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Ensuring optimal food quality is essential in the dynamic realm of online food delivery services, as it directly impacts consumer satisfaction and provides a competitive edge. This study examines the utilization of Multi-Criteria Decision Analysis (MCDA) techniques, namely the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assess and prioritize online food delivery services according to different quality characteristics, with Food Temperature upon Arrival (FT) being identified as the most crucial component, followed by Freshness of Ingredients (FI), Presentation of Food (PF), Accuracy of Order (AO), and Portion Size (PS). The AHP research indicates that FT and FI have the most significant influence on customer satisfaction, whereas PF, AO, and PS have less significant impacts. The research employs the TOPSIS approach to evaluate and prioritize three prominent online meal delivery platforms—Zomato, Swiggy, and Uber Eats—using a normalized decision matrix created from the selected parameters. The Weighted Normalized Matrix derived from TOPSIS analysis demonstrates that Zomato outperforms the other alternatives in most parameters. The results emphasize the need of giving priority to food temperature and freshness to improve the quality of delivery. This research enhances the field by showcasing a successful method for evaluating the quality of service in the online food delivery industry.

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Multi-criteria Decision Analysis for Food Quality in Online Delivery: AHP and Topsis Perspectives

  • 章节

  • 发表日期：

    2026年

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  • Dsouza Prima Frederick,
  • Arlene Shalma Fernandes,
  • Reema Agnes Frank,
  • Mohammad Omar Sabri,
  • Manoj Kumar,
  • Shilpi Rana,
  • Jayant Giri,
  • Pallavi Giri

摘要

Ensuring optimal food quality is essential in the dynamic realm of online food delivery services, as it directly impacts consumer satisfaction and provides a competitive edge. This study examines the utilization of Multi-Criteria Decision Analysis (MCDA) techniques, namely the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assess and prioritize online food delivery services according to different quality characteristics, with Food Temperature upon Arrival (FT) being identified as the most crucial component, followed by Freshness of Ingredients (FI), Presentation of Food (PF), Accuracy of Order (AO), and Portion Size (PS). The AHP research indicates that FT and FI have the most significant influence on customer satisfaction, whereas PF, AO, and PS have less significant impacts. The research employs the TOPSIS approach to evaluate and prioritize three prominent online meal delivery platforms—Zomato, Swiggy, and Uber Eats—using a normalized decision matrix created from the selected parameters. The Weighted Normalized Matrix derived from TOPSIS analysis demonstrates that Zomato outperforms the other alternatives in most parameters. The results emphasize the need of giving priority to food temperature and freshness to improve the quality of delivery. This research enhances the field by showcasing a successful method for evaluating the quality of service in the online food delivery industry.