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This paper focuses on analyzing the vehicle insurance ecosystem in Estonia that integrated Actor Network Theory (ANT) as theoretical lens to investigate potential transformations in the said ecosystem and to gauge the influence of insurance-oriented telematics technology. The paper combined an interpretive approach applied to a case study method. The study offers insights which are useful to facilitate the alignment of insurance-oriented telematics technology and its practical implementation within social systems. This study is one of the first to examine insurance-oriented telematics technology as a socio-technical process in Estonia.
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European Scientific Journal September 2018 edition Vol.14, No.26 ISSN: 1857 7881 (Print) e - ISSN 1857- 7431
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Analyzing the Vehicle Insurance Ecosystem in Estonia
using Actor-Network Theory
Andrew Adjah Sai
Lecturer and Researcher at Estonian Business School, Tallinn, Estonia
Anna Naroznaja
Partner at Plottera
Doi:10.19044/esj.2018.v14n26p45 URL:http://dx.doi.org/10.19044/esj.2018.v14n26p45
Abstract
This paper focuses on analyzing the vehicle insurance ecosystem in
Estonia that integrated Actor Network Theory (ANT) as theoretical lens to
investigate potential transformations in the said ecosystem and to gauge the
influence of insurance-oriented telematics technology. The paper combined an
interpretive approach applied to a case study method. The study offers insights
which are useful to facilitate the alignment of insurance-oriented telematics
technology and its practical implementation within social systems. This study
is one of the first to examine insurance-oriented telematics technology as a
socio-technical process in Estonia.
Keywords: Actor network theory, Estonia, insurance-oriented telematics
technology
1 Introduction
The insurance industry is a heavily regulated and data-driven industry.
The rise of what is known within the insurance circles as “Insurtech” promises
to significantly disrupt traditional insurance practices (Lee & Shin, 2017).
However, this is especially due to the adoption of sensors worldwide which
has a growing significant impact on different industries (Xu & Li, 2014).
Insurtech is a set of innovative business models, platforms that bring in a new
customer experience by applying innovative technologies in the insurance
world. Insurtech thrives under the bigger umbrella of the fintech industry.
Fintech is an emerging financial services sector that includes third-party
payment, money market funds (MMF), insurance products, risk management,
authentication, and peer-to-peer (P2P) lending (Barberis, 2014, as cited in
Shim and Shin, 2016). They noted that the financial sector has been relying on
internet-based technology to bring new services to markets for some time.
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Also, they agreed that the offline industry is gradually integrating more and
more with online technologies.
The traditional vehicle insurance calculation methodology was mostly
based on general factors as vehicle characteristics and driver’s socio-
demographical data (Segovia-Vargas et al., 2015). However, the competition
between insurance firms is increasing and many companies are trying to
reduce costs; due to this, the Usage- Based-Insurance (UBI) was developed
(Troncoso et al., 2007), which is based on innovative internet-based telematics
technology. Telematics technology involves data collection, transmission, and
analysis of data collected from a device installed in a motor vehicle. The
technology generates usage-based driving information such as vehicle speed,
acceleration, and location. Personal driving data is also generated which can
serve as a basis for optimizing driving styles and other social facets. Several
studies have been conducted about the efficiency of telematics technology and
transport, for example the effective implementation of telematics solutions
aimed in the streamlining and optimization of urban freight transport systems
(Iwan, 2016). Consequently, another research about urban traffic speed and
maximization of fuel economy (Zhang et al., 2015), particularly telematics
solutions, have become more substantial in having a significant influence on
vehicle pollution reduction (Walker & Manson, 2014).
Actor Network Theory (ANT) is a socio-technical approach that
researchers employ to gain a better understanding of social and technological
developments in a social system. ANT focuses on the relationships between
the human and non-human (Papadopoulos, 2007), and it evolved from the
work of Michel Callon, John Law, and Bruno Latour (Graham & Marvin,
2001). According to Latour (1999), to bypass the divide between nature and
culture, ANT theorist should concentrate on the inclusion of non-humans into
the social sphere and humans into the natural sphere. Therefore, this
fundamental position has been supported by many scholars. For example,
Pinch (1996) argued that technology goes through and through the social,
while Bijker (1987) asserted that technology and society exist as a seamless
web.
To understand the insurance ecosystem in Estonia, therefore, the
interweaving of social and technological dimensions has become significant.
An attempt has been made to show the insurance ecosystem through ANT.
Using the ANT approach, all the factors (both human and non-human)
influencing the insurance ecosystem in Estonia are seen as actors and a
combination of all these in terms of networks. Consequently, the actor network
illustration would help to visualize all the key players in the ecosystem and
bring out their interconnections. It has been argued among scholars in this field
that ANT theory provides a powerful tool to better reveal the complexities and
dynamics of a technology driven industry.
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The Estonian insurance ecosystem has been thriving since re-
independence in 1991. Traditional insurance practices have dominated the
insurance industry. Consequently, the insurance industry, though heavily
regulated in Estonia, has not employed insurance-oriented telematics
technology. Records indicate that accident rates are on the high. At both
individual ownership and firm levels, little can be done to regularize driver
behaviour and to become more accurate in terms of premium pricing, among
others.
The primary research question is “How can telematics-oriented
technology form an actor network for re-configuring the Estonian insurance
ecosystem?” For this case study, we employ actor network theory as a lens
and interpretation tool. We first show the ecosystem without telematics
technology, and the situation after telematics were brought into the picture and
to substantiate the findings with data analyzed from actual tests conducted.
The following research questions guide this study:
1. Who are the focal actors in the Estonian insurance ecosystem and how
have they done so far?
2. What are the implications of re-configuring the insurance ecosystem
with telematics technology?
2 Theoretical Framework
2.1 Telematics Technology
Telematics technology as a concept has received little attention in both
research and practice. The few studies conducted about “fintech” and new
technologies such as “telematics” have not adequately defined the scope and
parameters of its usage. However, some scholars have attempted to do so. For
example, Nora and Minc in 1978 asserted that “telematics is not a distinct
technology or technology standard” (Nora & Minc, 1978). Other scholars
described the combination of telecommunication and information processing,
as predominantly referring to information and communications technology
within road vehicles (Nijkamp et al., 1996; Van Der Laan et al., 1997).
Husnjak, et al. (2015) posited that Global Positioning System (GPS)
technology is used in car telematics together with other sensor devices to
monitor driving distance, speed, and style. The influence of these same
technologies and devices on driving behaviour has been confirmed by Ayuso,
et al. (2014), regardless that there is no peer reviewed evidence about the
effects of GPS and sensor devices on outcomes such as road injury.
Prior research in other contexts, using various research methods
(Husnjak, Peraković, Forenbacher, & Mumdziev, 2015), have highlighted the
importance of telematics technology generated data for both the policyholder
and insurance companies. Other studies highlighted include the observations
of motor insurance pricing in India and the need for a radical innovation to
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disrupt the status quo (Rejikumar, 2013). Mihaela David reviewed theoretical
concepts about non-life insurance pricing and noted that one of the major
concerns for the insurance companies is the design of tariff structure that will
fairly distribute the burden of claim among policyholders. He observed in that
same study that the calculation of a differentiated premium within the
insurance portfolio is almost non-existent such that a flat risk profile is
assumed for the determination of reasonable insurance premiums (David,
2015). Marika Azzopardi et al also used a SWOT-analysis to navigate Usage-
Based Insurance and conventional rating methods for automotive covers and
concluded that telematics enhances fleet management and mitigates risk
(Azzopardi & Cortis, 2013). A study in China, focused on risks in the
proceedings of underwriting, established a scientific and practical model to
enhance motor insurance underwriting risk management process (Chehui,
Zhangjiwu, & Zhangxingyang, 2011). One noteworthy study proved the value
of telematics-based data in the risk selection process of an insurance company,
where three models including a logistic regression, random forests, and
artificial neural networks model are built and compared (Baecke, 2017). Yu-
Luen Ma et al confirm that mileage, peak time travel as well as driving
behavior such as braking or starting habits are highly correlated with accident
rate. They also confirm that contextual driving factors such as speeding and
relative speed are important risk factors (Ma et al., 2018). Another interesting
study described a partnership between one of Italy’s largest auto insurers
(Unipol) and a systems integrator (Octo Telematics) that yielded a new and
very profitable customer value proposition for the insurer based on customer’s
driving and risk profiles (Peppard et al., 2011).
2.2 An Actor Network Theory (ANT) Perspective
Young et al. (2010) have argued that ANT regards networks as
dynamic processes, while other theories such as stakeholder theory, agency
theory, and social network theory, among others, limited the focus on the
interests of human participants in the network.
According to Callon (1986), translation and inscription are two crucial
processes in ANT. He defined inscription as referring to the process of creating
technical artifacts that ensure the protection of an actor’s interests. Cressman
(2009) also asserted that within ANT, translation is a concept that bridges gaps
between the varied aspects that are combined in technology. Callon’s four
moments are applied widely in ANT research and is employed in this study.
To Callon, op. cit., translation of an actor or actors into a network is achieved
through four moments of translation: (1) problematisation, (2) interessement,
(3) enrollment, and (4) mobilisation. In the first moment, focal actors define
their interest based on the problem they face in achieving their goal, and they
aim to establish themselves as an obligatory passage point (OPP) through
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which the other actors must pass. This is a situation that has to occur for all
the actors to satisfy their interests (Callon, 1986). In the second moment,
interessement, focal actors impose and stabilize other actors’ identity. Focal
actors attempt to define and interrelate the various roles taken up by other
actors in enrollment, the third moment. In the final moment of translation,
mobilisation, actors are persuaded that their interests are aligned with those of
the focal actor, thereby avoiding betrayal and allowing the network to be
maintained (Callon, 1986).
2.3 Estonia A Small Baltic Country with Big Technology Advancements
Few studies have focused on the Estonian financial industry, not to
speak of insurance industry. However, some studies exist on the social system
of Estonia and technological change (Sai & Boadi, 2017); ANT and urban
planning and development in Tallinn (Pulk & Murumägi, 2013). Sai et al.
(2017) argued that Estonia has remained a giant in terms of technological
advancements over the last few decades since re-independence in 1991 from
the Soviet era. For 50 years, Estonia was under the Soviet regime. Sai, et al,
op. cit., further argued that the changes occasioned in Estonia are partly a
result of the social system being densely homogenous. The country is located
in Eastern Europe and has a population of about 1.3 million people. The
Estonian social system can be characterized as homophilous (Lazarfeld &
Merton, 1964, p. 23 as cited in Sai, et al, op cit.). Homophilous social systems
tend toward system norms. They posited that interaction in such social systems
is between people of similar backgrounds, while people and ideas that differ
from the norm are seen as strange and undesirable. Even though Lazarfeld et
al have argued that homophilous social systems are often less innovative, the
case is not same with Estonia. Estonia has chalked major successes on various
technology and country development indicators over a relatively short period
of time. At the same time, while telematics technology has received wide
acceptance in central and northern Europe, no such evidence exists about its
use in Estonia. For example, Peppard et al., op. cit., have provided insights
into the Italian case.
However, the total land area is 45,336 km2 (17,504 square miles) with
a population density of about 30 inhabitants per km2 (75 inhabitants per square
mile). Administratively, the country is divided into 15 counties, 213
administrative units, including 30 cities and 183 rural municipalities. The
official language is Estonian. However, English, Russian, Finnish, and
German are widely spoken as well. Tallinn is the capital city. Other principal
cities in Estonia are becoming more and more urbanized, while the Estonian
society is undergoing considerable change with increasing levels of
stratification and distribution of family income (Sai & Boadi, 2017).
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3 Methodology
The paper opted for qualitative methods and draws from the
interpretive paradigm, as suggested by Dudovskiy (2018). The theoretical
framework lies on actor network theory. The assumption behind actor network
theory is that the process of interest alignment and associations are examined
from the perspective of the network (Monteiro, et al., as cited in Pulk and
Murumagi, 2013). In this sense, the role of the researcher is to examine and
record the network’s elements, investigate the way that aligned networks are
created, and explore the stability and irreversibility of the network (Tsohou,
2012); treat all actors the same way irrespective of their human or non human
nature. The study approach was inductive, which is suitable for case studies
and actor network theory methods. According to Eisenhardt and Graebner
(2007), case studies allow us to develop theory inductively by accommodating
variable number of sources. In the paper, the researchers combined actor
network theory with exploratory case study method. Exploratory case method
was suitable as it aims to find answers to the questions of “what” or
“who” (Dodovskiy, 2018). Conducting a case study research involves the use
of some combination of different methods and procedures for collecting and
analyzing data (Yin, 2009). Both the quantitative and qualitative methods can
serve as the basic methods for carrying out the research and to describe in
detail a social phenomenon. Data was sourced via primary and secondary
sources. The primary data was collected from Estonian Telematics company,
Plottera. The data assembled by telematics boxes were discretely installed on
customer cars along with Global Positioning System (GPS) and General
Packet Radio Service (GPRS) antennas, and they are connected to the CAN
(Controller Area Network) bus of vehicles. Secondary data sources were
sourced from Estonian Insurance Fund, published materials, and documents.
4 Analyses
4.1 Observed Interaction between Actors in the Insurance Ecosystem
Before analyzing the insurance ecosystem, it is imperative to identify
the focal actor. In this paper, we identify insurance-oriented telematics
technology as the focal actor. All other actors in the network are fully
acknowledged. These include the Government (represented by the sector
Minister); legislation (the Motor Insurance Act, 2014); The insurance
regulators (Motor Insurance Fund (LKF)); Insurers; Vehicles and their
Drivers. These actants are elaborated on in the ensuing section. Meanwhile,
other representative actors, such as Motor Insurance Register Data and Police,
are noted as well.
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4.2 The Insurance Ecosystem without Telematics Technology
Legislation: The current Motor Insurance Act of Estonia (amended)
came into force fully in October 2014. This amended version repealed relevant
sections of the Motor Insurance Act (RT I 2007, 55, 368) accordingly. This
Act regulates insurance of civil liability arising from damage caused by the
use of a vehicle in Estonia and it is compulsory per the Act. The compulsory
aspect of the motor insurance is regulated by the provisions of the Laws of
Obligations Act while automatic motor insurance is regulated by the
provisions of the Administrative Procedure Act (Motor Insurance Act, 2014).
Regulator (The Motor Insurance Fund): In Estonia, a body charged
with the responsibility over insurance oversight is the Estonian Traffic
Insurance Fund. This non-profit body serves as a motor insurance guarantee,
compensation entity, and is also responsible for the Estonian Green Card
bureau. The LKF ensures the proper functioning of the motor insurance system
by performing functions arising from the Motor Insurance Act and managing
contracts entered into with the state and its articles of association, among other
responsibilities. All insurers that offer motor insurance in Estonia are members
of the LKF.
Insurer: The insurer is the principal contact for obtaining an insurance
contact in Estonia. The Insurer is required by the Act to submit, to the Motor
Insurance Register, data about contracts concluded and policies issued to
insured entities; withdrawal from and cancellation of contracts; reports on
insured events received and registration of same; decisions by the Insurer to
compensate or refuse compensation for damages; recovery claims filed by the
Insurer and vehicles destroyed due to insured events. Therefore, these duties
are required under the Act.
Insured: The Insured is either an owner of a vehicle or an authorized
user of a vehicle (Driver of vehicle).
Vehicle: All motor vehicles stated under the Act that are required to
be covered under motor insurance.
Territorial Validity of Insurance Cover: Motor vehicles are
registered by the state. Motor insurance cover is generally recognized
regardless of the county (administrative zone or jurisdiction) where the motor
vehicle was registered. That notwithstanding, an insured event is considered
based on the location the event occurred and in which county the event
occurred based on circumstances and prescriptions of the Act.
Notification of Insured Event: Insured events must be reported to the
insurer of the person who caused damage or to the Insurer of an injured party,
according to the Act. Further, the law specifies how issues of compensation
and other notifications about insured events must be communicated, which
includes via post and/or email.
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Access to Motor Insurance Register Data: Insurers are required by
the Act, in the event of assessing the insured risk, and concluding a contract
and its performance to access the Register via a Chief Officer.
Police: The same Act admonishes the Police to receive notification of
insured events and also act as agents for enforcement of specific aspects of the
Act on the field. The Police was extended to include all emergency services
agencies and allied organizations that serve as first responders to accident
events.
A schema illustrating the actors in the network without telematics
technology is shown in Figure 1.
Figure 1. The Insurance ecosystem in Estonia without Telematics Technology. Source:
Authors
4.3 First Moment of Translation: Problematization Phase
As pointed out earlier in this paper, the insurance industry in Estonia
is lagging behind other countries in terms of application of telematics-oriented
vehicle insurance technologies to streamline the industry and, consequently,
face significant problems as a result. This is the first phase in translation where
the focal actor(s) define their interest in the problem they face in achieving
their goal, and aim to establish themselves as an obligatory passage point
(OPP) through which the other actors must pass (Callon, 1986). Some of the
problems are discussed below:
4.3.1 Vehicular Accidents in Estonia
Between the period 2009 and 2016, the recorded vehicle accidents
show that Mondays and Fridays recorded the highest number of vehicular
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accidents, at 30,786 accidents and 34,707 accidents respectively. The analyses
were based on datasets from the Estonian Insurance Fund (LKF). Several
factors account for this scale of accidents, some of which are discussed later
in the paper. In terms of distribution by month, December and January
recorded the highest number of accidents (19,227 and 17,673 respectively).
For distribution by time of the day, 6:30am to 23:00 accounted for the most
accidents, however peaking at 17:00. Figure 2 shows below the hourly
distribution.
Figure 2. Distribution of accidents by hour, 2009-2016. Dataset from LKF Estonia
4.3.2 Cost of Vehicular Accidents 2009 2016
According to the LKF, an overwhelming amount of payments were
made for insured events during the 2009 to 2016 period. Thus, most payments
were made for cases at intersections, amounting to about 87.1 million euros.
Other insured events occurred at same direction cases: parking related,
opposite direction accidents, unspecified as well as special cases. Figure 3
summarizes this.
Figure 3. Payments for Accidents by type. Dataset from LKF Estonia
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4.3.3 Number of Accidents by County
Figure 4 shows accident distribution by counties. In the last six months
of 2016, the highest occurrence of car accidents were registered in Harju
county (10156 cases), while the smallest car damages were registered in Hiiu
county 25 and Põlva county– 93 (LKF, 2017). The LKF further reports that
most road accidents recorded in 2016 were caused by drivers, citing driver
behavior as key triggers to these occurrences. These include over speeding,
unsafe overtaking, and misjudgment of traffic and road conditions. The most
frequent accidents were reported to have occurred at crossroads or
intersections and parking lots in Tallinn, which is the capital city. The
commonly cited trigger of these occurrences has been alcohol use and drugs
(LKF, 2016).
Figure 4. Event distribution by counties. Dataset from LKF Estonia
Subsequently, the most frequent accidents between the second half of
2016 and first half of 2017 occurred at crossroads and parking lots as earlier
mentioned. The Rocca al Mare ring which is in the Mustamae district of the
Harju country recorded the most damages from car accidents. 136 insured
events amounting to losses of 0.18 million euros were made just from events
at the said ring. For the same Harju county, Sõpruse pst., and Tammsaare
crossroads, recorded 112 cases at 0.18 million euros losses. Ülemiste
crossroad accounted for 109 cases, with pay-outs amounting to 0.14 million
euros; Järvevana road, Tammsaare road and Pärnu mnt cross road accounted
for 104 cases and losses by way of 0.19 million euros; Viru intersection
accounted for 85 cases, with pay-out of 0.11 million euros. Others included
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Ülemiste keskus (155 cases, 0.16 million euros in pay-outs), Järve Keskus
(109 cases, 0.1 million euros in pay-outs), Kristiine Keskus and
Lasnamae Centrum (77 cases and 4 cases respectively, each paying out 0.06
million euros each).
The situation was not any different in the second largest city of Estonia,
Tartu. Events were registered mostly at parking lots at Lõunakeskus in Tartu
(112 cases, 0.14 euros paid out). The intersection at Riga in Tartu accounted
for 114 cases at a cost of 0.16 million euros in pay-outs for insured events.
4.4 Second Moment of Translation: Interessement Phase
Callon (1986) has said that, at this phase, the focal actor convinces
other actors to agree on and accept the definition.
In the first place, the current situation in the Estonian insurance
ecosystem is fraught with several challenges as earlier discussed. Firstly, it has
been established that driver behaviour is the main trigger for the countless
losses made over the period. However, there is really no established
mechanism to monitor such behaviour, given the current ecosystem. Secondly,
the cases noted and reported by the LKF are collected and retrieved from data
repositories, one of which is the Motor Insurance Data Register. Per the Motor
Insurance Act, in the event of insured cases or their adjudication, as the case
may be, a request has to be made to a principal officer at the Motor Insurance
Data Registry. In addition, archived information together with reports from
other actors, such as the Police, are released to affected parties, all within the
remit of the law. In the event that these data are in some way adjusted either
knowingly or unintentionally, the effect on a determination by a court of law
is enormous on all parties. Thirdly, even though efforts have been made to
identify risky zones, the data is not conclusive nor available. For example,
there are fragmented data on risky zones, but there is no real-time information
to address driver’s needs.
Figure 5. An example of real-time account of accident events generated from Plottera.
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Fourthly, the current situation prescribes a universal insurance
coverage and territorial validity. This means notwithstanding the county where
one subscribed to an insurance cover or location of insured event, risk
premium calculation approaches are based on individual risk profiles (some of
which may be inconclusive) and are not reflective of the actual situation.
4.5 Third Moment of Translation: Enrollment Phase
Callon, op cit., described this phase of translation as a “group of
multilateral negotiation” (Callon, 1986). We put across what changes
telematics technologies could make on the current ecosystem in Estonia in a
bid to build a path to lead to enrolment from interessement, in the previous
phase. The introduction of telematics could impact significantly on the future
of the insurance ecosystem in Estonia. Firstly, telematic technology could be
used to analyze vehicle initial registration points and where the vehicles were
actually used. For instance, Hiiu county is a low-risk zone and, therefore,
insurance premiums are lower. Vehicles are originally registered in Hiiu
county at low fees and actually driven in high risk zone (for example, Tallinn
in Harju county). Real-time data on driving locations can be shared with the
Insurer and can serve as a basis for driver profiling and risk assessment and
also the territorial validity of insurance cover. This could inure in a switch to
usage-based mechanisms for premium calculations, among others and also
classification schemes such as higher scores for driving within higher risk
zones.
Further, traffic patterns at the moment are obtained from, again,
fragmented online resources and maps. The use of telematics and its associated
devices will provide real-time notifications to all parties in the ecosystem and
would, therefore, reduce probable traffic jams. Furthermore, the work of the
Police in the chain of events will become simpler as data generated from
telematics technology actually showcases in graphical format how an accident
occurred. It also shows how it reduces conflicts and their determination which
sometimes is inaccurate (see Figure 5 above).
Telematics can also ease the work of the oversight body, the
Government and its representative the sector Minister. Accurate data about
happenings on the ground can inform which portions of the current amended
Act have to be revised, in addition to changing driving examination
approaches to counter the gaps identified in the system. Laws about roads and
their layouts in planning could be adjusted accordingly using such
information. For example, most drunk and reckless driving cases have
emanated from younger people within the population who are either
inexperienced drivers or just run into these events as a result of youthful
exuberance. This will mean channeling resources based on actual reports to a
targeted audience within the public on extensive sensitization drives using
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images of actual occurrences. This approach from prior research has been
known to be effective in communicating messages about such situations to
ward off deviant behaviour.
4.6 Fourth Moment of Translation: Mobilization Phase
Latour (1987) argued that the outcome of successful negotiations is an
actor-network characterized by aligned interests and when these interests have
been successfully translated. This final phase in the translation endeavor is
where the focal actor seeks to secure continued support to its goals and interest
from the enrolled actors through multiple negotiations (Callon, op cit.). Figure
6 shows how heterogenous actors come together to form a network where
Obligatory Passage Point (OPP) is telematics technology, through which other
actors must pass. The OPP is the situation that has to occur for all the actors
to satisfy their interests (Callon, op cit.).
Figure 6. The Insurance ecosystem with Telematics Technology. Source: Authors
In an attempt to show this further and to establish the place of
telematics technology in the Estonian insurance ecosystem, we selected an
insurance solutions partner, Plottera (a software development company
specializing in fleet management solutions based on global positioning
systems (GPS) technology). We then established some criteria to select
vehicles in Estonia, and we also tested the telematics technology under
different conditions with the selected vehicles. The results are presented
below.
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4.7 Preparation for Tests
The testing of telematics solutions for the insurance industry using
Plottera began with the installation of GPS tackers on the selected vehicles.
The devices are connected to the vehicles via OBD2, and the installation took
up to ten minutes to complete. For purposes of better results, the tests were
conducted at different times within the year and it varied from two to four
months each.
4.7.1 Solutions Partner
In Figure 6 above, telematics technology is represented as one
Obligatory Passage Point. Regardless of this, telematics technology
implementation may require a solutions partner, software, and devices. The
analytics partner is Plottera as stated earlier. Plottera offers pay-as-you drive
and smart insurance solutions to insurance companies with a focus on
optimizing fleet and work processes to help its customers reduce cost and
increase profits. The company from its operations is able to generate, but not
limited to, real-time data on the following:
1. Driving time (day or night);
2. Driving speed on different road conditions;
3. Driving behavior;
4. Breaks or stoppages in-between journeys;
5. Driving mileage reports;
6. Total trips made.
4.7.2 Software
The partner solutions company, Plottera, houses data and other
resources from the Plottera web interface and applications which can easily be
transmitted to insurance systems via APIs. Figure 7 shows the likely
relationship in the ecosystem when telematics is introduced into the network.
Figure 7. Illustration of the Plottera interface and applications. Source: Authors
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The interface generates real-time notifications which the vehicle driver
also sees instantaneously. Figure 8 and 9 shows sample notification and the
Plottera data distribution scheme.
Figure 8. Plottera Data Distribution Scheme
Figure 9. Sample of Notification
A mobile application from Plottera informs drivers in parking spots
about the risks that may arise. For example, narrow parking spaces is based on
the real width of the client's car (illustrated in Figure 8), or about a place with
a high risk of theft, among others. Intelligent notifications from the plotters
are dynamic and they work only under certain conditions (specific time,
location, etc.). Therefore, the driver receives only relevant and important
notifications.
The Plottera database consists of 350,000 accident cases in Estonia.
Based on algorithms written for this purpose, various areas in Estonia have
been zoned and risk calculations have been undertaken using machine learning
systems and tools. In addition, the various zones based on analytics have been
classified as either low, moderate, high or extreme, per every 100m2 in
Estonia. The premise of the classifications included data on driving behavior
(braking, acceleration, cornering, location, weather conditions and other
events in the area). Others include rush hour feeds, narrow parking spots and
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wild animal locations, etc. Figure 10 shows the different risk zones based on
Plottera analytics.
Figure 10. Risk zones based on Plottera analytics. Source: Authors
4.7.3 Devices
To collect data from the vehicles selected, GPS tracker, Teltonika
FM1010, and FM1000 were employed. The reason for using these devices is
that, firstly, the price is not high and insurance companies can invest their own
resources in obtaining same. Secondly, the use of two different types of
loggers allows for a wider scope of options between smart GPS and simple
GPS. The third reason is that vehicle diagnostics data can easily be read as
these devices use the modern GPS systems. For example in Russia, the Global
Navigation Satellite System (GLONASS)/GPS -module with enhanced
sensitivity is used; in Europe generally, the Galileo is commonly used; in
China BeiDou; in India Indian Regional Navigation Satellite System
(IRNSS); and in Japan Quasi-Zenith Satellite System (QZSS) (Parkinson &
Spilker, 1996). Extensive studies have been conducted about automotive
telematics types, fleet management, telematics security, GPS, remote
diagnostics including the built-in diagnostic system (OBD2); sensors, satellite
navigation and data transmission (see Minter, 2017; Hobba, 2016; Parkinson
& Spilker, 1996 as cited in Paefgen et al., 2013; Fleming, 2001; Dietz, 2007;
Angelovi & Jablonický, 2014; McLoad, 2005; and Heijden & Marchau,
2002).
4.7.4 Criteria for Selection of Vehicles
Based on LKF data, 5 vehicles were selected from different risk
groups, in an initial pilot project assessment. The list of vehicles was narrowed
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to 3 after careful consideration in order to cover adequately, all risk segments.
Table 1 shows the details of the 3 selected vehicles for the tests.
Table 1. Car List for the Test
Car Type
Car Manufacturer and Model
Engine Power
Year
Risk by LKF1
SUV
BMW X5
230 kW
2017
6,91
Small Car
Mazda 2
94 kW
2015
5,5
Small Car
Volkswagen Passat
110 kW
2016
4,54
1Risk by LKF Density of car accidents. This means that every X car out of 100 were
involved in accidents (adapted based on LKF 2016 report).
4.8 Test of Insurance-Oriented Telematics Technology
4.8.0 Results of Tests
Car Manufacturer and
Model
Engine
Power
Year
Risk by
LKF1
Driving Score from
Plottera2
Distance
(km)
BMW X5
230 kW
2017
6,91
6,2
6754
Mazda 2
94 kW
2015
5,5
8,2
3710
Volkswagen Passat
110 kW
2016
4,54
6,7
7940
Table 2. Consolidated results of test
Figure 11. Summary of vehicle data from Plottera
4.8.1 Vehicle 1: Volkswagen Passat 2016
The pilot project period with Volkswagen Passat was 09.06.2017
17:11 5.09.2017 13:00.
Figure 11 summarizes results about vehicle 1. Total distance covered
by Volkswagen Passat during the pilot project was 7,940 km. The maximum
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speed was 167 km/h and the average speed was 58.6 km/h for speed statistics.
The average speed indicates that the car was driving around 60% of total time
out of city. 49 cases of high levels of speeding above the maximum allowed
speeds in Estonia were recorded, while the average speed of 50km/h indicated
low risk speed.
Trip duration of more than 80% was less than 1 hour. This duration of
trips is optimal, at which the probability of accidents due to fatigue is minimal.
In addition to trip duration, trip distance was also optimal. The risk of
accidents was low. The test results show that vehicle 1 was driven at high risk
times in summer between 16:00 and 18:00 hours. The most trips occurred on
work days. Wednesdays recorded the highest number of trips (61 trips or
16.40% of all trips completed), while Saturdays recorded the lowest number
(41 trips or 11.02% of all trips completed).
4.8.2 Vehicle 2: BMW X5 2017
The pilot project period with BMW X5 was 12.06.2017 13:00
21.09.2017 10:00.
Figure 11 summarizes results about vehicle 2. Total distance covered
by BMW X5 during the pilot project was 6,754 km. The maximum speed was
190 km/h and the average speed was 56.5 km/h for speed statistics. The
average speed indicates that the car was driving around 57% of total time out
of city. The vehicle travelled twice to Finland and Latvia. 85 cases of high
levels of speeding above the maximum allowed speeds in Estonia were
recorded, while the average speed of 50km/h indicated low risk speed.
Trip duration of more than 73% was less than 1 hour. This duration of
trips is optimal, at which the probability of accidents due to fatigue is minimal.
In addition to trip duration, trip distance was also optimal. The risk of
accidents was low. The test results show that vehicle 2 was driven at high risk
times in summer between 16:00 and 18:00 hours. The most trips occurred on
work days. Fridays recorded the highest number of trips (83 trips or 15.84%
of all trips completed) while Sundays recorded the lowest number (63 trips or
12.02% of all trips completed).
4.8.3 Vehicle 3: 2016 Mazda 2 2017
Figure 11 summarizes results about vehicle 3. Total distance covered
by Mazda 2 during the pilot project was 3,710 km. The maximum speed was
150 km/h and the average speed was 42.5 km/h for speed statistics. The
average speed indicates that the car was driving around 90% of total time out
of city. 19 cases of high levels of speeding above the maximum allowed speeds
in Estonia were recorded, while the average speed of 50km/h indicated low
risk speed.
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Trip duration of more than 85% was less than 1 hour. This duration of
trips is optimal, at which the probability of accidents due to fatigue is minimal.
In addition to trip duration, trip distance was also optimal. The risk of
accidents was low. The test results show that vehicle 3 was driven at high risk
times in summer between 16:00 and 18:00 hours (24% of total trips
completed). The most trips occurred on work days. Saturday recorded the
highest number of trips (59 trips or 17.25% of all trips completed), while
Wednesdays recorded the lowest number (39 trips or 11.4% of all trips
completed) for vehicle 3.
Figure 11 presents Plottera driving score by weeks. During the tests,
the driving score was increased in all cars. The highest change was 60% with
Volkswagen, and the smallest 20% with Mazda.
Figure 11. Test car Plottera driving score by weeks
5 Discussion
The primary research question is “How can telematics-oriented
technology form an actor network for re-configuring the Estonian insurance
ecosystem?” For this case study, we employ actor network theory as a lens
and interpretation tool. ANT is used for analyzing the insurance ecosystem in
Estonia as a trajectory of transformations, identifying stakeholders and their
perspectives and interests throughout the process, and examining how these
interests can be (or cannot be) aligned within a common goal to form a
network of allies in agreement with the study of Tsohou, Karyda, Kokolakis,
and Kiountouzis (2012). The authors also look to the main advantage of ANT
in terms of providing a valuable analyzing instrument for exploring the role of
various facets in socio-technical networks. The authors first show the
ecosystem without telematics technology and the situation after telematics is
brought into the picture and the findings is substantiated with data analyzed
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from actual tests conducted to address the following research questions in this
study:
1. Who are the focal actors in the Estonian insurance ecosystem and how
have they done so far?
2. What are the implications of re-configuring the insurance ecosystem
with telematics technology?
ANT has been found to be a useful lens to explore the potential of
telematics technology in Estonia as well as to examine its use in insurance
field. This paper has studied stakeholder interactions with the telematics
system by analyzing each in a temporary implementation project.
To tackle the first research question, the authors have shown that
telematics technology, in the context of this research paper, is a focal actor and
obligatory passage point (OPP). The representative actors are the
Insured/Driver, Insurer, Motor Insurance Fund (LKF), Legislation (Act 2014)
among others. As noted earlier in this paper, the original ecosystem is fraught
with challenges, most of which have been exhausted earlier. The paper and
our empirical analysis highlight the roles of each of the actants in the original
network. Table 3 provides a summary of the main components of ANT theory
(Heterogenous network; Tokens; Punctualization; OPP; Irreversibility; and
Translation) as employed in this study to analyze the Estonian insurance
ecosystem and the results of the study. The convergence provides the
following potential outcomes of some confirming prior studies. It also extends
theoretical contributions to this research stream, as detailed in Table 3, while
also positing an answer to the second part of the first research question:
Actor Network: It is a heterogenous network comprising of all the key
players, the focal actor, OPP, and representative actors. Telematics
technology was successful in re-aligning myraid elements in the
original network during the tests on the vehicles.
Tokens/Quasi Objects: These were not identified during the trials
(See Table 3).
Punctualization: It can be seen as a condition is met.
Concept of Irreversibility: The changes telematics technology will
introduce will re-configure, stabilize, and form an enduring network
that can be sustained.
The Translation Process: The translation process shows four phases
to be walked through: problematization; intressement; enrollment; and
mobilization. All conditions reviewed in prior literature and that are
relevant to this study were analyzed and considered satisfactory.
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Translatio
n
Mobilizatio
n Phase
Theory: This phase requires that the network is formalized and stabilized with all the
defined actors, their interests, and roles.
Result: As noted earlier, major policy and legislative amendments are required to
formalize this stage in Estonia. The tests revealed that this is feasible and will create
jobs for the solutions’ start-ups in Estonia to thrive.
Enrolment
Phase
Theory: Actors in this phase accept and align themselves with their allocated roles and
defined interests.
Result: This was adequately achieved during the pilot phase with the trials on the three
vehicles. Telematics reset and redefined roles of all Actants in the network.
Intresseme
nt Phase
Theory: The focal Actor initiates the process of locking the other actors into the
proposed roles in the new network.
Result: This condition was satisfied. (See Figure 6).
Problemati
zation
Phase
Theory: The focal Actor defines the nature of the problem in a specific situation with
other actors through negotiating about OPP in the forming network.
Result: Condition satisfied.
Irreversib
ility
Theory: The concept of irreversibility states that the degree of irreversibility depends
on, first, the extent to which it is subsequently impossible to go back to a point where
translation was only among others and, second, the extent to which it shapes and
determines subsequent translations.
Result: Telematics technology will re-orient the insurance and financial technology
industries and therefore major changes, including policy and legislation, are required to
satisfy this condition.
OPP
Theory: The focal Actor defines the OPP through which all other Actors must pass and
by which focal Actor becomes indispensable (Callon, 1986).
Result: The Focal Actor, Telematics, and its allied forces becomes the OPP in the trials,
satisfying this condition.
Punctuali
zation
Theory: Punctualization in ANT is when, within the domain, every actor in the web of
relations is connected to others and as a whole will be considered as a single object or
concept. ANT requires all Actors or sections of the network to perform required tasks
and thus maintain the web of relations.
Result: All other Actors, besides the OPP are considered representative Actors in the
network and therefore played varied roles during the trials, satisfying the
punctualization condition in ANT.
Tokens/Q
uasi
Objects
Theory: Tokens are created through the successful interaction of actors/actants in a
network and are passed between Actors within the network. These token, as they are
increasingly transmitted or passed through the network, become punctualized and
materialized (Wickramasinghe, et al., 2012).
Result: During the trials, no such tokens were identified. It is suggested that quasi
objects may include premiums, which are generated as a result of risk profiles based on
telematics data.
Heterogen
ous
network
Theory: Heterogenous networks have been defined by Latour (1996, 2005) and
Wickramasinghe, et al. (2012) as a network of materially heterogenous actors that is
achieved by a great deal of work that shapes those various social and non-social
elements and “disciplines” them so that they work together, instead of “making off on
their own”.
Result: Telematics technology re-configured myriad elements in the original network,
during the trials on the vehicles.
Descriptio
n
Focal
Actor
Represent
ative
Actor
Represent
ative
Actor
Represent
ative
Actor
Represent
ative
Actor
Represent
ative
Actor
Represen
tative
Actor
Actant
Telema
tic
Techno
logy
Legislati
on
(Act
2014)
Insured/
Driver
The
Minister
(oversigh
t)
Police/
Medics
Insurer
Motor
Insuranc
e Fund
(LKF)
Table 3. Telematics technology meets insurance in Estonia
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The second research question is addressed under Research
Implications below. The authors look to Policy and other Practical
Implications that could change the entire landscape of the Estonian insurance
ecosystem.
6 Implications for Practice
The traditional motor insurance does not consider the driving behavior
of the customer, which does not allow assess objectively to the risk of using
each particular vehicle. Accordingly, there is no possibility to provide the
optimal cost of insurance for a specific customer. Further, the asymmetry of
the information between all actors in the network is more likely. Moreover,
clients of traditional vehicle insurance coverage generally have three touch
points with their insurers: when signing-off a coverage policy; when
cancelling such policies; and after an insured event or accident, in case of
claims. Using telematics, there are modifications in this customer interaction
model: clients may regularly login to their coverage portal or use an
application provided via their insurers that allows them to see their ultimate
journeys or current driving behavior (Carbone, 2016b). This integrative
approach to insurance provides safety nets for providers to be able to
differentiate their products and services and make a positive impact on their
clients rather than just serving as a point of registration for coverage, bill
conveyance, and other peripheral activities in the chain. Furthermore,
insurance companies that use telematics stand to gain exceptional precision
and enhanced accuracy in data management, while significantly reducing
information asymmetry due to interaction with telematics data and platforms.
In addition to the foregoing, telematics solutions offer insurance
companies the opportunity to tailor-make insurance solutions, products, and
services that respond to actual conditions in the environment, instead of
hypothesized methods. Driving behavior and other data could inure to the
design of reasonable discount schemes, reduction of insurance premiums
based on lower risks profiles, and a more accurate usage- based mechanisms
for arriving at calculations of premiums due. Customer data algorithms could
be designed for specific markets and these algorithms and models can be
dynamically updated. This was done as a result of geo-specific functions,
which is a feature that is non-existent in the traditional insurance package
offerings. Such companies, still offering traditional products with the
deployment of this technology on a full scale, stand to become marginalized
and irrelevant because customers are more likely to switch to enjoy packages
based on more intelligent technology solutions and meet their personal needs
and preferences.
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Therefore, the study points to the benefits and opportunities (summarized)
of deploying insurance-oriented telematics technology in the Estonian
insurance ecosystem, as follows:
1. The ability to create new products based on the principles of insurance
telematics Pay-As-You-Drive and Pay-How-You-Drive calculation
pricing schemes and, therefore, customize offerings and tariff plans for
each client based on accurate individual risk accounting and profiling
methods using telematics. It is also likely that more companies will
enroll their fleets with the design of tailor-made offering to meet such
specific demand.
2. An increased probability for the Insurer to establish fault; determine
insurance fraud and cut out revenue losses based on false information
sometimes provided about the insured event; reduce cycle times in
claims processing.
3. Enhanced communication and engagement between the Insured and
Insurer via mobile applications.
Silvello (2016) observed in a similar study that the foregoing factors
could contribute to increased profits for the Insurer. In addition to that benefit,
there is the possibility of attracting new customers. Meanwhile, insurance
companies can focus on risk mitigation as noted earlier when onboarding new
clients or when updating insurance policies.
On the Insured side, the following gains can be obtained. These
include: personal control over cost of insurance; transparency of pricing and
calculation mechanisms, and possibly bonuses for good driver behavior and
loyalty; Improved and safer driver behavior, reducing frequency and severity
of accidents; instant interaction with insurance stakeholders about an insured
event, so that the driver or passers-by may not need to necessarily contact these
agencies under such traumatic conditions; reduction in fuel consumption and
potential decrease in automobile wear and tear.
6.1 Social Implications
The Estonian social set-up stands to gain from the re-configuration of
the insurance ecosystem. These can be considered from several perspectives.
Firstly, from the general social system, telematics technology, when
employed, could increase road and traffic safety due to improved driver
behavior and efficient tracking and recovery of vehicles. This means enhanced
levels of citizen and pedestrian safety, lower social expenses, and lower life
losses. According to the World Health Organisation (WHO), technology-
assisted and autonomous driving will cut frequency and costs of road accidents
(WHO, 2015). Furthermore, the millions of euros paid out for accidents and
other insured occurrences could be reduced. For example, the 87.1 million
euros in payments recorded by the LKF for just cases at intersections, over
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2009 to 2016, could be halved or reduced to less than 30%, culminating in
saving to the state, which can be channeled into other development activities.
The ANIA has resoundingly noted that the use of smart devices could drop
motor insurance claims by 15-25% (ANIA, 2016).
Environmentally, CO2 emissions will be lowered on one end with a
reduction in fuel consumption, allowing for a cleaner environment overall.
Some of the gains to the Estonia besides the many economic benefits
and gains enumerated earlier, include political gains such as virtual violation
detection and instant notification about deviant behavior to relevant
stakeholders as well as a connected law enforcement organization, who can
also call for policy and other legislative amendments to improve other laws
not only about vehicles and vehicular arrangements, but also about driver
licensing regimes and laws. Jobs could also be created for many software and
application developers and technology companies in Estonia and beyond, who
would drive further innovation for the insurtech and fintech industries, sector,
and the country.
7 Research Limitations and Future Research
The authors consider some key limitations to the study.
Firstly, the role of researchers has been acknowledged to impact the
actor-network and its conceptualization (Cresswell et al., 2011), which may
limit the generalizability of the findings. In any case, the study is focused
within a particular context.
Secondly, the findings from the study may be considered exploratory
since the implementation of the telematics technology on the three vehicles
are not entirely representative of the causal factors discussed in the paper and
the results of the test thereof. ANT has received criticisms for the lack of
explanatory or critical power (Cresswell, et al., 2010; Mitev, 2009). The idea
of using ANT in combination with other theories has been suggested
(Cresswell, et al., 2011; Greenhalgh & Stones, 2010; Mitev, 2009), which
could add a more critical lens to ANT theory. Further empirical confirmatory
research is required to cover aspect of this research stream not addressed in
this study.
8 Conclusion
In this paper, the specific case of the Estonian insurance ecosystem
was analyzed from the theoretical perspective of ANT. Since the aim was to
contribute to the current discourse on the application of ANT to the field of
information systems, the concluding section of the paper discusses the analysis
and delves into specific aspects of how ANT can be applied. Also, it extends
established applications of ANT-related concepts while bringing out
interconnections. The insurtech and fintech landscapes are evolving really
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fast, and Estonia cannot be left behind this wind of technological inertia that
is blowing over the European region. Scholars and practitioners alike propose
that successful transitions to new solutions, such as that suggested in this
paper, should be accompanied by a deep understanding of technology and
detailed analysis of the processes and policy and legislative changes, which
are feasible given the transactional cost of implementing such projects in the
society. The Estonian society has seen many technological amendments since
re-independence in agreement with the studies of Sai, et al. (2017). The
potential result of using telematics will be the emergence of new players in the
Estonian financial markets who are more technology-oriented with deeper
understanding of its associated risks. Lastly, the use of telematics will bring to
the fore the subject of Big Data, which will mean better infrastructure and
equipped personnel to deal with it. The records show, as at end of 2015, that
29% of Estonian enterprises have vast experience in big data analysis of
information and communication, and 21% in financial and insurance data
analysis (Sai, et al., 2017, p. 6). Therefore, these percentages could markedly
move up.
References:
1. Angelovi M. & Jablonický, J. (2014). The Effect of Alternative Fuel
on the On-Board Diagnostics System at Compression-Ignition (Diesel)
Combustion Engines. 2014, p 358.
2. ANIA (2016). How the connected world is changing the insurance
business: Lessons from Italy, in Insurance Analytics Europe Summit.
Retrieved 01 24, 2016 from
http://www.ania.it/export/sites/default/it/pubblicazioni/monografie-e-
interventi/Dario-Focarelli_Londra_5_6_ottobre.pdf
3. Azzopardi, M. & Cortis, D. (2013). Implementing automotive
telematics for insurance covers of fleets. Journal of Technology
Management and Innovation, 8(4), 59
67. https://doi.org/10.4067/S0718-27242013000500005
4. Baecke, P. & Bocca, L. (2017). The value of vehicle telematics data in
insurance risk selection processes. Decis. Support Syst. 98(C), 6979.
5. Bijker, W.E., Hughes, T.P., & Pinch, T.J. (Eds) (1987). The Social
Construction of Technological Systems: New Directions in the
Sociology and History of Technology, MIT Press, Cambridge, MA.
6. Callon, M. (1986). Some elements of a sociology of translation:
domestication of the scallops and the fishermen of St Brieuc Bay. In J.
Law(Ed.), Power, action and belief: A new sociology of knowledge?
(pp. 196223). London: Routledge.
7. Carbone, M. (2016b). Connected Insurance Observatory The future
of insurance is Insur- tech. Retrieved 01 27, 2016
European Scientific Journal September 2018 edition Vol.14, No.26 ISSN: 1857 7881 (Print) e - ISSN 1857- 7431
70
from https://www.linkedin.com/pulse/future-insurance-insurtech-
matteo-carbone/
8. Chehui, Zhangjiwu, & Zhangxingyang (2011). Research on motor
vehicle insurance underwriting risk management model. Procedia
Engineering, 15, 49734977.
https://doi.org/10.1016/j.proeng.2011.08.924
9. Cressman, D. (2009). A brief overview of actor network theory:
Punctualization, heterogeneous engineering & translation.
Vancouver: ACT Lab/Center for Policy Research on Science &
Technology (CPROST) School of Communication, Simon Fraser
University (Working Paper).
10. Cresswell, K., Worth, A., & Sheikh, A. (2011). Implementing and
adopting electronic health record systems. Clinical Governance: An
International Journal, 16(4), 320- 336. doi:
doi:10.1108/14777271111175369
11. David, M. (2015). A Review of Theoretical Concepts and Empirical
Literature of Non-life Insurance Pricing. Procedia Economics and
Finance, 20(15), 157162. https://doi.org/10.1016/S2212-
5671(15)00060-X
12. David, M. (2015). Auto Insurance Premium Calculation Using
Generalized Linear Models. Procedia Economics and Finance,
20(15), 147156. https://doi.org/10.1016/S2212-5671(15)00059-3
13. Dietz, U. (2007). Turning eCall into a basis for future telematic
services. Paper presented at the 14th World Congress on Intelligent
Transportation Systems. [Online]. https://trid.trb.org/view/883532
14. Dudoskiy, J. (2018). The Ultimate Guide to Writing a Dissertation in
Business Studies: A Step-by-Step Assistance, Research-
methodology.net
15. Estonian Traffic Insurance Fund (LKF) Reports (2016) (2017).
Available [online] https://www.lkf.ee/et
16. Fleming, W. J. (2001). Overview of Automotive Sensors. IEEE
Sensors Journal, 1(4), pp. 296-308.
17. Heijden van der, R. & Marchau, V. (2002). Innovating road traffic
management by ITS: a future perspective. International Journal of
Technology, Policy and Management, vol. 2, no. 1, pp. 2039, 2002.
18. Husnjak, S., Peraković, D., Forenbacher, I., & Mumdziev, M. (2015).
Telematics system in usage based motor insurance. Procedia
Engineering, 100(January), 816
825. https://doi.org/10.1016/j.proeng.2015.01.436
19. Iwan, S. (2016). ‘Implementation of telematics-based good practices
to support urban freight transport systems, applying a city’s
adaptability level’, Int. J. Shipping and Transport Logistics, Vol. 8,
European Scientific Journal September 2018 edition Vol.14, No.26 ISSN: 1857 7881 (Print) e - ISSN 1857- 7431
71
No. 5, pp.531551.
20. Latour, B. (1999). Pandora’s Hope: Essays on the Reality of Science
Studies, Harvard University Press, Cambridge, MA.
21. Latour, B. (2006). Reassembling the Social. Politica y Sociedad (Vol.
43). https://doi.org/10.1163/156913308X336453
22. Lee, I. & Shin, Y. J. (2017). Fintech: Ecosystem, business models,
investment decisions, and challenges. Business
Horizons. https://doi.org/10.1016/j.bushor.2017.09.003
23. Ma, Y-L., Zhu X., Hu, X., & Chiu, Y-C. (2018). The use of context-
sensitive insurance telematics data in auto insurance rate making.
Transportation Research Part A: Policy and Practice, 113, 243-258.
DOI: 10.1016/j.tra.2018.04.013
24. Mintsis, G., Basbas, S., Papaioannou, P., Taxiltaris, C., & Tziavos, I.
N. (2004). Applications of GPS technology in the land transportation
system. European Journal of Operational Research, vol. 152, no. 2,
pp. 399409.
25. Mitev, N. (2009). In and out of actor-network theory: a necessary but
insufficient journey, Information Technology & People, Vol. 22 No. 1,
pp. 9-25.
26. Motor Insurance Act (2014 amended). Estonia. Available
[online] https://www.riigiteataja.ee/en/eli/506012015001/consolide
27. Nijkamp, P., Pepping, G., & Banister, D. (1996). Telematics and
Transport Behavior. New York (USA): Springer.
28. Nora, S. & Minc, A. (1978). L'informatisation de la société. Paris
(France): Documentation française cited in Ippisch, T. (2010).
Telematics Data in Motor Insurance: Creating Value by Understanding
the Impact of Accidents on Vehicle Use. Framework, (3829), 187.
29. Papadopoulos, T. (2007). Constructing and translating a socio-
technical innovation using Actor Network Theory. 16th EDAMBA
Summer Academy, Soreze, France.
30. Peppard, J., Edwards, C., & Lambert, R. (2011). MIS Quarterly
Executive. MIS Quarterly, 10(2), 115117.
https://doi.org/10.1108/02635570910926564
31. Pulk, K. & Murumägi, M. (2013). The Network of Different Actors
Influencing the Process of Urban Planning and Development The
Case of Tallinn City Hall, Journal of Management and Change, EBS
(30).
32. Rejikumar, G. (2013). A pre-launch exploration of customer
acceptance of usage based vehicle insurance policy, IIMB
Management Review, 25(1), 1927.
[online] https://doi.org/10.1016/j.iimb.2012.11.002
European Scientific Journal September 2018 edition Vol.14, No.26 ISSN: 1857 7881 (Print) e - ISSN 1857- 7431
72
33. Sai Andrew, A. & Boadi, P. (2017). A Bundled Approach to
Explaining Technological Change: The Case of e-Estonia, European
Journal of Business and Management
[online] http://iiste.org/Journals/index.php/EJBM/article/view/39126
34. Segovia-Vargas, M.J., Camacho-Minano, MDM., & Pascual-Ezama,
D. (2015). Risk factors selection in automobile insurance policies: a
way to improve the bottom line of insurance companies. Review of
Business Man- agement, 17, 12281245.
doi:10.7819/rbgn.v17i57.1741.
35. Shim, Y. & Shin, D. H. (2016). Analyzing China’s Fintech Industry
from the Perspective of Actor-Network Theory. Telecommunications
Policy, 40(23), 168
181. https://doi.org/10.1016/j.telpol.2015.11.005
36. Silvello, A. (2016). The three pillars of connected insurance.
Retrieved 01 27, 2016 from http://harvardecon.org/?p=3310
37. McLoad, T. (2005). Fleet Management Systems: e Future is Here,
Fleet Owner.
38. Troncoso, C., Danezis, G., Kosta, E., & Preneel, B. (2007). Pri- payd:
privacy friendly pay-as-you-drive insurance. Proceedings of the 2007
ACM workshop on Privacy in electronic society, (pp. 99107).
doi:10.1145/1314333.1314353.
39. Tsohou, A., Karyda, M., Kokolakis, S., & Kiountouzis, E. (2012).
Analyzing trajectories of information security awareness. Information
Technology & People, 25(3), 327
352. https://doi.org/10.1108/09593841211254358
40. Van Der Laan, J. D., Heino, A., & De Waard, D. (1997). A simple
procedure for the assessment of acceptance of advanced transport
telematics. Transportation Research Part C: Emerging Technologies,
5(1), pp. 1-10.
41. Walker, G. & Manson, A. (2014). ‘Telematics, urban freight logistics
and low carbon road networks’, Journal of Transport Geography,
Elsevier, Vol. 37, pp. 7481.
42. Wickramasinghe, N., Tatnall, A., & Goldberg, S. (2012).
Understanding the Advantages of Mobile Solutions for Chronic
Disease Management: The Role of ANT as a Rich Theoretical Lens.
International Journal of Actor-Network Theory and Technological
Innovation (IJANTTI), 4(1), 12.
43. World Health Organization (WHO) (2015). Road Safety. Fact Sheet,
Retrieved 09 25, 2016
from http://www.wpro.who.int/mediacentre/factsheets/fs_20130627/
en/
44. Xu, L. D., He, W., & Li, S. (2014). Internet of things in industries: A
European Scientific Journal September 2018 edition Vol.14, No.26 ISSN: 1857 7881 (Print) e - ISSN 1857- 7431
73
survey. IEEE Transactions on Industrial Informatics, 10, 22332243.
doi:10.1109/TII.2014.2300753.
45. Young, D., Borland, R., & Coghill, K. (2010). An actor network theory
analysis of policy innovation for smoke-free places: understanding
change in complex systems. American public Health, 7(100), 1208
1217, http://dx.doi.org/10.2105/AJPH.2009.184705.
46. Zhang, D., Ivanco, A., & Filipi, Z. (2015). ‘An averaging approach to
estimate urban traffic speed using large-scale origin-destination data’,
Int. J. Powertrains, Vol. 4, No. 2, pp.126140.
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