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1. On-Demand Delivery Platforms and Restaurant Sales

   https://doi.org/10.1287/mnsc.2021.01010  

   Li, Zhuoxin; Wang, Gang (October 2024, Management Science)

   Restaurants are increasingly relying on on-demand delivery platforms (e.g., DoorDash, Grubhub, and Uber Eats) to reach customers and fulfill takeout orders. Although on-demand delivery is a valuable option for consumers, whether restaurants benefit from or are being hurt by partnering with these platforms remains unclear. This paper investigates whether and to what extent the platform delivery channel substitutes restaurants’ own takeout/dine-in channels and the net impact on restaurant revenue. Empirical analyses show that restaurants overall benefit from on-demand delivery platforms—these platforms increase restaurants’ total takeout sales while creating positive spillovers to customer dine-in visits. However, the platform effects are substantially heterogeneous, depending on the type of restaurants (independent versus chain) and the type of customer channels (takeout versus dine-in). The overall positive effect on fast-food chains is four times as large as that on independent restaurants. For takeout, delivery platforms substitute independent restaurants’ but complement chain restaurants’ own takeout sales. For dine-in, delivery platforms increase both independent and chain restaurants’ dine-in visits by a similar magnitude. Therefore, the value of delivery platforms to independent restaurants mostly comes from the increase in dine-in visits, whereas the value to chain restaurants primarily comes from the gain in takeout sales. Further, the platform delivery channel facilitates price competition and reduces the opportunity for independent restaurants to differentiate with premium services and dine-in experience, which may explain why independent restaurants do not benefit as much from on-demand delivery platforms. This paper was accepted by D. J. Wu, information systems. Funding: Z. Li is grateful to the National Science Foundation Division of Social and Economic Sciences for support provided through the CAREER award [Grant 2243736]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01010 . 

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2. Assessing the Effects of Limited Curbside Pickup Capacity in Meal Delivery Operations for Increased Safety during a Pandemic

   https://doi.org/10.1177/0361198121991840  

   Fotouhi, Hossein; Mori, Nicholas; Miller-Hooks, Elise; Sokolov, Vadim; Sahasrabudhe, Sagar (February 2021, Transportation Research Record: Journal of the Transportation Research Board)

   Meal delivery has become increasingly popular in past years and of great importance in past months during the COVID-19 pandemic. Sustaining such services depends on maintaining provider profitability and reduced cost to consumers while continuing to support autonomy and independence for customers, restaurants, and delivery drivers (here crowdsourced drivers). This paper investigates the possible enactment of curbside regulations in the U.S. that limit the number of drivers simultaneously waiting at restaurants to pick up meals for delivery on both public safety and delivery efficiency. Curbside regulations would aim to increase safety by enabling social distancing between delivery personnel at pickup locations and have a secondary benefit of improving local traffic flows, which are sometimes impeded in busier, urban locations. Curbside space limits are studied in relation to their impacts on consumer-related performance measures: freshness of the food on delivery and click-to-door time. This investigation is enabled through a proposed hybrid discrete-event and time-advanced simulation platform that replicates meal delivery service calls and pickup and delivery operations across a region built on data from a leading meal delivery company. Embedded within the simulation is an integer program that optimally assigns orders to drivers in a dynamically changing environment. Order assignments are constrained by imposed curbside capacity limits at the restaurants, and potential efficiencies and curbside violation reductions from bundling orders are assessed. Results of analyses from numerical experiments provide insights to state and local communities in designing curbside restrictions that reduce curbside crowding yet enable delivery companies to retain their profitability. 

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3. Growing Platforms by Adding Complementors Without a Contract

   https://doi.org/10.1287/isre.2023.0237  

   Mayya, Raveesh; Li, Zhuoxin (February 2025, Information Systems Research)

   Practitioner-Oriented Abstract Online platforms often face challenges in sustaining growth, especially in competitive markets such as food delivery. This paper examines a novel strategy in which platforms list nonpartnered restaurants, allowing consumers to order from them via third-party deliverers. Whereas these restaurants gain visibility without paying commissions, concerns arise about potential harm because of lack of control over menus and pricing. We analyze the impact of this strategy using data from Grubhub and a California policy change that banned nonpartnered listings. We find that being listed as nonpartnered boosts takeout revenue for these restaurants, particularly independent ones. Additionally, there’s a positive spillover effect on partnered restaurants. However, regulatory delisting reverses these gains, highlighting the delicate balance between platform growth strategies and regulatory actions. For platform owners, this study underscores the potential of noncontracted partnerships as a growth strategy, especially if there are third-party enablers on the platform such as deliverers. However, it also cautions against potential disruptions from regulatory changes, urging businesses to adapt their strategies accordingly. For restaurant owners, our finding emphasizes the importance of adapting to changes by enhancing operational readiness to capitalize on increased visibility. They should advocate for regulations that enhance their choices and overall transparency, not inadvertently decrease them. Policy-Oriented Abstract In the competitive landscape of online platforms, the pursuit of growth often necessitates innovative strategies. Whereas such strategies are deemed controversial and get pushbacks from some participants, how should policymakers respond? This research provides insights into such a scenario by investigating a novel growth strategy in which food delivery platforms onboarded nonpartnered restaurants, enabling consumer interactions without formal contracts, promising an increased visibility without commission fees. Leveraging data from Grubhub and a policy change in California, we find that being listed boosts takeout revenue significantly, especially for independent restaurants, with positive spillovers on partnered restaurants. However, delisting reverses these gains. For policymakers, these findings challenge the narrative that nonpartnered listings are inherently harmful. They highlight the potential of such arrangements to increase market access and revenue, particularly for independent businesses. However, the study also underscores the need for balanced regulation. Whereas protecting restaurants is crucial, overly restrictive policies can limit their choices and growth opportunities. We recommend a policy approach that empowers restaurants by requiring platforms to obtain explicit consent before listing them. Additionally, enhancing transparency around nonpartnered listings and associated fees can enable customers to make informed choices. This approach fosters a fair market, preserving the benefits of platform-enabled growth for all stakeholders. 

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4. Leveraging data analytics to understand the relationship between restaurants’ safety violations and COVID-19 transmission

   Arthur Huang a, Aarash Baktash (July 2022, International journal of hospitality management)

   This paper leverages natural language processing, spatial analysis, and statistical analysis to examine the relationship between restaurants’ safety violations and COVID-19 cases. We used location-based consumers’ complaints data during the early stage of business reopening in Florida, USA. First, statistical analysis was conducted to examine the correlation between restaurants’ safety violations and COVID-19 transmission. Second, a neural network-based deep learning model was developed to perform topic modeling based on consumers’ complaints. Third, spatial modeling of the complaints’ geographic distributions was performed to identify the hotspots of consumers’ complaints and COVID-19 cases. The results reveal a positive relationship between consumers’ complaints about restaurants’ safety violations and COVID-19 cases. In particular, consumers’ complaints about personal protection measures had the highest correlation with COVID-19 cases, followed by environmental safety measures. Our analytical methods and findings shed light on customers’ behavioral shifts and hospitality businesses’ adaptive practices during a pandemic. 

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5. Increasing driver flexibility through personalized menus and incentives in ridesharing and crowdsourced delivery platforms

   https://doi.org/10.1002/nav.22212  

   Horner, Hannah; Pazour, Jennifer; Mitchell, John_E (July 2024, Naval Research Logistics (NRL))

   Abstract This article formulates and solves a stochastic optimization model to investigate the impact of crowdsourced platforms (e.g., ridesharing, on‐demand delivery, volunteer food rescue, and carpooling) offering small, personalized menus of requests and incentive offers for drivers to choose from. To circumvent nonlinear variable relationships, we exploit model structure to formulate the program as a stochastic linear integer program. The proposed solution approach models stochastic responses as a sample of variable and fixed scenarios, and to counterbalance solution overfitting, uses a participation ratio parameter. The problem is also decomposed and iterated among two separate subproblems, one which optimizes menus, and another, which optimizes incentives. Computational experiments, based on a ride sharing application using occasional drivers demonstrate the importance of using multiple scenarios to capture stochastic driver behavior. Our method provides robust performance even when discrepancies between predicted and observed driver behaviors exist. Computational results show that offering menus and personalized incentives can significantly increase match rates and platform profit compared to recommending a single request to each driver. Further, compared to the menu‐only model, the average driver income is increased, and more customer requests are matched. By strategically using personalized incentives to prioritize promising matches and to increase drivers' willingness to accept requests, our approach benefits both drivers and customers. Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver‐request pairs less likely to be accepted. 

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