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Abstract: Digital technologies are recognized as a potential enabler for circular economy since they can support companies in the adoption of managerial practices for a circular business model design. However, which technologies can be used for this purpose, and with special reference for the adoption of design for X practices within the value creation dimension of the circular business model, as well as the adoption of product service system and take back system within the value capture dimension of the circular business model, is an under-researched management topic that deserves further investigation. Accordingly, the paper takes a first step towards closing the gap in the existing research on circular business model by answering the question of which digital technologies can support the adoption of design for X, product service system, and take back system managerial practices enabling circular business model design.
Keywords: Circular Economy, Circular Business Models, Digital Technologies, Correspondence Analysis
1Introduction
Circular Economy (CE) is a novel industrial approach that has been introduced to overcome the issues characterizing the traditional linear "take-make-dispose" economic model, by minimizing the utilization of raw materials, enhancing the product lifetime to exploit the maximum value from it, and, once a product reaches the end of life, reusing spare parts and components to decrease the total demand of raw materials (Kirchherr et al., 2017; Stahel, 2013).
To implement CE principles, companies are called to innovate their business model and strategic positioning into the market (Geissdoerfer et al., 2018), i.e., by redesigning the logics through which they create, transfer, and capture value, thus creating a circular business model (CBM) (Centobelli et al., 2020). Existing literature has shown the relevance of such a redesign process, with particular reference to value creation and value capture (Ünal et al., 2019a).
In this context, digital technologies are recognized as a potential enabler for CE (Uçar et al., 2020; Wilson et al., 2021), since they can support companies in adopting managerial practices for CBMs (Chiaroni et al., 2020; Ranta et al., 2021; Rosa et al., 2020). For example, big data are used to assess potential pathways for secondary materials (Davis et al., 2017; Jose & Ramakrishna, 2018), or industrial symbiosis (Song et al., 2017), while simulation systems allow optimizing the performance of supply chains and...