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626 Case 4 ƒ Uber: Competing as Market Leader in the US versus Being a Distant Second in China
CASE Uber: Competing as Market Leader
in the US versus Being a Distant
Second in China
Jochen Wirtz and Christopher Tang
Uber allowed people to book and share rides in private
cars via their smartphones. With its headquarters in the
US, it operates in 60 countries and has a strong presence
in the Asia–Pacic region. is case study explores Uber’s
development and growth, rst in the US, then its global
expansion and subsequent foray into China. Despite
enjoying international success with deep penetration
in major cities, Uber opped in the Chinese market.
What were the reasons for its failure in China, given its
spectacular performance in many other countries?
Uber was founded in 2009 by Travis Kalanick (current
Chief Executive Ocer) and Garrett Camp (Co-Founder)
in San Francisco. Its business model rested on the use
of an app to call for a driver at any time and location
(Exhibit 1). Uber managed to build a spectacular network
of drivers and passengers in just three years, thriving
in what some people term as an “instant-gratication
economy”, powered by the smartphone as the remote
control for life. “If we can get you a car in ve minutes,
we can get you anything in ve minutes,” Kalanick said1.
© 2016 by Jochen Wirtz and Christopher Tang. Jochen Wirtz is
Professor of Marketing at the National University of Singapore, and
Christopher Tang is a UCLA Distinguished Professor and the holder
of the Edward W. Carter Chain in Business Administration.
The authors thank Chia En Celeste for her excellent assistance with
the data collection, analysis, and writing of this case study.
1 Vanity Fair covered an interview with Travis Kalanick in December
2014; this article’s insights can be found throughout the Uber
case study:
Uber sets prices
for rides.
Uber splits ride
receipts with drivers,
keeping an average
of 20%.
Customers pay less for
Uber than traditional
Drivers have access to
more income.
Uber uses revenues
to cover expenses;
customer acquisition,
tech development,
infrastructure, etc.
To grow, Uber invests in
R&D and acquisitions.
Fundraising from
investors conducted
Based on distance, car type,
demand period
Local companies may be acquired
to gain a foothold in new market
Uber reduces this percentage in
cities with strong competition
Regulatory and legal issues
increases costs
Exhibit 1 Uber’s Business Model
disruptive-cab-ride-to-riches-the-uber-payo; accessed October 27, 2015.
Case Studies 627
Expanding outside of the US, Uber was a threat to taxi
services in Europe and Asia, triggering protests in France,
Germany, and India. Despite resulting government
scrutiny, tighter regulations and disputes with local taxi
companies, Uber’s disruptive business model successfully
posed an eective challenge to taxi monopolies in the
countries it operated in. As of August 2015, Uber clinched
the title of the most valuable startup in the world, valued
at $51 billion.
Enjoying first mover advantage in app-enabled
transportation services and ridesharing, Uber was far
more successful in its number of users and drivers than
its main American competitor, Lyft. Lyft positioned
itself as a more informal, community-centered way to
travel, with the expectation that drivers and shotgun-
riding passengers would strike up a conversation during
the ride. By being a late entrant to the market entering
three years aer Uber, Ly managed to operate in only
65 American cities by the end of 2015. In contrast, Uber
had been operating in a total of 300 large cities in 60
countries. Both companies oered a myriad of services
at dierent price points (Exhibit 2).
China, with a projection of 221 cities containing a
population of one million or more, was a highly attractive
market for any internationally-minded taxi company.
Uber pioneered its taxi service in Shanghai in 2013.
Entering dicult markets was not new to Uber, which
had previously successfully navigated diverse markets
in the UK, India, and South Africa. Nevertheless, Uber
encountered unique roadblocks in China — strong
competitors, existing low-cost taxi services, and a lack of
know-how to navigate around local regulations and even
corrupt ocials. Uber also faced tough competition from
a much larger local player, Didi-Kuaidi (known locally as
ૺૺӯԊ). Didi boasted more than one million drivers
in 360 cities in China, whereas Uber only had about
100,000 drivers in 20 cities.
Exhibit 2 A Comparison of Uber and Lyft’s Services in the US
Uber Lyft
The least expensive Uber service. Seats four riders. Drivers use
everyday cars that are 2,000 or newer
The lowest cost service. A request for a Lyft will send to you a
four-seater car
Seats at least six passengers. An UberXL car will be an SUV or a
Minivan. Higher fare price than UberX
Lyft Plus
A car that seats six or more passengers. Slightly more expensive
than Lyft
Share your ride with another person and split the cost
Lyft Line
A ridesharing service that pairs you with other passengers who
are traveling along the same route. Similar to a carpool
A luxury sedan that seats up to four riders. Expect a BMW,
Mercedes, Audi, etc., with a leather interior
Uber's executive luxury service. Commercially registered and
insured livery vehicles, typically a black SUV or luxury sedan
Services are sorted according to fares in ascending order. Information adapted from
628 Case 4 ƒ Uber: Competing as Market Leader in the US versus Being a Distant Second in China
The first conceptualization of Uber’s business model
started in Paris in 2008, when founders Kalanick
and Camp could not get a cab aer returning from a
conference. e two discussed solving the problem with
a mobile app — push a button and get a car.
In 2009, UberCab was born. After downloading its
app, registering and entering credit-card information,
customers could summon a car with the press of a
button. G.P.S. took care of the location, and the cost was
automatically charged to the customer’s credit card, with
tips included. It did not take long for the company to run
into regulatory issues when the San Francisco Municipal
Transportation Agency objected to the use of “cab” in
UberCab’s name a few months aer its launch, given its
operation without a taxi license.
After changing its name to Uber, things went on an
upward trajectory. Valued at $60 million aer only six
months of operation, Uber received support not just
from angel investors and venture capitalists, but also from
prominent celebrities like Ashton Kutcher (founder of
A-Grade Investments), Jay Z (co-founder of Roc-A-Fella
Records), and Je Bezos (founder of Amazon).
Uber faced many obstacles and criticism in its early years.
One criticism was directed at the “surge pricing” model,
which referred to the practice of charging customers
higher prices at peak hours. It garnered a lot of attention
during a snowstorm in New York in December 2013,
when rates increased up to eight times its standard rates,
attracting a ood of negative publicity. Kalanick defended
this practice with economics — it reected demand and
supply at any given point in time, and eectively allocated
capacity to customers who were willing to pay even
during super-peak periods. To ameliorate public outrage,
Uber eventually tweaked its pricing model and limited
fare hikes to a maximum of 2.8 times the normal fares
in the face of snowstorms in New York2. Uber proudly
announced in January 2015 that it had more than 160,000
active drivers in the US who provided more than a million
rides a day.
Uber’s operations covered 75% of the US population,
and even as it sets its sights on international markets, it
remained focused on growth at home. Its eorts were
mainly channeled towards building a strong network of
drivers and improving service for consumers. ese eorts
paid o — 40,000 US drivers joined Uber in December
2014 alone; service eciency saw improvements with
91% of UberX rides arriving in less than 10 minutes in
Philadelphia; and the demand for Uber peaked when
people celebrate and consume alcohol, testifying to Uber’s
position as a “better late-night option. Uber also started
to pay more attention to corporate social responsibility.
For example, its program UberMILITARY led to the
hiring of 10,000 veterans — ex-military personnel — as
drivers, while the use of UberPOOL was calculated to
save more than 13,000 gallons of fuel each month in San
Francisco alone3. By stretching its network of drivers
to dierent demographic segments in society, oering
alternative ridesharing options and reducing waiting
time, Uber was able to build on network eects for drivers
and loyalty among consumers, making it dicult for
competitors to enter and grow in its markets.
Ly was founded in 2012 by John Zimmer and Logan
Green, launched primarily as a low-cost competitor to
Uber. Its focus was on short, urban rides. Ly logged
an impressive 2.2 million rides in December 2014, with
revenues for that year estimated at $130 million. In
May 2015, Ly was valued at $2.5 billion4, its promising
growth bolstered by estimates of 2015 revenues to be $796
million, an impressive 512% jump from 2014.
While Uber touted its iconic black cars to dierentiate its
luxury services for professionals (Exhibit 2), Ly adorned
its cars with a pink moustache (Exhibit 3), which had
become an identifying factor for the company when
driving down the streets of San Francisco5. This was
accompanied by the greeting of all Ly passengers with a
st bump. While these tongue-in-cheek communications
2 The Guardian covered the revision in surge pricing by Uber in
3 Uber Expansion, not officially affiliated with Uber, provides a
range of statistics pertaining to Uber’s expansion in this page:
4 Business Insider website reported on the $2.5 billion valuation
on 15 May 2015; the whole article can be found here: http://
5 Wired Magazine reports on the changing of Lyft’s most prominent
quirks in January 2015, with reasons: http://www.wired.
Case Studies 629
were successful in positioning Ly dierently, Ly’s top
management announced plans to tone down the carstache
and scrap the st bump practice in January 2015. is
decision was made with the realization that what worked
in the West Coast would not work in Lyfts plans to
expand to other cities in the US, or even internationally.
Regardless of Ly toning down its practices, it still prided
itself on its friendliness and laidback driving experience
when compared to Uber. An internal presentation from
March 2015 that was leaked to Bloomberg revealed its
criticisms of Uber for its “top-down model”, “exclusive
mentality”, and “anti-social culture”6. On the other hand,
Lyft claimed its growth to be bottom-up and led by
drivers through positive word-of-mouth marketing, 32%
of whom were female. All in all, Ly believed itself to be
a “trusted brand” delivering a “social experience” with
memorable quirks — the carstache being one of them.
Apart from its more relaxed brand image, Ly mainly
positioned itself as a lower-cost alternative to Uber. Since
2014, the company announced big price cuts — they rst
cut prices by 20% in early 2014 and then reduced them
again by 10% in May7. Ly also used a surge-pricing
Exhibit 3 Lyft’s Pink Carstaches
The original version of the carstache. Retrieved from http://cdn.
Lyft’s new moustache, termed a “glowstache”. Retrieved from http://
6 Eric Newcomer and Leslie Picker (2015), “Leaked Lyft Document
Reveals a Costly Battle With Uber”, Bloomberg Businessweek.
Retrieved from 2015-
7 Vator talks about Lyft’s model with respect to prices and Uber’s
response in 2 articles written in 2014:
04-24-lyft-takes-off-hits-24-new-cities-in-one-day and http://
model; to ward off potential criticism, it provided
discounts of 10–15% during o-peak hours. While both
companies engaged in aggressive price cutting strategies
whenever they operated in the same city, Ly drivers
typically charged — and earned — less than Uber drivers
(Exhibit 4), which was consistent with Ly’s positioning
of being a lower cost alternative.
While Ly enjoyed strong branding and was expected to
spend a generous 60.5% of its revenue on marketing in
December 2015, its operations were not as entrenched
as Uber’s. One example can be seen in its attempts to
break into New York’s tight network of taxis in July
2014, where Uber had already operated for three years.
A public exposé occurred, in which the company was
issued a cease-and-desist letter by the New York State
Department of Financial Services just days before it
planned to open operations8, for non-compliance with
safety requirements and licensing criteria. Uber also
aggressively cut the price of its UberX service by 20% that
week, to price itself signicantly lower than regular taxis
just before Ly entered the market. e bottom line of
Ly and Uber’s rivalry was that the latter enjoyed a rst-
mover advantage and, having established a presence in
major cities beforehand, beneted from network eects
and sucient margins which allowed it to cut prices when
needed, to erect barriers to entry and slow down the
gr owt h of compe tit ors . Ub er’s si gni ca ntl y hi ghe r ma rke t
valuation also helped to raise more capital each funding
8 To follow Lyft’s saga in New York City in July 2014, the time period
it decided to offer its services to Hong Kong, read Bloomberg
Businessweek’s exposé on the issue:
630 Case 4 ƒ Uber: Competing as Market Leader in the US versus Being a Distant Second in China
round — it raised $1 billion in July 2015 while Ly raised
only half the amount in the same year. is helped sustain
any losses in operations in an era of price cuts.
Finally, Ly tried to expand fast — it raised $250 million
in 2014 and another $530 million in March 2015, with
the main goal of expanding internationally and entering
less competitive markets without already entrenched
In China, Uber found itself in the position of the much
smaller late entrant. Here, Didi was the clear leader. Didi-
Kuaidi, referred to as Didi by the public, was the product
of a merger between Didi Dache and Kuaidi Dache, two
of Chinas leading taxi-hailing apps. In February 2015, the
merged entity was valued at $6 billion, and doubled to
$12 billion by September in the same year9. Didis services
covered 80% of Chinas huge market of 800 million city
dwellers (Exhibit 5), being a deep-pocketed dominant
player reaping the network-leveraging dividends of
having drivers and customers hooked on to its product
9 Gerry Shih (2015), “China taxi apps Didi Dache and Kuaidi Dache
announce $6 billion tie-up”, Reuters. Retrieved from http://
Exhibit 4 Average Income of Uber and Lyft Drivers per Trip in Selected Cities.
Adapted from
per-trip; accessed October 27, 2015.
Didi Uber Others
Exhibit 5 Market Share of App-Based Car-Hire Services in China
Adapted from
waterloo, accessed October 10, 2015.
Case Studies 631
Didi was also far more successful than Uber in the
aspect of legal legitimacy, acquired from its local
connections10. Didi enjoyed backing from powerful
Chinese government investors, the most notable one
being the China Investment Corporation, China’s
sovereign fund in charge of managing foreign exchange
reserves. These well-connected investors opened up
opportunities for Didi at the expense of its competitors,
which included working with regulators. A success was
commemorated in October 2015, when Didi became the
rst car-hailing app to be awarded an ocial license in
Shanghai. is authorization was hailed as a landmark
decision, allowing Didi to operate its ride-hailing business
in the city without any fear of legal infringements11. It
assuaged concerns among taxi drivers, as one revealed
in September 2015, “I worry all the time about being
caught and ned by the government. My biggest concern
is policy uncertainties.” With this formal recognition,
more drivers were certain to sign on with Didi vis-à-vis
its competitors, which could not provide the same level
of regulatory security.
From the beginning, Didi pursued an aggressive strategy
to lure as many drivers to its app as possible. Didi spent
$700 million on rewards to taxi drivers between 2013 and
2014, attracting both new drivers and switching drivers
from existing taxi companies with monetary incentives.
So important were taxi companies as a source of growth
that the sales team in Didi even went to the streets to
promote their app to cabbies. By allowing its mobile
apps to be used by taxi drivers as an additional channel
to attract more passengers, Didi sought to convert these
drivers to work for them exclusively during peak hours by
oering more attractive rates and bonuses. is method
of attracting and converting drivers with the use of
incentives allowed Didi to swily convert a large number
of taxi drivers, quickly scaling their operations in other
cities. It also highlights the main dierence between Didi
and Uber’s business model — Didi started out with taxi
drivers adopting its app, before adding non-traditional
transport services to its portfolio while Uber started out
with the intention of disrupting the taxi industry itself
by replacing its services.
10 Deborah Findling (2015), “What stands between Uber and
success in China?”, CNBC. Retrieved from http://www.cnbc.
11 “In the race for legal legitimacy,” (2015) Wall Street Journal.
Retrieved from
Part of Didi’s fast growth was also due to tweaking and
expanding its business model to meet unique local
demands. For example, urban dwellers frequently
looked for a compromise between overcrowded public
transportation and the high cost of driving to work
themselves, which led Didi to introduce Hitch as a
service oering in its app, which was a group ride-sharing
service along preset routes. Hitch was for casual drivers
who wanted to recoup some gas money and toll fees on
their daily commute — by inputting their start and end
points into the app, Hitch connected them with nearby
passengers heading in the same direction, allowing
them to share the ride. is was dierent from the more
traditional taxi-type service as drivers had control over
where the ride ended, and they did not make a prot o
the service — passengers only paid for the cost of gas
and tolls. is allowed for fares that were 30–40% lower
than those of regular taxis. For Didi, Hitch encouraged
consumers to try Didi’s services at a low cost, therefore,
opening a pathway for them to convert to the more
expensive for-prot taxi service eventually.
Clearly, Didi understood the local market’s needs well
enough to carry out eective customer segmentation to
target the dierentiated needs in its product development.
is allowed for the building of customer loyalty to the
main corporate brand, and the greater willingness to try
and switch between Didi’s various services, depending
on the occasion of travel.
Uber had prioritized China as a key market for expansion,
and it was befuddling to the company to be in a distantly
second position. Uber to Didi in China was like Ly to
Uber in the US. In a cruel twist of fate, Didi recently
invested $100 million in Ly in September 2015, forming
an international ride-sharing partnership.
Uber managed to capture only 11.5% of the Chinese
market, but experts did not find it surprising given
China’s unique institutional structures. Greg Tarr, partner
at CrossPacic Capital, commented, “When you have
great technology and a great business model but don’t
understand some of those local business premises... West
Coast aggressiveness will only get you so far. China is
such a dierent animal in terms of dealing with the local
culture, the protectionism and the fact that you don’t
have local investors.” is demonstrated the need for
632 Case 4 ƒ Uber: Competing as Market Leader in the US versus Being a Distant Second in China
Uber to better understand the Chinese market, rather
than merely transplanting its San Francisco model of
attracting American drivers and dealing with local
regulations. Uber thus attempted to work closely with
Chinas Ministry of Transport by setting up servers in
China, in an eort to obtain an internet service company
license by sharing data with local transport authorities.
Reformation in Uber’s marketing strategy in China was
a priority, and steps were taken to set up local teams
to localize logistics, including language and support
services. At consumers’ requests, Uber strategically
partnered with Chinese search giant Baidu, ditching
Google Maps for Baidu maps into its app. Baidu also
prominently advertised Uber on its main page with a
prominent “Get a Car” button, linking it to Uber’s app.
Partnerships with Alibaba also allowed Uber to use the
simpler and non-credit card-based payment mechanism
of Alipay12. is was important as many Chinese residents
did not own credit cards.
Uber competed vigorously with Didi on many other
fronts to attract drivers to sign on with their companies.
Both oered bonuses for drivers who hit ride targets, in
a bid to extend geographical coverage and reduce wait
times. is was based on an industry-wide understanding
12 The Market Mogul covers Didi’s triumph over Uber in the
Chinese market in a short read:
that spending cash to build an operational base as
quickly as possible leveraging on economies of scale was
the only way to win in China. As Didi’s President, Jean
Liu, revealed, “By using subsidies to get more cars on
the road… waiting times were shortened, fares became
cheaper, more users were drawn on to the platform and
drivers on the platform. We have already created such a
virtuous circle of increased orders, customer retention.13
To try and respond more eectively to Didi’s diversication
of services, Uber looked beyond its typical car-ordering
model that worked so well in other international markets.
In August 2014, Uber announced the implementation of
Peoples Uber, where drivers oered “non-prot” rides
to carpooling passengers who only paid for the cost of
gas and maintenance. is was Uber’s version of Didi’s
Hitch, competing directly to attract people who wanted
low cost rides.
Uber seemed to be playing catch-up rather than setting
trends in the China market. e race to grab market share
was critical because it was understood that whoever got
ahead rst would remain the dominant player for a long
time. Uber had to decide how to eectively compete with
a much larger competitor, where to side-step competition
and innovate new services, and where and how to go
head-on with Didi.
13 Financial Times reports on the intensive cash burning on
subsidies in the China market by Didi and Uber in http://www.
Study Questions
1. How could Uber retain its dominant position in the US market? Are there services and/or geographic
niche markets where Uber should accommodate Lyft?
2. How could Uber effectively compete with Didi? Should it compete head-on in China, or should it side-
step competition by focusing on niche markets through service innovation, and geographic expansion
within China?
This case is published in:
Jochen Wirtz and Christopher Lovelock (2016),
Services Marketing: People, Technology,
Strategy, 8th edition, World Scientific.
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... Although Uber and Didi Chuxing in China share many similar design features, they adopt the radically different models of accepting the orders: Uber in China uses the PDI model in which the destination information can be seen after the drivers accept the consumers' requests (Jin et al. 2017). Didi Chuxing uses the ADI model in which the information will be observed before the drivers confirm the acceptance of the orders, which is similar to the traditional taxi model (Wirtz and Tang 2016). The different models have their own characteristics: Under the PDI model, all the passengers' requests can be met and the platform will obtain more revenue. ...
... Evidently, the cost c s and subsidy θ i (s) are both increasing function of the distance s. To attract more drivers at an early stage of development, the platform needs to provide so generous subsidies to enhance competitiveness (Wirtz and Tang 2016) and this behavior also triggers a subsidy war between the platforms. Thus, we define θ i (s) = z i s 2 , z i ∈ (0, 1), z A = z, z P = z + z and c s = ks 2 , k ∈ (0, 1) 8 for the first time. ...
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The two leading on-demand service ride-sharing platforms in China have used very different destination information sharing models in the past period of time: Didi Chuxing uses an ex-ante destination information model (ADI) in which drivers can acquire the passengers’ destination information before receiving the orders, whereas Uber in China uses an ex-post destination information model (PDI) in which participants can only obtain the information after receiving the requests. This work compares ADI and PDI to study their impacts on the decisions as well as revenue/welfare of all stakeholders. We show that the PDI model generates more revenue for the platform than the ADI model in most cases, but it undermines the welfare of the passengers. This stands in sharp contrast with the existing views which argue that the ADI model can result in lower consumer surplus. Moreover, the platform can attract participants to choose the PDI model by increasing subsidies or degree of subsidies. Drivers are better off under the PDI model in most scenarios. The only exception is when both the opportunity cost and subsidy are lower or the subsidy is higher. Under this condition, the drivers can be worse off under the PDI model. Finally, if the platform adopts a new subsidy scheme which is related to the income of the participants, drivers are always better off under the PDI model but this model is still not good for the passengers. Besides, the higher demand state and equitable payoff are, the more social welfare under the ADI model it will generate.
... The unique ride-sharing service being distributed via the mobile phone application with dynamic pricing that is often below market rates makes Uber a formidable competitor compared to any other offering in the United States market at the time of entry (Wirtz and Tang 2016). In addition, other dimensions that are not highly differentiated still work to make Uber more entrepreneurial. ...
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This paper presents arguments toward developing a theory of entrepreneurial differentiation. The comprehensive theory enables the distinction between entrepreneurial and non-entrepreneurial firms by providing a clear framework based on the idea of differentiation. In developing our framework, we relied heavily on previous research and prior theoretical developments as well as established understanding of entrepreneurial phenomena. Our theory delineates eight specific dimensions of differentiation we utilize to determine and assess the entrepreneurial profile of the firm. We argue that these eight dimensions work together to increase the overall amount of differentiation of the firm, and therefore, entrepreneurial nature of the firm. We then present formal propositions of the Theory of Entrepreneurial Differentiation as we relate our eight dimensions of differentiation to entrepreneurial orientation, management objectives, and organizational outcomes with specific relationships between each of these constructs. The resulting framework provides a comprehensive explanation of how entrepreneurial firms compete. We then assess how these theoretical conceptualizations impact existing research and how future research might proceed given these ideas.
... In China, the major ride-sharing business is operated by Didi Chuxing (abbreviated as Didi in this paper) who has monopolized the market since 2016 (Wirtz & Tang, 2016). Although transportationrelated agents have put large efforts into seeking solutions for traffic optimization, the substantial increase in demand, particularly during peak periods, has resulted in problems with queueing for potential travellers in China (Fang et al., 2013). ...
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Rapid development of the ride-sharing economy has led to a rising need to better understand travellers’ decision making regarding their travel time and cost. The present study conducted a travel choice experiment using smartphone applications, based on data collected from 532 respondents and 2128 stated-preference surveys in China. Based on prospect theory, the experiment utilized a coupon reward policy to analyze how much ride-sharing platforms might influence travellers’ choices in both work and leisure contexts. The results of an ordered logit model revealed that older residents were likely to pay more to reduce waiting time. It was further found that tourists had significantly higher probabilities to take expensive alternatives with shorter queueing time. The tourists’ value of time was higher than that of residents, while the reward policy employed was found to increase the residents’ value of time. Specific theoretical and managerial implications of the findings are discussed. Highlights • It conducted stated-preference surveys regarding travellers’ ride-sharing choices in China • The choice experiment employed four scenarios: rewarded residents, non-rewarded residents, rewarded tourists, and non-rewarded tourists • The logit model revealed older residents were likely to pay more to reduce waiting time • The tourists’ value of time was higher than that of residents • The reward policy increased the residents’ value of time, but not that of the tourists
... Watanabe et al. [14] present a common evolutionary pattern about society, economy, and technology in the ride-hailing market by summarizing the development path of Uber. Wirtz and Tang [25] review the development of Uber in America and its global expansion and analyze the competition between Uber and Didi Chuxing in China. From the perspective of drivers and TNCs, Beer et al. [26] make the comparative analysis about ride-hailing regulation policy of major cities in the United States. ...
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With the popularity of the sharing economy, ride-hailing services have greatly affected people’s travel and become a new travel mode for urban residents. However, the lack of effective industry regulation has resulted in serious operational problems and growing difficulties in the furthering development of ride-hailing services in China. Therefore, it is necessary to study the regulation strategies of multiple subjects involved in ride-hailing industry. Based on evolutionary game theory, the paper establishes the tripartite evolution game model about regulation strategies of ride-hailing industry. The theoretical research and simulation results show that the evolutionarily stable strategy of a single subject (Transportation Network Company, driver or passenger) is affected by the strategies of other two subjects together. Moreover, when making the decision, the Transportation Network Companies (TNCs) need to consider the difference between benefits and costs, user scale, incentives and penalties from the government. Drivers need to consider their benefits and costs, travel user scale and penalties from the government and the TNCs. Besides, the benefits and costs, and the harmony of ride-hailing industry need to be considered for passengers. Potential policy implications are proposed.
... Tan (2016) concentrates on the Uber's practical development experience in China from the perspective of the sharing economy. Wirtz and Tang (2016) reviews the development of Uber in America and its global expansion in addition to analysing the competition between Uber and Didi Chuxing in China. ...
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The ridesourcing services market in China has recently experienced significant changes, which stem from its legalization and management policy. These changes impact multiple stakeholders of this market (e.g., drivers, passengers, government, competing services) and present them with new opportunities and challenges. This paper develops an evolutionary game model to analyse the Evolutionary Stable Strategy (ESS) between the Transportation Network Companies (TNCs) and drivers. The new model is explored and analysed with simulation experiments to observe the dynamic route of multiple stakeholders. The theoretical research and simulation results indicate that under the authorities’ control over the TNCs, when the net income under strict management is higher than that of the loose management for the TNCs, the final ESS is “Legal Operation, Strict Management”. When the net income under strict management is less than that of the loose management for the THCs, the strategy of “Illegal Operation, Loose Management” may gain popularity and continue to grow; in this case, the ESS may also not exist. The model indicates the strength of the government’s control plays a significant role in leading the achievement of “Legal Operation, Strict Management”. As a consequence, to achieve the perfect evolution of “Legal Operation, Strict Management”, it is necessary for the government to impose a greater penalty on illegal drivers and ensure appropriate compensation measures. The results of the study provide a useful reference for the sustainable development of the ridesourcing services market.
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This article investigates a long-term optimal spatial pricing strategy for a ride-sourcing platform that serves a particular (possibly populated) area with profit-driven service providers (i.e., drivers) and time- and price-sensitive customers (i.e., passengers). By observing that oftentimes, the price strategy is anisotropic and spatial-dependent, both the supply and request are endogenous, and we build an analytical bi-level optimization mode. In the upper-level formulation, the ride-sourcing platform aims at setting up the spatially heterogeneous pricing strategy to maximize its total profit. However, in the lower level, we solve the trip distribution model that characterizes the flow rates among zones given the travel demand rate at each zone. We prove that when the platform seeks to expand its business, the optimal number of participating drivers and their optimal wages will be influenced not only by the pricing strategy but also by the level of service of the entire platform. Our further investigation shows that the profit at a particular zone can be influenced by the potential customers’ service requests from other zones. Finally, we use the real-world data provided by DiDi Chuxing to numerically illustrate our model and theoretical results.
Prospect theory can systematically explain the decision biases caused by newsvendors' reference behavior in uncertain demand. Unlike the traditional newsvendor problems, the newsvendor in the on-demand services can manage the capacity depending on the price because of the self-scheduling participants. To bridge this gap, this work investigates the adequate capacity pool of eligible agents in an on-demand service newsvendor whose objective is to choose price to maximize the expected utility in uncertain demand settings. We consider two scenarios: With reference-dependent preferences and without reference dependence, and use the reference point of prospect theory to explain the on-demand service newsvendor's behavior. We show that the scenario with reference-dependent preferences affects the capacity pool of newsvendor and the eligible participant's revenue, where the subsidy and commission ratios play important roles. When the subsidy ratio is lower and commission ratio is higher, the eligible participants are better off in the scenario without reference-dependent preference. If the newsvendor needs to change the subsidy strategy after the price is determined, a lower bound on the subsidy ratio can be set to ensure the operations. Surprisingly, we find that the newsvendor considering reference payoff into the decision objective is worse off in profits because the pervasive "pull-to-center"-like phenomena in the price. Then, we examine the robustness of our results by considering a cap on the number of participants, the multiplier-based pricing in the peak periods, an alternative reference point, and a fixed price. The finding shows that our key result continues to hold.
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Connected vehicles and fully automated driving systems are the main objectives of the future transportation system. A safe interactive system that interacts with people and things is essential to achieve these objectives. In this context, a crowd intelligence system plays a key role in interactive system development. Crowd intelligence is a combined method of data collection, integration and analysis from devices such as the smartphones, wearables, vehicles and a wide range of Internet of Things applications to use them as sensors. This collective feedback-driven interactive method is opportunistic for the development of the future transportation system. In this study, a survey is conducted considering crowd-intelligence techniques for the transportation system. From this survey, various challenges of the intelligent transportation system have been outlined and crowd-intelligent solutions have been discussed. A layered structure of transportation system architecture is suggested considering various problems in each layer and its crowd-intelligent solutions. The crowd-intelligence-based mobility, traffic control, traffic prediction, parking solutions have been discussed in this survey. Moreover, the importance of crowd-intelligent techniques and its applicability is discussed for sustainable development of futuristic transport infrastructure.
Better understanding of the impacts of new mobility services (NMS) is needed to inform evidence-based policy, but cities and researchers are hindered by a lack of access to detailed system data. Application programming interface (API) services can be a medium for real-time data sharing and access, and have been used for data collection in the past, but the literature lacks a systematic examination of the potential value of publicly available API data for extracting policy-relevant information, specifically supply and demand, on NMS. The objectives of this study are: 1) to catalogue all the publicly available API data streams for NMS in three major cities known as the Cascadia Corridor (Vancouver, British Columbia; Seattle, Washington; and Portland, Oregon); 2) to create, apply, and share web data extraction tools (Python scripts) for each API; and 3) to assess the usefulness of the extracted data in quantifying supply and demand for each service. Results reveal some measures of supply and demand that can be extracted from API data and be useful in future analysis (mostly for bikeshare and carshare services, not ridesourcing). However, important information on supply and demand of most of the NMS in these cities cannot be obtained through API data extraction. Stronger open data policies for mobility services are therefore needed if policymakers want to obtain useful and independent insights on the usage of these services.
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E-hailing cars have become an important innovation in transportation operations. The E-hailing originated in the US and has developed rapidly around the world. As a laggard, China has experienced remarkable progress in exploring development period, rapid expansion period and adjustment period. The main reason is that China has a large user base, a sufficient labor force, a good network innovation environment, and a relatively relaxed regulatory environment. The new policy formulated by the Chinese government has caused a relatively large constraint on the Ehailing industry. Whether the E-hailing platform can maintain neutrality business and whether it can safeguarding consumer rights while developing the economy is an important challenge.
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