ASSESSING THE INFLUENCE OF CLOUD KITCHEN AND FOOD AGGREGATORS ON TRADITIONAL RESTAURANT BUSINESS MODELS

Abstract

This study examines how cloud kitchens and food aggregator platforms influence traditional restaurant business models. Cloud kitchens and services such as GrabFood, Uber Eats, and Foodpanda offer convenience and low overhead, reshaping consumer expectations. Traditional restaurants face challenges in customer retention, branding, and operational efficiency due to digital competition, high delivery fees, and evolving ordering behaviors. The aim is to understand these impacts and identify strategies that enable traditional operators to compete effectively in the digital marketplace. A quantitative descriptive design is employed to evaluate the effects on Max’s Restaurant in Cubao, Quezon City. Purposive sampling selects twenty employees with direct experience in operations, marketing, and delivery coordination. A researcher‑developed survey questionnaire gathers data on revenue shifts, customer loyalty, pricing strategies, operational adjustments, and technology adoption. Responses are analyzed using descriptive statistics to reveal trends and operational implications without manipulating any variables. Cloud kitchens and aggregator partnerships significantly impacted Max’s Restaurant, reducing in‑store market share while boosting overall order volume through delivery channels. Operational efficiency improved through streamlined kitchen workflows, but customer loyalty shifted toward aggregator platforms due to ease of ordering. Dynamic pricing models emerged in response to intensified price competition. Cloud kitchens demonstrated higher profitability per order, whereas traditional menu quality and packaging standards required adaptation. Digital adoption challenges included limited in‑house delivery infrastructure and the need to optimize online menus for aggregator interfaces. The study found that cloud kitchens and food aggregators disrupted traditional restaurant performance by enhancing convenience at the expense of dine‑in market share. As a result, technology integration—such as proprietary online ordering systems—was recommended, along with partnerships that reduce delivery fees and co‑branding initiatives on aggregator platforms. Menu redesign for packaging integrity and dynamic pricing strategies was also advised to maintain competitiveness. Limitations included sample size and single‑location scope; future research could expand to multiple brands and regions.