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 

10

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 

13

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. 

15

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

20

 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

Bibliography

Dianoux, C., Linhart, Z. The effectiveness of female nudity in advertising in three European countries,

International Marketing Review, 27, 5, 562-578, 2010.

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

Hoon, H., Scott, H. Introduction: Innovation and identity in next-generation smart cities, City, Culture and

Society, 12, 1-4, 2018

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

Forbes, The Smartest Cities In The World In 2018 [Online]. Available at

https://www.forbes.com/sites/iese/2018/07/13/the-smartest-cities-in-the-world-in-2018/#4bea87d82efc

[Accessed 19 August 2018].

Lutz, Richard J., "Affective and Cognitive Antecedents of Attitude towards the Ad: A conceptual framework,"

Psychological Processes and Advertising Effects: Theory, Research, and Applications, Linda Allowed and

Andrew Mitchell, eds, Hillsdale, NJ. 1985.

Kirmani, A., & Campbell, M. C.. “Taking the target’s perspective: The persuasion knowledge model“. In:

Wänke, M. Social Psycholog y of Consumer Behavior. New York: Taylor & Francis, 2009.

Lutz, R. J. MacKenzie, S. B., & Belch, G. E. Attitude toward the ad as a mediator of advertising effectiveness:

Determinants and consequences. Advances in consumer research, Hillsdale, NJ, 1983

Shimp, T. A, 1981. Attitude toward the Ad as a Mediator of Consumer Brand Choice [Online] Available at.

http://dx.doi.org/10.1080/00913367.1981.10672756 [Accessed 19 August 2018].

Baker, W. E., & Lutz, R. J, 2000. An Empirical Test of an Updated RelevanceAccessibility Model of Advertising

Effectiveness [Online] Available at. http:// dx.doi.org/10.1080/00913367.2000.10673599 [Accessed 19 August

2018].

Dianoux, C., Linhart, 2012 The Attitude toward advertising in general and Attitude toward specific ads: is it the

same influence whatever the countries? [Online] Available at

https://www.researchgate.net/publication/281448511_The_Attitude_toward_advertising_in_general_and_Attitud

e_toward_specific_ads_is_it_the_same_influence_whatever_the_countries [Accessed 24 August 2018]

Vilhelmson, B. ‘Daily Mobility and the Use of Time for Different Activities. Sweden: GeoJournal, 1999.

Castells, M.. The Rise of the Network Society: The Information Age: Economy, Society, and Culture. New

Jersey: John Wiley & Sons, 2011.

Hjorthol, R., and M. Gripsrud. Home as a Communication Hub: The Domestic Use of ICT. Australia: Journal of

Transport Geography, ICT and the Shaping of Access, Mobility and Everyday Life, 2009.

Banister, Cities, Mobility and Climate Change. United Kingdom: Journal of Transport Geography, Special

section on Alternative Travel futures, 2011.

Schwanen, T., and M. Kwan, The Internet, Mobile Phone and Space-Time Constraints. Australia: Geoforum,

2008.

Dal Fiore, F., P. L. Mokhtarian, I. Salomon, and M. E. Singer. “Nomads at Last”? A Set of Perspectives on How

Mobile Technology May Affect Travel, Michigan: Journal of Transport Geography, 2014.

Cresswell, T, Towards a Politics of Mobility. Environment and Planning, England: Society and Space, 2012.

37

Røe, P, Qualitative Research on Intra-Urban Travel: An Alternative Approach, Oslo: Journal of Transport

Geography, 2000.

Jain, J., and G. Lyons, The Gift of Travel Time. United Kingdom: Journal of Transport Geography, 2008.

Wegener, M, Overview of Land-Use Transport Models, Transport Geography and Spatial Systems, UK:

Pergamon/Elsevier Science, 2004.

Böcker, L., J. Prillwitz, and M. Dijst, Climate Change Impacts on Mode Choices and Travelled Distances: A

Comparison of Present with 2050 Weather Conditions for the Randstad Holland, Netherlands: Journal of

Transport Geography, 2013.

Røe, P., and I. Saglie, Minicities in Suburbia – A Model for Urban Sustainability?, Oslo: Journal for Design and

Design Education, 2011.

Muller, P. O, Transportation and Urban Form-Stages in the Spatial Evolution of the American Metropolis, San

Francisco: Guilford Publications, 2004.

Garreau, J, Edge City: Life on the New Frontier. United States: Anchor, 2011

Dieleman, F. M., M. Dijst, and G. Burghouwt, Urban Form and Travel Behaviour: Micro-Level Household

Attributes and Residential Context, Netherlands: Urban Studies, 2002.

Cao, X. (Jason), P. L. Mokhtarian, and S. L. Handy, Examining the Impacts of Residential Self‐Selection on

Travel Behaviour: A Focus on Empirical Findings, Minnesota: Transport Reviews, 2009.

Cervero, R., and K. Kockelman, Travel Demand and the 3Ds: Density, Diversity, and Design, California:

Elsevier Ltd, 1997.

Ewing, R., and R. Cervero, Travel and the Built Environment, Utah: Journal of the American Planning

Association, 2010.

Westford, P, 2010. Neighborhood Design and Travel : A Study of Residential Quality, Child Leisure Activity and

Trips to School, [Online]. Available at: http://www.diva-portal.org/smash/record.jsf?pid=diva2:293921

[Accessed 3 Aug 2018].

Engebretsen, Ø., and P. Christiansen, Bystruktur Og Transport: En Studie Av Personreiser I Byer Og Tettsteder’.

Oslo: Transportøkonomisk Institutt, 2011.

Hensher, D. A, Why Is Light Rail Starting to Dominate Bus Rapid Transit Yet Again?, Australia: Transport

Reviews, 2016.

Holden, E., and I. T. Norland, Three Challenges for the Compact City as a Sustainable Urban Form: Household

Consumption of Energy and Transport in Eight Residential Areas in the Greater Oslo Region, Osli: Urban

Studies, 2005.

Cass, N., E. Shove, and J. Urry, Social exclusion, mobility and access, Lancaster: The sociological review, 2005.

Boschmann, E. E., and M. P. Kwan, Toward socially sustainable urban transportation: Progress and potentials,

Denver: International Journal of Sustainable Transportation, 2008.

Bakker, S., Maat, K. and van Wee, B, Stakeholders interests, expectations, and strategies regarding the

development and implementation of electric vehicles: The case of the Netherlands, Netherlands: Transportation

Research, 2014.

Wockatz, P. and Schartau, P, IM Traveller Needs and UK Capability Study: Supporting the Realisation of

Intelligent Mobility in the UK, United Kingdom: Transport Systems Catapult, Milton Keynes, 2015.

38

Le Vine et al., S. Le Vine, M. Lee-Gosselin, A. Sivakumar, J. Polak, A new approach to predict the market and

impacts of round-trip and point-to-point carsharing systems: case study of London, United Kingdom:

Transportation Research, 2014.

Bilger, B, (2013) Auto-Correct: Has the self-driving car at last arrived?, [Online]. Available at:

http://www.newyorker.com/magazine/2013/11/25/auto-correct [Accessed 23 Aug 2018].

Gössling, S. and Cohen, S, Why sustainable transport policies will fail: EU climate policy in the light of

transport taboos, United Kingdom: Journal of Transport Geography, 2012.

Yianni, S, Foreword in Wockatz, P. and Schartau, P, IM Traveller Needs and UK Capability Study: Supporting

the Realisation of Intelligent Mobility in the UK, United Kingdom: Transport Systems Catapult, Milton Keynes,

2015.

The Insider, 2018, What is the Internet of Things (IoT)? [Online]. Available at:

https://www.thisisinsider.com/what-is-the-internet-of-things-definition-2016-8 [Accessed 23 Aug 2018].

IBM, 2016, The 4 big ways the IoT is impacting design and construction [Online]. Available at:

https://www.ibm.com/blogs/internet-ofthings/4-big-ways-the-iot-is-impacting-design-and-construction/

[Accessed 19 Aug 2018].

The Guardian, 2016 Uber hails victory after Transport for London drops restrictions. [Online]. Available at:

http://www.theguardian.com/technology/2016/jan/20/uber-claims-victory-aftertfl-drops-proposed-restrictions

[Accessed 29 Aug 2018].

Macaulay, J., Buckalew, L., & Chung, G, Internet of Things in Logistics. Germany: DHL Trend Research, 2015.

Gartner, 2017. Internet of Things [Online]. Available at: https://www.gartner.com/it-glossary/internet-of-things/

[Accessed 30 Aug 2018].

ITU, 2005. The Internet of Things [Online] Itu Internet Repor. Available at:

https://doi.org/10.2139/ssrn.2324902 [Accessed 12 Aug 2018].

Cisco, 2016. Smart Traffic Management With Real Time Data Analysis [Online]. Available at:

https://www.cisco.com/c/en_in/about/knowledge-network/smart-traffic.html [Accessed 12 Aug 2018].

Ziegeldorf, J. H., Morchon, O. G., & Wehrle, K, 2014. Privacy in the internet of things: Threats and challenges.

Security and Communication Networks [Online]. Available at: https://doi.org/10.1002/sec.795 [Accessed 12 Aug

2018].

Roger Clarcke, 2009. PIA Origins and Development [Online]. Available at:

http://www.rogerclarke.com/DV/PIAHist-08.html [Accessed 19 Aug 2018].

Gubbi, J., Buyya, R., & Marusic, 2013. Internet of Things (IoT): A vision, architectural elements, and future

directions [Online]. Available at: https://doi.org/10.1016/j.future.2013.01.010 [Accessed 19 Aug 2018].

Rose, R., Getting things done in an anti-modern society: Social capital networks in Russia. Washington: The

World bank, 2000.