacArthur, 2013). Within the academic discourse, Kirchherr etal. (2017) synthesized 114 CE definitions and proposed a unifying definition. Recently Kirchherr etal., (2023a, 2023b) revisited and extended a unifying definition of a CE. For our study, we largely follow this definition and sharpen it by additionally including the 10 circularity principles in the definition. Hence, based on recent definitions (Geissdoerfer etal., 2017; Kirchherr etal., 2023a, 2023b; Potting etal., 2017), we conceptualize CE as a regenerative economic system which necessitates a paradigm shift to replace the “end of life” concept and minimize resource input, waste, emission, and energy leakage through the 10 circularity principles ranging between refuse and recover to foster value maintenance and sustainable development, enabled by stakeholder alliances, their technological innovations and capabilities. In line with this definition, with its diverse principles, CE affects processes throughout the entire life cycle of a product (Andersen etal., 2023; Geissdoerfer etal., 2020; van Loon & van Wassenhove, 2020), as shown in Fig.1. There is also a growing narrative that CE is a systemic approach and that reaching the higher-order circularity principles requires inter-organizational collaboration (Geisendorf &Pietrulla, 2018; Stahel, 2016). To achieve this, existing research shows that the realization of CE can benefit significantly from using digital technologies and closing data gaps. In particular, data processing and utilizing information flows are important (Chauhan etal., 2022; Jäger-Roschko & Petersen, 2022). For example, waste-toresource matches can benefit significantly from both descriptive and prescriptive data analytics. Effective localization of products for take-back and redistribution for additional lifecycles also relies on accurate data (Petrik etal., 2025). This harnessing of data and digital technologies for their generation or processing to promote CE is also known as smart CE (Kristoffersen etal., 2020; Zeiss, 2019). However, traditional IT systems struggle to manage inter-organizational data streams due to interoperability limitations, which is why digital platforms are considered promising to support circular value streams (Antikainen etal., 2018; Heinz etal., 2024; Zeiss etal., 2021). Digital platforms foracircular economy In the context of smart CE, digital platforms are viewed as digital technologies that can process and organize data streams through open interfaces and their layered architecture (Cusumano etal., 2019; Yoo etal., 2010). However, a purely technical view of digital platforms is insufficient to understand their impact on value creation. Platforms can simultaneously support transactions between actors (i.e., supply and demand) and form the architectural basis for innovations and digital services that utilize the platform functionalities (Cusumano etal., 2019; Hein etal., 2020). Thus, a distinction can be made between innovation and transaction platforms, depending on their predominant value facilitation mechanism. When such platforms are accessible to third parties, they catalyze the emergence of Inbound logistics Take-back Redistribution Use in thefirst lifecycle Use in then lifecycle Use in thesecond lifecycle Reuse Remanufacture Rethink/ Recycle / Recover Pre-use In-use Post-use Waste and emission leakage Repair/ Refurbish Disposal Human resources Procurement Refuse Rethink Reduce Outbound logistics Marketing and Sales Manufacturing Service Technology and product development Fig. 1 Conceptualization of a circular economy Electronic Markets (2025) 35:60 60 Page 4 of 25 platform-based ecosystems (Gawer, 2014; Jacobides etal., 2018). Such platforms generate different leverage effects, with platform-based output exceeding the input (Thomas etal., 2014) and are subject to network effects that increase value for platform users (Körppen etal., 2024; McIntyre & Srinivasan, 2017). Following the logic described above, digital platforms can also be used to navigate the circular value flows between stakeholders. Business ecosystems based on platforms are known to promote collective action at a system level, fostering collaboration between organizations (Ritala, 2024). For example, a critical success factor for increasing the use of secondary resources is the existence of a market, which digital platforms can create (Henry etal., 2020; Knoth etal., 2022; Kumar etal., 2023). In addition to acting as an intermediary, the potential of digital platforms in the context of CE also relates to the joint collection of data related to circularity principles or co-creation (Konietzko etal., 2019). Platforms can also generate network effects in the CE context, for example, by increasing the number of circular solutions. This leads to an improved offering for the demand side (Körppen etal., 2024), increasing the platform value and facilitating the embracement of CE. Therefore, platforms should be recognized as sociotechnical digital artifacts that not only offer technical features but also develop business value that can be used for the embracement of CE and guide further morphological analysis (Kosmol & Leyh, 2020; Zeiss etal., 2021). In this context, the concept of ontological reversal provides a valuable lens for understanding the role of digital platforms in the CE. It was introduced by Baskerville etal. (2020) to emphasize that digital technologies no longer merely represent physical reality but increasingly shape it. By acting as intermediaries and processing large amounts of data from the platform-based ecosystem, platforms can act as constitutive elements in organizing the CE practices and creating circular markets. Berg and Wilts (2019) reflect on the existing deficits of resource supply markets and reason that digital platforms can help solve them, noting that typical platform issues such as data quality, algorithm performance, user protection, information asymmetries, property rights, lock-ins, and gatekeeping must be managed by platform providers. Balder etal. (2023) propose a process model for shifting CE transactions to a digital platform. In addition to this, Budde etal. (2024) develop a process model that depicts the transformation of a recycler into a digital platform provider with an increasing number of different platform user groups. He etal. (2021) study the dilemmas of platform user participation. Kosmol and Leyh (2020) present a tool for building digital platforms to support industrial symbiosis. Marantes etal. (2023) present a sociotechnical framework to support the development and deployment of CE platforms. Several papers show insights from technical implementations of platforms. Aivaliotis etal. (2021) and Ambrosio etal. (2021) outline the architectures of IoT-enabled platforms for smart waste management. Boukhatmi etal. (2023) conduct a design science research study and derive design principles, focusing on data architecture. Fozza etal. (2023) develop a platform architecture electrical and electronic waste logistics requirements. Soldatos etal. (2021) present a logical and data architecture of a platform to enable crosssectoral reuse of resources. In terms of business-oriented research, Hirota etal., (2022, 2023) present business architectures of platforms, detailing the business processes these architectures should support. Konietzko etal. (2019) investigate the different roles of digital platforms and find that platforms support markets, operations, and co-creation. Blackburn etal. (2023) conceptualize platforms as meta-organizations for orchestrating circular value creation. Schwanholz and Leipold (2020) perform a business model analysis of platforms that support sharing and relate to CE, distinguishing three types of platform business models that aim either at social interaction, profit and sustainability, or at mixed goals. Hence, despite these two exceptions (Blackburn etal., 2023; Schwanholz &Leipold, 2020), most existing studies report single implementation cases. In summary, the review of existing work indicates that the studies mainly focus on the potentials, challenges, and technical realization of digital platforms for CE. This suggests a lack of a comprehensive overview of the solution space for configuring CE platforms, although these studies are later used to develop the taxonomy. Methodology Taxonomy development To delineate the solution space of CE platforms, we performed a morphological analysis and organized it into a taxonomy. Taxonomies are valuable for structuring descriptive knowledge and giving order to complex research objects (Glass & Vessey, 1995). In addition, taxonomies are purposeful in delineating solution spaces, as they can be used as descriptive theories (Gregor, 2006) storing multidimensional empirical knowledge applicable in business, for instance, as configurational models (Lüdeke‐Freund etal., 2019; Möller etal., 2022). The taxonomy development approach utilized the method proposed by Nickerson etal. (2013) for two reasons. First, it supports a sequential use of conceptual data from the scientific literature and empirical data from realworld scenarios. Second, it is iterative and provides guidance for defining ending conditions. Electronic Markets (2025) 35:60 Page 5 of 25 60 Meta-characteristic: The first step of the taxonomy development approach is to formulate the meta-characteristic of the taxonomy, which defines its purpose and the target user groups. The taxonomy aims to systematize the characteristics of digital platforms for CE. As the taxonomy provides an overview of the possible characteristics, the resulting solution space aims to guide research at the intersection of platform and CE while supporting decision-makers in the process of configuring digital platforms through a make approach or assessing existing market solutions through a buy approach. Ending conditions: The second step requires to define the ending conditions. We rely on the eight objective criteria proposed by Nickerson etal. (2013) and five subjective ending conditions proposed by Szopinski etal. (2020): (S1) it is concise, with a number of dimensions and characteristics that do not create mental overload when working with the taxonomy; (S2) it is robust, allowing a clear differentiation of the diverse platform configurations; (S3) it is comprehensive, enabling the classification of all platform cases from the empirical web search; (S4) it can be extended due to the relative novelty of the application of platform technologies for CE; (S5) it is sufficiently explanatory, helping to explain the different platform configurations for CE. Taxonomy design approach: The next steps, 3 to 7, require choosing the design approach. The development of the taxonomy went through four iterations described in Table1. Systematic literature review: Given the scholarly interest in the topic, we first performed a conceptual-to-empirical approach. We conducted a systematic literature analysis at the database level to achieve extensive coverage of research related to digital platforms for CE. After testing different search strings, we opted for an inclusive search string that included a diverse spectrum of relevant literature (Larsen etal., 2019). Thus, we defined a search string combining the keywords “circular economy” with the combination of keywords “platform” OR “ecosystem.” This string was used for the search in nine scientific databases: EBSCOhost, Web of Science, ScienceDirect, ACM, IEEE Explore, AiSeL, Springerlink, Taylor&Francis, and Wiley. The search was performed in November 2023 and repeated in June 2024 since academic research on CE is rapidly evolving. Figure2 illustrates the steps of building a literature sample by following the PRISMA guidelines. The PRISMA guidelines represent an established standard for documenting the screening and analysis of a literature sample retrieved from the databases (Page etal., 2021). The literature sample creation was guided by inclusion and exclusion criteria to minimize the sampling bias, which are included on the right side of Fig.2. In particular, only peer-reviewed journal or conference papers published in English were considered for screening. The titles and abstracts of all identified papers were screened to estimate the relevance of each paper to the research topic. This was done by assessing whether the papers conducted empirical or conceptual research using the concept of digital platforms, which was significantly shaped by Cusumano etal. (2019), distinguishing the facilitation of innovation or transactions, and Gawer (2014), highlighting the supply-chain or industry level of openness. To be considered relevant, papers had to explore these platform concepts and link them to the contextualization of CE presented earlier. Although we did not find any deviations in the definition of CE, we excluded many papers during the screening process that pursued a technically different notion of platforms (e.g., Ivanov etal., 2022), examined CBM or circular ecosystems that do not rely significantly on platforms (e.g., Thakur & Wilson, 2024), examined consumer behavior from social media platforms (e.g., Lima, 2022), or, for example, referred to biorefineries or other chemical plants as platforms (e.g., Leong & Chang, 2023). Given the conceptual compatibility between the CE and the sharing economy (Henry etal., 2020), studies on platforms that promote sharing were also included in the sample. We also excluded publications that were not research papers in journals or conferences, MDPI publications, and papers that were not written in English. During the full-text analysis, we excluded further papers that deviate conceptually from our understanding of CE and platforms or if they only occasionally mentioned the terms “CE” or “platform.” Subsequent content analysis of the full texts reduced the final sample to 19 papers. A backward search yielded three additional papers. During the repeated search, one paper was added. Appendix 1 contains the entire sample. Table 1 Overview of the iterations performed to build the taxonomy Iteration Approach Sample Result 1 Conceptual-to-empirical 14 papers Two meta-dimensions and six dimensions 2 Empirical-to-conceptual 50 websites, randomized First version of the taxonomy with an additional meta-dimension 3 Empirical-to-conceptual 80 remaining websites Refined version of the taxonomy 4 Empirical-to-conceptual 130 websites End of the development process as no changes have been made Electronic Markets (2025) 35:60 60 Page 6 of 25 First iteration: Relying on a mix of an inductive category formation, as proposed by Mayring (2022), and the deductive search based on the pre-codification scheme, as proposed by Bandara etal. (2015), our literature analysis enabled us to characterize CE platforms and define an initial set of metadimensions: (1) scope and (2) platform. This initial analysis led to the identification of five primary dimensions, integrating the platform value creation mechanisms of innovation and transaction platforms as described by Blackburn etal. (2023), circular strategies based on the 9R framework by Potting etal. (2017), value purpose as outlined by Atif (2023), and two scope dimensions targeting matchmaking to differentiate between B2B and B2 C, and the business structure, which can be for-profit or non-profit, as discussed by Ciulli etal. (2020) as well as Schwanholz and Leipold (2020). However, our search also revealed that the research on digital platforms for CE is still in its infancy, with only a few case studies. This suggests that, at the time of the search, the research landscape lacked a comprehensive view of the solution space for digital platforms for CE and did not offer a morphological analysis of NOITACIFITNEDI EBSCO host n = 222 GNINEERCSYTILIBIGILEDEDULCNI Removed duplicates before screening: n = 202 Web of Science n = 139 Science Direct n = 340 IEEE Explore n = 29 ACM Digital n = 1 Springer Link n = 2453 Taylor& Francis n = 17 Records identified from the databases: n = 3377 Wiley n = 6 Records screened (title and abstract): n = 3175 Records removed after the screening procedure: n = 2948 •Workshop papers, posters, interviews or TREOs •Papers published by MDPI •Papers written in languages other than English •Papers dealing with different digital technologies as influencing factors on CE without a focus on platforms •Paper analyzing CBM, CE ecosystems without focusing on CE platforms •Papers analyzing user behavior based on data from social media platforms •Conceptual deviation from the platform concept coined by Cusumano et al. (2019) and Gawer (2014) Records read (full-text): n = 227 Records removed after reading and analyzing fulltexts: n = 208 •Focus on other sustainability practices such as sustainability accounting instead of CE •Occasional mentions of platforms or CE without an integrated analysis of both concepts •Conceptual deviation from the platform concept coined by Cusumano et al. (2019) and Gawer (2014) Relevant records after full-text reading: n = 19 Records added after backward search: n = 3 Final sample of academic literature: n = 22 Extended sample after the repeated search in November 2024: n = 23 AiSeL n = 170 Fig. 2 Overview of the literature sample building process based on PRISMA guidelines Electronic Markets (2025) 35:60 Page 7 of 25 60 platform archetypes that facilitate CE. Based on this finding, we conducted an additional empirical web search to create a database of real-world CE platform examples, extend the data foundation, and improve the taxonomy. Analysis of real-world objects: The web search was performed in February 2024. The sample was built by explorative search for platforms via Crunchbase and LinkedIn. Crunchbase is a relevant source of business data, providing firms an environment to present themselves and search for investors. To mitigate the bias of using only one source, LinkedIn built an additional data source chosen because it is the largest professional social network, with 850 million members in around 200 countries. For the CE platform search, we used similar keywords “circular economy” AND “platform.” The search led to the identification of 697 firms on Crunchbase and 1000 additional hits on LinkedIn. Not all hits corresponded to our defined concept of CE platforms, which is presented in “Methodology”, so each website had to be assessed to confirm whether they represent platforms with open access to third parties to form a CE platform ecosystem. Consultancies or funding initiatives that advise or fund platform providers were excluded from the sample. Furthermore, platformless communities were also excluded. To increase the validity of our results, three researchers from the team independently adjusted and compared the sample, discussing discrepancies until a consensus was reached. This reduced the sample to 151 potential platforms. After eliminating duplicates from LinkedIn previously identified on Crunchbase, an additional 45 platforms were included, bringing the total to 196 suspected CE platforms. Each platform was further analyzed by team members using website information. This analysis, which involved inductively deriving categories for taxonomy organization, excluded some solutions that appeared to be platforms but only supported internal analytics. We also excluded websites that were no longer online. The refined and final sample comprised 129 platforms, listed in Appendix 2. Second iteration: Once the empirical sample was finalized, four iterations were performed to create the final taxonomy and characterize the platforms from the sample. Figure3 outlines the evolution of the dimensions during the four iterations. The sample was randomized using Microsoft Excel’s randomization function and consisted of 50 platforms, representing a relative proportion of 38%. This sub-sample was used for the second iteration to derive the dimensions and their characteristics inductively forming the second version of the taxonomy. From a research method perspective, we performed electronic document analysis (Bowen, 2009) to derive relevant platform characteristics, as companies usually communicate the core elements of their platforms (Teece, 2010). Each researcher proposed the characteristics of CE platforms by studying the sub-sample independently. Open coding was used to extract platform characteristics, guided by the expertise of the research team. A total of five additional dimensions were defined. Four dimensions were distributed across the meta-dimensions identified in the first iteration. The need for an additional meta-dimension, called technology, was recognized to organize the complementary technologies. As additional meta-dimensions and dimensions were added to the taxonomy, a third iteration was performed. Third iteration: During the third iteration, the taxonomy was used to characterize the remaining 80 platforms, during which an additional dimension with two characteristics was added to the taxonomy. This change was also defined by consensus and categorized within the taxonomy according to its order and affiliation with the three meta-dimensions. The addition of another dimension meant that the object ending condition proposed by Nickerson etal. (2013) was not met, necessitating a further iteration. Fourth iteration: During this last iteration, the entire sample was characterized by applying the taxonomy. No changes were made during this iteration, which indicates the robustness and completeness of the taxonomy and the fulfilment of the objective ending conditions proposed by Nickerson etal. (2013). Appendix 3 presents the ending conditions, while Appendix 4 presents the data structure tables and an excerpt of the coding scheme. When deciding how to visualize the taxonomy, a morphology was chosen from the existing options (Szopinski etal., 2020) for two reasons. First, morphologies are well suited to enable taxonomy users to visualize and compare different platform configurations. Second, morphologies can group dimensions into meta-dimensions, allowing multiple options to be logically organized at a higher level (Möller etal., 2022). Given the interpretative nature of the sample construction (Bowen, 2009; Cram etal., 2020) and the taxonomy development process, category building was performed independently by each researcher and then discussed. The names of the dimensions and their characteristics, as well as their granularity, exclusivity, and sequence in the taxonomy, were discussed and assigned to the meta-dimensions by consensus. This helped to ensure the general objectivity of the taxonomy. To further improve the usability of the taxonomy as a means to structure the solution space, we decided that certain dimensions should not be mutually exclusive, as otherwise the possibilities to analyze existing platforms from the field or to design a platform would be hampered and become impractical for use in platform configuration (Möller etal., 2019). Evaluation: To improve the rigor of the taxonomy development and to gain deeper insights into how potential users understand and rate the usefulness of the taxonomy, we conducted interviews (Szopinski etal., 2020). Initially, we presented the taxonomy to the students of a university Master’s level sustainability course. We asked them whether they Electronic Markets (2025) 35:60 60 Page 8 of 25 found the taxonomy understandable enough and whether it helped them to characterize certain platforms from the sample introduced during the course. We assessed the overlap with the characterization by the researchers. Afterward, we consulted 16 experts from practice and research to evaluate the taxonomy. To be considered for the evaluation, we ensured that the experts were potential users of the taxonomy. According to Szopinski etal. (2019), such a sample size is appropriate for evaluating the taxonomy. The interviews lasted 1h each. In the first half hour, the purpose of the taxonomy and all dimensions and characteristics were explained. The experts were able to comment iteratively on each component of the taxonomy. In the next half hour, we used a questionnaire and asked the interviewees to explain how they would use the results and evaluated it based on a predefined set of criteria. In line with Rosemann and Vessey (2008) and Szopinski etal. (2020), the criteria aimed to capture feedback on the relevance, novelty, applicability, accessibility, and confidence in the use of the taxonomy. We used ordinal scales with ratings of “1” to “5” to rate the criteria and requested qualitative statements to justify each rating. With representatives from CE platforms, we additionally conducted a self-assessment, which added a criterion on the perceived experience of applying the taxonomy. We also asked the experts about limitations and potential for improvement in order to learn about the potential uses and current limitations of our taxonomy as a descriptive artifact. Appendix 5 provides further details on the evaluation, the experts, and the questionnaire design. Archetype identification After developing the taxonomy, we used the empirical sample to perform cluster analysis and identify CE platform archetypes as configuration patterns based on the taxonomy. Archetypes represent a typical configuration or a pattern for an object of interest, such as CE platform designs, from which copies can be made (Johnson, 1994). In our context, as described by Möller etal. (2019), the morphological visualization of the taxonomy can create patterns, and the central configurations represent archetypes. Cluster analysis helps to group similar objects based on the manifestations of their characteristics to derive archetypes. Cluster analysis uses statistical methods to achieve high internal homogeneity Iteration 1Iteration 2Iteration 3 Conceptual-to-empirical Empirical-to-conceptualEmpirical-to-conceptual ScopeCircularity principles Technology Circularity principles Value object Value object Business model Technologies Targeted matchmaking Proximity Organizational form Iteration 4 Empirical-to-conceptual Circularity principles Value object Business model Technologies Targeted matchmaking Proximity Organizational form Complementary Technologies Targeted matchmaking Proximity Business structure Circularity principles Dimension or characteristic not changed New meta-dimension New dimension Dimension renamed or number of characteristics changed Legend: Targeted matchmaking Business structure PlatformValue-creating mechanism Value-creating mechanism Value purpose Area of application Supported capabilities Partner network Value-creating mechanism Value purpose Area of application Supported capabilities Partner network Value-creating mechanism Value purpose Area of application Degree of openness Partner network Value purpose Area of application Fig. 3 Overview of the taxonomy changes with each iteration Electronic Markets (2025) 35:60 Page 15 of 25 60 Cluster 1Cluster 2 Cluster 3Cluster 4 Cluster 5 Cluster 6 ecnatsiD Dendogram Fig. 6 Cluster dendrogram with six cluster groups Table 3 Distribution of the CE platform characteristics Circular marketplaces Circular operations enablers Packaging tracking platforms Waste management platformsWaste marketplaces Tracking platforms 67 8224 81 1 Materials12% 0% 0% 21%13% 45% Products 12% 88%0%50% 13%64% Packaging3% 0% 100% 17%0%0% Waste6% 25%0%67% 100% 9% Intantgibles 4% 25%0%0%0%18% People3% 0% 0% 0% 0% 0% Refuse 3% 0% 0% 8% 0% 0% Rethink6% 0% 0% 33%0%0% Reduce6% 13%0%63% 0% 27% Reuse84% 75%0%21% 25%82% Repair 30% 25%100%4%0%45% Refurbish24% 0% 0% 8% 0% 36% Remanufacture6% 0% 0% 0% 0% 45% Repurpose 13% 13%0%21% 0% 45% Recycle24% 0% 0% 58%25% 55% Recover6% 0% 0% 17%38% 0% B2B43% 88%0%100%88% 100% B2C37% 100% 100% 8% 0% 0% B2G1% 0% 0% 8% 13%9% B2B2C7% 0% 100% 0% 0% 0% C2C24% 13%0%0%0%0% C2G4% 0% 0% 0% 0% 0% B2B2G1% 0% 0% 8% 0% 9% Local 1% 25%100%4%38% 0% National 39% 25%0%25% 25%0% International57% 63%100%75% 63%100% Non-profit 1% 0% 0% 0% 0% 0% For-profit99% 100% 100% 100% 100% 100% Platform access 100%25% 100% 100% 100% 9% Platform license0% 75%0%0%0%91% Innovation6% 38%50% 21%50% 82% Transaction97% 88%50% 83%63% 73% Collective intelligence 7% 0% 0% 0% 13%0% Information provisioning 7% 38%0%71% 25%18% Operations 25% 50%50% 75%63% 45% Tracking 9% 13%50% 58%38% 91% Connecting93% 13%0%13% 38%55% Design 4% 0% 0% 13%13% 0% Manufacturing3% 0% 0% 33%25% 36% Logistics9% 25%100%67% 38%55% Sales55% 88%0%8%0%27% Service40% 63%50% 100% 50%73% Knowledge13% 38%0%58% 13%45% Reporting 7% 0% 50%25% 50%18% Benchmarking 0% 0% 0% 13%13% 9% Financing3% 0% 0% 4% 0% 0% Within one organization 10% 88%0%88% 0% Closely connected network or supply chain3% 38%100%0%0%100% Loosely coupled ecosystem 87% 0% 0% 13%88% 0% Visible28% 25%0%25% 13%9% Hidden72% 75%100%75% 25%91% None 93% 88%0%50% 75%36% For data integrity6% 0% 0% 0% 100% 9% For data collection0% 0% 100% 8% 0% 9% For data processing6% 13%0%38% 0% 45% For data sharing0% 0% 0% 4% 0% 9% Complementary echnologies Partner network Degree of openness Area of application Value purpose Value-creating mechanism Business model Platform Technology Business structure Proximity Targeted matchmaking Circularity principles Arhetypes Number of cases for each cluster Scope Value object Metadimensions Dimensions Characteristic Electronic Markets (2025) 35:60 60 Page 16 of 25 AI capabilities. While platforms, such as “Cloud Cycle,” focus on material logistics, others such as “Katalx” focus on advanced supply chain analytics. By enabling take-back, these platforms support repair, refurbishment, remanufacturing, reuse, or repurposing, depending on the nature and value of the tangibles. Six platforms in this archetype are hybrids, combining innovations with transaction mechanisms. Rather than establishing full circular ecosystems, most platforms focus on transforming supply chains. For instance, “Block Materials” utilizes blockchain to make information about building materials immutable to promote material reuse, mitigating the problem of lemon markets and reducing the problems of information asymmetries and uncertain residual value. Outliers as distinctive platforms: Although outliers reduce the robustness of the derived archetypes, they represent valuable niches addressed by CE platforms that deserve attention from a business perspective. The platform “Capture,” for instance, supports the dissemination of knowledge and the integration of the latest research results on plastics, water, and CO2 into business processes, while the “Design for Circularity” platform promotes circular design and knowledge about product design for circular ecosystems. “Deply” and “lablaco” are platforms for managing DPPs throughout the product life cycle. “Investment Ready” connects investors and founders with ideas for CBMs, building a foundation for an entrepreneurship ecosystem focusing on CE. Although these platforms were excluded during the clustering procedure, they can be seen as pioneers of innovative CE platform archetypes in the future. Evaluation Participants in the evaluation confirmed the usefulness of the taxonomy and perceived it as relevant. Accordingly, the relevance was rated 4.19/5. Representatives of CE platform providers helped us to understand that the taxonomy can be used to guide further platform development, in addition to providing configurational guidance during platform launch: “It is useful to use it in developing a platform or in rethinking it. It’s like time to time you stop and you say okay what are we doing in which direction. It’s not something you use daily.” We also learned that it can be particularly useful in discussions with investors and shareholders to justify decisions on further development goals. Industrial company representatives, who are more likely to take on the role of platform user, found the taxonomy effective and efficient for identifying the best-matching platform offerings on the market for their CE plans and for initiating requests for proposals or tenders. A representative from the industry said: “…It simply helps me to achieve transparency in strategic decisions and to justify them. That's often the question that comes from the top down—why and how. And then, of course, I can then get several arguments … or think through (the platform selection) and then use it (taxonomy) as a strategic decision-making tool.” Similarly, researchers noted that the taxonomy enables a transparent, criteria-based justification for selecting a platform to share product data with partners in an ongoing research project. State agency representatives stated that the taxonomy would improve their understanding of the CE platform business models of companies applying for funding, thereby enabling them to make better funding decisions — especially given that many business models are complex and not all companies claiming to develop a platform actually do so. Participants also perceived the taxonomy as very novel, rating it 4.69/5 and admitting that they do not yet know a taxonomy focusing on CE platforms. One researcher stated: “I was familiar with the principles of the circular economy and the platform categories, but not the way you have brought them together.” Most practitioners familiar with the CE and digital platforms found the choice of a morphological box and the used terminology quite easy to understand, resulting in an accessibility rating of 4.34/5. One practitioner stated: “The criteria are self-explanatory for me. So if you read them and are a bit familiar with the subject matter, you understand what it’s all about.” However, practitioners without prior knowledge noted that a glossary was needed to clarify individual characteristics, such as the differences between innovation and transaction platforms, as well as the associated transaction mechanisms and the observed nature of innovation through enterprise business processes. One practitioner from the industry explained: “You just need someone to explain the taxonomy or a corresponding paper. Because otherwise you just have a bunch of categories or characteristics and don’t know what’s behind them.” Despite this suggestion, we have retained the original terminology as it was judged to be well-chosen by the participating researchers. The applicability was rated 3.94/5, indicating that the participants found the taxonomy quite applicable. The use of a morphological box to draw signal paths and assess market-ready platforms based on their alignment with the desired path was commended. One representative of an industrial company said: “I’m a fan of desk research, and of course, there are now tools like the Co-Pilot, but as an engineer, I always think in these morphological boxes. And I always try to give the whole thing a structure. And when I put a signal path in the (morphological) box and succeed in hitting it with a platform assessment — then I can say that’s the platform that I take a closer look at.” To further enhance the applicability, practitioners demanded a prescriptive extension, suggesting to implement the taxonomy as a digital assessment tool with a linked database of archetypes and interactive tooltips for each path to better understand the consequences of a desired CE platform configuration. Electronic Markets (2025) 35:60 Page 17 of 25 60 The experts did not identify any gaps or aspects not covered by the dimensions of the taxonomy presented, indicating its conceptual completeness and validity. Showing the archetypes has further strengthened the practitioners’ confidence in the application of the taxonomy. One representative from the industry stated: “It would definitely make my decision easier. That’s why I would always rely on something like that and trust it. You still have to make the decision yourself. But to say, with what logic do I now approach the decision? I would definitely say that this is a good basis.” One participant commented that it would be beneficial to consider and bundle the CE aspects with carbon accounting in more detail, although reporting as an application area is included. Given the differences in the carbon accounting support enabled by platforms, we concluded that a stand-alone taxonomy could be dedicated to carbon accounting and decided not to extend our taxonomy. Multiple participants also noted that the taxonomy’s functional logic is only a starting point, offering initial guidance. It can be combined with additional tools, such as a weighted scoring model for value analysis during platform selection and tendering: “When it comes to evaluation, some kind of weighting of the dimensions would be helpful.” A platform provider confirmed this, explaining that decisions regarding further platform development are investment decisions — where the taxonomy would clarify what is intended but not why and what the consequences may be. This, however, fits with the descriptive aim of the taxonomy. In addition, consultants commended the taxonomy for its utility in platform ideation workshops to extend traditional business models with digital and circular options, and it was considered applicable for technical due diligence in platform mergers and acquisitions. This strong confidence in the taxonomy’s application is reflected in its high rating of 4.44/5. Further illustrative statements can be found in Appendix 5. Discussion With the growing awareness of sustainable value creation, there is an increasing need to use digital technologies to leverage CE (Chauhan etal., 2022; Winkelmann etal., 2024). With its long tradition in advancing the knowledge on digital platforms (Hein etal., 2020; Reuver etal., 2018), IS research is predestined to contribute to the knowledge base of how digital platforms can help leverage the CE (Heinz etal., 2024; Schoormann etal., 2025; Zeiss etal., 2021). Against this background, the study examines the configurational aspects of CE platforms by proposing a taxonomy to answer RQ1. Furthermore, the study explores their distinctive archetypes to answer RQ2 and support the circular transformation of organizations. Both results respond to recent calls for empirical research on CE realization through the platform lens (Alt, 2020; Martín-Peña etal., 2024; Suchek etal., 2021). Considering the ability of digital platforms to bridge organizations and close data gaps between them (Blackburn etal., 2023; Cusumano etal., 2019), our study advances the knowledge of the realization of smart CE approaches with digital platforms as means for creating, processing, and sharing data between organizations and decision-makers (Kristoffersen etal., 2020). In this context, the archetypes show how digital platforms can contribute to CE, and the taxonomy summarizes different features of CE platforms for a flexible configuration. Both contributions provide empirical evidence on the existing CE platforms and their variety. Considering the challenges that organizations face in the circular transformation of operations, the establishment of circular business models, and the difficulties of establishing a digital infrastructure for this (van Loon etal., 2022), our study shows that digital platforms play a decisive role in the implementation and promotion of a CE by providing the technological infrastructure for the networking of stakeholders for circular value streams. In particular, our study shows the versatility of transaction platforms that facilitate participation in the CE via user-friendly interfaces, enabling different types of transactions such as selling, lending, sharing, or bartering. Our study shows that platforms can help collect and analyze different types of data, offering valuable datadriven insights into the composition of waste streams, supply chains, or customer behavior, going beyond the matchmaking mechanisms. Moreover, platforms can significantly help with inventory management, product tracking, reverse logistics, and sustainability reporting. As platform capabilities evolve, they will enable further benefits, as indicated by the distinctive niche platforms (i.e., declared as outliers). These niche platforms are of particular interest for platform-based CE innovation and research on the uptake of new circular business models. Research implications An uptake of CE requires digitalization and remains a largely unexplored problem space in IS research (Reich etal., 2025; Zeiss etal., 2021). Since archetypes have a generalized nature and the taxonomy offers the possibility to illustrate options, our paper provides a descriptive theory on business model innovation through CE platforms (Antikainen & Valkokari, 2016; Geissdoerfer etal., 2023; Gregor, 2006). The taxonomy aligns with the pattern of identifying characteristics of real-world instances (Schoormann etal., 2023) to build an empirical understanding of CE platforms’ main features, thereby complementing existing taxonomical research in the context of CE (Lüdeke‐ Freund etal., 2019). From the perspective of CBM research (Lüdeke‐Freund etal., 2019), the theoretical contribution Electronic Markets (2025) 35:60 60 Page 18 of 25 of our study grounds in the created overview of the already launched CE platform archetypes. Complementing existing overviews of sharing platforms (Schwanholz &Leipold, 2020) and CE start-ups (Henry etal., 2020), as well as single cases of CE platforms (Balder etal., 2023; Budde etal., 2024; Fozza etal., 2023), our study provides a structural analysis of CE platforms through the definition of six archetypes. Furthermore, the taxonomy supports a consolidation of the vibrant landscape of CE platforms. As mentioned by Lüdeke‐Freund etal. (2019), taxonomies offer researchers analytical clarity about the possible and existing CE platform configurations, giving researchers a foundation for empirical studies of certain CE platform archetypes. For example, since we have identified platforms that reward sustainable behavior, researchers can start researching the design patterns of IS with a long-term effect of keeping users engaged with sustainable behavior (Ixmeier etal., 2024), building upon the identified archetypes and their characteristics. Previous platform research has recognized that research results can contradict each other depending on the application domain of the platform (Reuver etal., 2018) and that there are many examples of the failure of digital platforms. Considering contextual specifics is therefore essential when establishing digital platforms. Hence, from the platform research perspective, our study offers new insights into the nascent instantiation of digital platforms for CE (Heinz etal., 2024; Körppen etal., 2024). With the archetypes and the related examples of CE platforms, our study showcases multiple pathways to how digital platforms can be used to create capabilities to manage circularity, serving multiple customers of just one physical object instead of just facilitating sales. Interestingly, in the context of CE, transaction platforms can promote reuse not only through sales but also through leasing and sharing, whereby sales can also be supported in an innovative way through tracking and lifecycle capabilities, which have so far received little attention in the platform context. Overall, our study shows that digital platforms can intervene in multiple ways throughout the entire product lifecycle to enable circular value creation. Using the identified archetypes and a descriptive artifact to classify CE platforms and demarcate archetypes, we provide new insights in response to the recent IS calls on how digital platforms can be designed and leveraged for the CE (Zeiss etal., 2021). The ability of digital platforms to form ecosystems and act as meta-organizations (Blackburn etal., 2023) can also help organizations overcome the challenges of CE transformation (Takacs etal., 2022) and was present in all CE platform archetypes. In particular, the realization that there are platforms that enable other companies to own platforms also ties in with the realization that platforms help companies to build a platform from existing business and build capabilities for twin transformation (Budde etal., 2024; Christmann etal., 2024). Our data also indicates that some platform providers want to reduce information asymmetries and reduce the lemon market problem by having the platform providers check or repair the traded products themselves or, in some cases, even use immutable technologies such as blockchain. Hence, our results also contribute to the discussion around the role of centralized and decentralized approaches as our dataset suggests that there are central platforms for different capabilities (Reuver etal., 2024). Furthermore, the identified archetypes of different CE platforms on the market suggest that platforms cause the ontological reversal in the realm of digital sustainability (Baskerville etal., 2020; Kotlarsky etal., 2023). The platforms embed rules, facilitate informed decisionmaking, create new processes, and orchestrate collaboration at scale. All these functions help to close loops and realize a CE. By integrating ontological reversal into the CE discourse, it becomes evident that digital platforms are central to the creation of circular ecosystems, which helps to understand how digital sustainability is supported by platforms (Schoormann etal., 2025). In addition, our study offers empirical data for the discourse on platform distinctiveness (Durand & Haans, 2022) as the taxonomy, archetypes, and cluster distribution help identify platforms already widely offered and analyze gaps in the platform-based landscape. This helps researchers conceptualize new types of CBM based on digital platforms and practitioners to ensure competitive advantage through a sufficiently large distinctiveness to other already launched platforms. CE platform providers can use the taxonomy to assess and reflect on their platforms, and the archetypes can also help with platform redesign. In the future, the symbiosis between digital platforms and DPPs can be explored to unleash circularity principles (Langley etal., 2023) and the alignment of circular ecosystems (Jensen etal., 2024) through better information sharing. This is particularly important because DPPs are becoming mandatory for new products in various industries and can improve data sharing between stakeholders in addition to platforms. Similarly, based on our study, design options for platforms for managing DPPs can be researched, as this could represent a new archetype in the future. We are also confident that interested researchers and practitioners can use the taxonomy and the empirical dataset to create a database for the pattern-based development of CE platforms (Drewel etal., 2021). Pattern-based platform design is considered meaningful, especially when assisting practitioners who lack profound experience with the platform business model elements and the aligned building blocks of the platform architecture. Electronic Markets (2025) 35:60 Page 19 of 25 60 Practical implications Furthermore, our results offer valuable practical insights. The taxonomy and the archetypes can strengthen the dissemination of CE platforms in practice to move from localized industrial symbiosis (Chertow, 2007) to more flexible, geographically dispersed, and digitally connected loops. As empirically confirmed, the taxonomy is suitable for describing and characterizing CE platforms to help organizations intending to become CE platform providers with decision-making when navigating the vast solution space of CE platforms (Möller etal., 2022). In particular, the taxonomy can provide analytical support and systematize alternative scenarios by comparing possible platform configurations when launching a platform. For platform providers, the taxonomy can also be used to characterize existing platforms and, following the concept of distinctiveness (Durand &Haans, 2022), to find market entry points and distinguish a platform-based CBM from the already existing ones. Thus, the taxonomy and the archetypes assist decision-makers in practice in the search for market niches. Especially since platforms are constantly evolving digital artifacts (Kallinikos etal., 2013), our results can support decision-makers in the further development of a platform after its introduction. The taxonomy also aligns well with the work of Parida etal. (2019) to complement orchestration mechanisms for circular ecosystems with an appropriate platform configuration. In addition, the archetypes can be used by platform launching organizations to communicate their own CE platform strategy to other stakeholders, such as funding agencies or investors. For industrial organizations that want to make their operations more sustainable through CE but do not want to become CE platform providers, our taxonomy, combined with the archetypes, can help them understand the complex market for CE platforms and find platforms that optimally fit their needs. Similarly, state agencies can use both results to better understand start-ups seeking platform funding and compare them more effectively with previously funded ventures. For decision-makers from the practice, a customizable template that flexibly supports the aforementioned scenarios can be downloaded from the following URL: http:// bit. ly/ 415XM DV. Conclusions andoutlook Limitations Given the qualitative-interpretative nature of our study, potential limitations need to be discussed. We took an outsider’s perspective and our analysis relies, to a significant extent, on publicly available platform provider’s self-descriptions. To mitigate this threat to validity, we triangulated data from Crunchbase and LinkedIn descriptions, platform websites, and external reports where available to code the characteristics. Despite the systematic approach and triangulation, the reliance on company self-descriptions available on LinkedIn, Crunchbase, and official websites may introduce self-reporting bias. Thus, the classification of CE platforms depended on publicly available information, which may not accurately reflect the internal functionality or actual platform status. The real-world objects were independently examined and coded by the research team members. Each researcher also independently wrote the descriptions of the archetypes, and they were compared and discussed. To enhance the validity of both the taxonomy and the archetype, repeated discussions were held until a consensus was reached on the derivation of characteristics, their classification into dimensions, and the description of archetypes. Furthermore, the taxonomy was empirically evaluated by potential users and subsequently refined to improve its usefulness and applicability. Initial taxonomy application with students and a subsequent external evaluation with practitioners and academics have shown that the taxonomy is accessible, consistent, complete, and has external validity. In particular, confidence in the validity of the taxonomy has been confirmed as the taxonomy integrates the 9R framework (Potting etal., 2017) and captures different areas that can be positively impacted by the use of CE platforms. While the taxonomy provides a configurational tool, it does not specify which configurations should be adopted under specific conditions, thus limiting the contextual knowledge derived from our study. By identifying archetypes that can be understood as established configurations, we have made a first attempt to mitigate this limitation. Consequently, future research is needed to advance our knowledge about how to gain a competitive advantage through CE platforms (Konietzko etal., 2019; Suchek etal., 2021). It remains an open question whether CE platforms should mimic the archetypes or differ from them for long-term success (Durand &Haans, 2022). Taking the outsider’s perspective, we did not have access to the financial data of the platforms in the sample. Thus, we cannot assess the economic performance of specific platform archetypes under certain conditions or how economically sustainable certain archetypes are. Our dataset reveals that most CE platforms are recently launched micro or small enterprises. Some of the examined platforms ceased operations during our analysis, indicating a short lifespan of some CE platforms. This fast-moving evolution of the CE platform landscape poses a limitation for any taxonomy, which effectively captures only a snapshot in time (Nickerson etal., 2013). It is reasonable to assume that that future developments — especially evolving sustainability legislations in the Electronic Markets (2025) 35:60 60 Page 20 of 25 European Union — will likely introduce new characteristics and dimensions, affecting purposeful configuration of platforms (Chaudhuri etal., 2024; Operato etal., 2025). Nevertheless, we chose a morphological box to represent the taxonomy because it is flexible and can be updated in response to the changing regulatory requirements and new CE platform configurations that may not be fully characterized by the taxonomy. We also cannot guarantee that our sample is exhaustive despite using two sources and identifying numerous duplicates. Some CE platforms may not be listed on LinkedIn or Crunchbase or may not use the term “platform” in their self-description. In addition, our study is subject to language bias, as we were only able to analyze platforms with English websites. Our sample contains few platforms from Asia or the Global South. Given the platform potential for development (Bonina etal., 2021), further research focusing on continents other than Europe and North America could help to discover new value-adding activities (Hiller etal., 2022) that can be digitally supported by platforms and reveal new platform configurations. We, therefore, encourage researchers to explore the intersection of CE and digital platforms in other regions to improve our understanding of the impact of platforms in supporting the CE. Another limitation relates to clustering. While the archetypes are generally reliable, with the large number of dimensions, it cannot be ruled out that the cluster assignment is not optimal. We have tried to mitigate this limitation by combining different clustering methods and performing a qualitative plausibility check for the clusters. Future research Following the limitations, we see several opportunities for future research. The landscape of CE platforms is still nascent, and our analysis revealed dynamism. Considering this, a better understanding of which archetypes and configuration patterns sustain in the long term represents a promising avenue for further research. Future research should revisit the CE platform archetypes and check whether novel archetypes that were not covered by our sample emerge. To address the limitation of relying on publicly available self-descriptions, we encourage future studies to focus on in-depth analysis of specific platform clusters or individual cases through interviews with key informants and qualitative case studies. Using our taxonomy as a starting point, such research could explore the processes and value creation mechanisms of CE platforms in more detail. Modeling techniques such as e3-value could be applied to systematically capture platform configurations and value exchange from the interview data (Hiller etal., 2022; Schultze & Avital, 2011). This would allow for a more detailed understanding of how CE platform archetypes manifest themselves, support their validation and refinement, and potentially reveal new patterns, as well as consequences that cannot be identified through desk research (Schoormann etal., 2025). Due to the outsider’s perspective and lack of insight into the financial data of the CE platforms, there are opportunities for research on CBMs with a particular focus on the platforms. The taxonomy and the archetypes provide a starting point for adopting an insider perspective, allowing case study research and direct engagement with key informants to identify success factors and deepen our understanding of platform archetypes that effectively foster circularity. Analyzing business processes or microfoundations would also advance our knowledge of how circularity principles are leveraged by digital platforms (Bingham etal., 2019; Khan etal., 2020). In this context, future research should also investigate the value capture mechanisms of CE platforms, given the complexity of capturing value from platforms as indicated by previous work (Petrik etal., 2024; Schreieck etal., 2017). Following the evaluation feedback, an extension of the taxonomy to create an integrated view of platform-based support for CE and carbon accounting is another purposeful opportunity for future research. Given that platforms promote power and information asymmetries (Pauli etal., 2021; Zhu, 2019) and sometimes fail to create vibrant ecosystems, particularly observed in B2B domains with high criticality of data (Jesus etal., 2024; Pauli etal., 2021). The reluctance to share critical data further hampers the uptake of CE (Hoppe etal., 2024). Future research can use the taxonomy to observe dialectical tensions (Recker etal., 2024), investigate the risks of platform failure in the context of smart CE, and formulate strategies to overcome them (Kristoffersen etal., 2020). The developed taxonomy provides a foundation for studying success and failure factors in shifting CE on platforms to close data gaps and create appropriate information flows to scale CE (Jäger-Roschko &Petersen, 2022; Reich etal., 2025; Rich, 1992). Following evaluation feedback regarding the taxonomy’s descriptive nature, we recognized the need for a comprehensive prescriptive method to support decision-making for implementing CE via platforms (March & Smith, 1995). Consequently, developing a software-supported method for self-assessment, which provides grounded recommendations, explains the impact of configurational decisions and implementation options, and integrates a data model for existing CE platform configurations, representing a promising design-oriented research opportunity. In this respect, domain-specific adaptations of the taxonomy are also possible. Electronic Markets (2025) 35:60 Page 21 of 25 60 Concluding remarks The study presents a morphological analysis of existing literature and 129 real-world cases of CE platforms. The resulting taxonomy comprises 12 dimensions with 56 characteristics and addresses RQ1 by providing a comprehensive overview of the solution space of CE platforms. As the external evaluation has shown, researchers and decisionmakers can use this taxonomy as a configurational model for analytical clarity within the CE platform solution space. Furthermore, the taxonomy can assist in the configuration of one’s own CE platform in the search for suitable platforms to join. Additionally, we applied the taxonomy to realworld cases to address RQ2, deriving six archetypes that provide an overview of existing CE platforms, reveal the CE mechanisms they facilitate, and identify gaps to guide future research. Overall, the study advances our understanding of CE platforms and provides a foundation for in-depth exploration of the existing CE platform archetypes, their business models, and the opportunities and challenges associated with facilitating CE through platforms. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1252502500792-w. Funding Open Access funding enabled and organized by Projekt DEAL. This work was supported by the Ministry of Science, Research and the Arts of the State of Baden-Wurttemberg within the sustainability support of the projects of the Exzellenzinitiative II. Ministerium für Wissenschaft,Forschung und Kunst BadenWürttemberg,Exzellenzinitiative II,Dimitri Petrik Declarations Competing interests The authors declare no competing interests. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References Aarikka-Stenroos,L., Ritala,P., & D. W. Thomas,L. (2021). Circular economy ecosystems: a typology, definitions, and implications. 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