Academic literature on the topic 'Food Delivery Riders'

Ordering food through smartphones brings millions of laborers into a new occupation -food delivery rider. To date, insufficient research has focused on this group of population in platform economy. This paper examines the management mechanism of Chinese food delivery industry based on Marxist labor process theory and its extensions. Three main findings are revealed. First, the food delivery platform strictly set online and off-line institutions to manage food delivery riders; second, riders are involved in an illusion of flexitime but indeed provide more labor forces; third, riders are not free as they are constantly monitored by platform’s algorithm driven by big data. Given this, the conclusion suggests that all platform enterprises should abide professional ethnics and undertake social responsibility and to liberate food delivery riders’ nature.

So far, food-delivery riders have been addressed in reference to broad socio-economic processes related to the diffusion of digital labour platforms. Adopting a dispositional approach, this paper frames the rider's occupation as a specific and open-ended occupation. We try to shed light on the dynamics of differentiation and positioning within this occupational space, thus recomposing its heterogeneity. The article is based on a mixed-methods research carried out in Milan in 2020, centred on an "observant participation" of seven months, during which the re-searcher worked as a rider.

Services such as Meituan and Uber Eats have revolutionized the way the customer can find and order from restaurants. Numerous independent restaurants are competing for orders placed by customers via online food ordering platforms. Ordering takeout food on smartphone apps has become more and more prevalent in recent years. There are some operational challenges that takeout food service providers have to deal with, e.g., customer demand fluctuates over time and region. In this sense, the service providers sometimes ignore the fact that some riders may be idle in several periods in regions, while, in contrast, there may be a shortage of riders in other situations. In order to address this problem, we introduce a two-stage model to optimize scheduling of riders for instant food deliveries. A service provider platform expectantly schedules the least quantity of riders to deliver within expected arrival time to satisfy customer demand in different regions and time periods. We introduce a two-stage model that adopts the method of mixed-integer programming (MIP), characterize relevant aspects of the scenario, and propose an optimization algorithm for scheduling riders. We also divide the delivery service region and time into smaller parts in terms of granularity. The large neighborhood search algorithm is validated through numerical experiments and is shown to meet the design objectives. Furthermore, this study reveals that the optimization of rider resource is beneficial to reduce overall cost of the delivery. Takeout food service platforms decide scheduling shifts (start time and duration) of the riders to achieve a service level target at minimum cost. Additional sensitivity analyses, such as the tightness of the order time windows associated with the orders and riders’ familiarity with delivery regions, are also discussed

In the emerging business of food delivery, rider traffic accidents raise financial cost and social traffic burden. Although there has been much effort on traffic accident forecasting using temporal-spatial prediction models, none of the existing work studies the problem of detecting the takeaway rider accidents based on food delivery trajectory data. In this article, we aim to detect whether a takeaway rider meets an accident on a certain time period based on trajectories of food delivery and riders’ contextual information. The food delivery data has a heterogeneous information structure and carries contextual information such as weather and delivery history, and trajectory data are collected as a spatial-temporal sequence. In this article, we propose a TakeAway Rider Accident detection fusion network TARA-Net to jointly model these heterogeneous and spatial-temporal sequence data. We utilize the residual network to extract basic contextual information features and take advantage of a transformer encoder to capture trajectory features. These embedding features are concatenated into a pyramidal feed-forward neural network. We jointly train the above three components to combine the benefits of spatial-temporal trajectory data and sparse basic contextual data for early detecting traffic accidents. Furthermore, although traffic accidents rarely happen in food delivery, we propose a sampling mechanism to alleviate the imbalance of samples when training the model. We evaluate the model on a transportation mode classification dataset Geolife and a real-world Ele.me dataset with over 3 million riders. The experimental results show that the proposed model is superior to the state-of-the-art.

In this paper, we study the delivery order pattern of take-out riders in Chinese society, adopt a time window function to give a reward and punishment measure for the complete quality of riders under various factors, and adopt hierarchical analysis to obtain and quantify the degree of influence of different factors on delivery, and get the commission scheme design so that the riders can get the maximum commission. Then, from the perspective of game theory, we establish a two-stage game model under the non-cooperative game, with the first stage of the game being the takeaway demand realizer and the second stage of the game being the takeaway demander and the contacting platform, and finally select a win-win solution with a balanced game. Finally, the pricing model proposed in the previous paper is optimized iteratively, considering the addition of long-distance as well as cross-regional distance quantification factors, and considering the case of dedicated personnel working exclusively. This paper proposes an equilibrium state for the four ends of the platform, merchants, consumers, and riders, and proposes a cross-regional and long-distance delivery scheme with certain feasibility, giving the platform and riders the possibility to expand the sales area and delivery area.

Online platforms are revolutionizing our daily lives in an attempt to make it easier by offering innovative services. They also have introduced radical new business models which provide a new type of flexible working, facilitating employment. While platforms are revolutionary vehicles, they also denied workers status, resulting in food delivery riders facing precarious working conditions. The current regulatory framework is underdeveloped and unable to guarantee basic social rights to platform workers, except for Spain. At the same time, delivery workers are fighting to get some form of recognition and protection. Consequently, courts have been increasingly requested to determine the riders’ legal status. However, courts are struggling in characterizing those employment relationships resulting in disparities. For instance, the Cour de Cassation in France has established that an employer-employee relationship existed while the UK High Court denied worker status to Deliveroo riders. This lack of harmonization and different rulings could result in the application of EU rules in some countries but not others. It might, therefore, be time for the EU to start recognizing and regulating these jobs to offer better worker protections.

With the prosperity of the platform economy, assorted new forms of businesses have emerged, which are seemingly different from traditional employees, have literally emerged. In addition, Chinese labor laws split workers into laborers and non-laborers, only when a worker is a laborer can he or she be governed by labor laws. Thus, whether such workers are laborers has caused a definitional maze in both judicial practice and academia. One of the most representative of new business patterns is the rider of the food delivery platform. This article takes the food delivery rider who best represents one of the new forms of employment as a theme for analysis, and adopts the method of case analysis and comparative research. This paper holds the view that online platform enterprises have the following essential characteristics: online platform enterprises tend to abandon their fixed assets; they take more control of riders with the blessing of algorithms; and they skirt the law to mask real legal relationships. These features all contribute to proving more difficult to determine whether a rider is a laborer. In response to the policy of building harmonious labor relations and to protect riders’ rights they deserve, this essay studies the ABC test established by the California State Supreme Court, by discussing the backdrop and merits of the ABC test, together with the reasons why it can be used for reference, to provide threads for China’s judicial practice.

This article studies the temporality of platform labor through the specific lens of Meituan, the leading food delivery platform in China featuring an algorithm-driven dispatch system. This paper widens the existing research on platform labor from the perspective of time and speed. The on-demand food delivery service cultivates consumers’ expectations of timely satisfaction, nonetheless, it builds upon the rush, strictness, and flexibility of digital labor’s temporality which is often called a “mission impossible at times”. With the rhetoric of ‘Flexible work hours,’ the platform acts as an intermediary in the contradictory costumer-rider temporal relationship and prioritizes the customer's position within the temporal orders, where ICTs such as real-time tracking systems play a key role in sense-making. As the article will show, Meituan’s ETA (Estimated Time of Arrival) algorithm distributes the common interests of efficiency with the "invisible hand of value," in the quaternary relation between riders, customers, suppliers, and platform. While workers' time experience and negotiating ability are diminished, the platform gains the ultimate capacity to exploit platform labor systematically. The coordination of all platform algorithms normalizes class divisions and unequal power structures, interpreting the asymmetrical power between capital and labor in the platform economy.

Background:  Despite the utilization of algorithms as data management tools, they are increasingly used as people management tools to allocate, optimize and evaluate workers. This is especially popular among digital labor platforms of the gig economy as it is seen as one of the core innovations that enabled such platforms. Usually, these platform workers are self-employed, which results in an apparent autonomy while working under a rigid algorithm. For those workers, proactively shaping the job according to their own needs and abilities, commonly known as job crafting, may be increasingly important. As research suggests that job crafting occurs across professions and industries, how is it possible under the constraints of algorithmic management?  Purpose:  This thesis investigates the abilities of German food delivery riders of the company Lieferando to perform job crafting while being managed by an algorithm.  Method:  To meet the purpose of this study, the authors conducted a qualitative study. The data was collected through technology-mediated interviews with riders of the company Lieferando in Germany. The authors applied an online recruitment strategy through various social media websites to find suitable interviewees. Interview partners were picked with a random sampling strategy. The interviews were semi-structured, and the researchers guided the interviewees through a previously prepared topic guide with open-ended questions.  Conclusion:  The results of this study provide empirical evidence that riders of the food delivery company Lieferando engage in job crafting activities although working under the constraints of algorithmic management. The outcomes further show that all riders performed task crafting and cognitive crafting in various ways, whereas engagement in relational crafting was less developed. Riders not only have the ability to modify their work but also enrich it.  The findings of this study allow to draw several theoretical and managerial implications as well as provide possible research opportunities for future studies.

This doctoral research examines the approaches that gig workers in the Australian states of New South Wales and Queensland have taken to improve their working conditions by working together. The research focuses on three types of gig workers: rideshare drivers, food delivery riders, and on-demand in-home manual workers. The thesis argues that gig workers show varying levels of interest in working collectively to take action to improve their working conditions. The differences are partly explained by the ways in which they variously interpret and respond to the signals that push them to act with others.

What do we mean when we speak of algorithmic management? Starting from the case of digital food delivery platforms, this chapter proposes some theoretical and interpretative hypotheses regarding the introduction of algorithmic management systems. In particular, the authors argue that this management model can be described as not completely new, since it includes both elements of the industrial capitalism, based on direct and disciplinary control, and of the managerial model typical of post-Fordism, centred instead on the subsumption of subjectivity, which – by leveraging workers’ request for greater autonomy – enhances the subjects’ very passions and desires. Drawing on a co-research carried out with food delivery riders who took part in the mobilizations organized in the city of Turin (Italy), this chapter investigates the processes giving shape to the forms of algorithmic management and analyse both the various control methods put into practice through the platforms and the answers the workers find to work in these complex environments while at the same time questioning this management model.

‘Transport Platform Workers’ focuses on the struggles of transport workers on platforms. It discusses food delivery workers, starting with the strikes of Deliveroo riders in London in 2016. This draws on the longest-running project that has contributed to the book. The dynamics of these struggles are analysed through the framework of class composition, unpicking the changing technical, social, and political composition of this work. It then moves through examples of subsequent waves of strikes across Europe, as well as the formation of the Transnational Couriers Federation. This analysis is then developed through an increasingly global network of food delivery driver organisations (including both unions and networks), reflecting on the successes and limitations of different models of resistance and organisation that drivers are experimenting with. The chapter then moves on to discuss private hire drivers, highlighting the coordinated strikes and protests in the run-up to Uber’s IPO. These went beyond the coordination of days of action or alignment of strikes, leading to the formation of international networks.

The collaborative, or sharing, economy has experienced tremendous growth. For instance, in an often cited study, PWC suggests that by 2025 sales revenue in the sharing economy will rise to 335 billion dollars from 15 billion in 2013 (PWC, 2015), with the effects of COVID-19 on the sharing economy yet to be ascertained. Uber, the ride hailing app, has increased trips per year from 140 million in 2014 to 6.9 billion in 2019 (Iqbal, 2021). Since its launch in 2017, Lime Scooters, who rent e-scooters, is now operating in more than 120 cities across more than 30 countries (Glasner, 2020). Airbnb is another prime example of this success (Dolnicar, 2020; Fagerstrøm et al., 2017; Oskam & Boswijk, 2016; Prothero et al., 2011). Yet, the vulnerability of the sharing economy has been exposed since COVID-19 started in 2020. For example, evidence from Australia suggests that the financial loss as result of the pandemic is around AUS $14 million for Airbnb hosts (89.5% reduction in income) from January to August 2020, suggesting that hosts suffered 6.5 times more than the Airbnb platform itself (Chen et al., 2020). On the contrary, UberEats and the other three main food delivery apps in the US (DoorDash, Grubhub and Postmates) saw a collective rise in revenue of more than US $3 billion during the pandemic (Sumagaysay, 2020). These suggest that the effects of the pandemic on the sharing economy are not always negative. This chapter focuses on one specific player in the sharing economy, Airbnb, with implications for other peer-to-peer accommodation providers and beyond.

With the huge variables ushering in the Chinese takeaway market “COVID-19 pandemic”, there are more sinking user markets that continue to flood into it and the takeaway industry is booming. After the epidemic, the meaning of the word "takeaway" was extended from catering to food delivery, medicine delivery, and all kinds of necessities, and the takeaway delivery gradually accompanied the new retail consumption habits developed by users during the epidemic. In the whole service system of take-out delivery, with the increasing number of take-out delivery personnel, the take-out delivery e-bikes used are still ordinary travel e-bikes, lacking consideration and design of user behavior and usage scenarios. In the context of the new national standard "electric bicycle safety technical specifications" policy, there are still many takeaway delivery using electric bicycles do not meet the specifications, due to the delivery of limited time to arrive, a one-time delivery of overloaded goods and other cases of traffic accidents caused by frequent. This study introduces the KANO model and hierarchical analysis and TRIZ theory to study the real needs of users, and seeks to conduct innovative design research on take-away delivery electric bicycles.In the preliminary stage of the study, the secondary data research method was used to conduct preliminary summary research on a variety of businesses extending from the take-out industry, and the delivery process of riders in the take-out industry service system after receiving orders was derived through observation, interview, focus group discussion, and questionnaire research methods. Based on the KANO model framework to classify and prioritize user requirements, the non-linear relationship between product features and user satisfaction is identified based on the analysis of the impact of user requirements on user satisfaction. Recognize product and service requirements from the user's perspective, design and publish a valid questionnaire, summarize and classify the survey results, and build a quality model. Further research and analysis of the quality model to identify the sensitivity of specific measurement indicators. Combined with the hierarchical analysis method, the elements within the same level are compared in two-by-two analysis to determine their respective weights for the previous level, and finally give the importance ranking of all factors relative to the total goal, and finally tap into the real user needs. The TRIZ theory is then used to provide systematic theoretical and methodological tools for problem transformation, analysis and solution of needs creatively using innovative thinking methods and problem analysis methods, following the laws of technological system evolution. The KANO model and hierarchical analysis method are effectively combined with TRIZ theory to gradually quantify the qualitative analysis, which can generate more objective research data conclusions and provide innovative ideas for the design research of electric bicycle for delivery.In this study, the different needs of users are organized and differentiated in a hierarchical manner through KANO model analysis, combined with different emotional hierarchical need points of users. Predict the direction of users' emotional needs for the product, and match their emotional requirements with the product at different levels. Combined with the interview research, the design study was conducted taking into account the human-machine factors for the take-away delivery electric bicycle. In line with the new national standard policy, the delivery time of the rider is guaranteed at the same time, the area of the delivery goods and pick-up efficiency is improved, and the riding experience and safety of the rider are also taken into account. This is a research exploration of the takeaway delivery industry.This study combines the KANO model, hierarchical analysis and TRIZ theory to design a study on the process of users for take-out delivery. Through an in-depth study of user behavior, combined with the quantitative study of hierarchical analysis, different levels of demand are derived. Then, through TRIZ theory and emotional design, the take-out delivery service system was optimized and the innovative design of the take-out delivery bicycle was studied. In this study, based on the KANO model approach, the visual experience is designed from the appearance and user experience, giving the electric bicycle emotional factors to form a design that meets the user's needs. The whole design process also explored the correlation between design emotional needs and design form. The design solution evaluation results verified the effectiveness and feasibility of the KANO model approach to user needs analysis in product design and provided an effective solution for the design innovation of electric bicycle for take-away delivery.

During COVID 19 pandemic the global needs for online shopping, ride-sharing transportation, and food delivery services have been dramatically increased. The drivers who suffer from diabetes especially low blood sugar level (Hypoglycemia) are more likely at risk than others. Earlier literature has revealed that hypoglycemic issues in patients with diabetes are correlated with significant changes in scalp electroencephalography (EEG); signals amplitude (time domain) or power spectral density (frequency domain). In addition, Haret rate variability HRV which reflects the balance between the sympathetic and parasympathetic nervous system has been proven as one of the indicators of Hypoglycemia. The aim of this paper to propose a conceptual design of a Vehicle detection system using both EEG and HRV measures at the same time in real time feed using non-invasive sensors to reduce the potential of driver’s cognitive dysfunction.