Circular Economy, Sustainability, Data-driven Business Models, Data Sources, Circular Business Models
Extended Abstract
Introduction and Background
Through external pressures from policy makers, consumers and internal pressures from leaders, the concept of sustainability has become increasingly important to businesses which is at the same time a challenge to them (Dubey et al., 2019), since they are struggling with meeting their sustainability targets (Gleissdorf et al., 2018). The circular economy (CE), offers a response by progressively decoupling growth from the consumption of finite resources with the goal of reducing waste and pollution, keeping products and materials in use, and regenerating natural systems (EllenMacArthurFoundation, 2019), leading to environmental, social, and economic benefits (Bressanelli et al., 2018). Through experimentations with, and the implementation of circular business models (CBMs) in last years, CBMs gained rapid traction containing the substitution of virgin materials with bioderived alternatives, extending the lifecycle through resale, recycling, and including strategies that focus on reducing greenhouse gas emissions and waste (Charnley et al., 2022). While many definitions of CE exist, Kirchherr et al. (2017) defined CE within their iteratively developed coding 9R framework as “an economic system that replaces the “end-of-life” concept with reducing, alternatively reusing, recycling and recovering materials in production/distribution and consumption processes.” Thus, attracting interest from academia, companies, and policymakers is a promising approach promoting sustainability and competitiveness (Bressanelli et al., 2018).
Furthermore, Big Data have been proposed as one the driving force of the new circular economy (Awan et al., 2021) with technological innovation as a key factor for instance to connect assets that enable predictive maintenance to prolong the asset life; blockchain technology that can create traceability and transparency in supply chains to reduce waste and 3D printing spare parts that make repairing easier (Akinode et al., 2020 & Charnley et al., 2022). It can help in overcoming barriers to CBMs, facilitate the operationalization of circular material, components, and product flows, offer the far-reaching potential for comprehensive networking of “smart” circular economy strategies from analysis to artificial intelligence (AI) supported prediction of data, and can be seen as “glue” between value chain partners (Baumgartner et al., 2022 & Kristoffersen et al., 2020). Data sources are one of the key elements of data-driven solutions, defined by George et al. (2014) as an increasing plurality of sources, including user-generated content, business transactions, operations management and sensor network data. Furthermore, Breitfuss et al. (2020) identified 10 frequently used data sources (weather data, user-generated data, product-generated data and more) for developing data driven use cases that were assigned in their “Data Source Category”. This category is one among 5 categories that are important to generate a data driven use case.
Because CE and DDBM, which support businesses becoming circular are emerging fields, there are few systematic guidance in the literature (Kristoffersen et al., 2020). Rusch et al. (2021) conducted a systematic literature review using collecting examples (n=146) to provide a more comprehensive overview of current and potential examples of DDBM in CE. Another systematic literature review on the role and value of data in realizing circular business models was conducted by Luoma et al. (2021). However, there is still an overall and holistic overview missing on how data driven business models can contribute to circularity in practice and what data sources are relevant for that.
Therefore, the goals of this paper are to (1) collect use cases that contain data driven business models that contributes to CE (2) analyse those use cases to (3) identify circular strategies in businesses and (4) display the connection of them with data sources. With that we want to address the research questions:
What are the dominant circular strategies in data driven circular business models?
What data sources are used for circular business models?
Method
For this study 130 Use Cases which are derived from real business cases were collected. Based on existing literature we combined it with exploratory research to gather secondary data from the internet through online research (Saunders & Lewis, 2012). We build on the use cases that were already conducted by Kristoffersen et al. (2020) and Potting et al. (2017). For our research only use cases containing circular business models that were driven by data and technologies were chosen. After this preliminary selection the next step was to further analyze those use cases that fulfilled three criteria, that were chosen by us, which we defined as important for a meaningful analysis to contribute to our research topic:
1. Degree of renowned business: established SME’s or large companies with sophisticated websites reports or were very widely known.
2. Strong direct connection between data and circularity: high degrees of data contributing to their circular business model and significance level of data for circularity and
3. Simplicity of Business Model: we focused on business models that were straightforward and easy to understand so we were able to identify data sources and their connection to CE.
47 use cases fulfilled all three criteria and were examined more in-depth by three different researchers. The goal was to find data sources that this business in our selected use cases used that matches one or more of the 10 cards in the Data Source category by (Breitfuss et al., 2020) and with one or more 9Rs of (Potting et al., 2017) framework.
Results
We modified the 9R framework to represent the circular economy in companies (Potting et al., 2017 & Vermeulen et al., 2019), resulting in Reduce, Rethink, Recycle, Refuse, Repair, Refurbish, Repurpose and Remanufacture, ranked by highest to lowest frequency. As shown in Fig.1, Reduce had the highest number of matching use cases (42), followed by Rethink (19) and Recycle (13). All other Rs occurred in less than 20% of use cases examined.
Figure 1 Frequency of Circular Strategies analyzed by 47 most relevant use cases
Furthermore, we used the scatter plot method to show the relationship between the data sources and the 9R’s in the circular strategy.
Figure 2 Correlation between Data Source and Circular Strategy
This scatter plot shows the correlation of 5 Data Sources which are 1. Product-generated Data, 2. Process Data, 3. Open Data, 4. Geographic Data, and 5. User-generated Data, that were matched with the 47 in depth analyzed use cases that fulfilled the chosen three criteria, and the adapted 9R (ordered by descending frequency). Recover, one of the Rs in the 9R framework of (Potting et al., 2017 & Vermeulen et al., 2019) was excluded as it did not appear in any of the 47 use cases analyzed. Finally, it turned out that all 5 Data Sources found contain the three dominant circular strategies Reduce, Rethink and Recycle.
Discussion
As examined by Liu et al. (2022) “Reduce is the most frequently discussed topic in our reviewed literature, in which sense it is clearly seen as the most feasible way to adopt DDBM for CE.” Also, in Figure 1 our analysis of 47 use cases shows the most frequently used circular strategy in our research which indicates that most of businesses are reducing CO2 emissions in their circular strategies. Since CE indicators are mainly of quantitative nature (Moraga et al., 2020) it makes sense that their focus lies on that strategy. Additionally, it is noted by Potting et al. (2017) that high-level CE strategies require socio-institutional changes in the product chain, which increases the complexity of the CE assessment process and shows that the Reduce strategy is easier for companies to apply. Luoma et al. (2021) discovered that user-generated or product-generated data can be valuable in the context of circular business models. Our research shows that in the analysed use cases, most of the data sources used were also product-generated data. Thus, this paper recommends businesses focusing on collecting product- and user-generated data for implementing circular strategies in their business model.
Conclusion
This research analysed 47 most relevant use cases of real businesses through a combination of literature review and exploratory research. Through that 47 relevant use cases that fulfilled the criteria 1. Degree of renowned business, 2. Strong direct connection between data and circularity, and 3. Simplicity of Business Model could be found. Our findings indicate that for circular strategies like Reduce, Rethink and Recycle mainly data sources like: Product-generated Data, Process Data, Open Data, Geographic Data and User-generated Data are used. In a next research step, we plan to analyse additional data analytics and services that can be derived by our use cases and connected to our 9 circular strategies. However, the limitations of this research are its explorative nature and the limited number of the use cases. In future we want to expand our research by finding additional data sources from all 130 use cases that have been collected.
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