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Citizen Empowerment in Newly Born Smart Cities in
Mauritius
Nawaz Mohamudally
University of Technology, Mauritius
La Tour Koenig, Pointe-aux-Sables
Sandhya Armoogum
University of Technology, Mauritius
La Tour Koenig, Pointe-aux-Sables
ABSTRACT
Advances in technology are quickly paving the way for smart
cities. The Government of Mauritius has set up the Smart City
Scheme to provide an enabling framework and a package of
attractive fiscal and non-fiscal incentives to investors for the
development of smart cities across the island. However, prior to
the design and implementation of such technologies, it is
important to predict the behavioural intention to use such
technology so that smart city technologies effectively empower
citizens and improve the quality of life of citizens. In this research
work, it is proposed to use the TAM to effectively assess the
perception and readiness and the perceived usefulness of certain
smart city technologies such as for transportation as well as
identifying key smart city applications for Mauritius. The aim of
this research project is to evaluate and assess the different factors
and condition that can have an impact on the perceived ease of use
(PEOU), perceived usefulness (PU), behavioural intention (BI) to
use and actual use (AU) of smart city technologies. For smart
cities to become a reality in Mauritius, smart city applications and
services should be citizen-centred and relevant for ordinary
citizens in their everyday life. This research work allows assessing
the readiness of Mauritian citizens for Smart city applications, as
well as identifying applications which can empower citizen.
CCS Concepts
• General and reference~Surveys and overviews
Keywords
Smart City; Technology Acceptance; Mobile Applications;
Citizen Empowerment.
1. INTRODUCTION
Smart Cities is becoming more of a reality every day, and smart
cities are being set up around the world. Over the last decade,
research into technologies needed to build and support Smart
Cities have matured. Today, with the use of information and
communications technologies (ICT), many solutions to manage
the Smart City infrastructure such as energy monitoring, water
management, transport management, traffic control and parking
space management, have been proposed and is being
implemented. However, such technological advances may not be
enough for Smart Cities to be a successful reality. In the zeal for
technological advancement, “who” Smart Cities are built for
should not be neglected. Smart Cities should be created and
deployed for humans first; then only will the Smart City be of real
value and the engineering efforts used towards the enhancement
of the human experience of the Smart City.
According to International Telecommunication Union
(ITU)'s report "Measuring Information Society" published in 2017
[1], the ICT Development Index IDI (based on ICT Access, ICT
Use and ICT Skills) for Europe is highest (average IDI value at
7.50). The high IDI values in Europe reflects on the access to high
bandwidth connectivity, high adult literacy rate and the and better
ICT use. This certainly explains why many of the smart cities
today such as Barcelona, Copenhagen, Helsinki, and Vienna are
found in Europe. Africa has by far the lowest average IDI value at
2.64, which is not more than half the global average. In the
African region, Mauritius is ranked first with an IDI value of 5.88,
followed by Seychelles and South Africa. According to research
by Frost & Sullivan in its report "The Telecommunications
Market in Southern Africa" [2], a new techno park, a third
submarine fibre optic cable as well as enhanced integration of ICT
and business in the country is boosting competitiveness in the
Information Technology sector in Mauritius. The Smart City
Scheme of the government of Mauritius already aims at
leveraging digitalization and new technologies for the
development of Smart Cities and Techno poles.
The disparity between Europe and Africa is obvious.
However, according to a Deloitte report [3], Africa is ready to
leapfrog the competition through smart cities technology but only
with successful adoption and appropriate selection of technology.
Smart City solutions that are successful in Europe may not be
successful in Africa and Mauritius. In [4], the author investigates
the causes of E-Government failure in developing countries.
Similarly, in [5], the author argues that the high failure rate of E-
government projects in Africa results in waste of financial
resources, which African countries can't afford. Users and the
information systems designed to support their needs and
behaviours are becoming increasingly complex [6]. For Smart
Cities to become a reality, it is important that citizens adopt and
accept smart city technologies.
The technology acceptance model (TAM) [7] which consists
of two major constructs: perceived ease of use (PEOU) and
perceived usefulness (PU) is often used to study and determine the
Republic of Mauritius
Republic of Mauritius
adoption of new solutions. In [8], the authors use TAM to assess
and improve M-Learning. In [9], the authors use the Modified
Unified Theory of Acceptance and Use of Technology 2
(UTAUT2) model to study the factors that influenced the adoption
of TV streaming by Internet users in Indonesia. A study [10] uses
Unified Theory of Acceptance and Use of Technology (UTAUT)
to investigate what impacts people to adopt mobile banking and
why it is underused. Findings from such research are useful in
providing guidance to developers for designing appropriate
solutions. To the best of our knowledge, apart from some research
conducted in the area of e/m-learning, e-government, mobile
payments and mobile banking, not much research has been
conducted in Africa to evaluate the level of participation and
adoption of smart cities solutions. No such research has been
conducted in Mauritius. The acceptance and adoption of smart
cities solutions is fundamental as the technological innovations
alone are not sufficient. Variables and behaviours that influence
acceptance and usage of such technologies, potential limitations
and gaps have to be identified and analysed especially in this
context where citizens may be called upon to participate heavily
and provide information to the entire community.
For smart cities to become a reality in Mauritius, smart city
applications and services should be citizen-centred and relevant
for ordinary citizens in their everyday life. Institutions such as
governments are the drivers of the Smart City. However, for the
success of the smart city initiatives, it is important to get feedback
from the citizens who would form an integral part of the smart
city. This research will look into the Mauritian society’s readiness
for acceptance of new solutions and smart city technologies and
give an insight into the needs and demand of the society, as well
as the harmonization of individual and common demands. It also
allows performing analysis of the social and non-technological
aspects of smart city adoption.
This paper is organized as follows. In Section 2, an overview
of TAM and related research works is presented. The research
work involved the design and development of an integrated Smart
City for Mauritius. In Section 3, the Smart City App design and
development is described. Section 4 presents our methodology
and results. In Section 5 consist of discussions and conclusion.
2. RELATED WORK
Several models exist to study human acceptance behaviour of
Information technology and Information systems, such as the
Theory of Reasoned action (TRA), Theory of Planned Behaviour
(TPB), Technology Acceptance Model (TAM, TAM2) and
Unified Theory of Acceptance and Use of Technology (UTAUT)
[11]. TAM has been developed by Davis [12] and is one of the
most popular research models to predict use and acceptance of
information systems and technology by individual users. TAM
predicts acceptance based on the end-user's perceived usefulness
(PU) and perceived ease of use (PEOU) of the technology for a
specific purpose. As ICT is becoming increasingly complex and
central to organizational operations and managerial decision
making, technology acceptance is even more important.
TAM and its variants are one of the most popularly used
models for assessing acceptance of technology. In [13], the
authors examined the consumer acceptance of online banking
using an extension of the TAM model where they reported that
PU was more influential than PEOU in explaining technology
acceptance. Similarly in [14], mobile banking adoption was
studied using TAM and TPB.
In [15] an extended model based on TRA and TAM approach
was used to predict acceptance of e-shopping. It was observed that
PEOU and PU significantly determine individual attitudes toward
e-shopping. This study also suggests that user acceptance is a
better indicator of e-shopping intentions than user satisfaction. In
[16], an extension of the TAM to include the four variables
(process satisfaction, outcome satisfaction, expectations, and E-
commerce use) was used to assess e-commerce where it was
reported that the extended TAM explained actual behaviour in E-
commerce environments better than the original TAM. Scherer et
al [17] uses TAM to explain teachers’ adoption of digital
technology in education. It was concluded that using TAM is
relevant but the role of certain key constructs and the importance
of external variables contrast some existing beliefs about the
TAM. Given that no such work has been attempted in the past, in
this work, the classical TAM was adopted.
3. PROTOTYPE SMARTCITY APP
A prototype SmartCity app, which consisted of six main features,
was implemented as a showcase of a typical Smart City
application. Via a main menu anyone of the main features could
be accessed. The six main features were: (1) The Nearby Places;
(2) Next Buses; (3) Weather Info; (4) Parking; (5) News; and (6)
Complaint.
The Nearby places goal is to help users to find ‘nearby
places’ (gas stations, pharmacies, restaurants and shopping
centres) around them on a map and allows the user to get the route
to a particular “place” in their neighbourhood. The Next Bus
feature allows users to enter their start and destination points and
to find buses that they can take and also provide an estimated time
at which the bus would arrive on their selected starting point bus
stop of the journey. Walking directions to the appropriate bus stop
from their current location is also indicated to the user and a
countdown for the bus arrival. The Weather feature is meant to
give weather information at the current location of the user and
also gives a brief 10-day hourly weather forecast to the user.
Similarly, the News feature is to provide national information to
the user as well as local information based on the user’s location.
The Complaint feature of the SmartCity App is to allow users to
post any complaint or report any incident such as accident,
damage to public infrastructure etc. This can allow authorities to
apprise of incidents or the status of the public infrastructure. The
Parking feature of the app allows the citizen to find out about
parking availability in their neighbourhood or in any selected
region, as well as parking availability and payment information (if
applicable). Directions of how to reach a particular available
parking space is also shown on a Map.
4. METHODOLOGY
The quantitative method was selected for the study through the
use of a questionnaire as it is a suitable way to reach a
geographically dispersed audience at a relatively low cost. The
survey was administered to a sample of 200 citizens of Mauritius.
The convenience sampling method was used where citizens will
be approached online or through their work place. Respondents
were first shown a video of the SmartCity App and then attempt to
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DOI: http://dx.doi.org/10.1145/12345.67890
respond to the questions. The reliability of the questionnaire was
estimated by conducting a pre-test in which 10 questionnaires
were administered to respondents, who were then excluded from
the sample. The response was quite spontaneous and the
participants showed ease of understanding for the questionnaire.
Following the pilot test, the questionnaire was slightly modified
based on comments received from respondents.
The final questionnaire consisted of a short covering letter,
and directions on how to fill the questionnaire. The first section of
the questionnaire consists of 9 questions related to the profile of
the citizen. The second section of the questionnaire consists of 21
questions based on the six features of the SmartCity App which
was showcased to them through the video of the interaction with
the app. The last section of the questionnaire consists of some 30
questions to assess the acceptance as well as to get input about the
factors that may affect adoption such as internet connectivity.
Respondents were mainly asked to provide ratings on a 5-point
Likert Scale for most questions, which was deemed convenient.
The following hypotheses were formulated:
Hypothesis 1. Perceived usefulness (PU) has a positive effect
on citizen acceptance of the Smart City App.
Hypothesis 2. Perceived ease of use (PEOU) has a positive
effect on citizen acceptance of the Smart City App.
Hypothesis 2b. Perceived ease of use (PEOU) has a direct
effect on perceived usefulness (PU).
Hypothesis 3a.User Satisfaction (US) has a positive effect on
citizen acceptance of the Smart City App.
Hypothesis 3b. PU positively affects User Satisfaction (US) of
the Smart City App.
Hypothesis 4a. Perceived ease of use (PEOU) has a direct
effect on behavioural intention.
Hypothesis 4b. Perceived usefulness (PU) has a direct effect on
behavioural intention.
Hypothesis 5. The quality of the Internet connection has a
positive effect on citizen acceptance of SmartCity App.
Hypothesis 7. Citizen Experience has a positive effect on
acceptance of SmartCity App.
5. FINDINGS
5.1 Survey Response
Citizens of Mauritius were invited in this research work to
participate in the survey by two modes: (1) an email invitation
with a link to the online version on the survey hosted on Google
server (Google Forms) was sent to some organizations and to
students of the university asking them to forward the email to
their friends and families for higher rate of dissemination of the
survey request; (2) 100 printed copies of the survey was left with
various institutions to solicit their participation in the survey.
Participation was encouraged by allowing the participants to enter
into a drawing to win a power bank as a gift. Phone calls were
made to solicit the participation of citizens. 68 responses were
recorded online and 32 filled printed copies of the survey were
obtained. Overall only 100 filled questionnaires were obtained
though 200 were targeted.
5.2 Results: Citizen Profile
The respondents consisted of 41.2% female and 58.8% male.
57.4% of the respondents were of the age group 21-29 years old,
17.6% were of the age group 30-39, 11.8% were of the age group
18-20 years old and only 1.5% response were obtained from
senior citizens i.e. 60 years old and above. The majority
respondents were thus observed to be in the age group 21-29 years
old, followed by age group 30-39. Poor participation was
observed from age group above 50 years and below 17 years.
75% of the respondents had tertiary education level while
23% of the respondents had secondary education level. 81% of the
respondents reported that the smart phone is their preferred
communications device while 15% also used tablets as a
communications device given that tablets nowadays support 4G
and 5G network connectivity. Most respondents are using their
mobile phones for accessing Internet, which is a positive
indication towards the use of their smart phones for accessing
internet-based services such as the SmartCity app.
Regarding the type of Internet access used, it was found that
68.0% of people used both Wi-Fi when available and mobile data
connectivity otherwise. Regarding mobile data connectivity, most
respondents were found to spend between 10 MUR and 299
MUR. Respondents were also asked to indicate their preferences
regarding the following popular mobile applications: Facebook,
Twitter, Instagram, YouTube, Snap Chat, WhatsApp, Email,
Google Search, Online Shopping, Online Banking, News and
Games. The following applications were highly favoured
compared to the others: Facebook, YouTube, WhatsApp, Email,
and Google Search.
Thus, it can be said that young citizens mostly possess a smart
phone and they are already quite familiar with mobile application
for communications and information.
5.3 Results: SmartCity App features
Most respondents agreed that the six main features were
useful, easy to use and information provided via the app was
clear, useful and easy to understand. However, when asked to
rank these six features of the app, the Responses from citizens
also indicated the bus information feature was chosen to be the
most useful. The most valued feature of the SmartCity App listed
in descending order of preference is as follows: (1) Bus
Information; (2) Parking Information; (3) Search Nearby Places;
(4) News; (5) Complaint; and (5) Weather Information.
5.4 Results: SmartCity App Acceptance
It is believed that user readiness is important for technology
acceptance so that citizens can adopt Smart City applications
which can help in their day to day routine. The readiness was
assessed by studying the responses regarding the use of other
mobile based technologies such as Facebook for interaction with
peers etc. Table 1 below depicts the Mean, Median, Mode,
Standard Deviation and Coefficient of Variation of the use of
some existing popular mobile applications. Respondents were
asked to select from a scale of 1 to 5 their likeliness to use the
listed mobile applications daily whereby a value of 1 represents
low likelihood of usage and a value of 5 represents highest
likelihood of usage of the technology. It is observed that the Mode
for Facebook, YouTube, WhatsApp, Email and News are 5 which
mean that they are most popular.
Table 1. Likelihood of usage of popular mobile applications
Mean
Median
Mode
SD*
CV**
Facebook
3.69
4.00
5.00
1.46
0.40
Twitter
2.40
2.00
1.00
1.64
0.68
Instagram
2.89
3.00
1.00
1.70
0.59
YouTube
4.12
5.00
5.00
1.18
0.29
Snapchat
2.27
1.00
1.00
1.61
0.71
WhatsApp
4.31
5.00
5.00
1.17
0.27
Email
4.08
5.00
5.00
1.32
0.32
Google
3.90
4.00
4.00
1.37
0.35
Online
Shopping
2.99
3.00
3.00
1.45
0.48
Online
Banking
3.24
4.00
4.00
1.60
0.49
News
3.44
4.00
5.00
1.47
0.43
Games
2.88
3.00
1.00
1.68
0.58
*SD – Standard Deviation
**CV – Coefficient of Variation
Standard deviation being a popular measure of variability; it
shows how much variation (dispersion, spread, scatter) from the
mean exists. Usually, a low standard deviation indicates that the
data points tend to be very close to the mean. A high standard
deviation indicates that the data points are spread out over a large
range of values. To interpret the variation further, the coefficient
of variation (CV) was calculated. As a rule of thumb, a CV >= 1
indicates a relatively high variation, while a CV < 1 can be
considered low. Given that the CV is less than 1, it indicates that
data have a low variance and are therefore closer to the mean
value. Based on this, the order of preference of the three most
popular mobile applications in descending order are as follows:
WhatsApp, YouTube, and Email. Online banking and online
shopping with a mean of 3.24 and 2.99 respectively, and a median
of 4 and 3 respectively are still above and equal to the average
value of 3. It can thus be concluded that the respondents have a
readiness of average to high for mobile applications.
To use the SmartCity App, having internet connectivity is
crucial. Respondents were asked to provide feedback regarding
the quality of Internet access. 79.0% of respondents claimed to
have access to Wi-Fi in their work or home environment. In case,
Wi-Fi access is not available, the respondent’s intention to use
mobile data connectivity to use the SmartCity App was evaluated
using the Likert scale. More than half of the respondents indicated
a high likelihood of paying for mobile data connectivity to access
the SmartCity App. 48.0% respondents found the current cost of
accessing the Internet via mobile data connection expensive,
while 22.0% of respondents found the cost of mobile data
acceptable and 29.0% found the cost of Internet highly acceptable.
On average, users found the speed of accessing the Internet both
via Wi-Fi and mobile data connection acceptable. Response
indicates that the quality of both Wi-Fi and mobile data
connection tends to be good quality. Table 5.3 depicts the factor
analysis results for the Quality of Internet access. Thus, it can be
concluded that access to Internet connection for the SmartCity
App is not an issue and people find the quality of internet
connectivity to be acceptable. Quality of Internet increases user
satisfaction and consequently use of the SmartCity App. Table 2
below depicts the factor analysis of the Quality of Internet.
Table 2. Factor analysis results: Quality of Internet (QI)*
Factor
Factor
Loadings
Is Wi-Fi easily accessible in your area? (QI1)
On a scale of 0 to 5, how often do you receive Wi-
Fi access in your area? (QI2)
On a scale of 0 to 5, how likely are you to use
Mobile Data connectivity in case there is no Wi-Fi
access at all in order to use the app? (QI3)
I find the current costs of accessing the Internet via
Mobile Data acceptable. (QI4)
I accept the current network speed of the Internet
via Wi-Fi. (QI5)
I accept the current network speed of the Internet
via Mobile Data. (QI6)
On a scale of 0 to 5, how would you rate the
quality of Wi-Fi connection in your area? (QI7)
On a scale of 0 to 5, how would you rate the
quality of Mobile Data connection in your area?
(QI8)
.638
.955
.571
.949
.804
.821
.774
.825
*Items with Factor loadings below 0.3 are not shown.
Two factor analysis using the Principal Component Analysis
(PCA) with Varimax (orthogonal) rotation were carried out. All
items having a loading greater than 0.3 on each factor were
retained. Table 3 to 7 represent a rotated component matrix for
Perceived Usefulness (PU), Perceived Ease of Use (PEOU), User
Satisfaction (US), and Behavioural Intention (BI) respectively.
Table 3. Factor analysis results: Perceived Usefulness (PU)
Factor
Factor
Loadings
…this SmartCity App useful to me. (PU1)
…is functional. (PU2)
…will enhance my effectiveness. (PU3)
…help to increase my productivity. (PU4)
…make some tasks easier. (PU6)
…greater control over my schedule. (PU7)
.842
.775
.849
.736
.526
.505
Timeliness of information provided…(PU8)
.792
The two highest factor loadings, in Table 3, regarding perceived
usefulness were .849 (respondents believe that the SmartCity App
will enhance their effectiveness) and .842 which supports the fact
that respondents found the SmartCity App useful overall. This is a
strong indication of acceptance.
Table 4. Factor analysis results: Perceived Ease of Use
(PEOU)
Factor
Factor
Loadings
Interaction with this SmartCity App is
clear…(PEOU1)
Learning to use this… (PEOU2)
I find it easy to locate the information… (PEOU3)
.836
.853
.788
From Table 4, it can be observed that respondents found that it
was easy to interact with the app (.836) and that learning to use
this app is going to be easy (.853). This also contributes to
acceptance of the SmartCity app.
Table 5. Factor analysis results: User Satisfaction (US)
Factor
Factor
Loadings
Overall, I am satisfied…(US1)
I am satisfied with the features or services…(US2)
.743
.758
From Table 5, it can be observed that respondents indicated user
satisfaction from the features of the SmartCity app. Factor
loadings were greater than 0.7. This indicates the high likelihood
of acceptance of the SmartCity app if it were to become fully
available for daily use.
Table 6. Factor analysis results: Behavioural Intention
(BI)
Factor
Factor
Loadings
I will recommend others to use …(BI3)
When I need it again, I intend…(BI4)
The use of this… (BI7)
If I were asked to express my opinion…(BI8)
.823
.889
.843
.868
From Table 6, it clear that the respondents who viewed the demo
of the SmartCity app indicated strong behavioural intention to use
the app when required and that they would most definitely
recommend others to use the app. The factor loadings were
greater than 0.8 indicating strong consequent acceptance of the
app.
5.5 Findings of other factors related to TAM
TAM states that PU and PEOU impacts technology acceptance. In
the case of new born smart city application for the empowerment
of citizens in developing countries, it was observed that other
external factors also impact the acceptance of mobile based
application namely
(1) Quality of Internet which may not be a “sure thing”
unlike in developed countries,
(2) Citizen Experience. Given that citizens are not exposed
to many mobile applications e.g. for bus schedule etc. in
developing African countries, this can impact the
acceptance of new smart city applications.
(3) User Satisfaction of the features in the SmartCity App.
Not all the features were observed to be equally useful
to the respondents. This could be due to the fact that
they can rely on other sources for information regarding
weather, news etc. Bus schedule and real-time tracking
feature and Parking (location and availability) feature
were highly appreciated due to the lack of such
integrated platform for information about the
transportation. Additional features which could further
increase user satisfaction from the SmartCity app is to
be investigated.
Figure 1 below depicts the TAM model and its external factors to
be considered for acceptance in newly born smart cities based on
our findings.
Figure 1. Modified TAM
6. CONCLUSIONS
The aim of this research project is to evaluate and assess the
different factors and condition that can have an impact on the
perceived ease of use (PEOU), perceived usefulness (PU),
behavioural intention (BI) to use and actual use (AU) of smart city
technologies. The findings of this study have shown that there is
indeed a relationship between those variables and hence support
the initial hypotheses. Future work involves further analysis of
data collected and exploring other more advanced technology
acceptance model as well as identifying additional factors which
could impact acceptance of mobile based applications in newly
born Smart Cities.
7. ACKNOWLEDGMENTS
Our thanks to the Mauritius Research Council (MRC), for funding
this research under the Small Scale Research Scheme.
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