Optimal delivery time and subsidy for IT-enabled food delivery platforms considering negative externality and social welfare
| DOI | https://doi.org/10.1108/IMDS-09-2022-0554 |
| Published date | 14 February 2023 |
| Date | 14 February 2023 |
| Pages | 1336-1358 |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
| Author | Bin Zhao,Haoquan Tan,Chi Zhou,Haiyang Feng |
Optimal delivery time and subsidy
for IT-enabled food delivery
platforms considering negative
externality and social welfare
Bin Zhao, Haoquan Tan and Chi Zhou
School of Management, Tianjin University of Technology, Tianjin, China, and
Haiyang Feng
Laboratory of Computation and Analytics of Complex
Management Systems (CACMS), Tianjin University, Tianjin, China
Abstract
Purpose –Information technology-enabled gig platforms connect freelancers with consumers to provide
short-term services or asset sharing. The growth of gig economy, however, has been accompanied by
controversy, and, recently, food delivery platforms have been criticized for using data-driven techniques to set
strict delivery time limits, resulting in negative externality.This study aims to provide managerial implications
on the decisions of delivery time and subsidy for food delivery platforms.
Design/methodology/approach –The authors develop an analytical framework to investigate the optimal
delivery time and subsidy provided to delivery drivers to maximize the gig platform’s profit and compare the
results with those of a socially optimal outcome.
Findings –The study reveals that it is optimal for the platform to shorten the delivery time and raise the
subsidy when the food price becomes higher; nevertheless, the platform should shorten the delivery time and
lower the subsidy in response to a higher delivery fee. Increases in the food price or delivery fee have non-
monotonic effects on the number of fulfilled orders and the platform’s profit. In addition, the authors solve the
socially optimal outcome and find that a socially optimal delivery time is longer than the platform’s preferred
length when the delivery fee is high and the negative externality is strong.
Originality/value –The food delivery platform’soptimal decision on delivery time is derived after taking negative
externality into account, which is rarely considered in the prior literature but is a practically important problem.
Keywords IT-Enabled food delivery platform, Delivery time, Subsidy, Negative externality, Social welfare
Paper type Research paper
1. Introduction
Over the past decade, with the rapid evolution of digital technologies, the gig economy has been
developing rapidly around the world. In the United States, for example, the number of gigs
workers is forecasted to reach 90.1 million by 2028 (Ruby, 2022). The European Commission
estimates over 28 million people on the continent are self-employed on digital platforms, rising to
43 million by 2025 (Korpar, 2021). The gig economy involves electronic mediation of employment
arrangements, in which individuals find short-term tasks or projects via gig platforms that
connect them to clients and process payments (Kuhn and Galloway, 2019). Big data technology
fosters information sharing on gig platforms (e.g. TaskRabbit, Uber and Meituan), playing
importantroles in matching labor supply and demand (Friedman, 2014).
Despite the rapid growth of the gig economy, it has been subject to controversy. On gig
platforms, gig workers are individual operators rather than long-term employees of a company
(Tassinari and Maccarrone, 2017). This transfers more of the economic burden to workers and
IMDS
123,5
1336
This work was supported by the Ministry of Education in China Project of Humanities and Social
Sciences (No. 21YJA630120), Tianjin Philosophy and Social Science Planning Project (No. TJGL22-013)
and National Natural Science Foundation of China (Nos. 72022012 and 71971153).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 13 September 2022
Revised 14 December 2022
23 January 2023
Accepted 25 January 2023
Industrial Management & Data
Systems
Vol. 123 No. 5, 2023
pp. 1336-1358
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-09-2022-0554
excludes them from the insurance covered by the traditional companies for their employees
(Friedman, 2014). These problems are particularly severe on food delivery platforms. Due to the
delivery drivers’self-employed status, the food delivery platform controls the service process of
delivery drivers mainly through adjusting delivery time and salary. Meituan, a leading food
delivery platform in China, has been criticized for managing and monitoring delivery drivers
using bigdata technology andsetting a too-strictdelivery time limitfor them (Wang, 2021;Yin,
2020). In an attempt to avoid being fined for delayed delivery, the delivery drivers tend to break
traffic laws, which leads to more traffic accidents and threatens public safety. Delivery drivers
find themselves in a dilemma defined by delivery security and work opportunity. From the
platform’s perspective, providing subsidies to delivery drivers is a common means to improve
their engagement and satisfaction. For instance, Meituan and Ele.me, two major food delivery
platformsin China, startedsubsidizing delivery driverssince their platformlaunch (Van Doorn
and Chen, 2021). Motivated by the practical issues faced by Meituan and responding to the calls
(Global Times, 2021;Xia and Liang, 2021), this study investigates how a food delivery platform
optimally sets the delivery time and subsidy provided to the delivery drivers to maximize its
profit and further analyzes the socially optimal delivery time after taking into account the
negative externality resulting froma short delivery time.
The conflict between the delivery drivers’safety and the delivery time set by the algorithm
gives rise to a series of social controversies; thus, the setting of delivery wage and time is of
critical importance. Although research has focused on the optimal pricing strategy of
different types of sharing platforms, how to set gig workers’wages and amount of time spent
in piecework on gig platforms has not been addressed. Consequently, in the present study, we
concentrate mainly on how the platform sets the subsidy provided to the delivery drivers and
the delivery time. Further, given that gig platforms are multi-sided platforms, there is a
significant cross-side network effect (Chu and Manchanda, 2016), and we develop a two-sided
model that focuses on the interaction between delivery drivers and consumers: a larger
number of delivery drivers attract more consumers to join the platform and vice versa.
In our model, we consider a monopoly platform that connects restaurants, delivery drivers
and consumers. A consumer can decide whether to submit an order and ask for food delivery
service, and consumers pay a delivery fee for each fulfilled order. To boost the participation of the
delivery drivers, the platform may provide subsidies to them. Thus, the wage (per order) of the
delivery drivers consists of delivery fee paid by the consumer and the subsidy provided by
the platform. The delivery drivers evaluate the risk based on the delivery time and payment per
order to decide whether to join the platform. We study how the platform sets the subsidy offered
to the delivery drivers and delivery time to maximize its profit. Further, we solve the socially
optimal delivery time and the corresponding subsidy, taking into account the negative
externality and compare the platform’s preferred outcome with the socially optimal outcome.
We highlight a few major findings from our analysis. First, we uncover the conditions under
which the platform should provide subsidy. In particular, when the delivery fee is low, the gig
platform should subsidize the delivery drivers to attract them to work for the platform. Second,
after solving the platform’s optimal delivery time and subsidy, we find that, as the food price
becomes higher, it is optimal for the platform to shorten the delivery time and increase the subsidy
provided to the delivery drivers; as the delivery fee becomes higher, the platform is better off setting
a shorter delivery time and a lower subsidy. Third, under the platform’s preferred outcome, the
number of platform participants (consumers or drivers) and the platform profit first increase and
then decrease as the food price or the delivery fee increases. Fourth, when the delivery fee is high or
the negative externality is strong, the socially optimal delivery time is longer than the platform’s
preferred length, and the platform will provide a lower subsidy to the delivery drivers under the
socially optimal delivery time. Finally, we explore how the socially optimal outcome responds to
environmental changes and find that a higher delivery fee shortens the delivery time and increases
the subsidy, which is analogous to the platform’s responses to the changes in the food price.
IT-enabled
food delivery
platforms
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