report the factors influence food delivery...
TRANSCRIPT
DIPLOMA IN BUSINESS STUDIES
COMMERCE DEPARTMENT
REPORT
THE FACTORS INFLUENCE FOOD DELIVERY APPLICATIONS USAGE
AMONG STUDENTS OF POLITEKNIK SULTAN SALAHUDDIN ABDUL AZIZ
SHAH DURING MOVEMENT CONTROL ORDER (MCO)
No. NAME MATRIC NO.
1. NURUL SYAZWANI BINTI ZAKARIA 08DPM18F1040
2. NUR SYAFIQA BINTI ROSLI 08DPM18F1057
3. BATRISYIA BINTI SOBIE 08DPM18F1070
4. ANNUR BINTI ABDUL RAZAK 08DPM18F1077
I
DECLERATION OF ORIGINALITY
TITLE: THE FACTORS INFLUENCE FOOD DELIVERY APPLICATIONS USAGE
AMONG STUDENTS OF POLITEKNIK SULTAN SALAHUDDIN ABDUL AZIZ
SHAH DURING MOVEMENT CONTROL ORDER (MCO)
SESSION: JUNE 2020
1. We, a) NURUL SYAZWANI BINTI ZAKARIA (08DPM18F1040),
b) NUR SYAFIQA BINTI ROSLI (08DPM18F1057),
c) BATRISYIA BINTI SOBIE (08DPM18F1070),
d) ANNUR BINTI ABDUL RAZAK (08DPM18F1077),
Are the final year students of Diploma in Business Studies, Commerce Department,
Politeknik Sultan Salahuddin Abdul Aziz Shah, located at Persiaran Usahawan, Seksyen U1,
40150 Shah Alam, Selangor.
2. We ensure that ‘this project’ and its intellectual properties are original work without
plagiarism from any other sources.
3. In order to fulfil the requirement of being awarded a Diploma in Business Studies, we agree
to release the intellectual properties of the project to the said polytechnic above.
II
Prepared by:
a) NURUL SYAZWANI BINTI ZAKARIA
(Identity Card No.: 000115081112) NURUL SYAZWANI BINTI ZAKARIA
b) NUR SYAFIQA BINTI ROSLI
(Identity Card No.: 001228020724) NUR SYAFIQA BINTI ROSLI
c) BATRISYIA BINTI SOBIE
(Identity Card No.: 000612020900) BATRISYIA BINTI SOBIE
d) ANNUR BINTI ABDUL RAZAK
(Identity Card No.: 001004020776) ANNUR BINTI ABDUL RAZAK
In front of us,
HASNI BINTI HASHIM
As our Supervisor Date: HASNI BINTI HASHIM
III
ACKNOWLEDGEMENT
First for the wonderful opportunities and experiences throughout the five semesters we
went through, we would like to thank Allah SWT. Gratitude for the grace and encouragement
Allah SWT has given us in completing this study, to go through all the journeys of struggle.
With the guidance of Allah SWT, we might successfully solve this research problem together.
A journey toward achieving one of our lifetime goals is to aspire to succeed and complete this
subject.
Our first and deepest appreciation to our supervisors, the supervisor of this research
project, Puan Hasni Binti Hashim, who supervised, assisted and provided what was needed to
lead this project. It is very important to us without her superior and comprehensive knowledge
and experience to generate excellent project performance and therefore also her support. With
much respect and appreciation, thank you Puan Hasni Binti Hashim.
Special thanks also to Dr. Noordini Binti Abdullah, the DPB6043 Business Project’s
research coordinator, for her professionalism and encouragement from the beginning of this
study to the end of this project. Nevertheless, we express our gratitude to our family for their
kind cooperation and motivation that will enable us to complete this project.
Other than that, we would also like to thank all the respondents involved in the success
of this research project for their contribution to spending some time and struggling to cooperate
in the survey questionnaire.
We would like to thank Food Delivery Services in Malaysia who have supported us a
great deal by gathering information about the services offered so that we can conduct research
on their services. We also would like to thank the employees of the Food Delivery Service who
are able to share knowledge during the research with us.
May Allah SWT bless us all.
Thank you.
IV
THE FACTORS INFLUENCE FOOD DELIVERY APPLICATIONS USAGE
AMONG STUDENTS OF POLITEKNIK SULTAN SALAHUDDIN ABDUL AZIZ
SHAH DURING MOVEMENT CONTROL ORDER (MCO)
NURUL SYAZWANI BINTI ZAKARIA (08DPM18F1040)
NUR SYAFIQA BINTI ROSLI (08DPM18F1057)
BATRISYIA BINTI SOBIE (08DPM18F1070)
ANNUR BINTI ABDUL RAZAK (08DPM18F1077)
SUPERVISOR: PUAN HASNI BINTI HASHIM
Commerce Department
Politeknik Sultan Salahuddin Abdul Aziz Shah, Shah Alam, Selangor
Abstract - Food delivery application is a one of the mobile commerce and it is completed
through the mobile devices, like the smart phones or tablets. The customers can select the food
and make payment online, then the food will be delivered to the customers. During Movement
Control Order (MCO) in Malaysia, demand for food delivery applications increased as their
movement is limited. This purpose of this study is to study the factors influence food delivery
applications usage among students of Politeknik Sultan Salahuddin Abdul Aziz Shah during
Movement Control Order (MCO). Besides, the literature review will look further into the detail
of the effectiveness of using food delivery applications among students. The analysis of
research data will be carry out using Google form software. Data that relate to this assemble
were collected from 357 students from Politeknik Sultan Salahuddin Abdul Aziz Shah. The
result indicate that facilitating conditions had a strongest influence on the factors influence
followed by social influence and habit. Therefore, this research confirms its importance of
social influence, facilitating conditions and habit as a factor in encouraging users to use food
delivery applications. This research could make improvement to the food delivery applications
on how to attract customer among students of Politeknik Sultan Salahuddin Abdul Aziz Shah.
Keywords – food delivery applications, Movement Control Order (MCO), factors influence
V
TABLE OF CONTENTS
CONTENT PAGE
DECLARATION OF ORIGINALLY I – II
ACKNOWLEDGEMENT III
ABSTRACT IV
TABLE OF CONTENTS V - VII
LIST OF TABLES VIII - IX
CHAPTER 1: INTRODUCTION
1.1 INTRODUCTION 1 – 2
1.2 BACKGROUND OF RESEARCH 2 – 3
1.3 PROBLEM STATEMENT 3
1.4 RESEARCH OBJECTIVE 4
1.5 RESEARCH QUESTION 4
1.6 SCOPE OF RESEACH 4
1.7 SIGNIFICANT OF RESEARCH 5
1.8 DEFINITION OF TERM 5 – 6
1.9 SUMMARY 7
CHAPTER 2: LITERATURE REVIEW
2.1 INTRODUCTION 8
2.2 LITERATURE REVIEW AND HYPOTHESES 8
2.2.1 STUDENTS OF POLYTECHNIC 9
2.2.2 FOOD DELIVERY APPLICATIONS 10
2.2.3 SOCIAL INFLUENCE 11
2.2.4 FACILITATING CONDITIONS 12
2.2.5 HABIT 13
2.3 HYPOTHESES OF THE STUDY 14
2.3.1 SOCIAL INFLUENCE 14
VI
2.3.2 FACILITATING CONDITIONS 14
2.4.3 HABIT 15
2.4 THEORATICAL FRAMEWORK 16
2.5 SUMMARY 16
CHAPTER 3: RESEARCH METHODOLOGY
3.1 INTRODUCTION 17
3.2 RESEARCH DESIGN 17
3.3 DATA COLLECTION METHOD 18
3.3.1 PRIMARY DATA 18
3.3.2 SECONDARY DATA 19
3.4 RESEARCH INSTRUMENT 19 – 24
3.5 SAMPLING TECHNIQUE 25
3.6 DATA ANALYSIS METHOD 26
3.6.1 DESCRIPTIVE METHOD 26
3.7 SCALE MEASUREMENT 27
3.7.1 RELIABILITY TEST 27
3.7.2 PILOT TEST 28
3.8 SUMMARY 29
CHAPTER 4: DATA ANALYSIS
4.1 INTRODUCTION 30
4.2 DEMOGRAPHY PROFILE OF RESPONDENTS 30 – 32
4.3 GOODNESS MEASURE 33
4.3.1 DESCRIPTIVE ANALYSIS 33
4.4 RESEARCH FINDINGS 33
4.4.1 STATISTICAL SUMMARY 33 – 37
4.5 HYPOTHESES TESTING 38
VII
4.5.1 CORRELATION COEFFICIENTS RANGE 38
4.5.2 CORRELATION ANALYSIS 38
4.5.2.1 RELATIONSHIP BETWEEN FOOD
DELIVERY APPLICATIONS AND SOCIAL
INFLUENCE
39
4.5.2.2 RELATIONSHIP BETWEEN FOOD
DELIVERY APPLICATIONS AND
FACILITATING CONDITIONS
39
4.5.2.3 RELATIONSHIP BETWEEN FOOD
DELIVERY APPLICATIONS AND HABIT 39
4.6 SUMMARY 40
CHAPTER 5: DISCUSSION AND CONCLUSION
5.1 INTRODUCTION 41
5.2 RECAPITULATION OF THE STUDY 41
5.2.1 DISCUSSION OF MAJOR FINDINGS 42
5.3 RECOMMENDATION 43
5.3.1 RECOMMENDATION FOR FUTURE RESEARCH 43
5.3.2 RECOMMENDATION FOR FOOD DELIVERY
COMPANY AND STUDENTS 44
5.4 SUMMARY 45
VIII
LIST OF TABLES
TABLE/FIGURE PAGE
Figure 1 Research Framework 16
Table 3.1 Food Delivery Applications (Dependent Variable) 21
Table 3.2 Social Influence (1st Independent Variable) 22
Table 3.3 Facilitating Conditions (2nd Independent Variable) 23
Table 3.4 Habit (4th Independent Variable) 24
Table 3.5 Alpha Coefficient Range Strength of Association 27
Table 3.6 Reliability Test for 31 Respondents 28
Table 4.1 Demographic Profile 31
Table 4.2 General Information 32
Table 4.3 Overall Descriptive Analysis of the Variable 33
Table 4.4 Statistical Summary (Food Delivery Application) 34
Table 4.5 Statistical Summary (Social Influence) 35
Table 4.6 Statistical Summary (Facilitating Conditions) 36
Table 4.7 Statistical Summary (Habit) 37
Table 4.8 Pearson Correlation Coefficient (r) Strengths 38
Table 4.9 Pearson’s Correlation Coefficients of the Variables 38
IX
REFERENCES 46 – 51
APPENDIX A: 31 SET OF PILOT TEST 52
APPENDIX B: QUESTIONNAIRE 53 – 57
APPENDIX C: CRUCIAL INFORMATION 58 – 64
APPENDIX D: DESCRIPTIVE STATISTICS 65 – 68
APPENDIX E: RELIABILITY TEST 69
APPENDIX F: CORRELATION TEST 70
APPENDIX G: SWOT ANALYSIS 71
APPENDIX H: GANTT CHART 72
1
CHAPTER 1: INTRODUCTION
1.1 INTRODUCTION
An overview of the entire research project was given in this chapter. The goal of the
study is to investigate the factors influencing the use of food delivery applications among
students of Politeknik Sultan Salahuddin Abdul Aziz Shah during the Movement Control Order
(MCO). It consists of eight components of the studies which include the research background,
problem statement, research objectives, research questions, the scope of the research, the
significance of the research, definition of operational terms and summary. The first part of the
study was started with a research background, problem statement and objectives to give a basic
understanding of the overall study. Next, the research question offers arguments and inquiries
which are needed to examine for further investigation. The significance of the study was
explained the importance and contribution of the study. Lastly, the definition of the operational
statement was outlined and ended with the summary.
According to Tang (2020), Prime Minister Tan Sri Muhyiddin Yassin has declared that
the entire country will be on a movement control order starting from 18th March 2020 until
31st March 2020 to deal with the rise in Covid-19 cases. The decision was made under the
Prevention and Control of Infectious Diseases Act 1988 and the Police Act 1967.The
prohibition of movement and mass assembly nationwide would include all religious, sports,
social and cultural activities. The enforcement took place of all places of worship and business
premises must be closed except for supermarkets, public markets, grocery stores, and stores
selling basic necessities. When the movement control order (MCO) was enforced, Malaysians
staying home became dependent on food delivery riders — two major food delivery companies
even increased the number of riders working for them to cope with the spike in demand (Radzi,
2020).
On the other hand, despite the negative influence of COVID-19 significantly affecting
the supply and demand of the catering industry, it has changed the consumption habits of
residents and accelerated the transformation of catering enterprises from traditional in-store
service to online-to-offline service for surviving in the pandemic situation and maintaining
sustainable development (Zhao & Bacao, 2020).
2
The start of Movement Control Order (MCO) saw a large number of people ordering
through food delivery applications such as Grab Food and Food Panda. This comes back to a
trend that has seen changes in the scant few months of Malaysia’s first national-level pandemic
response — food delivery. This was the answer offered by the government during the MCO
for those who, due to various circumstances, were unable to cook while obeying the stay at
home order (Kong, 2020).
1.2 BACKGROUND RESEARCH
This study aims to investigate the factors affecting the use of food delivery applications
during the enforcement of the Movement Control Order (MCO). This analysis is devoted to
the students of Politeknik Sultan Salahuddin Abdul Aziz Shah.
Food delivery applications are becoming more popular in Malaysia with a population
of various races. Application Foodpanda was the first delivery company that started
aggressively in Malaysia in 2012 (Milo, 2018). Foodpanda is one of the leading online food
delivery marketplace and is spread out globally. Foodpanda also has become an instant success
because of their user-friendly, high quality and effective interface (Shona, 2016). After the
success of Foodpanda in food delivery service, there is more competitors try to defeating them
such as GrabFood, dahmakan, Bungkusit, DeliverEat and others.
Food delivery applications is a courier service in which stores, restaurants or third-party
applications delivers food to consumers on demand. These days, orders are executed through
mobile apps, websites or via telephone. Deliveries include cooked dishes as well as groceries
from supermarkets. Other methods of food delivery include catering or wholesale. The first
recorded instance of a meal delivery comes from Italy in 1889. King Umberto and his Queen
Margherita and called Raffaele Esposito, the creator of the Pizza Margherita, to deliver a pizza
to their palace in Naples (Viktor, 2020). Food delivery applications offer food service
customers the ability to search through diverse products and compare costs. Small-scale
restaurants that have lower advertising and marketing abilities can use delivery apps as a
convenient and highly efficient sales and marketing tool (Lee, Sung, & Jeon, Determinants of
Continuous Intention on Food Delivery Apps: Extending UTAUT2 with Information Quality,
2019).
3
This research therefore seeks to analyse on factors influence food delivery applications
usage among students of Politeknik Sultan Salahuddin Abdul Aziz Shah during Movement
Control Order (MCO).
1.3 PROBLEM STATEMENT
Individuals who are busy doing work or assignments at a given time having problems
and lead to the food delivery applications. According to Belanche, Flavián, & Pérez-Rueda
(2020), food delivery applications is perfect for people who are busy in their work life and the
applications suits the lifestyle of the customer such as eating at home and ordering via mobile
phone. They are willing not to go out to buy food. There are some students who are feeling
lazy to buy food. Besides, students will feel wasted time for waiting for food when there are
crowded.
However, during Movement Control Order (MCO), mostly people are likely to buy
their foods through food delivery applications. Because of that, food delivery applications are
intended to fulfil customers needs among students of Politeknik Sultan Salahuddin Abdul Aziz
Shah. Students can get a benefits from using food delivery applications which is the probability
of contracting Coronavirus was low.
Customers can access the website and choose the restaurant according to the real-time
customized using the system provided which is the food delivery service application
(Hendrickson, 2016).
Students also feel bored with the same food sometimes they want to try something
different especially when the food is viral. The visual effects of food will affect the expectations
on how the taste of food and can also affect the real taste (Gayler & Sas, 2017).
4
1.4 RESEARCH OBJECTIVES
The main objectives of this study are as per below:
1.4.1 To examine relationship between social influence with food delivery
applications usage among students of Politeknik Sultan Salahuddin Abdul Aziz
Shah.
1.4.2 To examine relationship between facilitating conditions with food delivery
applications usage among students of Politeknik Sultan Salahuddin Abdul Aziz
Shah.
1.4.3 To examine relationship between habit with food delivery applications usage
among students of Politeknik Sultan Salahuddin Abdul Aziz Shah.
1.5 RESEARCH QUESTIONS
The research question for this study are as per below:
1.5.1 Does social influence has relationship with food delivery applications usage
among students of Politeknik Sultan Salahuddin Abdul Aziz Shah.
1.5.2 Does facilitating conditions has relationship with food delivery applications
usage among students of Politeknik Sultan Salahuddin Abdul Aziz Shah.
1.5.3 Does habit has relationship with food delivery applications usage among
students of Politeknik Sultan Salahuddin Abdul Aziz Shah.
1.6 SCOPE OF RESEARCH
This research is perform on students of Politeknik Sultan Salahuddin Abdul Aziz Shah.
Therefore, this study can find out the factors influence food delivery application usage among
students of Politeknik Sultan Salahuddin Abdul Aziz Shah during Movement Control Order
(MCO). This research will also allow analysis and conclusions to be drawn based on the answer
of the students in the online questionnaire. The study results will help researchers identify the
factors influence food delivery applications usage among students of Politeknik Sultan
Salahuddin Abdul Aziz Shah during Movement Control Order (MCO).
5
1.7 SIGNIFICANT OF RESEARCH
The significant of this study are:
1.7.1 The researchers discover information related to factors influence students using
food delivery applications that have never been explored by others.
1.7.2 The students from Polytechnic, college or university who want to do a research
regarding the factors influence students using food delivery applications can use
this research as a reference.
1.7.3 The society able to know which factors the most influence students to meet their
needs using food delivery applications during Movement Control Order (MCO).
1.8 DEFINITION OF TERM
1.8.1 Pandemic – A pandemic is the worldwide spread of a new disease. An influenza
pandemic occurs when a new influenza virus emerges and spreads around the
world, and most people do not have immunity. Viruses that have caused past
pandemic typically originated from animal influenza viruses (World Health
Organization (WHO), 2010).
1.8.2 COVID-19 – Coronavirus disease 2019 (COVID-19) is defined as illness
caused by a novel coronavirus now called severe acute respiratory syndrome
coronavirus 2 (SARS CoV-2; formerly called 2019-nCoV), which was first
identified amid an outbreak of respiratory illness cases in Wuhan City, Hubei
Province China. It was initially reported to the WHO on December 31, 2019.
On January 30, 2020, the WHO declared the COVID-19 outbreak a global
health emergency. On March 11, 2020, the WHO declared COVID-19 a global
pandemic, its first such designation since declaring H1N1 influenza a pandemic
in 2009 (Cennimo, MD, FAAP, FACP, & AAHIVS, 2020).
1.8.3 Movement Control Order (MCO) – The Movement Control Order refers to
the Prevention and Control of Infectious Diseases (Declaration of Infected Local
Areas) Order 2020 (“PCID Order”) which was issued by Malaysian Prime
Minister, Tan Sri Muhyiddin Yassin on 16.3.2020 under the Prevention and
Control of Infectious Diseases Act 1988 (“PCID Act 1988”), after being
6
satisfied that all States and Federal Territories in Malaysia are threatened with
an epidemic of an infectious disease, namely Covid-19, a life-threatening
microbial infection specified in Part 1 of the First Schedule of PCID Act 1988
(MahWengKwai & Associates , 2020)
1.8.4 Food Delivery Applications – Technological evolution has completely
changed the entire scenario of the restaurant industry. It has uplifted the usage
of online food delivery services and enabled us to order food at the comfort of
our home, compare prices and conveniently access these services. These online
food delivery services are boosting the option of choosing meals from a wide
variety of restaurants with a single tap of our smartphones (Das & Ghose, 2019).
1.8.5 Social Influence – Social influence has been validated as significantly
determining users' intention to use an online-to-offline delivery service (Park,
2019). Social influence as an important variable in Unified Theory of
Acceptance and Use of Technology (UTAUT) has a significant impact on users'
intentions to continue using mobile technologies (Lai & Shi, 2015).
1.8.6 Facilitating Conditions – Facilitating conditions such as resources availability,
skills as well as technical infrastructure could play a significant role towards
digital library use among engineering lecturers. Given that an individual
perceives that using a system will improve his job performance represents
performance expectancy, while availability of technical and organisational
infrastructure required to use a system represents the facilitating conditions;
both performance expectancy and facilitating conditions could be said to play a
critical role and have direct impact on the use of any system (Hamzat &
Mabawonku, 2018)
1.8.7 Habit – According to the Limayem, Hirt, & Cheung (2007), habit is defined as
tendencies that are performed automatically through learning. In addition, habit
is sometimes affected by current environmental conditions or past experiences
and may not be conscious (Hsu, Chang, & Chuang, 2015).
7
1.9 SUMMARY
This chapter contained an introduction to research that researchers want to do about the
factors influence food delivery applications usage among students of Politeknik Sultan
Salahuddin Abdul Aziz Shah during Movement Control Order (MCO). This chapter also
contained the research’s background, problem statement, research objectives, research
questions, scope of research, significant of research as well as definition of operational terms.
In order to provide a clear explanation for the definition of this analysis and develop a
theoretical framework, Chapter 2 will continue the study by reviewing the literature for each
variable in this study.
8
CHAPTER 2: LITERATURE REVIEW
2.1 INTRODUCTION
The factors influence food delivery applications usage among students of Politeknik
Sultan Salahuddin Abdul Aziz Shah during Movement Control Order (MCO) will be discussed
in this chapter. The approach includes some study of the food delivery applications, social
influence, facilitating conditions, habit and students of Polytechnic. The literature review
showed further discussion on the feasibility of using food delivery applications among students
in depth.
Based on the survey held by Russ & Yus (2020), in particular, when the Covid-19
spreads in some countries, especially in China, the online food delivery can contribute to
keeping food quality control stable in the food health and food safety from the Convid-19, with
the “contactless” pickup and delivery services to make the deliver workers and customers safe,
this delivery can also make the food health and quality sustainable in the whole delivery
process.
Service and delivery service it is defined as the act, the process or the actuation those
are provided by the company or individuals. Comparing to the physical goods, the service is
the intangible actions in the economy and the market (Zeithaml, Bitner, & Gremler, 2014).
According to Kotler & Keller (2018), defined services as acts or performances provided by one
party to another.
In conclusion, during Movement Control Order (MCO) factors affect the use of food
delivery application among students of Politeknik Sultan Salahuddin Abdul Aziz Shah is social
influence, facilitating conditions and habit.
2.2 LITERATURE REVIEW
The goal of the literature review is to explore in more detail the factors affecting this
research. Other than that, literature reviews also known as secondary sources, only include
previous research and do not reveal any new or original research information
9
2.2.1 STUDENTS OF POLYTECHNIC
The establishment of Premier Polytechnics officially announced by The Minister of
Ministry of Higher Education during the launching of Polytechnic Transformation Plan on 25
February 2010. The three institutions chosen are Politeknik Sultan Salahuddin Abdul Aziz
Shah in Shah Alam, Selangor, Politeknik Ungku Omar in Ipoh, Perak and Politeknik Johor
Baru, Johor (Richard Lim, 2010).
The premier polytechnic is expected to lead the field of thrust programs (niche area)
and specific technology that can produce quality graduates with entrepreneurship abilities.
Graduates must be trained in entrepreneur skill in order for them to sustain in the respective
industries. Addition to the above, by developing an international reputation and being
preeminent national higher institution in TVET, polytechnic will become the attraction of
higher education choice for students locally and internationally (Shahul Hamid, 2010).
There is 4,730 active students of Politeknik Sultan Salahuddin Abdul Aziz Shah (Portal
Rasmi, 2020). Therefore, students of trade department Politeknik Sultan Salahuddin Abdul
Aziz Shah were respondents in this research. This study was focused on polytechnic students
who are aged between 18 to 25 years old.
10
2.2.2 FOOD DELIVERY APPLICATIONS
Jacob, Sreedharan & Sreena.K (2019), stated that online food delivery mobile
applications has become popular over these years. There are wide varieties of restaurants now
delivering online services at best offers and reasonable prices. The online food ordering system
sets up a food menu online with the help of mobile applications like Grab Food, Food Panda
and Dah Makan.
Das & Ghose (2019), pointed that the technological evolution has completely changed
the entire scenario of the restaurants industry. It has uplifted the usage of online food delivery
services and enabled us to order food at the comfort of our home, compare prices and
conveniently access these services. These online food delivery services are boosting the option
of choosing meals from a wide variety of restaurants with a single tap of our smartphones.
However, using these applications will provide tracking system where the customers
become more acquainted with each progression of delivering. In addition, these applications
also provide a feedbacks and recommendations, rate the food item and mode of delivering
(Jacob et. al, 2019). This will facilitate the affairs of students Politeknik Sultan Salahuddin
Abdul Aziz Shah in tracking their orders during Movement Control Order (MCO) because they
cannot go out regarding “stay at home” orders.
In conclusion, food delivery applications which is very popular nowadays giving
advantages to customers especially students because they can order their food at home without
having to go out due to the order of “stay at home” during Movement Control Order (MCO).
11
2.2.3 SOCIAL INFLUENCE
Social influence has been validated as significantly determining users' intention to use
an online-to-offline delivery service (Roh & Park, 2019). Social influence as an important
variable in Unified Theory of Acceptance and Use of Technology (UTAUT) has a significant
impact on users' intentions to continue using mobile technologies (Lai & Shi, 2015). This angle
has been supported in various aspects, such as mobile social network sites (Zhou & Li, 2014),
shopping apps (Chopdar & Sivakumar, 2018) and mobile payment systems (Zhu, Lan, &
Chang, 2017).
Furthermore, social influence not only directly determines users' continuance intention,
but also indirectly formulates users' intention to continuously use mobile technology by
affecting their satisfaction (Hsiao, Chang, & Tang, 2016). Veljko Marinković, Đorđević, &
Zoran Kalinić (2020), revised Unified Theory of Acceptance and Use of Technology (UTAUT)
to confirm that social influence has a significant effect on users' satisfaction towards
continuance intention of using mobile technology.
Social influence or subjective norms refers to the degree of individuals believe that
relevant others would approve of certain behaviour (Du, Zhu, Lv, & Sun, 2012).
Due to the social influence, people who are more important and close can be one of the
factors influence food delivery applications usage among the students of Politeknik Sultan
Salahuddin Abdul Aziz Shah during Movement Control Order (MCO). Besides that, the people
who influence someone behaviour can also influence the students to using the food delivery
applications during the Movement Control Order (MCO). Lastly, one factors that influence
customers perception on food delivery application usage among students of Politeknik Sultan
Salahuddin Abdul Aziz Shah during Movement Control Order (MCO) is people whose
opinions that the students value such as their parents, family and others.
12
2.2.4 FACILITATING CONDITIONS
Facilitating conditions such as resources availability, skills as well as technical
infrastructure could play a significant role towards digital library use among engineering
lecturers. Given that an individual perceives that using a system will improve his job
performance represents performance expectancy, while availability of technical and
organisational infrastructure required to use a system represents the facilitating conditions; both
performance expectancy and facilitating conditions could be said to play a critical role and
have direct impact on the use of any system (Hamzat et. al, 2018).
According to Liao & Cheung (2008), by performing online banking, transactions can
be performed without have to walk in personally over the counter. Meanwhile, 50% of
respondents in the study by Singhal & Padhmanabhan (2008) agreed that internet banking is
convenient and flexible ways of banking and it also have various transaction related benefits.
Due to facilitating conditions, the students of Politeknik Sultan Salahuddin Abdul Aziz
Shah have the knowledge necessary to use food delivery applications for purchasing their foods
during Movement Control Order (MCO). Mostly students of Politeknik Sultan Salahuddin
Abdul Aziz Shah feel more comfortable using food delivery applications for purchasing food
at their favourite eateries during Movement Control Order (MCO) because it is the most easier
and safer.
13
2.2.5 HABIT
The term ‘habit’ is widely used to predict and explain behaviour. The definition of
'habit' is an implied disorder and is not related to theory. Habit is the context and implicit
response that develops through the learning of repetitive rewards (Wood & Runger, 2016).
Habit also a modern personality of psychology and is largely a social loss (Wood, 2017).
Consumer behaviour is a situation in which the customer, groups or organizations
select, purchase, use, and differentiate the ideas, goods, and services for their needs and wants.
It refers to consumer actions in the market and the basis of one’s actions (Ayush, Rubi,
Manisha, Kumari Vishaka, & Sonam, 2019).
Population consumption habits are changed and transformation is rushing from
traditional business to online business to survive the epidemic of maintaining business despite
the negative influence of Covid-19 pandemic throughout the Movement Control Order (MCO).
From the end of February 2020 to the beginning of March 2020 there were 71.7% of the 15,263
participants using the food delivery applications and 41.6% of the population prefers to use
online to offline services for buy daily supplies during Covid-19 pandemic in China. The food
delivery applications become an increasingly popular and most useful platform for the food
service industry throughout covid-19 as well as growing steadily. Customers can enjoy food at
home and at the same time be able to protect themselves from the Covid-19 pandemic which
also formulates new consumption habits for sustainable use (Zhao et. al, 2020).
Based on Prabowo & Nugroho (2018), there are various food delivery services through
internet and mobile applications as food delivery services are growing. Due to the busy
schedule and congested traffic causing people to choose to order food using smartphones and
delivered immediately and this become trend nowadays.
In a nutshell, there are variety of habit among customers. Based on the research, most
habit that customers have is they don’t have time to cook because of their busyness.
14
2.3 HYPOTHESES OF THE STUDY
2.3.1 SOCIAL INFLUENCE
According to the Singh & Matsui (2017), they found that there is a significant
relationship between social influence and food delivery applications usage. This is because
they stated if socially connected people who are using newer web technologies for shopping,
then this could be a factor of influence for intention to use. Based on the research by Lee, Sung
& Jeon (2019) also stated that social influence has been validated as positively influencing
users’ behavioural intentions for new technologies, products and services. However, the more
favourable the social influence of a behaviour, the stronger an individual’s intention to perform
it (Bendegul, Faizan, Anil, & Bulent Ozturk, 2018). Regarding independent variable for social
influence, the following is the proposed hypotheses:
H1: There is a significant relationship between social influence and food delivery applications
usage.
2.3.2 FACILITATING CONDITIONS
According to the Venkatesh, Thong & Xu (2012), they found the significant
relationship between facilitating conditions and food delivery application usage. This is
because they stated that users who deem facilitating conditions to be adequate are less averse
to using a new service, thus strengthening their use intentions. However, based on the research
of Lakhal, Khechine & Pascot (2013) verified facilitating conditions as a significant
determinants of students’ intentions to use. In another study, Unified Theory of Acceptance
and Use of Technology (UTAUT) did not consider the relationship between facilitating
conditions and usage intention, but, due to their important role, several previous studies have
tested and confirmed the effect of facilitating conditions on acceptance and usage of systems
(Ali, Pradeep & Kashif, 2016). Regarding independent variable for facilitating conditions, the
following is the proposed hypotheses:
H2: There is a significant relationship between facilitating conditions and food delivery
applications usage.
15
2.3.3 HABIT
According to the Singh & Matsui (2017) mentioned habitual users of online shopping
will tend to browse online stores as part of their natural behavior. Habit can influence on both
intention to use and use behavior. It showed this study found the significant relationship
between habit and food delivery applications usage. In another study, habit is sometimes
affected by current environmental conditions or past experiences and not be concious (Hsu,
Chang & Chuang, 2015). Based on the research of Venkatesh, Thong & Xu (2012) stated that
prior experience use is a prerequisite for habit to influence the use and that habit was a key
factor in future acceptance. Regarding independent variable for habit, the following is the
proposed hypotheses:
H3: There is a significant relationship between habit and food delivery applications usage.
16
2.4 THEORATICAL FRAMEWORK
Figure 1: Research Framework
Sources: (Lee, Sung, & Jeon, Determinants of Continuous Intention on Food Delivery Apps:
Extending UTAUT2 with Information Quality, 2019)
The purpose of this research is to study the factors influence food delivery applications
usage among students of Politeknik Sultan Salahuddin Abdul Aziz Shah during Movement
Control Order (MCO). There are three independent variables (IV) chosen by the researcher,
which are social influence, facilitating conditions and habit while the dependent variable (DV)
is factors influence food delivery applications usage.
2.5 SUMMARY
Basically, in this chapter provide more comprehensive information about food delivery
applications, social influence, facilitating conditions, habit and our respondents which is
students of Polytechnic. However, in this chapter also tells about the hypotheses regarding the
relationship between each independent variable (IV) and food delivery applications usage.
Habit
(IV)
Facilitating Conditions
(IV)
Social Influence
(IV)
Food Delivery
Applications
(DV)
H1
H2
H3
17
CHAPTER 3: RESEARCH METHODOLOGY
3.1 INTRODUCTION
Methodology is research where certain researchers should take appropriate measures to
examine the problem of research and certain methods are used to classify, select, process and
evaluate data. To understand the issues, all the approaches that are used will be used. It is
intended for the reader to estimate the figure and to be able to trust it. The research methodology
are divided into two sections which are how was the data collected or created? And, how was
it investigated? This research is done by using quantitative research that allows researchers to
identify social influence, facilitating conditions and habit with food delivery applications usage
among students of Politeknik Sultan Salahuddin Abdul Aziz Shah during Movement Control
Order (MCO). This chapter included the contents which are the methods that are chosen to do
the research design, methods on data collection, how the sample was selected, what the
instrument used in research, what the techniques used in sampling data and analysis such as
how the questionnaire was developed.
3.2 RESEARCH DESIGN
The summary of the defining framework, plan or strategy analysis procedure is shown
in the design of the research. The content of this chapter will cover the research approach,
sampling method, how to collect data, technique for processing and analysing data and report
writing.
This research would be conducted by using a quantitative approach. The survey
instrument was customized to identify on social influence, facilitating conditions and habit with
food delivery applications usage among students of Politeknik Sultan Salahuddin Abdul Aziz
Shah during Movement Control Order (MCO).
A sample of 357 students was selected randomly using Krejcie and Morgan Table
(KENPRO, 2012) based on the population of average students that use food delivery
applications which is 4730 students in Politeknik Sultan Salahuddin Abdul Aziz Shah (Portal
Rasmi, 2020). This research was conducted to get the data through questionnaire that had been
give out to the students of Politeknik Sultan Salahuddin Abdul Aziz Shah. Questionnaires were
the popular and most productive method to collect data used by researchers.
18
3.3 DATA COLLECTION METHOD
Data collection is the process of gathering and measuring information on variables of
interest, in an established systematic fashion that enables one to answer stated research
questions, test hypotheses, and evaluate outcomes. The data collection component of research
is common to all fields of study including physical and social sciences, humanities, business,
etc (Syed Muhammad Sajjad, 2016).
Data collection methods can be divided into two categories which is primary data and
secondary data. Primary data consist of information collected for specific purposes and primary
data is also collected by surveys and analysis. Secondary data consist of information that
already exists that has been gathered for some purposes.
3.3.1 PRIMARY DATA
Data that has been collected from first-hand-experience is known as primary data.
Primary data has not been published yet and is more reliable, authentic and objective. Primary
data has not been changed or altered by human beings; therefore its validity is greater than
secondary data. Sources for primary data are limited and at times it becomes difficult to obtain
data from primary source because of either scarcity of population or lack of cooperation.
Following are some of the sources of primary data (Syed Muhammad Sajjad, 2016).
In this research, researcher has conducted a survey with students of Politeknik Sultan
Salahuddin Abdul Aziz Shah and distribute questionnaire to them using Google Form. The
questionnaire was distributed to the students through and it is includes the questions about their
general information and factors influence food delivery applications usage among students of
Politeknik Sultan Salahuddin Abdul Aziz Shah.
19
3.3.2 SECONDARY DATA
Data collected from a source that has already been published in any form is called as
secondary data. The review of literature in any research is based on secondary data. It is
collected by someone else for some other purpose but being utilized by the researcher for
another purpose. Secondary data is essential, since it is impossible to conduct a new survey
that can adequately capture past change and/or developments (Syed Muhammad Sajjad, 2016).
Researchers use the previous journal in this research as a reference to guide when doing
all the research.
3.4 RESEARCH INSTRUMENT
Research instrument involves questionnaire design, pilot test, and construct
measurement that discusses the instrument that will be used to measure on factors that influence
food delivery applications usage among students of Politeknik Sultan Salahuddin Abdul Aziz
Shah during Movement Control Order (MCO).
The structure of questionnaire was designed in English language because it is more
appropriate to communicate with our respondents. Moreover, the questionnaire was divided
into two parts. Section A is about demographic profile of respondents and Section B is about
dependent and independent variable questions.
In Section A, the questions help to identify the profiles of respondents such as gender,
age, marital status, source of income and course of study. Other than that, the questions in
Section A also asked to gain general information of respondents.
In Section B, much deeper and precise questions were asked regarding their social
influence, facilitating conditions and habit on food delivery applications. To identify, the
questions asked using the Likert Scale are as follow:
20
Section A: Demographic Profile
Please indicate (✔) in the appropriate information about yourself. Each question should only
have ONE answer.
1. Gender
Male Female
2. Age
18 and below 19 – 22 23 and above
3. Marital status
Single Married
4. Income
Below RM 400 RM 401 – RM 800
RM 801 – RM 1,200 RM 1,201 and above
5. Course of study
Business Non-business
6. Do you own a smartphone
Yes No
7. Have you ever downloaded applications for your mobile device?
Yes No
8. Do you know about Food Delivery Applications?
Yes No
9. Which food delivery applications have you downloaded?
Food Panda Dah Makan
Grab Food Others:_________________
21
Section B: Independent and Dependent Variables
Please tick (✔) your answer to each statement using Likert scale.
Table 3.1 Food Delivery Applications (Dependent Variable)
No. Questions Strongly
disagree Disagree Neutral Agree
Strongly
agree
1. I am familiar with food
delivery applications
2.
I know about food delivery
applications through
advertisement on social
media.
3.
I do realize about the
existence of food delivery
applications until now.
4. I do have an experience of
food delivery applications.
5. I have heard about food
delivery applications.
22
Table 3.2 Social Influence (1st Independent Variable)
No. Questions Strongly
disagree Disagree Neutral Agree
Strongly
agree
1.
People who are important to
me think that I should use
food delivery applications for
purchasing foods.
2.
People who influence my
behaviour think that I should
use food delivery applications
for purchasing foods.
3.
People whose opinions I value
prefer that I use food delivery
applications for purchasing
foods.
Source: (Lee, Sung, & Jeon, Determinants of Continuous Intention on Food Delivery Apps:
Extending UTAUT2 with Information Quality, 2019)
23
Table 3.3 Facilitating Conditions (2nd Independent Variable)
No. Questions Strongly
disagree Disagree Neutral Agree
Strongly
agree
1.
I have the knowledge
necessary to use food delivery
applications for purchasing
foods.
2.
I feel comfortable using food
delivery applications for
purchasing foods.
Source: (Lee, Sung, & Jeon, Determinants of Continuous Intention on Food Delivery Apps:
Extending UTAUT2 with Information Quality, 2019)
24
Table 3.4 Habit (4th Independent Variable)
No. Questions Strongly
disagree Disagree Neutral Agree
Strongly
agree
1.
Purchasing foods through
food delivery applications is
almost like a habit for me.
2.
I am addicted to using food
delivery applications for the
purchase of foods.
3.
I must use food delivery
applications for purchasing
foods.
4.
Using food delivery
applications for purchasing
foods has become natural to
me.
Source: (Lee, Sung, & Jeon, Determinants of Continuous Intention on Food Delivery Apps:
Extending UTAUT2 with Information Quality, 2019)
25
3.5 SAMPLING TECHNIQUE
According to Taherdoost (2016), the first stage in the sampling process is to clearly
define target population. A sampling frame is a list of the actual cases from which sample will
be drawn. The sampling frame must be representative of the population. Prior to examining the
various types of sampling method, it is worth noting what is meant by sampling, along with
reasons why researchers are likely to select a sample. Taking a subset from chosen sampling
frame or entire population is called sampling. Sampling can be used to make inference about a
population or to make generalization in relation to existing theory. In essence, this depends on
choice of sampling technique.
Sampling technique are one of the important parts of social research. In order to answer
the research questions, researches should be able to collect data by use the sampling technique
that specially made for information collection. Therefore, researchers use samples when
collecting data to answer problems or research questions.
A related idea of integrating probability and non-probability samples is also explored
in Sakshaug, Wis´niowski, Ruiz, & Blom (2019) who describe a simulation-based approach
rather than the simple and direct analytic derivations presented in this article. Multivariate-
estimates (e.g., regression coefficients), on the other hand, tend to be less susceptible to
discrepancies between probability and non-probability samples (Ansolabehere, S., & Rivers,
2013). According to Fahimi, Barlas, Gross, & Thomas (2014), propose a method they coin
“blended calibration,” where a probability sample weighted to known population totals is
combined with a counterweight non-probability sample, and the combined sample is calibrated
to differentiated variables in the probability-only sample.
In this research, simple random sampling was chosen. Simple random sampling takes
a small, random portion of the entire population to represent the entire data set, where each
member has an equal probability of being chosen (Adam, 2020). However, the population must
be homogenous i.e. every element contains same kind of characteristics that meets the
described criteria of target population (Alvi, 2016).
All targeted respondents will be ask to read and answer the questionnaire specially
made online in 7 - 10 minutes to complete it.
26
3.6 DATA ANALYSIS METHOD
When something is possible to collect all the data, the data will be turned into valuable
analysis knowledge. Data analysis is the process of systematically applying statistical or logical
technique to describe and illustrate, condense and recap and analyse data. Successfully
gathered data was analysed to assess how the sample for this study reacts to the items under
investigations. Using statistical summary tables and charts, the results of the analysis could
then be displayed.
3.6.1 DESCRIPTIVE ANALYSIS
Descriptive statistics are data analysis by percentage, frequency and by using Measure
of central tendency (MCT) - mean, mode and median. In descriptive statistics, the type of data
analysis often involves in variety analysis using only one variable.
According to Ethridge & D.E. (2004), descriptive research can be explained as a
statement of affairs as they are at present with the researcher having no control over variable.
Moreover, “descriptive studies may be characterized as simply the attempt to determine,
describe or identify what is, while analytic research attempts to establish why it is that way or
how it came to be.
According to Fox & Bayat (2007), descriptive research is “aimed at casting light on
current issues or problems through a process of data collection that enables them to describe
the situation more completely than was possible without employing this method. In its essence,
descriptive studies are used to describe various aspects of the phenomenon. In its popular
format, descriptive research is used to describe characteristics and/or behaviour of sample
population.
Frequency distribution is a mathematical division with the purpose of obtaining a count
of the number of responses connected with different values if one variable and to express these
counts in term of percentage. The purpose of frequency is to demonstrate the values such as
numbers and percentages for different categories of a single categorical variable.
27
3.7 SCALE MEASUREMENT
3.7.1 RELIABILITY TEST
Reliability is concerns the extent to which a measurement of a phenomenon give stable
and consist result (Carmines & Zeller, 1979). According to Huck (2007), testing for reliability
is important as it refers to the consistency across the parts of a measuring instrument. The most
commonly used internal consistency measure is the Cronbach Alpha coefficient. It is viewed
as the most appropriate measure of reliability when making use of Likert scales (Whitley, 2002
& Robinson, 2009).
According to Bruin (2011), Cronbach’s alpha is a measure of internal consistency, that
is, how closely related a set of items are as a group. A “high” value for alpha does not imply
that the measure is undimensional. Technically speaking, Cronbach’s alpha is not a statistical
test – it is a coefficient of reliability (or consistency).
Based on the Table 3.5, it showed the range of reliability and its coefficient of
Cronbach’s Alpha as a guide to determine the results of pilot test.
Table 3.5 Range of reliability and its coefficient of Cronbach’s alpha
No. Coefficient of Cronbach’s Alpha Reliability Level
1. More than 0.90 Excellent
2. 0.80 – 0.89 Good
3. 0.70 – 0.79 Acceptable
4. 0.60 – 0.69 Questionable
5. 0.50 – 0.59 Poor
6. Less than 0.59 Unacceptable
Source: Adapted from Khairul Zahreen, Syuhaida, & Abd Latif (2018)
28
3.7.2 PILOT TEST
Pilot test are implemented to determine whether problems exist that need to be
addressed prior to putting the production survey in the field (Lavrakas, 2008). Based on the
Table 3.5 above, if the reliability scale was greater than 0.70, it means the questionnaires are
good and reliable to use. But if the reliability scale showed less than 0.70, the questionnaire
were unreliable to be used in this research. Cronbach’s alpha test may be used in conditions
other than the production or testing of the questionnaire, there is little literature on its use in
those conditions (Mohamad Adam, Evi Diana, & Nur Akmal, 2018).
For this research, about 31 out of 357 respondents were distributed the questionnaires
for the pilot test requirements among students of Politeknik Sultan Salahuddin Abdul Aziz
Shah. The findings of the reliability test have been presented in the Table 3.6 below. Since
the Cronbach's Alpha valued more than 0.7 each, each test was significant.
Table 3.6 Reliability Test for 31 Respondents
Variables Items Cronbach’s Alpha
Dependent Food Delivery Applications 5 .827
Independent Social Influence 3 .804
Facilitating Conditions 2 .916
Habit 4 .930
After constructing for each variable, the outcome of the reliability test showed that the
habit is the highest, which is 0.930 and followed by facilitating conditions, which is 0.916.
Next is food delivery applications which is 0.827 and the last one is social influence which is
0.804. Based on the Alpha Coefficient Range Strength of Association, all of these variable
exceed 0.7 which is acceptable.
29
3.8 SUMMARY
Finally, it may concluded that research methodology have been used in gathering,
reviewing and simplifying data. This chapter given the summary about the methodology which
research design and research instrument is decided, data collection methods and data analysis
method are introduced, sampling technique and scale measurement are clarified. However, in
the earlier component of this chapter were mentioned about target population, sampling frame
and sampling size.
30
CHAPTER 4: ANALYSIS AND RESULT
4.1 INTRODUCTION
In this chapter, each results obtained from the questionnaire used for the survey
respondent’s data would be investigated. Data that researchers collected from 357 respondents
were investigated using Statistical Package for the Society Sciences (SPSS) Version 22
software program. Furthermore, elements that would be covered in this chapter include
demographic profile and descriptive analysis (food delivery applications, social influence,
facilitating conditions and habit). The results would kindly be presented in charts and tables.
Lastly, this chapter would be concluded with a summary on research findings.
4.2 DEMOGRAPHY PROFILE OF RESPONDENTS
A total of 357 of questionnaire were answered by the students of Politeknik Sultan
Salahuddin Abdul Aziz Shah through Google Form that researchers has created to conduct the
survey. From the amount of questionnaire answered, it has been answered excellently.
For demographic data, the questions were asked to the respondents such as gender, age,
marital status, income and course of study. All the demographic data has been analysed using
Statistical Package for the Society Sciences (SPSS) Version 22.
The total of all respondents obtained from the questionnaires through Google Form
were 357 respondents. By referring the Table 4.1, it showed the majority of the respondents
were female with 228 respondents (63.9%) and male were 129 respondents (36.1%).
The range age of the respondents was high at 19 – 22 years old comprised of 316
respondents (88.5%). Next, 18 years old and below with 34 respondents (9.5%) while 23 years
old and above with 7 respondents (2%).
For marital status, majority of the respondents (99.7%) are single and 1 of the
respondents (0.3%) is married.
As for income, more than half respondents which are 245 respondents (68.6%) are
below RM400. Other than that, 66 respondents (18.5%) are having a monthly income ranging
from RM401 – RM800 while 38 respondents (10.6%) having monthly income from RM801 –
31
RM 1,200. Only 8 respondents (2.2%) have monthly income from RM 1, 201 and above.
Last but not least, 199 respondents (55.7%) which are more than half respondents from
business students and 158 respondents (44.3%) are non-business students.
The profile of the respondents is shown in Table 4.1 and Appendix C.
Table 4.1 Demographic Profile
Respondent’s Demographic Frequency Percentage (%)
Gender Male 129 36.1
Female 228 63.9
Age 18 and below 34 9.5
19 – 22 years 316 88.5
23 and above 7 2.0
Marital status Single 356 99.7
Married 1 0.3
Income Below RM400 245 68.6
RM401 – RM800 66 18.5
RM801 – RM1,200 38 10.6
RM1,2001 and above 8 2.2
Course of study Business 199 55.7
Non-business 158 44.3
Source: Developed from the research
32
As shown in Table 4.2, all of the respondents owned a smartphone. Each of them has
downloaded applications in their smartphones. All the respondents know about food delivery
applications.
Most of the respondents had downloaded Food Panda applications with 56.9% which
is 203. Second highest in downloaded food delivery applications is Grab Food with 27.2%, 97
respondents. Third highest is Dah Makan with 49 at 13.7%. The results showed some
respondents downloaded BungkusIt applications with 1.1% which are 4.
Table 4.2 General Information
General Frequency (N) Percentage (%)
Do you own a smartphone? Yes 357 100.0
No 0 0
Have you ever downloaded Yes 357 100.0
applications for your mobile device? No 0 0
Do you know about Food Delivery Yes 357 100.0
Applications? No 0 0
Which food delivery applications Food Panda 203 56.9
have you downloaded? Grab Food 97 27.2
Dah Makan 49 13.7
BungkusIt 4 1.1
Others 4 1.1
Source: Developed from the research
33
4.3 GOODNESS MEASURE
4.3.1 DESCRIPTIVE ANALYSIS
Table 4.3 includes a summary of the descriptive analysis of the variables. In the 5-point
Likert scale, all variables were measured with 5 being strongly agreed.
Table 4.3 Overall Descriptive Analysis of the Variables
Mean Standard Deviation
Food Delivery Applications 4.46 .560
Social Influence 3.82 .783
Facilitating Conditions 4.36 .745
Habit 3.50 .964
4.4 RESEARCH FINDINGS
4.4.1 STATISTICAL SUMMARY
In order to determine the 'quality' of each decision variable, statistical measurements
were measured from deterministic sampling of available methods (Sun, Li, & Ernst, 2019).
That said however, the mean field theory is based on the central boundary theorem with the
parameters generated in a manner with finite variances (Karakida, Akaho, & Amari, 2019)
while standard deviation used to analyse a dataset's distribution according to the mean
(Hargrave, 2020)
34
Table 4.4 Food Delivery Applications
Code Statement Mean Standard Deviation
FDA 1 I am familiar with food delivery applications 4.38 .758
FDA 2 I know about food delivery applications through
advertisement on social media.
4.33 .784
FDA 3 I do realize about the existence of food delivery
applications until now.
4.56 .654
FDA 4 I do have an experience of food delivery applications. 4.37 .827
FDA 5 I have heard about food delivery applications. 4.64 .547
Based on Table 4.4, for food delivery applications, FDA 5 (I have heard about food
delivery applications) indicate the highest mean score with 4.64 and standard deviation at
0.547. On the other side, FDA 2 (I know about food delivery applications through
advertisements on social media) indicate the lowest mean score with 4.33 and standard
deviation at 0.784.
35
Table 4.5 Social Influence
Code Statement Mean Standard Deviation
SI 1 People who are important to me think that I should
use food delivery applications for purchasing foods.
4.06 .916
SI 2 People who influence my behaviour think that I
should use food delivery applications for purchasing
foods.
3.66 .915
SI 3 People whose opinions I value prefer that I use food
delivery applications for purchasing foods.
3.75 .880
As for the social influence in Table 4.5, SI 1 (People who are important to me think that
I should use food delivery applications for purchasing foods) indicate the highest mean with
4.06 and standard deviation 0.916 while SI 2 (People who influence my behaviour think that I
should use food delivery applications for purchasing foods) indicate the lowest mean with 3.66
and standard deviation at 0.915.
36
Table 4.6 Facilitating Conditions
Code Statement Mean Standard Deviation
FC 1 I have the knowledge necessary to use food delivery
applications for purchasing foods.
4.28 .841
FC 2 I feel comfortable using food delivery applications
for purchasing foods.
4.45 .750
Based on facilitating conditions from Table 4.6, FC 2 (I feel comfortable using food
delivery applications for purchasing foods) indicate the highest mean with 4.45 and standard
deviation at 0.750 and the lowest mean with 4.28 and standard deviation at 0.841 is FC 1 (I
have the knowledge necessary to use food delivery applications for purchasing foods).
37
Table 4.7 Habit
Code Statement Mean Standard Deviation
HABIT 1 Purchasing foods through food delivery applications is
almost like a habit for me.
3.18 1.161
HABIT 2 I am addicted to using food delivery applications for
the purchase of foods.
3.32 1.190
HABIT 3 I must use food delivery applications for purchasing
foods.
3.69 1.114
HABIT 4 Using food delivery applications for purchasing foods
has become natural to me.
3.79 1.103
Last but not least, habit for Table 4.7, HABIT 4 (Using food delivery applications for
purchasing foods has become natural to me) indicate the highest mean with 3.79 and standard
deviation at 1.103 while HABIT 1 (Purchasing foods through food delivery applications is
almost like a habit for me) indicate the lowest mean with 3.18 and standard deviation at 1.161.
38
4.5 HYPOTHESES TESTING
4.5.1 CORRELATION COEFFICIENTS RANGE
In order to evaluate the inter-correlations among all the research variables, Pearson
Product-Moment Correlation was used. Summaries of the results are given in Table 4.8 and
Appendix F.
Table 4.8 Pearson Correlation Coefficient (r) Strengths
Strength of Correlation Range of Absolute Correlation
Coefficient (r)
0.8 – 1.0 Very strong
0.6 – 0.79 Strong
0.4 – 0.59 Moderate
0.2 – 0.39 Weak
0 – 0.19 Very weak
Source: (Liang, et al., 2019)
4.5.2 CORRELATION ANALYSIS
Table 4.9 Pearson’s Correlation Coefficients of the Variables
Food Delivery
Applications
Social
Influence
Facilitating
Conditions Habit
Food Delivery Pearson Correlation 1
Applications Sig. (2-tailed)
N 357
Social Pearson Correlation .441** 1
Influence Sig. (2-tailed) .000
N 357 357
Facilitating Pearson Correlation .657** .436** 1
Conditions Sig. (2-tailed) .000 .000
N 357 357 357
Habit Pearson Correlation .417** .366** .359** 1
Sig. (2-tailed) .000 .000 .000
N 357 357 357 357
** Correlation is significant at the 0.01 level (2-tailed).
39
4.5.2.1 Relationship between food delivery applications and social
influence
Based on the Table 4.8, the result for the correlation coefficient of the pair between
food delivery applications and social influence was moderate strength which is r = 0.441 (p-
value = 0.000) (p < 0.005). The correlation coefficient value (r = 0.441) stated that there was a
positive relationship between this two variables. The results thus indicated that there was a
significant relationship between food delivery applications and social influence with a
significant value of 0.000.
4.5.2.2 Relationship between food delivery applications and facilitating
conditions
Based on the Table 4.8, the result for the correlation coefficient of the pair between
food delivery applications and facilitating conditions was moderate and the highest strength
which is r = 0.657 (p-value = 0.000) (p < 0.005). The correlation coefficient value (r = 0.657)
showed that there was a positive relationship between this two variables. The results thus
specified that there was a significant relationship between food delivery applications and
facilitating conditions with a significant value of 0.000.
4.5.2.3 Relationship between food delivery applications and habit
Based on the Table 4.8, the result for the correlation coefficient of the pair between
food delivery applications and habit was moderate and the lowest strength which is r = 0.417
(p-value = 0.000) (p < 0.005). The correlation coefficient value (r = 0.417) revealed that there
was a positive relationship between this two variables. The results thus stipulated that there
was a significant relationship between food delivery applications and habit with a significant
value of 0.000.
40
4.6 SUMMARY
In conclusion, the purpose of this chapter was to present the analyses and results
obtained by data collection using questionnaires given to the respondents. Not just that, all
frameworks were already tested for reliability. The use of food delivery applications among
learners can be seen in this chapter.
41
CHAPTER 5: DISCUSSION AND CONCLUSION
5.1 INTRODUCTION
This chapter will include a summary of the main results by listing out the recapitulation
of the report, providing a detailed conclusion for the descriptive research of Chapter 4, and
providing suggestions for future research.
5.2 RECAPITULATION OF THE STUDY
This study aims to find out the extent to which food delivery applications help its users
to obtain food through the food delivery applications available during the period of Movement
Control Order (MCO). In order to substantiate the research problem, three Independent
Variable - Social Influence, Facilitating Conditions and Habit. The findings of the study will
eventually answer the following questions.
1) Does social influence affects food delivery applications usage among students of
Politeknik Sultan Salahuddin Abdul Aziz Shah?
2) Does facilitating conditions affects food delivery applications usage among students of
Politeknik Sultan Salahuddin Abdul Aziz Shah?
3) Does habit affects food delivery applications usage among students of Politeknik Sultan
Salahuddin Abdul Aziz Shah?
There were several hypotheses developed to test the relationship between the
independents variables and the dependent variable. The first set of hypotheses was developed
to identify the relationship between factors influencing which consist of the social influence,
facilitating conditions and habit in the use of food delivery applications during the period of
Movement Control Order (MCO).
42
5.2.1 DISCUSSION OF MAJOR FINDINGS
The study has shown that social influence has a strongest positive relationship with food
delivery applications perceived ease of use. Social influence was the most salient determinant
of perceived ease of use. This is in line with the research conducted by Park (2019), Lai & Shi
(2015), Hsiao, Chang & Tang (2015), Du, Zhu, LV & Sun (2012). Social influence has been
validated as significantly determining users’ intentions to use an online-to-offline delivery
service. Social influence as an important variable in UTAUT has a significant impact on users’
intentions to continue using mobile technologies. Social influence not only directly determines
users’ continuance intention, but also indirectly formulates users’ intention to continuously use
mobile technology by affecting their satisfaction. Social influence or subjective norms refers
to the degree of individuals believe that relevant others would approve of certain behavior.
Facilitating conditions also has a strong positive relationship with food delivery
applications perceived ease of use. Although 100% of the respondent had a smart phone, the
respondents think that more facilitating resources like smart phone and network connection
will be a key factor in influencing ease of using food delivery applications. According to Liao
& Cheung (2018), by performing online banking, transactions can be performed without have
to walk in personally over the counter. Meanwhile, 50% of respondents in the study by Divya
& V, (2008) agreed that internet banking is convenient and flexible ways of banking and it also
have various transaction related benefits.
Habit also has a strong positive relationship with food delivery applications perceived
ease of use. This is in line with the research conducted by Zhao & Bacao, (2020). Population
consumption habit are changed and transformation is rushing from traditional business to
online business to survive the epidemic of maintaining business despite the negative influence
of Covid-19 pandemic throughout the Movement Control Order (MCO). From the end of
February to the beginning of March 2020 there were 71.7% of the 15,263 participants using
food delivery applications and 41.6% of the population prefer to use online-to-offline services
for buy daily supplies during Covid-19 pandemic in China. The food delivery applications
become an increasingly popular and most useful platform for the food service industry
throughout Covid-19 as well as growing steadily. Customers can enjoy food at home and at the
same time be able to protect themselves from the Covid-19 pandemic which also formulates
new consumption habit for sustainable use.
43
5.3 RECOMMENDATION
5.3.1 RECOMMENDATION FOR FUTURE RESEARCH
This research only discussed and study briefly without much deeper study as finding
factors that would influence food delivery applications usage during Movement Control Order
(MCO). Therefore, hopefully that this research could be a benefit for future research on
improving the model by incorporating other relevant dependent and independent variables
based on new findings from latest literature at the time.
Although it might take a longer time, it would allow the researchers to get a better
understanding and acknowledgement regarding all factors that might be included. Thus, the
research would be provided with wider perspectives.
However, future research could help expand this research to include the effect and
experience on the food delivery applications usage during Movement Control Order (MCO).
This will help for a better analysis.
Last but surely not the least, hopefully this research could be a help for any others food
delivery applications to make a further research in future or a reference to make a better service.
44
5.3.2 RECOMMENDATION FOR FOOD DELIVERY COMPANY AND
STUDENTS
Based on the findings, analysis and conclusion done in this research, there were a few
recommendations could be considered. Company of Food delivery should make a wider
advertisement such as on newspaper, television and any others social media. Despite of having
a high percentage of user acknowledgement towards food delivery applications among students
of Politeknik Sultan Salahuddin Abdul Aziz Shah during Movement Control Order (MCO),
does not mean it is the same situation in any other polytechnic students.
Next, company of Food Delivery should add on more features or service that could ease
users and attract them all at once. Combine with all food delivery providers such as Grab Food,
Food Panda, Dah Makan, BungkusIt and others could be one of the ways to add on more
features or services in one applications. Also, food delivery applications should provide guides
and information for students who just get to know about the food delivery applications during
Movement Control Order (MCO). This could help them to get a better understanding and be
aware of the benefits and services that food delivery applications could provide.
Moreover, during the time of emergency this applications could help the students of
Politeknik Sultan Salahuddin Abdul Aziz Shah in so many ways during Movement Control
Order (MCO). As of during this research was being analysed, a virus disease called Covid-19
that was widely spreading has becoming a problem for all people around the globe. For the
students of Politeknik Sultan Salahuddin Abdul Aziz Shah, going out from their home even for
a moment could be risky and worrying as they might get infected by the virus as the infected
cases were rapidly increasing. This includes going out to buy foods. Thus, food delivery
applications play a crucial role in helping and easing most of the students. They would not have
the need to go out as they can do so many things even by staying home. For example, they can
just order their foods through food delivery applications on their mobile phones.
45
5.4 SUMMARY
The objectives were achieved based on the results and discussions of the study. The
researcher found that the factors influence food delivery applications usage among students of
Politeknik Sultan Salahuddin Abdul Aziz Shah were indeed significant.
46
REFERENCES
Adam, H. (2020, September 22). Simple Random Sample. Retrieved from Investopedia:
https://www.investopedia.com/terms/s/simple-random-
sample.asp#:~:text=A%20simple%20random%20sample%20takes,like%20lotteries%
20or%20random%20draws.
Akroush, M., & Al-Debei, M. (2015). An integrated model of factors affecting consumer
attitudes towards online shopping. Business Process Management, 1353-1376.
Ali, F., Pradeep, K., & Kashif, H. (2016). An Assessment of Students' Acceptance and Usage
of Computer Supported Collaborative Classrooms in Hospitality and Tourism Schools.
Journal of Hospitality, Leisure, Sport & Tourism Education, Vol 18, 51-60.
Alvi, M. (2016). A Manual for Selecting Sampling Technique in Research. MPRA Munich
RePEc Archive, 16-17.
Ansolabehere, S., & Rivers, D. (2013). Cooperative Survey Research. Annual Review of
Political Science, 16, 307–329.
Ayush, B., Rubi, K., Manisha, V., Kumari Vishaka, N., & Sonam, S. (2019). Satisfaction Of
Consumers By Using Online Food Services. International Journal of Humanities, 35-
43.
Bendegul, O., Faizan, A., Anil, B., & Bulent Ozturk, A. (2018). Psychological Factors
Influencing Customers' Acceptance of Smartphone Diet Apps When Ordering Food at
Restaurant. International Journal of Hospitality Management, 67-77.
Bernard, H. (2011). Research Methods in Anthropology: Qualitative and Quantitative
Approaches.
Carmines, E., & Zeller, R. (1979). Reliability and validity assessment.
Cennimo, D. J., MD, FAAP, FACP, & AAHIVS. (2020, September 4). What is COVID-19?
Retrieved from Medscape: https://www.medscape.com/answers/2500114-
197401/what-is-covid-19
Chopdar, P. K., & Sivakumar, V. J. (2018). Understanding continuance usage of mobile
shopping applications in India: the role of espoused cultural values and perceived risk.
47
Understanding continuance usage of mobile shopping applications in India: the role of
espoused cultural values and perceived risk, 42-64.
Das, S., & Ghose, D. (2019). Influence Of Online Food Delivery Apps On The Operations Of
The Restaurant Business . International Journal Of Scientific & Technology Research
Volume 8, Issue 12, 1372-1377.
Du, H., Zhu, G., Lv, T., & Sun, X. (2012). Factors Affecting Purchase Intention on 3G Value-
added Services. Jindal Journal of Business Research, 1, 2, 139-152.
Duyck, P., Pynoo, B., & Devolder, P. (2010). Monitoring the PACS Implementation Process
in a Large University Hospital - Discrepancies Between Radiologists and Physicians. J
Digit Imaging 23, 73-80.
Gayler, T., & Sas, C. (2017). An Exploration of Taste–Emotion Mappings from the Perspective
of Food Design Practitioners. Proceedings of the 2nd ACM SIGCHI International
Workshop on Multisensory Approaches to Human-Food Interaction, 2141-2153.
Hamzat, S. A., & Mabawonku, I. (2018). Influence of Performance Expectancy and Facilitating
Conditions on use of Digital Library by Engineering Lecturers in universities in South-
west, Nigeria. e Collection Development and Management Commons, and the
Information Literacy, 1-16.
Hargrave, M. (2020, September 19). Standard Deviation Definition. Retrieved from
Investopedia: https://www.investopedia.com/terms/s/standarddeviation.asp
Hendrickson, B. M. (2016). System and Method for Improving Customer Wait Time, Customer
Service, and Marketing Efficiency in the Restaurant, Retail, Hospitality, Travel, and
Entertainment Industries. United States Patent, 1-38.
Hsiao, C.-H., Chang, J.-J., & Tang, K.-Y. (2016). Exploring the influential factors in
continuance usage of mobile social Apps: Satisfaction, habit, and customer value
perspectives. Telematics and Informatics, Volume 33, Issue 2, 342-355.
Hsu, M.-H., Chang, C.-M., & Chuang, L.-W. (2015). Understanding the determinants of online
repeat purchase intention & moderating role of habit, the case of online group-buying
in Taiwan. International Journal of Information Management, 45-56.
Karakida, R., Akaho, S., & Amari, S.-i. (2019). Deep Neural Networks: Mean Field Approach.
Universal Statistics of Fisher Information.
48
KENPRO. (2012, August 25). Sample Size Determination Using Krejcie and Morgan Table.
Retrieved from http://www.kenpro.org/sample-size-determination-using-krejcie-and-
morgan-table/
Khairul Zahreen, M. A., Syuhaida, I., & Abd Latif, S. (2018). Contractor’s Performance
Appraisal System in the Malaysian Construction Industry: Current Practice, Perception
and Understanding. International Journal of Engineering & Technology, 7 (3.9), 46-
51.
Kong, X. (2020, May 13). Food delivery: Changing trends between early and late MCO.
Retrieved from Focus Malaysia Business & Beyond:
https://focusmalaysia.my/mainstream/food-delivery-changing-trends-between-early-
and-late-mco/
Krishna. (2020). Reliability Testing Tutorial: What is, Methods, Tools, Example. Retrieved
from Guru99: https://www.guru99.com/reliability-testing.html
Lai, I., & Shi, G. (2015). The impact of privacy concerns on the intention for continued use of
an integrated mobile instant messaging and social network platform. International
Journal of Mobile Communications.
Lakhal, S., Khechine , H., & Pascot, D. (2013). Student Behavioural Intentions to Use Desktop
Video Conferencing in a Distance Course: Integration of Autonomy to the UTAUT
Model. Journal of Computing in Higher Education 25, 93-121.
Lavrakas, P. (2008). Pilot Test. Encyclopedia of Survey Research Methods.
Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of Continuous Intention on Food
Delivery Apps: Extending UTAUT2 with Information Quality. Sustainability, 1-15.
Retrieved from Sustainability.
Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of Continuous Intention on Food
Delivery Apps: Extending UTAUT2 with Information Quality. Sustainability, 2-5.
Liang, Y., Abbott, D., Howard, N., Lim, K., Ward, R., & Elgendi, M. (2019). How Effective
Is Pulse Arrival Time for Evaluating Blood Pressure? Challenges and
Recommendations from a Study Using the MIMIC Database. Journal of Clinical
Medicine, 9.
49
MahWengKwai & Associates . (2020, April 9). FAQs on The Movement Control Order (MCO)
and What Happens If You Breach It. Retrieved from https://mahwengkwai.com/the-
movement-control-order-mco-and-what-happens-if-you-breach-it/
Milo, E. (2018, February 1). The food delivery battle has just begun in Malaysia. Retrieved
from ec Insider: https://www.ecinsider.my/2018/02/food-delivery-companies-
malaysia.html
Mohamad Adam, B., Evi Diana, O., & Nur Akmal, B. (2018). A Review on Sample Size
Determination for Cronbach’s Alpha Test: A Simple Guide for Researchers. The
Malaysian Journal of Medical Sciences, 85-99.
Portal Rasmi. (2020). Portal Rasmi Politeknik Premier Sultan Salahuddin Abdul Aziz Shah.
Retrieved from https://psa.mypolycc.edu.my/
Radzi, R. (2020, May 8). Demand still high for food delivery riders even under CMCO as most
still prefer using online delivery service. Retrieved from Malay Mail:
https://www.malaymail.com/news/malaysia/2020/05/08/demand-still-high-for-food-
delivery-riders-even-under-cmco-as-most-still-pr/1864147
Ranaweera, C., & Karjaluoto, H. (2017). The impact of service bundles on the mechanism
through which functional value and price value affect WOM intent. Journal of Service
Management, 1-35.
Richard Lim. (2010, February 25). Malaysia's three premier polytechnics named. Retrieved
from The Star Online:
https://www.thestar.com.my/news/nation/2010/02/25/malaysias-three-premier-
polytechnics-named
Roh, M., & Park, K. (2019). Adoption of O2O food delivery services in South Korea: The
moderating role of moral obligation in meal preparation. Internal Journal of
Information Management, 262-273.
Shahul Hamid, A. W. (2010). Transformational of Malaysian’s Polytechnic into University
College in 2015: Issues and Challenges for Malaysian Technical and Vocational
Education. Transformational of Malaysian’s Polytechnic into University College in
2015: Issues and Challenges for Malaysian Technical and Vocational Education, 10-
11.
50
Shona. (2016, April 6). Foodpanda – Asia’s Food Delivery Platform. Retrieved from
DEMYSTIFY ASIA: http://www.demystifyasia.com/foodpanda/
Singh, M., & Matsui, Y. (2017). How Long Tail and Trust Affect Online Shopping Behavior:
An Extension to UTAUT2 Framework. Pasific Asia Journal of the Association for
Information Systems , 1-7.
Sun, Y., Li, X., & Ernst, A. (2019). Using Statistical Measures and Machine Learning for
Graph Reduction to Solve Maximum Weight Clique Problems. Ieee Transactions on
Pattern Analysis and Machine Intelligence, 1-15.
Syed Muhammad Sajjad, K. (2016). Methods of data collection. 201-275.
Tamilmani, K., Rana, N., Dwivedi, Y., Sahu, G., & and Roderick, S. (2018). Exploring the
Role of 'Price Value' for Understanding Consumer Adoption of Technology: A Review
and Meta-analysis of UTAUT2 based Empirical Studies. Twenty-Second Pacific Asia
Conference on Information Systems.
Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer Acceptance and Use of Information
Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS
Q., 157-178.
Viktor. (2020). The Food Delivery Business Model – A Complete Guide. Retrieved from
productmint: https://productmint.com/the-food-delivery-business-model-a-complete-
guide/
Wood, W. (2017). Habit in Personality and Social Psychology. Personality and Social
Psychology Review, 1-15.
Wood, W., & Runger, D. (2016). Psychology of Habit. Annual Review of Psychology, 289-
314.
Workplace Testing. (2018, July 15). Pilot Test. Retrieved from Workplace Testing.Com:
https://www.workplacetesting.com/definition/368/pilot-test-research
World Health Organization (WHO). (2010, February 24). What is a pandemic? Retrieved from
World Health Organization:
https://www.who.int/csr/disease/swineflu/frequently_asked_questions/pandemic/en/
51
Zeithaml, V. A., Bitner, M. J., & Gremler, D. D. (2014). Marketing de serviços: a empresa com
foco no cliente. Bookman, Porto Alegre.
Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food
delivery apps during 2019 novel Coronavirus pandemic period? International Journal
of Hospitality Management.
Zhou, T., & Li, H. (2014). Understanding mobile SNS continuance usage in China from the
perspectives of social influence and privacy concern. Computers in Human Behavior,
283-289.
Zhu, D. H., Lan, L., & Chang, Y. (2017). Understanding the Intention to Continue Use of a
Mobile Payment Provider: An Examination of Alipay Wallet in China. The
International Journal of Business and Information.
52
APPENDIX A: 31 SET OF PILOT TEST
Food Delivery Applications
Reliability Statistics
Cronbach's
Alpha
Cronbach's Alpha Based on
Standardized Items N of Items
.827 .842 5
Social Influence
Reliability Statistics
Cronbach's
Alpha
Cronbach's Alpha Based on
Standardized Items N of Items
.804 .811 3
Facilitating Conditions
Reliability Statistics
Cronbach's
Alpha
Cronbach's Alpha Based on
Standardized Items N of Items
.916 .916 2
Habit
Reliability Statistics
Cronbach's
Alpha
Cronbach's Alpha Based on
Standardized Items N of Items
.930 .931 4
53
APPENDIX B: QUESTIONNAIRE
TITLE: THE FACTORS INFLUENCE FOOD DELIVERY APPLICATIONS USAGE
AMONG STUDENTS OF POLITEKNIK SULTAN SALAHUDDIN ABDUL AZIZ
SHAH DURING MOVEMENT CONTROL ORDER (MCO)
Dear respondents,
We are Diploma in Business Studies Students from Commerce Department, Politeknik
Sultan Salahuddin Abdul Aziz Shah. We are conducting survey to complete our Final Year
Project. The survey is about The Factors Influence Food Delivery Applications Usage Among
Students of Politeknik Sultan Salahuddin Abdul Aziz Shah During Movement Control Order
(MCO).
The purpose of this study is to study on Factors Influence Food Delivery Applications
Usage Among Students of Politeknik Sultan Salahuddin Abdul Aziz Shah During Movement
Control Order (MCO).
We can assure that all the answers provided by you will be strict confidentially and
used for our future research. Here, we would like to take this opportunity to thank you for your
precious time in giving us your feedback in this survey/questionnaire.
NAME MATRIC NO.
NURUL SYAZWANI BINTI ZAKARIA 08DPM18F1040
NUR SYAFIQA BINTI ROSLI 08DPM18F1057
BATRISYIA BINTI SOBIE 08DPM18F1070
ANNUR BINTI ABDUL RAZAK 08DPM18F1077
54
RESEARCH TOPIC:
The Factors Influence Food Delivery Applications Usage among Students of Politeknik
Sultan Salahuddin Abdul Aziz Shah during Movement Control Order (MCO)
Survey Questionnaire
Dear respondent, The purpose of this survey is to study The Factors Influence Food
Delivery Applications Usage among Students of Politeknik Sultan Salahuddin Abdul
Aziz Shah during Movement Control Order (MCO).
Thank you for your participation.
Instruction:
1) There are TWO (2) sections in this questionnaire. Please answer ALL questions in
ALL sections.
2) Completion of this form will take approximately 15 minutes.
3) The contents of this questionnaire will be kept strict confidential
55
Section A: Demographic Profile
Please indicate (✔) in the appropriate information about yourself. Each question should only
have ONE answer.
2. Gender
Male Female
2. Age
18 and below 19 – 22 23 and above
3. Marital status
Single Married
4. Income
Below RM 400 RM 401 – RM 800
RM 801 – RM 1,200 RM 1,201 and above
5. Course of study
Business Non-business
6. Do you own a smartphone
Yes No
7. Have you ever downloaded applications for your mobile device?
Yes No
8. Do you know about Food Delivery Applications?
Yes No
9. Which food delivery applications have you downloaded?
Food Panda Dah Makan
Grab Food Others:_________________
56
Section B: Independent and Dependent Variables
Please indicate how strongly you agree or disagree with the statement given on the following
Likert scale. Please circle the number which best describes your response.
(1) = strongly disagree; (2) = disagree; (3) = neutral; (4) = agree; (5) = strongly agree
Food Delivery Applications (Dependent Variable)
No. Question 1 2 3 4 5
1. I am familiar with food delivery applications. 1 2 3 4 5
2. I know about food delivery applications through
advertisement on social media. 1 2 3 4 5
3. I do realize about the existence of food delivery
applications until now. 1 2 3 4 5
4. I do have an experience of food delivery applications. 1 2 3 4 5
5. I have heard about food delivery applications. 1 2 3 4 5
Social Influence (1st Independent Variable)
No. Question 1 2 3 4 5
1. People who are important to me think that I should use
food delivery applications for purchasing foods. 1 2 3 4 5
2. People who influence my behaviour think that I should
use food delivery applications for purchasing foods.
1 2 3 4 5
3. People whose opinions I value prefer that I use food
delivery applications for purchasing foods. 1 2 3 4 5
57
Facilitating Conditions (2nd Independent Variable)
No. Question 1 2 3 4 5
1. I have the knowledge necessary to use food delivery
applications for purchasing foods. 1 2 3 4 5
2. I feel comfortable using food delivery applications for
purchasing foods.
1 2 3 4 5
Habit (4th Independent Variable)
No. Question 1 2 3 4 5
1. Purchasing foods through food delivery applications is
almost like a habit for me.
1 2 3 4 5
2. I am addicted to using food delivery applications for the
purchase of foods.
1 2 3 4 5
3. I must use food delivery applications for purchasing
foods.
1 2 3 4 5
4. Using food delivery applications for purchasing foods
has become natural to me.
1 2 3 4 5
58
APPENDIX C: CRUCIAL INFORMATION
FDA 1 (I am familiar with food delivery applications)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Disagree 9 2.5 2.5 2.5
Neutral 33 9.2 9.2 11.8
Agree 127 35.6 35.6 47.3
Strongly agree 188 52.7 52.7 100.0
Total 357 100.0 100.0
FDA 2 (I know about food delivery applications through advertisements on social
media)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Strongly disagree 1 .3 .3 .3
Disagree 8 2.2 2.2 2.5
Neutral 40 11.2 11.2 13.7
Agree 131 36.7 36.7 50.4
Strongly agree 177 49.6 49.6 100.0
Total 357 100.0 100.0
59
FDA 3 (I do realize about the existence of food delivery applications until now)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Disagree 6 1.7 1.7 1.7
Neutral 14 3.9 3.9 5.6
Agree 112 31.4 31.4 37.0
Strongly agree 225 63.0 63.0 100.0
Total 357 100.0 100.0
FDA 4 (I do have an experience of food delivery applications)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Strongly disagree 3 .8 .8 .8
Disagree 5 1.4 1.4 2.2
Neutral 47 13.2 13.2 15.4
Agree 103 28.9 28.9 44.3
Strongly agree 199 55.7 55.7 100.0
Total 357 100.0 100.0
FDA 5 (I have heard about food delivery applications)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Disagree 2 .6 .6 .6
Neutral 6 1.7 1.7 2.2
Agree 111 31.1 31.1 33.3
Strongly agree 238 66.7 66.7 100.0
Total 357 100.0 100.0
60
SI 1 (People who are important to me think that I should use food delivery applications
for purchasing foods)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Strongly disagree 2 .6 .6 .6
Disagree 15 4.2 4.2 4.8
Neutral 83 23.2 23.2 28.0
Agree 118 33.1 33.1 61.1
Strongly agree 139 38.9 38.9 100.0
Total 357 100.0 100.0
SI 2 (People who influence my behaviour think that I should use food delivery
applications for purchasing foods)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Strongly disagree 5 1.4 1.4 1.4
Disagree 31 8.7 8.7 10.1
Neutral 108 30.3 30.3 40.3
Agree 150 42.0 42.0 82.4
Strongly agree 63 17.6 17.6 100.0
Total 357 100.0 100.0
61
SI 3 (People whose opinions I value prefer that I use food delivery applications for
purchasing foods)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Strongly disagree 5 1.4 1.4 1.4
Disagree 21 5.9 5.9 7.3
Neutral 102 28.6 28.6 35.9
Agree 161 45.1 45.1 81.0
Strongly agree 68 19.0 19.0 100.0
Total 357 100.0 100.0
FC 1 (I have the knowledge necessary to use food delivery applications for purchasing
foods)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Strongly disagree 4 1.1 1.1 1.1
Disagree 12 3.4 3.4 4.5
Neutral 30 8.4 8.4 12.9
Agree 146 40.9 40.9 53.8
Strongly agree 165 46.2 46.2 100.0
Total 357 100.0 100.0
62
FC 2 (I feel comfortable using food delivery applications for purchasing foods)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Disagree 12 3.4 3.4 3.4
Neutral 20 5.6 5.6 9.0
Agree 122 34.2 34.2 43.1
Strongly agree 203 56.9 56.9 100.0
Total 357 100.0 100.0
HABIT 1 (Purchasing foods through food delivery applications is almost like a habit for
me)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Strongly disagree 24 6.7 6.7 6.7
Disagree 88 24.6 24.6 31.4
Neutral 100 28.0 28.0 59.4
Agree 91 25.5 25.5 84.9
Strongly agree 54 15.1 15.1 100.0
Total 357 100.0 100.0
63
HABIT 2 (I am addicted to using food delivery applications for the purchase of foods)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Strongly disagree 25 7.0 7.0 7.0
Disagree 63 17.6 17.6 24.6
Neutral 115 32.2 32.2 56.9
Agree 79 22.1 22.1 79.0
Strongly agree 75 21.0 21.0 100.0
Total 357 100.0 100.0
HABIT 3 (I must use food delivery applications for purchasing foods)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Strongly disagree 10 2.8 2.8 2.8
Disagree 38 10.6 10.6 13.4
Neutral 119 33.3 33.3 46.8
Agree 75 21.0 21.0 67.8
Strongly agree 115 32.2 32.2 100.0
Total 357 100.0 100.0
64
HABIT 4 (Using food delivery applications for purchasing foods has become natural to
me)
Frequency Percent Valid Percent Cumulative
Percent
Valid
Strongly disagree 4 1.1 1.1 1.1
Disagree 48 13.4 13.4 14.6
Neutral 94 26.3 26.3 40.9
Agree 84 23.5 23.5 64.4
Strongly agree 127 35.6 35.6 100.0
Total 357 100.0 100.0
65
APPENDIX D: DESCRIPTIVE STATISTICS
Food Delivery Applications (Dependent Variable)
Descriptive Statistic
N Minimum Maximum Mean Std. Deviation
I am familiar with food delivery
applications. 357 2 5 4.38 .758
I know about food delivery
applications through
advertisement on social media.
357 1 5 4.33 .784
I do realize about the existence
of food delivery applications
until now.
357 2 5 4.56 .654
I do have an experience of food
delivery applications. 357 1 5 4.37 .827
I have heard about food
delivery applications. 357 2 5 4.64 .547
66
Social Influence (1st Independent Variable)
Descriptive Statistic
N Minimum Maximum Mean Std. Deviation
People who are important to me
think that I should use food
delivery applications for
purchasing foods.
357 1 5 4.06 .916
People who influence my
behaviour think that I should
use food delivery applications
for purchasing foods.
357 1 5 3.66 .915
People whose opinions I value
prefer that I use food delivery
applications for purchasing
foods.
357 1 5 3.75 .880
67
Facilitating Conditions (2nd Independent Variable)
Descriptive Statistic
N Minimum Maximum Mean Std. Deviation
I have the knowledge necessary
to use food delivery
applications for purchasing
foods.
357 1 5 4.28 .841
I feel comfortable using food
delivery applications for
purchasing foods.
357 2 5 4.45 .750
68
Habit (3th Independent Variable)
Descriptive Statistic
N Minimum Maximum Mean Std. Deviation
Purchasing foods through food
delivery applications is almost
like a habit for me.
357 1 5 3.18 1.161
I am addicted to using food
delivery applications for the
purchase of foods.
357 1 5 3.32 1.190
I must use food delivery
applications for purchasing
foods.
357 1 5 3.69 1.114
Using food delivery
applications for purchasing
foods has become natural to
me.
357 1 5 3.79 1.103
69
APPENDIX E: RELIABILITY TEST
Food Delivery Applications
N Valid 357
Missing 0
Mean 4.46
Std. Deviation .560
Social Influence
N Valid 357
Missing 0
Mean 3.50
Std. Deviation .964
Facilitating Conditions
N Valid 357
Missing 0
Mean 4.36
Std. Deviation .745
Habit
N Valid 357
Missing 0
Mean 3.82
Std. Deviation .783
70
APPENDIX F: CORRELATION TEST
Correlations
TFDA TSI TFC THABIT
FOOD
DELIVERY
APPLICATION
Pearson Correlation 1 .441** .657** .417**
Sig. (2-tailed) .000 .000 .000
N
357 357 357 357
SOCIAL
INFLUENCE
Pearson Correlation .441** 1 .436** .366**
Sig. (2-tailed) .000 .000 .000
N 357 357 357 357
FACILITATING
CONDITION
Pearson Correlation .657** .436** 1 .359**
Sig. (2-tailed) .000 .000 .000
N 357 357 357 357
HABIT Pearson Correlation .417** .366** .359** 1
Sig. (2-tailed) .000 .000 .000
N 357 357 357 357
**. Correlation is significant at the 0.01 level (2-tailed).
71
APPENDIX G: SWOT ANALYSIS
STRENGTHS
Well-known applications
Variety of products
High demand during pandemic
Covid-19
WEAKNESS
Many competitors
OPPORTUNITIES
Market development
THREATS
Instability economy
Fluctuating currencies
72
APPENDIX H: GANTT CHART
Activities Week
1
Week
2
Week
3
Week
4
Week
5
Week
6
Week
7
Week
8
Week
9
Week
10
Week
11
Week
12
Week
13
Week
14
Week
15
Form a group
Choose title
Determine supervisor ✔
Discuss about the issue
Discuss proposal introduction ✔
Checking Chapter 1 with supervisor ✔
Discuss about research objectives
Discuss about Chapter 2 ✔
Discuss about Chapter 1 until Chapter 3 ✔
Discuss about Topic 1 until Topic 3
Discuss about questionnaire ✔
Checked proposal Chapter 1 until
Chapter 3 ✔
Present the proposal to supervisor
Checked the proposal with supervisor ✔
Discuss about hypothesis
Do questionnaire ✔
Discuss and checked questionnaire ✔
Develop questionnaire ✔
Checked pilot test using SPSS
Proceed to analysed population
Reliable data
✔
Distributed actual data among Students of
Politeknik Sultan Salahuddin Abdul Aziz
Shah
✔
Analysis data ✔ ✔
Prepared report ✔
Final report ✔
Present Final Year Project (FYP) ✔