Market Research about Hong Kong Online Food Delivery Services

The research aimed to examine the food industry in the Hong Kong to understand the current scenario in the market, preference of the consumers and the way the market trends are changing. The literature review evaluated the different internal and external factors affecting the consumer repurchase intention to understand the factors that has to be focused on by the companies in the market.

Running head: MARKET RESEARCH
Market research about Hong Kong online food
delivery services
Name of the student
Name of the university
Author note

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Declaration
I hereby declare that the project titled “Market research about Hong Kong
online food delivery services” is my own work and I have carried this out
under the supervision of _________.
I also certify that the work is my own original work and the work has not been
submitted to any other university. The guidelines mentioned by the University
has been followed in the report. We have provided credit to the authors by
citing their work at all times.

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Abstract
The research aimed to examine the food industry in the Hong Kong to
understand the current scenario in the market, preference of the consumers
and the way the market trends are changing. The literature review evaluated
the different internal and external factors affecting the consumer repurchase
intention to understand the factors that has to be focused on by the
companies in the market. The research used a mono method where primary
data has been collected to perform quantitative through of 100 respondents in
Hong Kong. The findings have tested all the hypotheses mentioned in the
literature review section and had identified factors relevant to the evaluated
market.

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Acknowledgements
I would like to express my gratitude and appreciation to my supervisor,
__________ who has supported and advised at all times throughout the
course of the study. I would also like to give special thanks to my unit
coordinator, _________ for assisting and encouraging me to take up this
research topic. My special thanks to the respondents for cooperative with me
and helping me to complete this survey.

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Table of Contents
Chapter One: Introduction.................................................................................7
1.0 Introduction..............................................................................................7
1.1 Background of the study..........................................................................7
1.2 Research problem....................................................................................8
1.3 Research rationale...................................................................................9
1.4 Research objective.................................................................................10
1.5 Research questions...............................................................................10
1.6 Organisation of the study.......................................................................10
1.7 Summary................................................................................................12
Chapter Two: Literature review.......................................................................13
2.1 Introduction............................................................................................13
2.2 Global online food delivery market........................................................13
2.3 Online Food delivery services................................................................16
2.4 Consumer Perception on food delivery services...................................18
2.4.1 Hedonic motivation.............................................................................19
2.4.2 Prior experience of online purchase...................................................20
2.4.3 Time saving orientation.......................................................................22
2.4.4 Price saving orientation.......................................................................23
2.5 Other Critical success factors in the industry.........................................24
2.5.1 Information..........................................................................................24
2.5.2 System quality.....................................................................................25
2.5.3 Design Quality.....................................................................................26
2.5.4 Technology Acceptance model...........................................................26
2.6 Conceptual Framework..........................................................................28
2.7 Literature gap.........................................................................................28
2.8 Summary................................................................................................29

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Chapter three: Research Methodology...........................................................30
3.1 Introduction............................................................................................30
3.2 Research Philosophy.............................................................................30
3.3 Research Approach...............................................................................31
3.4 Research Design....................................................................................32
3.5 Data collection and Analysis..................................................................32
3.6 Sampling process...................................................................................33
3.7 Reliability and Validity............................................................................33
3.8 Ethical consideration..............................................................................34
3.9 Summary................................................................................................34
Chapter Four: Data Description.......................................................................36
4.1 Reliability and Validity............................................................................36
4.2 Descriptive Data.....................................................................................39
4.3 Correlation Matrix...................................................................................44
4.4 Regression analysis and hypothesis testing..........................................48
4.4.1 Hypothesis 1.......................................................................................48
4.4.2 Hypothesis 2.......................................................................................50
4.4.3 Hypothesis 3.......................................................................................51
4.4.4 Hypothesis 4.......................................................................................52
4.4.5 Hypothesis 5.......................................................................................53
4.4.6 Hypothesis 6.......................................................................................54
4.4.7 Hypothesis 7.......................................................................................55
4.4.7 Hypothesis 8.......................................................................................56
Chapter Five: Data Analysis............................................................................58
Chapter Six: Conclusions and Recommendations..........................................64
6.1 Conclusion.............................................................................................64
6.2 Key Findings..........................................................................................65

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6.3 Recommendation...................................................................................66
References.......................................................................................................67
Appendix..........................................................................................................73

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CHAPTER ONE: INTRODUCTION
1.0 INTRODUCTION
This study aims to examine the food industry in the Hong Kong to
understand the current scenario in the market, preference of the consumers
and the way the market trends are changing. China is one of the largest
growing economies in the world and there has been significant increase in
competition in the market due to the presence of strong competitors in the
market. Hong Kong is one of the leading tourists’ spots for Asian countries
with diversity in culture and food. This study highlights the road map for
success in this highly competitive environment by performing extensive
research on the given topic.
1.1 BACKGROUND OF THE STUDY
In the past few years, the digital food courier services have expanded
significantly in Hong Kong. Foodpanda is the restaurant aggregators for
Rocket Internet at the global platform and they have been able to expand their
business significantly which is represented by the US$60 million acquired
from existing and new investors for growth and development (Woodhouse,
2016). The arrival of multiple online food ordering services have fuelled up a
new trend in the city. This has also affected the local diners working as it has
affected the way they are ordering food due to the access they are getting to
diverse cuisines (In, 2014). The online delivery service have come a long way
after entering the Hong Honk food and beverage industry in the past five to six
years. Hong Kong, being one of the most lucrative places for food and
beverage services, the competition has increased significantly and with the
market entry of another industry giant, British company, Deliveroo has entered
the market (Leung, 2019). Moreover, the company offered subscription plans
for the consumers at HK$98 which waives of all the monthly delivery cost
(Leung, 2019). Consumers in Hong Kong are also price sensitive, even
though they have high disposable income.
Uber Eats, Foodpanda and Deliveroo can be considered as the three
major players in the Hong Kong market where Deliveroo enjoy the lions share
in the market. However, the new subscription services has been developed to

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increase the market share significantly. Currently, the overall food delivery
market in Hong Kong generates US$615m which shows that a year on year
growth rate of almost 10% and the number of active users consists of 2.3
million consumers (Woodhouse, 2016). Moreover, due to the increase in the
number or companies providing delivery services to the consumers,
restaurants are able to negotiate the fees which is lowering and squeezing the
margin. Moreover, as the market size of Hong Kong is smaller compared to
the other parts in China, there are limits to the extent to which an organisation
can grow in market. Therefore, role of technology and infrastructure is critical
in improving the growth of various companies in the market, otherwise it will
become more difficult to increase profit margin.
1.2 RESEARCH PROBLEM
In order to understand the problem area of research, it is key to
understand the current scenario in the Hong market and the changes
experienced in the behaviour of the consumers. Internet of things and other
technologies have changed the way businesses operate in a market along
with the change in the consumer outlook. Ecommerce and transactions of
various types of online platforms has made it all about value for money and
convenience as key factors to change (Yeo, Goh & Rezaei, 2017). Therefore,
online food and beverage delivery services has also taken a turn as it
facilitates two way communication and is more convenient. This has
encouraged majority of the companies in using Internet of Things into their
marketing mix for luring consumers in making online purchase (Yeo, Goh &
Rezaei, 2017). However, as the market in Hong Kong is smaller compared to
other global market, it has become imperative for the companies to take up
new and innovative strategies to increase their market share (Soo &
Woodhouse, 2016). Moreover, as the number of companies have increased,
the bargaining power lies in the hands of the suppliers (the restaurants) that
are aiming to form partnerships at lower prices. This affects the overall profit
margin of the companies, like Foodpanda, UberEats and Deliveroo as it is
difficult to grow in market having smaller number of target consumers (Leung,
2019). Moreover, there has been a significant change in the buying behaviour
of the consumers due to emergence of online food delivery applications.

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The market has seen the big companies falter and take serious
measures to get back on track in a crowded food delivery market. Foodpanda,
one of the early movers into the Hong Kong food delivery market were forced
to lay off more than 15 per cent of their staffs so that they could revamp their
existing strategy in the market (Soo & Woodhouse, 2016). This was an
attempt to reduce the operation cost for the organisation and improve profit
margin in a highly competitive market. Foodpanda has struggled to turn a
profit and reducing cost by using penetration price strategy to lure in more
consumers (Soo & Woodhouse, 2016). These efforts were made for the
company to remain competitive and offer similar prices with respect to the
other companies in the market. The competition is not limited to the pricing
strategy but also the time taken for delivery and overall consumer experience.
Deliveroo has earned good reputation in the market and all the companies are
always to outdo one another which has resulted in shorter competitive
advantage for all the companies. This is the reason it is essential to evaluate
the current scenario in the market to develop strategy that could bring
sustainable competitive advantage for the companies for a longer period of
time.
Thus, it has become mandatory for the companies to develop
innovative strategies to lure in more consumers due to the intensity of
competition in the global market. The study aims to address and evaluate this
aspect of the study by assessing the current market scenario along with the
changes purchase behaviour of the consumers. This will successfully provide
an overall analysis of the overall Hong Kong which can be used as a guideline
for gaining sustainable competitive advantage.
1.3 RESEARCH RATIONALE
This research aims to bring awareness to the management of different
food delivery services by providing them with in-depth knowledge and data on
the Hong Kong market and the changing needs of the consumers. The
companies have been facing difficulties in holding to on the sales volume
significantly. The amount of switching segment is higher than the loyal
consumers of each brand. The majority of the section are switching
consumers that look great opportunity to order their favourite food. Moreover,

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the quality of the service is another critical factor in effectively contributing to
the change in the industry as the consumers are very particular about the
service they are receiving. This study will critically examine and highlight the
switching segment and strategies needed to turn them into loyal consumers in
the market. Thus, the research aims to make breakthrough in understanding
the dimension of online food delivery market in Hong Kong.
1.4 RESEARCH OBJECTIVE
The research objective of the current studies are as follows:
 To examine the current market scenario in respect to online food
delivery services in Hong Kong
 To identify the key challenges and opportunities in the Hong Kong
market
 To understand the consumer perception on the food delivery services
 To recommend suitable strategies to be implemented by the
companies for generating sustainable competitive advantage in the
market
1.5 RESEARCH QUESTIONS
Primary Research questions:
 What is the current scenario in the Hong Kong foo delivery market?
 What are the key challenges and opportunities that can be capitalised
by the companies in Hong Kong?
 How does the consumer purchase intention in changing in respect to
the online food delivery services
Secondary Research question:
 What are the necessary strategies for developing sustainable
competitive advantage in the market?
1.6 ORGANISATION OF THE STUDY
The study has been divided into primarily six chapters and each
chapter is a continuation of the previous one and linked together to develop a
suitable research report. The first chapter is the introduction which provides a
detailed background of the research context along with the significance and

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the contribution of the research. The research objectives and questions are
formulated to depict the outline and scope of the current research. The next
chapter is the literature review which provides a detailed analysis of the past
literature of the research which includes analysis of the market scenario in
Hong Kong in respect to the online food delivery services and the changes in
the perception of the consumers. This has facilitated in understanding the
current drawbacks and gaps in literature which can be addressed using the
current study.
The third chapter is the research methodology section which creates a
specific research design to fulfil the goals and objectives of the current
research. It has described the research methods, frameworks and processes
used to collect and analyse data. The fourth chapter is the data description
chapter which presents the collected data by using the suggested method.
This will help in understanding the demographics of the participants and other
relevant data using tables, graphs and charts like scatter plots. The fifth
chapter is the continuation of the previous chapter where the data presented
will be analysed to uncover valid findings. The interpretation of the described
data has been carried in this section to address the research objectives and
questions. The final chapter provides a link between the previous chapters
and highlight key conclusions. It will also provide a summary of the objectives,
goals and methods used to address the study. The practical implication has
been described in this research and provide a detailed analysis of the
recommended strategies that can be used. The limitation of the overall
research has also been portrayed to understand the future scope of research.
Chapter 1: Introduction
Chapter 5: Data Analysis
Chapter 2: Literature Review
Chapter 3: Research Methodology
Chapter 4: Data Description

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Figure 1: Dissertation Structure
Source: (As created by author)
1.7 SUMMARY
This first chapter has introduced the background of the research
context to provide the key highlights off the online food delivery market in
Hong Kong. The introduction chapter highlighted the competitiveness of the
market and the major players in the industry. It has also highlighted few of the
challenges and opportunities in the market to define the research problem
effectively. A research rationale has been provided to understand the reason
for choosing the study and the way it is expected to contribute towards
research in marketing. The research objectives and the research questions
have been formulated to define the scope of the study. This is critically the
most significant stepping stone of the research as it defines the direction and
flow of the overall research.
The chapter has also provided a detailed outline of the overall research
by describing the purpose of each of the chapter and the logical flow.
Introduction chapter lays upon the basic foundation of conducting the
research for providing direction. The research shows that the market in Hong
Kong is highly competitive in nature and due to the smaller size of the market
in respect to other parts of China, the competition in intense. The major global
players have been struggling to manage their competitive positioning and
faced varies issues such as laying off of employees to reduce operational
cost. This shows that the margin of operation is very thin and organisations
require better strategies in regards to innovation and economies of scale to
improve profit margin and develop sustainable competitive advantage. The
bargaining power of the consumers and suppliers are higher due to the
availability of the large number of the companies which has been reducing the
profit margin of the company. Moreover, Foodpanda has been forced to lay off
employees due to this reason. The next chapter will critically analyse the past
literature on consumer perception, online food delivery services and other
relevant factors to identify the gap in literature.
Recommendation

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CHAPTER TWO: LITERATURE REVIEW
2.1 INTRODUCTION
This chapter critically evaluates past literature on online food delivery
services and consumer perception to highlight the characteristics of the
industry. This also includes the critical success factors necessary for gaining
competitive advantage in the market. The research has also identified various
issues affecting the operation of the companies. The gap in the literature is
also identified which facilitates in understanding the areas to be addressed in
this research.
2.2 GLOBAL ONLINE FOOD DELIVERY MARKET
The online food delivery services have revolutionised the whole
industry by effectively using Internet of Things to allow consumers to order
from websites and applications. The development of this market has resulted
in solving for various issues such as decrease in waiting time for ordered food
and has also reduced the long queues ((Hirschberg et al., 2016)). The
performance of the industry has been good taking into account the fact that
the industry is relatively new. The major players in the industry has been
using and relying strategies such as acquisition and partnerships for
improving their growth in the market. However, there are multiple factors
which has affected the rise of the global online food industry such as the
surge in food industry and the increase in the working population along with
the penetration of Internet of Things into the global market (Hirschberg et al.,
2016). Moreover, the dependency of the consumers on mobile phones has
also been a key factor in contributing to the growth of the industry. The global
online food delivery industry has expanded and reached a market value of US
$84.6 Billion in the year of 2018 (Research, 2019). Moreover, the
development of the industry is based on the convenience in service provided
by these major companies. The popularity of this concept has increased
significantly as it is mutually beneficial for the restaurants and the consumers.
The majority of the consumers are adopting this food delivery services
because it is precise, less time consuming and easy to order. On the other
hand, the food delivery providers consider it to be a suitable option as it is
helps in improving revenue by reduction of errors and labour expenses.

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The global market have seen an increase in this trend where more
consumers are preferring to order food using online services. The increase in
the trend is due to the penetration of internet connections and smartphone
technology in the market (Hirschberg et al., 2016). The online food delivery
service provides a variety of options to all the consumers where they can
compare and choose products based on the ratings and cost. This service
has provided value for money for the consumers and at the same time, it
saves the logistic cost for the restaurants. The forecasted data shows that the
global food delivery market is expected to reach $ 161.74 Billion by the end of
2023. The non-family segment generates majority of the revenue share which
was more than 70% in the year of 2017 (Hirschberg et al., 2016). This
segment is expected to grow further consisting of the millennial and the
Generation Z that spends majority of their time on the internet. The forecast
shows that the revenue of this segment is expected to reach USD 115.43
billion with a CAGR of 16.2% in the next four years ((Hirschberg et al., 2016)).
However, this segment are heavily influenced by the word of mouth and
reviews of peer groups on the social media websites.
The growth of digital technology has resulted in transformation and
reshaping of the total market due to the change in needs of the consumers.
The emergence of ecommerce has made the consumer habituated with using
online platforms of shopping where they have better transparency and
convenience and expecting the same thing from the food delivery services
resulted in the growth of this industry (Research, 2019). However, there are
mainly two type of the platforms have risen to fulfil this market gap where one
are the aggregators which emerged a decade back and the new delivery
application services which arrived within the last few years. The major
difference between both this types of delivery categories is the fact that the
new delivery services have their own logistics networks which has helped
them in including the restaurants that have their own drivers (Research,
2019). This has helped them to open up restaurants that belong to an entirely
new segment. This has resulted in attracting more investment into this
segment and expand their business. These new delivery applications have
become brands in no time and acquired a large segment of the consumers.

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This market is expected to grow even further as there is no limit to the number
of restaurants these platforms can form partnership with.
Figure 2: Potential growth
Source: (Hirschberg et al., 2016)
Figure 3: Growth in two types of business models

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Source: (Statistacloudfront.s3.amazonaws.com, 2019).
The above show that growth of the two business models used in delivery
services, one is the restaurant to delivery and the second is the platform to
delivery services. It shows that even though the platform to delivery services
is still a minor segment in terms of the total food and delivery services in the
world, the potential for growth for each of the company is huge.
Figure 4: Growth in terms of regions
Source: (Statistacloudfront.s3.amazonaws.com, 2019).
The evaluation of growth in terms of global region shows that China is
not far behind the European regions and is expecting nearly similar growth.
The growth rate of 9.4% shows that the platform to consumer delivery
services is expected to take a front seat in the global food delivery services
(Statistacloudfront.s3.amazonaws.com, 2019). However, the data shows that
China is the most dominant market and is expected to grow even further.
2.3 ONLINE FOOD DELIVERY SERVICES
As stated by Yeo, Goh, and Rezaei, (2017), there has been significant
on-going shift between the different method of food distribution and marketing
in organisations where traditional food delivery services are moving to online

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delivery system for food and beverages. The restaurants offer multiple
options to their consumers to make things easier which includes pick up,
drive-through, home delivery, phone based ordering services and online
shopping. Chen et al., (2014) claimed that preferences and habits of
consumers have been evolving at a rapid pace which makes it necessary for
the business entities to innovative and make use of digitisation to transform
their products and services. According to Pigatto et al., (2017), the consumer
have been immensely impacted by the information and digital transformation.
Web 2.0 has also impacted business where ecommerce sector is dealing with
variety of services starting from books, apparels and food services. The
implementation of internet and digital technology has become a part of
majority of the business sectors and slowly changing the classical approaches
to conducting business. Yeo, Goh, and Rezaei, (2017) states that these
innovations and changes are a result of the benefits needed by the modern
consumers where time and convenience has become a crucial aspect of
service in business. The implementation of internet on the food industry has
resulted in significant positive as it has been able to address the market gap
and both the consumers and the businesses have benefitted from them.
On the contrary, Chen et al., (2014) argued that even though there are
plenty of benefits attached to this type of delivery systems but there are
drawbacks which may affect certain segment of the consumers. Various
consumers seek personal contact as one of the key factor while developing
food on the online platform. On the other hand, there are segment of
consumers that are not comfortable or familiar with the modern technology.
These factors are supposed to have significant impact on satisfaction level of
the consumers. Nguyen, de Leeuw and Dullaert, (2018) suggests that the
type of service expected by the consumers results in the development of
business and various consumers are reluctant to use self-services that value
interaction with the staffs and employees. This means that the consistency
and performance of the platform is a critical aspect of online food delivery
systems.

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2.4 CONSUMER PERCEPTION ON FOOD DELIVERY SERVICES
As stated by Tan, Oriade and Fallon, (2014), the increase in number of
women at workplace, urbanization and increase in disposable income in
families have resulted in change in food habits of consumers. People prefer
eating out more often than it was in the past and average expenses of people
eating out also increased significantly. Shih-Tse Wang and Tsai, (2014)
suggested that habit of people eating out have increased among the younger
generations having a significant higher disposable income and living in the
urban areas. Moreover, these consumers prefer having food with higher
calorie content. The concept of eating out is one of the founding stones of this
online delivery business. This helps in determining the purchase intention of
the consumers and classify them in respect to their eating habits. Yeo, Goh,
and Rezaei, (2017) opined that globalisation has had profound impact in
eating habits, culture and lifestyle due to the technological transformation
which resulted in the cultural and political changes. The daily routines of the
general population changed significantly due to the shortage of time everyone
had in their life. As stated by Cho and Sagynov (2015), saving time and
convenience are two of the most significant factors among the consumers for
using a technology based service.
According to Shih-Tse Wang and Tsai, (2014), individuals have their
own online behaviour if we keep aside the cultural and social trend which
depends on the demographic data of the individual such as Age, income, sex
and ethnicity (Tan, Oriade & Fallon, 2014). Similarly, the attitude of the
individuals towards technology and the notion it is contributing positive to their
lives plays a crucial in the overall process of exchange where consumers
develop their urge to search for more information. Behavioural change of the
consumers also depends on the various features during the process of
exchange such as time consumed in the overall process, the ease of use and
psychological cost (Nguyen, de Leeuw & Dullaert, 2018). In this context, the
use of smartphone has a significant role in providing more ease and
accessibility by reducing the overall search time in comparison to that of the
using computers. Moreover, the websites define the overall psychological cost

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attached to the process as people prefer using user friendly websites so that
they can save a significant amount of time.
According to Cho and Sagynov (2015), the development and growth of
the service industry has influenced the consumers in preferring fast food
where technology has provided the user friendly experience by reducing the
amount of time spent behind orders and delivery times. Moreover, it also
provided the consumers a variety of options which was one of the most
important factor affecting the change in the consumer behaviour (Pigatto et
al., 2017). Smartphones have become one of the major means of gathering
information using global searches on different products and services where
they can gather variety of information and compare with other options to make
their choices. Similarly, the business entities have better opportunity to reach
out to consumers at any time and place.
As stated by Teo et al., (2015), this has resulted in the emergence of a
new consumer type which is considering different types of electronic media
and is multifaceted. This consumers shuffle among websites and are difficult
to be identified on online platforms. Therefore, it is essential for the
businesses to understand the personal motivation behind the change in
purchasing behaviour and develop marketing strategies to improve consumer
loyalty towards a brand (Pigatto et al., 2017). These consumers constantly
search for information in the online platform and it is necessary for
organisation to engage consumers effectively with information that attracts
them towards business offerings. The next section aims to examine the
motivating factors affecting the perception of the consumers in respect to the
online foo delivery market.
2.4.1 HEDONIC MOTIVATION
According to Alalwan et al., (2015), in respect to the utilitarian
perspective, shopping for products and services has always been considered
to be a rational process. Therefore, using one’s work mentality to purchase
and acquire products and services is a rational process of shopping. On the
contrary, the concept of hedonism is a completely opposite processes which
represents the desire of having fun and being playful. As stated by Labbe et

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al., (2015), consumers are always searching for sensory stimulation while
making a purchase of products and services where fun or symbolism is a
critical factor. Consumers wants to experience pleasure or fun while making
the purchase and using the product. However, this does not change the
traditional theories of consumption but is an extension of the previous
theories. It also includes the overall joy felt by the consumers throughout the
process of purchase starting from the recognition of need and finally post
purchase behaviour. Yim et al., (2014) states this experiential view is used to
take a broader approach to consumption and purchase process. This also
considers the processing of information for problem solving and cognitive
rational. The overall motivation of making a purchase and searching is derived
from hedonism as it results in emotional arousal. Davis, Lang and San Diego,
(2014) has examined the role of hedonism in ecommerce and found out that
in majority of the cases, hedonism affects trust, flow and brand equity and
results in consumer loyalty.
As stated by Yim et al., (2014), a visit to websites depends on the
enjoyment felt by the consumers throughout the whole process. This can
understood by the actual state in respect to the preferred state and the
desired state. Therefore, when the consumer’s actual state meets the desired
or preferred state then the consumer has reached a certain level of
satisfaction while making the purchase. Similarly, in case of the food delivery
services, hedonic motivation determines the feelings of the users towards
product offered. When the quality of the food and services meet the
expectation of the consumers, then organisations experience positive attitude
from the consumers.
H01: There is no significant relationship between hedonic motivation and
consumer purchase intention
H11: There is a significant positive relationship between hedonic motivation
and consumer purchase intention
2.4.2 PRIOR EXPERIENCE OF ONLINE PURCHASE
Lin and Lekhawipat, (2014) states that online purchase can be defined
as the process of exchanging money, effort and time on the online platform. ()

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argued that online purchasing of products is still considered risky when
compared to the traditional methods due to the lack of interaction with the
original product. As opined by Karimi, Papamichail and Holland, (2015), the
users that have positive and satisfactory past online experience would
develop a level of confidence on the products offered and services offered by
the website and develop repurchase intention among these consumers when
there is a generation of need and want. Therefore, prior positive experience
results in repurchase of products and services on the online platform as trust
is one of the critical component of developing a health relationship with the
consumers.
O. Pappas et al., (2014) states that the online experience of a person
includes cognitive process, flow, schema and involvement. As stated by Hsu,
Chang and Chuang, (2015), it is natural for the consumers to learn by
interacting with the online environment. Online experiences are mainly divided
into direct online experience and the second is indirect online experience.
Direct online experience is experienced when the consumer comes in direct
contact with the product. On the contrary, in case of the indirect online
experience consumers only with the advertisements (Lin and Lekhawipat,
2014). This leads to post purchase adoption and pre purchase adoption which
depends on the type of interaction between the consumer and the products. In
case of the direct online interaction, the customer experiences post purchase
adoption while pre purchase adoption is the case in case of indirect
interaction. Moreover, the overall online buying experiences also determines
the level of consumer satisfaction and chances of developing loyalty towards
the business is high. Karimi, Papamichail and Holland, (2015) conducted a
research on prior purchase experience which shows that positive experience
results in increase in consumer purchase intention. Post purchase
experiences helps in reducing the level of anxiety of the consumers so that
the websites can reduce the ambiguity by making the interaction effective.
On the other hand, the previous purchase experience of the consumers
determines the level of the expectation, the consumers will have from the
future transactions. The ease of user experience perceived by the consumers
change when there is a significant positive experience in the past and it

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improves the chances of revisit from the consumers. Hasan, (2016) states
that repurchase intention of the consumers are higher in cases when the past
experience has matched up to the perceived experience. Moreover, the
collection of experience makes it easier for the consumers to operate on the
platform and a higher degree of satisfaction. This can be used to understand
the fact that prior experience of the consumer directly impacts the repeat
purchase intention of the consumers.
H02: There is no significant relationship between prior purchase experience
and repeat purchase intention
H12: There is significant positive relationship between prior purchase
experience and repeat purchase intention
2.4.3 TIME SAVING ORIENTATION
As stated by Elizabeth Lloyd et al., (2014), in modern times, majority of
the people do not have the time to wait outside restaurants in long queues
while going out to eat and online delivery services have address this gap
where these consumer can enjoy their favourite cuisine without being have to
wait for long hours. Therefore, it is essential to get the job done in the
minimum time possible so time saving is a key area to be focused which
affects the perception of the consumers (Elizabeth Lloyd et al., 2014). This is
the reason that the takeaway and delivery industry has witnessed significant
in the global market. However, the overall market is highly fragmented in
nature and there are innumerable amount of choice and types of food. Kim,
Lee and Park, (2014) claimed that consumers prefer online delivery services
as it is fast, precise and convenient and author suggest that more than 65% of
the orders are delivered at home. This shows that majority of the households
are focusing mainly on the household segment.
As stated by Dholakia et al., (2016), the perception of the buyers are
captured by the perceived usefulness which means that consumers consider
technology helps them to shop effectively. The amount of time a consumer
saves using online shopping is a gain for them and consumers consider it
more useful as it reduces the effort and products are available all the time
without any traditional store timings. Elizabeth Lloyd et al., (2014) states

23MARKET RESEARCH
opportunity costs makes the high income consumers value time more
sincerely. This is the reason that people having high disposable income but
less time prefer using online shopping as it saves them time. Moreover, Kim,
Lee and Park, (2014) suggested that saving time has a strong influence while
measure the usefulness of the services and products as consumers get the
convenient payment methods and options to choose from. This shows that
the time saving dimension has significant influence post usage usefulness
which impacts the attitude and perception of the consumers. Elizabeth Lloyd
et al., (2014) states that the ease of use of the application also is associated
with the time saved by the consumers. The perception suggest that consumer
ease to use websites saves browsing time for the consumers and makes it
more appealing. Dholakia et al., (2016) conducted a research to examine the
time saving dimension in case of online which showed that consumers are
conscious about the time spent over the purchase and the effectiveness of the
time spent. Thus, it can also be said that there is significant positive
relationship between time saving orientation and repeat purchase of products
where the role of technology is significantly important.
H03: There is no significant relationship between time saving orientation and
repeat purchase intention
H13: There is significant positive relationship between time saving orientation
and repeat purchase intention
2.4.4 PRICE SAVING ORIENTATION
As stated by Alavi, Wieseke and Guba, (2016), the consumers in the
modern era have become sensitive to the price changes and consumers are
constantly looking for ways to save as much possible on their purchases
through discounts and other offers available to them. Zielke and Komor,
(2015) performed a study which showed that consumers are willing to travel
extra mile in order to avail discounts of goods and products. This implies that
price is a critical factor which affects the purchase intention of the consumers.
Tan, Oriade and Fallon, (2014) performed a study which shows that lower
competitive pricing increases the sales volumes for business entities and its
shows that need for having value for money. The consumers are willing to pay

24MARKET RESEARCH
the price which they perceive to be the adequate price for a product or
service.
Pigatto et al., (2017) states that consumers have the option of
evaluating prices of products using different websites and as there are
numerous options available in the market competitive pricing is a key
component in the online foo delivery services. On the other hand, Alavi,
Wieseke and Guba, (2016) suggests that these companies have supply chain
and logistics costs which makes is necessary to develop innovative ways and
implementation to technology to improve their profit margin in the long run.
Yeo, Goh, and Rezaei, (2017) states that consumers prefer using online
applications and websites as provides them with variety of price ranges and
they are able to quickly filter out the options suitable to their needs. Zielke and
Komor, (2015) conducted studies in the past which showed pricing orientation
has significant impact on the purchase intention of the consumers.
H04: There is no significant relationship between price saving orientation and
repeat purchase intention
H14: There is significant positive relationship between price saving orientation
and repeat purchase intention
2.5 OTHER CRITICAL SUCCESS FACTORS IN THE INDUSTRY
In continuation of the above factors mentioned in the study, there are other
factors from the perspective of the food delivery application and services
which are key for the success of the business entities in the modern global
environment. These are factors which the ease of use and time saving aspect
which are the main advantages of online delivery systems in respect to the
traditional models.
2.5.1 INFORMATION
Almaiah, Jalil and Man, (2016) states that the quality of the information
perceived by the consumers determines the decision making of the
consumers. According to Kallweit, Spreer and Toporowski, (2014), the service
quality, perceived value and the information quality determines the perceived
usefulness and ease of use in respect to the technology. Mohammadi, (2015)
conducted a study where they measured the perceived usefulness of the

25MARKET RESEARCH
collected information. The results showed that the nature of the perceived
information determines the usefulness of the application.
Past studies have classified information that has significant impact on
consumers into two major categories and they are user generated content
and the firm-generated content. Almaiah, Jalil and Man, (2016) states that in
case of the purchase of a product, asymmetric information gained by the
seller and the buyer leads to exposure to additional risks for the consumers.
This happens generally when there are no adequate information on products
and the consumers have to rely on inadequate information while making the
purchase of the products. Kallweit, Spreer and Toporowski, (2014) states that
consumers rely on consumer reviews in such cases while making purchase
which helps them to gain trust and credibility. The asymmetry of information
can be reduced using this process. On the contrary, the perception of the
consumers regarding the information as useful, irrespective of the fact that the
review is positive or negative.
H05: There is no significant impact of user generated and firm generated
information on the purchase intention of the consumers
H15: There is significant impact of user generated and firm generated
information on the purchase intention of the consumers
2.5.2 SYSTEM QUALITY
In respect to the technology acceptance model, system quality has
been considered as an external variable by majority of the researchers.
Almaiah, Jalil and Man, (2016) performed a study and found out that the
quality of the systems provided by the online food delivery services influences
the perceived ease of use and the usefulness. On the other hand, Calisir et
al., (2014) used the technology acceptance model to examine the ecommerce
of all online stores in Turkey and identified five elements that significantly
affected the perceived usefulness and the perceived ease of use. These
factors are service quality, information quality, enjoyment, trust and system
quality. Fathema, Shannon and Ross, (2015) also utilised the technology
acceptance model to examine the enterprise resource system of companies
to understand the role of technology. The study concluded the ease of use

26MARKET RESEARCH
and usefulness depends on the technological characteristics and system
quality. This shows that system quality is a crucial external variable that
impacts the ease of use and perception of the consumers.
H06: There is no significant impact of system quality on the purchase intention
of the consumers
H16: There is significant impact of system quality information on the purchase
intention of the consumers
2.5.3 DESIGN QUALITY
In terms of the technology acceptance model, design quality is another
factor which has a strong influence on the perception of the consumers.
Almaiah, Jalil and Man, (2016) measured the effectiveness of website design
in respect to the Chinese business to consumer websites and the results
showed that design has significantly influence on the perceived ease of use
and perceived usefulness. On the contrary, Kallweit, Spreer and Toporowski,
(2014), integrated the quality of e shopping, trust and enjoyment into the
technology acceptance model to measure the acceptance level of the
consumers in electronic shopping. The results were as expected and the
quality of service had significant impact on the ease of use. Calisir et al.,
(2014) also focused on the relevance of the system design into the technology
acceptance model to measure the factors affecting intention of the
consumers. The results suggests that there is significant impact of the quality
of the website and application design on the perceived ease of use of the
consumers.
H07: There is no significant impact of design quality on the purchase intention
of the consumers
H17: There is significant impact of design quality information on the purchase
intention of the consumers
2.5.4 TECHNOLOGY ACCEPTANCE MODEL
The technology acceptance model developed was developed by Davis
(1986) and it is one of the most widely acceptance model. The theory suggest
that the in the technology acceptance model which determines the behaviour

27MARKET RESEARCH
intention of using the system and this is severely influenced by the attitude of
the user. Kallweit, Spreer and Toporowski, (2014), have extended the
applicability of technology acceptance model by addition of the external
variables which measures the impact of the external factors on the perceived
use and ease of use. Almaiah, Jalil and Man, (2016) conducted a study to
examine the impact of perceived usefulness and perceived ease of use on
adoption of ecommerce. The results were positive and showed that perceived
ease of use indirectly impact the buying behaviour of the consumers.
On the other hand, the expansion of ecommerce on mart phone
devices have resulted in extension of the technology acceptance model.
Mohammadi, (2015) performed a research on the perceived usefulness and
ease of use of mobile devices and the attitude of the consumers towards the
purchase behaviour of the consumers. The results showed that the flexibility
of the mobile applications greatly influence the perception of the consumers.
H08: There is no significant impact of perceived quality of the consumers on
the attitude towards online food delivery services
H18: There is significant impact of perceived quality of the consumers on the
attitude towards online food delivery services

28MARKET RESEARCH
2.6 CONCEPTUAL FRAMEWORK
Figure 5: Conceptual framework
Source:
The conceptual framework describes the overall concept of the study
which describes both the internal factors and the external factors that affect
the perception of the consumers towards the online food delivery service and
the different factors affecting this relationship. The different hypothesis have
been developed based on the relationship identified from the past literature.
These factors has been put to test to identify the ones that have significantly
contribute the scenario in Hong Kong food delivery industry.
2.7 LITERATURE GAP
The evaluation of the past literature clearly shows that there has been
significant arguments for and against the effectiveness of these factors on the
consumer perception. Moreover, the majority of the studies have not tested
External Factors
Consumer
perception
Hedonic Motivation
Prior purchase
experience
Price Saving Orientation
Time saving orientation
Design quality
System quality
Information
TAM
Internal Factors

29MARKET RESEARCH
these factors on the online food delivery industry and services so that it is
difficult to conclude whether these factors will be applicable for the food
delivery industry or not. Moreover, the majority of the research has just
conceptualised the models but very few have used quantitative analysis to
examine these hypothesis and theories. On the other hand, the majority of the
studies have either considered the internal factors or the external factors but
this has taken both into account to measure the perception and relationship.
2.8 SUMMARY
This chapter has critically evaluates the current scenario in the global
food delivery industry and describes the online food delivery services. The
perception of the consumers towards online delivery services have been
examined using both internal and external factors to identify the factors that
are essential for the companies to gain sustainable competitive advantage in
the Hong Kong Food delivery market. Hedonic motivation, prior experience of
online purchase, time saving orientation and price saving orientation are the
internal factors chosen in the research. On the other hand, the external
factors affecting the perception and ease of use of the consumers are
information, system quality, design quality and technology acceptance model.

30MARKET RESEARCH
CHAPTER THREE: RESEARCH METHODOLOGY
3.1 INTRODUCTION
This chapter develops the frameworks, methods and instruments
required for data collection and analysis. Research methodology can be
defined as the process of formulating an effective research design for
effectively addressing the goals and objective of the research (Mackey &
Gass, 2015). The research design has been developed based on the
Saunders research onion which consists of the six layers (Saunders et al.,
2015). Each layer is closely linked to each other and unfolds the methods
required to successfully collect and analyse data in the research. The
research methodology section also describes the advantages and
disadvantages of using each of the method. The primary goals of any
research are description of behaviour, prediction of one variable to another
variable and measurement of causal relationship between two variables. In
this research, all the above mentioned aims has been addressed using a
mono method analysis. The research also is a cross sectional study so the
data has been collected only once. The study is an applied research and has
been able to identify critical factors that are contributing to perception of the
consumers.
3.2 RESEARCH PHILOSOPHY
The research is defined at different levels by using a research
philosophy which includes sets of ideas, assumptions and values. This also
helps in development of knowledge. Assumptions are made at every stage in
a research and even if the researchers are not aware it, it facilitates in
shaping up the research question. There are different types of assumptions
made in a research and they are mainly influence of values on research
(axiological assumptions), encountered realities (ontological assumptions)
and assumptions based on human knowledge (epistemological assumptions)
(Saunders et al., 2015). These assumptions are necessary for providing
direction to the study by streamlining the research question and developing
methods to address these questions. Pragmatism positivism, realism and
interpretivism are the different types of research philosophies used in

31MARKET RESEARCH
marketing research. Generally, the majority of the researches uses an
interpretivist or positivist approach and this study is no different from the
others. The current study has chosen positivism as the research philosophy
(Padilla-Díaz, 2015). Positivism only emphasises on factual knowledge by
making observations and measurement. However, these observations and
measurements need to be trustworthy. The role of researcher in this kind of
study is limited to data collection and analysis using a objective method. The
results obtained from positivism philosophy are quantifiable and observable.
The positivism philosophy is dependent on statistical analysis as the
data collected is quantifiable. As per the empiricist, development of
knowledge depends on human experience and uses an ontological view
(Padilla-Díaz, 2015). It implies that the philosophy considers that the world
consists of observable and discrete events that intermingle in a determined,
regular and observable manner. In this study, the positivism philosophy is
most suited as it will facilitate in testing the developed hypothesis using
statistical methods. Moreover, its helps in keeping the view of the researcher
out of the study.
3.3 RESEARCH APPROACH
The concept of research approach includes two essential parts one is
the logical reasoning and the other is process of data collection. Research
approach defines the process of choosing appropriate data and evaluating it.
Inductive and deductive are the two primary approaches used in market
research (Sekaran & Bougie, 2016). As it has been already mentioned that
each layer of the onion is related to the previous layer, it can be said that the
approach should be in accordance to the positivism philosophy. In this study,
deductive approach has been chosen as the research approach. The
deductive approach mainly deals with the process of hypothesis development
and testing of the hypotheses mentioned in the study by developing an
effective research strategy. As per the common definition, deductive approach
develops general results from particular premises. It is essential for measuring
causal relationship between two variables and link it to existing mentioned in
the literature section in the research. The deductive helps in making the data

32MARKET RESEARCH
quantifiable in the research and generalise the findings of the study up to an
extent.
3.4 RESEARCH DESIGN
Research has been define diversely by different authors. Some authors
consider it as the whole plan of collecting and evaluating data. On the hand,
some consider it as the method of choosing between qualitative and
quantitative method in the research. There are mainly three types of designs
used in marketing research and they are explanatory, descriptive and
exploratory research design (Meyers, Gamst & Guarino 2016). However, this
study as chosen the explanatory research which is also known as the causal
research design. As the name suggests, it is used for measuring the causal
relationship between two variables and uses a high structured design to
quantify the relationship. The causal research design facilitates in focusing on
a particular issue to explain the patterns of association between independent
and dependent variables. The causal design helps in measuring the
relationship between the seven hypotheses developed in the research.
3.5 DATA COLLECTION AND ANALYSIS
The process of data collection consists of two possibilities, one is
primary data collection and another is secondary data collection. It also
defines whether the data collected is quantitative or qualitative in nature. This
research has collected primary quantitative data where the survey
questionnaire has been used as a research instrument (Palinkas et al., 2015).
The survey questionnaire consists of demographic questions and question of
each of factors mentioned in the literature review. The questions are
developed on nominal scale, ordinal scale and five point Likert scale. This
questionnaire is an original questionnaire and has not been adopted from any
tested questionnaire. The questionnaire not only addresses the preferred
online food delivery services but also includes the perception of the
consumers and the factors affecting them.
The data collected has been analysed using statistical tools and
techniques such as MS Excel and SPSS (statistical tool for social sciences).
The data collected has been represented in graphs and tables to highlight the

33MARKET RESEARCH
response frequencies and the demographic composition of the respondents.
The study has used Pearson’s correlation matrix to examine the correlation
between the chosen variables. On the other hand, the study has a regression
analysis to perform a trend analysis to develop a relationship between the
variables. The ANOVA test has been conducted to test the hypotheses
developed in the literature review section in the research.
3.6 SAMPLING PROCESS
Sampling is the process of choosing elements from the total population
when the target population is large. This facilitates in reducing the
complication in the research and reduces the overall cost and saves time. The
process of sampling starts defining the target population in the research and
consists of the population that are best suited to address the objective of the
research. In this sample population consists of the population in Hong Kong.
The next step is choosing the sampling frame in the research and consists of
the elements that can contribute effectively to the research. The study has
chosen consumers that are frequent users of online food delivery services.
The next step is determining the sampling size in the research and the current
study has chosen 100 respondents in Hong Kong. The final step is choosing
the sampling technique required to choose the sampling population. The
sampling techniques are divided into probabilistic and non-probabilistic
sampling. Probabilistic sampling is divided into cluster random sampling,
stratified random sampling, simple random sampling and systematic random
sampling (Martínez-Mesa et al., 2016). On the other hand, the non-
probabilistic sampling has been divided into quota sampling, purposive
sampling, volunteer sampling and haphazard sampling. The major difference
between the two techniques is that the probabilistic sampling uses
randomisation to select sample population. In this study, stratified random
sampling has been chosen as the method of sampling which divides the
target population into strata and population is selected randomly from each
strata. This facilitates in avoiding bias in the research. However, as the
sample size is relatively small, the sampling error is expected to be around
10%.

34MARKET RESEARCH
3.7 RELIABILITY AND VALIDITY
Reliability is the process of measuring the ability of the study to
replicate the results using different sets of data. The reliability of the
questionnaire has been measured using a pilot study conducted on 10
respondents to evaluate the quality of data collected. On the other hand, the
reliability of the collected data has been measured using Cronbach’s Alpha
test which measures the internal consistency and the scale reliability (Bonett
& Wright, 2015). On the contrary, validity is the measure of the
appropriateness of the instruments used in this research. The validity of the
instruments has been measured using construct validity. On the contrary, the
validity of the collected data has been measured using Bartlett test of
sphericity and the Kaiser Meyer Olkin test of sampling adequacy (Turkyilmaz,
Erdem & Uslu, 2015). The KMO test measures the proportion of variance and
sampling adequacy of the collected data. The Bartlett test measures the
significance of the collected data and whether the data collected are related or
not.
3.8 ETHICAL CONSIDERATION
The current research has adhered to all the ethical guidelines
mentioned in the process. The major principles of research has been
conformed to which has ensured that the three cardinal sins have been
avoided. This means that none of the data is plagiarised and credit has been
given to the authors by citing their work accurately. The data collected has not
been fabricated which means that none of the data has been omitted to obtain
a desired result. Similarly, none of the data has been falsified or manipulated
to achieve the expected results. Informed consent is one of the critical aspect
of any research and the study has made sure that all the participants have
made voluntary participation. The research has also followed the data
protection act of 1998 where the privacy of the respondents have been kept.
This has been done by keeping anonymity of their personal data. The data
collected has not been used for any other purpose and the data collected has
been stored securely and will be deleted as soon as the purpose of the study
is fulfilled. The study has taken verbal consent from the respondents so that

35MARKET RESEARCH
they can leave at any point of time. The questionnaire has not used any
discriminatory or abusive language which may hurt or offend any respondent.
3.9 SUMMARY
The research has clearly discussed the different research methods and
instrumented used in the research to obtain relevant data. The chapter
described the overall plan for gathering and examining data effectively. The
current study has chosen positivism as the research philosophy as it is
dependent on statistical analysis as the data collected is quantifiable.
Deductive approach has been chosen as the research approach as it helps in
the process of hypothesis development and testing of the hypotheses
mentioned in the study by developing an effective research strategy. This
study as chosen the explanatory research which is also known as the causal
research design. The causal research design facilitates in focusing on a
particular issue to explain the patterns of association between independent
and dependent variables. This research has collected primary quantitative
data where the survey questionnaire has been used as a research instrument.
This questionnaire is an original questionnaire and has not been adopted from
any tested questionnaire. The questionnaire not only addresses the preferred
online food delivery services but also includes the perception of the
consumers and the factors affecting them. The study has used Pearson’s
correlation matrix to examine the correlation between the chosen variables.
On the other hand, the study has a regression analysis to perform a trend
analysis to develop a relationship between the variables. The ANOVA test has
been conducted to test the hypotheses developed in the literature review
section in the research. The current study has chosen 100 respondents in
Hong Kong and stratified random sampling has been chosen as the method of
sampling which divides the target population into strata and population is
selected randomly from each strata. The reliability of the collected data has
been measured using Cronbach’s Alpha test which measures the internal
consistency and the scale reliability. The validity of the collected data has
been measured using Bartlett test of sphericity and the Kaiser Meyer Olkin
test of sampling adequacy. The current research has adhered to all the ethical
guidelines mentioned in the process.

36MARKET RESEARCH
CHAPTER FOUR: DATA DESCRIPTION
4.1 RELIABILITY AND VALIDITY
Reliability Statistics
Cronbach's
Alpha
N of Items
.987 12
Table 1: Reliability statistics
Source: (As created by author)
Item Statistics
Mean Std. Deviation N
Hedonic motivation 2.24 1.296 100
Prior purchase 2.22 1.211 100
Time saving 2.22 1.260 100
offers and discounts 2.19 1.277 100
Information 2.18 1.258 100
easy to use 2.25 1.290 100
better design 2.21 1.266 100
delivery time 2.21 1.241 100
variety in menu 2.28 1.173 100
quality expectations 2.22 1.260 100
attitude 2.19 1.253 100
repeat purchase 2.26 1.260 100
Table 2: Item statistics
Source: (As created by author)
Item-Total Statistics
Scale Mean if
Item Deleted
Scale
Variance if
Item Deleted
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted
Hedonic motivation 24.43 165.015 .929 .985
Prior purchase 24.45 167.341 .920 .986

37MARKET RESEARCH
Time saving 24.45 166.311 .915 .986
offers and discounts 24.48 165.646 .924 .985
Information 24.49 166.111 .923 .985
easy to use 24.42 165.499 .918 .986
better design 24.46 165.827 .927 .985
delivery time 24.46 166.514 .923 .985
variety in menu 24.39 168.483 .912 .986
quality expectations 24.45 166.048 .924 .985
attitude 24.48 166.596 .911 .986
repeat purchase 24.41 165.861 .930 .985
Table 3: Item total statistics
Source: (As created by author)
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .968
Bartlett's Test of Sphericity Approx. Chi-Square 1906.860
df 66
Sig. .000
Table 4: KMO and Bartlett's Test
Source: (As created by author)
Communalities
Initial Extractio
n
Hedonic motivation 1.000 .886
Prior purchase 1.000 .871
Time saving 1.000 .863
offers and discounts 1.000 .877
Information 1.000 .876
easy to use 1.000 .868
better design 1.000 .882
delivery time 1.000 .876
variety in menu 1.000 .859
quality expectations 1.000 .877
attitude 1.000 .857
repeat purchase 1.000 .887
Extraction Method: Principal Component Analysis.
Table 5: Communalities

38MARKET RESEARCH
Source: (As created by author)
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
1 10.477 87.306 87.306 10.477 87.306 87.306
2 .228 1.904 89.210
3 .204 1.697 90.907
4 .192 1.602 92.510
5 .172 1.433 93.943
6 .139 1.157 95.100
7 .135 1.129 96.229
8 .118 .982 97.210
9 .112 .936 98.147
10 .085 .710 98.857
11 .072 .598 99.455
12 .065 .545 100.000
Extraction Method: Principal Component Analysis.
Table 5: Total Variance
Source: (As created by author)
Component Matrixa
Component
1
Hedonic motivation .941
Prior purchase .933
Time saving .929
offers and discounts .936
Information .936
easy to use .931
better design .939
delivery time .936
variety in menu .927
quality expectations .937
attitude .926
repeat purchase .942
Table 5: Component Matrixa

39MARKET RESEARCH
Source: (As created by author)
Extraction Method: Principal Component
Analysis.a
a. 1 components extracted.
4.2 DESCRIPTIVE DATA
Gender
Frequenc
y
Percent Valid Percent Cumulative
Percent
Valid Male 47 47.0 47.0 47.0
Fem
ale
53 53.0 53.0 100.0
Total 100 100.0 100.0
Table 6: Gender
Source: (As created by author)
Age
Frequenc
y
Percent Valid Percent Cumulative
Percent
Valid 18-
24
21 21.0 21.0 21.0
25-
30
16 16.0 16.0 37.0
31-
35
26 26.0 26.0 63.0
36-
40
17 17.0 17.0 80.0
41-
49
20 20.0 20.0 100.0
Total 100 100.0 100.0
Table 7: Age
Source: (As created by author)
length of use
Frequenc
y
Percent Valid Percent Cumulative
Percent
Valid 0-2 18 18.0 18.0 18.0
2-3 21 21.0 21.0 39.0

40MARKET RESEARCH
3-5 18 18.0 18.0 57.0
5-7 25 25.0 25.0 82.0
7
and
abov
e
18 18.0 18.0 100.0
Total 100 100.0 100.0
Table 8: Age
Source: (As created by author)
Preference between food delivery applications
Frequency Percent Valid Percent Cumulative
Percent
Vali
d
Foodpanda 34 34.0 34.0 34.0
Deliveroo 29 29.0 29.0 63.0
UberEats 18 18.0 18.0 81.0
Others 19 19.0 19.0 100.0
Total 100 100.0 100.0
Table 8: Preference between food delivery applications
Source: (As created by author)
Hedonic motivation
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 35 35.0 35.0 35.0
Agree 34 34.0 34.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 6 6.0 6.0 89.0
Strongly
Disagree
11 11.0 11.0 100.0
Total 100 100.0 100.0
Table 9: Hedonic motivation
Source: (As created by author)
Prior purchase
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 33 33.0 33.0 33.0
Agree 36 36.0 36.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 10 10.0 10.0 93.0

41MARKET RESEARCH
Strongly
Disagree
7 7.0 7.0 100.0
Total 100 100.0 100.0
Table 10: Prior purchase
Source: (As created by author)
Time saving
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 35 35.0 35.0 35.0
Agree 34 34.0 34.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 8 8.0 8.0 91.0
Strongly
Disagree
9 9.0 9.0 100.0
Total 100 100.0 100.0
Table 11: Time saving
Source: (As created by author)
offers and discounts
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 38 38.0 38.0 38.0
Agree 31 31.0 31.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 8 8.0 8.0 91.0
Strongly
Disagree
9 9.0 9.0 100.0
Total 100 100.0 100.0
Table 12: Offers and Discounts
Source: (As created by author)
Information
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 38 38.0 38.0 38.0
Agree 31 31.0 31.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 9 9.0 9.0 92.0
Strongly
Disagree
8 8.0 8.0 100.0
Total 100 100.0 100.0

42MARKET RESEARCH
Table 13: Information
Source: (As created by author)
easy to use
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 34 34.0 34.0 34.0
Agree 35 35.0 35.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 6 6.0 6.0 89.0
Strongly
Disagree
11 11.0 11.0 100.0
Total 100 100.0 100.0
Table 14: easy to use
Source: (As created by author)
better design
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 36 36.0 36.0 36.0
Agree 33 33.0 33.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 8 8.0 8.0 91.0
Strongly
Disagree
9 9.0 9.0 100.0
Total 100 100.0 100.0
Table 14: better design
Source: (As created by author)
delivery time
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 35 35.0 35.0 35.0
Agree 34 34.0 34.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 9 9.0 9.0 92.0
Strongly
Disagree
8 8.0 8.0 100.0
Total 100 100.0 100.0
Table 15: delivery time
Source: (As created by author)

43MARKET RESEARCH
variety in menu
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 27 27.0 27.0 27.0
Agree 42 42.0 42.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 10 10.0 10.0 93.0
Strongly
Disagree
7 7.0 7.0 100.0
Total 100 100.0 100.0
Table 16: variety in menu
Source: (As created by author)
quality expectations
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 35 35.0 35.0 35.0
Agree 34 34.0 34.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 8 8.0 8.0 91.0
Strongly
Disagree
9 9.0 9.0 100.0
Total 100 100.0 100.0
Table 17: quality expectations
Source: (As created by author)
attitude
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 37 37.0 37.0 37.0
Agree 32 32.0 32.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 9 9.0 9.0 92.0
Strongly
Disagree
8 8.0 8.0 100.0
Total 100 100.0 100.0
Table 18: attitude
Source: (As created by author)
repeat purchase
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly agree 32 32.0 32.0 32.0

44MARKET RESEARCH
Agree 37 37.0 37.0 69.0
Neutral 14 14.0 14.0 83.0
Disagree 7 7.0 7.0 90.0
Strongly
Disagree
10 10.0 10.0 100.0
Total 100 100.0 100.0
Table 19: repeat purchase
Source: (As created by author)
4.3 CORRELATION MATRIX
Correlations
Hedonic
motivation
Prior
purchase
Time
saving
offers
and
discounts
Information
Hedonic
motivation
Pearson
Correlation
1 .893** .877** .882** .853**
Sig. (2-tailed) .000 .000 .000 .000
N 100 100 100 100 100
Prior
purchase
Pearson
Correlation
.893** 1 .855** .874** .875**
Sig. (2-tailed) .000 .000 .000 .000
N 100 100 100 100 100
Time saving Pearson
Correlation
.877** .855** 1 .853** .873**
Sig. (2-tailed) .000 .000 .000 .000
N 100 100 100 100 100
offers and
discounts
Pearson
Correlation
.882** .874** .853** 1 .852**
Sig. (2-tailed) .000 .000 .000 .000
N 100 100 100 100 100
Information Pearson
Correlation
.853** .875** .873** .852** 1
Sig. (2-tailed) .000 .000 .000 .000

45MARKET RESEARCH
N 100 100 100 100 100
easy to use Pearson
Correlation
.870** .844** .867** .866** .862**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 100 100 100 100 100
better design Pearson
Correlation
.880** .872** .845** .875** .896**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 100 100 100 100 100
delivery time Pearson
Correlation
.860** .863** .848** .841** .881**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 100 100 100 100 100
variety in
menu
Pearson
Correlation
.839** .838** .847** .834** .869**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 100 100 100 100 100
quality
expectations
Pearson
Correlation
.858** .849** .841** .878** .848**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 100 100 100 100 100
attitude Pearson
Correlation
.868** .824** .863** .842** .824**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 100 100 100 100 100
repeat
purchase
Pearson
Correlation
.871** .876** .848** .898** .862**
Sig. (2-tailed) .000 .000 .000 .000 .000
N 100 100 100 100 100
Table 20: Correlation matrix
Source: (As created by author)
Correlations
easy
to
use
better
design
delivery
time
variety
in
menu
quality
expectations
attitude
Hedonic
motivation
Pearson
Correlatio
n
.870** .880** .860** .839** .858** .868**
Sig. (2-
tailed)
.000 .000 .000 .000 .000 .000
N 100 100 100 100 100 100

46MARKET RESEARCH
Prior
purchase
Pearson
Correlatio
n
.844** .872** .863** .838** .849** .824**
Sig. (2-
tailed)
.000 .000 .000 .000 .000 .000
N 100 100 100 100 100 100
Time saving Pearson
Correlatio
n
.867** .845** .848** .847** .841** .863**
Sig. (2-
tailed)
.000 .000 .000 .000 .000 .000
N 100 100 100 100 100 100
offers and
discounts
Pearson
Correlatio
n
.866** .875** .841** .834** .878** .842**
Sig. (2-
tailed)
.000 .000 .000 .000 .000 .000
N 100 100 100 100 100 100
Information Pearson
Correlatio
n
.862** .896** .881** .869** .848** .824**
Sig. (2-
tailed)
.000 .000 .000 .000 .000 .000
N 100 100 100 100 100 100
easy to use Pearson
Correlatio
n
1 .840** .869** .841** .867** .870**
Sig. (2-
tailed)
.000 .000 .000 .000 .000
N 100 100 100 100 100 100
better
design
Pearson
Correlatio
n
.840** 1 .872** .858** .883** .841**
Sig. (2-
tailed)
.000 .000 .000 .000 .000
N 100 100 100 100 100 100
delivery
time
Pearson
Correlatio
n
.869** .872** 1 .847** .887** .851**
Sig. (2-
tailed)
.000 .000 .000 .000 .000
N 100 100 100 100 100 100

47MARKET RESEARCH
variety in
menu
Pearson
Correlatio
n
.841** .858** .847** 1 .867** .878**
Sig. (2-
tailed)
.000 .000 .000 .000 .000
N 100 100 100 100 100 100
quality
expectation
s
Pearson
Correlatio
n
.867** .883** .887** .867** 1 .850**
Sig. (2-
tailed)
.000 .000 .000 .000 .000
N 100 100 100 100 100 100
attitude Pearson
Correlatio
n
.870** .841** .851** .878** .850** 1
Sig. (2-
tailed)
.000 .000 .000 .000 .000
N 100 100 100 100 100 100
repeat
purchase
Pearson
Correlatio
n
.848** .865** .875** .873** .873** .870**
Sig. (2-
tailed)
.000 .000 .000 .000 .000 .000
N 100 100 100 100 100 100
Table 21: Correlation matrix
Source: (As created by author)
Correlations
repeat purchase
Hedonic motivation Pearson Correlation .871**
Sig. (2-tailed) .000
N 100
Prior purchase Pearson Correlation .876**
Sig. (2-tailed) .000
N 100
Time saving Pearson Correlation .848**
Sig. (2-tailed) .000
N 100
offers and discounts Pearson Correlation .898**
Sig. (2-tailed) .000

48MARKET RESEARCH
N 100
Information Pearson Correlation .862**
Sig. (2-tailed) .000
N 100
easy to use Pearson Correlation .848**
Sig. (2-tailed) .000
N 100
better design Pearson Correlation .865**
Sig. (2-tailed) .000
N 100
delivery time Pearson Correlation .875**
Sig. (2-tailed) .000
N 100
variety in menu Pearson Correlation .873**
Sig. (2-tailed) .000
N 100
quality expectations Pearson Correlation .873**
Sig. (2-tailed) .000
N 100
attitude Pearson Correlation .870**
Sig. (2-tailed) .000
N 100
repeat purchase Pearson Correlation 1
Sig. (2-tailed)
N 100
Table 22: Correlation matrix
Source: (As created by author)
**. Correlation is significant at the 0.01 level (2-tailed).
4.4 REGRESSION ANALYSIS AND HYPOTHESIS TESTING
4.4.1 HYPOTHESIS 1
Table 23: Regression Analysis 1
Source: (As created by author)
Model Summaryb
Mode R R Square Adjusted R Std. Error of Durbin-

49MARKET RESEARCH
l Square the Estimate Watson
1 .871a .758 .756 .623 2.350
a. Predictors: (Constant), Hedonic motivation
b. Dependent Variable: repeat purchase
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 119.185 1 119.185 306.932 .000b
Residual 38.055 98 .388
Total 157.240 99
a. Dependent Variable: repeat purchase
b. Predictors: (Constant), Hedonic motivation
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .363 .125 2.909 .004
Hedonic motivation .847 .048 .871 17.519 .000
a. Dependent Variable: repeat purchase
Residuals Statisticsa
Minimum Maximu
m
Mean Std.
Deviation
N
Predicted Value 1.21 4.60 2.26 1.097 100
Residual -1.057 1.250 .000 .620 100
Std. Predicted Value -.957 2.130 .000 1.000 100
Std. Residual -1.696 2.006 .000 .995 100
a. Dependent Variable: repeat purchase

50MARKET RESEARCH
Figure 6: Scatterplot
Source:
4.4.2 HYPOTHESIS 2
Table 24: Regression Analysis 2
Source: (As created by author)
Model Summaryb
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .876a .767 .764 .612 1.922
a. Predictors: (Constant), Prior purchase
b. Dependent Variable: repeat purchase
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 120.543 1 120.543 321.910 .000b
Residual 36.697 98 .374
Total 157.240 99

51MARKET RESEARCH
a. Dependent Variable: repeat purchase
b. Predictors: (Constant), Prior purchase
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .237 .128 1.847 .068
Prior purchase .911 .051 .876 17.942 .000
a. Dependent Variable: repeat purchase
Residuals Statisticsa
Minimum Maximu
m
Mean Std.
Deviation
N
Predicted Value 1.15 4.79 2.26 1.103 100
Residual -1.060 1.118 .000 .609 100
Std. Predicted Value -1.008 2.296 .000 1.000 100
Std. Residual -1.731 1.827 .000 .995 100
a. Dependent Variable: repeat purchase
4.4.3 HYPOTHESIS 3
Table 24: Regression Analysis 3
Source: (As created by author)
Model Summaryb
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .848a .719 .716 .672 2.256
a. Predictors: (Constant), Time saving
b. Dependent Variable: repeat purchase
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 113.028 1 113.028 250.541 .000b
Residual 44.212 98 .451
Total 157.240 99

52MARKET RESEARCH
a. Dependent Variable: repeat purchase
b. Predictors: (Constant), Time saving
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .377 .137 2.762 .007
Time saving .848 .054 .848 15.828 .000
a. Dependent Variable: repeat purchase
Residuals Statisticsa
Minimum Maximu
m
Mean Std.
Deviation
N
Predicted Value 1.23 4.62 2.26 1.069 100
Residual -1.073 1.230 .000 .668 100
Std. Predicted Value -.968 2.206 .000 1.000 100
Std. Residual -1.598 1.832 .000 .995 100
a. Dependent Variable: repeat purchase
4.4.4 HYPOTHESIS 4
Table 25: Regression Analysis 4
Source: (As created by author)
Model Summaryb
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .898a .806 .805 .557 2.229
a. Predictors: (Constant), offers and discounts
b. Dependent Variable: repeat purchase
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.

53MARKET RESEARCH
1 Regression 126.812 1 126.812 408.423 .000b
Residual 30.428 98 .310
Total 157.240 99
a. Dependent Variable: repeat purchase
b. Predictors: (Constant), offers and discounts
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .319 .111 2.870 .005
offers and discounts .886 .044 .898 20.209 .000
a. Dependent Variable: repeat purchase
Residuals Statisticsa
Minimum Maximu
m
Mean Std.
Deviation
N
Predicted Value 1.21 4.75 2.26 1.132 100
Residual -1.092 1.136 .000 .554 100
Std. Predicted Value -.932 2.201 .000 1.000 100
Std. Residual -1.959 2.038 .000 .995 100
a. Dependent Variable: repeat purchase
4.4.5 HYPOTHESIS 5
Table 26: Regression Analysis 5
Source: (As created by author)
Model Summaryb
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .862a .743 .740 .642 1.705
a. Predictors: (Constant), Information

54MARKET RESEARCH
b. Dependent Variable: repeat purchase
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 116.812 1 116.812 283.163 .000b
Residual 40.428 98 .413
Total 157.240 99
a. Dependent Variable: repeat purchase
b. Predictors: (Constant), Information
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .378 .129 2.932 .004
Information .863 .051 .862 16.827 .000
a. Dependent Variable: repeat purchase
Residuals Statisticsa
Minimum Maximu
m
Mean Std.
Deviation
N
Predicted Value 1.24 4.69 2.26 1.086 100
Residual -1.105 1.169 .000 .639 100
Std. Predicted Value -.938 2.241 .000 1.000 100
Std. Residual -1.720 1.820 .000 .995 100
a. Dependent Variable: repeat purchase
4.4.6 HYPOTHESIS 6
Table 27: Regression Analysis 6
Source: (As created by author)
Model Summaryb
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .848a .719 .716 .671 2.284

55MARKET RESEARCH
a. Predictors: (Constant), easy to use
b. Dependent Variable: repeat purchase
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 113.094 1 113.094 251.059 .000b
Residual 44.146 98 .450
Total 157.240 99
a. Dependent Variable: repeat purchase
b. Predictors: (Constant), easy to use
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .396 .135 2.922 .004
easy to use .829 .052 .848 15.845 .000
a. Dependent Variable: repeat purchase
Residuals Statisticsa
Minimum Maximu
m
Mean Std.
Deviation
N
Predicted Value 1.22 4.54 2.26 1.069 100
Residual -1.053 1.290 .000 .668 100
Std. Predicted Value -.969 2.132 .000 1.000 100
Std. Residual -1.569 1.922 .000 .995 100
a. Dependent Variable: repeat purchase
4.4.7 HYPOTHESIS 7
Table 28: Regression Analysis 7
Source: (As created by author)
Model Summaryb

56MARKET RESEARCH
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .865a .748 .745 .636 2.253
a. Predictors: (Constant), better design
b. Dependent Variable: repeat purchase
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 117.556 1 117.556 290.303 .000b
Residual 39.684 98 .405
Total 157.240 99
a. Dependent Variable: repeat purchase
b. Predictors: (Constant), better design
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .357 .129 2.780 .007
better design .861 .051 .865 17.038 .000
a. Dependent Variable: repeat purchase
Residuals Statisticsa
Minimum Maximu
m
Mean Std.
Deviation
N
Predicted Value 1.22 4.66 2.26 1.090 100
Residual -1.079 1.199 .000 .633 100
Std. Predicted Value -.956 2.204 .000 1.000 100
Std. Residual -1.696 1.884 .000 .995 100
a. Dependent Variable: repeat purchase
4.4.7 HYPOTHESIS 8
Table 29: Regression Analysis 8

57MARKET RESEARCH
Source: (As created by author)
Model Summaryb
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .850a .722 .720 .663 2.064
a. Predictors: (Constant), quality expectations
b. Dependent Variable: attitude
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 112.250 1 112.250 254.992 .000b
Residual 43.140 98 .440
Total 155.390 99
a. Dependent Variable: attitude
b. Predictors: (Constant), quality expectations
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .314 .135 2.326 .022
quality expectations .845 .053 .850 15.968 .000
a. Dependent Variable: attitude
Residuals Statisticsa
Minimum Maximu
m
Mean Std.
Deviation
N
Predicted Value 1.16 4.54 2.19 1.065 100
Residual -1.004 1.306 .000 .660 100
Std. Predicted Value -.968 2.206 .000 1.000 100
Std. Residual -1.513 1.968 .000 .995 100
a. Dependent Variable: attitude

58MARKET RESEARCH

59MARKET RESEARCH
CHAPTER FIVE: DATA ANALYSIS
This chapter critically analyses the data described in the last chapter to
shed light on the findings developed from the collected data. The research
has used statistical techniques and tools to quantitative test the hypotheses
and evaluate the reliability and validity of the collected data.
The reliability of the collected data measures the internal consistency
and scale reliability of the collected data. The value of alpha is acceptable
when it is greater than 0.6 and in this study, the value of alpha is .987 which is
high. This implies that the internal consistency among the collected data is
high and the data set is acceptable. The item total statistics measures the
value of alpha when one of the items are deleted. It can be seen that the
value of alpha decreases when one of the item is deleted. This signifies that
all the variables are significantly contributing to the study and should not be
eliminated.
The next section measures the validity of the data collected using KMO
statistics and Bartlett test of sphericity. The KMO value is acceptable at a
range of 0.8 to 1. A value below that range in not acceptable and in this study,
the value of KMO is .968 which is high. This signifies that the value is
acceptable and the proportion of variance among the data is low. It also
shows that sampling of the collected data is adequate for the given study. The
value of Bartlett test of sphericity is .000 which is less than 0.05 and this
means that the values are related. This proves that the data collected is valid.
In terms of preference of choosing an online food delivery service, 34%
of the respondents have chosen Foodpanda as their preference and 29%
have chosen Deliveroo. On the other hand, only 18% have chosen UberEats.
This shows that Foodpanda is still leading in the market but Deliveroo is quite
close to them and could take the lead at any point of time. However, UberEats
have not been able to keep up with these two and needs to develop strategies
to increase their share in the market. However, it is shocking to see that the
other smaller food delivery services occupy significant share in the market.
This shows that trust is one of the critical factor and consumers stick to their
favoured app service once they incur good food and services.

60MARKET RESEARCH
The correlation matrix shows that there is significant positive
relationship among all the elements mentioned in the research. These
elements are closely linked with each other as these are critical factors in
achieving sustainability and competitive advantage in the market. The
correlation ranges within 0.8 to 0.9 which shows a high positive correlation.
These values are significant at two tail which means that the relationship
bidirectional in nature.
The regression analysis is conducted to develop a trend equation
between two variables. This research has used a linear regression to
measure the relationship between repeat purchase intention and hedonic
motivation of the consumers. The value of R in the research is .871 which
defines the correlation between the two variables. This signifies that the
predictability of the model is high. The value of R square is .758 and the
values is acceptable at 0.7. The R square is the coefficient of determination
which measures the goodness of fit of the model. It also measures the
explaining capability of the independent variable on the dependent variable. in
this study, the explaining capability of hedonic motivation is 75.8% which is
relatively high and this means that change in hedonic motivation will affect the
purchase intention of the consumers. The value of Durbin Watson in this
research is 2.350 which determines the first order autocorrelation between the
variables. As the value lies within 1.7 to 2.5, there is no first order
autocorrelation between the independent and the dependent variable. The
ANOVA test shows that the value of F statistics is .000 which is less than the
p value of 0.05 and thus the null hypothesis can be rejected. This means that
there is significant positive relationship between hedonic motivation and
consumer repeat purchase intention.
CRPI= .363+.847HM,
Where CRPI= consumer repeat purchase intention, HM =hedonic motivation
When the value of HM is replaced by 1, then the value of CPI becomes 1.21.
This implies that with 1 unit increase in value of hedonic motivation, there is
1.21 unit increase in consumer repeat purchase intention.

61MARKET RESEARCH
In case of the second regression, the value of R is .876 which defines
the correlation between the two variables. This signifies that the predictability
of the model is high. The value of R square is .767 and the values is
acceptable at 0.7. The R square is the coefficient of determination which
measures the goodness of fit of the model. It also measures the explaining
capability of the independent variable on the dependent variable. in this study,
the explaining capability of prior purchase intention is 76.7% which is
relatively high and this means that change in prior purchase intention will
affect the purchase intention of the consumers. The value of Durbin Watson
in this research is 1.922 which determines the first order autocorrelation
between the variables. As the value lies within 1.7 to 2.5, there is no first order
autocorrelation between the independent and the dependent variable. The
ANOVA test shows that the value of F statistics is .000 which is less than the
p value of 0.05 and thus the null hypothesis can be rejected. This means that
there is significant positive relationship between prior purchase experience
and consumer repeat purchase intention.
CRPI= .237+.911PP,
Where CRPI= consumer repeat purchase intention, PP =prior purchase
experience
When the value of PP is replaced by 1, then the value of CPI becomes 1.148.
This implies that with 1 unit increase in value of prior purchase experience,
there is 1.148 unit increase in consumer repeat purchase intention.
In case of the third regression, the value of R is .848 which defines the
correlation between the two variables. This signifies that the predictability of
the model is high. The value of R square is .719 and the values is acceptable
at 0.7. The R square is the coefficient of determination which measures the
goodness of fit of the model. It also measures the explaining capability of the
independent variable on the dependent variable. In this study, the explaining
capability of time saving orientation is 71.9% which is relatively high and this
means that change in time saving orientation will affect the purchase intention
of the consumers. The value of Durbin Watson in this research is 2.256 which
determines the first order autocorrelation between the variables. As the value

62MARKET RESEARCH
lies within 1.7 to 2.5, there is no first order autocorrelation between the
independent and the dependent variable. The ANOVA test shows that the
value of F statistics is .000 which is less than the p value of 0.05 and thus the
null hypothesis can be rejected. This means that there is significant positive
relationship between time saving orientation and consumer repeat purchase
intention.
CRPI= .337+.848TS,
Where CRPI= consumer repeat purchase intention, TS = Time saving
When the value of TS is replaced by 1, then the value of CPI becomes 1.185.
This implies that with 1 unit increase in value of time saving orientation, there
is 1.185 unit increase in consumer repeat purchase intention.
In case of the fourth regression, the value of R is .898 which defines
the correlation between the two variables. This signifies that the predictability
of the model is high. The value of R square is .806 and the values is
acceptable at 0.7. The R square is the coefficient of determination which
measures the goodness of fit of the model. It also measures the explaining
capability of the independent variable on the dependent variable. In this study,
the explaining capability of price saving orientation is 80.6% which is relatively
high and this means that change in price saving orientation will affect the
purchase intention of the consumers. The value of Durbin Watson in this
research is 2.229 which determines the first order autocorrelation between the
variables. As the value lies within 1.7 to 2.5, there is no first order
autocorrelation between the independent and the dependent variable. The
ANOVA test shows that the value of F statistics is .000 which is less than the
p value of 0.05 and thus the null hypothesis can be rejected. This means that
there is significant positive relationship between price saving orientation and
consumer repeat purchase intention.
CRPI= .319+.886PS,
Where CRPI= consumer repeat purchase intention, PS = Price saving

63MARKET RESEARCH
When the value of PS is replaced by 1, then the value of CPI becomes 1.205.
This implies that with 1 unit increase in value of time saving orientation, there
is 1.205 unit increase in consumer repeat purchase intention.
In case of the fifth regression, the value of R is .862 which defines the
correlation between the two variables. This signifies that the predictability of
the model is high. The value of R square is .743 and the values is acceptable
at 0.7. The R square is the coefficient of determination which measures the
goodness of fit of the model. It also measures the explaining capability of the
independent variable on the dependent variable. In this study, the explaining
capability of information quality is 74.3% which is relatively high and this
means that change in information quality will affect the purchase intention of
the consumers. The value of Durbin Watson in this research is 1.705 which
determines the first order autocorrelation between the variables. As the value
lies within 1.7 to 2.5, there is no first order autocorrelation between the
independent and the dependent variable. The ANOVA test shows that the
value of F statistics is .000 which is less than the p value of 0.05 and thus the
null hypothesis can be rejected. This means that there is significant positive
relationship between information quality and consumer repeat purchase
intention.
CRPI= .378+.863IQ,
Where CRPI= consumer repeat purchase intention, IQ = Information quality
When the value of IQ is replaced by 1, then the value of CPI becomes 1.241.
This implies that with 1 unit increase in value of information quality, there is
1.241 unit increase in consumer repeat purchase intention.
In case of the sixth regression, the value of R is .848 which defines the
correlation between the two variables. This signifies that the predictability of
the model is high. The value of R square is .719 and the values is acceptable
at 0.7. The R square is the coefficient of determination which measures the
goodness of fit of the model. It also measures the explaining capability of the
independent variable on the dependent variable. In this study, the explaining
capability of system quality is 71.9% which is relatively high and this means
that change in system quality will affect the purchase intention of the

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consumers. The value of Durbin Watson in this research is 2.284 which
determines the first order autocorrelation between the variables. As the value
lies within 1.7 to 2.5, there is no first order autocorrelation between the
independent and the dependent variable. The ANOVA test shows that the
value of F statistics is .000 which is less than the p value of 0.05 and thus the
null hypothesis can be rejected. This means that there is significant positive
relationship between system quality and consumer repeat purchase intention.
CRPI= .396+.829SQ,
Where CRPI= consumer repeat purchase intention, SQ = System quality
When the value of SQ is replaced by 1, then the value of CPI becomes 1.225.
This implies that with 1 unit increase in value of system quality, there is 1.225
unit increase in consumer repeat purchase intention.
Similarly, in case of the other two hypothesis, the null hypothesis has
been successfully rejected and alternative hypothesis have been accepted.
This shows that all the factors both internal and external significantly
contribute to the perception of the consumers and also affect their purchase
intention.

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CHAPTER SIX: CONCLUSIONS AND RECOMMENDATIONS
6.1 CONCLUSION
The research aimed to examine the food industry in the Hong Kong to
understand the current scenario in the market, preference of the consumers
and the way the market trends are changing. The digital food courier services
have expanded significantly in Hong Kong. The online delivery service have
come a long way after entering the Hong Honk food and beverage industry in
the past five to six years. Hong Kong, being one of the most lucrative places
for food and beverage services, the competition has increased significantly
and with the market entry of another industry giant, British company,
Deliveroo has entered the market. Ecommerce and transactions of various
types of online platforms has made it all about value for money and
convenience as key factors to change. Therefore, online food and beverage
delivery services has also taken a turn as it facilitates two way communication
and is more convenient. As the number of companies have increased, the
bargaining power lies in the hands of the suppliers (the restaurants) that are
aiming to form partnerships at lower prices.
The literature review chapter has critically evaluates the current
scenario in the global food delivery industry and describes the online food
delivery services. The perception of the consumers towards online delivery
services have been examined using both internal and external factors to
identify the factors that are essential for the companies to gain sustainable
competitive advantage in the Hong Kong Food delivery market. Hedonic
motivation, prior experience of online purchase, time saving orientation and
price saving orientation are the internal factors chosen in the research. On the
other hand, the external factors affecting the perception and ease of use of
the consumers are information, system quality, design quality and technology
acceptance model. In the third chapter, the research has clearly discussed the
different research methods and instrumented used in the research to obtain
relevant data. The chapter described the overall plan for gathering and
examining data effectively. This study as chosen the explanatory research
which is also known as the causal research design. This research has

66MARKET RESEARCH
collected primary quantitative data where the survey questionnaire has been
used as a research instrument. The questionnaire not only addresses the
preferred online food delivery services but also includes the perception of the
consumers and the factors affecting them. The study has a regression
analysis to perform a trend analysis to develop a relationship between the
variables. The ANOVA test has been conducted to test the hypotheses
developed in the literature review section in the research. The current study
has chosen 100 respondents in Hong Kong and stratified random sampling
has been chosen as the method of sampling which divides the target
population into strata and population is selected randomly from each strata.
The reliability of the collected data has been measured using Cronbach’s
Alpha test which measures the internal consistency and the scale reliability.
The validity of the collected data has been measured using Bartlett test of
sphericity and the Kaiser Meyer Olkin test of sampling adequacy.
6.2 KEY FINDINGS
The key findings of the study are as follows:
 In terms of preference of choosing an online food delivery service,
34% of the respondents have chosen Foodpanda as their preference
and 29% have chosen Deliveroo. On the other hand, only 18% have
chosen UberEats. This shows that Foodpanda is still leading in the
market but Deliveroo is quite close to them and could take the lead at
any point of time. However, UberEats have not been able to keep up
with these two and needs to develop strategies to increase their share
in the market.
 The ANOVA test shows that the value of F statistics is .000 which is
less than the p value of 0.05 and thus the null hypothesis can be
rejected. This means that there is significant positive relationship
between hedonic motivation and consumer repeat purchase intention.
 The ANOVA test also showed that in case of prior purchase
experience, the value of F statistics is .000 which is less than the p
value of 0.05 and thus the null hypothesis can be rejected. This means
that there is significant positive relationship between prior purchase
experience and consumer repeat purchase intention.

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 The ANOVA test showed that in case of time saving orientation, the
value of F statistics is .000 which is less than the p value of 0.05 and
thus the null hypothesis can be rejected. This means that there is
significant positive relationship between time saving orientation and
consumer repeat purchase intention.
 The findings remaining true for all the internal and external factors in
the research.
6.3 RECOMMENDATION
The study has provided following recommendation based on the
findings:
 The food delivery services should improve on the service quality by
focusing on factors such as system quality, design quality and
information quality. The consumers prefer using systems that are easy
to use and save time.
 The food delivery services should focus on the price they offer to the
consumers as majority of the consumers are highly sensitive to price
changes.
 Food delivery services should maintain consistency in the quality of
product and services as prior experience of the consumers have
significant impact on their next purchase.
 Moreover, it is seen that consumers switch applications based on
discounts and offers.

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REFERENCES
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., Lal, B., & Williams, M. D. (2015).
Consumer adoption of Internet banking in Jordan: Examining the role
of hedonic motivation, habit, self-efficacy and trust. Journal of Financial
Services Marketing, 20(2), 145-157.
Alavi, S., Wieseke, J., & Guba, J. H. (2016). Saving on discounts through
accurate sensing–salespeople's estimations of customer price
importance and their effects on negotiation success. Journal of
Retailing, 92(1), 40-55.
Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Extending the TAM to examine
the effects of quality features on mobile learning acceptance. Journal
of Computers in Education, 3(4), 453-485.
Bonett, D. G., & Wright, T. A. (2015). Cronbach's alpha reliability: Interval
estimation, hypothesis testing, and sample size planning. Journal of
Organizational Behavior, 36(1), 3-15.
Calisir, F., Altin Gumussoy, C., Bayraktaroglu, A. E., & Karaali, D. (2014).
Predicting the intention to use a web‐based learning system: Perceived
content quality, anxiety, perceived system quality, image, and the
technology acceptance model. Human Factors and Ergonomics in
Manufacturing & Service Industries, 24(5), 515-531.
Chen, M. C., Hsu, C. L., Hsu, C. M., & Lee, Y. Y. (2014). Ensuring the quality
of e-shopping specialty foods through efficient logistics service. Trends
in Food Science & Technology, 35(1), 69-82.
Cho, Y. C., & Sagynov, E. (2015). Exploring factors that affect usefulness,
ease of use, trust, and purchase intention in the online
environment. International Journal of Management & Information
Systems (IJMIS), 19(1), 21-36.
Davis, R., Lang, B., & San Diego, J. (2014). How gender affects the
relationship between hedonic shopping motivation and purchase
intentions?. Journal of Consumer Behaviour, 13(1), 18-30.

69MARKET RESEARCH
Dholakia, U., Tam, L., Yoon, S., & Wong, N. (2016). The ant and the
grasshopper: understanding personal saving orientation of
consumers. Journal of Consumer Research, 43(1), 134-155.
Elizabeth Lloyd, A., YK Chan, R., SC Yip, L., & Chan, A. (2014). Time buying
and time saving: effects on service convenience and the shopping
experience at the mall. Journal of Services Marketing, 28(1), 36-49.
Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the Technology
Acceptance Model (TAM) to examine faculty use of Learning
Management Systems (LMSs) in higher education institutions. Journal
of Online Learning & Teaching, 11(2).
Hasan, B. (2016). Perceived irritation in online shopping: The impact of
website design characteristics. Computers in Human Behavior, 54,
224-230.
Hirschberg, C., Rajko, A., Schumacher, T., & Wrulich, M. (2016). The
changing market for food delivery. Retrieved 7 August 2019, from
https://www.mckinsey.com/industries/high-tech/our-insights/the-
changing-market-for-food-delivery
Hsu, M. H., Chang, C. M., & Chuang, L. W. (2015). Understanding the
determinants of online repeat purchase intention and moderating role
of habit: The case of online group-buying in Taiwan. International
Journal of Information Management, 35(1), 45-56.
In, N. (2014). Guide to the city's online food courier services. Retrieved 5
August 2019, from
https://www.scmp.com/lifestyle/food-wine/article/1578481/our-guide-
internet-food-courier-services-hong-kong
Kallweit, K., Spreer, P., & Toporowski, W. (2014). Why do customers use self-
service information technologies in retail? The mediating effect of
perceived service quality. Journal of retailing and consumer
services, 21(3), 268-276.

70MARKET RESEARCH
Karimi, S., Papamichail, K. N., & Holland, C. P. (2015). The effect of prior
knowledge and decision-making style on the online purchase decision-
making process: A typology of consumer shopping behaviour. Decision
Support Systems, 77, 137-147.
Kim, Y. K., Lee, M. Y., & Park, S. H. (2014). Shopping value orientation:
Conceptualization and measurement. Journal of Business
Research, 67(1), 2884-2890.
Labbe, D., Ferrage, A., Rytz, A., Pace, J., & Martin, N. (2015). Pleasantness,
emotions and perceptions induced by coffee beverage experience
depend on the consumption motivation (hedonic or utilitarian). Food
quality and preference, 44, 56-61.
Leung, K. (2019). Hong Kong's food delivery battle intensifies as Deliveroo
launches subscription service to fend off Foodpanda and Uber Eats.
Retrieved 5 August 2019, from
https://www.msn.com/g00/en-sg/News/other/hong-kong-s-food-
delivery-battle-intensifies-as-deliveroo-launches-subscription-service-
to-fend-off-foodpanda-and-uber-eats/ar-BBVPcNl?
i10c.ua=1&i10c.encReferrer=aHR0cHM6Ly93d3cubXNuLmNvbS9lbi1z
Zy9OZXdzL%3290aGVyL2hvbmcta29uZy1zLWZvb2QtZGVsaXZlcn
%6btYmF0dGxlLWludGVuc2lmaWVzLWFzLWRlbGl2ZXJ
%76by1sYXVuY2hlcy1zdWJzY3JpcHRpb24tc2Vydmlj
%5aS10by1mZW5kLW9mZi1mb29kcGFuZGEtYW5kLXViZ
%58ItZWF0cy9hci1CQlZQY05s&i10c.dv=12#page=2
Lin, C., & Lekhawipat, W. (2014). Factors affecting online repurchase
intention. Industrial Management & Data Systems, 114(4), 597-611.
Mackey, A., & Gass, S. M. (2015). Second language research: Methodology
and design. Routledge.
Martínez-Mesa, J., González-Chica, D. A., Duquia, R. P., Bonamigo, R. R., &
Bastos, J. L. (2016). Sampling: how to select participants in my
research study?. Anais brasileiros de dermatologia, 91(3), 326-330.

71MARKET RESEARCH
Meyers, L. S., Gamst, G., & Guarino, A. J. (2016). Applied multivariate
research: Design and interpretation. Sage publications.
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An
integration of TAM and IS success model. Computers in Human
Behavior, 45, 359-374.
Nguyen, D. H., de Leeuw, S., & Dullaert, W. E. (2018). Consumer behaviour
and order fulfilment in online retailing: a systematic
review. International Journal of Management Reviews, 20(2), 255-276.
O. Pappas, I., G. Pateli, A., N. Giannakos, M., & Chrissikopoulos, V. (2014).
Moderating effects of online shopping experience on customer
satisfaction and repurchase intentions. International Journal of Retail &
Distribution Management, 42(3), 187-204.
Padilla-Díaz, M. (2015). Phenomenology in educational qualitative research:
Philosophy as science or philosophical science. International Journal
of Educational Excellence, 1(2), 101-110.
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., &
Hoagwood, K. (2015). Purposeful sampling for qualitative data
collection and analysis in mixed method implementation
research. Administration and policy in mental health and mental health
services research, 42(5), 533-544.
Pigatto, G., Machado, J. G. D. C. F., Negreti, A. D. S., & Machado, L. M.
(2017). Have you chosen your request? Analysis of online food delivery
companies in Brazil. British Food Journal, 119(3), 639-657.
Research, A. (2019). Online Food Delivery Market to Hit $161.74 Billion by
2023 - Global and Regional (US, UK, China, India, etc.) Industry
Strategy and Statistic Analysis: Adroit Market Research. Retrieved 7
August 2019, from
https://www.globenewswire.com/news-release/2019/04/16/1804428/0/e
n/Online-Food-Delivery-Market-to-Hit-161-74-Billion-by-2023-Global-
and-Regional-US-UK-China-India-etc-Industry-Strategy-and-Statistic-
Analysis-Adroit-Market-Research.html

72MARKET RESEARCH
Saunders, M. N., Lewis, P., Thornhill, A., & Bristow, A. (2015). Understanding
research philosophy and approaches to theory development.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill
building approach. John Wiley & Sons.
Shih-Tse Wang, E., & Tsai, B. K. (2014). Consumer response to retail
performance of organic food retailers. British Food Journal, 116(2),
212-227.
Soo, Z., & Woodhouse, A. (2016). Reality bites: meal delivery company
Foodpanda lays off 15 per cent of its Hong Kong staff. Retrieved 5
August 2019, from https://www.scmp.com/tech/article/1913304/reality-
bites-meal-delivery-company-foodpanda-lays-15-cent-its-hong-kong-
staff
Statistacloudfront.s3.amazonaws.com. (2019). Retrieved 7 August 2019, from
https://statistacloudfront.s3.amazonaws.com/download/pdf/OnlineFood
Delivery_Preview.pdf
Tan, Q., Oriade, A., & Fallon, P. (2014). Service quality and customer
satisfaction in Chinese fast food sector: A proposal for
CFFRSERV. Advances in Hospitality and Tourism Research
(AHTR), 2(1), 30-53.
Teo, A. C., Tan, G. W. H., Ooi, K. B., Hew, T. S., & Yew, K. T. (2015). The
effects of convenience and speed in m-payment. Industrial
Management & Data Systems, 115(2), 311-331.
Turkyilmaz, C. A., Erdem, S., & Uslu, A. (2015). The effects of personality
traits and website quality on online impulse buying. Procedia-Social
and Behavioral Sciences, 175, 98-105.
Woodhouse, A. (2016). ‘It’s basically not been touched’: foodpanda and
Deliveroo join menagerie of online food delivery start-ups vying for
Hong Kong’s nearly virgin market. Retrieved 5 August 2019, from
https://www.scmp.com/tech/start-ups/article/1901187/its-basically-not-
been-touched-foodpanda-and-deliveroo-join-menagerie

73MARKET RESEARCH
Yeo, V. C. S., Goh, S. K., & Rezaei, S. (2017). Consumer experiences,
attitude and behavioral intention toward online food delivery (OFD)
services. Journal of Retailing and Consumer Services, 35, 150-162.
Yeo, V. C. S., Goh, S. K., & Rezaei, S. (2017). Consumer experiences,
attitude and behavioral intention toward online food delivery (OFD)
services. Journal of Retailing and Consumer Services, 35, 150-162.
Yim, M. Y. C., Yoo, S. C., Sauer, P. L., & Seo, J. H. (2014). Hedonic shopping
motivation and co-shopper influence on utilitarian grocery shopping in
superstores. Journal of the Academy of Marketing Science, 42(5), 528-
544.
Zielke, S., & Komor, M. (2015). Cross-national differences in price–role
orientation and their impact on retail markets. Journal of the Academy
of Marketing Science, 43(2), 159-180.

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APPENDIX
SURVEY QUESTIONNAIRE
My name is ___________ and I am pursuing my major in
__________. This survey is for educational purpose and all the responses will
not be used for any other purpose. Participation in this study is voluntary and
you can leave the survey at any point of time. Kindly help me out with this
survey.
1. Gender?
Male Female
2. Age?
18- 24 25-30 31-35 36-40 41-49
3. How long have you been using smart phone?
0-2 2-3 3-5 5-7 7 and above
4. Kindly choose among the following online food delivery services?
Foodpanda Deliveroo UberEats
Others
5. Kindly rate the following statement starting from 1 being strongly agree to 5
being strongly disagree.
Internal Factors 1 2 3 4 5
You order food from the online platform as it is fun and
provides pleasure
Your prior purchase experience affects your application
choice
You order food from online delivery applications to save
time
You choose platforms based on the offers and discounts
available on it