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Type: Article
Title: Competitiveness of the Shared Economy Model for Sustainable Management of Logistics Systems PDF Article
Author: Keisha LaRaine Ingram
On-line: 31-August-2022
Metrics: Applied Business: Issues & Solutions 1(2022)10-16 – ISSN 2783-6967.
DOI: 10.57005/ab.2022.1.2
Abstract. This paper analyses the shared economy model to sustainably manage the tangible and in- tangible resources of logistics systems. A critical review of literature on the current supply chain management policy and technological platform in current use that supports it was done to suggest a new conceptual framework for logistics processes' sustainable management on a shared economy platform. This was examined according to how the concepts of new technologies influence logistics and the role of sustainable management platforms of the shared economy in enabling greater improved logistics processes. The inductive methodology approach was applied using multi-criteria analysis interpretive research method. The impact of the shared business model on each stakeholder and beneficiary varies according to how resources are consumed and its adoption according to the core business models requirements of each. Current scientific literature does not identify the impact this phenomenon has on companies in different sectors, as there is a lack of detailed analysis and evidence to fill this gap, particularly as the Internet of Things (IoT) monetize digital assets autonomously through the Economy of Things (EoT) marketplaces. From the analysis conducted, the findings provide a concept of the prototype framework required for the shared economy in the e-logistics' ecosystems rather than traditional ones, modelled using multi-criteria analysis interpretive methods as a strategic resource within the shared economy of supply chain management systems.
JEL: Q01.
Keywords: crowdsourcing; sustainable management; sharing economy; digital assets.
Citation: Keisha LaRaine Ingram (2022) Competitiveness of the Shared Economy Model for Sustainable Management of Logistics Systems. – Applied Business: Issues & Solutions 1(2022)10–16 – ISSN 2783-6967.

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