The role and value of data in circular business models: A systematic literature review

Luoma, Päivi; Toppinen, Anne; Penttinen, Esko Article The role and value of data in circular business models: A systematic literature review Journal of Business Models (JOBM) Provided in Cooperation with: Aalborg University, Aalborg Suggested Citation: Luoma, Päivi; Toppinen, Anne; Penttinen, Esko (2021) : The role and value of data in circular business models: A systematic literature review, Journal of Business Models (JOBM), ISSN 2246-2465, Aalborg University Open Publishing, Aalborg, Vol. 9, Iss. 2, pp. 44-71, https://doi.org/10.5278/jbm.v9i2.3448 This Version is available at: https://hdl.handle.net/10419/318958 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. https://creativecommons.org/licenses/by-nc-nd/3.0/ Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 44 The Role and Value of Data in Realising Circular Business Models – a Systematic Literature Review Päivi Luoma1, Anne Toppinen2, and Esko Penttinen3 Abstract Purpose: A systematic review of the literature on circular business models was performed, for synthesis of what it reveals about the role and value of data in those models. The increasing quantity of supply-chain and life-cycle data available has potential to be a significant driver of circular business models. The paper describes the current state of knowledge and identifies avenues for further research related to use of various forms of data in the models. Design: A systematic review of literature on the use of data in circular business models was carried out, to inform understanding of the state of knowledge and provide a firm foundation for further research. Findings: The literature reviewed points to fragmented understanding of the role and value of data in circular business models. Nonetheless, scholars and practitioners commonly see data as a driver and enabler of circular economy. The article identifies two distinct approaches to value for data as presented in the corpus and discusses what types of data seem to be valuable in a circular business-model context. Among the further research opportunities are work on data as a source of business-model innovation and on collaboration in capturing the value of data in circular business models. Value: The study provides new insight on the nexus of circular business models and data, and it represents one of the first comprehensive reviews addressing data’s value in a networked circular-economy context. Please cite this paper as: Luoma, P., Toppinen, A., and Penttinen, E. (2021), The Role and Value of Data in Realising Circular Business Models – a Systematic Literature Review, Journal of Business Models, Vol. 9, No. 2, pp. 44-71 Keywords: business models, circular economy, value of data, data-driven, sustainability 1 Faculty of Agriculture and Forestry, Dept. of Forest Sciences, University of Helsinki, Finland, [email protected] 2 Faculty of Agriculture and Forestry, Dept. of Forest Sciences, Helsinki Institute of Sustainability Science, University of Helsinki, Finland 3 School of Business, Department of Information and Service Economy, Aalto University, Finland Acknowledgements: Luoma’s part of the work has been funded by a grant from Metsämiesten Säätiö Foundation for her doctoral dissertation. DOI: https://doi.org/10.5278/jbm.v9i2.3448 Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 45 Introduction Scarcity of natural resources is among the most significant factors defining the landscape where today’s companies do business and create value. Population growth and climate change create rising pressure related to the use of natural resources (IPCC, 2019) and call for intelligent decisions for efficient allocation, use, and conservation of valuable resources. For companies, resource scarcity is not only a source of risk and concern (e.g., Gaustad et al., 2018) but, through circular business models, also an opportunity to pursue new revenue streams and market segments, along with enhanced customer experience (e.g., Lüdeke-Freund et al., 2019; Stahel, 2016; Tukker, 2015). In the context of circular economy, new innovative business models are needed for closing resource loops, slowing the cycle, and narrowing the loops, by such means as extended customer experience, longlife goods, product-life extension, recycling, reuse of materials, and resource-efficiency (e.g., Bocken et al., 2016). Circular business models are aimed at resolving environmental sustainability challenges by turning linear resource flows into loops (Stahel, 1997). The goal is to get more value from the resources and simultaneously improve the sustainability of production and consumption. At the same time, the burgeoning availability of data is transforming how businesses operate, and data’s utility in generating knowledge and insight to improve decision-making is seen as a potentially powerful source of creation of both economic and social value (Grover et al., 2018). More efficient use of data can serve as a significant driver and enabler of circular economy (Frishammar and Parida, 2019; Gupta et al., 2018; Stahel, 2016), and interesting examples of datadriven circular business models, such as performance contracts, sharing models, and digital marketplaces for resources and waste streams, are already emerging (Ellen MacArthur Foundation, 2019; World Economic Forum, 2016). Circular economy requires better understanding of (often complex) flows and loops of resources, their value, and environmental impacts in contexts of complex value chains and networks. At the same time, these phenomena extend across borders between technologies, actors, and industries and over the full lifetime of products and services. Particularly in light of this complexity, data might be of help in considering how to realise circular economy. Recent years have witnessed growing interest in sustainable business models and related innovations (e.g., Dentchev et al., 2018; Wirtz et al., 2016), with circular business models being no exception (e.g., Brown, 2019; Lüdeke-Freund et al., 2019; Manninen et al., 2018; Pieroni et al., 2019). However, previous studies have not specifically considered the role and value that the wealth of data can have at the core of circular business models and related decision-making. Research on the intersection of data and circular business models has remained scarce (for exceptions, see Bressanelli et al., 2018; Tseng et al., 2018), and more insight into this nexus is needed, for understanding of how data can support creation of sustainable business. Accordingly, we identified two research questions, formulated thus: 1) In what ways does literature on circular business models inform about the role and value of data in this set of models? 2) Through a review, can one identify possible paths for further research related to the use of various forms of data in circular business models? The presentation of the systematic review begins in Section 2, laying out the conceptual background with regard to circular business models and the value of data therein. Then, Section 3 describes the research design and Section 4 presents the findings from the literature review. We conclude the paper by offering final thoughts and identifying further research opportunities. Conceptual Background Circular Business Models The aim in employing circular business models is to address environmental sustainability challenges by transforming linear resource flows into loops, giving them circular form (Bocken et al., 2016; Stahel, 2016; Tukker, 2015). The goal is to obtain greater value from the resource use and increase the sustainability of production and consumption. In circular business models, value is created in three ways: closing resource loops through reuse and recycling of materials, slowing the Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 46 loops by designing long-life goods and extending products’ service life, and narrowing the resource flows via resource-efficiency (Bocken et al., 2016). To move from linear business models to circular ones, companies must redesign their value-creation logic, covering value propositions, the value-creation infrastructure, and the value-capture models (Hofmann, 2019). For this paper, a business model is defined as describing the logic or design of how a business creates value and delivers it to the customers while also outlining the architecture of the revenues, costs, and profits associated with the company delivering that value (Teece, 2010). It is seen to include the following components: the value offered to customers (the value proposition), how the value is created and delivered to customers (value’s creation and delivery), and how profit is generated (value capture) (Bocken et al., 2014; Richardson, 2008; Teece, 2010). However, the concept of the business model is versatile, and it is defined and conceptualised in numerous ways (e.g., Al-Debei and Avison, 2010; Lüdeke-Freund et al., 2019; Zott et al., 2011). At base, such a model provides an abstract understanding of the relevant organisation’s business logic in a somewhat descriptive manner (Al-Debei and Avison, 2010). In practice, business models are systems that exhibit complex interdependencies among these elements (Massa et al., 2018). They are often industry-specific and depend also on the company context and business maturity in how they are designed to yield competitive advantage for the organisation in question. In this paper, a circular business model is defined as a business model that helps companies to create value by means of using resources in multiple cycles, thus reducing both waste and consumption (Lüdeke-Freund et al., 2019). In the context of circular business models, several approaches have been taken to apprehend the core of the model, with reasoning based on various taxonomies of the value-creation rationale (Ellen MacArthur Foundation, 2015), strategies (Bocken et al., 2016), and patterns (Lüdeke-Freund et al., 2019) represented by the business models. For this paper, the classification of circular business patterns developed by LüdekeFreund et al. (2019) was used for categorisation of the literature in the circular business model context. In this classification, the following six patterns are considered: repair and maintenance, reuse and redistribution, refurbishment and remanufacturing, recycling, cascading and repurposing, and organic feedstock. The value expected to arise via circular business models encompasses not just economic value and direct value created for the customer (through means such as savings on production costs and materials and greater ‘value-in-use’) but also societal value (Lüdeke-Freund et al., 2019; Stahel, 2016). As a concept, circular economy has strong connections with sustainability, and this concept is evolving, manifesting various definitions, boundaries, principles, and associated practices as it does so (Merli et al., 2018). That said, from a sustainability point of view, the concept has, in general, been claimed to be more environmentally driven, with only a tenuous link to social sustainability (e.g., D’Amato et al., 2017). Likewise, the value is characterised as created primarily on foundations of an environmental value proposition (Manninen et al., 2018), and some have argued that circular business models might not always be able to capture the full scale of sustainability (Geissdoerfer et al., 2018). In these models, the value is often co-created over the entire supply chain: customers, suppliers, manufacturers, retailers, etc. (Manninen et al., 2018; Urbinati et al., 2017). Although not unambiguously defined or conceptualised, circular business models facilitate reflection on how companies can reach sustainability objectives in a way that makes good business sense. Hence, the insights from the review presented here are clearly relevant not only for academia but also for companies striving for circular-economy objectives. Business models and innovation in them have been subject to increasing research efforts in recent years (e.g., Foss and Saebi, 2017; Massa et al., 2018; Nielsen et al., 2018), and, their conceptual fuzziness notwithstanding, they have turned out to be a helpful tool for understanding how companies do business and create value. Paying attention to business models can aid in rethinking and redesigning how companies reach their goals, understanding new types of innovation, and drawing attention to creation of social and environmental value alongside the economic (Massa et al., 2018). There is a growing body of research on sustainable business models and related innovations (e.g., Dentchev et al., 2018; Wirtz et al., 2016) – of which examination of Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 47 circular business models forms a key part (e.g., Brown, 2019; Lüdeke-Freund et al., 2019; Manninen et al., 2018; Pieroni et al., 2019) – and on what kinds of inherent uncertainties these entail (Linder and Williander, 2017). While a few authors have cited data as a potential driver and enabler of circular economy and related business models (e.g., Frishammar and Parida, 2019; Gupta et al., 2018), the role and value of data in circular business models remains largely uncharted territory. Understanding the Value of Data Growth in the volume of data is changing how businesses operate, and the power of data in generating insight to support better decision-making is seen as a potentially vast source of customer, economic, and social value (Grover et al., 2018), where one can define data as objective facts about events and observations about the state of the world (Davenport and Prusak, 1998) or as symbols that represent properties of objects, events, and their environments (Ackoff, 1989). Said data may be either structured or unstructured, although the application of analytics to extract value from data usually assumes availability of sufficiently structured data – normalised records in a database with a rigid and regular structure (Abiteboul, 1997; McCallum, 2005). However, vast volumes of data are being generated in unstructured form, such as humangenerated e-mail messages and their attachment files, photos, videos, voice recordings, and social-media content. This limits the direct applicability of traditional analytics. Through data’s integration, discovery, and exploitation (e.g., Miller and Mork, 2013), one can turn data into valuable information and knowledge. That insight holds promise for improving decisions and yielding such results as better utilisation of assets, greater operation efficiency, cost savings, and extended customer experience (e.g., Chen et al., 2015; Günther et al., 2017). Through data’s potential contribution to uncovering hidden patterns and heretofore unknown correlations (Chen et al., 2015), this resource could aid in increasing understanding of circular phenomena and in realising circular economy. In this paper, we focus on which circular business models and strategies are seen as specifically benefiting from data and how the data may be conceptualised as a source of value under circular business models. More efficient use of data may help to turn the visions behind these models into reality by refining the valuecreation logic, including decisions on how value is created, offered, and delivered to customers and how profit is generated. Those classes of business models that rely on data may be termed data-driven business models (Hartmann, 2016). However, data might not always represent the world accurately, as it is easier to capture data from readily quantifiable phenomena (Jones, 2018). Structured and quantifiable data might be more readily available, as well as more attractive to use, than unstructured and non-quantifiable data. Data that could yield understanding of often complex circular phenomena might not be available, at least in relevant form, and a less accurate view of the phenomena might be produced. Such a picture may have much less value in decision-making. In addition, value may be lost through delays in extracting data, transforming the data into usable information, and deciding how to act on the information (Pigni, 2016). For example, either the absence of data indicating a need for maintenance or non-response to such data can lead to equipment breakdowns, production downtime, and other waste. Also, some use of data can have adverse impacts, which may run counter to circular-economy objectives. Even if handled responsibly and well, exploitation of data often requires extensive investments in management, technology, and other capabilities (Akter et al., 2016). General rationales related to data-driven value creation may be applicable in circular business models. More efficient use of data can add value by affording transparency of information and greater access to it, discovery and experimentation, prediction and optimisation, rapid adaptation and learning, customisation of products and services, and deeper understanding of customers (Chen et al., 2015). Value can be extracted from data streams through initiation of action on the basis of real-time data or via merging of multiple data streams (Pigni, 2016). For example, real-time data on products’ use and performance can prompt initiation of predictive maintenance measures, and demand for Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 48 ride-sharing services can be forecast from considering weather data in combination with details of mobility demands. Data can be accumulated for information services, refined into insights and decision support, aggregated to inform existing services and enable new ones, and utilised for tracking and optimising operations and performance (Pigni, 2016). Better use of data can lead to innovation in product, service, and business models and thereby transform businesses’ operations (Grover et al., 2018; Hartmann, 2016). Reaping the full benefits of data often demands a change in business model, however (Buhl et al., 2013). Prior research offers insight pertaining to data-driven business models and the benefits and value of data in general (e.g., Chen et al., 2015; Grover et al., 2018; Hartmann, 2016). Yet, while some authors have identified data as a potential driver and enabler of circular economy (de Mattos and de Albuquerque, 2018; Frishammar and Parida, 2019; Gupta et al., 2018; Tura et al., 2019), little work has addressed the role and value of data specifically in relation to circular business models (for exceptions, see Bressanelli et al., 2018; Tseng et al., 2018). Nonetheless, further research addressing it is seen as important (Alcayaga et al., 2019; Rajala et al., 2018). This area represents a significant gap in scholarly understanding of data’s potential to support development of circular economy. The Research Design To understand what the existing body of research indicates about the role and value of data in realisation of circular business models, we identified, reviewed, and formed a synthesis of the relevant literature. The literature review represents a method suited to systematic understanding of an existing body of knowledge and to providing a firm foundation for further research (Levy and Ellis, 2006). The search was limited to peer-reviewed scholarly articles found in academic databases (Scopus and EBSCO Business Source Complete) and published in this millennium. For emphasis on the business context, the search used the term ‘circular’ in combination with either ‘business model’ or ‘value creation’, in the title, abstract, key words, or subject (stemming and Boolean operators were used thus: ‘circular’ AND ‘business model*’ OR ‘value creat*’), where ‘data’ was used in any of the text. These search terms had been identified as having appropriate breadth and depth for answering our first research question (Levy and Ellis, 2006; Okoli, 2015). Additional criteria were used to screen the literature: publication language (English) and publication date (1.1.2000–30.8.2019). After removal of duplicates, the total number of articles was 147, and 39 papers from this set were identified as relevant for understanding the role and value of data in circular business models. To be deemed relevant, the content had to speak to the research questions. There were no criteria related to research design or the context of the research. This search was complemented with forward and backward searches because the key words taken as search terms might have a limited ‘lifetime’ and alternative terms may have been used (Levy and Ellis, 2006). The forward and backward search yielded five further articles. Therefore, the final sample consisted of 44 articles. The full text of each article selected was systematically reviewed with regard to the theoretical, conceptual, and empirical contribution to answering research question 1. Relevant material was collected manually and documented systematically in Excel sheets. The perspective of the articles on data and data’s value was assessed and the link to circular business models identified. The type and sources of data dealt with, the nature of the data-driven activities considered, and the benefits and impacts of data identified as expected and/or realised were identified as the main themes in the course of the analysis. This enabled classifying and comparing the content of the articles and systematically synthesising the findings within a conceptual framework. The development of our conceptual framework was based on the results of the literature review and reflects the conceptual background for our work also. Finally, further research opportunities were identified on the basis of the outcomes from the literature review. Figure 1 summarises the research design. Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 49 Results of the Literature Review In the corpus, data and related information technologies, services, and platforms are commonly presented as drivers and enablers of circular economy (e.g., de Mattos and de Albuquerque, 2018; Tura et al., 2019), and lack of data is often cited as a barrier to circular business models (e.g., Saidani et al., 2018; Vermunt et al., 2019). A summary table covering all 44 articles is presented in Annex 1, and Annex 2 lists the context of each piece, its perspective on the relevant data, and the business models and strategies discussed. All the articles matching the criteria used for our review are quite recent, published between 2016 and 2019. This attests to a strong upswing of attention to the subject, with growing interest in understanding the nexus of circular business models and digital technologies. In total, the sources feature 340 articles published on circular business models and value creation during the time span considered, so about 10% of the model-related papers deal with the role of data in one way or another. As a whole, the body of literature reviewed indicates that the state of understanding of the intersection of circular business models and data is highly fragmented. The articles show wide variety in the circular business models addressed. In addition, diverse contexts and industries, among them manufacturing, waste management, and digitalisation, are covered. In some articles, the data or related factors are at the core of the discussion, while they are presented as a minor issue in others. Perspectives on the data were found to vary too, from perceiving the data as input to modelling, through applying life-cycle assessment of information flows in the supply chain, to expressing more general views on unlocking the potential of circular economy. Below, we discuss the ways in which the literature on circular business models informs us about circular business models’ relationship with data (including the associated strategies for exploitation of data) and what specific use data may have in circular business models. In addition, we identify two approaches to value of data that were articulated in the corpus and discuss which sorts of data seem the most valuable in this context. Connecting circular business models to the role and value of data The articles reviewed cover a broad spectrum of circular business models. Table 1 presents examples of this breadth with regard to the potential role and value of data, reflecting the various circular business model patterns introduced by Lüdeke-Freund et al. (2019). Many of the articles show connections with several business-model patterns, not least because roughly half of the papers express a general perspective on circular business models, without considering any specific ones. Many of the models discussed in the literature represent a high-level strategy or approach rather than a ready-to-apply model that could easily be classified as a specific business-model pattern. Several articles cite opportunities in servitization and product–service systems, providing customers with service and performance rather than products (Alcayaga et al., 2019; Bressanelli et al., 2018; Frishammar and Parida, 2019; Khan et al., 2018; Pialot et al., 2017; Spring and Araujo, 2017). While this prominence might A literature search of academic databases: Scopus and EBSCO Business Source Complete Was limited to peer-reviewed articles (in English) published on 1.1.2000-30.8.2019 Search terms using the term ‘circular’ with either ‘business model*’ or ‘value creat*’ and also ‘data’ Resulted in 147 articles Identification of 39 articles as relevant Five additional articles Yielded, in total, 44 articles Systematic review of the articles for their theoretical, conceptual, and empirical contribution, for answering the research question Conclusion and identification of future research opportunities on the basis of the results of the litrature review 1 2 3 4 5 Figure 1: The research design for the literature review Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 50 Circular business model pattern (Lüdeke-Freund et al. , 2019)Potential role and value of data Examples from the literature Repair and maintenance Through repair and maintenance services, companies can extend product life. This necessitates customer-centred services, expertise in the products, ability to solve problems ‘on the fly’, and corresponding forward and reverse logistics. • End-to-end product and service data, real-time and historical, are needed for design support and for provision of long-life products and their repair and maintenance. Both understanding of customers’ behaviour and preferences and the real-time visibility of the usage of a product seem crucial for increasing value for the customer. • There is potential value in data on the use, status, condition, location, and operation of products and services. Both real-time and historical data for the products or services’ full service life and on customers’ behaviour and preferences could be relevant. The data may be either useror product-generated. • Several articles point to opportunities for product– service systems to provide customers with service and performance instead of products. These can extend companies’ ownership of products over the full service life. This potential encourages companies to optimise the design, maintenance, and service-life management. Product–service systems’ creation requires good understanding and evidence of customer behaviour and preferences. Alcayaga et al. (2019) Bressanelli et al. (2018) Pialot et al. (2017) Spring and Araujo (2017) Zhang et al. (2017) Reuse and redistribution Through reuse and redistribution, customers can be given access to used products, possibly with minor enhancement or modifications. This might require evaluating the products’ market value and creating suitable marketplaces. • Product lifetime data is a pre requisite for supporting the design and provision of long-life products that can be reused and redistributed. Digital platforms could serve as marketplaces. Both understanding of customers’ behaviour and preferences and clarity as to the usage of a product seem crucial. • Data on the use, status, condition, location, and operation of products and services may be of value. Both real-time and historical data for their full lifetime and details on customers’ behaviour and preferences may be relevant. The data may be either useror product-generated. Alcayaga et al. (2019) Nascimento et al. (2019) Saidani et al. (2018) Refurbishment and remanufacturing Refurbishing and remanufacturing products – e.g., repairing or replacing components – can extend product life. This requires combining repair and maintenance capacity with reuse and redistribu tion capabilities in various ways, including reverse and forward logistics and applying technical expertise about products and their refurbishment and remanufacturing. • Data for the products’ full lifetime performance can be used to adjust design, operation, and disposal strategies for refurbishment and remanufacturing. Tools for product design can assist with assessing refurbishment and remanufacturing potential but might demand prohibitive quantities of product data. For a summary of potentially valuable data, see ‘Repair and maintenance’ and ‘Reuse and redistribution’, above. Favi et al. (2019) Jensen et al. (2019) Khan et al. (2018) Matsumoto et al. (2016) Table1: The potential role and value of data in circular business models Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 51 Circular business model pattern (Lüdeke-Freund et al. , 2019)Potential role and value of data Examples from the literature Recycling Used materials can be converted into materials of lower value or into higher-quality materials for improved functionality. This requires knowledge of product design, material sciences, and the materials’ physical and chemical properties, along with solid ability to arrange reverse logistics. • Data on material flows and on waste streams are of potential value. In addition, product-design data and data covering the entire service life (from the materials used to end-of-life contamination) are of importance for understanding recyclability and the recovery options. Alcayaga et al. (2019) de Mattos and de Albuquerque (2018) Favi et al. (2019) Mishra et al. (2018) Niero and Olsen (2016) Cascading and repurposing Organisations can apply iterative use of the energy and materials within physical objects, including biological nutrients. Exploiting this pattern demands facilitating material flows and supporting industrial symbiosis networks. • Real-time and historical data on the whole life cycle and details of material flows, environmental impact, performance, etc. are seen as relevant. Valuable data may pertain to condition, operation, status, location, use, and the surrounding system. Information flows in the supply chain appear crucial. • Articles referring to closed-loop systems and industrial symbiosis are classified as articulating a cas cading and repurposing business model, as they often focus on facilitating material flows and sup porting industrial symbiosis net works. However, they may be crucial for any of the models in enabling forward and reverse logistics. Aid et al. (2017) Fisher et al. (2018) Rajala et al. (2018) Tseng et al. (2018) Organic feedstock This pattern involves processing organic residuals, via biomass conversion or anaerobic digestion, for use as production inputs or safe disposal in the biosphere. Corresponding reverse flows, alongside conversion, must be arranged and managed. Material compositions might be complex and the residues contaminated. • The articles reviewed do not specifically address a business model based on organic feedstock. However, some do focus on cloud manufacturing, the sharing of manufacturing capabilities and resources on a cloud platform, which might be valuable in this context. Among the potential benefits are greater process resilience and improved waste reduction, reuse, and recovery. Fisher et al. (2018) Lindström et al. (2018) Table1: The potential role and value of data in circular business models (Continued) be connected with the popularity of these models in writings on circular business models, it also ties in with the role that data could take specifically in such systems. Product–service systems of this nature show links to several business models (repair and maintenance, reuse and redistribution, refurbishment and remanufacturing, and recycling). Exploiting data for product–service systems should encourage companies to optimise their products’ design, maintenance, and lifetime management to support a long service life, easy reuse, and recyclability, alongside other circulareconomy-related objectives. Several articles refer to closed-loop supply chains and product systems (Aid et al., 2017; de Mattos and de Albuquerque, 2018; Mishra et al., 2018; Niero and Olsen, 2016; Rajala et al., 2018; Tseng et al., 2018), bringing in discussion of cross-industry networks needed for reverse logistics, with links to many of the business models. Said articles are classified as representing a cascading and repurposing business model (just as the articles dealing with industrial symbiosis are), although networks of this sort may offer value under any of the models presented. These papers indicate that data could be of particular value with regard to orchestrating Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 58 consistent with what is visible for more general datadriven business models and the related notion that value of data is produced in activities involving other stakeholders in the data ecosystem (Bharadwaj et al., 2013; Thomas and Leiponen, 2016). In circular business models, the impetus for collaboration can arise from such angles as a need to understand complex crosscutting systems, such as global supply chains, along with shared risks, critical leverage points, and technical barriers (Brown, 2019). Company reluctance to share data for reason of privacy, security, or competitiveness concerns is not specific to circular business. Digital trust is necessary between any collaboration partners (Rajala et al., 2018), and data access may be controlled via formal contracts or selling of data alongside explicit specification of data ownership and rights (Günther et al., 2017). Proposition 3: Data Can Yield Insight on How to Co-create Value with Customers The literature shows that several types of circular business model are aimed at changing the role of the customer in the value creation. This may occur, for example, when one provides the customer with service, access, or performance instead of product ownership. As evidenced by the literature, the middle stretch of a product’s life (i.e., the use of products and services) is receiving growing interest. There is awareness also that data on customers’ behaviour and preferences and lifelong data on products and services can be of great value for understanding how to design circular products, services, and business models that all extend service life or how to provide a personalised offering that reduces users’ consumption of resources. However, a gap is visible with regard to research into the customer’s changing role in circular business models and how data can be used in response. Circular business models, when extending a company’s responsibility for the ownership of products over their entire life, increase interaction with customers (Lewandowski, 2016). The interactions are a possible source for additional valuable data, of use for enhancing customers’ experience and customer relations. 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Zott, C., Amit, R. and Massa, L. (2011). The business model: Recent developments and future research, Journal of Management 37(4): 1019–1042. Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 64 Summary of the articles reviewed Years of publication 20 15 10 5 0 2016 2017 2018 2019 Number of articles No relevant articles were published before 2016. The bar for 2019 covers the months from January to August. Journals 0 5 10 15 Journal of Cleaner Production Resources, Conservation and Recycling Sustainability California Management Review Journal of Industrial Ecology Technology Forecasting and Social Change Other journals Number of articles The ‘other journals’ category covers Advanced Engineering Informatics, Annals of Operations Research, Annual Review of Materials Research, Applied Sciences, Autonomous Agents and Multi-Agent Systems, Industrial Marketing Management, International Journal of Information Management, International Journal of Management Cases, International Journal of Precision Engineering and Manufacturing – Green Technology, Journal of Manufacturing Systems, Journal of Manufacturing Technology Management, Management Decision, Marine Policy, Production Planning & Control, and Social Sciences, with one article each. Annex 1: Summary of the literature reviewed Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 65 Summary of the articles reviewed Research methods Case Study 41% Other 9% Interview 7% Modelling 11% Literature Review 31% The other research methods were action research, questionnaire-based surveys, preparation of perspective papers, and multi-method approaches. Authors Title Journal Year Method Context Perspective on the data Business models and strategies discussed Aid, G., Eklund, M., Anderberg, S., & Baas, L. ‘Expanding roles for the Swedish waste management sector in inter-organizational resource management’ Resources, Conservation and Recycling 2017 Interviews Waste management Material flow and environmental impact data Industrial symbiosis Alcayaga, A., Wiener, M., & Hansen, E. ‘Towards a framework of smart-circular systems: An integrative literature review’ Journal of Cleaner Production 2019 Literature review Smart circular systems Products’ lifetime data Product–service systems; maintenance; reuse; remanufacturing; recycling Asif, F., Lieder, M., & Rashid, A. ‘Multi-method simulation based tool to evaluate economic and environmental performance of circular product systems’ Journal of Cleaner Production 2016 Modelling Circular product systems Data as input to a software tool Circular product systems Bressanelli, G., Adrodegari, F., Perona, M., & Saccani, N. ‘Exploring how usage-focused business models enable circular economy through digital technologies’ Sustainability 2018 Case study Circular economy; digital technologies Digital technologies Servitized business models Camacho-Otero, J., Boks, C., & Pettersen, I. ‘Consumption in the circular economy: A literature review’ Sustainability 2018 Literature review Circular economy; consumption Customer data No specific model or strategy Annex 1: Summary of the literature reviewed (Continued) Annex 2: Articles included in the literature review Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 66 Authors Title Journal Year Method Context Perspective on the data Business models and strategies discussed Cezarino, L., Liboni, L., Oliveira Stefanelli, N., Oliveira, B., & Stocco, L. ‘Diving into emerging economies bottleneck: Industry 4.0 and implications for circular economy’ Management Decision 2019 Literature review Circular economy; industry 4.0 Opportunities and limitations connected with industry 4.0 No specific model or strategy de Mattos, C., & de Albuquerque, T. ‘Enabling factors and strategies for the transition toward a circular economy (CE)’ Sustainability 2018 Case study Circular economy Data as a key aspect of circular business models Industrial symbiosis; extending resource value; reverse supply chain Favi, C., Marconi, M., Germani, M., & Mandolini, M. ‘A design for [a] disassembly tool oriented to mechatronic product de-manufacturing and recycling’ Advanced Engineering Informatics 2019 Modelling Disassemblability and recyclability Data as input to a software tool Disassemblability; recyclability Fisher, O., Watson, N., Porcu, L., Bacon, D., Rigley, M., & Gomes, R. L. ‘Cloud manufacturing as a sustainable process manufacturing route’ Journal of Manufacturing Systems 2018 Literature review Cloud manufacturing Cloud manufacturing as a mechanism to share and exploit data Automation, process resilience, waste reduction, reuse, and recovery Frishammar, J., & Parida, V. ‘Circular business model transformation: A roadmap for incumbent firms’ California Management Review 2019 Case study Circular business transformation The potential role of software and data-analytics specialists Product–service systems Garcia-Muiña, F., González-Sánchez, R., Ferrari, A., & Settembre-Blundo, D. ‘The paradigms of Industry 4.0 and circular economy as enabling drivers for the competitiveness of businesses and territories: The case of an Italian ceramic tiles manufacturing company’ Social Sciences 2018 Case study Circular economy; industry 4.0 Industry 4.0 in support of collecting, storing, and processing of data No specific model or strategy Gilbert, P., Wilson, P., Walsh, C., & Hodgson, P. ‘The role of material efficiency to reduce CO2 emissions during ship manufacture: A life cycle approach’ Marine Policy 2017 Modelling Life-cycle analysis Data as input to life-cycle analysis Material-efficiency Gupta, S., Chen, H., Hazen, B., Kaur, S., & Santibañez Gonzalez, E. ‘Circular economy and big data analytics: A stakeholder perspective’ Technology Forecasting and Social Change 2018 Interviews Circular economy; Big Data analytics Big Data as a facilitator of circular economy No specific model or strategy Heyes, G., Sharmina, M., Mendoza, J., Gallego-Schmid, A., & Azapagic, A. ‘Developing and implementing circular economy business models in service-oriented technology companies’ Journal of Cleaner Production 2019 Case study Circular business models Data’s monitoring and analysis as an attractive business model for IT companies Data’s monitoring and analysis Hofmann, F. ‘Circular business models: Business approach as driver or obstructer of sustainability transitions?’ Journal of Cleaner Production 2019 Literature review Circular business models Digital technologies supporting circular business models No specific model or strategy Hopkinson, P., Zils, M., Hawkins, P., & Roper, S. ‘Managing a complex global circular economy business model: Opportunities and challenges’ California Management Review 2018 Case study Circular business models Asset-tracking tools; real-time visibility No specific model or strategy Annex 2: Articles included in the literature review (Continued) Journal of Business Models (2021), Vol. 9, No. 2, pp. 44-71 67 Authors Title Journal Year Method Context Perspective on the data Business models and strategies discussed Jabbour, C., Lopes De Sousa Jabbour, A., Sarkis, J., & Filho, M. ‘Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda’ Technology Forecasting and Social Change 2019 Literature review Circular economy; Big Data Big Data in unlocking the potential of circular economy ‘Regenerate, share, optimize, loop, virtualize, exchange’ Jensen, J., Prendeville, S., Bocken, N., & Peck, D. ‘Creating sustainable value through remanufacturing: Three industry cases’ Journal of Cleaner Production 2019 Case study Sustainable remanufacturing Data in assessment of environmental and economic performance Remanufacturing Khan, M., Mittal, S., West, S., & Wuest, T. ‘Review on upgradability – a product lifetime extension strategy in the context of product service systems’ Journal of Cleaner Production 2018 Literature review Upgrading; extending products’ service life Data to support designing of upgradable services Product–service systems Leising, E., Quist, J., & Bocken, N. ‘Circular Economy in the building sector: Three cases and a collaboration tool’ Journal of Cleaner Production 2018 Case study Circular economy Information flow in the supply chain No specific model or strategy Lieder, M., Asif, F., & Rashid, A. ‘Towards Circular Economy implementation: An agentbased simulation approach for business model changes’ Autonomous Agents and Multi-Agent Systems 2017 Modelling Circular business models Data as input to understanding customers’ behaviour and preferences No specific model or strategy Lindström, J., Hermanson, A., Blomstedt, F., & Kyösti, P. ‘A multi-usable cloud service platform: A case study on improved development pace and efficiency’ Applied Sciences 2018 Case study Cloud service platforms Data collection and analytics in Big Data operations No specific model or strategy Lopes De Sousa Jabbour, A., Jabbour, C., Godinho, F., & Roubaud, D. ‘Industry 4.0 and the circular economy: A proposed research agenda and original roadmap for sustainable operations’ Annals of Operations Research 2018 Literature review Circular economy; industry 4.0 Industry 4.0’s technologies to collect, analyse, and act on data ‘Regenerate, share, optimize, loop, virtualize, exchange’ Lüdeke-Freund, F., Gold, S., & Bocken, N. ‘A review and typology of circular economy business model patterns’ Journal of Industrial Ecology 2019 Literature review Circular business models’ design Identifying equipment databases as an example of auxiliary services No specific model or strategy Manninen, K., Koskela, S., Antikainen, R., Bocken, N., Dahlbo, H., & Aminoff, A. ‘Do circular economy business models capture intended environmental value propositions?’ Journal of Cleaner Production 2018 Case study Circular business models Lack of data for verifying the environmental benefits of circular business models No specific model or strategy Matsumoto, M., Yang, S., Martinsen, K., & Kainuma, Y. ‘Trends and research challenges in remanufacturing’ International Journal of Precision Engineering and Manufacturing – Green Technology 2016 Literature review Remanufacturing Design tools as requiring significant quantities of product data Remanufacturing Merli, R., Preziosi, M., & Acampora, A. ‘How do scholars approach the circular economy? A systematic literature review’ Journal of Cleaner Production 2018 Literature review Circular economy Linking of Big Data and the Internet of Things to circular economy No specific model or strategy Annex 2: Articles included in the literature review (Continued)