UNIVERSITY OF ECONOMICS AND MANAGEMENT
Nárožní 2600/9a, 158 00 Praha 5
DIPLOMA THESIS
MASTER OF BUSINESS ADMINISTRATION
University of Economics and Management
This email address is being protected from spambots. You need JavaScript enabled to view it. / www.vsem.cz
TITLE OF DIPLOMA THESIS
Attitudes Towards City e-Mobility Services
DATE OF GRADUATION AND DIPLOMA THESIS DEFENCE (MONTH/YEAR)
October/2018
NAME AND SURNAME OF THE STUDENT/ STUDY GROUP
Prashant Nagle/ MBA EN03
NAME OF THE SUPERVISOR
Doc. Ing. Zdeněk Linhart, CSc.
STUDENT’S DECLARATION
I declare that this Diploma thesis is my own work, and the bibliography contains all the literature that I have referred to in
writing of the thesis.
I am aware of the fact that this work will be published in accordance with the §47b of the Higher Education Act, and I agree
with that publication, regardless of the result of the defended thesis.
I declare that the information I used in the thesis come from legitimate sources, ie. in particular that it is not subject to state,
professional or business secrets or other confidential sources, which I wouldn’t have the rights to use or publish.
Date and Place: 31/08/2018, Prague
ACKNOWLEDGEMENT
I would first like to thank my thesis supervisor Doc. Ing. Zdeněk Linhart, CSc. from the University of Economics and
Management [VSEM].
SUMMARY
1. Main objective:
The main objective is to help advertising readers of both Soft-Sell and Hard-Sell Ads on Smart-Mobility to
improve ROI of investments into e-mobility and to find the general attitude of people on IoT.
2. Research methods:
Questionnaire will find countries with Inverse Preference of AG & AAG related to commuting.
Linkert Online Questionnaire on a seven-point scale was used to collect answers and Paired T-Test analysis
to process AG and Aad differences between countries.
Total 171 people responded, out of which 90 Respondents for Soft-Sell Ad and 81 Respondents for the
Hard-Sell Ad on Smart-Mobility.
3. Result of research:
For Soft-Sell Ad, AG has changed due to AAD, people are interested to accept Smart Mobility when
advertised with social aspects, which they can feel and experience. Thus the Soft-Sell Ads are recommended
for Smart-Mobility.
For Hard-Sell Ad, Ag has not changed due to AAD, complexities in the advertisement may confuse the
people about the benefits Smart-Mobility brings to their lives.
The hypothesis stands false that the countries with very dense population with experience from blocked
traffic will prefer public transport. Thus, it is recommended to promote the self-commute features in the
countries with high density of population and public transport in the countries with low density.
The overall General Attitude towards IoT is Good, Favourable and Positive. Thus, promotion of the benefits
of IoT with Smart Mobility is recommended.
4. Conclusions and recommendation:
The hard-sell advertisement had little to no impact on the attitude of people towards smart-mobility, soft-sell
ad was able to affect and change the attitude of people towards smart-mobility. The change in AG reflects
the effectivity of the soft-sell ad, and no change in AG in hard-sell ad shows the ineffectivity. The attitude in
general towards the IoT devices was good, favourable and positive. So the investors
(Transportation/Metro/Train manufacturers and the Automotive Manufacturers) in smart-mobility are
recommended to utilize the soft-sell advertisement with the smart features IoT brings to the life of the
people.
It was also found that the countries with dense population prefers to self-commute as opposed to the
hypothesis. So the investors are recommended to promote their products related to self-commute in the
countries which are densely populated.
KEYWORDS
AG, AAD, Smart Mobility, IoT, Attitude, Survey
JEL CLASSIFICATION
M37 Advertising, O35 Social Innovation, Q55Technological Innovation
UNIVERSITY OF ECONOMICS AND
MANAGEMENT
Nárožní 2600/9a, 158 00 Praha 5, Czech Republic
DIPLOMA THESIS ASSIGNMENT
Name and surname: Prashant Nagle
Study program: Master of Business Administration Eng (MBA)
Study group: MBA EN03
Title of the thesis: Attitudes Towards City e-Mobility Services
Content of the thesis: 1. Introduction – Problem Declaration, Aim, Methods,
Structure of the Thesis
2. Theoretical-Methodological Part
2.1 Attitudes towards mass and individual transport,
2.1.1 Attitude towards IOT Devices
2.2 Taxation
2.3 Mediation of attitudes
2.4 Methodology
3. Analytical part
3.1 CPM characteristics
3.2 Answered hypotheses
3.3 Recommendations with BEP
4. Conclusions
References:
(at least 4 sources)
Bajada, T., Titheridge, H. The attitudes of tourists
towards a bus service: implications for policy from a
Maltese case study. Transportation Research Procedia,
25, 4110-4129, 2017.
Dianoux, C., Linhart, Z. The effectiveness of female
nudity in advertising in three European countries.
International Marketing Review, 27(5), 562-578, 2010.
Hoon, H., Scott, H. Introduction: Innovation and identity
in next-generation smart cities. City, Culture and Society,
12, 1-4, 2018.
Sofana Reka, S., Dragicevic, T. Future effectual role of
energy delivery: A comprehensive review of Internet of
Things and smart grid. Renewable and Sustainable
Energy Reviews, 91, 90-108, 2018.
Schedule: Aim and methods till: 05.05.2018
Theoretical part till: do 01.06.2018
Results till: 01.07.2018
Final version till: 01.09.2018
Supervisor: doc. Ing. Zdeněk Linhart, CSc.
Prof. Ing. Milan Žák, CSc.
rector
In Prague 01. 04. 2018
Prof. Ing.
Milan
Žák CSc.
Digitálně podepsal Prof. Ing.
Milan Žák CSc.
DN: cn=Prof. Ing. Milan Žák
CSc., c=CZ, o=Vysoká škola
ekonomie a managementu,
a.s., givenName=Milan,
sn=Žák, serialNumber=ICA -
10393535
Datum: 2018.04.26 10:21:50
+02'00'
Table of Contents
1 Introduction ........................................................................................................................... 1
2. Theoretical-Methodological Part ........................................................................................ 3
2.1 Attitudes towards mass and individual transport ........................................................................................... 3
2.1.1 Attitude towards IOT Devices .............................................................................................................. 12
2.2 Taxation ...................................................................................................................................................... 17
2.3 Mediation of attitudes ................................................................................................................................. 18
2.4 Methodology ............................................................................................................................................... 19
3. Analytical part .................................................................................................................... 23
3.1 CPM characteristics .................................................................................................................................... 23
3.2 Answered hypotheses .................................................................................................................................. 24
3.2.1 Internation Comparison of Commute Preferences ............................................................................... 24
3.2.2 Answers to the Soft-Sell Advertisement .............................................................................................. 27
3.2.3 The Paired T-Test Analysis & Conclusions from Soft-Sell Ad responses ........................................... 28
3.2.4 Answers to the Hard-Sell Advertisement ............................................................................................. 30
3.2.5 The Paired T-Test Analysis & Conclusion from Hard-Sell Ad responses ........................................... 31
3.3 Recommendations with BEP ....................................................................................................................... 33
4. Conclusions ......................................................................................................................... 34
Abstract .................................................................................................................................... 35
Bibliography ............................................................................................................................. 36
1
1 Introduction
The cities in the different nations are facing a lot of challenges with regards to the commute
and transportation (public and self-commute) of its citizens. There has been recent attempts of
improvising the quality of transportation services with the introduction of City e-Mobility
services or commonly known as Smart Mobility which is part of the Smart City services.
Forbes lists top 10 Smart Cities in the world in 2018 namely, New York securing the smartest
city in the world, London on second place, Paris on third, Tokyo (4), Reykjavik (5),
Singapore (6), Seoul (7), Toronto (8), Hong Kong (9) and Amsterdam (10.). Europe, with 12
cities ranking among the top 25, is once again the top-performing geographical area. It is
followed by North America, with six; Asia, with four (all in the top 10); and Oceania, with
three. This was analysed considered key to being a smart, sustainable city: human capital
(developing, attracting and nurturing talent), social cohesion (consensus among the different
social groups in a city), economy, environment, governance, urban planning, international
outreach, technology, and e-mobility and transportation (ease of movement and access to
public services) (Forbes, 2018).
There is an active contemporary debate about how emerging technologies such as automated
vehicles, peer-to-peer sharing applications and the ‘internet of things’ will revolutionise
individual and mass mobility. Indeed, it is argued that the so-called ‘Smart Mobility’
transition, in which these technologies combine to transform how the mobility system is
organised and operates, has already begun. The way people move within the city context
changes with the development of transportation systems, information and communication
technologies. In our project, we investigate new ways of urban mobility from both a cognitive
and a transportation perspective.
Attitudes towards advertising in general were found to influence the effectiveness of specific
ads. Attitudes towards advertising in general were expected to influence the success of any
particular advertising. It seemed reasonable to anticipate a person's predisposition to respond
consistently towards advertising in general, either favorably or unfavorably, would mediate
the effectiveness of any given ad. Interest in the attitudes-towards-advertising-in-general
construct gained momentum as researchers showed it was an important underlying
determinant of attitude-towards-the-ad (Dianoux, C., Linhart, 2012).
This thesis examines based on international research the differences between results of studies
focused on consumers’ attitude and their preferences towards advertising on Smart Mobility,
general attitude towards IoT and to evaluate the reasons for such preferences.
The hypothesis is that the densly populated countries will prefer public transport. For example
France will prefere public transport and Czech Republic will prefere private transport. And to
find the reasons for such opposite preferences. For example, very densed population with
experience from blocked traffic will prefer public transport. Then, it may be evaluated if this
general opinion will be changed due to smart features of transport by displaying Soft-sell and
Hard-sell ads. The abbreviations used for attitudes towards specific ads in general (ASG),
attitudes toward advertising (Aad) and attitudes toward ads in general (AG).
An survey was carried out across several countries exposing consumers to the experimental
advertisements on Smart Mobility with both Hard-Sell Ad, showing the technological and
direct benefits and the Soft-Sell Ad, featuring the social aspects that comes with Smart
Mobility.
2
The set of questionaire was created for each hard-sell and soft-sell ads in two parts, Part A
and Part B. The three questions about attitude general are about institution of e-mobility
(Smart Mobility). Therefore, the questions were asked to the respondents before and at the
end of Part B questionnaire.
1. Overall, mobility is good
2. Overall, mobility is favourable
3. Overall, mobility is positive
These three questions were repeated in the end of B part of questionnaire to see if AG has
changed due to Aad.
Two pictures of smart mobility were created. Each picture was shown to different group of
respondents. Then the aim is to compare the assign differences in answers to the shown
pictures.
This thesis is started with the theoretical background to clarify the key constructs of attitude
toward advertising in general and attitude toward an ad, Smart Mobility as well as their
relationship. The paper is focused on the specific area, where only mentioned surveys and
researches can be deeply analyzed. Moreover the paper presents new factors which may
influence resultant relationships observed by different authors all over the world. Thus, in
light of our theoretical background and empirical evidence, the international context is
presented the research questions were developed accordingly.
This thesis first introduces with the theoritical concepts of attitudes towards specific ads in
general (ASG), attitudes toward advertising (Aad) and attitudes toward ads in general (AG),
Smart Mobility, Internet of Things devices (IoT), Taxation Problems in select few countries
towards the innovations, Mediation of Attitudes, CPM Characteristiscs, Answered
Hypotheses and Recommendation with BEP.
3
2. Theoretical-Methodological Part
Attitudes toward an ad (Aad) can be define as thoughts and emotions of consumer related to
the ad (Kirmani a Campbell, 2009). Other authors define Aad as emotional reaction of
consumer (interesting/boring, symphatic/annoying etc.) (Lutz et al., 1983; MacKenzie, 1986).
It is also possible to mention that there are another two aspects of ad perception – cognitive
and emotional (Shimp, 1981). These attitudes can obtain also emotional reactions (luck,
happiness etc.) and evaluation reaction (trustfulness or information bareness) (Baker a Lutz,
2000).
Lutz defines attitude towards the ad in general (AG) as thought predisposition of reaction
(positive or negative) based on the shown advertisements.
2.1 Attitudes towards mass and individual transport
Starting with a theoretical background to clarify the key constructs of attitude toward
advertising in general and attitude toward an ad, as well as their relationship and then about
the Smart Mobility and the factors affecting commute.
Humans always needed to commute. To visit friends and family, to go to work, to do travel
and leisure activities, to go shopping (Vilhelmson, 1999). Basically, to live our lives, to
participate in society, we need to commute. It is next to impossible to do all activities on the
very same spot, even such vital activities as sleeping and going to the toilet. Humans have
throughout all time needed to travel, but more so in a contemporary society which is
increasingly network-oriented (Castells, 2011), and seldom restricted to the local
neighbourhood, village or fixated small-sized places. Nowadays, one person’s network of
family, friends, colleagues, workplace and so on may span entire cities, regions, countries,
even across continents. As societies become increasingly network-oriented, the amount of
mobility will tend to increase. Internet and communication technologies (ICT) has been
suggested to replace or to reduce the need to travel, but that has, for the time being, not been
the case (Banister, 2011; Hjorthol and Gripsrud, 2009). ICT has, arguably, on the contrary,
enhanced mobility and resulted in increased mobility (Schwanen and Kwan, 2008; Dal Fiore
et al., 2014). Constraints, such as not knowing how to get somewhere, or not knowing about
the opportunities elsewhere and far-off, are easily reduced or eliminated by simply using a
smartphone (Dal Fiore et al., 2014).
Mobility, the way it has been described so far, is understood as a derived demand in an
activity approach (Vilhelmson, 1999). People do not commute for the sake of commuting in
itself, but to participate in activities at locations elsewhere. Commuting is, in other words, the
side effect of participating in society. This has been the usual conceptualising of travelling in
transport disciplines, such as transport geography, throughout the years, but especially back in
the golden age of spatial sciences (Cresswell, 2010). This paradigm can in many regards be
considered as the cultural turn in social sciences finally catching up with the last ‘positivist’
stronghold, at least in human geography, namely transport studies (Røe, 2000).
Understandings of commute as a ‘gift in itself’ (Jain and Lyons, 2008) and as consisting of
both physical movements, practices and representations embedded with cultural meaning
(Cresswell, 2010), point out that how people practice, experience and perceive trips can have
a major impact on how they travel. Albeit these elements are likely to influence what
commute mode the commuters use and how much they commute, they are not included in this
thesis. The use of quantitative interviews, surveys with questions about attitudes, cultural
4
meaning and experiences, and technology fieldwork could have addressed these issues (Røe
2000).
e-Mobility will in this thesis be understood as a derived demand, and moreover, daily urban
mobility is the form of mobility that is at the locus. Besides, in a sustainability perspective,
the scope of city regions seems to be the most relevant for policymaking. While international
climate change agreements have had a hard time to succeed, there appears to be a willingness
at the level of cities, and city regions, to address climate change through actual measures
(Banister, 2011). That mobility is more than just from A to B, commute in itself, experience,
meaning, etc.
Several indicators can be applied to address commute behaviour, such as trip frequency, trip
distances, transport mode choice, total distance travelled, and transport-related energy use.
Total distance travelled by car and transport mode choice are chosen as indicators of commute
choices in this master thesis. The commute choices also directly relatable to other important
matters, such as congestion and local pollution. This indicator does not distinguish between
how far and how often people commute but is deemed to be more relevant to policy-making.
Commute mode choice is chosen because it does a good job of addressing the decisionmaking process specifically, which becomes even more important when it comes to
decoupling population growth from growth in car use.
Daily e-mobility, or commute, is one of eight urban subsystems, or processes, that is
identified in the urban system. The urban must, according to Wegener, be understood as an
urban totality that is not static but ever changing due to fluctuations and modifications in these
subsystems. The systems change, however, at different rates. The two subsystems physical
networks – e-Mobility, communications and utility networks – and the overall land use
change very slowly. The two following subsystems, workplaces and housing, change not as
slowly as physical networks and the land use, but still slowly. The fifth and sixth subsystem,
employment and population change fast, while (passenger) commute and goods transports can
change immediately. A flow perspective (Dijst, 2013) that is inspired by Wegener’s urban
subsystems, also include the extremely fluctuate and ever-changing flows of information,
knowledge and money. The flow perspective also includes large-scale natural processes in the
Earth system, such as climate change, as a part of the urban system. In that sense, the urban
transcends the scales of local and global.
All of these urban systems are linked together, and how the ‘land-use transport feedback
cycle’ is used in planning literature to explain these relations. In short, the distribution of land
use determines the locations of activities. Human activities need transport infrastructure –
remember how a person is not able to do all activities at the very same spot. The transport
infrastructures result in accessibility. The effects of uneven spatial distribution of accessibility
results in relocations and real estate development in the most accessible areas. These changes
in land use and location of activities will yet again result in new shifts in the transport
infrastructure, and so on this feedback cycle continues. These relationships and the
subsystems are market driven and subject to policy making.
Cities are heterogeneous with various types of districts, such as downtowns, central business
districts, inner city areas, outer city areas, industry zones, and residential locations. The
picture gets even more complicated as cities have turned into large heterogeneous city
regions, with multiple regional centres, mini-cities (Røe and Saglie, 2011), in a polycentric
pattern. The introduction of the car as the dominant transport mode allowed cities to sprawl,
5
making the cities, and societies, arguably car-dependent. (Sub)urban sprawl has been a larger
issue in the US than in Europe. One reason to this is that a large share of the expansion of
European cities found place during the 19th century before the car was the dominant transport
mode, while US cities expanded mostly after the second world war when the car was the
dominating transport mode (Muller, 2004). Initial waves of residential relocations to the
outskirts of cities, i.e. a suburban sprawl, have been followed by waves of relocating
businesses, workplaces, and eventually shopping malls to the suburbs – turning cities inside
out, rendering suburbs into postsuburban landscapes, and cities and countrysides into complex
metropolitan areas (Garreau, 2011).
It is important to know how city structures, such as population density and proximity to the
city centre influence the total distance travelled by car since it is directly relatable not only to
the CO2 emissions from car use, but also other important aspects, such as congestion, local
pollution and public health. Other aspects of travel behaviour, such as what transport mode
people choose to travel with, must be used to delve into how trip destination locations
influence travel behaviour. A common finding in quantitative land use/transport studies is
that car ownership/access overshadows the influence of all other observable factors
(Dieleman, Dijst, and Burghouwt, 2002), both socioeconomic and demographic attributes and
urban structures, on travel behaviour. A major limitation in most of these studies is that car
ownership is treated as independent from both urban structures and sociodemographic
attributes of individuals and households, when these elements most likely are closely
interlinked. It is usual in transport studies to distinguish between commute and non-commute
trips. Utilitarian (commute) trips are assumed, and found (Vilhelmson, 1999), to be more
governed by rational decision-making, and therefore more influenced by urban structures than
non-commute trips.
To address another well-known issue in transport geography – both residential and travelrelated self-selection – that people dwell in certain areas and have certain types of travel
behaviour because they have different preferences (Cao, Mokhtarian, and Handy, 2009). Car
ownership tracks back, arguably, to people’s preferences of both city structures – they might
have to use the car to live the place they want to live – and travel behaviour – they simply
enjoy taking a ride with the car. Moreover, people may not prefer to dwell or travel the way
they do, but they are selected into neighbourhoods that influence their car ownership and how
they travel
It is usual in commute studies to distinguish between commute and non-commute trips.
Commute trips are assumed, and found (Vilhelmson, 1999), to be more governed by rational
decision-making, and therefore more influenced by urban structures than non-commute trips.
The five important Ds of Commute
There are two traditions on how to study the importance of the local neighbourhood for
Commute behaviour. The first one, which is usual in American studies, is to examine the
effect of the urban structures within the local neighbourhood on travel behaviour. The second
tradition is to investigate the importance of the location of a neighbourhood relative to the city
centre of the city or closest second-tier and regional centres.
Within the local neighbourhood, The ‘three Ds’ – density, diversity, and design – as the urban
structures that influence travel behaviour the most (Cervero and Kockelman, 1997). Three
other Ds, destination accessibility, distance to public transport, and demand management,
6
have in later years been added to the list of influential urban structures. All of the Ds, except
for demand management will subsequently be presented and used in this thesis. Demand
management addresses mostly economic incentives to regulate supply and demand, such as
parking supply, road pricing, etc, and is strictly speaking not a characteristic of the spatial
urban structures.
1. Density
Density has always been regarded as a key characteristic that influences travel
behaviour, as shown in Newman and Kenworthy’s well-known study. The density
indicates the intensity of land use and activity within the neighbourhood. Previous
studies have measured the effect of both population density, workplace density, and a
combined population/workplace density. Workplace density is, however, labelled as
an indicator of diversity in this thesis, however. The reason to this is explained in the
following diversity section.
Density in the local neighbourhood is important due to three reasons. First, higher
density shortens distances between origins and destinations, which again is assumed to
make people use non-motorized modes. The local density of each neighbourhood in
the city adds up to the overall density of the city. In a city with overall high density,
distances will be shorter than in a city with low density and equally large population.
Second, high population density indicates a good market. Second, many people
concentrated in a small is area is the same as many potential public transport users,
customers, workers, and public service users. The expenses of constructing any
infrastructure or service are lower per customer or user when they are concentrated
and not dispersed. This supports a higher density of shops and services, thereby also
contributing to mixed activities and workplaces in the local area. Concentrated flows
of public transport passengers allow for higher frequencies, higher station density and
an increasing competitiveness towards the car.
Third, high density can simultaneously prove to be negative for car use since it
contributes to bottlenecks and congestion, and fewer parking opportunities.
In a meta-analysis of more than 50 quantitative studies, most of them American,
Ewing and Cervero (2010) found that population density and workplace density only
have a very weak effect on how much people travel. Increased density, of both kinds,
is also associated with slightly increased shares of walking and public transport use.
Increased residential population density reduces the distance travelled by car slightly.
Ewing and Cervero suspect that high multicollinearity among urban structures in the
quantitative models is the reason to the low contribution and that density, in reality,
has a larger impact than predicted by the models. The idea is that the city structures
are too interrelated. Instead of getting one clean and definite effect from one urban
structure on commute behaviour, they muddle and render the effect of several urban
structures to be weak or insignificant.
The interwoven relationship between the distance from the residential location to the
city centre and population density at the residential location. The population density at
the residential location apparently does not contribute much in explaining commute
behaviour. The local density of each neighbourhood in the city adds up to the overall
density of the city. Moreover, in a city with overall high density, distances will be
shorter than in a city with low density and equally large population.
7
2. Design
Design addresses specifically the built environment in the local neighbourhood. Is it
the street infrastructure designed in such a way that it promotes walking, or not? How
pedestrian-oriented is the street design? Design was originally understood as the
placement of parking lots and the placement of shade trees (Cervero and Kockelman
1997) but has over the years moved over to address the characteristics of street
networks (Ewing and Cervero 2010). One major distinction has been whether the
streets network in the local neighbourhood is cul-de-sac-oriented, with curvilinear
roads, few intersections, and many dead-end streets – which are often found in
suburban areas – or is the street network grid-oriented, just as in urban, central areas.
Grid-oriented networks are assumed to offer direct routes in most directions, and
promote walking, while cul-de-sac-shaped patterns discourage walking. A grid-shaped
street network will not reduce car use on its own. Yes, grid-shaped streets reduce the
cost of walking and cycling, but it also reduces the cost of using the car. Moreover, the
reduced travel cost can result in a rebound effect, that people travel more because it is
cheaper. To succeed in reducing car use, grid-shaped street networks must be
combined with regulations and incentives, i.e. demand management, according to
Crane. Intersection density has often been used as a design indicator, but also block
size, street and pavement connectivity, and pavement coverage and metres have also
been used.
Most often intersection density – were found in the international meta-analysis (Ewing
and Cervero, 2010) to have a larger, negative, impact on both density and diversity.
Moreover, no other city structure had a larger impact on walking and public transport
use than design. The association between design and travelling by car was
insignificant when the distance from the place of residence to the city centre. The
other study (Westford, 2010), from Stockholm, found that children are less likely to
walk to the school in a neighbourhood with grid streets and mixed traffic than three
other neighbourhoods with separate roads for active modes (walking, cycling) and
motorised modes.
3. Diversity
Diversity, refers to the variation and amount of activities in the local neighbourhood.
A large range of diversity indicators have been used in previous studies, all from
employment and floor area to different entropy measures with low values if the land
use or activity in the local neighbourhood is monotone, and high values if the land use
is diverse (Ewing and Cervero, 2010). Having an extensive range of facilities and
services nearby, will assumedly reduce trip distances and thereby increase the
likeliness of walking and cycling (Cervero and Kockelman, 1997). Diversity is also
assumed to increase public transport use, albeit this association is not as obvious as the
influence on cycling and walking. The assumption is that people are more inclined to
use public transport if they can combine the trip with other activities, such as visiting
the grocery store before/after they use public transport on the way to/from work.
High degree of diversity, indicated by factors such as land use mix and job-housing
balance, were found to have a positive effect on walking and public transport use in
international studies (Ewing and Cervero, 2010). The reason assumed for it is an
8
assumption among the researchers that local job opportunities are of little interest
among most people in an increasingly specialised workforce. They work elsewhere
anyway. Not only population density but also workplace density in the residential
neighbourhood have a larger impact than proximity to the city centre on car use. Not
an indicator of diversity in the location neighbourhood, however the accessibility to
local service facilities reduced the distance people travelled by motorised modes and
the energy use people spend on travelling.
4. Distance to public transport
Increased distance to public transport makes it, assumedly, less likely to use public
transport, while short distances make it more liable to use public transport (Ewing and
Cervero, 2010). Moreover, the distance to public transport may not only have an
impact on public transport use in itself but also what mode people use as access and
egress modes on their way to and from public transport. People that dwell several
kilometres from the public transport, but use it to commute to work in the city centre,
for example, may be more likely to use the car to get to the public transport station
than the person who lives next to the bus stop. People have in several contexts also
been found to have a preference for rail-oriented public transport over bus-oriented
public transport (Hensher, 2016).
In international studies (Ewing and Cervero, 2010), proximity to public transport
increased the shares of walking and, not surprisingly, public transport use. Public
transport provision near the residence turned out to have some effect on total distance
travelled by motorised mode, but no effect on the modal split. The distance to public
transport had no significant effect on whether married men or women use the car or
not to commute trips. What did matter, though, was the public transport frequency.
This is a reminder that not only spatial configurations but also organisational
configurations have an impact on how people travel.
5. Destination accessibility
Destination accessibility addresses how easy people can get to their desired
destinations. As previously mentioned, proximity to the concentration of facilities is
more important than proximity to the single closest facility. Commute distance to the
city centre, which has a high concentration of facilities, has often been used as an
indicator of destination accessibility. Short distances are supposed to increase
walkability, and cycling, while longer distances are more likely to increase the use of
motorised transport modes, especially car use, and trip distances. Proximity to subcentres has also been used as an indicator of destination accessibility and may be even
more important to use to when one measures accessibility in studies of large
polycentric city regions, with regional centres in the suburban areas, as well as in
‘exurban’ areas. These indicators address destination accessibility at a regional scale,
and it is important to take them into consideration as the workforce get increasingly
specialised. Besides, if people want to go the cinema or go out for dinner, or go
shopping, then these kinds of leisure-oriented services also tend to be concentrated in
central district. Distance to local centres, on the other hand, can be relevant when
people are to carry out mundane everyday activities or have non-specialized work.
Ewing and Cervero (2010) found in their meta-analysis that travel distance to the city
centre is the most important city structure and public transport use, while also having a
9
noteworthy effect on walking. People travel less by car and more by public transport
and walking when they live nearby the city centre.
The overall trend is that people tend to travel more and longer by motorised transport
modes the further they live away from the city centre. People travel longer by
motorised modes the further they live from not only the city centre but also from local
service facilities. People in the outer parts of the city region commuted in average
longer and more often by car than city and inner city dwellers. Holden and Norland
(2005) found that distance from residential location to the city centre had an effect on
the respondents’ everyday travel energy use, but the distance to local subcentre also
had an impact.
Ewing and Cervero (2010) argue that distance to the city centre is a proxy for the other
Ds that characterise the activities within the local neighbourhoods, and in that way
also explain the low contribution of density to travel behaviour. It is the local withinneighbourhood Ds – density, diversity, design – that are proxies for the distance to the
city centre of Oslo. It is more plausible, that the travel distance to the major
concentrations of workplaces, services and facilities in the city centre matters more
than the street design and the number of intersections in the residential neighbourhood.
Travel distances and car use increases outwards to these tipping points. Beyond these
tipping points, people start to travel less. The assumption is that people live that far
away from the assumedly most attractive concentrations of facilities, that they choose
the second-best option within acceptable travel distance. The transport rationale of
reducing transport costs outweighs the need of getting to the best facilities.
Access to recreational green areas has often been neglected in transport/land use
studies, but green recreational areas, such as parks and forests, has proven in some
studies (Holden and Norland, 2005) to have a significant effect on people’s leisure,
non-commute trips. One assumption is that people that dwell in dense and ‘grey’
inner-city areas compensate for the lack of nearby green leisure areas by carrying out
longer leisure, non-commute trips, especially in the weekends.
Yet, inner-city dwellers seem to make medium-long leisure trips more often on the
weekends, indicating a certain compensation effect.
Holden and Norland (2005) found that people in neighbourhoods with a high density
of dwellings spend more energy on flights. They also found that residents with access
to a private garden spend less transport-related energy on long leisure trips by car and
plane. Holden and Norland (2005) stresses that the relationship between urban
structures and especially long-distance leisure trips, such as flights, most likely is not
causal, but rather spurious. A more plausible explanation, which needs further
research, is that people with an urban and cosmopolitan lifestyle prefer to both live in
urban areas and travel more by plane.
Strategies of sustainable City e-Mobility
In a compact city, the local neighbourhoods – and thereby the entire city – are densely
populated, diverse and with pedestrian-oriented street design. In a dense city, distances to the
city centre and subcentres will be short. Short walking distances to public transport also
ensure a transit-oriented development. In that way, the compact development ensures that
commute, in general, are shorter and therefore can be made by walking and cycling. The
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transit-oriented development is intended to make it easier to undertake long-distance trips by
public transport instead of using the car.
These strategies are also assumed to cause not only environmentally friendly mobility, but
also socially equitable mobility (Cass, Shove, and Urry, 2005; Boschmann and Kwan, 2008).
In an ideal compact transit-oriented city, people are not required to own a car. One issue that
challenges this is that accessible locations equal to attractive and thereby expensive locations.
That is why Banister (2001) suggests that policy-makers should emphasise a development of
not only attractive but also affordable locations in the cities. Besides, the configurations of
density, diversity and design in the compact city coincide particularly well with the
configurations that will ensure livable streets full of activity – the ‘sideway ballet’ – in cities.
The challenge with the compact city strategy is that (sub)urban sprawl has resulted in large
polycentric car-dependent city regions with long distances between functions. The influence
of proximity to not only the main city centre but also regional sub-centres that the urban
development should be decentralised at the regional scale, while development should be
centralised and compact within the cities and neighbourhoods at the local scale.
In their solution to render car-dependent urban regions into sustainable configurations, so it
could be strategise that basically a combination of the compact city strategy, and transitoriented development. They envision that urban development should be dense and diverse
around the transport hubs throughout the region, in so-called ‘urban villages.’, that are wellconnected by the public transport infrastructure.
What is ‘Smart Mobility’?
In order to begin the task of thinking through the implications of smart mobility that actors
and institutions of governance will be confronted with, it is helpful to identify some key
building blocks that are common to different views of the future as they are being debated
today, especially those changes that are either already emerging or which are the subject of
the most intense R&D effort, e.g.:
The shift towards ‘mobility as a service’, where individuals’ ownership of vehicles is
increasingly replaced by “usership”, that is the ability to purchase access rights to an
interoperable package of mobility services (car, taxi, bus, rail, bike share) owned by
others. This is facilitated by integrated aggregation and payment platforms, with
intensive processing of ‘big data’ to match provision to demand in real time;
Autonomous vehicles that do not require ‘driving’ by any of the passengers, and
which enable all occupants of the vehicle to focus on other tasks whilst they are in
motion; New user-generated and user-centred information which is context specific
and integrates mobility and non-mobility options, which draws upon;
Increasingly ‘intelligent’ infrastructure which derives operational information from
users and provides feedback in real-time to influence of traveller behaviour and
optimise system performance;
The electrification of the vehicle fleet using battery power, plug-in hybrid and/or other
new technologies. Combined with a smart energy distribution grid, electric vehicles
could be both emission free at the point of use (thus satisfying consumer desire for
‘sustainable’ mobility, see Bakker et al, 2014) and also be part of the electricity
storage solution for the widespread adoption of renewables more generally.
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The list is not comprehensive of today’s opportunities and new ideas will surely emerge.
Nonetheless, some key elements of the socio-technical transition that appear in the more
technology-led imaginings of smart mobility futures. First, there is the transition from
ownership to “usership” identified as a critical innovation by advocates of smart mobility
(Wocartz and Schartau, 2015). This transition is already apparent: car share clubs had almost
5 million members and 92,000 vehicles worldwide in 2014, an increase of more than ten fold
over a decade previously (Le Vine et al, 2014). Given that the average car today is parked for
96% of the time there is very significant potential to unlock efficiencies by reducing the
amount of time expensive assets are not actually mobile or under occupied.
Furthermore, apps such as Uber also work on the principle of better matching user demand
and vehicle supply in space and time increasing the utilisation of drivers and reducing wait
times for passengers. Combining these attributes provides the most optimistic (corporate)
vision of the smart mobility future.
“… if cars could drive themselves, there would be no need for most people to own them. A
fleet of vehicles could operate as a personalized publictransportation system, picking people
up and dropping them off independently, waiting at parking lots between calls. … Streets
would clear, highways shrink, parking lots turn to parkland.” (Bilger, B, 2013, Adams, J.,
2015).
Second is a transition in the definition of the marketplace that is ‘mobility’. Today this market
is dominated by private vehicle ownership, roads funded by the state (usually through general
taxation) and a public transport system which, to varying degrees in different places, has some
form of state direction and support. The transition to a new smart model of mobility therefore
implies that this traditional business model for the public private allocation of tasks across the
mobility system will evolve. As one recent study into the market for intelligent mobility put it
“value in mobility is derived from traveller spend, whether this means spend on travel tickets,
vehicle ownership, or services and apps.” (Wocartz and Schartau, 2015). Fundamentally, the
commoditisation of individual journeys and the journey time of users is what makes ‘smart
mobility’ pay for itself, and represents a continuation of the longstanding trend towards the
neo-liberalisation of the transport system (Gössling and Cohen, 2014).
Whilst these innovations may also create public value for society and the state these are
usually treated as secondary or residual impacts by the technology sector pushing the smart
transition. More important for smart mobility proponents is the potential to grow the market
by more effectively “address(ing) significant unmet lifestyle needs across a range of traveller
types” (Wocartz and Schartau, 2015) thus neatly revealing the essential paradox of much
smart mobility rhetoric at present, i.e. that the smart transition will simultaneously create the
promise of a system that can reduce demand, whilst at the same time fulfilling previously
unmet demand.
Third is the greater convenience and comprehensiveness of inter-modality or “from the
current ‘modal-centric’ to future ‘user-centric’ transport system” identified as an important
benefit of this more marketised approach to accessing mobility services (Yianni, 2015,
Hietenan, 2014) sets out his view of future mobility as seeing “the whole transport sector as a
co-operative, interconnected eco-system, providing services reflecting the needs of customers.
The boundaries between different transport modes are blurred or disappear completely. The
ecosystem consists of transport infrastructure, transportation services, transport information
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and payment services.” Crucially, this transition requires the emergence of new integrated
mobility aggregators, smart intermediaries that match mobility supply to demand in real time
to tailor services to the needs of the travellers. The new role of aggregator, which iseffectively
a form of arbitrage for mobility, is one if not the most important changeelements in the smart
mobility system of the future. We return to the question of the implications of this role being
played by the state or private firms below.
Fourth, there is a transition in the role of the citizen in the transport system. This is both as a
source and recipient of information through mobile communication and through bringing their
resources to the shared mobility platforms. This has so far manifested itself in people using
their vehicles as part of ride-share systems, as vehicles on-demand for Uber and Lyft and by
renting out driveways for other users. This is part of a wider transition away from the state as
the prime source of information to being one of many actors feeding information into the
mobility system.
2.1.1 Attitude towards IOT Devices
Although every technology expert has his own definition of the Internet of Things (IoT), they
all believe that it will transform the world as we know it. The world has changed a lot since
1995, the year where the world wide web was introduced. The IoT is expected to make an
even bigger impact on our lives. As was the same for the internet, it is important that we get a
clear vision on what it is and how it can create value to our society. This technological
revolution will connect everyone, everything, and everywhere. This makes it hard to define
IoT, since everyone looks at it from his own point of view. Quality engineers could use IoT as
a tool to monitor and improve their products, the government to build smart cities, others
could use it to create ambient intelligence. These are only a few examples of the many
opportunities that we are facing today. This thesis is also aim to guage the general attitude of
people towards IoT.
Business Insider defines IoT as “A network of internet-connected objects able to collect and
exchange data using embedded sensors” (Insider, 2016). IBM focuses on the virtualisation of
real world objects, they see IoT as “the creation of a digital twin of physical objects, it
transforms the real physical world into a virtual world, where everything is connected”.
These physical objects become objects with embedded electronics that can transfer data over a
network without any human interaction (IBM, 2016). The guardian referes, just like Forbes,
on the connectivity. They say it's about “connecting devices over the internet, letting them talk
to us, applications, and each other” (The Guardian, 2016. IoT transforms physical items into
smart items. Items that have the ability to capture context data and provide information
systems with a representation of 'things'. They clearly focus on the information IoT generates.
Cisco has a similar definition as Leiria. They also define IoT as a transformation of physical
items into smart items. Cisco says IoT connects previously unconnected devices (Macaulay,
Buckalew, & Chung, 2015). Some researchers stress the value of the information IoT
generates. They describe it as a new information system which includes more objects than
ever before. Gartner defines IoT as “the network of physical objects that contain embedded
technology to communicate and sense or interact with their internal states or the external
environment.” (Gartner, 2017). Another, more general definition is Internet of Things as the
technology which is broadly used to refer to both: “ (i) the resulting global network
interconnecting smart objects by means of extended Internet technologies, (ii) the set of
supporting technologies necessary to realize such a vision (including, e.g., RFIDs,
sensor/actuators, machine-to-machine communication devices, etc.) and (iii) the ensemble of
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applications and services leveraging such technologies to open new business and market
opportunities” (ITU, 2005). Most of these experts have similar definitions, only with a
different focus. They agree that IoT is a network of interconnected physical objects that
collect and exchange data. The main difference is the value delivery they expect that IoT will
bring.
Traffic Management With the increasing congestions, more attention is been given to traffic
management. It is important that there is an integrated system which manages traffic flows,
parking spaces, emergency interventions, etc. Connecting the Traffic Management System
with a GIS enabled digital road map of the city and using the power of data analytics is key to
smoothen traffic management. IoT provides us with real time data which enables us to
manage traffic flows much better. This real time data, combined with GIS mapping and
parking management, provides information to the drivers which not only reduces congestions
but also saves fuel and time. There are three ways to lower traffic congestion. The most
obvious way is to lower traffic in general. A second would be to increase the capacity of the
roads and infrastructure. The third is to optimise the traffic flows. All three of them can be
implemented by using IoT. According to the United States department of Transportation, the
majority of daily trips in the United States in 2001 occurred in personal vehicles, 87 percent in
total. About 38 percent of all trips were personal vehicle trips without passengers besides the
driver. This means that there is huge amount of lost capacity due to unused chairs. Internet of
Things can enable app developers to create apps which allows people to use those free spaces.
This combined with an improved public transport should be able to significantly reduce
traffic.
There is a lot of traffic in the city centre by people who are looking to park their car. IoT can
enable a sharing economy which can reduce the need for parking place significantly.
Therefore, fewer cars are needed so it will be easier to park your car in the city centre.
Complementary, IoT can increase capacity of the roads and infrastructure. Internet of Things
enables us to measure the traffic flows. We can use this data to build a better/smoother road
infrastructure. The same measurements can be used real time to divert traffic and solve traffic
congestions real time. Following tables explain how this is done, these examples are
suggested by Cisco. (Cisco, 2016)
Congestion
Number of Vehicles Sensors, connected to traffic signals, send information to
a central server about the number of vehicles at a certain
traffic signal
Data analysis Informations system gets real-time data from sensors
about traffic signals within some distance of the specific
junction
Inform about congestion When a threshold is reached, analytics software send a
message to traffic displays 1km before the signal
Driver diverts When the number of vehicles at the signals decreases
below threshold, message flashed on the displays stops
urging drivers to drive towards signal
Integrated system Installing similar systems across the city makes all
traffic signals congestion free
Table 1 Traffic Congestions Remedies (Cisco, 2016)
Traffic Emergency
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Ambulance interventi on Ambulance carrying a critical patient is driving at full
speed towards the hospital
Data analysis Information system gets real time data from sensors,
traffic signals on the way to hospital and GIS mapping
of all roads leading to hospital
Inform route to
ambulance
A message is sent to the ambulance display panel in
front of the driver informing him which road to take
Manipulat e traffic All signals towards the hospital are asked to be
manipulated, allowing the ambulance to pass through
Inform hospital A message is also sent to hospital system urging them to
be ready, including an auto message to the doctor's
phone to rush back if he is out
Table 2 Traffic Emergency (Cisco, 2016)
Criminal act prevention
Potential criminal activity Someone places a suspicious bag near a bus stop
Capturing data CCTV camera records all activities near bus stop
Data analysis All information from CCTV, sensors on the road,
criminal database and information from police command
center is analysed and decisions are being taken
Inform police and public Based on the analysis, a message is send to the police
command centre and the nearest public display asking
public to remain away from the site
Police Interventi on Police squad is dispatched to site to check bag contents
and take necessary action
Inform about criminal Video of person placing bag is send across the police
stations by the command centre
Table 3 Criminal act prevention (Cisco, 2016)
“The increasingly invisible, dense and pervasive collection, processing and dissemination of
data in the midst of people’s private lives gives rise to serious privacy issues” (Ziegeldorf,
Morchon, & Wehrle, 2014). When IoT breaks through, data will be generated from objects
that are private and personal to us. Our car will send data about where and how fast you are
driving, your home will send data about the lighting and heating system, your fridge could tell
what you eat or drink etc. Imagine what warehouses would do to get to this information.
There hasn’t been a good understanding about what privacy is and how we should control it.
Privacy in an Internet of Things environment (Ziegeldorf et al., 2014) implies that there is
Awareness of privacy risks imposed by smart things and services surrounding the data
subject
Individual control over the collection and processing of personal information by the
surrounding smart objects
Awareness and control of subsequent use and distribution of personal information to
anyone outside the subject’s personal control sphere It is not clear what exactly
personal information is since privacy is a social concept which is subjected to the
individuals perception and believes.
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Hence, we must take care about the sensitivity of the involved information and the relating
user requirements when designing new systems and products. Lately, companies are taking
PIAs (Privacy Impact Assessments) to see how their projects affect the stakeholders’ privacy.
(Roger Clarcke, 2009)
Interoperability
An another big challenge of IoT is the interoperability. Interoperability is necessary for using
IoT at its full capacities. Applications can't be built anymore like they were used to, as
standalone systems. Companies will have to work together to create applications that can
work with each other, share data etc. It is the interoperability that is the real value of IoT. Data
will be used by different applications and industries which opens a lot of opportunities. Since
interoperability is crucial to the value delivery of IoT, it is important that we pay attention to
this challenge. This comprises data sharing and thus standardisation as well as being able to
process big data
Standardisation is necessary to guarantee the interoperability of several objects. Since the
data will be used by other instances, clear definitions on how data should be created and
processed must be made. When data has to be standardised, it sets a framework which
restricts or makes it harder to make innovative and creative applications. Nonetheless, it is
important to facilitate the interoperability. Many experts believe that most of the value will be
delivered by the sharing of data. Some data will even be shared across different industries.
Although the links between these cross functional areas are not clear right now, in the future
they will be prominent in the value chain. Currently, the government is debating about how
data should be managed. They are debating about the difference between data ownership and
data usage and how to enable sharing of private data. A solution for this threat can be to
implement an open API. This enables programmers to write their program as they wish. They
are only constrained to the library for more abstract tasks. For example, a word processor
doesn’t need to know how exactly to operate a printer. The only thing it needs to be able to do
is call for a specific part in the programming library. IIt allows the programmer to use features
without understanding how these features work. In other words, it creates some form of
abstraction. (Gubbi, Buyya, & Marusic, 2013).
The IoT Explosion
The number of online capable devices increased to 8.4 billion in 2017, and it is estimated that
it will consist of about 30 billion objects by 2020. These devices include physical items,
vehicles, home appliances and other objects that are embedded with electronics, software,
sensors, actuators, and characterized by its connectivity to internet. Internet of Things (IoT) is
a network of such devices through which they can exchange data and command. In the
context of smart grid, IoT is at the pinnacle of its expansion stage as it offers a promising
future with smart analytics. Energy based analytics data provided from the user to utility
could potentially significantly enhance the efficiency and reduce congestions in the smart
grid, thus contributing to the improvement power supply reliability in the future 100%
renewable energy scenario. Globally, the path of a smart grid offers far reaching parallels in
the evolution to smart cities and progress towards IoT. Information and communications
technologies has transformed user's lives dramatically in all ventures since the past decade
where the utility providers face a diverse challenge in achieving better customer relationships.
The prospects of IoT and IoT enabled applications are limitless with the possibilities of
virtually connecting all the providers to the consumers and where communication is more
prompt. This complete interface and interconnectivity eases the processes improving the
16
productivity on a larger scale. The interconnectivity through communication such as mobile
phones is possible with swift decision- making through social collaboration comprising IoT
reducing application TCO (Total cost of ownership). There are many benefits of the cloud at
the financial outlook that becomes apparent where the TCO of a particular solution is met
from its purchase, considering the outcomes of both the service and operating expenses. Most
companies put little effort to solely improve the errors and promptly offers service to an
application on a complete cycle. In contrary, when the companies for their requirements, get a
workplace software from the cloud, there is a possibility of obtaining these services at a fixed
price for the entire contract period without due consideration of any hidden costs.
The part of this thesis was also to understand the Attitude in General towards IoT, so the set
of questionnaire were created on the 7-point scale with 1 Surely disagree / 7 Surely agree and
the opinions were taken.
Scale
1. Surely disagree
2. Middle disagreement
3. Slightly disagree
4. Neither agree nor disagree
5. Slightly agree
6. Middle agreement
7. Surely agree
Questions
a. Confidentiality can be managed with stringent laws
b. IoT needs better government regulating laws
c. Interoperability of IoT Devices & Apps from the different suppliers is a huge
challenge
d. It is easy to connect things together, but much harder to decide what data should be
allowed to read
e. IoT adversely affects the employment rate
f. Better services to the citizens is more important than the confidentiality of data
g. IoT makes us vulnerable to cyber attacks
The three questions about attitude general (Muehling, 1987) are about institution of IoT.
Therefore, the questions were;
1. Overall, IoT is good
2. Overall, IoT is favourable
3. Overall, Smart mobility is positive
The responses to IoT questions from the Soft-Sell Ad are as below
Questions Surely
disagree
Middle
disagreement
Slightly
disagree
Neither agree
nor disagree
Slightly
agree
Middle
agreement
Surely
agree
IoT needs better government
regulating laws 0% 1% 3% 1% 17% 21% 57%
IoT makes us vulnerable to cyber
attacks 1% 0% 6% 8% 20% 22% 43%
17
It is easy to connect things together,
but much harder to decide what data
should be allowed to read
0% 3% 4% 6% 14% 28% 44%
Better services to the citizens is more
important than the confidentiality of
data
1% 0% 11% 2% 14% 24% 47%
Confidentiality can be managed with
stringent laws 12% 8% 1% 6% 4% 14% 54%
IoT adversely affects the employment
rate 11% 14% 3% 6% 14% 14% 37%
Interoperability of IoT Devices &
Apps from the different suppliers is a
huge challenge
0% 0% 2% 8% 22% 28% 40%
Overall, IoT is good 0% 1% 0% 1% 7% 14% 77%
Overall, IoT is favorable 0% 1% 1% 2% 9% 19% 68%
Overall, IoT is positive 0% 1% 0% 1% 8% 16% 74%
Table 4 Responses for IoT questionnaire
Picture 1 Graph generated from the responses of the respondents for IoT questionnaire
Conclusion: The overall General Attitude towards IoT is Good, Favourable and Positive.
2.2 Taxation
In the standard theory of tax evasion, individuals and corporations pay taxes only because
they are forced to (i.e., because they believe that if they did not, they would be liable to
prosecution by the state). If this were the case, it would be essential that the probability of
being discovered for tax evasion, and the size of the penalty if caught and convicted are
sufficiently large to deter evasion. One problem with the standard view isthat forsome taxes
such as self-reported income taxes, it is hard to believe that the probability of being caught for
evasion is very large. In fact, all countries do encounter tax evasion, even those with the most
sophisticated systems for gaining compliance. To illustrate, the United States Internal
Revenue Service estimates that the proportion of all individual tax returns that are audited was
0.8% in 1990 (down from 4.75% in 1965). Civil penaltiesrange from 20% of the portion of
the underpayment resulting from a specific misconduct such as negligence or substantial
understatement to 75% if there is evidence of substantial intentional wrongdoing. In very
serious cases, criminal penalties may be applied. However, in 1995, only 4.1% of all U.S.
taxpayers who were reassessed following an audit received any penalty at all. Yet, the IRS
estimatesthat, for tax year 1992, 91.7% of income thatshould have been reported wasin fact
reported
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The standard view of tax compliance in tax theory is that taxes are a ‘burden’ or windfall
harm. Individuals do not consider taxesin relation to the otherside of the government ledger -
expenditures. The chief problem in normative taxation theory is to devise taxes which
minimize the ‘excess burden’ , i.e, how to minimize the total burden of taxation. Asis now
common in the literature on tax evasion, the model visualizes an individual taxpayer facing a
tax rate t on own income Y. If she chooses to evade taxes, she faces a punishment ftE where E
is the amount of unreported income and f is the size of the punishment (the fine rate) if
caught. In one sense, the model adapts the standard crime model of Becker (1968) to the
taxation case. In another sense, tax evasion is part of optimal portfolio choice: the individual
who chooses to evade taxesin effect makes a risky bet thatshe will not be caught and
convicted. However, the logic is simple once one realizes that tax evasion is treated as a risky
gamble or a problem in optimal portfolio choice. The penalty if an individual is caught, ftE,
issimply a constant multiple of the amount of tax evaded tE. Thus, if the tax rate rises, both
the gainsfrom evasion and the penalty rise by the same proportion, and there is no substitution
effect for or away from evasion. There is an income effect, however: the individual is poorer
as a result of the possibility of paying a higher penalty. This will make her take less risk,
hence evade less at higher tax rates. Of course this relationship is derived from individual
behavior and only holds at the individual level. The aggregate level of evasion may well move
in a different direction as the level of tax affects the number of taxpayers who choose to
evade.
Given that the problem of tax evasion appears to be more substantial in institutionally less
developed countries (i.e., transition countries), and since in this paper we intend to look at the
role of informal institutions on the decision to evade taxes, transition countries provide an
excellent test bed for our ideas. About a decade ago, these countries went through an
institutional shock, caused by the collapse of former communist regime. The level of the
institutional shock varied per country, depending on the type of regime. On one hand, the
communist regime was over-organized, where bureaucratic orders and ideological repression
determined what individuals had to do. On the other hand, it was characterized by
organizational failure, which motivated individualsto create and rely on informal networks.
“Such a ‘dual society’ of formal versus informal networks [institutions] was far more
developed in the Soviet Union, where it had been in place for more than 70 years, than in the
Czech Republic [for example]” (Rose, 2000). In Eastern Europe,similar characteristics were
observed in Albania, where the totalitarian regime lasted for more than 40 years. As a
consequence, these societies experienced significant distrust in the government and formal
institutions. The substitute was found in family-, friends- or local networks. After the collapse
of communism, in countries where the ‘dual society’ was dominant, and where in addition the
new governments did not manage to function properly, trust has eroded even further, forcing
people to invest and rely more on networks.
Indeed, the level of trust in the Russian government appears to be extremely low based on
survey data used in international comparisons. Only 3.4% of the respondents think that they
can trust the state. Only 25% of people appear to trust public institutions. The highest level of
trust is expressed towards family members.
2.3 Mediation of attitudes
Mediator style has been defined as both a set of strategies and tactics that characterize the
conduct of a case and as the role mediators perceive themselves to play in the mediation of a
conflict. Mediator styles that have received the most attention in the practitioner literature
include the evaluative, facilitative, and transformative styles. Moreover, mediator style is of
19
particular interest to researchers and practitioners alike because of its presumed influence on
the process and outcomes of mediation and the disputing parties’ satisfaction with mediation
services.
Despite its central importance, however, research on mediation style has been relatively
meager and methodologically haphazard. Although mediation style is analogous to the major
models used in psychotherapy (e.g., the cognitive and behavioral models of practice),
variation among mediator styles has not been systematically measured. The opposite can be
said in the field of psychotherapy wherein differing models of practice have been measured
using psychometrically valid scales and these efforts have furthered the theory building
process in psychotherapy and strongly influenced research on outcome comparisons among
the different styles. As a result, there is no agreed upon metric for assessing mediator style,
thus retarding efforts to systematically assess its impact on the delivery of mediation services.
Field studies of mediator style. Few studies have explored the relationship between global
mediator stylistic thinking and mediator behavior. These studies have also used various
methods to examine mediator style: observing mediation sessions, interviewing mediators
postsession, case studies, and self-report questionnaires. Mediators adopting this approach
encouraged parties to engage in a full expression of their feelings and attitudes. Emphasizing
empathy, exploring past relationships and discussing issues not readily raised by the parties
were key behaviors of therapeutic mediators. Therapeutic mediators believed these cathartic
techniques would lead to a resolution.
In this thesis, the mediation of attitude is deeply analysed in the section 3.2.2 The Paired TTest Analysis of Soft-Sell Ad and the section 3.2.4 The Paired T-Test Analysis of the HardSell Ad.
2.4 Methodology
A web-based survey format was used distribute the questionnaire created. There are several
advantages to using a web-based format:
1. Dramatically decreased response times. Typical turnaround time is four to six weeks
with traditional mail surveys, two to three weeks for telephone surveys, and only 2 to
3 days for web-based surveys
2. Reduced cost. Costs for e-mail and web-based surveys can be substantially lower than
for traditional mail surveys because there are no printing, postage, or stationery costs.
3. Web-based surveys are 50% less expensive to implement than telephone surveys, and
20% less expensive than mail surveys
4. Efficient data entry. An electronic survey can be configured to send data to a database
or spreadsheet, eliminating the need for manual data entry
Starting with the filtering questions including;
1. Email address *
2. Are you a *
Working Professional
Home Maker
Student
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Retired Individual
3. What is your gender?
Female
Male
4. What is your age group?
18-30 Years
31-40 Years
41-50 Years
51-60 Years
60+ Years
5. Which country are you living in?
6. I predominantly travel by *
Public Transport
Self Commute
Both
The Part A was to create and share the Attitude General Questions on the 7-point scale with 1
Surely disagree / 7 Surely agree
1. Surely disagree
2. Middle disagreement
3. Slightly disagree
4. Neither agree nor disagree
5. Slightly agree
6. Middle agreement
7. Surely agree
The Attitude Generaal questions were
Smart Mobility is a future fantasy
My general opinion about Smart Mobility is unfavorable
Smart Mobility helps raise our standard of living
Overall, I do want the Public Transport to improve
Present infrastructure is sufficient to sustain mass commute
It provides high quality of commute
Overall, I do want the Self Commute to improve
The three questions about attitude general (Muehling, 1987) are about institution of Smartmobility. Therefore, the questions were;
4. Overall, Smart mobility is good (strongly disagree = bad --- strongly agree = good)
5. Overall, Smart mobility is favourable (strongly disagree = unfavourable --- strongly
agree = favourable)
6. Overall, Smart mobility is positive (strongly disagree = negative --- strongly agree =
positive)
21
These three questions were repeat in the end of B part of questionnaire to see if AG has
changed due to Aad. Two pictures of smart mobility were created. Each picture was shown to
different group of respondents. Then the aim was to compare the assign differences in
answers to shown picture.
The Soft-Sell advertisement depicted the socializing with Smart-Mobility
Picture 2 Soft-Sell ad of Smart-Mobility used in the questionnaire
And the Hard-Sell advertisement with the description of performance parameters in
Smart-Mobility
Picture 3 Hard-Sell ad of Smart-Mobility used in the questionnaire
The Part B of the questions asked are as follows, on the 7-point scale with 1 surely disagree / 7
surely agree.
22
Scale
Degree Linkert Scale to Perform Paired T-Test Analysis
Surely disagree 1
Middle disagreement 2
Slightly disagree 3
Neither agree nor disagree 4
Slightly agree 5
Middle agreement 6
Surely agree 7
Table 5 Linkert 7-point Scale used in the questionnaire
Questions
It improves the general mobility of the citizens
It negatively impact Taxation
Laws and legal aspects are complicated
It increases un-employment
It utilizes clean energy hence reduced emissions
It is cost effective in a long run
It manages my speed limits notifications automatically
Smart Mobility is about high comfort commute
It contribute to increase my overall personal productivity
Smart Mobility Infrastructure is also about better utilization of land
It automates my travel bills
It helps to improve my schedule
It helps reducing accidents
I like to drive myself than sitting in an Autonomous vehicle
It is energy efficient
It offers no delays in the schedule
It gives me the flexibility with availability of best-fitting transport mode
It requires minimum governance
It provides added value services such as internet or emergency services etc.
It is about high safety travel
It is connected with other smart services (Smart City, Smart Grid, Smart Education,
Smart Waste Management etc.)
It helps reduce traffic congestion
It gives me analytics & reports of my commute
It is environment friendly
It reduces my stress when I commute
There are enough budget and funds to implement the Smart Mobility infrastructure
It needs a solid strategy before implementations
Smart Parking diminishes parking issues
The responses were recorded and analysed in Sectio 3. Analytical Part.
23
3. Analytical part
3.1 CPM characteristics
CPM (Critical Path Method) network planning is a technique used in the analysis. This
technique "of the work done towards the realization of a project, when to start, and what bits
work as well as when and what to do with the" grid presents visual information to the
manager (Mccahon and Lee,1989). CPM, the duration of activity is assumed to be constant
when the deterministic method (Meyer, Loch, Pich, 2014). In this study, the problem of CPM
subjective interest based merging method is used with the membership functions. In this
method, the formula of triangular fuzzy numbers,
(a+2b+c)/4
each with a designated representative values and values that are greater among themselves
pessimistic, optimistic median optimal value was considered as the lowest value were
calculated CPM (Baykasoglu, Gokcen, 2012).
For this thesis, for example commuters may need a website interface to help them for general
e-mobility services. Following are the assumptions taken for such web portal:
Assumptions
1. Website will have 100 e-Mobility services to cater like e-billing, reporting etc.
2. The main site, as well as forum and community pages will have administrative panel
with the capability to add/remove/edit any content in a non-technical fashion.
3. The city staff will have the ability to maintain the site content (including the product
database) without the help of programmers.
Activities Duration
(weeks)
Predecessor
Initializing A 1 -
Planning B 2 A
Design and requirements analysis C 1 B
Prepare schedule D 1 C
Website development E 4 D
Create product catalogue F 2 A
Customize engine G 3 A
Graphical Design (Web) H 3 E, F
Monitoring and Controlling I 4 G, H
Prepare and present report to stakeholders J 4 I
Closing K 1 J
Table 6 Activities based on assumption
24
Picture 4 Critical Path generated for the assumed activities
3.2 Answered hypotheses
The total number of responses recorded were 171, which included 90 responses for the SoftSell Ad and 81 responses for the Hard-Sell Ad.
3.2.1 Internation Comparison of Commute Preferences
Profession For Hard-Sell Ad For Soft-Sell Ad
Home Maker 7 12
Retired Individual 7 4
Student 22 23
Working Professional 45 51
Grand Total 81 90
Table 7 responses to the question about the profession of the respondents
Gender For Hard-Sell Ad For Soft-Sell Ad
Female 38 44
Male 43 46
Grand Total 81 90
Table 8 responses to the question about the gender of the respondents
I predominantly travel by For Hard-Sell Ad For Soft-Sell Ad
Both 14 14
Public Transport 43 38
Self-Commute 24 38
Grand Total 81 90
Table 9 responses to the question about the commute preferences of the respondents
Age Groups For Hard-Sell Ad For Soft-Sell Ad
18-30 Years 35 31
31-40 Years 28 43
41-50 Years 8 10
51-60 Years 8 5
60+ Years 2
Grand Total 81 89
Table 10 responses to the question about the age-groups of the respondents
25
For Hard-Sell Ad For Soft-Sell Ad
Armenia 1 Austria 3
Australia 1 Canada 5
Bhutan 1 Czech Republic 26
Czech Republic 29 France 1
Egypt 1 Germany 3
Finland 1 Great Britain 6
France 5 India 26
Germany 1 Malta 1
Great Britain 2 United States of America (USA) 15
India 12 Uruguay 1
Nepal 1 (blank) 3
Philippines 1 Grand Total 90
United States of America (USA) 23
(blank) 2
Grand Total 81
Table 11 responses to the question about the country of the respondents
3.2.1.1 Countries with inverse preferences of commute
Picture 5 Result of the Inverse Commute Preferences from the responses of the Soft-Sell Ad
26
Picture 6 Result of the Inverse Commute Preferences from the responses of the Hard-Sell Ad
3.2.1.1 Population density (people per sq. km of land area)
Population density is midyear population divided by land area in square kilometers.
Population is based on the de facto definition of population, which counts all residents
regardless of legal status or citizenship--except for refugees not permanently settled in the
country of asylum, who are generally considered part of the population of their country of
origin. Land area is a country's total area, excluding area under inland water bodies, national
claims to continental shelf, and exclusive economic zones. In most cases the definition of
inland water bodies includes major rivers and lakes.
The following chart represents the population explosion in Czech Republic, India and USA
Picture 7 Graph generated for the population density in Czech Republic, India and USA (Worldbank, 2018)
Country Name Year 2017
India 450.418617
Czech Republic 137.175534
United States 35.6077646
27
Table 12 Population density in India, Czech Republic and USA (Worldbank, 2018)
Conclusion: Thus, the hypothesis that countries very dense population with experience from
blocked traffic will prefers to self-commute which is opposite to the hypothesis.
3.2.2 Answers to the Soft-Sell Advertisement
Questions
No of
Respo
nses
Surely
disagree
(%)
Middle
disagre
ement
(%)
Slightly
disagree
(%)
Neither
agree nor
disagree
(%)
Slightly
agree
(%)
Middle
agreement
(%)
Surely
agree
(%)
Total
(%)
Part A Questionnaire
My general opinion about Smart
Mobility is unfavourable 90 18% 19% 14% 3% 2% 11% 32% 100%
Overall, I do want the Public Transport
to improve 90 1% 1% 0% 3% 16% 24% 54% 100%
Overall, I do want the Self Commute to
improve 90 1% 0% 0% 6% 20% 27% 47% 100%
Smart Mobility is a future fantasy 90 8% 10% 4% 7% 9% 28% 34% 100%
Present infrastructure is sufficient to
sustain mass commute 90 11% 8% 16% 7% 7% 17% 36% 100%
Smart Mobility helps raise our standard
of living 90 0% 0% 1% 6% 21% 30% 42% 100%
It provides high quality of commute 90 0% 0% 3% 8% 26% 24% 39% 100%
Overall, Smart Mobility is Good 90 0% 0% 0% 0% 12% 23% 64% 100%
Overall, Smart Mobility is Favourable 90 0% 1% 0% 0% 13% 17% 69% 100%
Overall, Smart Mobility is Positive 90 0% 0% 0% 1% 13% 19% 67% 100%
Part B Questionnaire
Smart Mobility is about high comfort
commute 90 0% 1% 4% 4% 24% 30% 36% 100%
It is about high safety travel 90 0% 0% 1% 9% 19% 24% 47% 100%
It offers no delays in the schedule 90 0% 0% 7% 14% 18% 28% 33% 100%
It helps to improve my schedule 90 0% 0% 2% 9% 26% 24% 39% 100%
It is environment friendly 90 0% 0% 2% 0% 22% 42% 33% 100%
It utilizes clean energy hence reduced
emissions 90 0% 1% 1% 7% 20% 28% 43% 100%
It is connected with other smart services
(Smart City, Smart Grid, Smart
Education, Smart Waste Management
etc.)
90 0% 0% 1% 2% 10% 32% 54% 100%
It provides added value services such as
internet or emergency services etc. 90 0% 1% 0% 4% 16% 32% 47% 100%
It improves the general mobility of the
citizens 90 0% 0% 2% 3% 17% 43% 34% 100%
It gives me analytics & reports of my
commute 90 0% 0% 1% 11% 22% 27% 39% 100%
It helps reduce traffic congestion 90 0% 0% 4% 7% 16% 32% 41% 100%
It is cost effective in a long run 90 0% 0% 1% 2% 19% 34% 43% 100%
It requires minimum governance 90 3% 1% 6% 14% 17% 29% 30% 100%
Smart Parking diminishes parking issues 90 0% 2% 1% 4% 19% 41% 32% 100%
It contribute to increase my overall
personal productivity 90 0% 0% 1% 9% 9% 41% 40% 100%
It reduces my stress when I commute 90 0% 0% 1% 8% 14% 22% 54% 100%
Smart Mobility Infrastructure is also
about better utilization of land 90 0% 0% 3% 3% 17% 32% 44% 100%
28
It manages my speed limits notifications
automatically 90 1% 0% 1% 10% 22% 30% 36% 100%
It automates my travel bills 90 0% 1% 3% 12% 16% 19% 49% 100%
It gives me the flexibility with
availability of best-fitting transport
mode
90 0% 0% 1% 3% 23% 27% 46% 100%
It is energy efficient 90 0% 0% 0% 4% 23% 33% 39% 100%
It negatively impact Taxation 90 11% 10% 17% 8% 8% 26% 21% 100%
It increases un-employment 90 11% 12% 9% 3% 10% 20% 34% 100%
Laws and legal aspects are complicated 90 2% 9% 6% 12% 13% 21% 37% 100%
There are enough budget and funds to
implement the Smart Mobility
infrastructure
90 2% 3% 2% 7% 8% 28% 50% 100%
I like to drive myself than sitting in an
Autonomous vehicle 90 2% 2% 7% 11% 18% 29% 31% 100%
It needs a solid strategy before
implementations 90 0% 1% 2% 3% 11% 29% 53% 100%
It helps reducing accidents 90 0% 0% 2% 8% 21% 30% 39% 100%
After seeing Soft-Sell Ad, Overall,
Smart Mobility is Good 90 0% 0% 0% 2% 3% 19% 76% 100%
After seeing Soft-Sell Ad, Overall,
Smart Mobility is Favourable 90 0% 0% 0% 1% 7% 19% 73% 100%
After seeing Soft-Sell Ad Overall, Smart
Mobility is Positive 90 0% 0% 1% 0% 7% 12% 80% 100%
Questions on IoT
IoT needs better government regulating
laws 90 0% 1% 3% 1% 17% 21% 57% 100%
IoT makes us vulnarable to cyber attacks 90 1% 0% 6% 8% 20% 22% 43% 100%
It is easy to connect things together, but
much harder to decide what data should
be allowed to read
90 0% 3% 4% 6% 14% 28% 44% 100%
Better services to the citizens is more
important than the confidentiality of data 90 1% 0% 11% 2% 14% 24% 47% 100%
Confidentiality can be managed with
stringent laws 90 12% 8% 1% 6% 4% 14% 54% 100%
IoT adversly affects the employment rate 90 11% 14% 3% 6% 14% 14% 37% 100%
Interoperability of IoT Devices & Apps
from the different suppliers is a huge
challenge
90 0% 0% 2% 8% 22% 28% 40% 100%
Overall, IoT is good 90 0% 1% 0% 1% 7% 14% 77% 100%
Overall, IoT is favorable 90 0% 1% 1% 2% 9% 19% 68% 100%
Overall, IoT is positive 90 0% 1% 0% 1% 8% 16% 74% 100%
Table 13 responses to the soft-sell ad
3.2.3 The Paired T-Test Analysis & Conclusions from Soft-Sell Ad responses
t-Test: Paired Two Sample for
Means
Overall, Smart Mobility is Good
Before After
Mean 6.522222 6.677777778
Variance 0.499501 0.42309613
Observations 90 90
Pearson Correlation 0.345707
Hypothesized Mean Difference 0
df 89
29
t Stat -1.89767
P(T<=t) one-tail 0.030491
t Critical one-tail 1.662155
P(T<=t) two-tail 0.060981
t Critical two-tail 1.986979
Table 14 The Paired T-Test Analysis from Soft-Sell Ad responses
Conclusion:For Soft-Sell Ad, Overall, Smart Mobility is Good
The difference between the t Stat is smaller than t Critical one-tail indicates that the AG has notchanged due to AAD.
Since the value of P(T<=t) one-tail is 0.030491, If the calculated P-value is less than 0.05 (in this
case it is less), the conclusion is that, statistically, the mean difference between the paired
observations is significantly different from 0, thus AG has changed due to AAD.
t-Test: Paired Two Sample for
Means
Overall, Smart Mobility is Favorable
Before After
Mean 6.511111111 6.644444444
Variance 0.747066167 0.433957553
Observations 90 90
Pearson Correlation 0.599025098
Hypothesized Mean Difference 0
df 89
t Stat
-
1.790867725
P(T<=t) one-tail 0.038357153
t Critical one-tail 1.662155326
P(T<=t) two-tail 0.076714306
t Critical two-tail 1.9869787
Table 15 The Paired T-Test Analysis from Soft-Sell Ad responses
Conclusion:For Soft-Sell Ad, Overall, Smart Mobility is Favorable
The difference between the t Stat is greater than t Critical one-tail indicates that the AG has
changed due to AAD.
Since the value of P(T<=t) one-tail is 0.038357153, If the calculated P-value is less than 0.05 (in this
case it is less), the conclusion is that, statistically, the mean difference between the paired
observations is significantly different from 0, thus AG has changed due to AAD.
t-Test: Paired Two Sample for
Means
Overall Smart Mobility is Positive
Before After
Mean 6.511111111 6.7
Variance 0.589762797 0.482022472
Observations 90 90
Pearson Correlation 0.396182705
Hypothesized Mean Difference 0
df 89
t Stat
-
2.223824435
P(T<=t) one-tail 0.014345635
30
t Critical one-tail 1.662155326
P(T<=t) two-tail 0.028691269
t Critical two-tail 1.9869787
Table 16 The Paired T-Test Analysis from Soft-Sell Ad responses
Conclusion: For Soft-Sell Ad, Overall, Smart Mobility is Positive
The difference between the t Stat is greater than t Critical one-tail indicates that the AG has
changed due to AAD.
Since the value of P(T<=t) one-tail is 0.014345635, If the calculated P-value is less than 0.05 (in this
case it is less), the conclusion is that, statistically, the mean difference between the paired
observations is significantly different from 0, thus AG has changed due to AAD.
3.2.4 Answers to the Hard-Sell Advertisement
Questions Total
Surely
disagree
(%)
Middle
disagre
ement
(%)
Slightly
disagree
(%)
Neither
agree
nor
disagree
(%)
Slightly
agree
(%)
Middle
agreemen
t (%)
Surel
y
agree
(%)
Total
(%)
Part A Questionnaire
My general opinion about Smart
Mobility is unfavourable 81 22% 28% 21% 12% 9% 4% 4% 100%
Overall, I do want the Public Transport
to improve 81 0% 7% 10% 12% 19% 22% 30% 100%
Overall, I do want the Self Commute to
improve 81 4% 9% 4% 4% 25% 30% 26% 100%
Smart Mobility is a future fantasy 81 19% 19% 12% 10% 14% 19% 9% 100%
Present infrastructure is sufficient to
sustain mass commute 81 21% 16% 9% 9% 20% 12% 14% 100%
Smart Mobility helps raise our standard
of living 81 1% 6% 5% 14% 25% 31% 19% 100%
It provides high quality of commute 81 4% 6% 1% 14% 26% 26% 23% 100%
Overall, Smart Mobility is Good 81 2% 5% 9% 11% 19% 17% 37% 100%
Overall, Smart Mobility is Favourable 81 2% 5% 9% 11% 19% 25% 30% 100%
Overall, Smart Mobility is Positive 81 2% 6% 10% 7% 16% 25% 33% 100%
Part B Questionnaire
Smart Mobility is about high comfort
commute 81 1% 6% 9% 11% 26% 26% 21% 100%
It is about high safety travel 81 0% 5% 5% 9% 36% 25% 21% 100%
It offers no delays in the schedule 81 0% 6% 9% 17% 25% 30% 14% 100%
It helps to improve my schedule 81 1% 4% 9% 20% 31% 16% 20% 100%
It is environment friendly 81 2% 11% 5% 14% 19% 31% 19% 100%
It utilizes clean energy hence reduced
emmissions 81 0% 6% 9% 17% 20% 25% 23% 100%
It is connected with other smart services
(Smart City, Smart Grid, Smart
Education, Smart Waste Management
etc.) 81 0% 6% 5% 15% 21% 27% 26% 100%
It provides added value services such as
internet or emergency services etc. 81 1% 4% 6% 11% 30% 22% 26% 100%
It improves the general mobility of the
citizens 81 1% 4% 6% 14% 23% 27% 25% 100%
It gives me analytics & reports of my
commute 81 1% 10% 6% 15% 27% 23% 17% 100%
It helps reduce traffic congestion 81 1% 6% 6% 7% 31% 31% 17% 100%
It is cost effective in a long run 81 1% 4% 9% 15% 25% 26% 21% 100%
31
It requires minimum governance 81 2% 5% 14% 20% 27% 19% 14% 100%
Smart Parking diminishes parking issues 81 1% 2% 10% 15% 26% 28% 17% 100%
It contribute to increase my overall
personal productivity 81 0% 5% 9% 12% 28% 27% 19% 100%
It reduces my stress when I commute 81 2% 1% 6% 11% 27% 26% 26% 100%
Smart Mobility Infrastructure is also
about better utilization of land 81 0% 9% 11% 16% 22% 28% 14% 100%
It manages my speed limits notifications
automatically 81 0% 9% 4% 25% 22% 21% 20% 100%
It automates my travel bills 81 2% 6% 9% 10% 33% 19% 21% 100%
It gives me the flexibility with
availability of best-fiting transport mode 81 1% 4% 14% 14% 26% 19% 23% 100%
It is energy efficient 81 1% 5% 7% 16% 25% 22% 23% 100%
It negatively impact Taxation 81 14% 26% 11% 25% 4% 14% 7% 100%
It increases un-employment 81 11% 23% 21% 14% 16% 12% 2% 100%
Laws and legal aspects are complicated 81 11% 23% 11% 21% 10% 16% 7% 100%
There are enough budget and funds to
implement the Smart Mobility
infrastructure 81 4% 6% 11% 14% 22% 21% 22% 100%
I like to drive myself than sitting in an
Autonomous vehicle 81 1% 6% 19% 21% 28% 9% 16% 100%
It needs a solid strategy before
implementations 81 1% 7% 7% 6% 30% 16% 32% 100%
It helps reducing accidents 81 1% 6% 5% 19% 28% 27% 14% 100%
After seeing the Hard-Sell Ad, Overall,
Smart Mobility is Good 81 2% 4% 15% 6% 10% 22% 41% 100%
After seeing the Hard-Sell Ad, Overall,
Smart Mobility is Favourable 81 0% 5% 15% 11% 15% 20% 35% 100%
After seeing the Hard-Sell Ad, Overall,
Smart Mobility is Positive 81 1% 5% 7% 6% 16% 23% 41% 100%
Questions on IoT
IoT needs better government regulating
laws 81 1% 7% 11% 16% 19% 20% 26% 100%
IoT makes us vulnarable to cyber attacks 81 5% 5% 19% 19% 7% 7% 38% 100%
It is easy to connect things together, but
much harder to decide what data should
be allowed to read 81 5% 12% 15% 17% 20% 12% 19% 100%
Better services to the citizens is more
important than the confidentiality of data 81 21% 11% 10% 9% 17% 11% 21% 100%
Confidentiality can be managed with
stringent laws 81 20% 10% 12% 12% 20% 4% 22% 100%
IoT adversly affects the employment rate 81 25% 16% 27% 14% 4% 7% 7% 100%
Interoperability of IoT Devices & Apps
from the different suppliers is a huge
challenge 81 4% 10% 12% 16% 28% 11% 19% 100%
Overall, IoT is good 81 1% 5% 6% 16% 10% 26% 36% 100%
Overall, IoT is favorable 81 1% 6% 12% 15% 10% 28% 27% 100%
Overall, IoT is positive 81 1% 4% 11% 7% 12% 32% 32% 100%
Table 17 The responses for the Hard-Sell Ad
3.2.5 The Paired T-Test Analysis & Conclusion from Hard-Sell Ad responses
t-Test: Paired Two Sample for Means
Overall Smart Mobility is Good
Before After
Mean 5.382716049 5.469135802
32
Variance 2.839197531 3.052160494
Observations 81 81
Pearson Correlation 0.62614267
Hypothesized Mean Difference 0
df 80
t Stat -0.523790133
P(T<=t) one-tail 0.300936302
t Critical one-tail 1.664124579
P(T<=t) two-tail 0.601872605
t Critical two-tail 1.990063421
Table 18 The Paired T-Test Analysis from Hard-Sell Ad responses
Conclusion: For Hard-Sell Ad, Overall, Smart Mobility is good
The difference between the t Stat is smaller than t Critical one-tail indicates that the AG has notchanged due to AAD.
Since the value of P(T<=t) one-tail is 0.300936302, If the calculated P-value is less than 0.05 (in this
case it is more), the conclusion is that, statistically, the mean difference between the paired
observations is significantly different from 0, thus there is no change in the opinion in AG due to AAD.
t-Test: Paired Two Sample for Means
Overall, Smart Mobility is Favorable
Before After
Mean 5.308641975 5.333333333
Variance 2.666049383 2.65
Observations 81 81
Pearson Correlation 0.689738396
Hypothesized Mean Difference 0
df 80
t Stat -0.173032135
P(T<=t) one-tail 0.431531605
t Critical one-tail 1.664124579
P(T<=t) two-tail 0.863063211
t Critical two-tail 1.990063421
Table 19 The Paired T-Test Analysis from Hard-Sell Ad responses
Conclusion: For Hard-Sell Ad, Overall, Smart Mobility is Favorable
The difference between the t Stat is smaller than t Critical one-tail indicates that the AG has notchanged due to AAD.
Since the value of P(T<=t) one-tail is 0.431531605, If the calculated P-value is less than 0.05 (in this
case it is more), the conclusion is that, statistically, the mean difference between the paired
observations is significantly different from 0, thus there is no change in the opinion in AG due to AAD.
t-Test: Paired Two Sample for Means
Overall Smart Mobility is Positive
Before After
Mean 5.358024691 5.641975309
Variance 2.907716049 2.482716049
Observations 81 81
Pearson Correlation 0.634497369
Hypothesized Mean Difference 0
df 80
t Stat -1.815758038
P(T<=t) one-tail 0.036577105
33
t Critical one-tail 1.664124579
P(T<=t) two-tail 0.073154211
t Critical two-tail 1.990063421
Table 20 The Paired T-Test Analysis from Hard-Sell Ad responses
Conclusion: For Hard-Sell Ad, Overall, Smart Mobility is Positive
The difference between the t Stat is smaller than t Critical one-tail indicates that the AG has notchanged due to AAD.
Since the value of P(T<=t) one-tail is 0.036577105, If the calculated P-value is less than 0.05 (in this
case it is less), the conclusion is that, statistically, the mean difference between the paired
observations is significantly different from 0, thus AG has changed due to AAD.
3.3 Recommendations with BEP
From the conclusions of the chapter 3.2.1 and subchapter 3.2.1.1 above, the hypothesis
was proved to be false and people in the densely populated country prefers selfcommute. It is recommended to promote IoT based cars in the densly populated
countries.
From the conclusion of the chapter 2.1.1, it was found that the overall General
Attitude towards IoT is Good, Favourable and Positive. Thus, promotion of the
benefits of IoT with Smart Mobility (Self Commute or Public Transport) is
recommended.
From the conclusions of the chapter 3.2.3 above, Soft-Sell Ads are recommended for
Smart-Mobility.
From the conclusions of the chapter 3.2.5 above, it was understood that the
complexities in the hard-sell advertisement may confuse the people about the benefits
Smart-Mobility brings to their lives and soft-sell ads was percieved as hard-sell.
34
4. Conclusions
This thesis was based on the hypothesis that more densely populated countries prefers public
transport, but it was found that people in the densly populated countries prefers to selcommute than taking public transport. This finding is important to understand the return of
investment to the Automotive companies to promote the upcoming car models equipped with
smart-mobility features.
Furthermore, the evidence that there was no change found in the opinion in AG due to AAD
in the hard-sell ads and AG was changed due to AAD in the soft-sell ads, results in the
recommendations the type of Ads Transportaion and Automotive companies can choose to
influence return of investments in their products.
The general attitude of people towards IoT was favourable, good and positive. Thus it would
be reasonable to promote IoT, for the Transportation and Automotive manufacturers, through
test drivings because the latest models of cars and transportation might be expensive.
35
Abstract
This document provides an overview of the analysis of the General Attitude of people towards
Smart-Mobility and their attitude towards advertisement. The Seven point Linkert scale
survey was used and Paired T-Test Analysis was performed. The General Attitude towards
IoT was evaluated. Two advertisements questionnaire on Smart Mobility were created, one of
which was Hard-Sell and the other was Soft-Sell and distributed between two different groups
of respondants. Total 171 responses were analysed out of which 90 responses were recorded
for Soft-Sell ad and 81 were recorded for Hard-Sell ad.
Keywords: AG, AAD, Smart Mobility, IoT, Attitude, Survey
JEL Classification Codes:
1. M37 Advertising
2. O35 Social Innovation
3. Q55 Technological Innovation
36
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