On-demand Food Research Articles

From ride hailing to food hailing: Understanding on-demand food delivery through platform urbanism and urban policy in Canadian cities

ABSTRACT This paper examines the pivot that took place during the COVID-19 pandemic from ride hailing to food hailing. In 2020, the global spread of COVID-19 challenged urban life, raising questions about the prospects for digital mobility platforms. Pandemic restrictions resulted in dramatic changes in mobility patterns globally, including significant declines in demand for ride hailing. Concurrently, with restaurants closed to indoor and outdoor dining for extended periods in many cities around the world, restaurateurs worked diligently to adjust their business models. During the early days of the COVID-19 lockdown, ride hailing firms such as Uber shifted their efforts from moving people to moving food and other goods. With a particular focus on Canada, we document this pivot and analyze its significance using evidence from case studies and interviews with multiple actors involved in the food and platform delivery ecosystem in major North American cities. We discovered both mutually beneficial and friction-filled relationships in the business, social and organizational logistics of digital food delivery. These results have implications for theories of platform urbanism and urban policy including highlighting new forms of competition that prioritize the role of urban infrastructure for creating value for platform firms.

Precarity and agency in youthspaces of work: The case of food delivery platform workers in Athens, Greece

The growth of digital platforms is spawning new, if often precarious, forms of work. One such type of work that has particularly grown in recent years, especially during the COVID-19 pandemic, is that of digital platform-facilitated on-demand food delivery services. Such deliveries are often undertaken by younger workers. Given this, here we explore the following research questions: (i) what forms of labour precarity are found and reproduced in the physical and digital spaces that young food delivery platform (FDP) workers create and inhabit?; and (ii) what types of agency do FDP workers develop within these spaces in order to navigate – and sometimes resist – precarity? Drawing upon concepts from critical Youth Studies, Geographical Political Economy and Labour Geography, the article develops the concept of precarious youthspaces of work to investigate the connections between youths’ everyday lives and the production of space. To do so we present a qualitative study conducted in Athens, Greece, that sought to capture the multifaceted working arrangements of FDP workers, both offline and online. Our analysis reveals that FDP workers suffer various forms of labour precarity which they must navigate. But such workers are not just accommodating to precarity. They have also generated forms of collective oppositional agency that are reshaping local labour markets. However, whilst their agency has sometimes generated solidarity and robust collective movements it has also led to divisions that have limited cooperation amongst FDP workers.

3D soft material printer as a mesoscale additive biomanufacturing platform for in-space manufacturing

With the burgeoning in-space manufacturing (ISM) industry, developing an on-demand additive manufacturing (AM) platform will be crucial for long-term space habitation. However, acute space boundary conditions, such as limited physical space, microgravity, vacuum, and others pose unique challenges for designing the printing process, the platform’s structure, and the materials’ printability. An AM platform operable in a space environment would enable production at the point of need (PoN), for example, on-demand food, nutrition, and pharmaceutical products. This research is focused on the design, fabrication, and testing of a 3D printer confined within CubeSat boundaries to study the feasibility of soft material printing aimed toward potential ISM applications. The printer unit was built using components off the shelf (COTS) while adhering to the severe spatial boundary conditions posed by the CubeSat dimensions and was tested using an edible material ink to demonstrate multi-layer prints of soft materials. Printing in ambient Earth conditions as well as under vacuum displayed consistent layer cohesion and comparison to 3D model data although vacuum prints showed visibly dehydrated prints owing to outgassing of air bubbles. The printer equipment’s structural integrity was validated under simulated launch and operation conditions using a vibration testing setup according to the NASA-recommended microsatellites standards. The results indicated that the printer assembly maintained its structural and operational integrity during and after testing. Using soft materials as the basis of testing allows scalability when expanding to more complex and structural materials to produce spare parts using a frugally engineered modular manufacturing platform.

Order Dispatching Via GNN-Based Optimization Algorithm for On-Demand Food Delivery

Order Dispatching Via GNN-Based Optimization Algorithm for On-Demand Food Delivery

Will e-consumers wait longer for a more sustainable on-demand food delivery? A choice experiment using green information

ABSTRACT The rapid growth of on-demand food delivery has sparked concerns about its societal and environmental impact. While alternative solutions like group ordering and electric bikes aim to address these issues, their adoption is hindered by inconveniences, mainly due to the longer delivery times, which are critical in food delivery. Price incentives such as discounts have traditionally been used as compensation, but questions about their long-term feasibility persist. This study assesses whether providing carbon footprint information can reduce the reliance on price incentives, especially when compensating for longer delivery times. Through a mixed-design experiment with 215 e-consumers in Greater Jakarta, we found that green information effectively encourages more sustainable delivery choices, similar to discounts. However, combining green information with discounts adds little value, suggesting that emphasizing green information alone could reduce financial costs and foster intrinsic motivation. Additionally, female and younger consumers are more responsive to green information, indicating the potential for targeted sustainability efforts in the on-demand food delivery sector.

Uncovering merchants’ willingness to wait in on-demand food delivery markets

While traditional on-demand food delivery services help restaurants reach more customers and enable doorstep deliveries, they also come with drawbacks, such as high commission fees and limited control over the delivery process. White-label food delivery services have emerged as an alternative, ready-to-use platform for restaurants to arrange delivery for customer orders received through their applications or websites, without the constraints imposed by traditional on-demand food delivery platforms or the need to develop an in-house delivery operation. Although several studies have investigated consumer behavior when using traditional on-demand food delivery services, there is limited research on merchants’ behavior when adopting white-label food delivery services. In this research, we develop a non-parametric survival model to estimate merchants’ willingness to wait when using white-label food delivery services and examine how various factors, such as delivery fees, the number of placed orders, and average waiting time, affect merchants’ willingness to wait, drawing on a dataset of both delivered and canceled orders from a crowd-sourcing delivery platform in Singapore. The empirical results show that merchants’ willingness to wait has a non-linear relationship with their average waiting time; it initially increases and then decreases with average waiting time. Moreover, the relationship between merchants’ willingness to wait during the pick-up stage and their average waiting times in the matching stage follows a similar non-linear trend. Merchants who have experienced lengthy waiting times on average in the matching stage tend to be less patient in the pick-up stage. This research sheds light on the stage-specific dynamics of merchants waiting behavior in white-label delivery service and provides insights for delivery platforms to optimize their operational strategies and enhance user experiences.

Quantifying consumers’ cost-value trade-offs on on-demand food delivery services: Value-of-time framework and partitioned pricing

PurposeThis study aims to quantitatively characterize consumers' value-cost trade-offs in on-demand food delivery (ODFD) services. On the value side, the study endeavors to monetize the convenience value of ODFD with a Value-of-Time (VOT) framework, while on the cost side, this study investigates the implication of ODFD's partitioned pricing structure with the theoretical tenet of Attribution Theory. Design/methodology/approachA stated choice experiment is developed and distributed to an online panel in Singapore with 580 valid responses collected. The data is then analyzed with a mixed logit model. FindingsThe results show that cost, time, and health-related factors significantly influence consumers' dining channel choices. In terms of time, consumers' differentiated time sensitivities are revealed. Specifically, consumers are most sensitive to walking time, followed by waiting time at food establishments and at their own places. Also, the food type and dining locations affect consumers’ time perceptions. Regarding cost, consumers exhibit an inflated price sensitivity toward food premiums compared to delivery fees. Originality/valueThis study addresses the lack of quantitative investigations into the value-cost tradeoffs in ODFD services. ODFD's convenience values are quantified and monetized with a novel VOT-based framework. Besides, the study sheds light on the underexplored partitioner pricing scheme with the theoretical tenet of the Attribution Theory. Overall, the research provides behavioral insights to support practitioners' operational decision-making.

Fostering humanistic algorithmic management: A process of enacting human-algorithm complementarity

Unlike traditional employer-employee relationships, contemporary digital platforms use algorithms to control and regulate the crowd workforce. Although prior research has expressed concerns over dehumanization stemming from algorithmic management, limited scholarly attention has been dedicated to exploring how human management can complement algorithmic approaches to address these concerns. Leveraging a case study of an on-demand food delivery platform during the COVID-19 pandemic, we developed a process theoretical model that uncovers several drivers and mechanisms essential for the transition from mechanistic algorithmic management to humanistic algorithmic management. This model elucidates the dynamic substitution and complementarity of human and algorithmic management through four key mechanisms: replacing and dampening, compensating, enabling, and synergizing. It also delineates effective humanistic management actions in scenarios in which algorithmic decisions are insufficient, which contributes to both performance and humanistic outcomes. In the realm of contemporary crowd workforce management, where digital platforms employ algorithms, this research sheds light on the unique and timely insights into the role of human managers in enhancing the strategic and humanistic values of algorithmic technology.

The Lesser of Two Evils? Explaining Chinese Rural Migrant Workers’ Preference for On-demand Food Delivery Work With Reference to the Legal Framework

Drawing on a series of interviews with on-demand food delivery riders, who are rural migrant workers, this paper seeks to explain the workers’ decision to give up stable jobs in factories in favour of largely unregulated and precarious on-demand work. Focusing on those aspects of the legal framework which shape workers’ decisions, it presents the explanation under the dual banners of ‘income’ and ‘freedom and flexibility’. In terms of income, Chinese law often enables low factory wages and a reliance on overtime; migrant workers view social insurance contributions as a loss; issues with payroll and wage arrears are significant. In terms of freedom and flexibility, insufficient rest rights lead to inadequate breaks for assembly line workers and, compounded by unspecified ‘special working time’ permit physically unbearable shifts without extra compensation. Age and gender discrimination are prevalent both in factories and various sectors. In contrast, on-demand delivery work has relatively low barriers to entry.

A Matching Algorithm with Reinforcement Learning and Decoupling Strategy for Order Dispatching in On-Demand Food Delivery

A Matching Algorithm with Reinforcement Learning and Decoupling Strategy for Order Dispatching in On-Demand Food Delivery

Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

How Information and Communications Technology Affects the Micro-Location Choices of Stores on On-Demand Food Delivery Platforms: Evidence from Xinjiekou’s Central Business District in Nanjing

The digitization of consumption, led by information and communications technology (ICT), has reshaped the urban commercial spatial structure (UCSS) of restaurants and retailers. However, the impacts of ICT on UCSS and location selection remain unclear. In this study, based on on-demand food delivery data and real-time traffic data, we used two types of machine learning algorithms, random forest regression (RFR) and the density-based spatial clustering of applications with noise (DBSCAN), to study the spatial distribution patterns, driving factors, and new geographical location phenomena of ‘brick-and-click’ (B&C) stores in Xinjiekou’s central business district (CBD) in Nanjing, China. The results show that the UCSS in the CBD is being decentralized, but the degree of influence is related to the business type. Additionally, the scale of demand and the distance from core commercial nodes greatly affect the scales of B&C stores. Moreover, the agglomeration of high-sales B&C stores seems to indicate a micro-location advantage, characterized by the concentration of delivery riders, which is usually located in the commercial hinterland with dense traffic. This makes stores situated in traditionally advantageous locations more attractive for online sales. Thus, ICT enhances the Matthew effect in business competition. These findings deepen our understanding of urban digital planning management and business systems.

Consumer Intentions to Switch On-Demand Food Delivery Platforms: A Perspective from Push-Pull-Mooring Theory

With a burgeoning market and a multitude of on-demand food delivery (OFD) platforms offering diverse options, comprehending the reasons that drive consumers to switch between platforms is paramount. The push-pull-mooring (PPM) theory provides a comprehensive framework for assessing why and how consumers navigate, guiding strategic decisions for service providers seeking to optimize their offerings and retain their customer base. This research employs the PPM theory to rigorously analyze how these elements influence consumers’ intentions to switch between OFD platforms in Taiwan. Findings from a comprehensive survey of 441 OFD users reveal that both pull and mooring factors exert a significant influence on consumers’ inclination to switch platforms, collectively explaining about 42% of the switching intention. Recognizing these critical factors empowers managers to make judicious decisions aimed at enhancing platform offerings and refining marketing strategies, ultimately fortifying customer retention and bolstering satisfaction levels.

Short-Term Demand Prediction for On-Demand Food Delivery with Attention-Based Convolutional LSTM

Demand prediction for on-demand food delivery (ODFD) is of great importance to the operation and transportation resource utilization of ODFD platforms. This paper addresses short-term ODFD demand prediction using an end-to-end deep learning architecture. The problem is formulated as a spatial–temporal prediction. The proposed model is composed of convolutional long short-term memory (ConvLSTM), and convolutional neural network (CNN) units with encoder–decoder structure. Specifically, long short-term memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems. The convolution unit is responsible for capturing spatial attributes, while the LSTM part is adopted to learn temporal attributes. Additionally, an attentional model is designed and integrated to enhance the prediction performance by addressing the spatial variation in demand. The proposed approach is compared to several baseline models using a historical ODFD dataset from Shenzhen, China. Results indicate that the proposed model obtains the highest prediction accuracy by capturing both spatial and temporal correlations with attention information focusing on different parts of the input series.

Analysis of an on-demand food delivery platform: Participatory equilibrium and two-sided pricing strategy

The on-demand food delivery platform assigns orders from customers to independent couriers, known as agents. An agent’s decision to provide services depends on the wage and the order assignment. The platform can adjust the actual supply and demand through two-sided pricing, such that an equilibrium between customers and agents that maximizes the platform’s profit can be formed. We develop a stylized model to investigate the optimal price and wage of a platform facing delay-sensitive customers and income-sensitive agents. We find three possibilities for participatory equilibrium in the platform as the pair of wage and price changes. Meanwhile, all participatory equilibrium regions have two asymptotically stable equilibria, one of which is a nonparticipatory equilibrium. The platform can form a participatory equilibrium only when it attracts enough customers and agents in the initial phase. Our analysis shows that the optimal pricing strategy and the maximum revenue for a platform depend on the valuation and delay sensitivity of the target customers. Furthermore, contradicting the intuition, our results show that the optimal wage is non-increasing in the total demand rate and that the optimal price is non-decreasing in the service capacity of the platform.

Sharing economy in the food sector: A systematic literature review and future research agenda

In recent years, the sharing economy (SE) has proliferated in various domains, causing a shift from traditional consumption patterns. However, within the hospitality sector, studies have predominantly addressed the accommodation domain while neglecting the ‘food’ sector. This low focus may have been compounded by the lack of a standard definition of SE. Therefore, using a five-faceted standard definition, this study extends the SE lens to the food sector in the hospitality domain. A literature review was conducted to identify relevant studies on food-based SE services aligned with the five defining characteristics of SE: technology-based matching platform, access-oriented, crowdsourced supply, enhanced customer role, and economically substantive. Ninety-six articles were selected and synthesized to identify three food-sector SE services: on-demand food delivery (ODFD), meal-sharing, and food-sharing, along with their associated stakeholders (customers, service providers, complementors, and platform providers). Extant literature on each food-sector SE service was synthesized to obtain their research profile, and extract the antecedent & consequence variables, which led to research gaps. This study makes a novel contribution to the SE domain by consolidating the literature on one of its relatively unaddressed sectors, and setting a future research agenda.

Model-Based Vehicle-Miles Traveled and Emission Evaluation of On-Demand Food Delivery Considering the Impact of COVID-19 Pandemic

The market for on-demand food delivery (ODFD) has increased considerably, especially during the COVID-19 pandemic. It is crucial for transportation and environmental agencies to understand how ODFD has reshaped the travel patterns of people, affecting vehicle-miles traveled (VMT) as well as pollutant emissions in the transportation system. However, the lack of public data from food delivery companies makes it challenging to quantify the impact of on-demand delivery on the real-world transportation network. In this research, we propose a comprehensive framework to quantify the VMT and emissions incurred by ODFD with three main components: (i) a daily activity generation tool, Comprehensive Econometric Micro-simulator for Daily Activity-travel Patterns, to create a simulation scenario of ODFD behaviors based on a real-world roadway network and population demographics in the City of Riverside, California; (ii) an efficient order dispatching and routing algorithm, adaptive large neighborhood search, to obtain a high quality order dispatching and routing plan; (iii) an emission evaluation model, emission factor (EMFAC), to evaluate pollutant emissions from all dining-related trips. Both short-term and long-term impacts of the COVID-19 pandemic are evaluated. Experimental results show that ODFD has great potential to reduce the dining-related VMT and emissions. The total dining-related VMT in the during-pandemic case decreased by 38% and in the after-pandemic case reduced by 6% to 9%, and the corresponding environmental impacts were reduced accordingly. Meanwhile, emissions reduced significantly with more electric vehicles involved in food delivery. With 100% electric delivery fleet, the ODFD service can save 14% to 22% of emissions after the COVID-19 pandemic.

An Online Deep Reinforcement Learning-Based Order Recommendation Framework for Rider-Centered Food Delivery System

As an important part of intelligent transportation systems, On-demand Food Delivery (OFD) becomes a prevalent logistics service in modern society. With the continuously increasing scale of transactions, rider-centered assignment manner is gaining more attraction than traditional platform-centered assignment among food delivery companies. However, problems such as dynamic arrivals of orders, uncertain rider behaviors and various false-negative feedbacks inhibit the platform to make a proper decision in the interaction process with riders. To address such issues, we propose an online Deep Reinforcement Learning-based Order Recommendation (DRLOR) framework to solve the decision-making problem in the scenario of OFD. The problem is modeled as a Markov Decision Process (MDP). The DRLOR framework mainly consists of three networks, i.e., the actor-critic network that learns an optimal order ranking policy at each interaction step, the rider behavior prediction network that predicts the grabbing behavior of riders and the feedback correlation network based on attention mechanism that identifies valid feedback information from false feedbacks and learns a high-dimensional state embedding to represent the states of riders. Extensive offline and online experiments are conducted on Meituan delivery platform and the results demonstrate that the proposed DRLOR framework can significantly shorten the length of interactions between riders and the platform, leading to a better experience of both riders and customers.

TECHNOLOGICAL CHALLENGES IN LAST MILE ON DEMAND FOOD DELIVERY : PERSPECTIVE FROM FOOD DELIVERY RIDERS WITH SPECIAL REFERENCE TO COIMBATORE CITY, TAMILNADU, INDIA

On-demand food delivery workers play an important role in the ODFD (On-Demand Food Delivery) supply chain. This study investigates the technological problems that riders encountered while delivering meals. A questionnaire distribution was done in order to gather information from users of the Swiggy and Zomato platforms, and 100 people participated in the study. The study's ndings imply that the respondents are dealing with a variety of difculties, including technological ones brought on by the device (a mobile phone) and platform algorithmic setup..The key problems are the overall setup of the algorithm, its boss-like forcefulness, lack of safety as a result algorithmic setup, and its boss-like setup.

Optimization of route distance using k-NN algorithm for on-demand food delivery

Customers are now more able to purchase goods over the phone or the Internet, and the ability for those purchases to be delivered safely to the customer’s location is proliferating. On-request meal delivery, where customers submit their food orders online, and riders deliver them, is growing in popularity. The cutting-edge urban food application necessitates incredibly efficient and adaptable continuous delivery administrations toward quick delivery with the shortest route. However, signing up enough food parcels and training them to use such food-seeking frameworks is challenging. This article describes a publicly supported web-based food delivery system. IoT (Internet of Things) and 3G, 4G, or 5G developments can attract public riders to act as publicly sponsored riders delivering meals using shared bikes or electric vehicles. The publicly funded riders are gradually distributed among several food suppliers for food delivery. This investigation promotes an online food ordering system and uses K-Nearest Neighbor calculations to address the Traveling Salesman Problem (TSP) in directing progress. The framework also uses the Global Positioning System (GPS) on Android-compatible mobile devices and the TOM-TOM Routing API to obtain coordinates for planning purposes. To evaluate the presentation of the proposed approach, recreated limited scope and certifiable enormous scope on-request food delivery occurrences are used. Compared to the conventional methodology, the proposed strategy reduces the delay time. Each rider will receive the most direct route to the order delivery address. The delivery delay time is reduced by approximately 10–15 minutes for every order. The food supplier can determine whether an item is available to the rider; thus, the food supplier can add an order to the rider having the shortest way.