"Unveiling Risk Strategies in the Online Food Delivery Platforms" by Maryam Mahdikhani, Jiyoon An et al.
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Publication Date

March 2025

Abstract

Online Food Delivery Platforms (OFDPs) have faced unique challenges related to uncertainty and global risk in recent decades. To address these risks, this study utilized machine learning (ML) techniques, including Latent Dirichlet Allocation (LDA) and Word2Vec, to extract and cluster OFDP-related risk factors. The analysis was conducted using unstructured text data from the annual reports of two prominent OFDP companies—Delivery Hero (DH) and Grubhub (GH)—from 2017 to 2020. We also examined the multidimensional RepRisk Index (RRI) for these companies and developed a two-dimensional matrix to evaluate preparedness and responsiveness across various risk categories in the OFDP sector. The results indicate that the OFDP companies exhibited greater resilience in managing economic and governmental risks, while their approaches towards technological, environmental, and service quality risks were more oriented toward innovation.

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