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Dissertation 4.3

Sustainability Adoption in Logistics Hubs in Europe: Data Quality And Bias — A propensity score matching

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~ 22–32 mins
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Logistics Metaverse RPA
Abstract

This dissertation investigates “Sustainability Adoption in Logistics Hubs in Europe: Data Quality And Bias — A propensity score matching” using a propensity score matching. Through a behavioral lens, the analysis integrates multi-source data to derive a practical decision framework for researchers and practitioners.

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Sustainability Adoption in Logistics Hubs in Europe: Data Quality And Bias — A propensity score matching

ABSTRACT
Sustainability Adoption in Logistics Hubs in Europe: Data Quality And Bias — A propensity score matching is unpacked across themes: change enablement, governance, interoperability, and risks. Limitations and future research paths are noted.
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