Understanding ride-sourcing drivers' behaviour and preferences

Delft University of Technology

Insights from focus groups analysis

Ashkrof, Peyman; Homem de Almeida Correia, Gonçalo ; Cats, Oded; van Arem, Bart

DOI

10.1016/j.rtbm.2020.100516

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2020

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Final published version

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Research in Transportation Business and Management

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Ashkrof, P., Homem de Almeida Correia, G., Cats, O., & van Arem, B. (2020). Understanding ride-sourcing

drivers' behaviour and preferences: Insights from focus groups analysis.

Research in Transportation

Business and Management

, 1-15. Article 100516. https://doi.org/10.1016/j.rtbm.2020.100516

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Research in Transportation Business & Management

journal homepage: www.elsevier.com/locate/rtbm

Understanding ride-sourcing drivers' behaviour and preferences: Insights

from focus groups analysis

Peyman Ashkrof

⁎

, Gonçalo Homem de Almeida Correia, Oded Cats, Bart van Arem

Department of Transport and Planning, Delft University of Technology, Stevinweg 1, Delft, Netherlands

ARTICLE INFO

Keywords:

Ride-sourcing

Transport Network Companies

Shared Mobility

Driver's Behaviour

Qualitative Research

ABSTRACT

Ride-sourcing has recently been at the centre of attention as the most disruptive mode of transport associated

with the so-called shared mobility era. Drivers, riders, the platform, policymakers, and the general public are

considered as the main stakeholders of the system. While ride-sourcing platforms have been growing, so did the

heightened tension between them and their drivers. That is why understanding drivers' behaviour and pre-

ferences is of key importance to ride-sourcing companies in managing their relationship with drivers (also

known as driver-partners) and in retaining them in the presence of competence. Ride-sourcing drivers are not

only chauﬀeurs but ﬂeet owners. They can make various operational and tactical decisions that directly inﬂu-

ence other stakeholders and the transport system performance as a whole. Conducting a series of focus groups

with ride-sourcing drivers in the Netherlands, we have studied their opinions about the system functionalities as

well as their possible interactions with the platform and wishes for changes. The focus group results suggest that

the main decisions of drivers, which are ride acceptance, relocation strategies, working shift and area in which to

work, could be aﬀected by many elements depending on platform strategies, drivers' characteristics, riders'

attributes, and exogenous factors. We ﬁnd that part-time and full-time drivers, as well as experienced and be-

ginning drivers, are characterized by distinctive behaviour. Flexibility and freedom were mentioned as the key

reasons for joining the platform while an unfair reputation system, unreliable navigation algorithm, high

competition between drivers, passenger-oriented platform, high-commission fee, and misleading guidance were

acknowledged as being the main system drawbacks. Based on our ﬁndings, we propose a conceptual model that

frames the relationship between the tactical and operational decisions of drivers and related factors.

1. Introduction

Technology development in the transportation sector has changed

the mobility boundaries and introduced new transport possibilities to

address transport-related issues such as traﬃc congestion, parking

scarcity, climate change, hyper-urbanization, and also demographic

and societal changes. Ride-sourcing companies, also known as

“Transportation Network Companies (TNCs)”, have emerged as one of

the frontiers in the shared mobility space and can potentially shift

mobility from a vehicle ownership model to service-based operations.

By deﬁnition, ride-sourcing is a digital platform supplied by private car

owners to oﬀer on-demand door-to-door transport services to users

requesting rides. Therefore, it is able to possibly address the transpor-

tation needs of travellers by oﬀering seamless and eﬃcient mobility

solutions. Notwithstanding, there are also intense debates concerning

the deﬁciencies and pitfalls of ride-sourcing services such as their

contribution to traﬃc congestion, discrimination, and air pollution

(Shen, Zou, Lin, & Liu, 2020). This raises awareness of the possible

system issues and the relevance of these services as well as the opera-

tions and potential regulation thereof for developing a sustainable

urban mobility policy.

Various stakeholders with diverse objectives and decisions are in-

volved in the ride-sourcing system. Drivers/service providers, riders/

passengers, the platform, policymakers, and the general public are the

main stakeholders of the ride-sourcing system. Given that each party

pursues various objectives that may conﬂict with the others' interests,

unravelling their behaviour and decisions is crucial for studying and

potentially shaping this complex system.

From the supply-side perspective, drivers are not only chauﬀeurs

but semi-independent ﬂeet owners. Given that working relationship in

the gig economy (i.e., a labour market system supplied by independent

contractors/freelancers) between the platform and digital workers is

characterized by mistrust (Wentrup, Nakamura, & Ström, 2019), drivers

are in the heart of these two-sided platforms since they oﬀer their

https://doi.org/10.1016/j.rtbm.2020.100516

Received 16 March 2020; Received in revised form 9 June 2020; Accepted 11 June 2020

⁎

Corresponding author.

E-mail addresses: [email protected] (P. Ashkrof), [email protected] (G.H.d.A. Correia), [email protected] (O. Cats), [email protected] (B. van Arem).

Research in Transportation Business & Management 37 (2020) 100516

Available online 03 July 2020

(http://creativecommons.org/licenses/by/4.0/).

private cars to transport passengers.

One of the main concerns that may hinder the platform objectives

and the potential beneﬁts associated with ride-sourcing is the heigh-

tened tensions between drivers as service suppliers and companies as

platform owners due to the dissatisfaction of the drivers regarding their

working conditions (Nicolas Vega (New York Post), 2019; Wang &

Smart, 2020). Thousands of drivers frequently strike for improving their

working conditions all around the world. They believe that their needs

and expectations are overlooked by the platform. Therefore, under-

standing drivers' behaviour and preferences is of key importance to

ride-sourcing companies (i.e. TNCs) in managing their relationship with

the so-called driver-partners (this term used by TNCs refers to drivers

who partner with these companies) and in retaining them in the pre-

sence of competence.

Previous studies on the supply side have covered various topics

including estimated time of travel (Wang, Fu, & Ye, 2018) pricing

strategies (Cachon, Daniels, & Lobel, 2017; Zha, Yin, & Du, 2018),

matching strategies (Zha, Yin, & Xu, 2018), repositioning guidance

(Vazifeh, Santi, Resta, Strogatz, & Ratti, 2018), policies and regulations

(Zha, Yin, & Yang, 2016). They have mostly assumed that drivers are

fully compliant with the platform or considered a few monetary vari-

ables including hourly income as the factors inﬂuencing their choices

though many variables such as the cumulative revenue, working shift,

the aversion to long working hours, driving costs, information sharing

and incentives may presumably impact the decisions of drivers, yet

remaining hitherto unexplored in the literature. This arguably stems

from the lack of knowledge on the aspects considered by drivers and

their potential impact on their behaviour and decisions. Furthermore,

many studies have hypothesized that on-demand transport services are

operated by a centrally fully automated ﬂeet of vehicles, so-called taxi

robots (Ciari, Janzen, & Ziemlicki, 2020; Hörl, Ruch, Becker, Frazzoli, &

Axhausen, 2019; Levin, 2017; Liang, de Almeida Correi, An, & van

Arem, 2020; Oh, Seshadri, Le, Zegras, & Ben-Akiva, 2020; Winter, Cats,

Martens, & van Arem, 2020). Current ﬂeets are not automated at this

time and the literature suggests that automated vehicles seem not to be

introduced to the market in the near future (SAE International, 2018).

Furthermore, there is a growing body of literature on driver supply

properties such as elasticity, wage, and incentives. Wang and Smart

(2020) analysed an extracted sample of 18,399 for-hire vehicle drivers

working in the United States from 12-year Integrated Public Use Mi-

crodata Series data. They report that the hourly income of for-hire

vehicle drivers has decreased since the entry of Uber to the market. The

key objective of modelling driver supply is to investigate the main

reasons why drivers join the system. Analysing the characteristics of

Uber drivers through the Uber administrative data and surveys, Hall

and Krueger (2018) conclude that ﬂexibility is the main factor at-

tracting drivers to work for Uber to start with. With regard to supply

elasticity, the eﬀects of monetary measures such as hourly income on

the working shift of drivers are studied. Cahuc, Carcillo, and Zylberberg

(2014) argue that income rate impacts both the decision to join the

platform as well as the number of working hours. Using New York City

taxi driver data, Farber (2015) found out that drivers have a positive

elasticity which means that they typically work longer hours when in-

come rates are higher in line with expectations. Moreover, several

studies have investigated the eﬀect of wages and incentives on the

supply-side operation of ride-sourcing platforms. For instance, Leng,

Du, Wang, Li, and Xiong (2016) analyses the response of drivers to

monetary promotions given by two competing ride-sourcing platforms

in China. They reported that the number of trips per day increases and

the idle time decreases during the promotion.

Most of the abovementioned studies are based on several assump-

tions concerning drivers' behaviour which have not been insofar thor-

oughly studied. In general, drivers are free to decide whether and when

to join the system, to accept/decline ride requests, and about their re-

location strategies in order to cover more proﬁt/satisfying periods. This

freedom provides drivers with a range of choices that can directly

inﬂuence their income level as well as system performance. For ex-

ample, the low ride request acceptance rate of drivers in a region might

increase the waiting time for riders in that area (lower level of service).

In another scenario, if no driver accepts an incoming ride request or to

be available at a particular location/time, the request is aborted re-

sulting in the dissatisfaction of the client. This highlights the funda-

mental role of service suppliers in the ride-sourcing environment.

Hence, in order to control the supply-side dynamics, the drivers' be-

haviour and perceptions towards the platform strategies need to be

unravelled. This also provides an opportunity to address the issues that

drivers face which could lead to decreasing the existing tensions with

the platforms and thus break the barriers to fully realize the potential

beneﬁts of ride-sourcing. To this end, this study aims at gaining in-

depth knowledge about ride-sourcing drivers' decisions and their rela-

tions with system functionalities.

We conducted three focus group interviews with Uber drivers in the

Netherlands. In our analysis, we classify the results into drivers' (i)

understanding of the system operations, (ii) behaviour and (iii) ex-

pectations in order to shed light on the ride-sourcing drivers' role. In the

following sections, details on the focus group execution (Section 2) are

given, followed by a discussion of our ﬁndings (Section 3). We propose

a conceptual model for drivers' main behavioural elements and their

connections in Section 4 and conclude with a discussion of this study's

implications pointing also for directions for further research (Section 5).

2. Methodology

2.1. Focus group characteristics

Given that the knowledge about the social reality of ride-sourcing

drivers is limited due to the non-transparent characteristics of the gig

economy practices, focus group as a form of empirical qualitative re-

search is adopted as the research method in this study. This approach

allows gaining deep insights into drivers' perspective of the system

operations and unravelling their interactions with the platform in order

to comprehend their views and behaviour.

Focus groups enable the exploration of the topic of interest by

providing qualitative information by means of a focused discussion

between a limited number of people who on the one hand possess

certain common characteristics and on the other hand exhibit diversity

with regard to other key characteristics (Krueger & Casey, 2014). In the

context of transport innovations, focus groups have mostly been used

for studying the views of travellers and policymakers concerning

emerging mobility technologies (Carvalho, Costa, Simoes, Silva, &

Silva, 2016; Davison, Enoch, Ryley, Quddus, & Wang, 2012; Faber &

van Lierop, 2020; Ferrer & Ruiz, 2018; Jacobsson, Arnäs, & Stefansson,

2017; Li, 2018; Nikitas, Wang, & Knamiller, 2019; Pudāne et al., 2018).

The method of focus group strives to provide a dynamic informal

group discussion amongst participants to freely share their ideas and

learn from or contrast each other's perspectives thanks to the sense of

cohesiveness as being a member of a group (Peters, 1993). This enables

the researcher to consider the variation in the opinions, generation of

new ideas as well as possible solutions, the evolution of the ideas during

the discussion, and evaluate the discussed topics in order to capture the

main themes eﬃciently. The main potential pitfalls of focus groups are

potential participants/moderator bias, ungeneralizable outcomes and

time-sensitive results (i.e. dependent on the time of the study).

The main reasons for adopting a focus group as the research method

in this study are: i) The knowledge about drivers' perception of the

system operations and their interactions with the platform is limited

and scarce; ii) Qualitative research can explore the opinions and feel-

ings of drivers; iii) The focus group ﬁndings can facilitate the prior-

itization and design of future quantitative research.

P. Ashkrof, et al.

Research in Transportation Business & Management 37 (2020) 100516

2.2. Focus group design and sessions

Before describing the focus group set-up, it is important to provide a

brief description of the research context. This study is conducted in the

Netherlands in which high-quality public transport services are pro-

vided and two ride-sourcing companies, namely Uber and ViaVan, are

currently active. Uber started operating in Amsterdam in 2012 and

currently provides two private-ride products, i.e. UberX and UberBlack

in more than ﬁve cities. ViaVan has only recently entered the market

(early 2018), oﬀering solely shared rides and its operations are limited

to the Amsterdam area. Ride-sourcing is generally more regulated in

Europe than elsewhere, especially in the Netherlands where drivers

need to be registered as professional drivers. Therefore, Uber drivers

working in the Netherlands were identiﬁed as the target group.

Placing emphasis upon the individual heterogeneity, Wang, Zhang,

Fu, Li, and Liu (2020) concluded that classifying the taxi users into

diﬀerent groups is necessary when studying their behaviour. Given that

this heterogeneity may exist between drivers, several categories can be

investigated. As ride-sourcing drivers are free to decide about their

working patterns, it is assumed that full-time and part-time drivers have

distinctive behaviour given that part-time drivers might have some

other scheduled activities limiting their freedom. Part-time drivers are

deﬁned as the ones who have other occupations while full-time drivers

spend their whole working time in the platform. Furthermore, more

experienced drivers are expected to decide diﬀerently compared to

beginning drivers. Hence, working full-time/part-time and being an

experienced/beginning driver were deﬁned as the screening criteria for

the participants.

Based on the ﬁndings of Krueger et al. (2014), focus group sessions

should be small enough to enable the participants to share their ideas

yet large enough to provide a diversity of perceptions. On the other

hand, since dominant participants may inﬂuence others within the

group, it is recommended to have more than one group session.

Moreover, collecting data from several group discussions enables the

researcher to compare and contrast data across groups. To this end, we

decided to hold three sessions with 4–7 drivers in each group.

The focus group meetings took place in Amsterdam on 22, 25, and

29 July 2019 in a standard meeting room where the conversations (in

Dutch) were audio-recorded. Each session lasted two hours and was led

by a professional moderator hired for this purpose who was not in-

volved with the research beforehand. This was a deliberate choice to

minimize the moderator bias which could unnecessarily redirect the

discussions into the moderator's topics of interest. On the other hand,

prior knowledge of the moderator can have some added value to foster

the group dynamics. In order to obtain a balance between the mod-

erator bias and having enough background knowledge, we had several

joint meetings with the independent moderator to brief her and also

provided her with a semi-structured moderation guide to ensure the

research objectives could be achieved. Besides, the ﬁrst author followed

all the focus groups' discussions in an observation room in real-time. He

was able to see and hear the participants while they could not see him

thanks to a one-way mirror. In several situations during the sessions,

the ﬁrst author contacted the moderator for asking some follow-up

questions. However, she was fully authorized to refuse to ask any

leading questions raised by him during the discussions. It should be

noted that at the beginning of each session, participants were informed

about his presence (as a researcher from a Dutch university) behind the

one-way mirror and the relevant reasons for that. Fig. 1 indicates the

meeting room from the perspective of the ﬁrst author in the observation

room.

Each session started with a short introduction to the topic. Although

the identity of the research team was not revealed, it was emphasized

that the research is conducted by a Dutch university for academic

purposes. The idea behind this was to prevent potentially underlying

concerns by participants that may hinder them from expressing them-

selves freely and possibly giving biased and strategic responses.

After the introduction, the focus group rules and conditions in-

cluding conﬁdentiality, having no right or wrong answers, respecting

the opinions of each other, the session duration, and eventual in-

centives were explained. Then, the drivers were asked to introduce

themselves and summarize their perception of the platform perfor-

mance in one word as an icebreaker. Following the group introduction

and based on the moderation guide, the general open-ended questions

were asked to initiate the discussion, and then follow-up questions,

probes, and prompts were raised to saturate the topic. Table 1 shows

the topics and the main associated questions.

2.3. Sample characteristics

A panel provider was hired to reach out to the target group. Using

snowball sampling, they recruited 16 Uber drivers complying with the

screening criteria (full-time/part-time and experienced/beginning dri-

vers). Even though the focus group sample is not required to represent

the population in terms of neither socioeconomic characteristics nor

working behaviour (Marshall, 1996), Table 2 contains information

about the drivers' proﬁle to allow for additional insights when dis-

cussing the ﬁndings.

It can be seen that out of the 16 drivers, most of them were male

whereas two females participated. The number of part-time drivers was

slightly higher than the number of full-time ones (9 part-time drivers).

Most of the participants were UberX drivers while one of them was

working as UberX as well as UberBlack driver simultaneously. Their

working experience diﬀered from 1 month to 5 years. In this study,

drivers with more than two years of driving experience with the plat-

form are considered experienced drivers. Each driver is identiﬁed by a

speciﬁc code which starts with D (driver) followed by the participant

number within the respective focus group session (from 1 to 6), their

employment status (F for full-time, P for part-time), the session number

(from 1 to 3). For example, D2F3 refers to Driver number 2 who is a

Female and participated in the third session.

2.4. Data analysis

The transcript-based analysis is considered as the most robust

method of analysing qualitative data (Onwuegbuzie, Dickinson, Leech,

& Zoran, 2009). The qualitative content analysis principle was used to

analyse the focus group transcripts obtained from the audio-recorded

conversations. Based on the research framework, this systematic

bottom-up approach aims at providing a comprehensive description of

the phenomenon at the theoretical level through inductive or deductive

category development (Elo & Kyngäs, 2008; Mayring, 2000;

Williamson, Given, & Sciﬂeet, 2018). The collected data is the primary

source of identifying concepts, themes, and categories in inductive

analysis processes while deductive content analysis is carried out based

on the prior formulated knowledge (Kyngäs, 2020; Mayring, 2000). In

this study, the majority of analysis is conducted deductively because the

existing literature contributed to deﬁning the study assumptions and

deriving most of the categories. However, some themes were identiﬁed

independently of the literature given that background knowledge is

limited and fragmented in this ﬁeld.

The analysis process comprises three main phases including pre-

paration, organizing and reporting (Elo & Kyngäs, 2008). The tran-

scripts are scrupulously reviewed word for word several times for

making sense of the data and ensuring accuracy. Then, the text is coded

by writing notes and headings in shorthand words in the margin and

also keywords and sentences are highlighted. After that, the data is

classiﬁed into several groups in accordance with the identiﬁed cate-

gories in the literature. Next, those groups were categorized under

higher-order headings in order to reduce the number of topics, extract

the themes, and increase the understanding of the phenomenon. Fi-

nally, the identiﬁed categories and sub-categories are integrated, ana-

lysed, and interpreted in order to explain the drivers' decisions and

P. Ashkrof, et al.

Research in Transportation Business & Management 37 (2020) 100516

behaviour using the relevant highlighted quotes. To increase the re-

liability of the ﬁndings, the moderator was also requested to provide a

top-line report in order to enable the cross-checking of the identiﬁed

themes with an independent coder, therefore, minimizing the re-

searcher bias in the analysis process. The next section reports the focus

group ﬁndings.

3. Findings

We report the ﬁndings in three main categories: system operations

(3.1), drivers' behaviour (3.2), and drivers' expectations (3.3). The ﬁrst

section discusses the drivers' perspectives on ride-sourcing system

components. Then, the decisions of drivers as well as the corresponding

attributes are explained in the second section. The last part elaborates

on the needs, preferences, and expectation of the focus group drivers.

3.1. System operations

Here we describe the ride-sourcing platform functionality as

Fig. 1. Focus group meeting room, two perspectives taken from the observation room.

Table 1

Main questions of the moderation guide.

No. Main Questions⁎ Topic Category

1 How happy are you on a scale 1–10 (10 is the highest) as a driver? Starter

2 Why did you choose Uber? Background

3 What are the diﬀerences between a taxi driver and an Uber driver? Taxi vs Uber

4 Describe a typical workday. What are your activities? Working pattern

5 How many of you have a ﬁxed/ﬂexible working shift, and why have you selected this working shift?

6 How much time do you ride with and without a passenger within a weekday? What about weekends? Empty rides

7 Is there any way to reduce your empty trips? How?

8 What kind of information are you shown when a request comes? Requests

9 Based on what factors do you consider accepting or rejecting a request?

10 What is your opinion about having a menu of trip requests to select between them?

11 Did you ever feel uncomfortable during working hours? Or experience any passengers' misbehaviour? If yes, in what way? Safety

12 When you ﬁnish up a trip during your shift, what do you do? (Do you stop there and wait for the next possible passenger?) Relocation

13 What have you ﬁgured out about the platform pricing mechanism? Pricing

14 What would be the minimum hourly net income that you expect to earn? Minimum income

15 What do you think about providing service in low demand areas such as suburban or oﬀering rides in the middle of the night? Incentive

16 Imagine you will be the CEO of Uber as from tomorrow, what are the ﬁrst things you would change? Expectations

⁎

Each main question had a set of what-if scenarios, follow-up questions, and probes in order to ensure the topic is saturated.

Table 2

Sample characteristics.

Driver code Gender Age Employment

status

Service Experience

D1F1 Male 24 Full-time UberX 6 months

D2P1 Male 41 Part-time UberX 4 years

D3F1 Male 66 Full-time UberX 4 years

D4F1 Female 28 Full-time UberX 2 years

D5F1 Female 29 Full-time UberX 2 years

D6P1 Male 28 Part-time UberX 6 months

D1F2 Male 22 Full-time UberX 6 months

D2P2 Male 22 Part-time UberX 2 years

D3F2 Male 22 Full-time UberX and

UberBlack

3 years

D4P2 Male 22 Part-time UberX 2 years

D1P3 Male 39 Part-time UberX 3 years

D2F3 Male 36 Full-time UberX 5 years

D3P3 Male 31 Part-time UberX 3 years

D4P3 Male 42 Part-time UberX 1 month

D5P3 Male 25 Part-time UberX 1 month

D6P3 Male 25 Part-time UberX 3 years

P. Ashkrof, et al.

Research in Transportation Business & Management 37 (2020) 100516

experienced and perceived by the drivers. We structure the discussion

of these ﬁndings into the following sections: ride requests, working shift

and area, utilization rate, rematch, reputation system and tips, navi-

gation, manipulation, and riders.

3.1.1. Ride requests

When a ride is requested by a rider, the app sends the request to

nearby drivers. Drivers have the choice of either accepting or declining

the request. If a driver decides to accept the request, he needs to pick up

the rider at his pick-up point. Even after accepting the request, the

driver can cancel it. However, the cancellation has some consequences

(to be discussed below). In case of not accepting the request, the driver

waits for the next possible request or ends his working shift. The main

question is that what kind of information is shown to drivers when a

request appears? In the focus group, we asked the drivers to clarify it

and express their opinions.

a) Information sharing policy: Currently, drivers are provided with

limited information. They are able to see the pick-up point address,

the distance and predicted travel time between their location and

the pick-up point, and the rider's rating. Trip fare and the ﬁnal

destination are not shown to drivers. They cannot see the destina-

tion immediately after accepting the request. Instead, the destina-

tion pops up when the driver approaches the rider. This is pre-

sumably because the probability of cancelling the request by the

driver decreases given that he has already driven some kilometres to

pick-up the passenger. Thus, if he/she cancels the request at this

stage, he/she has earned nothing. Most drivers stated that they

found it diﬃcult to make a decision about the request given the

limited data available upfront.

“The given information is the distance from the client and the rate. That's

it.” D1F2

“... you don't know where someone is going. But it can be hard to decide

sometimes...” D6P1

Many drivers said that having no information about the ride desti-

nation before accepting requests is problematic as they may end up

with a short-distance ride which is even shorter than the distance be-

tween the drivers' location and the pick-up point.

“Prior to a ride, you don't know the destination. Sometimes you drive for

15 kilometres and ﬁnd out someone only has to be 200 meters down the

road. That's really a problem.” D1F1

There is also other information that is occasionally indicated such as

surge pricing (dynamic pricing), trips longer than 30 min, and pre-

booked rides. Surge pricing is a pricing strategy based on the local ratio

between supply and demand. It results in higher fares for riders and

thus higher income for drivers. Both drivers and riders can see a mul-

tiplier applied on top of the standard rates in the application in case of

surge pricing.

Moreover, a special icon (+30) appears in the driver's application to

indicate in case a trip duration that is longer than 30 min. Drivers are

also informed if a request is a pre-booked ride. Since they cannot see

pre-ride requests much in advance, one of the drivers found this feature

unnecessary. There is no diﬀerence for drivers whether a request is pre-

booked or not when they are not able to see it in advance, so it does not

have any eﬀect on the drivers' decisions.

“[drivers can see] if they [riders] have booked it [the ride] in advance

[pre-booked ride] ... It doesn't make a diﬀerence. It's unnecessary in-

formation.” D4P2

b) Declining and cancelling requests: There is a clear distinction be-

tween declining and cancelling requests. The former implies that the

request is never accepted by drivers while the latter means that an

accepted request is cancelled by either drivers or riders. In contrast

to declining, which could be done without any ramiﬁcations for the

driver, cancellation has some consequences. There is a threshold of a

maximum three cancellations per day and drivers need to explain

why an accepted request has been cancelled. If a driver exceeds the

maximum, he/she gets a warning. After receiving three warnings,

the application is deactivated and he/she needs to go to the head-

quarters to get briefed and in some cases, the driver may get blocked

either temporarily or permanently.

The more experienced the drivers, the more selective they are with

accepting requests. Experienced drivers believed that only some of the

requests should be accepted based on several criteria depending on the

driver's experience in order to maximize the proﬁt. They usually stop

somewhere and wait for the next trip. In contrast, beginning drivers

prefer to accept most of the requests and then drive empty to receive a

request.

“I think you can cancel three times a day. If you cancel more, you'll get a

warning... You can decline as much as you want. But you can't cancel as

much as you want... I used to accept everything as well, but after some

time you learn how to work with Uber… it's better not to accept every-

thing. Otherwise, you work really hard, and only take rides for 4, 5 or 6

euros. I'd rather wait for a ride of 30 or 20 [minutes]...” D2F3

Drivers may also cancel a request if either the pick-up point seems to

be risky in terms of getting ﬁned or the rider looks problematic or the

trip characteristics including trip distance/fare are not appealing. Risky

pick-up point was the most typical reason for cancelling a request.

“Many cancellations. Because of the wrong pick up locations…” D3F2

“You must also pay attention to the places where you are allowed to

stand still or park. Pick up points. For example, if I look at Utrecht near

the station… pick up points are really bad.” D2P1

If the request is cancelled by the rider after two minutes or by the

driver because of the riders' issues (e.g., not showing up, too many

people, etc.), the rider has to compensate for it.

“If you wait for the client... If the client is not there and you already

called… Then you will get a refund for the waiting time... Not only that,

but it's also when the client is with too many people.” D2P2

However, the cancellation feature could cause some disputes be-

tween riders and drivers when they try to shirk the responsibility for the

cancellation. Many drivers believed that Uber supports the rider in all

cases even if they are mistaken.

“During disagreements between drivers and clients, Uber always picks the

side of the client. And even if they don't, they often make a double

commitment. Then, they tell the client they chose their side, and they tell

us the same. And ultimately, they give us compensation, but the customer

won't get banned. So, clients will never have any consequences of their

wrongful behaviour" D1F1

c) Preferred destination: Drivers can set their preferred destination and

have a higher chance of getting requests heading in the same di-

rection as their destination. They are allowed to set their preferred

destination twice a day. Most drivers were satisﬁed with this feature

and use it when they intend to ﬁnish their shift. They usually set the

destination to their home and get the ﬁltered requests.

“It's like a bonus. Because I also think that there's a higher chance for you

to get that ride, over other drivers. I don't know exactly how that works,

but I think it's something like that.” D1F2

A few drivers did not ﬁnd it helpful because they believed they

might miss some proﬁtable requests in other directions.

“...You won't get oﬀered any rides that go in another direction. So, you'll

be empty way more often…” D3P3

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3.1.2. Working shift and area

Gig-economy ﬁrms are renowned for giving the labour the freedom

to choose their working shift due to the fact that they do not have direct

employment relations but are rather considered as independent con-

tractors.

a) Flexible working patterns: Flexibility, freedom, and independence

were acknowledged by all drivers as the main motivation for joining

Uber. Drivers can work as much or as little as they desire. They are

able to independently decide when and where to start and ﬁnish

their work without requiring to explain to an employer. The feeling

of not having a boss can provide drivers with a sense of in-

dependence.

“You decide about your own working time. You decide if you are going to

work at all or not. I don't have to call someone if I am not going to work

… You get everything in control… It doesn't matter where you are in the

Netherlands, you can always go online and work if you want to…” D4P2

“You can more or less decide yourself how much you earn, how many

hours you work... And you're independent.” D5F1

This option can enable labour supply to work dynamically based on

their preferences. That is why many Uber drivers work as part-time

workers meaning that they have another source of income at the same

time.

b) Maximum working hours: A new rule has recently been made that

sets a maximum of 12 working hours per day for drivers. Based on

Uber, from May 2018, the driver application is deactivated after

12 hours of driving with Uber and will be activated after 6 hours of a

continuous break. This working time limit excludes the period when

the driver is oﬄine or he/she stops somewhere and wait for the next

trip. Some drivers pointed to this rule as a strict policy which re-

duces their ﬂexibility, but it seems that there is a misunderstanding.

They thought that the maximum working hours rule was applied

even in the oﬄine mode within the shift.

“I would also like those broken shifts. Often, I only have a couple of

regular customers, but you are forced to use your driving time im-

mediately.” D1F1

“Now you can work 12 hours from the moment that you are logged in...

Sometimes I don't feel like working yet or there are no rides, but then I

can't take an evening shift because I am then over the maximum number

of hours. Because the clock just keeps counting. That has to do with the

safety of the driver.” D5F1

It appears that Uber needs to adopt measures to adequately inform

drivers about the new rules to avoid undesired consequences and allow

drivers to eﬀectively use the platform and schedule their working hours

accordingly.

3.1.3. Utilization rate

The working shift duration includes all trips with passengers, empty

trips (deadhead trips), and waiting time. As Uber drivers are paid based

on the kilometres travelled with riders, it is crucial to draw a distinction

between rides with passenger(s) and empty rides. That is why the uti-

lization rate is an indicator that shows the percentage of mileage with

passengers. It is calculated by dividing the amount of time the vehicle is

occupied by the total working shift duration.

a) Weekday versus weekend: The most typical utilization rate reported

in the focus group was 60%. Some drivers reported that their utili-

zation rate was higher on weekends than weekdays while others

stated that although the occupancy rate was the same on both

weekend and weekdays, the riders' characteristics were distinct.

“For me, there is not really a real diﬀerence between the week or the

weekend. The riders are diﬀerent though. But the occupancy rate isn't.”

D2P1

“During the week, I have around two rides per hour, sometimes three.

During the weekend, it's almost always three per hour. On average, one

ride is around 15 minutes.” D6P1

b) Seasonality: A few drivers believed that driving for Uber could be a

seasonal job when they have plenty of rides in the summer (high

utilization rate) and not many rides in winter, especially in the

period after Christmas (low utilization rate). Therefore, given that

the utilization rate ﬂuctuates during the year, they did not feel that

the job was ﬁnancially secure.

“During the summertime, there are so many tourists. And there's so much

going on. But the period after Christmas… There's such a decline in in-

come for those months… you almost can't compensate for it in the busier

months.” D1P3

“It's not always secure. In the winter it can be that you will leave really

early and don't drive a lot, and if you get a ﬁne or get an accident... That

can happen. You will have a lot of costs and no income.” D4P2

c) Ride-sourcing versus taxi: Despite the unstable utilization rate, one

of the drivers who was working for normal taxi companies con-

ﬁrmed that the utilization rate of Uber is much higher than normal

taxis.

“In a normal taxi, I'm empty way more. If I drive for Uber, I can have up

to three customers per hour.” D4F1

This is in line with the ﬁndings of Cramer and Krueger (2016). Using

data from ﬁve cities in the US, they concluded that the utilization rate

of ride-sourcing platforms is higher than taxis due to the larger scale of

ride-sourcing platforms, more eﬃcient matching and pricing strategies

and also ﬂexible labour model. Contreras and Paz (2018) also conﬁrm

that ride-sourcing has negative and signiﬁcant impacts on taxicab ri-

dership.

3.1.4. Rematch

Rematch is a new matching strategy implemented in some airports

to help reduce the number of cars in the terminals, riders' wait time,

and the number of drivers waiting in the airport parking lots. When

drivers drop oﬀ passengers at the airport, they can immediately receive

an on-site pick-up request as available, so they do not need to drive to

the parking lots and wait there for the next possible request. If no re-

quest pops up within a certain time window (2–3 min), they are no

longer eligible for Rematch and can either go to the waiting queue or

exit the airport.

Drivers who want to work in the Amsterdam airport (Schiphol) need

to deposit 100 euros to receive a special pass called “Schiphol Pass”.

There is a virtual waiting queue in the airport for the drivers who have

the pass. While many drivers were unaware of Rematch, a few drivers

conﬁrmed that Rematch can help them earn more money thanks to the

higher utilization rate at the airport. They reported that when a trip is

ﬁnished at the airport, the next ride request instantly appears, there-

fore, no waiting time.

“You don't have to wait there [Schiphol] anymore. I've had Rematch a

couple of times. It's great for your income. If it works, it really makes

sense to go there... Nowadays you have the rematch system which im-

proves your chances of getting a ride back immediately.” D3P3

One of the drivers who had not noticed Rematch accused Uber of

discriminating between drivers because he thought only some drivers

(e.g., the ones joining the platform at early stages or drivers who accept

more rides) would beneﬁt from this feature. This, again, stresses the

necessity of having eﬀective communication between drivers and the

platform in order to ensure drivers are fully updated about the new

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features and also the platform can receive drivers' feedback for further

improvement. It helps eliminate possible misunderstandings and de-

velop trust, as one of the main components in any sustainable business,

between suppliers/workers and the platform.

3.1.5. Reputation system and tips

Drivers and riders are able to anonymously rate each other from 1

(the lowest) to 5 (the highest) to quantify the service quality based on

the trip experience after ﬁnishing a ride through the application. This

feature, which is the so-called two-way (bilateral) rating system or re-

putation system, can intensify the interaction between drivers and ri-

ders and may enhance trust-building between them, particularly since

they usually do not know each other. On the other hand, the platform

can use the reputation system to control drivers/riders and monitor

their behaviour given that the working relationship between the plat-

form and digital workers is characterized by mistrust (Wentrup et al.,

2019). A beginning driver/rider starts with ﬁve stars and then the

rating is adjusted according to the feedback, so the overall rating is an

average of accumulated individual ratings. The reputational feedback

mechanism can potentially inﬂuence the behaviour of both riders and

drivers given the consequences of having a low rating, especially for

drivers.

a) Unfair rating system: Many drivers stated that they perceived the

rating system to be unfair because of two key reasons: Firstly, the

riders' rating is less reliable than drivers' rating since most of the

riders do not travel as much as drivers, hence their ratings are based

on fewer records. Secondly, the riders' rating is not considered as

important as drivers' rating given that drivers are banned by Uber

either temporarily or permanently if their rating is below what is

considered by Uber as a minimum rating in that region while a low

rating does not have any consequences for riders. In other words,

riders play the role of middle managers over drivers given that their

feedback is a key element for drivers (Rosenblat & Stark, 2015), in a

manner similar to other two-sided platforms such as Airbnb and

TripAdvisor.

“If a client has a low rating, that doesn't carry any consequences. But, it

does to us. And that client can keep behaving the same. That's a diﬀer-

ence. Under 4.6, you can't even drive for Uber. But a client with a rating

of 4.0 can still order an Uber.” D6P1

Analysing the data from ride-sourcing platform in India, Kapoor and

Tucker (2017) argued that drivers are stimulated to leave the platform

by an unfair rating system.

Some drivers mentioned that when heading towards riders with a

poor rating they adjust their expectations and can experience anxiety.

“I'm really on edge when I see that my next client has a low rating. I make

sure I'm ready for it and expect the worst.” D3F1

The reputation system can, therefore, be considered as a scare tactic

to address the mistrust issue between all parties, particularly for drivers

who are constantly under the risk of being deactivated.

Tipping: Riders can also give a tip to drivers in the application after

each trip as they wish. Some drivers pointed out they did not rely on

this option though and perceived it as a bonus promoted by Uber.

“Clients often don't have any cash with them. That's the concept of Uber

as well, so it's a good thing that they can also give a tip digitally.” D2P2

Chandar, Gneezy, List, and Muir (2019) pointed to the gender dif-

ferences in tipping and being tipped. They found that men leave more

tips while women are tipped more.

3.1.6. Navigation

For drivers, the quality of navigation is crucial due to the fact that it

is not only about getting from point A to point B, but ﬁnding and

picking up their riders. Using the Estimated Time of Arrival (ETA), Uber

navigates drivers through the fastest path between the driver's location

and the pick-up point(s) as well as between the pick-up point(s) and the

destination(s). Decreasing the travel cost for riders, energy consump-

tion, and vehicular pollution, a reliable ETA can improve system eﬃ-

ciency. However, an accurate ETA depends on many factors such as

spatial-temporal dependencies, traﬃc congestion and weather condi-

tion (D. Wang, Zhang, Cao, Li, & Zheng, 2018).

a) Unreliable ETA: Although Uber has recently redesigned its naviga-

tion system, a few drivers said that the ETA does not work precisely.

“It doesn't consider traﬃc. So, therefore arrival times often are incorrect

in busy areas like the centre.” D3F1

b) Re-routing issues: Uber recommends drivers to ask riders about their

preferred route which may cause some problems for drivers. A few

drivers said that if they re-routed the trip due to some justiﬁable

reasons like the rider's preference, the platform did not auto-

matically consider it. This was not desirable for drivers, especially

when they had to take a longer route. In this case, drivers need to

email the customer service to explain what happened in order to

claim the extra kilometres travelled.

“They see the route you took and based on that they might think you

should've done it diﬀerently. So, automatically if you have driven 5

kilometres too long, they will take that from your ﬁnal earnings, even

though you might have had a good reason.” D2F3

3.1.7. (Mis)information and asymmetric relations

a) Misleading: Many drivers claimed to be misled even before starting

the job. They believed that Uber manipulated them. They were told

that they could earn around 1000 euros per week.

“They made all these great promises, like earning 1000 euros per week

and that all sounded great so I thought: let's do that... With these adver-

tisements they've attracted drivers, that's really misleading.” D6P3

Many drivers emphasized that the application sometimes misleads

them by showing the surge pricing areas or high-demand areas where

they are supposed to have more demand while in many cases drivers

who follow the application recommendations are not able to get any

requests.

“There is a dynamic rate. But it is there for nothing because you don't get

any rides. While it says it's really busy. You could be at home looking at

the rate, and because you think it's busy you will go to work. But then it

will be for nothing... You could be in an area that's very red. But then you

could also have no rides for half an hour. These are the moments I get

really annoyed.” D1F2

Although the mismatch information about surge pricing and high

demand areas has caused a feeling of mistrust for many drivers, some

more experienced drivers believed that this might be due to the fact

that drivers compete with each other to reach the recommended areas,

then those locations will no longer be undersupplied. They also stated

that the platform might be aiming to attract drivers to a certain area for

diﬀerent reasons such as decreasing the passengers' waiting time.

“...I think everyone just has the same mentality. Everyone just goes there,

if there's surge there.” D6P3

b) Strong competition: Some drivers believed that oversupply is one of

the main reasons that they cannot earn more money as much as

Uber promised. There exists a strong competition between drivers to

get rides which leads to lower utilization rate and therefore lower

income.

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“There is a lot of competition... Uber does not have a maximum number

of drivers, so anyone can register. And now the supply and demand are

no longer at a good proportion. So, there's too much competition.” D6P1

c) Monopolization: Despite the low income and the feeling of mistrust

as well as being manipulated, a few drivers stated that Uber has a

monopoly position as there is no competing company receiving as

many as Uber ride requests, so they felt forced to work with Uber.

“It's like you don't have a better option than Uber. They've taken over the

complete market and just forced everyone to join them.” D5F1

3.1.8. Riders

a) Rider-oriented platform: Many drivers said that in case of any

conﬂict between drivers and riders, Uber mostly takes the riders'

side. Drivers believed that Uber is biased towards riders at the cost

of drivers which can even lead to rider’ misbehaviour. Some drivers

mentioned issues caused by riders including vomiting in the car,

eating or drinking, unpleasant smell, smashing the door, touching

buttons, and hyper-critical people.

“They [riders] think they can do anything in the car… Not all of them.

But there are a lot of clients who think they can do anything.” D3F2

b) Ride-sourcing riders versus taxi passengers: Some drivers pointed

out that Uber riders were more cautious than passengers picked up

at random from the street. This is because riders know that their

identity can be traced if needed thanks to the cash-free transactions

and self-identiﬁcation procedure for activating the application.

“There are a lot of diﬀerences between Uber and Taxi clients because all

Uber customers are registered. If a customer [Uber rider] gets into your

car, they've already given their credit card details. So, the customer won't

misbehave as much. Because they know Uber can ﬁnd them…” D4P2

The diﬀerence between taxi and ride-sourcing users is also high-

lighted by Rayle, Dai, Chan, Cervero, and Shaheen (2016). Comparing

the results of a survey of ride-sourcing users in San Francisco with a

previous taxi survey and taxi trip logs, they conclude that younger and

well-educated passengers who seek short waiting times and fast point-

to-point trips tend to use ride-sourcing services.

3.2. Drivers' behaviour

Drivers' behaviour stems from their operational and tactical deci-

sions which are based on their understanding of the system operations

and preferences/aversions. In general, drivers are able to make deci-

sions about accepting/declining/cancelling requests, relocation (re-

positioning), working shift and area. Decisions related to requests and

relocation can be associated with operational decisions while selecting

the working shift and area are categorized as tactical decisions. This

section describes the factors which are taken into account by the drivers

when making decisions. The ﬁndings are presented in three sub-sec-

tions: ride acceptance, relocation strategies, working shift and area.

3.2.1. Ride acceptance

Once a request appears in the application, drivers are given a few

seconds to decide whether to accept or decline (dismissing, not ac-

cepting) the request. Although the given information seems to be lim-

ited for making an informed decision, many requests are declined by

drivers. Romanyuk (2016) argues that in a two-sided platform with a

matching algorithm, the probability of rejecting a request by a seller is

higher when the full information disclosure is available. Drivers are

shown the pick-up point address, the distance and time between the

driver's location and the pick-up point, and the rider's rating before

accepting the requests which can lead to blind passenger acceptance

when they do not have any information about the trip fare and the ﬁnal

destination. In case of accepting, the fastest route to the rider is given

while the driver is still not able to ﬁnd the ﬁnal ride destination. The

ﬁnal destination is shown when the driver approaches the rider and

pick him/her up. Some additive information is given as necessary, for

example, if the request is within surge pricing, the trip is longer than

30 min, and the ride is pre-booked.

a) Pick-up point location: In the focus group meetings, the drivers

discussed their criteria for making decisions with regards to in-

coming requests. All the drivers unanimously believed that the re-

quests with risky pick-up points located mostly in the city centre

should be declined due to the high risk of getting ﬁned by police

while there is no support from neither the platform nor the rider.

Getting ﬁned leads to increasing the operational costs, therefore,

less proﬁt.

“...you can't wait there [risky spots], so you'll get a ﬁne. And then your

customers get in and laugh while you get a ﬁne. I don't feel like doing

that. In the center there are a lot of places like that…” D3P3

“...If I am not allowed to stand still, I will decline it [the request]. I

already got a ﬁne, and that's a loss of money…” D1F2

b) Distance and time to the pick-up point: The distance and travel time

between the driver's location and the pick-up point appear to be an

inﬂuential factor. Given that drivers are not able to see the ride

destination, a few drivers said they did not tend to accept the re-

quests in which their pick-up points were located far from their

current location. This is because there is a risk of ending up with a

short-distance ride after driving to the faraway pick-up point.

“You just shouldn't accept some rides. I mean, you know how much time

it may take you to get to the customer. For me, if it's more than 8 minutes

I say: No, thank you!... The location is decisive. Rides on Dam Square or

Damrak Street, I also refuse.” D3F1

“… If I have to drive a long way to pick up the client. Is it only a little, or

a lot? If it's a lot then I will refuse. Because if the ride is only 2 kilometres,

I drive there for almost nothing.” D3F2

c) Rider's rating: Rider's rating is another factor that is always shown to

drivers. In contrast to drivers who are not able to work for the

platform when their rating is less than a certain threshold, riders can

request rides regardless of their rating. Some drivers stated that they

preferred not to accept the requests of the riders who have a low

rating. The high risk of misbehaving as well as giving the driver a

low rating was mentioned by some drivers as the main reason for

declining those requests.

“If I see the client has a rating of 3.7, that means a lot of drivers gave a

bad rating. If I see that, I refuse.” D3F2

d) Surge pricing: Amongst the additive information, surge pricing may

indirectly lead to declining many requests. Both riders and drivers

are informed if the price of a request has surged that means higher

income for drivers. That is why drivers try to enter those areas and

receive promoted requests. Some drivers reported that they did not

accept the requests with standard pricing when they were close or

on the way of surge pricing areas.

“When I drive somewhere that there's no surge, but I'm close to it, I'll

reject other rides. I prefer to go to the surge area and take a ride there

rather than taking a ride away from the surge area.” D6P1

“If there is surge pricing, and I will get a request without surge pricing,

then I will refuse. I won't take it. Because I know a little bit further on, I

can get a ride for 2-3 times of the normal price.” D3F2

One of the drivers who was working for both UberX and UberBlack

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Research in Transportation Business & Management 37 (2020) 100516

stated neither the rider's rating nor surge pricing was decisive for him

when working as an UberBlack driver. This is because there is usually

no surge pricing for UberBlack since the price is already higher for

UberBlack. On the other hand, UberBlack riders are more desirable and

generous in terms of leaving tip.

“… I also drive for UberBlack... You won't have diﬃcult clients... There

is almost no surge pricing because it's already higher....” D3F2

e) Long-distance rides: Many drivers mentioned that they avoided

short-distance trips since the trip fare was low. It could be worse if a

short-distance trip is combined with traﬃc congestion given that

drivers are paid based on kilometres travelled. Long-distance rides

are appealing for drivers since they can drive longer without any

stop, therefore, higher earning.

“... It's really bad to be in a traﬃc jam for a long time for a short ride. For

example, it's 2 kilometres and you will be in a traﬃc jam for 20 min-

utes.” D1F1

“Your income is really low. Most of the time you get short rides.” D1P3

Many drivers said they are more likely to accept requests indicated

by 30+ in the application, indicating that the ride takes more than

30 minutes. Long-distance rides which are complemented by surge

pricing were appreciated by all drivers as the best rides.

“I prefer 30+ next to a ride if it's longer than 30 minutes. So, you im-

mediately know it's a long ride.” D4F1

“Long journeys are equivalent to good rides, so that's great. And it's even

better if you also get a dynamic rate.” D6P1

f) Destination prediction: It appears that drivers can predict some

characteristics of the ride in order to make a decision about the

request. The plausible destination, for example, was mentioned by

some drivers as one of the criteria. The most experienced drivers

said that they predicted the ﬁnal destination of the requests based

on the rider origin and the request arrival time, so the requests

which seemed to be short rides were declined.

“If it's [pick-up point] a hotel and also time of the day. Very early in the

morning you just know for sure it [the ride] will be to Schiphol. But when

it's 8 am, your chances go down to 50/50. Because a lot of people also go

to their oﬃce then... based on that I decide if I want to reject or accept.”

D2F3

g) Experience: Based on the discussion in the focus group meetings, we

can conclude that the experienced drivers were more selective in

choosing whether to accept requests. They do not usually accept all

requests: since they are familiar with the areas and the character-

istics of the requests, they are able to assess if a request is worth

being accepted. The most experienced driver (5 years) had an ac-

ceptance rate of 10% while new joiners (less than 6 months) had

90% on average.

“When I'm in the city I get a new request every 10 seconds or every

minute. And in other busy areas around every 2 minutes. And I think I

accept about 1 out of every 10.” D2F3

“I also accept almost everything.” D5P3

h) Cancellation criteria: The other choice made by drivers is to cancel a

trip after accepting its request even though it is preferred not to

cancel trips due to the possible consequences. Risky pick-up points,

short-distance rides, and problematic riders were mentioned as the

main reasons for the cancellations. Some inexperienced drivers

stated that despite the fact that they are shown the pick-up address

before accepting the request, they could not recognize if it has a risk

of getting ﬁned. That is why they accepted the ride, approached the

address to assess the pick-up point. If they found it risky to stop,

they cancelled the ride.

“You will have to cancel because you cannot stop there [risky pick-up

points].” D1F2

3.2.2. Relocation strategies

When the rider is dropped oﬀ, the ride is ﬁnished. Drivers, there-

fore, have three so-called relocation strategies options if they tend to

continue their shift. They can either wait at certain places or cruise to

some random places or drive to some target areas where more demand

is expected. Although drivers pursue the same objective which is

maximizing the occupancy rate, therefore, proﬁt, their relocation

strategies diﬀer depending on several factors such as their attitudes and

experience.

a) Experience: Most beginning drivers preferred not to wait because

they enjoyed moving and also, they want to avoid having to pay for

parking; Otherwise, they might get ﬁned. Therefore, they drive

around or drive somewhere until a request appears. This behaviour

can increase the empty rides and cause some environmental issues

due to the risk of increasing vehicle kilometres travelled.

“...me neither [I never park]. I'm on the side with my car lights on… and

then you just hope that your customer comes quickly... I never stand still

whenever I have to wait for a ride. I just drive around.” D6P1

“I like to keep on moving actually. I just follow certain routes. For in-

stance, when I'm in the West… I just go in the direction of Schiphol. And

if I get a ride along the way I take it…” D6P3

In contrast, experienced drivers tended to wait in order to decrease

their empty trips. They know the safe places to park without paying for

it and getting ﬁned.

“I've stopped driving around for a year. Whenever I drop oﬀ someone at

the Prinsengracht [a neighbourhood in Amsterdam], I just know where I

can stay and I stay there until a new ride comes in…” D2F3

b) Surge pricing area: Given that drivers are able to see the surge

pricing areas on the map, some beginning drivers said that they

tracked them. While more experienced drivers stated that they did

not follow those areas since many drivers competed to reach there

and got the potential promoted requests which led to oversupply

and consequently no ride. Furthermore, they believed that the ap-

plication deliberately does not show the surge area in real-time in

order to gather drivers in a certain area. The reason might be for

improving the level of service for passengers (shorter waiting times).

“You see surge pricing on the map. Then, you drive where there are red

spots. You will see 1.6 in this area, so you know if you get a ride there,

the price will be times 1.6. If you get a request, you will also see 1.6 on

the bottom right of the screen. And if you don't see this, but you know it's

there, then it's not smart to take it... It could be that you are two streets

outside of this area.” D1F2

“I never drive to the surge.” D3F1

These statements conﬁrm the ﬁndings of Jiao (2018). He concluded

that the ambiguity and unforeseeability of the surge pricing mechanism

pose signiﬁcant challenges for the system stakeholders.

c) High-demand area: There is an icon like a ﬂashlight in the app that

shows the areas in which the demand is higher, but it is not surge

pricing. A few drivers said that they did take it into consideration for

repositioning while some drivers believed that there is no point to

follow it.

“There's also an icon that means that if you go to a certain place there's a

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Research in Transportation Business & Management 37 (2020) 100516

higher chance of getting a ride. It's a blue icon.” D2P2

“I don't look at it [high demand icon] normally. It isn't an important

factor for me.” D4P2

d) Rider's application: After ﬁnishing a trip, some drivers turn on the

rider's application in order to check the number of drivers in the

area. Then, if there is intense competition, they tend to relocate to

places where the chance of getting a ride is higher.

“I drive around the corner, stop there for a bit and launch the Uber app.

The one for passengers, which allows me to see how many Uber drivers

are there and if there are many in the area, I'll drive somewhere else. But

if there's not much competition, I stay there for a little bit. So, I actually

look through the app of the customer.” D3F1

e) Spatial position: The distance from the centre is another inﬂuential

factor. A few drivers said that if they end up with a location where is

further away from the centre, they can wait more in order to reduce

empty trips.

“…If you have to go somewhere outside of Amsterdam. Then, I wait there

for a bit, and I don't drive back immediately.” D4P2

f) Temporal status: It appears that relocation strategies are time-de-

pendent and have strong temporal patterns. A driver said that at

night, he did not wait after ﬁnishing a trip in a residential area and

immediately drove back to busier areas while in the afternoon/

evening, he preferred to wait for a few minutes at the location of the

previous ride to ﬁnd the next passenger. The reason is that the

probability of getting a ride in a place out of the centre is lower at

night.

“During night time you don't have to wait in a residential area, you would

just drive back. But at 7 pm or 3 pm, chances are higher.” D6P1

3.2.3. Working shift and area

The most important advantage of the platform mentioned by all

Uber drivers is the ﬂexibility to select the working schedule and service

area. This was the key reason for many drivers to join Uber. The de-

cision regarding the working shift is heavily dependent on the drivers'

employment status (whether a full-time or part-time Uber driver) and

preferences/aversions.

a) Preferences/aversions: Some drivers said that they preferred to work

in the evening because they were not morning persons. While some

stated they tended to work in the morning to avoid drunk/mis-

behaving riders given that the probability of having those riders is

much higher in the evening/night. A few drivers added that they did

not like spending the whole evening working instead of having some

social activities, so the morning shift was their preference. It appears

that the drivers gave priority to their aversions to decide about their

working shift.

“I work from 3 pm to 10 pm. Almost always. I really hate the alarm clock

in the morning. I like to stay in bed late. So, in the mornings, I just do my

things, and when it gets a little later in the afternoon, I think… let's get

going…” D3F1

“...nights are not for me. All those drunk people...” D2F3

b) Demand activity pattern: Working hours in mid-week days may

diﬀer from weekends. This is because commuting trips are per-

formed in the morning during a week while leisure rides, as the

main trips on weekends, are more requested in the evening. Thus,

demand activity pattern would be an inﬂuential factor for choosing

the working schedule.

“...on weekdays, I work during the daytime more, while on the weekends

I work more in the evenings.” D4F1

c) Demand prediction: Most of the drivers believed that the city center

is one of the main spots where the chance of receiving ride requests

is higher. This is because many rides are requested by tourists at

hotels located in the city center and also commuters who enter and

exit the area.

“Mostly I go to the city centre. There are the biggest chances of getting a

ride… I live in Amsterdam, but not in the centre. So, most of the times I

will go to the city centre... mostly in the mornings. Most hotels are in the

centre. But it's not only tourists who take Uber… Also working people.

People who live and work there.” D2P2

Some drivers do not prefer requests from the city centre because

they think that most of them are short rides which are not desirable for

drivers. Long trips are mentioned by all drivers as the most attractive

rides, especially when combined with surge pricing.

“I'll move around the edges of the center. Because in the center itself,

people just stay there [short rides]. But in IJburg or Zuid [neighbour-

hoods outside of the city center], you know for sure that people will go

towards the center. Those are longer rides.” D4P3

Weather condition, as well as the operation of public transportation

and ﬂights, can also inﬂuence the drivers' decisions on their spatial-

temporal coverage. Many drivers reported that demand is higher on

rainy, snowy, and cold days and also in case of a disruption in public

transport or ﬂights. An underlying distinction can be drawn between

part-time and full-time drivers in this case. As the part-time drivers

were less ﬂexible than full-time drivers due to other activities/com-

mitments, they did not tend to change their schedule and service area

because of the external factors. While many full-time drivers followed

the weather condition and public transport operations through either

Uber application or weather forecast/planner applications or their

community in order to decide when and where to work.

“Disruptions in public transport are also really important. During those

moments you'll get a lot of clients.” D5F1

“Those kinds of things [disruptions in Schiphol or PT] don't happen a

lot... I don't change my whole schedule just because something is hap-

pening.” D4P2

Moreover, events such as concerts and festivals can potentially

impact the drivers' working schedule and area. Drivers are informed

about planned events on a weekly basis through a newsletter sent by

Uber every Monday morning. Therefore, they can make an informed

decision about their working plan.

“If there's a party or festival somewhere. Most of the time I'll make sure to

be there.” D5P3

d) Surge pricing: Although many experienced drivers believed that

surge pricing area is not reliable, some beginning drivers said they

checked the application and if surge pricing appeared, they go on-

line. A few drivers reported that the information shown in the oﬄine

and online status is diﬀerent. Sometimes, the oﬄine application

overestimates the demand in order to encourage drivers to join the

system resulting in larger ﬂeet sizes for the platform.

“You also have dynamic prices. You'll see that on your screen. You can

also decide what hours you work based on those prices. They try to

manipulate it sometimes.” D1F1

e) Experience: Drivers gradually learn when and where to work for

earning more money based on their experiences. Thus, experienced

drivers could ﬁnd the places where the probability of getting their

favourite trips (e.g., long-distance rides) is higher.

P. Ashkrof, et al.

Research in Transportation Business & Management 37 (2020) 100516

“It's just experience. I've driven for Uber for so long and I've driven as a

street taxi, so I know everything. You have so many hotels around there.

90 percent of my rides go to Schiphol. You just have to know where to

stand. And don't accept everything... I know all the addresses of the

hotels...” D2F3

3.3. Drivers' expectations

The interaction of drivers with the platform is based on their

knowledge about the system environment and their experience as a

professional driver. The more drivers are familiar with the business

context, the more informed decisions they can make, so their expecta-

tions appear to be more well-grounded. In this section, the expectations

and preferences of the drivers are described in four categories including

requests, shared rides, income, and low-demand areas.

3.3.1. Requests

a) Ride destination: Many drivers believed that they should have been

able to see the ride destination before accepting the request so that

they could consciously incorporate this information into their de-

cision making. Despite the fact that it is desirable for drivers to have

as much information as possible, a few drivers argued that it is not

reasonable (given the platform's objectives) to expect to see the

destination in advance since most of the short rides might be de-

clined.

“What's important for me is that you could see the destination before you

accept. Because now, you only see that afterwards.” D1F1

“...Then all short rides would be refused. So, they'll never do that.” D3F1

b) Additional information: Some drivers would like to have more de-

tailed information to enable making a more informed decision on

accepting/declining requests and ﬁnding a suitable spot to pick-up

passenger(s). For instance, the luggage characteristics including size

and weight, number of passengers, if the rider has a pet or baby, and

so on.

“Luggage is very important. Sometimes, they have so many bags that

don't even ﬁt... An icon on your app for dogs and babies...” D2F3

“How many people you will pick up, I'd like to know that... I'd like to

know if they have children. But I would still pick them up. I have a kid

myself; I'd like to do that. [if you know there is a baby] you know that

you have to ﬁnd a good spot to stop.” D3P3

c) Rider's photo: Riders are able to see the driver's photo when they

request a ride, while drivers do not have access to the rider's photo.

Some drivers stated they should have been able to see riders' photo

to recognize them and pick them up more conveniently. Otherwise,

they argued, riders should not be able to see drivers' photo because

if it is about privacy, it has to be a mutual protocol to avoid dis-

crimination.

“I think the proﬁle picture needs to be private. We also don't see the

picture of the client. They can make a screenshot for example... why only

us and not them?” D1F2

Some studies argue that using the name and photos in the proﬁle is a

double-edged sword. On the one hand, it can build trust between two

sides, but on the other hand, it may lead to gender and racial dis-

crimination (Fistman & Luca, 2016; Ge, Knittel, MacKenzie, & Zoepf,

2016).

d) Rider's live location: A few drivers believed that it would be really

helpful if they could see the live location of riders. Then, they would

manage to pick up the rider more eﬃciently given that the expected

pick-up location is sometimes diﬀerent from the actual pick-up

point.

“The client could be able to choose to share a live location. It would be

nice to always see the live location. Sometimes it's a tourist, and then it

could be diﬃcult to explain where he could stand, especially when he

doesn't speak English very well…” D2P2

f) Pre-booked rides: At this moment, drivers are allowed to see if the

received request is pre-booked. One of the drivers suggested that it

could be helpful if when she dropped oﬀ a passenger, she would be

able to see all pre-book trips in that area in advance. It could enable

drivers to decide whether to wait there or move to another location.

“They [drivers] would use pre-booking. Then, they would know which

rides will come... It would help you decide whether you'll wait or not.”

D5F1

g) Relocating riders: One of the main concerns of drivers is risky pick-

up points. Relocating riders could be a solution to convince drivers

to accept requests which appear to have risky pick-up spots. A few

drivers suggested that Uber should relocate riders and ask them to

ﬁnd a safer place for being picked up.

“You're not allowed to stop there [Dam square – a risky pick-up point].

They should send the clients somewhere you are allowed to stop. And

then we can pick them up there.” D5F1

3.3.2. Shared rides

Uber does not oﬀer yet its pooled trips product (i.e. Uber Pool) in

the Netherlands. Notwithstanding, many drivers disliked the idea of

shared rides and the associated matching and pricing mechanisms.

Some drivers were familiar with the concept of pooled rides through

another ride-sourcing company, namely “ViaVan” which exclusively

provides on-demand shared transit services in Amsterdam.

a) Pricing: Based on the drivers' understanding of the ViaVan pricing

strategy, drivers are paid based on the kilometres travelled, re-

gardless of the number of passengers. Therefore, additional pas-

sengers do not necessarily lead to higher earning. Most drivers said

shared rides would be appealing if passengers would have paid se-

parately. One driver said extra pick-up travel time and embarking

fee should be considered in the trip fare for each passenger.

“...you really have to get both the embarking fee as well as the extra time.

So, you can really see it as a separate customer.” D6P1

b) More frequent stops: It is not desirable for drivers to stop because

every stop can increase the operational costs as well as the risk of

getting ﬁned. A few drivers asserted that they preferred to stop as

little as possible and were concerned that shared trips would in-

crease the number of stops.

“It's more about that you would have to stop more often, which is already

diﬃcult because you are not allowed to stop in many places. The best is to

stop the least possible and being able to drive on.” D3F2

c) Conﬂicts between riders: Some drivers pointed to the possible con-

ﬂicts which may arise amongst riders and between riders and the

driver especially when one of the riders is in a rush. Despite the fact

that the riders requesting shared ride are aware of some possible

delays and deviations, there is still, for example, a chance of conﬂict

between riders especially when a rider is in rush and the driver

needs to pick up another passenger who has requested a ride, but

he/she is not at the pick-up point. A few drivers believed the rider

who is in a hurry may put some pressure on the driver which could

be stressful for the driver and aﬀect the driver's rating given by the

riders.

“You are with a client in the car, and you need to pick up the other one.

P. Ashkrof, et al.

Research in Transportation Business & Management 37 (2020) 100516

And this person is not at the location, and the other client is in a rush...

That's a hassle.” D2P2

“... you'll get some pressure from the person that's going to be late... Yeah,

you make one mistake and everyone's day is ruined. Take the wrong exit

and someone is late and the other as well. It will only get worse…” D5P3

3.3.3. Income

a) Low income: Most drivers complained about their low income due

to the platform's high commission fee (25% of each trip) and strong

competition between drivers. They suggested that the commission

fee should be lowered and the competition between drivers needs to

be controlled by imposing a constraint on the maximum number of

active drivers in the region.

“I [as Uber CEO] would set a maximum of drivers in all big cities. So, a

max in Amsterdam and Utrecht. There shouldn't be too many drivers.”

D2P1

This is in line with the For-Hire Vehicles (FHV) regulations which

have recently been introduced in New York City. In order to comply

with the new regulations that aim to increase driver's income and re-

lieve congestion in Manhattan, ride-sourcing platforms have limited the

access of drivers to the application in some areas.

b) Minimum age: The other measure proposed by some drivers for

decreasing the competition and operational costs was to set a

minimum age for Uber drivers. After this suggestion, a discussion

was initiated about the consequences faced by experienced drivers

because of irresponsibility and the lack of experience of young dri-

vers. The logic behind it is that young drivers cause a lot of accidents

which can have ramiﬁcations like damaging Uber's reputation and

increasing the insurance fees. Surprisingly, one of the youngest

drivers accepted the criticism during the discussion.

“...there are so many drivers of 18 to 21…. They're still in school. And

during the summer they start working for Uber and then they hit bikers or

even kill people. And we have to deal with the consequences for the rest

of the year, while they just go back to school.” D4F1

“One of the reasons that insurances are so expensive now, is that because

so many inexperienced drivers are now on the road. Me as well...

therefore more experienced drivers like this gentleman, or that lady have

to pay a lot for the insurance, so they are really a victim of that.” D1F1

c) Minimum wage: Many drivers stated that they were promised to

earn 1000 euros per week, while it was not realistic. They believed

that this misleading and incorrect information is spread by the

platform in order to attract more drivers and oversaturate the

market at drivers' expense.

“I just want to get what they promised, which is about 1000 euros per

week.” D1P3

Some of them believed that a minimum wage per hour should be set

and if they reach that point, the shift can end. It appears to be a feasible

regulatory measure given that ride-sourcing drivers working in NYC

beneﬁt from a minimum income of $17.22 per hour (after expenses)

following the recent introduction of the FHV regulations in NYC.

3.3.4. Low demand areas

a) Spatial bonus: Making a ride to a low-demand area could potentially

decrease the utilization rate of drivers given that the probability of

receiving a ride is lower there. This is why the spatial bonus is

needed to balance between demand and supply and hence reduce

spatial disparities by supporting trips to low-demand areas. Some

drivers pointed out that this risk needs to be compensated in order

to persuade them to accept those rides. For example, a bonus should

be set for a certain number of trips to low-demand areas or the

commission fee could be lower in some areas (dynamic commission

fee).

“If you have done 10 rides from a certain place, you'll get a 100 euros

bonus. For once, or every 10 rides. I mean, if it's a place where nobody

else comes… I think that for certain cities, they should have no com-

mission. If you pick someone up there, you don't have to pay the 25

percent.” D2F3

b) Guaranteed hourly income: Another driver argued that since he

could not trust Uber to give him a bonus for 10 rides, he preferred to

have a guaranteed hourly income to oﬀer a service in low-demand

areas. This comment can also stress the necessity of dealing with the

persistent strong mistrust.

“If they want me to go to Lemsterhoek [a low demand area in

Rotterdam], I just want a guaranteed hourly rate. Not 10 rides. Then, I

do only 9 and the system might reject me after that. I don't trust that.”

D3P3

4. Discussion

While we make no claim as to the generalisability of the qualitative

results, we propose, as a mean to synthesize our ﬁndings, a conceptual

model that can be used as further reference for future research. It

provides a framework by which it is possible to characterize the main

components of the behaviour of these important agents in the ride-

sourcing environment. Based on the identiﬁed themes in the focus

group sessions, Fig. 2 illustrates the relationship between the tactical

and operational decisions of drivers and the factors aﬀecting them.

The decisions of ride-sourcing drivers are divided into working shift,

relocation strategies, and ride acceptance. These can be inﬂuenced by a

set of factors categorized into platform strategies, drivers' character-

istics, riders' attributes, and exogenous factors (this is depicted by using

diﬀerent colours). The items are also grouped based on the associated

decision(s) that they aﬀect. The middle-dotted box represents the fac-

tors that aﬀect all the three types of decisions. Platform's incentive

schemes and pricing strategies, drivers' experience, understanding of

the system operations, socio-demographic characteristics, attitudes, and

rider's interaction with drivers impact the working shift, relocation

strategies, and ride acceptance behaviour of drivers.

Moreover, the platform information sharing policy, destination

prediction by drivers, rider's pick-up point, rating, and willingness to

share additional information such as luggage characteristics and the

number of passengers are likely to play a crucial role in the ride ac-

ceptance behaviour. Relocation strategies might be inﬂuenced by the

platform repositioning guidance, pre-booked rides, drivers' spatial-

temporal status after ﬁnishing a trip, and the level of competition be-

tween drivers which can be checked by the rider's application. At the

upper-level, platform employment regulations (e.g., maximum working

hours), demand pattern, weather condition, scheduled events such as

concerts, the level of service and operations of public transport as well

as ﬂights are, in addition to those factors that apply to all decision di-

mensions, relevant for the drivers to decide on their working shift.

Both tactical and operational decisions are reciprocally connected.

Taking into account that the choice of a relocation strategy is time-

dependent and that drivers tend to reduce the idle time within their

working hours, the relationship between working shift and relocation

strategies can be governed by the utilization rate which is the ratio

between the occupied time and the working shift. Moreover, working

shift and ride acceptance might be linked by the served demand so that

drivers assess the shift proﬁtability based on the earned income which

is dependent on the characteristics of the accepted rides during the

selected working schedule. The operational decisions could also be

P. Ashkrof, et al.

Research in Transportation Business & Management 37 (2020) 100516

related based on the incoming demand given that drivers choose a re-

positioning tactic to ﬁnd ride requests whereas if they do not receive

desirable requests, they may adapt their relocation strategies.

The relative importance of the identiﬁed determinants, as well as

the inter-dependency between the diﬀerent driver decision dimensions,

should be subject to future research. On the other hand, more items and

links can be added to this framework given that some topics have not

been covered in the focus group sessions; for instance, refuelling stra-

tegies, multi-homing issues (i.e., drivers are connected with more than

one ride-sourcing platform at the same time), drivers' car ownership

(owning or leasing the car?) and their implications. We believe that the

ﬁndings from this qualitative research provide input into setting a re-

search agenda focusing on the supply-side dynamics of the ride-sour-

cing double-sided platform.

5. Conclusions

Ride-sourcing platforms have been rapidly introduced in recent

years in cities around the globe. As a two-sided platform with gig

economy business models, ride-sourcing companies match drivers with

passengers' requests. While the interactions between individual drivers

and the platform determine the supply-side dynamics, drivers also di-

rectly interact with passengers. As such, drivers are in the heart of the

ride-sourcing system, yet very limited research attention has been de-

voted to understanding their motives and perceptions. This is of parti-

cular relevance given the existing tension between drivers and the

platforms in several countries where these companies operate. To this

end, we have conducted a series of focus groups with Uber drivers

working in the Netherlands in order to gain deep insights into drivers'

perceptions of the system operations and their interactions with the

platform.

We found that while all drivers strive to maximize their revenue

their strategies can be signiﬁcantly diﬀerent amongst each other. The

focus group insights indicate that the behaviour of ride-sourcing drivers

can be aﬀected by many exogenous and endogenous elements de-

pending on platform strategies, drivers' characteristics, riders' attri-

butes, and exogenous factors.

Ride-sourcing drivers have several main decisions during the course

of their work: ride acceptance, relocation strategies, working shift and

geographical area. Drivers need to decide whether to accept/decline a

ride request based on the limited information provisioned. Although

some beginning drivers found it extremely challenging to make an in-

formed decision on requests, most of the experienced drivers believed

that many requests should be declined based on some criteria such as

pick-up point location, distance to the rider or rider's rating. However,

having access to more detailed information about the request's char-

acteristics such as the ﬁnal destination, trip fare, the number of pas-

sengers, and luggage speciﬁcations was considered desirable but not

available yet in the platform.

The level of experience was also found to be an inﬂuential factor in

drivers' relocation strategies in which many beginning drivers followed

the platform repositioning guidance whereas more experienced drivers

did not trust the application recommendations such as surge pricing

areas and high-demand spots.

The ﬂexibility in choosing a working shift and area in which to

operate was appreciated by all drivers as the key reason for joining the

system. This freedom enables drivers to plan their working schedule

based on their preferences. Given that part-time drivers had less ﬂex-

ibility due to their other commitments and activities, a sharp distinction

between part-time and full-time drivers in their decisions on working

shift and their will and ability to respond to prevailing conditions was

identiﬁed.

Given that ride-sourcing platforms constantly introduce new fea-

tures such as Rematch and maximum working hours, it appears to be

crucial to ensure that drivers are adequately briefed on new function-

alities. Otherwise, there might be a high risk of misunderstanding of the

system operation which leads to unexpected and seemingly irrational

behaviour of drivers. Moreover, we observed a strong mistrust of the

drivers in the platform due to what was perceived by the focus groups

as an unfair reputation system, unreliable navigation algorithm, high

competition between drivers, a passenger-oriented platform, high

commission fees and misleading tactics.

Following the insights gained in this study, future research should

examine the determinants of drivers' operational and tactical decisions

Utilization Rate

Served Demand

Incoming Demand

Ride Acceptance

(Operational decision)

Rating

Additional information

(baggage characteristics, number

of passengers)

Employment

regulations

Demand

pattern

Weather

condition

Public transport/flight operation

Events

Repositioning guidance

Drivers’ spatial-temporal status

Using riders’ application

Pricing strategies

Incentive

schemes

Interaction with drivers

Understanding of the

system operations

Socio-demographic

characteristics

Experience

Attitudes

Relocation Strategies

(Operational decision)

Working Shift

(Tactical decision)

Pre-booked rides

• Platform strategies • Drivers’ characteristics • Riders’ attributes • Exogenous factors

Fig. 2. Conceptual model of tactical and operational decisions of ride-sourcing drivers.

P. Ashkrof, et al.

Research in Transportation Business & Management 37 (2020) 100516

by means of either stated preferences choice experiments or ﬁeld ob-

servations of revealed preferences for ride-sourcing drivers. Estimating

choice models for explaining driver's decisions (e.g. joining the plat-

form, working shift, rebalancing, ride acceptance) will facilitate the

assessment of the impacts of diﬀerent policies and system conditions on

supply-side dynamics and system performance. This study was con-

ducted in the Netherlands where there is a single ride-sourcing platform

(Uber) that dominates the market. An important research direction

would be to replicate such a study in a more competitive environment

in which several ride-sourcing companies are trying to attract both

users and drivers. It should be noted that the data collection was con-

ducted prior to the COVID-19 pandemic. Further insight is required to

understand the possible changes to drivers' behaviour due to the new

demand patterns, changes in users' travel behaviour, and public health

risks. It is also recommended to look at this system through the lens of

other stakeholders including platform providers, policymakers, and

users to explore their attitudes, preferences, concerns, and limitations.

Then, a comprehensive conceptual model may be developed to explain

the dynamics between all the agents. Last but not least, the approach

used in this research can be applied to study the ecosystem of other gig

economy businesses such as delivery and freelancer services.

Acknowledgement

This research was supported by the CriticalMaaS project (grant

number 804469), which is ﬁnanced by the European Research Council

and the Amsterdam Institute for Advanced Metropolitan Solutions.

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